From mdekauwe at gmail.com  Sat Aug  1 06:23:47 2009
From: mdekauwe at gmail.com (Martin)
Date: Sat, 1 Aug 2009 03:23:47 -0700 (PDT)
Subject: [SciPy-User] sorting an array
Message-ID: <b6655146-359b-40a3-bb52-8275783f7216@24g2000yqm.googlegroups.com>

Hi,

I would ideally like to sort an multidimensional array by  the first
column, but I can only manage to adjust the whole array. For
example...

>a = array([(1,0,2,5),(1,5,6,2),(1,2,8,7),(1,4,4,6),(1,3,2,3),(1,11,2,0),(1,10,1,3),(1,9,0,4),(1,8,9,6)])
>a
array([[ 1,  0,  2,  5],
       [ 1,  5,  6,  2],
       [ 1,  2,  8,  7],
       [ 1,  4,  4,  6],
       [ 1,  3,  2,  3],
       [ 1, 11,  2,  0],
       [ 1, 10,  1,  3],
       [ 1,  9,  0,  4],
       [ 1,  8,  9,  6]])
>sort(a, axis = 0)
array([[ 1,  0,  0,  0],
       [ 1,  2,  1,  2],
       [ 1,  3,  2,  3],
       [ 1,  4,  2,  3],
       [ 1,  5,  2,  4],
       [ 1,  8,  4,  5],
       [ 1,  9,  6,  6],
       [ 1, 10,  8,  6],
       [ 1, 11,  9,  7]])

When really what i wanted was not for the 3rd and 4th columns to also
be sorted nunmerically in that way. In unix I would reformat the first
two columns in gawk i.e. gawk '{printf("%2.3d %2.3d, %d, %d\n", $1,
$2, $3, $4)}' | sort -n -k 1

Any help would be appreciated!

Thanks

Martin


From josef.pktd at gmail.com  Sat Aug  1 07:44:28 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sat, 1 Aug 2009 07:44:28 -0400
Subject: [SciPy-User] sorting an array
In-Reply-To: <b6655146-359b-40a3-bb52-8275783f7216@24g2000yqm.googlegroups.com>
References: <b6655146-359b-40a3-bb52-8275783f7216@24g2000yqm.googlegroups.com>
Message-ID: <1cd32cbb0908010444m29fec4f3v864a14bf5d58ca16@mail.gmail.com>

On Sat, Aug 1, 2009 at 6:23 AM, Martin<mdekauwe at gmail.com> wrote:
> Hi,
>
> I would ideally like to sort an multidimensional array by ?the first
> column, but I can only manage to adjust the whole array. For
> example...
>
>>a = array([(1,0,2,5),(1,5,6,2),(1,2,8,7),(1,4,4,6),(1,3,2,3),(1,11,2,0),(1,10,1,3),(1,9,0,4),(1,8,9,6)])
>>a
> array([[ 1, ?0, ?2, ?5],
> ? ? ? [ 1, ?5, ?6, ?2],
> ? ? ? [ 1, ?2, ?8, ?7],
> ? ? ? [ 1, ?4, ?4, ?6],
> ? ? ? [ 1, ?3, ?2, ?3],
> ? ? ? [ 1, 11, ?2, ?0],
> ? ? ? [ 1, 10, ?1, ?3],
> ? ? ? [ 1, ?9, ?0, ?4],
> ? ? ? [ 1, ?8, ?9, ?6]])
>>sort(a, axis = 0)
> array([[ 1, ?0, ?0, ?0],
> ? ? ? [ 1, ?2, ?1, ?2],
> ? ? ? [ 1, ?3, ?2, ?3],
> ? ? ? [ 1, ?4, ?2, ?3],
> ? ? ? [ 1, ?5, ?2, ?4],
> ? ? ? [ 1, ?8, ?4, ?5],
> ? ? ? [ 1, ?9, ?6, ?6],
> ? ? ? [ 1, 10, ?8, ?6],
> ? ? ? [ 1, 11, ?9, ?7]])
>
> When really what i wanted was not for the 3rd and 4th columns to also
> be sorted nunmerically in that way. In unix I would reformat the first
> two columns in gawk i.e. gawk '{printf("%2.3d %2.3d, %d, %d\n", $1,
> $2, $3, $4)}' | sort -n -k 1
>
> Any help would be appreciated!

If you only want to base the sorting on one column, then you can use
argsort on the column and reorder the entire array.
If you want to sort by rows then a thread last december 21, 2008 on
the numpy discussion list on sortrows would help.

(as a windows user, I have no idea what your gawk command means)

Josef

>
> Thanks
>
> Martin
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From emmanuelle.gouillart at normalesup.org  Sat Aug  1 07:47:08 2009
From: emmanuelle.gouillart at normalesup.org (Emmanuelle Gouillart)
Date: Sat, 1 Aug 2009 13:47:08 +0200
Subject: [SciPy-User] sorting an array
In-Reply-To: <b6655146-359b-40a3-bb52-8275783f7216@24g2000yqm.googlegroups.com>
References: <b6655146-359b-40a3-bb52-8275783f7216@24g2000yqm.googlegroups.com>
Message-ID: <20090801114708.GB10485@phare.normalesup.org>

	Hi Martin,

	I'm not sure if I understand what you mean, but does the
following solve your problem?

>>> a = np.array([(1,0,2,5),(1,5,6,2),(1,2,8,7),(1,4,4,6),(1,3,2,3),(1,11,2,0),(1,10,1,3),(1,9,0,4),(1,8,9,6)])
>>> a
array([[ 1,  0,  2,  5],
       [ 1,  5,  6,  2],
       [ 1,  2,  8,  7],
       [ 1,  4,  4,  6],
       [ 1,  3,  2,  3],
       [ 1, 11,  2,  0],
       [ 1, 10,  1,  3],
       [ 1,  9,  0,  4],
       [ 1,  8,  9,  6]])
>>> a[:,:2] = np.sort(a[:,:2], axis=0)
>>> a
array([[ 1,  0,  2,  5],
       [ 1,  2,  6,  2],
       [ 1,  3,  8,  7],
       [ 1,  4,  4,  6],
       [ 1,  5,  2,  3],
       [ 1,  8,  2,  0],
       [ 1,  9,  1,  3],
       [ 1, 10,  0,  4],
       [ 1, 11,  9,  6]])

	Cheers,

	Emmanuelle

On Sat, Aug 01, 2009 at 03:23:47AM -0700, Martin wrote:
> Hi,

> I would ideally like to sort an multidimensional array by  the first
> column, but I can only manage to adjust the whole array. For
> example...

> >a = array([(1,0,2,5),(1,5,6,2),(1,2,8,7),(1,4,4,6),(1,3,2,3),(1,11,2,0),(1,10,1,3),(1,9,0,4),(1,8,9,6)])
> >a
> array([[ 1,  0,  2,  5],
>        [ 1,  5,  6,  2],
>        [ 1,  2,  8,  7],
>        [ 1,  4,  4,  6],
>        [ 1,  3,  2,  3],
>        [ 1, 11,  2,  0],
>        [ 1, 10,  1,  3],
>        [ 1,  9,  0,  4],
>        [ 1,  8,  9,  6]])
> >sort(a, axis = 0)
> array([[ 1,  0,  0,  0],
>        [ 1,  2,  1,  2],
>        [ 1,  3,  2,  3],
>        [ 1,  4,  2,  3],
>        [ 1,  5,  2,  4],
>        [ 1,  8,  4,  5],
>        [ 1,  9,  6,  6],
>        [ 1, 10,  8,  6],
>        [ 1, 11,  9,  7]])

> When really what i wanted was not for the 3rd and 4th columns to also
> be sorted nunmerically in that way. In unix I would reformat the first
> two columns in gawk i.e. gawk '{printf("%2.3d %2.3d, %d, %d\n", $1,
> $2, $3, $4)}' | sort -n -k 1

> Any help would be appreciated!

> Thanks

> Martin
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user


From josef.pktd at gmail.com  Sat Aug  1 08:00:45 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sat, 1 Aug 2009 08:00:45 -0400
Subject: [SciPy-User] sorting an array
In-Reply-To: <20090801114708.GB10485@phare.normalesup.org>
References: <b6655146-359b-40a3-bb52-8275783f7216@24g2000yqm.googlegroups.com>
	<20090801114708.GB10485@phare.normalesup.org>
Message-ID: <1cd32cbb0908010500j7468463axf89a0988047fbda5@mail.gmail.com>

On Sat, Aug 1, 2009 at 7:47 AM, Emmanuelle
Gouillart<emmanuelle.gouillart at normalesup.org> wrote:
> ? ? ? ?Hi Martin,
>
> ? ? ? ?I'm not sure if I understand what you mean, but does the
> following solve your problem?
>
>>>> a = np.array([(1,0,2,5),(1,5,6,2),(1,2,8,7),(1,4,4,6),(1,3,2,3),(1,11,2,0),(1,10,1,3),(1,9,0,4),(1,8,9,6)])
>>>> a
> array([[ 1, ?0, ?2, ?5],
> ? ? ? [ 1, ?5, ?6, ?2],
> ? ? ? [ 1, ?2, ?8, ?7],
> ? ? ? [ 1, ?4, ?4, ?6],
> ? ? ? [ 1, ?3, ?2, ?3],
> ? ? ? [ 1, 11, ?2, ?0],
> ? ? ? [ 1, 10, ?1, ?3],
> ? ? ? [ 1, ?9, ?0, ?4],
> ? ? ? [ 1, ?8, ?9, ?6]])
>>>> a[:,:2] = np.sort(a[:,:2], axis=0)
>>>> a
> array([[ 1, ?0, ?2, ?5],
> ? ? ? [ 1, ?2, ?6, ?2],
> ? ? ? [ 1, ?3, ?8, ?7],
> ? ? ? [ 1, ?4, ?4, ?6],
> ? ? ? [ 1, ?5, ?2, ?3],
> ? ? ? [ 1, ?8, ?2, ?0],
> ? ? ? [ 1, ?9, ?1, ?3],
> ? ? ? [ 1, 10, ?0, ?4],
> ? ? ? [ 1, 11, ?9, ?6]])
>
> ? ? ? ?Cheers,
>
> ? ? ? ?Emmanuelle
>
> On Sat, Aug 01, 2009 at 03:23:47AM -0700, Martin wrote:
>> Hi,
>
>> I would ideally like to sort an multidimensional array by ?the first
>> column, but I can only manage to adjust the whole array. For
>> example...
>
>> >a = array([(1,0,2,5),(1,5,6,2),(1,2,8,7),(1,4,4,6),(1,3,2,3),(1,11,2,0),(1,10,1,3),(1,9,0,4),(1,8,9,6)])
>> >a
>> array([[ 1, ?0, ?2, ?5],
>> ? ? ? ?[ 1, ?5, ?6, ?2],
>> ? ? ? ?[ 1, ?2, ?8, ?7],
>> ? ? ? ?[ 1, ?4, ?4, ?6],
>> ? ? ? ?[ 1, ?3, ?2, ?3],
>> ? ? ? ?[ 1, 11, ?2, ?0],
>> ? ? ? ?[ 1, 10, ?1, ?3],
>> ? ? ? ?[ 1, ?9, ?0, ?4],
>> ? ? ? ?[ 1, ?8, ?9, ?6]])
>> >sort(a, axis = 0)
>> array([[ 1, ?0, ?0, ?0],
>> ? ? ? ?[ 1, ?2, ?1, ?2],
>> ? ? ? ?[ 1, ?3, ?2, ?3],
>> ? ? ? ?[ 1, ?4, ?2, ?3],
>> ? ? ? ?[ 1, ?5, ?2, ?4],
>> ? ? ? ?[ 1, ?8, ?4, ?5],
>> ? ? ? ?[ 1, ?9, ?6, ?6],
>> ? ? ? ?[ 1, 10, ?8, ?6],
>> ? ? ? ?[ 1, 11, ?9, ?7]])
>
>> When really what i wanted was not for the 3rd and 4th columns to also
>> be sorted nunmerically in that way. In unix I would reformat the first
>> two columns in gawk i.e. gawk '{printf("%2.3d %2.3d, %d, %d\n", $1,
>> $2, $3, $4)}' | sort -n -k 1
>
>> Any help would be appreciated!
>
>> Thanks
>
>> Martin
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>

or maybe: sorting rows by the last column (since it is not sorted already)

>>> a = np.array([(1,0,2,5),(1,5,6,2),(1,2,8,7),(1,4,4,6),(1,3,2,3),(1,11,2,0),(1,10,1,3),(1,9,0,4),(1,8,9,6)])
>>> ind = np.argsort(a[:,-1])
>>> a[ind,:]
array([[ 1, 11,  2,  0],
       [ 1,  5,  6,  2],
       [ 1,  3,  2,  3],
       [ 1, 10,  1,  3],
       [ 1,  9,  0,  4],
       [ 1,  0,  2,  5],
       [ 1,  4,  4,  6],
       [ 1,  8,  9,  6],
       [ 1,  2,  8,  7]])

Josef


From emmanuelle.gouillart at normalesup.org  Sat Aug  1 10:08:02 2009
From: emmanuelle.gouillart at normalesup.org (Emmanuelle Gouillart)
Date: Sat, 1 Aug 2009 16:08:02 +0200
Subject: [SciPy-User] Filling a function with values
In-Reply-To: <91d218430907310959q5590b5d7kd29a97689a27a5e@mail.gmail.com>
References: <91d218430907310959q5590b5d7kd29a97689a27a5e@mail.gmail.com>
Message-ID: <20090801140802.GD10485@phare.normalesup.org>

	Hi Jose,

	I can see two types of answers, depending on what's inside your
function MyFunction. 

	Either the function contains only simple operations like basic
arithmetic operations, or more generally ufuncs (numpy's "universal
functions"), then it's possible that you can take advantage of
broadcasting (see
http://www.scipy.org/Tentative_NumPy_Tutorial#head-081b5e0ff3963e102f4bb39b39261750b507895e
for more information about broadcasting). Here is a small example

>>> import numpy as np
>>> Y, X = np.ogrid[0:5, 0:6]
>>> X
array([[0, 1, 2, 3, 4, 5]])
>>> Y
array([[0],
       [1],
       [2],
       [3],
       [4]])
>>> def add_plus_one(x, y, a=1):
    return x + y + a
... 
>>> add_plus_one(X, Y)
array([[ 1,  2,  3,  4,  5,  6],
       [ 2,  3,  4,  5,  6,  7],
       [ 3,  4,  5,  6,  7,  8],
       [ 4,  5,  6,  7,  8,  9],
       [ 5,  6,  7,  8,  9, 10]])

Note that I have used ogrid instead of mgrid here, because I knew the
flat arrays would be broadcast to 5x6 arrays. Computations can be much faster
when you use broadcasting.

However, if you do more complex operations on p1, p2, p3 you can't always use
braodcasting. Then you can create a new "vectorized" function which will
be an instance of the numpy.vectorize class. Here'a another example
(quite far-feched, I'm afraid...):

>>> show()
>>> def sig_rand(n):
    a = np.random.rand(n)
    return a.std()
... 
>>> vec_sig = np.vectorize(sig_rand)
>>> vec_sig(np.arange(10))
array([ 0.        ,  0.        ,  0.09590652,  0.24932485,  0.22653281,
        0.19517703,  0.30371537,  0.24547412,  0.31915861,  0.27624239])

Nevertheless, I don't think vectorize speeds up the code compared to a
for loop (unless it can do some broadcasting). It just allows to write
more compact code. 

If you can't use broadcasting, you were right to use mgrid instead of
ogrid, but check first if you can't do some broadcasting.

Cheers,

Emmanuelle

On Fri, Jul 31, 2009 at 05:59:51PM +0100, Jose Gomez-Dans wrote:
>    Hi,
>    This is a really quick and stupid question. Let's say I have a function
>    and I want to calculate its value for a set of points. What's the best way
>    of quickly doing this avoiding loops?

>    def MyFunction ( p1, p2, p3, t, y)
>    ? return *some_value*

>    t = numpy.array ( ..... )
>    y = numpy.array ( ....)
>    a1,a2,a3 = numpy.mgrid [ 0:100, 0:1:100j, 0:100 ]

>    evaluated_function = MyFunction (a1, a2, a3, t, y)

>    evaluated_function.shape would be (100,100,100), and evaluated_function
>    [m, n, k] should be equal to
>    MyFunction ( a1[m], a2[n], a3[k], t, y )

>    I think mgrid (ogrid) are the right tools, but I don't really understand
>    how I can get it to do what I want. I have read
>    <[1]http://docs.scipy.org/doc/scipy/reference/tutorial/basic.html#id5>,
>    but that doesn't clear things up. I guess that what I want to do is just
>    mentioned at the end of this Section of the tutorial, but can anyone maybe
>    give an example?

>    many thanks!
>    J

> References

>    Visible links
>    1. http://docs.scipy.org/doc/scipy/reference/tutorial/basic.html#id5

> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user



From zunzun at zunzun.com  Sat Aug  1 10:58:14 2009
From: zunzun at zunzun.com (James Phillips)
Date: Sat, 1 Aug 2009 09:58:14 -0500
Subject: [SciPy-User] Attached multiprocessing parallel Differential
	Evolution GA code
Message-ID: <268756d30908010758q14c9bca2w7b1d6b06e76db707@mail.gmail.com>

Below and attached is my first cut at a Python multiprocessing module
parallel implementation of the Differential Evolution genetic algorithm.
The code is explicitly in the public domain.

While this does run in parallel processes, I do not see 100% CPU utilization
for the child processes on my 4-core test server.  I suspect this is due to
locking on the multiprocessing.Array objects, where the entire array is
being locked at each access rather than only the requested elements in the
array.

Note the "coefficient polishing on" option's use of scipy.optimize.fmin at
each new "best solution" during iteration of the genetic algorithm, this
vastly speeds up the GA runs.

I did not cross-post to the numpy discussion list, not sure if I should.

     James Phillips
     zunzun AT zunzun.com
     http://zunzun.com


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

#    testParallelDESolver.py
#
#    Placed into the public domain 1 August, 2009
#    James R. Phillips
#    2548 Vera Cruz Drive
#    Birmingham, AL 35235 USA
#    email: zunzun at zunzun.com
#    http://zunzun.com

import ParallelDESolver, numpy, time

class TestSolver(ParallelDESolver.ParallelDESolver):

    #some test data
    xData = numpy.array([5.357, 9.861, 5.457, 5.936, 6.161, 6.731])
    yData = numpy.array([0.376, 7.104, 0.489, 1.049, 1.327, 2.077])


    def externalEnergyFunction(self, trial):
        # inverse exponential with offset, y = a * exp(b/x) + c
        predicted = trial[0] * numpy.exp(trial[1] / self.xData) + trial[2]

        # sum of squared error
        error = predicted - self.yData
        return numpy.sum(error*error)



if __name__ == '__main__':

    print
    print '  Running test generating new random numbers and coefficient
polisher off, estimated worst case time'
    solver = TestSolver(4, 600, 600, -10, 10, "Rand2Exp", 0.7, 0.6, 0.01,
False, False)
    tStart = time.time()
    atSolution, generations, bestEnergy, bestSolution = solver.Solve()
    print ' ', time.time() - tStart, 'seconds elapsed.  atSolution =',
atSolution, ':', generations, 'generations passed, bestEnergy =', bestEnergy
    print '  coefficients:',
    for i in bestSolution:
        print i,
    print
    print

    print '  Running test generating new random numbers and coefficient
polisher on, estimated medium case time'
    solver = TestSolver(4, 600, 600, -10, 10, "Rand2Exp", 0.7, 0.6, 0.01,
False, True)
    tStart = time.time()
    atSolution, generations, bestEnergy, bestSolution = solver.Solve()
    print ' ', time.time() - tStart, 'seconds elapsed.  atSolution =',
atSolution, ':', generations, 'generations passed, bestEnergy =', bestEnergy
    print '  coefficients:',
    for i in bestSolution:
        print i,
    print
    print

    print '  Running test recycling random numbers and coefficient polisher
on, estimated best case time'
    solver = TestSolver(4, 600, 600, -10, 10, "Rand2Exp", 0.7, 0.6, 0.01,
True, True)
    tStart = time.time()
    atSolution, generations, bestEnergy, bestSolution = solver.Solve()
    print ' ', time.time() - tStart, 'seconds elapsed.  atSolution =',
atSolution, ':', generations, 'generations passed, bestEnergy =', bestEnergy
    print '  coefficients:',
    for i in bestSolution:
        print i,
    print
    print


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


#    ParallelDESolver.py
#
#    Placed into the public domain 1 August, 2009
#    James R. Phillips
#    2548 Vera Cruz Drive
#    Birmingham, AL 35235 USA
#    email: zunzun at zunzun.com
#    http://zunzun.com


import sys, os, numpy, random, scipy.optimize, multiprocessing


def parallelProcessPoolInitializer(in_sharedMemoryPopulation,
in_sharedMemoryPopulationEnergies, in_sharedMemoryBestEnergy,
in_sharedMemoryBestSolution, in_sharedMemoryAtSolution,
in_sharedMemoryGeneration, in_solver):
    global sharedMemoryPopulation
    global sharedMemoryPopulationEnergies
    global sharedMemoryBestEnergy
    global sharedMemoryBestSolution
    global sharedMemoryAtSolution
    global sharedMemoryGeneration
    global solver

    sharedMemoryPopulation = in_sharedMemoryPopulation
    sharedMemoryPopulationEnergies = in_sharedMemoryPopulationEnergies
    sharedMemoryBestEnergy = in_sharedMemoryBestEnergy
    sharedMemoryBestSolution = in_sharedMemoryBestSolution
    sharedMemoryAtSolution = in_sharedMemoryAtSolution
    sharedMemoryGeneration = in_sharedMemoryGeneration
    solver = in_solver


def parallelProcessFunction(candidate):
    global sharedMemoryPopulation
    global sharedMemoryPopulationEnergies
    global sharedMemoryBestEnergy
    global sharedMemoryBestSolution
    global sharedMemoryAtSolution
    global sharedMemoryGeneration
    global solver

    if sharedMemoryAtSolution.value != 0: # has some other process found a
solution?
        return

    eval('solver.' + solver.deStrategy + '(candidate)')
    trialEnergy, atSolution = solver.EnergyFunction(solver.trialSolution)

    if solver.polishTheBestTrials == True and trialEnergy <
sharedMemoryBestEnergy.value and sharedMemoryGeneration.value > 0: # not the
first generation
        # try to polish these new coefficients a bit.
        solver.trialSolution =
scipy.optimize.fmin(solver.externalEnergyFunction, solver.trialSolution,
disp = 0) # don't print warning messages to stdout
        trialEnergy, atSolution =
solver.EnergyFunction(solver.trialSolution) # recalc with polished
coefficients

    if trialEnergy < sharedMemoryPopulationEnergies[candidate]: # setting
two shared memory objects, need to prevent contention here
        sharedMemoryPopulationEnergies[candidate] = trialEnergy
        for i in range(len(solver.trialSolution)):
            sharedMemoryPopulation[candidate * solver.parameterCount + i] =
solver.trialSolution[i]

        # If at an all-time low, save to "best"
        if trialEnergy < sharedMemoryBestEnergy.value: # setting two shared
memory objects, need to prevent contention here
            #print 'New best energy in generation',
sharedMemoryGeneration.value, 'os.getpid() =', os.getpid(), 'new best
energy', trialEnergy
            sharedMemoryBestEnergy.value = trialEnergy
            for i in range(len(solver.trialSolution)):
                sharedMemoryBestSolution[i] = solver.trialSolution[i]

    # if we've reached a sufficient solution, let the other processes know
    if atSolution == True:
        #print os.getpid(), 'At solution.'
        sharedMemoryAtSolution.value = 1


class ParallelDESolver:

    def __init__(self, parameterCount, populationSize, maxGenerations,
minInitialValue, maxInitialValue, deStrategy, diffScale, crossoverProb,
cutoffEnergy, useClassRandomNumberMethods, polishTheBestTrials):

        self.polishTheBestTrials = polishTheBestTrials # see the Solve
method where this flag is used
        self.maxGenerations      = maxGenerations
        self.parameterCount      = parameterCount
        self.populationSize      = populationSize
        self.cutoffEnergy        = cutoffEnergy
        self.minInitialValue     = minInitialValue
        self.maxInitialValue     = maxInitialValue
        self.deStrategy          = deStrategy # deStrategy is the name of
the DE function to use
        self.useClassRandomNumberMethods = useClassRandomNumberMethods

        self.scale = diffScale
        self.crossOverProbability = crossoverProb

        self.generation = 1 # for testing, actual value set on the Solve()
method

        if True == self.useClassRandomNumberMethods:
            self.SetupClassRandomNumberMethods(3)


    def Solve(self):

        # a random initial population in shared memory
        sharedMemoryPopulation = multiprocessing.Array('d', [0.0] *
(self.populationSize * self.parameterCount))
        for i in range(self.populationSize * self.parameterCount):
            sharedMemoryPopulation[i] = random.uniform(self.minInitialValue,
self.maxInitialValue)

        # initial population assumed to have high energies
        sharedMemoryPopulationEnergies = multiprocessing.Array('d',
[1.0E300] * self.populationSize)

        # a shared memory "best energy" for internal comparisons
        sharedMemoryBestEnergy = multiprocessing.Value('d', 1.0E300)

        # best solution, will be copied into by worker processes
        sharedMemoryBestSolution = multiprocessing.Array('d', [0.0] *
self.parameterCount)

        # if solution has been reached, we are done
        sharedMemoryAtSolution = multiprocessing.Value('i', 0)

        # allows check for "generation 0"
        sharedMemoryGeneration = multiprocessing.Value('i', 0)


        initialArguments = [sharedMemoryPopulation,
sharedMemoryPopulationEnergies, sharedMemoryBestEnergy,
sharedMemoryBestSolution, sharedMemoryAtSolution, sharedMemoryGeneration,
self]
        processPool =
multiprocessing.Pool(initializer=parallelProcessPoolInitializer,
initargs=initialArguments)

        for sharedMemoryGeneration.value in range(self.maxGenerations):
            #print 'new gen'
            if sharedMemoryAtSolution.value != 0:
                break
            processPool.map(parallelProcessFunction,
range(self.populationSize))


        return sharedMemoryAtSolution.value, sharedMemoryGeneration.value,
sharedMemoryBestEnergy.value, sharedMemoryBestSolution


    def SetupClassRandomNumberMethods(self, in_RandomSeed):
        #print 'Generating random numbers for recycling'
        numpy.random.seed(in_RandomSeed) # this yields same results each
time Solve() is run
        self.nonStandardRandomCount = self.populationSize *
self.parameterCount * 3 + 1 # the "+1" makes an odd number of array items
        if self.nonStandardRandomCount < 523: # set a minimum number of
random numbers
            self.nonStandardRandomCount = 523

        self.ArrayOfRandomIntegersBetweenZeroAndParameterCount =
numpy.random.random_integers(0, self.parameterCount-1,
size=(self.nonStandardRandomCount))
        self.ArrayOfRandomRandomFloatBetweenZeroAndOne =
numpy.random.uniform(size=(self.nonStandardRandomCount))
        self.ArrayOfRandomIntegersBetweenZeroAndPopulationSize =
numpy.random.random_integers(0, self.populationSize-1,
size=(self.nonStandardRandomCount))
        self.randCounter1 = 0
        self.randCounter2 = 0
        self.randCounter3 = 0


    def GetClassRandomIntegerBetweenZeroAndParameterCount(self):
        self.randCounter1 += 1
        if self.randCounter1 >= self.nonStandardRandomCount:
            self.randCounter1 = 0
        return
self.ArrayOfRandomIntegersBetweenZeroAndParameterCount[self.randCounter1]

    def GetClassRandomFloatBetweenZeroAndOne(self):
        self.randCounter2 += 1
        if self.randCounter2 >= self.nonStandardRandomCount:
            self.randCounter2 = 0
        return
self.ArrayOfRandomRandomFloatBetweenZeroAndOne[self.randCounter2]

    def GetClassRandomIntegerBetweenZeroAndPopulationSize(self):
        self.randCounter3 += 1
        if self.randCounter3 >= self.nonStandardRandomCount:
            self.randCounter3 = 0
        return
self.ArrayOfRandomIntegersBetweenZeroAndPopulationSize[self.randCounter3]


    # this class might normally be subclassed and this method overridden, or
the
    # externalEnergyFunction set and this method used directly
    def EnergyFunction(self, trial):
        try:
            energy = self.externalEnergyFunction(trial)
        except ArithmeticError:
            energy = 1.0E300 # high energies for arithmetic exceptions
        except FloatingPointError:
            energy = 1.0E300 # high energies for floating point exceptions

        # we will be "done" if the energy is less than or equal to the
cutoff energy
        if energy <= self.cutoffEnergy:
            return energy, True
        else:
            return energy, False

    def Best1Exp(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,0,0,0)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate *
self.parameterCount  :  candidate * self.parameterCount +
self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] = self.bestSolution[n] + self.scale *
(sharedMemoryPopulation[r1 * self.parameterCount + n] -
sharedMemoryPopulation[r2 * self.parameterCount + n])
            n = (n + 1) % self.parameterCount
            i += 1


    def Rand1Exp(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,1,0,0)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate *
self.parameterCount  :  candidate * self.parameterCount +
self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] = sharedMemoryPopulation[r1 *
self.parameterCount + n] + self.scale * (sharedMemoryPopulation[r2 *
self.parameterCount + n] - sharedMemoryPopulation[r3 * self.parameterCount +
n])
            n = (n + 1) % self.parameterCount
            i += 1


    def RandToBest1Exp(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,0,0,0)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate *
self.parameterCount  :  candidate * self.parameterCount +
self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] += self.scale * (self.bestSolution[n] -
self.trialSolution[n]) + self.scale * (sharedMemoryPopulation[r1 *
self.parameterCount + n] - sharedMemoryPopulation[r2 * self.parameterCount +
n])
            n = (n + 1) % self.parameterCount
            i += 1


    def Best2Exp(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,1,1,0)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate *
self.parameterCount  :  candidate * self.parameterCount +
self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] = self.bestSolution[n] + self.scale *
(sharedMemoryPopulation[r1 * self.parameterCount + n] +
sharedMemoryPopulation[r2 * self.parameterCount + n] -
sharedMemoryPopulation[r3 * self.parameterCount + n] -
sharedMemoryPopulation[r4 * self.parameterCount + n])
            n = (n + 1) % self.parameterCount
            i += 1


    def Rand2Exp(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,1,1,1)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate *
self.parameterCount  :  candidate * self.parameterCount +
self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] = sharedMemoryPopulation[r1 *
self.parameterCount + n] + self.scale * (sharedMemoryPopulation[r2 *
self.parameterCount + n] + sharedMemoryPopulation[r3 * self.parameterCount +
n] - sharedMemoryPopulation[r4 * self.parameterCount + n] -
sharedMemoryPopulation[r5 * self.parameterCount + n])
            n = (n + 1) % self.parameterCount
            i += 1


    def Best1Bin(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,0,0,0)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate *
self.parameterCount  :  candidate * self.parameterCount +
self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] = self.bestSolution[n] + self.scale *
(sharedMemoryPopulation[r1 * self.parameterCount + n] -
sharedMemoryPopulation[r2 * self.parameterCount + n])
            n = (n + 1) % self.parameterCount
            i += 1


    def Rand1Bin(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,1,0,0)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate *
self.parameterCount  :  candidate * self.parameterCount +
self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] = sharedMemoryPopulation[r1 *
self.parameterCount + n] + self.scale * (sharedMemoryPopulation[r2 *
self.parameterCount + n] - sharedMemoryPopulation[r3 * self.parameterCount +
n])
            n = (n + 1) % self.parameterCount
            i += 1


    def RandToBest1Bin(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,0,0,0)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate *
self.parameterCount  :  candidate * self.parameterCount +
self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] += self.scale * (self.bestSolution[n] -
self.trialSolution[n]) + self.scale * (sharedMemoryPopulation[r1 *
self.parameterCount + n] - sharedMemoryPopulation[r2 * self.parameterCount +
n])
            n = (n + 1) % self.parameterCount
            i += 1


    def Best2Bin(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,1,1,0)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate *
self.parameterCount  :  candidate * self.parameterCount +
self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] = self.bestSolution[n] + self.scale *
(sharedMemoryPopulation[r1 * self.parameterCount + n] +
sharedMemoryPopulation[r2 * self.parameterCount + n] -
sharedMemoryPopulation[r3 * self.parameterCount + n] -
sharedMemoryPopulation[r4 * self.parameterCount + n])
            n = (n + 1) % self.parameterCount
            i += 1


    def Rand2Bin(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,1,1,1)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate *
self.parameterCount  :  candidate * self.parameterCount +
self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] = sharedMemoryPopulation[r1 *
self.parameterCount + n] + self.scale * (sharedMemoryPopulation[r2 *
self.parameterCount + n] + sharedMemoryPopulation[r3 * self.parameterCount +
n] - sharedMemoryPopulation[r4 * self.parameterCount + n] -
sharedMemoryPopulation[r5 * self.parameterCount + n])
            n = (n + 1) % self.parameterCount
            i += 1


    def SelectSamples(self, candidate, r1, r2, r3, r4, r5):
        if r1:
            while(1):
                if True == self.useClassRandomNumberMethods:
                    r1 =
self.GetClassRandomIntegerBetweenZeroAndPopulationSize()
                else:
                    r1 = random.randint(0, self.populationSize-1)
                if r1 != candidate:
                    break
        if r2:
            while(1):
                if True == self.useClassRandomNumberMethods:
                    r2 =
self.GetClassRandomIntegerBetweenZeroAndPopulationSize()
                else:
                    r2 = random.randint(0, self.populationSize-1)
                if r2 != candidate and r2 != r1:
                    break
        if r3:
            while(1):
                if True == self.useClassRandomNumberMethods:
                    r3 =
self.GetClassRandomIntegerBetweenZeroAndPopulationSize()
                else:
                    r3 = random.randint(0, self.populationSize-1)
                if r3 != candidate and r3 != r1 and r3 != r2:
                    break
        if r4:
            while(1):
                if True == self.useClassRandomNumberMethods:
                    r4 =
self.GetClassRandomIntegerBetweenZeroAndPopulationSize()
                else:
                    r4 = random.randint(0, self.populationSize-1)
                if r4 != candidate and r4 != r1 and r4 != r2 and r4 != r3:
                    break
        if r5:
            while(1):
                if True == self.useClassRandomNumberMethods:
                    r5 =
self.GetClassRandomIntegerBetweenZeroAndPopulationSize()
                else:
                    r5 = random.randint(0, self.populationSize-1)
                if r5 != candidate and r5 != r1 and r5 != r2 and r5 != r3
and r5 != r4:
                    break

        return r1, r2, r3, r4, r5
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From ebonak at hotmail.com  Sat Aug  1 12:13:35 2009
From: ebonak at hotmail.com (Esmail)
Date: Sat, 01 Aug 2009 12:13:35 -0400
Subject: [SciPy-User] gnuplot.py xrange/shifting graph not working
Message-ID: <h51pjg$coc$1@ger.gmane.org>

Hi

Need some basic help with Gnuplot.py - it must a simple thing I'm not
getting.


I can't get xrange to work as expected, the axis shifts, but the data
doesn't get plotted accordingly. Best explained with an example. If
you enter the following in gnuplot directly/interactively:

----------------------
gnuplot> set xrange[-5:5]
gnuplot> plot x*x
----------------------


This is what I want to be able to do via a python script where I pass in
the data for x and y in list.

I have the following code:

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

#!/usr/bin/env python

import Gnuplot

g = Gnuplot.Gnuplot(debug=1)

x = range(-5,5)
y = []
for i in x:
     y.append(i*i)

g('set style data linespoint')
#g('set xrange[-5:5]')
g.plot(y)

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

the plot is centered around 5 (tick marks go from 0 to 10). If I use

g('set xrange[-5:5]')

to center it around 0, I only get half of the graph (tick marks go
from -5 to 5).

I also tried g.plot(x, y) w/o luck

Is there a way to get this to work as the interactive gnuplot example
above does?

I think I may be missing something obvious ...

Thanks!



From cournape at gmail.com  Sat Aug  1 12:21:10 2009
From: cournape at gmail.com (David Cournapeau)
Date: Sun, 2 Aug 2009 01:21:10 +0900
Subject: [SciPy-User] SciPy Foundation
In-Reply-To: <wl8fxcc6aua.fsf@glup.physics.ucf.edu>
References: <wl8fxcc6aua.fsf@glup.physics.ucf.edu>
Message-ID: <5b8d13220908010921m49b218f2ga13b5b75a2aebaff@mail.gmail.com>

Hi Joe,

On Sat, Aug 1, 2009 at 2:06 AM, Joe Harrington<jh at physics.ucf.edu> wrote:

>
> I define success as popular adoption in preference to commercial
> packages. ?I believe in vote-with-your-feet: this goal will not be
> reached until all aspects of the package and its presentation to the
> world exceed those of our commercial competition. ?Scipy is now a
> grass roots effort, but that takes it only so far. ?Other projects,
> such as OpenOffice and Sage, don't follow this model and do produce
> quality products that compete with commercial offerings, at least on
> open-source platforms.

I am not sure openoffice is a good example, but I share the sentiment
that something is missing in the organization of the community.

I think it is very important to keep in mind that in any open source
project, telling people what to do does not work well. Not everybody
will share the same goals, are interested in scipy in the same way,
etc...  So any structure should help people doing what they want for
scipy's sake, but above all, should not alienate anyone who would have
worked on scipy otherwise. It may just be rhetoric, but saying that
"it would be nice for scipy to have this goal" instead of "we should
do this" matters IMHO.

Some of the things I am missing:
 - no quantifiable feedback from users: if we want to work on a set of
features, we cannot prioritize.  Likewise, we have very little
statistics on usage, platforms, etc... OTOH, this is often hard to
obtain for open source projects.
 - a scipy foundation: several times already, I have been asked
privately to do add some feature to scipy, generally things which
takes a few hours max, in exchange for some money. It is too much of a
hassle to set up things to get money for a few hours work, and
frankly, for a few hours, I would prefer to ask people to give money
to a scipy foundation instead. Something like the R foundation
(http://www.r-project.org/foundation/main.html). A foundation with a
legal status would make the situation much easier w.r.t donations I
believe. It should not be that hard to set up.
 - website: I think the root of the problem is lack of a dedicated
person for it, a person with design skills ideally, to design a
coherent graphic "chart" (not sure about the exact English word),
etc... I don't know how to get volunteers for this: it seems like many
projects manage to have such volunteers.

About the more particular points you raised:

> - Packaging
> ?- Personal Package Archive or equivalent for every release of every
> ? ?OS for the full toolstack (There are tools that do this but we
> ? ?don't use them. ?NSF requires Metronome - http://nmi.cs.wisc.edu/
> ? ?- for funding most development grants, so right now we're not even
> ? ?on NSF's radar.)
> ?- Track record of having the whole toolstack installation "just
> ? ?work" in a few command lines or clicks for *everyone*
> ?- Regular, scheduled releases of numpy and scipy
> ?- Coordinated releases of numpy, scipy, and stable scikits into PPA system

The problem of packaging is that it is hard to do well, but has no
technically challenging part in it. And it usually does not fall into
the "scratching ones' itch", because once you know how to build the
software, you are done and usually want to start using the damn thing.
Worse, it needs to be done every-time (every release). So this is
fundamentally different than doc: having done a great packaging work
for version N is useless after N+1 is out. It does not make sense to
pay someone to do it once.

Having some infrastructure would help: for example, something which
automatically builds packages on a set of supported platforms. It has
to be 100 % automatic, so that pushing one button get you the sources,
build the package, install it, and test it. This costs money and time
to set up.

> - Public communication
> ?- A real marketing plan
> ?- Executing on that plan
> ?- Web site geared toward multiple audiences, run by experts at that
> ? ?kind of communication
> ?- More webinars, conference booths, training, aimed at all levels
> ?- Demos, testimonials, topical forums, all showcased

Concerning communication with users, I think that the mailing lists do
not work well. It is ok for development, but it kinda sucks for
helping average users. Since I have been working on the dark side for
numpy/scipy- windows, I have been regularly using stackoverflow to ask
for some obscure windows stuff. stackoverflow is a a mix between a FAQ
and wikipedia. It works extremely well, and the user experience is way
above anything I have seen in this vein. Something like this to use
for scipy/numpy would be extremely useful I believe. It is vastly
superior to ML or wiki for focused problems ("how to do this in
matlab", "how to install on this linux distribution", etc...).

As an example of usage, R has recently used the main website so that
the most upvoted N R questions would be answered by R core developers
(during a R conference I believe). This all feels much better than ML
to me (again, as far as average user usage is concerned, not for
developer communication).

One website to handle all the user community, no need for complicated
forum rules and all (everything works with search and tags).
Stackoverflow works without any fixed hierarchy for many times more
participants that we will ever have, and much broader topics than us.

They will have soon a dedicated solution for custom websites using the
same stack - maybe something can be worked on as a open source
project.

David


From ebonak at hotmail.com  Sat Aug  1 12:36:51 2009
From: ebonak at hotmail.com (Esmail)
Date: Sat, 01 Aug 2009 12:36:51 -0400
Subject: [SciPy-User] gnuplot.py xrange/shifting graph not working
In-Reply-To: <h51pjg$coc$1@ger.gmane.org>
References: <h51pjg$coc$1@ger.gmane.org>
Message-ID: <h51qv3$ft2$1@ger.gmane.org>

Well .. technically, this

x = range(-5,5)

should really be

x = range(-5,6)

but still doesn't solve the problem ...



From vanforeest at gmail.com  Sat Aug  1 16:19:11 2009
From: vanforeest at gmail.com (nicky van foreest)
Date: Sat, 1 Aug 2009 22:19:11 +0200
Subject: [SciPy-User] gnuplot.py xrange/shifting graph not working
In-Reply-To: <h51qv3$ft2$1@ger.gmane.org>
References: <h51pjg$coc$1@ger.gmane.org> <h51qv3$ft2$1@ger.gmane.org>
Message-ID: <fa510ff80908011319u5c25448es58f8d08b1e4a9793@mail.gmail.com>

This works for me. Mind that data is a list of lists, while your y is
just a list. Hence gnuplot plotted y as a function of its index, not
of x.


#!/usr/bin/env python

import Gnuplot

g = Gnuplot.Gnuplot()

x = range(-5,6)
data = []
for i in x:
    y.append(i*i)
    data.append([i,i*i])

g('set style data linespoint')
#g('set xrange[-5:5]')
g.plot(data)
g.hardcopy("elephant.ps")


For some reason Gnuplot.Gnuplot(persist = 1) does not work for me.
However, I view my results with gv. For this set

chuck~>cat .gv
GV.antialias:           True
GV.watchFile:           True
GV.watchFileFrequency:  500
GV.version:             gv 3.6.5.91
chuck~>

Hope this helps

Nicky



2009/8/1 Esmail <ebonak at hotmail.com>:
> Well .. technically, this
>
> x = range(-5,5)
>
> should really be
>
> x = range(-5,6)
>
> but still doesn't solve the problem ...
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From pgmdevlist at gmail.com  Sat Aug  1 16:44:11 2009
From: pgmdevlist at gmail.com (Pierre GM)
Date: Sat, 1 Aug 2009 16:44:11 -0400
Subject: [SciPy-User] expectation function for stats.distributions
In-Reply-To: <1cd32cbb0907311310g59c4f2a5uf9f328acba0bad07@mail.gmail.com>
References: <1cd32cbb0907310901oe3e7ca4r74586845222acdbc@mail.gmail.com>
	<fa510ff80907311210k7f0cd0a9y798e1bbeeeba471e@mail.gmail.com>
	<1cd32cbb0907311310g59c4f2a5uf9f328acba0bad07@mail.gmail.com>
Message-ID: <EDABE9A4-0155-43D4-9E20-D8501E91AA97@gmail.com>


On Jul 31, 2009, at 4:10 PM, josef.pktd at gmail.com wrote:

> On Fri, Jul 31, 2009 at 3:10 PM, nicky van foreest<vanforeest at gmail.com 
> > wrote:
>> Hi Josef,
>>
>> I would like such a function.
>
> Thanks for the interest.

Count me in as well

> To the name: I'm up for ideas

rv_generic.expect ?

In other stats.distributions news:
* I added some extra distributions to hydroclimpy (http://hydroclimpy.sourceforge.net/ 
), along wrappings around Hosking's routines for the computation of L- 
moments. Any interest to put some of it in scipy.stats.distributions ?
* Do we have some kind of template to write the documentation of  
distributions ? Something like mathworld/wikipedia ?



From josef.pktd at gmail.com  Sat Aug  1 17:39:40 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sat, 1 Aug 2009 17:39:40 -0400
Subject: [SciPy-User] expectation function for stats.distributions
In-Reply-To: <EDABE9A4-0155-43D4-9E20-D8501E91AA97@gmail.com>
References: <1cd32cbb0907310901oe3e7ca4r74586845222acdbc@mail.gmail.com>
	<fa510ff80907311210k7f0cd0a9y798e1bbeeeba471e@mail.gmail.com>
	<1cd32cbb0907311310g59c4f2a5uf9f328acba0bad07@mail.gmail.com>
	<EDABE9A4-0155-43D4-9E20-D8501E91AA97@gmail.com>
Message-ID: <1cd32cbb0908011439x1cce637ep304ee32b269a1f47@mail.gmail.com>

On Sat, Aug 1, 2009 at 4:44 PM, Pierre GM<pgmdevlist at gmail.com> wrote:
>
> On Jul 31, 2009, at 4:10 PM, josef.pktd at gmail.com wrote:
>
>> On Fri, Jul 31, 2009 at 3:10 PM, nicky van foreest<vanforeest at gmail.com
>> > wrote:
>>> Hi Josef,
>>>
>>> I would like such a function.
>>
>> Thanks for the interest.
>
> Count me in as well
>
>> To the name: I'm up for ideas
>
> rv_generic.expect ?

I think that's my favorite so far.
It will be attached to continuous and discrete separately (not to
generic) because of the difference in implementation.
I added loc and scale to the expectation function.
but for discrete distributions following the pattern of _drv2_moment
for discrete might not be the best implementation.

>
> In other stats.distributions news:
> * I added some extra distributions to hydroclimpy (http://hydroclimpy.sourceforge.net/
> ), along wrappings around Hosking's routines for the computation of L-
> moments. Any interest to put some of it in scipy.stats.distributions ?

Yes, extra distributions, if they follow the standard pattern, could
be included in the tests by adding the name and one set of parameters.

I also have skewnormal and skewt, and there are some interesting ones
(some multidimensional) in pymc. But I'm faster fixing bugs than
adding new things, and my time is still mostly tied up with the
stats.models rewrite in the gsoc.

A not fully cleaned up version of my code is at
http://bazaar.launchpad.net/~josef-pktd/scipy/scipytrunkwork2/annotate/head%3A/scipy/stats/distribution_extras.py

> * Do we have some kind of template to write the documentation of
> distributions ? Something like mathworld/wikipedia ?

Currently, the only distribution specific information is in the
extradocs. The main background information are the two pdf files by
Travis.

I was just wondering how we could include them in the docs. I needed
them to find the exact definition of the ppf for the discrete case,
since I never remember which way the inequalities go.

Josef

>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From neilcrighton at gmail.com  Sat Aug  1 18:26:52 2009
From: neilcrighton at gmail.com (Neil Crighton)
Date: Sat, 1 Aug 2009 22:26:52 +0000 (UTC)
Subject: [SciPy-User] sorting an array
References: <b6655146-359b-40a3-bb52-8275783f7216@24g2000yqm.googlegroups.com>
Message-ID: <loom.20090801T221407-521@post.gmane.org>


Martin <mdekauwe <at> gmail.com> writes:

> When really what i wanted was not for the 3rd and 4th columns to also
> be sorted nunmerically in that way. In unix I would reformat the first
> two columns in gawk i.e. gawk '{printf("%2.3d %2.3d, %d, %d\n", $1,
> $2, $3, $4)}' | sort -n -k 1
> 

I think lexsort does what you need. Get the sorted row indices:

>>> a
array([[ 1,  0,  2,  5],
       [ 1,  5,  6,  2],
       [ 1,  2,  8,  7],
       [ 1,  4,  4,  6],
       [ 1,  3,  2,  3],
       [ 1, 11,  2,  0],
       [ 1, 10,  1,  3],
       [ 1,  9,  0,  4],
       [ 1,  8,  9,  6]])
>>> ind = np.lexsort(a.T[::-1])

a.T gives an array of columns, and reversing them with ::-1 sorts by the
first column values, then the second column values if the first column 
values are equal, and so on. Now index the the original array:

>>> ind
array([0, 2, 4, 3, 1, 8, 7, 6, 5])
>>> a[ind]
array([[ 1,  0,  2,  5],
       [ 1,  2,  8,  7],
       [ 1,  3,  2,  3],
       [ 1,  4,  4,  6],
       [ 1,  5,  6,  2],
       [ 1,  8,  9,  6],
       [ 1,  9,  0,  4],
       [ 1, 10,  1,  3],
       [ 1, 11,  2,  0]])


Neil






From ebonak at hotmail.com  Sat Aug  1 19:19:56 2009
From: ebonak at hotmail.com (Esmail)
Date: Sat, 01 Aug 2009 19:19:56 -0400
Subject: [SciPy-User] gnuplot.py xrange/shifting graph not working
In-Reply-To: <fa510ff80908011319u5c25448es58f8d08b1e4a9793@mail.gmail.com>
References: <h51pjg$coc$1@ger.gmane.org> <h51qv3$ft2$1@ger.gmane.org>
	<fa510ff80908011319u5c25448es58f8d08b1e4a9793@mail.gmail.com>
Message-ID: <h52iit$6rp$1@ger.gmane.org>

Hi Nicky,

nicky van foreest wrote:
> This works for me. Mind that data is a list of lists, while your y is
> just a list. Hence gnuplot plotted y as a function of its index, not
> of x.

ah .. got it!

> Hope this helps

It sure did .. it works great.

I have a quick follow up though if you don't mind.
Here is a 3D version.
------------------

#!/usr/bin/env python

import Gnuplot

g = Gnuplot.Gnuplot(debug=1)
x = range(-3,4)
data = []

for i in x:
     for j in x:
         data.append([i, j, i*i])

for i in data:
     print i[0], i[1], i[2]

g('set hidden3d')
g('set style data linespoint')
g.splot(data)

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

the

     g('set style data linespoint')

leads to lines connecting that ought not to. To see what I mean run
the above code, and then compare this with plotting the data below
straight from gnuplot. It is the same as the program generates during
the run, all I have done is inserted blank lines for each block and
then saved the file under the name t2data


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

gnuplot> set style data linespoint
gnuplot> splot './t2data'

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

Is there a way to mimic this with the data list? I tried appending '\n' to
the data list, but that didn't work. If there is no easy work around I may
just write the data to a file (with the blank lines) and then read it
in again - probably not the most efficient way to do this, but it'll
work :-) ... plus that seems to be going on in the background already
anyway.


Thanks,
Esmail


--- t2data file ----
-3 -3 9
-3 -2 9
-3 -1 9
-3 0 9
-3 1 9
-3 2 9
-3 3 9

-2 -3 4
-2 -2 4
-2 -1 4
-2 0 4
-2 1 4
-2 2 4
-2 3 4

-1 -3 1
-1 -2 1
-1 -1 1
-1 0 1
-1 1 1
-1 2 1
-1 3 1

0 -3 0
0 -2 0
0 -1 0
0 0 0
0 1 0
0 2 0
0 3 0

1 -3 1
1 -2 1
1 -1 1
1 0 1
1 1 1
1 2 1
1 3 1

2 -3 4
2 -2 4
2 -1 4
2 0 4
2 1 4
2 2 4
2 3 4

3 -3 9
3 -2 9
3 -1 9
3 0 9
3 1 9
3 2 9
3 3 9



From ebonak at hotmail.com  Sat Aug  1 19:23:35 2009
From: ebonak at hotmail.com (Esmail)
Date: Sat, 01 Aug 2009 19:23:35 -0400
Subject: [SciPy-User] putting a dot on a graph with gnuplot.py
Message-ID: <h52ipo$78t$1@ger.gmane.org>

Hello all,

I am optimizing some functions using Python that I am then
plotting with gnuplot.py using the plot and splot commands.

I can generate the function graphs/surfaces ok, but I am at
a loss on how I can put a circle or some sort of marker at
a specific place on the graph or the surface (to indicate the
maximum).

Is there a way to do this? I've been using gnuplot itself for
a while, but I have no idea how to do this.

Thanks,
Esmail



From mdekauwe at gmail.com  Sat Aug  1 19:31:26 2009
From: mdekauwe at gmail.com (Martin)
Date: Sat, 1 Aug 2009 16:31:26 -0700 (PDT)
Subject: [SciPy-User] sorting an array
In-Reply-To: <loom.20090801T221407-521@post.gmane.org>
References: <b6655146-359b-40a3-bb52-8275783f7216@24g2000yqm.googlegroups.com>
	<loom.20090801T221407-521@post.gmane.org>
Message-ID: <19f36f93-9511-4442-85cf-707a262edf34@d4g2000yqa.googlegroups.com>

Hi all,

Thanks for the replies...perhaps if I explained the context it might
help (or it might not!). Basically it is an image processing problem
so the array is rows, columns. So I want to sort the array such that

row col
1   1
1   2
1   3
etc
2   1
2   2
2   3

then I can use the other columns to create images safe in the
knowledge they are in the correct row, column 2D space. I think
Emmanuelle solution does the trick from what I can see, I am just
trying it out. Although I have never seen [:,:2] what does the :2 bit
mean?

Neil your solution results in column 4 changing some of the ordering
from what I can see (the zero in the fourth column).

Thanks a lot for the suggestions

Martin



On Aug 1, 11:26?pm, Neil Crighton <neilcrigh... at gmail.com> wrote:
> Martin <mdekauwe <at> gmail.com> writes:
>
> > When really what i wanted was not for the 3rd and 4th columns to also
> > be sorted nunmerically in that way. In unix I would reformat the first
> > two columns in gawk i.e. gawk '{printf("%2.3d %2.3d, %d, %d\n", $1,
> > $2, $3, $4)}' | sort -n -k 1
>
> I think lexsort does what you need. Get the sorted row indices:
>
> >>> a
>
> array([[ 1, ?0, ?2, ?5],
> ? ? ? ?[ 1, ?5, ?6, ?2],
> ? ? ? ?[ 1, ?2, ?8, ?7],
> ? ? ? ?[ 1, ?4, ?4, ?6],
> ? ? ? ?[ 1, ?3, ?2, ?3],
> ? ? ? ?[ 1, 11, ?2, ?0],
> ? ? ? ?[ 1, 10, ?1, ?3],
> ? ? ? ?[ 1, ?9, ?0, ?4],
> ? ? ? ?[ 1, ?8, ?9, ?6]])
>
> >>> ind = np.lexsort(a.T[::-1])
>
> a.T gives an array of columns, and reversing them with ::-1 sorts by the
> first column values, then the second column values if the first column
> values are equal, and so on. Now index the the original array:
>
> >>> ind
>
> array([0, 2, 4, 3, 1, 8, 7, 6, 5])>>> a[ind]
>
> array([[ 1, ?0, ?2, ?5],
> ? ? ? ?[ 1, ?2, ?8, ?7],
> ? ? ? ?[ 1, ?3, ?2, ?3],
> ? ? ? ?[ 1, ?4, ?4, ?6],
> ? ? ? ?[ 1, ?5, ?6, ?2],
> ? ? ? ?[ 1, ?8, ?9, ?6],
> ? ? ? ?[ 1, ?9, ?0, ?4],
> ? ? ? ?[ 1, 10, ?1, ?3],
> ? ? ? ?[ 1, 11, ?2, ?0]])
>
> Neil
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-U... at scipy.orghttp://mail.scipy.org/mailman/listinfo/scipy-user


From mdekauwe at gmail.com  Sat Aug  1 19:48:59 2009
From: mdekauwe at gmail.com (Martin)
Date: Sat, 1 Aug 2009 16:48:59 -0700 (PDT)
Subject: [SciPy-User] putting a dot on a graph with gnuplot.py
In-Reply-To: <h52ipo$78t$1@ger.gmane.org>
References: <h52ipo$78t$1@ger.gmane.org>
Message-ID: <ee6c6a40-5bf4-41de-bc0f-be89cbe57a2d@o6g2000yqj.googlegroups.com>

Not sure about a circle, but set label "some crap" at 2, 0 will put
text on a graph. My guess is the gnuplot group would be more likely to
have a solution for you.

Martin

On Aug 2, 12:23?am, Esmail <ebo... at hotmail.com> wrote:
> Hello all,
>
> I am optimizing some functions using Python that I am then
> plotting with gnuplot.py using the plot and splot commands.
>
> I can generate the function graphs/surfaces ok, but I am at
> a loss on how I can put a circle or some sort of marker at
> a specific place on the graph or the surface (to indicate the
> maximum).
>
> Is there a way to do this? I've been using gnuplot itself for
> a while, but I have no idea how to do this.
>
> Thanks,
> Esmail
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-U... at scipy.orghttp://mail.scipy.org/mailman/listinfo/scipy-user


From ebonak at hotmail.com  Sat Aug  1 19:52:44 2009
From: ebonak at hotmail.com (Esmail)
Date: Sat, 01 Aug 2009 19:52:44 -0400
Subject: [SciPy-User] putting a dot on a graph with gnuplot.py
In-Reply-To: <ee6c6a40-5bf4-41de-bc0f-be89cbe57a2d@o6g2000yqj.googlegroups.com>
References: <h52ipo$78t$1@ger.gmane.org>
	<ee6c6a40-5bf4-41de-bc0f-be89cbe57a2d@o6g2000yqj.googlegroups.com>
Message-ID: <h52kgc$9gs$2@ger.gmane.org>

Martin wrote:
> Not sure about a circle, but set label "some crap" at 2, 0 will put
> text on a graph. My guess is the gnuplot group would be more likely to
> have a solution for you.

Thanks Martin .. I just discovered the gnuplot group and posted a similar
query there.  I should have poked around looking for a group first,

Regards,
Esmail



From dwf at cs.toronto.edu  Sat Aug  1 21:26:22 2009
From: dwf at cs.toronto.edu (David Warde-Farley)
Date: Sat, 1 Aug 2009 21:26:22 -0400
Subject: [SciPy-User] SciPy Foundation
In-Reply-To: <5b8d13220908010921m49b218f2ga13b5b75a2aebaff@mail.gmail.com>
References: <wl8fxcc6aua.fsf@glup.physics.ucf.edu>
	<5b8d13220908010921m49b218f2ga13b5b75a2aebaff@mail.gmail.com>
Message-ID: <75285228-7E31-4659-A780-F79C9E659509@cs.toronto.edu>

On 1-Aug-09, at 12:21 PM, David Cournapeau wrote:

> One website to handle all the user community, no need for complicated
> forum rules and all (everything works with search and tags).
> Stackoverflow works without any fixed hierarchy for many times more
> participants that we will ever have, and much broader topics than us.
>
> They will have soon a dedicated solution for custom websites using the
> same stack - maybe something can be worked on as a open source
> project.

I agree: StackOverflow is a great model. FWIW I've seen another place  
where a similar model is available and can be deployed on a project/ 
product specific basis: http://getsatisfaction.com - they explicitly  
say they welcome open source projects. Others may exist as well.

Regards,

David


From robert.kern at gmail.com  Sun Aug  2 00:48:40 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Sat, 1 Aug 2009 23:48:40 -0500
Subject: [SciPy-User] SciPy Foundation
In-Reply-To: <75285228-7E31-4659-A780-F79C9E659509@cs.toronto.edu>
References: <wl8fxcc6aua.fsf@glup.physics.ucf.edu>
	<5b8d13220908010921m49b218f2ga13b5b75a2aebaff@mail.gmail.com> 
	<75285228-7E31-4659-A780-F79C9E659509@cs.toronto.edu>
Message-ID: <3d375d730908012148r233fb0bel1ead4106e8a12f23@mail.gmail.com>

On Sat, Aug 1, 2009 at 20:26, David Warde-Farley<dwf at cs.toronto.edu> wrote:
> On 1-Aug-09, at 12:21 PM, David Cournapeau wrote:
>
>> One website to handle all the user community, no need for complicated
>> forum rules and all (everything works with search and tags).
>> Stackoverflow works without any fixed hierarchy for many times more
>> participants that we will ever have, and much broader topics than us.
>>
>> They will have soon a dedicated solution for custom websites using the
>> same stack - maybe something can be worked on as a open source
>> project.
>
> I agree: StackOverflow is a great model. FWIW I've seen another place
> where a similar model is available and can be deployed on a project/
> product specific basis: http://getsatisfaction.com - they explicitly
> say they welcome open source projects. Others may exist as well.

cnprog is a StackOverflow clone written for a Chinese programming
community using Django. It appears to be reasonably complete and has
English localizations. It took me about 5 minutes to get it running
for playing around. However, getting it truly ready for use will take
longer than that.

  http://github.com/cnprog/CNPROG/tree/master

I'm afraid that we probably don't have the spare IT cycles to spare at
Enthought currently to implement this properly right now, but if
anyone wants to present us with a fait accompli, we'd be happy to host
it.

-- 
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


From dwf at cs.toronto.edu  Sun Aug  2 02:13:37 2009
From: dwf at cs.toronto.edu (David Warde-Farley)
Date: Sun, 2 Aug 2009 02:13:37 -0400
Subject: [SciPy-User] sorting an array
In-Reply-To: <19f36f93-9511-4442-85cf-707a262edf34@d4g2000yqa.googlegroups.com>
References: <b6655146-359b-40a3-bb52-8275783f7216@24g2000yqm.googlegroups.com>
	<loom.20090801T221407-521@post.gmane.org>
	<19f36f93-9511-4442-85cf-707a262edf34@d4g2000yqa.googlegroups.com>
Message-ID: <F3C7A691-EE56-48C2-AB7D-C812345AC80D@cs.toronto.edu>

On 1-Aug-09, at 7:31 PM, Martin wrote:

>  Although I have never seen [:,:2] what does the :2 bit
> mean?

Up to, but not including, the third column of a 2D array.

In [6]: x = arange(16).reshape(4,4)

In [7]: x
Out[7]:
array([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11],
        [12, 13, 14, 15]])

In [8]: x[:,:2]
Out[8]:
array([[ 0,  1],
        [ 4,  5],
        [ 8,  9],
        [12, 13]])



From neilcrighton at gmail.com  Sun Aug  2 04:43:49 2009
From: neilcrighton at gmail.com (Neil Crighton)
Date: Sun, 2 Aug 2009 08:43:49 +0000 (UTC)
Subject: [SciPy-User] sorting an array
References: <b6655146-359b-40a3-bb52-8275783f7216@24g2000yqm.googlegroups.com>
	<loom.20090801T221407-521@post.gmane.org>
	<19f36f93-9511-4442-85cf-707a262edf34@d4g2000yqa.googlegroups.com>
Message-ID: <loom.20090802T083844-480@post.gmane.org>

Martin <mdekauwe <at> gmail.com> writes:

> Hi all,
> 
> Thanks for the replies...perhaps if I explained the context it might
> help (or it might not!). Basically it is an image processing problem
> so the array is rows, columns. So I want to sort the array such that
> ...
> Neil your solution results in column 4 changing some of the ordering
> from what I can see (the zero in the fourth column).
> 

I think I see now - you want the values in the third and fourth columns to be
unchanged after sorting?  Then, yeah, I think Emmanuelle's version is the 
way to go.

I was confused by the first sentence in your original post. To me, 'sort an 
array by the first column' means sort every column using the ordering of 
values in the first column.

Neil




From mdekauwe at gmail.com  Sun Aug  2 08:51:24 2009
From: mdekauwe at gmail.com (Martin)
Date: Sun, 2 Aug 2009 05:51:24 -0700 (PDT)
Subject: [SciPy-User] sorting an array
In-Reply-To: <F3C7A691-EE56-48C2-AB7D-C812345AC80D@cs.toronto.edu>
References: <b6655146-359b-40a3-bb52-8275783f7216@24g2000yqm.googlegroups.com>
	<loom.20090801T221407-521@post.gmane.org>
	<19f36f93-9511-4442-85cf-707a262edf34@d4g2000yqa.googlegroups.com>
	<F3C7A691-EE56-48C2-AB7D-C812345AC80D@cs.toronto.edu>
Message-ID: <3474c0db-e3ad-4d3b-8211-de46b958ffac@h31g2000yqd.googlegroups.com>

Thanks David - that is very useful.

On Aug 2, 7:13?am, David Warde-Farley <d... at cs.toronto.edu> wrote:
> On 1-Aug-09, at 7:31 PM, Martin wrote:
>
> > ?Although I have never seen [:,:2] what does the :2 bit
> > mean?
>
> Up to, but not including, the third column of a 2D array.
>
> In [6]: x = arange(16).reshape(4,4)
>
> In [7]: x
> Out[7]:
> array([[ 0, ?1, ?2, ?3],
> ? ? ? ? [ 4, ?5, ?6, ?7],
> ? ? ? ? [ 8, ?9, 10, 11],
> ? ? ? ? [12, 13, 14, 15]])
>
> In [8]: x[:,:2]
> Out[8]:
> array([[ 0, ?1],
> ? ? ? ? [ 4, ?5],
> ? ? ? ? [ 8, ?9],
> ? ? ? ? [12, 13]])
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-U... at scipy.orghttp://mail.scipy.org/mailman/listinfo/scipy-user


From mdekauwe at gmail.com  Sun Aug  2 09:11:05 2009
From: mdekauwe at gmail.com (Martin)
Date: Sun, 2 Aug 2009 06:11:05 -0700 (PDT)
Subject: [SciPy-User] sorting an array
In-Reply-To: <loom.20090802T083844-480@post.gmane.org>
References: <b6655146-359b-40a3-bb52-8275783f7216@24g2000yqm.googlegroups.com>
	<loom.20090801T221407-521@post.gmane.org>
	<19f36f93-9511-4442-85cf-707a262edf34@d4g2000yqa.googlegroups.com>
	<loom.20090802T083844-480@post.gmane.org>
Message-ID: <00b0e83d-aa89-4529-b23a-9cfe65f13b44@b15g2000yqd.googlegroups.com>

Apologies Neil! Your solution was in fact exactly what I wanted...I
don't know why I said otherwise yesterday (tiredness). As you said
what I wanted to do was to sort by the first and then second cols,
moving the third and fourth columns to their respective places. What I
didn't want was for these cols(3 and 4) to also be numerically sorted.

I am trying it now.

Thanks



On Aug 2, 9:43?am, Neil Crighton <neilcrigh... at gmail.com> wrote:
> Martin <mdekauwe <at> gmail.com> writes:
>
> > Hi all,
>
> > Thanks for the replies...perhaps if I explained the context it might
> > help (or it might not!). Basically it is an image processing problem
> > so the array is rows, columns. So I want to sort the array such that
> > ...
> > Neil your solution results in column 4 changing some of the ordering
> > from what I can see (the zero in the fourth column).
>
> I think I see now - you want the values in the third and fourth columns to be
> unchanged after sorting? ?Then, yeah, I think Emmanuelle's version is the
> way to go.
>
> I was confused by the first sentence in your original post. To me, 'sort an
> array by the first column' means sort every column using the ordering of
> values in the first column.
>
> Neil
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-U... at scipy.orghttp://mail.scipy.org/mailman/listinfo/scipy-user


From josef.pktd at gmail.com  Sun Aug  2 09:41:02 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sun, 2 Aug 2009 09:41:02 -0400
Subject: [SciPy-User] adding distributions from hydroclimpy to
	stats.distributions
Message-ID: <1cd32cbb0908020641k4649315ahbd1a7ba986fd9ef9@mail.gmail.com>

On Sat, Aug 1, 2009 at 5:39 PM, <josef.pktd at gmail.com> wrote:
> On Sat, Aug 1, 2009 at 4:44 PM, Pierre GM<pgmdevlist at gmail.com> wrote:

>> In other stats.distributions news:
>> * I added some extra distributions to hydroclimpy (http://hydroclimpy.sourceforge.net/
>> ), along wrappings around Hosking's routines for the computation of L-
>> moments. Any interest to put some of it in scipy.stats.distributions ?
>
> Yes, extra distributions, if they follow the standard pattern, could
> be included in the tests by adding the name and one set of parameters.
>

I looked briefly at the distributions in hydroclimpy
http://projects.scipy.org/scikits/browser/trunk/hydroclimpy/scikits/hydroclimpy/stats/extradistributions.py

my first impression:

kappa, glogistic, gennorm and wakeby
can be added almost without changes to stats distributions, since they
are already in the standard format
cosmetic changes: add longname and extradocs (from module docstring)

pearson3
This one overwrites the main public methods, pdf, cdf, ...,
Can this be rewritten to define only the private, distribution
specific methods, _cdf, _pdf, or is there a special reason for the
public methods?

ztnbinom and logseries look like duplicates of stats.nbinom and stats.logser
ztnbinom uses a different way to calculate stats
logseries adds a fit function
Is there a difference that I'm missing after my only brief look?

I don't know anything about L moments and only briefly looked up the
definitions. Is there a generic method, that works (reasonably well)
for all distribution?
I assume the main work would be to make sure that adding a new method
would work with all distributions. I would gladly review a patch, but
I don't have the time to do the integration into stats.distributions
and the testing myself.

Josef


From gav451 at gmail.com  Sun Aug  2 11:14:11 2009
From: gav451 at gmail.com (Gerard Vermeulen)
Date: Sun, 2 Aug 2009 17:14:11 +0200
Subject: [SciPy-User] PyQwt-5.2.0 released
Message-ID: <20090802171411.41ec88ad@jupiter.rozan.fr>

What is PyQwt ( http://pyqwt.sourceforge.net ) ?

- it is a set of Python bindings for the Qwt C++ class library which
  extends the Qt framework with widgets for scientific and engineering
  applications.   It provides a 2-dimensional plotting widget and
  various widgets to display and control bounded or unbounded floating
  point values.

- it requires and extends PyQt, a set of Python bindings for Qt.

- it supports the use of PyQt, Qt, Qwt, and optionally NumPy or SciPy
  in a GUI Python application or in an interactive Python session.

- it runs on POSIX, Mac OS X and Windows platforms (practically any
  platform supported by Qt and Python).

- it plots fast: fairly good hardware allows a rate of 100,000
  points/second. (PyQwt with Qt-3 is faster than with Qt-4).

- it is licensed under the GPL with an exception to allow dynamic
  linking with non-free releases of Qt and PyQt. 


The most important new features of PyQwt v5.2.0 are:
- support for Qwt v5.2.0
- support for PyQt4 upto v4.5.4, PyQt3 upto v3.18.1, and SIP upto
  v4.8.2.
- switch to documentation generated by Sphinx.
- provide a normal qwt plugin for the pyuic4 user interface compiler
  instead of the anormal qwt plugin included in PyQt. 

The most important bug fixes in PyQwt-5.2.0 are:
- fixed crashes in the QImage-array conversion functions.
- fixed three transfer of ownership bugs.


PyQwt-5.2.0 supports:
1. Python v2.6.x and v2.5.x.
2. PyQt v3.18.1 downto v3.17.5.
3  PyQt v4.5.x, v4.4.x.
4  SIP v4.8.x downto v4.7.3.
5. Qt v3.3.x.
6. Qt v4.5.x, v4.4.x, and v4.3.x.
7. Qwt v5.2.x, v5.1.x, and v5.0.x.
8. Recent versions of NumPy, numarray, and/or Numeric.


Enjoy -- Gerard Vermeulen


From pgmdevlist at gmail.com  Sun Aug  2 15:52:56 2009
From: pgmdevlist at gmail.com (Pierre GM)
Date: Sun, 2 Aug 2009 15:52:56 -0400
Subject: [SciPy-User] adding distributions from hydroclimpy to
	stats.distributions
In-Reply-To: <1cd32cbb0908020641k4649315ahbd1a7ba986fd9ef9@mail.gmail.com>
References: <1cd32cbb0908020641k4649315ahbd1a7ba986fd9ef9@mail.gmail.com>
Message-ID: <82156633-5678-4485-9ECA-FB33CB540691@gmail.com>


On Aug 2, 2009, at 9:41 AM, josef.pktd at gmail.com wrote:
>
> I looked briefly at the distributions in hydroclimpy
> http://projects.scipy.org/scikits/browser/trunk/hydroclimpy/scikits/hydroclimpy/stats/extradistributions.py
>
> my first impression:
>
> kappa, glogistic, gennorm and wakeby
> can be added almost without changes to stats distributions, since they
> are already in the standard format
> cosmetic changes: add longname and extradocs (from module docstring)

Agreed. I still have an issue about defining a proper template for  
describing the distributions (eqns for pdf/cdf/ppf, example of usage,  
plots...), hence the nudge. What are our doc exegetes' recommendations ?


> pearson3
> This one overwrites the main public methods, pdf, cdf, ...,
> Can this be rewritten to define only the private, distribution
> specific methods, _cdf, _pdf, or is there a special reason for the
> public methods?

Depending on the values of the parameters, Pearson III can reduce to a  
normal. Overwriting .pdf and .cdf was IMHO more efficient than trying  
to stick to the _pdf/_cdf methods. The same problem arises when a  
distribution reduces to another in some particular cases.



> ztnbinom and logseries look like duplicates of stats.nbinom and  
> stats.logser
> ztnbinom uses a different way to calculate stats

ztnbinom is the zero-truncated negative binomial distribution, a  
particular case of the negative binomial where support is restricted  
to integers larger or equal than 1 (no zero class). Yes, the stats are  
slightly different because of the truncation. Similarly, we can define  
a zero-inflated Poisson.

I considered developing a generic trunc_dist class from rv_discrete to  
handle arbitrary truncation, but realize that the scope was too large  
for me to handle, and I've already far enough on my plate(s) for now.

> logseries adds a fit function
> Is there a difference that I'm missing after my only brief look?

I had overlooked the logser distribution (silly me). Adding the fit  
method is required for my own applications (analyzing dry/wet spells  
distributions). I'm about to add fit methods to other discrete  
distribution as I need them.



> I don't know anything about L moments and only briefly looked up the
> definitions. Is there a generic method, that works (reasonably well)
> for all distribution?

L-moments are defined for continuous distributions only. You can find  
a nice description of their definition and use here:
http://www.research.ibm.com/people/h/hosking/lmoments.html
In short, they tend to be more robust that the classical moments. The  
facts that the L-kurtosis and L-skewness are in the interval [-1;+1]  
simplifies the comparisons between different distributions when trying  
to define the most adequate one.
L-moments of some specific distributions have an explicit formulation  
that can help estimating the parameters of these distributions (hence  
the whole lmoments.py module).


> I assume the main work would be to make sure that adding a new method
> would work with all distributions. I would gladly review a patch, but
> I don't have the time to do the integration into stats.distributions
> and the testing myself.

OK, what about we keep them on the backburner for now ? Hopefully I'll  
have more time to deal with polishing the docs and adding more tests  
soon. My advertising these new distributions was primarily to let  
other users know that they're already implemented somewhere, to  
illustrate the need for a doc template



From pgmdevlist at gmail.com  Sun Aug  2 16:05:59 2009
From: pgmdevlist at gmail.com (Pierre GM)
Date: Sun, 2 Aug 2009 16:05:59 -0400
Subject: [SciPy-User] An extra parameter to stats.chisquare ?
Message-ID: <32A899D6-8D41-458B-B31C-2A9717E04509@gmail.com>

All,
stats.chisquare requires a mandatory parameter (frequency of  
observations) and an optional argument (theoretical frequencies). In  
that second case, I think we have to introduce yet another parameter,  
p, corresponding to the number of parameters of the theoretical  
distribution: the number of degrees-of-freedom for the chisqprob would  
then be k-p (with k the sample size), instead of the current k-1. Of  
course, we can set p=1 by default.
Comments ?
P.


From vanforeest at gmail.com  Sun Aug  2 16:06:42 2009
From: vanforeest at gmail.com (nicky van foreest)
Date: Sun, 2 Aug 2009 22:06:42 +0200
Subject: [SciPy-User] gnuplot.py xrange/shifting graph not working
In-Reply-To: <h52iit$6rp$1@ger.gmane.org>
References: <h51pjg$coc$1@ger.gmane.org> <h51qv3$ft2$1@ger.gmane.org>
	<fa510ff80908011319u5c25448es58f8d08b1e4a9793@mail.gmail.com>
	<h52iit$6rp$1@ger.gmane.org>
Message-ID: <fa510ff80908021306n652c00b9qde16dc94675817ea@mail.gmail.com>

Hi,
> Here is a 3D version.
> ------------------
>
> #!/usr/bin/env python
>
> import Gnuplot
>
> g = Gnuplot.Gnuplot(debug=1)
> x = range(-3,4)
> data = []
>
> for i in x:
> ? ? for j in x:
> ? ? ? ? data.append([i, j, i*i])

I cannot help you right away this time. This is what I know:

I checked the demo.py file that comes along with python-gnuplot. There
the plotting starts with the z-values, then the x, and then the y
values: This is not what you seem to be doing.
See the line: g.splot(Gnuplot.GridData(m,x,y, binary=0))

Besides this, I recall from the not the frequently asked questions
site of gnuplot that interlacing spaces has some effect on the
plotting of data, which effect I forgot. This may also affect your
plot.

Sorry that I cannot be of further help this time.


>
> for i in data:
> ? ? print i[0], i[1], i[2]
>
> g('set hidden3d')
> g('set style data linespoint')
> g.splot(data)
>
> ------------------
>
> the
>
> ? ? g('set style data linespoint')
>
> leads to lines connecting that ought not to. To see what I mean run
> the above code, and then compare this with plotting the data below
> straight from gnuplot. It is the same as the program generates during
> the run, all I have done is inserted blank lines for each block and
> then saved the file under the name t2data
>
>
> ------------------
>
> gnuplot> set style data linespoint
> gnuplot> splot './t2data'
>
> ------------------
>
> Is there a way to mimic this with the data list? I tried appending '\n' to
> the data list, but that didn't work. If there is no easy work around I may
> just write the data to a file (with the blank lines) and then read it
> in again - probably not the most efficient way to do this, but it'll
> work :-) ... plus that seems to be going on in the background already
> anyway.
>
>
> Thanks,
> Esmail
>
>
> --- t2data file ----
> -3 -3 9
> -3 -2 9
> -3 -1 9
> -3 0 9
> -3 1 9
> -3 2 9
> -3 3 9
>
> -2 -3 4
> -2 -2 4
> -2 -1 4
> -2 0 4
> -2 1 4
> -2 2 4
> -2 3 4
>
> -1 -3 1
> -1 -2 1
> -1 -1 1
> -1 0 1
> -1 1 1
> -1 2 1
> -1 3 1
>
> 0 -3 0
> 0 -2 0
> 0 -1 0
> 0 0 0
> 0 1 0
> 0 2 0
> 0 3 0
>
> 1 -3 1
> 1 -2 1
> 1 -1 1
> 1 0 1
> 1 1 1
> 1 2 1
> 1 3 1
>
> 2 -3 4
> 2 -2 4
> 2 -1 4
> 2 0 4
> 2 1 4
> 2 2 4
> 2 3 4
>
> 3 -3 9
> 3 -2 9
> 3 -1 9
> 3 0 9
> 3 1 9
> 3 2 9
> 3 3 9
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From josef.pktd at gmail.com  Sun Aug  2 17:18:41 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sun, 2 Aug 2009 17:18:41 -0400
Subject: [SciPy-User] An extra parameter to stats.chisquare ?
In-Reply-To: <32A899D6-8D41-458B-B31C-2A9717E04509@gmail.com>
References: <32A899D6-8D41-458B-B31C-2A9717E04509@gmail.com>
Message-ID: <1cd32cbb0908021418ib347190u1fc5184050d05a28@mail.gmail.com>

On Sun, Aug 2, 2009 at 4:05 PM, Pierre GM<pgmdevlist at gmail.com> wrote:
> All,
> stats.chisquare requires a mandatory parameter (frequency of
> observations) and an optional argument (theoretical frequencies). In
> that second case, I think we have to introduce yet another parameter,
> p, corresponding to the number of parameters of the theoretical
> distribution: the number of degrees-of-freedom for the chisqprob would
> then be k-p (with k the sample size), instead of the current k-1. Of
> course, we can set p=1 by default.
> Comments ?
> P.

No disagreement with adding e.g. "ddof" as additional keyword
parameter. This might be also relevant for other tests where the data
can be based on prior estimation. (The same problem shows up with
tests after regression.)

For the chisquare test, I'm not sure about the theory, since I only
used chisquare without estimating parameters. Wikipedia seems a bit
ambiguous:

"The chi-square statistic can then be used to calculate a p-value by
comparing the value of the statistic to a chi-square distribution. The
number of degrees of freedom is equal to the number of cells n, minus
the reduction in degrees of freedom, p.
More generally however, when maximum likelihood estimation does not
coincide with minimum chi-square estimation, the distribution will lie
somewhere between a chi-square distribution with n - 1 - p and n - 1
degrees of freedom (See for instance Chernoff and Lehmann 1954)."
(http://en.wikipedia.org/wiki/Pearson%27s_chi-square_test)


If you need goodness-of-fit tests, then you could also try my
implementation of a more general class of gof statistics (power
discrepancy). I sent it to the mailing list a while ago.

Josef


> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From ebonak at hotmail.com  Sun Aug  2 19:59:14 2009
From: ebonak at hotmail.com (Esmail)
Date: Sun, 02 Aug 2009 19:59:14 -0400
Subject: [SciPy-User] gnuplot.py xrange/shifting graph not working
In-Reply-To: <fa510ff80908021306n652c00b9qde16dc94675817ea@mail.gmail.com>
References: <h51pjg$coc$1@ger.gmane.org>
	<h51qv3$ft2$1@ger.gmane.org>	<fa510ff80908011319u5c25448es58f8d08b1e4a9793@mail.gmail.com>	<h52iit$6rp$1@ger.gmane.org>
	<fa510ff80908021306n652c00b9qde16dc94675817ea@mail.gmail.com>
Message-ID: <h5598i$1pj$1@ger.gmane.org>

nicky van foreest wrote:
> 
> I cannot help you right away this time. This is what I know:
> 
> I checked the demo.py file that comes along with python-gnuplot. There
> the plotting starts with the z-values, then the x, and then the y
> values: This is not what you seem to be doing.
> See the line: g.splot(Gnuplot.GridData(m,x,y, binary=0))
> 
> Besides this, I recall from the not the frequently asked questions
> site of gnuplot that interlacing spaces has some effect on the
> plotting of data, which effect I forgot. This may also affect your
> plot.
> 
> Sorry that I cannot be of further help this time.

Not at all, no reason to be sorry, thanks for the help and the
pointers to the demo file. I will take a closer look at that file
and the test.py file too.

Thanks again!

Esmail



From ebonak at hotmail.com  Sun Aug  2 20:22:52 2009
From: ebonak at hotmail.com (Esmail)
Date: Sun, 02 Aug 2009 20:22:52 -0400
Subject: [SciPy-User] putting a dot on a graph with gnuplot.py
In-Reply-To: <ee6c6a40-5bf4-41de-bc0f-be89cbe57a2d@o6g2000yqj.googlegroups.com>
References: <h52ipo$78t$1@ger.gmane.org>
	<ee6c6a40-5bf4-41de-bc0f-be89cbe57a2d@o6g2000yqj.googlegroups.com>
Message-ID: <h55akt$4e3$1@ger.gmane.org>

Martin wrote:
> Not sure about a circle, but set label "some crap" at 2, 0 will put
> text on a graph. My guess is the gnuplot group would be more likely to
> have a solution for you.
> 
> Martin

Hi Martin,

Just another quick note to let you know that your suggestion was
very helpful and worked.

Thanks,
Esmail



From pgmdevlist at gmail.com  Sun Aug  2 22:43:47 2009
From: pgmdevlist at gmail.com (Pierre GM)
Date: Sun, 2 Aug 2009 22:43:47 -0400
Subject: [SciPy-User] An extra parameter to stats.chisquare ?
In-Reply-To: <1cd32cbb0908021418ib347190u1fc5184050d05a28@mail.gmail.com>
References: <32A899D6-8D41-458B-B31C-2A9717E04509@gmail.com>
	<1cd32cbb0908021418ib347190u1fc5184050d05a28@mail.gmail.com>
Message-ID: <57170D20-8B86-4DA6-B5B5-6C2B49FD6EB9@gmail.com>


On Aug 2, 2009, at 5:18 PM, josef.pktd at gmail.com wrote:

> On Sun, Aug 2, 2009 at 4:05 PM, Pierre GM<pgmdevlist at gmail.com> wrote:
>> All,
>> stats.chisquare requires a mandatory parameter (frequency of
>> observations) and an optional argument (theoretical frequencies). In
>> that second case, I think we have to introduce yet another parameter,
>> p, corresponding to the number of parameters of the theoretical
>> distribution: the number of degrees-of-freedom for the chisqprob  
>> would
>> then be k-p (with k the sample size), instead of the current k-1. Of
>> course, we can set p=1 by default.
>> Comments ?
>> P.
>
> No disagreement with adding e.g. "ddof" as additional keyword
> parameter. This might be also relevant for other tests where the data
> can be based on prior estimation. (The same problem shows up with
> tests after regression.)
>
> For the chisquare test, I'm not sure about the theory, since I only
> used chisquare without estimating parameters. Wikipedia seems a bit
> ambiguous:

Well, I guess we'd need a "real" statistician. From what I gathered,  
when you fit your N observations to a distribution with p parameters  
(eg, 2 for normal, 1 for logseries), the ddof is N-(p+1): http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm
However, that works as long as all the parameters are independent. If  
one depends from the others or can be related to the others, we switch  
from p independent parameters to (p-1), thus giving a dof of N-p. So,  
wikipedia looks right.

> If you need goodness-of-fit tests, then you could also try my
> implementation of a more general class of gof statistics (power
> discrepancy). I sent it to the mailing list a while ago.

 From 04/27 ? I def'ny gonna give it a try, thanks a lot. 


From josef.pktd at gmail.com  Mon Aug  3 00:20:01 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Mon, 3 Aug 2009 00:20:01 -0400
Subject: [SciPy-User] adding distributions from hydroclimpy to
	stats.distributions
In-Reply-To: <82156633-5678-4485-9ECA-FB33CB540691@gmail.com>
References: <1cd32cbb0908020641k4649315ahbd1a7ba986fd9ef9@mail.gmail.com>
	<82156633-5678-4485-9ECA-FB33CB540691@gmail.com>
Message-ID: <1cd32cbb0908022120i160ca529gd7942e4a76acee73@mail.gmail.com>

On Sun, Aug 2, 2009 at 3:52 PM, Pierre GM<pgmdevlist at gmail.com> wrote:
>
> On Aug 2, 2009, at 9:41 AM, josef.pktd at gmail.com wrote:
>>
>> I looked briefly at the distributions in hydroclimpy
>> http://projects.scipy.org/scikits/browser/trunk/hydroclimpy/scikits/hydroclimpy/stats/extradistributions.py
>>
>> my first impression:
>>
>> kappa, glogistic, gennorm and wakeby
>> can be added almost without changes to stats distributions, since they
>> are already in the standard format
>> cosmetic changes: add longname and extradocs (from module docstring)
>
> Agreed. I still have an issue about defining a proper template for
> describing the distributions (eqns for pdf/cdf/ppf, example of usage,
> plots...), hence the nudge. What are our doc exegetes' recommendations ?

currently only the class docstring gets distribution specific
information, done in rv_generic.__init__,
replacing name of distribution and shape parameters, and adding
extradocs. The example in the class docstring is just a generic
example. More description of the distributions could simply be added
to the extradocs, which show up in the help at the bottom of the
individual distribution class/instance docstrings. For example the
available formulas for the distribution pdf, cdf, sf, ..., (info from
Travis manual) could be included in the extradoc.)

For methods there is currently no setup for individualized information
and examples. But this could be changed, if we really want to.
However, since methods for all distribution follow the same pattern, I
don't see really the need. Better overall help and examples, e.g. in
the tutorial, would be useful.

Any good ideas for improvements?







>
>
>> pearson3
>> This one overwrites the main public methods, pdf, cdf, ...,
>> Can this be rewritten to define only the private, distribution
>> specific methods, _cdf, _pdf, or is there a special reason for the
>> public methods?
>
> Depending on the values of the parameters, Pearson III can reduce to a
> normal. Overwriting .pdf and .cdf was IMHO more efficient than trying
> to stick to the _pdf/_cdf methods. The same problem arises when a
> distribution reduces to another in some particular cases.
>

I will have to look how you did it.
Per Brodtkorb implemented some changes where distribution in the limit
with respect to changes in the parameters are included. In this case
the methods get a bit heavy in conditional assignments, but still
follow the standard pattern.

Your description of Pearson III could be a bit more explicit about the
relationship to the gamma distribution. From searching on the
internet, it isn't clear to me whether gamma is the same as Pearson
III, hinted at by Wikipedia and it looks this way also in
http://mathworld.wolfram.com/PearsonSystem.html
or whether there are different versions.

>
>
>> ztnbinom and logseries look like duplicates of stats.nbinom and
>> stats.logser
>> ztnbinom uses a different way to calculate stats
>
> ztnbinom is the zero-truncated negative binomial distribution, a
> particular case of the negative binomial where support is restricted
> to integers larger or equal than 1 (no zero class). Yes, the stats are
> slightly different because of the truncation.

Initially, I didn't look carefully enough to see that there is the
scaling term in the cdf and pdf. The extradoc string still has 0<=k
and rvs doesn't remove the zeros in the numpy.random numbers (?).

> Similarly, we can define
> a zero-inflated Poisson.
>
> I considered developing a generic trunc_dist class from rv_discrete to
> handle arbitrary truncation, but realize that the scope was too large
> for me to handle, and I've already far enough on my plate(s) for now.

For the continuous distribution, I wanted to do this in a similar way
as I did for creating distributions based on non-linear
transformations. But I played only a bit with it for the discrete case
when I checked the correctness of the expect function.
A basic version that is fully functional and uses the generic methods
should be pretty easy to write, but testing, optimizing, ...


>
>> logseries adds a fit function
>> Is there a difference that I'm missing after my only brief look?
>
> I had overlooked the logser distribution (silly me). Adding the fit
> method is required for my own applications (analyzing dry/wet spells
> distributions). I'm about to add fit methods to other discrete
> distribution as I need them.
>
>
>
>> I don't know anything about L moments and only briefly looked up the
>> definitions. Is there a generic method, that works (reasonably well)
>> for all distribution?
>
> L-moments are defined for continuous distributions only. You can find
> a nice description of their definition and use here:
> http://www.research.ibm.com/people/h/hosking/lmoments.html
> In short, they tend to be more robust that the classical moments. The
> facts that the L-kurtosis and L-skewness are in the interval [-1;+1]
> simplifies the comparisons between different distributions when trying
> to define the most adequate one.
> L-moments of some specific distributions have an explicit formulation
> that can help estimating the parameters of these distributions (hence
> the whole lmoments.py module).
>

I looked at this and similar references, and it looks interesting. Do
I have to be a bit suspicious because there are almost no statistics
journals in the list of references on the ibm page? Did L-moments only
become popular in hydrology?
Just a quick check: I didn't find L-moments in the SAS help, but R has
a package for it, and the fortran code by Hosking is BSD (or similar).

>
>> I assume the main work would be to make sure that adding a new method
>> would work with all distributions. I would gladly review a patch, but
>> I don't have the time to do the integration into stats.distributions
>> and the testing myself.
>
> OK, what about we keep them on the backburner for now ? Hopefully I'll
> have more time to deal with polishing the docs and adding more tests
> soon. My advertising these new distributions was primarily to let
> other users know that they're already implemented somewhere, to
> illustrate the need for a doc template
>

I had already looked a bit closer at your lmoments, and they are
already pretty well integrated with the stats.distribution code. It
should be possible to add some generic tests that work for the
distributions that are not included in your tests.

A python question, since I'm never sure about the details of monkey
patching and it always takes a while to track down the correct
references

In lmoments you attach functions to distribution classes directly.
>From my (possibly wrong) understanding these functions should remain
functions and not turn into instance methods. However, it looks like
you use them as instance methods in the tests. Does self get replaced
by the instance in the call?
I thought that to attach a function as an instance method, either
new.instancemethod or types.MethodType have to be used.
What am I missing?

http://projects.scipy.org/scikits/browser/trunk/hydroclimpy/scikits/hydroclimpy/stats/lmoments.py

340	# moment from definition
341	def _lmomg(self, m, *args):
342	    "Compute the mth L-moment with a Legendre polynomial."
343	    P_r = special.sh_legendre(m-1)
344	    func = lambda x : self._ppf(x, *args) * P_r(x)
345	    return integrate.quad(func, 0, 1)[0]
346	dist.rv_continuous._lmomg = _lmomg
347	
348	def _lmomg_frozen(self, nmom):
349	    return self.dist._lmomg(nmom, *self.args, **self.kwds)
350	dist.rv_frozen._lmomg = _lmomg_frozen

and

508	dist.expon_gen.lstats = _lstats_direct(_lmoments.lmrexp)
509	dist.gamma_gen.lstats = _lstats_direct(_lmoments.lmrgam)
510	dist.genextreme_gen.lstats = _lstats_direct(_lmoments.lmrgev)
511	extradist.glogistic_gen.lstats = _lstats_direct(_lmoments.lmrglo)


BTW:
I like it that you started writing some developer notes on
stats.distribution. There are many things that took me a long time to
figure out, and developer notes explaining the internals would have
come in handy. The main is the state preserving call to _argcheck when
the bounds depend on parameters, which creates some entertaining,
state dependent bugs.
I wish distributions were classes instead of class instances.

Josef

> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From pgmdevlist at gmail.com  Mon Aug  3 02:53:04 2009
From: pgmdevlist at gmail.com (Pierre GM)
Date: Mon, 3 Aug 2009 02:53:04 -0400
Subject: [SciPy-User] adding distributions from hydroclimpy to
	stats.distributions
In-Reply-To: <1cd32cbb0908022120i160ca529gd7942e4a76acee73@mail.gmail.com>
References: <1cd32cbb0908020641k4649315ahbd1a7ba986fd9ef9@mail.gmail.com>
	<82156633-5678-4485-9ECA-FB33CB540691@gmail.com>
	<1cd32cbb0908022120i160ca529gd7942e4a76acee73@mail.gmail.com>
Message-ID: <40EC9782-3B5E-4EE2-8D3E-F11178962FF7@gmail.com>


On Aug 3, 2009, at 12:20 AM, josef.pktd at gmail.com wrote:

> On Sun, Aug 2, 2009 at 3:52 PM, Pierre GM<pgmdevlist at gmail.com> wrote:
>>
>> ... I still have an issue about defining a proper template for
>> describing the distributions (eqns for pdf/cdf/ppf, example of usage,
>> plots...), hence the nudge. What are our doc exegetes'  
>> recommendations ?
>
> currently only the class docstring gets distribution specific
> information, done in rv_generic.__init__,
> replacing name of distribution and shape parameters, and adding
> extradocs. The example in the class docstring is just a generic
> example. More description of the distributions could simply be added
> to the extradocs, which show up in the help at the bottom of the
> individual distribution class/instance docstrings. For example the
> available formulas for the distribution pdf, cdf, sf, ..., (info from
> Travis manual) could be included in the extradoc.)

Well, that's a start indeed. I'd still like to see some real graphs in  
the docs. Bah, details at this point.

> For methods there is currently no setup for individualized information
> and examples. But this could be changed, if we really want to.
> However, since methods for all distribution follow the same pattern, I
> don't see really the need. Better overall help and examples, e.g. in
> the tutorial, would be useful.

Agreed. There could be a starting page in the doc that lists all the  
methods and attributes  available to all distributions, and we could  
focus on the particularities of each distribution in its own  
docstring. I'd still like something more encyclopedic, but that's once  
again just a detail.

>>> pearson3
>>> This one overwrites the main public methods, pdf, cdf, ...,
>>> Can this be rewritten to define only the private, distribution
>>> specific methods, _cdf, _pdf, or is there a special reason for the
>>> public methods?
>>
>> Depending on the values of the parameters, Pearson III can reduce  
>> to a
>> normal. Overwriting .pdf and .cdf was IMHO more efficient than trying
>> to stick to the _pdf/_cdf methods. The same problem arises when a
>> distribution reduces to another in some particular cases.
>>
>
> I will have to look how you did it.
> Per Brodtkorb implemented some changes where distribution in the limit
> with respect to changes in the parameters are included. In this case
> the methods get a bit heavy in conditional assignments, but still
> follow the standard pattern.

I remember comments of yours about the use of numpy.where (you're  
right, where evaluates both expressions, which can be a problem).
Anyhow, standards are good but should not go in the way: I'm happier  
with my implementation than with using _pdf/_cdf.


> Your description of Pearson III could be a bit more explicit about the
> relationship to the gamma distribution. From searching on the
> internet, it isn't clear to me whether gamma is the same as Pearson
> III, hinted at by Wikipedia and it looks this way also in
> http://mathworld.wolfram.com/PearsonSystem.html
> or whether there are different versions.

It's roughly a gamma that can transform to a normal when needed. It's  
a function used in hydrology. Actually, I need to implement the log  
Pearson III, which is the standard used by the USGS to estimate flood  
return periods. Anyhow, it inherits from gamma...

>> ztnbinom is the zero-truncated negative binomial distribution, a
>> particular case of the negative binomial where support is restricted
>> to integers larger or equal than 1 (no zero class). Yes, the stats  
>> are
>> slightly different because of the truncation.
>
> Initially, I didn't look carefully enough to see that there is the
> scaling term in the cdf and pdf. The extradoc string still has 0<=k
> and rvs doesn't remove the zeros in the numpy.random numbers (?).

Sorry for the extradoc. And good point for the rvs, I hadn't try it  
(no need). I'll correct that.


>> I considered developing a generic trunc_dist class from rv_discrete  
>> to
>> handle arbitrary truncation, but realize that the scope was too large
>> for me to handle, and I've already far enough on my plate(s) for now.
>
> For the continuous distribution, I wanted to do this in a similar way
> as I did for creating distributions based on non-linear
> transformations. But I played only a bit with it for the discrete case
> when I checked the correctness of the expect function.
> A basic version that is fully functional and uses the generic methods
> should be pretty easy to write, but testing, optimizing, ...

Sooo, we'll put that on the TODO list.

>>
>>
>>> I don't know anything about L moments and only briefly looked up the
>>> definitions. Is there a generic method, that works (reasonably well)
>>> for all distribution?
>>
>> L-moments are defined for continuous distributions only. You can find
>> a nice description of their definition and use here:
>> http://www.research.ibm.com/people/h/hosking/lmoments.html
>
> I looked at this and similar references, and it looks interesting. Do
> I have to be a bit suspicious because there are almost no statistics
> journals in the list of references on the ibm page? Did L-moments only
> become popular in hydrology?

Well, I'm no specialist, but they're indeed used in hydrology as an  
easy way to estimate distribution parameters from a finite  sample of  
observations. It's a really useful tricks, but nothing out of the  
ordinary, probably why you had difficulties finding references in  
statistical journals

> Just a quick check: I didn't find L-moments in the SAS help, but R has
> a package for it, and the fortran code by Hosking is BSD (or similar).

I guess there are a couple (L-moment, lmomco), and yes, the code is  
BSD (I checked it beforehand)

> I had already looked a bit closer at your lmoments, and they are
> already pretty well integrated with the stats.distribution code.

Eh... I figured it'd be easier that way. FYI, I started learning  
Python and numpy 4 years ago, and the L-moments were the first thing I  
implemented around fortran. I left the code gather dust for four years  
but just recently came to need it again, so I reimplemented things  
from scratch...


> It
> should be possible to add some generic tests that work for the
> distributions that are not included in your tests.

Oh yes. sure. Not a top priority for me right now, though.

> A python question, since I'm never sure about the details of monkey
> patching and it always takes a while to track down the correct
> references
>
> In lmoments you attach functions to distribution classes directly.
>> From my (possibly wrong) understanding these functions should remain
> functions and not turn into instance methods.

Why not ? You never wrote a function that later on should turn into a  
method ? We used that in several places in scikits.timeseries.


> However, it looks like
> you use them as instance methods in the tests. Does self get replaced
> by the instance in the call?

Yes. Try it.

> I thought that to attach a function as an instance method, either
> new.instancemethod or types.MethodType have to be used.
> What am I missing?

Well, as soon as a function is attached to a class, it becomes a  
method (provided the first argument of the function is an instance of  
the class you attach it to), right ? At least, it works...

> 508	dist.expon_gen.lstats = _lstats_direct(_lmoments.lmrexp)

OK, here, it's a tad tricky, but not much: _lstats_direct is a class  
that implements a __call__ method. Therefore, any instance of  
_lstats_direct is callable and behave as a function. Or as a method if  
you attach it to a class. Here, we initialize _lstats_direct with a  
fortran function: the result is an instance of _lstats_direct, which  
is callable and to all mean behaves like a function, we attach it to  
dist.expon_gen and et voil?, it's a method.

> BTW:
> I like it that you started writing some developer notes on
> stats.distribution. There are many things that took me a long time to
> figure out, and developer notes explaining the internals would have
> come in handy.

Well, I figured somebody would need them as well. I know I will. It's  
far from enough, but it's a start.

> The main is the state preserving call to _argcheck when
> the bounds depend on parameters, which creates some entertaining,
> state dependent bugs.
> I wish distributions were classes instead of class instances.

Ah, don't get me started on that either...



From mdekauwe at gmail.com  Mon Aug  3 04:09:29 2009
From: mdekauwe at gmail.com (Martin)
Date: Mon, 3 Aug 2009 01:09:29 -0700 (PDT)
Subject: [SciPy-User] putting a dot on a graph with gnuplot.py
In-Reply-To: <h55akt$4e3$1@ger.gmane.org>
References: <h52ipo$78t$1@ger.gmane.org>
	<ee6c6a40-5bf4-41de-bc0f-be89cbe57a2d@o6g2000yqj.googlegroups.com>
	<h55akt$4e3$1@ger.gmane.org>
Message-ID: <c5480b32-0acb-4f0e-880e-92aa7817cebd@c2g2000yqi.googlegroups.com>

Glad to hear it but that wouldn't put a circle on the graph? Unless
you made a large "." I guess?

Martin

On Aug 3, 1:22?am, Esmail <ebo... at hotmail.com> wrote:
> Martin wrote:
> > Not sure about a circle, but set label "some crap" at 2, 0 will put
> > text on a graph. My guess is the gnuplot group would be more likely to
> > have a solution for you.
>
> > Martin
>
> Hi Martin,
>
> Just another quick note to let you know that your suggestion was
> very helpful and worked.
>
> Thanks,
> Esmail
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-U... at scipy.orghttp://mail.scipy.org/mailman/listinfo/scipy-user


From mdekauwe at gmail.com  Mon Aug  3 04:09:55 2009
From: mdekauwe at gmail.com (Martin)
Date: Mon, 3 Aug 2009 01:09:55 -0700 (PDT)
Subject: [SciPy-User] sorting an array
In-Reply-To: <00b0e83d-aa89-4529-b23a-9cfe65f13b44@b15g2000yqd.googlegroups.com>
References: <b6655146-359b-40a3-bb52-8275783f7216@24g2000yqm.googlegroups.com>
	<loom.20090801T221407-521@post.gmane.org>
	<19f36f93-9511-4442-85cf-707a262edf34@d4g2000yqa.googlegroups.com>
	<loom.20090802T083844-480@post.gmane.org>
	<00b0e83d-aa89-4529-b23a-9cfe65f13b44@b15g2000yqd.googlegroups.com>
Message-ID: <31fdc25a-553b-4a90-87d7-26786cb225c0@o15g2000yqm.googlegroups.com>

Works like a treat thanks Neil.

On Aug 2, 2:11?pm, Martin <mdeka... at gmail.com> wrote:
> Apologies Neil! Your solution was in fact exactly what I wanted...I
> don't know why I said otherwise yesterday (tiredness). As you said
> what I wanted to do was to sort by the first and then second cols,
> moving the third and fourth columns to their respective places. What I
> didn't want was for these cols(3 and 4) to also be numerically sorted.
>
> I am trying it now.
>
> Thanks
>
> On Aug 2, 9:43?am, Neil Crighton <neilcrigh... at gmail.com> wrote:
>
> > Martin <mdekauwe <at> gmail.com> writes:
>
> > > Hi all,
>
> > > Thanks for the replies...perhaps if I explained the context it might
> > > help (or it might not!). Basically it is an image processing problem
> > > so the array is rows, columns. So I want to sort the array such that
> > > ...
> > > Neil your solution results in column 4 changing some of the ordering
> > > from what I can see (the zero in the fourth column).
>
> > I think I see now - you want the values in the third and fourth columns to be
> > unchanged after sorting? ?Then, yeah, I think Emmanuelle's version is the
> > way to go.
>
> > I was confused by the first sentence in your original post. To me, 'sort an
> > array by the first column' means sort every column using the ordering of
> > values in the first column.
>
> > Neil
>
> > _______________________________________________
> > SciPy-User mailing list
> > SciPy-U... at scipy.orghttp://mail.scipy.org/mailman/listinfo/scipy-user
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-U... at scipy.orghttp://mail.scipy.org/mailman/listinfo/scipy-user


From cournape at gmail.com  Mon Aug  3 08:24:03 2009
From: cournape at gmail.com (David Cournapeau)
Date: Mon, 3 Aug 2009 21:24:03 +0900
Subject: [SciPy-User] SciPy Foundation
In-Reply-To: <3d375d730908012148r233fb0bel1ead4106e8a12f23@mail.gmail.com>
References: <wl8fxcc6aua.fsf@glup.physics.ucf.edu>
	<5b8d13220908010921m49b218f2ga13b5b75a2aebaff@mail.gmail.com>
	<75285228-7E31-4659-A780-F79C9E659509@cs.toronto.edu>
	<3d375d730908012148r233fb0bel1ead4106e8a12f23@mail.gmail.com>
Message-ID: <5b8d13220908030524l2c20b6a8o8c297f2b3ea6a562@mail.gmail.com>

On Sun, Aug 2, 2009 at 1:48 PM, Robert Kern<robert.kern at gmail.com> wrote:

>
> cnprog is a StackOverflow clone written for a Chinese programming
> community using Django. It appears to be reasonably complete and has
> English localizations. It took me about 5 minutes to get it running
> for playing around. However, getting it truly ready for use will take
> longer than that.
>
> ?http://github.com/cnprog/CNPROG/tree/master

Ah, nice, I was aware of cnprog, but did not know they opened the
source. I will look at it, then,

thanks,

David


From aisaac at american.edu  Mon Aug  3 09:34:10 2009
From: aisaac at american.edu (Alan G Isaac)
Date: Mon, 03 Aug 2009 09:34:10 -0400
Subject: [SciPy-User] Attached multiprocessing parallel
 Differential	Evolution GA code
In-Reply-To: <268756d30908010758q14c9bca2w7b1d6b06e76db707@mail.gmail.com>
References: <268756d30908010758q14c9bca2w7b1d6b06e76db707@mail.gmail.com>
Message-ID: <4A76E752.3060008@american.edu>

On 8/1/2009 10:58 AM James Phillips apparently wrote:
> Below and attached is my first cut at a Python multiprocessing module
> parallel implementation of the Differential Evolution genetic
> algorithm.  The code is explicitly in the public domain.


Thanks for posting this.  I confess I was unaware of the
differential evolution approach to optimization.

Are you proposing adding this functionality to scipy.optimize?
Or what? And if so, how to proceed?

Alan Isaac




From ebonak at hotmail.com  Mon Aug  3 09:52:05 2009
From: ebonak at hotmail.com (Esmail)
Date: Mon, 03 Aug 2009 09:52:05 -0400
Subject: [SciPy-User] works in gnuplot,
 but not quite in Python/Gnuplot.py - splot (sin(x)/x) * (sin(y)/y)
Message-ID: <h56q26$off$1@ger.gmane.org>

Hello all,

I am trying to generate a plot with Python/Gnuplot.py that I can
create interactively with gnuplot.

----------

   gnuplot> set isosample 40
   gnuplot> set hidden3d
   gnuplot> splot (sin(x)/x) * (sin(y)/y)

----------


gives me a *beautiful* continuous plot.

I am trying to do the same with Python/Gnuplot.py - but no joy.

I have the following short Python code to generate my data which I
then funnel into a file called 'somb.dat'

----------

#!/usr/bin/env python

# somb.py
# compute the sombrero function

from math import sin
from numpy import arange

def f(x, y):
     return (sin(x)/x) * (sin(y)/y)


for x in arange(-10, 11):
     for y in arange(-10, 11):
         print '%3d  %3d   %f' % (x, y, f(x, y))
     print

----------

e.g.,

    somb.py > somb.dat

and I use this to plot the data:

----------
#!/usr/bin/env python

import Gnuplot
g = Gnuplot.Gnuplot(debug=1)

g('set hidden3d')
g('set isosample 40')
g('set datafile missing "nan"')
g.splot(Gnuplot.File('somb.dat', with_='lines linewidth 1'))
raw_input()  # to pause

----------

Is there a way to achieve the same sort of continuous output that
gnuplot generates? I tried using 0.1 as 3rd parameter for arange, but
it didn't help. I suspect the problem is with the nan values (and/or
the numeric representation in Python)?. How does gnuplot handle
this? How can I achieve the same with Python/Gnuplot.py?

Any suggestions/hints?

(I am thrilled that I can use gnuplot via Python)

Thanks,
Esmail



From josef.pktd at gmail.com  Mon Aug  3 11:12:18 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Mon, 3 Aug 2009 11:12:18 -0400
Subject: [SciPy-User] An extra parameter to stats.chisquare ?
In-Reply-To: <57170D20-8B86-4DA6-B5B5-6C2B49FD6EB9@gmail.com>
References: <32A899D6-8D41-458B-B31C-2A9717E04509@gmail.com>
	<1cd32cbb0908021418ib347190u1fc5184050d05a28@mail.gmail.com>
	<57170D20-8B86-4DA6-B5B5-6C2B49FD6EB9@gmail.com>
Message-ID: <1cd32cbb0908030812i1ece6567r86e167ac2ab9a1d9@mail.gmail.com>

On Sun, Aug 2, 2009 at 10:43 PM, Pierre GM<pgmdevlist at gmail.com> wrote:
>
> On Aug 2, 2009, at 5:18 PM, josef.pktd at gmail.com wrote:
>
>> On Sun, Aug 2, 2009 at 4:05 PM, Pierre GM<pgmdevlist at gmail.com> wrote:
>>> All,
>>> stats.chisquare requires a mandatory parameter (frequency of
>>> observations) and an optional argument (theoretical frequencies). In
>>> that second case, I think we have to introduce yet another parameter,
>>> p, corresponding to the number of parameters of the theoretical
>>> distribution: the number of degrees-of-freedom for the chisqprob
>>> would
>>> then be k-p (with k the sample size), instead of the current k-1. Of
>>> course, we can set p=1 by default.
>>> Comments ?
>>> P.
>>
>> No disagreement with adding e.g. "ddof" as additional keyword
>> parameter. This might be also relevant for other tests where the data
>> can be based on prior estimation. (The same problem shows up with
>> tests after regression.)
>>
>> For the chisquare test, I'm not sure about the theory, since I only
>> used chisquare without estimating parameters. Wikipedia seems a bit
>> ambiguous:
>
> Well, I guess we'd need a "real" statistician. From what I gathered,
> when you fit your N observations to a distribution with p parameters
> (eg, 2 for normal, 1 for logseries), the ddof is N-(p+1): http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm
> However, that works as long as all the parameters are independent. If
> one depends from the others or can be related to the others, we switch
> from p independent parameters to (p-1), thus giving a dof of N-p. So,
> wikipedia looks right.


how about this change, then

def chisquare(f_obs, f_exp=None, ddof=0):
      ....
      return chisq, chisqprob(chisq, k-1-ddof)

default is when no parameters are estimated (dof=k-1), e.g. create
random sample and compare to distribution with *given* parameters.
I didn't find a reference for your statement that the parameter
(estimators) have to be independent. I only found more references to
efficient maximum likelihood estimation, in which case it is k-1-p. If
the parameters are estimated in a different way, then then the dof
should be between k-1-p and k-1, or it is possible that the asymptotic
distributions is not a chisquare, in which case the this test is not
appropriate.

We could add something like this in the notes.

>
>> If you need goodness-of-fit tests, then you could also try my
>> implementation of a more general class of gof statistics (power
>> discrepancy). I sent it to the mailing list a while ago.
>
> ?From 04/27 ? I def'ny gonna give it a try, thanks a lot.

If you are testing discrete distribution, then there is also a helper function
in test_discrete_basic.py in stats/tests

def check_discrete_chisquare(distfn, arg, rvs, alpha, msg):
    '''perform chisquare test for random sample of a discrete distribution

The main point of the function is to do a equal weight binning, to
maintain a minimum expected frequency in each cell, which is
recommended (>=5 expected observations for the chisquare distribution
to be an appropriate approximation).
(Note: the function is not fully cleaned up, and not tested on its
own, but used for all discrete distributions in the stats.tests)

Josef


> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From robert.kern at gmail.com  Mon Aug  3 11:58:04 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Mon, 3 Aug 2009 10:58:04 -0500
Subject: [SciPy-User] SciPy Foundation
In-Reply-To: <5b8d13220908030524l2c20b6a8o8c297f2b3ea6a562@mail.gmail.com>
References: <wl8fxcc6aua.fsf@glup.physics.ucf.edu>
	<5b8d13220908010921m49b218f2ga13b5b75a2aebaff@mail.gmail.com> 
	<75285228-7E31-4659-A780-F79C9E659509@cs.toronto.edu>
	<3d375d730908012148r233fb0bel1ead4106e8a12f23@mail.gmail.com> 
	<5b8d13220908030524l2c20b6a8o8c297f2b3ea6a562@mail.gmail.com>
Message-ID: <3d375d730908030858q62eab09v5f414cefcc45b740@mail.gmail.com>

On Mon, Aug 3, 2009 at 07:24, David Cournapeau<cournape at gmail.com> wrote:
> On Sun, Aug 2, 2009 at 1:48 PM, Robert Kern<robert.kern at gmail.com> wrote:
>
>>
>> cnprog is a StackOverflow clone written for a Chinese programming
>> community using Django. It appears to be reasonably complete and has
>> English localizations. It took me about 5 minutes to get it running
>> for playing around. However, getting it truly ready for use will take
>> longer than that.
>>
>> ?http://github.com/cnprog/CNPROG/tree/master
>
> Ah, nice, I was aware of cnprog, but did not know they opened the
> source. I will look at it, then,

I have a branch here:

  http://github.com/rkern/CNPROG/tree/master

that pulls in the English translations from the branch that runs

  http://qa.nmrwiki.org/

-- 
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


From bsouthey at gmail.com  Mon Aug  3 12:37:55 2009
From: bsouthey at gmail.com (Bruce Southey)
Date: Mon, 03 Aug 2009 11:37:55 -0500
Subject: [SciPy-User] adding distributions from hydroclimpy to
	stats.distributions
In-Reply-To: <1cd32cbb0908022120i160ca529gd7942e4a76acee73@mail.gmail.com>
References: <1cd32cbb0908020641k4649315ahbd1a7ba986fd9ef9@mail.gmail.com>	<82156633-5678-4485-9ECA-FB33CB540691@gmail.com>
	<1cd32cbb0908022120i160ca529gd7942e4a76acee73@mail.gmail.com>
Message-ID: <4A771263.1040209@gmail.com>

On 08/02/2009 11:20 PM, josef.pktd at gmail.com wrote:
> On Sun, Aug 2, 2009 at 3:52 PM, Pierre GM<pgmdevlist at gmail.com>  wrote:
>    
>> On Aug 2, 2009, at 9:41 AM, josef.pktd at gmail.com wrote:
>>      
>>> I looked briefly at the distributions in hydroclimpy
>>> http://projects.scipy.org/scikits/browser/trunk/hydroclimpy/scikits/hydroclimpy/stats/extradistributions.py
>>>
>>> my first impression:
>>>
>>> kappa, glogistic, gennorm and wakeby
>>> can be added almost without changes to stats distributions, since they
>>> are already in the standard format
>>> cosmetic changes: add longname and extradocs (from module docstring)
>>>        
>> Agreed. I still have an issue about defining a proper template for
>> describing the distributions (eqns for pdf/cdf/ppf, example of usage,
>> plots...), hence the nudge. What are our doc exegetes' recommendations ?
>>      
>
> currently only the class docstring gets distribution specific
> information, done in rv_generic.__init__,
> replacing name of distribution and shape parameters, and adding
> extradocs. The example in the class docstring is just a generic
> example. More description of the distributions could simply be added
> to the extradocs, which show up in the help at the bottom of the
> individual distribution class/instance docstrings. For example the
> available formulas for the distribution pdf, cdf, sf, ..., (info from
> Travis manual) could be included in the extradoc.)
>
> For methods there is currently no setup for individualized information
> and examples. But this could be changed, if we really want to.
> However, since methods for all distribution follow the same pattern, I
> don't see really the need. Better overall help and examples, e.g. in
> the tutorial, would be useful.
>
> Any good ideas for improvements?
>    
(Not sure why this is on Scipy-users list becasue it is more appropriate 
on the developers list). There really are no nice ways to document 
distributions because of the conflict between the similarity of 
distributions and unique aspects of each distribution.  Also, there has 
to be a way to adequately describe the parameterization of the 
distribution especially when distributions such as the gamma vary in 
parameterization.

A generic description of a distribution class has the advantage that it 
tends to minimize the repetitive description of the common features (and 
functions) of distributions which makes the general usage very easy to 
describe. For example, SAS has generic PDF and CDF functions where the 
distribution is an argument:
http://support.sas.com/onlinedoc/913/getDoc/en/lrdict.hlp/a000270634.htm
http://support.sas.com/onlinedoc/913/getDoc/en/lrdict.hlp/a000208980.htm

But the problem with that is that it avoids that the fact that the 
actual details of these features and hence functions vary between 
distributions. Thus you are forced to look at each distribution to 
understand how to use these functions for that specific distribution 
which removes the advantage of the generic classes.

Alternatively there is the distribution orientated approach used by R 
where you describe the background of the distribution and provide the 
associated functions. For example, in R '?Normal' and '?FDist' return 
the normal and F distributions, respectively, and provide associated 
functions. This allows the unique features of each distribution but 
tends to lose the generic features of the distribution.  But the problem 
I find with R's approach is that you can not get specific help on 
individual functions. For example, at the R prompt, '?pf' goes back to 
the help on the F distribution as a whole and you have to find the 
actual pf function in all that material.

Note that SAS provides common names to widely used probability functions 
and inverses such as probit and probnorm for the Normal distribution, 
finv and probf for the F Distribution, etc. I find this approach 
consistent with numpy/scipy approach where the help on the function 
provide the what the functions does, the arguments and return values but 
does not provide any background of the distribution. For example, the 
SAS probf function 'Returns the probability from an F distribution':
http://support.sas.com/onlinedoc/913/getDoc/en/lrdict.hlp/a000245930.htm

A possible approach would be to have the following structure:
1) Generic documentation orientated to 'power users' that provides the 
basic documentation as included in distributions.py but ignores  
distribution-specific details. This tends to follow SAS's approach where 
the distribution is an argument to the generic function.

2) Distribution-specific documentation that adapts the generic 
documentation to the include the distribution-specific parameters like R 
does. The problem here for the current code is that each distribution 
must have it's own documentation.

3) Documentation on widely used functions that provide probabilities 
(and associated inverse functions) for widely used distributions like 
SAS does. (Okay these functions do not really exist in distribution.py 
but chisqprob, zprob and fprob exist in stats.py.)

It may be possible to combine 1) and 2) by having each distribution 
extend the generic documentation to include the relevant 
distribution-specific parameters. But that may be too complex to create.

>> I wish distributions were classes instead of class instances.
> Ah, don't get me started on that either...
>    
I have only briefly browsed the distribution code but I can see that 
number of distributions involved makes the original design cumbersome 
especially adding less common and more unusual distributions.  So my 2 
cents on this is that you both have the opportunity to propose a scipy 
PEP on the developers list to describe why it should be changed. That is 
also likely to influence the documentation.

Regards
Bruce


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From amd405 at psu.edu  Mon Aug  3 12:49:25 2009
From: amd405 at psu.edu (Ashley DaSilva)
Date: Mon, 3 Aug 2009 12:49:25 -0400
Subject: [SciPy-User] fsolve with restriction on variable
Message-ID: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>

Hello,

I am using fsolve to solve a function, f(v), v=[x,y,z] is a list of three
variables. However, I have a factor in f which contains (1-x**2)**(7./2).
So, when I do the following,
fsolve(f,x0)
the code eventually tries x= -1.57, which clearly produces an error in my
function due to the power 7./2.

I know that the solution for x should be between 0 and 1, is there a way to
put this restriction on x while using fsolve?
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From sccolbert at gmail.com  Mon Aug  3 13:00:32 2009
From: sccolbert at gmail.com (Chris Colbert)
Date: Mon, 3 Aug 2009 13:00:32 -0400
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
Message-ID: <7f014ea60908031000ob5da59ei2aedae593d6771db@mail.gmail.com>

you can use one of the constrained solvers here:
http://docs.scipy.org/doc/scipy/reference/optimize.html


On Mon, Aug 3, 2009 at 12:49 PM, Ashley DaSilva<amd405 at psu.edu> wrote:
> Hello,
>
> I am using fsolve to solve a function, f(v), v=[x,y,z] is a list of three
> variables. However, I have a factor in f which contains (1-x**2)**(7./2).
> So, when I do the following,
> fsolve(f,x0)
> the code eventually tries x= -1.57, which clearly produces an error in my
> function due to the power 7./2.
>
> I know that the solution for x should be between 0 and 1, is there a way to
> put this restriction on x while using fsolve?
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>


From ebonak at hotmail.com  Mon Aug  3 13:17:12 2009
From: ebonak at hotmail.com (Esmail)
Date: Mon, 03 Aug 2009 13:17:12 -0400
Subject: [SciPy-User] works in gnuplot,
 but not quite in Python/Gnuplot.py - splot (sin(x)/x) * (sin(y)/y)
In-Reply-To: <h56q26$off$1@ger.gmane.org>
References: <h56q26$off$1@ger.gmane.org>
Message-ID: <h5762o$1jl$1@ger.gmane.org>

oops .. never mind, I found the problem, it had to
do with my print statement.



From amd405 at psu.edu  Mon Aug  3 13:24:40 2009
From: amd405 at psu.edu (Ashley DaSilva)
Date: Mon, 3 Aug 2009 13:24:40 -0400
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <7f014ea60908031000ob5da59ei2aedae593d6771db@mail.gmail.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
	<7f014ea60908031000ob5da59ei2aedae593d6771db@mail.gmail.com>
Message-ID: <f1dbe1be0908031024q334c7366s1d1e5b2f34047ebd@mail.gmail.com>

Thanks for the reply.

I see that there are some constrained optimization techniques, but I don't
want to find the minimum/maximum of my function, I want to find the root. I
also see that there are some scalar root finding techniques that allow
constraints (brentq,brenth,ridder,bisect). But my function is multivariate,
so these will not work for me.



On Mon, Aug 3, 2009 at 1:00 PM, Chris Colbert <sccolbert at gmail.com> wrote:

> you can use one of the constrained solvers here:
> http://docs.scipy.org/doc/scipy/reference/optimize.html
>
>
> On Mon, Aug 3, 2009 at 12:49 PM, Ashley DaSilva<amd405 at psu.edu> wrote:
> > Hello,
> >
> > I am using fsolve to solve a function, f(v), v=[x,y,z] is a list of three
> > variables. However, I have a factor in f which contains (1-x**2)**(7./2).
> > So, when I do the following,
> > fsolve(f,x0)
> > the code eventually tries x= -1.57, which clearly produces an error in my
> > function due to the power 7./2.
> >
> > I know that the solution for x should be between 0 and 1, is there a way
> to
> > put this restriction on x while using fsolve?
> >
> > _______________________________________________
> > SciPy-User mailing list
> > SciPy-User at scipy.org
> > http://mail.scipy.org/mailman/listinfo/scipy-user
> >
> >
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From prabhu at aero.iitb.ac.in  Mon Aug  3 13:20:28 2009
From: prabhu at aero.iitb.ac.in (Prabhu Ramachandran)
Date: Mon, 03 Aug 2009 22:50:28 +0530
Subject: [SciPy-User] [SciPy-dev] SciPy Foundation
In-Reply-To: <wl8fxcc6aua.fsf@glup.physics.ucf.edu>
References: <wl8fxcc6aua.fsf@glup.physics.ucf.edu>
Message-ID: <4A771C5C.4060702@aero.iitb.ac.in>

On 07/31/09 22:36, Joe Harrington wrote:
> About sixteen months ago, I launched the SciPy Documentation Project
> and its Marathon.  Dozens pitched in and now numpy docs are rapidly
> approaching a professional level.  The "pink wave" ("Needs Review"
> status) is at 56% today!  There is consensus among doc writers that
> much of the rest can be labeled in the "unimportant" category, so
> we're close to starting the review push (hold your fire, there is a
> web site mod to be done first).
> 
> We're also nearing the end of the summer, and it's time to look ahead.
> The path for docs is clear, but the path for SciPy is not.  I think
> our weakest area right now is organization of the project.  There is
> no consensus-based plan for improvement of the whole toward a stated
> goal, no centralized coordination of work, and no funded work focused
> on many of our weaknesses, notwithstanding my doc effort and what
> Enthought does for code.

Thank you for your efforts!

I believe I will be able to help this effort in various ways over the 
next few years from India as part of a large government grant.  I do not 
have the time to discuss it here at the moment but I will be at SciPy09 
and would love to discuss it there in person.  I will also be talking 
briefly about our overall goals there.  Specifically see:

  http://conference.scipy.org/abstract?id=13

regards,
prabhu


From harald.schilly at gmail.com  Mon Aug  3 13:27:31 2009
From: harald.schilly at gmail.com (Harald Schilly)
Date: Mon, 3 Aug 2009 19:27:31 +0200
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <f1dbe1be0908031024q334c7366s1d1e5b2f34047ebd@mail.gmail.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com> 
	<7f014ea60908031000ob5da59ei2aedae593d6771db@mail.gmail.com> 
	<f1dbe1be0908031024q334c7366s1d1e5b2f34047ebd@mail.gmail.com>
Message-ID: <20548feb0908031027l39255170x996c903ad9c51a40@mail.gmail.com>

On Mon, Aug 3, 2009 at 19:24, Ashley DaSilva<amd405 at psu.edu> wrote:
> t I don't
> want to find the minimum/maximum of my function, I want to find the root.

Very generally speaking, you can always find the root by minimization,
if you square your function (simply because no negative values are
possible)!

H


From awesomeashley527 at gmail.com  Mon Aug  3 13:34:24 2009
From: awesomeashley527 at gmail.com (Ashley DaSilva)
Date: Mon, 3 Aug 2009 13:34:24 -0400
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <20548feb0908031027l39255170x996c903ad9c51a40@mail.gmail.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
	<7f014ea60908031000ob5da59ei2aedae593d6771db@mail.gmail.com>
	<f1dbe1be0908031024q334c7366s1d1e5b2f34047ebd@mail.gmail.com>
	<20548feb0908031027l39255170x996c903ad9c51a40@mail.gmail.com>
Message-ID: <f1dbe1be0908031034g95081c4sb0bc21fb7325262f@mail.gmail.com>

Ahh, of course. I should have thought of this  :)   I will try it, thanks
for the suggestion.


Ashley



On Mon, Aug 3, 2009 at 1:27 PM, Harald Schilly <harald.schilly at gmail.com>wrote:

> On Mon, Aug 3, 2009 at 19:24, Ashley DaSilva<amd405 at psu.edu> wrote:
> > t I don't
> > want to find the minimum/maximum of my function, I want to find the root.
>
> Very generally speaking, you can always find the root by minimization,
> if you square your function (simply because no negative values are
> possible)!
>
> H
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From warren.weckesser at enthought.com  Mon Aug  3 13:55:24 2009
From: warren.weckesser at enthought.com (Warren Weckesser)
Date: Mon, 03 Aug 2009 12:55:24 -0500
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <20548feb0908031027l39255170x996c903ad9c51a40@mail.gmail.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
	<7f014ea60908031000ob5da59ei2aedae593d6771db@mail.gmail.com>
	<f1dbe1be0908031024q334c7366s1d1e5b2f34047ebd@mail.gmail.com>
	<20548feb0908031027l39255170x996c903ad9c51a40@mail.gmail.com>
Message-ID: <4A77248C.1050202@enthought.com>

Harald Schilly wrote:
> On Mon, Aug 3, 2009 at 19:24, Ashley DaSilva<amd405 at psu.edu> wrote:
>   
>> t I don't
>> want to find the minimum/maximum of my function, I want to find the root.
>>     
>
> Very generally speaking, you can always find the root by minimization,
> if you square your function (simply because no negative values are
> possible)!
>
> H
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>   
But this is generally a poor method for finding a root.  It kills the
quadratic convergence of Newton's method, which is at the heart (in some
form or another) of most good root-finding algorithms.

Warren


-- 
Warren Weckesser
Enthought, Inc.
515 Congress Avenue, Suite 2100
Austin, TX  78701
512-536-1057



From josef.pktd at gmail.com  Mon Aug  3 14:07:21 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Mon, 3 Aug 2009 14:07:21 -0400
Subject: [SciPy-User] adding distributions from hydroclimpy to
	stats.distributions
In-Reply-To: <4A771263.1040209@gmail.com>
References: <1cd32cbb0908020641k4649315ahbd1a7ba986fd9ef9@mail.gmail.com>
	<82156633-5678-4485-9ECA-FB33CB540691@gmail.com>
	<1cd32cbb0908022120i160ca529gd7942e4a76acee73@mail.gmail.com>
	<4A771263.1040209@gmail.com>
Message-ID: <1cd32cbb0908031107t28479cd9ucc34688385861277@mail.gmail.com>

On Mon, Aug 3, 2009 at 12:37 PM, Bruce Southey<bsouthey at gmail.com> wrote:
> On 08/02/2009 11:20 PM, josef.pktd at gmail.com wrote:
>
> On Sun, Aug 2, 2009 at 3:52 PM, Pierre GM<pgmdevlist at gmail.com> wrote:
>
>
> On Aug 2, 2009, at 9:41 AM, josef.pktd at gmail.com wrote:
>
>
> I looked briefly at the distributions in hydroclimpy
> http://projects.scipy.org/scikits/browser/trunk/hydroclimpy/scikits/hydroclimpy/stats/extradistributions.py
>
> my first impression:
>
> kappa, glogistic, gennorm and wakeby
> can be added almost without changes to stats distributions, since they
> are already in the standard format
> cosmetic changes: add longname and extradocs (from module docstring)
>
>
> Agreed. I still have an issue about defining a proper template for
> describing the distributions (eqns for pdf/cdf/ppf, example of usage,
> plots...), hence the nudge. What are our doc exegetes' recommendations ?
>
>
> currently only the class docstring gets distribution specific
> information, done in rv_generic.__init__,
> replacing name of distribution and shape parameters, and adding
> extradocs. The example in the class docstring is just a generic
> example. More description of the distributions could simply be added
> to the extradocs, which show up in the help at the bottom of the
> individual distribution class/instance docstrings. For example the
> available formulas for the distribution pdf, cdf, sf, ..., (info from
> Travis manual) could be included in the extradoc.)
>
> For methods there is currently no setup for individualized information
> and examples. But this could be changed, if we really want to.
> However, since methods for all distribution follow the same pattern, I
> don't see really the need. Better overall help and examples, e.g. in
> the tutorial, would be useful.
>
> Any good ideas for improvements?
>
>
> (Not sure why this is on Scipy-users list becasue it is more appropriate on
> the developers list). There really are no nice ways to document
> distributions because of the conflict between the similarity of
> distributions and unique aspects of each distribution.? Also, there has to
> be a way to adequately describe the parameterization of the distribution
> especially when distributions such as the gamma vary in parameterization.
>
> A generic description of a distribution class has the advantage that it
> tends to minimize the repetitive description of the common features (and
> functions) of distributions which makes the general usage very easy to
> describe. For example, SAS has generic PDF and CDF functions where the
> distribution is an argument:
> http://support.sas.com/onlinedoc/913/getDoc/en/lrdict.hlp/a000270634.htm
> http://support.sas.com/onlinedoc/913/getDoc/en/lrdict.hlp/a000208980.htm
>
> But the problem with that is that it avoids that the fact that the actual
> details of these features and hence functions vary between distributions.
> Thus you are forced to look at each distribution to understand how to use
> these functions for that specific distribution which removes the advantage
> of the generic classes.
>
> Alternatively there is the distribution orientated approach used by R where
> you describe the background of the distribution and provide the associated
> functions. For example, in R '?Normal' and '?FDist' return the normal and F
> distributions, respectively, and provide associated functions. This allows
> the unique features of each distribution but tends to lose the generic
> features of the distribution.? But the problem I find with R's approach is
> that you can not get specific help on individual functions. For example, at
> the R prompt, '?pf' goes back to the help on the F distribution as a whole
> and you have to find the actual pf function in all that material.
>
> Note that SAS provides common names to widely used probability functions and
> inverses such as probit and probnorm for the Normal distribution, finv and
> probf for the F Distribution, etc. I find this approach consistent with
> numpy/scipy approach where the help on the function provide the what the
> functions does, the arguments and return values but does not provide any
> background of the distribution. For example, the SAS probf function 'Returns
> the probability from an F distribution':
> http://support.sas.com/onlinedoc/913/getDoc/en/lrdict.hlp/a000245930.htm
>
> A possible approach would be to have the following structure:
> 1) Generic documentation orientated to 'power users' that provides the basic
> documentation as included in distributions.py but ignores
> distribution-specific details. This tends to follow SAS's approach where the
> distribution is an argument to the generic function.
>
> 2) Distribution-specific documentation that adapts the generic documentation
> to the include the distribution-specific parameters like R does. The problem
> here for the current code is that each distribution must have it's own
> documentation.
>
> 3) Documentation on widely used functions that provide probabilities (and
> associated inverse functions) for widely used distributions like SAS does.
> (Okay these functions do not really exist in distribution.py but chisqprob,
> zprob and fprob exist in stats.py.)
>
> It may be possible to combine 1) and 2) by having each distribution extend
> the generic documentation to include the relevant distribution-specific
> parameters. But that may be too complex to create.
>
> I wish distributions were classes instead of class instances.
>
> Ah, don't get me started on that either...
>
>
> I have only briefly browsed the distribution code but I can see that number
> of distributions involved makes the original design cumbersome especially
> adding less common and more unusual distributions.? So my 2 cents on this is
> that you both have the opportunity to propose a scipy PEP on the developers
> list to describe why it should be changed. That is also likely to influence
> the documentation.
>
> Regards
> Bruce
>

Thanks, for the comparison and links. The two SAS pages are pretty
good and might give me some outside benchmark for the distribution
tests.

just a few quick comments
I started the threads on the user list to see if I get more response
on new functions and distributions, although now it turned into a
developer discussion.

I like in scipy ,and it looks this way also in SAS, that the
distributions are in one consistent structure and location. In R it
always took me some time to find a less common distribution, since
they are spread over several packages.

To your points 1 and 2: I also think we should include more
distribution-specific information, the question is where to put it:
a) just expand current extradocs and keep it attached to
class/instance docstring
b) add automatically adjusted "extradocs" also to each method
c) add additional information to a location in the docs (as rst) but
not in the docstrings, incorporating if possible the pdf/lynx files by
Travis, and some nice graphs

(
and a more generic scipy proposal:
d) add a "demo" feature to scipy, i.e. examples that are in the path
and can be run just by calling them, as in matlab and R
)

I'm in favor of location a) and c) and don't think b) is necessary
because it would be mostly repetitive, but it would be feasible. Given
the work b) would require (writing additional distribution specific
docstrings) I would only expect it to be used for a few popular
distributions. The actual formulas (for pdf, cdf,...) I would prefer
to be in one place.

your point 3): I'm not much in favor of aliases,
the short description for probf in SAS: "Returns the probability from
an F distribution"
is pretty uninformative, which probability, pdf, cdf, sf ?
In contrast, stats.f.cdf  has all the information I need in its name,
and makes reading the code much more informative.

SEP: turning distributions into usable classes.
The main short term fix, I thought about, is to add an __init__ method
to the individual distributions and add the instantiation information
in it. Then the classes could be instantiated and subclassed without
any cut and paste. And different instances could maintain their state
instead of having it spill over if there is a mistake in the program.
However, when it looked liked that Per Brodtkorb and I were the only
ones actively working on the distributions code, I didn't want to risk
breaking existing code.

Josef


From awesomeashley527 at gmail.com  Mon Aug  3 14:13:03 2009
From: awesomeashley527 at gmail.com (Ashley DaSilva)
Date: Mon, 3 Aug 2009 14:13:03 -0400
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <4A77248C.1050202@enthought.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
	<7f014ea60908031000ob5da59ei2aedae593d6771db@mail.gmail.com>
	<f1dbe1be0908031024q334c7366s1d1e5b2f34047ebd@mail.gmail.com>
	<20548feb0908031027l39255170x996c903ad9c51a40@mail.gmail.com>
	<4A77248C.1050202@enthought.com>
Message-ID: <f1dbe1be0908031113l466262b5s8123fefd275ceffd@mail.gmail.com>

Warren, thanks for your input.

Do you know a way to add constraints to fsolve, or some other root finding
technique? If there's no other option, I'll have to go with Harald's
suggestion, even if it is slow to converge.

Also, does anyone know what the input format is for these minimization
techniques (fmin_l_bfgs_b, fmin_tnc, fmin_cobyla), I tried
def f(x):


On Mon, Aug 3, 2009 at 1:55 PM, Warren Weckesser <
warren.weckesser at enthought.com> wrote:

> Harald Schilly wrote:
> > On Mon, Aug 3, 2009 at 19:24, Ashley DaSilva<amd405 at psu.edu> wrote:
> >
> >> t I don't
> >> want to find the minimum/maximum of my function, I want to find the
> root.
> >>
> >
> > Very generally speaking, you can always find the root by minimization,
> > if you square your function (simply because no negative values are
> > possible)!
> >
> > H
> > _______________________________________________
> > SciPy-User mailing list
> > SciPy-User at scipy.org
> > http://mail.scipy.org/mailman/listinfo/scipy-user
> >
> But this is generally a poor method for finding a root.  It kills the
> quadratic convergence of Newton's method, which is at the heart (in some
> form or another) of most good root-finding algorithms.
>
> Warren
>
>
> --
> Warren Weckesser
> Enthought, Inc.
> 515 Congress Avenue, Suite 2100
> Austin, TX  78701
> 512-536-1057
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From awesomeashley527 at gmail.com  Mon Aug  3 14:17:05 2009
From: awesomeashley527 at gmail.com (Ashley DaSilva)
Date: Mon, 3 Aug 2009 14:17:05 -0400
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <f1dbe1be0908031113l466262b5s8123fefd275ceffd@mail.gmail.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
	<7f014ea60908031000ob5da59ei2aedae593d6771db@mail.gmail.com>
	<f1dbe1be0908031024q334c7366s1d1e5b2f34047ebd@mail.gmail.com>
	<20548feb0908031027l39255170x996c903ad9c51a40@mail.gmail.com>
	<4A77248C.1050202@enthought.com>
	<f1dbe1be0908031113l466262b5s8123fefd275ceffd@mail.gmail.com>
Message-ID: <f1dbe1be0908031117s719fc385sbc674b9b647e6c5@mail.gmail.com>

Sorry I pressed send by accident.

Warren, thanks for your input.
>
> Do you know a way to add constraints to fsolve, or some other root finding
> technique? If there's no other option, I'll have to go with Harald's
> suggestion, even if it is slow to converge.
>
> Also, does anyone know what the input format is for these minimization
> techniques (fmin_l_bfgs_b, fmin_tnc, fmin_cobyla), I tried:

def f(x):
   return [ n-P(x), n*u-Q(x), n-N(x)]

where P(x), Q(x), N(x) are defined functions and n,u are global constants. I
get the error
TypeError: unsupported operand type(s) for -: 'list' and 'list'
which I think is referring to the numerical approximation to the gradient,
when it tries to subtract f(x)-f0


Ashley



>
>
>
> On Mon, Aug 3, 2009 at 1:55 PM, Warren Weckesser <
> warren.weckesser at enthought.com> wrote:
>
>> Harald Schilly wrote:
>> > On Mon, Aug 3, 2009 at 19:24, Ashley DaSilva<amd405 at psu.edu> wrote:
>> >
>> >> t I don't
>> >> want to find the minimum/maximum of my function, I want to find the
>> root.
>> >>
>> >
>> > Very generally speaking, you can always find the root by minimization,
>> > if you square your function (simply because no negative values are
>> > possible)!
>> >
>> > H
>> > _______________________________________________
>> > SciPy-User mailing list
>> > SciPy-User at scipy.org
>> > http://mail.scipy.org/mailman/listinfo/scipy-user
>> >
>> But this is generally a poor method for finding a root.  It kills the
>> quadratic convergence of Newton's method, which is at the heart (in some
>> form or another) of most good root-finding algorithms.
>>
>> Warren
>>
>>
>> --
>> Warren Weckesser
>> Enthought, Inc.
>> 515 Congress Avenue, Suite 2100
>> Austin, TX  78701
>> 512-536-1057
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>
>
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From roban at astro.columbia.edu  Mon Aug  3 14:21:12 2009
From: roban at astro.columbia.edu (Roban Hultman Kramer)
Date: Mon, 3 Aug 2009 14:21:12 -0400
Subject: [SciPy-User] better cumulative integration routines (like cumtrapz)
Message-ID: <463180e60908031121m284ad40aie789c50bcb88a270@mail.gmail.com>

Hi,

I'm wondering if anyone can recommend a routine for one-dimensional
cumulative numerical integrals. Obviously there is
scipy.integrate.cumtrapz, but I was wondering if anyone knows of an
implementation of a more sophisticated numerical integration algorithm
that can take not just end points, but a whole series of points, and
efficiently approximate the definite integral at each point. In other
words, I have a function f(x), and want the value of the definite
integral from x0 to [x1, x2, x3, x4, ...]

Ideally I would want to be able to specify the desired precision of
the results, and also get an estimate of the achieved precision.
Essentially I'm asking for scipy.integrate.quad, but optimized for a
whole set of points. For the most part my functions are well behaved,
so I don't need fancy singularity-handling, though built-in
infinite-limit handling is great.

The obvious hack is to compute the definite integral separately for
each interval in my series of points, then add them together, but I
was wondering if anyone knows of an algorithm/implementation that does
this efficiently. I'd be willing to work on implementing something
myself, if you have pointers to an algorithm.

Thanks!

-Roban


From robert.kern at gmail.com  Mon Aug  3 14:21:14 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Mon, 3 Aug 2009 13:21:14 -0500
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <f1dbe1be0908031117s719fc385sbc674b9b647e6c5@mail.gmail.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com> 
	<7f014ea60908031000ob5da59ei2aedae593d6771db@mail.gmail.com> 
	<f1dbe1be0908031024q334c7366s1d1e5b2f34047ebd@mail.gmail.com> 
	<20548feb0908031027l39255170x996c903ad9c51a40@mail.gmail.com> 
	<4A77248C.1050202@enthought.com>
	<f1dbe1be0908031113l466262b5s8123fefd275ceffd@mail.gmail.com> 
	<f1dbe1be0908031117s719fc385sbc674b9b647e6c5@mail.gmail.com>
Message-ID: <3d375d730908031121k2e6f96d9j26fdf18791114d05@mail.gmail.com>

On Mon, Aug 3, 2009 at 13:17, Ashley DaSilva<awesomeashley527 at gmail.com> wrote:
> Sorry I pressed send by accident.
>
>> Warren, thanks for your input.
>>
>> Do you know a way to add constraints to fsolve, or some other root finding
>> technique? If there's no other option, I'll have to go with Harald's
>> suggestion, even if it is slow to converge.
>>
>> Also, does anyone know what the input format is for these minimization
>> techniques (fmin_l_bfgs_b, fmin_tnc, fmin_cobyla), I tried:
>
> def f(x):
> ?? return [ n-P(x), n*u-Q(x), n-N(x)]
>
> where P(x), Q(x), N(x) are defined functions and n,u are global constants. I
> get the error
> TypeError: unsupported operand type(s) for -: 'list' and 'list'
> which I think is referring to the numerical approximation to the gradient,
> when it tries to subtract f(x)-f0

Always copy-and-paste the traceback, not just the final message. For
the fmin_cobyla constraints, you don't pass a function that returns a
list. You pass a list of functions.

-- 
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


From pgmdevlist at gmail.com  Mon Aug  3 14:29:20 2009
From: pgmdevlist at gmail.com (Pierre GM)
Date: Mon, 3 Aug 2009 14:29:20 -0400
Subject: [SciPy-User] An extra parameter to stats.chisquare ?
In-Reply-To: <1cd32cbb0908030812i1ece6567r86e167ac2ab9a1d9@mail.gmail.com>
References: <32A899D6-8D41-458B-B31C-2A9717E04509@gmail.com>
	<1cd32cbb0908021418ib347190u1fc5184050d05a28@mail.gmail.com>
	<57170D20-8B86-4DA6-B5B5-6C2B49FD6EB9@gmail.com>
	<1cd32cbb0908030812i1ece6567r86e167ac2ab9a1d9@mail.gmail.com>
Message-ID: <37B9B008-CD93-4735-AFE3-4A28F8BC42E1@gmail.com>


On Aug 3, 2009, at 11:12 AM, josef.pktd at gmail.com wrote:

> On Sun, Aug 2, 2009 at 10:43 PM, Pierre GM<pgmdevlist at gmail.com>  
> wrote:
>>
>>
>> Well, I guess we'd need a "real" statistician. From what I gathered,
>> when you fit your N observations to a distribution with p parameters
>> (eg, 2 for normal, 1 for logseries), the ddof is N-(p+1): http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm
>> However, that works as long as all the parameters are independent. If
>> one depends from the others or can be related to the others, we  
>> switch
>> from p independent parameters to (p-1), thus giving a dof of N-p. So,
>> wikipedia looks right.
>
>
> how about this change, then
>
> def chisquare(f_obs, f_exp=None, ddof=0):
>      ....
>      return chisq, chisqprob(chisq, k-1-ddof)
>
> default is when no parameters are estimated (dof=k-1), e.g. create
> random sample and compare to distribution with *given* parameters.

Looks cool.

> I didn't find a reference for your statement that the parameter
> (estimators) have to be independent.

Check the example here:
http://books.google.com/books?id=3rC5A1doUcwC&pg=PA12

The model has 2 parameters, but only one is important, so the final  
ddof is k-2 instead of k-(2+1)

> If you are testing discrete distribution, then there is also a  
> helper function
> in test_discrete_basic.py in stats/tests

Oh OK. Didn't know about that.

> def check_discrete_chisquare(distfn, arg, rvs, alpha, msg):
>    '''perform chisquare test for random sample of a discrete  
> distribution
>
> The main point of the function is to do a equal weight binning, to
> maintain a minimum expected frequency in each cell, which is
> recommended (>=5 expected observations for the chisquare distribution
> to be an appropriate approximation).

Bah, this binning doesn't really matter when you wanna use X2 to  
compare a sample to an actual distribution, does it ? And anyway, this  
function suffers from the same problem as stats.chisquare: the ddof  
are not taken into account.
All in all, I like you chisquare function best.




From awesomeashley527 at gmail.com  Mon Aug  3 14:42:33 2009
From: awesomeashley527 at gmail.com (Ashley DaSilva)
Date: Mon, 3 Aug 2009 14:42:33 -0400
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <3d375d730908031121k2e6f96d9j26fdf18791114d05@mail.gmail.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
	<7f014ea60908031000ob5da59ei2aedae593d6771db@mail.gmail.com>
	<f1dbe1be0908031024q334c7366s1d1e5b2f34047ebd@mail.gmail.com>
	<20548feb0908031027l39255170x996c903ad9c51a40@mail.gmail.com>
	<4A77248C.1050202@enthought.com>
	<f1dbe1be0908031113l466262b5s8123fefd275ceffd@mail.gmail.com>
	<f1dbe1be0908031117s719fc385sbc674b9b647e6c5@mail.gmail.com>
	<3d375d730908031121k2e6f96d9j26fdf18791114d05@mail.gmail.com>
Message-ID: <f1dbe1be0908031142l4a286f21l9a081e4a9004ae73@mail.gmail.com>

Sorry, I will send the traceback.
I am using fmin_tnc, where I have: import fmin_tnc as fmin

Traceback (most recent call last):
  File "hf_improved.py", line 190, in <module>

solution=fmin(f2,x0,args=(eE,),bounds=[(0,1),(0,None),(0,None)],approx_grad=True)
  File "/usr/lib/python2.5/site-packages/scipy/optimize/tnc.py", line 246,
in fmin_tnc
    fmin, ftol, xtol, pgtol, rescale)
  File "/usr/lib/python2.5/site-packages/scipy/optimize/tnc.py", line 200,
in func_and_grad
    g = approx_fprime(x, func, epsilon, *args)
  File "/usr/lib/python2.5/site-packages/scipy/optimize/optimize.py", line
617, in approx_fprime
    grad[k] = (f(*((xk+ei,)+args)) - f0)/epsilon
TypeError: unsupported operand type(s) for -: 'list' and 'list'


Other information that might be useful:
def f2(v,E):
   u=v[0]
   return [(E-P(v))**2, (E*u-Q(v))**2, (1-N(v))**2]

where P, Q, N, are defined functions.


Ashley


On Mon, Aug 3, 2009 at 2:21 PM, Robert Kern <robert.kern at gmail.com> wrote:

> On Mon, Aug 3, 2009 at 13:17, Ashley DaSilva<awesomeashley527 at gmail.com>
> wrote:
> > Sorry I pressed send by accident.
> >
> >> Warren, thanks for your input.
> >>
> >> Do you know a way to add constraints to fsolve, or some other root
> finding
> >> technique? If there's no other option, I'll have to go with Harald's
> >> suggestion, even if it is slow to converge.
> >>
> >> Also, does anyone know what the input format is for these minimization
> >> techniques (fmin_l_bfgs_b, fmin_tnc, fmin_cobyla), I tried:
> >
> > def f(x):
> >    return [ n-P(x), n*u-Q(x), n-N(x)]
> >
> > where P(x), Q(x), N(x) are defined functions and n,u are global
> constants. I
> > get the error
> > TypeError: unsupported operand type(s) for -: 'list' and 'list'
> > which I think is referring to the numerical approximation to the
> gradient,
> > when it tries to subtract f(x)-f0
>
> Always copy-and-paste the traceback, not just the final message. For
> the fmin_cobyla constraints, you don't pass a function that returns a
> list. You pass a list of functions.
>
> --
> 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
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From robert.kern at gmail.com  Mon Aug  3 14:44:56 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Mon, 3 Aug 2009 13:44:56 -0500
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <f1dbe1be0908031142l4a286f21l9a081e4a9004ae73@mail.gmail.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com> 
	<7f014ea60908031000ob5da59ei2aedae593d6771db@mail.gmail.com> 
	<f1dbe1be0908031024q334c7366s1d1e5b2f34047ebd@mail.gmail.com> 
	<20548feb0908031027l39255170x996c903ad9c51a40@mail.gmail.com> 
	<4A77248C.1050202@enthought.com>
	<f1dbe1be0908031113l466262b5s8123fefd275ceffd@mail.gmail.com> 
	<f1dbe1be0908031117s719fc385sbc674b9b647e6c5@mail.gmail.com> 
	<3d375d730908031121k2e6f96d9j26fdf18791114d05@mail.gmail.com> 
	<f1dbe1be0908031142l4a286f21l9a081e4a9004ae73@mail.gmail.com>
Message-ID: <3d375d730908031144w5ffeb25jbed3426f8afafe35@mail.gmail.com>

On Mon, Aug 3, 2009 at 13:42, Ashley DaSilva<awesomeashley527 at gmail.com> wrote:
> Sorry, I will send the traceback.
> I am using fmin_tnc, where I have: import fmin_tnc as fmin
>
> Traceback (most recent call last):
> ? File "hf_improved.py", line 190, in <module>
>
> solution=fmin(f2,x0,args=(eE,),bounds=[(0,1),(0,None),(0,None)],approx_grad=True)
> ? File "/usr/lib/python2.5/site-packages/scipy/optimize/tnc.py", line 246,
> in fmin_tnc
> ??? fmin, ftol, xtol, pgtol, rescale)
> ? File "/usr/lib/python2.5/site-packages/scipy/optimize/tnc.py", line 200,
> in func_and_grad
> ??? g = approx_fprime(x, func, epsilon, *args)
> ? File "/usr/lib/python2.5/site-packages/scipy/optimize/optimize.py", line
> 617, in approx_fprime
> ??? grad[k] = (f(*((xk+ei,)+args)) - f0)/epsilon
> TypeError: unsupported operand type(s) for -: 'list' and 'list'
>
>
> Other information that might be useful:
> def f2(v,E):
> ?? u=v[0]
> ?? return [(E-P(v))**2, (E*u-Q(v))**2, (1-N(v))**2]
>
> where P, Q, N, are defined functions.

Oh, you mean for the function itself. Return an array, not a list.

-- 
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


From pgmdevlist at gmail.com  Mon Aug  3 14:45:55 2009
From: pgmdevlist at gmail.com (Pierre GM)
Date: Mon, 3 Aug 2009 14:45:55 -0400
Subject: [SciPy-User] Documenting distributions {was Re: adding
	distributions from hydroclimpy to stats.distributions}
In-Reply-To: <1cd32cbb0908031107t28479cd9ucc34688385861277@mail.gmail.com>
References: <1cd32cbb0908020641k4649315ahbd1a7ba986fd9ef9@mail.gmail.com>
	<82156633-5678-4485-9ECA-FB33CB540691@gmail.com>
	<1cd32cbb0908022120i160ca529gd7942e4a76acee73@mail.gmail.com>
	<4A771263.1040209@gmail.com>
	<1cd32cbb0908031107t28479cd9ucc34688385861277@mail.gmail.com>
Message-ID: <FA1591B7-7BD9-468A-9662-206811ECAA89@gmail.com>

All,
I'm afraid we're hitting a wall here. No matter how smart we are, and  
how convenient it is, an automatic documentation is *not* the panacea.  
I don't think we should pursue in Josef's direction of overloading the  
'extradoc' parameter.

I think we should on the contrary the docstring of a distribution  
quite short, and move the details to an independent page in the docs.
Josef's idea of a demo is something I'd like to see in the doc, and  
yes, we could implement a function to plot one or several graphs  
automatically. But this should go on a documentation, not in a  
docstring.

On Aug 3, 2009, at 2:07 PM, josef.pktd at gmail.com wrote:
>>
>> A generic description of a distribution class has the advantage  
>> that it
>> tends to minimize the repetitive description of the common features  
>> (and
>> functions) of distributions which makes the general usage very easy  
>> to
>> describe. For example, SAS has generic PDF and CDF functions where  
>> the
>> distribution is an argument:
>> http://support.sas.com/onlinedoc/913/getDoc/en/lrdict.hlp/a000270634.htm
>> http://support.sas.com/onlinedoc/913/getDoc/en/lrdict.hlp/a000208980.htm
>>
>> But the problem with that is that it avoids that the fact that the  
>> actual
>> details of these features and hence functions vary between  
>> distributions.
>> Thus you are forced to look at each distribution to understand how  
>> to use
>> these functions for that specific distribution which removes the  
>> advantage
>> of the generic classes.
>>
>> Alternatively there is the distribution orientated approach used by  
>> R where
>> you describe the background of the distribution and provide the  
>> associated
>> functions. For example, in R '?Normal' and '?FDist' return the  
>> normal and F
>> distributions, respectively, and provide associated functions. This  
>> allows
>> the unique features of each distribution but tends to lose the  
>> generic
>> features of the distribution.  But the problem I find with R's  
>> approach is
>> that you can not get specific help on individual functions. For  
>> example, at
>> the R prompt, '?pf' goes back to the help on the F distribution as  
>> a whole
>> and you have to find the actual pf function in all that material.
>>
>> Note that SAS provides common names to widely used probability  
>> functions and
>> inverses such as probit and probnorm for the Normal distribution,  
>> finv and
>> probf for the F Distribution, etc. I find this approach consistent  
>> with
>> numpy/scipy approach where the help on the function provide the  
>> what the
>> functions does, the arguments and return values but does not  
>> provide any
>> background of the distribution. For example, the SAS probf function  
>> 'Returns
>> the probability from an F distribution':
>> http://support.sas.com/onlinedoc/913/getDoc/en/lrdict.hlp/a000245930.htm
>>
>> A possible approach would be to have the following structure:
>> 1) Generic documentation orientated to 'power users' that provides  
>> the basic
>> documentation as included in distributions.py but ignores
>> distribution-specific details. This tends to follow SAS's approach  
>> where the
>> distribution is an argument to the generic function.
>>
>> 2) Distribution-specific documentation that adapts the generic  
>> documentation
>> to the include the distribution-specific parameters like R does.  
>> The problem
>> here for the current code is that each distribution must have it's  
>> own
>> documentation.
>>
>> 3) Documentation on widely used functions that provide  
>> probabilities (and
>> associated inverse functions) for widely used distributions like  
>> SAS does.
>> (Okay these functions do not really exist in distribution.py but  
>> chisqprob,
>> zprob and fprob exist in stats.py.)
>>
>> It may be possible to combine 1) and 2) by having each distribution  
>> extend
>> the generic documentation to include the relevant distribution- 
>> specific
>> parameters. But that may be too complex to create.
>>
>> I wish distributions were classes instead of class instances.
>>
>> Ah, don't get me started on that either...
>>
>>
>> I have only briefly browsed the distribution code but I can see  
>> that number
>> of distributions involved makes the original design cumbersome  
>> especially
>> adding less common and more unusual distributions.  So my 2 cents  
>> on this is
>> that you both have the opportunity to propose a scipy PEP on the  
>> developers
>> list to describe why it should be changed. That is also likely to  
>> influence
>> the documentation.
>>
>> Regards
>> Bruce
>>
>
> Thanks, for the comparison and links. The two SAS pages are pretty
> good and might give me some outside benchmark for the distribution
> tests.
>
> just a few quick comments
> I started the threads on the user list to see if I get more response
> on new functions and distributions, although now it turned into a
> developer discussion.
>
> I like in scipy ,and it looks this way also in SAS, that the
> distributions are in one consistent structure and location. In R it
> always took me some time to find a less common distribution, since
> they are spread over several packages.
>
> To your points 1 and 2: I also think we should include more
> distribution-specific information, the question is where to put it:
> a) just expand current extradocs and keep it attached to
> class/instance docstring
> b) add automatically adjusted "extradocs" also to each method
> c) add additional information to a location in the docs (as rst) but
> not in the docstrings, incorporating if possible the pdf/lynx files by
> Travis, and some nice graphs
>
> (
> and a more generic scipy proposal:
> d) add a "demo" feature to scipy, i.e. examples that are in the path
> and can be run just by calling them, as in matlab and R
> )
>
> I'm in favor of location a) and c) and don't think b) is necessary
> because it would be mostly repetitive, but it would be feasible. Given
> the work b) would require (writing additional distribution specific
> docstrings) I would only expect it to be used for a few popular
> distributions. The actual formulas (for pdf, cdf,...) I would prefer
> to be in one place.
>
> your point 3): I'm not much in favor of aliases,
> the short description for probf in SAS: "Returns the probability from
> an F distribution"
> is pretty uninformative, which probability, pdf, cdf, sf ?
> In contrast, stats.f.cdf  has all the information I need in its name,
> and makes reading the code much more informative.
>

And now, for something completely different

> SEP: turning distributions into usable classes.
> The main short term fix, I thought about, is to add an __init__ method
> to the individual distributions and add the instantiation information
> in it. Then the classes could be instantiated and subclassed without
> any cut and paste.

Describe a bit more. I don't really see the need for subclassing a  
normal, for example. It's already doable right now (as Pearson III)


> And different instances could maintain their state
> instead of having it spill over if there is a mistake in the program.
> However, when it looked liked that Per Brodtkorb and I were the only
> ones actively working on the distributions code, I didn't want to risk
> breaking existing code.

That's why I don't think we should push too hard. The current  
implementation is a bit abstruse, yes, it's not obvious to implement  
new distributions but we can document how. 


From josef.pktd at gmail.com  Mon Aug  3 15:10:36 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Mon, 3 Aug 2009 15:10:36 -0400
Subject: [SciPy-User] An extra parameter to stats.chisquare ?
In-Reply-To: <37B9B008-CD93-4735-AFE3-4A28F8BC42E1@gmail.com>
References: <32A899D6-8D41-458B-B31C-2A9717E04509@gmail.com>
	<1cd32cbb0908021418ib347190u1fc5184050d05a28@mail.gmail.com>
	<57170D20-8B86-4DA6-B5B5-6C2B49FD6EB9@gmail.com>
	<1cd32cbb0908030812i1ece6567r86e167ac2ab9a1d9@mail.gmail.com>
	<37B9B008-CD93-4735-AFE3-4A28F8BC42E1@gmail.com>
Message-ID: <1cd32cbb0908031210y3098db23jc7f3d0aed9534222@mail.gmail.com>

On Mon, Aug 3, 2009 at 2:29 PM, Pierre GM<pgmdevlist at gmail.com> wrote:
>
> On Aug 3, 2009, at 11:12 AM, josef.pktd at gmail.com wrote:
>
>> On Sun, Aug 2, 2009 at 10:43 PM, Pierre GM<pgmdevlist at gmail.com>
>> wrote:
>>>
>>>
>>> Well, I guess we'd need a "real" statistician. From what I gathered,
>>> when you fit your N observations to a distribution with p parameters
>>> (eg, 2 for normal, 1 for logseries), the ddof is N-(p+1): http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm
>>> However, that works as long as all the parameters are independent. If
>>> one depends from the others or can be related to the others, we
>>> switch
>>> from p independent parameters to (p-1), thus giving a dof of N-p. So,
>>> wikipedia looks right.
>>
>>
>> how about this change, then
>>
>> def chisquare(f_obs, f_exp=None, ddof=0):
>> ? ? ?....
>> ? ? ?return chisq, chisqprob(chisq, k-1-ddof)
>>
>> default is when no parameters are estimated (dof=k-1), e.g. create
>> random sample and compare to distribution with *given* parameters.
>
> Looks cool.

I will prepare the change and check whether chisquare is fully tested.

>
>> I didn't find a reference for your statement that the parameter
>> (estimators) have to be independent.
>
> Check the example here:
> http://books.google.com/books?id=3rC5A1doUcwC&pg=PA12
>
> The model has 2 parameters, but only one is important, so the final
> ddof is k-2 instead of k-(2+1)

Ok, I misinterpreted the term independent, I thought it means
statistically independent (like beta and sigma estimates in ols), and
not independent, as in one parameter is just a deterministic function
of the other ones.

The reference looks good, it uses power discrepancy in the following section.

>
>> If you are testing discrete distribution, then there is also a
>> helper function
>> in test_discrete_basic.py in stats/tests
>
> Oh OK. Didn't know about that.
>
>> def check_discrete_chisquare(distfn, arg, rvs, alpha, msg):
>> ? ?'''perform chisquare test for random sample of a discrete
>> distribution
>>
>> The main point of the function is to do a equal weight binning, to
>> maintain a minimum expected frequency in each cell, which is
>> recommended (>=5 expected observations for the chisquare distribution
>> to be an appropriate approximation).
>
> Bah, this binning doesn't really matter when you wanna use X2 to
> compare a sample to an actual distribution, does it ?

Yes it does.
If there is not a minimum number of expected frequency counts in each
cell, then the chisquare distribution is not a good approximation for
the distribution of the test statistic.
In your book example the expected cell count is around 8. If there
were fewer observations so that the expected cell count drops below 5,
then the literature recommends combining cells.

The worse case are discrete distributions with unbound support, e.g.
poisson, then there will always be integers in the tail(s) without
observations, and observations have to be binned. Similar the
continuous distribution case, you cannot compare the pdf/pmf pointwise
in the chisquare test if the probability of each point is very small.

> And anyway, this
> function suffers from the same problem as stats.chisquare: the ddof
> are not taken into account.

None, of my function takes estimated parameters into account since I
didn't think about this issue, but they can easily be changed in the
same way as stats.chisquare.

> All in all, I like you chisquare function best.
stats.chisquare is inherited not mine.

Josef
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From pgmdevlist at gmail.com  Mon Aug  3 15:27:17 2009
From: pgmdevlist at gmail.com (Pierre GM)
Date: Mon, 3 Aug 2009 15:27:17 -0400
Subject: [SciPy-User] An extra parameter to stats.chisquare ?
In-Reply-To: <1cd32cbb0908031210y3098db23jc7f3d0aed9534222@mail.gmail.com>
References: <32A899D6-8D41-458B-B31C-2A9717E04509@gmail.com>
	<1cd32cbb0908021418ib347190u1fc5184050d05a28@mail.gmail.com>
	<57170D20-8B86-4DA6-B5B5-6C2B49FD6EB9@gmail.com>
	<1cd32cbb0908030812i1ece6567r86e167ac2ab9a1d9@mail.gmail.com>
	<37B9B008-CD93-4735-AFE3-4A28F8BC42E1@gmail.com>
	<1cd32cbb0908031210y3098db23jc7f3d0aed9534222@mail.gmail.com>
Message-ID: <E5FF254C-160A-4FD9-AC34-8EBEC8724ABC@gmail.com>


On Aug 3, 2009, at 3:10 PM, josef.pktd at gmail.com wrote:
>>> def chisquare(f_obs, f_exp=None, ddof=0):
>>>      ....
>>>      return chisq, chisqprob(chisq, k-1-ddof)
>>>
>>> default is when no parameters are estimated (dof=k-1), e.g. create
>>> random sample and compare to distribution with *given* parameters.
>>
>> Looks cool.
>
> I will prepare the change and check whether chisquare is fully tested

OK, fab'.


>>>
>>> The main point of the function is to do a equal weight binning, to
>>> maintain a minimum expected frequency in each cell, which is
>>> recommended (>=5 expected observations for the chisquare  
>>> distribution
>>> to be an appropriate approximation).
>>
>> Bah, this binning doesn't really matter when you wanna use X2 to
>> compare a sample to an actual distribution, does it ?
>
> Yes it does.
> If there is not a minimum number of expected frequency counts in each
> cell, then the chisquare distribution is not a good approximation for
> the distribution of the test statistic.

Well, like I said, I'm no statistician by trade. So OK


> In your book example the expected cell count is around 8. If there
> were fewer observations so that the expected cell count drops below 5,
> then the literature recommends combining cells.
>
> The worse case are discrete distributions with unbound support, e.g.
> poisson, then there will always be integers in the tail(s) without
> observations, and observations have to be binned. Similar the
> continuous distribution case, you cannot compare the pdf/pmf pointwise
> in the chisquare test if the probability of each point is very small.

Dang. More to read. See, this kind of info would be great in a  
documentation ;)



From josef.pktd at gmail.com  Mon Aug  3 15:55:17 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Mon, 3 Aug 2009 15:55:17 -0400
Subject: [SciPy-User] adding distributions from hydroclimpy to
	stats.distributions
In-Reply-To: <1cd32cbb0908031107t28479cd9ucc34688385861277@mail.gmail.com>
References: <1cd32cbb0908020641k4649315ahbd1a7ba986fd9ef9@mail.gmail.com>
	<82156633-5678-4485-9ECA-FB33CB540691@gmail.com>
	<1cd32cbb0908022120i160ca529gd7942e4a76acee73@mail.gmail.com>
	<4A771263.1040209@gmail.com>
	<1cd32cbb0908031107t28479cd9ucc34688385861277@mail.gmail.com>
Message-ID: <1cd32cbb0908031255v33f56061sc05b9445e23dedef@mail.gmail.com>

On Mon, Aug 3, 2009 at 2:07 PM, <josef.pktd at gmail.com> wrote:
> On Mon, Aug 3, 2009 at 12:37 PM, Bruce Southey<bsouthey at gmail.com> wrote:
>> On 08/02/2009 11:20 PM, josef.pktd at gmail.com wrote:
>>
>> On Sun, Aug 2, 2009 at 3:52 PM, Pierre GM<pgmdevlist at gmail.com> wrote:
>>
>>
>> On Aug 2, 2009, at 9:41 AM, josef.pktd at gmail.com wrote:
>>
>>
>> I looked briefly at the distributions in hydroclimpy
>> http://projects.scipy.org/scikits/browser/trunk/hydroclimpy/scikits/hydroclimpy/stats/extradistributions.py
>>
>> my first impression:
>>
>> kappa, glogistic, gennorm and wakeby
>> can be added almost without changes to stats distributions, since they
>> are already in the standard format
>> cosmetic changes: add longname and extradocs (from module docstring)
>>
>>
>> Agreed. I still have an issue about defining a proper template for
>> describing the distributions (eqns for pdf/cdf/ppf, example of usage,
>> plots...), hence the nudge. What are our doc exegetes' recommendations ?
>>
>>
>> currently only the class docstring gets distribution specific
>> information, done in rv_generic.__init__,
>> replacing name of distribution and shape parameters, and adding
>> extradocs. The example in the class docstring is just a generic
>> example. More description of the distributions could simply be added
>> to the extradocs, which show up in the help at the bottom of the
>> individual distribution class/instance docstrings. For example the
>> available formulas for the distribution pdf, cdf, sf, ..., (info from
>> Travis manual) could be included in the extradoc.)
>>
>> For methods there is currently no setup for individualized information
>> and examples. But this could be changed, if we really want to.
>> However, since methods for all distribution follow the same pattern, I
>> don't see really the need. Better overall help and examples, e.g. in
>> the tutorial, would be useful.
>>
>> Any good ideas for improvements?
>>
>>
>> (Not sure why this is on Scipy-users list becasue it is more appropriate on
>> the developers list). There really are no nice ways to document
>> distributions because of the conflict between the similarity of
>> distributions and unique aspects of each distribution.? Also, there has to
>> be a way to adequately describe the parameterization of the distribution
>> especially when distributions such as the gamma vary in parameterization.
>>
>> A generic description of a distribution class has the advantage that it
>> tends to minimize the repetitive description of the common features (and
>> functions) of distributions which makes the general usage very easy to
>> describe. For example, SAS has generic PDF and CDF functions where the
>> distribution is an argument:
>> http://support.sas.com/onlinedoc/913/getDoc/en/lrdict.hlp/a000270634.htm
>> http://support.sas.com/onlinedoc/913/getDoc/en/lrdict.hlp/a000208980.htm
>>
>> But the problem with that is that it avoids that the fact that the actual
>> details of these features and hence functions vary between distributions.
>> Thus you are forced to look at each distribution to understand how to use
>> these functions for that specific distribution which removes the advantage
>> of the generic classes.
>>
>> Alternatively there is the distribution orientated approach used by R where
>> you describe the background of the distribution and provide the associated
>> functions. For example, in R '?Normal' and '?FDist' return the normal and F
>> distributions, respectively, and provide associated functions. This allows
>> the unique features of each distribution but tends to lose the generic
>> features of the distribution.? But the problem I find with R's approach is
>> that you can not get specific help on individual functions. For example, at
>> the R prompt, '?pf' goes back to the help on the F distribution as a whole
>> and you have to find the actual pf function in all that material.
>>
>> Note that SAS provides common names to widely used probability functions and
>> inverses such as probit and probnorm for the Normal distribution, finv and
>> probf for the F Distribution, etc. I find this approach consistent with
>> numpy/scipy approach where the help on the function provide the what the
>> functions does, the arguments and return values but does not provide any
>> background of the distribution. For example, the SAS probf function 'Returns
>> the probability from an F distribution':
>> http://support.sas.com/onlinedoc/913/getDoc/en/lrdict.hlp/a000245930.htm
>>
>> A possible approach would be to have the following structure:
>> 1) Generic documentation orientated to 'power users' that provides the basic
>> documentation as included in distributions.py but ignores
>> distribution-specific details. This tends to follow SAS's approach where the
>> distribution is an argument to the generic function.
>>
>> 2) Distribution-specific documentation that adapts the generic documentation
>> to the include the distribution-specific parameters like R does. The problem
>> here for the current code is that each distribution must have it's own
>> documentation.
>>
>> 3) Documentation on widely used functions that provide probabilities (and
>> associated inverse functions) for widely used distributions like SAS does.
>> (Okay these functions do not really exist in distribution.py but chisqprob,
>> zprob and fprob exist in stats.py.)
>>
>> It may be possible to combine 1) and 2) by having each distribution extend
>> the generic documentation to include the relevant distribution-specific
>> parameters. But that may be too complex to create.
>>
>> I wish distributions were classes instead of class instances.
>>
>> Ah, don't get me started on that either...
>>
>>
>> I have only briefly browsed the distribution code but I can see that number
>> of distributions involved makes the original design cumbersome especially
>> adding less common and more unusual distributions.? So my 2 cents on this is
>> that you both have the opportunity to propose a scipy PEP on the developers
>> list to describe why it should be changed. That is also likely to influence
>> the documentation.
>>
>> Regards
>> Bruce
>>
>
> Thanks, for the comparison and links. The two SAS pages are pretty
> good and might give me some outside benchmark for the distribution
> tests.
>
> just a few quick comments
> I started the threads on the user list to see if I get more response
> on new functions and distributions, although now it turned into a
> developer discussion.
>
> I like in scipy ,and it looks this way also in SAS, that the
> distributions are in one consistent structure and location. In R it
> always took me some time to find a less common distribution, since
> they are spread over several packages.
>
> To your points 1 and 2: I also think we should include more
> distribution-specific information, the question is where to put it:
> a) just expand current extradocs and keep it attached to
> class/instance docstring
> b) add automatically adjusted "extradocs" also to each method
> c) add additional information to a location in the docs (as rst) but
> not in the docstrings, incorporating if possible the pdf/lynx files by
> Travis, and some nice graphs
>
> (
> and a more generic scipy proposal:
> d) add a "demo" feature to scipy, i.e. examples that are in the path
> and can be run just by calling them, as in matlab and R
> )
>
> I'm in favor of location a) and c) and don't think b) is necessary
> because it would be mostly repetitive, but it would be feasible. Given
> the work b) would require (writing additional distribution specific
> docstrings) I would only expect it to be used for a few popular
> distributions. The actual formulas (for pdf, cdf,...) I would prefer
> to be in one place.
>
> your point 3): I'm not much in favor of aliases,
> the short description for probf in SAS: "Returns the probability from
> an F distribution"
> is pretty uninformative, which probability, pdf, cdf, sf ?
> In contrast, stats.f.cdf ?has all the information I need in its name,
> and makes reading the code much more informative.
>
> SEP: turning distributions into usable classes.
> The main short term fix, I thought about, is to add an __init__ method
> to the individual distributions and add the instantiation information
> in it. Then the classes could be instantiated and subclassed without
> any cut and paste. And different instances could maintain their state
> instead of having it spill over if there is a mistake in the program.
> However, when it looked liked that Per Brodtkorb and I were the only
> ones actively working on the distributions code, I didn't want to risk
> breaking existing code.
>
> Josef
>

to keep the code discussion separate from the documentation discussion
(some copy and paste):

On Mon, Aug 3, 2009 at 2:45 PM, Pierre GM<pgmdevlist at gmail.com> wrote:

> And now, for something completely different
>
>> SEP: turning distributions into usable classes.
>> The main short term fix, I thought about, is to add an __init__ method
>> to the individual distributions and add the instantiation information
>> in it. Then the classes could be instantiated and subclassed without
>> any cut and paste.
>
> Describe a bit more. I don't really see the need for subclassing a
> normal, for example. It's already doable right now (as Pearson III)

(just briefly, since I'm supposed to spend my free time reviewing the
stats.models code)

I thought about subclasses and creating new distribution classes more
in the context of generated classes, as for example new distributions
based on non-linear transformations (or based on truncation) and in
the context of freezing some parameters, especially for the maximum
likelihood estimation with fit.
(e.g. pymvpa has a semifrozen distribution class). I thought being
forced to create new classes based on the current
distribution-is-an-instance pattern is a bit messy. But I don't
remember all the details without checking my example code and notes
again. Nothing that cannot be worked around, but not really "clean"
for my taste.

>
>> And different instances could maintain their state
>> instead of having it spill over if there is a mistake in the program.
>> However, when it looked liked that Per Brodtkorb and I were the only
>> ones actively working on the distributions code, I didn't want to risk
>> breaking existing code.
>
> That's why I don't think we should push too hard. The current
> implementation is a bit abstruse, yes, it's not obvious to implement
> new distributions but we can document how.

My opinion also.
Especially since there are still other things that are more important
that are on the waiting list for enhancing the distributions.
(Per Brodtkorbs enhancements and new estimator is waiting for review
and reworking, my previous proposal "improving ML estimation of
distributions" to fix some parameters didn't get a response, and now
also L-moments, which overall is a lot of work in the queue)

Josef

> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From tsyu80 at gmail.com  Mon Aug  3 16:22:54 2009
From: tsyu80 at gmail.com (Tony Yu)
Date: Mon, 3 Aug 2009 16:22:54 -0400
Subject: [SciPy-User] ndimage filters overflow for default image format
	(uint8)
Message-ID: <0D3C80A2-D6D1-4C2B-B56F-A1C0358A3835@gmail.com>

I've been struggling to get ndimage filters to give the expected  
output. It turns out that my images were getting imported as unsigned  
integers (uint8) using both chaco's ImageData importer and PIL (with  
an additional call to numpy.asarray). As a result, edge filters--- 
which often return negative values---were returning overflowed arrays.  
Here's a simple example using the correlate1d function (which is used  
by the edge filters):

 >>> im = np.array([1, 2, 3, 4, 4, 3, 2, 1], dtype=np.uint8)
 >>> weights = np.array([-1, 0, 1])
 >>> print ndimage.correlate1d(im, weights)
[  1   2   2   1 255 254 254 255]

This result is not a huge surprise *if* you expect the output of the  
filter to be the same dtype as your input image, but this wasn't  
apparent to me until I really dug into the problem. (I'd expect the  
same dtype for filters that don't return values outside the range of  
input values, e.g. a median filter).

I realize guessing the "best" output format is difficult (b/c of  
memory considerations and variable input formats), but it seems like  
the current behavior would cause problems for a lot people. Maybe  
there could be a dtype parameter to specify the output dtype. For  
filters that can return values outside the input range (e.g < 0, >  
255), the default dtype could be int32. This of course assumes the  
input is uint8, but maybe that's a good assumption since that seems to  
be the default for PIL. Or there's probably a smarter way of choosing  
the output format.

I realize my suggestion could cause more problems than it's worth. If  
so, maybe just adding some very prominent warnings in the filter  
docstrings would do the trick.

Best,
-Tony

PS. Sorry if this post shows up twice, I've been having trouble  
posting with my other email address.


From josef.pktd at gmail.com  Mon Aug  3 16:50:40 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Mon, 3 Aug 2009 16:50:40 -0400
Subject: [SciPy-User] expectation function for stats.distributions
In-Reply-To: <1cd32cbb0908011439x1cce637ep304ee32b269a1f47@mail.gmail.com>
References: <1cd32cbb0907310901oe3e7ca4r74586845222acdbc@mail.gmail.com>
	<fa510ff80907311210k7f0cd0a9y798e1bbeeeba471e@mail.gmail.com>
	<1cd32cbb0907311310g59c4f2a5uf9f328acba0bad07@mail.gmail.com>
	<EDABE9A4-0155-43D4-9E20-D8501E91AA97@gmail.com>
	<1cd32cbb0908011439x1cce637ep304ee32b269a1f47@mail.gmail.com>
Message-ID: <1cd32cbb0908031350s3b839107r51b60508f28d29b8@mail.gmail.com>

On Sat, Aug 1, 2009 at 5:39 PM, <josef.pktd at gmail.com> wrote:
> On Sat, Aug 1, 2009 at 4:44 PM, Pierre GM<pgmdevlist at gmail.com> wrote:
>>
>> On Jul 31, 2009, at 4:10 PM, josef.pktd at gmail.com wrote:
>>
>>> On Fri, Jul 31, 2009 at 3:10 PM, nicky van foreest<vanforeest at gmail.com
>>> > wrote:
>>>> Hi Josef,
>>>>
>>>> I would like such a function.
>>>
>>> Thanks for the interest.
>>
>> Count me in as well
>>
>>> To the name: I'm up for ideas
>>
>> rv_generic.expect ?
>
> I think that's my favorite so far.
> It will be attached to continuous and discrete separately (not to
> generic) because of the difference in implementation.
> I added loc and scale to the expectation function.
> but for discrete distributions following the pattern of _drv2_moment
> for discrete might not be the best implementation.
>

Here's an updated version, one function for continuous, one for
discrete distributions
includes loc and scale,
expect_discrete is tested with all discrete distribution in stats for
mean and variance and is very accurate except for zipf (only 1e-5)
can be used as function or attached to the distributions as method.

Josef
-------------- next part --------------
'''
copy from webpage, where is the original?

Author jpktd

'''


import numpy as np
from scipy import stats, integrate

def expect(self, fn=None, args=(), loc=0, scale=1, lb=None, ub=None, conditional=False):
    '''calculate expected value of a function with respect to the distribution

    location and scale only tested on a few examples

    Parameters
    ----------
        all parameters are keyword parameters
        fn : function (default: identity mapping)
           Function for which integral is calculated. Takes only one argument.
        args : tuple
           argument (parameters) of the distribution
        lb, ub : numbers
           lower and upper bound for integration, default is set to the support
           of the distribution
        conditional : boolean (False)
           If true then the integral is corrected by the conditional probability
           of the integration interval. The return value is the expectation
           of the function, conditional on being in the given interval.

    Returns
    -------
        expected value : float
    '''
    if fn is None:
        def fun(x, *args):
            return x*self.pdf(x, loc=loc, scale=scale,*args)
    else:
        def fun(x, *args):
            return fn(x)*self.pdf(x, loc=loc, scale=scale, *args)
    if lb is None:
        lb = (self.a - loc)/(1.0*scale)
    if ub is None:
        ub = (self.b - loc)/(1.0*scale)
    if conditional:
        invfac = self.sf(lb,*args) - self.sf(ub,*args)
    else:
        invfac = 1.0
    return integrate.quad(fun, lb, ub,
                                args=args)[0]/invfac


print expect(stats.norm, lambda(x): (x)**4)
print expect(stats.norm, lambda(x): min((x)**2,1.5**2))

#Jonathans version
from scipy.stats import norm as Gaussian
c = 1.5 # our "cutoff" point
c = 0.5 # try another value
tmp = 2 * Gaussian.cdf(c) - 1
gamma = tmp + c**2 * (1 - tmp) - 2 * c * Gaussian.pdf(c)
print gamma

print expect(stats.norm, lambda(x): min((x)**2,c**2))

print 'check loc and scale - continuous'

m = expect(stats.norm, lambda(x): (x), loc=2, scale=2)
mnc2 = expect(stats.norm, lambda(x): (x)**2, loc=2, scale=2)
v = mnc2 - m**2
print m, v
print stats.norm.stats(loc=2, scale=2)
print expect(stats.norm, lambda(x): (x)**2, scale=2)
print expect(stats.norm, lambda(x): (x), loc=2)









### for discrete distributions

#based on _drv2_moment(self, n, *args), but streamlined
def expect_discrete(self, fn=None, args=(), loc=0, lb=None, ub=None, conditional=False):
    '''calculate expected value of a function with respect to the distribution
    for discrete distribution

    Parameters
    ----------
        (self : distribution instance as defined in scipy stats)
        fn : function (default: identity mapping)
           Function for which integral is calculated. Takes only one argument.
        args : tuple
           argument (parameters) of the distribution
        optional keyword parameters
        lb, ub : numbers
           lower and upper bound for integration, default is set to the support
           of the distribution, lb and ub are inclusive (ul<=k<=ub)
        conditional : boolean (False)
           If true then the expectation is corrected by the conditional
           probability of the integration interval. The return value is the
           expectation of the function, conditional on being in the given
           interval (k such that ul<=k<=ub).

    Returns
    -------
        expected value : float

    Notes
    -----
    * function is not vectorized
    * accuracy: uses self.moment_tol as stop criteria
        for heavy tailed distribution e.g. zipf(4), accuracy for
        mean, var example is only 1e-5,
        increasing precision (moment_tol) makes zipf very slow
    * suppnmin=100 internal parameter for minimum number of points to evaluate
        could be added as keyword parameter, to evaluate functions with
        non-monotonic shapes, points include integers in (-suppnmin, suppnmin)
    * uses maxcount=1000 limits the number of points that are evaluated
        to break loop for infinite sums
        (maximum of suppnmin+1000 positive plus suppnmin+1000 negative integers
        are evaluated)
    
    
    '''

    #moment_tol = 1e-12 # increase compared to self.moment_tol,
    # too slow for only small gain in precision for zipf

    #avoid endless loop with unbound integral, eg. var of zipf(2)
    maxcount = 1000
    suppnmin = 100  #minimum number of points to evaluate (+ and -)
    
    if fn is None:
        def fun(x):
            #loc and args from outer scope
            return (x+loc)*self._pmf(x, *args)
    else:
        def fun(x):
            #loc and args from outer scope
            return fn(x+loc)*self._pmf(x, *args)
    # used pmf because _pmf does not check support in randint
    # and there might be problems(?) with correct self.a, self.b at this stage
    # maybe not anymore, seems to work now with _pmf

    self._argcheck(*args) # (re)generate scalar self.a and self.b
    if lb is None:
        lb = (self.a)
    if ub is None:
        ub = (self.b)
    if conditional:
        invfac = self.sf(lb,*args) - self.sf(ub+1,*args)
    else:
        invfac = 1.0
        
    tot = 0.0
    low, upp = self._ppf(0.001, *args), self._ppf(0.999, *args)
    low = max(min(-suppnmin, low), lb)
    upp = min(max(suppnmin, upp), ub)
    supp = np.arange(low, upp+1, self.inc) #check limits
    #print 'low, upp', low, upp
    tot = np.sum(fun(supp))
    diff = 1e100
    pos = upp + self.inc
    count = 0
    
    #handle cases with infinite support

    while (pos <= ub) and (diff > self.moment_tol) and count <= maxcount: 
        diff = fun(pos)
        tot += diff
        pos += self.inc
        count += 1

    if self.a < 0: #handle case when self.a = -inf
        diff = 1e100
        pos = low - self.inc
        while (pos >= lb) and (diff > self.moment_tol) and count <= maxcount: 
            diff = fun(pos)
            tot += diff
            pos -= self.inc
            count += 1
    if count > maxcount:
        # replace with proper warning
        print 'sum did not converge'
    return tot/invfac


distdiscrete = [
    ['bernoulli',(0.3,)],
    ['binom',    (5, 0.4)],
    ['boltzmann',(1.4, 19)],
    ['dlaplace', (0.8,)], #0.5
    ['geom',     (0.5,)],
    ['hypergeom',(30, 12, 6)],
    ['hypergeom',(21,3,12)],  #numpy.random (3,18,12) numpy ticket:921
    ['hypergeom',(21,18,11)],  #numpy.random (18,3,11) numpy ticket:921
    ['logser',   (0.6,)],  # reenabled, numpy ticket:921
    ['nbinom',   (5, 0.5)],
    ['nbinom',   (0.4, 0.4)], #from tickets: 583
    ['planck',   (0.51,)],   #4.1
    ['poisson',  (0.6,)],
    ['randint',  (7, 31)],
    ['zipf',     (4,)],
    ['zipf',     (8,)],
    ['zipf',     (3,)]]


distdiscrete_ = [
    ['randint',  (7, 31)]
    ]

for distname, args in distdiscrete:
    distfn = getattr(stats, distname)
    print '\n', distname, args
    m0 = expect_discrete(distfn, lambda(x): 1, args)
    m = expect_discrete(distfn, lambda(x): x, args)
    mnc2 = expect_discrete(distfn, lambda(x): x**2, args)
    v = mnc2 - m*m
    md, vd = distfn.stats(*args)

    print m0, 'm0', m0-1
    print m, v, 'm, v'
    print md[()], vd[()], 'md, vd'
    print m - md, v - vd, 'm - md, v - vd'
    if (np.abs(m-md) > distfn.moment_tol) or (np.abs(v-vd) > distfn.moment_tol):
        print '************ diff too large'
    loc = 1
    ml = expect_discrete(distfn, lambda(x): x, args, loc=loc)
    mnc2l = expect_discrete(distfn, lambda(x): x**2, args, loc=loc)
    vl = mnc2l - ml*ml
    print ml, vl, 'ml, vl'
    if (np.abs(ml-md-loc) > distfn.moment_tol) or (np.abs(vl-vd) > distfn.moment_tol):
        print '************ diff too large'
    
print 'check scale, limits and conditional'
print expect_discrete(stats.randint, lambda(x): x, (0,10)),
print expect_discrete(stats.randint, lambda(x): x-1, (1,11)),
print np.dot(np.arange(10),stats.randint.pmf(np.arange(10),0,10))

print expect_discrete(stats.randint, lambda(x): x, (0,5)),
print expect_discrete(stats.randint, lambda(x): x, (0,10), ub=4,
                      conditional=True)
print expect_discrete(stats.randint, lambda(x): x, (0,10), ub=4,
                      conditional=False),
print np.dot(np.arange(5),stats.randint.pmf(np.arange(5),0,10))
print expect_discrete(stats.randint, lambda(x): x, (0,10), lb=2, ub=6,
                      conditional=True),
print expect_discrete(stats.randint, lambda(x): x, (2,7))
#Note: ub is inclusive (ul<=k<=ub), max in randint is exclusive (min<=k<max)







From sebastian.walter at gmail.com  Mon Aug  3 17:23:21 2009
From: sebastian.walter at gmail.com (Sebastian Walter)
Date: Mon, 3 Aug 2009 23:23:21 +0200
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <4A77248C.1050202@enthought.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
	<7f014ea60908031000ob5da59ei2aedae593d6771db@mail.gmail.com>
	<f1dbe1be0908031024q334c7366s1d1e5b2f34047ebd@mail.gmail.com>
	<20548feb0908031027l39255170x996c903ad9c51a40@mail.gmail.com>
	<4A77248C.1050202@enthought.com>
Message-ID: <ec9f80fa0908031423l16ff7562xbb41d4845ed28153@mail.gmail.com>

well, I agree that is not such a clever idea to solve nonlinear
systems by reformulating it as a NLP.
But if the function F(x) =0 is uniquely solvable and f is twice
continuously differentiable, then
f(x) := |F(x)|^2 is also C^2 and should have a strict local minimum.
Then Newton's method should locally converge quadratically.

Am I missing something here?




On Mon, Aug 3, 2009 at 7:55 PM, Warren
Weckesser<warren.weckesser at enthought.com> wrote:
> Harald Schilly wrote:
>> On Mon, Aug 3, 2009 at 19:24, Ashley DaSilva<amd405 at psu.edu> wrote:
>>
>>> t I don't
>>> want to find the minimum/maximum of my function, I want to find the root.
>>>
>>
>> Very generally speaking, you can always find the root by minimization,
>> if you square your function (simply because no negative values are
>> possible)!
>>
>> H
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
> But this is generally a poor method for finding a root. ?It kills the
> quadratic convergence of Newton's method, which is at the heart (in some
> form or another) of most good root-finding algorithms.
>
> Warren
>
>
> --
> Warren Weckesser
> Enthought, Inc.
> 515 Congress Avenue, Suite 2100
> Austin, TX ?78701
> 512-536-1057
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From Omer.Khalid at cern.ch  Mon Aug  3 17:26:41 2009
From: Omer.Khalid at cern.ch (Omer Khalid)
Date: Mon, 3 Aug 2009 23:26:41 +0200
Subject: [SciPy-User] Dimensions of x and y are incompatible
Message-ID: <77e5896b0908031426w5d66a96egb58e5b74b94866b@mail.gmail.com>

Hi,

I am trying to plot two curves on a same graph. The two input datasets are
xdata, ydata and they range between (0.01, 1]  for xdata and (0.1, 1] for
ydata.

I am constantly getting this error when plotting "Dimensions of x and y are
incompatible"

                plt.figure()
                xdata= array(xd)
                ydata = array(xd)

                N=len(xdata)
                print "N: %d" % (N)
                x=arange(0.0, 1.0+0.01, 0.01)
                M=len(ydata)
                print "M: %d" % (M)
                y=arange(0.0, 1.0+0.1, 0.1)
                plt.plot(x,xdata,'r')
                plt.plot(y,ydata,'b')
                plot.legend((xl,yl))
                plt.grid(True)
                plt.title(title)
                plt.xlabel('Value Ratio')
                plt.ylabel('Simulation Time Unit')
                pdf_name = '%s-%s.pdf' %
(str(filename),strftime("%m%d-%M-%S"))
                pwd = os.getcwd()
                path = os.path.join(pwd, exp, pdf_name)
                plt.savefig(path)

I have also tried:
x=arange(1,N)
y=arange(1,M)

But it didn't work. Any help would be much appreciated.

Thanks,
Omer
-------------- next part --------------
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From robert.kern at gmail.com  Mon Aug  3 17:39:27 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Mon, 3 Aug 2009 16:39:27 -0500
Subject: [SciPy-User] Dimensions of x and y are incompatible
In-Reply-To: <77e5896b0908031426w5d66a96egb58e5b74b94866b@mail.gmail.com>
References: <77e5896b0908031426w5d66a96egb58e5b74b94866b@mail.gmail.com>
Message-ID: <3d375d730908031439l35869cf8m9c992fe3e201a6ac@mail.gmail.com>

On Mon, Aug 3, 2009 at 16:26, Omer Khalid<Omer.Khalid at cern.ch> wrote:
> Hi,
>
> I am trying to plot two curves on a same graph. The two input datasets are
> xdata, ydata and they range between (0.01, 1]? for xdata and (0.1, 1] for
> ydata.

The matplotlib list is over here:

  https://lists.sourceforge.net/lists/listinfo/matplotlib-users

-- 
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


From pgmdevlist at gmail.com  Mon Aug  3 17:42:37 2009
From: pgmdevlist at gmail.com (Pierre GM)
Date: Mon, 3 Aug 2009 17:42:37 -0400
Subject: [SciPy-User] Dimensions of x and y are incompatible
In-Reply-To: <77e5896b0908031426w5d66a96egb58e5b74b94866b@mail.gmail.com>
References: <77e5896b0908031426w5d66a96egb58e5b74b94866b@mail.gmail.com>
Message-ID: <71113A1C-6976-4B8C-A6C4-79FA14304D51@gmail.com>


On Aug 3, 2009, at 5:26 PM, Omer Khalid wrote:

> Hi,

Omer,
You do realize this is the Scipy mailing list, right ? You should ask  
matplotlib related questions to the matplotlib mailing list:
matplotlib-users at lists.sourceforge.net

Anyhow:
> "Dimensions of x and y are incompatible"
means what it means: you're trying to plot y (size 11) with x of size  
(110). That won't do: your x and y must have the same size (else, what  
happens to the extras x or y ?)
Moreover, you may want to use np.linspace instead of np.arange for that:
np.linspace(0,1,11) gives you 11 points regularly spaced between 0 and  
1.


> I am trying to plot two curves on a same graph. The two input  
> datasets are xdata, ydata and they range between (0.01, 1]  for  
> xdata and (0.1, 1] for ydata.
>
> I am constantly getting this error when plotting "Dimensions of x  
> and y are incompatible"
>
>                 plt.figure()
>                 xdata= array(xd)
>                 ydata = array(xd)
>
>                 N=len(xdata)
>                 print "N: %d" % (N)
>                 x=arange(0.0, 1.0+0.01, 0.01)
>                 M=len(ydata)
>                 print "M: %d" % (M)
>                 y=arange(0.0, 1.0+0.1, 0.1)
>                 plt.plot(x,xdata,'r')
>                 plt.plot(y,ydata,'b')
>                 plot.legend((xl,yl))
>                 plt.grid(True)
>                 plt.title(title)
>                 plt.xlabel('Value Ratio')
>                 plt.ylabel('Simulation Time Unit')
>                 pdf_name = '%s-%s.pdf' % (str(filename),strftime("%m 
> %d-%M-%S"))
>                 pwd = os.getcwd()
>                 path = os.path.join(pwd, exp, pdf_name)
>                 plt.savefig(path)
>
> I have also tried:
> x=arange(1,N)
> y=arange(1,M)
>
> But it didn't work. Any help would be much appreciated.
>
> Thanks,
> Omer
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user



From robert.kern at gmail.com  Mon Aug  3 20:56:59 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Mon, 3 Aug 2009 19:56:59 -0500
Subject: [SciPy-User] Scipy and statistics: probability density function
In-Reply-To: <1cd32cbb0907271345v7ce27a5frfd0513ce3a62c20a@mail.gmail.com>
References: <C1C4F27B-636C-4A00-A169-A50440DEF3E4@imperial.ac.uk> 
	<3d375d730907270859w42327684gb726549eea094147@mail.gmail.com> 
	<1cd32cbb0907271345v7ce27a5frfd0513ce3a62c20a@mail.gmail.com>
Message-ID: <3d375d730908031756i700e5d51y9981524a491d3ebe@mail.gmail.com>

On Mon, Jul 27, 2009 at 15:45, <josef.pktd at gmail.com> wrote:
> On Mon, Jul 27, 2009 at 11:59 AM, Robert Kern<robert.kern at gmail.com> wrote:
>> On Mon, Jul 27, 2009 at 02:33, Daniel J
>> Farrell<daniel.farrell at imperial.ac.uk> wrote:
>>> Dear list,
>>>
>>> I am looking for some of the functionality provided by the GNU
>>> Scientific Library histogram module (http://www.gnu.org/software/gsl/manual/html_node/Histograms.html
>>> ).
>>>
>>> In particular, a need to be able to create a probability density
>>> function from my histogram of data. This will allow inverse look-ups
>>> to be performed, i.e. for a random number (0-->1) find the associated
>>> probability (http://www.gnu.org/software/gsl/manual/html_node/The-histogram-probability-distribution-struct.html
>>> ). This allows the distribution and for samples to be returned
>>> weighted by the probability of the distribution -- which is a common
>>> task!
>>
>> It looks like you want a CDF (or rather it's inverse, the PPF) rather
>> than a PDF. Anyways, this is straightforward. Compute the histogram
>> using normed=False. Find the cumulative sum and divide by the sum to
>> get the (smoothed) empirical CDF. Prepend a 0.0 to this, and then this
>> will align with the edges array that is also returned. Then you can
>> use linear interpolation to do lookups. If you use the edges array as
>> "X" and the empirical CDF as "Y", then this is a CDF. If you use the
>> empircal CDF array as "X" and the edges as "Y", then this is a PPF.
>>
>>
>> In [12]: x = np.random.uniform(0, 10, size=1000)
>>
>> In [13]: hist, edges = np.histogram(x)
>>
>> In [15]: hist
>> Out[15]: array([100, 101, 104, 108, ?80, 111, ?96, ?88, 108, 104])
>>
>> In [16]: edges
>> Out[16]:
>> array([ ?3.53879571e-04, ? 1.00026423e+00, ? 2.00017458e+00,
>> ? ? ? ? 3.00008493e+00, ? 3.99999528e+00, ? 4.99990563e+00,
>> ? ? ? ? 5.99981598e+00, ? 6.99972633e+00, ? 7.99963668e+00,
>> ? ? ? ? 8.99954704e+00, ? 9.99945739e+00])
>>
>> In [18]: ecdf = np.hstack([0.0, hist.cumsum() / float(hist.sum())])
>>
>> In [19]: ecdf
>> Out[19]:
>> array([ 0. ? , ?0.1 ?, ?0.201, ?0.305, ?0.413, ?0.493, ?0.604, ?0.7 ?,
>> ? ? ? ?0.788, ?0.896, ?1. ? ])
>>
>> In [20]: np.interp(np.linspace(0, 10), edges, ecdf)
>> Out[20]:
>> array([ 0. ? ? ? ?, ?0.0203746 , ?0.04078459, ?0.06119459, ?0.08160458,
>> ? ? ? ?0.10203472, ?0.12264881, ?0.14326291, ?0.163877 ?, ?0.18449109,
>> ? ? ? ?0.20522712, ?0.22645351, ?0.24767991, ?0.2689063 , ?0.29013269,
>> ? ? ? ?0.31160366, ?0.33364646, ?0.35568925, ?0.37773204, ?0.39977483,
>> ? ? ? ?0.41953158, ?0.43585957, ?0.45218756, ?0.46851556, ?0.48484355,
>> ? ? ? ?0.50433802, ?0.52699311, ?0.54964821, ?0.5723033 , ?0.59495839,
>> ? ? ? ?0.61577382, ?0.63536742, ?0.65496101, ?0.6745546 , ?0.6941482 ,
>> ? ? ? ?0.71259664, ?0.73055743, ?0.74851823, ?0.76647902, ?0.78443982,
>> ? ? ? ?0.80567348, ?0.82771627, ?0.84975906, ?0.87180185, ?0.89384465,
>> ? ? ? ?0.91515087, ?0.93637726, ?0.95760365, ?0.97883004, ?1. ? ? ? ?])
>>
>> In [21]: np.interp(np.linspace(0, 1), ecdf, edges)
>> Out[21]:
>> array([ ?3.53879571e-04, ? 2.04417216e-01, ? 4.08480553e-01,
>> ? ? ? ? 6.12543890e-01, ? 8.16607227e-01, ? 1.02046852e+00,
>> ? ? ? ? 1.22251143e+00, ? 1.42455434e+00, ? 1.62659724e+00,
>> ? ? ? ? 1.82864015e+00, ? 2.02980301e+00, ? 2.22601775e+00,
>> ? ? ? ? 2.42223250e+00, ? 2.61844725e+00, ? 2.81466200e+00,
>> ? ? ? ? 3.01047705e+00, ? 3.19942458e+00, ? 3.38837211e+00,
>> ? ? ? ? 3.57731965e+00, ? 3.76626718e+00, ? 3.95521472e+00,
>> ? ? ? ? 4.19462069e+00, ? 4.44969986e+00, ? 4.70477903e+00,
>> ? ? ? ? 4.95985820e+00, ? 5.15488346e+00, ? 5.33872431e+00,
>> ? ? ? ? 5.52256515e+00, ? 5.70640600e+00, ? 5.89024684e+00,
>> ? ? ? ? 6.08569264e+00, ? 6.29825861e+00, ? 6.51082459e+00,
>> ? ? ? ? 6.72339057e+00, ? 6.93595654e+00, ? 7.16204944e+00,
>> ? ? ? ? 7.39393960e+00, ? 7.62582975e+00, ? 7.85771991e+00,
>> ? ? ? ? 8.07294833e+00, ? 8.26189586e+00, ? 8.45084340e+00,
>> ? ? ? ? 8.63979093e+00, ? 8.82873846e+00, ? 9.01838365e+00,
>> ? ? ? ? 9.21459840e+00, ? 9.41081315e+00, ? 9.60702789e+00,
>> ? ? ? ? 9.80324264e+00, ? 9.99945739e+00])
>>
>
>
> This, together with the answer last week to a piecewise constant pdf,
> would make most of the elements for a distribution class, similar to
> the generic discrete distribution only for the continuous case.
>
> It could take as constructor either a data array (and use histogram as
> in this example, with theoretically optimal bin size) or a set of
> points and pdf values as in last weeks example.
>
> Does quad work correctly for integration of a piecewise function?
> If yes, then we could just use the generic methods by subclassing rv_continuous.

I suspect that it would be worthwhile to implement at least the CDF.

> Is it worth collecting these results?

Sure!

-- 
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


From eadrogue at gmx.net  Mon Aug  3 21:33:17 2009
From: eadrogue at gmx.net (Ernest =?iso-8859-1?Q?Adrogu=E9?=)
Date: Tue, 4 Aug 2009 03:33:17 +0200
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
Message-ID: <20090804013317.GA5376@doriath.local>

 3/08/09 @ 12:49 (-0400), thus spake Ashley DaSilva:
> Hello,
> 
> I am using fsolve to solve a function, f(v), v=[x,y,z] is a list of three
> variables. However, I have a factor in f which contains (1-x**2)**(7./2).
> So, when I do the following,
> fsolve(f,x0)
> the code eventually tries x= -1.57, which clearly produces an error in my
> function due to the power 7./2.
> 
> I know that the solution for x should be between 0 and 1, is there a way to
> put this restriction on x while using fsolve?

I ran into the same problem.
One thing you can try is add a couple of lines to your function
so that it returns a constant value (which is not a solution to your
problem) when (1-x**2) < 0, instead of the actual calculation.
In my case it did the trick.

Ernest



From warren.weckesser at enthought.com  Mon Aug  3 22:13:38 2009
From: warren.weckesser at enthought.com (Warren Weckesser)
Date: Mon, 03 Aug 2009 21:13:38 -0500
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <ec9f80fa0908031423l16ff7562xbb41d4845ed28153@mail.gmail.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>	<7f014ea60908031000ob5da59ei2aedae593d6771db@mail.gmail.com>	<f1dbe1be0908031024q334c7366s1d1e5b2f34047ebd@mail.gmail.com>	<20548feb0908031027l39255170x996c903ad9c51a40@mail.gmail.com>	<4A77248C.1050202@enthought.com>
	<ec9f80fa0908031423l16ff7562xbb41d4845ed28153@mail.gmail.com>
Message-ID: <4A779952.7050608@enthought.com>

Sebastian Walter wrote:
> well, I agree that is not such a clever idea to solve nonlinear
> systems by reformulating it as a NLP.
> But if the function F(x) =0 is uniquely solvable and f is twice
> continuously differentiable, then
> f(x) := |F(x)|^2 is also C^2 and should have a strict local minimum.
>   

Yes.
> Then Newton's method should locally converge quadratically.
>   

No,  because the derivative of the function being minimized is zero at 
the root.  In this case the convergence of Newton's method is only linear.


Warren

>
> On Mon, Aug 3, 2009 at 7:55 PM, Warren
> Weckesser<warren.weckesser at enthought.com> wrote:
>   
>> Harald Schilly wrote:
>>     
>>> On Mon, Aug 3, 2009 at 19:24, Ashley DaSilva<amd405 at psu.edu> wrote:
>>>
>>>       
>>>> t I don't
>>>> want to find the minimum/maximum of my function, I want to find the root.
>>>>
>>>>         
>>> Very generally speaking, you can always find the root by minimization,
>>> if you square your function (simply because no negative values are
>>> possible)!
>>>
>>> H
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>
>>>       
>> But this is generally a poor method for finding a root.  It kills the
>> quadratic convergence of Newton's method, which is at the heart (in some
>> form or another) of most good root-finding algorithms.
>>
>> Warren
>>
>>
>> --
>> Warren Weckesser
>> Enthought, Inc.
>> 515 Congress Avenue, Suite 2100
>> Austin, TX  78701
>> 512-536-1057
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>>     
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>   



From gokhansever at gmail.com  Mon Aug  3 22:37:07 2009
From: gokhansever at gmail.com (=?UTF-8?Q?G=C3=B6khan_Sever?=)
Date: Mon, 3 Aug 2009 21:37:07 -0500
Subject: [SciPy-User] Ubuntu vs Fedora for scientific work?
In-Reply-To: <4A6DC6A2.63BA.009B.0@twdb.state.tx.us>
References: <4A6DC6A2.63BA.009B.0@twdb.state.tx.us>
Message-ID: <49d6b3500908031937v549a5d30n246ee52018e07d47@mail.gmail.com>

On Mon, Jul 27, 2009 at 3:24 PM, Dharhas Pothina <
Dharhas.Pothina at twdb.state.tx.us> wrote:

> Hi All,
>
> This is slightly off topic but I felt that this lists membership would have
> good input on this question. I am presently running fedora 8 on my main
> workstation and it is getting a bit long in the tooth. Back when I set this
> machine up, RHEL was too much of a pain to use because scientific packages
> were always extremely outdated and difficult to install. Ubuntu was nice to
> use at home but installing scientific packages like the Intel Fortran
> compiler etc was complicated (not impossible, just more work than I wanted).
> So I went with Fedora which has worked pretty well so far.
>
> Recently, I've noticed that a lot of scientific packages now have ubuntu
> repositories and even the intel compiler has an ubuntu option. So I'm trying
> to decide whether to go with Fedora 11 or Ubuntu Jaunty.
>
> I'm not trying to start a flame war but I'm interested in what people's
> experience has been.
>
> thanks,
>
> - dharhas
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>

Hey Dharhas,

Your message seemingly delivered after 7 days :) There might be an issue
with your mail server.

I started my Linux life with Red Hat Enterprise 5. Right after I realized
the immensity of open-source world I quickly switched to Fedora 10. After 5
months of journey with FC10 a weeks ago I upgrade to FC11. Yes, I know it is
like a never ending quest.

Even though the very bizarre errors -like the ones mostly associated with
display drivers; I haven't seen any Python related tool not working in
Fedora 11. FC10 was the same as well.

I use repositories to install very essential tools, like C and Fortran
compilers, and installation of critical libraries. However when it comes to
Python, I usually access the codes from their repositories and make a manual
installation. Once I full-fill dependency requirements they nicely build and
install themselves.

Probably, I will keep working with Fedora until it gives me a very big
headache or if they stop releasing new versions.

To me, it would be better to stay on Fedora line since you have already had
experience with a Fedora. However, Ubuntu is a free OS too, you might
download the ISO and burn into your flash drive and give it a test drive
--installations are super easy.

Another funny note to add, when you settled with the OS decision, you will
find yourself trying to decide whether to use matplotlib or chaco, or which
backend should you use wx or Qt. Sometimes having too many options can cause
confusions as in these cases :)

-- 
G?khan
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From offonoffoffonoff at gmail.com  Mon Aug  3 23:40:22 2009
From: offonoffoffonoff at gmail.com (...)
Date: Mon, 3 Aug 2009 22:40:22 -0500
Subject: [SciPy-User] optical ray tracing
In-Reply-To: <4A6E2A33.6030202@ru.nl>
References: <a07a0f1c0907270934w4cd87d5eufb78f8efe8c0d057@mail.gmail.com>
	<4A6E2A33.6030202@ru.nl>
Message-ID: <572c50b30908032040j13b06832ue501b34f6a3870e9@mail.gmail.com>

> This might be interesting
> ?http://www-ee.eng.hawaii.edu/~zqyun/caevp.html

wow.  That looks like it does even too much for me.

actually, it looks like scipy but with a souped up graphics interface
instead of matplotlib.


From gael.varoquaux at normalesup.org  Tue Aug  4 01:27:12 2009
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Tue, 4 Aug 2009 07:27:12 +0200
Subject: [SciPy-User] Ubuntu vs Fedora for scientific work?
In-Reply-To: <4A6DC6A2.63BA.009B.0@twdb.state.tx.us>
References: <4A6DC6A2.63BA.009B.0@twdb.state.tx.us>
Message-ID: <20090804052712.GD28215@phare.normalesup.org>

On Mon, Jul 27, 2009 at 03:24:19PM -0500, Dharhas Pothina wrote:
> Recently, I've noticed that a lot of scientific packages now have ubuntu repositories and even the intel compiler has an ubuntu option. So I'm trying to decide whether to go with Fedora 11 or Ubuntu Jaunty. 

If you want ready-to-install packages, my experience is that you will get
more with Ubuntu. That's a big deal to me: I don't want to be offering
compilation support to my friends and colleagues.

Ga?l


From cournape at gmail.com  Tue Aug  4 01:37:37 2009
From: cournape at gmail.com (David Cournapeau)
Date: Tue, 4 Aug 2009 14:37:37 +0900
Subject: [SciPy-User] Ubuntu vs Fedora for scientific work?
In-Reply-To: <4A6DC6A2.63BA.009B.0@twdb.state.tx.us>
References: <4A6DC6A2.63BA.009B.0@twdb.state.tx.us>
Message-ID: <5b8d13220908032237t34e74fceq859fadb725996789@mail.gmail.com>

Hi Dharhas,

On Tue, Jul 28, 2009 at 5:24 AM, Dharhas
Pothina<Dharhas.Pothina at twdb.state.tx.us> wrote:
> Hi All,
>
> This is slightly off topic but I felt that this lists membership would have good input on this question. I am presently running fedora 8 on my main workstation and it is getting a bit long in the tooth. Back when I set this machine up, RHEL was too much of a pain to use because scientific packages were always extremely outdated and difficult to install. Ubuntu was nice to use at home but installing scientific packages like the Intel Fortran compiler etc was complicated (not impossible, just more work than I wanted). So I went with Fedora which has worked pretty well so far.

Distribution choices are kind of religious choices, almost like vim
vs. Emacs kind of things. In my opinion:
  - Knowing one well matters much more than which one you choose.
  - If you work with other people, particularly people more
knowledgable than you, choosing the same platform as them is better,
everything else being equal.

Installing the intel compiler is not so difficult anymore on Ubuntu -
Ubuntu being popular as it is now, I think most projects will support
Ubuntu (at least the LTS version) as a second platform after RHEL.

Also, the current availability of virtual machines means that as long
as you have enough memory, having several OS at the same time is much
easier than it used to be,

cheers,

David


From daniel.farrell at imperial.ac.uk  Tue Aug  4 04:11:55 2009
From: daniel.farrell at imperial.ac.uk (Daniel J Farrell)
Date: Tue, 4 Aug 2009 09:11:55 +0100
Subject: [SciPy-User] optical ray tracing
In-Reply-To: <4A6E2A33.6030202@ru.nl>
References: <a07a0f1c0907270934w4cd87d5eufb78f8efe8c0d057@mail.gmail.com>
	<4A6E2A33.6030202@ru.nl>
Message-ID: <953F07D8-2E37-4C6F-92A2-1BF2AFE89D5F@imperial.ac.uk>

Hi folks,

There has been a lot of interest in optical design/statistical ray  
tracing written in python recently and many different groups are  
writing code that does almost the same thing.

As a community we have 3 projects so far (sorry if I have left you  
project out, please correct me!),

http://github.com/jasminef/tracer/tree/optic_tests
http://bitbucket.org/bryancole/raytrace/overview/
http://www-ee.eng.hawaii.edu/~zqyun/rtplayground.html

I have created a Google group so, as a community, we can exchange  
knowledge, ideas and code. I'm sure this is of benefit to all:

http://groups.google.com/group/python-ray-tracing-community

You might also be interested in Bryan Cole's RayTrace project here  
Google Group,

http://groups.google.com/group/python-raytrace

But let's not clutter that up with non-specific posts!

So please head over to,

http://groups.google.com/group/python-ray-tracing-community

and write your introductions.

Cheers,

Dan


From baker.alexander at gmail.com  Tue Aug  4 04:21:33 2009
From: baker.alexander at gmail.com (alexander baker)
Date: Tue, 4 Aug 2009 09:21:33 +0100
Subject: [SciPy-User] Ubuntu vs Fedora for scientific work?
In-Reply-To: <5b8d13220908032237t34e74fceq859fadb725996789@mail.gmail.com>
References: <4A6DC6A2.63BA.009B.0@twdb.state.tx.us>
	<5b8d13220908032237t34e74fceq859fadb725996789@mail.gmail.com>
Message-ID: <270620220908040121o57436c87p244950d3d5dd5eec@mail.gmail.com>

Some very interesting points from David, especially the virtual machine
point.

Having a large number of historic scripts, sensitive to library versions,
upgrading major libraries has to be a careful but necessary process,
especially to take advantage of some of the new features like the time
series objects from scikits.

To minimise the impact, vmware on Ubuntu offers an easy way to create any
flavour/type OS. I keep a replica of the parent machine and test upgrades on
the virtual machine ahead of any upgrades to the parent libraries, ironing
out any wrinkles first. Ubuntu and vmware is almost painless, this is a big
plus for me.

Alex Baker

Mobile: 07788 872118
Blog: www.alexfb.com

--
All science is either physics or stamp collecting.


2009/8/4 David Cournapeau <cournape at gmail.com>

> Hi Dharhas,
>
> On Tue, Jul 28, 2009 at 5:24 AM, Dharhas
> Pothina<Dharhas.Pothina at twdb.state.tx.us> wrote:
> > Hi All,
> >
> > This is slightly off topic but I felt that this lists membership would
> have good input on this question. I am presently running fedora 8 on my main
> workstation and it is getting a bit long in the tooth. Back when I set this
> machine up, RHEL was too much of a pain to use because scientific packages
> were always extremely outdated and difficult to install. Ubuntu was nice to
> use at home but installing scientific packages like the Intel Fortran
> compiler etc was complicated (not impossible, just more work than I wanted).
> So I went with Fedora which has worked pretty well so far.
>
> Distribution choices are kind of religious choices, almost like vim
> vs. Emacs kind of things. In my opinion:
>  - Knowing one well matters much more than which one you choose.
>  - If you work with other people, particularly people more
> knowledgable than you, choosing the same platform as them is better,
> everything else being equal.
>
> Installing the intel compiler is not so difficult anymore on Ubuntu -
> Ubuntu being popular as it is now, I think most projects will support
> Ubuntu (at least the LTS version) as a second platform after RHEL.
>
> Also, the current availability of virtual machines means that as long
> as you have enough memory, having several OS at the same time is much
> easier than it used to be,
>
> cheers,
>
> David
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From sebastian.walter at gmail.com  Tue Aug  4 04:30:17 2009
From: sebastian.walter at gmail.com (Sebastian Walter)
Date: Tue, 4 Aug 2009 10:30:17 +0200
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <4A779952.7050608@enthought.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
	<7f014ea60908031000ob5da59ei2aedae593d6771db@mail.gmail.com>
	<f1dbe1be0908031024q334c7366s1d1e5b2f34047ebd@mail.gmail.com>
	<20548feb0908031027l39255170x996c903ad9c51a40@mail.gmail.com>
	<4A77248C.1050202@enthought.com>
	<ec9f80fa0908031423l16ff7562xbb41d4845ed28153@mail.gmail.com>
	<4A779952.7050608@enthought.com>
Message-ID: <ec9f80fa0908040130h15d0d248g6660e14d6df2879f@mail.gmail.com>

well, of course the derivative is zero at the root, that's the
definition of a KKT point.
I think you confuse Newton's method to solve nonlinear systems and
Newton's method for nonlinear optimization.

Yes, if you have a f(x) = f'(x) = 0 then you destroy the local
quadratic convergence of Newton's method.
But in nonlinear programming you do:

f(x) = | F(x)|^2

x_* = argmin_x f(x)

Then you define the first order necessary conditions for optimality:

0 = df(x)/dx

Therefore

0 =G(x) :=  2 dF/dx F

I.e. you get a nonlinear system that you can solve with Newton's method.
If you differentiate once more you get

H = 2 (d^2/dx F) F + 2 (d/dx F)^2

and use the update rule

x_+  = x - H(x)^-1 G(x)

in our case H is symmetric positive definite and therefore Newton
should converge quadratically.




On Tue, Aug 4, 2009 at 4:13 AM, Warren
Weckesser<warren.weckesser at enthought.com> wrote:
> Sebastian Walter wrote:
>> well, I agree that is not such a clever idea to solve nonlinear
>> systems by reformulating it as a NLP.
>> But if the function F(x) =0 is uniquely solvable and f is twice
>> continuously differentiable, then
>> f(x) := |F(x)|^2 is also C^2 and should have a strict local minimum.
>>
>
> Yes.
>> Then Newton's method should locally converge quadratically.
>>
>
> No,  because the derivative of the function being minimized is zero at
> the root.  In this case the convergence of Newton's method is only linear.
>
>
> Warren
>
>>
>> On Mon, Aug 3, 2009 at 7:55 PM, Warren
>> Weckesser<warren.weckesser at enthought.com> wrote:
>>
>>> Harald Schilly wrote:
>>>
>>>> On Mon, Aug 3, 2009 at 19:24, Ashley DaSilva<amd405 at psu.edu> wrote:
>>>>
>>>>
>>>>> t I don't
>>>>> want to find the minimum/maximum of my function, I want to find the root.
>>>>>
>>>>>
>>>> Very generally speaking, you can always find the root by minimization,
>>>> if you square your function (simply because no negative values are
>>>> possible)!
>>>>
>>>> H
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at scipy.org
>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>>
>>>>
>>> But this is generally a poor method for finding a root.  It kills the
>>> quadratic convergence of Newton's method, which is at the heart (in some
>>> form or another) of most good root-finding algorithms.
>>>
>>> Warren
>>>
>>>
>>> --
>>> Warren Weckesser
>>> Enthought, Inc.
>>> 515 Congress Avenue, Suite 2100
>>> Austin, TX  78701
>>> 512-536-1057
>>>
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>
>>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From sebastian.walter at gmail.com  Tue Aug  4 04:49:02 2009
From: sebastian.walter at gmail.com (Sebastian Walter)
Date: Tue, 4 Aug 2009 10:49:02 +0200
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <20090804013317.GA5376@doriath.local>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
	<20090804013317.GA5376@doriath.local>
Message-ID: <ec9f80fa0908040149m44e9f98fx4b887f8b3043d2f1@mail.gmail.com>

what you can try to do is using a penalty method:

x_* = argmin_x f(x)
subject to  g(x) <= 0

then you can try to do:

x_* = argmin_x f(x) + \rho (max(g(x), 0))^p
where e.g. p =2, and make \rho large

problem: badly conditioned for \rho very large.

But that's still much better than adding a constant when g(x) > 0!



2009/8/4 Ernest Adrogu? <eadrogue at gmx.net>:
>  3/08/09 @ 12:49 (-0400), thus spake Ashley DaSilva:
>> Hello,
>>
>> I am using fsolve to solve a function, f(v), v=[x,y,z] is a list of three
>> variables. However, I have a factor in f which contains (1-x**2)**(7./2).
>> So, when I do the following,
>> fsolve(f,x0)
>> the code eventually tries x= -1.57, which clearly produces an error in my
>> function due to the power 7./2.
>>
>> I know that the solution for x should be between 0 and 1, is there a way to
>> put this restriction on x while using fsolve?
>
> I ran into the same problem.
> One thing you can try is add a couple of lines to your function
> so that it returns a constant value (which is not a solution to your
> problem) when (1-x**2) < 0, instead of the actual calculation.
> In my case it did the trick.
>
> Ernest
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From gilles.rochefort at gmail.com  Tue Aug  4 05:43:39 2009
From: gilles.rochefort at gmail.com (Gilles Rochefort)
Date: Tue, 04 Aug 2009 11:43:39 +0200
Subject: [SciPy-User] ndimage filters overflow for default image format
 (uint8)
In-Reply-To: <0D3C80A2-D6D1-4C2B-B56F-A1C0358A3835@gmail.com>
References: <0D3C80A2-D6D1-4C2B-B56F-A1C0358A3835@gmail.com>
Message-ID: <4A7802CB.9020802@gmail.com>

Hi Tony,
> I've been struggling to get ndimage filters to give the expected  
> output. It turns out that my images were getting imported as unsigned  
> integers (uint8) using both chaco's ImageData importer and PIL (with  
> an additional call to numpy.asarray). As a result, edge filters--- 
> which often return negative values---were returning overflowed arrays.  
>   
As you may know, grayscale images are represented with uint8 because of
a low memory footprint.
By the way, if you intent to perform some calculations, you need to be
careful.

Two different goals may be achieved :
- Getting the best precision you can get
- lowering the memory footprint as much as you can !
> Here's a simple example using the correlate1d function (which is used  
> by the edge filters):
>
>  >>> im = np.array([1, 2, 3, 4, 4, 3, 2, 1], dtype=np.uint8)
>  >>> weights = np.array([-1, 0, 1])
>  >>> print ndimage.correlate1d(im, weights)
> [  1   2   2   1 255 254 254 255]
>   
So, If you need precision first and you are not concerned by memory, you
can do something like :
>>> im = np.array([1, 2, 3, 4, 4, 3, 2, 1], dtype=np.uint8)
>>> weights = np.array([-1, 0, 1])
>>> z = ndimage.correlate1d( np.asarray(im,dtype=int32), weights)
>>> print z

[  1   2   2   1  -1 -2 -2 -1]

and 

>>> print z.dtype
int32

I choose int32 but I should have chosen something like double, float,
and even int16 until it is able to store the result of that filter.

Now, if you are more concerned about memory footpring than precision,
you can re-consider your example :

 >>> im = np.array([1, 2, 3, 4, 4, 3, 2, 1], dtype=np.uint8)
 >>> weights = np.array([-1, 0, 1])
 >>> z= ndimage.correlate1d(im, weights)
 >>> print z
[  1   2   2   1 255 254 254 255]

 >>> z .dtype = int8
 >>> print z

[  1   2   2   1  -1 -2 -2 -1]


Note that changing dtype don't realloc or copy datas, that is just
another way to view them.

Ok, one may argue that changing dtype is not enought, and that's right.
Filtered datas have a range from
-255 up to 254 but int8 can only represent integer from -128 to 127 so ?

 >>> im = np.array([1, 2, 3, 4, 4, 3, 2, 1], dtype=np.uint8)
 >>> weights = np.array([-.5, 0, .5])
 >>> z= ndimage.correlate1d(im, weights)
 >>> print 2*z
[  0   2   2   0 0 -2 -2 0]

So, now filtered datas may be stored as int8, but you loose half the precision (dividing by two filter coefficients). Most
 applications won't suffer from a limited precision. 


In my opinion, as a programmer you have to know the filtered datas range. So, you just have to decide if precision does care or not. 
You can't expect correlate1d to guess your intent for the resulting range. Just for thought : are you processing vga images in real time,
or satellite image of 10Mpx ? 


Best regards,

Gilles Rochefort





> This result is not a huge surprise *if* you expect the output of the  
> filter to be the same dtype as your input image, but this wasn't  
> apparent to me until I really dug into the problem. (I'd expect the  
> same dtype for filters that don't return values outside the range of  
> input values, e.g. a median filter).
>
> I realize guessing the "best" output format is difficult (b/c of  
> memory considerations and variable input formats), but it seems like  
> the current behavior would cause problems for a lot people. Maybe  
> there could be a dtype parameter to specify the output dtype. For  
> filters that can return values outside the input range (e.g < 0, >  
> 255), the default dtype could be int32. This of course assumes the  
> input is uint8, but maybe that's a good assumption since that seems to  
> be the default for PIL. Or there's probably a smarter way of choosing  
> the output format.
>
> I realize my suggestion could cause more problems than it's worth. If  
> so, maybe just adding some very prominent warnings in the filter  
> docstrings would do the trick.
>
> Best,
> -Tony
>
> PS. Sorry if this post shows up twice, I've been having trouble  
> posting with my other email address.
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>   



From eadrogue at gmx.net  Tue Aug  4 07:23:55 2009
From: eadrogue at gmx.net (Ernest =?iso-8859-1?Q?Adrogu=E9?=)
Date: Tue, 4 Aug 2009 13:23:55 +0200
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <ec9f80fa0908040149m44e9f98fx4b887f8b3043d2f1@mail.gmail.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
	<20090804013317.GA5376@doriath.local>
	<ec9f80fa0908040149m44e9f98fx4b887f8b3043d2f1@mail.gmail.com>
Message-ID: <20090804112355.GA6395@doriath.local>

 4/08/09 @ 10:49 (+0200), thus spake Sebastian Walter:
> what you can try to do is using a penalty method:
> 
> x_* = argmin_x f(x)
> subject to  g(x) <= 0
> 
> then you can try to do:
> 
> x_* = argmin_x f(x) + \rho (max(g(x), 0))^p
> where e.g. p =2, and make \rho large
> 
> problem: badly conditioned for \rho very large.
> 
> But that's still much better than adding a constant when g(x) > 0!

But... this won't prevent the function from being evaluated
outside of its domain, which is the real issue here, will it?


Ernest


From sebastian.walter at gmail.com  Tue Aug  4 08:47:58 2009
From: sebastian.walter at gmail.com (Sebastian Walter)
Date: Tue, 4 Aug 2009 14:47:58 +0200
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <20090804112355.GA6395@doriath.local>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
	<20090804013317.GA5376@doriath.local>
	<ec9f80fa0908040149m44e9f98fx4b887f8b3043d2f1@mail.gmail.com>
	<20090804112355.GA6395@doriath.local>
Message-ID: <ec9f80fa0908040547r7cd6e017ye955ee14445e107f@mail.gmail.com>

uhmm, you are completely right... penalty methods are useless in that case.
Then,  interior points algorithms as implemented in IPOPT should be
method of choice.
They have the nice property to stay feasible (granted you can find a
feasible starting value ...)




2009/8/4 Ernest Adrogu? <eadrogue at gmx.net>:
>  4/08/09 @ 10:49 (+0200), thus spake Sebastian Walter:
>> what you can try to do is using a penalty method:
>>
>> x_* = argmin_x f(x)
>> subject to  g(x) <= 0
>>
>> then you can try to do:
>>
>> x_* = argmin_x f(x) + \rho (max(g(x), 0))^p
>> where e.g. p =2, and make \rho large
>>
>> problem: badly conditioned for \rho very large.
>>
>> But that's still much better than adding a constant when g(x) > 0!
>
> But... this won't prevent the function from being evaluated
> outside of its domain, which is the real issue here, will it?
>
>
> Ernest
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From Dharhas.Pothina at twdb.state.tx.us  Tue Aug  4 09:00:49 2009
From: Dharhas.Pothina at twdb.state.tx.us (Dharhas Pothina)
Date: Tue, 04 Aug 2009 08:00:49 -0500
Subject: [SciPy-User] Ubuntu vs Fedora for scientific work?
In-Reply-To: <49d6b3500908031937v549a5d30n246ee52018e07d47@mail.gmail.com>
References: <4A6DC6A2.63BA.009B.0@twdb.state.tx.us>
	<49d6b3500908031937v549a5d30n246ee52018e07d47@mail.gmail.com>
Message-ID: <4A77EAB1.63BA.009B.0@twdb.state.tx.us>


> Your message seemingly delivered after 7 days :) There might be an issue
> with your mail server.

yeah we had some mailserver issues. Our agency was forced to outsource IT related stuff to IBM ... 

> Even though the very bizarre errors -like the ones mostly associated with
> display drivers; I haven't seen any Python related tool not working in
> Fedora 11. FC10 was the same as well.

I seem to have display driver issues whichever distribution I use... I rarely upgrade kernels anymore unless I have a spare day to mess with video issues.

> I use repositories to install very essential tools, like C and Fortran
> compilers, and installation of critical libraries. However when it comes to
> Python, I usually access the codes from their repositories and make a manual
> installation. Once I full-fill dependency requirements they nicely build and
> install themselves.

This is my main concern. I don't mind building packages myself, with scipy/numpy it seems to be required since they keep adding useful features at a faster rate than the distributions seem to package them. I do want to be able to pull dependencies from the repos though.

> Probably, I will keep working with Fedora until it gives me a very big
> headache or if they stop releasing new versions.

The main things that made me consider Ubuntu, where that it seems easier to upgrade to a new version, increasingly scientific projects have an ubuntu repo and some multimedia stuff like watching flash/quicktime etc seems to be easier to set up. 

> To me, it would be better to stay on Fedora line since you have already had
> experience with a Fedora. However, Ubuntu is a free OS too, you might
> download the ISO and burn into your flash drive and give it a test drive
> --installations are super easy.

I've used Ubuntu at home so I'm familiar with it. I've just never used it for any scientific work.

> Another funny note to add, when you settled with the OS decision, you will
> find yourself trying to decide whether to use matplotlib or chaco, or which
> backend should you use wx or Qt. Sometimes having too many options can cause
> confusions as in these cases :)

In general I like the choice. One size doesn't fit all. I'm using matplotlib extensively but I'm trying to learn Chaco for an gui application I need to develop.

btw I use emacs not vi :)

- dharhas



From Dharhas.Pothina at twdb.state.tx.us  Tue Aug  4 09:08:48 2009
From: Dharhas.Pothina at twdb.state.tx.us (Dharhas Pothina)
Date: Tue, 04 Aug 2009 08:08:48 -0500
Subject: [SciPy-User] Ubuntu vs Fedora for scientific work?
In-Reply-To: <270620220908040121o57436c87p244950d3d5dd5eec@mail.gmail.com>
References: <4A6DC6A2.63BA.009B.0@twdb.state.tx.us>
	<5b8d13220908032237t34e74fceq859fadb725996789@mail.gmail.com>
	<270620220908040121o57436c87p244950d3d5dd5eec@mail.gmail.com>
Message-ID: <4A77EC90.63BA.009B.0@twdb.state.tx.us>


>>> alexander baker <baker.alexander at gmail.com> 8/4/2009 3:21 AM >>>
> Some very interesting points from David, especially the virtual machine
> point.

So is there much of a performance hit using a virtual machine, or is it pretty much unnoticable on a modern machine? 

- dharhas




From warren.weckesser at enthought.com  Tue Aug  4 09:24:50 2009
From: warren.weckesser at enthought.com (Warren Weckesser)
Date: Tue, 04 Aug 2009 08:24:50 -0500
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <ec9f80fa0908040130h15d0d248g6660e14d6df2879f@mail.gmail.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>	<7f014ea60908031000ob5da59ei2aedae593d6771db@mail.gmail.com>	<f1dbe1be0908031024q334c7366s1d1e5b2f34047ebd@mail.gmail.com>	<20548feb0908031027l39255170x996c903ad9c51a40@mail.gmail.com>	<4A77248C.1050202@enthought.com>	<ec9f80fa0908031423l16ff7562xbb41d4845ed28153@mail.gmail.com>	<4A779952.7050608@enthought.com>
	<ec9f80fa0908040130h15d0d248g6660e14d6df2879f@mail.gmail.com>
Message-ID: <4A7836A2.7000905@enthought.com>

Sebastian Walter wrote:
> well, of course the derivative is zero at the root, that's the
> definition of a KKT point.
> I think you confuse Newton's method to solve nonlinear systems and
> Newton's method for nonlinear optimization.
>   

Yes. :)

> Yes, if you have a f(x) = f'(x) = 0 then you destroy the local
> quadratic convergence of Newton's method.
> But in nonlinear programming you do:
>
> f(x) = | F(x)|^2
>
> x_* = argmin_x f(x)
>
> Then you define the first order necessary conditions for optimality:
>
> 0 = df(x)/dx
>
> Therefore
>
> 0 =G(x) :=  2 dF/dx F
>
> I.e. you get a nonlinear system that you can solve with Newton's method.
> If you differentiate once more you get
>
> H = 2 (d^2/dx F) F + 2 (d/dx F)^2
>
> and use the update rule
>
> x_+  = x - H(x)^-1 G(x)
>
> in our case H is symmetric positive definite and therefore Newton
> should converge quadratically.
>
>
>
>
> On Tue, Aug 4, 2009 at 4:13 AM, Warren
> Weckesser<warren.weckesser at enthought.com> wrote:
>   
>> Sebastian Walter wrote:
>>     
>>> well, I agree that is not such a clever idea to solve nonlinear
>>> systems by reformulating it as a NLP.
>>> But if the function F(x) =0 is uniquely solvable and f is twice
>>> continuously differentiable, then
>>> f(x) := |F(x)|^2 is also C^2 and should have a strict local minimum.
>>>
>>>       
>> Yes.
>>     
>>> Then Newton's method should locally converge quadratically.
>>>
>>>       
>> No,  because the derivative of the function being minimized is zero at
>> the root.  In this case the convergence of Newton's method is only linear.
>>
>>
>> Warren
>>
>>     
>>> On Mon, Aug 3, 2009 at 7:55 PM, Warren
>>> Weckesser<warren.weckesser at enthought.com> wrote:
>>>
>>>       
>>>> Harald Schilly wrote:
>>>>
>>>>         
>>>>> On Mon, Aug 3, 2009 at 19:24, Ashley DaSilva<amd405 at psu.edu> wrote:
>>>>>
>>>>>
>>>>>           
>>>>>> t I don't
>>>>>> want to find the minimum/maximum of my function, I want to find the root.
>>>>>>
>>>>>>
>>>>>>             
>>>>> Very generally speaking, you can always find the root by minimization,
>>>>> if you square your function (simply because no negative values are
>>>>> possible)!
>>>>>
>>>>> H
>>>>> _______________________________________________
>>>>> SciPy-User mailing list
>>>>> SciPy-User at scipy.org
>>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>>>
>>>>>
>>>>>           
>>>> But this is generally a poor method for finding a root.  It kills the
>>>> quadratic convergence of Newton's method, which is at the heart (in some
>>>> form or another) of most good root-finding algorithms.
>>>>
>>>> Warren
>>>>
>>>>
>>>> --
>>>> Warren Weckesser
>>>> Enthought, Inc.
>>>> 515 Congress Avenue, Suite 2100
>>>> Austin, TX  78701
>>>> 512-536-1057
>>>>
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at scipy.org
>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>>
>>>>
>>>>         
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>
>>>       
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>>     
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>   



From baker.alexander at gmail.com  Tue Aug  4 10:10:44 2009
From: baker.alexander at gmail.com (alexander baker)
Date: Tue, 4 Aug 2009 15:10:44 +0100
Subject: [SciPy-User] Ubuntu vs Fedora for scientific work?
In-Reply-To: <4A77EC90.63BA.009B.0@twdb.state.tx.us>
References: <4A6DC6A2.63BA.009B.0@twdb.state.tx.us>
	<5b8d13220908032237t34e74fceq859fadb725996789@mail.gmail.com>
	<270620220908040121o57436c87p244950d3d5dd5eec@mail.gmail.com>
	<4A77EC90.63BA.009B.0@twdb.state.tx.us>
Message-ID: <270620220908040710s15d08255t17ee6726442a087e@mail.gmail.com>

You can tune the amount of resources for each virtual machine, I use a
console only os to reduce overheads. Have not noticed machine is any more
sluggish than normal. The VM machine performance is also very swift, handles
just like typical install on remote machine.

Mobile: 07788 872118
Blog: www.alexfb.com

--
All science is either physics or stamp collecting.


2009/8/4 Dharhas Pothina <Dharhas.Pothina at twdb.state.tx.us>

>
> >>> alexander baker <baker.alexander at gmail.com> 8/4/2009 3:21 AM >>>
> > Some very interesting points from David, especially the virtual machine
> > point.
>
> So is there much of a performance hit using a virtual machine, or is it
> pretty much unnoticable on a modern machine?
>
> - dharhas
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From eadrogue at gmx.net  Tue Aug  4 10:38:04 2009
From: eadrogue at gmx.net (Ernest =?iso-8859-1?Q?Adrogu=E9?=)
Date: Tue, 4 Aug 2009 16:38:04 +0200
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <ec9f80fa0908040547r7cd6e017ye955ee14445e107f@mail.gmail.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
	<20090804013317.GA5376@doriath.local>
	<ec9f80fa0908040149m44e9f98fx4b887f8b3043d2f1@mail.gmail.com>
	<20090804112355.GA6395@doriath.local>
	<ec9f80fa0908040547r7cd6e017ye955ee14445e107f@mail.gmail.com>
Message-ID: <20090804143804.GA7021@doriath.local>

 4/08/09 @ 14:47 (+0200), thus spake Sebastian Walter:
> uhmm, you are completely right... penalty methods are useless in that case.
> Then,  interior points algorithms as implemented in IPOPT should be
> method of choice.

i would like to try ipopt, unfortunately it depends on third-party
libraries with draconian licenses. only mumps seems to actually be
"free", and yet in order to get it you have to fill a form and wait
until they send you the software by mail!!

Ernest


From P.Schellart at student.science.ru.nl  Tue Aug  4 11:29:24 2009
From: P.Schellart at student.science.ru.nl (Pim Schellart)
Date: Tue, 4 Aug 2009 17:29:24 +0200
Subject: [SciPy-User] Installing latest svn version UMFPACK issue
Message-ID: <7d3dada90908040829m47af06uef827f8a650ab7aa@mail.gmail.com>

Dear Scipy users/developers,

I want to install the latest svn version of scipy because I need the
curve_fit routine in minpack.
This routine (although not very robust) makes curve fitting much
easier, and I would like to see it included in the release version of
scipy soon :)
However when I try to install the latest svn checkout I get an error
that the installation cannot find UMFPACK on my system (OSX).
I did not get this error with previous installations and I am
wondering why I do get it now.
Is this a newly added dependency (in which case please tell me how to
install it since it seems to be designed to be integrated with
matlab), if not why does this problem occur (is there perhaps
something wrong with the installation script).

Kind regards,

Pim Schellart


From gael.varoquaux at normalesup.org  Tue Aug  4 11:36:00 2009
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Tue, 4 Aug 2009 17:36:00 +0200
Subject: [SciPy-User] Installing latest svn version UMFPACK issue
In-Reply-To: <7d3dada90908040829m47af06uef827f8a650ab7aa@mail.gmail.com>
References: <7d3dada90908040829m47af06uef827f8a650ab7aa@mail.gmail.com>
Message-ID: <20090804153600.GQ17519@phare.normalesup.org>

On Tue, Aug 04, 2009 at 05:29:24PM +0200, Pim Schellart wrote:
> Dear Scipy users/developers,

> I want to install the latest svn version of scipy because I need the
> curve_fit routine in minpack.
> This routine (although not very robust) makes curve fitting much
> easier, and I would like to see it included in the release version of
> scipy soon :)
> However when I try to install the latest svn checkout I get an error
> that the installation cannot find UMFPACK on my system (OSX).
> I did not get this error with previous installations and I am
> wondering why I do get it now.

AFAIK, it is a newly-added warning, for an old dependency. In other
words, this used to fail similarly, but you weren't aware. What you don't
know can't hurt you, right!

Ga?l


From scott.sinclair.za at gmail.com  Tue Aug  4 12:13:12 2009
From: scott.sinclair.za at gmail.com (Scott Sinclair)
Date: Tue, 4 Aug 2009 18:13:12 +0200
Subject: [SciPy-User] Dimensions of x and y are incompatible
In-Reply-To: <77e5896b0908031426w5d66a96egb58e5b74b94866b@mail.gmail.com>
References: <77e5896b0908031426w5d66a96egb58e5b74b94866b@mail.gmail.com>
Message-ID: <6a17e9ee0908040913j2cb91309pe700d96ef55177b9@mail.gmail.com>

> 2009/8/3 Omer Khalid <Omer.Khalid at cern.ch>:
> I am constantly getting this error when plotting "Dimensions of x and y are
> incompatible"

The x and y here are refering to the values of the parameters inside
the plt.plot() function, *not* out in your code, so it's a bit
confusing...

> ??????????????? plt.figure()
> ??????????????? xdata= array(xd)
> ??????????????? ydata = array(xd)

The first problem is that you haven't told us how big your xd is..

> ??????????????? N=len(xdata)
> ??????????????? print "N: %d" % (N)
> ??????????????? x=arange(0.0, 1.0+0.01, 0.01)

Unless you're lucky, your x probably doesn't have length N

> ??????????????? M=len(ydata)
> ??????????????? print "M: %d" % (M)
> ??????????????? y=arange(0.0, 1.0+0.1, 0.1)

Again, y probably doesn't have length M (which equals N..)

> ??????????????? plt.plot(x,xdata,'r')

So (unless you're lucky) the call above will fail because x and xdata
don't have the same length.

> ??????????????? plt.plot(y,ydata,'b')

If you were lucky before, the call above will certainly fail because y
does not have the same length as ydata, x or xdata.

> But it didn't work. Any help would be much appreciated.

I hope you've managed to work it out now :)

Cheers,
Scott


From sebastian.walter at gmail.com  Tue Aug  4 12:53:38 2009
From: sebastian.walter at gmail.com (Sebastian Walter)
Date: Tue, 4 Aug 2009 18:53:38 +0200
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <20090804143804.GA7021@doriath.local>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
	<20090804013317.GA5376@doriath.local>
	<ec9f80fa0908040149m44e9f98fx4b887f8b3043d2f1@mail.gmail.com>
	<20090804112355.GA6395@doriath.local>
	<ec9f80fa0908040547r7cd6e017ye955ee14445e107f@mail.gmail.com>
	<20090804143804.GA7021@doriath.local>
Message-ID: <ec9f80fa0908040953x671bf2a0g838613ac0363b20d@mail.gmail.com>

Yeah, the licences of the linear solvers used in IPOPT are really too
restrictive.

I use the ma27 (I think it is called) as academic user. It's not as
bad as you wrote above:
You register, you get a confirmation mail with a password, then you
can log in an download the code as a couple of fortran source code
files.
So, not such a big deal.









2009/8/4 Ernest Adrogu? <eadrogue at gmx.net>:
> ?4/08/09 @ 14:47 (+0200), thus spake Sebastian Walter:
>> uhmm, you are completely right... penalty methods are useless in that case.
>> Then, ?interior points algorithms as implemented in IPOPT should be
>> method of choice.
>
> i would like to try ipopt, unfortunately it depends on third-party
> libraries with draconian licenses. only mumps seems to actually be
> "free", and yet in order to get it you have to fill a form and wait
> until they send you the software by mail!!
>
> Ernest
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From pav+sp at iki.fi  Tue Aug  4 13:06:33 2009
From: pav+sp at iki.fi (Pauli Virtanen)
Date: Tue, 4 Aug 2009 17:06:33 +0000 (UTC)
Subject: [SciPy-User] Installing latest svn version UMFPACK issue
References: <7d3dada90908040829m47af06uef827f8a650ab7aa@mail.gmail.com>
	<20090804153600.GQ17519@phare.normalesup.org>
Message-ID: <h59pqp$4fd$1@ger.gmane.org>

On 2009-08-04, Gael Varoquaux <gael.varoquaux at normalesup.org> wrote:
> On Tue, Aug 04, 2009 at 05:29:24PM +0200, Pim Schellart wrote:
>> Dear Scipy users/developers,
>
>> I want to install the latest svn version of scipy because I need the
>> curve_fit routine in minpack.
>> This routine (although not very robust) makes curve fitting much
>> easier, and I would like to see it included in the release version of
>> scipy soon :)
>> However when I try to install the latest svn checkout I get an error
>> that the installation cannot find UMFPACK on my system (OSX).
>> I did not get this error with previous installations and I am
>> wondering why I do get it now.
>
> AFAIK, it is a newly-added warning, for an old dependency. In other
> words, this used to fail similarly, but you weren't aware. What you don't
> know can't hurt you, right!

Exactly, it's a warning. UMFPACK is an optional component, so you 
should be able to build Scipy also without it. If you can't then 
there's probably a mistake somewhere.

-- 
Pauli Virtanen



From zunzun at zunzun.com  Tue Aug  4 14:21:15 2009
From: zunzun at zunzun.com (James Phillips)
Date: Tue, 4 Aug 2009 18:21:15 +0000 (UTC)
Subject: [SciPy-User] Attached multiprocessing parallel Differential
	Evolution GA code
References: <268756d30908010758q14c9bca2w7b1d6b06e76db707@mail.gmail.com>
	<4A76E752.3060008@american.edu>
Message-ID: <loom.20090804T181841-497@post.gmane.org>

Alan G Isaac <aisaac <at> american.edu> writes:
> 
> Are you proposing adding this functionality to scipy.optimize?
> Or what? And if so, how to proceed?

I'm not sure where to go with it from here, which is why I "public domain"-ed it.

     James





From robert.kern at gmail.com  Tue Aug  4 14:42:22 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Tue, 4 Aug 2009 13:42:22 -0500
Subject: [SciPy-User] Attached multiprocessing parallel Differential
	Evolution GA code
In-Reply-To: <4A76E752.3060008@american.edu>
References: <268756d30908010758q14c9bca2w7b1d6b06e76db707@mail.gmail.com> 
	<4A76E752.3060008@american.edu>
Message-ID: <3d375d730908041142x7a6b6aa5g565b8e879539afeb@mail.gmail.com>

On Mon, Aug 3, 2009 at 08:34, Alan G Isaac<aisaac at american.edu> wrote:
> On 8/1/2009 10:58 AM James Phillips apparently wrote:
>> Below and attached is my first cut at a Python multiprocessing module
>> parallel implementation of the Differential Evolution genetic
>> algorithm. ?The code is explicitly in the public domain.
>
>
> Thanks for posting this. ?I confess I was unaware of the
> differential evolution approach to optimization.
>
> Are you proposing adding this functionality to scipy.optimize?
> Or what? And if so, how to proceed?

A multiprocessing version might not be ideal to integrate into
scipy.optimize by itself, but I do have a single-process version here:

http://svn.scipy.org/svn/scipy/branches/sandbox/scipy/sandbox/rkern/diffev.py

It's quite a bit more numpy-idiomatic than James' version, which
follows the C++ reference implementation of DE fairly closely.

-- 
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


From rex at nosyntax.net  Tue Aug  4 14:46:53 2009
From: rex at nosyntax.net (rex)
Date: Tue, 4 Aug 2009 11:46:53 -0700
Subject: [SciPy-User] book on scipy
In-Reply-To: <9457e7c80907311448med24475leebdfccef15e8f9c@mail.gmail.com>
References: <288df32a0907310100p354d5e81hafd366618a219870@mail.gmail.com>
	<20090731080522.GB9207@phare.normalesup.org>
	<7f014ea60907310147k780dbc43xa6acd045ed6cae19@mail.gmail.com>
	<20090731084952.GC9207@phare.normalesup.org>
	<4A72D47C.1060002@lpta.in2p3.fr> <4A72F006.5040206@american.edu>
	<20090731133733.GA23447@phare.normalesup.org>
	<20090731154612.GN10466@nosyntax.net>
	<9457e7c80907311448med24475leebdfccef15e8f9c@mail.gmail.com>
Message-ID: <20090804184653.GC10466@nosyntax.net>

St?fan van der Walt <stefan at sun.ac.za> [2009-07-31 14:48]:
>
>2009/7/31 rex <rex at nosyntax.net>:
>> Speaking of which, this example below does not work correctly with
>> matplotlib-0.98.1-1+lenny4 (the most recent in the Debian repository).
>
>I'm glad you spotted this problem.  Would you mind creating an account
>on docs.scipy.org and fixing it?

Hi St?fan,

I created an account (and sent you an email about it), but cannot
figure out how to edit the document. This is what I see after logging in:

==================================================================================
Numpy documentation editor

    * Wiki
    * Docstrings
    * Changes
    * Milestones
    * Search
    * Stats
    * Patch
    * ?
    * rex:
    * Profile
    * Log out

numpy.random.mtrand.RandomState.lognormal
View Log Diff to SVN Discussion Source

    * Review status: Proofed

Numpy manual contents ? NumPy Reference ? Routines ? Random sampling (:mod:`numpy.random`) ? 
[...]
=============================================================================================

If I log out and view the page again, it's the same except for the
logged-in status:

================================================================================
Numpy documentation editor

    * Wiki
    * Docstrings
    * Changes
    * Milestones
    * Search
    * Stats
    * Patch
    * ?
    * Log in

numpy.random.mtrand.RandomState.lognormal
View Log Diff to SVN Discussion Source

    * Review status: Proofed

Numpy manual contents ? NumPy Reference ? Routines ? Random sampling (:mod:`numpy.random`) ?
[...]
===========================================================================================

Does this mean someone has to enable editing for me, after which
an "Edit" option will appear?

Thanks,

-rex
-- 
The M isn't missing from BSD, it's implied by default...


From zunzun at zunzun.com  Tue Aug  4 15:10:22 2009
From: zunzun at zunzun.com (James Phillips)
Date: Tue, 4 Aug 2009 19:10:22 +0000 (UTC)
Subject: [SciPy-User]
	=?utf-8?q?Attached_multiprocessing_parallel_Differen?=
	=?utf-8?q?tial=09Evolution_GA_code?=
References: <268756d30908010758q14c9bca2w7b1d6b06e76db707@mail.gmail.com>
	<4A76E752.3060008@american.edu>
	<3d375d730908041142x7a6b6aa5g565b8e879539afeb@mail.gmail.com>
Message-ID: <loom.20090804T190713-8@post.gmane.org>

Robert Kern <robert.kern <at> gmail.com> writes:
> 
> A multiprocessing version might not be ideal to integrate into
> scipy.optimize by itself, but I do have a single-process version here:
> 
> http://svn.scipy.org/svn/scipy/branches/sandbox/scipy/sandbox/rkern/diffev.py
> 
> It's quite a bit more numpy-idiomatic than James' version, which
> follows the C++ reference implementation of DE fairly closely.


May I try my hand at parallelizing your code?  If so, under what license would
you like me to release the result?

     James







From robert.kern at gmail.com  Tue Aug  4 15:17:43 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Tue, 4 Aug 2009 14:17:43 -0500
Subject: [SciPy-User] Attached multiprocessing parallel Differential
	Evolution GA code
In-Reply-To: <loom.20090804T190713-8@post.gmane.org>
References: <268756d30908010758q14c9bca2w7b1d6b06e76db707@mail.gmail.com> 
	<4A76E752.3060008@american.edu>
	<3d375d730908041142x7a6b6aa5g565b8e879539afeb@mail.gmail.com> 
	<loom.20090804T190713-8@post.gmane.org>
Message-ID: <3d375d730908041217y5f8cc666w73ba590b5e27915@mail.gmail.com>

On Tue, Aug 4, 2009 at 14:10, James Phillips<zunzun at zunzun.com> wrote:
> Robert Kern <robert.kern <at> gmail.com> writes:
>>
>> A multiprocessing version might not be ideal to integrate into
>> scipy.optimize by itself, but I do have a single-process version here:
>>
>> http://svn.scipy.org/svn/scipy/branches/sandbox/scipy/sandbox/rkern/diffev.py
>>
>> It's quite a bit more numpy-idiomatic than James' version, which
>> follows the C++ reference implementation of DE fairly closely.
>
> May I try my hand at parallelizing your code?

By all means.

>?If so, under what license would
> you like me to release the result?

BSD, please.

-- 
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


From gael.varoquaux at normalesup.org  Tue Aug  4 15:42:46 2009
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Tue, 4 Aug 2009 21:42:46 +0200
Subject: [SciPy-User] book on scipy
In-Reply-To: <20090804184653.GC10466@nosyntax.net>
References: <288df32a0907310100p354d5e81hafd366618a219870@mail.gmail.com>
	<20090731080522.GB9207@phare.normalesup.org>
	<7f014ea60907310147k780dbc43xa6acd045ed6cae19@mail.gmail.com>
	<20090731084952.GC9207@phare.normalesup.org>
	<4A72D47C.1060002@lpta.in2p3.fr> <4A72F006.5040206@american.edu>
	<20090731133733.GA23447@phare.normalesup.org>
	<20090731154612.GN10466@nosyntax.net>
	<9457e7c80907311448med24475leebdfccef15e8f9c@mail.gmail.com>
	<20090804184653.GC10466@nosyntax.net>
Message-ID: <20090804194246.GE11772@phare.normalesup.org>

On Tue, Aug 04, 2009 at 11:46:53AM -0700, rex wrote:
> St?fan van der Walt <stefan at sun.ac.za> [2009-07-31 14:48]:

> >2009/7/31 rex <rex at nosyntax.net>:
> >> Speaking of which, this example below does not work correctly with
> >> matplotlib-0.98.1-1+lenny4 (the most recent in the Debian repository).

> >I'm glad you spotted this problem.  Would you mind creating an account
> >on docs.scipy.org and fixing it?

> Hi St?fan,

> I created an account (and sent you an email about it), but cannot
> figure out how to edit the document. This is what I see after logging in:

That's because we need to give you permissions.

It's done. Thanks for you interest.

Ga?l


From P.Schellart at student.science.ru.nl  Tue Aug  4 16:53:06 2009
From: P.Schellart at student.science.ru.nl (Pim Schellart)
Date: Tue, 4 Aug 2009 22:53:06 +0200
Subject: [SciPy-User] Installing latest svn version UMFPACK issue
Message-ID: <7d3dada90908041353q74dcd361ue8e9633738d07fe3@mail.gmail.com>

Dear All,

That might be just a warning but my installation aborts with the
following message:

Warning: No configuration returned, assuming unavailable.
blas_opt_info:
  FOUND:
    extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
    define_macros = [('NO_ATLAS_INFO', 3)]
    extra_compile_args = ['-msse3',
'-I/System/Library/Frameworks/vecLib.framework/Headers']

lapack_opt_info:
  FOUND:
    extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
    define_macros = [('NO_ATLAS_INFO', 3)]
    extra_compile_args = ['-msse3']

umfpack_info:
  libraries umfpack not found in
/Applications/scisoft/i386/Packages/Python-2.5.4/Python.framework/Versions/2.5/lib
  libraries umfpack not found in /usr/local/lib
  libraries umfpack not found in /usr/lib
/Applications/scisoft/i386/Packages/Python-2.5.4/Python.framework/Versions/2.5/lib/python2.5/site-packages/numpy/distutils/system_info.py:452:
UserWarning:
    UMFPACK sparse solver (http://www.cise.ufl.edu/research/sparse/umfpack/)
    not found. Directories to search for the libraries can be specified in the
    numpy/distutils/site.cfg file (section [umfpack]) or by setting
    the UMFPACK environment variable.
  warnings.warn(self.notfounderror.__doc__)
  NOT AVAILABLE

Traceback (most recent call last):
  File "setup.py", line 160, in <module>
    setup_package()
  File "setup.py", line 152, in setup_package
    configuration=configuration )
  File "/Applications/scisoft/i386/Packages/Python-2.5.4/Python.framework/Versions/2.5/lib/python2.5/site-packages/numpy/distutils/core.py",
line 150, in setup
    config = configuration()
  File "setup.py", line 118, in configuration
    config.add_subpackage('scipy')
  File "/Applications/scisoft/i386/Packages/Python-2.5.4/Python.framework/Versions/2.5/lib/python2.5/site-packages/numpy/distutils/misc_util.py",
line 852, in add_subpackage
    caller_level = 2)
  File "/Applications/scisoft/i386/Packages/Python-2.5.4/Python.framework/Versions/2.5/lib/python2.5/site-packages/numpy/distutils/misc_util.py",
line 835, in get_subpackage
    caller_level = caller_level + 1)
  File "/Applications/scisoft/i386/Packages/Python-2.5.4/Python.framework/Versions/2.5/lib/python2.5/site-packages/numpy/distutils/misc_util.py",
line 782, in _get_configuration_from_setup_py
    config = setup_module.configuration(*args)
  File "scipy/setup.py", line 20, in configuration
    config.add_subpackage('special')
  File "/Applications/scisoft/i386/Packages/Python-2.5.4/Python.framework/Versions/2.5/lib/python2.5/site-packages/numpy/distutils/misc_util.py",
line 852, in add_subpackage
    caller_level = 2)
  File "/Applications/scisoft/i386/Packages/Python-2.5.4/Python.framework/Versions/2.5/lib/python2.5/site-packages/numpy/distutils/misc_util.py",
line 835, in get_subpackage
    caller_level = caller_level + 1)
  File "/Applications/scisoft/i386/Packages/Python-2.5.4/Python.framework/Versions/2.5/lib/python2.5/site-packages/numpy/distutils/misc_util.py",
line 767, in _get_configuration_from_setup_py
    ('.py', 'U', 1))
  File "scipy/special/setup.py", line 7, in <module>
    from numpy.distutils.misc_util import get_numpy_include_dirs, get_info
ImportError: cannot import name get_info

Does anyone have any idea what is going wrong here?

Kind regards,

Pim Schellart

> AFAIK, it is a newly-added warning, for an old dependency. In other
> words, this used to fail similarly, but you weren't aware. What you don't
> know can't hurt you, right!


From robert.kern at gmail.com  Tue Aug  4 16:59:13 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Tue, 4 Aug 2009 15:59:13 -0500
Subject: [SciPy-User] Installing latest svn version UMFPACK issue
In-Reply-To: <7d3dada90908041353q74dcd361ue8e9633738d07fe3@mail.gmail.com>
References: <7d3dada90908041353q74dcd361ue8e9633738d07fe3@mail.gmail.com>
Message-ID: <3d375d730908041359yba64aabve556f9c1fe07048a@mail.gmail.com>

On Tue, Aug 4, 2009 at 15:53, Pim
Schellart<P.Schellart at student.science.ru.nl> wrote:
> Dear All,
>
> That might be just a warning but my installation aborts with the
> following message:
>
> Warning: No configuration returned, assuming unavailable.
> blas_opt_info:
> ?FOUND:
> ? ?extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
> ? ?define_macros = [('NO_ATLAS_INFO', 3)]
> ? ?extra_compile_args = ['-msse3',
> '-I/System/Library/Frameworks/vecLib.framework/Headers']
>
> lapack_opt_info:
> ?FOUND:
> ? ?extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']
> ? ?define_macros = [('NO_ATLAS_INFO', 3)]
> ? ?extra_compile_args = ['-msse3']
>
> umfpack_info:
> ?libraries umfpack not found in
> /Applications/scisoft/i386/Packages/Python-2.5.4/Python.framework/Versions/2.5/lib
> ?libraries umfpack not found in /usr/local/lib
> ?libraries umfpack not found in /usr/lib
> /Applications/scisoft/i386/Packages/Python-2.5.4/Python.framework/Versions/2.5/lib/python2.5/site-packages/numpy/distutils/system_info.py:452:
> UserWarning:
> ? ?UMFPACK sparse solver (http://www.cise.ufl.edu/research/sparse/umfpack/)
> ? ?not found. Directories to search for the libraries can be specified in the
> ? ?numpy/distutils/site.cfg file (section [umfpack]) or by setting
> ? ?the UMFPACK environment variable.
> ?warnings.warn(self.notfounderror.__doc__)
> ?NOT AVAILABLE
>
> Traceback (most recent call last):

> ?File "scipy/special/setup.py", line 7, in <module>
> ? ?from numpy.distutils.misc_util import get_numpy_include_dirs, get_info
> ImportError: cannot import name get_info
>
> Does anyone have any idea what is going wrong here?

Fixed in numpy r7252. Nothing to do with UMFPACK.

-- 
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


From tsyu80 at gmail.com  Wed Aug  5 02:47:42 2009
From: tsyu80 at gmail.com (Tony Yu)
Date: Wed, 5 Aug 2009 02:47:42 -0400
Subject: [SciPy-User] ndimage filters overflow for default image format
	(uint8)
In-Reply-To: <0D3C80A2-D6D1-4C2B-B56F-A1C0358A3835@gmail.com>
References: <0D3C80A2-D6D1-4C2B-B56F-A1C0358A3835@gmail.com>
Message-ID: <2FC9B155-121B-43AB-BD83-BB118A07C925@gmail.com>

Gilles Rochefort <gilles.rochefort at gmail.com> wrote:
> As you may know, grayscale images are represented with uint8 because  
> of
> a low memory footprint.
[Snip]
> So, now filtered datas may be stored as int8, but you loose half the  
> precision (dividing by two filter coefficients). Most applications  
> won't suffer from a limited precision.

Thanks for your suggestions. As I mentioned, I understand why scipy  
behaves the way it does (from a programming perspective); I'm just not  
sure if it makes sense from a user's perspective.

> In my opinion, as a programmer you have to know the filtered datas  
> range. So, you just have to decide if precision does care or not.
> You can't expect correlate1d to guess your intent for the resulting  
> range.

Actually, I didn't intend correlate1d to guess the resulting range,  
but instead, certain filters to guess (those filters that return  
values outside the input range). Most filters return outputs within  
the range of the input, and should behave as they currently do. In any  
case you're probably right: I don't think my solution was really the  
appropriate one.

I agree that a programmer should know the filtered data range, but my  
main complaint is that someone should be making noise if I forget that  
a filter returns values outside the valid range. To me, this behavior  
is similar to using a "bare except" (an except clause without an  
expression): code ends up failing silently. When using edge detection  
filters on uint8 images (the reason I ran into this problem), overflow  
resulted in erroneous output that **looked like valid output**. If I  
had known an  overflowed occurred, I would have found my problem much  
sooner.

Basically: I often do stupid things, and I want someone tell me I'm  
being stupid rather than nod in agreement. ;)

TSY


From david at ar.media.kyoto-u.ac.jp  Wed Aug  5 06:29:07 2009
From: david at ar.media.kyoto-u.ac.jp (David Cournapeau)
Date: Wed, 05 Aug 2009 19:29:07 +0900
Subject: [SciPy-User] Ubuntu vs Fedora for scientific work?
In-Reply-To: <4A77EC90.63BA.009B.0@twdb.state.tx.us>
References: <4A6DC6A2.63BA.009B.0@twdb.state.tx.us>	<5b8d13220908032237t34e74fceq859fadb725996789@mail.gmail.com>	<270620220908040121o57436c87p244950d3d5dd5eec@mail.gmail.com>
	<4A77EC90.63BA.009B.0@twdb.state.tx.us>
Message-ID: <4A795EF3.30708@ar.media.kyoto-u.ac.jp>

Dharhas Pothina wrote:
>>>> alexander baker <baker.alexander at gmail.com> 8/4/2009 3:21 AM >>>
>>>>         
>> Some very interesting points from David, especially the virtual machine
>> point.
>>     
>
> So is there much of a performance hit using a virtual machine, or is it pretty much unnoticable on a modern machine? 
>   

Well, numpy is faster on a Linux VM  in vmware fusion than on mac os x
on my macbook, because of more recent compiler and recent ATLAS I guess.
So unless you are IO heavy, the main drawback is memory - I bought a new
macbook for the explicit purpose of more available memory. Even  8-16 Gb
start to become affordable for a conventional workstation,

cheers,

David


> - dharhas
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
>   



From jackliddlephysics at googlemail.com  Wed Aug  5 08:44:18 2009
From: jackliddlephysics at googlemail.com (Jack Liddle)
Date: Wed, 5 Aug 2009 14:44:18 +0200
Subject: [SciPy-User] Mapping objects to numpy array
Message-ID: <6ebdbcf10908050544y4996f57k89a0b421ad5944c1@mail.gmail.com>

Hi

I'm trying to map some objects to a numpy array, so I can solve some
ode's with them and then work on the results with my objects.

Here's some code.

from numpy import array

class Particle(object):
   def __init__(self,x,v):
      self.x = x
      self.v = v

p1 = Particle(1,0)
a  = array(( p1.x, p1.v))
a[0] = 5
print p1.x

This prints 1 instead of 5.  How do I make the array 'a' share the
same memory as the p1 attributes?

Any ideas, hope this makes sense

Thanks

Jack Liddle


From sccolbert at gmail.com  Wed Aug  5 09:02:33 2009
From: sccolbert at gmail.com (Chris Colbert)
Date: Wed, 5 Aug 2009 09:02:33 -0400
Subject: [SciPy-User] Ubuntu vs Fedora for scientific work?
In-Reply-To: <4A795EF3.30708@ar.media.kyoto-u.ac.jp>
References: <4A6DC6A2.63BA.009B.0@twdb.state.tx.us>
	<5b8d13220908032237t34e74fceq859fadb725996789@mail.gmail.com>
	<270620220908040121o57436c87p244950d3d5dd5eec@mail.gmail.com>
	<4A77EC90.63BA.009B.0@twdb.state.tx.us>
	<4A795EF3.30708@ar.media.kyoto-u.ac.jp>
Message-ID: <7f014ea60908050602s65377b2ej5c63631d171f57b8@mail.gmail.com>

what macbook supports 16GB of memory?


On Wed, Aug 5, 2009 at 6:29 AM, David
Cournapeau<david at ar.media.kyoto-u.ac.jp> wrote:
> Dharhas Pothina wrote:
>>>>> alexander baker <baker.alexander at gmail.com> 8/4/2009 3:21 AM >>>
>>>>>
>>> Some very interesting points from David, especially the virtual machine
>>> point.
>>>
>>
>> So is there much of a performance hit using a virtual machine, or is it pretty much unnoticable on a modern machine?
>>
>
> Well, numpy is faster on a Linux VM ?in vmware fusion than on mac os x
> on my macbook, because of more recent compiler and recent ATLAS I guess.
> So unless you are IO heavy, the main drawback is memory - I bought a new
> macbook for the explicit purpose of more available memory. Even ?8-16 Gb
> start to become affordable for a conventional workstation,
>
> cheers,
>
> David
>
>
>> - dharhas
>>
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>>
>>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From emmanuelle.gouillart at normalesup.org  Wed Aug  5 09:05:22 2009
From: emmanuelle.gouillart at normalesup.org (Emmanuelle Gouillart)
Date: Wed, 5 Aug 2009 15:05:22 +0200
Subject: [SciPy-User] Mapping objects to numpy array
In-Reply-To: <6ebdbcf10908050544y4996f57k89a0b421ad5944c1@mail.gmail.com>
References: <6ebdbcf10908050544y4996f57k89a0b421ad5944c1@mail.gmail.com>
Message-ID: <20090805130522.GB15962@phare.normalesup.org>

	Hi Jack,

	I don't think you can do it this way. The reason is that p1.x and
p1.v can be anywhere in the memory, maybe at very different places. So
np.array makes a *copy* when you pass ( p1.x, p1.v) as arguments. I tried
setting copy=False as a keyword argument of np.array but it doesn't
change the result; apparently a copy has to be made. Why don't you define
an array inside the class Particle, instead of different attributes? 

	Cheers,

	Emmanuelle

On Wed, Aug 05, 2009 at 02:44:18PM +0200, Jack Liddle wrote:
> Hi

> I'm trying to map some objects to a numpy array, so I can solve some
> ode's with them and then work on the results with my objects.

> Here's some code.

> from numpy import array

> class Particle(object):
>    def __init__(self,x,v):
>       self.x = x
>       self.v = v

> p1 = Particle(1,0)
> a  = array(( p1.x, p1.v))
> a[0] = 5
> print p1.x

> This prints 1 instead of 5.  How do I make the array 'a' share the
> same memory as the p1 attributes?

> Any ideas, hope this makes sense

> Thanks

> Jack Liddle
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user


From sccolbert at gmail.com  Wed Aug  5 09:27:31 2009
From: sccolbert at gmail.com (Chris Colbert)
Date: Wed, 5 Aug 2009 09:27:31 -0400
Subject: [SciPy-User] Mapping objects to numpy array
In-Reply-To: <20090805130522.GB15962@phare.normalesup.org>
References: <6ebdbcf10908050544y4996f57k89a0b421ad5944c1@mail.gmail.com>
	<20090805130522.GB15962@phare.normalesup.org>
Message-ID: <7f014ea60908050627t72f83ad8m498ac48552baeff5@mail.gmail.com>

its for the same reason you cant do this:

In [1]: a = 1

In [2]: b = a

In [3]: a
Out[3]: 1

In [4]: b
Out[4]: 1

In [5]: a = 2

In [6]: a
Out[6]: 2

In [7]: b
Out[7]: 1


but you CAN do this:

In [8]: a = [1, 2]

In [9]: b = a

In [10]: a
Out[10]: [1, 2]

In [11]: b
Out[11]: [1, 2]

In [12]: a[0] = 0

In [13]: a
Out[13]: [0, 2]

In [14]: b
Out[14]: [0, 2]


floats and ints are immutables in python.
you'll need a mutable container to store the values how you are wanting.

That said, you still cant map to them with a numpy array like you want (AFAIK)

You may want to look into subclassing ndarray or reformulating your
problem so you dont need this requirement.


On Wed, Aug 5, 2009 at 9:05 AM, Emmanuelle
Gouillart<emmanuelle.gouillart at normalesup.org> wrote:
> ? ? ? ?Hi Jack,
>
> ? ? ? ?I don't think you can do it this way. The reason is that p1.x and
> p1.v can be anywhere in the memory, maybe at very different places. So
> np.array makes a *copy* when you pass ( p1.x, p1.v) as arguments. I tried
> setting copy=False as a keyword argument of np.array but it doesn't
> change the result; apparently a copy has to be made. Why don't you define
> an array inside the class Particle, instead of different attributes?
>
> ? ? ? ?Cheers,
>
> ? ? ? ?Emmanuelle
>
> On Wed, Aug 05, 2009 at 02:44:18PM +0200, Jack Liddle wrote:
>> Hi
>
>> I'm trying to map some objects to a numpy array, so I can solve some
>> ode's with them and then work on the results with my objects.
>
>> Here's some code.
>
>> from numpy import array
>
>> class Particle(object):
>> ? ?def __init__(self,x,v):
>> ? ? ? self.x = x
>> ? ? ? self.v = v
>
>> p1 = Particle(1,0)
>> a ?= array(( p1.x, p1.v))
>> a[0] = 5
>> print p1.x
>
>> This prints 1 instead of 5. ?How do I make the array 'a' share the
>> same memory as the p1 attributes?
>
>> Any ideas, hope this makes sense
>
>> Thanks
>
>> Jack Liddle
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From josef.pktd at gmail.com  Wed Aug  5 10:11:17 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Wed, 5 Aug 2009 10:11:17 -0400
Subject: [SciPy-User] Mapping objects to numpy array
In-Reply-To: <7f014ea60908050627t72f83ad8m498ac48552baeff5@mail.gmail.com>
References: <6ebdbcf10908050544y4996f57k89a0b421ad5944c1@mail.gmail.com>
	<20090805130522.GB15962@phare.normalesup.org>
	<7f014ea60908050627t72f83ad8m498ac48552baeff5@mail.gmail.com>
Message-ID: <1cd32cbb0908050711k335d3e95q9ad1078043672c78@mail.gmail.com>

On Wed, Aug 5, 2009 at 9:27 AM, Chris Colbert<sccolbert at gmail.com> wrote:
> its for the same reason you cant do this:
>
> In [1]: a = 1
>
> In [2]: b = a
>
> In [3]: a
> Out[3]: 1
>
> In [4]: b
> Out[4]: 1
>
> In [5]: a = 2
>
> In [6]: a
> Out[6]: 2
>
> In [7]: b
> Out[7]: 1
>
>
> but you CAN do this:
>
> In [8]: a = [1, 2]
>
> In [9]: b = a
>
> In [10]: a
> Out[10]: [1, 2]
>
> In [11]: b
> Out[11]: [1, 2]
>
> In [12]: a[0] = 0
>
> In [13]: a
> Out[13]: [0, 2]
>
> In [14]: b
> Out[14]: [0, 2]
>
>
> floats and ints are immutables in python.
> you'll need a mutable container to store the values how you are wanting.
>
> That said, you still cant map to them with a numpy array like you want (AFAIK)
>
> You may want to look into subclassing ndarray or reformulating your
> problem so you dont need this requirement.
>
>
> On Wed, Aug 5, 2009 at 9:05 AM, Emmanuelle
> Gouillart<emmanuelle.gouillart at normalesup.org> wrote:
>> ? ? ? ?Hi Jack,
>>
>> ? ? ? ?I don't think you can do it this way. The reason is that p1.x and
>> p1.v can be anywhere in the memory, maybe at very different places. So
>> np.array makes a *copy* when you pass ( p1.x, p1.v) as arguments. I tried
>> setting copy=False as a keyword argument of np.array but it doesn't
>> change the result; apparently a copy has to be made. Why don't you define
>> an array inside the class Particle, instead of different attributes?
>>
>> ? ? ? ?Cheers,
>>
>> ? ? ? ?Emmanuelle
>>
>> On Wed, Aug 05, 2009 at 02:44:18PM +0200, Jack Liddle wrote:
>>> Hi
>>
>>> I'm trying to map some objects to a numpy array, so I can solve some
>>> ode's with them and then work on the results with my objects.
>>
>>> Here's some code.
>>
>>> from numpy import array
>>
>>> class Particle(object):
>>> ? ?def __init__(self,x,v):
>>> ? ? ? self.x = x
>>> ? ? ? self.v = v
>>
>>> p1 = Particle(1,0)
>>> a ?= array(( p1.x, p1.v))
>>> a[0] = 5
>>> print p1.x
>>
>>> This prints 1 instead of 5. ?How do I make the array 'a' share the
>>> same memory as the p1 attributes?
>>
>>> Any ideas, hope this makes sense
>>
>>> Thanks
>>
>>> Jack Liddle
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>

I was trying to do it from scratch, but I got confused about class variables.
Can class variables only be accessed through the class self.__class__
? I seem to get rusty in my python knowledge.

the following seems to do what Emmanuelle proposed, but there might be
better examples already in the mailing lists.

Josef


class Particle(object):
  a  = np.empty((3,2))
  def __init__(self,k,x,v):
     self.k = k
     #why do I need self.__class__ for class variable
     self.__class__.a[k,0] = x
     self.__class__.a[k,1] = v

  def _getx(self):
      return self.__class__.a[self.k,0]
  def _setx(self, value):
      self.__class__.a[self.k,0] = value

  x = property(_getx, _setx)


aa  = Particle.a
print aa
p1 = Particle(0, 1, 2)
print p1
p2 = Particle(1, 2, 3)
p3 = Particle(2, 3, 4)
print aa
aa[:,:] = 1
print aa
print p2._getx(), p2.x
aa[0,0] = 5
print p1.x
p2.x = 10
print aa
print Particle.a
print vars(p2)


From Jim.Vickroy at noaa.gov  Wed Aug  5 11:32:17 2009
From: Jim.Vickroy at noaa.gov (Jim Vickroy)
Date: Wed, 05 Aug 2009 09:32:17 -0600
Subject: [SciPy-User] Mapping objects to numpy array
In-Reply-To: <1cd32cbb0908050711k335d3e95q9ad1078043672c78@mail.gmail.com>
References: <6ebdbcf10908050544y4996f57k89a0b421ad5944c1@mail.gmail.com>
	<20090805130522.GB15962@phare.normalesup.org>
	<7f014ea60908050627t72f83ad8m498ac48552baeff5@mail.gmail.com>
	<1cd32cbb0908050711k335d3e95q9ad1078043672c78@mail.gmail.com>
Message-ID: <4A79A601.4080101@noaa.gov>

josef.pktd at gmail.com wrote:
> On Wed, Aug 5, 2009 at 9:27 AM, Chris Colbert<sccolbert at gmail.com> wrote:
>   
>> its for the same reason you cant do this:
>>
>> In [1]: a = 1
>>
>> In [2]: b = a
>>
>> In [3]: a
>> Out[3]: 1
>>
>> In [4]: b
>> Out[4]: 1
>>
>> In [5]: a = 2
>>
>> In [6]: a
>> Out[6]: 2
>>
>> In [7]: b
>> Out[7]: 1
>>
>>
>> but you CAN do this:
>>
>> In [8]: a = [1, 2]
>>
>> In [9]: b = a
>>
>> In [10]: a
>> Out[10]: [1, 2]
>>
>> In [11]: b
>> Out[11]: [1, 2]
>>
>> In [12]: a[0] = 0
>>
>> In [13]: a
>> Out[13]: [0, 2]
>>
>> In [14]: b
>> Out[14]: [0, 2]
>>
>>
>> floats and ints are immutables in python.
>> you'll need a mutable container to store the values how you are wanting.
>>
>> That said, you still cant map to them with a numpy array like you want (AFAIK)
>>
>> You may want to look into subclassing ndarray or reformulating your
>> problem so you dont need this requirement.
>>
>>
>> On Wed, Aug 5, 2009 at 9:05 AM, Emmanuelle
>> Gouillart<emmanuelle.gouillart at normalesup.org> wrote:
>>     
>>>        Hi Jack,
>>>
>>>        I don't think you can do it this way. The reason is that p1.x and
>>> p1.v can be anywhere in the memory, maybe at very different places. So
>>> np.array makes a *copy* when you pass ( p1.x, p1.v) as arguments. I tried
>>> setting copy=False as a keyword argument of np.array but it doesn't
>>> change the result; apparently a copy has to be made. Why don't you define
>>> an array inside the class Particle, instead of different attributes?
>>>
>>>        Cheers,
>>>
>>>        Emmanuelle
>>>
>>> On Wed, Aug 05, 2009 at 02:44:18PM +0200, Jack Liddle wrote:
>>>       
>>>> Hi
>>>>         
>>>> I'm trying to map some objects to a numpy array, so I can solve some
>>>> ode's with them and then work on the results with my objects.
>>>>         
>>>> Here's some code.
>>>>         
>>>> from numpy import array
>>>>         
>>>> class Particle(object):
>>>>    def __init__(self,x,v):
>>>>       self.x = x
>>>>       self.v = v
>>>>         
>>>> p1 = Particle(1,0)
>>>> a  = array(( p1.x, p1.v))
>>>> a[0] = 5
>>>> print p1.x
>>>>         
>>>> This prints 1 instead of 5.  How do I make the array 'a' share the
>>>> same memory as the p1 attributes?
>>>>         
>>>> Any ideas, hope this makes sense
>>>>         
>>>> Thanks
>>>>         
>>>> Jack Liddle
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at scipy.org
>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>>         
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>
>>>       
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>>     
>
> I was trying to do it from scratch, but I got confused about class variables.
> Can class variables only be accessed through the class self.__class__
> ? I seem to get rusty in my python knowledge.
>
> the following seems to do what Emmanuelle proposed, but there might be
> better examples already in the mailing lists.
>
> Josef
>
>
> class Particle(object):
>   a  = np.empty((3,2))
>   
The above definition means that all instances of your Particle class 
will share the same /*"a".
*/If you create instances p1 = Particle(0,1,2) and p2 = Particle(3,4,5), 
p1.a and p2.a reference the same /*"a"*/.
Is that your intent?
>   def __init__(self,k,x,v):
>      self.k = k
>      #why do I need self.__class__ for class variable
>   
self.a should work just fine.  Did you try it without specifying the 
__class__ qualifier?
>      self.__class__.a[k,0] = x
>      self.__class__.a[k,1] = v
>
>   def _getx(self):
>       return self.__class__.a[self.k,0]
>   def _setx(self, value):
>       self.__class__.a[self.k,0] = value
>
>   x = property(_getx, _setx)
>
>
> aa  = Particle.a
> print aa
> p1 = Particle(0, 1, 2)
> print p1
> p2 = Particle(1, 2, 3)
> p3 = Particle(2, 3, 4)
> print aa
> aa[:,:] = 1
> print aa
> print p2._getx(), p2.x
> aa[0,0] = 5
> print p1.x
> p2.x = 10
> print aa
> print Particle.a
> print vars(p2)
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>   

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From josef.pktd at gmail.com  Wed Aug  5 11:55:15 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Wed, 5 Aug 2009 11:55:15 -0400
Subject: [SciPy-User] Mapping objects to numpy array
In-Reply-To: <4A79A601.4080101@noaa.gov>
References: <6ebdbcf10908050544y4996f57k89a0b421ad5944c1@mail.gmail.com>
	<20090805130522.GB15962@phare.normalesup.org>
	<7f014ea60908050627t72f83ad8m498ac48552baeff5@mail.gmail.com>
	<1cd32cbb0908050711k335d3e95q9ad1078043672c78@mail.gmail.com>
	<4A79A601.4080101@noaa.gov>
Message-ID: <1cd32cbb0908050855t3931432cqe3f54058c9af8b46@mail.gmail.com>

On Wed, Aug 5, 2009 at 11:32 AM, Jim Vickroy<Jim.Vickroy at noaa.gov> wrote:
> josef.pktd at gmail.com wrote:
>
> On Wed, Aug 5, 2009 at 9:27 AM, Chris Colbert<sccolbert at gmail.com> wrote:
>
>
> its for the same reason you cant do this:
>
> In [1]: a = 1
>
> In [2]: b = a
>
> In [3]: a
> Out[3]: 1
>
> In [4]: b
> Out[4]: 1
>
> In [5]: a = 2
>
> In [6]: a
> Out[6]: 2
>
> In [7]: b
> Out[7]: 1
>
>
> but you CAN do this:
>
> In [8]: a = [1, 2]
>
> In [9]: b = a
>
> In [10]: a
> Out[10]: [1, 2]
>
> In [11]: b
> Out[11]: [1, 2]
>
> In [12]: a[0] = 0
>
> In [13]: a
> Out[13]: [0, 2]
>
> In [14]: b
> Out[14]: [0, 2]
>
>
> floats and ints are immutables in python.
> you'll need a mutable container to store the values how you are wanting.
>
> That said, you still cant map to them with a numpy array like you want
> (AFAIK)
>
> You may want to look into subclassing ndarray or reformulating your
> problem so you dont need this requirement.
>
>
> On Wed, Aug 5, 2009 at 9:05 AM, Emmanuelle
> Gouillart<emmanuelle.gouillart at normalesup.org> wrote:
>
>
> ? ? ? ?Hi Jack,
>
> ? ? ? ?I don't think you can do it this way. The reason is that p1.x and
> p1.v can be anywhere in the memory, maybe at very different places. So
> np.array makes a *copy* when you pass ( p1.x, p1.v) as arguments. I tried
> setting copy=False as a keyword argument of np.array but it doesn't
> change the result; apparently a copy has to be made. Why don't you define
> an array inside the class Particle, instead of different attributes?
>
> ? ? ? ?Cheers,
>
> ? ? ? ?Emmanuelle
>
> On Wed, Aug 05, 2009 at 02:44:18PM +0200, Jack Liddle wrote:
>
>
> Hi
>
>
> I'm trying to map some objects to a numpy array, so I can solve some
> ode's with them and then work on the results with my objects.
>
>
> Here's some code.
>
>
> from numpy import array
>
>
> class Particle(object):
> ? ?def __init__(self,x,v):
> ? ? ? self.x = x
> ? ? ? self.v = v
>
>
> p1 = Particle(1,0)
> a ?= array(( p1.x, p1.v))
> a[0] = 5
> print p1.x
>
>
> This prints 1 instead of 5. ?How do I make the array 'a' share the
> same memory as the p1 attributes?
>
>
> Any ideas, hope this makes sense
>
>
> Thanks
>
>
> Jack Liddle
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
>
> I was trying to do it from scratch, but I got confused about class
> variables.
> Can class variables only be accessed through the class self.__class__
> ? I seem to get rusty in my python knowledge.
>
> the following seems to do what Emmanuelle proposed, but there might be
> better examples already in the mailing lists.
>
> Josef
>
>
> class Particle(object):
>   a  = np.empty((3,2))
>
>

> The above definition means that all instances of your Particle class will
> share the same "a".
> If you create instances p1 = Particle(0,1,2) and p2 = Particle(3,4,5), p1.a
> and p2.a reference the same "a".
> Is that your intent?

Yes, that's the point of the exercise, to have the data of all
particles accessible through one array, and the individual data points
accessible through the instance attribute.

>
>   def __init__(self,k,x,v):
>      self.k = k
>      #why do I need self.__class__ for class variable
>
>
> self.a should work just fine.? Did you try it without specifying the
> __class__ qualifier?

Thanks for the hint.
Yes, it works, and no, I didn't try.
I was worried about class versus instance variable, the python help
only used the class to access the class variable, and I didn't want to
spend too much time on the exercise.

When I actually need it, I will worry about the other details, like
how to preallocate and grow the class array `a` and to automatically
assign indices.

Josef


>
>      self.__class__.a[k,0] = x
>      self.__class__.a[k,1] = v
>
>   def _getx(self):
>       return self.__class__.a[self.k,0]
>   def _setx(self, value):
>       self.__class__.a[self.k,0] = value
>
>   x = property(_getx, _setx)
>
>
> aa  = Particle.a
> print aa
> p1 = Particle(0, 1, 2)
> print p1
> p2 = Particle(1, 2, 3)
> p3 = Particle(2, 3, 4)
> print aa
> aa[:,:] = 1
> print aa
> print p2._getx(), p2.x
> aa[0,0] = 5
> print p1.x
> p2.x = 10
> print aa
> print Particle.a
> print vars(p2)
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>


From P.Schellart at student.science.ru.nl  Wed Aug  5 11:56:21 2009
From: P.Schellart at student.science.ru.nl (Pim Schellart)
Date: Wed, 5 Aug 2009 17:56:21 +0200
Subject: [SciPy-User] curve_fit errors
Message-ID: <7d3dada90908050856k49791722xe8711486c16a44@mail.gmail.com>

Hi Everyone,

I am using the curve_fit wrapper around optimize.leastsq but I have a
few minor problems.
Even for a simple line fit the fitting does not produce a solution
unless I first shift the curve so that it's flat.
For a Gaussian fit a solution is not found unless I shift the curve
such that the maximum/minimum of the Gaussian is around zero.
Both of these errors occur whether I supply start values for the
parameters or not.
I get the following error:

RuntimeError: Optimal parameters not found: Both actual and predicted
relative reductions in the sum of squares
  are at most 0.000000 and the relative error between two consecutive
iterates is at
  most 0.000000

The following code produces this error:

import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
from scipy.optimize.minpack import curve_fit

def f(x,a,b):
    return a*x+b

x=np.array([1,10,20,21,30,31,39,40,49,50,59,60
,69,70,4212,4213,4222,4223,4232,4233,4281,4302,4311,4312
,4320,4321,4330,4331,4399,4400,4408,4409,4418,11086,11087,11095
,11096,11105,11106,11115,11116,11117,36400,36401])

y=f(x,0.101083824,82352.234)
y=y+np.random.randn(len(y))

plt.plot(x,y,'r+')

# Specify initial guess values for the parameters
p0=((y[-1]-y[0])/(x[-1]-x[0]),y[0])

# Compute the fit without using the errors
popt,pcov=curve_fit(f,x,y,p0)

However if I shift the curve with the guess values e.g.
y=y-((y[-1]-y[0])/(x[-1]-x[0]))*x-y[0]
than the fit does work.
Does anyone have any idea why this is and how to improve the
reliability of the curve_fit routine?

Kind regards,

Pim Schellart


From josef.pktd at gmail.com  Wed Aug  5 13:31:09 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Wed, 5 Aug 2009 13:31:09 -0400
Subject: [SciPy-User] curve_fit errors
In-Reply-To: <7d3dada90908050856k49791722xe8711486c16a44@mail.gmail.com>
References: <7d3dada90908050856k49791722xe8711486c16a44@mail.gmail.com>
Message-ID: <1cd32cbb0908051031n17262cear8befcef40fedaefd@mail.gmail.com>

On Wed, Aug 5, 2009 at 11:56 AM, Pim
Schellart<P.Schellart at student.science.ru.nl> wrote:
> Hi Everyone,
>
> I am using the curve_fit wrapper around optimize.leastsq but I have a
> few minor problems.
> Even for a simple line fit the fitting does not produce a solution
> unless I first shift the curve so that it's flat.
> For a Gaussian fit a solution is not found unless I shift the curve
> such that the maximum/minimum of the Gaussian is around zero.
> Both of these errors occur whether I supply start values for the
> parameters or not.
> I get the following error:
>
> RuntimeError: Optimal parameters not found: Both actual and predicted
> relative reductions in the sum of squares
> ?are at most 0.000000 and the relative error between two consecutive
> iterates is at
> ?most 0.000000
>
> The following code produces this error:
>
> import numpy as np
> import scipy as sp
> import matplotlib.pyplot as plt
> from scipy.optimize.minpack import curve_fit
>
> def f(x,a,b):
> ? ?return a*x+b
>
> x=np.array([1,10,20,21,30,31,39,40,49,50,59,60
> ,69,70,4212,4213,4222,4223,4232,4233,4281,4302,4311,4312
> ,4320,4321,4330,4331,4399,4400,4408,4409,4418,11086,11087,11095
> ,11096,11105,11106,11115,11116,11117,36400,36401])
>
> y=f(x,0.101083824,82352.234)
> y=y+np.random.randn(len(y))
>
> plt.plot(x,y,'r+')
>
> # Specify initial guess values for the parameters
> p0=((y[-1]-y[0])/(x[-1]-x[0]),y[0])
>
> # Compute the fit without using the errors
> popt,pcov=curve_fit(f,x,y,p0)
>
> However if I shift the curve with the guess values e.g.
> y=y-((y[-1]-y[0])/(x[-1]-x[0]))*x-y[0]
> than the fit does work.
> Does anyone have any idea why this is and how to improve the
> reliability of the curve_fit routine?

It looks like curve_fit is raising an error/exception when it shouldn't

    if ier != 1:
        raise RuntimeError, "Optimal parameters not found: " + mesg

variations that work, see below:

* for a linear curve, linalg.lstsq is better
* using optimize.leastsq directly works
* if the noise is larger and with adjustments to the data it works
* adding optimize.leastsq options didn't work to remove ier !=1, at
least the ones I tried


filing a ticket, and preparing a patch (looking at the return codes)
and adding this as a test case would be very helpful for getting this
fixed. Assuming of course my diagnosis is correct.

Making non-linear estimation numerically more robust for all kinds of
functions might be difficult, but I don't know much about that, (in
contrast to the linear case)

Josef


X = np.column_stack((np.ones(len(y)),x))
Y = y[:,np.newaxis]
print 'ols'
print np.dot(np.linalg.inv(np.dot(X.T,X)),np.dot(X.T,Y))

print 'lstsq'
print linalg.lstsq(X, Y)

print 'using leastsq directly'
def f2((a,b),x,y):return y-f(x,a,b)
popt,pcov=optimize.leastsq(f2,p0,(x,y))
print popt[::-1]

print 'larger noise and constant correction'
y2=f(x,0.101083824,82352.234)
y2=y2+np.random.randn(len(y2))*100
popt,pcov=curve_fit(f,x,y2-y2.min(),p0)
print popt[1]+y2.min(), popt[0]



>
> Kind regards,
>
> Pim Schellart
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From josef.pktd at gmail.com  Wed Aug  5 13:53:01 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Wed, 5 Aug 2009 13:53:01 -0400
Subject: [SciPy-User] curve_fit errors
In-Reply-To: <1cd32cbb0908051031n17262cear8befcef40fedaefd@mail.gmail.com>
References: <7d3dada90908050856k49791722xe8711486c16a44@mail.gmail.com>
	<1cd32cbb0908051031n17262cear8befcef40fedaefd@mail.gmail.com>
Message-ID: <1cd32cbb0908051053i7968eecdte7a8f5a3b9451c88@mail.gmail.com>

On Wed, Aug 5, 2009 at 1:31 PM, <josef.pktd at gmail.com> wrote:
> On Wed, Aug 5, 2009 at 11:56 AM, Pim
> Schellart<P.Schellart at student.science.ru.nl> wrote:
>> Hi Everyone,
>>
>> I am using the curve_fit wrapper around optimize.leastsq but I have a
>> few minor problems.
>> Even for a simple line fit the fitting does not produce a solution
>> unless I first shift the curve so that it's flat.
>> For a Gaussian fit a solution is not found unless I shift the curve
>> such that the maximum/minimum of the Gaussian is around zero.
>> Both of these errors occur whether I supply start values for the
>> parameters or not.
>> I get the following error:
>>
>> RuntimeError: Optimal parameters not found: Both actual and predicted
>> relative reductions in the sum of squares
>> ?are at most 0.000000 and the relative error between two consecutive
>> iterates is at
>> ?most 0.000000
>>
>> The following code produces this error:
>>
>> import numpy as np
>> import scipy as sp
>> import matplotlib.pyplot as plt
>> from scipy.optimize.minpack import curve_fit
>>
>> def f(x,a,b):
>> ? ?return a*x+b
>>
>> x=np.array([1,10,20,21,30,31,39,40,49,50,59,60
>> ,69,70,4212,4213,4222,4223,4232,4233,4281,4302,4311,4312
>> ,4320,4321,4330,4331,4399,4400,4408,4409,4418,11086,11087,11095
>> ,11096,11105,11106,11115,11116,11117,36400,36401])
>>
>> y=f(x,0.101083824,82352.234)
>> y=y+np.random.randn(len(y))
>>
>> plt.plot(x,y,'r+')
>>
>> # Specify initial guess values for the parameters
>> p0=((y[-1]-y[0])/(x[-1]-x[0]),y[0])
>>
>> # Compute the fit without using the errors
>> popt,pcov=curve_fit(f,x,y,p0)
>>
>> However if I shift the curve with the guess values e.g.
>> y=y-((y[-1]-y[0])/(x[-1]-x[0]))*x-y[0]
>> than the fit does work.
>> Does anyone have any idea why this is and how to improve the
>> reliability of the curve_fit routine?
>
> It looks like curve_fit is raising an error/exception when it shouldn't
>
> ? ?if ier != 1:
> ? ? ? ?raise RuntimeError, "Optimal parameters not found: " + mesg

optimize.leastsq is less picky about the solution:

    errors = {0:["Improper input parameters.", TypeError],
              1:["Both actual and predicted relative reductions in the
sum of squares\n  are at most %f" % ftol, None],
              2:["The relative error between two consecutive iterates
is at most %f" % xtol, None],
              3:["Both actual and predicted relative reductions in the
sum of squares\n  are at most %f and the relative error between two
consecutive iterates is at \n  most %f" % (ftol,xtol), None],
              4:["The cosine of the angle between func(x) and any
column of the\n  Jacobian is at most %f in absolute value" % gtol,
None],
              5:["Number of calls to function has reached maxfev =
%d." % maxfev, ValueError],
              6:["ftol=%f is too small, no further reduction in the
sum of squares\n  is possible.""" % ftol, ValueError],
              7:["xtol=%f is too small, no further improvement in the
approximate\n  solution is possible." % xtol, ValueError],
              8:["gtol=%f is too small, func(x) is orthogonal to the
columns of\n  the Jacobian to machine precision." % gtol, ValueError],
              'unknown':["Unknown error.", TypeError]}

    info = retval[-1]    # The FORTRAN return value

    if (info not in [1,2,3,4] and not full_output):
        if info in [5,6,7,8]:
            if warning:  print "Warning: " + errors[info][0]
        else:
            try:
                raise errors[info][1], errors[info][0]
            except KeyError:
                raise errors['unknown'][1], errors['unknown'][0]


I think curve_fit should just print a warning, instead of raising a
runtime error and throwing away potentially correct and useful
results.


Josef



>
> variations that work, see below:
>
> * for a linear curve, linalg.lstsq is better
> * using optimize.leastsq directly works
> * if the noise is larger and with adjustments to the data it works
> * adding optimize.leastsq options didn't work to remove ier !=1, at
> least the ones I tried
>
>
> filing a ticket, and preparing a patch (looking at the return codes)
> and adding this as a test case would be very helpful for getting this
> fixed. Assuming of course my diagnosis is correct.
>
> Making non-linear estimation numerically more robust for all kinds of
> functions might be difficult, but I don't know much about that, (in
> contrast to the linear case)
>
> Josef
>
>
> X = np.column_stack((np.ones(len(y)),x))
> Y = y[:,np.newaxis]
> print 'ols'
> print np.dot(np.linalg.inv(np.dot(X.T,X)),np.dot(X.T,Y))
>
> print 'lstsq'
> print linalg.lstsq(X, Y)
>
> print 'using leastsq directly'
> def f2((a,b),x,y):return y-f(x,a,b)
> popt,pcov=optimize.leastsq(f2,p0,(x,y))
> print popt[::-1]
>
> print 'larger noise and constant correction'
> y2=f(x,0.101083824,82352.234)
> y2=y2+np.random.randn(len(y2))*100
> popt,pcov=curve_fit(f,x,y2-y2.min(),p0)
> print popt[1]+y2.min(), popt[0]
>
>
>
>>
>> Kind regards,
>>
>> Pim Schellart
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>


From sccolbert at gmail.com  Wed Aug  5 16:32:43 2009
From: sccolbert at gmail.com (Chris Colbert)
Date: Wed, 5 Aug 2009 16:32:43 -0400
Subject: [SciPy-User] Mapping objects to numpy array
In-Reply-To: <1cd32cbb0908050855t3931432cqe3f54058c9af8b46@mail.gmail.com>
References: <6ebdbcf10908050544y4996f57k89a0b421ad5944c1@mail.gmail.com>
	<20090805130522.GB15962@phare.normalesup.org>
	<7f014ea60908050627t72f83ad8m498ac48552baeff5@mail.gmail.com>
	<1cd32cbb0908050711k335d3e95q9ad1078043672c78@mail.gmail.com>
	<4A79A601.4080101@noaa.gov>
	<1cd32cbb0908050855t3931432cqe3f54058c9af8b46@mail.gmail.com>
Message-ID: <7f014ea60908051332l2f928a4q9bfffd09ed91db5d@mail.gmail.com>

You cannot use self.a, as this creates an instance attribute that
hides the class attribute. You have to access through the __class__
variable of the instance, or access the class attribute directly. See
below:

In [7]: class Test(object):
   ...:     a = 1
   ...:     def __init__(self, val):
   ...:         self.b = val
   ...:
   ...:     def set(self, val):
   ...:         self.a = val
   ...:
   ...:

In [8]: t1 = Test(4)

In [9]: t1.a
Out[9]: 1

In [10]: t1.b
Out[10]: 4

In [11]: t2 = Test(5)

In [12]: t1.a
Out[12]: 1

In [13]: t2.a
Out[13]: 1

In [14]: t2.b
Out[14]: 5

In [15]: t1.set(4)

In [16]: t1.a
Out[16]: 4

In [17]: t1.b
Out[17]: 4

In [18]: t2.a
Out[18]: 1

In [19]: t2.b
Out[19]: 5

In [20]: t3 = Test(6)

In [21]: t4 = Test(7)

In [22]: t3.a
Out[22]: 1

In [23]: t4.a
Out[23]: 1

In [24]: Test.a = 9

In [25]: t3.a
Out[25]: 9

In [26]: t4.a
Out[26]: 9


On Wed, Aug 5, 2009 at 11:55 AM, <josef.pktd at gmail.com> wrote:
> On Wed, Aug 5, 2009 at 11:32 AM, Jim Vickroy<Jim.Vickroy at noaa.gov> wrote:
>> josef.pktd at gmail.com wrote:
>>
>> On Wed, Aug 5, 2009 at 9:27 AM, Chris Colbert<sccolbert at gmail.com> wrote:
>>
>>
>> its for the same reason you cant do this:
>>
>> In [1]: a = 1
>>
>> In [2]: b = a
>>
>> In [3]: a
>> Out[3]: 1
>>
>> In [4]: b
>> Out[4]: 1
>>
>> In [5]: a = 2
>>
>> In [6]: a
>> Out[6]: 2
>>
>> In [7]: b
>> Out[7]: 1
>>
>>
>> but you CAN do this:
>>
>> In [8]: a = [1, 2]
>>
>> In [9]: b = a
>>
>> In [10]: a
>> Out[10]: [1, 2]
>>
>> In [11]: b
>> Out[11]: [1, 2]
>>
>> In [12]: a[0] = 0
>>
>> In [13]: a
>> Out[13]: [0, 2]
>>
>> In [14]: b
>> Out[14]: [0, 2]
>>
>>
>> floats and ints are immutables in python.
>> you'll need a mutable container to store the values how you are wanting.
>>
>> That said, you still cant map to them with a numpy array like you want
>> (AFAIK)
>>
>> You may want to look into subclassing ndarray or reformulating your
>> problem so you dont need this requirement.
>>
>>
>> On Wed, Aug 5, 2009 at 9:05 AM, Emmanuelle
>> Gouillart<emmanuelle.gouillart at normalesup.org> wrote:
>>
>>
>> ? ? ? ?Hi Jack,
>>
>> ? ? ? ?I don't think you can do it this way. The reason is that p1.x and
>> p1.v can be anywhere in the memory, maybe at very different places. So
>> np.array makes a *copy* when you pass ( p1.x, p1.v) as arguments. I tried
>> setting copy=False as a keyword argument of np.array but it doesn't
>> change the result; apparently a copy has to be made. Why don't you define
>> an array inside the class Particle, instead of different attributes?
>>
>> ? ? ? ?Cheers,
>>
>> ? ? ? ?Emmanuelle
>>
>> On Wed, Aug 05, 2009 at 02:44:18PM +0200, Jack Liddle wrote:
>>
>>
>> Hi
>>
>>
>> I'm trying to map some objects to a numpy array, so I can solve some
>> ode's with them and then work on the results with my objects.
>>
>>
>> Here's some code.
>>
>>
>> from numpy import array
>>
>>
>> class Particle(object):
>> ? ?def __init__(self,x,v):
>> ? ? ? self.x = x
>> ? ? ? self.v = v
>>
>>
>> p1 = Particle(1,0)
>> a ?= array(( p1.x, p1.v))
>> a[0] = 5
>> print p1.x
>>
>>
>> This prints 1 instead of 5. ?How do I make the array 'a' share the
>> same memory as the p1 attributes?
>>
>>
>> Any ideas, hope this makes sense
>>
>>
>> Thanks
>>
>>
>> Jack Liddle
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>>
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>>
>>
>> I was trying to do it from scratch, but I got confused about class
>> variables.
>> Can class variables only be accessed through the class self.__class__
>> ? I seem to get rusty in my python knowledge.
>>
>> the following seems to do what Emmanuelle proposed, but there might be
>> better examples already in the mailing lists.
>>
>> Josef
>>
>>
>> class Particle(object):
>> ? a ?= np.empty((3,2))
>>
>>
>
>> The above definition means that all instances of your Particle class will
>> share the same "a".
>> If you create instances p1 = Particle(0,1,2) and p2 = Particle(3,4,5), p1.a
>> and p2.a reference the same "a".
>> Is that your intent?
>
> Yes, that's the point of the exercise, to have the data of all
> particles accessible through one array, and the individual data points
> accessible through the instance attribute.
>
>>
>> ? def __init__(self,k,x,v):
>> ? ? ?self.k = k
>> ? ? ?#why do I need self.__class__ for class variable
>>
>>
>> self.a should work just fine.? Did you try it without specifying the
>> __class__ qualifier?
>
> Thanks for the hint.
> Yes, it works, and no, I didn't try.
> I was worried about class versus instance variable, the python help
> only used the class to access the class variable, and I didn't want to
> spend too much time on the exercise.
>
> When I actually need it, I will worry about the other details, like
> how to preallocate and grow the class array `a` and to automatically
> assign indices.
>
> Josef
>
>
>>
>> ? ? ?self.__class__.a[k,0] = x
>> ? ? ?self.__class__.a[k,1] = v
>>
>> ? def _getx(self):
>> ? ? ? return self.__class__.a[self.k,0]
>> ? def _setx(self, value):
>> ? ? ? self.__class__.a[self.k,0] = value
>>
>> ? x = property(_getx, _setx)
>>
>>
>> aa ?= Particle.a
>> print aa
>> p1 = Particle(0, 1, 2)
>> print p1
>> p2 = Particle(1, 2, 3)
>> p3 = Particle(2, 3, 4)
>> print aa
>> aa[:,:] = 1
>> print aa
>> print p2._getx(), p2.x
>> aa[0,0] = 5
>> print p1.x
>> p2.x = 10
>> print aa
>> print Particle.a
>> print vars(p2)
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From josef.pktd at gmail.com  Wed Aug  5 16:48:50 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Wed, 5 Aug 2009 16:48:50 -0400
Subject: [SciPy-User] Mapping objects to numpy array
In-Reply-To: <7f014ea60908051332l2f928a4q9bfffd09ed91db5d@mail.gmail.com>
References: <6ebdbcf10908050544y4996f57k89a0b421ad5944c1@mail.gmail.com>
	<20090805130522.GB15962@phare.normalesup.org>
	<7f014ea60908050627t72f83ad8m498ac48552baeff5@mail.gmail.com>
	<1cd32cbb0908050711k335d3e95q9ad1078043672c78@mail.gmail.com>
	<4A79A601.4080101@noaa.gov>
	<1cd32cbb0908050855t3931432cqe3f54058c9af8b46@mail.gmail.com>
	<7f014ea60908051332l2f928a4q9bfffd09ed91db5d@mail.gmail.com>
Message-ID: <1cd32cbb0908051348p68e2e3efmb1e7dd20f8395f64@mail.gmail.com>

On Wed, Aug 5, 2009 at 4:32 PM, Chris Colbert<sccolbert at gmail.com> wrote:
> You cannot use self.a, as this creates an instance attribute that
> hides the class attribute. You have to access through the __class__
> variable of the instance, or access the class attribute directly. See
> below:
>
> In [7]: class Test(object):
> ? ...: ? ? a = 1
> ? ...: ? ? def __init__(self, val):
> ? ...: ? ? ? ? self.b = val
> ? ...:
> ? ...: ? ? def set(self, val):
> ? ...: ? ? ? ? self.a = val

here you are redefining `a`, turning it into an instance variable as you said.
I guess, I was right in being worried about the issue.

However, in my case `a` is a mutable array and my assignment is

  def _setx(self, value):
      self.a[self.k,0] = value

Which just changes one value in an existing array, it would be the
same if `a` where a python list.

When I run my example with self.a, it works in the same way as with
self.__class__.a


all aa, p1.a, and p2.a are changed after the assignment p2.x = 10

Josef

> ? ...:
> ? ...:
>
> In [8]: t1 = Test(4)
>
> In [9]: t1.a
> Out[9]: 1
>
> In [10]: t1.b
> Out[10]: 4
>
> In [11]: t2 = Test(5)
>
> In [12]: t1.a
> Out[12]: 1
>
> In [13]: t2.a
> Out[13]: 1
>
> In [14]: t2.b
> Out[14]: 5
>
> In [15]: t1.set(4)
>
> In [16]: t1.a
> Out[16]: 4
>
> In [17]: t1.b
> Out[17]: 4
>
> In [18]: t2.a
> Out[18]: 1
>
> In [19]: t2.b
> Out[19]: 5
>
> In [20]: t3 = Test(6)
>
> In [21]: t4 = Test(7)
>
> In [22]: t3.a
> Out[22]: 1
>
> In [23]: t4.a
> Out[23]: 1
>
> In [24]: Test.a = 9
>
> In [25]: t3.a
> Out[25]: 9
>
> In [26]: t4.a
> Out[26]: 9
>
>
> On Wed, Aug 5, 2009 at 11:55 AM, <josef.pktd at gmail.com> wrote:
>> On Wed, Aug 5, 2009 at 11:32 AM, Jim Vickroy<Jim.Vickroy at noaa.gov> wrote:
>>> josef.pktd at gmail.com wrote:
>>>
>>> On Wed, Aug 5, 2009 at 9:27 AM, Chris Colbert<sccolbert at gmail.com> wrote:
>>>
>>>
>>> its for the same reason you cant do this:
>>>
>>> In [1]: a = 1
>>>
>>> In [2]: b = a
>>>
>>> In [3]: a
>>> Out[3]: 1
>>>
>>> In [4]: b
>>> Out[4]: 1
>>>
>>> In [5]: a = 2
>>>
>>> In [6]: a
>>> Out[6]: 2
>>>
>>> In [7]: b
>>> Out[7]: 1
>>>
>>>
>>> but you CAN do this:
>>>
>>> In [8]: a = [1, 2]
>>>
>>> In [9]: b = a
>>>
>>> In [10]: a
>>> Out[10]: [1, 2]
>>>
>>> In [11]: b
>>> Out[11]: [1, 2]
>>>
>>> In [12]: a[0] = 0
>>>
>>> In [13]: a
>>> Out[13]: [0, 2]
>>>
>>> In [14]: b
>>> Out[14]: [0, 2]
>>>
>>>
>>> floats and ints are immutables in python.
>>> you'll need a mutable container to store the values how you are wanting.
>>>
>>> That said, you still cant map to them with a numpy array like you want
>>> (AFAIK)
>>>
>>> You may want to look into subclassing ndarray or reformulating your
>>> problem so you dont need this requirement.
>>>
>>>
>>> On Wed, Aug 5, 2009 at 9:05 AM, Emmanuelle
>>> Gouillart<emmanuelle.gouillart at normalesup.org> wrote:
>>>
>>>
>>> ? ? ? ?Hi Jack,
>>>
>>> ? ? ? ?I don't think you can do it this way. The reason is that p1.x and
>>> p1.v can be anywhere in the memory, maybe at very different places. So
>>> np.array makes a *copy* when you pass ( p1.x, p1.v) as arguments. I tried
>>> setting copy=False as a keyword argument of np.array but it doesn't
>>> change the result; apparently a copy has to be made. Why don't you define
>>> an array inside the class Particle, instead of different attributes?
>>>
>>> ? ? ? ?Cheers,
>>>
>>> ? ? ? ?Emmanuelle
>>>
>>> On Wed, Aug 05, 2009 at 02:44:18PM +0200, Jack Liddle wrote:
>>>
>>>
>>> Hi
>>>
>>>
>>> I'm trying to map some objects to a numpy array, so I can solve some
>>> ode's with them and then work on the results with my objects.
>>>
>>>
>>> Here's some code.
>>>
>>>
>>> from numpy import array
>>>
>>>
>>> class Particle(object):
>>> ? ?def __init__(self,x,v):
>>> ? ? ? self.x = x
>>> ? ? ? self.v = v
>>>
>>>
>>> p1 = Particle(1,0)
>>> a ?= array(( p1.x, p1.v))
>>> a[0] = 5
>>> print p1.x
>>>
>>>
>>> This prints 1 instead of 5. ?How do I make the array 'a' share the
>>> same memory as the p1 attributes?
>>>
>>>
>>> Any ideas, hope this makes sense
>>>
>>>
>>> Thanks
>>>
>>>
>>> Jack Liddle
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>
>>>
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>
>>>
>>>
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>
>>>
>>>
>>> I was trying to do it from scratch, but I got confused about class
>>> variables.
>>> Can class variables only be accessed through the class self.__class__
>>> ? I seem to get rusty in my python knowledge.
>>>
>>> the following seems to do what Emmanuelle proposed, but there might be
>>> better examples already in the mailing lists.
>>>
>>> Josef
>>>
>>>
>>> class Particle(object):
>>> ? a ?= np.empty((3,2))
>>>
>>>
>>
>>> The above definition means that all instances of your Particle class will
>>> share the same "a".
>>> If you create instances p1 = Particle(0,1,2) and p2 = Particle(3,4,5), p1.a
>>> and p2.a reference the same "a".
>>> Is that your intent?
>>
>> Yes, that's the point of the exercise, to have the data of all
>> particles accessible through one array, and the individual data points
>> accessible through the instance attribute.
>>
>>>
>>> ? def __init__(self,k,x,v):
>>> ? ? ?self.k = k
>>> ? ? ?#why do I need self.__class__ for class variable
>>>
>>>
>>> self.a should work just fine.? Did you try it without specifying the
>>> __class__ qualifier?
>>
>> Thanks for the hint.
>> Yes, it works, and no, I didn't try.
>> I was worried about class versus instance variable, the python help
>> only used the class to access the class variable, and I didn't want to
>> spend too much time on the exercise.
>>
>> When I actually need it, I will worry about the other details, like
>> how to preallocate and grow the class array `a` and to automatically
>> assign indices.
>>
>> Josef
>>
>>
>>>
>>> ? ? ?self.__class__.a[k,0] = x
>>> ? ? ?self.__class__.a[k,1] = v
>>>
>>> ? def _getx(self):
>>> ? ? ? return self.__class__.a[self.k,0]
>>> ? def _setx(self, value):
>>> ? ? ? self.__class__.a[self.k,0] = value
>>>
>>> ? x = property(_getx, _setx)
>>>
>>>
>>> aa ?= Particle.a
>>> print aa
>>> p1 = Particle(0, 1, 2)
>>> print p1
>>> p2 = Particle(1, 2, 3)
>>> p3 = Particle(2, 3, 4)
>>> print aa
>>> aa[:,:] = 1
>>> print aa
>>> print p2._getx(), p2.x
>>> aa[0,0] = 5
>>> print p1.x
>>> p2.x = 10
>>> print aa
>>> print Particle.a
>>> print vars(p2)
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>
>>>
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>
>>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From sccolbert at gmail.com  Wed Aug  5 17:04:32 2009
From: sccolbert at gmail.com (Chris Colbert)
Date: Wed, 5 Aug 2009 17:04:32 -0400
Subject: [SciPy-User] Mapping objects to numpy array
In-Reply-To: <1cd32cbb0908051348p68e2e3efmb1e7dd20f8395f64@mail.gmail.com>
References: <6ebdbcf10908050544y4996f57k89a0b421ad5944c1@mail.gmail.com>
	<20090805130522.GB15962@phare.normalesup.org>
	<7f014ea60908050627t72f83ad8m498ac48552baeff5@mail.gmail.com>
	<1cd32cbb0908050711k335d3e95q9ad1078043672c78@mail.gmail.com>
	<4A79A601.4080101@noaa.gov>
	<1cd32cbb0908050855t3931432cqe3f54058c9af8b46@mail.gmail.com>
	<7f014ea60908051332l2f928a4q9bfffd09ed91db5d@mail.gmail.com>
	<1cd32cbb0908051348p68e2e3efmb1e7dd20f8395f64@mail.gmail.com>
Message-ID: <7f014ea60908051404u5f3aac48vabf881b315f48ea3@mail.gmail.com>

Ahh, yeah, I missed that part.

However, I think using self.__class__  is more understandable when
reading the code, as it very obviously accesses a class attribute.


On Wed, Aug 5, 2009 at 4:48 PM, <josef.pktd at gmail.com> wrote:
> On Wed, Aug 5, 2009 at 4:32 PM, Chris Colbert<sccolbert at gmail.com> wrote:
>> You cannot use self.a, as this creates an instance attribute that
>> hides the class attribute. You have to access through the __class__
>> variable of the instance, or access the class attribute directly. See
>> below:
>>
>> In [7]: class Test(object):
>> ? ...: ? ? a = 1
>> ? ...: ? ? def __init__(self, val):
>> ? ...: ? ? ? ? self.b = val
>> ? ...:
>> ? ...: ? ? def set(self, val):
>> ? ...: ? ? ? ? self.a = val
>
> here you are redefining `a`, turning it into an instance variable as you said.
> I guess, I was right in being worried about the issue.
>
> However, in my case `a` is a mutable array and my assignment is
>
> ?def _setx(self, value):
> ? ? ?self.a[self.k,0] = value
>
> Which just changes one value in an existing array, it would be the
> same if `a` where a python list.
>
> When I run my example with self.a, it works in the same way as with
> self.__class__.a
>
>
> all aa, p1.a, and p2.a are changed after the assignment p2.x = 10
>
> Josef
>
>> ? ...:
>> ? ...:
>>
>> In [8]: t1 = Test(4)
>>
>> In [9]: t1.a
>> Out[9]: 1
>>
>> In [10]: t1.b
>> Out[10]: 4
>>
>> In [11]: t2 = Test(5)
>>
>> In [12]: t1.a
>> Out[12]: 1
>>
>> In [13]: t2.a
>> Out[13]: 1
>>
>> In [14]: t2.b
>> Out[14]: 5
>>
>> In [15]: t1.set(4)
>>
>> In [16]: t1.a
>> Out[16]: 4
>>
>> In [17]: t1.b
>> Out[17]: 4
>>
>> In [18]: t2.a
>> Out[18]: 1
>>
>> In [19]: t2.b
>> Out[19]: 5
>>
>> In [20]: t3 = Test(6)
>>
>> In [21]: t4 = Test(7)
>>
>> In [22]: t3.a
>> Out[22]: 1
>>
>> In [23]: t4.a
>> Out[23]: 1
>>
>> In [24]: Test.a = 9
>>
>> In [25]: t3.a
>> Out[25]: 9
>>
>> In [26]: t4.a
>> Out[26]: 9
>>
>>
>> On Wed, Aug 5, 2009 at 11:55 AM, <josef.pktd at gmail.com> wrote:
>>> On Wed, Aug 5, 2009 at 11:32 AM, Jim Vickroy<Jim.Vickroy at noaa.gov> wrote:
>>>> josef.pktd at gmail.com wrote:
>>>>
>>>> On Wed, Aug 5, 2009 at 9:27 AM, Chris Colbert<sccolbert at gmail.com> wrote:
>>>>
>>>>
>>>> its for the same reason you cant do this:
>>>>
>>>> In [1]: a = 1
>>>>
>>>> In [2]: b = a
>>>>
>>>> In [3]: a
>>>> Out[3]: 1
>>>>
>>>> In [4]: b
>>>> Out[4]: 1
>>>>
>>>> In [5]: a = 2
>>>>
>>>> In [6]: a
>>>> Out[6]: 2
>>>>
>>>> In [7]: b
>>>> Out[7]: 1
>>>>
>>>>
>>>> but you CAN do this:
>>>>
>>>> In [8]: a = [1, 2]
>>>>
>>>> In [9]: b = a
>>>>
>>>> In [10]: a
>>>> Out[10]: [1, 2]
>>>>
>>>> In [11]: b
>>>> Out[11]: [1, 2]
>>>>
>>>> In [12]: a[0] = 0
>>>>
>>>> In [13]: a
>>>> Out[13]: [0, 2]
>>>>
>>>> In [14]: b
>>>> Out[14]: [0, 2]
>>>>
>>>>
>>>> floats and ints are immutables in python.
>>>> you'll need a mutable container to store the values how you are wanting.
>>>>
>>>> That said, you still cant map to them with a numpy array like you want
>>>> (AFAIK)
>>>>
>>>> You may want to look into subclassing ndarray or reformulating your
>>>> problem so you dont need this requirement.
>>>>
>>>>
>>>> On Wed, Aug 5, 2009 at 9:05 AM, Emmanuelle
>>>> Gouillart<emmanuelle.gouillart at normalesup.org> wrote:
>>>>
>>>>
>>>> ? ? ? ?Hi Jack,
>>>>
>>>> ? ? ? ?I don't think you can do it this way. The reason is that p1.x and
>>>> p1.v can be anywhere in the memory, maybe at very different places. So
>>>> np.array makes a *copy* when you pass ( p1.x, p1.v) as arguments. I tried
>>>> setting copy=False as a keyword argument of np.array but it doesn't
>>>> change the result; apparently a copy has to be made. Why don't you define
>>>> an array inside the class Particle, instead of different attributes?
>>>>
>>>> ? ? ? ?Cheers,
>>>>
>>>> ? ? ? ?Emmanuelle
>>>>
>>>> On Wed, Aug 05, 2009 at 02:44:18PM +0200, Jack Liddle wrote:
>>>>
>>>>
>>>> Hi
>>>>
>>>>
>>>> I'm trying to map some objects to a numpy array, so I can solve some
>>>> ode's with them and then work on the results with my objects.
>>>>
>>>>
>>>> Here's some code.
>>>>
>>>>
>>>> from numpy import array
>>>>
>>>>
>>>> class Particle(object):
>>>> ? ?def __init__(self,x,v):
>>>> ? ? ? self.x = x
>>>> ? ? ? self.v = v
>>>>
>>>>
>>>> p1 = Particle(1,0)
>>>> a ?= array(( p1.x, p1.v))
>>>> a[0] = 5
>>>> print p1.x
>>>>
>>>>
>>>> This prints 1 instead of 5. ?How do I make the array 'a' share the
>>>> same memory as the p1 attributes?
>>>>
>>>>
>>>> Any ideas, hope this makes sense
>>>>
>>>>
>>>> Thanks
>>>>
>>>>
>>>> Jack Liddle
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at scipy.org
>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>>
>>>>
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at scipy.org
>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>>
>>>>
>>>>
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at scipy.org
>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>>
>>>>
>>>>
>>>> I was trying to do it from scratch, but I got confused about class
>>>> variables.
>>>> Can class variables only be accessed through the class self.__class__
>>>> ? I seem to get rusty in my python knowledge.
>>>>
>>>> the following seems to do what Emmanuelle proposed, but there might be
>>>> better examples already in the mailing lists.
>>>>
>>>> Josef
>>>>
>>>>
>>>> class Particle(object):
>>>> ? a ?= np.empty((3,2))
>>>>
>>>>
>>>
>>>> The above definition means that all instances of your Particle class will
>>>> share the same "a".
>>>> If you create instances p1 = Particle(0,1,2) and p2 = Particle(3,4,5), p1.a
>>>> and p2.a reference the same "a".
>>>> Is that your intent?
>>>
>>> Yes, that's the point of the exercise, to have the data of all
>>> particles accessible through one array, and the individual data points
>>> accessible through the instance attribute.
>>>
>>>>
>>>> ? def __init__(self,k,x,v):
>>>> ? ? ?self.k = k
>>>> ? ? ?#why do I need self.__class__ for class variable
>>>>
>>>>
>>>> self.a should work just fine.? Did you try it without specifying the
>>>> __class__ qualifier?
>>>
>>> Thanks for the hint.
>>> Yes, it works, and no, I didn't try.
>>> I was worried about class versus instance variable, the python help
>>> only used the class to access the class variable, and I didn't want to
>>> spend too much time on the exercise.
>>>
>>> When I actually need it, I will worry about the other details, like
>>> how to preallocate and grow the class array `a` and to automatically
>>> assign indices.
>>>
>>> Josef
>>>
>>>
>>>>
>>>> ? ? ?self.__class__.a[k,0] = x
>>>> ? ? ?self.__class__.a[k,1] = v
>>>>
>>>> ? def _getx(self):
>>>> ? ? ? return self.__class__.a[self.k,0]
>>>> ? def _setx(self, value):
>>>> ? ? ? self.__class__.a[self.k,0] = value
>>>>
>>>> ? x = property(_getx, _setx)
>>>>
>>>>
>>>> aa ?= Particle.a
>>>> print aa
>>>> p1 = Particle(0, 1, 2)
>>>> print p1
>>>> p2 = Particle(1, 2, 3)
>>>> p3 = Particle(2, 3, 4)
>>>> print aa
>>>> aa[:,:] = 1
>>>> print aa
>>>> print p2._getx(), p2.x
>>>> aa[0,0] = 5
>>>> print p1.x
>>>> p2.x = 10
>>>> print aa
>>>> print Particle.a
>>>> print vars(p2)
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at scipy.org
>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>>
>>>>
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at scipy.org
>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>>
>>>>
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From Jim.Vickroy at noaa.gov  Wed Aug  5 17:09:14 2009
From: Jim.Vickroy at noaa.gov (Jim Vickroy)
Date: Wed, 05 Aug 2009 15:09:14 -0600
Subject: [SciPy-User] Mapping objects to numpy array
In-Reply-To: <1cd32cbb0908051348p68e2e3efmb1e7dd20f8395f64@mail.gmail.com>
References: <6ebdbcf10908050544y4996f57k89a0b421ad5944c1@mail.gmail.com>
	<20090805130522.GB15962@phare.normalesup.org>
	<7f014ea60908050627t72f83ad8m498ac48552baeff5@mail.gmail.com>
	<1cd32cbb0908050711k335d3e95q9ad1078043672c78@mail.gmail.com>
	<4A79A601.4080101@noaa.gov>
	<1cd32cbb0908050855t3931432cqe3f54058c9af8b46@mail.gmail.com>
	<7f014ea60908051332l2f928a4q9bfffd09ed91db5d@mail.gmail.com>
	<1cd32cbb0908051348p68e2e3efmb1e7dd20f8395f64@mail.gmail.com>
Message-ID: <4A79F4FA.1050304@noaa.gov>

josef.pktd at gmail.com wrote:
> On Wed, Aug 5, 2009 at 4:32 PM, Chris Colbert<sccolbert at gmail.com> wrote:
>   
>> You cannot use self.a, as this creates an instance attribute that
>> hides the class attribute. You have to access through the __class__
>> variable of the instance, or access the class attribute directly. See
>> below:
>>
>> In [7]: class Test(object):
>>   ...:     a = 1
>>   ...:     def __init__(self, val):
>>   ...:         self.b = val
>>   ...:
>>   ...:     def set(self, val):
>>   ...:         self.a = val
>>     
>
> here you are redefining `a`, turning it into an instance variable as you said.
> I guess, I was right in being worried about the issue.
>   
You only need be concerned if you wish to also introduce an instance 
attribute with the same name; otherwise, as you have discovered, it 
works as expected.
Even with an instance attribute of the same name, your class wide 
attribute is still readily accessible via the __class__ qualifier (e.g., 
self.__class__.a) or via the class name qualifier (e.g., Test.a).
> However, in my case `a` is a mutable array and my assignment is
>
>   def _setx(self, value):
>       self.a[self.k,0] = value
>
> Which just changes one value in an existing array, it would be the
> same if `a` where a python list.
>
> When I run my example with self.a, it works in the same way as with
> self.__class__.a
>
>
> all aa, p1.a, and p2.a are changed after the assignment p2.x = 10
>
> Josef
>
>   
>>   ...:
>>   ...:
>>
>> In [8]: t1 = Test(4)
>>
>> In [9]: t1.a
>> Out[9]: 1
>>
>> In [10]: t1.b
>> Out[10]: 4
>>
>> In [11]: t2 = Test(5)
>>
>> In [12]: t1.a
>> Out[12]: 1
>>
>> In [13]: t2.a
>> Out[13]: 1
>>
>> In [14]: t2.b
>> Out[14]: 5
>>
>> In [15]: t1.set(4)
>>
>> In [16]: t1.a
>> Out[16]: 4
>>
>> In [17]: t1.b
>> Out[17]: 4
>>
>> In [18]: t2.a
>> Out[18]: 1
>>
>> In [19]: t2.b
>> Out[19]: 5
>>
>> In [20]: t3 = Test(6)
>>
>> In [21]: t4 = Test(7)
>>
>> In [22]: t3.a
>> Out[22]: 1
>>
>> In [23]: t4.a
>> Out[23]: 1
>>
>> In [24]: Test.a = 9
>>
>> In [25]: t3.a
>> Out[25]: 9
>>
>> In [26]: t4.a
>> Out[26]: 9
>>
>>
>> On Wed, Aug 5, 2009 at 11:55 AM, <josef.pktd at gmail.com> wrote:
>>     
>>> On Wed, Aug 5, 2009 at 11:32 AM, Jim Vickroy<Jim.Vickroy at noaa.gov> wrote:
>>>       
>>>> josef.pktd at gmail.com wrote:
>>>>
>>>> On Wed, Aug 5, 2009 at 9:27 AM, Chris Colbert<sccolbert at gmail.com> wrote:
>>>>
>>>>
>>>> its for the same reason you cant do this:
>>>>
>>>> In [1]: a = 1
>>>>
>>>> In [2]: b = a
>>>>
>>>> In [3]: a
>>>> Out[3]: 1
>>>>
>>>> In [4]: b
>>>> Out[4]: 1
>>>>
>>>> In [5]: a = 2
>>>>
>>>> In [6]: a
>>>> Out[6]: 2
>>>>
>>>> In [7]: b
>>>> Out[7]: 1
>>>>
>>>>
>>>> but you CAN do this:
>>>>
>>>> In [8]: a = [1, 2]
>>>>
>>>> In [9]: b = a
>>>>
>>>> In [10]: a
>>>> Out[10]: [1, 2]
>>>>
>>>> In [11]: b
>>>> Out[11]: [1, 2]
>>>>
>>>> In [12]: a[0] = 0
>>>>
>>>> In [13]: a
>>>> Out[13]: [0, 2]
>>>>
>>>> In [14]: b
>>>> Out[14]: [0, 2]
>>>>
>>>>
>>>> floats and ints are immutables in python.
>>>> you'll need a mutable container to store the values how you are wanting.
>>>>
>>>> That said, you still cant map to them with a numpy array like you want
>>>> (AFAIK)
>>>>
>>>> You may want to look into subclassing ndarray or reformulating your
>>>> problem so you dont need this requirement.
>>>>
>>>>
>>>> On Wed, Aug 5, 2009 at 9:05 AM, Emmanuelle
>>>> Gouillart<emmanuelle.gouillart at normalesup.org> wrote:
>>>>
>>>>
>>>>        Hi Jack,
>>>>
>>>>        I don't think you can do it this way. The reason is that p1.x and
>>>> p1.v can be anywhere in the memory, maybe at very different places. So
>>>> np.array makes a *copy* when you pass ( p1.x, p1.v) as arguments. I tried
>>>> setting copy=False as a keyword argument of np.array but it doesn't
>>>> change the result; apparently a copy has to be made. Why don't you define
>>>> an array inside the class Particle, instead of different attributes?
>>>>
>>>>        Cheers,
>>>>
>>>>        Emmanuelle
>>>>
>>>> On Wed, Aug 05, 2009 at 02:44:18PM +0200, Jack Liddle wrote:
>>>>
>>>>
>>>> Hi
>>>>
>>>>
>>>> I'm trying to map some objects to a numpy array, so I can solve some
>>>> ode's with them and then work on the results with my objects.
>>>>
>>>>
>>>> Here's some code.
>>>>
>>>>
>>>> from numpy import array
>>>>
>>>>
>>>> class Particle(object):
>>>>    def __init__(self,x,v):
>>>>       self.x = x
>>>>       self.v = v
>>>>
>>>>
>>>> p1 = Particle(1,0)
>>>> a  = array(( p1.x, p1.v))
>>>> a[0] = 5
>>>> print p1.x
>>>>
>>>>
>>>> This prints 1 instead of 5.  How do I make the array 'a' share the
>>>> same memory as the p1 attributes?
>>>>
>>>>
>>>> Any ideas, hope this makes sense
>>>>
>>>>
>>>> Thanks
>>>>
>>>>
>>>> Jack Liddle
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at scipy.org
>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>>
>>>>
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at scipy.org
>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>>
>>>>
>>>>
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at scipy.org
>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>>
>>>>
>>>>
>>>> I was trying to do it from scratch, but I got confused about class
>>>> variables.
>>>> Can class variables only be accessed through the class self.__class__
>>>> ? I seem to get rusty in my python knowledge.
>>>>
>>>> the following seems to do what Emmanuelle proposed, but there might be
>>>> better examples already in the mailing lists.
>>>>
>>>> Josef
>>>>
>>>>
>>>> class Particle(object):
>>>>   a  = np.empty((3,2))
>>>>
>>>>
>>>>         
>>>> The above definition means that all instances of your Particle class will
>>>> share the same "a".
>>>> If you create instances p1 = Particle(0,1,2) and p2 = Particle(3,4,5), p1.a
>>>> and p2.a reference the same "a".
>>>> Is that your intent?
>>>>         
>>> Yes, that's the point of the exercise, to have the data of all
>>> particles accessible through one array, and the individual data points
>>> accessible through the instance attribute.
>>>
>>>       
>>>>   def __init__(self,k,x,v):
>>>>      self.k = k
>>>>      #why do I need self.__class__ for class variable
>>>>
>>>>
>>>> self.a should work just fine.  Did you try it without specifying the
>>>> __class__ qualifier?
>>>>         
>>> Thanks for the hint.
>>> Yes, it works, and no, I didn't try.
>>> I was worried about class versus instance variable, the python help
>>> only used the class to access the class variable, and I didn't want to
>>> spend too much time on the exercise.
>>>
>>> When I actually need it, I will worry about the other details, like
>>> how to preallocate and grow the class array `a` and to automatically
>>> assign indices.
>>>
>>> Josef
>>>
>>>
>>>       
>>>>      self.__class__.a[k,0] = x
>>>>      self.__class__.a[k,1] = v
>>>>
>>>>   def _getx(self):
>>>>       return self.__class__.a[self.k,0]
>>>>   def _setx(self, value):
>>>>       self.__class__.a[self.k,0] = value
>>>>
>>>>   x = property(_getx, _setx)
>>>>
>>>>
>>>> aa  = Particle.a
>>>> print aa
>>>> p1 = Particle(0, 1, 2)
>>>> print p1
>>>> p2 = Particle(1, 2, 3)
>>>> p3 = Particle(2, 3, 4)
>>>> print aa
>>>> aa[:,:] = 1
>>>> print aa
>>>> print p2._getx(), p2.x
>>>> aa[0,0] = 5
>>>> print p1.x
>>>> p2.x = 10
>>>> print aa
>>>> print Particle.a
>>>> print vars(p2)
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at scipy.org
>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>>
>>>>
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at scipy.org
>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>>
>>>>
>>>>         
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>
>>>       
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>>     
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>   

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From fperez.net at gmail.com  Wed Aug  5 18:20:06 2009
From: fperez.net at gmail.com (Fernando Perez)
Date: Wed, 5 Aug 2009 15:20:06 -0700
Subject: [SciPy-User] Wiki page: food options at the SciPy'09 conference
Message-ID: <db6b5ecc0908051520j18878f72ycea97661e702de9f@mail.gmail.com>

Hi all,

this is a message mostly for those attending the conference who know
Caltech and its surroundings well.

We've created a page to list easy-to-access food options from the
campus, but I don't really know what to put there.  Anyone who has
some knowledge of local options is welcome to add to this wiki page,
and will earn the gratitude of all attendees:

http://conference.scipy.org/food

Thanks!

Cheers,

f


From wizzard028wise at gmail.com  Wed Aug  5 18:54:17 2009
From: wizzard028wise at gmail.com (Dorian)
Date: Thu, 6 Aug 2009 00:54:17 +0200
Subject: [SciPy-User] Wiki page: food options at the SciPy'09 conference
In-Reply-To: <db6b5ecc0908051520j18878f72ycea97661e702de9f@mail.gmail.com>
References: <db6b5ecc0908051520j18878f72ycea97661e702de9f@mail.gmail.com>
Message-ID: <674a602a0908051554h3c35645coa65c31faf90f7f2b@mail.gmail.com>

Hi,
When reading the subject of this email I was happy thinking that the free
open food project to fight against starvation similar to the one of a free
software has started already. Anyways I hope that it will come in near
future.

Best wishes to all people attending the conference

Dorian

On Thu, Aug 6, 2009 at 12:20 AM, Fernando Perez <fperez.net at gmail.com>wrote:

> Hi all,
>
> this is a message mostly for those attending the conference who know
> Caltech and its surroundings well.
>
> We've created a page to list easy-to-access food options from the
> campus, but I don't really know what to put there.  Anyone who has
> some knowledge of local options is welcome to add to this wiki page,
> and will earn the gratitude of all attendees:
>
> http://conference.scipy.org/food
>
> Thanks!
>
> Cheers,
>
> f
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From cycomanic at gmail.com  Wed Aug  5 20:11:16 2009
From: cycomanic at gmail.com (Jochen)
Date: Thu, 6 Aug 2009 10:11:16 +1000
Subject: [SciPy-User] 2d interpolation
Message-ID: <20090806101116.7da50f61@cudos0803>

Hi all,

I've got a 2D field z which is a function of arrays x and y. Now I want to
interpolate it to two new arrays xn and yn. I've tried using
scipy.interpolate.interp2d, but I get some weird artifacts for smaller
fields or for larger fields (200x200) it takes so long that I have to
kill it after a while. Is it expected to be this slow? Currently I've
hacked a solution using scipy.ndimage.interpolation.map_coordinates
which is fast but seems quite hackish to me? Anybody have a better
solution to do this?

Cheers
Jochen



From david at ar.media.kyoto-u.ac.jp  Wed Aug  5 22:23:45 2009
From: david at ar.media.kyoto-u.ac.jp (David Cournapeau)
Date: Thu, 06 Aug 2009 11:23:45 +0900
Subject: [SciPy-User] Ubuntu vs Fedora for scientific work?
In-Reply-To: <7f014ea60908050602s65377b2ej5c63631d171f57b8@mail.gmail.com>
References: <4A6DC6A2.63BA.009B.0@twdb.state.tx.us>	<5b8d13220908032237t34e74fceq859fadb725996789@mail.gmail.com>	<270620220908040121o57436c87p244950d3d5dd5eec@mail.gmail.com>	<4A77EC90.63BA.009B.0@twdb.state.tx.us>	<4A795EF3.30708@ar.media.kyoto-u.ac.jp>
	<7f014ea60908050602s65377b2ej5c63631d171f57b8@mail.gmail.com>
Message-ID: <4A7A3EB1.6030309@ar.media.kyoto-u.ac.jp>

Chris Colbert wrote:
> what macbook supports 16GB of memory?
>   

None that I am aware of - I don't know if you can find 4 and 8 SO-DIMM
anyway. I was speaking about regular desktop PC, where 8 and even 16 Gb
are not unusual anymore, are supported by many motherboards, and are
affordable.

David


From cohen at lpta.in2p3.fr  Wed Aug  5 23:21:20 2009
From: cohen at lpta.in2p3.fr (Johann Cohen-Tanugi)
Date: Thu, 06 Aug 2009 05:21:20 +0200
Subject: [SciPy-User] 2d interpolation
In-Reply-To: <20090806101116.7da50f61@cudos0803>
References: <20090806101116.7da50f61@cudos0803>
Message-ID: <4A7A4C30.4070403@lpta.in2p3.fr>

Hi Jochen,
best would probably be to post your code somewhere. With the scarse 
decription below, it is hard to give you potentially useful feedback :)
best,
Johann


Jochen wrote:
> Hi all,
>
> I've got a 2D field z which is a function of arrays x and y. Now I want to
> interpolate it to two new arrays xn and yn. I've tried using
> scipy.interpolate.interp2d, but I get some weird artifacts for smaller
> fields or for larger fields (200x200) it takes so long that I have to
> kill it after a while. Is it expected to be this slow? Currently I've
> hacked a solution using scipy.ndimage.interpolation.map_coordinates
> which is fast but seems quite hackish to me? Anybody have a better
> solution to do this?
>
> Cheers
> Jochen
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>   


From cycomanic at gmail.com  Thu Aug  6 00:51:40 2009
From: cycomanic at gmail.com (Jochen)
Date: Thu, 6 Aug 2009 14:51:40 +1000
Subject: [SciPy-User] 2d interpolation
In-Reply-To: <4A7A4C30.4070403@lpta.in2p3.fr>
References: <20090806101116.7da50f61@cudos0803>
	<4A7A4C30.4070403@lpta.in2p3.fr>
Message-ID: <20090806145140.7d181884@cudos0803>

Hi Johann,

my current code looks like this:

def resample(I, t, f, N=256, fcentre=None, tcentre=None):
    """
          Resample the spectrogramm so that we are on a dt=1/(N*df) grid.
          Returns the resampled spectrogramm, time and frequency array.
          Parameters:

          M         --  the spectrogramm [array(len(t),len(f))]
          t         --  the original time vector of the spectrogram
          f         --  the original frequency vector of the spectrogram
          N         --  number of points to resample to to [int] (default=256)
          fcenter   --  index of the centre frequency of the spectrogramm
                        if None take len(f)/2 (default=None)
          tcentre   --  index of the centre time of the spectrogramm
                        if None take len(t)/2 (default=None)

    """
    tmax = abs(t[-1]-t[0])
    tnew = np.linspace(t[0], t[-1], N)
    fnew = np.arange(-N/2,N/2)*(1./tmax)
    if fcentre:
        fnew = fnew + f[fcentre]
    else:
        fnew = fnew + f[len(f)/2]
    if tcentre:
        tnew = tnew - tnew[len(tnew)/2] + t[tcentre]
    ind_tn = interp(tnew, t, np.arange(len(t)),left=-1,right=len(t)+1)
    ind_fn = interp(fnew, f[::-1], np.arange(len(f)),left=-1, right=len(f)+1)
    Ind_tn, Ind_fn = np.meshgrid(ind_tn,ind_fn)
    Inew = map_coordinates(I, [Ind_tn, Ind_fn])
    return Inew, tnew ,fnew[::-1]

I tried simply using something like
inter = interp2d(t,f,I)
Inew = inter(tnew,fnew)

but the creation of the interpolation object is so slow it's unusable.

Cheers
Jochen

On Thu, 06 Aug 2009 05:21:20 +0200
Johann Cohen-Tanugi <cohen at lpta.in2p3.fr> wrote:

> Hi Jochen,
> best would probably be to post your code somewhere. With the scarse 
> decription below, it is hard to give you potentially useful
> feedback :) best,
> Johann
> 
> 
> Jochen wrote:
> > Hi all,
> >
> > I've got a 2D field z which is a function of arrays x and y. Now I
> > want to interpolate it to two new arrays xn and yn. I've tried using
> > scipy.interpolate.interp2d, but I get some weird artifacts for
> > smaller fields or for larger fields (200x200) it takes so long that
> > I have to kill it after a while. Is it expected to be this slow?
> > Currently I've hacked a solution using
> > scipy.ndimage.interpolation.map_coordinates which is fast but seems
> > quite hackish to me? Anybody have a better solution to do this?
> >
> > Cheers
> > Jochen
> >
> > _______________________________________________
> > SciPy-User mailing list
> > SciPy-User at scipy.org
> > http://mail.scipy.org/mailman/listinfo/scipy-user
> >   
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user



From oliphant at enthought.com  Thu Aug  6 01:32:42 2009
From: oliphant at enthought.com (Travis Oliphant)
Date: Wed, 5 Aug 2009 23:32:42 -0600
Subject: [SciPy-User] 2d interpolation
In-Reply-To: <4A7A4C30.4070403@lpta.in2p3.fr>
References: <20090806101116.7da50f61@cudos0803>
	<4A7A4C30.4070403@lpta.in2p3.fr>
Message-ID: <55096849-53B0-48DB-A5AA-96C64013AB54@enthought.com>

Interp2d is not very robust in my experience.

Interpolation is an area that needs some cohesion to bring all the  
Possible ways to do interpolation together.

The code you are using from ndimage is a good choice.  You could also  
use the delaunay package or radial basis functions from scikits

Sent from my iPhone

On Aug 5, 2009, at 9:21 PM, Johann Cohen-Tanugi <cohen at lpta.in2p3.fr>  
wrote:

> Hi Jochen,
> best would probably be to post your code somewhere. With the scarse
> decription below, it is hard to give you potentially useful  
> feedback :)
> best,
> Johann
>
>
> Jochen wrote:
>> Hi all,
>>
>> I've got a 2D field z which is a function of arrays x and y. Now I  
>> want to
>> interpolate it to two new arrays xn and yn. I've tried using
>> scipy.interpolate.interp2d, but I get some weird artifacts for  
>> smaller
>> fields or for larger fields (200x200) it takes so long that I have to
>> kill it after a while. Is it expected to be this slow? Currently I've
>> hacked a solution using scipy.ndimage.interpolation.map_coordinates
>> which is fast but seems quite hackish to me? Anybody have a better
>> solution to do this?
>>
>> Cheers
>> Jochen
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user


From eadrogue at gmx.net  Thu Aug  6 06:43:54 2009
From: eadrogue at gmx.net (Ernest =?iso-8859-1?Q?Adrogu=E9?=)
Date: Thu, 6 Aug 2009 12:43:54 +0200
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <ec9f80fa0908040953x671bf2a0g838613ac0363b20d@mail.gmail.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
	<20090804013317.GA5376@doriath.local>
	<ec9f80fa0908040149m44e9f98fx4b887f8b3043d2f1@mail.gmail.com>
	<20090804112355.GA6395@doriath.local>
	<ec9f80fa0908040547r7cd6e017ye955ee14445e107f@mail.gmail.com>
	<20090804143804.GA7021@doriath.local>
	<ec9f80fa0908040953x671bf2a0g838613ac0363b20d@mail.gmail.com>
Message-ID: <20090806104354.GB19081@doriath.local>

 4/08/09 @ 18:53 (+0200), thus spake Sebastian Walter:
> Yeah, the licences of the linear solvers used in IPOPT are really too
> restrictive.
> 
> I use the ma27 (I think it is called) as academic user. It's not as
> bad as you wrote above:
> You register, you get a confirmation mail with a password, then you
> can log in an download the code as a couple of fortran source code
> files.
> So, not such a big deal.

In all fairness, a little while later I received an e-mail with the link to
download MUMPS.

The README file states that the software is free of charge and in the public
domain. Why they don't put the download link directly on the web I don't know.

Ernest


From sebastian.walter at gmail.com  Thu Aug  6 10:38:47 2009
From: sebastian.walter at gmail.com (Sebastian Walter)
Date: Thu, 6 Aug 2009 16:38:47 +0200
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <20090806104354.GB19081@doriath.local>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
	<20090804013317.GA5376@doriath.local>
	<ec9f80fa0908040149m44e9f98fx4b887f8b3043d2f1@mail.gmail.com>
	<20090804112355.GA6395@doriath.local>
	<ec9f80fa0908040547r7cd6e017ye955ee14445e107f@mail.gmail.com>
	<20090804143804.GA7021@doriath.local>
	<ec9f80fa0908040953x671bf2a0g838613ac0363b20d@mail.gmail.com>
	<20090806104354.GB19081@doriath.local>
Message-ID: <ec9f80fa0908060738o3ef314faq10765d71afe71ee8@mail.gmail.com>

well, is MUMPS directly supported by IPOPT? I thought, only a couple
of interfaces are defined.
Some collegues of mine use UMFPACK to solve sparse linear systems, as
far as i know.


2009/8/6 Ernest Adrogu? <eadrogue at gmx.net>:
> ?4/08/09 @ 18:53 (+0200), thus spake Sebastian Walter:
>> Yeah, the licences of the linear solvers used in IPOPT are really too
>> restrictive.
>>
>> I use the ma27 (I think it is called) as academic user. It's not as
>> bad as you wrote above:
>> You register, you get a confirmation mail with a password, then you
>> can log in an download the code as a couple of fortran source code
>> files.
>> So, not such a big deal.
>
> In all fairness, a little while later I received an e-mail with the link to
> download MUMPS.
>
> The README file states that the software is free of charge and in the public
> domain. Why they don't put the download link directly on the web I don't know.
>
> Ernest
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From lucia.morganti at gmail.com  Thu Aug  6 11:05:24 2009
From: lucia.morganti at gmail.com (Lucia)
Date: Thu, 6 Aug 2009 17:05:24 +0200
Subject: [SciPy-User] troubles installing scipy (swing)
Message-ID: <c1912ec60908060805u214ddaddgefbb514c646fd421@mail.gmail.com>

Hello,
I am not an expert, and I am desperate, so please help me!
I am trying to install numpy and scipy on my Mac (OS X Version
10.5.7), following the instructions I found here:
http://www.scipy.org/Installing_SciPy/Mac_OS_X (using SVN).
Before doing it, I had installed python 2.6 through the macports.

I typed
export MACOSX_DEPLOYMENT_TARGET=10.5
in the scipy directory, before trying to build it, but still I have
the following error message:

swig: scipy/sparse/linalg/dsolve/umfpack/umfpack.i
swig -python -o
build/src.macosx-10.5-i386-2.6/scipy/sparse/linalg/dsolve/umfpack/_umfpack_wrap.c
-outdir build/src.macosx-10.5-i386-2.6/scipy/sparse/linalg/dsolve/umfpack
scipy/sparse/linalg/dsolve/umfpack/umfpack.i
scipy/sparse/linalg/dsolve/umfpack/umfpack.i:192: Error: Unable to
find 'umfpack.h'
scipy/sparse/linalg/dsolve/umfpack/umfpack.i:193: Error: Unable to
find 'umfpack_solve.h'
scipy/sparse/linalg/dsolve/umfpack/umfpack.i:194: Error: Unable to
find 'umfpack_defaults.h'
scipy/sparse/linalg/dsolve/umfpack/umfpack.i:195: Error: Unable to
find 'umfpack_triplet_to_col.h'
scipy/sparse/linalg/dsolve/umfpack/umfpack.i:196: Error: Unable to
find 'umfpack_col_to_triplet.h'
scipy/sparse/linalg/dsolve/umfpack/umfpack.i:197: Error: Unable to
find 'umfpack_transpose.h'
scipy/sparse/linalg/dsolve/umfpack/umfpack.i:198: Error: Unable to
find 'umfpack_scale.h'
scipy/sparse/linalg/dsolve/umfpack/umfpack.i:200: Error: Unable to
find 'umfpack_report_symbolic.h'
scipy/sparse/linalg/dsolve/umfpack/umfpack.i:201: Error: Unable to
find 'umfpack_report_numeric.h'
scipy/sparse/linalg/dsolve/umfpack/umfpack.i:202: Error: Unable to
find 'umfpack_report_info.h'
scipy/sparse/linalg/dsolve/umfpack/umfpack.i:203: Error: Unable to
find 'umfpack_report_control.h'
scipy/sparse/linalg/dsolve/umfpack/umfpack.i:215: Error: Unable to
find 'umfpack_symbolic.h'
scipy/sparse/linalg/dsolve/umfpack/umfpack.i:216: Error: Unable to
find 'umfpack_numeric.h'
scipy/sparse/linalg/dsolve/umfpack/umfpack.i:225: Error: Unable to
find 'umfpack_free_symbolic.h'
scipy/sparse/linalg/dsolve/umfpack/umfpack.i:226: Error: Unable to
find 'umfpack_free_numeric.h'
scipy/sparse/linalg/dsolve/umfpack/umfpack.i:248: Error: Unable to
find 'umfpack_get_lunz.h'
scipy/sparse/linalg/dsolve/umfpack/umfpack.i:272: Error: Unable to
find 'umfpack_get_numeric.h'
error: command 'swig' failed with exit status 1

What should  I do?
I know that the same problems has already appeared on the mailing
list, but either
1) I cannot understand the proposed solutions
2) the proposed solutions don't work.
Help me, please!!!!!!!!!!


From mglerner at gmail.com  Thu Aug  6 13:07:42 2009
From: mglerner at gmail.com (Michael Lerner)
Date: Thu, 6 Aug 2009 13:07:42 -0400
Subject: [SciPy-User] 2d version of fftfreq?
Message-ID: <e4c539ed0908061007m6f8b6d49y703349fe32e7631e@mail.gmail.com>

Hi,

I have a function z = f(x,y). I compute the Fourier transform via fft2(z).
Is there a 2d version of fftfreq that I can use?

Thanks,

-michael

-- 
Michael Lerner, Ph.D.
IRTA Postdoctoral Fellow
Laboratory of Computational Biology NIH/NHLBI
5635 Fishers Lane, Room T909, MSC 9314
Rockville, MD 20852 (UPS/FedEx/Reality)
Bethesda MD 20892-9314 (USPS)
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From robert.kern at gmail.com  Thu Aug  6 13:45:57 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Thu, 6 Aug 2009 12:45:57 -0500
Subject: [SciPy-User] 2d version of fftfreq?
In-Reply-To: <e4c539ed0908061007m6f8b6d49y703349fe32e7631e@mail.gmail.com>
References: <e4c539ed0908061007m6f8b6d49y703349fe32e7631e@mail.gmail.com>
Message-ID: <3d375d730908061045vf84160cvcf0a759b46b2c835@mail.gmail.com>

On Thu, Aug 6, 2009 at 12:07, Michael Lerner<mglerner at gmail.com> wrote:
> Hi,
>
> I have a function z = f(x,y). I compute the Fourier transform via fft2(z).
> Is there a 2d version of fftfreq that I can use?

It would just be the combination of fftfreq(z.shape[0]) and fftfreq(z.shape[1]).

-- 
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


From gilles.rochefort at gmail.com  Thu Aug  6 16:41:37 2009
From: gilles.rochefort at gmail.com (Gilles Rochefort)
Date: Thu, 06 Aug 2009 22:41:37 +0200
Subject: [SciPy-User] ndimage filters overflow for default image format
 (uint8)
In-Reply-To: <2FC9B155-121B-43AB-BD83-BB118A07C925@gmail.com>
References: <0D3C80A2-D6D1-4C2B-B56F-A1C0358A3835@gmail.com>
	<2FC9B155-121B-43AB-BD83-BB118A07C925@gmail.com>
Message-ID: <4A7B4001.2020807@gmail.com>

Tony Yu a ?crit :
> Gilles Rochefort <gilles.rochefort at gmail.com> wrote:
>   
>> As you may know, grayscale images are represented with uint8 because  
>> of
>> a low memory footprint.
>>     
> [Snip]
>   
>> So, now filtered datas may be stored as int8, but you loose half the  
>> precision (dividing by two filter coefficients). Most applications  
>> won't suffer from a limited precision.
>>     
>
> Thanks for your suggestions. As I mentioned, I understand why scipy  
> behaves the way it does (from a programming perspective); I'm just not  
> sure if it makes sense from a user's perspective.
>
>   
>> In my opinion, as a programmer you have to know the filtered datas  
>> range. So, you just have to decide if precision does care or not.
>> You can't expect correlate1d to guess your intent for the resulting  
>> range.
>>     
>
> Actually, I didn't intend correlate1d to guess the resulting range,  
> but instead, certain filters to guess (those filters that return  
> values outside the input range). Most filters return outputs within  
> the range of the input, and should behave as they currently do. In any  
> case you're probably right: I don't think my solution was really the  
> appropriate one.
>
> I agree that a programmer should know the filtered data range, but my  
> main complaint is that someone should be making noise if I forget that  
> a filter returns values outside the valid range. To me, this behavior  
> is similar to using a "bare except" (an except clause without an  
> expression): code ends up failing silently. When using edge detection  
> filters on uint8 images (the reason I ran into this problem), overflow  
> resulted in erroneous output that **looked like valid output**. If I  
> had known an  overflowed occurred, I would have found my problem much  
> sooner.
>
> Basically: I often do stupid things, and I want someone tell me I'm  
> being stupid rather than nod in agreement. ;)
>   
I understand your point of view.
Well, I imagine that raising an exception on a edge detection filter is
easy to do. You just have to check once if output type is
compatible (meaning twice the input maximum number and not unsigned type).
But doing such a validation in a general filtering is either painfull 
or either overkilling.

Reading docs for exception in python, stands for :

/exception /OverflowError?
<http://docs.python.org/library/exceptions.html#exceptions.OverflowError>
    Raised when the result of an arithmetic operation is too large to be
    represented. This cannot occur for long integers (which would rather
    raise MemoryError
    <http://docs.python.org/library/exceptions.html#exceptions.MemoryError>
    than give up) and for most operations with plain integers, which
    return a long integer instead. Because of the lack of
    standardization of floating point exception handling in C, most
    floating point operations also aren't checked.
/exception /FloatingPointError?
<http://docs.python.org/library/exceptions.html#exceptions.FloatingPointError>
    Raised when a floating point operation fails. This exception is
    always defined, but can only be raised when Python is configured
    with the /--with-fpectl/ option, or the WANT_SIGFPE_HANDLER symbol
    is defined in the pyconfig.h file.

Regards,
Gilles Rochefort
> TSY
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>   

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From david at ar.media.kyoto-u.ac.jp  Thu Aug  6 23:30:25 2009
From: david at ar.media.kyoto-u.ac.jp (David Cournapeau)
Date: Fri, 07 Aug 2009 12:30:25 +0900
Subject: [SciPy-User] troubles installing scipy (swing)
In-Reply-To: <c1912ec60908060805u214ddaddgefbb514c646fd421@mail.gmail.com>
References: <c1912ec60908060805u214ddaddgefbb514c646fd421@mail.gmail.com>
Message-ID: <4A7B9FD1.3090309@ar.media.kyoto-u.ac.jp>

Lucia wrote:
> Hello,
> I am not an expert, and I am desperate, so please help me!
> I am trying to install numpy and scipy on my Mac (OS X Version
> 10.5.7), following the instructions I found here:
> http://www.scipy.org/Installing_SciPy/Mac_OS_X (using SVN).
>   

Is there a reason why you don't use the binary installers ? They won't
work with the fink/macport python, but you won't have to worry about any
building problems: install the python from python.org, and then numpy
and scipy. You need numpy 1.3.0 and scipy 0.7.1 for python 2.6:

http://sourceforge.net/projects/numpy/files/NumPy/
http://sourceforge.net/projects/scipy/files/

> swig: scipy/sparse/linalg/dsolve/umfpack/umfpack.i
> swig -python -o
> build/src.macosx-10.5-i386-2.6/scipy/sparse/linalg/dsolve/umfpack/_umfpack_wrap.c
> -outdir build/src.macosx-10.5-i386-2.6/scipy/sparse/linalg/dsolve/umfpack
> scipy/sparse/linalg/dsolve/umfpack/umfpack.i
> scipy/sparse/linalg/dsolve/umfpack/umfpack.i:192: Error: Unable to
> find 'umfpack.h'
> scipy/sparse/linalg/dsolve/umfpack/umfpack.i:193: Error: Unable to
> find 'umfpack_solve.h'
> scipy/sparse/linalg/dsolve/umfpack/umfpack.i:194: Error: Unable to
> find 'umfpack_defaults.h'
> scipy/sparse/linalg/dsolve/umfpack/umfpack.i:195: Error: Unable to
> find 'umfpack_triplet_to_col.h'
> scipy/sparse/linalg/dsolve/umfpack/umfpack.i:196: Error: Unable to
> find 'umfpack_col_to_triplet.h'
> scipy/sparse/linalg/dsolve/umfpack/umfpack.i:197: Error: Unable to
> find 'umfpack_transpose.h'
> scipy/sparse/linalg/dsolve/umfpack/umfpack.i:198: Error: Unable to
> find 'umfpack_scale.h'
> scipy/sparse/linalg/dsolve/umfpack/umfpack.i:200: Error: Unable to
> find 'umfpack_report_symbolic.h'
> scipy/sparse/linalg/dsolve/umfpack/umfpack.i:201: Error: Unable to
> find 'umfpack_report_numeric.h'
> scipy/sparse/linalg/dsolve/umfpack/umfpack.i:202: Error: Unable to
> find 'umfpack_report_info.h'
> scipy/sparse/linalg/dsolve/umfpack/umfpack.i:203: Error: Unable to
> find 'umfpack_report_control.h'
> scipy/sparse/linalg/dsolve/umfpack/umfpack.i:215: Error: Unable to
> find 'umfpack_symbolic.h'
> scipy/sparse/linalg/dsolve/umfpack/umfpack.i:216: Error: Unable to
> find 'umfpack_numeric.h'
> scipy/sparse/linalg/dsolve/umfpack/umfpack.i:225: Error: Unable to
> find 'umfpack_free_symbolic.h'
> scipy/sparse/linalg/dsolve/umfpack/umfpack.i:226: Error: Unable to
> find 'umfpack_free_numeric.h'
> scipy/sparse/linalg/dsolve/umfpack/umfpack.i:248: Error: Unable to
> find 'umfpack_get_lunz.h'
> scipy/sparse/linalg/dsolve/umfpack/umfpack.i:272: Error: Unable to
> find 'umfpack_get_numeric.h'
> error: command 'swig' failed with exit status 1
>   

You could try UMFPACK=None python setup.py build. But really, installing
the binaries is easier.

cheers,

David


From jr at sun.ac.za  Fri Aug  7 03:31:20 2009
From: jr at sun.ac.za (Johann Rohwer)
Date: Fri, 7 Aug 2009 09:31:20 +0200
Subject: [SciPy-User] Scipy build failure
Message-ID: <200908070931.21115.jr@sun.ac.za>

I've tried to send this previously with a 17kb logfile attached, but 
the message doesn't seem to make it to the list. Here it is without 
the attachment:

I'm getting the following build failure in 
scipy/sparse/linalg/dsolve/SuperLU:

gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/util.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/util.c: In function 
'superlu_abort_and_exit':
scipy/sparse/linalg/dsolve/SuperLU/SRC/util.c:31: error: format not a 
string literal and no format arguments
scipy/sparse/linalg/dsolve/SuperLU/SRC/util.c: In function 
'superlu_abort_and_exit':
scipy/sparse/linalg/dsolve/SuperLU/SRC/util.c:31: error: format not a 
string literal and no format arguments
error: Command "gcc -pthread -fno-strict-aliasing -DNDEBUG -O2 -g -
pipe -Wformat -Werror=format-security -Wp,-D_FORTIFY_SOURCE=2 -
fexceptions -fstack-protector --param=ssp-buffer-size=4 -g -fPIC -
DUSE_VENDOR_BLAS=1 -I/usr/lib64/python2.6/site-
packages/numpy/core/include -c 
scipy/sparse/linalg/dsolve/SuperLU/SRC/util.c -o build/temp.linux-
x86_64-2.6/scipy/sparse/linalg/dsolve/SuperLU/SRC/util.o" failed with 
exit status 1

System is Linux x86_64 (gcc 4.3.2), building Scipy against self-
compiled ATLAS 3.8.0. The relevant parts of the build log are attached 
(as they contain some warnings which might be helpful). The error 
occurs with SVN as well as 0.7.0 and 0.7.1 source. ATLAS builds and 
tests fine. Numpy SVN compiles and installs fine as well and passes 
tests.

Any ideas?
Johann



From david at ar.media.kyoto-u.ac.jp  Fri Aug  7 04:05:32 2009
From: david at ar.media.kyoto-u.ac.jp (David Cournapeau)
Date: Fri, 07 Aug 2009 17:05:32 +0900
Subject: [SciPy-User] Scipy build failure
In-Reply-To: <200908070931.21115.jr@sun.ac.za>
References: <200908070931.21115.jr@sun.ac.za>
Message-ID: <4A7BE04C.8010403@ar.media.kyoto-u.ac.jp>

Johann Rohwer wrote:
> I've tried to send this previously with a 17kb logfile attached, but 
> the message doesn't seem to make it to the list. Here it is without 
> the attachment:
>
> I'm getting the following build failure in 
> scipy/sparse/linalg/dsolve/SuperLU:
>
> gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/util.c
> scipy/sparse/linalg/dsolve/SuperLU/SRC/util.c: In function 
> 'superlu_abort_and_exit':
> scipy/sparse/linalg/dsolve/SuperLU/SRC/util.c:31: error: format not a 
> string literal and no format arguments
> scipy/sparse/linalg/dsolve/SuperLU/SRC/util.c: In function 
> 'superlu_abort_and_exit':
> scipy/sparse/linalg/dsolve/SuperLU/SRC/util.c:31: error: format not a 
> string literal and no format arguments
> error: Command "gcc -pthread -fno-strict-aliasing -DNDEBUG -O2 -g -
> pipe -Wformat -Werror=format-security -Wp,-D_FORTIFY_SOURCE=2 -
> fexceptions -fstack-protector --param=ssp-buffer-size=4 -g -fPIC -
> DUSE_VENDOR_BLAS=1 -I/usr/lib64/python2.6/site-
> packages/numpy/core/include -c 
> scipy/sparse/linalg/dsolve/SuperLU/SRC/util.c -o build/temp.linux-
> x86_64-2.6/scipy/sparse/linalg/dsolve/SuperLU/SRC/util.o" failed with 
> exit status 1
>   

The problem is most likely the flags set up by your distribution (I
guess this is RHEL from the security enhanced flags), or more exactly,
the flags show dubious code in superlu.

I applied a quick fix that seems to work when I use your flags - can you
check it up ? (it is r5892).

cheers,

David


From jr at sun.ac.za  Fri Aug  7 07:06:36 2009
From: jr at sun.ac.za (Johann Rohwer)
Date: Fri, 7 Aug 2009 13:06:36 +0200
Subject: [SciPy-User] Scipy build failure
In-Reply-To: <4A7BE04C.8010403@ar.media.kyoto-u.ac.jp>
References: <200908070931.21115.jr@sun.ac.za>
	<4A7BE04C.8010403@ar.media.kyoto-u.ac.jp>
Message-ID: <200908071306.36378.jr@sun.ac.za>

On Friday 07 August 2009, David Cournapeau wrote:
> Johann Rohwer wrote:
> > I've tried to send this previously with a 17kb logfile attached,
> > but the message doesn't seem to make it to the list. Here it is
> > without the attachment:
> >
> > I'm getting the following build failure in
> > scipy/sparse/linalg/dsolve/SuperLU:
> >
>
> The problem is most likely the flags set up by your distribution (I
> guess this is RHEL from the security enhanced flags), or more
> exactly, the flags show dubious code in superlu.
>
> I applied a quick fix that seems to work when I use your flags -
> can you check it up ? (it is r5892).

Thanks, r5892 compiles without errors now!
Incidentally my distro is Mandriva 2009.1, but I know too little about 
security enhanced flags to contribute here...

Johann


From eadrogue at gmx.net  Fri Aug  7 10:23:30 2009
From: eadrogue at gmx.net (Ernest =?iso-8859-1?Q?Adrogu=E9?=)
Date: Fri, 7 Aug 2009 16:23:30 +0200
Subject: [SciPy-User] fsolve with restriction on variable
In-Reply-To: <ec9f80fa0908060738o3ef314faq10765d71afe71ee8@mail.gmail.com>
References: <f1dbe1be0908030949n110822dlee4796edf32e89c7@mail.gmail.com>
	<20090804013317.GA5376@doriath.local>
	<ec9f80fa0908040149m44e9f98fx4b887f8b3043d2f1@mail.gmail.com>
	<20090804112355.GA6395@doriath.local>
	<ec9f80fa0908040547r7cd6e017ye955ee14445e107f@mail.gmail.com>
	<20090804143804.GA7021@doriath.local>
	<ec9f80fa0908040953x671bf2a0g838613ac0363b20d@mail.gmail.com>
	<20090806104354.GB19081@doriath.local>
	<ec9f80fa0908060738o3ef314faq10765d71afe71ee8@mail.gmail.com>
Message-ID: <20090807142330.GA7673@doriath.local>

 6/08/09 @ 16:38 (+0200), thus spake Sebastian Walter:
> well, is MUMPS directly supported by IPOPT? I thought, only a couple
> of interfaces are defined.
> Some collegues of mine use UMFPACK to solve sparse linear systems, as
> far as i know.

From the ipopt website:
"""
Currently, the following linear solvers can be used:

    * MA27 from the Harwell Subroutine Library
      (see http://www.cse.clrc.ac.uk/nag/hsl/).
    * MA57 from the Harwell Subroutine Library
      (see http://www.cse.clrc.ac.uk/nag/hsl/).
    * MUMPS (MUltifrontal Massively Parallel sparse direct Solver)
      (see http://graal.ens-lyon.fr/MUMPS/)
    * The Parallel Sparse Direct Solver (PARDISO)
      (see http://www.computational.unibas.ch/cs/scicomp/software/pardiso/).
      Note: The Pardiso version in Intel's MKL library does not yet support the features necessary for IPOPT.
    * The Watson Sparse Matrix Package (WSMP)
      (see http://www-users.cs.umn.edu/~agupta/wsmp.html)

You should include at least one of the linear solvers above in order to run IPOPT, and if you want to be able to switch easily between different alternatives, you can compile IPOPT with all of them. 
"""
So, I guess, yes, it is supported. Can't tell you for certain
because the Debian distribution that I'm using lacks a certain header
file from the metis/scotch library which is required to compile MUMPS,
so haven't had a chance to try it out yet.

Ernest


From josephsmidt at gmail.com  Fri Aug  7 11:44:08 2009
From: josephsmidt at gmail.com (Joseph Smidt)
Date: Fri, 7 Aug 2009 08:44:08 -0700
Subject: [SciPy-User] How to average different pieces or an array?
Message-ID: <142682e10908070844t32426100g85f6d022cd028209@mail.gmail.com>

      I'm sure this is easy I just can't think of how to do it without
a bunch of for loops which I would like to avoid since they take up so
much computational time.

Say I have an array x, with 100 entries.   Lets say I have another
array y that looks like this:

y = [0,5,10,20,40,100]  which specified which ranges of x I would like
to average.

In other words, I want elements 0 - 4  in x to be replaced by their
average value. 5-9 replaced with their average value. 10-19 replaced
by their averaged value, etc...

    Effectively this will give me a new array with one 100 entries
with different sized binning along the array.

    If anyone knows how to do this without using for loops the whole
way I would appreciate it.

     Example:  I will make it simpler in case my above description was
confusing.  Say x = [1,3,2,6,7,4,5,4,9,4] and I passed in y =
[0,2,4,10].  The above description would return a new X:

X = [2,2,4,4,5.5,5.5,5.5,5.5,5.5,5.5]

    So that the apprpriate bins are averaged.

                                       Joseph Smidt
-- 
------------------------------------------------------------------------
Joseph Smidt <josephsmidt at gmail.com>

Physics and Astronomy
4129 Frederick Reines Hall
Irvine, CA 92697-4575
Office: 949-824-3269


From emmanuelle.gouillart at normalesup.org  Fri Aug  7 12:20:49 2009
From: emmanuelle.gouillart at normalesup.org (Emmanuelle Gouillart)
Date: Fri, 7 Aug 2009 18:20:49 +0200
Subject: [SciPy-User] How to average different pieces or an array?
In-Reply-To: <142682e10908070844t32426100g85f6d022cd028209@mail.gmail.com>
References: <142682e10908070844t32426100g85f6d022cd028209@mail.gmail.com>
Message-ID: <20090807162049.GB11622@phare.normalesup.org>

Hey,

this doesn't solve all of your problem but it might help

>>> import numpy as np
>>> x = np.array([1,3,2,6,7,4,5,4,9,4])
>>> y = np.array([0,2,4,10])
>>> Y, X = np.mgrid[0:np.alen(y), 0:dmax]
>>> mask = (X>=y[Y])[:-1]*(X[:-1]<y[(Y+1)[:-1]])
>>> print mask
--> print(mask)
[[ True  True False False False False False False False False]
 [False False  True  True False False False False False False]
 [False False False False  True  True  True  True  True  True]]
>>> means = (x*m).sum(axis=1)/(np.diff(y)).astype(float)
>>> means
array([ 2. ,  4. ,  5.5])


I just don't have the time to see how to repeat the means! (maybe later
:D)

Cheers,

Emmanuelle

On Fri, Aug 07, 2009 at 08:44:08AM -0700, Joseph Smidt wrote:
>       I'm sure this is easy I just can't think of how to do it without
> a bunch of for loops which I would like to avoid since they take up so
> much computational time.

> Say I have an array x, with 100 entries.   Lets say I have another
> array y that looks like this:

> y = [0,5,10,20,40,100]  which specified which ranges of x I would like
> to average.

> In other words, I want elements 0 - 4  in x to be replaced by their
> average value. 5-9 replaced with their average value. 10-19 replaced
> by their averaged value, etc...

>     Effectively this will give me a new array with one 100 entries
> with different sized binning along the array.

>     If anyone knows how to do this without using for loops the whole
> way I would appreciate it.

>      Example:  I will make it simpler in case my above description was
> confusing.  Say x = [1,3,2,6,7,4,5,4,9,4] and I passed in y =
> [0,2,4,10].  The above description would return a new X:

> X = [2,2,4,4,5.5,5.5,5.5,5.5,5.5,5.5]

>     So that the apprpriate bins are averaged.

>                                        Joseph Smidt


From gilles.rochefort at gmail.com  Fri Aug  7 12:37:32 2009
From: gilles.rochefort at gmail.com (gilles Rochefort)
Date: Fri, 07 Aug 2009 18:37:32 +0200
Subject: [SciPy-User] How to average different pieces or an array?
In-Reply-To: <142682e10908070844t32426100g85f6d022cd028209@mail.gmail.com>
References: <142682e10908070844t32426100g85f6d022cd028209@mail.gmail.com>
Message-ID: <4A7C584C.7050103@gmail.com>

Hi,

Not sure to answer well to the question (indeed there is loops)  but
have you tried
 something like this :

for s in [ slice(a,b) for a,b in zip(y[:-1],y[1:]) ]:
    x[s] = mean(x[s])

Regards,
Gilles Rochefort.
>       I'm sure this is easy I just can't think of how to do it without
> a bunch of for loops which I would like to avoid since they take up so
> much computational time.
>
> Say I have an array x, with 100 entries.   Lets say I have another
> array y that looks like this:
>
> y = [0,5,10,20,40,100]  which specified which ranges of x I would like
> to average.
>
> In other words, I want elements 0 - 4  in x to be replaced by their
> average value. 5-9 replaced with their average value. 10-19 replaced
> by their averaged value, etc...
>
>     Effectively this will give me a new array with one 100 entries
> with different sized binning along the array.
>
>     If anyone knows how to do this without using for loops the whole
> way I would appreciate it.
>
>      Example:  I will make it simpler in case my above description was
> confusing.  Say x = [1,3,2,6,7,4,5,4,9,4] and I passed in y =
> [0,2,4,10].  The above description would return a new X:
>
> X = [2,2,4,4,5.5,5.5,5.5,5.5,5.5,5.5]
>
>     So that the apprpriate bins are averaged.
>
>                                        Joseph Smidt
>   



From emmanuelle.gouillart at normalesup.org  Fri Aug  7 13:04:36 2009
From: emmanuelle.gouillart at normalesup.org (Emmanuelle Gouillart)
Date: Fri, 7 Aug 2009 19:04:36 +0200
Subject: [SciPy-User] How to average different pieces or an array?
In-Reply-To: <20090807162049.GB11622@phare.normalesup.org>
References: <142682e10908070844t32426100g85f6d022cd028209@mail.gmail.com>
	<20090807162049.GB11622@phare.normalesup.org>
Message-ID: <20090807170436.GC11622@phare.normalesup.org>

First of all I apologize about my previous mail which was full of typos
(I cut & pasted a bit of code with variables that had been defined
earlier...). Sorry.

Here's the complete solution. I find it very ugly :D but it does the job
without loops. If you have a large distribution of sizes of the bins,
Gilles's solution may be faster...

>>> import numpy as np
>>> x = np.array([1,3,2,6,7,4,5,4,9,4])
>>> y = np.array([0,2,4,10])
>>> Y, X = np.mgrid[0:np.alen(y), 0:np.alen(x)]
>>> mask = (X>=y[Y])[:-1]*(X[:-1]<y[(Y+1)[:-1]]) 
>>> means = (x*mask).sum(axis=1)/(np.diff(y)).astype(float)
>>> means
array([ 2. ,  4. ,  5.5])
>>> diffy = np.diff(y)
>>> Y, X = np.ogrid[0:np.alen(means), 0:np.max(np.diff(y))]
>>> other_mask = X<diffy[Y]
>>> results = other_mask*means[:,None] 
>>> res = np.ravel(results)[np.ravel(other_mask)]
>>> res
array([ 2. ,  2. ,  4. ,  4. ,  5.5,  5.5,  5.5,  5.5,  5.5,  5.5])

Cheers,

Emmanuelle

On Fri, Aug 07, 2009 at 06:20:49PM +0200, Emmanuelle Gouillart wrote:
> Hey,

> this doesn't solve all of your problem but it might help

> >>> import numpy as np
> >>> x = np.array([1,3,2,6,7,4,5,4,9,4])
> >>> y = np.array([0,2,4,10])
> >>> Y, X = np.mgrid[0:np.alen(y), 0:dmax]
> >>> mask = (X>=y[Y])[:-1]*(X[:-1]<y[(Y+1)[:-1]])
> >>> print mask
> --> print(mask)
> [[ True  True False False False False False False False False]
>  [False False  True  True False False False False False False]
>  [False False False False  True  True  True  True  True  True]]
> >>> means = (x*m).sum(axis=1)/(np.diff(y)).astype(float)
> >>> means
> array([ 2. ,  4. ,  5.5])


> I just don't have the time to see how to repeat the means! (maybe later
> :D)

> Cheers,

> Emmanuelle

> On Fri, Aug 07, 2009 at 08:44:08AM -0700, Joseph Smidt wrote:
> >       I'm sure this is easy I just can't think of how to do it without
> > a bunch of for loops which I would like to avoid since they take up so
> > much computational time.

> > Say I have an array x, with 100 entries.   Lets say I have another
> > array y that looks like this:

> > y = [0,5,10,20,40,100]  which specified which ranges of x I would like
> > to average.

> > In other words, I want elements 0 - 4  in x to be replaced by their
> > average value. 5-9 replaced with their average value. 10-19 replaced
> > by their averaged value, etc...

> >     Effectively this will give me a new array with one 100 entries
> > with different sized binning along the array.

> >     If anyone knows how to do this without using for loops the whole
> > way I would appreciate it.

> >      Example:  I will make it simpler in case my above description was
> > confusing.  Say x = [1,3,2,6,7,4,5,4,9,4] and I passed in y =
> > [0,2,4,10].  The above description would return a new X:

> > X = [2,2,4,4,5.5,5.5,5.5,5.5,5.5,5.5]

> >     So that the apprpriate bins are averaged.

> >                                        Joseph Smidt
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user


From josef.pktd at gmail.com  Fri Aug  7 13:20:42 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Fri, 7 Aug 2009 13:20:42 -0400
Subject: [SciPy-User] How to average different pieces or an array?
In-Reply-To: <20090807170436.GC11622@phare.normalesup.org>
References: <142682e10908070844t32426100g85f6d022cd028209@mail.gmail.com>
	<20090807162049.GB11622@phare.normalesup.org>
	<20090807170436.GC11622@phare.normalesup.org>
Message-ID: <1cd32cbb0908071020t6fb4b53ekd77722deed89ebbd@mail.gmail.com>

On Fri, Aug 7, 2009 at 1:04 PM, Emmanuelle
Gouillart<emmanuelle.gouillart at normalesup.org> wrote:
> First of all I apologize about my previous mail which was full of typos
> (I cut & pasted a bit of code with variables that had been defined
> earlier...). Sorry.
>
> Here's the complete solution. I find it very ugly :D but it does the job
> without loops. If you have a large distribution of sizes of the bins,
> Gilles's solution may be faster...
>
>>>> import numpy as np
>>>> x = np.array([1,3,2,6,7,4,5,4,9,4])
>>>> y = np.array([0,2,4,10])
>>>> Y, X = np.mgrid[0:np.alen(y), 0:np.alen(x)]
>>>> mask = (X>=y[Y])[:-1]*(X[:-1]<y[(Y+1)[:-1]])
>>>> means = (x*mask).sum(axis=1)/(np.diff(y)).astype(float)
>>>> means
> array([ 2. , ?4. , ?5.5])
>>>> diffy = np.diff(y)
>>>> Y, X = np.ogrid[0:np.alen(means), 0:np.max(np.diff(y))]
>>>> other_mask = X<diffy[Y]
>>>> results = other_mask*means[:,None]
>>>> res = np.ravel(results)[np.ravel(other_mask)]
>>>> res
> array([ 2. , ?2. , ?4. , ?4. , ?5.5, ?5.5, ?5.5, ?5.5, ?5.5, ?5.5])

this might blow up because of the size of the intermediate arrays if
the number of bins is large (len of array by number of bins ?)

I think, Gilles answer might be the fastest, if the bins are given by
the indices.

If the bins are given as labels
e.g. [0,0,1,1,2,2,2,2,2,2]
then np.bincount or scipy.ndimage can be used to calculate the means,
which are much faster for a large number of bins and large arrays.

Josef

>
> Cheers,
>
> Emmanuelle
>
> On Fri, Aug 07, 2009 at 06:20:49PM +0200, Emmanuelle Gouillart wrote:
>> Hey,
>
>> this doesn't solve all of your problem but it might help
>
>> >>> import numpy as np
>> >>> x = np.array([1,3,2,6,7,4,5,4,9,4])
>> >>> y = np.array([0,2,4,10])
>> >>> Y, X = np.mgrid[0:np.alen(y), 0:dmax]
>> >>> mask = (X>=y[Y])[:-1]*(X[:-1]<y[(Y+1)[:-1]])
>> >>> print mask
>> --> print(mask)
>> [[ True ?True False False False False False False False False]
>> ?[False False ?True ?True False False False False False False]
>> ?[False False False False ?True ?True ?True ?True ?True ?True]]
>> >>> means = (x*m).sum(axis=1)/(np.diff(y)).astype(float)
>> >>> means
>> array([ 2. , ?4. , ?5.5])
>
>
>> I just don't have the time to see how to repeat the means! (maybe later
>> :D)
>
>> Cheers,
>
>> Emmanuelle
>
>> On Fri, Aug 07, 2009 at 08:44:08AM -0700, Joseph Smidt wrote:
>> > ? ? ? I'm sure this is easy I just can't think of how to do it without
>> > a bunch of for loops which I would like to avoid since they take up so
>> > much computational time.
>
>> > Say I have an array x, with 100 entries. ? Lets say I have another
>> > array y that looks like this:
>
>> > y = [0,5,10,20,40,100] ?which specified which ranges of x I would like
>> > to average.
>
>> > In other words, I want elements 0 - 4 ?in x to be replaced by their
>> > average value. 5-9 replaced with their average value. 10-19 replaced
>> > by their averaged value, etc...
>
>> > ? ? Effectively this will give me a new array with one 100 entries
>> > with different sized binning along the array.
>
>> > ? ? If anyone knows how to do this without using for loops the whole
>> > way I would appreciate it.
>
>> > ? ? ?Example: ?I will make it simpler in case my above description was
>> > confusing. ?Say x = [1,3,2,6,7,4,5,4,9,4] and I passed in y =
>> > [0,2,4,10]. ?The above description would return a new X:
>
>> > X = [2,2,4,4,5.5,5.5,5.5,5.5,5.5,5.5]
>
>> > ? ? So that the apprpriate bins are averaged.
>
>> > ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?Joseph Smidt
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From ebonak at hotmail.com  Fri Aug  7 13:28:29 2009
From: ebonak at hotmail.com (Esmail)
Date: Fri, 07 Aug 2009 13:28:29 -0400
Subject: [SciPy-User] putting a dot on a graph with gnuplot.py
In-Reply-To: <c5480b32-0acb-4f0e-880e-92aa7817cebd@c2g2000yqi.googlegroups.com>
References: <h52ipo$78t$1@ger.gmane.org>	<ee6c6a40-5bf4-41de-bc0f-be89cbe57a2d@o6g2000yqj.googlegroups.com>	<h55akt$4e3$1@ger.gmane.org>
	<c5480b32-0acb-4f0e-880e-92aa7817cebd@c2g2000yqi.googlegroups.com>
Message-ID: <h5ho7s$i6r$1@ger.gmane.org>

Martin wrote:
> Glad to hear it but that wouldn't put a circle on the graph? Unless
> you made a large "." I guess?

Yep, this is what I used:

set label 1 "" center front at 3.292516, 129.960684  point pt 7 ps 1'




From boris.burle at univ-provence.fr  Fri Aug  7 13:38:54 2009
From: boris.burle at univ-provence.fr (=?ISO-8859-1?Q?Bor=EDs_BURLE?=)
Date: Fri, 07 Aug 2009 19:38:54 +0200
Subject: [SciPy-User] How to average different pieces or an array?
In-Reply-To: <4A7C584C.7050103@gmail.com>
References: <142682e10908070844t32426100g85f6d022cd028209@mail.gmail.com>
	<4A7C584C.7050103@gmail.com>
Message-ID: <4A7C66AE.2020005@univ-provence.fr>

Hi,

Or another version, close to Gilles' one:

x = array([1,3,2,6,7,4,5,4,9,4])
y = [0,2,4,10]
z = x


for i in arange(len(y)-1):
   z[y[i]:y[i+1]] = mean(x[y[i]:y[i+1]])


hope this helps!

    B.

gilles Rochefort a ?crit :
> Hi,
>
> Not sure to answer well to the question (indeed there is loops)  but
> have you tried
>  something like this :
>
> for s in [ slice(a,b) for a,b in zip(y[:-1],y[1:]) ]:
>     x[s] = mean(x[s])
>
> Regards,
> Gilles Rochefort.
>   
>>       I'm sure this is easy I just can't think of how to do it without
>> a bunch of for loops which I would like to avoid since they take up so
>> much computational time.
>>
>> Say I have an array x, with 100 entries.   Lets say I have another
>> array y that looks like this:
>>
>> y = [0,5,10,20,40,100]  which specified which ranges of x I would like
>> to average.
>>
>> In other words, I want elements 0 - 4  in x to be replaced by their
>> average value. 5-9 replaced with their average value. 10-19 replaced
>> by their averaged value, etc...
>>
>>     Effectively this will give me a new array with one 100 entries
>> with different sized binning along the array.
>>
>>     If anyone knows how to do this without using for loops the whole
>> way I would appreciate it.
>>
>>      Example:  I will make it simpler in case my above description was
>> confusing.  Say x = [1,3,2,6,7,4,5,4,9,4] and I passed in y =
>> [0,2,4,10].  The above description would return a new X:
>>
>> X = [2,2,4,4,5.5,5.5,5.5,5.5,5.5,5.5]
>>
>>     So that the apprpriate bins are averaged.
>>
>>                                        Joseph Smidt
>>   
>>     
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>   

-- 
Bor?s BURLE
Laboratoire de Neurobiologie de la Cognition
CNRS et Universit? de Provence
tel: (+33) 4 88 57 68 79
fax: (+33) 4 88 57 68 72
web page:  http://www.up.univ-mrs.fr/lnc/ACT/act-fr.html

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From josef.pktd at gmail.com  Fri Aug  7 13:59:24 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Fri, 7 Aug 2009 13:59:24 -0400
Subject: [SciPy-User] How to average different pieces or an array?
In-Reply-To: <4A7C66AE.2020005@univ-provence.fr>
References: <142682e10908070844t32426100g85f6d022cd028209@mail.gmail.com>
	<4A7C584C.7050103@gmail.com> <4A7C66AE.2020005@univ-provence.fr>
Message-ID: <1cd32cbb0908071059mc756ebbj246ad20d53a045ff@mail.gmail.com>

On Fri, Aug 7, 2009 at 1:38 PM, Bor?s BURLE<boris.burle at univ-provence.fr> wrote:
> Hi,
>
> Or another version, close to Gilles' one:
>
> x = array([1,3,2,6,7,4,5,4,9,4])
> y = [0,2,4,10]
> z = x
>
>
> for i in arange(len(y)-1):
> ?? z[y[i]:y[i+1]] = mean(x[y[i]:y[i+1]])
>
>
> hope this helps!
>
> ??? B.
>
> gilles Rochefort a ?crit?:
>
> Hi,
>
> Not sure to answer well to the question (indeed there is loops)  but
> have you tried
>  something like this :
>
> for s in [ slice(a,b) for a,b in zip(y[:-1],y[1:]) ]:
>     x[s] = mean(x[s])
>
> Regards,
> Gilles Rochefort.
>
>
>       I'm sure this is easy I just can't think of how to do it without
> a bunch of for loops which I would like to avoid since they take up so
> much computational time.
>
> Say I have an array x, with 100 entries.   Lets say I have another
> array y that looks like this:
>
> y = [0,5,10,20,40,100]  which specified which ranges of x I would like
> to average.
>
> In other words, I want elements 0 - 4  in x to be replaced by their
> average value. 5-9 replaced with their average value. 10-19 replaced
> by their averaged value, etc...
>
>     Effectively this will give me a new array with one 100 entries
> with different sized binning along the array.
>
>     If anyone knows how to do this without using for loops the whole
> way I would appreciate it.
>
>      Example:  I will make it simpler in case my above description was
> confusing.  Say x = [1,3,2,6,7,4,5,4,9,4] and I passed in y =
> [0,2,4,10].  The above description would return a new X:
>
> X = [2,2,4,4,5.5,5.5,5.5,5.5,5.5,5.5]
>
>     So that the apprpriate bins are averaged.
>
>                                        Joseph Smidt
>
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
>
> --
> Bor?s BURLE
> Laboratoire de Neurobiologie de la Cognition
> CNRS et Universit? de Provence
> tel: (+33) 4 88 57 68 79
> fax: (+33) 4 88 57 68 72
> web page:  http://www.up.univ-mrs.fr/lnc/ACT/act-fr.html
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>

Using the loop of Boris to build the reusable label/indicator array
and bincount. labels may be slower because they don't assume that the
array is sorted by label.

>>> x = np.array([1,3,2,6,7,4,5,4,9,4])
>>> y = np.array([0,2,4,10])
>>> ind = np.empty(x.shape, int)
>>> for i in np.arange(len(y)-1): ind[y[i]:y[i+1]] = i

>>> means = np.bincount(ind,x)/np.bincount(ind)
>>> meanarr = means[ind]
>>> meanarr
array([ 2. ,  2. ,  4. ,  4. ,  5.5,  5.5,  5.5,  5.5,  5.5,  5.5])

Josef


From josephsmidt at gmail.com  Fri Aug  7 14:06:38 2009
From: josephsmidt at gmail.com (Joseph Smidt)
Date: Fri, 7 Aug 2009 11:06:38 -0700
Subject: [SciPy-User] How to average different pieces or an array?
In-Reply-To: <1cd32cbb0908071059mc756ebbj246ad20d53a045ff@mail.gmail.com>
References: <142682e10908070844t32426100g85f6d022cd028209@mail.gmail.com>
	<4A7C584C.7050103@gmail.com> <4A7C66AE.2020005@univ-provence.fr>
	<1cd32cbb0908071059mc756ebbj246ad20d53a045ff@mail.gmail.com>
Message-ID: <142682e10908071106t37774045icdec9f2e537ffb9@mail.gmail.com>

Thank you all.  Your suggestions were very helpful.

On Fri, Aug 7, 2009 at 10:59 AM, <josef.pktd at gmail.com> wrote:
> On Fri, Aug 7, 2009 at 1:38 PM, Bor?s BURLE<boris.burle at univ-provence.fr> wrote:
>> Hi,
>>
>> Or another version, close to Gilles' one:
>>
>> x = array([1,3,2,6,7,4,5,4,9,4])
>> y = [0,2,4,10]
>> z = x
>>
>>
>> for i in arange(len(y)-1):
>> ?? z[y[i]:y[i+1]] = mean(x[y[i]:y[i+1]])
>>
>>
>> hope this helps!
>>
>> ??? B.
>>
>> gilles Rochefort a ?crit?:
>>
>> Hi,
>>
>> Not sure to answer well to the question (indeed there is loops) ?but
>> have you tried
>> ?something like this :
>>
>> for s in [ slice(a,b) for a,b in zip(y[:-1],y[1:]) ]:
>> ? ? x[s] = mean(x[s])
>>
>> Regards,
>> Gilles Rochefort.
>>
>>
>> ? ? ? I'm sure this is easy I just can't think of how to do it without
>> a bunch of for loops which I would like to avoid since they take up so
>> much computational time.
>>
>> Say I have an array x, with 100 entries. ? Lets say I have another
>> array y that looks like this:
>>
>> y = [0,5,10,20,40,100] ?which specified which ranges of x I would like
>> to average.
>>
>> In other words, I want elements 0 - 4 ?in x to be replaced by their
>> average value. 5-9 replaced with their average value. 10-19 replaced
>> by their averaged value, etc...
>>
>> ? ? Effectively this will give me a new array with one 100 entries
>> with different sized binning along the array.
>>
>> ? ? If anyone knows how to do this without using for loops the whole
>> way I would appreciate it.
>>
>> ? ? ?Example: ?I will make it simpler in case my above description was
>> confusing. ?Say x = [1,3,2,6,7,4,5,4,9,4] and I passed in y =
>> [0,2,4,10]. ?The above description would return a new X:
>>
>> X = [2,2,4,4,5.5,5.5,5.5,5.5,5.5,5.5]
>>
>> ? ? So that the apprpriate bins are averaged.
>>
>> ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?Joseph Smidt
>>
>>
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>>
>>
>> --
>> Bor?s BURLE
>> Laboratoire de Neurobiologie de la Cognition
>> CNRS et Universit? de Provence
>> tel: (+33) 4 88 57 68 79
>> fax: (+33) 4 88 57 68 72
>> web page: ?http://www.up.univ-mrs.fr/lnc/ACT/act-fr.html
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>>
>
> Using the loop of Boris to build the reusable label/indicator array
> and bincount. labels may be slower because they don't assume that the
> array is sorted by label.
>
>>>> x = np.array([1,3,2,6,7,4,5,4,9,4])
>>>> y = np.array([0,2,4,10])
>>>> ind = np.empty(x.shape, int)
>>>> for i in np.arange(len(y)-1): ind[y[i]:y[i+1]] = i
>
>>>> means = np.bincount(ind,x)/np.bincount(ind)
>>>> meanarr = means[ind]
>>>> meanarr
> array([ 2. , ?2. , ?4. , ?4. , ?5.5, ?5.5, ?5.5, ?5.5, ?5.5, ?5.5])
>
> Josef
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>



-- 
------------------------------------------------------------------------
Joseph Smidt <josephsmidt at gmail.com>

Physics and Astronomy
4129 Frederick Reines Hall
Irvine, CA 92697-4575
Office: 949-824-3269


From kuiper at jpl.nasa.gov  Fri Aug  7 14:11:15 2009
From: kuiper at jpl.nasa.gov (Tom Kuiper)
Date: Fri, 7 Aug 2009 11:11:15 -0700
Subject: [SciPy-User] food options at the SciPy'09
Message-ID: <4A7C6E43.2070304@jpl.nasa.gov>

If this appears twice, forgive me.  I sent it previously (yesterday at 
10:27 am PDT) via a browser interface to JPL's Office Outlook.  I have 
doubts about this system.  This time, from Iceweasel through our SMTP 
server.

I don't know how to edit the wiki but here is another food option which I
will use.

In the parking lot east of Moore and north-east of Powell-Booth there is
a catering truck at lunch time.  Sandwiches and Mexican food.  Very good
and very cheap.  I don't know the hours exactly but someone with an
office in Powell-Booth or Steele or Moore will know.  Long lines form
but the caterers pass along bowls of taco chips and quacamole.




From gilles.rochefort at gmail.com  Fri Aug  7 14:47:00 2009
From: gilles.rochefort at gmail.com (Gilles Rochefort)
Date: Fri, 07 Aug 2009 20:47:00 +0200
Subject: [SciPy-User] How to average different pieces or an array?
In-Reply-To: <4A7C66AE.2020005@univ-provence.fr>
References: <142682e10908070844t32426100g85f6d022cd028209@mail.gmail.com>	<4A7C584C.7050103@gmail.com>
	<4A7C66AE.2020005@univ-provence.fr>
Message-ID: <4A7C76A4.7050309@gmail.com>


> Hi,
>
> Or another version, close to Gilles' one:
>
> x = array([1,3,2,6,7,4,5,4,9,4])
> y = [0,2,4,10]
> z = x
>
>
> for i in arange(len(y)-1):
>   z[y[i]:y[i+1]] = mean(x[y[i]:y[i+1]])
>
Sure, there is no need to build a list of slices... sometimes I have
overkilling habits ;-)
In order to go in the same way as yours, may I suggest you to use xrange
rather than arange.

xrange is a generator and prevents to build the whole array as arange does.

Gilles.

>
> hope this helps!
>
>    B.
>
> gilles Rochefort a ?crit :
>> Hi,
>>
>> Not sure to answer well to the question (indeed there is loops)  but
>> have you tried
>>  something like this :
>>
>> for s in [ slice(a,b) for a,b in zip(y[:-1],y[1:]) ]:
>>     x[s] = mean(x[s])
>>
>> Regards,
>> Gilles Rochefort.
>>  
>>>       I'm sure this is easy I just can't think of how to do it without
>>> a bunch of for loops which I would like to avoid since they take up so
>>> much computational time.
>>>
>>> Say I have an array x, with 100 entries.   Lets say I have another
>>> array y that looks like this:
>>>
>>> y = [0,5,10,20,40,100]  which specified which ranges of x I would like
>>> to average.
>>>
>>> In other words, I want elements 0 - 4  in x to be replaced by their
>>> average value. 5-9 replaced with their average value. 10-19 replaced
>>> by their averaged value, etc...
>>>
>>>     Effectively this will give me a new array with one 100 entries
>>> with different sized binning along the array.
>>>
>>>     If anyone knows how to do this without using for loops the whole
>>> way I would appreciate it.
>>>
>>>      Example:  I will make it simpler in case my above description was
>>> confusing.  Say x = [1,3,2,6,7,4,5,4,9,4] and I passed in y =
>>> [0,2,4,10].  The above description would return a new X:
>>>
>>> X = [2,2,4,4,5.5,5.5,5.5,5.5,5.5,5.5]
>>>
>>>     So that the apprpriate bins are averaged.
>>>
>>>                                        Joseph Smidt
>>>       
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>>   
>
> ------------------------------------------------------------------------
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>   



From fperez.net at gmail.com  Fri Aug  7 18:10:01 2009
From: fperez.net at gmail.com (Fernando Perez)
Date: Fri, 7 Aug 2009 15:10:01 -0700
Subject: [SciPy-User] food options at the SciPy'09
In-Reply-To: <4A7C6E43.2070304@jpl.nasa.gov>
References: <4A7C6E43.2070304@jpl.nasa.gov>
Message-ID: <db6b5ecc0908071510y5fb61e94hc69d8a3819acd144@mail.gmail.com>

On Fri, Aug 7, 2009 at 11:11 AM, Tom Kuiper<kuiper at jpl.nasa.gov> wrote:
>
> I don't know how to edit the wiki but here is another food option which I
> will use.

Thanks!  I just added your note to the wiki page, much appreciated.

Cheers,

f


From emmanuelle.gouillart at normalesup.org  Sat Aug  8 05:48:46 2009
From: emmanuelle.gouillart at normalesup.org (Emmanuelle Gouillart)
Date: Sat, 8 Aug 2009 11:48:46 +0200
Subject: [SciPy-User] How to average different pieces or an array?
In-Reply-To: <1cd32cbb0908071020t6fb4b53ekd77722deed89ebbd@mail.gmail.com>
References: <142682e10908070844t32426100g85f6d022cd028209@mail.gmail.com>
	<20090807162049.GB11622@phare.normalesup.org>
	<20090807170436.GC11622@phare.normalesup.org>
	<1cd32cbb0908071020t6fb4b53ekd77722deed89ebbd@mail.gmail.com>
Message-ID: <20090808094846.GA21264@phare.normalesup.org>

[..]

> this might blow up because of the size of the intermediate arrays if
> the number of bins is large (len of array by number of bins ?)

> I think, Gilles answer might be the fastest, if the bins are given by
> the indices.

> If the bins are given as labels
> e.g. [0,0,1,1,2,2,2,2,2,2]
> then np.bincount or scipy.ndimage can be used to calculate the means,
> which are much faster for a large number of bins and large arrays.

Sure, the only advantage of my solution is that it satisfies the
constraint "without any for loop" :D ... which is often a very silly
constraint, but sometimes it's just fun looking for a solution without
loops!

Emmanuelle


From josef.pktd at gmail.com  Sat Aug  8 09:56:54 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sat, 8 Aug 2009 09:56:54 -0400
Subject: [SciPy-User] Documenting distributions {was Re: adding
	distributions from hydroclimpy to stats.distributions}
In-Reply-To: <FA1591B7-7BD9-468A-9662-206811ECAA89@gmail.com>
References: <1cd32cbb0908020641k4649315ahbd1a7ba986fd9ef9@mail.gmail.com>
	<82156633-5678-4485-9ECA-FB33CB540691@gmail.com>
	<1cd32cbb0908022120i160ca529gd7942e4a76acee73@mail.gmail.com>
	<4A771263.1040209@gmail.com>
	<1cd32cbb0908031107t28479cd9ucc34688385861277@mail.gmail.com>
	<FA1591B7-7BD9-468A-9662-206811ECAA89@gmail.com>
Message-ID: <1cd32cbb0908080656q67aa6e09q516293a531b3f4f1@mail.gmail.com>

On Mon, Aug 3, 2009 at 2:45 PM, Pierre GM<pgmdevlist at gmail.com> wrote:
> All,
> I'm afraid we're hitting a wall here. No matter how smart we are, and
> how convenient it is, an automatic documentation is *not* the panacea.
> I don't think we should pursue in Josef's direction of overloading the
> 'extradoc' parameter.
>
> I think we should on the contrary the docstring of a distribution
> quite short, and move the details to an independent page in the docs.
> Josef's idea of a demo is something I'd like to see in the doc, and
> yes, we could implement a function to plot one or several graphs
> automatically. But this should go on a documentation, not in a
> docstring.

I was looking for the best location to put additional doc information,
that doesn't fit into the docstrings of the functions and classes.
Currently the only additional location in scipy docs is the tutorial,
but that is not a good location for (technical) reference material.

I think the best location would be to add additional ReST files/trees
on the top level of the subpackage (scipy.stats), similar to the
toolbox documentation in matlab.

For example for the distributions we could add a rst file Distribution
Reference as in

http://www.mathworks.com/access/helpdesk/help/toolbox/stats/index.html?/access/helpdesk/help/toolbox/stats/&http://www.mathworks.com/support/product/product.html?product=ST

As structure, I would think that we need one rst file per distribution
linked to from an index page..
(It would also possible  to have one page for popular distributions or
distribution with a lot of information, and a single page for other
distributions where we have only the basic formulas from Travis'
notes.)

Adding this additional pages with background information would be
useful for other areas in scipy as well.


Josef


>
> On Aug 3, 2009, at 2:07 PM, josef.pktd at gmail.com wrote:
>>>
>>> A generic description of a distribution class has the advantage
>>> that it
>>> tends to minimize the repetitive description of the common features
>>> (and
>>> functions) of distributions which makes the general usage very easy
>>> to
>>> describe. For example, SAS has generic PDF and CDF functions where
>>> the
>>> distribution is an argument:
>>> http://support.sas.com/onlinedoc/913/getDoc/en/lrdict.hlp/a000270634.htm
>>> http://support.sas.com/onlinedoc/913/getDoc/en/lrdict.hlp/a000208980.htm
>>>
>>> But the problem with that is that it avoids that the fact that the
>>> actual
>>> details of these features and hence functions vary between
>>> distributions.
>>> Thus you are forced to look at each distribution to understand how
>>> to use
>>> these functions for that specific distribution which removes the
>>> advantage
>>> of the generic classes.
>>>
>>> Alternatively there is the distribution orientated approach used by
>>> R where
>>> you describe the background of the distribution and provide the
>>> associated
>>> functions. For example, in R '?Normal' and '?FDist' return the
>>> normal and F
>>> distributions, respectively, and provide associated functions. This
>>> allows
>>> the unique features of each distribution but tends to lose the
>>> generic
>>> features of the distribution. ?But the problem I find with R's
>>> approach is
>>> that you can not get specific help on individual functions. For
>>> example, at
>>> the R prompt, '?pf' goes back to the help on the F distribution as
>>> a whole
>>> and you have to find the actual pf function in all that material.
>>>
>>> Note that SAS provides common names to widely used probability
>>> functions and
>>> inverses such as probit and probnorm for the Normal distribution,
>>> finv and
>>> probf for the F Distribution, etc. I find this approach consistent
>>> with
>>> numpy/scipy approach where the help on the function provide the
>>> what the
>>> functions does, the arguments and return values but does not
>>> provide any
>>> background of the distribution. For example, the SAS probf function
>>> 'Returns
>>> the probability from an F distribution':
>>> http://support.sas.com/onlinedoc/913/getDoc/en/lrdict.hlp/a000245930.htm
>>>
>>> A possible approach would be to have the following structure:
>>> 1) Generic documentation orientated to 'power users' that provides
>>> the basic
>>> documentation as included in distributions.py but ignores
>>> distribution-specific details. This tends to follow SAS's approach
>>> where the
>>> distribution is an argument to the generic function.
>>>
>>> 2) Distribution-specific documentation that adapts the generic
>>> documentation
>>> to the include the distribution-specific parameters like R does.
>>> The problem
>>> here for the current code is that each distribution must have it's
>>> own
>>> documentation.
>>>
>>> 3) Documentation on widely used functions that provide
>>> probabilities (and
>>> associated inverse functions) for widely used distributions like
>>> SAS does.
>>> (Okay these functions do not really exist in distribution.py but
>>> chisqprob,
>>> zprob and fprob exist in stats.py.)
>>>
>>> It may be possible to combine 1) and 2) by having each distribution
>>> extend
>>> the generic documentation to include the relevant distribution-
>>> specific
>>> parameters. But that may be too complex to create.
>>>
>>> I wish distributions were classes instead of class instances.
>>>
>>> Ah, don't get me started on that either...
>>>
>>>
>>> I have only briefly browsed the distribution code but I can see
>>> that number
>>> of distributions involved makes the original design cumbersome
>>> especially
>>> adding less common and more unusual distributions. ?So my 2 cents
>>> on this is
>>> that you both have the opportunity to propose a scipy PEP on the
>>> developers
>>> list to describe why it should be changed. That is also likely to
>>> influence
>>> the documentation.
>>>
>>> Regards
>>> Bruce
>>>
>>
>> Thanks, for the comparison and links. The two SAS pages are pretty
>> good and might give me some outside benchmark for the distribution
>> tests.
>>
>> just a few quick comments
>> I started the threads on the user list to see if I get more response
>> on new functions and distributions, although now it turned into a
>> developer discussion.
>>
>> I like in scipy ,and it looks this way also in SAS, that the
>> distributions are in one consistent structure and location. In R it
>> always took me some time to find a less common distribution, since
>> they are spread over several packages.
>>
>> To your points 1 and 2: I also think we should include more
>> distribution-specific information, the question is where to put it:
>> a) just expand current extradocs and keep it attached to
>> class/instance docstring
>> b) add automatically adjusted "extradocs" also to each method
>> c) add additional information to a location in the docs (as rst) but
>> not in the docstrings, incorporating if possible the pdf/lynx files by
>> Travis, and some nice graphs
>>
>> (
>> and a more generic scipy proposal:
>> d) add a "demo" feature to scipy, i.e. examples that are in the path
>> and can be run just by calling them, as in matlab and R
>> )
>>
>> I'm in favor of location a) and c) and don't think b) is necessary
>> because it would be mostly repetitive, but it would be feasible. Given
>> the work b) would require (writing additional distribution specific
>> docstrings) I would only expect it to be used for a few popular
>> distributions. The actual formulas (for pdf, cdf,...) I would prefer
>> to be in one place.
>>
>> your point 3): I'm not much in favor of aliases,
>> the short description for probf in SAS: "Returns the probability from
>> an F distribution"
>> is pretty uninformative, which probability, pdf, cdf, sf ?
>> In contrast, stats.f.cdf ?has all the information I need in its name,
>> and makes reading the code much more informative.
>>
>
> And now, for something completely different
>
>> SEP: turning distributions into usable classes.
>> The main short term fix, I thought about, is to add an __init__ method
>> to the individual distributions and add the instantiation information
>> in it. Then the classes could be instantiated and subclassed without
>> any cut and paste.
>
> Describe a bit more. I don't really see the need for subclassing a
> normal, for example. It's already doable right now (as Pearson III)
>
>
>> And different instances could maintain their state
>> instead of having it spill over if there is a mistake in the program.
>> However, when it looked liked that Per Brodtkorb and I were the only
>> ones actively working on the distributions code, I didn't want to risk
>> breaking existing code.
>
> That's why I don't think we should push too hard. The current
> implementation is a bit abstruse, yes, it's not obvious to implement
> new distributions but we can document how.
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From agile.aspect at gmail.com  Sat Aug  8 21:46:41 2009
From: agile.aspect at gmail.com (Agile)
Date: Sat, 8 Aug 2009 18:46:41 -0700 (PDT)
Subject: [SciPy-User] building scipy with
Message-ID: <f25e36ca-c14d-454e-ac9b-e62eb9e7b613@d36g2000prb.googlegroups.com>

Hi - I've built Python 2.5.4 with gcc 4.1.2 and with numpy 1.3.0 on 32
bit machine running
CentOS 5.3 using the following instructions for ATLAS officially
sanctioned by SciPy:

http://www.scipy.org/Installing_SciPy/Linux#head-40c26a5b93b9afc7e3241e1d7fd84fe9326402e7

That is, I'm using the DavidCournapeau method. This is the method
where one ends up having just
2 libraries installed in /usr/lib/atlas/sse2, namely

   libblas.so.3.0
   liblapack.so.3

Numpy works fine, and using Nose, it was tested with 2030 tests and
finished with the following
message

   [OK] (KNOWNFAIL=1)

I'm not really sure what this means, but it suggests that 1 out of
2030 tests failed - I'm going
to ignore the failure for now (since there was no clue as to which
tested failed anyway.)  Hence
I can safely say Numpy is working properly for at 2029 tests.

The problem is I can't even begin to build Scipy because it craps out
immediately claiming it
can't find the ATLAS libraries (and suggests setting BLAS_SRC and
LAPACK_SRC environment
variables for a non-optimized build.)

My question is, since it's recommend method of building ATLAS for
Scipy, what do I have to do
to make it work with Scipy?

Here's the current Numpy's site.cfg.

   [DEFAULT]
   library_dirs = /usr/devtools/lib:/usr/lib/atlas/sse2
   include_dirs = /usr/devtools/include:/usr/include
   [blas_opt]
   #libraries = f77blas, cblas, atlas
   libraries = blas
   [lapack_opt]
   #libraries = lapack, f77blas, cblas, atlas
   libraries = lapack
   [fftw]
   libraries = fftw3

Any help would greatly appreciated.

-- Agile



From paul at boehm.org  Sun Aug  9 01:30:55 2009
From: paul at boehm.org (=?UTF-8?Q?Paul_B=C3=B6hm?=)
Date: Sat, 8 Aug 2009 22:30:55 -0700
Subject: [SciPy-User] scipy in google wave (screenshot attached)
In-Reply-To: <faeee03b0908082230v2b24f6b2o6f25bb12d7b4103b@mail.gmail.com>
References: <faeee03b0908082230v2b24f6b2o6f25bb12d7b4103b@mail.gmail.com>
Message-ID: <faeee03b0908082230kf7ffde3tc1b297cb0bcc3876@mail.gmail.com>

http://dl.getdropbox.com/u/22117/scipy_in_google_wave.png

we just made a demo of scipy in google wave 8)
-> collaborative scipy

we'll blog about it soonish too (more info etc.)


From paul at boehm.org  Sun Aug  9 01:30:09 2009
From: paul at boehm.org (=?UTF-8?Q?Paul_B=C3=B6hm?=)
Date: Sat, 8 Aug 2009 22:30:09 -0700
Subject: [SciPy-User] scipy in google wave (screenshot attached)
Message-ID: <faeee03b0908082230v2b24f6b2o6f25bb12d7b4103b@mail.gmail.com>

http://dl.getdropbox.com/u/22117/scipy_in_google_wave.png

we just made a demo of scipy in google wave 8)
-> collaborative scipy

we'll blog about it soonish too (more info etc.)


From cohen at lpta.in2p3.fr  Sun Aug  9 03:24:33 2009
From: cohen at lpta.in2p3.fr (Johann Cohen-Tanugi)
Date: Sun, 09 Aug 2009 09:24:33 +0200
Subject: [SciPy-User] building scipy with
In-Reply-To: <f25e36ca-c14d-454e-ac9b-e62eb9e7b613@d36g2000prb.googlegroups.com>
References: <f25e36ca-c14d-454e-ac9b-e62eb9e7b613@d36g2000prb.googlegroups.com>
Message-ID: <4A7E79B1.7030507@lpta.in2p3.fr>

Hi Agile,
Did you modify the scipy site.cfg as well? You arementioning only the 
numpy site.cfg file below.....
best,
Johann

Agile wrote:
> Hi - I've built Python 2.5.4 with gcc 4.1.2 and with numpy 1.3.0 on 32
> bit machine running
> CentOS 5.3 using the following instructions for ATLAS officially
> sanctioned by SciPy:
>
> http://www.scipy.org/Installing_SciPy/Linux#head-40c26a5b93b9afc7e3241e1d7fd84fe9326402e7
>
> That is, I'm using the DavidCournapeau method. This is the method
> where one ends up having just
> 2 libraries installed in /usr/lib/atlas/sse2, namely
>
>    libblas.so.3.0
>    liblapack.so.3
>
> Numpy works fine, and using Nose, it was tested with 2030 tests and
> finished with the following
> message
>
>    [OK] (KNOWNFAIL=1)
>
> I'm not really sure what this means, but it suggests that 1 out of
> 2030 tests failed - I'm going
> to ignore the failure for now (since there was no clue as to which
> tested failed anyway.)  Hence
> I can safely say Numpy is working properly for at 2029 tests.
>
> The problem is I can't even begin to build Scipy because it craps out
> immediately claiming it
> can't find the ATLAS libraries (and suggests setting BLAS_SRC and
> LAPACK_SRC environment
> variables for a non-optimized build.)
>
> My question is, since it's recommend method of building ATLAS for
> Scipy, what do I have to do
> to make it work with Scipy?
>
> Here's the current Numpy's site.cfg.
>
>    [DEFAULT]
>    library_dirs = /usr/devtools/lib:/usr/lib/atlas/sse2
>    include_dirs = /usr/devtools/include:/usr/include
>    [blas_opt]
>    #libraries = f77blas, cblas, atlas
>    libraries = blas
>    [lapack_opt]
>    #libraries = lapack, f77blas, cblas, atlas
>    libraries = lapack
>    [fftw]
>    libraries = fftw3
>
> Any help would greatly appreciated.
>
> -- Agile
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>   


From fperez.net at gmail.com  Sun Aug  9 05:12:25 2009
From: fperez.net at gmail.com (Fernando Perez)
Date: Sun, 9 Aug 2009 02:12:25 -0700
Subject: [SciPy-User] Sanity checklist for those attending the SciPy'09
	tutorials
Message-ID: <db6b5ecc0908090212q52aecc4cq230cedd4d88ceb4d@mail.gmail.com>

Hi all,

[ sorry for spamming the list, but even though I sent this to all the
email addresses I have on file for tutorial attendees, I know I am
missing a few, so I hope they see this message. ]

In order to make your experience at the scipy tutorials as smooth as
possible, we strongly recommend that you take a little time to install
the necessary tools in advance.

For both introductory and advanced  tutorials:

http://conference.scipy.org/intro_tutorial
http://conference.scipy.org/advanced_tutorials

you will find instructions on what to install and where to download it
from.  In addition (this is also mentioned on those pages), we
encourage you to run according to your tutorial of choice, a little
checklist script:

https://cirl.berkeley.edu/fperez/tmp/intro_tut_checklist.py
https://cirl.berkeley.edu/fperez/tmp/adv_tut_checklist.py

This will try to  spot any problems early, and we'll do our best  to
help you with them before you arrive to the conference.

Best regards,

Dave Peterson and Fernando Perez.

ps - for those of you who may find fixes for the checklist scripts,
the sources are hosted on github:

http://github.com/fperez/scipytut/


From josef.pktd at gmail.com  Sun Aug  9 07:34:59 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sun, 9 Aug 2009 07:34:59 -0400
Subject: [SciPy-User] Sanity checklist for those attending the SciPy'09
	tutorials
In-Reply-To: <db6b5ecc0908090212q52aecc4cq230cedd4d88ceb4d@mail.gmail.com>
References: <db6b5ecc0908090212q52aecc4cq230cedd4d88ceb4d@mail.gmail.com>
Message-ID: <1cd32cbb0908090434m2fe57da1xbadc7e1657b19694@mail.gmail.com>

On Sun, Aug 9, 2009 at 5:12 AM, Fernando Perez<fperez.net at gmail.com> wrote:
> Hi all,
>
> [ sorry for spamming the list, but even though I sent this to all the
> email addresses I have on file for tutorial attendees, I know I am
> missing a few, so I hope they see this message. ]
>
> In order to make your experience at the scipy tutorials as smooth as
> possible, we strongly recommend that you take a little time to install
> the necessary tools in advance.
>
> For both introductory and advanced ?tutorials:
>
> http://conference.scipy.org/intro_tutorial
> http://conference.scipy.org/advanced_tutorials
>
> you will find instructions on what to install and where to download it
> from. ?In addition (this is also mentioned on those pages), we
> encourage you to run according to your tutorial of choice, a little
> checklist script:
>
> https://cirl.berkeley.edu/fperez/tmp/intro_tut_checklist.py
> https://cirl.berkeley.edu/fperez/tmp/adv_tut_checklist.py
>
> This will try to ?spot any problems early, and we'll do our best ?to
> help you with them before you arrive to the conference.
>
> Best regards,
>
> Dave Peterson and Fernando Perez.
>
> ps - for those of you who may find fixes for the checklist scripts,
> the sources are hosted on github:
>
> http://github.com/fperez/scipytut/
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>

is "Availability: recent flavors of Unix. "  required (python 2.5 help
for os.uname)

Josef

==================
System information
==================
os.name      : nt
os.uname     :
Traceback (most recent call last):
  File "C:\Josef\work-oth\adv_tut_checklist.py", line 317, in <module>
    main()
  File "C:\Josef\work-oth\adv_tut_checklist.py", line 312, in main
    sys_info()
  File "C:\Josef\work-oth\adv_tut_checklist.py", line 43, in sys_info
    print 'os.uname     :',os.uname()
AttributeError: 'module' object has no attribute 'uname'


From fperez.net at gmail.com  Sun Aug  9 13:46:49 2009
From: fperez.net at gmail.com (Fernando Perez)
Date: Sun, 9 Aug 2009 10:46:49 -0700
Subject: [SciPy-User] [Numpy-discussion] Sanity checklist for those
	attending the SciPy'09 tutorials
In-Reply-To: <1cd32cbb0908090434m2fe57da1xbadc7e1657b19694@mail.gmail.com>
References: <db6b5ecc0908090212q52aecc4cq230cedd4d88ceb4d@mail.gmail.com> 
	<1cd32cbb0908090434m2fe57da1xbadc7e1657b19694@mail.gmail.com>
Message-ID: <db6b5ecc0908091046l3bcf587dxcdaa6c6aad163114@mail.gmail.com>

On Sun, Aug 9, 2009 at 4:34 AM, <josef.pktd at gmail.com> wrote:
> is "Availability: recent flavors of Unix. " ?required (python 2.5 help
> for os.uname)
>

Thanks for the catch, sorry about that.  My unix-isms showing through...

Updated.

f


From jdh2358 at gmail.com  Sun Aug  9 14:56:07 2009
From: jdh2358 at gmail.com (John Hunter)
Date: Sun, 9 Aug 2009 13:56:07 -0500
Subject: [SciPy-User] scipy in google wave (screenshot attached)
In-Reply-To: <faeee03b0908082230v2b24f6b2o6f25bb12d7b4103b@mail.gmail.com>
References: <faeee03b0908082230v2b24f6b2o6f25bb12d7b4103b@mail.gmail.com>
Message-ID: <88e473830908091156k228600c3v46f7f9b589c8f03a@mail.gmail.com>

On Sun, Aug 9, 2009 at 12:30 AM, Paul B?hm<paul at boehm.org> wrote:
> http://dl.getdropbox.com/u/22117/scipy_in_google_wave.png
>
> we just made a demo of scipy in google wave 8)
> -> collaborative scipy

Interesting -- is the python code run the the wave to generate the
figure?  If so, how did you get around the problem that only pure
python code can run in the appengine, so presumably numpy and mpl
would not work?  Are you passing the python code to another server
which serves up the png?

Looks interesting!
JDH


From dav at alum.mit.edu  Sun Aug  9 20:33:06 2009
From: dav at alum.mit.edu (Dav Clark)
Date: Sun, 9 Aug 2009 17:33:06 -0700
Subject: [SciPy-User] Installation checklist for SciPy tutorials
In-Reply-To: <96de71860908090201h7f8989bcm6fd72d9f97647b45@mail.gmail.com>
References: <96de71860908090201h7f8989bcm6fd72d9f97647b45@mail.gmail.com>
Message-ID: <C6CA2D6B-4E9C-4EAD-8523-5894D25FD406@alum.mit.edu>

Hey Fernando (and Ondrej and...),

I ran the adv_tut... script in EPD 4.3.0, and during the sympy test,  
it drops into pdb.  I'm guessing this is because you're using the -- 
pdb option in the argv to nose.runmodule, and so when you raise an  
exception, you drop the user into pdb.  Have a look at the transcript  
below... I think you'll agree that this output is pretty cryptic!

I'm running EPD 4.3.0, which the advanced tutorial (by Ondrej Certik)  
says is OK for sympy (even though it's not the "required" sympy  
version - I'm guessing there are some downstream fixes to the "0.6.2"  
version in EPD?).  You could figure out it's EPD 4.3.0 by parsing  
sys.version in validate_sympy.

But I think the deeper issue is that you're throwing an exception that  
for some reason doesn't get caught by nose, but rather drops the user  
into pdb.  Other "exceptions" (such as the nt.assert_true in  
validate_cython) do get handled in a sensible way by nose.  Should all  
exceptions be nose assertions? Or is there some deeper reason for  
having some tests actually raise {\tt Exceptions}? On the flipside,  
removing the '--pdb' option makes the script do what you expect a unit- 
test script to do.

Anyway, I don't really know what I'm doing with nose at this point,  
but I figured removing the '--pdb' makes pretty good sense.  I also  
made the change to look for EPD 4.3.0 in validate_sympy.  Ondrej (or  
anyone else), please correct me if my understanding of the tutorial  
description is incorrect! Inline, I've also included my transcript  
from your original script on EPD 4.3.0 (OSX intel) below...

Cheers,
Dav

In [2]: run adv_tut_checklist.py
Running tests:
__main__.test_imports('setuptools', None) ... MOD: setuptools,  
version: 0.6c9-s1
ok
__main__.test_imports('IPython', None) ... MOD: IPython, version: 0.9.1
ok
__main__.test_imports('numpy', None) ... MOD: numpy, version: 1.3.0
ok
__main__.test_imports('scipy', None) ... MOD: scipy, version:  
0.8.0.dev5698
ok
__main__.test_imports('scipy.io', None) ... MOD: scipy.io, version:  
*no info*
ok
__main__.test_imports('matplotlib', <function validate_mpl at  
0x186bf30>) ... MOD: matplotlib, version: 0.98.5.2
ok
__main__.test_imports('pylab', None) ... MOD: pylab, version: *no info*
ok
__main__.test_imports('enthought.mayavi.api', None) ... MOD:  
enthought.mayavi.api, version: 3.2.0n2
ok
__main__.test_imports('scipy.weave', None) ... MOD: scipy.weave,  
version: 0.4.9
ok
__main__.test_imports('sympy', <function validate_sympy at  
0x186bfb0>) ... > /Users/dav/Desktop/tmp-testdata-Leycyo/ 
adv_tut_checklist.py(90)validate_sympy()
-> (version, min_version))
(Pdb)

[That's where the script stops executing!  You can get a bt or  
similar, but that's pretty opaque too.  Note the explicit lack of  
actual error message... I guess this is how you test if we are really  
"advanced"?]

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From dav at alum.mit.edu  Sun Aug  9 21:16:41 2009
From: dav at alum.mit.edu (Dav Clark)
Date: Sun, 9 Aug 2009 18:16:41 -0700
Subject: [SciPy-User] Installation checklist for SciPy tutorials
In-Reply-To: <C6CA2D6B-4E9C-4EAD-8523-5894D25FD406@alum.mit.edu>
References: <96de71860908090201h7f8989bcm6fd72d9f97647b45@mail.gmail.com>
	<C6CA2D6B-4E9C-4EAD-8523-5894D25FD406@alum.mit.edu>
Message-ID: <FCCA9C20-4D36-477C-BD42-C14D4A89E18A@alum.mit.edu>

I accidentally sent the wrong version.  This script has a 'return'  
instead of a 'pass' when checking sympy version - so it actually  
bypasses the version test if it's EPD . Sorry about that.

DC

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From ondrej at certik.cz  Sun Aug  9 21:31:48 2009
From: ondrej at certik.cz (Ondrej Certik)
Date: Sun, 9 Aug 2009 19:31:48 -0600
Subject: [SciPy-User] Installation checklist for SciPy tutorials
In-Reply-To: <C6CA2D6B-4E9C-4EAD-8523-5894D25FD406@alum.mit.edu>
References: <96de71860908090201h7f8989bcm6fd72d9f97647b45@mail.gmail.com>
	<C6CA2D6B-4E9C-4EAD-8523-5894D25FD406@alum.mit.edu>
Message-ID: <85b5c3130908091831n6fc820bg3bf1d1cbddcd6cd3@mail.gmail.com>

On Sun, Aug 9, 2009 at 6:33 PM, Dav Clark<dav at alum.mit.edu> wrote:
> Hey Fernando (and Ondrej and...),
>
> I ran the adv_tut... script in EPD 4.3.0, and during the sympy test, it
> drops into pdb. ?I'm guessing this is because you're using the --pdb option
> in the argv to nose.runmodule, and so when you raise an exception, you drop
> the user into pdb. ?Have a look at the transcript below... I think you'll
> agree that this output is pretty cryptic!
>
> I'm running EPD 4.3.0, which the advanced tutorial (by Ondrej Certik) says
> is OK for sympy (even though it's not the "required" sympy version - I'm
> guessing there are some downstream fixes to the "0.6.2" version in EPD?).

sympy 0.6.4 would be better, but the version in EPD is ok for most of
the tutorial too.

Ondrej


From arokem at berkeley.edu  Sun Aug  9 21:53:55 2009
From: arokem at berkeley.edu (Ariel Rokem)
Date: Sun, 9 Aug 2009 18:53:55 -0700
Subject: [SciPy-User] Installation checklist for SciPy tutorials
In-Reply-To: <C6CA2D6B-4E9C-4EAD-8523-5894D25FD406@alum.mit.edu>
References: <96de71860908090201h7f8989bcm6fd72d9f97647b45@mail.gmail.com> 
	<C6CA2D6B-4E9C-4EAD-8523-5894D25FD406@alum.mit.edu>
Message-ID: <43958ee60908091853u439b6df9xa58a8f5243ecd7b7@mail.gmail.com>

Hey Dav and all,

if you type 'exit' at the Pdb prompt, it drops you out of Pdb and also
produces the full traceback, which allows you to find the component missing.


Cheers,

Ariel

On Sun, Aug 9, 2009 at 5:33 PM, Dav Clark <dav at alum.mit.edu> wrote:

> Hey Fernando (and Ondrej and...),
>
> I ran the adv_tut... script in EPD 4.3.0, and during the sympy test, it
> drops into pdb.  I'm guessing this is because you're using the --pdb option
> in the argv to nose.runmodule, and so when you raise an exception, you drop
> the user into pdb.  Have a look at the transcript below... I think you'll
> agree that this output is pretty cryptic!
>
> I'm running EPD 4.3.0, which the advanced tutorial (by Ondrej Certik) says
> is OK for sympy (even though it's not the "required" sympy version - I'm
> guessing there are some downstream fixes to the "0.6.2" version in EPD?).
>  You could figure out it's EPD 4.3.0 by parsing sys.version in
> validate_sympy.
>
> But I think the deeper issue is that you're throwing an exception that for
> some reason doesn't get caught by nose, but rather drops the user into pdb.
>  Other "exceptions" (such as the nt.assert_true in validate_cython) do get
> handled in a sensible way by nose.  Should all exceptions be nose
> assertions? Or is there some deeper reason for having some tests actually
> raise {\tt Exceptions}? On the flipside, removing the '--pdb' option makes
> the script do what you expect a unit-test script to do.
>
> Anyway, I don't really know what I'm doing with nose at this point, but I
> figured removing the '--pdb' makes pretty good sense.  I also made the
> change to look for EPD 4.3.0 in validate_sympy.  Ondrej (or anyone else),
> please correct me if my understanding of the tutorial description is
> incorrect! Inline, I've also included my transcript from your original
> script on EPD 4.3.0 (OSX intel) below...
>
> Cheers,
> Dav
>
> In [2]: run adv_tut_checklist.py
> Running tests:
> __main__.test_imports('setuptools', None) ... MOD: setuptools, version:
> 0.6c9-s1
> ok
> __main__.test_imports('IPython', None) ... MOD: IPython, version: 0.9.1
> ok
> __main__.test_imports('numpy', None) ... MOD: numpy, version: 1.3.0
> ok
> __main__.test_imports('scipy', None) ... MOD: scipy, version: 0.8.0.dev5698
> ok
> __main__.test_imports('scipy.io', None) ... MOD: scipy.io, version: *no
> info*
> ok
> __main__.test_imports('matplotlib', <function validate_mpl at 0x186bf30>)
> ... MOD: matplotlib, version: 0.98.5.2
> ok
> __main__.test_imports('pylab', None) ... MOD: pylab, version: *no info*
> ok
> __main__.test_imports('enthought.mayavi.api', None) ... MOD:
> enthought.mayavi.api, version: 3.2.0n2
> ok
> __main__.test_imports('scipy.weave', None) ... MOD: scipy.weave, version:
> 0.4.9
> ok
> __main__.test_imports('sympy', <function validate_sympy at 0x186bfb0>) ...
> >
> /Users/dav/Desktop/tmp-testdata-Leycyo/adv_tut_checklist.py(90)validate_sympy()
> -> (version, min_version))
> (Pdb)
>
> [That's where the script stops executing!  You can get a bt or similar, but
> that's pretty opaque too.  Note the explicit lack of actual error message...
> I guess this is how you test if we are really "advanced"?]
>
>
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>


-- 
Ariel Rokem
Helen Wills Neuroscience Institute
University of California, Berkeley
http://argentum.ucbso.berkeley.edu/ariel
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From paul at boehm.org  Mon Aug 10 02:39:13 2009
From: paul at boehm.org (=?UTF-8?Q?Paul_B=C3=B6hm?=)
Date: Sun, 9 Aug 2009 23:39:13 -0700
Subject: [SciPy-User] scipy in google wave (screenshot attached)
Message-ID: <faeee03b0908092339o48f33caqda274783e503e105@mail.gmail.com>

>Interesting -- is the python code run the the wave to generate the
>figure?  If so, how did you get around the problem that only pure
>python code can run in the appengine, so presumably numpy and mpl
>would not work?  Are you passing the python code to another server
>which serves up the png?

we ported the waveapi to ec2 (or just any machine), and then wrote an
appengine app that bridges the data out to us :p

if someone was seriously interested in developing a collaborative
scipy notebook, i'd love to help get that started. i think Wave would
work fabulously as a UI to scipy.
(i don't think i'll be the one to do it though, since other projects
occupy my time usually)

paul


From jdh2358 at gmail.com  Mon Aug 10 07:20:30 2009
From: jdh2358 at gmail.com (John Hunter)
Date: Mon, 10 Aug 2009 06:20:30 -0500
Subject: [SciPy-User] scipy in google wave (screenshot attached)
In-Reply-To: <faeee03b0908092339o48f33caqda274783e503e105@mail.gmail.com>
References: <faeee03b0908092339o48f33caqda274783e503e105@mail.gmail.com>
Message-ID: <88e473830908100420r215eaf2dnb2b3965391ceabc9@mail.gmail.com>

On Mon, Aug 10, 2009 at 1:39 AM, Paul B?hm<paul at boehm.org> wrote:
>>Interesting -- is the python code run the the wave to generate the
>>figure? ?If so, how did you get around the problem that only pure
>>python code can run in the appengine, so presumably numpy and mpl
>>would not work? ?Are you passing the python code to another server
>>which serves up the png?
>
> we ported the waveapi to ec2 (or just any machine), and then wrote an
> appengine app that bridges the data out to us :p
>
> if someone was seriously interested in developing a collaborative
> scipy notebook, i'd love to help get that started. i think Wave would
> work fabulously as a UI to scipy.
> (i don't think i'll be the one to do it though, since other projects
> occupy my time usually)

I'm pretty sure lots of people are interested in it.  Fernando Perez
has been talking about it for a decade, Robert Kern has implemented
one.  Lots of us will be together next week at scipy and would be
interested in hacking on this.  In addition to a notebook, this would
be a perfect tool for some hybrid wiki, faq, discussion board, irc
client where people could post questions as waves, with some code,
others could jump in with answers and more code and figures, and the
question would naturally evolve into a FAQ that lives in a wiki like
context.  I think something like that as a general python for science
forum would be really useful.

I have registered for a wave account and setup an experimental
appengine -- if you could help get me up to speed on what you've been
doing, I'm  sure there would be some people at scipy who could help
move the ball forward.

So by porting the app engine onto your own server, you are able to run
extension code on your server, eg numpy, scipy, mpl, from your wave?
Do you have a custom robot to execute python code and insert mpl
figures?  You say you ported over to ec2, but the URL in your
screenshot says wave.google.com?

Anyway, thanks for sharing.  This looks very exciting.

For those of you who haven't seen the wave demo yet, watch

  http://wave.google.com/

and imagine what can be done in a wave as mailing list with a working
python interpreter that can generate inline figures.  It's over an
hour long, and well worth the time.

JDH


From bsouthey at gmail.com  Mon Aug 10 10:37:08 2009
From: bsouthey at gmail.com (Bruce Southey)
Date: Mon, 10 Aug 2009 09:37:08 -0500
Subject: [SciPy-User] Documenting distributions {was Re: adding
 distributions from hydroclimpy to stats.distributions}
In-Reply-To: <1cd32cbb0908080656q67aa6e09q516293a531b3f4f1@mail.gmail.com>
References: <1cd32cbb0908020641k4649315ahbd1a7ba986fd9ef9@mail.gmail.com>	<82156633-5678-4485-9ECA-FB33CB540691@gmail.com>	<1cd32cbb0908022120i160ca529gd7942e4a76acee73@mail.gmail.com>	<4A771263.1040209@gmail.com>	<1cd32cbb0908031107t28479cd9ucc34688385861277@mail.gmail.com>	<FA1591B7-7BD9-468A-9662-206811ECAA89@gmail.com>
	<1cd32cbb0908080656q67aa6e09q516293a531b3f4f1@mail.gmail.com>
Message-ID: <4A803094.5030706@gmail.com>

On 08/08/2009 08:56 AM, josef.pktd at gmail.com wrote:
> On Mon, Aug 3, 2009 at 2:45 PM, Pierre GM<pgmdevlist at gmail.com>  wrote:
>    
>> All,
>> I'm afraid we're hitting a wall here. No matter how smart we are, and
>> how convenient it is, an automatic documentation is *not* the panacea.
>> I don't think we should pursue in Josef's direction of overloading the
>> 'extradoc' parameter.
>>
>> I think we should on the contrary the docstring of a distribution
>> quite short, and move the details to an independent page in the docs.
>> Josef's idea of a demo is something I'd like to see in the doc, and
>> yes, we could implement a function to plot one or several graphs
>> automatically. But this should go on a documentation, not in a
>> docstring.
>>      
>
> I was looking for the best location to put additional doc information,
> that doesn't fit into the docstrings of the functions and classes.
> Currently the only additional location in scipy docs is the tutorial,
> but that is not a good location for (technical) reference material.
>
> I think the best location would be to add additional ReST files/trees
> on the top level of the subpackage (scipy.stats), similar to the
> toolbox documentation in matlab.
>
> For example for the distributions we could add a rst file Distribution
> Reference as in
>
> http://www.mathworks.com/access/helpdesk/help/toolbox/stats/index.html?/access/helpdesk/help/toolbox/stats/&http://www.mathworks.com/support/product/product.html?product=ST
>
> As structure, I would think that we need one rst file per distribution
> linked to from an index page..
> (It would also possible  to have one page for popular distributions or
> distribution with a lot of information, and a single page for other
> distributions where we have only the basic formulas from Travis'
> notes.)
>
> Adding this additional pages with background information would be
> useful for other areas in scipy as well.
>
>
> Josef
>
>
>    
Hi,

I agree.

There is an important requirement to provide more than just a 
summarization of functions as described in the doc string PEP 0257:
http://www.python.org/dev/peps/pep-0257/
"The docstring for a function or method should summarize its behavior 
and document its arguments, return value(s), side effects, exceptions 
raised, and restrictions on when it can be called (all if applicable). 
Optional arguments should be indicated. It should be documented whether 
keyword arguments are part of the interface."

Probably the place to do that is using a reference or technical 
subdirectory of the scipy doc directory and within that subdirectory 
have additional directories for different scipy components not just 
stats. It would also be a place to include additional documentation like 
the Cookbook.


Bruce


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From josef.pktd at gmail.com  Mon Aug 10 10:59:08 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Mon, 10 Aug 2009 10:59:08 -0400
Subject: [SciPy-User] How to average different pieces or an array?
In-Reply-To: <20090808094846.GA21264@phare.normalesup.org>
References: <142682e10908070844t32426100g85f6d022cd028209@mail.gmail.com>
	<20090807162049.GB11622@phare.normalesup.org>
	<20090807170436.GC11622@phare.normalesup.org>
	<1cd32cbb0908071020t6fb4b53ekd77722deed89ebbd@mail.gmail.com>
	<20090808094846.GA21264@phare.normalesup.org>
Message-ID: <1cd32cbb0908100759x5dd269ebg249b7acc87066a3e@mail.gmail.com>

On Sat, Aug 8, 2009 at 5:48 AM, Emmanuelle
Gouillart<emmanuelle.gouillart at normalesup.org> wrote:
> [..]
>
>> this might blow up because of the size of the intermediate arrays if
>> the number of bins is large (len of array by number of bins ?)
>
>> I think, Gilles answer might be the fastest, if the bins are given by
>> the indices.
>
>> If the bins are given as labels
>> e.g. [0,0,1,1,2,2,2,2,2,2]
>> then np.bincount or scipy.ndimage can be used to calculate the means,
>> which are much faster for a large number of bins and large arrays.
>
> Sure, the only advantage of my solution is that it satisfies the
> constraint "without any for loop" :D ... which is often a very silly
> constraint, but sometimes it's just fun looking for a solution without
> loops!
>
> Emmanuelle
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>

Here's a version without python loop, and with shape of intermediate
arrays same as x. In some primitive timing, this version is 2 to 50
times faster than the python loop of Boris (using xrange).

Josef


import numpy as np

x = np.array([1,3,2,6,7,4,5,4,9,4])
y = np.array([0,2,4,10])

#construct label index
ind2 = np.zeros(x.shape, int)
ind2[y[1:-1]] = 1   # assumes boundary indices are included in y
ind = ind2.cumsum()

means = np.bincount(ind,x)/np.bincount(ind)
meanarr = means[ind]
print meanarr


From mglerner at gmail.com  Mon Aug 10 16:38:39 2009
From: mglerner at gmail.com (Michael Lerner)
Date: Mon, 10 Aug 2009 16:38:39 -0400
Subject: [SciPy-User] Indexing question
Message-ID: <e4c539ed0908101338s5a4597cepddfff6415f25b23@mail.gmail.com>

Hi,

If I have a 1-dimensional array and I want to increment some values, I can
do this:

In [1]: a = array([0,0,0,0,0,0,0,0])

In [2]: b = array([1,2,4])

In [3]: a[b] += 1

In [4]: a
Out[4]: array([0, 1, 1, 0, 1, 0, 0, 0])

I actually have a 3-dimensional array and a list of cells within it that I'd
like to increment. I can't quite seem to get the syntax right, e.g.

In [5]: a = zeros([3,3,3])

In [6]: b = array([[0,0,0],[1,0,0]])

In [7]: a[b] += 1

In [8]: a
Out[8]:
array([[[ 1.,  1.,  1.],
        [ 1.,  1.,  1.],
        [ 1.,  1.,  1.]],

       [[ 1.,  1.,  1.],
        [ 1.,  1.,  1.],
        [ 1.,  1.,  1.]],

       [[ 0.,  0.,  0.],
        [ 0.,  0.,  0.],
        [ 0.,  0.,  0.]]])

whereas I'd like to end up with

In [10]: desireda
Out[10]:
array([[[ 1.,  0.,  0.],
        [ 0.,  0.,  0.],
        [ 0.,  0.,  0.]],

       [[ 1.,  0.,  0.],
        [ 0.,  0.,  0.],
        [ 0.,  0.,  0.]],

       [[ 0.,  0.,  0.],
        [ 0.,  0.,  0.],
        [ 0.,  0.,  0.]]])

I think speed matters too. In the worst case, I'll have an array with
dimensions (400,400,30) and a list of around a million indices that I'd like
to increment.

Thanks,

-Michael

-- 
Michael Lerner, Ph.D.
IRTA Postdoctoral Fellow
Laboratory of Computational Biology NIH/NHLBI
5635 Fishers Lane, Room T909, MSC 9314
Rockville, MD 20852 (UPS/FedEx/Reality)
Bethesda MD 20892-9314 (USPS)
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From hazelnusse at gmail.com  Mon Aug 10 17:28:05 2009
From: hazelnusse at gmail.com (Luke)
Date: Mon, 10 Aug 2009 14:28:05 -0700
Subject: [SciPy-User] vectorizing a function with non scalar arguments
Message-ID: <99214b470908101428r4c6023d0qf64d40ecf58db7a7@mail.gmail.com>

I have a function of the following form:
def f(q, qd, parameter_list):
    # Unpacking the parameters
    m, g, I11, I22, I33 = parameter_list
    # Unpacking the qdots
    q1, q2, q3, q4, q5, q6 = q
    q1p, q2p, q3p, q4p, q5p, q6p = qd
    ....Some calculations to determine u1,u2,u3,u4,u5,u6....
    return [u1, u2, u3, u4, u5, u6]

As is visible above, q and qdot need to be length 6 tuples/lists,
while parameter_list needs to be length 5.

I would like to vectorize this function so that I could pass it:
1)  q and qd as a 2-d numpy arrays of shape (n, 6)
2) parameter_list as a 1-d numpy array of shape (5,)

and it would return a 2-d numpy array of shape (n, 6)

parameter_list is a bunch of constants that don't change, while q and
qd are the things that are different between say q[n, :] and q[n+1,
:].

I realize that I can use a for loop to loop from 0 to n-1.

My question is whether there a way to use vectorize (or some other
decorator), to obtain the behavior?  Or is there another nice method
that people might be able to recommend?

The documentation for vectorize is very sparse and seems like it is
geared only towards function which have scalar arguments.

Thanks,
~Luke


From dav at alum.mit.edu  Mon Aug 10 18:50:24 2009
From: dav at alum.mit.edu (Dav Clark)
Date: Mon, 10 Aug 2009 15:50:24 -0700
Subject: [SciPy-User] Indexing question
In-Reply-To: <e4c539ed0908101338s5a4597cepddfff6415f25b23@mail.gmail.com>
References: <e4c539ed0908101338s5a4597cepddfff6415f25b23@mail.gmail.com>
Message-ID: <979B2C27-C30A-4A86-A3F4-954A3DD1E9BB@alum.mit.edu>

You should give help(nipy.docs.indexing) a read... there you will find  
that this is the way to do what you're trying to do:

a = zeros([3,3,3])
a[[0, 1], 0, 0] += 1

# Or, if you want to do more typing
b = [array([0, 1]), array([0, 0]), array([0, 0])]
a[b] += 1

Basically, the nth dimension of a should be indexed by the nth entry/ 
entries in a python sequence b.  In your code, you are passing in a  
single array, which gets interpreted as a (very fancy) index into the  
first dimension of a, so you get the 0th and 1st slab of your data  
(the former repeated many times).  Your b is equivalent to  
array([0,1]) for assignment (though not for reference - it gives a  
different shape).  This actually points out an obscure feature, which  
is that even if you have entries that are doubly (or n-ably)  
referenced in your indexing, each element in the lvalue will only get  
operated on once.  Thus, while I might have expected the following to  
yield a = [2, 0], it actually yields a = [1, 0]:

a = array([0, 0])
a[a] += 1 # a[0] incremented twice?  No!

In other words, we don't need to worry about double-counting in our  
lvalues, which I guess is OK.  It'd be a bit more intuitive for _me_  
the other way 'round.  Any logic behind that, or perhaps just  
implementational ease?

As an additional idea... you could get numpy to tell you how it would  
like to refer to a set of indices by using the where function, as in:

where(desireda)

Final tip - if you already have code constructing your 'b', you could  
coerce it to a list with a[list(b)]

Cheers,
Dav


On Aug 10, 2009, at 1:38 PM, Michael Lerner wrote:

> Hi,
>
> If I have a 1-dimensional array and I want to increment some values,  
> I can do this:
>
> In [1]: a = array([0,0,0,0,0,0,0,0])
>
> In [2]: b = array([1,2,4])
>
> In [3]: a[b] += 1
>
> In [4]: a
> Out[4]: array([0, 1, 1, 0, 1, 0, 0, 0])
>
> I actually have a 3-dimensional array and a list of cells within it  
> that I'd like to increment. I can't quite seem to get the syntax  
> right, e.g.
>
> In [5]: a = zeros([3,3,3])
>
> In [6]: b = array([[0,0,0],[1,0,0]])
>
> In [7]: a[b] += 1
>
> In [8]: a
> Out[8]:
> array([[[ 1.,  1.,  1.],
>         [ 1.,  1.,  1.],
>         [ 1.,  1.,  1.]],
>
>        [[ 1.,  1.,  1.],
>         [ 1.,  1.,  1.],
>         [ 1.,  1.,  1.]],
>
>        [[ 0.,  0.,  0.],
>         [ 0.,  0.,  0.],
>         [ 0.,  0.,  0.]]])
>
> whereas I'd like to end up with
>
> In [10]: desireda
> Out[10]:
> array([[[ 1.,  0.,  0.],
>         [ 0.,  0.,  0.],
>         [ 0.,  0.,  0.]],
>
>        [[ 1.,  0.,  0.],
>         [ 0.,  0.,  0.],
>         [ 0.,  0.,  0.]],
>
>        [[ 0.,  0.,  0.],
>         [ 0.,  0.,  0.],
>         [ 0.,  0.,  0.]]])
>
> I think speed matters too. In the worst case, I'll have an array  
> with dimensions (400,400,30) and a list of around a million indices  
> that I'd like to increment.
>
> Thanks,
>
> -Michael
>
> -- 
> Michael Lerner, Ph.D.
> IRTA Postdoctoral Fellow
> Laboratory of Computational Biology NIH/NHLBI
> 5635 Fishers Lane, Room T909, MSC 9314
> Rockville, MD 20852 (UPS/FedEx/Reality)
> Bethesda MD 20892-9314 (USPS)
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user



From dwf at cs.toronto.edu  Mon Aug 10 19:01:37 2009
From: dwf at cs.toronto.edu (David Warde-Farley)
Date: Mon, 10 Aug 2009 19:01:37 -0400
Subject: [SciPy-User] vectorizing a function with non scalar arguments
In-Reply-To: <99214b470908101428r4c6023d0qf64d40ecf58db7a7@mail.gmail.com>
References: <99214b470908101428r4c6023d0qf64d40ecf58db7a7@mail.gmail.com>
Message-ID: <BBA93F0C-4413-486F-99D9-48DC3632585B@cs.toronto.edu>

On 10-Aug-09, at 5:28 PM, Luke wrote:

> I have a function of the following form:
> def f(q, qd, parameter_list):
>    # Unpacking the parameters
>    m, g, I11, I22, I33 = parameter_list
>    # Unpacking the qdots
>    q1, q2, q3, q4, q5, q6 = q
>    q1p, q2p, q3p, q4p, q5p, q6p = qd
>    ....Some calculations to determine u1,u2,u3,u4,u5,u6....
>    return [u1, u2, u3, u4, u5, u6]

We'd really need to see "...Some calculations..." but if it's  
something that's expressible as simple arithmetic/calls to numpy  
ufuncs, then it's probable that you can pull this off.

> I would like to vectorize this function so that I could pass it:
> 1)  q and qd as a 2-d numpy arrays of shape (n, 6)
> 2) parameter_list as a 1-d numpy array of shape (5,)

hsplit() and hstack() to unpack/pack the 2D arrays, respectively  
(though there are more memory efficient/cache friendly ways, if you  
care about speed).

> and it would return a 2-d numpy array of shape (n, 6)
>
> parameter_list is a bunch of constants that don't change, while q and
> qd are the things that are different between say q[n, :] and q[n+1,
> :].

> I realize that I can use a for loop to loop from 0 to n-1.
>
> My question is whether there a way to use vectorize (or some other
> decorator), to obtain the behavior?  Or is there another nice method
> that people might be able to recommend?

Again, if you split into 1d arrays and are just doing arithmetic on  
them (or you use numpy functions that can take scalar or vector  
arguments) then it should "vectorize itself". But it might be helpful  
if you have any helper functions of one scalar variable that you need  
to evaluate across an entire array of q1's, etc.

> The documentation for vectorize is very sparse and seems like it is
> geared only towards function which have scalar arguments.

Yes, 'vectorize' won't do it for you in this case.

David


From josef.pktd at gmail.com  Mon Aug 10 19:05:17 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Mon, 10 Aug 2009 19:05:17 -0400
Subject: [SciPy-User] Indexing question
In-Reply-To: <979B2C27-C30A-4A86-A3F4-954A3DD1E9BB@alum.mit.edu>
References: <e4c539ed0908101338s5a4597cepddfff6415f25b23@mail.gmail.com>
	<979B2C27-C30A-4A86-A3F4-954A3DD1E9BB@alum.mit.edu>
Message-ID: <1cd32cbb0908101605ue84d9a8j81143e62f64b1679@mail.gmail.com>

On Mon, Aug 10, 2009 at 6:50 PM, Dav Clark<dav at alum.mit.edu> wrote:
> You should give help(nipy.docs.indexing) a read... there you will find
> that this is the way to do what you're trying to do:
>
> a = zeros([3,3,3])
> a[[0, 1], 0, 0] += 1
>
> # Or, if you want to do more typing
> b = [array([0, 1]), array([0, 0]), array([0, 0])]
> a[b] += 1
>
> Basically, the nth dimension of a should be indexed by the nth entry/
> entries in a python sequence b. ?In your code, you are passing in a
> single array, which gets interpreted as a (very fancy) index into the
> first dimension of a, so you get the 0th and 1st slab of your data
> (the former repeated many times). ?Your b is equivalent to
> array([0,1]) for assignment (though not for reference - it gives a
> different shape). ?This actually points out an obscure feature, which
> is that even if you have entries that are doubly (or n-ably)
> referenced in your indexing, each element in the lvalue will only get
> operated on once. ?Thus, while I might have expected the following to
> yield a = [2, 0], it actually yields a = [1, 0]:
>
> a = array([0, 0])
> a[a] += 1 # a[0] incremented twice? ?No!
>
> In other words, we don't need to worry about double-counting in our
> lvalues, which I guess is OK. ?It'd be a bit more intuitive for _me_
> the other way 'round. ?Any logic behind that, or perhaps just
> implementational ease?
>
> As an additional idea... you could get numpy to tell you how it would
> like to refer to a set of indices by using the where function, as in:
>
> where(desireda)
>
> Final tip - if you already have code constructing your 'b', you could
> coerce it to a list with a[list(b)]
>
> Cheers,
> Dav
>
>
> On Aug 10, 2009, at 1:38 PM, Michael Lerner wrote:
>
>> Hi,
>>
>> If I have a 1-dimensional array and I want to increment some values,
>> I can do this:
>>
>> In [1]: a = array([0,0,0,0,0,0,0,0])
>>
>> In [2]: b = array([1,2,4])
>>
>> In [3]: a[b] += 1
>>
>> In [4]: a
>> Out[4]: array([0, 1, 1, 0, 1, 0, 0, 0])
>>
>> I actually have a 3-dimensional array and a list of cells within it
>> that I'd like to increment. I can't quite seem to get the syntax
>> right, e.g.
>>
>> In [5]: a = zeros([3,3,3])
>>
>> In [6]: b = array([[0,0,0],[1,0,0]])
>>
>> In [7]: a[b] += 1
>>
>> In [8]: a
>> Out[8]:
>> array([[[ 1., ?1., ?1.],
>> ? ? ? ? [ 1., ?1., ?1.],
>> ? ? ? ? [ 1., ?1., ?1.]],
>>
>> ? ? ? ?[[ 1., ?1., ?1.],
>> ? ? ? ? [ 1., ?1., ?1.],
>> ? ? ? ? [ 1., ?1., ?1.]],
>>
>> ? ? ? ?[[ 0., ?0., ?0.],
>> ? ? ? ? [ 0., ?0., ?0.],
>> ? ? ? ? [ 0., ?0., ?0.]]])
>>
>> whereas I'd like to end up with
>>
>> In [10]: desireda
>> Out[10]:
>> array([[[ 1., ?0., ?0.],
>> ? ? ? ? [ 0., ?0., ?0.],
>> ? ? ? ? [ 0., ?0., ?0.]],
>>
>> ? ? ? ?[[ 1., ?0., ?0.],
>> ? ? ? ? [ 0., ?0., ?0.],
>> ? ? ? ? [ 0., ?0., ?0.]],
>>
>> ? ? ? ?[[ 0., ?0., ?0.],
>> ? ? ? ? [ 0., ?0., ?0.],
>> ? ? ? ? [ 0., ?0., ?0.]]])
>>
>> I think speed matters too. In the worst case, I'll have an array
>> with dimensions (400,400,30) and a list of around a million indices
>> that I'd like to increment.
>>
>> Thanks,
>>
>> -Michael

and an example as illustration

Josef

>>> a = np.zeros([3,3,3])
>>> b = np.array([[0,0,0],[1,0,0]])
>>> a[b[:,0],b[:,1],b[:,2]] += 1
>>> a
array([[[ 1.,  0.,  0.],
        [ 0.,  0.,  0.],
        [ 0.,  0.,  0.]],

       [[ 1.,  0.,  0.],
        [ 0.,  0.,  0.],
        [ 0.,  0.,  0.]],

       [[ 0.,  0.,  0.],
        [ 0.,  0.,  0.],
        [ 0.,  0.,  0.]]])
>>> bz = zip([0,0,0],[1,0,0])
>>> a[bz] += 1
>>> a
array([[[ 2.,  0.,  0.],
        [ 0.,  0.,  0.],
        [ 0.,  0.,  0.]],

       [[ 2.,  0.,  0.],
        [ 0.,  0.,  0.],
        [ 0.,  0.,  0.]],

       [[ 0.,  0.,  0.],
        [ 0.,  0.,  0.],
        [ 0.,  0.,  0.]]])

>>
>> --
>> Michael Lerner, Ph.D.
>> IRTA Postdoctoral Fellow
>> Laboratory of Computational Biology NIH/NHLBI
>> 5635 Fishers Lane, Room T909, MSC 9314
>> Rockville, MD 20852 (UPS/FedEx/Reality)
>> Bethesda MD 20892-9314 (USPS)
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From washakie at gmail.com  Mon Aug 10 20:07:24 2009
From: washakie at gmail.com (John [H2O])
Date: Mon, 10 Aug 2009 17:07:24 -0700 (PDT)
Subject: [SciPy-User] [SciPy-user] 2d interpolation, non-regular lat/lon grid
Message-ID: <24909685.post@talk.nabble.com>


Hello,

I have a data set of x,y,z values. The y values are regularly spaced (half
degree latitude), the x values are irregular (10 degrees north of 70, 2
degrees between 60 and 70N, and half degree from 50 to 70N.). The extents of
my grid is 50-90N, and -180,180 (W-E)

I am trying to resample this to a .5 degree regular grid, but I haven't had
any success thus far. Here is my code:
NOTE:
    y is a vector of latitudes at 0.5 degree from 50-90N (containing
repeated values)
    x is a vector with lon values for every y, not regular. (containing
repeated values)
    z is a vector of z values for each x,y pair

SO:
    #Create half degree lat,lon grids
    lons = np.arange(x.min(),x.max(),0.5,'f')
    lats = np.arange(y.min(),y.max(),0.5,'f')

    # create interpolator
    z_interp = interp2d(x,y,z)
    Z = z_interp(lons,lats)

But when I plot my Z it looks strange, and does not line up with my expected
values. Does anyone know another better approach?



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From mglerner at gmail.com  Mon Aug 10 20:15:13 2009
From: mglerner at gmail.com (Michael Lerner)
Date: Mon, 10 Aug 2009 20:15:13 -0400
Subject: [SciPy-User] Indexing question
In-Reply-To: <1cd32cbb0908101605ue84d9a8j81143e62f64b1679@mail.gmail.com>
References: <e4c539ed0908101338s5a4597cepddfff6415f25b23@mail.gmail.com>
	<979B2C27-C30A-4A86-A3F4-954A3DD1E9BB@alum.mit.edu>
	<1cd32cbb0908101605ue84d9a8j81143e62f64b1679@mail.gmail.com>
Message-ID: <e4c539ed0908101715x46b536cfo98d4d080eee21b39@mail.gmail.com>

Dav and Josef-

Thanks!

I had looked at the indexing docs, but I apparently missed the relevant
section for multidimensional indexing. I see it now.

FWIW, your explanation, coupled with the example, is crystal clear.

I was also surprised by the double-counting oddity that you mention. I had
to rephrase my problem a bit in order to deal with it.

Thanks again,

-michael

On Mon, Aug 10, 2009 at 7:05 PM, <josef.pktd at gmail.com> wrote:

> On Mon, Aug 10, 2009 at 6:50 PM, Dav Clark<dav at alum.mit.edu> wrote:
> > You should give help(nipy.docs.indexing) a read... there you will find
> > that this is the way to do what you're trying to do:
> >
> > a = zeros([3,3,3])
> > a[[0, 1], 0, 0] += 1
> >
> > # Or, if you want to do more typing
> > b = [array([0, 1]), array([0, 0]), array([0, 0])]
> > a[b] += 1
> >
> > Basically, the nth dimension of a should be indexed by the nth entry/
> > entries in a python sequence b.  In your code, you are passing in a
> > single array, which gets interpreted as a (very fancy) index into the
> > first dimension of a, so you get the 0th and 1st slab of your data
> > (the former repeated many times).  Your b is equivalent to
> > array([0,1]) for assignment (though not for reference - it gives a
> > different shape).  This actually points out an obscure feature, which
> > is that even if you have entries that are doubly (or n-ably)
> > referenced in your indexing, each element in the lvalue will only get
> > operated on once.  Thus, while I might have expected the following to
> > yield a = [2, 0], it actually yields a = [1, 0]:
> >
> > a = array([0, 0])
> > a[a] += 1 # a[0] incremented twice?  No!
> >
> > In other words, we don't need to worry about double-counting in our
> > lvalues, which I guess is OK.  It'd be a bit more intuitive for _me_
> > the other way 'round.  Any logic behind that, or perhaps just
> > implementational ease?
> >
> > As an additional idea... you could get numpy to tell you how it would
> > like to refer to a set of indices by using the where function, as in:
> >
> > where(desireda)
> >
> > Final tip - if you already have code constructing your 'b', you could
> > coerce it to a list with a[list(b)]
> >
> > Cheers,
> > Dav
> >
> >
> > On Aug 10, 2009, at 1:38 PM, Michael Lerner wrote:
> >
> >> Hi,
> >>
> >> If I have a 1-dimensional array and I want to increment some values,
> >> I can do this:
> >>
> >> In [1]: a = array([0,0,0,0,0,0,0,0])
> >>
> >> In [2]: b = array([1,2,4])
> >>
> >> In [3]: a[b] += 1
> >>
> >> In [4]: a
> >> Out[4]: array([0, 1, 1, 0, 1, 0, 0, 0])
> >>
> >> I actually have a 3-dimensional array and a list of cells within it
> >> that I'd like to increment. I can't quite seem to get the syntax
> >> right, e.g.
> >>
> >> In [5]: a = zeros([3,3,3])
> >>
> >> In [6]: b = array([[0,0,0],[1,0,0]])
> >>
> >> In [7]: a[b] += 1
> >>
> >> In [8]: a
> >> Out[8]:
> >> array([[[ 1.,  1.,  1.],
> >>         [ 1.,  1.,  1.],
> >>         [ 1.,  1.,  1.]],
> >>
> >>        [[ 1.,  1.,  1.],
> >>         [ 1.,  1.,  1.],
> >>         [ 1.,  1.,  1.]],
> >>
> >>        [[ 0.,  0.,  0.],
> >>         [ 0.,  0.,  0.],
> >>         [ 0.,  0.,  0.]]])
> >>
> >> whereas I'd like to end up with
> >>
> >> In [10]: desireda
> >> Out[10]:
> >> array([[[ 1.,  0.,  0.],
> >>         [ 0.,  0.,  0.],
> >>         [ 0.,  0.,  0.]],
> >>
> >>        [[ 1.,  0.,  0.],
> >>         [ 0.,  0.,  0.],
> >>         [ 0.,  0.,  0.]],
> >>
> >>        [[ 0.,  0.,  0.],
> >>         [ 0.,  0.,  0.],
> >>         [ 0.,  0.,  0.]]])
> >>
> >> I think speed matters too. In the worst case, I'll have an array
> >> with dimensions (400,400,30) and a list of around a million indices
> >> that I'd like to increment.
> >>
> >> Thanks,
> >>
> >> -Michael
>
> and an example as illustration
>
> Josef
>
> >>> a = np.zeros([3,3,3])
> >>> b = np.array([[0,0,0],[1,0,0]])
> >>> a[b[:,0],b[:,1],b[:,2]] += 1
> >>> a
> array([[[ 1.,  0.,  0.],
>        [ 0.,  0.,  0.],
>        [ 0.,  0.,  0.]],
>
>       [[ 1.,  0.,  0.],
>        [ 0.,  0.,  0.],
>        [ 0.,  0.,  0.]],
>
>       [[ 0.,  0.,  0.],
>        [ 0.,  0.,  0.],
>        [ 0.,  0.,  0.]]])
> >>> bz = zip([0,0,0],[1,0,0])
> >>> a[bz] += 1
> >>> a
> array([[[ 2.,  0.,  0.],
>        [ 0.,  0.,  0.],
>         [ 0.,  0.,  0.]],
>
>       [[ 2.,  0.,  0.],
>        [ 0.,  0.,  0.],
>        [ 0.,  0.,  0.]],
>
>       [[ 0.,  0.,  0.],
>        [ 0.,  0.,  0.],
>        [ 0.,  0.,  0.]]])
>
> >>
> >> --
> >> Michael Lerner, Ph.D.
> >> IRTA Postdoctoral Fellow
> >> Laboratory of Computational Biology NIH/NHLBI
> >> 5635 Fishers Lane, Room T909, MSC 9314
> >> Rockville, MD 20852 (UPS/FedEx/Reality)
> >> Bethesda MD 20892-9314 (USPS)
> >> _______________________________________________
> >> SciPy-User mailing list
> >> SciPy-User at scipy.org
> >> http://mail.scipy.org/mailman/listinfo/scipy-user
> >
> > _______________________________________________
> > SciPy-User mailing list
> > SciPy-User at scipy.org
> > http://mail.scipy.org/mailman/listinfo/scipy-user
> >
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>



-- 
Michael Lerner, Ph.D.
IRTA Postdoctoral Fellow
Laboratory of Computational Biology NIH/NHLBI
5635 Fishers Lane, Room T909, MSC 9314
Rockville, MD 20852 (UPS/FedEx/Reality)
Bethesda MD 20892-9314 (USPS)
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From washakie at gmail.com  Tue Aug 11 09:09:40 2009
From: washakie at gmail.com (John [H2O])
Date: Tue, 11 Aug 2009 06:09:40 -0700 (PDT)
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
	non-regular lat/lon grid
In-Reply-To: <24909685.post@talk.nabble.com>
References: <24909685.post@talk.nabble.com>
Message-ID: <24917338.post@talk.nabble.com>


A picture is worth a thousand words...

I've posted a zoom showing the plotted raw data m.plot(x,y,color=z) and
behind it the interpolated data...  the second image is just the raw data.
Note the red dots over the blue fill from the interpolation... this is
because data is spaced only every 10 degrees lon at the pole, and the
underlying grid is 0.5 degree resolution.

http://www.picupine.com/f3c7b1fx-1

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From washakie at gmail.com  Tue Aug 11 09:54:33 2009
From: washakie at gmail.com (John [H2O])
Date: Tue, 11 Aug 2009 06:54:33 -0700 (PDT)
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
 non-regular lat/lon grid - help with delauney/natgrid??
In-Reply-To: <24909685.post@talk.nabble.com>
References: <24909685.post@talk.nabble.com>
Message-ID: <24918109.post@talk.nabble.com>


Answering my own question, but seeking comments.

Apparently matplotlib.mlab.griddata does exactly what I need.

However, I would be interested in know more about how to use this, and to
exert a little tighter control over it. One issue, as can be seen below, is
that there are clearly artifacts. I would like to create a masking array,
based on my raw data array, but as it has a highly irregular shape, this
doesn't seem trivial. Suggestions?

http://www.nabble.com/file/p24918109/fyi.png 



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From robert.kern at gmail.com  Tue Aug 11 13:58:31 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Tue, 11 Aug 2009 12:58:31 -0500
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
	non-regular lat/lon 	grid - help with delauney/natgrid??
In-Reply-To: <24918109.post@talk.nabble.com>
References: <24909685.post@talk.nabble.com> <24918109.post@talk.nabble.com>
Message-ID: <3d375d730908111058m2c0fc5daw16fe9add8936d4ec@mail.gmail.com>

On Tue, Aug 11, 2009 at 08:54, John [H2O]<washakie at gmail.com> wrote:
>
> Answering my own question, but seeking comments.
>
> Apparently matplotlib.mlab.griddata does exactly what I need.
>
> However, I would be interested in know more about how to use this, and to
> exert a little tighter control over it. One issue, as can be seen below, is
> that there are clearly artifacts. I would like to create a masking array,
> based on my raw data array, but as it has a highly irregular shape, this
> doesn't seem trivial. Suggestions?

Are you using lat/lon as X/Y for griddata? Or are you projecting it
first? You should project. Not seeing your code or data, I'm not sure
I can diagnose what is going wrong with the interpolation.

-- 
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


From jkington at wisc.edu  Tue Aug 11 15:11:31 2009
From: jkington at wisc.edu (Joe Kington)
Date: Tue, 11 Aug 2009 14:11:31 -0500
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
	non-regular lat/lon 	grid - help with delauney/natgrid??
In-Reply-To: <24918109.post@talk.nabble.com>
References: <24909685.post@talk.nabble.com> <24918109.post@talk.nabble.com>
Message-ID: <f6951adf0908111211w1bc145aarc00dd8312802a2af@mail.gmail.com>

It looks like it's definitely projected before interpolation.  You'll need
more control that mlab.griddata can give you to make a grid with fewer
artifacts

If you want more control over interpolation, you're going to need to look
into more flexible methods than spline (which is what I think griddata
uses).

Interpolation is 75% art and 25% science.  Basically, if you know roughly
what your interpolated data should look like, you can make it look like
that, without many artifacts, but it's going to take some work.  No one
interpolation method works best everywhere (spline is a good first choice
for smooth data, though, which is why it's used so much).

Actually, a lot of the artifacts can be reduced by fine tuning the search
radius and shape.  Unfortunately, I don't think anything currently in scipy
gives you the ability to do this (radius, maybe, but not shape... If I'm
wrong, please let me know!).  You can do a lot with spline if you can fine
tune the search neighborhood, without having to go to more complex methods.

The various kriging methods are the most flexible interpolation methods, but
also the most complex and slowest.  However, if you're willing to put in the
time you can do a really nice job.

If you want to look into it, read up on geostatistics.  A fairly nice open
source geostats program is SGeMS.  It does support python scripting, though
I've never done much from that side of it.  I've also never been able to get
it to build on linux, but the windows executable works perfectly in wine.
If you don't want to take that route, I've got several python kriging
routines written (with weave, etc, so they're reasonably fast).  I can send
them your way if you like. Unfortunately they're in pretty rough shape, so
it's probably best to use something more refined (and less brittle)...

Sorry, that wasn't at all what you were asking, but if you do want to try
more flexible methods, it will probably pay off.

Anyway, as far as masking your data goes, the simplest thing to do would be
to mask any interpolated points more than x distance away from a data
point.  That shouldn't be too hard... (Though I can't think of how to do it
in just a couple of lines...)  Something along these lines should work, but
I haven't actually tested it...

# Input: dataX, dataY, gridX, gridY, grid, maxDist
from scipy import spatial

# Assumes dataX and dataY are row vectors
dataXY = np.vstack((dataX, dataY)).T
dataQuadtree = spatial.KDTree(dataXY)

xx,yy = np.meshgrid(gridX, gridY)
xx,yy = xx.flatten, yy.flatten
gridPoints = np.vstack((xx,yy)).T

dists, indexes = dataQuadtree.query(gridPoints, k=1,
distance_upper_bound=maxDist)
mask = dists < maxDist
mask = mask.reshape(grid.shape)
grid = np.ma.masked_array(grid, mask)

(That may not run, I'm probably doing something stupid somewhere, but it's
the general idea, anyway...)

Hope that helps,
-Joe

On Tue, Aug 11, 2009 at 8:54 AM, John [H2O] <washakie at gmail.com> wrote:

>
> Answering my own question, but seeking comments.
>
> Apparently matplotlib.mlab.griddata does exactly what I need.
>
> However, I would be interested in know more about how to use this, and to
> exert a little tighter control over it. One issue, as can be seen below, is
> that there are clearly artifacts. I would like to create a masking array,
> based on my raw data array, but as it has a highly irregular shape, this
> doesn't seem trivial. Suggestions?
>
> http://www.nabble.com/file/p24918109/fyi.png
>
>
>
> --
> View this message in context:
> http://www.nabble.com/2d-interpolation%2C-non-regular-lat-lon-grid-tp24909685p24918109.html
> Sent from the Scipy-User mailing list archive at Nabble.com.
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From jkington at wisc.edu  Tue Aug 11 16:16:25 2009
From: jkington at wisc.edu (Joe Kington)
Date: Tue, 11 Aug 2009 15:16:25 -0500
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
	non-regular lat/lon 	grid - help with delauney/natgrid??
In-Reply-To: <f6951adf0908111211w1bc145aarc00dd8312802a2af@mail.gmail.com>
References: <24909685.post@talk.nabble.com> <24918109.post@talk.nabble.com>
	<f6951adf0908111211w1bc145aarc00dd8312802a2af@mail.gmail.com>
Message-ID: <f6951adf0908111316r30b529f6xcd123e78e3e2af5@mail.gmail.com>

Woops, nevermind, I just looked at your other post.  I suppose I should
listen to Mr. Kern before speaking up! :) I guess you're not projecting
before interpolation?  You'll definitely need to do that in order to avoid
the "dot" problems you mentioned there.   Projecting your data into a polar
stereographic (or similar) projection before gridding (and basing the grid
on the projected coordinates) should fix most of your problems.

I had assumed you were talking about the spline gridding artifacts present
away from data points in the figure you showed in this post.  Those are much
harder to avoid.

On Tue, Aug 11, 2009 at 2:11 PM, Joe Kington <jkington at wisc.edu> wrote:

> It looks like it's definitely projected before interpolation.  You'll need
> more control that mlab.griddata can give you to make a grid with fewer
> artifacts
>
> If you want more control over interpolation, you're going to need to look
> into more flexible methods than spline (which is what I think griddata
> uses).
>
> Interpolation is 75% art and 25% science.  Basically, if you know roughly
> what your interpolated data should look like, you can make it look like
> that, without many artifacts, but it's going to take some work.  No one
> interpolation method works best everywhere (spline is a good first choice
> for smooth data, though, which is why it's used so much).
>
> Actually, a lot of the artifacts can be reduced by fine tuning the search
> radius and shape.  Unfortunately, I don't think anything currently in scipy
> gives you the ability to do this (radius, maybe, but not shape... If I'm
> wrong, please let me know!).  You can do a lot with spline if you can fine
> tune the search neighborhood, without having to go to more complex methods.
>
> The various kriging methods are the most flexible interpolation methods,
> but also the most complex and slowest.  However, if you're willing to put in
> the time you can do a really nice job.
>
> If you want to look into it, read up on geostatistics.  A fairly nice open
> source geostats program is SGeMS.  It does support python scripting, though
> I've never done much from that side of it.  I've also never been able to get
> it to build on linux, but the windows executable works perfectly in wine.
> If you don't want to take that route, I've got several python kriging
> routines written (with weave, etc, so they're reasonably fast).  I can send
> them your way if you like. Unfortunately they're in pretty rough shape, so
> it's probably best to use something more refined (and less brittle)...
>
> Sorry, that wasn't at all what you were asking, but if you do want to try
> more flexible methods, it will probably pay off.
>
> Anyway, as far as masking your data goes, the simplest thing to do would be
> to mask any interpolated points more than x distance away from a data
> point.  That shouldn't be too hard... (Though I can't think of how to do it
> in just a couple of lines...)  Something along these lines should work, but
> I haven't actually tested it...
>
> # Input: dataX, dataY, gridX, gridY, grid, maxDist
> from scipy import spatial
>
> # Assumes dataX and dataY are row vectors
> dataXY = np.vstack((dataX, dataY)).T
> dataQuadtree = spatial.KDTree(dataXY)
>
> xx,yy = np.meshgrid(gridX, gridY)
> xx,yy = xx.flatten, yy.flatten
> gridPoints = np.vstack((xx,yy)).T
>
> dists, indexes = dataQuadtree.query(gridPoints, k=1,
> distance_upper_bound=maxDist)
> mask = dists < maxDist
> mask = mask.reshape(grid.shape)
> grid = np.ma.masked_array(grid, mask)
>
> (That may not run, I'm probably doing something stupid somewhere, but it's
> the general idea, anyway...)
>
> Hope that helps,
> -Joe
>
>
> On Tue, Aug 11, 2009 at 8:54 AM, John [H2O] <washakie at gmail.com> wrote:
>
>>
>> Answering my own question, but seeking comments.
>>
>> Apparently matplotlib.mlab.griddata does exactly what I need.
>>
>> However, I would be interested in know more about how to use this, and to
>> exert a little tighter control over it. One issue, as can be seen below,
>> is
>> that there are clearly artifacts. I would like to create a masking array,
>> based on my raw data array, but as it has a highly irregular shape, this
>> doesn't seem trivial. Suggestions?
>>
>> http://www.nabble.com/file/p24918109/fyi.png
>>
>>
>>
>> --
>> View this message in context:
>> http://www.nabble.com/2d-interpolation%2C-non-regular-lat-lon-grid-tp24909685p24918109.html
>> Sent from the Scipy-User mailing list archive at Nabble.com.
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>
>
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From washakie at gmail.com  Tue Aug 11 17:13:35 2009
From: washakie at gmail.com (John [H2O])
Date: Tue, 11 Aug 2009 14:13:35 -0700 (PDT)
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
 non-regular lat/lon 	grid - help with delauney/natgrid??
In-Reply-To: <3d375d730908111058m2c0fc5daw16fe9add8936d4ec@mail.gmail.com>
References: <24909685.post@talk.nabble.com> <24918109.post@talk.nabble.com>
	<3d375d730908111058m2c0fc5daw16fe9add8936d4ec@mail.gmail.com>
Message-ID: <24925884.post@talk.nabble.com>


My code is below. I am not projecting first. I tried just now, but I don't
think I know exactly how I would do that, could you please elaborate? I can
imagine x,y = m(x,y) ... but what about the lons, lats? They are not the
same size.

    # Set up a basemap and get a figure
## This is just a convenience function to return an m instance and a fig
handle
    fig,m = mp.get_base1(region='NPOLE')

    ## create interpolator
    print "creating interpolators"
    Z = mlab.griddata(x,y,z,lons,lats)
    #where x,y are in lon,lat for my data and
    #where lons,lats  are from [-180,180], [50,90] regularly spaced at 0.5
degrees
    
    #transform to nx x ny regularly spaced native projection grid
    dx = 2.*np.pi*m.rmajor/len(lons)
    nx = int((m.xmax-m.xmin)/dx)+1; ny = int((m.ymax-m.ymin)/dx)+1
    imdat = m.transform_scalar(Z,lons,lats,nx,ny)

   m.imshow(imdat)

-- 
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From washakie at gmail.com  Tue Aug 11 17:49:10 2009
From: washakie at gmail.com (John [H2O])
Date: Tue, 11 Aug 2009 14:49:10 -0700 (PDT)
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
 non-regular lat/lon 	grid - help with delauney/natgrid??
In-Reply-To: <24925884.post@talk.nabble.com>
References: <24909685.post@talk.nabble.com> <24918109.post@talk.nabble.com>
	<3d375d730908111058m2c0fc5daw16fe9add8936d4ec@mail.gmail.com>
	<24925884.post@talk.nabble.com>
Message-ID: <24926398.post@talk.nabble.com>


A new result based on the code below... not sure if it's better ;)

It does seem to be an improvement, but the problem is I still ultimately
need the data to be on a 0.5x0.5 degree grid. Also, this would clearly still
need masking at some locations. 

    # Set up a basemap and interpolate data
    fig,m = mp.get_base1(region='NPOLE')

    #transform to nx x ny regularly spaced native projection grid
    dx = 2.*np.pi*m.rmajor/len(lons)
    nx = int((m.xmax-m.xmin)/dx)+1; ny = int((m.ymax-m.ymin)/dx)+1
    # Project data into basemap
    ## create interpolator
    print "creating interpolators"
    #Z = mlab.griddata(x,y,z,lons,lats)
    x,y = m(x,y)
    newx= np.arange(m.xmin,m.xmax,(m.xmax-m.xmin)/100)
    newy= np.arange(m.ymin,m.ymax,(m.ymax-m.ymin)/100)
    Z = mlab.griddata(x,y,z,newx,newy)


http://www.nabble.com/file/p24926398/regrid_projection_first.png 
-- 
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From Dwf at cs.toronto.edu  Wed Aug 12 14:03:44 2009
From: Dwf at cs.toronto.edu (David Warde-Farley)
Date: Wed, 12 Aug 2009 14:03:44 -0400
Subject: [SciPy-User] machine learning/probabilistic modeling BOF?
Message-ID: <9F4C917C-B31C-4589-80FC-93CFF26E7A4B@cs.toronto.edu>

Since David Cournapeau noted that no one had proposed a BOF he was  
excited about attending (tsk tsk), I wonder if there'd be interest in  
a machine learning/probabilistic modeling BOF session.

What I'm interested in is primarily reusable tools for doing {maximum  
likelihood, Bayesian} probabilistic modeling in Python. Lately a lot  
of options have popped up for quickly specifying and fitting/ 
simulating models, including:

- PyMC 2.0 - http://pymc.googlecode.com/
- Infer.NET - http://research.microsoft.com/en-us/um/cambridge/projects/infernet/
- Hierarchical Bayesian Compiler - http://www.cs.utah.edu/~hal/HBC/

Obviously PyMC is the most promising given our preferred programming  
language but the others include interesting ideas too, for example  
Infer.NET features a compilatio

Some possible topics might include:
- The learn scikit - what's it good at, what's it missing?
- The maxentropy module - it took me a while to stumble upon it and  
recognize it as what I know as exponential family models. Could this  
code be reused, expanded, made more flexible?
- The old idea of porting Kevin Murphy's Bayes net toolbox, which  
DavidC got permission for but none of us actually got around to  
doing. :)
- Things that make complicated models easier to implement and test,  
i.e. automatic differentiation
- New algorithms that might deserve a place in scipy.cluster, ways of  
improving the existing implementations
- Package interoperability, data formats
- ??? (your idea here)

David


From aleck at marlboro.edu  Wed Aug 12 14:49:04 2009
From: aleck at marlboro.edu (Alec Koumjian)
Date: Wed, 12 Aug 2009 14:49:04 -0400
Subject: [SciPy-User] scikits.timeseries frequencies faster than seconds?
Message-ID: <61a4c0ba0908121149t3101c97fxca746990d3d7d240@mail.gmail.com>

I've been using the timeseries objects for a project that analyzes wind
data.  A lot of the data I use has a frequency of about 10 minutes, and so
the package works wonderfully.
However, some of the data has a frequency of 20Hz and I would like to
package it inside a timeseries for the sake of uniformity. Does anyone have
experience creating timeseries with these "invalid" frequencies?
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From robert.kern at gmail.com  Wed Aug 12 14:55:19 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Wed, 12 Aug 2009 13:55:19 -0500
Subject: [SciPy-User] Indexing question
In-Reply-To: <979B2C27-C30A-4A86-A3F4-954A3DD1E9BB@alum.mit.edu>
References: <e4c539ed0908101338s5a4597cepddfff6415f25b23@mail.gmail.com> 
	<979B2C27-C30A-4A86-A3F4-954A3DD1E9BB@alum.mit.edu>
Message-ID: <3d375d730908121155u72cab5bepbcf3e7bc7d1e6125@mail.gmail.com>

On Mon, Aug 10, 2009 at 17:50, Dav Clark<dav at alum.mit.edu> wrote:
>?This actually points out an obscure feature, which
> is that even if you have entries that are doubly (or n-ably)
> referenced in your indexing, each element in the lvalue will only get
> operated on once. ?Thus, while I might have expected the following to
> yield a = [2, 0], it actually yields a = [1, 0]:
>
> a = array([0, 0])
> a[a] += 1 # a[0] incremented twice? ?No!
>
> In other words, we don't need to worry about double-counting in our
> lvalues, which I guess is OK. ?It'd be a bit more intuitive for _me_
> the other way 'round. ?Any logic behind that, or perhaps just
> implementational ease?

Hard limit imposed by the semantics of +=, as discussed several times
previously on this list.

-- 
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


From pgmdevlist at gmail.com  Wed Aug 12 14:59:45 2009
From: pgmdevlist at gmail.com (Pierre GM)
Date: Wed, 12 Aug 2009 14:59:45 -0400
Subject: [SciPy-User] scikits.timeseries frequencies faster than seconds?
In-Reply-To: <61a4c0ba0908121149t3101c97fxca746990d3d7d240@mail.gmail.com>
References: <61a4c0ba0908121149t3101c97fxca746990d3d7d240@mail.gmail.com>
Message-ID: <D79FB1AA-7474-4989-952A-8C6A43D535DD@gmail.com>


On Aug 12, 2009, at 2:49 PM, Alec Koumjian wrote:
>
> However, some of the data has a frequency of 20Hz and I would like  
> to package it inside a timeseries for the sake of uniformity. Does  
> anyone have experience creating timeseries with these "invalid"  
> frequencies?

Alec,
Unfortunately, there's no way yet to define frequencies higher than  
seconds. This would require some significant changes in the C section  
of the code (adding the frequency themselves, but also the conversion  
funtions...) and I'm afraid lack of time will prevent me (and most  
likely Matt Knox as well) to work on it in the next future.
If you like implementing it, by all means ! Don't hesitate to contact  
us off-list.


From niall.moran at gmail.com  Wed Aug 12 17:58:23 2009
From: niall.moran at gmail.com (Niall Moran)
Date: Wed, 12 Aug 2009 22:58:23 +0100
Subject: [SciPy-User] Sparse complex eigensolver
Message-ID: <dbcf15060908121458h2a067541t6421ee1ac81e775f@mail.gmail.com>

Hi,

I am trying to partially diagonalise a sparse complex matrix using
scipy. I could not find a routine in the stable version 0.6.0 that
would do it so I installed the development version 0.8.0.dev5798 and
have been trying to use the routine sparse.linalg.speigs.ARPACK_eigs.
It works fine when my matrix is not complex but when there are complex
elements it seems to just ignore them and only take the real matrix
elements. The returned eigenvalues are real (which I expect in any
case) but the vectors are real also (which I know from other sources
should be complex).

My matrix is stored as a scipy.sparse.csr.csr_matrix (with type
numpy.complex128) and I have tried passing the matvec and _mul_vector
routine from this class to this function as the matrix vector
multiplication routine. Any ideas or hints would be much appreciated.

Many thanks,

Niall.


From jturner at gemini.edu  Wed Aug 12 18:14:29 2009
From: jturner at gemini.edu (James Turner)
Date: Wed, 12 Aug 2009 18:14:29 -0400
Subject: [SciPy-User] Astronomy BoF meeting at SciPy?
Message-ID: <4A833EC5.1080600@gemini.edu>

Hi everyone,

I haven't heard anything regarding an astronomy BoF session since
Joe's AstroPy post on 26 June regarding astronomy libraries. However,
at least Joe, Perry and I are interested in talking and I suspect
there are others (AplPy people?). Up for discussion will be issues
like co-ordinating library development, as different groups help ramp
up the functionality available to match IRAF or IDL.

Who else is interested? Do you have existing plans to meet at SciPy?
What days are you available? So far I believe the three of us are
available on Thursday and Friday. How about Thursday evening? Does
anyone know what time the dinner on Thursday is?

Thanks,

James.


From fperez.net at gmail.com  Wed Aug 12 18:27:22 2009
From: fperez.net at gmail.com (Fernando Perez)
Date: Wed, 12 Aug 2009 15:27:22 -0700
Subject: [SciPy-User] SciPy tutorials and talks will be recorded and posted
	online!
Message-ID: <db6b5ecc0908121527q28adf2e5k390547e2bd96ddb5@mail.gmail.com>

Hi all,

as you may recall, there have been recently a number of requests for
videotaping the conference.

I am very happy to announce that we will indeed have full video
coverage this year of both tutorial tracks as well as the main talks
(minus any specific talk where a speaker may object to being taped,
since we'll respect such objections if they are made).

Jeff Teeters and Kilian Koepsell from UC Berkeley, who have in the
past made recordings like these of one of my workshops and a recent
talk by Gael :

http://www.archive.org/search.php?query=Fernando+Perez+scientific+python
http://www.archive.org/details/ucb_py4science_2009_07_14_Gael_Varoquaux

are going to perform the work.

I'd like to sincerely thank:

- Jeff and Kilian for offering to do this work and providing some of
the recording equipment.

- The Redwood Center for Theoretical Neuroscience, which provided
other equipment.

- Enthought, who are funding this on very short notice!!!  (Especially
Dave Peterson and Eric Jones, who tolerated my last minute nags very
graciously).  Without Enthought's last-minute support, this would
simply not be happening.


I really appreciate that everyone involved worked on short notice to
make this possible, I hope the entire community will benefit from
these resources being available.

Best regards,

f


From robert.kern at gmail.com  Wed Aug 12 18:33:46 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Wed, 12 Aug 2009 17:33:46 -0500
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
	non-regular lat/lon 	grid - help with delauney/natgrid??
In-Reply-To: <24926398.post@talk.nabble.com>
References: <24909685.post@talk.nabble.com> <24918109.post@talk.nabble.com> 
	<3d375d730908111058m2c0fc5daw16fe9add8936d4ec@mail.gmail.com> 
	<24925884.post@talk.nabble.com> <24926398.post@talk.nabble.com>
Message-ID: <3d375d730908121533g53068e76w39fef3b79997989e@mail.gmail.com>

On Tue, Aug 11, 2009 at 16:49, John [H2O]<washakie at gmail.com> wrote:
>
> A new result based on the code below... not sure if it's better ;)
>
> It does seem to be an improvement, but the problem is I still ultimately
> need the data to be on a 0.5x0.5 degree grid.

Create the 0.5x0.5 degree lat,lon grid and project it to the X,Y
coordinate system in place of newx,newy.

-- 
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


From washakie at gmail.com  Wed Aug 12 19:06:32 2009
From: washakie at gmail.com (John [H2O])
Date: Wed, 12 Aug 2009 16:06:32 -0700 (PDT)
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
 non-regular lat/lon 	grid - help with delauney/natgrid??
In-Reply-To: <3d375d730908111058m2c0fc5daw16fe9add8936d4ec@mail.gmail.com>
References: <24909685.post@talk.nabble.com> <24918109.post@talk.nabble.com>
	<3d375d730908111058m2c0fc5daw16fe9add8936d4ec@mail.gmail.com>
Message-ID: <24943646.post@talk.nabble.com>



Robert Kern-2 wrote:
> 
> Are you using lat/lon as X/Y for griddata? Or are you projecting it
> first? You should project. Not seeing your code or data, I'm not sure
> I can diagnose what is going wrong with the interpolation.
> 
> -- 
> Robert Kern
> 
> 
I have been trying to follow your suggestion of projecting first.. see
example later, but I'm not sure if I am doing it correctly. One question is
how then would I return the interpolated array back to a lat/lon grid... it
seems transform_scalar is set up to go from lat/lon TO projection. I guess
it could be used to 'unproject' the data as well?

Thanks!
-- 
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From robert.kern at gmail.com  Wed Aug 12 19:11:05 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Wed, 12 Aug 2009 18:11:05 -0500
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
	non-regular lat/lon 	grid - help with delauney/natgrid??
In-Reply-To: <24943646.post@talk.nabble.com>
References: <24909685.post@talk.nabble.com> <24918109.post@talk.nabble.com> 
	<3d375d730908111058m2c0fc5daw16fe9add8936d4ec@mail.gmail.com> 
	<24943646.post@talk.nabble.com>
Message-ID: <3d375d730908121611p1b8eb33cof3dc3ba831b8e7b1@mail.gmail.com>

On Wed, Aug 12, 2009 at 18:06, John [H2O]<washakie at gmail.com> wrote:
>
>
> Robert Kern-2 wrote:
>>
>> Are you using lat/lon as X/Y for griddata? Or are you projecting it
>> first? You should project. Not seeing your code or data, I'm not sure
>> I can diagnose what is going wrong with the interpolation.
>>
>> --
>> Robert Kern
>>
>>
> I have been trying to follow your suggestion of projecting first.. see
> example later, but I'm not sure if I am doing it correctly. One question is
> how then would I return the interpolated array back to a lat/lon grid... it
> seems transform_scalar is set up to go from lat/lon TO projection. I guess
> it could be used to 'unproject' the data as well?

The output Z values will be in the same order as the inputs. There is
nothing to do.

-- 
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


From chanley at stsci.edu  Wed Aug 12 19:21:52 2009
From: chanley at stsci.edu (Christopher Hanley)
Date: Wed, 12 Aug 2009 19:21:52 -0400
Subject: [SciPy-User] Astronomy BoF meeting at SciPy?
In-Reply-To: <4A833EC5.1080600@gemini.edu>
References: <4A833EC5.1080600@gemini.edu>
Message-ID: <A95D44F7-1F14-4390-B382-50C618B07894@stsci.edu>

I would prefer to meet Thursday after dinner.

Chris


-- 
Christopher Hanley
Senior Systems Software Engineer
Space Telescope Science Institute
3700 San Martin Drive
Baltimore MD, 21218
(410) 338-4338

On Aug 12, 2009, at 6:14 PM, James Turner wrote:

> Hi everyone,
>
> I haven't heard anything regarding an astronomy BoF session since
> Joe's AstroPy post on 26 June regarding astronomy libraries. However,
> at least Joe, Perry and I are interested in talking and I suspect
> there are others (AplPy people?). Up for discussion will be issues
> like co-ordinating library development, as different groups help ramp
> up the functionality available to match IRAF or IDL.
>
> Who else is interested? Do you have existing plans to meet at SciPy?
> What days are you available? So far I believe the three of us are
> available on Thursday and Friday. How about Thursday evening? Does
> anyone know what time the dinner on Thursday is?
>
> Thanks,
>
> James.
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user



From dwf at cs.toronto.edu  Wed Aug 12 19:29:34 2009
From: dwf at cs.toronto.edu (David Warde-Farley)
Date: Wed, 12 Aug 2009 19:29:34 -0400
Subject: [SciPy-User] machine learning/probabilistic modeling BOF?
In-Reply-To: <9F4C917C-B31C-4589-80FC-93CFF26E7A4B@cs.toronto.edu>
References: <9F4C917C-B31C-4589-80FC-93CFF26E7A4B@cs.toronto.edu>
Message-ID: <FE0FE54C-E223-43D5-AC50-42470208D5C4@cs.toronto.edu>


On 12-Aug-09, at 2:03 PM, David Warde-Farley wrote:

> Obviously PyMC is the most promising given our preferred programming
> language but the others include interesting ideas too, for example
> Infer.NET features a compilatio

Wow, as usual I hit send to early. That should've read "Infer.NET  
features a native compilation step for fast evaluation." Come to think  
of it, so does HBC (though it can be simulated without).

David


From fperez.net at gmail.com  Wed Aug 12 19:59:09 2009
From: fperez.net at gmail.com (Fernando Perez)
Date: Wed, 12 Aug 2009 16:59:09 -0700
Subject: [SciPy-User] machine learning/probabilistic modeling BOF?
In-Reply-To: <9F4C917C-B31C-4589-80FC-93CFF26E7A4B@cs.toronto.edu>
References: <9F4C917C-B31C-4589-80FC-93CFF26E7A4B@cs.toronto.edu>
Message-ID: <db6b5ecc0908121659q5e6f9680jab13a2cfc8bdb065@mail.gmail.com>

Hi David,

On Wed, Aug 12, 2009 at 11:03 AM, David Warde-Farley<Dwf at cs.toronto.edu> wrote:
> Since David Cournapeau noted that no one had proposed a BOF he was
> excited about attending (tsk tsk), I wonder if there'd be interest in
> a machine learning/probabilistic modeling BOF session.
>

Is there a BOF wiki page already?  If not, why don't you go ahead and
put one up with this one on it? That will make it easier to collect
the other proposed bofs and coordinate room allocations on-site.

FWIW, I'd be very interested in the one you're proposing...

Cheers,

f


From washakie at gmail.com  Thu Aug 13 04:19:20 2009
From: washakie at gmail.com (John [H2O])
Date: Thu, 13 Aug 2009 01:19:20 -0700 (PDT)
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
 non-regular lat/lon 	grid - help with delauney/natgrid??
In-Reply-To: <3d375d730908121611p1b8eb33cof3dc3ba831b8e7b1@mail.gmail.com>
References: <24909685.post@talk.nabble.com> <24918109.post@talk.nabble.com>
	<3d375d730908111058m2c0fc5daw16fe9add8936d4ec@mail.gmail.com>
	<24943646.post@talk.nabble.com>
	<3d375d730908121611p1b8eb33cof3dc3ba831b8e7b1@mail.gmail.com>
Message-ID: <24950836.post@talk.nabble.com>


I'm sorry, pleading ignorance here... 

But is my approach correct? Could you possibly provide a link to an example
where data is projected first before the interpolation? Or, alternatively, a
brief example (if only in sudo code).

I just am not sure what I am doing here is correct:

149         # In this approach we work with projected data
150         x,y = m(x,y)
151         xres,yres = res
152         newx= np.arange(m.xmin,m.xmax,(m.xmax-m.xmin)/xres)
153         newy= np.arange(m.ymin,m.ymax,(m.ymax-m.ymin)/yres)
154         Znew = mlab.griddata(x,y,z,newx,newy)

It seems to work, yes, but what do you mean there is nothing to do? Should
my 'xres' simply be the lengths of the original x,y which are lon,lat ?

Thanks again!






Robert Kern-2 wrote:
> 
> On Wed, Aug 12, 2009 at 18:06, John [H2O]<washakie at gmail.com> wrote:
>>
>>
>> Robert Kern-2 wrote:
>>>
>>> Are you using lat/lon as X/Y for griddata? Or are you projecting it
>>> first? You should project. Not seeing your code or data, I'm not sure
>>> I can diagnose what is going wrong with the interpolation.
>>>
>>> --
>>> Robert Kern
>>>
>>>
>> I have been trying to follow your suggestion of projecting first.. see
>> example later, but I'm not sure if I am doing it correctly. One question
>> is
>> how then would I return the interpolated array back to a lat/lon grid...
>> it
>> seems transform_scalar is set up to go from lat/lon TO projection. I
>> guess
>> it could be used to 'unproject' the data as well?
> 
> The output Z values will be in the same order as the inputs. There is
> nothing to do.
> 
> -- 
> 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
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
> 
> 

-- 
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From scott.sinclair.za at gmail.com  Thu Aug 13 05:20:19 2009
From: scott.sinclair.za at gmail.com (Scott Sinclair)
Date: Thu, 13 Aug 2009 11:20:19 +0200
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
	non-regular lat/lon 	grid - help with delauney/natgrid??
In-Reply-To: <24950836.post@talk.nabble.com>
References: <24909685.post@talk.nabble.com> <24918109.post@talk.nabble.com>
	<3d375d730908111058m2c0fc5daw16fe9add8936d4ec@mail.gmail.com>
	<24943646.post@talk.nabble.com>
	<3d375d730908121611p1b8eb33cof3dc3ba831b8e7b1@mail.gmail.com>
	<24950836.post@talk.nabble.com>
Message-ID: <6a17e9ee0908130220i24cad4cfx5ad556f5751bac07@mail.gmail.com>

> 2009/8/13 John [H2O] <washakie at gmail.com>:
>
> I'm sorry, pleading ignorance here...
>
> But is my approach correct? Could you possibly provide a link to an example
> where data is projected first before the interpolation? Or, alternatively, a
> brief example (if only in sudo code).
>
> I just am not sure what I am doing here is correct:
>
> 149 ? ? ? ? # In this approach we work with projected data
> 150 ? ? ? ? x,y = m(x,y)
> 151 ? ? ? ? xres,yres = res
> 152 ? ? ? ? newx= np.arange(m.xmin,m.xmax,(m.xmax-m.xmin)/xres)
> 153 ? ? ? ? newy= np.arange(m.ymin,m.ymax,(m.ymax-m.ymin)/yres)
> 154 ? ? ? ? Znew = mlab.griddata(x,y,z,newx,newy)
>
> It seems to work, yes, but what do you mean there is nothing to do? Should
> my 'xres' simply be the lengths of the original x,y which are lon,lat ?

I think what Robert is suggesting you try is to interpolate the data
onto your 0.5x0.5 degree grid after projecting the grid *and* data
locations into your map projection i.e.

m = Basemap(# projection parameters)

# your data locations in proj co-ordinate system are x, y
# your data locs in degrees are lon, lat
x, y = m(lon, lat)

# create your lon-lat grid in degrees using e.g. np.mgrid
# you might need to be careful doing this step
# if your grid straddles the international dateline
grid_lon, grid_lat = np.mgrid[min_lon:max_lon:0.5, min_lat:max_lat:0.5]

# find the projected co-ordinates for the grid
grid_x, grid_y = m(grid_lon.ravel(), grid_lat.ravel())

# interpolate on projected grid
Znew = mlab.griddata(x, y, z, grid_x, grid_y)

There is "nothing to do" because Znew has same the same size and order
as grid_x, grid_y, grid_lon.ravel() & grid_lat.ravel() so you can plot
the data in either co-ordinate system (obviously being aware of the
co-ordinate space where the interpolation was made).

>
> Robert Kern-2 wrote:
>>
>> On Wed, Aug 12, 2009 at 18:06, John [H2O]<washakie at gmail.com> wrote:
>>>
>>>
>>> Robert Kern-2 wrote:
>>>>
>>>> Are you using lat/lon as X/Y for griddata? Or are you projecting it
>>>> first? You should project. Not seeing your code or data, I'm not sure
>>>> I can diagnose what is going wrong with the interpolation.
>>>>
>>>> --
>>>> Robert Kern
>>>>
>>>>
>>> I have been trying to follow your suggestion of projecting first.. see
>>> example later, but I'm not sure if I am doing it correctly. One question
>>> is
>>> how then would I return the interpolated array back to a lat/lon grid...
>>> it
>>> seems transform_scalar is set up to go from lat/lon TO projection. I
>>> guess
>>> it could be used to 'unproject' the data as well?
>>
>> The output Z values will be in the same order as the inputs. There is
>> nothing to do.
>>
>> --
>> Robert Kern

Cheers,
Scott


From washakie at gmail.com  Thu Aug 13 05:32:37 2009
From: washakie at gmail.com (John [H2O])
Date: Thu, 13 Aug 2009 02:32:37 -0700 (PDT)
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
 non-regular lat/lon 	grid - help with delauney/natgrid??
In-Reply-To: <6a17e9ee0908130220i24cad4cfx5ad556f5751bac07@mail.gmail.com>
References: <24909685.post@talk.nabble.com> <24918109.post@talk.nabble.com>
	<3d375d730908111058m2c0fc5daw16fe9add8936d4ec@mail.gmail.com>
	<24943646.post@talk.nabble.com>
	<3d375d730908121611p1b8eb33cof3dc3ba831b8e7b1@mail.gmail.com>
	<24950836.post@talk.nabble.com>
	<6a17e9ee0908130220i24cad4cfx5ad556f5751bac07@mail.gmail.com>
Message-ID: <24951861.post@talk.nabble.com>




Scott Sinclair-4 wrote:
> 
> 
> # find the projected co-ordinates for the grid
> grid_x, grid_y = m(grid_lon.ravel(), grid_lat.ravel())
> 
> 


Thank you. It was the use of mgrid I wasn't aware of...
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From washakie at gmail.com  Thu Aug 13 05:55:32 2009
From: washakie at gmail.com (John [H2O])
Date: Thu, 13 Aug 2009 02:55:32 -0700 (PDT)
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
 non-regular lat/lon 	grid - help with delauney/natgrid??
In-Reply-To: <6a17e9ee0908130220i24cad4cfx5ad556f5751bac07@mail.gmail.com>
References: <24909685.post@talk.nabble.com> <24918109.post@talk.nabble.com>
	<3d375d730908111058m2c0fc5daw16fe9add8936d4ec@mail.gmail.com>
	<24943646.post@talk.nabble.com>
	<3d375d730908121611p1b8eb33cof3dc3ba831b8e7b1@mail.gmail.com>
	<24950836.post@talk.nabble.com>
	<6a17e9ee0908130220i24cad4cfx5ad556f5751bac07@mail.gmail.com>
Message-ID: <24952162.post@talk.nabble.com>


Still problems...

I tried something similar before with meshgrid (how is this different from
mgrid?), but I got the same error as below using the method you outline. Is
this a function of the 'dateline' issue you mention? My lons go from
-180,180 and lats 50,90. I've tried shifting from 0,360 for the lons, but I
think the problem is that in projected coordinates I don't have a
monotonically increasing x.

    170 
    171         # interpolate on projected grid

--> 172         Z0 = mlab.griddata(x, y, z, grid_x, grid_y)
    173 

/wrk/bin64/site-packages/matplotlib/mlab.py in griddata(x, y, z, xi, yi,
interp)
   2700         yo = yi.astype(np.float)
   2701         if min(xo[1:]-xo[0:-1]) < 0 or min(yo[1:]-yo[0:-1]) < 0:
-> 2702             raise ValueError, 'output grid defined by xi,yi must be
monotone increasing'
   2703         # allocate array for output (buffer will be overwritten by
nagridd)

   2704         zo = np.empty((yo.shape[0],xo.shape[0]), np.float)

ValueError: output grid defined by xi,yi must be monotone increasing


And my code:
161         x,y = m(lon,lat)
162         # create your lon-lat grid in degrees using e.g. np.mgrid
163         # you might need to be careful doing this step
164         # if your grid straddles the international dateline
165         grid_lon, grid_lat = np.mgrid[lon.min():lon.max():dres, 
166                 lat.min():lat.max():dres]
167 
168         # find the projected co-ordinates for the grid
169         grid_x, grid_y = m(grid_lon.ravel(), grid_lat.ravel())
170         
171         # interpolate on projected grid
172         Z0 = mlab.griddata(x, y, z, grid_x, grid_y) is my code:



Scott Sinclair-4 wrote:
> 
> # create your lon-lat grid in degrees using e.g. np.mgrid
> # you might need to be careful doing this step
> # if your grid straddles the international dateline
> grid_lon, grid_lat = np.mgrid[min_lon:max_lon:0.5, min_lat:max_lat:0.5]
> 
> # find the projected co-ordinates for the grid
> grid_x, grid_y = m(grid_lon.ravel(), grid_lat.ravel())
> 
> # interpolate on projected grid
> Znew = mlab.griddata(x, y, z, grid_x, grid_y)
> Cheers,
> Scott
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
> 
> 

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From scott.sinclair.za at gmail.com  Thu Aug 13 08:48:02 2009
From: scott.sinclair.za at gmail.com (Scott Sinclair)
Date: Thu, 13 Aug 2009 14:48:02 +0200
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
	non-regular lat/lon 	grid - help with delauney/natgrid??
In-Reply-To: <24952162.post@talk.nabble.com>
References: <24909685.post@talk.nabble.com> <24918109.post@talk.nabble.com>
	<3d375d730908111058m2c0fc5daw16fe9add8936d4ec@mail.gmail.com>
	<24943646.post@talk.nabble.com>
	<3d375d730908121611p1b8eb33cof3dc3ba831b8e7b1@mail.gmail.com>
	<24950836.post@talk.nabble.com>
	<6a17e9ee0908130220i24cad4cfx5ad556f5751bac07@mail.gmail.com>
	<24952162.post@talk.nabble.com>
Message-ID: <6a17e9ee0908130548k6413dedfiaaee91e6410d296f@mail.gmail.com>

>2009/8/13 John [H2O] <washakie at gmail.com>:
>
> Still problems...

Ok. I probably should have read this
http://www.scipy.org/Cookbook/Matplotlib/Gridding_irregularly_spaced_data
and the docstring for mlab.griddata

Does it work if you do:

grid_lon = np.arange(lon.min(), lon.max()+dres, dres)
grid_lat = np.arange(lat.min(), lat.max()+dres, dres)

grid_x, grid_y = m(grid_lon, grid_lat)

Z0 = mlab.griddata(x, y, z, grid_x, grid_y)

Z0.shape should now == (grid_lat.size, grid_lon.size) and you can plot
accordingly (using imshow, contour, scatter or whatever).

Cheers,
Scott


From washakie at gmail.com  Thu Aug 13 09:47:56 2009
From: washakie at gmail.com (John [H2O])
Date: Thu, 13 Aug 2009 06:47:56 -0700 (PDT)
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
 non-regular lat/lon 	grid - help with delauney/natgrid??
In-Reply-To: <6a17e9ee0908130548k6413dedfiaaee91e6410d296f@mail.gmail.com>
References: <24909685.post@talk.nabble.com> <24918109.post@talk.nabble.com>
	<3d375d730908111058m2c0fc5daw16fe9add8936d4ec@mail.gmail.com>
	<24943646.post@talk.nabble.com>
	<3d375d730908121611p1b8eb33cof3dc3ba831b8e7b1@mail.gmail.com>
	<24950836.post@talk.nabble.com>
	<6a17e9ee0908130220i24cad4cfx5ad556f5751bac07@mail.gmail.com>
	<24952162.post@talk.nabble.com>
	<6a17e9ee0908130548k6413dedfiaaee91e6410d296f@mail.gmail.com>
Message-ID: <24954551.post@talk.nabble.com>


YOU should've read the (FM)?? Or I? ;)

Actually, I have referred to both docs, but I'm just missing something. &-(

Unfortunately, it seems now the problem is that m (basemap instance) expects
grid_lon, grid_lat to be of the same length. I tried to convert them into
meshgrid objects, but then I get the error I received in the prior message
about monotonically increasing axes...

Otherwise, the error is:

RuntimeError: Buffer lengths not the same


Scott Sinclair-4 wrote:
> 
>>2009/8/13 John [H2O] <washakie at gmail.com>:
>>
>> Still problems...
> 
> Ok. I probably should have read this
> http://www.scipy.org/Cookbook/Matplotlib/Gridding_irregularly_spaced_data
> and the docstring for mlab.griddata
> 
> Does it work if you do:
> 
> grid_lon = np.arange(lon.min(), lon.max()+dres, dres)
> grid_lat = np.arange(lat.min(), lat.max()+dres, dres)
> 
> grid_x, grid_y = m(grid_lon, grid_lat)
> 
> Z0 = mlab.griddata(x, y, z, grid_x, grid_y)
> 
> Z0.shape should now == (grid_lat.size, grid_lon.size) and you can plot
> accordingly (using imshow, contour, scatter or whatever).
> 
> Cheers,
> Scott
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
> 
> 

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From washakie at gmail.com  Thu Aug 13 12:15:50 2009
From: washakie at gmail.com (John [H2O])
Date: Thu, 13 Aug 2009 09:15:50 -0700 (PDT)
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
 non-regular lat/lon 	grid - help with delauney/natgrid??
In-Reply-To: <6a17e9ee0908130548k6413dedfiaaee91e6410d296f@mail.gmail.com>
References: <24909685.post@talk.nabble.com> <24918109.post@talk.nabble.com>
	<3d375d730908111058m2c0fc5daw16fe9add8936d4ec@mail.gmail.com>
	<24943646.post@talk.nabble.com>
	<3d375d730908121611p1b8eb33cof3dc3ba831b8e7b1@mail.gmail.com>
	<24950836.post@talk.nabble.com>
	<6a17e9ee0908130220i24cad4cfx5ad556f5751bac07@mail.gmail.com>
	<24952162.post@talk.nabble.com>
	<6a17e9ee0908130548k6413dedfiaaee91e6410d296f@mail.gmail.com>
Message-ID: <24955376.post@talk.nabble.com>


Not sure why... but it seems this message hasn't been posting. Trying one
more send.. apologies if its flooding the list at all?

Scott,
YOU should've read the (FM)?? Or I? ;)  I think you seem to have a pretty
good handle on it.

Actually, I have referred to both docs, but I'm just missing something.

Unfortunately, it seems now the problem is that m (basemap instance) expects
grid_lon, grid_lat to be of the same length. I tried to convert them into
meshgrid objects, but then I get the error I received in the prior message
about monotonically increasing axes...

Otherwise, the error is:

RuntimeError: Buffer lengths not the same 
-- 
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From kuiper at jpl.nasa.gov  Thu Aug 13 12:23:03 2009
From: kuiper at jpl.nasa.gov (Tom Kuiper)
Date: Thu, 13 Aug 2009 09:23:03 -0700
Subject: [SciPy-User] SciPy-User Digest, Vol 72, Issue 32
In-Reply-To: <mailman.497.1250119329.2350.scipy-user@scipy.org>
References: <mailman.497.1250119329.2350.scipy-user@scipy.org>
Message-ID: <4A843DE7.2050102@jpl.nasa.gov>

I'm interested in observing (in the United Nations sense of the word) 
this session.  Thursday after the dinner is a bit of a strain since I 
need to drive back to West LA (30 min) and be up again at 6:30 the next 
morning.  If it's not many people, we could get an early lunch (about 
11:30 I think) from the catering truck and find a table in the courtyard 
of Watson, adjacent to Steele.

Tom
> Message: 11
> Date: Wed, 12 Aug 2009 19:21:52 -0400
> From: Christopher Hanley <chanley at stsci.edu>
> Subject: Re: [SciPy-User] Astronomy BoF meeting at SciPy?
> To: SciPy Users List <scipy-user at scipy.org>
> Cc: Christopher Hanley <chanley at stsci.edu>
> Message-ID: <A95D44F7-1F14-4390-B382-50C618B07894 at stsci.edu>
> Content-Type: text/plain; charset=US-ASCII; format=flowed
>
> I would prefer to meet Thursday after dinner.
>
> Chris
>
>
> --
> Christopher Hanley
> Senior Systems Software Engineer
> Space Telescope Science Institute
> 3700 San Martin Drive
> Baltimore MD, 21218
> (410) 338-4338
>
> On Aug 12, 2009, at 6:14 PM, James Turner wrote:
>
>   
>> Hi everyone,
>>
>> I haven't heard anything regarding an astronomy BoF session since
>> Joe's AstroPy post on 26 June regarding astronomy libraries. However,
>> at least Joe, Perry and I are interested in talking and I suspect
>> there are others (AplPy people?). Up for discussion will be issues
>> like co-ordinating library development, as different groups help ramp
>> up the functionality available to match IRAF or IDL.
>>
>> Who else is interested? Do you have existing plans to meet at SciPy?
>> What days are you available? So far I believe the three of us are
>> available on Thursday and Friday. How about Thursday evening? Does
>> anyone know what time the dinner on Thursday is?
>>
>> Thanks,
>>
>> James.
>>     



From robert.kern at gmail.com  Thu Aug 13 12:27:46 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Thu, 13 Aug 2009 11:27:46 -0500
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
	non-regular lat/lon 	grid - help with delauney/natgrid??
In-Reply-To: <24954551.post@talk.nabble.com>
References: <24909685.post@talk.nabble.com> <24918109.post@talk.nabble.com> 
	<3d375d730908111058m2c0fc5daw16fe9add8936d4ec@mail.gmail.com> 
	<24943646.post@talk.nabble.com>
	<3d375d730908121611p1b8eb33cof3dc3ba831b8e7b1@mail.gmail.com> 
	<24950836.post@talk.nabble.com>
	<6a17e9ee0908130220i24cad4cfx5ad556f5751bac07@mail.gmail.com> 
	<24952162.post@talk.nabble.com>
	<6a17e9ee0908130548k6413dedfiaaee91e6410d296f@mail.gmail.com> 
	<24954551.post@talk.nabble.com>
Message-ID: <3d375d730908130927x65ed0d6fh2db206fa5afa0ad7@mail.gmail.com>

On Thu, Aug 13, 2009 at 08:47, John [H2O]<washakie at gmail.com> wrote:
>
> YOU should've read the (FM)?? Or I? ;)
>
> Actually, I have referred to both docs, but I'm just missing something. &-(
>
> Unfortunately, it seems now the problem is that m (basemap instance) expects
> grid_lon, grid_lat to be of the same length. I tried to convert them into
> meshgrid objects, but then I get the error I received in the prior message
> about monotonically increasing axes...

Ah, yes. griddata() only handles regular grids for some reason, not
arbitrary interpolation points. You will have to use the underlying
delaunay package to interpolate arbitrary points. Using your variable
names:

        # triangulate data
        tri = delaunay.Triangulation(x,y)
        # interpolate data
        interp = tri.nn_interpolator(z)
        Z0 = interp(gridx, gridy)

-- 
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


From scott.sinclair.za at gmail.com  Thu Aug 13 12:37:25 2009
From: scott.sinclair.za at gmail.com (Scott Sinclair)
Date: Thu, 13 Aug 2009 18:37:25 +0200
Subject: [SciPy-User] [SciPy-user] 2d interpolation,
	non-regular lat/lon 	grid - help with delauney/natgrid??
In-Reply-To: <3d375d730908130927x65ed0d6fh2db206fa5afa0ad7@mail.gmail.com>
References: <24909685.post@talk.nabble.com>
	<3d375d730908111058m2c0fc5daw16fe9add8936d4ec@mail.gmail.com>
	<24943646.post@talk.nabble.com>
	<3d375d730908121611p1b8eb33cof3dc3ba831b8e7b1@mail.gmail.com>
	<24950836.post@talk.nabble.com>
	<6a17e9ee0908130220i24cad4cfx5ad556f5751bac07@mail.gmail.com>
	<24952162.post@talk.nabble.com>
	<6a17e9ee0908130548k6413dedfiaaee91e6410d296f@mail.gmail.com>
	<24954551.post@talk.nabble.com>
	<3d375d730908130927x65ed0d6fh2db206fa5afa0ad7@mail.gmail.com>
Message-ID: <6a17e9ee0908130937r6d09d406jc047f37442c340a9@mail.gmail.com>

> 2009/8/13 Robert Kern <robert.kern at gmail.com>:
> On Thu, Aug 13, 2009 at 08:47, John [H2O]<washakie at gmail.com> wrote:
>>
>> YOU should've read the (FM)?? Or I? ;)

Looks like everyone needs to :)

>> Actually, I have referred to both docs, but I'm just missing something. &-(
>>
>> Unfortunately, it seems now the problem is that m (basemap instance) expects
>> grid_lon, grid_lat to be of the same length. I tried to convert them into
>> meshgrid objects, but then I get the error I received in the prior message
>> about monotonically increasing axes...
>
> Ah, yes. griddata() only handles regular grids for some reason, not
> arbitrary interpolation points. You will have to use the underlying
> delaunay package to interpolate arbitrary points. Using your variable
> names:
>
> ? ? ? ?# triangulate data
> ? ? ? ?tri = delaunay.Triangulation(x,y)
> ? ? ? ?# interpolate data
> ? ? ? ?interp = tri.nn_interpolator(z)
> ? ? ? ?Z0 = interp(gridx, gridy)

Cool, I didn't know about going directly to the delaunay package!

I think the original problem was with the large area of missing data
in the plot. John?

This is just a result of the transformation from a regular grid in
long/lat to the projection co-ordinates. The projected grid is no
longer a regular rectangular grid in the polar projections co-ordinate
system. Have a look at the attached script and set proj_grid to True
or False before running the script, to see the difference between
interpolating from scattered points to a grid that is regular and
rectangular in long/lat and one that is regular in a polar projection.

Cheers,
Scott
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From gael.varoquaux at normalesup.org  Thu Aug 13 13:01:24 2009
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Thu, 13 Aug 2009 19:01:24 +0200
Subject: [SciPy-User] machine learning/probabilistic modeling BOF?
In-Reply-To: <9F4C917C-B31C-4589-80FC-93CFF26E7A4B@cs.toronto.edu>
References: <9F4C917C-B31C-4589-80FC-93CFF26E7A4B@cs.toronto.edu>
Message-ID: <20090813170124.GM4037@phare.normalesup.org>

On Wed, Aug 12, 2009 at 02:03:44PM -0400, David Warde-Farley wrote:
> Since David Cournapeau noted that no one had proposed a BOF he was  
> excited about attending (tsk tsk), I wonder if there'd be interest in  
> a machine learning/probabilistic modeling BOF session.

I am interested.

For what its worth, my group is hiring an engineer to work for two years
on QAing and release machine-learning/statistical-modeling code that we
have developed internally in Python. I can't announce the name of the
enginner yet, as the contract is not fully signed, but we should be
hiring a very talented young fellow who has already contributed to
scientific Python project (let the speculations begin!).

Ga?l


From hazelnusse at gmail.com  Thu Aug 13 15:34:04 2009
From: hazelnusse at gmail.com (Luke)
Date: Thu, 13 Aug 2009 12:34:04 -0700
Subject: [SciPy-User] scipy odeint question
Message-ID: <99214b470908131234gaf8f735o264ecf87775e91fa@mail.gmail.com>

I am generating some ODE's that I integrate with scipy.odeint, and
everything is working well, but there is something that I would like
to do that I'm not sure if odeint can currently handle.

The function representing the right hand side of the ODE's I generate
are of the standard form that scipy needs, namely:
def f(t, x, params):
   ....
   return dxdt

In the ... there are many (sometimes thousands or tens of thousands of
lines) intermediate variables that are used for the final calculation
of dxdt.  The calculations generally involve the basic arithmetic
operators (+-*/**) and some trig functions (sin, cos, tan).
There are several other functions of t, x, and params that I
would like to evaluate *only at the integration step times specified
by the user*, and I would like to be able make use of the fact that
the computation of the intermediate variables has already been done
for the time step that is to be returned in the x array.

Here is an example of what I am trying to acheive:

def f(t, x, params):
   z1 = ...
   z2 = ...   (z1, z2, z3 would be in reality be more like z1, ...,
z1000, all functions of t, x, params)
   ...
   zn = ...
   dxdt = [z1+z2**2, z2*z3-z1**3, ...]
   # The following only to be evaluated at time points in the user
specified vector, e.g.,  t = arange(t_i, t_f, t_s)
   eqn_1 = f_1(z1,...., zn)
   eqn_2 = f_2(z1, ..., zn)
   ....
   eqn_n = f_n(z1, ..., zn)
   return dxdt

>From a practical point of view, I can easily integrate the equations,
determine the state trajectory, then call a function that evaluates
the other quantities eqn1, eqn2... *after* numerical integration.  But
for certain applications, such as realtime control applications, it is
useful to avoid repeated calculations (as would happen if I just
called a function that took that state trajectory and calculated the
eqn1, eqn2).

I can see a couple of approaches that might work:
1)  Make the time vector a global variable, and use an if statement
inside of f(t, x, params) like:
if t in t_span:
   eqn_1 = ...
   eqn_2 = ...
   ....
   eqn_n = ...
   accumulate_eqns(eqn1, ..., eqn_n)   # accumulate_eqns could be
either a function passed to f through pararms, or a global function

2) Make eqn_1, ..., eqn_n globals, and only integrate one time step at
a time so that they can be stored at the end of each integration step.
 In this way, odeint would be called in a for loop that would use a
time vector that had only two elements (their difference would be the
step time specified by the user)

I'm not 100% sure the above methods would work because I don't know if
globals in the namespace which calls odeint would be viewable to
globals declared in the function f.

What would be really awesome would be if odeint could hold on to any
variables in the f namespace when it reaches a time step that is
requested, and then call another function to evaluate the output
equations, but that function would have access to the state of all the
variables in the f namespace *at the time point* where the state x is
to be evaluated.  Ideally, the extra equations wouldn't be computed
except at the time points which are specified by the time vector
specified by the user.

Examples of use would be computing the linear/angular momentum of a
system of rigid bodies and particles, center of mass of the bodies,
total energy (kinetic and potential), and the value of constraints /
conserved quantities during the numerical integration of the
equations of motion.  The equations of motion can be integrated
without computing these quantities, but they are very useful for
checking that the numerical integration is well behaved.  For
complicated systems such as spacecraft, these equations can be
extremely long, and it can be very computationally advantageous to
make use of these intermediate quantities rather than having to be
recalculated after the numerical integration is complete.

Any ideas?

Sincerely,
~Luke


From hazelnusse at gmail.com  Thu Aug 13 15:50:05 2009
From: hazelnusse at gmail.com (Luke)
Date: Thu, 13 Aug 2009 12:50:05 -0700
Subject: [SciPy-User]  vectorizing a function with non scalar arguments
Message-ID: <99214b470908131250j1a7ca450v25cfb97377d36c97@mail.gmail.com>

Thanks for the replies!

>On 10-Aug-09, at 5:28 PM, Luke wrote:
>
>> I have a function of the following form:
>> def f(q, qd, parameter_list):
>>    # Unpacking the parameters
>>    m, g, I11, I22, I33 = parameter_list
>>    # Unpacking the qdots
>>    q1, q2, q3, q4, q5, q6 = q
>>    q1p, q2p, q3p, q4p, q5p, q6p = qd
>>    ....Some calculations to determine u1,u2,u3,u4,u5,u6....
>>    return [u1, u2, u3, u4, u5, u6]
>
>We'd really need to see "...Some calculations..." but if it's
>something that's expressible as simple arithmetic/calls to numpy
>ufuncs, then it's probable that you can pull this off.

The calculations would involve the basic arithmetic operators (+-*/**)
and some trigonmetric functions (sin,cos,tan), and maybe abs, so yes,
they could be expressed exclusively as Numpy ufuncs.

>
>> I would like to vectorize this function so that I could pass it:
>> 1)  q and qd as a 2-d numpy arrays of shape (n, 6)
>> 2) parameter_list as a 1-d numpy array of shape (5,)
>
>hsplit() and hstack() to unpack/pack the 2D arrays, respectively
>(though there are more memory efficient/cache friendly ways, if you
>care about speed).

I was hoping to code it in such a way that if I could still call the
function using 1-d lists, thus making it versatile enough to handle
both a single set of q and qd or a set of n q and qd.

>
>> and it would return a 2-d numpy array of shape (n, 6)
>>
>> parameter_list is a bunch of constants that don't change, while q and
>> qd are the things that are different between say q[n, :] and q[n+1,
>> :].
>
>> I realize that I can use a for loop to loop from 0 to n-1.
>>
>> My question is whether there a way to use vectorize (or some other
>> decorator), to obtain the behavior?  Or is there another nice method
>> that people might be able to recommend?
>
>Again, if you split into 1d arrays and are just doing arithmetic on
>them (or you use numpy functions that can take scalar or vector
>arguments) then it should "vectorize itself". But it might be helpful
>if you have any helper functions of one scalar variable that you need
>to evaluate across an entire array of q1's, etc.
>
>> The documentation for vectorize is very sparse and seems like it is
>> geared only towards function which have scalar arguments.
>
>Yes, 'vectorize' won't do it for you in this case.
>
Ok.  Thanks!

>David


From gokhansever at gmail.com  Thu Aug 13 15:56:59 2009
From: gokhansever at gmail.com (=?UTF-8?Q?G=C3=B6khan_Sever?=)
Date: Thu, 13 Aug 2009 14:56:59 -0500
Subject: [SciPy-User] lnpymath cannot find error during scipy installation
Message-ID: <49d6b3500908131256j4ab0918dkdd20563df9b7088c@mail.gmail.com>

/usr/bin/ld: cannot find -lnpymath
collect2: ld returned 1 exit status
error: Command "/usr/bin/g77 -g -Wall -g -Wall -shared
build/temp.linux-i686-2.6/scipy/special/_cephesmodule.o
build/temp.linux-i686-2.6/scipy/special/amos_wrappers.o
build/temp.linux-i686-2.6/scipy/special/specfun_wrappers.o
build/temp.linux-i686-2.6/scipy/special/toms_wrappers.o
build/temp.linux-i686-2.6/scipy/special/cdf_wrappers.o
build/temp.linux-i686-2.6/scipy/special/ufunc_extras.o
-L/usr/lib/python2.6/site-packages/numpy/core/lib -L/usr/lib
-Lbuild/temp.linux-i686-2.6 -lsc_amos -lsc_toms -lsc_c_misc -lsc_cephes
-lsc_mach -lsc_cdf -lsc_specfun -lnpymath -lm -lpython2.6 -lg2c -o
scipy/special/_cephes.so" failed with exit status 1

Did a python setupegg.py develop install for numpy and when I tried to do
the same for scipy I stuck at that error. I know there is a libnpymath file
under one of the numpy build dirs, however not sure how to show that path
for the linker.

Any ideas?

Thank you


##############################################
python -c 'from numpy.f2py.diagnose import run; run()'
##############################################
------
os.name='posix'
------
sys.platform='linux2'
------
sys.version:
2.6 (r26:66714, Jun  8 2009, 16:07:26)
[GCC 4.4.0 20090506 (Red Hat 4.4.0-4)]
------
sys.prefix:
/usr
------
sys.path=':/usr/lib/python2.6/site-packages/foolscap-0.4.2-py2.6.egg:/usr/lib/python2.6/site-packages/Twisted-8.2.0-py2.6-linux-i686.egg:/home/gsever/Desktop/python-repo/ipython:/home/gsever/Desktop/python-repo/numpy:/home/gsever/Desktop/python-repo/matplotlib/lib:/usr/lib/python2.6/site-packages/Sphinx-0.6.2-py2.6.egg:/usr/lib/python2.6/site-packages/docutils-0.5-py2.6.egg:/usr/lib/python2.6/site-packages/Jinja2-2.1.1-py2.6-linux-i686.egg:/usr/lib/python2.6/site-packages/Pygments-1.0-py2.6.egg:/usr/lib/python2.6/site-packages/xlwt-0.7.2-py2.6.egg:/usr/lib/python2.6/site-packages/spyder-1.0.0beta1-py2.6.egg:/usr/lib/python26.zip:/usr/lib/python2.6:/usr/lib/python2.6/plat-linux2:/usr/lib/python2.6/lib-tk:/usr/lib/python2.6/lib-old:/usr/lib/python2.6/lib-dynload:/usr/lib/python2.6/site-packages:/usr/lib/python2.6/site-packages/Numeric:/usr/lib/python2.6/site-packages/PIL:/usr/lib/python2.6/site-packages/gst-0.10:/usr/lib/python2.6/site-packages/gtk-2.0:/usr/lib/python2.6/site-packages:/usr/lib/python2.6/site-packages/wx-2.8-gtk2-unicode'
------
Failed to import numarray: No module named numarray
Found Numeric version '24.2' in
/usr/lib/python2.6/site-packages/Numeric/Numeric.pyc
Found new numpy version '1.4.0.dev' in
/home/gsever/Desktop/python-repo/numpy/numpy/__init__.pyc
Found f2py2e version '2' in
/home/gsever/Desktop/python-repo/numpy/numpy/f2py/f2py2e.pyc
Found numpy.distutils version '0.4.0' in
'/home/gsever/Desktop/python-repo/numpy/numpy/distutils/__init__.pyc'
------
Importing numpy.distutils.fcompiler ... ok
------
Checking availability of supported Fortran compilers:
GnuFCompiler instance properties:
  archiver        = ['/usr/bin/g77', '-cr']
  compile_switch  = '-c'
  compiler_f77    = ['/usr/bin/g77', '-g', '-Wall', '-fno-second-
                    underscore', '-fPIC', '-O3', '-funroll-loops']
  compiler_f90    = None
  compiler_fix    = None
  libraries       = ['g2c']
  library_dirs    = []
  linker_exe      = ['/usr/bin/g77', '-g', '-Wall', '-g', '-Wall']
  linker_so       = ['/usr/bin/g77', '-g', '-Wall', '-g', '-Wall', '-
                    shared']
  object_switch   = '-o '
  ranlib          = ['/usr/bin/g77']
  version         = LooseVersion ('3.4.6')
  version_cmd     = ['/usr/bin/g77', '--version']
Gnu95FCompiler instance properties:
  archiver        = ['/usr/bin/gfortran', '-cr']
  compile_switch  = '-c'
  compiler_f77    = ['/usr/bin/gfortran', '-Wall', '-ffixed-form', '-fno-
                    second-underscore', '-fPIC', '-O3', '-funroll-loops']
  compiler_f90    = ['/usr/bin/gfortran', '-Wall', '-fno-second-underscore',
                    '-fPIC', '-O3', '-funroll-loops']
  compiler_fix    = ['/usr/bin/gfortran', '-Wall', '-ffixed-form', '-fno-
                    second-underscore', '-Wall', '-fno-second-underscore',
'-
                    fPIC', '-O3', '-funroll-loops']
  libraries       = ['gfortran']
  library_dirs    = []
  linker_exe      = ['/usr/bin/gfortran', '-Wall', '-Wall']
  linker_so       = ['/usr/bin/gfortran', '-Wall', '-Wall', '-shared']
  object_switch   = '-o '
  ranlib          = ['/usr/bin/gfortran']
  version         = LooseVersion ('4.4.0')
  version_cmd     = ['/usr/bin/gfortran', '--version']
Fortran compilers found:
  --fcompiler=gnu    GNU Fortran 77 compiler (3.4.6)
  --fcompiler=gnu95  GNU Fortran 95 compiler (4.4.0)
Compilers available for this platform, but not found:
  --fcompiler=absoft   Absoft Corp Fortran Compiler
  --fcompiler=compaq   Compaq Fortran Compiler
  --fcompiler=g95      G95 Fortran Compiler
  --fcompiler=intel    Intel Fortran Compiler for 32-bit apps
  --fcompiler=intele   Intel Fortran Compiler for Itanium apps
  --fcompiler=intelem  Intel Fortran Compiler for EM64T-based apps
  --fcompiler=lahey    Lahey/Fujitsu Fortran 95 Compiler
  --fcompiler=nag      NAGWare Fortran 95 Compiler
  --fcompiler=pg       Portland Group Fortran Compiler
  --fcompiler=vast     Pacific-Sierra Research Fortran 90 Compiler
Compilers not available on this platform:
  --fcompiler=hpux     HP Fortran 90 Compiler
  --fcompiler=ibm      IBM XL Fortran Compiler
  --fcompiler=intelev  Intel Visual Fortran Compiler for Itanium apps
  --fcompiler=intelv   Intel Visual Fortran Compiler for 32-bit apps
  --fcompiler=mips     MIPSpro Fortran Compiler
  --fcompiler=none     Fake Fortran compiler
  --fcompiler=sun      Sun or Forte Fortran 95 Compiler
For compiler details, run 'config_fc --verbose' setup command.
------
Importing numpy.distutils.cpuinfo ... ok
------
CPU information: CPUInfoBase__get_nbits getNCPUs has_mmx has_sse has_sse2
has_sse3 has_ssse3 is_32bit is_Intel is_i686 ------



-- 
G?khan
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From gokhansever at gmail.com  Thu Aug 13 16:37:06 2009
From: gokhansever at gmail.com (=?UTF-8?Q?G=C3=B6khan_Sever?=)
Date: Thu, 13 Aug 2009 15:37:06 -0500
Subject: [SciPy-User] Open Research and Python's role
Message-ID: <49d6b3500908131337v3e2b3388h8a0df90260144237@mail.gmail.com>

The SciPy09 conference will provide such a great opportunity to bring
users and scientists from varying disciplines. I am curious to know
whether there is a planned session for a discussion of philosophical
outcomes of using an open-source programming language and various
other open scientific tools for ones scientific data analysis,
visualization, and publishing needs rather than the practical aspects
of the subject.

Although not a direct Python specific discussion issue, topics such as:

- How computational sciences are evolving by the growing open-source
communities?
- The role of open languages and tools for the science education
- Reproducibility, open information, data etc.. (not to mention Popper)


You might consider seeing the following resources to get more in-depth
information on some of the aforementioned subjects:

http://www.openscience.org/blog/?p=269   (The OpenScience Project ?
What, exactly, is Open Science?)
http://hyfen.net/out/writing/2009-07/michael-nielsen-at-science20/
(Doing it in the open: Michael Nielsen at Science 2.0)
http://www.linux.com/news/software/applications/32332-open-source-open-research
http://www.stanford.edu/~vcs/    (Victoria Stodden's Webpage  -
various talks and papers related to the subject)


I still think that these could be an interesting lunch or dinner time
talk if not a panel discussion.


G?khan


From dwf at cs.toronto.edu  Thu Aug 13 17:20:25 2009
From: dwf at cs.toronto.edu (David Warde-Farley)
Date: Thu, 13 Aug 2009 17:20:25 -0400
Subject: [SciPy-User] SciPy2009 BoF Wiki Page
Message-ID: <0636F499-8CBC-4053-ACF9-7BA40E5D58D4@cs.toronto.edu>

I needed a short break from some heavy writing, so on Fernando's  
suggestion I took to the task of aggregating together mailing list  
traffic about the BoFs next week. So far, 4 have been proposed, and  
I've written down under "attendees" the names of anyone who has  
expressed interest (except in Perry's case, where I've only heard it  
via proxy). The page is at

	http://scipy.org/SciPy2009/BoF

I've created sections below that are hyperlink targets for the topic  
of the session, if someone more knowledgeable of that domain can fill  
in those sections, please do.

Edit away, and see you next week! (And if someone can forward this to  
the Matplotlib list, I'm not currently subscribed)

David


From dwf at cs.toronto.edu  Thu Aug 13 17:31:27 2009
From: dwf at cs.toronto.edu (David Warde-Farley)
Date: Thu, 13 Aug 2009 17:31:27 -0400
Subject: [SciPy-User] machine learning/probabilistic modeling BOF?
In-Reply-To: <9F4C917C-B31C-4589-80FC-93CFF26E7A4B@cs.toronto.edu>
References: <9F4C917C-B31C-4589-80FC-93CFF26E7A4B@cs.toronto.edu>
Message-ID: <09979F9A-6602-43EA-976C-1614D5B1DBB5@cs.toronto.edu>

On 12-Aug-09, at 2:03 PM, David Warde-Farley wrote:

> Since David Cournapeau noted that no one had proposed a BOF he was  
> excited about attending (tsk tsk),

And of course, I messed this up. It was David Goldsmith, not David  
Cournapeau, who said this. Too many Davids on this list.

Interestingly enough, David Goldsmith's name cannot be used on the  
wiki as "Gold" is a banned word for spam protection reasons, hence  
G*ldsmith on the BoF wiki page :P

David


From gael.varoquaux at normalesup.org  Thu Aug 13 17:41:08 2009
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Thu, 13 Aug 2009 23:41:08 +0200
Subject: [SciPy-User] [IPython-user] SciPy2009 BoF Wiki Page
In-Reply-To: <0636F499-8CBC-4053-ACF9-7BA40E5D58D4@cs.toronto.edu>
References: <0636F499-8CBC-4053-ACF9-7BA40E5D58D4@cs.toronto.edu>
Message-ID: <20090813214108.GA9220@phare.normalesup.org>

On Thu, Aug 13, 2009 at 05:20:25PM -0400, David Warde-Farley wrote:
> I needed a short break from some heavy writing, so on Fernando's  
> suggestion I took to the task of aggregating together mailing list  
> traffic about the BoFs next week. So far, 4 have been proposed, and  
> I've written down under "attendees" the names of anyone who has  
> expressed interest (except in Perry's case, where I've only heard it  
> via proxy). The page is at

> 	http://scipy.org/SciPy2009/BoF

Thank you, very useful. I have linked it from the BoF page on the
conference website.

Ga?l


From fperez.net at gmail.com  Thu Aug 13 18:59:21 2009
From: fperez.net at gmail.com (Fernando Perez)
Date: Thu, 13 Aug 2009 15:59:21 -0700
Subject: [SciPy-User] [IPython-user] SciPy2009 BoF Wiki Page
In-Reply-To: <0636F499-8CBC-4053-ACF9-7BA40E5D58D4@cs.toronto.edu>
References: <0636F499-8CBC-4053-ACF9-7BA40E5D58D4@cs.toronto.edu>
Message-ID: <db6b5ecc0908131559wf00f3e2jfdab4a3a5bba9ba0@mail.gmail.com>

On Thu, Aug 13, 2009 at 2:20 PM, David Warde-Farley<dwf at cs.toronto.edu> wrote:
> I needed a short break from some heavy writing, so on Fernando's
> suggestion I took to the task of aggregating together mailing list
> traffic about the BoFs next week. So far, 4 have been proposed, and
> I've written down under "attendees" the names of anyone who has
> expressed interest (except in Perry's case, where I've only heard it
> via proxy). The page is at
>
> ? ? ? ?http://scipy.org/SciPy2009/BoF
>
> I've created sections below that are hyperlink targets for the topic
> of the session, if someone more knowledgeable of that domain can fill
> in those sections, please do.

Fantastic!  Many thanks.

>
> Edit away, and see you next week! (And if someone can forward this to
> the Matplotlib list, I'm not currently subscribed)

I'll send that now.

Cheers,

f


From pav at iki.fi  Fri Aug 14 03:52:03 2009
From: pav at iki.fi (Pauli Virtanen)
Date: Fri, 14 Aug 2009 07:52:03 +0000 (UTC)
Subject: [SciPy-User] machine learning/probabilistic modeling BOF?
References: <9F4C917C-B31C-4589-80FC-93CFF26E7A4B@cs.toronto.edu>
	<09979F9A-6602-43EA-976C-1614D5B1DBB5@cs.toronto.edu>
Message-ID: <h63533$b21$2@ger.gmane.org>

Thu, 13 Aug 2009 17:31:27 -0400, David Warde-Farley kirjoitti:
[clip]
> Interestingly enough, David Goldsmith's name cannot be used on the wiki
> as "Gold" is a banned word for spam protection reasons, hence G*ldsmith
> on the BoF wiki page :P

Dear Sir, would you be interested selling your WoW Gold for Real Money 
Trade?

    ***

Anyway, I fixed that bit of the overzealous spam protection. Probably 
some others should also match only at word boundaries, too.

	Pauli



From hoontaechung at gmail.com  Fri Aug 14 07:35:47 2009
From: hoontaechung at gmail.com (Chung, Tae-Hoon)
Date: Fri, 14 Aug 2009 19:35:47 +0800
Subject: [SciPy-User] how to use stats.kruskal correctly?
Message-ID: <4A854C13.9020802@gmail.com>

Hi, All;

What's the correct way to put something into the stats.kruskal function?
Any examples?
I tried one list with multiple lists as input or several lists 
separately but none succeeded.
The reference contains only the following description, with no 
explanation on input arguments:

scipy.stats.kruskal(*args)

The Kruskal-Wallis H-test is a non-parametric ANOVA for 2 or more 
groups, requiring at least 5 subjects in each group. This function 
calculates the Kruskal-Wallis H and associated p-value for 2 or more 
independent samples.

Returns: H-statistic (corrected for ties), associated p-value

Thanks in advance,
Tae-Hoon Chung


From josef.pktd at gmail.com  Fri Aug 14 08:03:21 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Fri, 14 Aug 2009 08:03:21 -0400
Subject: [SciPy-User] how to use stats.kruskal correctly?
In-Reply-To: <4A854C13.9020802@gmail.com>
References: <4A854C13.9020802@gmail.com>
Message-ID: <1cd32cbb0908140503k26c53a11v341598d2e8dd92b8@mail.gmail.com>

On Fri, Aug 14, 2009 at 7:35 AM, Chung, Tae-Hoon<hoontaechung at gmail.com> wrote:
> Hi, All;
>
> What's the correct way to put something into the stats.kruskal function?
> Any examples?
> I tried one list with multiple lists as input or several lists
> separately but none succeeded.
> The reference contains only the following description, with no
> explanation on input arguments:
>
> scipy.stats.kruskal(*args)
>
> The Kruskal-Wallis H-test is a non-parametric ANOVA for 2 or more
> groups, requiring at least 5 subjects in each group. This function
> calculates the Kruskal-Wallis H and associated p-value for 2 or more
> independent samples.
>
> Returns: H-statistic (corrected for ties), associated p-value
>
> Thanks in advance,
> Tae-Hoon Chung
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>

I guess there is a check for input type, or conversion to array missing

>>> stats.kruskal([1,2,3,4],[4,6,7,9])
Traceback (most recent call last):
  File "<pyshell#0>", line 1, in <module>
    stats.kruskal([1,2,3,4],[4,6,7,9])
  File "C:\Josef\scipytrunkcopy\scipy\stats\stats.py", line 2437, in kruskal
    all.extend(args[i].tolist())
AttributeError: 'list' object has no attribute 'tolist'

>>> stats.kruskal(np.array([1,2,3,4]),np.array([4,6,7,9]))
(4.7439759036144578, 0.029401048190339632)

I will check this

Josef


From josef.pktd at gmail.com  Fri Aug 14 10:13:23 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Fri, 14 Aug 2009 10:13:23 -0400
Subject: [SciPy-User] [SciPy-user] scipy.stats.stats mannwhitneyu vs
	ranksums?
In-Reply-To: <4a4e2ca7.0c92100a.2f71.0392@mx.google.com>
References: <43958ee60906301516s637b180fm35b96d5b61a1b549@mail.gmail.com>
	<4A4A92A6.9070503@apstat.com>
	<43958ee60906301624l7fd7f445sebd0d69b4ac32e61@mail.gmail.com>
	<4a4b8c6f.1a135e0a.7a2d.0b82@mx.google.com>
	<5b8d13220907010933j7463ec93tb0bb92f02fbf9308@mail.gmail.com>
	<4a4cf1bf.05a4100a.7834.ffffcf2e@mx.google.com>
	<4F46BA01-652B-4AEA-84CE-277D54F26693@cs.toronto.edu>
	<4a4e2ca7.0c92100a.2f71.0392@mx.google.com>
Message-ID: <1cd32cbb0908140713wcb67a2cl20899bb7207e64cc@mail.gmail.com>

On Fri, Jul 3, 2009 at 12:06 PM, Elias Pampalk<elias.pampalk at gmail.com> wrote:

>
> I did a quick comparison between Matlab/stats (R14SP3), R (2.8.1), and
> Python/SciPy (0.7). Maybe this is somehow useful for others too.
>
...

> Elias

Thanks for doing this, this is very helpful. I attached the comparison
to ticket:901

Except for one case, the numbers look pretty good.
However, documentation is still weak, and some of the code duplication
could be removed.

Josef

(I didn't look at it closely before, because I was on vacation at that time.)


From josef.pktd at gmail.com  Fri Aug 14 10:26:11 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Fri, 14 Aug 2009 10:26:11 -0400
Subject: [SciPy-User] how to use stats.kruskal correctly?
In-Reply-To: <1cd32cbb0908140503k26c53a11v341598d2e8dd92b8@mail.gmail.com>
References: <4A854C13.9020802@gmail.com>
	<1cd32cbb0908140503k26c53a11v341598d2e8dd92b8@mail.gmail.com>
Message-ID: <1cd32cbb0908140726i9a40b72sc517c5e4267a7a3a@mail.gmail.com>

On Fri, Aug 14, 2009 at 8:03 AM, <josef.pktd at gmail.com> wrote:
> On Fri, Aug 14, 2009 at 7:35 AM, Chung, Tae-Hoon<hoontaechung at gmail.com> wrote:
>> Hi, All;
>>
>> What's the correct way to put something into the stats.kruskal function?
>> Any examples?
>> I tried one list with multiple lists as input or several lists
>> separately but none succeeded.
>> The reference contains only the following description, with no
>> explanation on input arguments:
>>
>> scipy.stats.kruskal(*args)
>>
>> The Kruskal-Wallis H-test is a non-parametric ANOVA for 2 or more
>> groups, requiring at least 5 subjects in each group. This function
>> calculates the Kruskal-Wallis H and associated p-value for 2 or more
>> independent samples.
>>
>> Returns: H-statistic (corrected for ties), associated p-value
>>
>> Thanks in advance,
>> Tae-Hoon Chung
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>
> I guess there is a check for input type, or conversion to array missing
>
>>>> stats.kruskal([1,2,3,4],[4,6,7,9])
> Traceback (most recent call last):
> ?File "<pyshell#0>", line 1, in <module>
> ? ?stats.kruskal([1,2,3,4],[4,6,7,9])
> ?File "C:\Josef\scipytrunkcopy\scipy\stats\stats.py", line 2437, in kruskal
> ? ?all.extend(args[i].tolist())
> AttributeError: 'list' object has no attribute 'tolist'
>
>>>> stats.kruskal(np.array([1,2,3,4]),np.array([4,6,7,9]))
> (4.7439759036144578, 0.029401048190339632)
>
> I will check this
>
> Josef
>

stats.kruskal needs a bit of a rewrite to use more of a numpy style
than the inherited python list style.

Until this is done the function assumes that the arguments are numpy
arrays, one for each data series or groups.
Currently all tests use numpy arrays.

For more examples on the usage of kruskal and related stats functions,
you can look at http://projects.scipy.org/scipy/ticket/901

Josef


From hoontaechung at gmail.com  Fri Aug 14 10:50:55 2009
From: hoontaechung at gmail.com (Chung Tae-Hoon)
Date: Fri, 14 Aug 2009 22:50:55 +0800
Subject: [SciPy-User] how to use stats.kruskal correctly? RESOLVED!
In-Reply-To: <1cd32cbb0908140726i9a40b72sc517c5e4267a7a3a@mail.gmail.com>
References: <4A854C13.9020802@gmail.com>	<1cd32cbb0908140503k26c53a11v341598d2e8dd92b8@mail.gmail.com>
	<1cd32cbb0908140726i9a40b72sc517c5e4267a7a3a@mail.gmail.com>
Message-ID: <001601ca1cee$a315f5c0$e941e140$@com>

Hi, All;

The posted problem is resolved.
Thank you very much, Josef!

Tae-Hoon Chung

-----Original Message-----
From: scipy-user-bounces at scipy.org [mailto:scipy-user-bounces at scipy.org] On
Behalf Of josef.pktd at gmail.com
Sent: Friday, 14 August, 2009 10:26 PM
To: SciPy Users List
Subject: Re: [SciPy-User] how to use stats.kruskal correctly?

On Fri, Aug 14, 2009 at 8:03 AM, <josef.pktd at gmail.com> wrote:
> On Fri, Aug 14, 2009 at 7:35 AM, Chung, Tae-Hoon<hoontaechung at gmail.com>
wrote:
>> Hi, All;
>>
>> What's the correct way to put something into the stats.kruskal function?
>> Any examples?
>> I tried one list with multiple lists as input or several lists
>> separately but none succeeded.
>> The reference contains only the following description, with no
>> explanation on input arguments:
>>
>> scipy.stats.kruskal(*args)
>>
>> The Kruskal-Wallis H-test is a non-parametric ANOVA for 2 or more
>> groups, requiring at least 5 subjects in each group. This function
>> calculates the Kruskal-Wallis H and associated p-value for 2 or more
>> independent samples.
>>
>> Returns: H-statistic (corrected for ties), associated p-value
>>
>> Thanks in advance,
>> Tae-Hoon Chung
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>
> I guess there is a check for input type, or conversion to array missing
>
>>>> stats.kruskal([1,2,3,4],[4,6,7,9])
> Traceback (most recent call last):
> ?File "<pyshell#0>", line 1, in <module>
> ? ?stats.kruskal([1,2,3,4],[4,6,7,9])
> ?File "C:\Josef\scipytrunkcopy\scipy\stats\stats.py", line 2437, in
kruskal
> ? ?all.extend(args[i].tolist())
> AttributeError: 'list' object has no attribute 'tolist'
>
>>>> stats.kruskal(np.array([1,2,3,4]),np.array([4,6,7,9]))
> (4.7439759036144578, 0.029401048190339632)
>
> I will check this
>
> Josef
>

stats.kruskal needs a bit of a rewrite to use more of a numpy style
than the inherited python list style.

Until this is done the function assumes that the arguments are numpy
arrays, one for each data series or groups.
Currently all tests use numpy arrays.

For more examples on the usage of kruskal and related stats functions,
you can look at http://projects.scipy.org/scipy/ticket/901

Josef
_______________________________________________
SciPy-User mailing list
SciPy-User at scipy.org
http://mail.scipy.org/mailman/listinfo/scipy-user



From josef.pktd at gmail.com  Fri Aug 14 11:49:37 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Fri, 14 Aug 2009 11:49:37 -0400
Subject: [SciPy-User] [SciPy-user] scipy.stats.stats mannwhitneyu vs
	ranksums?
In-Reply-To: <1cd32cbb0908140713wcb67a2cl20899bb7207e64cc@mail.gmail.com>
References: <43958ee60906301516s637b180fm35b96d5b61a1b549@mail.gmail.com>
	<4A4A92A6.9070503@apstat.com>
	<43958ee60906301624l7fd7f445sebd0d69b4ac32e61@mail.gmail.com>
	<4a4b8c6f.1a135e0a.7a2d.0b82@mx.google.com>
	<5b8d13220907010933j7463ec93tb0bb92f02fbf9308@mail.gmail.com>
	<4a4cf1bf.05a4100a.7834.ffffcf2e@mx.google.com>
	<4F46BA01-652B-4AEA-84CE-277D54F26693@cs.toronto.edu>
	<4a4e2ca7.0c92100a.2f71.0392@mx.google.com>
	<1cd32cbb0908140713wcb67a2cl20899bb7207e64cc@mail.gmail.com>
Message-ID: <1cd32cbb0908140849v3c35fe4fxd483e255b070c8d2@mail.gmail.com>

On Fri, Aug 14, 2009 at 10:13 AM, <josef.pktd at gmail.com> wrote:
> On Fri, Jul 3, 2009 at 12:06 PM, Elias Pampalk<elias.pampalk at gmail.com> wrote:
>
>>
>> I did a quick comparison between Matlab/stats (R14SP3), R (2.8.1), and
>> Python/SciPy (0.7). Maybe this is somehow useful for others too.
>>
> ...
>
>> Elias
>
> Thanks for doing this, this is very helpful. I attached the comparison
> to ticket:901
>
> Except for one case, the numbers look pretty good.
> However, documentation is still weak, and some of the code duplication
> could be removed.
>
> Josef
>
> (I didn't look at it closely before, because I was on vacation at that time.)
>

I wish we would get more contributions like this, there are still some
unverified functions in stats (and the rest of scipy).

Skipper and I (mostly Skipper) spend a lot of time this summer
comparing the reworked models code with R, Stata or SAS, and it can be
very time consuming to find out whether the differences are bugs, are
because of the use of different options or whether the functions are
based on different definitions. The good result is that we have almost
all the code verified against at least one other statistical package.

Josef


From gael.varoquaux at normalesup.org  Fri Aug 14 12:09:13 2009
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Fri, 14 Aug 2009 18:09:13 +0200
Subject: [SciPy-User] [SciPy-user] scipy.stats.stats mannwhitneyu
	vs	ranksums?
In-Reply-To: <1cd32cbb0908140849v3c35fe4fxd483e255b070c8d2@mail.gmail.com>
References: <43958ee60906301516s637b180fm35b96d5b61a1b549@mail.gmail.com>
	<4A4A92A6.9070503@apstat.com>
	<43958ee60906301624l7fd7f445sebd0d69b4ac32e61@mail.gmail.com>
	<4a4b8c6f.1a135e0a.7a2d.0b82@mx.google.com>
	<5b8d13220907010933j7463ec93tb0bb92f02fbf9308@mail.gmail.com>
	<4a4cf1bf.05a4100a.7834.ffffcf2e@mx.google.com>
	<4F46BA01-652B-4AEA-84CE-277D54F26693@cs.toronto.edu>
	<4a4e2ca7.0c92100a.2f71.0392@mx.google.com>
	<1cd32cbb0908140713wcb67a2cl20899bb7207e64cc@mail.gmail.com>
	<1cd32cbb0908140849v3c35fe4fxd483e255b070c8d2@mail.gmail.com>
Message-ID: <20090814160913.GE5678@phare.normalesup.org>

On Fri, Aug 14, 2009 at 11:49:37AM -0400, josef.pktd at gmail.com wrote:
> Skipper and I (mostly Skipper) spend a lot of time this summer
> comparing the reworked models code with R, Stata or SAS, and it can be
> very time consuming to find out whether the differences are bugs, are
> because of the use of different options or whether the functions are
> based on different definitions. The good result is that we have almost
> all the code verified against at least one other statistical package.

Thank you so much guys. I often cite your work to colleagues as an
example of why you would want to open source some code: some random guys
might come later and actually verify it (and find that you've done a lot
of bad things).

Ga?l


From dav at alum.mit.edu  Fri Aug 14 13:39:43 2009
From: dav at alum.mit.edu (Dav Clark)
Date: Fri, 14 Aug 2009 10:39:43 -0700
Subject: [SciPy-User] Behavioral science BoF?
Message-ID: <123A4568-806A-422D-B53F-C81B5EB71D4D@alum.mit.edu>

I know a number of neuroscience types will be coming, but we already  
talk.  So this may not be the best use of conference time.

But, I figured I'd see if anyone were interested in issues like:

  - Stimulus presentation (What do people use / need - I can share my  
experience creating a more semantic framework for this stuff on top of  
VisionEgg)
  - Data collection (I'd be happy to talk about my experiences with  
the excellent LabJack I/O box - this wouldn't have to be for  
behavioral data).
  -  Data marshalling, storage, retrieval, visualization, analysis -  
I'm having great success dealing with glitchy data using an HDF5  
container and a TraitsUI visualization tool to rapidly look through  
the many things that went wrong.

I'd be happy talking to people about ecologies or toothpaste or Luan  
or whatever, if they felt they had similar computational problems.  So  
don't take the "behavioral science" aspect too seriously.

Any takers?
Dav


From gokhansever at gmail.com  Fri Aug 14 16:05:05 2009
From: gokhansever at gmail.com (=?UTF-8?Q?G=C3=B6khan_Sever?=)
Date: Fri, 14 Aug 2009 15:05:05 -0500
Subject: [SciPy-User] Intro tutorial checklist failure
Message-ID: <49d6b3500908141305g2812d010we1239f5e1074b40b@mail.gmail.com>

Hello,

As posted below the output of the script seemingly there is something wrong
with the matplotlib tests. I have matplotlib installed and working properly
however the script fails to test this. Manual testing of the test plot
functions work properly as expected.

I don't have scipy and mayavi installed yet since there are two bizarre
installation issues that I have had and posted on the lists, and still
couldn't figure out. All were working extremely nicely on Fedora 10 (The
source code installations) but there must be somethings wrong with some
critical libraries or some tools' being so up-to-date.


[gsever at ccn python-repo]$ python intro_tut_checklist.py
Running tests:
__main__.test_imports('setuptools', None) ... MOD: setuptools, version:
0.6c9
ok
__main__.test_imports('IPython', None) ... MOD: IPython, version:
0.11.bzr.r1205
ok
__main__.test_imports('numpy', None) ... MOD: numpy, version: 1.4.0.dev
ok
__main__.test_imports('scipy', None) ... ERROR
__main__.test_imports('scipy.io', None) ... ERROR
__main__.test_imports('matplotlib', <function validate_mpl at 0xb7a83614>)
... ERROR
__main__.test_imports('pylab', None) ... ERROR
__main__.test_imports('enthought.mayavi.api', None) ... ERROR
__main__.test_loadtxt(array([[ 0.,  1.], ... ok
__main__.test_loadtxt(array([('M', 21, 72.0), ('F', 35, 58.0)], ... ok
__main__.test_loadtxt(array([ 1.,  3.]), array([ 1.,  3.])) ... ok
__main__.test_loadtxt(array([ 2.,  4.]), array([ 2.,  4.])) ... ok
Simple plot generation. ... ERROR
Plots with math ... ERROR

======================================================================
ERROR: __main__.test_imports('scipy', None)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.6/site-packages/nose/case.py", line 182, in runTest
    self.test(*self.arg)
  File "intro_tut_checklist.py", line 91, in check_import
    exec "import %s as m" % mnames
  File "<string>", line 1, in <module>
ImportError: No module named scipy

======================================================================
ERROR: __main__.test_imports('scipy.io', None)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.6/site-packages/nose/case.py", line 182, in runTest
    self.test(*self.arg)
  File "intro_tut_checklist.py", line 91, in check_import
    exec "import %s as m" % mnames
  File "<string>", line 1, in <module>
ImportError: No module named scipy.io

======================================================================
ERROR: __main__.test_imports('matplotlib', <function validate_mpl at
0xb7a83614>)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.6/site-packages/nose/case.py", line 182, in runTest
    self.test(*self.arg)
  File "intro_tut_checklist.py", line 106, in check_import
    validator(m)
  File "intro_tut_checklist.py", line 69, in validate_mpl
    m.use('Agg')
AttributeError: 'module' object has no attribute 'use'

======================================================================
ERROR: __main__.test_imports('pylab', None)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.6/site-packages/nose/case.py", line 182, in runTest
    self.test(*self.arg)
  File "intro_tut_checklist.py", line 91, in check_import
    exec "import %s as m" % mnames
  File "<string>", line 1, in <module>
  File "/home/gsever/Desktop/python-repo/matplotlib/lib/pylab.py", line 1,
in <module>
    from matplotlib.pylab import *
ImportError: No module named pylab

======================================================================
ERROR: __main__.test_imports('enthought.mayavi.api', None)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.6/site-packages/nose/case.py", line 182, in runTest
    self.test(*self.arg)
  File "intro_tut_checklist.py", line 91, in check_import
    exec "import %s as m" % mnames
  File "<string>", line 1, in <module>
ImportError: No module named mayavi.api

======================================================================
ERROR: Simple plot generation.
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.6/site-packages/nose/case.py", line 182, in runTest
    self.test(*self.arg)
  File "intro_tut_checklist.py", line 166, in test_plot
    from matplotlib import pyplot as plt
ImportError: cannot import name pyplot

======================================================================
ERROR: Plots with math
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.6/site-packages/nose/case.py", line 182, in runTest
    self.test(*self.arg)
  File "intro_tut_checklist.py", line 175, in test_plot_math
    from matplotlib import pyplot as plt
ImportError: cannot import name pyplot

----------------------------------------------------------------------
Ran 14 tests in 0.539s

FAILED (errors=7)
Cleanup - removing temp directory:
/home/gsever/Desktop/python-repo/tmp-testdata-h_qN8x

***************************************************************************
                           TESTS FINISHED
***************************************************************************

If the printout above did not finish in 'OK' but instead says 'FAILED', copy
and send the *entire* output, including the system information below, for
help.
We'll do our best to assist you.  You can send your message to the Scipy
user
mailing list:

    http://mail.scipy.org/mailman/listinfo/scipy-user

but feel free to also CC directly: Fernando.Perez at berkeley.edu

==================
System information
==================
os.name      : posix
os.uname     : ('Linux', 'ccn', '2.6.29.6-217.2.3.fc11.i686.PAE', '#1 SMP
Wed Jul 29 16:05:22 EDT 2009', 'i686')
platform     : linux2
platform+    :
Linux-2.6.29.6-217.2.3.fc11.i686.PAE-i686-with-fedora-11-Leonidas
prefix       : /usr
exec_prefix  : /usr
executable   : /usr/bin/python
version_info : (2, 6, 0, 'final', 0)
version      : 2.6 (r26:66714, Jun  8 2009, 16:07:26)
[GCC 4.4.0 20090506 (Red Hat 4.4.0-4)]


-- 
G?khan
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From gael.varoquaux at normalesup.org  Fri Aug 14 16:48:07 2009
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Fri, 14 Aug 2009 22:48:07 +0200
Subject: [SciPy-User] Intro tutorial checklist failure
In-Reply-To: <49d6b3500908141305g2812d010we1239f5e1074b40b@mail.gmail.com>
References: <49d6b3500908141305g2812d010we1239f5e1074b40b@mail.gmail.com>
Message-ID: <20090814204807.GC7131@phare.normalesup.org>

On Fri, Aug 14, 2009 at 03:05:05PM -0500, G?khan Sever wrote:
>    I don't have scipy and mayavi installed yet since there are two bizarre
>    installation issues that I have had and posted on the lists, and still
>    couldn't figure out. All were working extremely nicely on Fedora 10 (The
>    source code installations) but there must be somethings wrong with some
>    critical libraries or some tools' being so up-to-date.

Could you remind me (on the enthought-dev mailing list, if you will) the
installation problem with Mayavi. I must have lost the mail, probably in
the flood that is my mailbox.

Ga?l


From gokhansever at gmail.com  Fri Aug 14 17:03:09 2009
From: gokhansever at gmail.com (=?UTF-8?Q?G=C3=B6khan_Sever?=)
Date: Fri, 14 Aug 2009 16:03:09 -0500
Subject: [SciPy-User] lnpymath cannot find error during scipy
	installation
In-Reply-To: <49d6b3500908131256j4ab0918dkdd20563df9b7088c@mail.gmail.com>
References: <49d6b3500908131256j4ab0918dkdd20563df9b7088c@mail.gmail.com>
Message-ID: <49d6b3500908141403r35bf33a4u4dc582d06fa220a@mail.gmail.com>

Hello,

A solution form the self :)

I fixed this compilation error by putting a symbolic to the npy_math.o file
(/home/gsever/Desktop/python-repo/numpy/build/temp.linux-i686-2.6/numpy/core/src/npymath)
under /usr/lib as libnpymath.so  (the library files like having "so" postfix
in lib so I just follow the convention.)

When I issued python setupegg.py develop as a root user it was finally been
able to compiled and working properly.

Wondering it is something related to the new code in the SVN-bases since I
remember I successfully built scipy (on top of SVN-numpy) from the SVN
without encountering this missing library (or not properly placed) issue.

The way I do the check-outs is as a regular user and then invoke the python
setupegg.py develop commands (where available) as root to place the links or
files properly under /usr directories. Before I had given full read-write
access to all /usr directories and files, but had observed weird issues due
to this change.



On Thu, Aug 13, 2009 at 2:56 PM, G?khan Sever <gokhansever at gmail.com> wrote:

> /usr/bin/ld: cannot find -lnpymath
> collect2: ld returned 1 exit status
> error: Command "/usr/bin/g77 -g -Wall -g -Wall -shared
> build/temp.linux-i686-2.6/scipy/special/_cephesmodule.o
> build/temp.linux-i686-2.6/scipy/special/amos_wrappers.o
> build/temp.linux-i686-2.6/scipy/special/specfun_wrappers.o
> build/temp.linux-i686-2.6/scipy/special/toms_wrappers.o
> build/temp.linux-i686-2.6/scipy/special/cdf_wrappers.o
> build/temp.linux-i686-2.6/scipy/special/ufunc_extras.o
> -L/usr/lib/python2.6/site-packages/numpy/core/lib -L/usr/lib
> -Lbuild/temp.linux-i686-2.6 -lsc_amos -lsc_toms -lsc_c_misc -lsc_cephes
> -lsc_mach -lsc_cdf -lsc_specfun -lnpymath -lm -lpython2.6 -lg2c -o
> scipy/special/_cephes.so" failed with exit status 1
>
> Did a python setupegg.py develop install for numpy and when I tried to do
> the same for scipy I stuck at that error. I know there is a libnpymath file
> under one of the numpy build dirs, however not sure how to show that path
> for the linker.
>
> Any ideas?
>
> Thank you
>
>
> ##############################################
> python -c 'from numpy.f2py.diagnose import run; run()'
> ##############################################
> ------
> os.name='posix'
> ------
> sys.platform='linux2'
> ------
> sys.version:
> 2.6 (r26:66714, Jun  8 2009, 16:07:26)
> [GCC 4.4.0 20090506 (Red Hat 4.4.0-4)]
> ------
> sys.prefix:
> /usr
> ------
>
> sys.path=':/usr/lib/python2.6/site-packages/foolscap-0.4.2-py2.6.egg:/usr/lib/python2.6/site-packages/Twisted-8.2.0-py2.6-linux-i686.egg:/home/gsever/Desktop/python-repo/ipython:/home/gsever/Desktop/python-repo/numpy:/home/gsever/Desktop/python-repo/matplotlib/lib:/usr/lib/python2.6/site-packages/Sphinx-0.6.2-py2.6.egg:/usr/lib/python2.6/site-packages/docutils-0.5-py2.6.egg:/usr/lib/python2.6/site-packages/Jinja2-2.1.1-py2.6-linux-i686.egg:/usr/lib/python2.6/site-packages/Pygments-1.0-py2.6.egg:/usr/lib/python2.6/site-packages/xlwt-0.7.2-py2.6.egg:/usr/lib/python2.6/site-packages/spyder-1.0.0beta1-py2.6.egg:/usr/lib/python26.zip:/usr/lib/python2.6:/usr/lib/python2.6/plat-linux2:/usr/lib/python2.6/lib-tk:/usr/lib/python2.6/lib-old:/usr/lib/python2.6/lib-dynload:/usr/lib/python2.6/site-packages:/usr/lib/python2.6/site-packages/Numeric:/usr/lib/python2.6/site-packages/PIL:/usr/lib/python2.6/site-packages/gst-0.10:/usr/lib/python2.6/site-packages/gtk-2.0:/usr/lib/python2.6/site-packages:/usr/lib/python2.6/site-packages/wx-2.8-gtk2-unicode'
> ------
> Failed to import numarray: No module named numarray
> Found Numeric version '24.2' in
> /usr/lib/python2.6/site-packages/Numeric/Numeric.pyc
> Found new numpy version '1.4.0.dev' in
> /home/gsever/Desktop/python-repo/numpy/numpy/__init__.pyc
> Found f2py2e version '2' in
> /home/gsever/Desktop/python-repo/numpy/numpy/f2py/f2py2e.pyc
> Found numpy.distutils version '0.4.0' in
> '/home/gsever/Desktop/python-repo/numpy/numpy/distutils/__init__.pyc'
> ------
> Importing numpy.distutils.fcompiler ... ok
> ------
> Checking availability of supported Fortran compilers:
> GnuFCompiler instance properties:
>   archiver        = ['/usr/bin/g77', '-cr']
>   compile_switch  = '-c'
>   compiler_f77    = ['/usr/bin/g77', '-g', '-Wall', '-fno-second-
>                     underscore', '-fPIC', '-O3', '-funroll-loops']
>   compiler_f90    = None
>   compiler_fix    = None
>   libraries       = ['g2c']
>   library_dirs    = []
>   linker_exe      = ['/usr/bin/g77', '-g', '-Wall', '-g', '-Wall']
>   linker_so       = ['/usr/bin/g77', '-g', '-Wall', '-g', '-Wall', '-
>                     shared']
>   object_switch   = '-o '
>   ranlib          = ['/usr/bin/g77']
>   version         = LooseVersion ('3.4.6')
>   version_cmd     = ['/usr/bin/g77', '--version']
> Gnu95FCompiler instance properties:
>   archiver        = ['/usr/bin/gfortran', '-cr']
>   compile_switch  = '-c'
>   compiler_f77    = ['/usr/bin/gfortran', '-Wall', '-ffixed-form', '-fno-
>                     second-underscore', '-fPIC', '-O3', '-funroll-loops']
>   compiler_f90    = ['/usr/bin/gfortran', '-Wall',
> '-fno-second-underscore',
>                     '-fPIC', '-O3', '-funroll-loops']
>   compiler_fix    = ['/usr/bin/gfortran', '-Wall', '-ffixed-form', '-fno-
>                     second-underscore', '-Wall', '-fno-second-underscore',
> '-
>                     fPIC', '-O3', '-funroll-loops']
>   libraries       = ['gfortran']
>   library_dirs    = []
>   linker_exe      = ['/usr/bin/gfortran', '-Wall', '-Wall']
>   linker_so       = ['/usr/bin/gfortran', '-Wall', '-Wall', '-shared']
>   object_switch   = '-o '
>   ranlib          = ['/usr/bin/gfortran']
>   version         = LooseVersion ('4.4.0')
>   version_cmd     = ['/usr/bin/gfortran', '--version']
> Fortran compilers found:
>   --fcompiler=gnu    GNU Fortran 77 compiler (3.4.6)
>   --fcompiler=gnu95  GNU Fortran 95 compiler (4.4.0)
> Compilers available for this platform, but not found:
>   --fcompiler=absoft   Absoft Corp Fortran Compiler
>   --fcompiler=compaq   Compaq Fortran Compiler
>   --fcompiler=g95      G95 Fortran Compiler
>   --fcompiler=intel    Intel Fortran Compiler for 32-bit apps
>   --fcompiler=intele   Intel Fortran Compiler for Itanium apps
>   --fcompiler=intelem  Intel Fortran Compiler for EM64T-based apps
>   --fcompiler=lahey    Lahey/Fujitsu Fortran 95 Compiler
>   --fcompiler=nag      NAGWare Fortran 95 Compiler
>   --fcompiler=pg       Portland Group Fortran Compiler
>   --fcompiler=vast     Pacific-Sierra Research Fortran 90 Compiler
> Compilers not available on this platform:
>   --fcompiler=hpux     HP Fortran 90 Compiler
>   --fcompiler=ibm      IBM XL Fortran Compiler
>   --fcompiler=intelev  Intel Visual Fortran Compiler for Itanium apps
>   --fcompiler=intelv   Intel Visual Fortran Compiler for 32-bit apps
>   --fcompiler=mips     MIPSpro Fortran Compiler
>   --fcompiler=none     Fake Fortran compiler
>   --fcompiler=sun      Sun or Forte Fortran 95 Compiler
> For compiler details, run 'config_fc --verbose' setup command.
> ------
> Importing numpy.distutils.cpuinfo ... ok
> ------
> CPU information: CPUInfoBase__get_nbits getNCPUs has_mmx has_sse has_sse2
> has_sse3 has_ssse3 is_32bit is_Intel is_i686 ------
>
>
>
> --
> G?khan
>



-- 
G?khan
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From d_l_goldsmith at yahoo.com  Fri Aug 14 18:03:52 2009
From: d_l_goldsmith at yahoo.com (David Goldsmith)
Date: Fri, 14 Aug 2009 15:03:52 -0700 (PDT)
Subject: [SciPy-User] Problems installing scipy [was: Sanity checklist for
	SciPy'09 tutorials]
Message-ID: <429746.44978.qm@web52108.mail.re2.yahoo.com>

Hi!? Problem(s) installing "bleeding edge" svn checkout (done just now; no checkout problems reported) of scipy using "python setup.py install" on the (DOS Terminal) command line.

Platform: Windows Vista Home Premium SP1
Python: 2.6.2
(Numpy 1.3.0rc2 already installed and run successfully numerous times).

Output: (lengthy; questions follow)

C:\Python26\Lib\site-packages\scipy>python setup.py install
setup.py:63: UserWarning:? --- Could not run svn info ---
? warnings.warn(" --- Could not run svn info --- ")
Warning: No configuration returned, assuming unavailable.
blas_opt_info:
blas_mkl_info:
? libraries mkl,vml,guide not found in C:\Python26\lib
? libraries mkl,vml,guide not found in C:\
? libraries mkl,vml,guide not found in C:\Python26\libs
? NOT AVAILABLE

atlas_blas_threads_info:
Setting PTATLAS=ATLAS
? NOT AVAILABLE

atlas_blas_info:
? NOT AVAILABLE

C:\Python26\lib\site-packages\numpy\distutils\system_info.py:1383: UserWarning:

? ? Atlas (http://math-atlas.sourceforge.net/) libraries not found.
? ? Directories to search for the libraries can be specified in the
? ? numpy/distutils/site.cfg file (section [atlas]) or by setting
? ? the ATLAS environment variable.
? warnings.warn(AtlasNotFoundError.__doc__)
blas_info:
? libraries blas not found in C:\Python26\lib
? libraries blas not found in C:\
? libraries blas not found in C:\Python26\libs
? NOT AVAILABLE

C:\Python26\lib\site-packages\numpy\distutils\system_info.py:1392: UserWarning:

? ? Blas (http://www.netlib.org/blas/) libraries not found.
? ? Directories to search for the libraries can be specified in the
? ? numpy/distutils/site.cfg file (section [blas]) or by setting
? ? the BLAS environment variable.
? warnings.warn(BlasNotFoundError.__doc__)
blas_src_info:
? NOT AVAILABLE

C:\Python26\lib\site-packages\numpy\distutils\system_info.py:1395: UserWarning:

? ? Blas (http://www.netlib.org/blas/) sources not found.
? ? Directories to search for the sources can be specified in the
? ? numpy/distutils/site.cfg file (section [blas_src]) or by setting
? ? the BLAS_SRC environment variable.
? warnings.warn(BlasSrcNotFoundError.__doc__)
Traceback (most recent call last):
? File "setup.py", line 160, in <module>
? ? setup_package()
? File "setup.py", line 152, in setup_package
? ? configuration=configuration )
? File "C:\Python26\lib\site-packages\numpy\distutils\core.py", line 150, in set
up
? ? config = configuration()
? File "setup.py", line 118, in configuration
? ? config.add_subpackage('scipy')
? File "C:\Python26\lib\site-packages\numpy\distutils\misc_util.py", line 852, i
n add_subpackage
? ? caller_level = 2)
? File "C:\Python26\lib\site-packages\numpy\distutils\misc_util.py", line 835, i
n get_subpackage
? ? caller_level = caller_level + 1)
? File "C:\Python26\lib\site-packages\numpy\distutils\misc_util.py", line 782, i
n _get_configuration_from_setup_py
? ? config = setup_module.configuration(*args)
? File "scipy\setup.py", line 8, in configuration
? ? config.add_subpackage('integrate')
? File "C:\Python26\lib\site-packages\numpy\distutils\misc_util.py", line 852, i
n add_subpackage
? ? caller_level = 2)
? File "C:\Python26\lib\site-packages\numpy\distutils\misc_util.py", line 835, i
n get_subpackage
? ? caller_level = caller_level + 1)
? File "C:\Python26\lib\site-packages\numpy\distutils\misc_util.py", line 782, i
n _get_configuration_from_setup_py
? ? config = setup_module.configuration(*args)
? File "scipy\integrate\setup.py", line 10, in configuration
? ? blas_opt = get_info('blas_opt',notfound_action=2)
? File "C:\Python26\lib\site-packages\numpy\distutils\system_info.py", line 303,
 in get_info
? ? return cl().get_info(notfound_action)
? File "C:\Python26\lib\site-packages\numpy\distutils\system_info.py", line 454,
 in get_info
? ? raise self.notfounderror,self.notfounderror.__doc__
numpy.distutils.system_info.BlasNotFoundError:
? ? Blas (http://www.netlib.org/blas/) libraries not found.
? ? Directories to search for the libraries can be specified in the
? ? numpy/distutils/site.cfg file (section [blas]) or by setting
? ? the BLAS environment variable.

Does one need to install BLAS separately?  If so, that should be added to the Pre-requisites on http://conference.scipy.org/intro_tutorial.

Also vague from that page: 

Are iPython, matplotlib, and Mayavi separate download/installs (i.e., not part of scipy?  I thought, at least, matplotlib was included with scipy, no?)

Do they need to be installed in the order listed?  In particular, can EPD or Python(x,y) be installed before the others?

Finally, a comment/question: this is *a lot* of stuff - how necessary is it all, really?  In particular, I signed up for the "advanced" track ('cause, based on the advertised content, that's where I am vis-a-vis the numpy content, which appears to be a substantial portion of the tutorials), but I'm "intermediate" w/ matplotlib and "novice" w/ everything else on the list - is this going to be a problem?

DG 

--- On Sun, 8/9/09, Fernando Perez <fperez.net at gmail.com> wrote:

> From: Fernando Perez <fperez.net at gmail.com>
> Subject: [Numpy-discussion] Sanity checklist for those attending the SciPy'09 tutorials
> To: "Discussion of Numerical Python" <numpy-discussion at scipy.org>, "SciPy Users List" <scipy-user at scipy.org>
> Date: Sunday, August 9, 2009, 2:12 AM
> Hi all,
> 
> [ sorry for spamming the list, but even though I sent this
> to all the
> email addresses I have on file for tutorial attendees, I
> know I am
> missing a few, so I hope they see this message. ]
> 
> In order to make your experience at the scipy tutorials as
> smooth as
> possible, we strongly recommend that you take a little time
> to install
> the necessary tools in advance.
> 
> For both introductory and advanced? tutorials:
> 
> http://conference.scipy.org/intro_tutorial
> http://conference.scipy.org/advanced_tutorials
> 
> you will find instructions on what to install and where to
> download it
> from.? In addition (this is also mentioned on those
> pages), we
> encourage you to run according to your tutorial of choice,
> a little
> checklist script:
> 
> https://cirl.berkeley.edu/fperez/tmp/intro_tut_checklist.py
> https://cirl.berkeley.edu/fperez/tmp/adv_tut_checklist.py
> 
> This will try to? spot any problems early, and we'll
> do our best? to
> help you with them before you arrive to the conference.
> 
> Best regards,
> 
> Dave Peterson and Fernando Perez.
> 
> ps - for those of you who may find fixes for the checklist
> scripts,
> the sources are hosted on github:
> 
> http://github.com/fperez/scipytut/
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
> 



      


From questions.anon at gmail.com  Fri Aug 14 18:18:02 2009
From: questions.anon at gmail.com (questions anon)
Date: Fri, 14 Aug 2009 15:18:02 -0700
Subject: [SciPy-User] satellite imagery
Message-ID: <aa15d47e0908141518vc7f4a70x1b1e138ac8644ff@mail.gmail.com>

Can anyone suggest any good python books or tutorials for using satellite
imagery?
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From jturner at gemini.edu  Fri Aug 14 18:20:30 2009
From: jturner at gemini.edu (James Turner)
Date: Fri, 14 Aug 2009 18:20:30 -0400
Subject: [SciPy-User] Astronomy BoF meeting at SciPy?
In-Reply-To: <4A833EC5.1080600@gemini.edu>
References: <4A833EC5.1080600@gemini.edu>
Message-ID: <4A85E32E.9070905@gemini.edu>

Hello again,

Regarding the astronomy BoF, most people seem to prefer Thursday
evening. I'm told there will be an informal reception (not a full
dinner) at 18:30 that evening, so the BoF meeting would need to be
after that. I'm guessing by the time the reception fades out, people
will want to eat more, but if we leave another hour or two for
dinner, it's going to end up being rather late. So I'm wondering if
we can order in pizza, beer or whatever and do it the uncivilized
way :-). I'll talk to Gael and see what he thinks.

Anyway, please plan on meeting on Thursday evening, time TBC. I'll
be travelling to the US on Sunday-Monday, so might not answer emails
before the tutorials start on Tuesday.

Cheers,

James.



From jturner at gemini.edu  Fri Aug 14 18:20:44 2009
From: jturner at gemini.edu (James Turner)
Date: Fri, 14 Aug 2009 18:20:44 -0400
Subject: [SciPy-User] SciPy2009 BoF Wiki Page
In-Reply-To: <0636F499-8CBC-4053-ACF9-7BA40E5D58D4@cs.toronto.edu>
References: <0636F499-8CBC-4053-ACF9-7BA40E5D58D4@cs.toronto.edu>
Message-ID: <4A85E33C.5020509@gemini.edu>

Thanks!


From strawman at astraw.com  Fri Aug 14 18:58:14 2009
From: strawman at astraw.com (Andrew Straw)
Date: Fri, 14 Aug 2009 15:58:14 -0700
Subject: [SciPy-User] SciPy 2009 website suggestions
Message-ID: <4A85EC06.2060006@astraw.com>

1. Add a link to the schedule in the left-side navigation bar.
2. At the top of each day in the schedule page, list the location of the
events.


From gokhansever at gmail.com  Fri Aug 14 21:01:45 2009
From: gokhansever at gmail.com (=?UTF-8?Q?G=C3=B6khan_Sever?=)
Date: Fri, 14 Aug 2009 20:01:45 -0500
Subject: [SciPy-User] Behavioral science BoF?
In-Reply-To: <123A4568-806A-422D-B53F-C81B5EB71D4D@alum.mit.edu>
References: <123A4568-806A-422D-B53F-C81B5EB71D4D@alum.mit.edu>
Message-ID: <49d6b3500908141801h5d2f5dc8q9b9240fe827d4292@mail.gmail.com>

On Fri, Aug 14, 2009 at 12:39 PM, Dav Clark <dav at alum.mit.edu> wrote:

> I know a number of neuroscience types will be coming, but we already
> talk.  So this may not be the best use of conference time.
>
> But, I figured I'd see if anyone were interested in issues like:
>
>  - Stimulus presentation (What do people use / need - I can share my
> experience creating a more semantic framework for this stuff on top of
> VisionEgg)
>  - Data collection (I'd be happy to talk about my experiences with
> the excellent LabJack I/O box - this wouldn't have to be for
> behavioral data).
>  -  Data marshalling, storage, retrieval, visualization, analysis -
> I'm having great success dealing with glitchy data using an HDF5
> container and a TraitsUI visualization tool to rapidly look through
> the many things that went wrong.
>

Although I don't know much about behavioral sciences the subject about the
data acquisition and analysis parts interests me very much. That LabJack I/O
box seems interesting. For general data analysis netCDF and HDF5 deployed on
a Traits UI is something we consider implementing.

As an atmospheric sciences graduate student I would like to learn and
discuss other people's approach/solutions in the data acquisition issues.

Please let me know when you are going to get together for this initiative.



>
> I'd be happy talking to people about ecologies or toothpaste or Luan
> or whatever, if they felt they had similar computational problems.  So
> don't take the "behavioral science" aspect too seriously.
>
> Any takers?
> Dav
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>



-- 
G?khan
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From josef.pktd at gmail.com  Fri Aug 14 21:30:48 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Fri, 14 Aug 2009 21:30:48 -0400
Subject: [SciPy-User] Problems installing scipy [was: Sanity checklist
	for SciPy'09 tutorials]
In-Reply-To: <429746.44978.qm@web52108.mail.re2.yahoo.com>
References: <429746.44978.qm@web52108.mail.re2.yahoo.com>
Message-ID: <1cd32cbb0908141830u14cc7516xbff9f57cf1634d08@mail.gmail.com>

On Fri, Aug 14, 2009 at 6:03 PM, David Goldsmith<d_l_goldsmith at yahoo.com> wrote:
> Hi!? Problem(s) installing "bleeding edge" svn checkout (done just now; no checkout problems reported) of scipy using "python setup.py install" on the (DOS Terminal) command line.
>
> Platform: Windows Vista Home Premium SP1
> Python: 2.6.2
> (Numpy 1.3.0rc2 already installed and run successfully numerous times).

I'm on WindowsXP python 2.5.2, and never tried Vista or Python 2.6, so
I don't have version specific information.

Note: recent scipy svn requires recent svn of numpy. so you would also
have to rebuild numpy from svn.

Getting the "bleeding" edge installed, especially the first time, is
no fun (although not too bad on XP if you follow good instructions and
don't build lapack). If you can avoid it go with the latest official
installers. The tutorials don't require bleeding edge.

The main instructions for building scipy are here
http://scipy.org/Installing_SciPy/Windows#head-cd37d819e333227e327079e4c2a2298daf625624

The link goes to the Blas/Lapack part, from where I initially copied
the binaries.

Just one explanation to questions below:

scipy is only scipy, no other packages included. Separate installer
for windows for matplotlib, ipython, and so on are available and
usually work without problems,

EPD or python(xy) can give you the full stack although not at the
"bleeding" edge. Additionally, python(xy) has binary installers for
individual packages which is very nice and work without problems what
little I have tried.

So my recommendation with Windows is to go with the installers or full
distributions, unless you really need the latest svn/hg/git/bzr
versions, or you want to spend the time to get fully setup for
development.

I think Blas is not listed as a prerequisite of the tutorials, because
you only need it to build the prerequisites (and for building the full
stack you would need a lot more.)

Josef

>
> Output: (lengthy; questions follow)
>
> C:\Python26\Lib\site-packages\scipy>python setup.py install
> setup.py:63: UserWarning:? --- Could not run svn info ---
> ? warnings.warn(" --- Could not run svn info --- ")
> Warning: No configuration returned, assuming unavailable.
> blas_opt_info:
> blas_mkl_info:
> ? libraries mkl,vml,guide not found in C:\Python26\lib
> ? libraries mkl,vml,guide not found in C:\
> ? libraries mkl,vml,guide not found in C:\Python26\libs
> ? NOT AVAILABLE
>
> atlas_blas_threads_info:
> Setting PTATLAS=ATLAS
> ? NOT AVAILABLE
>
> atlas_blas_info:
> ? NOT AVAILABLE
>
> C:\Python26\lib\site-packages\numpy\distutils\system_info.py:1383: UserWarning:
>
> ? ? Atlas (http://math-atlas.sourceforge.net/) libraries not found.
> ? ? Directories to search for the libraries can be specified in the
> ? ? numpy/distutils/site.cfg file (section [atlas]) or by setting
> ? ? the ATLAS environment variable.
> ? warnings.warn(AtlasNotFoundError.__doc__)
> blas_info:
> ? libraries blas not found in C:\Python26\lib
> ? libraries blas not found in C:\
> ? libraries blas not found in C:\Python26\libs
> ? NOT AVAILABLE
>
> C:\Python26\lib\site-packages\numpy\distutils\system_info.py:1392: UserWarning:
>
> ? ? Blas (http://www.netlib.org/blas/) libraries not found.
> ? ? Directories to search for the libraries can be specified in the
> ? ? numpy/distutils/site.cfg file (section [blas]) or by setting
> ? ? the BLAS environment variable.
> ? warnings.warn(BlasNotFoundError.__doc__)
> blas_src_info:
> ? NOT AVAILABLE
>
> C:\Python26\lib\site-packages\numpy\distutils\system_info.py:1395: UserWarning:
>
> ? ? Blas (http://www.netlib.org/blas/) sources not found.
> ? ? Directories to search for the sources can be specified in the
> ? ? numpy/distutils/site.cfg file (section [blas_src]) or by setting
> ? ? the BLAS_SRC environment variable.
> ? warnings.warn(BlasSrcNotFoundError.__doc__)
> Traceback (most recent call last):
> ? File "setup.py", line 160, in <module>
> ? ? setup_package()
> ? File "setup.py", line 152, in setup_package
> ? ? configuration=configuration )
> ? File "C:\Python26\lib\site-packages\numpy\distutils\core.py", line 150, in set
> up
> ? ? config = configuration()
> ? File "setup.py", line 118, in configuration
> ? ? config.add_subpackage('scipy')
> ? File "C:\Python26\lib\site-packages\numpy\distutils\misc_util.py", line 852, i
> n add_subpackage
> ? ? caller_level = 2)
> ? File "C:\Python26\lib\site-packages\numpy\distutils\misc_util.py", line 835, i
> n get_subpackage
> ? ? caller_level = caller_level + 1)
> ? File "C:\Python26\lib\site-packages\numpy\distutils\misc_util.py", line 782, i
> n _get_configuration_from_setup_py
> ? ? config = setup_module.configuration(*args)
> ? File "scipy\setup.py", line 8, in configuration
> ? ? config.add_subpackage('integrate')
> ? File "C:\Python26\lib\site-packages\numpy\distutils\misc_util.py", line 852, i
> n add_subpackage
> ? ? caller_level = 2)
> ? File "C:\Python26\lib\site-packages\numpy\distutils\misc_util.py", line 835, i
> n get_subpackage
> ? ? caller_level = caller_level + 1)
> ? File "C:\Python26\lib\site-packages\numpy\distutils\misc_util.py", line 782, i
> n _get_configuration_from_setup_py
> ? ? config = setup_module.configuration(*args)
> ? File "scipy\integrate\setup.py", line 10, in configuration
> ? ? blas_opt = get_info('blas_opt',notfound_action=2)
> ? File "C:\Python26\lib\site-packages\numpy\distutils\system_info.py", line 303,
> ?in get_info
> ? ? return cl().get_info(notfound_action)
> ? File "C:\Python26\lib\site-packages\numpy\distutils\system_info.py", line 454,
> ?in get_info
> ? ? raise self.notfounderror,self.notfounderror.__doc__
> numpy.distutils.system_info.BlasNotFoundError:
> ? ? Blas (http://www.netlib.org/blas/) libraries not found.
> ? ? Directories to search for the libraries can be specified in the
> ? ? numpy/distutils/site.cfg file (section [blas]) or by setting
> ? ? the BLAS environment variable.
>
> Does one need to install BLAS separately? ?If so, that should be added to the Pre-requisites on http://conference.scipy.org/intro_tutorial.
>
> Also vague from that page:
>
> Are iPython, matplotlib, and Mayavi separate download/installs (i.e., not part of scipy? ?I thought, at least, matplotlib was included with scipy, no?)
>
> Do they need to be installed in the order listed? ?In particular, can EPD or Python(x,y) be installed before the others?
>
> Finally, a comment/question: this is *a lot* of stuff - how necessary is it all, really? ?In particular, I signed up for the "advanced" track ('cause, based on the advertised content, that's where I am vis-a-vis the numpy content, which appears to be a substantial portion of the tutorials), but I'm "intermediate" w/ matplotlib and "novice" w/ everything else on the list - is this going to be a problem?
>
> DG
>
> --- On Sun, 8/9/09, Fernando Perez <fperez.net at gmail.com> wrote:
>
>> From: Fernando Perez <fperez.net at gmail.com>
>> Subject: [Numpy-discussion] Sanity checklist for those attending the SciPy'09 tutorials
>> To: "Discussion of Numerical Python" <numpy-discussion at scipy.org>, "SciPy Users List" <scipy-user at scipy.org>
>> Date: Sunday, August 9, 2009, 2:12 AM
>> Hi all,
>>
>> [ sorry for spamming the list, but even though I sent this
>> to all the
>> email addresses I have on file for tutorial attendees, I
>> know I am
>> missing a few, so I hope they see this message. ]
>>
>> In order to make your experience at the scipy tutorials as
>> smooth as
>> possible, we strongly recommend that you take a little time
>> to install
>> the necessary tools in advance.
>>
>> For both introductory and advanced? tutorials:
>>
>> http://conference.scipy.org/intro_tutorial
>> http://conference.scipy.org/advanced_tutorials
>>
>> you will find instructions on what to install and where to
>> download it
>> from.? In addition (this is also mentioned on those
>> pages), we
>> encourage you to run according to your tutorial of choice,
>> a little
>> checklist script:
>>
>> https://cirl.berkeley.edu/fperez/tmp/intro_tut_checklist.py
>> https://cirl.berkeley.edu/fperez/tmp/adv_tut_checklist.py
>>
>> This will try to? spot any problems early, and we'll
>> do our best? to
>> help you with them before you arrive to the conference.
>>
>> Best regards,
>>
>> Dave Peterson and Fernando Perez.
>>
>> ps - for those of you who may find fixes for the checklist
>> scripts,
>> the sources are hosted on github:
>>
>> http://github.com/fperez/scipytut/
>> _______________________________________________
>> NumPy-Discussion mailing list
>> NumPy-Discussion at scipy.org
>> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>>
>
>
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From cournape at gmail.com  Fri Aug 14 22:20:46 2009
From: cournape at gmail.com (David Cournapeau)
Date: Fri, 14 Aug 2009 19:20:46 -0700
Subject: [SciPy-User] lnpymath cannot find error during scipy
	installation
In-Reply-To: <49d6b3500908131256j4ab0918dkdd20563df9b7088c@mail.gmail.com>
References: <49d6b3500908131256j4ab0918dkdd20563df9b7088c@mail.gmail.com>
Message-ID: <5b8d13220908141920k78533f5ej8f3efa1a13c0310@mail.gmail.com>

On Thu, Aug 13, 2009 at 12:56 PM, G?khan Sever<gokhansever at gmail.com> wrote:
> /usr/bin/ld: cannot find -lnpymath
> collect2: ld returned 1 exit status
> error: Command "/usr/bin/g77 -g -Wall -g -Wall -shared
> build/temp.linux-i686-2.6/scipy/special/_cephesmodule.o
> build/temp.linux-i686-2.6/scipy/special/amos_wrappers.o
> build/temp.linux-i686-2.6/scipy/special/specfun_wrappers.o
> build/temp.linux-i686-2.6/scipy/special/toms_wrappers.o
> build/temp.linux-i686-2.6/scipy/special/cdf_wrappers.o
> build/temp.linux-i686-2.6/scipy/special/ufunc_extras.o
> -L/usr/lib/python2.6/site-packages/numpy/core/lib -L/usr/lib
> -Lbuild/temp.linux-i686-2.6 -lsc_amos -lsc_toms -lsc_c_misc -lsc_cephes
> -lsc_mach -lsc_cdf -lsc_specfun -lnpymath -lm -lpython2.6 -lg2c -o
> scipy/special/_cephes.so" failed with exit status 1
>
> Did a python setupegg.py develop install for numpy and when I tried to do
> the same for scipy I stuck at that error. I know there is a libnpymath file
> under one of the numpy build dirs, however not sure how to show that path
> for the linker.

I did some changes to build_clib command, but I did not test the
develop command. I thought that making sure that the in place command
works was enough, but it looks like it is not true. I will look at it
when I have some time. Please report it as a bug on numpy trac,
otherwise I will forget about it,

cheers,

David


From d_l_goldsmith at yahoo.com  Fri Aug 14 23:02:35 2009
From: d_l_goldsmith at yahoo.com (David Goldsmith)
Date: Fri, 14 Aug 2009 20:02:35 -0700 (PDT)
Subject: [SciPy-User] Problems installing scipy [was: Sanity checklist
	for SciPy'09 tutorials]
In-Reply-To: <1cd32cbb0908141830u14cc7516xbff9f57cf1634d08@mail.gmail.com>
Message-ID: <500809.23688.qm@web52104.mail.re2.yahoo.com>

Thanks, Josef!

--- On Fri, 8/14/09, josef.pktd at gmail.com <josef.pktd at gmail.com> wrote:

> I'm on WindowsXP python 2.5.2, and never tried Vista or
> Python 2.6, so
> I don't have version specific information.
> 
> Note: recent scipy svn requires recent svn of numpy. so you
> would also
> have to rebuild numpy from svn.

OK, definitely don't want to introduce that complication. ;-)

> Getting the "bleeding" edge installed, especially the first
> time, is
> no fun (although not too bad on XP if you follow good
> instructions and
> don't build lapack). If you can avoid it go with the latest
> official
> installers. The tutorials don't require bleeding edge.

Good to know, thanks!

> The main instructions for building scipy are here
> http://scipy.org/Installing_SciPy/Windows#head-cd37d819e333227e327079e4c2a2298daf625624

Thanks!

> The link goes to the Blas/Lapack part, from where I
> initially copied
> the binaries.
> 
> Just one explanation to questions below:
> 
> scipy is only scipy, no other packages included. Separate
> installer
> for windows for matplotlib, ipython, and so on are
> available and
> usually work without problems,
> 
> EPD or python(xy) can give you the full stack although not
> at the
> "bleeding" edge. Additionally, python(xy) has binary
> installers for
> individual packages which is very nice and work without
> problems what
> little I have tried.

So is this last what you'd recommend I try first?

DG

> 
> So my recommendation with Windows is to go with the
> installers or full
> distributions, unless you really need the latest
> svn/hg/git/bzr
> versions, or you want to spend the time to get fully setup
> for
> development.
> 
> I think Blas is not listed as a prerequisite of the
> tutorials, because
> you only need it to build the prerequisites (and for
> building the full
> stack you would need a lot more.)
> 
> Josef
> 
> >
> > Output: (lengthy; questions follow)
> >
> > C:\Python26\Lib\site-packages\scipy>python setup.py
> install
> > setup.py:63: UserWarning:? --- Could not run svn info
> ---
> > ? warnings.warn(" --- Could not run svn info --- ")
> > Warning: No configuration returned, assuming
> unavailable.
> > blas_opt_info:
> > blas_mkl_info:
> > ? libraries mkl,vml,guide not found in
> C:\Python26\lib
> > ? libraries mkl,vml,guide not found in C:\
> > ? libraries mkl,vml,guide not found in
> C:\Python26\libs
> > ? NOT AVAILABLE
> >
> > atlas_blas_threads_info:
> > Setting PTATLAS=ATLAS
> > ? NOT AVAILABLE
> >
> > atlas_blas_info:
> > ? NOT AVAILABLE
> >
> >
> C:\Python26\lib\site-packages\numpy\distutils\system_info.py:1383:
> UserWarning:
> >
> > ? ? Atlas (http://math-atlas.sourceforge.net/) libraries not
> found.
> > ? ? Directories to search for the libraries can be
> specified in the
> > ? ? numpy/distutils/site.cfg file (section [atlas])
> or by setting
> > ? ? the ATLAS environment variable.
> > ? warnings.warn(AtlasNotFoundError.__doc__)
> > blas_info:
> > ? libraries blas not found in C:\Python26\lib
> > ? libraries blas not found in C:\
> > ? libraries blas not found in C:\Python26\libs
> > ? NOT AVAILABLE
> >
> >
> C:\Python26\lib\site-packages\numpy\distutils\system_info.py:1392:
> UserWarning:
> >
> > ? ? Blas (http://www.netlib.org/blas/) libraries
> not found.
> > ? ? Directories to search for the libraries can be
> specified in the
> > ? ? numpy/distutils/site.cfg file (section [blas])
> or by setting
> > ? ? the BLAS environment variable.
> > ? warnings.warn(BlasNotFoundError.__doc__)
> > blas_src_info:
> > ? NOT AVAILABLE
> >
> >
> C:\Python26\lib\site-packages\numpy\distutils\system_info.py:1395:
> UserWarning:
> >
> > ? ? Blas (http://www.netlib.org/blas/) sources not
> found.
> > ? ? Directories to search for the sources can be
> specified in the
> > ? ? numpy/distutils/site.cfg file (section
> [blas_src]) or by setting
> > ? ? the BLAS_SRC environment variable.
> > ? warnings.warn(BlasSrcNotFoundError.__doc__)
> > Traceback (most recent call last):
> > ? File "setup.py", line 160, in <module>
> > ? ? setup_package()
> > ? File "setup.py", line 152, in setup_package
> > ? ? configuration=configuration )
> > ? File
> "C:\Python26\lib\site-packages\numpy\distutils\core.py",
> line 150, in set
> > up
> > ? ? config = configuration()
> > ? File "setup.py", line 118, in configuration
> > ? ? config.add_subpackage('scipy')
> > ? File
> "C:\Python26\lib\site-packages\numpy\distutils\misc_util.py",
> line 852, i
> > n add_subpackage
> > ? ? caller_level = 2)
> > ? File
> "C:\Python26\lib\site-packages\numpy\distutils\misc_util.py",
> line 835, i
> > n get_subpackage
> > ? ? caller_level = caller_level + 1)
> > ? File
> "C:\Python26\lib\site-packages\numpy\distutils\misc_util.py",
> line 782, i
> > n _get_configuration_from_setup_py
> > ? ? config = setup_module.configuration(*args)
> > ? File "scipy\setup.py", line 8, in configuration
> > ? ? config.add_subpackage('integrate')
> > ? File
> "C:\Python26\lib\site-packages\numpy\distutils\misc_util.py",
> line 852, i
> > n add_subpackage
> > ? ? caller_level = 2)
> > ? File
> "C:\Python26\lib\site-packages\numpy\distutils\misc_util.py",
> line 835, i
> > n get_subpackage
> > ? ? caller_level = caller_level + 1)
> > ? File
> "C:\Python26\lib\site-packages\numpy\distutils\misc_util.py",
> line 782, i
> > n _get_configuration_from_setup_py
> > ? ? config = setup_module.configuration(*args)
> > ? File "scipy\integrate\setup.py", line 10, in
> configuration
> > ? ? blas_opt =
> get_info('blas_opt',notfound_action=2)
> > ? File
> "C:\Python26\lib\site-packages\numpy\distutils\system_info.py",
> line 303,
> > ?in get_info
> > ? ? return cl().get_info(notfound_action)
> > ? File
> "C:\Python26\lib\site-packages\numpy\distutils\system_info.py",
> line 454,
> > ?in get_info
> > ? ? raise
> self.notfounderror,self.notfounderror.__doc__
> > numpy.distutils.system_info.BlasNotFoundError:
> > ? ? Blas (http://www.netlib.org/blas/) libraries
> not found.
> > ? ? Directories to search for the libraries can be
> specified in the
> > ? ? numpy/distutils/site.cfg file (section [blas])
> or by setting
> > ? ? the BLAS environment variable.
> >
> > Does one need to install BLAS separately? ?If so,
> that should be added to the Pre-requisites on http://conference.scipy.org/intro_tutorial.
> >
> > Also vague from that page:
> >
> > Are iPython, matplotlib, and Mayavi separate
> download/installs (i.e., not part of scipy? ?I thought, at
> least, matplotlib was included with scipy, no?)
> >
> > Do they need to be installed in the order listed? ?In
> particular, can EPD or Python(x,y) be installed before the
> others?
> >
> > Finally, a comment/question: this is *a lot* of stuff
> - how necessary is it all, really? ?In particular, I signed
> up for the "advanced" track ('cause, based on the advertised
> content, that's where I am vis-a-vis the numpy content,
> which appears to be a substantial portion of the tutorials),
> but I'm "intermediate" w/ matplotlib and "novice" w/
> everything else on the list - is this going to be a
> problem?
> >
> > DG
> >
> > --- On Sun, 8/9/09, Fernando Perez <fperez.net at gmail.com>
> wrote:
> >
> >> From: Fernando Perez <fperez.net at gmail.com>
> >> Subject: [Numpy-discussion] Sanity checklist for
> those attending the SciPy'09 tutorials
> >> To: "Discussion of Numerical Python" <numpy-discussion at scipy.org>,
> "SciPy Users List" <scipy-user at scipy.org>
> >> Date: Sunday, August 9, 2009, 2:12 AM
> >> Hi all,
> >>
> >> [ sorry for spamming the list, but even though I
> sent this
> >> to all the
> >> email addresses I have on file for tutorial
> attendees, I
> >> know I am
> >> missing a few, so I hope they see this message. ]
> >>
> >> In order to make your experience at the scipy
> tutorials as
> >> smooth as
> >> possible, we strongly recommend that you take a
> little time
> >> to install
> >> the necessary tools in advance.
> >>
> >> For both introductory and advanced? tutorials:
> >>
> >> http://conference.scipy.org/intro_tutorial
> >> http://conference.scipy.org/advanced_tutorials
> >>
> >> you will find instructions on what to install and
> where to
> >> download it
> >> from.? In addition (this is also mentioned on
> those
> >> pages), we
> >> encourage you to run according to your tutorial of
> choice,
> >> a little
> >> checklist script:
> >>
> >> https://cirl.berkeley.edu/fperez/tmp/intro_tut_checklist.py
> >> https://cirl.berkeley.edu/fperez/tmp/adv_tut_checklist.py
> >>
> >> This will try to? spot any problems early, and
> we'll
> >> do our best? to
> >> help you with them before you arrive to the
> conference.
> >>
> >> Best regards,
> >>
> >> Dave Peterson and Fernando Perez.
> >>
> >> ps - for those of you who may find fixes for the
> checklist
> >> scripts,
> >> the sources are hosted on github:
> >>
> >> http://github.com/fperez/scipytut/
> >> _______________________________________________
> >> NumPy-Discussion mailing list
> >> NumPy-Discussion at scipy.org
> >> http://mail.scipy.org/mailman/listinfo/numpy-discussion
> >>
> >
> >
> >
> >
> > _______________________________________________
> > SciPy-User mailing list
> > SciPy-User at scipy.org
> > http://mail.scipy.org/mailman/listinfo/scipy-user
> >
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
> 

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From fperez.net at gmail.com  Sat Aug 15 01:11:51 2009
From: fperez.net at gmail.com (Fernando Perez)
Date: Fri, 14 Aug 2009 22:11:51 -0700
Subject: [SciPy-User] Problems installing scipy [was: Sanity checklist
	for SciPy'09 tutorials]
In-Reply-To: <500809.23688.qm@web52104.mail.re2.yahoo.com>
References: <1cd32cbb0908141830u14cc7516xbff9f57cf1634d08@mail.gmail.com> 
	<500809.23688.qm@web52104.mail.re2.yahoo.com>
Message-ID: <db6b5ecc0908142211i30c46c24s1e1a9f13865433e2@mail.gmail.com>

Hi David,

On Fri, Aug 14, 2009 at 8:02 PM, David Goldsmith<d_l_goldsmith at yahoo.com> wrote:
>
>>
>> EPD or python(xy) can give you the full stack although not
>> at the
>> "bleeding" edge. Additionally, python(xy) has binary
>> installers for
>> individual packages which is very nice and work without
>> problems what
>> little I have tried.
>
> So is this last what you'd recommend I try first?

Most definitely.  I emailed a note last night to the tutorial
attendees indicating how to update Sympy and Cython, the only two
things for which you need a newer version than what's on EPD.

But unless you are very used to building everything from source and
adept at debugging strange compiler errors, I'd very strongly suggest
you use EPD (or on Windows, you can also use Python(x,y), which also
carries the tools needed for the intro tutorial).

I think this will save you a lot of time and headaches and let you
focus on learning now rather than building software and babysitting
compilers and linkers (a skill which most of us have acquired because
we had no other option, not because we like it).

Cheers,

f


From dwf at cs.toronto.edu  Sat Aug 15 03:01:49 2009
From: dwf at cs.toronto.edu (David Warde-Farley)
Date: Sat, 15 Aug 2009 03:01:49 -0400
Subject: [SciPy-User] satellite imagery
In-Reply-To: <aa15d47e0908141518vc7f4a70x1b1e138ac8644ff@mail.gmail.com>
References: <aa15d47e0908141518vc7f4a70x1b1e138ac8644ff@mail.gmail.com>
Message-ID: <A9AC9E96-3171-42A8-9111-EF4DC299BAEB@cs.toronto.edu>

On 14-Aug-09, at 6:18 PM, questions anon wrote:

> Can anyone suggest any good python books or tutorials for using  
> satellite imagery?

And doing what with it? You'll have to be more specific.


From stef.mientki at gmail.com  Sat Aug 15 13:19:08 2009
From: stef.mientki at gmail.com (Stef Mientki)
Date: Sat, 15 Aug 2009 19:19:08 +0200
Subject: [SciPy-User] scipy.signal.ss2tf nests numerator 1 deeper,
	is this a bug ?
Message-ID: <4A86EE0C.3090109@gmail.com>

hello,

creating a module that accepts any LTI representation,
my program didn't work, because ss2tf nests the numerator 1 deeper.

Is this a bug ?

from scipy import signal
from scipy.signal import tf2ss, ss2tf

print '======== HighPass Filter ========'
print 'Filter-1: IIR (Butterworth), 0.06*fn, -50dB'
filt = signal.iirdesign( 0.06, 0.002, 1, 50, 0, 'butter')

Space_State = tf2ss ( *filt )
filt2 = ss2tf ( *Space_State )
print 'orginal transfer function', filt
print 'tf --> Space_State --> tf', filt2


OUTPUT:
======== HighPass Filter ========
Filter-1: IIR (Butterworth), 0.06*fn, -50dB
orginal transfer function (array([ 0.92410511, -1.84821022,  
0.92410511]), array([ 1.        , -1.84244187,  0.85397858]))
tf --> Space_State --> tf (array([[ 0.92410511, -1.84821022,  
0.92410511]]), array([ 1.        , -1.84244187,  0.85397858]))


Is there a handy work around ?
One of things I want to do is to calculate the frequency response
(but I guess more things that I want will raise problems :-)
So this generates an error:
 
h, w = signal.freqz ( *filt2 )

And I have to use this statement:

h, w = signal.freqz ( filt2 [0] [0], filt2 [1] )

thanks,
Stef Mientki





From gokhansever at gmail.com  Sat Aug 15 15:31:05 2009
From: gokhansever at gmail.com (=?UTF-8?Q?G=C3=B6khan_Sever?=)
Date: Sat, 15 Aug 2009 14:31:05 -0500
Subject: [SciPy-User] Intro tutorial checklist failure
In-Reply-To: <96de71860908142202v79e5b20bv83257db348290264@mail.gmail.com>
References: <49d6b3500908141305g2812d010we1239f5e1074b40b@mail.gmail.com>
	<96de71860908142202v79e5b20bv83257db348290264@mail.gmail.com>
Message-ID: <49d6b3500908151231s6f8b6fa1m8f74efcf3a3cd7fe@mail.gmail.com>

On Sat, Aug 15, 2009 at 12:02 AM, Fernando Perez <
Fernando.Perez at berkeley.edu> wrote:

> Hi Gokhan,
>
> On Fri, Aug 14, 2009 at 1:05 PM, G?khan Sever<gokhansever at gmail.com>
> wrote:
> > As posted below the output of the script seemingly there is something
> wrong
> > with the matplotlib tests. I have matplotlib installed and working
> properly
> > however the script fails to test this. Manual testing of the test plot
> > functions work properly as expected.
> >
> > I don't have scipy and mayavi installed yet since there are two bizarre
> > installation issues that I have had and posted on the lists, and still
> > couldn't figure out. All were working extremely nicely on Fedora 10 (The
> > source code installations) but there must be somethings wrong with some
> > critical libraries or some tools' being so up-to-date.
>
> That is very weird, I have no idea why this could be presenting this
> behavior.  If you want, you can always try to run the script in
> ipython via
>
> run -n intro_tut_checklist.py
>
> and then call the tests individually:
>
>
> In [18]: run -n intro_tut_checklist.py
>
> In [19]: [ t[0](*t[1:]) for t in test_imports() ]
> MOD: setuptools, version: 0.6c9
> MOD: IPython, version: 0.10
> MOD: numpy, version: 1.4.0.dev7303
> MOD: scipy, version: 0.8.0.dev5764
> MOD: scipy.io, version: *no info*
> MOD: matplotlib, version: 1.0.svn
> MOD: pylab, version: *no info*
> MOD: enthought.mayavi.api, version: 3.1.0
> Out[19]: [None, None, None, None, None, None, None, None]
>
> In [20]: [ t[0](*t[1:]) for t in test_loadtxt() ]
> Out[20]: [None, None, None, None]
>
> In [21]: test_plot()
>
> In [22]: test_plot_math()
>
>
>
> If this works for you, I wouldn't worry too much for now...
>
> Cheers,
>
> f
>


Very bizarre :) All checks are OK with Ipython's run command, however mpl
tests still fail with python intro_tut_checklist.py

The only thing left is pycuda, I installed it before after a little struggle
on Fedora 10. Now it's time to install to Fedora 11.

To me, on Linux boxes with some extra curiosity in hand, development
checkouts and installations (with python setupegg.py python) is one of the
most natural way of bringing Python tools alive into one's system.

In a traditional way, I used to do yum search matplotlib, and yum install
matplotlib_result. But since 2-3 months I do svn co matplotlib_trunk, and
then a python setupegg.py develop (not the most stable but the most
up-to-date is under my hands.) A bug is discovered and patched, a new
functionality added just do an "svn up" do another develop if necessary
otherwise keep discovering things on the fly.

Although cheap breaks when it is most needed, it is better not to settle
with snake oil while I can have the whole snake :) (I love these quotes)



-- 
G?khan
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From andreas.matthias at gmail.com  Sat Aug 15 16:11:07 2009
From: andreas.matthias at gmail.com (Andreas Matthias)
Date: Sat, 15 Aug 2009 22:11:07 +0200
Subject: [SciPy-User] numpy: loadtxt with default value
Message-ID: <sbhjl6-k7b.ln1@fawkes.hogwarts>

The documentation of numpy.loadtxt says that the `converters'
parameter can be used to provide a default value for missing
data. However the following doesn't work.


import numpy as np
from StringIO import StringIO

s = StringIO('111  222')
a = np.loadtxt(s,
               dtype = {'names': ('a', 'b', 'c'),
                        'formats': (float, float, float)},
               converters = {2: lambda s: float(s or 0)}
               )


What's wrong with this code?

Ciao
Andreas

 



From pgmdevlist at gmail.com  Sat Aug 15 16:22:06 2009
From: pgmdevlist at gmail.com (Pierre GM)
Date: Sat, 15 Aug 2009 16:22:06 -0400
Subject: [SciPy-User] numpy: loadtxt with default value
In-Reply-To: <sbhjl6-k7b.ln1@fawkes.hogwarts>
References: <sbhjl6-k7b.ln1@fawkes.hogwarts>
Message-ID: <C38CC85F-D3A1-4DC9-99D9-2BDEAB0E19AA@gmail.com>


On Aug 15, 2009, at 4:11 PM, Andreas Matthias wrote:

> The documentation of numpy.loadtxt says that the `converters'
> parameter can be used to provide a default value for missing
> data. However the following doesn't work.
>
>
> import numpy as np
> from StringIO import StringIO
>
> s = StringIO('111  222')
> a = np.loadtxt(s,
>               dtype = {'names': ('a', 'b', 'c'),
>                        'formats': (float, float, float)},
>               converters = {2: lambda s: float(s or 0)}
>               )
>
>
> What's wrong with this code?

You have only 2 columns in s, and gave the converter (and name and  
format) for a third one ?


From andreas.matthias at gmail.com  Sat Aug 15 16:50:19 2009
From: andreas.matthias at gmail.com (Andreas Matthias)
Date: Sat, 15 Aug 2009 22:50:19 +0200
Subject: [SciPy-User] numpy: loadtxt with default value
References: <sbhjl6-k7b.ln1@fawkes.hogwarts>
	<C38CC85F-D3A1-4DC9-99D9-2BDEAB0E19AA@gmail.com>
Message-ID: <bljjl6-5qb.ln1@fawkes.hogwarts>

Pierre GM wrote:

> On Aug 15, 2009, at 4:11 PM, Andreas Matthias wrote:
> 
>> The documentation of numpy.loadtxt says that the `converters'
>> parameter can be used to provide a default value for missing
>> data. However the following doesn't work.
>> 
>> 
>> import numpy as np
>> from StringIO import StringIO
>> 
>> s = StringIO('111  222')
>> a = np.loadtxt(s,
>>               dtype = {'names': ('a', 'b', 'c'),
>>                        'formats': (float, float, float)},
>>               converters = {2: lambda s: float(s or 0)}
>>               )
>> 
>> 
>> What's wrong with this code?
> 
> You have only 2 columns in s, and gave the converter (and name and  
> format) for a third one ?

Yes. This was intended. This should be the case of a "default
value for missing data" as described in the documentation

    converters : {}
        A dictionary mapping column number to a function that will convert
        that column to a float.  E.g., if column 0 is a date string:
        ``converters = {0: datestr2num}``. Converters can also be used to
        provide a default value for missing data:
        ``converters = {3: lambda s: float(s or 0)}``.


Ciao
Andreas



From pgmdevlist at gmail.com  Sat Aug 15 18:14:00 2009
From: pgmdevlist at gmail.com (Pierre GM)
Date: Sat, 15 Aug 2009 18:14:00 -0400
Subject: [SciPy-User] numpy: loadtxt with default value
In-Reply-To: <bljjl6-5qb.ln1@fawkes.hogwarts>
References: <sbhjl6-k7b.ln1@fawkes.hogwarts>
	<C38CC85F-D3A1-4DC9-99D9-2BDEAB0E19AA@gmail.com>
	<bljjl6-5qb.ln1@fawkes.hogwarts>
Message-ID: <40E62F0C-767F-4A10-A96D-7583822643CC@gmail.com>


On Aug 15, 2009, at 4:50 PM, Andreas Matthias wrote:

> Pierre GM wrote:
>
>> On Aug 15, 2009, at 4:11 PM, Andreas Matthias wrote:
>>
>>> The documentation of numpy.loadtxt says that the `converters'
>>> parameter can be used to provide a default value for missing
>>> data. However the following doesn't work.
>>>
>>>
>>> import numpy as np
>>> from StringIO import StringIO
>>>
>>> s = StringIO('111  222')
>>> a = np.loadtxt(s,
>>>              dtype = {'names': ('a', 'b', 'c'),
>>>                       'formats': (float, float, float)},
>>>              converters = {2: lambda s: float(s or 0)}
>>>              )
>>
>> You have only 2 columns in s, and gave the converter (and name and
>> format) for a third one ?
>
> Yes. This was intended. This should be the case of a "default
> value for missing data" as described in the documentation

Mmh, I see what you mean: you want to be able to specify in advance  
the number of columns through the dtype (in your case, 3), and use  
some default values when a line has less than the required nb of rows...
That won't work in the current implementation: each line should have  
the proper nb of column, even if the column is empty.
In your example, you use spaces as delimiters, but that interferes  
with the EOL. Try to use a different delimiter if you can.
For example,

s = StringIO('111,  222,')
a = np.loadtxt(s,
               dtype = {'names': ('a', 'b', 'c'),
                        'formats': (float, float, float)},
               converters = {2: lambda s: float(s or 0)},
               delimiter=","
               )

should work



From hxianping at gmail.com  Sat Aug 15 21:43:10 2009
From: hxianping at gmail.com (Steve Han)
Date: Sun, 16 Aug 2009 09:43:10 +0800
Subject: [SciPy-User] Chinese scipy user group needs help
Message-ID: <702805a90908151843p4c9ee4c7m2d1b0b58a155673b@mail.gmail.com>

Hi list,

We are a numpy/scipy user group in Shandong Province ,China.Our interest is
at using Numpy/Scipy and Python multiprocessing to leverage scientifc and
engineering computing in day to day work.We need the help from the community
of Scipy,first we mean establish a local scipy community,then the other.Any
suggestion?
Thanks a lot!

-- 
Best Regards,
Steve Han
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From d_l_goldsmith at yahoo.com  Sun Aug 16 00:32:00 2009
From: d_l_goldsmith at yahoo.com (David Goldsmith)
Date: Sun, 16 Aug 2009 04:32:00 +0000 (GMT)
Subject: [SciPy-User] Tutorial Prep: one IntroTest Failure after Python(x,
	y) install
Message-ID: <114269.83988.qm@web52108.mail.re2.yahoo.com>

Hi!? I installed Python(x,y) (successfully, I believe) but when I run the Intro Test script I get:

Running tests:
__main__.test_imports('setuptools', None) ... MOD: setuptools, version: 0.6c9
ok
__main__.test_imports('IPython', None) ... MOD: IPython, version: 0.9.1
ok
__main__.test_imports('numpy', None) ... MOD: numpy, version: 1.3.0
ok

<snip>

Plots with math ... ok

======================================================================
ERROR: __main__.test_imports('enthought.mayavi.api', None)
----------------------------------------------------------------------
Traceback (most recent call last):
? File "C:\Python25\lib\site-packages\nose\case.py", line 183, in runTest
? ? self.test(*self.arg)
? File "IntroTests.py", line 91, in check_import
? ? exec "import %s as m" % mnames
? File "<string>", line 1, in <module>
ImportError: No module named enthought.mayavi.api

----------------------------------------------------------------------
Ran 14 tests in 6.198s

FAILED (errors=1)

So at:

http://code.enthought.com/projects/mayavi/docs/development/html/mayavi/installation.html

I read:

"Under Window [sic] the best way to install Mayavi is to install a full Python distribution, such as EPD or Pythonxy. Note that Pythonxy has a special download which provides a complete installer for Mayavi and all its dependencies and is a much smaller download than EPD or the full Pythonxy install."

OK, so I click on "special download" and run that.  No (reported) problems.  I try the Intro Tests again and get:

======================================================================
ERROR: __main__.test_imports('enthought.mayavi.api', None)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\Python25\lib\site-packages\nose\case.py", line 183, in runTest
    self.test(*self.arg)
  File "IntroTests.py", line 91, in check_import
    exec "import %s as m" % mnames
  File "<string>", line 1, in <module>
  File "c:\python25\lib\site-packages\Mayavi-3.0.2-py2.5-win32.egg\enthought\mayavi\api.py", line 2, in <module>
    from enthought.mayavi.core.engine import Engine
  File "c:\python25\lib\site-packages\Mayavi-3.0.2-py2.5-win32.egg\enthought\mayavi\core\engine.py", line 24, in <module>
    from enthought.traits.ui.api import View, Item
  File "c:\python25\lib\site-packages\Traits-3.0.2-py2.5-win32.egg\enthought\traits\ui\api.py", line 104, in <module>
    _constants  = toolkit().constants()
  File "c:\python25\lib\site-packages\Traits-3.0.2-py2.5-win32.egg\enthought\traits\ui\toolkit.py", line 369, in constants
    raise NotImplementedError
NotImplementedError

----------------------------------------------------------------------
Ran 14 tests in 8.628s

FAILED (errors=1)

I guess that's progress, but I'm not sure what to do next.

DG


      


From paul at boehm.org  Sun Aug 16 03:00:01 2009
From: paul at boehm.org (=?UTF-8?Q?Paul_B=C3=B6hm?=)
Date: Sun, 16 Aug 2009 00:00:01 -0700
Subject: [SciPy-User] scipy in google wave (screenshot attached)
In-Reply-To: <88e473830908100420r215eaf2dnb2b3965391ceabc9@mail.gmail.com>
References: <faeee03b0908092339o48f33caqda274783e503e105@mail.gmail.com> 
	<88e473830908100420r215eaf2dnb2b3965391ceabc9@mail.gmail.com>
Message-ID: <faeee03b0908160000n2c511c73rdf56f69a0ce664d2@mail.gmail.com>

Hey John,

I've met some ppl now who to a guy at an sf event, who are interested
in developing something like MatWave.

i've started a mailinglist so we can discuss further:
http://groups.google.com/group/matwave

setting up the dev environment is not trivial. so first thing on the
agenda should be getting ppl settled into a dev environment, which is
not as easy as local software. in theory we could even work on matwave
from within wave - we've written a collaborative wavebot-editor for
that

paul

On Mon, Aug 10, 2009 at 4:20 AM, John Hunter<jdh2358 at gmail.com> wrote:
> On Mon, Aug 10, 2009 at 1:39 AM, Paul B?hm<paul at boehm.org> wrote:
>>>Interesting -- is the python code run the the wave to generate the
>>>figure? ?If so, how did you get around the problem that only pure
>>>python code can run in the appengine, so presumably numpy and mpl
>>>would not work? ?Are you passing the python code to another server
>>>which serves up the png?
>>
>> we ported the waveapi to ec2 (or just any machine), and then wrote an
>> appengine app that bridges the data out to us :p
>>
>> if someone was seriously interested in developing a collaborative
>> scipy notebook, i'd love to help get that started. i think Wave would
>> work fabulously as a UI to scipy.
>> (i don't think i'll be the one to do it though, since other projects
>> occupy my time usually)
>
> I'm pretty sure lots of people are interested in it. ?Fernando Perez
> has been talking about it for a decade, Robert Kern has implemented
> one. ?Lots of us will be together next week at scipy and would be
> interested in hacking on this. ?In addition to a notebook, this would
> be a perfect tool for some hybrid wiki, faq, discussion board, irc
> client where people could post questions as waves, with some code,
> others could jump in with answers and more code and figures, and the
> question would naturally evolve into a FAQ that lives in a wiki like
> context. ?I think something like that as a general python for science
> forum would be really useful.
>
> I have registered for a wave account and setup an experimental
> appengine -- if you could help get me up to speed on what you've been
> doing, I'm ?sure there would be some people at scipy who could help
> move the ball forward.
>
> So by porting the app engine onto your own server, you are able to run
> extension code on your server, eg numpy, scipy, mpl, from your wave?
> Do you have a custom robot to execute python code and insert mpl
> figures? ?You say you ported over to ec2, but the URL in your
> screenshot says wave.google.com?
>
> Anyway, thanks for sharing. ?This looks very exciting.
>
> For those of you who haven't seen the wave demo yet, watch
>
> ?http://wave.google.com/
>
> and imagine what can be done in a wave as mailing list with a working
> python interpreter that can generate inline figures. ?It's over an
> hour long, and well worth the time.
>
> JDH
>


From emmanuelle.gouillart at normalesup.org  Sun Aug 16 08:59:17 2009
From: emmanuelle.gouillart at normalesup.org (Emmanuelle Gouillart)
Date: Sun, 16 Aug 2009 14:59:17 +0200
Subject: [SciPy-User] Chinese scipy user group needs help
In-Reply-To: <702805a90908151843p4c9ee4c7m2d1b0b58a155673b@mail.gmail.com>
References: <702805a90908151843p4c9ee4c7m2d1b0b58a155673b@mail.gmail.com>
Message-ID: <20090816125917.GD13497@phare.normalesup.org>

	Hello Steve,

	it is very nice to learn from Scipy users in China! 

	Below are some suggestions that might help you. If you want more
specific answers, you should give more details about 

* Who are the members of the group? Academic staff? Students? Engineers
  working in industry? 

* What are your goals? Creating a local user group to organize
  installations and training parties? Developing the use of Python e.g. at university,
  so that companies using Python/Scipy can hire graduates that already
  have a knowledge of the language? On the long term, contributing to
  numpy/scipy's code for some specific aspects?

Fostering a local community
---------------------------

If you want to create a local community, you'll need a good website, a
mailing-list and... a lot of energy to organize events (food and drinks,
etc.). Do you have access to a server that could host a website, e.g. a
blog, and a mail server? 

You should spend some time looking at the
http://wiki.python.org/moin/LocalUserGroups page that lists Python User
Groups round the world, to see how the local Python groups work. I don't
think there exist that many Scipy-only user groups. In fact, if you want
to reach the critical mass to have enough momentum in you user group, you
may consider widening the scope of the user group to the Python language.

I have seen in Dakar (Senegal) the birth of a Python User Group thanks to
the local Linux User Group, that had provided a server and a mailing-list
(as well as the help of a bunch of enthusiastic and efficient people), so
that the Python User Group could start off very rapidly and at almost no
cost. If there exists already a Linux User Group in your area and you
think they may be willing to help you, that could be an option.

Getting more users
------------------

If you want to develop the use of Scipy in your region, you'll have to
advertise Scipy a lot, and to organize training courses. A good target
for that purpose is universities and engineering schools, where there is
often a course on an interpreted scripting language (Matlab/IDL/R/Octave)
being taught. If you can convince a professor that the framework
Python/Numpy/Scipy is highly interesting for performing all the tasks of
scientific computing, then it's a done deal. The best way to convert
professors to Python is sometimes to show them that they can use it for
their own research and not only in their courses. If you work for a company,
you can give some feedback to the university that you may be interested in
hiring graduated trained in Python/Scipy.

Of course, this requires quite a bit of lobbying! You can also organize
yourself some training parties, and then count on word to mouth to get
more users. Some seminar/course material is available on the web. Take a
look at http://docs.scipy.org/doc/ and
http://www.scipy.org/Additional_Documentation?action=show
There is a whole course about Scipy in
http://www.rexx.com/~dkuhlman/scipy_course_01.html
There is a whole textbook about Scientific Computing with Python by Hans
Petter Langtangen
(http://www.amazon.com/Python-Scripting-Computational-Science-Engineering/dp/3540294155)
There is also a variety of slides available, e.g. on
https://cirl.berkeley.edu/fperez/py4science/2008_siam/

I don't know if the tutorials given the coming week at Scipy 2009
Conference will be posted online, you can check the page
http://conference.scipy.org/tutorials later this month. 

Getting help
------------

As the organizer of the User Group you will get a lot of questions about
Numpy/Scipy, some of which you obviously  won't know how to answer. You
should encourage strongly the users to suscribe to the classical
numpy-discussion and scipy-user mailing lists in addition to a local
mailing-list. The local list is useful for organizing the life of the
group, but questions related only to Numpy and Scipy should rather be
asked on the classical lists. Take a look at a recent thread on this
subject
http://www.nabble.com/Numpy-Scipy-and-the-Python-African-Tour-td24626720.html. 

	Don't hesitate to ask more questions, and please let us know
about the development of your User Group!

	Cheers,

	Emmanuelle

On Sun, Aug 16, 2009 at 09:43:10AM +0800, Steve Han wrote:
>    Hi list,
>    ?
>    We are a numpy/scipy user group in Shandong Province ,China.Our interest
>    is at using Numpy/Scipy and Python multiprocessing to leverage scientifc
>    and engineering computing in day to day work.We need the help from the
>    community of Scipy,first we mean?establish a local scipy community,then
>    the other.Any suggestion?
>    Thanks a lot!


From sturla at molden.no  Sun Aug 16 20:33:00 2009
From: sturla at molden.no (Sturla Molden)
Date: Sun, 16 Aug 2009 17:33:00 -0700
Subject: [SciPy-User] [SciPy-user] scipy.stats.stats mannwhitneyu
	vs	ranksums?
In-Reply-To: <1cd32cbb0908140713wcb67a2cl20899bb7207e64cc@mail.gmail.com>
References: <43958ee60906301516s637b180fm35b96d5b61a1b549@mail.gmail.com>	<4A4A92A6.9070503@apstat.com>	<43958ee60906301624l7fd7f445sebd0d69b4ac32e61@mail.gmail.com>	<4a4b8c6f.1a135e0a.7a2d.0b82@mx.google.com>	<5b8d13220907010933j7463ec93tb0bb92f02fbf9308@mail.gmail.com>	<4a4cf1bf.05a4100a.7834.ffffcf2e@mx.google.com>	<4F46BA01-652B-4AEA-84CE-277D54F26693@cs.toronto.edu>	<4a4e2ca7.0c92100a.2f71.0392@mx.google.com>
	<1cd32cbb0908140713wcb67a2cl20899bb7207e64cc@mail.gmail.com>
Message-ID: <4A88A53C.7040201@molden.no>


Josef,

I believe we had an discussion about various versions of Wilcoxon and 
Mann-Whitney some months ago. I find a discussion of this from february. 
We (or I alone?) also wrote Cython versions of the test, which I cannot 
find now. I should have filed a ticket then. :-(

Regards,
Sturla Molden





josef.pktd at gmail.com skrev:
> On Fri, Jul 3, 2009 at 12:06 PM, Elias Pampalk<elias.pampalk at gmail.com> wrote:
>
>   
>> I did a quick comparison between Matlab/stats (R14SP3), R (2.8.1), and
>> Python/SciPy (0.7). Maybe this is somehow useful for others too.
>>
>>     
> ...
>
>   
>> Elias
>>     
>
> Thanks for doing this, this is very helpful. I attached the comparison
> to ticket:901
>
> Except for one case, the numbers look pretty good.
> However, documentation is still weak, and some of the code duplication
> could be removed.
>
> Josef
>
> (I didn't look at it closely before, because I was on vacation at that time.)
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>   



From sturla at molden.no  Sun Aug 16 20:54:15 2009
From: sturla at molden.no (Sturla Molden)
Date: Sun, 16 Aug 2009 17:54:15 -0700
Subject: [SciPy-User] [SciPy-user] scipy.stats.stats
	mannwhitneyu	vs	ranksums?
In-Reply-To: <4A88A53C.7040201@molden.no>
References: <43958ee60906301516s637b180fm35b96d5b61a1b549@mail.gmail.com>	<4A4A92A6.9070503@apstat.com>	<43958ee60906301624l7fd7f445sebd0d69b4ac32e61@mail.gmail.com>	<4a4b8c6f.1a135e0a.7a2d.0b82@mx.google.com>	<5b8d13220907010933j7463ec93tb0bb92f02fbf9308@mail.gmail.com>	<4a4cf1bf.05a4100a.7834.ffffcf2e@mx.google.com>	<4F46BA01-652B-4AEA-84CE-277D54F26693@cs.toronto.edu>	<4a4e2ca7.0c92100a.2f71.0392@mx.google.com>	<1cd32cbb0908140713wcb67a2cl20899bb7207e64cc@mail.gmail.com>
	<4A88A53C.7040201@molden.no>
Message-ID: <4A88AA37.1080902@molden.no>


My memory serves me badly, that was Kendall's tau. It is still pending 
review though.

http://projects.scipy.org/scipy/ticket/893

Sturla




Sturla Molden skrev:
> Josef,
>
> I believe we had an discussion about various versions of Wilcoxon and 
> Mann-Whitney some months ago. I find a discussion of this from february. 
> We (or I alone?) also wrote Cython versions of the test, which I cannot 
> find now. I should have filed a ticket then. :-(
>
> Regards,
> Sturla Molden
>
>
>
>
>
> josef.pktd at gmail.com skrev:
>   
>> On Fri, Jul 3, 2009 at 12:06 PM, Elias Pampalk<elias.pampalk at gmail.com> wrote:
>>
>>   
>>     
>>> I did a quick comparison between Matlab/stats (R14SP3), R (2.8.1), and
>>> Python/SciPy (0.7). Maybe this is somehow useful for others too.
>>>
>>>     
>>>       
>> ...
>>
>>   
>>     
>>> Elias
>>>     
>>>       
>> Thanks for doing this, this is very helpful. I attached the comparison
>> to ticket:901
>>
>> Except for one case, the numbers look pretty good.
>> However, documentation is still weak, and some of the code duplication
>> could be removed.
>>
>> Josef
>>
>> (I didn't look at it closely before, because I was on vacation at that time.)
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>   
>>     
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>   



From ehauksson at gmail.com  Sun Aug 16 15:13:05 2009
From: ehauksson at gmail.com (Egill Hauksson)
Date: Sun, 16 Aug 2009 12:13:05 -0700
Subject: [SciPy-User] FAIL: __main__.test_loadtxt(array([('M', 21, 72.0),
	('F', 35, 58.0)], 
In-Reply-To: <96de71860908161159s5db4314es2cea1e6d994a1656@mail.gmail.com>
References: <db390ba40908161058k5f8c40bod893ecc3c1b3d6fb@mail.gmail.com>
	<96de71860908161159s5db4314es2cea1e6d994a1656@mail.gmail.com>
Message-ID: <db390ba40908161213o4e719bb0kb10d3c63b97fa53f@mail.gmail.com>

Fernando,

When I run the 2nd script I get one more error -- FAIL: Test basic Cython
sanity --see below.

Thanks
Egill

In [7]: %run adv_2.py
Running tests:
__main__.test_imports('setuptools', None) ... MOD: setuptools, version:
0.6c9-s1
ok
__main__.test_imports('IPython', None) ... MOD: IPython, version: 0.9.1
ok
__main__.test_imports('numpy', None) ... MOD: numpy, version: 1.3.0
ok
__main__.test_imports('scipy', None) ... MOD: scipy, version: 0.8.0.dev5698
ok
__main__.test_imports('scipy.io', None) ... MOD: scipy.io, version: *no
info*
ok
__main__.test_imports('matplotlib', <function validate_mpl at 0xa065cf0>)
...
/Library/Frameworks/Python.framework/Versions/4.3.0/lib/python2.5/site-packages/matplotlib-0.98.5.2n2-py2.5-macosx-10.3-fat.egg/matplotlib/__init__.py:818:
UserWarning:  This call to matplotlib.use() has no effect
because the the backend has already been chosen;
matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
or matplotlib.backends is imported for the first time.

  if warn: warnings.warn(_use_error_msg)
MOD: matplotlib, version: 0.98.5.2
ok
__main__.test_imports('pylab', None) ... MOD: pylab, version: *no info*
ok
__main__.test_imports('enthought.mayavi.api', None) ... MOD:
enthought.mayavi.api, version: 3.2.0n2
ok
__main__.test_imports('scipy.weave', None) ... MOD: scipy.weave, version:
0.4.9
ok
__main__.test_imports('sympy', <function validate_sympy at 0xa065d70>) ...
MOD: sympy, version: 0.6.5
ok
__main__.test_imports('Cython', None) ... MOD: Cython, version: *no info*
ok
__main__.test_imports('Cython.Distutils.build_ext', None) ... MOD:
build_ext, version: *no info*
ok
__main__.test_imports('enthought.traits.api', None) ... MOD:
enthought.traits.api, version: 3.1.0n1
ok
__main__.test_imports('enthought.traits.ui.api', None) ... MOD:
enthought.traits.ui.api, version: *no info*
ok
__main__.test_imports(('enthought.traits.ui.wx', 'enthought.traits.ui.qt4'),
None) ... MOD: enthought.traits.ui.wx, version: *no info*
ok
Simple code compilation and execution via scipy's weave ... ok
Test basic Cython sanity ... FAIL
__main__.test_loadtxt(array([[ 0.,  1.], ... ok
__main__.test_loadtxt(array([('M', 21, 72.0), ('F', 35, 58.0)], ... FAIL
__main__.test_loadtxt(array([ 1.,  3.]), array([ 1.,  3.])) ... ok
__main__.test_loadtxt(array([ 2.,  4.]), array([ 2.,  4.])) ... ok
Simple plot generation. ... ok
Plots with math ... ok

======================================================================
FAIL: Test basic Cython sanity
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/Library/Frameworks/Python.framework/Versions/4.3.0/lib/python2.5/site-packages/nose-0.10.3n1-py2.5.egg/nose/case.py",
line 182, in runTest
    self.test(*self.arg)
  File "adv_2.py", line 183, in test_cython
    validate_cython(None)
  File "adv_2.py", line 82, in validate_cython
    nt.assert_true(cython_version >= min_version, msg)
AssertionError: Cython version (0, 9, 8, 1, 1) found, at least (0, 11, 2)
required

======================================================================
FAIL: __main__.test_loadtxt(array([('M', 21, 72.0), ('F', 35, 58.0)],
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/Library/Frameworks/Python.framework/Versions/4.3.0/lib/python2.5/site-packages/nose-0.10.3n1-py2.5.egg/nose/case.py",
line 182, in runTest
    self.test(*self.arg)
  File
"/Library/Frameworks/Python.framework/Versions/4.3.0/lib/python2.5/site-packages/numpy-1.3.0n1-py2.5-macosx-10.3-fat.egg/numpy/testing/utils.py",
line 463, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File
"/Library/Frameworks/Python.framework/Versions/4.3.0/lib/python2.5/site-packages/numpy-1.3.0n1-py2.5-macosx-10.3-fat.egg/numpy/testing/utils.py",
line 395, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not equal

(mismatch 100.0%)
 x: array([('M', 21, 72.0), ('F', 35, 58.0)],
      dtype=[('gender', '|S1'), ('age', '>i4'), ('weight', '>f4')])
 y: array([('M', 21, 72.0), ('F', 35, 58.0)],
      dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')])

----------------------------------------------------------------------
Ran 23 tests in 30.818s

FAILED (failures=2)

Cleanup - removing temp directory: /Users/hauksson/SciPy/tmp-testdata-7pCnbL

***************************************************************************
                           TESTS FINISHED
***************************************************************************

If the printout above did not finish in 'OK' but instead says 'FAILED', copy
and send the *entire* output, including the system information below, for
help.
We'll do our best to assist you.  You can send your message to the Scipy
user
mailing list:

    http://mail.scipy.org/mailman/listinfo/scipy-user

but feel free to also CC directly: Fernando.Perez at berkeley.edu

==================
System information
==================
os.name      : posix
os.uname     : ('Darwin', 'egill-haukssons-powerbook-g4-15.local', '9.7.0',
'Darwin Kernel Version 9.7.0: Tue Mar 31 22:54:29 PDT 2009;
root:xnu-1228.12.14~1/RELEASE_PPC', 'Power Macintosh')
platform     : darwin
platform+    : Darwin-9.7.0-Power_Macintosh-powerpc-32bit
prefix       : /Library/Frameworks/Python.framework/Versions/4.3.0
exec_prefix  : /Library/Frameworks/Python.framework/Versions/4.3.0
executable   :
/Library/Frameworks/Python.framework/Versions/4.3.0/Resources/Python.app/Contents/MacOS/Python
version_info : (2, 5, 4, 'final', 0)
version      : 2.5.4 |EPD_Py25 4.3.0| (r254:67916, May 17 2009, 20:07:12)
[GCC 4.0.1 (Apple Computer, Inc. build 5370)]
==================

In [8]:


On Sun, Aug 16, 2009 at 11:59 AM, Fernando Perez <
Fernando.Perez at berkeley.edu> wrote:

> Hi Egill,
>
> On Sun, Aug 16, 2009 at 10:58 AM, Egill Hauksson<ehauksson at gmail.com>
> wrote:
> > To all,
> >
> > Any ideas -- see below for one FAIL.
> >
> > Thanks
> > Egill
> >
> > In [13]: %run intro.py
> [...]
> > AssertionError:
> > Arrays are not equal
> >
> > (mismatch 100.0%)
> >  x: array([('M', 21, 72.0), ('F', 35, 58.0)],
> >       dtype=[('gender', '|S1'), ('age', '>i4'), ('weight', '>f4')])
> >  y: array([('M', 21, 72.0), ('F', 35, 58.0)],
> >       dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')])
>
>
> Ah, don't worry. You must be on a power PC Mac instead of an Intel
> one, and the mismatch is simply in the endianness of the data.  I just
> failed to account for the possibility of a Big Endian chip in the
> tests.
>
> So the failure is just in my test, your setup is OK. Sorry about that!
>
> Cheers,
>
> f
>
>
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From kuiper at jpl.nasa.gov  Sun Aug 16 16:17:12 2009
From: kuiper at jpl.nasa.gov (Tom Kuiper)
Date: Sun, 16 Aug 2009 13:17:12 -0700
Subject: [SciPy-User] Some updates on the tutorial test scripts and
 versions of tools needed
In-Reply-To: <96de71860908140059v7ddcc93ew7cf051cbdd5d8088@mail.gmail.com>
References: <96de71860908140059v7ddcc93ew7cf051cbdd5d8088@mail.gmail.com>
Message-ID: <4A886948.1080903@jpl.nasa.gov>

Fernando Perez wrote:
> here are some notes regarding the tutorial requirements.  I am sending this to
> all of you, though some of it may only apply to those attending the advanced
> track.
>
> - If you had troubles with the testing scripts, please note that I've fixed a
>   few small issues.  The current versions are (intro, advanced and
>   pycuda-specific):
>
>   https://cirl.berkeley.edu/fperez/tmp/intro_tut_checklist.py
>   https://cirl.berkeley.edu/fperez/tmp/adv_tut_checklist.py
>   https://cirl.berkeley.edu/fperez/tmp/pycuda_tut_checklist.py
>   
...
The stable Debian version of cython is 0.9.8.  I just downloaded it.

"AssertionError: Cython version (0, 9, 8) found, at least (0, 11, 2) 
required"

It seems that 0.11.2 is in the unstable Debian distribution.  Can 
someone suggest how to get that package without having to upgrade to 
unstable?

Thanks

Tom


From gokhansever at gmail.com  Sun Aug 16 16:25:17 2009
From: gokhansever at gmail.com (=?UTF-8?Q?G=C3=B6khan_Sever?=)
Date: Sun, 16 Aug 2009 15:25:17 -0500
Subject: [SciPy-User] Some updates on the tutorial test scripts and
	versions of tools needed
In-Reply-To: <4A886948.1080903@jpl.nasa.gov>
References: <96de71860908140059v7ddcc93ew7cf051cbdd5d8088@mail.gmail.com>
	<4A886948.1080903@jpl.nasa.gov>
Message-ID: <49d6b3500908161325i17334f87q41ddef4c08cbb4ce@mail.gmail.com>

On Sun, Aug 16, 2009 at 3:17 PM, Tom Kuiper <kuiper at jpl.nasa.gov> wrote:

> Fernando Perez wrote:
> > here are some notes regarding the tutorial requirements.  I am sending
> this to
> > all of you, though some of it may only apply to those attending the
> advanced
> > track.
> >
> > - If you had troubles with the testing scripts, please note that I've
> fixed a
> >   few small issues.  The current versions are (intro, advanced and
> >   pycuda-specific):
> >
> >   https://cirl.berkeley.edu/fperez/tmp/intro_tut_checklist.py
> >   https://cirl.berkeley.edu/fperez/tmp/adv_tut_checklist.py
> >   https://cirl.berkeley.edu/fperez/tmp/pycuda_tut_checklist.py
> >
> ...
> The stable Debian version of cython is 0.9.8.  I just downloaded it.
>
> "AssertionError: Cython version (0, 9, 8) found, at least (0, 11, 2)
> required"
>
> It seems that 0.11.2 is in the unstable Debian distribution.  Can
> someone suggest how to get that package without having to upgrade to
> unstable?
>
> Thanks
>
> Tom
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


Hi Tom,

This is Fedora 11. You might try to make a source installation. It just
works fine here. (Might be a good idea to remove the old Cython package to
eliminate the conflicts.)

First install Mercurial to use hg command, then do:

hg clone http://hg.cython.org/cython/

go in to the cython directory and issue a "python setup.py install."

That should bring you up-to-date.
-- 
G?khan
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From kuiper at jpl.nasa.gov  Sun Aug 16 16:31:53 2009
From: kuiper at jpl.nasa.gov (Tom Kuiper)
Date: Sun, 16 Aug 2009 13:31:53 -0700
Subject: [SciPy-User] cython from Debian unstable
In-Reply-To: <49d6b3500908161325i17334f87q41ddef4c08cbb4ce@mail.gmail.com>
References: <96de71860908140059v7ddcc93ew7cf051cbdd5d8088@mail.gmail.com>	
	<4A886948.1080903@jpl.nasa.gov>
	<49d6b3500908161325i17334f87q41ddef4c08cbb4ce@mail.gmail.com>
Message-ID: <4A886CB9.1020802@jpl.nasa.gov>

That would be a last resort as it would break the Debian package system.

Tom

G?khan Sever wrote:
>
>
> On Sun, Aug 16, 2009 at 3:17 PM, Tom Kuiper <kuiper at jpl.nasa.gov 
> <mailto:kuiper at jpl.nasa.gov>> wrote:
>
>     Fernando Perez wrote:
>     > here are some notes regarding the tutorial requirements.  I am
>     sending this to
>     > all of you, though some of it may only apply to those attending
>     the advanced
>     > track.
>     >
>     > - If you had troubles with the testing scripts, please note that
>     I've fixed a
>     >   few small issues.  The current versions are (intro, advanced and
>     >   pycuda-specific):
>     >
>     >   https://cirl.berkeley.edu/fperez/tmp/intro_tut_checklist.py
>     >   https://cirl.berkeley.edu/fperez/tmp/adv_tut_checklist.py
>     >   https://cirl.berkeley.edu/fperez/tmp/pycuda_tut_checklist.py
>     >
>     ...
>     The stable Debian version of cython is 0.9.8.  I just downloaded it.
>
>     "AssertionError: Cython version (0, 9, 8) found, at least (0, 11, 2)
>     required"
>
>     It seems that 0.11.2 is in the unstable Debian distribution.  Can
>     someone suggest how to get that package without having to upgrade to
>     unstable?
>
>     Thanks
>
>     Tom
>     _______________________________________________
>     SciPy-User mailing list
>     SciPy-User at scipy.org <mailto:SciPy-User at scipy.org>
>     http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
>
> Hi Tom,
>
> This is Fedora 11. You might try to make a source installation. It 
> just works fine here. (Might be a good idea to remove the old Cython 
> package to eliminate the conflicts.)
>
> First install Mercurial to use hg command, then do:
>
> hg clone http://hg.cython.org/cython/
>
> go in to the cython directory and issue a "python setup.py install."
>
> That should bring you up-to-date.
> -- 
> G?khan

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From robert.kern at gmail.com  Sun Aug 16 16:33:19 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Sun, 16 Aug 2009 15:33:19 -0500
Subject: [SciPy-User] cython from Debian unstable
In-Reply-To: <4A886CB9.1020802@jpl.nasa.gov>
References: <96de71860908140059v7ddcc93ew7cf051cbdd5d8088@mail.gmail.com> 
	<4A886948.1080903@jpl.nasa.gov>
	<49d6b3500908161325i17334f87q41ddef4c08cbb4ce@mail.gmail.com> 
	<4A886CB9.1020802@jpl.nasa.gov>
Message-ID: <3d375d730908161333x52a2032ax36a99b0ff8df43bf@mail.gmail.com>

On Sun, Aug 16, 2009 at 15:31, Tom Kuiper<kuiper at jpl.nasa.gov> wrote:
> That would be a last resort as it would break the Debian package system.

$ sudo python setup.py install --prefix=/usr/local

-- 
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


From zunzun at zunzun.com  Sun Aug 16 17:51:54 2009
From: zunzun at zunzun.com (James Phillips)
Date: Sun, 16 Aug 2009 16:51:54 -0500
Subject: [SciPy-User] Additional Pearu-fication of zunzun.com (spline curves
	and surfaces)
Message-ID: <268756d30908161451m456bcc01gdd7bf9c8f9fd380b@mail.gmail.com>

Added 2d spline curves: http://zunzun.com/Equation/2/Spline/Spline/

and 3D spline surfaces: http://zunzun.com/Equation/3/Spline/Spline/

to http://zunzun.com - the automatically generated Python spline curve and
surface evaluation code was partially based on:

http://svn.scipy.org/svn/scipy/trunk/scipy/interpolate/fitpack/splev.f
http://svn.scipy.org/svn/scipy/trunk/scipy/interpolate/fitpack/fpbisp.f
http://svn.scipy.org/svn/scipy/trunk/scipy/interpolate/fitpack/fpbspl.f

as noted in the evaluation output source code comments.  I plan to use the
existing Python output code to generate C++, Java, C#, etc. later on - at
least end users have something for now if they require evaluation code in
those languages in the immediate future.


My 2D source code browsable at:
http://code.google.com/p/pythonequations/source/browse/trunk/pythonequations/Equations2D/Spline.py

My 3D source code browsable at:
http://code.google.com/p/pythonequations/source/browse/trunk/pythonequations/Equations3D/Spline.py

Pearu, thank you very much for the Fortran work that allowed me to
automatically generate the Python evaluation code for end users.

Now on to parallel processing for Robert Kern's DE code!

     James
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From cycomanic at gmail.com  Sun Aug 16 19:02:06 2009
From: cycomanic at gmail.com (Jochen)
Date: Mon, 17 Aug 2009 09:02:06 +1000
Subject: [SciPy-User] Some updates on the tutorial test scripts and
 versions of tools needed
In-Reply-To: <4A886948.1080903@jpl.nasa.gov>
References: <96de71860908140059v7ddcc93ew7cf051cbdd5d8088@mail.gmail.com>
	<4A886948.1080903@jpl.nasa.gov>
Message-ID: <20090817090206.76ab7935@cudos0803>

On Sun, 16 Aug 2009 13:17:12 -0700
Tom Kuiper <kuiper at jpl.nasa.gov> wrote:

> Fernando Perez wrote:
> > here are some notes regarding the tutorial requirements.  I am
> > sending this to all of you, though some of it may only apply to
> > those attending the advanced track.
> >
> > - If you had troubles with the testing scripts, please note that
> > I've fixed a few small issues.  The current versions are (intro,
> > advanced and pycuda-specific):
> >
> >   https://cirl.berkeley.edu/fperez/tmp/intro_tut_checklist.py
> >   https://cirl.berkeley.edu/fperez/tmp/adv_tut_checklist.py
> >   https://cirl.berkeley.edu/fperez/tmp/pycuda_tut_checklist.py
> >   
> ...
> The stable Debian version of cython is 0.9.8.  I just downloaded it.
> 
> "AssertionError: Cython version (0, 9, 8) found, at least (0, 11, 2) 
> required"
> 
> It seems that 0.11.2 is in the unstable Debian distribution.  Can 
> someone suggest how to get that package without having to upgrade to 
> unstable?

Hi Tom,

you could try to pin to the cython package from unstable. However you
might need to pull in quite a few dependencies from unstable. Have a
look at the debian apt howto:
http://www.debian.org/doc/manuals/apt-howto/index.en.html#contents
specifically sections 
3.8 How to keep a mixed system 
and 
3.10 How to keep specific versions of packages installed (complex)

Cheers
Jochen

> 
> Thanks
> 
> Tom
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user



From amcmorl at gmail.com  Sun Aug 16 19:41:28 2009
From: amcmorl at gmail.com (Angus McMorland)
Date: Sun, 16 Aug 2009 19:41:28 -0400
Subject: [SciPy-User] cython from Debian unstable
In-Reply-To: <4A886CB9.1020802@jpl.nasa.gov>
References: <96de71860908140059v7ddcc93ew7cf051cbdd5d8088@mail.gmail.com>
	<4A886948.1080903@jpl.nasa.gov>
	<49d6b3500908161325i17334f87q41ddef4c08cbb4ce@mail.gmail.com>
	<4A886CB9.1020802@jpl.nasa.gov>
Message-ID: <e85f13c40908161641x6eb66e72hd815e91bd5b94333@mail.gmail.com>

2009/8/16 Tom Kuiper <kuiper at jpl.nasa.gov>:
>> It seems that 0.11.2 is in the unstable Debian distribution. ?Can
>> someone suggest how to get that package without having to upgrade to
>> unstable?

In case you're still looking for a solution, and disclaimer: this is
from memory as I use Ubuntu now, you can temporarily add the unstable
repositories into your apt sources list, and use `apt-get install -t
unstable cython` to just get that version. It will, I believe, pull in
any dependencies from the unstable distribution that it requires as
well, but not generally upgrade the system. Then I've commented the
unstable repositories out again to ensure that I don't pull in any
more unstable packages than I need in subsequent operations.

Angus.
-- 
AJC McMorland
Post-doctoral research fellow
Neurobiology, University of Pittsburgh


From dmccully at mail.nih.gov  Sun Aug 16 21:27:04 2009
From: dmccully at mail.nih.gov (McCully, Dwayne (NIH/NLM/LHC) [C])
Date: Sun, 16 Aug 2009 21:27:04 -0400
Subject: [SciPy-User] Scipy Test failures
Message-ID: <8A8D24241B862C41B5D5EADB47CE39E2D6D83898@NIHMLBX01.nih.gov>

Hello Everyone,

This is my first time sending e-mail to the scipy user community.   I have a small problem and I would like some help from anyone:

Why does the scipy.test() fail to find omp_set_lock when its compiled into ATLAS?  My ATLAS variable is set correctly.  See below

Dwayne

[root at niamssolexa lib]# env | grep ATLAS
ATLAS=/usr/local/atlas/lib

[root at niamssolexa lib]# ls
libatlas.a  libatlas.so  libcblas.a  libcblas.so  libf77blas.a  libf77blas.so  liblapack.a  liblapack.so  libptcblas.a  libptcblas.so  libptf77blas.a  libptf77blas.so
[root at niamssolexa lib]# strings libatlas.a | grep omp_set_lock
omp_set_lock
omp_set_lock
omp_set_lock
omp_set_lock
omp_set_lock
[root at niamssolexa lib]# strings libatlas.so | grep omp_set_lock
omp_set_lock


[root at niamssolexa ~]# python
Python 2.5.2 (r252:60911, Mar  5 2008, 20:59:51)
[GCC 4.1.2 20070626 (Red Hat 4.1.2-14)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import scipy
>>> scipy.test()
Running unit tests for scipy
NumPy version 1.2.1
NumPy is installed in /usr/local/lib/python2.5/site-packages/numpy
SciPy version 0.7.1
SciPy is installed in /usr/local/lib/python2.5/site-packages/scipy
Python version 2.5.2 (r252:60911, Mar  5 2008, 20:59:51) [GCC 4.1.2 20070626 (Red Hat 4.1.2-14)]
nose version 0.11.0
/usr/local/lib/python2.5/site-packages/scipy/linsolve/__init__.py:4: DeprecationWarning: scipy.linsolve has moved to scipy.sparse.linalg.dsolve
  warn('scipy.linsolve has moved to scipy.sparse.linalg.dsolve', DeprecationWarning)
............................................................................................................................................................................................................EE......................../usr/local/lib/python2.5/site-packages/scipy/io/matlab/mio.py:111: Warning: Unreadable variable "testfunc", because "Cannot read matlab functions"
  matfile_dict = MR.get_variables()
........../usr/local/lib/python2.5/site-packages/scipy/io/matlab/mio.py:165: FutureWarning: Using oned_as default value ('column') This will change to 'row' in future versions
  oned_as=oned_as)
........................./usr/local/lib/python2.5/site-packages/scipy/io/matlab/tests/test_mio.py:438: FutureWarning: Using oned_as default value ('column') This will change to 'row' in future versions
  mfw = MatFile5Writer(StringIO())
......../usr/local/lib/python2.5/site-packages/scipy/io/matlab/mio.py:84: FutureWarning: Using struct_as_record default value (False) This will change to True in future versions
  return MatFile5Reader(byte_stream, **kwargs)
...............................Warning: 1000000 bytes requested, 20 bytes read.
./usr/local/lib/python2.5/site-packages/numpy/lib/utils.py:110: DeprecationWarning: write_array is deprecated
  warnings.warn(str1, DeprecationWarning)
/usr/local/lib/python2.5/site-packages/numpy/lib/utils.py:110: DeprecationWarning: read_array is deprecated
  warnings.warn(str1, DeprecationWarning)
..E..................../usr/local/lib/python2.5/site-packages/numpy/lib/utils.py:110: DeprecationWarning: npfile is deprecated
  warnings.warn(str1, DeprecationWarning)
.........EEEEE.................................................................................................................................................................................................................................................................................................................................................................................................................................EEEEE............................................................................................................................................................................................................................................................................................................................................K.K................................................................................................................................................................................................................................................................................................................E...warning: specified build_dir '_bad_path_' does not exist or is not writable. Trying default locations
.....warning: specified build_dir '_bad_path_' does not exist or is not writable. Trying default locations
...............................building extensions here: /root/.python25_compiled/m15
................................................................................................
======================================================================
ERROR: Failure: ImportError (/usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/nose/loader.py", line 379, in loadTestsFromName
    addr.filename, addr.module)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 39, in importFromPath
    return self.importFromDir(dir_path, fqname)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 86, in importFromDir
    mod = load_module(part_fqname, fh, filename, desc)
  File "/usr/local/lib/python2.5/site-packages/scipy/integrate/__init__.py", line 10, in <module>
    from odepack import *
  File "/usr/local/lib/python2.5/site-packages/scipy/integrate/odepack.py", line 7, in <module>
    import _odepack
ImportError: /usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock

======================================================================
ERROR: Failure: ImportError (/usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/nose/loader.py", line 379, in loadTestsFromName
    addr.filename, addr.module)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 39, in importFromPath
    return self.importFromDir(dir_path, fqname)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 86, in importFromDir
    mod = load_module(part_fqname, fh, filename, desc)
  File "/usr/local/lib/python2.5/site-packages/scipy/interpolate/__init__.py", line 13, in <module>
    from rbf import Rbf
  File "/usr/local/lib/python2.5/site-packages/scipy/interpolate/rbf.py", line 47, in <module>
    from scipy import linalg
  File "/usr/local/lib/python2.5/site-packages/scipy/linalg/__init__.py", line 8, in <module>
    from basic import *
  File "/usr/local/lib/python2.5/site-packages/scipy/linalg/basic.py", line 17, in <module>
    from lapack import get_lapack_funcs
  File "/usr/local/lib/python2.5/site-packages/scipy/linalg/lapack.py", line 17, in <module>
    from scipy.linalg import flapack
ImportError: /usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock

======================================================================
ERROR: test_integer (test_array_import.TestReadArray)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/io/tests/test_array_import.py", line 52, in test_integer
    from scipy import stats
  File "/usr/local/lib/python2.5/site-packages/scipy/stats/__init__.py", line 7, in <module>
    from stats import *
  File "/usr/local/lib/python2.5/site-packages/scipy/stats/stats.py", line 199, in <module>
    import scipy.linalg as linalg
  File "/usr/local/lib/python2.5/site-packages/scipy/linalg/__init__.py", line 8, in <module>
    from basic import *
  File "/usr/local/lib/python2.5/site-packages/scipy/linalg/basic.py", line 17, in <module>
    from lapack import get_lapack_funcs
  File "/usr/local/lib/python2.5/site-packages/scipy/linalg/lapack.py", line 17, in <module>
    from scipy.linalg import flapack
ImportError: /usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock

======================================================================
ERROR: Failure: ImportError (/usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/nose/loader.py", line 379, in loadTestsFromName
    addr.filename, addr.module)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 39, in importFromPath
    return self.importFromDir(dir_path, fqname)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 86, in importFromDir
    mod = load_module(part_fqname, fh, filename, desc)
  File "/usr/local/lib/python2.5/site-packages/scipy/lib/blas/__init__.py", line 9, in <module>
    import fblas
ImportError: /usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock

======================================================================
ERROR: Failure: ImportError (/usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/nose/loader.py", line 379, in loadTestsFromName
    addr.filename, addr.module)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 39, in importFromPath
    return self.importFromDir(dir_path, fqname)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 86, in importFromDir
    mod = load_module(part_fqname, fh, filename, desc)
  File "/usr/local/lib/python2.5/site-packages/scipy/lib/lapack/__init__.py", line 9, in <module>
    import calc_lwork
ImportError: /usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock

======================================================================
ERROR: Failure: ImportError (/usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/nose/loader.py", line 379, in loadTestsFromName
    addr.filename, addr.module)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 39, in importFromPath
    return self.importFromDir(dir_path, fqname)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 86, in importFromDir
    mod = load_module(part_fqname, fh, filename, desc)
  File "/usr/local/lib/python2.5/site-packages/scipy/linalg/__init__.py", line 8, in <module>
    from basic import *
  File "/usr/local/lib/python2.5/site-packages/scipy/linalg/basic.py", line 17, in <module>
    from lapack import get_lapack_funcs
  File "/usr/local/lib/python2.5/site-packages/scipy/linalg/lapack.py", line 17, in <module>
    from scipy.linalg import flapack
ImportError: /usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock

======================================================================
ERROR: Failure: ImportError (/usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/nose/loader.py", line 379, in loadTestsFromName
    addr.filename, addr.module)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 39, in importFromPath
    return self.importFromDir(dir_path, fqname)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 86, in importFromDir
    mod = load_module(part_fqname, fh, filename, desc)
  File "/usr/local/lib/python2.5/site-packages/scipy/linsolve/__init__.py", line 6, in <module>
    from scipy.sparse.linalg.dsolve import *
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/linalg/__init__.py", line 5, in <module>
    from isolve import *
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/linalg/isolve/__init__.py", line 4, in <module>
    from iterative import *
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/linalg/isolve/iterative.py", line 5, in <module>
    import _iterative
ImportError: /usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock

======================================================================
ERROR: Failure: ImportError (/usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/nose/loader.py", line 379, in loadTestsFromName
    addr.filename, addr.module)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 39, in importFromPath
    return self.importFromDir(dir_path, fqname)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 86, in importFromDir
    mod = load_module(part_fqname, fh, filename, desc)
  File "/usr/local/lib/python2.5/site-packages/scipy/maxentropy/__init__.py", line 9, in <module>
    from maxentropy import *
  File "/usr/local/lib/python2.5/site-packages/scipy/maxentropy/maxentropy.py", line 74, in <module>
    from scipy import optimize
  File "/usr/local/lib/python2.5/site-packages/scipy/optimize/__init__.py", line 11, in <module>
    from lbfgsb import fmin_l_bfgs_b
  File "/usr/local/lib/python2.5/site-packages/scipy/optimize/lbfgsb.py", line 30, in <module>
    import _lbfgsb
ImportError: /usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock

======================================================================
ERROR: Failure: ImportError (/usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/nose/loader.py", line 379, in loadTestsFromName
    addr.filename, addr.module)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 39, in importFromPath
    return self.importFromDir(dir_path, fqname)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 86, in importFromDir
    mod = load_module(part_fqname, fh, filename, desc)
  File "/usr/local/lib/python2.5/site-packages/scipy/odr/__init__.py", line 11, in <module>
    import odrpack
  File "/usr/local/lib/python2.5/site-packages/scipy/odr/odrpack.py", line 103, in <module>
    from scipy.odr import __odrpack
ImportError: /usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock

======================================================================
ERROR: Failure: ImportError (/usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/nose/loader.py", line 379, in loadTestsFromName
    addr.filename, addr.module)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 39, in importFromPath
    return self.importFromDir(dir_path, fqname)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 86, in importFromDir
    mod = load_module(part_fqname, fh, filename, desc)
  File "/usr/local/lib/python2.5/site-packages/scipy/optimize/__init__.py", line 11, in <module>
    from lbfgsb import fmin_l_bfgs_b
  File "/usr/local/lib/python2.5/site-packages/scipy/optimize/lbfgsb.py", line 30, in <module>
    import _lbfgsb
ImportError: /usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock

======================================================================
ERROR: Failure: ImportError (/usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/nose/loader.py", line 379, in loadTestsFromName
    addr.filename, addr.module)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 39, in importFromPath
    return self.importFromDir(dir_path, fqname)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 86, in importFromDir
    mod = load_module(part_fqname, fh, filename, desc)
  File "/usr/local/lib/python2.5/site-packages/scipy/signal/__init__.py", line 10, in <module>
    from filter_design import *
  File "/usr/local/lib/python2.5/site-packages/scipy/signal/filter_design.py", line 12, in <module>
    from scipy import special, optimize
  File "/usr/local/lib/python2.5/site-packages/scipy/optimize/__init__.py", line 11, in <module>
    from lbfgsb import fmin_l_bfgs_b
  File "/usr/local/lib/python2.5/site-packages/scipy/optimize/lbfgsb.py", line 30, in <module>
    import _lbfgsb
ImportError: /usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock

======================================================================
ERROR: Failure: ImportError (/usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/nose/loader.py", line 379, in loadTestsFromName
    addr.filename, addr.module)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 39, in importFromPath
    return self.importFromDir(dir_path, fqname)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 86, in importFromDir
    mod = load_module(part_fqname, fh, filename, desc)
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/linalg/__init__.py", line 5, in <module>
    from isolve import *
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/linalg/isolve/__init__.py", line 4, in <module>
    from iterative import *
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/linalg/isolve/iterative.py", line 5, in <module>
    import _iterative
ImportError: /usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock

======================================================================
ERROR: Failure: ImportError (/usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/nose/loader.py", line 379, in loadTestsFromName
    addr.filename, addr.module)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 39, in importFromPath
    return self.importFromDir(dir_path, fqname)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 86, in importFromDir
    mod = load_module(part_fqname, fh, filename, desc)
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/tests/test_base.py", line 31, in <module>
    from scipy.sparse.linalg import splu
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/linalg/__init__.py", line 5, in <module>
    from isolve import *
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/linalg/isolve/__init__.py", line 4, in <module>
    from iterative import *
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/linalg/isolve/iterative.py", line 5, in <module>
    import _iterative
ImportError: /usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock

======================================================================
ERROR: Failure: ImportError (/usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/nose/loader.py", line 379, in loadTestsFromName
    addr.filename, addr.module)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 39, in importFromPath
    return self.importFromDir(dir_path, fqname)
  File "/usr/local/lib/python2.5/site-packages/nose/importer.py", line 86, in importFromDir
    mod = load_module(part_fqname, fh, filename, desc)
  File "/usr/local/lib/python2.5/site-packages/scipy/stats/__init__.py", line 7, in <module>
    from stats import *
  File "/usr/local/lib/python2.5/site-packages/scipy/stats/stats.py", line 199, in <module>
    import scipy.linalg as linalg
  File "/usr/local/lib/python2.5/site-packages/scipy/linalg/__init__.py", line 8, in <module>
    from basic import *
  File "/usr/local/lib/python2.5/site-packages/scipy/linalg/basic.py", line 17, in <module>
    from lapack import get_lapack_funcs
  File "/usr/local/lib/python2.5/site-packages/scipy/linalg/lapack.py", line 17, in <module>
    from scipy.linalg import flapack
ImportError: /usr/local/atlas/lib/libatlas.so: undefined symbol: omp_set_lock

----------------------------------------------------------------------
Ran 1539 tests in 27.082s

FAILED (KNOWNFAIL=2, errors=14)
<nose.result.TextTestResult run=1539 errors=14 failures=0>
>>>
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From loretta at vol.at  Mon Aug 17 01:39:20 2009
From: loretta at vol.at (loretta)
Date: Mon, 17 Aug 2009 07:39:20 +0200
Subject: [SciPy-User] How to build simulation software like Simulink using
	SciPy?
Message-ID: <200908170739.20954.loretta@vol.at>

I wonder how simulation software could be built using SciPy. I tried to do so, 
but get into troubles as soon as feedback pathes are involved.

What I want to achieve is a package, that can be called periodically every T 
seconds, where all blocks (which are connected to each other) are calculated 
and results are recorded in blocks that can display signals (scopes). Between 
the coarse grained T seconds, I create a time vector for odeint, to calculate 
state vectors between the coarse grained i*T.

Without odeint I would have to choose a very small T to approach continuous 
time situation. I want odeint to do that (and it does), because it's so much 
faster than some homebrew ode solver.

The rule I've implemented to solve a net of blocks is: A block may be 
calculated as soon as all its predecessors have been calculated.

Everything works as expected, if I don't create loops. If I create loops, I 
only get at the output results of each block at i*T. What I need is a method 
to get at the output results of each blocks at the times j*Tau (where Tau < T 
and j = range( T/Tau)) odeint is solving the 1st order differential eq.

While having all these difficulties to get at proper results, I really wonder 
how Simulink is doing all this. 

Anyone willing to shed some light on this?

Kind regards
Loretta


From karl.young at ucsf.edu  Mon Aug 17 02:06:51 2009
From: karl.young at ucsf.edu (Young, Karl)
Date: Sun, 16 Aug 2009 23:06:51 -0700
Subject: [SciPy-User] SciPy2009 BoF Wiki Page
In-Reply-To: <0636F499-8CBC-4053-ACF9-7BA40E5D58D4@cs.toronto.edu>
References: <0636F499-8CBC-4053-ACF9-7BA40E5D58D4@cs.toronto.edu>
Message-ID: <72BBA065386338429D2C4E83E442CD4E0FE5393514@EX02.net.ucsf.edu>


Hi David,

I forgot my SciPy username (:-)) and couldn't edit the Wiki re. adding my name but am interested in the machine learning BOF if you guys are counting heads.

-- Karl

________________________________________
From: scipy-user-bounces at scipy.org [scipy-user-bounces at scipy.org] On Behalf Of David Warde-Farley [dwf at cs.toronto.edu]
Sent: Thursday, August 13, 2009 2:20 PM
To: SciPy Users List; Discussion of Numerical Python; ipython-user at scipy.net
Subject: [SciPy-User] SciPy2009 BoF Wiki Page

I needed a short break from some heavy writing, so on Fernando's
suggestion I took to the task of aggregating together mailing list
traffic about the BoFs next week. So far, 4 have been proposed, and
I've written down under "attendees" the names of anyone who has
expressed interest (except in Perry's case, where I've only heard it
via proxy). The page is at

        http://scipy.org/SciPy2009/BoF

I've created sections below that are hyperlink targets for the topic
of the session, if someone more knowledgeable of that domain can fill
in those sections, please do.

Edit away, and see you next week! (And if someone can forward this to
the Matplotlib list, I'm not currently subscribed)

David
_______________________________________________
SciPy-User mailing list
SciPy-User at scipy.org
http://mail.scipy.org/mailman/listinfo/scipy-user


From gokhansever at gmail.com  Mon Aug 17 08:07:10 2009
From: gokhansever at gmail.com (=?UTF-8?Q?G=C3=B6khan_Sever?=)
Date: Mon, 17 Aug 2009 07:07:10 -0500
Subject: [SciPy-User] How to build simulation software like Simulink
	using SciPy?
In-Reply-To: <200908170739.20954.loretta@vol.at>
References: <200908170739.20954.loretta@vol.at>
Message-ID: <49d6b3500908170507k11e6cff7h6cf724415116e24c@mail.gmail.com>

You may want to see this page: http://mgltools.scripps.edu/packages/vision

And for extra tutorials search for "vision" on showmedo.com



On Mon, Aug 17, 2009 at 12:39 AM, loretta <loretta at vol.at> wrote:

> I wonder how simulation software could be built using SciPy. I tried to do
> so,
> but get into troubles as soon as feedback pathes are involved.
>
> What I want to achieve is a package, that can be called periodically every
> T
> seconds, where all blocks (which are connected to each other) are
> calculated
> and results are recorded in blocks that can display signals (scopes).
> Between
> the coarse grained T seconds, I create a time vector for odeint, to
> calculate
> state vectors between the coarse grained i*T.
>
> Without odeint I would have to choose a very small T to approach continuous
> time situation. I want odeint to do that (and it does), because it's so
> much
> faster than some homebrew ode solver.
>
> The rule I've implemented to solve a net of blocks is: A block may be
> calculated as soon as all its predecessors have been calculated.
>
> Everything works as expected, if I don't create loops. If I create loops, I
> only get at the output results of each block at i*T. What I need is a
> method
> to get at the output results of each blocks at the times j*Tau (where Tau <
> T
> and j = range( T/Tau)) odeint is solving the 1st order differential eq.
>
> While having all these difficulties to get at proper results, I really
> wonder
> how Simulink is doing all this.
>
> Anyone willing to shed some light on this?
>
> Kind regards
> Loretta
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>



-- 
G?khan, on my way to SciPy09
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From s.mientki at ru.nl  Mon Aug 17 08:27:29 2009
From: s.mientki at ru.nl (Stef Mientki)
Date: Mon, 17 Aug 2009 14:27:29 +0200
Subject: [SciPy-User] How to build simulation software like
 Simulink	using SciPy?
In-Reply-To: <49d6b3500908170507k11e6cff7h6cf724415116e24c@mail.gmail.com>
References: <200908170739.20954.loretta@vol.at>
	<49d6b3500908170507k11e6cff7h6cf724415116e24c@mail.gmail.com>
Message-ID: <4A894CB1.7030400@ru.nl>

or look at PyLab_Works
http://mientki.ruhosting.nl/data_www/pylab_works/pw_animations_screenshots.html

One of the tricks for closed loop with visual feedback,
is to perform more than 1 network calculations in one display loop.
So let's say 50 frames per second fr visulization, and 1000 calculations 
per second.

cheers,
Stef

G?khan Sever wrote:
> You may want to see this page: http://mgltools.scripps.edu/packages/vision
>
> And for extra tutorials search for "vision" on showmedo.com 
> <http://showmedo.com>
>
>
>
> On Mon, Aug 17, 2009 at 12:39 AM, loretta <loretta at vol.at 
> <mailto:loretta at vol.at>> wrote:
>
>     I wonder how simulation software could be built using SciPy. I
>     tried to do so,
>     but get into troubles as soon as feedback pathes are involved.
>
>     What I want to achieve is a package, that can be called
>     periodically every T
>     seconds, where all blocks (which are connected to each other) are
>     calculated
>     and results are recorded in blocks that can display signals
>     (scopes). Between
>     the coarse grained T seconds, I create a time vector for odeint,
>     to calculate
>     state vectors between the coarse grained i*T.
>
>     Without odeint I would have to choose a very small T to approach
>     continuous
>     time situation. I want odeint to do that (and it does), because
>     it's so much
>     faster than some homebrew ode solver.
>
>     The rule I've implemented to solve a net of blocks is: A block may be
>     calculated as soon as all its predecessors have been calculated.
>
>     Everything works as expected, if I don't create loops. If I create
>     loops, I
>     only get at the output results of each block at i*T. What I need
>     is a method
>     to get at the output results of each blocks at the times j*Tau
>     (where Tau < T
>     and j = range( T/Tau)) odeint is solving the 1st order
>     differential eq.
>
>     While having all these difficulties to get at proper results, I
>     really wonder
>     how Simulink is doing all this.
>
>     Anyone willing to shed some light on this?
>
>     Kind regards
>     Loretta
>     _______________________________________________
>     SciPy-User mailing list
>     SciPy-User at scipy.org <mailto:SciPy-User at scipy.org>
>     http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
>
>
> -- 
> G?khan, on my way to SciPy09
> ------------------------------------------------------------------------
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>   


From gael.varoquaux at normalesup.org  Mon Aug 17 10:28:37 2009
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Mon, 17 Aug 2009 16:28:37 +0200
Subject: [SciPy-User] Tutorial Prep: one IntroTest Failure
	after	Python(x, y) install
In-Reply-To: <114269.83988.qm@web52108.mail.re2.yahoo.com>
References: <114269.83988.qm@web52108.mail.re2.yahoo.com>
Message-ID: <20090817142837.GB30571@phare.normalesup.org>

On Sun, Aug 16, 2009 at 04:32:00AM +0000, David Goldsmith wrote:
> Hi!? I installed Python(x,y) (successfully, I believe) but when I run the Intro Test script I get:

> ======================================================================
> ERROR: __main__.test_imports('enthought.mayavi.api', None)
> ----------------------------------------------------------------------
> Traceback (most recent call last):
> ? File "C:\Python25\lib\site-packages\nose\case.py", line 183, in runTest
> ? ? self.test(*self.arg)
> ? File "IntroTests.py", line 91, in check_import
> ? ? exec "import %s as m" % mnames
> ? File "<string>", line 1, in <module>
> ImportError: No module named enthought.mayavi.api

> ----------------------------------------------------------------------
> Ran 14 tests in 6.198s

Yes, it seems that Mayavi is no longer installed by default with
Python(x,y). Can you contact Pierre Raybault (contact at pythonxy.com) to
ask for more details. I don't have Windows, so I cannot test things
myself.

Ga?l


From josef.pktd at gmail.com  Mon Aug 17 10:29:14 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Mon, 17 Aug 2009 10:29:14 -0400
Subject: [SciPy-User] [SciPy-user] scipy.stats.stats mannwhitneyu vs
	ranksums?
In-Reply-To: <4A88AA37.1080902@molden.no>
References: <43958ee60906301516s637b180fm35b96d5b61a1b549@mail.gmail.com>
	<43958ee60906301624l7fd7f445sebd0d69b4ac32e61@mail.gmail.com>
	<4a4b8c6f.1a135e0a.7a2d.0b82@mx.google.com>
	<5b8d13220907010933j7463ec93tb0bb92f02fbf9308@mail.gmail.com>
	<4a4cf1bf.05a4100a.7834.ffffcf2e@mx.google.com>
	<4F46BA01-652B-4AEA-84CE-277D54F26693@cs.toronto.edu>
	<4a4e2ca7.0c92100a.2f71.0392@mx.google.com>
	<1cd32cbb0908140713wcb67a2cl20899bb7207e64cc@mail.gmail.com>
	<4A88A53C.7040201@molden.no> <4A88AA37.1080902@molden.no>
Message-ID: <1cd32cbb0908170729h238828deo3541c649a136d56e@mail.gmail.com>

On Sun, Aug 16, 2009 at 8:54 PM, Sturla Molden<sturla at molden.no> wrote:
>
> My memory serves me badly, that was Kendall's tau. It is still pending
> review though.
>
> http://projects.scipy.org/scipy/ticket/893
>
> Sturla
>

I'm aware of the waiting list for stats enhancement tickets, but I'm
only slowly getting used to assigning correct labels.

I should have moved Ticket 893 to the "needs work" status. The main
part, that prevents it from quick inclusion, is the missing variance
calculation to be able to calculate the pvalue. I looked at it at the
end of our long discussion, but I only left my comments in the threat.

I guess, I was to tired from several days of struggling with
kendalltau, mannwhitneyu and friends, that once the confusion was
cleared up and the bugs fixed, I wasn't in the "mood" of struggling to
program in cython and get a cython based enhancement into svn (a first
for me)  (and I needed to get busy with other things).

I started this summer to go through (some of) the stats tickets. But
with the work on stats.models and other things that I'm interested in,
eg. some extensions to the distributions, I don't have much time to
finish up the missing work in "needs work" enhancement tickets. Bug
fixes still have top priority, and filling in missing tests is second.

Any help is very welcome.

Josef


>
>
>
> Sturla Molden skrev:
>> Josef,
>>
>> I believe we had an discussion about various versions of Wilcoxon and
>> Mann-Whitney some months ago. I find a discussion of this from february.
>> We (or I alone?) also wrote Cython versions of the test, which I cannot
>> find now. I should have filed a ticket then. :-(
>>
>> Regards,
>> Sturla Molden
>>
>>
>>
>>
>>
>> josef.pktd at gmail.com skrev:
>>
>>> On Fri, Jul 3, 2009 at 12:06 PM, Elias Pampalk<elias.pampalk at gmail.com> wrote:
>>>
>>>
>>>
>>>> I did a quick comparison between Matlab/stats (R14SP3), R (2.8.1), and
>>>> Python/SciPy (0.7). Maybe this is somehow useful for others too.
>>>>
>>>>
>>>>
>>> ...
>>>
>>>
>>>
>>>> Elias
>>>>
>>>>
>>> Thanks for doing this, this is very helpful. I attached the comparison
>>> to ticket:901
>>>
>>> Except for one case, the numbers look pretty good.
>>> However, documentation is still weak, and some of the code duplication
>>> could be removed.
>>>
>>> Josef
>>>
>>> (I didn't look at it closely before, because I was on vacation at that time.)
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>
>>>
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From roban at astro.columbia.edu  Mon Aug 17 10:32:25 2009
From: roban at astro.columbia.edu (Roban Hultman Kramer)
Date: Mon, 17 Aug 2009 10:32:25 -0400
Subject: [SciPy-User] SciPy2009 BoF Wiki Page
In-Reply-To: <0636F499-8CBC-4053-ACF9-7BA40E5D58D4@cs.toronto.edu>
References: <0636F499-8CBC-4053-ACF9-7BA40E5D58D4@cs.toronto.edu>
Message-ID: <463180e60908170732w2cf67695m3e7689b8c54a789c@mail.gmail.com>

Unfortunately, I won't be able to make it to the SciPy conference, but
apropos of the Astronomy BoF session, I thought some people might be
interested in a project I hope to start.

I've just released a package of some simple cosmological routines in the
hope that it will form the basis of a community-maintained library:

http://roban.github.com/CosmoloPy/

There are some examples and tests in the git tree (or tar.gz, or zip
archive) that should get you started if you have any interest in using or
contributing to the project. Join the discussion group at

http://groups.google.com/group/cosmolopy-devel

Thanks!

-Roban
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From gael.varoquaux at normalesup.org  Mon Aug 17 10:37:00 2009
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Mon, 17 Aug 2009 16:37:00 +0200
Subject: [SciPy-User] cython from Debian unstable
In-Reply-To: <e85f13c40908161641x6eb66e72hd815e91bd5b94333@mail.gmail.com>
References: <96de71860908140059v7ddcc93ew7cf051cbdd5d8088@mail.gmail.com>
	<4A886948.1080903@jpl.nasa.gov>
	<49d6b3500908161325i17334f87q41ddef4c08cbb4ce@mail.gmail.com>
	<4A886CB9.1020802@jpl.nasa.gov>
	<e85f13c40908161641x6eb66e72hd815e91bd5b94333@mail.gmail.com>
Message-ID: <20090817143659.GD30571@phare.normalesup.org>

On Sun, Aug 16, 2009 at 07:41:28PM -0400, Angus McMorland wrote:
> 2009/8/16 Tom Kuiper <kuiper at jpl.nasa.gov>:
> >> It seems that 0.11.2 is in the unstable Debian distribution. ?Can
> >> someone suggest how to get that package without having to upgrade to
> >> unstable?

> In case you're still looking for a solution, and disclaimer: this is
> from memory as I use Ubuntu now, you can temporarily add the unstable
> repositories into your apt sources list, and use `apt-get install -t
> unstable cython` to just get that version. It will, I believe, pull in
> any dependencies from the unstable distribution that it requires as
> well, but not generally upgrade the system. Then I've commented the
> unstable repositories out again to ensure that I don't pull in any
> more unstable packages than I need in subsequent operations.

Don't do that. Use Robert's solution. It has no chances of breaking
your install.

Ga?l


From Fernando.Perez at berkeley.edu  Fri Aug 14 02:47:23 2009
From: Fernando.Perez at berkeley.edu (Fernando Perez)
Date: Thu, 13 Aug 2009 23:47:23 -0700
Subject: [SciPy-User] Checklist script
In-Reply-To: <f5014d110908131129x351a0dcahe6eedac46427f9f2@mail.gmail.com>
References: <f5014d110908131129x351a0dcahe6eedac46427f9f2@mail.gmail.com>
Message-ID: <96de71860908132347u2f3da8b8i270247fce9b05d6e@mail.gmail.com>

Hi David,

On Thu, Aug 13, 2009 at 11:29 AM, David Kim<davidkim05 at gmail.com> wrote:
> Can I get a little help? see output below...
>

I'll send in a minute an email with some instructions regarding this,
you're not the only one seeing the issue.

Cheers,

f


From Fernando.Perez at berkeley.edu  Sat Aug 15 01:02:51 2009
From: Fernando.Perez at berkeley.edu (Fernando Perez)
Date: Fri, 14 Aug 2009 22:02:51 -0700
Subject: [SciPy-User] Intro tutorial checklist failure
In-Reply-To: <49d6b3500908141305g2812d010we1239f5e1074b40b@mail.gmail.com>
References: <49d6b3500908141305g2812d010we1239f5e1074b40b@mail.gmail.com>
Message-ID: <96de71860908142202v79e5b20bv83257db348290264@mail.gmail.com>

Hi Gokhan,

On Fri, Aug 14, 2009 at 1:05 PM, G?khan Sever<gokhansever at gmail.com> wrote:
> As posted below the output of the script seemingly there is something wrong
> with the matplotlib tests. I have matplotlib installed and working properly
> however the script fails to test this. Manual testing of the test plot
> functions work properly as expected.
>
> I don't have scipy and mayavi installed yet since there are two bizarre
> installation issues that I have had and posted on the lists, and still
> couldn't figure out. All were working extremely nicely on Fedora 10 (The
> source code installations) but there must be somethings wrong with some
> critical libraries or some tools' being so up-to-date.

That is very weird, I have no idea why this could be presenting this
behavior.  If you want, you can always try to run the script in
ipython via

run -n intro_tut_checklist.py

and then call the tests individually:


In [18]: run -n intro_tut_checklist.py

In [19]: [ t[0](*t[1:]) for t in test_imports() ]
MOD: setuptools, version: 0.6c9
MOD: IPython, version: 0.10
MOD: numpy, version: 1.4.0.dev7303
MOD: scipy, version: 0.8.0.dev5764
MOD: scipy.io, version: *no info*
MOD: matplotlib, version: 1.0.svn
MOD: pylab, version: *no info*
MOD: enthought.mayavi.api, version: 3.1.0
Out[19]: [None, None, None, None, None, None, None, None]

In [20]: [ t[0](*t[1:]) for t in test_loadtxt() ]
Out[20]: [None, None, None, None]

In [21]: test_plot()

In [22]: test_plot_math()



If this works for you, I wouldn't worry too much for now...

Cheers,

f


From Fernando.Perez at berkeley.edu  Sun Aug 16 14:59:07 2009
From: Fernando.Perez at berkeley.edu (Fernando Perez)
Date: Sun, 16 Aug 2009 11:59:07 -0700
Subject: [SciPy-User] FAIL: __main__.test_loadtxt(array([('M', 21, 72.0),
	('F', 35, 58.0)], 
In-Reply-To: <db390ba40908161058k5f8c40bod893ecc3c1b3d6fb@mail.gmail.com>
References: <db390ba40908161058k5f8c40bod893ecc3c1b3d6fb@mail.gmail.com>
Message-ID: <96de71860908161159s5db4314es2cea1e6d994a1656@mail.gmail.com>

Hi Egill,

On Sun, Aug 16, 2009 at 10:58 AM, Egill Hauksson<ehauksson at gmail.com> wrote:
> To all,
>
> Any ideas -- see below for one FAIL.
>
> Thanks
> Egill
>
> In [13]: %run intro.py
[...]
> AssertionError:
> Arrays are not equal
>
> (mismatch 100.0%)
> ?x: array([('M', 21, 72.0), ('F', 35, 58.0)],
> ????? dtype=[('gender', '|S1'), ('age', '>i4'), ('weight', '>f4')])
> ?y: array([('M', 21, 72.0), ('F', 35, 58.0)],
> ????? dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')])


Ah, don't worry. You must be on a power PC Mac instead of an Intel
one, and the mismatch is simply in the endianness of the data.  I just
failed to account for the possibility of a Big Endian chip in the
tests.

So the failure is just in my test, your setup is OK. Sorry about that!

Cheers,

f


From Fernando.Perez at berkeley.edu  Sun Aug 16 15:43:05 2009
From: Fernando.Perez at berkeley.edu (Fernando Perez)
Date: Sun, 16 Aug 2009 12:43:05 -0700
Subject: [SciPy-User] FAIL: __main__.test_loadtxt(array([('M', 21, 72.0),
	('F', 35, 58.0)], 
In-Reply-To: <db390ba40908161213o4e719bb0kb10d3c63b97fa53f@mail.gmail.com>
References: <db390ba40908161058k5f8c40bod893ecc3c1b3d6fb@mail.gmail.com> 
	<96de71860908161159s5db4314es2cea1e6d994a1656@mail.gmail.com> 
	<db390ba40908161213o4e719bb0kb10d3c63b97fa53f@mail.gmail.com>
Message-ID: <96de71860908161243t2b5e71ebvd62bdc74200fc3e5@mail.gmail.com>

Hi Egill,

On Sun, Aug 16, 2009 at 12:13 PM, Egill Hauksson<ehauksson at gmail.com> wrote:
> Fernando,
>
> When I run the 2nd script I get one more error -- FAIL: Test basic Cython
> sanity --see below.

======================================================================
> FAIL: Test basic Cython sanity
> ----------------------------------------------------------------------
> Traceback (most recent call last):
> ? File
> "/Library/Frameworks/Python.framework/Versions/4.3.0/lib/python2.5/site-packages/nose-0.10.3n1-py2.5.egg/nose/case.py",
> line 182, in runTest
> ??? self.test(*self.arg)
> ? File "adv_2.py", line 183, in test_cython
> ??? validate_cython(None)
> ? File "adv_2.py", line 82, in validate_cython
> ??? nt.assert_true(cython_version >= min_version, msg)
> AssertionError: Cython version (0, 9, 8, 1, 1) found, at least (0, 11, 2)
> required

OK, this one you do need to fix.  I'm attaching below the instructions
on how to fix this...  I'll be offline for a few hours, but let us
know if you have any problems, I should be back in the evening.

Cheers,

f

ar tutorial attendees,

here are some notes regarding the tutorial requirements.  I am sending this to
all of you, though some of it may only apply to those attending the advanced
track.

- If you had troubles with the testing scripts, please note that I've fixed a
  few small issues.  The current versions are (intro, advanced and
  pycuda-specific):

  https://cirl.berkeley.edu/fperez/tmp/intro_tut_checklist.py
  https://cirl.berkeley.edu/fperez/tmp/adv_tut_checklist.py
  https://cirl.berkeley.edu/fperez/tmp/pycuda_tut_checklist.py

- For those of you on OSX, you may need to install Apple's XCode to get a
  working compiler on your system.  XCode is free to download, but it's a very
  large download and you do need to register in their developer site to access
  the file.

- For those of you using the Enthought Python Distribution (EPD henceforth),
  and attending the advanced tutorial track, you may need to manually add
  updated versions of Cython and Sympy, as those included by default are a bit
  old to demo all the features the speakers want to cover.  Below are the
  instructions for this (if you are attending the introductory tutorials, the
  default EPD install is sufficient).

- If you have a laptop with a CUDA-capable GPU, and you install the necessary
  requirements, you'll be able to follow the interactive parts of the
  tutorial.  The tutorials page has links to all necessary prerequisites:

  http://conference.scipy.org/advanced_tutorials

================================================================
 Instructions for an EPD-based setup for the advanced tutorials
================================================================

The current version of EPD has most of what you need, except for updated SymPy
and Cython.

1. Install sympy 0.6.5, available at:

  - Source: http://sympy.googlecode.com/files/sympy-0.6.5.tar.gz
  - Windows installer: http://sympy.googlecode.com/files/sympy-0.6.5.win32.exe

2. Install Cython 0.11.2.  On OSX you can do a source build, for Windows, binary
  installers are available:

  - Source: http://cython.org/Cython-0.11.2.tar.gz
  - Windows installer:
https://cirl.berkeley.edu/fperez/tmp/Cython-0.11.2.win32-py2.5.exe

3. Then, edit the easy-install.pth file for EPD, located at:

  - Windows: c:/Python25/Lib/site-packages/easy-install.pth

  - OSX: /Library/Frameworks/Python.framework/Versions/4.3.0/lib/python2.5/site-packages/easy-install.pth

Comment out the lines that read:

./cython-0.9.8.1.1n1-py2.5-win32.egg
./sympy-0.6.2n2-py2.5.egg

By prepending a # mark at the beginning.


4. At this point, the advanced tutorials checklist script:

https://cirl.berkeley.edu/fperez/tmp/adv_tut_checklist.py

should run to completion without errors, let us know otherwise.


From Fernando.Perez at berkeley.edu  Mon Aug 17 03:45:19 2009
From: Fernando.Perez at berkeley.edu (Fernando Perez)
Date: Mon, 17 Aug 2009 00:45:19 -0700
Subject: [SciPy-User] Some updates on the tutorial test scripts and
	versions of tools needed
In-Reply-To: <4A886948.1080903@jpl.nasa.gov>
References: <96de71860908140059v7ddcc93ew7cf051cbdd5d8088@mail.gmail.com> 
	<4A886948.1080903@jpl.nasa.gov>
Message-ID: <96de71860908170045gc54d412g9afb7d8eea3acbf5@mail.gmail.com>

Hi Tom,

On Sun, Aug 16, 2009 at 1:17 PM, Tom Kuiper<kuiper at jpl.nasa.gov> wrote:
> It seems that 0.11.2 is in the unstable Debian distribution. ?Can someone
> suggest how to get that package without having to upgrade to unstable?
>

Besides the suggestion Andrew made, I can only say what my approach is
on such matters: I use ubuntu (basically debian stable from X months
ago), and then I keep in ~/usr/local a fully configured subsystem with
$PATH, $PYTHONPATH, etc, and then do

python setup.py install --prefix=~/usr/local/

for all packages that I need in newer versions than what's in ubuntu
(numpy,  scipy, cython, matplotlib, networkx, sympy).

Cheers,

f


From questions.anon at gmail.com  Mon Aug 17 13:04:34 2009
From: questions.anon at gmail.com (questions anon)
Date: Mon, 17 Aug 2009 10:04:34 -0700
Subject: [SciPy-User] satellite imagery
In-Reply-To: <A9AC9E96-3171-42A8-9111-EF4DC299BAEB@cs.toronto.edu>
References: <aa15d47e0908141518vc7f4a70x1b1e138ac8644ff@mail.gmail.com>
	<A9AC9E96-3171-42A8-9111-EF4DC299BAEB@cs.toronto.edu>
Message-ID: <aa15d47e0908171004x3273b948p4950a0209c70918b@mail.gmail.com>

Thanks for responding. Some of the processing I would like to do include:
Import a hdf file, open calibrated data (VSWIR to TIR) and display image
using different band combinations.
>From the hdf open the pixel latitude and longitude and georeference the
image.
Process the images to apparent reflectance, emissivity and brightness
temperature
Process the images to certain indices using simple band math.
Overlay a vector file and calculate the mean values of the raster within the
vector region.
I would like to do this for many different files images.
Thanks

On Sat, Aug 15, 2009 at 12:01 AM, David Warde-Farley <dwf at cs.toronto.edu>wrote:

> On 14-Aug-09, at 6:18 PM, questions anon wrote:
>
> > Can anyone suggest any good python books or tutorials for using
> > satellite imagery?
>
> And doing what with it? You'll have to be more specific.
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From jgomezdans at gmail.com  Mon Aug 17 16:53:52 2009
From: jgomezdans at gmail.com (Jose Gomez-Dans)
Date: Mon, 17 Aug 2009 21:53:52 +0100
Subject: [SciPy-User] satellite imagery
In-Reply-To: <aa15d47e0908171004x3273b948p4950a0209c70918b@mail.gmail.com>
References: <aa15d47e0908141518vc7f4a70x1b1e138ac8644ff@mail.gmail.com>
	<A9AC9E96-3171-42A8-9111-EF4DC299BAEB@cs.toronto.edu>
	<aa15d47e0908171004x3273b948p4950a0209c70918b@mail.gmail.com>
Message-ID: <91d218430908171353j3d4b43ectb44001e3b39fd9@mail.gmail.com>

Hi,

2009/8/17 questions anon <questions.anon at gmail.com>

> Thanks for responding. Some of the processing I would like to do include:
> Import a hdf file, open calibrated data (VSWIR to TIR) and display image
> using different band combinations.
> From the hdf open the pixel latitude and longitude and georeference the
> image.
> Process the images to apparent reflectance, emissivity and brightness
> temperature
> Process the images to certain indices using simple band math.
> Overlay a vector file and calculate the mean values of the raster within
> the vector region.
> I would like to do this for many different files images.


This is fairly straightforward stuff (in principle!). You can use GDAL's
python bindings to open HDF files (your data looks like ASTER), and then use
matplotlib to plot colour composites. Any further image manipulations are
quite easy as everything is an array. You might need to watch memory usage,
and do image processing by blocks.

J
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From vanleeuwen.martin at gmail.com  Mon Aug 17 17:10:18 2009
From: vanleeuwen.martin at gmail.com (Martin81)
Date: Mon, 17 Aug 2009 14:10:18 -0700 (PDT)
Subject: [SciPy-User] [SciPy-user] set difference on meshes
Message-ID: <25014257.post@talk.nabble.com>


Hi,

I have a couple of cones generated. These cones are build from meshes and
displayed in mayavi using mlab.mesh(). The tops of the cones are
non-overlapping but as you gradually move downwards the cones do overlap. I
would like to remove any overlap. Thus I am looking to find the set
difference of the cones.

I tried:

1) masking out from the 2D meshed and generate again through mlab.mesh() -
doesn't show anything
2) removing any point inside another cone and redraw using
mlab.triangular_mesh() but don't know how to use delaunay2d() to build the
topology between points - no documentation found at all on the internet
3) using GTS (GNU Triangulated Surface Library) but couldn't build it on my
Windows machine.

Would anyone know how to do this?

Thanks
Martin
-- 
View this message in context: http://www.nabble.com/set-difference-on-meshes-tp25014257p25014257.html
Sent from the Scipy-User mailing list archive at Nabble.com.



From questions.anon at gmail.com  Mon Aug 17 17:41:13 2009
From: questions.anon at gmail.com (questions anon)
Date: Mon, 17 Aug 2009 14:41:13 -0700
Subject: [SciPy-User] satellite imagery
In-Reply-To: <91d218430908171353j3d4b43ectb44001e3b39fd9@mail.gmail.com>
References: <aa15d47e0908141518vc7f4a70x1b1e138ac8644ff@mail.gmail.com>
	<A9AC9E96-3171-42A8-9111-EF4DC299BAEB@cs.toronto.edu>
	<aa15d47e0908171004x3273b948p4950a0209c70918b@mail.gmail.com>
	<91d218430908171353j3d4b43ectb44001e3b39fd9@mail.gmail.com>
Message-ID: <aa15d47e0908171441s43459814x8a318e1223c018fc@mail.gmail.com>

Thanks. Are there any books/tutorials/examples on these and other processing
steps?

On Mon, Aug 17, 2009 at 1:53 PM, Jose Gomez-Dans <jgomezdans at gmail.com>wrote:

> Hi,
>
> 2009/8/17 questions anon <questions.anon at gmail.com>
>
>> Thanks for responding. Some of the processing I would like to do include:
>> Import a hdf file, open calibrated data (VSWIR to TIR) and display image
>> using different band combinations.
>> From the hdf open the pixel latitude and longitude and georeference the
>> image.
>> Process the images to apparent reflectance, emissivity and brightness
>> temperature
>> Process the images to certain indices using simple band math.
>> Overlay a vector file and calculate the mean values of the raster within
>> the vector region.
>> I would like to do this for many different files images.
>
>
> This is fairly straightforward stuff (in principle!). You can use GDAL's
> python bindings to open HDF files (your data looks like ASTER), and then use
> matplotlib to plot colour composites. Any further image manipulations are
> quite easy as everything is an array. You might need to watch memory usage,
> and do image processing by blocks.
>
> J
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
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From robince at gmail.com  Mon Aug 17 17:47:15 2009
From: robince at gmail.com (Robin)
Date: Mon, 17 Aug 2009 23:47:15 +0200
Subject: [SciPy-User] problem with discrete rvs?
Message-ID: <b14b627d0908171447q1430cd82tc2e5dcc5a7ece33e@mail.gmail.com>

Hi,

If I understand the scipy.stats package I should be able to create my
own arbitrary discrete distribution and generate samples from it... I
am trying this but am having the following problem. I define my
discrete distribution with the following:

vals = (array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13,
14, 15, 16,
       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]),
 array([ 0.31518555,  0.08191701,  0.0817977 ,  0.02212394,  0.08149974,
        0.02204335,  0.02201125,  0.00595341,  0.0817977 ,  0.02212394,
        0.02209172,  0.00597518,  0.02201125,  0.00595341,  0.00594474,
        0.00160788,  0.08161887,  0.02207557,  0.02204342,  0.00596212,
        0.02196313,  0.0059404 ,  0.00593175,  0.00160437,  0.02204342,
        0.00596212,  0.00595343,  0.00161023,  0.00593175,  0.00160437,
        0.00160203,  0.0004333 ]))

Then I do
rv = rv_discrete(name='test',values=vals)

and then try to generate samples. When I plot the results of this for
large number of samples, it seems to match pretty well but the last 4
or 5 values are never generated and have zero probability, even with
enough samples to really capture them.

In [742]: (te.rvs(size=1000000)==30).sum()
Out[742]: 0
In [743]: (te.rvs(size=1000000)==29).sum()
Out[743]: 0
In [744]: (te.rvs(size=1000000)==27).sum()
Out[744]: 0
In [745]: (te.rvs(size=1000000)==20).sum()
Out[745]: 21884

Does anyone have any ideas? Is this a bug in rv_discrete.rvs?

In [746]: scipy.__version__
Out[746]: '0.8.0.dev5825'
In [747]: numpy.__version__
Out[747]: '1.4.0.dev7039'

Cheers

Robin


From josef.pktd at gmail.com  Mon Aug 17 18:10:30 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Mon, 17 Aug 2009 18:10:30 -0400
Subject: [SciPy-User] problem with discrete rvs?
In-Reply-To: <b14b627d0908171447q1430cd82tc2e5dcc5a7ece33e@mail.gmail.com>
References: <b14b627d0908171447q1430cd82tc2e5dcc5a7ece33e@mail.gmail.com>
Message-ID: <1cd32cbb0908171510u2ad640dfjcd20892a4e9d8f74@mail.gmail.com>

On Mon, Aug 17, 2009 at 5:47 PM, Robin<robince at gmail.com> wrote:
> Hi,
>
> If I understand the scipy.stats package I should be able to create my
> own arbitrary discrete distribution and generate samples from it... I
> am trying this but am having the following problem. I define my
> discrete distribution with the following:
>
> vals = (array([ 0, ?1, ?2, ?3, ?4, ?5, ?6, ?7, ?8, ?9, 10, 11, 12, 13,
> 14, 15, 16,
> ? ? ? 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]),
> ?array([ 0.31518555, ?0.08191701, ?0.0817977 , ?0.02212394, ?0.08149974,
> ? ? ? ?0.02204335, ?0.02201125, ?0.00595341, ?0.0817977 , ?0.02212394,
> ? ? ? ?0.02209172, ?0.00597518, ?0.02201125, ?0.00595341, ?0.00594474,
> ? ? ? ?0.00160788, ?0.08161887, ?0.02207557, ?0.02204342, ?0.00596212,
> ? ? ? ?0.02196313, ?0.0059404 , ?0.00593175, ?0.00160437, ?0.02204342,
> ? ? ? ?0.00596212, ?0.00595343, ?0.00161023, ?0.00593175, ?0.00160437,
> ? ? ? ?0.00160203, ?0.0004333 ]))
>
> Then I do
> rv = rv_discrete(name='test',values=vals)
>
> and then try to generate samples. When I plot the results of this for
> large number of samples, it seems to match pretty well but the last 4
> or 5 values are never generated and have zero probability, even with
> enough samples to really capture them.
>
> In [742]: (te.rvs(size=1000000)==30).sum()
> Out[742]: 0
> In [743]: (te.rvs(size=1000000)==29).sum()
> Out[743]: 0
> In [744]: (te.rvs(size=1000000)==27).sum()
> Out[744]: 0
> In [745]: (te.rvs(size=1000000)==20).sum()
> Out[745]: 21884
>
> Does anyone have any ideas? Is this a bug in rv_discrete.rvs?
>
> In [746]: scipy.__version__
> Out[746]: '0.8.0.dev5825'
> In [747]: numpy.__version__
> Out[747]: '1.4.0.dev7039'

just a quick check

>>> vals[1].cumsum()
array([ 0.31518555,  0.39710256,  0.47890026,  0.5010242 ,  0.58252394,
        0.60456729,  0.62657854,  0.63253195,  0.71432965,  0.73645359,
        0.75854531,  0.76452049,  0.78653174,  0.79248515,  0.79842989,
        0.80003777,  0.88165664,  0.90373221,  0.92577563,  0.93173775,
        0.95370088,  0.95964128,  0.96557303,  0.9671774 ,  0.98922082,
        0.99518294,  1.00113637,  1.0027466 ,  1.00867835,  1.01028272,
        1.01188475,  1.01231805])


your probabilities don't add up to one.

I haven't checked if this is the source of the problem

Josef


>
> Cheers
>
> Robin
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From josef.pktd at gmail.com  Mon Aug 17 18:28:27 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Mon, 17 Aug 2009 18:28:27 -0400
Subject: [SciPy-User] problem with discrete rvs?
In-Reply-To: <1cd32cbb0908171510u2ad640dfjcd20892a4e9d8f74@mail.gmail.com>
References: <b14b627d0908171447q1430cd82tc2e5dcc5a7ece33e@mail.gmail.com>
	<1cd32cbb0908171510u2ad640dfjcd20892a4e9d8f74@mail.gmail.com>
Message-ID: <1cd32cbb0908171528s1a308ef1j9b46b59ac8afd4fa@mail.gmail.com>

On Mon, Aug 17, 2009 at 6:10 PM, <josef.pktd at gmail.com> wrote:
> On Mon, Aug 17, 2009 at 5:47 PM, Robin<robince at gmail.com> wrote:
>> Hi,
>>
>> If I understand the scipy.stats package I should be able to create my
>> own arbitrary discrete distribution and generate samples from it... I
>> am trying this but am having the following problem. I define my
>> discrete distribution with the following:
>>
>> vals = (array([ 0, ?1, ?2, ?3, ?4, ?5, ?6, ?7, ?8, ?9, 10, 11, 12, 13,
>> 14, 15, 16,
>> ? ? ? 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]),
>> ?array([ 0.31518555, ?0.08191701, ?0.0817977 , ?0.02212394, ?0.08149974,
>> ? ? ? ?0.02204335, ?0.02201125, ?0.00595341, ?0.0817977 , ?0.02212394,
>> ? ? ? ?0.02209172, ?0.00597518, ?0.02201125, ?0.00595341, ?0.00594474,
>> ? ? ? ?0.00160788, ?0.08161887, ?0.02207557, ?0.02204342, ?0.00596212,
>> ? ? ? ?0.02196313, ?0.0059404 , ?0.00593175, ?0.00160437, ?0.02204342,
>> ? ? ? ?0.00596212, ?0.00595343, ?0.00161023, ?0.00593175, ?0.00160437,
>> ? ? ? ?0.00160203, ?0.0004333 ]))
>>
>> Then I do
>> rv = rv_discrete(name='test',values=vals)
>>
>> and then try to generate samples. When I plot the results of this for
>> large number of samples, it seems to match pretty well but the last 4
>> or 5 values are never generated and have zero probability, even with
>> enough samples to really capture them.
>>
>> In [742]: (te.rvs(size=1000000)==30).sum()
>> Out[742]: 0
>> In [743]: (te.rvs(size=1000000)==29).sum()
>> Out[743]: 0
>> In [744]: (te.rvs(size=1000000)==27).sum()
>> Out[744]: 0
>> In [745]: (te.rvs(size=1000000)==20).sum()
>> Out[745]: 21884
>>
>> Does anyone have any ideas? Is this a bug in rv_discrete.rvs?
>>
>> In [746]: scipy.__version__
>> Out[746]: '0.8.0.dev5825'
>> In [747]: numpy.__version__
>> Out[747]: '1.4.0.dev7039'
>
> just a quick check
>
>>>> vals[1].cumsum()
> array([ 0.31518555, ?0.39710256, ?0.47890026, ?0.5010242 , ?0.58252394,
> ? ? ? ?0.60456729, ?0.62657854, ?0.63253195, ?0.71432965, ?0.73645359,
> ? ? ? ?0.75854531, ?0.76452049, ?0.78653174, ?0.79248515, ?0.79842989,
> ? ? ? ?0.80003777, ?0.88165664, ?0.90373221, ?0.92577563, ?0.93173775,
> ? ? ? ?0.95370088, ?0.95964128, ?0.96557303, ?0.9671774 , ?0.98922082,
> ? ? ? ?0.99518294, ?1.00113637, ?1.0027466 , ?1.00867835, ?1.01028272,
> ? ? ? ?1.01188475, ?1.01231805])
>
>
> your probabilities don't add up to one.
>
> I haven't checked if this is the source of the problem
>
> Josef

I don't know what your te is. but the following looks ok

Josef


import numpy as np
from scipy import stats


vals = [np.array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13,
14, 15, 16,
      17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]),
 np.array([ 0.31518555,  0.08191701,  0.0817977 ,  0.02212394,  0.08149974,
       0.02204335,  0.02201125,  0.00595341,  0.0817977 ,  0.02212394,
       0.02209172,  0.00597518,  0.02201125,  0.00595341,  0.00594474,
       0.00160788,  0.08161887,  0.02207557,  0.02204342,  0.00596212,
       0.02196313,  0.0059404 ,  0.00593175,  0.00160437,  0.02204342,
       0.00596212,  0.00595343,  0.00161023,  0.00593175,  0.00160437,
       0.00160203,  0.0004333 ])]

vals[1] /= vals[1].sum()


te = stats.rv_discrete(name='test',values=vals)

nsample = 10000
xrvs =te.rvs(size=nsample)
for i in range(25,31):
    print i, (xrvs==i).sum(), nsample * vals[1][i]


########## result

25 43 58.8957195814
26 56 58.8098769947
27 19 15.9063646055
28 62 58.5957150522
29 17 15.8484776598
30 11 15.8253623948

>
>>
>> Cheers
>>
>> Robin
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>


From timmichelsen at gmx-topmail.de  Mon Aug 17 18:37:46 2009
From: timmichelsen at gmx-topmail.de (Tim Michelsen)
Date: Tue, 18 Aug 2009 00:37:46 +0200
Subject: [SciPy-User] satellite imagery
In-Reply-To: <aa15d47e0908171004x3273b948p4950a0209c70918b@mail.gmail.com>
References: <aa15d47e0908141518vc7f4a70x1b1e138ac8644ff@mail.gmail.com>	<A9AC9E96-3171-42A8-9111-EF4DC299BAEB@cs.toronto.edu>
	<aa15d47e0908171004x3273b948p4950a0209c70918b@mail.gmail.com>
Message-ID: <h6cm3q$fmt$1@ger.gmane.org>

questions anon schrieb:
> Thanks for responding. Some of the processing I would like to do include:
> Import a hdf file, open calibrated data (VSWIR to TIR) and display image 
> using different band combinations.
>  From the hdf open the pixel latitude and longitude and georeference the 
> image.
> Process the images to apparent reflectance, emissivity and brightness 
> temperature
> Process the images to certain indices using simple band math.
> Overlay a vector file and calculate the mean values of the raster within 
> the vector region.
> I would like to do this for many different files images.
> Thanks
> 
> On Sat, Aug 15, 2009 at 12:01 AM, David Warde-Farley <dwf at cs.toronto.edu 
> <mailto:dwf at cs.toronto.edu>> wrote:
> 
>     On 14-Aug-09, at 6:18 PM, questions anon wrote:
> 
>      > Can anyone suggest any good python books or tutorials for using
>      > satellite imagery?
I never done any sat. imagery with python only.
But the GFOSS GRASS has al modules/tool chains you need
http://grass.osgeo.org/

It's a GIS programme which can be used within a scriptable environment:
http://grass.osgeo.org/wiki/GRASS_and_Python

Good luck.



From questions.anon at gmail.com  Mon Aug 17 19:55:18 2009
From: questions.anon at gmail.com (questions anon)
Date: Mon, 17 Aug 2009 16:55:18 -0700
Subject: [SciPy-User] satellite imagery
In-Reply-To: <h6cm3q$fmt$1@ger.gmane.org>
References: <aa15d47e0908141518vc7f4a70x1b1e138ac8644ff@mail.gmail.com>
	<A9AC9E96-3171-42A8-9111-EF4DC299BAEB@cs.toronto.edu>
	<aa15d47e0908171004x3273b948p4950a0209c70918b@mail.gmail.com>
	<h6cm3q$fmt$1@ger.gmane.org>
Message-ID: <aa15d47e0908171655o6dad89a1j784a07f18eb0a43c@mail.gmail.com>

Thanks for getting back to me, but the only image processing tutorial for
GRASS and python that I could find from that link was one that said it is
'slightly outdated'. I am looking for lots of examples as I am only a
beginner. I have access to all the ENthought tools, can this all be done in
SciPy?? Specifically for satellite and airborne remotely sensed data.
Really looking for examples/tutorials/books.
Thanks

On Mon, Aug 17, 2009 at 3:37 PM, Tim Michelsen
<timmichelsen at gmx-topmail.de>wrote:

> questions anon schrieb:
> > Thanks for responding. Some of the processing I would like to do include:
> > Import a hdf file, open calibrated data (VSWIR to TIR) and display image
> > using different band combinations.
> >  From the hdf open the pixel latitude and longitude and georeference the
> > image.
> > Process the images to apparent reflectance, emissivity and brightness
> > temperature
> > Process the images to certain indices using simple band math.
> > Overlay a vector file and calculate the mean values of the raster within
> > the vector region.
> > I would like to do this for many different files images.
> > Thanks
> >
> > On Sat, Aug 15, 2009 at 12:01 AM, David Warde-Farley <dwf at cs.toronto.edu
> > <mailto:dwf at cs.toronto.edu>> wrote:
> >
> >     On 14-Aug-09, at 6:18 PM, questions anon wrote:
> >
> >      > Can anyone suggest any good python books or tutorials for using
> >      > satellite imagery?
> I never done any sat. imagery with python only.
> But the GFOSS GRASS has al modules/tool chains you need
> http://grass.osgeo.org/
>
> It's a GIS programme which can be used within a scriptable environment:
> http://grass.osgeo.org/wiki/GRASS_and_Python
>
> Good luck.
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From josef.pktd at gmail.com  Mon Aug 17 20:05:00 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Mon, 17 Aug 2009 20:05:00 -0400
Subject: [SciPy-User] problem with discrete rvs?
In-Reply-To: <1cd32cbb0908171528s1a308ef1j9b46b59ac8afd4fa@mail.gmail.com>
References: <b14b627d0908171447q1430cd82tc2e5dcc5a7ece33e@mail.gmail.com>
	<1cd32cbb0908171510u2ad640dfjcd20892a4e9d8f74@mail.gmail.com>
	<1cd32cbb0908171528s1a308ef1j9b46b59ac8afd4fa@mail.gmail.com>
Message-ID: <1cd32cbb0908171705s4844bd48q127756c9d47d90@mail.gmail.com>

On Mon, Aug 17, 2009 at 6:28 PM, <josef.pktd at gmail.com> wrote:
> On Mon, Aug 17, 2009 at 6:10 PM, <josef.pktd at gmail.com> wrote:
>> On Mon, Aug 17, 2009 at 5:47 PM, Robin<robince at gmail.com> wrote:
>>> Hi,
>>>
>>> If I understand the scipy.stats package I should be able to create my
>>> own arbitrary discrete distribution and generate samples from it... I
>>> am trying this but am having the following problem. I define my
>>> discrete distribution with the following:
>>>
>>> vals = (array([ 0, ?1, ?2, ?3, ?4, ?5, ?6, ?7, ?8, ?9, 10, 11, 12, 13,
>>> 14, 15, 16,
>>> ? ? ? 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]),
>>> ?array([ 0.31518555, ?0.08191701, ?0.0817977 , ?0.02212394, ?0.08149974,
>>> ? ? ? ?0.02204335, ?0.02201125, ?0.00595341, ?0.0817977 , ?0.02212394,
>>> ? ? ? ?0.02209172, ?0.00597518, ?0.02201125, ?0.00595341, ?0.00594474,
>>> ? ? ? ?0.00160788, ?0.08161887, ?0.02207557, ?0.02204342, ?0.00596212,
>>> ? ? ? ?0.02196313, ?0.0059404 , ?0.00593175, ?0.00160437, ?0.02204342,
>>> ? ? ? ?0.00596212, ?0.00595343, ?0.00161023, ?0.00593175, ?0.00160437,
>>> ? ? ? ?0.00160203, ?0.0004333 ]))
>>>
>>> Then I do
>>> rv = rv_discrete(name='test',values=vals)
>>>
>>> and then try to generate samples. When I plot the results of this for
>>> large number of samples, it seems to match pretty well but the last 4
>>> or 5 values are never generated and have zero probability, even with
>>> enough samples to really capture them.
>>>
>>> In [742]: (te.rvs(size=1000000)==30).sum()
>>> Out[742]: 0
>>> In [743]: (te.rvs(size=1000000)==29).sum()
>>> Out[743]: 0
>>> In [744]: (te.rvs(size=1000000)==27).sum()
>>> Out[744]: 0
>>> In [745]: (te.rvs(size=1000000)==20).sum()
>>> Out[745]: 21884
>>>
>>> Does anyone have any ideas? Is this a bug in rv_discrete.rvs?
>>>
>>> In [746]: scipy.__version__
>>> Out[746]: '0.8.0.dev5825'
>>> In [747]: numpy.__version__
>>> Out[747]: '1.4.0.dev7039'
>>
>> just a quick check
>>
>>>>> vals[1].cumsum()
>> array([ 0.31518555, ?0.39710256, ?0.47890026, ?0.5010242 , ?0.58252394,
>> ? ? ? ?0.60456729, ?0.62657854, ?0.63253195, ?0.71432965, ?0.73645359,
>> ? ? ? ?0.75854531, ?0.76452049, ?0.78653174, ?0.79248515, ?0.79842989,
>> ? ? ? ?0.80003777, ?0.88165664, ?0.90373221, ?0.92577563, ?0.93173775,
>> ? ? ? ?0.95370088, ?0.95964128, ?0.96557303, ?0.9671774 , ?0.98922082,
>> ? ? ? ?0.99518294, ?1.00113637, ?1.0027466 , ?1.00867835, ?1.01028272,
>> ? ? ? ?1.01188475, ?1.01231805])
>>
>>
>> your probabilities don't add up to one.
>>
>> I haven't checked if this is the source of the problem
>>
>> Josef
>
> I don't know what your te is. but the following looks ok
>
> Josef
>
>
> import numpy as np
> from scipy import stats
>
>
> vals = [np.array([ 0, ?1, ?2, ?3, ?4, ?5, ?6, ?7, ?8, ?9, 10, 11, 12, 13,
> 14, 15, 16,
> ? ? ?17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]),
> ?np.array([ 0.31518555, ?0.08191701, ?0.0817977 , ?0.02212394, ?0.08149974,
> ? ? ? 0.02204335, ?0.02201125, ?0.00595341, ?0.0817977 , ?0.02212394,
> ? ? ? 0.02209172, ?0.00597518, ?0.02201125, ?0.00595341, ?0.00594474,
> ? ? ? 0.00160788, ?0.08161887, ?0.02207557, ?0.02204342, ?0.00596212,
> ? ? ? 0.02196313, ?0.0059404 , ?0.00593175, ?0.00160437, ?0.02204342,
> ? ? ? 0.00596212, ?0.00595343, ?0.00161023, ?0.00593175, ?0.00160437,
> ? ? ? 0.00160203, ?0.0004333 ])]
>
> vals[1] /= vals[1].sum()
>
>
> te = stats.rv_discrete(name='test',values=vals)
>
> nsample = 10000
> xrvs =te.rvs(size=nsample)
> for i in range(25,31):
> ? ?print i, (xrvs==i).sum(), nsample * vals[1][i]
>
>
> ########## result
>
> 25 43 58.8957195814
> 26 56 58.8098769947
> 27 19 15.9063646055
> 28 62 58.5957150522
> 29 17 15.8484776598
> 30 11 15.8253623948
>
>>
>>>
>>> Cheers
>>>
>>> Robin
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>
>>
>

and here is the chisquare test, which has a pvalue of 0.6, in my
previous example, and so it looks pretty good

>>> count = np.bincount(xrvs)
>>> count
array([3119,  803,  826,  199,  815,  218,  201,   65,  815,  215,  213,
         66,  222,   78,   53,    9,  777,  209,  230,   60,  238,   53,
         60,   20,  223,   43,   56,   19,   62,   17,   11,    5])
>>> stats.chisquare(count, nsample * vals[1])
(28.074609178075399, 0.61731069879207001)

There is an example in the stats tutorial (which is only in the doc
editor and never has been cleaned up), that shows as an example a
discrete approximation to the truncated standard normal using
rv_discrete.

Thanks for reporting suspicious behavior. I'm always interested in
where the bugs are hiding.
 I will check how easy it is to verify that the probabilities add up
to one (up to floating point precision), and raise a value error in
case they don't.

Josef


From questions.anon at gmail.com  Mon Aug 17 20:54:02 2009
From: questions.anon at gmail.com (questions anon)
Date: Mon, 17 Aug 2009 17:54:02 -0700
Subject: [SciPy-User] <matplotlib.image.AxesImage object at 0x032EDB50>????
Message-ID: <aa15d47e0908171754xaecf585v7049405de4ed197b@mail.gmail.com>

*Following: http://pysclint.sourceforge.net/pyhdf/example.html*

**

*I have opened HDF, then opened calibrated data, and then sliced one of the
bands*

*B9= [:,9,:]*

*I would then like to display the image using*

*from matplotlib.pyplot import imshow*

*imshow(b9.view())*

* *

*However any of the dir(b9) functions I try come up with this:*

*<matplotlib.image.AxesImage object at 0x032EDB50>*

*What does this mean?*
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From vanleeuwen.martin at gmail.com  Mon Aug 17 21:18:06 2009
From: vanleeuwen.martin at gmail.com (Martin van Leeuwen)
Date: Mon, 17 Aug 2009 18:18:06 -0700
Subject: [SciPy-User] <matplotlib.image.AxesImage object at
	0x032EDB50>????
In-Reply-To: <aa15d47e0908171754xaecf585v7049405de4ed197b@mail.gmail.com>
References: <aa15d47e0908171754xaecf585v7049405de4ed197b@mail.gmail.com>
Message-ID: <b6bdeb060908171818w43b79e76r4661be305f216c81@mail.gmail.com>

Maybe try:

imshow(B9)
show()



2009/8/17 questions anon <questions.anon at gmail.com>:
> Following: http://pysclint.sourceforge.net/pyhdf/example.html
>
> I have opened HDF, then opened calibrated data, and then sliced one of the
> bands
>
> B9= [:,9,:]
>
> I would then like to display the image using
>
> from matplotlib.pyplot import imshow
>
> imshow(b9.view())
>
>
>
> However any of the dir(b9) functions I try come up with this:
>
> <matplotlib.image.AxesImage object at 0x032EDB50>
>
> What does this mean?
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>


From gael.varoquaux at normalesup.org  Mon Aug 17 21:45:23 2009
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Tue, 18 Aug 2009 03:45:23 +0200
Subject: [SciPy-User] [SciPy-user] set difference on meshes
In-Reply-To: <25014257.post@talk.nabble.com>
References: <25014257.post@talk.nabble.com>
Message-ID: <20090818014523.GB29150@phare.normalesup.org>

On Mon, Aug 17, 2009 at 02:10:18PM -0700, Martin81 wrote:
> I have a couple of cones generated. These cones are build from meshes
> and displayed in mayavi using mlab.mesh(). The tops of the cones are
> non-overlapping but as you gradually move downwards the cones do
> overlap. I would like to remove any overlap. Thus I am looking to find
> the set difference of the cones.

> I tried:

> 1) masking out from the 2D meshed and generate again through mlab.mesh() -
> doesn't show anything
> 2) removing any point inside another cone and redraw using
> mlab.triangular_mesh() but don't know how to use delaunay2d() to build the
> topology between points - no documentation found at all on the internet
> 3) using GTS (GNU Triangulated Surface Library) but couldn't build it on my
> Windows machine.

> Would anyone know how to do this?

So, it seems to me that you are trying to do geometry operations on mesh.
That is not a totally trivial problem, and Mayavi is not well set to deal
with this (it seems that VTK does a bit of boolean operations though).

I am not sure I am getting the big picture, but let me try to explain
what I see would be possible.

If I get it right, you could picture your problem as a height map: you
would have a function that associate z to (x, y), where z could be eg NaN
for no points. This is actually a simplification of the problem, and it
might not suit you, but the boolean operation would be trivial on such a
problem: it would be a question of taking the max of both (or rather the
nanmax, because max of (float, nan) will give you nan). You should
probably take a adapated sampling of your problem rather than a regular
grid: a set of polar coordinnates starting from the centers of the cones.

What I have been describing above seems like a gross hack, but I have no
other ideas, and I am really not an expert on the problem.

Hope this helps,

Ga?l


From vanleeuwen.martin at gmail.com  Mon Aug 17 22:22:13 2009
From: vanleeuwen.martin at gmail.com (Martin van Leeuwen)
Date: Mon, 17 Aug 2009 19:22:13 -0700
Subject: [SciPy-User] [SciPy-user] set difference on meshes
In-Reply-To: <20090818014523.GB29150@phare.normalesup.org>
References: <25014257.post@talk.nabble.com>
	<20090818014523.GB29150@phare.normalesup.org>
Message-ID: <b6bdeb060908171922u2e2bb60p330098fae6e63fce@mail.gmail.com>

Thanks Gael,

There would be two uses for it. One is a height map as you described,
the other is as truely 3D meshes, with objects underneath the cones.
But the height map idea is good for now.
Since I am working in UTM coordinates, zero is not an X or Y
coordinate value. So I can reserve a value zero for any element that
is inside another mesh.
I managed to do so by by taking a top of one cone, calculate the angle
any point would make with the zenith line of that top and if it is
inside the cone, set its coordinates to zero, go to the next cone top,
repeat.
So mesh X and Y have some zeros in them, Z is the height mesh and also
has some zeros but those have real meaning (means height = zero
meters).

If I could only use the option of mesh() to mask out any points, that
would be great. It wouldn't interpolate any data points along the
intersection but that is fine.
And in fact, mesh() has got a mask argument, however, I doesn't seem
to do anything.
Is there a limit to the number of data points you can mask out?

Cheers
Martin

2009/8/17 Gael Varoquaux <gael.varoquaux at normalesup.org>:
> On Mon, Aug 17, 2009 at 02:10:18PM -0700, Martin81 wrote:
>> I have a couple of cones generated. These cones are build from meshes
>> and displayed in mayavi using mlab.mesh(). The tops of the cones are
>> non-overlapping but as you gradually move downwards the cones do
>> overlap. I would like to remove any overlap. Thus I am looking to find
>> the set difference of the cones.
>
>> I tried:
>
>> 1) masking out from the 2D meshed and generate again through mlab.mesh() -
>> doesn't show anything
>> 2) removing any point inside another cone and redraw using
>> mlab.triangular_mesh() but don't know how to use delaunay2d() to build the
>> topology between points - no documentation found at all on the internet
>> 3) using GTS (GNU Triangulated Surface Library) but couldn't build it on my
>> Windows machine.
>
>> Would anyone know how to do this?
>
> So, it seems to me that you are trying to do geometry operations on mesh.
> That is not a totally trivial problem, and Mayavi is not well set to deal
> with this (it seems that VTK does a bit of boolean operations though).
>
> I am not sure I am getting the big picture, but let me try to explain
> what I see would be possible.
>
> If I get it right, you could picture your problem as a height map: you
> would have a function that associate z to (x, y), where z could be eg NaN
> for no points. This is actually a simplification of the problem, and it
> might not suit you, but the boolean operation would be trivial on such a
> problem: it would be a question of taking the max of both (or rather the
> nanmax, because max of (float, nan) will give you nan). You should
> probably take a adapated sampling of your problem rather than a regular
> grid: a set of polar coordinnates starting from the centers of the cones.
>
> What I have been describing above seems like a gross hack, but I have no
> other ideas, and I am really not an expert on the problem.
>
> Hope this helps,
>
> Ga?l
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From gael.varoquaux at normalesup.org  Mon Aug 17 22:45:55 2009
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Tue, 18 Aug 2009 04:45:55 +0200
Subject: [SciPy-User] [SciPy-user] set difference on meshes
In-Reply-To: <b6bdeb060908171922u2e2bb60p330098fae6e63fce@mail.gmail.com>
References: <25014257.post@talk.nabble.com>
	<20090818014523.GB29150@phare.normalesup.org>
	<b6bdeb060908171922u2e2bb60p330098fae6e63fce@mail.gmail.com>
Message-ID: <20090818024555.GC29150@phare.normalesup.org>

On Mon, Aug 17, 2009 at 07:22:13PM -0700, Martin van Leeuwen wrote:
> If I could only use the option of mesh() to mask out any points, that
> would be great. It wouldn't interpolate any data points along the
> intersection but that is fine.
> And in fact, mesh() has got a mask argument, however, I doesn't seem
> to do anything.
> Is there a limit to the number of data points you can mask out?

Mask doesn't work right with mesh. I am sorry. If you can use surf, it
works better.

HTH,

Ga?l


From vanleeuwen.martin at gmail.com  Mon Aug 17 23:03:10 2009
From: vanleeuwen.martin at gmail.com (Martin van Leeuwen)
Date: Mon, 17 Aug 2009 20:03:10 -0700
Subject: [SciPy-User] [SciPy-user] set difference on meshes
In-Reply-To: <20090818024555.GC29150@phare.normalesup.org>
References: <25014257.post@talk.nabble.com>
	<20090818014523.GB29150@phare.normalesup.org>
	<b6bdeb060908171922u2e2bb60p330098fae6e63fce@mail.gmail.com>
	<20090818024555.GC29150@phare.normalesup.org>
Message-ID: <b6bdeb060908172003s74a7a7a0madb76a6f6f3dcf02@mail.gmail.com>

Alright, that is ok.

Could I also make a new triangulation all points of one cone that are
not inside another cone?

I was looking at delaunay2d(). The following website says something about that:

http://code.enthought.com/projects/mayavi/docs/development/html/mayavi/mlab.html

, under: "From data points to surfaces." But I don't see how
delaunay2d() needs to be implemented to return the trianges that
triangular_mesh() needs.

Thanks again!

Martin



2009/8/17 Gael Varoquaux <gael.varoquaux at normalesup.org>:
> On Mon, Aug 17, 2009 at 07:22:13PM -0700, Martin van Leeuwen wrote:
>> If I could only use the option of mesh() to mask out any points, that
>> would be great. It wouldn't interpolate any data points along the
>> intersection but that is fine.
>> And in fact, mesh() has got a mask argument, however, I doesn't seem
>> to do anything.
>> Is there a limit to the number of data points you can mask out?
>
> Mask doesn't work right with mesh. I am sorry. If you can use surf, it
> works better.
>
> HTH,
>
> Ga?l
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From scott.sinclair.za at gmail.com  Tue Aug 18 01:33:07 2009
From: scott.sinclair.za at gmail.com (Scott Sinclair)
Date: Tue, 18 Aug 2009 07:33:07 +0200
Subject: [SciPy-User] <matplotlib.image.AxesImage object at
	0x032EDB50>????
In-Reply-To: <aa15d47e0908171754xaecf585v7049405de4ed197b@mail.gmail.com>
References: <aa15d47e0908171754xaecf585v7049405de4ed197b@mail.gmail.com>
Message-ID: <6a17e9ee0908172233p552f6b26k33501297426c32cc@mail.gmail.com>

> 2009/8/18 questions anon <questions.anon at gmail.com>:
> Following: http://pysclint.sourceforge.net/pyhdf/example.html
>
> I have opened HDF, then opened calibrated data, and then sliced one of the
> bands
>
> B9= [:,9,:]
>
> I would then like to display the image using
>
> from matplotlib.pyplot import imshow
>
> imshow(b9.view())
>
>
>
> However any of the dir(b9) functions I try come up with this:
>
> <matplotlib.image.AxesImage object at 0x032EDB50>
>
> What does this mean?

Most of the pyplot functions return some kind of plot object. You
don't have to care too much right now. Basically the call to imshow()
creates the necessary plot object and then you need to call
pyplot.show() to make the plot appear. If your working environment is
set up in a particular way, it may not be necessary to call show(),
which is probably why the example you are following doesn't include a
call to show().

Questions related to plotting with Matplotlib are best asked on the
matplotlib-users mailing list, you can subscribe here
http://sourceforge.net/mail/?group_id=80706

Also have a look at the pyplot tutorial
http://matplotlib.sourceforge.net/users/pyplot_tutorial.html

Cheers,
Scott


From robince at gmail.com  Tue Aug 18 01:46:34 2009
From: robince at gmail.com (Robin)
Date: Tue, 18 Aug 2009 07:46:34 +0200
Subject: [SciPy-User] problem with discrete rvs?
In-Reply-To: <1cd32cbb0908171705s4844bd48q127756c9d47d90@mail.gmail.com>
References: <b14b627d0908171447q1430cd82tc2e5dcc5a7ece33e@mail.gmail.com>
	<1cd32cbb0908171510u2ad640dfjcd20892a4e9d8f74@mail.gmail.com>
	<1cd32cbb0908171528s1a308ef1j9b46b59ac8afd4fa@mail.gmail.com>
	<1cd32cbb0908171705s4844bd48q127756c9d47d90@mail.gmail.com>
Message-ID: <b14b627d0908172246i2a09e2f5ya6581cf1b9616560@mail.gmail.com>

On Tue, Aug 18, 2009 at 2:05 AM, <josef.pktd at gmail.com> wrote:
>>>
>>> your probabilities don't add up to one.
>>>

Hi,

Thanks very much for your quick response... of course I should have
checked that! (but it was late etc...) Actually it was a bug somewhere
else in my code - that distribution is generated and should be
normalised correctly but there was a typo in that line.

As you say it seems to be working perfectly now.

Thanks again,

Robin


From gael.varoquaux at normalesup.org  Tue Aug 18 01:56:24 2009
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Tue, 18 Aug 2009 07:56:24 +0200
Subject: [SciPy-User] [SciPy-user] set difference on meshes
In-Reply-To: <b6bdeb060908172003s74a7a7a0madb76a6f6f3dcf02@mail.gmail.com>
References: <25014257.post@talk.nabble.com>
	<20090818014523.GB29150@phare.normalesup.org>
	<b6bdeb060908171922u2e2bb60p330098fae6e63fce@mail.gmail.com>
	<20090818024555.GC29150@phare.normalesup.org>
	<b6bdeb060908172003s74a7a7a0madb76a6f6f3dcf02@mail.gmail.com>
Message-ID: <20090818055624.GA8281@phare.normalesup.org>

On Mon, Aug 17, 2009 at 08:03:10PM -0700, Martin van Leeuwen wrote:
> http://code.enthought.com/projects/mayavi/docs/development/html/mayavi/mlab.html

> , under: "From data points to surfaces." But I don't see how
> delaunay2d() needs to be implemented to return the trianges that
> triangular_mesh() needs.

It doesn't. It returns a data structure on which you can apply the
surface module, amongst other things, with:

mlab.pipeline.surface(obj)

where obj is returned by mlab.pipeline.delaunay2d.

You can also get the data as arrays as in the following:

In [1]: from enthought.mayavi import mlab

In [2]: import numpy as np

In [3]: x, y, z, s = np.random.random((4, 10))

In [4]: src = mlab.pipeline.scalar_scatter(x, y, z, s)

In [5]: mesh = mlab.pipeline.delaunay2d(src)

In [6]: surf = mlab.pipeline.surface(mesh)

In [7]: mesh.outputs[0].polys.to_array()
Out[7]: 
array([3, 9, 4, 3, 3, 5, 3, 2, 3, 8, 7, 0, 3, 6, 3, 1, 3, 4, 2, 3, 3, 5, 1,
       3, 3, 6, 1, 0, 3, 7, 3, 6, 3, 7, 6, 0, 3, 9, 3, 8, 3, 8, 3, 7, 3, 9,
       2, 4])

In [8]: mesh.outputs[0].points.to_array()
Out[8]: 
array([[ 0.94464944,  0.47644135,  0.72179666],
       [ 0.74063624,  0.09717904,  0.81146878],
       [ 0.17031379,  0.73111888,  0.62790633],
       [ 0.50215195,  0.54242855,  0.20974343],
       [ 0.27069343,  0.83886682,  0.38808641],
       [ 0.15944449,  0.21834495,  0.39882205],
       [ 0.78877187,  0.34613708,  0.79911974],
       [ 0.86809041,  0.48792871,  0.65521137],
       [ 0.85557844,  0.57255716,  0.6484326 ],
       [ 0.30144246,  0.96996277,  0.15670321]])

In [9]: mesh.outputs[0].point_data.scalars.to_array()
Out[9]: 
array([ 0.18869998,  0.87639289,  0.51392855,  0.30845021,  0.57186958,
        0.74023876,  0.41477073,  0.50683163,  0.4276263 ,  0.80960671])

HTH,

Ga?l


From vanleeuwen.martin at gmail.com  Tue Aug 18 08:05:29 2009
From: vanleeuwen.martin at gmail.com (Martin van Leeuwen)
Date: Tue, 18 Aug 2009 05:05:29 -0700
Subject: [SciPy-User] [SciPy-user] set difference on meshes
In-Reply-To: <20090818055624.GA8281@phare.normalesup.org>
References: <25014257.post@talk.nabble.com>
	<20090818014523.GB29150@phare.normalesup.org>
	<b6bdeb060908171922u2e2bb60p330098fae6e63fce@mail.gmail.com>
	<20090818024555.GC29150@phare.normalesup.org>
	<b6bdeb060908172003s74a7a7a0madb76a6f6f3dcf02@mail.gmail.com>
	<20090818055624.GA8281@phare.normalesup.org>
Message-ID: <b6bdeb060908180505t2460ac15rd38a5b3ba21baaa7@mail.gmail.com>

Alright thanks a lot. I will make a nice surface out of it for now and
will have a look at doing geometric operations in VTK later.

Thanks for your time!

Cheers, Martin

2009/8/17 Gael Varoquaux <gael.varoquaux at normalesup.org>:
> On Mon, Aug 17, 2009 at 08:03:10PM -0700, Martin van Leeuwen wrote:
>> http://code.enthought.com/projects/mayavi/docs/development/html/mayavi/mlab.html
>
>> , under: "From data points to surfaces." But I don't see how
>> delaunay2d() needs to be implemented to return the trianges that
>> triangular_mesh() needs.
>
> It doesn't. It returns a data structure on which you can apply the
> surface module, amongst other things, with:
>
> mlab.pipeline.surface(obj)
>
> where obj is returned by mlab.pipeline.delaunay2d.
>
> You can also get the data as arrays as in the following:
>
> In [1]: from enthought.mayavi import mlab
>
> In [2]: import numpy as np
>
> In [3]: x, y, z, s = np.random.random((4, 10))
>
> In [4]: src = mlab.pipeline.scalar_scatter(x, y, z, s)
>
> In [5]: mesh = mlab.pipeline.delaunay2d(src)
>
> In [6]: surf = mlab.pipeline.surface(mesh)
>
> In [7]: mesh.outputs[0].polys.to_array()
> Out[7]:
> array([3, 9, 4, 3, 3, 5, 3, 2, 3, 8, 7, 0, 3, 6, 3, 1, 3, 4, 2, 3, 3, 5, 1,
> ? ? ? 3, 3, 6, 1, 0, 3, 7, 3, 6, 3, 7, 6, 0, 3, 9, 3, 8, 3, 8, 3, 7, 3, 9,
> ? ? ? 2, 4])
>
> In [8]: mesh.outputs[0].points.to_array()
> Out[8]:
> array([[ 0.94464944, ?0.47644135, ?0.72179666],
> ? ? ? [ 0.74063624, ?0.09717904, ?0.81146878],
> ? ? ? [ 0.17031379, ?0.73111888, ?0.62790633],
> ? ? ? [ 0.50215195, ?0.54242855, ?0.20974343],
> ? ? ? [ 0.27069343, ?0.83886682, ?0.38808641],
> ? ? ? [ 0.15944449, ?0.21834495, ?0.39882205],
> ? ? ? [ 0.78877187, ?0.34613708, ?0.79911974],
> ? ? ? [ 0.86809041, ?0.48792871, ?0.65521137],
> ? ? ? [ 0.85557844, ?0.57255716, ?0.6484326 ],
> ? ? ? [ 0.30144246, ?0.96996277, ?0.15670321]])
>
> In [9]: mesh.outputs[0].point_data.scalars.to_array()
> Out[9]:
> array([ 0.18869998, ?0.87639289, ?0.51392855, ?0.30845021, ?0.57186958,
> ? ? ? ?0.74023876, ?0.41477073, ?0.50683163, ?0.4276263 , ?0.80960671])
>
> HTH,
>
> Ga?l
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From matthieu.brucher at gmail.com  Tue Aug 18 11:09:24 2009
From: matthieu.brucher at gmail.com (Matthieu Brucher)
Date: Tue, 18 Aug 2009 17:09:24 +0200
Subject: [SciPy-User] Optimization scikits (reloaded)
Message-ID: <e76aa17f0908180809u5a2779dbt86eaca0608b45d80@mail.gmail.com>

Hi,

I've taken some time to extract the generic optimization framework
from the old openopt scikit. It's now available as
scikits.optimization.

I didn't start cleaning the tutorial on the trac website, perhaps it
could be done somewhere else (the new portal? my own blog?).
The next step is to fix the manifold learning sub-scikit so that it
uses the new location. Then, openopt will be removed from the svn.

I don't know if I'mm be able to put much in developing new modules,
but who knows? Maybe one day I'll have to use it for my work...
If you have any questions, fill free to ask me.

Matthieu Brucher
-- 
Information System Engineer, Ph.D.
Website: http://matthieu-brucher.developpez.com/
Blogs: http://matt.eifelle.com and http://blog.developpez.com/?blog=92
LinkedIn: http://www.linkedin.com/in/matthieubrucher


From questions.anon at gmail.com  Tue Aug 18 11:15:18 2009
From: questions.anon at gmail.com (questions anon)
Date: Tue, 18 Aug 2009 08:15:18 -0700
Subject: [SciPy-User] <matplotlib.image.AxesImage object at
	0x032EDB50>????
In-Reply-To: <6a17e9ee0908172233p552f6b26k33501297426c32cc@mail.gmail.com>
References: <aa15d47e0908171754xaecf585v7049405de4ed197b@mail.gmail.com>
	<6a17e9ee0908172233p552f6b26k33501297426c32cc@mail.gmail.com>
Message-ID: <aa15d47e0908180815r5aa4a631re36b7641571c3434@mail.gmail.com>

thanks but I receive the same message

>>> imshow(b9)
<matplotlib.image.AxesImage object at 0x032ED9B0>
>>> show()

Traceback (most recent call last):
  File "<pyshell#54>", line 1, in <module>
    show()
NameError: name 'show' is not defined
>>>

On Mon, Aug 17, 2009 at 10:33 PM, Scott Sinclair <scott.sinclair.za@
gmail.com> wrote:

> > 2009/8/18 questions anon <questions.anon at gmail.com>:
> > Following: http://pysclint.sourceforge.net/pyhdf/example.html
> >
> > I have opened HDF, then opened calibrated data, and then sliced one of
> the
> > bands
> >
> > B9= [:,9,:]
> >
> > I would then like to display the image using
> >
> > from matplotlib.pyplot import imshow
> >
> > imshow(b9.view())
> >
> >
> >
> > However any of the dir(b9) functions I try come up with this:
> >
> > <matplotlib.image.AxesImage object at 0x032EDB50>
> >
> > What does this mean?
>
> Most of the pyplot functions return some kind of plot object. You
> don't have to care too much right now. Basically the call to imshow()
> creates the necessary plot object and then you need to call
> pyplot.show() to make the plot appear. If your working environment is
> set up in a particular way, it may not be necessary to call show(),
> which is probably why the example you are following doesn't include a
> call to show().
>
> Questions related to plotting with Matplotlib are best asked on the
> matplotlib-users mailing list, you can subscribe here
> http://sourceforge.net/mail/?group_id=80706
>
> Also have a look at the pyplot tutorial
> http://matplotlib.sourceforge.net/users/pyplot_tutorial.html
>
> Cheers,
> Scott
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From josef.pktd at gmail.com  Tue Aug 18 11:19:55 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Tue, 18 Aug 2009 11:19:55 -0400
Subject: [SciPy-User] [SciPy-dev] Optimization scikits (reloaded)
In-Reply-To: <e76aa17f0908180809u5a2779dbt86eaca0608b45d80@mail.gmail.com>
References: <e76aa17f0908180809u5a2779dbt86eaca0608b45d80@mail.gmail.com>
Message-ID: <1cd32cbb0908180819k1f265b9fv10b36dfad7134ed7@mail.gmail.com>

On Tue, Aug 18, 2009 at 11:09 AM, Matthieu
Brucher<matthieu.brucher at gmail.com> wrote:
> Hi,
>
> I've taken some time to extract the generic optimization framework
> from the old openopt scikit. It's now available as
> scikits.optimization.
>
> I didn't start cleaning the tutorial on the trac website, perhaps it
> could be done somewhere else (the new portal? my own blog?).
> The next step is to fix the manifold learning sub-scikit so that it
> uses the new location. Then, openopt will be removed from the svn.
>
> I don't know if I'mm be able to put much in developing new modules,
> but who knows? Maybe one day I'll have to use it for my work...
> If you have any questions, fill free to ask me.
>
> Matthieu Brucher
> --
> Information System Engineer, Ph.D.
> Website: http://matthieu-brucher.developpez.com/
> Blogs: http://matt.eifelle.com and http://blog.developpez.com/?blog=92
> LinkedIn: http://www.linkedin.com/in/matthieubrucher
> _______________________________________________
> Scipy-dev mailing list
> Scipy-dev at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-dev
>

Could you also clarify the boost dependency of the manifold learning
sub-scikits?
I had a hard time building it and was/am not sure if boost is a
requirement. I didn't see anything in the installation instructions.

However, I haven't looked or tried in a long time, so my information
might be outdated.

Thanks,

Josef


From jgomezdans at gmail.com  Tue Aug 18 11:31:45 2009
From: jgomezdans at gmail.com (Jose Gomez-Dans)
Date: Tue, 18 Aug 2009 16:31:45 +0100
Subject: [SciPy-User] satellite imagery
In-Reply-To: <aa15d47e0908171004x3273b948p4950a0209c70918b@mail.gmail.com>
References: <aa15d47e0908141518vc7f4a70x1b1e138ac8644ff@mail.gmail.com>
	<A9AC9E96-3171-42A8-9111-EF4DC299BAEB@cs.toronto.edu>
	<aa15d47e0908171004x3273b948p4950a0209c70918b@mail.gmail.com>
Message-ID: <91d218430908180831q1795325g5527d6e66ff19c2d@mail.gmail.com>

Hi,

2009/8/17 questions anon <questions.anon at gmail.com>

> Thanks for responding. Some of the processing I would like to do include:
> Import a hdf file, open calibrated data (VSWIR to TIR) and display image
> using different band combinations.
> From the hdf open the pixel latitude and longitude and georeference the
> image.
> Process the images to apparent reflectance, emissivity and brightness
> temperature
> Process the images to certain indices using simple band math.
> Overlay a vector file and calculate the mean values of the raster within
> the vector region.


But for the last point, most of the stuff is demonstrated in a web page I've
just put up. I focus on using GDAL in combination with the scipy/numpy/pylab
stack.
<
http://sites.google.com/site/spatialpython/processing-aster-with-python-numpy-and-gdal
>

The last point is relatively straightforward to do with python, but I
haven't yet got round to writing it up.

Hope it helps,
Jose
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From timmichelsen at gmx-topmail.de  Tue Aug 18 14:09:43 2009
From: timmichelsen at gmx-topmail.de (Tim Michelsen)
Date: Tue, 18 Aug 2009 20:09:43 +0200
Subject: [SciPy-User] satellite imagery
In-Reply-To: <aa15d47e0908171655o6dad89a1j784a07f18eb0a43c@mail.gmail.com>
References: <aa15d47e0908141518vc7f4a70x1b1e138ac8644ff@mail.gmail.com>	<A9AC9E96-3171-42A8-9111-EF4DC299BAEB@cs.toronto.edu>	<aa15d47e0908171004x3273b948p4950a0209c70918b@mail.gmail.com>	<h6cm3q$fmt$1@ger.gmane.org>
	<aa15d47e0908171655o6dad89a1j784a07f18eb0a43c@mail.gmail.com>
Message-ID: <h6eqp8$c03$1@ger.gmane.org>

> Thanks for getting back to me, but the only image processing tutorial 
> for GRASS and python that I could find from that link was one that said 
> it is 'slightly outdated'. I am looking for lots of examples as I am 
> only a beginner. I have access to all the ENthought tools, can this all 
> be done in SciPy?? Specifically for satellite and airborne remotely 
> sensed data.
> Really looking for examples/tutorials/books.
as others said:
you need to specify a bit more on what's your objective.

Due to -- as I think -- historical reasons -- the remote sensing 
commuity has been using IDL for long.

See for instance this here: http://radartools.berlios.de/

Is this waht you are aiming at?
Do you neet just to process some data (use GRASS standalone) or want to 
set up a automatic processing chain? You could do thins python or bash 
or ... after your algorithms prove to give results.

The books and examples really depend on where you wanna go and what your 
topic is.

Keep us posted!



From questions.anon at gmail.com  Tue Aug 18 14:20:30 2009
From: questions.anon at gmail.com (questions anon)
Date: Tue, 18 Aug 2009 11:20:30 -0700
Subject: [SciPy-User] satellite imagery
In-Reply-To: <h6eqp8$c03$1@ger.gmane.org>
References: <aa15d47e0908141518vc7f4a70x1b1e138ac8644ff@mail.gmail.com>
	<A9AC9E96-3171-42A8-9111-EF4DC299BAEB@cs.toronto.edu>
	<aa15d47e0908171004x3273b948p4950a0209c70918b@mail.gmail.com>
	<h6cm3q$fmt$1@ger.gmane.org>
	<aa15d47e0908171655o6dad89a1j784a07f18eb0a43c@mail.gmail.com>
	<h6eqp8$c03$1@ger.gmane.org>
Message-ID: <aa15d47e0908181120i5018b2ccrc4bad47c24d9cf60@mail.gmail.com>

Thanks again.
The example that Jose sent through is really what I am looking for. I
haven't had a chance to try it yet, but those are the steps I am looking to
use. I am very much a beginner at programming so any examples like this are
greatly appreciated.
Thanks again
Sarah

On Tue, Aug 18, 2009 at 11:09 AM, Tim Michelsen <timmichelsen at gmx-topmail.de
> wrote:

> > Thanks for getting back to me, but the only image processing tutorial
> > for GRASS and python that I could find from that link was one that said
> > it is 'slightly outdated'. I am looking for lots of examples as I am
> > only a beginner. I have access to all the ENthought tools, can this all
> > be done in SciPy?? Specifically for satellite and airborne remotely
> > sensed data.
> > Really looking for examples/tutorials/books.
> as others said:
> you need to specify a bit more on what's your objective.
>
> Due to -- as I think -- historical reasons -- the remote sensing
> commuity has been using IDL for long.
>
> See for instance this here: http://radartools.berlios.de/
>
> Is this waht you are aiming at?
> Do you neet just to process some data (use GRASS standalone) or want to
> set up a automatic processing chain? You could do thins python or bash
> or ... after your algorithms prove to give results.
>
> The books and examples really depend on where you wanna go and what your
> topic is.
>
> Keep us posted!
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From questions.anon at gmail.com  Tue Aug 18 16:10:21 2009
From: questions.anon at gmail.com (questions anon)
Date: Tue, 18 Aug 2009 13:10:21 -0700
Subject: [SciPy-User] <matplotlib.image.AxesImage object at
	0x032EDB50>????
In-Reply-To: <6a17e9ee0908172233p552f6b26k33501297426c32cc@mail.gmail.com>
References: <aa15d47e0908171754xaecf585v7049405de4ed197b@mail.gmail.com>
	<6a17e9ee0908172233p552f6b26k33501297426c32cc@mail.gmail.com>
Message-ID: <aa15d47e0908181310g5d4bbc41wa37d3fd4841a9e48@mail.gmail.com>

great thank you! the following worked
import numpy, pylab
pylab.imshow(band9)
pylab.show()

On Mon, Aug 17, 2009 at 10:33 PM, Scott Sinclair <scott.sinclair.za@
gmail.com> wrote:

> > 2009/8/18 questions anon <questions.anon at gmail.com>:
> > Following: http://pysclint.sourceforge.net/pyhdf/example.html
> >
> > I have opened HDF, then opened calibrated data, and then sliced one of
> the
> > bands
> >
> > B9= [:,9,:]
> >
> > I would then like to display the image using
> >
> > from matplotlib.pyplot import imshow
> >
> > imshow(b9.view())
> >
> >
> >
> > However any of the dir(b9) functions I try come up with this:
> >
> > <matplotlib.image.AxesImage object at 0x032EDB50>
> >
> > What does this mean?
>
> Most of the pyplot functions return some kind of plot object. You
> don't have to care too much right now. Basically the call to imshow()
> creates the necessary plot object and then you need to call
> pyplot.show() to make the plot appear. If your working environment is
> set up in a particular way, it may not be necessary to call show(),
> which is probably why the example you are following doesn't include a
> call to show().
>
> Questions related to plotting with Matplotlib are best asked on the
> matplotlib-users mailing list, you can subscribe here
> http://sourceforge.net/mail/?group_id=80706
>
> Also have a look at the pyplot tutorial
> http://matplotlib.sourceforge.net/users/pyplot_tutorial.html
>
> Cheers,
> Scott
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From Kristian.Sandberg at Colorado.EDU  Tue Aug 18 21:12:08 2009
From: Kristian.Sandberg at Colorado.EDU (Kristian Hans Sandberg)
Date: Tue, 18 Aug 2009 19:12:08 -0600 (MDT)
Subject: [SciPy-User] Problem importing weave
Message-ID: <20090818191208.AKO46840@joker.int.colorado.edu>

When I do

from scipy import weave

on my Mandriva 2009.1 (64-bit) installation, I get the error

DistutilsPlatformError: invalid Python installation: unable to open /usr/lib64/python2.6/config/Makefile (No such file or directory)

I can see that the config directory is indeed missing. I've installed both the numpy and numpy-devel packages, and numpy and scipy works without any problems except for that I cannot import weave.

Where should the config directory come from?

Thanks!
Kristian


Kristian Sandberg, Ph.D.
 
 Dept. of Applied Mathematics and
 The Boulder Laboratory for 3-D Electron Microscopy of Cells
 University of Colorado at Boulder
 Campus Box 526
 Boulder, CO 80309-0526, USA
 
 Phone: (303) 492 0593 (work)
        (303) 499 4404 (home)
        (303) 547 6290 (cell)
 
 Home page: http://amath.colorado.edu/faculty/sandberg


From cournape at gmail.com  Wed Aug 19 01:07:51 2009
From: cournape at gmail.com (David Cournapeau)
Date: Tue, 18 Aug 2009 22:07:51 -0700
Subject: [SciPy-User] Problem importing weave
In-Reply-To: <20090818191208.AKO46840@joker.int.colorado.edu>
References: <20090818191208.AKO46840@joker.int.colorado.edu>
Message-ID: <5b8d13220908182207v1d5d8ef0kcd732390c7c5ea7a@mail.gmail.com>

On Tue, Aug 18, 2009 at 6:12 PM, Kristian Hans
Sandberg<Kristian.Sandberg at colorado.edu> wrote:
> When I do
>
> from scipy import weave
>
> on my Mandriva 2009.1 (64-bit) installation, I get the error
>
> DistutilsPlatformError: invalid Python installation: unable to open /usr/lib64/python2.6/config/Makefile (No such file or directory)

Have you tried installing python-devel ?

David


From bruce at clearscienceinc.com  Wed Aug 19 12:21:21 2009
From: bruce at clearscienceinc.com (Bruce Ford)
Date: Wed, 19 Aug 2009 12:21:21 -0400
Subject: [SciPy-User] NetCDF to GRIB2 Conversion
Message-ID: <bebb7fa10908190921j743c25b7s6635cbb5bd1266f6@mail.gmail.com>

My searches on this topic have been unfruitful.  I'm new to the SciPy
community, so I might not be looking in the right places.

I need to bulk convert files from NetCDF to GRIB2 files.  Has anyone had to
deal do this kind of conversion?

Any nudges in the right direction would be appreciated.

Bruce
---------------------------------------
Bruce W. Ford
Clear Science, Inc.
bruce at clearscienceinc.com
bruce.w.ford.ctr at navy.smil.mil
http://www.ClearScienceInc.com
Phone/Fax: 904-379-9704
8241 Parkridge Circle N.
Jacksonville, FL  32211
Skype:  bruce.w.ford
Google Talk: fordbw at gmail.com
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From tim.whitcomb at nrlmry.navy.mil  Wed Aug 19 12:48:06 2009
From: tim.whitcomb at nrlmry.navy.mil (Whitcomb, Mr. Tim)
Date: Wed, 19 Aug 2009 09:48:06 -0700
Subject: [SciPy-User] NetCDF to GRIB2 Conversion
In-Reply-To: <bebb7fa10908190921j743c25b7s6635cbb5bd1266f6@mail.gmail.com>
References: <bebb7fa10908190921j743c25b7s6635cbb5bd1266f6@mail.gmail.com>
Message-ID: <B07C05C39FD05C49973EB16F68A6DDEE0A51B2@pallas.w2k.nrlmry.navy.mil>

 
> I need to bulk convert files from NetCDF to GRIB2 files.  Has anyone
had to deal do this kind of conversion?

There are a few things that come up on a web search, but we're in the
process of writing some GRIB converters now.  You're going to need some
sort of mapping between variable types, level types, the center/model
that the data is from, but when it comes to actually reading and writing
the data with Python, there are two modules that I've found useful:

For reading and writing GRIB2 files:
http://code.google.com/p/pygrib2/

For dealing with NetCDF files:
http://code.google.com/p/netcdf4-python/

I use NumPy to handle any of the numeric operations required.

Hope this helps - and good luck.

Tim


From chanley at stsci.edu  Wed Aug 19 13:05:04 2009
From: chanley at stsci.edu (Christopher Hanley)
Date: Wed, 19 Aug 2009 13:05:04 -0400
Subject: [SciPy-User] Scipy 2009 BOFs time/location
Message-ID: <915126EF-5D00-442D-A4B5-9A5326DBF1CC@stsci.edu>

Hi,

Have any decisions been made on the time/location for any of the BOFs  
at Scipy this year?  Are there going to be any tonight or will they  
all be tomorrow and Friday?

Just curious.

Thanks,
Chris


-- 
Christopher Hanley
Senior Systems Software Engineer
Space Telescope Science Institute
3700 San Martin Drive
Baltimore MD, 21218
(410) 338-4338



From jturner at gemini.edu  Wed Aug 19 13:08:30 2009
From: jturner at gemini.edu (James Turner)
Date: Wed, 19 Aug 2009 13:08:30 -0400
Subject: [SciPy-User] Scipy 2009 BOFs time/location
In-Reply-To: <915126EF-5D00-442D-A4B5-9A5326DBF1CC@stsci.edu>
References: <915126EF-5D00-442D-A4B5-9A5326DBF1CC@stsci.edu>
Message-ID: <4A8C318E.3010302@gemini.edu>

> Have any decisions been made on the time/location for any of the BOFs  
> at Scipy this year?  Are there going to be any tonight or will they  
> all be tomorrow and Friday?

Gael said the astronomy BoF would be booked for 19:30 on Thursday.
I'll try to confirm that today, or Gael can reply.

Cheers,

James.


From rmay31 at gmail.com  Wed Aug 19 13:14:57 2009
From: rmay31 at gmail.com (Ryan May)
Date: Wed, 19 Aug 2009 12:14:57 -0500
Subject: [SciPy-User] NetCDF to GRIB2 Conversion
In-Reply-To: <bebb7fa10908190921j743c25b7s6635cbb5bd1266f6@mail.gmail.com>
References: <bebb7fa10908190921j743c25b7s6635cbb5bd1266f6@mail.gmail.com>
Message-ID: <c7b95a020908191014p3a3c899akea1346f740570647@mail.gmail.com>

On Wed, Aug 19, 2009 at 11:21 AM, Bruce Ford <bruce at clearscienceinc.com>wrote:

> My searches on this topic have been unfruitful.  I'm new to the SciPy
> community, so I might not be looking in the right places.
>
> I need to bulk convert files from NetCDF to GRIB2 files.  Has anyone had to
> deal do this kind of conversion?
>
> Any nudges in the right direction would be appreciated.


There's the PyNIO module, which will read/write netcdf and grib2 through a
common interface: http://www.pyngl.ucar.edu/Nio.shtml

Ryan

-- 
Ryan May
Graduate Research Assistant
School of Meteorology
University of Oklahoma
Sent from Pasadena, California, United States
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From d_l_goldsmith at yahoo.com  Wed Aug 19 13:51:45 2009
From: d_l_goldsmith at yahoo.com (David Goldsmith)
Date: Wed, 19 Aug 2009 10:51:45 -0700 (PDT)
Subject: [SciPy-User] Scipy 2009 BOFs time/location
In-Reply-To: <915126EF-5D00-442D-A4B5-9A5326DBF1CC@stsci.edu>
Message-ID: <280587.75080.qm@web52110.mail.re2.yahoo.com>

Pending confirmation from Joe, I've tentatively decided on Friday, 1:30-2:30 for the Documentation BoF; now I need to wrangle a venue: anyone know who I talk to about that?  Thanks!

DG

--- On Wed, 8/19/09, Christopher Hanley <chanley at stsci.edu> wrote:

> From: Christopher Hanley <chanley at stsci.edu>
> Subject: [SciPy-User] Scipy 2009 BOFs time/location
> To: "SciPy Users List" <scipy-user at scipy.org>
> Date: Wednesday, August 19, 2009, 10:05 AM
> Hi,
> 
> Have any decisions been made on the time/location for any
> of the BOFs? 
> at Scipy this year?? Are there going to be any tonight
> or will they? 
> all be tomorrow and Friday?
> 
> Just curious.
> 
> Thanks,
> Chris
> 
> 
> -- 
> Christopher Hanley
> Senior Systems Software Engineer
> Space Telescope Science Institute
> 3700 San Martin Drive
> Baltimore MD, 21218
> (410) 338-4338
> 
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
> 

__________________________________________________
Do You Yahoo!?
Tired of spam?  Yahoo! Mail has the best spam protection around 
http://mail.yahoo.com 


From d_l_goldsmith at yahoo.com  Wed Aug 19 14:25:14 2009
From: d_l_goldsmith at yahoo.com (David Goldsmith)
Date: Wed, 19 Aug 2009 11:25:14 -0700 (PDT)
Subject: [SciPy-User] Documentation BOF time confirmation
In-Reply-To: <280587.75080.qm@web52110.mail.re2.yahoo.com>
Message-ID: <345951.72519.qm@web52105.mail.re2.yahoo.com>

13:30-14:30 PDT (20:30 UTC, I believe) Friday confirmed.

DG

--- On Wed, 8/19/09, David Goldsmith <d_l_goldsmith at yahoo.com> wrote:

> From: David Goldsmith <d_l_goldsmith at yahoo.com>
> Subject: Re: [SciPy-User] Scipy 2009 BOFs time/location
> To: "SciPy Users List" <scipy-user at scipy.org>
> Date: Wednesday, August 19, 2009, 10:51 AM
> Pending confirmation from Joe, I've
> tentatively decided on Friday, 1:30-2:30 for the
> Documentation BoF; now I need to wrangle a venue: anyone
> know who I talk to about that?? Thanks!
> 
> DG
> 
> --- On Wed, 8/19/09, Christopher Hanley <chanley at stsci.edu>
> wrote:
> 
> > From: Christopher Hanley <chanley at stsci.edu>
> > Subject: [SciPy-User] Scipy 2009 BOFs time/location
> > To: "SciPy Users List" <scipy-user at scipy.org>
> > Date: Wednesday, August 19, 2009, 10:05 AM
> > Hi,
> > 
> > Have any decisions been made on the time/location for
> any
> > of the BOFs? 
> > at Scipy this year?? Are there going to be any
> tonight
> > or will they? 
> > all be tomorrow and Friday?
> > 
> > Just curious.
> > 
> > Thanks,
> > Chris
> > 
> > 
> > -- 
> > Christopher Hanley
> > Senior Systems Software Engineer
> > Space Telescope Science Institute
> > 3700 San Martin Drive
> > Baltimore MD, 21218
> > (410) 338-4338
> > 
> > _______________________________________________
> > SciPy-User mailing list
> > SciPy-User at scipy.org
> > http://mail.scipy.org/mailman/listinfo/scipy-user
> > 
> 
> __________________________________________________
> Do You Yahoo!?
> Tired of spam?? Yahoo! Mail has the best spam
> protection around 
> http://mail.yahoo.com 
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
> 


      


From bruce at clearscienceinc.com  Wed Aug 19 14:42:08 2009
From: bruce at clearscienceinc.com (Bruce Ford)
Date: Wed, 19 Aug 2009 14:42:08 -0400
Subject: [SciPy-User] NetCDF to GRIB2 Conversion
In-Reply-To: <B07C05C39FD05C49973EB16F68A6DDEE0A51B2@pallas.w2k.nrlmry.navy.mil>
References: <bebb7fa10908190921j743c25b7s6635cbb5bd1266f6@mail.gmail.com>
	<B07C05C39FD05C49973EB16F68A6DDEE0A51B2@pallas.w2k.nrlmry.navy.mil>
Message-ID: <bebb7fa10908191142h3746a8f8oaa9f23bf19f9fc5b@mail.gmail.com>

Thanks Tim!

I have been looking at these two modules.  I'm most familiar with the NetCDF
format, so I'll get comfortable with netcdf4-python first.

Thanks!

Bruce
---------------------------------------
Bruce W. Ford
Clear Science, Inc.
bruce at clearscienceinc.com
bruce.w.ford.ctr at navy.smil.mil
http://www.ClearScienceInc.com
Phone/Fax: 904-379-9704
8241 Parkridge Circle N.
Jacksonville, FL  32211
Skype:  bruce.w.ford
Google Talk: fordbw at gmail.com


On Wed, Aug 19, 2009 at 12:48 PM, Whitcomb, Mr. Tim <
tim.whitcomb at nrlmry.navy.mil> wrote:

>
> > I need to bulk convert files from NetCDF to GRIB2 files.  Has anyone
> had to deal do this kind of conversion?
>
> There are a few things that come up on a web search, but we're in the
> process of writing some GRIB converters now.  You're going to need some
> sort of mapping between variable types, level types, the center/model
> that the data is from, but when it comes to actually reading and writing
> the data with Python, there are two modules that I've found useful:
>
> For reading and writing GRIB2 files:
> http://code.google.com/p/pygrib2/
>
> For dealing with NetCDF files:
> http://code.google.com/p/netcdf4-python/
>
> I use NumPy to handle any of the numeric operations required.
>
> Hope this helps - and good luck.
>
> Tim
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From bruce at clearscienceinc.com  Wed Aug 19 14:40:17 2009
From: bruce at clearscienceinc.com (Bruce Ford)
Date: Wed, 19 Aug 2009 14:40:17 -0400
Subject: [SciPy-User] NetCDF to GRIB2 Conversion
In-Reply-To: <c7b95a020908191014p3a3c899akea1346f740570647@mail.gmail.com>
References: <bebb7fa10908190921j743c25b7s6635cbb5bd1266f6@mail.gmail.com>
	<c7b95a020908191014p3a3c899akea1346f740570647@mail.gmail.com>
Message-ID: <bebb7fa10908191140v39e53a74vda7c2afaf66273e6@mail.gmail.com>

Thanks Ryan!

It looks like PyNIO doesn't yet write to GRIB2...which is precisely what I
need.  Maybe soon.

Bruce
---------------------------------------
Bruce W. Ford
Clear Science, Inc.
bruce at clearscienceinc.com
bruce.w.ford.ctr at navy.smil.mil
http://www.ClearScienceInc.com
Phone/Fax: 904-379-9704
8241 Parkridge Circle N.
Jacksonville, FL  32211
Skype:  bruce.w.ford
Google Talk: fordbw at gmail.com


On Wed, Aug 19, 2009 at 1:14 PM, Ryan May <rmay31 at gmail.com> wrote:

> On Wed, Aug 19, 2009 at 11:21 AM, Bruce Ford <bruce at clearscienceinc.com>wrote:
>
>> My searches on this topic have been unfruitful.  I'm new to the SciPy
>> community, so I might not be looking in the right places.
>>
>> I need to bulk convert files from NetCDF to GRIB2 files.  Has anyone had
>> to deal do this kind of conversion?
>>
>> Any nudges in the right direction would be appreciated.
>
>
> There's the PyNIO module, which will read/write netcdf and grib2 through a
> common interface: http://www.pyngl.ucar.edu/Nio.shtml
>
> Ryan
>
> --
> Ryan May
> Graduate Research Assistant
> School of Meteorology
> University of Oklahoma
> Sent from Pasadena, California, United States
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
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From destroooooy at gmail.com  Wed Aug 19 15:28:59 2009
From: destroooooy at gmail.com (DEMOLISHOR! the Demolishor)
Date: Wed, 19 Aug 2009 15:28:59 -0400
Subject: [SciPy-User] worst-case scenario installation
Message-ID: <3d95192b0908191228k3900d1aaqb825a4a4c46c3a0f@mail.gmail.com>

Folks,
  There is mounting evidence to suggest that I cannot install SciPy/ Numpy
under these restrictions:

  - I do not have root access to my machine
  - I use a later version of Python than my distribution provides (2.6
instead of 2.4--I am using RHEL5), built locally and installed in $HOME/bin
-- this prevents me from asking my sysadmin to install
numpy and scipy from the YUM repositories.
  - both g77 and gfortran are installed in /usr/bin and thus I cannot modify
my path to exclude g77 without fear of crippling the rest of the build
system in mysterious ways.

So here's what happens:
  - Using hand-built versions of ATLAS and LAPACK (following the
instructions on the ATLAS website) result in undefined reference errors in
liblapack.so to function calls that live in libblas.so
  - Using RPM versions of ATLAS and LAPACK from a RPM search site (I just
extracted and copied the libraries and headers into the relevant
directories) breaks down in the same way, and no RPM exists with these
libraries
built with g77.
  - Building ATLAS by hand using the instructions on the SciPy website  (
http://www.scipy.org/Installing_SciPy/Linux#head-89e1f6afaa3314d98a22c79b063cceee2cc6313c)
also breaks down because I cannot avoid having g77 in my path. Also I can no
longer build LAPACK with g77 (
http://www.netlib.org/lapack/lapack-3.2.html#_7_install_procedure).

The most aggravating part of this entire process is that I am going through
all these gyrations to avoid the Numpy setup script's obsession with g77. I
do not understand why the `--fcompiler' option doesn't work , or even
putting gfortran ahead of g77 in my PATH. I realize I am not a typical user,
but these things seem really simple to fix for someone that knows where to
look.

And as I was writing this, I figured this out:
I ended up linking *everything* in /usr/bin to ~/bin, removing /usr/bin from
my PATH, and then removing the links to g77 and f77 from ~/bin, and
rebuilding. Now Numpy fails 1 test, and SciPy fails 4, and I guess if I am
lucky I'll never need the stuff that doesn't pass the tests. So it was *not*
impossible to install an at least partial version of Scipy/Numpy on my
system.

Bottom line: weird people like me shouldn't suffer because of unimplemented
command-line options. That said, weird people are like me are grateful that
stuff like Scipy and Numpy exist because they keep me from having to use
ROOT.

Thanks,
Craig
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From robert.kern at gmail.com  Wed Aug 19 15:38:05 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Wed, 19 Aug 2009 12:38:05 -0700
Subject: [SciPy-User] worst-case scenario installation
In-Reply-To: <3d95192b0908191228k3900d1aaqb825a4a4c46c3a0f@mail.gmail.com>
References: <3d95192b0908191228k3900d1aaqb825a4a4c46c3a0f@mail.gmail.com>
Message-ID: <3d375d730908191238s24a8dd4dkc53a8b462e5c5adb@mail.gmail.com>

On Wed, Aug 19, 2009 at 12:28, DEMOLISHOR! the
Demolishor<destroooooy at gmail.com> wrote:

> The most aggravating part of this entire process is that I am going through
> all these gyrations to avoid the Numpy setup script's obsession with g77. I
> do not understand why the `--fcompiler' option doesn't work , or even
> putting gfortran ahead of g77 in my PATH. I realize I am not a typical user,
> but these things seem really simple to fix for someone that knows where to
> look.

What did you do? What results did you get? Copy-and-paste, please.
AFAIK, properly using --fcompiler=gnu95 with all of the appropriate
setup.py commands will work.

-- 
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


From harald.schilly at gmail.com  Wed Aug 19 15:48:00 2009
From: harald.schilly at gmail.com (Harald Schilly)
Date: Wed, 19 Aug 2009 21:48:00 +0200
Subject: [SciPy-User] worst-case scenario installation
In-Reply-To: <3d95192b0908191228k3900d1aaqb825a4a4c46c3a0f@mail.gmail.com>
References: <3d95192b0908191228k3900d1aaqb825a4a4c46c3a0f@mail.gmail.com>
Message-ID: <20548feb0908191248q38951752r7db0e070c1496c98@mail.gmail.com>

On Wed, Aug 19, 2009 at 21:28, DEMOLISHOR! the
Demolishor<destroooooy at gmail.com> wrote:
> Folks,
> ? There is mounting evidence to suggest that I cannot install SciPy/ Numpy
> under these restrictions:....

Sage contains numpy+scipy, with it's own python and atlas. Give it a
try, it should do everything for you, http://www.sagemath.org
Then do "./sage -(i)python" to start (i)python and do whatever you like.

H


From cohen at lpta.in2p3.fr  Wed Aug 19 16:42:25 2009
From: cohen at lpta.in2p3.fr (Johann Cohen-Tanugi)
Date: Wed, 19 Aug 2009 22:42:25 +0200
Subject: [SciPy-User] worst-case scenario installation
In-Reply-To: <3d95192b0908191228k3900d1aaqb825a4a4c46c3a0f@mail.gmail.com>
References: <3d95192b0908191228k3900d1aaqb825a4a4c46c3a0f@mail.gmail.com>
Message-ID: <4A8C63B1.1000907@lpta.in2p3.fr>

We need some more info like your LD_LIBRARY_PATH and the output of ldd 
on the various libraries that have problems. One thing you can try to do 
is make sure that you dont link to unwanted versions of BLAS etc... by 
creating soft links in $HOME/lib and resetting your LD_LIBRARY_PATH
The fact that g77 and gfortran are both in /usr/bin is completely 
inocuous, as Robert mentioned : it is probably the case for many if not 
all of us.

and yes..... directly building sage is a very nice way to avoid this 
definitely slightly tricky part of the built.

Johann

DEMOLISHOR! the Demolishor wrote:
> Folks,
>   There is mounting evidence to suggest that I cannot install SciPy/ 
> Numpy under these restrictions:
>
>   - I do not have root access to my machine
>   - I use a later version of Python than my distribution provides (2.6 
> instead of 2.4--I am using RHEL5), built locally and installed in 
> $HOME/bin -- this prevents me from asking my sysadmin to install
> numpy and scipy from the YUM repositories.
>   - both g77 and gfortran are installed in /usr/bin and thus I cannot 
> modify my path to exclude g77 without fear of crippling the rest of 
> the build system in mysterious ways.
>
> So here's what happens:
>   - Using hand-built versions of ATLAS and LAPACK (following the 
> instructions on the ATLAS website) result in undefined reference 
> errors in liblapack.so to function calls that live in libblas.so
>   - Using RPM versions of ATLAS and LAPACK from a RPM search site (I 
> just extracted and copied the libraries and headers into the relevant 
> directories) breaks down in the same way, and no RPM exists with these 
> libraries
> built with g77.
>   - Building ATLAS by hand using the instructions on the SciPy 
> website  
> (http://www.scipy.org/Installing_SciPy/Linux#head-89e1f6afaa3314d98a22c79b063cceee2cc6313c) 
> also breaks down because I cannot avoid having g77 in my path. Also I 
> can no longer build LAPACK with g77 
> (http://www.netlib.org/lapack/lapack-3.2.html#_7_install_procedure).
>
> The most aggravating part of this entire process is that I am going 
> through all these gyrations to avoid the Numpy setup script's 
> obsession with g77. I do not understand why the `--fcompiler' option 
> doesn't work , or even putting gfortran ahead of g77 in my PATH. I 
> realize I am not a typical user, but these things seem really simple 
> to fix for someone that knows where to look.
>
> And as I was writing this, I figured this out:
> I ended up linking *everything* in /usr/bin to ~/bin, removing 
> /usr/bin from my PATH, and then removing the links to g77 and f77 from 
> ~/bin, and rebuilding. Now Numpy fails 1 test, and SciPy fails 4, and 
> I guess if I am lucky I'll never need the stuff that doesn't pass the 
> tests. So it was *not* impossible to install an at least partial 
> version of Scipy/Numpy on my system.
>
> Bottom line: weird people like me shouldn't suffer because of 
> unimplemented command-line options. That said, weird people are like 
> me are grateful that stuff like Scipy and Numpy exist because they 
> keep me from having to use ROOT.
>
> Thanks,
> Craig
> ------------------------------------------------------------------------
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>   


From cohen at lpta.in2p3.fr  Wed Aug 19 16:45:14 2009
From: cohen at lpta.in2p3.fr (Johann Cohen-Tanugi)
Date: Wed, 19 Aug 2009 22:45:14 +0200
Subject: [SciPy-User] worst-case scenario installation
In-Reply-To: <4A8C63B1.1000907@lpta.in2p3.fr>
References: <3d95192b0908191228k3900d1aaqb825a4a4c46c3a0f@mail.gmail.com>
	<4A8C63B1.1000907@lpta.in2p3.fr>
Message-ID: <4A8C645A.2010503@lpta.in2p3.fr>

we would probably need your site.cfg file as well.... did you modify it?
Johann

Johann Cohen-Tanugi wrote:
> We need some more info like your LD_LIBRARY_PATH and the output of ldd 
> on the various libraries that have problems. One thing you can try to do 
> is make sure that you dont link to unwanted versions of BLAS etc... by 
> creating soft links in $HOME/lib and resetting your LD_LIBRARY_PATH
> The fact that g77 and gfortran are both in /usr/bin is completely 
> inocuous, as Robert mentioned : it is probably the case for many if not 
> all of us.
>
> and yes..... directly building sage is a very nice way to avoid this 
> definitely slightly tricky part of the built.
>
> Johann
>
> DEMOLISHOR! the Demolishor wrote:
>   
>> Folks,
>>   There is mounting evidence to suggest that I cannot install SciPy/ 
>> Numpy under these restrictions:
>>
>>   - I do not have root access to my machine
>>   - I use a later version of Python than my distribution provides (2.6 
>> instead of 2.4--I am using RHEL5), built locally and installed in 
>> $HOME/bin -- this prevents me from asking my sysadmin to install
>> numpy and scipy from the YUM repositories.
>>   - both g77 and gfortran are installed in /usr/bin and thus I cannot 
>> modify my path to exclude g77 without fear of crippling the rest of 
>> the build system in mysterious ways.
>>
>> So here's what happens:
>>   - Using hand-built versions of ATLAS and LAPACK (following the 
>> instructions on the ATLAS website) result in undefined reference 
>> errors in liblapack.so to function calls that live in libblas.so
>>   - Using RPM versions of ATLAS and LAPACK from a RPM search site (I 
>> just extracted and copied the libraries and headers into the relevant 
>> directories) breaks down in the same way, and no RPM exists with these 
>> libraries
>> built with g77.
>>   - Building ATLAS by hand using the instructions on the SciPy 
>> website  
>> (http://www.scipy.org/Installing_SciPy/Linux#head-89e1f6afaa3314d98a22c79b063cceee2cc6313c) 
>> also breaks down because I cannot avoid having g77 in my path. Also I 
>> can no longer build LAPACK with g77 
>> (http://www.netlib.org/lapack/lapack-3.2.html#_7_install_procedure).
>>
>> The most aggravating part of this entire process is that I am going 
>> through all these gyrations to avoid the Numpy setup script's 
>> obsession with g77. I do not understand why the `--fcompiler' option 
>> doesn't work , or even putting gfortran ahead of g77 in my PATH. I 
>> realize I am not a typical user, but these things seem really simple 
>> to fix for someone that knows where to look.
>>
>> And as I was writing this, I figured this out:
>> I ended up linking *everything* in /usr/bin to ~/bin, removing 
>> /usr/bin from my PATH, and then removing the links to g77 and f77 from 
>> ~/bin, and rebuilding. Now Numpy fails 1 test, and SciPy fails 4, and 
>> I guess if I am lucky I'll never need the stuff that doesn't pass the 
>> tests. So it was *not* impossible to install an at least partial 
>> version of Scipy/Numpy on my system.
>>
>> Bottom line: weird people like me shouldn't suffer because of 
>> unimplemented command-line options. That said, weird people are like 
>> me are grateful that stuff like Scipy and Numpy exist because they 
>> keep me from having to use ROOT.
>>
>> Thanks,
>> Craig
>> ------------------------------------------------------------------------
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>   
>>     
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>   


From timmichelsen at gmx-topmail.de  Wed Aug 19 17:06:01 2009
From: timmichelsen at gmx-topmail.de (Tim Michelsen)
Date: Wed, 19 Aug 2009 23:06:01 +0200
Subject: [SciPy-User] satellite imagery
In-Reply-To: <aa15d47e0908171655o6dad89a1j784a07f18eb0a43c@mail.gmail.com>
References: <aa15d47e0908141518vc7f4a70x1b1e138ac8644ff@mail.gmail.com>	<A9AC9E96-3171-42A8-9111-EF4DC299BAEB@cs.toronto.edu>	<aa15d47e0908171004x3273b948p4950a0209c70918b@mail.gmail.com>	<h6cm3q$fmt$1@ger.gmane.org>
	<aa15d47e0908171655o6dad89a1j784a07f18eb0a43c@mail.gmail.com>
Message-ID: <h6hpfq$ta5$1@ger.gmane.org>

Hello,
I liked Jose's examples.
As I work with imagery here and there, I got interested and checked it 
out myself.

Here is what I found:

Howard Butler (http://twitter.com/howardbutler) had given a great 
tutorial together with Sean Gilles [3] at OSGEO 2005 [1] . 
Unfortunately, materials have been taken offline. I have 6 PDF files of 
these which I can mail you if you sent me a private mail.

Another one seems to be here:
Shoaib Burq - Open Source Python GIS Hacks
http://www.osdc.com.au/papers/tutorials.html#open
and materials at:
http://geospatial.nomad-labs.com/2006/12/05/open-source-developers-conference-2006-melbourne-australia/ 


On the other hand: why are you trying to reinvent?
FOSS4G or OSGEO have evolved as the body for this stuff:
* http://wiki.osgeo.org/wiki/OSGeo_Python_Library
* http://grass.osgeo.org/wiki/GRASS_and_Python
* more on the mailing list:
 
http://n2.nabble.com/forum/Search.jtp?forum=1837860&local=y&query=python
* nearly all programs at http://www.osgeo.org/ have a python or jython 
interface

others have gatherd around:
* http://pywps.wald.intevation.org/
* http://pywms.wald.intevation.org/

@Jose and others:
Jose did some stuff nice python & gdal - mpl stuff.
But generally, it seems that few people are working with python and 
imagery directly:
http://pypi.python.org/pypi?%3Aaction=search&term=satellite

The general GIS stuff gets more matches:
http://pypi.python.org/pypi?:action=browse&c=391

Here is what I found:
* http://cosmicproject.org/links.html
* [Image-SIG] working with multi-spectral imagery, PIL, and NumPy
    http://mail.python.org/pipermail/image-sig/2007-May/004430.html
     => seems to have writen a tutorial.
* http://www.cs.unc.edu/~isenburg/lastools/
    for Python see:
    - http://mateusz.loskot.net/category/projects/liblas-projects/
    - http://liblas.org/ => http://liblas.org/wiki/PythonTutorial
    - http://pypi.python.org/pypi/libLAS
* http://geospatialpython.com/
* Case study
 
http://vnoel.wordpress.com/2008/05/03/bye-matlab-hello-python-thanks-sage/
* organisations
    http://wiki.python.org/moin/OrganizationsUsingPython
* Python in remote sensing: http://www.smhi.se/saf/about_py.pdf

[1] http://old-mapserver.gis.umn.edu/mum/agenda2005.html
[2] http://hobu.biz/index_html/OSGIS_hacks
[3] http://sgillies.net/blog / http://trac.gispython.org/lab

I'd like to ask you to summarise your findings also in a wiki. Either 
scipy cookbook or osgeo wiki.
At the osgeo lists / wiki you may also finde more infos.

Please keep us posted.

Regards,
Timmie



From gael.varoquaux at normalesup.org  Wed Aug 19 17:25:07 2009
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Wed, 19 Aug 2009 23:25:07 +0200
Subject: [SciPy-User] Scipy 2009 BOFs time/location
In-Reply-To: <915126EF-5D00-442D-A4B5-9A5326DBF1CC@stsci.edu>
References: <915126EF-5D00-442D-A4B5-9A5326DBF1CC@stsci.edu>
Message-ID: <20090819212507.GC10463@phare.normalesup.org>

On Wed, Aug 19, 2009 at 01:05:04PM -0400, Christopher Hanley wrote:
> Have any decisions been made on the time/location for any of the BOFs  
> at Scipy this year?  Are there going to be any tonight or will they  
> all be tomorrow and Friday?

Yes, I have been doing a terrible job about that.

So, we have a lot of BoFs. It is quite a struggle to get everything fit
together without too much overlap, especially with the different
constraints. Here is a suggestion:

Thursday:

  *  After the lightning talks

	* PDE BoF, Powell Booth room 100

	* Documentation, Powell Booth, room 120

	* Cython, Powell Booth, library (upstairs)


  * After dinner (around 9PM)

    * Machine Learning/Probabilistic Modeling, Poweel Booth, library
      (upstairs)

    * Astronomy, Powell Booth, room 100


Friday after the ask the experts session:

    * Organization, Funding, and Future Direction of SciPy

    * Behavior science

    * C coders

Sorry, we have so many BoFs that we had to have two sessions on Thursday
evening.

Ga?l


From gael.varoquaux at normalesup.org  Wed Aug 19 17:28:03 2009
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Wed, 19 Aug 2009 23:28:03 +0200
Subject: [SciPy-User] Scipy 2009 BOFs time/location
In-Reply-To: <280587.75080.qm@web52110.mail.re2.yahoo.com>
References: <915126EF-5D00-442D-A4B5-9A5326DBF1CC@stsci.edu>
	<280587.75080.qm@web52110.mail.re2.yahoo.com>
Message-ID: <20090819212803.GD10463@phare.normalesup.org>

On Wed, Aug 19, 2009 at 10:51:45AM -0700, David Goldsmith wrote:
> Pending confirmation from Joe, I've tentatively decided on Friday, 1:30-2:30 for the Documentation BoF; now I need to wrangle a venue: anyone know who I talk to about that?  Thanks!

To me. :)

I just announced another time. I am happy changing to the time you
suggested though.

Ga?l


From gael.varoquaux at normalesup.org  Wed Aug 19 18:21:25 2009
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Thu, 20 Aug 2009 00:21:25 +0200
Subject: [SciPy-User] Scipy 2009 BOFs time/location
In-Reply-To: <20090819212507.GC10463@phare.normalesup.org>
References: <915126EF-5D00-442D-A4B5-9A5326DBF1CC@stsci.edu>
	<20090819212507.GC10463@phare.normalesup.org>
Message-ID: <20090819222125.GE10463@phare.normalesup.org>

On Wed, Aug 19, 2009 at 11:25:07PM +0200, Gael Varoquaux wrote:
> On Wed, Aug 19, 2009 at 01:05:04PM -0400, Christopher Hanley wrote:
> > Have any decisions been made on the time/location for any of the BOFs  
> > at Scipy this year?  Are there going to be any tonight or will they  
> > all be tomorrow and Friday?

> Yes, I have been doing a terrible job about that.

> So, we have a lot of BoFs. It is quite a struggle to get everything fit
> together without too much overlap, especially with the different
> constraints. Here is a suggestion:

OK, I was able to get an extra room. So I have rescheduled BoFs to avoid
having BoFs during the reception on Thursday evening. The schedule page
has now the information.

http://conference.scipy.org/schedule

Ga?l


From questions.anon at gmail.com  Wed Aug 19 18:45:56 2009
From: questions.anon at gmail.com (questions anon)
Date: Wed, 19 Aug 2009 15:45:56 -0700
Subject: [SciPy-User] satellite imagery
In-Reply-To: <h6hpfq$ta5$1@ger.gmane.org>
References: <aa15d47e0908141518vc7f4a70x1b1e138ac8644ff@mail.gmail.com>
	<A9AC9E96-3171-42A8-9111-EF4DC299BAEB@cs.toronto.edu>
	<aa15d47e0908171004x3273b948p4950a0209c70918b@mail.gmail.com>
	<h6cm3q$fmt$1@ger.gmane.org>
	<aa15d47e0908171655o6dad89a1j784a07f18eb0a43c@mail.gmail.com>
	<h6hpfq$ta5$1@ger.gmane.org>
Message-ID: <aa15d47e0908191545h434b4de0n91553ca71b8c249d@mail.gmail.com>

Wow thanks Timmie, those links should get me started!
Thank you for your help. I will let you know how I go.



On Wed, Aug 19, 2009 at 2:06 PM, Tim Michelsen
<timmichelsen at gmx-topmail.de>wrote:

> Hello,
> I liked Jose's examples.
> As I work with imagery here and there, I got interested and checked it
> out myself.
>
> Here is what I found:
>
> Howard Butler (http://twitter.com/howardbutler) had given a great
> tutorial together with Sean Gilles [3] at OSGEO 2005 [1] .
> Unfortunately, materials have been taken offline. I have 6 PDF files of
> these which I can mail you if you sent me a private mail.
>
> Another one seems to be here:
> Shoaib Burq - Open Source Python GIS Hacks
> http://www.osdc.com.au/papers/tutorials.html#open
> and materials at:
>
> http://geospatial.nomad-labs.com/2006/12/05/open-source-developers-conference-2006-melbourne-australia/
>
>
> On the other hand: why are you trying to reinvent?
> FOSS4G or OSGEO have evolved as the body for this stuff:
> * http://wiki.osgeo.org/wiki/OSGeo_Python_Library
> * http://grass.osgeo.org/wiki/GRASS_and_Python
> * more on the mailing list:
>
> http://n2.nabble.com/forum/Search.jtp?forum=1837860&local=y&query=python
> * nearly all programs at http://www.osgeo.org/ have a python or jython
> interface
>
> others have gatherd around:
> * http://pywps.wald.intevation.org/
> * http://pywms.wald.intevation.org/
>
> @Jose and others:
> Jose did some stuff nice python & gdal - mpl stuff.
> But generally, it seems that few people are working with python and
> imagery directly:
> http://pypi.python.org/pypi?%3Aaction=search&term=satellite
>
> The general GIS stuff gets more matches:
> http://pypi.python.org/pypi?:action=browse&c=391
>
> Here is what I found:
> * http://cosmicproject.org/links.html
> * [Image-SIG] working with multi-spectral imagery, PIL, and NumPy
>    http://mail.python.org/pipermail/image-sig/2007-May/004430.html
>     => seems to have writen a tutorial.
> * http://www.cs.unc.edu/~isenburg/lastools/<http://www.cs.unc.edu/%7Eisenburg/lastools/>
>    for Python see:
>    - http://mateusz.loskot.net/category/projects/liblas-projects/
>    - http://liblas.org/ => http://liblas.org/wiki/PythonTutorial
>    - http://pypi.python.org/pypi/libLAS
> * http://geospatialpython.com/
> * Case study
>
> http://vnoel.wordpress.com/2008/05/03/bye-matlab-hello-python-thanks-sage/
> * organisations
>    http://wiki.python.org/moin/OrganizationsUsingPython
> * Python in remote sensing: http://www.smhi.se/saf/about_py.pdf
>
> [1] http://old-mapserver.gis.umn.edu/mum/agenda2005.html
> [2] http://hobu.biz/index_html/OSGIS_hacks
> [3] http://sgillies.net/blog / http://trac.gispython.org/lab
>
> I'd like to ask you to summarise your findings also in a wiki. Either
> scipy cookbook or osgeo wiki.
> At the osgeo lists / wiki you may also finde more infos.
>
> Please keep us posted.
>
> Regards,
> Timmie
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
-------------- next part --------------
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From dwf at cs.toronto.edu  Wed Aug 19 20:36:34 2009
From: dwf at cs.toronto.edu (David Warde-Farley)
Date: Wed, 19 Aug 2009 17:36:34 -0700
Subject: [SciPy-User] Scipy 2009 BOFs time/location
In-Reply-To: <20090819212507.GC10463@phare.normalesup.org>
References: <915126EF-5D00-442D-A4B5-9A5326DBF1CC@stsci.edu>
	<20090819212507.GC10463@phare.normalesup.org>
Message-ID: <19F7BF8F-763F-4035-9977-B01E4A918E90@cs.toronto.edu>


On 19-Aug-09, at 2:25 PM, Gael Varoquaux wrote:

> Thursday:
>
>  *  After the lightning talks
>
> 	* PDE BoF, Powell Booth room 100
>
> 	* Documentation, Powell Booth, room 120
>
> 	* Cython, Powell Booth, library (upstairs)
>
>
>  * After dinner (around 9PM)
>
>    * Machine Learning/Probabilistic Modeling, Poweel Booth, library
>      (upstairs)
>
>    * Astronomy, Powell Booth, room 100
>
>
> Friday after the ask the experts session:
>
>    * Organization, Funding, and Future Direction of SciPy
>
>    * Behavior science
>
>    * C coders

Looks good!

David


From dmccully at mail.nih.gov  Wed Aug 19 20:49:42 2009
From: dmccully at mail.nih.gov (McCully, Dwayne (NIH/NLM/LHC) [C])
Date: Wed, 19 Aug 2009 20:49:42 -0400
Subject: [SciPy-User] Removing numpy 1.2.1 to install 1.3.0
Message-ID: <8A8D24241B862C41B5D5EADB47CE39E2D8C8102E@NIHMLBX01.nih.gov>

Does anyone know the best method to remove numpy 1.2.1 before installing numpy 1.3.0?
Scipy seems to need numpy 1.3.0 included in my Python 2.5.2.

There is only the "python setup.py install" for Numpy 1.3.0

Dwayne

From robert.kern at gmail.com  Wed Aug 19 20:53:40 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Wed, 19 Aug 2009 17:53:40 -0700
Subject: [SciPy-User] Removing numpy 1.2.1 to install 1.3.0
In-Reply-To: <8A8D24241B862C41B5D5EADB47CE39E2D8C8102E@NIHMLBX01.nih.gov>
References: <8A8D24241B862C41B5D5EADB47CE39E2D8C8102E@NIHMLBX01.nih.gov>
Message-ID: <3d375d730908191753u3e72a109sf5e376ff815a738d@mail.gmail.com>

On Wed, Aug 19, 2009 at 17:49, McCully, Dwayne (NIH/NLM/LHC)
[C]<dmccully at mail.nih.gov> wrote:
> Does anyone know the best method to remove numpy 1.2.1 before installing numpy 1.3.0?

Delete it.

-- 
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


From Kristian.Sandberg at Colorado.EDU  Thu Aug 20 13:34:43 2009
From: Kristian.Sandberg at Colorado.EDU (Kristian Hans Sandberg)
Date: Thu, 20 Aug 2009 11:34:43 -0600 (MDT)
Subject: [SciPy-User] Problem importing weave
In-Reply-To: <5b8d13220908182207v1d5d8ef0kcd732390c7c5ea7a@mail.gmail.com>
References: <20090818191208.AKO46840@joker.int.colorado.edu>
	<5b8d13220908182207v1d5d8ef0kcd732390c7c5ea7a@mail.gmail.com>
Message-ID: <20090820113443.AKP32783@joker.int.colorado.edu>


Thanks, that helped. The package is apparently called lib64python2.6-devel in Mandriva (in earlier distributions this seems to have been installed by default).




 
 Home page: http://amath.colorado.edu/faculty/sandberg


---- Original message ----
>Date: Tue, 18 Aug 2009 22:07:51 -0700
>From: scipy-user-bounces at scipy.org (on behalf of David Cournapeau <cournape at gmail.com>)
>Subject: Re: [SciPy-User] Problem importing weave  
>To: SciPy Users List <scipy-user at scipy.org>
>
>On Tue, Aug 18, 2009 at 6:12 PM, Kristian Hans
>Sandberg<Kristian.Sandberg at colorado.edu> wrote:
>> When I do
>>
>> from scipy import weave
>>
>> on my Mandriva 2009.1 (64-bit) installation, I get the error
>>
>> DistutilsPlatformError: invalid Python installation: unable to open /usr/lib64/python2.6/config/Makefile (No such file or directory)
>
>Have you tried installing python-devel ?
>
>David
>_______________________________________________
>SciPy-User mailing list
>SciPy-User at scipy.org
>http://mail.scipy.org/mailman/listinfo/scipy-user


From cohen at lpta.in2p3.fr  Thu Aug 20 15:07:01 2009
From: cohen at lpta.in2p3.fr (Johann Cohen-Tanugi)
Date: Thu, 20 Aug 2009 21:07:01 +0200
Subject: [SciPy-User] worst-case scenario installation
In-Reply-To: <3d95192b0908200840qfa65462nf32f3cb124c9044d@mail.gmail.com>
References: <3d95192b0908191228k3900d1aaqb825a4a4c46c3a0f@mail.gmail.com>	
	<4A8C63B1.1000907@lpta.in2p3.fr> <4A8C645A.2010503@lpta.in2p3.fr>
	<3d95192b0908200840qfa65462nf32f3cb124c9044d@mail.gmail.com>
Message-ID: <4A8D9ED5.1060303@lpta.in2p3.fr>



DEMOLISHOR! the Demolishor wrote:
> Okay so I went through the steps again and I saw Numpy build with 
> gfortran--I must have been misled by the initial lines of output where 
> it says it can't find a Fortran 90 compiler. It tested with just one 
> known fail. Scipy segfaulted during tests so I built it with 
> --fcompiler=gnu95 as well and then I got 4 known fails during testing. 
> I think some of my earlier problems could have been caused by not  
> completely cleaning up between each build attempt.
>
> I would like to avoid kitchen-sink-style packages like Sage because I 
> am still recovering from years of sufferring at the hands of Rene and 
> Fons with ROOT. All I want are tools that help me write the scripts I 
> want to write--I don't want an all-inclusive interactive environment.
Welcome aboard, I know the pain of ROOT.... Sage is not exactly the same 
: yes it can be an all inclusive software for doing math, but what you 
can do is just launch ipython from within it, and it will come with all 
the right dependencies for scipy/matplotlib etc..., provided that the 
build was ok of course. In other words, it is also a scipy building 
machine, and you can rely on it just for that.

Johann

>
> I've attached a complete transcript of this build. Thanks in advance.
>
> --cb
>
> On Wed, Aug 19, 2009 at 4:45 PM, Johann Cohen-Tanugi 
> <cohen at lpta.in2p3.fr <mailto:cohen at lpta.in2p3.fr>> wrote:
>
>     we would probably need your site.cfg file as well.... did you
>     modify it?
>     Johann
>
>     Johann Cohen-Tanugi wrote:
>     > We need some more info like your LD_LIBRARY_PATH and the output
>     of ldd
>     > on the various libraries that have problems. One thing you can
>     try to do
>     > is make sure that you dont link to unwanted versions of BLAS
>     etc... by
>     > creating soft links in $HOME/lib and resetting your LD_LIBRARY_PATH
>     > The fact that g77 and gfortran are both in /usr/bin is completely
>     > inocuous, as Robert mentioned : it is probably the case for many
>     if not
>     > all of us.
>     >
>     > and yes..... directly building sage is a very nice way to avoid this
>     > definitely slightly tricky part of the built.
>     >
>     > Johann
>     >
>     > DEMOLISHOR! the Demolishor wrote:
>     >
>     >> Folks,
>     >>   There is mounting evidence to suggest that I cannot install
>     SciPy/
>     >> Numpy under these restrictions:
>     >>
>     >>   - I do not have root access to my machine
>     >>   - I use a later version of Python than my distribution
>     provides (2.6
>     >> instead of 2.4--I am using RHEL5), built locally and installed in
>     >> $HOME/bin -- this prevents me from asking my sysadmin to install
>     >> numpy and scipy from the YUM repositories.
>     >>   - both g77 and gfortran are installed in /usr/bin and thus I
>     cannot
>     >> modify my path to exclude g77 without fear of crippling the rest of
>     >> the build system in mysterious ways.
>     >>
>     >> So here's what happens:
>     >>   - Using hand-built versions of ATLAS and LAPACK (following the
>     >> instructions on the ATLAS website) result in undefined reference
>     >> errors in liblapack.so to function calls that live in libblas.so
>     >>   - Using RPM versions of ATLAS and LAPACK from a RPM search
>     site (I
>     >> just extracted and copied the libraries and headers into the
>     relevant
>     >> directories) breaks down in the same way, and no RPM exists
>     with these
>     >> libraries
>     >> built with g77.
>     >>   - Building ATLAS by hand using the instructions on the SciPy
>     >> website
>     >>
>     (http://www.scipy.org/Installing_SciPy/Linux#head-89e1f6afaa3314d98a22c79b063cceee2cc6313c)
>     >> also breaks down because I cannot avoid having g77 in my path.
>     Also I
>     >> can no longer build LAPACK with g77
>     >>
>     (http://www.netlib.org/lapack/lapack-3.2.html#_7_install_procedure).
>     >>
>     >> The most aggravating part of this entire process is that I am going
>     >> through all these gyrations to avoid the Numpy setup script's
>     >> obsession with g77. I do not understand why the `--fcompiler'
>     option
>     >> doesn't work , or even putting gfortran ahead of g77 in my PATH. I
>     >> realize I am not a typical user, but these things seem really
>     simple
>     >> to fix for someone that knows where to look.
>     >>
>     >> And as I was writing this, I figured this out:
>     >> I ended up linking *everything* in /usr/bin to ~/bin, removing
>     >> /usr/bin from my PATH, and then removing the links to g77 and
>     f77 from
>     >> ~/bin, and rebuilding. Now Numpy fails 1 test, and SciPy fails
>     4, and
>     >> I guess if I am lucky I'll never need the stuff that doesn't
>     pass the
>     >> tests. So it was *not* impossible to install an at least partial
>     >> version of Scipy/Numpy on my system.
>     >>
>     >> Bottom line: weird people like me shouldn't suffer because of
>     >> unimplemented command-line options. That said, weird people are
>     like
>     >> me are grateful that stuff like Scipy and Numpy exist because they
>     >> keep me from having to use ROOT.
>     >>
>     >> Thanks,
>     >> Craig
>     >>
>     ------------------------------------------------------------------------
>     >>
>     >> _______________________________________________
>     >> SciPy-User mailing list
>     >> SciPy-User at scipy.org <mailto:SciPy-User at scipy.org>
>     >> http://mail.scipy.org/mailman/listinfo/scipy-user
>     >>
>     >>
>     > _______________________________________________
>     > SciPy-User mailing list
>     > SciPy-User at scipy.org <mailto:SciPy-User at scipy.org>
>     > http://mail.scipy.org/mailman/listinfo/scipy-user
>     >
>     _______________________________________________
>     SciPy-User mailing list
>     SciPy-User at scipy.org <mailto:SciPy-User at scipy.org>
>     http://mail.scipy.org/mailman/listinfo/scipy-user
>
>


From robince at gmail.com  Thu Aug 20 15:51:01 2009
From: robince at gmail.com (Robin)
Date: Thu, 20 Aug 2009 21:51:01 +0200
Subject: [SciPy-User] worst-case scenario installation
In-Reply-To: <3d95192b0908191228k3900d1aaqb825a4a4c46c3a0f@mail.gmail.com>
References: <3d95192b0908191228k3900d1aaqb825a4a4c46c3a0f@mail.gmail.com>
Message-ID: <b14b627d0908201251w709f59d7v840e453e08bdb8d9@mail.gmail.com>

On Wed, Aug 19, 2009 at 9:28 PM, DEMOLISHOR! the
Demolishor<destroooooy at gmail.com> wrote:
> Folks,
> ? There is mounting evidence to suggest that I cannot install SciPy/ Numpy
> under these restrictions:
>
> ? - I do not have root access to my machine
> ? - I use a later version of Python than my distribution provides (2.6
> instead of 2.4--I am using RHEL5), built locally and installed in $HOME/bin
> -- this prevents me from asking my sysadmin to install
> numpy and scipy from the YUM repositories.
> ? - both g77 and gfortran are installed in /usr/bin and thus I cannot modify
> my path to exclude g77 without fear of crippling the rest of the build
> system in mysterious ways.
>
> So here's what happens:
> ? - Using hand-built versions of ATLAS and LAPACK (following the
> instructions on the ATLAS website) result in undefined reference errors in
> liblapack.so to function calls that live in libblas.so
> ? - Using RPM versions of ATLAS and LAPACK from a RPM search site (I just
> extracted and copied the libraries and headers into the relevant
> directories) breaks down in the same way, and no RPM exists with these
> libraries
> built with g77.
> ? - Building ATLAS by hand using the instructions on the SciPy website
> (http://www.scipy.org/Installing_SciPy/Linux#head-89e1f6afaa3314d98a22c79b063cceee2cc6313c)
> also breaks down because I cannot avoid having g77 in my path. Also I can no
> longer build LAPACK with g77
> (http://www.netlib.org/lapack/lapack-3.2.html#_7_install_procedure).
>
> The most aggravating part of this entire process is that I am going through
> all these gyrations to avoid the Numpy setup script's obsession with g77. I
> do not understand why the `--fcompiler' option doesn't work , or even
> putting gfortran ahead of g77 in my PATH. I realize I am not a typical user,
> but these things seem really simple to fix for someone that knows where to
> look.

It is possible - I've done this, but the easiest way is just to use
g77 I think (with an older version of lapack that supports it
(3.1.1)). Then as long as you use g77 for everything it should be
fine.

I have also observed the behaviour that it is impossible to get
numpy/scipy build to use gfortran if g77 is in the path - I have only
been able to install using gfortran on machines which I do have root
access and can uninstall g77 completely.

Cheers

Robin


From cournape at gmail.com  Thu Aug 20 18:37:19 2009
From: cournape at gmail.com (David Cournapeau)
Date: Thu, 20 Aug 2009 15:37:19 -0700
Subject: [SciPy-User] worst-case scenario installation
In-Reply-To: <b14b627d0908201251w709f59d7v840e453e08bdb8d9@mail.gmail.com>
References: <3d95192b0908191228k3900d1aaqb825a4a4c46c3a0f@mail.gmail.com>
	<b14b627d0908201251w709f59d7v840e453e08bdb8d9@mail.gmail.com>
Message-ID: <5b8d13220908201537q3b45fb9bq211e65bb855f15bd@mail.gmail.com>

On Thu, Aug 20, 2009 at 12:51 PM, Robin<robince at gmail.com> wrote:

> I have also observed the behaviour that it is impossible to get
> numpy/scipy build to use gfortran if g77 is in the path - I have only
> been able to install using gfortran on machines which I do have root
> access and can uninstall g77 completely.

Could you give us the output of python setup.py build_ext
--fcompiler=gnu95 ? This should helps us understanding why you cannot
invoke gfortran

cheers,

David


From amenity at enthought.com  Fri Aug 21 17:32:42 2009
From: amenity at enthought.com (Amenity Applewhite)
Date: Fri, 21 Aug 2009 16:32:42 -0500
Subject: [SciPy-User] August SCP Webinar: SciPy 2009
Message-ID: <F1215525-82FD-4469-AD55-CA659272D1ED@enthought.com>

Having trouble viewing this email? Click here

Hello,
We are pleased to announce out August Scientific Computing with Python  
webinar!

August 28: A review of SciPy 2009

The Enthought office in Austin has been pretty tranquil this week as  
many of our developers are out at the 2009 Python for Scientific  
Computing Conference at Caltech. The tutorial videos got posted  
yesterday, and slides are being uploaded as well.

Keynote speakers included Peter Norvig and Jon Guyer, the SciPy 2009  
BoF sessions covered topics ranging from Astronomy to Machine  
Learning. Needless to say, we expect there will be a lot to report  
back on! That?s why we?ve decided to dedicate next Friday?s Scientific  
Computing with Python Webinar to SciPy 2009.

Scientific Computing with Python Webinar: SciPy 2009

Friday, August 28

1pm CDT/6pm UTC

Register at GoToMeeting

Hope to see you there!

Thanks, and see you Friday!


--
Amenity Applewhite
Enthought, Inc.
Scientific Computing Solutions
www.enthought.com









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From hnry2k at hotmail.com  Fri Aug 21 20:51:19 2009
From: hnry2k at hotmail.com (=?iso-8859-1?Q?Jorge_E._=B4Sanchez_Sanchez?=)
Date: Fri, 21 Aug 2009 19:51:19 -0500
Subject: [SciPy-User] satellite imagery
In-Reply-To: <h6hpfq$ta5$1@ger.gmane.org>
References: <aa15d47e0908141518vc7f4a70x1b1e138ac8644ff@mail.gmail.com>
	<A9AC9E96-3171-42A8-9111-EF4DC299BAEB@cs.toronto.edu>
	<aa15d47e0908171004x3273b948p4950a0209c70918b@mail.gmail.com>
	<h6cm3q$fmt$1@ger.gmane.org>
	<aa15d47e0908171655o6dad89a1j784a07f18eb0a43c@mail.gmail.com> 
	<h6hpfq$ta5$1@ger.gmane.org>
Message-ID: <COL104-W3BD7CB679C23FDFE5F0719BFB0@phx.gbl>


Dear Tim,

 

   I am doing some meteorological data analysis and I am interested also in the Howard Butler's tutorial, could you please send to me also those 6 PDF files, I would appreciate it very much.

 

Thanks in advance

   Jorge
 
> To: scipy-user at scipy.org
> From: timmichelsen at gmx-topmail.de
> Date: Wed, 19 Aug 2009 23:06:01 +0200
> Subject: Re: [SciPy-User] satellite imagery
> 
> Hello,
> I liked Jose's examples.
> As I work with imagery here and there, I got interested and checked it 
> out myself.
> 
> Here is what I found:
> 
> Howard Butler (http://twitter.com/howardbutler) had given a great 
> tutorial together with Sean Gilles [3] at OSGEO 2005 [1] . 
> Unfortunately, materials have been taken offline. I have 6 PDF files of 
> these which I can mail you if you sent me a private mail.
> 
> Another one seems to be here:
> Shoaib Burq - Open Source Python GIS Hacks
> http://www.osdc.com.au/papers/tutorials.html#open
> and materials at:
> http://geospatial.nomad-labs.com/2006/12/05/open-source-developers-conference-2006-melbourne-australia/ 
> 
> 
> On the other hand: why are you trying to reinvent?
> FOSS4G or OSGEO have evolved as the body for this stuff:
> * http://wiki.osgeo.org/wiki/OSGeo_Python_Library
> * http://grass.osgeo.org/wiki/GRASS_and_Python
> * more on the mailing list:
> 
> http://n2.nabble.com/forum/Search.jtp?forum=1837860&local=y&query=python
> * nearly all programs at http://www.osgeo.org/ have a python or jython 
> interface
> 
> others have gatherd around:
> * http://pywps.wald.intevation.org/
> * http://pywms.wald.intevation.org/
> 
> @Jose and others:
> Jose did some stuff nice python & gdal - mpl stuff.
> But generally, it seems that few people are working with python and 
> imagery directly:
> http://pypi.python.org/pypi?%3Aaction=search&term=satellite
> 
> The general GIS stuff gets more matches:
> http://pypi.python.org/pypi?:action=browse&c=391
> 
> Here is what I found:
> * http://cosmicproject.org/links.html
> * [Image-SIG] working with multi-spectral imagery, PIL, and NumPy
> http://mail.python.org/pipermail/image-sig/2007-May/004430.html
> => seems to have writen a tutorial.
> * http://www.cs.unc.edu/~isenburg/lastools/
> for Python see:
> - http://mateusz.loskot.net/category/projects/liblas-projects/
> - http://liblas.org/ => http://liblas.org/wiki/PythonTutorial
> - http://pypi.python.org/pypi/libLAS
> * http://geospatialpython.com/
> * Case study
> 
> http://vnoel.wordpress.com/2008/05/03/bye-matlab-hello-python-thanks-sage/
> * organisations
> http://wiki.python.org/moin/OrganizationsUsingPython
> * Python in remote sensing: http://www.smhi.se/saf/about_py.pdf
> 
> [1] http://old-mapserver.gis.umn.edu/mum/agenda2005.html
> [2] http://hobu.biz/index_html/OSGIS_hacks
> [3] http://sgillies.net/blog / http://trac.gispython.org/lab
> 
> I'd like to ask you to summarise your findings also in a wiki. Either 
> scipy cookbook or osgeo wiki.
> At the osgeo lists / wiki you may also finde more infos.
> 
> Please keep us posted.
> 
> Regards,
> Timmie
> 
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user

_________________________________________________________________
Feliz aniversario Messenger!
www.aniversariomessenger.com.mx
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From colinjcotter at gmail.com  Sat Aug 22 12:44:10 2009
From: colinjcotter at gmail.com (Colin Cotter)
Date: Sat, 22 Aug 2009 17:44:10 +0100
Subject: [SciPy-User] signal handling inside integrate.odeint
Message-ID: <19c5f0af0908220944o11a31bf7xa5ee754be5df8430@mail.gmail.com>

Dear SciPy-user list,

  I have a question about handling signals with integrate.odeint. I am
developing some Monte-Carlo code that involves repeated calls off this
subroutine, but I seem to be doing something wrong with the signal
handling because if I hit ctrl-c during runtime, the code just jumps
out of odeint and continues with the loop. What is the best way to
deal with this i.e. how do I modify the code so that if I hit ctrl-c
during runtime the python script exits?

I have attached a piece of test code which exhibits this behaviour at
the bottom of this email.

best wishes
--Colin Cotter

from scipy import *

def myvec(x,t):
    for a in range(1000):
        pass
    return sin(x)

Y0 = 0.1*2*pi*arange(10.0)
while(True):
    Y1 = integrate.odeint(myvec, Y0, t=array([0,1.0]),
                          rtol=1.0e-1, atol=1.0e-1, \
                          h0=0.01, hmax=0.2)


From stefan at sun.ac.za  Sat Aug 22 16:19:51 2009
From: stefan at sun.ac.za (=?ISO-8859-1?Q?St=E9fan_van_der_Walt?=)
Date: Sat, 22 Aug 2009 13:19:51 -0700
Subject: [SciPy-User] Sprinting at SciPy2009 today and tomorrow
Message-ID: <9457e7c80908221319peab26e8we224d5a7a6d3bebf@mail.gmail.com>

Hey everyone,

The SciPy2009 sprints are underway, and you are welcome to take part!
Topics include NumPy, SciPy, Mayavi, Traits, IPython, Documentation
and the image processing toolbox.

Join us on irc in channel #scipy, server irc.freenode.net.

The timezone here is GMT-7, and we'll be around both days from 10:00am
till late.

See you there,

St?fan


From lucadeluge at gmail.com  Sun Aug 23 03:56:17 2009
From: lucadeluge at gmail.com (Luca Delucchi)
Date: Sun, 23 Aug 2009 09:56:17 +0200
Subject: [SciPy-User] problem with interpolate.splrep
In-Reply-To: <b7d609c00908230050h604e1f2bs8ecfa83ab1946742@mail.gmail.com>
References: <b7d609c00908230050h604e1f2bs8ecfa83ab1946742@mail.gmail.com>
Message-ID: <b7d609c00908230056q39b9850dk3f64dca9fb812574@mail.gmail.com>

I everybody i try to interpolate a point cloud with interpolate.splrep
but i have this error

luca at LucaCasa:/media/disk-1/python_gpx$ ./terzo.py
Traceback (most recent call last):
?File "./terzo.py", line 94, in <module>
? ?tckp = splrep(x,y,task=0,s=s,k=k,xb=xb,xe=xe)
?File "/usr/lib/python2.5/site-packages/scipy/interpolate/fitpack.py",
line 393, in splrep
? ?raise _iermess[ier][1],_iermess[ier][0]
ValueError: ? ? Error on input data

my code is

#!/usr/bin/python

import sys,os
import xml.etree.cElementTree as ET
import string
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import splrep, splev


def punti(nome):
? ?mainNS=string.Template("{http://www.topografix.com/GPX/1/0}$tag")

? ?trkTag=mainNS.substitute(tag="trkpt")
? ?nameTag=mainNS.substitute(tag="ele")

? ?track=[]
? ?for ins in nome:
? ? ? ?#print ins
? ? ? ?et=ET.parse(open(ins))
# ? ?wptinfo=[]
? ? ? ?for wpt in et.findall("//"+trkTag):
? ? ? ? ? ? ? ?wptinfo=[]
? ? ? ? ? ? ? ?wptinfo.append(wpt.get("lat"))
? ? ? ? ? ? ? ?wptinfo.append(wpt.get("lon"))
? ? ? ? ? ? ? ?wptinfo.append(wpt.findtext(nameTag))
? ? ? ? ? ? ? ?track.append(wptinfo)
? ? ? ?#print "file terminato"
? ?return track
? ? ? ?#print ",".join(wptinfo)

def ascesa(list):
? ? ? ?x=1
? ? ? ?asc=0
? ? ? ?while x < len(coor):
? ? ? ? ? ? ? ?if float(coor[x][2])>float(coor[x-1][2]):
? ? ? ? ? ? ? ? ? ? ? ?asc=asc+(float(coor[x][2])-float(coor[x-1][2]))
? ? ? ? ? ? ? ?x=x+1
? ? ? ?print asc

#filein=("affi_bobbio2009-08-13_TRK.gpx","peschiera-bobbio_trk.gpx")
#filein=("../gpxosm/brenta/ale_Dolomity_mapping.gpx","../gpxosm/brenta/ale_Dolomity_mapping2.gpx","../gpxosm/brenta/ale_Dolomity_mapping3.gpx","../gpxosm/brenta/luca2009-08-03_TRK.gpx")
filein=("ale_Dolomity_mapping.gpx","luca_diviso.gpx")

coor=punti(filein)
#print coor
x=[]
y=[]
i=0
while i < len(coor):
? ? ? ?x.append(float(coor[i][1]))
? ? ? ?y.append(float(coor[i][0]))
? ? ? ?i=i+1

# spline parameters
s=3.0 # smoothness parameter
k=2 # spline order
nest=-1 # estimate of number of knots needed (-1 = maximal)

tckp = splrep(x,y,task=0,s=s,k=k)

xnew,ynew = splev(linspace(0,1,400),tckp)

data,=plt.plot(x,y,'bo-',label='data')
fit,=plt.plot(xnew,ynew,'r-',label='fit')
plt.legend()
plt.xlabel('x')
plt.ylabel('y')
plt.show()

here [1][2] you can found the files gpx that i use like source for the
point cloud

thank's
Luca

[1] http://www.lucadelu.org/out/luca_diviso.gpx
[2] http://www.lucadelu.org/out/ale_Dolomity_mapping.gpx


From josef.pktd at gmail.com  Sun Aug 23 04:20:01 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sun, 23 Aug 2009 04:20:01 -0400
Subject: [SciPy-User] problem with interpolate.splrep
In-Reply-To: <b7d609c00908230056q39b9850dk3f64dca9fb812574@mail.gmail.com>
References: <b7d609c00908230050h604e1f2bs8ecfa83ab1946742@mail.gmail.com>
	<b7d609c00908230056q39b9850dk3f64dca9fb812574@mail.gmail.com>
Message-ID: <1cd32cbb0908230120q7fbdc7c0oa00ef520fe3f2dfe@mail.gmail.com>

On Sun, Aug 23, 2009 at 3:56 AM, Luca Delucchi<lucadeluge at gmail.com> wrote:
> I everybody i try to interpolate a point cloud with interpolate.splrep
> but i have this error
>
> luca at LucaCasa:/media/disk-1/python_gpx$ ./terzo.py
> Traceback (most recent call last):
> ?File "./terzo.py", line 94, in <module>
> ? ?tckp = splrep(x,y,task=0,s=s,k=k,xb=xb,xe=xe)
> ?File "/usr/lib/python2.5/site-packages/scipy/interpolate/fitpack.py",
> line 393, in splrep
> ? ?raise _iermess[ier][1],_iermess[ier][0]
> ValueError: ? ? Error on input data
>
> my code is
>
> #!/usr/bin/python
>
> import sys,os
> import xml.etree.cElementTree as ET
> import string
> import numpy as np
> import matplotlib.pyplot as plt
> from scipy.interpolate import splrep, splev
>
>
> def punti(nome):
> ? ?mainNS=string.Template("{http://www.topografix.com/GPX/1/0}$tag")
>
> ? ?trkTag=mainNS.substitute(tag="trkpt")
> ? ?nameTag=mainNS.substitute(tag="ele")
>
> ? ?track=[]
> ? ?for ins in nome:
> ? ? ? ?#print ins
> ? ? ? ?et=ET.parse(open(ins))
> # ? ?wptinfo=[]
> ? ? ? ?for wpt in et.findall("//"+trkTag):
> ? ? ? ? ? ? ? ?wptinfo=[]
> ? ? ? ? ? ? ? ?wptinfo.append(wpt.get("lat"))
> ? ? ? ? ? ? ? ?wptinfo.append(wpt.get("lon"))
> ? ? ? ? ? ? ? ?wptinfo.append(wpt.findtext(nameTag))
> ? ? ? ? ? ? ? ?track.append(wptinfo)
> ? ? ? ?#print "file terminato"
> ? ?return track
> ? ? ? ?#print ",".join(wptinfo)
>
> def ascesa(list):
> ? ? ? ?x=1
> ? ? ? ?asc=0
> ? ? ? ?while x < len(coor):
> ? ? ? ? ? ? ? ?if float(coor[x][2])>float(coor[x-1][2]):
> ? ? ? ? ? ? ? ? ? ? ? ?asc=asc+(float(coor[x][2])-float(coor[x-1][2]))
> ? ? ? ? ? ? ? ?x=x+1
> ? ? ? ?print asc
>
> #filein=("affi_bobbio2009-08-13_TRK.gpx","peschiera-bobbio_trk.gpx")
> #filein=("../gpxosm/brenta/ale_Dolomity_mapping.gpx","../gpxosm/brenta/ale_Dolomity_mapping2.gpx","../gpxosm/brenta/ale_Dolomity_mapping3.gpx","../gpxosm/brenta/luca2009-08-03_TRK.gpx")
> filein=("ale_Dolomity_mapping.gpx","luca_diviso.gpx")
>
> coor=punti(filein)
> #print coor
> x=[]
> y=[]
> i=0
> while i < len(coor):
> ? ? ? ?x.append(float(coor[i][1]))
> ? ? ? ?y.append(float(coor[i][0]))
> ? ? ? ?i=i+1
>
> # spline parameters
> s=3.0 # smoothness parameter
> k=2 # spline order
> nest=-1 # estimate of number of knots needed (-1 = maximal)
>
> tckp = splrep(x,y,task=0,s=s,k=k)
>
> xnew,ynew = splev(linspace(0,1,400),tckp)
>
> data,=plt.plot(x,y,'bo-',label='data')
> fit,=plt.plot(xnew,ynew,'r-',label='fit')
> plt.legend()
> plt.xlabel('x')
> plt.ylabel('y')
> plt.show()
>
> here [1][2] you can found the files gpx that i use like source for the
> point cloud
>
> thank's
> Luca
>
> [1] http://www.lucadelu.org/out/luca_diviso.gpx
> [2] http://www.lucadelu.org/out/ale_Dolomity_mapping.gpx
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


I don't know if this is documented, but from a simple example I would
think that splrep requires data sorted by x.

Can you try sorting your data? maybe it helps

Josef

>>> tckp = interpolate.splrep([1.,2.,3.,4.],[1.,3.,4.,4.5],task=0,s=3,k=2)

>>> tckp = interpolate.splrep([5.,2.,3.,4.],[1.,3.,4.,4.5],task=0,s=3,k=2)
Traceback (most recent call last):
  File "<pyshell#41>", line 1, in <module>
    tckp = interpolate.splrep([5.,2.,3.,4.],[1.,3.,4.,4.5],task=0,s=3,k=2)
  File "C:\Josef\scipy\interpolate\fitpack.py", line 418, in splrep
    raise _iermess[ier][1],_iermess[ier][0]
ValueError:     Error on input data


From butterw at gmail.com  Sun Aug 23 06:35:18 2009
From: butterw at gmail.com (butterw)
Date: Sun, 23 Aug 2009 03:35:18 -0700 (PDT)
Subject: [SciPy-User] [SciPy-user] SciPy 2009 Videos
In-Reply-To: <F1215525-82FD-4469-AD55-CA659272D1ED@enthought.com>
References: <F1215525-82FD-4469-AD55-CA659272D1ED@enthought.com>
Message-ID: <25102074.post@talk.nabble.com>



Enthought, Inc. wrote:
> 
> The Enthought office in Austin has been pretty tranquil this week as  
> many of our developers are out at the 2009 Python for Scientific  
> Computing Conference at Caltech. The tutorial videos got posted  
> yesterday, and slides are being uploaded as well.
> 

The Scipy 2009 conference videos are a great resource.

however I'm having trouble with the SciPy 2009 Advanced Tutorial 8 -
Designing scientific interfaces with Traits by Eric Jones
the file is corrupt at 2 minutes.
http://www.archive.org/details/scipy09_advancedTutorial_8
-- 
View this message in context: http://www.nabble.com/August-SCP-Webinar%3A-SciPy-2009-tp25087643p25102074.html
Sent from the Scipy-User mailing list archive at Nabble.com.



From tpk at kraussfamily.org  Sun Aug 23 09:46:14 2009
From: tpk at kraussfamily.org (Tom K.)
Date: Sun, 23 Aug 2009 06:46:14 -0700 (PDT)
Subject: [SciPy-User] [SciPy-user] signal handling inside
	integrate.odeint
In-Reply-To: <19c5f0af0908220944o11a31bf7xa5ee754be5df8430@mail.gmail.com>
References: <19c5f0af0908220944o11a31bf7xa5ee754be5df8430@mail.gmail.com>
Message-ID: <25103457.post@talk.nabble.com>



Colin Cotter wrote:
> 
>   I have a question about handling signals with integrate.odeint. I am
> developing some Monte-Carlo code that involves repeated calls off this
> subroutine, but I seem to be doing something wrong with the signal
> handling because if I hit ctrl-c during runtime, the code just jumps
> out of odeint and continues with the loop. What is the best way to
> deal with this i.e. how do I modify the code so that if I hit ctrl-c
> during runtime the python script exits?
> 

I don't think you are doing anything wrong, except perhaps expecting a
different behavior.

It looks like odeint calls some FORTRAN routines (LSODA, STODA) that don't
have any error handling.  So, the C code wrapper that calls the python
function notices the error and prints a message (multipack.h, line 178) and
returns NULL, but the FORTRAN code just continues as if there's nothing
wrong.

I don't see a way to fix this without modifying the FORTRAN code.  Perhaps I
am wrong?

I was able to get back to the command line on my machine by causing an
interrupt within the interrupt (holding down control-c).
-- 
View this message in context: http://www.nabble.com/signal-handling-inside-integrate.odeint-tp25095497p25103457.html
Sent from the Scipy-User mailing list archive at Nabble.com.



From gokhansever at gmail.com  Sun Aug 23 18:29:32 2009
From: gokhansever at gmail.com (=?UTF-8?Q?G=C3=B6khan_Sever?=)
Date: Sun, 23 Aug 2009 17:29:32 -0500
Subject: [SciPy-User] [SciPy-user] SciPy 2009 Videos
In-Reply-To: <25102074.post@talk.nabble.com>
References: <F1215525-82FD-4469-AD55-CA659272D1ED@enthought.com>
	<25102074.post@talk.nabble.com>
Message-ID: <49d6b3500908231529wb921d7aha2c69bb4dad19629@mail.gmail.com>

Confirmed here too.

No mencoder or ffmeg mp4 to avi conversations work on the file
(scipy09_advancedTutorial_8_512kb.mp4) either.

Hopefully the original file itself is good.

On Sun, Aug 23, 2009 at 5:35 AM, butterw <butterw at gmail.com> wrote:

>
>
> Enthought, Inc. wrote:
> >
> > The Enthought office in Austin has been pretty tranquil this week as
> > many of our developers are out at the 2009 Python for Scientific
> > Computing Conference at Caltech. The tutorial videos got posted
> > yesterday, and slides are being uploaded as well.
> >
>
> The Scipy 2009 conference videos are a great resource.
>
> however I'm having trouble with the SciPy 2009 Advanced Tutorial 8 -
> Designing scientific interfaces with Traits by Eric Jones
> the file is corrupt at 2 minutes.
> http://www.archive.org/details/scipy09_advancedTutorial_8
> --
> View this message in context:
> http://www.nabble.com/August-SCP-Webinar%3A-SciPy-2009-tp25087643p25102074.html
> Sent from the Scipy-User mailing list archive at Nabble.com.
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>



-- 
G?khan
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From fredmfp at gmail.com  Sun Aug 23 18:45:38 2009
From: fredmfp at gmail.com (fred)
Date: Mon, 24 Aug 2009 00:45:38 +0200
Subject: [SciPy-User] [SciPy-user] SciPy 2009 Videos
In-Reply-To: <25102074.post@talk.nabble.com>
References: <F1215525-82FD-4469-AD55-CA659272D1ED@enthought.com>
	<25102074.post@talk.nabble.com>
Message-ID: <4A91C692.3080806@gmail.com>

butterw a ?crit :

> however I'm having trouble with the SciPy 2009 Advanced Tutorial 8 -
> Designing scientific interfaces with Traits by Eric Jones
> the file is corrupt at 2 minutes.
> http://www.archive.org/details/scipy09_advancedTutorial_8
I can confirm :-(

Cheers,

-- 
Fred, Traits(UI) user.


From wolf.aarons at gmail.com  Mon Aug 24 13:40:29 2009
From: wolf.aarons at gmail.com (Aaron S. Wolf)
Date: Mon, 24 Aug 2009 10:40:29 -0700
Subject: [SciPy-User] Uniformity metric for a periodic signal
Message-ID: <4d11446f0908241040s19dc9e6apde27d7ee4368a29d@mail.gmail.com>

Dear scipy-user list,
    I have a signal processing question and would really appreciate any
insights anyone has. I am mostly looking for a reference or keywords to look
up. I am writing software that needs to determine how uniform or
'constant-valued' the brightness of pixels around a ring are. Essentially
what I need is a metric of the uniformity of an arbitrary non-negative
periodic function. I have come up with a couple of heuristic metrics, but I
am wondering whether anyone has an idea that stands on more formal footing.
    Here are the ideas I have had thusfar: clearly, this measure should be
relative to the mean value of the signal (i.e. small departures relative to
the mean value represent a nearly uniform signal), and thus I am normalizing
the signal by its mean. This metric should show that short wavelength
variations are more uniform than long wavelength ones (if we were to examine
the same signal with lower resolution, the short wavelength variations would
average out). For this reason, I am guessing that taking a power spectrum of
the signal is the right way to go. Consider a power spectrum composed of N
frequencies. We can then use the spectrum to construct N low pass filtered
versions of the original signal. The first signal in this set is the
original, the next has the highest freq component filtered out, the next has
the 2 highest freq filtered out, and so on, until the final one just has the
lowest freq component remaining. This set of signals represents the gradual
removal of lower and lower frequency components to the signal. Now if we can
measure the uniformity of each of these signals, we might be able to
construct an overall metric. For arbitrarily shaped probability
distributions, the absolute mean difference (see
http://en.wikipedia.org/wiki/Mean_difference) is a good measure of the level
of variation within a distribution. Thus we could calculate the mean
difference for all N filtered signals. Then we plot the mean difference as a
function of the how many frequencies have been filtered out. If the original
signal is dominated by high frequency components, then the mean difference
will drop to low values quickly with increasing filtering. If long frequency
components are present, however, large values for the mean difference will
persist until the highly filtered end of the graph. Thus I propose that the
area under this curve is a decent metric for how "uniform" a periodic signal
is.
    There is also another variation on this perscription which seems nearly
as good. Instead of using the elements of a power spectrum, we could obtain
our N filtered signals by applying a moving average filter of increasing
width to the periodic signal (with a box 1 element wide, then 2, 3,... up to
N elements). This is just another method for progressively filtering out
lower frequencies.
    This type of procedure seems somewhat reasonable to me, but I totally
made it up. To my knowledge, it has no rigorous basis. If anyone has any
more rigorous metrics, or ideas for equally useful heuristics, I would
greatly appreciate hearing about them. Thanks in advance!

Best,
Aaron
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From gokhansever at gmail.com  Mon Aug 24 22:06:06 2009
From: gokhansever at gmail.com (=?UTF-8?Q?G=C3=B6khan_Sever?=)
Date: Mon, 24 Aug 2009 21:06:06 -0500
Subject: [SciPy-User] [SciPy-user] SciPy 2009 Videos
In-Reply-To: <25102074.post@talk.nabble.com>
References: <F1215525-82FD-4469-AD55-CA659272D1ED@enthought.com>
	<25102074.post@talk.nabble.com>
Message-ID: <49d6b3500908241906t7b7b796fs4f1233c26d6c0e64@mail.gmail.com>

Hey,

Try downloading the big file there. It is super high quality and it works
without any problems.

On Sun, Aug 23, 2009 at 5:35 AM, butterw <butterw at gmail.com> wrote:

>
>
> Enthought, Inc. wrote:
> >
> > The Enthought office in Austin has been pretty tranquil this week as
> > many of our developers are out at the 2009 Python for Scientific
> > Computing Conference at Caltech. The tutorial videos got posted
> > yesterday, and slides are being uploaded as well.
> >
>
> The Scipy 2009 conference videos are a great resource.
>
> however I'm having trouble with the SciPy 2009 Advanced Tutorial 8 -
> Designing scientific interfaces with Traits by Eric Jones
> the file is corrupt at 2 minutes.
> http://www.archive.org/details/scipy09_advancedTutorial_8
> --
> View this message in context:
> http://www.nabble.com/August-SCP-Webinar%3A-SciPy-2009-tp25087643p25102074.html
> Sent from the Scipy-User mailing list archive at Nabble.com.
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>



-- 
G?khan
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From humongo.shi at gmail.com  Tue Aug 25 00:23:27 2009
From: humongo.shi at gmail.com (Hugo Shi)
Date: Mon, 24 Aug 2009 23:23:27 -0500
Subject: [SciPy-User] coby-la in optimize package - constraints ignored,
	is this a bug?
In-Reply-To: <1251170272.28966.2.camel@gigoblob>
References: <1251170272.28966.2.camel@gigoblob>
Message-ID: <1251174207.28966.3.camel@gigoblob>


This code returns 257, which is a violation of the constraint.  Is this
a bug? or am I doing something stupid?

thanks!

import numpy as np
import scipy as sp
import scipy.optimize

def myfunc(x):
    return -x[0]**2

def constraints(x):
    if x[0]>10:
        return -1.0
    else:
        return 1.0

retval=scipy.optimize.fmin_cobyla(myfunc,np.array([0]),constraints)





From robert.kern at gmail.com  Tue Aug 25 00:27:21 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Mon, 24 Aug 2009 23:27:21 -0500
Subject: [SciPy-User] coby-la in optimize package - constraints ignored,
	is this a bug?
In-Reply-To: <1251174207.28966.3.camel@gigoblob>
References: <1251170272.28966.2.camel@gigoblob>
	<1251174207.28966.3.camel@gigoblob>
Message-ID: <3d375d730908242127y3ce6facdr564c7a4fe29420bc@mail.gmail.com>

On Mon, Aug 24, 2009 at 23:23, Hugo Shi<humongo.shi at gmail.com> wrote:
>
> This code returns 257, which is a violation of the constraint. ?Is this
> a bug? or am I doing something stupid?
>
> thanks!
>
> import numpy as np
> import scipy as sp
> import scipy.optimize
>
> def myfunc(x):
> ? ?return -x[0]**2
>
> def constraints(x):
> ? ?if x[0]>10:
> ? ? ? ?return -1.0
> ? ?else:
> ? ? ? ?return 1.0

COBYLA will have problems with this and any other discontinuous
constraint. It tries to use derivative information with the constraint
function in order to determine how it can fix the problem.

In [2]: def myfunc(x):
   ...:     return -x[0]**2
   ...:

In [3]: def constraints(x):
   ...:     return 10.0 - x[0]
   ...:

In [4]: optimize.fmin_cobyla(myfunc, np.array([0.0]), constraints)

   Normal return from subroutine COBYLA

   NFVALS =   16   F =-1.000000E+02    MAXCV = 0.000000E+00
   X = 1.000000E+01
Out[4]: array([ 10.])

-- 
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


From fredmfp at gmail.com  Tue Aug 25 03:26:16 2009
From: fredmfp at gmail.com (fred)
Date: Tue, 25 Aug 2009 09:26:16 +0200
Subject: [SciPy-User] [SciPy-user] SciPy 2009 Videos
In-Reply-To: <49d6b3500908241906t7b7b796fs4f1233c26d6c0e64@mail.gmail.com>
References: <F1215525-82FD-4469-AD55-CA659272D1ED@enthought.com>	<25102074.post@talk.nabble.com>
	<49d6b3500908241906t7b7b796fs4f1233c26d6c0e64@mail.gmail.com>
Message-ID: <4A939218.1030404@gmail.com>

G?khan Sever a ?crit :
> Hey,
> 
> Try downloading the big file there. It is super high quality and it
> works without any problems.
Thanks for the info, G?khan! ;-)

-- 
Fred


From timmichelsen at gmx-topmail.de  Tue Aug 25 04:23:34 2009
From: timmichelsen at gmx-topmail.de (Tim Michelsen)
Date: Tue, 25 Aug 2009 08:23:34 +0000 (UTC)
Subject: [SciPy-User] satellite imagery
References: <aa15d47e0908141518vc7f4a70x1b1e138ac8644ff@mail.gmail.com>
	<A9AC9E96-3171-42A8-9111-EF4DC299BAEB@cs.toronto.edu>
	<aa15d47e0908171004x3273b948p4950a0209c70918b@mail.gmail.com>
	<h6cm3q$fmt$1@ger.gmane.org>
	<aa15d47e0908171655o6dad89a1j784a07f18eb0a43c@mail.gmail.com>
	<h6hpfq$ta5$1@ger.gmane.org>
	<COL104-W3BD7CB679C23FDFE5F0719BFB0@phx.gbl>
Message-ID: <loom.20090825T102212-681@post.gmane.org>

> ???I am doing some meteorological data analysis and I am interested also in
the Howard Butler's tutorial, could you please send to me also those 6 PDF
files,?I would appreciate it very much.
I have asked the permission of the authors to redistribute and got.

The files are now here:
http://wiki.osgeo.org/wiki/Educational_Content_Inventory#OSGEO_2005:_Open_Source_Python_GIS_Hacks

Happy hacking!



From gruben at bigpond.net.au  Tue Aug 25 09:50:59 2009
From: gruben at bigpond.net.au (Gary Ruben)
Date: Tue, 25 Aug 2009 23:50:59 +1000
Subject: [SciPy-User] possible bug in scipy.ndimage.interpolation.shift
Message-ID: <4A93EC43.5060808@bigpond.net.au>

I've just used scipy.ndimage.interpolation.shift to align some greyscale 
images represented as float arrays to sub-pixel accuracy which generally 
works well. However, I discovered that the resulting image array 
sometimes contained some negative values. I think there were about 7 or 
8 so they are probably occurring near the corners. For the moment I just 
forced any -ve values to 0 as a workaround. I don't feel that I have 
time to produce a minimal example at the moment because I'm writing a 
thesis but I thought it better to flag it here than keep silent. I'll 
keep this message in my inbox and when I'm finished in a month or so 
I'll raise a ticket.

Gary


From zachary.pincus at yale.edu  Tue Aug 25 10:30:35 2009
From: zachary.pincus at yale.edu (Zachary Pincus)
Date: Tue, 25 Aug 2009 10:30:35 -0400
Subject: [SciPy-User] possible bug in scipy.ndimage.interpolation.shift
In-Reply-To: <4A93EC43.5060808@bigpond.net.au>
References: <4A93EC43.5060808@bigpond.net.au>
Message-ID: <F02A00D1-A7AE-458E-A37F-B50E02A7DFE5@yale.edu>

Hi Gary,

I think that ringing is a general feature of higher-order spline  
interpolation, which ndimage uses by default for all its resampling  
operations. There are a few tickets about this, but in general I think  
the consensus was that it's, well, not a feature, but not exactly a  
bug either. Just something inherent in the system.

You can suppress this by choosing order=1 for the spline so you get  
linear interpolation, but then you don't get the smoothness of the 3rd  
order splines. But if you want smoothness, I think you more or less  
have to be prepared for the fact that smooth curves of that sort  
sometimes need to drop below zero to stay smooth. Manually suppressing  
the negative values seems reasonable...

Also, if your image is non-zero, especially at the edges, and you  
choose a constant-zero boundary condition, then you will be provoking  
this sort of ringing by introducing a steep step function at the  
edges, which is particularly hard to approximate with splines. Try the  
mirror boundary conditions, maybe. (A pity that there's no zero-flux  
-- AKA clamp -- boundary condition available, which would be useful  
too.)

Zach



On Aug 25, 2009, at 9:50 AM, Gary Ruben wrote:

> I've just used scipy.ndimage.interpolation.shift to align some  
> greyscale
> images represented as float arrays to sub-pixel accuracy which  
> generally
> works well. However, I discovered that the resulting image array
> sometimes contained some negative values. I think there were about 7  
> or
> 8 so they are probably occurring near the corners. For the moment I  
> just
> forced any -ve values to 0 as a workaround. I don't feel that I have
> time to produce a minimal example at the moment because I'm writing a
> thesis but I thought it better to flag it here than keep silent. I'll
> keep this message in my inbox and when I'm finished in a month or so
> I'll raise a ticket.
>
> Gary
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user



From d_l_goldsmith at yahoo.com  Tue Aug 25 11:57:30 2009
From: d_l_goldsmith at yahoo.com (David Goldsmith)
Date: Tue, 25 Aug 2009 08:57:30 -0700 (PDT)
Subject: [SciPy-User] possible bug in scipy.ndimage.interpolation.shift
In-Reply-To: <F02A00D1-A7AE-458E-A37F-B50E02A7DFE5@yale.edu>
Message-ID: <344766.50572.qm@web52111.mail.re2.yahoo.com>

--- On Tue, 8/25/09, Zachary Pincus <zachary.pincus at yale.edu> wrote:

> order splines. But if you want smoothness, I think you more
> or less? 
> have to be prepared for the fact that smooth curves of that
> sort? 
> sometimes need to drop below zero to stay smooth. Manually
> suppressing? 
> the negative values seems reasonable...

However, perhaps another optional argument, e.g., "floor={True,False}" say, (and code to implement, of course) could be added to the function?

DG

> Also, if your image is non-zero, especially at the edges,
> and you? 
> choose a constant-zero boundary condition, then you will be
> provoking? 
> this sort of ringing by introducing a steep step function
> at the? 
> edges, which is particularly hard to approximate with
> splines. Try the? 
> mirror boundary conditions, maybe. (A pity that there's no
> zero-flux? 
> -- AKA clamp -- boundary condition available, which would
> be useful? 
> too.)
> 
> Zach
> 
> 
> 
> On Aug 25, 2009, at 9:50 AM, Gary Ruben wrote:
> 
> > I've just used scipy.ndimage.interpolation.shift to
> align some? 
> > greyscale
> > images represented as float arrays to sub-pixel
> accuracy which? 
> > generally
> > works well. However, I discovered that the resulting
> image array
> > sometimes contained some negative values. I think
> there were about 7? 
> > or
> > 8 so they are probably occurring near the corners. For
> the moment I? 
> > just
> > forced any -ve values to 0 as a workaround. I don't
> feel that I have
> > time to produce a minimal example at the moment
> because I'm writing a
> > thesis but I thought it better to flag it here than
> keep silent. I'll
> > keep this message in my inbox and when I'm finished in
> a month or so
> > I'll raise a ticket.
> >
> > Gary
> > _______________________________________________
> > SciPy-User mailing list
> > SciPy-User at scipy.org
> > http://mail.scipy.org/mailman/listinfo/scipy-user
> 
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
> 


      


From ecarlson at eng.ua.edu  Tue Aug 25 13:18:57 2009
From: ecarlson at eng.ua.edu (Eric Carlson)
Date: Tue, 25 Aug 2009 12:18:57 -0500
Subject: [SciPy-User] SciPy09 Video page direct links
Message-ID: <h716e0$n6$1@ger.gmane.org>

Hello,

I had some issues searching for SciPy 09 video files this morning. 
Although the search was broken, at the same time it was still possible 
to link directly to the video pages.

The attached basic html file (no javascript or images) has the tutorial 
and conference schedule and direct links to corresponding videos. You 
should be able to save the file and open locally.

Cheers,
Eric Carlson
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From zachary.pincus at yale.edu  Tue Aug 25 13:29:43 2009
From: zachary.pincus at yale.edu (Zachary Pincus)
Date: Tue, 25 Aug 2009 13:29:43 -0400
Subject: [SciPy-User] possible bug in scipy.ndimage.interpolation.shift
In-Reply-To: <344766.50572.qm@web52111.mail.re2.yahoo.com>
References: <344766.50572.qm@web52111.mail.re2.yahoo.com>
Message-ID: <D38FA3C6-B5CB-4B6D-B44F-C8923B5CFD58@yale.edu>

> However, perhaps another optional argument, e.g.,  
> "floor={True,False}" say, (and code to implement, of course) could  
> be added to the function?

Eh, ndimage does the right thing when using integral-type outputs,  
even unsigned ones. And if you're using floating-point images, zero  
isn't necessarily a meaningful "floor". (Most of my floating-point  
images are centered around zero, for example.)

It's probably OK the way it is, just with the caveats that usually  
apply to fancy spline interpolation, which is that there will be  
ringing at sharp edges that may go outside of the range of the  
original values (whatever that range is).

Zach


In : ndimage.shift([0,8,0,5,0,3,0,10], -.75, output=float)
array([ 6.65579912,  1.6582038 ,  3.94576069,  1.04312844,  2.60047557,
        -0.3512807 ,  8.69527224,  0.        ])

In : ndimage.shift([0,8,0,5,0,3,0,10], -.75, output=int)
array([7, 2, 4, 1, 3, 0, 9, 0])




From akoumjian at gmail.com  Tue Aug 25 14:10:43 2009
From: akoumjian at gmail.com (Alec Koumjian)
Date: Tue, 25 Aug 2009 14:10:43 -0400
Subject: [SciPy-User] Subsecond frequencies in Scikits.timeseries
Message-ID: <61a4c0ba0908251110g4d7375d1ib4349557545476bb@mail.gmail.com>

I didn't get any responses last time, but thought I would bring it up again.

Does anyone have any insight as to adding subsecond frequencies into the
Timeseries object?
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From robert.kern at gmail.com  Tue Aug 25 14:15:10 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Tue, 25 Aug 2009 11:15:10 -0700
Subject: [SciPy-User] Subsecond frequencies in Scikits.timeseries
In-Reply-To: <61a4c0ba0908251110g4d7375d1ib4349557545476bb@mail.gmail.com>
References: <61a4c0ba0908251110g4d7375d1ib4349557545476bb@mail.gmail.com>
Message-ID: <3d375d730908251115r252c6eb6tfebef36dbb7f162b@mail.gmail.com>

On Tue, Aug 25, 2009 at 11:10, Alec Koumjian<akoumjian at gmail.com> wrote:
> I didn't get any responses last time, but thought I would bring it up again.

Pierre GM responded:

"""
Alec,
Unfortunately, there's no way yet to define frequencies higher than
seconds. This would require some significant changes in the C section
of the code (adding the frequency themselves, but also the conversion
funtions...) and I'm afraid lack of time will prevent me (and most
likely Matt Knox as well) to work on it in the next future.
If you like implementing it, by all means ! Don't hesitate to contact
us off-list.
"""

-- 
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


From dwf at cs.toronto.edu  Tue Aug 25 15:02:04 2009
From: dwf at cs.toronto.edu (David Warde-Farley)
Date: Tue, 25 Aug 2009 15:02:04 -0400
Subject: [SciPy-User] Subsecond frequencies in Scikits.timeseries
In-Reply-To: <61a4c0ba0908251110g4d7375d1ib4349557545476bb@mail.gmail.com>
References: <61a4c0ba0908251110g4d7375d1ib4349557545476bb@mail.gmail.com>
Message-ID: <1CE139B8-A713-46C1-A910-F67ED77BC092@cs.toronto.edu>

On 25-Aug-09, at 2:10 PM, Alec Koumjian wrote:

> I didn't get any responses last time, but thought I would bring it  
> up again.
>
> Does anyone have any insight as to adding subsecond frequencies into  
> the
> Timeseries object?

As I recall the inability to do this was one of the main motivations  
for the nipy.timeseries package. See Ariel's presentation at last  
week's conference:

	http://www.archive.org/details/scipy09_day1_04-Ariel_Rokem

David


From pgmdevlist at gmail.com  Tue Aug 25 15:27:38 2009
From: pgmdevlist at gmail.com (Pierre GM)
Date: Tue, 25 Aug 2009 15:27:38 -0400
Subject: [SciPy-User] Subsecond frequencies in Scikits.timeseries
In-Reply-To: <1CE139B8-A713-46C1-A910-F67ED77BC092@cs.toronto.edu>
References: <61a4c0ba0908251110g4d7375d1ib4349557545476bb@mail.gmail.com>
	<1CE139B8-A713-46C1-A910-F67ED77BC092@cs.toronto.edu>
Message-ID: <A051934E-BAF7-4862-855A-87F1CF107D71@gmail.com>


On Aug 25, 2009, at 3:02 PM, David Warde-Farley wrote:

> On 25-Aug-09, at 2:10 PM, Alec Koumjian wrote:
>
>> I didn't get any responses last time, but thought I would bring it
>> up again.
>>
>> Does anyone have any insight as to adding subsecond frequencies into
>> the
>> Timeseries object?
>
> As I recall the inability to do this was one of the main motivations
> for the nipy.timeseries package. See Ariel's presentation at last
> week's conference:
>
> 	http://www.archive.org/details/scipy09_day1_04-Ariel_Rokem

Great link indeed. Ariel's description of the limitation of  
scikits.timeseries (~4:00) is quite accurate: Matt is a financial  
analyst, I'm an hydrologist, our time scales are usually daily up to  
annual, our series usually have missing values, and the operations we  
are mostly interested in is switching from one freq to another...
Now, is the timeseries module part of nipy already ? I can't find it  
documented (I tried that: http://neuroimaging.scipy.org/site/doc/manual/html/contents.html) 
. I'd be interested in checking what is done so that we can improve on  
scikits.timeseries


From josef.pktd at gmail.com  Tue Aug 25 15:35:37 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Tue, 25 Aug 2009 15:35:37 -0400
Subject: [SciPy-User] Subsecond frequencies in Scikits.timeseries
In-Reply-To: <A051934E-BAF7-4862-855A-87F1CF107D71@gmail.com>
References: <61a4c0ba0908251110g4d7375d1ib4349557545476bb@mail.gmail.com>
	<1CE139B8-A713-46C1-A910-F67ED77BC092@cs.toronto.edu>
	<A051934E-BAF7-4862-855A-87F1CF107D71@gmail.com>
Message-ID: <1cd32cbb0908251235u5360a60dld19167891be3a0bf@mail.gmail.com>

On Tue, Aug 25, 2009 at 3:27 PM, Pierre GM<pgmdevlist at gmail.com> wrote:
>
> On Aug 25, 2009, at 3:02 PM, David Warde-Farley wrote:
>
>> On 25-Aug-09, at 2:10 PM, Alec Koumjian wrote:
>>
>>> I didn't get any responses last time, but thought I would bring it
>>> up again.
>>>
>>> Does anyone have any insight as to adding subsecond frequencies into
>>> the
>>> Timeseries object?
>>
>> As I recall the inability to do this was one of the main motivations
>> for the nipy.timeseries package. See Ariel's presentation at last
>> week's conference:
>>
>> ? ? ? http://www.archive.org/details/scipy09_day1_04-Ariel_Rokem
>
> Great link indeed. Ariel's description of the limitation of
> scikits.timeseries (~4:00) is quite accurate: Matt is a financial
> analyst, I'm an hydrologist, our time scales are usually daily up to
> annual, our series usually have missing values, and the operations we
> are mostly interested in is switching from one freq to another...
> Now, is the timeseries module part of nipy already ? I can't find it
> documented (I tried that: http://neuroimaging.scipy.org/site/doc/manual/html/contents.html)
> . I'd be interested in checking what is done so that we can improve on
> scikits.timeseries
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>

As far as I know it is still in the development branch at
https://code.launchpad.net/~nipy-developers/nipy/trunk-timeseries

Josef


From pgmdevlist at gmail.com  Tue Aug 25 15:44:42 2009
From: pgmdevlist at gmail.com (Pierre GM)
Date: Tue, 25 Aug 2009 15:44:42 -0400
Subject: [SciPy-User] Subsecond frequencies in Scikits.timeseries
In-Reply-To: <1cd32cbb0908251235u5360a60dld19167891be3a0bf@mail.gmail.com>
References: <61a4c0ba0908251110g4d7375d1ib4349557545476bb@mail.gmail.com>
	<1CE139B8-A713-46C1-A910-F67ED77BC092@cs.toronto.edu>
	<A051934E-BAF7-4862-855A-87F1CF107D71@gmail.com>
	<1cd32cbb0908251235u5360a60dld19167891be3a0bf@mail.gmail.com>
Message-ID: <BF2537FE-3834-42B4-B355-9B81416CF049@gmail.com>


On Aug 25, 2009, at 3:35 PM, josef.pktd at gmail.com wrote:
>
> As far as I know it is still in the development branch at
> https://code.launchpad.net/~nipy-developers/nipy/trunk-timeseries

Thx ! I'll get a look ASIC.


From fperez.net at gmail.com  Tue Aug 25 15:55:51 2009
From: fperez.net at gmail.com (Fernando Perez)
Date: Tue, 25 Aug 2009 12:55:51 -0700
Subject: [SciPy-User] Subsecond frequencies in Scikits.timeseries
In-Reply-To: <BF2537FE-3834-42B4-B355-9B81416CF049@gmail.com>
References: <61a4c0ba0908251110g4d7375d1ib4349557545476bb@mail.gmail.com> 
	<1CE139B8-A713-46C1-A910-F67ED77BC092@cs.toronto.edu>
	<A051934E-BAF7-4862-855A-87F1CF107D71@gmail.com> 
	<1cd32cbb0908251235u5360a60dld19167891be3a0bf@mail.gmail.com> 
	<BF2537FE-3834-42B4-B355-9B81416CF049@gmail.com>
Message-ID: <db6b5ecc0908251255n7fc33c95u16f82d1f7a49ed85@mail.gmail.com>

On Tue, Aug 25, 2009 at 12:44 PM, Pierre GM<pgmdevlist at gmail.com> wrote:
>> As far as I know it is still in the development branch at
>> https://code.launchpad.net/~nipy-developers/nipy/trunk-timeseries
>
> Thx ! I'll get a look ASIC.

Great.  I should add that we have every interest in possibly
integrating with the scikit in the future.  But our needs were driven
by the analysis of experimental data, and there are several groups at
UC Berkeley working with code of this nature. We looked at the scikit
carefully, and decided that (considering the fact that we have
research work that needed this code to be functional in a reasonable
timeframe) the impedance mismatch between our needs and the scikit was
large enough to justify a separate effort.  And I think it has paid
off, because by working from scratch, we've been able to address some
design problems in a way that I'm actually happy with, and this would
have been harder to do within an existing codebase.

But, we certainly would love it if this can lead eventually to an
integrated TS codebase that fits everyone's needs, it's just that our
resources weren't sufficient to try to do everything in one shot.

I should also mention that in the next couple of weeks (I'm in
semi-vacation away from my office, Ariel and the others at Berkeley
working on these related problems) we intend to  make this code
available on its own, outside of the nipy trunk tree.  This will make
it  easier to install/use by anyone who doesn't need the nipy core
code (only those who want to use actual nipy-specifiic features would
need nipy, not everyone looking for the timeseries stuff).  We'll make
sure to post about that here so anyone interested can track it with
less dependencies.

Regards,

f


From bruce at clearscienceinc.com  Tue Aug 25 17:33:38 2009
From: bruce at clearscienceinc.com (Bruce Ford)
Date: Tue, 25 Aug 2009 17:33:38 -0400
Subject: [SciPy-User] shiftgrid difficulties
In-Reply-To: <bebb7fa10908251412k7ddf48delcf588f1259f7e00b@mail.gmail.com>
References: <bebb7fa10908251412k7ddf48delcf588f1259f7e00b@mail.gmail.com>
Message-ID: <bebb7fa10908251433l546762a0t7dd30f956242030f@mail.gmail.com>

mpl_toolkits_basemap.shiftgrid looks straight forward enough, but I cannot
find a working combination.  Let me explain:

I'm trying to plot a variable that I've extracted from a NetCDF file.

When I plot it, I get something that looks like the attached file...it only
plots in the Eastern hemisphere (0-180).

The longitude array is [   0.    1.    2.    ...  356.  357.  358.  359.]

When trying "sig_wav_ht,lon = shiftgrid(180,sig_wav_ht,lon,start=False)" or
any other combination using different lon0 I get the same error

"cyclic point not included"

What am I missing?

Some of the code prior to the shiftgrid method:

file1 = sc_settings.data_dir + "/ww3/ww3.199301.nc"
nc = NetCDFFile(file1)
for var in nc.variables:
    print var
sig_wav_ht = nc.variables['sig_wav_ht'][1,:,:]
lon = nc.variables['longitude'][:]
print lon
lat = nc.variables['latitude'][:]
sig_wav_ht,lon = shiftgrid(180,sig_wav_ht,lon,start=False)

Any help would be appreciated.

Bruce


---------------------------------------
Bruce W. Ford
Clear Science, Inc.
bruce at clearscienceinc.com
bruce.w.ford.ctr at navy.smil.mil
http://www.ClearScienceInc.com
Phone/Fax: 904-379-9704
8241 Parkridge Circle N.
Jacksonville, FL  32211
Skype:  bruce.w.ford
Google Talk: fordbw at gmail.com
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From permafacture at gmail.com  Tue Aug 25 17:55:43 2009
From: permafacture at gmail.com (Permafacture)
Date: Tue, 25 Aug 2009 14:55:43 -0700 (PDT)
Subject: [SciPy-User] Solve an arbitrary function
In-Reply-To: <fa510ff80907311206o3d13f42ap19a7dafaab256bb1@mail.gmail.com>
References: <a850cb31-9d79-41f3-8c26-e21164e37fb9@d32g2000yqh.googlegroups.com>
	<3d375d730907302117n78a97481w80f9742233ffdba9@mail.gmail.com> 
	<fa510ff80907310019u1075e274uf128844999db1c79@mail.gmail.com> 
	<20090731080332.GA9207@phare.normalesup.org>
	<fa510ff80907311206o3d13f42ap19a7dafaab256bb1@mail.gmail.com>
Message-ID: <0a22b0ec-b758-4396-b7bd-0ddc7286e719@24g2000yqm.googlegroups.com>

Thanks!

hopefully i can improve this example, either by showing the way to do
this or by being shown :)

Say I want a function to solve for the intersection between a line and
a parabola.  It is not practical to define a new function to pass to
fsolve for every instance of this class intersections.  But, i could
pass it an instance of a class...

import numpy
from scipy.optimize import fsolve


class parabaline():

    """provide a method to pass fsolve the values of arbitrary lines
and parabolas"""

    def __init__(self, slope, yint, A, B, C):
        self.m = slope
        self.q = yint
        self.a = A
        self.b = B
        self.c = C

    def f(self, xy):
        x,y=xy
        z = numpy.array([y - self.m*x - self.q, y - self.a*x**2 -
self.b*x - self.c])
        return z


test = parabaline(0,1.,1., 0., 0.)

c= fsolve(test.f,[-1.,0.])
d = fsolve(test.f,[1.,0.])

print c
print d


This is my solution for my situation.  Is there a better way to do
this?


From sccolbert at gmail.com  Tue Aug 25 18:06:33 2009
From: sccolbert at gmail.com (Chris Colbert)
Date: Tue, 25 Aug 2009 18:06:33 -0400
Subject: [SciPy-User] Solve an arbitrary function
In-Reply-To: <0a22b0ec-b758-4396-b7bd-0ddc7286e719@24g2000yqm.googlegroups.com>
References: <a850cb31-9d79-41f3-8c26-e21164e37fb9@d32g2000yqh.googlegroups.com>
	<3d375d730907302117n78a97481w80f9742233ffdba9@mail.gmail.com>
	<fa510ff80907310019u1075e274uf128844999db1c79@mail.gmail.com>
	<20090731080332.GA9207@phare.normalesup.org>
	<fa510ff80907311206o3d13f42ap19a7dafaab256bb1@mail.gmail.com>
	<0a22b0ec-b758-4396-b7bd-0ddc7286e719@24g2000yqm.googlegroups.com>
Message-ID: <7f014ea60908251506p7cedb68doc2925d167eae9bcb@mail.gmail.com>

you can pass any number of arguments to your solving function as you want. i.e.

def f(self, xy, args):
      x, y = xy
      m, q, a, b, c = args
      <--snip--->


called as:

fsolve(f, [1., 0.], (slope, yint, A, B, C))


On Tue, Aug 25, 2009 at 5:55 PM, Permafacture<permafacture at gmail.com> wrote:
> Thanks!
>
> hopefully i can improve this example, either by showing the way to do
> this or by being shown :)
>
> Say I want a function to solve for the intersection between a line and
> a parabola. ?It is not practical to define a new function to pass to
> fsolve for every instance of this class intersections. ?But, i could
> pass it an instance of a class...
>
> import numpy
> from scipy.optimize import fsolve
>
>
> class parabaline():
>
> ? ?"""provide a method to pass fsolve the values of arbitrary lines
> and parabolas"""
>
> ? ?def __init__(self, slope, yint, A, B, C):
> ? ? ? ?self.m = slope
> ? ? ? ?self.q = yint
> ? ? ? ?self.a = A
> ? ? ? ?self.b = B
> ? ? ? ?self.c = C
>
> ? ?def f(self, xy):
> ? ? ? ?x,y=xy
> ? ? ? ?z = numpy.array([y - self.m*x - self.q, y - self.a*x**2 -
> self.b*x - self.c])
> ? ? ? ?return z
>
>
> test = parabaline(0,1.,1., 0., 0.)
>
> c= fsolve(test.f,[-1.,0.])
> d = fsolve(test.f,[1.,0.])
>
> print c
> print d
>
>
> This is my solution for my situation. ?Is there a better way to do
> this?
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From destroooooy at gmail.com  Thu Aug 20 11:40:43 2009
From: destroooooy at gmail.com (DEMOLISHOR! the Demolishor)
Date: Thu, 20 Aug 2009 11:40:43 -0400
Subject: [SciPy-User] worst-case scenario installation
In-Reply-To: <4A8C645A.2010503@lpta.in2p3.fr>
References: <3d95192b0908191228k3900d1aaqb825a4a4c46c3a0f@mail.gmail.com>
	<4A8C63B1.1000907@lpta.in2p3.fr> <4A8C645A.2010503@lpta.in2p3.fr>
Message-ID: <3d95192b0908200840qfa65462nf32f3cb124c9044d@mail.gmail.com>

Okay so I went through the steps again and I saw Numpy build with
gfortran--I must have been misled by the initial lines of output where it
says it can't find a Fortran 90 compiler. It tested with just one known
fail. Scipy segfaulted during tests so I built it with --fcompiler=gnu95 as
well and then I got 4 known fails during testing. I think some of my earlier
problems could have been caused by not  completely cleaning up between each
build attempt.

I would like to avoid kitchen-sink-style packages like Sage because I am
still recovering from years of sufferring at the hands of Rene and Fons with
ROOT. All I want are tools that help me write the scripts I want to write--I
don't want an all-inclusive interactive environment.

I've attached a complete transcript of this build. Thanks in advance.

--cb

On Wed, Aug 19, 2009 at 4:45 PM, Johann Cohen-Tanugi <cohen at lpta.in2p3.fr>wrote:

> we would probably need your site.cfg file as well.... did you modify it?
> Johann
>
> Johann Cohen-Tanugi wrote:
> > We need some more info like your LD_LIBRARY_PATH and the output of ldd
> > on the various libraries that have problems. One thing you can try to do
> > is make sure that you dont link to unwanted versions of BLAS etc... by
> > creating soft links in $HOME/lib and resetting your LD_LIBRARY_PATH
> > The fact that g77 and gfortran are both in /usr/bin is completely
> > inocuous, as Robert mentioned : it is probably the case for many if not
> > all of us.
> >
> > and yes..... directly building sage is a very nice way to avoid this
> > definitely slightly tricky part of the built.
> >
> > Johann
> >
> > DEMOLISHOR! the Demolishor wrote:
> >
> >> Folks,
> >>   There is mounting evidence to suggest that I cannot install SciPy/
> >> Numpy under these restrictions:
> >>
> >>   - I do not have root access to my machine
> >>   - I use a later version of Python than my distribution provides (2.6
> >> instead of 2.4--I am using RHEL5), built locally and installed in
> >> $HOME/bin -- this prevents me from asking my sysadmin to install
> >> numpy and scipy from the YUM repositories.
> >>   - both g77 and gfortran are installed in /usr/bin and thus I cannot
> >> modify my path to exclude g77 without fear of crippling the rest of
> >> the build system in mysterious ways.
> >>
> >> So here's what happens:
> >>   - Using hand-built versions of ATLAS and LAPACK (following the
> >> instructions on the ATLAS website) result in undefined reference
> >> errors in liblapack.so to function calls that live in libblas.so
> >>   - Using RPM versions of ATLAS and LAPACK from a RPM search site (I
> >> just extracted and copied the libraries and headers into the relevant
> >> directories) breaks down in the same way, and no RPM exists with these
> >> libraries
> >> built with g77.
> >>   - Building ATLAS by hand using the instructions on the SciPy
> >> website
> >> (
> http://www.scipy.org/Installing_SciPy/Linux#head-89e1f6afaa3314d98a22c79b063cceee2cc6313c
> )
> >> also breaks down because I cannot avoid having g77 in my path. Also I
> >> can no longer build LAPACK with g77
> >> (http://www.netlib.org/lapack/lapack-3.2.html#_7_install_procedure).
> >>
> >> The most aggravating part of this entire process is that I am going
> >> through all these gyrations to avoid the Numpy setup script's
> >> obsession with g77. I do not understand why the `--fcompiler' option
> >> doesn't work , or even putting gfortran ahead of g77 in my PATH. I
> >> realize I am not a typical user, but these things seem really simple
> >> to fix for someone that knows where to look.
> >>
> >> And as I was writing this, I figured this out:
> >> I ended up linking *everything* in /usr/bin to ~/bin, removing
> >> /usr/bin from my PATH, and then removing the links to g77 and f77 from
> >> ~/bin, and rebuilding. Now Numpy fails 1 test, and SciPy fails 4, and
> >> I guess if I am lucky I'll never need the stuff that doesn't pass the
> >> tests. So it was *not* impossible to install an at least partial
> >> version of Scipy/Numpy on my system.
> >>
> >> Bottom line: weird people like me shouldn't suffer because of
> >> unimplemented command-line options. That said, weird people are like
> >> me are grateful that stuff like Scipy and Numpy exist because they
> >> keep me from having to use ROOT.
> >>
> >> Thanks,
> >> Craig
> >> ------------------------------------------------------------------------
> >>
> >> _______________________________________________
> >> SciPy-User mailing list
> >> SciPy-User at scipy.org
> >> http://mail.scipy.org/mailman/listinfo/scipy-user
> >>
> >>
> > _______________________________________________
> > SciPy-User mailing list
> > SciPy-User at scipy.org
> > http://mail.scipy.org/mailman/listinfo/scipy-user
> >
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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[craigb at fsul1 test_numpy]$ which python
/home/craigb/bin/python
[craigb at fsul1 test_numpy]$ python   
Python 2.6.2 (r262:71600, Jul 28 2009, 10:47:31) 
[GCC 4.1.2 20080704 (Red Hat 4.1.2-44)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> 
[craigb at fsul1 test_numpy]$ 
[craigb at fsul1 test_numpy]$ wget http://downloads.sourceforge.net/project/numpy/NumPy/1.3.0/numpy-1.3.0.tar.gz
--22:49:33--  http://downloads.sourceforge.net/project/numpy/NumPy/1.3.0/numpy-1.3.0.tar.gz
Resolving downloads.sourceforge.net... 216.34.181.59
Connecting to downloads.sourceforge.net|216.34.181.59|:80... connected.
HTTP request sent, awaiting response... 302 Found
Location: http://voxel.dl.sourceforge.net/project/numpy/NumPy/1.3.0/numpy-1.3.0.tar.gz [following]
--22:49:33--  http://voxel.dl.sourceforge.net/project/numpy/NumPy/1.3.0/numpy-1.3.0.tar.gz
Resolving voxel.dl.sourceforge.net... 74.63.52.169, 69.9.191.18, 69.9.191.19, ...
Connecting to voxel.dl.sourceforge.net|74.63.52.169|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1995868 (1.9M) [application/x-gzip]
Saving to: `numpy-1.3.0.tar.gz'

100%[=======================================>] 1,995,868   5.38M/s   in 0.4s   

22:49:33 (5.38 MB/s) - `numpy-1.3.0.tar.gz' saved [1995868/1995868]

[craigb at fsul1 test_numpy]$ tar -xzf numpy-1.3.0.tar.gz 
[craigb at fsul1 test_numpy]$ cd numpy-1.3.0 

[craigb at fsul1 numpy-1.3.0]$ 
[craigb at fsul1 numpy-1.3.0]$ ls /local/scratch/lib/    
libatlas.so                   libwx_gtk2u_adv-2.8.so.0.5.0
libatlas.so.3                 libwx_gtk2u_aui-2.8.so
libatlas.so.3.0               libwx_gtk2u_aui-2.8.so.0
libblas.so                    libwx_gtk2u_aui-2.8.so.0.5.0
libblas.so.3                  libwx_gtk2u_core-2.8.so
libblas.so.3.0                libwx_gtk2u_core-2.8.so.0
libcblas.so                   libwx_gtk2u_core-2.8.so.0.5.0
libcblas.so.3                 libwx_gtk2u_gizmos-2.8.so
libcblas.so.3.0               libwx_gtk2u_gizmos-2.8.so.0
libf77blas.so                 libwx_gtk2u_gizmos-2.8.so.0.5.0
libf77blas.so.3               libwx_gtk2u_gizmos_xrc-2.8.so
libf77blas.so.3.0             libwx_gtk2u_gizmos_xrc-2.8.so.0
libg2c.so                     libwx_gtk2u_gizmos_xrc-2.8.so.0.5.0
liblapack_atlas.so            libwx_gtk2u_html-2.8.so
liblapack_atlas.so.3          libwx_gtk2u_html-2.8.so.0
liblapack_atlas.so.3.0        libwx_gtk2u_html-2.8.so.0.5.0
liblapack.so                  libwx_gtk2u_qa-2.8.so
liblapack.so.3                libwx_gtk2u_qa-2.8.so.0
liblapack.so.3.0              libwx_gtk2u_qa-2.8.so.0.5.0
libMinuit2.a                  libwx_gtk2u_richtext-2.8.so
libMinuit2.la                 libwx_gtk2u_richtext-2.8.so.0
libMinuit2.so                 libwx_gtk2u_richtext-2.8.so.0.5.0
libMinuit2.so.0               libwx_gtk2u_stc-2.8.so
libMinuit2.so.0.0.0           libwx_gtk2u_stc-2.8.so.0
libwx_baseu-2.8.so            libwx_gtk2u_stc-2.8.so.0.5.0
libwx_baseu-2.8.so.0          libwx_gtk2u_xrc-2.8.so
libwx_baseu-2.8.so.0.5.0      libwx_gtk2u_xrc-2.8.so.0
libwx_baseu_net-2.8.so        libwx_gtk2u_xrc-2.8.so.0.5.0
libwx_baseu_net-2.8.so.0      python2.5
libwx_baseu_net-2.8.so.0.5.0  python2.6
libwx_baseu_xml-2.8.so        root
libwx_baseu_xml-2.8.so.0      sipdistutils.py
libwx_baseu_xml-2.8.so.0.5.0  sip.so
libwx_gtk2u_adv-2.8.so        wx
libwx_gtk2u_adv-2.8.so.0
[craigb at fsul1 numpy-1.3.0]$ 
[craigb at fsul1 numpy-1.3.0]$ cp site.cfg.example site.cfg         
[craigb at fsul1 numpy-1.3.0]$ emacs -nw site.cfg
[craigb at fsul1 numpy-1.3.0]$ cat site.cfg
# Adapted from the installation instructions for Ubuntu on the SciPy webpage

[DEFAULT]
library_dirs = /local/scratch/lib
include_dirs = /local/scratch/include

[atlas]
atlas_libs = lapack, f77blas, cblas, atlas 
 
[craigb at fsul1 numpy-1.3.0]$ 
[craigb at fsul1 numpy-1.3.0]$ python setup.py build --fcompiler=gnu95
Running from numpy source directory.
F2PY Version 2
blas_opt_info:
blas_mkl_info:
  libraries mkl,vml,guide not found in /local/scratch/lib
  NOT AVAILABLE

atlas_blas_threads_info:
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
  FOUND:
    libraries = ['lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = c
    include_dirs = ['/local/scratch/include/atlas']

/local/scratch/test_numpy/numpy-1.3.0/numpy/distutils/command/config.py:361: DeprecationWarning: 
+++++++++++++++++++++++++++++++++++++++++++++++++
Usage of get_output is deprecated: please do not 
use it anymore, and avoid configuration checks 
involving running executable on the target machine.
+++++++++++++++++++++++++++++++++++++++++++++++++

  DeprecationWarning)
customize GnuFCompiler
Found executable /usr/bin/g77
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using config
compiling '_configtest.c':

/* This file is generated from numpy/distutils/system_info.py */
void ATL_buildinfo(void);
int main(void) {
  ATL_buildinfo();
  return 0;
}
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-c'
gcc: _configtest.c
gcc -pthread _configtest.o -L/local/scratch/lib -llapack -lf77blas -lcblas -latlas -o _configtest
ATLAS version 3.6.0 built by camm on Tue Jun 15 03:19:44 UTC 2004:
   UNAME    : Linux intech131 2.4.20 #1 SMP Thu Jan 23 11:24:05 EST 2003 i686 GNU/Linux
   INSTFLG  : 
   MMDEF    : 
   ARCHDEF  : 
   F2CDEFS  : -DAdd__ -DStringSunStyle
   CACHEEDGE: 196608
   F77      : /usr/bin/g77, version GNU Fortran (GCC) 3.3.5 (Debian 1:3.3.5-1)
   F77FLAGS : -O
   CC       : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   CC FLAGS : -fomit-frame-pointer -O3 -funroll-all-loops
   MCC      : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   MCCFLAGS : -fomit-frame-pointer -O
success!
removing: _configtest.c _configtest.o _configtest
  FOUND:
    libraries = ['lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = c
    define_macros = [('ATLAS_INFO', '"\\"3.6.0\\""')]
    include_dirs = ['/local/scratch/include/atlas']

lapack_opt_info:
lapack_mkl_info:
mkl_info:
  libraries mkl,vml,guide not found in /local/scratch/lib
  NOT AVAILABLE

  NOT AVAILABLE

atlas_threads_info:
Setting PTATLAS=ATLAS
numpy.distutils.system_info.atlas_threads_info
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
  FOUND:
    libraries = ['lapack', 'lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = f77
    include_dirs = ['/local/scratch/include/atlas']

customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using config
compiling '_configtest.c':

/* This file is generated from numpy/distutils/system_info.py */
void ATL_buildinfo(void);
int main(void) {
  ATL_buildinfo();
  return 0;
}
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-c'
gcc: _configtest.c
gcc -pthread _configtest.o -L/local/scratch/lib -llapack -llapack -lf77blas -lcblas -latlas -o _configtest
ATLAS version 3.6.0 built by camm on Tue Jun 15 03:19:44 UTC 2004:
   UNAME    : Linux intech131 2.4.20 #1 SMP Thu Jan 23 11:24:05 EST 2003 i686 GNU/Linux
   INSTFLG  : 
   MMDEF    : 
   ARCHDEF  : 
   F2CDEFS  : -DAdd__ -DStringSunStyle
   CACHEEDGE: 196608
   F77      : /usr/bin/g77, version GNU Fortran (GCC) 3.3.5 (Debian 1:3.3.5-1)
   F77FLAGS : -O
   CC       : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   CC FLAGS : -fomit-frame-pointer -O3 -funroll-all-loops
   MCC      : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   MCCFLAGS : -fomit-frame-pointer -O
success!
removing: _configtest.c _configtest.o _configtest
  FOUND:
    libraries = ['lapack', 'lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = f77
    define_macros = [('ATLAS_INFO', '"\\"3.6.0\\""')]
    include_dirs = ['/local/scratch/include/atlas']

running build
running config_cc
unifing config_cc, config, build_clib, build_ext, build commands --compiler options
running config_fc
unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options
running build_src
building py_modules sources
creating build
creating build/src.linux-i686-2.6
creating build/src.linux-i686-2.6/numpy
creating build/src.linux-i686-2.6/numpy/distutils
building library "npymath" sources
creating build/src.linux-i686-2.6/numpy/core
creating build/src.linux-i686-2.6/numpy/core/src
conv_template:> build/src.linux-i686-2.6/numpy/core/src/npy_math.c
building extension "numpy.core._sort" sources
Generating build/src.linux-i686-2.6/numpy/core/include/numpy/config.h
customize Gnu95FCompiler
Found executable /usr/bin/gfortran
customize Gnu95FCompiler using config
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
_configtest.c: In function 'main':
_configtest.c:5: error: size of array 'test_array' is negative
_configtest.c:4: warning: function declaration isn't a prototype
_configtest.c: In function 'main':
_configtest.c:5: error: size of array 'test_array' is negative
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
_configtest.c: In function 'main':
_configtest.c:5: error: size of array 'test_array' is negative
_configtest.c:4: warning: function declaration isn't a prototype
_configtest.c: In function 'main':
_configtest.c:5: error: size of array 'test_array' is negative
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:5: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:5: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
/local/scratch/test_numpy/numpy-1.3.0/numpy/distutils/command/config.py:39: DeprecationWarning: 
+++++++++++++++++++++++++++++++++++++++++++++++++
Usage of try_run is deprecated: please do not 
use it anymore, and avoid configuration checks 
involving running executable on the target machine.
+++++++++++++++++++++++++++++++++++++++++++++++++

  DeprecationWarning)
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
gcc -pthread _configtest.o -o _configtest
_configtest.o: In function `main':
/local/scratch/test_numpy/numpy-1.3.0/_configtest.c:5: undefined reference to `exp'
collect2: ld returned 1 exit status
_configtest.o: In function `main':
/local/scratch/test_numpy/numpy-1.3.0/_configtest.c:5: undefined reference to `exp'
collect2: ld returned 1 exit status
failure.
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
gcc -pthread _configtest.o -lm -o _configtest
_configtest
success!
removing: _configtest.c _configtest.o _configtest
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:1: warning: conflicting types for built-in function 'asin'
_configtest.c:2: warning: conflicting types for built-in function 'cos'
_configtest.c:3: warning: conflicting types for built-in function 'log'
_configtest.c:4: warning: conflicting types for built-in function 'fabs'
_configtest.c:5: warning: conflicting types for built-in function 'tanh'
_configtest.c:6: warning: conflicting types for built-in function 'atan'
_configtest.c:7: warning: conflicting types for built-in function 'acos'
_configtest.c:8: warning: conflicting types for built-in function 'floor'
_configtest.c:9: warning: conflicting types for built-in function 'fmod'
_configtest.c:10: warning: conflicting types for built-in function 'sqrt'
_configtest.c:11: warning: conflicting types for built-in function 'cosh'
_configtest.c:12: warning: conflicting types for built-in function 'modf'
_configtest.c:13: warning: conflicting types for built-in function 'sinh'
_configtest.c:14: warning: conflicting types for built-in function 'frexp'
_configtest.c:15: warning: conflicting types for built-in function 'exp'
_configtest.c:16: warning: conflicting types for built-in function 'tan'
_configtest.c:17: warning: conflicting types for built-in function 'ceil'
_configtest.c:18: warning: conflicting types for built-in function 'log10'
_configtest.c:19: warning: conflicting types for built-in function 'sin'
_configtest.c:20: warning: conflicting types for built-in function 'ldexp'
gcc -pthread _configtest.o -lm -o _configtest
success!
removing: _configtest.c _configtest.o _configtest
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:1: warning: conflicting types for built-in function 'rint'
_configtest.c:2: warning: conflicting types for built-in function 'log2'
_configtest.c:3: warning: conflicting types for built-in function 'exp2'
_configtest.c:4: warning: conflicting types for built-in function 'trunc'
gcc -pthread _configtest.o -lm -o _configtest
success!
removing: _configtest.c _configtest.o _configtest
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:1: warning: conflicting types for built-in function 'cosf'
_configtest.c:2: warning: conflicting types for built-in function 'coshf'
_configtest.c:3: warning: conflicting types for built-in function 'rintf'
_configtest.c:4: warning: conflicting types for built-in function 'fabsf'
_configtest.c:5: warning: conflicting types for built-in function 'floorf'
_configtest.c:6: warning: conflicting types for built-in function 'tanhf'
_configtest.c:7: warning: conflicting types for built-in function 'log10f'
_configtest.c:8: warning: conflicting types for built-in function 'logf'
_configtest.c:9: warning: conflicting types for built-in function 'sinhf'
_configtest.c:10: warning: conflicting types for built-in function 'acosf'
_configtest.c:11: warning: conflicting types for built-in function 'sqrtf'
_configtest.c:12: warning: conflicting types for built-in function 'ldexpf'
_configtest.c:13: warning: conflicting types for built-in function 'hypotf'
_configtest.c:14: warning: conflicting types for built-in function 'log2f'
_configtest.c:15: warning: conflicting types for built-in function 'exp2f'
_configtest.c:16: warning: conflicting types for built-in function 'atanf'
_configtest.c:17: warning: conflicting types for built-in function 'fmodf'
_configtest.c:18: warning: conflicting types for built-in function 'atan2f'
_configtest.c:19: warning: conflicting types for built-in function 'modff'
_configtest.c:20: warning: conflicting types for built-in function 'ceilf'
_configtest.c:21: warning: conflicting types for built-in function 'log1pf'
_configtest.c:22: warning: conflicting types for built-in function 'asinf'
_configtest.c:23: warning: conflicting types for built-in function 'acoshf'
_configtest.c:24: warning: conflicting types for built-in function 'sinf'
_configtest.c:25: warning: conflicting types for built-in function 'tanf'
_configtest.c:26: warning: conflicting types for built-in function 'atanhf'
_configtest.c:27: warning: conflicting types for built-in function 'truncf'
_configtest.c:28: warning: conflicting types for built-in function 'asinhf'
_configtest.c:29: warning: conflicting types for built-in function 'frexpf'
_configtest.c:30: warning: conflicting types for built-in function 'powf'
_configtest.c:31: warning: conflicting types for built-in function 'expf'
_configtest.c:32: warning: conflicting types for built-in function 'expm1f'
gcc -pthread _configtest.o -lm -o _configtest
success!
removing: _configtest.c _configtest.o _configtest
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:1: warning: conflicting types for built-in function 'tanhl'
_configtest.c:2: warning: conflicting types for built-in function 'log10l'
_configtest.c:3: warning: conflicting types for built-in function 'coshl'
_configtest.c:4: warning: conflicting types for built-in function 'cosl'
_configtest.c:5: warning: conflicting types for built-in function 'floorl'
_configtest.c:6: warning: conflicting types for built-in function 'rintl'
_configtest.c:7: warning: conflicting types for built-in function 'fabsl'
_configtest.c:8: warning: conflicting types for built-in function 'acosl'
_configtest.c:9: warning: conflicting types for built-in function 'ldexpl'
_configtest.c:10: warning: conflicting types for built-in function 'sqrtl'
_configtest.c:11: warning: conflicting types for built-in function 'logl'
_configtest.c:12: warning: conflicting types for built-in function 'expm1l'
_configtest.c:13: warning: conflicting types for built-in function 'hypotl'
_configtest.c:14: warning: conflicting types for built-in function 'log2l'
_configtest.c:15: warning: conflicting types for built-in function 'exp2l'
_configtest.c:16: warning: conflicting types for built-in function 'atanl'
_configtest.c:17: warning: conflicting types for built-in function 'frexpl'
_configtest.c:18: warning: conflicting types for built-in function 'atan2l'
_configtest.c:19: warning: conflicting types for built-in function 'sinhl'
_configtest.c:20: warning: conflicting types for built-in function 'fmodl'
_configtest.c:21: warning: conflicting types for built-in function 'log1pl'
_configtest.c:22: warning: conflicting types for built-in function 'asinl'
_configtest.c:23: warning: conflicting types for built-in function 'ceill'
_configtest.c:24: warning: conflicting types for built-in function 'sinl'
_configtest.c:25: warning: conflicting types for built-in function 'acoshl'
_configtest.c:26: warning: conflicting types for built-in function 'atanhl'
_configtest.c:27: warning: conflicting types for built-in function 'tanl'
_configtest.c:28: warning: conflicting types for built-in function 'truncl'
_configtest.c:29: warning: conflicting types for built-in function 'powl'
_configtest.c:30: warning: conflicting types for built-in function 'expl'
_configtest.c:31: warning: conflicting types for built-in function 'modfl'
_configtest.c:32: warning: conflicting types for built-in function 'asinhl'
gcc -pthread _configtest.o -lm -o _configtest
success!
removing: _configtest.c _configtest.o _configtest
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
success!
removing: _configtest.c _configtest.o
File: build/src.linux-i686-2.6/numpy/core/include/numpy/config.h
#define SIZEOF_SHORT 2
#define SIZEOF_INT 4
#define SIZEOF_LONG 4
#define SIZEOF_FLOAT 4
#define SIZEOF_DOUBLE 8
#define SIZEOF_PY_INTPTR_T 4
#define SIZEOF_PY_LONG_LONG 8
#define MATHLIB m
#define HAVE_SIN
#define HAVE_COS
#define HAVE_TAN
#define HAVE_SINH
#define HAVE_COSH
#define HAVE_TANH
#define HAVE_FABS
#define HAVE_FLOOR
#define HAVE_CEIL
#define HAVE_SQRT
#define HAVE_LOG10
#define HAVE_LOG
#define HAVE_EXP
#define HAVE_ASIN
#define HAVE_ACOS
#define HAVE_ATAN
#define HAVE_FMOD
#define HAVE_MODF
#define HAVE_FREXP
#define HAVE_LDEXP
#define HAVE_RINT
#define HAVE_TRUNC
#define HAVE_EXP2
#define HAVE_LOG2
#define HAVE_SINF
#define HAVE_COSF
#define HAVE_TANF
#define HAVE_SINHF
#define HAVE_COSHF
#define HAVE_TANHF
#define HAVE_FABSF
#define HAVE_FLOORF
#define HAVE_CEILF
#define HAVE_RINTF
#define HAVE_TRUNCF
#define HAVE_SQRTF
#define HAVE_LOG10F
#define HAVE_LOGF
#define HAVE_LOG1PF
#define HAVE_EXPF
#define HAVE_EXPM1F
#define HAVE_ASINF
#define HAVE_ACOSF
#define HAVE_ATANF
#define HAVE_ASINHF
#define HAVE_ACOSHF
#define HAVE_ATANHF
#define HAVE_HYPOTF
#define HAVE_ATAN2F
#define HAVE_POWF
#define HAVE_FMODF
#define HAVE_MODFF
#define HAVE_FREXPF
#define HAVE_LDEXPF
#define HAVE_EXP2F
#define HAVE_LOG2F
#define HAVE_SINL
#define HAVE_COSL
#define HAVE_TANL
#define HAVE_SINHL
#define HAVE_COSHL
#define HAVE_TANHL
#define HAVE_FABSL
#define HAVE_FLOORL
#define HAVE_CEILL
#define HAVE_RINTL
#define HAVE_TRUNCL
#define HAVE_SQRTL
#define HAVE_LOG10L
#define HAVE_LOGL
#define HAVE_LOG1PL
#define HAVE_EXPL
#define HAVE_EXPM1L
#define HAVE_ASINL
#define HAVE_ACOSL
#define HAVE_ATANL
#define HAVE_ASINHL
#define HAVE_ACOSHL
#define HAVE_ATANHL
#define HAVE_HYPOTL
#define HAVE_ATAN2L
#define HAVE_POWL
#define HAVE_FMODL
#define HAVE_MODFL
#define HAVE_FREXPL
#define HAVE_LDEXPL
#define HAVE_EXP2L
#define HAVE_LOG2L
#define HAVE_DECL_ISNAN
#define HAVE_DECL_ISINF
#define HAVE_DECL_SIGNBIT
#define HAVE_DECL_ISFINITE
#ifndef __cplusplus
/* #undef inline */
#endif

EOF
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h' to sources.
Generating build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:5: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
success!
removing: _configtest.c _configtest.o
File: build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h
#define NPY_SIZEOF_SHORT 2
#define NPY_SIZEOF_INT 4
#define NPY_SIZEOF_LONG 4
#define NPY_SIZEOF_FLOAT 4
#define NPY_SIZEOF_DOUBLE 8
#define NPY_SIZEOF_LONGDOUBLE 12
#define NPY_SIZEOF_PY_INTPTR_T 4
#define NPY_SIZEOF_PY_LONG_LONG 8
#define NPY_SIZEOF_LONGLONG 8
#define NPY_NO_SMP 0
#define NPY_HAVE_DECL_ISNAN
#define NPY_HAVE_DECL_ISINF
#define NPY_HAVE_DECL_SIGNBIT
#define NPY_HAVE_DECL_ISFINITE
#define NPY_USE_C99_FORMATS 1
#define NPY_INLINE inline

#ifndef __STDC_FORMAT_MACROS
#define __STDC_FORMAT_MACROS 1
#endif

EOF
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h' to sources.
numpy/core/code_generators/genapi.py:9: DeprecationWarning: the md5 module is deprecated; use hashlib instead
  import md5
executing numpy/core/code_generators/generate_numpy_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h' to sources.
conv_template:> build/src.linux-i686-2.6/numpy/core/src/_sortmodule.c
numpy.core - nothing done with h_files = ['build/src.linux-i686-2.6/numpy/core/include/numpy/config.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h']
building extension "numpy.core.multiarray" sources
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h' to sources.
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h' to sources.
executing numpy/core/code_generators/generate_numpy_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h' to sources.
conv_template:> build/src.linux-i686-2.6/numpy/core/src/scalartypes.inc
  adding 'build/src.linux-i686-2.6/numpy/core/src' to include_dirs.
conv_template:> build/src.linux-i686-2.6/numpy/core/src/arraytypes.inc
numpy.core - nothing done with h_files = ['build/src.linux-i686-2.6/numpy/core/src/scalartypes.inc', 'build/src.linux-i686-2.6/numpy/core/src/arraytypes.inc', 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h']
building extension "numpy.core.umath" sources
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h' to sources.
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h' to sources.
executing numpy/core/code_generators/generate_ufunc_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__ufunc_api.h' to sources.
conv_template:> build/src.linux-i686-2.6/numpy/core/src/umathmodule.c
  adding 'build/src.linux-i686-2.6/numpy/core/src' to include_dirs.
conv_template:> build/src.linux-i686-2.6/numpy/core/src/umath_funcs.inc
conv_template:> build/src.linux-i686-2.6/numpy/core/src/umath_loops.inc
numpy.core - nothing done with h_files = ['build/src.linux-i686-2.6/numpy/core/src/scalartypes.inc', 'build/src.linux-i686-2.6/numpy/core/src/arraytypes.inc', 'build/src.linux-i686-2.6/numpy/core/src/umath_funcs.inc', 'build/src.linux-i686-2.6/numpy/core/src/umath_loops.inc', 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__ufunc_api.h']
building extension "numpy.core.scalarmath" sources
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h' to sources.
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h' to sources.
executing numpy/core/code_generators/generate_numpy_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h' to sources.
executing numpy/core/code_generators/generate_ufunc_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__ufunc_api.h' to sources.
conv_template:> build/src.linux-i686-2.6/numpy/core/src/scalarmathmodule.c
numpy.core - nothing done with h_files = ['build/src.linux-i686-2.6/numpy/core/include/numpy/config.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__ufunc_api.h']
building extension "numpy.core._dotblas" sources
  adding 'numpy/core/blasdot/_dotblas.c' to sources.
building extension "numpy.core.umath_tests" sources
conv_template:> build/src.linux-i686-2.6/numpy/core/src/umath_tests.c
building extension "numpy.lib._compiled_base" sources
building extension "numpy.numarray._capi" sources
building extension "numpy.fft.fftpack_lite" sources
building extension "numpy.linalg.lapack_lite" sources
creating build/src.linux-i686-2.6/numpy/linalg
  adding 'numpy/linalg/lapack_litemodule.c' to sources.
  adding 'numpy/linalg/python_xerbla.c' to sources.
building extension "numpy.random.mtrand" sources
creating build/src.linux-i686-2.6/numpy/random
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
gcc -pthread _configtest.o -o _configtest
_configtest
failure.
removing: _configtest.c _configtest.o _configtest
building data_files sources
running build_py
creating build/lib.linux-i686-2.6
creating build/lib.linux-i686-2.6/numpy
copying numpy/dual.py -> build/lib.linux-i686-2.6/numpy
copying numpy/ctypeslib.py -> build/lib.linux-i686-2.6/numpy
copying numpy/setupscons.py -> build/lib.linux-i686-2.6/numpy
copying numpy/add_newdocs.py -> build/lib.linux-i686-2.6/numpy
copying numpy/_import_tools.py -> build/lib.linux-i686-2.6/numpy
copying numpy/matlib.py -> build/lib.linux-i686-2.6/numpy
copying numpy/version.py -> build/lib.linux-i686-2.6/numpy
copying numpy/setup.py -> build/lib.linux-i686-2.6/numpy
copying numpy/__init__.py -> build/lib.linux-i686-2.6/numpy
copying build/src.linux-i686-2.6/numpy/__config__.py -> build/lib.linux-i686-2.6/numpy
creating build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/lib2def.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/numpy_distribution.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/log.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/setupscons.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/misc_util.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/line_endings.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/environment.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/mingw32ccompiler.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/system_info.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/unixccompiler.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/cpuinfo.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/__version__.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/ccompiler.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/from_template.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/conv_template.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/setup.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/interactive.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/exec_command.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/__init__.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/core.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/intelccompiler.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/info.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/extension.py -> build/lib.linux-i686-2.6/numpy/distutils
copying build/src.linux-i686-2.6/numpy/distutils/__config__.py -> build/lib.linux-i686-2.6/numpy/distutils
creating build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/install.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/build_ext.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/build_py.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/config_compiler.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/autodist.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/build_src.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/config.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/egg_info.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/install_data.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/develop.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/bdist_rpm.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/build.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/sdist.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/install_headers.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/build_clib.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/scons.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/__init__.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/build_scripts.py -> build/lib.linux-i686-2.6/numpy/distutils/command
creating build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/hpux.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/intel.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/g95.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/mips.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/pg.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/absoft.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/lahey.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/sun.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/ibm.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/nag.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/compaq.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/gnu.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/none.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/__init__.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/vast.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
creating build/lib.linux-i686-2.6/numpy/testing
copying numpy/testing/setupscons.py -> build/lib.linux-i686-2.6/numpy/testing
copying numpy/testing/nosetester.py -> build/lib.linux-i686-2.6/numpy/testing
copying numpy/testing/utils.py -> build/lib.linux-i686-2.6/numpy/testing
copying numpy/testing/noseclasses.py -> build/lib.linux-i686-2.6/numpy/testing
copying numpy/testing/decorators.py -> build/lib.linux-i686-2.6/numpy/testing
copying numpy/testing/setup.py -> build/lib.linux-i686-2.6/numpy/testing
copying numpy/testing/__init__.py -> build/lib.linux-i686-2.6/numpy/testing
copying numpy/testing/numpytest.py -> build/lib.linux-i686-2.6/numpy/testing
copying numpy/testing/nulltester.py -> build/lib.linux-i686-2.6/numpy/testing
creating build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/rules.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/cb_rules.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/setupscons.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/common_rules.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/f2py_testing.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/capi_maps.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/f2py2e.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/use_rules.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/crackfortran.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/__version__.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/cfuncs.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/setup.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/func2subr.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/auxfuncs.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/__init__.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/f90mod_rules.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/diagnose.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/info.py -> build/lib.linux-i686-2.6/numpy/f2py
creating build/lib.linux-i686-2.6/numpy/core
copying numpy/core/defmatrix.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/scons_support.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/records.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/fromnumeric.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/_internal.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/setupscons.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/numerictypes.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/numeric.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/memmap.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/setup.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/defchararray.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/__init__.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/arrayprint.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/info.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/setup_common.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/code_generators/generate_numpy_api.py -> build/lib.linux-i686-2.6/numpy/core
creating build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/io.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/function_base.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/shape_base.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/user_array.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/ufunclike.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/_datasource.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/machar.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/setupscons.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/getlimits.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/_iotools.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/arraysetops.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/utils.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/type_check.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/twodim_base.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/arrayterator.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/stride_tricks.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/financial.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/scimath.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/index_tricks.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/setup.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/format.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/__init__.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/polynomial.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/recfunctions.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/info.py -> build/lib.linux-i686-2.6/numpy/lib
creating build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/matrix.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/fft.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/alter_code1.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/user_array.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/rng.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/rng_stats.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/alter_code2.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/random_array.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/setupscons.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/arrayfns.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/ufuncs.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/compat.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/array_printer.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/misc.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/mlab.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/ma.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/precision.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/fix_default_axis.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/setup.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/functions.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/linear_algebra.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/__init__.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/typeconv.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
creating build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/matrix.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/fft.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/alter_code1.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/nd_image.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/image.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/convolve.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/alter_code2.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/random_array.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/setupscons.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/session.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/ufuncs.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/compat.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/numerictypes.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/mlab.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/ma.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/util.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/setup.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/functions.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/linear_algebra.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/__init__.py -> build/lib.linux-i686-2.6/numpy/numarray
creating build/lib.linux-i686-2.6/numpy/fft
copying numpy/fft/helper.py -> build/lib.linux-i686-2.6/numpy/fft
copying numpy/fft/setupscons.py -> build/lib.linux-i686-2.6/numpy/fft
copying numpy/fft/setup.py -> build/lib.linux-i686-2.6/numpy/fft
copying numpy/fft/fftpack.py -> build/lib.linux-i686-2.6/numpy/fft
copying numpy/fft/__init__.py -> build/lib.linux-i686-2.6/numpy/fft
copying numpy/fft/info.py -> build/lib.linux-i686-2.6/numpy/fft
creating build/lib.linux-i686-2.6/numpy/linalg
copying numpy/linalg/setupscons.py -> build/lib.linux-i686-2.6/numpy/linalg
copying numpy/linalg/linalg.py -> build/lib.linux-i686-2.6/numpy/linalg
copying numpy/linalg/setup.py -> build/lib.linux-i686-2.6/numpy/linalg
copying numpy/linalg/__init__.py -> build/lib.linux-i686-2.6/numpy/linalg
copying numpy/linalg/info.py -> build/lib.linux-i686-2.6/numpy/linalg
creating build/lib.linux-i686-2.6/numpy/random
copying numpy/random/setupscons.py -> build/lib.linux-i686-2.6/numpy/random
copying numpy/random/setup.py -> build/lib.linux-i686-2.6/numpy/random
copying numpy/random/__init__.py -> build/lib.linux-i686-2.6/numpy/random
copying numpy/random/info.py -> build/lib.linux-i686-2.6/numpy/random
creating build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/timer_comparison.py -> build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/bench.py -> build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/setupscons.py -> build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/testutils.py -> build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/extras.py -> build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/version.py -> build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/setup.py -> build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/mrecords.py -> build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/__init__.py -> build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/core.py -> build/lib.linux-i686-2.6/numpy/ma
creating build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/glossary.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/io.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/broadcasting.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/basics.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/constants.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/creation.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/jargon.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/indexing.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/ufuncs.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/misc.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/structured_arrays.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/subclassing.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/howtofind.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/methods_vs_functions.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/internals.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/performance.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/__init__.py -> build/lib.linux-i686-2.6/numpy/doc
running build_clib
customize UnixCCompiler
customize UnixCCompiler using build_clib
building 'npymath' library
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6
creating build/temp.linux-i686-2.6/build
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/numpy
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/numpy/core
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/numpy/core/src
compile options: '-Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/numpy/core/src/npy_math.c
ar: adding 1 object files to build/temp.linux-i686-2.6/libnpymath.a
running build_ext
customize UnixCCompiler
customize UnixCCompiler using build_ext
customize Gnu95FCompiler
customize Gnu95FCompiler using build_ext
building 'numpy.core._sort' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/numpy/core/src/_sortmodule.c
gcc -pthread -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/numpy/core/src/_sortmodule.o -Lbuild/temp.linux-i686-2.6 -lm -o build/lib.linux-i686-2.6/numpy/core/_sort.so
building 'numpy.core.multiarray' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/numpy
creating build/temp.linux-i686-2.6/numpy/core
creating build/temp.linux-i686-2.6/numpy/core/src
compile options: '-Ibuild/src.linux-i686-2.6/numpy/core/src -Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: numpy/core/src/multiarraymodule.c
gcc -pthread -shared build/temp.linux-i686-2.6/numpy/core/src/multiarraymodule.o -Lbuild/temp.linux-i686-2.6 -lnpymath -lm -o build/lib.linux-i686-2.6/numpy/core/multiarray.so
building 'numpy.core.umath' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Ibuild/src.linux-i686-2.6/numpy/core/src -Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/numpy/core/src/umathmodule.c
gcc -pthread -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/numpy/core/src/umathmodule.o -Lbuild/temp.linux-i686-2.6 -lnpymath -lm -o build/lib.linux-i686-2.6/numpy/core/umath.so
building 'numpy.core.scalarmath' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/numpy/core/src/scalarmathmodule.c
gcc -pthread -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/numpy/core/src/scalarmathmodule.o -Lbuild/temp.linux-i686-2.6 -lm -o build/lib.linux-i686-2.6/numpy/core/scalarmath.so
building 'numpy.core._dotblas' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/numpy/core/blasdot
compile options: '-DATLAS_INFO="\"3.6.0\"" -Inumpy/core/blasdot -I/local/scratch/include/atlas -Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: numpy/core/blasdot/_dotblas.c
gcc -pthread -shared build/temp.linux-i686-2.6/numpy/core/blasdot/_dotblas.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -lf77blas -lcblas -latlas -o build/lib.linux-i686-2.6/numpy/core/_dotblas.so
building 'numpy.core.umath_tests' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/numpy/core/src/umath_tests.c
gcc -pthread -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/numpy/core/src/umath_tests.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/numpy/core/umath_tests.so
building 'numpy.lib._compiled_base' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/numpy/lib
creating build/temp.linux-i686-2.6/numpy/lib/src
compile options: '-Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: numpy/lib/src/_compiled_base.c
gcc -pthread -shared build/temp.linux-i686-2.6/numpy/lib/src/_compiled_base.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/numpy/lib/_compiled_base.so
building 'numpy.numarray._capi' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/numpy/numarray
compile options: '-Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: numpy/numarray/_capi.c
gcc -pthread -shared build/temp.linux-i686-2.6/numpy/numarray/_capi.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/numpy/numarray/_capi.so
building 'numpy.fft.fftpack_lite' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/numpy/fft
compile options: '-Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: numpy/fft/fftpack_litemodule.c
gcc: numpy/fft/fftpack.c
gcc -pthread -shared build/temp.linux-i686-2.6/numpy/fft/fftpack_litemodule.o build/temp.linux-i686-2.6/numpy/fft/fftpack.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/numpy/fft/fftpack_lite.so
building 'numpy.linalg.lapack_lite' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/numpy/linalg
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: numpy/linalg/lapack_litemodule.c
gcc: numpy/linalg/python_xerbla.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/numpy/linalg/lapack_litemodule.o build/temp.linux-i686-2.6/numpy/linalg/python_xerbla.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/numpy/linalg/lapack_lite.so
building 'numpy.random.mtrand' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/numpy/random
creating build/temp.linux-i686-2.6/numpy/random/mtrand
compile options: '-Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: numpy/random/mtrand/distributions.c
gcc: numpy/random/mtrand/initarray.c
gcc: numpy/random/mtrand/randomkit.c
gcc: numpy/random/mtrand/mtrand.c
gcc -pthread -shared build/temp.linux-i686-2.6/numpy/random/mtrand/mtrand.o build/temp.linux-i686-2.6/numpy/random/mtrand/randomkit.o build/temp.linux-i686-2.6/numpy/random/mtrand/initarray.o build/temp.linux-i686-2.6/numpy/random/mtrand/distributions.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/numpy/random/mtrand.so
running scons
running build_scripts
creating build/scripts.linux-i686-2.6
Creating build/scripts.linux-i686-2.6/f2py
  adding 'build/scripts.linux-i686-2.6/f2py' to scripts
changing mode of build/scripts.linux-i686-2.6/f2py from 644 to 755

[craigb at fsul1 numpy-1.3.0]$ python setup.py install --prefix=/local/scratch/test_numpy/ 
Running from numpy source directory.
F2PY Version 2
blas_opt_info:
blas_mkl_info:
  libraries mkl,vml,guide not found in /local/scratch/lib
  NOT AVAILABLE

atlas_blas_threads_info:
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
  FOUND:
    libraries = ['lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = c
    include_dirs = ['/local/scratch/include/atlas']

/local/scratch/test_numpy/numpy-1.3.0/numpy/distutils/command/config.py:361: DeprecationWarning: 
+++++++++++++++++++++++++++++++++++++++++++++++++
Usage of get_output is deprecated: please do not 
use it anymore, and avoid configuration checks 
involving running executable on the target machine.
+++++++++++++++++++++++++++++++++++++++++++++++++

  DeprecationWarning)
customize GnuFCompiler
Found executable /usr/bin/g77
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using config
compiling '_configtest.c':

/* This file is generated from numpy/distutils/system_info.py */
void ATL_buildinfo(void);
int main(void) {
  ATL_buildinfo();
  return 0;
}
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-c'
gcc: _configtest.c
gcc -pthread _configtest.o -L/local/scratch/lib -llapack -lf77blas -lcblas -latlas -o _configtest
ATLAS version 3.6.0 built by camm on Tue Jun 15 03:19:44 UTC 2004:
   UNAME    : Linux intech131 2.4.20 #1 SMP Thu Jan 23 11:24:05 EST 2003 i686 GNU/Linux
   INSTFLG  : 
   MMDEF    : 
   ARCHDEF  : 
   F2CDEFS  : -DAdd__ -DStringSunStyle
   CACHEEDGE: 196608
   F77      : /usr/bin/g77, version GNU Fortran (GCC) 3.3.5 (Debian 1:3.3.5-1)
   F77FLAGS : -O
   CC       : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   CC FLAGS : -fomit-frame-pointer -O3 -funroll-all-loops
   MCC      : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   MCCFLAGS : -fomit-frame-pointer -O
success!
removing: _configtest.c _configtest.o _configtest
  FOUND:
    libraries = ['lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = c
    define_macros = [('ATLAS_INFO', '"\\"3.6.0\\""')]
    include_dirs = ['/local/scratch/include/atlas']

lapack_opt_info:
lapack_mkl_info:
mkl_info:
  libraries mkl,vml,guide not found in /local/scratch/lib
  NOT AVAILABLE

  NOT AVAILABLE

atlas_threads_info:
Setting PTATLAS=ATLAS
numpy.distutils.system_info.atlas_threads_info
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
  FOUND:
    libraries = ['lapack', 'lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = f77
    include_dirs = ['/local/scratch/include/atlas']

customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using config
compiling '_configtest.c':

/* This file is generated from numpy/distutils/system_info.py */
void ATL_buildinfo(void);
int main(void) {
  ATL_buildinfo();
  return 0;
}
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-c'
gcc: _configtest.c
gcc -pthread _configtest.o -L/local/scratch/lib -llapack -llapack -lf77blas -lcblas -latlas -o _configtest
ATLAS version 3.6.0 built by camm on Tue Jun 15 03:19:44 UTC 2004:
   UNAME    : Linux intech131 2.4.20 #1 SMP Thu Jan 23 11:24:05 EST 2003 i686 GNU/Linux
   INSTFLG  : 
   MMDEF    : 
   ARCHDEF  : 
   F2CDEFS  : -DAdd__ -DStringSunStyle
   CACHEEDGE: 196608
   F77      : /usr/bin/g77, version GNU Fortran (GCC) 3.3.5 (Debian 1:3.3.5-1)
   F77FLAGS : -O
   CC       : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   CC FLAGS : -fomit-frame-pointer -O3 -funroll-all-loops
   MCC      : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   MCCFLAGS : -fomit-frame-pointer -O
success!
removing: _configtest.c _configtest.o _configtest
  FOUND:
    libraries = ['lapack', 'lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = f77
    define_macros = [('ATLAS_INFO', '"\\"3.6.0\\""')]
    include_dirs = ['/local/scratch/include/atlas']

running install
running build
running config_cc
unifing config_cc, config, build_clib, build_ext, build commands --compiler options
running config_fc
unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options
running build_src
building py_modules sources
building library "npymath" sources
building extension "numpy.core._sort" sources
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h' to sources.
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h' to sources.
numpy/core/code_generators/genapi.py:9: DeprecationWarning: the md5 module is deprecated; use hashlib instead
  import md5
executing numpy/core/code_generators/generate_numpy_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h' to sources.
numpy.core - nothing done with h_files = ['build/src.linux-i686-2.6/numpy/core/include/numpy/config.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h']
building extension "numpy.core.multiarray" sources
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h' to sources.
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h' to sources.
executing numpy/core/code_generators/generate_numpy_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h' to sources.
  adding 'build/src.linux-i686-2.6/numpy/core/src' to include_dirs.
numpy.core - nothing done with h_files = ['build/src.linux-i686-2.6/numpy/core/src/scalartypes.inc', 'build/src.linux-i686-2.6/numpy/core/src/arraytypes.inc', 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h']
building extension "numpy.core.umath" sources
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h' to sources.
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h' to sources.
executing numpy/core/code_generators/generate_ufunc_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__ufunc_api.h' to sources.
  adding 'build/src.linux-i686-2.6/numpy/core/src' to include_dirs.
numpy.core - nothing done with h_files = ['build/src.linux-i686-2.6/numpy/core/src/scalartypes.inc', 'build/src.linux-i686-2.6/numpy/core/src/arraytypes.inc', 'build/src.linux-i686-2.6/numpy/core/src/umath_funcs.inc', 'build/src.linux-i686-2.6/numpy/core/src/umath_loops.inc', 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__ufunc_api.h']
building extension "numpy.core.scalarmath" sources
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h' to sources.
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h' to sources.
executing numpy/core/code_generators/generate_numpy_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h' to sources.
executing numpy/core/code_generators/generate_ufunc_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__ufunc_api.h' to sources.
numpy.core - nothing done with h_files = ['build/src.linux-i686-2.6/numpy/core/include/numpy/config.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__ufunc_api.h']
building extension "numpy.core._dotblas" sources
  adding 'numpy/core/blasdot/_dotblas.c' to sources.
building extension "numpy.core.umath_tests" sources
building extension "numpy.lib._compiled_base" sources
building extension "numpy.numarray._capi" sources
building extension "numpy.fft.fftpack_lite" sources
building extension "numpy.linalg.lapack_lite" sources
  adding 'numpy/linalg/lapack_litemodule.c' to sources.
  adding 'numpy/linalg/python_xerbla.c' to sources.
building extension "numpy.random.mtrand" sources
/local/scratch/test_numpy/numpy-1.3.0/numpy/distutils/command/config.py:39: DeprecationWarning: 
+++++++++++++++++++++++++++++++++++++++++++++++++
Usage of try_run is deprecated: please do not 
use it anymore, and avoid configuration checks 
involving running executable on the target machine.
+++++++++++++++++++++++++++++++++++++++++++++++++

  DeprecationWarning)
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using config
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
gcc -pthread _configtest.o -o _configtest
_configtest
failure.
removing: _configtest.c _configtest.o _configtest
building data_files sources
running build_py
copying numpy/version.py -> build/lib.linux-i686-2.6/numpy
copying build/src.linux-i686-2.6/numpy/__config__.py -> build/lib.linux-i686-2.6/numpy
copying build/src.linux-i686-2.6/numpy/distutils/__config__.py -> build/lib.linux-i686-2.6/numpy/distutils
running build_clib
customize UnixCCompiler
customize UnixCCompiler using build_clib
running build_ext
customize UnixCCompiler
customize UnixCCompiler using build_ext
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using build_ext
running scons
running build_scripts
  adding 'build/scripts.linux-i686-2.6/f2py' to scripts
running install_lib
creating /local/scratch/test_numpy/lib
creating /local/scratch/test_numpy/lib/python2.6
creating /local/scratch/test_numpy/lib/python2.6/site-packages
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/glossary.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/io.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/broadcasting.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/basics.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/constants.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/creation.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/jargon.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/indexing.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/ufuncs.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/misc.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/structured_arrays.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/subclassing.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/howtofind.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/methods_vs_functions.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/internals.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/performance.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/testing/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/testing/nosetester.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/testing/utils.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/testing/noseclasses.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/testing/decorators.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/testing/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/testing/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/testing/numpytest.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/testing/nulltester.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/dual.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/linalg
copying build/lib.linux-i686-2.6/numpy/linalg/lapack_lite.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/linalg
copying build/lib.linux-i686-2.6/numpy/linalg/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/linalg
copying build/lib.linux-i686-2.6/numpy/linalg/linalg.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/linalg
copying build/lib.linux-i686-2.6/numpy/linalg/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/linalg
copying build/lib.linux-i686-2.6/numpy/linalg/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/linalg
copying build/lib.linux-i686-2.6/numpy/linalg/info.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/linalg
copying build/lib.linux-i686-2.6/numpy/ctypeslib.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/lib2def.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/numpy_distribution.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/log.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/__config__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/misc_util.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/line_endings.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/environment.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/mingw32ccompiler.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/system_info.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/unixccompiler.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/cpuinfo.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/__version__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/ccompiler.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/from_template.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/conv_template.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/interactive.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/exec_command.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/core.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/intelccompiler.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/hpux.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/intel.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/g95.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/mips.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/pg.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/absoft.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/lahey.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/sun.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/ibm.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/nag.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/compaq.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/gnu.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/none.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/vast.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/info.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/install.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/build_ext.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/build_py.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/config_compiler.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/autodist.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/build_src.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/config.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/egg_info.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/install_data.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/develop.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/bdist_rpm.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/build.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/sdist.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/install_headers.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/build_clib.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/scons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/build_scripts.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/extension.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/timer_comparison.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/bench.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/testutils.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/extras.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/version.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/mrecords.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/core.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/matrix.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/fft.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/alter_code1.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/nd_image.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/image.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/convolve.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/alter_code2.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/random_array.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/session.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/ufuncs.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/compat.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/numerictypes.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/mlab.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/ma.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/util.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/functions.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/linear_algebra.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/_capi.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/io.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/function_base.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/shape_base.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/user_array.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/ufunclike.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/_datasource.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/machar.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/getlimits.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/_iotools.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/arraysetops.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/utils.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/type_check.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/twodim_base.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/arrayterator.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/stride_tricks.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/financial.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/scimath.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/_compiled_base.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/index_tricks.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/format.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/polynomial.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/recfunctions.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/info.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/__config__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
copying build/lib.linux-i686-2.6/numpy/add_newdocs.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/matrix.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/fft.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/alter_code1.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/user_array.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/rng.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/rng_stats.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/alter_code2.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/random_array.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/arrayfns.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/ufuncs.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/compat.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/array_printer.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/misc.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/mlab.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/ma.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/precision.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/fix_default_axis.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/functions.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/linear_algebra.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/typeconv.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/rules.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/cb_rules.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/common_rules.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/f2py_testing.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/capi_maps.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/f2py2e.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/use_rules.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/crackfortran.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/__version__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/cfuncs.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/func2subr.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/auxfuncs.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/f90mod_rules.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/diagnose.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/info.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/_import_tools.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
copying build/lib.linux-i686-2.6/numpy/matlib.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
copying build/lib.linux-i686-2.6/numpy/version.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
copying build/lib.linux-i686-2.6/numpy/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/fft
copying build/lib.linux-i686-2.6/numpy/fft/helper.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/fft
copying build/lib.linux-i686-2.6/numpy/fft/fftpack_lite.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/fft
copying build/lib.linux-i686-2.6/numpy/fft/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/fft
copying build/lib.linux-i686-2.6/numpy/fft/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/fft
copying build/lib.linux-i686-2.6/numpy/fft/fftpack.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/fft
copying build/lib.linux-i686-2.6/numpy/fft/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/fft
copying build/lib.linux-i686-2.6/numpy/fft/info.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/fft
copying build/lib.linux-i686-2.6/numpy/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/random
copying build/lib.linux-i686-2.6/numpy/random/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/random
copying build/lib.linux-i686-2.6/numpy/random/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/random
copying build/lib.linux-i686-2.6/numpy/random/mtrand.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/random
copying build/lib.linux-i686-2.6/numpy/random/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/random
copying build/lib.linux-i686-2.6/numpy/random/info.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/random
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/defmatrix.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/scons_support.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/records.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/scalarmath.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/_dotblas.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/fromnumeric.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/_internal.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/umath.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/numerictypes.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/generate_numpy_api.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/numeric.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/memmap.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/umath_tests.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/multiarray.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/defchararray.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/_sort.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/arrayprint.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/info.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/setup_common.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
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copying numpy/lib/tests/test__iotools.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib/tests/
copying numpy/lib/tests/test_index_tricks.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib/tests/
copying numpy/lib/tests/test_io.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib/tests/
copying numpy/lib/tests/test_arraysetops.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib/tests/
copying numpy/lib/tests/test_ufunclike.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib/tests/
copying numpy/lib/tests/test_format.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib/tests/
copying numpy/lib/tests/test__datasource.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib/tests/
copying numpy/lib/tests/test_function_base.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib/tests/
copying numpy/lib/tests/test_arrayterator.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib/tests/
copying numpy/lib/tests/test_shape_base.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib/tests/
copying numpy/lib/tests/test_stride_tricks.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib/tests/
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/mingw
copying numpy/distutils/mingw/gfortran_vs2003_hack.c -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/mingw
copying numpy/distutils/tests/swig_ext/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/swig_ext
copying numpy/distutils/tests/swig_ext/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/swig_ext
copying site.cfg -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/tests/swig_ext/src
copying numpy/distutils/tests/swig_ext/src/example.i -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/swig_ext/src
copying numpy/distutils/tests/swig_ext/src/zoo.i -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/swig_ext/src
copying numpy/distutils/tests/swig_ext/src/zoo.cc -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/swig_ext/src
copying numpy/distutils/tests/swig_ext/src/zoo.h -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/swig_ext/src
copying numpy/distutils/tests/swig_ext/src/example.c -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/swig_ext/src
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/tests
copying numpy/tests/test_ctypeslib.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/tests/
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/tests/pyrex_ext/tests
copying numpy/distutils/tests/pyrex_ext/tests/test_primes.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/pyrex_ext/tests
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/tests/gen_ext
copying numpy/distutils/tests/gen_ext/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/gen_ext
copying numpy/distutils/tests/gen_ext/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/gen_ext
copying numpy/distutils/tests/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/
copying numpy/distutils/tests/test_fcompiler_gnu.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/
copying numpy/distutils/tests/test_misc_util.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/numarray/numpy
copying numpy/numarray/numpy/cfunc.h -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray/numpy
copying numpy/numarray/numpy/ieeespecial.h -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray/numpy
copying numpy/numarray/numpy/numcomplex.h -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray/numpy
copying numpy/numarray/numpy/arraybase.h -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray/numpy
copying numpy/numarray/numpy/libnumarray.h -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray/numpy
copying numpy/numarray/numpy/nummacro.h -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray/numpy
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/oldnumeric/tests
copying numpy/oldnumeric/tests/test_oldnumeric.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric/tests/
copying numpy/core/tests/test_ufunc.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/tests/
copying numpy/core/tests/test_regression.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/tests/
copying numpy/core/tests/test_blasdot.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/tests/
copying numpy/core/tests/test_unicode.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/tests/
copying numpy/core/tests/test_dtype.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/tests/
copying numpy/core/tests/test_defchararray.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/tests/
copying numpy/core/tests/test_umath.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/tests/
copying numpy/core/tests/test_multiarray.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/tests/
copying numpy/core/tests/test_print.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/tests/
copying numpy/core/tests/test_records.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/tests/
copying numpy/core/tests/test_numeric.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/tests/
copying numpy/core/tests/test_defmatrix.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/tests/
copying numpy/core/tests/test_numerictypes.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/tests/
copying numpy/core/tests/test_scalarmath.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/tests/
copying numpy/core/tests/test_memmap.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/tests/
copying numpy/core/tests/test_errstate.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/tests/
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/testing/tests
copying numpy/testing/tests/test_decorators.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing/tests/
copying numpy/testing/tests/test_utils.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing/tests/
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/ma/tests
copying numpy/ma/tests/test_old_ma.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma/tests/
copying numpy/ma/tests/test_core.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma/tests/
copying numpy/ma/tests/test_extras.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma/tests/
copying numpy/ma/tests/test_mrecords.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma/tests/
copying numpy/ma/tests/test_subclassing.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma/tests/
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/tests/f2py_ext
copying numpy/distutils/tests/f2py_ext/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/f2py_ext
copying numpy/distutils/tests/f2py_ext/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/f2py_ext
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/linalg/tests
copying numpy/linalg/tests/test_regression.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/linalg/tests/
copying numpy/linalg/tests/test_linalg.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/linalg/tests/
copying numpy/linalg/tests/test_build.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/linalg/tests/
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/tests/gen_ext/tests
copying numpy/distutils/tests/gen_ext/tests/test_fib3.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/gen_ext/tests
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/tests/f2py_ext/src
copying numpy/distutils/tests/f2py_ext/src/fib2.pyf -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/f2py_ext/src
copying numpy/distutils/tests/f2py_ext/src/fib1.f -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/f2py_ext/src
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/tests/f2py_f90_ext/include
copying numpy/distutils/tests/f2py_f90_ext/include/body.f90 -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/f2py_f90_ext/include
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/tests/f2py_f90_ext/tests
copying numpy/distutils/tests/f2py_f90_ext/tests/test_foo.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/f2py_f90_ext/tests
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/tests/f2py_ext/tests
copying numpy/distutils/tests/f2py_ext/tests/test_fib2.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/f2py_ext/tests
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/tests/f2py_f90_ext/src
copying numpy/distutils/tests/f2py_f90_ext/src/foo_free.f90 -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/f2py_f90_ext/src
copying build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/include/numpy
copying build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/include/numpy
copying build/src.linux-i686-2.6/numpy/core/include/numpy/multiarray_api.txt -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/include/numpy
copying build/src.linux-i686-2.6/numpy/core/include/numpy/__ufunc_api.h -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/include/numpy
copying build/src.linux-i686-2.6/numpy/core/include/numpy/ufunc_api.txt -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/include/numpy
running install_egg_info
Writing /local/scratch/test_numpy//lib/python2.6/site-packages/numpy-1.3.0-py2.6.egg-info

[craigb at fsul1 numpy-1.3.0]$ 
[craigb at fsul1 numpy-1.3.0]$ cd ..

[craigb at fsul1 test_numpy]$ wget http://downloads.sourceforge.net/project/scipy/scipy/0.7.1/scipy-0.7.1.tar.gz
--23:00:05--  http://downloads.sourceforge.net/project/scipy/scipy/0.7.1/scipy-0.7.1.tar.gz
Resolving downloads.sourceforge.net... 216.34.181.59
Connecting to downloads.sourceforge.net|216.34.181.59|:80... connected.
HTTP request sent, awaiting response... 302 Found
Location: http://voxel.dl.sourceforge.net/project/scipy/scipy/0.7.1/scipy-0.7.1.tar.gz [following]
--23:00:06--  http://voxel.dl.sourceforge.net/project/scipy/scipy/0.7.1/scipy-0.7.1.tar.gz
Resolving voxel.dl.sourceforge.net... 74.63.52.167, 74.63.52.168, 74.63.52.169, ...
Connecting to voxel.dl.sourceforge.net|74.63.52.167|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 4538765 (4.3M) [application/x-gzip]
Saving to: `scipy-0.7.1.tar.gz'

100%[=======================================>] 4,538,765   5.64M/s   in 0.8s   

23:00:07 (5.64 MB/s) - `scipy-0.7.1.tar.gz' saved [4538765/4538765]

[craigb at fsul1 test_numpy]$ 
[craigb at fsul1 test_numpy]$ tar -xzf scipy-0.7.1.tar.gz 
[craigb at fsul1 test_numpy]$ cd scipy-0.7.1

[craigb at fsul1 scipy-0.7.1]$ python setup.py build --fcompiler=gnu95

Warning: No configuration returned, assuming unavailable.
blas_opt_info:
blas_mkl_info:
  libraries mkl,vml,guide not found in /local/scratch/lib
  NOT AVAILABLE

atlas_blas_threads_info:
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
  FOUND:
    libraries = ['lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = c
    include_dirs = ['/local/scratch/include/atlas']

/local/scratch/lib/python2.6/site-packages/numpy/distutils/command/config.py:361: DeprecationWarning: 
+++++++++++++++++++++++++++++++++++++++++++++++++
Usage of get_output is deprecated: please do not 
use it anymore, and avoid configuration checks 
involving running executable on the target machine.
+++++++++++++++++++++++++++++++++++++++++++++++++

  DeprecationWarning)
customize GnuFCompiler
Found executable /usr/bin/g77
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using config
compiling '_configtest.c':

/* This file is generated from numpy/distutils/system_info.py */
void ATL_buildinfo(void);
int main(void) {
  ATL_buildinfo();
  return 0;
}
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-c'
gcc: _configtest.c
gcc -pthread _configtest.o -L/local/scratch/lib -llapack -lf77blas -lcblas -latlas -o _configtest
ATLAS version 3.6.0 built by camm on Tue Jun 15 03:19:44 UTC 2004:
   UNAME    : Linux intech131 2.4.20 #1 SMP Thu Jan 23 11:24:05 EST 2003 i686 GNU/Linux
   INSTFLG  : 
   MMDEF    : 
   ARCHDEF  : 
   F2CDEFS  : -DAdd__ -DStringSunStyle
   CACHEEDGE: 196608
   F77      : /usr/bin/g77, version GNU Fortran (GCC) 3.3.5 (Debian 1:3.3.5-1)
   F77FLAGS : -O
   CC       : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   CC FLAGS : -fomit-frame-pointer -O3 -funroll-all-loops
   MCC      : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   MCCFLAGS : -fomit-frame-pointer -O
success!
removing: _configtest.c _configtest.o _configtest
  FOUND:
    libraries = ['lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = c
    define_macros = [('ATLAS_INFO', '"\\"3.6.0\\""')]
    include_dirs = ['/local/scratch/include/atlas']

ATLAS version 3.6.0
lapack_opt_info:
lapack_mkl_info:
mkl_info:
  libraries mkl,vml,guide not found in /local/scratch/lib
  NOT AVAILABLE

  NOT AVAILABLE

atlas_threads_info:
Setting PTATLAS=ATLAS
numpy.distutils.system_info.atlas_threads_info
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
  FOUND:
    libraries = ['lapack', 'lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = f77
    include_dirs = ['/local/scratch/include/atlas']

customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using config
compiling '_configtest.c':

/* This file is generated from numpy/distutils/system_info.py */
void ATL_buildinfo(void);
int main(void) {
  ATL_buildinfo();
  return 0;
}
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-c'
gcc: _configtest.c
gcc -pthread _configtest.o -L/local/scratch/lib -llapack -llapack -lf77blas -lcblas -latlas -o _configtest
ATLAS version 3.6.0 built by camm on Tue Jun 15 03:19:44 UTC 2004:
   UNAME    : Linux intech131 2.4.20 #1 SMP Thu Jan 23 11:24:05 EST 2003 i686 GNU/Linux
   INSTFLG  : 
   MMDEF    : 
   ARCHDEF  : 
   F2CDEFS  : -DAdd__ -DStringSunStyle
   CACHEEDGE: 196608
   F77      : /usr/bin/g77, version GNU Fortran (GCC) 3.3.5 (Debian 1:3.3.5-1)
   F77FLAGS : -O
   CC       : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   CC FLAGS : -fomit-frame-pointer -O3 -funroll-all-loops
   MCC      : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   MCCFLAGS : -fomit-frame-pointer -O
success!
removing: _configtest.c _configtest.o _configtest
  FOUND:
    libraries = ['lapack', 'lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = f77
    define_macros = [('ATLAS_INFO', '"\\"3.6.0\\""')]
    include_dirs = ['/local/scratch/include/atlas']

ATLAS version 3.6.0
ATLAS version 3.6.0
umfpack_info:
  libraries umfpack not found in /local/scratch/lib
/local/scratch/lib/python2.6/site-packages/numpy/distutils/system_info.py:452: UserWarning: 
    UMFPACK sparse solver (http://www.cise.ufl.edu/research/sparse/umfpack/)
    not found. Directories to search for the libraries can be specified in the
    numpy/distutils/site.cfg file (section [umfpack]) or by setting
    the UMFPACK environment variable.
  warnings.warn(self.notfounderror.__doc__)
  NOT AVAILABLE

running build
running config_cc
unifing config_cc, config, build_clib, build_ext, build commands --compiler options
running config_fc
unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options
running build_src
building py_modules sources
creating build
creating build/src.linux-i686-2.6
creating build/src.linux-i686-2.6/scipy
building library "dfftpack" sources
building library "linpack_lite" sources
building library "mach" sources
building library "quadpack" sources
building library "odepack" sources
building library "fitpack" sources
building library "odrpack" sources
building library "minpack" sources
building library "rootfind" sources
building library "superlu_src" sources
building library "arpack" sources
building library "sc_c_misc" sources
building library "sc_cephes" sources
building library "sc_mach" sources
building library "sc_toms" sources
building library "sc_amos" sources
building library "sc_cdf" sources
building library "sc_specfun" sources
building library "statlib" sources
building extension "scipy.cluster._vq" sources
building extension "scipy.cluster._hierarchy_wrap" sources
building extension "scipy.fftpack._fftpack" sources
creating build/src.linux-i686-2.6/scipy/fftpack
f2py options: []
f2py: scipy/fftpack/fftpack.pyf
Reading fortran codes...
	Reading file 'scipy/fftpack/fftpack.pyf' (format:free)
Post-processing...
	Block: _fftpack
			Block: zfft
			Block: drfft
			Block: zrfft
			Block: zfftnd
			Block: destroy_zfft_cache
			Block: destroy_zfftnd_cache
			Block: destroy_drfft_cache
Post-processing (stage 2)...
Building modules...
	Building module "_fftpack"...
		Constructing wrapper function "zfft"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zfft(x,[n,direction,normalize,overwrite_x])
		Constructing wrapper function "drfft"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = drfft(x,[n,direction,normalize,overwrite_x])
		Constructing wrapper function "zrfft"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zrfft(x,[n,direction,normalize,overwrite_x])
		Constructing wrapper function "zfftnd"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zfftnd(x,[s,direction,normalize,overwrite_x])
		Constructing wrapper function "destroy_zfft_cache"...
		  destroy_zfft_cache()
		Constructing wrapper function "destroy_zfftnd_cache"...
		  destroy_zfftnd_cache()
		Constructing wrapper function "destroy_drfft_cache"...
		  destroy_drfft_cache()
	Wrote C/API module "_fftpack" to file "build/src.linux-i686-2.6/scipy/fftpack/_fftpackmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
copying /local/scratch/lib/python2.6/site-packages/numpy/f2py/src/fortranobject.c -> build/src.linux-i686-2.6
copying /local/scratch/lib/python2.6/site-packages/numpy/f2py/src/fortranobject.h -> build/src.linux-i686-2.6
building extension "scipy.fftpack.convolve" sources
f2py options: []
f2py: scipy/fftpack/convolve.pyf
Reading fortran codes...
	Reading file 'scipy/fftpack/convolve.pyf' (format:free)
Post-processing...
	Block: convolve__user__routines
			Block: kernel_func
	Block: convolve
			Block: init_convolution_kernel
			Block: destroy_convolve_cache
			Block: convolve
			Block: convolve_z
Post-processing (stage 2)...
Building modules...
	Constructing call-back function "cb_kernel_func_in_convolve__user__routines"
	  def kernel_func(k): return kernel_func
	Building module "convolve"...
		Constructing wrapper function "init_convolution_kernel"...
		  omega = init_convolution_kernel(n,kernel_func,[d,zero_nyquist,kernel_func_extra_args])
		Constructing wrapper function "destroy_convolve_cache"...
		  destroy_convolve_cache()
		Constructing wrapper function "convolve"...
		  y = convolve(x,omega,[swap_real_imag,overwrite_x])
		Constructing wrapper function "convolve_z"...
		  y = convolve_z(x,omega_real,omega_imag,[overwrite_x])
	Wrote C/API module "convolve" to file "build/src.linux-i686-2.6/scipy/fftpack/convolvemodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.integrate._quadpack" sources
building extension "scipy.integrate._odepack" sources
building extension "scipy.integrate.vode" sources
creating build/src.linux-i686-2.6/scipy/integrate
f2py options: []
f2py: scipy/integrate/vode.pyf
Reading fortran codes...
	Reading file 'scipy/integrate/vode.pyf' (format:free)
Post-processing...
	Block: dvode__user__routines
		Block: dvode_user_interface
			Block: f
			Block: jac
	Block: zvode__user__routines
		Block: zvode_user_interface
			Block: f
			Block: jac
	Block: vode
			Block: dvode
			Block: zvode
Post-processing (stage 2)...
Building modules...
	Constructing call-back function "cb_f_in_dvode__user__routines"
	  def f(t,y): return ydot
	Constructing call-back function "cb_jac_in_dvode__user__routines"
	  def jac(t,y): return jac
	Constructing call-back function "cb_f_in_zvode__user__routines"
	  def f(t,y): return ydot
	Constructing call-back function "cb_jac_in_zvode__user__routines"
	  def jac(t,y): return jac
	Building module "vode"...
		Constructing wrapper function "dvode"...
warning: callstatement is defined without callprotoargument
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y,t,istate = dvode(f,jac,y,t,tout,rtol,atol,itask,istate,rwork,iwork,mf,[f_extra_args,jac_extra_args,overwrite_y])
		Constructing wrapper function "zvode"...
warning: callstatement is defined without callprotoargument
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y,t,istate = zvode(f,jac,y,t,tout,rtol,atol,itask,istate,zwork,rwork,iwork,mf,[f_extra_args,jac_extra_args,overwrite_y])
	Wrote C/API module "vode" to file "build/src.linux-i686-2.6/scipy/integrate/vodemodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.interpolate._fitpack" sources
building extension "scipy.interpolate.dfitpack" sources
creating build/src.linux-i686-2.6/scipy/interpolate
creating build/src.linux-i686-2.6/scipy/interpolate/src
f2py options: []
f2py: scipy/interpolate/src/fitpack.pyf
Reading fortran codes...
	Reading file 'scipy/interpolate/src/fitpack.pyf' (format:free)
Post-processing...
	Block: dfitpack
			Block: splev
			Block: splder
			Block: splint
			Block: sproot
			Block: spalde
			Block: curfit
			Block: percur
			Block: parcur
			Block: fpcurf0
			Block: fpcurf1
			Block: fpcurfm1
			Block: bispev
			Block: bispeu
			Block: surfit_smth
			Block: surfit_lsq
			Block: regrid_smth
			Block: dblint
Post-processing (stage 2)...
Building modules...
	Building module "dfitpack"...
		Constructing wrapper function "splev"...
		  y = splev(t,c,k,x)
		Constructing wrapper function "splder"...
		  y = splder(t,c,k,x,[nu])
		Creating wrapper for Fortran function "splint"("splint")...
		Constructing wrapper function "splint"...
		  splint = splint(t,c,k,a,b)
		Constructing wrapper function "sproot"...
		  zero,m,ier = sproot(t,c,[mest])
		Constructing wrapper function "spalde"...
		  d,ier = spalde(t,c,k,x)
		Constructing wrapper function "curfit"...
		  n,c,fp,ier = curfit(iopt,x,y,w,t,wrk,iwrk,[xb,xe,k,s])
		Constructing wrapper function "percur"...
		  n,c,fp,ier = percur(iopt,x,y,w,t,wrk,iwrk,[k,s])
		Constructing wrapper function "parcur"...
		  n,c,fp,ier = parcur(iopt,ipar,idim,u,x,w,ub,ue,t,wrk,iwrk,[k,s])
		Constructing wrapper function "fpcurf0"...
		  x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier = fpcurf0(x,y,k,[w,xb,xe,s,nest])
		Constructing wrapper function "fpcurf1"...
		  x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier = fpcurf1(x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier,[overwrite_x,overwrite_y,overwrite_w,overwrite_t,overwrite_c,overwrite_fpint,overwrite_nrdata])
		Constructing wrapper function "fpcurfm1"...
		  x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier = fpcurfm1(x,y,k,t,[w,xb,xe,overwrite_t])
		Constructing wrapper function "bispev"...
		  z,ier = bispev(tx,ty,c,kx,ky,x,y)
		Constructing wrapper function "bispeu"...
		  z,ier = bispeu(tx,ty,c,kx,ky,x,y)
		Constructing wrapper function "surfit_smth"...
		  nx,tx,ny,ty,c,fp,wrk1,ier = surfit_smth(x,y,z,[w,xb,xe,yb,ye,kx,ky,s,nxest,nyest,eps,lwrk2])
		Constructing wrapper function "surfit_lsq"...
		  tx,ty,c,fp,ier = surfit_lsq(x,y,z,tx,ty,[w,xb,xe,yb,ye,kx,ky,eps,lwrk2,overwrite_tx,overwrite_ty])
		Constructing wrapper function "regrid_smth"...
		  nx,tx,ny,ty,c,fp,ier = regrid_smth(x,y,z,[xb,xe,yb,ye,kx,ky,s])
		Creating wrapper for Fortran function "dblint"("dblint")...
		Constructing wrapper function "dblint"...
		  dblint = dblint(tx,ty,c,kx,ky,xb,xe,yb,ye)
	Wrote C/API module "dfitpack" to file "build/src.linux-i686-2.6/scipy/interpolate/src/dfitpackmodule.c"
	Fortran 77 wrappers are saved to "build/src.linux-i686-2.6/scipy/interpolate/src/dfitpack-f2pywrappers.f"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/scipy/interpolate/src/dfitpack-f2pywrappers.f' to sources.
building extension "scipy.interpolate._interpolate" sources
building extension "scipy.io.numpyio" sources
building extension "scipy.lib.blas.fblas" sources
creating build/src.linux-i686-2.6/scipy/lib
creating build/src.linux-i686-2.6/scipy/lib/blas
from_template:> build/src.linux-i686-2.6/scipy/lib/blas/fblas.pyf
Including file scipy/lib/blas/fblas_l1.pyf.src
Including file scipy/lib/blas/fblas_l2.pyf.src
Including file scipy/lib/blas/fblas_l3.pyf.src
Mismatch in number of replacements (base <prefix=s,d,c,z>) for <__l1=->. Ignoring.
from_template:> build/src.linux-i686-2.6/scipy/lib/blas/fblaswrap.f
creating build/src.linux-i686-2.6/build
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas
f2py options: ['skip:', ':']
f2py: build/src.linux-i686-2.6/scipy/lib/blas/fblas.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/lib/blas/fblas.pyf' (format:free)
Post-processing...
	Block: fblas
			Block: srotg
			Block: drotg
			Block: crotg
			Block: zrotg
			Block: srotmg
			Block: drotmg
			Block: srot
			Block: drot
			Block: csrot
			Block: zdrot
			Block: srotm
			Block: drotm
			Block: sswap
			Block: dswap
			Block: cswap
			Block: zswap
			Block: sscal
			Block: dscal
			Block: cscal
			Block: zscal
			Block: csscal
			Block: zdscal
			Block: scopy
			Block: dcopy
			Block: ccopy
			Block: zcopy
			Block: saxpy
			Block: daxpy
			Block: caxpy
			Block: zaxpy
			Block: sdot
			Block: ddot
			Block: cdotu
			Block: zdotu
			Block: cdotc
			Block: zdotc
			Block: snrm2
			Block: dnrm2
			Block: scnrm2
			Block: dznrm2
			Block: sasum
			Block: dasum
			Block: scasum
			Block: dzasum
			Block: isamax
			Block: idamax
			Block: icamax
			Block: izamax
			Block: sgemv
			Block: dgemv
			Block: cgemv
			Block: zgemv
			Block: ssymv
			Block: dsymv
			Block: chemv
			Block: zhemv
			Block: strmv
			Block: dtrmv
			Block: ctrmv
			Block: ztrmv
			Block: sger
			Block: dger
			Block: cgeru
			Block: zgeru
			Block: cgerc
			Block: zgerc
			Block: sgemm
			Block: dgemm
			Block: cgemm
			Block: zgemm
Post-processing (stage 2)...
Building modules...
	Building module "fblas"...
		Constructing wrapper function "srotg"...
		  c,s = srotg(a,b)
		Constructing wrapper function "drotg"...
		  c,s = drotg(a,b)
		Constructing wrapper function "crotg"...
		  c,s = crotg(a,b)
		Constructing wrapper function "zrotg"...
		  c,s = zrotg(a,b)
		Constructing wrapper function "srotmg"...
		  param = srotmg(d1,d2,x1,y1)
		Constructing wrapper function "drotmg"...
		  param = drotmg(d1,d2,x1,y1)
		Constructing wrapper function "srot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = srot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "drot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = drot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "csrot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = csrot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "zdrot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = zdrot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "srotm"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = srotm(x,y,param,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "drotm"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = drotm(x,y,param,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "sswap"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = sswap(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "dswap"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = dswap(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "cswap"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = cswap(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "zswap"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = zswap(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "sscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = sscal(a,x,[n,offx,incx])
		Constructing wrapper function "dscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = dscal(a,x,[n,offx,incx])
		Constructing wrapper function "cscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = cscal(a,x,[n,offx,incx])
		Constructing wrapper function "zscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = zscal(a,x,[n,offx,incx])
		Constructing wrapper function "csscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = csscal(a,x,[n,offx,incx,overwrite_x])
		Constructing wrapper function "zdscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = zdscal(a,x,[n,offx,incx,overwrite_x])
		Constructing wrapper function "scopy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = scopy(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "dcopy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = dcopy(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "ccopy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = ccopy(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "zcopy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zcopy(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "saxpy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  z = saxpy(x,y,[n,a,offx,incx,offy,incy])
		Constructing wrapper function "daxpy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  z = daxpy(x,y,[n,a,offx,incx,offy,incy])
		Constructing wrapper function "caxpy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  z = caxpy(x,y,[n,a,offx,incx,offy,incy])
		Constructing wrapper function "zaxpy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  z = zaxpy(x,y,[n,a,offx,incx,offy,incy])
		Creating wrapper for Fortran function "sdot"("sdot")...
		Constructing wrapper function "sdot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = sdot(x,y,[n,offx,incx,offy,incy])
		Creating wrapper for Fortran function "ddot"("ddot")...
		Constructing wrapper function "ddot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = ddot(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "cdotu"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = cdotu(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "zdotu"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = zdotu(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "cdotc"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = cdotc(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "zdotc"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = zdotc(x,y,[n,offx,incx,offy,incy])
		Creating wrapper for Fortran function "snrm2"("snrm2")...
		Constructing wrapper function "snrm2"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  n2 = snrm2(x,[n,offx,incx])
		Creating wrapper for Fortran function "dnrm2"("dnrm2")...
		Constructing wrapper function "dnrm2"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  n2 = dnrm2(x,[n,offx,incx])
		Creating wrapper for Fortran function "scnrm2"("scnrm2")...
		Constructing wrapper function "scnrm2"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  n2 = scnrm2(x,[n,offx,incx])
		Creating wrapper for Fortran function "dznrm2"("dznrm2")...
		Constructing wrapper function "dznrm2"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  n2 = dznrm2(x,[n,offx,incx])
		Creating wrapper for Fortran function "sasum"("sasum")...
		Constructing wrapper function "sasum"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  s = sasum(x,[n,offx,incx])
		Creating wrapper for Fortran function "dasum"("dasum")...
		Constructing wrapper function "dasum"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  s = dasum(x,[n,offx,incx])
		Creating wrapper for Fortran function "scasum"("scasum")...
		Constructing wrapper function "scasum"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  s = scasum(x,[n,offx,incx])
		Creating wrapper for Fortran function "dzasum"("dzasum")...
		Constructing wrapper function "dzasum"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  s = dzasum(x,[n,offx,incx])
		Constructing wrapper function "isamax"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  k = isamax(x,[n,offx,incx])
		Constructing wrapper function "idamax"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  k = idamax(x,[n,offx,incx])
		Constructing wrapper function "icamax"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  k = icamax(x,[n,offx,incx])
		Constructing wrapper function "izamax"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  k = izamax(x,[n,offx,incx])
		Constructing wrapper function "sgemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = sgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])
		Constructing wrapper function "dgemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = dgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])
		Constructing wrapper function "cgemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = cgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])
		Constructing wrapper function "zgemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])
		Constructing wrapper function "ssymv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = ssymv(alpha,a,x,[beta,y,offx,incx,offy,incy,lower,overwrite_y])
		Constructing wrapper function "dsymv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = dsymv(alpha,a,x,[beta,y,offx,incx,offy,incy,lower,overwrite_y])
		Constructing wrapper function "chemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = chemv(alpha,a,x,[beta,y,offx,incx,offy,incy,lower,overwrite_y])
		Constructing wrapper function "zhemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zhemv(alpha,a,x,[beta,y,offx,incx,offy,incy,lower,overwrite_y])
		Constructing wrapper function "strmv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = strmv(a,x,[offx,incx,lower,trans,unitdiag,overwrite_x])
		Constructing wrapper function "dtrmv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = dtrmv(a,x,[offx,incx,lower,trans,unitdiag,overwrite_x])
		Constructing wrapper function "ctrmv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = ctrmv(a,x,[offx,incx,lower,trans,unitdiag,overwrite_x])
		Constructing wrapper function "ztrmv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = ztrmv(a,x,[offx,incx,lower,trans,unitdiag,overwrite_x])
		Constructing wrapper function "sger"...
		  a = sger(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "dger"...
		  a = dger(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "cgeru"...
		  a = cgeru(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "zgeru"...
		  a = zgeru(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "cgerc"...
		  a = cgerc(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "zgerc"...
		  a = zgerc(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "sgemm"...
		  c = sgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])
		Constructing wrapper function "dgemm"...
		  c = dgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])
		Constructing wrapper function "cgemm"...
		  c = cgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])
		Constructing wrapper function "zgemm"...
		  c = zgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])
	Wrote C/API module "fblas" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/fblasmodule.c"
	Fortran 77 wrappers are saved to "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/fblas-f2pywrappers.f"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/fblas-f2pywrappers.f' to sources.
building extension "scipy.lib.blas.cblas" sources
  adding 'scipy/lib/blas/cblas.pyf.src' to sources.
from_template:> build/src.linux-i686-2.6/scipy/lib/blas/cblas.pyf
Including file scipy/lib/blas/cblas_l1.pyf.src
f2py options: ['skip:', ':']
f2py: build/src.linux-i686-2.6/scipy/lib/blas/cblas.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/lib/blas/cblas.pyf' (format:free)
Line #33 in build/src.linux-i686-2.6/scipy/lib/blas/cblas.pyf:"  intent(c)"
	All arguments will have attribute intent(c)
Line #57 in build/src.linux-i686-2.6/scipy/lib/blas/cblas.pyf:"  intent(c)"
	All arguments will have attribute intent(c)
Line #81 in build/src.linux-i686-2.6/scipy/lib/blas/cblas.pyf:"  intent(c)"
	All arguments will have attribute intent(c)
Line #105 in build/src.linux-i686-2.6/scipy/lib/blas/cblas.pyf:"  intent(c)"
	All arguments will have attribute intent(c)
Post-processing...
	Block: cblas
			Block: saxpy
			Block: daxpy
			Block: caxpy
			Block: zaxpy
Post-processing (stage 2)...
Building modules...
	Building module "cblas"...
		Constructing wrapper function "saxpy"...
		  z = saxpy(x,y,[n,a,incx,incy,overwrite_y])
		Constructing wrapper function "daxpy"...
		  z = daxpy(x,y,[n,a,incx,incy,overwrite_y])
		Constructing wrapper function "caxpy"...
		  z = caxpy(x,y,[n,a,incx,incy,overwrite_y])
		Constructing wrapper function "zaxpy"...
		  z = zaxpy(x,y,[n,a,incx,incy,overwrite_y])
	Wrote C/API module "cblas" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/cblasmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.lib.lapack.flapack" sources
creating build/src.linux-i686-2.6/scipy/lib/lapack
from_template:> build/src.linux-i686-2.6/scipy/lib/lapack/flapack.pyf
Including file scipy/lib/lapack/flapack_user.pyf.src
Including file scipy/lib/lapack/flapack_le.pyf.src
Including file scipy/lib/lapack/flapack_lls.pyf.src
Including file scipy/lib/lapack/flapack_esv.pyf.src
Including file scipy/lib/lapack/flapack_gesv.pyf.src
Including file scipy/lib/lapack/flapack_lec.pyf.src
Including file scipy/lib/lapack/flapack_llsc.pyf.src
Including file scipy/lib/lapack/flapack_sevc.pyf.src
Including file scipy/lib/lapack/flapack_evc.pyf.src
Including file scipy/lib/lapack/flapack_svdc.pyf.src
Including file scipy/lib/lapack/flapack_gsevc.pyf.src
Including file scipy/lib/lapack/flapack_gevc.pyf.src
Including file scipy/lib/lapack/flapack_aux.pyf.src
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack
f2py options: ['skip:', ':']
f2py: build/src.linux-i686-2.6/scipy/lib/lapack/flapack.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/lib/lapack/flapack.pyf' (format:free)
Post-processing...
	Block: flapack
			Block: gees__user__routines
				Block: gees_user_interface
					Block: sselect
					Block: dselect
					Block: cselect
					Block: zselect
			Block: sgesv
			Block: dgesv
			Block: cgesv
			Block: zgesv
			Block: sgbsv
			Block: dgbsv
			Block: cgbsv
			Block: zgbsv
			Block: sposv
			Block: dposv
			Block: cposv
			Block: zposv
			Block: sgelss
			Block: dgelss
			Block: cgelss
			Block: zgelss
			Block: ssyev
			Block: dsyev
			Block: cheev
			Block: zheev
			Block: ssyevd
			Block: dsyevd
			Block: cheevd
			Block: zheevd
			Block: ssyevr
			Block: dsyevr
			Block: cheevr
			Block: zheevr
			Block: sgees
			Block: dgees
			Block: cgees
			Block: zgees
			Block: sgeev
			Block: dgeev
			Block: cgeev
			Block: zgeev
			Block: sgesdd
			Block: dgesdd
			Block: cgesdd
			Block: zgesdd
			Block: ssygv
			Block: dsygv
			Block: chegv
			Block: zhegv
			Block: ssygvd
			Block: dsygvd
			Block: chegvd
			Block: zhegvd
			Block: sggev
			Block: dggev
			Block: cggev
			Block: zggev
			Block: sgetrf
			Block: dgetrf
			Block: cgetrf
			Block: zgetrf
			Block: spotrf
			Block: dpotrf
			Block: cpotrf
			Block: zpotrf
			Block: sgetrs
			Block: dgetrs
			Block: cgetrs
			Block: zgetrs
			Block: spotrs
			Block: dpotrs
			Block: cpotrs
			Block: zpotrs
			Block: sgetri
			Block: dgetri
			Block: cgetri
			Block: zgetri
			Block: spotri
			Block: dpotri
			Block: cpotri
			Block: zpotri
			Block: strtri
			Block: dtrtri
			Block: ctrtri
			Block: ztrtri
			Block: sgeqrf
			Block: dgeqrf
			Block: cgeqrf
			Block: zgeqrf
			Block: sorgqr
			Block: dorgqr
			Block: cungqr
			Block: zungqr
			Block: sgehrd
			Block: dgehrd
			Block: cgehrd
			Block: zgehrd
			Block: sgebal
			Block: dgebal
			Block: cgebal
			Block: zgebal
			Block: slauum
			Block: dlauum
			Block: clauum
			Block: zlauum
			Block: slaswp
			Block: dlaswp
			Block: claswp
			Block: zlaswp
Post-processing (stage 2)...
Building modules...
	Constructing call-back function "cb_sselect_in_gees__user__routines"
	  def sselect(arg1,arg2): return sselect
	Constructing call-back function "cb_dselect_in_gees__user__routines"
	  def dselect(arg1,arg2): return dselect
	Constructing call-back function "cb_cselect_in_gees__user__routines"
	  def cselect(arg): return cselect
	Constructing call-back function "cb_zselect_in_gees__user__routines"
	  def zselect(arg): return zselect
	Building module "flapack"...
		Constructing wrapper function "sgesv"...
		  lu,piv,x,info = sgesv(a,b,[overwrite_a,overwrite_b])
		Constructing wrapper function "dgesv"...
		  lu,piv,x,info = dgesv(a,b,[overwrite_a,overwrite_b])
		Constructing wrapper function "cgesv"...
		  lu,piv,x,info = cgesv(a,b,[overwrite_a,overwrite_b])
		Constructing wrapper function "zgesv"...
		  lu,piv,x,info = zgesv(a,b,[overwrite_a,overwrite_b])
		Constructing wrapper function "sgbsv"...
		  lub,piv,x,info = sgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])
		Constructing wrapper function "dgbsv"...
		  lub,piv,x,info = dgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])
		Constructing wrapper function "cgbsv"...
		  lub,piv,x,info = cgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])
		Constructing wrapper function "zgbsv"...
		  lub,piv,x,info = zgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])
		Constructing wrapper function "sposv"...
		  c,x,info = sposv(a,b,[lower,overwrite_a,overwrite_b])
		Constructing wrapper function "dposv"...
		  c,x,info = dposv(a,b,[lower,overwrite_a,overwrite_b])
		Constructing wrapper function "cposv"...
		  c,x,info = cposv(a,b,[lower,overwrite_a,overwrite_b])
		Constructing wrapper function "zposv"...
		  c,x,info = zposv(a,b,[lower,overwrite_a,overwrite_b])
		Constructing wrapper function "sgelss"...
		  v,x,s,rank,info = sgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "dgelss"...
		  v,x,s,rank,info = dgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "cgelss"...
		  v,x,s,rank,info = cgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "zgelss"...
		  v,x,s,rank,info = zgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "ssyev"...
		  w,v,info = ssyev(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "dsyev"...
		  w,v,info = dsyev(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "cheev"...
		  w,v,info = cheev(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "zheev"...
		  w,v,info = zheev(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "ssyevd"...
		  w,v,info = ssyevd(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "dsyevd"...
		  w,v,info = dsyevd(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "cheevd"...
		  w,v,info = cheevd(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "zheevd"...
		  w,v,info = zheevd(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "ssyevr"...
		  w,v,info = ssyevr(a,[compute_v,lower,vrange,irange,atol,lwork,overwrite_a])
		Constructing wrapper function "dsyevr"...
		  w,v,info = dsyevr(a,[compute_v,lower,vrange,irange,atol,lwork,overwrite_a])
		Constructing wrapper function "cheevr"...
		  w,v,info = cheevr(a,[compute_v,lower,vrange,irange,atol,lwork,overwrite_a])
		Constructing wrapper function "zheevr"...
		  w,v,info = zheevr(a,[compute_v,lower,vrange,irange,atol,lwork,overwrite_a])
		Constructing wrapper function "sgees"...
		  t,sdim,wr,wi,vs,info = sgees(sselect,a,[compute_v,sort_t,lwork,sselect_extra_args,overwrite_a])
		Constructing wrapper function "dgees"...
		  t,sdim,wr,wi,vs,info = dgees(dselect,a,[compute_v,sort_t,lwork,dselect_extra_args,overwrite_a])
		Constructing wrapper function "cgees"...
		  t,sdim,w,vs,info = cgees(cselect,a,[compute_v,sort_t,lwork,cselect_extra_args,overwrite_a])
		Constructing wrapper function "zgees"...
		  t,sdim,w,vs,info = zgees(zselect,a,[compute_v,sort_t,lwork,zselect_extra_args,overwrite_a])
		Constructing wrapper function "sgeev"...
		  wr,wi,vl,vr,info = sgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])
		Constructing wrapper function "dgeev"...
		  wr,wi,vl,vr,info = dgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])
		Constructing wrapper function "cgeev"...
		  w,vl,vr,info = cgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])
		Constructing wrapper function "zgeev"...
		  w,vl,vr,info = zgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])
		Constructing wrapper function "sgesdd"...
		  u,s,vt,info = sgesdd(a,[compute_uv,lwork,overwrite_a])
		Constructing wrapper function "dgesdd"...
		  u,s,vt,info = dgesdd(a,[compute_uv,lwork,overwrite_a])
		Constructing wrapper function "cgesdd"...
		  u,s,vt,info = cgesdd(a,[compute_uv,lwork,overwrite_a])
		Constructing wrapper function "zgesdd"...
		  u,s,vt,info = zgesdd(a,[compute_uv,lwork,overwrite_a])
		Constructing wrapper function "ssygv"...
		  w,v,info = ssygv(a,b,[itype,compute_v,lower,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "dsygv"...
		  w,v,info = dsygv(a,b,[itype,compute_v,lower,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "chegv"...
		  w,v,info = chegv(a,b,[itype,compute_v,lower,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "zhegv"...
		  w,v,info = zhegv(a,b,[itype,compute_v,lower,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "ssygvd"...
		  w,v,info = ssygvd(a,b,[itype,compute_v,lower,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "dsygvd"...
		  w,v,info = dsygvd(a,b,[itype,compute_v,lower,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "chegvd"...
		  w,v,info = chegvd(a,b,[itype,compute_v,lower,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "zhegvd"...
		  w,v,info = zhegvd(a,b,[itype,compute_v,lower,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "sggev"...
		  alphar,alphai,beta,vl,vr,info = sggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "dggev"...
		  alphar,alphai,beta,vl,vr,info = dggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "cggev"...
		  alpha,beta,vl,vr,info = cggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "zggev"...
		  alpha,beta,vl,vr,info = zggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "sgetrf"...
		  lu,piv,info = sgetrf(a,[overwrite_a])
		Constructing wrapper function "dgetrf"...
		  lu,piv,info = dgetrf(a,[overwrite_a])
		Constructing wrapper function "cgetrf"...
		  lu,piv,info = cgetrf(a,[overwrite_a])
		Constructing wrapper function "zgetrf"...
		  lu,piv,info = zgetrf(a,[overwrite_a])
		Constructing wrapper function "spotrf"...
		  c,info = spotrf(a,[lower,clean,overwrite_a])
		Constructing wrapper function "dpotrf"...
		  c,info = dpotrf(a,[lower,clean,overwrite_a])
		Constructing wrapper function "cpotrf"...
		  c,info = cpotrf(a,[lower,clean,overwrite_a])
		Constructing wrapper function "zpotrf"...
		  c,info = zpotrf(a,[lower,clean,overwrite_a])
		Constructing wrapper function "sgetrs"...
		  x,info = sgetrs(lu,piv,b,[trans,overwrite_b])
		Constructing wrapper function "dgetrs"...
		  x,info = dgetrs(lu,piv,b,[trans,overwrite_b])
		Constructing wrapper function "cgetrs"...
		  x,info = cgetrs(lu,piv,b,[trans,overwrite_b])
		Constructing wrapper function "zgetrs"...
		  x,info = zgetrs(lu,piv,b,[trans,overwrite_b])
		Constructing wrapper function "spotrs"...
		  x,info = spotrs(c,b,[lower,overwrite_b])
		Constructing wrapper function "dpotrs"...
		  x,info = dpotrs(c,b,[lower,overwrite_b])
		Constructing wrapper function "cpotrs"...
		  x,info = cpotrs(c,b,[lower,overwrite_b])
		Constructing wrapper function "zpotrs"...
		  x,info = zpotrs(c,b,[lower,overwrite_b])
		Constructing wrapper function "sgetri"...
		  inv_a,info = sgetri(lu,piv,[lwork,overwrite_lu])
		Constructing wrapper function "dgetri"...
		  inv_a,info = dgetri(lu,piv,[lwork,overwrite_lu])
		Constructing wrapper function "cgetri"...
		  inv_a,info = cgetri(lu,piv,[lwork,overwrite_lu])
		Constructing wrapper function "zgetri"...
		  inv_a,info = zgetri(lu,piv,[lwork,overwrite_lu])
		Constructing wrapper function "spotri"...
		  inv_a,info = spotri(c,[lower,overwrite_c])
		Constructing wrapper function "dpotri"...
		  inv_a,info = dpotri(c,[lower,overwrite_c])
		Constructing wrapper function "cpotri"...
		  inv_a,info = cpotri(c,[lower,overwrite_c])
		Constructing wrapper function "zpotri"...
		  inv_a,info = zpotri(c,[lower,overwrite_c])
		Constructing wrapper function "strtri"...
		  inv_c,info = strtri(c,[lower,unitdiag,overwrite_c])
		Constructing wrapper function "dtrtri"...
		  inv_c,info = dtrtri(c,[lower,unitdiag,overwrite_c])
		Constructing wrapper function "ctrtri"...
		  inv_c,info = ctrtri(c,[lower,unitdiag,overwrite_c])
		Constructing wrapper function "ztrtri"...
		  inv_c,info = ztrtri(c,[lower,unitdiag,overwrite_c])
		Constructing wrapper function "sgeqrf"...
		  qr,tau,info = sgeqrf(a,[lwork,overwrite_a])
		Constructing wrapper function "dgeqrf"...
		  qr,tau,info = dgeqrf(a,[lwork,overwrite_a])
		Constructing wrapper function "cgeqrf"...
		  qr,tau,info = cgeqrf(a,[lwork,overwrite_a])
		Constructing wrapper function "zgeqrf"...
		  qr,tau,info = zgeqrf(a,[lwork,overwrite_a])
		Constructing wrapper function "sorgqr"...
		  q,info = sorgqr(qr,tau,[lwork,overwrite_qr,overwrite_tau])
		Constructing wrapper function "dorgqr"...
		  q,info = dorgqr(qr,tau,[lwork,overwrite_qr,overwrite_tau])
		Constructing wrapper function "cungqr"...
		  q,info = cungqr(qr,tau,[lwork,overwrite_qr,overwrite_tau])
		Constructing wrapper function "zungqr"...
		  q,info = zungqr(qr,tau,[lwork,overwrite_qr,overwrite_tau])
		Constructing wrapper function "sgehrd"...
		  ht,tau,info = sgehrd(a,[lo,hi,lwork,overwrite_a])
		Constructing wrapper function "dgehrd"...
		  ht,tau,info = dgehrd(a,[lo,hi,lwork,overwrite_a])
		Constructing wrapper function "cgehrd"...
		  ht,tau,info = cgehrd(a,[lo,hi,lwork,overwrite_a])
		Constructing wrapper function "zgehrd"...
		  ht,tau,info = zgehrd(a,[lo,hi,lwork,overwrite_a])
		Constructing wrapper function "sgebal"...
		  ba,lo,hi,pivscale,info = sgebal(a,[scale,permute,overwrite_a])
		Constructing wrapper function "dgebal"...
		  ba,lo,hi,pivscale,info = dgebal(a,[scale,permute,overwrite_a])
		Constructing wrapper function "cgebal"...
		  ba,lo,hi,pivscale,info = cgebal(a,[scale,permute,overwrite_a])
		Constructing wrapper function "zgebal"...
		  ba,lo,hi,pivscale,info = zgebal(a,[scale,permute,overwrite_a])
		Constructing wrapper function "slauum"...
		  a,info = slauum(c,[lower,overwrite_c])
		Constructing wrapper function "dlauum"...
		  a,info = dlauum(c,[lower,overwrite_c])
		Constructing wrapper function "clauum"...
		  a,info = clauum(c,[lower,overwrite_c])
		Constructing wrapper function "zlauum"...
		  a,info = zlauum(c,[lower,overwrite_c])
		Constructing wrapper function "slaswp"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  a = slaswp(a,piv,[k1,k2,off,inc,overwrite_a])
		Constructing wrapper function "dlaswp"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  a = dlaswp(a,piv,[k1,k2,off,inc,overwrite_a])
		Constructing wrapper function "claswp"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  a = claswp(a,piv,[k1,k2,off,inc,overwrite_a])
		Constructing wrapper function "zlaswp"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  a = zlaswp(a,piv,[k1,k2,off,inc,overwrite_a])
	Wrote C/API module "flapack" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack/flapackmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.lib.lapack.clapack" sources
  adding 'scipy/lib/lapack/clapack.pyf.src' to sources.
from_template:> build/src.linux-i686-2.6/scipy/lib/lapack/clapack.pyf
f2py options: ['skip:', ':']
f2py: build/src.linux-i686-2.6/scipy/lib/lapack/clapack.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/lib/lapack/clapack.pyf' (format:free)
Post-processing...
	Block: clapack
			Block: sgesv
			Block: dgesv
			Block: cgesv
			Block: zgesv
			Block: sgetrf
			Block: dgetrf
			Block: cgetrf
			Block: zgetrf
			Block: sgetrs
			Block: dgetrs
			Block: cgetrs
			Block: zgetrs
			Block: sgetri
			Block: dgetri
			Block: cgetri
			Block: zgetri
			Block: sposv
			Block: dposv
			Block: cposv
			Block: zposv
			Block: spotrf
			Block: dpotrf
			Block: cpotrf
			Block: zpotrf
			Block: spotrs
			Block: dpotrs
			Block: cpotrs
			Block: zpotrs
			Block: spotri
			Block: dpotri
			Block: cpotri
			Block: zpotri
			Block: slauum
			Block: dlauum
			Block: clauum
			Block: zlauum
			Block: strtri
			Block: dtrtri
			Block: ctrtri
			Block: ztrtri
Post-processing (stage 2)...
Building modules...
	Building module "clapack"...
		Constructing wrapper function "sgesv"...
		  lu,piv,x,info = sgesv(a,b,[rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "dgesv"...
		  lu,piv,x,info = dgesv(a,b,[rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "cgesv"...
		  lu,piv,x,info = cgesv(a,b,[rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "zgesv"...
		  lu,piv,x,info = zgesv(a,b,[rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "sgetrf"...
		  lu,piv,info = sgetrf(a,[rowmajor,overwrite_a])
		Constructing wrapper function "dgetrf"...
		  lu,piv,info = dgetrf(a,[rowmajor,overwrite_a])
		Constructing wrapper function "cgetrf"...
		  lu,piv,info = cgetrf(a,[rowmajor,overwrite_a])
		Constructing wrapper function "zgetrf"...
		  lu,piv,info = zgetrf(a,[rowmajor,overwrite_a])
		Constructing wrapper function "sgetrs"...
		  x,info = sgetrs(lu,piv,b,[trans,rowmajor,overwrite_b])
		Constructing wrapper function "dgetrs"...
		  x,info = dgetrs(lu,piv,b,[trans,rowmajor,overwrite_b])
		Constructing wrapper function "cgetrs"...
		  x,info = cgetrs(lu,piv,b,[trans,rowmajor,overwrite_b])
		Constructing wrapper function "zgetrs"...
		  x,info = zgetrs(lu,piv,b,[trans,rowmajor,overwrite_b])
		Constructing wrapper function "sgetri"...
		  inv_a,info = sgetri(lu,piv,[rowmajor,overwrite_lu])
		Constructing wrapper function "dgetri"...
		  inv_a,info = dgetri(lu,piv,[rowmajor,overwrite_lu])
		Constructing wrapper function "cgetri"...
		  inv_a,info = cgetri(lu,piv,[rowmajor,overwrite_lu])
		Constructing wrapper function "zgetri"...
		  inv_a,info = zgetri(lu,piv,[rowmajor,overwrite_lu])
		Constructing wrapper function "sposv"...
		  c,x,info = sposv(a,b,[lower,rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "dposv"...
		  c,x,info = dposv(a,b,[lower,rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "cposv"...
		  c,x,info = cposv(a,b,[lower,rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "zposv"...
		  c,x,info = zposv(a,b,[lower,rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "spotrf"...
		  c,info = spotrf(a,[lower,clean,rowmajor,overwrite_a])
		Constructing wrapper function "dpotrf"...
		  c,info = dpotrf(a,[lower,clean,rowmajor,overwrite_a])
		Constructing wrapper function "cpotrf"...
		  c,info = cpotrf(a,[lower,clean,rowmajor,overwrite_a])
		Constructing wrapper function "zpotrf"...
		  c,info = zpotrf(a,[lower,clean,rowmajor,overwrite_a])
		Constructing wrapper function "spotrs"...
		  x,info = spotrs(c,b,[lower,rowmajor,overwrite_b])
		Constructing wrapper function "dpotrs"...
		  x,info = dpotrs(c,b,[lower,rowmajor,overwrite_b])
		Constructing wrapper function "cpotrs"...
		  x,info = cpotrs(c,b,[lower,rowmajor,overwrite_b])
		Constructing wrapper function "zpotrs"...
		  x,info = zpotrs(c,b,[lower,rowmajor,overwrite_b])
		Constructing wrapper function "spotri"...
		  inv_a,info = spotri(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "dpotri"...
		  inv_a,info = dpotri(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "cpotri"...
		  inv_a,info = cpotri(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "zpotri"...
		  inv_a,info = zpotri(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "slauum"...
		  a,info = slauum(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "dlauum"...
		  a,info = dlauum(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "clauum"...
		  a,info = clauum(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "zlauum"...
		  a,info = zlauum(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "strtri"...
		  inv_c,info = strtri(c,[lower,unitdiag,rowmajor,overwrite_c])
		Constructing wrapper function "dtrtri"...
		  inv_c,info = dtrtri(c,[lower,unitdiag,rowmajor,overwrite_c])
		Constructing wrapper function "ctrtri"...
		  inv_c,info = ctrtri(c,[lower,unitdiag,rowmajor,overwrite_c])
		Constructing wrapper function "ztrtri"...
		  inv_c,info = ztrtri(c,[lower,unitdiag,rowmajor,overwrite_c])
	Wrote C/API module "clapack" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack/clapackmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.lib.lapack.calc_lwork" sources
f2py options: []
f2py:> build/src.linux-i686-2.6/scipy/lib/lapack/calc_lworkmodule.c
Reading fortran codes...
	Reading file 'scipy/lib/lapack/calc_lwork.f' (format:fix,strict)
Post-processing...
	Block: calc_lwork
			Block: gehrd
			Block: gesdd
			Block: gelss
			Block: getri
			Block: geev
			Block: heev
			Block: syev
			Block: gees
			Block: geqrf
			Block: gqr
Post-processing (stage 2)...
Building modules...
	Building module "calc_lwork"...
		Constructing wrapper function "gehrd"...
		  minwrk,maxwrk = gehrd(prefix,n,[lo,hi])
		Constructing wrapper function "gesdd"...
		  minwrk,maxwrk = gesdd(prefix,m,n,[compute_uv])
		Constructing wrapper function "gelss"...
		  minwrk,maxwrk = gelss(prefix,m,n,nrhs)
		Constructing wrapper function "getri"...
		  minwrk,maxwrk = getri(prefix,n)
		Constructing wrapper function "geev"...
		  minwrk,maxwrk = geev(prefix,n,[compute_vl,compute_vr])
		Constructing wrapper function "heev"...
		  minwrk,maxwrk = heev(prefix,n,[lower])
		Constructing wrapper function "syev"...
		  minwrk,maxwrk = syev(prefix,n,[lower])
		Constructing wrapper function "gees"...
		  minwrk,maxwrk = gees(prefix,n,[compute_v])
		Constructing wrapper function "geqrf"...
		  minwrk,maxwrk = geqrf(prefix,m,n)
		Constructing wrapper function "gqr"...
		  minwrk,maxwrk = gqr(prefix,m,n)
	Wrote C/API module "calc_lwork" to file "build/src.linux-i686-2.6/scipy/lib/lapack/calc_lworkmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.lib.lapack.atlas_version" sources
building extension "scipy.linalg.fblas" sources
creating build/src.linux-i686-2.6/scipy/linalg
generating fblas interface
23
  adding 'build/src.linux-i686-2.6/scipy/linalg/fblas.pyf' to sources.
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg
f2py options: []
f2py: build/src.linux-i686-2.6/scipy/linalg/fblas.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/linalg/fblas.pyf' (format:free)
Post-processing...
	Block: fblas
			Block: srotg
			Block: drotg
			Block: crotg
			Block: zrotg
			Block: srotmg
			Block: drotmg
			Block: srot
			Block: drot
			Block: csrot
			Block: zdrot
			Block: srotm
			Block: drotm
			Block: sswap
			Block: dswap
			Block: cswap
			Block: zswap
			Block: sscal
			Block: dscal
			Block: cscal
			Block: zscal
			Block: csscal
			Block: zdscal
			Block: scopy
			Block: dcopy
			Block: ccopy
			Block: zcopy
			Block: saxpy
			Block: daxpy
			Block: caxpy
			Block: zaxpy
			Block: cdotu
			Block: zdotu
			Block: cdotc
			Block: zdotc
			Block: sgemv
			Block: dgemv
			Block: cgemv
			Block: zgemv
			Block: chemv
			Block: zhemv
			Block: ssymv
			Block: dsymv
			Block: strmv
			Block: dtrmv
			Block: ctrmv
			Block: ztrmv
			Block: sger
			Block: dger
			Block: cgeru
			Block: zgeru
			Block: cgerc
			Block: zgerc
			Block: sgemm
			Block: dgemm
			Block: cgemm
			Block: zgemm
			Block: sdot
			Block: ddot
			Block: snrm2
			Block: dnrm2
			Block: scnrm2
			Block: dznrm2
			Block: sasum
			Block: dasum
			Block: scasum
			Block: dzasum
			Block: isamax
			Block: idamax
			Block: icamax
			Block: izamax
Post-processing (stage 2)...
Building modules...
	Building module "fblas"...
		Constructing wrapper function "srotg"...
		  c,s = srotg(a,b)
		Constructing wrapper function "drotg"...
		  c,s = drotg(a,b)
		Constructing wrapper function "crotg"...
		  c,s = crotg(a,b)
		Constructing wrapper function "zrotg"...
		  c,s = zrotg(a,b)
		Constructing wrapper function "srotmg"...
		  param = srotmg(d1,d2,x1,y1)
		Constructing wrapper function "drotmg"...
		  param = drotmg(d1,d2,x1,y1)
		Constructing wrapper function "srot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = srot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "drot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = drot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "csrot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = csrot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "zdrot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = zdrot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "srotm"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = srotm(x,y,param,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "drotm"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = drotm(x,y,param,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "sswap"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = sswap(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "dswap"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = dswap(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "cswap"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = cswap(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "zswap"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = zswap(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "sscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = sscal(a,x,[n,offx,incx])
		Constructing wrapper function "dscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = dscal(a,x,[n,offx,incx])
		Constructing wrapper function "cscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = cscal(a,x,[n,offx,incx])
		Constructing wrapper function "zscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = zscal(a,x,[n,offx,incx])
		Constructing wrapper function "csscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = csscal(a,x,[n,offx,incx,overwrite_x])
		Constructing wrapper function "zdscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = zdscal(a,x,[n,offx,incx,overwrite_x])
		Constructing wrapper function "scopy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = scopy(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "dcopy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = dcopy(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "ccopy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = ccopy(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "zcopy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zcopy(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "saxpy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = saxpy(x,y,[n,a,offx,incx,offy,incy])
		Constructing wrapper function "daxpy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = daxpy(x,y,[n,a,offx,incx,offy,incy])
		Constructing wrapper function "caxpy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = caxpy(x,y,[n,a,offx,incx,offy,incy])
		Constructing wrapper function "zaxpy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zaxpy(x,y,[n,a,offx,incx,offy,incy])
		Constructing wrapper function "cdotu"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = cdotu(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "zdotu"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = zdotu(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "cdotc"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = cdotc(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "zdotc"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = zdotc(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "sgemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = sgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])
		Constructing wrapper function "dgemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = dgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])
		Constructing wrapper function "cgemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = cgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])
		Constructing wrapper function "zgemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])
		Constructing wrapper function "chemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = chemv(alpha,a,x,beta,y,[offx,incx,offy,incy,lower,overwrite_y])
		Constructing wrapper function "zhemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zhemv(alpha,a,x,beta,y,[offx,incx,offy,incy,lower,overwrite_y])
		Constructing wrapper function "ssymv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = ssymv(alpha,a,x,beta,y,[offx,incx,offy,incy,lower,overwrite_y])
		Constructing wrapper function "dsymv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = dsymv(alpha,a,x,beta,y,[offx,incx,offy,incy,lower,overwrite_y])
		Constructing wrapper function "strmv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = strmv(a,x,[offx,incx,lower,trans,unitdiag,overwrite_x])
		Constructing wrapper function "dtrmv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = dtrmv(a,x,[offx,incx,lower,trans,unitdiag,overwrite_x])
		Constructing wrapper function "ctrmv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = ctrmv(a,x,[offx,incx,lower,trans,unitdiag,overwrite_x])
		Constructing wrapper function "ztrmv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = ztrmv(a,x,[offx,incx,lower,trans,unitdiag,overwrite_x])
		Constructing wrapper function "sger"...
		  a = sger(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "dger"...
		  a = dger(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "cgeru"...
		  a = cgeru(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "zgeru"...
		  a = zgeru(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "cgerc"...
		  a = cgerc(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "zgerc"...
		  a = zgerc(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "sgemm"...
		  c = sgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])
		Constructing wrapper function "dgemm"...
		  c = dgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])
		Constructing wrapper function "cgemm"...
		  c = cgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])
		Constructing wrapper function "zgemm"...
		  c = zgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])
		Creating wrapper for Fortran function "sdot"("sdot")...
		Constructing wrapper function "sdot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = sdot(x,y,[n,offx,incx,offy,incy])
		Creating wrapper for Fortran function "ddot"("ddot")...
		Constructing wrapper function "ddot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = ddot(x,y,[n,offx,incx,offy,incy])
		Creating wrapper for Fortran function "snrm2"("snrm2")...
		Constructing wrapper function "snrm2"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  n2 = snrm2(x,[n,offx,incx])
		Creating wrapper for Fortran function "dnrm2"("dnrm2")...
		Constructing wrapper function "dnrm2"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  n2 = dnrm2(x,[n,offx,incx])
		Creating wrapper for Fortran function "scnrm2"("scnrm2")...
		Constructing wrapper function "scnrm2"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  n2 = scnrm2(x,[n,offx,incx])
		Creating wrapper for Fortran function "dznrm2"("dznrm2")...
		Constructing wrapper function "dznrm2"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  n2 = dznrm2(x,[n,offx,incx])
		Creating wrapper for Fortran function "sasum"("sasum")...
		Constructing wrapper function "sasum"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  s = sasum(x,[n,offx,incx])
		Creating wrapper for Fortran function "dasum"("dasum")...
		Constructing wrapper function "dasum"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  s = dasum(x,[n,offx,incx])
		Creating wrapper for Fortran function "scasum"("scasum")...
		Constructing wrapper function "scasum"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  s = scasum(x,[n,offx,incx])
		Creating wrapper for Fortran function "dzasum"("dzasum")...
		Constructing wrapper function "dzasum"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  s = dzasum(x,[n,offx,incx])
		Constructing wrapper function "isamax"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  k = isamax(x,[n,offx,incx])
		Constructing wrapper function "idamax"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  k = idamax(x,[n,offx,incx])
		Constructing wrapper function "icamax"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  k = icamax(x,[n,offx,incx])
		Constructing wrapper function "izamax"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  k = izamax(x,[n,offx,incx])
	Wrote C/API module "fblas" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/fblasmodule.c"
	Fortran 77 wrappers are saved to "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/fblas-f2pywrappers.f"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/fblas-f2pywrappers.f' to sources.
building extension "scipy.linalg.cblas" sources
generating cblas interface
2
  adding 'build/src.linux-i686-2.6/scipy/linalg/cblas.pyf' to sources.
f2py options: []
f2py: build/src.linux-i686-2.6/scipy/linalg/cblas.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/linalg/cblas.pyf' (format:free)
Line #19 in build/src.linux-i686-2.6/scipy/linalg/cblas.pyf:"  intent(c)"
	All arguments will have attribute intent(c)
Line #42 in build/src.linux-i686-2.6/scipy/linalg/cblas.pyf:"  intent(c)"
	All arguments will have attribute intent(c)
Line #65 in build/src.linux-i686-2.6/scipy/linalg/cblas.pyf:"  intent(c)"
	All arguments will have attribute intent(c)
Line #88 in build/src.linux-i686-2.6/scipy/linalg/cblas.pyf:"  intent(c)"
	All arguments will have attribute intent(c)
Post-processing...
	Block: cblas
			Block: saxpy
			Block: daxpy
			Block: caxpy
			Block: zaxpy
Post-processing (stage 2)...
Building modules...
	Building module "cblas"...
		Constructing wrapper function "saxpy"...
		  z = saxpy(a,x,y,[n,incx,incy,overwrite_y])
		Constructing wrapper function "daxpy"...
		  z = daxpy(a,x,y,[n,incx,incy,overwrite_y])
		Constructing wrapper function "caxpy"...
		  z = caxpy(a,x,y,[n,incx,incy,overwrite_y])
		Constructing wrapper function "zaxpy"...
		  z = zaxpy(a,x,y,[n,incx,incy,overwrite_y])
	Wrote C/API module "cblas" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/cblasmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.linalg.flapack" sources
generating flapack interface
61
  adding 'build/src.linux-i686-2.6/scipy/linalg/flapack.pyf' to sources.
f2py options: []
f2py: build/src.linux-i686-2.6/scipy/linalg/flapack.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/linalg/flapack.pyf' (format:free)
Post-processing...
	Block: cgees__user__routines
		Block: cgees_user_interface
			Block: cselect
	Block: dgees__user__routines
		Block: dgees_user_interface
			Block: dselect
	Block: sgees__user__routines
		Block: sgees_user_interface
			Block: sselect
	Block: zgees__user__routines
		Block: zgees_user_interface
			Block: zselect
	Block: flapack
			Block: spbtrf
			Block: dpbtrf
			Block: cpbtrf
			Block: zpbtrf
			Block: spbsv
			Block: dpbsv
			Block: cpbsv
			Block: zpbsv
			Block: sgebal
			Block: dgebal
			Block: cgebal
			Block: zgebal
			Block: sgehrd
			Block: dgehrd
			Block: cgehrd
			Block: zgehrd
			Block: sgbsv
			Block: dgbsv
			Block: cgbsv
			Block: zgbsv
			Block: sgesv
			Block: dgesv
			Block: cgesv
			Block: zgesv
			Block: sgetrf
			Block: dgetrf
			Block: cgetrf
			Block: zgetrf
			Block: sgetrs
			Block: dgetrs
			Block: cgetrs
			Block: zgetrs
			Block: sgetri
			Block: dgetri
			Block: cgetri
			Block: zgetri
			Block: sgesdd
			Block: dgesdd
			Block: cgesdd
			Block: zgesdd
			Block: sgelss
			Block: dgelss
			Block: cgelss
			Block: zgelss
			Block: sgeqrf
			Block: dgeqrf
			Block: cgeqrf
			Block: zgeqrf
			Block: sgerqf
			Block: dgerqf
			Block: cgerqf
			Block: zgerqf
			Block: sorgqr
			Block: dorgqr
			Block: cungqr
			Block: zungqr
			Block: sgeev
			Block: dgeev
			Block: cgeev
			Block: zgeev
			Block: sgegv
			Block: dgegv
			Block: cgegv
			Block: zgegv
			Block: ssyev
			Block: dsyev
			Block: cheev
			Block: zheev
			Block: sposv
			Block: dposv
			Block: cposv
			Block: zposv
			Block: spotrf
			Block: dpotrf
			Block: cpotrf
			Block: zpotrf
			Block: spotrs
			Block: dpotrs
			Block: cpotrs
			Block: zpotrs
			Block: spotri
			Block: dpotri
			Block: cpotri
			Block: zpotri
			Block: slauum
			Block: dlauum
			Block: clauum
			Block: zlauum
			Block: strtri
			Block: dtrtri
			Block: ctrtri
			Block: ztrtri
			Block: slaswp
			Block: dlaswp
			Block: claswp
			Block: zlaswp
			Block: cgees
			Block: zgees
			Block: dgees
			Block: sgees
			Block: sggev
			Block: dggev
			Block: cggev
			Block: zggev
			Block: ssbev
			Block: dsbev
			Block: ssbevd
			Block: dsbevd
			Block: ssbevx
			Block: dsbevx
			Block: chbevd
			Block: zhbevd
			Block: chbevx
			Block: zhbevx
			Block: sgbtrf
			Block: dgbtrf
			Block: cgbtrf
			Block: zgbtrf
			Block: sgbtrs
			Block: dgbtrs
			Block: cgbtrs
			Block: zgbtrs
			Block: ssyevr
			Block: dsyevr
			Block: cheevr
			Block: zheevr
			Block: ssygv
			Block: dsygv
			Block: chegv
			Block: zhegv
			Block: ssygvd
			Block: dsygvd
			Block: chegvd
			Block: zhegvd
			Block: ssygvx
			Block: dsygvx
			Block: chegvx
			Block: zhegvx
			Block: slamch
			Block: dlamch
Post-processing (stage 2)...
Building modules...
	Constructing call-back function "cb_cselect_in_cgees__user__routines"
	  def cselect(e_w__i__e): return cselect
	Constructing call-back function "cb_dselect_in_dgees__user__routines"
	  def dselect(e_wr__i__e,e_wi__i__e): return dselect
	Constructing call-back function "cb_sselect_in_sgees__user__routines"
	  def sselect(e_wr__i__e,e_wi__i__e): return sselect
	Constructing call-back function "cb_zselect_in_zgees__user__routines"
	  def zselect(e_w__i__e): return zselect
	Building module "flapack"...
		Constructing wrapper function "spbtrf"...
		  c,info = spbtrf(ab,[lower,ldab,overwrite_ab])
		Constructing wrapper function "dpbtrf"...
		  c,info = dpbtrf(ab,[lower,ldab,overwrite_ab])
		Constructing wrapper function "cpbtrf"...
		  c,info = cpbtrf(ab,[lower,ldab,overwrite_ab])
		Constructing wrapper function "zpbtrf"...
		  c,info = zpbtrf(ab,[lower,ldab,overwrite_ab])
		Constructing wrapper function "spbsv"...
		  c,x,info = spbsv(ab,b,[lower,ldab,overwrite_ab,overwrite_b])
		Constructing wrapper function "dpbsv"...
		  c,x,info = dpbsv(ab,b,[lower,ldab,overwrite_ab,overwrite_b])
		Constructing wrapper function "cpbsv"...
		  c,x,info = cpbsv(ab,b,[lower,ldab,overwrite_ab,overwrite_b])
		Constructing wrapper function "zpbsv"...
		  c,x,info = zpbsv(ab,b,[lower,ldab,overwrite_ab,overwrite_b])
		Constructing wrapper function "sgebal"...
		  ba,lo,hi,pivscale,info = sgebal(a,[scale,permute,overwrite_a])
		Constructing wrapper function "dgebal"...
		  ba,lo,hi,pivscale,info = dgebal(a,[scale,permute,overwrite_a])
		Constructing wrapper function "cgebal"...
		  ba,lo,hi,pivscale,info = cgebal(a,[scale,permute,overwrite_a])
		Constructing wrapper function "zgebal"...
		  ba,lo,hi,pivscale,info = zgebal(a,[scale,permute,overwrite_a])
		Constructing wrapper function "sgehrd"...
		  ht,tau,info = sgehrd(a,[lo,hi,lwork,overwrite_a])
		Constructing wrapper function "dgehrd"...
		  ht,tau,info = dgehrd(a,[lo,hi,lwork,overwrite_a])
		Constructing wrapper function "cgehrd"...
		  ht,tau,info = cgehrd(a,[lo,hi,lwork,overwrite_a])
		Constructing wrapper function "zgehrd"...
		  ht,tau,info = zgehrd(a,[lo,hi,lwork,overwrite_a])
		Constructing wrapper function "sgbsv"...
		  lub,piv,x,info = sgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])
		Constructing wrapper function "dgbsv"...
		  lub,piv,x,info = dgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])
		Constructing wrapper function "cgbsv"...
		  lub,piv,x,info = cgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])
		Constructing wrapper function "zgbsv"...
		  lub,piv,x,info = zgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])
		Constructing wrapper function "sgesv"...
		  lu,piv,x,info = sgesv(a,b,[overwrite_a,overwrite_b])
		Constructing wrapper function "dgesv"...
		  lu,piv,x,info = dgesv(a,b,[overwrite_a,overwrite_b])
		Constructing wrapper function "cgesv"...
		  lu,piv,x,info = cgesv(a,b,[overwrite_a,overwrite_b])
		Constructing wrapper function "zgesv"...
		  lu,piv,x,info = zgesv(a,b,[overwrite_a,overwrite_b])
		Constructing wrapper function "sgetrf"...
		  lu,piv,info = sgetrf(a,[overwrite_a])
		Constructing wrapper function "dgetrf"...
		  lu,piv,info = dgetrf(a,[overwrite_a])
		Constructing wrapper function "cgetrf"...
		  lu,piv,info = cgetrf(a,[overwrite_a])
		Constructing wrapper function "zgetrf"...
		  lu,piv,info = zgetrf(a,[overwrite_a])
		Constructing wrapper function "sgetrs"...
		  x,info = sgetrs(lu,piv,b,[trans,overwrite_b])
		Constructing wrapper function "dgetrs"...
		  x,info = dgetrs(lu,piv,b,[trans,overwrite_b])
		Constructing wrapper function "cgetrs"...
		  x,info = cgetrs(lu,piv,b,[trans,overwrite_b])
		Constructing wrapper function "zgetrs"...
		  x,info = zgetrs(lu,piv,b,[trans,overwrite_b])
		Constructing wrapper function "sgetri"...
		  inv_a,info = sgetri(lu,piv,[lwork,overwrite_lu])
		Constructing wrapper function "dgetri"...
		  inv_a,info = dgetri(lu,piv,[lwork,overwrite_lu])
		Constructing wrapper function "cgetri"...
		  inv_a,info = cgetri(lu,piv,[lwork,overwrite_lu])
		Constructing wrapper function "zgetri"...
		  inv_a,info = zgetri(lu,piv,[lwork,overwrite_lu])
		Constructing wrapper function "sgesdd"...
		  u,s,vt,info = sgesdd(a,[compute_uv,lwork,overwrite_a])
		Constructing wrapper function "dgesdd"...
		  u,s,vt,info = dgesdd(a,[compute_uv,lwork,overwrite_a])
		Constructing wrapper function "cgesdd"...
		  u,s,vt,info = cgesdd(a,[compute_uv,lwork,overwrite_a])
		Constructing wrapper function "zgesdd"...
		  u,s,vt,info = zgesdd(a,[compute_uv,lwork,overwrite_a])
		Constructing wrapper function "sgelss"...
		  v,x,s,rank,info = sgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "dgelss"...
		  v,x,s,rank,info = dgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "cgelss"...
		  v,x,s,rank,info = cgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "zgelss"...
		  v,x,s,rank,info = zgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "sgeqrf"...
		  qr,tau,work,info = sgeqrf(a,[lwork,overwrite_a])
		Constructing wrapper function "dgeqrf"...
		  qr,tau,work,info = dgeqrf(a,[lwork,overwrite_a])
		Constructing wrapper function "cgeqrf"...
		  qr,tau,work,info = cgeqrf(a,[lwork,overwrite_a])
		Constructing wrapper function "zgeqrf"...
		  qr,tau,work,info = zgeqrf(a,[lwork,overwrite_a])
		Constructing wrapper function "sgerqf"...
		  qr,tau,work,info = sgerqf(a,[lwork,overwrite_a])
		Constructing wrapper function "dgerqf"...
		  qr,tau,work,info = dgerqf(a,[lwork,overwrite_a])
		Constructing wrapper function "cgerqf"...
		  qr,tau,work,info = cgerqf(a,[lwork,overwrite_a])
		Constructing wrapper function "zgerqf"...
		  qr,tau,work,info = zgerqf(a,[lwork,overwrite_a])
		Constructing wrapper function "sorgqr"...
		  q,work,info = sorgqr(a,tau,[lwork,overwrite_a])
		Constructing wrapper function "dorgqr"...
		  q,work,info = dorgqr(a,tau,[lwork,overwrite_a])
		Constructing wrapper function "cungqr"...
		  q,work,info = cungqr(a,tau,[lwork,overwrite_a])
		Constructing wrapper function "zungqr"...
		  q,work,info = zungqr(a,tau,[lwork,overwrite_a])
		Constructing wrapper function "sgeev"...
		  wr,wi,vl,vr,info = sgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])
		Constructing wrapper function "dgeev"...
		  wr,wi,vl,vr,info = dgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])
		Constructing wrapper function "cgeev"...
		  w,vl,vr,info = cgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])
		Constructing wrapper function "zgeev"...
		  w,vl,vr,info = zgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])
		Constructing wrapper function "sgegv"...
		  alphar,alphai,beta,vl,vr,info = sgegv(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "dgegv"...
		  alphar,alphai,beta,vl,vr,info = dgegv(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "cgegv"...
		  alpha,beta,vl,vr,info = cgegv(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "zgegv"...
		  alpha,beta,vl,vr,info = zgegv(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "ssyev"...
		  w,v,info = ssyev(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "dsyev"...
		  w,v,info = dsyev(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "cheev"...
		  w,v,info = cheev(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "zheev"...
		  w,v,info = zheev(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "sposv"...
		  c,x,info = sposv(a,b,[lower,overwrite_a,overwrite_b])
		Constructing wrapper function "dposv"...
		  c,x,info = dposv(a,b,[lower,overwrite_a,overwrite_b])
		Constructing wrapper function "cposv"...
		  c,x,info = cposv(a,b,[lower,overwrite_a,overwrite_b])
		Constructing wrapper function "zposv"...
		  c,x,info = zposv(a,b,[lower,overwrite_a,overwrite_b])
		Constructing wrapper function "spotrf"...
		  c,info = spotrf(a,[lower,clean,overwrite_a])
		Constructing wrapper function "dpotrf"...
		  c,info = dpotrf(a,[lower,clean,overwrite_a])
		Constructing wrapper function "cpotrf"...
		  c,info = cpotrf(a,[lower,clean,overwrite_a])
		Constructing wrapper function "zpotrf"...
		  c,info = zpotrf(a,[lower,clean,overwrite_a])
		Constructing wrapper function "spotrs"...
		  x,info = spotrs(c,b,[lower,overwrite_b])
		Constructing wrapper function "dpotrs"...
		  x,info = dpotrs(c,b,[lower,overwrite_b])
		Constructing wrapper function "cpotrs"...
		  x,info = cpotrs(c,b,[lower,overwrite_b])
		Constructing wrapper function "zpotrs"...
		  x,info = zpotrs(c,b,[lower,overwrite_b])
		Constructing wrapper function "spotri"...
		  inv_a,info = spotri(c,[lower,overwrite_c])
		Constructing wrapper function "dpotri"...
		  inv_a,info = dpotri(c,[lower,overwrite_c])
		Constructing wrapper function "cpotri"...
		  inv_a,info = cpotri(c,[lower,overwrite_c])
		Constructing wrapper function "zpotri"...
		  inv_a,info = zpotri(c,[lower,overwrite_c])
		Constructing wrapper function "slauum"...
		  a,info = slauum(c,[lower,overwrite_c])
		Constructing wrapper function "dlauum"...
		  a,info = dlauum(c,[lower,overwrite_c])
		Constructing wrapper function "clauum"...
		  a,info = clauum(c,[lower,overwrite_c])
		Constructing wrapper function "zlauum"...
		  a,info = zlauum(c,[lower,overwrite_c])
		Constructing wrapper function "strtri"...
		  inv_c,info = strtri(c,[lower,unitdiag,overwrite_c])
		Constructing wrapper function "dtrtri"...
		  inv_c,info = dtrtri(c,[lower,unitdiag,overwrite_c])
		Constructing wrapper function "ctrtri"...
		  inv_c,info = ctrtri(c,[lower,unitdiag,overwrite_c])
		Constructing wrapper function "ztrtri"...
		  inv_c,info = ztrtri(c,[lower,unitdiag,overwrite_c])
		Constructing wrapper function "slaswp"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  a = slaswp(a,piv,[k1,k2,off,inc,overwrite_a])
		Constructing wrapper function "dlaswp"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  a = dlaswp(a,piv,[k1,k2,off,inc,overwrite_a])
		Constructing wrapper function "claswp"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  a = claswp(a,piv,[k1,k2,off,inc,overwrite_a])
		Constructing wrapper function "zlaswp"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  a = zlaswp(a,piv,[k1,k2,off,inc,overwrite_a])
		Constructing wrapper function "cgees"...
		  t,sdim,w,vs,work,info = cgees(cselect,a,[compute_v,sort_t,lwork,cselect_extra_args,overwrite_a])
		Constructing wrapper function "zgees"...
		  t,sdim,w,vs,work,info = zgees(zselect,a,[compute_v,sort_t,lwork,zselect_extra_args,overwrite_a])
		Constructing wrapper function "dgees"...
		  t,sdim,wr,wi,vs,work,info = dgees(dselect,a,[compute_v,sort_t,lwork,dselect_extra_args,overwrite_a])
		Constructing wrapper function "sgees"...
		  t,sdim,wr,wi,vs,work,info = sgees(sselect,a,[compute_v,sort_t,lwork,sselect_extra_args,overwrite_a])
		Constructing wrapper function "sggev"...
		  alphar,alphai,beta,vl,vr,work,info = sggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "dggev"...
		  alphar,alphai,beta,vl,vr,work,info = dggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "cggev"...
		  alpha,beta,vl,vr,work,info = cggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "zggev"...
		  alpha,beta,vl,vr,work,info = zggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "ssbev"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,info = ssbev(ab,[compute_v,lower,ldab,overwrite_ab])
		Constructing wrapper function "dsbev"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,info = dsbev(ab,[compute_v,lower,ldab,overwrite_ab])
		Constructing wrapper function "ssbevd"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,info = ssbevd(ab,[compute_v,lower,ldab,liwork,overwrite_ab])
		Constructing wrapper function "dsbevd"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,info = dsbevd(ab,[compute_v,lower,ldab,liwork,overwrite_ab])
		Constructing wrapper function "ssbevx"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,m,ifail,info = ssbevx(ab,vl,vu,il,iu,[ldab,compute_v,range,lower,abstol,mmax,overwrite_ab])
		Constructing wrapper function "dsbevx"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,m,ifail,info = dsbevx(ab,vl,vu,il,iu,[ldab,compute_v,range,lower,abstol,mmax,overwrite_ab])
		Constructing wrapper function "chbevd"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,info = chbevd(ab,[compute_v,lower,ldab,lrwork,liwork,overwrite_ab])
		Constructing wrapper function "zhbevd"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,info = zhbevd(ab,[compute_v,lower,ldab,lrwork,liwork,overwrite_ab])
		Constructing wrapper function "chbevx"...
warning: callstatement is defined without callprotoargument
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,m,ifail,info = chbevx(ab,vl,vu,il,iu,[ldab,compute_v,range,lower,abstol,mmax,overwrite_ab])
		Constructing wrapper function "zhbevx"...
warning: callstatement is defined without callprotoargument
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,m,ifail,info = zhbevx(ab,vl,vu,il,iu,[ldab,compute_v,range,lower,abstol,mmax,overwrite_ab])
		Constructing wrapper function "sgbtrf"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  lu,ipiv,info = sgbtrf(ab,kl,ku,[m,n,ldab,overwrite_ab])
		Constructing wrapper function "dgbtrf"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  lu,ipiv,info = dgbtrf(ab,kl,ku,[m,n,ldab,overwrite_ab])
		Constructing wrapper function "cgbtrf"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  lu,ipiv,info = cgbtrf(ab,kl,ku,[m,n,ldab,overwrite_ab])
		Constructing wrapper function "zgbtrf"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  lu,ipiv,info = zgbtrf(ab,kl,ku,[m,n,ldab,overwrite_ab])
		Constructing wrapper function "sgbtrs"...
warning: callstatement is defined without callprotoargument
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,info = sgbtrs(ab,kl,ku,b,ipiv,[trans,n,ldab,ldb,overwrite_b])
		Constructing wrapper function "dgbtrs"...
warning: callstatement is defined without callprotoargument
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,info = dgbtrs(ab,kl,ku,b,ipiv,[trans,n,ldab,ldb,overwrite_b])
		Constructing wrapper function "cgbtrs"...
warning: callstatement is defined without callprotoargument
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,info = cgbtrs(ab,kl,ku,b,ipiv,[trans,n,ldab,ldb,overwrite_b])
		Constructing wrapper function "zgbtrs"...
warning: callstatement is defined without callprotoargument
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,info = zgbtrs(ab,kl,ku,b,ipiv,[trans,n,ldab,ldb,overwrite_b])
		Constructing wrapper function "ssyevr"...
		  w,z,info = ssyevr(a,[jobz,range,uplo,il,iu,lwork,overwrite_a])
		Constructing wrapper function "dsyevr"...
		  w,z,info = dsyevr(a,[jobz,range,uplo,il,iu,lwork,overwrite_a])
		Constructing wrapper function "cheevr"...
		  w,z,info = cheevr(a,[jobz,range,uplo,il,iu,lwork,overwrite_a])
		Constructing wrapper function "zheevr"...
		  w,z,info = zheevr(a,[jobz,range,uplo,il,iu,lwork,overwrite_a])
		Constructing wrapper function "ssygv"...
		  a,w,info = ssygv(a,b,[itype,jobz,uplo,overwrite_a,overwrite_b])
		Constructing wrapper function "dsygv"...
		  a,w,info = dsygv(a,b,[itype,jobz,uplo,overwrite_a,overwrite_b])
		Constructing wrapper function "chegv"...
		  a,w,info = chegv(a,b,[itype,jobz,uplo,overwrite_a,overwrite_b])
		Constructing wrapper function "zhegv"...
		  a,w,info = zhegv(a,b,[itype,jobz,uplo,overwrite_a,overwrite_b])
		Constructing wrapper function "ssygvd"...
		  a,w,info = ssygvd(a,b,[itype,jobz,uplo,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "dsygvd"...
		  a,w,info = dsygvd(a,b,[itype,jobz,uplo,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "chegvd"...
		  a,w,info = chegvd(a,b,[itype,jobz,uplo,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "zhegvd"...
		  a,w,info = zhegvd(a,b,[itype,jobz,uplo,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "ssygvx"...
		  w,z,ifail,info = ssygvx(a,b,iu,[itype,jobz,uplo,il,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "dsygvx"...
		  w,z,ifail,info = dsygvx(a,b,iu,[itype,jobz,uplo,il,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "chegvx"...
		  w,z,ifail,info = chegvx(a,b,iu,[itype,jobz,uplo,il,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "zhegvx"...
		  w,z,ifail,info = zhegvx(a,b,iu,[itype,jobz,uplo,il,lwork,overwrite_a,overwrite_b])
		Creating wrapper for Fortran function "slamch"("slamch")...
		Constructing wrapper function "slamch"...
		  slamch = slamch(cmach)
		Creating wrapper for Fortran function "dlamch"("dlamch")...
		Constructing wrapper function "dlamch"...
		  dlamch = dlamch(cmach)
	Wrote C/API module "flapack" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c"
	Fortran 77 wrappers are saved to "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapack-f2pywrappers.f"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapack-f2pywrappers.f' to sources.
building extension "scipy.linalg.clapack" sources
generating clapack interface
11
  adding 'build/src.linux-i686-2.6/scipy/linalg/clapack.pyf' to sources.
f2py options: []
f2py: build/src.linux-i686-2.6/scipy/linalg/clapack.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/linalg/clapack.pyf' (format:free)
Post-processing...
	Block: clapack
			Block: sgesv
			Block: dgesv
			Block: cgesv
			Block: zgesv
			Block: sgetrf
			Block: dgetrf
			Block: cgetrf
			Block: zgetrf
			Block: sgetrs
			Block: dgetrs
			Block: cgetrs
			Block: zgetrs
			Block: sgetri
			Block: dgetri
			Block: cgetri
			Block: zgetri
			Block: sposv
			Block: dposv
			Block: cposv
			Block: zposv
			Block: spotrf
			Block: dpotrf
			Block: cpotrf
			Block: zpotrf
			Block: spotrs
			Block: dpotrs
			Block: cpotrs
			Block: zpotrs
			Block: spotri
			Block: dpotri
			Block: cpotri
			Block: zpotri
			Block: slauum
			Block: dlauum
			Block: clauum
			Block: zlauum
			Block: strtri
			Block: dtrtri
			Block: ctrtri
			Block: ztrtri
Post-processing (stage 2)...
Building modules...
	Building module "clapack"...
		Constructing wrapper function "sgesv"...
		  lu,piv,x,info = sgesv(a,b,[rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "dgesv"...
		  lu,piv,x,info = dgesv(a,b,[rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "cgesv"...
		  lu,piv,x,info = cgesv(a,b,[rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "zgesv"...
		  lu,piv,x,info = zgesv(a,b,[rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "sgetrf"...
		  lu,piv,info = sgetrf(a,[rowmajor,overwrite_a])
		Constructing wrapper function "dgetrf"...
		  lu,piv,info = dgetrf(a,[rowmajor,overwrite_a])
		Constructing wrapper function "cgetrf"...
		  lu,piv,info = cgetrf(a,[rowmajor,overwrite_a])
		Constructing wrapper function "zgetrf"...
		  lu,piv,info = zgetrf(a,[rowmajor,overwrite_a])
		Constructing wrapper function "sgetrs"...
		  x,info = sgetrs(lu,piv,b,[trans,rowmajor,overwrite_b])
		Constructing wrapper function "dgetrs"...
		  x,info = dgetrs(lu,piv,b,[trans,rowmajor,overwrite_b])
		Constructing wrapper function "cgetrs"...
		  x,info = cgetrs(lu,piv,b,[trans,rowmajor,overwrite_b])
		Constructing wrapper function "zgetrs"...
		  x,info = zgetrs(lu,piv,b,[trans,rowmajor,overwrite_b])
		Constructing wrapper function "sgetri"...
		  inv_a,info = sgetri(lu,piv,[rowmajor,overwrite_lu])
		Constructing wrapper function "dgetri"...
		  inv_a,info = dgetri(lu,piv,[rowmajor,overwrite_lu])
		Constructing wrapper function "cgetri"...
		  inv_a,info = cgetri(lu,piv,[rowmajor,overwrite_lu])
		Constructing wrapper function "zgetri"...
		  inv_a,info = zgetri(lu,piv,[rowmajor,overwrite_lu])
		Constructing wrapper function "sposv"...
		  c,x,info = sposv(a,b,[lower,rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "dposv"...
		  c,x,info = dposv(a,b,[lower,rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "cposv"...
		  c,x,info = cposv(a,b,[lower,rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "zposv"...
		  c,x,info = zposv(a,b,[lower,rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "spotrf"...
		  c,info = spotrf(a,[lower,clean,rowmajor,overwrite_a])
		Constructing wrapper function "dpotrf"...
		  c,info = dpotrf(a,[lower,clean,rowmajor,overwrite_a])
		Constructing wrapper function "cpotrf"...
		  c,info = cpotrf(a,[lower,clean,rowmajor,overwrite_a])
		Constructing wrapper function "zpotrf"...
		  c,info = zpotrf(a,[lower,clean,rowmajor,overwrite_a])
		Constructing wrapper function "spotrs"...
		  x,info = spotrs(c,b,[lower,rowmajor,overwrite_b])
		Constructing wrapper function "dpotrs"...
		  x,info = dpotrs(c,b,[lower,rowmajor,overwrite_b])
		Constructing wrapper function "cpotrs"...
		  x,info = cpotrs(c,b,[lower,rowmajor,overwrite_b])
		Constructing wrapper function "zpotrs"...
		  x,info = zpotrs(c,b,[lower,rowmajor,overwrite_b])
		Constructing wrapper function "spotri"...
		  inv_a,info = spotri(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "dpotri"...
		  inv_a,info = dpotri(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "cpotri"...
		  inv_a,info = cpotri(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "zpotri"...
		  inv_a,info = zpotri(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "slauum"...
		  a,info = slauum(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "dlauum"...
		  a,info = dlauum(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "clauum"...
		  a,info = clauum(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "zlauum"...
		  a,info = zlauum(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "strtri"...
		  inv_c,info = strtri(c,[lower,unitdiag,rowmajor,overwrite_c])
		Constructing wrapper function "dtrtri"...
		  inv_c,info = dtrtri(c,[lower,unitdiag,rowmajor,overwrite_c])
		Constructing wrapper function "ctrtri"...
		  inv_c,info = ctrtri(c,[lower,unitdiag,rowmajor,overwrite_c])
		Constructing wrapper function "ztrtri"...
		  inv_c,info = ztrtri(c,[lower,unitdiag,rowmajor,overwrite_c])
	Wrote C/API module "clapack" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/clapackmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.linalg._flinalg" sources
f2py options: []
f2py:> build/src.linux-i686-2.6/scipy/linalg/_flinalgmodule.c
Reading fortran codes...
	Reading file 'scipy/linalg/src/det.f' (format:fix,strict)
	Reading file 'scipy/linalg/src/lu.f' (format:fix,strict)
Post-processing...
	Block: _flinalg
			Block: ddet_c
			Block: ddet_r
			Block: sdet_c
			Block: sdet_r
			Block: zdet_c
			Block: zdet_r
			Block: cdet_c
			Block: cdet_r
			Block: dlu_c
			Block: zlu_c
			Block: slu_c
			Block: clu_c
Post-processing (stage 2)...
Building modules...
	Building module "_flinalg"...
		Constructing wrapper function "ddet_c"...
		  det,info = ddet_c(a,[overwrite_a])
		Constructing wrapper function "ddet_r"...
		  det,info = ddet_r(a,[overwrite_a])
		Constructing wrapper function "sdet_c"...
		  det,info = sdet_c(a,[overwrite_a])
		Constructing wrapper function "sdet_r"...
		  det,info = sdet_r(a,[overwrite_a])
		Constructing wrapper function "zdet_c"...
		  det,info = zdet_c(a,[overwrite_a])
		Constructing wrapper function "zdet_r"...
		  det,info = zdet_r(a,[overwrite_a])
		Constructing wrapper function "cdet_c"...
		  det,info = cdet_c(a,[overwrite_a])
		Constructing wrapper function "cdet_r"...
		  det,info = cdet_r(a,[overwrite_a])
		Constructing wrapper function "dlu_c"...
		  p,l,u,info = dlu_c(a,[permute_l,overwrite_a])
		Constructing wrapper function "zlu_c"...
		  p,l,u,info = zlu_c(a,[permute_l,overwrite_a])
		Constructing wrapper function "slu_c"...
		  p,l,u,info = slu_c(a,[permute_l,overwrite_a])
		Constructing wrapper function "clu_c"...
		  p,l,u,info = clu_c(a,[permute_l,overwrite_a])
	Wrote C/API module "_flinalg" to file "build/src.linux-i686-2.6/scipy/linalg/_flinalgmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.linalg.calc_lwork" sources
f2py options: []
f2py:> build/src.linux-i686-2.6/scipy/linalg/calc_lworkmodule.c
Reading fortran codes...
	Reading file 'scipy/linalg/src/calc_lwork.f' (format:fix,strict)
Post-processing...
	Block: calc_lwork
			Block: gehrd
			Block: gesdd
			Block: gelss
			Block: getri
			Block: geev
			Block: heev
			Block: syev
			Block: gees
			Block: geqrf
			Block: gqr
Post-processing (stage 2)...
Building modules...
	Building module "calc_lwork"...
		Constructing wrapper function "gehrd"...
		  minwrk,maxwrk = gehrd(prefix,n,lo,hi)
		Constructing wrapper function "gesdd"...
		  minwrk,maxwrk = gesdd(prefix,m,n,compute_uv)
		Constructing wrapper function "gelss"...
		  minwrk,maxwrk = gelss(prefix,m,n,nrhs)
		Constructing wrapper function "getri"...
		  minwrk,maxwrk = getri(prefix,n)
		Constructing wrapper function "geev"...
		  minwrk,maxwrk = geev(prefix,n,[compute_vl,compute_vr])
		Constructing wrapper function "heev"...
		  minwrk,maxwrk = heev(prefix,n,[lower])
		Constructing wrapper function "syev"...
		  minwrk,maxwrk = syev(prefix,n,[lower])
		Constructing wrapper function "gees"...
		  minwrk,maxwrk = gees(prefix,n,[compute_v])
		Constructing wrapper function "geqrf"...
		  minwrk,maxwrk = geqrf(prefix,m,n)
		Constructing wrapper function "gqr"...
		  minwrk,maxwrk = gqr(prefix,m,n)
	Wrote C/API module "calc_lwork" to file "build/src.linux-i686-2.6/scipy/linalg/calc_lworkmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.linalg.atlas_version" sources
building extension "scipy.odr.__odrpack" sources
building extension "scipy.optimize._minpack" sources
building extension "scipy.optimize._zeros" sources
building extension "scipy.optimize._lbfgsb" sources
creating build/src.linux-i686-2.6/scipy/optimize
creating build/src.linux-i686-2.6/scipy/optimize/lbfgsb
f2py options: []
f2py: scipy/optimize/lbfgsb/lbfgsb.pyf
Reading fortran codes...
	Reading file 'scipy/optimize/lbfgsb/lbfgsb.pyf' (format:free)
Post-processing...
	Block: _lbfgsb
			Block: setulb
Post-processing (stage 2)...
Building modules...
	Building module "_lbfgsb"...
		Constructing wrapper function "setulb"...
		  setulb(m,x,l,u,nbd,f,g,factr,pgtol,wa,iwa,task,iprint,csave,lsave,isave,dsave,[n])
	Wrote C/API module "_lbfgsb" to file "build/src.linux-i686-2.6/scipy/optimize/lbfgsb/_lbfgsbmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.optimize.moduleTNC" sources
building extension "scipy.optimize._cobyla" sources
creating build/src.linux-i686-2.6/scipy/optimize/cobyla
f2py options: []
f2py: scipy/optimize/cobyla/cobyla.pyf
Reading fortran codes...
	Reading file 'scipy/optimize/cobyla/cobyla.pyf' (format:free)
Post-processing...
	Block: _cobyla__user__routines
		Block: _cobyla_user_interface
			Block: calcfc
	Block: _cobyla
			Block: minimize
Post-processing (stage 2)...
Building modules...
	Constructing call-back function "cb_calcfc_in__cobyla__user__routines"
	  def calcfc(x,con): return f
	Building module "_cobyla"...
		Constructing wrapper function "minimize"...
		  x = minimize(calcfc,m,x,rhobeg,rhoend,[iprint,maxfun,calcfc_extra_args])
	Wrote C/API module "_cobyla" to file "build/src.linux-i686-2.6/scipy/optimize/cobyla/_cobylamodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.optimize.minpack2" sources
creating build/src.linux-i686-2.6/scipy/optimize/minpack2
f2py options: []
f2py: scipy/optimize/minpack2/minpack2.pyf
Reading fortran codes...
	Reading file 'scipy/optimize/minpack2/minpack2.pyf' (format:free)
Post-processing...
	Block: minpack2
			Block: dcsrch
			Block: dcstep
Post-processing (stage 2)...
Building modules...
	Building module "minpack2"...
		Constructing wrapper function "dcsrch"...
		  stp,f,g,task = dcsrch(stp,f,g,ftol,gtol,xtol,task,stpmin,stpmax,isave,dsave)
		Constructing wrapper function "dcstep"...
		  stx,fx,dx,sty,fy,dy,stp,brackt = dcstep(stx,fx,dx,sty,fy,dy,stp,fp,dp,brackt,stpmin,stpmax)
	Wrote C/API module "minpack2" to file "build/src.linux-i686-2.6/scipy/optimize/minpack2/minpack2module.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.optimize._slsqp" sources
creating build/src.linux-i686-2.6/scipy/optimize/slsqp
f2py options: []
f2py: scipy/optimize/slsqp/slsqp.pyf
Reading fortran codes...
	Reading file 'scipy/optimize/slsqp/slsqp.pyf' (format:free)
Post-processing...
	Block: _slsqp
			Block: slsqp
Post-processing (stage 2)...
Building modules...
	Building module "_slsqp"...
		Constructing wrapper function "slsqp"...
		  slsqp(m,meq,x,xl,xu,f,c,g,a,acc,iter,mode,w,jw,[la,n,l_w,l_jw])
	Wrote C/API module "_slsqp" to file "build/src.linux-i686-2.6/scipy/optimize/slsqp/_slsqpmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.optimize._nnls" sources
creating build/src.linux-i686-2.6/scipy/optimize/nnls
f2py options: []
f2py: scipy/optimize/nnls/nnls.pyf
Reading fortran codes...
	Reading file 'scipy/optimize/nnls/nnls.pyf' (format:free)
crackline: groupcounter=1 groupname={0: '', 1: 'python module', 2: 'interface', 3: 'subroutine'}
crackline: Mismatch of blocks encountered. Trying to fix it by assuming "end" statement.
Post-processing...
	Block: _nnls
			Block: nnls
Post-processing (stage 2)...
Building modules...
	Building module "_nnls"...
		Constructing wrapper function "nnls"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,rnorm,mode = nnls(a,m,n,b,w,zz,index_bn,[mda,overwrite_a,overwrite_b])
	Wrote C/API module "_nnls" to file "build/src.linux-i686-2.6/scipy/optimize/nnls/_nnlsmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.signal.sigtools" sources
building extension "scipy.signal.spline" sources
building extension "scipy.sparse.linalg.isolve._iterative" sources
creating build/src.linux-i686-2.6/scipy/sparse
creating build/src.linux-i686-2.6/scipy/sparse/linalg
creating build/src.linux-i686-2.6/scipy/sparse/linalg/isolve
creating build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/STOPTEST2.f
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/getbreak.f
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/BiCGREVCOM.f
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/BiCGSTABREVCOM.f
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/CGREVCOM.f
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/CGSREVCOM.f
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/GMRESREVCOM.f
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/QMRREVCOM.f
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/_iterative.pyf
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative
f2py options: []
f2py: build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/_iterative.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/_iterative.pyf' (format:free)
Post-processing...
	Block: _iterative
			Block: sbicgrevcom
			Block: dbicgrevcom
			Block: cbicgrevcom
			Block: zbicgrevcom
			Block: sbicgstabrevcom
			Block: dbicgstabrevcom
			Block: cbicgstabrevcom
			Block: zbicgstabrevcom
			Block: scgrevcom
			Block: dcgrevcom
			Block: ccgrevcom
			Block: zcgrevcom
			Block: scgsrevcom
			Block: dcgsrevcom
			Block: ccgsrevcom
			Block: zcgsrevcom
			Block: sqmrrevcom
			Block: dqmrrevcom
			Block: cqmrrevcom
			Block: zqmrrevcom
			Block: sgmresrevcom
			Block: dgmresrevcom
			Block: cgmresrevcom
			Block: zgmresrevcom
			Block: sstoptest2
			Block: dstoptest2
			Block: cstoptest2
			Block: zstoptest2
Post-processing (stage 2)...
Building modules...
	Building module "_iterative"...
		Constructing wrapper function "sbicgrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = sbicgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "dbicgrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = dbicgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "cbicgrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = cbicgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "zbicgrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = zbicgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "sbicgstabrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = sbicgstabrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "dbicgstabrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = dbicgstabrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "cbicgstabrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = cbicgstabrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "zbicgstabrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = zbicgstabrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "scgrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = scgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "dcgrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = dcgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "ccgrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = ccgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "zcgrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = zcgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "scgsrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = scgsrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "dcgsrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = dcgsrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "ccgsrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = ccgsrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "zcgsrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = zcgsrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "sqmrrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = sqmrrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "dqmrrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = dqmrrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "cqmrrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = cqmrrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "zqmrrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = zqmrrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "sgmresrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = sgmresrevcom(b,x,restrt,work,work2,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "dgmresrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = dgmresrevcom(b,x,restrt,work,work2,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "cgmresrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = cgmresrevcom(b,x,restrt,work,work2,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "zgmresrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = zgmresrevcom(b,x,restrt,work,work2,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "sstoptest2"...
		  bnrm2,resid,info = sstoptest2(r,b,bnrm2,tol,info)
		Constructing wrapper function "dstoptest2"...
		  bnrm2,resid,info = dstoptest2(r,b,bnrm2,tol,info)
		Constructing wrapper function "cstoptest2"...
		  bnrm2,resid,info = cstoptest2(r,b,bnrm2,tol,info)
		Constructing wrapper function "zstoptest2"...
		  bnrm2,resid,info = zstoptest2(r,b,bnrm2,tol,info)
	Wrote C/API module "_iterative" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/_iterativemodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.sparse.linalg.dsolve._zsuperlu" sources
building extension "scipy.sparse.linalg.dsolve._dsuperlu" sources
building extension "scipy.sparse.linalg.dsolve._csuperlu" sources
building extension "scipy.sparse.linalg.dsolve._ssuperlu" sources
building extension "scipy.sparse.linalg.dsolve.umfpack.__umfpack" sources
creating build/src.linux-i686-2.6/scipy/sparse/linalg/dsolve
creating build/src.linux-i686-2.6/scipy/sparse/linalg/dsolve/umfpack
building extension "scipy.sparse.linalg.eigen.arpack._arpack" sources
creating build/src.linux-i686-2.6/scipy/sparse/linalg/eigen
creating build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/arpack.pyf
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
f2py options: []
f2py: build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/arpack.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/arpack.pyf' (format:free)
Post-processing...
	Block: _arpack
			Block: ssaupd
			Block: dsaupd
			Block: sseupd
			Block: dseupd
			Block: snaupd
			Block: dnaupd
			Block: sneupd
			Block: dneupd
			Block: cnaupd
			Block: znaupd
			Block: cneupd
			Block: zneupd
Post-processing (stage 2)...
Building modules...
	Building module "_arpack"...
		Constructing wrapper function "ssaupd"...
		  ido,resid,v,iparam,ipntr,info = ssaupd(ido,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[n,ncv,ldv,lworkl])
		Constructing wrapper function "dsaupd"...
		  ido,resid,v,iparam,ipntr,info = dsaupd(ido,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[n,ncv,ldv,lworkl])
		Constructing wrapper function "sseupd"...
		  d,z,info = sseupd(rvec,howmny,select,sigma,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[ldz,n,ncv,ldv,lworkl])
		Constructing wrapper function "dseupd"...
		  d,z,info = dseupd(rvec,howmny,select,sigma,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[ldz,n,ncv,ldv,lworkl])
		Constructing wrapper function "snaupd"...
		  ido,resid,v,iparam,ipntr,info = snaupd(ido,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[n,ncv,ldv,lworkl])
		Constructing wrapper function "dnaupd"...
		  ido,resid,v,iparam,ipntr,info = dnaupd(ido,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[n,ncv,ldv,lworkl])
		Constructing wrapper function "sneupd"...
		  dr,di,z,info = sneupd(rvec,howmny,select,sigmar,sigmai,workev,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[ldz,n,ncv,ldv,lworkl])
		Constructing wrapper function "dneupd"...
		  dr,di,z,info = dneupd(rvec,howmny,select,sigmar,sigmai,workev,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[ldz,n,ncv,ldv,lworkl])
		Constructing wrapper function "cnaupd"...
		  ido,resid,v,iparam,ipntr,info = cnaupd(ido,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,rwork,info,[n,ncv,ldv,lworkl])
		Constructing wrapper function "znaupd"...
		  ido,resid,v,iparam,ipntr,info = znaupd(ido,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,rwork,info,[n,ncv,ldv,lworkl])
		Constructing wrapper function "cneupd"...
		  d,z,info = cneupd(rvec,howmny,select,sigma,workev,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,rwork,info,[ldz,n,ncv,ldv,lworkl])
		Constructing wrapper function "zneupd"...
		  d,z,info = zneupd(rvec,howmny,select,sigma,workev,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,rwork,info,[ldz,n,ncv,ldv,lworkl])
		Constructing COMMON block support for "debug"...
		  logfil,ndigit,mgetv0,msaupd,msaup2,msaitr,mseigt,msapps,msgets,mseupd,mnaupd,mnaup2,mnaitr,mneigh,mnapps,mngets,mneupd,mcaupd,mcaup2,mcaitr,mceigh,mcapps,mcgets,mceupd
		Constructing COMMON block support for "timing"...
		  nopx,nbx,nrorth,nitref,nrstrt,tsaupd,tsaup2,tsaitr,tseigt,tsgets,tsapps,tsconv,tnaupd,tnaup2,tnaitr,tneigh,tngets,tnapps,tnconv,tcaupd,tcaup2,tcaitr,tceigh,tcgets,tcapps,tcconv,tmvopx,tmvbx,tgetv0,titref,trvec
	Wrote C/API module "_arpack" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpackmodule.c"
	Fortran 77 wrappers are saved to "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpack-f2pywrappers.f"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpack-f2pywrappers.f' to sources.
building extension "scipy.sparse.sparsetools._csr" sources
building extension "scipy.sparse.sparsetools._csc" sources
building extension "scipy.sparse.sparsetools._coo" sources
building extension "scipy.sparse.sparsetools._bsr" sources
building extension "scipy.sparse.sparsetools._dia" sources
building extension "scipy.spatial.ckdtree" sources
building extension "scipy.spatial._distance_wrap" sources
building extension "scipy.special._cephes" sources
building extension "scipy.special.specfun" sources
creating build/src.linux-i686-2.6/scipy/special
f2py options: ['--no-wrap-functions']
f2py: scipy/special/specfun.pyf
Reading fortran codes...
	Reading file 'scipy/special/specfun.pyf' (format:free)
Post-processing...
	Block: specfun
			Block: clqmn
			Block: lqmn
			Block: clpmn
			Block: jdzo
			Block: bernob
			Block: bernoa
			Block: csphjy
			Block: lpmns
			Block: eulera
			Block: clqn
			Block: airyzo
			Block: eulerb
			Block: cva1
			Block: lqnb
			Block: lamv
			Block: lagzo
			Block: legzo
			Block: pbdv
			Block: cerzo
			Block: lamn
			Block: clpn
			Block: lqmns
			Block: chgm
			Block: lpmn
			Block: fcszo
			Block: aswfb
			Block: lqna
			Block: cpbdn
			Block: lpn
			Block: fcoef
			Block: sphi
			Block: rcty
			Block: lpni
			Block: cyzo
			Block: csphik
			Block: sphj
			Block: othpl
			Block: klvnzo
			Block: jyzo
			Block: rctj
			Block: herzo
			Block: sphk
			Block: pbvv
			Block: segv
			Block: sphy
Post-processing (stage 2)...
Building modules...
	Building module "specfun"...
		Constructing wrapper function "clqmn"...
		  cqm,cqd = clqmn(m,n,z)
		Constructing wrapper function "lqmn"...
		  qm,qd = lqmn(m,n,x)
		Constructing wrapper function "clpmn"...
		  cpm,cpd = clpmn(m,n,x,y)
		Constructing wrapper function "jdzo"...
		  n,m,pcode,zo = jdzo(nt)
		Constructing wrapper function "bernob"...
		  bn = bernob(n)
		Constructing wrapper function "bernoa"...
		  bn = bernoa(n)
		Constructing wrapper function "csphjy"...
		  nm,csj,cdj,csy,cdy = csphjy(n,z)
		Constructing wrapper function "lpmns"...
		  pm,pd = lpmns(m,n,x)
		Constructing wrapper function "eulera"...
		  en = eulera(n)
		Constructing wrapper function "clqn"...
		  cqn,cqd = clqn(n,z)
		Constructing wrapper function "airyzo"...
		  xa,xb,xc,xd = airyzo(nt,[kf])
		Constructing wrapper function "eulerb"...
		  en = eulerb(n)
		Constructing wrapper function "cva1"...
		  cv = cva1(kd,m,q)
		Constructing wrapper function "lqnb"...
		  qn,qd = lqnb(n,x)
		Constructing wrapper function "lamv"...
		  vm,vl,dl = lamv(v,x)
		Constructing wrapper function "lagzo"...
		  x,w = lagzo(n)
		Constructing wrapper function "legzo"...
		  x,w = legzo(n)
		Constructing wrapper function "pbdv"...
		  dv,dp,pdf,pdd = pbdv(v,x)
		Constructing wrapper function "cerzo"...
		  zo = cerzo(nt)
		Constructing wrapper function "lamn"...
		  nm,bl,dl = lamn(n,x)
		Constructing wrapper function "clpn"...
		  cpn,cpd = clpn(n,z)
		Constructing wrapper function "lqmns"...
		  qm,qd = lqmns(m,n,x)
		Constructing wrapper function "chgm"...
		  hg = chgm(a,b,x)
		Constructing wrapper function "lpmn"...
		  pm,pd = lpmn(m,n,x)
		Constructing wrapper function "fcszo"...
		  zo = fcszo(kf,nt)
		Constructing wrapper function "aswfb"...
		  s1f,s1d = aswfb(m,n,c,x,kd,cv)
		Constructing wrapper function "lqna"...
		  qn,qd = lqna(n,x)
		Constructing wrapper function "cpbdn"...
		  cpb,cpd = cpbdn(n,z)
		Constructing wrapper function "lpn"...
		  pn,pd = lpn(n,x)
		Constructing wrapper function "fcoef"...
		  fc = fcoef(kd,m,q,a)
		Constructing wrapper function "sphi"...
		  nm,si,di = sphi(n,x)
		Constructing wrapper function "rcty"...
		  nm,ry,dy = rcty(n,x)
		Constructing wrapper function "lpni"...
		  pn,pd,pl = lpni(n,x)
		Constructing wrapper function "cyzo"...
		  zo,zv = cyzo(nt,kf,kc)
		Constructing wrapper function "csphik"...
		  nm,csi,cdi,csk,cdk = csphik(n,z)
		Constructing wrapper function "sphj"...
		  nm,sj,dj = sphj(n,x)
		Constructing wrapper function "othpl"...
		  pl,dpl = othpl(kf,n,x)
		Constructing wrapper function "klvnzo"...
		  zo = klvnzo(nt,kd)
		Constructing wrapper function "jyzo"...
		  rj0,rj1,ry0,ry1 = jyzo(n,nt)
		Constructing wrapper function "rctj"...
		  nm,rj,dj = rctj(n,x)
		Constructing wrapper function "herzo"...
		  x,w = herzo(n)
		Constructing wrapper function "sphk"...
		  nm,sk,dk = sphk(n,x)
		Constructing wrapper function "pbvv"...
		  vv,vp,pvf,pvd = pbvv(v,x)
		Constructing wrapper function "segv"...
		  cv,eg = segv(m,n,c,kd)
		Constructing wrapper function "sphy"...
		  nm,sy,dy = sphy(n,x)
	Wrote C/API module "specfun" to file "build/src.linux-i686-2.6/scipy/special/specfunmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.stats.statlib" sources
creating build/src.linux-i686-2.6/scipy/stats
f2py options: ['--no-wrap-functions']
f2py: scipy/stats/statlib.pyf
Reading fortran codes...
	Reading file 'scipy/stats/statlib.pyf' (format:free)
Post-processing...
	Block: statlib
			Block: swilk
			Block: wprob
			Block: gscale
			Block: prho
Post-processing (stage 2)...
Building modules...
	Building module "statlib"...
		Constructing wrapper function "swilk"...
		  a,w,pw,ifault = swilk(x,a,[init,n1])
		Constructing wrapper function "wprob"...
		  astart,a1,ifault = wprob(test,other)
		Constructing wrapper function "gscale"...
		  astart,a1,ifault = gscale(test,other)
		Constructing wrapper function "prho"...
		  ifault = prho(n,is)
	Wrote C/API module "statlib" to file "build/src.linux-i686-2.6/scipy/stats/statlibmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.stats.vonmises_cython" sources
building extension "scipy.stats.futil" sources
f2py options: []
f2py:> build/src.linux-i686-2.6/scipy/stats/futilmodule.c
Reading fortran codes...
	Reading file 'scipy/stats/futil.f' (format:fix,strict)
Post-processing...
	Block: futil
			Block: dqsort
			Block: dfreps
Post-processing (stage 2)...
Building modules...
	Building module "futil"...
		Constructing wrapper function "dqsort"...
		  arr = dqsort(arr,[overwrite_arr])
		Constructing wrapper function "dfreps"...
		  replist,repnum,nlist = dfreps(arr)
	Wrote C/API module "futil" to file "build/src.linux-i686-2.6/scipy/stats/futilmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.stats.mvn" sources
f2py options: []
f2py: scipy/stats/mvn.pyf
Reading fortran codes...
	Reading file 'scipy/stats/mvn.pyf' (format:free)
Post-processing...
	Block: mvn
			Block: mvnun
			Block: mvndst
Post-processing (stage 2)...
Building modules...
	Building module "mvn"...
		Constructing wrapper function "mvnun"...
		  value,inform = mvnun(lower,upper,means,covar,[maxpts,abseps,releps])
		Constructing wrapper function "mvndst"...
		  error,value,inform = mvndst(lower,upper,infin,correl,[maxpts,abseps,releps])
		Constructing COMMON block support for "dkblck"...
		  ivls
	Wrote C/API module "mvn" to file "build/src.linux-i686-2.6/scipy/stats/mvnmodule.c"
	Fortran 77 wrappers are saved to "build/src.linux-i686-2.6/scipy/stats/mvn-f2pywrappers.f"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/scipy/stats/mvn-f2pywrappers.f' to sources.
building extension "scipy.ndimage._nd_image" sources
building data_files sources
running build_py
creating build/lib.linux-i686-2.6
creating build/lib.linux-i686-2.6/scipy
copying scipy/setupscons.py -> build/lib.linux-i686-2.6/scipy
copying scipy/version.py -> build/lib.linux-i686-2.6/scipy
copying scipy/setup.py -> build/lib.linux-i686-2.6/scipy
copying scipy/__init__.py -> build/lib.linux-i686-2.6/scipy
copying build/src.linux-i686-2.6/scipy/__config__.py -> build/lib.linux-i686-2.6/scipy
creating build/lib.linux-i686-2.6/scipy/cluster
copying scipy/cluster/setupscons.py -> build/lib.linux-i686-2.6/scipy/cluster
copying scipy/cluster/vq.py -> build/lib.linux-i686-2.6/scipy/cluster
copying scipy/cluster/hierarchy.py -> build/lib.linux-i686-2.6/scipy/cluster
copying scipy/cluster/setup.py -> build/lib.linux-i686-2.6/scipy/cluster
copying scipy/cluster/__init__.py -> build/lib.linux-i686-2.6/scipy/cluster
copying scipy/cluster/info.py -> build/lib.linux-i686-2.6/scipy/cluster
creating build/lib.linux-i686-2.6/scipy/constants
copying scipy/constants/constants.py -> build/lib.linux-i686-2.6/scipy/constants
copying scipy/constants/codata.py -> build/lib.linux-i686-2.6/scipy/constants
copying scipy/constants/setup.py -> build/lib.linux-i686-2.6/scipy/constants
copying scipy/constants/__init__.py -> build/lib.linux-i686-2.6/scipy/constants
creating build/lib.linux-i686-2.6/scipy/fftpack
copying scipy/fftpack/helper.py -> build/lib.linux-i686-2.6/scipy/fftpack
copying scipy/fftpack/setupscons.py -> build/lib.linux-i686-2.6/scipy/fftpack
copying scipy/fftpack/fftpack_version.py -> build/lib.linux-i686-2.6/scipy/fftpack
copying scipy/fftpack/basic.py -> build/lib.linux-i686-2.6/scipy/fftpack
copying scipy/fftpack/pseudo_diffs.py -> build/lib.linux-i686-2.6/scipy/fftpack
copying scipy/fftpack/setup.py -> build/lib.linux-i686-2.6/scipy/fftpack
copying scipy/fftpack/__init__.py -> build/lib.linux-i686-2.6/scipy/fftpack
copying scipy/fftpack/info.py -> build/lib.linux-i686-2.6/scipy/fftpack
creating build/lib.linux-i686-2.6/scipy/integrate
copying scipy/integrate/odepack.py -> build/lib.linux-i686-2.6/scipy/integrate
copying scipy/integrate/quadpack.py -> build/lib.linux-i686-2.6/scipy/integrate
copying scipy/integrate/setupscons.py -> build/lib.linux-i686-2.6/scipy/integrate
copying scipy/integrate/ode.py -> build/lib.linux-i686-2.6/scipy/integrate
copying scipy/integrate/quadrature.py -> build/lib.linux-i686-2.6/scipy/integrate
copying scipy/integrate/setup.py -> build/lib.linux-i686-2.6/scipy/integrate
copying scipy/integrate/__init__.py -> build/lib.linux-i686-2.6/scipy/integrate
copying scipy/integrate/info.py -> build/lib.linux-i686-2.6/scipy/integrate
creating build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/fitpack2.py -> build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/setupscons.py -> build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/rbf.py -> build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/fitpack.py -> build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/interpolate_wrapper.py -> build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/interpolate.py -> build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/polyint.py -> build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/setup.py -> build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/__init__.py -> build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/info.py -> build/lib.linux-i686-2.6/scipy/interpolate
creating build/lib.linux-i686-2.6/scipy/io
copying scipy/io/dumbdbm_patched.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/dumb_shelve.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/pickler.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/fopen.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/npfile.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/data_store.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/setupscons.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/netcdf.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/wavfile.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/recaster.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/array_import.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/mmio.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/setup.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/__init__.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/info.py -> build/lib.linux-i686-2.6/scipy/io
creating build/lib.linux-i686-2.6/scipy/io/matlab
copying scipy/io/matlab/mio5.py -> build/lib.linux-i686-2.6/scipy/io/matlab
copying scipy/io/matlab/miobase.py -> build/lib.linux-i686-2.6/scipy/io/matlab
copying scipy/io/matlab/mio4.py -> build/lib.linux-i686-2.6/scipy/io/matlab
copying scipy/io/matlab/setup.py -> build/lib.linux-i686-2.6/scipy/io/matlab
copying scipy/io/matlab/mio.py -> build/lib.linux-i686-2.6/scipy/io/matlab
copying scipy/io/matlab/byteordercodes.py -> build/lib.linux-i686-2.6/scipy/io/matlab
copying scipy/io/matlab/__init__.py -> build/lib.linux-i686-2.6/scipy/io/matlab
creating build/lib.linux-i686-2.6/scipy/io/arff
copying scipy/io/arff/utils.py -> build/lib.linux-i686-2.6/scipy/io/arff
copying scipy/io/arff/myfunctools.py -> build/lib.linux-i686-2.6/scipy/io/arff
copying scipy/io/arff/arffread.py -> build/lib.linux-i686-2.6/scipy/io/arff
copying scipy/io/arff/setup.py -> build/lib.linux-i686-2.6/scipy/io/arff
copying scipy/io/arff/__init__.py -> build/lib.linux-i686-2.6/scipy/io/arff
creating build/lib.linux-i686-2.6/scipy/lib
copying scipy/lib/setupscons.py -> build/lib.linux-i686-2.6/scipy/lib
copying scipy/lib/setup.py -> build/lib.linux-i686-2.6/scipy/lib
copying scipy/lib/__init__.py -> build/lib.linux-i686-2.6/scipy/lib
copying scipy/lib/info.py -> build/lib.linux-i686-2.6/scipy/lib
creating build/lib.linux-i686-2.6/scipy/lib/blas
copying scipy/lib/blas/scons_support.py -> build/lib.linux-i686-2.6/scipy/lib/blas
copying scipy/lib/blas/setupscons.py -> build/lib.linux-i686-2.6/scipy/lib/blas
copying scipy/lib/blas/setup.py -> build/lib.linux-i686-2.6/scipy/lib/blas
copying scipy/lib/blas/__init__.py -> build/lib.linux-i686-2.6/scipy/lib/blas
copying scipy/lib/blas/info.py -> build/lib.linux-i686-2.6/scipy/lib/blas
creating build/lib.linux-i686-2.6/scipy/lib/lapack
copying scipy/lib/lapack/scons_support.py -> build/lib.linux-i686-2.6/scipy/lib/lapack
copying scipy/lib/lapack/setupscons.py -> build/lib.linux-i686-2.6/scipy/lib/lapack
copying scipy/lib/lapack/setup.py -> build/lib.linux-i686-2.6/scipy/lib/lapack
copying scipy/lib/lapack/__init__.py -> build/lib.linux-i686-2.6/scipy/lib/lapack
copying scipy/lib/lapack/info.py -> build/lib.linux-i686-2.6/scipy/lib/lapack
creating build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/lapack.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/scons_support.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/interface_gen.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/matfuncs.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/decomp.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/setupscons.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/blas.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/basic.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/setup_atlas_version.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/linalg_version.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/setup.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/__init__.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/iterative.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/info.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/flinalg.py -> build/lib.linux-i686-2.6/scipy/linalg
creating build/lib.linux-i686-2.6/scipy/linsolve
copying scipy/linsolve/__init__.py -> build/lib.linux-i686-2.6/scipy/linsolve
creating build/lib.linux-i686-2.6/scipy/maxentropy
copying scipy/maxentropy/setupscons.py -> build/lib.linux-i686-2.6/scipy/maxentropy
copying scipy/maxentropy/maxentropy.py -> build/lib.linux-i686-2.6/scipy/maxentropy
copying scipy/maxentropy/maxentutils.py -> build/lib.linux-i686-2.6/scipy/maxentropy
copying scipy/maxentropy/setup.py -> build/lib.linux-i686-2.6/scipy/maxentropy
copying scipy/maxentropy/__init__.py -> build/lib.linux-i686-2.6/scipy/maxentropy
copying scipy/maxentropy/info.py -> build/lib.linux-i686-2.6/scipy/maxentropy
creating build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/setupscons.py -> build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/helpmod.py -> build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/common.py -> build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/limits.py -> build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/ppimport.py -> build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/pilutil.py -> build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/setup.py -> build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/__init__.py -> build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/pexec.py -> build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/info.py -> build/lib.linux-i686-2.6/scipy/misc
creating build/lib.linux-i686-2.6/scipy/odr
copying scipy/odr/setupscons.py -> build/lib.linux-i686-2.6/scipy/odr
copying scipy/odr/models.py -> build/lib.linux-i686-2.6/scipy/odr
copying scipy/odr/odrpack.py -> build/lib.linux-i686-2.6/scipy/odr
copying scipy/odr/setup.py -> build/lib.linux-i686-2.6/scipy/odr
copying scipy/odr/__init__.py -> build/lib.linux-i686-2.6/scipy/odr
copying scipy/odr/info.py -> build/lib.linux-i686-2.6/scipy/odr
creating build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/linesearch.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/anneal.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/lbfgsb.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/nonlin.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/minpack.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/setupscons.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/nnls.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/cobyla.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/zeros.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/_tstutils.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/optimize.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/tnc.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/setup.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/__init__.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/info.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/slsqp.py -> build/lib.linux-i686-2.6/scipy/optimize
creating build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/ltisys.py -> build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/filter_design.py -> build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/signaltools.py -> build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/setupscons.py -> build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/waveforms.py -> build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/wavelets.py -> build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/setup.py -> build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/__init__.py -> build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/bsplines.py -> build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/info.py -> build/lib.linux-i686-2.6/scipy/signal
creating build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/data.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/compressed.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/coo.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/setupscons.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/spfuncs.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/construct.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/sputils.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/csr.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/dia.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/base.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/setup.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/bsr.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/csc.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/extract.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/__init__.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/dok.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/lil.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/info.py -> build/lib.linux-i686-2.6/scipy/sparse
creating build/lib.linux-i686-2.6/scipy/sparse/linalg
copying scipy/sparse/linalg/setupscons.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg
copying scipy/sparse/linalg/interface.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg
copying scipy/sparse/linalg/setup.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg
copying scipy/sparse/linalg/__init__.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg
copying scipy/sparse/linalg/info.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg
creating build/lib.linux-i686-2.6/scipy/sparse/linalg/isolve
copying scipy/sparse/linalg/isolve/setupscons.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/isolve
copying scipy/sparse/linalg/isolve/utils.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/isolve
copying scipy/sparse/linalg/isolve/minres.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/isolve
copying scipy/sparse/linalg/isolve/setup.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/isolve
copying scipy/sparse/linalg/isolve/__init__.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/isolve
copying scipy/sparse/linalg/isolve/iterative.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/isolve
creating build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve
copying scipy/sparse/linalg/dsolve/linsolve.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve
copying scipy/sparse/linalg/dsolve/setupscons.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve
copying scipy/sparse/linalg/dsolve/setup.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve
copying scipy/sparse/linalg/dsolve/__init__.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve
copying scipy/sparse/linalg/dsolve/_superlu.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve
copying scipy/sparse/linalg/dsolve/info.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve
creating build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/umfpack
copying scipy/sparse/linalg/dsolve/umfpack/umfpack.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/umfpack
copying scipy/sparse/linalg/dsolve/umfpack/setupscons.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/umfpack
copying scipy/sparse/linalg/dsolve/umfpack/setup.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/umfpack
copying scipy/sparse/linalg/dsolve/umfpack/__init__.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/umfpack
copying scipy/sparse/linalg/dsolve/umfpack/info.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/umfpack
creating build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen
copying scipy/sparse/linalg/eigen/setupscons.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen
copying scipy/sparse/linalg/eigen/setup.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen
copying scipy/sparse/linalg/eigen/__init__.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen
copying scipy/sparse/linalg/eigen/info.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen
creating build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
copying scipy/sparse/linalg/eigen/arpack/setupscons.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
copying scipy/sparse/linalg/eigen/arpack/speigs.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
copying scipy/sparse/linalg/eigen/arpack/arpack.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
copying scipy/sparse/linalg/eigen/arpack/setup.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
copying scipy/sparse/linalg/eigen/arpack/__init__.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
copying scipy/sparse/linalg/eigen/arpack/info.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
creating build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/lobpcg
copying scipy/sparse/linalg/eigen/lobpcg/lobpcg.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/lobpcg
copying scipy/sparse/linalg/eigen/lobpcg/setupscons.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/lobpcg
copying scipy/sparse/linalg/eigen/lobpcg/setup.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/lobpcg
copying scipy/sparse/linalg/eigen/lobpcg/__init__.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/lobpcg
copying scipy/sparse/linalg/eigen/lobpcg/info.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/lobpcg
creating build/lib.linux-i686-2.6/scipy/sparse/sparsetools
copying scipy/sparse/sparsetools/coo.py -> build/lib.linux-i686-2.6/scipy/sparse/sparsetools
copying scipy/sparse/sparsetools/setupscons.py -> build/lib.linux-i686-2.6/scipy/sparse/sparsetools
copying scipy/sparse/sparsetools/csr.py -> build/lib.linux-i686-2.6/scipy/sparse/sparsetools
copying scipy/sparse/sparsetools/dia.py -> build/lib.linux-i686-2.6/scipy/sparse/sparsetools
copying scipy/sparse/sparsetools/setup.py -> build/lib.linux-i686-2.6/scipy/sparse/sparsetools
copying scipy/sparse/sparsetools/bsr.py -> build/lib.linux-i686-2.6/scipy/sparse/sparsetools
copying scipy/sparse/sparsetools/csc.py -> build/lib.linux-i686-2.6/scipy/sparse/sparsetools
copying scipy/sparse/sparsetools/__init__.py -> build/lib.linux-i686-2.6/scipy/sparse/sparsetools
creating build/lib.linux-i686-2.6/scipy/spatial
copying scipy/spatial/setupscons.py -> build/lib.linux-i686-2.6/scipy/spatial
copying scipy/spatial/distance.py -> build/lib.linux-i686-2.6/scipy/spatial
copying scipy/spatial/kdtree.py -> build/lib.linux-i686-2.6/scipy/spatial
copying scipy/spatial/setup.py -> build/lib.linux-i686-2.6/scipy/spatial
copying scipy/spatial/__init__.py -> build/lib.linux-i686-2.6/scipy/spatial
copying scipy/spatial/info.py -> build/lib.linux-i686-2.6/scipy/spatial
creating build/lib.linux-i686-2.6/scipy/special
copying scipy/special/orthogonal.py -> build/lib.linux-i686-2.6/scipy/special
copying scipy/special/gendoc.py -> build/lib.linux-i686-2.6/scipy/special
copying scipy/special/setupscons.py -> build/lib.linux-i686-2.6/scipy/special
copying scipy/special/basic.py -> build/lib.linux-i686-2.6/scipy/special
copying scipy/special/special_version.py -> build/lib.linux-i686-2.6/scipy/special
copying scipy/special/spfun_stats.py -> build/lib.linux-i686-2.6/scipy/special
copying scipy/special/setup.py -> build/lib.linux-i686-2.6/scipy/special
copying scipy/special/__init__.py -> build/lib.linux-i686-2.6/scipy/special
copying scipy/special/info.py -> build/lib.linux-i686-2.6/scipy/special
creating build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/mstats_basic.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/setupscons.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/mstats.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/vonmises.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/rv.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/distributions.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/mstats_extras.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/stats.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/morestats.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/kde.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/setup.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/__init__.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/info.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/_support.py -> build/lib.linux-i686-2.6/scipy/stats
creating build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/_ni_support.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/filters.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/setupscons.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/fourier.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/doccer.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/measurements.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/morphology.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/setup.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/interpolation.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/__init__.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/info.py -> build/lib.linux-i686-2.6/scipy/ndimage
creating build/lib.linux-i686-2.6/scipy/stsci
copying scipy/stsci/setupscons.py -> build/lib.linux-i686-2.6/scipy/stsci
copying scipy/stsci/setup.py -> build/lib.linux-i686-2.6/scipy/stsci
copying scipy/stsci/__init__.py -> build/lib.linux-i686-2.6/scipy/stsci
creating build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/platform_info.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/cpp_namespace_spec.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/inline_tools.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/swigptr.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/common_info.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/standard_array_spec.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/setupscons.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/ext_tools.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/blitz_tools.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/build_tools.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/ast_tools.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/c_spec.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/base_spec.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/swig2_spec.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/catalog.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/converters.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/blitz_spec.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/base_info.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/swigptr2.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/weave_version.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/accelerate_tools.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/md5_load.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/setup.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/size_check.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/__init__.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/vtk_spec.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/bytecodecompiler.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/info.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/slice_handler.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/numpy_scalar_spec.py -> build/lib.linux-i686-2.6/scipy/weave
running build_clib
customize UnixCCompiler
customize UnixCCompiler using build_clib
customize Gnu95FCompiler
Found executable /usr/bin/gfortran
customize Gnu95FCompiler using build_clib
building 'dfftpack' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6
creating build/temp.linux-i686-2.6/scipy
creating build/temp.linux-i686-2.6/scipy/fftpack
creating build/temp.linux-i686-2.6/scipy/fftpack/src
creating build/temp.linux-i686-2.6/scipy/fftpack/src/dfftpack
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/fftpack/src/dfftpack/zffti1.f
scipy/fftpack/src/dfftpack/zffti1.f: In function 'zffti1':
scipy/fftpack/src/dfftpack/zffti1.f:11: warning: 'ntry' may be used uninitialized in this function
gfortran:f77: scipy/fftpack/src/dfftpack/dfftf.f
gfortran:f77: scipy/fftpack/src/dfftpack/zfftf.f
gfortran:f77: scipy/fftpack/src/dfftpack/dsint1.f
gfortran:f77: scipy/fftpack/src/dfftpack/dffti.f
gfortran:f77: scipy/fftpack/src/dfftpack/dsinqi.f
gfortran:f77: scipy/fftpack/src/dfftpack/dfftb.f
gfortran:f77: scipy/fftpack/src/dfftpack/dsint.f
gfortran:f77: scipy/fftpack/src/dfftpack/dcosti.f
gfortran:f77: scipy/fftpack/src/dfftpack/dcost.f
gfortran:f77: scipy/fftpack/src/dfftpack/dcosqi.f
gfortran:f77: scipy/fftpack/src/dfftpack/dffti1.f
scipy/fftpack/src/dfftpack/dffti1.f: In function 'dffti1':
scipy/fftpack/src/dfftpack/dffti1.f:11: warning: 'ntry' may be used uninitialized in this function
gfortran:f77: scipy/fftpack/src/dfftpack/dsinqb.f
gfortran:f77: scipy/fftpack/src/dfftpack/zffti.f
gfortran:f77: scipy/fftpack/src/dfftpack/dsinqf.f
gfortran:f77: scipy/fftpack/src/dfftpack/dfftb1.f
gfortran:f77: scipy/fftpack/src/dfftpack/zfftf1.f
gfortran:f77: scipy/fftpack/src/dfftpack/dfftf1.f
gfortran:f77: scipy/fftpack/src/dfftpack/zfftb1.f
gfortran:f77: scipy/fftpack/src/dfftpack/zfftb.f
gfortran:f77: scipy/fftpack/src/dfftpack/dcosqb.f
gfortran:f77: scipy/fftpack/src/dfftpack/dsinti.f
gfortran:f77: scipy/fftpack/src/dfftpack/dcosqf.f
ar: adding 23 object files to build/temp.linux-i686-2.6/libdfftpack.a
building 'linpack_lite' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/integrate
creating build/temp.linux-i686-2.6/scipy/integrate/linpack_lite
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/integrate/linpack_lite/zgbfa.f
gfortran:f77: scipy/integrate/linpack_lite/zgbsl.f
gfortran:f77: scipy/integrate/linpack_lite/dgesl.f
gfortran:f77: scipy/integrate/linpack_lite/zgesl.f
gfortran:f77: scipy/integrate/linpack_lite/dgbsl.f
gfortran:f77: scipy/integrate/linpack_lite/dgbfa.f
gfortran:f77: scipy/integrate/linpack_lite/dgtsl.f
gfortran:f77: scipy/integrate/linpack_lite/zgefa.f
gfortran:f77: scipy/integrate/linpack_lite/dgefa.f
ar: adding 9 object files to build/temp.linux-i686-2.6/liblinpack_lite.a
building 'mach' library
using additional config_fc from setup script for fortran compiler: {'noopt': ('scipy/integrate/setup.pyc', 1)}
customize Gnu95FCompiler
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC
creating build/temp.linux-i686-2.6/scipy/integrate/mach
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/integrate/mach/i1mach.f
gfortran:f77: scipy/integrate/mach/d1mach.f
gfortran:f77: scipy/integrate/mach/xerror.f
 In file scipy/integrate/mach/xerror.f:1

      SUBROUTINE XERROR(MESS,NMESS,L1,L2)                               
                                       1
Warning: Unused variable l2 declared at (1)
 In file scipy/integrate/mach/xerror.f:1

      SUBROUTINE XERROR(MESS,NMESS,L1,L2)                               
                                    1
Warning: Unused variable l1 declared at (1)
gfortran:f77: scipy/integrate/mach/r1mach.f
ar: adding 4 object files to build/temp.linux-i686-2.6/libmach.a
building 'quadpack' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/integrate/quadpack
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/integrate/quadpack/dqaws.f
gfortran:f77: scipy/integrate/quadpack/dqawse.f
gfortran:f77: scipy/integrate/quadpack/dqk21.f
gfortran:f77: scipy/integrate/quadpack/dqawc.f
gfortran:f77: scipy/integrate/quadpack/dqawo.f
gfortran:f77: scipy/integrate/quadpack/dqpsrt.f
gfortran:f77: scipy/integrate/quadpack/dqk41.f
gfortran:f77: scipy/integrate/quadpack/dqk61.f
gfortran:f77: scipy/integrate/quadpack/dqwgtf.f
 In file scipy/integrate/quadpack/dqwgtf.f:1

      double precision function dqwgtf(x,omega,p2,p3,p4,integr)         
                                                   1
Warning: Unused variable p3 declared at (1)
 In file scipy/integrate/quadpack/dqwgtf.f:1

      double precision function dqwgtf(x,omega,p2,p3,p4,integr)         
                                                1
Warning: Unused variable p2 declared at (1)
 In file scipy/integrate/quadpack/dqwgtf.f:1

      double precision function dqwgtf(x,omega,p2,p3,p4,integr)         
                                                      1
Warning: Unused variable p4 declared at (1)
gfortran:f77: scipy/integrate/quadpack/dqng.f
scipy/integrate/quadpack/dqng.f: In function 'dqng':
scipy/integrate/quadpack/dqng.f:80: warning: 'resabs' may be used uninitialized in this function
scipy/integrate/quadpack/dqng.f:80: warning: 'resasc' may be used uninitialized in this function
scipy/integrate/quadpack/dqng.f:80: warning: 'res43' may be used uninitialized in this function
scipy/integrate/quadpack/dqng.f:82: warning: 'ipx' may be used uninitialized in this function
scipy/integrate/quadpack/dqng.f:80: warning: 'res21' may be used uninitialized in this function
gfortran:f77: scipy/integrate/quadpack/dqk51.f
gfortran:f77: scipy/integrate/quadpack/dqelg.f
gfortran:f77: scipy/integrate/quadpack/dqags.f
gfortran:f77: scipy/integrate/quadpack/dqagp.f
gfortran:f77: scipy/integrate/quadpack/dqc25c.f
gfortran:f77: scipy/integrate/quadpack/dqmomo.f
 In file scipy/integrate/quadpack/dqmomo.f:126

   90 return                                                            
    1
Warning: Label 90 at (1) defined but not used
gfortran:f77: scipy/integrate/quadpack/dqawfe.f
scipy/integrate/quadpack/dqawfe.f: In function 'dqawfe':
scipy/integrate/quadpack/dqawfe.f:203: warning: 'll' may be used uninitialized in this function
scipy/integrate/quadpack/dqawfe.f:200: warning: 'drl' may be used uninitialized in this function
gfortran:f77: scipy/integrate/quadpack/dqagpe.f
scipy/integrate/quadpack/dqagpe.f: In function 'dqagpe':
scipy/integrate/quadpack/dqagpe.f:196: warning: 'k' may be used uninitialized in this function
scipy/integrate/quadpack/dqagpe.f:191: warning: 'correc' may be used uninitialized in this function
gfortran:f77: scipy/integrate/quadpack/dqk15w.f
gfortran:f77: scipy/integrate/quadpack/dqcheb.f
gfortran:f77: scipy/integrate/quadpack/dqc25s.f
gfortran:f77: scipy/integrate/quadpack/dqawf.f
gfortran:f77: scipy/integrate/quadpack/dqag.f
gfortran:f77: scipy/integrate/quadpack/dqc25f.f
scipy/integrate/quadpack/dqc25f.f: In function 'dqc25f':
scipy/integrate/quadpack/dqc25f.f:103: warning: 'm' may be used uninitialized in this function
gfortran:f77: scipy/integrate/quadpack/dqagie.f
scipy/integrate/quadpack/dqagie.f: In function 'dqagie':
scipy/integrate/quadpack/dqagie.f:154: warning: 'small' may be used uninitialized in this function
scipy/integrate/quadpack/dqagie.f:153: warning: 'ertest' may be used uninitialized in this function
scipy/integrate/quadpack/dqagie.f:152: warning: 'erlarg' may be used uninitialized in this function
scipy/integrate/quadpack/dqagie.f:151: warning: 'correc' may be used uninitialized in this function
gfortran:f77: scipy/integrate/quadpack/dqk15i.f
gfortran:f77: scipy/integrate/quadpack/dqage.f
gfortran:f77: scipy/integrate/quadpack/dqk31.f
gfortran:f77: scipy/integrate/quadpack/dqwgtc.f
 In file scipy/integrate/quadpack/dqwgtc.f:1

      double precision function dqwgtc(x,c,p2,p3,p4,kp)                 
                                               1
Warning: Unused variable p3 declared at (1)
 In file scipy/integrate/quadpack/dqwgtc.f:1

      double precision function dqwgtc(x,c,p2,p3,p4,kp)                 
                                                     1
Warning: Unused variable kp declared at (1)
 In file scipy/integrate/quadpack/dqwgtc.f:1

      double precision function dqwgtc(x,c,p2,p3,p4,kp)                 
                                            1
Warning: Unused variable p2 declared at (1)
 In file scipy/integrate/quadpack/dqwgtc.f:1

      double precision function dqwgtc(x,c,p2,p3,p4,kp)                 
                                                  1
Warning: Unused variable p4 declared at (1)
gfortran:f77: scipy/integrate/quadpack/dqwgts.f
gfortran:f77: scipy/integrate/quadpack/dqagse.f
scipy/integrate/quadpack/dqagse.f: In function 'dqagse':
scipy/integrate/quadpack/dqagse.f:153: warning: 'small' may be used uninitialized in this function
scipy/integrate/quadpack/dqagse.f:152: warning: 'ertest' may be used uninitialized in this function
scipy/integrate/quadpack/dqagse.f:151: warning: 'erlarg' may be used uninitialized in this function
scipy/integrate/quadpack/dqagse.f:150: warning: 'correc' may be used uninitialized in this function
gfortran:f77: scipy/integrate/quadpack/dqk15.f
gfortran:f77: scipy/integrate/quadpack/dqawce.f
gfortran:f77: scipy/integrate/quadpack/dqagi.f
gfortran:f77: scipy/integrate/quadpack/dqawoe.f
scipy/integrate/quadpack/dqawoe.f: In function 'dqawoe':
scipy/integrate/quadpack/dqawoe.f:208: warning: 'ertest' may be used uninitialized in this function
scipy/integrate/quadpack/dqawoe.f:207: warning: 'erlarg' may be used uninitialized in this function
scipy/integrate/quadpack/dqawoe.f:206: warning: 'correc' may be used uninitialized in this function
ar: adding 35 object files to build/temp.linux-i686-2.6/libquadpack.a
building 'odepack' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/integrate/odepack
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/integrate/odepack/intdy.f
gfortran:f77: scipy/integrate/odepack/bnorm.f
gfortran:f77: scipy/integrate/odepack/mdm.f
gfortran:f77: scipy/integrate/odepack/prjs.f
gfortran:f77: scipy/integrate/odepack/stode.f
scipy/integrate/odepack/stode.f: In function 'stode':
scipy/integrate/odepack/stode.f:14: warning: 'rh' may be used uninitialized in this function
scipy/integrate/odepack/stode.f:9: warning: 'iredo' may be used uninitialized in this function
scipy/integrate/odepack/stode.f:13: warning: 'dsm' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/lsode.f
scipy/integrate/odepack/lsode.f: In function 'lsode':
scipy/integrate/odepack/lsode.f:959: warning: 'ihit' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/prepji.f
gfortran:f77: scipy/integrate/odepack/fnorm.f
gfortran:f77: scipy/integrate/odepack/sro.f
gfortran:f77: scipy/integrate/odepack/vode.f
 In file scipy/integrate/odepack/vode.f:2373

 700  R = ONE/TQ(2)                                                     
   1
Warning: Label 700 at (1) defined but not used
 In file scipy/integrate/odepack/vode.f:2739

      SUBROUTINE DVNLSD (Y, YH, LDYH, VSAV, SAVF, EWT, ACOR, IWM, WM,   
                                         1
Warning: Unused variable vsav declared at (1)
 In file scipy/integrate/odepack/vode.f:3495

      DOUBLE PRECISION FUNCTION D1MACH (IDUM)                           
                                           1
Warning: Unused variable idum declared at (1)
 In file scipy/integrate/odepack/vode.f:3514

      SUBROUTINE XERRWD (MSG, NMES, NERR, LEVEL, NI, I1, I2, NR, R1, R2)
                                       1
Warning: Unused variable nerr declared at (1)
scipy/integrate/odepack/vode.f: In function 'ixsav':
scipy/integrate/odepack/vode.f:3610: warning: '__result_ixsav' may be used uninitialized in this function
scipy/integrate/odepack/vode.f: In function 'dvode':
scipy/integrate/odepack/vode.f:1055: warning: 'ihit' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/prep.f
gfortran:f77: scipy/integrate/odepack/lsodar.f
scipy/integrate/odepack/lsodar.f: In function 'lsodar':
scipy/integrate/odepack/lsodar.f:1096: warning: 'lenwm' may be used uninitialized in this function
scipy/integrate/odepack/lsodar.f:1108: warning: 'ihit' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/ainvg.f
gfortran:f77: scipy/integrate/odepack/stodi.f
scipy/integrate/odepack/stodi.f: In function 'stodi':
scipy/integrate/odepack/stodi.f:15: warning: 'rh' may be used uninitialized in this function
scipy/integrate/odepack/stodi.f:13: warning: 'dsm' may be used uninitialized in this function
scipy/integrate/odepack/stodi.f:9: warning: 'iredo' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/prja.f
gfortran:f77: scipy/integrate/odepack/nnfc.f
gfortran:f77: scipy/integrate/odepack/pjibt.f
gfortran:f77: scipy/integrate/odepack/lsoibt.f
scipy/integrate/odepack/lsoibt.f: In function 'lsoibt':
scipy/integrate/odepack/lsoibt.f:1207: warning: 'ihit' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/solsy.f
 In file scipy/integrate/odepack/solsy.f:1

      subroutine solsy (wm, iwm, x, tem)                                
                                      1
Warning: Unused variable tem declared at (1)
gfortran:f77: scipy/integrate/odepack/blkdta000.f
gfortran:f77: scipy/integrate/odepack/ddassl.f
 In file scipy/integrate/odepack/ddassl.f:1605

      SUBROUTINE DDAWTS (NEQ, IWT, RTOL, ATOL, Y, WT, RPAR, IPAR)       
                                                         1
Warning: Unused variable rpar declared at (1)
 In file scipy/integrate/odepack/ddassl.f:1605

      SUBROUTINE DDAWTS (NEQ, IWT, RTOL, ATOL, Y, WT, RPAR, IPAR)       
                                                               1
Warning: Unused variable ipar declared at (1)
 In file scipy/integrate/odepack/ddassl.f:1647

      DOUBLE PRECISION FUNCTION DDANRM (NEQ, V, WT, RPAR, IPAR)         
                                                       1
Warning: Unused variable rpar declared at (1)
 In file scipy/integrate/odepack/ddassl.f:1647

      DOUBLE PRECISION FUNCTION DDANRM (NEQ, V, WT, RPAR, IPAR)         
                                                             1
Warning: Unused variable ipar declared at (1)
 In file scipy/integrate/odepack/ddassl.f:3153

   30 IF (LEVEL.LE.0 .OR. (LEVEL.EQ.1 .AND. MKNTRL.LE.1)) RETURN        
    1
Warning: Label 30 at (1) defined but not used
 In file scipy/integrate/odepack/ddassl.f:3170

      SUBROUTINE XERHLT (MESSG)                                         
                             1
Warning: Unused variable messg declared at (1)
scipy/integrate/odepack/ddassl.f: In function 'ddastp':
scipy/integrate/odepack/ddassl.f:2122: warning: 'terkm1' may be used uninitialized in this function
scipy/integrate/odepack/ddassl.f:2121: warning: 'oldnrm' may be used uninitialized in this function
scipy/integrate/odepack/ddassl.f:2117: warning: 'knew' may be used uninitialized in this function
scipy/integrate/odepack/ddassl.f:2121: warning: 'est' may be used uninitialized in this function
scipy/integrate/odepack/ddassl.f:2120: warning: 'erkm1' may be used uninitialized in this function
scipy/integrate/odepack/ddassl.f: In function 'ddaini':
scipy/integrate/odepack/ddassl.f:1758: warning: 's' may be used uninitialized in this function
scipy/integrate/odepack/ddassl.f:1758: warning: 'oldnrm' may be used uninitialized in this function
scipy/integrate/odepack/ddassl.f:1758: warning: 'err' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/jgroup.f
gfortran:f77: scipy/integrate/odepack/iprep.f
gfortran:f77: scipy/integrate/odepack/solbt.f
gfortran:f77: scipy/integrate/odepack/ddasrt.f
 In file scipy/integrate/odepack/ddasrt.f:1022

300   CONTINUE                                                          
  1
Warning: Label 300 at (1) defined but not used
 In file scipy/integrate/odepack/ddasrt.f:1080

360   ITEMP = LPHI + NEQ                                                
  1
Warning: Label 360 at (1) defined but not used
 In file scipy/integrate/odepack/ddasrt.f:1538

770   MSG = 'DASSL--  RUN TERMINATED. APPARENT INFINITE LOOP'           
  1
Warning: Label 770 at (1) defined but not used
 In file scipy/integrate/odepack/ddasrt.f:1932

      SUBROUTINE XERRWV (MSG, NMES, NERR, LEVEL, NI, I1, I2, NR, R1, R2)
                                       1
Warning: Unused variable nerr declared at (1)
gfortran:f77: scipy/integrate/odepack/aigbt.f
gfortran:f77: scipy/integrate/odepack/md.f
gfortran:f77: scipy/integrate/odepack/xsetf.f
gfortran:f77: scipy/integrate/odepack/mdu.f
gfortran:f77: scipy/integrate/odepack/mdp.f
scipy/integrate/odepack/mdp.f: In function 'mdp':
scipy/integrate/odepack/mdp.f:8: warning: 'free' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/cntnzu.f
gfortran:f77: scipy/integrate/odepack/rchek.f
gfortran:f77: scipy/integrate/odepack/srcar.f
gfortran:f77: scipy/integrate/odepack/adjlr.f
gfortran:f77: scipy/integrate/odepack/xsetun.f
gfortran:f77: scipy/integrate/odepack/decbt.f
gfortran:f77: scipy/integrate/odepack/lsodi.f
scipy/integrate/odepack/lsodi.f: In function 'lsodi':
scipy/integrate/odepack/lsodi.f:1143: warning: 'lenwm' may be used uninitialized in this function
scipy/integrate/odepack/lsodi.f:1150: warning: 'ihit' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/cfode.f
gfortran:f77: scipy/integrate/odepack/vmnorm.f
gfortran:f77: scipy/integrate/odepack/zvode.f
 In file scipy/integrate/odepack/zvode.f:2394

 700  R = ONE/TQ(2)                                                     
   1
Warning: Label 700 at (1) defined but not used
 In file scipy/integrate/odepack/zvode.f:2760

      SUBROUTINE ZVNLSD (Y, YH, LDYH, VSAV, SAVF, EWT, ACOR, IWM, WM,   
                                         1
Warning: Unused variable vsav declared at (1)
scipy/integrate/odepack/zvode.f: In function 'zvode':
scipy/integrate/odepack/zvode.f:1067: warning: 'ihit' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/slss.f
 In file scipy/integrate/odepack/slss.f:1

      subroutine slss (wk, iwk, x, tem)                                 
                                     1
Warning: Unused variable tem declared at (1)
gfortran:f77: scipy/integrate/odepack/xerrwv.f
 In file scipy/integrate/odepack/xerrwv.f:1

      subroutine xerrwv (msg, nmes, nerr, level, ni, i1, i2, nr, r1, r2)
                                       1
Warning: Unused variable nerr declared at (1)
gfortran:f77: scipy/integrate/odepack/ewset.f
gfortran:f77: scipy/integrate/odepack/srcms.f
gfortran:f77: scipy/integrate/odepack/stoda.f
scipy/integrate/odepack/stoda.f: In function 'stoda':
scipy/integrate/odepack/stoda.f:18: warning: 'rh' may be used uninitialized in this function
scipy/integrate/odepack/stoda.f:19: warning: 'pdh' may be used uninitialized in this function
scipy/integrate/odepack/stoda.f:10: warning: 'iredo' may be used uninitialized in this function
scipy/integrate/odepack/stoda.f:17: warning: 'dsm' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/prepj.f
gfortran:f77: scipy/integrate/odepack/cdrv.f
gfortran:f77: scipy/integrate/odepack/lsoda.f
scipy/integrate/odepack/lsoda.f: In function 'lsoda':
scipy/integrate/odepack/lsoda.f:978: warning: 'lenwm' may be used uninitialized in this function
scipy/integrate/odepack/lsoda.f:988: warning: 'ihit' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/vnorm.f
gfortran:f77: scipy/integrate/odepack/srcom.f
gfortran:f77: scipy/integrate/odepack/nntc.f
gfortran:f77: scipy/integrate/odepack/odrv.f
gfortran:f77: scipy/integrate/odepack/nroc.f
gfortran:f77: scipy/integrate/odepack/slsbt.f
 In file scipy/integrate/odepack/slsbt.f:1

      subroutine slsbt (wm, iwm, x, tem)                                
                                      1
Warning: Unused variable tem declared at (1)
gfortran:f77: scipy/integrate/odepack/nsfc.f
gfortran:f77: scipy/integrate/odepack/lsodes.f
scipy/integrate/odepack/lsodes.f: In function 'lsodes':
scipy/integrate/odepack/lsodes.f:1245: warning: 'ihit' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/roots.f
gfortran:f77: scipy/integrate/odepack/srcma.f
gfortran:f77: scipy/integrate/odepack/nnsc.f
gfortran:f77: scipy/integrate/odepack/mdi.f
ar: adding 50 object files to build/temp.linux-i686-2.6/libodepack.a
ar: adding 10 object files to build/temp.linux-i686-2.6/libodepack.a
building 'fitpack' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/interpolate
creating build/temp.linux-i686-2.6/scipy/interpolate/fitpack
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/interpolate/fitpack/fpintb.f
scipy/interpolate/fitpack/fpintb.f: In function 'fpintb':
scipy/interpolate/fitpack/fpintb.f:26: warning: 'ia' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fptrpe.f
 In file scipy/interpolate/fitpack/fptrpe.f:17

      integer i,iband,irot,it,ii,i2,i3,j,jj,l,mid,nmd,m2,m3,            
                    1
Warning: Unused variable iband declared at (1)
scipy/interpolate/fitpack/fptrpe.f: In function 'fptrpe':
scipy/interpolate/fitpack/fptrpe.f:16: warning: 'pinv' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpchep.f
gfortran:f77: scipy/interpolate/fitpack/profil.f
gfortran:f77: scipy/interpolate/fitpack/insert.f
gfortran:f77: scipy/interpolate/fitpack/concur.f
 In file scipy/interpolate/fitpack/concur.f:287

      real*8 tol,dist                                                   
                    1
Warning: Unused variable dist declared at (1)
gfortran:f77: scipy/interpolate/fitpack/fpgrdi.f
 In file scipy/interpolate/fitpack/fpgrdi.f:296

 400    if(nrold.eq.number) go to 420                                   
   1
Warning: Label 400 at (1) defined but not used
scipy/interpolate/fitpack/fpgrdi.f: In function 'fpgrdi':
scipy/interpolate/fitpack/fpgrdi.f:16: warning: 'pinv' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fptrnp.f
scipy/interpolate/fitpack/fptrnp.f: In function 'fptrnp':
scipy/interpolate/fitpack/fptrnp.f:15: warning: 'pinv' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpgrre.f
scipy/interpolate/fitpack/fpgrre.f: In function 'fpgrre':
scipy/interpolate/fitpack/fpgrre.f:16: warning: 'pinv' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fprppo.f
scipy/interpolate/fitpack/fprppo.f: In function 'fprppo':
scipy/interpolate/fitpack/fprppo.f:12: warning: 'j' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpinst.f
gfortran:f77: scipy/interpolate/fitpack/fppasu.f
scipy/interpolate/fitpack/fppasu.f: In function 'fppasu':
scipy/interpolate/fitpack/fppasu.f:15: warning: 'peru' may be used uninitialized in this function
scipy/interpolate/fitpack/fppasu.f:15: warning: 'perv' may be used uninitialized in this function
scipy/interpolate/fitpack/fppasu.f:19: warning: 'nve' may be used uninitialized in this function
scipy/interpolate/fitpack/fppasu.f:19: warning: 'nue' may be used uninitialized in this function
scipy/interpolate/fitpack/fppasu.f:18: warning: 'nmaxu' may be used uninitialized in this function
scipy/interpolate/fitpack/fppasu.f:18: warning: 'nmaxv' may be used uninitialized in this function
scipy/interpolate/fitpack/fppasu.f:14: warning: 'acc' may be used uninitialized in this function
scipy/interpolate/fitpack/fppasu.f:14: warning: 'fpms' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpclos.f
scipy/interpolate/fitpack/fpclos.f: In function 'fpclos':
scipy/interpolate/fitpack/fpclos.f:17: warning: 'nplus' may be used uninitialized in this function
scipy/interpolate/fitpack/fpclos.f:17: warning: 'new' may be used uninitialized in this function
scipy/interpolate/fitpack/fpclos.f:18: warning: 'n10' may be used uninitialized in this function
scipy/interpolate/fitpack/fpclos.f:16: warning: 'i1' may be used uninitialized in this function
scipy/interpolate/fitpack/fpclos.f:13: warning: 'fpold' may be used uninitialized in this function
scipy/interpolate/fitpack/fpclos.f:13: warning: 'fp0' may be used uninitialized in this function
scipy/interpolate/fitpack/fpclos.f:13: warning: 'fpms' may be used uninitialized in this function
scipy/interpolate/fitpack/fpclos.f:17: warning: 'nmax' may be used uninitialized in this function
scipy/interpolate/fitpack/fpclos.f:13: warning: 'acc' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/surev.f
gfortran:f77: scipy/interpolate/fitpack/fpdisc.f
gfortran:f77: scipy/interpolate/fitpack/fppola.f
 In file scipy/interpolate/fitpack/fppola.f:377

 370      in = nummer(in)                                               
   1
Warning: Label 370 at (1) defined but not used
 In file scipy/interpolate/fitpack/fppola.f:440

 440    do 450 i=1,nrint                                                
   1
Warning: Label 440 at (1) defined but not used
 In file scipy/interpolate/fitpack/fppola.f:23

     * iter,i1,i2,i3,j,jl,jrot,j1,j2,k,l,la,lf,lh,ll,lu,lv,lwest,l1,l2, 
                        1
Warning: Unused variable jl declared at (1)
scipy/interpolate/fitpack/fppola.f: In function 'fppola':
scipy/interpolate/fitpack/fppola.f:19: warning: 'fpms' may be used uninitialized in this function
scipy/interpolate/fitpack/fppola.f:19: warning: 'acc' may be used uninitialized in this function
scipy/interpolate/fitpack/fppola.f:23: warning: 'lwest' may be used uninitialized in this function
scipy/interpolate/fitpack/fppola.f:24: warning: 'nv4' may be used uninitialized in this function
scipy/interpolate/fitpack/fppola.f:24: warning: 'nu4' may be used uninitialized in this function
scipy/interpolate/fitpack/fppola.f:25: warning: 'iband1' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fppocu.f
gfortran:f77: scipy/interpolate/fitpack/splder.f
 In file scipy/interpolate/fitpack/splder.f:79

  30  ier = 0                                                           
   1
Warning: Label 30 at (1) defined but not used
scipy/interpolate/fitpack/splder.f: In function 'splder':
scipy/interpolate/fitpack/splder.f:117: warning: 'k2' is used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpsurf.f
 In file scipy/interpolate/fitpack/fpsurf.f:245

 240      in = nummer(in)                                               
   1
Warning: Label 240 at (1) defined but not used
 In file scipy/interpolate/fitpack/fpsurf.f:305

 310    do 320 i=1,nrint                                                
   1
Warning: Label 310 at (1) defined but not used
scipy/interpolate/fitpack/fpsurf.f: In function 'fpsurf':
scipy/interpolate/fitpack/fpsurf.f:16: warning: 'acc' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsurf.f:21: warning: 'lwest' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsurf.f:22: warning: 'nyy' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsurf.f:21: warning: 'nk1y' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsurf.f:21: warning: 'nk1x' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsurf.f:19: warning: 'iband1' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsurf.f:16: warning: 'fpms' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/splev.f
 In file scipy/interpolate/fitpack/splev.f:75

  30  ier = 0                                                           
   1
Warning: Label 30 at (1) defined but not used
gfortran:f77: scipy/interpolate/fitpack/cualde.f
gfortran:f77: scipy/interpolate/fitpack/concon.f
gfortran:f77: scipy/interpolate/fitpack/percur.f
gfortran:f77: scipy/interpolate/fitpack/fprank.f
scipy/interpolate/fitpack/fprank.f: In function 'fprank':
scipy/interpolate/fitpack/fprank.f:25: warning: 'j3' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/sphere.f
 In file scipy/interpolate/fitpack/sphere.f:318

     * lbp,lco,lf,lff,lfp,lh,lq,lst,lsp,lwest,maxit,ncest,ncc,ntt,      
         1
Warning: Unused variable jlbp declared at (1)
gfortran:f77: scipy/interpolate/fitpack/evapol.f
gfortran:f77: scipy/interpolate/fitpack/fpdeno.f
gfortran:f77: scipy/interpolate/fitpack/fpcyt1.f
gfortran:f77: scipy/interpolate/fitpack/fpbfout.f
scipy/interpolate/fitpack/fpbfout.f: In function 'fpbfou':
scipy/interpolate/fitpack/fpbfout.f:35: warning: 'term' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpperi.f
scipy/interpolate/fitpack/fpperi.f: In function 'fpperi':
scipy/interpolate/fitpack/fpperi.f:17: warning: 'new' may be used uninitialized in this function
scipy/interpolate/fitpack/fpperi.f:17: warning: 'nplus' may be used uninitialized in this function
scipy/interpolate/fitpack/fpperi.f:18: warning: 'n10' may be used uninitialized in this function
scipy/interpolate/fitpack/fpperi.f:16: warning: 'i1' may be used uninitialized in this function
scipy/interpolate/fitpack/fpperi.f:13: warning: 'fpold' may be used uninitialized in this function
scipy/interpolate/fitpack/fpperi.f:13: warning: 'fp0' may be used uninitialized in this function
scipy/interpolate/fitpack/fpperi.f:13: warning: 'fpms' may be used uninitialized in this function
scipy/interpolate/fitpack/fpperi.f:17: warning: 'nmax' may be used uninitialized in this function
scipy/interpolate/fitpack/fpperi.f:13: warning: 'acc' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpback.f
gfortran:f77: scipy/interpolate/fitpack/fpcsin.f
gfortran:f77: scipy/interpolate/fitpack/parcur.f
gfortran:f77: scipy/interpolate/fitpack/fpspgr.f
scipy/interpolate/fitpack/fpspgr.f: In function 'fpspgr':
scipy/interpolate/fitpack/fpspgr.f:21: warning: 'nvmax' may be used uninitialized in this function
scipy/interpolate/fitpack/fpspgr.f:21: warning: 'nve' may be used uninitialized in this function
scipy/interpolate/fitpack/fpspgr.f:21: warning: 'nue' may be used uninitialized in this function
scipy/interpolate/fitpack/fpspgr.f:21: warning: 'numax' may be used uninitialized in this function
scipy/interpolate/fitpack/fpspgr.f:16: warning: 'acc' may be used uninitialized in this function
scipy/interpolate/fitpack/fpspgr.f:20: warning: 'nplu' may be used uninitialized in this function
scipy/interpolate/fitpack/fpspgr.f:16: warning: 'fpms' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fprati.f
gfortran:f77: scipy/interpolate/fitpack/fpcurf.f
scipy/interpolate/fitpack/fpcurf.f: In function 'fpcurf':
scipy/interpolate/fitpack/fpcurf.f:15: warning: 'nplus' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcurf.f:12: warning: 'fpold' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcurf.f:12: warning: 'fpms' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcurf.f:12: warning: 'fp0' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcurf.f:15: warning: 'nmax' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcurf.f:12: warning: 'acc' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/bispev.f
gfortran:f77: scipy/interpolate/fitpack/surfit.f
gfortran:f77: scipy/interpolate/fitpack/dblint.f
gfortran:f77: scipy/interpolate/fitpack/fpcons.f
scipy/interpolate/fitpack/fpcons.f: In function 'fpcons':
scipy/interpolate/fitpack/fpcons.f:15: warning: 'nplus' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcons.f:15: warning: 'nk1' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcons.f:12: warning: 'fpms' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcons.f:12: warning: 'fp0' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcons.f:12: warning: 'fpold' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcons.f:15: warning: 'nmax' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcons.f:15: warning: 'mm' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcons.f:12: warning: 'acc' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/regrid.f
gfortran:f77: scipy/interpolate/fitpack/fpknot.f
scipy/interpolate/fitpack/fpknot.f: In function 'fpknot':
scipy/interpolate/fitpack/fpknot.f:21: warning: 'number' may be used uninitialized in this function
scipy/interpolate/fitpack/fpknot.f:20: warning: 'maxbeg' may be used uninitialized in this function
scipy/interpolate/fitpack/fpknot.f:20: warning: 'maxpt' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fprpsp.f
gfortran:f77: scipy/interpolate/fitpack/fpcuro.f
gfortran:f77: scipy/interpolate/fitpack/fpbspl.f
gfortran:f77: scipy/interpolate/fitpack/fpgrpa.f
gfortran:f77: scipy/interpolate/fitpack/curev.f
gfortran:f77: scipy/interpolate/fitpack/fporde.f
gfortran:f77: scipy/interpolate/fitpack/spgrid.f
gfortran:f77: scipy/interpolate/fitpack/fpopdi.f
gfortran:f77: scipy/interpolate/fitpack/cocosp.f
gfortran:f77: scipy/interpolate/fitpack/fpsysy.f
gfortran:f77: scipy/interpolate/fitpack/fpchec.f
gfortran:f77: scipy/interpolate/fitpack/fprota.f
gfortran:f77: scipy/interpolate/fitpack/fpcyt2.f
gfortran:f77: scipy/interpolate/fitpack/curfit.f
gfortran:f77: scipy/interpolate/fitpack/parsur.f
gfortran:f77: scipy/interpolate/fitpack/polar.f
 In file scipy/interpolate/fitpack/polar.f:353

     * lbv,lco,lf,lff,lfp,lh,lq,lsu,lsv,lwest,maxit,ncest,ncc,nuu,      
         1
Warning: Unused variable jlbv declared at (1)
gfortran:f77: scipy/interpolate/fitpack/fpbisp.f
gfortran:f77: scipy/interpolate/fitpack/parder.f
gfortran:f77: scipy/interpolate/fitpack/splint.f
gfortran:f77: scipy/interpolate/fitpack/pogrid.f
gfortran:f77: scipy/interpolate/fitpack/fpregr.f
scipy/interpolate/fitpack/fpregr.f: In function 'fpregr':
scipy/interpolate/fitpack/fpregr.f:19: warning: 'nye' may be used uninitialized in this function
scipy/interpolate/fitpack/fpregr.f:19: warning: 'nxe' may be used uninitialized in this function
scipy/interpolate/fitpack/fpregr.f:18: warning: 'nmaxy' may be used uninitialized in this function
scipy/interpolate/fitpack/fpregr.f:18: warning: 'nmaxx' may be used uninitialized in this function
scipy/interpolate/fitpack/fpregr.f:15: warning: 'acc' may be used uninitialized in this function
scipy/interpolate/fitpack/fpregr.f:15: warning: 'fpms' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/bispeu.f
 In file scipy/interpolate/fitpack/bispeu.f:50

      integer i,iw,lwest                                                
                 1
Warning: Unused variable iw declared at (1)
 In file scipy/interpolate/fitpack/bispeu.f:44

      integer nx,ny,kx,ky,m,lwrk,kwrk,ier                               
                                    1
Warning: Unused variable kwrk declared at (1)
gfortran:f77: scipy/interpolate/fitpack/fpsuev.f
gfortran:f77: scipy/interpolate/fitpack/fpgivs.f
scipy/interpolate/fitpack/fpgivs.f: In function 'fpgivs':
scipy/interpolate/fitpack/fpgivs.f:8: warning: 'dd' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpader.f
gfortran:f77: scipy/interpolate/fitpack/spalde.f
gfortran:f77: scipy/interpolate/fitpack/fourco.f
gfortran:f77: scipy/interpolate/fitpack/fpseno.f
gfortran:f77: scipy/interpolate/fitpack/fpopsp.f
 In file scipy/interpolate/fitpack/fpopsp.f:58

      real*8 res,sq,sqq,sq0,sq1,step1,step2,three                       
               1
Warning: Unused variable res declared at (1)
gfortran:f77: scipy/interpolate/fitpack/fppara.f
scipy/interpolate/fitpack/fppara.f: In function 'fppara':
scipy/interpolate/fitpack/fppara.f:15: warning: 'nplus' may be used uninitialized in this function
scipy/interpolate/fitpack/fppara.f:12: warning: 'fpms' may be used uninitialized in this function
scipy/interpolate/fitpack/fppara.f:12: warning: 'fpold' may be used uninitialized in this function
scipy/interpolate/fitpack/fppara.f:12: warning: 'fp0' may be used uninitialized in this function
scipy/interpolate/fitpack/fppara.f:15: warning: 'nmax' may be used uninitialized in this function
scipy/interpolate/fitpack/fppara.f:12: warning: 'acc' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpadno.f
gfortran:f77: scipy/interpolate/fitpack/fpfrno.f
scipy/interpolate/fitpack/fpfrno.f: In function 'fpfrno':
scipy/interpolate/fitpack/fpfrno.f:14: warning: 'k' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpbacp.f
gfortran:f77: scipy/interpolate/fitpack/fppogr.f
scipy/interpolate/fitpack/fppogr.f: In function 'fppogr':
scipy/interpolate/fitpack/fppogr.f:21: warning: 'nve' may be used uninitialized in this function
scipy/interpolate/fitpack/fppogr.f:21: warning: 'nvmax' may be used uninitialized in this function
scipy/interpolate/fitpack/fppogr.f:21: warning: 'nue' may be used uninitialized in this function
scipy/interpolate/fitpack/fppogr.f:21: warning: 'numax' may be used uninitialized in this function
scipy/interpolate/fitpack/fppogr.f:16: warning: 'acc' may be used uninitialized in this function
scipy/interpolate/fitpack/fppogr.f:20: warning: 'nplu' may be used uninitialized in this function
scipy/interpolate/fitpack/fppogr.f:16: warning: 'fpms' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpcoco.f
scipy/interpolate/fitpack/fpcoco.f: In function 'fpcoco':
scipy/interpolate/fitpack/fpcoco.f:12: warning: 'k' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpadpo.f
gfortran:f77: scipy/interpolate/fitpack/clocur.f
gfortran:f77: scipy/interpolate/fitpack/fpgrsp.f
 In file scipy/interpolate/fitpack/fpgrsp.f:348

 400    if(nrold.eq.number) go to 420                                   
   1
Warning: Label 400 at (1) defined but not used
scipy/interpolate/fitpack/fpgrsp.f: In function 'fpgrsp':
scipy/interpolate/fitpack/fpgrsp.f:17: warning: 'pinv' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpsphe.f
 In file scipy/interpolate/fitpack/fpsphe.f:327

 330      in = nummer(in)                                               
   1
Warning: Label 330 at (1) defined but not used
 In file scipy/interpolate/fitpack/fpsphe.f:390

 440    do 450 i=1,nrint                                                
   1
Warning: Label 440 at (1) defined but not used
scipy/interpolate/fitpack/fpsphe.f: In function 'fpsphe':
scipy/interpolate/fitpack/fpsphe.f:19: warning: 'fpms' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsphe.f:18: warning: 'acc' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsphe.f:22: warning: 'lwest' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsphe.f:23: warning: 'ntt' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsphe.f:23: warning: 'nt4' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsphe.f:23: warning: 'np4' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsphe.f:21: warning: 'iband1' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpched.f
gfortran:f77: scipy/interpolate/fitpack/sproot.f
gfortran:f77: scipy/interpolate/fitpack/fpcosp.f
ar: adding 50 object files to build/temp.linux-i686-2.6/libfitpack.a
ar: adding 34 object files to build/temp.linux-i686-2.6/libfitpack.a
building 'odrpack' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/odr
creating build/temp.linux-i686-2.6/scipy/odr/odrpack
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/odr/odrpack/d_odr.f
scipy/odr/odrpack/d_odr.f: In function 'dppt':
scipy/odr/odrpack/d_odr.f:9611: warning: '__result_dppt' may be used uninitialized in this function
scipy/odr/odrpack/d_odr.f: In function 'djckm':
scipy/odr/odrpack/d_odr.f:3547: warning: 'h' may be used uninitialized in this function
gfortran:f77: scipy/odr/odrpack/d_mprec.f
gfortran:f77: scipy/odr/odrpack/dlunoc.f
gfortran:f77: scipy/odr/odrpack/d_lpk.f
ar: adding 4 object files to build/temp.linux-i686-2.6/libodrpack.a
building 'minpack' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/optimize
creating build/temp.linux-i686-2.6/scipy/optimize/minpack
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/optimize/minpack/lmdif.f
scipy/optimize/minpack/lmdif.f: In function 'lmdif':
scipy/optimize/minpack/lmdif.f:193: warning: 'xnorm' may be used uninitialized in this function
scipy/optimize/minpack/lmdif.f:193: warning: 'temp' may be used uninitialized in this function
gfortran:f77: scipy/optimize/minpack/lmstr1.f
gfortran:f77: scipy/optimize/minpack/fdjac2.f
gfortran:f77: scipy/optimize/minpack/lmdif1.f
gfortran:f77: scipy/optimize/minpack/hybrj.f
scipy/optimize/minpack/hybrj.f: In function 'hybrj':
scipy/optimize/minpack/hybrj.f:155: warning: 'xnorm' may be used uninitialized in this function
gfortran:f77: scipy/optimize/minpack/enorm.f
scipy/optimize/minpack/enorm.f: In function 'enorm':
scipy/optimize/minpack/enorm.f:90: warning: '__result_enorm' may be used uninitialized in this function
gfortran:f77: scipy/optimize/minpack/fdjac1.f
gfortran:f77: scipy/optimize/minpack/chkder.f
gfortran:f77: scipy/optimize/minpack/qrsolv.f
gfortran:f77: scipy/optimize/minpack/lmstr.f
scipy/optimize/minpack/lmstr.f: In function 'lmstr':
scipy/optimize/minpack/lmstr.f:189: warning: 'xnorm' may be used uninitialized in this function
gfortran:f77: scipy/optimize/minpack/rwupdt.f
gfortran:f77: scipy/optimize/minpack/r1updt.f
gfortran:f77: scipy/optimize/minpack/dogleg.f
gfortran:f77: scipy/optimize/minpack/hybrj1.f
gfortran:f77: scipy/optimize/minpack/lmder.f
scipy/optimize/minpack/lmder.f: In function 'lmder':
scipy/optimize/minpack/lmder.f:189: warning: 'xnorm' may be used uninitialized in this function
scipy/optimize/minpack/lmder.f:189: warning: 'temp' may be used uninitialized in this function
gfortran:f77: scipy/optimize/minpack/qrfac.f
gfortran:f77: scipy/optimize/minpack/qform.f
gfortran:f77: scipy/optimize/minpack/dpmpar.f
gfortran:f77: scipy/optimize/minpack/lmpar.f
gfortran:f77: scipy/optimize/minpack/r1mpyq.f
scipy/optimize/minpack/r1mpyq.f: In function 'r1mpyq':
scipy/optimize/minpack/r1mpyq.f:54: warning: 'sin' may be used uninitialized in this function
scipy/optimize/minpack/r1mpyq.f:54: warning: 'cos' may be used uninitialized in this function
gfortran:f77: scipy/optimize/minpack/hybrd.f
scipy/optimize/minpack/hybrd.f: In function 'hybrd':
scipy/optimize/minpack/hybrd.f:169: warning: 'xnorm' may be used uninitialized in this function
gfortran:f77: scipy/optimize/minpack/hybrd1.f
gfortran:f77: scipy/optimize/minpack/lmder1.f
ar: adding 23 object files to build/temp.linux-i686-2.6/libminpack.a
building 'rootfind' library
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/optimize/Zeros
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gcc: scipy/optimize/Zeros/brenth.c
gcc: scipy/optimize/Zeros/brentq.c
gcc: scipy/optimize/Zeros/ridder.c
gcc: scipy/optimize/Zeros/bisect.c
scipy/optimize/Zeros/zeros.h:16: warning: 'dminarg1' defined but not used
scipy/optimize/Zeros/zeros.h:16: warning: 'dminarg2' defined but not used
ar: adding 4 object files to build/temp.linux-i686-2.6/librootfind.a
building 'superlu_src' library
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/sparse
creating build/temp.linux-i686-2.6/scipy/sparse/linalg
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/SuperLU
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/SuperLU/SRC
compile options: '-DUSE_VENDOR_BLAS=1 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cgscon.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zmemory.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zpivotgrowth.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zpivotgrowth.c: In function 'zPivotGrowth':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zpivotgrowth.c:59: warning: unused variable 'temp_comp'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sgssv.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgssv.c: In function 'sgssv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgssv.c:133: warning: 'AA' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/ccolumn_dfs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccolumn_dfs.c: In function 'ccolumn_dfs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccolumn_dfs.c:128: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccolumn_dfs.c:171: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cpruneL.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/scopy_to_ucol.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/scopy_to_ucol.c: In function 'scopy_to_ucol':
scipy/sparse/linalg/dsolve/SuperLU/SRC/scopy_to_ucol.c:79: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/scopy_to_ucol.c:82: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/colamd.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cgssvx.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgssvx.c: In function 'cgssvx':
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgssvx.c:347: warning: 'smlnum' may be used uninitialized in this function
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgssvx.c:347: warning: 'bignum' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dreadhb.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dreadhb.c: In function 'dreadhb':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dreadhb.c:180: warning: unused variable 'key'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrs.c: In function 'sgstrs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrs.c:107: warning: function declaration isn't a prototype
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrs.c:186: warning: implicit declaration of function 'strsm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrs.c:189: warning: implicit declaration of function 'sgemm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrs.c:93: warning: unused variable 'incy'
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrs.c:93: warning: unused variable 'incx'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zgssv.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgssv.c: In function 'zgssv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgssv.c:133: warning: 'AA' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sutil.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/sutil.c:472: warning: return type defaults to 'int'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_bmod.c: In function 'ssnode_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_bmod.c:95: warning: implicit declaration of function 'strsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_bmod.c:97: warning: implicit declaration of function 'sgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_bmod.c:50: warning: unused variable 'iptr'
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_bmod.c:50: warning: unused variable 'i'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dsp_blas3.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c: In function 'cPrint_SuperNode_Matrix':
scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c:243: warning: operation on 'd' may be undefined
scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c: At top level:
scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c:475: warning: return type defaults to 'int'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrf.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrf.c: In function 'cgstrf':
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrf.c:185: warning: 'iperm_r' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/xerbla.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/xerbla.c: In function 'xerbla_':
scipy/sparse/linalg/dsolve/SuperLU/SRC/xerbla.c:33: warning: implicit declaration of function 'printf'
scipy/sparse/linalg/dsolve/SuperLU/SRC/xerbla.c:33: warning: incompatible implicit declaration of built-in function 'printf'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrs.c: In function 'dgstrs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrs.c:107: warning: function declaration isn't a prototype
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrs.c:186: warning: implicit declaration of function 'dtrsm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrs.c:189: warning: implicit declaration of function 'dgemm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrs.c:93: warning: unused variable 'incy'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrs.c:93: warning: unused variable 'incx'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_dfs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_dfs.c: In function 'ssnode_dfs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_dfs.c:75: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_dfs.c:88: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dgssvx.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgssvx.c: In function 'dgssvx':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgssvx.c:347: warning: 'smlnum' may be used uninitialized in this function
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgssvx.c:347: warning: 'bignum' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_bmod.c: In function 'csnode_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_bmod.c:96: warning: implicit declaration of function 'ctrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_bmod.c:98: warning: implicit declaration of function 'cgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_bmod.c:51: warning: unused variable 'iptr'
scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_bmod.c:51: warning: unused variable 'i'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zutil.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zutil.c: In function 'zPrint_SuperNode_Matrix':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zutil.c:243: warning: operation on 'd' may be undefined
scipy/sparse/linalg/dsolve/SuperLU/SRC/zutil.c: At top level:
scipy/sparse/linalg/dsolve/SuperLU/SRC/zutil.c:475: warning: return type defaults to 'int'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dutil.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dutil.c:472: warning: return type defaults to 'int'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_bmod.c: In function 'zsnode_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_bmod.c:96: warning: implicit declaration of function 'ztrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_bmod.c:98: warning: implicit declaration of function 'zgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_bmod.c:51: warning: unused variable 'iptr'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_bmod.c:51: warning: unused variable 'i'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/csp_blas2.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/csp_blas2.c: In function 'sp_ctrsv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/csp_blas2.c:153: warning: implicit declaration of function 'ctrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/csp_blas2.c:156: warning: implicit declaration of function 'cgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/csp_blas2.c: In function 'sp_cgemv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/csp_blas2.c:477: warning: suggest parentheses around && within ||
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cpanel_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/cpanel_bmod.c:31: warning: function declaration isn't a prototype
scipy/sparse/linalg/dsolve/SuperLU/SRC/cpanel_bmod.c: In function 'cpanel_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/cpanel_bmod.c:229: warning: implicit declaration of function 'ctrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/cpanel_bmod.c:276: warning: implicit declaration of function 'cgemv_'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zpruneL.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/spivotL.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zpanel_dfs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dcopy_to_ucol.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcopy_to_ucol.c: In function 'dcopy_to_ucol':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcopy_to_ucol.c:79: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcopy_to_ucol.c:82: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zcolumn_dfs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcolumn_dfs.c: In function 'zcolumn_dfs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcolumn_dfs.c:128: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcolumn_dfs.c:171: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/scolumn_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/scolumn_bmod.c: In function 'scolumn_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/scolumn_bmod.c:215: warning: implicit declaration of function 'strsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/scolumn_bmod.c:226: warning: implicit declaration of function 'sgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/scolumn_bmod.c:269: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cgssv.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgssv.c: In function 'cgssv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgssv.c:133: warning: 'AA' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zsp_blas3.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrf.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrf.c: In function 'sgstrf':
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrf.c:185: warning: 'iperm_r' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/slamch.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/slamch.c: In function 'slamc2_':
scipy/sparse/linalg/dsolve/SuperLU/SRC/slamch.c:414: warning: unused variable 'c__1'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dzsum1.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrf.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrf.c: In function 'zgstrf':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrf.c:185: warning: 'iperm_r' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sp_coletree.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/spanel_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/spanel_bmod.c:31: warning: function declaration isn't a prototype
scipy/sparse/linalg/dsolve/SuperLU/SRC/spanel_bmod.c: In function 'spanel_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/spanel_bmod.c:215: warning: implicit declaration of function 'strsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/spanel_bmod.c:262: warning: implicit declaration of function 'sgemv_'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sgsrfs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zcopy_to_ucol.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcopy_to_ucol.c: In function 'zcopy_to_ucol':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcopy_to_ucol.c:79: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcopy_to_ucol.c:82: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cpanel_dfs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrs.c: In function 'cgstrs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrs.c:108: warning: function declaration isn't a prototype
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrs.c:188: warning: implicit declaration of function 'ctrsm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrs.c:191: warning: implicit declaration of function 'cgemm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrs.c:93: warning: unused variable 'incy'
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrs.c:93: warning: unused variable 'incx'
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrs.c: In function 'cprint_soln':
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrs.c:349: warning: format '%.4f' expects type 'double', but argument 3 has type 'complex'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zgsequ.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/spivotgrowth.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/ssp_blas3.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zlaqgs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_dfs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_dfs.c: In function 'dsnode_dfs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_dfs.c:75: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_dfs.c:88: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_dfs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_dfs.c: In function 'zsnode_dfs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_dfs.c:75: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_dfs.c:88: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dmemory.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrs.c: In function 'zgstrs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrs.c:108: warning: function declaration isn't a prototype
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrs.c:188: warning: implicit declaration of function 'ztrsm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrs.c:191: warning: implicit declaration of function 'zgemm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrs.c:93: warning: unused variable 'incy'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrs.c:93: warning: unused variable 'incx'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrs.c: In function 'zprint_soln':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrs.c:351: warning: format '%.4f' expects type 'double', but argument 3 has type 'doublecomplex'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dpanel_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dpanel_bmod.c:31: warning: function declaration isn't a prototype
scipy/sparse/linalg/dsolve/SuperLU/SRC/dpanel_bmod.c: In function 'dpanel_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dpanel_bmod.c:215: warning: implicit declaration of function 'dtrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dpanel_bmod.c:262: warning: implicit declaration of function 'dgemv_'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dsp_blas2.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsp_blas2.c: In function 'sp_dtrsv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsp_blas2.c:150: warning: implicit declaration of function 'dtrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsp_blas2.c:153: warning: implicit declaration of function 'dgemv_'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dpivotgrowth.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_bmod.c: In function 'dsnode_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_bmod.c:95: warning: implicit declaration of function 'dtrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_bmod.c:97: warning: implicit declaration of function 'dgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_bmod.c:50: warning: unused variable 'iptr'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_bmod.c:50: warning: unused variable 'i'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/icmax1.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/slacon.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/creadhb.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/creadhb.c: In function 'creadhb':
scipy/sparse/linalg/dsolve/SuperLU/SRC/creadhb.c:190: warning: unused variable 'key'
scipy/sparse/linalg/dsolve/SuperLU/SRC/creadhb.c: In function 'cReadValues':
scipy/sparse/linalg/dsolve/SuperLU/SRC/creadhb.c:87: warning: 'realpart' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotgrowth.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotgrowth.c: In function 'cPivotGrowth':
scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotgrowth.c:59: warning: unused variable 'temp_comp'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zreadhb.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zreadhb.c: In function 'zreadhb':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zreadhb.c:190: warning: unused variable 'key'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/csp_blas3.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_dfs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_dfs.c: In function 'csnode_dfs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_dfs.c:75: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_dfs.c:88: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsrfs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sp_preorder.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dcolumn_dfs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcolumn_dfs.c: In function 'dcolumn_dfs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcolumn_dfs.c:128: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcolumn_dfs.c:171: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsequ.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dgscon.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zsp_blas2.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsp_blas2.c: In function 'sp_ztrsv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsp_blas2.c:153: warning: implicit declaration of function 'ztrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsp_blas2.c:156: warning: implicit declaration of function 'zgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsp_blas2.c: In function 'sp_zgemv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsp_blas2.c:476: warning: suggest parentheses around && within ||
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c: In function 'c_div':
scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c:30: warning: implicit declaration of function 'exit'
scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c:30: warning: incompatible implicit declaration of built-in function 'exit'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/heap_relax_snode.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/spanel_dfs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dcomplex.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcomplex.c: In function 'z_div':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcomplex.c:30: warning: implicit declaration of function 'exit'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcomplex.c:30: warning: incompatible implicit declaration of built-in function 'exit'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dgsequ.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/lsame.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/lsame.c: In function 'lsame_':
scipy/sparse/linalg/dsolve/SuperLU/SRC/lsame.c:55: warning: suggest parentheses around && within ||
scipy/sparse/linalg/dsolve/SuperLU/SRC/lsame.c:56: warning: suggest parentheses around && within ||
scipy/sparse/linalg/dsolve/SuperLU/SRC/lsame.c:58: warning: suggest parentheses around && within ||
scipy/sparse/linalg/dsolve/SuperLU/SRC/lsame.c:59: warning: suggest parentheses around && within ||
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zlacon.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zlacon.c: In function 'zlacon_':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zlacon.c:150: warning: implicit declaration of function 'zcopy_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zlacon.c:156: warning: label 'L90' defined but not used
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dlacon.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zpivotL.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zgssvx.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgssvx.c: In function 'zgssvx':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgssvx.c:347: warning: 'smlnum' may be used uninitialized in this function
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgssvx.c:347: warning: 'bignum' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/superlu_timer.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/superlu_timer.c:37: warning: function declaration isn't a prototype
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/slaqgs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrsL.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrsL.c: In function 'dgstrsL':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrsL.c:95: warning: function declaration isn't a prototype
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrsL.c:170: warning: implicit declaration of function 'dtrsm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrsL.c:173: warning: implicit declaration of function 'dgemm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrsL.c:91: warning: unused variable 'jcol'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrsL.c:88: warning: unused variable 'Uval'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrsL.c:83: warning: unused variable 'incy'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrsL.c:83: warning: unused variable 'incx'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zgsrfs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c: In function 'clacon_':
scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c:150: warning: implicit declaration of function 'ccopy_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c:156: warning: label 'L90' defined but not used
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/scsum1.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zpanel_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zpanel_bmod.c:31: warning: function declaration isn't a prototype
scipy/sparse/linalg/dsolve/SuperLU/SRC/zpanel_bmod.c: In function 'zpanel_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zpanel_bmod.c:229: warning: implicit declaration of function 'ztrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zpanel_bmod.c:276: warning: implicit declaration of function 'zgemv_'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dlamch.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dlamch.c: In function 'dlamc2_':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dlamch.c:407: warning: unused variable 'c__1'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dcolumn_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcolumn_bmod.c: In function 'dcolumn_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcolumn_bmod.c:215: warning: implicit declaration of function 'dtrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcolumn_bmod.c:226: warning: implicit declaration of function 'dgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcolumn_bmod.c:269: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/ccopy_to_ucol.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccopy_to_ucol.c: In function 'ccopy_to_ucol':
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccopy_to_ucol.c:79: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccopy_to_ucol.c:82: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/slangs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/slangs.c: In function 'slangs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/slangs.c:61: warning: 'value' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/smemory.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dlangs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dlangs.c: In function 'dlangs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dlangs.c:61: warning: 'value' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/memory.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cmemory.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/izmax1.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/izmax1.c: In function 'izmax1_':
scipy/sparse/linalg/dsolve/SuperLU/SRC/izmax1.c:63: warning: implicit declaration of function 'abs'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/ccolumn_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccolumn_bmod.c: In function 'ccolumn_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccolumn_bmod.c:228: warning: implicit declaration of function 'ctrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccolumn_bmod.c:239: warning: implicit declaration of function 'cgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccolumn_bmod.c:282: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/ssp_blas2.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssp_blas2.c: In function 'sp_strsv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssp_blas2.c:150: warning: implicit declaration of function 'strsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssp_blas2.c:153: warning: implicit declaration of function 'sgemv_'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/spruneL.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sgsequ.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dGetDiagU.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zlangs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zlangs.c: In function 'zlangs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zlangs.c:61: warning: 'value' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrf.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrf.c: In function 'dgstrf':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrf.c:185: warning: 'iperm_r' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/get_perm_c.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/get_perm_c.c: In function 'get_perm_c':
scipy/sparse/linalg/dsolve/SuperLU/SRC/get_perm_c.c:366: warning: function declaration isn't a prototype
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zgscon.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dpivotL.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/claqgs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dpanel_dfs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/relax_snode.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dlaqgs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/clangs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/clangs.c: In function 'clangs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/clangs.c:61: warning: 'value' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dgsrfs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/mmd.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sreadhb.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/sreadhb.c: In function 'sreadhb':
scipy/sparse/linalg/dsolve/SuperLU/SRC/sreadhb.c:180: warning: unused variable 'key'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sgssvx.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgssvx.c: In function 'sgssvx':
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgssvx.c:347: warning: 'smlnum' may be used uninitialized in this function
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgssvx.c:347: warning: 'bignum' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zcolumn_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcolumn_bmod.c: In function 'zcolumn_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcolumn_bmod.c:230: warning: implicit declaration of function 'ztrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcolumn_bmod.c:241: warning: implicit declaration of function 'zgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcolumn_bmod.c:284: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/util.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotL.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dgssv.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgssv.c: In function 'dgssv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgssv.c:133: warning: 'AA' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sgscon.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sp_ienv.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/sp_ienv.c: In function 'sp_ienv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/sp_ienv.c:57: warning: implicit declaration of function 'xerbla_'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dpruneL.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/scolumn_dfs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/scolumn_dfs.c: In function 'scolumn_dfs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/scolumn_dfs.c:128: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/scolumn_dfs.c:171: warning: suggest parentheses around assignment used as truth value
ar: adding 50 object files to build/temp.linux-i686-2.6/libsuperlu_src.a
ar: adding 50 object files to build/temp.linux-i686-2.6/libsuperlu_src.a
ar: adding 23 object files to build/temp.linux-i686-2.6/libsuperlu_src.a
building 'arpack' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/eigen
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/ARPACK
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/ARPACK/SRC
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/ARPACK/FWRAPPERS
compile options: '-Iscipy/sparse/linalg/eigen/arpack/ARPACK/SRC -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaitr.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaitr.f:210

     &   (ido, bmat, n, k, np, nb, resid, rnorm, v, ldv, h, ldh,        
                                1
Warning: Unused variable nb declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaitr.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaitr.f:210

     &   (ido, bmat, n, k, np, nb, resid, rnorm, v, ldv, h, ldh,        
                                1
Warning: Unused variable nb declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cngets.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cngets.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cngets.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cngets.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cngets.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsortr.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/csortc.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnconv.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnconv.f:73

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnconv.f:73

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnconv.f:73

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnconv.f:73

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaupd.f:417

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaupd.f:417

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaupd.f:417

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaupd.f:417

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseigt.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseigt.f:124

      integer    i, k, msglvl                                           
                 1
Warning: Unused variable i declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseigt.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseigt.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseigt.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseigt.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstatn.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssapps.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssapps.f:139

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssapps.f:139

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssapps.f:139

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssapps.f:139

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snconv.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snconv.f:73

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snconv.f:73

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snconv.f:73

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snconv.f:73

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/slaqrb.f
scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/slaqrb.f: In function 'slaqrb':
scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/slaqrb.f:141: warning: 'i2' may be used uninitialized in this function
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneigh.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaitr.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaitr.f:209

     &   (ido, bmat, n, k, np, nb, resid, rnorm, v, ldv, h, ldh,        
                                1
Warning: Unused variable nb declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneigh.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsapps.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsapps.f:139

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsapps.f:139

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsapps.f:139

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsapps.f:139

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstatn.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dlaqrb.f
scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dlaqrb.f: In function 'dlaqrb':
scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dlaqrb.f:141: warning: 'i2' may be used uninitialized in this function
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssortc.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaup2.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaup2.f:324

   10 continue                                                          
    1
Warning: Label 10 at (1) defined but not used
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaup2.f:809

  130    continue                                                       
    1
Warning: Label 130 at (1) defined but not used
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaup2.f:180

     &   ( ido, bmat, n, which, nev, np, tol, resid, mode, iupd,        
                                                              1
Warning: Unused variable iupd declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaup2.f:189

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaup2.f:189

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneigh.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnapps.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnapps.f:143

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnapps.f:143

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnapps.f:143

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnapps.f:143

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssconv.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssconv.f:66

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssconv.f:66

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssconv.f:66

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssconv.f:66

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zsortc.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snapps.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snapps.f:152

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snapps.f:152

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snapps.f:152

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snapps.f:152

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssgets.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssgets.f:100

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssgets.f:100

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssgets.f:100

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssgets.f:100

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cgetv0.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cgetv0.f:116

     &   ( ido, bmat, itry, initv, n, j, v, ldv, resid, rnorm,          
                         1
Warning: Unused variable itry declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cgetv0.f:124

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cgetv0.f:124

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnapps.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnapps.f:152

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnapps.f:152

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnapps.f:152

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnapps.f:152

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaitr.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaup2.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaup2.f:324

   10 continue                                                          
    1
Warning: Label 10 at (1) defined but not used
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaup2.f:809

  130    continue                                                       
    1
Warning: Label 130 at (1) defined but not used
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaup2.f:180

     &   ( ido, bmat, n, which, nev, np, tol, resid, mode, iupd,        
                                                              1
Warning: Unused variable iupd declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaup2.f:189

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaup2.f:189

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsortc.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaupd.f:388

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaupd.f:422

     &           ldh, ldq, levec, mode, msglvl, mxiter, nb,             
                               1
Warning: Unused variable levec declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaupd.f:388

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaupd.f:388

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaupd.f:388

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znapps.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znapps.f:143

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znapps.f:143

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znapps.f:143

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znapps.f:143

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstats.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsesrt.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstqrb.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaupd.f:415

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaupd.f:415

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaupd.f:447

     &           ldh, ldq, levec, mode, msglvl, mxiter, nb,             
                               1
Warning: Unused variable levec declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaupd.f:415

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaupd.f:415

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cstatn.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zstatn.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaupd.f:417

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaupd.f:417

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaupd.f:417

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaupd.f:417

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstats.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sngets.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sngets.f:103

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sngets.f:96

     &                    shiftr, shifti )                              
                               1
Warning: Unused variable shiftr declared at (1)
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sngets.f:96

     &                    shiftr, shifti )                              
                                       1
Warning: Unused variable shifti declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sngets.f:103

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sngets.f:103

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sngets.f:103

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dgetv0.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dgetv0.f:120

     &   ( ido, bmat, itry, initv, n, j, v, ldv, resid, rnorm,          
                         1
Warning: Unused variable itry declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dgetv0.f:128

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dgetv0.f:128

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaup2.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaup2.f:322

   10 continue                                                          
    1
Warning: Label 10 at (1) defined but not used
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaup2.f:169

     &   ( ido, bmat, n, which, nev, np, tol, resid, mode, iupd,        
                                                              1
Warning: Unused variable iupd declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaup2.f:178

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaup2.f:178

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaup2.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaup2.f:316

   10 continue                                                          
    1
Warning: Label 10 at (1) defined but not used
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaup2.f:175

     &   ( ido, bmat, n, which, nev, np, tol, resid, mode, iupd,        
                                                              1
Warning: Unused variable iupd declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaup2.f:184

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaup2.f:184

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sgetv0.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sgetv0.f:120

     &   ( ido, bmat, itry, initv, n, j, v, ldv, resid, rnorm,          
                         1
Warning: Unused variable itry declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sgetv0.f:128

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sgetv0.f:128

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneigh.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dngets.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dngets.f:103

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dngets.f:96

     &                    shiftr, shifti )                              
                               1
Warning: Unused variable shiftr declared at (1)
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dngets.f:96

     &                    shiftr, shifti )                              
                                       1
Warning: Unused variable shifti declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dngets.f:103

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dngets.f:103

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dngets.f:103

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaitr.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaitr.f:209

     &   (ido, bmat, n, k, np, nb, resid, rnorm, v, ldv, h, ldh,        
                                1
Warning: Unused variable nb declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsgets.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsgets.f:100

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsgets.f:100

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsgets.f:100

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsgets.f:100

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssesrt.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaitr.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseigt.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseigt.f:124

      integer    i, k, msglvl                                           
                 1
Warning: Unused variable i declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseigt.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseigt.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseigt.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseigt.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaup2.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaup2.f:316

   10 continue                                                          
    1
Warning: Label 10 at (1) defined but not used
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaup2.f:175

     &   ( ido, bmat, n, which, nev, np, tol, resid, mode, iupd,        
                                                              1
Warning: Unused variable iupd declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaup2.f:184

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaup2.f:184

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssortr.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zngets.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zngets.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zngets.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zngets.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zngets.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaupd.f:388

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaupd.f:422

     &           ldh, ldq, levec, mode, msglvl, mxiter, nb,             
                               1
Warning: Unused variable levec declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaupd.f:388

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaupd.f:388

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaupd.f:388

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaupd.f:415

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaupd.f:415

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaupd.f:447

     &           ldh, ldq, levec, mode, msglvl, mxiter, nb,             
                               1
Warning: Unused variable levec declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaupd.f:415

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaupd.f:415

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstqrb.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsconv.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsconv.f:66

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsconv.f:66

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsconv.f:66

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsconv.f:66

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zgetv0.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zgetv0.f:116

     &   ( ido, bmat, itry, initv, n, j, v, ldv, resid, rnorm,          
                         1
Warning: Unused variable itry declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zgetv0.f:124

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zgetv0.f:124

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaup2.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaup2.f:322

   10 continue                                                          
    1
Warning: Label 10 at (1) defined but not used
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaup2.f:169

     &   ( ido, bmat, n, which, nev, np, tol, resid, mode, iupd,        
                                                              1
Warning: Unused variable iupd declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaup2.f:178

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaup2.f:178

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/zmout.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/svout.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/ivout.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/second.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/dvout.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/smout.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/iset.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/iset.f:6

      subroutine iset (n, value, array, inc)                            
                                          1
Warning: Unused variable inc declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/iswap.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/icopy.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/icnteq.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/cmout.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/dmout.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/cvout.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/zvout.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/zlahqr.f
scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/zlahqr.f: In function 'zlahqr':
scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/zlahqr.f:96: warning: 'i2' may be used uninitialized in this function
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/dlahqr.f
scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/dlahqr.f: In function 'dlahqr':
scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/dlahqr.f:102: warning: 'i2' may be used uninitialized in this function
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/clahqr.f
scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/clahqr.f: In function 'clahqr':
scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/clahqr.f:96: warning: 'i2' may be used uninitialized in this function
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/slahqr.f
scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/slahqr.f: In function 'slahqr':
scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/slahqr.f:102: warning: 'i2' may be used uninitialized in this function
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/FWRAPPERS/dummy.f
ar: adding 50 object files to build/temp.linux-i686-2.6/libarpack.a
ar: adding 37 object files to build/temp.linux-i686-2.6/libarpack.a
building 'sc_c_misc' library
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/special
creating build/temp.linux-i686-2.6/scipy/special/c_misc
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gcc: scipy/special/c_misc/gammaincinv.c
gcc: scipy/special/c_misc/besselpoly.c
gcc: scipy/special/c_misc/fsolve.c
scipy/special/c_misc/fsolve.c: In function 'false_position':
scipy/special/c_misc/fsolve.c:58: warning: 'f3' may be used uninitialized in this function
scipy/special/c_misc/fsolve.c:58: warning: 'x3' may be used uninitialized in this function
ar: adding 3 object files to build/temp.linux-i686-2.6/libsc_c_misc.a
building 'sc_cephes' library
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/special/cephes
compile options: '-I/home/craigb/include/python2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gcc: scipy/special/cephes/tukey.c
gcc: scipy/special/cephes/dawsn.c
gcc: scipy/special/cephes/tandg.c
gcc: scipy/special/cephes/k0.c
gcc: scipy/special/cephes/const.c
gcc: scipy/special/cephes/zetac.c
gcc: scipy/special/cephes/ellie.c
gcc: scipy/special/cephes/i0.c
gcc: scipy/special/cephes/zeta.c
gcc: scipy/special/cephes/kn.c
gcc: scipy/special/cephes/scipy_iv.c
scipy/special/cephes/scipy_iv.c: In function 'cephes_iv':
scipy/special/cephes/scipy_iv.c:90: warning: unused variable 'vp'
scipy/special/cephes/scipy_iv.c: In function 'iv_asymptotic':
scipy/special/cephes/scipy_iv.c:149: warning: unused variable 'mup'
gcc: scipy/special/cephes/mvmpy.c
gcc: scipy/special/cephes/sindg.c
gcc: scipy/special/cephes/exp10.c
gcc: scipy/special/cephes/beta.c
gcc: scipy/special/cephes/incbi.c
gcc: scipy/special/cephes/jv.c
scipy/special/cephes/jv.c: In function 'cephes_jv':
scipy/special/cephes/jv.c:196: warning: label 'underf' defined but not used
gcc: scipy/special/cephes/i1.c
gcc: scipy/special/cephes/stdtr.c
gcc: scipy/special/cephes/polyn.c
gcc: scipy/special/cephes/kolmogorov.c
gcc: scipy/special/cephes/ellpe.c
gcc: scipy/special/cephes/struve.c
gcc: scipy/special/cephes/fresnl.c
gcc: scipy/special/cephes/unity.c
gcc: scipy/special/cephes/incbet.c
gcc: scipy/special/cephes/fabs.c
gcc: scipy/special/cephes/psi.c
gcc: scipy/special/cephes/gamma.c
gcc: scipy/special/cephes/airy.c
gcc: scipy/special/cephes/mtherr.c
gcc: scipy/special/cephes/mmmpy.c
gcc: scipy/special/cephes/igam.c
gcc: scipy/special/cephes/nbdtr.c
gcc: scipy/special/cephes/chbevl.c
gcc: scipy/special/cephes/bdtr.c
gcc: scipy/special/cephes/hyp2f1.c
gcc: scipy/special/cephes/ndtr.c
gcc: scipy/special/cephes/k1.c
gcc: scipy/special/cephes/sici.c
gcc: scipy/special/cephes/setprec.c
gcc: scipy/special/cephes/simq.c
gcc: scipy/special/cephes/fdtr.c
gcc: scipy/special/cephes/polmisc.c
gcc: scipy/special/cephes/yn.c
gcc: scipy/special/cephes/exp2.c
gcc: scipy/special/cephes/rgamma.c
gcc: scipy/special/cephes/spence.c
gcc: scipy/special/cephes/round.c
gcc: scipy/special/cephes/ndtri.c
gcc: scipy/special/cephes/gdtr.c
gcc: scipy/special/cephes/sincos.c
gcc: scipy/special/cephes/j0.c
gcc: scipy/special/cephes/shichi.c
gcc: scipy/special/cephes/pdtr.c
gcc: scipy/special/cephes/btdtr.c
gcc: scipy/special/cephes/cbrt.c
gcc: scipy/special/cephes/hyperg.c
gcc: scipy/special/cephes/polevl.c
gcc: scipy/special/cephes/simpsn.c
gcc: scipy/special/cephes/igami.c
gcc: scipy/special/cephes/cpmul.c
gcc: scipy/special/cephes/mtransp.c
gcc: scipy/special/cephes/ellpk.c
gcc: scipy/special/cephes/isnan.c
/local/scratch/lib/python2.6/site-packages/numpy/core/include/numpy/__multiarray_api.h:969: warning: '_import_array' defined but not used
gcc: scipy/special/cephes/gels.c
gcc: scipy/special/cephes/ellpj.c
gcc: scipy/special/cephes/ellik.c
gcc: scipy/special/cephes/expn.c
gcc: scipy/special/cephes/powi.c
gcc: scipy/special/cephes/chdtr.c
gcc: scipy/special/cephes/j1.c
gcc: scipy/special/cephes/euclid.c
scipy/special/cephes/euclid.c:55: warning: 'radd' defined but not used
scipy/special/cephes/euclid.c:90: warning: 'rsub' defined but not used
scipy/special/cephes/euclid.c:124: warning: 'rmul' defined but not used
scipy/special/cephes/euclid.c:157: warning: 'rdiv' defined but not used
gcc: scipy/special/cephes/polrt.c
scipy/special/cephes/polrt.c: In function 'polrt':
scipy/special/cephes/polrt.c:71: warning: 'xsav.r' may be used uninitialized in this function
scipy/special/cephes/polrt.c:71: warning: 'xsav.i' may be used uninitialized in this function
ar: adding 50 object files to build/temp.linux-i686-2.6/libsc_cephes.a
ar: adding 24 object files to build/temp.linux-i686-2.6/libsc_cephes.a
building 'sc_mach' library
using additional config_fc from setup script for fortran compiler: {'noopt': ('scipy/special/setup.pyc', 1)}
customize Gnu95FCompiler
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC
creating build/temp.linux-i686-2.6/scipy/special/mach
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/special/mach/i1mach.f
gfortran:f77: scipy/special/mach/d1mach.f
gfortran:f77: scipy/special/mach/xerror.f
 In file scipy/special/mach/xerror.f:1

      SUBROUTINE XERROR(MESS,NMESS,L1,L2)                               
                                       1
Warning: Unused variable l2 declared at (1)
 In file scipy/special/mach/xerror.f:1

      SUBROUTINE XERROR(MESS,NMESS,L1,L2)                               
                                    1
Warning: Unused variable l1 declared at (1)
gfortran:f77: scipy/special/mach/r1mach.f
ar: adding 4 object files to build/temp.linux-i686-2.6/libsc_mach.a
building 'sc_toms' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/special/amos
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/special/amos/zshch.f
gfortran:f77: scipy/special/amos/zunhj.f
gfortran:f77: scipy/special/amos/zacai.f
gfortran:f77: scipy/special/amos/zs1s2.f
gfortran:f77: scipy/special/amos/zasyi.f
gfortran:f77: scipy/special/amos/zbesi.f
gfortran:f77: scipy/special/amos/zuni2.f
scipy/special/amos/zuni2.f: In function 'zuni2':
scipy/special/amos/zuni2.f:27: warning: 'iflag' may be used uninitialized in this function
gfortran:f77: scipy/special/amos/dgamln.f
scipy/special/amos/dgamln.f: In function 'dgamln':
scipy/special/amos/dgamln.f:41: warning: 'nz' may be used uninitialized in this function
scipy/special/amos/dgamln.f:138: warning: '__result_dgamln' may be used uninitialized in this function
gfortran:f77: scipy/special/amos/zbunk.f
gfortran:f77: scipy/special/amos/zexp.f
gfortran:f77: scipy/special/amos/zseri.f
scipy/special/amos/zseri.f: In function 'zseri':
scipy/special/amos/zseri.f:19: warning: 'ss' may be used uninitialized in this function
gfortran:f77: scipy/special/amos/zbesy.f
gfortran:f77: scipy/special/amos/zsqrt.f
gfortran:f77: scipy/special/amos/zacon.f
scipy/special/amos/zacon.f: In function 'zacon':
scipy/special/amos/zacon.f:22: warning: 'sc2r' may be used uninitialized in this function
scipy/special/amos/zacon.f:22: warning: 'sc2i' may be used uninitialized in this function
gfortran:f77: scipy/special/amos/dsclmr.f
gfortran:f77: scipy/special/amos/zbesh.f
gfortran:f77: scipy/special/amos/zunk1.f
scipy/special/amos/zunk1.f: In function 'zunk1':
scipy/special/amos/zunk1.f:23: warning: 'kflag' may be used uninitialized in this function
scipy/special/amos/zunk1.f:23: warning: 'iflag' may be used uninitialized in this function
gfortran:f77: scipy/special/amos/zairy.f
gfortran:f77: scipy/special/amos/zbesj.f
gfortran:f77: scipy/special/amos/zwrsk.f
gfortran:f77: scipy/special/amos/zmlri.f
gfortran:f77: scipy/special/amos/zbinu.f
gfortran:f77: scipy/special/amos/zbknu.f
scipy/special/amos/zbknu.f: In function 'zbknu':
scipy/special/amos/zbknu.f:15: warning: 'dnu2' may be used uninitialized in this function
scipy/special/amos/zbknu.f:13: warning: 'ckr' may be used uninitialized in this function
scipy/special/amos/zbknu.f:13: warning: 'cki' may be used uninitialized in this function
gfortran:f77: scipy/special/amos/zuni1.f
scipy/special/amos/zuni1.f: In function 'zuni1':
scipy/special/amos/zuni1.f:24: warning: 'iflag' may be used uninitialized in this function
gfortran:f77: scipy/special/amos/zuoik.f
scipy/special/amos/zuoik.f: In function 'zuoik':
scipy/special/amos/zuoik.f:30: warning: 'aarg' may be used uninitialized in this function
gfortran:f77: scipy/special/amos/fdump.f
gfortran:f77: scipy/special/amos/zunik.f
gfortran:f77: scipy/special/amos/zkscl.f
gfortran:f77: scipy/special/amos/zbuni.f
gfortran:f77: scipy/special/amos/zbesk.f
gfortran:f77: scipy/special/amos/zunk2.f
scipy/special/amos/zunk2.f: In function 'zunk2':
scipy/special/amos/zunk2.f:30: warning: 'kflag' may be used uninitialized in this function
scipy/special/amos/zunk2.f:30: warning: 'iflag' may be used uninitialized in this function
gfortran:f77: scipy/special/amos/zdiv.f
gfortran:f77: scipy/special/amos/zmlt.f
gfortran:f77: scipy/special/amos/zbiry.f
gfortran:f77: scipy/special/amos/zrati.f
gfortran:f77: scipy/special/amos/zlog.f
gfortran:f77: scipy/special/amos/zabs.f
gfortran:f77: scipy/special/amos/zuchk.f
ar: adding 38 object files to build/temp.linux-i686-2.6/libsc_toms.a
building 'sc_amos' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/special/toms
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/special/toms/wofz.f
scipy/special/toms/wofz.f: In function 'wofz':
scipy/special/toms/wofz.f:136: warning: 'h2' may be used uninitialized in this function
scipy/special/toms/wofz.f:143: warning: 'qlambda' may be used uninitialized in this function
scipy/special/toms/wofz.f:107: warning: 'v2' may be used uninitialized in this function
scipy/special/toms/wofz.f:106: warning: 'u2' may be used uninitialized in this function
ar: adding 1 object files to build/temp.linux-i686-2.6/libsc_amos.a
building 'sc_cdf' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/special/cdflib
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/special/cdflib/erf.f
gfortran:f77: scipy/special/cdflib/rlog1.f
gfortran:f77: scipy/special/cdflib/gamma_fort.f
scipy/special/cdflib/gamma_fort.f: In function 'gamma':
scipy/special/cdflib/gamma_fort.f:20: warning: 's' may be used uninitialized in this function
gfortran:f77: scipy/special/cdflib/gamln1.f
gfortran:f77: scipy/special/cdflib/cumbet.f
gfortran:f77: scipy/special/cdflib/ipmpar.f
gfortran:f77: scipy/special/cdflib/cumgam.f
gfortran:f77: scipy/special/cdflib/gam1.f
gfortran:f77: scipy/special/cdflib/bfrac.f
gfortran:f77: scipy/special/cdflib/dt1.f
gfortran:f77: scipy/special/cdflib/bgrat.f
gfortran:f77: scipy/special/cdflib/exparg.f
gfortran:f77: scipy/special/cdflib/cdfchi.f
scipy/special/cdflib/cdfchi.f: In function 'cdfchi':
scipy/special/cdflib/cdfchi.f:177: warning: 'porq' is used uninitialized in this function
gfortran:f77: scipy/special/cdflib/cdffnc.f
gfortran:f77: scipy/special/cdflib/cumchi.f
gfortran:f77: scipy/special/cdflib/cumt.f
gfortran:f77: scipy/special/cdflib/alnrel.f
gfortran:f77: scipy/special/cdflib/cdfbet.f
gfortran:f77: scipy/special/cdflib/gamln.f
gfortran:f77: scipy/special/cdflib/bratio.f
gfortran:f77: scipy/special/cdflib/cdfbin.f
gfortran:f77: scipy/special/cdflib/brcomp.f
gfortran:f77: scipy/special/cdflib/brcmp1.f
gfortran:f77: scipy/special/cdflib/psi_fort.f
gfortran:f77: scipy/special/cdflib/bcorr.f
gfortran:f77: scipy/special/cdflib/cumpoi.f
gfortran:f77: scipy/special/cdflib/alngam.f
gfortran:f77: scipy/special/cdflib/cumnbn.f
gfortran:f77: scipy/special/cdflib/fpser.f
gfortran:f77: scipy/special/cdflib/dinvr.f
 In file scipy/special/cdflib/dinvr.f:99

      ASSIGN 10 TO i99999                                               
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/special/cdflib/dinvr.f:105

      ASSIGN 20 TO i99999                                               
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/special/cdflib/dinvr.f:142

      ASSIGN 90 TO i99999                                               
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/special/cdflib/dinvr.f:167

      ASSIGN 130 TO i99999                                              
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/special/cdflib/dinvr.f:202

      ASSIGN 200 TO i99999                                              
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/special/cdflib/dinvr.f:237

      ASSIGN 270 TO i99999                                              
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/special/cdflib/dinvr.f:346

      GO TO i99999                                                      
                 1
Warning: Obsolete: Assigned GOTO statement at (1)
 In file scipy/special/cdflib/dinvr.f:102

   10 fsmall = fx                                                       
    1
Warning: Label 10 at (1) defined but not used
 In file scipy/special/cdflib/dinvr.f:108

   20 fbig = fx                                                         
    1
Warning: Label 20 at (1) defined but not used
 In file scipy/special/cdflib/dinvr.f:145

   90 yy = fx                                                           
    1
Warning: Label 90 at (1) defined but not used
 In file scipy/special/cdflib/dinvr.f:170

  130 yy = fx                                                           
    1
Warning: Label 130 at (1) defined but not used
 In file scipy/special/cdflib/dinvr.f:205

  200 yy = fx                                                           
    1
Warning: Label 200 at (1) defined but not used
 In file scipy/special/cdflib/dinvr.f:240

  270 CONTINUE                                                          
    1
Warning: Label 270 at (1) defined but not used
gfortran:f77: scipy/special/cdflib/cumfnc.f
gfortran:f77: scipy/special/cdflib/cumnor.f
gfortran:f77: scipy/special/cdflib/cdfchn.f
gfortran:f77: scipy/special/cdflib/dzror.f
 In file scipy/special/cdflib/dzror.f:92

      ASSIGN 10 TO i99999                                               
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/special/cdflib/dzror.f:100

      ASSIGN 20 TO i99999                                               
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/special/cdflib/dzror.f:181

      ASSIGN 200 TO i99999                                              
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/special/cdflib/dzror.f:281

      GO TO i99999                                                      
                 1
Warning: Obsolete: Assigned GOTO statement at (1)
 In file scipy/special/cdflib/dzror.f:95

   10 fb = fx                                                           
    1
Warning: Label 10 at (1) defined but not used
 In file scipy/special/cdflib/dzror.f:106

   20 IF (.NOT. (fb.LT.0.0D0)) GO TO 40                                 
    1
Warning: Label 20 at (1) defined but not used
 In file scipy/special/cdflib/dzror.f:184

  200 fb = fx                                                           
    1
Warning: Label 200 at (1) defined but not used
gfortran:f77: scipy/special/cdflib/cdft.f
gfortran:f77: scipy/special/cdflib/rlog.f
gfortran:f77: scipy/special/cdflib/devlpl.f
gfortran:f77: scipy/special/cdflib/spmpar.f
gfortran:f77: scipy/special/cdflib/algdiv.f
gfortran:f77: scipy/special/cdflib/stvaln.f
gfortran:f77: scipy/special/cdflib/cumf.f
gfortran:f77: scipy/special/cdflib/cdfpoi.f
gfortran:f77: scipy/special/cdflib/cdfnor.f
gfortran:f77: scipy/special/cdflib/rexp.f
gfortran:f77: scipy/special/cdflib/cdff.f
gfortran:f77: scipy/special/cdflib/cumchn.f
gfortran:f77: scipy/special/cdflib/bup.f
gfortran:f77: scipy/special/cdflib/cumbin.f
gfortran:f77: scipy/special/cdflib/gsumln.f
gfortran:f77: scipy/special/cdflib/gratio.f
gfortran:f77: scipy/special/cdflib/betaln.f
gfortran:f77: scipy/special/cdflib/apser.f
gfortran:f77: scipy/special/cdflib/cdftnc.f
gfortran:f77: scipy/special/cdflib/basym.f
gfortran:f77: scipy/special/cdflib/esum.f
gfortran:f77: scipy/special/cdflib/cdfnbn.f
gfortran:f77: scipy/special/cdflib/rcomp.f
gfortran:f77: scipy/special/cdflib/grat1.f
gfortran:f77: scipy/special/cdflib/gaminv.f
scipy/special/cdflib/gaminv.f: In function 'gaminv':
scipy/special/cdflib/gaminv.f:58: warning: 'b' may be used uninitialized in this function
gfortran:f77: scipy/special/cdflib/cumtnc.f
gfortran:f77: scipy/special/cdflib/erfc1.f
gfortran:f77: scipy/special/cdflib/cdfgam.f
gfortran:f77: scipy/special/cdflib/dinvnr.f
gfortran:f77: scipy/special/cdflib/bpser.f
ar: adding 50 object files to build/temp.linux-i686-2.6/libsc_cdf.a
ar: adding 14 object files to build/temp.linux-i686-2.6/libsc_cdf.a
building 'sc_specfun' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/special/specfun
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/special/specfun/specfun.f
scipy/special/specfun/specfun.f: In function 'stvhv':
scipy/special/specfun/specfun.f:12832: warning: 'bjv' may be used uninitialized in this function
'IMAGPART_EXPR <cw>scipy/special/specfun/specfun.f: In function 'cik01':
scipy/special/specfun/specfun.f:12519: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cw>scipy/special/specfun/specfun.f:12519: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cqf0>scipy/special/specfun/specfun.f: In function 'clqmn':
scipy/special/specfun/specfun.f:11981: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cqf0>scipy/special/specfun/specfun.f:11981: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cqf0>scipy/special/specfun/specfun.f: In function 'clqn':
scipy/special/specfun/specfun.f:2228: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cqf0>scipy/special/specfun/specfun.f:2228: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cf>scipy/special/specfun/specfun.f: In function 'cikna':
scipy/special/specfun/specfun.f:12280: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cf>scipy/special/specfun/specfun.f:12280: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cf>scipy/special/specfun/specfun.f: In function 'ciknb':
scipy/special/specfun/specfun.f:12170: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cf>scipy/special/specfun/specfun.f:12170: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cf>scipy/special/specfun/specfun.f: In function 'cikva':
scipy/special/specfun/specfun.f:11217: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cf>scipy/special/specfun/specfun.f:11217: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cf>scipy/special/specfun/specfun.f: In function 'cikvb':
scipy/special/specfun/specfun.f:11056: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cf>scipy/special/specfun/specfun.f:11056: warning: ' may be used uninitialized in this function
scipy/special/specfun/specfun.f: In function 'chgul':
scipy/special/specfun/specfun.f:9145: warning: 'r0' may be used uninitialized in this function
'IMAGPART_EXPR <cf>scipy/special/specfun/specfun.f: In function 'csphik':
scipy/special/specfun/specfun.f:10024: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cf>scipy/special/specfun/specfun.f:10024: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cs>scipy/special/specfun/specfun.f:10028: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cs>scipy/special/specfun/specfun.f:10028: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cf>scipy/special/specfun/specfun.f: In function 'cjynb':
scipy/special/specfun/specfun.f:6712: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cf>scipy/special/specfun/specfun.f:6712: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cf>scipy/special/specfun/specfun.f: In function 'cjyna':
scipy/special/specfun/specfun.f:6576: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cf>scipy/special/specfun/specfun.f:6576: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <ch0>scipy/special/specfun/specfun.f:6611: warning: ' may be used uninitialized in this function
'REALPART_EXPR <ch0>scipy/special/specfun/specfun.f:6611: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <zw>scipy/special/specfun/specfun.f: In function 'hygfz':
scipy/special/specfun/specfun.f:6118: warning: ' may be used uninitialized in this function
'REALPART_EXPR <zw>scipy/special/specfun/specfun.f:6118: warning: ' may be used uninitialized in this function
scipy/special/specfun/specfun.f:6093: warning: 'k' may be used uninitialized in this function
'IMAGPART_EXPR <chw>scipy/special/specfun/specfun.f: In function 'cchg':
scipy/special/specfun/specfun.f:5979: warning: ' may be used uninitialized in this function
'REALPART_EXPR <chw>scipy/special/specfun/specfun.f:5979: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cy1>scipy/special/specfun/specfun.f:6014: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cy1>scipy/special/specfun/specfun.f:6014: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cy0>scipy/special/specfun/specfun.f:6013: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cy0>scipy/special/specfun/specfun.f:6013: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cfi>scipy/special/specfun/specfun.f: In function 'ciklv':
scipy/special/specfun/specfun.f:5383: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cfi>scipy/special/specfun/specfun.f:5383: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cfk>scipy/special/specfun/specfun.f:5388: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cfk>scipy/special/specfun/specfun.f:5388: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cyv0>scipy/special/specfun/specfun.f: In function 'cjyvb':
scipy/special/specfun/specfun.f:3623: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cyv0>scipy/special/specfun/specfun.f:3623: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cf>scipy/special/specfun/specfun.f:3661: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cf>scipy/special/specfun/specfun.f:3661: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cjv0>scipy/special/specfun/specfun.f: In function 'cjyva':
scipy/special/specfun/specfun.f:3347: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cjv0>scipy/special/specfun/specfun.f:3347: warning: ' may be used uninitialized in this function
scipy/special/specfun/specfun.f:3347: warning: 'cjv0' may be used uninitialized in this function
'IMAGPART_EXPR <cyv0>scipy/special/specfun/specfun.f:3375: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cyv0>scipy/special/specfun/specfun.f:3375: warning: ' may be used uninitialized in this function
scipy/special/specfun/specfun.f:3375: warning: 'cyv0' may be used uninitialized in this function
'IMAGPART_EXPR <cyv1>scipy/special/specfun/specfun.f:3378: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cyv1>scipy/special/specfun/specfun.f:3378: warning: ' may be used uninitialized in this function
scipy/special/specfun/specfun.f:3378: warning: 'cyv1' may be used uninitialized in this function
'IMAGPART_EXPR <cju0>scipy/special/specfun/specfun.f:3396: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cju0>scipy/special/specfun/specfun.f:3396: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cf>scipy/special/specfun/specfun.f:3442: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cf>scipy/special/specfun/specfun.f:3442: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cs>scipy/special/specfun/specfun.f:3460: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cs>scipy/special/specfun/specfun.f:3460: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <ch0>scipy/special/specfun/specfun.f:3490: warning: ' may be used uninitialized in this function
'REALPART_EXPR <ch0>scipy/special/specfun/specfun.f:3490: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cfy>scipy/special/specfun/specfun.f: In function 'cjylv':
scipy/special/specfun/specfun.f:1437: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cfy>scipy/special/specfun/specfun.f:1437: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cfj>scipy/special/specfun/specfun.f:1432: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cfj>scipy/special/specfun/specfun.f:1432: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cs>scipy/special/specfun/specfun.f: In function 'csphjy':
scipy/special/specfun/specfun.f:1147: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cs>scipy/special/specfun/specfun.f:1147: warning: ' may be used uninitialized in this function
ar: adding 1 object files to build/temp.linux-i686-2.6/libsc_specfun.a
building 'statlib' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/stats
creating build/temp.linux-i686-2.6/scipy/stats/statlib
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/stats/statlib/swilk.f
gfortran:f77: scipy/stats/statlib/ansari.f
 In file scipy/stats/statlib/ansari.f:73

       IF (MM1) 1, 2, 3                                                 
                                                                       1
Warning: Obsolete: arithmetic IF statement at (1)
gfortran:f77: scipy/stats/statlib/spearman.f
 In file scipy/stats/statlib/spearman.f:12

      double precision zero, one, two, b, x, y, z, u, six,              
                                                1
Warning: Unused variable z declared at (1)
ar: adding 3 object files to build/temp.linux-i686-2.6/libstatlib.a
running build_ext
customize UnixCCompiler
customize UnixCCompiler using build_ext
resetting extension 'scipy.integrate._odepack' language from 'c' to 'f77'.
resetting extension 'scipy.integrate.vode' language from 'c' to 'f77'.
resetting extension 'scipy.lib.blas.fblas' language from 'c' to 'f77'.
resetting extension 'scipy.odr.__odrpack' language from 'c' to 'f77'.
extending extension 'scipy.sparse.linalg.dsolve._zsuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)]
extending extension 'scipy.sparse.linalg.dsolve._dsuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)]
extending extension 'scipy.sparse.linalg.dsolve._csuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)]
extending extension 'scipy.sparse.linalg.dsolve._ssuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)]
customize UnixCCompiler
customize UnixCCompiler using build_ext
customize Gnu95FCompiler
customize Gnu95FCompiler using build_ext
building 'scipy.cluster._vq' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/cluster
creating build/temp.linux-i686-2.6/scipy/cluster/src
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/cluster/src/vq_module.c
gcc: scipy/cluster/src/vq.c
/local/scratch/lib/python2.6/site-packages/numpy/core/include/numpy/__multiarray_api.h:969: warning: '_import_array' defined but not used
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/cluster/src/vq_module.o build/temp.linux-i686-2.6/scipy/cluster/src/vq.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/cluster/_vq.so
building 'scipy.cluster._hierarchy_wrap' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/cluster/src/hierarchy.c
gcc: scipy/cluster/src/hierarchy_wrap.c
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/cluster/src/hierarchy_wrap.o build/temp.linux-i686-2.6/scipy/cluster/src/hierarchy.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/cluster/_hierarchy_wrap.so
building 'scipy.fftpack._fftpack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/fftpack
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/fortranobject.c
gcc: build/src.linux-i686-2.6/scipy/fftpack/_fftpackmodule.c
gcc: scipy/fftpack/src/zfft.c
gcc: scipy/fftpack/src/zfftnd.c
gcc: scipy/fftpack/src/drfft.c
gcc: scipy/fftpack/src/zrfft.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/fftpack/_fftpackmodule.o build/temp.linux-i686-2.6/scipy/fftpack/src/zfft.o build/temp.linux-i686-2.6/scipy/fftpack/src/drfft.o build/temp.linux-i686-2.6/scipy/fftpack/src/zrfft.o build/temp.linux-i686-2.6/scipy/fftpack/src/zfftnd.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -Lbuild/temp.linux-i686-2.6 -ldfftpack -lgfortran -o build/lib.linux-i686-2.6/scipy/fftpack/_fftpack.so
building 'scipy.fftpack.convolve' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/fftpack/src/convolve.c
gcc: build/src.linux-i686-2.6/scipy/fftpack/convolvemodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/fftpack/convolvemodule.o build/temp.linux-i686-2.6/scipy/fftpack/src/convolve.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -Lbuild/temp.linux-i686-2.6 -ldfftpack -lgfortran -o build/lib.linux-i686-2.6/scipy/fftpack/convolve.so
building 'scipy.integrate._quadpack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/integrate/_quadpackmodule.c
In file included from scipy/integrate/_quadpackmodule.c:6:
scipy/integrate/__quadpack.h:40: warning: function declaration isn't a prototype
scipy/integrate/__quadpack.h:41: warning: function declaration isn't a prototype
scipy/integrate/__quadpack.h:42: warning: function declaration isn't a prototype
scipy/integrate/__quadpack.h:43: warning: function declaration isn't a prototype
scipy/integrate/__quadpack.h:44: warning: function declaration isn't a prototype
scipy/integrate/__quadpack.h:45: warning: function declaration isn't a prototype
scipy/integrate/__quadpack.h:46: warning: function declaration isn't a prototype
scipy/integrate/__quadpack.h: In function 'quad_function':
scipy/integrate/__quadpack.h:60: warning: unused variable 'nb'
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/integrate/_quadpackmodule.o -Lbuild/temp.linux-i686-2.6 -lquadpack -llinpack_lite -lmach -lgfortran -o build/lib.linux-i686-2.6/scipy/integrate/_quadpack.so
building 'scipy.integrate._odepack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/integrate/_odepackmodule.c
In file included from scipy/integrate/_odepackmodule.c:6:
scipy/integrate/__odepack.h:23: warning: function declaration isn't a prototype
scipy/integrate/multipack.h:114: warning: 'my_make_numpy_array' defined but not used
scipy/integrate/__odepack.h: In function 'odepack_odeint':
scipy/integrate/__odepack.h:218: warning: 'tcrit' may be used uninitialized in this function
scipy/integrate/__odepack.h:219: warning: 'wa' may be used uninitialized in this function
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/integrate/_odepackmodule.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -lodepack -llinpack_lite -lmach -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/integrate/_odepack.so
building 'scipy.integrate.vode' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/integrate
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/integrate/vodemodule.c
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:306: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:307: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c: In function 'cb_f_in_dvode__user__routines':
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:331: warning: unused variable 'ipar'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:330: warning: unused variable 'rpar'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c: In function 'cb_jac_in_dvode__user__routines':
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:463: warning: unused variable 'ipar'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:462: warning: unused variable 'rpar'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:460: warning: unused variable 'mu'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:459: warning: unused variable 'ml'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c: In function 'cb_f_in_zvode__user__routines':
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:591: warning: unused variable 'ipar'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:590: warning: unused variable 'rpar'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c: In function 'cb_jac_in_zvode__user__routines':
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:723: warning: unused variable 'ipar'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:722: warning: unused variable 'rpar'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:720: warning: unused variable 'mu'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:719: warning: unused variable 'ml'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c: At top level:
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:879: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:1208: warning: function declaration isn't a prototype
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/integrate/vodemodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -lodepack -llinpack_lite -lmach -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/integrate/vode.so
building 'scipy.interpolate._fitpack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/interpolate/src
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/interpolate/src/_fitpackmodule.c
scipy/interpolate/src/multipack.h:114: warning: 'my_make_numpy_array' defined but not used
scipy/interpolate/src/multipack.h:134: warning: 'call_python_function' defined but not used
scipy/interpolate/src/__fitpack.h: In function '_bspldismat':
scipy/interpolate/src/__fitpack.h:988: warning: 'dx' may be used uninitialized in this function
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/interpolate/src/_fitpackmodule.o -Lbuild/temp.linux-i686-2.6 -lfitpack -lgfortran -o build/lib.linux-i686-2.6/scipy/interpolate/_fitpack.so
building 'scipy.interpolate.dfitpack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/interpolate
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/interpolate/src
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/interpolate/src/dfitpackmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: build/src.linux-i686-2.6/scipy/interpolate/src/dfitpack-f2pywrappers.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/interpolate/src/dfitpackmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/interpolate/src/dfitpack-f2pywrappers.o -Lbuild/temp.linux-i686-2.6 -lfitpack -lgfortran -o build/lib.linux-i686-2.6/scipy/interpolate/dfitpack.so
building 'scipy.interpolate._interpolate' extension
compiling C++ sources
C compiler: g++ -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -fPIC

compile options: '-Iscipy/interpolate/src -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
g++: scipy/interpolate/src/_interpolate.cpp
g++ -pthread -shared build/temp.linux-i686-2.6/scipy/interpolate/src/_interpolate.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/interpolate/_interpolate.so
building 'scipy.io.numpyio' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/io
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/io/numpyiomodule.c
scipy/io/numpyiomodule.c: In function 'numpyio_fromfile':
scipy/io/numpyiomodule.c:85: warning: 'castfunc' may be used uninitialized in this function
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/io/numpyiomodule.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/io/numpyio.so
building 'scipy.lib.blas.fblas' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/fblasmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: build/src.linux-i686-2.6/scipy/lib/blas/fblaswrap.f
gfortran:f77: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/fblas-f2pywrappers.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/fblasmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/fblaswrap.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/fblas-f2pywrappers.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/lib/blas/fblas.so
building 'scipy.lib.blas.cblas' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/cblasmodule.c
gcc -pthread -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/cblasmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -lf77blas -lcblas -latlas -o build/lib.linux-i686-2.6/scipy/lib/blas/cblas.so
building 'scipy.lib.lapack.flapack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack/flapackmodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack/flapackmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/lib/lapack/flapack.so
building 'scipy.lib.lapack.clapack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack/clapackmodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack/clapackmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/lib/lapack/clapack.so
building 'scipy.lib.lapack.calc_lwork' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/lib/lapack/calc_lworkmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/lib
creating build/temp.linux-i686-2.6/scipy/lib/lapack
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/lib/lapack/calc_lwork.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack/calc_lworkmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/lib/lapack/calc_lwork.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/lib/lapack/calc_lwork.so
building 'scipy.lib.lapack.atlas_version' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/lib/lapack/atlas_version.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/lib/lapack/atlas_version.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/lib/lapack/atlas_version.so
building 'scipy.linalg.fblas' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/fblasmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/linalg
creating build/temp.linux-i686-2.6/scipy/linalg/src
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/linalg/src/fblaswrap.f
gfortran:f77: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/fblas-f2pywrappers.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/fblasmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/linalg/src/fblaswrap.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/fblas-f2pywrappers.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/linalg/fblas.so
building 'scipy.linalg.cblas' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/cblasmodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/cblasmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/linalg/cblas.so
building 'scipy.linalg.flapack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:543: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:544: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:549: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:550: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:551: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:552: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c: In function 'f2py_rout_flapack_cheev':
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:12222: warning: passing argument 6 of 'f2py_func' from incompatible pointer type
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c: In function 'f2py_rout_flapack_zheev':
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:12406: warning: passing argument 6 of 'f2py_func' from incompatible pointer type
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c: At top level:
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:20222: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:20558: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:21505: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:21698: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:21891: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:22084: warning: function declaration isn't a prototype
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapack-f2pywrappers.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapack-f2pywrappers.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/linalg/flapack.so
building 'scipy.linalg.clapack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/clapackmodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/clapackmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/linalg/clapack.so
building 'scipy.linalg._flinalg' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/linalg/_flinalgmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/linalg/src/det.f
gfortran:f77: scipy/linalg/src/lu.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/_flinalgmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/linalg/src/det.o build/temp.linux-i686-2.6/scipy/linalg/src/lu.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/linalg/_flinalg.so
building 'scipy.linalg.calc_lwork' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/linalg/calc_lworkmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/linalg/src/calc_lwork.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/calc_lworkmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/linalg/src/calc_lwork.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/linalg/calc_lwork.so
building 'scipy.linalg.atlas_version' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/linalg/atlas_version.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/linalg/atlas_version.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/linalg/atlas_version.so
building 'scipy.odr.__odrpack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -Iscipy/odr -I/local/scratch/include/atlas -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/odr/__odrpack.c
scipy/odr/__odrpack.c:1324: warning: 'check_args' defined but not used
scipy/odr/__odrpack.c: In function 'fcn_callback':
scipy/odr/__odrpack.c:47: warning: 'result' may be used uninitialized in this function
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/odr/__odrpack.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -lodrpack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/odr/__odrpack.so
building 'scipy.optimize._minpack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/optimize/_minpackmodule.c
In file included from scipy/optimize/_minpackmodule.c:7:
scipy/optimize/__minpack.h: In function 'minpack_hybrd':
scipy/optimize/__minpack.h:231: warning: unused variable 'store_multipack_globals3'
scipy/optimize/__minpack.h: In function 'minpack_lmdif':
scipy/optimize/__minpack.h:434: warning: unused variable 'store_multipack_globals3'
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/optimize/_minpackmodule.o -Lbuild/temp.linux-i686-2.6 -lminpack -lgfortran -o build/lib.linux-i686-2.6/scipy/optimize/_minpack.so
building 'scipy.optimize._zeros' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/optimize/zeros.c
scipy/optimize/Zeros/zeros.h:16: warning: 'dminarg1' defined but not used
scipy/optimize/Zeros/zeros.h:16: warning: 'dminarg2' defined but not used
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/optimize/zeros.o -Lbuild/temp.linux-i686-2.6 -lrootfind -o build/lib.linux-i686-2.6/scipy/optimize/_zeros.so
building 'scipy.optimize._lbfgsb' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/lbfgsb
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/optimize/lbfgsb/_lbfgsbmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/optimize/lbfgsb
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/optimize/lbfgsb/routines.f
 In file scipy/optimize/lbfgsb/routines.f:258

     +                  sgo, yg, ygo, index, iwhere, indx2, task,       
                          1
Warning: Unused variable sgo declared at (1)
 In file scipy/optimize/lbfgsb/routines.f:258

     +                  sgo, yg, ygo, index, iwhere, indx2, task,       
                                   1
Warning: Unused variable ygo declared at (1)
 In file scipy/optimize/lbfgsb/routines.f:257

     +                  sy, ss, yy, wt, wn, snd, z, r, d, t, wa, sg,    
                                 1
Warning: Unused variable yy declared at (1)
 In file scipy/optimize/lbfgsb/routines.f:1176

     +                  v, nint, sg, yg, iprint, sbgnrm, info, epsmch)  
                                  1
Warning: Unused variable sg declared at (1)
 In file scipy/optimize/lbfgsb/routines.f:1176

     +                  v, nint, sg, yg, iprint, sbgnrm, info, epsmch)  
                                      1
Warning: Unused variable yg declared at (1)
 In file scipy/optimize/lbfgsb/routines.f:2856

 3006 format (i5,2(1x,i4),2(1x,i6),(1x,i4),(1x,i5),7x,'-',10x,'-')      
    1
Warning: Label 3006 at (1) defined but not used
scipy/optimize/lbfgsb/routines.f: In function 'subsm':
scipy/optimize/lbfgsb/routines.f:3097: warning: 'ibd' may be used uninitialized in this function
scipy/optimize/lbfgsb/routines.f: In function 'cauchy':
scipy/optimize/lbfgsb/routines.f:1366: warning: 'tu' may be used uninitialized in this function
scipy/optimize/lbfgsb/routines.f:1366: warning: 'tl' may be used uninitialized in this function
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/lbfgsb/_lbfgsbmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/optimize/lbfgsb/routines.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/optimize/_lbfgsb.so
building 'scipy.optimize.moduleTNC' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/optimize/tnc
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/optimize/tnc/moduleTNC.c
gcc: scipy/optimize/tnc/tnc.c
scipy/optimize/tnc/tnc.c: In function 'linearSearch':
scipy/optimize/tnc/tnc.c:1415: warning: 'braktd' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1409: warning: 'scxbnd' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1409: warning: 'factor' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1409: warning: 'e' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1409: warning: 'b' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1409: warning: 'a' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1409: warning: 'step' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1409: warning: 'gmin' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1409: warning: 'oldf' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1408: warning: 'gtest2' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1408: warning: 'gtest1' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1408: warning: 'tol' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1408: warning: 'b1' may be used uninitialized in this function
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/optimize/tnc/moduleTNC.o build/temp.linux-i686-2.6/scipy/optimize/tnc/tnc.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/optimize/moduleTNC.so
building 'scipy.optimize._cobyla' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/cobyla
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/optimize/cobyla/_cobylamodule.c
build/src.linux-i686-2.6/scipy/optimize/cobyla/_cobylamodule.c: In function 'cb_calcfc_in__cobyla__user__routines':
build/src.linux-i686-2.6/scipy/optimize/cobyla/_cobylamodule.c:314: warning: unused variable 'f'
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/optimize/cobyla
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/optimize/cobyla/cobyla2.f
gfortran:f77: scipy/optimize/cobyla/trstlp.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/cobyla/_cobylamodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/optimize/cobyla/cobyla2.o build/temp.linux-i686-2.6/scipy/optimize/cobyla/trstlp.o -Lbuild/temp.linux-i686-2.6 -lgfortran -o build/lib.linux-i686-2.6/scipy/optimize/_cobyla.so
building 'scipy.optimize.minpack2' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/minpack2
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/optimize/minpack2/minpack2module.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/optimize/minpack2
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/optimize/minpack2/dcsrch.f
gfortran:f77: scipy/optimize/minpack2/dcstep.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/minpack2/minpack2module.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/optimize/minpack2/dcsrch.o build/temp.linux-i686-2.6/scipy/optimize/minpack2/dcstep.o -Lbuild/temp.linux-i686-2.6 -lgfortran -o build/lib.linux-i686-2.6/scipy/optimize/minpack2.so
building 'scipy.optimize._slsqp' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/slsqp
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/optimize/slsqp/_slsqpmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/optimize/slsqp
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/optimize/slsqp/slsqp_optmz.f
 In file scipy/optimize/slsqp/slsqp_optmz.f:257

      IF (mode) 260, 100, 220                                           
                                                                       1
Warning: Obsolete: arithmetic IF statement at (1)
 In file scipy/optimize/slsqp/slsqp_optmz.f:1901

   10 assign 30 to next                                                 
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/optimize/slsqp/slsqp_optmz.f:1906

   20    GO TO next,(30, 50, 70, 110)                                   
                  1
Warning: Obsolete: Assigned GOTO statement at (1)
 In file scipy/optimize/slsqp/slsqp_optmz.f:1908

      assign 50 to next                                                 
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/optimize/slsqp/slsqp_optmz.f:1918

      assign 70 to next                                                 
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/optimize/slsqp/slsqp_optmz.f:1924

      assign 110 to next                                                
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/optimize/slsqp/slsqp_optmz.f:1913

   50 IF( dx(i) .EQ. ZERO) GO TO 200                                    
    1
Warning: Label 50 at (1) defined but not used
 In file scipy/optimize/slsqp/slsqp_optmz.f:1932

   70 IF( ABS(dx(i)) .GT. cutlo ) GO TO 75                              
    1
Warning: Label 70 at (1) defined but not used
 In file scipy/optimize/slsqp/slsqp_optmz.f:1937

  110 IF( ABS(dx(i)) .LE. xmax ) GO TO 115                              
    1
Warning: Label 110 at (1) defined but not used
scipy/optimize/slsqp/slsqp_optmz.f: In function 'ldl':
scipy/optimize/slsqp/slsqp_optmz.f:1455: warning: 'tp' may be used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f: In function 'linmin':
scipy/optimize/slsqp/slsqp_optmz.f:1592: warning: 'e' is used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1604: warning: 'd' is used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1617: warning: 'u' is used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1638: warning: 'fx' is used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1639: warning: 'x' is used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1641: warning: 'w' is used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1642: warning: 'fw' is used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1651: warning: 'fv' is used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1651: warning: 'v' is used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1556: warning: 'b' may be used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1556: warning: 'a' may be used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f: In function 'dnrm2_':
scipy/optimize/slsqp/slsqp_optmz.f:1855: warning: 'xmax' may be used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f: In function 'hfti':
scipy/optimize/slsqp/slsqp_optmz.f:1244: warning: 'hmax' may be used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f: In function 'nnls':
scipy/optimize/slsqp/slsqp_optmz.f:1058: warning: 'izmax' may be used uninitialized in this function
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/slsqp/_slsqpmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/optimize/slsqp/slsqp_optmz.o -Lbuild/temp.linux-i686-2.6 -lgfortran -o build/lib.linux-i686-2.6/scipy/optimize/_slsqp.so
building 'scipy.optimize._nnls' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/nnls
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/optimize/nnls/_nnlsmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/optimize/nnls
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/optimize/nnls/nnls.f
 In file scipy/optimize/nnls/nnls.f:394

      IF (CL) 130,130,20                                                
                                                                       1
Warning: Obsolete: arithmetic IF statement at (1)
 In file scipy/optimize/nnls/nnls.f:400

      IF (U(1,LPIVOT)) 50,50,40                                         
                                                                       1
Warning: Obsolete: arithmetic IF statement at (1)
 In file scipy/optimize/nnls/nnls.f:407

   60 IF (CL) 130,130,70                                                
                                                                       1
Warning: Obsolete: arithmetic IF statement at (1)
 In file scipy/optimize/nnls/nnls.f:412

      IF (B) 80,130,130                                                 
                                                                       1
Warning: Obsolete: arithmetic IF statement at (1)
 In file scipy/optimize/nnls/nnls.f:424

          IF (SM) 100,120,100                                           
                                                                       1
Warning: Obsolete: arithmetic IF statement at (1)
scipy/optimize/nnls/nnls.f: In function 'nnls':
scipy/optimize/nnls/nnls.f:52: warning: 'izmax' may be used uninitialized in this function
scipy/optimize/nnls/nnls.f:52: warning: 'jj' may be used uninitialized in this function
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/nnls/_nnlsmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/optimize/nnls/nnls.o -Lbuild/temp.linux-i686-2.6 -lgfortran -o build/lib.linux-i686-2.6/scipy/optimize/_nnls.so
building 'scipy.signal.sigtools' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/signal
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/signal/firfilter.c
gcc: scipy/signal/medianfilter.c
gcc: scipy/signal/sigtoolsmodule.c
scipy/signal/sigtoolsmodule.c:1488: warning: 'Py_copy_info_vec' defined but not used
scipy/signal/lfilter.c: In function 'sigtools_linear_filter':
scipy/signal/lfilter.c:180: warning: 'itzf' may be used uninitialized in this function
scipy/signal/lfilter.c:180: warning: 'itzi' may be used uninitialized in this function
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/signal/sigtoolsmodule.o build/temp.linux-i686-2.6/scipy/signal/firfilter.o build/temp.linux-i686-2.6/scipy/signal/medianfilter.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/signal/sigtools.so
building 'scipy.signal.spline' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/signal/C_bspline_util.c
gcc: scipy/signal/splinemodule.c
gcc: scipy/signal/bspline_util.c
gcc: scipy/signal/D_bspline_util.c
gcc: scipy/signal/S_bspline_util.c
gcc: scipy/signal/Z_bspline_util.c
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/signal/splinemodule.o build/temp.linux-i686-2.6/scipy/signal/S_bspline_util.o build/temp.linux-i686-2.6/scipy/signal/D_bspline_util.o build/temp.linux-i686-2.6/scipy/signal/C_bspline_util.o build/temp.linux-i686-2.6/scipy/signal/Z_bspline_util.o build/temp.linux-i686-2.6/scipy/signal/bspline_util.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/signal/spline.so
building 'scipy.sparse.linalg.isolve._iterative' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/_iterativemodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/STOPTEST2.f
gfortran:f77: build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/getbreak.f
gfortran:f77: build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/BiCGREVCOM.f
gfortran:f77: build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/BiCGSTABREVCOM.f
gfortran:f77: build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/CGREVCOM.f
gfortran:f77: build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/CGSREVCOM.f
gfortran:f77: build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/GMRESREVCOM.f
 In file build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/GMRESREVCOM.f:474

     $     FUNCTION sAPPROXRES( I, H, S, GIVENS, LDG )                  
                                   1
Warning: Unused variable h declared at (1)
 In file build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/GMRESREVCOM.f:1064

     $     FUNCTION dAPPROXRES( I, H, S, GIVENS, LDG )                  
                                   1
Warning: Unused variable h declared at (1)
 In file build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/GMRESREVCOM.f:1654

     $     FUNCTION scAPPROXRES( I, H, S, GIVENS, LDG )                 
                                    1
Warning: Unused variable h declared at (1)
 In file build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/GMRESREVCOM.f:2244

     $     FUNCTION dzAPPROXRES( I, H, S, GIVENS, LDG )                 
                                    1
Warning: Unused variable h declared at (1)
gfortran:f77: build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/QMRREVCOM.f
 In file build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/QMRREVCOM.f:686

     $       EPSTOL, XITOL,                                             
                  1
Warning: Unused variable deltatolepstol declared at (1)
 In file build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/QMRREVCOM.f:1810

     $       EPSTOL, XITOL,                                             
                  1
Warning: Unused variable deltatolepstol declared at (1)
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/_iterativemodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/STOPTEST2.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/getbreak.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/BiCGREVCOM.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/BiCGSTABREVCOM.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/CGREVCOM.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/CGSREVCOM.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/GMRESREVCOM.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/QMRREVCOM.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/sparse/linalg/isolve/_iterative.so
building 'scipy.sparse.linalg.dsolve._zsuperlu' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -DUSE_VENDOR_BLAS=1 -I/local/scratch/include/atlas -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/sparse/linalg/dsolve/_zsuperlumodule.c
gcc: scipy/sparse/linalg/dsolve/_superlu_utils.c
gcc: scipy/sparse/linalg/dsolve/_superluobject.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_zsuperlumodule.o build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_superlu_utils.o build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_superluobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -lsuperlu_src -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/_zsuperlu.so
building 'scipy.sparse.linalg.dsolve._dsuperlu' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -DUSE_VENDOR_BLAS=1 -I/local/scratch/include/atlas -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/sparse/linalg/dsolve/_dsuperlumodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_dsuperlumodule.o build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_superlu_utils.o build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_superluobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -lsuperlu_src -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/_dsuperlu.so
building 'scipy.sparse.linalg.dsolve._csuperlu' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -DUSE_VENDOR_BLAS=1 -I/local/scratch/include/atlas -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/sparse/linalg/dsolve/_csuperlumodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_csuperlumodule.o build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_superlu_utils.o build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_superluobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -lsuperlu_src -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/_csuperlu.so
building 'scipy.sparse.linalg.dsolve._ssuperlu' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -DUSE_VENDOR_BLAS=1 -I/local/scratch/include/atlas -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/sparse/linalg/dsolve/_ssuperlumodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_ssuperlumodule.o build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_superlu_utils.o build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_superluobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -lsuperlu_src -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/_ssuperlu.so
building 'scipy.sparse.linalg.eigen.arpack._arpack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpackmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpack-f2pywrappers.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpackmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpack-f2pywrappers.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -larpack -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpack.so
building 'scipy.sparse.sparsetools._csr' extension
compiling C++ sources
C compiler: g++ -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -fPIC

creating build/temp.linux-i686-2.6/scipy/sparse/sparsetools
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
g++: scipy/sparse/sparsetools/csr_wrap.cxx
g++ -pthread -shared build/temp.linux-i686-2.6/scipy/sparse/sparsetools/csr_wrap.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_csr.so
building 'scipy.sparse.sparsetools._csc' extension
compiling C++ sources
C compiler: g++ -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
g++: scipy/sparse/sparsetools/csc_wrap.cxx
g++ -pthread -shared build/temp.linux-i686-2.6/scipy/sparse/sparsetools/csc_wrap.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_csc.so
building 'scipy.sparse.sparsetools._coo' extension
compiling C++ sources
C compiler: g++ -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
g++: scipy/sparse/sparsetools/coo_wrap.cxx
g++ -pthread -shared build/temp.linux-i686-2.6/scipy/sparse/sparsetools/coo_wrap.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_coo.so
building 'scipy.sparse.sparsetools._bsr' extension
compiling C++ sources
C compiler: g++ -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
g++: scipy/sparse/sparsetools/bsr_wrap.cxx
g++ -pthread -shared build/temp.linux-i686-2.6/scipy/sparse/sparsetools/bsr_wrap.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_bsr.so
building 'scipy.sparse.sparsetools._dia' extension
compiling C++ sources
C compiler: g++ -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
g++: scipy/sparse/sparsetools/dia_wrap.cxx
g++ -pthread -shared build/temp.linux-i686-2.6/scipy/sparse/sparsetools/dia_wrap.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_dia.so
building 'scipy.spatial.ckdtree' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/spatial
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/spatial/ckdtree.c
/local/scratch/lib/python2.6/site-packages/numpy/core/include/numpy/__multiarray_api.h:969: warning: '_import_array' defined but not used
scipy/spatial/ckdtree.c:1469: warning: '__pyx_doc_5scipy_7spatial_7ckdtree_7cKDTree___init__' defined but not used
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/spatial/ckdtree.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/spatial/ckdtree.so
building 'scipy.spatial._distance_wrap' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/spatial/src
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/spatial/src/distance.c
gcc: scipy/spatial/src/distance_wrap.c
scipy/spatial/src/distance_wrap.c: In function 'pdist_weighted_minkowski_wrap':
scipy/spatial/src/distance_wrap.c:866: warning: assignment discards qualifiers from pointer target type
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/spatial/src/distance_wrap.o build/temp.linux-i686-2.6/scipy/spatial/src/distance.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/spatial/_distance_wrap.so
building 'scipy.special._cephes' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/special/toms_wrappers.c
gcc: scipy/special/cdf_wrappers.c
gcc: scipy/special/ufunc_extras.c
gcc: scipy/special/amos_wrappers.c
gcc: scipy/special/specfun_wrappers.c
gcc: scipy/special/_cephesmodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/special/_cephesmodule.o build/temp.linux-i686-2.6/scipy/special/amos_wrappers.o build/temp.linux-i686-2.6/scipy/special/specfun_wrappers.o build/temp.linux-i686-2.6/scipy/special/toms_wrappers.o build/temp.linux-i686-2.6/scipy/special/cdf_wrappers.o build/temp.linux-i686-2.6/scipy/special/ufunc_extras.o -Lbuild/temp.linux-i686-2.6 -lsc_amos -lsc_toms -lsc_c_misc -lsc_cephes -lsc_mach -lsc_cdf -lsc_specfun -lgfortran -o build/lib.linux-i686-2.6/scipy/special/_cephes.so
building 'scipy.special.specfun' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/special
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/special/specfunmodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/special/specfunmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -Lbuild/temp.linux-i686-2.6 -lsc_specfun -lgfortran -o build/lib.linux-i686-2.6/scipy/special/specfun.so
building 'scipy.stats.statlib' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/stats
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/stats/statlibmodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/stats/statlibmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -Lbuild/temp.linux-i686-2.6 -lstatlib -lgfortran -o build/lib.linux-i686-2.6/scipy/stats/statlib.so
building 'scipy.stats.vonmises_cython' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/stats/vonmises_cython.c
/local/scratch/lib/python2.6/site-packages/numpy/core/include/numpy/__multiarray_api.h:969: warning: '_import_array' defined but not used
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/stats/vonmises_cython.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/stats/vonmises_cython.so
building 'scipy.stats.futil' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/stats/futilmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/stats/futil.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/stats/futilmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/stats/futil.o -Lbuild/temp.linux-i686-2.6 -lgfortran -o build/lib.linux-i686-2.6/scipy/stats/futil.so
building 'scipy.stats.mvn' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/stats/mvnmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/stats/mvndst.f
 In file scipy/stats/mvndst.f:1066

         IF ( R .LT. 0 ) BVN = -BVN + MAX( ZERO, MVNPHI(-H)-MVNPHI(-K) )
                                                                       1
Warning: Line truncated at (1)
 In file scipy/stats/mvndst.f:1093

     &    / 15485857, 17329489, 36312197, 55911127, 75906931, 96210113 /
                                                                       1
Warning: Line truncated at (1)
scipy/stats/mvndst.f: In function 'bvnmvn':
scipy/stats/mvndst.f:909: warning: '__result_bvnmvn' may be used uninitialized in this function
scipy/stats/mvndst.f: In function 'covsrt':
scipy/stats/mvndst.f:257: warning: 'bmin' may be used uninitialized in this function
scipy/stats/mvndst.f:257: warning: 'amin' may be used uninitialized in this function
gfortran:f77: build/src.linux-i686-2.6/scipy/stats/mvn-f2pywrappers.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/stats/mvnmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/stats/mvndst.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/stats/mvn-f2pywrappers.o -Lbuild/temp.linux-i686-2.6 -lgfortran -o build/lib.linux-i686-2.6/scipy/stats/mvn.so
building 'scipy.ndimage._nd_image' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/ndimage
creating build/temp.linux-i686-2.6/scipy/ndimage/src
compile options: '-Iscipy/ndimage/src -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/ndimage/src/ni_filters.c
gcc: scipy/ndimage/src/nd_image.c
gcc: scipy/ndimage/src/ni_measure.c
gcc: scipy/ndimage/src/ni_support.c
gcc: scipy/ndimage/src/ni_fourier.c
gcc: scipy/ndimage/src/ni_interpolation.c
gcc: scipy/ndimage/src/ni_morphology.c
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/ndimage/src/nd_image.o build/temp.linux-i686-2.6/scipy/ndimage/src/ni_filters.o build/temp.linux-i686-2.6/scipy/ndimage/src/ni_fourier.o build/temp.linux-i686-2.6/scipy/ndimage/src/ni_interpolation.o build/temp.linux-i686-2.6/scipy/ndimage/src/ni_measure.o build/temp.linux-i686-2.6/scipy/ndimage/src/ni_morphology.o build/temp.linux-i686-2.6/scipy/ndimage/src/ni_support.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/ndimage/_nd_image.so
running scons

Warning: No configuration returned, assuming unavailable.
blas_opt_info:
blas_mkl_info:
  libraries mkl,vml,guide not found in /local/scratch/lib
  NOT AVAILABLE

atlas_blas_threads_info:
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
  FOUND:
    libraries = ['lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = c
    include_dirs = ['/local/scratch/include/atlas']

/local/scratch/lib/python2.6/site-packages/numpy/distutils/command/config.py:361: DeprecationWarning: 
+++++++++++++++++++++++++++++++++++++++++++++++++
Usage of get_output is deprecated: please do not 
use it anymore, and avoid configuration checks 
involving running executable on the target machine.
+++++++++++++++++++++++++++++++++++++++++++++++++

  DeprecationWarning)
customize GnuFCompiler
Found executable /usr/bin/g77
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using config
compiling '_configtest.c':

/* This file is generated from numpy/distutils/system_info.py */
void ATL_buildinfo(void);
int main(void) {
  ATL_buildinfo();
  return 0;
}
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-c'
gcc: _configtest.c
gcc -pthread _configtest.o -L/local/scratch/lib -llapack -lf77blas -lcblas -latlas -o _configtest
ATLAS version 3.6.0 built by camm on Tue Jun 15 03:19:44 UTC 2004:
   UNAME    : Linux intech131 2.4.20 #1 SMP Thu Jan 23 11:24:05 EST 2003 i686 GNU/Linux
   INSTFLG  : 
   MMDEF    : 
   ARCHDEF  : 
   F2CDEFS  : -DAdd__ -DStringSunStyle
   CACHEEDGE: 196608
   F77      : /usr/bin/g77, version GNU Fortran (GCC) 3.3.5 (Debian 1:3.3.5-1)
   F77FLAGS : -O
   CC       : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   CC FLAGS : -fomit-frame-pointer -O3 -funroll-all-loops
   MCC      : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   MCCFLAGS : -fomit-frame-pointer -O
success!
removing: _configtest.c _configtest.o _configtest
  FOUND:
    libraries = ['lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = c
    define_macros = [('ATLAS_INFO', '"\\"3.6.0\\""')]
    include_dirs = ['/local/scratch/include/atlas']

ATLAS version 3.6.0
lapack_opt_info:
lapack_mkl_info:
mkl_info:
  libraries mkl,vml,guide not found in /local/scratch/lib
  NOT AVAILABLE

  NOT AVAILABLE

atlas_threads_info:
Setting PTATLAS=ATLAS
numpy.distutils.system_info.atlas_threads_info
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
  FOUND:
    libraries = ['lapack', 'lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = f77
    include_dirs = ['/local/scratch/include/atlas']

customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using config
compiling '_configtest.c':

/* This file is generated from numpy/distutils/system_info.py */
void ATL_buildinfo(void);
int main(void) {
  ATL_buildinfo();
  return 0;
}
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-c'
gcc: _configtest.c
gcc -pthread _configtest.o -L/local/scratch/lib -llapack -llapack -lf77blas -lcblas -latlas -o _configtest
ATLAS version 3.6.0 built by camm on Tue Jun 15 03:19:44 UTC 2004:
   UNAME    : Linux intech131 2.4.20 #1 SMP Thu Jan 23 11:24:05 EST 2003 i686 GNU/Linux
   INSTFLG  : 
   MMDEF    : 
   ARCHDEF  : 
   F2CDEFS  : -DAdd__ -DStringSunStyle
   CACHEEDGE: 196608
   F77      : /usr/bin/g77, version GNU Fortran (GCC) 3.3.5 (Debian 1:3.3.5-1)
   F77FLAGS : -O
   CC       : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   CC FLAGS : -fomit-frame-pointer -O3 -funroll-all-loops
   MCC      : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   MCCFLAGS : -fomit-frame-pointer -O
success!
removing: _configtest.c _configtest.o _configtest
  FOUND:
    libraries = ['lapack', 'lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = f77
    define_macros = [('ATLAS_INFO', '"\\"3.6.0\\""')]
    include_dirs = ['/local/scratch/include/atlas']

ATLAS version 3.6.0
ATLAS version 3.6.0
umfpack_info:
  libraries umfpack not found in /local/scratch/lib
/local/scratch/lib/python2.6/site-packages/numpy/distutils/system_info.py:452: UserWarning: 
    UMFPACK sparse solver (http://www.cise.ufl.edu/research/sparse/umfpack/)
    not found. Directories to search for the libraries can be specified in the
    numpy/distutils/site.cfg file (section [umfpack]) or by setting
    the UMFPACK environment variable.
  warnings.warn(self.notfounderror.__doc__)
  NOT AVAILABLE

running install
running build
running config_cc
unifing config_cc, config, build_clib, build_ext, build commands --compiler options
running config_fc
unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options
running build_src
building py_modules sources
building library "dfftpack" sources
building library "linpack_lite" sources
building library "mach" sources
building library "quadpack" sources
building library "odepack" sources
building library "fitpack" sources
building library "odrpack" sources
building library "minpack" sources
building library "rootfind" sources
building library "superlu_src" sources
building library "arpack" sources
building library "sc_c_misc" sources
building library "sc_cephes" sources
building library "sc_mach" sources
building library "sc_toms" sources
building library "sc_amos" sources
building library "sc_cdf" sources
building library "sc_specfun" sources
building library "statlib" sources
building extension "scipy.cluster._vq" sources
building extension "scipy.cluster._hierarchy_wrap" sources
building extension "scipy.fftpack._fftpack" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.fftpack.convolve" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.integrate._quadpack" sources
building extension "scipy.integrate._odepack" sources
building extension "scipy.integrate.vode" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.interpolate._fitpack" sources
building extension "scipy.interpolate.dfitpack" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/scipy/interpolate/src/dfitpack-f2pywrappers.f' to sources.
building extension "scipy.interpolate._interpolate" sources
building extension "scipy.io.numpyio" sources
building extension "scipy.lib.blas.fblas" sources
f2py options: ['skip:', ':']
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/fblas-f2pywrappers.f' to sources.
building extension "scipy.lib.blas.cblas" sources
  adding 'scipy/lib/blas/cblas.pyf.src' to sources.
f2py options: ['skip:', ':']
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.lib.lapack.flapack" sources
f2py options: ['skip:', ':']
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.lib.lapack.clapack" sources
  adding 'scipy/lib/lapack/clapack.pyf.src' to sources.
f2py options: ['skip:', ':']
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.lib.lapack.calc_lwork" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.lib.lapack.atlas_version" sources
building extension "scipy.linalg.fblas" sources
  adding 'build/src.linux-i686-2.6/scipy/linalg/fblas.pyf' to sources.
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/fblas-f2pywrappers.f' to sources.
building extension "scipy.linalg.cblas" sources
  adding 'build/src.linux-i686-2.6/scipy/linalg/cblas.pyf' to sources.
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.linalg.flapack" sources
  adding 'build/src.linux-i686-2.6/scipy/linalg/flapack.pyf' to sources.
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapack-f2pywrappers.f' to sources.
building extension "scipy.linalg.clapack" sources
  adding 'build/src.linux-i686-2.6/scipy/linalg/clapack.pyf' to sources.
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.linalg._flinalg" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.linalg.calc_lwork" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.linalg.atlas_version" sources
building extension "scipy.odr.__odrpack" sources
building extension "scipy.optimize._minpack" sources
building extension "scipy.optimize._zeros" sources
building extension "scipy.optimize._lbfgsb" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.optimize.moduleTNC" sources
building extension "scipy.optimize._cobyla" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.optimize.minpack2" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.optimize._slsqp" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.optimize._nnls" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.signal.sigtools" sources
building extension "scipy.signal.spline" sources
building extension "scipy.sparse.linalg.isolve._iterative" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.sparse.linalg.dsolve._zsuperlu" sources
building extension "scipy.sparse.linalg.dsolve._dsuperlu" sources
building extension "scipy.sparse.linalg.dsolve._csuperlu" sources
building extension "scipy.sparse.linalg.dsolve._ssuperlu" sources
building extension "scipy.sparse.linalg.dsolve.umfpack.__umfpack" sources
building extension "scipy.sparse.linalg.eigen.arpack._arpack" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpack-f2pywrappers.f' to sources.
building extension "scipy.sparse.sparsetools._csr" sources
building extension "scipy.sparse.sparsetools._csc" sources
building extension "scipy.sparse.sparsetools._coo" sources
building extension "scipy.sparse.sparsetools._bsr" sources
building extension "scipy.sparse.sparsetools._dia" sources
building extension "scipy.spatial.ckdtree" sources
building extension "scipy.spatial._distance_wrap" sources
building extension "scipy.special._cephes" sources
building extension "scipy.special.specfun" sources
f2py options: ['--no-wrap-functions']
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.stats.statlib" sources
f2py options: ['--no-wrap-functions']
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.stats.vonmises_cython" sources
building extension "scipy.stats.futil" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.stats.mvn" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/scipy/stats/mvn-f2pywrappers.f' to sources.
building extension "scipy.ndimage._nd_image" sources
building data_files sources
running build_py
copying scipy/version.py -> build/lib.linux-i686-2.6/scipy
copying build/src.linux-i686-2.6/scipy/__config__.py -> build/lib.linux-i686-2.6/scipy
running build_clib
customize UnixCCompiler
customize UnixCCompiler using build_clib
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using build_clib
running build_ext
customize UnixCCompiler
customize UnixCCompiler using build_ext
resetting extension 'scipy.integrate._odepack' language from 'c' to 'f77'.
resetting extension 'scipy.integrate.vode' language from 'c' to 'f77'.
resetting extension 'scipy.lib.blas.fblas' language from 'c' to 'f77'.
resetting extension 'scipy.odr.__odrpack' language from 'c' to 'f77'.
extending extension 'scipy.sparse.linalg.dsolve._zsuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)]
extending extension 'scipy.sparse.linalg.dsolve._dsuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)]
extending extension 'scipy.sparse.linalg.dsolve._csuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)]
extending extension 'scipy.sparse.linalg.dsolve._ssuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)]
customize UnixCCompiler
customize UnixCCompiler using build_ext
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using build_ext
running scons
running install_lib
copying build/lib.linux-i686-2.6/scipy/io/numpyio.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/io
copying build/lib.linux-i686-2.6/scipy/spatial/_distance_wrap.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/spatial
copying build/lib.linux-i686-2.6/scipy/spatial/ckdtree.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/spatial
copying build/lib.linux-i686-2.6/scipy/linalg/clapack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/linalg
copying build/lib.linux-i686-2.6/scipy/linalg/cblas.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/linalg
copying build/lib.linux-i686-2.6/scipy/linalg/fblas.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/linalg
copying build/lib.linux-i686-2.6/scipy/linalg/atlas_version.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/linalg
copying build/lib.linux-i686-2.6/scipy/linalg/_flinalg.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/linalg
copying build/lib.linux-i686-2.6/scipy/linalg/flapack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/linalg
copying build/lib.linux-i686-2.6/scipy/linalg/calc_lwork.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/linalg
copying build/lib.linux-i686-2.6/scipy/interpolate/_interpolate.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/interpolate
copying build/lib.linux-i686-2.6/scipy/interpolate/_fitpack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/interpolate
copying build/lib.linux-i686-2.6/scipy/interpolate/dfitpack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/interpolate
copying build/lib.linux-i686-2.6/scipy/fftpack/convolve.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/fftpack
copying build/lib.linux-i686-2.6/scipy/fftpack/_fftpack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/fftpack
copying build/lib.linux-i686-2.6/scipy/lib/blas/cblas.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/lib/blas
copying build/lib.linux-i686-2.6/scipy/lib/blas/fblas.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/lib/blas
copying build/lib.linux-i686-2.6/scipy/lib/lapack/clapack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/lib/lapack
copying build/lib.linux-i686-2.6/scipy/lib/lapack/atlas_version.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/lib/lapack
copying build/lib.linux-i686-2.6/scipy/lib/lapack/flapack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/lib/lapack
copying build/lib.linux-i686-2.6/scipy/lib/lapack/calc_lwork.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/lib/lapack
copying build/lib.linux-i686-2.6/scipy/__config__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy
copying build/lib.linux-i686-2.6/scipy/special/_cephes.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/special
copying build/lib.linux-i686-2.6/scipy/special/specfun.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/special
copying build/lib.linux-i686-2.6/scipy/sparse/linalg/isolve/_iterative.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/linalg/isolve
copying build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/_csuperlu.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/linalg/dsolve
copying build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/_zsuperlu.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/linalg/dsolve
copying build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/_ssuperlu.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/linalg/dsolve
copying build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/_dsuperlu.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/linalg/dsolve
copying build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/linalg/eigen/arpack
copying build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_bsr.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/sparsetools
copying build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_csr.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/sparsetools
copying build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_csc.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/sparsetools
copying build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_coo.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/sparsetools
copying build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_dia.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/sparsetools
copying build/lib.linux-i686-2.6/scipy/integrate/_quadpack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/integrate
copying build/lib.linux-i686-2.6/scipy/integrate/_odepack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/integrate
copying build/lib.linux-i686-2.6/scipy/integrate/vode.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/integrate
copying build/lib.linux-i686-2.6/scipy/stats/mvn.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/stats
copying build/lib.linux-i686-2.6/scipy/stats/vonmises_cython.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/stats
copying build/lib.linux-i686-2.6/scipy/stats/futil.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/stats
copying build/lib.linux-i686-2.6/scipy/stats/statlib.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/stats
copying build/lib.linux-i686-2.6/scipy/odr/__odrpack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/odr
copying build/lib.linux-i686-2.6/scipy/optimize/_minpack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/optimize
copying build/lib.linux-i686-2.6/scipy/optimize/_slsqp.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/optimize
copying build/lib.linux-i686-2.6/scipy/optimize/moduleTNC.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/optimize
copying build/lib.linux-i686-2.6/scipy/optimize/minpack2.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/optimize
copying build/lib.linux-i686-2.6/scipy/optimize/_zeros.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/optimize
copying build/lib.linux-i686-2.6/scipy/optimize/_cobyla.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/optimize
copying build/lib.linux-i686-2.6/scipy/optimize/_nnls.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/optimize
copying build/lib.linux-i686-2.6/scipy/optimize/_lbfgsb.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/optimize
copying build/lib.linux-i686-2.6/scipy/version.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy
copying build/lib.linux-i686-2.6/scipy/ndimage/_nd_image.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/ndimage
copying build/lib.linux-i686-2.6/scipy/signal/spline.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/signal
copying build/lib.linux-i686-2.6/scipy/signal/sigtools.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/signal
copying build/lib.linux-i686-2.6/scipy/cluster/_hierarchy_wrap.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/cluster
copying build/lib.linux-i686-2.6/scipy/cluster/_vq.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/cluster
byte-compiling /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/__config__.py to __config__.pyc
byte-compiling /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/version.py to version.pyc
running install_data
running install_egg_info
Removing /local/scratch/test_numpy//lib/python2.6/site-packages/scipy-0.7.1-py2.6.egg-info
Writing /local/scratch/test_numpy//lib/python2.6/site-packages/scipy-0.7.1-py2.6.egg-info

[craigb at fsul1 scipy-0.7.1]$ cd ..

[craigb at fsul1 test_numpy]$ unset PYTHONPATH
[craigb at fsul1 test_numpy]$ export PYTHONPATH=/local/scratch/test_numpy/lib/python2.6/site-packages/
[craigb at fsul1 test_numpy]$ 
[craigb at fsul1 test_numpy]$ python
Python 2.6.2 (r262:71600, Jul 28 2009, 10:47:31) 
[GCC 4.1.2 20080704 (Red Hat 4.1.2-44)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy
>>> numpy.test()
Running unit tests for numpy
NumPy version 1.3.0
NumPy is installed in /local/scratch/test_numpy/lib/python2.6/site-packages/numpy
Python version 2.6.2 (r262:71600, Jul 28 2009, 10:47:31) [GCC 4.1.2 20080704 (Red Hat 4.1.2-44)]
nose version 0.11.1
........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................K.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
----------------------------------------------------------------------
Ran 2030 tests in 7.947s

OK (KNOWNFAIL=1)
<nose.result.TextTestResult run=2030 errors=0 failures=0>
>>> import scipy
>>> scipy.test()
Running unit tests for scipy
NumPy version 1.3.0
NumPy is installed in /local/scratch/test_numpy/lib/python2.6/site-packages/numpy
SciPy version 0.7.1
SciPy is installed in /local/scratch/test_numpy/lib/python2.6/site-packages/scipy
Python version 2.6.2 (r262:71600, Jul 28 2009, 10:47:31) [GCC 4.1.2 20080704 (Red Hat 4.1.2-44)]
nose version 0.11.1
............................................................................................................................................................................................................................./local/scratch/test_numpy/lib/python2.6/site-packages/scipy/interpolate/fitpack2.py:498: UserWarning: 
The coefficients of the spline returned have been computed as the
minimal norm least-squares solution of a (numerically) rank deficient
system (deficiency=7). If deficiency is large, the results may be
inaccurate. Deficiency may strongly depend on the value of eps.
  warnings.warn(message)
...../local/scratch/test_numpy/lib/python2.6/site-packages/scipy/interpolate/fitpack2.py:439: UserWarning: 
The required storage space exceeds the available storage space: nxest
or nyest too small, or s too small.
The weighted least-squares spline corresponds to the current set of
knots.
  warnings.warn(message)
...........................................K..K..................................................................................................................................../local/scratch/test_numpy/lib/python2.6/site-packages/scipy/io/matlab/tests/test_mio.py:438: FutureWarning: Using oned_as default value ('column') This will change to 'row' in future versions
  mfw = MatFile5Writer(StringIO())
......../local/scratch/test_numpy/lib/python2.6/site-packages/scipy/io/matlab/mio.py:84: FutureWarning: Using struct_as_record default value (False) This will change to True in future versions
  return MatFile5Reader(byte_stream, **kwargs)
...............................Warning: 1000000 bytes requested, 20 bytes read.
./local/scratch/test_numpy/lib/python2.6/site-packages/numpy/lib/utils.py:108: DeprecationWarning: write_array is deprecated
  warnings.warn(str1, DeprecationWarning)
/local/scratch/test_numpy/lib/python2.6/site-packages/numpy/lib/utils.py:108: DeprecationWarning: read_array is deprecated
  warnings.warn(str1, DeprecationWarning)
......................./local/scratch/test_numpy/lib/python2.6/site-packages/numpy/lib/utils.py:108: DeprecationWarning: npfile is deprecated
  warnings.warn(str1, DeprecationWarning)
.........................................................................................................................................................SSSSSS......SSSSSS......SSSS....ATLAS version 3.6.0 built by camm on Tue Jun 15 03:19:44 UTC 2004:
   UNAME    : Linux intech131 2.4.20 #1 SMP Thu Jan 23 11:24:05 EST 2003 i686 GNU/Linux
   INSTFLG  : 
   MMDEF    : 
   ARCHDEF  : 
   F2CDEFS  : -DAdd__ -DStringSunStyle
   CACHEEDGE: 196608
   F77      : /usr/bin/g77, version GNU Fortran (GCC) 3.3.5 (Debian 1:3.3.5-1)
   F77FLAGS : -O
   CC       : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   CC FLAGS : -fomit-frame-pointer -O3 -funroll-all-loops
   MCC      : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   MCCFLAGS : -fomit-frame-pointer -O
.............................................................................................................../local/scratch/test_numpy/lib/python2.6/site-packages/scipy/linalg/decomp.py:1177: DeprecationWarning: qr econ argument will be removed after scipy 0.7. The economy transform will then be available through the mode='economic' argument.
  "the mode='economic' argument.", DeprecationWarning)
.............................................................................................................................................................................................................................................................................................Result may be inaccurate, approximate err = 2.76151934647e-09
...Result may be inaccurate, approximate err = 1.61696391382e-10
.....SSS..................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................SSSSSSSSSSS...........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................K.K.........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................../local/scratch/test_numpy/lib/python2.6/site-packages/numpy/lib/function_base.py:324: Warning: 
            The new semantics of histogram is now the default and the `new`
            keyword will be removed in NumPy 1.4.
            
  """, Warning)
....................................................................................S................................................................................../local/scratch/test_numpy/lib/python2.6/site-packages/scipy/stats/stats.py:420: DeprecationWarning: scipy.stats.mean is deprecated; please update your code to use numpy.mean.
Please note that:
    - numpy.mean axis argument defaults to None, not 0
    - numpy.mean has a ddof argument to replace bias in a more general manner.
      scipy.stats.mean(a, bias=True) can be replaced by numpy.mean(x,
axis=0, ddof=1).
  axis=0, ddof=1).""", DeprecationWarning)
./local/scratch/test_numpy/lib/python2.6/site-packages/scipy/stats/stats.py:1329: DeprecationWarning: scipy.stats.std is deprecated; please update your code to use numpy.std.
Please note that:
    - numpy.std axis argument defaults to None, not 0
    - numpy.std has a ddof argument to replace bias in a more general manner.
      scipy.stats.std(a, bias=True) can be replaced by numpy.std(x,
      axis=0, ddof=0), scipy.stats.std(a, bias=False) by numpy.std(x, axis=0,
      ddof=1).
  ddof=1).""", DeprecationWarning)
/local/scratch/test_numpy/lib/python2.6/site-packages/scipy/stats/stats.py:1305: DeprecationWarning: scipy.stats.var is deprecated; please update your code to use numpy.var.
Please note that:
    - numpy.var axis argument defaults to None, not 0
    - numpy.var has a ddof argument to replace bias in a more general manner.
      scipy.stats.var(a, bias=True) can be replaced by numpy.var(x,
      axis=0, ddof=0), scipy.stats.var(a, bias=False) by var(x, axis=0,
      ddof=1).
  ddof=1).""", DeprecationWarning)
./local/scratch/test_numpy/lib/python2.6/site-packages/scipy/stats/morestats.py:603: UserWarning: Ties preclude use of exact statistic.
  warnings.warn("Ties preclude use of exact statistic.")
....../local/scratch/test_numpy/lib/python2.6/site-packages/scipy/stats/stats.py:498: DeprecationWarning: scipy.stats.median is deprecated; please update your code to use numpy.median.
Please note that:
    - numpy.median axis argument defaults to None, not 0
    - numpy.median has a ddof argument to replace bias in a more general manner.
      scipy.stats.median(a, bias=True) can be replaced by numpy.median(x,
axis=0, ddof=1).
  axis=0, ddof=1).""", DeprecationWarning)
....................................................................................................................................................................................................Warning: friedmanchisquare test using Chisquared aproximation
...............warning: specified build_dir '_bad_path_' does not exist or is not writable. Trying default locations
.....warning: specified build_dir '_bad_path_' does not exist or is not writable. Trying default locations
...............................building extensions here: /home/craigb/.python26_compiled/m2
................................................................................................
----------------------------------------------------------------------
Ran 3488 tests in 46.805s

OK (KNOWNFAIL=4, SKIP=31)
<nose.result.TextTestResult run=3488 errors=0 failures=0>
>>> 




From destroooooy at gmail.com  Fri Aug 21 17:06:14 2009
From: destroooooy at gmail.com (DEMOLISHOR! the Demolishor)
Date: Fri, 21 Aug 2009 17:06:14 -0400
Subject: [SciPy-User] worst-case scenario installation
In-Reply-To: <5b8d13220908201537q3b45fb9bq211e65bb855f15bd@mail.gmail.com>
References: <3d95192b0908191228k3900d1aaqb825a4a4c46c3a0f@mail.gmail.com>
	<b14b627d0908201251w709f59d7v840e453e08bdb8d9@mail.gmail.com>
	<5b8d13220908201537q3b45fb9bq211e65bb855f15bd@mail.gmail.com>
Message-ID: <3d95192b0908211406w3dbee429i2ec5233f445fe098@mail.gmail.com>

Does the attached have the output you need?

On Thu, Aug 20, 2009 at 6:37 PM, David Cournapeau <cournape at gmail.com>wrote:

> On Thu, Aug 20, 2009 at 12:51 PM, Robin<robince at gmail.com> wrote:
>
> > I have also observed the behaviour that it is impossible to get
> > numpy/scipy build to use gfortran if g77 is in the path - I have only
> > been able to install using gfortran on machines which I do have root
> > access and can uninstall g77 completely.
>
> Could you give us the output of python setup.py build_ext
> --fcompiler=gnu95 ? This should helps us understanding why you cannot
> invoke gfortran
>
> cheers,
>
> David
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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[craigb at fsul1 test_numpy]$ which python
/home/craigb/bin/python
[craigb at fsul1 test_numpy]$ python   
Python 2.6.2 (r262:71600, Jul 28 2009, 10:47:31) 
[GCC 4.1.2 20080704 (Red Hat 4.1.2-44)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> 
[craigb at fsul1 test_numpy]$ 
[craigb at fsul1 test_numpy]$ wget http://downloads.sourceforge.net/project/numpy/NumPy/1.3.0/numpy-1.3.0.tar.gz
--22:49:33--  http://downloads.sourceforge.net/project/numpy/NumPy/1.3.0/numpy-1.3.0.tar.gz
Resolving downloads.sourceforge.net... 216.34.181.59
Connecting to downloads.sourceforge.net|216.34.181.59|:80... connected.
HTTP request sent, awaiting response... 302 Found
Location: http://voxel.dl.sourceforge.net/project/numpy/NumPy/1.3.0/numpy-1.3.0.tar.gz [following]
--22:49:33--  http://voxel.dl.sourceforge.net/project/numpy/NumPy/1.3.0/numpy-1.3.0.tar.gz
Resolving voxel.dl.sourceforge.net... 74.63.52.169, 69.9.191.18, 69.9.191.19, ...
Connecting to voxel.dl.sourceforge.net|74.63.52.169|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1995868 (1.9M) [application/x-gzip]
Saving to: `numpy-1.3.0.tar.gz'

100%[=======================================>] 1,995,868   5.38M/s   in 0.4s   

22:49:33 (5.38 MB/s) - `numpy-1.3.0.tar.gz' saved [1995868/1995868]

[craigb at fsul1 test_numpy]$ tar -xzf numpy-1.3.0.tar.gz 
[craigb at fsul1 test_numpy]$ cd numpy-1.3.0 

[craigb at fsul1 numpy-1.3.0]$ 
[craigb at fsul1 numpy-1.3.0]$ ls /local/scratch/lib/    
libatlas.so                   libwx_gtk2u_adv-2.8.so.0.5.0
libatlas.so.3                 libwx_gtk2u_aui-2.8.so
libatlas.so.3.0               libwx_gtk2u_aui-2.8.so.0
libblas.so                    libwx_gtk2u_aui-2.8.so.0.5.0
libblas.so.3                  libwx_gtk2u_core-2.8.so
libblas.so.3.0                libwx_gtk2u_core-2.8.so.0
libcblas.so                   libwx_gtk2u_core-2.8.so.0.5.0
libcblas.so.3                 libwx_gtk2u_gizmos-2.8.so
libcblas.so.3.0               libwx_gtk2u_gizmos-2.8.so.0
libf77blas.so                 libwx_gtk2u_gizmos-2.8.so.0.5.0
libf77blas.so.3               libwx_gtk2u_gizmos_xrc-2.8.so
libf77blas.so.3.0             libwx_gtk2u_gizmos_xrc-2.8.so.0
libg2c.so                     libwx_gtk2u_gizmos_xrc-2.8.so.0.5.0
liblapack_atlas.so            libwx_gtk2u_html-2.8.so
liblapack_atlas.so.3          libwx_gtk2u_html-2.8.so.0
liblapack_atlas.so.3.0        libwx_gtk2u_html-2.8.so.0.5.0
liblapack.so                  libwx_gtk2u_qa-2.8.so
liblapack.so.3                libwx_gtk2u_qa-2.8.so.0
liblapack.so.3.0              libwx_gtk2u_qa-2.8.so.0.5.0
libMinuit2.a                  libwx_gtk2u_richtext-2.8.so
libMinuit2.la                 libwx_gtk2u_richtext-2.8.so.0
libMinuit2.so                 libwx_gtk2u_richtext-2.8.so.0.5.0
libMinuit2.so.0               libwx_gtk2u_stc-2.8.so
libMinuit2.so.0.0.0           libwx_gtk2u_stc-2.8.so.0
libwx_baseu-2.8.so            libwx_gtk2u_stc-2.8.so.0.5.0
libwx_baseu-2.8.so.0          libwx_gtk2u_xrc-2.8.so
libwx_baseu-2.8.so.0.5.0      libwx_gtk2u_xrc-2.8.so.0
libwx_baseu_net-2.8.so        libwx_gtk2u_xrc-2.8.so.0.5.0
libwx_baseu_net-2.8.so.0      python2.5
libwx_baseu_net-2.8.so.0.5.0  python2.6
libwx_baseu_xml-2.8.so        root
libwx_baseu_xml-2.8.so.0      sipdistutils.py
libwx_baseu_xml-2.8.so.0.5.0  sip.so
libwx_gtk2u_adv-2.8.so        wx
libwx_gtk2u_adv-2.8.so.0
[craigb at fsul1 numpy-1.3.0]$ 
[craigb at fsul1 numpy-1.3.0]$ cp site.cfg.example site.cfg         
[craigb at fsul1 numpy-1.3.0]$ emacs -nw site.cfg
[craigb at fsul1 numpy-1.3.0]$ cat site.cfg
# Adapted from the installation instructions for Ubuntu on the SciPy webpage

[DEFAULT]
library_dirs = /local/scratch/lib
include_dirs = /local/scratch/include

[atlas]
atlas_libs = lapack, f77blas, cblas, atlas 
 
[craigb at fsul1 numpy-1.3.0]$ 
[craigb at fsul1 numpy-1.3.0]$ python setup.py build --fcompiler=gnu95
Running from numpy source directory.
F2PY Version 2
blas_opt_info:
blas_mkl_info:
  libraries mkl,vml,guide not found in /local/scratch/lib
  NOT AVAILABLE

atlas_blas_threads_info:
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
  FOUND:
    libraries = ['lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = c
    include_dirs = ['/local/scratch/include/atlas']

/local/scratch/test_numpy/numpy-1.3.0/numpy/distutils/command/config.py:361: DeprecationWarning: 
+++++++++++++++++++++++++++++++++++++++++++++++++
Usage of get_output is deprecated: please do not 
use it anymore, and avoid configuration checks 
involving running executable on the target machine.
+++++++++++++++++++++++++++++++++++++++++++++++++

  DeprecationWarning)
customize GnuFCompiler
Found executable /usr/bin/g77
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using config
compiling '_configtest.c':

/* This file is generated from numpy/distutils/system_info.py */
void ATL_buildinfo(void);
int main(void) {
  ATL_buildinfo();
  return 0;
}
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-c'
gcc: _configtest.c
gcc -pthread _configtest.o -L/local/scratch/lib -llapack -lf77blas -lcblas -latlas -o _configtest
ATLAS version 3.6.0 built by camm on Tue Jun 15 03:19:44 UTC 2004:
   UNAME    : Linux intech131 2.4.20 #1 SMP Thu Jan 23 11:24:05 EST 2003 i686 GNU/Linux
   INSTFLG  : 
   MMDEF    : 
   ARCHDEF  : 
   F2CDEFS  : -DAdd__ -DStringSunStyle
   CACHEEDGE: 196608
   F77      : /usr/bin/g77, version GNU Fortran (GCC) 3.3.5 (Debian 1:3.3.5-1)
   F77FLAGS : -O
   CC       : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   CC FLAGS : -fomit-frame-pointer -O3 -funroll-all-loops
   MCC      : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   MCCFLAGS : -fomit-frame-pointer -O
success!
removing: _configtest.c _configtest.o _configtest
  FOUND:
    libraries = ['lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = c
    define_macros = [('ATLAS_INFO', '"\\"3.6.0\\""')]
    include_dirs = ['/local/scratch/include/atlas']

lapack_opt_info:
lapack_mkl_info:
mkl_info:
  libraries mkl,vml,guide not found in /local/scratch/lib
  NOT AVAILABLE

  NOT AVAILABLE

atlas_threads_info:
Setting PTATLAS=ATLAS
numpy.distutils.system_info.atlas_threads_info
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
  FOUND:
    libraries = ['lapack', 'lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = f77
    include_dirs = ['/local/scratch/include/atlas']

customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using config
compiling '_configtest.c':

/* This file is generated from numpy/distutils/system_info.py */
void ATL_buildinfo(void);
int main(void) {
  ATL_buildinfo();
  return 0;
}
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-c'
gcc: _configtest.c
gcc -pthread _configtest.o -L/local/scratch/lib -llapack -llapack -lf77blas -lcblas -latlas -o _configtest
ATLAS version 3.6.0 built by camm on Tue Jun 15 03:19:44 UTC 2004:
   UNAME    : Linux intech131 2.4.20 #1 SMP Thu Jan 23 11:24:05 EST 2003 i686 GNU/Linux
   INSTFLG  : 
   MMDEF    : 
   ARCHDEF  : 
   F2CDEFS  : -DAdd__ -DStringSunStyle
   CACHEEDGE: 196608
   F77      : /usr/bin/g77, version GNU Fortran (GCC) 3.3.5 (Debian 1:3.3.5-1)
   F77FLAGS : -O
   CC       : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   CC FLAGS : -fomit-frame-pointer -O3 -funroll-all-loops
   MCC      : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   MCCFLAGS : -fomit-frame-pointer -O
success!
removing: _configtest.c _configtest.o _configtest
  FOUND:
    libraries = ['lapack', 'lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = f77
    define_macros = [('ATLAS_INFO', '"\\"3.6.0\\""')]
    include_dirs = ['/local/scratch/include/atlas']

running build
running config_cc
unifing config_cc, config, build_clib, build_ext, build commands --compiler options
running config_fc
unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options
running build_src
building py_modules sources
creating build
creating build/src.linux-i686-2.6
creating build/src.linux-i686-2.6/numpy
creating build/src.linux-i686-2.6/numpy/distutils
building library "npymath" sources
creating build/src.linux-i686-2.6/numpy/core
creating build/src.linux-i686-2.6/numpy/core/src
conv_template:> build/src.linux-i686-2.6/numpy/core/src/npy_math.c
building extension "numpy.core._sort" sources
Generating build/src.linux-i686-2.6/numpy/core/include/numpy/config.h
customize Gnu95FCompiler
Found executable /usr/bin/gfortran
customize Gnu95FCompiler using config
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
_configtest.c: In function 'main':
_configtest.c:5: error: size of array 'test_array' is negative
_configtest.c:4: warning: function declaration isn't a prototype
_configtest.c: In function 'main':
_configtest.c:5: error: size of array 'test_array' is negative
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
_configtest.c: In function 'main':
_configtest.c:5: error: size of array 'test_array' is negative
_configtest.c:4: warning: function declaration isn't a prototype
_configtest.c: In function 'main':
_configtest.c:5: error: size of array 'test_array' is negative
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:5: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:4: warning: function declaration isn't a prototype
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:5: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
/local/scratch/test_numpy/numpy-1.3.0/numpy/distutils/command/config.py:39: DeprecationWarning: 
+++++++++++++++++++++++++++++++++++++++++++++++++
Usage of try_run is deprecated: please do not 
use it anymore, and avoid configuration checks 
involving running executable on the target machine.
+++++++++++++++++++++++++++++++++++++++++++++++++

  DeprecationWarning)
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
gcc -pthread _configtest.o -o _configtest
_configtest.o: In function `main':
/local/scratch/test_numpy/numpy-1.3.0/_configtest.c:5: undefined reference to `exp'
collect2: ld returned 1 exit status
_configtest.o: In function `main':
/local/scratch/test_numpy/numpy-1.3.0/_configtest.c:5: undefined reference to `exp'
collect2: ld returned 1 exit status
failure.
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
gcc -pthread _configtest.o -lm -o _configtest
_configtest
success!
removing: _configtest.c _configtest.o _configtest
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:1: warning: conflicting types for built-in function 'asin'
_configtest.c:2: warning: conflicting types for built-in function 'cos'
_configtest.c:3: warning: conflicting types for built-in function 'log'
_configtest.c:4: warning: conflicting types for built-in function 'fabs'
_configtest.c:5: warning: conflicting types for built-in function 'tanh'
_configtest.c:6: warning: conflicting types for built-in function 'atan'
_configtest.c:7: warning: conflicting types for built-in function 'acos'
_configtest.c:8: warning: conflicting types for built-in function 'floor'
_configtest.c:9: warning: conflicting types for built-in function 'fmod'
_configtest.c:10: warning: conflicting types for built-in function 'sqrt'
_configtest.c:11: warning: conflicting types for built-in function 'cosh'
_configtest.c:12: warning: conflicting types for built-in function 'modf'
_configtest.c:13: warning: conflicting types for built-in function 'sinh'
_configtest.c:14: warning: conflicting types for built-in function 'frexp'
_configtest.c:15: warning: conflicting types for built-in function 'exp'
_configtest.c:16: warning: conflicting types for built-in function 'tan'
_configtest.c:17: warning: conflicting types for built-in function 'ceil'
_configtest.c:18: warning: conflicting types for built-in function 'log10'
_configtest.c:19: warning: conflicting types for built-in function 'sin'
_configtest.c:20: warning: conflicting types for built-in function 'ldexp'
gcc -pthread _configtest.o -lm -o _configtest
success!
removing: _configtest.c _configtest.o _configtest
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:1: warning: conflicting types for built-in function 'rint'
_configtest.c:2: warning: conflicting types for built-in function 'log2'
_configtest.c:3: warning: conflicting types for built-in function 'exp2'
_configtest.c:4: warning: conflicting types for built-in function 'trunc'
gcc -pthread _configtest.o -lm -o _configtest
success!
removing: _configtest.c _configtest.o _configtest
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:1: warning: conflicting types for built-in function 'cosf'
_configtest.c:2: warning: conflicting types for built-in function 'coshf'
_configtest.c:3: warning: conflicting types for built-in function 'rintf'
_configtest.c:4: warning: conflicting types for built-in function 'fabsf'
_configtest.c:5: warning: conflicting types for built-in function 'floorf'
_configtest.c:6: warning: conflicting types for built-in function 'tanhf'
_configtest.c:7: warning: conflicting types for built-in function 'log10f'
_configtest.c:8: warning: conflicting types for built-in function 'logf'
_configtest.c:9: warning: conflicting types for built-in function 'sinhf'
_configtest.c:10: warning: conflicting types for built-in function 'acosf'
_configtest.c:11: warning: conflicting types for built-in function 'sqrtf'
_configtest.c:12: warning: conflicting types for built-in function 'ldexpf'
_configtest.c:13: warning: conflicting types for built-in function 'hypotf'
_configtest.c:14: warning: conflicting types for built-in function 'log2f'
_configtest.c:15: warning: conflicting types for built-in function 'exp2f'
_configtest.c:16: warning: conflicting types for built-in function 'atanf'
_configtest.c:17: warning: conflicting types for built-in function 'fmodf'
_configtest.c:18: warning: conflicting types for built-in function 'atan2f'
_configtest.c:19: warning: conflicting types for built-in function 'modff'
_configtest.c:20: warning: conflicting types for built-in function 'ceilf'
_configtest.c:21: warning: conflicting types for built-in function 'log1pf'
_configtest.c:22: warning: conflicting types for built-in function 'asinf'
_configtest.c:23: warning: conflicting types for built-in function 'acoshf'
_configtest.c:24: warning: conflicting types for built-in function 'sinf'
_configtest.c:25: warning: conflicting types for built-in function 'tanf'
_configtest.c:26: warning: conflicting types for built-in function 'atanhf'
_configtest.c:27: warning: conflicting types for built-in function 'truncf'
_configtest.c:28: warning: conflicting types for built-in function 'asinhf'
_configtest.c:29: warning: conflicting types for built-in function 'frexpf'
_configtest.c:30: warning: conflicting types for built-in function 'powf'
_configtest.c:31: warning: conflicting types for built-in function 'expf'
_configtest.c:32: warning: conflicting types for built-in function 'expm1f'
gcc -pthread _configtest.o -lm -o _configtest
success!
removing: _configtest.c _configtest.o _configtest
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:1: warning: conflicting types for built-in function 'tanhl'
_configtest.c:2: warning: conflicting types for built-in function 'log10l'
_configtest.c:3: warning: conflicting types for built-in function 'coshl'
_configtest.c:4: warning: conflicting types for built-in function 'cosl'
_configtest.c:5: warning: conflicting types for built-in function 'floorl'
_configtest.c:6: warning: conflicting types for built-in function 'rintl'
_configtest.c:7: warning: conflicting types for built-in function 'fabsl'
_configtest.c:8: warning: conflicting types for built-in function 'acosl'
_configtest.c:9: warning: conflicting types for built-in function 'ldexpl'
_configtest.c:10: warning: conflicting types for built-in function 'sqrtl'
_configtest.c:11: warning: conflicting types for built-in function 'logl'
_configtest.c:12: warning: conflicting types for built-in function 'expm1l'
_configtest.c:13: warning: conflicting types for built-in function 'hypotl'
_configtest.c:14: warning: conflicting types for built-in function 'log2l'
_configtest.c:15: warning: conflicting types for built-in function 'exp2l'
_configtest.c:16: warning: conflicting types for built-in function 'atanl'
_configtest.c:17: warning: conflicting types for built-in function 'frexpl'
_configtest.c:18: warning: conflicting types for built-in function 'atan2l'
_configtest.c:19: warning: conflicting types for built-in function 'sinhl'
_configtest.c:20: warning: conflicting types for built-in function 'fmodl'
_configtest.c:21: warning: conflicting types for built-in function 'log1pl'
_configtest.c:22: warning: conflicting types for built-in function 'asinl'
_configtest.c:23: warning: conflicting types for built-in function 'ceill'
_configtest.c:24: warning: conflicting types for built-in function 'sinl'
_configtest.c:25: warning: conflicting types for built-in function 'acoshl'
_configtest.c:26: warning: conflicting types for built-in function 'atanhl'
_configtest.c:27: warning: conflicting types for built-in function 'tanl'
_configtest.c:28: warning: conflicting types for built-in function 'truncl'
_configtest.c:29: warning: conflicting types for built-in function 'powl'
_configtest.c:30: warning: conflicting types for built-in function 'expl'
_configtest.c:31: warning: conflicting types for built-in function 'modfl'
_configtest.c:32: warning: conflicting types for built-in function 'asinhl'
gcc -pthread _configtest.o -lm -o _configtest
success!
removing: _configtest.c _configtest.o _configtest
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:6: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
success!
removing: _configtest.c _configtest.o
File: build/src.linux-i686-2.6/numpy/core/include/numpy/config.h
#define SIZEOF_SHORT 2
#define SIZEOF_INT 4
#define SIZEOF_LONG 4
#define SIZEOF_FLOAT 4
#define SIZEOF_DOUBLE 8
#define SIZEOF_PY_INTPTR_T 4
#define SIZEOF_PY_LONG_LONG 8
#define MATHLIB m
#define HAVE_SIN
#define HAVE_COS
#define HAVE_TAN
#define HAVE_SINH
#define HAVE_COSH
#define HAVE_TANH
#define HAVE_FABS
#define HAVE_FLOOR
#define HAVE_CEIL
#define HAVE_SQRT
#define HAVE_LOG10
#define HAVE_LOG
#define HAVE_EXP
#define HAVE_ASIN
#define HAVE_ACOS
#define HAVE_ATAN
#define HAVE_FMOD
#define HAVE_MODF
#define HAVE_FREXP
#define HAVE_LDEXP
#define HAVE_RINT
#define HAVE_TRUNC
#define HAVE_EXP2
#define HAVE_LOG2
#define HAVE_SINF
#define HAVE_COSF
#define HAVE_TANF
#define HAVE_SINHF
#define HAVE_COSHF
#define HAVE_TANHF
#define HAVE_FABSF
#define HAVE_FLOORF
#define HAVE_CEILF
#define HAVE_RINTF
#define HAVE_TRUNCF
#define HAVE_SQRTF
#define HAVE_LOG10F
#define HAVE_LOGF
#define HAVE_LOG1PF
#define HAVE_EXPF
#define HAVE_EXPM1F
#define HAVE_ASINF
#define HAVE_ACOSF
#define HAVE_ATANF
#define HAVE_ASINHF
#define HAVE_ACOSHF
#define HAVE_ATANHF
#define HAVE_HYPOTF
#define HAVE_ATAN2F
#define HAVE_POWF
#define HAVE_FMODF
#define HAVE_MODFF
#define HAVE_FREXPF
#define HAVE_LDEXPF
#define HAVE_EXP2F
#define HAVE_LOG2F
#define HAVE_SINL
#define HAVE_COSL
#define HAVE_TANL
#define HAVE_SINHL
#define HAVE_COSHL
#define HAVE_TANHL
#define HAVE_FABSL
#define HAVE_FLOORL
#define HAVE_CEILL
#define HAVE_RINTL
#define HAVE_TRUNCL
#define HAVE_SQRTL
#define HAVE_LOG10L
#define HAVE_LOGL
#define HAVE_LOG1PL
#define HAVE_EXPL
#define HAVE_EXPM1L
#define HAVE_ASINL
#define HAVE_ACOSL
#define HAVE_ATANL
#define HAVE_ASINHL
#define HAVE_ACOSHL
#define HAVE_ATANHL
#define HAVE_HYPOTL
#define HAVE_ATAN2L
#define HAVE_POWL
#define HAVE_FMODL
#define HAVE_MODFL
#define HAVE_FREXPL
#define HAVE_LDEXPL
#define HAVE_EXP2L
#define HAVE_LOG2L
#define HAVE_DECL_ISNAN
#define HAVE_DECL_ISINF
#define HAVE_DECL_SIGNBIT
#define HAVE_DECL_ISFINITE
#ifndef __cplusplus
/* #undef inline */
#endif

EOF
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h' to sources.
Generating build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
_configtest.c:5: warning: function declaration isn't a prototype
success!
removing: _configtest.c _configtest.o
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
success!
removing: _configtest.c _configtest.o
File: build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h
#define NPY_SIZEOF_SHORT 2
#define NPY_SIZEOF_INT 4
#define NPY_SIZEOF_LONG 4
#define NPY_SIZEOF_FLOAT 4
#define NPY_SIZEOF_DOUBLE 8
#define NPY_SIZEOF_LONGDOUBLE 12
#define NPY_SIZEOF_PY_INTPTR_T 4
#define NPY_SIZEOF_PY_LONG_LONG 8
#define NPY_SIZEOF_LONGLONG 8
#define NPY_NO_SMP 0
#define NPY_HAVE_DECL_ISNAN
#define NPY_HAVE_DECL_ISINF
#define NPY_HAVE_DECL_SIGNBIT
#define NPY_HAVE_DECL_ISFINITE
#define NPY_USE_C99_FORMATS 1
#define NPY_INLINE inline

#ifndef __STDC_FORMAT_MACROS
#define __STDC_FORMAT_MACROS 1
#endif

EOF
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h' to sources.
numpy/core/code_generators/genapi.py:9: DeprecationWarning: the md5 module is deprecated; use hashlib instead
  import md5
executing numpy/core/code_generators/generate_numpy_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h' to sources.
conv_template:> build/src.linux-i686-2.6/numpy/core/src/_sortmodule.c
numpy.core - nothing done with h_files = ['build/src.linux-i686-2.6/numpy/core/include/numpy/config.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h']
building extension "numpy.core.multiarray" sources
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h' to sources.
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h' to sources.
executing numpy/core/code_generators/generate_numpy_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h' to sources.
conv_template:> build/src.linux-i686-2.6/numpy/core/src/scalartypes.inc
  adding 'build/src.linux-i686-2.6/numpy/core/src' to include_dirs.
conv_template:> build/src.linux-i686-2.6/numpy/core/src/arraytypes.inc
numpy.core - nothing done with h_files = ['build/src.linux-i686-2.6/numpy/core/src/scalartypes.inc', 'build/src.linux-i686-2.6/numpy/core/src/arraytypes.inc', 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h']
building extension "numpy.core.umath" sources
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h' to sources.
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h' to sources.
executing numpy/core/code_generators/generate_ufunc_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__ufunc_api.h' to sources.
conv_template:> build/src.linux-i686-2.6/numpy/core/src/umathmodule.c
  adding 'build/src.linux-i686-2.6/numpy/core/src' to include_dirs.
conv_template:> build/src.linux-i686-2.6/numpy/core/src/umath_funcs.inc
conv_template:> build/src.linux-i686-2.6/numpy/core/src/umath_loops.inc
numpy.core - nothing done with h_files = ['build/src.linux-i686-2.6/numpy/core/src/scalartypes.inc', 'build/src.linux-i686-2.6/numpy/core/src/arraytypes.inc', 'build/src.linux-i686-2.6/numpy/core/src/umath_funcs.inc', 'build/src.linux-i686-2.6/numpy/core/src/umath_loops.inc', 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__ufunc_api.h']
building extension "numpy.core.scalarmath" sources
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h' to sources.
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h' to sources.
executing numpy/core/code_generators/generate_numpy_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h' to sources.
executing numpy/core/code_generators/generate_ufunc_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__ufunc_api.h' to sources.
conv_template:> build/src.linux-i686-2.6/numpy/core/src/scalarmathmodule.c
numpy.core - nothing done with h_files = ['build/src.linux-i686-2.6/numpy/core/include/numpy/config.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__ufunc_api.h']
building extension "numpy.core._dotblas" sources
  adding 'numpy/core/blasdot/_dotblas.c' to sources.
building extension "numpy.core.umath_tests" sources
conv_template:> build/src.linux-i686-2.6/numpy/core/src/umath_tests.c
building extension "numpy.lib._compiled_base" sources
building extension "numpy.numarray._capi" sources
building extension "numpy.fft.fftpack_lite" sources
building extension "numpy.linalg.lapack_lite" sources
creating build/src.linux-i686-2.6/numpy/linalg
  adding 'numpy/linalg/lapack_litemodule.c' to sources.
  adding 'numpy/linalg/python_xerbla.c' to sources.
building extension "numpy.random.mtrand" sources
creating build/src.linux-i686-2.6/numpy/random
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
gcc -pthread _configtest.o -o _configtest
_configtest
failure.
removing: _configtest.c _configtest.o _configtest
building data_files sources
running build_py
creating build/lib.linux-i686-2.6
creating build/lib.linux-i686-2.6/numpy
copying numpy/dual.py -> build/lib.linux-i686-2.6/numpy
copying numpy/ctypeslib.py -> build/lib.linux-i686-2.6/numpy
copying numpy/setupscons.py -> build/lib.linux-i686-2.6/numpy
copying numpy/add_newdocs.py -> build/lib.linux-i686-2.6/numpy
copying numpy/_import_tools.py -> build/lib.linux-i686-2.6/numpy
copying numpy/matlib.py -> build/lib.linux-i686-2.6/numpy
copying numpy/version.py -> build/lib.linux-i686-2.6/numpy
copying numpy/setup.py -> build/lib.linux-i686-2.6/numpy
copying numpy/__init__.py -> build/lib.linux-i686-2.6/numpy
copying build/src.linux-i686-2.6/numpy/__config__.py -> build/lib.linux-i686-2.6/numpy
creating build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/lib2def.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/numpy_distribution.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/log.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/setupscons.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/misc_util.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/line_endings.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/environment.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/mingw32ccompiler.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/system_info.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/unixccompiler.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/cpuinfo.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/__version__.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/ccompiler.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/from_template.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/conv_template.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/setup.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/interactive.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/exec_command.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/__init__.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/core.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/intelccompiler.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/info.py -> build/lib.linux-i686-2.6/numpy/distutils
copying numpy/distutils/extension.py -> build/lib.linux-i686-2.6/numpy/distutils
copying build/src.linux-i686-2.6/numpy/distutils/__config__.py -> build/lib.linux-i686-2.6/numpy/distutils
creating build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/install.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/build_ext.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/build_py.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/config_compiler.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/autodist.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/build_src.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/config.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/egg_info.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/install_data.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/develop.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/bdist_rpm.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/build.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/sdist.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/install_headers.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/build_clib.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/scons.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/__init__.py -> build/lib.linux-i686-2.6/numpy/distutils/command
copying numpy/distutils/command/build_scripts.py -> build/lib.linux-i686-2.6/numpy/distutils/command
creating build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/hpux.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/intel.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/g95.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/mips.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/pg.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/absoft.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/lahey.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/sun.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/ibm.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/nag.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/compaq.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/gnu.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/none.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/__init__.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
copying numpy/distutils/fcompiler/vast.py -> build/lib.linux-i686-2.6/numpy/distutils/fcompiler
creating build/lib.linux-i686-2.6/numpy/testing
copying numpy/testing/setupscons.py -> build/lib.linux-i686-2.6/numpy/testing
copying numpy/testing/nosetester.py -> build/lib.linux-i686-2.6/numpy/testing
copying numpy/testing/utils.py -> build/lib.linux-i686-2.6/numpy/testing
copying numpy/testing/noseclasses.py -> build/lib.linux-i686-2.6/numpy/testing
copying numpy/testing/decorators.py -> build/lib.linux-i686-2.6/numpy/testing
copying numpy/testing/setup.py -> build/lib.linux-i686-2.6/numpy/testing
copying numpy/testing/__init__.py -> build/lib.linux-i686-2.6/numpy/testing
copying numpy/testing/numpytest.py -> build/lib.linux-i686-2.6/numpy/testing
copying numpy/testing/nulltester.py -> build/lib.linux-i686-2.6/numpy/testing
creating build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/rules.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/cb_rules.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/setupscons.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/common_rules.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/f2py_testing.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/capi_maps.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/f2py2e.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/use_rules.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/crackfortran.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/__version__.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/cfuncs.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/setup.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/func2subr.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/auxfuncs.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/__init__.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/f90mod_rules.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/diagnose.py -> build/lib.linux-i686-2.6/numpy/f2py
copying numpy/f2py/info.py -> build/lib.linux-i686-2.6/numpy/f2py
creating build/lib.linux-i686-2.6/numpy/core
copying numpy/core/defmatrix.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/scons_support.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/records.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/fromnumeric.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/_internal.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/setupscons.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/numerictypes.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/numeric.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/memmap.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/setup.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/defchararray.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/__init__.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/arrayprint.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/info.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/setup_common.py -> build/lib.linux-i686-2.6/numpy/core
copying numpy/core/code_generators/generate_numpy_api.py -> build/lib.linux-i686-2.6/numpy/core
creating build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/io.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/function_base.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/shape_base.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/user_array.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/ufunclike.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/_datasource.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/machar.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/setupscons.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/getlimits.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/_iotools.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/arraysetops.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/utils.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/type_check.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/twodim_base.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/arrayterator.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/stride_tricks.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/financial.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/scimath.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/index_tricks.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/setup.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/format.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/__init__.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/polynomial.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/recfunctions.py -> build/lib.linux-i686-2.6/numpy/lib
copying numpy/lib/info.py -> build/lib.linux-i686-2.6/numpy/lib
creating build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/matrix.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/fft.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/alter_code1.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/user_array.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/rng.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/rng_stats.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/alter_code2.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/random_array.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/setupscons.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/arrayfns.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/ufuncs.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/compat.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/array_printer.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/misc.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/mlab.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/ma.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/precision.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/fix_default_axis.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/setup.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/functions.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/linear_algebra.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/__init__.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
copying numpy/oldnumeric/typeconv.py -> build/lib.linux-i686-2.6/numpy/oldnumeric
creating build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/matrix.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/fft.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/alter_code1.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/nd_image.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/image.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/convolve.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/alter_code2.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/random_array.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/setupscons.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/session.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/ufuncs.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/compat.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/numerictypes.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/mlab.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/ma.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/util.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/setup.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/functions.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/linear_algebra.py -> build/lib.linux-i686-2.6/numpy/numarray
copying numpy/numarray/__init__.py -> build/lib.linux-i686-2.6/numpy/numarray
creating build/lib.linux-i686-2.6/numpy/fft
copying numpy/fft/helper.py -> build/lib.linux-i686-2.6/numpy/fft
copying numpy/fft/setupscons.py -> build/lib.linux-i686-2.6/numpy/fft
copying numpy/fft/setup.py -> build/lib.linux-i686-2.6/numpy/fft
copying numpy/fft/fftpack.py -> build/lib.linux-i686-2.6/numpy/fft
copying numpy/fft/__init__.py -> build/lib.linux-i686-2.6/numpy/fft
copying numpy/fft/info.py -> build/lib.linux-i686-2.6/numpy/fft
creating build/lib.linux-i686-2.6/numpy/linalg
copying numpy/linalg/setupscons.py -> build/lib.linux-i686-2.6/numpy/linalg
copying numpy/linalg/linalg.py -> build/lib.linux-i686-2.6/numpy/linalg
copying numpy/linalg/setup.py -> build/lib.linux-i686-2.6/numpy/linalg
copying numpy/linalg/__init__.py -> build/lib.linux-i686-2.6/numpy/linalg
copying numpy/linalg/info.py -> build/lib.linux-i686-2.6/numpy/linalg
creating build/lib.linux-i686-2.6/numpy/random
copying numpy/random/setupscons.py -> build/lib.linux-i686-2.6/numpy/random
copying numpy/random/setup.py -> build/lib.linux-i686-2.6/numpy/random
copying numpy/random/__init__.py -> build/lib.linux-i686-2.6/numpy/random
copying numpy/random/info.py -> build/lib.linux-i686-2.6/numpy/random
creating build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/timer_comparison.py -> build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/bench.py -> build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/setupscons.py -> build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/testutils.py -> build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/extras.py -> build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/version.py -> build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/setup.py -> build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/mrecords.py -> build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/__init__.py -> build/lib.linux-i686-2.6/numpy/ma
copying numpy/ma/core.py -> build/lib.linux-i686-2.6/numpy/ma
creating build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/glossary.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/io.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/broadcasting.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/basics.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/constants.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/creation.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/jargon.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/indexing.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/ufuncs.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/misc.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/structured_arrays.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/subclassing.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/howtofind.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/methods_vs_functions.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/internals.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/performance.py -> build/lib.linux-i686-2.6/numpy/doc
copying numpy/doc/__init__.py -> build/lib.linux-i686-2.6/numpy/doc
running build_clib
customize UnixCCompiler
customize UnixCCompiler using build_clib
building 'npymath' library
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6
creating build/temp.linux-i686-2.6/build
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/numpy
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/numpy/core
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/numpy/core/src
compile options: '-Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/numpy/core/src/npy_math.c
ar: adding 1 object files to build/temp.linux-i686-2.6/libnpymath.a
running build_ext
customize UnixCCompiler
customize UnixCCompiler using build_ext
customize Gnu95FCompiler
customize Gnu95FCompiler using build_ext
building 'numpy.core._sort' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/numpy/core/src/_sortmodule.c
gcc -pthread -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/numpy/core/src/_sortmodule.o -Lbuild/temp.linux-i686-2.6 -lm -o build/lib.linux-i686-2.6/numpy/core/_sort.so
building 'numpy.core.multiarray' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/numpy
creating build/temp.linux-i686-2.6/numpy/core
creating build/temp.linux-i686-2.6/numpy/core/src
compile options: '-Ibuild/src.linux-i686-2.6/numpy/core/src -Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: numpy/core/src/multiarraymodule.c
gcc -pthread -shared build/temp.linux-i686-2.6/numpy/core/src/multiarraymodule.o -Lbuild/temp.linux-i686-2.6 -lnpymath -lm -o build/lib.linux-i686-2.6/numpy/core/multiarray.so
building 'numpy.core.umath' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Ibuild/src.linux-i686-2.6/numpy/core/src -Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/numpy/core/src/umathmodule.c
gcc -pthread -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/numpy/core/src/umathmodule.o -Lbuild/temp.linux-i686-2.6 -lnpymath -lm -o build/lib.linux-i686-2.6/numpy/core/umath.so
building 'numpy.core.scalarmath' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/numpy/core/src/scalarmathmodule.c
gcc -pthread -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/numpy/core/src/scalarmathmodule.o -Lbuild/temp.linux-i686-2.6 -lm -o build/lib.linux-i686-2.6/numpy/core/scalarmath.so
building 'numpy.core._dotblas' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/numpy/core/blasdot
compile options: '-DATLAS_INFO="\"3.6.0\"" -Inumpy/core/blasdot -I/local/scratch/include/atlas -Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: numpy/core/blasdot/_dotblas.c
gcc -pthread -shared build/temp.linux-i686-2.6/numpy/core/blasdot/_dotblas.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -lf77blas -lcblas -latlas -o build/lib.linux-i686-2.6/numpy/core/_dotblas.so
building 'numpy.core.umath_tests' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/numpy/core/src/umath_tests.c
gcc -pthread -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/numpy/core/src/umath_tests.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/numpy/core/umath_tests.so
building 'numpy.lib._compiled_base' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/numpy/lib
creating build/temp.linux-i686-2.6/numpy/lib/src
compile options: '-Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: numpy/lib/src/_compiled_base.c
gcc -pthread -shared build/temp.linux-i686-2.6/numpy/lib/src/_compiled_base.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/numpy/lib/_compiled_base.so
building 'numpy.numarray._capi' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/numpy/numarray
compile options: '-Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: numpy/numarray/_capi.c
gcc -pthread -shared build/temp.linux-i686-2.6/numpy/numarray/_capi.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/numpy/numarray/_capi.so
building 'numpy.fft.fftpack_lite' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/numpy/fft
compile options: '-Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: numpy/fft/fftpack_litemodule.c
gcc: numpy/fft/fftpack.c
gcc -pthread -shared build/temp.linux-i686-2.6/numpy/fft/fftpack_litemodule.o build/temp.linux-i686-2.6/numpy/fft/fftpack.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/numpy/fft/fftpack_lite.so
building 'numpy.linalg.lapack_lite' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/numpy/linalg
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: numpy/linalg/lapack_litemodule.c
gcc: numpy/linalg/python_xerbla.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/numpy/linalg/lapack_litemodule.o build/temp.linux-i686-2.6/numpy/linalg/python_xerbla.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/numpy/linalg/lapack_lite.so
building 'numpy.random.mtrand' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/numpy/random
creating build/temp.linux-i686-2.6/numpy/random/mtrand
compile options: '-Inumpy/core/include -Ibuild/src.linux-i686-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: numpy/random/mtrand/distributions.c
gcc: numpy/random/mtrand/initarray.c
gcc: numpy/random/mtrand/randomkit.c
gcc: numpy/random/mtrand/mtrand.c
gcc -pthread -shared build/temp.linux-i686-2.6/numpy/random/mtrand/mtrand.o build/temp.linux-i686-2.6/numpy/random/mtrand/randomkit.o build/temp.linux-i686-2.6/numpy/random/mtrand/initarray.o build/temp.linux-i686-2.6/numpy/random/mtrand/distributions.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/numpy/random/mtrand.so
running scons
running build_scripts
creating build/scripts.linux-i686-2.6
Creating build/scripts.linux-i686-2.6/f2py
  adding 'build/scripts.linux-i686-2.6/f2py' to scripts
changing mode of build/scripts.linux-i686-2.6/f2py from 644 to 755

[craigb at fsul1 numpy-1.3.0]$ python setup.py install --prefix=/local/scratch/test_numpy/ 
Running from numpy source directory.
F2PY Version 2
blas_opt_info:
blas_mkl_info:
  libraries mkl,vml,guide not found in /local/scratch/lib
  NOT AVAILABLE

atlas_blas_threads_info:
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
  FOUND:
    libraries = ['lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = c
    include_dirs = ['/local/scratch/include/atlas']

/local/scratch/test_numpy/numpy-1.3.0/numpy/distutils/command/config.py:361: DeprecationWarning: 
+++++++++++++++++++++++++++++++++++++++++++++++++
Usage of get_output is deprecated: please do not 
use it anymore, and avoid configuration checks 
involving running executable on the target machine.
+++++++++++++++++++++++++++++++++++++++++++++++++

  DeprecationWarning)
customize GnuFCompiler
Found executable /usr/bin/g77
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using config
compiling '_configtest.c':

/* This file is generated from numpy/distutils/system_info.py */
void ATL_buildinfo(void);
int main(void) {
  ATL_buildinfo();
  return 0;
}
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-c'
gcc: _configtest.c
gcc -pthread _configtest.o -L/local/scratch/lib -llapack -lf77blas -lcblas -latlas -o _configtest
ATLAS version 3.6.0 built by camm on Tue Jun 15 03:19:44 UTC 2004:
   UNAME    : Linux intech131 2.4.20 #1 SMP Thu Jan 23 11:24:05 EST 2003 i686 GNU/Linux
   INSTFLG  : 
   MMDEF    : 
   ARCHDEF  : 
   F2CDEFS  : -DAdd__ -DStringSunStyle
   CACHEEDGE: 196608
   F77      : /usr/bin/g77, version GNU Fortran (GCC) 3.3.5 (Debian 1:3.3.5-1)
   F77FLAGS : -O
   CC       : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   CC FLAGS : -fomit-frame-pointer -O3 -funroll-all-loops
   MCC      : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   MCCFLAGS : -fomit-frame-pointer -O
success!
removing: _configtest.c _configtest.o _configtest
  FOUND:
    libraries = ['lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = c
    define_macros = [('ATLAS_INFO', '"\\"3.6.0\\""')]
    include_dirs = ['/local/scratch/include/atlas']

lapack_opt_info:
lapack_mkl_info:
mkl_info:
  libraries mkl,vml,guide not found in /local/scratch/lib
  NOT AVAILABLE

  NOT AVAILABLE

atlas_threads_info:
Setting PTATLAS=ATLAS
numpy.distutils.system_info.atlas_threads_info
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
  FOUND:
    libraries = ['lapack', 'lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = f77
    include_dirs = ['/local/scratch/include/atlas']

customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using config
compiling '_configtest.c':

/* This file is generated from numpy/distutils/system_info.py */
void ATL_buildinfo(void);
int main(void) {
  ATL_buildinfo();
  return 0;
}
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-c'
gcc: _configtest.c
gcc -pthread _configtest.o -L/local/scratch/lib -llapack -llapack -lf77blas -lcblas -latlas -o _configtest
ATLAS version 3.6.0 built by camm on Tue Jun 15 03:19:44 UTC 2004:
   UNAME    : Linux intech131 2.4.20 #1 SMP Thu Jan 23 11:24:05 EST 2003 i686 GNU/Linux
   INSTFLG  : 
   MMDEF    : 
   ARCHDEF  : 
   F2CDEFS  : -DAdd__ -DStringSunStyle
   CACHEEDGE: 196608
   F77      : /usr/bin/g77, version GNU Fortran (GCC) 3.3.5 (Debian 1:3.3.5-1)
   F77FLAGS : -O
   CC       : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   CC FLAGS : -fomit-frame-pointer -O3 -funroll-all-loops
   MCC      : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   MCCFLAGS : -fomit-frame-pointer -O
success!
removing: _configtest.c _configtest.o _configtest
  FOUND:
    libraries = ['lapack', 'lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = f77
    define_macros = [('ATLAS_INFO', '"\\"3.6.0\\""')]
    include_dirs = ['/local/scratch/include/atlas']

running install
running build
running config_cc
unifing config_cc, config, build_clib, build_ext, build commands --compiler options
running config_fc
unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options
running build_src
building py_modules sources
building library "npymath" sources
building extension "numpy.core._sort" sources
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h' to sources.
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h' to sources.
numpy/core/code_generators/genapi.py:9: DeprecationWarning: the md5 module is deprecated; use hashlib instead
  import md5
executing numpy/core/code_generators/generate_numpy_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h' to sources.
numpy.core - nothing done with h_files = ['build/src.linux-i686-2.6/numpy/core/include/numpy/config.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h']
building extension "numpy.core.multiarray" sources
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h' to sources.
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h' to sources.
executing numpy/core/code_generators/generate_numpy_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h' to sources.
  adding 'build/src.linux-i686-2.6/numpy/core/src' to include_dirs.
numpy.core - nothing done with h_files = ['build/src.linux-i686-2.6/numpy/core/src/scalartypes.inc', 'build/src.linux-i686-2.6/numpy/core/src/arraytypes.inc', 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h']
building extension "numpy.core.umath" sources
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h' to sources.
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h' to sources.
executing numpy/core/code_generators/generate_ufunc_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__ufunc_api.h' to sources.
  adding 'build/src.linux-i686-2.6/numpy/core/src' to include_dirs.
numpy.core - nothing done with h_files = ['build/src.linux-i686-2.6/numpy/core/src/scalartypes.inc', 'build/src.linux-i686-2.6/numpy/core/src/arraytypes.inc', 'build/src.linux-i686-2.6/numpy/core/src/umath_funcs.inc', 'build/src.linux-i686-2.6/numpy/core/src/umath_loops.inc', 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__ufunc_api.h']
building extension "numpy.core.scalarmath" sources
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/config.h' to sources.
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h' to sources.
executing numpy/core/code_generators/generate_numpy_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h' to sources.
executing numpy/core/code_generators/generate_ufunc_api.py
  adding 'build/src.linux-i686-2.6/numpy/core/include/numpy/__ufunc_api.h' to sources.
numpy.core - nothing done with h_files = ['build/src.linux-i686-2.6/numpy/core/include/numpy/config.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h', 'build/src.linux-i686-2.6/numpy/core/include/numpy/__ufunc_api.h']
building extension "numpy.core._dotblas" sources
  adding 'numpy/core/blasdot/_dotblas.c' to sources.
building extension "numpy.core.umath_tests" sources
building extension "numpy.lib._compiled_base" sources
building extension "numpy.numarray._capi" sources
building extension "numpy.fft.fftpack_lite" sources
building extension "numpy.linalg.lapack_lite" sources
  adding 'numpy/linalg/lapack_litemodule.c' to sources.
  adding 'numpy/linalg/python_xerbla.c' to sources.
building extension "numpy.random.mtrand" sources
/local/scratch/test_numpy/numpy-1.3.0/numpy/distutils/command/config.py:39: DeprecationWarning: 
+++++++++++++++++++++++++++++++++++++++++++++++++
Usage of try_run is deprecated: please do not 
use it anymore, and avoid configuration checks 
involving running executable on the target machine.
+++++++++++++++++++++++++++++++++++++++++++++++++

  DeprecationWarning)
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using config
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Inumpy/core/src -Inumpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: _configtest.c
gcc -pthread _configtest.o -o _configtest
_configtest
failure.
removing: _configtest.c _configtest.o _configtest
building data_files sources
running build_py
copying numpy/version.py -> build/lib.linux-i686-2.6/numpy
copying build/src.linux-i686-2.6/numpy/__config__.py -> build/lib.linux-i686-2.6/numpy
copying build/src.linux-i686-2.6/numpy/distutils/__config__.py -> build/lib.linux-i686-2.6/numpy/distutils
running build_clib
customize UnixCCompiler
customize UnixCCompiler using build_clib
running build_ext
customize UnixCCompiler
customize UnixCCompiler using build_ext
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using build_ext
running scons
running build_scripts
  adding 'build/scripts.linux-i686-2.6/f2py' to scripts
running install_lib
creating /local/scratch/test_numpy/lib
creating /local/scratch/test_numpy/lib/python2.6
creating /local/scratch/test_numpy/lib/python2.6/site-packages
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/glossary.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/io.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/broadcasting.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/basics.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/constants.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/creation.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/jargon.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/indexing.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/ufuncs.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/misc.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/structured_arrays.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/subclassing.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/howtofind.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/methods_vs_functions.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/internals.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/performance.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
copying build/lib.linux-i686-2.6/numpy/doc/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/testing/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/testing/nosetester.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/testing/utils.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/testing/noseclasses.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/testing/decorators.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/testing/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/testing/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/testing/numpytest.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/testing/nulltester.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/testing
copying build/lib.linux-i686-2.6/numpy/dual.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/linalg
copying build/lib.linux-i686-2.6/numpy/linalg/lapack_lite.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/linalg
copying build/lib.linux-i686-2.6/numpy/linalg/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/linalg
copying build/lib.linux-i686-2.6/numpy/linalg/linalg.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/linalg
copying build/lib.linux-i686-2.6/numpy/linalg/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/linalg
copying build/lib.linux-i686-2.6/numpy/linalg/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/linalg
copying build/lib.linux-i686-2.6/numpy/linalg/info.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/linalg
copying build/lib.linux-i686-2.6/numpy/ctypeslib.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/lib2def.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/numpy_distribution.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/log.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/__config__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/misc_util.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/line_endings.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/environment.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/mingw32ccompiler.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/system_info.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/unixccompiler.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/cpuinfo.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/__version__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/ccompiler.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/from_template.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/conv_template.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/interactive.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/exec_command.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/core.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
copying build/lib.linux-i686-2.6/numpy/distutils/intelccompiler.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/hpux.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/intel.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/g95.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/mips.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/pg.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/absoft.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/lahey.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/sun.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/ibm.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/nag.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/compaq.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/gnu.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/none.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/fcompiler/vast.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/fcompiler
copying build/lib.linux-i686-2.6/numpy/distutils/info.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/install.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/build_ext.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/build_py.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/config_compiler.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/autodist.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/build_src.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/config.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/egg_info.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/install_data.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/develop.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/bdist_rpm.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/build.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/sdist.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/install_headers.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/build_clib.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/scons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/command/build_scripts.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/command
copying build/lib.linux-i686-2.6/numpy/distutils/extension.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/timer_comparison.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/bench.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/testutils.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/extras.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/version.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/mrecords.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/ma/core.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/ma
copying build/lib.linux-i686-2.6/numpy/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/matrix.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/fft.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/alter_code1.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/nd_image.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/image.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/convolve.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/alter_code2.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/random_array.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/session.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/ufuncs.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/compat.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/numerictypes.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/mlab.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/ma.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/util.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/functions.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/linear_algebra.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/_capi.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
copying build/lib.linux-i686-2.6/numpy/numarray/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/numarray
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/io.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/function_base.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/shape_base.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/user_array.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/ufunclike.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/_datasource.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/machar.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/getlimits.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/_iotools.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/arraysetops.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/utils.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/type_check.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/twodim_base.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/arrayterator.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/stride_tricks.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/financial.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/scimath.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/_compiled_base.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/index_tricks.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/format.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/polynomial.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/recfunctions.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/lib/info.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/lib
copying build/lib.linux-i686-2.6/numpy/__config__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
copying build/lib.linux-i686-2.6/numpy/add_newdocs.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/matrix.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/fft.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/alter_code1.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/user_array.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/rng.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/rng_stats.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/alter_code2.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/random_array.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/arrayfns.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/ufuncs.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/compat.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/array_printer.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/misc.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/mlab.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/ma.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/precision.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/fix_default_axis.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/functions.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/linear_algebra.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
copying build/lib.linux-i686-2.6/numpy/oldnumeric/typeconv.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/oldnumeric
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/rules.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/cb_rules.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/common_rules.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/f2py_testing.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/capi_maps.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/f2py2e.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/use_rules.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/crackfortran.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/__version__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/cfuncs.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/func2subr.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/auxfuncs.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/f90mod_rules.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/diagnose.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/f2py/info.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/f2py
copying build/lib.linux-i686-2.6/numpy/_import_tools.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
copying build/lib.linux-i686-2.6/numpy/matlib.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
copying build/lib.linux-i686-2.6/numpy/version.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
copying build/lib.linux-i686-2.6/numpy/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/fft
copying build/lib.linux-i686-2.6/numpy/fft/helper.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/fft
copying build/lib.linux-i686-2.6/numpy/fft/fftpack_lite.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/fft
copying build/lib.linux-i686-2.6/numpy/fft/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/fft
copying build/lib.linux-i686-2.6/numpy/fft/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/fft
copying build/lib.linux-i686-2.6/numpy/fft/fftpack.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/fft
copying build/lib.linux-i686-2.6/numpy/fft/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/fft
copying build/lib.linux-i686-2.6/numpy/fft/info.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/fft
copying build/lib.linux-i686-2.6/numpy/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/random
copying build/lib.linux-i686-2.6/numpy/random/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/random
copying build/lib.linux-i686-2.6/numpy/random/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/random
copying build/lib.linux-i686-2.6/numpy/random/mtrand.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/random
copying build/lib.linux-i686-2.6/numpy/random/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/random
copying build/lib.linux-i686-2.6/numpy/random/info.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/random
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/defmatrix.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/scons_support.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/records.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/scalarmath.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/_dotblas.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/fromnumeric.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/_internal.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/setupscons.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/umath.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/numerictypes.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/generate_numpy_api.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/numeric.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/memmap.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/umath_tests.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/multiarray.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/setup.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/defchararray.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/__init__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/_sort.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/arrayprint.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/info.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
copying build/lib.linux-i686-2.6/numpy/core/setup_common.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core
byte-compiling /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/doc/glossary.py to glossary.pyc
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byte-compiling /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/interactive.py to interactive.pyc
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copying numpy/linalg/tests/test_build.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/linalg/tests/
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/tests/gen_ext/tests
copying numpy/distutils/tests/gen_ext/tests/test_fib3.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/gen_ext/tests
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/tests/f2py_ext/src
copying numpy/distutils/tests/f2py_ext/src/fib2.pyf -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/f2py_ext/src
copying numpy/distutils/tests/f2py_ext/src/fib1.f -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/f2py_ext/src
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/tests/f2py_f90_ext/include
copying numpy/distutils/tests/f2py_f90_ext/include/body.f90 -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/f2py_f90_ext/include
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/tests/f2py_f90_ext/tests
copying numpy/distutils/tests/f2py_f90_ext/tests/test_foo.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/f2py_f90_ext/tests
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/tests/f2py_ext/tests
copying numpy/distutils/tests/f2py_ext/tests/test_fib2.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/f2py_ext/tests
creating /local/scratch/test_numpy/lib/python2.6/site-packages/numpy/distutils/tests/f2py_f90_ext/src
copying numpy/distutils/tests/f2py_f90_ext/src/foo_free.f90 -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/distutils/tests/f2py_f90_ext/src
copying build/src.linux-i686-2.6/numpy/core/include/numpy/numpyconfig.h -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/include/numpy
copying build/src.linux-i686-2.6/numpy/core/include/numpy/__multiarray_api.h -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/include/numpy
copying build/src.linux-i686-2.6/numpy/core/include/numpy/multiarray_api.txt -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/include/numpy
copying build/src.linux-i686-2.6/numpy/core/include/numpy/__ufunc_api.h -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/include/numpy
copying build/src.linux-i686-2.6/numpy/core/include/numpy/ufunc_api.txt -> /local/scratch/test_numpy//lib/python2.6/site-packages/numpy/core/include/numpy
running install_egg_info
Writing /local/scratch/test_numpy//lib/python2.6/site-packages/numpy-1.3.0-py2.6.egg-info

[craigb at fsul1 numpy-1.3.0]$ 
[craigb at fsul1 numpy-1.3.0]$ cd ..

[craigb at fsul1 test_numpy]$ wget http://downloads.sourceforge.net/project/scipy/scipy/0.7.1/scipy-0.7.1.tar.gz
--23:00:05--  http://downloads.sourceforge.net/project/scipy/scipy/0.7.1/scipy-0.7.1.tar.gz
Resolving downloads.sourceforge.net... 216.34.181.59
Connecting to downloads.sourceforge.net|216.34.181.59|:80... connected.
HTTP request sent, awaiting response... 302 Found
Location: http://voxel.dl.sourceforge.net/project/scipy/scipy/0.7.1/scipy-0.7.1.tar.gz [following]
--23:00:06--  http://voxel.dl.sourceforge.net/project/scipy/scipy/0.7.1/scipy-0.7.1.tar.gz
Resolving voxel.dl.sourceforge.net... 74.63.52.167, 74.63.52.168, 74.63.52.169, ...
Connecting to voxel.dl.sourceforge.net|74.63.52.167|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 4538765 (4.3M) [application/x-gzip]
Saving to: `scipy-0.7.1.tar.gz'

100%[=======================================>] 4,538,765   5.64M/s   in 0.8s   

23:00:07 (5.64 MB/s) - `scipy-0.7.1.tar.gz' saved [4538765/4538765]

[craigb at fsul1 test_numpy]$ 
[craigb at fsul1 test_numpy]$ tar -xzf scipy-0.7.1.tar.gz 
[craigb at fsul1 test_numpy]$ cd scipy-0.7.1

[craigb at fsul1 scipy-0.7.1]$ python setup.py build --fcompiler=gnu95

Warning: No configuration returned, assuming unavailable.
blas_opt_info:
blas_mkl_info:
  libraries mkl,vml,guide not found in /local/scratch/lib
  NOT AVAILABLE

atlas_blas_threads_info:
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
  FOUND:
    libraries = ['lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = c
    include_dirs = ['/local/scratch/include/atlas']

/local/scratch/lib/python2.6/site-packages/numpy/distutils/command/config.py:361: DeprecationWarning: 
+++++++++++++++++++++++++++++++++++++++++++++++++
Usage of get_output is deprecated: please do not 
use it anymore, and avoid configuration checks 
involving running executable on the target machine.
+++++++++++++++++++++++++++++++++++++++++++++++++

  DeprecationWarning)
customize GnuFCompiler
Found executable /usr/bin/g77
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using config
compiling '_configtest.c':

/* This file is generated from numpy/distutils/system_info.py */
void ATL_buildinfo(void);
int main(void) {
  ATL_buildinfo();
  return 0;
}
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-c'
gcc: _configtest.c
gcc -pthread _configtest.o -L/local/scratch/lib -llapack -lf77blas -lcblas -latlas -o _configtest
ATLAS version 3.6.0 built by camm on Tue Jun 15 03:19:44 UTC 2004:
   UNAME    : Linux intech131 2.4.20 #1 SMP Thu Jan 23 11:24:05 EST 2003 i686 GNU/Linux
   INSTFLG  : 
   MMDEF    : 
   ARCHDEF  : 
   F2CDEFS  : -DAdd__ -DStringSunStyle
   CACHEEDGE: 196608
   F77      : /usr/bin/g77, version GNU Fortran (GCC) 3.3.5 (Debian 1:3.3.5-1)
   F77FLAGS : -O
   CC       : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   CC FLAGS : -fomit-frame-pointer -O3 -funroll-all-loops
   MCC      : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   MCCFLAGS : -fomit-frame-pointer -O
success!
removing: _configtest.c _configtest.o _configtest
  FOUND:
    libraries = ['lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = c
    define_macros = [('ATLAS_INFO', '"\\"3.6.0\\""')]
    include_dirs = ['/local/scratch/include/atlas']

ATLAS version 3.6.0
lapack_opt_info:
lapack_mkl_info:
mkl_info:
  libraries mkl,vml,guide not found in /local/scratch/lib
  NOT AVAILABLE

  NOT AVAILABLE

atlas_threads_info:
Setting PTATLAS=ATLAS
numpy.distutils.system_info.atlas_threads_info
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
  FOUND:
    libraries = ['lapack', 'lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = f77
    include_dirs = ['/local/scratch/include/atlas']

customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using config
compiling '_configtest.c':

/* This file is generated from numpy/distutils/system_info.py */
void ATL_buildinfo(void);
int main(void) {
  ATL_buildinfo();
  return 0;
}
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-c'
gcc: _configtest.c
gcc -pthread _configtest.o -L/local/scratch/lib -llapack -llapack -lf77blas -lcblas -latlas -o _configtest
ATLAS version 3.6.0 built by camm on Tue Jun 15 03:19:44 UTC 2004:
   UNAME    : Linux intech131 2.4.20 #1 SMP Thu Jan 23 11:24:05 EST 2003 i686 GNU/Linux
   INSTFLG  : 
   MMDEF    : 
   ARCHDEF  : 
   F2CDEFS  : -DAdd__ -DStringSunStyle
   CACHEEDGE: 196608
   F77      : /usr/bin/g77, version GNU Fortran (GCC) 3.3.5 (Debian 1:3.3.5-1)
   F77FLAGS : -O
   CC       : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   CC FLAGS : -fomit-frame-pointer -O3 -funroll-all-loops
   MCC      : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   MCCFLAGS : -fomit-frame-pointer -O
success!
removing: _configtest.c _configtest.o _configtest
  FOUND:
    libraries = ['lapack', 'lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = f77
    define_macros = [('ATLAS_INFO', '"\\"3.6.0\\""')]
    include_dirs = ['/local/scratch/include/atlas']

ATLAS version 3.6.0
ATLAS version 3.6.0
umfpack_info:
  libraries umfpack not found in /local/scratch/lib
/local/scratch/lib/python2.6/site-packages/numpy/distutils/system_info.py:452: UserWarning: 
    UMFPACK sparse solver (http://www.cise.ufl.edu/research/sparse/umfpack/)
    not found. Directories to search for the libraries can be specified in the
    numpy/distutils/site.cfg file (section [umfpack]) or by setting
    the UMFPACK environment variable.
  warnings.warn(self.notfounderror.__doc__)
  NOT AVAILABLE

running build
running config_cc
unifing config_cc, config, build_clib, build_ext, build commands --compiler options
running config_fc
unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options
running build_src
building py_modules sources
creating build
creating build/src.linux-i686-2.6
creating build/src.linux-i686-2.6/scipy
building library "dfftpack" sources
building library "linpack_lite" sources
building library "mach" sources
building library "quadpack" sources
building library "odepack" sources
building library "fitpack" sources
building library "odrpack" sources
building library "minpack" sources
building library "rootfind" sources
building library "superlu_src" sources
building library "arpack" sources
building library "sc_c_misc" sources
building library "sc_cephes" sources
building library "sc_mach" sources
building library "sc_toms" sources
building library "sc_amos" sources
building library "sc_cdf" sources
building library "sc_specfun" sources
building library "statlib" sources
building extension "scipy.cluster._vq" sources
building extension "scipy.cluster._hierarchy_wrap" sources
building extension "scipy.fftpack._fftpack" sources
creating build/src.linux-i686-2.6/scipy/fftpack
f2py options: []
f2py: scipy/fftpack/fftpack.pyf
Reading fortran codes...
	Reading file 'scipy/fftpack/fftpack.pyf' (format:free)
Post-processing...
	Block: _fftpack
			Block: zfft
			Block: drfft
			Block: zrfft
			Block: zfftnd
			Block: destroy_zfft_cache
			Block: destroy_zfftnd_cache
			Block: destroy_drfft_cache
Post-processing (stage 2)...
Building modules...
	Building module "_fftpack"...
		Constructing wrapper function "zfft"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zfft(x,[n,direction,normalize,overwrite_x])
		Constructing wrapper function "drfft"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = drfft(x,[n,direction,normalize,overwrite_x])
		Constructing wrapper function "zrfft"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zrfft(x,[n,direction,normalize,overwrite_x])
		Constructing wrapper function "zfftnd"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zfftnd(x,[s,direction,normalize,overwrite_x])
		Constructing wrapper function "destroy_zfft_cache"...
		  destroy_zfft_cache()
		Constructing wrapper function "destroy_zfftnd_cache"...
		  destroy_zfftnd_cache()
		Constructing wrapper function "destroy_drfft_cache"...
		  destroy_drfft_cache()
	Wrote C/API module "_fftpack" to file "build/src.linux-i686-2.6/scipy/fftpack/_fftpackmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
copying /local/scratch/lib/python2.6/site-packages/numpy/f2py/src/fortranobject.c -> build/src.linux-i686-2.6
copying /local/scratch/lib/python2.6/site-packages/numpy/f2py/src/fortranobject.h -> build/src.linux-i686-2.6
building extension "scipy.fftpack.convolve" sources
f2py options: []
f2py: scipy/fftpack/convolve.pyf
Reading fortran codes...
	Reading file 'scipy/fftpack/convolve.pyf' (format:free)
Post-processing...
	Block: convolve__user__routines
			Block: kernel_func
	Block: convolve
			Block: init_convolution_kernel
			Block: destroy_convolve_cache
			Block: convolve
			Block: convolve_z
Post-processing (stage 2)...
Building modules...
	Constructing call-back function "cb_kernel_func_in_convolve__user__routines"
	  def kernel_func(k): return kernel_func
	Building module "convolve"...
		Constructing wrapper function "init_convolution_kernel"...
		  omega = init_convolution_kernel(n,kernel_func,[d,zero_nyquist,kernel_func_extra_args])
		Constructing wrapper function "destroy_convolve_cache"...
		  destroy_convolve_cache()
		Constructing wrapper function "convolve"...
		  y = convolve(x,omega,[swap_real_imag,overwrite_x])
		Constructing wrapper function "convolve_z"...
		  y = convolve_z(x,omega_real,omega_imag,[overwrite_x])
	Wrote C/API module "convolve" to file "build/src.linux-i686-2.6/scipy/fftpack/convolvemodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.integrate._quadpack" sources
building extension "scipy.integrate._odepack" sources
building extension "scipy.integrate.vode" sources
creating build/src.linux-i686-2.6/scipy/integrate
f2py options: []
f2py: scipy/integrate/vode.pyf
Reading fortran codes...
	Reading file 'scipy/integrate/vode.pyf' (format:free)
Post-processing...
	Block: dvode__user__routines
		Block: dvode_user_interface
			Block: f
			Block: jac
	Block: zvode__user__routines
		Block: zvode_user_interface
			Block: f
			Block: jac
	Block: vode
			Block: dvode
			Block: zvode
Post-processing (stage 2)...
Building modules...
	Constructing call-back function "cb_f_in_dvode__user__routines"
	  def f(t,y): return ydot
	Constructing call-back function "cb_jac_in_dvode__user__routines"
	  def jac(t,y): return jac
	Constructing call-back function "cb_f_in_zvode__user__routines"
	  def f(t,y): return ydot
	Constructing call-back function "cb_jac_in_zvode__user__routines"
	  def jac(t,y): return jac
	Building module "vode"...
		Constructing wrapper function "dvode"...
warning: callstatement is defined without callprotoargument
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y,t,istate = dvode(f,jac,y,t,tout,rtol,atol,itask,istate,rwork,iwork,mf,[f_extra_args,jac_extra_args,overwrite_y])
		Constructing wrapper function "zvode"...
warning: callstatement is defined without callprotoargument
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y,t,istate = zvode(f,jac,y,t,tout,rtol,atol,itask,istate,zwork,rwork,iwork,mf,[f_extra_args,jac_extra_args,overwrite_y])
	Wrote C/API module "vode" to file "build/src.linux-i686-2.6/scipy/integrate/vodemodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.interpolate._fitpack" sources
building extension "scipy.interpolate.dfitpack" sources
creating build/src.linux-i686-2.6/scipy/interpolate
creating build/src.linux-i686-2.6/scipy/interpolate/src
f2py options: []
f2py: scipy/interpolate/src/fitpack.pyf
Reading fortran codes...
	Reading file 'scipy/interpolate/src/fitpack.pyf' (format:free)
Post-processing...
	Block: dfitpack
			Block: splev
			Block: splder
			Block: splint
			Block: sproot
			Block: spalde
			Block: curfit
			Block: percur
			Block: parcur
			Block: fpcurf0
			Block: fpcurf1
			Block: fpcurfm1
			Block: bispev
			Block: bispeu
			Block: surfit_smth
			Block: surfit_lsq
			Block: regrid_smth
			Block: dblint
Post-processing (stage 2)...
Building modules...
	Building module "dfitpack"...
		Constructing wrapper function "splev"...
		  y = splev(t,c,k,x)
		Constructing wrapper function "splder"...
		  y = splder(t,c,k,x,[nu])
		Creating wrapper for Fortran function "splint"("splint")...
		Constructing wrapper function "splint"...
		  splint = splint(t,c,k,a,b)
		Constructing wrapper function "sproot"...
		  zero,m,ier = sproot(t,c,[mest])
		Constructing wrapper function "spalde"...
		  d,ier = spalde(t,c,k,x)
		Constructing wrapper function "curfit"...
		  n,c,fp,ier = curfit(iopt,x,y,w,t,wrk,iwrk,[xb,xe,k,s])
		Constructing wrapper function "percur"...
		  n,c,fp,ier = percur(iopt,x,y,w,t,wrk,iwrk,[k,s])
		Constructing wrapper function "parcur"...
		  n,c,fp,ier = parcur(iopt,ipar,idim,u,x,w,ub,ue,t,wrk,iwrk,[k,s])
		Constructing wrapper function "fpcurf0"...
		  x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier = fpcurf0(x,y,k,[w,xb,xe,s,nest])
		Constructing wrapper function "fpcurf1"...
		  x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier = fpcurf1(x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier,[overwrite_x,overwrite_y,overwrite_w,overwrite_t,overwrite_c,overwrite_fpint,overwrite_nrdata])
		Constructing wrapper function "fpcurfm1"...
		  x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier = fpcurfm1(x,y,k,t,[w,xb,xe,overwrite_t])
		Constructing wrapper function "bispev"...
		  z,ier = bispev(tx,ty,c,kx,ky,x,y)
		Constructing wrapper function "bispeu"...
		  z,ier = bispeu(tx,ty,c,kx,ky,x,y)
		Constructing wrapper function "surfit_smth"...
		  nx,tx,ny,ty,c,fp,wrk1,ier = surfit_smth(x,y,z,[w,xb,xe,yb,ye,kx,ky,s,nxest,nyest,eps,lwrk2])
		Constructing wrapper function "surfit_lsq"...
		  tx,ty,c,fp,ier = surfit_lsq(x,y,z,tx,ty,[w,xb,xe,yb,ye,kx,ky,eps,lwrk2,overwrite_tx,overwrite_ty])
		Constructing wrapper function "regrid_smth"...
		  nx,tx,ny,ty,c,fp,ier = regrid_smth(x,y,z,[xb,xe,yb,ye,kx,ky,s])
		Creating wrapper for Fortran function "dblint"("dblint")...
		Constructing wrapper function "dblint"...
		  dblint = dblint(tx,ty,c,kx,ky,xb,xe,yb,ye)
	Wrote C/API module "dfitpack" to file "build/src.linux-i686-2.6/scipy/interpolate/src/dfitpackmodule.c"
	Fortran 77 wrappers are saved to "build/src.linux-i686-2.6/scipy/interpolate/src/dfitpack-f2pywrappers.f"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/scipy/interpolate/src/dfitpack-f2pywrappers.f' to sources.
building extension "scipy.interpolate._interpolate" sources
building extension "scipy.io.numpyio" sources
building extension "scipy.lib.blas.fblas" sources
creating build/src.linux-i686-2.6/scipy/lib
creating build/src.linux-i686-2.6/scipy/lib/blas
from_template:> build/src.linux-i686-2.6/scipy/lib/blas/fblas.pyf
Including file scipy/lib/blas/fblas_l1.pyf.src
Including file scipy/lib/blas/fblas_l2.pyf.src
Including file scipy/lib/blas/fblas_l3.pyf.src
Mismatch in number of replacements (base <prefix=s,d,c,z>) for <__l1=->. Ignoring.
from_template:> build/src.linux-i686-2.6/scipy/lib/blas/fblaswrap.f
creating build/src.linux-i686-2.6/build
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas
f2py options: ['skip:', ':']
f2py: build/src.linux-i686-2.6/scipy/lib/blas/fblas.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/lib/blas/fblas.pyf' (format:free)
Post-processing...
	Block: fblas
			Block: srotg
			Block: drotg
			Block: crotg
			Block: zrotg
			Block: srotmg
			Block: drotmg
			Block: srot
			Block: drot
			Block: csrot
			Block: zdrot
			Block: srotm
			Block: drotm
			Block: sswap
			Block: dswap
			Block: cswap
			Block: zswap
			Block: sscal
			Block: dscal
			Block: cscal
			Block: zscal
			Block: csscal
			Block: zdscal
			Block: scopy
			Block: dcopy
			Block: ccopy
			Block: zcopy
			Block: saxpy
			Block: daxpy
			Block: caxpy
			Block: zaxpy
			Block: sdot
			Block: ddot
			Block: cdotu
			Block: zdotu
			Block: cdotc
			Block: zdotc
			Block: snrm2
			Block: dnrm2
			Block: scnrm2
			Block: dznrm2
			Block: sasum
			Block: dasum
			Block: scasum
			Block: dzasum
			Block: isamax
			Block: idamax
			Block: icamax
			Block: izamax
			Block: sgemv
			Block: dgemv
			Block: cgemv
			Block: zgemv
			Block: ssymv
			Block: dsymv
			Block: chemv
			Block: zhemv
			Block: strmv
			Block: dtrmv
			Block: ctrmv
			Block: ztrmv
			Block: sger
			Block: dger
			Block: cgeru
			Block: zgeru
			Block: cgerc
			Block: zgerc
			Block: sgemm
			Block: dgemm
			Block: cgemm
			Block: zgemm
Post-processing (stage 2)...
Building modules...
	Building module "fblas"...
		Constructing wrapper function "srotg"...
		  c,s = srotg(a,b)
		Constructing wrapper function "drotg"...
		  c,s = drotg(a,b)
		Constructing wrapper function "crotg"...
		  c,s = crotg(a,b)
		Constructing wrapper function "zrotg"...
		  c,s = zrotg(a,b)
		Constructing wrapper function "srotmg"...
		  param = srotmg(d1,d2,x1,y1)
		Constructing wrapper function "drotmg"...
		  param = drotmg(d1,d2,x1,y1)
		Constructing wrapper function "srot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = srot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "drot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = drot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "csrot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = csrot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "zdrot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = zdrot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "srotm"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = srotm(x,y,param,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "drotm"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = drotm(x,y,param,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "sswap"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = sswap(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "dswap"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = dswap(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "cswap"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = cswap(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "zswap"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = zswap(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "sscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = sscal(a,x,[n,offx,incx])
		Constructing wrapper function "dscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = dscal(a,x,[n,offx,incx])
		Constructing wrapper function "cscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = cscal(a,x,[n,offx,incx])
		Constructing wrapper function "zscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = zscal(a,x,[n,offx,incx])
		Constructing wrapper function "csscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = csscal(a,x,[n,offx,incx,overwrite_x])
		Constructing wrapper function "zdscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = zdscal(a,x,[n,offx,incx,overwrite_x])
		Constructing wrapper function "scopy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = scopy(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "dcopy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = dcopy(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "ccopy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = ccopy(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "zcopy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zcopy(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "saxpy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  z = saxpy(x,y,[n,a,offx,incx,offy,incy])
		Constructing wrapper function "daxpy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  z = daxpy(x,y,[n,a,offx,incx,offy,incy])
		Constructing wrapper function "caxpy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  z = caxpy(x,y,[n,a,offx,incx,offy,incy])
		Constructing wrapper function "zaxpy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  z = zaxpy(x,y,[n,a,offx,incx,offy,incy])
		Creating wrapper for Fortran function "sdot"("sdot")...
		Constructing wrapper function "sdot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = sdot(x,y,[n,offx,incx,offy,incy])
		Creating wrapper for Fortran function "ddot"("ddot")...
		Constructing wrapper function "ddot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = ddot(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "cdotu"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = cdotu(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "zdotu"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = zdotu(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "cdotc"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = cdotc(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "zdotc"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = zdotc(x,y,[n,offx,incx,offy,incy])
		Creating wrapper for Fortran function "snrm2"("snrm2")...
		Constructing wrapper function "snrm2"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  n2 = snrm2(x,[n,offx,incx])
		Creating wrapper for Fortran function "dnrm2"("dnrm2")...
		Constructing wrapper function "dnrm2"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  n2 = dnrm2(x,[n,offx,incx])
		Creating wrapper for Fortran function "scnrm2"("scnrm2")...
		Constructing wrapper function "scnrm2"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  n2 = scnrm2(x,[n,offx,incx])
		Creating wrapper for Fortran function "dznrm2"("dznrm2")...
		Constructing wrapper function "dznrm2"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  n2 = dznrm2(x,[n,offx,incx])
		Creating wrapper for Fortran function "sasum"("sasum")...
		Constructing wrapper function "sasum"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  s = sasum(x,[n,offx,incx])
		Creating wrapper for Fortran function "dasum"("dasum")...
		Constructing wrapper function "dasum"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  s = dasum(x,[n,offx,incx])
		Creating wrapper for Fortran function "scasum"("scasum")...
		Constructing wrapper function "scasum"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  s = scasum(x,[n,offx,incx])
		Creating wrapper for Fortran function "dzasum"("dzasum")...
		Constructing wrapper function "dzasum"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  s = dzasum(x,[n,offx,incx])
		Constructing wrapper function "isamax"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  k = isamax(x,[n,offx,incx])
		Constructing wrapper function "idamax"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  k = idamax(x,[n,offx,incx])
		Constructing wrapper function "icamax"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  k = icamax(x,[n,offx,incx])
		Constructing wrapper function "izamax"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  k = izamax(x,[n,offx,incx])
		Constructing wrapper function "sgemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = sgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])
		Constructing wrapper function "dgemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = dgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])
		Constructing wrapper function "cgemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = cgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])
		Constructing wrapper function "zgemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])
		Constructing wrapper function "ssymv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = ssymv(alpha,a,x,[beta,y,offx,incx,offy,incy,lower,overwrite_y])
		Constructing wrapper function "dsymv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = dsymv(alpha,a,x,[beta,y,offx,incx,offy,incy,lower,overwrite_y])
		Constructing wrapper function "chemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = chemv(alpha,a,x,[beta,y,offx,incx,offy,incy,lower,overwrite_y])
		Constructing wrapper function "zhemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zhemv(alpha,a,x,[beta,y,offx,incx,offy,incy,lower,overwrite_y])
		Constructing wrapper function "strmv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = strmv(a,x,[offx,incx,lower,trans,unitdiag,overwrite_x])
		Constructing wrapper function "dtrmv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = dtrmv(a,x,[offx,incx,lower,trans,unitdiag,overwrite_x])
		Constructing wrapper function "ctrmv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = ctrmv(a,x,[offx,incx,lower,trans,unitdiag,overwrite_x])
		Constructing wrapper function "ztrmv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = ztrmv(a,x,[offx,incx,lower,trans,unitdiag,overwrite_x])
		Constructing wrapper function "sger"...
		  a = sger(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "dger"...
		  a = dger(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "cgeru"...
		  a = cgeru(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "zgeru"...
		  a = zgeru(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "cgerc"...
		  a = cgerc(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "zgerc"...
		  a = zgerc(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "sgemm"...
		  c = sgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])
		Constructing wrapper function "dgemm"...
		  c = dgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])
		Constructing wrapper function "cgemm"...
		  c = cgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])
		Constructing wrapper function "zgemm"...
		  c = zgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])
	Wrote C/API module "fblas" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/fblasmodule.c"
	Fortran 77 wrappers are saved to "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/fblas-f2pywrappers.f"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/fblas-f2pywrappers.f' to sources.
building extension "scipy.lib.blas.cblas" sources
  adding 'scipy/lib/blas/cblas.pyf.src' to sources.
from_template:> build/src.linux-i686-2.6/scipy/lib/blas/cblas.pyf
Including file scipy/lib/blas/cblas_l1.pyf.src
f2py options: ['skip:', ':']
f2py: build/src.linux-i686-2.6/scipy/lib/blas/cblas.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/lib/blas/cblas.pyf' (format:free)
Line #33 in build/src.linux-i686-2.6/scipy/lib/blas/cblas.pyf:"  intent(c)"
	All arguments will have attribute intent(c)
Line #57 in build/src.linux-i686-2.6/scipy/lib/blas/cblas.pyf:"  intent(c)"
	All arguments will have attribute intent(c)
Line #81 in build/src.linux-i686-2.6/scipy/lib/blas/cblas.pyf:"  intent(c)"
	All arguments will have attribute intent(c)
Line #105 in build/src.linux-i686-2.6/scipy/lib/blas/cblas.pyf:"  intent(c)"
	All arguments will have attribute intent(c)
Post-processing...
	Block: cblas
			Block: saxpy
			Block: daxpy
			Block: caxpy
			Block: zaxpy
Post-processing (stage 2)...
Building modules...
	Building module "cblas"...
		Constructing wrapper function "saxpy"...
		  z = saxpy(x,y,[n,a,incx,incy,overwrite_y])
		Constructing wrapper function "daxpy"...
		  z = daxpy(x,y,[n,a,incx,incy,overwrite_y])
		Constructing wrapper function "caxpy"...
		  z = caxpy(x,y,[n,a,incx,incy,overwrite_y])
		Constructing wrapper function "zaxpy"...
		  z = zaxpy(x,y,[n,a,incx,incy,overwrite_y])
	Wrote C/API module "cblas" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/cblasmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.lib.lapack.flapack" sources
creating build/src.linux-i686-2.6/scipy/lib/lapack
from_template:> build/src.linux-i686-2.6/scipy/lib/lapack/flapack.pyf
Including file scipy/lib/lapack/flapack_user.pyf.src
Including file scipy/lib/lapack/flapack_le.pyf.src
Including file scipy/lib/lapack/flapack_lls.pyf.src
Including file scipy/lib/lapack/flapack_esv.pyf.src
Including file scipy/lib/lapack/flapack_gesv.pyf.src
Including file scipy/lib/lapack/flapack_lec.pyf.src
Including file scipy/lib/lapack/flapack_llsc.pyf.src
Including file scipy/lib/lapack/flapack_sevc.pyf.src
Including file scipy/lib/lapack/flapack_evc.pyf.src
Including file scipy/lib/lapack/flapack_svdc.pyf.src
Including file scipy/lib/lapack/flapack_gsevc.pyf.src
Including file scipy/lib/lapack/flapack_gevc.pyf.src
Including file scipy/lib/lapack/flapack_aux.pyf.src
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack
f2py options: ['skip:', ':']
f2py: build/src.linux-i686-2.6/scipy/lib/lapack/flapack.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/lib/lapack/flapack.pyf' (format:free)
Post-processing...
	Block: flapack
			Block: gees__user__routines
				Block: gees_user_interface
					Block: sselect
					Block: dselect
					Block: cselect
					Block: zselect
			Block: sgesv
			Block: dgesv
			Block: cgesv
			Block: zgesv
			Block: sgbsv
			Block: dgbsv
			Block: cgbsv
			Block: zgbsv
			Block: sposv
			Block: dposv
			Block: cposv
			Block: zposv
			Block: sgelss
			Block: dgelss
			Block: cgelss
			Block: zgelss
			Block: ssyev
			Block: dsyev
			Block: cheev
			Block: zheev
			Block: ssyevd
			Block: dsyevd
			Block: cheevd
			Block: zheevd
			Block: ssyevr
			Block: dsyevr
			Block: cheevr
			Block: zheevr
			Block: sgees
			Block: dgees
			Block: cgees
			Block: zgees
			Block: sgeev
			Block: dgeev
			Block: cgeev
			Block: zgeev
			Block: sgesdd
			Block: dgesdd
			Block: cgesdd
			Block: zgesdd
			Block: ssygv
			Block: dsygv
			Block: chegv
			Block: zhegv
			Block: ssygvd
			Block: dsygvd
			Block: chegvd
			Block: zhegvd
			Block: sggev
			Block: dggev
			Block: cggev
			Block: zggev
			Block: sgetrf
			Block: dgetrf
			Block: cgetrf
			Block: zgetrf
			Block: spotrf
			Block: dpotrf
			Block: cpotrf
			Block: zpotrf
			Block: sgetrs
			Block: dgetrs
			Block: cgetrs
			Block: zgetrs
			Block: spotrs
			Block: dpotrs
			Block: cpotrs
			Block: zpotrs
			Block: sgetri
			Block: dgetri
			Block: cgetri
			Block: zgetri
			Block: spotri
			Block: dpotri
			Block: cpotri
			Block: zpotri
			Block: strtri
			Block: dtrtri
			Block: ctrtri
			Block: ztrtri
			Block: sgeqrf
			Block: dgeqrf
			Block: cgeqrf
			Block: zgeqrf
			Block: sorgqr
			Block: dorgqr
			Block: cungqr
			Block: zungqr
			Block: sgehrd
			Block: dgehrd
			Block: cgehrd
			Block: zgehrd
			Block: sgebal
			Block: dgebal
			Block: cgebal
			Block: zgebal
			Block: slauum
			Block: dlauum
			Block: clauum
			Block: zlauum
			Block: slaswp
			Block: dlaswp
			Block: claswp
			Block: zlaswp
Post-processing (stage 2)...
Building modules...
	Constructing call-back function "cb_sselect_in_gees__user__routines"
	  def sselect(arg1,arg2): return sselect
	Constructing call-back function "cb_dselect_in_gees__user__routines"
	  def dselect(arg1,arg2): return dselect
	Constructing call-back function "cb_cselect_in_gees__user__routines"
	  def cselect(arg): return cselect
	Constructing call-back function "cb_zselect_in_gees__user__routines"
	  def zselect(arg): return zselect
	Building module "flapack"...
		Constructing wrapper function "sgesv"...
		  lu,piv,x,info = sgesv(a,b,[overwrite_a,overwrite_b])
		Constructing wrapper function "dgesv"...
		  lu,piv,x,info = dgesv(a,b,[overwrite_a,overwrite_b])
		Constructing wrapper function "cgesv"...
		  lu,piv,x,info = cgesv(a,b,[overwrite_a,overwrite_b])
		Constructing wrapper function "zgesv"...
		  lu,piv,x,info = zgesv(a,b,[overwrite_a,overwrite_b])
		Constructing wrapper function "sgbsv"...
		  lub,piv,x,info = sgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])
		Constructing wrapper function "dgbsv"...
		  lub,piv,x,info = dgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])
		Constructing wrapper function "cgbsv"...
		  lub,piv,x,info = cgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])
		Constructing wrapper function "zgbsv"...
		  lub,piv,x,info = zgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])
		Constructing wrapper function "sposv"...
		  c,x,info = sposv(a,b,[lower,overwrite_a,overwrite_b])
		Constructing wrapper function "dposv"...
		  c,x,info = dposv(a,b,[lower,overwrite_a,overwrite_b])
		Constructing wrapper function "cposv"...
		  c,x,info = cposv(a,b,[lower,overwrite_a,overwrite_b])
		Constructing wrapper function "zposv"...
		  c,x,info = zposv(a,b,[lower,overwrite_a,overwrite_b])
		Constructing wrapper function "sgelss"...
		  v,x,s,rank,info = sgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "dgelss"...
		  v,x,s,rank,info = dgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "cgelss"...
		  v,x,s,rank,info = cgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "zgelss"...
		  v,x,s,rank,info = zgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "ssyev"...
		  w,v,info = ssyev(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "dsyev"...
		  w,v,info = dsyev(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "cheev"...
		  w,v,info = cheev(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "zheev"...
		  w,v,info = zheev(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "ssyevd"...
		  w,v,info = ssyevd(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "dsyevd"...
		  w,v,info = dsyevd(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "cheevd"...
		  w,v,info = cheevd(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "zheevd"...
		  w,v,info = zheevd(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "ssyevr"...
		  w,v,info = ssyevr(a,[compute_v,lower,vrange,irange,atol,lwork,overwrite_a])
		Constructing wrapper function "dsyevr"...
		  w,v,info = dsyevr(a,[compute_v,lower,vrange,irange,atol,lwork,overwrite_a])
		Constructing wrapper function "cheevr"...
		  w,v,info = cheevr(a,[compute_v,lower,vrange,irange,atol,lwork,overwrite_a])
		Constructing wrapper function "zheevr"...
		  w,v,info = zheevr(a,[compute_v,lower,vrange,irange,atol,lwork,overwrite_a])
		Constructing wrapper function "sgees"...
		  t,sdim,wr,wi,vs,info = sgees(sselect,a,[compute_v,sort_t,lwork,sselect_extra_args,overwrite_a])
		Constructing wrapper function "dgees"...
		  t,sdim,wr,wi,vs,info = dgees(dselect,a,[compute_v,sort_t,lwork,dselect_extra_args,overwrite_a])
		Constructing wrapper function "cgees"...
		  t,sdim,w,vs,info = cgees(cselect,a,[compute_v,sort_t,lwork,cselect_extra_args,overwrite_a])
		Constructing wrapper function "zgees"...
		  t,sdim,w,vs,info = zgees(zselect,a,[compute_v,sort_t,lwork,zselect_extra_args,overwrite_a])
		Constructing wrapper function "sgeev"...
		  wr,wi,vl,vr,info = sgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])
		Constructing wrapper function "dgeev"...
		  wr,wi,vl,vr,info = dgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])
		Constructing wrapper function "cgeev"...
		  w,vl,vr,info = cgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])
		Constructing wrapper function "zgeev"...
		  w,vl,vr,info = zgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])
		Constructing wrapper function "sgesdd"...
		  u,s,vt,info = sgesdd(a,[compute_uv,lwork,overwrite_a])
		Constructing wrapper function "dgesdd"...
		  u,s,vt,info = dgesdd(a,[compute_uv,lwork,overwrite_a])
		Constructing wrapper function "cgesdd"...
		  u,s,vt,info = cgesdd(a,[compute_uv,lwork,overwrite_a])
		Constructing wrapper function "zgesdd"...
		  u,s,vt,info = zgesdd(a,[compute_uv,lwork,overwrite_a])
		Constructing wrapper function "ssygv"...
		  w,v,info = ssygv(a,b,[itype,compute_v,lower,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "dsygv"...
		  w,v,info = dsygv(a,b,[itype,compute_v,lower,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "chegv"...
		  w,v,info = chegv(a,b,[itype,compute_v,lower,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "zhegv"...
		  w,v,info = zhegv(a,b,[itype,compute_v,lower,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "ssygvd"...
		  w,v,info = ssygvd(a,b,[itype,compute_v,lower,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "dsygvd"...
		  w,v,info = dsygvd(a,b,[itype,compute_v,lower,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "chegvd"...
		  w,v,info = chegvd(a,b,[itype,compute_v,lower,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "zhegvd"...
		  w,v,info = zhegvd(a,b,[itype,compute_v,lower,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "sggev"...
		  alphar,alphai,beta,vl,vr,info = sggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "dggev"...
		  alphar,alphai,beta,vl,vr,info = dggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "cggev"...
		  alpha,beta,vl,vr,info = cggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "zggev"...
		  alpha,beta,vl,vr,info = zggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "sgetrf"...
		  lu,piv,info = sgetrf(a,[overwrite_a])
		Constructing wrapper function "dgetrf"...
		  lu,piv,info = dgetrf(a,[overwrite_a])
		Constructing wrapper function "cgetrf"...
		  lu,piv,info = cgetrf(a,[overwrite_a])
		Constructing wrapper function "zgetrf"...
		  lu,piv,info = zgetrf(a,[overwrite_a])
		Constructing wrapper function "spotrf"...
		  c,info = spotrf(a,[lower,clean,overwrite_a])
		Constructing wrapper function "dpotrf"...
		  c,info = dpotrf(a,[lower,clean,overwrite_a])
		Constructing wrapper function "cpotrf"...
		  c,info = cpotrf(a,[lower,clean,overwrite_a])
		Constructing wrapper function "zpotrf"...
		  c,info = zpotrf(a,[lower,clean,overwrite_a])
		Constructing wrapper function "sgetrs"...
		  x,info = sgetrs(lu,piv,b,[trans,overwrite_b])
		Constructing wrapper function "dgetrs"...
		  x,info = dgetrs(lu,piv,b,[trans,overwrite_b])
		Constructing wrapper function "cgetrs"...
		  x,info = cgetrs(lu,piv,b,[trans,overwrite_b])
		Constructing wrapper function "zgetrs"...
		  x,info = zgetrs(lu,piv,b,[trans,overwrite_b])
		Constructing wrapper function "spotrs"...
		  x,info = spotrs(c,b,[lower,overwrite_b])
		Constructing wrapper function "dpotrs"...
		  x,info = dpotrs(c,b,[lower,overwrite_b])
		Constructing wrapper function "cpotrs"...
		  x,info = cpotrs(c,b,[lower,overwrite_b])
		Constructing wrapper function "zpotrs"...
		  x,info = zpotrs(c,b,[lower,overwrite_b])
		Constructing wrapper function "sgetri"...
		  inv_a,info = sgetri(lu,piv,[lwork,overwrite_lu])
		Constructing wrapper function "dgetri"...
		  inv_a,info = dgetri(lu,piv,[lwork,overwrite_lu])
		Constructing wrapper function "cgetri"...
		  inv_a,info = cgetri(lu,piv,[lwork,overwrite_lu])
		Constructing wrapper function "zgetri"...
		  inv_a,info = zgetri(lu,piv,[lwork,overwrite_lu])
		Constructing wrapper function "spotri"...
		  inv_a,info = spotri(c,[lower,overwrite_c])
		Constructing wrapper function "dpotri"...
		  inv_a,info = dpotri(c,[lower,overwrite_c])
		Constructing wrapper function "cpotri"...
		  inv_a,info = cpotri(c,[lower,overwrite_c])
		Constructing wrapper function "zpotri"...
		  inv_a,info = zpotri(c,[lower,overwrite_c])
		Constructing wrapper function "strtri"...
		  inv_c,info = strtri(c,[lower,unitdiag,overwrite_c])
		Constructing wrapper function "dtrtri"...
		  inv_c,info = dtrtri(c,[lower,unitdiag,overwrite_c])
		Constructing wrapper function "ctrtri"...
		  inv_c,info = ctrtri(c,[lower,unitdiag,overwrite_c])
		Constructing wrapper function "ztrtri"...
		  inv_c,info = ztrtri(c,[lower,unitdiag,overwrite_c])
		Constructing wrapper function "sgeqrf"...
		  qr,tau,info = sgeqrf(a,[lwork,overwrite_a])
		Constructing wrapper function "dgeqrf"...
		  qr,tau,info = dgeqrf(a,[lwork,overwrite_a])
		Constructing wrapper function "cgeqrf"...
		  qr,tau,info = cgeqrf(a,[lwork,overwrite_a])
		Constructing wrapper function "zgeqrf"...
		  qr,tau,info = zgeqrf(a,[lwork,overwrite_a])
		Constructing wrapper function "sorgqr"...
		  q,info = sorgqr(qr,tau,[lwork,overwrite_qr,overwrite_tau])
		Constructing wrapper function "dorgqr"...
		  q,info = dorgqr(qr,tau,[lwork,overwrite_qr,overwrite_tau])
		Constructing wrapper function "cungqr"...
		  q,info = cungqr(qr,tau,[lwork,overwrite_qr,overwrite_tau])
		Constructing wrapper function "zungqr"...
		  q,info = zungqr(qr,tau,[lwork,overwrite_qr,overwrite_tau])
		Constructing wrapper function "sgehrd"...
		  ht,tau,info = sgehrd(a,[lo,hi,lwork,overwrite_a])
		Constructing wrapper function "dgehrd"...
		  ht,tau,info = dgehrd(a,[lo,hi,lwork,overwrite_a])
		Constructing wrapper function "cgehrd"...
		  ht,tau,info = cgehrd(a,[lo,hi,lwork,overwrite_a])
		Constructing wrapper function "zgehrd"...
		  ht,tau,info = zgehrd(a,[lo,hi,lwork,overwrite_a])
		Constructing wrapper function "sgebal"...
		  ba,lo,hi,pivscale,info = sgebal(a,[scale,permute,overwrite_a])
		Constructing wrapper function "dgebal"...
		  ba,lo,hi,pivscale,info = dgebal(a,[scale,permute,overwrite_a])
		Constructing wrapper function "cgebal"...
		  ba,lo,hi,pivscale,info = cgebal(a,[scale,permute,overwrite_a])
		Constructing wrapper function "zgebal"...
		  ba,lo,hi,pivscale,info = zgebal(a,[scale,permute,overwrite_a])
		Constructing wrapper function "slauum"...
		  a,info = slauum(c,[lower,overwrite_c])
		Constructing wrapper function "dlauum"...
		  a,info = dlauum(c,[lower,overwrite_c])
		Constructing wrapper function "clauum"...
		  a,info = clauum(c,[lower,overwrite_c])
		Constructing wrapper function "zlauum"...
		  a,info = zlauum(c,[lower,overwrite_c])
		Constructing wrapper function "slaswp"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  a = slaswp(a,piv,[k1,k2,off,inc,overwrite_a])
		Constructing wrapper function "dlaswp"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  a = dlaswp(a,piv,[k1,k2,off,inc,overwrite_a])
		Constructing wrapper function "claswp"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  a = claswp(a,piv,[k1,k2,off,inc,overwrite_a])
		Constructing wrapper function "zlaswp"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  a = zlaswp(a,piv,[k1,k2,off,inc,overwrite_a])
	Wrote C/API module "flapack" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack/flapackmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.lib.lapack.clapack" sources
  adding 'scipy/lib/lapack/clapack.pyf.src' to sources.
from_template:> build/src.linux-i686-2.6/scipy/lib/lapack/clapack.pyf
f2py options: ['skip:', ':']
f2py: build/src.linux-i686-2.6/scipy/lib/lapack/clapack.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/lib/lapack/clapack.pyf' (format:free)
Post-processing...
	Block: clapack
			Block: sgesv
			Block: dgesv
			Block: cgesv
			Block: zgesv
			Block: sgetrf
			Block: dgetrf
			Block: cgetrf
			Block: zgetrf
			Block: sgetrs
			Block: dgetrs
			Block: cgetrs
			Block: zgetrs
			Block: sgetri
			Block: dgetri
			Block: cgetri
			Block: zgetri
			Block: sposv
			Block: dposv
			Block: cposv
			Block: zposv
			Block: spotrf
			Block: dpotrf
			Block: cpotrf
			Block: zpotrf
			Block: spotrs
			Block: dpotrs
			Block: cpotrs
			Block: zpotrs
			Block: spotri
			Block: dpotri
			Block: cpotri
			Block: zpotri
			Block: slauum
			Block: dlauum
			Block: clauum
			Block: zlauum
			Block: strtri
			Block: dtrtri
			Block: ctrtri
			Block: ztrtri
Post-processing (stage 2)...
Building modules...
	Building module "clapack"...
		Constructing wrapper function "sgesv"...
		  lu,piv,x,info = sgesv(a,b,[rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "dgesv"...
		  lu,piv,x,info = dgesv(a,b,[rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "cgesv"...
		  lu,piv,x,info = cgesv(a,b,[rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "zgesv"...
		  lu,piv,x,info = zgesv(a,b,[rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "sgetrf"...
		  lu,piv,info = sgetrf(a,[rowmajor,overwrite_a])
		Constructing wrapper function "dgetrf"...
		  lu,piv,info = dgetrf(a,[rowmajor,overwrite_a])
		Constructing wrapper function "cgetrf"...
		  lu,piv,info = cgetrf(a,[rowmajor,overwrite_a])
		Constructing wrapper function "zgetrf"...
		  lu,piv,info = zgetrf(a,[rowmajor,overwrite_a])
		Constructing wrapper function "sgetrs"...
		  x,info = sgetrs(lu,piv,b,[trans,rowmajor,overwrite_b])
		Constructing wrapper function "dgetrs"...
		  x,info = dgetrs(lu,piv,b,[trans,rowmajor,overwrite_b])
		Constructing wrapper function "cgetrs"...
		  x,info = cgetrs(lu,piv,b,[trans,rowmajor,overwrite_b])
		Constructing wrapper function "zgetrs"...
		  x,info = zgetrs(lu,piv,b,[trans,rowmajor,overwrite_b])
		Constructing wrapper function "sgetri"...
		  inv_a,info = sgetri(lu,piv,[rowmajor,overwrite_lu])
		Constructing wrapper function "dgetri"...
		  inv_a,info = dgetri(lu,piv,[rowmajor,overwrite_lu])
		Constructing wrapper function "cgetri"...
		  inv_a,info = cgetri(lu,piv,[rowmajor,overwrite_lu])
		Constructing wrapper function "zgetri"...
		  inv_a,info = zgetri(lu,piv,[rowmajor,overwrite_lu])
		Constructing wrapper function "sposv"...
		  c,x,info = sposv(a,b,[lower,rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "dposv"...
		  c,x,info = dposv(a,b,[lower,rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "cposv"...
		  c,x,info = cposv(a,b,[lower,rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "zposv"...
		  c,x,info = zposv(a,b,[lower,rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "spotrf"...
		  c,info = spotrf(a,[lower,clean,rowmajor,overwrite_a])
		Constructing wrapper function "dpotrf"...
		  c,info = dpotrf(a,[lower,clean,rowmajor,overwrite_a])
		Constructing wrapper function "cpotrf"...
		  c,info = cpotrf(a,[lower,clean,rowmajor,overwrite_a])
		Constructing wrapper function "zpotrf"...
		  c,info = zpotrf(a,[lower,clean,rowmajor,overwrite_a])
		Constructing wrapper function "spotrs"...
		  x,info = spotrs(c,b,[lower,rowmajor,overwrite_b])
		Constructing wrapper function "dpotrs"...
		  x,info = dpotrs(c,b,[lower,rowmajor,overwrite_b])
		Constructing wrapper function "cpotrs"...
		  x,info = cpotrs(c,b,[lower,rowmajor,overwrite_b])
		Constructing wrapper function "zpotrs"...
		  x,info = zpotrs(c,b,[lower,rowmajor,overwrite_b])
		Constructing wrapper function "spotri"...
		  inv_a,info = spotri(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "dpotri"...
		  inv_a,info = dpotri(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "cpotri"...
		  inv_a,info = cpotri(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "zpotri"...
		  inv_a,info = zpotri(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "slauum"...
		  a,info = slauum(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "dlauum"...
		  a,info = dlauum(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "clauum"...
		  a,info = clauum(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "zlauum"...
		  a,info = zlauum(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "strtri"...
		  inv_c,info = strtri(c,[lower,unitdiag,rowmajor,overwrite_c])
		Constructing wrapper function "dtrtri"...
		  inv_c,info = dtrtri(c,[lower,unitdiag,rowmajor,overwrite_c])
		Constructing wrapper function "ctrtri"...
		  inv_c,info = ctrtri(c,[lower,unitdiag,rowmajor,overwrite_c])
		Constructing wrapper function "ztrtri"...
		  inv_c,info = ztrtri(c,[lower,unitdiag,rowmajor,overwrite_c])
	Wrote C/API module "clapack" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack/clapackmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.lib.lapack.calc_lwork" sources
f2py options: []
f2py:> build/src.linux-i686-2.6/scipy/lib/lapack/calc_lworkmodule.c
Reading fortran codes...
	Reading file 'scipy/lib/lapack/calc_lwork.f' (format:fix,strict)
Post-processing...
	Block: calc_lwork
			Block: gehrd
			Block: gesdd
			Block: gelss
			Block: getri
			Block: geev
			Block: heev
			Block: syev
			Block: gees
			Block: geqrf
			Block: gqr
Post-processing (stage 2)...
Building modules...
	Building module "calc_lwork"...
		Constructing wrapper function "gehrd"...
		  minwrk,maxwrk = gehrd(prefix,n,[lo,hi])
		Constructing wrapper function "gesdd"...
		  minwrk,maxwrk = gesdd(prefix,m,n,[compute_uv])
		Constructing wrapper function "gelss"...
		  minwrk,maxwrk = gelss(prefix,m,n,nrhs)
		Constructing wrapper function "getri"...
		  minwrk,maxwrk = getri(prefix,n)
		Constructing wrapper function "geev"...
		  minwrk,maxwrk = geev(prefix,n,[compute_vl,compute_vr])
		Constructing wrapper function "heev"...
		  minwrk,maxwrk = heev(prefix,n,[lower])
		Constructing wrapper function "syev"...
		  minwrk,maxwrk = syev(prefix,n,[lower])
		Constructing wrapper function "gees"...
		  minwrk,maxwrk = gees(prefix,n,[compute_v])
		Constructing wrapper function "geqrf"...
		  minwrk,maxwrk = geqrf(prefix,m,n)
		Constructing wrapper function "gqr"...
		  minwrk,maxwrk = gqr(prefix,m,n)
	Wrote C/API module "calc_lwork" to file "build/src.linux-i686-2.6/scipy/lib/lapack/calc_lworkmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.lib.lapack.atlas_version" sources
building extension "scipy.linalg.fblas" sources
creating build/src.linux-i686-2.6/scipy/linalg
generating fblas interface
23
  adding 'build/src.linux-i686-2.6/scipy/linalg/fblas.pyf' to sources.
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg
f2py options: []
f2py: build/src.linux-i686-2.6/scipy/linalg/fblas.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/linalg/fblas.pyf' (format:free)
Post-processing...
	Block: fblas
			Block: srotg
			Block: drotg
			Block: crotg
			Block: zrotg
			Block: srotmg
			Block: drotmg
			Block: srot
			Block: drot
			Block: csrot
			Block: zdrot
			Block: srotm
			Block: drotm
			Block: sswap
			Block: dswap
			Block: cswap
			Block: zswap
			Block: sscal
			Block: dscal
			Block: cscal
			Block: zscal
			Block: csscal
			Block: zdscal
			Block: scopy
			Block: dcopy
			Block: ccopy
			Block: zcopy
			Block: saxpy
			Block: daxpy
			Block: caxpy
			Block: zaxpy
			Block: cdotu
			Block: zdotu
			Block: cdotc
			Block: zdotc
			Block: sgemv
			Block: dgemv
			Block: cgemv
			Block: zgemv
			Block: chemv
			Block: zhemv
			Block: ssymv
			Block: dsymv
			Block: strmv
			Block: dtrmv
			Block: ctrmv
			Block: ztrmv
			Block: sger
			Block: dger
			Block: cgeru
			Block: zgeru
			Block: cgerc
			Block: zgerc
			Block: sgemm
			Block: dgemm
			Block: cgemm
			Block: zgemm
			Block: sdot
			Block: ddot
			Block: snrm2
			Block: dnrm2
			Block: scnrm2
			Block: dznrm2
			Block: sasum
			Block: dasum
			Block: scasum
			Block: dzasum
			Block: isamax
			Block: idamax
			Block: icamax
			Block: izamax
Post-processing (stage 2)...
Building modules...
	Building module "fblas"...
		Constructing wrapper function "srotg"...
		  c,s = srotg(a,b)
		Constructing wrapper function "drotg"...
		  c,s = drotg(a,b)
		Constructing wrapper function "crotg"...
		  c,s = crotg(a,b)
		Constructing wrapper function "zrotg"...
		  c,s = zrotg(a,b)
		Constructing wrapper function "srotmg"...
		  param = srotmg(d1,d2,x1,y1)
		Constructing wrapper function "drotmg"...
		  param = drotmg(d1,d2,x1,y1)
		Constructing wrapper function "srot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = srot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "drot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = drot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "csrot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = csrot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "zdrot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = zdrot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "srotm"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = srotm(x,y,param,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "drotm"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = drotm(x,y,param,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])
		Constructing wrapper function "sswap"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = sswap(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "dswap"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = dswap(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "cswap"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = cswap(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "zswap"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,y = zswap(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "sscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = sscal(a,x,[n,offx,incx])
		Constructing wrapper function "dscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = dscal(a,x,[n,offx,incx])
		Constructing wrapper function "cscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = cscal(a,x,[n,offx,incx])
		Constructing wrapper function "zscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = zscal(a,x,[n,offx,incx])
		Constructing wrapper function "csscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = csscal(a,x,[n,offx,incx,overwrite_x])
		Constructing wrapper function "zdscal"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = zdscal(a,x,[n,offx,incx,overwrite_x])
		Constructing wrapper function "scopy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = scopy(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "dcopy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = dcopy(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "ccopy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = ccopy(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "zcopy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zcopy(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "saxpy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = saxpy(x,y,[n,a,offx,incx,offy,incy])
		Constructing wrapper function "daxpy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = daxpy(x,y,[n,a,offx,incx,offy,incy])
		Constructing wrapper function "caxpy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = caxpy(x,y,[n,a,offx,incx,offy,incy])
		Constructing wrapper function "zaxpy"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zaxpy(x,y,[n,a,offx,incx,offy,incy])
		Constructing wrapper function "cdotu"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = cdotu(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "zdotu"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = zdotu(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "cdotc"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = cdotc(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "zdotc"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = zdotc(x,y,[n,offx,incx,offy,incy])
		Constructing wrapper function "sgemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = sgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])
		Constructing wrapper function "dgemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = dgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])
		Constructing wrapper function "cgemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = cgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])
		Constructing wrapper function "zgemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])
		Constructing wrapper function "chemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = chemv(alpha,a,x,beta,y,[offx,incx,offy,incy,lower,overwrite_y])
		Constructing wrapper function "zhemv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = zhemv(alpha,a,x,beta,y,[offx,incx,offy,incy,lower,overwrite_y])
		Constructing wrapper function "ssymv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = ssymv(alpha,a,x,beta,y,[offx,incx,offy,incy,lower,overwrite_y])
		Constructing wrapper function "dsymv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  y = dsymv(alpha,a,x,beta,y,[offx,incx,offy,incy,lower,overwrite_y])
		Constructing wrapper function "strmv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = strmv(a,x,[offx,incx,lower,trans,unitdiag,overwrite_x])
		Constructing wrapper function "dtrmv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = dtrmv(a,x,[offx,incx,lower,trans,unitdiag,overwrite_x])
		Constructing wrapper function "ctrmv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = ctrmv(a,x,[offx,incx,lower,trans,unitdiag,overwrite_x])
		Constructing wrapper function "ztrmv"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x = ztrmv(a,x,[offx,incx,lower,trans,unitdiag,overwrite_x])
		Constructing wrapper function "sger"...
		  a = sger(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "dger"...
		  a = dger(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "cgeru"...
		  a = cgeru(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "zgeru"...
		  a = zgeru(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "cgerc"...
		  a = cgerc(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "zgerc"...
		  a = zgerc(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])
		Constructing wrapper function "sgemm"...
		  c = sgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])
		Constructing wrapper function "dgemm"...
		  c = dgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])
		Constructing wrapper function "cgemm"...
		  c = cgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])
		Constructing wrapper function "zgemm"...
		  c = zgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])
		Creating wrapper for Fortran function "sdot"("sdot")...
		Constructing wrapper function "sdot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = sdot(x,y,[n,offx,incx,offy,incy])
		Creating wrapper for Fortran function "ddot"("ddot")...
		Constructing wrapper function "ddot"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  xy = ddot(x,y,[n,offx,incx,offy,incy])
		Creating wrapper for Fortran function "snrm2"("snrm2")...
		Constructing wrapper function "snrm2"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  n2 = snrm2(x,[n,offx,incx])
		Creating wrapper for Fortran function "dnrm2"("dnrm2")...
		Constructing wrapper function "dnrm2"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  n2 = dnrm2(x,[n,offx,incx])
		Creating wrapper for Fortran function "scnrm2"("scnrm2")...
		Constructing wrapper function "scnrm2"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  n2 = scnrm2(x,[n,offx,incx])
		Creating wrapper for Fortran function "dznrm2"("dznrm2")...
		Constructing wrapper function "dznrm2"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  n2 = dznrm2(x,[n,offx,incx])
		Creating wrapper for Fortran function "sasum"("sasum")...
		Constructing wrapper function "sasum"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  s = sasum(x,[n,offx,incx])
		Creating wrapper for Fortran function "dasum"("dasum")...
		Constructing wrapper function "dasum"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  s = dasum(x,[n,offx,incx])
		Creating wrapper for Fortran function "scasum"("scasum")...
		Constructing wrapper function "scasum"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  s = scasum(x,[n,offx,incx])
		Creating wrapper for Fortran function "dzasum"("dzasum")...
		Constructing wrapper function "dzasum"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  s = dzasum(x,[n,offx,incx])
		Constructing wrapper function "isamax"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  k = isamax(x,[n,offx,incx])
		Constructing wrapper function "idamax"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  k = idamax(x,[n,offx,incx])
		Constructing wrapper function "icamax"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  k = icamax(x,[n,offx,incx])
		Constructing wrapper function "izamax"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  k = izamax(x,[n,offx,incx])
	Wrote C/API module "fblas" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/fblasmodule.c"
	Fortran 77 wrappers are saved to "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/fblas-f2pywrappers.f"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/fblas-f2pywrappers.f' to sources.
building extension "scipy.linalg.cblas" sources
generating cblas interface
2
  adding 'build/src.linux-i686-2.6/scipy/linalg/cblas.pyf' to sources.
f2py options: []
f2py: build/src.linux-i686-2.6/scipy/linalg/cblas.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/linalg/cblas.pyf' (format:free)
Line #19 in build/src.linux-i686-2.6/scipy/linalg/cblas.pyf:"  intent(c)"
	All arguments will have attribute intent(c)
Line #42 in build/src.linux-i686-2.6/scipy/linalg/cblas.pyf:"  intent(c)"
	All arguments will have attribute intent(c)
Line #65 in build/src.linux-i686-2.6/scipy/linalg/cblas.pyf:"  intent(c)"
	All arguments will have attribute intent(c)
Line #88 in build/src.linux-i686-2.6/scipy/linalg/cblas.pyf:"  intent(c)"
	All arguments will have attribute intent(c)
Post-processing...
	Block: cblas
			Block: saxpy
			Block: daxpy
			Block: caxpy
			Block: zaxpy
Post-processing (stage 2)...
Building modules...
	Building module "cblas"...
		Constructing wrapper function "saxpy"...
		  z = saxpy(a,x,y,[n,incx,incy,overwrite_y])
		Constructing wrapper function "daxpy"...
		  z = daxpy(a,x,y,[n,incx,incy,overwrite_y])
		Constructing wrapper function "caxpy"...
		  z = caxpy(a,x,y,[n,incx,incy,overwrite_y])
		Constructing wrapper function "zaxpy"...
		  z = zaxpy(a,x,y,[n,incx,incy,overwrite_y])
	Wrote C/API module "cblas" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/cblasmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.linalg.flapack" sources
generating flapack interface
61
  adding 'build/src.linux-i686-2.6/scipy/linalg/flapack.pyf' to sources.
f2py options: []
f2py: build/src.linux-i686-2.6/scipy/linalg/flapack.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/linalg/flapack.pyf' (format:free)
Post-processing...
	Block: cgees__user__routines
		Block: cgees_user_interface
			Block: cselect
	Block: dgees__user__routines
		Block: dgees_user_interface
			Block: dselect
	Block: sgees__user__routines
		Block: sgees_user_interface
			Block: sselect
	Block: zgees__user__routines
		Block: zgees_user_interface
			Block: zselect
	Block: flapack
			Block: spbtrf
			Block: dpbtrf
			Block: cpbtrf
			Block: zpbtrf
			Block: spbsv
			Block: dpbsv
			Block: cpbsv
			Block: zpbsv
			Block: sgebal
			Block: dgebal
			Block: cgebal
			Block: zgebal
			Block: sgehrd
			Block: dgehrd
			Block: cgehrd
			Block: zgehrd
			Block: sgbsv
			Block: dgbsv
			Block: cgbsv
			Block: zgbsv
			Block: sgesv
			Block: dgesv
			Block: cgesv
			Block: zgesv
			Block: sgetrf
			Block: dgetrf
			Block: cgetrf
			Block: zgetrf
			Block: sgetrs
			Block: dgetrs
			Block: cgetrs
			Block: zgetrs
			Block: sgetri
			Block: dgetri
			Block: cgetri
			Block: zgetri
			Block: sgesdd
			Block: dgesdd
			Block: cgesdd
			Block: zgesdd
			Block: sgelss
			Block: dgelss
			Block: cgelss
			Block: zgelss
			Block: sgeqrf
			Block: dgeqrf
			Block: cgeqrf
			Block: zgeqrf
			Block: sgerqf
			Block: dgerqf
			Block: cgerqf
			Block: zgerqf
			Block: sorgqr
			Block: dorgqr
			Block: cungqr
			Block: zungqr
			Block: sgeev
			Block: dgeev
			Block: cgeev
			Block: zgeev
			Block: sgegv
			Block: dgegv
			Block: cgegv
			Block: zgegv
			Block: ssyev
			Block: dsyev
			Block: cheev
			Block: zheev
			Block: sposv
			Block: dposv
			Block: cposv
			Block: zposv
			Block: spotrf
			Block: dpotrf
			Block: cpotrf
			Block: zpotrf
			Block: spotrs
			Block: dpotrs
			Block: cpotrs
			Block: zpotrs
			Block: spotri
			Block: dpotri
			Block: cpotri
			Block: zpotri
			Block: slauum
			Block: dlauum
			Block: clauum
			Block: zlauum
			Block: strtri
			Block: dtrtri
			Block: ctrtri
			Block: ztrtri
			Block: slaswp
			Block: dlaswp
			Block: claswp
			Block: zlaswp
			Block: cgees
			Block: zgees
			Block: dgees
			Block: sgees
			Block: sggev
			Block: dggev
			Block: cggev
			Block: zggev
			Block: ssbev
			Block: dsbev
			Block: ssbevd
			Block: dsbevd
			Block: ssbevx
			Block: dsbevx
			Block: chbevd
			Block: zhbevd
			Block: chbevx
			Block: zhbevx
			Block: sgbtrf
			Block: dgbtrf
			Block: cgbtrf
			Block: zgbtrf
			Block: sgbtrs
			Block: dgbtrs
			Block: cgbtrs
			Block: zgbtrs
			Block: ssyevr
			Block: dsyevr
			Block: cheevr
			Block: zheevr
			Block: ssygv
			Block: dsygv
			Block: chegv
			Block: zhegv
			Block: ssygvd
			Block: dsygvd
			Block: chegvd
			Block: zhegvd
			Block: ssygvx
			Block: dsygvx
			Block: chegvx
			Block: zhegvx
			Block: slamch
			Block: dlamch
Post-processing (stage 2)...
Building modules...
	Constructing call-back function "cb_cselect_in_cgees__user__routines"
	  def cselect(e_w__i__e): return cselect
	Constructing call-back function "cb_dselect_in_dgees__user__routines"
	  def dselect(e_wr__i__e,e_wi__i__e): return dselect
	Constructing call-back function "cb_sselect_in_sgees__user__routines"
	  def sselect(e_wr__i__e,e_wi__i__e): return sselect
	Constructing call-back function "cb_zselect_in_zgees__user__routines"
	  def zselect(e_w__i__e): return zselect
	Building module "flapack"...
		Constructing wrapper function "spbtrf"...
		  c,info = spbtrf(ab,[lower,ldab,overwrite_ab])
		Constructing wrapper function "dpbtrf"...
		  c,info = dpbtrf(ab,[lower,ldab,overwrite_ab])
		Constructing wrapper function "cpbtrf"...
		  c,info = cpbtrf(ab,[lower,ldab,overwrite_ab])
		Constructing wrapper function "zpbtrf"...
		  c,info = zpbtrf(ab,[lower,ldab,overwrite_ab])
		Constructing wrapper function "spbsv"...
		  c,x,info = spbsv(ab,b,[lower,ldab,overwrite_ab,overwrite_b])
		Constructing wrapper function "dpbsv"...
		  c,x,info = dpbsv(ab,b,[lower,ldab,overwrite_ab,overwrite_b])
		Constructing wrapper function "cpbsv"...
		  c,x,info = cpbsv(ab,b,[lower,ldab,overwrite_ab,overwrite_b])
		Constructing wrapper function "zpbsv"...
		  c,x,info = zpbsv(ab,b,[lower,ldab,overwrite_ab,overwrite_b])
		Constructing wrapper function "sgebal"...
		  ba,lo,hi,pivscale,info = sgebal(a,[scale,permute,overwrite_a])
		Constructing wrapper function "dgebal"...
		  ba,lo,hi,pivscale,info = dgebal(a,[scale,permute,overwrite_a])
		Constructing wrapper function "cgebal"...
		  ba,lo,hi,pivscale,info = cgebal(a,[scale,permute,overwrite_a])
		Constructing wrapper function "zgebal"...
		  ba,lo,hi,pivscale,info = zgebal(a,[scale,permute,overwrite_a])
		Constructing wrapper function "sgehrd"...
		  ht,tau,info = sgehrd(a,[lo,hi,lwork,overwrite_a])
		Constructing wrapper function "dgehrd"...
		  ht,tau,info = dgehrd(a,[lo,hi,lwork,overwrite_a])
		Constructing wrapper function "cgehrd"...
		  ht,tau,info = cgehrd(a,[lo,hi,lwork,overwrite_a])
		Constructing wrapper function "zgehrd"...
		  ht,tau,info = zgehrd(a,[lo,hi,lwork,overwrite_a])
		Constructing wrapper function "sgbsv"...
		  lub,piv,x,info = sgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])
		Constructing wrapper function "dgbsv"...
		  lub,piv,x,info = dgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])
		Constructing wrapper function "cgbsv"...
		  lub,piv,x,info = cgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])
		Constructing wrapper function "zgbsv"...
		  lub,piv,x,info = zgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])
		Constructing wrapper function "sgesv"...
		  lu,piv,x,info = sgesv(a,b,[overwrite_a,overwrite_b])
		Constructing wrapper function "dgesv"...
		  lu,piv,x,info = dgesv(a,b,[overwrite_a,overwrite_b])
		Constructing wrapper function "cgesv"...
		  lu,piv,x,info = cgesv(a,b,[overwrite_a,overwrite_b])
		Constructing wrapper function "zgesv"...
		  lu,piv,x,info = zgesv(a,b,[overwrite_a,overwrite_b])
		Constructing wrapper function "sgetrf"...
		  lu,piv,info = sgetrf(a,[overwrite_a])
		Constructing wrapper function "dgetrf"...
		  lu,piv,info = dgetrf(a,[overwrite_a])
		Constructing wrapper function "cgetrf"...
		  lu,piv,info = cgetrf(a,[overwrite_a])
		Constructing wrapper function "zgetrf"...
		  lu,piv,info = zgetrf(a,[overwrite_a])
		Constructing wrapper function "sgetrs"...
		  x,info = sgetrs(lu,piv,b,[trans,overwrite_b])
		Constructing wrapper function "dgetrs"...
		  x,info = dgetrs(lu,piv,b,[trans,overwrite_b])
		Constructing wrapper function "cgetrs"...
		  x,info = cgetrs(lu,piv,b,[trans,overwrite_b])
		Constructing wrapper function "zgetrs"...
		  x,info = zgetrs(lu,piv,b,[trans,overwrite_b])
		Constructing wrapper function "sgetri"...
		  inv_a,info = sgetri(lu,piv,[lwork,overwrite_lu])
		Constructing wrapper function "dgetri"...
		  inv_a,info = dgetri(lu,piv,[lwork,overwrite_lu])
		Constructing wrapper function "cgetri"...
		  inv_a,info = cgetri(lu,piv,[lwork,overwrite_lu])
		Constructing wrapper function "zgetri"...
		  inv_a,info = zgetri(lu,piv,[lwork,overwrite_lu])
		Constructing wrapper function "sgesdd"...
		  u,s,vt,info = sgesdd(a,[compute_uv,lwork,overwrite_a])
		Constructing wrapper function "dgesdd"...
		  u,s,vt,info = dgesdd(a,[compute_uv,lwork,overwrite_a])
		Constructing wrapper function "cgesdd"...
		  u,s,vt,info = cgesdd(a,[compute_uv,lwork,overwrite_a])
		Constructing wrapper function "zgesdd"...
		  u,s,vt,info = zgesdd(a,[compute_uv,lwork,overwrite_a])
		Constructing wrapper function "sgelss"...
		  v,x,s,rank,info = sgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "dgelss"...
		  v,x,s,rank,info = dgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "cgelss"...
		  v,x,s,rank,info = cgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "zgelss"...
		  v,x,s,rank,info = zgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "sgeqrf"...
		  qr,tau,work,info = sgeqrf(a,[lwork,overwrite_a])
		Constructing wrapper function "dgeqrf"...
		  qr,tau,work,info = dgeqrf(a,[lwork,overwrite_a])
		Constructing wrapper function "cgeqrf"...
		  qr,tau,work,info = cgeqrf(a,[lwork,overwrite_a])
		Constructing wrapper function "zgeqrf"...
		  qr,tau,work,info = zgeqrf(a,[lwork,overwrite_a])
		Constructing wrapper function "sgerqf"...
		  qr,tau,work,info = sgerqf(a,[lwork,overwrite_a])
		Constructing wrapper function "dgerqf"...
		  qr,tau,work,info = dgerqf(a,[lwork,overwrite_a])
		Constructing wrapper function "cgerqf"...
		  qr,tau,work,info = cgerqf(a,[lwork,overwrite_a])
		Constructing wrapper function "zgerqf"...
		  qr,tau,work,info = zgerqf(a,[lwork,overwrite_a])
		Constructing wrapper function "sorgqr"...
		  q,work,info = sorgqr(a,tau,[lwork,overwrite_a])
		Constructing wrapper function "dorgqr"...
		  q,work,info = dorgqr(a,tau,[lwork,overwrite_a])
		Constructing wrapper function "cungqr"...
		  q,work,info = cungqr(a,tau,[lwork,overwrite_a])
		Constructing wrapper function "zungqr"...
		  q,work,info = zungqr(a,tau,[lwork,overwrite_a])
		Constructing wrapper function "sgeev"...
		  wr,wi,vl,vr,info = sgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])
		Constructing wrapper function "dgeev"...
		  wr,wi,vl,vr,info = dgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])
		Constructing wrapper function "cgeev"...
		  w,vl,vr,info = cgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])
		Constructing wrapper function "zgeev"...
		  w,vl,vr,info = zgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])
		Constructing wrapper function "sgegv"...
		  alphar,alphai,beta,vl,vr,info = sgegv(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "dgegv"...
		  alphar,alphai,beta,vl,vr,info = dgegv(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "cgegv"...
		  alpha,beta,vl,vr,info = cgegv(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "zgegv"...
		  alpha,beta,vl,vr,info = zgegv(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "ssyev"...
		  w,v,info = ssyev(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "dsyev"...
		  w,v,info = dsyev(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "cheev"...
		  w,v,info = cheev(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "zheev"...
		  w,v,info = zheev(a,[compute_v,lower,lwork,overwrite_a])
		Constructing wrapper function "sposv"...
		  c,x,info = sposv(a,b,[lower,overwrite_a,overwrite_b])
		Constructing wrapper function "dposv"...
		  c,x,info = dposv(a,b,[lower,overwrite_a,overwrite_b])
		Constructing wrapper function "cposv"...
		  c,x,info = cposv(a,b,[lower,overwrite_a,overwrite_b])
		Constructing wrapper function "zposv"...
		  c,x,info = zposv(a,b,[lower,overwrite_a,overwrite_b])
		Constructing wrapper function "spotrf"...
		  c,info = spotrf(a,[lower,clean,overwrite_a])
		Constructing wrapper function "dpotrf"...
		  c,info = dpotrf(a,[lower,clean,overwrite_a])
		Constructing wrapper function "cpotrf"...
		  c,info = cpotrf(a,[lower,clean,overwrite_a])
		Constructing wrapper function "zpotrf"...
		  c,info = zpotrf(a,[lower,clean,overwrite_a])
		Constructing wrapper function "spotrs"...
		  x,info = spotrs(c,b,[lower,overwrite_b])
		Constructing wrapper function "dpotrs"...
		  x,info = dpotrs(c,b,[lower,overwrite_b])
		Constructing wrapper function "cpotrs"...
		  x,info = cpotrs(c,b,[lower,overwrite_b])
		Constructing wrapper function "zpotrs"...
		  x,info = zpotrs(c,b,[lower,overwrite_b])
		Constructing wrapper function "spotri"...
		  inv_a,info = spotri(c,[lower,overwrite_c])
		Constructing wrapper function "dpotri"...
		  inv_a,info = dpotri(c,[lower,overwrite_c])
		Constructing wrapper function "cpotri"...
		  inv_a,info = cpotri(c,[lower,overwrite_c])
		Constructing wrapper function "zpotri"...
		  inv_a,info = zpotri(c,[lower,overwrite_c])
		Constructing wrapper function "slauum"...
		  a,info = slauum(c,[lower,overwrite_c])
		Constructing wrapper function "dlauum"...
		  a,info = dlauum(c,[lower,overwrite_c])
		Constructing wrapper function "clauum"...
		  a,info = clauum(c,[lower,overwrite_c])
		Constructing wrapper function "zlauum"...
		  a,info = zlauum(c,[lower,overwrite_c])
		Constructing wrapper function "strtri"...
		  inv_c,info = strtri(c,[lower,unitdiag,overwrite_c])
		Constructing wrapper function "dtrtri"...
		  inv_c,info = dtrtri(c,[lower,unitdiag,overwrite_c])
		Constructing wrapper function "ctrtri"...
		  inv_c,info = ctrtri(c,[lower,unitdiag,overwrite_c])
		Constructing wrapper function "ztrtri"...
		  inv_c,info = ztrtri(c,[lower,unitdiag,overwrite_c])
		Constructing wrapper function "slaswp"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  a = slaswp(a,piv,[k1,k2,off,inc,overwrite_a])
		Constructing wrapper function "dlaswp"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  a = dlaswp(a,piv,[k1,k2,off,inc,overwrite_a])
		Constructing wrapper function "claswp"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  a = claswp(a,piv,[k1,k2,off,inc,overwrite_a])
		Constructing wrapper function "zlaswp"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  a = zlaswp(a,piv,[k1,k2,off,inc,overwrite_a])
		Constructing wrapper function "cgees"...
		  t,sdim,w,vs,work,info = cgees(cselect,a,[compute_v,sort_t,lwork,cselect_extra_args,overwrite_a])
		Constructing wrapper function "zgees"...
		  t,sdim,w,vs,work,info = zgees(zselect,a,[compute_v,sort_t,lwork,zselect_extra_args,overwrite_a])
		Constructing wrapper function "dgees"...
		  t,sdim,wr,wi,vs,work,info = dgees(dselect,a,[compute_v,sort_t,lwork,dselect_extra_args,overwrite_a])
		Constructing wrapper function "sgees"...
		  t,sdim,wr,wi,vs,work,info = sgees(sselect,a,[compute_v,sort_t,lwork,sselect_extra_args,overwrite_a])
		Constructing wrapper function "sggev"...
		  alphar,alphai,beta,vl,vr,work,info = sggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "dggev"...
		  alphar,alphai,beta,vl,vr,work,info = dggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "cggev"...
		  alpha,beta,vl,vr,work,info = cggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "zggev"...
		  alpha,beta,vl,vr,work,info = zggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "ssbev"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,info = ssbev(ab,[compute_v,lower,ldab,overwrite_ab])
		Constructing wrapper function "dsbev"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,info = dsbev(ab,[compute_v,lower,ldab,overwrite_ab])
		Constructing wrapper function "ssbevd"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,info = ssbevd(ab,[compute_v,lower,ldab,liwork,overwrite_ab])
		Constructing wrapper function "dsbevd"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,info = dsbevd(ab,[compute_v,lower,ldab,liwork,overwrite_ab])
		Constructing wrapper function "ssbevx"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,m,ifail,info = ssbevx(ab,vl,vu,il,iu,[ldab,compute_v,range,lower,abstol,mmax,overwrite_ab])
		Constructing wrapper function "dsbevx"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,m,ifail,info = dsbevx(ab,vl,vu,il,iu,[ldab,compute_v,range,lower,abstol,mmax,overwrite_ab])
		Constructing wrapper function "chbevd"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,info = chbevd(ab,[compute_v,lower,ldab,lrwork,liwork,overwrite_ab])
		Constructing wrapper function "zhbevd"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,info = zhbevd(ab,[compute_v,lower,ldab,lrwork,liwork,overwrite_ab])
		Constructing wrapper function "chbevx"...
warning: callstatement is defined without callprotoargument
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,m,ifail,info = chbevx(ab,vl,vu,il,iu,[ldab,compute_v,range,lower,abstol,mmax,overwrite_ab])
		Constructing wrapper function "zhbevx"...
warning: callstatement is defined without callprotoargument
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  w,z,m,ifail,info = zhbevx(ab,vl,vu,il,iu,[ldab,compute_v,range,lower,abstol,mmax,overwrite_ab])
		Constructing wrapper function "sgbtrf"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  lu,ipiv,info = sgbtrf(ab,kl,ku,[m,n,ldab,overwrite_ab])
		Constructing wrapper function "dgbtrf"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  lu,ipiv,info = dgbtrf(ab,kl,ku,[m,n,ldab,overwrite_ab])
		Constructing wrapper function "cgbtrf"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  lu,ipiv,info = cgbtrf(ab,kl,ku,[m,n,ldab,overwrite_ab])
		Constructing wrapper function "zgbtrf"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  lu,ipiv,info = zgbtrf(ab,kl,ku,[m,n,ldab,overwrite_ab])
		Constructing wrapper function "sgbtrs"...
warning: callstatement is defined without callprotoargument
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,info = sgbtrs(ab,kl,ku,b,ipiv,[trans,n,ldab,ldb,overwrite_b])
		Constructing wrapper function "dgbtrs"...
warning: callstatement is defined without callprotoargument
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,info = dgbtrs(ab,kl,ku,b,ipiv,[trans,n,ldab,ldb,overwrite_b])
		Constructing wrapper function "cgbtrs"...
warning: callstatement is defined without callprotoargument
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,info = cgbtrs(ab,kl,ku,b,ipiv,[trans,n,ldab,ldb,overwrite_b])
		Constructing wrapper function "zgbtrs"...
warning: callstatement is defined without callprotoargument
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,info = zgbtrs(ab,kl,ku,b,ipiv,[trans,n,ldab,ldb,overwrite_b])
		Constructing wrapper function "ssyevr"...
		  w,z,info = ssyevr(a,[jobz,range,uplo,il,iu,lwork,overwrite_a])
		Constructing wrapper function "dsyevr"...
		  w,z,info = dsyevr(a,[jobz,range,uplo,il,iu,lwork,overwrite_a])
		Constructing wrapper function "cheevr"...
		  w,z,info = cheevr(a,[jobz,range,uplo,il,iu,lwork,overwrite_a])
		Constructing wrapper function "zheevr"...
		  w,z,info = zheevr(a,[jobz,range,uplo,il,iu,lwork,overwrite_a])
		Constructing wrapper function "ssygv"...
		  a,w,info = ssygv(a,b,[itype,jobz,uplo,overwrite_a,overwrite_b])
		Constructing wrapper function "dsygv"...
		  a,w,info = dsygv(a,b,[itype,jobz,uplo,overwrite_a,overwrite_b])
		Constructing wrapper function "chegv"...
		  a,w,info = chegv(a,b,[itype,jobz,uplo,overwrite_a,overwrite_b])
		Constructing wrapper function "zhegv"...
		  a,w,info = zhegv(a,b,[itype,jobz,uplo,overwrite_a,overwrite_b])
		Constructing wrapper function "ssygvd"...
		  a,w,info = ssygvd(a,b,[itype,jobz,uplo,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "dsygvd"...
		  a,w,info = dsygvd(a,b,[itype,jobz,uplo,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "chegvd"...
		  a,w,info = chegvd(a,b,[itype,jobz,uplo,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "zhegvd"...
		  a,w,info = zhegvd(a,b,[itype,jobz,uplo,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "ssygvx"...
		  w,z,ifail,info = ssygvx(a,b,iu,[itype,jobz,uplo,il,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "dsygvx"...
		  w,z,ifail,info = dsygvx(a,b,iu,[itype,jobz,uplo,il,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "chegvx"...
		  w,z,ifail,info = chegvx(a,b,iu,[itype,jobz,uplo,il,lwork,overwrite_a,overwrite_b])
		Constructing wrapper function "zhegvx"...
		  w,z,ifail,info = zhegvx(a,b,iu,[itype,jobz,uplo,il,lwork,overwrite_a,overwrite_b])
		Creating wrapper for Fortran function "slamch"("slamch")...
		Constructing wrapper function "slamch"...
		  slamch = slamch(cmach)
		Creating wrapper for Fortran function "dlamch"("dlamch")...
		Constructing wrapper function "dlamch"...
		  dlamch = dlamch(cmach)
	Wrote C/API module "flapack" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c"
	Fortran 77 wrappers are saved to "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapack-f2pywrappers.f"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapack-f2pywrappers.f' to sources.
building extension "scipy.linalg.clapack" sources
generating clapack interface
11
  adding 'build/src.linux-i686-2.6/scipy/linalg/clapack.pyf' to sources.
f2py options: []
f2py: build/src.linux-i686-2.6/scipy/linalg/clapack.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/linalg/clapack.pyf' (format:free)
Post-processing...
	Block: clapack
			Block: sgesv
			Block: dgesv
			Block: cgesv
			Block: zgesv
			Block: sgetrf
			Block: dgetrf
			Block: cgetrf
			Block: zgetrf
			Block: sgetrs
			Block: dgetrs
			Block: cgetrs
			Block: zgetrs
			Block: sgetri
			Block: dgetri
			Block: cgetri
			Block: zgetri
			Block: sposv
			Block: dposv
			Block: cposv
			Block: zposv
			Block: spotrf
			Block: dpotrf
			Block: cpotrf
			Block: zpotrf
			Block: spotrs
			Block: dpotrs
			Block: cpotrs
			Block: zpotrs
			Block: spotri
			Block: dpotri
			Block: cpotri
			Block: zpotri
			Block: slauum
			Block: dlauum
			Block: clauum
			Block: zlauum
			Block: strtri
			Block: dtrtri
			Block: ctrtri
			Block: ztrtri
Post-processing (stage 2)...
Building modules...
	Building module "clapack"...
		Constructing wrapper function "sgesv"...
		  lu,piv,x,info = sgesv(a,b,[rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "dgesv"...
		  lu,piv,x,info = dgesv(a,b,[rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "cgesv"...
		  lu,piv,x,info = cgesv(a,b,[rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "zgesv"...
		  lu,piv,x,info = zgesv(a,b,[rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "sgetrf"...
		  lu,piv,info = sgetrf(a,[rowmajor,overwrite_a])
		Constructing wrapper function "dgetrf"...
		  lu,piv,info = dgetrf(a,[rowmajor,overwrite_a])
		Constructing wrapper function "cgetrf"...
		  lu,piv,info = cgetrf(a,[rowmajor,overwrite_a])
		Constructing wrapper function "zgetrf"...
		  lu,piv,info = zgetrf(a,[rowmajor,overwrite_a])
		Constructing wrapper function "sgetrs"...
		  x,info = sgetrs(lu,piv,b,[trans,rowmajor,overwrite_b])
		Constructing wrapper function "dgetrs"...
		  x,info = dgetrs(lu,piv,b,[trans,rowmajor,overwrite_b])
		Constructing wrapper function "cgetrs"...
		  x,info = cgetrs(lu,piv,b,[trans,rowmajor,overwrite_b])
		Constructing wrapper function "zgetrs"...
		  x,info = zgetrs(lu,piv,b,[trans,rowmajor,overwrite_b])
		Constructing wrapper function "sgetri"...
		  inv_a,info = sgetri(lu,piv,[rowmajor,overwrite_lu])
		Constructing wrapper function "dgetri"...
		  inv_a,info = dgetri(lu,piv,[rowmajor,overwrite_lu])
		Constructing wrapper function "cgetri"...
		  inv_a,info = cgetri(lu,piv,[rowmajor,overwrite_lu])
		Constructing wrapper function "zgetri"...
		  inv_a,info = zgetri(lu,piv,[rowmajor,overwrite_lu])
		Constructing wrapper function "sposv"...
		  c,x,info = sposv(a,b,[lower,rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "dposv"...
		  c,x,info = dposv(a,b,[lower,rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "cposv"...
		  c,x,info = cposv(a,b,[lower,rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "zposv"...
		  c,x,info = zposv(a,b,[lower,rowmajor,overwrite_a,overwrite_b])
		Constructing wrapper function "spotrf"...
		  c,info = spotrf(a,[lower,clean,rowmajor,overwrite_a])
		Constructing wrapper function "dpotrf"...
		  c,info = dpotrf(a,[lower,clean,rowmajor,overwrite_a])
		Constructing wrapper function "cpotrf"...
		  c,info = cpotrf(a,[lower,clean,rowmajor,overwrite_a])
		Constructing wrapper function "zpotrf"...
		  c,info = zpotrf(a,[lower,clean,rowmajor,overwrite_a])
		Constructing wrapper function "spotrs"...
		  x,info = spotrs(c,b,[lower,rowmajor,overwrite_b])
		Constructing wrapper function "dpotrs"...
		  x,info = dpotrs(c,b,[lower,rowmajor,overwrite_b])
		Constructing wrapper function "cpotrs"...
		  x,info = cpotrs(c,b,[lower,rowmajor,overwrite_b])
		Constructing wrapper function "zpotrs"...
		  x,info = zpotrs(c,b,[lower,rowmajor,overwrite_b])
		Constructing wrapper function "spotri"...
		  inv_a,info = spotri(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "dpotri"...
		  inv_a,info = dpotri(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "cpotri"...
		  inv_a,info = cpotri(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "zpotri"...
		  inv_a,info = zpotri(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "slauum"...
		  a,info = slauum(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "dlauum"...
		  a,info = dlauum(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "clauum"...
		  a,info = clauum(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "zlauum"...
		  a,info = zlauum(c,[lower,rowmajor,overwrite_c])
		Constructing wrapper function "strtri"...
		  inv_c,info = strtri(c,[lower,unitdiag,rowmajor,overwrite_c])
		Constructing wrapper function "dtrtri"...
		  inv_c,info = dtrtri(c,[lower,unitdiag,rowmajor,overwrite_c])
		Constructing wrapper function "ctrtri"...
		  inv_c,info = ctrtri(c,[lower,unitdiag,rowmajor,overwrite_c])
		Constructing wrapper function "ztrtri"...
		  inv_c,info = ztrtri(c,[lower,unitdiag,rowmajor,overwrite_c])
	Wrote C/API module "clapack" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/clapackmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.linalg._flinalg" sources
f2py options: []
f2py:> build/src.linux-i686-2.6/scipy/linalg/_flinalgmodule.c
Reading fortran codes...
	Reading file 'scipy/linalg/src/det.f' (format:fix,strict)
	Reading file 'scipy/linalg/src/lu.f' (format:fix,strict)
Post-processing...
	Block: _flinalg
			Block: ddet_c
			Block: ddet_r
			Block: sdet_c
			Block: sdet_r
			Block: zdet_c
			Block: zdet_r
			Block: cdet_c
			Block: cdet_r
			Block: dlu_c
			Block: zlu_c
			Block: slu_c
			Block: clu_c
Post-processing (stage 2)...
Building modules...
	Building module "_flinalg"...
		Constructing wrapper function "ddet_c"...
		  det,info = ddet_c(a,[overwrite_a])
		Constructing wrapper function "ddet_r"...
		  det,info = ddet_r(a,[overwrite_a])
		Constructing wrapper function "sdet_c"...
		  det,info = sdet_c(a,[overwrite_a])
		Constructing wrapper function "sdet_r"...
		  det,info = sdet_r(a,[overwrite_a])
		Constructing wrapper function "zdet_c"...
		  det,info = zdet_c(a,[overwrite_a])
		Constructing wrapper function "zdet_r"...
		  det,info = zdet_r(a,[overwrite_a])
		Constructing wrapper function "cdet_c"...
		  det,info = cdet_c(a,[overwrite_a])
		Constructing wrapper function "cdet_r"...
		  det,info = cdet_r(a,[overwrite_a])
		Constructing wrapper function "dlu_c"...
		  p,l,u,info = dlu_c(a,[permute_l,overwrite_a])
		Constructing wrapper function "zlu_c"...
		  p,l,u,info = zlu_c(a,[permute_l,overwrite_a])
		Constructing wrapper function "slu_c"...
		  p,l,u,info = slu_c(a,[permute_l,overwrite_a])
		Constructing wrapper function "clu_c"...
		  p,l,u,info = clu_c(a,[permute_l,overwrite_a])
	Wrote C/API module "_flinalg" to file "build/src.linux-i686-2.6/scipy/linalg/_flinalgmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.linalg.calc_lwork" sources
f2py options: []
f2py:> build/src.linux-i686-2.6/scipy/linalg/calc_lworkmodule.c
Reading fortran codes...
	Reading file 'scipy/linalg/src/calc_lwork.f' (format:fix,strict)
Post-processing...
	Block: calc_lwork
			Block: gehrd
			Block: gesdd
			Block: gelss
			Block: getri
			Block: geev
			Block: heev
			Block: syev
			Block: gees
			Block: geqrf
			Block: gqr
Post-processing (stage 2)...
Building modules...
	Building module "calc_lwork"...
		Constructing wrapper function "gehrd"...
		  minwrk,maxwrk = gehrd(prefix,n,lo,hi)
		Constructing wrapper function "gesdd"...
		  minwrk,maxwrk = gesdd(prefix,m,n,compute_uv)
		Constructing wrapper function "gelss"...
		  minwrk,maxwrk = gelss(prefix,m,n,nrhs)
		Constructing wrapper function "getri"...
		  minwrk,maxwrk = getri(prefix,n)
		Constructing wrapper function "geev"...
		  minwrk,maxwrk = geev(prefix,n,[compute_vl,compute_vr])
		Constructing wrapper function "heev"...
		  minwrk,maxwrk = heev(prefix,n,[lower])
		Constructing wrapper function "syev"...
		  minwrk,maxwrk = syev(prefix,n,[lower])
		Constructing wrapper function "gees"...
		  minwrk,maxwrk = gees(prefix,n,[compute_v])
		Constructing wrapper function "geqrf"...
		  minwrk,maxwrk = geqrf(prefix,m,n)
		Constructing wrapper function "gqr"...
		  minwrk,maxwrk = gqr(prefix,m,n)
	Wrote C/API module "calc_lwork" to file "build/src.linux-i686-2.6/scipy/linalg/calc_lworkmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.linalg.atlas_version" sources
building extension "scipy.odr.__odrpack" sources
building extension "scipy.optimize._minpack" sources
building extension "scipy.optimize._zeros" sources
building extension "scipy.optimize._lbfgsb" sources
creating build/src.linux-i686-2.6/scipy/optimize
creating build/src.linux-i686-2.6/scipy/optimize/lbfgsb
f2py options: []
f2py: scipy/optimize/lbfgsb/lbfgsb.pyf
Reading fortran codes...
	Reading file 'scipy/optimize/lbfgsb/lbfgsb.pyf' (format:free)
Post-processing...
	Block: _lbfgsb
			Block: setulb
Post-processing (stage 2)...
Building modules...
	Building module "_lbfgsb"...
		Constructing wrapper function "setulb"...
		  setulb(m,x,l,u,nbd,f,g,factr,pgtol,wa,iwa,task,iprint,csave,lsave,isave,dsave,[n])
	Wrote C/API module "_lbfgsb" to file "build/src.linux-i686-2.6/scipy/optimize/lbfgsb/_lbfgsbmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.optimize.moduleTNC" sources
building extension "scipy.optimize._cobyla" sources
creating build/src.linux-i686-2.6/scipy/optimize/cobyla
f2py options: []
f2py: scipy/optimize/cobyla/cobyla.pyf
Reading fortran codes...
	Reading file 'scipy/optimize/cobyla/cobyla.pyf' (format:free)
Post-processing...
	Block: _cobyla__user__routines
		Block: _cobyla_user_interface
			Block: calcfc
	Block: _cobyla
			Block: minimize
Post-processing (stage 2)...
Building modules...
	Constructing call-back function "cb_calcfc_in__cobyla__user__routines"
	  def calcfc(x,con): return f
	Building module "_cobyla"...
		Constructing wrapper function "minimize"...
		  x = minimize(calcfc,m,x,rhobeg,rhoend,[iprint,maxfun,calcfc_extra_args])
	Wrote C/API module "_cobyla" to file "build/src.linux-i686-2.6/scipy/optimize/cobyla/_cobylamodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.optimize.minpack2" sources
creating build/src.linux-i686-2.6/scipy/optimize/minpack2
f2py options: []
f2py: scipy/optimize/minpack2/minpack2.pyf
Reading fortran codes...
	Reading file 'scipy/optimize/minpack2/minpack2.pyf' (format:free)
Post-processing...
	Block: minpack2
			Block: dcsrch
			Block: dcstep
Post-processing (stage 2)...
Building modules...
	Building module "minpack2"...
		Constructing wrapper function "dcsrch"...
		  stp,f,g,task = dcsrch(stp,f,g,ftol,gtol,xtol,task,stpmin,stpmax,isave,dsave)
		Constructing wrapper function "dcstep"...
		  stx,fx,dx,sty,fy,dy,stp,brackt = dcstep(stx,fx,dx,sty,fy,dy,stp,fp,dp,brackt,stpmin,stpmax)
	Wrote C/API module "minpack2" to file "build/src.linux-i686-2.6/scipy/optimize/minpack2/minpack2module.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.optimize._slsqp" sources
creating build/src.linux-i686-2.6/scipy/optimize/slsqp
f2py options: []
f2py: scipy/optimize/slsqp/slsqp.pyf
Reading fortran codes...
	Reading file 'scipy/optimize/slsqp/slsqp.pyf' (format:free)
Post-processing...
	Block: _slsqp
			Block: slsqp
Post-processing (stage 2)...
Building modules...
	Building module "_slsqp"...
		Constructing wrapper function "slsqp"...
		  slsqp(m,meq,x,xl,xu,f,c,g,a,acc,iter,mode,w,jw,[la,n,l_w,l_jw])
	Wrote C/API module "_slsqp" to file "build/src.linux-i686-2.6/scipy/optimize/slsqp/_slsqpmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.optimize._nnls" sources
creating build/src.linux-i686-2.6/scipy/optimize/nnls
f2py options: []
f2py: scipy/optimize/nnls/nnls.pyf
Reading fortran codes...
	Reading file 'scipy/optimize/nnls/nnls.pyf' (format:free)
crackline: groupcounter=1 groupname={0: '', 1: 'python module', 2: 'interface', 3: 'subroutine'}
crackline: Mismatch of blocks encountered. Trying to fix it by assuming "end" statement.
Post-processing...
	Block: _nnls
			Block: nnls
Post-processing (stage 2)...
Building modules...
	Building module "_nnls"...
		Constructing wrapper function "nnls"...
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
getarrdims:warning: assumed shape array, using 0 instead of '*'
		  x,rnorm,mode = nnls(a,m,n,b,w,zz,index_bn,[mda,overwrite_a,overwrite_b])
	Wrote C/API module "_nnls" to file "build/src.linux-i686-2.6/scipy/optimize/nnls/_nnlsmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.signal.sigtools" sources
building extension "scipy.signal.spline" sources
building extension "scipy.sparse.linalg.isolve._iterative" sources
creating build/src.linux-i686-2.6/scipy/sparse
creating build/src.linux-i686-2.6/scipy/sparse/linalg
creating build/src.linux-i686-2.6/scipy/sparse/linalg/isolve
creating build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/STOPTEST2.f
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/getbreak.f
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/BiCGREVCOM.f
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/BiCGSTABREVCOM.f
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/CGREVCOM.f
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/CGSREVCOM.f
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/GMRESREVCOM.f
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/QMRREVCOM.f
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/_iterative.pyf
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative
f2py options: []
f2py: build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/_iterative.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/_iterative.pyf' (format:free)
Post-processing...
	Block: _iterative
			Block: sbicgrevcom
			Block: dbicgrevcom
			Block: cbicgrevcom
			Block: zbicgrevcom
			Block: sbicgstabrevcom
			Block: dbicgstabrevcom
			Block: cbicgstabrevcom
			Block: zbicgstabrevcom
			Block: scgrevcom
			Block: dcgrevcom
			Block: ccgrevcom
			Block: zcgrevcom
			Block: scgsrevcom
			Block: dcgsrevcom
			Block: ccgsrevcom
			Block: zcgsrevcom
			Block: sqmrrevcom
			Block: dqmrrevcom
			Block: cqmrrevcom
			Block: zqmrrevcom
			Block: sgmresrevcom
			Block: dgmresrevcom
			Block: cgmresrevcom
			Block: zgmresrevcom
			Block: sstoptest2
			Block: dstoptest2
			Block: cstoptest2
			Block: zstoptest2
Post-processing (stage 2)...
Building modules...
	Building module "_iterative"...
		Constructing wrapper function "sbicgrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = sbicgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "dbicgrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = dbicgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "cbicgrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = cbicgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "zbicgrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = zbicgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "sbicgstabrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = sbicgstabrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "dbicgstabrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = dbicgstabrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "cbicgstabrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = cbicgstabrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "zbicgstabrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = zbicgstabrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "scgrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = scgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "dcgrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = dcgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "ccgrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = ccgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "zcgrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = zcgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "scgsrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = scgsrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "dcgsrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = dcgsrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "ccgsrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = ccgsrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "zcgsrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = zcgsrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "sqmrrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = sqmrrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "dqmrrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = dqmrrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "cqmrrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = cqmrrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "zqmrrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = zqmrrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "sgmresrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = sgmresrevcom(b,x,restrt,work,work2,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "dgmresrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = dgmresrevcom(b,x,restrt,work,work2,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "cgmresrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = cgmresrevcom(b,x,restrt,work,work2,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "zgmresrevcom"...
		  x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = zgmresrevcom(b,x,restrt,work,work2,iter,resid,info,ndx1,ndx2,ijob)
		Constructing wrapper function "sstoptest2"...
		  bnrm2,resid,info = sstoptest2(r,b,bnrm2,tol,info)
		Constructing wrapper function "dstoptest2"...
		  bnrm2,resid,info = dstoptest2(r,b,bnrm2,tol,info)
		Constructing wrapper function "cstoptest2"...
		  bnrm2,resid,info = cstoptest2(r,b,bnrm2,tol,info)
		Constructing wrapper function "zstoptest2"...
		  bnrm2,resid,info = zstoptest2(r,b,bnrm2,tol,info)
	Wrote C/API module "_iterative" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/_iterativemodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.sparse.linalg.dsolve._zsuperlu" sources
building extension "scipy.sparse.linalg.dsolve._dsuperlu" sources
building extension "scipy.sparse.linalg.dsolve._csuperlu" sources
building extension "scipy.sparse.linalg.dsolve._ssuperlu" sources
building extension "scipy.sparse.linalg.dsolve.umfpack.__umfpack" sources
creating build/src.linux-i686-2.6/scipy/sparse/linalg/dsolve
creating build/src.linux-i686-2.6/scipy/sparse/linalg/dsolve/umfpack
building extension "scipy.sparse.linalg.eigen.arpack._arpack" sources
creating build/src.linux-i686-2.6/scipy/sparse/linalg/eigen
creating build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
from_template:> build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/arpack.pyf
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen
creating build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
f2py options: []
f2py: build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/arpack.pyf
Reading fortran codes...
	Reading file 'build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/arpack.pyf' (format:free)
Post-processing...
	Block: _arpack
			Block: ssaupd
			Block: dsaupd
			Block: sseupd
			Block: dseupd
			Block: snaupd
			Block: dnaupd
			Block: sneupd
			Block: dneupd
			Block: cnaupd
			Block: znaupd
			Block: cneupd
			Block: zneupd
Post-processing (stage 2)...
Building modules...
	Building module "_arpack"...
		Constructing wrapper function "ssaupd"...
		  ido,resid,v,iparam,ipntr,info = ssaupd(ido,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[n,ncv,ldv,lworkl])
		Constructing wrapper function "dsaupd"...
		  ido,resid,v,iparam,ipntr,info = dsaupd(ido,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[n,ncv,ldv,lworkl])
		Constructing wrapper function "sseupd"...
		  d,z,info = sseupd(rvec,howmny,select,sigma,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[ldz,n,ncv,ldv,lworkl])
		Constructing wrapper function "dseupd"...
		  d,z,info = dseupd(rvec,howmny,select,sigma,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[ldz,n,ncv,ldv,lworkl])
		Constructing wrapper function "snaupd"...
		  ido,resid,v,iparam,ipntr,info = snaupd(ido,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[n,ncv,ldv,lworkl])
		Constructing wrapper function "dnaupd"...
		  ido,resid,v,iparam,ipntr,info = dnaupd(ido,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[n,ncv,ldv,lworkl])
		Constructing wrapper function "sneupd"...
		  dr,di,z,info = sneupd(rvec,howmny,select,sigmar,sigmai,workev,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[ldz,n,ncv,ldv,lworkl])
		Constructing wrapper function "dneupd"...
		  dr,di,z,info = dneupd(rvec,howmny,select,sigmar,sigmai,workev,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[ldz,n,ncv,ldv,lworkl])
		Constructing wrapper function "cnaupd"...
		  ido,resid,v,iparam,ipntr,info = cnaupd(ido,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,rwork,info,[n,ncv,ldv,lworkl])
		Constructing wrapper function "znaupd"...
		  ido,resid,v,iparam,ipntr,info = znaupd(ido,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,rwork,info,[n,ncv,ldv,lworkl])
		Constructing wrapper function "cneupd"...
		  d,z,info = cneupd(rvec,howmny,select,sigma,workev,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,rwork,info,[ldz,n,ncv,ldv,lworkl])
		Constructing wrapper function "zneupd"...
		  d,z,info = zneupd(rvec,howmny,select,sigma,workev,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,rwork,info,[ldz,n,ncv,ldv,lworkl])
		Constructing COMMON block support for "debug"...
		  logfil,ndigit,mgetv0,msaupd,msaup2,msaitr,mseigt,msapps,msgets,mseupd,mnaupd,mnaup2,mnaitr,mneigh,mnapps,mngets,mneupd,mcaupd,mcaup2,mcaitr,mceigh,mcapps,mcgets,mceupd
		Constructing COMMON block support for "timing"...
		  nopx,nbx,nrorth,nitref,nrstrt,tsaupd,tsaup2,tsaitr,tseigt,tsgets,tsapps,tsconv,tnaupd,tnaup2,tnaitr,tneigh,tngets,tnapps,tnconv,tcaupd,tcaup2,tcaitr,tceigh,tcgets,tcapps,tcconv,tmvopx,tmvbx,tgetv0,titref,trvec
	Wrote C/API module "_arpack" to file "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpackmodule.c"
	Fortran 77 wrappers are saved to "build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpack-f2pywrappers.f"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpack-f2pywrappers.f' to sources.
building extension "scipy.sparse.sparsetools._csr" sources
building extension "scipy.sparse.sparsetools._csc" sources
building extension "scipy.sparse.sparsetools._coo" sources
building extension "scipy.sparse.sparsetools._bsr" sources
building extension "scipy.sparse.sparsetools._dia" sources
building extension "scipy.spatial.ckdtree" sources
building extension "scipy.spatial._distance_wrap" sources
building extension "scipy.special._cephes" sources
building extension "scipy.special.specfun" sources
creating build/src.linux-i686-2.6/scipy/special
f2py options: ['--no-wrap-functions']
f2py: scipy/special/specfun.pyf
Reading fortran codes...
	Reading file 'scipy/special/specfun.pyf' (format:free)
Post-processing...
	Block: specfun
			Block: clqmn
			Block: lqmn
			Block: clpmn
			Block: jdzo
			Block: bernob
			Block: bernoa
			Block: csphjy
			Block: lpmns
			Block: eulera
			Block: clqn
			Block: airyzo
			Block: eulerb
			Block: cva1
			Block: lqnb
			Block: lamv
			Block: lagzo
			Block: legzo
			Block: pbdv
			Block: cerzo
			Block: lamn
			Block: clpn
			Block: lqmns
			Block: chgm
			Block: lpmn
			Block: fcszo
			Block: aswfb
			Block: lqna
			Block: cpbdn
			Block: lpn
			Block: fcoef
			Block: sphi
			Block: rcty
			Block: lpni
			Block: cyzo
			Block: csphik
			Block: sphj
			Block: othpl
			Block: klvnzo
			Block: jyzo
			Block: rctj
			Block: herzo
			Block: sphk
			Block: pbvv
			Block: segv
			Block: sphy
Post-processing (stage 2)...
Building modules...
	Building module "specfun"...
		Constructing wrapper function "clqmn"...
		  cqm,cqd = clqmn(m,n,z)
		Constructing wrapper function "lqmn"...
		  qm,qd = lqmn(m,n,x)
		Constructing wrapper function "clpmn"...
		  cpm,cpd = clpmn(m,n,x,y)
		Constructing wrapper function "jdzo"...
		  n,m,pcode,zo = jdzo(nt)
		Constructing wrapper function "bernob"...
		  bn = bernob(n)
		Constructing wrapper function "bernoa"...
		  bn = bernoa(n)
		Constructing wrapper function "csphjy"...
		  nm,csj,cdj,csy,cdy = csphjy(n,z)
		Constructing wrapper function "lpmns"...
		  pm,pd = lpmns(m,n,x)
		Constructing wrapper function "eulera"...
		  en = eulera(n)
		Constructing wrapper function "clqn"...
		  cqn,cqd = clqn(n,z)
		Constructing wrapper function "airyzo"...
		  xa,xb,xc,xd = airyzo(nt,[kf])
		Constructing wrapper function "eulerb"...
		  en = eulerb(n)
		Constructing wrapper function "cva1"...
		  cv = cva1(kd,m,q)
		Constructing wrapper function "lqnb"...
		  qn,qd = lqnb(n,x)
		Constructing wrapper function "lamv"...
		  vm,vl,dl = lamv(v,x)
		Constructing wrapper function "lagzo"...
		  x,w = lagzo(n)
		Constructing wrapper function "legzo"...
		  x,w = legzo(n)
		Constructing wrapper function "pbdv"...
		  dv,dp,pdf,pdd = pbdv(v,x)
		Constructing wrapper function "cerzo"...
		  zo = cerzo(nt)
		Constructing wrapper function "lamn"...
		  nm,bl,dl = lamn(n,x)
		Constructing wrapper function "clpn"...
		  cpn,cpd = clpn(n,z)
		Constructing wrapper function "lqmns"...
		  qm,qd = lqmns(m,n,x)
		Constructing wrapper function "chgm"...
		  hg = chgm(a,b,x)
		Constructing wrapper function "lpmn"...
		  pm,pd = lpmn(m,n,x)
		Constructing wrapper function "fcszo"...
		  zo = fcszo(kf,nt)
		Constructing wrapper function "aswfb"...
		  s1f,s1d = aswfb(m,n,c,x,kd,cv)
		Constructing wrapper function "lqna"...
		  qn,qd = lqna(n,x)
		Constructing wrapper function "cpbdn"...
		  cpb,cpd = cpbdn(n,z)
		Constructing wrapper function "lpn"...
		  pn,pd = lpn(n,x)
		Constructing wrapper function "fcoef"...
		  fc = fcoef(kd,m,q,a)
		Constructing wrapper function "sphi"...
		  nm,si,di = sphi(n,x)
		Constructing wrapper function "rcty"...
		  nm,ry,dy = rcty(n,x)
		Constructing wrapper function "lpni"...
		  pn,pd,pl = lpni(n,x)
		Constructing wrapper function "cyzo"...
		  zo,zv = cyzo(nt,kf,kc)
		Constructing wrapper function "csphik"...
		  nm,csi,cdi,csk,cdk = csphik(n,z)
		Constructing wrapper function "sphj"...
		  nm,sj,dj = sphj(n,x)
		Constructing wrapper function "othpl"...
		  pl,dpl = othpl(kf,n,x)
		Constructing wrapper function "klvnzo"...
		  zo = klvnzo(nt,kd)
		Constructing wrapper function "jyzo"...
		  rj0,rj1,ry0,ry1 = jyzo(n,nt)
		Constructing wrapper function "rctj"...
		  nm,rj,dj = rctj(n,x)
		Constructing wrapper function "herzo"...
		  x,w = herzo(n)
		Constructing wrapper function "sphk"...
		  nm,sk,dk = sphk(n,x)
		Constructing wrapper function "pbvv"...
		  vv,vp,pvf,pvd = pbvv(v,x)
		Constructing wrapper function "segv"...
		  cv,eg = segv(m,n,c,kd)
		Constructing wrapper function "sphy"...
		  nm,sy,dy = sphy(n,x)
	Wrote C/API module "specfun" to file "build/src.linux-i686-2.6/scipy/special/specfunmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.stats.statlib" sources
creating build/src.linux-i686-2.6/scipy/stats
f2py options: ['--no-wrap-functions']
f2py: scipy/stats/statlib.pyf
Reading fortran codes...
	Reading file 'scipy/stats/statlib.pyf' (format:free)
Post-processing...
	Block: statlib
			Block: swilk
			Block: wprob
			Block: gscale
			Block: prho
Post-processing (stage 2)...
Building modules...
	Building module "statlib"...
		Constructing wrapper function "swilk"...
		  a,w,pw,ifault = swilk(x,a,[init,n1])
		Constructing wrapper function "wprob"...
		  astart,a1,ifault = wprob(test,other)
		Constructing wrapper function "gscale"...
		  astart,a1,ifault = gscale(test,other)
		Constructing wrapper function "prho"...
		  ifault = prho(n,is)
	Wrote C/API module "statlib" to file "build/src.linux-i686-2.6/scipy/stats/statlibmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.stats.vonmises_cython" sources
building extension "scipy.stats.futil" sources
f2py options: []
f2py:> build/src.linux-i686-2.6/scipy/stats/futilmodule.c
Reading fortran codes...
	Reading file 'scipy/stats/futil.f' (format:fix,strict)
Post-processing...
	Block: futil
			Block: dqsort
			Block: dfreps
Post-processing (stage 2)...
Building modules...
	Building module "futil"...
		Constructing wrapper function "dqsort"...
		  arr = dqsort(arr,[overwrite_arr])
		Constructing wrapper function "dfreps"...
		  replist,repnum,nlist = dfreps(arr)
	Wrote C/API module "futil" to file "build/src.linux-i686-2.6/scipy/stats/futilmodule.c"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.stats.mvn" sources
f2py options: []
f2py: scipy/stats/mvn.pyf
Reading fortran codes...
	Reading file 'scipy/stats/mvn.pyf' (format:free)
Post-processing...
	Block: mvn
			Block: mvnun
			Block: mvndst
Post-processing (stage 2)...
Building modules...
	Building module "mvn"...
		Constructing wrapper function "mvnun"...
		  value,inform = mvnun(lower,upper,means,covar,[maxpts,abseps,releps])
		Constructing wrapper function "mvndst"...
		  error,value,inform = mvndst(lower,upper,infin,correl,[maxpts,abseps,releps])
		Constructing COMMON block support for "dkblck"...
		  ivls
	Wrote C/API module "mvn" to file "build/src.linux-i686-2.6/scipy/stats/mvnmodule.c"
	Fortran 77 wrappers are saved to "build/src.linux-i686-2.6/scipy/stats/mvn-f2pywrappers.f"
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/scipy/stats/mvn-f2pywrappers.f' to sources.
building extension "scipy.ndimage._nd_image" sources
building data_files sources
running build_py
creating build/lib.linux-i686-2.6
creating build/lib.linux-i686-2.6/scipy
copying scipy/setupscons.py -> build/lib.linux-i686-2.6/scipy
copying scipy/version.py -> build/lib.linux-i686-2.6/scipy
copying scipy/setup.py -> build/lib.linux-i686-2.6/scipy
copying scipy/__init__.py -> build/lib.linux-i686-2.6/scipy
copying build/src.linux-i686-2.6/scipy/__config__.py -> build/lib.linux-i686-2.6/scipy
creating build/lib.linux-i686-2.6/scipy/cluster
copying scipy/cluster/setupscons.py -> build/lib.linux-i686-2.6/scipy/cluster
copying scipy/cluster/vq.py -> build/lib.linux-i686-2.6/scipy/cluster
copying scipy/cluster/hierarchy.py -> build/lib.linux-i686-2.6/scipy/cluster
copying scipy/cluster/setup.py -> build/lib.linux-i686-2.6/scipy/cluster
copying scipy/cluster/__init__.py -> build/lib.linux-i686-2.6/scipy/cluster
copying scipy/cluster/info.py -> build/lib.linux-i686-2.6/scipy/cluster
creating build/lib.linux-i686-2.6/scipy/constants
copying scipy/constants/constants.py -> build/lib.linux-i686-2.6/scipy/constants
copying scipy/constants/codata.py -> build/lib.linux-i686-2.6/scipy/constants
copying scipy/constants/setup.py -> build/lib.linux-i686-2.6/scipy/constants
copying scipy/constants/__init__.py -> build/lib.linux-i686-2.6/scipy/constants
creating build/lib.linux-i686-2.6/scipy/fftpack
copying scipy/fftpack/helper.py -> build/lib.linux-i686-2.6/scipy/fftpack
copying scipy/fftpack/setupscons.py -> build/lib.linux-i686-2.6/scipy/fftpack
copying scipy/fftpack/fftpack_version.py -> build/lib.linux-i686-2.6/scipy/fftpack
copying scipy/fftpack/basic.py -> build/lib.linux-i686-2.6/scipy/fftpack
copying scipy/fftpack/pseudo_diffs.py -> build/lib.linux-i686-2.6/scipy/fftpack
copying scipy/fftpack/setup.py -> build/lib.linux-i686-2.6/scipy/fftpack
copying scipy/fftpack/__init__.py -> build/lib.linux-i686-2.6/scipy/fftpack
copying scipy/fftpack/info.py -> build/lib.linux-i686-2.6/scipy/fftpack
creating build/lib.linux-i686-2.6/scipy/integrate
copying scipy/integrate/odepack.py -> build/lib.linux-i686-2.6/scipy/integrate
copying scipy/integrate/quadpack.py -> build/lib.linux-i686-2.6/scipy/integrate
copying scipy/integrate/setupscons.py -> build/lib.linux-i686-2.6/scipy/integrate
copying scipy/integrate/ode.py -> build/lib.linux-i686-2.6/scipy/integrate
copying scipy/integrate/quadrature.py -> build/lib.linux-i686-2.6/scipy/integrate
copying scipy/integrate/setup.py -> build/lib.linux-i686-2.6/scipy/integrate
copying scipy/integrate/__init__.py -> build/lib.linux-i686-2.6/scipy/integrate
copying scipy/integrate/info.py -> build/lib.linux-i686-2.6/scipy/integrate
creating build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/fitpack2.py -> build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/setupscons.py -> build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/rbf.py -> build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/fitpack.py -> build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/interpolate_wrapper.py -> build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/interpolate.py -> build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/polyint.py -> build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/setup.py -> build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/__init__.py -> build/lib.linux-i686-2.6/scipy/interpolate
copying scipy/interpolate/info.py -> build/lib.linux-i686-2.6/scipy/interpolate
creating build/lib.linux-i686-2.6/scipy/io
copying scipy/io/dumbdbm_patched.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/dumb_shelve.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/pickler.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/fopen.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/npfile.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/data_store.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/setupscons.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/netcdf.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/wavfile.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/recaster.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/array_import.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/mmio.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/setup.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/__init__.py -> build/lib.linux-i686-2.6/scipy/io
copying scipy/io/info.py -> build/lib.linux-i686-2.6/scipy/io
creating build/lib.linux-i686-2.6/scipy/io/matlab
copying scipy/io/matlab/mio5.py -> build/lib.linux-i686-2.6/scipy/io/matlab
copying scipy/io/matlab/miobase.py -> build/lib.linux-i686-2.6/scipy/io/matlab
copying scipy/io/matlab/mio4.py -> build/lib.linux-i686-2.6/scipy/io/matlab
copying scipy/io/matlab/setup.py -> build/lib.linux-i686-2.6/scipy/io/matlab
copying scipy/io/matlab/mio.py -> build/lib.linux-i686-2.6/scipy/io/matlab
copying scipy/io/matlab/byteordercodes.py -> build/lib.linux-i686-2.6/scipy/io/matlab
copying scipy/io/matlab/__init__.py -> build/lib.linux-i686-2.6/scipy/io/matlab
creating build/lib.linux-i686-2.6/scipy/io/arff
copying scipy/io/arff/utils.py -> build/lib.linux-i686-2.6/scipy/io/arff
copying scipy/io/arff/myfunctools.py -> build/lib.linux-i686-2.6/scipy/io/arff
copying scipy/io/arff/arffread.py -> build/lib.linux-i686-2.6/scipy/io/arff
copying scipy/io/arff/setup.py -> build/lib.linux-i686-2.6/scipy/io/arff
copying scipy/io/arff/__init__.py -> build/lib.linux-i686-2.6/scipy/io/arff
creating build/lib.linux-i686-2.6/scipy/lib
copying scipy/lib/setupscons.py -> build/lib.linux-i686-2.6/scipy/lib
copying scipy/lib/setup.py -> build/lib.linux-i686-2.6/scipy/lib
copying scipy/lib/__init__.py -> build/lib.linux-i686-2.6/scipy/lib
copying scipy/lib/info.py -> build/lib.linux-i686-2.6/scipy/lib
creating build/lib.linux-i686-2.6/scipy/lib/blas
copying scipy/lib/blas/scons_support.py -> build/lib.linux-i686-2.6/scipy/lib/blas
copying scipy/lib/blas/setupscons.py -> build/lib.linux-i686-2.6/scipy/lib/blas
copying scipy/lib/blas/setup.py -> build/lib.linux-i686-2.6/scipy/lib/blas
copying scipy/lib/blas/__init__.py -> build/lib.linux-i686-2.6/scipy/lib/blas
copying scipy/lib/blas/info.py -> build/lib.linux-i686-2.6/scipy/lib/blas
creating build/lib.linux-i686-2.6/scipy/lib/lapack
copying scipy/lib/lapack/scons_support.py -> build/lib.linux-i686-2.6/scipy/lib/lapack
copying scipy/lib/lapack/setupscons.py -> build/lib.linux-i686-2.6/scipy/lib/lapack
copying scipy/lib/lapack/setup.py -> build/lib.linux-i686-2.6/scipy/lib/lapack
copying scipy/lib/lapack/__init__.py -> build/lib.linux-i686-2.6/scipy/lib/lapack
copying scipy/lib/lapack/info.py -> build/lib.linux-i686-2.6/scipy/lib/lapack
creating build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/lapack.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/scons_support.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/interface_gen.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/matfuncs.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/decomp.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/setupscons.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/blas.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/basic.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/setup_atlas_version.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/linalg_version.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/setup.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/__init__.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/iterative.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/info.py -> build/lib.linux-i686-2.6/scipy/linalg
copying scipy/linalg/flinalg.py -> build/lib.linux-i686-2.6/scipy/linalg
creating build/lib.linux-i686-2.6/scipy/linsolve
copying scipy/linsolve/__init__.py -> build/lib.linux-i686-2.6/scipy/linsolve
creating build/lib.linux-i686-2.6/scipy/maxentropy
copying scipy/maxentropy/setupscons.py -> build/lib.linux-i686-2.6/scipy/maxentropy
copying scipy/maxentropy/maxentropy.py -> build/lib.linux-i686-2.6/scipy/maxentropy
copying scipy/maxentropy/maxentutils.py -> build/lib.linux-i686-2.6/scipy/maxentropy
copying scipy/maxentropy/setup.py -> build/lib.linux-i686-2.6/scipy/maxentropy
copying scipy/maxentropy/__init__.py -> build/lib.linux-i686-2.6/scipy/maxentropy
copying scipy/maxentropy/info.py -> build/lib.linux-i686-2.6/scipy/maxentropy
creating build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/setupscons.py -> build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/helpmod.py -> build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/common.py -> build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/limits.py -> build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/ppimport.py -> build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/pilutil.py -> build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/setup.py -> build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/__init__.py -> build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/pexec.py -> build/lib.linux-i686-2.6/scipy/misc
copying scipy/misc/info.py -> build/lib.linux-i686-2.6/scipy/misc
creating build/lib.linux-i686-2.6/scipy/odr
copying scipy/odr/setupscons.py -> build/lib.linux-i686-2.6/scipy/odr
copying scipy/odr/models.py -> build/lib.linux-i686-2.6/scipy/odr
copying scipy/odr/odrpack.py -> build/lib.linux-i686-2.6/scipy/odr
copying scipy/odr/setup.py -> build/lib.linux-i686-2.6/scipy/odr
copying scipy/odr/__init__.py -> build/lib.linux-i686-2.6/scipy/odr
copying scipy/odr/info.py -> build/lib.linux-i686-2.6/scipy/odr
creating build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/linesearch.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/anneal.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/lbfgsb.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/nonlin.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/minpack.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/setupscons.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/nnls.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/cobyla.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/zeros.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/_tstutils.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/optimize.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/tnc.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/setup.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/__init__.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/info.py -> build/lib.linux-i686-2.6/scipy/optimize
copying scipy/optimize/slsqp.py -> build/lib.linux-i686-2.6/scipy/optimize
creating build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/ltisys.py -> build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/filter_design.py -> build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/signaltools.py -> build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/setupscons.py -> build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/waveforms.py -> build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/wavelets.py -> build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/setup.py -> build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/__init__.py -> build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/bsplines.py -> build/lib.linux-i686-2.6/scipy/signal
copying scipy/signal/info.py -> build/lib.linux-i686-2.6/scipy/signal
creating build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/data.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/compressed.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/coo.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/setupscons.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/spfuncs.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/construct.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/sputils.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/csr.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/dia.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/base.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/setup.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/bsr.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/csc.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/extract.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/__init__.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/dok.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/lil.py -> build/lib.linux-i686-2.6/scipy/sparse
copying scipy/sparse/info.py -> build/lib.linux-i686-2.6/scipy/sparse
creating build/lib.linux-i686-2.6/scipy/sparse/linalg
copying scipy/sparse/linalg/setupscons.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg
copying scipy/sparse/linalg/interface.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg
copying scipy/sparse/linalg/setup.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg
copying scipy/sparse/linalg/__init__.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg
copying scipy/sparse/linalg/info.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg
creating build/lib.linux-i686-2.6/scipy/sparse/linalg/isolve
copying scipy/sparse/linalg/isolve/setupscons.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/isolve
copying scipy/sparse/linalg/isolve/utils.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/isolve
copying scipy/sparse/linalg/isolve/minres.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/isolve
copying scipy/sparse/linalg/isolve/setup.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/isolve
copying scipy/sparse/linalg/isolve/__init__.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/isolve
copying scipy/sparse/linalg/isolve/iterative.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/isolve
creating build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve
copying scipy/sparse/linalg/dsolve/linsolve.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve
copying scipy/sparse/linalg/dsolve/setupscons.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve
copying scipy/sparse/linalg/dsolve/setup.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve
copying scipy/sparse/linalg/dsolve/__init__.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve
copying scipy/sparse/linalg/dsolve/_superlu.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve
copying scipy/sparse/linalg/dsolve/info.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve
creating build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/umfpack
copying scipy/sparse/linalg/dsolve/umfpack/umfpack.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/umfpack
copying scipy/sparse/linalg/dsolve/umfpack/setupscons.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/umfpack
copying scipy/sparse/linalg/dsolve/umfpack/setup.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/umfpack
copying scipy/sparse/linalg/dsolve/umfpack/__init__.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/umfpack
copying scipy/sparse/linalg/dsolve/umfpack/info.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/umfpack
creating build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen
copying scipy/sparse/linalg/eigen/setupscons.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen
copying scipy/sparse/linalg/eigen/setup.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen
copying scipy/sparse/linalg/eigen/__init__.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen
copying scipy/sparse/linalg/eigen/info.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen
creating build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
copying scipy/sparse/linalg/eigen/arpack/setupscons.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
copying scipy/sparse/linalg/eigen/arpack/speigs.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
copying scipy/sparse/linalg/eigen/arpack/arpack.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
copying scipy/sparse/linalg/eigen/arpack/setup.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
copying scipy/sparse/linalg/eigen/arpack/__init__.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
copying scipy/sparse/linalg/eigen/arpack/info.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
creating build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/lobpcg
copying scipy/sparse/linalg/eigen/lobpcg/lobpcg.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/lobpcg
copying scipy/sparse/linalg/eigen/lobpcg/setupscons.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/lobpcg
copying scipy/sparse/linalg/eigen/lobpcg/setup.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/lobpcg
copying scipy/sparse/linalg/eigen/lobpcg/__init__.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/lobpcg
copying scipy/sparse/linalg/eigen/lobpcg/info.py -> build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/lobpcg
creating build/lib.linux-i686-2.6/scipy/sparse/sparsetools
copying scipy/sparse/sparsetools/coo.py -> build/lib.linux-i686-2.6/scipy/sparse/sparsetools
copying scipy/sparse/sparsetools/setupscons.py -> build/lib.linux-i686-2.6/scipy/sparse/sparsetools
copying scipy/sparse/sparsetools/csr.py -> build/lib.linux-i686-2.6/scipy/sparse/sparsetools
copying scipy/sparse/sparsetools/dia.py -> build/lib.linux-i686-2.6/scipy/sparse/sparsetools
copying scipy/sparse/sparsetools/setup.py -> build/lib.linux-i686-2.6/scipy/sparse/sparsetools
copying scipy/sparse/sparsetools/bsr.py -> build/lib.linux-i686-2.6/scipy/sparse/sparsetools
copying scipy/sparse/sparsetools/csc.py -> build/lib.linux-i686-2.6/scipy/sparse/sparsetools
copying scipy/sparse/sparsetools/__init__.py -> build/lib.linux-i686-2.6/scipy/sparse/sparsetools
creating build/lib.linux-i686-2.6/scipy/spatial
copying scipy/spatial/setupscons.py -> build/lib.linux-i686-2.6/scipy/spatial
copying scipy/spatial/distance.py -> build/lib.linux-i686-2.6/scipy/spatial
copying scipy/spatial/kdtree.py -> build/lib.linux-i686-2.6/scipy/spatial
copying scipy/spatial/setup.py -> build/lib.linux-i686-2.6/scipy/spatial
copying scipy/spatial/__init__.py -> build/lib.linux-i686-2.6/scipy/spatial
copying scipy/spatial/info.py -> build/lib.linux-i686-2.6/scipy/spatial
creating build/lib.linux-i686-2.6/scipy/special
copying scipy/special/orthogonal.py -> build/lib.linux-i686-2.6/scipy/special
copying scipy/special/gendoc.py -> build/lib.linux-i686-2.6/scipy/special
copying scipy/special/setupscons.py -> build/lib.linux-i686-2.6/scipy/special
copying scipy/special/basic.py -> build/lib.linux-i686-2.6/scipy/special
copying scipy/special/special_version.py -> build/lib.linux-i686-2.6/scipy/special
copying scipy/special/spfun_stats.py -> build/lib.linux-i686-2.6/scipy/special
copying scipy/special/setup.py -> build/lib.linux-i686-2.6/scipy/special
copying scipy/special/__init__.py -> build/lib.linux-i686-2.6/scipy/special
copying scipy/special/info.py -> build/lib.linux-i686-2.6/scipy/special
creating build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/mstats_basic.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/setupscons.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/mstats.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/vonmises.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/rv.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/distributions.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/mstats_extras.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/stats.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/morestats.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/kde.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/setup.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/__init__.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/info.py -> build/lib.linux-i686-2.6/scipy/stats
copying scipy/stats/_support.py -> build/lib.linux-i686-2.6/scipy/stats
creating build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/_ni_support.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/filters.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/setupscons.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/fourier.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/doccer.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/measurements.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/morphology.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/setup.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/interpolation.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/__init__.py -> build/lib.linux-i686-2.6/scipy/ndimage
copying scipy/ndimage/info.py -> build/lib.linux-i686-2.6/scipy/ndimage
creating build/lib.linux-i686-2.6/scipy/stsci
copying scipy/stsci/setupscons.py -> build/lib.linux-i686-2.6/scipy/stsci
copying scipy/stsci/setup.py -> build/lib.linux-i686-2.6/scipy/stsci
copying scipy/stsci/__init__.py -> build/lib.linux-i686-2.6/scipy/stsci
creating build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/platform_info.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/cpp_namespace_spec.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/inline_tools.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/swigptr.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/common_info.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/standard_array_spec.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/setupscons.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/ext_tools.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/blitz_tools.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/build_tools.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/ast_tools.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/c_spec.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/base_spec.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/swig2_spec.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/catalog.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/converters.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/blitz_spec.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/base_info.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/swigptr2.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/weave_version.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/accelerate_tools.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/md5_load.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/setup.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/size_check.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/__init__.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/vtk_spec.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/bytecodecompiler.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/info.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/slice_handler.py -> build/lib.linux-i686-2.6/scipy/weave
copying scipy/weave/numpy_scalar_spec.py -> build/lib.linux-i686-2.6/scipy/weave
running build_clib
customize UnixCCompiler
customize UnixCCompiler using build_clib
customize Gnu95FCompiler
Found executable /usr/bin/gfortran
customize Gnu95FCompiler using build_clib
building 'dfftpack' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6
creating build/temp.linux-i686-2.6/scipy
creating build/temp.linux-i686-2.6/scipy/fftpack
creating build/temp.linux-i686-2.6/scipy/fftpack/src
creating build/temp.linux-i686-2.6/scipy/fftpack/src/dfftpack
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/fftpack/src/dfftpack/zffti1.f
scipy/fftpack/src/dfftpack/zffti1.f: In function 'zffti1':
scipy/fftpack/src/dfftpack/zffti1.f:11: warning: 'ntry' may be used uninitialized in this function
gfortran:f77: scipy/fftpack/src/dfftpack/dfftf.f
gfortran:f77: scipy/fftpack/src/dfftpack/zfftf.f
gfortran:f77: scipy/fftpack/src/dfftpack/dsint1.f
gfortran:f77: scipy/fftpack/src/dfftpack/dffti.f
gfortran:f77: scipy/fftpack/src/dfftpack/dsinqi.f
gfortran:f77: scipy/fftpack/src/dfftpack/dfftb.f
gfortran:f77: scipy/fftpack/src/dfftpack/dsint.f
gfortran:f77: scipy/fftpack/src/dfftpack/dcosti.f
gfortran:f77: scipy/fftpack/src/dfftpack/dcost.f
gfortran:f77: scipy/fftpack/src/dfftpack/dcosqi.f
gfortran:f77: scipy/fftpack/src/dfftpack/dffti1.f
scipy/fftpack/src/dfftpack/dffti1.f: In function 'dffti1':
scipy/fftpack/src/dfftpack/dffti1.f:11: warning: 'ntry' may be used uninitialized in this function
gfortran:f77: scipy/fftpack/src/dfftpack/dsinqb.f
gfortran:f77: scipy/fftpack/src/dfftpack/zffti.f
gfortran:f77: scipy/fftpack/src/dfftpack/dsinqf.f
gfortran:f77: scipy/fftpack/src/dfftpack/dfftb1.f
gfortran:f77: scipy/fftpack/src/dfftpack/zfftf1.f
gfortran:f77: scipy/fftpack/src/dfftpack/dfftf1.f
gfortran:f77: scipy/fftpack/src/dfftpack/zfftb1.f
gfortran:f77: scipy/fftpack/src/dfftpack/zfftb.f
gfortran:f77: scipy/fftpack/src/dfftpack/dcosqb.f
gfortran:f77: scipy/fftpack/src/dfftpack/dsinti.f
gfortran:f77: scipy/fftpack/src/dfftpack/dcosqf.f
ar: adding 23 object files to build/temp.linux-i686-2.6/libdfftpack.a
building 'linpack_lite' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/integrate
creating build/temp.linux-i686-2.6/scipy/integrate/linpack_lite
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/integrate/linpack_lite/zgbfa.f
gfortran:f77: scipy/integrate/linpack_lite/zgbsl.f
gfortran:f77: scipy/integrate/linpack_lite/dgesl.f
gfortran:f77: scipy/integrate/linpack_lite/zgesl.f
gfortran:f77: scipy/integrate/linpack_lite/dgbsl.f
gfortran:f77: scipy/integrate/linpack_lite/dgbfa.f
gfortran:f77: scipy/integrate/linpack_lite/dgtsl.f
gfortran:f77: scipy/integrate/linpack_lite/zgefa.f
gfortran:f77: scipy/integrate/linpack_lite/dgefa.f
ar: adding 9 object files to build/temp.linux-i686-2.6/liblinpack_lite.a
building 'mach' library
using additional config_fc from setup script for fortran compiler: {'noopt': ('scipy/integrate/setup.pyc', 1)}
customize Gnu95FCompiler
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC
creating build/temp.linux-i686-2.6/scipy/integrate/mach
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/integrate/mach/i1mach.f
gfortran:f77: scipy/integrate/mach/d1mach.f
gfortran:f77: scipy/integrate/mach/xerror.f
 In file scipy/integrate/mach/xerror.f:1

      SUBROUTINE XERROR(MESS,NMESS,L1,L2)                               
                                       1
Warning: Unused variable l2 declared at (1)
 In file scipy/integrate/mach/xerror.f:1

      SUBROUTINE XERROR(MESS,NMESS,L1,L2)                               
                                    1
Warning: Unused variable l1 declared at (1)
gfortran:f77: scipy/integrate/mach/r1mach.f
ar: adding 4 object files to build/temp.linux-i686-2.6/libmach.a
building 'quadpack' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/integrate/quadpack
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/integrate/quadpack/dqaws.f
gfortran:f77: scipy/integrate/quadpack/dqawse.f
gfortran:f77: scipy/integrate/quadpack/dqk21.f
gfortran:f77: scipy/integrate/quadpack/dqawc.f
gfortran:f77: scipy/integrate/quadpack/dqawo.f
gfortran:f77: scipy/integrate/quadpack/dqpsrt.f
gfortran:f77: scipy/integrate/quadpack/dqk41.f
gfortran:f77: scipy/integrate/quadpack/dqk61.f
gfortran:f77: scipy/integrate/quadpack/dqwgtf.f
 In file scipy/integrate/quadpack/dqwgtf.f:1

      double precision function dqwgtf(x,omega,p2,p3,p4,integr)         
                                                   1
Warning: Unused variable p3 declared at (1)
 In file scipy/integrate/quadpack/dqwgtf.f:1

      double precision function dqwgtf(x,omega,p2,p3,p4,integr)         
                                                1
Warning: Unused variable p2 declared at (1)
 In file scipy/integrate/quadpack/dqwgtf.f:1

      double precision function dqwgtf(x,omega,p2,p3,p4,integr)         
                                                      1
Warning: Unused variable p4 declared at (1)
gfortran:f77: scipy/integrate/quadpack/dqng.f
scipy/integrate/quadpack/dqng.f: In function 'dqng':
scipy/integrate/quadpack/dqng.f:80: warning: 'resabs' may be used uninitialized in this function
scipy/integrate/quadpack/dqng.f:80: warning: 'resasc' may be used uninitialized in this function
scipy/integrate/quadpack/dqng.f:80: warning: 'res43' may be used uninitialized in this function
scipy/integrate/quadpack/dqng.f:82: warning: 'ipx' may be used uninitialized in this function
scipy/integrate/quadpack/dqng.f:80: warning: 'res21' may be used uninitialized in this function
gfortran:f77: scipy/integrate/quadpack/dqk51.f
gfortran:f77: scipy/integrate/quadpack/dqelg.f
gfortran:f77: scipy/integrate/quadpack/dqags.f
gfortran:f77: scipy/integrate/quadpack/dqagp.f
gfortran:f77: scipy/integrate/quadpack/dqc25c.f
gfortran:f77: scipy/integrate/quadpack/dqmomo.f
 In file scipy/integrate/quadpack/dqmomo.f:126

   90 return                                                            
    1
Warning: Label 90 at (1) defined but not used
gfortran:f77: scipy/integrate/quadpack/dqawfe.f
scipy/integrate/quadpack/dqawfe.f: In function 'dqawfe':
scipy/integrate/quadpack/dqawfe.f:203: warning: 'll' may be used uninitialized in this function
scipy/integrate/quadpack/dqawfe.f:200: warning: 'drl' may be used uninitialized in this function
gfortran:f77: scipy/integrate/quadpack/dqagpe.f
scipy/integrate/quadpack/dqagpe.f: In function 'dqagpe':
scipy/integrate/quadpack/dqagpe.f:196: warning: 'k' may be used uninitialized in this function
scipy/integrate/quadpack/dqagpe.f:191: warning: 'correc' may be used uninitialized in this function
gfortran:f77: scipy/integrate/quadpack/dqk15w.f
gfortran:f77: scipy/integrate/quadpack/dqcheb.f
gfortran:f77: scipy/integrate/quadpack/dqc25s.f
gfortran:f77: scipy/integrate/quadpack/dqawf.f
gfortran:f77: scipy/integrate/quadpack/dqag.f
gfortran:f77: scipy/integrate/quadpack/dqc25f.f
scipy/integrate/quadpack/dqc25f.f: In function 'dqc25f':
scipy/integrate/quadpack/dqc25f.f:103: warning: 'm' may be used uninitialized in this function
gfortran:f77: scipy/integrate/quadpack/dqagie.f
scipy/integrate/quadpack/dqagie.f: In function 'dqagie':
scipy/integrate/quadpack/dqagie.f:154: warning: 'small' may be used uninitialized in this function
scipy/integrate/quadpack/dqagie.f:153: warning: 'ertest' may be used uninitialized in this function
scipy/integrate/quadpack/dqagie.f:152: warning: 'erlarg' may be used uninitialized in this function
scipy/integrate/quadpack/dqagie.f:151: warning: 'correc' may be used uninitialized in this function
gfortran:f77: scipy/integrate/quadpack/dqk15i.f
gfortran:f77: scipy/integrate/quadpack/dqage.f
gfortran:f77: scipy/integrate/quadpack/dqk31.f
gfortran:f77: scipy/integrate/quadpack/dqwgtc.f
 In file scipy/integrate/quadpack/dqwgtc.f:1

      double precision function dqwgtc(x,c,p2,p3,p4,kp)                 
                                               1
Warning: Unused variable p3 declared at (1)
 In file scipy/integrate/quadpack/dqwgtc.f:1

      double precision function dqwgtc(x,c,p2,p3,p4,kp)                 
                                                     1
Warning: Unused variable kp declared at (1)
 In file scipy/integrate/quadpack/dqwgtc.f:1

      double precision function dqwgtc(x,c,p2,p3,p4,kp)                 
                                            1
Warning: Unused variable p2 declared at (1)
 In file scipy/integrate/quadpack/dqwgtc.f:1

      double precision function dqwgtc(x,c,p2,p3,p4,kp)                 
                                                  1
Warning: Unused variable p4 declared at (1)
gfortran:f77: scipy/integrate/quadpack/dqwgts.f
gfortran:f77: scipy/integrate/quadpack/dqagse.f
scipy/integrate/quadpack/dqagse.f: In function 'dqagse':
scipy/integrate/quadpack/dqagse.f:153: warning: 'small' may be used uninitialized in this function
scipy/integrate/quadpack/dqagse.f:152: warning: 'ertest' may be used uninitialized in this function
scipy/integrate/quadpack/dqagse.f:151: warning: 'erlarg' may be used uninitialized in this function
scipy/integrate/quadpack/dqagse.f:150: warning: 'correc' may be used uninitialized in this function
gfortran:f77: scipy/integrate/quadpack/dqk15.f
gfortran:f77: scipy/integrate/quadpack/dqawce.f
gfortran:f77: scipy/integrate/quadpack/dqagi.f
gfortran:f77: scipy/integrate/quadpack/dqawoe.f
scipy/integrate/quadpack/dqawoe.f: In function 'dqawoe':
scipy/integrate/quadpack/dqawoe.f:208: warning: 'ertest' may be used uninitialized in this function
scipy/integrate/quadpack/dqawoe.f:207: warning: 'erlarg' may be used uninitialized in this function
scipy/integrate/quadpack/dqawoe.f:206: warning: 'correc' may be used uninitialized in this function
ar: adding 35 object files to build/temp.linux-i686-2.6/libquadpack.a
building 'odepack' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/integrate/odepack
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/integrate/odepack/intdy.f
gfortran:f77: scipy/integrate/odepack/bnorm.f
gfortran:f77: scipy/integrate/odepack/mdm.f
gfortran:f77: scipy/integrate/odepack/prjs.f
gfortran:f77: scipy/integrate/odepack/stode.f
scipy/integrate/odepack/stode.f: In function 'stode':
scipy/integrate/odepack/stode.f:14: warning: 'rh' may be used uninitialized in this function
scipy/integrate/odepack/stode.f:9: warning: 'iredo' may be used uninitialized in this function
scipy/integrate/odepack/stode.f:13: warning: 'dsm' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/lsode.f
scipy/integrate/odepack/lsode.f: In function 'lsode':
scipy/integrate/odepack/lsode.f:959: warning: 'ihit' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/prepji.f
gfortran:f77: scipy/integrate/odepack/fnorm.f
gfortran:f77: scipy/integrate/odepack/sro.f
gfortran:f77: scipy/integrate/odepack/vode.f
 In file scipy/integrate/odepack/vode.f:2373

 700  R = ONE/TQ(2)                                                     
   1
Warning: Label 700 at (1) defined but not used
 In file scipy/integrate/odepack/vode.f:2739

      SUBROUTINE DVNLSD (Y, YH, LDYH, VSAV, SAVF, EWT, ACOR, IWM, WM,   
                                         1
Warning: Unused variable vsav declared at (1)
 In file scipy/integrate/odepack/vode.f:3495

      DOUBLE PRECISION FUNCTION D1MACH (IDUM)                           
                                           1
Warning: Unused variable idum declared at (1)
 In file scipy/integrate/odepack/vode.f:3514

      SUBROUTINE XERRWD (MSG, NMES, NERR, LEVEL, NI, I1, I2, NR, R1, R2)
                                       1
Warning: Unused variable nerr declared at (1)
scipy/integrate/odepack/vode.f: In function 'ixsav':
scipy/integrate/odepack/vode.f:3610: warning: '__result_ixsav' may be used uninitialized in this function
scipy/integrate/odepack/vode.f: In function 'dvode':
scipy/integrate/odepack/vode.f:1055: warning: 'ihit' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/prep.f
gfortran:f77: scipy/integrate/odepack/lsodar.f
scipy/integrate/odepack/lsodar.f: In function 'lsodar':
scipy/integrate/odepack/lsodar.f:1096: warning: 'lenwm' may be used uninitialized in this function
scipy/integrate/odepack/lsodar.f:1108: warning: 'ihit' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/ainvg.f
gfortran:f77: scipy/integrate/odepack/stodi.f
scipy/integrate/odepack/stodi.f: In function 'stodi':
scipy/integrate/odepack/stodi.f:15: warning: 'rh' may be used uninitialized in this function
scipy/integrate/odepack/stodi.f:13: warning: 'dsm' may be used uninitialized in this function
scipy/integrate/odepack/stodi.f:9: warning: 'iredo' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/prja.f
gfortran:f77: scipy/integrate/odepack/nnfc.f
gfortran:f77: scipy/integrate/odepack/pjibt.f
gfortran:f77: scipy/integrate/odepack/lsoibt.f
scipy/integrate/odepack/lsoibt.f: In function 'lsoibt':
scipy/integrate/odepack/lsoibt.f:1207: warning: 'ihit' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/solsy.f
 In file scipy/integrate/odepack/solsy.f:1

      subroutine solsy (wm, iwm, x, tem)                                
                                      1
Warning: Unused variable tem declared at (1)
gfortran:f77: scipy/integrate/odepack/blkdta000.f
gfortran:f77: scipy/integrate/odepack/ddassl.f
 In file scipy/integrate/odepack/ddassl.f:1605

      SUBROUTINE DDAWTS (NEQ, IWT, RTOL, ATOL, Y, WT, RPAR, IPAR)       
                                                         1
Warning: Unused variable rpar declared at (1)
 In file scipy/integrate/odepack/ddassl.f:1605

      SUBROUTINE DDAWTS (NEQ, IWT, RTOL, ATOL, Y, WT, RPAR, IPAR)       
                                                               1
Warning: Unused variable ipar declared at (1)
 In file scipy/integrate/odepack/ddassl.f:1647

      DOUBLE PRECISION FUNCTION DDANRM (NEQ, V, WT, RPAR, IPAR)         
                                                       1
Warning: Unused variable rpar declared at (1)
 In file scipy/integrate/odepack/ddassl.f:1647

      DOUBLE PRECISION FUNCTION DDANRM (NEQ, V, WT, RPAR, IPAR)         
                                                             1
Warning: Unused variable ipar declared at (1)
 In file scipy/integrate/odepack/ddassl.f:3153

   30 IF (LEVEL.LE.0 .OR. (LEVEL.EQ.1 .AND. MKNTRL.LE.1)) RETURN        
    1
Warning: Label 30 at (1) defined but not used
 In file scipy/integrate/odepack/ddassl.f:3170

      SUBROUTINE XERHLT (MESSG)                                         
                             1
Warning: Unused variable messg declared at (1)
scipy/integrate/odepack/ddassl.f: In function 'ddastp':
scipy/integrate/odepack/ddassl.f:2122: warning: 'terkm1' may be used uninitialized in this function
scipy/integrate/odepack/ddassl.f:2121: warning: 'oldnrm' may be used uninitialized in this function
scipy/integrate/odepack/ddassl.f:2117: warning: 'knew' may be used uninitialized in this function
scipy/integrate/odepack/ddassl.f:2121: warning: 'est' may be used uninitialized in this function
scipy/integrate/odepack/ddassl.f:2120: warning: 'erkm1' may be used uninitialized in this function
scipy/integrate/odepack/ddassl.f: In function 'ddaini':
scipy/integrate/odepack/ddassl.f:1758: warning: 's' may be used uninitialized in this function
scipy/integrate/odepack/ddassl.f:1758: warning: 'oldnrm' may be used uninitialized in this function
scipy/integrate/odepack/ddassl.f:1758: warning: 'err' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/jgroup.f
gfortran:f77: scipy/integrate/odepack/iprep.f
gfortran:f77: scipy/integrate/odepack/solbt.f
gfortran:f77: scipy/integrate/odepack/ddasrt.f
 In file scipy/integrate/odepack/ddasrt.f:1022

300   CONTINUE                                                          
  1
Warning: Label 300 at (1) defined but not used
 In file scipy/integrate/odepack/ddasrt.f:1080

360   ITEMP = LPHI + NEQ                                                
  1
Warning: Label 360 at (1) defined but not used
 In file scipy/integrate/odepack/ddasrt.f:1538

770   MSG = 'DASSL--  RUN TERMINATED. APPARENT INFINITE LOOP'           
  1
Warning: Label 770 at (1) defined but not used
 In file scipy/integrate/odepack/ddasrt.f:1932

      SUBROUTINE XERRWV (MSG, NMES, NERR, LEVEL, NI, I1, I2, NR, R1, R2)
                                       1
Warning: Unused variable nerr declared at (1)
gfortran:f77: scipy/integrate/odepack/aigbt.f
gfortran:f77: scipy/integrate/odepack/md.f
gfortran:f77: scipy/integrate/odepack/xsetf.f
gfortran:f77: scipy/integrate/odepack/mdu.f
gfortran:f77: scipy/integrate/odepack/mdp.f
scipy/integrate/odepack/mdp.f: In function 'mdp':
scipy/integrate/odepack/mdp.f:8: warning: 'free' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/cntnzu.f
gfortran:f77: scipy/integrate/odepack/rchek.f
gfortran:f77: scipy/integrate/odepack/srcar.f
gfortran:f77: scipy/integrate/odepack/adjlr.f
gfortran:f77: scipy/integrate/odepack/xsetun.f
gfortran:f77: scipy/integrate/odepack/decbt.f
gfortran:f77: scipy/integrate/odepack/lsodi.f
scipy/integrate/odepack/lsodi.f: In function 'lsodi':
scipy/integrate/odepack/lsodi.f:1143: warning: 'lenwm' may be used uninitialized in this function
scipy/integrate/odepack/lsodi.f:1150: warning: 'ihit' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/cfode.f
gfortran:f77: scipy/integrate/odepack/vmnorm.f
gfortran:f77: scipy/integrate/odepack/zvode.f
 In file scipy/integrate/odepack/zvode.f:2394

 700  R = ONE/TQ(2)                                                     
   1
Warning: Label 700 at (1) defined but not used
 In file scipy/integrate/odepack/zvode.f:2760

      SUBROUTINE ZVNLSD (Y, YH, LDYH, VSAV, SAVF, EWT, ACOR, IWM, WM,   
                                         1
Warning: Unused variable vsav declared at (1)
scipy/integrate/odepack/zvode.f: In function 'zvode':
scipy/integrate/odepack/zvode.f:1067: warning: 'ihit' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/slss.f
 In file scipy/integrate/odepack/slss.f:1

      subroutine slss (wk, iwk, x, tem)                                 
                                     1
Warning: Unused variable tem declared at (1)
gfortran:f77: scipy/integrate/odepack/xerrwv.f
 In file scipy/integrate/odepack/xerrwv.f:1

      subroutine xerrwv (msg, nmes, nerr, level, ni, i1, i2, nr, r1, r2)
                                       1
Warning: Unused variable nerr declared at (1)
gfortran:f77: scipy/integrate/odepack/ewset.f
gfortran:f77: scipy/integrate/odepack/srcms.f
gfortran:f77: scipy/integrate/odepack/stoda.f
scipy/integrate/odepack/stoda.f: In function 'stoda':
scipy/integrate/odepack/stoda.f:18: warning: 'rh' may be used uninitialized in this function
scipy/integrate/odepack/stoda.f:19: warning: 'pdh' may be used uninitialized in this function
scipy/integrate/odepack/stoda.f:10: warning: 'iredo' may be used uninitialized in this function
scipy/integrate/odepack/stoda.f:17: warning: 'dsm' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/prepj.f
gfortran:f77: scipy/integrate/odepack/cdrv.f
gfortran:f77: scipy/integrate/odepack/lsoda.f
scipy/integrate/odepack/lsoda.f: In function 'lsoda':
scipy/integrate/odepack/lsoda.f:978: warning: 'lenwm' may be used uninitialized in this function
scipy/integrate/odepack/lsoda.f:988: warning: 'ihit' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/vnorm.f
gfortran:f77: scipy/integrate/odepack/srcom.f
gfortran:f77: scipy/integrate/odepack/nntc.f
gfortran:f77: scipy/integrate/odepack/odrv.f
gfortran:f77: scipy/integrate/odepack/nroc.f
gfortran:f77: scipy/integrate/odepack/slsbt.f
 In file scipy/integrate/odepack/slsbt.f:1

      subroutine slsbt (wm, iwm, x, tem)                                
                                      1
Warning: Unused variable tem declared at (1)
gfortran:f77: scipy/integrate/odepack/nsfc.f
gfortran:f77: scipy/integrate/odepack/lsodes.f
scipy/integrate/odepack/lsodes.f: In function 'lsodes':
scipy/integrate/odepack/lsodes.f:1245: warning: 'ihit' may be used uninitialized in this function
gfortran:f77: scipy/integrate/odepack/roots.f
gfortran:f77: scipy/integrate/odepack/srcma.f
gfortran:f77: scipy/integrate/odepack/nnsc.f
gfortran:f77: scipy/integrate/odepack/mdi.f
ar: adding 50 object files to build/temp.linux-i686-2.6/libodepack.a
ar: adding 10 object files to build/temp.linux-i686-2.6/libodepack.a
building 'fitpack' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/interpolate
creating build/temp.linux-i686-2.6/scipy/interpolate/fitpack
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/interpolate/fitpack/fpintb.f
scipy/interpolate/fitpack/fpintb.f: In function 'fpintb':
scipy/interpolate/fitpack/fpintb.f:26: warning: 'ia' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fptrpe.f
 In file scipy/interpolate/fitpack/fptrpe.f:17

      integer i,iband,irot,it,ii,i2,i3,j,jj,l,mid,nmd,m2,m3,            
                    1
Warning: Unused variable iband declared at (1)
scipy/interpolate/fitpack/fptrpe.f: In function 'fptrpe':
scipy/interpolate/fitpack/fptrpe.f:16: warning: 'pinv' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpchep.f
gfortran:f77: scipy/interpolate/fitpack/profil.f
gfortran:f77: scipy/interpolate/fitpack/insert.f
gfortran:f77: scipy/interpolate/fitpack/concur.f
 In file scipy/interpolate/fitpack/concur.f:287

      real*8 tol,dist                                                   
                    1
Warning: Unused variable dist declared at (1)
gfortran:f77: scipy/interpolate/fitpack/fpgrdi.f
 In file scipy/interpolate/fitpack/fpgrdi.f:296

 400    if(nrold.eq.number) go to 420                                   
   1
Warning: Label 400 at (1) defined but not used
scipy/interpolate/fitpack/fpgrdi.f: In function 'fpgrdi':
scipy/interpolate/fitpack/fpgrdi.f:16: warning: 'pinv' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fptrnp.f
scipy/interpolate/fitpack/fptrnp.f: In function 'fptrnp':
scipy/interpolate/fitpack/fptrnp.f:15: warning: 'pinv' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpgrre.f
scipy/interpolate/fitpack/fpgrre.f: In function 'fpgrre':
scipy/interpolate/fitpack/fpgrre.f:16: warning: 'pinv' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fprppo.f
scipy/interpolate/fitpack/fprppo.f: In function 'fprppo':
scipy/interpolate/fitpack/fprppo.f:12: warning: 'j' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpinst.f
gfortran:f77: scipy/interpolate/fitpack/fppasu.f
scipy/interpolate/fitpack/fppasu.f: In function 'fppasu':
scipy/interpolate/fitpack/fppasu.f:15: warning: 'peru' may be used uninitialized in this function
scipy/interpolate/fitpack/fppasu.f:15: warning: 'perv' may be used uninitialized in this function
scipy/interpolate/fitpack/fppasu.f:19: warning: 'nve' may be used uninitialized in this function
scipy/interpolate/fitpack/fppasu.f:19: warning: 'nue' may be used uninitialized in this function
scipy/interpolate/fitpack/fppasu.f:18: warning: 'nmaxu' may be used uninitialized in this function
scipy/interpolate/fitpack/fppasu.f:18: warning: 'nmaxv' may be used uninitialized in this function
scipy/interpolate/fitpack/fppasu.f:14: warning: 'acc' may be used uninitialized in this function
scipy/interpolate/fitpack/fppasu.f:14: warning: 'fpms' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpclos.f
scipy/interpolate/fitpack/fpclos.f: In function 'fpclos':
scipy/interpolate/fitpack/fpclos.f:17: warning: 'nplus' may be used uninitialized in this function
scipy/interpolate/fitpack/fpclos.f:17: warning: 'new' may be used uninitialized in this function
scipy/interpolate/fitpack/fpclos.f:18: warning: 'n10' may be used uninitialized in this function
scipy/interpolate/fitpack/fpclos.f:16: warning: 'i1' may be used uninitialized in this function
scipy/interpolate/fitpack/fpclos.f:13: warning: 'fpold' may be used uninitialized in this function
scipy/interpolate/fitpack/fpclos.f:13: warning: 'fp0' may be used uninitialized in this function
scipy/interpolate/fitpack/fpclos.f:13: warning: 'fpms' may be used uninitialized in this function
scipy/interpolate/fitpack/fpclos.f:17: warning: 'nmax' may be used uninitialized in this function
scipy/interpolate/fitpack/fpclos.f:13: warning: 'acc' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/surev.f
gfortran:f77: scipy/interpolate/fitpack/fpdisc.f
gfortran:f77: scipy/interpolate/fitpack/fppola.f
 In file scipy/interpolate/fitpack/fppola.f:377

 370      in = nummer(in)                                               
   1
Warning: Label 370 at (1) defined but not used
 In file scipy/interpolate/fitpack/fppola.f:440

 440    do 450 i=1,nrint                                                
   1
Warning: Label 440 at (1) defined but not used
 In file scipy/interpolate/fitpack/fppola.f:23

     * iter,i1,i2,i3,j,jl,jrot,j1,j2,k,l,la,lf,lh,ll,lu,lv,lwest,l1,l2, 
                        1
Warning: Unused variable jl declared at (1)
scipy/interpolate/fitpack/fppola.f: In function 'fppola':
scipy/interpolate/fitpack/fppola.f:19: warning: 'fpms' may be used uninitialized in this function
scipy/interpolate/fitpack/fppola.f:19: warning: 'acc' may be used uninitialized in this function
scipy/interpolate/fitpack/fppola.f:23: warning: 'lwest' may be used uninitialized in this function
scipy/interpolate/fitpack/fppola.f:24: warning: 'nv4' may be used uninitialized in this function
scipy/interpolate/fitpack/fppola.f:24: warning: 'nu4' may be used uninitialized in this function
scipy/interpolate/fitpack/fppola.f:25: warning: 'iband1' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fppocu.f
gfortran:f77: scipy/interpolate/fitpack/splder.f
 In file scipy/interpolate/fitpack/splder.f:79

  30  ier = 0                                                           
   1
Warning: Label 30 at (1) defined but not used
scipy/interpolate/fitpack/splder.f: In function 'splder':
scipy/interpolate/fitpack/splder.f:117: warning: 'k2' is used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpsurf.f
 In file scipy/interpolate/fitpack/fpsurf.f:245

 240      in = nummer(in)                                               
   1
Warning: Label 240 at (1) defined but not used
 In file scipy/interpolate/fitpack/fpsurf.f:305

 310    do 320 i=1,nrint                                                
   1
Warning: Label 310 at (1) defined but not used
scipy/interpolate/fitpack/fpsurf.f: In function 'fpsurf':
scipy/interpolate/fitpack/fpsurf.f:16: warning: 'acc' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsurf.f:21: warning: 'lwest' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsurf.f:22: warning: 'nyy' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsurf.f:21: warning: 'nk1y' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsurf.f:21: warning: 'nk1x' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsurf.f:19: warning: 'iband1' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsurf.f:16: warning: 'fpms' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/splev.f
 In file scipy/interpolate/fitpack/splev.f:75

  30  ier = 0                                                           
   1
Warning: Label 30 at (1) defined but not used
gfortran:f77: scipy/interpolate/fitpack/cualde.f
gfortran:f77: scipy/interpolate/fitpack/concon.f
gfortran:f77: scipy/interpolate/fitpack/percur.f
gfortran:f77: scipy/interpolate/fitpack/fprank.f
scipy/interpolate/fitpack/fprank.f: In function 'fprank':
scipy/interpolate/fitpack/fprank.f:25: warning: 'j3' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/sphere.f
 In file scipy/interpolate/fitpack/sphere.f:318

     * lbp,lco,lf,lff,lfp,lh,lq,lst,lsp,lwest,maxit,ncest,ncc,ntt,      
         1
Warning: Unused variable jlbp declared at (1)
gfortran:f77: scipy/interpolate/fitpack/evapol.f
gfortran:f77: scipy/interpolate/fitpack/fpdeno.f
gfortran:f77: scipy/interpolate/fitpack/fpcyt1.f
gfortran:f77: scipy/interpolate/fitpack/fpbfout.f
scipy/interpolate/fitpack/fpbfout.f: In function 'fpbfou':
scipy/interpolate/fitpack/fpbfout.f:35: warning: 'term' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpperi.f
scipy/interpolate/fitpack/fpperi.f: In function 'fpperi':
scipy/interpolate/fitpack/fpperi.f:17: warning: 'new' may be used uninitialized in this function
scipy/interpolate/fitpack/fpperi.f:17: warning: 'nplus' may be used uninitialized in this function
scipy/interpolate/fitpack/fpperi.f:18: warning: 'n10' may be used uninitialized in this function
scipy/interpolate/fitpack/fpperi.f:16: warning: 'i1' may be used uninitialized in this function
scipy/interpolate/fitpack/fpperi.f:13: warning: 'fpold' may be used uninitialized in this function
scipy/interpolate/fitpack/fpperi.f:13: warning: 'fp0' may be used uninitialized in this function
scipy/interpolate/fitpack/fpperi.f:13: warning: 'fpms' may be used uninitialized in this function
scipy/interpolate/fitpack/fpperi.f:17: warning: 'nmax' may be used uninitialized in this function
scipy/interpolate/fitpack/fpperi.f:13: warning: 'acc' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpback.f
gfortran:f77: scipy/interpolate/fitpack/fpcsin.f
gfortran:f77: scipy/interpolate/fitpack/parcur.f
gfortran:f77: scipy/interpolate/fitpack/fpspgr.f
scipy/interpolate/fitpack/fpspgr.f: In function 'fpspgr':
scipy/interpolate/fitpack/fpspgr.f:21: warning: 'nvmax' may be used uninitialized in this function
scipy/interpolate/fitpack/fpspgr.f:21: warning: 'nve' may be used uninitialized in this function
scipy/interpolate/fitpack/fpspgr.f:21: warning: 'nue' may be used uninitialized in this function
scipy/interpolate/fitpack/fpspgr.f:21: warning: 'numax' may be used uninitialized in this function
scipy/interpolate/fitpack/fpspgr.f:16: warning: 'acc' may be used uninitialized in this function
scipy/interpolate/fitpack/fpspgr.f:20: warning: 'nplu' may be used uninitialized in this function
scipy/interpolate/fitpack/fpspgr.f:16: warning: 'fpms' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fprati.f
gfortran:f77: scipy/interpolate/fitpack/fpcurf.f
scipy/interpolate/fitpack/fpcurf.f: In function 'fpcurf':
scipy/interpolate/fitpack/fpcurf.f:15: warning: 'nplus' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcurf.f:12: warning: 'fpold' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcurf.f:12: warning: 'fpms' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcurf.f:12: warning: 'fp0' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcurf.f:15: warning: 'nmax' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcurf.f:12: warning: 'acc' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/bispev.f
gfortran:f77: scipy/interpolate/fitpack/surfit.f
gfortran:f77: scipy/interpolate/fitpack/dblint.f
gfortran:f77: scipy/interpolate/fitpack/fpcons.f
scipy/interpolate/fitpack/fpcons.f: In function 'fpcons':
scipy/interpolate/fitpack/fpcons.f:15: warning: 'nplus' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcons.f:15: warning: 'nk1' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcons.f:12: warning: 'fpms' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcons.f:12: warning: 'fp0' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcons.f:12: warning: 'fpold' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcons.f:15: warning: 'nmax' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcons.f:15: warning: 'mm' may be used uninitialized in this function
scipy/interpolate/fitpack/fpcons.f:12: warning: 'acc' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/regrid.f
gfortran:f77: scipy/interpolate/fitpack/fpknot.f
scipy/interpolate/fitpack/fpknot.f: In function 'fpknot':
scipy/interpolate/fitpack/fpknot.f:21: warning: 'number' may be used uninitialized in this function
scipy/interpolate/fitpack/fpknot.f:20: warning: 'maxbeg' may be used uninitialized in this function
scipy/interpolate/fitpack/fpknot.f:20: warning: 'maxpt' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fprpsp.f
gfortran:f77: scipy/interpolate/fitpack/fpcuro.f
gfortran:f77: scipy/interpolate/fitpack/fpbspl.f
gfortran:f77: scipy/interpolate/fitpack/fpgrpa.f
gfortran:f77: scipy/interpolate/fitpack/curev.f
gfortran:f77: scipy/interpolate/fitpack/fporde.f
gfortran:f77: scipy/interpolate/fitpack/spgrid.f
gfortran:f77: scipy/interpolate/fitpack/fpopdi.f
gfortran:f77: scipy/interpolate/fitpack/cocosp.f
gfortran:f77: scipy/interpolate/fitpack/fpsysy.f
gfortran:f77: scipy/interpolate/fitpack/fpchec.f
gfortran:f77: scipy/interpolate/fitpack/fprota.f
gfortran:f77: scipy/interpolate/fitpack/fpcyt2.f
gfortran:f77: scipy/interpolate/fitpack/curfit.f
gfortran:f77: scipy/interpolate/fitpack/parsur.f
gfortran:f77: scipy/interpolate/fitpack/polar.f
 In file scipy/interpolate/fitpack/polar.f:353

     * lbv,lco,lf,lff,lfp,lh,lq,lsu,lsv,lwest,maxit,ncest,ncc,nuu,      
         1
Warning: Unused variable jlbv declared at (1)
gfortran:f77: scipy/interpolate/fitpack/fpbisp.f
gfortran:f77: scipy/interpolate/fitpack/parder.f
gfortran:f77: scipy/interpolate/fitpack/splint.f
gfortran:f77: scipy/interpolate/fitpack/pogrid.f
gfortran:f77: scipy/interpolate/fitpack/fpregr.f
scipy/interpolate/fitpack/fpregr.f: In function 'fpregr':
scipy/interpolate/fitpack/fpregr.f:19: warning: 'nye' may be used uninitialized in this function
scipy/interpolate/fitpack/fpregr.f:19: warning: 'nxe' may be used uninitialized in this function
scipy/interpolate/fitpack/fpregr.f:18: warning: 'nmaxy' may be used uninitialized in this function
scipy/interpolate/fitpack/fpregr.f:18: warning: 'nmaxx' may be used uninitialized in this function
scipy/interpolate/fitpack/fpregr.f:15: warning: 'acc' may be used uninitialized in this function
scipy/interpolate/fitpack/fpregr.f:15: warning: 'fpms' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/bispeu.f
 In file scipy/interpolate/fitpack/bispeu.f:50

      integer i,iw,lwest                                                
                 1
Warning: Unused variable iw declared at (1)
 In file scipy/interpolate/fitpack/bispeu.f:44

      integer nx,ny,kx,ky,m,lwrk,kwrk,ier                               
                                    1
Warning: Unused variable kwrk declared at (1)
gfortran:f77: scipy/interpolate/fitpack/fpsuev.f
gfortran:f77: scipy/interpolate/fitpack/fpgivs.f
scipy/interpolate/fitpack/fpgivs.f: In function 'fpgivs':
scipy/interpolate/fitpack/fpgivs.f:8: warning: 'dd' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpader.f
gfortran:f77: scipy/interpolate/fitpack/spalde.f
gfortran:f77: scipy/interpolate/fitpack/fourco.f
gfortran:f77: scipy/interpolate/fitpack/fpseno.f
gfortran:f77: scipy/interpolate/fitpack/fpopsp.f
 In file scipy/interpolate/fitpack/fpopsp.f:58

      real*8 res,sq,sqq,sq0,sq1,step1,step2,three                       
               1
Warning: Unused variable res declared at (1)
gfortran:f77: scipy/interpolate/fitpack/fppara.f
scipy/interpolate/fitpack/fppara.f: In function 'fppara':
scipy/interpolate/fitpack/fppara.f:15: warning: 'nplus' may be used uninitialized in this function
scipy/interpolate/fitpack/fppara.f:12: warning: 'fpms' may be used uninitialized in this function
scipy/interpolate/fitpack/fppara.f:12: warning: 'fpold' may be used uninitialized in this function
scipy/interpolate/fitpack/fppara.f:12: warning: 'fp0' may be used uninitialized in this function
scipy/interpolate/fitpack/fppara.f:15: warning: 'nmax' may be used uninitialized in this function
scipy/interpolate/fitpack/fppara.f:12: warning: 'acc' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpadno.f
gfortran:f77: scipy/interpolate/fitpack/fpfrno.f
scipy/interpolate/fitpack/fpfrno.f: In function 'fpfrno':
scipy/interpolate/fitpack/fpfrno.f:14: warning: 'k' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpbacp.f
gfortran:f77: scipy/interpolate/fitpack/fppogr.f
scipy/interpolate/fitpack/fppogr.f: In function 'fppogr':
scipy/interpolate/fitpack/fppogr.f:21: warning: 'nve' may be used uninitialized in this function
scipy/interpolate/fitpack/fppogr.f:21: warning: 'nvmax' may be used uninitialized in this function
scipy/interpolate/fitpack/fppogr.f:21: warning: 'nue' may be used uninitialized in this function
scipy/interpolate/fitpack/fppogr.f:21: warning: 'numax' may be used uninitialized in this function
scipy/interpolate/fitpack/fppogr.f:16: warning: 'acc' may be used uninitialized in this function
scipy/interpolate/fitpack/fppogr.f:20: warning: 'nplu' may be used uninitialized in this function
scipy/interpolate/fitpack/fppogr.f:16: warning: 'fpms' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpcoco.f
scipy/interpolate/fitpack/fpcoco.f: In function 'fpcoco':
scipy/interpolate/fitpack/fpcoco.f:12: warning: 'k' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpadpo.f
gfortran:f77: scipy/interpolate/fitpack/clocur.f
gfortran:f77: scipy/interpolate/fitpack/fpgrsp.f
 In file scipy/interpolate/fitpack/fpgrsp.f:348

 400    if(nrold.eq.number) go to 420                                   
   1
Warning: Label 400 at (1) defined but not used
scipy/interpolate/fitpack/fpgrsp.f: In function 'fpgrsp':
scipy/interpolate/fitpack/fpgrsp.f:17: warning: 'pinv' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpsphe.f
 In file scipy/interpolate/fitpack/fpsphe.f:327

 330      in = nummer(in)                                               
   1
Warning: Label 330 at (1) defined but not used
 In file scipy/interpolate/fitpack/fpsphe.f:390

 440    do 450 i=1,nrint                                                
   1
Warning: Label 440 at (1) defined but not used
scipy/interpolate/fitpack/fpsphe.f: In function 'fpsphe':
scipy/interpolate/fitpack/fpsphe.f:19: warning: 'fpms' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsphe.f:18: warning: 'acc' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsphe.f:22: warning: 'lwest' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsphe.f:23: warning: 'ntt' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsphe.f:23: warning: 'nt4' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsphe.f:23: warning: 'np4' may be used uninitialized in this function
scipy/interpolate/fitpack/fpsphe.f:21: warning: 'iband1' may be used uninitialized in this function
gfortran:f77: scipy/interpolate/fitpack/fpched.f
gfortran:f77: scipy/interpolate/fitpack/sproot.f
gfortran:f77: scipy/interpolate/fitpack/fpcosp.f
ar: adding 50 object files to build/temp.linux-i686-2.6/libfitpack.a
ar: adding 34 object files to build/temp.linux-i686-2.6/libfitpack.a
building 'odrpack' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/odr
creating build/temp.linux-i686-2.6/scipy/odr/odrpack
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/odr/odrpack/d_odr.f
scipy/odr/odrpack/d_odr.f: In function 'dppt':
scipy/odr/odrpack/d_odr.f:9611: warning: '__result_dppt' may be used uninitialized in this function
scipy/odr/odrpack/d_odr.f: In function 'djckm':
scipy/odr/odrpack/d_odr.f:3547: warning: 'h' may be used uninitialized in this function
gfortran:f77: scipy/odr/odrpack/d_mprec.f
gfortran:f77: scipy/odr/odrpack/dlunoc.f
gfortran:f77: scipy/odr/odrpack/d_lpk.f
ar: adding 4 object files to build/temp.linux-i686-2.6/libodrpack.a
building 'minpack' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/optimize
creating build/temp.linux-i686-2.6/scipy/optimize/minpack
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/optimize/minpack/lmdif.f
scipy/optimize/minpack/lmdif.f: In function 'lmdif':
scipy/optimize/minpack/lmdif.f:193: warning: 'xnorm' may be used uninitialized in this function
scipy/optimize/minpack/lmdif.f:193: warning: 'temp' may be used uninitialized in this function
gfortran:f77: scipy/optimize/minpack/lmstr1.f
gfortran:f77: scipy/optimize/minpack/fdjac2.f
gfortran:f77: scipy/optimize/minpack/lmdif1.f
gfortran:f77: scipy/optimize/minpack/hybrj.f
scipy/optimize/minpack/hybrj.f: In function 'hybrj':
scipy/optimize/minpack/hybrj.f:155: warning: 'xnorm' may be used uninitialized in this function
gfortran:f77: scipy/optimize/minpack/enorm.f
scipy/optimize/minpack/enorm.f: In function 'enorm':
scipy/optimize/minpack/enorm.f:90: warning: '__result_enorm' may be used uninitialized in this function
gfortran:f77: scipy/optimize/minpack/fdjac1.f
gfortran:f77: scipy/optimize/minpack/chkder.f
gfortran:f77: scipy/optimize/minpack/qrsolv.f
gfortran:f77: scipy/optimize/minpack/lmstr.f
scipy/optimize/minpack/lmstr.f: In function 'lmstr':
scipy/optimize/minpack/lmstr.f:189: warning: 'xnorm' may be used uninitialized in this function
gfortran:f77: scipy/optimize/minpack/rwupdt.f
gfortran:f77: scipy/optimize/minpack/r1updt.f
gfortran:f77: scipy/optimize/minpack/dogleg.f
gfortran:f77: scipy/optimize/minpack/hybrj1.f
gfortran:f77: scipy/optimize/minpack/lmder.f
scipy/optimize/minpack/lmder.f: In function 'lmder':
scipy/optimize/minpack/lmder.f:189: warning: 'xnorm' may be used uninitialized in this function
scipy/optimize/minpack/lmder.f:189: warning: 'temp' may be used uninitialized in this function
gfortran:f77: scipy/optimize/minpack/qrfac.f
gfortran:f77: scipy/optimize/minpack/qform.f
gfortran:f77: scipy/optimize/minpack/dpmpar.f
gfortran:f77: scipy/optimize/minpack/lmpar.f
gfortran:f77: scipy/optimize/minpack/r1mpyq.f
scipy/optimize/minpack/r1mpyq.f: In function 'r1mpyq':
scipy/optimize/minpack/r1mpyq.f:54: warning: 'sin' may be used uninitialized in this function
scipy/optimize/minpack/r1mpyq.f:54: warning: 'cos' may be used uninitialized in this function
gfortran:f77: scipy/optimize/minpack/hybrd.f
scipy/optimize/minpack/hybrd.f: In function 'hybrd':
scipy/optimize/minpack/hybrd.f:169: warning: 'xnorm' may be used uninitialized in this function
gfortran:f77: scipy/optimize/minpack/hybrd1.f
gfortran:f77: scipy/optimize/minpack/lmder1.f
ar: adding 23 object files to build/temp.linux-i686-2.6/libminpack.a
building 'rootfind' library
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/optimize/Zeros
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gcc: scipy/optimize/Zeros/brenth.c
gcc: scipy/optimize/Zeros/brentq.c
gcc: scipy/optimize/Zeros/ridder.c
gcc: scipy/optimize/Zeros/bisect.c
scipy/optimize/Zeros/zeros.h:16: warning: 'dminarg1' defined but not used
scipy/optimize/Zeros/zeros.h:16: warning: 'dminarg2' defined but not used
ar: adding 4 object files to build/temp.linux-i686-2.6/librootfind.a
building 'superlu_src' library
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/sparse
creating build/temp.linux-i686-2.6/scipy/sparse/linalg
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/SuperLU
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/SuperLU/SRC
compile options: '-DUSE_VENDOR_BLAS=1 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cgscon.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zmemory.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zpivotgrowth.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zpivotgrowth.c: In function 'zPivotGrowth':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zpivotgrowth.c:59: warning: unused variable 'temp_comp'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sgssv.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgssv.c: In function 'sgssv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgssv.c:133: warning: 'AA' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/ccolumn_dfs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccolumn_dfs.c: In function 'ccolumn_dfs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccolumn_dfs.c:128: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccolumn_dfs.c:171: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cpruneL.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/scopy_to_ucol.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/scopy_to_ucol.c: In function 'scopy_to_ucol':
scipy/sparse/linalg/dsolve/SuperLU/SRC/scopy_to_ucol.c:79: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/scopy_to_ucol.c:82: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/colamd.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cgssvx.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgssvx.c: In function 'cgssvx':
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgssvx.c:347: warning: 'smlnum' may be used uninitialized in this function
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgssvx.c:347: warning: 'bignum' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dreadhb.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dreadhb.c: In function 'dreadhb':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dreadhb.c:180: warning: unused variable 'key'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrs.c: In function 'sgstrs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrs.c:107: warning: function declaration isn't a prototype
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrs.c:186: warning: implicit declaration of function 'strsm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrs.c:189: warning: implicit declaration of function 'sgemm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrs.c:93: warning: unused variable 'incy'
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrs.c:93: warning: unused variable 'incx'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zgssv.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgssv.c: In function 'zgssv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgssv.c:133: warning: 'AA' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sutil.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/sutil.c:472: warning: return type defaults to 'int'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_bmod.c: In function 'ssnode_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_bmod.c:95: warning: implicit declaration of function 'strsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_bmod.c:97: warning: implicit declaration of function 'sgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_bmod.c:50: warning: unused variable 'iptr'
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_bmod.c:50: warning: unused variable 'i'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dsp_blas3.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c: In function 'cPrint_SuperNode_Matrix':
scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c:243: warning: operation on 'd' may be undefined
scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c: At top level:
scipy/sparse/linalg/dsolve/SuperLU/SRC/cutil.c:475: warning: return type defaults to 'int'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrf.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrf.c: In function 'cgstrf':
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrf.c:185: warning: 'iperm_r' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/xerbla.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/xerbla.c: In function 'xerbla_':
scipy/sparse/linalg/dsolve/SuperLU/SRC/xerbla.c:33: warning: implicit declaration of function 'printf'
scipy/sparse/linalg/dsolve/SuperLU/SRC/xerbla.c:33: warning: incompatible implicit declaration of built-in function 'printf'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrs.c: In function 'dgstrs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrs.c:107: warning: function declaration isn't a prototype
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrs.c:186: warning: implicit declaration of function 'dtrsm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrs.c:189: warning: implicit declaration of function 'dgemm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrs.c:93: warning: unused variable 'incy'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrs.c:93: warning: unused variable 'incx'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_dfs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_dfs.c: In function 'ssnode_dfs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_dfs.c:75: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssnode_dfs.c:88: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dgssvx.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgssvx.c: In function 'dgssvx':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgssvx.c:347: warning: 'smlnum' may be used uninitialized in this function
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgssvx.c:347: warning: 'bignum' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_bmod.c: In function 'csnode_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_bmod.c:96: warning: implicit declaration of function 'ctrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_bmod.c:98: warning: implicit declaration of function 'cgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_bmod.c:51: warning: unused variable 'iptr'
scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_bmod.c:51: warning: unused variable 'i'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zutil.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zutil.c: In function 'zPrint_SuperNode_Matrix':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zutil.c:243: warning: operation on 'd' may be undefined
scipy/sparse/linalg/dsolve/SuperLU/SRC/zutil.c: At top level:
scipy/sparse/linalg/dsolve/SuperLU/SRC/zutil.c:475: warning: return type defaults to 'int'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dutil.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dutil.c:472: warning: return type defaults to 'int'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_bmod.c: In function 'zsnode_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_bmod.c:96: warning: implicit declaration of function 'ztrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_bmod.c:98: warning: implicit declaration of function 'zgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_bmod.c:51: warning: unused variable 'iptr'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_bmod.c:51: warning: unused variable 'i'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/csp_blas2.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/csp_blas2.c: In function 'sp_ctrsv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/csp_blas2.c:153: warning: implicit declaration of function 'ctrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/csp_blas2.c:156: warning: implicit declaration of function 'cgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/csp_blas2.c: In function 'sp_cgemv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/csp_blas2.c:477: warning: suggest parentheses around && within ||
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cpanel_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/cpanel_bmod.c:31: warning: function declaration isn't a prototype
scipy/sparse/linalg/dsolve/SuperLU/SRC/cpanel_bmod.c: In function 'cpanel_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/cpanel_bmod.c:229: warning: implicit declaration of function 'ctrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/cpanel_bmod.c:276: warning: implicit declaration of function 'cgemv_'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zpruneL.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/spivotL.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zpanel_dfs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dcopy_to_ucol.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcopy_to_ucol.c: In function 'dcopy_to_ucol':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcopy_to_ucol.c:79: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcopy_to_ucol.c:82: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zcolumn_dfs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcolumn_dfs.c: In function 'zcolumn_dfs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcolumn_dfs.c:128: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcolumn_dfs.c:171: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/scolumn_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/scolumn_bmod.c: In function 'scolumn_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/scolumn_bmod.c:215: warning: implicit declaration of function 'strsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/scolumn_bmod.c:226: warning: implicit declaration of function 'sgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/scolumn_bmod.c:269: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cgssv.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgssv.c: In function 'cgssv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgssv.c:133: warning: 'AA' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zsp_blas3.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrf.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrf.c: In function 'sgstrf':
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgstrf.c:185: warning: 'iperm_r' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/slamch.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/slamch.c: In function 'slamc2_':
scipy/sparse/linalg/dsolve/SuperLU/SRC/slamch.c:414: warning: unused variable 'c__1'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dzsum1.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrf.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrf.c: In function 'zgstrf':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrf.c:185: warning: 'iperm_r' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sp_coletree.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/spanel_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/spanel_bmod.c:31: warning: function declaration isn't a prototype
scipy/sparse/linalg/dsolve/SuperLU/SRC/spanel_bmod.c: In function 'spanel_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/spanel_bmod.c:215: warning: implicit declaration of function 'strsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/spanel_bmod.c:262: warning: implicit declaration of function 'sgemv_'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sgsrfs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zcopy_to_ucol.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcopy_to_ucol.c: In function 'zcopy_to_ucol':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcopy_to_ucol.c:79: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcopy_to_ucol.c:82: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cpanel_dfs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrs.c: In function 'cgstrs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrs.c:108: warning: function declaration isn't a prototype
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrs.c:188: warning: implicit declaration of function 'ctrsm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrs.c:191: warning: implicit declaration of function 'cgemm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrs.c:93: warning: unused variable 'incy'
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrs.c:93: warning: unused variable 'incx'
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrs.c: In function 'cprint_soln':
scipy/sparse/linalg/dsolve/SuperLU/SRC/cgstrs.c:349: warning: format '%.4f' expects type 'double', but argument 3 has type 'complex'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zgsequ.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/spivotgrowth.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/ssp_blas3.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zlaqgs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_dfs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_dfs.c: In function 'dsnode_dfs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_dfs.c:75: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_dfs.c:88: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_dfs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_dfs.c: In function 'zsnode_dfs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_dfs.c:75: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsnode_dfs.c:88: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dmemory.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrs.c: In function 'zgstrs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrs.c:108: warning: function declaration isn't a prototype
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrs.c:188: warning: implicit declaration of function 'ztrsm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrs.c:191: warning: implicit declaration of function 'zgemm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrs.c:93: warning: unused variable 'incy'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrs.c:93: warning: unused variable 'incx'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrs.c: In function 'zprint_soln':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgstrs.c:351: warning: format '%.4f' expects type 'double', but argument 3 has type 'doublecomplex'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dpanel_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dpanel_bmod.c:31: warning: function declaration isn't a prototype
scipy/sparse/linalg/dsolve/SuperLU/SRC/dpanel_bmod.c: In function 'dpanel_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dpanel_bmod.c:215: warning: implicit declaration of function 'dtrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dpanel_bmod.c:262: warning: implicit declaration of function 'dgemv_'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dsp_blas2.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsp_blas2.c: In function 'sp_dtrsv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsp_blas2.c:150: warning: implicit declaration of function 'dtrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsp_blas2.c:153: warning: implicit declaration of function 'dgemv_'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dpivotgrowth.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_bmod.c: In function 'dsnode_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_bmod.c:95: warning: implicit declaration of function 'dtrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_bmod.c:97: warning: implicit declaration of function 'dgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_bmod.c:50: warning: unused variable 'iptr'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_bmod.c:50: warning: unused variable 'i'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/icmax1.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/slacon.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/creadhb.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/creadhb.c: In function 'creadhb':
scipy/sparse/linalg/dsolve/SuperLU/SRC/creadhb.c:190: warning: unused variable 'key'
scipy/sparse/linalg/dsolve/SuperLU/SRC/creadhb.c: In function 'cReadValues':
scipy/sparse/linalg/dsolve/SuperLU/SRC/creadhb.c:87: warning: 'realpart' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotgrowth.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotgrowth.c: In function 'cPivotGrowth':
scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotgrowth.c:59: warning: unused variable 'temp_comp'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zreadhb.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zreadhb.c: In function 'zreadhb':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zreadhb.c:190: warning: unused variable 'key'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/csp_blas3.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_dfs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_dfs.c: In function 'csnode_dfs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_dfs.c:75: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/csnode_dfs.c:88: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsrfs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sp_preorder.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dcolumn_dfs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcolumn_dfs.c: In function 'dcolumn_dfs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcolumn_dfs.c:128: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcolumn_dfs.c:171: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cgsequ.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dgscon.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zsp_blas2.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsp_blas2.c: In function 'sp_ztrsv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsp_blas2.c:153: warning: implicit declaration of function 'ztrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsp_blas2.c:156: warning: implicit declaration of function 'zgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsp_blas2.c: In function 'sp_zgemv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zsp_blas2.c:476: warning: suggest parentheses around && within ||
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c: In function 'c_div':
scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c:30: warning: implicit declaration of function 'exit'
scipy/sparse/linalg/dsolve/SuperLU/SRC/scomplex.c:30: warning: incompatible implicit declaration of built-in function 'exit'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/heap_relax_snode.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/spanel_dfs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dcomplex.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcomplex.c: In function 'z_div':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcomplex.c:30: warning: implicit declaration of function 'exit'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcomplex.c:30: warning: incompatible implicit declaration of built-in function 'exit'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dgsequ.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/lsame.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/lsame.c: In function 'lsame_':
scipy/sparse/linalg/dsolve/SuperLU/SRC/lsame.c:55: warning: suggest parentheses around && within ||
scipy/sparse/linalg/dsolve/SuperLU/SRC/lsame.c:56: warning: suggest parentheses around && within ||
scipy/sparse/linalg/dsolve/SuperLU/SRC/lsame.c:58: warning: suggest parentheses around && within ||
scipy/sparse/linalg/dsolve/SuperLU/SRC/lsame.c:59: warning: suggest parentheses around && within ||
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zlacon.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zlacon.c: In function 'zlacon_':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zlacon.c:150: warning: implicit declaration of function 'zcopy_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zlacon.c:156: warning: label 'L90' defined but not used
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dlacon.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zpivotL.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zgssvx.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgssvx.c: In function 'zgssvx':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgssvx.c:347: warning: 'smlnum' may be used uninitialized in this function
scipy/sparse/linalg/dsolve/SuperLU/SRC/zgssvx.c:347: warning: 'bignum' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/superlu_timer.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/superlu_timer.c:37: warning: function declaration isn't a prototype
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/slaqgs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrsL.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrsL.c: In function 'dgstrsL':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrsL.c:95: warning: function declaration isn't a prototype
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrsL.c:170: warning: implicit declaration of function 'dtrsm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrsL.c:173: warning: implicit declaration of function 'dgemm_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrsL.c:91: warning: unused variable 'jcol'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrsL.c:88: warning: unused variable 'Uval'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrsL.c:83: warning: unused variable 'incy'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrsL.c:83: warning: unused variable 'incx'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zgsrfs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c: In function 'clacon_':
scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c:150: warning: implicit declaration of function 'ccopy_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/clacon.c:156: warning: label 'L90' defined but not used
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/scsum1.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zpanel_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zpanel_bmod.c:31: warning: function declaration isn't a prototype
scipy/sparse/linalg/dsolve/SuperLU/SRC/zpanel_bmod.c: In function 'zpanel_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zpanel_bmod.c:229: warning: implicit declaration of function 'ztrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zpanel_bmod.c:276: warning: implicit declaration of function 'zgemv_'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dlamch.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dlamch.c: In function 'dlamc2_':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dlamch.c:407: warning: unused variable 'c__1'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dcolumn_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcolumn_bmod.c: In function 'dcolumn_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcolumn_bmod.c:215: warning: implicit declaration of function 'dtrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcolumn_bmod.c:226: warning: implicit declaration of function 'dgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/dcolumn_bmod.c:269: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/ccopy_to_ucol.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccopy_to_ucol.c: In function 'ccopy_to_ucol':
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccopy_to_ucol.c:79: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccopy_to_ucol.c:82: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/slangs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/slangs.c: In function 'slangs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/slangs.c:61: warning: 'value' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/smemory.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dlangs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dlangs.c: In function 'dlangs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dlangs.c:61: warning: 'value' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/memory.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cmemory.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/izmax1.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/izmax1.c: In function 'izmax1_':
scipy/sparse/linalg/dsolve/SuperLU/SRC/izmax1.c:63: warning: implicit declaration of function 'abs'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/ccolumn_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccolumn_bmod.c: In function 'ccolumn_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccolumn_bmod.c:228: warning: implicit declaration of function 'ctrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccolumn_bmod.c:239: warning: implicit declaration of function 'cgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/ccolumn_bmod.c:282: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/ssp_blas2.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssp_blas2.c: In function 'sp_strsv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssp_blas2.c:150: warning: implicit declaration of function 'strsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/ssp_blas2.c:153: warning: implicit declaration of function 'sgemv_'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/spruneL.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sgsequ.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dGetDiagU.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zlangs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zlangs.c: In function 'zlangs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zlangs.c:61: warning: 'value' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrf.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrf.c: In function 'dgstrf':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgstrf.c:185: warning: 'iperm_r' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/get_perm_c.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/get_perm_c.c: In function 'get_perm_c':
scipy/sparse/linalg/dsolve/SuperLU/SRC/get_perm_c.c:366: warning: function declaration isn't a prototype
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zgscon.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dpivotL.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/claqgs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dpanel_dfs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/relax_snode.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dlaqgs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/clangs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/clangs.c: In function 'clangs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/clangs.c:61: warning: 'value' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dgsrfs.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/mmd.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sreadhb.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/sreadhb.c: In function 'sreadhb':
scipy/sparse/linalg/dsolve/SuperLU/SRC/sreadhb.c:180: warning: unused variable 'key'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sgssvx.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgssvx.c: In function 'sgssvx':
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgssvx.c:347: warning: 'smlnum' may be used uninitialized in this function
scipy/sparse/linalg/dsolve/SuperLU/SRC/sgssvx.c:347: warning: 'bignum' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/zcolumn_bmod.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcolumn_bmod.c: In function 'zcolumn_bmod':
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcolumn_bmod.c:230: warning: implicit declaration of function 'ztrsv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcolumn_bmod.c:241: warning: implicit declaration of function 'zgemv_'
scipy/sparse/linalg/dsolve/SuperLU/SRC/zcolumn_bmod.c:284: warning: suggest parentheses around assignment used as truth value
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/util.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/cpivotL.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dgssv.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgssv.c: In function 'dgssv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/dgssv.c:133: warning: 'AA' may be used uninitialized in this function
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sgscon.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/sp_ienv.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/sp_ienv.c: In function 'sp_ienv':
scipy/sparse/linalg/dsolve/SuperLU/SRC/sp_ienv.c:57: warning: implicit declaration of function 'xerbla_'
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/dpruneL.c
gcc: scipy/sparse/linalg/dsolve/SuperLU/SRC/scolumn_dfs.c
scipy/sparse/linalg/dsolve/SuperLU/SRC/scolumn_dfs.c: In function 'scolumn_dfs':
scipy/sparse/linalg/dsolve/SuperLU/SRC/scolumn_dfs.c:128: warning: suggest parentheses around assignment used as truth value
scipy/sparse/linalg/dsolve/SuperLU/SRC/scolumn_dfs.c:171: warning: suggest parentheses around assignment used as truth value
ar: adding 50 object files to build/temp.linux-i686-2.6/libsuperlu_src.a
ar: adding 50 object files to build/temp.linux-i686-2.6/libsuperlu_src.a
ar: adding 23 object files to build/temp.linux-i686-2.6/libsuperlu_src.a
building 'arpack' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/eigen
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/ARPACK
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/ARPACK/SRC
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK
creating build/temp.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/ARPACK/FWRAPPERS
compile options: '-Iscipy/sparse/linalg/eigen/arpack/ARPACK/SRC -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaitr.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaitr.f:210

     &   (ido, bmat, n, k, np, nb, resid, rnorm, v, ldv, h, ldh,        
                                1
Warning: Unused variable nb declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaitr.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaitr.f:210

     &   (ido, bmat, n, k, np, nb, resid, rnorm, v, ldv, h, ldh,        
                                1
Warning: Unused variable nb declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cngets.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cngets.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cngets.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cngets.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cngets.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsortr.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/csortc.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnconv.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnconv.f:73

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnconv.f:73

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnconv.f:73

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnconv.f:73

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaupd.f:417

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaupd.f:417

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaupd.f:417

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaupd.f:417

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseigt.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseigt.f:124

      integer    i, k, msglvl                                           
                 1
Warning: Unused variable i declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseigt.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseigt.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseigt.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseigt.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstatn.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssapps.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssapps.f:139

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssapps.f:139

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssapps.f:139

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssapps.f:139

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snconv.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snconv.f:73

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snconv.f:73

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snconv.f:73

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snconv.f:73

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/slaqrb.f
scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/slaqrb.f: In function 'slaqrb':
scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/slaqrb.f:141: warning: 'i2' may be used uninitialized in this function
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneigh.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaitr.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaitr.f:209

     &   (ido, bmat, n, k, np, nb, resid, rnorm, v, ldv, h, ldh,        
                                1
Warning: Unused variable nb declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneigh.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsapps.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsapps.f:139

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsapps.f:139

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsapps.f:139

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsapps.f:139

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstatn.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstatn.f:24

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dlaqrb.f
scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dlaqrb.f: In function 'dlaqrb':
scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dlaqrb.f:141: warning: 'i2' may be used uninitialized in this function
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssortc.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaup2.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaup2.f:324

   10 continue                                                          
    1
Warning: Label 10 at (1) defined but not used
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaup2.f:809

  130    continue                                                       
    1
Warning: Label 130 at (1) defined but not used
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaup2.f:180

     &   ( ido, bmat, n, which, nev, np, tol, resid, mode, iupd,        
                                                              1
Warning: Unused variable iupd declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaup2.f:189

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaup2.f:189

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneigh.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnapps.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnapps.f:143

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnapps.f:143

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnapps.f:143

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnapps.f:143

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssconv.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssconv.f:66

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssconv.f:66

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssconv.f:66

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssconv.f:66

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cneupd.f:260

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zsortc.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snapps.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snapps.f:152

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snapps.f:152

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snapps.f:152

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snapps.f:152

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssgets.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssgets.f:100

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssgets.f:100

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssgets.f:100

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssgets.f:100

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cgetv0.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cgetv0.f:116

     &   ( ido, bmat, itry, initv, n, j, v, ldv, resid, rnorm,          
                         1
Warning: Unused variable itry declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cgetv0.f:124

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cgetv0.f:124

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnapps.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnapps.f:152

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnapps.f:152

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnapps.f:152

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnapps.f:152

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaitr.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaup2.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaup2.f:324

   10 continue                                                          
    1
Warning: Label 10 at (1) defined but not used
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaup2.f:809

  130    continue                                                       
    1
Warning: Label 130 at (1) defined but not used
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaup2.f:180

     &   ( ido, bmat, n, which, nev, np, tol, resid, mode, iupd,        
                                                              1
Warning: Unused variable iupd declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaup2.f:189

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsaup2.f:189

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsortc.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaupd.f:388

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaupd.f:422

     &           ldh, ldq, levec, mode, msglvl, mxiter, nb,             
                               1
Warning: Unused variable levec declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaupd.f:388

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaupd.f:388

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaupd.f:388

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znapps.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znapps.f:143

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znapps.f:143

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znapps.f:143

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znapps.f:143

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstats.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsesrt.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstqrb.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaupd.f:415

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaupd.f:415

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaupd.f:447

     &           ldh, ldq, levec, mode, msglvl, mxiter, nb,             
                               1
Warning: Unused variable levec declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaupd.f:415

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaupd.f:415

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sneupd.f:313

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cstatn.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zstatn.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zstatn.f:16

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaupd.f:417

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaupd.f:417

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaupd.f:417

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaupd.f:417

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstats.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dstats.f:14

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sngets.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sngets.f:103

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sngets.f:96

     &                    shiftr, shifti )                              
                               1
Warning: Unused variable shiftr declared at (1)
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sngets.f:96

     &                    shiftr, shifti )                              
                                       1
Warning: Unused variable shifti declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sngets.f:103

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sngets.f:103

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sngets.f:103

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dgetv0.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dgetv0.f:120

     &   ( ido, bmat, itry, initv, n, j, v, ldv, resid, rnorm,          
                         1
Warning: Unused variable itry declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dgetv0.f:128

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dgetv0.f:128

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaup2.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaup2.f:322

   10 continue                                                          
    1
Warning: Label 10 at (1) defined but not used
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaup2.f:169

     &   ( ido, bmat, n, which, nev, np, tol, resid, mode, iupd,        
                                                              1
Warning: Unused variable iupd declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaup2.f:178

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaup2.f:178

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaup2.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaup2.f:316

   10 continue                                                          
    1
Warning: Label 10 at (1) defined but not used
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaup2.f:175

     &   ( ido, bmat, n, which, nev, np, tol, resid, mode, iupd,        
                                                              1
Warning: Unused variable iupd declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaup2.f:184

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaup2.f:184

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sgetv0.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sgetv0.f:120

     &   ( ido, bmat, itry, initv, n, j, v, ldv, resid, rnorm,          
                         1
Warning: Unused variable itry declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sgetv0.f:128

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sgetv0.f:128

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneigh.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zneigh.f:108

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dngets.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dngets.f:103

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dngets.f:96

     &                    shiftr, shifti )                              
                               1
Warning: Unused variable shiftr declared at (1)
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dngets.f:96

     &                    shiftr, shifti )                              
                                       1
Warning: Unused variable shifti declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dngets.f:103

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dngets.f:103

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dngets.f:103

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaitr.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaitr.f:209

     &   (ido, bmat, n, k, np, nb, resid, rnorm, v, ldv, h, ldh,        
                                1
Warning: Unused variable nb declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsgets.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsgets.f:100

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsgets.f:100

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsgets.f:100

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsgets.f:100

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssesrt.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssaitr.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseigt.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseigt.f:124

      integer    i, k, msglvl                                           
                 1
Warning: Unused variable i declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseigt.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseigt.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseigt.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseigt.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaup2.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaup2.f:316

   10 continue                                                          
    1
Warning: Label 10 at (1) defined but not used
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaup2.f:175

     &   ( ido, bmat, n, which, nev, np, tol, resid, mode, iupd,        
                                                              1
Warning: Unused variable iupd declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaup2.f:184

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dnaup2.f:184

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/ssortr.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zngets.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zngets.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zngets.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zngets.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zngets.f:95

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaupd.f:388

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaupd.f:422

     &           ldh, ldq, levec, mode, msglvl, mxiter, nb,             
                               1
Warning: Unused variable levec declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaupd.f:388

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaupd.f:388

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/znaupd.f:388

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaupd.f:415

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaupd.f:415

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaupd.f:447

     &           ldh, ldq, levec, mode, msglvl, mxiter, nb,             
                               1
Warning: Unused variable levec declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaupd.f:415

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/snaupd.f:415

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sstqrb.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsconv.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsconv.f:66

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsconv.f:66

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsconv.f:66

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dsconv.f:66

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/sseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zgetv0.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zgetv0.f:116

     &   ( ido, bmat, itry, initv, n, j, v, ldv, resid, rnorm,          
                         1
Warning: Unused variable itry declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zgetv0.f:124

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/zgetv0.f:124

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseupd.f
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                          1
Warning: Unused variable t2 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                  1
Warning: Unused variable t0 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                      1
Warning: Unused variable t1 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                              1
Warning: Unused variable t3 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/dseupd.f:230

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaup2.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaup2.f:322

   10 continue                                                          
    1
Warning: Label 10 at (1) defined but not used
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaup2.f:169

     &   ( ido, bmat, n, which, nev, np, tol, resid, mode, iupd,        
                                                              1
Warning: Unused variable iupd declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaup2.f:178

      real       t0, t1, t2, t3, t4, t5                                 
                                  1
Warning: Unused variable t4 declared at (1)
 In file stat.h:8

     Included at scipy/sparse/linalg/eigen/arpack/ARPACK/SRC/cnaup2.f:178

      real       t0, t1, t2, t3, t4, t5                                 
                                      1
Warning: Unused variable t5 declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/zmout.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/svout.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/ivout.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/second.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/dvout.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/smout.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/iset.f
 In file scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/iset.f:6

      subroutine iset (n, value, array, inc)                            
                                          1
Warning: Unused variable inc declared at (1)
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/iswap.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/icopy.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/icnteq.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/cmout.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/dmout.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/cvout.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/UTIL/zvout.f
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/zlahqr.f
scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/zlahqr.f: In function 'zlahqr':
scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/zlahqr.f:96: warning: 'i2' may be used uninitialized in this function
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/dlahqr.f
scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/dlahqr.f: In function 'dlahqr':
scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/dlahqr.f:102: warning: 'i2' may be used uninitialized in this function
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/clahqr.f
scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/clahqr.f: In function 'clahqr':
scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/clahqr.f:96: warning: 'i2' may be used uninitialized in this function
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/slahqr.f
scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/slahqr.f: In function 'slahqr':
scipy/sparse/linalg/eigen/arpack/ARPACK/LAPACK/slahqr.f:102: warning: 'i2' may be used uninitialized in this function
gfortran:f77: scipy/sparse/linalg/eigen/arpack/ARPACK/FWRAPPERS/dummy.f
ar: adding 50 object files to build/temp.linux-i686-2.6/libarpack.a
ar: adding 37 object files to build/temp.linux-i686-2.6/libarpack.a
building 'sc_c_misc' library
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/special
creating build/temp.linux-i686-2.6/scipy/special/c_misc
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gcc: scipy/special/c_misc/gammaincinv.c
gcc: scipy/special/c_misc/besselpoly.c
gcc: scipy/special/c_misc/fsolve.c
scipy/special/c_misc/fsolve.c: In function 'false_position':
scipy/special/c_misc/fsolve.c:58: warning: 'f3' may be used uninitialized in this function
scipy/special/c_misc/fsolve.c:58: warning: 'x3' may be used uninitialized in this function
ar: adding 3 object files to build/temp.linux-i686-2.6/libsc_c_misc.a
building 'sc_cephes' library
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/special/cephes
compile options: '-I/home/craigb/include/python2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gcc: scipy/special/cephes/tukey.c
gcc: scipy/special/cephes/dawsn.c
gcc: scipy/special/cephes/tandg.c
gcc: scipy/special/cephes/k0.c
gcc: scipy/special/cephes/const.c
gcc: scipy/special/cephes/zetac.c
gcc: scipy/special/cephes/ellie.c
gcc: scipy/special/cephes/i0.c
gcc: scipy/special/cephes/zeta.c
gcc: scipy/special/cephes/kn.c
gcc: scipy/special/cephes/scipy_iv.c
scipy/special/cephes/scipy_iv.c: In function 'cephes_iv':
scipy/special/cephes/scipy_iv.c:90: warning: unused variable 'vp'
scipy/special/cephes/scipy_iv.c: In function 'iv_asymptotic':
scipy/special/cephes/scipy_iv.c:149: warning: unused variable 'mup'
gcc: scipy/special/cephes/mvmpy.c
gcc: scipy/special/cephes/sindg.c
gcc: scipy/special/cephes/exp10.c
gcc: scipy/special/cephes/beta.c
gcc: scipy/special/cephes/incbi.c
gcc: scipy/special/cephes/jv.c
scipy/special/cephes/jv.c: In function 'cephes_jv':
scipy/special/cephes/jv.c:196: warning: label 'underf' defined but not used
gcc: scipy/special/cephes/i1.c
gcc: scipy/special/cephes/stdtr.c
gcc: scipy/special/cephes/polyn.c
gcc: scipy/special/cephes/kolmogorov.c
gcc: scipy/special/cephes/ellpe.c
gcc: scipy/special/cephes/struve.c
gcc: scipy/special/cephes/fresnl.c
gcc: scipy/special/cephes/unity.c
gcc: scipy/special/cephes/incbet.c
gcc: scipy/special/cephes/fabs.c
gcc: scipy/special/cephes/psi.c
gcc: scipy/special/cephes/gamma.c
gcc: scipy/special/cephes/airy.c
gcc: scipy/special/cephes/mtherr.c
gcc: scipy/special/cephes/mmmpy.c
gcc: scipy/special/cephes/igam.c
gcc: scipy/special/cephes/nbdtr.c
gcc: scipy/special/cephes/chbevl.c
gcc: scipy/special/cephes/bdtr.c
gcc: scipy/special/cephes/hyp2f1.c
gcc: scipy/special/cephes/ndtr.c
gcc: scipy/special/cephes/k1.c
gcc: scipy/special/cephes/sici.c
gcc: scipy/special/cephes/setprec.c
gcc: scipy/special/cephes/simq.c
gcc: scipy/special/cephes/fdtr.c
gcc: scipy/special/cephes/polmisc.c
gcc: scipy/special/cephes/yn.c
gcc: scipy/special/cephes/exp2.c
gcc: scipy/special/cephes/rgamma.c
gcc: scipy/special/cephes/spence.c
gcc: scipy/special/cephes/round.c
gcc: scipy/special/cephes/ndtri.c
gcc: scipy/special/cephes/gdtr.c
gcc: scipy/special/cephes/sincos.c
gcc: scipy/special/cephes/j0.c
gcc: scipy/special/cephes/shichi.c
gcc: scipy/special/cephes/pdtr.c
gcc: scipy/special/cephes/btdtr.c
gcc: scipy/special/cephes/cbrt.c
gcc: scipy/special/cephes/hyperg.c
gcc: scipy/special/cephes/polevl.c
gcc: scipy/special/cephes/simpsn.c
gcc: scipy/special/cephes/igami.c
gcc: scipy/special/cephes/cpmul.c
gcc: scipy/special/cephes/mtransp.c
gcc: scipy/special/cephes/ellpk.c
gcc: scipy/special/cephes/isnan.c
/local/scratch/lib/python2.6/site-packages/numpy/core/include/numpy/__multiarray_api.h:969: warning: '_import_array' defined but not used
gcc: scipy/special/cephes/gels.c
gcc: scipy/special/cephes/ellpj.c
gcc: scipy/special/cephes/ellik.c
gcc: scipy/special/cephes/expn.c
gcc: scipy/special/cephes/powi.c
gcc: scipy/special/cephes/chdtr.c
gcc: scipy/special/cephes/j1.c
gcc: scipy/special/cephes/euclid.c
scipy/special/cephes/euclid.c:55: warning: 'radd' defined but not used
scipy/special/cephes/euclid.c:90: warning: 'rsub' defined but not used
scipy/special/cephes/euclid.c:124: warning: 'rmul' defined but not used
scipy/special/cephes/euclid.c:157: warning: 'rdiv' defined but not used
gcc: scipy/special/cephes/polrt.c
scipy/special/cephes/polrt.c: In function 'polrt':
scipy/special/cephes/polrt.c:71: warning: 'xsav.r' may be used uninitialized in this function
scipy/special/cephes/polrt.c:71: warning: 'xsav.i' may be used uninitialized in this function
ar: adding 50 object files to build/temp.linux-i686-2.6/libsc_cephes.a
ar: adding 24 object files to build/temp.linux-i686-2.6/libsc_cephes.a
building 'sc_mach' library
using additional config_fc from setup script for fortran compiler: {'noopt': ('scipy/special/setup.pyc', 1)}
customize Gnu95FCompiler
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC
creating build/temp.linux-i686-2.6/scipy/special/mach
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/special/mach/i1mach.f
gfortran:f77: scipy/special/mach/d1mach.f
gfortran:f77: scipy/special/mach/xerror.f
 In file scipy/special/mach/xerror.f:1

      SUBROUTINE XERROR(MESS,NMESS,L1,L2)                               
                                       1
Warning: Unused variable l2 declared at (1)
 In file scipy/special/mach/xerror.f:1

      SUBROUTINE XERROR(MESS,NMESS,L1,L2)                               
                                    1
Warning: Unused variable l1 declared at (1)
gfortran:f77: scipy/special/mach/r1mach.f
ar: adding 4 object files to build/temp.linux-i686-2.6/libsc_mach.a
building 'sc_toms' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/special/amos
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/special/amos/zshch.f
gfortran:f77: scipy/special/amos/zunhj.f
gfortran:f77: scipy/special/amos/zacai.f
gfortran:f77: scipy/special/amos/zs1s2.f
gfortran:f77: scipy/special/amos/zasyi.f
gfortran:f77: scipy/special/amos/zbesi.f
gfortran:f77: scipy/special/amos/zuni2.f
scipy/special/amos/zuni2.f: In function 'zuni2':
scipy/special/amos/zuni2.f:27: warning: 'iflag' may be used uninitialized in this function
gfortran:f77: scipy/special/amos/dgamln.f
scipy/special/amos/dgamln.f: In function 'dgamln':
scipy/special/amos/dgamln.f:41: warning: 'nz' may be used uninitialized in this function
scipy/special/amos/dgamln.f:138: warning: '__result_dgamln' may be used uninitialized in this function
gfortran:f77: scipy/special/amos/zbunk.f
gfortran:f77: scipy/special/amos/zexp.f
gfortran:f77: scipy/special/amos/zseri.f
scipy/special/amos/zseri.f: In function 'zseri':
scipy/special/amos/zseri.f:19: warning: 'ss' may be used uninitialized in this function
gfortran:f77: scipy/special/amos/zbesy.f
gfortran:f77: scipy/special/amos/zsqrt.f
gfortran:f77: scipy/special/amos/zacon.f
scipy/special/amos/zacon.f: In function 'zacon':
scipy/special/amos/zacon.f:22: warning: 'sc2r' may be used uninitialized in this function
scipy/special/amos/zacon.f:22: warning: 'sc2i' may be used uninitialized in this function
gfortran:f77: scipy/special/amos/dsclmr.f
gfortran:f77: scipy/special/amos/zbesh.f
gfortran:f77: scipy/special/amos/zunk1.f
scipy/special/amos/zunk1.f: In function 'zunk1':
scipy/special/amos/zunk1.f:23: warning: 'kflag' may be used uninitialized in this function
scipy/special/amos/zunk1.f:23: warning: 'iflag' may be used uninitialized in this function
gfortran:f77: scipy/special/amos/zairy.f
gfortran:f77: scipy/special/amos/zbesj.f
gfortran:f77: scipy/special/amos/zwrsk.f
gfortran:f77: scipy/special/amos/zmlri.f
gfortran:f77: scipy/special/amos/zbinu.f
gfortran:f77: scipy/special/amos/zbknu.f
scipy/special/amos/zbknu.f: In function 'zbknu':
scipy/special/amos/zbknu.f:15: warning: 'dnu2' may be used uninitialized in this function
scipy/special/amos/zbknu.f:13: warning: 'ckr' may be used uninitialized in this function
scipy/special/amos/zbknu.f:13: warning: 'cki' may be used uninitialized in this function
gfortran:f77: scipy/special/amos/zuni1.f
scipy/special/amos/zuni1.f: In function 'zuni1':
scipy/special/amos/zuni1.f:24: warning: 'iflag' may be used uninitialized in this function
gfortran:f77: scipy/special/amos/zuoik.f
scipy/special/amos/zuoik.f: In function 'zuoik':
scipy/special/amos/zuoik.f:30: warning: 'aarg' may be used uninitialized in this function
gfortran:f77: scipy/special/amos/fdump.f
gfortran:f77: scipy/special/amos/zunik.f
gfortran:f77: scipy/special/amos/zkscl.f
gfortran:f77: scipy/special/amos/zbuni.f
gfortran:f77: scipy/special/amos/zbesk.f
gfortran:f77: scipy/special/amos/zunk2.f
scipy/special/amos/zunk2.f: In function 'zunk2':
scipy/special/amos/zunk2.f:30: warning: 'kflag' may be used uninitialized in this function
scipy/special/amos/zunk2.f:30: warning: 'iflag' may be used uninitialized in this function
gfortran:f77: scipy/special/amos/zdiv.f
gfortran:f77: scipy/special/amos/zmlt.f
gfortran:f77: scipy/special/amos/zbiry.f
gfortran:f77: scipy/special/amos/zrati.f
gfortran:f77: scipy/special/amos/zlog.f
gfortran:f77: scipy/special/amos/zabs.f
gfortran:f77: scipy/special/amos/zuchk.f
ar: adding 38 object files to build/temp.linux-i686-2.6/libsc_toms.a
building 'sc_amos' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/special/toms
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/special/toms/wofz.f
scipy/special/toms/wofz.f: In function 'wofz':
scipy/special/toms/wofz.f:136: warning: 'h2' may be used uninitialized in this function
scipy/special/toms/wofz.f:143: warning: 'qlambda' may be used uninitialized in this function
scipy/special/toms/wofz.f:107: warning: 'v2' may be used uninitialized in this function
scipy/special/toms/wofz.f:106: warning: 'u2' may be used uninitialized in this function
ar: adding 1 object files to build/temp.linux-i686-2.6/libsc_amos.a
building 'sc_cdf' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/special/cdflib
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/special/cdflib/erf.f
gfortran:f77: scipy/special/cdflib/rlog1.f
gfortran:f77: scipy/special/cdflib/gamma_fort.f
scipy/special/cdflib/gamma_fort.f: In function 'gamma':
scipy/special/cdflib/gamma_fort.f:20: warning: 's' may be used uninitialized in this function
gfortran:f77: scipy/special/cdflib/gamln1.f
gfortran:f77: scipy/special/cdflib/cumbet.f
gfortran:f77: scipy/special/cdflib/ipmpar.f
gfortran:f77: scipy/special/cdflib/cumgam.f
gfortran:f77: scipy/special/cdflib/gam1.f
gfortran:f77: scipy/special/cdflib/bfrac.f
gfortran:f77: scipy/special/cdflib/dt1.f
gfortran:f77: scipy/special/cdflib/bgrat.f
gfortran:f77: scipy/special/cdflib/exparg.f
gfortran:f77: scipy/special/cdflib/cdfchi.f
scipy/special/cdflib/cdfchi.f: In function 'cdfchi':
scipy/special/cdflib/cdfchi.f:177: warning: 'porq' is used uninitialized in this function
gfortran:f77: scipy/special/cdflib/cdffnc.f
gfortran:f77: scipy/special/cdflib/cumchi.f
gfortran:f77: scipy/special/cdflib/cumt.f
gfortran:f77: scipy/special/cdflib/alnrel.f
gfortran:f77: scipy/special/cdflib/cdfbet.f
gfortran:f77: scipy/special/cdflib/gamln.f
gfortran:f77: scipy/special/cdflib/bratio.f
gfortran:f77: scipy/special/cdflib/cdfbin.f
gfortran:f77: scipy/special/cdflib/brcomp.f
gfortran:f77: scipy/special/cdflib/brcmp1.f
gfortran:f77: scipy/special/cdflib/psi_fort.f
gfortran:f77: scipy/special/cdflib/bcorr.f
gfortran:f77: scipy/special/cdflib/cumpoi.f
gfortran:f77: scipy/special/cdflib/alngam.f
gfortran:f77: scipy/special/cdflib/cumnbn.f
gfortran:f77: scipy/special/cdflib/fpser.f
gfortran:f77: scipy/special/cdflib/dinvr.f
 In file scipy/special/cdflib/dinvr.f:99

      ASSIGN 10 TO i99999                                               
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/special/cdflib/dinvr.f:105

      ASSIGN 20 TO i99999                                               
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/special/cdflib/dinvr.f:142

      ASSIGN 90 TO i99999                                               
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/special/cdflib/dinvr.f:167

      ASSIGN 130 TO i99999                                              
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/special/cdflib/dinvr.f:202

      ASSIGN 200 TO i99999                                              
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/special/cdflib/dinvr.f:237

      ASSIGN 270 TO i99999                                              
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/special/cdflib/dinvr.f:346

      GO TO i99999                                                      
                 1
Warning: Obsolete: Assigned GOTO statement at (1)
 In file scipy/special/cdflib/dinvr.f:102

   10 fsmall = fx                                                       
    1
Warning: Label 10 at (1) defined but not used
 In file scipy/special/cdflib/dinvr.f:108

   20 fbig = fx                                                         
    1
Warning: Label 20 at (1) defined but not used
 In file scipy/special/cdflib/dinvr.f:145

   90 yy = fx                                                           
    1
Warning: Label 90 at (1) defined but not used
 In file scipy/special/cdflib/dinvr.f:170

  130 yy = fx                                                           
    1
Warning: Label 130 at (1) defined but not used
 In file scipy/special/cdflib/dinvr.f:205

  200 yy = fx                                                           
    1
Warning: Label 200 at (1) defined but not used
 In file scipy/special/cdflib/dinvr.f:240

  270 CONTINUE                                                          
    1
Warning: Label 270 at (1) defined but not used
gfortran:f77: scipy/special/cdflib/cumfnc.f
gfortran:f77: scipy/special/cdflib/cumnor.f
gfortran:f77: scipy/special/cdflib/cdfchn.f
gfortran:f77: scipy/special/cdflib/dzror.f
 In file scipy/special/cdflib/dzror.f:92

      ASSIGN 10 TO i99999                                               
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/special/cdflib/dzror.f:100

      ASSIGN 20 TO i99999                                               
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/special/cdflib/dzror.f:181

      ASSIGN 200 TO i99999                                              
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/special/cdflib/dzror.f:281

      GO TO i99999                                                      
                 1
Warning: Obsolete: Assigned GOTO statement at (1)
 In file scipy/special/cdflib/dzror.f:95

   10 fb = fx                                                           
    1
Warning: Label 10 at (1) defined but not used
 In file scipy/special/cdflib/dzror.f:106

   20 IF (.NOT. (fb.LT.0.0D0)) GO TO 40                                 
    1
Warning: Label 20 at (1) defined but not used
 In file scipy/special/cdflib/dzror.f:184

  200 fb = fx                                                           
    1
Warning: Label 200 at (1) defined but not used
gfortran:f77: scipy/special/cdflib/cdft.f
gfortran:f77: scipy/special/cdflib/rlog.f
gfortran:f77: scipy/special/cdflib/devlpl.f
gfortran:f77: scipy/special/cdflib/spmpar.f
gfortran:f77: scipy/special/cdflib/algdiv.f
gfortran:f77: scipy/special/cdflib/stvaln.f
gfortran:f77: scipy/special/cdflib/cumf.f
gfortran:f77: scipy/special/cdflib/cdfpoi.f
gfortran:f77: scipy/special/cdflib/cdfnor.f
gfortran:f77: scipy/special/cdflib/rexp.f
gfortran:f77: scipy/special/cdflib/cdff.f
gfortran:f77: scipy/special/cdflib/cumchn.f
gfortran:f77: scipy/special/cdflib/bup.f
gfortran:f77: scipy/special/cdflib/cumbin.f
gfortran:f77: scipy/special/cdflib/gsumln.f
gfortran:f77: scipy/special/cdflib/gratio.f
gfortran:f77: scipy/special/cdflib/betaln.f
gfortran:f77: scipy/special/cdflib/apser.f
gfortran:f77: scipy/special/cdflib/cdftnc.f
gfortran:f77: scipy/special/cdflib/basym.f
gfortran:f77: scipy/special/cdflib/esum.f
gfortran:f77: scipy/special/cdflib/cdfnbn.f
gfortran:f77: scipy/special/cdflib/rcomp.f
gfortran:f77: scipy/special/cdflib/grat1.f
gfortran:f77: scipy/special/cdflib/gaminv.f
scipy/special/cdflib/gaminv.f: In function 'gaminv':
scipy/special/cdflib/gaminv.f:58: warning: 'b' may be used uninitialized in this function
gfortran:f77: scipy/special/cdflib/cumtnc.f
gfortran:f77: scipy/special/cdflib/erfc1.f
gfortran:f77: scipy/special/cdflib/cdfgam.f
gfortran:f77: scipy/special/cdflib/dinvnr.f
gfortran:f77: scipy/special/cdflib/bpser.f
ar: adding 50 object files to build/temp.linux-i686-2.6/libsc_cdf.a
ar: adding 14 object files to build/temp.linux-i686-2.6/libsc_cdf.a
building 'sc_specfun' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/special/specfun
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/special/specfun/specfun.f
scipy/special/specfun/specfun.f: In function 'stvhv':
scipy/special/specfun/specfun.f:12832: warning: 'bjv' may be used uninitialized in this function
'IMAGPART_EXPR <cw>scipy/special/specfun/specfun.f: In function 'cik01':
scipy/special/specfun/specfun.f:12519: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cw>scipy/special/specfun/specfun.f:12519: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cqf0>scipy/special/specfun/specfun.f: In function 'clqmn':
scipy/special/specfun/specfun.f:11981: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cqf0>scipy/special/specfun/specfun.f:11981: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cqf0>scipy/special/specfun/specfun.f: In function 'clqn':
scipy/special/specfun/specfun.f:2228: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cqf0>scipy/special/specfun/specfun.f:2228: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cf>scipy/special/specfun/specfun.f: In function 'cikna':
scipy/special/specfun/specfun.f:12280: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cf>scipy/special/specfun/specfun.f:12280: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cf>scipy/special/specfun/specfun.f: In function 'ciknb':
scipy/special/specfun/specfun.f:12170: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cf>scipy/special/specfun/specfun.f:12170: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cf>scipy/special/specfun/specfun.f: In function 'cikva':
scipy/special/specfun/specfun.f:11217: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cf>scipy/special/specfun/specfun.f:11217: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cf>scipy/special/specfun/specfun.f: In function 'cikvb':
scipy/special/specfun/specfun.f:11056: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cf>scipy/special/specfun/specfun.f:11056: warning: ' may be used uninitialized in this function
scipy/special/specfun/specfun.f: In function 'chgul':
scipy/special/specfun/specfun.f:9145: warning: 'r0' may be used uninitialized in this function
'IMAGPART_EXPR <cf>scipy/special/specfun/specfun.f: In function 'csphik':
scipy/special/specfun/specfun.f:10024: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cf>scipy/special/specfun/specfun.f:10024: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cs>scipy/special/specfun/specfun.f:10028: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cs>scipy/special/specfun/specfun.f:10028: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cf>scipy/special/specfun/specfun.f: In function 'cjynb':
scipy/special/specfun/specfun.f:6712: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cf>scipy/special/specfun/specfun.f:6712: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cf>scipy/special/specfun/specfun.f: In function 'cjyna':
scipy/special/specfun/specfun.f:6576: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cf>scipy/special/specfun/specfun.f:6576: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <ch0>scipy/special/specfun/specfun.f:6611: warning: ' may be used uninitialized in this function
'REALPART_EXPR <ch0>scipy/special/specfun/specfun.f:6611: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <zw>scipy/special/specfun/specfun.f: In function 'hygfz':
scipy/special/specfun/specfun.f:6118: warning: ' may be used uninitialized in this function
'REALPART_EXPR <zw>scipy/special/specfun/specfun.f:6118: warning: ' may be used uninitialized in this function
scipy/special/specfun/specfun.f:6093: warning: 'k' may be used uninitialized in this function
'IMAGPART_EXPR <chw>scipy/special/specfun/specfun.f: In function 'cchg':
scipy/special/specfun/specfun.f:5979: warning: ' may be used uninitialized in this function
'REALPART_EXPR <chw>scipy/special/specfun/specfun.f:5979: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cy1>scipy/special/specfun/specfun.f:6014: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cy1>scipy/special/specfun/specfun.f:6014: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cy0>scipy/special/specfun/specfun.f:6013: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cy0>scipy/special/specfun/specfun.f:6013: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cfi>scipy/special/specfun/specfun.f: In function 'ciklv':
scipy/special/specfun/specfun.f:5383: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cfi>scipy/special/specfun/specfun.f:5383: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cfk>scipy/special/specfun/specfun.f:5388: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cfk>scipy/special/specfun/specfun.f:5388: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cyv0>scipy/special/specfun/specfun.f: In function 'cjyvb':
scipy/special/specfun/specfun.f:3623: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cyv0>scipy/special/specfun/specfun.f:3623: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cf>scipy/special/specfun/specfun.f:3661: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cf>scipy/special/specfun/specfun.f:3661: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cjv0>scipy/special/specfun/specfun.f: In function 'cjyva':
scipy/special/specfun/specfun.f:3347: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cjv0>scipy/special/specfun/specfun.f:3347: warning: ' may be used uninitialized in this function
scipy/special/specfun/specfun.f:3347: warning: 'cjv0' may be used uninitialized in this function
'IMAGPART_EXPR <cyv0>scipy/special/specfun/specfun.f:3375: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cyv0>scipy/special/specfun/specfun.f:3375: warning: ' may be used uninitialized in this function
scipy/special/specfun/specfun.f:3375: warning: 'cyv0' may be used uninitialized in this function
'IMAGPART_EXPR <cyv1>scipy/special/specfun/specfun.f:3378: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cyv1>scipy/special/specfun/specfun.f:3378: warning: ' may be used uninitialized in this function
scipy/special/specfun/specfun.f:3378: warning: 'cyv1' may be used uninitialized in this function
'IMAGPART_EXPR <cju0>scipy/special/specfun/specfun.f:3396: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cju0>scipy/special/specfun/specfun.f:3396: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cf>scipy/special/specfun/specfun.f:3442: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cf>scipy/special/specfun/specfun.f:3442: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cs>scipy/special/specfun/specfun.f:3460: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cs>scipy/special/specfun/specfun.f:3460: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <ch0>scipy/special/specfun/specfun.f:3490: warning: ' may be used uninitialized in this function
'REALPART_EXPR <ch0>scipy/special/specfun/specfun.f:3490: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cfy>scipy/special/specfun/specfun.f: In function 'cjylv':
scipy/special/specfun/specfun.f:1437: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cfy>scipy/special/specfun/specfun.f:1437: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cfj>scipy/special/specfun/specfun.f:1432: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cfj>scipy/special/specfun/specfun.f:1432: warning: ' may be used uninitialized in this function
'IMAGPART_EXPR <cs>scipy/special/specfun/specfun.f: In function 'csphjy':
scipy/special/specfun/specfun.f:1147: warning: ' may be used uninitialized in this function
'REALPART_EXPR <cs>scipy/special/specfun/specfun.f:1147: warning: ' may be used uninitialized in this function
ar: adding 1 object files to build/temp.linux-i686-2.6/libsc_specfun.a
building 'statlib' library
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/stats
creating build/temp.linux-i686-2.6/scipy/stats/statlib
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -c'
gfortran:f77: scipy/stats/statlib/swilk.f
gfortran:f77: scipy/stats/statlib/ansari.f
 In file scipy/stats/statlib/ansari.f:73

       IF (MM1) 1, 2, 3                                                 
                                                                       1
Warning: Obsolete: arithmetic IF statement at (1)
gfortran:f77: scipy/stats/statlib/spearman.f
 In file scipy/stats/statlib/spearman.f:12

      double precision zero, one, two, b, x, y, z, u, six,              
                                                1
Warning: Unused variable z declared at (1)
ar: adding 3 object files to build/temp.linux-i686-2.6/libstatlib.a
running build_ext
customize UnixCCompiler
customize UnixCCompiler using build_ext
resetting extension 'scipy.integrate._odepack' language from 'c' to 'f77'.
resetting extension 'scipy.integrate.vode' language from 'c' to 'f77'.
resetting extension 'scipy.lib.blas.fblas' language from 'c' to 'f77'.
resetting extension 'scipy.odr.__odrpack' language from 'c' to 'f77'.
extending extension 'scipy.sparse.linalg.dsolve._zsuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)]
extending extension 'scipy.sparse.linalg.dsolve._dsuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)]
extending extension 'scipy.sparse.linalg.dsolve._csuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)]
extending extension 'scipy.sparse.linalg.dsolve._ssuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)]
customize UnixCCompiler
customize UnixCCompiler using build_ext
customize Gnu95FCompiler
customize Gnu95FCompiler using build_ext
building 'scipy.cluster._vq' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/cluster
creating build/temp.linux-i686-2.6/scipy/cluster/src
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/cluster/src/vq_module.c
gcc: scipy/cluster/src/vq.c
/local/scratch/lib/python2.6/site-packages/numpy/core/include/numpy/__multiarray_api.h:969: warning: '_import_array' defined but not used
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/cluster/src/vq_module.o build/temp.linux-i686-2.6/scipy/cluster/src/vq.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/cluster/_vq.so
building 'scipy.cluster._hierarchy_wrap' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/cluster/src/hierarchy.c
gcc: scipy/cluster/src/hierarchy_wrap.c
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/cluster/src/hierarchy_wrap.o build/temp.linux-i686-2.6/scipy/cluster/src/hierarchy.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/cluster/_hierarchy_wrap.so
building 'scipy.fftpack._fftpack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/fftpack
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/fortranobject.c
gcc: build/src.linux-i686-2.6/scipy/fftpack/_fftpackmodule.c
gcc: scipy/fftpack/src/zfft.c
gcc: scipy/fftpack/src/zfftnd.c
gcc: scipy/fftpack/src/drfft.c
gcc: scipy/fftpack/src/zrfft.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/fftpack/_fftpackmodule.o build/temp.linux-i686-2.6/scipy/fftpack/src/zfft.o build/temp.linux-i686-2.6/scipy/fftpack/src/drfft.o build/temp.linux-i686-2.6/scipy/fftpack/src/zrfft.o build/temp.linux-i686-2.6/scipy/fftpack/src/zfftnd.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -Lbuild/temp.linux-i686-2.6 -ldfftpack -lgfortran -o build/lib.linux-i686-2.6/scipy/fftpack/_fftpack.so
building 'scipy.fftpack.convolve' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/fftpack/src/convolve.c
gcc: build/src.linux-i686-2.6/scipy/fftpack/convolvemodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/fftpack/convolvemodule.o build/temp.linux-i686-2.6/scipy/fftpack/src/convolve.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -Lbuild/temp.linux-i686-2.6 -ldfftpack -lgfortran -o build/lib.linux-i686-2.6/scipy/fftpack/convolve.so
building 'scipy.integrate._quadpack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/integrate/_quadpackmodule.c
In file included from scipy/integrate/_quadpackmodule.c:6:
scipy/integrate/__quadpack.h:40: warning: function declaration isn't a prototype
scipy/integrate/__quadpack.h:41: warning: function declaration isn't a prototype
scipy/integrate/__quadpack.h:42: warning: function declaration isn't a prototype
scipy/integrate/__quadpack.h:43: warning: function declaration isn't a prototype
scipy/integrate/__quadpack.h:44: warning: function declaration isn't a prototype
scipy/integrate/__quadpack.h:45: warning: function declaration isn't a prototype
scipy/integrate/__quadpack.h:46: warning: function declaration isn't a prototype
scipy/integrate/__quadpack.h: In function 'quad_function':
scipy/integrate/__quadpack.h:60: warning: unused variable 'nb'
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/integrate/_quadpackmodule.o -Lbuild/temp.linux-i686-2.6 -lquadpack -llinpack_lite -lmach -lgfortran -o build/lib.linux-i686-2.6/scipy/integrate/_quadpack.so
building 'scipy.integrate._odepack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/integrate/_odepackmodule.c
In file included from scipy/integrate/_odepackmodule.c:6:
scipy/integrate/__odepack.h:23: warning: function declaration isn't a prototype
scipy/integrate/multipack.h:114: warning: 'my_make_numpy_array' defined but not used
scipy/integrate/__odepack.h: In function 'odepack_odeint':
scipy/integrate/__odepack.h:218: warning: 'tcrit' may be used uninitialized in this function
scipy/integrate/__odepack.h:219: warning: 'wa' may be used uninitialized in this function
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/integrate/_odepackmodule.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -lodepack -llinpack_lite -lmach -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/integrate/_odepack.so
building 'scipy.integrate.vode' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/integrate
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/integrate/vodemodule.c
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:306: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:307: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c: In function 'cb_f_in_dvode__user__routines':
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:331: warning: unused variable 'ipar'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:330: warning: unused variable 'rpar'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c: In function 'cb_jac_in_dvode__user__routines':
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:463: warning: unused variable 'ipar'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:462: warning: unused variable 'rpar'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:460: warning: unused variable 'mu'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:459: warning: unused variable 'ml'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c: In function 'cb_f_in_zvode__user__routines':
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:591: warning: unused variable 'ipar'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:590: warning: unused variable 'rpar'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c: In function 'cb_jac_in_zvode__user__routines':
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:723: warning: unused variable 'ipar'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:722: warning: unused variable 'rpar'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:720: warning: unused variable 'mu'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:719: warning: unused variable 'ml'
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c: At top level:
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:879: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/scipy/integrate/vodemodule.c:1208: warning: function declaration isn't a prototype
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/integrate/vodemodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -lodepack -llinpack_lite -lmach -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/integrate/vode.so
building 'scipy.interpolate._fitpack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/interpolate/src
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/interpolate/src/_fitpackmodule.c
scipy/interpolate/src/multipack.h:114: warning: 'my_make_numpy_array' defined but not used
scipy/interpolate/src/multipack.h:134: warning: 'call_python_function' defined but not used
scipy/interpolate/src/__fitpack.h: In function '_bspldismat':
scipy/interpolate/src/__fitpack.h:988: warning: 'dx' may be used uninitialized in this function
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/interpolate/src/_fitpackmodule.o -Lbuild/temp.linux-i686-2.6 -lfitpack -lgfortran -o build/lib.linux-i686-2.6/scipy/interpolate/_fitpack.so
building 'scipy.interpolate.dfitpack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/interpolate
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/interpolate/src
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/interpolate/src/dfitpackmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: build/src.linux-i686-2.6/scipy/interpolate/src/dfitpack-f2pywrappers.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/interpolate/src/dfitpackmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/interpolate/src/dfitpack-f2pywrappers.o -Lbuild/temp.linux-i686-2.6 -lfitpack -lgfortran -o build/lib.linux-i686-2.6/scipy/interpolate/dfitpack.so
building 'scipy.interpolate._interpolate' extension
compiling C++ sources
C compiler: g++ -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -fPIC

compile options: '-Iscipy/interpolate/src -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
g++: scipy/interpolate/src/_interpolate.cpp
g++ -pthread -shared build/temp.linux-i686-2.6/scipy/interpolate/src/_interpolate.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/interpolate/_interpolate.so
building 'scipy.io.numpyio' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/io
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/io/numpyiomodule.c
scipy/io/numpyiomodule.c: In function 'numpyio_fromfile':
scipy/io/numpyiomodule.c:85: warning: 'castfunc' may be used uninitialized in this function
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/io/numpyiomodule.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/io/numpyio.so
building 'scipy.lib.blas.fblas' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/fblasmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: build/src.linux-i686-2.6/scipy/lib/blas/fblaswrap.f
gfortran:f77: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/fblas-f2pywrappers.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/fblasmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/fblaswrap.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/fblas-f2pywrappers.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/lib/blas/fblas.so
building 'scipy.lib.blas.cblas' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/cblasmodule.c
gcc -pthread -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/cblasmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -lf77blas -lcblas -latlas -o build/lib.linux-i686-2.6/scipy/lib/blas/cblas.so
building 'scipy.lib.lapack.flapack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack/flapackmodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack/flapackmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/lib/lapack/flapack.so
building 'scipy.lib.lapack.clapack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack/clapackmodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack/clapackmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/lib/lapack/clapack.so
building 'scipy.lib.lapack.calc_lwork' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/lib/lapack/calc_lworkmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/lib
creating build/temp.linux-i686-2.6/scipy/lib/lapack
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/lib/lapack/calc_lwork.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/lapack/calc_lworkmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/lib/lapack/calc_lwork.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/lib/lapack/calc_lwork.so
building 'scipy.lib.lapack.atlas_version' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/lib/lapack/atlas_version.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/lib/lapack/atlas_version.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/lib/lapack/atlas_version.so
building 'scipy.linalg.fblas' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/fblasmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/linalg
creating build/temp.linux-i686-2.6/scipy/linalg/src
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/linalg/src/fblaswrap.f
gfortran:f77: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/fblas-f2pywrappers.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/fblasmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/linalg/src/fblaswrap.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/fblas-f2pywrappers.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/linalg/fblas.so
building 'scipy.linalg.cblas' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/cblasmodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/cblasmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/linalg/cblas.so
building 'scipy.linalg.flapack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:543: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:544: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:549: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:550: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:551: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:552: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c: In function 'f2py_rout_flapack_cheev':
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:12222: warning: passing argument 6 of 'f2py_func' from incompatible pointer type
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c: In function 'f2py_rout_flapack_zheev':
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:12406: warning: passing argument 6 of 'f2py_func' from incompatible pointer type
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c: At top level:
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:20222: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:20558: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:21505: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:21698: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:21891: warning: function declaration isn't a prototype
build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.c:22084: warning: function declaration isn't a prototype
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapack-f2pywrappers.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapackmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapack-f2pywrappers.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/linalg/flapack.so
building 'scipy.linalg.clapack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/clapackmodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/clapackmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/linalg/clapack.so
building 'scipy.linalg._flinalg' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/linalg/_flinalgmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/linalg/src/det.f
gfortran:f77: scipy/linalg/src/lu.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/_flinalgmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/linalg/src/det.o build/temp.linux-i686-2.6/scipy/linalg/src/lu.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/linalg/_flinalg.so
building 'scipy.linalg.calc_lwork' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/linalg/calc_lworkmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/linalg/src/calc_lwork.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/calc_lworkmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/linalg/src/calc_lwork.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/linalg/calc_lwork.so
building 'scipy.linalg.atlas_version' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/linalg/atlas_version.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/linalg/atlas_version.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/linalg/atlas_version.so
building 'scipy.odr.__odrpack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -Iscipy/odr -I/local/scratch/include/atlas -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/odr/__odrpack.c
scipy/odr/__odrpack.c:1324: warning: 'check_args' defined but not used
scipy/odr/__odrpack.c: In function 'fcn_callback':
scipy/odr/__odrpack.c:47: warning: 'result' may be used uninitialized in this function
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/odr/__odrpack.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -lodrpack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/odr/__odrpack.so
building 'scipy.optimize._minpack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/optimize/_minpackmodule.c
In file included from scipy/optimize/_minpackmodule.c:7:
scipy/optimize/__minpack.h: In function 'minpack_hybrd':
scipy/optimize/__minpack.h:231: warning: unused variable 'store_multipack_globals3'
scipy/optimize/__minpack.h: In function 'minpack_lmdif':
scipy/optimize/__minpack.h:434: warning: unused variable 'store_multipack_globals3'
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/optimize/_minpackmodule.o -Lbuild/temp.linux-i686-2.6 -lminpack -lgfortran -o build/lib.linux-i686-2.6/scipy/optimize/_minpack.so
building 'scipy.optimize._zeros' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/optimize/zeros.c
scipy/optimize/Zeros/zeros.h:16: warning: 'dminarg1' defined but not used
scipy/optimize/Zeros/zeros.h:16: warning: 'dminarg2' defined but not used
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/optimize/zeros.o -Lbuild/temp.linux-i686-2.6 -lrootfind -o build/lib.linux-i686-2.6/scipy/optimize/_zeros.so
building 'scipy.optimize._lbfgsb' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/lbfgsb
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/optimize/lbfgsb/_lbfgsbmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/optimize/lbfgsb
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/optimize/lbfgsb/routines.f
 In file scipy/optimize/lbfgsb/routines.f:258

     +                  sgo, yg, ygo, index, iwhere, indx2, task,       
                          1
Warning: Unused variable sgo declared at (1)
 In file scipy/optimize/lbfgsb/routines.f:258

     +                  sgo, yg, ygo, index, iwhere, indx2, task,       
                                   1
Warning: Unused variable ygo declared at (1)
 In file scipy/optimize/lbfgsb/routines.f:257

     +                  sy, ss, yy, wt, wn, snd, z, r, d, t, wa, sg,    
                                 1
Warning: Unused variable yy declared at (1)
 In file scipy/optimize/lbfgsb/routines.f:1176

     +                  v, nint, sg, yg, iprint, sbgnrm, info, epsmch)  
                                  1
Warning: Unused variable sg declared at (1)
 In file scipy/optimize/lbfgsb/routines.f:1176

     +                  v, nint, sg, yg, iprint, sbgnrm, info, epsmch)  
                                      1
Warning: Unused variable yg declared at (1)
 In file scipy/optimize/lbfgsb/routines.f:2856

 3006 format (i5,2(1x,i4),2(1x,i6),(1x,i4),(1x,i5),7x,'-',10x,'-')      
    1
Warning: Label 3006 at (1) defined but not used
scipy/optimize/lbfgsb/routines.f: In function 'subsm':
scipy/optimize/lbfgsb/routines.f:3097: warning: 'ibd' may be used uninitialized in this function
scipy/optimize/lbfgsb/routines.f: In function 'cauchy':
scipy/optimize/lbfgsb/routines.f:1366: warning: 'tu' may be used uninitialized in this function
scipy/optimize/lbfgsb/routines.f:1366: warning: 'tl' may be used uninitialized in this function
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/lbfgsb/_lbfgsbmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/optimize/lbfgsb/routines.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/optimize/_lbfgsb.so
building 'scipy.optimize.moduleTNC' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/optimize/tnc
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/optimize/tnc/moduleTNC.c
gcc: scipy/optimize/tnc/tnc.c
scipy/optimize/tnc/tnc.c: In function 'linearSearch':
scipy/optimize/tnc/tnc.c:1415: warning: 'braktd' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1409: warning: 'scxbnd' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1409: warning: 'factor' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1409: warning: 'e' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1409: warning: 'b' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1409: warning: 'a' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1409: warning: 'step' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1409: warning: 'gmin' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1409: warning: 'oldf' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1408: warning: 'gtest2' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1408: warning: 'gtest1' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1408: warning: 'tol' may be used uninitialized in this function
scipy/optimize/tnc/tnc.c:1408: warning: 'b1' may be used uninitialized in this function
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/optimize/tnc/moduleTNC.o build/temp.linux-i686-2.6/scipy/optimize/tnc/tnc.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/optimize/moduleTNC.so
building 'scipy.optimize._cobyla' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/cobyla
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/optimize/cobyla/_cobylamodule.c
build/src.linux-i686-2.6/scipy/optimize/cobyla/_cobylamodule.c: In function 'cb_calcfc_in__cobyla__user__routines':
build/src.linux-i686-2.6/scipy/optimize/cobyla/_cobylamodule.c:314: warning: unused variable 'f'
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/optimize/cobyla
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/optimize/cobyla/cobyla2.f
gfortran:f77: scipy/optimize/cobyla/trstlp.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/cobyla/_cobylamodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/optimize/cobyla/cobyla2.o build/temp.linux-i686-2.6/scipy/optimize/cobyla/trstlp.o -Lbuild/temp.linux-i686-2.6 -lgfortran -o build/lib.linux-i686-2.6/scipy/optimize/_cobyla.so
building 'scipy.optimize.minpack2' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/minpack2
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/optimize/minpack2/minpack2module.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/optimize/minpack2
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/optimize/minpack2/dcsrch.f
gfortran:f77: scipy/optimize/minpack2/dcstep.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/minpack2/minpack2module.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/optimize/minpack2/dcsrch.o build/temp.linux-i686-2.6/scipy/optimize/minpack2/dcstep.o -Lbuild/temp.linux-i686-2.6 -lgfortran -o build/lib.linux-i686-2.6/scipy/optimize/minpack2.so
building 'scipy.optimize._slsqp' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/slsqp
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/optimize/slsqp/_slsqpmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/optimize/slsqp
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/optimize/slsqp/slsqp_optmz.f
 In file scipy/optimize/slsqp/slsqp_optmz.f:257

      IF (mode) 260, 100, 220                                           
                                                                       1
Warning: Obsolete: arithmetic IF statement at (1)
 In file scipy/optimize/slsqp/slsqp_optmz.f:1901

   10 assign 30 to next                                                 
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/optimize/slsqp/slsqp_optmz.f:1906

   20    GO TO next,(30, 50, 70, 110)                                   
                  1
Warning: Obsolete: Assigned GOTO statement at (1)
 In file scipy/optimize/slsqp/slsqp_optmz.f:1908

      assign 50 to next                                                 
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/optimize/slsqp/slsqp_optmz.f:1918

      assign 70 to next                                                 
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/optimize/slsqp/slsqp_optmz.f:1924

      assign 110 to next                                                
                                                                       1
Warning: Obsolete: ASSIGN statement at (1)
 In file scipy/optimize/slsqp/slsqp_optmz.f:1913

   50 IF( dx(i) .EQ. ZERO) GO TO 200                                    
    1
Warning: Label 50 at (1) defined but not used
 In file scipy/optimize/slsqp/slsqp_optmz.f:1932

   70 IF( ABS(dx(i)) .GT. cutlo ) GO TO 75                              
    1
Warning: Label 70 at (1) defined but not used
 In file scipy/optimize/slsqp/slsqp_optmz.f:1937

  110 IF( ABS(dx(i)) .LE. xmax ) GO TO 115                              
    1
Warning: Label 110 at (1) defined but not used
scipy/optimize/slsqp/slsqp_optmz.f: In function 'ldl':
scipy/optimize/slsqp/slsqp_optmz.f:1455: warning: 'tp' may be used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f: In function 'linmin':
scipy/optimize/slsqp/slsqp_optmz.f:1592: warning: 'e' is used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1604: warning: 'd' is used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1617: warning: 'u' is used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1638: warning: 'fx' is used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1639: warning: 'x' is used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1641: warning: 'w' is used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1642: warning: 'fw' is used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1651: warning: 'fv' is used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1651: warning: 'v' is used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1556: warning: 'b' may be used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f:1556: warning: 'a' may be used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f: In function 'dnrm2_':
scipy/optimize/slsqp/slsqp_optmz.f:1855: warning: 'xmax' may be used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f: In function 'hfti':
scipy/optimize/slsqp/slsqp_optmz.f:1244: warning: 'hmax' may be used uninitialized in this function
scipy/optimize/slsqp/slsqp_optmz.f: In function 'nnls':
scipy/optimize/slsqp/slsqp_optmz.f:1058: warning: 'izmax' may be used uninitialized in this function
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/slsqp/_slsqpmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/optimize/slsqp/slsqp_optmz.o -Lbuild/temp.linux-i686-2.6 -lgfortran -o build/lib.linux-i686-2.6/scipy/optimize/_slsqp.so
building 'scipy.optimize._nnls' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/nnls
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/optimize/nnls/_nnlsmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/scipy/optimize/nnls
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/optimize/nnls/nnls.f
 In file scipy/optimize/nnls/nnls.f:394

      IF (CL) 130,130,20                                                
                                                                       1
Warning: Obsolete: arithmetic IF statement at (1)
 In file scipy/optimize/nnls/nnls.f:400

      IF (U(1,LPIVOT)) 50,50,40                                         
                                                                       1
Warning: Obsolete: arithmetic IF statement at (1)
 In file scipy/optimize/nnls/nnls.f:407

   60 IF (CL) 130,130,70                                                
                                                                       1
Warning: Obsolete: arithmetic IF statement at (1)
 In file scipy/optimize/nnls/nnls.f:412

      IF (B) 80,130,130                                                 
                                                                       1
Warning: Obsolete: arithmetic IF statement at (1)
 In file scipy/optimize/nnls/nnls.f:424

          IF (SM) 100,120,100                                           
                                                                       1
Warning: Obsolete: arithmetic IF statement at (1)
scipy/optimize/nnls/nnls.f: In function 'nnls':
scipy/optimize/nnls/nnls.f:52: warning: 'izmax' may be used uninitialized in this function
scipy/optimize/nnls/nnls.f:52: warning: 'jj' may be used uninitialized in this function
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/optimize/nnls/_nnlsmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/optimize/nnls/nnls.o -Lbuild/temp.linux-i686-2.6 -lgfortran -o build/lib.linux-i686-2.6/scipy/optimize/_nnls.so
building 'scipy.signal.sigtools' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/signal
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/signal/firfilter.c
gcc: scipy/signal/medianfilter.c
gcc: scipy/signal/sigtoolsmodule.c
scipy/signal/sigtoolsmodule.c:1488: warning: 'Py_copy_info_vec' defined but not used
scipy/signal/lfilter.c: In function 'sigtools_linear_filter':
scipy/signal/lfilter.c:180: warning: 'itzf' may be used uninitialized in this function
scipy/signal/lfilter.c:180: warning: 'itzi' may be used uninitialized in this function
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/signal/sigtoolsmodule.o build/temp.linux-i686-2.6/scipy/signal/firfilter.o build/temp.linux-i686-2.6/scipy/signal/medianfilter.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/signal/sigtools.so
building 'scipy.signal.spline' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/signal/C_bspline_util.c
gcc: scipy/signal/splinemodule.c
gcc: scipy/signal/bspline_util.c
gcc: scipy/signal/D_bspline_util.c
gcc: scipy/signal/S_bspline_util.c
gcc: scipy/signal/Z_bspline_util.c
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/signal/splinemodule.o build/temp.linux-i686-2.6/scipy/signal/S_bspline_util.o build/temp.linux-i686-2.6/scipy/signal/D_bspline_util.o build/temp.linux-i686-2.6/scipy/signal/C_bspline_util.o build/temp.linux-i686-2.6/scipy/signal/Z_bspline_util.o build/temp.linux-i686-2.6/scipy/signal/bspline_util.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/signal/spline.so
building 'scipy.sparse.linalg.isolve._iterative' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/_iterativemodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/STOPTEST2.f
gfortran:f77: build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/getbreak.f
gfortran:f77: build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/BiCGREVCOM.f
gfortran:f77: build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/BiCGSTABREVCOM.f
gfortran:f77: build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/CGREVCOM.f
gfortran:f77: build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/CGSREVCOM.f
gfortran:f77: build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/GMRESREVCOM.f
 In file build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/GMRESREVCOM.f:474

     $     FUNCTION sAPPROXRES( I, H, S, GIVENS, LDG )                  
                                   1
Warning: Unused variable h declared at (1)
 In file build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/GMRESREVCOM.f:1064

     $     FUNCTION dAPPROXRES( I, H, S, GIVENS, LDG )                  
                                   1
Warning: Unused variable h declared at (1)
 In file build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/GMRESREVCOM.f:1654

     $     FUNCTION scAPPROXRES( I, H, S, GIVENS, LDG )                 
                                    1
Warning: Unused variable h declared at (1)
 In file build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/GMRESREVCOM.f:2244

     $     FUNCTION dzAPPROXRES( I, H, S, GIVENS, LDG )                 
                                    1
Warning: Unused variable h declared at (1)
gfortran:f77: build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/QMRREVCOM.f
 In file build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/QMRREVCOM.f:686

     $       EPSTOL, XITOL,                                             
                  1
Warning: Unused variable deltatolepstol declared at (1)
 In file build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/QMRREVCOM.f:1810

     $       EPSTOL, XITOL,                                             
                  1
Warning: Unused variable deltatolepstol declared at (1)
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/_iterativemodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/STOPTEST2.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/getbreak.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/BiCGREVCOM.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/BiCGSTABREVCOM.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/CGREVCOM.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/CGSREVCOM.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/GMRESREVCOM.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/isolve/iterative/QMRREVCOM.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/sparse/linalg/isolve/_iterative.so
building 'scipy.sparse.linalg.dsolve._zsuperlu' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -DUSE_VENDOR_BLAS=1 -I/local/scratch/include/atlas -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/sparse/linalg/dsolve/_zsuperlumodule.c
gcc: scipy/sparse/linalg/dsolve/_superlu_utils.c
gcc: scipy/sparse/linalg/dsolve/_superluobject.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_zsuperlumodule.o build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_superlu_utils.o build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_superluobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -lsuperlu_src -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/_zsuperlu.so
building 'scipy.sparse.linalg.dsolve._dsuperlu' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -DUSE_VENDOR_BLAS=1 -I/local/scratch/include/atlas -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/sparse/linalg/dsolve/_dsuperlumodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_dsuperlumodule.o build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_superlu_utils.o build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_superluobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -lsuperlu_src -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/_dsuperlu.so
building 'scipy.sparse.linalg.dsolve._csuperlu' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -DUSE_VENDOR_BLAS=1 -I/local/scratch/include/atlas -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/sparse/linalg/dsolve/_csuperlumodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_csuperlumodule.o build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_superlu_utils.o build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_superluobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -lsuperlu_src -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/_csuperlu.so
building 'scipy.sparse.linalg.dsolve._ssuperlu' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-DATLAS_INFO="\"3.6.0\"" -DUSE_VENDOR_BLAS=1 -I/local/scratch/include/atlas -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/sparse/linalg/dsolve/_ssuperlumodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_ssuperlumodule.o build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_superlu_utils.o build/temp.linux-i686-2.6/scipy/sparse/linalg/dsolve/_superluobject.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -lsuperlu_src -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/_ssuperlu.so
building 'scipy.sparse.linalg.eigen.arpack._arpack' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen
creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpackmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
compile options: '-DATLAS_INFO="\"3.6.0\"" -I/local/scratch/include/atlas -Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpack-f2pywrappers.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpackmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpack-f2pywrappers.o -L/local/scratch/lib -Lbuild/temp.linux-i686-2.6 -larpack -llapack -llapack -lf77blas -lcblas -latlas -lgfortran -o build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpack.so
building 'scipy.sparse.sparsetools._csr' extension
compiling C++ sources
C compiler: g++ -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -fPIC

creating build/temp.linux-i686-2.6/scipy/sparse/sparsetools
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
g++: scipy/sparse/sparsetools/csr_wrap.cxx
g++ -pthread -shared build/temp.linux-i686-2.6/scipy/sparse/sparsetools/csr_wrap.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_csr.so
building 'scipy.sparse.sparsetools._csc' extension
compiling C++ sources
C compiler: g++ -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
g++: scipy/sparse/sparsetools/csc_wrap.cxx
g++ -pthread -shared build/temp.linux-i686-2.6/scipy/sparse/sparsetools/csc_wrap.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_csc.so
building 'scipy.sparse.sparsetools._coo' extension
compiling C++ sources
C compiler: g++ -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
g++: scipy/sparse/sparsetools/coo_wrap.cxx
g++ -pthread -shared build/temp.linux-i686-2.6/scipy/sparse/sparsetools/coo_wrap.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_coo.so
building 'scipy.sparse.sparsetools._bsr' extension
compiling C++ sources
C compiler: g++ -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
g++: scipy/sparse/sparsetools/bsr_wrap.cxx
g++ -pthread -shared build/temp.linux-i686-2.6/scipy/sparse/sparsetools/bsr_wrap.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_bsr.so
building 'scipy.sparse.sparsetools._dia' extension
compiling C++ sources
C compiler: g++ -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
g++: scipy/sparse/sparsetools/dia_wrap.cxx
g++ -pthread -shared build/temp.linux-i686-2.6/scipy/sparse/sparsetools/dia_wrap.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_dia.so
building 'scipy.spatial.ckdtree' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/spatial
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/spatial/ckdtree.c
/local/scratch/lib/python2.6/site-packages/numpy/core/include/numpy/__multiarray_api.h:969: warning: '_import_array' defined but not used
scipy/spatial/ckdtree.c:1469: warning: '__pyx_doc_5scipy_7spatial_7ckdtree_7cKDTree___init__' defined but not used
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/spatial/ckdtree.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/spatial/ckdtree.so
building 'scipy.spatial._distance_wrap' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/spatial/src
compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/spatial/src/distance.c
gcc: scipy/spatial/src/distance_wrap.c
scipy/spatial/src/distance_wrap.c: In function 'pdist_weighted_minkowski_wrap':
scipy/spatial/src/distance_wrap.c:866: warning: assignment discards qualifiers from pointer target type
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/spatial/src/distance_wrap.o build/temp.linux-i686-2.6/scipy/spatial/src/distance.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/spatial/_distance_wrap.so
building 'scipy.special._cephes' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/special/toms_wrappers.c
gcc: scipy/special/cdf_wrappers.c
gcc: scipy/special/ufunc_extras.c
gcc: scipy/special/amos_wrappers.c
gcc: scipy/special/specfun_wrappers.c
gcc: scipy/special/_cephesmodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/scipy/special/_cephesmodule.o build/temp.linux-i686-2.6/scipy/special/amos_wrappers.o build/temp.linux-i686-2.6/scipy/special/specfun_wrappers.o build/temp.linux-i686-2.6/scipy/special/toms_wrappers.o build/temp.linux-i686-2.6/scipy/special/cdf_wrappers.o build/temp.linux-i686-2.6/scipy/special/ufunc_extras.o -Lbuild/temp.linux-i686-2.6 -lsc_amos -lsc_toms -lsc_c_misc -lsc_cephes -lsc_mach -lsc_cdf -lsc_specfun -lgfortran -o build/lib.linux-i686-2.6/scipy/special/_cephes.so
building 'scipy.special.specfun' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/special
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/special/specfunmodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/special/specfunmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -Lbuild/temp.linux-i686-2.6 -lsc_specfun -lgfortran -o build/lib.linux-i686-2.6/scipy/special/specfun.so
building 'scipy.stats.statlib' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/stats
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/stats/statlibmodule.c
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/stats/statlibmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o -Lbuild/temp.linux-i686-2.6 -lstatlib -lgfortran -o build/lib.linux-i686-2.6/scipy/stats/statlib.so
building 'scipy.stats.vonmises_cython' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/stats/vonmises_cython.c
/local/scratch/lib/python2.6/site-packages/numpy/core/include/numpy/__multiarray_api.h:969: warning: '_import_array' defined but not used
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/stats/vonmises_cython.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/stats/vonmises_cython.so
building 'scipy.stats.futil' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/stats/futilmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/stats/futil.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/stats/futilmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/stats/futil.o -Lbuild/temp.linux-i686-2.6 -lgfortran -o build/lib.linux-i686-2.6/scipy/stats/futil.so
building 'scipy.stats.mvn' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: build/src.linux-i686-2.6/scipy/stats/mvnmodule.c
compiling Fortran sources
Fortran f77 compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran f90 compiler: /usr/bin/gfortran -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
Fortran fix compiler: /usr/bin/gfortran -Wall -ffixed-form -fno-second-underscore -Wall -fno-second-underscore -fPIC -O3 -funroll-loops
compile options: '-Ibuild/src.linux-i686-2.6 -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gfortran:f77: scipy/stats/mvndst.f
 In file scipy/stats/mvndst.f:1066

         IF ( R .LT. 0 ) BVN = -BVN + MAX( ZERO, MVNPHI(-H)-MVNPHI(-K) )
                                                                       1
Warning: Line truncated at (1)
 In file scipy/stats/mvndst.f:1093

     &    / 15485857, 17329489, 36312197, 55911127, 75906931, 96210113 /
                                                                       1
Warning: Line truncated at (1)
scipy/stats/mvndst.f: In function 'bvnmvn':
scipy/stats/mvndst.f:909: warning: '__result_bvnmvn' may be used uninitialized in this function
scipy/stats/mvndst.f: In function 'covsrt':
scipy/stats/mvndst.f:257: warning: 'bmin' may be used uninitialized in this function
scipy/stats/mvndst.f:257: warning: 'amin' may be used uninitialized in this function
gfortran:f77: build/src.linux-i686-2.6/scipy/stats/mvn-f2pywrappers.f
/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/stats/mvnmodule.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/fortranobject.o build/temp.linux-i686-2.6/scipy/stats/mvndst.o build/temp.linux-i686-2.6/build/src.linux-i686-2.6/scipy/stats/mvn-f2pywrappers.o -Lbuild/temp.linux-i686-2.6 -lgfortran -o build/lib.linux-i686-2.6/scipy/stats/mvn.so
building 'scipy.ndimage._nd_image' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

creating build/temp.linux-i686-2.6/scipy/ndimage
creating build/temp.linux-i686-2.6/scipy/ndimage/src
compile options: '-Iscipy/ndimage/src -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/local/scratch/lib/python2.6/site-packages/numpy/core/include -I/home/craigb/include/python2.6 -c'
gcc: scipy/ndimage/src/ni_filters.c
gcc: scipy/ndimage/src/nd_image.c
gcc: scipy/ndimage/src/ni_measure.c
gcc: scipy/ndimage/src/ni_support.c
gcc: scipy/ndimage/src/ni_fourier.c
gcc: scipy/ndimage/src/ni_interpolation.c
gcc: scipy/ndimage/src/ni_morphology.c
gcc -pthread -shared build/temp.linux-i686-2.6/scipy/ndimage/src/nd_image.o build/temp.linux-i686-2.6/scipy/ndimage/src/ni_filters.o build/temp.linux-i686-2.6/scipy/ndimage/src/ni_fourier.o build/temp.linux-i686-2.6/scipy/ndimage/src/ni_interpolation.o build/temp.linux-i686-2.6/scipy/ndimage/src/ni_measure.o build/temp.linux-i686-2.6/scipy/ndimage/src/ni_morphology.o build/temp.linux-i686-2.6/scipy/ndimage/src/ni_support.o -Lbuild/temp.linux-i686-2.6 -o build/lib.linux-i686-2.6/scipy/ndimage/_nd_image.so
running scons

Warning: No configuration returned, assuming unavailable.
blas_opt_info:
blas_mkl_info:
  libraries mkl,vml,guide not found in /local/scratch/lib
  NOT AVAILABLE

atlas_blas_threads_info:
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
  FOUND:
    libraries = ['lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = c
    include_dirs = ['/local/scratch/include/atlas']

/local/scratch/lib/python2.6/site-packages/numpy/distutils/command/config.py:361: DeprecationWarning: 
+++++++++++++++++++++++++++++++++++++++++++++++++
Usage of get_output is deprecated: please do not 
use it anymore, and avoid configuration checks 
involving running executable on the target machine.
+++++++++++++++++++++++++++++++++++++++++++++++++

  DeprecationWarning)
customize GnuFCompiler
Found executable /usr/bin/g77
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using config
compiling '_configtest.c':

/* This file is generated from numpy/distutils/system_info.py */
void ATL_buildinfo(void);
int main(void) {
  ATL_buildinfo();
  return 0;
}
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-c'
gcc: _configtest.c
gcc -pthread _configtest.o -L/local/scratch/lib -llapack -lf77blas -lcblas -latlas -o _configtest
ATLAS version 3.6.0 built by camm on Tue Jun 15 03:19:44 UTC 2004:
   UNAME    : Linux intech131 2.4.20 #1 SMP Thu Jan 23 11:24:05 EST 2003 i686 GNU/Linux
   INSTFLG  : 
   MMDEF    : 
   ARCHDEF  : 
   F2CDEFS  : -DAdd__ -DStringSunStyle
   CACHEEDGE: 196608
   F77      : /usr/bin/g77, version GNU Fortran (GCC) 3.3.5 (Debian 1:3.3.5-1)
   F77FLAGS : -O
   CC       : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   CC FLAGS : -fomit-frame-pointer -O3 -funroll-all-loops
   MCC      : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   MCCFLAGS : -fomit-frame-pointer -O
success!
removing: _configtest.c _configtest.o _configtest
  FOUND:
    libraries = ['lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = c
    define_macros = [('ATLAS_INFO', '"\\"3.6.0\\""')]
    include_dirs = ['/local/scratch/include/atlas']

ATLAS version 3.6.0
lapack_opt_info:
lapack_mkl_info:
mkl_info:
  libraries mkl,vml,guide not found in /local/scratch/lib
  NOT AVAILABLE

  NOT AVAILABLE

atlas_threads_info:
Setting PTATLAS=ATLAS
numpy.distutils.system_info.atlas_threads_info
Setting PTATLAS=ATLAS
Setting PTATLAS=ATLAS
  FOUND:
    libraries = ['lapack', 'lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = f77
    include_dirs = ['/local/scratch/include/atlas']

customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using config
compiling '_configtest.c':

/* This file is generated from numpy/distutils/system_info.py */
void ATL_buildinfo(void);
int main(void) {
  ATL_buildinfo();
  return 0;
}
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC

compile options: '-c'
gcc: _configtest.c
gcc -pthread _configtest.o -L/local/scratch/lib -llapack -llapack -lf77blas -lcblas -latlas -o _configtest
ATLAS version 3.6.0 built by camm on Tue Jun 15 03:19:44 UTC 2004:
   UNAME    : Linux intech131 2.4.20 #1 SMP Thu Jan 23 11:24:05 EST 2003 i686 GNU/Linux
   INSTFLG  : 
   MMDEF    : 
   ARCHDEF  : 
   F2CDEFS  : -DAdd__ -DStringSunStyle
   CACHEEDGE: 196608
   F77      : /usr/bin/g77, version GNU Fortran (GCC) 3.3.5 (Debian 1:3.3.5-1)
   F77FLAGS : -O
   CC       : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   CC FLAGS : -fomit-frame-pointer -O3 -funroll-all-loops
   MCC      : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   MCCFLAGS : -fomit-frame-pointer -O
success!
removing: _configtest.c _configtest.o _configtest
  FOUND:
    libraries = ['lapack', 'lapack', 'f77blas', 'cblas', 'atlas']
    library_dirs = ['/local/scratch/lib']
    language = f77
    define_macros = [('ATLAS_INFO', '"\\"3.6.0\\""')]
    include_dirs = ['/local/scratch/include/atlas']

ATLAS version 3.6.0
ATLAS version 3.6.0
umfpack_info:
  libraries umfpack not found in /local/scratch/lib
/local/scratch/lib/python2.6/site-packages/numpy/distutils/system_info.py:452: UserWarning: 
    UMFPACK sparse solver (http://www.cise.ufl.edu/research/sparse/umfpack/)
    not found. Directories to search for the libraries can be specified in the
    numpy/distutils/site.cfg file (section [umfpack]) or by setting
    the UMFPACK environment variable.
  warnings.warn(self.notfounderror.__doc__)
  NOT AVAILABLE

running install
running build
running config_cc
unifing config_cc, config, build_clib, build_ext, build commands --compiler options
running config_fc
unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options
running build_src
building py_modules sources
building library "dfftpack" sources
building library "linpack_lite" sources
building library "mach" sources
building library "quadpack" sources
building library "odepack" sources
building library "fitpack" sources
building library "odrpack" sources
building library "minpack" sources
building library "rootfind" sources
building library "superlu_src" sources
building library "arpack" sources
building library "sc_c_misc" sources
building library "sc_cephes" sources
building library "sc_mach" sources
building library "sc_toms" sources
building library "sc_amos" sources
building library "sc_cdf" sources
building library "sc_specfun" sources
building library "statlib" sources
building extension "scipy.cluster._vq" sources
building extension "scipy.cluster._hierarchy_wrap" sources
building extension "scipy.fftpack._fftpack" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.fftpack.convolve" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.integrate._quadpack" sources
building extension "scipy.integrate._odepack" sources
building extension "scipy.integrate.vode" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.interpolate._fitpack" sources
building extension "scipy.interpolate.dfitpack" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/scipy/interpolate/src/dfitpack-f2pywrappers.f' to sources.
building extension "scipy.interpolate._interpolate" sources
building extension "scipy.io.numpyio" sources
building extension "scipy.lib.blas.fblas" sources
f2py options: ['skip:', ':']
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/lib/blas/fblas-f2pywrappers.f' to sources.
building extension "scipy.lib.blas.cblas" sources
  adding 'scipy/lib/blas/cblas.pyf.src' to sources.
f2py options: ['skip:', ':']
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.lib.lapack.flapack" sources
f2py options: ['skip:', ':']
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.lib.lapack.clapack" sources
  adding 'scipy/lib/lapack/clapack.pyf.src' to sources.
f2py options: ['skip:', ':']
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.lib.lapack.calc_lwork" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.lib.lapack.atlas_version" sources
building extension "scipy.linalg.fblas" sources
  adding 'build/src.linux-i686-2.6/scipy/linalg/fblas.pyf' to sources.
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/fblas-f2pywrappers.f' to sources.
building extension "scipy.linalg.cblas" sources
  adding 'build/src.linux-i686-2.6/scipy/linalg/cblas.pyf' to sources.
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.linalg.flapack" sources
  adding 'build/src.linux-i686-2.6/scipy/linalg/flapack.pyf' to sources.
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/linalg/flapack-f2pywrappers.f' to sources.
building extension "scipy.linalg.clapack" sources
  adding 'build/src.linux-i686-2.6/scipy/linalg/clapack.pyf' to sources.
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.linalg._flinalg" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.linalg.calc_lwork" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.linalg.atlas_version" sources
building extension "scipy.odr.__odrpack" sources
building extension "scipy.optimize._minpack" sources
building extension "scipy.optimize._zeros" sources
building extension "scipy.optimize._lbfgsb" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.optimize.moduleTNC" sources
building extension "scipy.optimize._cobyla" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.optimize.minpack2" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.optimize._slsqp" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.optimize._nnls" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.signal.sigtools" sources
building extension "scipy.signal.spline" sources
building extension "scipy.sparse.linalg.isolve._iterative" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.sparse.linalg.dsolve._zsuperlu" sources
building extension "scipy.sparse.linalg.dsolve._dsuperlu" sources
building extension "scipy.sparse.linalg.dsolve._csuperlu" sources
building extension "scipy.sparse.linalg.dsolve._ssuperlu" sources
building extension "scipy.sparse.linalg.dsolve.umfpack.__umfpack" sources
building extension "scipy.sparse.linalg.eigen.arpack._arpack" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/build/src.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpack-f2pywrappers.f' to sources.
building extension "scipy.sparse.sparsetools._csr" sources
building extension "scipy.sparse.sparsetools._csc" sources
building extension "scipy.sparse.sparsetools._coo" sources
building extension "scipy.sparse.sparsetools._bsr" sources
building extension "scipy.sparse.sparsetools._dia" sources
building extension "scipy.spatial.ckdtree" sources
building extension "scipy.spatial._distance_wrap" sources
building extension "scipy.special._cephes" sources
building extension "scipy.special.specfun" sources
f2py options: ['--no-wrap-functions']
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.stats.statlib" sources
f2py options: ['--no-wrap-functions']
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.stats.vonmises_cython" sources
building extension "scipy.stats.futil" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
building extension "scipy.stats.mvn" sources
f2py options: []
  adding 'build/src.linux-i686-2.6/fortranobject.c' to sources.
  adding 'build/src.linux-i686-2.6' to include_dirs.
  adding 'build/src.linux-i686-2.6/scipy/stats/mvn-f2pywrappers.f' to sources.
building extension "scipy.ndimage._nd_image" sources
building data_files sources
running build_py
copying scipy/version.py -> build/lib.linux-i686-2.6/scipy
copying build/src.linux-i686-2.6/scipy/__config__.py -> build/lib.linux-i686-2.6/scipy
running build_clib
customize UnixCCompiler
customize UnixCCompiler using build_clib
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using build_clib
running build_ext
customize UnixCCompiler
customize UnixCCompiler using build_ext
resetting extension 'scipy.integrate._odepack' language from 'c' to 'f77'.
resetting extension 'scipy.integrate.vode' language from 'c' to 'f77'.
resetting extension 'scipy.lib.blas.fblas' language from 'c' to 'f77'.
resetting extension 'scipy.odr.__odrpack' language from 'c' to 'f77'.
extending extension 'scipy.sparse.linalg.dsolve._zsuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)]
extending extension 'scipy.sparse.linalg.dsolve._dsuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)]
extending extension 'scipy.sparse.linalg.dsolve._csuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)]
extending extension 'scipy.sparse.linalg.dsolve._ssuperlu' defined_macros with [('USE_VENDOR_BLAS', 1)]
customize UnixCCompiler
customize UnixCCompiler using build_ext
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler
gnu: no Fortran 90 compiler found
gnu: no Fortran 90 compiler found
customize GnuFCompiler using build_ext
running scons
running install_lib
copying build/lib.linux-i686-2.6/scipy/io/numpyio.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/io
copying build/lib.linux-i686-2.6/scipy/spatial/_distance_wrap.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/spatial
copying build/lib.linux-i686-2.6/scipy/spatial/ckdtree.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/spatial
copying build/lib.linux-i686-2.6/scipy/linalg/clapack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/linalg
copying build/lib.linux-i686-2.6/scipy/linalg/cblas.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/linalg
copying build/lib.linux-i686-2.6/scipy/linalg/fblas.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/linalg
copying build/lib.linux-i686-2.6/scipy/linalg/atlas_version.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/linalg
copying build/lib.linux-i686-2.6/scipy/linalg/_flinalg.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/linalg
copying build/lib.linux-i686-2.6/scipy/linalg/flapack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/linalg
copying build/lib.linux-i686-2.6/scipy/linalg/calc_lwork.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/linalg
copying build/lib.linux-i686-2.6/scipy/interpolate/_interpolate.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/interpolate
copying build/lib.linux-i686-2.6/scipy/interpolate/_fitpack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/interpolate
copying build/lib.linux-i686-2.6/scipy/interpolate/dfitpack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/interpolate
copying build/lib.linux-i686-2.6/scipy/fftpack/convolve.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/fftpack
copying build/lib.linux-i686-2.6/scipy/fftpack/_fftpack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/fftpack
copying build/lib.linux-i686-2.6/scipy/lib/blas/cblas.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/lib/blas
copying build/lib.linux-i686-2.6/scipy/lib/blas/fblas.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/lib/blas
copying build/lib.linux-i686-2.6/scipy/lib/lapack/clapack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/lib/lapack
copying build/lib.linux-i686-2.6/scipy/lib/lapack/atlas_version.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/lib/lapack
copying build/lib.linux-i686-2.6/scipy/lib/lapack/flapack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/lib/lapack
copying build/lib.linux-i686-2.6/scipy/lib/lapack/calc_lwork.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/lib/lapack
copying build/lib.linux-i686-2.6/scipy/__config__.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy
copying build/lib.linux-i686-2.6/scipy/special/_cephes.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/special
copying build/lib.linux-i686-2.6/scipy/special/specfun.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/special
copying build/lib.linux-i686-2.6/scipy/sparse/linalg/isolve/_iterative.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/linalg/isolve
copying build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/_csuperlu.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/linalg/dsolve
copying build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/_zsuperlu.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/linalg/dsolve
copying build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/_ssuperlu.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/linalg/dsolve
copying build/lib.linux-i686-2.6/scipy/sparse/linalg/dsolve/_dsuperlu.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/linalg/dsolve
copying build/lib.linux-i686-2.6/scipy/sparse/linalg/eigen/arpack/_arpack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/linalg/eigen/arpack
copying build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_bsr.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/sparsetools
copying build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_csr.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/sparsetools
copying build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_csc.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/sparsetools
copying build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_coo.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/sparsetools
copying build/lib.linux-i686-2.6/scipy/sparse/sparsetools/_dia.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/sparse/sparsetools
copying build/lib.linux-i686-2.6/scipy/integrate/_quadpack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/integrate
copying build/lib.linux-i686-2.6/scipy/integrate/_odepack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/integrate
copying build/lib.linux-i686-2.6/scipy/integrate/vode.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/integrate
copying build/lib.linux-i686-2.6/scipy/stats/mvn.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/stats
copying build/lib.linux-i686-2.6/scipy/stats/vonmises_cython.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/stats
copying build/lib.linux-i686-2.6/scipy/stats/futil.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/stats
copying build/lib.linux-i686-2.6/scipy/stats/statlib.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/stats
copying build/lib.linux-i686-2.6/scipy/odr/__odrpack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/odr
copying build/lib.linux-i686-2.6/scipy/optimize/_minpack.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/optimize
copying build/lib.linux-i686-2.6/scipy/optimize/_slsqp.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/optimize
copying build/lib.linux-i686-2.6/scipy/optimize/moduleTNC.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/optimize
copying build/lib.linux-i686-2.6/scipy/optimize/minpack2.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/optimize
copying build/lib.linux-i686-2.6/scipy/optimize/_zeros.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/optimize
copying build/lib.linux-i686-2.6/scipy/optimize/_cobyla.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/optimize
copying build/lib.linux-i686-2.6/scipy/optimize/_nnls.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/optimize
copying build/lib.linux-i686-2.6/scipy/optimize/_lbfgsb.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/optimize
copying build/lib.linux-i686-2.6/scipy/version.py -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy
copying build/lib.linux-i686-2.6/scipy/ndimage/_nd_image.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/ndimage
copying build/lib.linux-i686-2.6/scipy/signal/spline.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/signal
copying build/lib.linux-i686-2.6/scipy/signal/sigtools.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/signal
copying build/lib.linux-i686-2.6/scipy/cluster/_hierarchy_wrap.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/cluster
copying build/lib.linux-i686-2.6/scipy/cluster/_vq.so -> /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/cluster
byte-compiling /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/__config__.py to __config__.pyc
byte-compiling /local/scratch/test_numpy//lib/python2.6/site-packages/scipy/version.py to version.pyc
running install_data
running install_egg_info
Removing /local/scratch/test_numpy//lib/python2.6/site-packages/scipy-0.7.1-py2.6.egg-info
Writing /local/scratch/test_numpy//lib/python2.6/site-packages/scipy-0.7.1-py2.6.egg-info

[craigb at fsul1 scipy-0.7.1]$ cd ..

[craigb at fsul1 test_numpy]$ unset PYTHONPATH
[craigb at fsul1 test_numpy]$ export PYTHONPATH=/local/scratch/test_numpy/lib/python2.6/site-packages/
[craigb at fsul1 test_numpy]$ 
[craigb at fsul1 test_numpy]$ python
Python 2.6.2 (r262:71600, Jul 28 2009, 10:47:31) 
[GCC 4.1.2 20080704 (Red Hat 4.1.2-44)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy
>>> numpy.test()
Running unit tests for numpy
NumPy version 1.3.0
NumPy is installed in /local/scratch/test_numpy/lib/python2.6/site-packages/numpy
Python version 2.6.2 (r262:71600, Jul 28 2009, 10:47:31) [GCC 4.1.2 20080704 (Red Hat 4.1.2-44)]
nose version 0.11.1
........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................K.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
----------------------------------------------------------------------
Ran 2030 tests in 7.947s

OK (KNOWNFAIL=1)
<nose.result.TextTestResult run=2030 errors=0 failures=0>
>>> import scipy
>>> scipy.test()
Running unit tests for scipy
NumPy version 1.3.0
NumPy is installed in /local/scratch/test_numpy/lib/python2.6/site-packages/numpy
SciPy version 0.7.1
SciPy is installed in /local/scratch/test_numpy/lib/python2.6/site-packages/scipy
Python version 2.6.2 (r262:71600, Jul 28 2009, 10:47:31) [GCC 4.1.2 20080704 (Red Hat 4.1.2-44)]
nose version 0.11.1
............................................................................................................................................................................................................................./local/scratch/test_numpy/lib/python2.6/site-packages/scipy/interpolate/fitpack2.py:498: UserWarning: 
The coefficients of the spline returned have been computed as the
minimal norm least-squares solution of a (numerically) rank deficient
system (deficiency=7). If deficiency is large, the results may be
inaccurate. Deficiency may strongly depend on the value of eps.
  warnings.warn(message)
...../local/scratch/test_numpy/lib/python2.6/site-packages/scipy/interpolate/fitpack2.py:439: UserWarning: 
The required storage space exceeds the available storage space: nxest
or nyest too small, or s too small.
The weighted least-squares spline corresponds to the current set of
knots.
  warnings.warn(message)
...........................................K..K..................................................................................................................................../local/scratch/test_numpy/lib/python2.6/site-packages/scipy/io/matlab/tests/test_mio.py:438: FutureWarning: Using oned_as default value ('column') This will change to 'row' in future versions
  mfw = MatFile5Writer(StringIO())
......../local/scratch/test_numpy/lib/python2.6/site-packages/scipy/io/matlab/mio.py:84: FutureWarning: Using struct_as_record default value (False) This will change to True in future versions
  return MatFile5Reader(byte_stream, **kwargs)
...............................Warning: 1000000 bytes requested, 20 bytes read.
./local/scratch/test_numpy/lib/python2.6/site-packages/numpy/lib/utils.py:108: DeprecationWarning: write_array is deprecated
  warnings.warn(str1, DeprecationWarning)
/local/scratch/test_numpy/lib/python2.6/site-packages/numpy/lib/utils.py:108: DeprecationWarning: read_array is deprecated
  warnings.warn(str1, DeprecationWarning)
......................./local/scratch/test_numpy/lib/python2.6/site-packages/numpy/lib/utils.py:108: DeprecationWarning: npfile is deprecated
  warnings.warn(str1, DeprecationWarning)
.........................................................................................................................................................SSSSSS......SSSSSS......SSSS....ATLAS version 3.6.0 built by camm on Tue Jun 15 03:19:44 UTC 2004:
   UNAME    : Linux intech131 2.4.20 #1 SMP Thu Jan 23 11:24:05 EST 2003 i686 GNU/Linux
   INSTFLG  : 
   MMDEF    : 
   ARCHDEF  : 
   F2CDEFS  : -DAdd__ -DStringSunStyle
   CACHEEDGE: 196608
   F77      : /usr/bin/g77, version GNU Fortran (GCC) 3.3.5 (Debian 1:3.3.5-1)
   F77FLAGS : -O
   CC       : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   CC FLAGS : -fomit-frame-pointer -O3 -funroll-all-loops
   MCC      : /usr/bin/gcc, version gcc (GCC) 3.3.5 (Debian 1:3.3.5-1)
   MCCFLAGS : -fomit-frame-pointer -O
.............................................................................................................../local/scratch/test_numpy/lib/python2.6/site-packages/scipy/linalg/decomp.py:1177: DeprecationWarning: qr econ argument will be removed after scipy 0.7. The economy transform will then be available through the mode='economic' argument.
  "the mode='economic' argument.", DeprecationWarning)
.............................................................................................................................................................................................................................................................................................Result may be inaccurate, approximate err = 2.76151934647e-09
...Result may be inaccurate, approximate err = 1.61696391382e-10
.....SSS..................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................SSSSSSSSSSS...........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................K.K.........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................../local/scratch/test_numpy/lib/python2.6/site-packages/numpy/lib/function_base.py:324: Warning: 
            The new semantics of histogram is now the default and the `new`
            keyword will be removed in NumPy 1.4.
            
  """, Warning)
....................................................................................S................................................................................../local/scratch/test_numpy/lib/python2.6/site-packages/scipy/stats/stats.py:420: DeprecationWarning: scipy.stats.mean is deprecated; please update your code to use numpy.mean.
Please note that:
    - numpy.mean axis argument defaults to None, not 0
    - numpy.mean has a ddof argument to replace bias in a more general manner.
      scipy.stats.mean(a, bias=True) can be replaced by numpy.mean(x,
axis=0, ddof=1).
  axis=0, ddof=1).""", DeprecationWarning)
./local/scratch/test_numpy/lib/python2.6/site-packages/scipy/stats/stats.py:1329: DeprecationWarning: scipy.stats.std is deprecated; please update your code to use numpy.std.
Please note that:
    - numpy.std axis argument defaults to None, not 0
    - numpy.std has a ddof argument to replace bias in a more general manner.
      scipy.stats.std(a, bias=True) can be replaced by numpy.std(x,
      axis=0, ddof=0), scipy.stats.std(a, bias=False) by numpy.std(x, axis=0,
      ddof=1).
  ddof=1).""", DeprecationWarning)
/local/scratch/test_numpy/lib/python2.6/site-packages/scipy/stats/stats.py:1305: DeprecationWarning: scipy.stats.var is deprecated; please update your code to use numpy.var.
Please note that:
    - numpy.var axis argument defaults to None, not 0
    - numpy.var has a ddof argument to replace bias in a more general manner.
      scipy.stats.var(a, bias=True) can be replaced by numpy.var(x,
      axis=0, ddof=0), scipy.stats.var(a, bias=False) by var(x, axis=0,
      ddof=1).
  ddof=1).""", DeprecationWarning)
./local/scratch/test_numpy/lib/python2.6/site-packages/scipy/stats/morestats.py:603: UserWarning: Ties preclude use of exact statistic.
  warnings.warn("Ties preclude use of exact statistic.")
....../local/scratch/test_numpy/lib/python2.6/site-packages/scipy/stats/stats.py:498: DeprecationWarning: scipy.stats.median is deprecated; please update your code to use numpy.median.
Please note that:
    - numpy.median axis argument defaults to None, not 0
    - numpy.median has a ddof argument to replace bias in a more general manner.
      scipy.stats.median(a, bias=True) can be replaced by numpy.median(x,
axis=0, ddof=1).
  axis=0, ddof=1).""", DeprecationWarning)
....................................................................................................................................................................................................Warning: friedmanchisquare test using Chisquared aproximation
...............warning: specified build_dir '_bad_path_' does not exist or is not writable. Trying default locations
.....warning: specified build_dir '_bad_path_' does not exist or is not writable. Trying default locations
...............................building extensions here: /home/craigb/.python26_compiled/m2
................................................................................................
----------------------------------------------------------------------
Ran 3488 tests in 46.805s

OK (KNOWNFAIL=4, SKIP=31)
<nose.result.TextTestResult run=3488 errors=0 failures=0>
>>> 




From gruben at bigpond.net.au  Tue Aug 25 19:37:41 2009
From: gruben at bigpond.net.au (Gary Ruben)
Date: Wed, 26 Aug 2009 09:37:41 +1000
Subject: [SciPy-User] possible bug in scipy.ndimage.interpolation.shift
In-Reply-To: <D38FA3C6-B5CB-4B6D-B44F-C8923B5CFD58@yale.edu>
References: <344766.50572.qm@web52111.mail.re2.yahoo.com>
	<D38FA3C6-B5CB-4B6D-B44F-C8923B5CFD58@yale.edu>
Message-ID: <4A9475C5.9080304@bigpond.net.au>

Thanks for the explanation Zach. I had suspected it might be spline 
related. Also I think you're right that I am provoking ringing at the 
edges in my case but I think I can improve that in my case.

Gary R.

Zachary Pincus wrote:
>> However, perhaps another optional argument, e.g.,  
>> "floor={True,False}" say, (and code to implement, of course) could  
>> be added to the function?
> 
> Eh, ndimage does the right thing when using integral-type outputs,  
> even unsigned ones. And if you're using floating-point images, zero  
> isn't necessarily a meaningful "floor". (Most of my floating-point  
> images are centered around zero, for example.)
> 
> It's probably OK the way it is, just with the caveats that usually  
> apply to fancy spline interpolation, which is that there will be  
> ringing at sharp edges that may go outside of the range of the  
> original values (whatever that range is).
> 
> Zach
> 
> 
> In : ndimage.shift([0,8,0,5,0,3,0,10], -.75, output=float)
> array([ 6.65579912,  1.6582038 ,  3.94576069,  1.04312844,  2.60047557,
>         -0.3512807 ,  8.69527224,  0.        ])
> 
> In : ndimage.shift([0,8,0,5,0,3,0,10], -.75, output=int)
> array([7, 2, 4, 1, 3, 0, 9, 0])



From robert.kern at gmail.com  Tue Aug 25 19:38:37 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Tue, 25 Aug 2009 16:38:37 -0700
Subject: [SciPy-User] worst-case scenario installation
In-Reply-To: <3d95192b0908200840qfa65462nf32f3cb124c9044d@mail.gmail.com>
References: <3d95192b0908191228k3900d1aaqb825a4a4c46c3a0f@mail.gmail.com> 
	<4A8C63B1.1000907@lpta.in2p3.fr> <4A8C645A.2010503@lpta.in2p3.fr> 
	<3d95192b0908200840qfa65462nf32f3cb124c9044d@mail.gmail.com>
Message-ID: <3d375d730908251638m336cd62exae95d0c6931efcbe@mail.gmail.com>

On Thu, Aug 20, 2009 at 08:40, DEMOLISHOR! the
Demolishor<destroooooy at gmail.com> wrote:
> I would like to avoid kitchen-sink-style packages like Sage because I am
> still recovering from years of sufferring at the hands of Rene and Fons with
> ROOT. All I want are tools that help me write the scripts I want to write--I
> don't want an all-inclusive interactive environment.

You may want to take a look at Ondrej's SPD:

  http://code.google.com/p/spdproject/

It takes the recipes for building scipy, etc., from Sage without all of Sage.

-- 
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


From permafacture at gmail.com  Tue Aug 25 19:48:01 2009
From: permafacture at gmail.com (Permafacture)
Date: Tue, 25 Aug 2009 16:48:01 -0700 (PDT)
Subject: [SciPy-User] fsolve, no solution and the ier flag
Message-ID: <19c69d4c-6c04-49b7-8c17-482f81d29442@32g2000yqj.googlegroups.com>

How do I check the ier flag for fsolve?

intersect = fsolve(f,[1.,1.], full_output=1)

this returned an extended output but there was no ier flag in there.

thanks

elliot


From permafacture at gmail.com  Tue Aug 25 19:48:01 2009
From: permafacture at gmail.com (Permafacture)
Date: Tue, 25 Aug 2009 16:48:01 -0700 (PDT)
Subject: [SciPy-User] fsolve, no solution and the ier flag
Message-ID: <19c69d4c-6c04-49b7-8c17-482f81d29442@32g2000yqj.googlegroups.com>

How do I check the ier flag for fsolve?

intersect = fsolve(f,[1.,1.], full_output=1)

this returned an extended output but there was no ier flag in there.

thanks

elliot


From pinto at mit.edu  Tue Aug 25 20:08:47 2009
From: pinto at mit.edu (Nicolas Pinto)
Date: Tue, 25 Aug 2009 20:08:47 -0400
Subject: [SciPy-User] worst-case scenario installation
In-Reply-To: <3d95192b0908211406w3dbee429i2ec5233f445fe098@mail.gmail.com>
References: <3d95192b0908191228k3900d1aaqb825a4a4c46c3a0f@mail.gmail.com> 
	<b14b627d0908201251w709f59d7v840e453e08bdb8d9@mail.gmail.com> 
	<5b8d13220908201537q3b45fb9bq211e65bb855f15bd@mail.gmail.com> 
	<3d95192b0908211406w3dbee429i2ec5233f445fe098@mail.gmail.com>
Message-ID: <954ae5aa0908251708t3de00d0eqa0a79e47d19a255d@mail.gmail.com>

Dear Demolishor,

I was able to install numpy/scipy with ATLAS/LAPACK from scratch on a
non-root Fedora cluster. See if the following helps:

# prepare ~/usr
mkdir -p ~/usr/lib
ln -sf  lib ~/usr/lib64

------------------------------
# lapack
mkdir -p ~/src && cd ~/src
wget http://www.netlib.org/lapack/lapack.tgz
tar xzvf lapack.tgz

# build lapack
cd ~/src/lapack-3.2.1
cp -vf make.inc.example make.inc
emacs -nw make.inc
# OPTS = -O2 -fPIC -m64
# NOOPT = -O0 -fPIC -m64
#the -m64 flags build 64-bit code (nothing for 32bit)
cd ~/src/lapack-3.2.1/SRC
make -j 4

# get atlas (3.8.3)
mkdir -p ~/src && cd ~/src
wget "
http://sourceforge.net/project/downloading.php?group_id=23725&filename=atlas3.8.3.tar.bz2&a=65663372
"
tar xjvf atlas3.8.3.tar.bz2

# build atlas
cd ~/src/ATLAS/
mkdir -p Linux_X64SSE2 && cd Linux_X64SSE2
../configure -b 64 -D c -DPentiumCPS=2216 -Fa alg -fPIC
--with-netlib-lapack=$HOME/src/lapack-3.2.1/lapack_LINUX.a -Si cputhrchk 0

make build
make check
make ptcheck
make time
cd lib
make shared
make ptshared
mkdir -p $HOME/usr/lib/ && cp -vf *.so  *.a $HOME/usr/lib/
export LD_LIBRARY_PATH=$HOME/usr/lib:$LD_LIBRARY_PATH

------------------------------
# setup python path
mkdir -p $HOME/usr/lib/python2.5/site-packages
emacs -nw ~/.bashrc
export PYTHONPATH=$HOME/usr/lib/python2.5/site-packages:$PYTHONPATH
source ~/.bashrc

------------------------------
# easy_install / setuptools
mkdir -p ~/src && cd ~/src
wget http://peak.telecommunity.com/dist/ez_setup.py
python ez_setup.py --prefix=$HOME/usr
emacs ~/.pydistutils.cfg
[easy_install]
prefix = /home/ac/npinto/usr

# ipython
easy_install --prefix=$HOME/usr -U ipython

------------------------------
# numpy

mkdir -p ~/src && cd ~/src
wget http://voxel.dl.sourceforge.net/sourceforge/numpy/numpy-1.3.0.tar.gz

tar xzvf numpy-1.3.0.tar.gz
cd ~/src/numpy-1.3.0
rm -rvf build
cp -vf site.cfg.example site.cfg
#[DEFAULT]
#library_dirs = /home/ac/npinto/usr/lib

#include_dirs = /home/ac/npinto/usr/include
#[blas_opt]
#libraries = ptf77blas, ptcblas, atlas
#
#[lapack_opt]
#libraries = lapack, ptf77blas, ptcblas, atlas

python setup.py build --fcompiler=gnu95
python setup.py install --prefix=$HOME/usr
export PYTHONPATH=$HOME/usr/lib/python2.5/site-packages:$PYTHONPATH
# verify install
(cd ~/ && python -c "import numpy; assert numpy.dot.__module__ ==
'numpy.core._dotblas'; from numpy.linalg import lapack_lite")

# scipy
mkdir -p ~/src; cd ~/src
wget http://voxel.dl.sourceforge.net/sourceforge/scipy/scipy-0.7.0.tar.gz

tar xzvf scipy-0.7.0.tar.gz
cd ~/src/scipy-0.7.0
rm -rvf build
python setup.py build
python setup.py install --prefix=$HOME/usr
export PYTHONPATH=$HOME/usr/lib/python2.5/site-packages:$PYTHONPATH

`cd ~/ && python -c "from scipy import linalg"`





On Fri, Aug 21, 2009 at 5:06 PM, DEMOLISHOR! the Demolishor <
destroooooy at gmail.com> wrote:

> Does the attached have the output you need?
>
>
> On Thu, Aug 20, 2009 at 6:37 PM, David Cournapeau <cournape at gmail.com>wrote:
>
>> On Thu, Aug 20, 2009 at 12:51 PM, Robin<robince at gmail.com> wrote:
>>
>> > I have also observed the behaviour that it is impossible to get
>> > numpy/scipy build to use gfortran if g77 is in the path - I have only
>> > been able to install using gfortran on machines which I do have root
>> > access and can uninstall g77 completely.
>>
>> Could you give us the output of python setup.py build_ext
>> --fcompiler=gnu95 ? This should helps us understanding why you cannot
>> invoke gfortran
>>
>> cheers,
>>
>> David
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>


-- 
Nicolas Pinto
Ph.D. Candidate, Brain & Computer Sciences
Massachusetts Institute of Technology, USA
http://web.mit.edu/pinto
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From josef.pktd at gmail.com  Tue Aug 25 23:19:57 2009
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Tue, 25 Aug 2009 23:19:57 -0400
Subject: [SciPy-User] example stats.probplot
Message-ID: <1cd32cbb0908252019s73097023w3cddcd77e046bfc7@mail.gmail.com>

Just an example of what's still waiting in scipy.stats.

Josef
-------------- next part --------------
'''Example: stats.probplot

'''

import numpy as np
from scipy import stats
import matplotlib.pyplot as plt

nsample = 100
np.random.seed(7654321)

plt.subplot(221)
# t distribution with small degrees of freedom
x = stats.t.rvs(3, size=nsample)
stats.probplot(x, plot=plt)

plt.subplot(222)
# t distribution with larger degrees of freedom
x = stats.t.rvs(25, size=nsample)
stats.probplot(x, plot=plt)

plt.subplot(223)
# mixture of 2 normal distributions with broadcasting
x = stats.norm.rvs(loc=[0,5], scale=[1,1.5], size=(nsample/2.,2)).ravel()
stats.probplot(x, plot=plt)

plt.subplot(224)
# standard normal distribution
x = stats.norm.rvs(loc=0, scale=1, size=nsample)
stats.probplot(x, plot=plt)

#plt.show()

From fredmfp at gmail.com  Wed Aug 26 07:32:48 2009
From: fredmfp at gmail.com (fred)
Date: Wed, 26 Aug 2009 13:32:48 +0200
Subject: [SciPy-User] computing local extrema
Message-ID: <4A951D60.7010104@gmail.com>

Hi,

I would like to compute local extrema of an array (2D/3D),
ie get a list of points (coords + value).

How could I do this?

Any hint?

TIA.

Cheers,

-- 
Fred


From markus.proeller at ifm.com  Wed Aug 26 07:31:34 2009
From: markus.proeller at ifm.com (markus.proeller at ifm.com)
Date: Wed, 26 Aug 2009 13:31:34 +0200
Subject: [SciPy-User] Usage of scipy.signal.resample
Message-ID: <OF3A387610.8DE1BB35-ONC125761E.003E60C1-C125761E.003F629D@ifm-electronic.com>

Hello,

I have a question concerning the resample function of scipy.
I have the following code:

from scipy.signal import resample
>>>x = array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>>resample(x,5)
array([ 2.5       ,  1.26393202,  4.5       ,  5.5       ,  8.73606798])

I don't understand the first value of 2.5.
My scipy version is 0.7.0

Thanks for help,

Markus
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From zachary.pincus at yale.edu  Wed Aug 26 10:03:35 2009
From: zachary.pincus at yale.edu (Zachary Pincus)
Date: Wed, 26 Aug 2009 10:03:35 -0400
Subject: [SciPy-User] computing local extrema
In-Reply-To: <4A951D60.7010104@gmail.com>
References: <4A951D60.7010104@gmail.com>
Message-ID: <6C7F0CA9-0792-487B-A981-EAF7A0D3539D@yale.edu>

> I would like to compute local extrema of an array (2D/3D),
> ie get a list of points (coords + value).
>
> How could I do this?
>
> Any hint?


A sort of old-school image-processing way of doing this is to  
calculate the maximum of a moving neighborhood (maximum filter:  
scipy.ndimage.maximum_filter), and then look to see where the original  
array is equal to the maximum in the neighborhood.

maxima = (a == scipy.ndimage.maximum_filter(a, 4))

will give a boolean array the same shape as a, with True in the  
positions where the points are the local maxima in a 4x4 (or 4x4x4,  
depending on if a is 2d or 3d, etc.) square window. You can use  
numpy.nonzero to get the coordinates out of the 'maxima' array, and  
can use fancy indexing (a[maxima] or a[numpy.nonzero(maxima])) to pull  
out the values.

Obviously this approach is a bit flaky -- boundary conditions can be a  
bit tricky, depending on what you want; also this it fails on  
plateaux, determining everything to be a maximum. Plus you have to  
choose a window size... this can be helpful for suppressing extrema  
produced by noise, though. A 3x3 window will give the most fine- 
grained account of the extremal (and plateau / saddle) points.

Zach




From ivo.maljevic at gmail.com  Wed Aug 26 10:11:29 2009
From: ivo.maljevic at gmail.com (Ivo Maljevic)
Date: Wed, 26 Aug 2009 10:11:29 -0400
Subject: [SciPy-User] Usage of scipy.signal.resample
In-Reply-To: <OF3A387610.8DE1BB35-ONC125761E.003E60C1-C125761E.003F629D@ifm-electronic.com>
References: <OF3A387610.8DE1BB35-ONC125761E.003E60C1-C125761E.003F629D@ifm-electronic.com>
Message-ID: <826c64da0908260711g6d77aa0bs8f7186b665402e5e@mail.gmail.com>

Unless you have a periodic function, I wouldn't rely much on the resample
function. It uses FFT approach, and the basic assumption is that x is
periodic. Without even trying to go into details, my first guess is that
what you see is the consequence
of aliasing.

A function that uses polyphase filter would do a better job, but it hasn't
been written yet :(
Until that is done, maybe you want to experiment by appending zeros to x,
resampling, and then discarding the last half:

>>> x=linspace(0,9,10)
>>> xx=r_[x, zeros(10)]
>>> yy=resample(xx,10)
>>> yy
array([ 0.16423509,  1.89281122,  4.1904564 ,  5.66068461,  8.68487231,
        2.33576491, -0.6288792 ,  0.3095436 , -0.16068461,  0.05119566])


>>> y=yy[0:5]
>>> y
array([ 0.16423509,  1.89281122,  4.1904564 ,  5.66068461,  8.68487231])

This is by no means a perfect solution, but I'm just throwing some ideas,
and you can try and see if that is good enough for you.

Ivo

2009/8/26 <markus.proeller at ifm.com>

>
> Hello,
>
> I have a question concerning the resample function of scipy.
> I have the following code:
>
> from scipy.signal import resample
> >>>x = array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
> >>>resample(x,5)
> array([ 2.5       ,  1.26393202,  4.5       ,  5.5       ,  8.73606798])
>
> I don't understand the first value of 2.5.
> My scipy version is 0.7.0
>
> Thanks for help,
>
> Markus
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
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From lev at columbia.edu  Wed Aug 26 10:44:16 2009
From: lev at columbia.edu (Lev Givon)
Date: Wed, 26 Aug 2009 10:44:16 -0400
Subject: [SciPy-User] Usage of scipy.signal.resample
In-Reply-To: <826c64da0908260711g6d77aa0bs8f7186b665402e5e@mail.gmail.com>
References: <OF3A387610.8DE1BB35-ONC125761E.003E60C1-C125761E.003F629D@ifm-electronic.com>
	<826c64da0908260711g6d77aa0bs8f7186b665402e5e@mail.gmail.com>
Message-ID: <20090826144416.GA20833@localhost.ee.columbia.edu>

Received from Ivo Maljevic on Wed, Aug 26, 2009 at 10:11:29AM EDT:

> 2009/8/26 <markus.proeller at ifm.com>
> 
> >
> > Hello,
> >
> > I have a question concerning the resample function of scipy.
> > I have the following code:
> >
> > from scipy.signal import resample
> > >>>x = array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
> > >>>resample(x,5)
> > array([ 2.5       ,  1.26393202,  4.5       ,  5.5       ,  8.73606798])
> >
> > I don't understand the first value of 2.5.
> > My scipy version is 0.7.0
> >
> > Thanks for help,
> >
> > Markus
>
> Unless you have a periodic function, I wouldn't rely much on the resample
> function. It uses FFT approach, and the basic assumption is that x is
> periodic. Without even trying to go into details, my first guess is that
> what you see is the consequence
> of aliasing.
> 
> A function that uses polyphase filter would do a better job, but it hasn't
> been written yet :(
> Until that is done, maybe you want to experiment by appending zeros to x,
> resampling, and then discarding the last half:
> 
> >>> x=linspace(0,9,10)
> >>> xx=r_[x, zeros(10)]
> >>> yy=resample(xx,10)
> >>> yy
> array([ 0.16423509,  1.89281122,  4.1904564 ,  5.66068461,  8.68487231,
>         2.33576491, -0.6288792 ,  0.3095436 , -0.16068461,  0.05119566])
> 
> 
> >>> y=yy[0:5]
> >>> y
> array([ 0.16423509,  1.89281122,  4.1904564 ,  5.66068461,  8.68487231])
> 
> This is by no means a perfect solution, but I'm just throwing some ideas,
> and you can try and see if that is good enough for you.
> 
> Ivo

You may also wish to check out the samplerate scikit by David
Cournapeau; it provides a Python interface to an eponymous library
that provides a more robust sample rate conversion facility than the
fft-based function provided by scipy.

http://pypi.python.org/pypi/scikits.samplerate

							L.G.


From vanleeuwen.martin at gmail.com  Wed Aug 26 12:50:26 2009
From: vanleeuwen.martin at gmail.com (Martin van Leeuwen)
Date: Wed, 26 Aug 2009 09:50:26 -0700
Subject: [SciPy-User] computing local extrema
In-Reply-To: <4A951D60.7010104@gmail.com>
References: <4A951D60.7010104@gmail.com>
Message-ID: <b6bdeb060908260950y3ba45890mabfa344bb06e849d@mail.gmail.com>

Hi,

You could create multiple thresholds along one axis of the array, and
get all elements above each threshold value (=region). You build a
region-stack from this; a 2D/3D boolean array that says which elements
are above the threshold value for each threshold you used. If a region
contains a region in a higher threshold value level (higher up in the
stack) that means you haven't found the local maximum yet; if it
doesn't, that is your top.

This method would allow flats to be detected as well.

The last thing you have to do is: for every remaining region that is a
local maximum, you compute the coordinates and value as the mean of
all coordinates or values contained in the region.

Hope this helps
Martin


2009/8/26 fred <fredmfp at gmail.com>:
> Hi,
>
> I would like to compute local extrema of an array (2D/3D),
> ie get a list of points (coords + value).
>
> How could I do this?
>
> Any hint?
>
> TIA.
>
> Cheers,
>
> --
> Fred
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From ndbecker2 at gmail.com  Wed Aug 26 13:36:21 2009
From: ndbecker2 at gmail.com (Neal Becker)
Date: Wed, 26 Aug 2009 13:36:21 -0400
Subject: [SciPy-User] Usage of scipy.signal.resample
References: <OF3A387610.8DE1BB35-ONC125761E.003E60C1-C125761E.003F629D@ifm-electronic.com>
	<826c64da0908260711g6d77aa0bs8f7186b665402e5e@mail.gmail.com>
	<20090826144416.GA20833@localhost.ee.columbia.edu>
Message-ID: <h73rql$kj9$1@ger.gmane.org>

Lev Givon wrote:

> Received from Ivo Maljevic on Wed, Aug 26, 2009 at 10:11:29AM EDT:
> 
>> 2009/8/26 <markus.proeller at ifm.com>
>> 
>> >
>> > Hello,
>> >
>> > I have a question concerning the resample function of scipy.
>> > I have the following code:
>> >
>> > from scipy.signal import resample
>> > >>>x = array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>> > >>>resample(x,5)
>> > array([ 2.5       ,  1.26393202,  4.5       ,  5.5       , 
>> > 8.73606798])
>> >
>> > I don't understand the first value of 2.5.
>> > My scipy version is 0.7.0
>> >
>> > Thanks for help,
>> >
>> > Markus
>>
>> Unless you have a periodic function, I wouldn't rely much on the resample
>> function. It uses FFT approach, and the basic assumption is that x is
>> periodic. Without even trying to go into details, my first guess is that
>> what you see is the consequence
>> of aliasing.
>> 
>> A function that uses polyphase filter would do a better job, but it
>> hasn't been written yet :(
>> Until that is done, maybe you want to experiment by appending zeros to x,
>> resampling, and then discarding the last half:
>> 
>> >>> x=linspace(0,9,10)
>> >>> xx=r_[x, zeros(10)]
>> >>> yy=resample(xx,10)
>> >>> yy
>> array([ 0.16423509,  1.89281122,  4.1904564 ,  5.66068461,  8.68487231,
>>         2.33576491, -0.6288792 ,  0.3095436 , -0.16068461,  0.05119566])
>> 
>> 
>> >>> y=yy[0:5]
>> >>> y
>> array([ 0.16423509,  1.89281122,  4.1904564 ,  5.66068461,  8.68487231])
>> 
>> This is by no means a perfect solution, but I'm just throwing some ideas,
>> and you can try and see if that is good enough for you.
>> 
>> Ivo
> 
> You may also wish to check out the samplerate scikit by David
> Cournapeau; it provides a Python interface to an eponymous library
> that provides a more robust sample rate conversion facility than the
> fft-based function provided by scipy.
> 
> http://pypi.python.org/pypi/scikits.samplerate
> 
> L.G.

Thanks for pointing to this.  I am quite interested in this subject.  I 
grabbed the samplerate source - it seems to be a wrapper on libsamplerate.  
I am quite interested in the reference http://www-isl.stanford.edu/~boyd/.

AFAICT, libsamplerate refers to the above article, but IIUC it doesn't 
actually use this technique at all.  Am I missing something?  (I'd like to 
try to get the software that was used for SOCP - I've been trying to find 
software for optimization of complex-value FIR filters)



From permafacture at gmail.com  Wed Aug 26 13:48:18 2009
From: permafacture at gmail.com (Permafacture)
Date: Wed, 26 Aug 2009 10:48:18 -0700 (PDT)
Subject: [SciPy-User] fsolve, no solution and the ier flag
In-Reply-To: <19c69d4c-6c04-49b7-8c17-482f81d29442@32g2000yqj.googlegroups.com>
References: <19c69d4c-6c04-49b7-8c17-482f81d29442@32g2000yqj.googlegroups.com>
Message-ID: <dac5ee11-5d71-4356-9782-8ffd19531fc6@c29g2000yqd.googlegroups.com>


I was wrong.  ier is in there.

in my case:

intersect[2] = 5


From cournape at gmail.com  Wed Aug 26 14:13:38 2009
From: cournape at gmail.com (David Cournapeau)
Date: Wed, 26 Aug 2009 13:13:38 -0500
Subject: [SciPy-User] Usage of scipy.signal.resample
In-Reply-To: <h73rql$kj9$1@ger.gmane.org>
References: <OF3A387610.8DE1BB35-ONC125761E.003E60C1-C125761E.003F629D@ifm-electronic.com>
	<826c64da0908260711g6d77aa0bs8f7186b665402e5e@mail.gmail.com>
	<20090826144416.GA20833@localhost.ee.columbia.edu>
	<h73rql$kj9$1@ger.gmane.org>
Message-ID: <5b8d13220908261113k60dfb807xe6465b28749dceb5@mail.gmail.com>

On Wed, Aug 26, 2009 at 12:36 PM, Neal Becker<ndbecker2 at gmail.com> wrote:
> Lev Givon wrote:
>
>> Received from Ivo Maljevic on Wed, Aug 26, 2009 at 10:11:29AM EDT:
>>
>>> 2009/8/26 <markus.proeller at ifm.com>
>>>
>>> >
>>> > Hello,
>>> >
>>> > I have a question concerning the resample function of scipy.
>>> > I have the following code:
>>> >
>>> > from scipy.signal import resample
>>> > >>>x = array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> > >>>resample(x,5)
>>> > array([ 2.5 ? ? ? , ?1.26393202, ?4.5 ? ? ? , ?5.5 ? ? ? ,
>>> > 8.73606798])
>>> >
>>> > I don't understand the first value of 2.5.
>>> > My scipy version is 0.7.0
>>> >
>>> > Thanks for help,
>>> >
>>> > Markus
>>>
>>> Unless you have a periodic function, I wouldn't rely much on the resample
>>> function. It uses FFT approach, and the basic assumption is that x is
>>> periodic. Without even trying to go into details, my first guess is that
>>> what you see is the consequence
>>> of aliasing.
>>>
>>> A function that uses polyphase filter would do a better job, but it
>>> hasn't been written yet :(
>>> Until that is done, maybe you want to experiment by appending zeros to x,
>>> resampling, and then discarding the last half:
>>>
>>> >>> x=linspace(0,9,10)
>>> >>> xx=r_[x, zeros(10)]
>>> >>> yy=resample(xx,10)
>>> >>> yy
>>> array([ 0.16423509, ?1.89281122, ?4.1904564 , ?5.66068461, ?8.68487231,
>>> ? ? ? ? 2.33576491, -0.6288792 , ?0.3095436 , -0.16068461, ?0.05119566])
>>>
>>>
>>> >>> y=yy[0:5]
>>> >>> y
>>> array([ 0.16423509, ?1.89281122, ?4.1904564 , ?5.66068461, ?8.68487231])
>>>
>>> This is by no means a perfect solution, but I'm just throwing some ideas,
>>> and you can try and see if that is good enough for you.
>>>
>>> Ivo
>>
>> You may also wish to check out the samplerate scikit by David
>> Cournapeau; it provides a Python interface to an eponymous library
>> that provides a more robust sample rate conversion facility than the
>> fft-based function provided by scipy.
>>
>> http://pypi.python.org/pypi/scikits.samplerate
>>
>> L.G.
>
> Thanks for pointing to this. ?I am quite interested in this subject. ?I
> grabbed the samplerate source - it seems to be a wrapper on libsamplerate.
> I am quite interested in the reference http://www-isl.stanford.edu/~boyd/.
>
> AFAICT, libsamplerate refers to the above article, but IIUC it doesn't
> actually use this technique at all.

Are you refering to the right link ? SRC is based on sinc
interpolation (band-limited interpolation), which is a well known
technique for high quality resampling for audio signals (the same
kinds of techniques are used in synthesizers, for example).

http://ccrma-www.stanford.edu/~jos/resample/

AFAIK, SRC is one of the best resampling implementation for audio
signals, and certainly the best available under an open source license
(GPL).

David


From ivo.maljevic at gmail.com  Wed Aug 26 16:20:25 2009
From: ivo.maljevic at gmail.com (Ivo Maljevic)
Date: Wed, 26 Aug 2009 16:20:25 -0400
Subject: [SciPy-User] Usage of scipy.signal.resample
In-Reply-To: <OF3A387610.8DE1BB35-ONC125761E.003E60C1-C125761E.003F629D@ifm-electronic.com>
References: <OF3A387610.8DE1BB35-ONC125761E.003E60C1-C125761E.003F629D@ifm-electronic.com>
Message-ID: <826c64da0908261320r50c7612o3d1ee7bfc685564d@mail.gmail.com>

Markus,
One question for you. If you are going to downsample your signal by an
integer value,
why don't you simply take every Mth sample (if M is your downsampling
ratio)?
Ivo

2009/8/26 <markus.proeller at ifm.com>

>
> Hello,
>
> I have a question concerning the resample function of scipy.
> I have the following code:
>
> from scipy.signal import resample
> >>>x = array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
> >>>resample(x,5)
> array([ 2.5       ,  1.26393202,  4.5       ,  5.5       ,  8.73606798])
>
> I don't understand the first value of 2.5.
> My scipy version is 0.7.0
>
> Thanks for help,
>
> Markus
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
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From cournape at gmail.com  Wed Aug 26 16:24:09 2009
From: cournape at gmail.com (David Cournapeau)
Date: Wed, 26 Aug 2009 15:24:09 -0500
Subject: [SciPy-User] Usage of scipy.signal.resample
In-Reply-To: <826c64da0908261320r50c7612o3d1ee7bfc685564d@mail.gmail.com>
References: <OF3A387610.8DE1BB35-ONC125761E.003E60C1-C125761E.003F629D@ifm-electronic.com>
	<826c64da0908261320r50c7612o3d1ee7bfc685564d@mail.gmail.com>
Message-ID: <5b8d13220908261324r1882baa9y3ffb80065016311f@mail.gmail.com>

On Wed, Aug 26, 2009 at 3:20 PM, Ivo Maljevic<ivo.maljevic at gmail.com> wrote:
> Markus,
> One question for you. If you are going to downsample your signal by an
> integer value,
> why don't you simply take every Mth sample (if M is your downsampling
> ratio)?

It would incure an awful lot of aliasing if you don't low pass at 1/M first.

cheers,

David


From ivo.maljevic at gmail.com  Wed Aug 26 16:30:05 2009
From: ivo.maljevic at gmail.com (Ivo Maljevic)
Date: Wed, 26 Aug 2009 16:30:05 -0400
Subject: [SciPy-User] Usage of scipy.signal.resample
In-Reply-To: <5b8d13220908261324r1882baa9y3ffb80065016311f@mail.gmail.com>
References: <OF3A387610.8DE1BB35-ONC125761E.003E60C1-C125761E.003F629D@ifm-electronic.com>
	<826c64da0908261320r50c7612o3d1ee7bfc685564d@mail.gmail.com>
	<5b8d13220908261324r1882baa9y3ffb80065016311f@mail.gmail.com>
Message-ID: <826c64da0908261330s198386a9w751a5b44e42b7d62@mail.gmail.com>

Right, right, I'm a bit rusty, haven't used this in a while. I'm glad to see
that there is a source code for the 'sinc' table based resampling, I used it
in the past but haven't saved to code.

2009/8/26 David Cournapeau <cournape at gmail.com>

> On Wed, Aug 26, 2009 at 3:20 PM, Ivo Maljevic<ivo.maljevic at gmail.com>
> wrote:
> > Markus,
> > One question for you. If you are going to downsample your signal by an
> > integer value,
> > why don't you simply take every Mth sample (if M is your downsampling
> > ratio)?
>
> It would incure an awful lot of aliasing if you don't low pass at 1/M
> first.
>
> cheers,
>
> David
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From david_baddeley at yahoo.com.au  Wed Aug 26 17:41:06 2009
From: david_baddeley at yahoo.com.au (David Baddeley)
Date: Wed, 26 Aug 2009 14:41:06 -0700 (PDT)
Subject: [SciPy-User] computing local extrema
In-Reply-To: <4A951D60.7010104@gmail.com>
References: <4A951D60.7010104@gmail.com>
Message-ID: <469625.15621.qm@web33004.mail.mud.yahoo.com>

Hi Fred,

you could try something along these lines (assuming your data is called 'data' and numpy is imported as np):

d1 = np.diff(np.sign(np.diff(data))

maxima_indices = np.where(d1 < -.5) + 1
minima_indices = np.where(d1 > .5) + 1

you can then get the values using the indices on your original data. 


best wishes,
David


----- Original Message ----
From: fred <fredmfp at gmail.com>
To: SciPy Users List <scipy-user at scipy.org>
Sent: Wednesday, 26 August, 2009 11:32:48 PM
Subject: [SciPy-User] computing local extrema

Hi,

I would like to compute local extrema of an array (2D/3D),
ie get a list of points (coords + value).

How could I do this?

Any hint?

TIA.

Cheers,

-- 
Fred
_______________________________________________
SciPy-User mailing list
SciPy-User at scipy.org
http://mail.scipy.org/mailman/listinfo/scipy-user



      


From rbroze at yahoo.com  Wed Aug 26 19:14:20 2009
From: rbroze at yahoo.com (robert roze)
Date: Wed, 26 Aug 2009 16:14:20 -0700 (PDT)
Subject: [SciPy-User] errors from scipy.test(), from import calc_lwork
Message-ID: <736000.79338.qm@web37106.mail.mud.yahoo.com>

I get 10 errors from the scipy.test() routine. Nine of them appear to happen when the linalg/basic.py module executes: 

from scipy.linalg import calc_lwork
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/net/easystreet/vol/homes/rroze/Python-2.6.2/lib/python2.6/site-packages/scipy/linalg/__init__.py", line 8, in <module>
    from basic import *
  File "/net/easystreet/vol/homes/rroze/Python-2.6.2/lib/python2.6/site-packages/scipy/linalg/basic.py", line 24, in <module>
    from scipy.linalg import calc_lwork
ImportError: cannot import name calc_lwork

It seems like all modules that import calc_lwork fail this way. At the python command line, I get the ImportError from all these modules:
 scipy.stats, scipy.signal, scipy.maxentropy, scipy.linalg, scipy.interpolate
All, complaining about "calc_lwork"

But the other modules import without complaint (scipy.sparse, scipy.optimize, etc...)

In
the site-packages/scipy/linalg directory, there is a file called
calc_lwork.so, so it appears to exist, but it is a binary file, I don't
know the type, why it wouldn't be import-able, and this is where my software skills start to fade. Does anyone have any idea why calc_lwork can't import?

Here is the info on my platform, versions, etc:

1) Platform information::
  posix linux2
  Linux ld23 2.6.9-78.0.5.ELsmp #1 SMP Wed Sep 24 05:40:24 EDT 2008 x86_64 x86_64 x86_64 GNU/Linux
  RedHad Enterprise version 3.4, and I do not have root

2) Information about C,C++,Fortran compilers/linkers as reported by
   the compilers when requesting their version information, e.g.,
   the output of
   ::
gcc -v
Reading specs from /usr/lib/gcc/x86_64-redhat-linux/3.4.6/specs
Configured
with: ../configure --prefix=/usr --mandir=/usr/share/man
--infodir=/usr/share/info --enable-shared --enable-threads=posix
--disable-checking --with-system-zlib --enable-__cxa_atexit
--disable-libunwind-exceptions --enable-java-awt=gtk
--host=x86_64-redhat-linux
Thread model: posix
gcc version 3.4.6 20060404 (Red Hat 3.4.6-8)

g77 --version
GNU Fortran (GCC) 3.4.6 20060404 (Red Hat 3.4.6-8)
Copyright (C) 2006 Free Software Foundation, Inc.

3) Python version::
python -c 'import sys;print sys.version'
2.6.2 (r262:71600, Aug 26 2009, 13:45:43)
[GCC 3.4.6 20060404 (Red Hat 3.4.6-8)]

4) NumPy version::
python -c 'import numpy;print numpy.__version__'
1.3.0

5) ATLAS version, the locations of atlas and lapack libraries, building
   information if any. If you have ATLAS version 3.3.6 or newer, then
   give the output of the last command in
   ::
     cd scipy/Lib/linalg

  I didn't find this directory, but found a similar one here:

scipy-0.7.1/scipy/linalg

  which contained the file, setup_atlas_version.py, so I ran it and got this error:

python setup_atlas_version.py build_ext --inplace --force
Traceback (most recent call last):
  File "setup_atlas_version.py", line 7, in <module>
    from numpy.distutils.misc_util import get_path, default_config_dict
ImportError: cannot import name get_path

python -c 'import atlas_version'
Traceback (most recent call last):
  File "<string>", line 1, in <module>
ImportError: No module named atlas_version

7)  The output of the following commands

python /net/easystreet/vol/homes/rroze/Python-2.6.2/lib/python2.6/site-packages/numpy/distutils/system_info.py
lapack_info:
  libraries lapack not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries lapack not found in /usr/local/lib64
  libraries lapack not found in /usr/local/lib
  FOUND:
    libraries = ['lapack']
    library_dirs = ['/usr/lib64']
    language = f77

lapack_opt_info:
lapack_mkl_info:
mkl_info:
  libraries mkl,vml,guide not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries mkl,vml,guide not found in /usr/local/lib64
  libraries mkl,vml,guide not found in /usr/local/lib
  libraries mkl,vml,guide not found in /usr/lib64
  libraries mkl,vml,guide not found in /usr/lib
  NOT AVAILABLE

  NOT AVAILABLE

atlas_threads_info:
Setting PTATLAS=ATLAS
  libraries ptf77blas,ptcblas,atlas not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries lapack_atlas not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries ptf77blas,ptcblas,atlas not found in /usr/local/lib64
  libraries lapack_atlas not found in /usr/local/lib64
  libraries ptf77blas,ptcblas,atlas not found in /usr/local/lib
  libraries lapack_atlas not found in /usr/local/lib
  libraries ptf77blas,ptcblas,atlas not found in /usr/lib64
  libraries lapack_atlas not found in /usr/lib64
  libraries ptf77blas,ptcblas,atlas not found in /usr/lib/sse2
  libraries lapack_atlas not found in /usr/lib/sse2
  libraries ptf77blas,ptcblas,atlas not found in /usr/lib
  libraries lapack_atlas not found in /usr/lib
__main__.atlas_threads_info
  NOT AVAILABLE

atlas_info:
  libraries f77blas,cblas,atlas not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries lapack_atlas not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries f77blas,cblas,atlas not found in /usr/local/lib64
  libraries lapack_atlas not found in /usr/local/lib64
  libraries f77blas,cblas,atlas not found in /usr/local/lib
  libraries lapack_atlas not found in /usr/local/lib
  libraries f77blas,cblas,atlas not found in /usr/lib64
  libraries lapack_atlas not found in /usr/lib64
  libraries f77blas,cblas,atlas not found in /usr/lib/sse2
  libraries lapack_atlas not found in /usr/lib/sse2
  libraries f77blas,cblas,atlas not found in /usr/lib
  libraries lapack_atlas not found in /usr/lib
__main__.atlas_info
  NOT AVAILABLE

/net/easystreet/vol/homes/rroze/Python-2.6.2/lib/python2.6/site-packages/numpy/distutils/system_info.py:1290: UserWarning:
    Atlas (http://math-atlas.sourceforge.net/) libraries not found.
    Directories to search for the libraries can be specified in the
    numpy/distutils/site.cfg file (section [atlas]) or by setting
    the ATLAS environment variable.
  warnings.warn(AtlasNotFoundError.__doc__)
blas_info:
  libraries blas not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries blas not found in /usr/local/lib64
  libraries blas not found in /usr/local/lib
  FOUND:
    libraries = ['blas']
    library_dirs = ['/usr/lib64']
    language = f77

  FOUND:
    libraries = ['lapack', 'blas']
    library_dirs = ['/usr/lib64']
    define_macros = [('NO_ATLAS_INFO', 1)]
    language = f77

wx_info:
Could not locate executable wx-config
File not found: None. Cannot determine wx info.
  NOT AVAILABLE

lapack_atlas_info:
  libraries lapack_atlas,f77blas,cblas,atlas not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries lapack_atlas not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries lapack_atlas,f77blas,cblas,atlas not found in /usr/local/lib64
  libraries lapack_atlas not found in /usr/local/lib64
  libraries lapack_atlas,f77blas,cblas,atlas not found in /usr/local/lib
  libraries lapack_atlas not found in /usr/local/lib
  libraries lapack_atlas,f77blas,cblas,atlas not found in /usr/lib64
  libraries lapack_atlas not found in /usr/lib64
  libraries lapack_atlas,f77blas,cblas,atlas not found in /usr/lib/sse2
  libraries lapack_atlas not found in /usr/lib/sse2
  libraries lapack_atlas,f77blas,cblas,atlas not found in /usr/lib
  libraries lapack_atlas not found in /usr/lib
__main__.lapack_atlas_info
  NOT AVAILABLE

umfpack_info:
  libraries umfpack not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries umfpack not found in /usr/local/lib64
  libraries umfpack not found in /usr/local/lib
  libraries umfpack not found in /usr/lib64
  libraries umfpack not found in /usr/lib
  NOT AVAILABLE

_pkg_config_info:
Found executable /usr/bin/pkg-config
  NOT AVAILABLE

lapack_atlas_threads_info:
Setting PTATLAS=ATLAS
  libraries lapack_atlas,ptf77blas,ptcblas,atlas not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries lapack_atlas not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries lapack_atlas,ptf77blas,ptcblas,atlas not found in /usr/local/lib64
  libraries lapack_atlas not found in /usr/local/lib64
  libraries lapack_atlas,ptf77blas,ptcblas,atlas not found in /usr/local/lib
  libraries lapack_atlas not found in /usr/local/lib
  libraries lapack_atlas,ptf77blas,ptcblas,atlas not found in /usr/lib64
  libraries lapack_atlas not found in /usr/lib64
  libraries lapack_atlas,ptf77blas,ptcblas,atlas not found in /usr/lib/sse2
  libraries lapack_atlas not found in /usr/lib/sse2
  libraries lapack_atlas,ptf77blas,ptcblas,atlas not found in /usr/lib
  libraries lapack_atlas not found in /usr/lib
__main__.lapack_atlas_threads_info
  NOT AVAILABLE

x11_info:
  FOUND:
    libraries = ['X11']
    library_dirs = ['/usr/X11R6/lib64']
    include_dirs = ['/usr/X11R6/include']

fftw_info:
  libraries fftw3 not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries fftw3 not found in /usr/local/lib64
  libraries fftw3 not found in /usr/local/lib
  libraries fftw3 not found in /usr/lib64
  libraries fftw3 not found in /usr/lib
  fftw3 not found
  libraries rfftw,fftw not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries rfftw,fftw not found in /usr/local/lib64
  libraries rfftw,fftw not found in /usr/local/lib
  libraries rfftw,fftw not found in /usr/lib64
  libraries rfftw,fftw not found in /usr/lib
  fftw2 not found
  NOT AVAILABLE

f2py_info:
  FOUND:
    sources = ['/net/easystreet/vol/homes/rroze/Python-2.6.2/lib/python2.6/site-packages/numpy/f2py/src/fortranobject.c']
    include_dirs = ['/net/easystreet/vol/homes/rroze/Python-2.6.2/lib/python2.6/site-packages/numpy/f2py/src']

gdk_pixbuf_xlib_2_info:
  NOT AVAILABLE

dfftw_threads_info:
  libraries drfftw_threads,dfftw_threads not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries drfftw_threads,dfftw_threads not found in /usr/local/lib64
  libraries drfftw_threads,dfftw_threads not found in /usr/local/lib
  libraries drfftw_threads,dfftw_threads not found in /usr/lib64
  libraries drfftw_threads,dfftw_threads not found in /usr/lib
  dfftw threads not found
  NOT AVAILABLE

atlas_blas_info:
  libraries f77blas,cblas,atlas not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries f77blas,cblas,atlas not found in /usr/local/lib64
  libraries f77blas,cblas,atlas not found in /usr/local/lib
  libraries f77blas,cblas,atlas not found in /usr/lib64
  libraries f77blas,cblas,atlas not found in /usr/lib/sse2
  libraries f77blas,cblas,atlas not found in /usr/lib
  NOT AVAILABLE

fftw3_info:
  libraries fftw3 not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries fftw3 not found in /usr/local/lib64
  libraries fftw3 not found in /usr/local/lib
  libraries fftw3 not found in /usr/lib64
  libraries fftw3 not found in /usr/lib
  fftw3 not found
  NOT AVAILABLE

blas_opt_info:
blas_mkl_info:
  libraries mkl,vml,guide not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries mkl,vml,guide not found in /usr/local/lib64
  libraries mkl,vml,guide not found in /usr/local/lib
  libraries mkl,vml,guide not found in /usr/lib64
  libraries mkl,vml,guide not found in /usr/lib
  NOT AVAILABLE

atlas_blas_threads_info:
Setting PTATLAS=ATLAS
  libraries ptf77blas,ptcblas,atlas not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries ptf77blas,ptcblas,atlas not found in /usr/local/lib64
  libraries ptf77blas,ptcblas,atlas not found in /usr/local/lib
  libraries ptf77blas,ptcblas,atlas not found in /usr/lib64
  libraries ptf77blas,ptcblas,atlas not found in /usr/lib/sse2
  libraries ptf77blas,ptcblas,atlas not found in /usr/lib
  NOT AVAILABLE

/net/easystreet/vol/homes/rroze/Python-2.6.2/lib/python2.6/site-packages/numpy/distutils/system_info.py:1383: UserWarning:
    Atlas (http://math-atlas.sourceforge.net/) libraries not found.
    Directories to search for the libraries can be specified in the
    numpy/distutils/site.cfg file (section [atlas]) or by setting
    the ATLAS environment variable.
  warnings.warn(AtlasNotFoundError.__doc__)
  FOUND:
    libraries = ['blas']
    library_dirs = ['/usr/lib64']
    define_macros = [('NO_ATLAS_INFO', 1)]
    language = f77

sfftw_info:
  libraries srfftw,sfftw not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries srfftw,sfftw not found in /usr/local/lib64
  libraries srfftw,sfftw not found in /usr/local/lib
  libraries srfftw,sfftw not found in /usr/lib64
  libraries srfftw,sfftw not found in /usr/lib
  sfftw not found
  NOT AVAILABLE

xft_info:
  FOUND:
    libraries = ['Xft', 'X11', 'freetype', 'Xrender', 'fontconfig']
    library_dirs = ['/usr/X11R6/lib64']
    define_macros = [('XFT_INFO', '"\\"2.1.2.2\\""'), ('XFT_VERSION_2_1_2_2', None)]
    include_dirs = ['/usr/X11R6/include', '/usr/include/freetype2', '/usr/include/freetype2/config']

fft_opt_info:
fftw2_info:
  libraries rfftw,fftw not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries rfftw,fftw not found in /usr/local/lib64
  libraries rfftw,fftw not found in /usr/local/lib
  libraries rfftw,fftw not found in /usr/lib64
  libraries rfftw,fftw not found in /usr/lib
  fftw2 not found
  NOT AVAILABLE

dfftw_info:
  libraries drfftw,dfftw not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries drfftw,dfftw not found in /usr/local/lib64
  libraries drfftw,dfftw not found in /usr/local/lib
  libraries drfftw,dfftw not found in /usr/lib64
  libraries drfftw,dfftw not found in /usr/lib
  dfftw not found
  NOT AVAILABLE

djbfft_info:
  NOT AVAILABLE

  NOT AVAILABLE

gdk_x11_2_info:
  NOT AVAILABLE

agg2_info:
  NOT AVAILABLE

numarray_info:
  NOT AVAILABLE

blas_src_info:
  NOT AVAILABLE

fftw_threads_info:
  libraries rfftw_threads,fftw_threads not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries rfftw_threads,fftw_threads not found in /usr/local/lib64
  libraries rfftw_threads,fftw_threads not found in /usr/local/lib
  libraries rfftw_threads,fftw_threads not found in /usr/lib64
  libraries rfftw_threads,fftw_threads not found in /usr/lib
  fftw threads not found
  NOT AVAILABLE

_numpy_info:
  NOT AVAILABLE

gdk_info:
  NOT AVAILABLE

gtkp_x11_2_info:
  NOT AVAILABLE

sfftw_threads_info:
  libraries srfftw_threads,sfftw_threads not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries srfftw_threads,sfftw_threads not found in /usr/local/lib64
  libraries srfftw_threads,sfftw_threads not found in /usr/local/lib
  libraries srfftw_threads,sfftw_threads not found in /usr/lib64
  libraries srfftw_threads,sfftw_threads not found in /usr/lib
  sfftw threads not found
  NOT AVAILABLE

boost_python_info:
  NOT AVAILABLE

freetype2_info:
  FOUND:
    libraries = ['freetype', 'z']
    define_macros = [('FREETYPE2_INFO', '"\\"9.7.3\\""'), ('FREETYPE2_VERSION_9_7_3', None)]
    include_dirs = ['/usr/include/freetype2']

gdk_2_info:
  NOT AVAILABLE

lapack_src_info:
  NOT AVAILABLE

gtkp_2_info:
  NOT AVAILABLE

gdk_pixbuf_2_info:
  NOT AVAILABLE

amd_info:
  libraries amd not found in /net/easystreet/vol/homes/rroze/Python-2.6.2/lib
  libraries amd not found in /usr/local/lib64
  libraries amd not found in /usr/local/lib
  libraries amd not found in /usr/lib64
  libraries amd not found in /usr/lib
  NOT AVAILABLE

Numeric_info:
  NOT AVAILABLE

numerix_info:
numpy_info:
  FOUND:
    define_macros = [('NUMPY_VERSION', '"\\"1.3.0\\""'), ('NUMPY', None)]

  FOUND:
    define_macros = [('NUMPY_VERSION', '"\\"1.3.0\\""'), ('NUMPY', None)]

9) ldd /path/to/ext_module.so
ldd: /path/to/ext_module.so: No such file or directory


      
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From david_baddeley at yahoo.com.au  Wed Aug 26 20:13:04 2009
From: david_baddeley at yahoo.com.au (David Baddeley)
Date: Wed, 26 Aug 2009 17:13:04 -0700 (PDT)
Subject: [SciPy-User] computing local extrema
In-Reply-To: <469625.15621.qm@web33004.mail.mud.yahoo.com>
References: <4A951D60.7010104@gmail.com>
	<469625.15621.qm@web33004.mail.mud.yahoo.com>
Message-ID: <666813.16131.qm@web33003.mail.mud.yahoo.com>

Oops,

overlooked the 2D/3D part, but the idea should be able to be generalised if you replace the 'diff' with a convolution with a differential kernel.
eg: scipy.ndimage.convolve(data, array([[0, -.5, 0], [-.5, 1, 0], [0,0,0]]))

David



----- Original Message ----
From: David Baddeley <david_baddeley at yahoo.com.au>
To: SciPy Users List <scipy-user at scipy.org>
Sent: Thursday, 27 August, 2009 9:41:06 AM
Subject: Re: [SciPy-User] computing local extrema

Hi Fred,

you could try something along these lines (assuming your data is called 'data' and numpy is imported as np):

d1 = np.diff(np.sign(np.diff(data))

maxima_indices = np.where(d1 < -.5) + 1
minima_indices = np.where(d1 > .5) + 1

you can then get the values using the indices on your original data. 


best wishes,
David


----- Original Message ----
From: fred <fredmfp at gmail.com>
To: SciPy Users List <scipy-user at scipy.org>
Sent: Wednesday, 26 August, 2009 11:32:48 PM
Subject: [SciPy-User] computing local extrema

Hi,

I would like to compute local extrema of an array (2D/3D),
ie get a list of points (coords + value).

How could I do this?

Any hint?

TIA.

Cheers,

-- 
Fred
_______________________________________________
SciPy-User mailing list
SciPy-User at scipy.org
http://mail.scipy.org/mailman/listinfo/scipy-user



      
_______________________________________________
SciPy-User mailing list
SciPy-User at scipy.org
http://mail.scipy.org/mailman/listinfo/scipy-user



      


From ivo.maljevic at gmail.com  Wed Aug 26 22:40:30 2009
From: ivo.maljevic at gmail.com (Ivo Maljevic)
Date: Wed, 26 Aug 2009 22:40:30 -0400
Subject: [SciPy-User] Usage of scipy.signal.resample
In-Reply-To: <OF3A387610.8DE1BB35-ONC125761E.003E60C1-C125761E.003F629D@ifm-electronic.com>
References: <OF3A387610.8DE1BB35-ONC125761E.003E60C1-C125761E.003F629D@ifm-electronic.com>
Message-ID: <826c64da0908261940r3ee80555xb6bdc3b6ce6fba5c@mail.gmail.com>

I guess I spoke too early, and since I haven't looked into resampling theory
for about 15 years, the whole discussion took a different path insted of
simply answering Markus's question. David Cournapeau, you are fresh from the
DSP theory, so correct me if what I say below is not completely accurate.

First of all, there is nothing wrong with the result that resample function
provides. The problem is that normally one wouldn't use artificially created
examples like the ramp function for resampling. In many applications, real
world signals that need to be downsampled by an integer value would either
be oversampled to begin with, and have no high frequency content, or are LP
filtered, amounting to the same. In that scenario, FFT based resampling
should be good enough, as it simply removes the high frequency content
(which is zero or almost zero anyway), and keeping only low-pass content
intact. If you use the resample call from SciPy on such signals, you should
be fine.

Example that you gave is different because the FFT of x has all frequencies
(high frequency terms are not zero), so when you remove the HF terms you end
up with a different signal. I'll first illustrate what happens with FFT
based resampling.

>>> x=linspace(0,9,10)
>>> X=fft(x)
>>> X
array([ 45. +0.00000000e+00j,  -5. +1.53884177e+01j,  -5. +6.88190960e+00j,
        -5. +3.63271264e+00j,  -5. +1.62459848e+00j,  -5. +4.44089210e-16j,
        -5. -1.62459848e+00j,  -5. -3.63271264e+00j,  -5. -6.88190960e+00j,
        -5. -1.53884177e+01j])

>>> Y=delete(X,[3,4,5,6,7])   <-- remove the HF content

>>> Y
array([ 45. +0.j        ,  -5.+15.38841769j,  -5. +6.8819096j ,
        -5. -6.8819096j ,  -5.-15.38841769j])

>>> real(1/2.0*ifft(Y))       <-- Go back to time domain, ignore imag part
array([ 2.5       ,  1.26393202,  4.5       ,  5.5       ,  8.73606798])

Now, if you had a different signal to begin with, one that has no HF
components:

>>> X[3:7]=0  <-- set HF content to zero
>>> X
array([ 45. +0.j        ,  -5.+15.38841769j,  -5. +6.8819096j ,
         0. +0.j        ,   0. +0.j        ,   0. +0.j        ,
         0. +0.j        ,  -5. -3.63271264j,  -5. -6.8819096j ,
        -5.-15.38841769j])
>>> xx=ifft(X)  <-- A new time domain signal that has no HF if FT domain
>>> xx
array([ 2.00000000 -3.63271264e-01j,  0.07294902 +5.87785252e-01j,
        1.88196601 +1.28785871e-15j,  3.30901699 -5.87785252e-01j,
        4.00000000 +3.63271264e-01j,  5.00000000 +3.63271264e-01j,
        5.69098301 -5.87785252e-01j,  7.11803399 +5.77315973e-16j,
        8.92705098 +5.87785252e-01j,  7.00000000 -3.63271264e-01j])
>>> resample(xx,5)
array([ 2.50000000 -1.24567023e-15j,  1.26393202 +2.39196503e-16j,
        4.50000000 +2.00297331e-15j,  5.50000000 -3.92675644e-16j,
        8.73606798 -6.14926162e-16j])

You will get the same result as above, even though xx is not the same as x.

I have checked, and Matlab produces almost the same result as SciPy with
your ramp example, even though it uses polyphase filters.
It would be interesting to see what David's script produces, but I believe
the result would be similar.

So, in short, if you have a signal with limited frequency content, and you
just need to downsample it by an integer value,
SciPy's resample should be good enough. If you have a more complex scenario,
or need fractional resampling ratio, then you are better
of using David's code.

Ivo




2009/8/26 <markus.proeller at ifm.com>

>
> Hello,
>
> I have a question concerning the resample function of scipy.
> I have the following code:
>
> from scipy.signal import resample
> >>>x = array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
> >>>resample(x,5)
> array([ 2.5       ,  1.26393202,  4.5       ,  5.5       ,  8.73606798])
>
> I don't understand the first value of 2.5.
> My scipy version is 0.7.0
>
> Thanks for help,
>
> Markus
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
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From cournape at gmail.com  Thu Aug 27 00:02:12 2009
From: cournape at gmail.com (David Cournapeau)
Date: Wed, 26 Aug 2009 23:02:12 -0500
Subject: [SciPy-User] Usage of scipy.signal.resample
In-Reply-To: <826c64da0908261940r3ee80555xb6bdc3b6ce6fba5c@mail.gmail.com>
References: <OF3A387610.8DE1BB35-ONC125761E.003E60C1-C125761E.003F629D@ifm-electronic.com>
	<826c64da0908261940r3ee80555xb6bdc3b6ce6fba5c@mail.gmail.com>
Message-ID: <5b8d13220908262102v39e9be45hee842aa49f486c29@mail.gmail.com>

On Wed, Aug 26, 2009 at 9:40 PM, Ivo Maljevic<ivo.maljevic at gmail.com> wrote:
> I guess I spoke too early, and since I haven't looked into resampling theory
> for about 15 years, the whole discussion took a different path insted of
> simply answering Markus's question. David Cournapeau, you are fresh from the
> DSP theory, so correct me if what I say below is not completely accurate.
>
> First of all, there is nothing wrong with the result that resample function
> provides. The problem is that normally one wouldn't use artificially created
> examples like the ramp function for resampling.

stricly speaking, a ramp signal has infinite bandwidth because of the
discontinuity :)

> In many applications, real
> world signals that need to be downsampled by an integer value would either
> be oversampled to begin with, and have no high frequency content, or are LP
> filtered, amounting to the same. In that scenario, FFT based resampling
> should be good enough, as it simply removes the high frequency content
> (which is zero or almost zero anyway), and keeping only low-pass content
> intact. If you use the resample call from SciPy on such signals, you should
> be fine.

It really depends on the application. "My" code (which is really just
a very simple wrapper around SRC) is useful when you need high quality
resampling, which is often needed for speech or audio processing. In
this respect, the square mean error is only one of the important
factor (the type of the error matters a lot: with simple scheme as
linear interpolation,  you will get a very "colorful" noise, which is
very audible).

> I have checked, and Matlab produces almost the same result as SciPy with
> your ramp example, even though it uses polyphase filters.

Polyphase filters are fundamentally an optimization of the
upsampling-dowsampling necessary for rational sampling rate
conversion. But the basics are pretty simple: upsample to N,
downsample to M for a M/N rate conversion.

> It would be interesting to see what David's script produces, but I believe
> the result would be similar.

They can actually be quite different from a perceptive point of view.
If you have a good soundcard and have a somewhat trained ear, the
difference on music are generally quite significant.

I would really like to have a polyphase system in scipy.signal, though,

David


From d_l_goldsmith at yahoo.com  Thu Aug 27 01:11:34 2009
From: d_l_goldsmith at yahoo.com (David Goldsmith)
Date: Wed, 26 Aug 2009 22:11:34 -0700 (PDT)
Subject: [SciPy-User] More elegant way to recover the last iterate of a
	non-converging newton?
Message-ID: <204794.69817.qm@web52112.mail.re2.yahoo.com>

Hi, folks!  Question re: my own work - I'm using scipy.optimize.minpack.newton with a rather low maxiter (10) so it's frequently terminating with the "Failed to converge after maxiter iterations, value is <last iterate>" RuntimeError, which isn't a problem for now.  What is a problem is I still want to use <last iterate>, and the best I've come up with for "catching" it is:

try:
    result = newton(f, z, fp, tol=tol, maxiter=maxiter)
except RuntimeError, e:
    result = np.complex( e.message.split( '(' )[-1].split( ')' )[0] )

There's got to be a better way, no?

DG


      


From vanforeest at gmail.com  Thu Aug 27 02:49:08 2009
From: vanforeest at gmail.com (nicky van foreest)
Date: Thu, 27 Aug 2009 08:49:08 +0200
Subject: [SciPy-User] More elegant way to recover the last iterate of a
	non-converging newton?
In-Reply-To: <204794.69817.qm@web52112.mail.re2.yahoo.com>
References: <204794.69817.qm@web52112.mail.re2.yahoo.com>
Message-ID: <fa510ff80908262349v67b6ecabhbe506ef8ae40d434@mail.gmail.com>

A silly suggestion perhaps, but why don't you just change the tolerance?

2009/8/27 David Goldsmith <d_l_goldsmith at yahoo.com>:
> Hi, folks! ?Question re: my own work - I'm using scipy.optimize.minpack.newton with a rather low maxiter (10) so it's frequently terminating with the "Failed to converge after maxiter iterations, value is <last iterate>" RuntimeError, which isn't a problem for now. ?What is a problem is I still want to use <last iterate>, and the best I've come up with for "catching" it is:
>
> try:
> ? ?result = newton(f, z, fp, tol=tol, maxiter=maxiter)
> except RuntimeError, e:
> ? ?result = np.complex( e.message.split( '(' )[-1].split( ')' )[0] )
>
> There's got to be a better way, no?
>
> DG
>
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From d_l_goldsmith at yahoo.com  Thu Aug 27 02:51:34 2009
From: d_l_goldsmith at yahoo.com (David Goldsmith)
Date: Wed, 26 Aug 2009 23:51:34 -0700 (PDT)
Subject: [SciPy-User] More elegant way to recover the last iterate of a
	non-converging newton?
In-Reply-To: <fa510ff80908262349v67b6ecabhbe506ef8ae40d434@mail.gmail.com>
Message-ID: <632137.41709.qm@web52109.mail.re2.yahoo.com>

'Cause I'm generating a fractal and, at least until I've found the window I want, speed is more important than precision.

DG

--- On Wed, 8/26/09, nicky van foreest <vanforeest at gmail.com> wrote:

> From: nicky van foreest <vanforeest at gmail.com>
> Subject: Re: [SciPy-User] More elegant way to recover the last iterate of a non-converging newton?
> To: "SciPy Users List" <scipy-user at scipy.org>
> Date: Wednesday, August 26, 2009, 11:49 PM
> A silly suggestion perhaps, but why
> don't you just change the tolerance?
> 
> 2009/8/27 David Goldsmith <d_l_goldsmith at yahoo.com>:
> > Hi, folks! ?Question re: my own work - I'm using
> scipy.optimize.minpack.newton with a rather low maxiter (10)
> so it's frequently terminating with the "Failed to converge
> after maxiter iterations, value is <last iterate>"
> RuntimeError, which isn't a problem for now. ?What is a
> problem is I still want to use <last iterate>, and the
> best I've come up with for "catching" it is:
> >
> > try:
> > ? ?result = newton(f, z, fp, tol=tol,
> maxiter=maxiter)
> > except RuntimeError, e:
> > ? ?result = np.complex( e.message.split( '('
> )[-1].split( ')' )[0] )
> >
> > There's got to be a better way, no?
> >
> > DG
> >
> >
> >
> > _______________________________________________
> > SciPy-User mailing list
> > SciPy-User at scipy.org
> > http://mail.scipy.org/mailman/listinfo/scipy-user
> >
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
> 


      


From markus.proeller at ifm.com  Thu Aug 27 04:13:09 2009
From: markus.proeller at ifm.com (markus.proeller at ifm.com)
Date: Thu, 27 Aug 2009 10:13:09 +0200
Subject: [SciPy-User] Usage of scipy.signal.resample
In-Reply-To: <5b8d13220908262102v39e9be45hee842aa49f486c29@mail.gmail.com>
Message-ID: <OF0F2AF3D6.98797621-ONC125761F.002B5196-C125761F.002D2D6F@ifm-electronic.com>

Hello David and Ivo,

thank you very much for your detailed help. I do have a ramp like signal, 
that I want to compress. Thats why I wanted to use the resample function 
to generate same interim values. It is decompressed by linear 
interpolation, so I think the best approach is to generate the interim 
values by linear interpolation as well. The downsample ratio is not an 
integer value.
To be honest I just used the resample function because it was just a 
function call, were I could enter the amount of interim values. I didn't 
take care of aliasing and filtering.
I have another question to David, if I had an integer value for the 
downsampling rate, for instance the ramp function from 0 to 10 and take 
let's say every second sample, so I got 0,2,4,8,10, where do I have my 
aliasing here? Because I can reconstruct my original array by simple 
linear interpolation.

Markus
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From timmichelsen at gmx-topmail.de  Thu Aug 27 08:13:12 2009
From: timmichelsen at gmx-topmail.de (Tim Michelsen)
Date: Thu, 27 Aug 2009 12:13:12 +0000 (UTC)
Subject: [SciPy-User] interactive tool with data
Message-ID: <loom.20090827T140752-725@post.gmane.org>

Hello,
is there any interactive tool for scientific python, that supports data import
by dialog windows?

I am thinking of something like R-Commander for R-Project:
http://yfrog.com/e4rcommanderdataimportj

For instance, IPython could have a magic:
%fileimport

This could open a dialog to select any file that can be read in with numpy,
scipy tools or any other library read in before (timeseries, hdf, pytables).

This could be convenient for interactive data analysis and for quick checking.

What to you think?

Best,
Timmie



From ivo.maljevic at gmail.com  Thu Aug 27 09:13:28 2009
From: ivo.maljevic at gmail.com (Ivo Maljevic)
Date: Thu, 27 Aug 2009 09:13:28 -0400
Subject: [SciPy-User] Usage of scipy.signal.resample
In-Reply-To: <OF0F2AF3D6.98797621-ONC125761F.002B5196-C125761F.002D2D6F@ifm-electronic.com>
References: <5b8d13220908262102v39e9be45hee842aa49f486c29@mail.gmail.com>
	<OF0F2AF3D6.98797621-ONC125761F.002B5196-C125761F.002D2D6F@ifm-electronic.com>
Message-ID: <826c64da0908270613m455027f2h910f1c940dd98908@mail.gmail.com>

Markus,
I'll not comment your question to David, even though it should be easy to
see why, but I will comment on the nature of the problem
you are dealing with.

In communication systems, sampling and resampling theory is based on the
assumption that we are interested at preserving the spectral content of the
'signal', and easy reconstruction of the original signal using simple linear
filtering.

Now, let's think in abstract terms about your problem. Take a time limited
ramp function. As David pointed out, it has discontinuity, and it is also
time-limited time, so for both of these reasons its spectrum will be
unlimited. According to sampling theory, we shouldn't be able to sample it.
Or, we can sample it if:
1) we relax the bandwidth criterion and say that bandwidth is defined as the
99th percentile of the energy content, or something similar.
2) we LP filter it first and limit the bandwidth
When you take a discrete version of it, like x=0,1,2, ..., 9 in your
example, you have sampled it and the signal suffers significant aliasing due
to spectrum repetition and overlap. But, in your application you don't care
about that, you are not concerned with preserving the spectral content, you
are just interested at preserving the 'shape' and being able to go back to
the original signal given the 'side information' about the original signal
(it being linear). If you now take every even sample, that is, 0,2,4,...,8,
you have even more aliasing with respect to the original signal, and the
spectrum is quite different from either the original signal or the one with
0,1,... samples. But, if your intent is to compress the signal, that is, to
use less samples, and you already know that linear interpolation should be
used to bring the signal back to its original form, than you really do not
care about the spectral characteristics.

The bottomline is, don't use the resample function from SciPy for the
problem that you have as it is intended for different purpose.

Hope this helps,
Ivo

2009/8/27 <markus.proeller at ifm.com>

>
> Hello David and Ivo,
>
> thank you very much for your detailed help. I do have a ramp like signal,
> that I want to compress. Thats why I wanted to use the resample function to
> generate same interim values. It is decompressed by linear interpolation, so
> I think the best approach is to generate the interim values by linear
> interpolation as well. The downsample ratio is not an integer value.
> To be honest I just used the resample function because it was just a
> function call, were I could enter the amount of interim values. I didn't
> take care of aliasing and filtering.
> I have another question to David, if I had an integer value for the
> downsampling rate, for instance the ramp function from 0 to 10 and take
> let's say every second sample, so I got 0,2,4,8,10, where do I have my
> aliasing here? Because I can reconstruct my original array by simple linear
> interpolation.
>
> Markus
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
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From markus.proeller at ifm.com  Thu Aug 27 09:43:40 2009
From: markus.proeller at ifm.com (markus.proeller at ifm.com)
Date: Thu, 27 Aug 2009 15:43:40 +0200
Subject: [SciPy-User] Antwort: Re:  Usage of scipy.signal.resample
In-Reply-To: <826c64da0908270613m455027f2h910f1c940dd98908@mail.gmail.com>
Message-ID: <OF24E23F61.B8E37A6F-ONC125761F.004B1371-C125761F.004B6FD5@ifm-electronic.com>

Okay,
thank you very much for your help, now I understand.

Markus
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From david.huard at gmail.com  Thu Aug 27 09:42:06 2009
From: david.huard at gmail.com (David Huard)
Date: Thu, 27 Aug 2009 09:42:06 -0400
Subject: [SciPy-User] shiftgrid difficulties
In-Reply-To: <bebb7fa10908251433l546762a0t7dd30f956242030f@mail.gmail.com>
References: <bebb7fa10908251412k7ddf48delcf588f1259f7e00b@mail.gmail.com>
	<bebb7fa10908251433l546762a0t7dd30f956242030f@mail.gmail.com>
Message-ID: <91cf711d0908270642i7c5b0172tec88c10f88fb510@mail.gmail.com>

Bruce,

You need to add a cyclic point first before applying shiftgrid.

See basemap.addcyclic


David



On Tue, Aug 25, 2009 at 5:33 PM, Bruce Ford <bruce at clearscienceinc.com>wrote:

>
> mpl_toolkits_basemap.shiftgrid looks straight forward enough, but I cannot
> find a working combination.  Let me explain:
>
> I'm trying to plot a variable that I've extracted from a NetCDF file.
>
> When I plot it, I get something that looks like the attached file...it only
> plots in the Eastern hemisphere (0-180).
>
> The longitude array is [   0.    1.    2.    ...  356.  357.  358.  359.]
>
> When trying "sig_wav_ht,lon = shiftgrid(180,sig_wav_ht,lon,start=False)" or
> any other combination using different lon0 I get the same error
>
> "cyclic point not included"
>
> What am I missing?
>
> Some of the code prior to the shiftgrid method:
>
> file1 = sc_settings.data_dir + "/ww3/ww3.199301.nc"
> nc = NetCDFFile(file1)
> for var in nc.variables:
>     print var
> sig_wav_ht = nc.variables['sig_wav_ht'][1,:,:]
> lon = nc.variables['longitude'][:]
> print lon
> lat = nc.variables['latitude'][:]
> sig_wav_ht,lon = shiftgrid(180,sig_wav_ht,lon,start=False)
>
> Any help would be appreciated.
>
> Bruce
>
>
> ---------------------------------------
> Bruce W. Ford
> Clear Science, Inc.
> bruce at clearscienceinc.com
> bruce.w.ford.ctr at navy.smil.mil
> http://www.ClearScienceInc.com
> Phone/Fax: 904-379-9704
> 8241 Parkridge Circle N.
> Jacksonville, FL  32211
> Skype:  bruce.w.ford
> Google Talk: fordbw at gmail.com
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
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From ndbecker2 at gmail.com  Thu Aug 27 09:50:29 2009
From: ndbecker2 at gmail.com (Neal Becker)
Date: Thu, 27 Aug 2009 09:50:29 -0400
Subject: [SciPy-User] Usage of scipy.signal.resample
References: <OF3A387610.8DE1BB35-ONC125761E.003E60C1-C125761E.003F629D@ifm-electronic.com>
	<826c64da0908260711g6d77aa0bs8f7186b665402e5e@mail.gmail.com>
	<20090826144416.GA20833@localhost.ee.columbia.edu>
	<h73rql$kj9$1@ger.gmane.org>
	<5b8d13220908261113k60dfb807xe6465b28749dceb5@mail.gmail.com>
Message-ID: <h762v5$lrc$1@ger.gmane.org>

David Cournapeau wrote:

> On Wed, Aug 26, 2009 at 12:36 PM, Neal Becker<ndbecker2 at gmail.com> wrote:
>> Lev Givon wrote:
>>
>>> Received from Ivo Maljevic on Wed, Aug 26, 2009 at 10:11:29AM EDT:
>>>
>>>> 2009/8/26 <markus.proeller at ifm.com>
>>>>
>>>> >
>>>> > Hello,
>>>> >
>>>> > I have a question concerning the resample function of scipy.
>>>> > I have the following code:
>>>> >
>>>> > from scipy.signal import resample
>>>> > >>>x = array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>>> > >>>resample(x,5)
>>>> > array([ 2.5       ,  1.26393202,  4.5       ,  5.5       ,
>>>> > 8.73606798])
>>>> >
>>>> > I don't understand the first value of 2.5.
>>>> > My scipy version is 0.7.0
>>>> >
>>>> > Thanks for help,
>>>> >
>>>> > Markus
>>>>
>>>> Unless you have a periodic function, I wouldn't rely much on the
>>>> resample function. It uses FFT approach, and the basic assumption is
>>>> that x is periodic. Without even trying to go into details, my first
>>>> guess is that what you see is the consequence
>>>> of aliasing.
>>>>
>>>> A function that uses polyphase filter would do a better job, but it
>>>> hasn't been written yet :(
>>>> Until that is done, maybe you want to experiment by appending zeros to
>>>> x, resampling, and then discarding the last half:
>>>>
>>>> >>> x=linspace(0,9,10)
>>>> >>> xx=r_[x, zeros(10)]
>>>> >>> yy=resample(xx,10)
>>>> >>> yy
>>>> array([ 0.16423509,  1.89281122,  4.1904564 ,  5.66068461,  8.68487231,
>>>> 2.33576491, -0.6288792 ,  0.3095436 , -0.16068461,  0.05119566])
>>>>
>>>>
>>>> >>> y=yy[0:5]
>>>> >>> y
>>>> array([ 0.16423509,  1.89281122,  4.1904564 ,  5.66068461, 
>>>> 8.68487231])
>>>>
>>>> This is by no means a perfect solution, but I'm just throwing some
>>>> ideas, and you can try and see if that is good enough for you.
>>>>
>>>> Ivo
>>>
>>> You may also wish to check out the samplerate scikit by David
>>> Cournapeau; it provides a Python interface to an eponymous library
>>> that provides a more robust sample rate conversion facility than the
>>> fft-based function provided by scipy.
>>>
>>> http://pypi.python.org/pypi/scikits.samplerate
>>>
>>> L.G.
>>
>> Thanks for pointing to this.  I am quite interested in this subject.  I
>> grabbed the samplerate source - it seems to be a wrapper on
>> libsamplerate. I am quite interested in the reference
>> http://www-isl.stanford.edu/~boyd/.
>>
>> AFAICT, libsamplerate refers to the above article, but IIUC it doesn't
>> actually use this technique at all.
> 
> Are you refering to the right link ? SRC is based on sinc
> interpolation (band-limited interpolation), which is a well known
> technique for high quality resampling for audio signals (the same
> kinds of techniques are used in synthesizers, for example).
> 
> http://ccrma-www.stanford.edu/~jos/resample/
> 
> AFAIK, SRC is one of the best resampling implementation for audio
> signals, and certainly the best available under an open source license
> (GPL).
> 
> David

I'm referring to http://www-isl.stanford.edu/~boyd/.  I'd like to get more 
info on 'second order cone programming' applied to design of arbitrary 
complex FIR filters.  Right now, the only way I have found to design complex 
FIR filters to meet arbitrary response is a window, which is far from 
optimal.




From karl.young at ucsf.edu  Thu Aug 27 15:45:51 2009
From: karl.young at ucsf.edu (Karl Young)
Date: Thu, 27 Aug 2009 12:45:51 -0700
Subject: [SciPy-User] installing pynifti under EPD
Message-ID: <4A96E26F.20405@ucsf.edu>


Sorry if this issue is slightly tangential to the interests of this 
group but I couldn't think of any place else to ask (e.g. I don't think 
the pynifti guys have a list). I'd like to read some analyze image files 
(Mayo clinic medical image file format) into numpy arrays and process 
them. This works fine in linux but I'm currently trying to use 
Enthought's EPD distribution under OS X (Python 2.5.4 |EPD_Py25 4.3.0| 
(r254:67916, May 17 2009, 20:07:12)) and having no luck installing 
pynifti to read the analyze files.

I downloaded and tried to install pynifti (pynifti-0.20090303.1) using 
the standard distutils mechanism
 but when I import nifti I got errors saying some of the symbols in 
_clib.so couldn't be found. So I downloaded and installed 
nifticlib-1.1.0 but that didn't help, I tried to copy the various 
nifticlib libraries into obvious places to no avail and there isn't much 
in the way of documentation on this so I was wondering if anyone had the 
magic incantation for installing pynifti under EPD on OS X; sorry again 
if this is slightly off topic.

-- Karl Young


From robert.kern at gmail.com  Thu Aug 27 15:48:35 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Thu, 27 Aug 2009 12:48:35 -0700
Subject: [SciPy-User] installing pynifti under EPD
In-Reply-To: <4A96E26F.20405@ucsf.edu>
References: <4A96E26F.20405@ucsf.edu>
Message-ID: <3d375d730908271248l3b350c32r730636de716629ed@mail.gmail.com>

On Thu, Aug 27, 2009 at 12:45, Karl Young<karl.young at ucsf.edu> wrote:
>
> Sorry if this issue is slightly tangential to the interests of this
> group but I couldn't think of any place else to ask (e.g. I don't think
> the pynifti guys have a list).

http://niftilib.sourceforge.net/pynifti/

It's at the bottom.

-- 
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


From karl.young at ucsf.edu  Thu Aug 27 15:59:01 2009
From: karl.young at ucsf.edu (Karl Young)
Date: Thu, 27 Aug 2009 12:59:01 -0700
Subject: [SciPy-User] installing pynifti under EPD
In-Reply-To: <3d375d730908271248l3b350c32r730636de716629ed@mail.gmail.com>
References: <4A96E26F.20405@ucsf.edu>
	<3d375d730908271248l3b350c32r730636de716629ed@mail.gmail.com>
Message-ID: <4A96E585.7090503@ucsf.edu>


> On Thu, Aug 27, 2009 at 12:45, Karl Young<karl.young at ucsf.edu> wrote:
>   
>> Sorry if this issue is slightly tangential to the interests of this
>> group but I couldn't think of any place else to ask (e.g. I don't think
>> the pynifti guys have a list).
>>     
>
> http://niftilib.sourceforge.net/pynifti/
>
> It's at the bottom.
>
>   


Oops, missed that; thanks Robert and sorry for the extra bandwidth.


From lorenzo.isella at gmail.com  Thu Aug 27 18:31:06 2009
From: lorenzo.isella at gmail.com (Lorenzo Isella)
Date: Fri, 28 Aug 2009 00:31:06 +0200
Subject: [SciPy-User] Finding Positions of Identical Entries in Arrays of
	Different Size
Message-ID: <4A97092A.7040200@gmail.com>

Dear All,
A problem which should be rather easy, but which is giving me a hard time.
(1) Consider for instance the following two arrays

A=[12,78,98,654,7665,1213,765] and
B=[98,765, 654]

Now, what I need is an efficient way of finding the positions of the 
entries of A equal to the entries of B, that is to say in the case of 
the two arrays above (counting from zero)

C=[2,6,3].
Note that in the example there is a single entry of A equal to the i-th 
entry of B.
I have tried using scipy.where, but unsuccessfully. Any ideas about how 
to code that efficiently?
(2) I would like to extend this to the case where A and B are m times n 
arrays, e.g. consider the case

A= 233 789
      987 734
      890 253
      876 321
      534 489

and

B= 987 734
      876 321
     
and C is now the 1D vector returning the positions of the rows of A 
equal to those of B, i.e.

C=[1, 3].

Any suggestions is really welcome.
Kind Regards

Lorenzo


From Kristian.Sandberg at Colorado.EDU  Thu Aug 27 18:35:15 2009
From: Kristian.Sandberg at Colorado.EDU (Kristian Hans Sandberg)
Date: Thu, 27 Aug 2009 16:35:15 -0600 (MDT)
Subject: [SciPy-User] Problem with scipy.special.chebyt
Message-ID: <20090827163515.AKT55498@joker.int.colorado.edu>

When I type

import scipy.special
scipy.special.chebyt(12)(-0.5)

in IPython, IPython crashes. (If I put this snippet in a file and run it outside IPython, Python also aborts, so it's not a problem with IPython.)

Can anyone else reproduce this?

I'm using Python 2.6.1 and scipy 0.7.0 on Mandriva 2009.1, 64-bit.

Thanks!
Kristian






Kristian Sandberg, Ph.D.
 
 Dept. of Applied Mathematics and
 The Boulder Laboratory for 3-D Electron Microscopy of Cells
 University of Colorado at Boulder
 Campus Box 526
 Boulder, CO 80309-0526, USA
 
 Phone: (303) 492 0593 (work)
        (303) 499 4404 (home)
        (303) 547 6290 (cell)
 
 Home page: http://amath.colorado.edu/faculty/sandberg


From robert.kern at gmail.com  Thu Aug 27 18:38:49 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Thu, 27 Aug 2009 15:38:49 -0700
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <20090827163515.AKT55498@joker.int.colorado.edu>
References: <20090827163515.AKT55498@joker.int.colorado.edu>
Message-ID: <3d375d730908271538w5ceef2ep40253624517f8e30@mail.gmail.com>

On Thu, Aug 27, 2009 at 15:35, Kristian Hans
Sandberg<Kristian.Sandberg at colorado.edu> wrote:
> When I type
>
> import scipy.special
> scipy.special.chebyt(12)(-0.5)
>
> in IPython, IPython crashes. (If I put this snippet in a file and run it outside IPython, Python also aborts, so it's not a problem with IPython.)
>
> Can anyone else reproduce this?

Can you provide a gdb backtrace?

-- 
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


From Kristian.Sandberg at Colorado.EDU  Thu Aug 27 18:54:43 2009
From: Kristian.Sandberg at Colorado.EDU (Kristian Hans Sandberg)
Date: Thu, 27 Aug 2009 16:54:43 -0600 (MDT)
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <3d375d730908271538w5ceef2ep40253624517f8e30@mail.gmail.com>
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<3d375d730908271538w5ceef2ep40253624517f8e30@mail.gmail.com>
Message-ID: <20090827165443.AKT56485@joker.int.colorado.edu>

How do I do that?


Kristian Sandberg, Ph.D.
 
 Dept. of Applied Mathematics and
 The Boulder Laboratory for 3-D Electron Microscopy of Cells
 University of Colorado at Boulder
 Campus Box 526
 Boulder, CO 80309-0526, USA
 
 Phone: (303) 492 0593 (work)
        (303) 499 4404 (home)
        (303) 547 6290 (cell)
 
 Home page: http://amath.colorado.edu/faculty/sandberg


---- Original message ----
>Date: Thu, 27 Aug 2009 15:38:49 -0700
>From: scipy-user-bounces at scipy.org (on behalf of Robert Kern <robert.kern at gmail.com>)
>Subject: Re: [SciPy-User] Problem with scipy.special.chebyt  
>To: SciPy Users List <scipy-user at scipy.org>
>
>On Thu, Aug 27, 2009 at 15:35, Kristian Hans
>Sandberg<Kristian.Sandberg at colorado.edu> wrote:
>> When I type
>>
>> import scipy.special
>> scipy.special.chebyt(12)(-0.5)
>>
>> in IPython, IPython crashes. (If I put this snippet in a file and run it outside IPython, Python also aborts, so it's not a problem with IPython.)
>>
>> Can anyone else reproduce this?
>
>Can you provide a gdb backtrace?
>
>-- 
>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
>_______________________________________________
>SciPy-User mailing list
>SciPy-User at scipy.org
>http://mail.scipy.org/mailman/listinfo/scipy-user


From eads at soe.ucsc.edu  Thu Aug 27 19:26:51 2009
From: eads at soe.ucsc.edu (Damian Eads)
Date: Thu, 27 Aug 2009 16:26:51 -0700
Subject: [SciPy-User] Finding Positions of Identical Entries in Arrays
	of Different Size
In-Reply-To: <4A97092A.7040200@gmail.com>
References: <4A97092A.7040200@gmail.com>
Message-ID: <91b4b1ab0908271626p25264ca1p615f68e8ab024de7@mail.gmail.com>

On Thu, Aug 27, 2009 at 3:31 PM, Lorenzo Isella<lorenzo.isella at gmail.com> wrote:
> Dear All,
> A problem which should be rather easy, but which is giving me a hard time.
> (1) Consider for instance the following two arrays
>
> A=[12,78,98,654,7665,1213,765] and
> B=[98,765, 654]

np.where(np.setmember1d(A,B))

>
> Now, what I need is an efficient way of finding the positions of the
> entries of A equal to the entries of B, that is to say in the case of
> the two arrays above (counting from zero)
>
> C=[2,6,3].
> Note that in the example there is a single entry of A equal to the i-th
> entry of B.
> I have tried using scipy.where, but unsuccessfully. Any ideas about how
> to code that efficiently?
> (2) I would like to extend this to the case where A and B are m times n
> arrays, e.g. consider the case
>
> A= 233 789
> ? ? ?987 734
> ? ? ?890 253
> ? ? ?876 321
> ? ? ?534 489
>
> and
>
> B= 987 734
> ? ? ?876 321
>
> and C is now the 1D vector returning the positions of the rows of A
> equal to those of B, i.e.
>
> C=[1, 3].
>

This is probably not the best solution but it works in cases where the
elements in B are not unique.

qmax=max(B.max(),A.max())
Bp=B[:,0].copy()
Ap=A[:,0].copy()
Bp+=B[:,1]*qmax
Ap+=A[:,1]*qmax
C=np.where(np.setmember1d(A,B))

I hope this helps.

Damian

> Any suggestions is really welcome.
> Kind Regards
>
> Lorenzo
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>



-- 
-----------------------------------------------------
Damian Eads                           Ph.D. Candidate
University of California             Computer Science
1156 High Street         Machine Learning Lab, E2-489
Santa Cruz, CA 95064    http://www.soe.ucsc.edu/~eads


From eads at soe.ucsc.edu  Thu Aug 27 19:27:51 2009
From: eads at soe.ucsc.edu (Damian Eads)
Date: Thu, 27 Aug 2009 16:27:51 -0700
Subject: [SciPy-User] Finding Positions of Identical Entries in Arrays
	of Different Size
In-Reply-To: <91b4b1ab0908271626p25264ca1p615f68e8ab024de7@mail.gmail.com>
References: <4A97092A.7040200@gmail.com>
	<91b4b1ab0908271626p25264ca1p615f68e8ab024de7@mail.gmail.com>
Message-ID: <91b4b1ab0908271627t278a71caxc1903e2f125ee58f@mail.gmail.com>

On Thu, Aug 27, 2009 at 4:26 PM, Damian Eads<eads at soe.ucsc.edu> wrote:
> On Thu, Aug 27, 2009 at 3:31 PM, Lorenzo Isella<lorenzo.isella at gmail.com> wrote:
>> Dear All,
>> A problem which should be rather easy, but which is giving me a hard time.
>> (1) Consider for instance the following two arrays
>>
>> A=[12,78,98,654,7665,1213,765] and
>> B=[98,765, 654]
>
> np.where(np.setmember1d(A,B))
>
>>
>> Now, what I need is an efficient way of finding the positions of the
>> entries of A equal to the entries of B, that is to say in the case of
>> the two arrays above (counting from zero)
>>
>> C=[2,6,3].
>> Note that in the example there is a single entry of A equal to the i-th
>> entry of B.
>> I have tried using scipy.where, but unsuccessfully. Any ideas about how
>> to code that efficiently?
>> (2) I would like to extend this to the case where A and B are m times n
>> arrays, e.g. consider the case
>>
>> A= 233 789
>> ? ? ?987 734
>> ? ? ?890 253
>> ? ? ?876 321
>> ? ? ?534 489
>>
>> and
>>
>> B= 987 734
>> ? ? ?876 321
>>
>> and C is now the 1D vector returning the positions of the rows of A
>> equal to those of B, i.e.
>>
>> C=[1, 3].
>>
>
> This is probably not the best solution but it works in cases where the
> elements in B are not unique.
>
> qmax=max(B.max(),A.max())
> Bp=B[:,0].copy()
> Ap=A[:,0].copy()
> Bp+=B[:,1]*qmax
> Ap+=A[:,1]*qmax
> C=np.where(np.setmember1d(A,B))

Err, I mean, C=np.where(np.setmember1d(Ap,Bp))

Damian


From jdh2358 at gmail.com  Thu Aug 27 20:43:54 2009
From: jdh2358 at gmail.com (John Hunter)
Date: Thu, 27 Aug 2009 19:43:54 -0500
Subject: [SciPy-User] SciPy09 Video page direct links
In-Reply-To: <h716e0$n6$1@ger.gmane.org>
References: <h716e0$n6$1@ger.gmane.org>
Message-ID: <88e473830908271743g428604c0h393174f33067b36c@mail.gmail.com>

On Tue, Aug 25, 2009 at 12:18 PM, Eric Carlson<ecarlson at eng.ua.edu> wrote:
> Hello,
>
> I had some issues searching for SciPy 09 video files this morning. Although
> the search was broken, at the same time it was still possible to link
> directly to the video pages.
>
> The attached basic html file (no javascript or images) has the tutorial and
> conference schedule and direct links to corresponding videos. You should be
> able to save the file and open locally.

Hey this is great - - the relevant sections of the scipy website are
written in ReST, I believe, so what would be most helpful would be a
ReST document that has the content you submitted in html.

  http://docutils.sourceforge.net/docs/ref/rst/introduction.html

The the website developers could just drop it into the conference pages.


From aisaac at american.edu  Thu Aug 27 21:10:00 2009
From: aisaac at american.edu (Alan G Isaac)
Date: Thu, 27 Aug 2009 21:10:00 -0400
Subject: [SciPy-User] SciPy09 Video page direct links
In-Reply-To: <88e473830908271743g428604c0h393174f33067b36c@mail.gmail.com>
References: <h716e0$n6$1@ger.gmane.org>
	<88e473830908271743g428604c0h393174f33067b36c@mail.gmail.com>
Message-ID: <4A972E68.9000804@american.edu>

On 8/27/2009 8:43 PM John Hunter apparently wrote:
> Hey this is great - - the relevant sections of the scipy website are
> written in ReST, I believe, so what would be most helpful would be a
> ReST document that has the content you submitted in html.
> 
>   http://docutils.sourceforge.net/docs/ref/rst/introduction.html
> 
> The the website developers could just drop it into the conference pages.


http://docutils.sourceforge.net/sandbox/cliechti/html2rst/html2rst.py

hth,
Alan Isaac



From nmb at wartburg.edu  Thu Aug 27 21:33:11 2009
From: nmb at wartburg.edu (Neil Martinsen-Burrell)
Date: Thu, 27 Aug 2009 20:33:11 -0500
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <20090827165443.AKT56485@joker.int.colorado.edu>
References: <20090827163515.AKT55498@joker.int.colorado.edu>	<3d375d730908271538w5ceef2ep40253624517f8e30@mail.gmail.com>
	<20090827165443.AKT56485@joker.int.colorado.edu>
Message-ID: <4A9733D7.6000004@wartburg.edu>

On 2009-08-27 17:54 , Kristian Hans Sandberg wrote:
> How do I do that?

Like this:

nmb at guttle[~]$ gdb python
GNU gdb 6.3.50-20050815 (Apple version gdb-966) (Tue Mar 10 02:43:13 UTC 
2009)
Copyright 2004 Free Software Foundation, Inc.
GDB is free software, covered by the GNU General Public License, and you are
welcome to change it and/or distribute copies of it under certain 
conditions.
Type "show copying" to see the conditions.
There is absolutely no warranty for GDB.  Type "show warranty" for details.
This GDB was configured as "i386-apple-darwin"...Reading symbols for 
shared libraries .. done

(gdb) run
Starting program: 
/Library/Frameworks/Python.framework/Versions/4.3.0/bin/python
Reading symbols for shared libraries +. done

Program received signal SIGTRAP, Trace/breakpoint trap.
0x8fe01010 in __dyld__dyld_start ()
(gdb) continue
Continuing.
Reading symbols for shared libraries . done
EPD_Py25 (4.3.0) -- http://www.enthought.com

Python 2.5.4 |EPD_Py25 4.3.0| (r254:67916, May 17 2009, 20:07:12)
[GCC 4.0.1 (Apple Computer, Inc. build 5370)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
Reading symbols for shared libraries .. done
 >>> import scipy.special
Reading symbols for shared libraries . done
[more of the same]
 >>> scipy.special.chebyt(12)(-0.5)
1.0000000000001745

Note that you have to "continue" to get to the python prompt.

-Neil


From dwf at cs.toronto.edu  Thu Aug 27 23:04:47 2009
From: dwf at cs.toronto.edu (David Warde-Farley)
Date: Thu, 27 Aug 2009 23:04:47 -0400
Subject: [SciPy-User] SciPy09 Video page direct links
In-Reply-To: <88e473830908271743g428604c0h393174f33067b36c@mail.gmail.com>
References: <h716e0$n6$1@ger.gmane.org>
	<88e473830908271743g428604c0h393174f33067b36c@mail.gmail.com>
Message-ID: <914CF16D-5606-4127-A901-1442F25FEB30@cs.toronto.edu>

On 27-Aug-09, at 8:43 PM, John Hunter wrote:

> Hey this is great - - the relevant sections of the scipy website are
> written in ReST, I believe, so what would be most helpful would be a
> ReST document that has the content you submitted in html.

They aren't, yet -- they're written in whatever awful wiki dialect  
Moin uses. Though that will be changing shortly.

I've put off announcing it to the list, but myself and a few others  
have been slowly converting all the useful (non-spam and non-personal  
page) content on the wiki to ReST, with the goal of moving the  
SciPy.org main page off of moin and onto Sphinx, and the Cookbook into  
pydocweb. You can take a look at the progress at http://github.com/dwf/rescued-scipy-wiki/

We'll certainly leave the current wiki intact for the time being, and  
there may be a fresh wiki instance (with CAPTCHAs to prevent the  
downward spiral we've seen within the current Moin instance) set up  
afterward, but it seems like the face of the community to the world  
ought to be one that is less of a mess, easily maintainable, and with  
comparatively little opportunity for vandalism.

David


From gael.varoquaux at normalesup.org  Fri Aug 28 01:27:12 2009
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Fri, 28 Aug 2009 07:27:12 +0200
Subject: [SciPy-User] SciPy09 Video page direct links
In-Reply-To: <914CF16D-5606-4127-A901-1442F25FEB30@cs.toronto.edu>
References: <h716e0$n6$1@ger.gmane.org>
	<88e473830908271743g428604c0h393174f33067b36c@mail.gmail.com>
	<914CF16D-5606-4127-A901-1442F25FEB30@cs.toronto.edu>
Message-ID: <20090828052712.GA16134@phare.normalesup.org>

On Thu, Aug 27, 2009 at 11:04:47PM -0400, David Warde-Farley wrote:
> On 27-Aug-09, at 8:43 PM, John Hunter wrote:

> > Hey this is great - - the relevant sections of the scipy website are
> > written in ReST, I believe, so what would be most helpful would be a
> > ReST document that has the content you submitted in html.

> They aren't, yet -- they're written in whatever awful wiki dialect  
> Moin uses. Though that will be changing shortly.

The scipy conference website are, actually, eventhough it is hidden to
the user.

So, I can back what John said, if you give us a ReST file, we will be
able to add it easily to the conference website, and be much grateful.

Ga?l


From dwf at cs.toronto.edu  Fri Aug 28 02:34:30 2009
From: dwf at cs.toronto.edu (David Warde-Farley)
Date: Fri, 28 Aug 2009 02:34:30 -0400
Subject: [SciPy-User] SciPy09 Video page direct links
In-Reply-To: <20090828052712.GA16134@phare.normalesup.org>
References: <h716e0$n6$1@ger.gmane.org>
	<88e473830908271743g428604c0h393174f33067b36c@mail.gmail.com>
	<914CF16D-5606-4127-A901-1442F25FEB30@cs.toronto.edu>
	<20090828052712.GA16134@phare.normalesup.org>
Message-ID: <2FA33339-D0FF-41FF-95BF-CB4F348A7C42@cs.toronto.edu>

On 28-Aug-09, at 1:27 AM, Gael Varoquaux wrote:

> The scipy conference website are, actually, eventhough it is hidden to
> the user.

My bad!

As a side note, there are previous years' conference wiki pages which  
have been (mostly) converted into ReST, and would probably be more at  
home on the conference web page than anywhere else. We'll keep fixing  
them up, but feel free to take the ReST files and throw them somewhere  
in the conference website if you have some cycles to spare.

David


From gael.varoquaux at normalesup.org  Fri Aug 28 03:19:51 2009
From: gael.varoquaux at normalesup.org (Gael Varoquaux)
Date: Fri, 28 Aug 2009 09:19:51 +0200
Subject: [SciPy-User] SciPy09 Video page direct links
In-Reply-To: <2FA33339-D0FF-41FF-95BF-CB4F348A7C42@cs.toronto.edu>
References: <h716e0$n6$1@ger.gmane.org>
	<88e473830908271743g428604c0h393174f33067b36c@mail.gmail.com>
	<914CF16D-5606-4127-A901-1442F25FEB30@cs.toronto.edu>
	<20090828052712.GA16134@phare.normalesup.org>
	<2FA33339-D0FF-41FF-95BF-CB4F348A7C42@cs.toronto.edu>
Message-ID: <20090828071951.GD9280@phare.normalesup.org>

On Fri, Aug 28, 2009 at 02:34:30AM -0400, David Warde-Farley wrote:
> On 28-Aug-09, at 1:27 AM, Gael Varoquaux wrote:

> > The scipy conference website are, actually, eventhough it is hidden to
> > the user.

> My bad!

> As a side note, there are previous years' conference wiki pages which  
> have been (mostly) converted into ReST, and would probably be more at  
> home on the conference web page than anywhere else. We'll keep fixing  
> them up, but feel free to take the ReST files and throw them somewhere  
> in the conference website if you have some cycles to spare.

I believe that you are overestimating the quality of the web application
behind the conference website. I don't think that we can easily to that.
The wiki engine powering the conference website is a very simple one, and
we can't add pages to the 'old conference' part :(.

Ga?l


From timmichelsen at gmx-topmail.de  Fri Aug 28 04:09:45 2009
From: timmichelsen at gmx-topmail.de (Tim Michelsen)
Date: Fri, 28 Aug 2009 08:09:45 +0000 (UTC)
Subject: [SciPy-User] SciPy09 Video page direct links
References: <h716e0$n6$1@ger.gmane.org>
	<88e473830908271743g428604c0h393174f33067b36c@mail.gmail.com>
	<914CF16D-5606-4127-A901-1442F25FEB30@cs.toronto.edu>
Message-ID: <loom.20090828T100810-716@post.gmane.org>

> I've put off announcing it to the list, but myself and a few others  
> have been slowly converting all the useful (non-spam and non-personal  
> page) content on the wiki to ReST, with the goal of moving the  
> SciPy.org main page off of moin and onto Sphinx, and the Cookbook into  
> pydocweb. You can take a look at the progress at
http://github.com/dwf/rescued-scipy-wiki/
I have done something similar recently.

I had good success using pandoc for html2rest or wiki to rest.

Hope that helps.

You may drop me a note as PM. I can send you my humble script.



From timmichelsen at gmx-topmail.de  Fri Aug 28 04:15:19 2009
From: timmichelsen at gmx-topmail.de (Tim Michelsen)
Date: Fri, 28 Aug 2009 08:15:19 +0000 (UTC)
Subject: [SciPy-User] SciPy09 Video page direct links
References: <h716e0$n6$1@ger.gmane.org>
	<88e473830908271743g428604c0h393174f33067b36c@mail.gmail.com>
	<914CF16D-5606-4127-A901-1442F25FEB30@cs.toronto.edu>
	<loom.20090828T100810-716@post.gmane.org>
Message-ID: <loom.20090828T101225-190@post.gmane.org>

> You may drop me a note as PM. I can send you my humble script.
Its actually here:
http://bazaar.launchpad.net/~timmie/web2py/web2py-appdocu/annotate/head%3A/doc/convert_faq.py

A very simple one. No science ;-(

here are the dialiects supported by pandoc:
http://johnmacfarlane.net/pandoc/


And taking this example: http://zwiki.org/MoinMoinMarkupExamples
moinmoin looks like mediawiki markup. But I am not shure I as I do not use wikis
that much...



From dwf at cs.toronto.edu  Fri Aug 28 05:06:29 2009
From: dwf at cs.toronto.edu (David Warde-Farley)
Date: Fri, 28 Aug 2009 05:06:29 -0400
Subject: [SciPy-User] SciPy09 Video page direct links
In-Reply-To: <loom.20090828T100810-716@post.gmane.org>
References: <h716e0$n6$1@ger.gmane.org>
	<88e473830908271743g428604c0h393174f33067b36c@mail.gmail.com>
	<914CF16D-5606-4127-A901-1442F25FEB30@cs.toronto.edu>
	<loom.20090828T100810-716@post.gmane.org>
Message-ID: <CD9D5CEE-8633-43C7-A5AA-76A0C7D45D29@cs.toronto.edu>

On 28-Aug-09, at 4:09 AM, Tim Michelsen wrote:

> I have done something similar recently.
>
> I had good success using pandoc for html2rest or wiki to rest.
>
> Hope that helps.

Hi Tim,

Thanks for the pointer. I've used pandoc in the past to convert the  
numarray ndimage docs ( http://stsdas.stsci.edu/numarray/Doc/module-numarray.ndimage.html 
  ) to the version that currently appears in the SciPy Reference  
Guide, and it worked quite well for that purpose. It doesn't look like  
it supports Moin wiki dialect, but I've been using a MoinMoin plugin  
that I resurrected from terminal bit rot, my modified version is at http://github.com/dwf/moin2rst/tree/master 
  .

It does an okay job, but doesn't handle everything. I've been using  
lots of ad hoc regexps to do the cleanup, and have been a little  
astonished at how successfully I've been at automating a lot of things.

The nice thing about moin2rst is that it's a MoinMoin plugin and can  
use Moin's own parser; the trouble is that Moin seems to change their  
API quite frequently and I had to diddle a lot of imports to get it to  
work. Ideally I (or someone) would expand my fork of moin2rst so that  
it does a really bang-up job of converting Moin pages to ReST, but at  
this point I don't have the time or inclination for such an effort.

Cheers,

David


From pav at iki.fi  Fri Aug 28 05:24:21 2009
From: pav at iki.fi (Pauli Virtanen)
Date: Fri, 28 Aug 2009 09:24:21 +0000 (UTC)
Subject: [SciPy-User] Problem with scipy.special.chebyt
References: <20090827163515.AKT55498@joker.int.colorado.edu>
Message-ID: <h787o5$fd4$1@ger.gmane.org>

Thu, 27 Aug 2009 16:35:15 -0600, Kristian Hans Sandberg kirjoitti:
[clip]
> import scipy.special
> scipy.special.chebyt(12)(-0.5)
> 
> in IPython, IPython crashes. (If I put this snippet in a file and run it
> outside IPython, Python also aborts, so it's not a problem with
> IPython.)
> 
> Can anyone else reproduce this?
> 
> I'm using Python 2.6.1 and scipy 0.7.0 on Mandriva 2009.1, 64-bit.

Some likely possibilities:

- Your ATLAS build is broken. If so, also eig() should crash.

- Your scipy.special build is broken. If so, special.gamma() should crash.

-- 
Pauli Virtanen



From ivo.maljevic at gmail.com  Fri Aug 28 07:15:49 2009
From: ivo.maljevic at gmail.com (Ivo Maljevic)
Date: Fri, 28 Aug 2009 07:15:49 -0400
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <h787o5$fd4$1@ger.gmane.org>
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<h787o5$fd4$1@ger.gmane.org>
Message-ID: <826c64da0908280415k6e3ad22fj382cd969ebbcb057@mail.gmail.com>

I can reproduce the original problem. Normally, I use Ubuntu, but recently I
swithced to openSUSE 11.1, 64 bit, just to see how it works.
The problem is, I cannot find python-dbg package under openSUSE, so I cannot
produce a proper backtrace.

However, I can illustrate what works, and what doesn't:

>>> from scipy.linalg import eig

>>> A=scipy.matrix([[1,1,1],[4,4,3],[7,8,5]])
>>> eig(A)
(array([ 10.57624887+0.j       ,  -0.28812444+0.1074048j,
        -0.28812444-0.1074048j]), array([[-0.14056873+0.j        ,
-0.58724949-0.17776272j,
        -0.58724949+0.17776272j],
       [-0.48042175+0.j        ,  0.00353210+0.16590709j,
         0.00353210-0.16590709j],
       [-0.86569936+0.j        ,  0.77201089+0.j        ,
0.77201089-0.j        ]]))


scipy.special.gamma(scipy.linspace(0,3,4),scipy.linspace(0,3,4))
array([  1.79769313e+308,   1.00000000e+000,   1.00000000e+000,
         2.00000000e+000])


>>> import scipy.special
>>> scipy.special.chebyt(12)(-0.5)
*** glibc detected *** python: malloc(): memory corruption:
0x0000000000afdf40 ***

Ivo


2009/8/28 Pauli Virtanen <pav at iki.fi>

> Thu, 27 Aug 2009 16:35:15 -0600, Kristian Hans Sandberg kirjoitti:
> [clip]
> > import scipy.special
> > scipy.special.chebyt(12)(-0.5)
> >
> > in IPython, IPython crashes. (If I put this snippet in a file and run it
> > outside IPython, Python also aborts, so it's not a problem with
> > IPython.)
> >
> > Can anyone else reproduce this?
> >
> > I'm using Python 2.6.1 and scipy 0.7.0 on Mandriva 2009.1, 64-bit.
>
> Some likely possibilities:
>
> - Your ATLAS build is broken. If so, also eig() should crash.
>
> - Your scipy.special build is broken. If so, special.gamma() should crash.
>
> --
> Pauli Virtanen
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
-------------- next part --------------
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URL: <http://mail.scipy.org/pipermail/scipy-user/attachments/20090828/09254eb6/attachment.html>

From Kristian.Sandberg at Colorado.EDU  Fri Aug 28 11:16:35 2009
From: Kristian.Sandberg at Colorado.EDU (Kristian Hans Sandberg)
Date: Fri, 28 Aug 2009 09:16:35 -0600 (MDT)
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <4A9733D7.6000004@wartburg.edu>
References: <20090827163515.AKT55498@joker.int.colorado.edu>	<3d375d730908271538w5ceef2ep40253624517f8e30@mail.gmail.com>
	<20090827165443.AKT56485@joker.int.colorado.edu>
	<4A9733D7.6000004@wartburg.edu>
Message-ID: <20090828091635.AKT86011@joker.int.colorado.edu>


Thank Neil! However, this is really funny: When I run it inside gdb, it works, but when I run it outside gdb, it crashes. See printout below:
 
-------------------

>> gdb python
GNU gdb 6.8-6mdv2009.1 (Mandriva Linux release 2009.1)
Copyright (C) 2008 Free Software Foundation, Inc.     
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
This is free software: you are free to change and redistribute it.           
There is NO WARRANTY, to the extent permitted by law.  Type "show copying"   
and "show warranty" for details.                                             
This GDB was configured as "x86_64-mandriva-linux-gnu"...                    
(no debugging symbols found)                                                 
Missing debug package(s), you should install: python-debug-2.6.1-6.1mdv2009.1.x86_64
(gdb) run
Starting program: /usr/bin/python
[Thread debugging using libthread_db enabled]
Python 2.6.1 (r261:67515, Aug 22 2009, 12:38:20)
[GCC 4.3.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
[New Thread 0x7ff75a2fc6f0 (LWP 13481)]
>>> import scipy.special
>>> scipy.special.chebyt(12)(-0.5)
0.99999999999973122
>>> quit
Use quit() or Ctrl-D (i.e. EOF) to exit
>>>

Program exited normally.
(gdb) quit
~/projects >> python
Python 2.6.1 (r261:67515, Aug 22 2009, 12:38:20)
[GCC 4.3.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import scipy.special
>>> scipy.special.chebyt(12)(-0.5)
Abort

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

Also, chebyt works consistently for degree 11 and lower, but fails consistently for degree 12 and above.

Kristian




Kristian Sandberg, Ph.D.
 
 Dept. of Applied Mathematics and
 The Boulder Laboratory for 3-D Electron Microscopy of Cells
 University of Colorado at Boulder
 Campus Box 526
 Boulder, CO 80309-0526, USA
 
 Phone: (303) 492 0593 (work)
        (303) 499 4404 (home)
        (303) 547 6290 (cell)
 
 Home page: http://amath.colorado.edu/faculty/sandberg


---- Original message ----
>Date: Thu, 27 Aug 2009 20:33:11 -0500
>From: scipy-user-bounces at scipy.org (on behalf of Neil Martinsen-Burrell <nmb at wartburg.edu>)
>Subject: Re: [SciPy-User] Problem with scipy.special.chebyt  
>To: SciPy Users List <scipy-user at scipy.org>
>
>On 2009-08-27 17:54 , Kristian Hans Sandberg wrote:
>> How do I do that?
>
>Like this:
>
>nmb at guttle[~]$ gdb python
>GNU gdb 6.3.50-20050815 (Apple version gdb-966) (Tue Mar 10 02:43:13 UTC 
>2009)
>Copyright 2004 Free Software Foundation, Inc.
>GDB is free software, covered by the GNU General Public License, and you are
>welcome to change it and/or distribute copies of it under certain 
>conditions.
>Type "show copying" to see the conditions.
>There is absolutely no warranty for GDB.  Type "show warranty" for details.
>This GDB was configured as "i386-apple-darwin"...Reading symbols for 
>shared libraries .. done
>
>(gdb) run
>Starting program: 
>/Library/Frameworks/Python.framework/Versions/4.3.0/bin/python
>Reading symbols for shared libraries +. done
>
>Program received signal SIGTRAP, Trace/breakpoint trap.
>0x8fe01010 in __dyld__dyld_start ()
>(gdb) continue
>Continuing.
>Reading symbols for shared libraries . done
>EPD_Py25 (4.3.0) -- http://www.enthought.com
>
>Python 2.5.4 |EPD_Py25 4.3.0| (r254:67916, May 17 2009, 20:07:12)
>[GCC 4.0.1 (Apple Computer, Inc. build 5370)] on darwin
>Type "help", "copyright", "credits" or "license" for more information.
>Reading symbols for shared libraries .. done
> >>> import scipy.special
>Reading symbols for shared libraries . done
>[more of the same]
> >>> scipy.special.chebyt(12)(-0.5)
>1.0000000000001745
>
>Note that you have to "continue" to get to the python prompt.
>
>-Neil
>_______________________________________________
>SciPy-User mailing list
>SciPy-User at scipy.org
>http://mail.scipy.org/mailman/listinfo/scipy-user


From pav at iki.fi  Fri Aug 28 11:38:56 2009
From: pav at iki.fi (Pauli Virtanen)
Date: Fri, 28 Aug 2009 15:38:56 +0000 (UTC)
Subject: [SciPy-User] Problem with scipy.special.chebyt
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<h787o5$fd4$1@ger.gmane.org>
	<826c64da0908280415k6e3ad22fj382cd969ebbcb057@mail.gmail.com>
Message-ID: <h78tmg$m3e$1@ger.gmane.org>

Fri, 28 Aug 2009 07:15:49 -0400, Ivo Maljevic kirjoitti:
>>>> import scipy.special
>>>> scipy.special.chebyt(12)(-0.5)
> *** glibc detected *** python: malloc(): memory corruption:
> 0x0000000000afdf40 ***

This particular glibc error does not necessarily mean that the problem is 
in chebyt -- glibc just notices the problem at this point, but the 
corruption may have occurred earlier. Can you try running eig() etc. 
multiple times?

The point is that scipy.special.chebyt is mostly pure-python code, which 
calls, among others, eig() and special.gamma(). It is very likely the 
error is in these lower-level routines. It'd be helpful if you were able 
to find out which routine bugs out.

-- 
Pauli Virtanen



From ivo.maljevic at gmail.com  Fri Aug 28 11:55:19 2009
From: ivo.maljevic at gmail.com (Ivo Maljevic)
Date: Fri, 28 Aug 2009 11:55:19 -0400
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <h78tmg$m3e$1@ger.gmane.org>
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<h787o5$fd4$1@ger.gmane.org>
	<826c64da0908280415k6e3ad22fj382cd969ebbcb057@mail.gmail.com>
	<h78tmg$m3e$1@ger.gmane.org>
Message-ID: <826c64da0908280855r6a1982c8o778ab530a1ff02f4@mail.gmail.com>

I'll do it when I get home. My work PC runs 32-bit version of Ubuntu, and no
problems there.

One question, though. I looked at the chebyt function, and it has two
returns. Why?

def chebyt(n,monic=0):
    """Return nth order Chebyshev polynomial of first kind, Tn(x).
Orthogonal
    over [-1,1] with weight function (1-x**2)**(-1/2).
    """
    assert(n>=0), "n must be nonnegative"
    wfunc = lambda x: 1.0/sqrt(1-x*x)
    if n==0:
        return orthopoly1d([],[],pi,1.0,wfunc,(-1,1),monic)
    n1 = n
    x,w,mu = t_roots(n1,mu=1)
    hn = pi/2
    kn = 2**(n-1)
    p = orthopoly1d(x,w,hn,kn,wfunc,(-1,1),monic)
    return p

    return jacobi(n,-0.5,-0.5,monic=monic)


2009/8/28 Pauli Virtanen <pav at iki.fi>

> Fri, 28 Aug 2009 07:15:49 -0400, Ivo Maljevic kirjoitti:
> >>>> import scipy.special
> >>>> scipy.special.chebyt(12)(-0.5)
> > *** glibc detected *** python: malloc(): memory corruption:
> > 0x0000000000afdf40 ***
>
> This particular glibc error does not necessarily mean that the problem is
> in chebyt -- glibc just notices the problem at this point, but the
> corruption may have occurred earlier. Can you try running eig() etc.
> multiple times?
>
> The point is that scipy.special.chebyt is mostly pure-python code, which
> calls, among others, eig() and special.gamma(). It is very likely the
> error is in these lower-level routines. It'd be helpful if you were able
> to find out which routine bugs out.
>
> --
> Pauli Virtanen
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From ecarlson at eng.ua.edu  Fri Aug 28 12:15:33 2009
From: ecarlson at eng.ua.edu (Eric Carlson)
Date: Fri, 28 Aug 2009 11:15:33 -0500
Subject: [SciPy-User] SciPy09 Video page direct links
In-Reply-To: <4A972E68.9000804@american.edu>
References: <h716e0$n6$1@ger.gmane.org>	<88e473830908271743g428604c0h393174f33067b36c@mail.gmail.com>
	<4A972E68.9000804@american.edu>
Message-ID: <h78vr6$u96$1@ger.gmane.org>

Alan G Isaac wrote:
> On 8/27/2009 8:43 PM John Hunter apparently wrote:
>> Hey this is great - - the relevant sections of the scipy website are
>> written in ReST, I believe, so what would be most helpful would be a
>> ReST document that has the content you submitted in html.
>>
>>   http://docutils.sourceforge.net/docs/ref/rst/introduction.html
>>
>> The the website developers could just drop it into the conference pages.
> 
> 
> http://docutils.sourceforge.net/sandbox/cliechti/html2rst/html2rst.py
> 
> hth,
> Alan Isaac
Per your request John, attached is an rst version that I think should 
work. I got it to build in Sphinx with only one warning about this doc 
not being in the TOC tree. To the best of my proofing, I think that all 
the links work, but ...

I used Alan's link as a starting point for the translation, and got to 
learn a considerable amount about string encoding (thanks to Gael's name 
it seems). html2rst gave me a useful starting point, but still left me a 
great opportunity to learn much about ReST. The program did a great job 
with many parts, so I presume most problems arose from problems with the 
original html.

Anyway, it was a good lesson for me and I hope the file will be helpful 
for others.

Cheers,
EC
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From silva at lma.cnrs-mrs.fr  Fri Aug 28 14:24:46 2009
From: silva at lma.cnrs-mrs.fr (Fabrice Silva)
Date: Fri, 28 Aug 2009 20:24:46 +0200
Subject: [SciPy-User] odeint : knowing time step that the solver tried to
	use...
Message-ID: <1251483886.2766.74.camel@localhost.localdomain>

Hi folks,
I am dealing with a resolution of a set of ODE which periodically
becomes to a stiff problem (but it isn't the trouble here). Odeint is
used.
There are some cases where the solver does not succeed to converge, a
lot of time step are done before reaching 'tout'.

Is there a way to get the unsuccessful time step? It may help us to
determine whether our model has a problem...

-- 
Fabrice Silva
Laboratory of Mechanics and Acoustics - CNRS
31 chemin Joseph Aiguier, 13402 Marseille, France.



From ivo.maljevic at gmail.com  Fri Aug 28 18:17:25 2009
From: ivo.maljevic at gmail.com (Ivo Maljevic)
Date: Fri, 28 Aug 2009 18:17:25 -0400
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <h78tmg$m3e$1@ger.gmane.org>
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<h787o5$fd4$1@ger.gmane.org>
	<826c64da0908280415k6e3ad22fj382cd969ebbcb057@mail.gmail.com>
	<h78tmg$m3e$1@ger.gmane.org>
Message-ID: <826c64da0908281517v3b504abeqde2ae04ee02da60b@mail.gmail.com>

Well, I've tried, and this is the best I could come up with.
Inside gen_roots_and_weights() function I added few lines for debugging:

    nn = np.arange(1.0,n)
    sqrt_bn = sqrt_bn_func(nn)
    an = an_func(np.concatenate(([0], nn)))

        print 'Step A (before eig)'
    print 'mat=', np.diagflat(an) +np.diagflat(sqrt_bn,1) +
np.diagflat(sqrt_bn,-1)
    print 'Step B (before eig)'

    x, v = eig((np.diagflat(an) +
                np.diagflat(sqrt_bn,1) +
                np.diagflat(sqrt_bn,-1)))

    print 'Step C (after eig)'

and step B is the last point where I get before the program breaks. But, if
I load the same matrix (result between A and B), eig() doesn't break.
Anyway, since this works on other distributions, I guess it is not scipy
error. You are probably correct to guess that it's the eignevalue function,
even though I'm not sure why doesn't it break consistently.

Ivo



2009/8/28 Pauli Virtanen <pav at iki.fi>

> Fri, 28 Aug 2009 07:15:49 -0400, Ivo Maljevic kirjoitti:
> >>>> import scipy.special
> >>>> scipy.special.chebyt(12)(-0.5)
> > *** glibc detected *** python: malloc(): memory corruption:
> > 0x0000000000afdf40 ***
>
> This particular glibc error does not necessarily mean that the problem is
> in chebyt -- glibc just notices the problem at this point, but the
> corruption may have occurred earlier. Can you try running eig() etc.
> multiple times?
>
> The point is that scipy.special.chebyt is mostly pure-python code, which
> calls, among others, eig() and special.gamma(). It is very likely the
> error is in these lower-level routines. It'd be helpful if you were able
> to find out which routine bugs out.
>
> --
> Pauli Virtanen
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From cournape at gmail.com  Sat Aug 29 01:45:46 2009
From: cournape at gmail.com (David Cournapeau)
Date: Sat, 29 Aug 2009 00:45:46 -0500
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <20090828091635.AKT86011@joker.int.colorado.edu>
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<3d375d730908271538w5ceef2ep40253624517f8e30@mail.gmail.com>
	<20090827165443.AKT56485@joker.int.colorado.edu>
	<4A9733D7.6000004@wartburg.edu>
	<20090828091635.AKT86011@joker.int.colorado.edu>
Message-ID: <5b8d13220908282245r3637d17u9c6c3b44a16f8154@mail.gmail.com>

On Fri, Aug 28, 2009 at 10:16 AM, Kristian Hans
Sandberg<Kristian.Sandberg at colorado.edu> wrote:
>
> Thank Neil! However, this is really funny: When I run it inside gdb, it works, but when I run it outside gdb, it crashes. See printout below:

Build a debug scipy (python setup.py build_ext -g install) and run the
test under valgrind, this should gives us useful information.

cheers,

David


From pav+sp at iki.fi  Sat Aug 29 10:25:56 2009
From: pav+sp at iki.fi (Pauli Virtanen)
Date: Sat, 29 Aug 2009 14:25:56 +0000 (UTC)
Subject: [SciPy-User] Problem with scipy.special.chebyt
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<h787o5$fd4$1@ger.gmane.org>
	<826c64da0908280415k6e3ad22fj382cd969ebbcb057@mail.gmail.com>
	<h78tmg$m3e$1@ger.gmane.org>
	<826c64da0908280855r6a1982c8o778ab530a1ff02f4@mail.gmail.com>
Message-ID: <h7bdpk$kqj$1@ger.gmane.org>

On 2009-08-28, Ivo Maljevic <ivo.maljevic at gmail.com> wrote:
[clip]
> One question, though. I looked at the chebyt function, and it has two
> returns. Why?
>
> def chebyt(n,monic=0):
>     """Return nth order Chebyshev polynomial of first kind, Tn(x).
> Orthogonal
>     over [-1,1] with weight function (1-x**2)**(-1/2).
>     """
>     assert(n>=0), "n must be nonnegative"
>     wfunc = lambda x: 1.0/sqrt(1-x*x)
>     if n==0:
>         return orthopoly1d([],[],pi,1.0,wfunc,(-1,1),monic)
>     n1 = n
>     x,w,mu = t_roots(n1,mu=1)
>     hn = pi/2
>     kn = 2**(n-1)
>     p = orthopoly1d(x,w,hn,kn,wfunc,(-1,1),monic)
>     return p
>
>     return jacobi(n,-0.5,-0.5,monic=monic)

Cut-and-paste leftovers, most probably. Removed.

-- 
Pauli Virtanen



From amenity at enthought.com  Sat Aug 29 12:55:05 2009
From: amenity at enthought.com (Amenity Applewhite)
Date: Sat, 29 Aug 2009 11:55:05 -0500
Subject: [SciPy-User] EPD Webinar September 4th: How do I...read data from
	all kinds of files?
Message-ID: <862C3BCD-5C2A-4A50-8B29-35C38315BBFA@enthought.com>

Having trouble viewing this email? Click here

Friday, September 4th 1pm CDT
How do I...read data from all kinds of files?  Working with various  
file formats.
Hello Leah,

We wanted to let you know that next week we'll host another  
installment of our popular EPD webinar series. Although only EPD Basic  
or above subscribers are guaranteed seats at EPD webinars, we invite  
non-subscribers to add their names to the waiting list for each event.  
If there are available seats, you will be notified by next Thursday  
and given access to the webinar.  Links to the waiting lists and  
upcoming topics are posted here: http://enthought.com/training/webinars.php

These events feature detailed demonstrations of powerful Python  
techniques that Enthought developers use to enhance our applications  
or development process. Participants are often invited to participate  
in the demonstration, and are welcome to join the interactive VOIP  
discussion later in the session. This is a great opportunity to learn  
new methods and interact with our expert developers.

If you have topics you'd like to see addressed during the webinar,  
feel free to let us know at media at enthought.com.
How do I...read data from all kinds of files?
Getting data from where it sits into NumPy so it can be manipulated  
using the wealth of libraries available in EPD is usually the first  
step in any workflow. In this webinar we will illustrate by example  
how to read (and write) different kinds of data files:  CSV, Excel,  
HDF, .MAT, and arbitrary ASCII and arbitrary binary files.  We will  
show how data can be accessed using either the filesystem or the world  
wide web.

Once again, to add your name to the wait-list:
http://enthought.com/training/webinars.php

We hope to see you there!

Thanks,
Enthought Media
Quick Links...
Enthought.com
Enthought Python Distribution (EPD)
Enthought Webinars

@Facebook
@Twitter

Forward email
--
Amenity Applewhite
Enthought, Inc.
Scientific Computing Solutions
www.enthought.com









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From tsyu80 at gmail.com  Sat Aug 29 13:00:22 2009
From: tsyu80 at gmail.com (Tony S Yu)
Date: Sat, 29 Aug 2009 13:00:22 -0400
Subject: [SciPy-User] bug in ndimage grey_dilation?
Message-ID: <A448FF20-F076-4A7B-A1FD-818843957D58@gmail.com>

ndimage.grey_dilation doesn't handle scalar size arguments (in  
contrast to other filters). For example:

 >>> ndimage.grey_dilation(np.ones((5,5)), size=2)
TypeError: 'int' object is unsubscriptable

Below is a simple patch which seems to fix the issue (I basically  
copied the behavior of ndimage.filters._min_or_max_filter). The diff  
below is taken against the 0.7.x branch.

I guess this may not technically be a bug since filters in  
morphology.py don't specify they can handle scalar size arguments  
(filters in filters.py do document this behavior). In any case, this  
fix would make the API more consistent.

Cheers,
-Tony

===================================================================
--- morphology.py	(revision 5911)
+++ morphology.py	(working copy)
@@ -347,13 +347,14 @@
          footprint = footprint[tuple([slice(None, None, -1)] *
                                      footprint.ndim)]
      input = numpy.asarray(input)
+    sizes = _ni_support._normalize_sequence(size, input.ndim)
      origin = _ni_support._normalize_sequence(origin, input.ndim)
      for ii in range(len(origin)):
          origin[ii] = -origin[ii]
          if footprint is not None:
              sz = footprint.shape[ii]
          else:
-            sz = size[ii]
+            sz = sizes[ii]
          if not sz & 1:
              origin[ii] -= 1
      return filters._min_or_max_filter(input, size, footprint,  
structure,



From ivo.maljevic at gmail.com  Sat Aug 29 14:05:21 2009
From: ivo.maljevic at gmail.com (Ivo Maljevic)
Date: Sat, 29 Aug 2009 14:05:21 -0400
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <5b8d13220908282245r3637d17u9c6c3b44a16f8154@mail.gmail.com>
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<3d375d730908271538w5ceef2ep40253624517f8e30@mail.gmail.com>
	<20090827165443.AKT56485@joker.int.colorado.edu>
	<4A9733D7.6000004@wartburg.edu>
	<20090828091635.AKT86011@joker.int.colorado.edu>
	<5b8d13220908282245r3637d17u9c6c3b44a16f8154@mail.gmail.com>
Message-ID: <826c64da0908291105w2f7a0449i481b907d971b6962@mail.gmail.com>

David, Pauli,
You guys are more versed with this. It should be fairly straighforward to
reproduce the problem. I just installed Mandriva 2009.1, 32 bit, as a
virtual machine, installed scimath package (which contains scipy), and the
problem show up right away. As Kristian said, when you run python in gdb,
the problem goes away on Mandriva. It doesn't go away on openSUSE, though.

So, the problem is not only on 64 bit distributions, and most likely it is
across  all the distributions, it is just that almost no one uses chebyt,
especially with these parameters. It doesn't fail with chebyt(11)(-0.5).
Also, it doesn't fail on Ubuntu, but I believe that is because scipy used
there is 0.6.

Ivo

2009/8/29 David Cournapeau <cournape at gmail.com>

> On Fri, Aug 28, 2009 at 10:16 AM, Kristian Hans
> Sandberg<Kristian.Sandberg at colorado.edu> wrote:
> >
> > Thank Neil! However, this is really funny: When I run it inside gdb, it
> works, but when I run it outside gdb, it crashes. See printout below:
>
> Build a debug scipy (python setup.py build_ext -g install) and run the
> test under valgrind, this should gives us useful information.
>
> cheers,
>
> David
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From humongo.shi at gmail.com  Sat Aug 29 14:24:50 2009
From: humongo.shi at gmail.com (Hugo Shi)
Date: Sat, 29 Aug 2009 13:24:50 -0500
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <826c64da0908291105w2f7a0449i481b907d971b6962@mail.gmail.com>
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<3d375d730908271538w5ceef2ep40253624517f8e30@mail.gmail.com>
	<20090827165443.AKT56485@joker.int.colorado.edu>
	<4A9733D7.6000004@wartburg.edu>
	<20090828091635.AKT86011@joker.int.colorado.edu>
	<5b8d13220908282245r3637d17u9c6c3b44a16f8154@mail.gmail.com>
	<826c64da0908291105w2f7a0449i481b907d971b6962@mail.gmail.com>
Message-ID: <1251570290.3972.0.camel@gigoblob>

running ubuntu 9.04, ran this

In [2]: import scipy.special

In [3]: scipy.special.chebyt(12)(-0.5)
Out[3]: 0.99999999999995792

It works in ubuntu, and ubuntu 9.04 is using scipy 0.7.0



On Sat, 2009-08-29 at 14:05 -0400, Ivo Maljevic wrote:
> David, Pauli, 
> You guys are more versed with this. It should be fairly straighforward
> to reproduce the problem. I just installed Mandriva 2009.1, 32 bit, as
> a virtual machine, installed scimath package (which contains scipy),
> and the problem show up right away. As Kristian said, when you run
> python in gdb, the problem goes away on Mandriva. It doesn't go away
> on openSUSE, though. 
> 
> So, the problem is not only on 64 bit distributions, and most likely
> it is across  all the distributions, it is just that almost no one
> uses chebyt, especially with these parameters. It doesn't fail with
> chebyt(11)(-0.5). Also, it doesn't fail on Ubuntu, but I believe that
> is because scipy used there is 0.6. 
> 
> Ivo
> 
> 2009/8/29 David Cournapeau <cournape at gmail.com>
>         On Fri, Aug 28, 2009 at 10:16 AM, Kristian Hans
>         Sandberg<Kristian.Sandberg at colorado.edu> wrote:
>         >
>         
>         > Thank Neil! However, this is really funny: When I run it
>         inside gdb, it works, but when I run it outside gdb, it
>         crashes. See printout below:
>         
>         
>         Build a debug scipy (python setup.py build_ext -g install) and
>         run the
>         test under valgrind, this should gives us useful information.
>         
>         cheers,
>         
>         David
>         
>         _______________________________________________
>         SciPy-User mailing list
>         SciPy-User at scipy.org
>         http://mail.scipy.org/mailman/listinfo/scipy-user
>         
> 
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user



From ivo.maljevic at gmail.com  Sat Aug 29 14:39:47 2009
From: ivo.maljevic at gmail.com (Ivo Maljevic)
Date: Sat, 29 Aug 2009 14:39:47 -0400
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <1251570290.3972.0.camel@gigoblob>
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<3d375d730908271538w5ceef2ep40253624517f8e30@mail.gmail.com>
	<20090827165443.AKT56485@joker.int.colorado.edu>
	<4A9733D7.6000004@wartburg.edu>
	<20090828091635.AKT86011@joker.int.colorado.edu>
	<5b8d13220908282245r3637d17u9c6c3b44a16f8154@mail.gmail.com>
	<826c64da0908291105w2f7a0449i481b907d971b6962@mail.gmail.com>
	<1251570290.3972.0.camel@gigoblob>
Message-ID: <826c64da0908291139ncc264f2se824a4fe189ad144@mail.gmail.com>

Sorry, you are right. Ubuntu does use 0.7.0, and it works for me as well:

>>> from scipy.special import chebyt
>>> chebyt(12)(-0.5)
0.99999999999992151

2009/8/29 Hugo Shi <humongo.shi at gmail.com>

> running ubuntu 9.04, ran this
>
> In [2]: import scipy.special
>
> In [3]: scipy.special.chebyt(12)(-0.5)
> Out[3]: 0.99999999999995792
>
> It works in ubuntu, and ubuntu 9.04 is using scipy 0.7.0
>
>
>
> On Sat, 2009-08-29 at 14:05 -0400, Ivo Maljevic wrote:
> > David, Pauli,
> > You guys are more versed with this. It should be fairly straighforward
> > to reproduce the problem. I just installed Mandriva 2009.1, 32 bit, as
> > a virtual machine, installed scimath package (which contains scipy),
> > and the problem show up right away. As Kristian said, when you run
> > python in gdb, the problem goes away on Mandriva. It doesn't go away
> > on openSUSE, though.
> >
> > So, the problem is not only on 64 bit distributions, and most likely
> > it is across  all the distributions, it is just that almost no one
> > uses chebyt, especially with these parameters. It doesn't fail with
> > chebyt(11)(-0.5). Also, it doesn't fail on Ubuntu, but I believe that
> > is because scipy used there is 0.6.
> >
> > Ivo
> >
> > 2009/8/29 David Cournapeau <cournape at gmail.com>
> >         On Fri, Aug 28, 2009 at 10:16 AM, Kristian Hans
> >         Sandberg<Kristian.Sandberg at colorado.edu> wrote:
> >         >
> >
> >         > Thank Neil! However, this is really funny: When I run it
> >         inside gdb, it works, but when I run it outside gdb, it
> >         crashes. See printout below:
> >
> >
> >         Build a debug scipy (python setup.py build_ext -g install) and
> >         run the
> >         test under valgrind, this should gives us useful information.
> >
> >         cheers,
> >
> >         David
> >
> >         _______________________________________________
> >         SciPy-User mailing list
> >         SciPy-User at scipy.org
> >         http://mail.scipy.org/mailman/listinfo/scipy-user
> >
> >
> > _______________________________________________
> > SciPy-User mailing list
> > SciPy-User at scipy.org
> > http://mail.scipy.org/mailman/listinfo/scipy-user
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From ivo.maljevic at gmail.com  Sat Aug 29 14:54:15 2009
From: ivo.maljevic at gmail.com (Ivo Maljevic)
Date: Sat, 29 Aug 2009 14:54:15 -0400
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <5b8d13220908282245r3637d17u9c6c3b44a16f8154@mail.gmail.com>
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<3d375d730908271538w5ceef2ep40253624517f8e30@mail.gmail.com>
	<20090827165443.AKT56485@joker.int.colorado.edu>
	<4A9733D7.6000004@wartburg.edu>
	<20090828091635.AKT86011@joker.int.colorado.edu>
	<5b8d13220908282245r3637d17u9c6c3b44a16f8154@mail.gmail.com>
Message-ID: <826c64da0908291154l37b626f5le6e42b92b5e69fb4@mail.gmail.com>

Is this helpful:

python setup.py build_ext -g install
non-existing path in 'cluster':
'src/vq_module.c'
non-existing path in 'cluster':
'src/vq.c'
non-existing path in 'cluster':
'src/hierarchy_wrap.c'
non-existing path in 'cluster':
'src/hierarchy.c'
Appending scipy.cluster configuration to
scipy
Ignoring attempt to set 'name' (from 'scipy' to
'scipy.cluster')
Warning: No configuration returned, assuming
unavailable.
Appending scipy.constants configuration to
scipy
Ignoring attempt to set 'name' (from 'scipy' to
'scipy.constants')
could not resolve pattern in 'fftpack':
'src/dfftpack/*.f'
non-existing path in 'fftpack':
'fftpack.pyf'
non-existing path in 'fftpack':
'src/zfft.c'
non-existing path in 'fftpack':
'src/drfft.c'
non-existing path in 'fftpack':
'src/zrfft.c'
non-existing path in 'fftpack':
'src/zfftnd.c'
non-existing path in 'fftpack':
'src/zfft_fftpack.c'
non-existing path in 'fftpack':
'src/drfft_fftpack.c'
non-existing path in 'fftpack':
'src/zfftnd_fftpack.c'
non-existing path in 'fftpack':
'convolve.pyf'
non-existing path in 'fftpack':
'src/convolve.c'
Appending scipy.fftpack configuration to
scipy
Ignoring attempt to set 'name' (from 'scipy' to
'scipy.fftpack')
/usr/lib64/python2.6/site-packages/numpy/distutils/command/config.py:361:
DeprecationWarning:
+++++++++++++++++++++++++++++++++++++++++++++++++

Usage of get_output is deprecated: please do
not
use it anymore, and avoid configuration
checks
involving running executable on the target
machine.
+++++++++++++++++++++++++++++++++++++++++++++++++


  DeprecationWarning)
Could not locate executable g77
Could not locate executable f77
Could not locate executable ifort
Could not locate executable ifc
Could not locate executable lf95
Could not locate executable pgf90
Could not locate executable pgf77
Could not locate executable f90
Could not locate executable f95
Could not locate executable fort
Could not locate executable efort
Could not locate executable efc
Found executable /usr/bin/gfortran
compiling '_configtest.c':

/* This file is generated from numpy/distutils/system_info.py */
void ATL_buildinfo(void);
int main(void) {
  ATL_buildinfo();
  return 0;
}
ATLAS version 3.8.3 built by abuild on Fri Mar 13 05:13:09 UTC 2009:
   UNAME    : Linux build20 2.6.27 #1 SMP 2009-02-28 04:40:21 +0100 x86_64
x86_64 x86_64 GNU/Linux
   INSTFLG  : -1 0 -a
1
   ARCHDEFS : -DATL_OS_Linux -DATL_ARCH_AMD64K10h -DATL_CPUMHZ=2310
-DATL_SSE3 -DATL_SSE2 -DATL_SSE1 -DATL_3DNow -DATL_USE64BITS
-DATL_GAS_x8664

   F2CDEFS  : -DAdd_ -DF77_INTEGER=int
-DStringSunStyle
   CACHEEDGE:
1048576

   F77      : gfortran, version GNU Fortran (SUSE Linux) 4.3.2
[gcc-4_3-branch revision 141291]
   F77FLAGS : -fomit-frame-pointer -mfpmath=sse -msse3 -O2 -falign-loops=32
-fPIC -m64
   SMC      : gcc, version gcc (SUSE Linux) 4.3.2 [gcc-4_3-branch revision
141291]
   SMCFLAGS : -fomit-frame-pointer -mfpmath=sse -msse3 -O2 -falign-loops=32
-fPIC -m64
   SKC      : gcc, version gcc (SUSE Linux) 4.3.2 [gcc-4_3-branch revision
141291]
   SKCFLAGS : -fomit-frame-pointer -mfpmath=sse -msse3 -O2 -falign-loops=32
-fPIC -m64
could not resolve pattern in 'integrate':
'linpack_lite/*.f'
could not resolve pattern in 'integrate':
'mach/*.f'
could not resolve pattern in 'integrate':
'quadpack/*.f'
could not resolve pattern in 'integrate':
'odepack/*.f'
non-existing path in 'integrate':
'_quadpackmodule.c'
non-existing path in 'integrate':
'quadpack.h'
non-existing path in 'integrate':
'__quadpack.h'
non-existing path in 'integrate':
'_odepackmodule.c'
non-existing path in 'integrate':
'__odepack.h'
non-existing path in 'integrate':
'multipack.h'
non-existing path in 'integrate':
'vode.pyf'
Appending scipy.integrate configuration to
scipy
Ignoring attempt to set 'name' (from 'scipy' to
'scipy.integrate')
could not resolve pattern in 'interpolate':
'fitpack/*.f'
non-existing path in 'interpolate':
'src/_fitpackmodule.c'
non-existing path in 'interpolate':
'src/__fitpack.h'
non-existing path in 'interpolate':
'src/multipack.h'
non-existing path in 'interpolate':
'src/fitpack.pyf'
non-existing path in 'interpolate':
'src/_interpolate.cpp'
non-existing path in 'interpolate':
'src/interpolate.h'
non-existing path in 'interpolate':
'src'
Appending scipy.interpolate configuration to
scipy
Ignoring attempt to set 'name' (from 'scipy' to
'scipy.interpolate')
non-existing path in 'io':
'numpyiomodule.c'

Appending scipy.io.matlab configuration to scipy.io

Ignoring attempt to set 'name' (from 'scipy.io' to
'scipy.io.matlab')
Appending scipy.io.arff configuration to scipy.io

Ignoring attempt to set 'name' (from 'scipy.io' to
'scipy.io.arff')
Appending scipy.io configuration to
scipy
Ignoring attempt to set 'name' (from 'scipy' to 'scipy.io')

ATLAS version
3.8.3

non-existing path in 'lib/blas':
'fblas.pyf.src'

non-existing path in 'lib/blas':
'fblaswrap.f.src'

could not resolve pattern in 'lib/blas':
'fblas_l?.pyf.src'
non-existing path in 'lib/blas':
'fblas.pyf.src'

non-existing path in 'lib/blas':
'fblaswrap.f.src'

non-existing path in 'lib/blas':
'fblaswrap_veclib_c.c.src'

non-existing path in 'lib/blas':
'cblas.pyf.src'

could not resolve pattern in 'lib/blas':
'cblas_l?.pyf.src'
Appending scipy.lib.blas configuration to
scipy.lib
Ignoring attempt to set 'name' (from 'scipy.lib' to
'scipy.lib.blas')
/usr/lib64/python2.6/site-packages/numpy/distutils/system_info.py:967:
UserWarning:
*********************************************************************

    Could not find lapack library within the ATLAS installation.

*********************************************************************


  warnings.warn(message)
/usr/lib64/python2.6/site-packages/numpy/distutils/system_info.py:1301:
UserWarning:
    Lapack (http://www.netlib.org/lapack/) libraries not
found.
    Directories to search for the libraries can be specified in
the
    numpy/distutils/site.cfg file (section [lapack]) or by
setting
    the LAPACK environment
variable.

warnings.warn(LapackNotFoundError.__doc__)

/usr/lib64/python2.6/site-packages/numpy/distutils/system_info.py:1304:
UserWarning:
    Lapack (http://www.netlib.org/lapack/) sources not
found.
    Directories to search for the sources can be specified in
the
    numpy/distutils/site.cfg file (section [lapack_src]) or by
setting
    the LAPACK_SRC environment
variable.

warnings.warn(LapackSrcNotFoundError.__doc__)

Traceback (most recent call
last):
  File "setup.py", line 32, in
<module>

setup(**configuration(top_path='').todict())

  File "setup.py", line 11, in
configuration

config.add_subpackage('lib')

  File "/usr/lib64/python2.6/site-packages/numpy/distutils/misc_util.py",
line 852, in add_subpackage
    caller_level =
2)

  File "/usr/lib64/python2.6/site-packages/numpy/distutils/misc_util.py",
line 835, in get_subpackage
    caller_level = caller_level +
1)
  File "/usr/lib64/python2.6/site-packages/numpy/distutils/misc_util.py",
line 782, in _get_configuration_from_setup_py
    config = setup_module.configuration(*args)
  File "/usr/lib64/python2.6/site-packages/scipy/lib/setup.py", line 8, in
configuration
    config.add_subpackage('lapack')
  File "/usr/lib64/python2.6/site-packages/numpy/distutils/misc_util.py",
line 852, in add_subpackage
    caller_level = 2)
  File "/usr/lib64/python2.6/site-packages/numpy/distutils/misc_util.py",
line 835, in get_subpackage
    caller_level = caller_level + 1)
  File "/usr/lib64/python2.6/site-packages/numpy/distutils/misc_util.py",
line 782, in _get_configuration_from_setup_py
    config = setup_module.configuration(*args)
  File "/usr/lib64/python2.6/site-packages/scipy/lib/lapack/setup.py", line
32, in configuration
    lapack_opt = get_info('lapack_opt',notfound_action=2)
  File "/usr/lib64/python2.6/site-packages/numpy/distutils/system_info.py",
line 303, in get_info
    return cl().get_info(notfound_action)
  File "/usr/lib64/python2.6/site-packages/numpy/distutils/system_info.py",
line 454, in get_info
    raise self.notfounderror,self.notfounderror.__doc__
numpy.distutils.system_info.LapackNotFoundError:
    Lapack (http://www.netlib.org/lapack/) libraries not found.
    Directories to search for the libraries can be specified in the
    numpy/distutils/site.cfg file (section [lapack]) or by setting
    the LAPACK environment variable.



2009/8/29 David Cournapeau <cournape at gmail.com>

> On Fri, Aug 28, 2009 at 10:16 AM, Kristian Hans
> Sandberg<Kristian.Sandberg at colorado.edu> wrote:
> >
> > Thank Neil! However, this is really funny: When I run it inside gdb, it
> works, but when I run it outside gdb, it crashes. See printout below:
>
> Build a debug scipy (python setup.py build_ext -g install) and run the
> test under valgrind, this should gives us useful information.
>
> cheers,
>
> David
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From zachary.pincus at yale.edu  Sat Aug 29 15:30:59 2009
From: zachary.pincus at yale.edu (Zachary Pincus)
Date: Sat, 29 Aug 2009 15:30:59 -0400
Subject: [SciPy-User] bug in ndimage grey_dilation?
In-Reply-To: <A448FF20-F076-4A7B-A1FD-818843957D58@gmail.com>
References: <A448FF20-F076-4A7B-A1FD-818843957D58@gmail.com>
Message-ID: <C36F4C5B-58CA-40A7-87C4-4485490140D6@yale.edu>

That looks good and reasonable!

Any other morphological filters that have this inconsistency?

Zach


On Aug 29, 2009, at 1:00 PM, Tony S Yu wrote:

> ndimage.grey_dilation doesn't handle scalar size arguments (in
> contrast to other filters). For example:
>
>>>> ndimage.grey_dilation(np.ones((5,5)), size=2)
> TypeError: 'int' object is unsubscriptable
>
> Below is a simple patch which seems to fix the issue (I basically
> copied the behavior of ndimage.filters._min_or_max_filter). The diff
> below is taken against the 0.7.x branch.
>
> I guess this may not technically be a bug since filters in
> morphology.py don't specify they can handle scalar size arguments
> (filters in filters.py do document this behavior). In any case, this
> fix would make the API more consistent.
>
> Cheers,
> -Tony
>
> ===================================================================
> --- morphology.py	(revision 5911)
> +++ morphology.py	(working copy)
> @@ -347,13 +347,14 @@
>          footprint = footprint[tuple([slice(None, None, -1)] *
>                                      footprint.ndim)]
>      input = numpy.asarray(input)
> +    sizes = _ni_support._normalize_sequence(size, input.ndim)
>      origin = _ni_support._normalize_sequence(origin, input.ndim)
>      for ii in range(len(origin)):
>          origin[ii] = -origin[ii]
>          if footprint is not None:
>              sz = footprint.shape[ii]
>          else:
> -            sz = size[ii]
> +            sz = sizes[ii]
>          if not sz & 1:
>              origin[ii] -= 1
>      return filters._min_or_max_filter(input, size, footprint,
> structure,
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user



From ivo.maljevic at gmail.com  Sat Aug 29 16:20:32 2009
From: ivo.maljevic at gmail.com (Ivo Maljevic)
Date: Sat, 29 Aug 2009 16:20:32 -0400
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <1251570290.3972.0.camel@gigoblob>
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<3d375d730908271538w5ceef2ep40253624517f8e30@mail.gmail.com>
	<20090827165443.AKT56485@joker.int.colorado.edu>
	<4A9733D7.6000004@wartburg.edu>
	<20090828091635.AKT86011@joker.int.colorado.edu>
	<5b8d13220908282245r3637d17u9c6c3b44a16f8154@mail.gmail.com>
	<826c64da0908291105w2f7a0449i481b907d971b6962@mail.gmail.com>
	<1251570290.3972.0.camel@gigoblob>
Message-ID: <826c64da0908291320h3bb5a6c5tf33867078a409d5a@mail.gmail.com>

I remembered that there was something different between Ubuntu and openSUSE.
It's numpy.

aptitude show python-numpy
Package: python-numpy
State: installed
Automatically installed: yes
Version: 1:1.2.1-1ubuntu1


zypper if python-numpy
Loading repository data...
Reading installed packages...
Information for package python-numpy:
Repository: @System
Name: python-numpy
Version: 1.3.0-3.1
Arch: x86_64
Vendor: openSUSE-Education

2009/8/29 Hugo Shi <humongo.shi at gmail.com>

> running ubuntu 9.04, ran this
>
> In [2]: import scipy.special
>
> In [3]: scipy.special.chebyt(12)(-0.5)
> Out[3]: 0.99999999999995792
>
> It works in ubuntu, and ubuntu 9.04 is using scipy 0.7.0
>
>
>
> On Sat, 2009-08-29 at 14:05 -0400, Ivo Maljevic wrote:
> > David, Pauli,
> > You guys are more versed with this. It should be fairly straighforward
> > to reproduce the problem. I just installed Mandriva 2009.1, 32 bit, as
> > a virtual machine, installed scimath package (which contains scipy),
> > and the problem show up right away. As Kristian said, when you run
> > python in gdb, the problem goes away on Mandriva. It doesn't go away
> > on openSUSE, though.
> >
> > So, the problem is not only on 64 bit distributions, and most likely
> > it is across  all the distributions, it is just that almost no one
> > uses chebyt, especially with these parameters. It doesn't fail with
> > chebyt(11)(-0.5). Also, it doesn't fail on Ubuntu, but I believe that
> > is because scipy used there is 0.6.
> >
> > Ivo
> >
> > 2009/8/29 David Cournapeau <cournape at gmail.com>
> >         On Fri, Aug 28, 2009 at 10:16 AM, Kristian Hans
> >         Sandberg<Kristian.Sandberg at colorado.edu> wrote:
> >         >
> >
> >         > Thank Neil! However, this is really funny: When I run it
> >         inside gdb, it works, but when I run it outside gdb, it
> >         crashes. See printout below:
> >
> >
> >         Build a debug scipy (python setup.py build_ext -g install) and
> >         run the
> >         test under valgrind, this should gives us useful information.
> >
> >         cheers,
> >
> >         David
> >
> >         _______________________________________________
> >         SciPy-User mailing list
> >         SciPy-User at scipy.org
> >         http://mail.scipy.org/mailman/listinfo/scipy-user
> >
> >
> > _______________________________________________
> > SciPy-User mailing list
> > SciPy-User at scipy.org
> > http://mail.scipy.org/mailman/listinfo/scipy-user
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From ivo.maljevic at gmail.com  Sat Aug 29 17:18:29 2009
From: ivo.maljevic at gmail.com (Ivo Maljevic)
Date: Sat, 29 Aug 2009 17:18:29 -0400
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <826c64da0908291320h3bb5a6c5tf33867078a409d5a@mail.gmail.com>
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<3d375d730908271538w5ceef2ep40253624517f8e30@mail.gmail.com>
	<20090827165443.AKT56485@joker.int.colorado.edu>
	<4A9733D7.6000004@wartburg.edu>
	<20090828091635.AKT86011@joker.int.colorado.edu>
	<5b8d13220908282245r3637d17u9c6c3b44a16f8154@mail.gmail.com>
	<826c64da0908291105w2f7a0449i481b907d971b6962@mail.gmail.com>
	<1251570290.3972.0.camel@gigoblob>
	<826c64da0908291320h3bb5a6c5tf33867078a409d5a@mail.gmail.com>
Message-ID: <826c64da0908291418n35604dabqabfa68dbd3cfb697@mail.gmail.com>

Don't know if anybody is still interested in this. I don't even use this
function, but since I started with a reproduction of the bug I couldn't let
it go.
I believe I found a solution that works, at least for me.

/usr/lib64/python/site-packages/scipy/special

I modified orthogonal.py as shown below (the eigenvalue call):

# Scipy imports.
import numpy as np
from numpy import all, any, exp, inf, pi, sqrt
#from numpy.dual import eig
from scipy.linalg import eig

and the problem went away:

>>> from scipy.special import chebyt
>>> chebyt(12)(-0.5)
0.99999999999985179

I confirmed with Mandrake as well. I do not plan to check and see what are
the differences between numpy.dual and scipy.linalg, I'm sure
there are people who know this better.

Ivo
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From fperez.net at gmail.com  Sat Aug 29 17:18:08 2009
From: fperez.net at gmail.com (Fernando Perez)
Date: Sat, 29 Aug 2009 14:18:08 -0700
Subject: [SciPy-User] SciPy09 Video page direct links
In-Reply-To: <2FA33339-D0FF-41FF-95BF-CB4F348A7C42@cs.toronto.edu>
References: <h716e0$n6$1@ger.gmane.org>
	<88e473830908271743g428604c0h393174f33067b36c@mail.gmail.com> 
	<914CF16D-5606-4127-A901-1442F25FEB30@cs.toronto.edu>
	<20090828052712.GA16134@phare.normalesup.org> 
	<2FA33339-D0FF-41FF-95BF-CB4F348A7C42@cs.toronto.edu>
Message-ID: <db6b5ecc0908291418k6c2f0af0l8d5efe3e4c77645c@mail.gmail.com>

On Thu, Aug 27, 2009 at 11:34 PM, David Warde-Farley<dwf at cs.toronto.edu> wrote:
> As a side note, there are previous years' conference wiki pages which
> have been (mostly) converted into ReST, and would probably be more at

As a side-side note, it's worth remembering that moin understands reST
already.  I've been recently making all new pages in moin wikis using
this:

{{{
#!rst

...

}}}

I'm not sure it catches 100% of reST and in the long run we do want to
transition to a native-reST solution, but tagging existing pages like
this may help contain the spread of Moin markup further...

Cheers,

f


From tjhnson at gmail.com  Sat Aug 29 18:21:34 2009
From: tjhnson at gmail.com (T J)
Date: Sat, 29 Aug 2009 15:21:34 -0700
Subject: [SciPy-User] symmetric lil_matrix
Message-ID: <b02c60870908291521y1bf09c0fic5c4306ae8550603@mail.gmail.com>

In constructing a scipy.sparse.lil_matrix, is there any built-in
functionality to support symmetric matrices?

>>> m = sparse.lil_matrix((3,3))
>>> m[1,2] = 3
>>> m
<3x3 sparse matrix of type '<type 'numpy.float64'>'
	with 1 stored elements in LInked List format>
>>> m[2,1] = m[1,2]
<3x3 sparse matrix of type '<type 'numpy.float64'>'
	with 2 stored elements in LInked List format>

Was wondering if m[2,1] could be automatically added if one declared
the lil_matrix as symmetric.  Also, the data structure says it has 2
stored elements.  Is it actually storing two elements?  My hope is
that additional "sparseness" could be gained if it is known that the
matrix is symmetric.


From chris.michalski at gmail.com  Sat Aug 29 21:48:10 2009
From: chris.michalski at gmail.com (Chris Michalski)
Date: Sat, 29 Aug 2009 18:48:10 -0700
Subject: [SciPy-User] Monotonic Piecewise Cubic Hermite Interpolator (Matlab
	pchip() equivalent).
Message-ID: <C2CC09DF-37FD-41E1-946D-98996DCA15B1@gmail.com>

Recently, I had a need for a monotonic piece-wise cubic Hermite  
interpolator.   Matlab provides the function "pchip" (Piecewise Cubic  
Hermite Interpolator), but when I Googled I didn't find any Python  
equivalent.   I tried "interp1d()" from scipy.interpolate but this was  
a standard cubic spline using all of the data - not a piece-wise cubic  
spline.  This type of interpolator wasn't up to my needs because it  
tends to ring between points where there are sharp transitions because  
it isn't monotonic and it uses all of the data - which makes the  
interpolator "miss" sharp breaks in the data.

I had access to Matlab documentation, so I spent a some time tracing  
through the code to figure out how I might write a Python duplicate.   
This was an massive exercise in frustration and a potent reminder on  
why I love Python and use Matlab only under duress.  I find typical  
Matlab code is poorly documented (if at all) and that apparently  
includes the code included in their official releases.  I also find  
Matlab syntax ?dated? and the code very difficult to ?read?.

Wikipedia to the rescue:

http://en.wikipedia.org/wiki/Cubic_Hermite_spline
http://en.wikipedia.org/wiki/Monotone_cubic_interpolation

Not to be deterred, these two very well written Wikipedia entries  
explained in simple language how to compute the interpolant.  Hats off  
to whoever wrote these entries ? they are excellent.  The result was a  
surprising small amount of code considering the Matlab code was  
approaching 10 pages of incomprehensible code. Again - strong evidence  
that things are just better in Python...

The resulting code:

along with a matplotlib plot showing the results:
As the plot shows, the "pchip" does preserve monotonicity compared to  
a "straight" piecewise cubic Hermite interpolation.  In addition, both  
of the piecewise interpolators result in less "ringing" away from  
transitions because they only consider the data points in the  
immediate vicinity of the desired output point.

Hope this is helpful in filling the void that several have asked about.
















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From dwf at cs.toronto.edu  Sat Aug 29 22:32:20 2009
From: dwf at cs.toronto.edu (David Warde-Farley)
Date: Sat, 29 Aug 2009 22:32:20 -0400
Subject: [SciPy-User] symmetric lil_matrix
In-Reply-To: <b02c60870908291521y1bf09c0fic5c4306ae8550603@mail.gmail.com>
References: <b02c60870908291521y1bf09c0fic5c4306ae8550603@mail.gmail.com>
Message-ID: <AEA7D97D-AA1A-4213-94FC-28D95F476AE3@cs.toronto.edu>

On 29-Aug-09, at 6:21 PM, T J wrote:

> In constructing a scipy.sparse.lil_matrix, is there any built-in
> functionality to support symmetric matrices?

No, but such functionality would be easy to add by subclassing.

import scipy.sparse

class sym_lil_matrix(scipy.sparse.lil_matrix):
	def __setitem__(self, index, x):
		super(sym_lil_matrix, self).__setitem__(index, x)
		super(sym_lil_matrix, self).__setitem__(index[::-1], x)


You could add a custom constructor but it's unclear what the semantics  
ought to be there.

> Was wondering if m[2,1] could be automatically added if one declared
> the lil_matrix as symmetric.  Also, the data structure says it has 2
> stored elements.  Is it actually storing two elements?

Yes.

> My hope is that additional "sparseness" could be gained if it is known
> that the matrix is symmetric.

That would require considerably more thought (in order to implement  
all of the interface that needs to be aware of this duplication) but  
is certainly possible. It hardly seems worth it unless your matrices  
are positively gigantic and can't fit into memory.

David


From tsyu80 at gmail.com  Sat Aug 29 23:58:01 2009
From: tsyu80 at gmail.com (Tony S Yu)
Date: Sat, 29 Aug 2009 23:58:01 -0400
Subject: [SciPy-User] bug in ndimage grey_dilation?
In-Reply-To: <mailman.1006.1251596899.2350.scipy-user@scipy.org>
References: <mailman.1006.1251596899.2350.scipy-user@scipy.org>
Message-ID: <209CE9FD-0505-4CD3-90EF-A9AA764C81A7@gmail.com>


On Aug 29, 2009, at 9:48 PM, scipy-user-request at scipy.org wrote:

> Message: 1
> Date: Sat, 29 Aug 2009 15:30:59 -0400
> From: Zachary Pincus <zachary.pincus at yale.edu>
> Subject: Re: [SciPy-User] bug in ndimage grey_dilation?
> To: SciPy Users List <scipy-user at scipy.org>
> Message-ID: <C36F4C5B-58CA-40A7-87C4-4485490140D6 at yale.edu>
> Content-Type: text/plain; charset=US-ASCII; format=flowed
>
> That looks good and reasonable!

Oops! I really didn't test my fix. This patch causes a failure if  
footprint is specified without specifying a size (which should be  
allowed). Also, the function seems to fail (both before and after the  
patch) if the structure is specified without specifying a size (which  
the docstring suggests is allowed).

I'll work on another patch, but it may require me to understand the  
code a bit better (I don't even understand the difference between  
footprint and structure at this point). (Also, ``if not sz & 1``---at  
the bottom of the diff---seems really weird to me).

>
> Any other morphological filters that have this inconsistency?

This seems to be the only filter affected with this inconsistency b/c  
all other filters delegate their handling of size/footprint/structure  
arguments to filters._min_or_max_filter (which handles the scalar  
sizes without problems).

-Tony

>
> Zach
>
>
> On Aug 29, 2009, at 1:00 PM, Tony S Yu wrote:
>
>> ndimage.grey_dilation doesn't handle scalar size arguments (in
>> contrast to other filters). For example:
>>
>>>>> ndimage.grey_dilation(np.ones((5,5)), size=2)
>> TypeError: 'int' object is unsubscriptable
>>
>> Below is a simple patch which seems to fix the issue (I basically
>> copied the behavior of ndimage.filters._min_or_max_filter). The diff
>> below is taken against the 0.7.x branch.
>>
>> I guess this may not technically be a bug since filters in
>> morphology.py don't specify they can handle scalar size arguments
>> (filters in filters.py do document this behavior). In any case, this
>> fix would make the API more consistent.
>>
>> Cheers,
>> -Tony
>>
>> ===================================================================
>> --- morphology.py	(revision 5911)
>> +++ morphology.py	(working copy)
>> @@ -347,13 +347,14 @@
>>         footprint = footprint[tuple([slice(None, None, -1)] *
>>                                     footprint.ndim)]
>>     input = numpy.asarray(input)
>> +    sizes = _ni_support._normalize_sequence(size, input.ndim)
>>     origin = _ni_support._normalize_sequence(origin, input.ndim)
>>     for ii in range(len(origin)):
>>         origin[ii] = -origin[ii]
>>         if footprint is not None:
>>             sz = footprint.shape[ii]
>>         else:
>> -            sz = size[ii]
>> +            sz = sizes[ii]
>>         if not sz & 1:
>>             origin[ii] -= 1
>>     return filters._min_or_max_filter(input, size, footprint,
>> structure,
>

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From pav at iki.fi  Sun Aug 30 10:20:59 2009
From: pav at iki.fi (Pauli Virtanen)
Date: Sun, 30 Aug 2009 14:20:59 +0000 (UTC)
Subject: [SciPy-User] Problem with scipy.special.chebyt
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<3d375d730908271538w5ceef2ep40253624517f8e30@mail.gmail.com>
	<20090827165443.AKT56485@joker.int.colorado.edu>
	<4A9733D7.6000004@wartburg.edu>
	<20090828091635.AKT86011@joker.int.colorado.edu>
	<5b8d13220908282245r3637d17u9c6c3b44a16f8154@mail.gmail.com>
	<826c64da0908291105w2f7a0449i481b907d971b6962@mail.gmail.com>
	<1251570290.3972.0.camel@gigoblob>
	<826c64da0908291320h3bb5a6c5tf33867078a409d5a@mail.gmail.com>
	<826c64da0908291418n35604dabqabfa68dbd3cfb697@mail.gmail.com>
Message-ID: <h7e1sa$aq4$1@ger.gmane.org>

On 2009-08-29, Ivo Maljevic <ivo.maljevic at gmail.com> wrote:
> Don't know if anybody is still interested in this.

In general, we are interested in bugs.

However, this type of crashes in linear algebra routines 
typically do not come from errors in Numpy or Scipy, but from a 
mis-compilation/linking/compiler choice in the build of one of 
the libraries. They are also difficult to reproduce on other 
platforms or Linux distributions.

For example, I cannot reproduce this particular issue on any of 
the machines I have access to.

> I don't even use this function, but since I started with a 
> reproduction of the bug I couldn't let it go. I believe I found 
> a solution that works, at least for me.
>
> /usr/lib64/python/site-packages/scipy/special
>
> I modified orthogonal.py as shown below (the eigenvalue call):
>
> # Scipy imports.
> import numpy as np
> from numpy import all, any, exp, inf, pi, sqrt
> #from numpy.dual import eig
> from scipy.linalg import eig
>
> and the problem went away:
>
>>>> from scipy.special import chebyt
>>>> chebyt(12)(-0.5)
> 0.99999999999985179
>
> I confirmed with Mandrake as well. I do not plan to check and see what are
> the differences between numpy.dual and scipy.linalg, I'm sure
> there are people who know this better.

It very much looks like scipy.linalg is broken on Mandrake. Some 
possible causes: miscompiled ATLAS, etc. I believe this bug is 
quite specific to the release of Mandrake you have, but is 
probably not present on other platforms.

You can try to verify this by checking if

	scipy.linalg.eig

fails. The eig from numpy.dual is an alias for this (when Scipy 
is available).

The best thing to do in any case may be to send a bug report to 
the Mandrake developers.

-- 
Pauli Virtanen




From ivo.maljevic at gmail.com  Sun Aug 30 10:46:10 2009
From: ivo.maljevic at gmail.com (Ivo Maljevic)
Date: Sun, 30 Aug 2009 10:46:10 -0400
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <h7e1sa$aq4$1@ger.gmane.org>
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<20090827165443.AKT56485@joker.int.colorado.edu>
	<4A9733D7.6000004@wartburg.edu>
	<20090828091635.AKT86011@joker.int.colorado.edu>
	<5b8d13220908282245r3637d17u9c6c3b44a16f8154@mail.gmail.com>
	<826c64da0908291105w2f7a0449i481b907d971b6962@mail.gmail.com>
	<1251570290.3972.0.camel@gigoblob>
	<826c64da0908291320h3bb5a6c5tf33867078a409d5a@mail.gmail.com>
	<826c64da0908291418n35604dabqabfa68dbd3cfb697@mail.gmail.com>
	<h7e1sa$aq4$1@ger.gmane.org>
Message-ID: <826c64da0908300746u711270e6wbec19a136b21555b@mail.gmail.com>

Pauli, I'm surprised that you cannot reproduce this problem. It is not
unique to Mandriva, as I repeatedly said that I reproduced the problem on
openSUSE first. In both cases, I didn't do a custom build, just a fresh
install with both installations. I don't know why nobody from scipy wants to
try a fresh install on a virtual machine maybe, as I am sure you would be
able to see the problem right away. I agree that this is most likely not
numpy/scipy problem, but it would still be helpful for other users out there
if somebody actually tried this with a clean distro.

In my previous email I specified that using the eig function from
scipy.linalg solves the problem, and using the eig from numpy.dual brings it
back. That's how I found the workaround in the first place, because I wanted
to replicate what exactly happens in chebyt function (replaced some
numerical values with expressions as it was easy for me to recognize
0.707... values). If, as you are saying, scipy.linalg and numpy.dual are the
same thing, then how to explain the following. I used the following matrix:

A= mat([[ 0., 1.0/sqrt(2.0), 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.
],
 [ 1.0/sqrt(2.0), 0.,  1.0/2.0,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.
],
 [ 0.,  1.0/2.0,  0.,  1.0/2.0,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0. ],
 [ 0.,  0.,  1.0/2.0,  0.,  1.0/2.0,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
 [ 0.,  0.,  0.,  1.0/2.0,  0.,  1.0/2.0,  0.,  0.,  0.,  0.,  0.,  0.],
 [ 0.,  0.,  0.,  0.,  1.0/2.0,  0.,  1.0/2.0,  0.,  0.,  0.,  0.,  0.],
 [ 0.,  0.,  0.,  0.,  0.,  1.0/2.0,  0. , 1.0/2.0,  0.,  0.,  0.,  0.],
 [ 0.,  0.,  0.,  0.,  0.,  0.,  1.0/2.0,  0.,  1.0/2.0,  0.,  0.,  0.],
 [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.0/2.0,  0.,  1.0/2.0,  0.,  0.],
 [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.0/2.0,  0.,  1.0/2.0,  0.],
 [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.0/2.0,  0.,  1.0/2.0],
 [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.0/2.0,  0. ]])

and when I use eig from numpy.dual:

from numpy.dual import eig,det

C,B=eig(A)
print C
print 'det(A)=%g' % det(A)

I get:

[ivom at localhost ~]$ python cheby.py
[ 0.99144486  0.92387953  0.79335334  0.60876143  0.38268343 -0.99144486
  0.13052619 -0.92387953 -0.13052619 -0.79335334 -0.38268343 -0.60876143]
det(A)=0.000488281
*Segmentation fault*

On the other hand, with:

from scipy.linalg import eig,det

I get:

[ivom at localhost ~]$ python cheby.py
[ 0.99144486+0.j  0.92387953+0.j  0.79335334+0.j  0.60876143+0.j
  0.38268343+0.j -0.99144486+0.j  0.13052619+0.j -0.92387953+0.j
 -0.13052619+0.j -0.79335334+0.j -0.38268343+0.j -0.60876143+0.j]
det(A)=0.000488281

Ivo

2009/8/30 Pauli Virtanen <pav at iki.fi>

> On 2009-08-29, Ivo Maljevic <ivo.maljevic at gmail.com> wrote:
> > Don't know if anybody is still interested in this.
>
> In general, we are interested in bugs.
>
> However, this type of crashes in linear algebra routines
> typically do not come from errors in Numpy or Scipy, but from a
> mis-compilation/linking/compiler choice in the build of one of
> the libraries. They are also difficult to reproduce on other
> platforms or Linux distributions.
>
> For example, I cannot reproduce this particular issue on any of
> the machines I have access to.
>
> > I don't even use this function, but since I started with a
> > reproduction of the bug I couldn't let it go. I believe I found
> > a solution that works, at least for me.
> >
> > /usr/lib64/python/site-packages/scipy/special
> >
> > I modified orthogonal.py as shown below (the eigenvalue call):
> >
> > # Scipy imports.
> > import numpy as np
> > from numpy import all, any, exp, inf, pi, sqrt
> > #from numpy.dual import eig
> > from scipy.linalg import eig
> >
> > and the problem went away:
> >
> >>>> from scipy.special import chebyt
> >>>> chebyt(12)(-0.5)
> > 0.99999999999985179
> >
> > I confirmed with Mandrake as well. I do not plan to check and see what
> are
> > the differences between numpy.dual and scipy.linalg, I'm sure
> > there are people who know this better.
>
> It very much looks like scipy.linalg is broken on Mandrake. Some
> possible causes: miscompiled ATLAS, etc. I believe this bug is
> quite specific to the release of Mandrake you have, but is
> probably not present on other platforms.
>
> You can try to verify this by checking if
>
>        scipy.linalg.eig
>
> fails. The eig from numpy.dual is an alias for this (when Scipy
> is available).
>
> The best thing to do in any case may be to send a bug report to
> the Mandrake developers.
>
> --
> Pauli Virtanen
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From dmitrey at mail.ua  Sun Aug 30 10:50:14 2009
From: dmitrey at mail.ua (Dmitrey Kroshko)
Date: Sun, 30 Aug 2009 17:50:14 +0300
Subject: [SciPy-User] =?utf-8?q?_ANN=3A_FuncDesigner_0=2E15RC2=2C_DerAppro?=
 =?utf-8?q?ximator_0=2E15RC2=2C_OpenOpt_0=2E25RC2?=
Message-ID: <1251643814.A7c125e24.4046@mail.ua>

Hi all, 
 (sorry if you get the message for twice - there were some problems occured)

I'm glad to inform you about new packages - FuncDesigner and DerApproximator. They have been extracted from OpenOpt into standalone Python modules. 
 
FuncDesigner is a convenient tool for building functions and getting their derivatives via Automatic differentiation ( http://en.wikipedia.org/wiki/Automatic_differentiation ), not to be confused with numerical differentiation via finite-differences derivatives approximation and symbolic differentiation provided by Maxima, SymPy etc. 
So, you can optimize models coded in FuncDesigner by OpenOpt w/o providing user-supplied derivatives. 
For more details see http://openopt.org/FuncDesigner 
 
DerApproximator is a small yet important package for getting/checking derivatives (currently only 1st ones), extracted from OpenOpt framework to be standalone Python module. It is required by FuncDesigner (for obtaining derivatives of oofuns beyond standard set without routines to yield them directly) and some OpenOpt solvers (when there are some functions without user-supplied derivatives). 
See http://openopt.org/DerApproximator for more details. 
 
For more info about these RCs read 
 http://forum.openopt.org/viewtopic.php?id=131 
 
Regards, D. 
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From dmitrey at mail.ua  Sun Aug 30 10:51:32 2009
From: dmitrey at mail.ua (dmitrey at mail.ua)
Date: Sun, 30 Aug 2009 17:51:32 +0300
Subject: [SciPy-User] =?utf-8?b?INCS0LDRiCDQu9C40YHRgiDQv9C+0YLRgNCw0L8=?=
 =?utf-8?b?0LjQsiDRgyDQv9Cw0L/QutGDICLQodC/0LDQvCIuINCS0Lgg0LzQvtC20LU=?=
 =?utf-8?b?0YLQtSAi0L/RgNC+0YjRgtC+0LLRhdC90YPRgtC4IiDQudC+0LPQviDQvtC0?=
 =?utf-8?b?0L3QuNC8INC60LvRltC60L7QvC4=?=
Message-ID: <1251643892.4E1D49060.23812@mail.ua>

(see english version below)

?????? Dmitrey Kroshko <dmitrey at mail.ua>,	SciPy Users List <scipy-user at scipy.org> ???????? ? ???? ???????? ????? ? ???? ?????? ????? ???? ???????? ? ????? "????".

??? ????????? ("????????????") ???? ? "??????" ???????? ?? ????????? ???? ? ??????? ???, ?????????? ?? ???????:
http://www.dmitrey.mail.ua/confirm/1251643845.H305026P3954.mx.mail.ua

?? ???????? ????????? ???????? ???? ???. ????? ????? ??? ?????, ????????? ? ?????? Dmitrey Kroshko <dmitrey at mail.ua>,	SciPy Users List <scipy-user at scipy.org>, ?????? ??????? ???????????? ? "??????" ??? ?????????? ?????????.

??? ???????????? ???? ??????????? ?? ????? ?????? ???????????? ???? ???????????? ????????? (????????) ?? ???????:
http://www.dmitrey.mail.ua
??? ?????? ????? ??????? ??? ???????? ???? ????????? e-mail ?????? ??????????? (????????? ? ????????-????).

? ???????, ?????????? ?? ?????????
Dmitrey Kroshko dmitrey at mail.ua


-------------------------- english -------------------------------

My antispam system detected your message as possible SPAM.

To confirm that your message is not a spam use this link:
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With the respect, I hope for the understanding
Dmitrey Kroshko dmitrey at mail.ua

From zachary.pincus at yale.edu  Sun Aug 30 12:14:27 2009
From: zachary.pincus at yale.edu (Zachary Pincus)
Date: Sun, 30 Aug 2009 12:14:27 -0400
Subject: [SciPy-User] bug in ndimage grey_dilation?
In-Reply-To: <209CE9FD-0505-4CD3-90EF-A9AA764C81A7@gmail.com>
References: <mailman.1006.1251596899.2350.scipy-user@scipy.org>
	<209CE9FD-0505-4CD3-90EF-A9AA764C81A7@gmail.com>
Message-ID: <E5ED36AA-3097-4144-8821-C6AEDAF99BBC@yale.edu>

> I'll work on another patch, but it may require me to understand the  
> code a bit better (I don't even understand the difference between  
> footprint and structure at this point).

Probably a good idea, though it looks like this will take digging some  
into the C code...

 From what I can tell, the "structure" array allows for a non-binary  
morphological kernel, whereas the footprint is binary. Not exactly  
sure what's done with the non-binary values in the structure array as  
part of the min/max operation, though -- I think the values therein  
are added/subtracted to/from the respective values in the input array  
before the max/min operations (respectively).

>> Any other morphological filters that have this inconsistency?
>
> This seems to be the only filter affected with this inconsistency b/ 
> c all other filters delegate their handling of size/footprint/ 
> structure arguments to filters._min_or_max_filter (which handles the  
> scalar sizes without problems).

Presumably there's some reason that grey_opening doesn't delegate this  
as do the rest, but this is a bit of a surprise. Maybe it could?

Zach


From ivo.maljevic at gmail.com  Sun Aug 30 13:03:11 2009
From: ivo.maljevic at gmail.com (Ivo Maljevic)
Date: Sun, 30 Aug 2009 13:03:11 -0400
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <h7e1sa$aq4$1@ger.gmane.org>
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<20090827165443.AKT56485@joker.int.colorado.edu>
	<4A9733D7.6000004@wartburg.edu>
	<20090828091635.AKT86011@joker.int.colorado.edu>
	<5b8d13220908282245r3637d17u9c6c3b44a16f8154@mail.gmail.com>
	<826c64da0908291105w2f7a0449i481b907d971b6962@mail.gmail.com>
	<1251570290.3972.0.camel@gigoblob>
	<826c64da0908291320h3bb5a6c5tf33867078a409d5a@mail.gmail.com>
	<826c64da0908291418n35604dabqabfa68dbd3cfb697@mail.gmail.com>
	<h7e1sa$aq4$1@ger.gmane.org>
Message-ID: <826c64da0908301003k6d2f8d08k72ff0d2b160946c3@mail.gmail.com>

>
> It very much looks like scipy.linalg is broken on Mandrake. Some
> possible causes: miscompiled ATLAS, etc. I believe this bug is
> quite specific to the release of Mandrake you have, but is
> probably not present on other platforms.
>
> You can try to verify this by checking if
>
>        scipy.linalg.eig
>
> fails. The eig from numpy.dual is an alias for this (when Scipy
> is available).
>


I probably shouldn't bother with this as you clearly have your mind set that
there is no problem,
but your statement does not seem to be correct. In numpy's dual, there is a
line:

import numpy.linalg as linpkg

and then:

eig = linpkg.eig

and then in numpy/linalg/linalg.py

there is a completely different 'eig' implementation than the one in
scipy.linalg.

To add to that, numpy's version of linalg clearly and explicitly says the
following:

"""Lite version of scipy.linalg.

Notes
-----
This module is a lite version of the linalg.py module in SciPy which
contains high-level Python interface to the LAPACK library.  The lite
version only accesses the following LAPACK functions: dgesv, zgesv,
dgeev, zgeev, dgesdd, zgesdd, dgelsd, zgelsd, dsyevd, zheevd, dgetrf,
zgetrf, dpotrf, zpotrf, dgeqrf, zgeqrf, zungqr, dorgqr.






>
> The best thing to do in any case may be to send a bug report to
> the Mandrake developers.
>
> --
> Pauli Virtanen
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From cournape at gmail.com  Sun Aug 30 13:27:14 2009
From: cournape at gmail.com (David Cournapeau)
Date: Sun, 30 Aug 2009 12:27:14 -0500
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <826c64da0908300746u711270e6wbec19a136b21555b@mail.gmail.com>
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<4A9733D7.6000004@wartburg.edu>
	<20090828091635.AKT86011@joker.int.colorado.edu>
	<5b8d13220908282245r3637d17u9c6c3b44a16f8154@mail.gmail.com>
	<826c64da0908291105w2f7a0449i481b907d971b6962@mail.gmail.com>
	<1251570290.3972.0.camel@gigoblob>
	<826c64da0908291320h3bb5a6c5tf33867078a409d5a@mail.gmail.com>
	<826c64da0908291418n35604dabqabfa68dbd3cfb697@mail.gmail.com>
	<h7e1sa$aq4$1@ger.gmane.org>
	<826c64da0908300746u711270e6wbec19a136b21555b@mail.gmail.com>
Message-ID: <5b8d13220908301027q61ae6f67q6b7072f580148e76@mail.gmail.com>

On Sun, Aug 30, 2009 at 9:46 AM, Ivo Maljevic<ivo.maljevic at gmail.com> wrote:
> Pauli,
> I'm surprised that you cannot reproduce this problem. It is not unique to
> Mandriva, as I repeatedly said that I reproduced the problem on openSUSE
> first. In both cases, I didn't do a custom build, just a fresh install with
> both installations. I don't know why nobody from scipy wants to try a fresh
> install on a virtual machine maybe, as I am sure you would be able to see
> the problem right away.

Because it takes a lot of time, and very few developers use those distributions.

> I agree that this is most likely not numpy/scipy
> problem, but it would still be helpful for other users out there if somebody
> actually tried this with a clean distro.
> In my previous email I specified that using the eig function from
> scipy.linalg solves the problem, and using the eig from numpy.dual brings it
> back.

What does ldd return on numpy/linalg/lapack_lite.so and
scipy/linalg/flapack.so|_flinalg.so|fblas.so ?

Most likely, numpy and scipy do not link to the same libraries, and
your distribution ship some broken packages.

cheers,

David


From ivo.maljevic at gmail.com  Sun Aug 30 13:45:26 2009
From: ivo.maljevic at gmail.com (Ivo Maljevic)
Date: Sun, 30 Aug 2009 13:45:26 -0400
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <5b8d13220908301027q61ae6f67q6b7072f580148e76@mail.gmail.com>
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<20090828091635.AKT86011@joker.int.colorado.edu>
	<5b8d13220908282245r3637d17u9c6c3b44a16f8154@mail.gmail.com>
	<826c64da0908291105w2f7a0449i481b907d971b6962@mail.gmail.com>
	<1251570290.3972.0.camel@gigoblob>
	<826c64da0908291320h3bb5a6c5tf33867078a409d5a@mail.gmail.com>
	<826c64da0908291418n35604dabqabfa68dbd3cfb697@mail.gmail.com>
	<h7e1sa$aq4$1@ger.gmane.org>
	<826c64da0908300746u711270e6wbec19a136b21555b@mail.gmail.com>
	<5b8d13220908301027q61ae6f67q6b7072f580148e76@mail.gmail.com>
Message-ID: <826c64da0908301045u5c416510xa006f1f9add8a39c@mail.gmail.com>

David,
Yes, I figured out that among the other differences between scipy.linalg eig
and numpy.dual eig
evaluations, there is this lite library.

But, regardless of whether this works on some distributions or not, wouldn't
it be appropriate to
use scipy.linalg.eig instead of numpy.dual.eig when evaluating scipy.special
functions?

I cannot see the differences
between the loaded libraries (except for the addresses), but the
implementation itself is different. Here is one output you requested:

numpy/linalg # ldd lapack_lite.so
        linux-vdso.so.1 =>
(0x00007fff443ff000)
        liblapack.so.3 => /usr/lib64/liblapack.so.3
(0x00007fec746dd000)
        libblas.so.3 => /usr/lib64/libblas.so.3 (0x00007fec743bd000)
        libpython2.6.so.1.0 => /usr/lib64/libpython2.6.so.1.0
(0x00007fec74024000)
        libgfortran.so.3 => /usr/lib64/libgfortran.so.3 (0x00007fec73d48000)
        libm.so.6 => /lib64/libm.so.6 (0x00007fec73af2000)
        libgcc_s.so.1 => /lib64/libgcc_s.so.1 (0x00007fec738d9000)
        libc.so.6 => /lib64/libc.so.6 (0x00007fec73580000)
        libpthread.so.0 => /lib64/libpthread.so.0 (0x00007fec73364000)
        libdl.so.2 => /lib64/libdl.so.2 (0x00007fec7315f000)
        libutil.so.1 => /lib64/libutil.so.1 (0x00007fec72f5c000)
        /lib64/ld-linux-x86-64.so.2 (0x00007fec75506000)

ldd ../../scipy/linalg/flapack.so
        linux-vdso.so.1 =>  (0x00007fffeb7ff000)
        liblapack.so.3 => /usr/lib64/liblapack.so.3 (0x00007f502374f000)
        libblas.so.3 => /usr/lib64/libblas.so.3 (0x00007f502342f000)
        libpython2.6.so.1.0 => /usr/lib64/libpython2.6.so.1.0
(0x00007f5023096000)
        libgfortran.so.3 => /usr/lib64/libgfortran.so.3 (0x00007f5022dba000)
        libm.so.6 => /lib64/libm.so.6 (0x00007f5022b64000)
        libgcc_s.so.1 => /lib64/libgcc_s.so.1 (0x00007f502294b000)
        libc.so.6 => /lib64/libc.so.6 (0x00007f50225f2000)
        libpthread.so.0 => /lib64/libpthread.so.0 (0x00007f50223d6000)
        libdl.so.2 => /lib64/libdl.so.2 (0x00007f50221d1000)
        libutil.so.1 => /lib64/libutil.so.1 (0x00007f5021fce000)
        /lib64/ld-linux-x86-64.so.2 (0x00007f50245eb000)



2009/8/30 David Cournapeau <cournape at gmail.com>

> On Sun, Aug 30, 2009 at 9:46 AM, Ivo Maljevic<ivo.maljevic at gmail.com>
> wrote:
> > Pauli,
> > I'm surprised that you cannot reproduce this problem. It is not unique to
> > Mandriva, as I repeatedly said that I reproduced the problem on openSUSE
> > first. In both cases, I didn't do a custom build, just a fresh install
> with
> > both installations. I don't know why nobody from scipy wants to try a
> fresh
> > install on a virtual machine maybe, as I am sure you would be able to see
> > the problem right away.
>
> Because it takes a lot of time, and very few developers use those
> distributions.
>
> > I agree that this is most likely not numpy/scipy
> > problem, but it would still be helpful for other users out there if
> somebody
> > actually tried this with a clean distro.
> > In my previous email I specified that using the eig function from
> > scipy.linalg solves the problem, and using the eig from numpy.dual brings
> it
> > back.
>
> What does ldd return on numpy/linalg/lapack_lite.so and
> scipy/linalg/flapack.so|_flinalg.so|fblas.so ?
>
> Most likely, numpy and scipy do not link to the same libraries, and
> your distribution ship some broken packages.
>
> cheers,
>
> David
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From pav at iki.fi  Sun Aug 30 13:55:30 2009
From: pav at iki.fi (Pauli Virtanen)
Date: Sun, 30 Aug 2009 17:55:30 +0000 (UTC)
Subject: [SciPy-User] Problem with scipy.special.chebyt
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<20090827165443.AKT56485@joker.int.colorado.edu>
	<4A9733D7.6000004@wartburg.edu>
	<20090828091635.AKT86011@joker.int.colorado.edu>
	<5b8d13220908282245r3637d17u9c6c3b44a16f8154@mail.gmail.com>
	<826c64da0908291105w2f7a0449i481b907d971b6962@mail.gmail.com>
	<1251570290.3972.0.camel@gigoblob>
	<826c64da0908291320h3bb5a6c5tf33867078a409d5a@mail.gmail.com>
	<826c64da0908291418n35604dabqabfa68dbd3cfb697@mail.gmail.com>
	<h7e1sa$aq4$1@ger.gmane.org>
	<826c64da0908300746u711270e6wbec19a136b21555b@mail.gmail.com>
Message-ID: <h7eeeh$dfs$1@ger.gmane.org>

On 2009-08-30, Ivo Maljevic <ivo.maljevic at gmail.com> wrote:
[clip]
> install with both installations. I don't know why nobody from 
> scipy wants to try a fresh install on a virtual machine maybe, 
> as I am sure you would be able to see the problem right away. I 
> agree that this is most likely not numpy/scipy problem, but it 
> would still be helpful for other users out there if somebody 
> actually tried this with a clean distro.

Sorry, but I don't have enough time to spend on this at the 
moment -- maybe later on. Tracking down this kind of 
platform-specific issues often means hours of gruelling work.

Feel free to file a bug ticket to Numpy's Trac so that this won't 
be forgotten. Also, I'd suggest letting the Mandriva/Opensuse 
people know there may be something wrong in their packages.

> In my previous email I specified that using the eig function 
> from scipy.linalg solves the problem, and using the eig from 
> numpy.dual brings it back.
[clip]

I mistyped scipy.linalg where I meant numpy.linalg. As David 
said, numpy and scipy may possibly use different lower-level 
libraries, which explains the difference.

All this seems to point out that something is wrong on Mandriva 
and Opensuse with how Numpy links to the underlying lapack 
library.

-- 
Pauli Virtanen



From ivo.maljevic at gmail.com  Sun Aug 30 14:16:40 2009
From: ivo.maljevic at gmail.com (Ivo Maljevic)
Date: Sun, 30 Aug 2009 14:16:40 -0400
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <h7eeeh$dfs$1@ger.gmane.org>
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<20090828091635.AKT86011@joker.int.colorado.edu>
	<5b8d13220908282245r3637d17u9c6c3b44a16f8154@mail.gmail.com>
	<826c64da0908291105w2f7a0449i481b907d971b6962@mail.gmail.com>
	<1251570290.3972.0.camel@gigoblob>
	<826c64da0908291320h3bb5a6c5tf33867078a409d5a@mail.gmail.com>
	<826c64da0908291418n35604dabqabfa68dbd3cfb697@mail.gmail.com>
	<h7e1sa$aq4$1@ger.gmane.org>
	<826c64da0908300746u711270e6wbec19a136b21555b@mail.gmail.com>
	<h7eeeh$dfs$1@ger.gmane.org>
Message-ID: <826c64da0908301116m1595263etbc0b5417a4f8139a@mail.gmail.com>

Pauli, Whatever the case is, I would suggest you to consider using
scipy.linalg eigenvalue function inside scipy.special. I see no point
in using numpy.dual version of it. They are clearly differently implemented,
and scipy's eig function solves the problem even in these distributions, and
moves the problem from scipy to numpy.
Ivo

2009/8/30 Pauli Virtanen <pav at iki.fi>

> On 2009-08-30, Ivo Maljevic <ivo.maljevic at gmail.com> wrote:
> [clip]
> > install with both installations. I don't know why nobody from
> > scipy wants to try a fresh install on a virtual machine maybe,
> > as I am sure you would be able to see the problem right away. I
> > agree that this is most likely not numpy/scipy problem, but it
> > would still be helpful for other users out there if somebody
> > actually tried this with a clean distro.
>
> Sorry, but I don't have enough time to spend on this at the
> moment -- maybe later on. Tracking down this kind of
> platform-specific issues often means hours of gruelling work.
>
> Feel free to file a bug ticket to Numpy's Trac so that this won't
> be forgotten. Also, I'd suggest letting the Mandriva/Opensuse
> people know there may be something wrong in their packages.
>
> > In my previous email I specified that using the eig function
> > from scipy.linalg solves the problem, and using the eig from
> > numpy.dual brings it back.
> [clip]
>
> I mistyped scipy.linalg where I meant numpy.linalg. As David
> said, numpy and scipy may possibly use different lower-level
> libraries, which explains the difference.
>
> All this seems to point out that something is wrong on Mandriva
> and Opensuse with how Numpy links to the underlying lapack
> library.
>
> --
> Pauli Virtanen
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From pav at iki.fi  Sun Aug 30 14:45:08 2009
From: pav at iki.fi (Pauli Virtanen)
Date: Sun, 30 Aug 2009 18:45:08 +0000 (UTC)
Subject: [SciPy-User] Problem with scipy.special.chebyt
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<20090828091635.AKT86011@joker.int.colorado.edu>
	<5b8d13220908282245r3637d17u9c6c3b44a16f8154@mail.gmail.com>
	<826c64da0908291105w2f7a0449i481b907d971b6962@mail.gmail.com>
	<1251570290.3972.0.camel@gigoblob>
	<826c64da0908291320h3bb5a6c5tf33867078a409d5a@mail.gmail.com>
	<826c64da0908291418n35604dabqabfa68dbd3cfb697@mail.gmail.com>
	<h7e1sa$aq4$1@ger.gmane.org>
	<826c64da0908300746u711270e6wbec19a136b21555b@mail.gmail.com>
	<h7eeeh$dfs$1@ger.gmane.org>
	<826c64da0908301116m1595263etbc0b5417a4f8139a@mail.gmail.com>
Message-ID: <h7ehbk$jt0$1@ger.gmane.org>

On 2009-08-30, Ivo Maljevic <ivo.maljevic at gmail.com> wrote:
[clip]
> Pauli, Whatever the case is, I would suggest you to consider using
> scipy.linalg eigenvalue function inside scipy.special. I see no point
> in using numpy.dual version of it. They are clearly differently implemented,
> and scipy's eig function solves the problem even in these distributions, and
> moves the problem from scipy to numpy.

I believe the original intent in using numpy.dual at all was here 
to avoid coupling between different scipy submodules (see r2920 
-- it's pretty explicit). How desirable this is is then a 
different question.

Anyway, the right fix is to track down why numpy.linalg.eig fails 
on Mandrake/Opensuse.

-- 
Pauli Virtanen



From ivo.maljevic at gmail.com  Sun Aug 30 14:57:08 2009
From: ivo.maljevic at gmail.com (Ivo Maljevic)
Date: Sun, 30 Aug 2009 14:57:08 -0400
Subject: [SciPy-User] Problem with scipy.special.chebyt
In-Reply-To: <h7ehbk$jt0$1@ger.gmane.org>
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<826c64da0908291105w2f7a0449i481b907d971b6962@mail.gmail.com>
	<1251570290.3972.0.camel@gigoblob>
	<826c64da0908291320h3bb5a6c5tf33867078a409d5a@mail.gmail.com>
	<826c64da0908291418n35604dabqabfa68dbd3cfb697@mail.gmail.com>
	<h7e1sa$aq4$1@ger.gmane.org>
	<826c64da0908300746u711270e6wbec19a136b21555b@mail.gmail.com>
	<h7eeeh$dfs$1@ger.gmane.org>
	<826c64da0908301116m1595263etbc0b5417a4f8139a@mail.gmail.com>
	<h7ehbk$jt0$1@ger.gmane.org>
Message-ID: <826c64da0908301157x46984ae3y6679d3bbf35dc4be@mail.gmail.com>

OK, let me know what I can do then to help. For starters, here is the output
from Mandriva. Keep in mind that I'm using mandriva for the first time in my
life, so I'm not quite versed in using it:.

I changed the colour to red for the library part.

(gdb) run cheby.py
Starting program: /usr/bin/python cheby.py
[Thread debugging using libthread_db enabled]
[New Thread 0xb7c056c0 (LWP 3763)]
[ 0.99144486  0.92387953  0.79335334  0.60876143  0.38268343 -0.99144486
  0.13052619 -0.92387953 -0.13052619 -0.79335334 -0.38268343 -0.60876143]
det(A)=0.000488281

Program received signal SIGSEGV, Segmentation fault.
0xb7e89ed8 in ?? () from /usr/lib/libpython2.6.so.1.0


2009/8/30 Pauli Virtanen <pav at iki.fi>

> On 2009-08-30, Ivo Maljevic <ivo.maljevic at gmail.com> wrote:
> [clip]
> > Pauli, Whatever the case is, I would suggest you to consider using
> > scipy.linalg eigenvalue function inside scipy.special. I see no point
> > in using numpy.dual version of it. They are clearly differently
> implemented,
> > and scipy's eig function solves the problem even in these distributions,
> and
> > moves the problem from scipy to numpy.
>
> I believe the original intent in using numpy.dual at all was here
> to avoid coupling between different scipy submodules (see r2920
> -- it's pretty explicit). How desirable this is is then a
> different question.
>
> Anyway, the right fix is to track down why numpy.linalg.eig fails
> on Mandrake/Opensuse.
>
> --
> Pauli Virtanen
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From pav at iki.fi  Sun Aug 30 16:13:34 2009
From: pav at iki.fi (Pauli Virtanen)
Date: Sun, 30 Aug 2009 20:13:34 +0000 (UTC)
Subject: [SciPy-User] Problem with scipy.special.chebyt
References: <20090827163515.AKT55498@joker.int.colorado.edu>
	<826c64da0908291105w2f7a0449i481b907d971b6962@mail.gmail.com>
	<1251570290.3972.0.camel@gigoblob>
	<826c64da0908291320h3bb5a6c5tf33867078a409d5a@mail.gmail.com>
	<826c64da0908291418n35604dabqabfa68dbd3cfb697@mail.gmail.com>
	<h7e1sa$aq4$1@ger.gmane.org>
	<826c64da0908300746u711270e6wbec19a136b21555b@mail.gmail.com>
	<h7eeeh$dfs$1@ger.gmane.org>
	<826c64da0908301116m1595263etbc0b5417a4f8139a@mail.gmail.com>
	<h7ehbk$jt0$1@ger.gmane.org>
	<826c64da0908301157x46984ae3y6679d3bbf35dc4be@mail.gmail.com>
Message-ID: <h7emhd$3ee$1@ger.gmane.org>

On 2009-08-30, Ivo Maljevic <ivo.maljevic at gmail.com> wrote:
[clip]
> OK, let me know what I can do then to help. For starters, here is the output
> from Mandriva. Keep in mind that I'm using mandriva for the first time in my
> life, so I'm not quite versed in using it:.
>
> I changed the colour to red for the library part.
>
> (gdb) run cheby.py
> Starting program: /usr/bin/python cheby.py
> [Thread debugging using libthread_db enabled]
> [New Thread 0xb7c056c0 (LWP 3763)]
> [ 0.99144486  0.92387953  0.79335334  0.60876143  0.38268343 -0.99144486
>   0.13052619 -0.92387953 -0.13052619 -0.79335334 -0.38268343 -0.60876143]
> det(A)=0.000488281
>
> Program received signal SIGSEGV, Segmentation fault.
> 0xb7e89ed8 in ?? () from /usr/lib/libpython2.6.so.1.0

This probably gets a bit advanced now that the problematic area 
is pinpointed. Some knowledge / willingness to learn 
independently about C and LAPACK/BLAS may be required.

We need to move this discussion to Numpy Trac,

	http://projects.scipy.org/numpy/ticket/1211

so that we don't spam scipy.users list needlessly with this.

    ***

My checklist for the bug would be this -- as I don't know exactly 
what's wrong it's best to check many things and hope to hit 
something:


  Q: Is it really LAPACK or BLAS, and if yes, which one?

- install a different BLAS library, and use 
  "export LD_PRELOAD=/usr/lib/libANOTHERBLAS.so.1"
  to switch to using it. Does the crash persist?
- if you are using an architecture-optimized ATLAS library, try installing
  a less optimized one
- try to recompile LAPACK, and again try to use it. Does the crash persist?
- write a fortran/C-only test case. does it crash, too?


  Q: Are the input parameters to LAPACK sane?

- insert printf statements before and after DGEEV calls in lapack_litemodule.c,
  to print all parameters passed in to DGEEV
- check against lapack manual that the parameters passed to LAPACK are valid
- check that array sizes are large enough
- what does LAPACK report as its lwork? is it sane?
- check if data alignment can be a problem, cf.
  http://projects.scipy.org/numpy/ticket/551
  although I don't really believe this is the problem here.


  Q: Can the crash be traced better?

- insert "print 'PRE'" and "print 'POST'" before and after eig()
- run it under valgrind: are there warnings between PRE/POST?

  Pass --suppressions=valgrind-python.supp to valgrind to 
  suppress some common Python warnings, the suppression file is here:
  http://svn.python.org/projects/python/trunk/Misc/valgrind-python.supp


  Q: Are other things failing, too?

- Try running "numpy.test()" and "scipy.test()".


Other things to do:

- Write this as a suitable regression test in numpy/linalg/tests/test_regression.py
  so that this problem cannot recur silently in future releases.

  Submit it as a patch to be committed to SVN.


-- 
Pauli Virtanen



From lorenzo.isella at gmail.com  Sun Aug 30 16:35:01 2009
From: lorenzo.isella at gmail.com (Lorenzo Isella)
Date: Sun, 30 Aug 2009 22:35:01 +0200
Subject: [SciPy-User] Efficient "Interpolation"
Message-ID: <4A9AE275.3090104@gmail.com>

Dear All,
What I am after is not exactly an interpolation in the usual sense. 
Consider the following arrays

A=[1,2,4,5,6,8,9]
B=[2,4,5,8]
C=[24,45,77,99]

The array A stands for a set of discrete times (NB: some values may be 
missing), B is a subset of A  and C is a set of values of a variable Q 
taken at times in B.
I would like to create an array D with the values the values of Q at the 
times given by A according to this rule (not exactly a mathematical 
interpolation):  if A[i] falls in the right-open interval [B[j],B[j+1]) 
for some j, then D[i]=C[j].
To avoid troubles for the end values (using Python array indexing), 
D[0]=C[0] and if B[j+1] does not exist, then D[i]=C[-1].
For the arrays given above, I would have

D=[24, 24,45,77,77,99,99].

Does anyone know how to code this efficiently?
Many thanks

Lorenzo




From tsyu80 at gmail.com  Sun Aug 30 18:04:15 2009
From: tsyu80 at gmail.com (Tony S Yu)
Date: Sun, 30 Aug 2009 18:04:15 -0400
Subject: [SciPy-User] bug in ndimage grey_dilation?
In-Reply-To: <mailman.1.1251651602.25414.scipy-user@scipy.org>
References: <mailman.1.1251651602.25414.scipy-user@scipy.org>
Message-ID: <1CD9CA8C-976C-4039-97D0-FACF33971B77@gmail.com>


On Aug 30, 2009, at 1:00 PM, scipy-user-request at scipy.org wrote:

> Message: 3
> Date: Sun, 30 Aug 2009 12:14:27 -0400
> From: Zachary Pincus <zachary.pincus at yale.edu>
> Subject: Re: [SciPy-User] bug in ndimage grey_dilation?
> To: SciPy Users List <scipy-user at scipy.org>
> Message-ID: <E5ED36AA-3097-4144-8821-C6AEDAF99BBC at yale.edu>
> Content-Type: text/plain; charset=US-ASCII; format=flowed; delsp=yes
>
>>> Any other morphological filters that have this inconsistency?
>>
>> This seems to be the only filter affected with this inconsistency b/
>> c all other filters delegate their handling of size/footprint/
>> structure arguments to filters._min_or_max_filter (which handles the
>> scalar sizes without problems).
>
> Presumably there's some reason that grey_opening doesn't delegate this
> as do the rest, but this is a bit of a surprise. Maybe it could?
>
> Zach

It turns out that all the code before the call to  
filters._min_or_max_filter handles a shift of the origin parameter  
when the size/footprint/structure has even numbered sides (e.g. a 2x2  
footprint has no real "center" to place the origin). For some reason,  
the grey_erosion function works just fine without any special argument  
handling, but grey_dilation does need it (I should know, I tried  
removing all of it).

Here's a test script to test grey_dilation and grey_erosion against  
binary_dilation and binary_erosion, respectively:



Without any changes to scipy 0.7.1, the only failures are for dilation  
using scalar size arguments or structure arguments (dilation with a  
structure will pass if you also give it a size tuple). Note, the  
erosion tests all pass, as well.

After the fix below, all tests pass. Note, however, that I only test  
the degenerate case, i.e. when the structure is binary. Also, in the  
patch, if both a structure and footprint are specified, then size will  
equal the shape of the structure---maybe this should be reversed.  
Finally, I'm only testing 2D arrays, so this may need more testing...

Let me know what you think,
-Tony

Index: morphology.py
===================================================================
--- morphology.py	(revision 5911)
+++ morphology.py	(working copy)
@@ -339,21 +339,16 @@
      value when mode is equal to 'constant'.
      """
      if structure is not None:
-        structure = numpy.asarray(structure)
-        structure = structure[tuple([slice(None, None, -1)] *
-                                    structure.ndim)]
-    if footprint is not None:
-        footprint = numpy.asarray(footprint)
-        footprint = footprint[tuple([slice(None, None, -1)] *
-                                    footprint.ndim)]
-    input = numpy.asarray(input)
+        size = numpy.asarray(structure).shape
+    elif footprint is not None:
+        size = numpy.asarray(footprint).shape
+    elif size is None:
+        raise RuntimeError("size, footprint, or structure must be  
specified")
+    sizes = _ni_support._normalize_sequence(size, input.ndim)
      origin = _ni_support._normalize_sequence(origin, input.ndim)
      for ii in range(len(origin)):
          origin[ii] = -origin[ii]
-        if footprint is not None:
-            sz = footprint.shape[ii]
-        else:
-            sz = size[ii]
+        sz = sizes[ii]
          if not sz & 1:
              origin[ii] -= 1
      return filters._min_or_max_filter(input, size, footprint,  
structure,

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From jsseabold at gmail.com  Mon Aug 31 15:38:55 2009
From: jsseabold at gmail.com (Skipper Seabold)
Date: Mon, 31 Aug 2009 15:38:55 -0400
Subject: [SciPy-User] [ANN] scikits.statsmodels 0.1.0b1 release
Message-ID: <c048da1c0908311238s7b9ff3aeg755793b5fea984c0@mail.gmail.com>

Hello all,

As many of you know, Josef and I have been working hard to get a
release ready for general consumption for the models code (now
statsmodels).  Well, we're happy to announce that a (very) beta
release is ready.  Please, read on.

Background
==========

The statsmodels code was started by Jonathan Taylor and was formerly
included as part of scipy.  It was taken up to be tested, corrected, and
extended as part of the Google Summer of Code 2009.

What it is
==========

We are now releasing the efforts of the last few months under the scikits
namespace as scikits.statsmodels.  Statsmodels is a pure python package
that requires numpy and scipy.  It offers a convenient interface for
fitting parameterized statistical models with growing support for
displaying univariate and multivariate summary statistics, regression
summaries, and (postestimation) statistical tests.

Main Feautures
==============

* regression: Generalized least squares (including weighted least squares
    and least squares with autoregressive errors), ordinary least squares.
* glm: Generalized linear models with support for all of the one-parameter
    exponential family distributions.
* rlm: Robust linear models with support for several M-estimators.
* datasets: Datasets to be distributed and used for examples and in
    testing.

There is also a sandbox which contains code for generalized additive models
(untested), mixed effects models, cox proportional hazards model (both are
untested and still dependent on the nipy formula framework), generating
descriptive statistics, and printing table output to ascii, latex, and html.
None of this code is considered "production ready".

Where to get it
===============

Development branches will be on LaunchPad.  This is where to go to get the
most up to date code in the trunk branch.  Experimental code will also
be hosted here in different branches.

<https://code.launchpad.net/statsmodels>

Source download of stable tags will be on SourceForge.

<https://sourceforge.net/projects/statsmodels/>

or

PyPi: <http://pypi.python.org/pypi/scikits.statsmodels/>

License
=======

Simplified BSD

Documentation
=============

The official documentation is hosted on SourceForge.

<http://statsmodels.sourceforge.net/>

The sphinx docs are currently undergoing a lot of work.  They are not yet
comprehensive, but should get you started.

There is some more information on the project blog, which will be updated as we
make progress on the code.

<http://scipystats.blogspot.com>

Discussion and Development
==========================

All chatter will take place on the or scipy-user mailing list.  We are very
interested in receiving feedback about usability, suggestions for
improvements, and bug reports via the mailing list or the bug tracker at
<https://bugs.launchpad.net/statsmodels>.

Cheers,

The Current statsmodels maintainers

Josef Perktold and Skipper Seabold


From robert.kern at gmail.com  Mon Aug 31 18:55:55 2009
From: robert.kern at gmail.com (Robert Kern)
Date: Mon, 31 Aug 2009 17:55:55 -0500
Subject: [SciPy-User] Efficient "Interpolation"
In-Reply-To: <4A9AE275.3090104@gmail.com>
References: <4A9AE275.3090104@gmail.com>
Message-ID: <3d375d730908311555p1a061ceag4b0f442e39f0282e@mail.gmail.com>

On Sun, Aug 30, 2009 at 15:35, Lorenzo Isella<lorenzo.isella at gmail.com> wrote:
> Dear All,
> What I am after is not exactly an interpolation in the usual sense.
> Consider the following arrays
>
> A=[1,2,4,5,6,8,9]
> B=[2,4,5,8]
> C=[24,45,77,99]
>
> The array A stands for a set of discrete times (NB: some values may be
> missing), B is a subset of A ?and C is a set of values of a variable Q
> taken at times in B.
> I would like to create an array D with the values the values of Q at the
> times given by A according to this rule (not exactly a mathematical
> interpolation): ?if A[i] falls in the right-open interval [B[j],B[j+1])
> for some j, then D[i]=C[j].
> To avoid troubles for the end values (using Python array indexing),
> D[0]=C[0] and if B[j+1] does not exist, then D[i]=C[-1].
> For the arrays given above, I would have
>
> D=[24, 24,45,77,77,99,99].
>
> Does anyone know how to code this efficiently?

This corresponds to the "block" mode of interpolation that we at
Enthought have been using for well log data. We have had interns try
to clean it up for contribution to scipy, but it is still in a rough
state:

http://svn.scipy.org/svn/scipy/branches/interpolate/

-- 
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


From dmccully at mail.nih.gov  Tue Aug 25 20:20:51 2009
From: dmccully at mail.nih.gov (McCully, Dwayne (NIH/NLM/LHC) [C])
Date: Wed, 26 Aug 2009 00:20:51 -0000
Subject: [SciPy-User] Scipy Test Failures - scipy.test()
In-Reply-To: <954ae5aa0908251708t3de00d0eqa0a79e47d19a255d@mail.gmail.com>
References: <3d95192b0908191228k3900d1aaqb825a4a4c46c3a0f@mail.gmail.com>
	<b14b627d0908201251w709f59d7v840e453e08bdb8d9@mail.gmail.com>
	<5b8d13220908201537q3b45fb9bq211e65bb855f15bd@mail.gmail.com>
	<3d95192b0908211406w3dbee429i2ec5233f445fe098@mail.gmail.com>
	<954ae5aa0908251708t3de00d0eqa0a79e47d19a255d@mail.gmail.com>
Message-ID: <8A8D24241B862C41B5D5EADB47CE39E2D8C6E277@NIHMLBX01.nih.gov>

Hello everyone and Scipy developers unparticular,

A number of test failures are occurring during the simple scipy.test().
Could someone elaborate after reviewing the below run?   Is this normal?

Thank you.

Dwayne

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

Python 2.5.2 (r252:60911, Mar  5 2008, 20:59:51)
[GCC 4.1.2 20070626 (Red Hat 4.1.2-14)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import scipy
>>> scipy.test()
Running unit tests for scipy
NumPy version 1.4.0.dev7308
NumPy is installed in /usr/local/lib/python2.5/site-packages/numpy
SciPy version 0.8.0.dev5893
SciPy is installed in /usr/local/lib/python2.5/site-packages/scipy
Python version 2.5.2 (r252:60911, Mar  5 2008, 20:59:51) [GCC 4.1.2 20070626 (Red Hat 4.1.2-14)]
nose version 0.11.0
....................................................................................................................................................................................................................................................................../usr/local/lib/python2.5/site-packages/scipy/interpolate/fitpack2.py:512: UserWarning:
The coefficients of the spline returned have been computed as the
minimal norm least-squares solution of a (numerically) rank deficient
system (deficiency=7). If deficiency is large, the results may be
inaccurate. Deficiency may strongly depend on the value of eps.
  warnings.warn(message)
...../usr/local/lib/python2.5/site-packages/scipy/interpolate/fitpack2.py:453: UserWarning:
The required storage space exceeds the available storage space: nxest
or nyest too small, or s too small.
The weighted least-squares spline corresponds to the current set of
knots.
  warnings.warn(message)
...........................................K..K................................................................................................./usr/local/lib/python2.5/site-packages/scipy/io/matlab/mio.py:134: Warning: Unreadable variable "testfunc", because "Cannot read matlab functions"
  matfile_dict = MR.get_variables()
........../usr/local/lib/python2.5/site-packages/scipy/io/matlab/mio.py:190: FutureWarning: Using oned_as default value ('column') This will change to 'row' in future versions
  oned_as=oned_as)
........................./usr/local/lib/python2.5/site-packages/scipy/io/matlab/tests/test_mio.py:438: FutureWarning: Using oned_as default value ('column') This will change to 'row' in future versions
  mfw = MatFile5Writer(StringIO())
......../usr/local/lib/python2.5/site-packages/scipy/io/matlab/mio.py:99: FutureWarning: Using struct_as_record default value (False) This will change to True in future versions
  return MatFile5Reader(byte_stream, **kwargs)
...............................Warning: 1000000 bytes requested, 20 bytes read.
./usr/local/lib/python2.5/site-packages/numpy/lib/utils.py:130: DeprecationWarning: write_array is deprecated
  warnings.warn(str1, DeprecationWarning)
/usr/local/lib/python2.5/site-packages/numpy/lib/utils.py:130: DeprecationWarning: read_array is deprecated
  warnings.warn(str1, DeprecationWarning)
......................../usr/local/lib/python2.5/site-packages/numpy/lib/utils.py:130: DeprecationWarning: npfile is deprecated
  warnings.warn(str1, DeprecationWarning)
.......................................................................................................................................................SSSSSS......SSSSSS......SSSS..................................................................................................................F/usr/local/lib/python2.5/site-packages/scipy/linalg/decomp.py:1177: DeprecationWarning: qr econ argument will be removed after scipy 0.7. The economy transform will then be available through the mode='economic' argument.
  "the mode='economic' argument.", DeprecationWarning)
FF...........................................................................................................................................................................................................................................................................................Result may be inaccurate, approximate err = 6.90714725568e-09
...Result may be inaccurate, approximate err = 1.87387729512e-10
.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................SSSSSSSSSSS.........EE.............F...........F......................................F..........F....................................F............F................................................F............F................................................F.......F....................................F........F.........................................F.......F....................................................F.F..........................................................................................................................................................................................................................................................................................................................................K.K............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................/usr/local/lib/python2.5/site-packages/numpy/lib/function_base.py:325: Warning:
            The new semantics of histogram is now the default and the `new`
            keyword will be removed in NumPy 1.4.

  """, Warning)
.................................................................................................................S....................................................................................../usr/local/lib/python2.5/site-packages/scipy/stats/morestats.py:603: UserWarning: Ties preclude use of exact statistic.
  warnings.warn("Ties preclude use of exact statistic.")
...........................................................................................................................................................................................................Warning: friedmanchisquare test using Chisquared aproximation
.......................................................................warning: specified build_dir '_bad_path_' does not exist or is not writable. Trying default locations
.....warning: specified build_dir '_bad_path_' does not exist or is not writable. Trying default locations
...............................building extensions here: /root/.python25_compiled/m43
................................................................................................
======================================================================
ERROR: test_lobpcg.test_ElasticRod
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/nose/case.py", line 183, in runTest
    self.test(*self.arg)
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/linalg/eigen/lobpcg/tests/test_lobpcg.py", line 74, in test_ElasticRod
    compare_solutions(A,B,20)
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/linalg/eigen/lobpcg/tests/test_lobpcg.py", line 49, in compare_solutions
    eigs,vecs = lobpcg(A, X, B=B, tol=1e-5, maxiter=30)
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/linalg/eigen/lobpcg/lobpcg.py", line 391, in lobpcg
    aux = b_orthonormalize( B, activeBlockVectorR )
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/linalg/eigen/lobpcg/lobpcg.py", line 130, in b_orthonormalize
    gramVBV = sla.cholesky( gramVBV )
  File "/usr/local/lib/python2.5/site-packages/scipy/linalg/decomp.py", line 1017, in cholesky
    a1 = asarray_chkfinite(a)
  File "/usr/local/lib/python2.5/site-packages/numpy/lib/function_base.py", line 709, in asarray_chkfinite
    raise ValueError, "array must not contain infs or NaNs"
ValueError: array must not contain infs or NaNs

======================================================================
ERROR: test_lobpcg.test_MikotaPair
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/nose/case.py", line 183, in runTest
    self.test(*self.arg)
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/linalg/eigen/lobpcg/tests/test_lobpcg.py", line 78, in test_MikotaPair
    compare_solutions(A,B,20)
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/linalg/eigen/lobpcg/tests/test_lobpcg.py", line 49, in compare_solutions
    eigs,vecs = lobpcg(A, X, B=B, tol=1e-5, maxiter=30)
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/linalg/eigen/lobpcg/lobpcg.py", line 391, in lobpcg
    aux = b_orthonormalize( B, activeBlockVectorR )
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/linalg/eigen/lobpcg/lobpcg.py", line 130, in b_orthonormalize
    gramVBV = sla.cholesky( gramVBV )
  File "/usr/local/lib/python2.5/site-packages/scipy/linalg/decomp.py", line 1023, in cholesky
    if info>0: raise LinAlgError, "matrix not positive definite"
LinAlgError: matrix not positive definite

======================================================================
FAIL: test_random_tall (test_decomp.TestQR)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/linalg/tests/test_decomp.py", line 880, in test_random_tall
    assert_array_almost_equal(dot(transpose(q),q),identity(m))
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 726, in assert_array_almost_equal
    header='Arrays are not almost equal')
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 571, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not almost equal

(mismatch 0.36%)
 x: array([[  1.00000000e+00,   4.42354486e-17,  -1.73472348e-17, ...,
          0.00000000e+00,   0.00000000e+00,   0.00000000e+00],
       [  3.03576608e-17,   1.00000000e+00,   1.34441069e-17, ...,...
 y: array([[ 1.,  0.,  0., ...,  0.,  0.,  0.],
       [ 0.,  1.,  0., ...,  0.,  0.,  0.],
       [ 0.,  0.,  1., ...,  0.,  0.,  0.],...

======================================================================
FAIL: test_random_tall_e (test_decomp.TestQR)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/linalg/tests/test_decomp.py", line 890, in test_random_tall_e
    assert_array_almost_equal(dot(transpose(q),q),identity(n))
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 726, in assert_array_almost_equal
    header='Arrays are not almost equal')
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 571, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not almost equal

(mismatch 41.32%)
 x: array([[ 0.60600496,  0.02506279,  0.05475682, ...,  0.        ,
         0.        ,  0.        ],
       [ 0.02506279,  0.58317258,  0.01339485, ...,  0.        ,...
 y: array([[ 1.,  0.,  0., ...,  0.,  0.,  0.],
       [ 0.,  1.,  0., ...,  0.,  0.,  0.],
       [ 0.,  0.,  1., ...,  0.,  0.,  0.],...

======================================================================
FAIL: test_random_trap (test_decomp.TestQR)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/linalg/tests/test_decomp.py", line 901, in test_random_trap
    assert_array_almost_equal(dot(transpose(q),q),identity(m))
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 726, in assert_array_almost_equal
    header='Arrays are not almost equal')
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 571, in assert_array_compare
    raise AssertionError(msg)
AssertionError:
Arrays are not almost equal

(mismatch 0.36%)
 x: array([[  1.00000000e+00,   1.04083409e-17,   1.38777878e-17, ...,
          0.00000000e+00,   0.00000000e+00,   0.00000000e+00],
       [  1.04083409e-17,   1.00000000e+00,   2.77555756e-17, ...,...
 y: array([[ 1.,  0.,  0., ...,  0.,  0.,  0.],
       [ 0.,  1.,  0., ...,  0.,  0.,  0.],
       [ 0.,  0.,  1., ...,  0.,  0.,  0.],...

======================================================================
FAIL: test_asfptype (test_base.TestBSR)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/tests/test_base.py", line 242, in test_asfptype
    assert_equal( A.dtype , 'int32' )
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 274, in assert_equal
    raise AssertionError(msg)
AssertionError:
Items are not equal:
 ACTUAL: dtype('int32')
 DESIRED: 'int32'

======================================================================
FAIL: Test manipulating empty matrices. Fails in SciPy SVN <= r1768
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/tests/test_base.py", line 78, in test_empty_arithmetic
    assert_equal(m.dtype,mytype)
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 274, in assert_equal
    raise AssertionError(msg)
AssertionError:
Items are not equal:
 ACTUAL: dtype('int32')
 DESIRED: 'int32'

======================================================================
FAIL: test_asfptype (test_base.TestCOO)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/tests/test_base.py", line 242, in test_asfptype
    assert_equal( A.dtype , 'int32' )
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 274, in assert_equal
    raise AssertionError(msg)
AssertionError:
Items are not equal:
 ACTUAL: dtype('int32')
 DESIRED: 'int32'

======================================================================
FAIL: Test manipulating empty matrices. Fails in SciPy SVN <= r1768
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/tests/test_base.py", line 78, in test_empty_arithmetic
    assert_equal(m.dtype,mytype)
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 274, in assert_equal
    raise AssertionError(msg)
AssertionError:
Items are not equal:
 ACTUAL: dtype('int32')
 DESIRED: 'int32'

======================================================================
FAIL: test_asfptype (test_base.TestCSC)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/tests/test_base.py", line 242, in test_asfptype
    assert_equal( A.dtype , 'int32' )
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 274, in assert_equal
    raise AssertionError(msg)
AssertionError:
Items are not equal:
 ACTUAL: dtype('int32')
 DESIRED: 'int32'

======================================================================
FAIL: Test manipulating empty matrices. Fails in SciPy SVN <= r1768
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/tests/test_base.py", line 78, in test_empty_arithmetic
    assert_equal(m.dtype,mytype)
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 274, in assert_equal
    raise AssertionError(msg)
AssertionError:
Items are not equal:
 ACTUAL: dtype('int32')
 DESIRED: 'int32'

======================================================================
FAIL: test_asfptype (test_base.TestCSR)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/tests/test_base.py", line 242, in test_asfptype
    assert_equal( A.dtype , 'int32' )
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 274, in assert_equal
    raise AssertionError(msg)
AssertionError:
Items are not equal:
 ACTUAL: dtype('int32')
 DESIRED: 'int32'

======================================================================
FAIL: Test manipulating empty matrices. Fails in SciPy SVN <= r1768
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/tests/test_base.py", line 78, in test_empty_arithmetic
    assert_equal(m.dtype,mytype)
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 274, in assert_equal
    raise AssertionError(msg)
AssertionError:
Items are not equal:
 ACTUAL: dtype('int32')
 DESIRED: 'int32'

======================================================================
FAIL: test_asfptype (test_base.TestDIA)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/tests/test_base.py", line 242, in test_asfptype
    assert_equal( A.dtype , 'int32' )
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 274, in assert_equal
    raise AssertionError(msg)
AssertionError:
Items are not equal:
 ACTUAL: dtype('int32')
 DESIRED: 'int32'

======================================================================
FAIL: Test manipulating empty matrices. Fails in SciPy SVN <= r1768
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/tests/test_base.py", line 78, in test_empty_arithmetic
    assert_equal(m.dtype,mytype)
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 274, in assert_equal
    raise AssertionError(msg)
AssertionError:
Items are not equal:
 ACTUAL: dtype('int32')
 DESIRED: 'int32'

======================================================================
FAIL: test_asfptype (test_base.TestDOK)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/tests/test_base.py", line 242, in test_asfptype
    assert_equal( A.dtype , 'int32' )
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 274, in assert_equal
    raise AssertionError(msg)
AssertionError:
Items are not equal:
 ACTUAL: dtype('int32')
 DESIRED: 'int32'

======================================================================
FAIL: Test manipulating empty matrices. Fails in SciPy SVN <= r1768
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/tests/test_base.py", line 78, in test_empty_arithmetic
    assert_equal(m.dtype,mytype)
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 274, in assert_equal
    raise AssertionError(msg)
AssertionError:
Items are not equal:
 ACTUAL: dtype('int32')
 DESIRED: 'int32'

======================================================================
FAIL: test_asfptype (test_base.TestLIL)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/tests/test_base.py", line 242, in test_asfptype
    assert_equal( A.dtype , 'int32' )
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 274, in assert_equal
    raise AssertionError(msg)
AssertionError:
Items are not equal:
 ACTUAL: dtype('int32')
 DESIRED: 'int32'

======================================================================
FAIL: Test manipulating empty matrices. Fails in SciPy SVN <= r1768
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/tests/test_base.py", line 78, in test_empty_arithmetic
    assert_equal(m.dtype,mytype)
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 274, in assert_equal
    raise AssertionError(msg)
AssertionError:
Items are not equal:
 ACTUAL: dtype('int32')
 DESIRED: 'int32'

======================================================================
FAIL: test_eye (test_construct.TestConstructUtils)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/tests/test_construct.py", line 84, in test_eye
    assert_equal(eye(3,3,dtype='int16').dtype, 'int16')
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 274, in assert_equal
    raise AssertionError(msg)
AssertionError:
Items are not equal:
 ACTUAL: dtype('int16')
 DESIRED: 'int16'

======================================================================
FAIL: test_identity (test_construct.TestConstructUtils)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.5/site-packages/scipy/sparse/tests/test_construct.py", line 70, in test_identity
    assert_equal( I.dtype, 'int8' )
  File "/usr/local/lib/python2.5/site-packages/numpy/testing/utils.py", line 274, in assert_equal
    raise AssertionError(msg)
AssertionError:
Items are not equal:
 ACTUAL: dtype('int8')
 DESIRED: 'int8'

----------------------------------------------------------------------
Ran 3810 tests in 95.884s

FAILED (KNOWNFAIL=4, SKIP=28, errors=2, failures=19)
<nose.result.TextTestResult run=3810 errors=2 failures=19>
>>>
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