From keith.hughitt at gmail.com  Tue May  1 12:36:32 2012
From: keith.hughitt at gmail.com (Keith Hughitt)
Date: Tue, 1 May 2012 12:36:32 -0400
Subject: [SciPy-User] building on Ubuntu Linux from github source : BLAS
 and LAPACK not getting picked up from environment variable
In-Reply-To: <CAFiDpRWvkVtPigGvpn7Gwd2NXzWAm=wQMQPD+SP=nnuxvdD=QQ@mail.gmail.com>
References: <CAFiDpRWvkVtPigGvpn7Gwd2NXzWAm=wQMQPD+SP=nnuxvdD=QQ@mail.gmail.com>
Message-ID: <CAOJcpR-_DcP+LWNO=L896Qfcpx36Rimmh4Scb_D9gRiNcNRJHg@mail.gmail.com>

Hi hari,

Try:

sudo apt-get build-dep python-scipy

That should fetch and install all of the dependencies needed to build SciPy.

Best,
Keith

On Fri, Apr 20, 2012 at 11:53 AM, hari jayaram <harijay at gmail.com> wrote:

> Hi
> I am on 64 bit Ubuntu , python  2.6.5 , GCC 4.4.3. I want to compile
> my own scipy since the ubuntu package on lucid installs a scipy which
> does not have the scipy.optimize.curve_fit
>
> I followed the build instructions at the url below to install from the
> git source .
>
> http://www.scipy.org/Installing_SciPy/BuildingGeneral
>
> I could install numpy from the git source . Then I installed lapack
> and blas libraries in my home directory following the instructions at
> BuildingGeneral.
> I set the environment variables BLAS and LAPACK in my ~/.bashrc and
> sourced them and then tried the setup.py for scipy
>
> sudo python setup.py install
>
>
> I get the following errors implying that BLAS was not getting picked
> up.  I even copied the libblas.a and liblapack.a files to /usr/lib,
> linked it to liblapack.so ...but none of these helped find lapack or
> blas
>
>
> "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."
>
> I am wondering if  the build instructions are different for the github
> tree. The detailed error is given below.
> Thanks for your help
>
> Hari
>
>
>
>
>
>
>
>
> hari at hari:~/scipy$ sudo python setup.py install --prefix=/usr/local
> blas_opt_info:
> blas_mkl_info:
>  libraries mkl,vml,guide not found in ['/usr/local/lib',
> '/usr/lib64', '/usr/lib']
>  NOT AVAILABLE
>
> atlas_blas_threads_info:
> Setting PTATLAS=ATLAS
>  libraries ptf77blas,ptcblas,atlas not found in ['/usr/local/lib',
> '/usr/lib64', '/usr/lib']
>  NOT AVAILABLE
>
> atlas_blas_info:
>  libraries f77blas,cblas,atlas not found in ['/usr/local/lib',
> '/usr/lib64', '/usr/lib']
>  NOT AVAILABLE
>
> /usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py:1474:
> 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 ['/usr/local/lib', '/usr/lib64', '/usr/lib']
>  NOT AVAILABLE
>
> /usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py:1483:
> 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
>
> /usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py:1486:
> 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 208, in <module>
>    setup_package()
>  File "setup.py", line 199, in setup_package
>    configuration=configuration )
>  File "/usr/local/lib/python2.6/dist-packages/numpy/distutils/core.py",
> line 152, in setup
>    config = configuration()
>  File "setup.py", line 136, in configuration
>    config.add_subpackage('scipy')
>  File
> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
> line 1002, in add_subpackage
>    caller_level = 2)
>  File
> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
> line 971, in get_subpackage
>    caller_level = caller_level + 1)
>  File
> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
> line 908, in _get_configuration_from_setup_py
>    config = setup_module.configuration(*args)
>  File "scipy/setup.py", line 8, in configuration
>    config.add_subpackage('integrate')
>  File
> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
> line 1002, in add_subpackage
>    caller_level = 2)
>  File
> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
> line 971, in get_subpackage
>    caller_level = caller_level + 1)
>  File
> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
> line 908, in _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
> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py",
> line 325, in get_info
>    return cl().get_info(notfound_action)
>  File
> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py",
> line 484, 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.
> hari at hari:~/scipy$ export BLAS=/home/hari/blas/BLAS/libblas.a
> hari at hari:~/scipy$ export
> LAPACK=/home/hari/LAPACK/lapack-3.4.0/liblapack.a
> hari at hari:~/scipy$ sudo python setup.py install --prefix=/usr/local
> blas_opt_info:
> blas_mkl_info:
>  libraries mkl,vml,guide not found in ['/usr/local/lib',
> '/usr/lib64', '/usr/lib']
>  NOT AVAILABLE
>
> atlas_blas_threads_info:
> Setting PTATLAS=ATLAS
>  libraries ptf77blas,ptcblas,atlas not found in ['/usr/local/lib',
> '/usr/lib64', '/usr/lib']
>  NOT AVAILABLE
>
> atlas_blas_info:
>  libraries f77blas,cblas,atlas not found in ['/usr/local/lib',
> '/usr/lib64', '/usr/lib']
>  NOT AVAILABLE
>
> /usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py:1474:
> 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 ['/usr/local/lib', '/usr/lib64', '/usr/lib']
>  NOT AVAILABLE
>
> /usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py:1483:
> 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
>
> /usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py:1486:
> 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 208, in <module>
>    setup_package()
>  File "setup.py", line 199, in setup_package
>    configuration=configuration )
>  File "/usr/local/lib/python2.6/dist-packages/numpy/distutils/core.py",
> line 152, in setup
>    config = configuration()
>  File "setup.py", line 136, in configuration
>    config.add_subpackage('scipy')
>  File
> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
> line 1002, in add_subpackage
>    caller_level = 2)
>  File
> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
> line 971, in get_subpackage
>    caller_level = caller_level + 1)
>  File
> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
> line 908, in _get_configuration_from_setup_py
>    config = setup_module.configuration(*args)
>  File "scipy/setup.py", line 8, in configuration
>    config.add_subpackage('integrate')
>  File
> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
> line 1002, in add_subpackage
>    caller_level = 2)
>  File
> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
> line 971, in get_subpackage
>    caller_level = caller_level + 1)
>  File
> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
> line 908, in _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
> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py",
> line 325, in get_info
>    return cl().get_info(notfound_action)
>  File
> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py",
> line 484, 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.
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From camillechambon at yahoo.fr  Tue May  1 13:31:20 2012
From: camillechambon at yahoo.fr (camille chambon)
Date: Tue, 1 May 2012 18:31:20 +0100 (BST)
Subject: [SciPy-User] Least-square fitting of a 3rd degree polynomial
Message-ID: <1335893480.26559.YahooMailNeo@web171302.mail.ir2.yahoo.com>

Hello,

I would like to fit 'a' such as y = a * x**3 + b * x**2 + 
c * x + d, 
where x and y are measured data,
and b = 0.0, c = -0.00458844157413 and d = 5.8 are fixed. 

According to http://www.scipy.org/Cookbook/FittingData#head-27373a786baa162a2e8a910ee0b8a48838082262, I try to use scipy.optimize.leastsq fit routine like that: 

#### CODE ##### 

from numpy import *
from pylab import *
from scipy import optimize

# My data points
x = array([1078.0, 1117.0, 1212.1, 1368.7, 1686.8, 1880.0])
y = array([5.8, 5.6, 5.4, 4.9, 2.4, 0.0])

b, c, d =? 0.0, -0.00458844157413, 5.8 # Fixed parameters

fitfunc = lambda p, x: poly1d([p[0], b, c, d])(x) # Target function
errfunc = lambda p, x, y: fitfunc(p, x) - y # Distance to the target function

p0 = [-4.0E-09] # Initial guess for the parameters
p1, success = optimize.leastsq(errfunc, p0[:], args=(x, y))

time = linspace(x.min(), x.max(), 100)
plot(x, y, "ro", time, fitfunc(p1, time), "r-") # Plot of the data and the fit

title("a fitting")
xlabel("time [day]")
ylabel("number []")
legend(('x position', 'x fit', 'y position', 'y fit'))

show()

################################

But the fit does not work (as one can see on the attached image).

Does someone have any idea of what I'm doing wrong?

Thanks in advance for your help.

Cheers,

Camille
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From josef.pktd at gmail.com  Tue May  1 13:44:01 2012
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Tue, 1 May 2012 13:44:01 -0400
Subject: [SciPy-User] Least-square fitting of a 3rd degree polynomial
In-Reply-To: <1335893480.26559.YahooMailNeo@web171302.mail.ir2.yahoo.com>
References: <1335893480.26559.YahooMailNeo@web171302.mail.ir2.yahoo.com>
Message-ID: <CAMMTP+Du3N3m1oC9L0ee9SgNaTatQa+e4hwmMoQ=Sob3zxPj2g@mail.gmail.com>

On Tue, May 1, 2012 at 1:31 PM, camille chambon <camillechambon at yahoo.fr> wrote:
> Hello,
>
> I would like to fit 'a' such as y = a * x**3 + b * x**2 + c * x + d,
> where x and y are measured data, and b = 0.0, c = -0.00458844157413 and d =
> 5.8 are fixed.
>
> According to
> http://www.scipy.org/Cookbook/FittingData#head-27373a786baa162a2e8a910ee0b8a48838082262,
> I try to use scipy.optimize.leastsq fit routine like that:
>
> #### CODE #####
>
> from numpy import *
> from pylab import *
> from scipy import optimize
>
> # My data points
> x = array([1078.0, 1117.0, 1212.1, 1368.7, 1686.8, 1880.0])
> y = array([5.8, 5.6, 5.4, 4.9, 2.4, 0.0])
>
> b, c, d =? 0.0, -0.00458844157413, 5.8 # Fixed parameters
>
> fitfunc = lambda p, x: poly1d([p[0], b, c, d])(x) # Target function
> errfunc = lambda p, x, y: fitfunc(p, x) - y # Distance to the target
> function
>
> p0 = [-4.0E-09] # Initial guess for the parameters
> p1, success = optimize.leastsq(errfunc, p0[:], args=(x, y))
>
> time = linspace(x.min(), x.max(), 100)
> plot(x, y, "ro", time, fitfunc(p1, time), "r-") # Plot of the data and the
> fit
>
> title("a fitting")
> xlabel("time [day]")
> ylabel("number []")
> legend(('x position', 'x fit', 'y position', 'y fit'))
>
> show()
>
> ################################
>
> But the fit does not work (as one can see on the attached image).
>
> Does someone have any idea of what I'm doing wrong?
>
> Thanks in advance for your help.

my guess would be that the scale is awful, the x are very large

if you only need the to estimate "a", then it's just a very simple
linear regression problem dot(x,x)/dot(x,y) or something like this.

as aside numpy.polynomial works very well for fitting a polynomial,
but doesn't allow for missing terms

Josef

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


From charlesr.harris at gmail.com  Tue May  1 13:45:55 2012
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Tue, 1 May 2012 11:45:55 -0600
Subject: [SciPy-User] Least-square fitting of a 3rd degree polynomial
In-Reply-To: <1335893480.26559.YahooMailNeo@web171302.mail.ir2.yahoo.com>
References: <1335893480.26559.YahooMailNeo@web171302.mail.ir2.yahoo.com>
Message-ID: <CAB6mnx+p1UbESagbF3m83t==Tr3AW2ZpAQht4qZWtCYJ3417Lw@mail.gmail.com>

On Tue, May 1, 2012 at 11:31 AM, camille chambon <camillechambon at yahoo.fr>wrote:

> Hello,
>
> I would like to fit 'a' such as y = a * x**3 + b * x**2 + c * x + d,
> where x and y are measured data, and b = 0.0, c = -0.00458844157413 and d
> = 5.8 are fixed.
>
> According to
> http://www.scipy.org/Cookbook/FittingData#head-27373a786baa162a2e8a910ee0b8a48838082262,
> I try to use scipy.optimize.leastsq fit routine like that:
>
> #### CODE #####
>
> from numpy import *
> from pylab import *
> from scipy import optimize
>
> # My data points
> x = array([1078.0, 1117.0, 1212.1, 1368.7, 1686.8, 1880.0])
> y = array([5.8, 5.6, 5.4, 4.9, 2.4, 0.0])
>
> b, c, d =  0.0, -0.00458844157413, 5.8 # Fixed parameters
>
> fitfunc = lambda p, x: poly1d([p[0], b, c, d])(x) # Target function
> errfunc = lambda p, x, y: fitfunc(p, x) - y # Distance to the target
> function
>
> p0 = [-4.0E-09] # Initial guess for the parameters
> p1, success = optimize.leastsq(errfunc, p0[:], args=(x, y))
>
> time = linspace(x.min(), x.max(), 100)
> plot(x, y, "ro", time, fitfunc(p1, time), "r-") # Plot of the data and the
> fit
>
> title("a fitting")
> xlabel("time [day]")
> ylabel("number []")
> legend(('x position', 'x fit', 'y position', 'y fit'))
>
> show()
>
> ################################
>
> But the fit does not work (as one can see on the attached image).
>
> Does someone have any idea of what I'm doing wrong?
>
> Thanks in advance for your help.
>
> Cheers,
>
> Camille
>
>
You can use numpy for this.

In [1]: from numpy.polynomial import Polynomial as P

In [2]: x = array([1078.0, 1117.0, 1212.1, 1368.7, 1686.8, 1880.0])

In [3]: y = array([5.8, 5.6, 5.4, 4.9, 2.4, 0.0])

In [4]: p = P.fit(x,y,3)

In [5]: plot(*p.linspace())
Out[5]: [<matplotlib.lines.Line2D at 0x2e51ad0>]

In [6]: plot(x, y, '.')
Out[6]: [<matplotlib.lines.Line2D at 0x2e62c90>]

In [7]: p.mapparms()
Out[7]: (-3.6882793017456361, 0.0024937655860349127)

Note two things, the coefficients go from degree zero upwards and you need
to make the substitution x <- -3.6882793017456361 + 0024937655860349127*x
if you want to use them in a publication.

Chuck
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From charlesr.harris at gmail.com  Tue May  1 13:47:23 2012
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Tue, 1 May 2012 11:47:23 -0600
Subject: [SciPy-User] Least-square fitting of a 3rd degree polynomial
In-Reply-To: <CAMMTP+Du3N3m1oC9L0ee9SgNaTatQa+e4hwmMoQ=Sob3zxPj2g@mail.gmail.com>
References: <1335893480.26559.YahooMailNeo@web171302.mail.ir2.yahoo.com>
	<CAMMTP+Du3N3m1oC9L0ee9SgNaTatQa+e4hwmMoQ=Sob3zxPj2g@mail.gmail.com>
Message-ID: <CAB6mnx+z5Rqe1OE58L4duAVHvxMnXKxOtXGQR5rqs=VBbeEDww@mail.gmail.com>

On Tue, May 1, 2012 at 11:44 AM, <josef.pktd at gmail.com> wrote:

> On Tue, May 1, 2012 at 1:31 PM, camille chambon <camillechambon at yahoo.fr>
> wrote:
> > Hello,
> >
> > I would like to fit 'a' such as y = a * x**3 + b * x**2 + c * x + d,
> > where x and y are measured data, and b = 0.0, c = -0.00458844157413 and
> d =
> > 5.8 are fixed.
> >
> > According to
> >
> http://www.scipy.org/Cookbook/FittingData#head-27373a786baa162a2e8a910ee0b8a48838082262
> ,
> > I try to use scipy.optimize.leastsq fit routine like that:
> >
> > #### CODE #####
> >
> > from numpy import *
> > from pylab import *
> > from scipy import optimize
> >
> > # My data points
> > x = array([1078.0, 1117.0, 1212.1, 1368.7, 1686.8, 1880.0])
> > y = array([5.8, 5.6, 5.4, 4.9, 2.4, 0.0])
> >
> > b, c, d =  0.0, -0.00458844157413, 5.8 # Fixed parameters
> >
> > fitfunc = lambda p, x: poly1d([p[0], b, c, d])(x) # Target function
> > errfunc = lambda p, x, y: fitfunc(p, x) - y # Distance to the target
> > function
> >
> > p0 = [-4.0E-09] # Initial guess for the parameters
> > p1, success = optimize.leastsq(errfunc, p0[:], args=(x, y))
> >
> > time = linspace(x.min(), x.max(), 100)
> > plot(x, y, "ro", time, fitfunc(p1, time), "r-") # Plot of the data and
> the
> > fit
> >
> > title("a fitting")
> > xlabel("time [day]")
> > ylabel("number []")
> > legend(('x position', 'x fit', 'y position', 'y fit'))
> >
> > show()
> >
> > ################################
> >
> > But the fit does not work (as one can see on the attached image).
> >
> > Does someone have any idea of what I'm doing wrong?
> >
> > Thanks in advance for your help.
>
> my guess would be that the scale is awful, the x are very large
>
> if you only need the to estimate "a", then it's just a very simple
> linear regression problem dot(x,x)/dot(x,y) or something like this.
>
> as aside numpy.polynomial works very well for fitting a polynomial,
> but doesn't allow for missing terms
>
>
It does recognize NA in devel, but we will see where that goes ;)

Chuck
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From harijay at gmail.com  Tue May  1 14:56:13 2012
From: harijay at gmail.com (hari jayaram)
Date: Tue, 1 May 2012 14:56:13 -0400
Subject: [SciPy-User] building on Ubuntu Linux from github source : BLAS
 and LAPACK not getting picked up from environment variable
In-Reply-To: <CAOJcpR-_DcP+LWNO=L896Qfcpx36Rimmh4Scb_D9gRiNcNRJHg@mail.gmail.com>
References: <CAFiDpRWvkVtPigGvpn7Gwd2NXzWAm=wQMQPD+SP=nnuxvdD=QQ@mail.gmail.com>
	<CAOJcpR-_DcP+LWNO=L896Qfcpx36Rimmh4Scb_D9gRiNcNRJHg@mail.gmail.com>
Message-ID: <CAFiDpRV+dcP972+eqG2g80Ft2Ea5+5P0oPm0A+znZpV7yCfj3w@mail.gmail.com>

Thanks Keith,
My problem was that I had installed numpy from the git repository
before I had installed BLAS.

Once I uninstalled numpy , then installed LAPACK and BLAS.
Then I could install numpy and scipy using "sudo python setup.py install"

Thanks
Hari

On Tue, May 1, 2012 at 12:36 PM, Keith Hughitt <keith.hughitt at gmail.com> wrote:
> Hi hari,
>
> Try:
>
> sudo apt-get build-dep python-scipy
>
> That should fetch and install all of the dependencies needed to build SciPy.
>
> Best,
> Keith
>
> On Fri, Apr 20, 2012 at 11:53 AM, hari jayaram <harijay at gmail.com> wrote:
>>
>> Hi
>> I am on 64 bit Ubuntu , python ?2.6.5 , GCC 4.4.3. I want to compile
>> my own scipy since the ubuntu package on lucid installs a scipy which
>> does not have the scipy.optimize.curve_fit
>>
>> I followed the build instructions at the url below to install from the
>> git source .
>>
>> http://www.scipy.org/Installing_SciPy/BuildingGeneral
>>
>> I could install numpy from the git source . Then I installed lapack
>> and blas libraries in my home directory following the instructions at
>> BuildingGeneral.
>> I set the environment variables BLAS and LAPACK in my ~/.bashrc and
>> sourced them and then tried the setup.py for scipy
>>
>> sudo python setup.py install
>>
>>
>> I get the following errors implying that BLAS was not getting picked
>> up. ?I even copied the libblas.a and liblapack.a files to /usr/lib,
>> linked it to liblapack.so ...but none of these helped find lapack or
>> blas
>>
>>
>> "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."
>>
>> I am wondering if ?the build instructions are different for the github
>> tree. The detailed error is given below.
>> Thanks for your help
>>
>> Hari
>>
>>
>>
>>
>>
>>
>>
>>
>> hari at hari:~/scipy$ sudo python setup.py install --prefix=/usr/local
>> blas_opt_info:
>> blas_mkl_info:
>> ?libraries mkl,vml,guide not found in ['/usr/local/lib',
>> '/usr/lib64', '/usr/lib']
>> ?NOT AVAILABLE
>>
>> atlas_blas_threads_info:
>> Setting PTATLAS=ATLAS
>> ?libraries ptf77blas,ptcblas,atlas not found in ['/usr/local/lib',
>> '/usr/lib64', '/usr/lib']
>> ?NOT AVAILABLE
>>
>> atlas_blas_info:
>> ?libraries f77blas,cblas,atlas not found in ['/usr/local/lib',
>> '/usr/lib64', '/usr/lib']
>> ?NOT AVAILABLE
>>
>>
>> /usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py:1474:
>> 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 ['/usr/local/lib', '/usr/lib64', '/usr/lib']
>> ?NOT AVAILABLE
>>
>>
>> /usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py:1483:
>> 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
>>
>>
>> /usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py:1486:
>> 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 208, in <module>
>> ? ?setup_package()
>> ?File "setup.py", line 199, in setup_package
>> ? ?configuration=configuration )
>> ?File "/usr/local/lib/python2.6/dist-packages/numpy/distutils/core.py",
>> line 152, in setup
>> ? ?config = configuration()
>> ?File "setup.py", line 136, in configuration
>> ? ?config.add_subpackage('scipy')
>> ?File
>> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
>> line 1002, in add_subpackage
>> ? ?caller_level = 2)
>> ?File
>> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
>> line 971, in get_subpackage
>> ? ?caller_level = caller_level + 1)
>> ?File
>> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
>> line 908, in _get_configuration_from_setup_py
>> ? ?config = setup_module.configuration(*args)
>> ?File "scipy/setup.py", line 8, in configuration
>> ? ?config.add_subpackage('integrate')
>> ?File
>> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
>> line 1002, in add_subpackage
>> ? ?caller_level = 2)
>> ?File
>> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
>> line 971, in get_subpackage
>> ? ?caller_level = caller_level + 1)
>> ?File
>> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
>> line 908, in _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
>> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py",
>> line 325, in get_info
>> ? ?return cl().get_info(notfound_action)
>> ?File
>> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py",
>> line 484, 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.
>> hari at hari:~/scipy$ export BLAS=/home/hari/blas/BLAS/libblas.a
>> hari at hari:~/scipy$ export
>> LAPACK=/home/hari/LAPACK/lapack-3.4.0/liblapack.a
>> hari at hari:~/scipy$ sudo python setup.py install --prefix=/usr/local
>> blas_opt_info:
>> blas_mkl_info:
>> ?libraries mkl,vml,guide not found in ['/usr/local/lib',
>> '/usr/lib64', '/usr/lib']
>> ?NOT AVAILABLE
>>
>> atlas_blas_threads_info:
>> Setting PTATLAS=ATLAS
>> ?libraries ptf77blas,ptcblas,atlas not found in ['/usr/local/lib',
>> '/usr/lib64', '/usr/lib']
>> ?NOT AVAILABLE
>>
>> atlas_blas_info:
>> ?libraries f77blas,cblas,atlas not found in ['/usr/local/lib',
>> '/usr/lib64', '/usr/lib']
>> ?NOT AVAILABLE
>>
>>
>> /usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py:1474:
>> 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 ['/usr/local/lib', '/usr/lib64', '/usr/lib']
>> ?NOT AVAILABLE
>>
>>
>> /usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py:1483:
>> 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
>>
>>
>> /usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py:1486:
>> 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 208, in <module>
>> ? ?setup_package()
>> ?File "setup.py", line 199, in setup_package
>> ? ?configuration=configuration )
>> ?File "/usr/local/lib/python2.6/dist-packages/numpy/distutils/core.py",
>> line 152, in setup
>> ? ?config = configuration()
>> ?File "setup.py", line 136, in configuration
>> ? ?config.add_subpackage('scipy')
>> ?File
>> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
>> line 1002, in add_subpackage
>> ? ?caller_level = 2)
>> ?File
>> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
>> line 971, in get_subpackage
>> ? ?caller_level = caller_level + 1)
>> ?File
>> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
>> line 908, in _get_configuration_from_setup_py
>> ? ?config = setup_module.configuration(*args)
>> ?File "scipy/setup.py", line 8, in configuration
>> ? ?config.add_subpackage('integrate')
>> ?File
>> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
>> line 1002, in add_subpackage
>> ? ?caller_level = 2)
>> ?File
>> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
>> line 971, in get_subpackage
>> ? ?caller_level = caller_level + 1)
>> ?File
>> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/misc_util.py",
>> line 908, in _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
>> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py",
>> line 325, in get_info
>> ? ?return cl().get_info(notfound_action)
>> ?File
>> "/usr/local/lib/python2.6/dist-packages/numpy/distutils/system_info.py",
>> line 484, 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.
>> _______________________________________________
>> 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 ralf.gommers at googlemail.com  Tue May  1 15:53:59 2012
From: ralf.gommers at googlemail.com (Ralf Gommers)
Date: Tue, 1 May 2012 21:53:59 +0200
Subject: [SciPy-User] scipy sprint @ EuroSciPy '12?
Message-ID: <CABL7CQiThTVnkHvE3whxkGT87JRsc-OXZeZu-3TGi7FiCPrVAQ@mail.gmail.com>

Hi all,

Would people be interested in having a scipy sprint at EuroSciPy this year?
Last year we tried to do a last minute mini-sprint and ended up hunting for
wifi access for half the time, so it would be good to organize things
better this time around. I'm thinking a one-day sprint on Wed Aug 22nd
would be good.

If there's interest, I'm happy to organize (contact the conference
organizers for a room, create a wiki page, etc.).

Ralf
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From camillechambon at yahoo.fr  Wed May  2 03:35:16 2012
From: camillechambon at yahoo.fr (camille chambon)
Date: Wed, 2 May 2012 08:35:16 +0100 (BST)
Subject: [SciPy-User] Re : Least-square fitting of a 3rd degree polynomial
In-Reply-To: <CAMMTP+Du3N3m1oC9L0ee9SgNaTatQa+e4hwmMoQ=Sob3zxPj2g@mail.gmail.com>
References: <1335893480.26559.YahooMailNeo@web171302.mail.ir2.yahoo.com>
	<CAMMTP+Du3N3m1oC9L0ee9SgNaTatQa+e4hwmMoQ=Sob3zxPj2g@mail.gmail.com>
Message-ID: <1335944116.84181.YahooMailNeo@web171304.mail.ir2.yahoo.com>

Thanks for your answer.

I tried to reduce my problem to a linear regression problem:

x = array([1078.0, 1117.0, 1212.1, 1368.7, 1686.8, 1880.0])
y = array([5.8, 5.6, 5.4, 4.9, 2.4, 0.0])
b, c, d =? 0.0, -0.00458844157413, 5.8

z = y - b * x**2 - c * x
p = numpy.polyfit(x, z, 3)
time = linspace(x.min(), x.max(), 100)
plot(x, y, "ro", time, numpy.poly1d([p[0], b, c, d])(time), "r-")

but it doesn't work (see attached image).

Where am I wrong?

Cheers,

Camille



________________________________
 De?: "josef.pktd at gmail.com" <josef.pktd at gmail.com>
??: camille chambon <camillechambon at yahoo.fr>; SciPy Users List <scipy-user at scipy.org> 
Envoy? le : Mardi 1 mai 2012 19h44
Objet?: Re: [SciPy-User] Least-square fitting of a 3rd degree polynomial
 
On Tue, May 1, 2012 at 1:31 PM, camille chambon <camillechambon at yahoo.fr> wrote:
> Hello,
>
> I would like to fit 'a' such as y = a * x**3 + b * x**2 + c * x + d,
> where x and y are measured data, and b = 0.0, c = -0.00458844157413 and d =
> 5.8 are fixed.
>
> According to
> http://www.scipy.org/Cookbook/FittingData#head-27373a786baa162a2e8a910ee0b8a48838082262,
> I try to use scipy.optimize.leastsq fit routine like that:
>
> #### CODE #####
>
> from numpy import *
> from pylab import *
> from scipy import optimize
>
> # My data points
> x = array([1078.0, 1117.0, 1212.1, 1368.7, 1686.8, 1880.0])
> y = array([5.8, 5.6, 5.4, 4.9, 2.4, 0.0])
>
> b, c, d =? 0.0, -0.00458844157413, 5.8 # Fixed parameters
>
> fitfunc = lambda p, x: poly1d([p[0], b, c, d])(x) # Target function
> errfunc = lambda p, x, y: fitfunc(p, x) - y # Distance to the target
> function
>
> p0 = [-4.0E-09] # Initial guess for the parameters
> p1, success = optimize.leastsq(errfunc, p0[:], args=(x, y))
>
> time = linspace(x.min(), x.max(), 100)
> plot(x, y, "ro", time, fitfunc(p1, time), "r-") # Plot of the data and the
> fit
>
> title("a fitting")
> xlabel("time [day]")
> ylabel("number []")
> legend(('x position', 'x fit', 'y position', 'y fit'))
>
> show()
>
> ################################
>
> But the fit does not work (as one can see on the attached image).
>
> Does someone have any idea of what I'm doing wrong?
>
> Thanks in advance for your help.

my guess would be that the scale is awful, the x are very large

if you only need the to estimate "a", then it's just a very simple
linear regression problem dot(x,x)/dot(x,y) or something like this.

as aside numpy.polynomial works very well for fitting a polynomial,
but doesn't allow for missing terms

Josef

>
> Cheers,
>
> Camille
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From camillechambon at yahoo.fr  Wed May  2 03:39:12 2012
From: camillechambon at yahoo.fr (camille chambon)
Date: Wed, 2 May 2012 08:39:12 +0100 (BST)
Subject: [SciPy-User] Re : Least-square fitting of a 3rd degree polynomial
In-Reply-To: <CAB6mnx+p1UbESagbF3m83t==Tr3AW2ZpAQht4qZWtCYJ3417Lw@mail.gmail.com>
References: <1335893480.26559.YahooMailNeo@web171302.mail.ir2.yahoo.com>
	<CAB6mnx+p1UbESagbF3m83t==Tr3AW2ZpAQht4qZWtCYJ3417Lw@mail.gmail.com>
Message-ID: <1335944352.68951.YahooMailNeo@web171306.mail.ir2.yahoo.com>

Thanks for your answer.

But I would like to fit 'a' while 'b', 'c' and 'd' are fixed.Your method gives me another b, c and d. Is there any way to fit a polynomial fixing some coefficients?


Cheers,

Camille



________________________________
 De?: Charles R Harris <charlesr.harris at gmail.com>
??: camille chambon <camillechambon at yahoo.fr>; SciPy Users List <scipy-user at scipy.org> 
Envoy? le : Mardi 1 mai 2012 19h45
Objet?: Re: [SciPy-User] Least-square fitting of a 3rd degree polynomial
 




On Tue, May 1, 2012 at 11:31 AM, camille chambon <camillechambon at yahoo.fr> wrote:

Hello,
>
>I would like to fit 'a' such as y = a * x**3 + b * x**2 + 
c * x + d, 
>where x and y are measured data,
and b = 0.0, c = -0.00458844157413 and d = 5.8 are fixed. 
>
>According to http://www.scipy.org/Cookbook/FittingData#head-27373a786baa162a2e8a910ee0b8a48838082262, I try to use scipy.optimize.leastsq fit routine like that: 
>
>#### CODE ##### 
>
>from numpy import *
>from pylab import *
>from scipy import optimize
>
># My data points
>x = array([1078.0, 1117.0, 1212.1, 1368.7, 1686.8, 1880.0])
>y = array([5.8, 5.6, 5.4, 4.9, 2.4, 0.0])
>
>b, c, d =? 0.0, -0.00458844157413, 5.8 # Fixed parameters
>
>fitfunc = lambda p, x: poly1d([p[0], b, c, d])(x) # Target function
>errfunc = lambda p, x, y: fitfunc(p, x) - y # Distance to the target function
>
>p0 = [-4.0E-09] # Initial guess for the parameters
>p1, success = optimize.leastsq(errfunc, p0[:], args=(x, y))
>
>time = linspace(x.min(), x.max(), 100)
>plot(x, y, "ro", time, fitfunc(p1, time), "r-") # Plot of the data and the fit
>
>title("a fitting")
>xlabel("time [day]")
>ylabel("number []")
>legend(('x position', 'x fit', 'y position', 'y fit'))
>
>show()
>
>################################
>
>But the fit does not work (as one can see on the attached image).
>
>Does someone
 have any idea of what I'm doing wrong?
>
>Thanks in advance for your help.
>
>Cheers,
>
>Camille
>
>

You can use numpy for this.

In [1]: from numpy.polynomial import Polynomial as P

In [2]: x = array([1078.0, 1117.0, 1212.1, 1368.7, 1686.8, 1880.0])

In [3]: y = array([5.8, 5.6, 5.4, 4.9, 2.4, 0.0])

In [4]: p = P.fit(x,y,3)

In [5]: plot(*p.linspace())
Out[5]: [<matplotlib.lines.Line2D at 0x2e51ad0>]

In [6]: plot(x, y, '.')
Out[6]: [<matplotlib.lines.Line2D at 0x2e62c90>]

In [7]: p.mapparms()
Out[7]: (-3.6882793017456361, 0.0024937655860349127)

Note two things, the coefficients go from degree zero upwards and you need to make the substitution x <- -3.6882793017456361 + 0024937655860349127*x if you want to use them in a publication.

Chuck 
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From camillechambon at yahoo.fr  Wed May  2 03:51:21 2012
From: camillechambon at yahoo.fr (camille chambon)
Date: Wed, 2 May 2012 08:51:21 +0100 (BST)
Subject: [SciPy-User] Re : Least-square fitting of a 3rd degree polynomial
In-Reply-To: <CAB6mnx+z5Rqe1OE58L4duAVHvxMnXKxOtXGQR5rqs=VBbeEDww@mail.gmail.com>
References: <1335893480.26559.YahooMailNeo@web171302.mail.ir2.yahoo.com>
	<CAMMTP+Du3N3m1oC9L0ee9SgNaTatQa+e4hwmMoQ=Sob3zxPj2g@mail.gmail.com>
	<CAB6mnx+z5Rqe1OE58L4duAVHvxMnXKxOtXGQR5rqs=VBbeEDww@mail.gmail.com>
Message-ID: <1335945081.81241.YahooMailNeo@web171301.mail.ir2.yahoo.com>

I tried to set a polynomial with a NA coefficient:
from numpy.polynomial import Polynomial as P
import numpy
x = numpy.array([1078.0, 1117.0, 1212.1, 1368.7, 1686.8, 1880.0])
y = numpy.array([5.8, 5.6, 5.4, 4.9, 2.4, 0.0])

b, c, d =? 0.0, -0.00458844157413, 5.8

my_poly = P([d, c, b, numpy.nan], [x.min(), x.max()])
print my_poly # print "poly([? 5.80000000e+00? -4.58844157e-03?? 0.00000000e+00????????????? nan], [ 1078.? 1880.])"
p = my_poly.fit(x,y,3)
print p # print "poly([ 4.20020036 -2.66837734 -1.33882427 -0.20317739], [ 1078.? 1880.])"


but new coefficients are calculated. Does that mean it doesn't work?

Cheers,

Camille



________________________________
 De?: Charles R Harris <charlesr.harris at gmail.com>
??: SciPy Users List <scipy-user at scipy.org> 
Envoy? le : Mardi 1 mai 2012 19h47
Objet?: Re: [SciPy-User] Least-square fitting of a 3rd degree polynomial
 




On Tue, May 1, 2012 at 11:44 AM, <josef.pktd at gmail.com> wrote:

On Tue, May 1, 2012 at 1:31 PM, camille chambon <camillechambon at yahoo.fr> wrote:
>> Hello,
>>
>> I would like to fit 'a' such as y = a * x**3 + b * x**2 + c * x + d,
>> where x and y are measured data, and b = 0.0, c = -0.00458844157413 and d =
>> 5.8 are fixed.
>>
>> According to
>> http://www.scipy.org/Cookbook/FittingData#head-27373a786baa162a2e8a910ee0b8a48838082262,
>> I try to use scipy.optimize.leastsq fit routine like that:
>>
>> #### CODE #####
>>
>> from numpy import *
>> from pylab import *
>> from scipy import optimize
>>
>> # My data points
>> x = array([1078.0, 1117.0, 1212.1, 1368.7, 1686.8, 1880.0])
>> y = array([5.8, 5.6, 5.4, 4.9, 2.4, 0.0])
>>
>> b, c, d =? 0.0, -0.00458844157413, 5.8 # Fixed parameters
>>
>> fitfunc = lambda p, x: poly1d([p[0], b, c, d])(x) # Target function
>> errfunc = lambda p, x, y: fitfunc(p, x) - y # Distance to the target
>> function
>>
>> p0 = [-4.0E-09] # Initial guess for the parameters
>> p1, success = optimize.leastsq(errfunc, p0[:], args=(x, y))
>>
>> time = linspace(x.min(), x.max(), 100)
>> plot(x, y, "ro", time, fitfunc(p1, time), "r-") # Plot of the data and the
>> fit
>>
>> title("a fitting")
>> xlabel("time [day]")
>> ylabel("number []")
>> legend(('x position', 'x fit', 'y position', 'y fit'))
>>
>> show()
>>
>> ################################
>>
>> But the fit does not work (as one can see on the attached image).
>>
>> Does someone have any idea of what I'm doing wrong?
>>
>> Thanks in advance for your help.
>
>my guess would be that the scale is awful, the x are very large
>
>if you only need the to estimate "a", then it's just a very simple
>linear regression problem dot(x,x)/dot(x,y) or something like this.
>
>as aside numpy.polynomial works very well for fitting a polynomial,
>but doesn't allow for missing terms
>
>

It does recognize NA in devel, but we will see where that goes ;)

Chuck 


_______________________________________________
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SciPy-User at scipy.org
http://mail.scipy.org/mailman/listinfo/scipy-user
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From josef.pktd at gmail.com  Wed May  2 06:45:43 2012
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Wed, 2 May 2012 06:45:43 -0400
Subject: [SciPy-User] Re : Least-square fitting of a 3rd degree
	polynomial
In-Reply-To: <1335944116.84181.YahooMailNeo@web171304.mail.ir2.yahoo.com>
References: <1335893480.26559.YahooMailNeo@web171302.mail.ir2.yahoo.com>
	<CAMMTP+Du3N3m1oC9L0ee9SgNaTatQa+e4hwmMoQ=Sob3zxPj2g@mail.gmail.com>
	<1335944116.84181.YahooMailNeo@web171304.mail.ir2.yahoo.com>
Message-ID: <CAMMTP+C-Zb6RTr-0mhM3dSOC8XjwWrn7U4ZzK6r8wUu6Wqak2w@mail.gmail.com>

On Wed, May 2, 2012 at 3:35 AM, camille chambon <camillechambon at yahoo.fr> wrote:
> Thanks for your answer.
>
> I tried to reduce my problem to a linear regression problem:
>
> x = array([1078.0, 1117.0, 1212.1, 1368.7, 1686.8, 1880.0])
> y = array([5.8, 5.6, 5.4, 4.9, 2.4, 0.0])
> b, c, d =? 0.0, -0.00458844157413, 5.8
> z = y - b * x**2 - c * x
> p = numpy.polyfit(x, z, 3)
> time = linspace(x.min(), x.max(), 100)
> plot(x, y, "ro", time, numpy.poly1d([p[0], b, c, d])(time), "r-")
>
> but it doesn't work (see attached image).
>
> Where am I wrong?

Are you sure you got your fixed values b,c,d right?

y2 = y - d - c * x - b * x**2
>>> y2
array([ 4.94634002,  4.92528924,  5.16165003,  5.38019998,  4.33978325,
        2.82627016])

It doesn't look like you can fit y2  as a function of x**3 and get a
good result.

I get a similar plot to your original leastsq solution.


import numpy as np
import matplotlib.pyplot as plt


x = np.array([1078.0, 1117.0, 1212.1, 1368.7, 1686.8, 1880.0])
y = np.array([5.8, 5.6, 5.4, 4.9, 2.4, 0.0])
b, c, d =  0.0, -0.00458844157413, 5.8
y2 = y - d - c * x - b * x**2
x2 = x**3
a = np.dot(x2, y2) / np.dot(x2, x2)

#check with statsmodels
import statsmodels.api as sm
a2 = sm.OLS(y2, x2).fit().params
print a, a2[0]

xx = np.linspace(x.min(), x.max())
yhat = d + c * xx + b * xx**2 + a * xx**3
plt.plot(x, y, 'o')
plt.plot(xx, yhat, '-')

plt.figure()
plt.plot(x, y2, 'o')
plt.show()


Josef

>
> Cheers,
>
> Camille
>
> ________________________________
> De?: "josef.pktd at gmail.com" <josef.pktd at gmail.com>
> ??: camille chambon <camillechambon at yahoo.fr>; SciPy Users List
> <scipy-user at scipy.org>
> Envoy? le : Mardi 1 mai 2012 19h44
> Objet?: Re: [SciPy-User] Least-square fitting of a 3rd degree polynomial
>
> On Tue, May 1, 2012 at 1:31 PM, camille chambon <camillechambon at yahoo.fr>
> wrote:
>> Hello,
>>
>> I would like to fit 'a' such as y = a * x**3 + b * x**2 + c * x + d,
>> where x and y are measured data, and b = 0.0, c = -0.00458844157413 and d
>> =
>> 5.8 are fixed.
>>
>> According to
>>
>> http://www.scipy.org/Cookbook/FittingData#head-27373a786baa162a2e8a910ee0b8a48838082262,
>> I try to use scipy.optimize.leastsq fit routine like that:
>>
>> #### CODE #####
>>
>> from numpy import *
>> from pylab import *
>> from scipy import optimize
>>
>> # My data points
>> x = array([1078.0, 1117.0, 1212.1, 1368.7, 1686.8, 1880.0])
>> y = array([5.8, 5.6, 5.4, 4.9, 2.4, 0.0])
>>
>> b, c, d =? 0.0, -0.00458844157413, 5.8 # Fixed parameters
>>
>> fitfunc = lambda p, x: poly1d([p[0], b, c, d])(x) # Target function
>> errfunc = lambda p, x, y: fitfunc(p, x) - y # Distance to the target
>> function
>>
>> p0 = [-4.0E-09] # Initial guess for the parameters
>> p1, success = optimize.leastsq(errfunc, p0[:], args=(x, y))
>>
>> time = linspace(x.min(), x.max(), 100)
>> plot(x, y, "ro", time, fitfunc(p1, time), "r-") # Plot of the data and the
>> fit
>>
>> title("a fitting")
>> xlabel("time [day]")
>> ylabel("number []")
>> legend(('x position', 'x fit', 'y position', 'y fit'))
>>
>> show()
>>
>> ################################
>>
>> But the fit does not work (as one can see on the attached image).
>>
>> Does someone have any idea of what I'm doing wrong?
>>
>> Thanks in advance for your help.
>
> my guess would be that the scale is awful, the x are very large
>
> if you only need the to estimate "a", then it's just a very simple
> linear regression problem dot(x,x)/dot(x,y) or something like this.
>
> as aside numpy.polynomial works very well for fitting a polynomial,
> but doesn't allow for missing terms
>
> Josef
>
>>
>> Cheers,
>>
>> Camille
>>
>>
>> _______________________________________________
>> 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 camillechambon at yahoo.fr  Wed May  2 13:04:28 2012
From: camillechambon at yahoo.fr (camille chambon)
Date: Wed, 2 May 2012 18:04:28 +0100 (BST)
Subject: [SciPy-User] Re : Re : Least-square fitting of a 3rd degree
	polynomial
References: <1335893480.26559.YahooMailNeo@web171302.mail.ir2.yahoo.com>
	<CAMMTP+Du3N3m1oC9L0ee9SgNaTatQa+e4hwmMoQ=Sob3zxPj2g@mail.gmail.com>
	<1335944116.84181.YahooMailNeo@web171304.mail.ir2.yahoo.com>
	<CAMMTP+C-Zb6RTr-0mhM3dSOC8XjwWrn7U4ZzK6r8wUu6Wqak2w@mail.gmail.com>
Message-ID: <1335978268.79539.YahooMailNeo@web171305.mail.ir2.yahoo.com>

Thanks for your answer.

I realized I made a mistake with my data. It works much better now. Sorry, I have my head in the clouds...

Cheers,

Camille



________________________________
 De?: "josef.pktd at gmail.com" <josef.pktd at gmail.com>
??: SciPy Users List <scipy-user at scipy.org> 
Envoy? le : Mercredi 2 mai 2012 12h45
Objet?: Re: [SciPy-User] Re : Least-square fitting of a 3rd degree polynomial
 
On Wed, May 2, 2012 at 3:35 AM, camille chambon <camillechambon at yahoo.fr> wrote:
> Thanks for your answer.
>
> I tried to reduce my problem to a
 linear regression problem:
>
> x = array([1078.0, 1117.0, 1212.1, 1368.7, 1686.8, 1880.0])
> y = array([5.8, 5.6, 5.4, 4.9, 2.4, 0.0])
> b, c, d =? 0.0, -0.00458844157413, 5.8
> z = y - b * x**2 - c * x
> p = numpy.polyfit(x, z, 3)
> time = linspace(x.min(), x.max(), 100)
> plot(x, y, "ro", time, numpy.poly1d([p[0], b, c, d])(time), "r-")
>
> but it doesn't work (see attached image).
>
> Where am I wrong?

Are you sure you got your fixed values b,c,d right?

y2 = y - d - c * x - b * x**2
>>> y2
array([ 4.94634002,? 4.92528924,? 5.16165003,? 5.38019998,? 4.33978325,
? ? ? ? 2.82627016])

It doesn't look like you can fit y2? as a function of x**3 and get a
good result.

I get a similar plot to your original leastsq solution.


import numpy as np
import matplotlib.pyplot as
 plt


x = np.array([1078.0, 1117.0, 1212.1, 1368.7, 1686.8, 1880.0])
y = np.array([5.8, 5.6, 5.4, 4.9, 2.4, 0.0])
b, c, d =? 0.0, -0.00458844157413, 5.8
y2 = y - d - c * x - b * x**2
x2 = x**3
a = np.dot(x2, y2) / np.dot(x2, x2)

#check with statsmodels
import statsmodels.api as sm
a2 = sm.OLS(y2, x2).fit().params
print a, a2[0]

xx = np.linspace(x.min(), x.max())
yhat = d + c * xx + b * xx**2 + a * xx**3
plt.plot(x, y, 'o')
plt.plot(xx, yhat, '-')

plt.figure()
plt.plot(x, y2, 'o')
plt.show()


Josef

>
> Cheers,
>
> Camille
>
> ________________________________
> De?: "josef.pktd at gmail.com" <josef.pktd at gmail.com>
> ??: camille chambon <camillechambon at yahoo.fr>; SciPy Users List
> <scipy-user at scipy.org>
> Envoy? le : Mardi 1 mai 2012 19h44
> Objet?: Re: [SciPy-User] Least-square fitting of a 3rd degree polynomial
>
> On Tue, May 1, 2012 at 1:31 PM, camille chambon <camillechambon at yahoo.fr>
> wrote:
>> Hello,
>>
>> I would like to fit 'a' such as y = a * x**3 + b * x**2 + c * x + d,
>> where x and y are measured data, and b = 0.0, c = -0.00458844157413 and d
>> =
>> 5.8 are fixed.
>>
>> According to
>>
>> http://www.scipy.org/Cookbook/FittingData#head-27373a786baa162a2e8a910ee0b8a48838082262,
>> I try to use scipy.optimize.leastsq fit routine like that:
>>
>> #### CODE #####
>>
>> from numpy import *
>> from pylab import *
>> from scipy import optimize
>>
>> # My data points
>> x = array([1078.0, 1117.0, 1212.1, 1368.7, 1686.8, 1880.0])
>> y = array([5.8, 5.6, 5.4, 4.9, 2.4, 0.0])
>>
>> b, c, d =? 0.0, -0.00458844157413, 5.8 # Fixed parameters
>>
>> fitfunc = lambda p, x: poly1d([p[0], b, c, d])(x) # Target function
>> errfunc = lambda p, x, y: fitfunc(p, x) - y # Distance to the target
>> function
>>
>> p0 = [-4.0E-09] # Initial guess for the parameters
>>
 p1, success = optimize.leastsq(errfunc, p0[:], args=(x, y))
>>
>> time = linspace(x.min(), x.max(), 100)
>> plot(x, y, "ro", time, fitfunc(p1, time), "r-") # Plot of the data and the
>> fit
>>
>> title("a fitting")
>> xlabel("time [day]")
>> ylabel("number []")
>> legend(('x position', 'x fit', 'y position', 'y fit'))
>>
>> show()
>>
>> ################################
>>
>> But the fit does not work (as one can see on the attached image).
>>
>> Does someone have any idea of what I'm doing wrong?
>>
>> Thanks in advance for your help.
>
> my guess would be that the scale is awful, the x are very large
>
> if you only need the to estimate "a", then it's just a very simple
> linear regression problem dot(x,x)/dot(x,y) or something like this.
>
>
 as aside numpy.polynomial works very well for fitting a polynomial,
> but doesn't allow for missing terms
>
> Josef
>
>>
>> Cheers,
>>
>> Camille
>>
>>
>> _______________________________________________
>> 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 otrov at hush.ai  Wed May  2 16:45:20 2012
From: otrov at hush.ai (Kliment)
Date: Wed, 02 May 2012 22:45:20 +0200
Subject: [SciPy-User] ... numpy/linalg/lapack_lite.so: undefined symbol:
	zungqr_
Message-ID: <20120502204520.C52B310E2D8@smtp.hushmail.com>

Hi,

I compiled lapack, atlas, umfpack, fftw in local folder, in similar 
way as described here: http://www.scipy.org/Installing_SciPy/Linux 
on 32bit Ubuntu Precise

In ~/.local/lib I have:
========================================
libamd.2.2.3.a
libamd.a -> libamd.2.2.3.a
libatlas.a
libcblas.a
libf77blas.a
libfftw3.a
libfftw3.la*
liblapack.a
librefblas.a
libsatlas.so*
libtmglib.a
libumfpack.5.5.2.a
libumfpack.a -> libumfpack.5.5.2.a
========================================

In ~/.local/include I have:
========================================
amd.h
atlas/
cblas.h
clapack.h
fftw3.f
fftw3.f03
fftw3.h
fftw3l.f03
fftw3q.f03
UFconfig.h
umfpack.h
<various umfpack_[...].h files>
========================================

My site.cfg looks like this:
========================================
[DEFAULT]
library_dirs = $HOME/.local/lib
include_dirs = $HOME/.local/include

[atlas]
atlas_libs = lapack, f77blas, cblas, atlas

[amd]
amd_libs = amd

[umfpack]
umfpack_libs = umfpack, gfortran

[fftw]
libraries = fftw3
========================================

I extracted numpy and run:

python setup.py build --fcompiler=gnu95
python setup.py install --prefix=$HOME/.local

I then run python interpreter and try to import numpy, when I 
receive import error:

ImportError: /home/vlad/.local/lib/python2.7/site-
packages/numpy/linalg/lapack_lite.so: undefined symbol: zungqr_

What did I do wrong?



From cournape at gmail.com  Thu May  3 13:11:56 2012
From: cournape at gmail.com (David Cournapeau)
Date: Thu, 3 May 2012 18:11:56 +0100
Subject: [SciPy-User] ... numpy/linalg/lapack_lite.so: undefined symbol:
	zungqr_
In-Reply-To: <20120502204520.C52B310E2D8@smtp.hushmail.com>
References: <20120502204520.C52B310E2D8@smtp.hushmail.com>
Message-ID: <CAGY4rcVtf21JiE30DZ-_RhB83C4G8CZKMO1pcpcNx+Tpqa655w@mail.gmail.com>

On Wed, May 2, 2012 at 9:45 PM, Kliment <otrov at hush.ai> wrote:

> Hi,
>
> I compiled lapack, atlas, umfpack, fftw in local folder, in similar
> way as described here: http://www.scipy.org/Installing_SciPy/Linux
> on 32bit Ubuntu Precise
>
> In ~/.local/lib I have:
> ========================================
> libamd.2.2.3.a
> libamd.a -> libamd.2.2.3.a
> libatlas.a
> libcblas.a
> libf77blas.a
> libfftw3.a
> libfftw3.la*
> liblapack.a
> librefblas.a
> libsatlas.so*
> libtmglib.a
> libumfpack.5.5.2.a
> libumfpack.a -> libumfpack.5.5.2.a
> ========================================
>
> In ~/.local/include I have:
> ========================================
> amd.h
> atlas/
> cblas.h
> clapack.h
> fftw3.f
> fftw3.f03
> fftw3.h
> fftw3l.f03
> fftw3q.f03
> UFconfig.h
> umfpack.h
> <various umfpack_[...].h files>
> ========================================
>
> My site.cfg looks like this:
> ========================================
> [DEFAULT]
> library_dirs = $HOME/.local/lib
> include_dirs = $HOME/.local/include
>
> [atlas]
> atlas_libs = lapack, f77blas, cblas, atlas
>
> [amd]
> amd_libs = amd
>
> [umfpack]
> umfpack_libs = umfpack, gfortran
>
> [fftw]
> libraries = fftw3
> ========================================
>
> I extracted numpy and run:
>
> python setup.py build --fcompiler=gnu95
> python setup.py install --prefix=$HOME/.local
>
> I then run python interpreter and try to import numpy, when I
> receive import error:
>
> ImportError: /home/vlad/.local/lib/python2.7/site-
> packages/numpy/linalg/lapack_lite.so: undefined symbol: zungqr_
>

Most likely you did not build atlas and/or lapack correctly.

Unless you are familiar with debugging those kind of issues, I strongly
recommend you to build numpy against atlas as given by ubuntu (something
like apt-get install libatlas-base-dev should do the trick).

David
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From otrov at hush.ai  Thu May  3 13:36:05 2012
From: otrov at hush.ai (Kliment)
Date: Thu, 3 May 2012 17:36:05 +0000 (UTC)
Subject: [SciPy-User]
	=?utf-8?q?=2E=2E=2E_numpy/linalg/lapack=5Flite=2Eso?=
	=?utf-8?q?=3A_undefined_symbol=3A=09zungqr=5F?=
References: <20120502204520.C52B310E2D8@smtp.hushmail.com>
	<CAGY4rcVtf21JiE30DZ-_RhB83C4G8CZKMO1pcpcNx+Tpqa655w@mail.gmail.com>
Message-ID: <loom.20120503T193320-486@post.gmane.org>

David Cournapeau <cournape <at> gmail.com> writes:
> 
> Most likely you did not build atlas and/or lapack correctly.
> 
> Unless you are familiar with debugging those kind of issues, I strongly
recommend you to build numpy against atlas as given by ubuntu (something like
apt-get install libatlas-base-dev should do the trick).
> 
> David?
 

That much I do know myself, obviously

Thanks for trying to communicate thou




From zfyuan at mail.ustc.edu.cn  Fri May  4 09:03:21 2012
From: zfyuan at mail.ustc.edu.cn (=?UTF-8?B?6KKB5oyv6aOe?=)
Date: Fri, 04 May 2012 21:03:21 +0800
Subject: [SciPy-User] Is there any quantile function in scipy.stats module?
Message-ID: <4FA3D399.4030605@mail.ustc.edu.cn>

Hi, all.

     My work involved in some probability calculation, which needs the 
quantile
function of distributions like standard normal and t. However, I looked 
into
scipy.stats.t, there's only pdf and cdf functions, and didn't see quantile
function. What can I do with this problem?

     I want your help, thanks a lot !

Best wishes: )

-- 
Jeffrey


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From josef.pktd at gmail.com  Fri May  4 09:09:41 2012
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Fri, 4 May 2012 09:09:41 -0400
Subject: [SciPy-User] Is there any quantile function in scipy.stats
	module?
In-Reply-To: <4FA3D399.4030605@mail.ustc.edu.cn>
References: <4FA3D399.4030605@mail.ustc.edu.cn>
Message-ID: <CAMMTP+DzawnRgHnkkMQdNBHgKmamV-647VsRBpV43U2Ykb_DRg@mail.gmail.com>

On Fri, May 4, 2012 at 9:03 AM, ??? <zfyuan at mail.ustc.edu.cn> wrote:
> Hi, all.
>
> ??? My work involved in some probability calculation, which needs the
> quantile
> function of distributions like standard normal and t. However, I looked into
> scipy.stats.t, there's only pdf and cdf functions, and didn't see quantile
> function. What can I do with this problem?

stats.norm.ppf
stats.t.ppf
gives the quantiles for a specific values (can be list or array)

.isf is the quantile for the survival function, and is for some
distributions more precise for upper tails than using ppf.

Josef



>
> ??? I want your help, thanks a lot !
>
> Best wishes: )
>
> --
> Jeffrey
>
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>


From cmueller_dev at yahoo.com  Thu May  3 12:44:15 2012
From: cmueller_dev at yahoo.com (Chris Mueller)
Date: Thu, 3 May 2012 09:44:15 -0700 (PDT)
Subject: [SciPy-User] 2012 SciPy Bioinformatics Workshop
Message-ID: <1336063455.23270.YahooMailNeo@web111204.mail.gq1.yahoo.com>

We are pleased to announce the 2012 SciPy Bioinformatics Workshop held in conjunction with SciPy 2012 this July in Austin, TX.

Python in biology is not dead yet... in fact, it's alive and well!

Remember just a few short years ago when BioPerl ruled the world? ?Just one minor paradigm shift* later and Python now has a commanding presence in bioinformatics. From Python bindings to common tools all the way to entire Python-based informatics platforms, Python is used everywhere** in modern bioinformatics.

If you use Python for bioinformatics or just want to learn more about how its being used, join us at the 2012 SciPy Bioinformatics Workshop. We will have speakers from both academia and industry showcasing how Python is enabling biologists to effectively work with large, complex data sets.

The workshop will be held the evening of July 19 from 5-6:30.

More information about SciPy is available on the conference site:
? http://conference.scipy.org/scipy2012/

!! Participate !!

Are you using Python in bioinformatics? ?We'd love to have you share your story. ?We are looking for 3-4 speakers to share their experiences using Python for bioinformatics. ?

Please contact Chris Mueller at chris.mueller [at] lab7.io and Ray Roberts at rroberts [at] enthought.com to volunteer. Please include a brief description or link to a paper/topic which you would like to discuss. Presentations will last for 15 minutes each and will be followed by a panel Q&A.

--
* That would be next generation sequencing
** Yes, we aRe awaRe of that otheR language used eveRywhere, but let's celebRate Python Right now.



From zfyuan at mail.ustc.edu.cn  Fri May  4 11:23:50 2012
From: zfyuan at mail.ustc.edu.cn (=?UTF-8?B?6KKB5oyv6aOe?=)
Date: Fri, 04 May 2012 23:23:50 +0800
Subject: [SciPy-User] Is there any quantile function in scipy.stats
	module?
In-Reply-To: <CAMMTP+DzawnRgHnkkMQdNBHgKmamV-647VsRBpV43U2Ykb_DRg@mail.gmail.com>
References: <4FA3D399.4030605@mail.ustc.edu.cn>
	<CAMMTP+DzawnRgHnkkMQdNBHgKmamV-647VsRBpV43U2Ykb_DRg@mail.gmail.com>
Message-ID: <4FA3F486.2060309@mail.ustc.edu.cn>

? 2012?05?04? 21:09, josef.pktd at gmail.com ??:
> On Fri, May 4, 2012 at 9:03 AM,<zfyuan at mail.ustc.edu.cn>  wrote:
>> Hi, all.
>>
>>      My work involved in some probability calculation, which needs the
>> quantile
>> function of distributions like standard normal and t. However, I looked into
>> scipy.stats.t, there's only pdf and cdf functions, and didn't see quantile
>> function. What can I do with this problem?
> stats.norm.ppf
> stats.t.ppf
> gives the quantiles for a specific values (can be list or array)
>
> .isf is the quantile for the survival function, and is for some
> distributions more precise for upper tails than using ppf.
>
> Josef
>
>>      I want your help, thanks a lot !
>>
>> Best wishes: )
>>
>> --
>> Jeffrey
>>
>>
>>
>> _______________________________________________
>> 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 see. Thanks a lot

-- 
Jeffrey




From zfyuan at mail.ustc.edu.cn  Fri May  4 23:04:09 2012
From: zfyuan at mail.ustc.edu.cn (Jeffrey)
Date: Sat, 05 May 2012 11:04:09 +0800
Subject: [SciPy-User] scipy.integrate with a function vector
Message-ID: <4FA498A9.5050406@mail.ustc.edu.cn>

Hi, all.

     My problem came with an integration problem. Below is a brief sample:

 >>>    e1=numpy.arange(10)
 >>>    d1=e1/10
 >>>    def e2(w):
 >>>        return w*e1
 >>>    def d2(w):
 >>>        return w*d1
 >>>
 >>>    scipy.integrate.quad(lambda x: e1(x)-d1(x), 0 , 1)

     Since e1,d1 are array with length 10, d2(w) and e2(w) will also 
each return an array of length 10. However scipy.integrate.quad only 
supports a scale function.

     Is there any integrating function in scipy, which could solve this 
problem?
     Or my code writes wrongly? Any alternative way to solve this problem?

     Thanks a lot.

-- 
Jeffrey




From otrov at hush.ai  Sat May  5 01:44:13 2012
From: otrov at hush.ai (Kliment)
Date: Sat, 5 May 2012 05:44:13 +0000 (UTC)
Subject: [SciPy-User]
	=?utf-8?q?=2E=2E=2Enumpy/linalg/lapack=5Flite=2Eso?=
	=?utf-8?q?=3A_undefined_symbol=3A=09zungqr=5F?=
References: <20120502204520.C52B310E2D8@smtp.hushmail.com>
	<CAGY4rcVtf21JiE30DZ-_RhB83C4G8CZKMO1pcpcNx+Tpqa655w@mail.gmail.com>
	<loom.20120503T193320-486@post.gmane.org>
Message-ID: <loom.20120505T073519-521@post.gmane.org>

In case anyone gets here from Google:

I managed to build it successfully, by following this recipe:
http://mbudisic.wordpress.com/2010/08/12/installing-atlas-with-full-lapack-on-64-bit-linux/
and additionally using latest stable ATLAS (instead latest dev version) paired
with LAPACK 3.3.1 (instead latest 3.4.1), as apparently not any LAPACK can be
build with any ATLAS, but perhaps pairing roughly dates should go fine.

Method described in above post differs from one provided on Scipy page, by the
way LAPACK is set, which is not just linked to ATLAS build process with
downloaded archive.
Looking now, from first post is evident that LAPACK library is striped down
version, which is what I got by following instructions on Scipy page.

Hope this help someone



From ralf.gommers at googlemail.com  Sat May  5 14:15:47 2012
From: ralf.gommers at googlemail.com (Ralf Gommers)
Date: Sat, 5 May 2012 20:15:47 +0200
Subject: [SciPy-User] ANN: NumPy 1.6.2 release candidate 1
Message-ID: <CABL7CQju9HqYapdJGTb=TVpOHZn8J7wwzRROtKxW-JhawD6A2A@mail.gmail.com>

Hi,

I'm pleased to announce the availability of the first release candidate of
NumPy 1.6.2.  This is a maintenance release. Due to the delay of the NumPy
1.7.0, this release contains far more fixes than a regular NumPy bugfix
release.  It also includes a number of documentation and build improvements.

Sources and binary installers can be found at
https://sourceforge.net/projects/numpy/files/NumPy/1.6.2rc1/

Please test this release and report any issues on the numpy-discussion
mailing list.

Cheers,
Ralf



``numpy.core`` issues fixed
---------------------------

#2063  make unique() return consistent index
#1138  allow creating arrays from empty buffers or empty slices
#1446  correct note about correspondence vstack and concatenate
#1149  make argmin() work for datetime
#1672  fix allclose() to work for scalar inf
#1747  make np.median() work for 0-D arrays
#1776  make complex division by zero to yield inf properly
#1675  add scalar support for the format() function
#1905  explicitly check for NaNs in allclose()
#1952  allow floating ddof in std() and var()
#1948  fix regression for indexing chararrays with empty list
#2017  fix type hashing
#2046  deleting array attributes causes segfault
#2033  a**2.0 has incorrect type
#2045  make attribute/iterator_element deletions not segfault
#2021  fix segfault in searchsorted()
#2073  fix float16 __array_interface__ bug


``numpy.lib`` issues fixed
--------------------------

#2048  break reference cycle in NpzFile
#1573  savetxt() now handles complex arrays
#1387  allow bincount() to accept empty arrays
#1899  fixed histogramdd() bug with empty inputs
#1793  fix failing npyio test under py3k
#1936  fix extra nesting for subarray dtypes
#1848  make tril/triu return the same dtype as the original array
#1918  use Py_TYPE to access ob_type, so it works also on Py3


``numpy.f2py`` changes
----------------------

ENH:   Introduce new options extra_f77_compiler_args and
extra_f90_compiler_args
BLD:   Improve reporting of fcompiler value
BUG:   Fix f2py test_kind.py test


``numpy.poly`` changes
----------------------

ENH:   Add some tests for polynomial printing
ENH:   Add companion matrix functions
DOC:   Rearrange the polynomial documents
BUG:   Fix up links to classes
DOC:   Add version added to some of the polynomial package modules
DOC:   Document xxxfit functions in the polynomial package modules
BUG:   The polynomial convenience classes let different types interact
DOC:   Document the use of the polynomial convenience classes
DOC:   Improve numpy reference documentation of polynomial classes
ENH:   Improve the computation of polynomials from roots
STY:   Code cleanup in polynomial [*]fromroots functions
DOC:   Remove references to cast and NA, which were added in 1.7


``numpy.distutils`` issues fixed
-------------------------------

#1261  change compile flag on AIX from -O5 to -O3
#1377  update HP compiler flags
#1383  provide better support for C++ code on HPUX
#1857  fix build for py3k + pip
BLD:   raise a clearer warning in case of building without cleaning up first
BLD:   follow build_ext coding convention in build_clib
BLD:   fix up detection of Intel CPU on OS X in system_info.py
BLD:   add support for the new X11 directory structure on Ubuntu & co.
BLD:   add ufsparse to the libraries search path.
BLD:   add 'pgfortran' as a valid compiler in the Portland Group
BLD:   update version match regexp for IBM AIX Fortran compilers.
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From josef.pktd at gmail.com  Sat May  5 16:20:27 2012
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sat, 5 May 2012 16:20:27 -0400
Subject: [SciPy-User] scipy.stats.distributions.erlang - "boring" consensus
	building in a ticket
Message-ID: <CAMMTP+AwoWP3akZfaAGtEuEq+2my-uNb4n2grrDtajQ0AMvXqQ@mail.gmail.com>

Should we restrict the shape parameter to be an integer instead of a float?

http://projects.scipy.org/scipy/ticket/1647

Let's ask the users:

Does anyone want an exception if the shape parameter is not an integer?
Is there a demand or use case for estimating the shape parameter as an
integer instead of a float?

right now erlang and gamma are essentially the same, as far as I can see

Josef


From josef.pktd at gmail.com  Sat May  5 16:22:15 2012
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sat, 5 May 2012 16:22:15 -0400
Subject: [SciPy-User] scipy.stats.distributions.erlang - "boring"
 consensus building in a ticket
In-Reply-To: <CAMMTP+AwoWP3akZfaAGtEuEq+2my-uNb4n2grrDtajQ0AMvXqQ@mail.gmail.com>
References: <CAMMTP+AwoWP3akZfaAGtEuEq+2my-uNb4n2grrDtajQ0AMvXqQ@mail.gmail.com>
Message-ID: <CAMMTP+C_vZ_MZzD5=6Kwa-ziDy8=cq23CXpzC01n2ygv8nqB-w@mail.gmail.com>

On Sat, May 5, 2012 at 4:20 PM,  <josef.pktd at gmail.com> wrote:
> Should we restrict the shape parameter to be an integer instead of a float?
>
> http://projects.scipy.org/scipy/ticket/1647
>
> Let's ask the users:
>
> Does anyone want an exception if the shape parameter is not an integer?
sorry correction, not an exception, if the parameter is invalid, an
nan is returned

> Is there a demand or use case for estimating the shape parameter as an
> integer instead of a float?
>
> right now erlang and gamma are essentially the same, as far as I can see
>
> Josef


From vanforeest at gmail.com  Sat May  5 17:59:52 2012
From: vanforeest at gmail.com (nicky van foreest)
Date: Sat, 5 May 2012 23:59:52 +0200
Subject: [SciPy-User] scipy.stats.distributions.erlang - "boring"
 consensus building in a ticket
In-Reply-To: <CAMMTP+AwoWP3akZfaAGtEuEq+2my-uNb4n2grrDtajQ0AMvXqQ@mail.gmail.com>
References: <CAMMTP+AwoWP3akZfaAGtEuEq+2my-uNb4n2grrDtajQ0AMvXqQ@mail.gmail.com>
Message-ID: <CAK_GA2EwpnSx05TS5HZQgHbSQEPULig6VtzOd+qq0WZOW78B5w@mail.gmail.com>

Hi,

I just looked through the discussion in the ticket. Both sides (1: the
scale k should be an int, 2) allow k to be a float) make sense. From
my background in queueing I am inclined to say that k should be
restricted to the integers as in queueing theory the Erlang-k
distribution is used to model some distribution for which k can only
be an int. On the other hand, I am unsure whether a user of the Erlang
distribution should be protected from filling in a float. In all (?)
books on queueing and probability it is written that the Erlang
distribution is a special case of the gamma distribution, so users of
the Erlang distribution should know this (.... kind of, hopefully).

>From an implementation point of view I think that aliasing Erlang to
the gamma distribution makes good sense, and I don't believe that the
users or the Erlang distribution require to be protected against
filling in floats. Perhaps a sentence in the docstring of the Erlang
distribution that it is an alias of the gamma distribution, hence does
not check on the scale being an int, will prevent some potential
misuse.

Nicky



On 5 May 2012 22:20,  <josef.pktd at gmail.com> wrote:
> Should we restrict the shape parameter to be an integer instead of a float?
>
> http://projects.scipy.org/scipy/ticket/1647
>
> Let's ask the users:
>
> Does anyone want an exception if the shape parameter is not an integer?
> Is there a demand or use case for estimating the shape parameter as an
> integer instead of a float?
>
> right now erlang and gamma are essentially the same, as far as I can see
>
> Josef
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user


From cp3028 at googlemail.com  Fri May  4 16:57:07 2012
From: cp3028 at googlemail.com (cp3028)
Date: Fri, 4 May 2012 13:57:07 -0700 (PDT)
Subject: [SciPy-User] Using Scipy Sparse Matrices and Weave Inline possible?
Message-ID: <71ebd09a-6bdb-4247-8659-db0e1c179fc6@n1g2000vby.googlegroups.com>

Hello Everyone,

I have to assemble a very large sparse matrix from small dense
matrices. Is there a way to use weave.inlne to speed up the process?
The problem is, the entries of the small dense matrices are
distributed very irregular on the large sparse matrice. Until now I
loop over all dense matrices and every entry and assign it to the
corresponding position in the sparse large matrice through a lookup
table.

Is there a faster way?

Cheers!



From klonuo at gmail.com  Sun May  6 02:39:20 2012
From: klonuo at gmail.com (klo uo)
Date: Sun, 6 May 2012 08:39:20 +0200
Subject: [SciPy-User] ndimage/morphology - binary dilation and erosion?
In-Reply-To: <3847715.zaxhO1o2BG@rabbit>
References: <CAA-8Ld-u-WDE=0ahAXnYZndm_=r7te_Bc-T5F5eivk=LvWmQHA@mail.gmail.com>
	<CAEym_Hq-wHXVe+Q9LcdqeB4L66pCAaZSp0Mh1dTfemLDqy9oog@mail.gmail.com>
	<CAA-8Ld_mf99UuioRq2YgM0Y-AwTcoaGyN4FPzQqF6+s3PZsJEQ@mail.gmail.com>
	<3847715.zaxhO1o2BG@rabbit>
Message-ID: <CAA-8Ld-HRH+XCbcVp+BC+sJeM9-VQkqtpDRFG2BpTDOg3cZFXg@mail.gmail.com>

Hi Luis,

a bit prolonged reply, but as I remembered this messages thought to
maybe get to you :)

I wanted to try next on, with "skeleton-ing" some images, but
"ndimage" doesn't have this function AFAIK. Then I googled and pointed
to "pymorph" which I assume you are the author.

So I'm curious, why did you recommended "mohotas", when "pymorph"
seems like made for morphological analysis?


Regards,
klo

On Mon, Apr 2, 2012 at 6:25 PM, Luis Pedro Coelho <lpc at cmu.edu> wrote:
> On Saturday, March 31, 2012 08:08:41 PM klo uo wrote:
>> ?I tried grey opening on sample image with both modules. Approach seems
>> good and result is bit identical with both modules (footprint=square(3)),
>> and I thought to comment on differences on both modules:
>>
>> ?- skimage requires converting data type to 'uint8' and won't accept
>> anything less
>> ?- ndimage grey opening is 3 times faster on my PC
>
> Mahotas (which I wrote): http://luispedro.org/software/mahotas is closer in
> implementation to ndimage and should be as fast (as well as supporting
> multiple types).
>
> It doesn't have the open() operation, but you can dilate() & erode() yourself:
>
> def open(f, Bc, output=None):
> ? ? ? output = mahotas.dilate(f, Bc, output=output)
> ? ? ? return mahotas.erode(f, Bc, output=output)
>
> (Also, I think that the skimage erode() & dilate() are for flat structuring
> elements only, but that doesn't seem to be an issue for you).
>
> HTH,
> --
> Luis Pedro Coelho | Institute for Molecular Medicine | http://luispedro.org
>


From klonuo at gmail.com  Sun May  6 02:47:29 2012
From: klonuo at gmail.com (klo uo)
Date: Sun, 6 May 2012 08:47:29 +0200
Subject: [SciPy-User] ndimage/morphology - binary dilation and erosion?
In-Reply-To: <CAA-8Ld-HRH+XCbcVp+BC+sJeM9-VQkqtpDRFG2BpTDOg3cZFXg@mail.gmail.com>
References: <CAA-8Ld-u-WDE=0ahAXnYZndm_=r7te_Bc-T5F5eivk=LvWmQHA@mail.gmail.com>
	<CAEym_Hq-wHXVe+Q9LcdqeB4L66pCAaZSp0Mh1dTfemLDqy9oog@mail.gmail.com>
	<CAA-8Ld_mf99UuioRq2YgM0Y-AwTcoaGyN4FPzQqF6+s3PZsJEQ@mail.gmail.com>
	<3847715.zaxhO1o2BG@rabbit>
	<CAA-8Ld-HRH+XCbcVp+BC+sJeM9-VQkqtpDRFG2BpTDOg3cZFXg@mail.gmail.com>
Message-ID: <CAA-8Ld8QTH2O3jrNHFn6m+0vAs+yBxF6uaYpxJLcU74popipHg@mail.gmail.com>

> So I'm curious, why did you recommended "mohotas", when "pymorph"
> seems like made for morphological analysis?

Just couple of minutes later, I read this
(http://luispedro.org/software/pymorph#see-also):

"mahotas is another Python image processing package, but most of its
functions are in C++. It is, therefore, much faster for the operations
it supports."

So I assume that will be the answer ;)


From klonuo at gmail.com  Sun May  6 04:49:19 2012
From: klonuo at gmail.com (klo uo)
Date: Sun, 6 May 2012 10:49:19 +0200
Subject: [SciPy-User] ndimage/morphology - binary dilation and erosion?
In-Reply-To: <CAA-8Ld8QTH2O3jrNHFn6m+0vAs+yBxF6uaYpxJLcU74popipHg@mail.gmail.com>
References: <CAA-8Ld-u-WDE=0ahAXnYZndm_=r7te_Bc-T5F5eivk=LvWmQHA@mail.gmail.com>
	<CAEym_Hq-wHXVe+Q9LcdqeB4L66pCAaZSp0Mh1dTfemLDqy9oog@mail.gmail.com>
	<CAA-8Ld_mf99UuioRq2YgM0Y-AwTcoaGyN4FPzQqF6+s3PZsJEQ@mail.gmail.com>
	<3847715.zaxhO1o2BG@rabbit>
	<CAA-8Ld-HRH+XCbcVp+BC+sJeM9-VQkqtpDRFG2BpTDOg3cZFXg@mail.gmail.com>
	<CAA-8Ld8QTH2O3jrNHFn6m+0vAs+yBxF6uaYpxJLcU74popipHg@mail.gmail.com>
Message-ID: <CAA-8Ld-BRqbkiZejzSNYc4YHPk4x3LJKZa9NtcKRS7TnBNdKmw@mail.gmail.com>

> (http://luispedro.org/software/pymorph#see-also):
>
> "mahotas is another Python image processing package, but most of its
> functions are in C++. It is, therefore, much faster for the operations
> it supports."
>

Skeletonization with "pymorph" is indeed very very slow

I found very nice example how to do this just with "ndimage" and in a
blink of a second time:

========================================
def sk(i, r):
    x = ndimage.distance_transform_edt(i)
    y = ndimage.morphological_laplace(x, (r, r))
    return y < y.min()/2
========================================

For example in pylab mode:

In [1]: from scipy import ndimage
In [2]: im = imread('clip.png')[:,:,0]
In [3]: imshow(sk(im, 5), cmap='binary', interpolation='nearest')


From tsyu80 at gmail.com  Sun May  6 08:24:53 2012
From: tsyu80 at gmail.com (Tony Yu)
Date: Sun, 6 May 2012 08:24:53 -0400
Subject: [SciPy-User] ndimage/morphology - binary dilation and erosion?
In-Reply-To: <CAA-8Ld-BRqbkiZejzSNYc4YHPk4x3LJKZa9NtcKRS7TnBNdKmw@mail.gmail.com>
References: <CAA-8Ld-u-WDE=0ahAXnYZndm_=r7te_Bc-T5F5eivk=LvWmQHA@mail.gmail.com>
	<CAEym_Hq-wHXVe+Q9LcdqeB4L66pCAaZSp0Mh1dTfemLDqy9oog@mail.gmail.com>
	<CAA-8Ld_mf99UuioRq2YgM0Y-AwTcoaGyN4FPzQqF6+s3PZsJEQ@mail.gmail.com>
	<3847715.zaxhO1o2BG@rabbit>
	<CAA-8Ld-HRH+XCbcVp+BC+sJeM9-VQkqtpDRFG2BpTDOg3cZFXg@mail.gmail.com>
	<CAA-8Ld8QTH2O3jrNHFn6m+0vAs+yBxF6uaYpxJLcU74popipHg@mail.gmail.com>
	<CAA-8Ld-BRqbkiZejzSNYc4YHPk4x3LJKZa9NtcKRS7TnBNdKmw@mail.gmail.com>
Message-ID: <CAEym_HpvCMuc7kN8h_tEuMAUVdGLW9R2MUYCU+L+76S8g0AYSQ@mail.gmail.com>

On Sun, May 6, 2012 at 4:49 AM, klo uo <klonuo at gmail.com> wrote:

> > (http://luispedro.org/software/pymorph#see-also):
> >
> > "mahotas is another Python image processing package, but most of its
> > functions are in C++. It is, therefore, much faster for the operations
> > it supports."
> >
>
> Skeletonization with "pymorph" is indeed very very slow
>
> I found very nice example how to do this just with "ndimage" and in a
> blink of a second time:
>
> ========================================
> def sk(i, r):
>    x = ndimage.distance_transform_edt(i)
>    y = ndimage.morphological_laplace(x, (r, r))
>    return y < y.min()/2
> ========================================
>
> For example in pylab mode:
>
> In [1]: from scipy import ndimage
> In [2]: im = imread('clip.png')[:,:,0]
> In [3]: imshow(sk(im, 5), cmap='binary', interpolation='nearest')
> _______________________________________________
>
>
There's also a couple of skeletonize functions in scikits-image:
skeletonize<http://scikits-image.org/docs/dev/auto_examples/plot_skeleton.html>
and
medial_axis<http://scikits-image.org/docs/dev/auto_examples/plot_medial_transform.html>.
But I'm not sure how the performance compares to the other solutions you've
found.

Cheers,
-Tony
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From josef.pktd at gmail.com  Sun May  6 10:46:38 2012
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sun, 6 May 2012 10:46:38 -0400
Subject: [SciPy-User] scipy.stats.distributions.erlang - "boring"
 consensus building in a ticket
In-Reply-To: <CAK_GA2EwpnSx05TS5HZQgHbSQEPULig6VtzOd+qq0WZOW78B5w@mail.gmail.com>
References: <CAMMTP+AwoWP3akZfaAGtEuEq+2my-uNb4n2grrDtajQ0AMvXqQ@mail.gmail.com>
	<CAK_GA2EwpnSx05TS5HZQgHbSQEPULig6VtzOd+qq0WZOW78B5w@mail.gmail.com>
Message-ID: <CAMMTP+CgJaNMFK=FL=W2RJDTvy7roeHgL-=vQzj4KOmgbPUbVA@mail.gmail.com>

On Sat, May 5, 2012 at 5:59 PM, nicky van foreest <vanforeest at gmail.com> wrote:
> Hi,
>
> I just looked through the discussion in the ticket. Both sides (1: the
> scale k should be an int, 2) allow k to be a float) make sense. From
> my background in queueing I am inclined to say that k should be
> restricted to the integers as in queueing theory the Erlang-k
> distribution is used to model some distribution for which k can only
> be an int. On the other hand, I am unsure whether a user of the Erlang
> distribution should be protected from filling in a float. In all (?)
> books on queueing and probability it is written that the Erlang
> distribution is a special case of the gamma distribution, so users of
> the Erlang distribution should know this (.... kind of, hopefully).
>
> >From an implementation point of view I think that aliasing Erlang to
> the gamma distribution makes good sense, and I don't believe that the
> users or the Erlang distribution require to be protected against
> filling in floats. Perhaps a sentence in the docstring of the Erlang
> distribution that it is an alias of the gamma distribution, hence does
> not check on the scale being an int, will prevent some potential
> misuse.

Thanks for the comments.

Do you think it would be useful to have fit() restrict to integers?

I guess currently nobody uses erlang.fit() because, at least in the
example, it doesn't work with default parameters.

import numpy as np
from scipy import stats

#add fitstart to erlang, otherwise it doesn't work
stats.erlang._fitstart = stats.gamma._fitstart

np.random.seed(876589)
rvs = stats.erlang.rvs(5, size=500)

for dist in [stats.erlang, stats.gamma]:
    print '\n', dist.name
    p0 = dist.fit(rvs)
    print stats.gamma.nnlf(p0, rvs), p0
    print

    for k in range(10):
        p = dist.fit(rvs, f0=k)
        print dist.nnlf(p, rvs), p

Josef

>
> Nicky
>
>
>
> On 5 May 2012 22:20, ?<josef.pktd at gmail.com> wrote:
>> Should we restrict the shape parameter to be an integer instead of a float?
>>
>> http://projects.scipy.org/scipy/ticket/1647
>>
>> Let's ask the users:
>>
>> Does anyone want an exception if the shape parameter is not an integer?
>> Is there a demand or use case for estimating the shape parameter as an
>> integer instead of a float?
>>
>> right now erlang and gamma are essentially the same, as far as I can see
>>
>> Josef
>> _______________________________________________
>> 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 klonuo at gmail.com  Sun May  6 11:31:29 2012
From: klonuo at gmail.com (klo uo)
Date: Sun, 6 May 2012 17:31:29 +0200
Subject: [SciPy-User] ndimage/morphology - binary dilation and erosion?
In-Reply-To: <CAEym_HpvCMuc7kN8h_tEuMAUVdGLW9R2MUYCU+L+76S8g0AYSQ@mail.gmail.com>
References: <CAA-8Ld-u-WDE=0ahAXnYZndm_=r7te_Bc-T5F5eivk=LvWmQHA@mail.gmail.com>
	<CAEym_Hq-wHXVe+Q9LcdqeB4L66pCAaZSp0Mh1dTfemLDqy9oog@mail.gmail.com>
	<CAA-8Ld_mf99UuioRq2YgM0Y-AwTcoaGyN4FPzQqF6+s3PZsJEQ@mail.gmail.com>
	<3847715.zaxhO1o2BG@rabbit>
	<CAA-8Ld-HRH+XCbcVp+BC+sJeM9-VQkqtpDRFG2BpTDOg3cZFXg@mail.gmail.com>
	<CAA-8Ld8QTH2O3jrNHFn6m+0vAs+yBxF6uaYpxJLcU74popipHg@mail.gmail.com>
	<CAA-8Ld-BRqbkiZejzSNYc4YHPk4x3LJKZa9NtcKRS7TnBNdKmw@mail.gmail.com>
	<CAEym_HpvCMuc7kN8h_tEuMAUVdGLW9R2MUYCU+L+76S8g0AYSQ@mail.gmail.com>
Message-ID: <CAA-8Ld9CREAQa4pR2iZNEkVLXCtaAe8hDx+vNM7qR_qgiu-wtg@mail.gmail.com>

> There's also a couple of skeletonize functions in scikits-image:
> skeletonize?and medial_axis. But I'm not sure how the performance compares
> to the other solutions you've found.

Well, it can't be compared :)
"skimage" skeletonize() executes almost instantly. There must be
something wrong "pymorph" skelm() other then requirement to convert
numpy array in boolean dtype ;)

It just showed first in Google, I should have tried harder and check skimage

Thanks Tony


From david_baddeley at yahoo.com.au  Sun May  6 18:03:33 2012
From: david_baddeley at yahoo.com.au (David Baddeley)
Date: Sun, 6 May 2012 15:03:33 -0700 (PDT)
Subject: [SciPy-User] scipy.stats.distributions.erlang - "boring"
	consensus building in a ticket
In-Reply-To: <CAMMTP+CgJaNMFK=FL=W2RJDTvy7roeHgL-=vQzj4KOmgbPUbVA@mail.gmail.com>
References: <CAMMTP+AwoWP3akZfaAGtEuEq+2my-uNb4n2grrDtajQ0AMvXqQ@mail.gmail.com>
	<CAK_GA2EwpnSx05TS5HZQgHbSQEPULig6VtzOd+qq0WZOW78B5w@mail.gmail.com>
	<CAMMTP+CgJaNMFK=FL=W2RJDTvy7roeHgL-=vQzj4KOmgbPUbVA@mail.gmail.com>
Message-ID: <1336341813.68989.YahooMailNeo@web113410.mail.gq1.yahoo.com>

I don't use the Erlang distribution myself, but would be curious to know how you would go about restricting the scale parameter to being an integer when doing the fit. This kind of restriction sound like it could be useful in a number of different problems, but I can't think of any easy way to do it (other than to use some form of brute force optimisation over the integer parameter).

cheers,
David


________________________________
 From: "josef.pktd at gmail.com" <josef.pktd at gmail.com>
To: SciPy Users List <scipy-user at scipy.org> 
Sent: Monday, 7 May 2012 2:46 AM
Subject: Re: [SciPy-User] scipy.stats.distributions.erlang - "boring" consensus building in a ticket
 
On Sat, May 5, 2012 at 5:59 PM, nicky van foreest <vanforeest at gmail.com> wrote:
> Hi,
>
> I just looked through the discussion in the ticket. Both sides (1: the
> scale k should be an int, 2) allow k to be a float) make sense. From
> my background in queueing I am inclined to say that k should be
> restricted to the integers as in queueing theory the Erlang-k
> distribution is used to model some distribution for which k can only
> be an int. On the other hand, I am unsure whether a user of the Erlang
> distribution should be protected from filling in a float. In all (?)
> books on queueing and probability it is written that the Erlang
> distribution is a special case of the gamma distribution, so users of
> the Erlang distribution should know this (.... kind of, hopefully).
>
> >From an implementation point of view I think that aliasing Erlang to
> the gamma distribution makes good sense, and I don't believe that the
> users or the Erlang distribution require to be protected against
> filling in floats. Perhaps a sentence in the docstring of the Erlang
> distribution that it is an alias of the gamma distribution, hence does
> not check on the scale being an int, will prevent some potential
> misuse.

Thanks for the comments.

Do you think it would be useful to have fit() restrict to integers?

I guess currently nobody uses erlang.fit() because, at least in the
example, it doesn't work with default parameters.

import numpy as np
from scipy import stats

#add fitstart to erlang, otherwise it doesn't work
stats.erlang._fitstart = stats.gamma._fitstart

np.random.seed(876589)
rvs = stats.erlang.rvs(5, size=500)

for dist in [stats.erlang, stats.gamma]:
? ? print '\n', dist.name
? ? p0 = dist.fit(rvs)
? ? print stats.gamma.nnlf(p0, rvs), p0
? ? print

? ? for k in range(10):
? ? ? ? p = dist.fit(rvs, f0=k)
? ? ? ? print dist.nnlf(p, rvs), p

Josef

>
> Nicky
>
>
>
> On 5 May 2012 22:20, ?<josef.pktd at gmail.com> wrote:
>> Should we restrict the shape parameter to be an integer instead of a float?
>>
>> http://projects.scipy.org/scipy/ticket/1647
>>
>> Let's ask the users:
>>
>> Does anyone want an exception if the shape parameter is not an integer?
>> Is there a demand or use case for estimating the shape parameter as an
>> integer instead of a float?
>>
>> right now erlang and gamma are essentially the same, as far as I can see
>>
>> Josef
>> _______________________________________________
>> 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 josef.pktd at gmail.com  Sun May  6 18:20:08 2012
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sun, 6 May 2012 18:20:08 -0400
Subject: [SciPy-User] scipy.stats.distributions.erlang - "boring"
 consensus building in a ticket
In-Reply-To: <1336341813.68989.YahooMailNeo@web113410.mail.gq1.yahoo.com>
References: <CAMMTP+AwoWP3akZfaAGtEuEq+2my-uNb4n2grrDtajQ0AMvXqQ@mail.gmail.com>
	<CAK_GA2EwpnSx05TS5HZQgHbSQEPULig6VtzOd+qq0WZOW78B5w@mail.gmail.com>
	<CAMMTP+CgJaNMFK=FL=W2RJDTvy7roeHgL-=vQzj4KOmgbPUbVA@mail.gmail.com>
	<1336341813.68989.YahooMailNeo@web113410.mail.gq1.yahoo.com>
Message-ID: <CAMMTP+BNzMdAjh601Qfu16Uho8x_FcVX3WMLb5gRS2Ne2N1USQ@mail.gmail.com>

On Sun, May 6, 2012 at 6:03 PM, David Baddeley
<david_baddeley at yahoo.com.au> wrote:
> I don't use the Erlang distribution myself, but would be curious to know how
> you would go about restricting the scale parameter to being an integer when
> doing the fit. This kind of restriction sound like it could be useful in a
> number of different problems, but I can't think of any easy way to do it
> (other than to use some form of brute force optimisation over the integer
> parameter).

As in my previous example, I see two possibilities

1) two step optimization, with grid search on the outside and
continuous fit for fixed integer parameter on the inside. This works
now that constraint fitting is working correctly.
2) run continuous parameter optimization, given that the function is
defined on the real line/space, and then go to 1) in the integer
neighborhood of the continuous result

(other possibility go for openopt or similar with mixed integer
programming. I have no idea and never tried)

My guess  wabout whether 1) or 2) is faster depends on how large the
possible range of integers to search in is, and how nice the curvature
is to use fmin_bfgs for example.

Cheers,

Josef

>
> cheers,
> David
>
> ________________________________
> From: "josef.pktd at gmail.com" <josef.pktd at gmail.com>
> To: SciPy Users List <scipy-user at scipy.org>
> Sent: Monday, 7 May 2012 2:46 AM
> Subject: Re: [SciPy-User] scipy.stats.distributions.erlang - "boring"
> consensus building in a ticket
>
> On Sat, May 5, 2012 at 5:59 PM, nicky van foreest <vanforeest at gmail.com>
> wrote:
>> Hi,
>>
>> I just looked through the discussion in the ticket. Both sides (1: the
>> scale k should be an int, 2) allow k to be a float) make sense. From
>> my background in queueing I am inclined to say that k should be
>> restricted to the integers as in queueing theory the Erlang-k
>> distribution is used to model some distribution for which k can only
>> be an int. On the other hand, I am unsure whether a user of the Erlang
>> distribution should be protected from filling in a float. In all (?)
>> books on queueing and probability it is written that the Erlang
>> distribution is a special case of the gamma distribution, so users of
>> the Erlang distribution should know this (.... kind of, hopefully).
>>
>> >From an implementation point of view I think that aliasing Erlang to
>> the gamma distribution makes good sense, and I don't believe that the
>> users or the Erlang distribution require to be protected against
>> filling in floats. Perhaps a sentence in the docstring of the Erlang
>> distribution that it is an alias of the gamma distribution, hence does
>> not check on the scale being an int, will prevent some potential
>> misuse.
>
> Thanks for the comments.
>
> Do you think it would be useful to have fit() restrict to integers?
>
> I guess currently nobody uses erlang.fit() because, at least in the
> example, it doesn't work with default parameters.
>
> import numpy as np
> from scipy import stats
>
> #add fitstart to erlang, otherwise it doesn't work
> stats.erlang._fitstart = stats.gamma._fitstart
>
> np.random.seed(876589)
> rvs = stats.erlang.rvs(5, size=500)
>
> for dist in [stats.erlang, stats.gamma]:
> ? ? print '\n', dist.name
> ? ? p0 = dist.fit(rvs)
> ? ? print stats.gamma.nnlf(p0, rvs), p0
> ? ? print
>
> ? ? for k in range(10):
> ? ? ? ? p = dist.fit(rvs, f0=k)
> ? ? ? ? print dist.nnlf(p, rvs), p
>
> Josef
>
>>
>> Nicky
>>
>>
>>
>> On 5 May 2012 22:20, ?<josef.pktd at gmail.com> wrote:
>>> Should we restrict the shape parameter to be an integer instead of a
>>> float?
>>>
>>> http://projects.scipy.org/scipy/ticket/1647
>>>
>>> Let's ask the users:
>>>
>>> Does anyone want an exception if the shape parameter is not an integer?
>>> Is there a demand or use case for estimating the shape parameter as an
>>> integer instead of a float?
>>>
>>> right now erlang and gamma are essentially the same, as far as I can see
>>>
>>> Josef
>>> _______________________________________________
>>> 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 lpc at cmu.edu  Sun May  6 18:21:12 2012
From: lpc at cmu.edu (Luis Pedro Coelho)
Date: Sun, 06 May 2012 23:21:12 +0100
Subject: [SciPy-User] ndimage/morphology - binary dilation and erosion?
In-Reply-To: <CAA-8Ld9CREAQa4pR2iZNEkVLXCtaAe8hDx+vNM7qR_qgiu-wtg@mail.gmail.com>
References: <CAA-8Ld-u-WDE=0ahAXnYZndm_=r7te_Bc-T5F5eivk=LvWmQHA@mail.gmail.com>
	<CAEym_HpvCMuc7kN8h_tEuMAUVdGLW9R2MUYCU+L+76S8g0AYSQ@mail.gmail.com>
	<CAA-8Ld9CREAQa4pR2iZNEkVLXCtaAe8hDx+vNM7qR_qgiu-wtg@mail.gmail.com>
Message-ID: <3305104.yBzU5AVMy4@rabbit>

On Sunday, May 06, 2012 05:31:29 PM klo uo wrote:
> > There's also a couple of skeletonize functions in scikits-image:
> > skeletonize and medial_axis. But I'm not sure how the performance compares
> > to the other solutions you've found.
> 
> Well, it can't be compared :)
> "skimage" skeletonize() executes almost instantly. There must be
> something wrong "pymorph" skelm() other then requirement to convert
> numpy array in boolean dtype ;)

There is nothing wrong, it's just Python instead of compiled code.

I maintain both mahotas and pymorph and, increasingly, there is little point 
to using pymorph. Mahotas is starting to do it all and it does it much, much, 
faster.

Mahotas has thin() which is a really well-tuned implementation.

> It just showed first in Google, I should have tried harder and check skimage

If you're interested, check out the pythonvision google group for more 
computer vision related questions.


https://groups.google.com/forum/#!forum/pythonvision
-- 
Luis Pedro Coelho | Institute for Molecular Medicine | http://luispedro.org
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From vanforeest at gmail.com  Mon May  7 07:13:09 2012
From: vanforeest at gmail.com (nicky van foreest)
Date: Mon, 7 May 2012 13:13:09 +0200
Subject: [SciPy-User] scipy.stats.distributions.erlang - "boring"
 consensus building in a ticket
In-Reply-To: <CAMMTP+CgJaNMFK=FL=W2RJDTvy7roeHgL-=vQzj4KOmgbPUbVA@mail.gmail.com>
References: <CAMMTP+AwoWP3akZfaAGtEuEq+2my-uNb4n2grrDtajQ0AMvXqQ@mail.gmail.com>
	<CAK_GA2EwpnSx05TS5HZQgHbSQEPULig6VtzOd+qq0WZOW78B5w@mail.gmail.com>
	<CAMMTP+CgJaNMFK=FL=W2RJDTvy7roeHgL-=vQzj4KOmgbPUbVA@mail.gmail.com>
Message-ID: <CAK_GA2FT_j7RGUT4MGu0iWVVBN8qCDjJ4r4bVhT7Q9NhQWdZ0Q@mail.gmail.com>

> Do you think it would be useful to have fit() restrict to integers?
>
> I guess currently nobody uses erlang.fit() because, at least in the
> example, it doesn't work with default parameters.
>
> import numpy as np
> from scipy import stats
>
> #add fitstart to erlang, otherwise it doesn't work
> stats.erlang._fitstart = stats.gamma._fitstart
>
> np.random.seed(876589)
> rvs = stats.erlang.rvs(5, size=500)
>
> for dist in [stats.erlang, stats.gamma]:
> ? ?print '\n', dist.name
> ? ?p0 = dist.fit(rvs)
> ? ?print stats.gamma.nnlf(p0, rvs), p0
> ? ?print
>
> ? ?for k in range(10):
> ? ? ? ?p = dist.fit(rvs, f0=k)
> ? ? ? ?print dist.nnlf(p, rvs), p

I think it returning an int is here to be exptected by an Erlang user.
Hence, yes.

I ran the above code, but got some warnings:

Warning: invalid value encountered in subtract

I did not pursue the origin of this though.

>
> Josef
>
>>
>> Nicky
>>
>>
>>
>> On 5 May 2012 22:20, ?<josef.pktd at gmail.com> wrote:
>>> Should we restrict the shape parameter to be an integer instead of a float?
>>>
>>> http://projects.scipy.org/scipy/ticket/1647
>>>
>>> Let's ask the users:
>>>
>>> Does anyone want an exception if the shape parameter is not an integer?
>>> Is there a demand or use case for estimating the shape parameter as an
>>> integer instead of a float?
>>>
>>> right now erlang and gamma are essentially the same, as far as I can see
>>>
>>> Josef
>>> _______________________________________________
>>> 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 ralf.gommers at googlemail.com  Mon May  7 12:03:23 2012
From: ralf.gommers at googlemail.com (Ralf Gommers)
Date: Mon, 7 May 2012 18:03:23 +0200
Subject: [SciPy-User] scipy.sparse.csgraph merged
Message-ID: <CABL7CQjsCGcAtPi1cbfZvdj8R-rKJRuqwn_hgCQhW8siyvhG8g@mail.gmail.com>

Hi,

I finally merged https://github.com/scipy/scipy/pull/119, the sparse graph
algorithms module written by Jake Vanderplas. This will certainly by one of
the highlights of the 0.11 release (which shouldn't be too far off). Thanks
again to Jake for all the work he put in.

Ralf
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From moise at aims.ac.za  Tue May  8 07:53:54 2012
From: moise at aims.ac.za (=?ISO-8859-1?Q?Guy_Mo=EFse_Dongho_Nguimdo?=)
Date: Tue, 8 May 2012 13:53:54 +0200
Subject: [SciPy-User] scipy.integrate.dblquad with variable boundary
Message-ID: <CADKn56hOXgGbcMNCotYwNfn5ke9JiHacjn3kpLZYPsL_aq0pag@mail.gmail.com>

I am trying to use dblquad to do double integration, but one of the
boundaries of the integrale is also a variable.
This is the integral: $\int^b_a \int^y_c f(x,y) dydx$
May some one help?
Thanks

-- 
GM DONGHO NGUIMDO
---------------------------------------------------------------------------------------------------------------------------
"When it is obvious that the goals cannot be reached, don't adjust the
goals, adjust the action steps." *-Confucius*
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From cynthia.crooks at bp.com  Tue May  8 15:25:38 2012
From: cynthia.crooks at bp.com (Crooks, Cynthia J)
Date: Tue, 8 May 2012 20:25:38 +0100
Subject: [SciPy-User] Linux compile questions for SciPy
Message-ID: <829E7BF743AC4E48932E140E9F36219003FFAB96@BP1XEUEX054-C.bp1.ad.bp.com>

I am trying to compile SciPy for our Linux systems.  I have been able to
compile NumPy  and ATLAS.  These have been installed in user specified
directories, /hpc/soft/NumPy and /hpc/soft/atlas, using the -prefix
option.    When I try to build SciPy it seems to be picking up an older
version of numpy installed in the system directories.  How do I point
the SciPy build to look for the version I have compiled?

OS	SLES SP 1

uname -a 	Linux hpcp8002 2.6.32.12-0.7-default #1 SMP 2010-05-20
11:14:20 +0200 x86_64 x86_64 x86_64 GNU/Linux

numpy version	1.6.1	**The version I compiled
numpy version 1.3.0	**Version install in system directories
scipy version 0.10.1
atlas version 3.9.74
python version 2.6

I have attached the screen output from the scipy build attempt.

Any help would be appreciated.  Thank you.

Cindy <<scipy.build.log>> 

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From lists at hilboll.de  Wed May  9 03:18:27 2012
From: lists at hilboll.de (Andreas H.)
Date: Wed, 09 May 2012 09:18:27 +0200
Subject: [SciPy-User] Linux compile questions for SciPy
In-Reply-To: <829E7BF743AC4E48932E140E9F36219003FFAB96@BP1XEUEX054-C.bp1.ad.bp.com>
References: <829E7BF743AC4E48932E140E9F36219003FFAB96@BP1XEUEX054-C.bp1.ad.bp.com>
Message-ID: <4FAA1A43.8010105@hilboll.de>

Am Di 08 Mai 2012 21:25:38 CEST schrieb Crooks, Cynthia J:
> I am trying to compile SciPy for our Linux systems.  I have been able
> to compile NumPy  and ATLAS.  These have been installed in user
> specified directories, /hpc/soft/NumPy and /hpc/soft/atlas, using the
> ?prefix option.   When I try to build SciPy it seems to be picking up
> an older version of numpy installed in the system directories.  How do
> I point the SciPy build to look for the version I have compiled?
>
> OS      SLESSP 1
>
> uname ?a       Linux hpcp8002 2.6.32.12-0.7-default #1 SMP 2010-05-20
> 11:14:20 +0200 x86_64 x86_64 x86_64 GNU/Linux
>
> numpy version  1.6.1   **The version I compiled
>
> numpy version 1.3.0     **Version install in system directories
>
> scipy version 0.10.1
>
> atlas version3.9.74
>
> python version 2.6
>
> I have attached the screen output from the scipy build attempt.
>
> Any help would be appreciated.  Thank you.
>
> Cindy<<scipy.build.log>>
>
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user

Make sure your PYTHONPATH environment variable is set to include the 
site-packages folder from /hpc/soft/NumPy/ before the system-wide 
site-packages.

Andreas.


From matheusmacedof at hotmail.com  Sun May  6 18:49:59 2012
From: matheusmacedof at hotmail.com (=?utf-8?B?TWF0aGV1cyBNYWNlZMOY?=)
Date: Sun, 6 May 2012 19:49:59 -0300
Subject: [SciPy-User]  Python 2.7 / 64-bit
In-Reply-To: BANLkTikLa-+S0hHHd4NuDvWyk-NK+shY6A@mail.gmail.com
Message-ID: <COL110-DS902A889578CB82DF4FCA5DB110@phx.gbl>

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From matheusmacedof at hotmail.com  Sun May  6 18:52:50 2012
From: matheusmacedof at hotmail.com (=?utf-8?B?TWF0aGV1cyBNYWNlZMOY?=)
Date: Sun, 6 May 2012 19:52:50 -0300
Subject: [SciPy-User]  Python 2.7 / 64-bit
In-Reply-To: BANLkTikLa-+S0hHHd4NuDvWyk-NK+shY6A@mail.gmail.com
Message-ID: <COL110-DS993C4B042CFB20020BC68DB110@phx.gbl>

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From ralf.gommers at googlemail.com  Wed May  9 15:01:05 2012
From: ralf.gommers at googlemail.com (Ralf Gommers)
Date: Wed, 9 May 2012 21:01:05 +0200
Subject: [SciPy-User] Using Scipy Sparse Matrices and Weave Inline
	possible?
In-Reply-To: <71ebd09a-6bdb-4247-8659-db0e1c179fc6@n1g2000vby.googlegroups.com>
References: <71ebd09a-6bdb-4247-8659-db0e1c179fc6@n1g2000vby.googlegroups.com>
Message-ID: <CABL7CQgyc1+GO7SBSJXCaA30CU9+1XwsJ+1dgvBkyO9+=_HH1A@mail.gmail.com>

On Fri, May 4, 2012 at 10:57 PM, cp3028 <cp3028 at googlemail.com> wrote:

> Hello Everyone,
>
> I have to assemble a very large sparse matrix from small dense
> matrices. Is there a way to use weave.inlne to speed up the process?
> The problem is, the entries of the small dense matrices are
> distributed very irregular on the large sparse matrice. Until now I
> loop over all dense matrices and every entry and assign it to the
> corresponding position in the sparse large matrice through a lookup
> table.
>
> Is there a faster way?
>

I don't have a faster way, but do want to recommend not using weave for any
new code you're writing (because it's unmaintained). Better to use
cython.inline if you can.

Ralf
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From sturla at molden.no  Thu May 10 09:39:30 2012
From: sturla at molden.no (Sturla Molden)
Date: Thu, 10 May 2012 15:39:30 +0200
Subject: [SciPy-User] OT: Python and C syntax highlighting with LaTeX
Message-ID: <4FABC512.2000702@molden.no>

I know this is slightly OT on the scipy list, but it applies to 
scientific computing nevertheless.

Do you have suggesting for a LaTeX package to do consistent syntax 
highlighting of Python and C (preferably C99)?

Colors are ok, but I want C and Python to look about the same style wise.


Regards,
Sturla







From alan.isaac at gmail.com  Thu May 10 10:07:40 2012
From: alan.isaac at gmail.com (Alan G Isaac)
Date: Thu, 10 May 2012 10:07:40 -0400
Subject: [SciPy-User] OT: Python and C syntax highlighting with LaTeX
In-Reply-To: <4FABC512.2000702@molden.no>
References: <4FABC512.2000702@molden.no>
Message-ID: <4FABCBAC.7050402@gmail.com>

On 5/10/2012 9:39 AM, Sturla Molden wrote:
> a LaTeX package to do consistent syntax
> highlighting of Python and C (preferably C99)


Is the ``listings`` package not adequate?
http://get-software.net/macros/latex/contrib/listings/listings.pdf

Alan Isaac


From scott.sinclair.za at gmail.com  Thu May 10 10:18:05 2012
From: scott.sinclair.za at gmail.com (Scott Sinclair)
Date: Thu, 10 May 2012 16:18:05 +0200
Subject: [SciPy-User] OT: Python and C syntax highlighting with LaTeX
In-Reply-To: <4FABC512.2000702@molden.no>
References: <4FABC512.2000702@molden.no>
Message-ID: <CA+nsYDvu5cZhBBBO7Sta5Nc28AUv6OA5p=ufUiqNC6OyhMQy3Q@mail.gmail.com>

On 10 May 2012 15:39, Sturla Molden <sturla at molden.no> wrote:
> Do you have suggesting for a LaTeX package to do consistent syntax
> highlighting of Python and C (preferably C99)?

I've had some nice results with the minted package
(http://tug.ctan.org/pkg/minted).

Cheers,
Scott


From josef.pktd at gmail.com  Thu May 10 20:04:44 2012
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Thu, 10 May 2012 20:04:44 -0400
Subject: [SciPy-User] why do the discrete distributions not have a `fit`?
Message-ID: <CAMMTP+AUuv3LJjt59twnmOdKmnjG0UcndE3UoLwn_hxZ=a+2vw@mail.gmail.com>

Why do the discrete distributions not have a `fit` method like the
continuous distributions?

currently it's a bug in the documentation
http://projects.scipy.org/scipy/ticket/1659

in statsmodels, we fit several of the discrete distributions.

How about discrete parameters?   (in analogy to the erlang discussion)

hypergeom is based on a story about marbles or balls
http://en.wikipedia.org/wiki/Hypergeometric_distribution#Application_and_example
but why should we care, it's just a discrete distribution with 3 shape
parameters, isn't it?

fractional marbles ?

>>> nn = np.linspace(4.5, 8, 101)
>>> pmf = [stats.hypergeom.pmf(5, 10.8, n, 8.5) for n in nn]

>>> plt.plot(nn, pmf, '-o')
>>> plt.title("pmf of hypergeom as function of parameter n")

Doesn't look like there are any problems, and the likelihood function
is nicely concave.

conclusion: scipy.stats doesn't have a hypergeometric distribution,
but a generalized version that is defined on a real parameter space.

Josef
(so what's the point? Sorry, I was just getting distracted while
looking for `fit`.)
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From ben.root at ou.edu  Fri May 11 10:05:17 2012
From: ben.root at ou.edu (Benjamin Root)
Date: Fri, 11 May 2012 10:05:17 -0400
Subject: [SciPy-User] [Numpy-discussion] ANN: NumPy 1.6.2 release
	candidate 1
In-Reply-To: <CABL7CQju9HqYapdJGTb=TVpOHZn8J7wwzRROtKxW-JhawD6A2A@mail.gmail.com>
References: <CABL7CQju9HqYapdJGTb=TVpOHZn8J7wwzRROtKxW-JhawD6A2A@mail.gmail.com>
Message-ID: <CANNq6FkmEjFk9TpQqPMq46vDbRV0icEqy2+pU5=4E_BV_Chx8w@mail.gmail.com>

On Sat, May 5, 2012 at 2:15 PM, Ralf Gommers <ralf.gommers at googlemail.com>wrote:

> Hi,
>
> I'm pleased to announce the availability of the first release candidate of
> NumPy 1.6.2.  This is a maintenance release. Due to the delay of the NumPy
> 1.7.0, this release contains far more fixes than a regular NumPy bugfix
> release.  It also includes a number of documentation and build improvements.
>
> Sources and binary installers can be found at
> https://sourceforge.net/projects/numpy/files/NumPy/1.6.2rc1/
>
> Please test this release and report any issues on the numpy-discussion
> mailing list.
>
> Cheers,
> Ralf
>
>
>
> ``numpy.core`` issues fixed
> ---------------------------
>
> #2063  make unique() return consistent index
> #1138  allow creating arrays from empty buffers or empty slices
> #1446  correct note about correspondence vstack and concatenate
> #1149  make argmin() work for datetime
> #1672  fix allclose() to work for scalar inf
> #1747  make np.median() work for 0-D arrays
> #1776  make complex division by zero to yield inf properly
> #1675  add scalar support for the format() function
> #1905  explicitly check for NaNs in allclose()
> #1952  allow floating ddof in std() and var()
> #1948  fix regression for indexing chararrays with empty list
> #2017  fix type hashing
> #2046  deleting array attributes causes segfault
> #2033  a**2.0 has incorrect type
> #2045  make attribute/iterator_element deletions not segfault
> #2021  fix segfault in searchsorted()
> #2073  fix float16 __array_interface__ bug
>
>
> ``numpy.lib`` issues fixed
> --------------------------
>
> #2048  break reference cycle in NpzFile
> #1573  savetxt() now handles complex arrays
> #1387  allow bincount() to accept empty arrays
> #1899  fixed histogramdd() bug with empty inputs
> #1793  fix failing npyio test under py3k
> #1936  fix extra nesting for subarray dtypes
> #1848  make tril/triu return the same dtype as the original array
> #1918  use Py_TYPE to access ob_type, so it works also on Py3
>
>
> ``numpy.f2py`` changes
> ----------------------
>
> ENH:   Introduce new options extra_f77_compiler_args and
> extra_f90_compiler_args
> BLD:   Improve reporting of fcompiler value
> BUG:   Fix f2py test_kind.py test
>
>
> ``numpy.poly`` changes
> ----------------------
>
> ENH:   Add some tests for polynomial printing
> ENH:   Add companion matrix functions
> DOC:   Rearrange the polynomial documents
> BUG:   Fix up links to classes
> DOC:   Add version added to some of the polynomial package modules
> DOC:   Document xxxfit functions in the polynomial package modules
> BUG:   The polynomial convenience classes let different types interact
> DOC:   Document the use of the polynomial convenience classes
> DOC:   Improve numpy reference documentation of polynomial classes
> ENH:   Improve the computation of polynomials from roots
> STY:   Code cleanup in polynomial [*]fromroots functions
> DOC:   Remove references to cast and NA, which were added in 1.7
>
>
> ``numpy.distutils`` issues fixed
> -------------------------------
>
> #1261  change compile flag on AIX from -O5 to -O3
> #1377  update HP compiler flags
> #1383  provide better support for C++ code on HPUX
> #1857  fix build for py3k + pip
> BLD:   raise a clearer warning in case of building without cleaning up
> first
> BLD:   follow build_ext coding convention in build_clib
> BLD:   fix up detection of Intel CPU on OS X in system_info.py
> BLD:   add support for the new X11 directory structure on Ubuntu & co.
> BLD:   add ufsparse to the libraries search path.
> BLD:   add 'pgfortran' as a valid compiler in the Portland Group
> BLD:   update version match regexp for IBM AIX Fortran compilers.
>
>
>
I just noticed that my fix for the np.gradient() function isn't listed.
https://github.com/numpy/numpy/pull/167

Not critical, but if a second rc is needed for any reason, it would be nice
to have that in there.

Thanks!
Ben Root
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From cp3028 at googlemail.com  Wed May  9 13:44:26 2012
From: cp3028 at googlemail.com (cp3028)
Date: Wed, 9 May 2012 10:44:26 -0700 (PDT)
Subject: [SciPy-User] How to assemble large sparse matrices effectively
Message-ID: <a84280ca-6647-495d-94c5-09e9ba481759@u4g2000vbm.googlegroups.com>

Hello everyone,

I am working on an FEM project using Scipy. Now my problem is, that
the assembly of the sparse matrices is to slow. I compute the
contribution of every element in dense small matrices (one for each
element). For the assembly of the global matrices I loop over all
small dense matrices and set the matrice entries the following way:
...
[i,j] = someList[k][l]
Mglobal[i,j] = Mglobal[i,j] + Mlocal[k,l]
...

Mglobal is a lil_matrice of appropriate size, someList maps the
indexing variables.

Of course this is rather slow and consumes most of the matrice
assembly time. Is there a better way to assemble a large sparse matrix
from many small dense matrices? I tried scipy.weave but it doesn't
seem to work with sparse matrices

Yours,

cm


From cp3028 at googlemail.com  Wed May  9 14:34:58 2012
From: cp3028 at googlemail.com (cp3028)
Date: Wed, 9 May 2012 20:34:58 +0200
Subject: [SciPy-User] How to assemble large sparse matrices effectively
Message-ID: <985C3F2D-4449-4946-A348-B731660D798D@gmail.com>

Hello everyone,

I am working on an FEM project using Scipy. Now my problem is, that
the assembly of the sparse matrices is to slow. I compute the
contribution of every element in dense small matrices (one for each
element). For the assembly of the global matrices I loop over all
small dense matrices and set the matrice entries the following way:
...
[i,j] = someList[k][l]
Mglobal[i,j] = Mglobal[i,j] + Mlocal[k,l]
...

Mglobal is a lil_matrice of appropriate size, someList maps the
indexing variables.

Of course this is rather slow and consumes most of the matrice
assembly time. Is there a better way to assemble a large sparse matrix
from many small dense matrices? I tried scipy.weave but it doesn't
seem to work with sparse matrices

Yours,

cm


From cp3028 at googlemail.com  Wed May  9 14:36:39 2012
From: cp3028 at googlemail.com (cp3028)
Date: Wed, 9 May 2012 11:36:39 -0700 (PDT)
Subject: [SciPy-User] How to assemble large sparse matrices effectively
Message-ID: <681b81d4-491f-4b4b-b201-df6bc1fbcce7@m10g2000vbb.googlegroups.com>

Hello everyone,

I am working on an FEM project using Scipy. Now my problem is, that
the assembly of the sparse matrices is to slow. I compute the
contribution of every element in dense small matrices (one for each
element). For the assembly of the global matrices I loop over all
small dense matrices and set the matrice entries the following way:
...
[i,j] = someList[k][l]
Mglobal[i,j] = Mglobal[i,j] + Mlocal[k,l]
...

Mglobal is a lil_matrice of appropriate size, someList maps the
indexing variables.

Of course this is rather slow and consumes most of the matrice
assembly time. Is there a better way to assemble a large sparse matrix
from many small dense matrices? I tried scipy.weave but it doesn't
seem to work with sparse matrices

Yours,

cm


From erik.kastman at gmail.com  Thu May 10 17:15:43 2012
From: erik.kastman at gmail.com (Erik Kastman)
Date: Thu, 10 May 2012 17:15:43 -0400
Subject: [SciPy-User] Simple ndarray dim question?
Message-ID: <448F2446-BEA6-4850-A64A-830578984DD9@gmail.com>

Hi all,

Using SciPy and Matlab, I'm having trouble reconstructing an array to match what is given from a matlab cell array loaded using scipy.io.loadmat().

For example, say I create a cell containing a pair of double arrays in matlab and then load it using scipy.io (I'm using SPM to do imaging analyses in conjunction with pynifti and the like)

Matlab

    >> onsets{1} = [0 30 60 90]
    >> onsets{2} = [15 45 75 105]

Python

    >>> import scipy.io as scio
    >>> mat = scio.loadmat('onsets.mat')
    >>> mat['onsets'][0]
    array([[[ 0 30 60 90]], [[ 15  45  75 105]]], dtype=object)
    
    >>> mat['onsets'][0].shape
    
    (2,)

My question is this: **Why does this numpy array have the shape (2,) instead of (2,1,4)**? In real life I'm trying to use Python to parse a logfile and build these onsets cell arrays, so I'd like to be able to build them from scratch.

When I try to build the same array from the printed output, I get a different shape back:

    >>> new_onsets = array([[[ 0, 30, 60, 90]], [[ 15,  45,  75, 105]]], dtype=object)
    array([[[0, 30, 60, 90]],
    
           [[15, 45, 75, 105]]], dtype=object)
    
    >>> new_onsets.shape
    (2,1,4)

Unfortunately, the shape (vectors of doubles in a cell array) is coded in a spec upstream, so I need to be able to get this saved exactly in this format. Of course, it's not a big deal since I could just write the parser in matlab, but it would be nice to figure out what's going on and add a little to my [minuscule] knowledge of numpy.

Thanks in advance for any suggestions,
Erik

cross-posted to stack-overflow: http://stackoverflow.com/questions/10542263

From charlesr.harris at gmail.com  Fri May 11 12:01:59 2012
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Fri, 11 May 2012 10:01:59 -0600
Subject: [SciPy-User] [Numpy-discussion] ANN: NumPy 1.6.2 release
 candidate 1
In-Reply-To: <CANNq6FkmEjFk9TpQqPMq46vDbRV0icEqy2+pU5=4E_BV_Chx8w@mail.gmail.com>
References: <CABL7CQju9HqYapdJGTb=TVpOHZn8J7wwzRROtKxW-JhawD6A2A@mail.gmail.com>
	<CANNq6FkmEjFk9TpQqPMq46vDbRV0icEqy2+pU5=4E_BV_Chx8w@mail.gmail.com>
Message-ID: <CAB6mnxJuwG6-xGuOQdOGvSzHAW6a7F9djAMgWZjiAbFA1p+qmA@mail.gmail.com>

On Fri, May 11, 2012 at 8:05 AM, Benjamin Root <ben.root at ou.edu> wrote:

>
>
> On Sat, May 5, 2012 at 2:15 PM, Ralf Gommers <ralf.gommers at googlemail.com>wrote:
>
>> Hi,
>>
>> I'm pleased to announce the availability of the first release candidate
>> of NumPy 1.6.2.  This is a maintenance release. Due to the delay of the
>> NumPy 1.7.0, this release contains far more fixes than a regular NumPy
>> bugfix release.  It also includes a number of documentation and build
>> improvements.
>>
>> Sources and binary installers can be found at
>> https://sourceforge.net/projects/numpy/files/NumPy/1.6.2rc1/
>>
>> Please test this release and report any issues on the numpy-discussion
>> mailing list.
>>
>> Cheers,
>> Ralf
>>
>>
>>
>> ``numpy.core`` issues fixed
>> ---------------------------
>>
>> #2063  make unique() return consistent index
>> #1138  allow creating arrays from empty buffers or empty slices
>> #1446  correct note about correspondence vstack and concatenate
>> #1149  make argmin() work for datetime
>> #1672  fix allclose() to work for scalar inf
>> #1747  make np.median() work for 0-D arrays
>> #1776  make complex division by zero to yield inf properly
>> #1675  add scalar support for the format() function
>> #1905  explicitly check for NaNs in allclose()
>> #1952  allow floating ddof in std() and var()
>> #1948  fix regression for indexing chararrays with empty list
>> #2017  fix type hashing
>> #2046  deleting array attributes causes segfault
>> #2033  a**2.0 has incorrect type
>> #2045  make attribute/iterator_element deletions not segfault
>> #2021  fix segfault in searchsorted()
>> #2073  fix float16 __array_interface__ bug
>>
>>
>> ``numpy.lib`` issues fixed
>> --------------------------
>>
>> #2048  break reference cycle in NpzFile
>> #1573  savetxt() now handles complex arrays
>> #1387  allow bincount() to accept empty arrays
>> #1899  fixed histogramdd() bug with empty inputs
>> #1793  fix failing npyio test under py3k
>> #1936  fix extra nesting for subarray dtypes
>> #1848  make tril/triu return the same dtype as the original array
>> #1918  use Py_TYPE to access ob_type, so it works also on Py3
>>
>>
>> ``numpy.f2py`` changes
>> ----------------------
>>
>> ENH:   Introduce new options extra_f77_compiler_args and
>> extra_f90_compiler_args
>> BLD:   Improve reporting of fcompiler value
>> BUG:   Fix f2py test_kind.py test
>>
>>
>> ``numpy.poly`` changes
>> ----------------------
>>
>> ENH:   Add some tests for polynomial printing
>> ENH:   Add companion matrix functions
>> DOC:   Rearrange the polynomial documents
>> BUG:   Fix up links to classes
>> DOC:   Add version added to some of the polynomial package modules
>> DOC:   Document xxxfit functions in the polynomial package modules
>> BUG:   The polynomial convenience classes let different types interact
>> DOC:   Document the use of the polynomial convenience classes
>> DOC:   Improve numpy reference documentation of polynomial classes
>> ENH:   Improve the computation of polynomials from roots
>> STY:   Code cleanup in polynomial [*]fromroots functions
>> DOC:   Remove references to cast and NA, which were added in 1.7
>>
>>
>> ``numpy.distutils`` issues fixed
>> -------------------------------
>>
>> #1261  change compile flag on AIX from -O5 to -O3
>> #1377  update HP compiler flags
>> #1383  provide better support for C++ code on HPUX
>> #1857  fix build for py3k + pip
>> BLD:   raise a clearer warning in case of building without cleaning up
>> first
>> BLD:   follow build_ext coding convention in build_clib
>> BLD:   fix up detection of Intel CPU on OS X in system_info.py
>> BLD:   add support for the new X11 directory structure on Ubuntu & co.
>> BLD:   add ufsparse to the libraries search path.
>> BLD:   add 'pgfortran' as a valid compiler in the Portland Group
>> BLD:   update version match regexp for IBM AIX Fortran compilers.
>>
>>
>>
> I just noticed that my fix for the np.gradient() function isn't listed.
> https://github.com/numpy/numpy/pull/167
>
> Not critical, but if a second rc is needed for any reason, it would be
> nice to have that in there.
>
>
If there is a second rc I'll put in in.

Chuck
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From gregor.thalhammer at gmail.com  Fri May 11 15:29:51 2012
From: gregor.thalhammer at gmail.com (Gregor Thalhammer)
Date: Fri, 11 May 2012 21:29:51 +0200
Subject: [SciPy-User] 2D phase unwrapping
Message-ID: <FEE7C41D-5649-4A2B-A928-5A29F39259BC@gmail.com>

Hi all,

I have beend searching for an implementation for phase unwrapping in 2D (and possibly also 3D). I found this old thread in the numpy mailing list

http://mail.scipy.org/pipermail/numpy-discussion/2008-November/038873.html

which mentions the C implementation from GERI: http://www.ljmu.ac.uk/GERI/90202.htm

While searching I found remarks by the authors that these algorithms have been incorporated into scipy, however I am unable to find them in current scipy or numpy. Am I missing something obvious? 
Instead I found this wrapper: https://github.com/pointtonull/pyunwrap
This seems to be based on a wrapper already mentioned in the above mentioned discussion, but the links mentioned there are dead. I added some setup.py, and with some small modifications I managed to compile the extension both on OS X and Windows. 

I would like to see these algorithms included in scipy, and I am willing to work on this. So now my questions: In the old numpy-discussion thread licensing issues are raised, can anybody tell more? On the GERI homepage they distribute their code under a non-commercial-use license, but the authors seem to agree on incorporating their code into scipy. 
Except from this, what else would be required?

Thanks for any advice
Gregor

From travis at continuum.io  Fri May 11 16:02:10 2012
From: travis at continuum.io (Travis Oliphant)
Date: Fri, 11 May 2012 15:02:10 -0500
Subject: [SciPy-User] Simple ndarray dim question?
In-Reply-To: <448F2446-BEA6-4850-A64A-830578984DD9@gmail.com>
References: <448F2446-BEA6-4850-A64A-830578984DD9@gmail.com>
Message-ID: <83D62D0E-5765-4744-B30D-4A24A1DDA180@continuum.io>


On May 10, 2012, at 4:15 PM, Erik Kastman wrote:

> Hi all,
> 
> Using SciPy and Matlab, I'm having trouble reconstructing an array to match what is given from a matlab cell array loaded using scipy.io.loadmat().
> 
> For example, say I create a cell containing a pair of double arrays in matlab and then load it using scipy.io (I'm using SPM to do imaging analyses in conjunction with pynifti and the like)
> 
> Matlab
> 
>>> onsets{1} = [0 30 60 90]
>>> onsets{2} = [15 45 75 105]
> 
> Python
> 
>>>> import scipy.io as scio
>>>> mat = scio.loadmat('onsets.mat')
>>>> mat['onsets'][0]
>    array([[[ 0 30 60 90]], [[ 15  45  75 105]]], dtype=object)
> 
>>>> mat['onsets'][0].shape
> 
>    (2,)
> 
> My question is this: **Why does this numpy array have the shape (2,) instead of (2,1,4)**? In real life I'm trying to use Python to parse a logfile and build these onsets cell arrays, so I'd like to be able to build them from scratch.

loadmat returns an array of *Python objects* because you have cell arrays.    Each object in the outer NumPy array is probably a (1,4) NumPy array of a different type.    To be sure look at

type(mat['onsets'][0][0])

and 

mat['onsets'][0].shape

if the result of the first expression is an ndarray. 


> 
> When I try to build the same array from the printed output, I get a different shape back:
> 
>>>> new_onsets = array([[[ 0, 30, 60, 90]], [[ 15,  45,  75, 105]]], dtype=object)
>    array([[[0, 30, 60, 90]],
> 
>           [[15, 45, 75, 105]]], dtype=object)
> 
>>>> new_onsets.shape
>    (2,1,4)

Now you have an "object" array of shape (2,1,4) where each object happens to be a Python int.   Normally you would make an array of "integers" or "floating point values".   

You could build what you saw before with: 

new_onsets = empty((2,), dtype=object)
new_onsets[0] = array([[0, 30, 60, 90]])
new_onsets[1] = array([[15, 45, 75, 105]])


-Travis




From cimrman3 at ntc.zcu.cz  Sat May 12 04:54:42 2012
From: cimrman3 at ntc.zcu.cz (Robert Cimrman)
Date: Sat, 12 May 2012 10:54:42 +0200
Subject: [SciPy-User] How to assemble large sparse matrices effectively
In-Reply-To: <a84280ca-6647-495d-94c5-09e9ba481759@u4g2000vbm.googlegroups.com>
References: <a84280ca-6647-495d-94c5-09e9ba481759@u4g2000vbm.googlegroups.com>
Message-ID: <4FAE2552.2050905@ntc.zcu.cz>

Hello,

On 05/09/2012 07:44 PM, cp3028 wrote:
> Hello everyone,
>
> I am working on an FEM project using Scipy. Now my problem is, that
> the assembly of the sparse matrices is to slow. I compute the
> contribution of every element in dense small matrices (one for each
> element). For the assembly of the global matrices I loop over all
> small dense matrices and set the matrice entries the following way:
> ...
> [i,j] = someList[k][l]
> Mglobal[i,j] = Mglobal[i,j] + Mlocal[k,l]
> ...
>
> Mglobal is a lil_matrice of appropriate size, someList maps the
> indexing variables.

You can try to adapt [1], which is in Cython, and works with the CSR 
matrices.

Example usage:
assemble_matrix(m.data, m.indptr, m.indices, mtx_in_els,
                 element_indices, sign, row_connectivity,
                 column_connectivity)

- m is the global matrix in CSR format
- mtx_in_els is an array of element matrices of shape (n_elements, 1, 
n_rows, n_columns)
- element_indices are indices (of length n_elements) into the row and 
column element connectivity arrays.

r.

[1] 
https://github.com/sfepy/sfepy/blob/master/sfepy/fem/extmods/assemble.pyx



From ralf.gommers at googlemail.com  Sat May 12 05:58:17 2012
From: ralf.gommers at googlemail.com (Ralf Gommers)
Date: Sat, 12 May 2012 11:58:17 +0200
Subject: [SciPy-User] 2D phase unwrapping
In-Reply-To: <FEE7C41D-5649-4A2B-A928-5A29F39259BC@gmail.com>
References: <FEE7C41D-5649-4A2B-A928-5A29F39259BC@gmail.com>
Message-ID: <CABL7CQhLUrs_M2n0-3iv9nzbz-E-LGfX9JLpAsX2+UQCqM8D6A@mail.gmail.com>

On Fri, May 11, 2012 at 9:29 PM, Gregor Thalhammer <
gregor.thalhammer at gmail.com> wrote:

> Hi all,
>
> I have beend searching for an implementation for phase unwrapping in 2D
> (and possibly also 3D). I found this old thread in the numpy mailing list
>
> http://mail.scipy.org/pipermail/numpy-discussion/2008-November/038873.html
>
> which mentions the C implementation from GERI:
> http://www.ljmu.ac.uk/GERI/90202.htm
>
> While searching I found remarks by the authors that these algorithms have
> been incorporated into scipy, however I am unable to find them in current
> scipy or numpy. Am I missing something obvious?
> Instead I found this wrapper: https://github.com/pointtonull/pyunwrap
> This seems to be based on a wrapper already mentioned in the above
> mentioned discussion, but the links mentioned there are dead. I added some
> setup.py, and with some small modifications I managed to compile the
> extension both on OS X and Windows.
>
> I would like to see these algorithms included in scipy, and I am willing
> to work on this.


Great!


> So now my questions: In the old numpy-discussion thread licensing issues
> are raised, can anybody tell more? On the GERI homepage they distribute
> their code under a non-commercial-use license, but the authors seem to
> agree on incorporating their code into scipy.
> Except from this, what else would be required?
>

The emails about the license seem to be very clear to me, there's no issue.
So all it would take is someone submitting a complete wrapper (with the
normal requirements on docs/tests). The only things to decide are then: do
we want it in scipy (+1 from me), where to put it and what the API should
look like.

Ralf
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From friedrichromstedt at gmail.com  Sat May 12 06:30:03 2012
From: friedrichromstedt at gmail.com (Friedrich Romstedt)
Date: Sat, 12 May 2012 12:30:03 +0200
Subject: [SciPy-User] 2D phase unwrapping
In-Reply-To: <FEE7C41D-5649-4A2B-A928-5A29F39259BC@gmail.com>
References: <FEE7C41D-5649-4A2B-A928-5A29F39259BC@gmail.com>
Message-ID: <51478D12-D06D-449B-BE37-901E93C72061@gmail.com>

Am 11.05.2012 um 21:29 schrieb Gregor Thalhammer <gregor.thalhammer at gmail.com>:

> Hi all,
> 
> I have beend searching for an implementation for phase unwrapping in 2D (and possibly also 3D). I found this old thread in the numpy mailing list
> 
> http://mail.scipy.org/pipermail/numpy-discussion/2008-November/038873.html
> 
> which mentions the C implementation from GERI: http://www.ljmu.ac.uk/GERI/90202.htm
> 
> While searching I found remarks by the authors that these algorithms have been incorporated into scipy, however I am unable to find them in current scipy or numpy. Am I missing something obvious? 
> Instead I found this wrapper: https://github.com/pointtonull/pyunwrap
> This seems to be based on a wrapper already mentioned in the above mentioned discussion, but the links mentioned there are dead. I added some setup.py, and with some small modifications I managed to compile the extension both on OS X and Windows. 
> 
> I would like to see these algorithms included in scipy, and I am willing to work on this. So now my questions: In the old numpy-discussion thread licensing issues are raised, can anybody tell more? On the GERI homepage they distribute their code under a non-commercial-use license, but the authors seem to agree on incorporating their code into scipy. 
> Except from this, what else would be required?
> 
> Thanks for any advice
> Gregor

Hi,

I worked pretty hard on an unwrapping algorithm for Fourier tranform results. By this I noticed the following points:

? Phases are angles, so they are just identical, if their angle "value" is identical modulus 2 pi. There's nothing to tell them apart. The numerical value is just an insufficient model and we fight these insufficiencies when trying to "unwrap". They are already unwrapped. We just don't see it anymore. 

? For the special case of adding complex numbers, as in Fourier theory, I noticed that there is no mathematically consistent way of determining the 2 pi component of the sum of two complex numbers w.r.t. the angular numbers of the summands. It just depends on how you work it out to be calculated. This is because adding complex numbers works on real and imaginary part. For multiplication it's just fine to add the angles, as multiplication of complex numbers works on complex modulus and phase. 

? I suggest to leave the numerical value behind, or to use it in a way consistent with what it is supposed to model. This means to see it as a distance on the line of a circle, not on a normal cartesian axis. So if the number reaches 2 pi, it's just exactly the same point on the circle. From distances between points on the circle phase differences can be calculated easily by using school math. Of course it will still be undetermined up to 2 pi. 

I think if the problem requires unwrapping still, working on the way of posing the problem and understanding its kernel better might prove more useful in the long run that trying to fight noise which makes unwrapping algorithms break down. But this is just a gut feeling. It builds on that if some angular value requires to be interpreted as if on a cartesian scale there must be something very wrong with the interpretation after all. Angles are not like that as I mentioned in the beginning. 

This is a theoretical post and I'm not about to have any effect on what goes into scipy and what does not. :-)

Friedrich

From robert.kern at gmail.com  Sat May 12 06:49:19 2012
From: robert.kern at gmail.com (Robert Kern)
Date: Sat, 12 May 2012 11:49:19 +0100
Subject: [SciPy-User] 2D phase unwrapping
In-Reply-To: <51478D12-D06D-449B-BE37-901E93C72061@gmail.com>
References: <FEE7C41D-5649-4A2B-A928-5A29F39259BC@gmail.com>
	<51478D12-D06D-449B-BE37-901E93C72061@gmail.com>
Message-ID: <CAF6FJit+iQw569UTu2t+2LSBkKRG2EE6ZdEn8iNVYFYihzO7EA@mail.gmail.com>

On Sat, May 12, 2012 at 11:30 AM, Friedrich Romstedt
<friedrichromstedt at gmail.com> wrote:

> I worked pretty hard on an unwrapping algorithm for Fourier tranform results. By this I noticed the following points:
>
> ? Phases are angles, so they are just identical, if their angle "value" is identical modulus 2 pi. There's nothing to tell them apart. The numerical value is just an insufficient model and we fight these insufficiencies when trying to "unwrap". They are already unwrapped. We just don't see it anymore.

Usually, the point of phase unwrapping is to try to recover an
underlying linear variable in the range (-inf, inf) that we are
observing through the lens of a complex phase. For example, when doing
Interferometric Synthetic Aperture RADAR (InSAR), the setup is to fly
a satellite doing SAR twice over a given area. SAR gives you a complex
image of the ground surface. The complex phase is related to the
two-way travel time of the RADAR signal. The phase of a single SAR
image is essentially meaningless because it depends on every little
detail on the surface, but if the surface did not change much in its
fine details (seriously, plants growing on just the order of
centimeters is a problem) but moved in the direction of the RADAR
signal due to seismic events, the difference in the phases
(interferometry!) is linearly related to the distance that the ground
moved. Except that the phase difference, the only thing we can
directly observe, gets wrapped. There *is* an underlying non-phase
linear variable here that we are trying to estimate via phase
unwrapping. When we're unwrapping phase, we're not really interested
in the phase itself. It's just the only thing that we can observe.
There are better and worse ways to unwrap phase in order to estimate
these underlying variables, but it's not a theoretically doomed
effort.

-- 
Robert Kern


From friedrichromstedt at gmail.com  Sat May 12 12:43:07 2012
From: friedrichromstedt at gmail.com (Friedrich Romstedt)
Date: Sat, 12 May 2012 18:43:07 +0200
Subject: [SciPy-User] 2D phase unwrapping
In-Reply-To: <CAF6FJit+iQw569UTu2t+2LSBkKRG2EE6ZdEn8iNVYFYihzO7EA@mail.gmail.com>
References: <FEE7C41D-5649-4A2B-A928-5A29F39259BC@gmail.com>
	<51478D12-D06D-449B-BE37-901E93C72061@gmail.com>
	<CAF6FJit+iQw569UTu2t+2LSBkKRG2EE6ZdEn8iNVYFYihzO7EA@mail.gmail.com>
Message-ID: <0EEF82BB-29B0-4795-B6EE-A45C5F24F885@gmail.com>

Hi Robert,

This post contains reasoning which is OT to the problem at hand. It is related, but it does not solve anything about this "unwrapping" process. Rather it tries to understand what we are doing here and where it might lead to, what the aiming is. :-)

If the reader is only interested in getting his data done, he better does not read it. I've done it because I found there was be something to it (for me), and I wanted to know what it was. This means to be able to formulate it. I wanted to know what thing it is that I apparently did not take into account, and how it relates to the problem of the OP, and what the whole story looks like. If the reader finds this an interesting attitude, he might want to eventually read on. Otherwise, storage is cheap these days. I won't waste much. :-)

I know this is not a philosophy mailing list. It's just the best I can do at the moment. I found the thing interesting and gave my contribution. If someone complains enough that thinking like this does absolutely not fit here, I might want to unsubscribe. :-) Just be open. :-)

Am 12.05.2012 um 12:49 schrieb Robert Kern <robert.kern at gmail.com>:

> On Sat, May 12, 2012 at 11:30 AM, Friedrich Romstedt
> <friedrichromstedt at gmail.com> wrote:
> 
>> I worked pretty hard on an unwrapping algorithm for Fourier tranform results. By this I noticed the following points:
>> 
>> ? Phases are angles, so they are just identical, if their angle "value" is identical modulus 2 pi. There's nothing to tell them apart. The numerical value is just an insufficient model and we fight these insufficiencies when trying to "unwrap". They are already unwrapped. We just don't see it anymore.
> 
> Usually, the point of phase unwrapping is to try to recover an
> underlying linear variable in the range (-inf, inf) that we are
> observing through the lens of a complex phase. For example, when doing
> Interferometric Synthetic Aperture RADAR (InSAR), the setup is to fly
> a satellite doing SAR twice over a given area. SAR gives you a complex
> image of the ground surface. The complex phase is related to the
> two-way travel time of the RADAR signal. The phase of a single SAR
> image is essentially meaningless because it depends on every little
> detail on the surface, but if the surface did not change much in its
> fine details (seriously, plants growing on just the order of
> centimeters is a problem) but moved in the direction of the RADAR
> signal due to seismic events, the difference in the phases
> (interferometry!) is linearly related to the distance that the ground
> moved. Except that the phase difference, the only thing we can
> directly observe, gets wrapped. There *is* an underlying non-phase
> linear variable here that we are trying to estimate via phase
> unwrapping. When we're unwrapping phase, we're not really interested
> in the phase itself. It's just the only thing that we can observe.
> There are better and worse ways to unwrap phase in order to estimate
> these underlying variables, but it's not a theoretically doomed
> effort.

Yes. 

[I concentrate in the following on InSAR]

This is a descriptive data analysis problem. I'm not really interested in that kind of problems, although I'm always tempted to be. Two different people with two different interests met here. 

As I see it, the theory is just about modeling how height change and lateral displacement (when moving along the map), as well as any other effect, comprise the net interferometric phase. Since the lateral displacement (when moving along the map) is encoded in the pixel coordinate (or the spacial extent of the image), it can be resolved. Now all the other effects remain. 

The theory now does not make any judgement on the probability of all the different explanations which are possible due to the 2 pi "ambiguity" of the phase (more precise, of the fact that this ambiguity arises when representing phase as a number). So the theory would be happy with all these rather rough height maps which incorporate jumps of a kilometer from pixel to pixel. Because the theory yields the sum of all this different height maps, all of them at the same time. 

It is now hence a matter of a-priori information which continuous interpretation of these contraint done by the measurement we prefer or like best. It's no longer up to the theory to give this. It's a judgement on the constraint done by the measurement, taking into account that it should be continuous, s.t. jumps of a kilometer are not that likely. We shrink the theoretically possible explanations down using a-priori information. 

If there was a jump of one kilometer from one scanline to the next it would still be there on earth, but the interpretation would fail to find this out. Because it has not an eye for it. It would prefer the explanation where the jump is the remaining 2 cm downwards or so (just an example). 

As I said, this is not the kind of problems I'm interested in. I'm not a believer of realism. :-). It's fine if the interpretation yields this 2 cm jump from one scanline to the next. It will create a world for you where you can build on. You might want to refine it by going down there, finding that 1 km jump, or not. Although it might have been preferrable for an alien doing InSAR to just agree on that he would not know what he will see. To be precise, the alien [having InSAR eyes] would have no interest in assuming that there would be a continuous surface. :-)

So it's rather "space unwrapping" or "how to choose the continuous surface" than "phase unwrapping". And I'm not going into that here, I just have not studied what others did about it so I'll better hide my childish ideas how it might be done. :-)

Friedrich

[some content deleted :-)]

P.S.: And if course, a-priori information involves theoretical notions. With "theory" I meant the theory of InSAR, implying phases starting from height and other information. The InSAR process is interesting because it brings together two different theories: First, that of how spacial coordinates etc. relate to phase, and second, the model of a continuous surface. It's an assumption that they hold at the same time. But if they hold at the same time, it won't create anything new on the basis of other things, and that's why I'm not interested in it. So much of text just to find that out :-/. 

And the process of choosing the surface is just the attempt to see if the two theories mentioned can hold at the same time. 

From drwells at vt.edu  Sat May 12 15:36:40 2012
From: drwells at vt.edu (Dave Wells)
Date: Sat, 12 May 2012 15:36:40 -0400
Subject: [SciPy-User] How to assemble large sparse matrices
Message-ID: <CALa6-KoTmmY7QyXSPWJnXZctOYJPujRccJHssMOXv1UPqtCPww@mail.gmail.com>

cm,

The trick is to use the IJV storage format. This is a trio of three arrays
where the first one contains row indicies, the second has column indicies, and
the third has the values of the matrix at that location. This is the best way
to build finite element matricies (or any sparse matrix in my opinion) as access
to the first format is really fast (just filling an an array) and
conversion is also
very fast.

In scipy this is called the COO matrix. It sums entries with the same indicies
and can be converted to any other format fast. It ignores zeros too (so it
does 'the right things' for finite elements).

For finite elements, you can estimate the size of the three arrays by something
like

size = number_of_elements * number_of_basis_functions**2

so if you have 2D quadratics you would do number_of_elements * 36, for example.
This approach is convenient because if you have local matricies you definitely
have the global numbers and entry values: exactly what you need for building
the three IJV arrays.

I hope this helps,
Dave Wells
Virginia Tech

> Message: 4
> Date: Wed, 9 May 2012 11:36:39 -0700 (PDT)
> From: cp3028 <cp3028 at googlemail.com>
> Subject: [SciPy-User] How to assemble large sparse matrices
>        effectively
> To: scipy-user at scipy.org
> Message-ID:
>        <681b81d4-491f-4b4b-b201-df6bc1fbcce7 at m10g2000vbb.googlegroups.com>
> Content-Type: text/plain; charset=ISO-8859-1
>
> Hello everyone,
>
> I am working on an FEM project using Scipy. Now my problem is, that
> the assembly of the sparse matrices is to slow. I compute the
> contribution of every element in dense small matrices (one for each
> element). For the assembly of the global matrices I loop over all
> small dense matrices and set the matrice entries the following way:
> ...
> [i,j] = someList[k][l]
> Mglobal[i,j] = Mglobal[i,j] + Mlocal[k,l]
> ...
>
> Mglobal is a lil_matrice of appropriate size, someList maps the
> indexing variables.
>
> Of course this is rather slow and consumes most of the matrice
> assembly time. Is there a better way to assemble a large sparse matrix
> from many small dense matrices? I tried scipy.weave but it doesn't
> seem to work with sparse matrices
>
> Yours,
>
> cm


From zachary.pincus at yale.edu  Sun May 13 13:07:02 2012
From: zachary.pincus at yale.edu (Zachary Pincus)
Date: Sun, 13 May 2012 13:07:02 -0400
Subject: [SciPy-User] Weighted KDE
Message-ID: <0CE25948-3ED7-4928-9C63-4F44366C3AF5@yale.edu>

Hello all,

A while ago, someone asked on this list about whether it would be simple to modify scipy.stats.kde.gaussian_kde to deal with weighted data:
http://mail.scipy.org/pipermail/scipy-user/2008-November/018578.html

Anne and Robert assured the writer that this was pretty simple (modulo bandwidth selection), though I couldn't find any code that the original author may have generated based on that advice.

I've got a problem that could (perhaps) be solved neatly with weighed KDE, so I'd like to give this a go. I assume that at a minimum, to get basic gaussian_kde.evaluate() functionality:

(1) The covariance calculation would need to be replaced by a weighted-covariance calculation. (Simple enough.)

(2) In evaluate(), the critical part looks like this (and a similar stanza that loops over the points instead):
# if there are more points than data, so loop over data
for i in range(self.n):
    diff = self.dataset[:, i, newaxis] - points
    tdiff = dot(self.inv_cov, diff)
    energy = sum(diff*tdiff,axis=0) / 2.0
    result = result + exp(-energy)

I assume that, further, the 'diff' values ought to be scaled by the weights, too. Is this all that would need to be done? (For the integration and resampling, obviously, there would be a bit of other work...)

Thanks,
Zach

From josef.pktd at gmail.com  Sun May 13 14:17:30 2012
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sun, 13 May 2012 14:17:30 -0400
Subject: [SciPy-User] Weighted KDE
In-Reply-To: <0CE25948-3ED7-4928-9C63-4F44366C3AF5@yale.edu>
References: <0CE25948-3ED7-4928-9C63-4F44366C3AF5@yale.edu>
Message-ID: <CAMMTP+BA1J0Lw8K4KyLimQgYBo69M1i2vU5ZaYajvT16YHcWkw@mail.gmail.com>

On Sun, May 13, 2012 at 1:07 PM, Zachary Pincus <zachary.pincus at yale.edu> wrote:
> Hello all,
>
> A while ago, someone asked on this list about whether it would be simple to modify scipy.stats.kde.gaussian_kde to deal with weighted data:
> http://mail.scipy.org/pipermail/scipy-user/2008-November/018578.html
>
> Anne and Robert assured the writer that this was pretty simple (modulo bandwidth selection), though I couldn't find any code that the original author may have generated based on that advice.
>
> I've got a problem that could (perhaps) be solved neatly with weighed KDE, so I'd like to give this a go. I assume that at a minimum, to get basic gaussian_kde.evaluate() functionality:
>
> (1) The covariance calculation would need to be replaced by a weighted-covariance calculation. (Simple enough.)
>
> (2) In evaluate(), the critical part looks like this (and a similar stanza that loops over the points instead):
> # if there are more points than data, so loop over data
> for i in range(self.n):
> ? ?diff = self.dataset[:, i, newaxis] - points
> ? ?tdiff = dot(self.inv_cov, diff)
> ? ?energy = sum(diff*tdiff,axis=0) / 2.0
> ? ?result = result + exp(-energy)
>
> I assume that, further, the 'diff' values ought to be scaled by the weights, too. Is this all that would need to be done? (For the integration and resampling, obviously, there would be a bit of other work...)

it looks to me that way, scaled according to weight by dataset points

I don't see what the norm_factor should be:
      self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n
there should be the weights somewhere in there, maybe just replace
self.n by sum(weights) given a constant covariance

sampling doesn't look difficult, if we want biased sampling, then
instead of randint, we would need weighted randint (non-uniform)

integration might require more work, or not (I never tried to understand them)

(I don't know if kde in statsmodels has weights on the schedule.)

Josef
mostly guessing

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


From markus.baden at gmail.com  Sun May 13 23:25:41 2012
From: markus.baden at gmail.com (Markus Baden)
Date: Mon, 14 May 2012 11:25:41 +0800
Subject: [SciPy-User] scipy.odr - Goodness of fit and parameter estimation
 for explicit orthogonal distance regression
Message-ID: <CABo_DbCsygHe3nADrrBsA-+Khees2SMSPvEn3QJCbqU+X2RPaQ@mail.gmail.com>

Hi list,

Currently, I am trying to fit a quadratic curve to a data set which has
much larger errors in the x than in the y direction. My errors are assumed
to be normally distributed and I want to estimate the confidence interval
of the fitted parameters. I have fitted the data two different ways. 1) I
neglect the x errors and fit the quadratic by minimizing the weighted
residuals (y -f) / sig_y via scipy.optimize.leastsq and 2) I use scipy.odr
to fit the parameters. Both result similar fitted parameters.

Now I am stuck with estimating the confidence intervals on these errors and
I have a couple of questions. For method 1) the reduced chi squared is bad
(much larger then 1), because when neglecting the x errors, none of the
points actually lie within a couple of standard deviations on the line.
However when including the x-errors they all fall nicely onto the line.

Is there a way for me to include the x errors into my minimization and
goodness of fit estimation via the reduced chi-squared? I came across a
remark in the CERN minuit documentation [1], that seemed to approximate the
function by a line over the point and then use this to convert x to y
errors. I also read something like this in numerical recipes [2], but not
for general least squares (only for the case of linear data). Does anyone
have any pointers into that direction?

My second question is related to method 2). Is there a way of accessing the
goodness of fit for ODR, similar to calculating the reduced chi-squared for
a fit that only has errors in the y? Another question along this line
concerns the scipy.odr.ODR.Output.sd_beta attribute. In the docstring it
says it is the standard error of the parameter, does that mean a 1 standard
deviation confidence interval? And how exactly are they calculated. Tried
to look at the source and in the odrpack guide, but unfortunately couldn't
figure that out.

I somehow have the feeling, that my problem should be quite standard
(goodness of fit with both x and y errors), but so far I could not find a
good explanation. Any pointers to textbooks or resources on the web would
be greatly appreciated.

Best regards,

Markus

[1] http://wwwasd.web.cern.ch/wwwasd/cgi-bin/listpawfaqs.pl/27
[2] Numerical Recipes in C, Second Edition, Chapter 15.3 "Straight-Line
Data with Errors in Both Coordinate"
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From jdgleeson at mac.com  Mon May 14 01:01:47 2012
From: jdgleeson at mac.com (John Gleeson)
Date: Sun, 13 May 2012 23:01:47 -0600
Subject: [SciPy-User] scipy.odr - Goodness of fit and parameter
 estimation for explicit orthogonal distance regression
In-Reply-To: <CABo_DbCsygHe3nADrrBsA-+Khees2SMSPvEn3QJCbqU+X2RPaQ@mail.gmail.com>
References: <CABo_DbCsygHe3nADrrBsA-+Khees2SMSPvEn3QJCbqU+X2RPaQ@mail.gmail.com>
Message-ID: <38210107-A9CA-40E5-80F1-10825C23DB1A@mac.com>

Perhaps the module kmpfit from the kapetyn package would be helpful to  
you.
http://www.astro.rug.nl/software/kapteyn/kmpfit.html

Don't miss the tutorial
http://www.astro.rug.nl/software/kapteyn/kmpfittutorial.html
which has a section "Fitting data when both variables have  
uncertainties"
with subsection "Effective variance method for various models: Model  
parabola"


From markus.baden at gmail.com  Mon May 14 03:36:47 2012
From: markus.baden at gmail.com (Markus Baden)
Date: Mon, 14 May 2012 15:36:47 +0800
Subject: [SciPy-User] scipy.odr - Goodness of fit and parameter
 estimation for explicit orthogonal distance regression
In-Reply-To: <38210107-A9CA-40E5-80F1-10825C23DB1A@mac.com>
References: <CABo_DbCsygHe3nADrrBsA-+Khees2SMSPvEn3QJCbqU+X2RPaQ@mail.gmail.com>
	<38210107-A9CA-40E5-80F1-10825C23DB1A@mac.com>
Message-ID: <CABo_DbAu7LM3ZZA0S5UqOhNgUgwt0yEykVFRN92Tq-vjYQ+KaA@mail.gmail.com>

> Don't miss the tutorial
> http://www.astro.rug.nl/software/kapteyn/kmpfittutorial.html
> which has a section "Fitting data when both variables have
> uncertainties"
>

Thanks a lot, the tutorial and references therein are very useful.

Regards,

Markus
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From robert.kern at gmail.com  Mon May 14 06:10:48 2012
From: robert.kern at gmail.com (Robert Kern)
Date: Mon, 14 May 2012 11:10:48 +0100
Subject: [SciPy-User] scipy.odr - Goodness of fit and parameter
 estimation for explicit orthogonal distance regression
In-Reply-To: <CABo_DbCsygHe3nADrrBsA-+Khees2SMSPvEn3QJCbqU+X2RPaQ@mail.gmail.com>
References: <CABo_DbCsygHe3nADrrBsA-+Khees2SMSPvEn3QJCbqU+X2RPaQ@mail.gmail.com>
Message-ID: <CAF6FJivY3Q0rZco+KVx1TkqddxA7XG0Z2EZSWXtOL0rx0tX4tQ@mail.gmail.com>

On Mon, May 14, 2012 at 4:25 AM, Markus Baden <markus.baden at gmail.com> wrote:
> Hi list,
>
> Currently, I am trying to fit a quadratic curve to a data set which has much
> larger errors in the x than in the y direction. My errors are assumed to be
> normally distributed and I want to estimate the confidence interval of the
> fitted parameters. I have fitted the data two different ways. 1) I neglect
> the x errors and fit the quadratic by minimizing the weighted residuals (y
> -f) / sig_y via scipy.optimize.leastsq and 2) I use scipy.odr to fit the
> parameters. Both result similar fitted parameters.
>
> Now I am stuck with estimating the confidence intervals on these errors and
> I have a couple of questions.

scipy.odr provides an estimate of the covariance matrix and standard
deviations of the parameter estimates. Getting the confidence interval
for a parameter is just a matter of scaling up the standard deviations
by the appropriate t-distribution value with nobs-nparams degrees of
freedom. A paper by the ODRPACK implementors gives the formula
explicitly on page 6:

  http://www.mechanicalkern.com/static/odr_vcv.pdf

It also has more information on how the covariance matrix is calculated.

> My second question is related to method 2). Is there a way of accessing the
> goodness of fit for ODR, similar to calculating the reduced chi-squared for
> a fit that only has errors in the y?

Output.res_var is the reduced Chi-square. But you can compute it from
scratch using the raw residuals. Output.eps are the differences in Y
and Output.delta are the differences in X. Square each and multiply by
their weights (a.k.a. divide by the data variances), then add up all
of them. Divide by (nobs - nparams).

> Another question along this line
> concerns the scipy.odr.ODR.Output.sd_beta attribute. In the docstring it
> says it is the standard error of the parameter, does that mean a 1 standard
> deviation confidence interval?

Yes.

> And how exactly are they calculated. Tried to
> look at the source and in the odrpack guide, but unfortunately couldn't
> figure that out.

As given in the odr_vcv.pdf paper. Some slightly sparser details are
also in the ODRPACK Guide in section "4.B. Covariance Matrix".

-- 
Robert Kern


From gerrit.holl at ltu.se  Mon May 14 10:51:54 2012
From: gerrit.holl at ltu.se (Gerrit Holl)
Date: Mon, 14 May 2012 16:51:54 +0200
Subject: [SciPy-User] Using scipy.io.loadmat to read Matlab containers.Map
	object?
Message-ID: <CACtgQxxVfELAep0GHPKKeAB+h0poAuLQQQaSme_r2647vj7CXw@mail.gmail.com>

Hi,

is it possible to use scipy.io.loadmat to read a Matlab containers.Map object?

>> cm = containers.Map();
>> cm('abc') = 42;
>> save('/tmp/test.mat', 'cm');


In [15]: M = scipy.io.loadmat('/tmp/test.mat')

In [16]: M.keys()
Out[16]: ['__function_workspace__', 'None', '__version__',
'__header__', '__globals__']

In [17]: M["None"]
Out[17]:
MatlabOpaque([ ('cm', 'MCOS', 'containers.Map', [[3707764736L], [2L],
[1L], [1L], [1L], [1L]])],
      dtype=[('s0', '|O8'), ('s1', '|O8'), ('s2', '|O8'), ('arr', '|O8')])
In [15]: M = scipy.io.loadmat('/tmp/test.mat')

In [16]: M.keys()
Out[16]: ['__function_workspace__', 'None', '__version__',
'__header__', '__globals__']

In [17]: M["None"]
Out[17]:
MatlabOpaque([ ('cm', 'MCOS', 'containers.Map', [[3707764736L], [2L],
[1L], [1L], [1L], [1L]])],
      dtype=[('s0', '|O8'), ('s1', '|O8'), ('s2', '|O8'), ('arr', '|O8')])



regards,
Gerrit.

-- 
Gerrit Holl
PhD student at Division of Space Technology, Lule? University of
Technology, Kiruna, Sweden
http://www.sat.ltu.se/members/gerrit/


From gregor.thalhammer at gmail.com  Mon May 14 11:11:37 2012
From: gregor.thalhammer at gmail.com (Gregor Thalhammer)
Date: Mon, 14 May 2012 17:11:37 +0200
Subject: [SciPy-User] 2D phase unwrapping
In-Reply-To: <CABL7CQhLUrs_M2n0-3iv9nzbz-E-LGfX9JLpAsX2+UQCqM8D6A@mail.gmail.com>
References: <FEE7C41D-5649-4A2B-A928-5A29F39259BC@gmail.com>
	<CABL7CQhLUrs_M2n0-3iv9nzbz-E-LGfX9JLpAsX2+UQCqM8D6A@mail.gmail.com>
Message-ID: <5B6184CF-C72B-4B6D-AABE-306A7FD69EB7@gmail.com>


Am 12.5.2012 um 11:58 schrieb Ralf Gommers:

> 
> 
> On Fri, May 11, 2012 at 9:29 PM, Gregor Thalhammer <gregor.thalhammer at gmail.com> wrote:
> Hi all,
> 
> I have beend searching for an implementation for phase unwrapping in 2D (and possibly also 3D). I found this old thread in the numpy mailing list
> 
> http://mail.scipy.org/pipermail/numpy-discussion/2008-November/038873.html
> 
> which mentions the C implementation from GERI: http://www.ljmu.ac.uk/GERI/90202.htm
> 
> While searching I found remarks by the authors that these algorithms have been incorporated into scipy, however I am unable to find them in current scipy or numpy. Am I missing something obvious?
> Instead I found this wrapper: https://github.com/pointtonull/pyunwrap
> This seems to be based on a wrapper already mentioned in the above mentioned discussion, but the links mentioned there are dead. I added some setup.py, and with some small modifications I managed to compile the extension both on OS X and Windows.
> 
> I would like to see these algorithms included in scipy, and I am willing to work on this.
> 
> Great!
>  
> So now my questions: In the old numpy-discussion thread licensing issues are raised, can anybody tell more? On the GERI homepage they distribute their code under a non-commercial-use license, but the authors seem to agree on incorporating their code into scipy.
> Except from this, what else would be required?
> 
> The emails about the license seem to be very clear to me, there's no issue. So all it would take is someone submitting a complete wrapper (with the normal requirements on docs/tests). The only things to decide are then: do we want it in scipy (+1 from me), where to put it and what the API should look like.

Over the weekend I worked on the 2D and 3D phase unwrappers. I put a first version on github: 
https://github.com/geggo/phase-unwrap
git://github.com/geggo/phase-unwrap.git
I tested it on OS X with gcc-4.2, Python 2.7 and recent cython. (it crashes on Windows (MSVC 9), probably related to cython, but thats another story). 

Seems to work ok, only one basic test, no docs yet.

The interface is quite simple:

def unwrap(wrapped_array, 
           wrap_around_axis_0 = False, 
           wrap_around_axis_1 = False, 
           wrap_around_axis_2 = False):

it accepts an 2d or 3d numpy array or an masked array, and returns an masked array if one was given. A fresh float32 array is returned. The additional arguments can be used to specify cyclic boundary conditions along the given axis. The calculations are internally performed with float32. It should be quite straightforward to extend this to other types, but I think for the most common use cases for this algorithm (unwrapping of noisy data) a float32 array is sufficient.

I guess a proper place in scipy would be to add this extension to scipy.ndimage.

Please, any comments are welcome. And also some hints, why it crashes on Windows - or how to debug.

Gregor

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From erik.kastman at gmail.com  Mon May 14 11:31:08 2012
From: erik.kastman at gmail.com (Erik)
Date: Mon, 14 May 2012 15:31:08 +0000 (UTC)
Subject: [SciPy-User] Simple ndarray dim question?
References: <448F2446-BEA6-4850-A64A-830578984DD9@gmail.com>
	<83D62D0E-5765-4744-B30D-4A24A1DDA180@continuum.io>
Message-ID: <loom.20120514T172700-161@post.gmane.org>

Travis Oliphant <travis <at> continuum.io> writes:
> You could build what you saw before with: 
>
> new_onsets = empty((2,), dtype=object)
> new_onsets[0] = array([[0, 30, 60, 90]])
> new_onsets[1] = array([[15, 45, 75, 105]])
> 

That works like a charm. I guess I was trying to build an outer array
by gluing the two together, but building the structure first 
(empty(shape)) and then inserting the data arrays actually makes
more sense. Thanks for your help Travis!

Erik



From sara2411 at gmail.com  Mon May 14 12:59:58 2012
From: sara2411 at gmail.com (Sara Gallian)
Date: Mon, 14 May 2012 18:59:58 +0200
Subject: [SciPy-User] Trajectory Integration via scipy.integrate or PyDSTool?
Message-ID: <F3F6A793-5AA5-4930-8FB9-AC19444D7767@gmail.com>

Hi all,
I'm trying to integrate particle trajectories in a finite domain, and I need to make them "reflect" at the boundaries (i.e. reverse the velocity perpendicular to the plane they collided with). I started by using  scipy.integrate.odeint , but since the integration steps are variable, simply checking the position and reversing the velocity won't work!
Can anyone suggest the quickest way to obtain this? would Vode be able to handle this, or should I try to learn to use  PyDSTool?
I'm running pretty late on a deadline, so any suggestion is more than appreciated :)
Thanks!
Sara

From rob.clewley at gmail.com  Mon May 14 13:22:37 2012
From: rob.clewley at gmail.com (Rob Clewley)
Date: Mon, 14 May 2012 13:22:37 -0400
Subject: [SciPy-User] Trajectory Integration via scipy.integrate or
	PyDSTool?
In-Reply-To: <F3F6A793-5AA5-4930-8FB9-AC19444D7767@gmail.com>
References: <F3F6A793-5AA5-4930-8FB9-AC19444D7767@gmail.com>
Message-ID: <CA+7tCySWORaJhba=eDZLrh-UyFv5XXMW+pdXEE_zHDuXU4vhAQ@mail.gmail.com>

Hi Sara,

I think you should use a tool that's appropriate for the task. With an
hour of work and help on this list or the PyDSTool forum at
http://sourceforge.net/projects/pydstool/forums/forum/472291 you
should be able to get your problem working quickly with PyDSTool. I
expect your problem is easy to solve in PyDSTool, and difficult to do
with odeint.

You can still use scipy's VODE in PyDSTool and you won't need any
fancy installation. Check out the file vode_event_test1.py in the
tests directory. Writing the vector field is simple, but in case you
have specific problems setting up yours then you should send me your
code on list so that I can help. I'd be interested to see what the
equations look like.

It's very simple to define a terminal event, just copy and adapt the
code in the file. You can write a loop so that whenever integration
has stopped because of an event, you reverse the velocity IC and
restart.

-Rob

On Mon, May 14, 2012 at 12:59 PM, Sara Gallian <sara2411 at gmail.com> wrote:
> Hi all,
> I'm trying to integrate particle trajectories in a finite domain, and I need to make them "reflect" at the boundaries (i.e. reverse the velocity perpendicular to the plane they collided with). I started by using ?scipy.integrate.odeint , but since the integration steps are variable, simply checking the position and reversing the velocity won't work!
> Can anyone suggest the quickest way to obtain this? would Vode be able to handle this, or should I try to learn to use ?PyDSTool?
> I'm running pretty late on a deadline, so any suggestion is more than appreciated :)
> Thanks!
> Sara
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user



-- 
Robert Clewley, Ph.D.
Assistant Professor
Neuroscience Institute and
Department of Mathematics and Statistics
Georgia State University
PO Box 5030
Atlanta, GA 30302, USA

tel: 404-413-6420 fax: 404-413-5446
http://www2.gsu.edu/~matrhc
http://neuroscience.gsu.edu/rclewley.html


From sara2411 at gmail.com  Mon May 14 14:03:28 2012
From: sara2411 at gmail.com (Sara Gallian)
Date: Mon, 14 May 2012 20:03:28 +0200
Subject: [SciPy-User] Trajectory Integration via scipy.integrate or
	PyDSTool?
In-Reply-To: <CA+7tCySWORaJhba=eDZLrh-UyFv5XXMW+pdXEE_zHDuXU4vhAQ@mail.gmail.com>
References: <F3F6A793-5AA5-4930-8FB9-AC19444D7767@gmail.com>
	<CA+7tCySWORaJhba=eDZLrh-UyFv5XXMW+pdXEE_zHDuXU4vhAQ@mail.gmail.com>
Message-ID: <CC803233-7FB7-4EA7-B851-D96B26E4005A@gmail.com>

Dear Rob,

thank you for the quick reply.
Unfortunately - I must say I'm  a bit ashamed of that - I have troubles with installing PyDSTool! :(
I'm using a Mac with Lion version 10.7.3 installed.
I copied the unzipped folder and changed the permission.
than added the path to my .profle file

PYTHONPATH="/usr/local/PyDSTool:${PYTHONPATH}"
export PYTHONPATH

The python version I'm using though, is the Enthough Distribution (EPD-7.1-2) , which path
PATH="/Library/Frameworks/EPD64.framework/Versions/Current/bin:${PATH}"
export PATH
So I can't set the variable PYTHON to /sw/bin/python2.6..

Is there a conflict with EPD version I have installed? Or am I just getting it totally wrong?
Thank you!

Sara

On May 14, 2012, at 7:22 PM, Rob Clewley wrote:

> Hi Sara,
> 
> I think you should use a tool that's appropriate for the task. With an
> hour of work and help on this list or the PyDSTool forum at
> http://sourceforge.net/projects/pydstool/forums/forum/472291 you
> should be able to get your problem working quickly with PyDSTool. I
> expect your problem is easy to solve in PyDSTool, and difficult to do
> with odeint.
> 
> You can still use scipy's VODE in PyDSTool and you won't need any
> fancy installation. Check out the file vode_event_test1.py in the
> tests directory. Writing the vector field is simple, but in case you
> have specific problems setting up yours then you should send me your
> code on list so that I can help. I'd be interested to see what the
> equations look like.
> 
> It's very simple to define a terminal event, just copy and adapt the
> code in the file. You can write a loop so that whenever integration
> has stopped because of an event, you reverse the velocity IC and
> restart.
> 
> -Rob
> 
> On Mon, May 14, 2012 at 12:59 PM, Sara Gallian <sara2411 at gmail.com> wrote:
>> Hi all,
>> I'm trying to integrate particle trajectories in a finite domain, and I need to make them "reflect" at the boundaries (i.e. reverse the velocity perpendicular to the plane they collided with). I started by using  scipy.integrate.odeint , but since the integration steps are variable, simply checking the position and reversing the velocity won't work!
>> Can anyone suggest the quickest way to obtain this? would Vode be able to handle this, or should I try to learn to use  PyDSTool?
>> I'm running pretty late on a deadline, so any suggestion is more than appreciated :)
>> Thanks!
>> Sara
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
> 
> 
> 
> -- 
> Robert Clewley, Ph.D.
> Assistant Professor
> Neuroscience Institute and
> Department of Mathematics and Statistics
> Georgia State University
> PO Box 5030
> Atlanta, GA 30302, USA
> 
> tel: 404-413-6420 fax: 404-413-5446
> http://www2.gsu.edu/~matrhc
> http://neuroscience.gsu.edu/rclewley.html
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user



From lou_boog2000 at yahoo.com  Mon May 14 14:03:42 2012
From: lou_boog2000 at yahoo.com (Lou Pecora)
Date: Mon, 14 May 2012 11:03:42 -0700 (PDT)
Subject: [SciPy-User] Trajectory Integration via scipy.integrate or
	PyDSTool?
In-Reply-To: <CA+7tCySWORaJhba=eDZLrh-UyFv5XXMW+pdXEE_zHDuXU4vhAQ@mail.gmail.com>
References: <F3F6A793-5AA5-4930-8FB9-AC19444D7767@gmail.com>
	<CA+7tCySWORaJhba=eDZLrh-UyFv5XXMW+pdXEE_zHDuXU4vhAQ@mail.gmail.com>
Message-ID: <1337018622.1991.YahooMailNeo@web160306.mail.bf1.yahoo.com>

Hi, Sara,

Can you give more information? ?

Are the particles in a potential or is it free flight between hard wall collisions? ?Do the particles interact? ?I assume the dynamics are conservative (Hamiltonian). Is that correct?
?
-- Lou Pecora,   my views are my own.


________________________________
 From: Rob Clewley <rob.clewley at gmail.com>
To: SciPy Users List <scipy-user at scipy.org> 
Sent: Monday, May 14, 2012 1:22 PM
Subject: Re: [SciPy-User] Trajectory Integration via scipy.integrate or PyDSTool?
 
Hi Sara,

I think you should use a tool that's appropriate for the task. With an
hour of work and help on this list or the PyDSTool forum at
http://sourceforge.net/projects/pydstool/forums/forum/472291 you
should be able to get your problem working quickly with PyDSTool. I
expect your problem is easy to solve in PyDSTool, and difficult to do
with odeint.

You can still use scipy's VODE in PyDSTool and you won't need any
fancy installation. Check out the file vode_event_test1.py in the
tests directory. Writing the vector field is simple, but in case you
have specific problems setting up yours then you should send me your
code on list so that I can help. I'd be interested to see what the
equations look like.

It's very simple to define a terminal event, just copy and adapt the
code in the file. You can write a loop so that whenever integration
has stopped because of an event, you reverse the velocity IC and
restart.

-Rob

On Mon, May 14, 2012 at 12:59 PM, Sara Gallian <sara2411 at gmail.com> wrote:
> Hi all,
> I'm trying to integrate particle trajectories in a finite domain, and I need to make them "reflect" at the boundaries (i.e. reverse the velocity perpendicular to the plane they collided with). I started by using ?scipy.integrate.odeint , but since the integration steps are variable, simply checking the position and reversing the velocity won't work!
> Can anyone suggest the quickest way to obtain this? would Vode be able to handle this, or should I try to learn to use ?PyDSTool?
> I'm running pretty late on a deadline, so any suggestion is more than appreciated :)
> Thanks!
> Sara
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user



-- 
Robert Clewley, Ph.D.
Assistant Professor
Neuroscience Institute and
Department of Mathematics and Statistics
Georgia State University
PO Box 5030
Atlanta, GA 30302, USA

tel: 404-413-6420 fax: 404-413-5446
http://www2.gsu.edu/~matrhc
http://neuroscience.gsu.edu/rclewley.html
_______________________________________________
SciPy-User mailing list
SciPy-User at scipy.org
http://mail.scipy.org/mailman/listinfo/scipy-user
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From sara2411 at gmail.com  Mon May 14 14:12:33 2012
From: sara2411 at gmail.com (Sara Gallian)
Date: Mon, 14 May 2012 20:12:33 +0200
Subject: [SciPy-User] Trajectory Integration via scipy.integrate or
	PyDSTool?
In-Reply-To: <1337018622.1991.YahooMailNeo@web160306.mail.bf1.yahoo.com>
References: <F3F6A793-5AA5-4930-8FB9-AC19444D7767@gmail.com>
	<CA+7tCySWORaJhba=eDZLrh-UyFv5XXMW+pdXEE_zHDuXU4vhAQ@mail.gmail.com>
	<1337018622.1991.YahooMailNeo@web160306.mail.bf1.yahoo.com>
Message-ID: <EC45B2EE-527E-4FA6-BCAA-63AFD2BE9B8A@gmail.com>

Dear Lou, 
I'm just moving a bunch of electrons in a static b field
dv/dt = q/m v x B
B depends on the position, in general
They move in this field, without interacting, till they bounce off a wall, that specularly reflects them
Yes, the dynamic is conservative.
I could just write a RK4 and check the position at every ts, but I was hoping to be able to use a better tool
Thanks
Sara

Sent from my iPhone

On 14.05.2012, at 20:03, Lou Pecora <lou_boog2000 at yahoo.com> wrote:

> Hi, Sara,
> 
> Can you give more information?  
> 
> Are the particles in a potential or is it free flight between hard wall collisions?  Do the particles interact?  I assume the dynamics are conservative (Hamiltonian). Is that correct?
>  
> -- Lou Pecora, my views are my own.
> From: Rob Clewley <rob.clewley at gmail.com>
> To: SciPy Users List <scipy-user at scipy.org> 
> Sent: Monday, May 14, 2012 1:22 PM
> Subject: Re: [SciPy-User] Trajectory Integration via scipy.integrate or PyDSTool?
> 
> Hi Sara,
> 
> I think you should use a tool that's appropriate for the task. With an
> hour of work and help on this list or the PyDSTool forum at
> http://sourceforge.net/projects/pydstool/forums/forum/472291 you
> should be able to get your problem working quickly with PyDSTool. I
> expect your problem is easy to solve in PyDSTool, and difficult to do
> with odeint.
> 
> You can still use scipy's VODE in PyDSTool and you won't need any
> fancy installation. Check out the file vode_event_test1.py in the
> tests directory. Writing the vector field is simple, but in case you
> have specific problems setting up yours then you should send me your
> code on list so that I can help. I'd be interested to see what the
> equations look like.
> 
> It's very simple to define a terminal event, just copy and adapt the
> code in the file. You can write a loop so that whenever integration
> has stopped because of an event, you reverse the velocity IC and
> restart.
> 
> -Rob
> 
> On Mon, May 14, 2012 at 12:59 PM, Sara Gallian <sara2411 at gmail.com> wrote:
> > Hi all,
> > I'm trying to integrate particle trajectories in a finite domain, and I need to make them "reflect" at the boundaries (i.e. reverse the velocity perpendicular to the plane they collided with). I started by using  scipy.integrate.odeint , but since the integration steps are variable, simply checking the position and reversing the velocity won't work!
> > Can anyone suggest the quickest way to obtain this? would Vode be able to handle this, or should I try to learn to use  PyDSTool?
> > I'm running pretty late on a deadline, so any suggestion is more than appreciated :)
> > Thanks!
> > Sara
> > _______________________________________________
> > SciPy-User mailing list
> > SciPy-User at scipy.org
> > http://mail.scipy.org/mailman/listinfo/scipy-user
> 
> 
> 
> -- 
> Robert Clewley, Ph.D.
> Assistant Professor
> Neuroscience Institute and
> Department of Mathematics and Statistics
> Georgia State University
> PO Box 5030
> Atlanta, GA 30302, USA
> 
> tel: 404-413-6420 fax: 404-413-5446
> http://www2.gsu.edu/~matrhc
> http://neuroscience.gsu.edu/rclewley.html
> _______________________________________________
> 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 matthew.brett at gmail.com  Mon May 14 14:36:56 2012
From: matthew.brett at gmail.com (Matthew Brett)
Date: Mon, 14 May 2012 11:36:56 -0700
Subject: [SciPy-User] Using scipy.io.loadmat to read Matlab
	containers.Map object?
In-Reply-To: <CACtgQxxVfELAep0GHPKKeAB+h0poAuLQQQaSme_r2647vj7CXw@mail.gmail.com>
References: <CACtgQxxVfELAep0GHPKKeAB+h0poAuLQQQaSme_r2647vj7CXw@mail.gmail.com>
Message-ID: <CAH6Pt5rcHrncqdVZAcCCrNnwdH4qxY1tWEn4H11iChUk9zWrxg@mail.gmail.com>

Hi,

On Mon, May 14, 2012 at 7:51 AM, Gerrit Holl <gerrit.holl at ltu.se> wrote:
> Hi,
>
> is it possible to use scipy.io.loadmat to read a Matlab containers.Map object?
>
>>> cm = containers.Map();
>>> cm('abc') = 42;
>>> save('/tmp/test.mat', 'cm');
>
>
> In [15]: M = scipy.io.loadmat('/tmp/test.mat')
>
> In [16]: M.keys()
> Out[16]: ['__function_workspace__', 'None', '__version__',
> '__header__', '__globals__']
>
> In [17]: M["None"]
> Out[17]:
> MatlabOpaque([ ('cm', 'MCOS', 'containers.Map', [[3707764736L], [2L],
> [1L], [1L], [1L], [1L]])],
> ? ? ?dtype=[('s0', '|O8'), ('s1', '|O8'), ('s2', '|O8'), ('arr', '|O8')])

Just because I didn't know what a container.Map was, it looks like
it's a MATLAB dict equivalent:

http://www.mathworks.com/help/techdoc/matlab_prog/brqqo69.html

I see that I or someone thought that types labeled as MatlabOpaque
were MATLAB functions:

https://github.com/scipy/scipy/blob/master/scipy/io/matlab/mio5_params.py#L51

but at least, the internal structure looks rather confusing.  Do you
recognize any of the structure in what got loaded?  Does it load OK
when loaded in Octave?

Best,

Matthew


From lou_boog2000 at yahoo.com  Mon May 14 14:46:15 2012
From: lou_boog2000 at yahoo.com (Lou Pecora)
Date: Mon, 14 May 2012 11:46:15 -0700 (PDT)
Subject: [SciPy-User] Trajectory Integration via scipy.integrate or
	PyDSTool?
In-Reply-To: <EC45B2EE-527E-4FA6-BCAA-63AFD2BE9B8A@gmail.com>
References: <F3F6A793-5AA5-4930-8FB9-AC19444D7767@gmail.com>
	<CA+7tCySWORaJhba=eDZLrh-UyFv5XXMW+pdXEE_zHDuXU4vhAQ@mail.gmail.com>
	<1337018622.1991.YahooMailNeo@web160306.mail.bf1.yahoo.com>
	<EC45B2EE-527E-4FA6-BCAA-63AFD2BE9B8A@gmail.com>
Message-ID: <1337021175.94572.YahooMailNeo@web160301.mail.bf1.yahoo.com>

Position dependent B makes it hard otherwise it could all be done geometrically (I'm sure you know that). ?I don't see how you can avoid checking for boundary collisions on each time step. Well, you would probably leave the boundaries out of the dynamics, but check for boundary crossing at each time step, then do an adaptive time step to get up to the boundary for a reflection (I hope that makes sense). ?If the boundaries are not complicated, checking for crossings is pretty straightforward.
?
-- Lou Pecora,   my views are my own.


________________________________
 From: Sara Gallian <sara2411 at gmail.com>
To: Lou Pecora <lou_boog2000 at yahoo.com>; SciPy Users List <scipy-user at scipy.org> 
Sent: Monday, May 14, 2012 2:12 PM
Subject: Re: [SciPy-User] Trajectory Integration via scipy.integrate or PyDSTool?
 

Dear Lou,?
I'm just moving a bunch of electrons in a static b field
dv/dt = q/m v x B
B depends on the position, in general
They move in this field, without interacting, till they bounce off a wall, that specularly reflects them
Yes, the dynamic is conservative.
I could just write a RK4 and check the position at every ts, but I was hoping to be able to use a better tool
Thanks
Sara

Sent from my iPhone

On 14.05.2012, at 20:03, Lou Pecora <lou_boog2000 at yahoo.com> wrote:


Hi, Sara,
>
>
>Can you give more information? ?
>
>
>Are the particles in a potential or is it free flight between hard wall collisions? ?Do the particles interact? ?I assume the dynamics are conservative (Hamiltonian). Is that correct?
>?
>-- Lou Pecora,   my views are my own.
>
>
>________________________________
> From: Rob Clewley <rob.clewley at gmail.com>
>To: SciPy Users List <scipy-user at scipy.org> 
>Sent: Monday, May 14, 2012 1:22 PM
>Subject: Re: [SciPy-User] Trajectory Integration via scipy.integrate or PyDSTool?
> 
>Hi Sara,
>
>I think you should use a tool that's appropriate for the task. With an
>hour of work and help on this list or the PyDSTool forum at
>http://sourceforge.net/projects/pydstool/forums/forum/472291 you
>should be able to get your problem working quickly with PyDSTool. I
>expect your problem is easy to solve in PyDSTool, and difficult to do
>with odeint.
>
>You can still use scipy's VODE in PyDSTool and you won't need any
>fancy installation. Check out the file vode_event_test1.py in the
>tests directory. Writing the vector field is simple, but in case you
>have specific problems setting up yours then you should send me your
>code on list so that I can help. I'd be interested to see what the
>equations look like.
>
>It's very simple to define a terminal event, just copy and adapt the
>code in the file. You can write a loop so that whenever integration
>has
 stopped because of an event, you reverse the velocity IC and
>restart.
>
>-Rob
>
>On Mon, May 14, 2012 at 12:59 PM, Sara Gallian <sara2411 at gmail.com> wrote:
>> Hi all,
>> I'm trying to integrate particle trajectories in a finite domain, and I need to make them "reflect" at the boundaries (i.e. reverse the velocity perpendicular to the plane they collided with). I started by using ?scipy.integrate.odeint , but since the integration steps are variable, simply checking the position and reversing the velocity won't work!
>> Can anyone suggest the quickest way to obtain this? would Vode be able to handle this, or should I try to learn to use ?PyDSTool?
>> I'm running pretty late on a deadline, so any suggestion is more than appreciated :)
>> Thanks!
>> Sara
>> _______________________________________________
>>
 SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
>
>-- 
>Robert Clewley, Ph.D.
>Assistant Professor
>Neuroscience Institute and
>Department of Mathematics and Statistics
>Georgia State University
>PO Box 5030
>Atlanta, GA 30302, USA
>
>tel: 404-413-6420 fax: 404-413-5446
>http://www2.gsu.edu/~matrhc
>http://neuroscience.gsu.edu/rclewley.html
>_______________________________________________
>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 rob.clewley at gmail.com  Mon May 14 16:28:56 2012
From: rob.clewley at gmail.com (Rob Clewley)
Date: Mon, 14 May 2012 16:28:56 -0400
Subject: [SciPy-User] Trajectory Integration via scipy.integrate or
	PyDSTool?
In-Reply-To: <EC45B2EE-527E-4FA6-BCAA-63AFD2BE9B8A@gmail.com>
References: <F3F6A793-5AA5-4930-8FB9-AC19444D7767@gmail.com>
	<CA+7tCySWORaJhba=eDZLrh-UyFv5XXMW+pdXEE_zHDuXU4vhAQ@mail.gmail.com>
	<1337018622.1991.YahooMailNeo@web160306.mail.bf1.yahoo.com>
	<EC45B2EE-527E-4FA6-BCAA-63AFD2BE9B8A@gmail.com>
Message-ID: <CA+7tCyRTAbMp7QY=Z=YhqiqNDsyLs4-ywbQZCkRy+W=XBv4Qpw@mail.gmail.com>

Sara,

Your Hamiltonian system really requires a symplectic integrator or
similar, as Lou mentions, that truly respects energy conservation. A
regular ODE integrator will create numerical error in the total
energy, as it is oblivious. In lieu of such a thing, at the very least
you can make a correction at those detected events in your loop to
restore the total energy.

Anyway, regarding your installation. You should change your PATH to
point to whichever version of python you are using, i.e. the version
that has numpy, scipy etc. installed through EPD. It looks like you
are using a Mac, so if you've installed EPD you should point to that
version of python with your PATH. /sw/ is not a valid directory with
EPD. If EPD installs outside of the Mac Framework then you will need
to change the path, but if it uses the Framework python then there is
nothing to change.

Also, and I haven't given this much thought, but in the absence of a
geometric integrator, you could possibly even solve the system more
accurately as a differential-algebraic system (DAE), using the energy
constant as the algebraic constraint. But there might be more
mathematical problems with that approach that I'm not seeing (I'm not
an expert). At least you could try setting such a thing with the Radau
integrator in PyDSTool, and there are DAE examples provided.

Feel free to share your equations and code in more detail if you're
still struggling. With small integration steps you might be fine using
RK4 with the energy correction at boundaries.

-Rob

On Mon, May 14, 2012 at 2:12 PM, Sara Gallian <sara2411 at gmail.com> wrote:
> Dear Lou,
> I'm just moving a bunch of electrons in a static b field
> dv/dt = q/m v x B
> B depends on the position, in general
> They move in this field, without interacting, till they bounce off a wall,
> that specularly reflects them
> Yes, the dynamic is conservative.
> I could just write a RK4 and check the position at every ts, but I was
> hoping to be able to use a better tool
> Thanks
> Sara
>
> Sent from my iPhone
>
> On 14.05.2012, at 20:03, Lou Pecora <lou_boog2000 at yahoo.com> wrote:
>
> Hi, Sara,
>
> Can you give more information?
>
> Are the particles in a potential or is it free flight between hard wall
> collisions? ?Do the particles interact? ?I assume the dynamics are
> conservative (Hamiltonian). Is that correct?
>
> -- Lou Pecora, my views are my own.
> ________________________________
> From: Rob Clewley <rob.clewley at gmail.com>
> To: SciPy Users List <scipy-user at scipy.org>
> Sent: Monday, May 14, 2012 1:22 PM
> Subject: Re: [SciPy-User] Trajectory Integration via scipy.integrate or
> PyDSTool?
>
> Hi Sara,
>
> I think you should use a tool that's appropriate for the task. With an
> hour of work and help on this list or the PyDSTool forum at
> http://sourceforge.net/projects/pydstool/forums/forum/472291 you
> should be able to get your problem working quickly with PyDSTool. I
> expect your problem is easy to solve in PyDSTool, and difficult to do
> with odeint.
>
> You can still use scipy's VODE in PyDSTool and you won't need any
> fancy installation. Check out the file vode_event_test1.py in the
> tests directory. Writing the vector field is simple, but in case you
> have specific problems setting up yours then you should send me your
> code on list so that I can help. I'd be interested to see what the
> equations look like.
>
> It's very simple to define a terminal event, just copy and adapt the
> code in the file. You can write a loop so that whenever integration
> has stopped because of an event, you reverse the velocity IC and
> restart.
>
> -Rob
>
> On Mon, May 14, 2012 at 12:59 PM, Sara Gallian <sara2411 at gmail.com> wrote:
>> Hi all,
>> I'm trying to integrate particle trajectories in a finite domain, and I
>> need to make them "reflect" at the boundaries (i.e. reverse the velocity
>> perpendicular to the plane they collided with). I started by using
>> ?scipy.integrate.odeint , but since the integration steps are variable,
>> simply checking the position and reversing the velocity won't work!
>> Can anyone suggest the quickest way to obtain this? would Vode be able to
>> handle this, or should I try to learn to use ?PyDSTool?
>> I'm running pretty late on a deadline, so any suggestion is more than
>> appreciated :)
>> Thanks!
>> Sara
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
>
> --
> Robert Clewley, Ph.D.
> Assistant Professor
> Neuroscience Institute and
> Department of Mathematics and Statistics
> Georgia State University
> PO Box 5030
> Atlanta, GA 30302, USA
>
> tel: 404-413-6420 fax: 404-413-5446
> http://www2.gsu.edu/~matrhc
> http://neuroscience.gsu.edu/rclewley.html
> _______________________________________________
> 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
>



-- 
Robert Clewley, Ph.D.
Assistant Professor
Neuroscience Institute and
Department of Mathematics and Statistics
Georgia State University
PO Box 5030
Atlanta, GA 30302, USA

tel: 404-413-6420 fax: 404-413-5446
http://www2.gsu.edu/~matrhc
http://neuroscience.gsu.edu/rclewley.html


From sara2411 at gmail.com  Mon May 14 16:47:10 2012
From: sara2411 at gmail.com (Sara Gallian)
Date: Mon, 14 May 2012 22:47:10 +0200
Subject: [SciPy-User] Trajectory Integration via scipy.integrate or
	PyDSTool?
In-Reply-To: <CA+7tCyRTAbMp7QY=Z=YhqiqNDsyLs4-ywbQZCkRy+W=XBv4Qpw@mail.gmail.com>
References: <F3F6A793-5AA5-4930-8FB9-AC19444D7767@gmail.com>
	<CA+7tCySWORaJhba=eDZLrh-UyFv5XXMW+pdXEE_zHDuXU4vhAQ@mail.gmail.com>
	<1337018622.1991.YahooMailNeo@web160306.mail.bf1.yahoo.com>
	<EC45B2EE-527E-4FA6-BCAA-63AFD2BE9B8A@gmail.com>
	<CA+7tCyRTAbMp7QY=Z=YhqiqNDsyLs4-ywbQZCkRy+W=XBv4Qpw@mail.gmail.com>
Message-ID: <D695A080-268D-401E-B468-696A16B08A23@gmail.com>

Thank you all for the suggestions! 

Indeed I know RK4 is not the best integrator for orbital-like problems, I just needed a "quick and dirty" solution to begin with (and show a few pictures :) ). I will definitely refine my little code later..

PyDSTool seems a really cool module, at any rate, but still I am a little confused. 
I have already set both paths in my .profile :

PYTHONPATH="/usr/local/PyDSTool:${PYTHONPATH}"
export PYTHONPATH

and 

PATH="/Library/Frameworks/EPD64.framework/Versions/Current/bin:${PATH}"
export PATH

but I receive an error when I try to import the module. 
I don't understand why.. 

Thank you very much for the patience!
Sara

On 14.05.2012, at 22:28, Rob Clewley wrote:

> Sara,
> 
> Your Hamiltonian system really requires a symplectic integrator or
> similar, as Lou mentions, that truly respects energy conservation. A
> regular ODE integrator will create numerical error in the total
> energy, as it is oblivious. In lieu of such a thing, at the very least
> you can make a correction at those detected events in your loop to
> restore the total energy.
> 
> Anyway, regarding your installation. You should change your PATH to
> point to whichever version of python you are using, i.e. the version
> that has numpy, scipy etc. installed through EPD. It looks like you
> are using a Mac, so if you've installed EPD you should point to that
> version of python with your PATH. /sw/ is not a valid directory with
> EPD. If EPD installs outside of the Mac Framework then you will need
> to change the path, but if it uses the Framework python then there is
> nothing to change.
> 
> Also, and I haven't given this much thought, but in the absence of a
> geometric integrator, you could possibly even solve the system more
> accurately as a differential-algebraic system (DAE), using the energy
> constant as the algebraic constraint. But there might be more
> mathematical problems with that approach that I'm not seeing (I'm not
> an expert). At least you could try setting such a thing with the Radau
> integrator in PyDSTool, and there are DAE examples provided.
> 
> Feel free to share your equations and code in more detail if you're
> still struggling. With small integration steps you might be fine using
> RK4 with the energy correction at boundaries.
> 
> -Rob
> 
> On Mon, May 14, 2012 at 2:12 PM, Sara Gallian <sara2411 at gmail.com> wrote:
>> Dear Lou,
>> I'm just moving a bunch of electrons in a static b field
>> dv/dt = q/m v x B
>> B depends on the position, in general
>> They move in this field, without interacting, till they bounce off a wall,
>> that specularly reflects them
>> Yes, the dynamic is conservative.
>> I could just write a RK4 and check the position at every ts, but I was
>> hoping to be able to use a better tool
>> Thanks
>> Sara
>> 
>> Sent from my iPhone
>> 
>> On 14.05.2012, at 20:03, Lou Pecora <lou_boog2000 at yahoo.com> wrote:
>> 
>> Hi, Sara,
>> 
>> Can you give more information?
>> 
>> Are the particles in a potential or is it free flight between hard wall
>> collisions?  Do the particles interact?  I assume the dynamics are
>> conservative (Hamiltonian). Is that correct?
>> 
>> -- Lou Pecora, my views are my own.
>> ________________________________
>> From: Rob Clewley <rob.clewley at gmail.com>
>> To: SciPy Users List <scipy-user at scipy.org>
>> Sent: Monday, May 14, 2012 1:22 PM
>> Subject: Re: [SciPy-User] Trajectory Integration via scipy.integrate or
>> PyDSTool?
>> 
>> Hi Sara,
>> 
>> I think you should use a tool that's appropriate for the task. With an
>> hour of work and help on this list or the PyDSTool forum at
>> http://sourceforge.net/projects/pydstool/forums/forum/472291 you
>> should be able to get your problem working quickly with PyDSTool. I
>> expect your problem is easy to solve in PyDSTool, and difficult to do
>> with odeint.
>> 
>> You can still use scipy's VODE in PyDSTool and you won't need any
>> fancy installation. Check out the file vode_event_test1.py in the
>> tests directory. Writing the vector field is simple, but in case you
>> have specific problems setting up yours then you should send me your
>> code on list so that I can help. I'd be interested to see what the
>> equations look like.
>> 
>> It's very simple to define a terminal event, just copy and adapt the
>> code in the file. You can write a loop so that whenever integration
>> has stopped because of an event, you reverse the velocity IC and
>> restart.
>> 
>> -Rob
>> 
>> On Mon, May 14, 2012 at 12:59 PM, Sara Gallian <sara2411 at gmail.com> wrote:
>>> Hi all,
>>> I'm trying to integrate particle trajectories in a finite domain, and I
>>> need to make them "reflect" at the boundaries (i.e. reverse the velocity
>>> perpendicular to the plane they collided with). I started by using
>>>  scipy.integrate.odeint , but since the integration steps are variable,
>>> simply checking the position and reversing the velocity won't work!
>>> Can anyone suggest the quickest way to obtain this? would Vode be able to
>>> handle this, or should I try to learn to use  PyDSTool?
>>> I'm running pretty late on a deadline, so any suggestion is more than
>>> appreciated :)
>>> Thanks!
>>> Sara
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>> 
>> 
>> 
>> --
>> Robert Clewley, Ph.D.
>> Assistant Professor
>> Neuroscience Institute and
>> Department of Mathematics and Statistics
>> Georgia State University
>> PO Box 5030
>> Atlanta, GA 30302, USA
>> 
>> tel: 404-413-6420 fax: 404-413-5446
>> http://www2.gsu.edu/~matrhc
>> http://neuroscience.gsu.edu/rclewley.html
>> _______________________________________________
>> 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
>> 
> 
> 
> 
> -- 
> Robert Clewley, Ph.D.
> Assistant Professor
> Neuroscience Institute and
> Department of Mathematics and Statistics
> Georgia State University
> PO Box 5030
> Atlanta, GA 30302, USA
> 
> tel: 404-413-6420 fax: 404-413-5446
> http://www2.gsu.edu/~matrhc
> http://neuroscience.gsu.edu/rclewley.html
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user



From cgohlke at uci.edu  Mon May 14 17:47:06 2012
From: cgohlke at uci.edu (Christoph Gohlke)
Date: Mon, 14 May 2012 14:47:06 -0700
Subject: [SciPy-User] 2D phase unwrapping
In-Reply-To: <5B6184CF-C72B-4B6D-AABE-306A7FD69EB7@gmail.com>
References: <FEE7C41D-5649-4A2B-A928-5A29F39259BC@gmail.com>
	<CABL7CQhLUrs_M2n0-3iv9nzbz-E-LGfX9JLpAsX2+UQCqM8D6A@mail.gmail.com>
	<5B6184CF-C72B-4B6D-AABE-306A7FD69EB7@gmail.com>
Message-ID: <4FB17D5A.40806@uci.edu>



On 5/14/2012 8:11 AM, Gregor Thalhammer wrote:
>
> Am 12.5.2012 um 11:58 schrieb Ralf Gommers:
>
>>
>>
>> On Fri, May 11, 2012 at 9:29 PM, Gregor Thalhammer
>> <gregor.thalhammer at gmail.com <mailto:gregor.thalhammer at gmail.com>> wrote:
>>
>>     Hi all,
>>
>>     I have beend searching for an implementation for phase unwrapping
>>     in 2D (and possibly also 3D). I found this old thread in the numpy
>>     mailing list
>>
>>     http://mail.scipy.org/pipermail/numpy-discussion/2008-November/038873.html
>>
>>     which mentions the C implementation from GERI:
>>     http://www.ljmu.ac.uk/GERI/90202.htm
>>
>>     While searching I found remarks by the authors that these
>>     algorithms have been incorporated into scipy, however I am unable
>>     to find them in current scipy or numpy. Am I missing something
>>     obvious?
>>     Instead I found this wrapper: https://github.com/pointtonull/pyunwrap
>>     This seems to be based on a wrapper already mentioned in the above
>>     mentioned discussion, but the links mentioned there are dead. I
>>     added some setup.py, and with some small modifications I managed
>>     to compile the extension both on OS X and Windows.
>>
>>     I would like to see these algorithms included in scipy, and I am
>>     willing to work on this.
>>
>>
>> Great!
>>
>>     So now my questions: In the old numpy-discussion thread licensing
>>     issues are raised, can anybody tell more? On the GERI homepage
>>     they distribute their code under a non-commercial-use license, but
>>     the authors seem to agree on incorporating their code into scipy.
>>     Except from this, what else would be required?
>>
>>
>> The emails about the license seem to be very clear to me, there's no
>> issue. So all it would take is someone submitting a complete wrapper
>> (with the normal requirements on docs/tests). The only things to
>> decide are then: do we want it in scipy (+1 from me), where to put it
>> and what the API should look like.
>
> Over the weekend I worked on the 2D and 3D phase unwrappers. I put a
> first version on github:
> https://github.com/geggo/phase-unwrap
> git://github.com/geggo/phase-unwrap.git
> I tested it on OS X with gcc-4.2, Python 2.7 and recent cython. (it
> crashes on Windows (MSVC 9), probably related to cython, but thats
> another story).
>
> Seems to work ok, only one basic test, no docs yet.
>
> The interface is quite simple:
>
> defunwrap(wrapped_array,
>
> wrap_around_axis_0 = False,
> wrap_around_axis_1 = False,
> wrap_around_axis_2 = False):
>
> it accepts an 2d or 3d numpy array or an masked array, and returns an
> masked array if one was given. A fresh float32 array is returned. The
> additional arguments can be used to specify cyclic boundary conditions
> along the given axis. The calculations are internally performed with
> float32. It should be quite straightforward to extend this to other
> types, but I think for the most common use cases for this algorithm
> (unwrapping of noisy data) a float32 array is sufficient.
>
> I guess a proper place in scipy would be to add this extension to
> scipy.ndimage.
>
> Please, any comments are welcome. And also some hints, why it crashes on
> Windows - or how to debug.
>
> Gregor
>

Explicitly defining the C function return types (e.g. as void) fixes the 
crash for me.

Christoph
-------------- next part --------------
diff --git a/unwrap2D/Hussein_3D_unwrapper_with_mask_and_wrap_around_option.c b/unwrap2D/Hussein_3D_unwrapper_with_mask_and_wrap_around_option.c
index e0e4cb9..08f51aa 100755
--- a/unwrap2D/Hussein_3D_unwrapper_with_mask_and_wrap_around_option.c
+++ b/unwrap2D/Hussein_3D_unwrapper_with_mask_and_wrap_around_option.c
@@ -1011,6 +1011,7 @@ void  returnVolume(VOXELM *voxel, float *unwrappedVolume, int volume_width, int
 }
 
 //the main function of the unwrapper
+void
 unwrap3D(float* wrapped_volume, float* unwrapped_volume, unsigned char* input_mask,
 	 int volume_width, int volume_height, int volume_depth,
 	 int wrap_around_x, int wrap_around_y, int wrap_around_z)
diff --git a/unwrap2D/Miguel_2D_unwrapper_with_mask_and_wrap_around_option.c b/unwrap2D/Miguel_2D_unwrapper_with_mask_and_wrap_around_option.c
index 3017c52..2e851eb 100755
--- a/unwrap2D/Miguel_2D_unwrapper_with_mask_and_wrap_around_option.c
+++ b/unwrap2D/Miguel_2D_unwrapper_with_mask_and_wrap_around_option.c
@@ -680,6 +680,7 @@ void  returnImage(PIXELM *pixel, float *unwrapped_image, int image_width, int im
 }
 
 //the main function of the unwrapper
+void
 unwrap2D(float* wrapped_image, float* UnwrappedImage, unsigned char* input_mask, 
 	 int image_width, int image_height, 
 	 int wrap_around_x, int wrap_around_y)
diff --git a/unwrap2D/unwrap2D.pyx b/unwrap2D/unwrap2D.pyx
index 9bd901d..dab5216 100755
--- a/unwrap2D/unwrap2D.pyx
+++ b/unwrap2D/unwrap2D.pyx
@@ -1,4 +1,4 @@
-cdef extern unwrap2D(float* wrapped_image, 
+cdef extern void unwrap2D(float* wrapped_image, 
                      float* unwrapped_image, 
                      unsigned char* input_mask, 
                      int image_width, int image_height,
diff --git a/unwrap2D/unwrap3D.pyx b/unwrap2D/unwrap3D.pyx
index 7a0aee9..aa18b37 100644
--- a/unwrap2D/unwrap3D.pyx
+++ b/unwrap2D/unwrap3D.pyx
@@ -1,4 +1,4 @@
-cdef extern unwrap3D(float* wrapped_volume, 
+cdef extern void unwrap3D(float* wrapped_volume, 
                      float* unwrapped_volume, 
                      unsigned char* input_mask, 
                      int image_width, int image_height, int volume_depth,

From rob.clewley at gmail.com  Mon May 14 17:56:04 2012
From: rob.clewley at gmail.com (Rob Clewley)
Date: Mon, 14 May 2012 17:56:04 -0400
Subject: [SciPy-User] Trajectory Integration via scipy.integrate or
	PyDSTool?
In-Reply-To: <D695A080-268D-401E-B468-696A16B08A23@gmail.com>
References: <F3F6A793-5AA5-4930-8FB9-AC19444D7767@gmail.com>
	<CA+7tCySWORaJhba=eDZLrh-UyFv5XXMW+pdXEE_zHDuXU4vhAQ@mail.gmail.com>
	<1337018622.1991.YahooMailNeo@web160306.mail.bf1.yahoo.com>
	<EC45B2EE-527E-4FA6-BCAA-63AFD2BE9B8A@gmail.com>
	<CA+7tCyRTAbMp7QY=Z=YhqiqNDsyLs4-ywbQZCkRy+W=XBv4Qpw@mail.gmail.com>
	<D695A080-268D-401E-B468-696A16B08A23@gmail.com>
Message-ID: <CA+7tCyQt9KLs11hxAeLM5zULjmb1aUjdTMRrDYn4uv667exJgw@mail.gmail.com>

On Mon, May 14, 2012 at 4:47 PM, Sara Gallian <sara2411 at gmail.com> wrote:
> I have already set both paths in my .profile :
[SNIP]
> but I receive an error when I try to import the module.
> I don't understand why..

For purely installation issues you should post to the PyDSTool user
forum in any follow up questions you still have. I don't think the
scipy list wants to hear about this non-scipy issue!

Briefly, though, if you look at the installation notes, it also shows
how you should write and place a PyDSTool.pth file in python's
site-packages folder. That is quite possibly your remaining problem.

-Rob



> On 14.05.2012, at 22:28, Rob Clewley wrote:
>
>> Sara,
>>
>> Your Hamiltonian system really requires a symplectic integrator or
>> similar, as Lou mentions, that truly respects energy conservation. A
>> regular ODE integrator will create numerical error in the total
>> energy, as it is oblivious. In lieu of such a thing, at the very least
>> you can make a correction at those detected events in your loop to
>> restore the total energy.
>>
>> Anyway, regarding your installation. You should change your PATH to
>> point to whichever version of python you are using, i.e. the version
>> that has numpy, scipy etc. installed through EPD. It looks like you
>> are using a Mac, so if you've installed EPD you should point to that
>> version of python with your PATH. /sw/ is not a valid directory with
>> EPD. If EPD installs outside of the Mac Framework then you will need
>> to change the path, but if it uses the Framework python then there is
>> nothing to change.
>>
>> Also, and I haven't given this much thought, but in the absence of a
>> geometric integrator, you could possibly even solve the system more
>> accurately as a differential-algebraic system (DAE), using the energy
>> constant as the algebraic constraint. But there might be more
>> mathematical problems with that approach that I'm not seeing (I'm not
>> an expert). At least you could try setting such a thing with the Radau
>> integrator in PyDSTool, and there are DAE examples provided.
>>
>> Feel free to share your equations and code in more detail if you're
>> still struggling. With small integration steps you might be fine using
>> RK4 with the energy correction at boundaries.
>>
>> -Rob
>>
>> On Mon, May 14, 2012 at 2:12 PM, Sara Gallian <sara2411 at gmail.com> wrote:
>>> Dear Lou,
>>> I'm just moving a bunch of electrons in a static b field
>>> dv/dt = q/m v x B
>>> B depends on the position, in general
>>> They move in this field, without interacting, till they bounce off a wall,
>>> that specularly reflects them
>>> Yes, the dynamic is conservative.
>>> I could just write a RK4 and check the position at every ts, but I was
>>> hoping to be able to use a better tool
>>> Thanks
>>> Sara
>>>
>>> Sent from my iPhone
>>>
>>> On 14.05.2012, at 20:03, Lou Pecora <lou_boog2000 at yahoo.com> wrote:
>>>
>>> Hi, Sara,
>>>
>>> Can you give more information?
>>>
>>> Are the particles in a potential or is it free flight between hard wall
>>> collisions? ?Do the particles interact? ?I assume the dynamics are
>>> conservative (Hamiltonian). Is that correct?
>>>
>>> -- Lou Pecora, my views are my own.
>>> ________________________________
>>> From: Rob Clewley <rob.clewley at gmail.com>
>>> To: SciPy Users List <scipy-user at scipy.org>
>>> Sent: Monday, May 14, 2012 1:22 PM
>>> Subject: Re: [SciPy-User] Trajectory Integration via scipy.integrate or
>>> PyDSTool?
>>>
>>> Hi Sara,
>>>
>>> I think you should use a tool that's appropriate for the task. With an
>>> hour of work and help on this list or the PyDSTool forum at
>>> http://sourceforge.net/projects/pydstool/forums/forum/472291 you
>>> should be able to get your problem working quickly with PyDSTool. I
>>> expect your problem is easy to solve in PyDSTool, and difficult to do
>>> with odeint.
>>>
>>> You can still use scipy's VODE in PyDSTool and you won't need any
>>> fancy installation. Check out the file vode_event_test1.py in the
>>> tests directory. Writing the vector field is simple, but in case you
>>> have specific problems setting up yours then you should send me your
>>> code on list so that I can help. I'd be interested to see what the
>>> equations look like.
>>>
>>> It's very simple to define a terminal event, just copy and adapt the
>>> code in the file. You can write a loop so that whenever integration
>>> has stopped because of an event, you reverse the velocity IC and
>>> restart.
>>>
>>> -Rob
>>>
>>> On Mon, May 14, 2012 at 12:59 PM, Sara Gallian <sara2411 at gmail.com> wrote:
>>>> Hi all,
>>>> I'm trying to integrate particle trajectories in a finite domain, and I
>>>> need to make them "reflect" at the boundaries (i.e. reverse the velocity
>>>> perpendicular to the plane they collided with). I started by using
>>>> ?scipy.integrate.odeint , but since the integration steps are variable,
>>>> simply checking the position and reversing the velocity won't work!
>>>> Can anyone suggest the quickest way to obtain this? would Vode be able to
>>>> handle this, or should I try to learn to use ?PyDSTool?
>>>> I'm running pretty late on a deadline, so any suggestion is more than
>>>> appreciated :)
>>>> Thanks!
>>>> Sara
>>>> _______________________________________________
>>>> SciPy-User mailing list
>>>> SciPy-User at scipy.org
>>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>
>>>
>>>
>>> --
>>> Robert Clewley, Ph.D.
>>> Assistant Professor
>>> Neuroscience Institute and
>>> Department of Mathematics and Statistics
>>> Georgia State University
>>> PO Box 5030
>>> Atlanta, GA 30302, USA
>>>
>>> tel: 404-413-6420 fax: 404-413-5446
>>> http://www2.gsu.edu/~matrhc
>>> http://neuroscience.gsu.edu/rclewley.html
>>> _______________________________________________
>>> 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
>>>
>>
>>
>>
>> --
>> Robert Clewley, Ph.D.
>> Assistant Professor
>> Neuroscience Institute and
>> Department of Mathematics and Statistics
>> Georgia State University
>> PO Box 5030
>> Atlanta, GA 30302, USA
>>
>> tel: 404-413-6420 fax: 404-413-5446
>> http://www2.gsu.edu/~matrhc
>> http://neuroscience.gsu.edu/rclewley.html
>> _______________________________________________
>> 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



-- 
Robert Clewley, Ph.D.
Assistant Professor
Neuroscience Institute and
Department of Mathematics and Statistics
Georgia State University
PO Box 5030
Atlanta, GA 30302, USA

tel: 404-413-6420 fax: 404-413-5446
http://www2.gsu.edu/~matrhc
http://neuroscience.gsu.edu/rclewley.html


From gregor.thalhammer at gmail.com  Tue May 15 03:17:19 2012
From: gregor.thalhammer at gmail.com (Gregor Thalhammer)
Date: Tue, 15 May 2012 09:17:19 +0200
Subject: [SciPy-User] 2D phase unwrapping
In-Reply-To: <4FB17D5A.40806@uci.edu>
References: <FEE7C41D-5649-4A2B-A928-5A29F39259BC@gmail.com>
	<CABL7CQhLUrs_M2n0-3iv9nzbz-E-LGfX9JLpAsX2+UQCqM8D6A@mail.gmail.com>
	<5B6184CF-C72B-4B6D-AABE-306A7FD69EB7@gmail.com>
	<4FB17D5A.40806@uci.edu>
Message-ID: <2049A88E-4CB3-444E-B239-1D122D865967@gmail.com>


Am 14.5.2012 um 23:47 schrieb Christoph Gohlke:

> 
> 
> On 5/14/2012 8:11 AM, Gregor Thalhammer wrote:
>> 
>> Am 12.5.2012 um 11:58 schrieb Ralf Gommers:
>> 
>>> 
>>> 
>>> On Fri, May 11, 2012 at 9:29 PM, Gregor Thalhammer
>>> <gregor.thalhammer at gmail.com <mailto:gregor.thalhammer at gmail.com>> wrote:
>>> 
>>>    Hi all,
>>> 
>>>    I have beend searching for an implementation for phase unwrapping
>>>    in 2D (and possibly also 3D). I found this old thread in the numpy
>>>    mailing list
>>> 
>>>    http://mail.scipy.org/pipermail/numpy-discussion/2008-November/038873.html
>>> 
>>>    which mentions the C implementation from GERI:
>>>    http://www.ljmu.ac.uk/GERI/90202.htm
>>> 
>>>    While searching I found remarks by the authors that these
>>>    algorithms have been incorporated into scipy, however I am unable
>>>    to find them in current scipy or numpy. Am I missing something
>>>    obvious?
>>>    Instead I found this wrapper: https://github.com/pointtonull/pyunwrap
>>>    This seems to be based on a wrapper already mentioned in the above
>>>    mentioned discussion, but the links mentioned there are dead. I
>>>    added some setup.py, and with some small modifications I managed
>>>    to compile the extension both on OS X and Windows.
>>> 
>>>    I would like to see these algorithms included in scipy, and I am
>>>    willing to work on this.
>>> 
>>> 
>>> Great!
>>> 
>>>    So now my questions: In the old numpy-discussion thread licensing
>>>    issues are raised, can anybody tell more? On the GERI homepage
>>>    they distribute their code under a non-commercial-use license, but
>>>    the authors seem to agree on incorporating their code into scipy.
>>>    Except from this, what else would be required?
>>> 
>>> 
>>> The emails about the license seem to be very clear to me, there's no
>>> issue. So all it would take is someone submitting a complete wrapper
>>> (with the normal requirements on docs/tests). The only things to
>>> decide are then: do we want it in scipy (+1 from me), where to put it
>>> and what the API should look like.
>> 
>> Over the weekend I worked on the 2D and 3D phase unwrappers. I put a
>> first version on github:
>> https://github.com/geggo/phase-unwrap
>> git://github.com/geggo/phase-unwrap.git
>> I tested it on OS X with gcc-4.2, Python 2.7 and recent cython. (it
>> crashes on Windows (MSVC 9), probably related to cython, but thats
>> another story).
>> 
>> Seems to work ok, only one basic test, no docs yet.
>> 
>> The interface is quite simple:
>> 
>> defunwrap(wrapped_array,
>> 
>> wrap_around_axis_0 = False,
>> wrap_around_axis_1 = False,
>> wrap_around_axis_2 = False):
>> 
>> it accepts an 2d or 3d numpy array or an masked array, and returns an
>> masked array if one was given. A fresh float32 array is returned. The
>> additional arguments can be used to specify cyclic boundary conditions
>> along the given axis. The calculations are internally performed with
>> float32. It should be quite straightforward to extend this to other
>> types, but I think for the most common use cases for this algorithm
>> (unwrapping of noisy data) a float32 array is sufficient.
>> 
>> I guess a proper place in scipy would be to add this extension to
>> scipy.ndimage.
>> 
>> Please, any comments are welcome. And also some hints, why it crashes on
>> Windows - or how to debug.
>> 
>> Gregor
>> 
> 
> Explicitly defining the C function return types (e.g. as void) fixes the crash for me.

> 
> Christoph


Great! Thanks a lot for spotting this. I would have never found this by myself.

Gregor




From a.klein at science-applied.nl  Tue May 15 03:55:01 2012
From: a.klein at science-applied.nl (Almar Klein)
Date: Tue, 15 May 2012 09:55:01 +0200
Subject: [SciPy-User] ANN: IEP 3.0 - the Interactive Editor for Python
Message-ID: <CAB_T6=k253AQcKxPxagKAfC_ohsXWn6m1k-MNsS0cq6DxHjKrQ@mail.gmail.com>

Dear all,

We're pleased to announce version 3.0 of the Interactive Editor for Python.

IEP is a cross-platform Python IDE focused on interactivity and
introspection, which makes it very suitable for scientific computing. Its
practical design is aimed at simplicity and efficiency. IEP is written in
Python 3 and Qt. Binaries are available for Windows, Linux, and Mac.

Website: http://code.google.com/p/iep/
Discussion group: http://groups.google.com/group/iep_
Release notes:
http://code.google.com/p/iep/wiki/Release#version_3.0_(14-05-2012)

Regards,
  Rob, Ludo & Almar
-------------- next part --------------
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From gerrit.holl at ltu.se  Tue May 15 04:17:37 2012
From: gerrit.holl at ltu.se (Gerrit Holl)
Date: Tue, 15 May 2012 10:17:37 +0200
Subject: [SciPy-User] Using scipy.io.loadmat to read Matlab
	containers.Map object?
In-Reply-To: <CAH6Pt5rcHrncqdVZAcCCrNnwdH4qxY1tWEn4H11iChUk9zWrxg@mail.gmail.com>
References: <CACtgQxxVfELAep0GHPKKeAB+h0poAuLQQQaSme_r2647vj7CXw@mail.gmail.com>
	<CAH6Pt5rcHrncqdVZAcCCrNnwdH4qxY1tWEn4H11iChUk9zWrxg@mail.gmail.com>
Message-ID: <CACtgQxwtVmak4242aW2Lem0KSakURp1136bBU-Wsv4i5i3d9sA@mail.gmail.com>

On 14 May 2012 20:36, Matthew Brett <matthew.brett at gmail.com> wrote:
> Hi,
>
> On Mon, May 14, 2012 at 7:51 AM, Gerrit Holl <gerrit.holl at ltu.se> wrote:
>> Hi,
>>
>> is it possible to use scipy.io.loadmat to read a Matlab containers.Map object?
>>
>>>> cm = containers.Map();
>>>> cm('abc') = 42;
>>>> save('/tmp/test.mat', 'cm');
>>
>>
>> In [15]: M = scipy.io.loadmat('/tmp/test.mat')
>>
>> In [16]: M.keys()
>> Out[16]: ['__function_workspace__', 'None', '__version__',
>> '__header__', '__globals__']
>>
>> In [17]: M["None"]
>> Out[17]:
>> MatlabOpaque([ ('cm', 'MCOS', 'containers.Map', [[3707764736L], [2L],
>> [1L], [1L], [1L], [1L]])],
>> ? ? ?dtype=[('s0', '|O8'), ('s1', '|O8'), ('s2', '|O8'), ('arr', '|O8')])
>
> Just because I didn't know what a container.Map was, it looks like
> it's a MATLAB dict equivalent:
>
> http://www.mathworks.com/help/techdoc/matlab_prog/brqqo69.html
>
> I see that I or someone thought that types labeled as MatlabOpaque
> were MATLAB functions:
>
> https://github.com/scipy/scipy/blob/master/scipy/io/matlab/mio5_params.py#L51
>
> but at least, the internal structure looks rather confusing. ?Do you
> recognize any of the structure in what got loaded? ?Does it load OK
> when loaded in Octave?

I don't recognise anything, and nor does Octave.

containers.Map are a relatively new type in Matlab, not very well
known and quite underused. They're indeed very close to being a Python
dict, except that all keys must be of the same type and can only be
numeric or character arrays. But that means that Matlab->Python is a
well-defined translation.

It appears the answer to my question is "no". Is this something to
file an 'issue' for, I suppose it would be desirable to be able to
read those?

Gerrit.

-- 
Gerrit Holl
PhD student at Division of Space Technology, Lule? University of
Technology, Kiruna, Sweden
http://www.sat.ltu.se/members/gerrit/


From matthew.brett at gmail.com  Tue May 15 04:37:58 2012
From: matthew.brett at gmail.com (Matthew Brett)
Date: Tue, 15 May 2012 01:37:58 -0700
Subject: [SciPy-User] Using scipy.io.loadmat to read Matlab
	containers.Map object?
In-Reply-To: <CACtgQxwtVmak4242aW2Lem0KSakURp1136bBU-Wsv4i5i3d9sA@mail.gmail.com>
References: <CACtgQxxVfELAep0GHPKKeAB+h0poAuLQQQaSme_r2647vj7CXw@mail.gmail.com>
	<CAH6Pt5rcHrncqdVZAcCCrNnwdH4qxY1tWEn4H11iChUk9zWrxg@mail.gmail.com>
	<CACtgQxwtVmak4242aW2Lem0KSakURp1136bBU-Wsv4i5i3d9sA@mail.gmail.com>
Message-ID: <CAH6Pt5qpizDLYBGyEGHi3J6ZtC_22anVEtj-4i7AqKic2HsRhA@mail.gmail.com>

Hi,

On Tue, May 15, 2012 at 1:17 AM, Gerrit Holl <gerrit.holl at ltu.se> wrote:
> On 14 May 2012 20:36, Matthew Brett <matthew.brett at gmail.com> wrote:
>> Hi,
>>
>> On Mon, May 14, 2012 at 7:51 AM, Gerrit Holl <gerrit.holl at ltu.se> wrote:
>>> Hi,
>>>
>>> is it possible to use scipy.io.loadmat to read a Matlab containers.Map object?
>>>
>>>>> cm = containers.Map();
>>>>> cm('abc') = 42;
>>>>> save('/tmp/test.mat', 'cm');
>>>
>>>
>>> In [15]: M = scipy.io.loadmat('/tmp/test.mat')
>>>
>>> In [16]: M.keys()
>>> Out[16]: ['__function_workspace__', 'None', '__version__',
>>> '__header__', '__globals__']
>>>
>>> In [17]: M["None"]
>>> Out[17]:
>>> MatlabOpaque([ ('cm', 'MCOS', 'containers.Map', [[3707764736L], [2L],
>>> [1L], [1L], [1L], [1L]])],
>>> ? ? ?dtype=[('s0', '|O8'), ('s1', '|O8'), ('s2', '|O8'), ('arr', '|O8')])
>>
>> Just because I didn't know what a container.Map was, it looks like
>> it's a MATLAB dict equivalent:
>>
>> http://www.mathworks.com/help/techdoc/matlab_prog/brqqo69.html
>>
>> I see that I or someone thought that types labeled as MatlabOpaque
>> were MATLAB functions:
>>
>> https://github.com/scipy/scipy/blob/master/scipy/io/matlab/mio5_params.py#L51
>>
>> but at least, the internal structure looks rather confusing. ?Do you
>> recognize any of the structure in what got loaded? ?Does it load OK
>> when loaded in Octave?
>
> I don't recognise anything, and nor does Octave.
>
> containers.Map are a relatively new type in Matlab, not very well
> known and quite underused. They're indeed very close to being a Python
> dict, except that all keys must be of the same type and can only be
> numeric or character arrays. But that means that Matlab->Python is a
> well-defined translation.
>
> It appears the answer to my question is "no". Is this something to
> file an 'issue' for, I suppose it would be desirable to be able to
> read those?

Yes, er, the answer is 'no', sorry about that.  But, please do file an
issue for it.  It would be good to be able to read them, but I guess
it will be hard work getting there.  Do you know of any documentation
for the binary (.mat file) format of these things?  You might get an
idea of where we are by scanning the comments at the top of
https://github.com/scipy/scipy/blob/master/scipy/io/matlab/mio5.py

Cheers,

Matthew


From servant.mathieu at gmail.com  Wed May 16 12:20:27 2012
From: servant.mathieu at gmail.com (servant mathieu)
Date: Wed, 16 May 2012 18:20:27 +0200
Subject: [SciPy-User] is it possible to constrain the
	scipy.optimize.curve_fit function?
Message-ID: <CALnu5bM+c9L7taG_CBHdJhw7xpe5amHSVBRUEX7pa7gnRN+-7Q@mail.gmail.com>

Dear scipy users,

I'm trying to fit to data a power law of the form :




def func (x, a,b, r):

      return r + a*np.power(x,-b)




I would like to constrain the curve_fit routine to only allow
positive parameter values. How is it possible to do so?



Kind regards,

Mathieu
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From david_baddeley at yahoo.com.au  Wed May 16 17:02:30 2012
From: david_baddeley at yahoo.com.au (David Baddeley)
Date: Wed, 16 May 2012 14:02:30 -0700 (PDT)
Subject: [SciPy-User] is it possible to constrain the
	scipy.optimize.curve_fit function?
In-Reply-To: <CALnu5bM+c9L7taG_CBHdJhw7xpe5amHSVBRUEX7pa7gnRN+-7Q@mail.gmail.com>
References: <CALnu5bM+c9L7taG_CBHdJhw7xpe5amHSVBRUEX7pa7gnRN+-7Q@mail.gmail.com>
Message-ID: <1337202150.22855.YahooMailNeo@web113414.mail.gq1.yahoo.com>

The quick and dirty way is to do a variable?substitution with the square of your parameter?and fit, e.g. r + (a**2)*np.power(x, -b**2)
You can take the sqrt of the parameters later.


________________________________
 From: servant mathieu <servant.mathieu at gmail.com>
To: scipy-user at scipy.org 
Sent: Thursday, 17 May 2012 4:20 AM
Subject: [SciPy-User] is it possible to constrain the scipy.optimize.curve_fit function?
 

Dear scipy users,
?
I'm trying to fit to?data?a power law of the form :
?
?
def func (x, a,b, r): 
????? return r + a*np.power(x,-b)?
?
I would like to constrain the curve_fit routine to only allow positive?parameter values.?How is it possible to do so?
?
Kind regards,
Mathieu?
_______________________________________________
SciPy-User mailing list
SciPy-User at scipy.org
http://mail.scipy.org/mailman/listinfo/scipy-user
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From markus.baden at gmail.com  Wed May 16 20:35:52 2012
From: markus.baden at gmail.com (Markus Baden)
Date: Thu, 17 May 2012 08:35:52 +0800
Subject: [SciPy-User] scipy.odr - Goodness of fit and parameter
 estimation for explicit orthogonal distance regression
In-Reply-To: <CAF6FJivY3Q0rZco+KVx1TkqddxA7XG0Z2EZSWXtOL0rx0tX4tQ@mail.gmail.com>
References: <CABo_DbCsygHe3nADrrBsA-+Khees2SMSPvEn3QJCbqU+X2RPaQ@mail.gmail.com>
	<CAF6FJivY3Q0rZco+KVx1TkqddxA7XG0Z2EZSWXtOL0rx0tX4tQ@mail.gmail.com>
Message-ID: <CABo_DbCzknZJOhCiAVcfYJjt0d9qMYDhDG-tBBKUP8Sbir6jsA@mail.gmail.com>

> scipy.odr provides an estimate of the covariance matrix and standard
> deviations of the parameter estimates. Getting the confidence interval
> for a parameter is just a matter of scaling up the standard deviations
> by the appropriate t-distribution value with nobs-nparams degrees of
> freedom. A paper by the ODRPACK implementors gives the formula
> explicitly on page 6:
>
>  http://www.mechanicalkern.com/static/odr_vcv.pdf
>
> It also has more information on how the covariance matrix is calculated.
>

Thanks for the pointer to the paper. That is exactly what I was looking
for. Thanks also for the other clarification (and making scipy.odr!)

Regards,

Markus
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From servant.mathieu at gmail.com  Thu May 17 05:00:15 2012
From: servant.mathieu at gmail.com (servant mathieu)
Date: Thu, 17 May 2012 11:00:15 +0200
Subject: [SciPy-User] is it possible to constrain the
 scipy.optimize.curve_fit function?
In-Reply-To: <1337202150.22855.YahooMailNeo@web113414.mail.gq1.yahoo.com>
References: <CALnu5bM+c9L7taG_CBHdJhw7xpe5amHSVBRUEX7pa7gnRN+-7Q@mail.gmail.com>
	<1337202150.22855.YahooMailNeo@web113414.mail.gq1.yahoo.com>
Message-ID: <CALnu5bOdKxNQkZVAZRmiZ=6fau28+4ndDfNCzLOWbXDt72f4PA@mail.gmail.com>

Hi David,
That's really dirty but it works thanks :-). More generally, is there a
standard procedure to constrain a specific parameter to fall in a specific
range (e.g., [200, 400]) during the fitting process?

Mathieu

2012/5/16 David Baddeley <david_baddeley at yahoo.com.au>

> The quick and dirty way is to do a variable substitution with the square
> of your parameter and fit, e.g. r + (a**2)*np.power(x, -b**2)
> You can take the sqrt of the parameters later.
>
>   ------------------------------
> *From:* servant mathieu <servant.mathieu at gmail.com>
> *To:* scipy-user at scipy.org
> *Sent:* Thursday, 17 May 2012 4:20 AM
> *Subject:* [SciPy-User] is it possible to constrain the
> scipy.optimize.curve_fit function?
>
> Dear scipy users,
>
> I'm trying to fit to data a power law of the form :
>
>
> def func (x, a,b, r):
>       return r + a*np.power(x,-b)
>
>
> I would like to constrain the curve_fit routine to only allow
> positive parameter values. How is it possible to do so?
>
> Kind regards,
> Mathieu
>
> _______________________________________________
> 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 vaggi.federico at gmail.com  Thu May 17 05:51:05 2012
From: vaggi.federico at gmail.com (federico vaggi)
Date: Thu, 17 May 2012 11:51:05 +0200
Subject: [SciPy-User] is it possible to constrain the
	scipy.optimize.curve_fit function?
Message-ID: <CAGvd0=h3RfSkTp=qffzUDtG-SQdtMNH1u_ieVErJ4nYoH8kuSw@mail.gmail.com>

Afaik,

there was a big discussion about this a while ago, and the short answer is,
currently there is no 'automatic' way to do it.  However, in your case,
it's pretty easy.

Simply define:

def func (x, a,b, r):
     a = abs(a)
     b = abs(b)
     r = abs(r)
     return r + a*np.power(x,-b)

And that will do the trick.  If you need to more complex boundaries, you
can simply use a combination of period functions with a given amplitude or
what have you.  Alternatively, there are *a lot* of optimization libraries
available for Python that are not a part of scipy that offer the
possibility to specify boundaries.

For example:

http://newville.github.com/lmfit-py/
http://ab-initio.mit.edu/wiki/index.php/NLopt_Python_Reference

Federico



Date: Wed, 16 May 2012 18:20:27 +0200
> From: servant mathieu <servant.mathieu at gmail.com>
> Subject: [SciPy-User] is it possible to constrain the
>        scipy.optimize.curve_fit function?
> To: scipy-user at scipy.org
> Message-ID:
>        <CALnu5bM+c9L7taG_CBHdJhw7xpe5amHSVBRUEX7pa7gnRN+-7Q at mail.gmail.com
> >
> Content-Type: text/plain; charset="iso-8859-1"
>
> Dear scipy users,
>
> I'm trying to fit to data a power law of the form :
>
>
>
>
> def func (x, a,b, r):
>
>      return r + a*np.power(x,-b)
>
>
>
>
> I would like to constrain the curve_fit routine to only allow
> positive parameter values. How is it possible to do so?
>
>
>
> Kind regards,
>
> Mathieu
>
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From david_baddeley at yahoo.com.au  Thu May 17 15:47:43 2012
From: david_baddeley at yahoo.com.au (David Baddeley)
Date: Thu, 17 May 2012 12:47:43 -0700 (PDT)
Subject: [SciPy-User] is it possible to constrain the
	scipy.optimize.curve_fit function?
Message-ID: <1337284063.28406.BPMail_high_noncarrier@web113410.mail.gq1.yahoo.com>


I'd caution against using abs, as abs(x) is not differentiable around 0 and could cause a gradient descent solver to get stuck/confused. x**2 on the other hand is fully differentiable, but requires you to take the sqrt of the parameters after fitting.



------------------------------
On Thu, May 17, 2012 9:51 PM NZST federico vaggi wrote:

>Afaik,
>
>there was a big discussion about this a while ago, and the short answer is,
>currently there is no 'automatic' way to do it.  However, in your case,
>it's pretty easy.
>
>Simply define:
>
>def func (x, a,b, r):
>     a = abs(a)
>     b = abs(b)
>     r = abs(r)
>     return r + a*np.power(x,-b)
>
>And that will do the trick.  If you need to more complex boundaries, you
>can simply use a combination of period functions with a given amplitude or
>what have you.  Alternatively, there are *a lot* of optimization libraries
>available for Python that are not a part of scipy that offer the
>possibility to specify boundaries.
>
>For example:
>
>http://newville.github.com/lmfit-py/
>http://ab-initio.mit.edu/wiki/index.php/NLopt_Python_Reference
>
>Federico
>
>
>
>Date: Wed, 16 May 2012 18:20:27 +0200
>> From: servant mathieu <servant.mathieu at gmail.com>
>> Subject: [SciPy-User] is it possible to constrain the
>>        scipy.optimize.curve_fit function?
>> To: scipy-user at scipy.org
>> Message-ID:
>>        <CALnu5bM+c9L7taG_CBHdJhw7xpe5amHSVBRUEX7pa7gnRN+-7Q at mail.gmail.com
>> >
>> Content-Type: text/plain; charset="iso-8859-1"
>>
>> Dear scipy users,
>>
>> I'm trying to fit to data a power law of the form :
>>
>>
>>
>>
>> def func (x, a,b, r):
>>
>>      return r + a*np.power(x,-b)
>>
>>
>>
>>
>> I would like to constrain the curve_fit routine to only allow
>> positive parameter values. How is it possible to do so?
>>
>>
>>
>> Kind regards,
>>
>> Mathieu
>>



From pav at iki.fi  Thu May 17 17:02:12 2012
From: pav at iki.fi (Pauli Virtanen)
Date: Thu, 17 May 2012 23:02:12 +0200
Subject: [SciPy-User] is it possible to constrain the
	scipy.optimize.curve_fit function?
In-Reply-To: <CAGvd0=h3RfSkTp=qffzUDtG-SQdtMNH1u_ieVErJ4nYoH8kuSw@mail.gmail.com>
References: <CAGvd0=h3RfSkTp=qffzUDtG-SQdtMNH1u_ieVErJ4nYoH8kuSw@mail.gmail.com>
Message-ID: <jp3p0o$2fg$1@dough.gmane.org>

17.05.2012 11:51, federico vaggi kirjoitti:
[clip]
> And that will do the trick.  If you need to more complex boundaries, you
> can simply use a combination of period functions with a given amplitude
> or what have you.  Alternatively, there are *a lot* of optimization
> libraries available for Python that are not a part of scipy that offer
> the possibility to specify boundaries.

Note that Scipy has several solvers that support bounds in optimization
problems --- to use those for least squares, you'll just need to do
"return (r**2).sum()" yourself. This is AFAIK also what lmfit does, in
addition to clipping parameter values within the bounds in the residual
function (I'm not sure how robust the results such clipping produces are).

	Pauli



From sergio_r at mail.com  Fri May 18 16:13:00 2012
From: sergio_r at mail.com (Sergio Rojas)
Date: Fri, 18 May 2012 16:13:00 -0400
Subject: [SciPy-User] about scipy performance
Message-ID: <20120518201300.17900@gmx.com>

Running the performance test of scipy presented at
 [ http://software.intel.com/en-us/articles/numpy-scipy-with-mkl/ ] I found it strange a
 decrease in the time that takes the computation for a large K value (see for instance the value of
 tm corresponding to k=192 and k=200). Does this behaviour makes sense? Is there any test suite available
 to better check the performance of a scipy installation?

 Sergio
 PD. the output of np.show_config() is shown below the data.

 K TM GFLOPS
 64, 0.2182408, 34.91556
 80, 0.2414728, 39.50755
 96, 0.2986258, 38.37579
 104, 0.3231602, 38.43295
 112, 0.3429056, 39.01948
 120, 0.3645972, 39.33108
 128, 0.4074092, 37.55438
 144, 0.445568, 38.6473
 160, 0.4914636, 38.9449
 176, 0.5274322, 39.92931
 192, 0.6281674, 36.5826
 200, 0.6149654, 38.92902
 208, 0.6355928, 39.17603
 224, 0.673929, 39.79648
 240, 0.7244088, 39.67373
 256, 0.8612222, 35.60057
 384, 1.185954, 38.80421



 atlas_threads_info:
 libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
 library_dirs = ['/home/srojas/myPROG/LapackLib_gfortran/Atlas64b/lib']
 define_macros = [('ATLAS_INFO', '"\\"3.9.72\\""')]
 language = f77
 include_dirs = ['/home/srojas/myPROG/LapackLib_gfortran/Atlas64b/include']
 blas_opt_info:
 libraries = ['ptf77blas', 'ptcblas', 'atlas']
 library_dirs = ['/home/srojas/myPROG/LapackLib_gfortran/Atlas64b/lib']
 define_macros = [('ATLAS_INFO', '"\\"3.9.72\\""')]
 language = c
 include_dirs = ['/home/srojas/myPROG/LapackLib_gfortran/Atlas64b/include']
 atlas_blas_threads_info:
 libraries = ['ptf77blas', 'ptcblas', 'atlas']
 library_dirs = ['/home/srojas/myPROG/LapackLib_gfortran/Atlas64b/lib']
 define_macros = [('ATLAS_INFO', '"\\"3.9.72\\""')]
 language = c
 include_dirs = ['/home/srojas/myPROG/LapackLib_gfortran/Atlas64b/include']
 lapack_opt_info:
 libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
 library_dirs = ['/home/srojas/myPROG/LapackLib_gfortran/Atlas64b/lib']
 define_macros = [('ATLAS_INFO', '"\\"3.9.72\\""')]
 language = f77
 include_dirs = ['/home/srojas/myPROG/LapackLib_gfortran/Atlas64b/include']
 lapack_mkl_info:
 NOT AVAILABLE
 blas_mkl_info:
 NOT AVAILABLE
 mkl_info:
 NOT AVAILABLE
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From david_baddeley at yahoo.com.au  Fri May 18 16:28:19 2012
From: david_baddeley at yahoo.com.au (David Baddeley)
Date: Fri, 18 May 2012 13:28:19 -0700 (PDT)
Subject: [SciPy-User] about scipy performance
Message-ID: <1337372899.50497.BPMail_high_noncarrier@web113409.mail.gq1.yahoo.com>


Is this reproducible, or could it just be timing noise?

In general though, I'm not sure you can expect performance to be strictly monotonic in k - there are all sorts of issues (e.g. cache coherency) which can make some sizes more efficient than others. The departures from monotonicity that you see are really rather small compared to those which are present in e.g. fftw.

Cheers,
David

------------------------------
On Sat, May 19, 2012 8:13 AM NZST Sergio Rojas wrote:

>Running the performance test of scipy presented at
> [ http://software.intel.com/en-us/articles/numpy-scipy-with-mkl/ ] I found it strange a
> decrease in the time that takes the computation for a large K value (see for instance the value of
> tm corresponding to k=192 and k=200). Does this behaviour makes sense? Is there any test suite available
> to better check the performance of a scipy installation?
>
> Sergio
> PD. the output of np.show_config() is shown below the data.
>
> K TM GFLOPS
> 64, 0.2182408, 34.91556
> 80, 0.2414728, 39.50755
> 96, 0.2986258, 38.37579
> 104, 0.3231602, 38.43295
> 112, 0.3429056, 39.01948
> 120, 0.3645972, 39.33108
> 128, 0.4074092, 37.55438
> 144, 0.445568, 38.6473
> 160, 0.4914636, 38.9449
> 176, 0.5274322, 39.92931
> 192, 0.6281674, 36.5826
> 200, 0.6149654, 38.92902
> 208, 0.6355928, 39.17603
> 224, 0.673929, 39.79648
> 240, 0.7244088, 39.67373
> 256, 0.8612222, 35.60057
> 384, 1.185954, 38.80421
>
>
>
> atlas_threads_info:
> libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
> library_dirs = ['/home/srojas/myPROG/LapackLib_gfortran/Atlas64b/lib']
> define_macros = [('ATLAS_INFO', '"\\"3.9.72\\"')]
> language = f77
> include_dirs = ['/home/srojas/myPROG/LapackLib_gfortran/Atlas64b/include']
> blas_opt_info:
> libraries = ['ptf77blas', 'ptcblas', 'atlas']
> library_dirs = ['/home/srojas/myPROG/LapackLib_gfortran/Atlas64b/lib']
> define_macros = [('ATLAS_INFO', '"\\"3.9.72\\"')]
> language = c
> include_dirs = ['/home/srojas/myPROG/LapackLib_gfortran/Atlas64b/include']
> atlas_blas_threads_info:
> libraries = ['ptf77blas', 'ptcblas', 'atlas']
> library_dirs = ['/home/srojas/myPROG/LapackLib_gfortran/Atlas64b/lib']
> define_macros = [('ATLAS_INFO', '"\\"3.9.72\\"')]
> language = c
> include_dirs = ['/home/srojas/myPROG/LapackLib_gfortran/Atlas64b/include']
> lapack_opt_info:
> libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
> library_dirs = ['/home/srojas/myPROG/LapackLib_gfortran/Atlas64b/lib']
> define_macros = [('ATLAS_INFO', '"\\"3.9.72\\"')]
> language = f77
> include_dirs = ['/home/srojas/myPROG/LapackLib_gfortran/Atlas64b/include']
> lapack_mkl_info:
> NOT AVAILABLE
> blas_mkl_info:
> NOT AVAILABLE
> mkl_info:
> NOT AVAILABLE



From sergio_r at mail.com  Fri May 18 18:46:09 2012
From: sergio_r at mail.com (Sergio Rojas)
Date: Fri, 18 May 2012 18:46:09 -0400
Subject: [SciPy-User] scipy test error: undefined symbol: ATL_buildinfo
Message-ID: <20120518224609.17910@gmx.com>

Hello guys,

 After struggling for a while, I just finished the installation
 of scipy using the intel mkl library.

 Ungracefully the tests ended with an error regarding
 "undefined symbol: ATL_buildinfo" (see below)

 Can this scipy installation live with this error? or
 Is it prone to throw out wrong computations?

 Also, why the number of ran test is not a fix number
 (running the tests using a scipy/Atlas installation
 finished succesfully indicating: Ran 5832 tests in 422.155s
 OK (KNOWNFAIL=14, SKIP=42), but the scipy/mkl tests ended with
 Ran 5833 tests in 448.386s FAILED (KNOWNFAIL=14, SKIP=34, errors=1))?

 what is the meaning of KNOWNFAIL=14?

 Sergio

 >$ python_mkl
 Python 2.7.2 (default, May 16 2012, 13:06:17)
 [GCC 4.6.1] on linux3
 Type "help", "copyright", "credits" or "license" for more information.
 >>> import scipy
 >>> scipy.show_config()
 umfpack_info:
 NOT AVAILABLE
 lapack_opt_info:
 libraries = ['mkl_lapack95_ilp64', 'mkl_lapack95_lp64', 'mkl_rt', 'mkl_intel
 _lp64', 'mkl_intel_thread', 'mkl_core', 'pthread']
 library_dirs = ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/l
 ib/intel64']
 define_macros = [('SCIPY_MKL_H', None)]
 include_dirs = ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/i
 nclude']
 blas_opt_info:
 libraries = ['mkl_rt', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'pt
 hread']
 library_dirs = ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/l
 ib/intel64']
 define_macros = [('SCIPY_MKL_H', None)]
 include_dirs = ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/i
 nclude']
 lapack_mkl_info:
 libraries = ['mkl_lapack95_ilp64', 'mkl_lapack95_lp64', 'mkl_rt', 'mkl_intel
 _lp64', 'mkl_intel_thread', 'mkl_core', 'pthread']
 library_dirs = ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/l
 ib/intel64']
 define_macros = [('SCIPY_MKL_H', None)]
 include_dirs = ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/i
 nclude']
 blas_mkl_info:
 libraries = ['mkl_rt', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'pt
 hread']
 library_dirs = ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/l
 ib/intel64']
 define_macros = [('SCIPY_MKL_H', None)]
 include_dirs = ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/i
 nclude']
 mkl_info:
 libraries = ['mkl_rt', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'pt
 hread']
 library_dirs = ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/l
 ib/intel64']
 define_macros = [('SCIPY_MKL_H', None)]
 include_dirs = ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/i
 nclude']
 >>> scipy.test('full', verbose=2)
 Running unit tests for scipy
 NumPy version 1.6.1
 NumPy is installed in /home/srojas/myPROG/Python272GnuMKL/lib/python2.7/site-pac
 kages/numpy
 SciPy version 0.10.1
 SciPy is installed in /home/srojas/myPROG/Python272GnuMKL/lib/python2.7/site-pac
 kages/scipy
 Python version 2.7.2 (default, May 16 2012, 13:06:17) [GCC 4.6.1]
 nose version 1.1.2
 ...
 ... (DELETED OUTPUT)
 ...
 ======================================================================
 ERROR: Failure: ImportError (/home/srojas/myPROG/Python272GnuMKL/lib/python2.7/s
 ite-packages/scipy/linalg/atlas_version.so: undefined symbol: ATL_buildinfo)
 ----------------------------------------------------------------------
 Traceback (most recent call last):
 File "/home/srojas/myPROG/Python272GnuMKL/lib/python2.7/site-packages/nose/loa
 der.py", line 390, in loadTestsFromName
 addr.filename, addr.module)
 File "/home/srojas/myPROG/Python272GnuMKL/lib/python2.7/site-packages/nose/imp
 orter.py", line 39, in importFromPath
 return self.importFromDir(dir_path, fqname)
 File "/home/srojas/myPROG/Python272GnuMKL/lib/python2.7/site-packages/nose/imp
 orter.py", line 86, in importFromDir
 mod = load_module(part_fqname, fh, filename, desc)
 File "/home/srojas/myPROG/Python272GnuMKL/lib/python2.7/site-packages/scipy/li
 nalg/tests/test_atlas_version.py", line 6, in <module>
 import scipy.linalg.atlas_version
 ImportError: /home/srojas/myPROG/Python272GnuMKL/lib/python2.7/site-packages/sci
 py/linalg/atlas_version.so: undefined symbol: ATL_buildinfo

 ----------------------------------------------------------------------
 Ran 5833 tests in 448.386s

 FAILED (KNOWNFAIL=14, SKIP=34, errors=1)
 <nose.result.TextTestResult run=5833 errors=1 failures=0>
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From f_magician at mac.com  Sat May 19 11:09:39 2012
From: f_magician at mac.com (Magician)
Date: Sun, 20 May 2012 00:09:39 +0900
Subject: [SciPy-User] SciPy installation troubles on CentOS 6.2
Message-ID: <4CD3A5FA-D012-4EAF-82C3-53D56AAB19F6@mac.com>

Hi All,


I'm trying to build SciPy from source code,
but I have some troubles.

My environment is below:
> CentOS 6.2 on VMware Fusion 4.1.2 (CentOS was installed as Software Development WS)
> Python 2.7.3 (already built from sources, installed at /usr/local/python-2.7.3)
> NumPy 1.6.1, SciPy 0.10.1, ATLAS 3.8.4, Lapack 3.4.1 (now trying to build)
I and my colleagues (other users) want to use recent Python,
so I installed Python from sources, and I can't install SciPy
by using yum command.

Now I'm facing ATLAS compiling errors.
Configuration options are "--prefix=/usr/local/atlas-3.8.4 -Fa alg -fPIC".
I tried to build it for several times, and always I got errors as below:
> res/dgemvN_6_75 : VARIATION EXCEEDS TOLERENCE, RERUN WITH HIGHER REPS.
> 
> ATL_gemvN_mm.c : 1257.99
> ATL_gemvN_1x1_1.c : 581.74
> ATL_gemvN_1x1_1a.c : 1589.45
> ATL_gemvN_4x2_0.c : 813.13
> ATL_gemvN_4x4_1.c : 755.54
> make[3]: *** [res/dMVRES] Error 255
> make[3]: Leaving directory `/home/magician/Desktop/ATLAS/build/tune/blas/gemv'
> make[2]: *** [/home/magician/Desktop/ATLAS/build/tune/blas/gemv/res/dMVRES] Error 2
> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin'
> ERROR 734 DURING MVTUNE!!.  CHECK INSTALL_LOG/dMVTUNE.LOG FOR DETAILS.
> make[2]: Entering directory `/home/magician/Desktop/ATLAS/build/bin'
> cd /home/magician/Desktop/ATLAS/build ; make error_report
> make[3]: Entering directory `/home/magician/Desktop/ATLAS/build'
> make -f Make.top error_report
> make[4]: Entering directory `/home/magician/Desktop/ATLAS/build'
> uname -a 2>&1 >> bin/INSTALL_LOG/ERROR.LOG
> gcc -v 2>&1  >> bin/INSTALL_LOG/ERROR.LOG
> Using built-in specs.
> Target: x86_64-redhat-linux
> Configured with: ../configure --prefix=/usr --mandir=/usr/share/man --infodir=/usr/share/info --with-bugurl=http://bugzilla.redhat.com/bugzilla --enable-bootstrap --enable-shared --enable-threads=posix --enable-checking=release --with-system-zlib --enable-__cxa_atexit --disable-libunwind-exceptions --enable-gnu-unique-object --enable-languages=c,c++,objc,obj-c++,java,fortran,ada --enable-java-awt=gtk --disable-dssi --with-java-home=/usr/lib/jvm/java-1.5.0-gcj-1.5.0.0/jre --enable-libgcj-multifile --enable-java-maintainer-mode --with-ecj-jar=/usr/share/java/eclipse-ecj.jar --disable-libjava-multilib --with-ppl --with-cloog --with-tune=generic --with-arch_32=i686 --build=x86_64-redhat-linux
> Thread model: posix
> gcc version 4.4.6 20110731 (Red Hat 4.4.6-3) (GCC) 
> gcc -V 2>&1  >> bin/INSTALL_LOG/ERROR.LOG
> gcc: '-V' option must have argument
> make[4]: [error_report] Error 1 (ignored)
> gcc --version 2>&1  >> bin/INSTALL_LOG/ERROR.LOG
> tar cf error_Corei264SSE3.tar Make.inc bin/INSTALL_LOG/*
> gzip --best error_Corei264SSE3.tar
> mv error_Corei264SSE3.tar.gz error_Corei264SSE3.tgz
> make[4]: Leaving directory `/home/magician/Desktop/ATLAS/build'
> make[3]: Leaving directory `/home/magician/Desktop/ATLAS/build'
> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin'
> Error report error_<ARCH>.tgz has been created in your top-level ATLAS
> directory.  Be sure to include this file in any help request.
> cat: ../../CONFIG/error.txt: No such file or directory
> cat: ../../CONFIG/error.txt: No such file or directory
> make[1]: *** [build] Error 255
> make[1]: Leaving directory `/home/magician/Desktop/ATLAS/build'
> make: *** [build] Error 2

It's very troublesome for me to build ATLAS by myself.
My purpose is just using SciPy on my Python.
Even if it's optimized not so good for my environment, it's OK.

Is there an easy or a sure way to build and install SciPy?


Magician


From ralf.gommers at googlemail.com  Sun May 20 04:21:12 2012
From: ralf.gommers at googlemail.com (Ralf Gommers)
Date: Sun, 20 May 2012 10:21:12 +0200
Subject: [SciPy-User] SciPy installation troubles on CentOS 6.2
In-Reply-To: <4CD3A5FA-D012-4EAF-82C3-53D56AAB19F6@mac.com>
References: <4CD3A5FA-D012-4EAF-82C3-53D56AAB19F6@mac.com>
Message-ID: <CABL7CQh+WPwpiMtO0UDKo9G01Gj38kTaWV=bSvq9eCK_qihAfQ@mail.gmail.com>

On Sat, May 19, 2012 at 5:09 PM, Magician <f_magician at mac.com> wrote:

> Hi All,
>
>
> I'm trying to build SciPy from source code,
> but I have some troubles.
>
> My environment is below:
> > CentOS 6.2 on VMware Fusion 4.1.2 (CentOS was installed as Software
> Development WS)
> > Python 2.7.3 (already built from sources, installed at
> /usr/local/python-2.7.3)
> > NumPy 1.6.1, SciPy 0.10.1, ATLAS 3.8.4, Lapack 3.4.1 (now trying to
> build)
> I and my colleagues (other users) want to use recent Python,
> so I installed Python from sources, and I can't install SciPy
> by using yum command.
>
> Now I'm facing ATLAS compiling errors.
> Configuration options are "--prefix=/usr/local/atlas-3.8.4 -Fa alg -fPIC".
> I tried to build it for several times, and always I got errors as below:
> > res/dgemvN_6_75 : VARIATION EXCEEDS TOLERENCE, RERUN WITH HIGHER REPS.
> >
> > ATL_gemvN_mm.c : 1257.99
> > ATL_gemvN_1x1_1.c : 581.74
> > ATL_gemvN_1x1_1a.c : 1589.45
> > ATL_gemvN_4x2_0.c : 813.13
> > ATL_gemvN_4x4_1.c : 755.54
> > make[3]: *** [res/dMVRES] Error 255
> > make[3]: Leaving directory
> `/home/magician/Desktop/ATLAS/build/tune/blas/gemv'
> > make[2]: ***
> [/home/magician/Desktop/ATLAS/build/tune/blas/gemv/res/dMVRES] Error 2
> > make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin'
> > ERROR 734 DURING MVTUNE!!.  CHECK INSTALL_LOG/dMVTUNE.LOG FOR DETAILS.
> > make[2]: Entering directory `/home/magician/Desktop/ATLAS/build/bin'
> > cd /home/magician/Desktop/ATLAS/build ; make error_report
> > make[3]: Entering directory `/home/magician/Desktop/ATLAS/build'
> > make -f Make.top error_report
> > make[4]: Entering directory `/home/magician/Desktop/ATLAS/build'
> > uname -a 2>&1 >> bin/INSTALL_LOG/ERROR.LOG
> > gcc -v 2>&1  >> bin/INSTALL_LOG/ERROR.LOG
> > Using built-in specs.
> > Target: x86_64-redhat-linux
> > Configured with: ../configure --prefix=/usr --mandir=/usr/share/man
> --infodir=/usr/share/info --with-bugurl=
> http://bugzilla.redhat.com/bugzilla --enable-bootstrap --enable-shared
> --enable-threads=posix --enable-checking=release --with-system-zlib
> --enable-__cxa_atexit --disable-libunwind-exceptions
> --enable-gnu-unique-object
> --enable-languages=c,c++,objc,obj-c++,java,fortran,ada
> --enable-java-awt=gtk --disable-dssi
> --with-java-home=/usr/lib/jvm/java-1.5.0-gcj-1.5.0.0/jre
> --enable-libgcj-multifile --enable-java-maintainer-mode
> --with-ecj-jar=/usr/share/java/eclipse-ecj.jar --disable-libjava-multilib
> --with-ppl --with-cloog --with-tune=generic --with-arch_32=i686
> --build=x86_64-redhat-linux
> > Thread model: posix
> > gcc version 4.4.6 20110731 (Red Hat 4.4.6-3) (GCC)
> > gcc -V 2>&1  >> bin/INSTALL_LOG/ERROR.LOG
> > gcc: '-V' option must have argument
> > make[4]: [error_report] Error 1 (ignored)
> > gcc --version 2>&1  >> bin/INSTALL_LOG/ERROR.LOG
> > tar cf error_Corei264SSE3.tar Make.inc bin/INSTALL_LOG/*
> > gzip --best error_Corei264SSE3.tar
> > mv error_Corei264SSE3.tar.gz error_Corei264SSE3.tgz
> > make[4]: Leaving directory `/home/magician/Desktop/ATLAS/build'
> > make[3]: Leaving directory `/home/magician/Desktop/ATLAS/build'
> > make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin'
> > Error report error_<ARCH>.tgz has been created in your top-level ATLAS
> > directory.  Be sure to include this file in any help request.
> > cat: ../../CONFIG/error.txt: No such file or directory
> > cat: ../../CONFIG/error.txt: No such file or directory
> > make[1]: *** [build] Error 255
> > make[1]: Leaving directory `/home/magician/Desktop/ATLAS/build'
> > make: *** [build] Error 2
>
> It's very troublesome for me to build ATLAS by myself.
> My purpose is just using SciPy on my Python.
> Even if it's optimized not so good for my environment, it's OK.
>
> Is there an easy or a sure way to build and install SciPy?


Building ATLAS is much harder than building scipy, so you should try to
find some rpm's for it, like
http://linuxtoolkit.blogspot.com/2010/09/installing-lapack-blas-and-atlas-on.html.
There's no problem building scipy against ATLAS from a binary install.

Ralf
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From ralf.gommers at googlemail.com  Sun May 20 05:34:46 2012
From: ralf.gommers at googlemail.com (Ralf Gommers)
Date: Sun, 20 May 2012 11:34:46 +0200
Subject: [SciPy-User] ANN: NumPy 1.6.2 released
Message-ID: <CABL7CQiC08x-6wJW9uu6ibynOmds3RjwMvLrZf5KhgKViBuTrg@mail.gmail.com>

Hi,

I'm pleased to announce the availability of NumPy 1.6.2.  This is a
maintenance release. Due to the delay of the NumPy 1.7.0, this release
contains far more fixes than a regular NumPy bugfix release.  It also
includes a number of documentation and build improvements.

Sources and binary installers can be found at
<https://sourceforge.net/projects/numpy/files/NumPy/1.6.2rc1/>
http://sourceforge.net/projects/numpy/files/NumPy/1.6.2/, release notes are
copied below.

Thanks to everyone who contributed to this release.

Enjoy,
The NumPy developers



=========================
NumPy 1.6.2 Release Notes
=========================

This is a bugfix release in the 1.6.x series.  Due to the delay of the NumPy
1.7.0 release, this release contains far more fixes than a regular NumPy
bugfix
release.  It also includes a number of documentation and build improvements.


``numpy.core`` issues fixed
---------------------------

#2063  make unique() return consistent index
#1138  allow creating arrays from empty buffers or empty slices
#1446  correct note about correspondence vstack and concatenate
#1149  make argmin() work for datetime
#1672  fix allclose() to work for scalar inf
#1747  make np.median() work for 0-D arrays
#1776  make complex division by zero to yield inf properly
#1675  add scalar support for the format() function
#1905  explicitly check for NaNs in allclose()
#1952  allow floating ddof in std() and var()
#1948  fix regression for indexing chararrays with empty list
#2017  fix type hashing
#2046  deleting array attributes causes segfault
#2033  a**2.0 has incorrect type
#2045  make attribute/iterator_element deletions not segfault
#2021  fix segfault in searchsorted()
#2073  fix float16 __array_interface__ bug


``numpy.lib`` issues fixed
--------------------------

#2048  break reference cycle in NpzFile
#1573  savetxt() now handles complex arrays
#1387  allow bincount() to accept empty arrays
#1899  fixed histogramdd() bug with empty inputs
#1793  fix failing npyio test under py3k
#1936  fix extra nesting for subarray dtypes
#1848  make tril/triu return the same dtype as the original array
#1918  use Py_TYPE to access ob_type, so it works also on Py3


``numpy.f2py`` changes
----------------------

ENH:   Introduce new options extra_f77_compiler_args and
extra_f90_compiler_args
BLD:   Improve reporting of fcompiler value
BUG:   Fix f2py test_kind.py test


``numpy.poly`` changes
----------------------

ENH:   Add some tests for polynomial printing
ENH:   Add companion matrix functions
DOC:   Rearrange the polynomial documents
BUG:   Fix up links to classes
DOC:   Add version added to some of the polynomial package modules
DOC:   Document xxxfit functions in the polynomial package modules
BUG:   The polynomial convenience classes let different types interact
DOC:   Document the use of the polynomial convenience classes
DOC:   Improve numpy reference documentation of polynomial classes
ENH:   Improve the computation of polynomials from roots
STY:   Code cleanup in polynomial [*]fromroots functions
DOC:   Remove references to cast and NA, which were added in 1.7


``numpy.distutils`` issues fixed
-------------------------------

#1261  change compile flag on AIX from -O5 to -O3
#1377  update HP compiler flags
#1383  provide better support for C++ code on HPUX
#1857  fix build for py3k + pip
BLD:   raise a clearer warning in case of building without cleaning up first
BLD:   follow build_ext coding convention in build_clib
BLD:   fix up detection of Intel CPU on OS X in system_info.py
BLD:   add support for the new X11 directory structure on Ubuntu & co.
BLD:   add ufsparse to the libraries search path.
BLD:   add 'pgfortran' as a valid compiler in the Portland Group
BLD:   update version match regexp for IBM AIX Fortran compilers.


``numpy.random`` issues fixed
-----------------------------

BUG:  Use npy_intp instead of long in mtrand
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From ralf.gommers at googlemail.com  Sun May 20 06:44:34 2012
From: ralf.gommers at googlemail.com (Ralf Gommers)
Date: Sun, 20 May 2012 12:44:34 +0200
Subject: [SciPy-User] scipy test error: undefined symbol: ATL_buildinfo
In-Reply-To: <20120518224609.17910@gmx.com>
References: <20120518224609.17910@gmx.com>
Message-ID: <CABL7CQjrnrY6LHRv21tV5VY7AspXP5ctQcW-y1EJ1-b_UVwEaA@mail.gmail.com>

On Sat, May 19, 2012 at 12:46 AM, Sergio Rojas <sergio_r at mail.com> wrote:

>
> Hello guys,
>
> After struggling for a while, I just finished the installation
> of scipy using the intel mkl library.
>
> Ungracefully the tests ended with an error regarding
> "undefined symbol: ATL_buildinfo" (see below)


> Can this scipy installation live with this error? or
> Is it  prone to throw out wrong computations?
>

The only failure is a check of the ATLAS version, that shouldn't yield
incorrect results.


> Also, why the number of ran test is not a fix number
>

Not sure. Perhaps nose isn't counting the one ATLAS test when it passes,
due to that file not having any actual tests in it.


> (running the tests using a scipy/Atlas installation
> finished succesfully indicating: Ran 5832 tests in 422.155s
> OK (KNOWNFAIL=14, SKIP=42), but the scipy/mkl tests ended with
> Ran 5833 tests in 448.386s FAILED (KNOWNFAIL=14, SKIP=34, errors=1))?
>
> what is the meaning of KNOWNFAIL=14?
>

Knownfail indicates tests that are known to fail under some conditions
(usually hardware/compiler dependent), so they aren't reported as new
errors/failures. If we wouldn't do this, we would keep getting the same bug
reports over and over again.

Ralf



> Sergio
>
> >$ python_mkl
> Python 2.7.2 (default, May 16 2012, 13:06:17)
> [GCC 4.6.1] on linux3
> Type "help", "copyright", "credits" or "license" for more information.
> >>> import scipy
> >>> scipy.show_config()
> umfpack_info:
>   NOT AVAILABLE
> lapack_opt_info:
>     libraries = ['mkl_lapack95_ilp64', 'mkl_lapack95_lp64', 'mkl_rt',
> 'mkl_intel
> _lp64', 'mkl_intel_thread', 'mkl_core', 'pthread']
>     library_dirs =
> ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/l
> ib/intel64']
>     define_macros = [('SCIPY_MKL_H', None)]
>     include_dirs =
> ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/i
> nclude']
> blas_opt_info:
>     libraries = ['mkl_rt', 'mkl_intel_lp64', 'mkl_intel_thread',
> 'mkl_core', 'pt
> hread']
>     library_dirs =
> ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/l
> ib/intel64']
>     define_macros = [('SCIPY_MKL_H', None)]
>     include_dirs =
> ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/i
> nclude']
> lapack_mkl_info:
>     libraries = ['mkl_lapack95_ilp64', 'mkl_lapack95_lp64', 'mkl_rt',
> 'mkl_intel
> _lp64', 'mkl_intel_thread', 'mkl_core', 'pthread']
>     library_dirs =
> ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/l
> ib/intel64']
>     define_macros = [('SCIPY_MKL_H', None)]
>     include_dirs =
> ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/i
> nclude']
> blas_mkl_info:
>     libraries = ['mkl_rt', 'mkl_intel_lp64', 'mkl_intel_thread',
> 'mkl_core', 'pt
> hread']
>     library_dirs =
> ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/l
> ib/intel64']
>     define_macros = [('SCIPY_MKL_H', None)]
>     include_dirs =
> ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/i
> nclude']
> mkl_info:
>     libraries = ['mkl_rt', 'mkl_intel_lp64', 'mkl_intel_thread',
> 'mkl_core', 'pt
> hread']
>     library_dirs =
> ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/l
> ib/intel64']
>     define_macros = [('SCIPY_MKL_H', None)]
>     include_dirs =
> ['/home/srojas/myPROG/IntelC/composer_xe_2011_sp1.7.256/mkl/i
> nclude']
> >>> scipy.test('full', verbose=2)
> Running unit tests for scipy
> NumPy version 1.6.1
> NumPy is installed in
> /home/srojas/myPROG/Python272GnuMKL/lib/python2.7/site-pac
> kages/numpy
> SciPy version 0.10.1
> SciPy is installed in
> /home/srojas/myPROG/Python272GnuMKL/lib/python2.7/site-pac
> kages/scipy
> Python version 2.7.2 (default, May 16 2012, 13:06:17) [GCC 4.6.1]
> nose version 1.1.2
> ...
> ... (DELETED OUTPUT)
> ...
> ======================================================================
> ERROR: Failure: ImportError
> (/home/srojas/myPROG/Python272GnuMKL/lib/python2.7/s
> ite-packages/scipy/linalg/atlas_version.so: undefined symbol:
> ATL_buildinfo)
> ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File
> "/home/srojas/myPROG/Python272GnuMKL/lib/python2.7/site-packages/nose/loa
> der.py", line 390, in loadTestsFromName
>     addr.filename, addr.module)
>   File
> "/home/srojas/myPROG/Python272GnuMKL/lib/python2.7/site-packages/nose/imp
> orter.py", line 39, in importFromPath
>     return self.importFromDir(dir_path, fqname)
>   File
> "/home/srojas/myPROG/Python272GnuMKL/lib/python2.7/site-packages/nose/imp
> orter.py", line 86, in importFromDir
>     mod = load_module(part_fqname, fh, filename, desc)
>   File
> "/home/srojas/myPROG/Python272GnuMKL/lib/python2.7/site-packages/scipy/li
> nalg/tests/test_atlas_version.py", line 6, in <module>
>     import scipy.linalg.atlas_version
> ImportError:
> /home/srojas/myPROG/Python272GnuMKL/lib/python2.7/site-packages/sci
> py/linalg/atlas_version.so: undefined symbol: ATL_buildinfo
>
> ----------------------------------------------------------------------
> Ran 5833 tests in 448.386s
>
> FAILED (KNOWNFAIL=14, SKIP=34, errors=1)
> <nose.result.TextTestResult run=5833 errors=1 failures=0>
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
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From f_magician at mac.com  Sun May 20 10:16:48 2012
From: f_magician at mac.com (Magician)
Date: Sun, 20 May 2012 23:16:48 +0900
Subject: [SciPy-User] SciPy installation troubles on CentOS 6.2
In-Reply-To: <mailman.3432.1337510428.1208.scipy-user@scipy.org>
References: <mailman.3432.1337510428.1208.scipy-user@scipy.org>
Message-ID: <C3368B14-EED1-4E87-A7E9-80F76990A0E2@mac.com>

Hi Ralf,


Thanks for your advice.
I tried to install BLAS/Lapack/ATLAS as below:
> yum install blas-devel lapack-devel atlas-devel

Next I installed NumPy as below:
> tar xzvf numpy-1.6.1.tar.gz
> cd numpy-1.6.1
> export CFLAGS="-L/usr/local/python-2.7.3/lib"
> python setup.py build

But then I got those errors:
> building 'numpy.linalg.lapack_lite' extension
> compiling C sources
> C compiler: gcc -pthread -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -L/usr/local/python-2.7.3/lib -fPIC
> 
> creating build/temp.linux-x86_64-2.7/numpy/linalg
> compile options: '-DATLAS_INFO="\"3.8.4\"" -I/usr/include -Inumpy/core/include -Ibuild/src.linux-x86_64-2.7/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/include -I/usr/local/python-2.7.3/include/python2.7 -Ibuild/src.linux-x86_64-2.7/numpy/core/src/multiarray -Ibuild/src.linux-x86_64-2.7/numpy/core/src/umath -c'
> gcc: numpy/linalg/lapack_litemodule.c
> gcc: numpy/linalg/python_xerbla.c
> /usr/bin/gfortran -Wall -Wall -shared build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib64/atlas -L. -Lbuild/temp.linux-x86_64-2.7 -llapack -lptf77blas -lptcblas -latlas -lpython2.7 -lgfortran -o build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so
> /usr/bin/ld: cannot find -lpython2.7
> collect2: ld returned 1 exit status
> /usr/bin/ld: cannot find -lpython2.7
> collect2: ld returned 1 exit status
> error: Command "/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib64/atlas -L. -Lbuild/temp.linux-x86_64-2.7 -llapack -lptf77blas -lptcblas -latlas -lpython2.7 -lgfortran -o build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so" failed with exit status 1

If I haven't install BLAS/Lapack/ATLAS, NumPy will be
successfully built and installed.


Magician


On 2012/05/20, at 19:40, scipy-user-request at scipy.org wrote:

> Message: 1
> Date: Sun, 20 May 2012 10:21:12 +0200
> From: Ralf Gommers <ralf.gommers at googlemail.com>
> Subject: Re: [SciPy-User] SciPy installation troubles on CentOS 6.2
> To: SciPy Users List <scipy-user at scipy.org>
> Message-ID:
> 	<CABL7CQh+WPwpiMtO0UDKo9G01Gj38kTaWV=bSvq9eCK_qihAfQ at mail.gmail.com>
> Content-Type: text/plain; charset="iso-8859-1"
> 
> On Sat, May 19, 2012 at 5:09 PM, Magician <f_magician at mac.com> wrote:
> 
>> Hi All,
>> 
>> 
>> I'm trying to build SciPy from source code,
>> but I have some troubles.
>> 
>> My environment is below:
>>> CentOS 6.2 on VMware Fusion 4.1.2 (CentOS was installed as Software
>> Development WS)
>>> Python 2.7.3 (already built from sources, installed at
>> /usr/local/python-2.7.3)
>>> NumPy 1.6.1, SciPy 0.10.1, ATLAS 3.8.4, Lapack 3.4.1 (now trying to
>> build)
>> I and my colleagues (other users) want to use recent Python,
>> so I installed Python from sources, and I can't install SciPy
>> by using yum command.
>> 
>> Now I'm facing ATLAS compiling errors.
>> Configuration options are "--prefix=/usr/local/atlas-3.8.4 -Fa alg -fPIC".
>> I tried to build it for several times, and always I got errors as below:
>>> res/dgemvN_6_75 : VARIATION EXCEEDS TOLERENCE, RERUN WITH HIGHER REPS.
>>> 
>>> ATL_gemvN_mm.c : 1257.99
>>> ATL_gemvN_1x1_1.c : 581.74
>>> ATL_gemvN_1x1_1a.c : 1589.45
>>> ATL_gemvN_4x2_0.c : 813.13
>>> ATL_gemvN_4x4_1.c : 755.54
>>> make[3]: *** [res/dMVRES] Error 255
>>> make[3]: Leaving directory
>> `/home/magician/Desktop/ATLAS/build/tune/blas/gemv'
>>> make[2]: ***
>> [/home/magician/Desktop/ATLAS/build/tune/blas/gemv/res/dMVRES] Error 2
>>> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin'
>>> ERROR 734 DURING MVTUNE!!.  CHECK INSTALL_LOG/dMVTUNE.LOG FOR DETAILS.
>>> make[2]: Entering directory `/home/magician/Desktop/ATLAS/build/bin'
>>> cd /home/magician/Desktop/ATLAS/build ; make error_report
>>> make[3]: Entering directory `/home/magician/Desktop/ATLAS/build'
>>> make -f Make.top error_report
>>> make[4]: Entering directory `/home/magician/Desktop/ATLAS/build'
>>> uname -a 2>&1 >> bin/INSTALL_LOG/ERROR.LOG
>>> gcc -v 2>&1  >> bin/INSTALL_LOG/ERROR.LOG
>>> Using built-in specs.
>>> Target: x86_64-redhat-linux
>>> Configured with: ../configure --prefix=/usr --mandir=/usr/share/man
>> --infodir=/usr/share/info --with-bugurl=
>> http://bugzilla.redhat.com/bugzilla --enable-bootstrap --enable-shared
>> --enable-threads=posix --enable-checking=release --with-system-zlib
>> --enable-__cxa_atexit --disable-libunwind-exceptions
>> --enable-gnu-unique-object
>> --enable-languages=c,c++,objc,obj-c++,java,fortran,ada
>> --enable-java-awt=gtk --disable-dssi
>> --with-java-home=/usr/lib/jvm/java-1.5.0-gcj-1.5.0.0/jre
>> --enable-libgcj-multifile --enable-java-maintainer-mode
>> --with-ecj-jar=/usr/share/java/eclipse-ecj.jar --disable-libjava-multilib
>> --with-ppl --with-cloog --with-tune=generic --with-arch_32=i686
>> --build=x86_64-redhat-linux
>>> Thread model: posix
>>> gcc version 4.4.6 20110731 (Red Hat 4.4.6-3) (GCC)
>>> gcc -V 2>&1  >> bin/INSTALL_LOG/ERROR.LOG
>>> gcc: '-V' option must have argument
>>> make[4]: [error_report] Error 1 (ignored)
>>> gcc --version 2>&1  >> bin/INSTALL_LOG/ERROR.LOG
>>> tar cf error_Corei264SSE3.tar Make.inc bin/INSTALL_LOG/*
>>> gzip --best error_Corei264SSE3.tar
>>> mv error_Corei264SSE3.tar.gz error_Corei264SSE3.tgz
>>> make[4]: Leaving directory `/home/magician/Desktop/ATLAS/build'
>>> make[3]: Leaving directory `/home/magician/Desktop/ATLAS/build'
>>> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin'
>>> Error report error_<ARCH>.tgz has been created in your top-level ATLAS
>>> directory.  Be sure to include this file in any help request.
>>> cat: ../../CONFIG/error.txt: No such file or directory
>>> cat: ../../CONFIG/error.txt: No such file or directory
>>> make[1]: *** [build] Error 255
>>> make[1]: Leaving directory `/home/magician/Desktop/ATLAS/build'
>>> make: *** [build] Error 2
>> 
>> It's very troublesome for me to build ATLAS by myself.
>> My purpose is just using SciPy on my Python.
>> Even if it's optimized not so good for my environment, it's OK.
>> 
>> Is there an easy or a sure way to build and install SciPy?
> 
> 
> Building ATLAS is much harder than building scipy, so you should try to
> find some rpm's for it, like
> http://linuxtoolkit.blogspot.com/2010/09/installing-lapack-blas-and-atlas-on.html.
> There's no problem building scipy against ATLAS from a binary install.
> 
> Ralf



From ralf.gommers at googlemail.com  Sun May 20 12:51:12 2012
From: ralf.gommers at googlemail.com (Ralf Gommers)
Date: Sun, 20 May 2012 18:51:12 +0200
Subject: [SciPy-User] SciPy installation troubles on CentOS 6.2
In-Reply-To: <C3368B14-EED1-4E87-A7E9-80F76990A0E2@mac.com>
References: <mailman.3432.1337510428.1208.scipy-user@scipy.org>
	<C3368B14-EED1-4E87-A7E9-80F76990A0E2@mac.com>
Message-ID: <CABL7CQhZrSbwsYCyhK99m-Hf4ZPownOSNWjZvji_gM1UmKjsMQ@mail.gmail.com>

On Sun, May 20, 2012 at 4:16 PM, Magician <f_magician at mac.com> wrote:

> Hi Ralf,
>
>
> Thanks for your advice.
> I tried to install BLAS/Lapack/ATLAS as below:
> > yum install blas-devel lapack-devel atlas-devel
>
> Next I installed NumPy as below:
> > tar xzvf numpy-1.6.1.tar.gz
> > cd numpy-1.6.1
> > export CFLAGS="-L/usr/local/python-2.7.3/lib"
>

Note that this overrides CFLAGS instead of appending that flag to the rest.
If it doesn't work without that line, you have to either specify all cflags
or use numscons/bento.

Ralf


> > python setup.py build
>
> But then I got those errors:
> > building 'numpy.linalg.lapack_lite' extension
> > compiling C sources
> > C compiler: gcc -pthread -DNDEBUG -g -fwrapv -O3 -Wall
> -Wstrict-prototypes -L/usr/local/python-2.7.3/lib -fPIC
> >
> > creating build/temp.linux-x86_64-2.7/numpy/linalg
> > compile options: '-DATLAS_INFO="\"3.8.4\"" -I/usr/include
> -Inumpy/core/include -Ibuild/src.linux-x86_64-2.7/numpy/core/include/numpy
> -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core
> -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath
> -Inumpy/core/include -I/usr/local/python-2.7.3/include/python2.7
> -Ibuild/src.linux-x86_64-2.7/numpy/core/src/multiarray
> -Ibuild/src.linux-x86_64-2.7/numpy/core/src/umath -c'
> > gcc: numpy/linalg/lapack_litemodule.c
> > gcc: numpy/linalg/python_xerbla.c
> > /usr/bin/gfortran -Wall -Wall -shared
> build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o
> build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib64/atlas
> -L. -Lbuild/temp.linux-x86_64-2.7 -llapack -lptf77blas -lptcblas -latlas
> -lpython2.7 -lgfortran -o
> build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so
> > /usr/bin/ld: cannot find -lpython2.7
> > collect2: ld returned 1 exit status
> > /usr/bin/ld: cannot find -lpython2.7
> > collect2: ld returned 1 exit status
> > error: Command "/usr/bin/gfortran -Wall -Wall -shared
> build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o
> build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib64/atlas
> -L. -Lbuild/temp.linux-x86_64-2.7 -llapack -lptf77blas -lptcblas -latlas
> -lpython2.7 -lgfortran -o
> build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so" failed with exit
> status 1
>
> If I haven't install BLAS/Lapack/ATLAS, NumPy will be
> successfully built and installed.
>
>
> Magician
>
>
> On 2012/05/20, at 19:40, scipy-user-request at scipy.org wrote:
>
> > Message: 1
> > Date: Sun, 20 May 2012 10:21:12 +0200
> > From: Ralf Gommers <ralf.gommers at googlemail.com>
> > Subject: Re: [SciPy-User] SciPy installation troubles on CentOS 6.2
> > To: SciPy Users List <scipy-user at scipy.org>
> > Message-ID:
> >       <CABL7CQh+WPwpiMtO0UDKo9G01Gj38kTaWV=
> bSvq9eCK_qihAfQ at mail.gmail.com>
> > Content-Type: text/plain; charset="iso-8859-1"
> >
> > On Sat, May 19, 2012 at 5:09 PM, Magician <f_magician at mac.com> wrote:
> >
> >> Hi All,
> >>
> >>
> >> I'm trying to build SciPy from source code,
> >> but I have some troubles.
> >>
> >> My environment is below:
> >>> CentOS 6.2 on VMware Fusion 4.1.2 (CentOS was installed as Software
> >> Development WS)
> >>> Python 2.7.3 (already built from sources, installed at
> >> /usr/local/python-2.7.3)
> >>> NumPy 1.6.1, SciPy 0.10.1, ATLAS 3.8.4, Lapack 3.4.1 (now trying to
> >> build)
> >> I and my colleagues (other users) want to use recent Python,
> >> so I installed Python from sources, and I can't install SciPy
> >> by using yum command.
> >>
> >> Now I'm facing ATLAS compiling errors.
> >> Configuration options are "--prefix=/usr/local/atlas-3.8.4 -Fa alg
> -fPIC".
> >> I tried to build it for several times, and always I got errors as below:
> >>> res/dgemvN_6_75 : VARIATION EXCEEDS TOLERENCE, RERUN WITH HIGHER REPS.
> >>>
> >>> ATL_gemvN_mm.c : 1257.99
> >>> ATL_gemvN_1x1_1.c : 581.74
> >>> ATL_gemvN_1x1_1a.c : 1589.45
> >>> ATL_gemvN_4x2_0.c : 813.13
> >>> ATL_gemvN_4x4_1.c : 755.54
> >>> make[3]: *** [res/dMVRES] Error 255
> >>> make[3]: Leaving directory
> >> `/home/magician/Desktop/ATLAS/build/tune/blas/gemv'
> >>> make[2]: ***
> >> [/home/magician/Desktop/ATLAS/build/tune/blas/gemv/res/dMVRES] Error 2
> >>> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin'
> >>> ERROR 734 DURING MVTUNE!!.  CHECK INSTALL_LOG/dMVTUNE.LOG FOR DETAILS.
> >>> make[2]: Entering directory `/home/magician/Desktop/ATLAS/build/bin'
> >>> cd /home/magician/Desktop/ATLAS/build ; make error_report
> >>> make[3]: Entering directory `/home/magician/Desktop/ATLAS/build'
> >>> make -f Make.top error_report
> >>> make[4]: Entering directory `/home/magician/Desktop/ATLAS/build'
> >>> uname -a 2>&1 >> bin/INSTALL_LOG/ERROR.LOG
> >>> gcc -v 2>&1  >> bin/INSTALL_LOG/ERROR.LOG
> >>> Using built-in specs.
> >>> Target: x86_64-redhat-linux
> >>> Configured with: ../configure --prefix=/usr --mandir=/usr/share/man
> >> --infodir=/usr/share/info --with-bugurl=
> >> http://bugzilla.redhat.com/bugzilla --enable-bootstrap --enable-shared
> >> --enable-threads=posix --enable-checking=release --with-system-zlib
> >> --enable-__cxa_atexit --disable-libunwind-exceptions
> >> --enable-gnu-unique-object
> >> --enable-languages=c,c++,objc,obj-c++,java,fortran,ada
> >> --enable-java-awt=gtk --disable-dssi
> >> --with-java-home=/usr/lib/jvm/java-1.5.0-gcj-1.5.0.0/jre
> >> --enable-libgcj-multifile --enable-java-maintainer-mode
> >> --with-ecj-jar=/usr/share/java/eclipse-ecj.jar
> --disable-libjava-multilib
> >> --with-ppl --with-cloog --with-tune=generic --with-arch_32=i686
> >> --build=x86_64-redhat-linux
> >>> Thread model: posix
> >>> gcc version 4.4.6 20110731 (Red Hat 4.4.6-3) (GCC)
> >>> gcc -V 2>&1  >> bin/INSTALL_LOG/ERROR.LOG
> >>> gcc: '-V' option must have argument
> >>> make[4]: [error_report] Error 1 (ignored)
> >>> gcc --version 2>&1  >> bin/INSTALL_LOG/ERROR.LOG
> >>> tar cf error_Corei264SSE3.tar Make.inc bin/INSTALL_LOG/*
> >>> gzip --best error_Corei264SSE3.tar
> >>> mv error_Corei264SSE3.tar.gz error_Corei264SSE3.tgz
> >>> make[4]: Leaving directory `/home/magician/Desktop/ATLAS/build'
> >>> make[3]: Leaving directory `/home/magician/Desktop/ATLAS/build'
> >>> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin'
> >>> Error report error_<ARCH>.tgz has been created in your top-level ATLAS
> >>> directory.  Be sure to include this file in any help request.
> >>> cat: ../../CONFIG/error.txt: No such file or directory
> >>> cat: ../../CONFIG/error.txt: No such file or directory
> >>> make[1]: *** [build] Error 255
> >>> make[1]: Leaving directory `/home/magician/Desktop/ATLAS/build'
> >>> make: *** [build] Error 2
> >>
> >> It's very troublesome for me to build ATLAS by myself.
> >> My purpose is just using SciPy on my Python.
> >> Even if it's optimized not so good for my environment, it's OK.
> >>
> >> Is there an easy or a sure way to build and install SciPy?
> >
> >
> > Building ATLAS is much harder than building scipy, so you should try to
> > find some rpm's for it, like
> >
> http://linuxtoolkit.blogspot.com/2010/09/installing-lapack-blas-and-atlas-on.html
> .
> > There's no problem building scipy against ATLAS from a binary install.
> >
> > Ralf
>
>
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From ralf.gommers at googlemail.com  Sun May 20 13:14:41 2012
From: ralf.gommers at googlemail.com (Ralf Gommers)
Date: Sun, 20 May 2012 19:14:41 +0200
Subject: [SciPy-User] why do the discrete distributions not have a `fit`?
In-Reply-To: <CAMMTP+AUuv3LJjt59twnmOdKmnjG0UcndE3UoLwn_hxZ=a+2vw@mail.gmail.com>
References: <CAMMTP+AUuv3LJjt59twnmOdKmnjG0UcndE3UoLwn_hxZ=a+2vw@mail.gmail.com>
Message-ID: <CABL7CQiPXAKVKVpAAgOR0cAHQwRhJ+ufy5fYKAPi8x3u2pMcow@mail.gmail.com>

On Fri, May 11, 2012 at 2:04 AM, <josef.pktd at gmail.com> wrote:

> Why do the discrete distributions not have a `fit` method like the
> continuous distributions?
>
> currently it's a bug in the documentation
> http://projects.scipy.org/scipy/ticket/1659
>
> in statsmodels, we fit several of the discrete distributions.
>

Which ones? And do you then return non-integer parameters or not?


> How about discrete parameters?   (in analogy to the erlang discussion)
>
> hypergeom is based on a story about marbles or balls
>
> http://en.wikipedia.org/wiki/Hypergeometric_distribution#Application_and_example
> but why should we care, it's just a discrete distribution with 3 shape
> parameters, isn't it?
>
> fractional marbles ?
>
> >>> nn = np.linspace(4.5, 8, 101)
> >>> pmf = [stats.hypergeom.pmf(5, 10.8, n, 8.5) for n in nn]
>
> >>> plt.plot(nn, pmf, '-o')
> >>> plt.title("pmf of hypergeom as function of parameter n")
>
> Doesn't look like there are any problems, and the likelihood function
> is nicely concave.
>
> conclusion: scipy.stats doesn't have a hypergeometric distribution,
> but a generalized version that is defined on a real parameter space.
>
> Josef
> (so what's the point? Sorry, I was just getting distracted while
> looking for `fit`.)
>

For functions that work with continuous input, perhaps using the continuous
fit and then looking for the best-fit with integer params near the
continuous optimum would work. I looked for literature on this topic, but
didn't find anything useful yet.

Ralf
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From josef.pktd at gmail.com  Sun May 20 13:44:05 2012
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sun, 20 May 2012 13:44:05 -0400
Subject: [SciPy-User] why do the discrete distributions not have a `fit`?
In-Reply-To: <CABL7CQiPXAKVKVpAAgOR0cAHQwRhJ+ufy5fYKAPi8x3u2pMcow@mail.gmail.com>
References: <CAMMTP+AUuv3LJjt59twnmOdKmnjG0UcndE3UoLwn_hxZ=a+2vw@mail.gmail.com>
	<CABL7CQiPXAKVKVpAAgOR0cAHQwRhJ+ufy5fYKAPi8x3u2pMcow@mail.gmail.com>
Message-ID: <CAMMTP+DKR=RYnYNnQqdn4PL9qH567geOZZsWOC1f0Vc+Nr+LBA@mail.gmail.com>

On Sun, May 20, 2012 at 1:14 PM, Ralf Gommers
<ralf.gommers at googlemail.com> wrote:
>
>
> On Fri, May 11, 2012 at 2:04 AM, <josef.pktd at gmail.com> wrote:
>>
>> Why do the discrete distributions not have a `fit` method like the
>> continuous distributions?
>>
>> currently it's a bug in the documentation
>> http://projects.scipy.org/scipy/ticket/1659
>>
>> in statsmodels, we fit several of the discrete distributions.
>
>
> Which ones? And do you then return non-integer parameters or not?

statsmodels.discrete Logit, Poisson, Negative Binomial (in GLM, but
unfinished in discrete)
http://statsmodels.sourceforge.net/devel/discretemod.html

No integer parameters restriction since I never seen them in the literature.

>
>>
>> How about discrete parameters? ? (in analogy to the erlang discussion)
>>
>> hypergeom is based on a story about marbles or balls
>>
>> http://en.wikipedia.org/wiki/Hypergeometric_distribution#Application_and_example
>> but why should we care, it's just a discrete distribution with 3 shape
>> parameters, isn't it?
>>
>> fractional marbles ?
>>
>> >>> nn = np.linspace(4.5, 8, 101)
>> >>> pmf = [stats.hypergeom.pmf(5, 10.8, n, 8.5) for n in nn]
>>
>> >>> plt.plot(nn, pmf, '-o')
>> >>> plt.title("pmf of hypergeom as function of parameter n")
>>
>> Doesn't look like there are any problems, and the likelihood function
>> is nicely concave.
>>
>> conclusion: scipy.stats doesn't have a hypergeometric distribution,
>> but a generalized version that is defined on a real parameter space.
>>
>> Josef
>> (so what's the point? Sorry, I was just getting distracted while
>> looking for `fit`.)
>
>
> For functions that work with continuous input, perhaps using the continuous
> fit and then looking for the best-fit with integer params near the
> continuous optimum would work. I looked for literature on this topic, but
> didn't find anything useful yet.

If hypergeometric works (and I guess boltzmann and binom works from a
quick look) for continuous parameters, then I don't see much reason to
restrict parameters to integers.

I played a bit more with hypergeom.fit and the main problem is to find
starting parameters that are consistent with the data, since the
support moves with the parameters, even if loc and scale are fixed.

For many discrete distributions the problem might be more how to
restrict the parameter space, e.g. if shape parameter is in (0,1)
(Logit or Probit transformation for Bernoulli, or exp transformation
for shape parameters > 0 in Poisson.
Or maybe not, if the optimization routine always finds an interior solution.

Josef



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


From sergio_r at mail.com  Sun May 20 16:34:44 2012
From: sergio_r at mail.com (Sergio Rojas)
Date: Sun, 20 May 2012 16:34:44 -0400
Subject: [SciPy-User] SciPy installation troubles on CentOS 6.2
Message-ID: <20120520203445.17910@gmx.com>

Magician, since I did not see any mention of it, I think you are missing to include in your build up a "site.cfg" file (see for instance http://www.scipy.org/Installing_SciPy/Linux Step 4: Build numpy(1.5.0) ). In that file you need to specify where your libraries are. That file needs to be used in building both Numpy and Scipy. Sergio ---------------------------------------------------------------------- Message: 1 Date: Sun, 20 May 2012 23:16:48 +0900 From: Magician <f_magician at mac.com> Subject: Re: [SciPy-User] SciPy installation troubles on CentOS 6.2 To: ralf.gommers at googlemail.com Cc: scipy-user at scipy.org Message-ID: <C3368B14-EED1-4E87-A7E9-80F76990A0E2 at mac.com> Content-Type: text/plain; CHARSET=US-ASCII Hi Ralf, Thanks for your advice. I tried to install BLAS/Lapack/ATLAS as below: > yum install blas-devel lapack-devel atlas-devel Next I installed NumPy as below: > tar xzvf numpy-1.6.1.tar.gz > cd numpy-1.6.1 > export CFLAGS="-L/usr/local/python-2.7.3/lib" > python setup.py build But then I got those errors: > building 'numpy.linalg.lapack_lite' extension > compiling C sources > C compiler: gcc -pthread -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -L/usr/local/python-2.7.3/lib -fPIC > > creating build/temp.linux-x86_64-2.7/numpy/linalg > compile options: '-DATLAS_INFO="\"3.8.4\"" -I/usr/include -Inumpy/core/include -Ibuild/src.linux-x86_64-2.7/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/include -I/usr/local/python-2.7.3/include/python2.7 -Ibuild/src.linux-x86_64-2.7/numpy/core/src/multiarray -Ibuild/src.linux-x86_64-2.7/numpy/core/src/umath -c' > gcc: numpy/linalg/lapack_litemodule.c > gcc: numpy/linalg/python_xerbla.c > /usr/bin/gfortran -Wall -Wall -shared build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib64/atlas -L. -Lbuild/temp.linux-x86_64-2.7 -llapack -lptf77blas -lptcblas -latlas -lpython2.7 -lgfortran -o build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so > /usr/bin/ld: cannot find -lpython2.7 > collect2: ld returned 1 exit status > /usr/bin/ld: cannot find -lpython2.7 > collect2: ld returned 1 exit status > error: Command "/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib64/atlas -L. -Lbuild/temp.linux-x86_64-2.7 -llapack -lptf77blas -lptcblas -latlas -lpython2.7 -lgfortran -o build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so" failed with exit status 1 If I haven't install BLAS/Lapack/ATLAS, NumPy will be successfully built and installed. Magician On 2012/05/20, at 19:40, scipy-user-request at scipy.org wrote: > Message: 1 > Date: Sun, 20 May 2012 10:21:12 +0200 > From: Ralf Gommers <ralf.gommers at googlemail.com> > Subject: Re: [SciPy-User] SciPy installation troubles on CentOS 6.2 > To: SciPy Users List <scipy-user at scipy.org> > Message-ID: > <CABL7CQh+WPwpiMtO0UDKo9G01Gj38kTaWV=bSvq9eCK_qihAfQ at mail.gmail.com> > Content-Type: text/plain; charset="iso-8859-1" > > On Sat, May 19, 2012 at 5:09 PM, Magician <f_magician at mac.com> wrote: > >> Hi All, >> >> >> I'm trying to build SciPy from source code, >> but I have some troubles. >> >> My environment is below: >>> CentOS 6.2 on VMware Fusion 4.1.2 (CentOS was installed as Software >> Development WS) >>> Python 2.7.3 (already built from sources, installed at >> /usr/local/python-2.7.3) >>> NumPy 1.6.1, SciPy 0.10.1, ATLAS 3.8.4, Lapack 3.4.1 (now trying to >> build) >> I and my colleagues (other users) want to use recent Python, >> so I installed Python from sources, and I can't install SciPy >> by using yum command. >> >> Now I'm facing ATLAS compiling errors. >> Configuration options are "--prefix=/usr/local/atlas-3.8.4 -Fa alg -fPIC". >> I tried to build it for several times, and always I got errors as below: >>> res/dgemvN_6_75 : VARIATION EXCEEDS TOLERENCE, RERUN WITH HIGHER REPS. >>> >>> ATL_gemvN_mm.c : 1257.99 >>> ATL_gemvN_1x1_1.c : 581.74 >>> ATL_gemvN_1x1_1a.c : 1589.45 >>> ATL_gemvN_4x2_0.c : 813.13 >>> ATL_gemvN_4x4_1.c : 755.54 >>> make[3]: *** [res/dMVRES] Error 255 >>> make[3]: Leaving directory >> `/home/magician/Desktop/ATLAS/build/tune/blas/gemv' >>> make[2]: *** >> [/home/magician/Desktop/ATLAS/build/tune/blas/gemv/res/dMVRES] Error 2 >>> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin' >>> ERROR 734 DURING MVTUNE!!. CHECK INSTALL_LOG/dMVTUNE.LOG FOR DETAILS. >>> make[2]: Entering directory `/home/magician/Desktop/ATLAS/build/bin' >>> cd /home/magician/Desktop/ATLAS/build ; make error_report >>> make[3]: Entering directory `/home/magician/Desktop/ATLAS/build' >>> make -f Make.top error_report >>> make[4]: Entering directory `/home/magician/Desktop/ATLAS/build' >>> uname -a 2>&1 >> bin/INSTALL_LOG/ERROR.LOG >>> gcc -v 2>&1 >> bin/INSTALL_LOG/ERROR.LOG >>> Using built-in specs. >>> Target: x86_64-redhat-linux >>> Configured with: ../configure --prefix=/usr --mandir=/usr/share/man >> --infodir=/usr/share/info --with-bugurl= >> http://bugzilla.redhat.com/bugzilla --enable-bootstrap --enable-shared >> --enable-threads=posix --enable-checking=release --with-system-zlib >> --enable-__cxa_atexit --disable-libunwind-exceptions >> --enable-gnu-unique-object >> --enable-languages=c,c++,objc,obj-c++,java,fortran,ada >> --enable-java-awt=gtk --disable-dssi >> --with-java-home=/usr/lib/jvm/java-1.5.0-gcj-1.5.0.0/jre >> --enable-libgcj-multifile --enable-java-maintainer-mode >> --with-ecj-jar=/usr/share/java/eclipse-ecj.jar --disable-libjava-multilib >> --with-ppl --with-cloog --with-tune=generic --with-arch_32=i686 >> --build=x86_64-redhat-linux >>> Thread model: posix >>> gcc version 4.4.6 20110731 (Red Hat 4.4.6-3) (GCC) >>> gcc -V 2>&1 >> bin/INSTALL_LOG/ERROR.LOG >>> gcc: '-V' option must have argument >>> make[4]: [error_report] Error 1 (ignored) >>> gcc --version 2>&1 >> bin/INSTALL_LOG/ERROR.LOG >>> tar cf error_Corei264SSE3.tar Make.inc bin/INSTALL_LOG/* >>> gzip --best error_Corei264SSE3.tar >>> mv error_Corei264SSE3.tar.gz error_Corei264SSE3.tgz >>> make[4]: Leaving directory `/home/magician/Desktop/ATLAS/build' >>> make[3]: Leaving directory `/home/magician/Desktop/ATLAS/build' >>> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin' >>> Error report error_<ARCH>.tgz has been created in your top-level ATLAS >>> directory. Be sure to include this file in any help request. >>> cat: ../../CONFIG/error.txt: No such file or directory >>> cat: ../../CONFIG/error.txt: No such file or directory >>> make[1]: *** [build] Error 255 >>> make[1]: Leaving directory `/home/magician/Desktop/ATLAS/build' >>> make: *** [build] Error 2 >> >> It's very troublesome for me to build ATLAS by myself. >> My purpose is just using SciPy on my Python. >> Even if it's optimized not so good for my environment, it's OK. >> >> Is there an easy or a sure way to build and install SciPy? > > > Building ATLAS is much harder than building scipy, so you should try to > find some rpm's for it, like > http://linuxtoolkit.blogspot.com/2010/09/installing-lapack-blas-and-atlas-on.html. > There's no problem building scipy against ATLAS from a binary install. > > Ralf -
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From pierre.puiseux at univ-pau.fr  Mon May 21 05:56:10 2012
From: pierre.puiseux at univ-pau.fr (pierre puiseux UPPA)
Date: Mon, 21 May 2012 11:56:10 +0200
Subject: [SciPy-User] subclassing ndarray : i want slice to return ndarray,
	not subclass
Message-ID: <CB77D3A3-1C93-4172-91DE-6455521B63AE@univ-pau.fr>

Hello,
all my question is in the title. More precisely, like in scipy doc i try this :
======================================
import sys
import numpy as np

class ArrayChild(np.ndarray):

    def __new__(cls, array, info=None):
        obj = np.asarray(array).view(cls)
        obj.info = info
        return obj

    def __array_finalize__(self, obj):
        print>>sys.stderr, "__array_finalize__"
        if obj is None: return
        self.info = getattr(obj, 'info', None)

if __name__=='__main__':
    a = np.arange(6)
    a.shape=2,3
    a_child = ArrayChild(a)
    for x in a_child : 
        print>>sys.stderr, x, type(x)

=====================================
and i have the answer :
=====================================
__array_finalize__
__array_finalize__
[0 1 2] <class '__main__.ArrayChild'>
__array_finalize__
[3 4 5] <class '__main__.ArrayChild'>

=====================================
but i want
=====================================
__array_finalize__
__array_finalize__
[0 1 2] <class '__main__.np.ndarray'>
__array_finalize__
[3 4 5] <class '__main__.np.ndarray'>

=====================================
I've tried to redefine : __getslice__  like this, but it does not work.
=====================================
    def __getslice__(self, *args, **kwargs):
        return np.ndarray.__getslice__(self, *args, **kwargs)


=====================================
Some idea ?
Thanks to numpy/scipy team for that great job.
=====================================

Pierre Puiseux
Laboratoire de Math?matiques Appliqu?es
Universit? de Pau et des Pays de l'Adour
pierre.puiseux at univ-pau.fr
http://www.univ-pau.fr/~puiseux

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From d.s.seljebotn at astro.uio.no  Mon May 21 07:42:07 2012
From: d.s.seljebotn at astro.uio.no (Dag Sverre Seljebotn)
Date: Mon, 21 May 2012 13:42:07 +0200
Subject: [SciPy-User] subclassing ndarray : i want slice to return
 ndarray, not subclass
In-Reply-To: <CB77D3A3-1C93-4172-91DE-6455521B63AE@univ-pau.fr>
References: <CB77D3A3-1C93-4172-91DE-6455521B63AE@univ-pau.fr>
Message-ID: <4FBA2A0F.10003@astro.uio.no>

On 05/21/2012 11:56 AM, pierre puiseux UPPA wrote:
> Hello,
> all my question is in the title. More precisely, like in scipy doc i try
> this :
> ======================================
> importsys
> import numpy as np
>
> class ArrayChild(np.ndarray):

Subclassing ndarray is a very tricky business -- I did it once and 
regretted having done it for years, because there's so much you can't do 
etc.. You're almost certainly better off with embedding an array as an 
attribute, and then forward properties etc. to it.

But your answer: You want to override __getitem__, __getslice__ is 
deprecated in Python.

Dag

>
> def __new__(cls, array, info=None):
> obj = np.asarray(array).view(cls)
> obj.info = info
> return obj
>
> def __array_finalize__(self, obj):
> print>>sys.stderr, "__array_finalize__"
> if obj is None: return
> self.info = getattr(obj, 'info', None)
>
> if __name__=='__main__':
> a = np.arange(6)
> a.shape=2,3
> a_child = ArrayChild(a)
> for x in a_child :
> print>>sys.stderr, x, type(x)
>
> =====================================
> and i have the answer :
> =====================================
> __array_finalize__
> __array_finalize__
> [0 1 2] <class '__main__.ArrayChild'>
> __array_finalize__
> [3 4 5] <class '__main__.ArrayChild'>
>
> =====================================
> but i want
> =====================================
> __array_finalize__
> __array_finalize__
> [0 1 2] <class '__main__.np.ndarray'>
> __array_finalize__
> [3 4 5] <class '__main__.np.ndarray'>
>
> =====================================
> I've tried to redefine : __getslice__ like this, but it does not work.
> =====================================
> def __getslice__(self, *args, **kwargs):
> return np.ndarray.__getslice__(self, *args, **kwargs)



From tobias at ebockwurst.de  Mon May 21 07:36:13 2012
From: tobias at ebockwurst.de (Tobias)
Date: Mon, 21 May 2012 11:36:13 +0000 (UTC)
Subject: [SciPy-User] scipy.weave on windows vista compiling error
Message-ID: <loom.20120521T131903-9@post.gmane.org>

Hello!

I'm trying to bring scipy.weave to work on Windows Vista with MinGw. 
I use Python 2.7.1, Scipy 0.10.1 and MinGw 4.6.2
My code looks like the following:

 from scipy import weave
 weave.inline("""print('Hello World!');""", [], compiler = 'mingw32-gcc')

I get the following error message:

 DistutilsPlatformError: don't know how to compile C/C++ code on platform 'nt' 
 with 'mingw32-gcc' compiler

With MinGw come a lot of different compiler. If I choose not 'mingw32-gcc' but 
for example 'gcc' weave complains 

 ValueError: invalid version number '4.'

So I also downloaded the olf MinGW 2.9.5 Version. Then I also get the Error 
"don't know how to compile C/C++ code on platform 'nt'"

The PATH-Variables are set and I also made shure, that there is write access to 
the MinGw and Python folders. Searching for this error message gave no 
progress, but maybe you could help me. Would be great!

All the best
Tobi






From njs at pobox.com  Mon May 21 08:15:24 2012
From: njs at pobox.com (Nathaniel Smith)
Date: Mon, 21 May 2012 13:15:24 +0100
Subject: [SciPy-User] subclassing ndarray : i want slice to return
 ndarray, not subclass
In-Reply-To: <4FBA2A0F.10003@astro.uio.no>
References: <CB77D3A3-1C93-4172-91DE-6455521B63AE@univ-pau.fr>
	<4FBA2A0F.10003@astro.uio.no>
Message-ID: <CAPJVwBm=OMs-no5eGZeyw7-yz_akPogRJXyTg=VsXHuFj=ksLw@mail.gmail.com>

On Mon, May 21, 2012 at 12:42 PM, Dag Sverre Seljebotn
<d.s.seljebotn at astro.uio.no> wrote:
> On 05/21/2012 11:56 AM, pierre puiseux UPPA wrote:
>> Hello,
>> all my question is in the title. More precisely, like in scipy doc i try
>> this :
>> ======================================
>> importsys
>> import numpy as np
>>
>> class ArrayChild(np.ndarray):
>
> Subclassing ndarray is a very tricky business -- I did it once and
> regretted having done it for years, because there's so much you can't do
> etc.. You're almost certainly better off with embedding an array as an
> attribute, and then forward properties etc. to it.

Yes, it's almost always the wrong thing...

> But your answer: You want to override __getitem__, __getslice__ is
> deprecated in Python.

You have to override both, actually. After overriding __getitem__, then add
  def __getslice__(self, i, j):
    return self.__getitem__(slice(i, j))
or otherwise your __getitem__ will get skipped in some cases.

The other option would be to just accept that your subclass will be
returned from slices. If the only difference between your subclass and
the base ndarray is that your arrays have an extra ".info" attribute,
then you can just not define __array_finalize__ and check for the
presence of a ".info" attribute instead of checking for the subclass
directly. This depends on what your subclass is actually supposed to
do, obviously.

-- Nathaniel


From Jerome.Kieffer at esrf.fr  Mon May 21 15:00:06 2012
From: Jerome.Kieffer at esrf.fr (Jerome Kieffer)
Date: Mon, 21 May 2012 21:00:06 +0200
Subject: [SciPy-User] scipy.weave on windows vista compiling error
In-Reply-To: <loom.20120521T131903-9@post.gmane.org>
References: <loom.20120521T131903-9@post.gmane.org>
Message-ID: <20120521210006.ca8d3b07.Jerome.Kieffer@esrf.fr>


Hi Tobias,

> I'm trying to bring scipy.weave to work on Windows Vista with MinGw. 
> I use Python 2.7.1, Scipy 0.10.1 and MinGw 4.6.2

I thought weave never worked under windows ??? maybe with mingw it is better (but no under 64bits windows :(
This is the main reason why most people (like me) moved from weave to cython.

Cheers,

-- 
J?r?me Kieffer
Data analysis unit - ESRF


From cgohlke at uci.edu  Mon May 21 15:37:13 2012
From: cgohlke at uci.edu (Christoph Gohlke)
Date: Mon, 21 May 2012 12:37:13 -0700
Subject: [SciPy-User] scipy.weave on windows vista compiling error
In-Reply-To: <loom.20120521T131903-9@post.gmane.org>
References: <loom.20120521T131903-9@post.gmane.org>
Message-ID: <4FBA9969.5090002@uci.edu>



On 5/21/2012 4:36 AM, Tobias wrote:
> Hello!
>
> I'm trying to bring scipy.weave to work on Windows Vista with MinGw.
> I use Python 2.7.1, Scipy 0.10.1 and MinGw 4.6.2
> My code looks like the following:
>
>   from scipy import weave
>   weave.inline("""print('Hello World!');""", [], compiler = 'mingw32-gcc')
>
> I get the following error message:
>
>   DistutilsPlatformError: don't know how to compile C/C++ code on platform 'nt'
>   with 'mingw32-gcc' compiler
>
> With MinGw come a lot of different compiler. If I choose not 'mingw32-gcc' but
> for example 'gcc' weave complains
>
>   ValueError: invalid version number '4.'
>
> So I also downloaded the olf MinGW 2.9.5 Version. Then I also get the Error
> "don't know how to compile C/C++ code on platform 'nt'"
>
> The PATH-Variables are set and I also made shure, that there is write access to
> the MinGw and Python folders. Searching for this error message gave no
> progress, but maybe you could help me. Would be great!
>
> All the best
> Tobi
>
>

Try `weave.inline("""printf("Hello World!");""", [], compiler='mingw32')`

Worked for me with gcc 4.5.2 on win32-py2.7. The msvc9 compiler also 
works for 64 bit Python.

Christoph


From vaggi.federico at gmail.com  Mon May 21 18:13:30 2012
From: vaggi.federico at gmail.com (federico vaggi)
Date: Tue, 22 May 2012 00:13:30 +0200
Subject: [SciPy-User] scipy.weave on windows vista compiling error
Message-ID: <CAGvd0=iL0UfvuhBsfw8W4M+5tehkq8KOceREkDa9HC7TE5xkjQ@mail.gmail.com>

> Message: 2
> Date: Mon, 21 May 2012 11:36:13 +0000 (UTC)
> From: Tobias <tobias at ebockwurst.de>
> Subject: [SciPy-User] scipy.weave on windows vista compiling error
> To: scipy-user at scipy.org
> Message-ID: <loom.20120521T131903-9 at post.gmane.org>
> Content-Type: text/plain; charset=us-ascii
>
> Hello!
>
> I'm trying to bring scipy.weave to work on Windows Vista with MinGw.
> I use Python 2.7.1, Scipy 0.10.1 and MinGw 4.6.2
> My code looks like the following:
>
>  from scipy import weave
>  weave.inline("""print('Hello World!');""", [], compiler = 'mingw32-gcc')
>
> I get the following error message:
>
>  DistutilsPlatformError: don't know how to compile C/C++ code on platform
> 'nt'
>  with 'mingw32-gcc' compiler
>
> With MinGw come a lot of different compiler. If I choose not 'mingw32-gcc'
> but
> for example 'gcc' weave complains
>
>  ValueError: invalid version number '4.'
>
> So I also downloaded the olf MinGW 2.9.5 Version. Then I also get the Error
> "don't know how to compile C/C++ code on platform 'nt'"
>
> The PATH-Variables are set and I also made shure, that there is write
> access to
> the MinGw and Python folders. Searching for this error message gave no
> progress, but maybe you could help me. Would be great!
>
> All the best
> Tobi
>
>
Your C++ compiler has to match the C++ compiler used to compile Python -
which, I think, by default, on Vista is Visual Studio 2008.

However, the weave module is not actively maintained, and there are much
better options right now for interfacing with C/C++.  I would highly
suggest that if you have some performance-critical part of your code, you
either use Cython, or, if it is a simple numerical expression, use the
amazing numexpr package.

I warn you though - while pure Python code is very portable across
platforms, interfacing Python with other libraries (mostly, C and C++) is a
huge pain on Windows.  If you really want to continue, I strongly advise
you take advantage of Christoph Gohlke's work:

http://www.lfd.uci.edu/~gohlke/pythonlibs/

who has done all the hard work has made binary installers for most of the
hard-to-compile Python libraries with C and C++ dependencies.

Federico
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From mail.till at gmx.de  Tue May 22 10:57:18 2012
From: mail.till at gmx.de (Till Stensitzki)
Date: Tue, 22 May 2012 14:57:18 +0000 (UTC)
Subject: [SciPy-User] scipy.weave on windows vista compiling error
References: <loom.20120521T131903-9@post.gmane.org>
Message-ID: <loom.20120522T165635-820@post.gmane.org>

Have a look at the solution of:

http://stackoverflow.com/questions/10231572/f2py-returns-valueerror-invalid-version-number-4

It is a bug in distutils version comparison.




From tobias at ebockwurst.de  Tue May 22 15:07:48 2012
From: tobias at ebockwurst.de (Tobias)
Date: Tue, 22 May 2012 19:07:48 +0000 (UTC)
Subject: [SciPy-User] scipy.weave on windows vista compiling error
References: <loom.20120521T131903-9@post.gmane.org>
	<loom.20120522T165635-820@post.gmane.org>
Message-ID: <loom.20120522T210651-882@post.gmane.org>

Till Stensitzki <mail.till <at> gmx.de> writes:

> 
> Have a look at the solution of:
> 
> http://stackoverflow.com/questions/10231572/f2py-returns-valueerror-invalid-
version-number-4
> 
> It is a bug in distutils version comparison.
> 

Hey Till,

this really solved my problem!
Thank you very much

Tobi





From otti at gargamels-haus.net  Wed May 23 04:01:31 2012
From: otti at gargamels-haus.net (otti)
Date: Wed, 23 May 2012 10:01:31 +0200
Subject: [SciPy-User] Wrong Step Response
Message-ID: <4FBC995B.60400@gargamels-haus.net>

Hello everyone,

after reading some threads about step response problems in the mailing list
i couldnt come up with a proper solution for my problem.

I've got the following transfer function:

G = Vr/(sT1*(1+sT2))

Vr = 2/15
T1 = 0.2
T2 = 1

For this i like to plot the step response so i made the following script:

*---Begin Script*

# some imports
import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
from scipy import signal

# Constants
T1 = 0.2
T2 = 1
Vr = float(2)/15
Tsim = 50

# Denumerator
N0 = np.array([T1*T2 ,T1 , 0])

# numerator
Z0 = Vr

# create tfcn
sys = sp.signal.lti(Z0, N0)

# create the step response
t = np.linspace(0, Tsim, 1000)
u = np.arange(len(t))
u = np.ones_like(u)
yout = sp.signal.lsim2(sys, T=t, U=u)[1]

plt.figure(1)
plt.plot(t, u, t, yout/yout.max())
plt.grid("on")
plt.xlabel("t")
plt.ylabel("h(t)")
plt.title("Sprungantwort")
plt.show()

*---End Script*

This gives me the following response, which i know is not the rigth one 
because i made this one
already in a lab at university with MATLAB but i don't have MATLAB at 
home because i prefer to use Numpy+Scipy+Matplotlib.

wrong step response

Here is the correct response:

correct step response

After several hours of reading an trying different approaches im kind of 
frustrated ^^.

I would be really thankfull for any help so that i can keep on going the 
Scipy track.


Greetings,
William
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From silva at lma.cnrs-mrs.fr  Wed May 23 04:36:58 2012
From: silva at lma.cnrs-mrs.fr (Fabrice Silva)
Date: Wed, 23 May 2012 10:36:58 +0200
Subject: [SciPy-User] Wrong Step Response
In-Reply-To: <4FBC995B.60400@gargamels-haus.net>
References: <4FBC995B.60400@gargamels-haus.net>
Message-ID: <1337762218.3554.8.camel@sonsAA>

Le mercredi 23 mai 2012 ? 10:01 +0200, otti a ?crit :
> Hello everyone,
> 
> after reading some threads about step response problems in the mailing list
> i couldnt come up with a proper solution for my problem.
> 
> I've got the following transfer function:
> 
> G = Vr/(sT1*(1+sT2))
> 
> Vr = 2/15
> T1 = 0.2
> T2 = 1

This transfer function involves an integrator (leading to the slope
after the transient) and a first-order low-pass filter (leading to a
damped contribution, the transient). 

IMHO, the step_response.png file is more probably correct than the
TA1.jpg file, as there is no second-order term that would possibly lead
to the oscillation shown in the latter. Moreover, the action of the
integrator for a step response can not lead to a constant steady state.
Am I wrong?

Looking at the fonts, I believe the step_response.png file comes from
matplotlib, and then from your python script. What makes you sure the
matlab script is correct? 


-- 
Fabrice Silva



From otti at gargamels-haus.net  Wed May 23 06:00:37 2012
From: otti at gargamels-haus.net (William)
Date: Wed, 23 May 2012 10:00:37 +0000 (UTC)
Subject: [SciPy-User] Wrong Step Response
References: <4FBC995B.60400@gargamels-haus.net>
	<1337762218.3554.8.camel@sonsAA>
Message-ID: <loom.20120523T113147-351@post.gmane.org>

Fabrice Silva <silva <at> lma.cnrs-mrs.fr> writes:

> This transfer function involves an integrator (leading to the slope
> after the transient) and a first-order low-pass filter (leading to a
> damped contribution, the transient). 
> 
> IMHO, the step_response.png file is more probably correct than the
> TA1.jpg file, as there is no second-order term that would possibly lead
> to the oscillation shown in the latter. Moreover, the action of the
> integrator for a step response can not lead to a constant steady state.
> Am I wrong?
> 
> Looking at the fonts, I believe the step_response.png file comes from
> matplotlib, and then from your python script. What makes you sure the
> matlab script is correct? 
> 



The integrator is used to reduce the overall error of the response so that it
reaches the constant steady state 1.
The low-pass is used to reduce the overshoot caused by the integrator.
If i didn't get it wrong in the control engineering class.
Probably have to check the notes again and run thru the MATLAB script again.

The response of TA1.jpg was made during a Lab (control engineering) at
University and the Prof checked it.





From silva at lma.cnrs-mrs.fr  Wed May 23 06:16:05 2012
From: silva at lma.cnrs-mrs.fr (Fabrice Silva)
Date: Wed, 23 May 2012 12:16:05 +0200
Subject: [SciPy-User] Wrong Step Response
In-Reply-To: <loom.20120523T113147-351@post.gmane.org>
References: <4FBC995B.60400@gargamels-haus.net>
	<1337762218.3554.8.camel@sonsAA>
	<loom.20120523T113147-351@post.gmane.org>
Message-ID: <1337768165.3554.12.camel@sonsAA>

> The integrator is used to reduce the overall error of the response so that it
> reaches the constant steady state 1.
> The low-pass is used to reduce the overshoot caused by the integrator.
> If i didn't get it wrong in the control engineering class.
> Probably have to check the notes again and run thru the MATLAB script again.
> 
> The response of TA1.jpg was made during a Lab (control engineering) at
> University and the Prof checked it.

Are you sure you are not confusing the step response of a transfer
function with the step response of a looped system containing an
integrator ?

Once you close the loop, the global transfer function is not the one you
mentionned but
H = G/(1+G)
for a unity feedback (for example), which exhibits the oscillation and
the unity steady state.

What you did in your control engineering classes may be the closed-loop
system...
-- 
Fabrice Silva



From otti at gargamels-haus.net  Wed May 23 06:23:53 2012
From: otti at gargamels-haus.net (William)
Date: Wed, 23 May 2012 10:23:53 +0000 (UTC)
Subject: [SciPy-User] Wrong Step Response
References: <4FBC995B.60400@gargamels-haus.net>
	<1337762218.3554.8.camel@sonsAA>
	<loom.20120523T113147-351@post.gmane.org>
	<1337768165.3554.12.camel@sonsAA>
Message-ID: <loom.20120523T122014-712@post.gmane.org>

Fabrice Silva <silva <at> lma.cnrs-mrs.fr> writes:

> Are you sure you are not confusing the step response of a transfer
> function with the step response of a looped system containing an
> integrator ?
> 
> Once you close the loop, the global transfer function is not the one you
> mentionned but
> H = G/(1+G)
> for a unity feedback (for example), which exhibits the oscillation and
> the unity steady state.


Ah thanks alot Fabrice, youre right!!

Got things mixed up!




From l.ulferts at hs-osnabrueck.de  Wed May 23 10:06:43 2012
From: l.ulferts at hs-osnabrueck.de (Lothar Ulferts)
Date: Wed, 23 May 2012 14:06:43 +0000 (UTC)
Subject: [SciPy-User] Array Selection Help -Part2-
Message-ID: <loom.20120523T152130-734@post.gmane.org>

Dear list,

based upon the thread "Array Selection Help"
http://thread.gmane.org/gmane.comp.python.scientific.user/19412 I would like to
modifiy the task a little bit:
The arr1 contains numbers (label) , arr2 floating points (values). But, here it
is different, zones shouldn't be 'Multiparts'. A zone should be split into its
untouched parts. 
For example:
label:
100 100 100 -99 -99 -99       100 100 100 -99 -99 -99
100 100 -99 -99 200 200       100 100 -99 -99 200 200
-99 -99 -99 -99 200 200  =>   -99 -99 -99 -99 200 200 
300 300 300 300 300 300       300 300 300 300 300 300
200 200 200 -99 100 100       300 400 400 -99 500 500
200 200 200 -99 100 100       400 400 400 -99 500 500

values:
1.5 1.9 1.8 0.3 0.1 0.1
1.5 1.7 0.6 0.3 2.5 2.9
0.6 0.6 0.8 0.4 2.1 2.1
3.1 3.2 3.3 3.4 3.5 3.6
4.7 4.7 4.0 0.1 1.0 1.4
4.3 4.0 4.9 0.3 1.2 1.1

Result of zonal min should be:
1.7 1.7 1.7 -99 -99 -99
1.7 1.7 -99 0.3 2.9 2.9
-99 -99 -99 -99 2.9 2.9
3.6 3.6 3.6 3.6 3.6 3.6
4.0 4.0 4.0 -99 1.0 1.0
4.0 4.0 4.0 -99 1.0 1.0


The question is, how do I tranform the left array to the right?

beste regards
Lothar



From pierre at puiseux.name  Mon May 21 09:57:05 2012
From: pierre at puiseux.name (pierre puiseux)
Date: Mon, 21 May 2012 15:57:05 +0200
Subject: [SciPy-User] subclassing ndarray : i want slice to return
	ndarray, not subclass
In-Reply-To: <CB77D3A3-1C93-4172-91DE-6455521B63AE@univ-pau.fr>
References: <CB77D3A3-1C93-4172-91DE-6455521B63AE@univ-pau.fr>
Message-ID: <94E136DD-35A8-4837-8F67-CF4F6A75C680@puiseux.name>

First, my problem is : 
Imagine ArrayChild is a subclass of np.ndarray and 'ac' is an instance of ArrayChild.
I call very often ac[i] or ac[1:12], which calls methods ArrayChild.__getislice__() or ArrayChild.__getiitem__() 
But for each call Python/NumPy create a new instance of ArrayChild, and calls my (very expansive) ArrayChild.__array_finalize__

I'd like to avoid this creation, and I want a more direct access to ArrayChild's slice or item

Finally, i've found a solution (I'm not sure it's less expansive, anyway) : 

first, i create a ndarray view of my ArrayChild in __array_finalize__.
where i add this line : 
======================================
self.array_view = obj.view(np.ndarray)
======================================

Then i define methods ArrayChild.__getslice__ and ArrayChild.__getitem__ to call the getters of array_view instead of default getters :

======================================
    def __getslice__(self, *args, **kwargs):
        return np.ndarray.__getslice__(self.array_view, *args, **kwargs)
                                       ---------------
    def __getitem__(self, *args, **kwargs):
        return np.ndarray.__getitem__(self.array_view, *args, **kwargs)
                                      ---------------
======================================
That's it, i have my results : subarray of ArrayChild are now np.ndarray s.
======================================
__array_finalize__
[0 1 2] <type 'numpy.ndarray'>
[3 4 5] <type 'numpy.ndarray'>
======================================

The final test program is :

======================================
import numpy as np

class ArrayChild(np.ndarray):

    def __new__(cls, array, info=None):
        obj = np.asarray(array).view(cls)
        obj.info = info
        return obj

    def __array_finalize__(self, obj):
        print>>sys.stderr, "__array_finalize__"
        if obj is None: return
        self.info = getattr(obj, 'info', None)
        self.array_view = obj.view(np.ndarray)
    
    def __getslice__(self, *args, **kwargs):
        return np.ndarray.__getslice__(self.array_view, *args, **kwargs)

    def __getitem__(self, *args, **kwargs):
        return np.ndarray.__getitem__(self.array_view, *args, **kwargs)

if __name__=='__main__':
    a = np.arange(6)
    a.shape=2,3
    a_child = ArrayChild(a)
    for x in a_child : 
        print>>sys.stderr, x, type(x)
======================================


Le 21 mai 2012 ? 11:56, pierre puiseux UPPA a ?crit :

> Hello,
> all my question is in the title. More precisely, like in scipy doc i try this :
> ======================================
> import sys
> import numpy as np
> 
> class ArrayChild(np.ndarray):
> 
>     def __new__(cls, array, info=None):
>         obj = np.asarray(array).view(cls)
>         obj.info = info
>         return obj
> 
>     def __array_finalize__(self, obj):
>         print>>sys.stderr, "__array_finalize__"
>         if obj is None: return
>         self.info = getattr(obj, 'info', None)
> 
> if __name__=='__main__':
>     a = np.arange(6)
>     a.shape=2,3
>     a_child = ArrayChild(a)
>     for x in a_child : 
>         print>>sys.stderr, x, type(x)
> 
> =====================================
> and i have the answer :
> =====================================
> __array_finalize__
> __array_finalize__
> [0 1 2] <class '__main__.ArrayChild'>
> __array_finalize__
> [3 4 5] <class '__main__.ArrayChild'>
> 
> =====================================
> but i want
> =====================================
> __array_finalize__
> __array_finalize__
> [0 1 2] <class '__main__.np.ndarray'>
> __array_finalize__
> [3 4 5] <class '__main__.np.ndarray'>
> 
> =====================================
> I've tried to redefine : __getslice__  like this, but it does not work.
> =====================================
>     def __getslice__(self, *args, **kwargs):
>         return np.ndarray.__getslice__(self, *args, **kwargs)
> 
> 
> =====================================
> Some idea ?
> Thanks to numpy/scipy team for that great job.
> =====================================
> 
> Pierre Puiseux
> Laboratoire de Math?matiques Appliqu?es
> Universit? de Pau et des Pays de l'Adour
> pierre.puiseux at univ-pau.fr
> http://www.univ-pau.fr/~puiseux
> 

pierre puiseux
pierre at puiseux.name
http://puiseux.name





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From alejandro.weinstein at gmail.com  Wed May 23 11:33:15 2012
From: alejandro.weinstein at gmail.com (Alejandro Weinstein)
Date: Wed, 23 May 2012 09:33:15 -0600
Subject: [SciPy-User] scipy.sparse.linalg.eigs is faster with k=8 than with
	k=1
Message-ID: <CAPFc=oz+ck-_6msNQ7SadNb69RH9zwTgKSj1U0PfdsckH5vydA@mail.gmail.com>

Hi:

I am using scipy.sparse.linalg.eigs with a 336x336 sparse matrix with
1144 nonzero entries. I only need the eigenvector corresponding to the
larger eigenvalue, so I was running the function with k=1. However, I
found that it is about 10 times faster to call the function with k=8.

I am testing this with the following code (available here:
https://gist.github.com/2775892):

#############################################################
import timeit

setup = """
import numpy as np
import scipy.sparse
import scipy.io
from scipy.sparse.linalg import eigs

P = scipy.io.mmread('P.mtx')
"""

n = 10
for k in range(1,20):
    code = 'eigs(P, k=%d)' % k
    t = timeit.timeit(stmt=code, setup=setup, number=n) / n
    print 'k: %2d, time: %5.1f ms' % (k, 1000*t)

#############################################################

The output is

k:  1, time: 301.7 ms
k:  2, time: 242.6 ms
k:  3, time: 352.0 ms
k:  4, time: 168.8 ms
k:  5, time: 148.1 ms
k:  6, time:  93.2 ms
k:  7, time:  70.0 ms
k:  8, time:  29.3 ms
k:  9, time:  45.2 ms
k: 10, time:  63.0 ms
k: 11, time: 209.1 ms
k: 12, time: 170.8 ms
k: 13, time: 120.2 ms
k: 14, time: 104.6 ms
k: 15, time: 115.0 ms
k: 16, time:  97.0 ms
k: 17, time:  94.0 ms
k: 18, time:  94.4 ms
k: 19, time:  74.3 ms

Is this behavior typical for eigs? In other words, should I always use
k set to values around 6 or 8, or is it matrix dependent?

I am also curious about this. I would expect that computing 1
eigenvector should never be slower than computing more eigenvectors.
Any about why is this happening?

Alejandro.


From pav at iki.fi  Wed May 23 13:15:53 2012
From: pav at iki.fi (Pauli Virtanen)
Date: Wed, 23 May 2012 17:15:53 +0000 (UTC)
Subject: [SciPy-User]
	=?utf-8?q?scipy=2Esparse=2Elinalg=2Eeigs_is_faster_w?=
	=?utf-8?q?ith_k=3D8_than_with=09k=3D1?=
References: <CAPFc=oz+ck-_6msNQ7SadNb69RH9zwTgKSj1U0PfdsckH5vydA@mail.gmail.com>
Message-ID: <loom.20120523T190859-703@post.gmane.org>

Alejandro Weinstein <alejandro.weinstein <at> gmail.com> writes:
[clip]
> I am using scipy.sparse.linalg.eigs with a 336x336 sparse matrix with
> 1144 nonzero entries. I only need the eigenvector corresponding to the
> larger eigenvalue, so I was running the function with k=1. However, I
> found that it is about 10 times faster to call the function with k=8.
[clip]
> Is this behavior typical for eigs? In other words, should I always use
> k set to values around 6 or 8, or is it matrix dependent?
> 
> I am also curious about this. I would expect that computing 1
> eigenvector should never be slower than computing more eigenvectors.
> Any about why is this happening?

The sparse eigenvalue solver, ARPACK, is a Krylov method, and the size of the
Krylov subspace depends on `k` (you can also adjust it by specifying the `ncv`
parameter). I don't see any straightforward ways to predict how the exact
performance depends on the Krylov subspace size --- bigger is better, but on the
other hand, involves more work. The performance will in any case depend on the
structure of the matrix.

More on ARPACK:
ftp://ftp.caam.rice.edu/pub/people/sorensen/ARPACK/ug.ps.gz



From vaggi.federico at gmail.com  Wed May 23 13:47:02 2012
From: vaggi.federico at gmail.com (federico vaggi)
Date: Wed, 23 May 2012 19:47:02 +0200
Subject: [SciPy-User] Linear algebra problem
Message-ID: <CAGvd0=g1f=0G_34anp-Bhcxrz-DNHg-ArZEwo8dYBrsRDrbq1Q@mail.gmail.com>

Hi,

I have a standard system, of type:

AX = B

Where:

X is a nxm matrix.

B is a mxn matrix.

A is a nxn matrix.

I have B and X, and I am trying to calculate A - however, B, is null matrix.

Using the default numpy solvers (
http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html#numpy.linalg.lstsq)
I obtain the trivial correct solution of A being a null matrix.

What's the most robust way to search for the solution which minimizes the
residuals, while still not returning a null matrix of A?  X, is usually
rank deficient, so I know I won't have an exact solution.

Thanks for all the help,

Federico
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From sergio_r at mail.com  Wed May 23 13:47:18 2012
From: sergio_r at mail.com (Sergio Rojas)
Date: Wed, 23 May 2012 13:47:18 -0400
Subject: [SciPy-User] scipy.sparse.linalg.eigs is faster with k=8 than
	with k=1
Message-ID: <20120523174718.17940@gmx.com>

I do not have an answer to your question Alejandro, but I am curious about why the timing
 changes each time the code is executed. Is there any random allocation in the process of solving
 your problem?

 Sergio

 ---------------------------------------------------------------------- Message: 1 Date: Wed, 23 May 2012 09:33:15 -0600 From: Alejandro Weinstein <alejandro.weinstein at gmail.com> Subject: [SciPy-User] scipy.sparse.linalg.eigs is faster with k=8 than with k=1 To: scipy-user at scipy.org Message-ID: <CAPFc=oz+ck-_6msNQ7SadNb69RH9zwTgKSj1U0PfdsckH5vydA at mail.gmail.com> Content-Type: text/plain; charset=ISO-8859-1 Hi: I am using scipy.sparse.linalg.eigs with a 336x336 sparse matrix with 1144 nonzero entries. I only need the eigenvector corresponding to the larger eigenvalue, so I was running the function with k=1. However, I found that it is about 10 times faster to call the function with k=8. I am testing this with the following code (available here: https://gist.github.com/2775892): ############################################################# import timeit setup = """ import numpy as np import scipy.sparse import scipy.io from scipy.sparse.linalg import eigs P = scipy.io.mmread('P.mtx') """ n = 10 for k in range(1,20): code = 'eigs(P, k=%d)' % k t = timeit.timeit(stmt=code, setup=setup, number=n) / n print 'k: %2d, time: %5.1f ms' % (k, 1000*t) ############################################################# The output is k: 1, time: 301.7 ms k: 2, time: 242.6 ms k: 3, time: 352.0 ms k: 4, time: 168.8 ms k: 5, time: 148.1 ms k: 6, time: 93.2 ms k: 7, time: 70.0 ms k: 8, time: 29.3 ms k: 9, time: 45.2 ms k: 10, time: 63.0 ms k: 11, time: 209.1 ms k: 12, time: 170.8 ms k: 13, time: 120.2 ms k: 14, time: 104.6 ms k: 15, time: 115.0 ms k: 16, time: 97.0 ms k: 17, time: 94.0 ms k: 18, time: 94.4 ms k: 19, time: 74.3 ms Is this behavior typical for eigs? In other words, should I always use k set to values around 6 or 8, or is it matrix dependent? I am also curious about this. I would expect that computing 1 eigenvector should never be slower than computing more eigenvectors. Any about why is this happening? Alejandro. ------------------------------
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From alejandro.weinstein at gmail.com  Wed May 23 14:00:11 2012
From: alejandro.weinstein at gmail.com (Alejandro Weinstein)
Date: Wed, 23 May 2012 12:00:11 -0600
Subject: [SciPy-User] scipy.sparse.linalg.eigs is faster with k=8 than
 with k=1
In-Reply-To: <loom.20120523T190859-703@post.gmane.org>
References: <CAPFc=oz+ck-_6msNQ7SadNb69RH9zwTgKSj1U0PfdsckH5vydA@mail.gmail.com>
	<loom.20120523T190859-703@post.gmane.org>
Message-ID: <CAPFc=ozbLs0kaHPx+UPs1Va6cDyxrAag8_vs22gt6KhCCyA-zQ@mail.gmail.com>

On Wed, May 23, 2012 at 11:15 AM, Pauli Virtanen <pav at iki.fi> wrote:
> The sparse eigenvalue solver, ARPACK, is a Krylov method, and the size of the
> Krylov subspace depends on `k` (you can also adjust it by specifying the `ncv`
> parameter). I don't see any straightforward ways to predict how the exact
> performance depends on the Krylov subspace size --- bigger is better, but on the
> other hand, involves more work. The performance will in any case depend on the
> structure of the matrix.

I see. Thanks for the answer.

Just for the record, if I set sigma=1, which means that eigs finds
eigenvalues near sigma using shift-invert mode, then the time
increases with k. These are the times (everything is the same as in
the original program, except that I use  code = 'eigs(P, k=%d,
sigma=1)' % k

Using sigma equal to 1.000000
k:  1, time:   3.1 ms
k:  2, time:   6.9 ms
k:  3, time:   5.4 ms
k:  4, time:   7.9 ms
k:  5, time:  16.8 ms
k:  6, time:  24.4 ms
k:  7, time:  17.1 ms
k:  8, time:  20.4 ms
k:  9, time:  21.2 ms
k: 10, time:  20.7 ms
k: 11, time:  23.5 ms
k: 12, time:  24.2 ms
k: 13, time:  32.0 ms
k: 14, time:  32.9 ms
k: 15, time:  23.3 ms
k: 16, time:  36.4 ms
k: 17, time:  49.4 ms
k: 18, time:  39.3 ms
k: 19, time:  42.2 ms

Alejandro.


From alejandro.weinstein at gmail.com  Wed May 23 14:06:16 2012
From: alejandro.weinstein at gmail.com (Alejandro Weinstein)
Date: Wed, 23 May 2012 12:06:16 -0600
Subject: [SciPy-User] scipy.sparse.linalg.eigs is faster with k=8 than
 with k=1
In-Reply-To: <20120523174718.17940@gmx.com>
References: <20120523174718.17940@gmx.com>
Message-ID: <CAPFc=owBqw39Wi0O2bhSCg4kQzj-UBfUtp6nZZfZPbX1UCvy_w@mail.gmail.com>

On Wed, May 23, 2012 at 11:47 AM, Sergio Rojas <sergio_r at mail.com> wrote:
>
> I do not have an answer to your question Alejandro, but I am curious about
> why the timing
> changes each time the code is executed. Is there any random allocation in
> the process of solving
> your problem?

There is nothing random on my side. I just load the matrix and call
eigs (the timing is only for the function call). But I don't see a
significant variation each time I call the program. I would say is the
natural variation you observe in a multitasking environment. (I am
running Linux, in case that matters).

Alejandro.


From pav at iki.fi  Wed May 23 16:12:59 2012
From: pav at iki.fi (Pauli Virtanen)
Date: Wed, 23 May 2012 22:12:59 +0200
Subject: [SciPy-User] Linear algebra problem
In-Reply-To: <CAGvd0=g1f=0G_34anp-Bhcxrz-DNHg-ArZEwo8dYBrsRDrbq1Q@mail.gmail.com>
References: <CAGvd0=g1f=0G_34anp-Bhcxrz-DNHg-ArZEwo8dYBrsRDrbq1Q@mail.gmail.com>
Message-ID: <jpjgcc$nhs$1@dough.gmane.org>

23.05.2012 19:47, federico vaggi kirjoitti:
[clip]
> AX = B
[clip]
> I have B and X, and I am trying to calculate A - however, B, is
> null matrix.
>
> What's the most robust way to search for the solution which minimizes
> the residuals, while still not returning a null matrix of A?  X, is
> usually rank deficient, so I know I won't have an exact solution.

This problem is equivalent to looking for the subspace of zero
eigenvalue. You can do an eigenvalue or SVD decomposition of X, and then
grab the solution from the eigen-/singular vectors corresponding to zero
or small eigen/singular values.

-- 
Pauli Virtanen



From warren.weckesser at enthought.com  Thu May 24 00:29:19 2012
From: warren.weckesser at enthought.com (Warren Weckesser)
Date: Wed, 23 May 2012 23:29:19 -0500
Subject: [SciPy-User] Linear algebra problem
In-Reply-To: <jpjgcc$nhs$1@dough.gmane.org>
References: <CAGvd0=g1f=0G_34anp-Bhcxrz-DNHg-ArZEwo8dYBrsRDrbq1Q@mail.gmail.com>
	<jpjgcc$nhs$1@dough.gmane.org>
Message-ID: <CAM-+wY_7gzMmS8TOsV5jBgWNKB=E6y7f4RP+PExUxg=xg1R6xg@mail.gmail.com>

On Wed, May 23, 2012 at 3:12 PM, Pauli Virtanen <pav at iki.fi> wrote:

> 23.05.2012 19:47, federico vaggi kirjoitti:
> [clip]
> > AX = B
> [clip]
> > I have B and X, and I am trying to calculate A - however, B, is
> > null matrix.
> >
> > What's the most robust way to search for the solution which minimizes
> > the residuals, while still not returning a null matrix of A?  X, is
> > usually rank deficient, so I know I won't have an exact solution.
>
> This problem is equivalent to looking for the subspace of zero
> eigenvalue. You can do an eigenvalue or SVD decomposition of X, and then
> grab the solution from the eigen-/singular vectors corresponding to zero
> or small eigen/singular values.
>
>

Another way to say this is you want the nullspace of X.T.  See the SciPy
Cookbook entry http://www.scipy.org/Cookbook/RankNullspace for an example
of a function that computes the nullspace using the singular value
decomposition.

For example, in the following, X is 4 by 2:

In [57]: import rank_nullspace as rn

In [58]: X = array([[1,2],[3,4],[5,6],[7,8]])

In [59]: X
Out[59]:
array([[1, 2],
       [3, 4],
       [5, 6],
       [7, 8]])

In [60]: A = rn.nullspace(X.T).T

In [61]: A
Out[61]:
array([[-0.39450102,  0.24279655,  0.69790998, -0.5462055 ],
       [-0.37995913,  0.80065588, -0.46143436,  0.04073761]])


We get a 2 by 4 result; we can add trivial rows of zeros to make A 4 by 4:


In [62]: A = vstack((A, zeros((2,4))))

In [63]: A
Out[63]:
array([[-0.39450102,  0.24279655,  0.69790998, -0.5462055 ],
       [-0.37995913,  0.80065588, -0.46143436,  0.04073761],
       [ 0.        ,  0.        ,  0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.        ,  0.        ]])


Verify that we have solved AX = B, where B is the 4 by 2 array of zeros:

In [64]: dot(A, X)
Out[64]:
array([[ -1.33226763e-15,   0.00000000e+00],
       [  0.00000000e+00,   3.33066907e-16],
       [  0.00000000e+00,   0.00000000e+00],
       [  0.00000000e+00,   0.00000000e+00]])


Warren
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From servant.mathieu at gmail.com  Thu May 24 04:15:51 2012
From: servant.mathieu at gmail.com (servant mathieu)
Date: Thu, 24 May 2012 10:15:51 +0200
Subject: [SciPy-User] efficiency of the simplex routine: R (optim) vs
	scipy.optimize.fmin
Message-ID: <CALnu5bOztD74TrEK=Gy5LZD6_zCgpXZ1tZkW=Hj08gRK6a36QA@mail.gmail.com>

Dear scipy users,

Again a question about optimization.

 I've just compared the efficiency of the simplex routine in R (optim) vs
scipy (fmin), when minimizing a chi-square. fmin is faster than optim,
but appears to be less efficient. In R, the value of the function is always
minimized step by step (there are of course some exceptions) while there is
lot of fluctuations in python. Given that the underlying simplex algorithm
is supposed to be the same, which mechanism is responsible for this
difference? Is it possible to constrain fmin so it could be more rigorous?

Cheers,
Mathieu
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From m_daoust at hotmail.com  Thu May 24 06:40:25 2012
From: m_daoust at hotmail.com (M Daoust)
Date: Thu, 24 May 2012 06:40:25 -0400
Subject: [SciPy-User] subclassing ndarray : i want slice to return >
 ndarray, not subclass
In-Reply-To: <mailman.11.1337619602.28301.scipy-user@scipy.org>
References: <mailman.11.1337619602.28301.scipy-user@scipy.org>
Message-ID: <SNT106-W12F09A9DD1DD93FCEA4DE6E5000@phx.gbl>


> > Subclassing ndarray is a very tricky business -- I did it once and
> > regretted having done it for years, because there's so much you can't do
> > etc.. You're almost certainly better off with embedding an array as an
> > attribute, and then forward properties etc. to it.
> 
> Yes, it's almost always the wrong thing...

I've sub-classes numpy.ndarray in some of my personal code, so I'm just 
hoping to understand why this is the wrong choice before I dig the 
hole any deeper.


So it's only wrong because it's hard to do right, and you can't do certain things? (which things?)

If you want a object to act mostly like a  numpy.ndarray, making a sub-class *should* be the right answer. 

Because isn't forwarding lookups to a wrapped class  just a re-implementation of the sub-classing mechanism?

Isn't it also tricky to get that "wrapping subclassing" right?

If you're going to sub-class something, it would be nice to use the built in sub-class machinery, for clarity... etc...


 		 	   		  
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From tmp50 at ukr.net  Thu May 24 07:32:29 2012
From: tmp50 at ukr.net (Dmitrey)
Date: Thu, 24 May 2012 14:32:29 +0300
Subject: [SciPy-User] Some numpy funcs for PyPy
Message-ID: <6355.1337859149.6100681629467803648@ffe6.ukr.net>




hi all,
maybe you're aware of numpypy - numpy port for pypy (pypy.org) - Python
language implementation with dynamic compilation.

Unfortunately, numpypy developmnent is very slow due to strict quality
standards and some other issues, so for my purposes I have provided some
missing numpypy funcs, in particular

* atleast_1d, atleast_2d, hstack, vstack, cumsum, isscalar, asscalar,
asfarray, flatnonzero, tile, zeros_like, ones_like, empty_like,
where, searchsorted

* with "axis" parameter: nan(arg)min, nan(arg)max, all, any

and have got some OpenOpt / FuncDesigner functionality working faster
than in CPython.

File with this functions you can get here

Also you may be interested in some info at http://openopt.org/PyPy
Regards, Dmitrey.
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From fccoelho at gmail.com  Thu May 24 07:43:45 2012
From: fccoelho at gmail.com (Flavio Coelho)
Date: Thu, 24 May 2012 08:43:45 -0300
Subject: [SciPy-User] Some numpy funcs for PyPy
In-Reply-To: <6355.1337859149.6100681629467803648@ffe6.ukr.net>
References: <6355.1337859149.6100681629467803648@ffe6.ukr.net>
Message-ID: <CAAbyzbSbNcAG7AROOfz7JzRzq0qCr2kVAzKs4X4JyemDgn9GJg@mail.gmail.com>

That's very usefull! I hope these features get included upstream in the
next release of numpypy.

thanks,

Fl?vio

On Thu, May 24, 2012 at 8:32 AM, Dmitrey <tmp50 at ukr.net> wrote:

>  hi all,
> maybe you're aware of numpypy - numpy port for pypy (pypy.org) - Python
> language implementation with dynamic compilation.
>
> Unfortunately, numpypy developmnent is very slow due to strict quality
> standards and some other issues, so for my purposes I have provided some
> missing numpypy funcs, in particular
>
>
>    - atleast_1d, atleast_2d, hstack, vstack, cumsum, isscalar, asscalar,
>    asfarray, flatnonzero, tile, zeros_like, ones_like, empty_like, where,
>    searchsorted
>    - with "axis" parameter: nan(arg)min, nan(arg)max, all, any
>
> and have got some OpenOpt / FuncDesigner functionality working faster than
> in CPython.
>
> File with this functions you can get here<http://trac.openopt.org/openopt/browser/numpy.py>
>
> Also you may be interested in some info at http://openopt.org/PyPy
> Regards, Dmitrey.
>
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>


-- 
Fl?vio Code?o Coelho
================
+55(21) 3799-5567
Professor
Escola de Matem?tica Aplicada
Funda??o Get?lio Vargas
Rio de Janeiro - RJ
Brasil
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From tmp50 at ukr.net  Thu May 24 09:07:25 2012
From: tmp50 at ukr.net (Dmitrey)
Date: Thu, 24 May 2012 16:07:25 +0300
Subject: [SciPy-User] [Numpy-discussion] Some numpy funcs for PyPy
In-Reply-To: <CAKVJ-_7KD0aj-G0gVgn_1+7NObyJ0Wb54E5BjtLNMvyvhs_yeA@mail.gmail.com>
References: <CAKVJ-_7KD0aj-G0gVgn_1+7NObyJ0Wb54E5BjtLNMvyvhs_yeA@mail.gmail.com>
	<6355.1337859149.6100681629467803648@ffe6.ukr.net>
Message-ID: <69589.1337864845.13913483336144781312@ffe16.ukr.net>




>

On your website you wrote:

>> From my (Dmitrey) point of view numpypy development is
>> very unfriendly for newcomers - PyPy developers say "provide
>> code, preferably in interpreter level instead of AppLevel,
>> provide whole test coverage for all possible corner cases,
>> provide hg diff for code, and then, maybe, it will be committed".
>> Probably this is the reason why so insufficient number of
>> developers work on numpypy.

I assume that is paraphrased with a little hyperbole, but it
isn't so different from numpy (other than using git), or many
other open source projects. 
> 

Of course, many opensource projects do like that, but in the case of
numpypy IMHO the things are especially bad.




>

Unit tests are important, and
taking patches without them is risky.
> 

Yes, but at first, things required from numpypy newcomers are TOO
complicated - and no guarrantee is provided, that elapsed efforts will
not be just a waste of time; at 2nd, the high-quality standards are
especially cynic when compared with their own code quality, e.g.
numpypy.all(True) doesn't work yet, despite it hangs in bug tracker for a
long time; a[a<0] = b[b<0] works incorrectly etc.
These are reasons that forced me to write some required for my purposes
missing funcs and some bug walkarounds (like for that one with
numpypy.all and any).




>

?I've been subscribed to the pypy-dev list for a while, 
> 

I had been subsribed IIRC for a couple of months




>

but I
don't recall seeing you posting there.
> 

I had made some, see my pypy activity here




>

?Have you tried to submit
any of your work to PyPy yet?
> 

yes: I had spent lots of time for concatenate() (pypy developers said
noone works on it) - and finally they have committed code for this func
from other trunc. Things like this were with some other my proposed code
for PyPy and all those days spent for it.




>

?Perhaps you should have
sent this message to pypy-dev instead?
> 

I had explained them my point of view in mail list and irc channel, their
answer was like "don't borther horses, why do you in a hurry? All will be
done during several months", but I see it (porting whole numpy)
definitely won't be done during the term. IIRC during ~ 2 months only ~10
new items were added to numpypy; also, lots of numpypy items, when
calling, e.g. searchsorted, just raise NotImplementedError: wainting for
interplevel routine, or don't work with high-dimensional arrays and/or
some other corner cases.

numpypy developers go (rather slowly) their own way, while I just propose
temporary alternative, till proper PyPy-numpy implementation

regards, D.
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From yildiz at strw.leidenuniv.nl  Thu May 24 09:50:52 2012
From: yildiz at strw.leidenuniv.nl (Umut Yildiz)
Date: Thu, 24 May 2012 13:50:52 +0000 (UTC)
Subject: [SciPy-User] griddata not working after update to Python 2.7
Message-ID: <loom.20120524T153737-172@post.gmane.org>

Dear All,

I used to use griddata in order to make my contourmaps. However, after I updated
my Python from 2.6 to 2.7 griddata is not working anymore.

I tried some workarounds but no success.

The countourmap that I produced before is here.
http://dl.dropbox.com/u/17983476/matplotlib/contour_dT_workingbefore.png

After the Python 2.7 update, it turns to the following.
http://dl.dropbox.com/u/17983476/matplotlib/contour_dT_broken.png

Here is the datafile.
http://dl.dropbox.com/u/17983476/matplotlib/contour_dT.dat

And the associated python script (which is also below).
http://dl.dropbox.com/u/17983476/matplotlib/contour_dT.py

The code that I was using before is here. I had to comment out #import griddata
line because this is the only way that it continues. Is this a bug in griddata,
or if there are new workarounds, I would be glad to know a new method to produce
my contourplots again.

Thanks a lot

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

import os
import sys
import math
from math import *
from numpy import *
#import griddata
from pylab import *
from matplotlib.ticker import FormatStrFormatter
params = {'axes.labelsize': 20,
		'text.fontsize': 15,
		'legend.fontsize': 14,
		'xtick.labelsize': 20,
		'ytick.labelsize': 20,
		'text.usetex': True }
rcParams.update(params)

par1 = []
par2 = []
chis = []

rfile = file('contour_dT.dat','r')
line = rfile.readline()
data = line.split()

while len(data) >1:
	par1.append(float(data[0]))
	par2.append(float(data[1]))
	chis.append(float(data[2]))
	line = rfile.readline()
	data = line.split()

par1 = array(par1)
par2 = array(par2)
chis = array(chis)

xi = linspace(3.2,7.8,50)
yi = linspace(15,300,50)
zi = griddata(par2,par1,chis,xi,yi)
levels = [0.4,0.5,0.6,0.7,0.8,0.9,1.0,1.2,1.5,2,3,4,6,10,12,15,20,25,30,40,50]
CS = contourf(xi,yi,zi,levels,cmap=cm.jet)
CS2 = contour(CS, levels=CS.levels[::2],
                        colors = 'r',
                        hold='on')

cbar = colorbar(CS)
cbar.add_lines(CS2)

savefig("contour_dT.png")
show()





From aldcroft at head.cfa.harvard.edu  Thu May 24 12:09:42 2012
From: aldcroft at head.cfa.harvard.edu (Tom Aldcroft)
Date: Thu, 24 May 2012 12:09:42 -0400
Subject: [SciPy-User] subclassing ndarray : i want slice to return >
 ndarray, not subclass
In-Reply-To: <SNT106-W12F09A9DD1DD93FCEA4DE6E5000@phx.gbl>
References: <mailman.11.1337619602.28301.scipy-user@scipy.org>
	<SNT106-W12F09A9DD1DD93FCEA4DE6E5000@phx.gbl>
Message-ID: <CAGsQ-NseKw3dud0h4ehnKQkfLqH9cCdJgHpx0fHjhTEqnK6jGA@mail.gmail.com>

On Thu, May 24, 2012 at 6:40 AM, M Daoust <m_daoust at hotmail.com> wrote:
>> > Subclassing ndarray is a very tricky business -- I did it once and
>> > regretted having done it for years, because there's so much you can't do
>> > etc.. You're almost certainly better off with embedding an array as an
>> > attribute, and then forward properties etc. to it.
>>
>> Yes, it's almost always the wrong thing...
>
> I've sub-classes numpy.ndarray in some of my personal code, so I'm just
> hoping to understand why this is the wrong choice before I dig the hole any
> deeper.
>
> So it's only wrong because it's hard to do right, and you can't do certain
> things? (which things?)
>
> If you want a object to act mostly like a? numpy.ndarray, making a sub-class
> *should* be the right answer.
>
> Because isn't forwarding lookups to a wrapped class? just a
> re-implementation of the sub-classing mechanism?
>
> Isn't it also tricky to get that "wrapping subclassing" right?
>
> If you're going to sub-class something, it would be nice to use the built in
> sub-class machinery, for clarity... etc...
>
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>

After seeing this thread I brought this point up on the
numpy-discussion list.  Here is a link to the thread:

http://mail.scipy.org/pipermail/numpy-discussion/2012-May/062451.html

- Tom


From pav at iki.fi  Thu May 24 14:09:20 2012
From: pav at iki.fi (Pauli Virtanen)
Date: Thu, 24 May 2012 20:09:20 +0200
Subject: [SciPy-User] griddata not working after update to Python 2.7
In-Reply-To: <loom.20120524T153737-172@post.gmane.org>
References: <loom.20120524T153737-172@post.gmane.org>
Message-ID: <jpltgg$t3t$1@dough.gmane.org>

24.05.2012 15:50, Umut Yildiz kirjoitti:
[clip]
> The code that I was using before is here. I had to comment out #import griddata
> line because this is the only way that it continues. Is this a bug in griddata,
> or if there are new workarounds, I would be glad to know a new method to produce
> my contourplots again.
[clip]
> zi = griddata(par2,par1,chis,xi,yi)

Note that this is the griddata function from matplotlib, so asking on
one of the Matplotlib lists could be useful.

-- 
Pauli Virtanen



From pierre.raybaut at gmail.com  Sat May 26 15:42:01 2012
From: pierre.raybaut at gmail.com (Pierre Raybaut)
Date: Sat, 26 May 2012 21:42:01 +0200
Subject: [SciPy-User] ANN: Spyder v2.1.10
Message-ID: <CAKegKuDQJpqv6C=eL5Et7SxG2k-JLp+0EypOSTijmMQ9b7-Hzw@mail.gmail.com>

Hi all,

On the behalf of Spyder's development team
(http://code.google.com/p/spyderlib/people/list), I'm pleased to
announce that Spyder v2.1.10 has been released and is available for
Windows XP/Vista/7, GNU/Linux and MacOS X:
http://code.google.com/p/spyderlib/

This is a pure maintenance release -- a lot of bugs were fixed since v2.1.9:
http://code.google.com/p/spyderlib/wiki/ChangeLog

Spyder is a free, open-source (MIT license) interactive development
environment for the Python language with advanced editing, interactive
testing, debugging and introspection features. Originally designed to
provide MATLAB-like features (integrated help, interactive console,
variable explorer with GUI-based editors for dictionaries, NumPy
arrays, ...), it is strongly oriented towards scientific computing and
software development.
Thanks to the `spyderlib` library, Spyder also provides powerful
ready-to-use widgets: embedded Python console (example:
http://packages.python.org/guiqwt/_images/sift3.png), NumPy array
editor (example: http://packages.python.org/guiqwt/_images/sift2.png),
dictionary editor, source code editor, etc.

Description of key features with tasty screenshots can be found at:
http://code.google.com/p/spyderlib/wiki/Features

On Windows platforms, Spyder is also available as a stand-alone
executable (don't forget to disable UAC on Vista/7). This all-in-one
portable version is still experimental (for example, it does not embed
sphinx -- meaning no rich text mode for the object inspector) but it
should provide a working version of Spyder for Windows platforms
without having to install anything else (except Python 2.x itself, of
course).

Don't forget to follow Spyder updates/news:
 * on the project website: http://code.google.com/p/spyderlib/
 * and on our official blog: http://spyder-ide.blogspot.com/

Last, but not least, we welcome any contribution that helps making
Spyder an efficient scientific development/computing environment. Join
us to help creating your favourite environment!
(http://code.google.com/p/spyderlib/wiki/NoteForContributors)

Enjoy!

-Pierre


From f_magician at mac.com  Sat May 26 22:05:27 2012
From: f_magician at mac.com (Magician)
Date: Sun, 27 May 2012 11:05:27 +0900
Subject: [SciPy-User] SciPy installation troubles on CentOS 6.2
In-Reply-To: <CABL7CQi9j-kQrJveHdasYTi2Q0KOO-rZiUcoKr0_pdAZrr_kzQ@mail.gmail.com>
References: <mailman.3432.1337510428.1208.scipy-user@scipy.org>
	<C3368B14-EED1-4E87-A7E9-80F76990A0E2@mac.com>
	<CABL7CQi9j-kQrJveHdasYTi2Q0KOO-rZiUcoKr0_pdAZrr_kzQ@mail.gmail.com>
Message-ID: <F7FF31A8-A9FA-4AA5-AA5D-0CB3824681D6@mac.com>

Hi Ralf and Sergio,


I thought my problems inclined to NumPy, and I posted to NumPy mailing list.
But I couldn't get any answers for several days, so I'd like to ask again.


I tried to build NumPy again with site.cfg, but I got these errors:
> building 'numpy.core._sort' extension
> compiling C sources
> C compiler: gcc -pthread -fno-strict-aliasing -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC
> 
> compile options: '-Inumpy/core/include -Ibuild/src.linux-x86_64-2.7/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/include -I/usr/local/python-2.7.3/include/python2.7 -Ibuild/src.linux-x86_64-2.7/numpy/core/src/multiarray -Ibuild/src.linux-x86_64-2.7/numpy/core/src/umath -c'
> gcc: build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.c
> gcc -pthread -shared build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.o -L. -Lbuild/temp.linux-x86_64-2.7 -lnpymath -lm -lpython2.7 -o build/lib.linux-x86_64-2.7/numpy/core/_sort.so
> /usr/bin/ld: cannot find -lpython2.7
> collect2: ld returned 1 exit status
> /usr/bin/ld: cannot find -lpython2.7
> collect2: ld returned 1 exit status
> error: Command "gcc -pthread -shared build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.o -L. -Lbuild/temp.linux-x86_64-2.7 -lnpymath -lm -lpython2.7 -o build/lib.linux-x86_64-2.7/numpy/core/_sort.so" failed with exit status 1

Before I built NumPy, I uncommented and modified site.cfg as below:
> [DEFAULT]
> library_dirs = /usr/local/python-2.7.3/lib:/usr/lib64:/usr/lib64/atlas
> include_dirs = /usr/local/python-2.7.3/include:/usr/include:/usr/include/atlas
> 
> [blas_opt]
> libraries = f77blas, cblas, atlas
> 
> [lapack_opt]
> libraries = lapack, f77blas, cblas, atlas

And "setup.py config" dumped these messages:
> Running from numpy source directory.F2PY Version 2
> blas_opt_info:
> blas_mkl_info:
>  libraries mkl,vml,guide not found in /usr/local/python-2.7.3/lib
>  libraries mkl,vml,guide not found in /usr/lib64
>  libraries mkl,vml,guide not found in /usr/lib64/atlas
>  NOT AVAILABLE
> 
> atlas_blas_threads_info:
> Setting PTATLAS=ATLAS
>  libraries ptf77blas,ptcblas,atlas not found in /usr/local/python-2.7.3/lib
> Setting PTATLAS=ATLAS
> customize GnuFCompiler
> Could not locate executable g77
> Could not locate executable f77
> customize IntelFCompiler
> Could not locate executable ifort
> Could not locate executable ifc
> customize LaheyFCompiler
> Could not locate executable lf95
> customize PGroupFCompiler
> Could not locate executable pgf90
> Could not locate executable pgf77
> customize AbsoftFCompiler
> Could not locate executable f90
> customize NAGFCompiler
> Found executable /usr/bin/f95
> customize VastFCompiler
> customize CompaqFCompiler
> Could not locate executable fort
> customize IntelItaniumFCompiler
> Could not locate executable efort
> Could not locate executable efc
> customize IntelEM64TFCompiler
> customize Gnu95FCompiler
> Found executable /usr/bin/gfortran
> customize Gnu95FCompiler
> customize Gnu95FCompiler 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 -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC
> 
> compile options: '-c'
> gcc: _configtest.c
> gcc -pthread _configtest.o -L/usr/lib64/atlas -lptf77blas -lptcblas -latlas -o _configtest
> ATLAS version 3.8.4 built by mockbuild on Wed Dec  7 18:04:21 GMT 2011:
>   UNAME    : Linux c6b5.bsys.dev.centos.org 2.6.32-44.2.el6.x86_64 #1 SMP Wed Jul 21 12:48:32 EDT 2010 x86_64 x86_64 x86_64 GNU/Linux
>   INSTFLG  : -1 0 -a 1
>   ARCHDEFS : -DATL_OS_Linux -DATL_ARCH_PII -DATL_CPUMHZ=2261 -DATL_SSE2 -DATL_SSE1 -DATL_USE64BITS -DATL_GAS_x8664
>   F2CDEFS  : -DAdd_ -DF77_INTEGER=int -DStringSunStyle
>   CACHEEDGE: 8388608
>   F77      : gfortran, version GNU Fortran (GCC) 4.4.6 20110731 (Red Hat 4.4.6-3)
>   F77FLAGS : -fomit-frame-pointer -mfpmath=387 -O2 -falign-loops=4 -g -Wa,--noexecstack -fPIC -m64
>   SMC      : gcc, version gcc (GCC) 4.4.6 20110731 (Red Hat 4.4.6-3)
>   SMCFLAGS : -fomit-frame-pointer -mfpmath=387 -O2 -falign-loops=4 -g -Wa,--noexecstack -fPIC -m64
>   SKC      : gcc, version gcc (GCC) 4.4.6 20110731 (Red Hat 4.4.6-3)
>   SKCFLAGS : -fomit-frame-pointer -mfpmath=387 -O2 -falign-loops=4 -g -Wa,--noexecstack -fPIC -m64
> success!
> removing: _configtest.c _configtest.o _configtest
> Setting PTATLAS=ATLAS
>  FOUND:
>    libraries = ['ptf77blas', 'ptcblas', 'atlas']
>    library_dirs = ['/usr/lib64/atlas']
>    language = c
>    define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')]
>    include_dirs = ['/usr/include']
> 
>  FOUND:
>    libraries = ['ptf77blas', 'ptcblas', 'atlas']
>    library_dirs = ['/usr/lib64/atlas']
>    language = c
>    define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')]
>    include_dirs = ['/usr/include']
> 
> lapack_opt_info:
> lapack_mkl_info:
> mkl_info:
>  libraries mkl,vml,guide not found in /usr/local/python-2.7.3/lib
>  libraries mkl,vml,guide not found in /usr/lib64
>  libraries mkl,vml,guide not found in /usr/lib64/atlas
>  NOT AVAILABLE
> 
>  NOT AVAILABLE
> 
> atlas_threads_info:
> Setting PTATLAS=ATLAS
>  libraries ptf77blas,ptcblas,atlas not found in /usr/local/python-2.7.3/lib
>  libraries lapack_atlas not found in /usr/local/python-2.7.3/lib
>  libraries lapack_atlas not found in /usr/lib64/atlas
> numpy.distutils.system_info.atlas_threads_info
> Setting PTATLAS=ATLAS
> Setting PTATLAS=ATLAS
>  FOUND:
>    libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
>    library_dirs = ['/usr/lib64/atlas']
>    language = f77
>    define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')]
>    include_dirs = ['/usr/include']
> 
>  FOUND:
>    libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
>    library_dirs = ['/usr/lib64/atlas']
>    language = f77
>    define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')]
>    include_dirs = ['/usr/include']
> 
> running config

I couldn't get enough informations about numscons/bento,
so I'm still trying to build it with site.cfg and CFLAGS.

How could I solve it?


Magician


On 2012/05/21, at 1:50, Ralf Gommers wrote:

> 
> 
> On Sun, May 20, 2012 at 4:16 PM, Magician <f_magician at mac.com> wrote:
> Hi Ralf,
> 
> 
> Thanks for your advice.
> I tried to install BLAS/Lapack/ATLAS as below:
> > yum install blas-devel lapack-devel atlas-devel
> 
> Next I installed NumPy as below:
> > tar xzvf numpy-1.6.1.tar.gz
> > cd numpy-1.6.1
> > export CFLAGS="-L/usr/local/python-2.7.3/lib"
> 
> Note that this overrides CFLAGS instead of appending that flag to the rest. If it doesn't work without that line, you have to either specify all cflags or use numscons/bento.
> 
> Ralf
> 
>  
> > python setup.py build
> 
> But then I got those errors:
> > building 'numpy.linalg.lapack_lite' extension
> > compiling C sources
> > C compiler: gcc -pthread -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -L/usr/local/python-2.7.3/lib -fPIC
> >
> > creating build/temp.linux-x86_64-2.7/numpy/linalg
> > compile options: '-DATLAS_INFO="\"3.8.4\"" -I/usr/include -Inumpy/core/include -Ibuild/src.linux-x86_64-2.7/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/include -I/usr/local/python-2.7.3/include/python2.7 -Ibuild/src.linux-x86_64-2.7/numpy/core/src/multiarray -Ibuild/src.linux-x86_64-2.7/numpy/core/src/umath -c'
> > gcc: numpy/linalg/lapack_litemodule.c
> > gcc: numpy/linalg/python_xerbla.c
> > /usr/bin/gfortran -Wall -Wall -shared build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib64/atlas -L. -Lbuild/temp.linux-x86_64-2.7 -llapack -lptf77blas -lptcblas -latlas -lpython2.7 -lgfortran -o build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so
> > /usr/bin/ld: cannot find -lpython2.7
> > collect2: ld returned 1 exit status
> > /usr/bin/ld: cannot find -lpython2.7
> > collect2: ld returned 1 exit status
> > error: Command "/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib64/atlas -L. -Lbuild/temp.linux-x86_64-2.7 -llapack -lptf77blas -lptcblas -latlas -lpython2.7 -lgfortran -o build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so" failed with exit status 1
> 
> If I haven't install BLAS/Lapack/ATLAS, NumPy will be
> successfully built and installed.
> 
> 
> Magician
> 
> 
> On 2012/05/20, at 19:40, scipy-user-request at scipy.org wrote:
> 
> > Message: 1
> > Date: Sun, 20 May 2012 10:21:12 +0200
> > From: Ralf Gommers <ralf.gommers at googlemail.com>
> > Subject: Re: [SciPy-User] SciPy installation troubles on CentOS 6.2
> > To: SciPy Users List <scipy-user at scipy.org>
> > Message-ID:
> >       <CABL7CQh+WPwpiMtO0UDKo9G01Gj38kTaWV=bSvq9eCK_qihAfQ at mail.gmail.com>
> > Content-Type: text/plain; charset="iso-8859-1"
> >
> > On Sat, May 19, 2012 at 5:09 PM, Magician <f_magician at mac.com> wrote:
> >
> >> Hi All,
> >>
> >>
> >> I'm trying to build SciPy from source code,
> >> but I have some troubles.
> >>
> >> My environment is below:
> >>> CentOS 6.2 on VMware Fusion 4.1.2 (CentOS was installed as Software
> >> Development WS)
> >>> Python 2.7.3 (already built from sources, installed at
> >> /usr/local/python-2.7.3)
> >>> NumPy 1.6.1, SciPy 0.10.1, ATLAS 3.8.4, Lapack 3.4.1 (now trying to
> >> build)
> >> I and my colleagues (other users) want to use recent Python,
> >> so I installed Python from sources, and I can't install SciPy
> >> by using yum command.
> >>
> >> Now I'm facing ATLAS compiling errors.
> >> Configuration options are "--prefix=/usr/local/atlas-3.8.4 -Fa alg -fPIC".
> >> I tried to build it for several times, and always I got errors as below:
> >>> res/dgemvN_6_75 : VARIATION EXCEEDS TOLERENCE, RERUN WITH HIGHER REPS.
> >>>
> >>> ATL_gemvN_mm.c : 1257.99
> >>> ATL_gemvN_1x1_1.c : 581.74
> >>> ATL_gemvN_1x1_1a.c : 1589.45
> >>> ATL_gemvN_4x2_0.c : 813.13
> >>> ATL_gemvN_4x4_1.c : 755.54
> >>> make[3]: *** [res/dMVRES] Error 255
> >>> make[3]: Leaving directory
> >> `/home/magician/Desktop/ATLAS/build/tune/blas/gemv'
> >>> make[2]: ***
> >> [/home/magician/Desktop/ATLAS/build/tune/blas/gemv/res/dMVRES] Error 2
> >>> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin'
> >>> ERROR 734 DURING MVTUNE!!.  CHECK INSTALL_LOG/dMVTUNE.LOG FOR DETAILS.
> >>> make[2]: Entering directory `/home/magician/Desktop/ATLAS/build/bin'
> >>> cd /home/magician/Desktop/ATLAS/build ; make error_report
> >>> make[3]: Entering directory `/home/magician/Desktop/ATLAS/build'
> >>> make -f Make.top error_report
> >>> make[4]: Entering directory `/home/magician/Desktop/ATLAS/build'
> >>> uname -a 2>&1 >> bin/INSTALL_LOG/ERROR.LOG
> >>> gcc -v 2>&1  >> bin/INSTALL_LOG/ERROR.LOG
> >>> Using built-in specs.
> >>> Target: x86_64-redhat-linux
> >>> Configured with: ../configure --prefix=/usr --mandir=/usr/share/man
> >> --infodir=/usr/share/info --with-bugurl=
> >> http://bugzilla.redhat.com/bugzilla --enable-bootstrap --enable-shared
> >> --enable-threads=posix --enable-checking=release --with-system-zlib
> >> --enable-__cxa_atexit --disable-libunwind-exceptions
> >> --enable-gnu-unique-object
> >> --enable-languages=c,c++,objc,obj-c++,java,fortran,ada
> >> --enable-java-awt=gtk --disable-dssi
> >> --with-java-home=/usr/lib/jvm/java-1.5.0-gcj-1.5.0.0/jre
> >> --enable-libgcj-multifile --enable-java-maintainer-mode
> >> --with-ecj-jar=/usr/share/java/eclipse-ecj.jar --disable-libjava-multilib
> >> --with-ppl --with-cloog --with-tune=generic --with-arch_32=i686
> >> --build=x86_64-redhat-linux
> >>> Thread model: posix
> >>> gcc version 4.4.6 20110731 (Red Hat 4.4.6-3) (GCC)
> >>> gcc -V 2>&1  >> bin/INSTALL_LOG/ERROR.LOG
> >>> gcc: '-V' option must have argument
> >>> make[4]: [error_report] Error 1 (ignored)
> >>> gcc --version 2>&1  >> bin/INSTALL_LOG/ERROR.LOG
> >>> tar cf error_Corei264SSE3.tar Make.inc bin/INSTALL_LOG/*
> >>> gzip --best error_Corei264SSE3.tar
> >>> mv error_Corei264SSE3.tar.gz error_Corei264SSE3.tgz
> >>> make[4]: Leaving directory `/home/magician/Desktop/ATLAS/build'
> >>> make[3]: Leaving directory `/home/magician/Desktop/ATLAS/build'
> >>> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin'
> >>> Error report error_<ARCH>.tgz has been created in your top-level ATLAS
> >>> directory.  Be sure to include this file in any help request.
> >>> cat: ../../CONFIG/error.txt: No such file or directory
> >>> cat: ../../CONFIG/error.txt: No such file or directory
> >>> make[1]: *** [build] Error 255
> >>> make[1]: Leaving directory `/home/magician/Desktop/ATLAS/build'
> >>> make: *** [build] Error 2
> >>
> >> It's very troublesome for me to build ATLAS by myself.
> >> My purpose is just using SciPy on my Python.
> >> Even if it's optimized not so good for my environment, it's OK.
> >>
> >> Is there an easy or a sure way to build and install SciPy?
> >
> >
> > Building ATLAS is much harder than building scipy, so you should try to
> > find some rpm's for it, like
> > http://linuxtoolkit.blogspot.com/2010/09/installing-lapack-blas-and-atlas-on.html.
> > There's no problem building scipy against ATLAS from a binary install.
> >
> > Ralf


From ralf.gommers at googlemail.com  Sun May 27 04:16:23 2012
From: ralf.gommers at googlemail.com (Ralf Gommers)
Date: Sun, 27 May 2012 10:16:23 +0200
Subject: [SciPy-User] SciPy installation troubles on CentOS 6.2
In-Reply-To: <F7FF31A8-A9FA-4AA5-AA5D-0CB3824681D6@mac.com>
References: <mailman.3432.1337510428.1208.scipy-user@scipy.org>
	<C3368B14-EED1-4E87-A7E9-80F76990A0E2@mac.com>
	<CABL7CQi9j-kQrJveHdasYTi2Q0KOO-rZiUcoKr0_pdAZrr_kzQ@mail.gmail.com>
	<F7FF31A8-A9FA-4AA5-AA5D-0CB3824681D6@mac.com>
Message-ID: <CABL7CQj3AnbtPD6r_Eg6dYnUuVSYnB1Zhk+UwQTotrkhN04Wjw@mail.gmail.com>

On Sun, May 27, 2012 at 4:05 AM, Magician <f_magician at mac.com> wrote:

> Hi Ralf and Sergio,
>
>
> I thought my problems inclined to NumPy, and I posted to NumPy mailing
> list.
> But I couldn't get any answers for several days, so I'd like to ask again.
>
>
> I tried to build NumPy again with site.cfg, but I got these errors:
> > building 'numpy.core._sort' extension
> > compiling C sources
> > C compiler: gcc -pthread -fno-strict-aliasing -g -O2 -DNDEBUG -g -fwrapv
> -O3 -Wall -Wstrict-prototypes -fPIC
> >
> > compile options: '-Inumpy/core/include
> -Ibuild/src.linux-x86_64-2.7/numpy/core/include/numpy
> -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core
> -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath
> -Inumpy/core/include -I/usr/local/python-2.7.3/include/python2.7
> -Ibuild/src.linux-x86_64-2.7/numpy/core/src/multiarray
> -Ibuild/src.linux-x86_64-2.7/numpy/core/src/umath -c'
> > gcc: build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.c
> > gcc -pthread -shared
> build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.o
> -L. -Lbuild/temp.linux-x86_64-2.7 -lnpymath -lm -lpython2.7 -o
> build/lib.linux-x86_64-2.7/numpy/core/_sort.so
> > /usr/bin/ld: cannot find -lpython2.7
> > collect2: ld returned 1 exit status
> > /usr/bin/ld: cannot find -lpython2.7
> > collect2: ld returned 1 exit status
> > error: Command "gcc -pthread -shared
> build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.o
> -L. -Lbuild/temp.linux-x86_64-2.7 -lnpymath -lm -lpython2.7 -o
> build/lib.linux-x86_64-2.7/numpy/core/_sort.so" failed with exit status 1
>
> Before I built NumPy, I uncommented and modified site.cfg as below:
> > [DEFAULT]
> > library_dirs = /usr/local/python-2.7.3/lib:/usr/lib64:/usr/lib64/atlas
> > include_dirs =
> /usr/local/python-2.7.3/include:/usr/include:/usr/include/atlas
> >
> > [blas_opt]
> > libraries = f77blas, cblas, atlas
> >
> > [lapack_opt]
> > libraries = lapack, f77blas, cblas, atlas
>
> And "setup.py config" dumped these messages:
> > Running from numpy source directory.F2PY Version 2
> > blas_opt_info:
> > blas_mkl_info:
> >  libraries mkl,vml,guide not found in /usr/local/python-2.7.3/lib
> >  libraries mkl,vml,guide not found in /usr/lib64
> >  libraries mkl,vml,guide not found in /usr/lib64/atlas
> >  NOT AVAILABLE
> >
> > atlas_blas_threads_info:
> > Setting PTATLAS=ATLAS
> >  libraries ptf77blas,ptcblas,atlas not found in
> /usr/local/python-2.7.3/lib
> > Setting PTATLAS=ATLAS
> > customize GnuFCompiler
> > Could not locate executable g77
> > Could not locate executable f77
> > customize IntelFCompiler
> > Could not locate executable ifort
> > Could not locate executable ifc
> > customize LaheyFCompiler
> > Could not locate executable lf95
> > customize PGroupFCompiler
> > Could not locate executable pgf90
> > Could not locate executable pgf77
> > customize AbsoftFCompiler
> > Could not locate executable f90
> > customize NAGFCompiler
> > Found executable /usr/bin/f95
> > customize VastFCompiler
> > customize CompaqFCompiler
> > Could not locate executable fort
> > customize IntelItaniumFCompiler
> > Could not locate executable efort
> > Could not locate executable efc
> > customize IntelEM64TFCompiler
> > customize Gnu95FCompiler
> > Found executable /usr/bin/gfortran
> > customize Gnu95FCompiler
> > customize Gnu95FCompiler 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 -g -O2 -DNDEBUG -g -fwrapv
> -O3 -Wall -Wstrict-prototypes -fPIC
> >
> > compile options: '-c'
> > gcc: _configtest.c
> > gcc -pthread _configtest.o -L/usr/lib64/atlas -lptf77blas -lptcblas
> -latlas -o _configtest
> > ATLAS version 3.8.4 built by mockbuild on Wed Dec  7 18:04:21 GMT 2011:
> >   UNAME    : Linux c6b5.bsys.dev.centos.org 2.6.32-44.2.el6.x86_64 #1
> SMP Wed Jul 21 12:48:32 EDT 2010 x86_64 x86_64 x86_64 GNU/Linux
> >   INSTFLG  : -1 0 -a 1
> >   ARCHDEFS : -DATL_OS_Linux -DATL_ARCH_PII -DATL_CPUMHZ=2261 -DATL_SSE2
> -DATL_SSE1 -DATL_USE64BITS -DATL_GAS_x8664
> >   F2CDEFS  : -DAdd_ -DF77_INTEGER=int -DStringSunStyle
> >   CACHEEDGE: 8388608
> >   F77      : gfortran, version GNU Fortran (GCC) 4.4.6 20110731 (Red Hat
> 4.4.6-3)
> >   F77FLAGS : -fomit-frame-pointer -mfpmath=387 -O2 -falign-loops=4 -g
> -Wa,--noexecstack -fPIC -m64
> >   SMC      : gcc, version gcc (GCC) 4.4.6 20110731 (Red Hat 4.4.6-3)
> >   SMCFLAGS : -fomit-frame-pointer -mfpmath=387 -O2 -falign-loops=4 -g
> -Wa,--noexecstack -fPIC -m64
> >   SKC      : gcc, version gcc (GCC) 4.4.6 20110731 (Red Hat 4.4.6-3)
> >   SKCFLAGS : -fomit-frame-pointer -mfpmath=387 -O2 -falign-loops=4 -g
> -Wa,--noexecstack -fPIC -m64
> > success!
> > removing: _configtest.c _configtest.o _configtest
> > Setting PTATLAS=ATLAS
> >  FOUND:
> >    libraries = ['ptf77blas', 'ptcblas', 'atlas']
> >    library_dirs = ['/usr/lib64/atlas']
> >    language = c
> >    define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')]
> >    include_dirs = ['/usr/include']
> >
> >  FOUND:
> >    libraries = ['ptf77blas', 'ptcblas', 'atlas']
> >    library_dirs = ['/usr/lib64/atlas']
> >    language = c
> >    define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')]
> >    include_dirs = ['/usr/include']
> >
> > lapack_opt_info:
> > lapack_mkl_info:
> > mkl_info:
> >  libraries mkl,vml,guide not found in /usr/local/python-2.7.3/lib
> >  libraries mkl,vml,guide not found in /usr/lib64
> >  libraries mkl,vml,guide not found in /usr/lib64/atlas
> >  NOT AVAILABLE
> >
> >  NOT AVAILABLE
> >
> > atlas_threads_info:
> > Setting PTATLAS=ATLAS
> >  libraries ptf77blas,ptcblas,atlas not found in
> /usr/local/python-2.7.3/lib
> >  libraries lapack_atlas not found in /usr/local/python-2.7.3/lib
> >  libraries lapack_atlas not found in /usr/lib64/atlas
> > numpy.distutils.system_info.atlas_threads_info
> > Setting PTATLAS=ATLAS
> > Setting PTATLAS=ATLAS
> >  FOUND:
> >    libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
> >    library_dirs = ['/usr/lib64/atlas']
> >    language = f77
> >    define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')]
> >    include_dirs = ['/usr/include']
> >
> >  FOUND:
> >    libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
> >    library_dirs = ['/usr/lib64/atlas']
> >    language = f77
> >    define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')]
> >    include_dirs = ['/usr/include']
> >
> > running config
>
> I couldn't get enough informations about numscons/bento,
> so I'm still trying to build it with site.cfg and CFLAGS.
>

You specified the additional library dir you need in site.cfg. You
therefore don't need to use CFLAGS anymore. This is probably still what's
going wrong, because the build error says it can't find Python itself
anymore. So make sure that CFLAGS is not defined and try again.

Ralf
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From f_magician at mac.com  Sun May 27 07:12:37 2012
From: f_magician at mac.com (Magician)
Date: Sun, 27 May 2012 20:12:37 +0900
Subject: [SciPy-User] SciPy installation troubles on CentOS 6.2
In-Reply-To: <CABL7CQj3AnbtPD6r_Eg6dYnUuVSYnB1Zhk+UwQTotrkhN04Wjw@mail.gmail.com>
References: <mailman.3432.1337510428.1208.scipy-user@scipy.org>
	<C3368B14-EED1-4E87-A7E9-80F76990A0E2@mac.com>
	<CABL7CQi9j-kQrJveHdasYTi2Q0KOO-rZiUcoKr0_pdAZrr_kzQ@mail.gmail.com>
	<F7FF31A8-A9FA-4AA5-AA5D-0CB3824681D6@mac.com>
	<CABL7CQj3AnbtPD6r_Eg6dYnUuVSYnB1Zhk+UwQTotrkhN04Wjw@mail.gmail.com>
Message-ID: <601BA7AC-02E6-469D-AA51-535E52DE22E7@mac.com>

Hi Ralf,


Maybe I'm misunderstanding how to handle site.cfg, so I'm trying easy tests
about CFLAGS and site.cfg.
These tests are done on independent snapshots (already installed Python 2.7.3) of VMware.
My libpython2.7.so and libpython2.7.so.1.0 are on /usr/local/python-2.7.3/lib.
Lapack and ATLAS are not installed yet.

With CFLAGS:
> export CFLAGS="-L/usr/local/python-2.7.3/lib"
> python setup.py config
>> Running from numpy source directory.non-existing path in 'numpy/distutils': 'site.cfg'
>> F2PY Version 2
>> blas_opt_info:
>> blas_mkl_info:
>>   libraries mkl,vml,guide not found in /usr/local/python-2.7.3/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 /usr/local/python-2.7.3/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/atlas
>>   libraries ptf77blas,ptcblas,atlas not found in /usr/lib64/sse2
>>   libraries ptf77blas,ptcblas,atlas not found in /usr/lib64
>>   libraries ptf77blas,ptcblas,atlas not found in /usr/lib
>>   NOT AVAILABLE
>> 
>> atlas_blas_info:
>>   libraries f77blas,cblas,atlas not found in /usr/local/python-2.7.3/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/atlas
>>   libraries f77blas,cblas,atlas not found in /usr/lib64/sse2
>>   libraries f77blas,cblas,atlas not found in /usr/lib64
>>   libraries f77blas,cblas,atlas not found in /usr/lib
>>   NOT AVAILABLE
>> 
>> /home/magician/Desktop/numpy-1.6.1/numpy/distutils/system_info.py:1414: 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 /usr/local/python-2.7.3/lib
>>   libraries blas not found in /usr/local/lib64
>>   libraries blas not found in /usr/local/lib
>>   libraries blas not found in /usr/lib64
>>   libraries blas not found in /usr/lib
>>   NOT AVAILABLE
>> 
>> /home/magician/Desktop/numpy-1.6.1/numpy/distutils/system_info.py:1423: 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
>> 
>> /home/magician/Desktop/numpy-1.6.1/numpy/distutils/system_info.py:1426: 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__)
>>   NOT AVAILABLE
>> 
>> lapack_opt_info:
>> lapack_mkl_info:
>> mkl_info:
>>   libraries mkl,vml,guide not found in /usr/local/python-2.7.3/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 /usr/local/python-2.7.3/lib
>>   libraries lapack_atlas not found in /usr/local/python-2.7.3/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/atlas
>>   libraries lapack_atlas not found in /usr/lib64/atlas
>>   libraries ptf77blas,ptcblas,atlas not found in /usr/lib64/sse2
>>   libraries lapack_atlas not found in /usr/lib64/sse2
>>   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
>>   libraries lapack_atlas not found in /usr/lib
>> numpy.distutils.system_info.atlas_threads_info
>>   NOT AVAILABLE
>> 
>> atlas_info:
>>   libraries f77blas,cblas,atlas not found in /usr/local/python-2.7.3/lib
>>   libraries lapack_atlas not found in /usr/local/python-2.7.3/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/atlas
>>   libraries lapack_atlas not found in /usr/lib64/atlas
>>   libraries f77blas,cblas,atlas not found in /usr/lib64/sse2
>>   libraries lapack_atlas not found in /usr/lib64/sse2
>>   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
>>   libraries lapack_atlas not found in /usr/lib
>> numpy.distutils.system_info.atlas_info
>>   NOT AVAILABLE
>> 
>> /home/magician/Desktop/numpy-1.6.1/numpy/distutils/system_info.py:1330: 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__)
>> lapack_info:
>>   libraries lapack not found in /usr/local/python-2.7.3/lib
>>   libraries lapack not found in /usr/local/lib64
>>   libraries lapack not found in /usr/local/lib
>>   libraries lapack not found in /usr/lib64
>>   libraries lapack not found in /usr/lib
>>   NOT AVAILABLE
>> 
>> /home/magician/Desktop/numpy-1.6.1/numpy/distutils/system_info.py:1341: 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__)
>> lapack_src_info:
>>   NOT AVAILABLE
>> 
>> /home/magician/Desktop/numpy-1.6.1/numpy/distutils/system_info.py:1344: 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__)
>>   NOT AVAILABLE
>> 
>> running config
Then I executed "python setup.py build", NumPy was built successfully.

Next with site.cfg:
> cp site.cfg.example site.cfg
> vi site.cfg
>> [DEFAULT]
>> library_dirs = /usr/local/python-2.7.3/lib
>> include_dirs = /usr/local/python-2.7.3/include
> python setup.py config

>> Running from numpy source directory.F2PY Version 2
>> blas_opt_info:
>> blas_mkl_info:
>>   libraries mkl,vml,guide not found in /usr/local/python-2.7.3/lib
>>   NOT AVAILABLE
>> 
>> atlas_blas_threads_info:
>> Setting PTATLAS=ATLAS
>>   libraries ptf77blas,ptcblas,atlas not found in /usr/local/python-2.7.3/lib
>>   NOT AVAILABLE
>> 
>> atlas_blas_info:
>>   libraries f77blas,cblas,atlas not found in /usr/local/python-2.7.3/lib
>>   NOT AVAILABLE
>> 
>> /home/magician/Desktop/numpy-1.6.1/numpy/distutils/system_info.py:1414: 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 /usr/local/python-2.7.3/lib
>>   NOT AVAILABLE
>> 
>> /home/magician/Desktop/numpy-1.6.1/numpy/distutils/system_info.py:1423: 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
>> 
>> /home/magician/Desktop/numpy-1.6.1/numpy/distutils/system_info.py:1426: 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__)
>>   NOT AVAILABLE
>> 
>> lapack_opt_info:
>> lapack_mkl_info:
>> mkl_info:
>>   libraries mkl,vml,guide not found in /usr/local/python-2.7.3/lib
>>   NOT AVAILABLE
>> 
>>   NOT AVAILABLE
>> 
>> atlas_threads_info:
>> Setting PTATLAS=ATLAS
>>   libraries ptf77blas,ptcblas,atlas not found in /usr/local/python-2.7.3/lib
>>   libraries lapack_atlas not found in /usr/local/python-2.7.3/lib
>> numpy.distutils.system_info.atlas_threads_info
>>   NOT AVAILABLE
>> 
>> atlas_info:
>>   libraries f77blas,cblas,atlas not found in /usr/local/python-2.7.3/lib
>>   libraries lapack_atlas not found in /usr/local/python-2.7.3/lib
>> numpy.distutils.system_info.atlas_info
>>   NOT AVAILABLE
>> 
>> /home/magician/Desktop/numpy-1.6.1/numpy/distutils/system_info.py:1330: 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__)
>> lapack_info:
>>   libraries lapack not found in /usr/local/python-2.7.3/lib
>>   NOT AVAILABLE
>> 
>> /home/magician/Desktop/numpy-1.6.1/numpy/distutils/system_info.py:1341: 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__)
>> lapack_src_info:
>>   NOT AVAILABLE
>> 
>> /home/magician/Desktop/numpy-1.6.1/numpy/distutils/system_info.py:1344: 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__)
>>   NOT AVAILABLE
>> 
>> running config
Then I executed "python setup.py build", I got errors about -lpython which I posted before.

If I edit site.cfg as below, config messages become same as CFLAGS,
but same -lpython errors are dumped:
> vi site.cfg
>> [DEFAULT]
>> library_dirs = /usr/local/python-2.7.3/lib:/usr/local/lib64:/usr/local/lib:/usr/lib64:/usr/lib
>> include_dirs = /usr/local/python-2.7.3/include


Magician


On 2012/05/27, at 17:16, Ralf Gommers wrote:

> On Sun, May 27, 2012 at 4:05 AM, Magician <f_magician at mac.com> wrote:
>> Hi Ralf and Sergio,
>> 
>> 
>> I thought my problems inclined to NumPy, and I posted to NumPy mailing list.
>> But I couldn't get any answers for several days, so I'd like to ask again.
>> 
>> 
>> I tried to build NumPy again with site.cfg, but I got these errors:
>> > building 'numpy.core._sort' extension
>> > compiling C sources
>> > C compiler: gcc -pthread -fno-strict-aliasing -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC
>> >
>> > compile options: '-Inumpy/core/include -Ibuild/src.linux-x86_64-2.7/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/include -I/usr/local/python-2.7.3/include/python2.7 -Ibuild/src.linux-x86_64-2.7/numpy/core/src/multiarray -Ibuild/src.linux-x86_64-2.7/numpy/core/src/umath -c'
>> > gcc: build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.c
>> > gcc -pthread -shared build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.o -L. -Lbuild/temp.linux-x86_64-2.7 -lnpymath -lm -lpython2.7 -o build/lib.linux-x86_64-2.7/numpy/core/_sort.so
>> > /usr/bin/ld: cannot find -lpython2.7
>> > collect2: ld returned 1 exit status
>> > /usr/bin/ld: cannot find -lpython2.7
>> > collect2: ld returned 1 exit status
>> > error: Command "gcc -pthread -shared build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.o -L. -Lbuild/temp.linux-x86_64-2.7 -lnpymath -lm -lpython2.7 -o build/lib.linux-x86_64-2.7/numpy/core/_sort.so" failed with exit status 1
>> 
>> Before I built NumPy, I uncommented and modified site.cfg as below:
>> > [DEFAULT]
>> > library_dirs = /usr/local/python-2.7.3/lib:/usr/lib64:/usr/lib64/atlas
>> > include_dirs = /usr/local/python-2.7.3/include:/usr/include:/usr/include/atlas
>> >
>> > [blas_opt]
>> > libraries = f77blas, cblas, atlas
>> >
>> > [lapack_opt]
>> > libraries = lapack, f77blas, cblas, atlas
>> 
>> And "setup.py config" dumped these messages:
>> > Running from numpy source directory.F2PY Version 2
>> > blas_opt_info:
>> > blas_mkl_info:
>> >  libraries mkl,vml,guide not found in /usr/local/python-2.7.3/lib
>> >  libraries mkl,vml,guide not found in /usr/lib64
>> >  libraries mkl,vml,guide not found in /usr/lib64/atlas
>> >  NOT AVAILABLE
>> >
>> > atlas_blas_threads_info:
>> > Setting PTATLAS=ATLAS
>> >  libraries ptf77blas,ptcblas,atlas not found in /usr/local/python-2.7.3/lib
>> > Setting PTATLAS=ATLAS
>> > customize GnuFCompiler
>> > Could not locate executable g77
>> > Could not locate executable f77
>> > customize IntelFCompiler
>> > Could not locate executable ifort
>> > Could not locate executable ifc
>> > customize LaheyFCompiler
>> > Could not locate executable lf95
>> > customize PGroupFCompiler
>> > Could not locate executable pgf90
>> > Could not locate executable pgf77
>> > customize AbsoftFCompiler
>> > Could not locate executable f90
>> > customize NAGFCompiler
>> > Found executable /usr/bin/f95
>> > customize VastFCompiler
>> > customize CompaqFCompiler
>> > Could not locate executable fort
>> > customize IntelItaniumFCompiler
>> > Could not locate executable efort
>> > Could not locate executable efc
>> > customize IntelEM64TFCompiler
>> > customize Gnu95FCompiler
>> > Found executable /usr/bin/gfortran
>> > customize Gnu95FCompiler
>> > customize Gnu95FCompiler 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 -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC
>> >
>> > compile options: '-c'
>> > gcc: _configtest.c
>> > gcc -pthread _configtest.o -L/usr/lib64/atlas -lptf77blas -lptcblas -latlas -o _configtest
>> > ATLAS version 3.8.4 built by mockbuild on Wed Dec  7 18:04:21 GMT 2011:
>> >   UNAME    : Linux c6b5.bsys.dev.centos.org 2.6.32-44.2.el6.x86_64 #1 SMP Wed Jul 21 12:48:32 EDT 2010 x86_64 x86_64 x86_64 GNU/Linux
>> >   INSTFLG  : -1 0 -a 1
>> >   ARCHDEFS : -DATL_OS_Linux -DATL_ARCH_PII -DATL_CPUMHZ=2261 -DATL_SSE2 -DATL_SSE1 -DATL_USE64BITS -DATL_GAS_x8664
>> >   F2CDEFS  : -DAdd_ -DF77_INTEGER=int -DStringSunStyle
>> >   CACHEEDGE: 8388608
>> >   F77      : gfortran, version GNU Fortran (GCC) 4.4.6 20110731 (Red Hat 4.4.6-3)
>> >   F77FLAGS : -fomit-frame-pointer -mfpmath=387 -O2 -falign-loops=4 -g -Wa,--noexecstack -fPIC -m64
>> >   SMC      : gcc, version gcc (GCC) 4.4.6 20110731 (Red Hat 4.4.6-3)
>> >   SMCFLAGS : -fomit-frame-pointer -mfpmath=387 -O2 -falign-loops=4 -g -Wa,--noexecstack -fPIC -m64
>> >   SKC      : gcc, version gcc (GCC) 4.4.6 20110731 (Red Hat 4.4.6-3)
>> >   SKCFLAGS : -fomit-frame-pointer -mfpmath=387 -O2 -falign-loops=4 -g -Wa,--noexecstack -fPIC -m64
>> > success!
>> > removing: _configtest.c _configtest.o _configtest
>> > Setting PTATLAS=ATLAS
>> >  FOUND:
>> >    libraries = ['ptf77blas', 'ptcblas', 'atlas']
>> >    library_dirs = ['/usr/lib64/atlas']
>> >    language = c
>> >    define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')]
>> >    include_dirs = ['/usr/include']
>> >
>> >  FOUND:
>> >    libraries = ['ptf77blas', 'ptcblas', 'atlas']
>> >    library_dirs = ['/usr/lib64/atlas']
>> >    language = c
>> >    define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')]
>> >    include_dirs = ['/usr/include']
>> >
>> > lapack_opt_info:
>> > lapack_mkl_info:
>> > mkl_info:
>> >  libraries mkl,vml,guide not found in /usr/local/python-2.7.3/lib
>> >  libraries mkl,vml,guide not found in /usr/lib64
>> >  libraries mkl,vml,guide not found in /usr/lib64/atlas
>> >  NOT AVAILABLE
>> >
>> >  NOT AVAILABLE
>> >
>> > atlas_threads_info:
>> > Setting PTATLAS=ATLAS
>> >  libraries ptf77blas,ptcblas,atlas not found in /usr/local/python-2.7.3/lib
>> >  libraries lapack_atlas not found in /usr/local/python-2.7.3/lib
>> >  libraries lapack_atlas not found in /usr/lib64/atlas
>> > numpy.distutils.system_info.atlas_threads_info
>> > Setting PTATLAS=ATLAS
>> > Setting PTATLAS=ATLAS
>> >  FOUND:
>> >    libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
>> >    library_dirs = ['/usr/lib64/atlas']
>> >    language = f77
>> >    define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')]
>> >    include_dirs = ['/usr/include']
>> >
>> >  FOUND:
>> >    libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
>> >    library_dirs = ['/usr/lib64/atlas']
>> >    language = f77
>> >    define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')]
>> >    include_dirs = ['/usr/include']
>> >
>> > running config
>> 
>> I couldn't get enough informations about numscons/bento,
>> so I'm still trying to build it with site.cfg and CFLAGS.
> 
> You specified the additional library dir you need in site.cfg. You therefore don't need to use CFLAGS anymore. This is probably still what's going wrong, because the build error says it can't find Python itself anymore. So make sure that CFLAGS is not defined and try again.
> 
> Ralf 



From sergio_r at mail.com  Sun May 27 13:21:11 2012
From: sergio_r at mail.com (Sergio Rojas)
Date: Sun, 27 May 2012 13:21:11 -0400
Subject: [SciPy-User] SciPy installation troubles on CentOS 6.2
Message-ID: <20120527172112.17920@gmx.com>

> From: Magician
 > /usr/bin/ld: cannot find -lpython2.7 
 Magician,
 This error listed indicates that python libraries are not being found.
 Try this:

 linux> locate libpython2.7.so
 linux> echo $LD_LIBRARY_PATH

 and verify that in your library path are the lib and include directories corresponding
 to the python you want to built.

 I don't recall this but, did you built your own python?

 Sergio

----- Original Message -----
From: Magician
Sent: 05/26/12 10:05 PM
To: Ralf Gommers, sergio_r at mail.com
Subject: Re: [SciPy-User] SciPy installation troubles on CentOS 6.2

 Hi Ralf and Sergio, I thought my problems inclined to NumPy, and I posted to NumPy mailing list. But I couldn't get any answers for several days, so I'd like to ask again. I tried to build NumPy again with site.cfg, but I got these errors: > building 'numpy.core._sort' extension > compiling C sources > C compiler: gcc -pthread -fno-strict-aliasing -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC > > compile options: '-Inumpy/core/include -Ibuild/src.linux-x86_64-2.7/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/include -I/usr/local/python-2.7.3/include/python2.7 -Ibuild/src.linux-x86_64-2.7/numpy/core/src/multiarray -Ibuild/src.linux-x86_64-2.7/numpy/core/src/umath -c' > gcc: build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.c > gcc -pthread -shared build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.o -L. -Lbuild/temp.linux-x86_64-2.7 -lnpymath -lm -lpython2.7 -o build/lib.linux-x86_64-2.7/numpy/core/_sort.so > /usr/bin/ld: cannot find -lpython2.7 > collect2: ld returned 1 exit status > /usr/bin/ld: cannot find -lpython2.7 > collect2: ld returned 1 exit status > error: Command "gcc -pthread -shared build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.o -L. -Lbuild/temp.linux-x86_64-2.7 -lnpymath -lm -lpython2.7 -o build/lib.linux-x86_64-2.7/numpy/core/_sort.so" failed with exit status 1 Before I built NumPy, I uncommented and modified site.cfg as below: > [DEFAULT] > library_dirs = /usr/local/python-2.7.3/lib:/usr/lib64:/usr/lib64/atlas > include_dirs = /usr/local/python-2.7.3/include:/usr/include:/usr/include/atlas > > [blas_opt] > libraries = f77blas, cblas, atlas > > [lapack_opt] > libraries = lapack, f77blas, cblas, atlas And "setup.py config" dumped these messages: > Running from numpy source directory.F2PY Version 2 > blas_opt_info: > blas_mkl_info: > libraries mkl,vml,guide not found in /usr/local/python-2.7.3/lib > libraries mkl,vml,guide not found in /usr/lib64 > libraries mkl,vml,guide not found in /usr/lib64/atlas > NOT AVAILABLE > > atlas_blas_threads_info: > Setting PTATLAS=ATLAS > libraries ptf77blas,ptcblas,atlas not found in /usr/local/python-2.7.3/lib > Setting PTATLAS=ATLAS > customize GnuFCompiler > Could not locate executable g77 > Could not locate executable f77 > customize IntelFCompiler > Could not locate executable ifort > Could not locate executable ifc > customize LaheyFCompiler > Could not locate executable lf95 > customize PGroupFCompiler > Could not locate executable pgf90 > Could not locate executable pgf77 > customize AbsoftFCompiler > Could not locate executable f90 > customize NAGFCompiler > Found executable /usr/bin/f95 > customize VastFCompiler > customize CompaqFCompiler > Could not locate executable fort > customize IntelItaniumFCompiler > Could not locate executable efort > Could not locate executable efc > customize IntelEM64TFCompiler > customize Gnu95FCompiler > Found executable /usr/bin/gfortran > customize Gnu95FCompiler > customize Gnu95FCompiler 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 -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC > > compile options: '-c' > gcc: _configtest.c > gcc -pthread _configtest.o -L/usr/lib64/atlas -lptf77blas -lptcblas -latlas -o _configtest > ATLAS version 3.8.4 built by mockbuild on Wed Dec 7 18:04:21 GMT 2011: > UNAME : Linux c6b5.bsys.dev.centos.org 2.6.32-44.2.el6.x86_64 #1 SMP Wed Jul 21 12:48:32 EDT 2010 x86_64 x86_64 x86_64 GNU/Linux > INSTFLG : -1 0 -a 1 > ARCHDEFS : -DATL_OS_Linux -DATL_ARCH_PII -DATL_CPUMHZ=2261 -DATL_SSE2 -DATL_SSE1 -DATL_USE64BITS -DATL_GAS_x8664 > F2CDEFS : -DAdd_ -DF77_INTEGER=int -DStringSunStyle > CACHEEDGE: 8388608 > F77 : gfortran, version GNU Fortran (GCC) 4.4.6 20110731 (Red Hat 4.4.6-3) > F77FLAGS : -fomit-frame-pointer -mfpmath=387 -O2 -falign-loops=4 -g -Wa,--noexecstack -fPIC -m64 > SMC : gcc, version gcc (GCC) 4.4.6 20110731 (Red Hat 4.4.6-3) > SMCFLAGS : -fomit-frame-pointer -mfpmath=387 -O2 -falign-loops=4 -g -Wa,--noexecstack -fPIC -m64 > SKC : gcc, version gcc (GCC) 4.4.6 20110731 (Red Hat 4.4.6-3) > SKCFLAGS : -fomit-frame-pointer -mfpmath=387 -O2 -falign-loops=4 -g -Wa,--noexecstack -fPIC -m64 > success! > removing: _configtest.c _configtest.o _configtest > Setting PTATLAS=ATLAS > FOUND: > libraries = ['ptf77blas', 'ptcblas', 'atlas'] > library_dirs = ['/usr/lib64/atlas'] > language = c > define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')] > include_dirs = ['/usr/include'] > > FOUND: > libraries = ['ptf77blas', 'ptcblas', 'atlas'] > library_dirs = ['/usr/lib64/atlas'] > language = c > define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')] > include_dirs = ['/usr/include'] > > lapack_opt_info: > lapack_mkl_info: > mkl_info: > libraries mkl,vml,guide not found in /usr/local/python-2.7.3/lib > libraries mkl,vml,guide not found in /usr/lib64 > libraries mkl,vml,guide not found in /usr/lib64/atlas > NOT AVAILABLE > > NOT AVAILABLE > > atlas_threads_info: > Setting PTATLAS=ATLAS > libraries ptf77blas,ptcblas,atlas not found in /usr/local/python-2.7.3/lib > libraries lapack_atlas not found in /usr/local/python-2.7.3/lib > libraries lapack_atlas not found in /usr/lib64/atlas > numpy.distutils.system_info.atlas_threads_info > Setting PTATLAS=ATLAS > Setting PTATLAS=ATLAS > FOUND: > libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas'] > library_dirs = ['/usr/lib64/atlas'] > language = f77 > define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')] > include_dirs = ['/usr/include'] > > FOUND: > libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas'] > library_dirs = ['/usr/lib64/atlas'] > language = f77 > define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')] > include_dirs = ['/usr/include'] > > running config I couldn't get enough informations about numscons/bento, so I'm still trying to build it with site.cfg and CFLAGS. How could I solve it? Magician On 2012/05/21, at 1:50, Ralf Gommers wrote: > > > On Sun, May 20, 2012 at 4:16 PM, Magician <f_magician at mac.com> wrote: > Hi Ralf, > > > Thanks for your advice. > I tried to install BLAS/Lapack/ATLAS as below: > > yum install blas-devel lapack-devel atlas-devel > > Next I installed NumPy as below: > > tar xzvf numpy-1.6.1.tar.gz > > cd numpy-1.6.1 > > export CFLAGS="-L/usr/local/python-2.7.3/lib" > > Note that this overrides CFLAGS instead of appending that flag to the rest. If it doesn't work without that line, you have to either specify all cflags or use numscons/bento. > > Ralf > > > > python setup.py build > > But then I got those errors: > > building 'numpy.linalg.lapack_lite' extension > > compiling C sources > > C compiler: gcc -pthread -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -L/usr/local/python-2.7.3/lib -fPIC > > > > creating build/temp.linux-x86_64-2.7/numpy/linalg > > compile options: '-DATLAS_INFO="\"3.8.4\"" -I/usr/include -Inumpy/core/include -Ibuild/src.linux-x86_64-2.7/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/include -I/usr/local/python-2.7.3/include/python2.7 -Ibuild/src.linux-x86_64-2.7/numpy/core/src/multiarray -Ibuild/src.linux-x86_64-2.7/numpy/core/src/umath -c' > > gcc: numpy/linalg/lapack_litemodule.c > > gcc: numpy/linalg/python_xerbla.c > > /usr/bin/gfortran -Wall -Wall -shared build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib64/atlas -L. -Lbuild/temp.linux-x86_64-2.7 -llapack -lptf77blas -lptcblas -latlas -lpython2.7 -lgfortran -o build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so > > /usr/bin/ld: cannot find -lpython2.7 > > collect2: ld returned 1 exit status > > /usr/bin/ld: cannot find -lpython2.7 > > collect2: ld returned 1 exit status > > error: Command "/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib64/atlas -L. -Lbuild/temp.linux-x86_64-2.7 -llapack -lptf77blas -lptcblas -latlas -lpython2.7 -lgfortran -o build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so" failed with exit status 1 > > If I haven't install BLAS/Lapack/ATLAS, NumPy will be > successfully built and installed. > > > Magician > > > On 2012/05/20, at 19:40, scipy-user-request at scipy.org wrote: > > > Message: 1 > > Date: Sun, 20 May 2012 10:21:12 +0200 > > From: Ralf Gommers <ralf.gommers at googlemail.com> > > Subject: Re: [SciPy-User] SciPy installation troubles on CentOS 6.2 > > To: SciPy Users List <scipy-user at scipy.org> > > Message-ID: > > <CABL7CQh+WPwpiMtO0UDKo9G01Gj38kTaWV=bSvq9eCK_qihAfQ at mail.gmail.com> > > Content-Type: text/plain; charset="iso-8859-1" > > > > On Sat, May 19, 2012 at 5:09 PM, Magician <f_magician at mac.com> wrote: > > > >> Hi All, > >> > >> > >> I'm trying to build SciPy from source code, > >> but I have some troubles. > >> > >> My environment is below: > >>> CentOS 6.2 on VMware Fusion 4.1.2 (CentOS was installed as Software > >> Development WS) > >>> Python 2.7.3 (already built from sources, installed at > >> /usr/local/python-2.7.3) > >>> NumPy 1.6.1, SciPy 0.10.1, ATLAS 3.8.4, Lapack 3.4.1 (now trying to > >> build) > >> I and my colleagues (other users) want to use recent Python, > >> so I installed Python from sources, and I can't install SciPy > >> by using yum command. > >> > >> Now I'm facing ATLAS compiling errors. > >> Configuration options are "--prefix=/usr/local/atlas-3.8.4 -Fa alg -fPIC". > >> I tried to build it for several times, and always I got errors as below: > >>> res/dgemvN_6_75 : VARIATION EXCEEDS TOLERENCE, RERUN WITH HIGHER REPS. > >>> > >>> ATL_gemvN_mm.c : 1257.99 > >>> ATL_gemvN_1x1_1.c : 581.74 > >>> ATL_gemvN_1x1_1a.c : 1589.45 > >>> ATL_gemvN_4x2_0.c : 813.13 > >>> ATL_gemvN_4x4_1.c : 755.54 > >>> make[3]: *** [res/dMVRES] Error 255 > >>> make[3]: Leaving directory > >> `/home/magician/Desktop/ATLAS/build/tune/blas/gemv' > >>> make[2]: *** > >> [/home/magician/Desktop/ATLAS/build/tune/blas/gemv/res/dMVRES] Error 2 > >>> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin' > >>> ERROR 734 DURING MVTUNE!!. CHECK INSTALL_LOG/dMVTUNE.LOG FOR DETAILS. > >>> make[2]: Entering directory `/home/magician/Desktop/ATLAS/build/bin' > >>> cd /home/magician/Desktop/ATLAS/build ; make error_report > >>> make[3]: Entering directory `/home/magician/Desktop/ATLAS/build' > >>> make -f Make.top error_report > >>> make[4]: Entering directory `/home/magician/Desktop/ATLAS/build' > >>> uname -a 2>&1 >> bin/INSTALL_LOG/ERROR.LOG > >>> gcc -v 2>&1 >> bin/INSTALL_LOG/ERROR.LOG > >>> Using built-in specs. > >>> Target: x86_64-redhat-linux > >>> Configured with: ../configure --prefix=/usr --mandir=/usr/share/man > >> --infodir=/usr/share/info --with-bugurl= > >> http://bugzilla.redhat.com/bugzilla --enable-bootstrap --enable-shared > >> --enable-threads=posix --enable-checking=release --with-system-zlib > >> --enable-__cxa_atexit --disable-libunwind-exceptions > >> --enable-gnu-unique-object > >> --enable-languages=c,c++,objc,obj-c++,java,fortran,ada > >> --enable-java-awt=gtk --disable-dssi > >> --with-java-home=/usr/lib/jvm/java-1.5.0-gcj-1.5.0.0/jre > >> --enable-libgcj-multifile --enable-java-maintainer-mode > >> --with-ecj-jar=/usr/share/java/eclipse-ecj.jar --disable-libjava-multilib > >> --with-ppl --with-cloog --with-tune=generic --with-arch_32=i686 > >> --build=x86_64-redhat-linux > >>> Thread model: posix > >>> gcc version 4.4.6 20110731 (Red Hat 4.4.6-3) (GCC) > >>> gcc -V 2>&1 >> bin/INSTALL_LOG/ERROR.LOG > >>> gcc: '-V' option must have argument > >>> make[4]: [error_report] Error 1 (ignored) > >>> gcc --version 2>&1 >> bin/INSTALL_LOG/ERROR.LOG > >>> tar cf error_Corei264SSE3.tar Make.inc bin/INSTALL_LOG/* > >>> gzip --best error_Corei264SSE3.tar > >>> mv error_Corei264SSE3.tar.gz error_Corei264SSE3.tgz > >>> make[4]: Leaving directory `/home/magician/Desktop/ATLAS/build' > >>> make[3]: Leaving directory `/home/magician/Desktop/ATLAS/build' > >>> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin' > >>> Error report error_<ARCH>.tgz has been created in your top-level ATLAS > >>> directory. Be sure to include this file in any help request. > >>> cat: ../../CONFIG/error.txt: No such file or directory > >>> cat: ../../CONFIG/error.txt: No such file or directory > >>> make[1]: *** [build] Error 255 > >>> make[1]: Leaving directory `/home/magician/Desktop/ATLAS/build' > >>> make: *** [build] Error 2 > >> > >> It's very troublesome for me to build ATLAS by myself. > >> My purpose is just using SciPy on my Python. > >> Even if it's optimized not so good for my environment, it's OK. > >> > >> Is there an easy or a sure way to build and install SciPy? > > > > > > Building ATLAS is much harder than building scipy, so you should try to > > find some rpm's for it, like > > http://linuxtoolkit.blogspot.com/2010/09/installing-lapack-blas-and-atlas-on.html. > > There's no problem building scipy against ATLAS from a binary install. > > > > Ralf
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From sergio_r at mail.com  Sun May 27 14:10:41 2012
From: sergio_r at mail.com (Sergio Rojas)
Date: Sun, 27 May 2012 14:10:41 -0400
Subject: [SciPy-User] SciPy installation troubles on CentOS 6.2
Message-ID: <20120527181042.17890@gmx.com>

Magician,

 try this:
 linux> locate libtatlas.so
 the directory in which it appears and the corresponding include which contains
 atlas include headears (to find it try "locate cblas.h". In my system the include directory is at
 /usr/include/ and inside it there should be an atlas directory) are the ones you need in site.cfg

 Since I built my own atlas I have:

 [DEFAULT]
 library_dirs = /home/srojas/myPROG/LapackLib_gfortran/Atlas64b/lib
 include_dirs = /home/srojas/myPROG/LapackLib_gfortran/Atlas64b/include
 [blas_opt]
 libraries = ptf77blas, ptcblas, atlas
 [lapack_opt]
 libraries = lapack, ptf77blas, ptcblas, atlas

 to compile numpy I use:

 python setup.py build --fcompiler=gfortran

 (you need to use the same compiler used to built atlas)

 If finished sucessfully, then to verify atlas support, before installing, try:

 ldd ./build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so

 which should show links to the library.

 If still you have problems, maybe you need to start doing things
 from scratch, installing python and atlas in your own path.


 Sergio
----- Original Message -----
From: Magician
Sent: 05/26/12 10:05 PM
To: Ralf Gommers, sergio_r at mail.com
Subject: Re: [SciPy-User] SciPy installation troubles on CentOS 6.2

 Hi Ralf and Sergio, I thought my problems inclined to NumPy, and I posted to NumPy mailing list. But I couldn't get any answers for several days, so I'd like to ask again. I tried to build NumPy again with site.cfg, but I got these errors: > building 'numpy.core._sort' extension > compiling C sources > C compiler: gcc -pthread -fno-strict-aliasing -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC > > compile options: '-Inumpy/core/include -Ibuild/src.linux-x86_64-2.7/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/include -I/usr/local/python-2.7.3/include/python2.7 -Ibuild/src.linux-x86_64-2.7/numpy/core/src/multiarray -Ibuild/src.linux-x86_64-2.7/numpy/core/src/umath -c' > gcc: build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.c > gcc -pthread -shared build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.o -L. -Lbuild/temp.linux-x86_64-2.7 -lnpymath -lm -lpython2.7 -o build/lib.linux-x86_64-2.7/numpy/core/_sort.so > /usr/bin/ld: cannot find -lpython2.7 > collect2: ld returned 1 exit status > /usr/bin/ld: cannot find -lpython2.7 > collect2: ld returned 1 exit status > error: Command "gcc -pthread -shared build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.o -L. -Lbuild/temp.linux-x86_64-2.7 -lnpymath -lm -lpython2.7 -o build/lib.linux-x86_64-2.7/numpy/core/_sort.so" failed with exit status 1 Before I built NumPy, I uncommented and modified site.cfg as below: > [DEFAULT] > library_dirs = /usr/local/python-2.7.3/lib:/usr/lib64:/usr/lib64/atlas > include_dirs = /usr/local/python-2.7.3/include:/usr/include:/usr/include/atlas > > [blas_opt] > libraries = f77blas, cblas, atlas > > [lapack_opt] > libraries = lapack, f77blas, cblas, atlas And "setup.py config" dumped these messages: > Running from numpy source directory.F2PY Version 2 > blas_opt_info: > blas_mkl_info: > libraries mkl,vml,guide not found in /usr/local/python-2.7.3/lib > libraries mkl,vml,guide not found in /usr/lib64 > libraries mkl,vml,guide not found in /usr/lib64/atlas > NOT AVAILABLE > > atlas_blas_threads_info: > Setting PTATLAS=ATLAS > libraries ptf77blas,ptcblas,atlas not found in /usr/local/python-2.7.3/lib > Setting PTATLAS=ATLAS > customize GnuFCompiler > Could not locate executable g77 > Could not locate executable f77 > customize IntelFCompiler > Could not locate executable ifort > Could not locate executable ifc > customize LaheyFCompiler > Could not locate executable lf95 > customize PGroupFCompiler > Could not locate executable pgf90 > Could not locate executable pgf77 > customize AbsoftFCompiler > Could not locate executable f90 > customize NAGFCompiler > Found executable /usr/bin/f95 > customize VastFCompiler > customize CompaqFCompiler > Could not locate executable fort > customize IntelItaniumFCompiler > Could not locate executable efort > Could not locate executable efc > customize IntelEM64TFCompiler > customize Gnu95FCompiler > Found executable /usr/bin/gfortran > customize Gnu95FCompiler > customize Gnu95FCompiler 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 -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC > > compile options: '-c' > gcc: _configtest.c > gcc -pthread _configtest.o -L/usr/lib64/atlas -lptf77blas -lptcblas -latlas -o _configtest > ATLAS version 3.8.4 built by mockbuild on Wed Dec 7 18:04:21 GMT 2011: > UNAME : Linux c6b5.bsys.dev.centos.org 2.6.32-44.2.el6.x86_64 #1 SMP Wed Jul 21 12:48:32 EDT 2010 x86_64 x86_64 x86_64 GNU/Linux > INSTFLG : -1 0 -a 1 > ARCHDEFS : -DATL_OS_Linux -DATL_ARCH_PII -DATL_CPUMHZ=2261 -DATL_SSE2 -DATL_SSE1 -DATL_USE64BITS -DATL_GAS_x8664 > F2CDEFS : -DAdd_ -DF77_INTEGER=int -DStringSunStyle > CACHEEDGE: 8388608 > F77 : gfortran, version GNU Fortran (GCC) 4.4.6 20110731 (Red Hat 4.4.6-3) > F77FLAGS : -fomit-frame-pointer -mfpmath=387 -O2 -falign-loops=4 -g -Wa,--noexecstack -fPIC -m64 > SMC : gcc, version gcc (GCC) 4.4.6 20110731 (Red Hat 4.4.6-3) > SMCFLAGS : -fomit-frame-pointer -mfpmath=387 -O2 -falign-loops=4 -g -Wa,--noexecstack -fPIC -m64 > SKC : gcc, version gcc (GCC) 4.4.6 20110731 (Red Hat 4.4.6-3) > SKCFLAGS : -fomit-frame-pointer -mfpmath=387 -O2 -falign-loops=4 -g -Wa,--noexecstack -fPIC -m64 > success! > removing: _configtest.c _configtest.o _configtest > Setting PTATLAS=ATLAS > FOUND: > libraries = ['ptf77blas', 'ptcblas', 'atlas'] > library_dirs = ['/usr/lib64/atlas'] > language = c > define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')] > include_dirs = ['/usr/include'] > > FOUND: > libraries = ['ptf77blas', 'ptcblas', 'atlas'] > library_dirs = ['/usr/lib64/atlas'] > language = c > define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')] > include_dirs = ['/usr/include'] > > lapack_opt_info: > lapack_mkl_info: > mkl_info: > libraries mkl,vml,guide not found in /usr/local/python-2.7.3/lib > libraries mkl,vml,guide not found in /usr/lib64 > libraries mkl,vml,guide not found in /usr/lib64/atlas > NOT AVAILABLE > > NOT AVAILABLE > > atlas_threads_info: > Setting PTATLAS=ATLAS > libraries ptf77blas,ptcblas,atlas not found in /usr/local/python-2.7.3/lib > libraries lapack_atlas not found in /usr/local/python-2.7.3/lib > libraries lapack_atlas not found in /usr/lib64/atlas > numpy.distutils.system_info.atlas_threads_info > Setting PTATLAS=ATLAS > Setting PTATLAS=ATLAS > FOUND: > libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas'] > library_dirs = ['/usr/lib64/atlas'] > language = f77 > define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')] > include_dirs = ['/usr/include'] > > FOUND: > libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas'] > library_dirs = ['/usr/lib64/atlas'] > language = f77 > define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')] > include_dirs = ['/usr/include'] > > running config I couldn't get enough informations about numscons/bento, so I'm still trying to build it with site.cfg and CFLAGS. How could I solve it? Magician On 2012/05/21, at 1:50, Ralf Gommers wrote: > > > On Sun, May 20, 2012 at 4:16 PM, Magician <f_magician at mac.com> wrote: > Hi Ralf, > > > Thanks for your advice. > I tried to install BLAS/Lapack/ATLAS as below: > > yum install blas-devel lapack-devel atlas-devel > > Next I installed NumPy as below: > > tar xzvf numpy-1.6.1.tar.gz > > cd numpy-1.6.1 > > export CFLAGS="-L/usr/local/python-2.7.3/lib" > > Note that this overrides CFLAGS instead of appending that flag to the rest. If it doesn't work without that line, you have to either specify all cflags or use numscons/bento. > > Ralf > > > > python setup.py build > > But then I got those errors: > > building 'numpy.linalg.lapack_lite' extension > > compiling C sources > > C compiler: gcc -pthread -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -L/usr/local/python-2.7.3/lib -fPIC > > > > creating build/temp.linux-x86_64-2.7/numpy/linalg > > compile options: '-DATLAS_INFO="\"3.8.4\"" -I/usr/include -Inumpy/core/include -Ibuild/src.linux-x86_64-2.7/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/include -I/usr/local/python-2.7.3/include/python2.7 -Ibuild/src.linux-x86_64-2.7/numpy/core/src/multiarray -Ibuild/src.linux-x86_64-2.7/numpy/core/src/umath -c' > > gcc: numpy/linalg/lapack_litemodule.c > > gcc: numpy/linalg/python_xerbla.c > > /usr/bin/gfortran -Wall -Wall -shared build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib64/atlas -L. -Lbuild/temp.linux-x86_64-2.7 -llapack -lptf77blas -lptcblas -latlas -lpython2.7 -lgfortran -o build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so > > /usr/bin/ld: cannot find -lpython2.7 > > collect2: ld returned 1 exit status > > /usr/bin/ld: cannot find -lpython2.7 > > collect2: ld returned 1 exit status > > error: Command "/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib64/atlas -L. -Lbuild/temp.linux-x86_64-2.7 -llapack -lptf77blas -lptcblas -latlas -lpython2.7 -lgfortran -o build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so" failed with exit status 1 > > If I haven't install BLAS/Lapack/ATLAS, NumPy will be > successfully built and installed. > > > Magician > > > On 2012/05/20, at 19:40, scipy-user-request at scipy.org wrote: > > > Message: 1 > > Date: Sun, 20 May 2012 10:21:12 +0200 > > From: Ralf Gommers <ralf.gommers at googlemail.com> > > Subject: Re: [SciPy-User] SciPy installation troubles on CentOS 6.2 > > To: SciPy Users List <scipy-user at scipy.org> > > Message-ID: > > <CABL7CQh+WPwpiMtO0UDKo9G01Gj38kTaWV=bSvq9eCK_qihAfQ at mail.gmail.com> > > Content-Type: text/plain; charset="iso-8859-1" > > > > On Sat, May 19, 2012 at 5:09 PM, Magician <f_magician at mac.com> wrote: > > > >> Hi All, > >> > >> > >> I'm trying to build SciPy from source code, > >> but I have some troubles. > >> > >> My environment is below: > >>> CentOS 6.2 on VMware Fusion 4.1.2 (CentOS was installed as Software > >> Development WS) > >>> Python 2.7.3 (already built from sources, installed at > >> /usr/local/python-2.7.3) > >>> NumPy 1.6.1, SciPy 0.10.1, ATLAS 3.8.4, Lapack 3.4.1 (now trying to > >> build) > >> I and my colleagues (other users) want to use recent Python, > >> so I installed Python from sources, and I can't install SciPy > >> by using yum command. > >> > >> Now I'm facing ATLAS compiling errors. > >> Configuration options are "--prefix=/usr/local/atlas-3.8.4 -Fa alg -fPIC". > >> I tried to build it for several times, and always I got errors as below: > >>> res/dgemvN_6_75 : VARIATION EXCEEDS TOLERENCE, RERUN WITH HIGHER REPS. > >>> > >>> ATL_gemvN_mm.c : 1257.99 > >>> ATL_gemvN_1x1_1.c : 581.74 > >>> ATL_gemvN_1x1_1a.c : 1589.45 > >>> ATL_gemvN_4x2_0.c : 813.13 > >>> ATL_gemvN_4x4_1.c : 755.54 > >>> make[3]: *** [res/dMVRES] Error 255 > >>> make[3]: Leaving directory > >> `/home/magician/Desktop/ATLAS/build/tune/blas/gemv' > >>> make[2]: *** > >> [/home/magician/Desktop/ATLAS/build/tune/blas/gemv/res/dMVRES] Error 2 > >>> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin' > >>> ERROR 734 DURING MVTUNE!!. CHECK INSTALL_LOG/dMVTUNE.LOG FOR DETAILS. > >>> make[2]: Entering directory `/home/magician/Desktop/ATLAS/build/bin' > >>> cd /home/magician/Desktop/ATLAS/build ; make error_report > >>> make[3]: Entering directory `/home/magician/Desktop/ATLAS/build' > >>> make -f Make.top error_report > >>> make[4]: Entering directory `/home/magician/Desktop/ATLAS/build' > >>> uname -a 2>&1 >> bin/INSTALL_LOG/ERROR.LOG > >>> gcc -v 2>&1 >> bin/INSTALL_LOG/ERROR.LOG > >>> Using built-in specs. > >>> Target: x86_64-redhat-linux > >>> Configured with: ../configure --prefix=/usr --mandir=/usr/share/man > >> --infodir=/usr/share/info --with-bugurl= > >> http://bugzilla.redhat.com/bugzilla --enable-bootstrap --enable-shared > >> --enable-threads=posix --enable-checking=release --with-system-zlib > >> --enable-__cxa_atexit --disable-libunwind-exceptions > >> --enable-gnu-unique-object > >> --enable-languages=c,c++,objc,obj-c++,java,fortran,ada > >> --enable-java-awt=gtk --disable-dssi > >> --with-java-home=/usr/lib/jvm/java-1.5.0-gcj-1.5.0.0/jre > >> --enable-libgcj-multifile --enable-java-maintainer-mode > >> --with-ecj-jar=/usr/share/java/eclipse-ecj.jar --disable-libjava-multilib > >> --with-ppl --with-cloog --with-tune=generic --with-arch_32=i686 > >> --build=x86_64-redhat-linux > >>> Thread model: posix > >>> gcc version 4.4.6 20110731 (Red Hat 4.4.6-3) (GCC) > >>> gcc -V 2>&1 >> bin/INSTALL_LOG/ERROR.LOG > >>> gcc: '-V' option must have argument > >>> make[4]: [error_report] Error 1 (ignored) > >>> gcc --version 2>&1 >> bin/INSTALL_LOG/ERROR.LOG > >>> tar cf error_Corei264SSE3.tar Make.inc bin/INSTALL_LOG/* > >>> gzip --best error_Corei264SSE3.tar > >>> mv error_Corei264SSE3.tar.gz error_Corei264SSE3.tgz > >>> make[4]: Leaving directory `/home/magician/Desktop/ATLAS/build' > >>> make[3]: Leaving directory `/home/magician/Desktop/ATLAS/build' > >>> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin' > >>> Error report error_<ARCH>.tgz has been created in your top-level ATLAS > >>> directory. Be sure to include this file in any help request. > >>> cat: ../../CONFIG/error.txt: No such file or directory > >>> cat: ../../CONFIG/error.txt: No such file or directory > >>> make[1]: *** [build] Error 255 > >>> make[1]: Leaving directory `/home/magician/Desktop/ATLAS/build' > >>> make: *** [build] Error 2 > >> > >> It's very troublesome for me to build ATLAS by myself. > >> My purpose is just using SciPy on my Python. > >> Even if it's optimized not so good for my environment, it's OK. > >> > >> Is there an easy or a sure way to build and install SciPy? > > > > > > Building ATLAS is much harder than building scipy, so you should try to > > find some rpm's for it, like > > http://linuxtoolkit.blogspot.com/2010/09/installing-lapack-blas-and-atlas-on.html. > > There's no problem building scipy against ATLAS from a binary install. > > > > Ralf
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From wesmckinn at gmail.com  Sun May 27 16:25:34 2012
From: wesmckinn at gmail.com (Wes McKinney)
Date: Sun, 27 May 2012 16:25:34 -0400
Subject: [SciPy-User] SciPy install from source fails on fresh Python 3.2.3,
	64-bit Ubuntu
Message-ID: <CAJPUwMAZ8XpL11JHNr=dALX19WSP7nv+UZ0vRS1r-wA88-VaSg@mail.gmail.com>

I'm getting failure at the end of 2to3 on scipy 0.10.1, with
easy_install and also from git master / 0.10.1 tag. I'll try rolling
back to Python 3.2.2 (3.2.3 was released on 4/10) later today and see
if that works

RefactoringTool:
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/sparse/linalg/dsolve/umfpack/setup.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/ndimage/fourier.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/ndimage/interpolation.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/ndimage/measurements.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/ndimage/filters.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/ndimage/morphology.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/integrate/quadpack.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/integrate/_ode.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/integrate/odepack.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/lib/lapack/__init__.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/lib/blas/__init__.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/cluster/__init__.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/cluster/vq.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/cluster/hierarchy.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/stats/mstats_basic.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/stats/morestats.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/stats/distributions.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/stats/kde.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/stats/stats.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/interpolate/fitpack.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/interpolate/interpolate.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/interpolate/ndgriddata.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/interpolate/interpolate_wrapper.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/interpolate/fitpack2.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/io/__init__.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/io/matlab/mio4.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/io/matlab/mio5.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/io/matlab/miobase.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/io/matlab/mio5_params.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/optimize/zeros.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/optimize/nnls.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/optimize/cobyla.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/optimize/lbfgsb.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/optimize/minpack.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/optimize/slsqp.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/spatial/__init__.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/spatial/distance.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/special/__init__.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/special/basic.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/special/add_newdocs.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/special/orthogonal.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/linalg/decomp.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/linalg/basic.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/linalg/misc.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/linalg/flinalg.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/linalg/lapack.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/signal/__init__.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/signal/bsplines.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/signal/signaltools.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/signal/fir_filter_design.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/fftpack/basic.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/fftpack/pseudo_diffs.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/sparse/linalg/eigen/arpack/arpack.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/sparse/linalg/isolve/iterative.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/sparse/linalg/dsolve/linsolve.py
/tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/sparse/linalg/dsolve/umfpack/umfpack.py
Running from scipy source directory.
Traceback (most recent call last):
  File "/usr/local/bin/easy_install-3.2", line 9, in <module>
    load_entry_point('distribute==0.6.27', 'console_scripts',
'easy_install-3.2')()
  File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/command/easy_install.py",
line 1915, in main
    with_ei_usage(lambda:
  File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/command/easy_install.py",
line 1896, in with_ei_usage
    return f()
  File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/command/easy_install.py",
line 1919, in <lambda>
    distclass=DistributionWithoutHelpCommands, **kw
  File "/usr/local/lib/python3.2/distutils/core.py", line 148, in setup
    dist.run_commands()
  File "/usr/local/lib/python3.2/distutils/dist.py", line 917, in run_commands
    self.run_command(cmd)
  File "/usr/local/lib/python3.2/distutils/dist.py", line 936, in run_command
    cmd_obj.run()
  File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/command/easy_install.py",
line 350, in run
    self.easy_install(spec, not self.no_deps)
  File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/command/easy_install.py",
line 590, in easy_install
    return self.install_item(spec, dist.location, tmpdir, deps)
  File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/command/easy_install.py",
line 620, in install_item
    dists = self.install_eggs(spec, download, tmpdir)
  File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/command/easy_install.py",
line 814, in install_eggs
    return self.build_and_install(setup_script, setup_base)
  File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/command/easy_install.py",
line 1094, in build_and_install
    self.run_setup(setup_script, setup_base, args)
  File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/command/easy_install.py",
line 1080, in run_setup
    run_setup(setup_script, args)
  File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/sandbox.py",
line 31, in run_setup
    lambda: exec(compile(open(
  File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/sandbox.py",
line 79, in run
    return func()
  File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/sandbox.py",
line 34, in <lambda>
    {'__file__':setup_script, '__name__':'__main__'})
  File "setup.py", line 196, in <module>
  File "setup.py", line 187, in setup_package
  File "/usr/local/lib/python3.2/site-packages/numpy/distutils/core.py",
line 152, in setup
    config = configuration()
  File "setup.py", line 138, in configuration
  File "/usr/local/lib/python3.2/site-packages/numpy/distutils/misc_util.py",
line 1002, in add_subpackage
    caller_level = 2)
  File "/usr/local/lib/python3.2/site-packages/numpy/distutils/misc_util.py",
line 971, in get_subpackage
    caller_level = caller_level + 1)
  File "/usr/local/lib/python3.2/site-packages/numpy/distutils/misc_util.py",
line 908, in _get_configuration_from_setup_py
    config = setup_module.configuration(*args)
  File "scipy/setup.py", line 4, in configuration
  File "/usr/local/lib/python3.2/site-packages/numpy/distutils/misc_util.py",
line 743, in __init__
    raise ValueError("%r is not a directory" % (package_path,))
ValueError: 'build/py3k/scipy' is not a directory


From wesmckinn at gmail.com  Sun May 27 21:38:08 2012
From: wesmckinn at gmail.com (Wes McKinney)
Date: Sun, 27 May 2012 21:38:08 -0400
Subject: [SciPy-User] SciPy install from source fails on fresh Python
	3.2.3, 64-bit Ubuntu
In-Reply-To: <CAJPUwMAZ8XpL11JHNr=dALX19WSP7nv+UZ0vRS1r-wA88-VaSg@mail.gmail.com>
References: <CAJPUwMAZ8XpL11JHNr=dALX19WSP7nv+UZ0vRS1r-wA88-VaSg@mail.gmail.com>
Message-ID: <CAJPUwMCeyRkzGh1_Yzd5TzX034roHr8GifUJ0f0VGCWc+7D=wQ@mail.gmail.com>

On Sun, May 27, 2012 at 4:25 PM, Wes McKinney <wesmckinn at gmail.com> wrote:
> I'm getting failure at the end of 2to3 on scipy 0.10.1, with
> easy_install and also from git master / 0.10.1 tag. I'll try rolling
> back to Python 3.2.2 (3.2.3 was released on 4/10) later today and see
> if that works
>
> RefactoringTool:
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/sparse/linalg/dsolve/umfpack/setup.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/ndimage/fourier.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/ndimage/interpolation.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/ndimage/measurements.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/ndimage/filters.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/ndimage/morphology.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/integrate/quadpack.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/integrate/_ode.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/integrate/odepack.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/lib/lapack/__init__.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/lib/blas/__init__.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/cluster/__init__.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/cluster/vq.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/cluster/hierarchy.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/stats/mstats_basic.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/stats/morestats.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/stats/distributions.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/stats/kde.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/stats/stats.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/interpolate/fitpack.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/interpolate/interpolate.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/interpolate/ndgriddata.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/interpolate/interpolate_wrapper.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/interpolate/fitpack2.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/io/__init__.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/io/matlab/mio4.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/io/matlab/mio5.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/io/matlab/miobase.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/io/matlab/mio5_params.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/optimize/zeros.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/optimize/nnls.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/optimize/cobyla.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/optimize/lbfgsb.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/optimize/minpack.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/optimize/slsqp.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/spatial/__init__.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/spatial/distance.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/special/__init__.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/special/basic.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/special/add_newdocs.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/special/orthogonal.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/linalg/decomp.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/linalg/basic.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/linalg/misc.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/linalg/flinalg.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/linalg/lapack.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/signal/__init__.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/signal/bsplines.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/signal/signaltools.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/signal/fir_filter_design.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/fftpack/basic.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/fftpack/pseudo_diffs.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/sparse/linalg/eigen/arpack/arpack.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/sparse/linalg/isolve/iterative.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/sparse/linalg/dsolve/linsolve.py
> /tmp/easy_install-sv1v29/scipy-0.10.1/build/py3k/scipy/sparse/linalg/dsolve/umfpack/umfpack.py
> Running from scipy source directory.
> Traceback (most recent call last):
> ?File "/usr/local/bin/easy_install-3.2", line 9, in <module>
> ? ?load_entry_point('distribute==0.6.27', 'console_scripts',
> 'easy_install-3.2')()
> ?File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/command/easy_install.py",
> line 1915, in main
> ? ?with_ei_usage(lambda:
> ?File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/command/easy_install.py",
> line 1896, in with_ei_usage
> ? ?return f()
> ?File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/command/easy_install.py",
> line 1919, in <lambda>
> ? ?distclass=DistributionWithoutHelpCommands, **kw
> ?File "/usr/local/lib/python3.2/distutils/core.py", line 148, in setup
> ? ?dist.run_commands()
> ?File "/usr/local/lib/python3.2/distutils/dist.py", line 917, in run_commands
> ? ?self.run_command(cmd)
> ?File "/usr/local/lib/python3.2/distutils/dist.py", line 936, in run_command
> ? ?cmd_obj.run()
> ?File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/command/easy_install.py",
> line 350, in run
> ? ?self.easy_install(spec, not self.no_deps)
> ?File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/command/easy_install.py",
> line 590, in easy_install
> ? ?return self.install_item(spec, dist.location, tmpdir, deps)
> ?File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/command/easy_install.py",
> line 620, in install_item
> ? ?dists = self.install_eggs(spec, download, tmpdir)
> ?File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/command/easy_install.py",
> line 814, in install_eggs
> ? ?return self.build_and_install(setup_script, setup_base)
> ?File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/command/easy_install.py",
> line 1094, in build_and_install
> ? ?self.run_setup(setup_script, setup_base, args)
> ?File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/command/easy_install.py",
> line 1080, in run_setup
> ? ?run_setup(setup_script, args)
> ?File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/sandbox.py",
> line 31, in run_setup
> ? ?lambda: exec(compile(open(
> ?File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/sandbox.py",
> line 79, in run
> ? ?return func()
> ?File "/usr/local/lib/python3.2/site-packages/distribute-0.6.27-py3.2.egg/setuptools/sandbox.py",
> line 34, in <lambda>
> ? ?{'__file__':setup_script, '__name__':'__main__'})
> ?File "setup.py", line 196, in <module>
> ?File "setup.py", line 187, in setup_package
> ?File "/usr/local/lib/python3.2/site-packages/numpy/distutils/core.py",
> line 152, in setup
> ? ?config = configuration()
> ?File "setup.py", line 138, in configuration
> ?File "/usr/local/lib/python3.2/site-packages/numpy/distutils/misc_util.py",
> line 1002, in add_subpackage
> ? ?caller_level = 2)
> ?File "/usr/local/lib/python3.2/site-packages/numpy/distutils/misc_util.py",
> line 971, in get_subpackage
> ? ?caller_level = caller_level + 1)
> ?File "/usr/local/lib/python3.2/site-packages/numpy/distutils/misc_util.py",
> line 908, in _get_configuration_from_setup_py
> ? ?config = setup_module.configuration(*args)
> ?File "scipy/setup.py", line 4, in configuration
> ?File "/usr/local/lib/python3.2/site-packages/numpy/distutils/misc_util.py",
> line 743, in __init__
> ? ?raise ValueError("%r is not a directory" % (package_path,))
> ValueError: 'build/py3k/scipy' is not a directory

To follow up, it appears that something was borked with my git clone--
a fresh git clone seems to have solved the problem.


From numpy-discussion at maubp.freeserve.co.uk  Thu May 24 08:19:23 2012
From: numpy-discussion at maubp.freeserve.co.uk (Peter)
Date: Thu, 24 May 2012 13:19:23 +0100
Subject: [SciPy-User] [Numpy-discussion] Some numpy funcs for PyPy
In-Reply-To: <6355.1337859149.6100681629467803648@ffe6.ukr.net>
References: <6355.1337859149.6100681629467803648@ffe6.ukr.net>
Message-ID: <CAKVJ-_7KD0aj-G0gVgn_1+7NObyJ0Wb54E5BjtLNMvyvhs_yeA@mail.gmail.com>

On Thu, May 24, 2012 at 12:32 PM, Dmitrey <tmp50 at ukr.net> wrote:
> hi all,
> maybe you're aware of numpypy - numpy port for pypy (pypy.org) - Python
> language implementation with dynamic compilation.
>
> Unfortunately, numpypy developmnent is very slow due to strict quality
> standards and some other issues, so for my purposes I have provided some
> missing numpypy funcs, in particular
>
> atleast_1d, atleast_2d, hstack, vstack, cumsum, isscalar, asscalar,
> asfarray, flatnonzero, tile, zeros_like, ones_like, empty_like, where,
> searchsorted
> with "axis" parameter: nan(arg)min, nan(arg)max, all, any
>
> and have got some OpenOpt / FuncDesigner functionality working
> faster than in CPython.
>
> File with this functions you can get here
>
> Also you may be interested in some info at http://openopt.org/PyPy
> Regards, Dmitrey.

As a NumPy user interested in PyPy it is great to know more people
are trying to contribute in this area. I myself have only filed PyPy
bugs about missing NumPy features rendering the initial numpypy
support useless to me.

On your website you wrote:

>> From my (Dmitrey) point of view numpypy development is
>> very unfriendly for newcomers - PyPy developers say "provide
>> code, preferably in interpreter level instead of AppLevel,
>> provide whole test coverage for all possible corner cases,
>> provide hg diff for code, and then, maybe, it will be committed".
>> Probably this is the reason why so insufficient number of
>> developers work on numpypy.

I assume that is paraphrased with a little hyperbole, but it
isn't so different from numpy (other than using git), or many
other open source projects. Unit tests are important, and
taking patches without them is risky.

I've been subscribed to the pypy-dev list for a while, but I
don't recall seeing you posting there. Have you tried to submit
any of your work to PyPy yet? Perhaps you should have
sent this message to pypy-dev instead?

(I am trying to be constructive, not critical.)

Regards,

Peter


From numpy-discussion at maubp.freeserve.co.uk  Thu May 24 09:37:39 2012
From: numpy-discussion at maubp.freeserve.co.uk (Peter)
Date: Thu, 24 May 2012 14:37:39 +0100
Subject: [SciPy-User] [Numpy-discussion] Some numpy funcs for PyPy
In-Reply-To: <69589.1337864845.13913483336144781312@ffe16.ukr.net>
References: <6355.1337859149.6100681629467803648@ffe6.ukr.net>
	<CAKVJ-_7KD0aj-G0gVgn_1+7NObyJ0Wb54E5BjtLNMvyvhs_yeA@mail.gmail.com>
	<69589.1337864845.13913483336144781312@ffe16.ukr.net>
Message-ID: <CAKVJ-_78LvdqNK9pEM_6MLNABKdEcP7AG++qL_u0qTzDZgJAtw@mail.gmail.com>

On Thu, May 24, 2012 at 2:07 PM, Dmitrey <tmp50 at ukr.net> wrote:
>
> I had been subsribed IIRC for a couple of months

I don't follow the PyPy IRC so that would explain it. I don't know how
much they use that rather than their mailing list, but both seem a
better place to discuss their handling or external contributions than
on the numpy-discussion and scipy-user lists.

Still, I hope you are able to make some contributions to numpypy,
because so far I've also found PyPy's numpy implementation too
limited for my usage.

Regards,

Peter


From f_magician at mac.com  Mon May 28 11:09:53 2012
From: f_magician at mac.com (Magician)
Date: Tue, 29 May 2012 00:09:53 +0900
Subject: [SciPy-User] SciPy installation troubles on CentOS 6.2
In-Reply-To: <20120527172112.17920@gmx.com>
References: <20120527172112.17920@gmx.com>
Message-ID: <47F243F2-B8C8-4EB9-A52C-96913CD79CDF@mac.com>

Hi Sergio,


Thanks for your advices.
Your advices looks very important for my state.

When I tried "locate libpython2.7.so", I got an error as below:
> locate: can not stat () `/var/lib/mlocate/mlocate.db': No such file or directory
After I did updatedb, locate command dumped exact library paths.
> /usr/local/python-2.7.3/lib/libpython2.7.so
> /usr/local/python-2.7.3/lib/libpython2.7.so.1.0

LD_LIBRARY_PATH was empty.
But I already defined library paths at /etc/ld.conf.so (already done ldconfig).

Then I did "python setup.py build" without creating site.cfg, I got usual errors:
> compile options: '-Inumpy/core/include -Ibuild/src.linux-x86_64-2.7/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/include -I/usr/local/python-2.7.3/include/python2.7 -Ibuild/src.linux-x86_64-2.7/numpy/core/src/multiarray -Ibuild/src.linux-x86_64-2.7/numpy/core/src/umath -c'
> gcc: build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.c
> gcc -pthread -shared build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.o -L. -Lbuild/temp.linux-x86_64-2.7 -lnpymath -lm -lpython2.7 -o build/lib.linux-x86_64-2.7/numpy/core/_sort.so
> /usr/bin/ld: cannot find -lpython2.7
> collect2: ld returned 1 exit status
> /usr/bin/ld: cannot find -lpython2.7
> collect2: ld returned 1 exit status
> error: Command "gcc -pthread -shared build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.o -L. -Lbuild/temp.linux-x86_64-2.7 -lnpymath -lm -lpython2.7 -o build/lib.linux-x86_64-2.7/numpy/core/_sort.so" failed with exit status 1

I have to do a bit more try....


Magician


On 2012/05/28, at 2:21, Sergio Rojas wrote:

> 
> > From: Magician
>  
> > /usr/bin/ld: cannot find -lpython2.7 
> 
> 
> Magician,
> This error listed  indicates that python libraries are not being found.
>   Try this:
> 
> linux> locate libpython2.7.so
> linux> echo $LD_LIBRARY_PATH
> 
> and verify that in your library path are the lib and include directories corresponding
> to the python you want to built.
> 
> I don't recall this but, did you built your own python?
> 
> Sergio
>  
>  
>> ----- Original Message -----
>> From: Magician
>> Sent: 05/26/12 10:05 PM
>> To: Ralf Gommers, sergio_r at mail.com
>> Subject: Re: [SciPy-User] SciPy installation troubles on CentOS 6.2
>> 
>>  
>> Hi Ralf and Sergio, 
>> 
>> 
>> I thought my problems inclined to NumPy, and I posted to NumPy mailing list. 
>> But I couldn't get any answers for several days, so I'd like to ask again. 
>> 
>> 
>> I tried to build NumPy again with site.cfg, but I got these errors: 
>> > building 'numpy.core._sort' extension 
>> > compiling C sources 
>> > C compiler: gcc -pthread -fno-strict-aliasing -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC 
>> > 
>> > compile options: '-Inumpy/core/include -Ibuild/src.linux-x86_64-2.7/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/include -I/usr/local/python-2.7.3/include/python2.7 -Ibuild/src.linux-x86_64-2.7/numpy/core/src/multiarray -Ibuild/src.linux-x86_64-2.7/numpy/core/src/umath -c' 
>> > gcc: build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.c 
>> > gcc -pthread -shared build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.o -L. -Lbuild/temp.linux-x86_64-2.7 -lnpymath -lm -lpython2.7 -o build/lib.linux-x86_64-2.7/numpy/core/_sort.so 
>> > /usr/bin/ld: cannot find -lpython2.7 
>> > collect2: ld returned 1 exit status 
>> > /usr/bin/ld: cannot find -lpython2.7 
>> > collect2: ld returned 1 exit status 
>> > error: Command "gcc -pthread -shared build/temp.linux-x86_64-2.7/build/src.linux-x86_64-2.7/numpy/core/src/_sortmodule.o -L. -Lbuild/temp.linux-x86_64-2.7 -lnpymath -lm -lpython2.7 -o build/lib.linux-x86_64-2.7/numpy/core/_sort.so" failed with exit status 1 
>> 
>> Before I built NumPy, I uncommented and modified site.cfg as below: 
>> > [DEFAULT] 
>> > library_dirs = /usr/local/python-2.7.3/lib:/usr/lib64:/usr/lib64/atlas 
>> > include_dirs = /usr/local/python-2.7.3/include:/usr/include:/usr/include/atlas 
>> > 
>> > [blas_opt] 
>> > libraries = f77blas, cblas, atlas 
>> > 
>> > [lapack_opt] 
>> > libraries = lapack, f77blas, cblas, atlas 
>> 
>> And "setup.py config" dumped these messages: 
>> > Running from numpy source directory.F2PY Version 2 
>> > blas_opt_info: 
>> > blas_mkl_info: 
>> >  libraries mkl,vml,guide not found in /usr/local/python-2.7.3/lib 
>> >  libraries mkl,vml,guide not found in /usr/lib64 
>> >  libraries mkl,vml,guide not found in /usr/lib64/atlas 
>> >  NOT AVAILABLE 
>> > 
>> > atlas_blas_threads_info: 
>> > Setting PTATLAS=ATLAS 
>> >  libraries ptf77blas,ptcblas,atlas not found in /usr/local/python-2.7.3/lib 
>> > Setting PTATLAS=ATLAS 
>> > customize GnuFCompiler 
>> > Could not locate executable g77 
>> > Could not locate executable f77 
>> > customize IntelFCompiler 
>> > Could not locate executable ifort 
>> > Could not locate executable ifc 
>> > customize LaheyFCompiler 
>> > Could not locate executable lf95 
>> > customize PGroupFCompiler 
>> > Could not locate executable pgf90 
>> > Could not locate executable pgf77 
>> > customize AbsoftFCompiler 
>> > Could not locate executable f90 
>> > customize NAGFCompiler 
>> > Found executable /usr/bin/f95 
>> > customize VastFCompiler 
>> > customize CompaqFCompiler 
>> > Could not locate executable fort 
>> > customize IntelItaniumFCompiler 
>> > Could not locate executable efort 
>> > Could not locate executable efc 
>> > customize IntelEM64TFCompiler 
>> > customize Gnu95FCompiler 
>> > Found executable /usr/bin/gfortran 
>> > customize Gnu95FCompiler 
>> > customize Gnu95FCompiler 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 -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC 
>> > 
>> > compile options: '-c' 
>> > gcc: _configtest.c 
>> > gcc -pthread _configtest.o -L/usr/lib64/atlas -lptf77blas -lptcblas -latlas -o _configtest 
>> > ATLAS version 3.8.4 built by mockbuild on Wed Dec  7 18:04:21 GMT 2011: 
>> >   UNAME    : Linux c6b5.bsys.dev.centos.org 2.6.32-44.2.el6.x86_64 #1 SMP Wed Jul 21 12:48:32 EDT 2010 x86_64 x86_64 x86_64 GNU/Linux 
>> >   INSTFLG  : -1 0 -a 1 
>> >   ARCHDEFS : -DATL_OS_Linux -DATL_ARCH_PII -DATL_CPUMHZ=2261 -DATL_SSE2 -DATL_SSE1 -DATL_USE64BITS -DATL_GAS_x8664 
>> >   F2CDEFS  : -DAdd_ -DF77_INTEGER=int -DStringSunStyle 
>> >   CACHEEDGE: 8388608 
>> >   F77      : gfortran, version GNU Fortran (GCC) 4.4.6 20110731 (Red Hat 4.4.6-3) 
>> >   F77FLAGS : -fomit-frame-pointer -mfpmath=387 -O2 -falign-loops=4 -g -Wa,--noexecstack -fPIC -m64 
>> >   SMC      : gcc, version gcc (GCC) 4.4.6 20110731 (Red Hat 4.4.6-3) 
>> >   SMCFLAGS : -fomit-frame-pointer -mfpmath=387 -O2 -falign-loops=4 -g -Wa,--noexecstack -fPIC -m64 
>> >   SKC      : gcc, version gcc (GCC) 4.4.6 20110731 (Red Hat 4.4.6-3) 
>> >   SKCFLAGS : -fomit-frame-pointer -mfpmath=387 -O2 -falign-loops=4 -g -Wa,--noexecstack -fPIC -m64 
>> > success! 
>> > removing: _configtest.c _configtest.o _configtest 
>> > Setting PTATLAS=ATLAS 
>> >  FOUND: 
>> >    libraries = ['ptf77blas', 'ptcblas', 'atlas'] 
>> >    library_dirs = ['/usr/lib64/atlas'] 
>> >    language = c 
>> >    define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')] 
>> >    include_dirs = ['/usr/include'] 
>> > 
>> >  FOUND: 
>> >    libraries = ['ptf77blas', 'ptcblas', 'atlas'] 
>> >    library_dirs = ['/usr/lib64/atlas'] 
>> >    language = c 
>> >    define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')] 
>> >    include_dirs = ['/usr/include'] 
>> > 
>> > lapack_opt_info: 
>> > lapack_mkl_info: 
>> > mkl_info: 
>> >  libraries mkl,vml,guide not found in /usr/local/python-2.7.3/lib 
>> >  libraries mkl,vml,guide not found in /usr/lib64 
>> >  libraries mkl,vml,guide not found in /usr/lib64/atlas 
>> >  NOT AVAILABLE 
>> > 
>> >  NOT AVAILABLE 
>> > 
>> > atlas_threads_info: 
>> > Setting PTATLAS=ATLAS 
>> >  libraries ptf77blas,ptcblas,atlas not found in /usr/local/python-2.7.3/lib 
>> >  libraries lapack_atlas not found in /usr/local/python-2.7.3/lib 
>> >  libraries lapack_atlas not found in /usr/lib64/atlas 
>> > numpy.distutils.system_info.atlas_threads_info 
>> > Setting PTATLAS=ATLAS 
>> > Setting PTATLAS=ATLAS 
>> >  FOUND: 
>> >    libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas'] 
>> >    library_dirs = ['/usr/lib64/atlas'] 
>> >    language = f77 
>> >    define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')] 
>> >    include_dirs = ['/usr/include'] 
>> > 
>> >  FOUND: 
>> >    libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas'] 
>> >    library_dirs = ['/usr/lib64/atlas'] 
>> >    language = f77 
>> >    define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')] 
>> >    include_dirs = ['/usr/include'] 
>> > 
>> > running config 
>> 
>> I couldn't get enough informations about numscons/bento, 
>> so I'm still trying to build it with site.cfg and CFLAGS. 
>> 
>> How could I solve it? 
>> 
>> 
>> Magician 
>> 
>> 
>> On 2012/05/21, at 1:50, Ralf Gommers wrote: 
>> 
>> > 
>> > 
>> > On Sun, May 20, 2012 at 4:16 PM, Magician <f_magician at mac.com> wrote: 
>> > Hi Ralf, 
>> > 
>> > 
>> > Thanks for your advice. 
>> > I tried to install BLAS/Lapack/ATLAS as below: 
>> > > yum install blas-devel lapack-devel atlas-devel 
>> > 
>> > Next I installed NumPy as below: 
>> > > tar xzvf numpy-1.6.1.tar.gz 
>> > > cd numpy-1.6.1 
>> > > export CFLAGS="-L/usr/local/python-2.7.3/lib" 
>> > 
>> > Note that this overrides CFLAGS instead of appending that flag to the rest. If it doesn't work without that line, you have to either specify all cflags or use numscons/bento. 
>> > 
>> > Ralf 
>> > 
>> >  
>> > > python setup.py build 
>> > 
>> > But then I got those errors: 
>> > > building 'numpy.linalg.lapack_lite' extension 
>> > > compiling C sources 
>> > > C compiler: gcc -pthread -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -L/usr/local/python-2.7.3/lib -fPIC 
>> > > 
>> > > creating build/temp.linux-x86_64-2.7/numpy/linalg 
>> > > compile options: '-DATLAS_INFO="\"3.8.4\"" -I/usr/include -Inumpy/core/include -Ibuild/src.linux-x86_64-2.7/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/include -I/usr/local/python-2.7.3/include/python2.7 -Ibuild/src.linux-x86_64-2.7/numpy/core/src/multiarray -Ibuild/src.linux-x86_64-2.7/numpy/core/src/umath -c' 
>> > > gcc: numpy/linalg/lapack_litemodule.c 
>> > > gcc: numpy/linalg/python_xerbla.c 
>> > > /usr/bin/gfortran -Wall -Wall -shared build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib64/atlas -L. -Lbuild/temp.linux-x86_64-2.7 -llapack -lptf77blas -lptcblas -latlas -lpython2.7 -lgfortran -o build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so 
>> > > /usr/bin/ld: cannot find -lpython2.7 
>> > > collect2: ld returned 1 exit status 
>> > > /usr/bin/ld: cannot find -lpython2.7 
>> > > collect2: ld returned 1 exit status 
>> > > error: Command "/usr/bin/gfortran -Wall -Wall -shared build/temp.linux-x86_64-2.7/numpy/linalg/lapack_litemodule.o build/temp.linux-x86_64-2.7/numpy/linalg/python_xerbla.o -L/usr/lib64/atlas -L. -Lbuild/temp.linux-x86_64-2.7 -llapack -lptf77blas -lptcblas -latlas -lpython2.7 -lgfortran -o build/lib.linux-x86_64-2.7/numpy/linalg/lapack_lite.so" failed with exit status 1 
>> > 
>> > If I haven't install BLAS/Lapack/ATLAS, NumPy will be 
>> > successfully built and installed. 
>> > 
>> > 
>> > Magician 
>> > 
>> > 
>> > On 2012/05/20, at 19:40, scipy-user-request at scipy.org wrote: 
>> > 
>> > > Message: 1 
>> > > Date: Sun, 20 May 2012 10:21:12 +0200 
>> > > From: Ralf Gommers <ralf.gommers at googlemail.com> 
>> > > Subject: Re: [SciPy-User] SciPy installation troubles on CentOS 6.2 
>> > > To: SciPy Users List <scipy-user at scipy.org> 
>> > > Message-ID: 
>> > >       <CABL7CQh+WPwpiMtO0UDKo9G01Gj38kTaWV=bSvq9eCK_qihAfQ at mail.gmail.com> 
>> > > Content-Type: text/plain; charset="iso-8859-1" 
>> > > 
>> > > On Sat, May 19, 2012 at 5:09 PM, Magician <f_magician at mac.com> wrote: 
>> > > 
>> > >> Hi All, 
>> > >> 
>> > >> 
>> > >> I'm trying to build SciPy from source code, 
>> > >> but I have some troubles. 
>> > >> 
>> > >> My environment is below: 
>> > >>> CentOS 6.2 on VMware Fusion 4.1.2 (CentOS was installed as Software 
>> > >> Development WS) 
>> > >>> Python 2.7.3 (already built from sources, installed at 
>> > >> /usr/local/python-2.7.3) 
>> > >>> NumPy 1.6.1, SciPy 0.10.1, ATLAS 3.8.4, Lapack 3.4.1 (now trying to 
>> > >> build) 
>> > >> I and my colleagues (other users) want to use recent Python, 
>> > >> so I installed Python from sources, and I can't install SciPy 
>> > >> by using yum command. 
>> > >> 
>> > >> Now I'm facing ATLAS compiling errors. 
>> > >> Configuration options are "--prefix=/usr/local/atlas-3.8.4 -Fa alg -fPIC". 
>> > >> I tried to build it for several times, and always I got errors as below: 
>> > >>> res/dgemvN_6_75 : VARIATION EXCEEDS TOLERENCE, RERUN WITH HIGHER REPS. 
>> > >>> 
>> > >>> ATL_gemvN_mm.c : 1257.99 
>> > >>> ATL_gemvN_1x1_1.c : 581.74 
>> > >>> ATL_gemvN_1x1_1a.c : 1589.45 
>> > >>> ATL_gemvN_4x2_0.c : 813.13 
>> > >>> ATL_gemvN_4x4_1.c : 755.54 
>> > >>> make[3]: *** [res/dMVRES] Error 255 
>> > >>> make[3]: Leaving directory 
>> > >> `/home/magician/Desktop/ATLAS/build/tune/blas/gemv' 
>> > >>> make[2]: *** 
>> > >> [/home/magician/Desktop/ATLAS/build/tune/blas/gemv/res/dMVRES] Error 2 
>> > >>> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin' 
>> > >>> ERROR 734 DURING MVTUNE!!.  CHECK INSTALL_LOG/dMVTUNE.LOG FOR DETAILS. 
>> > >>> make[2]: Entering directory `/home/magician/Desktop/ATLAS/build/bin' 
>> > >>> cd /home/magician/Desktop/ATLAS/build ; make error_report 
>> > >>> make[3]: Entering directory `/home/magician/Desktop/ATLAS/build' 
>> > >>> make -f Make.top error_report 
>> > >>> make[4]: Entering directory `/home/magician/Desktop/ATLAS/build' 
>> > >>> uname -a 2>&1 >> bin/INSTALL_LOG/ERROR.LOG 
>> > >>> gcc -v 2>&1  >> bin/INSTALL_LOG/ERROR.LOG 
>> > >>> Using built-in specs. 
>> > >>> Target: x86_64-redhat-linux 
>> > >>> Configured with: ../configure --prefix=/usr --mandir=/usr/share/man 
>> > >> --infodir=/usr/share/info --with-bugurl= 
>> > >> http://bugzilla.redhat.com/bugzilla --enable-bootstrap --enable-shared 
>> > >> --enable-threads=posix --enable-checking=release --with-system-zlib 
>> > >> --enable-__cxa_atexit --disable-libunwind-exceptions 
>> > >> --enable-gnu-unique-object 
>> > >> --enable-languages=c,c++,objc,obj-c++,java,fortran,ada 
>> > >> --enable-java-awt=gtk --disable-dssi 
>> > >> --with-java-home=/usr/lib/jvm/java-1.5.0-gcj-1.5.0.0/jre 
>> > >> --enable-libgcj-multifile --enable-java-maintainer-mode 
>> > >> --with-ecj-jar=/usr/share/java/eclipse-ecj.jar --disable-libjava-multilib 
>> > >> --with-ppl --with-cloog --with-tune=generic --with-arch_32=i686 
>> > >> --build=x86_64-redhat-linux 
>> > >>> Thread model: posix 
>> > >>> gcc version 4.4.6 20110731 (Red Hat 4.4.6-3) (GCC) 
>> > >>> gcc -V 2>&1  >> bin/INSTALL_LOG/ERROR.LOG 
>> > >>> gcc: '-V' option must have argument 
>> > >>> make[4]: [error_report] Error 1 (ignored) 
>> > >>> gcc --version 2>&1  >> bin/INSTALL_LOG/ERROR.LOG 
>> > >>> tar cf error_Corei264SSE3.tar Make.inc bin/INSTALL_LOG/* 
>> > >>> gzip --best error_Corei264SSE3.tar 
>> > >>> mv error_Corei264SSE3.tar.gz error_Corei264SSE3.tgz 
>> > >>> make[4]: Leaving directory `/home/magician/Desktop/ATLAS/build' 
>> > >>> make[3]: Leaving directory `/home/magician/Desktop/ATLAS/build' 
>> > >>> make[2]: Leaving directory `/home/magician/Desktop/ATLAS/build/bin' 
>> > >>> Error report error_<ARCH>.tgz has been created in your top-level ATLAS 
>> > >>> directory.  Be sure to include this file in any help request. 
>> > >>> cat: ../../CONFIG/error.txt: No such file or directory 
>> > >>> cat: ../../CONFIG/error.txt: No such file or directory 
>> > >>> make[1]: *** [build] Error 255 
>> > >>> make[1]: Leaving directory `/home/magician/Desktop/ATLAS/build' 
>> > >>> make: *** [build] Error 2 
>> > >> 
>> > >> It's very troublesome for me to build ATLAS by myself. 
>> > >> My purpose is just using SciPy on my Python. 
>> > >> Even if it's optimized not so good for my environment, it's OK. 
>> > >> 
>> > >> Is there an easy or a sure way to build and install SciPy? 
>> > > 
>> > > 
>> > > Building ATLAS is much harder than building scipy, so you should try to 
>> > > find some rpm's for it, like 
>> > > http://linuxtoolkit.blogspot.com/2010/09/installing-lapack-blas-and-atlas-on.html. 
>> > > There's no problem building scipy against ATLAS from a binary install. 
>> > > 
>> > > Ralf 


From cimrman3 at ntc.zcu.cz  Tue May 29 09:28:26 2012
From: cimrman3 at ntc.zcu.cz (Robert Cimrman)
Date: Tue, 29 May 2012 15:28:26 +0200
Subject: [SciPy-User] ANN: SfePy 2012.2
Message-ID: <4FC4CEFA.3030500@ntc.zcu.cz>

I am pleased to announce release 2012.2 of SfePy.

Description
-----------
SfePy (simple finite elements in Python) is a software for solving
systems of coupled partial differential equations by the finite element
method. The code is based on NumPy and SciPy packages. It is distributed
under the new BSD license.

Home page: http://sfepy.org
Downloads, mailing list, wiki: http://code.google.com/p/sfepy/
Git (source) repository, issue tracker: http://github.com/sfepy

Highlights of this release
--------------------------
- reimplemented acoustic band gaps code using the homogenization engine
- high order quadrature rules
- unified dot product and mass terms, lots of other term updates/fixes,
- updated the PDE solver application

For full release notes see http://docs.sfepy.org/doc/release_notes.html#id1
(rather long and technical).

Best regards,
Robert Cimrman and Contributors (*)

(*) Contributors to this release (alphabetical order):

Vladim?r Luke?, Andre Smit


From bouloumag at gmail.com  Thu May 31 09:18:10 2012
From: bouloumag at gmail.com (Darcoux Christine)
Date: Thu, 31 May 2012 09:18:10 -0400
Subject: [SciPy-User] 2-D data interpolation
Message-ID: <CAD6uFgmENmbAZL6Zj-YemhD8LjjuOuLPCEc1ACktM_Egfricaw@mail.gmail.com>

Hi,

I am converting a matlab code to python and I am looking for a
function like interp2 [1] for 2-D data interpolation.

My matlab code has calls like

M = interp2(x,y,z, xi,yi, 'cubic')

where x, y and z describe a surface function. The interp2 function
returns a matrix M corresponding to the elements of xi and yi and
determined by cubic interpolation.

Any help would be greatly appreciated,

[1] http://www.mathworks.com/help/techdoc/ref/interp2.html


From lists at hilboll.de  Thu May 31 09:27:33 2012
From: lists at hilboll.de (Andreas H.)
Date: Thu, 31 May 2012 15:27:33 +0200
Subject: [SciPy-User] 2-D data interpolation
In-Reply-To: <CAD6uFgmENmbAZL6Zj-YemhD8LjjuOuLPCEc1ACktM_Egfricaw@mail.gmail.com>
References: <CAD6uFgmENmbAZL6Zj-YemhD8LjjuOuLPCEc1ACktM_Egfricaw@mail.gmail.com>
Message-ID: <eb265c4da99629d704f60aac3697c5d7.squirrel@srv2.s4y.tournesol-consulting.eu>

> Hi,
>
> I am converting a matlab code to python and I am looking for a
> function like interp2 [1] for 2-D data interpolation.
>
> My matlab code has calls like
>
> M = interp2(x,y,z, xi,yi, 'cubic')
>
> where x, y and z describe a surface function. The interp2 function
> returns a matrix M corresponding to the elements of xi and yi and
> determined by cubic interpolation.
>
> Any help would be greatly appreciated,

You have several choices in scipy.interpolate, see
http://docs.scipy.org/doc/scipy/reference/interpolate.html#multivariate-interpolation.

Probably, you're looking for the two subclasses of BivariateSpline, namely
LSQBivariateSpline and SmoothBivariateSpline, the first doind
least-squares fitting, while the latter does some smoothing.

Unfortunately, there's no examples in the docstrings as of now.

Cheers,
Andreas.



From nadavh at visionsense.com  Thu May 31 14:34:26 2012
From: nadavh at visionsense.com (Nadav Horesh)
Date: Thu, 31 May 2012 18:34:26 +0000
Subject: [SciPy-User] 2-D data interpolation
Message-ID: <F656855EF0FAB246A0AEC45F7D5D132407174ADB@SN2PRD0802MB100.namprd08.prod.outlook.com>

You can try also scipy.interpolate.griddata (not the pylab's griddata!)

  Nadav
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From david_baddeley at yahoo.com.au  Thu May 31 15:44:38 2012
From: david_baddeley at yahoo.com.au (David Baddeley)
Date: Thu, 31 May 2012 12:44:38 -0700 (PDT)
Subject: [SciPy-User] 2-D data interpolation
Message-ID: <1338493478.77964.BPMail_high_noncarrier@web113409.mail.gq1.yahoo.com>


It's a bit of a special case, but if your starting values are already on a regular grid scipy.ndimage.map_coordinates is what you're after.

Cheers,
David


------------------------------
On Fri, Jun 1, 2012 1:18 AM NZST Darcoux Christine wrote:

>Hi,
>
>I am converting a matlab code to python and I am looking for a
>function like interp2 [1] for 2-D data interpolation.
>
>My matlab code has calls like
>
>M = interp2(x,y,z, xi,yi, 'cubic')
>
>where x, y and z describe a surface function. The interp2 function
>returns a matrix M corresponding to the elements of xi and yi and
>determined by cubic interpolation.
>
>Any help would be greatly appreciated,
>
>[1] http://www.mathworks.com/help/techdoc/ref/interp2.html
>_______________________________________________
>SciPy-User mailing list
>SciPy-User at scipy.org
>http://mail.scipy.org/mailman/listinfo/scipy-user



From markus.baden at gmail.com  Thu May 31 23:50:30 2012
From: markus.baden at gmail.com (Markus Baden)
Date: Fri, 1 Jun 2012 11:50:30 +0800
Subject: [SciPy-User] leastsq - When to scale covariance matrix by reduced
 chi square for confidence interval estimation
Message-ID: <CABo_DbAbu+JWFCW9Nv2-+0E7gSXH8cZ=RT7ggurKa-bLzm7TaQ@mail.gmail.com>

Hi List,

I'm trying to get my head around, when to scale the covariance matrix by
the reduced chi square of the problem for getting an estimate of the error
of a parameter obtained via fitting. I'm kinda stuck and would appreciate
any pointers to an answer. From the documentation of scipy.optimize.leastsq
and scipy.curve_fit, as well as from some old threads on this mailing list
[1, 2] it seems the procedure in scipy is the following

1) Create estimate of the Hessian matrix based on the Jacobian at the final
value, which is called the covariance matrix cov_x and is the curvature of
the fitting parameters around the minimum

2) Calculate the reduced chi square, red_chi_2 = sum( (w_i *(y_i - f_i))**2
) / dof, where w_i are the weights, y_i the data, f_i the fit and dof the
degrees of freedom (number of knobs)

3) Get parameter estimates by  calculating sqrt(diag(cov_x) * red_chi_2)

4) Scale confidence interval by appropriate value of student t
distribution, e.g. when predicting the standard deviation of a single value
just *= 1

So far, so good. However in the literature [3, 4] I often find that steps 2
and 3 are skipped when the data is weighted by errors in the individual
observation. Obviously for a good fit with red_chi_2 = 1 both ways of
getting an error are the same. [3] and [4] caution that the method they are
using assume among others normality and a reduced chi square of about 1 and
discourage the use of estimating the error in the fit for bad fits. However
it seems that the method currently in scipy somehow is more robust. Take
for example data similiar to the one I am currently working with [5]. The
fit has a reduced chi square of about one, and hence the errors of both the
scipy method and the literature method agree. If I make my reduced chi
square worse by scaling the error bars, the method in the literature gives
either very, very small errors or very, very large ones. The scipy method
however always produces about the same error estimate. Here is the output
of [5]

Errors scaled by:       1
Offset:                 -71.0
Chi 2:                  19.1752
Red. Chi 2:             1.0092
Error literature        2.8426
Error scipy             2.8557

Errors scaled by:       100.0
Offset:                 -71.0077
Chi 2:                  0.0019
Red. Chi 2:             0.0001
Error literature        283.9153
Error scipy             2.8563

Errors scaled by:       0.01
Offset:                 -69.0724
Chi 2:                  9797.5838
Red. Chi 2:             515.6623
Error literature        0.1235
Error scipy             2.8036

Now in the particular problem I am working at, we have a couple of fits
like [5] and some of them have a slightly worse reduced chi square of say
about 1.4 or 0.7. At this point the two methods start to deviate and I am
wondering which would be the correct way of quoting the errors estimated
from the fit. Even a basic reference to some text book that explains the
method used in scipy would be very helpful.

Thanks a lot,

Markus

P.S. On a related side remark: What happened to the benchmark tests against
NIST certified non linear regression data sets mentioned in [1]. Are they
still in the code base? And are there similiar benchmark data sets for non
linear regression with observations that have error bars on them?

[1] http://thread.gmane.org/gmane.comp.python.scientific.user/19482<http://article.gmane.org/gmane.comp.python.scientific.user/19482>
[2]
http://thread.gmane.org/gmane.comp.python.scientific.user/31023/focus=31024
[3] Numerical Recipes in C, 2nd edition, chapter 15.6 , especially p.
695-698
[4] Data Reduction and Error Analysis for the physical sciences, 3rd
edition, chapter 8.5, p. 157
[5] Gist of self-contained example at https://gist.github.com/2848439
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From chris.barker at noaa.gov  Wed May 30 19:45:14 2012
From: chris.barker at noaa.gov (Chris Barker)
Date: Wed, 30 May 2012 16:45:14 -0700
Subject: [SciPy-User] Scientific Software and Web Developer Needed
Message-ID: <CALGmxELtKDTYB2XbJVBWNdT9Q3G+AZtHuRV+1fPXApipzhof6g@mail.gmail.com>

Scientific Software and Web Developer Needed

NOAA Emergency Response Division

Help us develop our next-generation oil spill transport model.

Background:
The Emergency Response Division (ERD) of NOAA's Office of Response and
Restoration (OR&R) provides scientific expertise to support the
response to oil and chemical spills in the coastal environment. We
played a major role in the recent Deepwater Horizon oil spill in the
Gulf of Mexico.  In order to fulfill our mission, we develop many of
the software tools and models required to support a response to
hazardous material spills. We are currently in the middle of a program
to develop our next-generation oil spill transport model, taking into
account lessons learned from years of response and recent major
incidents.

There are currently two positions available: one will focus on on the
computational code in C++, Pyhton and Cython, and the other on a new
web front end (Python, HTML, CSS, Javascript).


General Characteristics:
The incumbents in this position will provide software development
services to support the mission of the Emergency Response Division of
NOAA's Office of Response and Restoration. As part of his/her efforts,
independent evaluation and application of development techniques,
algorithms, software architecture, and programming patterns will be
required.  The incumbent will work with the staff of ERD to provide
analysis on user needs and software, GUI, and library design. He/she
will be expect to work primarily on site at NOAA's facility in
Seattle.

Knowledge:
The incumbent must be able to apply modern concepts of software
engineering and design to the development of computational code, web
applications, and libraries. The incumbent will need to be able to
design, write, refactor, and implement code for a complex  web
application and/or computational library.  The incumbent will work
with a multi-disciplinary team including scientists, users, and other
developers, utilizing software development practices such as usability
design, version control, bug and issue tracking, and unit testing.
Good communication skills and the knowledge of working as part of a
team are required.

Direction received:
The incumbent will participate on various research and development
teams.  While endpoints will be identified through Division management
and some direct supervision will be provided, the incumbent will be
responsible for progressively being able to take input from team
meetings and design objectives and propose strategies for reaching
endpoints.

Typical duties and responsibilities:
The incumbent will work with the oil and chemical spill modeling team
to improve and develop new tools and models used in fate and transport
forecasting.  Different components of the project will be written in
C++, Python, and Javascript.

Education requirement, minimum:
Bachelor's degree in a technical discipline.

Experience requirement, minimum:
One to five years experience in development of complex software
systems in one or more full-featured programming languages (C, C++,
Java, Python, Javaascript, Ruby,  Fortran, etc.)

The team requires experience in the following languages/disciplines.
Each incumbent will need experience in some subset:

 * Computational/Scientific programming
 * Numerical Analysis/Methods
 * Parallel processing
 * Desktop GUI
 * Web services
 * Web clients: HTML/CSS/Javascript
 * Python
 * C/C++
 * Python--C/C++ integration
 * Software development team leadership

While the incumbent will work on-site at NOAA, directly with the NOAA
team, this is a contract position with General Dynamics Information
Technology:

http://www.gdit.com/default.aspx

For more information and to apply, use the GDIT web site:
http://www.resumeware.net/gdns_rw/gdns_web/job_detail.cfm?recnum=1&totalrecs=1&start=1&pagestart=1

If that long url doesn't work, try:

http://www.resumeware.net/gdns_rw/gdns_web/job_search.cfm

and search for job ID:  199765

You can also send questions about employment issues to:

   Susan Bowley:  susan.bowley at gdit.com

And questions about the nature of the work to:

   Chris Barker: Chris.Barker at noaa.gov




-- 

Christopher Barker, Ph.D.
Oceanographer

Emergency Response Division
NOAA/NOS/OR&R ? ? ? ? ? ?(206) 526-6959?? voice
7600 Sand Point Way NE ??(206) 526-6329?? fax
Seattle, WA ?98115 ? ? ??(206) 526-6317?? main reception

Chris.Barker at noaa.gov


From gregory.mosby at gmail.com  Thu May 31 00:19:09 2012
From: gregory.mosby at gmail.com (Greg Mosby)
Date: Wed, 30 May 2012 23:19:09 -0500
Subject: [SciPy-User] SciPy Install on Cygwin (Successful Once)
Message-ID: <CABU-BjozpCK2QAX5es6zDkbuH_QfA7LMqiOO43ehHq4QJgwavg@mail.gmail.com>

Hi,

I'm installing SciPy on my Win7 machine using Cygwin and its Python 2.6.7
version. I actually managed to successfully install Scipy not to long ago,
but I had some rebasing issues that ended with me having to reinstall
cygwin and those directories. I followed the basic outline of installing
ATLAS plus LAPACK. I have Numpy. I made a site.cfg for my unzipped
scipy-0.10.1 directory to make sure it would look in the right spots for
the lapack, atlas, etc libraries. It's the same as the one I used for my
successful install. But when I run:

python setup.py config_fc --fcompiler=gnu95 install

I get undefined reference errors. I can tell they are somehow due to the
install script not including a clapack.h file. The location of the file is
in my environment path, but perhaps not my python sys.path. However, it's
in one of the include directories I place in the site.cfg too, so I'm not
sure why the install is missing it. The last of the undefined references is:

"build/temp.cygwin-1.7.15-i686-2.6/build/src.cygwin-1.7.15-i686-2.6/build/src.cygwin-1.7.15-i686-2.6/scipy/lib/lapack/clapackmodule.o:clapackmodule.c:(.data+0x1414):
undefined reference to `_clapack_ztrtri'
collect2: ld returned 1 exit status
error: Command "/usr/bin/gfortran -Wall -Wall -shared
build/temp.cygwin-1.7.15-i686-2.6/build/src.cygwin-1.7.15-i686-2.6/build/src.cygwin-1.7.15-i686-2.6/scipy/lib/lapack/clapackmodule.o
build/temp.cygwin-1.7.15-i686-2.6/build/src.cygwin-1.7.15-i686-2.6/fortranobject.o
-L/usr/local/atlas/lib -L/usr/lib/gcc/i686-pc-cygwin/4.5.3
-L/usr/lib/python2.6/config -Lbuild/temp.cygwin-1.7.15-i686-2.6 -llapack
-lptf77blas -lptcblas -latlas -lpython2.6 -lgfortran -o
build/lib.cygwin-1.7.15-i686-2.6/scipy/lib/lapack/clapack.dll" failed with
exit status 1"

I'll attach a copy of the results from: python -c 'from numpy.f2py.diagnose
import run; run()'.

Thanks in advance.



-- 
Best,
Greg
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os.name='posix'
------
sys.platform='cygwin'
------
sys.version:
2.6.7 (r267:88850, Feb  2 2012, 23:50:20) 
[GCC 4.5.3]
------
sys.prefix:
/usr
------
sys.path=':/usr/lib/python2.6/site-packages/setuptools-0.6c11-py2.6.egg:/usr/lib/python2.6/site-packages/nose-1.1.2-py2.6.egg:/usr/lib/python2.6/site-packages/pyfits-3.0.7-py2.6-cygwin-1.7.15-i686.egg:/usr/lib/python2.6/site-packages/matplotlib-1.1.0-py2.6-cygwin-1.7.15-i686.egg:/usr/lib/python26.zip:/usr/lib/python2.6:/usr/lib/python2.6/plat-cygwin:/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/PIL:/usr/lib/python2.6/site-packages/gtk-2.0'
------
Found new numpy version '1.6.2' in /usr/lib/python2.6/site-packages/numpy/__init__.pyc
Found f2py2e version '2' in /usr/lib/python2.6/site-packages/numpy/f2py/f2py2e.pyc
Found numpy.distutils version '0.4.0' in '/usr/lib/python2.6/site-packages/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', '-O3', '-funroll-loops']
  compiler_f90    = None
  compiler_fix    = None
  libraries       = ['g2c']
  library_dirs    = ['/usr/lib/gcc/i686-pc-cygwin/3.4.4']
  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 ('3.4.4')
  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', '-O3', '-
                    funroll-loops']
  compiler_f90    = ['/usr/bin/gfortran', '-Wall', '-O3', '-funroll-loops']
  compiler_fix    = ['/usr/bin/gfortran', '-Wall', '-ffixed-form', '-Wall',
                    '-O3', '-funroll-loops']
  libraries       = ['gfortran']
  library_dirs    = ['/usr/lib/gcc/i686-pc-cygwin/4.5.3']
  linker_exe      = ['/usr/bin/gfortran', '-Wall', '-Wall']
  linker_so       = ['/usr/bin/gfortran', '-Wall', '-Wall', '-shared']
  object_switch   = '-o '
  ranlib          = ['/usr/bin/gfortran']
  version         = LooseVersion ('4.5.3')
  version_cmd     = ['/usr/bin/gfortran', '--version']
Fortran compilers found:
  --fcompiler=gnu    GNU Fortran 77 compiler (3.4.4)
  --fcompiler=gnu95  GNU Fortran 95 compiler (4.5.3)
Compilers available for this platform, but not found:
  --fcompiler=absoft   Absoft Corp Fortran Compiler
  --fcompiler=compaqv  DIGITAL or Compaq Visual Fortran Compiler
  --fcompiler=g95      G95 Fortran Compiler
  --fcompiler=intelev  Intel Visual Fortran Compiler for Itanium apps
  --fcompiler=intelv   Intel Visual Fortran Compiler for 32-bit apps
Compilers not available on this platform:
  --fcompiler=compaq    Compaq Fortran Compiler
  --fcompiler=hpux      HP Fortran 90 Compiler
  --fcompiler=ibm       IBM XL 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 64-bit apps
  --fcompiler=intelvem  Intel Visual Fortran Compiler for 64-bit apps
  --fcompiler=lahey     Lahey/Fujitsu Fortran 95 Compiler
  --fcompiler=mips      MIPSpro Fortran Compiler
  --fcompiler=nag       NAGWare Fortran 95 Compiler
  --fcompiler=none      Fake Fortran compiler
  --fcompiler=pathf95   PathScale Fortran Compiler
  --fcompiler=pg        Portland Group Fortran Compiler
  --fcompiler=sun       Sun or Forte Fortran 95 Compiler
  --fcompiler=vast      Pacific-Sierra Research Fortran 90 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 ------