From josef.pktd at gmail.com  Fri Mar  1 00:20:30 2013
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Fri, 1 Mar 2013 00:20:30 -0500
Subject: [SciPy-User] optimize.brentq and step function
In-Reply-To: <CAB6mnxJvAnd7AZNkjg2TwnmjVPbNvr22H4CarsBTwdHJ6GBqQA@mail.gmail.com>
References: <CAMMTP+AGsqCmHHYsP_YrqxJsWWDGGBm29=JiNE9MLzNnT_sg9w@mail.gmail.com>
	<CAB6mnxKS5DA+ChBOxs3kStB-waNJL1AraJwYowc=AMNg9BrH6g@mail.gmail.com>
	<CAB6mnxJvAnd7AZNkjg2TwnmjVPbNvr22H4CarsBTwdHJ6GBqQA@mail.gmail.com>
Message-ID: <CAMMTP+DgD+Ae69GqjwXBSLwZykDQbcLK8HznFeeEaH3YF-DQ7A@mail.gmail.com>

On Thu, Feb 28, 2013 at 11:57 PM, Charles R Harris
<charlesr.harris at gmail.com> wrote:
>
>
> On Thu, Feb 28, 2013 at 9:43 PM, Charles R Harris
> <charlesr.harris at gmail.com> wrote:
>>
>>
>>
>> On Thu, Feb 28, 2013 at 8:44 PM, <josef.pktd at gmail.com> wrote:
>>>
>>> brentq documentation says "f must be a continuous function"
>>>
>>> I forgot that I have a step function and tried brentq and it worked.
>>> Is this an accident or a feature?
>>>
>>
>> Feature, the documentation is off. Brentq falls back on bisection when it
>> converges too slowly. And there is some subtlety in 'slowly', but it does
>> work find a point of sign where the function changes sign, all that is
>> required is the the function be defined everywhere on the interval and there
>> be a finite number of 'zeros'. If you know you have a discontinuity, plain
>> old bisection is probably faster, but one of the best things about brentq is
>> its generality.
>
>
> And the finite part is wrong. Bisection will always find a `zero` if the
> ends have opposite signs since the interval is halved on every iteration and
> it will terminate when the interval is sufficiently small. But all you know
> at that point is that the ends have different signs, not that the function
> is almost zero there. So continuity is required for the function to actually
> be close to zero.

Thanks for the explanation, all clear.
I also tried optimize.bisect with similar results as brentq.

Related: Is there a general interest in an integer bisection? function
possibly defined only at integer points

There is one in stats.distributions, I fixed it, but I haven't read
anything about bisections (at least not since college).

Josef

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


From sguo at bournemouth.ac.uk  Fri Mar  1 07:11:55 2013
From: sguo at bournemouth.ac.uk (SHIHUI GUO)
Date: Fri, 1 Mar 2013 12:11:55 +0000
Subject: [SciPy-User] _dop.error: failed in processing argument list for
	call-back fcn.
In-Reply-To: <CAGzF1ue4vMTvBk1krzTTH5K+1s7NDOZ3JSw9L5nDmh8sEL4jxA@mail.gmail.com>
References: <CAGRXGSow0vHVHMWRB+Q7YeoEWZwjKuQM5gi4sd1tZeubBvz1Mw@mail.gmail.com><CAGzF1udvUx-sQV_7w2WC2hzccTbJpBnN9BjBFKChf7zyec_SZw@mail.gmail.com><CAGRXGSrMw1cuBXPDw+vEfijndWaKE4mGLf2unG0fwdh+bKGyzA@mail.gmail.com><CAGzF1ue4vMTvBk1krzTTH5K+1s7NDOZ3JSw9L5nDmh8sEL4jxA@mail.gmail.com>
Message-ID: <CAGRXGSrkE2GeA52rQx8YLQrNOwKUVDJ00jRAW479O2BXMbeQbg@mail.gmail.com>

Thanks Warren.

You are so helpful!

Shihui


On 1 March 2013 04:57, Warren Weckesser <warren.weckesser at gmail.com> wrote:

> On 2/26/13, SHIHUI GUO <sguo at bournemouth.ac.uk> wrote:
> > Hi Warren,
> >
> > Thanks for this. The major issue is that I declared the "param" in
> function
> > definition, but didn't pass that when calling.
> >
> > Another question, when we do:
> > ==========================
> > while test.successful() and test.t<t1:
> >         test.integrate(test.t+dt)
> > ==========================
> > the result is returned after each time step. Is there any default way I
> > could specify the time span and get the result over the whole time span,
> > instead of individual integrative step?
> >
>
> Yes, in fact, that is what it is doing, but the loop is set up to get
> a value every `dt` time units.  If you want just the final value,
> eliminate the loop, and just give the final time as the argument to
> test.integrate.
>
> For example, the following computes the solution to dy/dt = -y with
> y(0) = 1 at time t=10:
>
> -----
> In [34]: def func(t, y):
>    ....:     return -y
>    ....:
>
> In [35]: solver = ode(f=func)
>
> In [36]: solver.set_integrator("lsoda")
> Out[36]: <scipy.integrate._ode.ode at 0x2b59350>
>
> In [37]: solver.set_initial_value(1.0)
> Out[37]: <scipy.integrate._ode.ode at 0x2b59350>
>
> In [38]: result = solver.integrate(10.0)   # Get the solution at t=10.
>
> In [39]: result
> Out[39]: array([  4.53998024e-05])
>
> In [40]: np.exp(-10)
> Out[40]: 4.5399929762484854e-05
>
> In [41]: solver.successful()
> Out[41]: True
> -----
>
> Warren
>
>
> > Thanks.
> >
> > Shihui
> >
> >
> > On 26 February 2013 11:16, Warren Weckesser
> > <warren.weckesser at gmail.com>wrote:
> >
> >> On 2/26/13, SHIHUI GUO <sguo at bournemouth.ac.uk> wrote:
> >> > HI all,
> >> >
> >> > I want to use scipy to implement an oscillator, ie. solving a
> >> > secod-order
> >> > ordinary differential equation, my code is:
> >> > ===================================
> >> > from scipy.integrate import ode
> >> >
> >> > y0,t0 = [0, 1], 0
> >> >
> >> > def fun(t, y, params):
> >> >     rou = 1
> >> >     omega = 10
> >> >     sigma = 1
> >> >     # convergence rate, ie, lambda
> >> >     conrate = 10
> >> >     temp = -conrate*((y[0]^2+y[1]^2)/rou^2-sigma)
> >> >     dy = temp*y[0] - omega*y[1]
> >> >     ddy = omega*y[0] + temp*y[1]
> >> >     return [dy, ddy]
> >> >
> >> > test = ode(fun).set_integrator('dopri5')
> >> > test.set_initial_value(y0, t0)
> >> > t1 = 10
> >> > dt = 0.1
> >> >
> >> > while test.successful() and test.t<t1:
> >> >     test.integrate(test.t+dt)
> >> >     print test.t, test.yy
> >> > ===================================
> >> >
> >> > ===================================
> >> > The error says:
> >> > _dop.error: failed in processing argument list for call-back fcn.
> >> > File "/home/shepherd/python/research/testode.py", line 23, in <module>
> >> >   test.integrate(test.t+dt)
> >> > File
> >> >
> >>
> "/home/shepherd/epd/epd_free-7.3-2-rh5-x86/lib/python2.7/site-packages/scipy/integrate/_ode.py",
> >> > line 333, in integrate
> >> >   self.f_params, self.jac_params)
> >> > File
> >> >
> >>
> "/home/shepherd/epd/epd_free-7.3-2-rh5-x86/lib/python2.7/site-packages/scipy/integrate/_ode.py",
> >> > line 827, in run
> >> >   tuple(self.call_args) + (f_params,)))
> >> > ===================================
> >> >
> >> > Previously I use the ubuntu default python, and scipy is 0.9.0. Some
> >> thread
> >> > says it is a bug and has been fixed in 0.10.0, so I switched to
> >> enthought,
> >> > now the scipy is newest version, but the error remains.
> >> >
> >> > Thanks for any help.
> >> >
> >> > Shihui
> >> >
> >>
> >>
> >> There are a few problems in your code.
> >>
> >> You haven't told the `ode` object that your function accepts the extra
> >> argument `params`.  Normally you would do this with
> >> `test.set_f_params(...)`, but since your function doesn't actually use
> >> `params`, it is simpler to just change the function signature to
> >>
> >>     def fun(t, y):
> >>
> >> Next, in Python, the operator to raise a value to a power is **, not
> >> ^, so the formula for `temp` should be:
> >>
> >>     temp = -conrate*((y[0]**2+y[1]**2)/rou**2-sigma)
> >>
> >> Finally, the attribute for the solution is `y`, not `yy`, so the last
> >> line should be:
> >>
> >>     print test.t, test.y
> >>
> >>
> >> Cheers,
> >>
> >> Warren
> >>
> >>
> >> > --
> >> > *
> >> >
> >>
> ---------------------------------------------------------------------------------------
> >> > *
> >> >
> >> > SHIHUI GUO
> >> > National Center for Computer Animation
> >> > Bournemouth University
> >> > United Kingdom
> >> >
> >> > BU is a Disability Two Ticks Employer and has signed up to the Mindful
> >> > Employer charter. Information about the accessibility of University
> >> > buildings can be found on the BU DisabledGo webpages [
> >> > http://www.disabledgo.com/en/org/bournemouth-university ]
> >> > This email is intended only for the person to whom it is addressed and
> >> may
> >> > contain confidential information. If you have received this email in
> >> error,
> >> > please notify the sender and delete this email, which must not be
> >> > copied,
> >> > distributed or disclosed to any other person.
> >> > Any views or opinions presented are solely those of the author and do
> >> > not
> >> > necessarily represent those of Bournemouth University or its
> subsidiary
> >> > companies. Nor can any contract be formed on behalf of the University
> >> > or
> >> its
> >> > subsidiary companies via email.
> >> >
> >> >
> >> >
> >> _______________________________________________
> >> SciPy-User mailing list
> >> SciPy-User at scipy.org
> >> http://mail.scipy.org/mailman/listinfo/scipy-user
> >>
> >>
> >>
> >
> >
> > --
> > *
> >
> ---------------------------------------------------------------------------------------
> > *
> >
> > SHIHUI GUO
> > National Center for Computer Animation
> > Bournemouth University
> > United Kingdom
> >
> > BU is a Disability Two Ticks Employer and has signed up to the Mindful
> > Employer charter. Information about the accessibility of University
> > buildings can be found on the BU DisabledGo webpages [
> > http://www.disabledgo.com/en/org/bournemouth-university ]
> > This email is intended only for the person to whom it is addressed and
> may
> > contain confidential information. If you have received this email in
> error,
> > please notify the sender and delete this email, which must not be copied,
> > distributed or disclosed to any other person.
> > Any views or opinions presented are solely those of the author and do not
> > necessarily represent those of Bournemouth University or its subsidiary
> > companies. Nor can any contract be formed on behalf of the University or
> its
> > subsidiary companies via email.
> >
> >
> >
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
>


-- 
*
---------------------------------------------------------------------------------------
*

SHIHUI GUO
National Center for Computer Animation
Bournemouth University
United Kingdom

BU is a Disability Two Ticks Employer and has signed up to the Mindful Employer charter. Information about the accessibility of University buildings can be found on the BU DisabledGo webpages [ http://www.disabledgo.com/en/org/bournemouth-university ]
This email is intended only for the person to whom it is addressed and may contain confidential information. If you have received this email in error, please notify the sender and delete this email, which must not be copied, distributed or disclosed to any other person.
Any views or opinions presented are solely those of the author and do not necessarily represent those of Bournemouth University or its subsidiary companies. Nor can any contract be formed on behalf of the University or its subsidiary companies via email.

-- 
This message has been scanned for viruses and
dangerous content by MailScanner, and is
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From evilper at gmail.com  Fri Mar  1 14:24:13 2013
From: evilper at gmail.com (Per Nielsen)
Date: Fri, 1 Mar 2013 11:24:13 -0800
Subject: [SciPy-User] List of functions with different arguments
Message-ID: <CAGtYnKihrN7QUc80_p9MRGQ022+J3hc82bJBXFy9-DdBJuGgvA@mail.gmail.com>

Hi all

I apologize if this is too off topic, but I figured it was common in
scientific computing.

I am trying to make a list of copies of the same function, but with
different extra input arguments. However, I am having problems getting the
wanted output. I thought it was a problem with the way I refered to the
function, but I tried a deepcopy and also the functions seem to have
different ids.

Here is my output from the attached script:

simple
[3, 3, 3]
[4549677376, 4549677496, 4549677616]
deepcopy
[3, 3, 3]
[4549677736, 4549677856, 4549677976]
wanted/expected output: [1, 2, 3]

Any help would be greatly appreciated :)

Cheers,
Per
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From warren.weckesser at gmail.com  Fri Mar  1 14:53:39 2013
From: warren.weckesser at gmail.com (Warren Weckesser)
Date: Fri, 1 Mar 2013 14:53:39 -0500
Subject: [SciPy-User] List of functions with different arguments
In-Reply-To: <CAGtYnKihrN7QUc80_p9MRGQ022+J3hc82bJBXFy9-DdBJuGgvA@mail.gmail.com>
References: <CAGtYnKihrN7QUc80_p9MRGQ022+J3hc82bJBXFy9-DdBJuGgvA@mail.gmail.com>
Message-ID: <CAGzF1ue8CCuHydCkprJdvN-Y_JFYxkh8P+qbF9_ECQMuU0AzBw@mail.gmail.com>

On Fri, Mar 1, 2013 at 2:24 PM, Per Nielsen <evilper at gmail.com> wrote:

> Hi all
>
> I apologize if this is too off topic, but I figured it was common in
> scientific computing.
>
> I am trying to make a list of copies of the same function, but with
> different extra input arguments. However, I am having problems getting the
> wanted output. I thought it was a problem with the way I refered to the
> function, but I tried a deepcopy and also the functions seem to have
> different ids.
>
> Here is my output from the attached script:
>
> simple
> [3, 3, 3]
> [4549677376, 4549677496, 4549677616]
> deepcopy
> [3, 3, 3]
> [4549677736, 4549677856, 4549677976]
> wanted/expected output: [1, 2, 3]
>
> Any help would be greatly appreciated :)
>
> Cheers,
> Per
>
>

The problem is this:

fct_list = [lambda x: fct(x, aa) for aa in range(1, 4)]

does not do what you want it to.  The use of `aa` in the lambda expression
is not replaced by the values of `aa` in the list comprehension.  Instead,
your lambdas will use whatever value the name 'aa' has when they are
called.  In this case, after the list comprehension is done, `aa` is 3, so
they always get a=3.  In fact, if immediately after you define `fct_list`
you do `del aa`, you'll get an error when you try to call fct_list[0](1).

One alternative is to import `partial` from `functools`:

from functools import partial
fct_list = [partial(fct, a=aa) for aa in range(1, 4)]


Warren



> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
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From robert.kern at gmail.com  Fri Mar  1 15:01:47 2013
From: robert.kern at gmail.com (Robert Kern)
Date: Fri, 1 Mar 2013 20:01:47 +0000
Subject: [SciPy-User] List of functions with different arguments
In-Reply-To: <CAGtYnKihrN7QUc80_p9MRGQ022+J3hc82bJBXFy9-DdBJuGgvA@mail.gmail.com>
References: <CAGtYnKihrN7QUc80_p9MRGQ022+J3hc82bJBXFy9-DdBJuGgvA@mail.gmail.com>
Message-ID: <CAF6FJisrsauW7sKBbrh8k9cyL57X5KU9RqACdX-e7n5G2kbp5w@mail.gmail.com>

On Fri, Mar 1, 2013 at 7:24 PM, Per Nielsen <evilper at gmail.com> wrote:
> Hi all
>
> I apologize if this is too off topic, but I figured it was common in
> scientific computing.
>
> I am trying to make a list of copies of the same function, but with
> different extra input arguments. However, I am having problems getting the
> wanted output. I thought it was a problem with the way I refered to the
> function, but I tried a deepcopy and also the functions seem to have
> different ids.
>
> Here is my output from the attached script:
>
> simple
> [3, 3, 3]
> [4549677376, 4549677496, 4549677616]
> deepcopy
> [3, 3, 3]
> [4549677736, 4549677856, 4549677976]
> wanted/expected output: [1, 2, 3]
>
> Any help would be greatly appreciated :)

You probably want to use functools.partial() for this.

  http://docs.python.org/2/library/functools#functools.partial

The problem you are encountering is that the lambda code block looks
up the variable `aa` from the global namespace at runtime, not
definition-time. The inner `for` loops for the list comprehensions
leak the `aa` variable so you end up with the last one in the global
namespace.

An alternative is to specify and bind the `aa` value as a keyword
argument to the lambda:

  fct_list = [lambda x, aa=aa: fct(x, aa) for aa in range(1, 4)]

--
Robert Kern


From michael.aye at ucla.edu  Fri Mar  1 19:18:39 2013
From: michael.aye at ucla.edu (Michael Aye)
Date: Fri, 1 Mar 2013 16:18:39 -0800
Subject: [SciPy-User] Spline interpolation array instead of loop?
Message-ID: <kgrggo$iee$1@ger.gmane.org>

Hi!

So, I have to do the same spine interpolation (same=same x-axis) for 
189 data channels, so I was wonderding if there is a way to store the 
189 measurements into a 2D array and to set that at once to an 
interpolator, is that possible somehow?

I noticed that InterpolatedUnivariateSpline does not allow me to do 
that, it seems.

So, in other words, what I would like to be able to do is:

len(x) = 20

len(y_i) = 20

i = 1 ..189

y.shape == (20,189)

s = UnivariateSpline(x, y)


res = s(new_x)

res.shape == (20, 189)


Does something like that exist?

Have a nice weekend!
Michael




From charlesr.harris at gmail.com  Fri Mar  1 22:07:46 2013
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Fri, 1 Mar 2013 20:07:46 -0700
Subject: [SciPy-User] Spline interpolation array instead of loop?
In-Reply-To: <kgrggo$iee$1@ger.gmane.org>
References: <kgrggo$iee$1@ger.gmane.org>
Message-ID: <CAB6mnxJniYi6tOH2Qi16yd2CA9qm5-5ULDQ5XEVqOQMs8OQtVA@mail.gmail.com>

On Fri, Mar 1, 2013 at 5:18 PM, Michael Aye <michael.aye at ucla.edu> wrote:

> Hi!
>
> So, I have to do the same spine interpolation (same=same x-axis) for
> 189 data channels, so I was wonderding if there is a way to store the
> 189 measurements into a 2D array and to set that at once to an
> interpolator, is that possible somehow?
>
> I noticed that InterpolatedUnivariateSpline does not allow me to do
> that, it seems.
>
> So, in other words, what I would like to be able to do is:
>
> len(x) = 20
>
> len(y_i) = 20
>
> i = 1 ..189
>
> y.shape == (20,189)
>
> s = UnivariateSpline(x, y)
>
>
> res = s(new_x)
>
> res.shape == (20, 189)
>
>
> Does something like that exist?
>
>
Well, I'm working on an spline interpolater for scipy and it works with
vector valued splines, so that should cover your case. At the moment it is
in pure python prototype form, so pretty slow. You might have a look if you
are interested and see if it is in the right direction. It is
here<https://github.com/charris/bsplines>.
Not much documented at the moment since I've only started on it.

Chuck
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From charlesr.harris at gmail.com  Fri Mar  1 22:15:25 2013
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Fri, 1 Mar 2013 20:15:25 -0700
Subject: [SciPy-User] Spline interpolation array instead of loop?
In-Reply-To: <CAB6mnxJniYi6tOH2Qi16yd2CA9qm5-5ULDQ5XEVqOQMs8OQtVA@mail.gmail.com>
References: <kgrggo$iee$1@ger.gmane.org>
	<CAB6mnxJniYi6tOH2Qi16yd2CA9qm5-5ULDQ5XEVqOQMs8OQtVA@mail.gmail.com>
Message-ID: <CAB6mnxKHC_86pa20UeVG9voOUruRkqnbL8QwnjjQqr9Bt6nK0g@mail.gmail.com>

On Fri, Mar 1, 2013 at 8:07 PM, Charles R Harris
<charlesr.harris at gmail.com>wrote:

>
>
> On Fri, Mar 1, 2013 at 5:18 PM, Michael Aye <michael.aye at ucla.edu> wrote:
>
>> Hi!
>>
>> So, I have to do the same spine interpolation (same=same x-axis) for
>> 189 data channels, so I was wonderding if there is a way to store the
>> 189 measurements into a 2D array and to set that at once to an
>> interpolator, is that possible somehow?
>>
>> I noticed that InterpolatedUnivariateSpline does not allow me to do
>> that, it seems.
>>
>> So, in other words, what I would like to be able to do is:
>>
>> len(x) = 20
>>
>> len(y_i) = 20
>>
>> i = 1 ..189
>>
>> y.shape == (20,189)
>>
>> s = UnivariateSpline(x, y)
>>
>>
>> res = s(new_x)
>>
>> res.shape == (20, 189)
>>
>>
>> Does something like that exist?
>>
>>
> Well, I'm working on an spline interpolater for scipy and it works with
> vector valued splines, so that should cover your case. At the moment it is
> in pure python prototype form, so pretty slow. You might have a look if you
> are interested and see if it is in the right direction. It is here<https://github.com/charris/bsplines>.
> Not much documented at the moment since I've only started on it.
>
>
If you do try it out, and it would be a favor to me if you did ;), feel
free to email me directly if you need help setting it up.

Chuck
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From charlesr.harris at gmail.com  Fri Mar  1 22:24:44 2013
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Fri, 1 Mar 2013 20:24:44 -0700
Subject: [SciPy-User] Spline interpolation array instead of loop?
In-Reply-To: <CAB6mnxKHC_86pa20UeVG9voOUruRkqnbL8QwnjjQqr9Bt6nK0g@mail.gmail.com>
References: <kgrggo$iee$1@ger.gmane.org>
	<CAB6mnxJniYi6tOH2Qi16yd2CA9qm5-5ULDQ5XEVqOQMs8OQtVA@mail.gmail.com>
	<CAB6mnxKHC_86pa20UeVG9voOUruRkqnbL8QwnjjQqr9Bt6nK0g@mail.gmail.com>
Message-ID: <CAB6mnx+Xf34YkmFN+oj+jDUU=htSLSizS4Q93RKTavvMf67Jgg@mail.gmail.com>

On Fri, Mar 1, 2013 at 8:15 PM, Charles R Harris
<charlesr.harris at gmail.com>wrote:

>
>
> On Fri, Mar 1, 2013 at 8:07 PM, Charles R Harris <
> charlesr.harris at gmail.com> wrote:
>
>>
>>
>> On Fri, Mar 1, 2013 at 5:18 PM, Michael Aye <michael.aye at ucla.edu> wrote:
>>
>>> Hi!
>>>
>>> So, I have to do the same spine interpolation (same=same x-axis) for
>>> 189 data channels, so I was wonderding if there is a way to store the
>>> 189 measurements into a 2D array and to set that at once to an
>>> interpolator, is that possible somehow?
>>>
>>> I noticed that InterpolatedUnivariateSpline does not allow me to do
>>> that, it seems.
>>>
>>> So, in other words, what I would like to be able to do is:
>>>
>>> len(x) = 20
>>>
>>> len(y_i) = 20
>>>
>>> i = 1 ..189
>>>
>>> y.shape == (20,189)
>>>
>>> s = UnivariateSpline(x, y)
>>>
>>>
>>> res = s(new_x)
>>>
>>> res.shape == (20, 189)
>>>
>>>
>>> Does something like that exist?
>>>
>>>
>> Well, I'm working on an spline interpolater for scipy and it works with
>> vector valued splines, so that should cover your case. At the moment it is
>> in pure python prototype form, so pretty slow. You might have a look if you
>> are interested and see if it is in the right direction. It is here<https://github.com/charris/bsplines>.
>> Not much documented at the moment since I've only started on it.
>>
>>
> If you do try it out, and it would be a favor to me if you did ;), feel
> free to email me directly if you need help setting it up.
>

And now I remember that I don't do spline creation yet. What sort of
boundary conditions are you using? I also plan to solve for the spline
weights for vector valued data, I'll rough that out this weekend if you are
interested.

Chuck
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From josef.pktd at gmail.com  Sat Mar  2 09:22:41 2013
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sat, 2 Mar 2013 09:22:41 -0500
Subject: [SciPy-User] TODO: more one-sided options in statistical tests
Message-ID: <CAMMTP+Bzd6pO9yvv+2YuQY5k6eu0-GMbrBCcdxg=kF_diHd4AQ@mail.gmail.com>

options that would be nice to have, if someone has a few spare cycles.

most statistical tests in scipy stats don't have an option for a
one-sided alternative.

I could use a one-sided ``binom_test`` right now.
http://ugrad.stat.ubc.ca/R/library/stats/html/binom.test.html
My guess is, it's at most of few lines, but it takes time to find them
and to write the tests.

For example, a ttest_ind that allows for one-sided alternatives and
for a Null hypothesis that has a mean difference different from zero
https://github.com/statsmodels/statsmodels/pull/535/files#L13R461
(I needed it to write an equivalence test)

Josef


From member at linkedin.com  Sat Mar  2 22:15:54 2013
From: member at linkedin.com (Harshad Surdi via LinkedIn)
Date: Sun, 3 Mar 2013 03:15:54 +0000 (UTC)
Subject: [SciPy-User] Invitation to connect on LinkedIn
Message-ID: <1166090592.7356722.1362280554692.JavaMail.app@ela4-app2321.prod>

LinkedIn
------------




    Harshad Surdi requested to add you as a connection on LinkedIn:
  

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

Jose,

I'd like to add you to my professional network on LinkedIn.

- Harshad

Accept invitation from Harshad Surdi
http://www.linkedin.com/e/-3wy1w2-hdtmo3b0-4v/Q6WKH0LACopGJkAw_6fSqajo6R7VMvIz/blk/I698614865_20/3wOtCVFbmdxnSVFbm8JrnpKqlZJrmZzbmNJpjRQnOpBtn9QfmhBt71BoSd1p65Lr6lOfP0OnPkSe3gNdzwVdAALiRBmiz1qul0Lc34VdjkTcj0Qcj4LrCBxbOYWrSlI/eml-comm_invm-b-in_ac-inv28/?hs=false&tok=0mdUF0CgYzsBE1

View profile of Harshad Surdi
http://www.linkedin.com/e/-3wy1w2-hdtmo3b0-4v/rso/235918465/oHlG/name/21555906_I698614865_20/?hs=false&tok=00RJEblQEzsBE1
------------------------------------------
You are receiving Invitation emails.


This email was intended for Jose Gomez-Dans.
Learn why this is included: http://www.linkedin.com/e/-3wy1w2-hdtmo3b0-4v/plh/http%3A%2F%2Fhelp%2Elinkedin%2Ecom%2Fapp%2Fanswers%2Fdetail%2Fa_id%2F4788/-GXI/?hs=false&tok=0aCl8mwbwzsBE1

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From nicole.haenni at gmail.com  Sat Mar  2 21:40:17 2013
From: nicole.haenni at gmail.com (Nicole Haenni)
Date: Sun, 3 Mar 2013 03:40:17 +0100
Subject: [SciPy-User] Survey for framework and library developers:
 "Information needs in software ecosystems"
In-Reply-To: <CAEM6MMzJYaUFgxV0d328KAmgp20q8yVs5SkHotoYUcbaQoKRLg@mail.gmail.com>
References: <CAEM6MMw1_-2NPu74f8=ju98LTV3ja6eAGr5Z5uP_5r+JfRhKEw@mail.gmail.com>
	<CAEM6MMy5t_+-HPgU6Gdapf=8t+V5FD=hXH47Kd139g0ceZxOjw@mail.gmail.com>
	<CAEM6MMw7Pq0mo4XKPbmFrwXzZCNpqYsq4E1V-+c_bodRFzdUVA@mail.gmail.com>
	<CAEM6MMzaz01nJ1SOmR-xU9aA4tavNaJz2PXxWU=VCCPQcF1tVg@mail.gmail.com>
	<CAEM6MMyOKkPY69u==J8qK1OD1tmqEHer5tavjqLA+6baEG3E0Q@mail.gmail.com>
	<CAEM6MMyOBsWimPFAD1S3N05h+AnCZ4SQMwitd2BG6DoU3f7Rkg@mail.gmail.com>
	<CAEM6MMzJYaUFgxV0d328KAmgp20q8yVs5SkHotoYUcbaQoKRLg@mail.gmail.com>
Message-ID: <CAEM6MMz0p-buhxPD_3Q83JuoZHmjabia6osrpaPP-bHGTjqvdA@mail.gmail.com>

Hi,

I?m Nicole Haenni and I'm doing research for my thesis at the University of
Berne (scg.unibe.ch) with Mircea Lungu and Niko Schwarz. We are researching
on monitoring the activity in software ecosystems. This is a study about
information needs that arise in such software ecosystems. I need your help
to fill out the survey below. It takes about 10 minutes to complete it.

A software ecosystem can be a project repository like GitHub, an open
source community (e.g. the Apache community) or a language-based community
(e.g. Smalltalk has Squeaksource, Ruby has Rubyforge).

We formulate our research question as follows:
"What information needs arise when developers use code from other projects,
or see their own code used elsewhere."

Survey link: http://bit.ly/14Zc71N
or original link:
https://docs.google.com/spreadsheet/viewform?formkey=dFBJUmVodVU1V3BMMGRPRWxBdjdDbVE6MA

Thank you for your support!

Nicole Haenni

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

Software Composition Group
Institut f?r Informatik
Universit?t Bern
Neubr?ckstrasse 10
CH-3012 Bern
SWITZERLAND
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From opossumnano at gmail.com  Mon Mar  4 05:37:53 2013
From: opossumnano at gmail.com (Tiziano Zito)
Date: Mon, 4 Mar 2013 11:37:53 +0100
Subject: [SciPy-User] EuroSciPy 2013 Call for Abstracts
Message-ID: <20130304103753.GB30426@bio230.biologie.hu-berlin.de>

Dear Scientist using Python,

EuroSciPy 2013, the Sixth Annual Conference on Python in Science, takes place in
Brussels on 21 - 24 August 2013. The conference features two days of tutorials
followed by two days of scientific talks that start with our keynote speakers,
Cameron Neylon and Peter Wang.

The topics presented at EuroSciPy are very diverse, with a focus on advanced
software engineering and original uses of Python and its scientific libraries,
either in theoretical or experimental research, from both academia and the
industry. The program includes contributed talks and posters.

Submissions for talks and posters are welcome on our website
(http://www.euroscipy.org/). Authors must use the web interface to submit an
abstract to the conference. In your abstract, please provide details on what
Python tools are being employed, and how.

The deadline for submission is 28 April 2013.

Until 31 March 2013, you can apply for a sprint session on 25 August 2013.
Also, potential organizers for EuroSciPy 2014 are welcome to contact the
conference committee.

SciPythonic Regards,
The EuroSciPy 2013 Committee
http://www.euroscipy.org/

Conference Chairs: Pierre de Buyl and Nicolas Pettiaux, Universit? libre de
Bruxelles, Belgium
Tutorial Chair: Nicolas Rougier, INRIA, Nancy, France
Program Chair: Tiziano Zito, Humboldt-Universit?t zu Berlin, Germany

Program Committee
Ralf Gommers, ASML, The Netherlands
Emmanuelle Gouillart, Joint Unit CNRS/Saint-Gobain, France
Kael Hanson, Universit? Libre de Bruxelles, Belgium
Konrad Hinsen, Centre National de la Recherche Scientifique (CNRS), France
Hans Petter Langtangen, Simula and University of Oslo, Norway
Mike M?ller, Python Academy, Germany
Raphael Ritz, International Neuroinformatics Coordinating Facility, Stockholm,
Sweden
St?fan van der Walt, Applied Mathematics, Stellenbosch University, South Africa
Ga?l Varoquaux, INRIA Parietal, Saclay, France
Nelle Varoquaux, Mines ParisTech, France
Pauli Virtanen, Aalto University, Finland

Organizing Committee
Nicolas Chauvat, Logilab, France
Emmanuelle Gouillart, Joint Unit CNRS/Saint-Gobain, France
Kael Hanson, Universit? Libre de Bruxelles, Belgium
Renaud Lambiotte, University of Namur, Belgium
Thomas Lecocq, Royal Observatory of Belgium
Mike M?ller, Python Academy, Germany
Didrik Pinte, Enthought Europe
Ga?l Varoquaux, INRIA Parietal, Saclay, France
Nelle Varoquaux, Mines ParisTech, France


From daniele at grinta.net  Mon Mar  4 08:24:15 2013
From: daniele at grinta.net (Daniele Nicolodi)
Date: Mon, 04 Mar 2013 14:24:15 +0100
Subject: [SciPy-User] Allan Variance
Message-ID: <5134A07F.4050907@grinta.net>

Hello,

does anyone know or have written and is willing to share some code to
compute the Allan variance, modified Allan variance and friends, ideally
both from frequency than from phase data?

I'm writing my own code, it is not that difficult, but I would prefer
the time required for testing and validation doing something more fun.

If there is no available code implementing Allan variance, would it be
interesting to include in in SciPy?

Thank you so much.

Best,
Daniele


From jschoenberger at demuc.de  Mon Mar  4 15:38:05 2013
From: jschoenberger at demuc.de (=?iso-8859-1?Q?Johannes_Sch=F6nberger?=)
Date: Mon, 4 Mar 2013 21:38:05 +0100
Subject: [SciPy-User] Announcement: Release of scikits-image 0.8.0
Message-ID: <6466270E-ED8B-49FB-9ECD-EB383D6C1B14@demuc.de>

Announcement: scikits-image 0.8.0
=================================

We're happy to announce the 8th version of scikit-image!

scikit-image is an image processing toolbox for SciPy that includes algorithms
for segmentation, geometric transformations, color space manipulation,
analysis, filtering, morphology, feature detection, and more.

For more information, examples, and documentation, please visit our website:

    http://scikit-image.org


New Features
------------

- New rank filter package with many new functions and a very fast underlying
  local histogram algorithm, especially for large structuring elements
  `skimage.filter.rank.*`
- New function for small object removal
  `skimage.morphology.remove_small_objects`
- New circular hough transformation `skimage.transform.hough_circle`
- New function to draw circle perimeter `skimage.draw.circle_perimeter` and
  ellipse perimeter `skimage.draw.ellipse_perimeter`
- New dense DAISY feature descriptor `skimage.feature.daisy`
- New bilateral filter `skimage.filter.denoise_bilateral`
- New faster TV denoising filter based on split-Bregman algorithm
  `skimage.filter.denoise_tv_bregman`
- New linear hough peak detection `skimage.transform.hough_peaks`
- New Scharr edge detection `skimage.filter.scharr`
- New geometric image scaling as convenience function
  `skimage.transform.rescale`
- New theme for documentation and website
- Faster median filter through vectorization `skimage.filter.median_filter`
- Grayscale images supported for SLIC segmentation
- Unified peak detection with more options `skimage.feature.peak_local_max`
- `imread` can read images via URL and knows more formats `skimage.io.imread`

Additionally, this release adds lots of bug fixes, new examples, and
performance enhancements.


Contributors to this release
----------------------------

This release was only possible due to the efforts of many contributors, both
new and old.

- Adam Ginsburg
- Anders Boesen Lindbo Larsen
- Andreas Mueller
- Christoph Gohlke
- Christos Psaltis
- Colin Lea
- Fran?ois Boulogne
- Jan Margeta
- Johannes Sch?nberger
- Josh Warner (Mac)
- Juan Nunez-Iglesias
- Luis Pedro Coelho
- Marianne Corvellec
- Matt McCormick
- Nicolas Pinto
- Olivier Debeir
- Paul Ivanov
- Sergey Karayev
- Stefan van der Walt
- Steven Silvester
- Thouis (Ray) Jones
- Tony S Yu



From ndbecker2 at gmail.com  Tue Mar  5 07:13:29 2013
From: ndbecker2 at gmail.com (Neal Becker)
Date: Tue, 05 Mar 2013 07:13:29 -0500
Subject: [SciPy-User] Allan Variance
References: <5134A07F.4050907@grinta.net>
Message-ID: <kh4nh6$4i5$2@ger.gmane.org>

Daniele Nicolodi wrote:

> Hello,
> 
> does anyone know or have written and is willing to share some code to
> compute the Allan variance, modified Allan variance and friends, ideally
> both from frequency than from phase data?
> 
> I'm writing my own code, it is not that difficult, but I would prefer
> the time required for testing and validation doing something more fun.
> 
> If there is no available code implementing Allan variance, would it be
> interesting to include in in SciPy?
> 
> Thank you so much.
> 
> Best,
> Daniele

Do you mean, given phase noise power spectral density?



From daniele at grinta.net  Tue Mar  5 07:25:37 2013
From: daniele at grinta.net (Daniele Nicolodi)
Date: Tue, 05 Mar 2013 13:25:37 +0100
Subject: [SciPy-User] Allan Variance
In-Reply-To: <kh4nh6$4i5$2@ger.gmane.org>
References: <5134A07F.4050907@grinta.net> <kh4nh6$4i5$2@ger.gmane.org>
Message-ID: <5135E441.80504@grinta.net>

On 05/03/2013 13:13, Neal Becker wrote:
> Daniele Nicolodi wrote:
> 
>> Hello,
>>
>> does anyone know or have written and is willing to share some code to
>> compute the Allan variance, modified Allan variance and friends, ideally
>> both from frequency than from phase data?
>>
>> I'm writing my own code, it is not that difficult, but I would prefer
>> the time required for testing and validation doing something more fun.
>>
>> If there is no available code implementing Allan variance, would it be
>> interesting to include in in SciPy?
>>
>> Thank you so much.
>>
>> Best,
>> Daniele
> 
> Do you mean, given phase noise power spectral density?

I mean given phase or frequency time series.  But also code that
computes Allan variation from power spectral density may come handy for
me in future.

Cheers,
Daniele



From ndbecker2 at gmail.com  Tue Mar  5 08:29:35 2013
From: ndbecker2 at gmail.com (Neal Becker)
Date: Tue, 05 Mar 2013 08:29:35 -0500
Subject: [SciPy-User] Allan Variance
References: <5134A07F.4050907@grinta.net> <kh4nh6$4i5$2@ger.gmane.org>
	<5135E441.80504@grinta.net>
Message-ID: <kh4rvs$joa$1@ger.gmane.org>

Daniele Nicolodi wrote:

> On 05/03/2013 13:13, Neal Becker wrote:
>> Daniele Nicolodi wrote:
>> 
>>> Hello,
>>>
>>> does anyone know or have written and is willing to share some code to
>>> compute the Allan variance, modified Allan variance and friends, ideally
>>> both from frequency than from phase data?
>>>
>>> I'm writing my own code, it is not that difficult, but I would prefer
>>> the time required for testing and validation doing something more fun.
>>>
>>> If there is no available code implementing Allan variance, would it be
>>> interesting to include in in SciPy?
>>>
>>> Thank you so much.
>>>
>>> Best,
>>> Daniele
>> 
>> Do you mean, given phase noise power spectral density?
> 
> I mean given phase or frequency time series.  But also code that
> computes Allan variation from power spectral density may come handy for
> me in future.
> 
> Cheers,
> Daniele

I don't have that - but do have code to generate samples of blocks of phase 
noise from a given spectral mask.



From preetigupta25 at gmail.com  Mon Mar  4 13:24:38 2013
From: preetigupta25 at gmail.com (Preeti)
Date: Mon, 4 Mar 2013 10:24:38 -0800 (PST)
Subject: [SciPy-User] pupynere/scipy.io.netcdf
In-Reply-To: <496B8AD0.2000600@gmail.com>
References: <496B8AD0.2000600@gmail.com>
Message-ID: <1362421478675-17963.post@n7.nabble.com>

Hello,

Can someone tell me how to use this package? I got the source but where do I
put it on my mac?



--
View this message in context: http://scipy-user.10969.n7.nabble.com/pupynere-scipy-io-netcdf-tp9235p17963.html
Sent from the Scipy-User mailing list archive at Nabble.com.


From ralf.gommers at gmail.com  Wed Mar  6 12:41:18 2013
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Wed, 6 Mar 2013 18:41:18 +0100
Subject: [SciPy-User] pupynere/scipy.io.netcdf
In-Reply-To: <1362421478675-17963.post@n7.nabble.com>
References: <496B8AD0.2000600@gmail.com>
	<1362421478675-17963.post@n7.nabble.com>
Message-ID: <CABL7CQjzt34Xi2RoAqSfQZVOJQzgAJyf4w8md3bUhTDjvUj_vg@mail.gmail.com>

On Mon, Mar 4, 2013 at 7:24 PM, Preeti <preetigupta25 at gmail.com> wrote:

> Hello,
>
> Can someone tell me how to use this package? I got the source but where do
> I
> put it on my mac?
>

Don't you have scipy installed? Then no need to download a separate source
and put it somewhere. Just do:

>>> from scipy.io import netcdf
>>>  f = netcdf.netcdf_file('myfilename.nc')
>>> f.<TAB>  # look at methods to explore contents in file

Ralf
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From mooyix at gmail.com  Wed Mar  6 20:00:19 2013
From: mooyix at gmail.com (Brendan Dolan-Gavitt)
Date: Wed, 6 Mar 2013 20:00:19 -0500
Subject: [SciPy-User] Efficiently adding a vector to every row of a sparse
	CSR matrix?
Message-ID: <CAD2=zRDqoLr=8HFYVRt2b8L1s+6nULxy0u338bwqoTpE8=yObQ@mail.gmail.com>

Hi,

As part of implementing a batch calculation of Jensen-Shannon divergence, I
need to take a (sparse) 65536-element vector "V" and add it to every row of
a (sparse) 500000x65536 matrix "O" of observations. Is there any way to do
this that is both space and time efficient? The usual O+V tries to convert
O to a dense matrix, which fails because O is too big to fit in memory (it
would take up ~120 GB!).

I also can't do it slowly via iteration, because it looks like it's not
possible to update a sparse matrix in place.

My current solution is to tile V into a new 500000x65536 matrix and then
add:

import numpy as np
import sparse as sp
[...]
V = sp.csr_matrix(V)
# Create the CSR matrix directly
Vindptr = np.arange(0, len(V.indices)*O.shape[0]+1, len(V.indices),
dtype=np.int32)
Vindices = np.tile(V.indices, O.shape[0])
Vdata = np.tile(V.data, O.shape[0])
mV = sp.csr_matrix((Vdata, Vindices, Vindptr), shape=O.shape)
result = O+mV

This is reasonably fast (though creating mV takes around 6 seconds on its
own), but takes up a lot of memory to store even though there's a ton of
duplicate data.

Is there any way to do this efficiently? It seems like there ought to be...

-Brendan
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From pav at iki.fi  Thu Mar  7 11:00:04 2013
From: pav at iki.fi (Pauli Virtanen)
Date: Thu, 7 Mar 2013 16:00:04 +0000 (UTC)
Subject: [SciPy-User]
	=?utf-8?q?Efficiently_adding_a_vector_to_every_row_o?=
	=?utf-8?q?f_a_sparse=09CSR_matrix=3F?=
References: <CAD2=zRDqoLr=8HFYVRt2b8L1s+6nULxy0u338bwqoTpE8=yObQ@mail.gmail.com>
Message-ID: <loom.20130307T162846-691@post.gmane.org>

Brendan Dolan-Gavitt <mooyix <at> gmail.com> writes:
> As part of implementing a batch calculation of
> Jensen-Shannon divergence, I need to take a (sparse)
> 65536-element vector "V" and add it to every row of
> a (sparse) 500000x65536 matrix "O" of observations.
> Is there any way to do this that is both space and
> time efficient? The usual O+V tries to convert O to
> a dense matrix, which fails because O is too big to
> fit in memory (it would take up ~120 GB!).

What do you need to do with the final M = O + 1 V^T matrix? If you
need it for matrix-vector products (e.g. sparse SVD), it will be cheaper
to keep M around as an abstract linear operator rather than to 
actually form the sparse matrix (which will, in the end, contain
n=500000 times duplicated information).

I don't think Scipy has a specialized routine for adding 
a vector to each row of the sparse matrix. To speed up the
computation of O + 1 V^T over what you have, you can try
writing your own routine for that.

This will cut the memory requirements probably by a factor
of 2, and probably also speed up the computation by some factor.

-- 
Pauli Virtanen



From vanleeuwen.martin at gmail.com  Thu Mar  7 18:35:53 2013
From: vanleeuwen.martin at gmail.com (Martin van Leeuwen)
Date: Thu, 7 Mar 2013 15:35:53 -0800
Subject: [SciPy-User] Question about the position and orientation of the
 2dcircle in Mayavi's quiver3d plotting function
Message-ID: <CAP7q0gw9zfFVkJhrCctOEh5-Nnu8_JEHKrq5F7hOAAcSk4y8DQ@mail.gmail.com>

Dear Python users,

I am using the Mayavi toolkit quite a bit, because it is a very nicely
packaged 3D visualization toolkit. I use it mainly for displaying triangle
meshes and point cloud data, but recently I found it necessary to visualize
disks as well. The mayavi.mlab.quiver3d() function seems to be adequate for
this, however its '2dcircles' are oriented contrary to what I expected. I
expected that these 2dcircles would be oriented so that their normal
vectors would point in the direction that a normal '2darrow' would point
to, but it doesn't. Instead, the normal vector of the 2dcircle is
orthogonal to the direction of the 2darray. In addition the 2dcircle is not
centered on the 3D point (specified by the x,y,z input arrays), but touches
it. Is there a way around this? Or is there another function I overlooked
that centers the circles onto a 3D coordinate?

Thanks in advance! Any help is appreciated.

Martin
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From afraser at lanl.gov  Thu Mar  7 23:42:24 2013
From: afraser at lanl.gov (Fraser, Andy)
Date: Fri, 8 Mar 2013 04:42:24 +0000
Subject: [SciPy-User] Efficiently adding a vector to every row of a
 sparse	CSR matrix?
In-Reply-To: <CAD2=zRDqoLr=8HFYVRt2b8L1s+6nULxy0u338bwqoTpE8=yObQ@mail.gmail.com>
References: <CAD2=zRDqoLr=8HFYVRt2b8L1s+6nULxy0u338bwqoTpE8=yObQ@mail.gmail.com>
Message-ID: <EC43C431666E4F478A52330A1FC0967E1A69AB4B@ECS-EXG-P-MB07.win.lanl.gov>

If you don't need the 500000x65536 result as a stored object but can use the results one at a time, you can write a function that digs into the internal representation of the sparse matrix and "yields" the result (I think as an iterator).  If you use cython, it will be pretty fast.  Let me know if that approach will help.  If so, I can make a more detailed suggestion.

Andy
________________________________
From: scipy-user-bounces at scipy.org [scipy-user-bounces at scipy.org] on behalf of Brendan Dolan-Gavitt [mooyix at gmail.com]
Sent: Wednesday, March 06, 2013 6:00 PM
To: scipy-user at scipy.org
Subject: [SciPy-User] Efficiently adding a vector to every row of a sparse CSR matrix?

Hi,

As part of implementing a batch calculation of Jensen-Shannon divergence, I need to take a (sparse) 65536-element vector "V" and add it to every row of a (sparse) 500000x65536 matrix "O" of observations. [...]
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From robert.kern at gmail.com  Fri Mar  8 05:24:37 2013
From: robert.kern at gmail.com (Robert Kern)
Date: Fri, 8 Mar 2013 10:24:37 +0000
Subject: [SciPy-User] Question about the position and orientation of the
 2dcircle in Mayavi's quiver3d plotting function
In-Reply-To: <CAP7q0gw9zfFVkJhrCctOEh5-Nnu8_JEHKrq5F7hOAAcSk4y8DQ@mail.gmail.com>
References: <CAP7q0gw9zfFVkJhrCctOEh5-Nnu8_JEHKrq5F7hOAAcSk4y8DQ@mail.gmail.com>
Message-ID: <CAF6FJitzzN1A0PG6eofwJg7SbYZ7dP9XYs6=LWZ9Ht0VLgmuBQ@mail.gmail.com>

On Thu, Mar 7, 2013 at 11:35 PM, Martin van Leeuwen
<vanleeuwen.martin at gmail.com> wrote:
> Dear Python users,
>
> I am using the Mayavi toolkit quite a bit, because it is a very nicely
> packaged 3D visualization toolkit. I use it mainly for displaying triangle
> meshes and point cloud data, but recently I found it necessary to visualize
> disks as well. The mayavi.mlab.quiver3d() function seems to be adequate for
> this, however its '2dcircles' are oriented contrary to what I expected. I
> expected that these 2dcircles would be oriented so that their normal vectors
> would point in the direction that a normal '2darrow' would point to, but it
> doesn't. Instead, the normal vector of the 2dcircle is orthogonal to the
> direction of the 2darray. In addition the 2dcircle is not centered on the 3D
> point (specified by the x,y,z input arrays), but touches it. Is there a way
> around this? Or is there another function I overlooked that centers the
> circles onto a 3D coordinate?
>
> Thanks in advance! Any help is appreciated.

Questions about Mayavi are best asked on enthought-dev or StackOverflow.

  https://mail.enthought.com/mailman/listinfo/enthought-dev

--
Robert Kern


From martin.james.harrison at gmail.com  Thu Mar  7 09:00:53 2013
From: martin.james.harrison at gmail.com (Martin Harrison)
Date: Thu, 7 Mar 2013 06:00:53 -0800 (PST)
Subject: [SciPy-User] A NonLinearModelFit algorithm
Message-ID: <a27cd54b-f64b-4bb1-9606-c0d786e634d5@googlegroups.com>

Hi All - I am very new to scipy so bare with me please :-)

I have been using mathematica recently to mess around with my data. I have 
a method of calculating an x,y coordinate from 2 or more distance 
measurements coming from static devices (also x,y coords).

The function I use to do this most effectively with the data I have is the 
mathematica function:

NonlinearModelFit[data, Norm[{x, y} - {x0, y0}], {x0, y0}, {x, y}, Weights 
-> 1/observations^2]

where...

data = {{548189.217202, 5912779.96059, 93}, {548236.967784, 5912717.80716, 
39}, {548359.406452, 5912752.54022, 88}, {548358.636206, 5912690.89573, 
97}};

observations = {93, 39, 88, 97};

x0, y0 is my solution

The mathematica output to the above is:

FittedModel[{"Nonlinear", {x0 -> *548334.5910278788*, y0 -> *
5.912703316316227*^6*}, {{x, y}, Sqrt[Abs[x - x0]^2 + Abs[y - y0]^2]}}, 

  {{1/9604, 1/16900, 1/3249, 1/676, 1/900}}, {{548236.967784, 
5.91271780716*^6, 98}, {548236.967784, 5.91271780716*^6, 130}, 

   {548359.406452, 5.91275254022*^6, 57}, {548358.636206, 5.91269089573*^6, 
26}, {548358.636206, 5.91269089573*^6, 30}}, 

  Function[Null, Internal`LocalizedBlock[{x, x0, y, y0}, #1], {HoldAll}]]

Figures in bold are my x0,y0 solution.


So I am not fitting a curve but fitting to a point (with weights inversely 
proportional to the squared distance). I have looked around on google but 
am simply not sure where to start with this.

Any help with this would be very much appreciated.


So why am I doing this if mathematica does it? Well to call 
mathematicascript (using subprocess module) from python code is too slow 
for what I want to do with live data so want to try rewriting in python to 
see if the speed can be improved....
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From mdroe at stsci.edu  Thu Mar  7 18:54:23 2013
From: mdroe at stsci.edu (Michael Droettboom)
Date: Thu, 7 Mar 2013 18:54:23 -0500
Subject: [SciPy-User] SciPy John Hunter Excellence in Plotting Contest
In-Reply-To: <CAJd6-48F_r3NpnkZW_eA=Z3APu3ULrOXn15aLHcxFayqMqUJng@mail.gmail.com>
References: <CAJd6-48F_r3NpnkZW_eA=Z3APu3ULrOXn15aLHcxFayqMqUJng@mail.gmail.com>
Message-ID: <513928AF.7010201@stsci.edu>

Apologies for any accidental cross-posting.

Email not displaying correctly? View it in your browser. 
<http://us1.campaign-archive1.com/?u=e91b4574d5d1709a9dc4f7ab7&id=999d7ba343&e=7c1fb2879c> 


Scientific Computing with Python-Austin, Texas-June 24-29, 2013


  SciPy John Hunter Excellence in Plotting Contest

In memory of John Hunter, we are pleased to announce the first SciPy 
John Hunter Excellence in Plotting Competition. This open competition 
aims to highlight the importance of quality plotting to scientific 
progress and showcase the capabilities of the current generation of 
plotting software. Participants are invited to submit scientific plots 
to be judged by a panel. The winning entries will be announced and 
displayed at the conference.

NumFOCUS is graciously sponsoring cash prizes for the winners in the 
following amounts:

  * 1st prize: $500
  * 2nd prize: $200
  * 3rd prize: $100


    Instructions

  * Entries must be submitted by April 3 via e-mail
    <mailto:plotting-contest at scipy.org>.
  * Plots may be produced with any combination of Python-based tools (it
    is not required that they use matplotlib, for example).
  * Source code for the plot must be provided, along with a rendering of
    the plot in a vector format (PDF, PS, etc.). If the data can not be
    shared for reasons of size or licensing, "fake" data may be
    substituted, along with an image of the plot using real data.
  * Entries will be judged on their clarity, innovation and aesthetics,
    but most importantly for their effectiveness in illuminating real
    scientific work. Entrants are encouraged to submit plots that were
    used during the course of research, rather than merely being
    hypothetical.
  * SciPy reserves the right to display the entry at the conference, use
    in any materials or on its website, providing attribution to the
    original author(s).


    Important dates:

  * April 3rd: Plotting submissions due
  * Monday-Tuesday, June 24 - 25: SciPy 2013 Tutorials, Austin TX
  * Wednesday-Thursday, June 26 - 27: SciPy 2013 Conference, Austin TX *
    Winners will be announced during the conference days
  * Friday-Saturday, June 27 - 28: SciPy 2013 Sprints, Austin TX & remote

We look forward to exciting submissions that push the boundaries of 
plotting, in this, our first attempt at this kind of competition.

The SciPy Plotting Contest Organizer

-Michael Droettboom, Space Telescope Science Institute
You are receiving this email because you subscribed to the mailing list 
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-- 
Michael Droettboom
http://www.droettboom.com/




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From mdroe at stsci.edu  Fri Mar  8 09:33:43 2013
From: mdroe at stsci.edu (Michael Droettboom)
Date: Fri, 8 Mar 2013 09:33:43 -0500
Subject: [SciPy-User] Fwd: SciPy John Hunter Excellence in Plotting Contest
In-Reply-To: <513928AF.7010201@stsci.edu>
References: <513928AF.7010201@stsci.edu>
Message-ID: <5139F6C7.7020005@stsci.edu>


Apologies for any accidental cross-posting.

Email not displaying correctly? View it in your browser. 
<http://us1.campaign-archive1.com/?u=e91b4574d5d1709a9dc4f7ab7&id=999d7ba343&e=7c1fb2879c> 


Scientific Computing with Python-Austin, Texas-June 24-29, 2013


  SciPy John Hunter Excellence in Plotting Contest

In memory of John Hunter, we are pleased to announce the first SciPy 
John Hunter Excellence in Plotting Competition. This open competition 
aims to highlight the importance of quality plotting to scientific 
progress and showcase the capabilities of the current generation of 
plotting software. Participants are invited to submit scientific plots 
to be judged by a panel. The winning entries will be announced and 
displayed at the conference.

NumFOCUS is graciously sponsoring cash prizes for the winners in the 
following amounts:

  * 1st prize: $500
  * 2nd prize: $200
  * 3rd prize: $100


    Instructions

  * Entries must be submitted by April 3 via e-mail
    <mailto:plotting-contest at scipy.org>.
  * Plots may be produced with any combination of Python-based tools (it
    is not required that they use matplotlib, for example).
  * Source code for the plot must be provided, along with a rendering of
    the plot in a vector format (PDF, PS, etc.). If the data can not be
    shared for reasons of size or licensing, "fake" data may be
    substituted, along with an image of the plot using real data.
  * Entries will be judged on their clarity, innovation and aesthetics,
    but most importantly for their effectiveness in illuminating real
    scientific work. Entrants are encouraged to submit plots that were
    used during the course of research, rather than merely being
    hypothetical.
  * SciPy reserves the right to display the entry at the conference, use
    in any materials or on its website, providing attribution to the
    original author(s).


    Important dates:

  * April 3rd: Plotting submissions due
  * Monday-Tuesday, June 24 - 25: SciPy 2013 Tutorials, Austin TX
  * Wednesday-Thursday, June 26 - 27: SciPy 2013 Conference, Austin TX *
    Winners will be announced during the conference days
  * Friday-Saturday, June 27 - 28: SciPy 2013 Sprints, Austin TX & remote

We look forward to exciting submissions that push the boundaries of 
plotting, in this, our first attempt at this kind of competition.

The SciPy Plotting Contest Organizer

-Michael Droettboom, Space Telescope Science Institute
You are receiving this email because you subscribed to the mailing list 
or registered for the SciPy 2010 or SciPy 2011 conference in Austin, TX.

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From member at linkedin.com  Sun Mar 10 00:53:31 2013
From: member at linkedin.com (Harshad Surdi via LinkedIn)
Date: Sun, 10 Mar 2013 05:53:31 +0000 (UTC)
Subject: [SciPy-User] Invitation to connect on LinkedIn
Message-ID: <1511225485.15765842.1362894811338.JavaMail.app@ela4-app2318.prod>

LinkedIn
------------




    Harshad Surdi requested to add you as a connection on LinkedIn:
  

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

Jose,

I'd like to add you to my professional network on LinkedIn.

- Harshad

Accept invitation from Harshad Surdi
http://www.linkedin.com/e/-3wy1w2-he3sdqrd-33/Q6WKH0LACopGJkAw_6fSqajo6R7VMvIz/blk/I706575952_20/3wOtCVFbmdxnSVFbm8JrnpKqlZJrmZzbmNJpjRQnOpBtn9QfmhBt71BoSd1p65Lr6lOfP0OnP8RejkTdjoMdQALiRBmiz1qul0Lc34VdjkTcj0Qcj4LrCBxbOYWrSlI/eml-comm_invm-b-in_ac-inv28/?hs=false&tok=0BgZf2x1QxCBE1

View profile of Harshad Surdi
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You are receiving Invitation emails.


This email was intended for Jose Gomez-Dans.
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From imrehg at gmail.com  Sun Mar 10 22:39:11 2013
From: imrehg at gmail.com (Gergely Imreh)
Date: Mon, 11 Mar 2013 10:39:11 +0800
Subject: [SciPy-User] Allan Variance
In-Reply-To: <kh4rvs$joa$1@ger.gmane.org>
References: <5134A07F.4050907@grinta.net> <kh4nh6$4i5$2@ger.gmane.org>
	<5135E441.80504@grinta.net> <kh4rvs$joa$1@ger.gmane.org>
Message-ID: <CAJ3iQcqgG90SdU4C8osvoEo7J4JZNBw=iD1wZWqdq==CpyjLgA@mail.gmail.com>

Hey,

I have written such code before for frequency time series, the (not very
clean) source code is here:
allan function on
https://github.com/imrehg/physicscalc/blob/master/allan/allanmulti.py
partallan function in
https://github.com/imrehg/physicscalc/blob/master/allan/allanmulti3.py

There are some caveats, though, it's been a while and I remember that one
has to be careful how you calculate things if comparing to Allan variance
from instruments. I think the first mentioned function is the simple way.
The second mentioned function is replicating the calculation of one our
instruments in the lab, that uses overlapping time windows, thus the
calculation needs to be different too. But it's better if you check.

Probably will still adjust to your need, though.

Cheers,
   Greg


On 5 March 2013 21:29, Neal Becker <ndbecker2 at gmail.com> wrote:

> Daniele Nicolodi wrote:
>
> > On 05/03/2013 13:13, Neal Becker wrote:
> >> Daniele Nicolodi wrote:
> >>
> >>> Hello,
> >>>
> >>> does anyone know or have written and is willing to share some code to
> >>> compute the Allan variance, modified Allan variance and friends,
> ideally
> >>> both from frequency than from phase data?
> >>>
> >>> I'm writing my own code, it is not that difficult, but I would prefer
> >>> the time required for testing and validation doing something more fun.
> >>>
> >>> If there is no available code implementing Allan variance, would it be
> >>> interesting to include in in SciPy?
> >>>
> >>> Thank you so much.
> >>>
> >>> Best,
> >>> Daniele
> >>
> >> Do you mean, given phase noise power spectral density?
> >
> > I mean given phase or frequency time series.  But also code that
> > computes Allan variation from power spectral density may come handy for
> > me in future.
> >
> > Cheers,
> > Daniele
>
> I don't have that - but do have code to generate samples of blocks of phase
> noise from a given spectral mask.
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From mdekauwe at gmail.com  Mon Mar 11 01:30:58 2013
From: mdekauwe at gmail.com (mdekauwe)
Date: Sun, 10 Mar 2013 22:30:58 -0700 (PDT)
Subject: [SciPy-User] maximising a function
Message-ID: <1362979858440-17980.post@n7.nabble.com>

Hi,

I am trying to use the scipy library to optimise 4 parameters so that they
return the maximum for a given function. To do this I am using the COBYLA
function and setting a few constraints, essentially that these parameter
values are positive and that when summed they are less than a given value
(N_p). Can I check what I have is the correct implementation (see below)? 

The second issue is that part of the function depends on the solution to a
quadratic. If I don't resolve positive roots I want to be able to return
some value to let the minimiser know that these are bad values. I can't work
out how to do this? I tried returning a very large value but this didn't see
to work, so then I tried to use NaNs. What is the correct way to do this?

thanks,

Code (this will run).

#!/usr/bin/env python

"""
Optimise the distribution of nitrogen within the photosynthetic system to 
maximise photosynthesis for a given PAR and CO2 concentration.

Reference:
=========
* Medlyn (1996) The Optimal Allocation of Nitrogen within the C3
Photsynthetic 
  System at Elevated CO2. Australian Journal of Plant Physiology, 23,
593-603.

"""

import sys
import numpy as np
import scipy.optimize

class PhotosynthesisModel(object):
    """
    Photosynthesis model based on Evans (1989)
    
    References:
    -----------
    * Evans, 1989
    """
    def __init__(self, alpha=0.425, beta=0.7, g_m=0.4, theta=0.7,
K_cat=24.0,
                 K_s=1.25E-04, MAXIMISE=False):
        """
        Parameters
        ----------
        alpha : float
            quantum yield of electron transport (mol mol-1) 
        beta : float
            constant of proportionality between atmospheric and
intercellular 
            CO2 concentrations (-)
        g_m : float
            Conductance to CO2 transfer between intercellular spaces and
sites 
            of carboxylation (mol m-2 s-1 bar-1) 
        theta : float
            curvature of the light response (-)    
        K_cat : float
            specific activitt of Rubisco (mol CO2 mol-1 Rubisco s-1)
        K_s : float
            constant of proportionality between N_s and Jmax (g N m2 s
umol-1)
        MAXIMISE : logical
            if we want to minimise a function we need to return f(x), if we
            want to maximise a function we return -f(x)
        """
        self.alpha = alpha
        self.beta = beta
        self.g_m = g_m
        self.theta = theta
        self.K_cat = K_cat
        self.K_s = K_s
        self.sign = 1.0
        if MAXIMISE:
            self.sign = -1.0
        else:
            self.sign = 1.0
        
    def calc_photosynthesis(self, N_pools=None, par=None, Km=None, Rd=None, 
                            gamma_star=None, Ca=None, N_p=None,
error=False):
        """
        Leaf temperature is assumed to be constant = 25 deg C.
        
        
        Parameters
        ----------
        N_pools : list of floats
            list containing the 5 N pools: 
            N_c - amount of N in chlorophyll (mol m-2)
            N_s - amount of N in soluble protein other than Rubisco (mol
m-2)
            N_e - amount of N in electron transport components (mol m-2)
            N_r - amount of N in Rubisco (mol m-2)
            N_other - amount of leaf N not involved in photosynthesis (mol
m-2)
            
        par : float
            incident PAR (umol m-2 s-1)
        Km : float
            effective Michalis-Menten coefficent of Rubisco (umol mol-1)   
        Rd : float
            rate of dark respiration (umol m2 s-1)
        gamma_star : float
            leaf CO2 compensation point (umol mol-1)
        Ca : float
            atmospheric CO2 concentration (umol mol-1)
        N_p : float
            total amount of leaf N to be distrubuted (mol m-2)
        
        Returns:
        --------
        An : float
            Net leaf assimilation rate [umol m-2 s-1] 
          
        """
        # unpack...we need to pass as a list for the optimisation step
        (N_e, N_c, N_r, N_other) = N_pools
        
        # Leaf absorptance depends on the chlorophyll protein complexes
(N_c)
        absorptance = self.calc_absorptance(N_c)
        
        # Max rate of electron transport (umol m-2 s-1) 
        Jmax = self.calc_jmax(N_e, N_c)
        
        # Max rate of carboxylation velocity (umol m-2 s-1) 
        Vcmax = self.calc_vcmax(N_r)
        N_s = self.calc_n_alloc_soluble_protein(Jmax)
        
        # store values so I can get them all later
        self.N_pool_store = np.array([N_e, N_c, N_r, N_s, N_other])
        
        # rate of electron transport, a saturating function of absorbed PAR
        J, error = self.quadratic(a=self.theta, 
                                  b=-(self.alpha * absorptance * par +
Jmax), 
                                  c=self.alpha * absorptance * par * Jmax)
        if error:
            return None, error
        
        
        # CO2 concentration in the intercellular air spaces 
        Ci = self.beta * Ca
        
        # Photosynthesis when Rubisco is limiting
        a = 1. / self.g_m
        b = (Rd - Vcmax) / self.g_m - Ci - Km
        c = Vcmax * (Ci - gamma_star) - Rd * (Ci + Km)
        Wc, error = self.quadratic(a=a, b=b, c=c)
        if error:
            return None, error

        # Photosynthesis when electron transport is limiting
        a = -4. / self.g_m
        b = (Rd - J / 4.0) / self.g_m - Ci - 2.0 * gamma_star
        c = J / 4.0 * (Ci - gamma_star) - Rd * (Ci + 2.0 * gamma_star)
        Wj, error = self.quadratic(a=a, b=b, c=c)
        if error:
            return None, error
        
        Aleaf = np.minimum(Wc, Wj)
        
        Cc = Ci - (Aleaf / self.g_m);
        An = (1.0 - (gamma_star / Cc)) * Aleaf - Rd
        
        
        return An, error
 
        
    def assim(self, Cc, a1, a2):
        """Photosynthesis with the limitation defined by the variables
passed 
        as a1 and a2, i.e. if we are calculating vcmax or jmax limited.
        
        Parameters:
        ----------
        Cc : float
            CO2 concentration at the site of of carboxylation
        gamma_star : float
            CO2 compensation point in the abscence of mitochondrial
respiration
        a1 : float
            variable depends on whether the calculation is light or rubisco 
            limited.
        a2 : float
            variable depends on whether the calculation is light or rubisco 
            limited.

        Returns:
        -------
        assimilation_rate : float
            assimilation rate assuming either light or rubisco limitation.
        """
        return a1 * (Cc / (Cc + a2))
    
    def quadratic(self, a=None, b=None, c=None, error=False):
        """ minimilist quadratic solution as root for J solution should
always
        be positive, so I have excluded other quadratic solution steps. If 
        roots for J are negative or equal to zero then the data is bogus 
        
        Parameters:
        ----------
        a : float
            co-efficient 
        b : float
            co-efficient
        c : float
            co-efficient
        
        Returns:
        -------
        val : float
            positive root
        """
        d = b**2 - 4.0 * a * c # discriminant
        if np.any(d <= 0.0) or np.any(np.isnan(d)):
            error = True
            root1 = -999.9
            root2 = -999.9 
        else:
            root1 = (-b + np.sqrt(d)) / (2.0 * a)
            root2 = (-b - np.sqrt(d)) / (2.0 * a)
            
        return np.minimum(root1, root2), error
        
    
    def calc_jmax(self, N_e, N_c, a_j=15870.0, b_j=2775.0):
        """ Evans (1989( found a linear reln between the rate of electron
        transport and the total amount of nitrogen in the thylakoids per
unit
        chlorophyll 
        
        Parameters:
        ----------
        N_e : float
            amount of N in electron transport components (mol m-2)
        N_c : float
            amount of N in chlorophyll (mol m-2)
        a_j : float
            umol (mol N)-1 s-1
        b_j : float
            umol (mol N)-1 s-1
            
        Returns:
        --------
        Jmax : float
            Max rate of electron transport (umol m-2 s-1)
        """
        Jmax = a_j * N_e + b_j * N_c
        
        return Jmax
    
    def calc_vcmax(self, N_r):
        """ 
        Calculate Rubisco activity 
        
        Parameters:
        ----------
        N_r : float
            amount of N in Rubisco (mol m-2)
        
        Returns:
        --------
        Vcmax : float
            Max rate of carboxylation velocity (umol m-2 s-1) 
        """
        conv = 7000.0 / 44.0 # mol N to mol Rubiso
        
        return self.K_cat * conv * N_r
        
    
    def calc_absorptance(self, N_c):
        """ Calculate absorptance in the leaf based on N in the chlorophyll
        protein complexes
        
        Parameters:
        ----------
        N_c : float
            amount of N in chlorophyll (mol m-2)
            
        Returns:
        --------
        absorptance : float
            Leaf absorptance (-)
        """
        return (25.0 * N_c) / (25.0 * N_c + 0.076)
    
    def calc_n_alloc_soluble_protein(self, Jmax):
        """ Calculate N allocated to soluble protein
        
        Parameters:
        --------
        Jmax : float
            Max rate of electron transport (umol m-2 s-1)
        
        Returns:
        --------
        N_s : float
            amount of N in soluble protein other than Rubisco (mol m-2)
        """
        return self.K_s * Jmax 
  
    def optimise_func(self, x0, par=None, Km=None, Rd=None, 
                   gamma_star=None, Ca=None, N_p=None):
        """ Minimisation and maximisation are equivalent, to get the global
        maximium we just need to return -f(x) instead of f(x) """
        
        An, error = self.calc_photosynthesis(x0, par, Km, Rd, gamma_star,
Ca, N_p)
        
        # I want only positive values for xopt
        if np.any(x0<0.0):
            return np.nan
        
        (N_e, N_c, N_r, N_other) = x0
        
        # Leaf absorptance depends on the chlorophyll protein complexes
(N_c)
        absorptance = self.calc_absorptance(N_c)
        
        # Max rate of electron transport (umol m-2 s-1) 
        Jmax = self.calc_jmax(N_e, N_c)
        
        # Max rate of carboxylation velocity (umol m-2 s-1) 
        Vcmax = self.calc_vcmax(N_r)
        N_s = self.calc_n_alloc_soluble_protein(Jmax)
        
        # xopt can't be > N_p
        if (N_s + N_e + N_r + N_c + N_other) > N_p:
           return np.nan
        
        An, err = self.calc_photosynthesis(x0, par, Km, Rd, gamma_star, Ca,
N_p)
        if err:
            return np.nan
        else:
            return self.sign * np.mean(An)
       
    
    
    def constraint1(self, x, *args):
        """ N_s + N_e + N_r + N_c < N_p 
        
        returns a positive number if within bound and 0.0 it is exactly on
the 
        edge of the bound
        """
        
        (par, Km, Rd, gamma_star, Ca, N_p) = args
        
        # unpack...we need to pass as a list for the optimisation step
        (N_e, N_c, N_r, N_other) = x
        
        # Leaf absorptance depends on the chlorophyll protein complexes
(N_c)
        absorptance = self.calc_absorptance(N_c)
        
        # Max rate of electron transport (umol m-2 s-1) 
        Jmax = self.calc_jmax(N_e, N_c)
        
        # Max rate of carboxylation velocity (umol m-2 s-1) 
        Vcmax = self.calc_vcmax(N_r)
        N_s = self.calc_n_alloc_soluble_protein(Jmax)
        
        return N_p - (N_s + N_e + N_r + N_c + N_other)

    
    def constraint2(self, x, *args):
        """ N_e > 0.0 
        
        returns a positive number if within bound and 0.0 it is exactly on
the 
        edge of the bound
        """
        return x[0]
        
    def constraint3(self, x, *args):
        """ N_c > 0.0 
        
        returns a positive number if within bound and 0.0 it is exactly on
the 
        edge of the bound
        """
        return x[1]
    
    def constraint4(self, x, *args):
        """ N_r > 0.0 
        
        returns a positive number if within bound and 0.0 it is exactly on
the 
        edge of the bound
        """
        return x[2]
    
    def constraint5(self, x, *args):
        """ N_other > 0.0 
        
        returns a positive number if within bound and 0.0 it is exactly on
the 
        edge of the bound
        """
        return x[3]

 
    

def optimise_test():
    # default values
    Km = 544.
    Rd = 0.5
    gamma_star = 38.6
    
    par = np.arange(10.0, 1500.0, 20.0)
    Ca = np.ones(len(par)) * 350.0
    
    P = PhotosynthesisModel(MAXIMISE=True)
    func = P.optimise_func # short name for func
    
    # initial guess
    # Testing, just equally divide up the total N available
    N_p = 0.09
    x0 = np.array([N_p/5.0, N_p/5.0, N_p/5.0, N_p/5.0])
    args = (par, Km, Rd, gamma_star, Ca, N_p)

    cons = ({'type': 'ineq', 'fun': P.constraint1, 'args': args},
            {'type': 'ineq', 'fun': P.constraint2, 'args': args},
            {'type': 'ineq', 'fun': P.constraint3, 'args': args},
            {'type': 'ineq', 'fun': P.constraint4, 'args': args},
            {'type': 'ineq', 'fun': P.constraint5, 'args': args})
    result = scipy.optimize.minimize(func, x0, method="COBYLA", 
                                     constraints=cons, args=args)
    if result["success"]:
        print result.x
      

if __name__ == "__main__":
    
    optimise_test()



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From mdekauwe at gmail.com  Mon Mar 11 02:21:21 2013
From: mdekauwe at gmail.com (mdekauwe)
Date: Sun, 10 Mar 2013 23:21:21 -0700 (PDT)
Subject: [SciPy-User] maximising a function
In-Reply-To: <1362979858440-17980.post@n7.nabble.com>
References: <1362979858440-17980.post@n7.nabble.com>
Message-ID: <1362982881134-17981.post@n7.nabble.com>

Apologies I just realised np.minimum did not do what I thought, which was
messing up the quadratic calculation. I think this seems like it is working.
I am still not sure if there is a better way to set up the constraints? But
I guess this seems OK.

thanks.




#!/usr/bin/env python

"""
Belinda's optimal N allocation model.

Optimise the distribution of nitrogen within the photosynthetic system to 
maximise photosynthesis for a given PAR and CO2 concentration.

Reference:
=========
* Medlyn (1996) The Optimal Allocation of Nitrogen within the C3
Photsynthetic 
  System at Elevated CO2. Australian Journal of Plant Physiology, 23,
593-603.

"""
__author__ = "Martin De Kauwe"
__version__ = "1.0 (05.03.2013)"
__email__ = "mdekauwe at gmail.com"

import sys
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import fsolve
import scipy.optimize
import warnings

class PhotosynthesisModel(object):
    """
    Photosynthesis model based on Evans (1989)
    
    References:
    -----------
    * Evans, 1989
    """
    def __init__(self, alpha=0.425, beta=0.7, g_m=0.4, theta=0.7,
K_cat=24.0,
                 K_s=1.25E-04, MAXIMISE=False):
        """
        Parameters
        ----------
        alpha : float
            quantum yield of electron transport (mol mol-1) 
        beta : float
            constant of proportionality between atmospheric and
intercellular 
            CO2 concentrations (-)
        g_m : float
            Conductance to CO2 transfer between intercellular spaces and
sites 
            of carboxylation (mol m-2 s-1 bar-1) 
        theta : float
            curvature of the light response (-)    
        K_cat : float
            specific activitt of Rubisco (mol CO2 mol-1 Rubisco s-1)
        K_s : float
            constant of proportionality between N_s and Jmax (g N m2 s
umol-1)
        MAXIMISE : logical
            if we want to minimise a function we need to return f(x), if we
            want to maximise a function we return -f(x)
        """
        self.alpha = alpha
        self.beta = beta
        self.g_m = g_m
        self.theta = theta
        self.K_cat = K_cat
        self.K_s = K_s
        self.sign = 1.0
        if MAXIMISE:
            self.sign = -1.0
        else:
            self.sign = 1.0
        
    def calc_photosynthesis(self, N_pools=None, par=None, Km=None, Rd=None, 
                            gamma_star=None, Ca=None, N_p=None,
error=False):
        """
        Leaf temperature is assumed to be constant = 25 deg C.
        
        
        Parameters
        ----------
        N_pools : list of floats
            list containing the 5 N pools: 
            N_c - amount of N in chlorophyll (mol m-2)
            N_s - amount of N in soluble protein other than Rubisco (mol
m-2)
            N_e - amount of N in electron transport components (mol m-2)
            N_r - amount of N in Rubisco (mol m-2)
            N_other - amount of leaf N not involved in photosynthesis (mol
m-2)
            
        par : float
            incident PAR (umol m-2 s-1)
        Km : float
            effective Michalis-Menten coefficent of Rubisco (umol mol-1)   
        Rd : float
            rate of dark respiration (umol m2 s-1)
        gamma_star : float
            leaf CO2 compensation point (umol mol-1)
        Ca : float
            atmospheric CO2 concentration (umol mol-1)
        N_p : float
            total amount of leaf N to be distrubuted (mol m-2)
        
        Returns:
        --------
        An : float
            Net leaf assimilation rate [umol m-2 s-1] 
          
        """
        # unpack...we need to pass as a list for the optimisation step
        (N_e, N_c, N_r, N_other) = N_pools
        
        # Leaf absorptance depends on the chlorophyll protein complexes
(N_c)
        absorptance = self.calc_absorptance(N_c)
        
        # Max rate of electron transport (umol m-2 s-1) 
        Jmax = self.calc_jmax(N_e, N_c)
        
        # Max rate of carboxylation velocity (umol m-2 s-1) 
        Vcmax = self.calc_vcmax(N_r)
        N_s = self.calc_n_alloc_soluble_protein(Jmax)
       
        self.N_pool_store = np.array([N_e, N_c, N_r, N_s, N_other])
        
        # rate of electron transport, a saturating function of absorbed PAR
        J, error = self.quadratic(a=self.theta, 
                                  b=-(self.alpha * absorptance * par +
Jmax), 
                                  c=self.alpha * absorptance * par * Jmax)
        if error:
            return J, error
        
        
        # CO2 concentration in the intercellular air spaces 
        Ci = self.beta * Ca
        
        # Photosynthesis when Rubisco is limiting
        a = 1. / self.g_m
        b = (Rd - Vcmax) / self.g_m - Ci - Km
        c = Vcmax * (Ci - gamma_star) - Rd * (Ci + Km)
        Wc, error = self.quadratic(a=a, b=b, c=c)
        if error:
            return Wc, error
        
        # Photosynthesis when electron transport is limiting
        a = -4. / self.g_m
        b = (Rd - J / 4.0) / self.g_m - Ci - 2.0 * gamma_star
        c = J / 4.0 * (Ci - gamma_star) - Rd * (Ci + 2.0 * gamma_star)
        Wj, error = self.quadratic(a=a, b=b, c=c)
        if error:
            return Wj, error
        
        Aleaf = np.minimum(Wc, Wj)
        Cc = Ci - (Aleaf / self.g_m);
        An = (1.0 - (gamma_star / Cc)) * Aleaf - Rd
        
        
        return An, error
 
        
    def assim(self, Cc, a1, a2):
        """Photosynthesis with the limitation defined by the variables
passed 
        as a1 and a2, i.e. if we are calculating vcmax or jmax limited.
        
        Parameters:
        ----------
        Cc : float
            CO2 concentration at the site of of carboxylation
        gamma_star : float
            CO2 compensation point in the abscence of mitochondrial
respiration
        a1 : float
            variable depends on whether the calculation is light or rubisco 
            limited.
        a2 : float
            variable depends on whether the calculation is light or rubisco 
            limited.

        Returns:
        -------
        assimilation_rate : float
            assimilation rate assuming either light or rubisco limitation.
        """
        return a1 * (Cc / (Cc + a2))
    
    def quadratic(self, a=None, b=None, c=None, error=False):
        """ minimilist quadratic solution as root for J solution should
always
        be positive, so I have excluded other quadratic solution steps. I am 
        only returning the smallest of the two roots 
        
        Parameters:
        ----------
        a : float
            co-efficient 
        b : float
            co-efficient
        c : float
            co-efficient
        
        Returns:
        -------
        val : float
            positive root
        """
        
        d = b**2 - 4.0 * a * c # discriminant
        if np.any(d <= 0.0) or np.any(np.isnan(d)):
            error = True
            root1 = 0.0
            root2 = 0.0
            warnings.warn("Imaginary root found")
        else:
            root1 = (-b - np.sqrt(d)) / (2.0 * a)
            #root2 = (-b + np.sqrt(d)) / (2.0 * a) # don't want to return
this
            
        return root1, error
        
    
    def calc_jmax(self, N_e, N_c, a_j=15870.0, b_j=2775.0):
        """ Evans (1989( found a linear reln between the rate of electron
        transport and the total amount of nitrogen in the thylakoids per
unit
        chlorophyll 
        
        Parameters:
        ----------
        N_e : float
            amount of N in electron transport components (mol m-2)
        N_c : float
            amount of N in chlorophyll (mol m-2)
        a_j : float
            umol (mol N)-1 s-1
        b_j : float
            umol (mol N)-1 s-1
            
        Returns:
        --------
        Jmax : float
            Max rate of electron transport (umol m-2 s-1)
        """
        Jmax = a_j * N_e + b_j * N_c
        
        return Jmax
    
    def calc_vcmax(self, N_r):
        """ 
        Calculate Rubisco activity 
        
        Parameters:
        ----------
        N_r : float
            amount of N in Rubisco (mol m-2)
        
        Returns:
        --------
        Vcmax : float
            Max rate of carboxylation velocity (umol m-2 s-1) 
        """
        conv = 7000.0 / 44.0 # mol N to mol Rubiso
        
        return self.K_cat * conv * N_r
        
    
    def calc_absorptance(self, N_c):
        """ Calculate absorptance in the leaf based on N in the chlorophyll
        protein complexes
        
        Parameters:
        ----------
        N_c : float
            amount of N in chlorophyll (mol m-2)
            
        Returns:
        --------
        absorptance : float
            Leaf absorptance (-)
        """
        return (25.0 * N_c) / (25.0 * N_c + 0.076)
    
    def calc_n_alloc_soluble_protein(self, Jmax):
        """ Calculate N allocated to soluble protein
        
        Parameters:
        --------
        Jmax : float
            Max rate of electron transport (umol m-2 s-1)
        
        Returns:
        --------
        N_s : float
            amount of N in soluble protein other than Rubisco (mol m-2)
        """
        return self.K_s * Jmax 
  
    def optimise_func(self, x0, par=None, Km=None, Rd=None, 
                   gamma_star=None, Ca=None, N_p=None):
        """ Minimisation and maximisation are equivalent, to get the global
        maximium we just need to return -f(x) instead of f(x) """
        An, err = self.calc_photosynthesis(x0, par, Km, Rd, gamma_star, Ca,
N_p)

        return self.sign * np.mean(An)
    
    def penalty(self, x0, par=None, Km=None, Rd=None, gamma_star=None,
Ca=None, 
                N_p=None):
    
        
        An, error = self.calc_photosynthesis(x0, par, Km, Rd, gamma_star,
Ca, N_p)
        if np.any(x0<0.0):
            
            return np.nan
        
        (N_e, N_c, N_r, N_other) = x0
        # Leaf absorptance depends on the chlorophyll protein complexes
(N_c)
        absorptance = self.calc_absorptance(N_c)
        
        # Max rate of electron transport (umol m-2 s-1) 
        Jmax = self.calc_jmax(N_e, N_c)
        
        # Max rate of carboxylation velocity (umol m-2 s-1) 
        Vcmax = self.calc_vcmax(N_r)
        N_s = self.calc_n_alloc_soluble_protein(Jmax)
        
        if (N_s + N_e + N_r + N_c + N_other) > N_p:
           
            return np.nan
        
        An, error = self.calc_photosynthesis(x0, par, Km, Rd, gamma_star,
Ca, N_p)
        if error:
            
            return np.nan
        else:
            return self.sign * np.mean(An)
        
        
        #return self.sign * np.mean(An)
    
    
    
    
    def constraint1(self, x, *args):
        """ N_s + N_e + N_r + N_c < N_p 
        
        returns a positive number if within bound and 0.0 it is exactly on
the 
        edge of the bound
        """
        
        (par, Km, Rd, gamma_star, Ca, N_p) = args
        
        # unpack...we need to pass as a list for the optimisation step
        (N_e, N_c, N_r, N_other) = x
        
        # Leaf absorptance depends on the chlorophyll protein complexes
(N_c)
        absorptance = self.calc_absorptance(N_c)
        
        # Max rate of electron transport (umol m-2 s-1) 
        Jmax = self.calc_jmax(N_e, N_c)
        
        # Max rate of carboxylation velocity (umol m-2 s-1) 
        Vcmax = self.calc_vcmax(N_r)
        N_s = self.calc_n_alloc_soluble_protein(Jmax)
        
        return N_p - (N_s + N_e + N_r + N_c + N_other)

    
    def constraint2(self, x, *args):
        """ N_e > 0.0 
        
        returns a positive number if within bound and 0.0 it is exactly on
the 
        edge of the bound
        """
        return x[0]
        
    def constraint3(self, x, *args):
        """ N_c > 0.0 
        
        returns a positive number if within bound and 0.0 it is exactly on
the 
        edge of the bound
        """
        return x[1]
    
    def constraint4(self, x, *args):
        """ N_r > 0.0 
        
        returns a positive number if within bound and 0.0 it is exactly on
the 
        edge of the bound
        """
        return x[2]
    
    def constraint5(self, x, *args):
        """ N_other > 0.0 
        
        returns a positive number if within bound and 0.0 it is exactly on
the 
        edge of the bound
        """
        return x[3]


def optimise_test():
    # default values
    Km = 544.
    Rd = 0.5
    gamma_star = 38.6
    
    par = np.arange(10.0, 1500.0, 20.0)
    Ca = np.ones(len(par)) * 350.0
    
    P = PhotosynthesisModel(MAXIMISE=True)
    func = P.calc_photosynthesis
    #F = P.penalty # short name for func
    F = P.optimise_func
    
    # initial guess
    # Testing, just equally divide up the total N available
    N_p = 0.09
    x0 = np.array([N_p/5.0, N_p/5.0, N_p/5.0, N_p/5.0])
    args = (par, Km, Rd, gamma_star, Ca, N_p)
    
    
    
    cons = ({'type': 'ineq', 'fun': P.constraint1, 'args': args},
            {'type': 'ineq', 'fun': P.constraint2, 'args': args},
            {'type': 'ineq', 'fun': P.constraint3, 'args': args},
            {'type': 'ineq', 'fun': P.constraint4, 'args': args},
            {'type': 'ineq', 'fun': P.constraint5, 'args': args})
    result = scipy.optimize.minimize(F, x0, method="COBYLA",
constraints=cons, 
                                     args=args)
    if result["success"]:
        print result
        (N_c, N_e, N_r, N_s, N_other) = P.N_pool_store
        fitted_x0 = np.array([N_c, N_e, N_r, N_other])
    
        # bit of a hack as there can be small rounding errors,
        # not sure of a better solution?
        diff = np.sum(P.N_pool_store) - N_p
        print "Diff", diff
        print fitted_x0
    
        An, err = func(fitted_x0, par, Km, Rd, gamma_star, Ca, N_p)
        print err
        print np.mean(An)
        plt.plot(par, An, label="Cobyla")
        plt.legend(loc="best", numpoints=1)
        plt.show()


if __name__ == "__main__":
    
    
    optimise_test()
    
    



--
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From lorenzo.isella at gmail.com  Mon Mar 11 02:54:22 2013
From: lorenzo.isella at gmail.com (Lorenzo Isella)
Date: Mon, 11 Mar 2013 07:54:22 +0100
Subject: [SciPy-User] Python for Card Game Modeling
Message-ID: <op.wtrr4wfxzqkd1e@boh>

Dear All,
Unfortunately, I cannot come up with a script or a concrete example at
this stage.
I am trying to investigate statistically a card game (not a game like
poker, but something more like Magic).
There are various kinds of cards (agents, missions, victory points) each
obeying a given set of rules.
The purpose of the statistical analysis is to come up with a measure of
the strength of each card.
Any idea if "out there" there is any Python (better if using SciPy/Numpy)  
package/script which can help me?
Any suggestion is welcome

Lorenzo


From subhabangalore at gmail.com  Mon Mar 11 15:18:10 2013
From: subhabangalore at gmail.com (Subhabrata Banerjee)
Date: Mon, 11 Mar 2013 12:18:10 -0700 (PDT)
Subject: [SciPy-User] HMM in Python
Message-ID: <20896bfc-7b82-423b-baff-8dfa91f39d80@googlegroups.com>

Dear Group,

I do not know whether it is an out of box question but as you are great 
scientific computing people so I thought to post it here.

I am looking a simple example work out for forward probability of Hidden 
Markov Model(HMM) on the problem of sequence labelling.

If any one can kindly help me out.

Regards,
Subhabrata. 
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From afraser at lanl.gov  Mon Mar 11 16:28:20 2013
From: afraser at lanl.gov (Andrew Fraser)
Date: Mon, 11 Mar 2013 14:28:20 -0600
Subject: [SciPy-User] HMM in Python
In-Reply-To: <20896bfc-7b82-423b-baff-8dfa91f39d80@googlegroups.com>
References: <20896bfc-7b82-423b-baff-8dfa91f39d80@googlegroups.com>
Message-ID: <513E3E64.6020502@lanl.gov>

I am rewriting the code for my book "Hidden Markov Models and Dynamical
Systems" (see http://www.siam.org/books/ot107/) in python3.  The code
for the basic algorithms (including what you ask for) is written.  I am
in the process of getting permission from my employer to distribute the
code under GPL.  I hope to get permission this week.

Andy

On 03/11/2013 01:18 PM, Subhabrata Banerjee wrote:
> Dear Group,
>
> I do not know whether it is an out of box question but as you are
> great scientific computing people so I thought to post it here.
>
> I am looking a simple example work out for forward probability of
> Hidden Markov Model(HMM) on the problem of sequence labelling.
>
> If any one can kindly help me out.
>
> Regards,
> Subhabrata.
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user

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From subhabangalore at gmail.com  Mon Mar 11 18:39:36 2013
From: subhabangalore at gmail.com (Subhabrata Banerjee)
Date: Tue, 12 Mar 2013 04:09:36 +0530
Subject: [SciPy-User] HMM in Python
In-Reply-To: <513E3E64.6020502@lanl.gov>
References: <20896bfc-7b82-423b-baff-8dfa91f39d80@googlegroups.com>
	<513E3E64.6020502@lanl.gov>
Message-ID: <CADBiVjF54-xHHzZ1oYEqYvHPiqQC=hqdfQJuGoK03ip2O5r6vA@mail.gmail.com>

Dear Sir,

Thank you for your kind note. I read a material from Devert Alexandre and
problem is generally more or less solved. But knowing a great peer is
always helpful. We are expecting your code a great lunch and lot of mind
blowing discussion.

Regards,
Subhabrata.

On Tue, Mar 12, 2013 at 1:58 AM, Andrew Fraser <afraser at lanl.gov> wrote:

> **
> I am rewriting the code for my book "Hidden Markov Models and Dynamical
> Systems" (see http://www.siam.org/books/ot107/) in python3.  The code for
> the basic algorithms (including what you ask for) is written.  I am in the
> process of getting permission from my employer to distribute the code under
> GPL.  I hope to get permission this week.
>
> Andy
>
> On 03/11/2013 01:18 PM, Subhabrata Banerjee wrote:
>
> Dear Group,
>
> I do not know whether it is an out of box question but as you are great
> scientific computing people so I thought to post it here.
>
> I am looking a simple example work out for forward probability of Hidden
> Markov Model(HMM) on the problem of sequence labelling.
>
> If any one can kindly help me out.
>
> Regards,
> Subhabrata.
>
>
> _______________________________________________
> SciPy-User mailing listSciPy-User at scipy.orghttp://mail.scipy.org/mailman/listinfo/scipy-user
>
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>


-- 
*Sree Subhabrata Banerjee*
*PDF(2007-2010)[IISc, Bangalore]*
Call:09873237945
Member, IEEE(USA), ACM(USA).
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From denis-bz-py at t-online.de  Tue Mar 12 13:14:08 2013
From: denis-bz-py at t-online.de (denis)
Date: Tue, 12 Mar 2013 17:14:08 +0000 (UTC)
Subject: [SciPy-User] fast N-d interpolators Intergrid and Barypol
Message-ID: <loom.20130312T180539-669@post.gmane.org>

Folks,
  two small fast interpolators:

[Intergrid](http://denis-bz.github.com/docs/intergrid.html):
interpolate in an N-d box grid, uniform or non-uniform.  
This is just a wrapper for scipy.ndimage.map_coordinates and numpy.interp.

[Barypol](http://denis-bz.github.com/docs/barypol.html):
interpolate in a uniform N-d box grid, using d + 1 corners of a simplex  
(triangle, tetrahedron ...) around each query point.  
>From Munos and Moore, "Variable Resolution Discretization in Optimal Control",  
1999, 24p; see the pictures and example on pp. 4-5.  
It's implemented in a C++ header barypol.h, with a Cython wrapper
(both of which more knowledgeable people could certainly improve).

In 4d, 5d, 6d, Intergrid does around 3M, 2M, .8M interpolations / second.  
Barypol is ~ 5 times faster, but not as smooth.  
(Both will of course become cache-bound for large grids.)  
See interpol/test/*.log
and http://github.com/denis-bz/interpol .

Comments are welcome, testcases most welcome.

cheers
  -- denis




From sunnychugh87 at gmail.com  Wed Mar 13 04:08:51 2013
From: sunnychugh87 at gmail.com (sunnychugh87)
Date: Wed, 13 Mar 2013 01:08:51 -0700 (PDT)
Subject: [SciPy-User] [SciPy-user] Installing Polymode
In-Reply-To: <51099FAA.3010604@uci.edu>
References: <DDC57E45FF69594583AE92F69DCF2B2D0D091996@CCEXCHMBX-1.central.cranfield.ac.uk>
	<51099FAA.3010604@uci.edu>
Message-ID: <35168533.post@talk.nabble.com>


i have installed numpy mkl and scipy

but when i import polymode in program,,it gives this error---

Traceback (most recent call last):
  File "C:/Documents and Settings/student/Desktop/a1.py", line 1, in
<module>
    import Polymode
  File "C:\Python26\lib\site-packages\Polymode\__init__.py", line 47, in
<module>
    from .mathlink import coordinates, constants
  File "C:\Python26\lib\site-packages\Polymode\mathlink\__init__.py", line
37, in <module>
    from .bessel_ratios import besselj_ratio, hankel1_ratio
  File "numpy.pxd", line 119, in init Polymode.mathlink.bessel_ratios
(Polymode\mathlink\bessel_ratios.cpp:5165)
ValueError: numpy.dtype does not appear to be the correct type object


can u help...i need it bad




Christoph Gohlke wrote:
> 
> On 1/29/2013 8:33 AM, Manders, Mark wrote:
>> I was trying to install polymode into my Python 2.7 (32bit), for some
>> reason the following errors come up. It can?t seem to find the numpy
>> ?lapack? libraries even though I have numpy & scipy installed. I also
>> have boost_python installed but it can?t find anything from that either.
>>
> 
> Try <http://www.lfd.uci.edu/~gohlke/pythonlibs/#polymode>. It requires 
> Numpy-MKL and Scipy from the same website.
> 
> Christoph
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
> 
> 
-- 
View this message in context: http://old.nabble.com/Installing-Polymode-tp34958853p35168533.html
Sent from the Scipy-User mailing list archive at Nabble.com.



From sunnychugh87 at gmail.com  Wed Mar 13 04:10:11 2013
From: sunnychugh87 at gmail.com (sunnychugh87)
Date: Wed, 13 Mar 2013 01:10:11 -0700 (PDT)
Subject: [SciPy-User] [SciPy-user] Installing Polymode
In-Reply-To: <51099FAA.3010604@uci.edu>
References: <DDC57E45FF69594583AE92F69DCF2B2D0D091996@CCEXCHMBX-1.central.cranfield.ac.uk>
	<51099FAA.3010604@uci.edu>
Message-ID: <35168539.post@talk.nabble.com>


i have installed numpy mkl and scipy

but when i import polymode in program,,it gives this error---

Traceback (most recent call last):
  File "C:/Documents and Settings/student/Desktop/a1.py", line 1, in
<module>
    import Polymode
  File "C:\Python26\lib\site-packages\Polymode\__init__.py", line 47, in
<module>
    from .mathlink import coordinates, constants
  File "C:\Python26\lib\site-packages\Polymode\mathlink\__init__.py", line
37, in <module>
    from .bessel_ratios import besselj_ratio, hankel1_ratio
  File "numpy.pxd", line 119, in init Polymode.mathlink.bessel_ratios
(Polymode\mathlink\bessel_ratios.cpp:5165)
ValueError: numpy.dtype does not appear to be the correct type object


can u help...i need it bad


Christoph Gohlke wrote:
> 
> On 1/29/2013 8:33 AM, Manders, Mark wrote:
>> I was trying to install polymode into my Python 2.7 (32bit), for some
>> reason the following errors come up. It can?t seem to find the numpy
>> ?lapack? libraries even though I have numpy & scipy installed. I also
>> have boost_python installed but it can?t find anything from that either.
>>
> 
> Try <http://www.lfd.uci.edu/~gohlke/pythonlibs/#polymode>. It requires 
> Numpy-MKL and Scipy from the same website.
> 
> Christoph
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
> 
> 

-- 
View this message in context: http://old.nabble.com/Installing-Polymode-tp34958853p35168539.html
Sent from the Scipy-User mailing list archive at Nabble.com.



From sunnychugh87 at gmail.com  Wed Mar 13 04:10:56 2013
From: sunnychugh87 at gmail.com (sunnychugh87)
Date: Wed, 13 Mar 2013 01:10:56 -0700 (PDT)
Subject: [SciPy-User] [SciPy-user] Installing Polymode
In-Reply-To: <CABL7CQhwAtic3zof=x0vn-LRnv06fXfk0JckAWDcy+0D3-H+4w@mail.gmail.com>
References: <DDC57E45FF69594583AE92F69DCF2B2D0D091996@CCEXCHMBX-1.central.cranfield.ac.uk>
	<CABL7CQhwAtic3zof=x0vn-LRnv06fXfk0JckAWDcy+0D3-H+4w@mail.gmail.com>
Message-ID: <35168541.post@talk.nabble.com>


i have installed numpy mkl and scipy

but when i import polymode in program,,it gives this error---

Traceback (most recent call last):
  File "C:/Documents and Settings/student/Desktop/a1.py", line 1, in
<module>
    import Polymode
  File "C:\Python26\lib\site-packages\Polymode\__init__.py", line 47, in
<module>
    from .mathlink import coordinates, constants
  File "C:\Python26\lib\site-packages\Polymode\mathlink\__init__.py", line
37, in <module>
    from .bessel_ratios import besselj_ratio, hankel1_ratio
  File "numpy.pxd", line 119, in init Polymode.mathlink.bessel_ratios
(Polymode\mathlink\bessel_ratios.cpp:5165)
ValueError: numpy.dtype does not appear to be the correct type object


can u help...i need it bad


Ralf Gommers-3 wrote:
> 
> On Tue, Jan 29, 2013 at 5:33 PM, Manders, Mark
> <m.manders at cranfield.ac.uk>wrote:
> 
>>  I was trying to install polymode into my Python 2.7 (32bit), for some
>> reason the following errors come up. It can?t seem to find the numpy
>> ?lapack? libraries even though I have numpy & scipy installed. I also
>> have
>> boost_python installed but it can?t find anything from that either.****
>>
> 
> Did you install numpy/scipy from source or with a binary install? If the
> latter, you probably don't have a separate BLAS/LAPACK on your system.
> Either way, you need a setup.cfg to point setup.py to whereever your
> BLAS/LAPACK is on your system.
> 
> Note that Polymode is provided as Python(x,y) plugin as well. So if you
> use
> Python(x,y) that would be the way to go.
> 
> Ralf
> 
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
> 
> 

-- 
View this message in context: http://old.nabble.com/Installing-Polymode-tp34958853p35168541.html
Sent from the Scipy-User mailing list archive at Nabble.com.



From cgohlke at uci.edu  Wed Mar 13 11:49:29 2013
From: cgohlke at uci.edu (Christoph Gohlke)
Date: Wed, 13 Mar 2013 08:49:29 -0700
Subject: [SciPy-User] [SciPy-user] Installing Polymode
In-Reply-To: <35168533.post@talk.nabble.com>
References: <DDC57E45FF69594583AE92F69DCF2B2D0D091996@CCEXCHMBX-1.central.cranfield.ac.uk>
	<51099FAA.3010604@uci.edu> <35168533.post@talk.nabble.com>
Message-ID: <5140A009.6040302@uci.edu>

On 3/13/2013 1:08 AM, sunnychugh87 wrote:
>
> i have installed numpy mkl and scipy
>
> but when i import polymode in program,,it gives this error---
>
> Traceback (most recent call last):
>    File "C:/Documents and Settings/student/Desktop/a1.py", line 1, in
> <module>
>      import Polymode
>    File "C:\Python26\lib\site-packages\Polymode\__init__.py", line 47, in
> <module>
>      from .mathlink import coordinates, constants
>    File "C:\Python26\lib\site-packages\Polymode\mathlink\__init__.py", line
> 37, in <module>
>      from .bessel_ratios import besselj_ratio, hankel1_ratio
>    File "numpy.pxd", line 119, in init Polymode.mathlink.bessel_ratios
> (Polymode\mathlink\bessel_ratios.cpp:5165)
> ValueError: numpy.dtype does not appear to be the correct type object
>
>
> can u help...i need it bad
>
>
>
>
> Christoph Gohlke wrote:
>>
>> On 1/29/2013 8:33 AM, Manders, Mark wrote:
>>> I was trying to install polymode into my Python 2.7 (32bit), for some
>>> reason the following errors come up. It can?t seem to find the numpy
>>> ?lapack? libraries even though I have numpy & scipy installed. I also
>>> have boost_python installed but it can?t find anything from that either.
>>>
>>
>> Try <http://www.lfd.uci.edu/~gohlke/pythonlibs/#polymode>. It requires
>> Numpy-MKL and Scipy from the same website.
>>

Either downgrade to numpy 1.6 or use the updated builds at 
<http://www.lfd.uci.edu/~gohlke/pythonlibs/#polymode> with numpy 1.7.

Christoph


From a.klein at science-applied.nl  Wed Mar 13 16:31:57 2013
From: a.klein at science-applied.nl (Almar Klein)
Date: Wed, 13 Mar 2013 21:31:57 +0100
Subject: [SciPy-User] ANN: IEP 3.2 - the Interactive Editor for Python
Message-ID: <CAB_T6=m+VLXceMcWNe+pUWY84xQziRUj7tGBLASa0DnoiardKQ@mail.gmail.com>

Dear all,

We're pleased to announce version 3.2 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.

Binaries are available for Windows, Linux, and Mac.

A selection of the changes since 3.1:


   - This is the first release for which all binaries are build with Python
   3.3 and PySide? <https://code.google.com/p/iep/w/edit/PySide>.
   - New file browser tool, which replaces the old file browser and project
   manager tools. It combines the power of both in one simple interface. It
   also has functionality for peeking inside python modules. Since its design
   uses a generalization of the file system, implementing alternative file
   systems (like zip files or remote machines) should be quite easy in the
   future.
   - IEP now also comes with two fonts:
'DejaVu?<https://code.google.com/p/iep/w/edit/DejaVu>
    Sans Mono' (the default font) and Adobes new 'Source code pro'.
   - IEP now supports multiple languages. Translations for Dutch, French,
   Spanish and Catalan are available.

Website: http://code.google.com/p/iep/
Discussion group: http://groups.google.com/group/iep_
Release notes: https://code.google.com/p/iep/wiki/Release<https://code.google.com/p/iep/wiki/Release?ts=1363206546&updated=Release#version_3.2_(13-03-2013)>

Happy coding!
  Almar
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From eric.emsellem at eso.org  Thu Mar 14 04:34:52 2013
From: eric.emsellem at eso.org (Eric Emsellem)
Date: Thu, 14 Mar 2013 09:34:52 +0100
Subject: [SciPy-User] Problem with ndimage.interpolation.zoom
Message-ID: <51418BAC.70504@eso.org>

Hi,

I have a problem with the zoom function in the ndimage module of scipy.

I am trying to "expand/zoom" an array by a factor of e.g., 3 or 4.

I noticed that when the input array has a shape with odd number of e.g. 
lines/rows etc, something odd happens.

When you take a random 3d array of 2x2x2 it works, and with a times-3 
zoom, it tri-plicates each value in the output array. However, with the 
following example you'll see that the border values are replicated only 
twice and the central value 4 times... What is happening and how can I 
just get an array with just n times each input value?

# Try:
test = random.random(9)
test3 = test.reshape((3,3))
test3z = scipy.ndimage.interpolation.zoom(test3, 3, mode='constant', 
prefilter=False, order=0)

# and look at the output test3z... the border values are only there 
twice. Not three times... The central value is there 4 times...

Thanks for any input.

Eric



From denis-bz-py at t-online.de  Thu Mar 14 07:33:11 2013
From: denis-bz-py at t-online.de (denis)
Date: Thu, 14 Mar 2013 11:33:11 +0000 (UTC)
Subject: [SciPy-User] Problem with ndimage.interpolation.zoom
References: <51418BAC.70504@eso.org>
Message-ID: <loom.20130314T122635-921@post.gmane.org>

Eric Emsellem <eric.emsellem <at> eso.org> writes:
 
> I have a problem with the zoom function in the ndimage module of scipy.
> I am trying to "expand/zoom" an array by a factor of e.g., 3 or 4.
> I noticed that when the input array has a shape with odd number of e.g.

Hi Eric,

1) try it with floats, as below; `zoom` uses floats internally,
then converts to (in your case) ints when done, confusing.

2) consider zoom=2 of an arange in 1d:

    0            1           2
    y0   y1   y2   y3   y4   y5

To get y0 = 0 and y5 = 2,
yj must be (5/2) * xj, *not* 2 * xj.

(For zooming by ints just np.repeat
or maybe you want a http://en.wikipedia.org/wiki/Reconstruction_filter ?)

cheers
  -- denis


    # zoom.py: print ndimage.zoom
    from __future__ import division
    import sys
    import numpy as np
    from scipy.ndimage import zoom  # $scipy/ndimage/interpolation.py

    side = 3
    exec( "\n".join( sys.argv[1:] ))  # run this.py side= ...  from sh or ipytho
    np.set_printoptions( 1, threshold=100, edgeitems=10, suppress=True )

    for dim in [1, 2]:
        shape = [side] * dim
        A = np.arange( np.prod(shape), dtype=float ).reshape(shape)
        print "in:\n", A
        print "\nzoom:\n", zoom( A, zoom=2 )
 






From sunnychugh87 at gmail.com  Thu Mar 14 02:15:15 2013
From: sunnychugh87 at gmail.com (sunnychugh87)
Date: Wed, 13 Mar 2013 23:15:15 -0700 (PDT)
Subject: [SciPy-User] [SciPy-user] Installing Polymode
In-Reply-To: <5140A009.6040302@uci.edu>
References: <DDC57E45FF69594583AE92F69DCF2B2D0D091996@CCEXCHMBX-1.central.cranfield.ac.uk>
	<51099FAA.3010604@uci.edu> <35168533.post@talk.nabble.com>
	<5140A009.6040302@uci.edu>
Message-ID: <35172793.post@talk.nabble.com>


appreciate ur reply


do i have to use numpy mkl-1.6(as it is not available at ur site) or normal
numpy 1.6 from main site will work...
also i don't understand updated builds with numpy 1.7???



Christoph Gohlke wrote:
> 
> On 3/13/2013 1:08 AM, sunnychugh87 wrote:
>>
>> i have installed numpy mkl and scipy
>>
>> but when i import polymode in program,,it gives this error---
>>
>> Traceback (most recent call last):
>>    File "C:/Documents and Settings/student/Desktop/a1.py", line 1, in
>> <module>
>>      import Polymode
>>    File "C:\Python26\lib\site-packages\Polymode\__init__.py", line 47, in
>> <module>
>>      from .mathlink import coordinates, constants
>>    File "C:\Python26\lib\site-packages\Polymode\mathlink\__init__.py",
>> line
>> 37, in <module>
>>      from .bessel_ratios import besselj_ratio, hankel1_ratio
>>    File "numpy.pxd", line 119, in init Polymode.mathlink.bessel_ratios
>> (Polymode\mathlink\bessel_ratios.cpp:5165)
>> ValueError: numpy.dtype does not appear to be the correct type object
>>
>>
>> can u help...i need it bad
>>
>>
>>
>>
>> Christoph Gohlke wrote:
>>>
>>> On 1/29/2013 8:33 AM, Manders, Mark wrote:
>>>> I was trying to install polymode into my Python 2.7 (32bit), for some
>>>> reason the following errors come up. It can?t seem to find the numpy
>>>> ?lapack? libraries even though I have numpy & scipy installed. I also
>>>> have boost_python installed but it can?t find anything from that
>>>> either.
>>>>
>>>
>>> Try <http://www.lfd.uci.edu/~gohlke/pythonlibs/#polymode>. It requires
>>> Numpy-MKL and Scipy from the same website.
>>>
> 
> Either downgrade to numpy 1.6 or use the updated builds at 
> <http://www.lfd.uci.edu/~gohlke/pythonlibs/#polymode> with numpy 1.7.
> 
> Christoph
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
> 
> 

-- 
View this message in context: http://old.nabble.com/Installing-Polymode-tp34958853p35172793.html
Sent from the Scipy-User mailing list archive at Nabble.com.



From tmp50 at ukr.net  Fri Mar 15 09:21:21 2013
From: tmp50 at ukr.net (Dmitrey)
Date: Fri, 15 Mar 2013 15:21:21 +0200
Subject: [SciPy-User] OpenOpt Suite release 0.45
Message-ID: <1991.1363353681.6942528722304958464@ffe16.ukr.net>




Hi all,

>

I'm glad to inform you about new OpenOpt Suite release 0.45
(2013-March-15):
* Essential improvements for FuncDesigner interval analysis (thus affect
interalg)
* Temporary walkaround for a serious bug in FuncDesigner automatic
differentiation kernel due to a bug in some versions of Python or NumPy,
may affect optimization problems, including (MI)LP, (MI)NLP, TSP etc
* Some other minor bugfixes and improvements

>


>

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

>

Regards, D.

> http://openopt.org/Dmitrey
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From a.klein at science-applied.nl  Sat Mar 16 18:31:15 2013
From: a.klein at science-applied.nl (Almar Klein)
Date: Sat, 16 Mar 2013 23:31:15 +0100
Subject: [SciPy-User] ANN: Pyzo distro 2013b
Message-ID: <CAB_T6=kYOCF38uoRin3CjmwF=ckWSW+cFyDyFNC=ZONYd23Kqw@mail.gmail.com>

Dear all,

We're pleased to announce release 2013b of Pyzo distro, a Python
distribution for scientific computing based on Python 3. For more
information, and to give it a try, visit http://www.pyzo.org.

The most notable changes since the last release:

   - Pyzo is also build for Mac.
   - Python 3.3 is now used.
   - Added an executable to launch IPython notebook.
   - New versions of several packages, including the IDE.

More information:

Pyzo is available for Windows, Mac and Linux. The distribution is portable,
thus providing a way to install a scientific Python stack on computers
without the need for admin rights.

Naturally, Pyzo distro is complient with the scipy-stack, and comes with
additional packages such as scikit-image and scikit-learn.

With Pyzo we want to make scientific computing in Python easier accessible.
We especially hope to make it easier for newcomers (such as Matlab
converts) to join our awesome community. Pyzo uses  IEP as the default
front-end IDE, and IPython (with notebook) is also available.

Happy coding,
  Almar
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From lanceboyle at qwest.net  Sat Mar 16 23:22:52 2013
From: lanceboyle at qwest.net (Jerry)
Date: Sat, 16 Mar 2013 20:22:52 -0700
Subject: [SciPy-User] ANN: Pyzo distro 2013b
In-Reply-To: <CAB_T6=kYOCF38uoRin3CjmwF=ckWSW+cFyDyFNC=ZONYd23Kqw@mail.gmail.com>
References: <CAB_T6=kYOCF38uoRin3CjmwF=ckWSW+cFyDyFNC=ZONYd23Kqw@mail.gmail.com>
Message-ID: <152D9C59-D775-4217-9A98-79F3CD6E71AE@qwest.net>

Pyzo crashed on OS X 10.7.5 in under three minutes while trying to run a hello world program.
Jerry

On Mar 16, 2013, at 3:31 PM, Almar Klein wrote:

> Dear all,
> 
> We're pleased to announce release 2013b of Pyzo distro, a Python distribution for scientific computing based on Python 3. For more information, and to give it a try, visit http://www.pyzo.org.
> 
> The most notable changes since the last release:
> Pyzo is also build for Mac.
> Python 3.3 is now used.
> Added an executable to launch IPython notebook.
> New versions of several packages, including the IDE.
> More information:
> 
> Pyzo is available for Windows, Mac and Linux. The distribution is portable, thus providing a way to install a scientific Python stack on computers without the need for admin rights.
> 
> Naturally, Pyzo distro is complient with the scipy-stack, and comes with additional packages such as scikit-image and scikit-learn.
> 
> With Pyzo we want to make scientific computing in Python easier accessible. We especially hope to make it easier for newcomers (such as Matlab converts) to join our awesome community. Pyzo uses  IEP as the default front-end IDE, and IPython (with notebook) is also available.
> 
> Happy coding,
>   Almar
> 
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user

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From a.klein at science-applied.nl  Sun Mar 17 12:14:48 2013
From: a.klein at science-applied.nl (Almar Klein)
Date: Sun, 17 Mar 2013 17:14:48 +0100
Subject: [SciPy-User] ANN: Pyzo distro 2013b
In-Reply-To: <152D9C59-D775-4217-9A98-79F3CD6E71AE@qwest.net>
References: <CAB_T6=kYOCF38uoRin3CjmwF=ckWSW+cFyDyFNC=ZONYd23Kqw@mail.gmail.com>
	<152D9C59-D775-4217-9A98-79F3CD6E71AE@qwest.net>
Message-ID: <CAB_T6==bzV8ofTxhZpxnZw_SmHVgKbDYf0zCUroDNh46Cb6chA@mail.gmail.com>

Were there any errors reported?

- Almar


On 17 March 2013 04:22, Jerry <lanceboyle at qwest.net> wrote:

> Pyzo crashed on OS X 10.7.5 in under three minutes while trying to run a
> hello world program.
> Jerry
>
> On Mar 16, 2013, at 3:31 PM, Almar Klein wrote:
>
> Dear all,
>
> We're pleased to announce release 2013b of Pyzo distro, a Python
> distribution for scientific computing based on Python 3. For more
> information, and to give it a try, visit http://www.pyzo.org.
>
> The most notable changes since the last release:
>
>    - Pyzo is also build for Mac.
>    - Python 3.3 is now used.
>    - Added an executable to launch IPython notebook.
>    - New versions of several packages, including the IDE.
>
> More information:
>
> Pyzo is available for Windows, Mac and Linux. The distribution is
> portable, thus providing a way to install a scientific Python stack on
> computers without the need for admin rights.
>
> Naturally, Pyzo distro is complient with the scipy-stack, and comes with
> additional packages such as scikit-image and scikit-learn.
>
> With Pyzo we want to make scientific computing in Python easier
> accessible. We especially hope to make it easier for newcomers (such as
> Matlab converts) to join our awesome community. Pyzo uses  IEP as the
> default front-end IDE, and IPython (with notebook) is also available.
>
> Happy coding,
>   Almar
>
> _______________________________________________
> 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
>
>


-- 
Almar Klein, PhD
Science Applied
phone: +31 6 19268652
e-mail: a.klein at science-applied.nl
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From lanceboyle at qwest.net  Sun Mar 17 19:47:13 2013
From: lanceboyle at qwest.net (Jerry)
Date: Sun, 17 Mar 2013 16:47:13 -0700
Subject: [SciPy-User] ANN: Pyzo distro 2013b
In-Reply-To: <CAB_T6==bzV8ofTxhZpxnZw_SmHVgKbDYf0zCUroDNh46Cb6chA@mail.gmail.com>
References: <CAB_T6=kYOCF38uoRin3CjmwF=ckWSW+cFyDyFNC=ZONYd23Kqw@mail.gmail.com>
	<152D9C59-D775-4217-9A98-79F3CD6E71AE@qwest.net>
	<CAB_T6==bzV8ofTxhZpxnZw_SmHVgKbDYf0zCUroDNh46Cb6chA@mail.gmail.com>
Message-ID: <2CFCF0FC-8668-4F24-8D4A-AB1A794E11CA@qwest.net>

Here's the crash log.


Process:         pyzo [14506]
Path:            /Applications/Programming/*/pyzo.app/Contents/MacOS/pyzo
Identifier:      pyzo
Version:         ??? (???)
Code Type:       X86 (Native)
Parent Process:  launchd [295]

Date/Time:       2013-03-16 20:20:50.983 -0700
OS Version:      Mac OS X 10.7.5 (11G63b)
Report Version:  9

Crashed Thread:  4

Exception Type:  EXC_BAD_ACCESS (SIGBUS)
Exception Codes: KERN_PROTECTION_FAILURE at 0x0000000000000038

VM Regions Near 0x38:
--> __PAGEZERO             0000000000000000-0000000000001000 [    4K] ---/--- SM=NUL  /Applications/Programming/*/pyzo.app/Contents/MacOS/pyzo
    __TEXT                 0000000000001000-0000000000004000 [   12K] r-x/rwx SM=COW  /Applications/Programming/*/pyzo.app/Contents/MacOS/pyzo

Application Specific Information:
objc[14506]: garbage collection is OFF

Thread 0:: Dispatch queue: com.apple.main-thread
0   QtCore                        	0x01e1f870 qGetCharAttributes(unsigned short const*, unsigned int, HB_ScriptItem const*, unsigned int, HB_CharAttributes*) + 0
1   QtGui                         	0x03fd7a99 QTextEngine::attributes() const + 633
2   QtGui                         	0x03fe30e8 QTextLine::layout_helper(int) + 584
3   QtGui                         	0x04238bc6 QPlainTextDocumentLayout::layoutBlock(QTextBlock const&) + 486
4   QtGui                         	0x0423a554 QPlainTextDocumentLayout::blockBoundingRect(QTextBlock const&) const + 420
5   QtGui                         	0x0423ba0c QPlainTextEditPrivate::_q_adjustScrollbars() + 988
6   QtCore                        	0x01ece63f QMetaObject::activate(QObject*, QMetaObject const*, int, void**) + 1407
7   QtGui                         	0x03fc063e QTextControl::documentSizeChanged(QSizeF const&) + 62
8   QtGui                         	0x03fc70d0 QTextControl::qt_static_metacall(QObject*, QMetaObject::Call, int, void**) + 480
9   QtCore                        	0x01ec962f QMetaCallEvent::placeMetaCall(QObject*) + 47
10  QtCore                        	0x01ecabd7 QObject::event(QEvent*) + 983
11  QtGui                         	0x03fc0398 QTextControl::event(QEvent*) + 24
12  QtGui                         	0x03d94c5c QApplicationPrivate::notify_helper(QObject*, QEvent*) + 188
13  QtGui                         	0x03d99863 QApplication::notify(QObject*, QEvent*) + 1187
14  QtGui.so                      	0x03076c14 QApplicationWrapper::notify(QObject*, QEvent*) + 292
15  QtCore                        	0x01eb69dc QCoreApplication::notifyInternal(QObject*, QEvent*) + 108
16  QtCore                        	0x01eb7df8 QCoreApplicationPrivate::sendPostedEvents(QObject*, int, QThreadData*) + 664
17  QtGui                         	0x03d4b66f QEventDispatcherMacPrivate::postedEventsSourcePerformCallback(void*) + 175
18  com.apple.CoreFoundation      	0x94a3c13f __CFRUNLOOP_IS_CALLING_OUT_TO_A_SOURCE0_PERFORM_FUNCTION__ + 15
19  com.apple.CoreFoundation      	0x94a3baf6 __CFRunLoopDoSources0 + 246
20  com.apple.CoreFoundation      	0x94a659c8 __CFRunLoopRun + 1112
21  com.apple.CoreFoundation      	0x94a651dc CFRunLoopRunSpecific + 332
22  com.apple.CoreFoundation      	0x94a65088 CFRunLoopRunInMode + 120
23  com.apple.HIToolbox           	0x90c10543 RunCurrentEventLoopInMode + 318
24  com.apple.HIToolbox           	0x90c178ab ReceiveNextEventCommon + 381
25  com.apple.HIToolbox           	0x90c1771a BlockUntilNextEventMatchingListInMode + 88
26  com.apple.AppKit              	0x990e8ee8 _DPSNextEvent + 678
27  com.apple.AppKit              	0x990e8752 -[NSApplication nextEventMatchingMask:untilDate:inMode:dequeue:] + 113
28  com.apple.AppKit              	0x990e4ac1 -[NSApplication run] + 911
29  QtGui                         	0x03d4b0ca QEventDispatcherMac::processEvents(QFlags<QEventLoop::ProcessEventsFlag>) + 1786
30  QtCore                        	0x01eb5891 QEventLoop::processEvents(QFlags<QEventLoop::ProcessEventsFlag>) + 65
31  QtCore                        	0x01eb5c6a QEventLoop::exec(QFlags<QEventLoop::ProcessEventsFlag>) + 314
32  QtCore                        	0x01eb8346 QCoreApplication::exec() + 182
33  QtGui.so                      	0x03075923 Sbk_QApplicationFunc_exec_ + 83
34  libpython                     	0x0027293a PyEval_EvalFrameEx + 28042
35  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
36  libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
37  libpython                     	0x00273ecf PyEval_EvalCode + 95
38  libpython                     	0x00269050 builtin_exec + 224
39  libpython                     	0x00272d48 PyEval_EvalFrameEx + 29080
40  libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
41  libpython                     	0x00273ecf PyEval_EvalCode + 95
42  pyzo                          	0x00002c55 main + 2261
43  pyzo                          	0x000021f5 start + 53

Thread 1:: Dispatch queue: com.apple.libdispatch-manager
0   libsystem_kernel.dylib        	0x9bad290a kevent + 10
1   libdispatch.dylib             	0x975dee04 _dispatch_mgr_invoke + 969
2   libdispatch.dylib             	0x975dd853 _dispatch_mgr_thread + 53

Thread 2:
0   libsystem_kernel.dylib        	0x9bad183e __psynch_cvwait + 10
1   libsystem_c.dylib             	0x9b222e21 _pthread_cond_wait + 827
2   libsystem_c.dylib             	0x9b1d33e0 pthread_cond_timedwait$UNIX2003 + 70
3   libpython                     	0x002a2b56 PyThread_acquire_lock_timed + 342
4   libpython                     	0x002a8315 acquire_timed + 229
5   libpython                     	0x002a86cb lock_PyThread_acquire_lock + 251
6   libpython                     	0x00272d48 PyEval_EvalFrameEx + 29080
7   libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
8   libpython                     	0x00272ece PyEval_EvalFrameEx + 29470
9   libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
10  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
11  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
12  libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
13  libpython                     	0x001e693e function_call + 158
14  libpython                     	0x001bb6c9 PyObject_Call + 89
15  libpython                     	0x001d332c method_call + 140
16  libpython                     	0x001bb6c9 PyObject_Call + 89
17  libpython                     	0x0026a20e PyEval_CallObjectWithKeywords + 78
18  libpython                     	0x002a7e2c t_bootstrap + 76
19  libsystem_c.dylib             	0x9b21eed9 _pthread_start + 335
20  libsystem_c.dylib             	0x9b2226de thread_start + 34

Thread 3:
0   libsystem_kernel.dylib        	0x9bad1b42 __select + 10
1   time.so                       	0x007dc1f3 time_sleep + 211
2   libpython                     	0x00272d48 PyEval_EvalFrameEx + 29080
3   libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
4   libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
5   libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
6   libpython                     	0x001e693e function_call + 158
7   libpython                     	0x001bb6c9 PyObject_Call + 89
8   libpython                     	0x001d332c method_call + 140
9   libpython                     	0x001bb6c9 PyObject_Call + 89
10  libpython                     	0x0026a20e PyEval_CallObjectWithKeywords + 78
11  libpython                     	0x002a7e2c t_bootstrap + 76
12  libsystem_c.dylib             	0x9b21eed9 _pthread_start + 335
13  libsystem_c.dylib             	0x9b2226de thread_start + 34

Thread 4 Crashed:
0   QtCore                        	0x01def84a QString::operator=(QString const&) + 26
1   QtGui                         	0x03fd254b QTextEngine::validate() const + 123
2   QtGui                         	0x03fd5ff0 QTextEngine::itemize() const + 48
3   QtGui                         	0x03fde668 QTextLayout::beginLayout() + 40
4   QtGui                         	0x04238b02 QPlainTextDocumentLayout::layoutBlock(QTextBlock const&) + 290
5   QtGui                         	0x0423a554 QPlainTextDocumentLayout::blockBoundingRect(QTextBlock const&) const + 420
6   QtGui                         	0x04031488 QTextCursorPrivate::blockLayout(QTextBlock&) const + 72
7   QtGui                         	0x0403337b QTextCursorPrivate::setX() + 123
8   QtGui                         	0x0403644e QTextCursor::insertText(QString const&, QTextCharFormat const&) + 846
9   QtGui.so                      	0x03568390 Sbk_QTextCursorFunc_insertText + 624
10  libpython                     	0x00272d48 PyEval_EvalFrameEx + 29080
11  libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
12  libpython                     	0x00272ece PyEval_EvalFrameEx + 29470
13  libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
14  libpython                     	0x001e693e function_call + 158
15  libpython                     	0x001bb6c9 PyObject_Call + 89
16  libpython                     	0x001d332c method_call + 140
17  libpython                     	0x001bb6c9 PyObject_Call + 89
18  libpython                     	0x0026a20e PyEval_CallObjectWithKeywords + 78
19  libpython                     	0x001e0309 PyFile_WriteObject + 137
20  libpython                     	0x00267821 builtin_print + 305
21  libpython                     	0x00272d48 PyEval_EvalFrameEx + 29080
22  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
23  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
24  libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
25  libpython                     	0x00272ece PyEval_EvalFrameEx + 29470
26  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
27  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
28  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
29  libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
30  libpython                     	0x001e693e function_call + 158
31  libpython                     	0x001bb6c9 PyObject_Call + 89
32  libpython                     	0x001d332c method_call + 140
33  libpython                     	0x001bb6c9 PyObject_Call + 89
34  libpython                     	0x0026a20e PyEval_CallObjectWithKeywords + 78
35  libpython                     	0x002a7e2c t_bootstrap + 76
36  libsystem_c.dylib             	0x9b21eed9 _pthread_start + 335
37  libsystem_c.dylib             	0x9b2226de thread_start + 34

Thread 5:: QFileInfoGatherer
0   libsystem_kernel.dylib        	0x9bad183e __psynch_cvwait + 10
1   libsystem_c.dylib             	0x9b222e21 _pthread_cond_wait + 827
2   libsystem_c.dylib             	0x9b1d342c pthread_cond_wait$UNIX2003 + 71
3   QtCore                        	0x01d97976 QWaitCondition::wait(QMutex*, unsigned long) + 294
4   QtGui                         	0x0428d9da QFileInfoGatherer::run() + 650
5   QtCore                        	0x01d969ea QThreadPrivate::start(void*) + 346
6   libsystem_c.dylib             	0x9b21eed9 _pthread_start + 335
7   libsystem_c.dylib             	0x9b2226de thread_start + 34

Thread 6:: QKqueueFileSystemWatcherEngine
0   libsystem_kernel.dylib        	0x9bad290a kevent + 10
1   QtCore                        	0x01e98a55 QKqueueFileSystemWatcherEngine::run() + 117
2   QtCore                        	0x01d969ea QThreadPrivate::start(void*) + 346
3   libsystem_c.dylib             	0x9b21eed9 _pthread_start + 335
4   libsystem_c.dylib             	0x9b2226de thread_start + 34

Thread 7:
0   libsystem_kernel.dylib        	0x9bad183e __psynch_cvwait + 10
1   libsystem_c.dylib             	0x9b222e21 _pthread_cond_wait + 827
2   libsystem_c.dylib             	0x9b1d33e0 pthread_cond_timedwait$UNIX2003 + 70
3   libpython                     	0x002a2b56 PyThread_acquire_lock_timed + 342
4   libpython                     	0x002a8315 acquire_timed + 229
5   libpython                     	0x002a86cb lock_PyThread_acquire_lock + 251
6   libpython                     	0x00272d48 PyEval_EvalFrameEx + 29080
7   libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
8   libpython                     	0x00272ece PyEval_EvalFrameEx + 29470
9   libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
10  libpython                     	0x00272ece PyEval_EvalFrameEx + 29470
11  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
12  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
13  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
14  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
15  libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
16  libpython                     	0x001e693e function_call + 158
17  libpython                     	0x001bb6c9 PyObject_Call + 89
18  libpython                     	0x001d332c method_call + 140
19  libpython                     	0x001bb6c9 PyObject_Call + 89
20  libpython                     	0x0026a20e PyEval_CallObjectWithKeywords + 78
21  libpython                     	0x002a7e2c t_bootstrap + 76
22  libsystem_c.dylib             	0x9b21eed9 _pthread_start + 335
23  libsystem_c.dylib             	0x9b2226de thread_start + 34

Thread 8:
0   libsystem_kernel.dylib        	0x9bad1b42 __select + 10
1   select.so                     	0x0279733d select_select + 461
2   libpython                     	0x00272d48 PyEval_EvalFrameEx + 29080
3   libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
4   libpython                     	0x00272ece PyEval_EvalFrameEx + 29470
5   libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
6   libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
7   libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
8   libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
9   libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
10  libpython                     	0x001e693e function_call + 158
11  libpython                     	0x001bb6c9 PyObject_Call + 89
12  libpython                     	0x001d332c method_call + 140
13  libpython                     	0x001bb6c9 PyObject_Call + 89
14  libpython                     	0x0026a20e PyEval_CallObjectWithKeywords + 78
15  libpython                     	0x002a7e2c t_bootstrap + 76
16  libsystem_c.dylib             	0x9b21eed9 _pthread_start + 335
17  libsystem_c.dylib             	0x9b2226de thread_start + 34

Thread 9:
0   libsystem_kernel.dylib        	0x9bad183e __psynch_cvwait + 10
1   libsystem_c.dylib             	0x9b222e21 _pthread_cond_wait + 827
2   libsystem_c.dylib             	0x9b1d33e0 pthread_cond_timedwait$UNIX2003 + 70
3   libpython                     	0x002a2b56 PyThread_acquire_lock_timed + 342
4   libpython                     	0x002a8315 acquire_timed + 229
5   libpython                     	0x002a86cb lock_PyThread_acquire_lock + 251
6   libpython                     	0x00272d48 PyEval_EvalFrameEx + 29080
7   libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
8   libpython                     	0x00272ece PyEval_EvalFrameEx + 29470
9   libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
10  libpython                     	0x00272ece PyEval_EvalFrameEx + 29470
11  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
12  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
13  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
14  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
15  libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
16  libpython                     	0x001e693e function_call + 158
17  libpython                     	0x001bb6c9 PyObject_Call + 89
18  libpython                     	0x001d332c method_call + 140
19  libpython                     	0x001bb6c9 PyObject_Call + 89
20  libpython                     	0x0026a20e PyEval_CallObjectWithKeywords + 78
21  libpython                     	0x002a7e2c t_bootstrap + 76
22  libsystem_c.dylib             	0x9b21eed9 _pthread_start + 335
23  libsystem_c.dylib             	0x9b2226de thread_start + 34

Thread 10:
0   libsystem_kernel.dylib        	0x9bad1b42 __select + 10
1   select.so                     	0x0279733d select_select + 461
2   libpython                     	0x00272d48 PyEval_EvalFrameEx + 29080
3   libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
4   libpython                     	0x00272ece PyEval_EvalFrameEx + 29470
5   libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
6   libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
7   libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
8   libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
9   libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
10  libpython                     	0x001e693e function_call + 158
11  libpython                     	0x001bb6c9 PyObject_Call + 89
12  libpython                     	0x001d332c method_call + 140
13  libpython                     	0x001bb6c9 PyObject_Call + 89
14  libpython                     	0x0026a20e PyEval_CallObjectWithKeywords + 78
15  libpython                     	0x002a7e2c t_bootstrap + 76
16  libsystem_c.dylib             	0x9b21eed9 _pthread_start + 335
17  libsystem_c.dylib             	0x9b2226de thread_start + 34

Thread 11:: com.apple.CFSocket.private
0   libsystem_kernel.dylib        	0x9bad1b42 __select + 10
1   com.apple.CoreFoundation      	0x94ab3e15 __CFSocketManager + 1557
2   libsystem_c.dylib             	0x9b21eed9 _pthread_start + 335
3   libsystem_c.dylib             	0x9b2226de thread_start + 34

Thread 12:
0   libsystem_kernel.dylib        	0x9bad2d4e __read + 10
1   libpython                     	0x002d5de4 fileio_read + 228
2   libpython                     	0x001bb6c9 PyObject_Call + 89
3   libpython                     	0x001bbdc2 call_function_tail + 66
4   libpython                     	0x001bbf9a callmethod + 74
5   libpython                     	0x001bc073 _PyObject_CallMethodId_SizeT + 67
6   libpython                     	0x002d447d iobase_readline + 429
7   libpython                     	0x00272d48 PyEval_EvalFrameEx + 29080
8   libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
9   libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
10  libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
11  libpython                     	0x001e693e function_call + 158
12  libpython                     	0x001bb6c9 PyObject_Call + 89
13  libpython                     	0x001d332c method_call + 140
14  libpython                     	0x001bb6c9 PyObject_Call + 89
15  libpython                     	0x0026a20e PyEval_CallObjectWithKeywords + 78
16  libpython                     	0x002a7e2c t_bootstrap + 76
17  libsystem_c.dylib             	0x9b21eed9 _pthread_start + 335
18  libsystem_c.dylib             	0x9b2226de thread_start + 34

Thread 13:
0   libsystem_kernel.dylib        	0x9bad183e __psynch_cvwait + 10
1   libsystem_c.dylib             	0x9b222e21 _pthread_cond_wait + 827
2   libsystem_c.dylib             	0x9b1d33e0 pthread_cond_timedwait$UNIX2003 + 70
3   libpython                     	0x002a2b56 PyThread_acquire_lock_timed + 342
4   libpython                     	0x002a8315 acquire_timed + 229
5   libpython                     	0x002a86cb lock_PyThread_acquire_lock + 251
6   libpython                     	0x00272d48 PyEval_EvalFrameEx + 29080
7   libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
8   libpython                     	0x00272ece PyEval_EvalFrameEx + 29470
9   libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
10  libpython                     	0x00272ece PyEval_EvalFrameEx + 29470
11  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
12  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
13  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
14  libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
15  libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
16  libpython                     	0x001e693e function_call + 158
17  libpython                     	0x001bb6c9 PyObject_Call + 89
18  libpython                     	0x001d332c method_call + 140
19  libpython                     	0x001bb6c9 PyObject_Call + 89
20  libpython                     	0x0026a20e PyEval_CallObjectWithKeywords + 78
21  libpython                     	0x002a7e2c t_bootstrap + 76
22  libsystem_c.dylib             	0x9b21eed9 _pthread_start + 335
23  libsystem_c.dylib             	0x9b2226de thread_start + 34

Thread 14:
0   libsystem_kernel.dylib        	0x9bad1b42 __select + 10
1   select.so                     	0x0279733d select_select + 461
2   libpython                     	0x00272d48 PyEval_EvalFrameEx + 29080
3   libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
4   libpython                     	0x00272ece PyEval_EvalFrameEx + 29470
5   libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
6   libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
7   libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
8   libpython                     	0x002730e7 PyEval_EvalFrameEx + 30007
9   libpython                     	0x00273de6 PyEval_EvalCodeEx + 2246
10  libpython                     	0x001e693e function_call + 158
11  libpython                     	0x001bb6c9 PyObject_Call + 89
12  libpython                     	0x001d332c method_call + 140
13  libpython                     	0x001bb6c9 PyObject_Call + 89
14  libpython                     	0x0026a20e PyEval_CallObjectWithKeywords + 78
15  libpython                     	0x002a7e2c t_bootstrap + 76
16  libsystem_c.dylib             	0x9b21eed9 _pthread_start + 335
17  libsystem_c.dylib             	0x9b2226de thread_start + 34

Thread 4 crashed with X86 Thread State (32-bit):
  eax: 0x0c2c5930  ebx: 0x00000038  ecx: 0x009d8500  edx: 0xb118b001
  edi: 0x06ee1fa4  esi: 0xb118b0ec  ebp: 0xb118b0b8  esp: 0xb118b0a0
   ss: 0x00000023  efl: 0x00010202  eip: 0x01def84a   cs: 0x0000001b
   ds: 0x00000023   es: 0x00000023   fs: 0x00000023   gs: 0x0000000f
  cr2: 0x00000038
Logical CPU: 1

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0x975dc000 - 0x975eafff  libdispatch.dylib (187.10.0 - compatibility 1.0.0) <1B857064-288D-3919-B81A-38E9F4D19B54> /usr/lib/system/libdispatch.dylib
0x975eb000 - 0x976bbffb  com.apple.ImageIO.framework (3.1.2 - 3.1.2) <2092785C-795A-3CDF-A1B4-6C80BA3726DD> /System/Library/Frameworks/ApplicationServices.framework/Versions/A/Frameworks/ImageIO.framework/Versions/A/ImageIO
0x976bc000 - 0x979ebff7  com.apple.FinderKit (1.0.5 - 1.0.5) <5F2FB244-8734-31FA-A957-0F4B603E02BB> /System/Library/PrivateFrameworks/FinderKit.framework/Versions/A/FinderKit
0x979ec000 - 0x9831772b  com.apple.CoreGraphics (1.600.0 - ???) <DD7CDD67-FC4F-36FE-962F-2EA7EF3FC780> /System/Library/Frameworks/ApplicationServices.framework/Versions/A/Frameworks/CoreGraphics.framework/Versions/A/CoreGraphics
0x98324000 - 0x98346ffe  com.apple.framework.familycontrols (3.0 - 300) <6735D7ED-7053-3AB8-B144-E7F70A124CCD> /System/Library/PrivateFrameworks/FamilyControls.framework/Versions/A/FamilyControls
0x98347000 - 0x9834effd  com.apple.NetFS (4.0 - 4.0) <AE731CFE-1B2E-3E46-8759-843F5FB8C24F> /System/Library/Frameworks/NetFS.framework/Versions/A/NetFS
0x9839b000 - 0x983a3ff5  libcopyfile.dylib (85.1.0 - compatibility 1.0.0) <BB0C7B49-600F-3551-A460-B7E36CA4C4A4> /usr/lib/system/libcopyfile.dylib
0x98481000 - 0x984c1ff7  libauto.dylib (??? - ???) <984C81BE-FA1C-3228-8F7E-2965E7E5EB85> /usr/lib/libauto.dylib
0x984c6000 - 0x9873aff3  com.apple.CoreImage (7.99.1 - 1.0.1) <C4B2DD2A-8E45-31CD-9B25-2AC1CA252B14> /System/Library/Frameworks/QuartzCore.framework/Versions/A/Frameworks/CoreImage.framework/Versions/A/CoreImage
0x9873b000 - 0x98a81fff  com.apple.MediaToolbox (1.0 - 705.94) <89D37021-C389-3CC5-A158-620ADCBD99EF> /System/Library/PrivateFrameworks/MediaToolbox.framework/Versions/A/MediaToolbox
0x98a82000 - 0x98aadfff  com.apple.GSS (2.2 - 2.0) <2C468B23-FA87-30B5-B9A6-8C5D1373AA30> /System/Library/Frameworks/GSS.framework/Versions/A/GSS
0x98aae000 - 0x98c04fff  com.apple.audio.toolbox.AudioToolbox (1.7.3 - 1.7.3) <F09C7075-2C4E-3A4F-A372-95D700125513> /System/Library/Frameworks/AudioToolbox.framework/Versions/A/AudioToolbox
0x98c05000 - 0x98c12fff  libGL.dylib (??? - ???) <A72F14F7-1836-34AB-9F08-67836CB966E4> /System/Library/Frameworks/OpenGL.framework/Versions/A/Libraries/libGL.dylib
0x98eec000 - 0x98f34ff7  com.apple.SystemConfiguration (1.11.3 - 1.11) <68B92FEA-F754-3E7E-B5E6-D512E26144E7> /System/Library/Frameworks/SystemConfiguration.framework/Versions/A/SystemConfiguration
0x98f35000 - 0x98f64ff7  libsystem_info.dylib (??? - ???) <37640811-445B-3BB7-9934-A7C99848250D> /usr/lib/system/libsystem_info.dylib
0x990b9000 - 0x990d1fff  com.apple.frameworks.preferencepanes (15.0 - 15.0) <A8CE15CF-2539-318A-910C-8789D45FFD69> /System/Library/Frameworks/PreferencePanes.framework/Versions/A/PreferencePanes
0x990d3000 - 0x990deffe  com.apple.NetAuth (3.2 - 3.2) <4377FB18-A550-35C6-BCD2-71C42134EEA6> /System/Library/PrivateFrameworks/NetAuth.framework/Versions/A/NetAuth
0x990df000 - 0x99b74ff6  com.apple.AppKit (6.7.5 - 1138.51) <B9D3DCA0-9765-354E-9730-75A45A97DDFD> /System/Library/Frameworks/AppKit.framework/Versions/C/AppKit
0x99b75000 - 0x99b91ff5  com.apple.GenerationalStorage (1.0 - 126.1) <E622F823-7D98-3D13-9C3D-7EA482567394> /System/Library/PrivateFrameworks/GenerationalStorage.framework/Versions/A/GenerationalStorage
0x99b92000 - 0x99bcffef  libGLImage.dylib (??? - ???) <FC13D46F-69C4-3BA1-A5E8-52C2EC8B7D58> /System/Library/Frameworks/OpenGL.framework/Versions/A/Libraries/libGLImage.dylib
0x99d05000 - 0x99d05fff  com.apple.Carbon (153 - 153) <63603A0C-723B-3968-B302-EBEEE6A14E97> /System/Library/Frameworks/Carbon.framework/Versions/A/Carbon
0x99d11000 - 0x99d61ffa  libTIFF.dylib (??? - ???) <CAD45E49-FD7C-37FA-A118-AEB526B90E67> /System/Library/Frameworks/ApplicationServices.framework/Versions/A/Frameworks/ImageIO.framework/Versions/A/Resources/libTIFF.dylib
0x99d62000 - 0x99e1fff3  ColorSyncDeprecated.dylib (4.6.0 - compatibility 1.0.0) <726898F5-E718-3F27-B415-D6FDCDE09174> /System/Library/Frameworks/ApplicationServices.framework/Frameworks/ColorSync.framework/Versions/A/Resources/ColorSyncDeprecated.dylib
0x99f6b000 - 0x99f85fff  com.apple.Kerberos (1.0 - 1) <D7920A1C-FEC4-3460-8DD0-D02491578CBB> /System/Library/Frameworks/Kerberos.framework/Versions/A/Kerberos
0x99f86000 - 0x9a612ff5  com.apple.CoreAUC (6.16.12 - 6.16.12) <9D51400F-B827-3BA7-87F5-954A1CDDAEA9> /System/Library/PrivateFrameworks/CoreAUC.framework/Versions/A/CoreAUC
0x9a613000 - 0x9a724ff7  libJP2.dylib (??? - ???) <2B5EB147-F845-30DF-87C4-D2D3C3D0680A> /System/Library/Frameworks/ApplicationServices.framework/Versions/A/Frameworks/ImageIO.framework/Versions/A/Resources/libJP2.dylib
0x9a725000 - 0x9aa27fff  com.apple.CoreServices.CarbonCore (960.25 - 960.25) <C613B0DA-B401-3DC7-B626-6E20D4DDC8A8> /System/Library/Frameworks/CoreServices.framework/Versions/A/Frameworks/CarbonCore.framework/Versions/A/CarbonCore
0x9aa28000 - 0x9aa29ffd  libCVMSPluginSupport.dylib (??? - ???) <4B0476F9-950D-3EB7-BD83-F65AF0B05F0E> /System/Library/Frameworks/OpenGL.framework/Versions/A/Libraries/libCVMSPluginSupport.dylib
0x9aa2a000 - 0x9aa38ff7  libxar-nossl.dylib (??? - ???) <5BF4DA8E-C319-354A-967E-A0C725DC8BA3> /usr/lib/libxar-nossl.dylib
0x9aa39000 - 0x9aa43ff2  com.apple.audio.SoundManager (3.9.4.1 - 3.9.4.1) <2A089CE8-9760-3F0F-B77D-29A78940EA17> /System/Library/Frameworks/Carbon.framework/Versions/A/Frameworks/CarbonSound.framework/Versions/A/CarbonSound
0x9aa44000 - 0x9aa45ff7  libsystem_sandbox.dylib (??? - ???) <5CFCCFB7-CF29-3E04-801D-8532AE004768> /usr/lib/system/libsystem_sandbox.dylib
0x9aaa0000 - 0x9aaaefff  com.apple.opengl (1.8.1 - 1.8.1) <766AFB12-A2CB-3A55-B662-FC9FFCAE0008> /System/Library/Frameworks/OpenGL.framework/Versions/A/OpenGL
0x9aaaf000 - 0x9aab7ff3  libunwind.dylib (30.0.0 - compatibility 1.0.0) <E8DA8CEC-12D6-3C8D-B2E2-5D567C8F3CB5> /usr/lib/system/libunwind.dylib
0x9aab8000 - 0x9ab35fff  com.apple.PDFKit (2.6.4 - 2.6.4) <F2B635FC-EDD0-3D80-AB43-F78CB883623A> /System/Library/Frameworks/Quartz.framework/Versions/A/Frameworks/PDFKit.framework/Versions/A/PDFKit
0x9ab36000 - 0x9ab9eff3  com.apple.ISSupport (1.9.8 - 56) <59225A65-41C1-35CA-9F29-229AC427B728> /System/Library/PrivateFrameworks/ISSupport.framework/Versions/A/ISSupport
0x9ab9f000 - 0x9abadfff  libz.1.dylib (1.2.5 - compatibility 1.0.0) <E73A4025-835C-3F73-9853-B08606E892DB> /usr/lib/libz.1.dylib
0x9adcb000 - 0x9af7fff3  libicucore.A.dylib (46.1.0 - compatibility 1.0.0) <4AFF6FC3-6283-3934-8EFC-CA227CA11164> /usr/lib/libicucore.A.dylib
0x9af80000 - 0x9afcfffb  com.apple.AppleVAFramework (5.0.16 - 5.0.16) <1188E7AB-76FE-343F-9108-30CD67E5A37B> /System/Library/PrivateFrameworks/AppleVA.framework/Versions/A/AppleVA
0x9afd0000 - 0x9b0dfff7  com.apple.DesktopServices (1.6.5 - 1.6.5) <CEC069D7-37A3-3D25-A3BB-39DE99FDA46E> /System/Library/PrivateFrameworks/DesktopServicesPriv.framework/Versions/A/DesktopServicesPriv
0x9b0e0000 - 0x9b1c1ff7  com.apple.DiscRecording (6.0.4 - 6040.4.1) <08BADDAD-FA79-3872-9387-EEE2A9FAA2F0> /System/Library/Frameworks/DiscRecording.framework/Versions/A/DiscRecording
0x9b1c2000 - 0x9b28dfff  libsystem_c.dylib (763.13.0 - compatibility 1.0.0) <52421B00-79C8-3727-94DE-62F6820B9C31> /usr/lib/system/libsystem_c.dylib
0x9b3bd000 - 0x9b3beff0  libunc.dylib (24.0.0 - compatibility 1.0.0) <2F4B35B2-706C-3383-AA86-DABA409FAE45> /usr/lib/system/libunc.dylib
0x9b3bf000 - 0x9b3bffff  com.apple.Accelerate.vecLib (3.7 - vecLib 3.7) <22997C20-BEB7-301D-86C5-5BFB3B06D212> /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/vecLib
0x9b3c0000 - 0x9b3cbff3  libCSync.A.dylib (600.0.0 - compatibility 64.0.0) <D6E17FD4-ECA0-3EEE-BFC5-F6A42A21AB5D> /System/Library/Frameworks/ApplicationServices.framework/Versions/A/Frameworks/CoreGraphics.framework/Versions/A/Resources/libCSync.A.dylib
0x9b3cc000 - 0x9b3cfff7  libmathCommon.A.dylib (2026.0.0 - compatibility 1.0.0) <69357047-7BE0-3360-A36D-000F55E39336> /usr/lib/system/libmathCommon.A.dylib
0x9b3d0000 - 0x9b3faff0  libpcre.0.dylib (1.1.0 - compatibility 1.0.0) <5CAA1478-97E0-31EA-8F50-BF09D665DD84> /usr/lib/libpcre.0.dylib
0x9b3fb000 - 0x9b3fbfff  com.apple.Cocoa (6.6 - ???) <5FAFE73E-6AF5-3D09-9191-0BDC8C6875CB> /System/Library/Frameworks/Cocoa.framework/Versions/A/Cocoa
0x9b3fc000 - 0x9b410fff  com.apple.CFOpenDirectory (10.7 - 146) <9149C1FE-865E-3A8D-B9D9-547384F553C8> /System/Library/Frameworks/OpenDirectory.framework/Versions/A/Frameworks/CFOpenDirectory.framework/Versions/A/CFOpenDirectory
0x9b411000 - 0x9b7ccffb  com.apple.SceneKit (125.3 - 125.8) <89008B87-87E7-3972-A274-30311497EE32> /System/Library/PrivateFrameworks/SceneKit.framework/Versions/A/SceneKit
0x9b915000 - 0x9b93aff9  libJPEG.dylib (??? - ???) <743578F6-8C0C-39CC-9F15-3A01E1616EAE> /System/Library/Frameworks/ApplicationServices.framework/Versions/A/Frameworks/ImageIO.framework/Versions/A/Resources/libJPEG.dylib
0x9b93b000 - 0x9ba13ff6  com.apple.QuickLookUIFramework (3.2 - 500.18) <CE281BD9-C1CF-3F87-BD22-412ED253580E> /System/Library/Frameworks/Quartz.framework/Versions/A/Frameworks/QuickLookUI.framework/Versions/A/QuickLookUI
0x9ba14000 - 0x9ba8fffb  com.apple.ApplicationServices.ATS (317.12.0 - ???) <4D124B65-3D43-32E9-B296-3671347BB888> /System/Library/Frameworks/ApplicationServices.framework/Versions/A/Frameworks/ATS.framework/Versions/A/ATS
0x9ba9b000 - 0x9bab8ff3  com.apple.openscripting (1.3.3 - ???) <0579A4CB-FD6F-3D7F-A17B-AC0F2CF11FC7> /System/Library/Frameworks/Carbon.framework/Versions/A/Frameworks/OpenScripting.framework/Versions/A/OpenScripting
0x9bab9000 - 0x9bad7ff7  libsystem_kernel.dylib (1699.32.7 - compatibility 1.0.0) <79179F83-457A-3539-A76B-E960D2108109> /usr/lib/system/libsystem_kernel.dylib
0x9bad8000 - 0x9bb2affb  com.apple.CoreMediaIO (216.0 - 3199.8) <A079E576-64FD-3069-846E-F3B67AF28E15> /System/Library/Frameworks/CoreMediaIO.framework/Versions/A/CoreMediaIO
0x9bb2b000 - 0x9bdfaffb  com.apple.security (7.0 - 55148.6) <8DF67BDD-C98F-3B7E-AC63-D468407FA82D> /System/Library/Frameworks/Security.framework/Versions/A/Security
0x9be54000 - 0x9bf2aaab  libobjc.A.dylib (228.0.0 - compatibility 1.0.0) <2E272DCA-38A0-3530-BBF4-47AE678D20D4> /usr/lib/libobjc.A.dylib
0x9bf2b000 - 0x9bfa3ff8  com.apple.CorePDF (3.1 - 3.1) <0074267B-F74A-30FC-8508-A14C821F0771> /System/Library/PrivateFrameworks/CorePDF.framework/Versions/A/CorePDF
0x9bfa4000 - 0x9c03bff3  com.apple.securityfoundation (5.0 - 55116) <EB53CEF7-4836-39FD-B012-6BC122ED4CE9> /System/Library/Frameworks/SecurityFoundation.framework/Versions/A/SecurityFoundation
0x9c03c000 - 0x9c08eff7  libFontRegistry.dylib (??? - ???) <C2B84661-A62D-3FFF-8D8C-BC697E9BDF4C> /System/Library/Frameworks/ApplicationServices.framework/Versions/A/Frameworks/ATS.framework/Versions/A/Resources/libFontRegistry.dylib
0x9c08f000 - 0x9c08ffff  com.apple.quartzframework (1.5 - 1.5) <49B5CA00-083A-3D4A-9A68-4759A5CC35A6> /System/Library/Frameworks/Quartz.framework/Versions/A/Quartz
0x9c090000 - 0x9c0b2ff8  com.apple.PerformanceAnalysis (1.11 - 11) <453463FF-7C42-3526-8C96-A9971EE07154> /System/Library/PrivateFrameworks/PerformanceAnalysis.framework/Versions/A/PerformanceAnalysis
0x9c0b3000 - 0x9c0b5ffb  libRadiance.dylib (??? - ???) <4721057E-5A1F-3083-911B-200ED1CE7678> /System/Library/Frameworks/ApplicationServices.framework/Versions/A/Frameworks/ImageIO.framework/Versions/A/Resources/libRadiance.dylib
0x9c0b6000 - 0x9c3c0ff3  com.apple.Foundation (6.7.2 - 833.25) <4C52ED74-A1FD-3087-A2E1-035AB3CF9610> /System/Library/Frameworks/Foundation.framework/Versions/C/Foundation
0x9c3c1000 - 0x9c3caff3  com.apple.CommonAuth (2.2 - 2.0) <C3FD6EC2-8EB3-38FB-BBB7-05009CA49024> /System/Library/PrivateFrameworks/CommonAuth.framework/Versions/A/CommonAuth
0x9c3cb000 - 0x9c3d2ff7  libsystem_notify.dylib (80.1.0 - compatibility 1.0.0) <47DB9E1B-A7D1-3818-A747-382B2C5D9E1B> /usr/lib/system/libsystem_notify.dylib
0x9c3d3000 - 0x9c3e4fff  libbsm.0.dylib (??? - ???) <54ACF696-87C6-3652-808A-17BE7275C230> /usr/lib/libbsm.0.dylib
0x9c3e5000 - 0x9c4d5ff1  libiconv.2.dylib (7.0.0 - compatibility 7.0.0) <9E5F86A3-8405-3774-9E0C-3A074273C96D> /usr/lib/libiconv.2.dylib
0x9c4e4000 - 0x9c50affb  com.apple.quartzfilters (1.7.0 - 1.7.0) <64AB163E-7E91-3028-8730-BE11BC1F5237> /System/Library/Frameworks/Quartz.framework/Versions/A/Frameworks/QuartzFilters.framework/Versions/A/QuartzFilters
0x9cda7000 - 0x9cdb2fff  libkxld.dylib (??? - ???) <14E79D7A-B6C2-35C5-B56D-D343BEC2A106> /usr/lib/system/libkxld.dylib
0x9cdb3000 - 0x9cdb3ff0  com.apple.ApplicationServices (41 - 41) <C48EF6B2-ABF9-35BD-A07A-A38EC0008294> /System/Library/Frameworks/ApplicationServices.framework/Versions/A/ApplicationServices
0x9cdd2000 - 0x9ce0dfff  com.apple.bom (11.0 - 183) <8E3F690E-4D23-3379-828C-5AB0453B9226> /System/Library/PrivateFrameworks/Bom.framework/Versions/A/Bom
0x9ce0e000 - 0x9ce38ff1  com.apple.CoreServicesInternal (113.19 - 113.19) <F7A309D1-DCB4-38DE-8248-E16D0182AA6C> /System/Library/PrivateFrameworks/CoreServicesInternal.framework/Versions/A/CoreServicesInternal
0x9ce39000 - 0x9cefcfff  com.apple.CoreServices.OSServices (478.49 - 478.49) <5AF33605-C893-3F60-89CF-1BC9C0BC35AF> /System/Library/Frameworks/CoreServices.framework/Versions/A/Frameworks/OSServices.framework/Versions/A/OSServices
0x9cefd000 - 0x9cf05ff3  liblaunch.dylib (392.39.0 - compatibility 1.0.0) <9E6135FF-C2B1-3BC9-A160-B32D71BFA77C> /usr/lib/system/liblaunch.dylib
0xba900000 - 0xba91bffd  libJapaneseConverter.dylib (54.0.0 - compatibility 1.0.0) <473499F3-8CB8-3372-98B0-8E3BCC1A3D80> /System/Library/CoreServices/Encodings/libJapaneseConverter.dylib

External Modification Summary:
  Calls made by other processes targeting this process:
    task_for_pid: 73
    thread_create: 0
    thread_set_state: 0
  Calls made by this process:
    task_for_pid: 0
    thread_create: 0
    thread_set_state: 0
  Calls made by all processes on this machine:
    task_for_pid: 52593
    thread_create: 0
    thread_set_state: 0

VM Region Summary:
ReadOnly portion of Libraries: Total=222.2M resident=108.1M(49%) swapped_out_or_unallocated=114.1M(51%)
Writable regions: Total=211.2M written=47.4M(22%) resident=84.6M(40%) swapped_out=108K(0%) unallocated=126.7M(60%)
 
REGION TYPE                      VIRTUAL
===========                      =======
ATS (font support)                 32.4M
CG backing stores                  9712K
CG image                            424K
CG raster data                      348K
CG shared images                   3576K
CoreGraphics                          8K
CoreServices                       3156K
MALLOC                             86.6M
MALLOC guard page                    64K
MALLOC_LARGE (reserved)             256K        reserved VM address space (unallocated)
Memory tag=240                        4K
Memory tag=242                       12K
OpenCL                               60K
SQLite page cache                    96K
Stack                             115.7M
VM_ALLOCATE                        16.4M
__CI_BITMAP                          80K
__DATA                             11.8M
__DATA/__OBJC                       224K
__IMAGE                             528K
__IMPORT                              4K
__LINKEDIT                         51.0M
__OBJC                             2936K
__OBJC/__DATA                        16K
__PAGEZERO                            4K
__TEXT                            171.2M
__UNICODE                           544K
mapped file                       117.5M
shared memory                      37.3M
shared pmap                        7296K
===========                      =======
TOTAL                             668.6M
TOTAL, minus reserved VM space    668.3M

 
On Mar 17, 2013, at 9:14 AM, Almar Klein wrote:

> Were there any errors reported?
> 
> - Almar
> 
> 
> On 17 March 2013 04:22, Jerry <lanceboyle at qwest.net> wrote:
> Pyzo crashed on OS X 10.7.5 in under three minutes while trying to run a hello world program.
> Jerry
> 
> On Mar 16, 2013, at 3:31 PM, Almar Klein wrote:
> 
>> Dear all,
>> 
>> We're pleased to announce release 2013b of Pyzo distro, a Python distribution for scientific computing based on Python 3. For more information, and to give it a try, visit http://www.pyzo.org.
>> 
>> The most notable changes since the last release:
>> 	? Pyzo is also build for Mac.
>> 	? Python 3.3 is now used.
>> 	? Added an executable to launch IPython notebook.
>> 	? New versions of several packages, including the IDE.
>> More information:
>> 
>> Pyzo is available for Windows, Mac and Linux. The distribution is portable, thus providing a way to install a scientific Python stack on computers without the need for admin rights.
>> 
>> Naturally, Pyzo distro is complient with the scipy-stack, and comes with additional packages such as scikit-image and scikit-learn.
>> 
>> With Pyzo we want to make scientific computing in Python easier accessible. We especially hope to make it easier for newcomers (such as Matlab converts) to join our awesome community. Pyzo uses  IEP as the default front-end IDE, and IPython (with notebook) is also available.
>> 
>> Happy coding,
>>   Almar
>> 
>> _______________________________________________
>> 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
> 
> 
> 
> 
> -- 
> Almar Klein, PhD
> Science Applied
> phone: +31 6 19268652
> e-mail: a.klein at science-applied.nl
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user



From guziy.sasha at gmail.com  Sun Mar 17 22:40:33 2013
From: guziy.sasha at gmail.com (Oleksandr Huziy)
Date: Sun, 17 Mar 2013 22:40:33 -0400
Subject: [SciPy-User] ANN: Pyzo distro 2013b
In-Reply-To: <CAB_T6==bzV8ofTxhZpxnZw_SmHVgKbDYf0zCUroDNh46Cb6chA@mail.gmail.com>
References: <CAB_T6=kYOCF38uoRin3CjmwF=ckWSW+cFyDyFNC=ZONYd23Kqw@mail.gmail.com>
	<152D9C59-D775-4217-9A98-79F3CD6E71AE@qwest.net>
	<CAB_T6==bzV8ofTxhZpxnZw_SmHVgKbDYf0zCUroDNh46Cb6chA@mail.gmail.com>
Message-ID: <CAN3p1sXVQ5skVKJyx8FtSJ8qqhd+XhF=hSoCrsbyO6wxFz6Zsw@mail.gmail.com>

Hi,

here's the error I get on linux(Linux san-laptop 3.5.0-26-generic
#42-Ubuntu SMP Fri Mar 8 23:18:20 UTC 2013 x86_64 x86_64 x86_64 GNU/Linux):

>./pyzo
Cannot mix incompatible Qt library (version 0x40803) with this library
(version 0x40804)
Aborted (core dumped)

cheers

--
Oleksandr (Sasha) Huziy



2013/3/17 Almar Klein <a.klein at science-applied.nl>

> Were there any errors reported?
>
> - Almar
>
>
> On 17 March 2013 04:22, Jerry <lanceboyle at qwest.net> wrote:
>
>> Pyzo crashed on OS X 10.7.5 in under three minutes while trying to run a
>> hello world program.
>> Jerry
>>
>> On Mar 16, 2013, at 3:31 PM, Almar Klein wrote:
>>
>> Dear all,
>>
>> We're pleased to announce release 2013b of Pyzo distro, a Python
>> distribution for scientific computing based on Python 3. For more
>> information, and to give it a try, visit http://www.pyzo.org.
>>
>> The most notable changes since the last release:
>>
>>    - Pyzo is also build for Mac.
>>    - Python 3.3 is now used.
>>    - Added an executable to launch IPython notebook.
>>    - New versions of several packages, including the IDE.
>>
>> More information:
>>
>> Pyzo is available for Windows, Mac and Linux. The distribution is
>> portable, thus providing a way to install a scientific Python stack on
>> computers without the need for admin rights.
>>
>> Naturally, Pyzo distro is complient with the scipy-stack, and comes with
>> additional packages such as scikit-image and scikit-learn.
>>
>> With Pyzo we want to make scientific computing in Python easier
>> accessible. We especially hope to make it easier for newcomers (such as
>> Matlab converts) to join our awesome community. Pyzo uses  IEP as the
>> default front-end IDE, and IPython (with notebook) is also available.
>>
>> Happy coding,
>>   Almar
>>
>> _______________________________________________
>> 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
>>
>>
>
>
> --
> Almar Klein, PhD
> Science Applied
> phone: +31 6 19268652
> e-mail: a.klein at science-applied.nl
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
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From a.klein at science-applied.nl  Mon Mar 18 06:56:55 2013
From: a.klein at science-applied.nl (Almar Klein)
Date: Mon, 18 Mar 2013 11:56:55 +0100
Subject: [SciPy-User] ANN: Pyzo distro 2013b
In-Reply-To: <152D9C59-D775-4217-9A98-79F3CD6E71AE@qwest.net>
References: <CAB_T6=kYOCF38uoRin3CjmwF=ckWSW+cFyDyFNC=ZONYd23Kqw@mail.gmail.com>
	<152D9C59-D775-4217-9A98-79F3CD6E71AE@qwest.net>
Message-ID: <CAB_T6=mbbtVj5R5+Bby+GVosL4NVunZgVbnzZNwW1vWztqMyhQ@mail.gmail.com>

> Pyzo crashed on OS X 10.7.5 in under three minutes while trying to run a
> hello world program.
>

I just found: "Mac OS X 10.7 "Lion" was the first version of OS X to drop
support for 32-bit Intel processors and run exclusively on 64-bit Intel
CPUs."
So, I'm sorry, but you'll have to wait for the next release.

The reason that the binaries are 32 bit is that I forgot to make the VM
64bit when I created it. Woops. I only found out much later (after all the
hard work of setting up the machine was done). So the blame's on me.

- Almar




>
> Dear all,
>
> We're pleased to announce release 2013b of Pyzo distro, a Python
> distribution for scientific computing based on Python 3. For more
> information, and to give it a try, visit http://www.pyzo.org.
>
> The most notable changes since the last release:
>
>    - Pyzo is also build for Mac.
>    - Python 3.3 is now used.
>    - Added an executable to launch IPython notebook.
>    - New versions of several packages, including the IDE.
>
> More information:
>
> Pyzo is available for Windows, Mac and Linux. The distribution is
> portable, thus providing a way to install a scientific Python stack on
> computers without the need for admin rights.
>
> Naturally, Pyzo distro is complient with the scipy-stack, and comes with
> additional packages such as scikit-image and scikit-learn.
>
> With Pyzo we want to make scientific computing in Python easier
> accessible. We especially hope to make it easier for newcomers (such as
> Matlab converts) to join our awesome community. Pyzo uses  IEP as the
> default front-end IDE, and IPython (with notebook) is also available.
>
> Happy coding,
>   Almar
>
> _______________________________________________
> 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
>
>


-- 
Almar Klein, PhD
Science Applied
phone: +31 6 19268652
e-mail: a.klein at science-applied.nl
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From a.klein at science-applied.nl  Mon Mar 18 07:15:53 2013
From: a.klein at science-applied.nl (Almar Klein)
Date: Mon, 18 Mar 2013 12:15:53 +0100
Subject: [SciPy-User] ANN: Pyzo distro 2013b
In-Reply-To: <CAN3p1sXVQ5skVKJyx8FtSJ8qqhd+XhF=hSoCrsbyO6wxFz6Zsw@mail.gmail.com>
References: <CAB_T6=kYOCF38uoRin3CjmwF=ckWSW+cFyDyFNC=ZONYd23Kqw@mail.gmail.com>
	<152D9C59-D775-4217-9A98-79F3CD6E71AE@qwest.net>
	<CAB_T6==bzV8ofTxhZpxnZw_SmHVgKbDYf0zCUroDNh46Cb6chA@mail.gmail.com>
	<CAN3p1sXVQ5skVKJyx8FtSJ8qqhd+XhF=hSoCrsbyO6wxFz6Zsw@mail.gmail.com>
Message-ID: <CAB_T6=kaTztd3bSSDWGgGtSNUwLFqRSzKeu0QRbd6f9NoX-w4g@mail.gmail.com>

> here's the error I get on linux(Linux san-laptop 3.5.0-26-generic
> #42-Ubuntu SMP Fri Mar 8 23:18:20 UTC 2013 x86_64 x86_64 x86_64 GNU/Linux):
>
> >./pyzo
> Cannot mix incompatible Qt library (version 0x40803) with this library
> (version 0x40804)
> Aborted (core dumped)
>

You can fix this by creating a file qt.conf in the root of the Pyzo
directory with these two lines:
[Paths]
Plugins = '.'

(Next Pyzo release will probably have this file by default).

- Almar




>
>
--
> Oleksandr (Sasha) Huziy
>
>
>
> 2013/3/17 Almar Klein <a.klein at science-applied.nl>
>
>> Were there any errors reported?
>>
>> - Almar
>>
>>
>> On 17 March 2013 04:22, Jerry <lanceboyle at qwest.net> wrote:
>>
>>> Pyzo crashed on OS X 10.7.5 in under three minutes while trying to run a
>>> hello world program.
>>> Jerry
>>>
>>> On Mar 16, 2013, at 3:31 PM, Almar Klein wrote:
>>>
>>> Dear all,
>>>
>>> We're pleased to announce release 2013b of Pyzo distro, a Python
>>> distribution for scientific computing based on Python 3. For more
>>> information, and to give it a try, visit http://www.pyzo.org.
>>>
>>> The most notable changes since the last release:
>>>
>>>    - Pyzo is also build for Mac.
>>>    - Python 3.3 is now used.
>>>    - Added an executable to launch IPython notebook.
>>>    - New versions of several packages, including the IDE.
>>>
>>> More information:
>>>
>>> Pyzo is available for Windows, Mac and Linux. The distribution is
>>> portable, thus providing a way to install a scientific Python stack on
>>> computers without the need for admin rights.
>>>
>>> Naturally, Pyzo distro is complient with the scipy-stack, and comes
>>> with additional packages such as scikit-image and scikit-learn.
>>>
>>> With Pyzo we want to make scientific computing in Python easier
>>> accessible. We especially hope to make it easier for newcomers (such as
>>> Matlab converts) to join our awesome community. Pyzo uses  IEP as the
>>> default front-end IDE, and IPython (with notebook) is also available.
>>>
>>> Happy coding,
>>>   Almar
>>>
>>> _______________________________________________
>>> 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
>>>
>>>
>>
>>
>> --
>> Almar Klein, PhD
>> Science Applied
>> phone: +31 6 19268652
>> e-mail: a.klein at science-applied.nl
>>
>> _______________________________________________
>> 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
>
>


-- 
Almar Klein, PhD
Science Applied
phone: +31 6 19268652
e-mail: a.klein at science-applied.nl
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From josef.pktd at gmail.com  Mon Mar 18 13:13:33 2013
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Mon, 18 Mar 2013 13:13:33 -0400
Subject: [SciPy-User] expanding optimize.brentq
Message-ID: <CAMMTP+C0uNsAsi2TgQj+M=o99+0Y+LC456YfR+gn-apyADY+Lg@mail.gmail.com>

Just a thought, given the problems with fsolve

In scipy.stats.distribution, we use an expanding brentq, which looks
like it works very well.
https://github.com/scipy/scipy/pull/216/files#L0R1175

A question given that I have not much experience with root finders:

Is there a general algorithm that has similar properties?


In these application we know that the function is strictly monotonic,
but we don't have a bound for brentq without checking. For some cases
I know one of the bounds (e.g. 0 for non-negative solutions).

(I could use that right now as a replacement for fsolve.)

Josef


From t.arvind at gmail.com  Mon Mar 18 14:46:06 2013
From: t.arvind at gmail.com (Arvind Thiagarajan)
Date: Mon, 18 Mar 2013 11:46:06 -0700
Subject: [SciPy-User] scipy.stats beginner: help with fitting distribution
Message-ID: <CAHbAt1p8eeB6OG3U9MeU2rw9jPXnSbpj5LqXc2UiGjuQ8pD55g@mail.gmail.com>

Hi all,

I'm a beginner with SciPy so this may be a basic question. I am trying
to fit a Rice distribution to some data using scipy.stats.

However, I first tried some test code which doesn't seem to give me a
very good fit. I tried the following code:

>>> b = [0.3,]
>>> samples = rice.rvs(b, loc=0, scale=1, size=1000)
>>> rice.fit(samples)
(0.0012012190480231357, -0.0023216862043629813, 1.024758538166374)

Though the loc and scale seem ok (close to 0 and 1 respectively), I
was hoping the first return value would be closer to 0.3 since the
shape parameter I supplied while generating random samples was 0.3 (in
the call to rvs).

I initially thought 1000 samples was perhaps too few, but get
similarly poor results with 10,000 samples as well. Concluded that I
must be doing something wrong, or that I'm misinterpreting the usage
for the rvs() or fit() functions. Is the fit function supposed to try
to approximately find the same shape parameter I passed in to rvs? Or
should I be interpreting the fit result differently?

Wondering if anyone has any ideas?

Thanks a lot,
Arvind


From charlesr.harris at gmail.com  Mon Mar 18 14:58:20 2013
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Mon, 18 Mar 2013 12:58:20 -0600
Subject: [SciPy-User] expanding optimize.brentq
In-Reply-To: <CAMMTP+C0uNsAsi2TgQj+M=o99+0Y+LC456YfR+gn-apyADY+Lg@mail.gmail.com>
References: <CAMMTP+C0uNsAsi2TgQj+M=o99+0Y+LC456YfR+gn-apyADY+Lg@mail.gmail.com>
Message-ID: <CAB6mnxJt+pZOnsEUo=kTRRqzos+Eu3CF25p3GPj=KvmNBjGU1w@mail.gmail.com>

On Mon, Mar 18, 2013 at 11:13 AM, <josef.pktd at gmail.com> wrote:

> Just a thought, given the problems with fsolve
>
> In scipy.stats.distribution, we use an expanding brentq, which looks
> like it works very well.
> https://github.com/scipy/scipy/pull/216/files#L0R1175
>
> A question given that I have not much experience with root finders:
>
> Is there a general algorithm that has similar properties?
>
>
> In these application we know that the function is strictly monotonic,
> but we don't have a bound for brentq without checking. For some cases
> I know one of the bounds (e.g. 0 for non-negative solutions).
>
> (I could use that right now as a replacement for fsolve.)
>
>
I assume you are talking about the 1-D case where you don't have bounding
intervals to start with. There are various search methods that can be used
to find such intervals, but they can't be guaranteed to work. They all come
down to making trials of various points looking for sign reversals, and the
searches are either subdividing a larger interval or creating larger and
larger intervals by, say, doubling each time around. The addition of such
methods would be useful, but they need a failure mode :(

Your application looks to be finding points on a monotone function on the
interval [0, inf]. For that you could use the interval [0, 1] and map it to
[0, inf] with the substitution x/1-x, although the point x == 1 will be a
bit tricky unless your function handles inf gracefully. The accuracy will
also tend to be relative rather than absolute, but that seems to be a given
in any case. I suppose this approach falls under the change of variables
method. Those tend to be problem specific, so I don't know if they would be
a good fit for scipy, but certainly they could work well in specialized
domains. It would also seem to be a good idea to match the asymptotic
behavior if possible, which will tend to linearize the problem. So other
options would be functions like log, erf, arctan, etc, for the
substitution, but in those cases you probably already have the the inverse
functions.

Chuck
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From jsseabold at gmail.com  Mon Mar 18 15:09:32 2013
From: jsseabold at gmail.com (Skipper Seabold)
Date: Mon, 18 Mar 2013 15:09:32 -0400
Subject: [SciPy-User] expanding optimize.brentq
In-Reply-To: <CAB6mnxJt+pZOnsEUo=kTRRqzos+Eu3CF25p3GPj=KvmNBjGU1w@mail.gmail.com>
References: <CAMMTP+C0uNsAsi2TgQj+M=o99+0Y+LC456YfR+gn-apyADY+Lg@mail.gmail.com>
	<CAB6mnxJt+pZOnsEUo=kTRRqzos+Eu3CF25p3GPj=KvmNBjGU1w@mail.gmail.com>
Message-ID: <CAKF=DjsJ1Le=pa4k4=i2_x_DnNMiYaF0pWiOTB1mSpVsxjF4uQ@mail.gmail.com>

On Mon, Mar 18, 2013 at 2:58 PM, Charles R Harris <charlesr.harris at gmail.com
> wrote:

>
>
> On Mon, Mar 18, 2013 at 11:13 AM, <josef.pktd at gmail.com> wrote:
>
>> Just a thought, given the problems with fsolve
>>
>> In scipy.stats.distribution, we use an expanding brentq, which looks
>> like it works very well.
>> https://github.com/scipy/scipy/pull/216/files#L0R1175
>>
>> A question given that I have not much experience with root finders:
>>
>> Is there a general algorithm that has similar properties?
>>
>>
>> In these application we know that the function is strictly monotonic,
>> but we don't have a bound for brentq without checking. For some cases
>> I know one of the bounds (e.g. 0 for non-negative solutions).
>>
>> (I could use that right now as a replacement for fsolve.)
>>
>
<snip>


> Your application looks to be finding points on a monotone function on the
> interval [0, inf]. For that you could use the interval [0, 1] and map it to
> [0, inf] with the substitution x/1-x, although the point x == 1 will be a
> bit tricky unless your function handles inf gracefully. The accuracy will
> also tend to be relative rather than absolute, but that seems to be a given
> in any case. I suppose this approach falls under the change of variables
> method.
>

Ah, right. Yes, I think this will work well for our current use case (if I
understand the scope of the problem sufficiently). At least it seems to do
the trick with the sticking points I was seeing. It also improves the
perfomance without the bounding interval methods - fsolve, etc.

Thanks, Chuck.

Skipper



> Those tend to be problem specific, so I don't know if they would be a good
> fit for scipy, but certainly they could work well in specialized domains.
> It would also seem to be a good idea to match the asymptotic behavior if
> possible, which will tend to linearize the problem. So other options would
> be functions like log, erf, arctan, etc, for the substitution, but in those
> cases you probably already have the the inverse functions.
>
> Chuck
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
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From josef.pktd at gmail.com  Mon Mar 18 15:23:19 2013
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Mon, 18 Mar 2013 15:23:19 -0400
Subject: [SciPy-User] expanding optimize.brentq
In-Reply-To: <CAB6mnxJt+pZOnsEUo=kTRRqzos+Eu3CF25p3GPj=KvmNBjGU1w@mail.gmail.com>
References: <CAMMTP+C0uNsAsi2TgQj+M=o99+0Y+LC456YfR+gn-apyADY+Lg@mail.gmail.com>
	<CAB6mnxJt+pZOnsEUo=kTRRqzos+Eu3CF25p3GPj=KvmNBjGU1w@mail.gmail.com>
Message-ID: <CAMMTP+AtKC-Qz2-bUOxP+ap0Yn5c0dJpp5KB_66vWiBkyCa7Pw@mail.gmail.com>

On Mon, Mar 18, 2013 at 2:58 PM, Charles R Harris
<charlesr.harris at gmail.com> wrote:
>
>
> On Mon, Mar 18, 2013 at 11:13 AM, <josef.pktd at gmail.com> wrote:
>>
>> Just a thought, given the problems with fsolve
>>
>> In scipy.stats.distribution, we use an expanding brentq, which looks
>> like it works very well.
>> https://github.com/scipy/scipy/pull/216/files#L0R1175
>>
>> A question given that I have not much experience with root finders:
>>
>> Is there a general algorithm that has similar properties?
>>
>>
>> In these application we know that the function is strictly monotonic,
>> but we don't have a bound for brentq without checking. For some cases
>> I know one of the bounds (e.g. 0 for non-negative solutions).
>>
>> (I could use that right now as a replacement for fsolve.)
>>
>
> I assume you are talking about the 1-D case where you don't have bounding
> intervals to start with. There are various search methods that can be used
> to find such intervals, but they can't be guaranteed to work. They all come
> down to making trials of various points looking for sign reversals, and the
> searches are either subdividing a larger interval or creating larger and
> larger intervals by, say, doubling each time around. The addition of such
> methods would be useful, but they need a failure mode :(

Yes that's pretty much what I would like, and use in the rewritten
scipy.stats.distribution function.
But I was hoping there might be something more systematic than "home-made".

>
> Your application looks to be finding points on a monotone function on the
> interval [0, inf]. For that you could use the interval [0, 1] and map it to
> [0, inf] with the substitution x/1-x, although the point x == 1 will be a
> bit tricky unless your function handles inf gracefully. The accuracy will
> also tend to be relative rather than absolute, but that seems to be a given
> in any case. I suppose this approach falls under the change of variables
> method. Those tend to be problem specific, so I don't know if they would be
> a good fit for scipy, but certainly they could work well in specialized
> domains. It would also seem to be a good idea to match the asymptotic
> behavior if possible, which will tend to linearize the problem. So other
> options would be functions like log, erf, arctan, etc, for the substitution,
> but in those cases you probably already have the the inverse functions.

That might be a good idea to get brentq to influence the selection of
subdivisions, exponential instead of linear. One problem with brentq
is that I would have to give one huge bound, but the most likely case
is much smaller.
(example find sample size for power identity, we should get an answer
below 1000 but in some cases it could be 100.000)

One related problem for the bounded case in (0,1) is the open
interval, I use 1e-8, some packages use 1e-6 to stay away from the
undefined boundary points.
But again, there could be some extreme cases where the solution is
closer to 0 or 1, than the 1e-8.
(I don't remember if we found a solution to a similar problem in the
stats.distributions, or if I mix up a case there.)

Josef

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


From josef.pktd at gmail.com  Mon Mar 18 15:28:32 2013
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Mon, 18 Mar 2013 15:28:32 -0400
Subject: [SciPy-User] scipy.stats beginner: help with fitting
	distribution
In-Reply-To: <CAHbAt1p8eeB6OG3U9MeU2rw9jPXnSbpj5LqXc2UiGjuQ8pD55g@mail.gmail.com>
References: <CAHbAt1p8eeB6OG3U9MeU2rw9jPXnSbpj5LqXc2UiGjuQ8pD55g@mail.gmail.com>
Message-ID: <CAMMTP+DvuRUAhMDop8feE=JXTEmOrr28gdVSXokjBnfgcHMD8Q@mail.gmail.com>

On Mon, Mar 18, 2013 at 2:46 PM, Arvind Thiagarajan <t.arvind at gmail.com> wrote:
> Hi all,
>
> I'm a beginner with SciPy so this may be a basic question. I am trying
> to fit a Rice distribution to some data using scipy.stats.
>
> However, I first tried some test code which doesn't seem to give me a
> very good fit. I tried the following code:
>
>>>> b = [0.3,]
>>>> samples = rice.rvs(b, loc=0, scale=1, size=1000)
>>>> rice.fit(samples)
> (0.0012012190480231357, -0.0023216862043629813, 1.024758538166374)
>
> Though the loc and scale seem ok (close to 0 and 1 respectively), I
> was hoping the first return value would be closer to 0.3 since the
> shape parameter I supplied while generating random samples was 0.3 (in
> the call to rvs).
>
> I initially thought 1000 samples was perhaps too few, but get
> similarly poor results with 10,000 samples as well. Concluded that I
> must be doing something wrong, or that I'm misinterpreting the usage
> for the rvs() or fit() functions. Is the fit function supposed to try
> to approximately find the same shape parameter I passed in to rvs? Or
> should I be interpreting the fit result differently?
>
> Wondering if anyone has any ideas?

general answer:
http://stackoverflow.com/questions/15468215/python-scipy-stats-pareto-fit-how-does-it-work

For some distributions it helps to try out different starting values.
For some distributions, the maximum likelihood estimation does not
work (like pareto if we want to estimate also loc), either curvature
problems or likelihood can go to infinite.

I don't know anything about the rice distributions, and don't know how
difficult it is to estimate the parameters.

Josef

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


From t.arvind at gmail.com  Mon Mar 18 15:57:13 2013
From: t.arvind at gmail.com (Arvind Thiagarajan)
Date: Mon, 18 Mar 2013 12:57:13 -0700
Subject: [SciPy-User] scipy.stats beginner: help with fitting
	distribution
In-Reply-To: <CAMMTP+DvuRUAhMDop8feE=JXTEmOrr28gdVSXokjBnfgcHMD8Q@mail.gmail.com>
References: <CAHbAt1p8eeB6OG3U9MeU2rw9jPXnSbpj5LqXc2UiGjuQ8pD55g@mail.gmail.com>
	<CAMMTP+DvuRUAhMDop8feE=JXTEmOrr28gdVSXokjBnfgcHMD8Q@mail.gmail.com>
Message-ID: <CAHbAt1o3qQJyYYpAnjG0196YCdz1ZVyc_2TW3b5g_-dchCq5Lw@mail.gmail.com>

Thanks Josef. I played around a bit more and in the Rice case, it
appears the estimation is accurate only if loc and scale are supplied
--- the fit is unable to estimate all three together accurately. Also,
the estimation seems only accurate for larger values of the shape
parameter. Just mentioning this for anyone else who may see similar
problems.

Thanks,
Arvind

On Mon, Mar 18, 2013 at 12:28 PM,  <josef.pktd at gmail.com> wrote:
> On Mon, Mar 18, 2013 at 2:46 PM, Arvind Thiagarajan <t.arvind at gmail.com> wrote:
>> Hi all,
>>
>> I'm a beginner with SciPy so this may be a basic question. I am trying
>> to fit a Rice distribution to some data using scipy.stats.
>>
>> However, I first tried some test code which doesn't seem to give me a
>> very good fit. I tried the following code:
>>
>>>>> b = [0.3,]
>>>>> samples = rice.rvs(b, loc=0, scale=1, size=1000)
>>>>> rice.fit(samples)
>> (0.0012012190480231357, -0.0023216862043629813, 1.024758538166374)
>>
>> Though the loc and scale seem ok (close to 0 and 1 respectively), I
>> was hoping the first return value would be closer to 0.3 since the
>> shape parameter I supplied while generating random samples was 0.3 (in
>> the call to rvs).
>>
>> I initially thought 1000 samples was perhaps too few, but get
>> similarly poor results with 10,000 samples as well. Concluded that I
>> must be doing something wrong, or that I'm misinterpreting the usage
>> for the rvs() or fit() functions. Is the fit function supposed to try
>> to approximately find the same shape parameter I passed in to rvs? Or
>> should I be interpreting the fit result differently?
>>
>> Wondering if anyone has any ideas?
>
> general answer:
> http://stackoverflow.com/questions/15468215/python-scipy-stats-pareto-fit-how-does-it-work
>
> For some distributions it helps to try out different starting values.
> For some distributions, the maximum likelihood estimation does not
> work (like pareto if we want to estimate also loc), either curvature
> problems or likelihood can go to infinite.
>
> I don't know anything about the rice distributions, and don't know how
> difficult it is to estimate the parameters.
>
> Josef
>
>>
>> Thanks a lot,
>> Arvind
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user


From josef.pktd at gmail.com  Mon Mar 18 16:37:28 2013
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Mon, 18 Mar 2013 16:37:28 -0400
Subject: [SciPy-User] scipy.stats beginner: help with fitting
	distribution
In-Reply-To: <CAHbAt1o3qQJyYYpAnjG0196YCdz1ZVyc_2TW3b5g_-dchCq5Lw@mail.gmail.com>
References: <CAHbAt1p8eeB6OG3U9MeU2rw9jPXnSbpj5LqXc2UiGjuQ8pD55g@mail.gmail.com>
	<CAMMTP+DvuRUAhMDop8feE=JXTEmOrr28gdVSXokjBnfgcHMD8Q@mail.gmail.com>
	<CAHbAt1o3qQJyYYpAnjG0196YCdz1ZVyc_2TW3b5g_-dchCq5Lw@mail.gmail.com>
Message-ID: <CAMMTP+AFCMQChaNdWwCTQzT_RThQfy=m9XPyT_YZsJn8pmOuEw@mail.gmail.com>

On Mon, Mar 18, 2013 at 3:57 PM, Arvind Thiagarajan <t.arvind at gmail.com> wrote:
> Thanks Josef. I played around a bit more and in the Rice case, it
> appears the estimation is accurate only if loc and scale are supplied
> --- the fit is unable to estimate all three together accurately. Also,
> the estimation seems only accurate for larger values of the shape
> parameter. Just mentioning this for anyone else who may see similar
> problems.

quick look:
http://en.wikipedia.org/wiki/Rice_distribution#Parameter_estimation_.28the_Koay_inversion_technique.29
maybe it should work if loc is fixed.

Josef


>
> Thanks,
> Arvind
>
> On Mon, Mar 18, 2013 at 12:28 PM,  <josef.pktd at gmail.com> wrote:
>> On Mon, Mar 18, 2013 at 2:46 PM, Arvind Thiagarajan <t.arvind at gmail.com> wrote:
>>> Hi all,
>>>
>>> I'm a beginner with SciPy so this may be a basic question. I am trying
>>> to fit a Rice distribution to some data using scipy.stats.
>>>
>>> However, I first tried some test code which doesn't seem to give me a
>>> very good fit. I tried the following code:
>>>
>>>>>> b = [0.3,]
>>>>>> samples = rice.rvs(b, loc=0, scale=1, size=1000)
>>>>>> rice.fit(samples)
>>> (0.0012012190480231357, -0.0023216862043629813, 1.024758538166374)
>>>
>>> Though the loc and scale seem ok (close to 0 and 1 respectively), I
>>> was hoping the first return value would be closer to 0.3 since the
>>> shape parameter I supplied while generating random samples was 0.3 (in
>>> the call to rvs).
>>>
>>> I initially thought 1000 samples was perhaps too few, but get
>>> similarly poor results with 10,000 samples as well. Concluded that I
>>> must be doing something wrong, or that I'm misinterpreting the usage
>>> for the rvs() or fit() functions. Is the fit function supposed to try
>>> to approximately find the same shape parameter I passed in to rvs? Or
>>> should I be interpreting the fit result differently?
>>>
>>> Wondering if anyone has any ideas?
>>
>> general answer:
>> http://stackoverflow.com/questions/15468215/python-scipy-stats-pareto-fit-how-does-it-work
>>
>> For some distributions it helps to try out different starting values.
>> For some distributions, the maximum likelihood estimation does not
>> work (like pareto if we want to estimate also loc), either curvature
>> problems or likelihood can go to infinite.
>>
>> I don't know anything about the rice distributions, and don't know how
>> difficult it is to estimate the parameters.
>>
>> Josef
>>
>>>
>>> Thanks a lot,
>>> Arvind
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user


From josef.pktd at gmail.com  Mon Mar 18 22:02:04 2013
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Mon, 18 Mar 2013 22:02:04 -0400
Subject: [SciPy-User] expanding optimize.brentq
In-Reply-To: <CAMMTP+AtKC-Qz2-bUOxP+ap0Yn5c0dJpp5KB_66vWiBkyCa7Pw@mail.gmail.com>
References: <CAMMTP+C0uNsAsi2TgQj+M=o99+0Y+LC456YfR+gn-apyADY+Lg@mail.gmail.com>
	<CAB6mnxJt+pZOnsEUo=kTRRqzos+Eu3CF25p3GPj=KvmNBjGU1w@mail.gmail.com>
	<CAMMTP+AtKC-Qz2-bUOxP+ap0Yn5c0dJpp5KB_66vWiBkyCa7Pw@mail.gmail.com>
Message-ID: <CAMMTP+BjxYT=nyOKBiLraVQnVSFk0_9bRimRYLjmAOF32pxgrg@mail.gmail.com>

On Mon, Mar 18, 2013 at 3:23 PM,  <josef.pktd at gmail.com> wrote:
> On Mon, Mar 18, 2013 at 2:58 PM, Charles R Harris
> <charlesr.harris at gmail.com> wrote:
>>
>>
>> On Mon, Mar 18, 2013 at 11:13 AM, <josef.pktd at gmail.com> wrote:
>>>
>>> Just a thought, given the problems with fsolve
>>>
>>> In scipy.stats.distribution, we use an expanding brentq, which looks
>>> like it works very well.
>>> https://github.com/scipy/scipy/pull/216/files#L0R1175
>>>
>>> A question given that I have not much experience with root finders:
>>>
>>> Is there a general algorithm that has similar properties?
>>>
>>>
>>> In these application we know that the function is strictly monotonic,
>>> but we don't have a bound for brentq without checking. For some cases
>>> I know one of the bounds (e.g. 0 for non-negative solutions).
>>>
>>> (I could use that right now as a replacement for fsolve.)
>>>
>>
>> I assume you are talking about the 1-D case where you don't have bounding
>> intervals to start with. There are various search methods that can be used
>> to find such intervals, but they can't be guaranteed to work. They all come
>> down to making trials of various points looking for sign reversals, and the
>> searches are either subdividing a larger interval or creating larger and
>> larger intervals by, say, doubling each time around. The addition of such
>> methods would be useful, but they need a failure mode :(
>
> Yes that's pretty much what I would like, and use in the rewritten
> scipy.stats.distribution function.
> But I was hoping there might be something more systematic than "home-made".

a "home-made" proof of concept

it works, but is still pretty ugly, and can only expand away from zero
(since this was the case that
we needed in scipy.stats.distribution)

I just kept piling on conditions until all my test cases finished
without exception.

Josef

------------------
# -*- coding: utf-8 -*-
"""

Created on Mon Mar 18 15:48:23 2013
Author: Josef Perktold

"""

import numpy as np
from scipy import optimize

# based on scipy.stats.distributions._ppf_single_call
def brentq_expanding(func, low=None, upp=None, args=(), xtol=1e-5,
                     start_low=None, start_upp=None, increasing=None):
    #assumes monotonic ``func``

    left, right = low, upp  #alias

    # start_upp first because of possible sl = -1 > upp
    if upp is not None:
        su = upp
    elif start_upp is not None:
        su = start_upp
        if start_upp < 0:
            print "raise ValueError('start_upp needs to be positive')"
    else:
        su = 1
        start_upp = 1


    if low is not None:
        sl = low
    elif start_low is not None:
        sl = start_low
        if start_low > 0:
            print "raise ValueError('start_low needs to be negative')"
    else:
        sl = min(-1, su - 1)
        start_low = sl

    # need sl < su
    if upp is None:
        su = max(su, sl + 1)


    # increasing or not ?
    if ((low is None) or (upp is None)) and increasing is None:
        assert sl < su
        f_low = func(sl, *args)
        f_upp = func(su, *args)
        increasing = (f_low < f_upp)

    print 'low, upp', low, upp
    print 'increasing', increasing
    print 'sl, su', sl, su


    start_low, start_upp = sl, su
    if not increasing:
        start_low, start_upp =  start_upp, start_low
        left, right = right, left

    max_it = 10
    n_it = 0
    factor = 10.
    if left is None:
        left = start_low #* factor
        while func(left, *args) > 0:
            right = left
            left *= factor
            if n_it >= max_it:
                break
            n_it += 1
        # left is now such that cdf(left) < q
    if right is None:
        right = start_upp #* factor
        while func(right, *args) < 0:
            left = right
            right *= factor
            if n_it >= max_it:
                break
            n_it += 1
        # right is now such that cdf(right) > q

#    if left > right:
#        left, right = right, left #swap
    return optimize.brentq(func, \
                           left, right, args=args, xtol=xtol)


def func(x, a):
    f = (x - a)**3
    print 'evaluating at %f, fval = %f' % (x, f)
    return f



def funcn(x, a):
    f = -(x - a)**3
    print 'evaluating at %f, fval = %f' % (x, f)
    return f

print brentq_expanding(func, args=(0,), increasing=True)

print brentq_expanding(funcn, args=(0,), increasing=False)
print brentq_expanding(funcn, args=(-50,), increasing=False)

print brentq_expanding(func, args=(20,))
print brentq_expanding(funcn, args=(20,))
print brentq_expanding(func, args=(500000,))

# one bound
print brentq_expanding(func, args=(500000,), low=10000)
print brentq_expanding(func, args=(-50000,), upp=-1000)

print brentq_expanding(funcn, args=(500000,), low=10000)
print brentq_expanding(funcn, args=(-50000,), upp=-1000)

# both bounds
# hits maxiter in brentq if bounds too wide
print brentq_expanding(func, args=(500000,), low=300000, upp=700000)
print brentq_expanding(func, args=(-50000,), low= -70000, upp=-1000)
print brentq_expanding(funcn, args=(500000,), low=300000, upp=700000)
print brentq_expanding(funcn, args=(-50000,), low= -70000, upp=-10000)
-----------------------------

>
>>
>> Your application looks to be finding points on a monotone function on the
>> interval [0, inf]. For that you could use the interval [0, 1] and map it to
>> [0, inf] with the substitution x/1-x, although the point x == 1 will be a
>> bit tricky unless your function handles inf gracefully. The accuracy will
>> also tend to be relative rather than absolute, but that seems to be a given
>> in any case. I suppose this approach falls under the change of variables
>> method. Those tend to be problem specific, so I don't know if they would be
>> a good fit for scipy, but certainly they could work well in specialized
>> domains. It would also seem to be a good idea to match the asymptotic
>> behavior if possible, which will tend to linearize the problem. So other
>> options would be functions like log, erf, arctan, etc, for the substitution,
>> but in those cases you probably already have the the inverse functions.
>
> That might be a good idea to get brentq to influence the selection of
> subdivisions, exponential instead of linear. One problem with brentq
> is that I would have to give one huge bound, but the most likely case
> is much smaller.
> (example find sample size for power identity, we should get an answer
> below 1000 but in some cases it could be 100.000)
>
> One related problem for the bounded case in (0,1) is the open
> interval, I use 1e-8, some packages use 1e-6 to stay away from the
> undefined boundary points.
> But again, there could be some extreme cases where the solution is
> closer to 0 or 1, than the 1e-8.
> (I don't remember if we found a solution to a similar problem in the
> stats.distributions, or if I mix up a case there.)
>
> Josef
>
>>
>> Chuck
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>


From josef.pktd at gmail.com  Mon Mar 18 22:32:22 2013
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Mon, 18 Mar 2013 22:32:22 -0400
Subject: [SciPy-User] expanding optimize.brentq
In-Reply-To: <CAMMTP+BjxYT=nyOKBiLraVQnVSFk0_9bRimRYLjmAOF32pxgrg@mail.gmail.com>
References: <CAMMTP+C0uNsAsi2TgQj+M=o99+0Y+LC456YfR+gn-apyADY+Lg@mail.gmail.com>
	<CAB6mnxJt+pZOnsEUo=kTRRqzos+Eu3CF25p3GPj=KvmNBjGU1w@mail.gmail.com>
	<CAMMTP+AtKC-Qz2-bUOxP+ap0Yn5c0dJpp5KB_66vWiBkyCa7Pw@mail.gmail.com>
	<CAMMTP+BjxYT=nyOKBiLraVQnVSFk0_9bRimRYLjmAOF32pxgrg@mail.gmail.com>
Message-ID: <CAMMTP+AQY1dCfLeKjyW-1hy-grrZauu03LoRhdqTafEai7BFmg@mail.gmail.com>

On Mon, Mar 18, 2013 at 10:02 PM,  <josef.pktd at gmail.com> wrote:
> On Mon, Mar 18, 2013 at 3:23 PM,  <josef.pktd at gmail.com> wrote:
>> On Mon, Mar 18, 2013 at 2:58 PM, Charles R Harris
>> <charlesr.harris at gmail.com> wrote:
>>>
>>>
>>> On Mon, Mar 18, 2013 at 11:13 AM, <josef.pktd at gmail.com> wrote:
>>>>
>>>> Just a thought, given the problems with fsolve
>>>>
>>>> In scipy.stats.distribution, we use an expanding brentq, which looks
>>>> like it works very well.
>>>> https://github.com/scipy/scipy/pull/216/files#L0R1175
>>>>
>>>> A question given that I have not much experience with root finders:
>>>>
>>>> Is there a general algorithm that has similar properties?
>>>>
>>>>
>>>> In these application we know that the function is strictly monotonic,
>>>> but we don't have a bound for brentq without checking. For some cases
>>>> I know one of the bounds (e.g. 0 for non-negative solutions).
>>>>
>>>> (I could use that right now as a replacement for fsolve.)
>>>>
>>>
>>> I assume you are talking about the 1-D case where you don't have bounding
>>> intervals to start with. There are various search methods that can be used
>>> to find such intervals, but they can't be guaranteed to work. They all come
>>> down to making trials of various points looking for sign reversals, and the
>>> searches are either subdividing a larger interval or creating larger and
>>> larger intervals by, say, doubling each time around. The addition of such
>>> methods would be useful, but they need a failure mode :(
>>
>> Yes that's pretty much what I would like, and use in the rewritten
>> scipy.stats.distribution function.
>> But I was hoping there might be something more systematic than "home-made".
>
> a "home-made" proof of concept
>
> it works, but is still pretty ugly, and can only expand away from zero
> (since this was the case that
> we needed in scipy.stats.distribution)
>
> I just kept piling on conditions until all my test cases finished
> without exception.

"silly" usecase: find the zero of a cubic function (x-a)**3 if the
root a is 1.234e30:

we need to specify that the function is increasing (and increase both maxiter)

print brentq_expanding(func, args=(1.234e30,), increasing=True, maxiter_bq=200)

first and last steps of the root-finding:

evaluating at -1.000000, fval = -1.87908e+90
evaluating at 1.000000, fval = -1.87908e+90
evaluating at 10.000000, fval = -1.87908e+90
evaluating at 100.000000, fval = -1.87908e+90
evaluating at 1000.000000, fval = -1.87908e+90
evaluating at 10000.000000, fval = -1.87908e+90
evaluating at 100000.000000, fval = -1.87908e+90
evaluating at 1000000.000000, fval = -1.87908e+90
evaluating at 10000000.000000, fval = -1.87908e+90
evaluating at 100000000.000000, fval = -1.87908e+90
.....
evaluating at 1233999999999999400000000000000.000000, fval =
-178405961588244990000000000000000000000000000.000000
evaluating at 1233999999999999700000000000000.000000, fval =
-22300745198530623000000000000000000000000000.000000
evaluating at 1234000000000000800000000000000.000000, fval =
602120120360326820000000000000000000000000000.000000
evaluating at 1234000000000000000000000000000.000000, fval = 0.000000
1.234e+30

Josef
fun
>
> Josef
>
> ------------------
> # -*- coding: utf-8 -*-
> """
>
> Created on Mon Mar 18 15:48:23 2013
> Author: Josef Perktold
>
> """
>
> import numpy as np
> from scipy import optimize
>
> # based on scipy.stats.distributions._ppf_single_call
> def brentq_expanding(func, low=None, upp=None, args=(), xtol=1e-5,
>                      start_low=None, start_upp=None, increasing=None):
>     #assumes monotonic ``func``
>
>     left, right = low, upp  #alias
>
>     # start_upp first because of possible sl = -1 > upp
>     if upp is not None:
>         su = upp
>     elif start_upp is not None:
>         su = start_upp
>         if start_upp < 0:
>             print "raise ValueError('start_upp needs to be positive')"
>     else:
>         su = 1
>         start_upp = 1
>
>
>     if low is not None:
>         sl = low
>     elif start_low is not None:
>         sl = start_low
>         if start_low > 0:
>             print "raise ValueError('start_low needs to be negative')"
>     else:
>         sl = min(-1, su - 1)
>         start_low = sl
>
>     # need sl < su
>     if upp is None:
>         su = max(su, sl + 1)
>
>
>     # increasing or not ?
>     if ((low is None) or (upp is None)) and increasing is None:
>         assert sl < su
>         f_low = func(sl, *args)
>         f_upp = func(su, *args)
>         increasing = (f_low < f_upp)
>
>     print 'low, upp', low, upp
>     print 'increasing', increasing
>     print 'sl, su', sl, su
>
>
>     start_low, start_upp = sl, su
>     if not increasing:
>         start_low, start_upp =  start_upp, start_low
>         left, right = right, left
>
>     max_it = 10
>     n_it = 0
>     factor = 10.
>     if left is None:
>         left = start_low #* factor
>         while func(left, *args) > 0:
>             right = left
>             left *= factor
>             if n_it >= max_it:
>                 break
>             n_it += 1
>         # left is now such that cdf(left) < q
>     if right is None:
>         right = start_upp #* factor
>         while func(right, *args) < 0:
>             left = right
>             right *= factor
>             if n_it >= max_it:
>                 break
>             n_it += 1
>         # right is now such that cdf(right) > q
>
> #    if left > right:
> #        left, right = right, left #swap
>     return optimize.brentq(func, \
>                            left, right, args=args, xtol=xtol)
>
>
> def func(x, a):
>     f = (x - a)**3
>     print 'evaluating at %f, fval = %f' % (x, f)
>     return f
>
>
>
> def funcn(x, a):
>     f = -(x - a)**3
>     print 'evaluating at %f, fval = %f' % (x, f)
>     return f
>
> print brentq_expanding(func, args=(0,), increasing=True)
>
> print brentq_expanding(funcn, args=(0,), increasing=False)
> print brentq_expanding(funcn, args=(-50,), increasing=False)
>
> print brentq_expanding(func, args=(20,))
> print brentq_expanding(funcn, args=(20,))
> print brentq_expanding(func, args=(500000,))
>
> # one bound
> print brentq_expanding(func, args=(500000,), low=10000)
> print brentq_expanding(func, args=(-50000,), upp=-1000)
>
> print brentq_expanding(funcn, args=(500000,), low=10000)
> print brentq_expanding(funcn, args=(-50000,), upp=-1000)
>
> # both bounds
> # hits maxiter in brentq if bounds too wide
> print brentq_expanding(func, args=(500000,), low=300000, upp=700000)
> print brentq_expanding(func, args=(-50000,), low= -70000, upp=-1000)
> print brentq_expanding(funcn, args=(500000,), low=300000, upp=700000)
> print brentq_expanding(funcn, args=(-50000,), low= -70000, upp=-10000)
> -----------------------------
>
>>
>>>
>>> Your application looks to be finding points on a monotone function on the
>>> interval [0, inf]. For that you could use the interval [0, 1] and map it to
>>> [0, inf] with the substitution x/1-x, although the point x == 1 will be a
>>> bit tricky unless your function handles inf gracefully. The accuracy will
>>> also tend to be relative rather than absolute, but that seems to be a given
>>> in any case. I suppose this approach falls under the change of variables
>>> method. Those tend to be problem specific, so I don't know if they would be
>>> a good fit for scipy, but certainly they could work well in specialized
>>> domains. It would also seem to be a good idea to match the asymptotic
>>> behavior if possible, which will tend to linearize the problem. So other
>>> options would be functions like log, erf, arctan, etc, for the substitution,
>>> but in those cases you probably already have the the inverse functions.
>>
>> That might be a good idea to get brentq to influence the selection of
>> subdivisions, exponential instead of linear. One problem with brentq
>> is that I would have to give one huge bound, but the most likely case
>> is much smaller.
>> (example find sample size for power identity, we should get an answer
>> below 1000 but in some cases it could be 100.000)
>>
>> One related problem for the bounded case in (0,1) is the open
>> interval, I use 1e-8, some packages use 1e-6 to stay away from the
>> undefined boundary points.
>> But again, there could be some extreme cases where the solution is
>> closer to 0 or 1, than the 1e-8.
>> (I don't remember if we found a solution to a similar problem in the
>> stats.distributions, or if I mix up a case there.)
>>
>> Josef
>>
>>>
>>> Chuck
>>>
>>> _______________________________________________
>>> SciPy-User mailing list
>>> SciPy-User at scipy.org
>>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>>


From lanceboyle at qwest.net  Tue Mar 19 03:02:20 2013
From: lanceboyle at qwest.net (Jerry)
Date: Tue, 19 Mar 2013 00:02:20 -0700
Subject: [SciPy-User] ANN: Pyzo distro 2013b
In-Reply-To: <CAB_T6=mbbtVj5R5+Bby+GVosL4NVunZgVbnzZNwW1vWztqMyhQ@mail.gmail.com>
References: <CAB_T6=kYOCF38uoRin3CjmwF=ckWSW+cFyDyFNC=ZONYd23Kqw@mail.gmail.com>
	<152D9C59-D775-4217-9A98-79F3CD6E71AE@qwest.net>
	<CAB_T6=mbbtVj5R5+Bby+GVosL4NVunZgVbnzZNwW1vWztqMyhQ@mail.gmail.com>
Message-ID: <0242EE41-9844-4EAB-9F3D-1FEEA4DBF124@qwest.net>


On Mar 18, 2013, at 3:56 AM, Almar Klein wrote:

> 
> Pyzo crashed on OS X 10.7.5 in under three minutes while trying to run a hello world program.
> 
> I just found: "Mac OS X 10.7 "Lion" was the first version of OS X to drop support for 32-bit Intel processors and run exclusively on 64-bit Intel CPUs." 
> So, I'm sorry, but you'll have to wait for the next release.
> 
> The reason that the binaries are 32 bit is that I forgot to make the VM 64bit when I created it. Woops. I only found out much later (after all the hard work of setting up the machine was done). So the blame's on me.
> 
> - Almar

Thanks for looking into this.
Jerry
> 
> 
>  
> 
>> Dear all,
>> 
>> We're pleased to announce release 2013b of Pyzo distro, a Python distribution for scientific computing based on Python 3. For more information, and to give it a try, visit http://www.pyzo.org.
>> 
>> The most notable changes since the last release:
>> Pyzo is also build for Mac.
>> Python 3.3 is now used.
>> Added an executable to launch IPython notebook.
>> New versions of several packages, including the IDE.
>> More information:
>> 
>> Pyzo is available for Windows, Mac and Linux. The distribution is portable, thus providing a way to install a scientific Python stack on computers without the need for admin rights.
>> 
>> Naturally, Pyzo distro is complient with the scipy-stack, and comes with additional packages such as scikit-image and scikit-learn.
>> 
>> With Pyzo we want to make scientific computing in Python easier accessible. We especially hope to make it easier for newcomers (such as Matlab converts) to join our awesome community. Pyzo uses  IEP as the default front-end IDE, and IPython (with notebook) is also available.
>> 
>> Happy coding,
>>   Almar
>> 
>> _______________________________________________
>> 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
> 
> 
> 
> 
> -- 
> Almar Klein, PhD
> Science Applied
> phone: +31 6 19268652
> e-mail: a.klein at science-applied.nl
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user

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From seb.haase at gmail.com  Tue Mar 19 03:57:05 2013
From: seb.haase at gmail.com (Sebastian Haase)
Date: Tue, 19 Mar 2013 08:57:05 +0100
Subject: [SciPy-User] ANN: Pyzo distro 2013b
In-Reply-To: <0242EE41-9844-4EAB-9F3D-1FEEA4DBF124@qwest.net>
References: <CAB_T6=kYOCF38uoRin3CjmwF=ckWSW+cFyDyFNC=ZONYd23Kqw@mail.gmail.com>
	<152D9C59-D775-4217-9A98-79F3CD6E71AE@qwest.net>
	<CAB_T6=mbbtVj5R5+Bby+GVosL4NVunZgVbnzZNwW1vWztqMyhQ@mail.gmail.com>
	<0242EE41-9844-4EAB-9F3D-1FEEA4DBF124@qwest.net>
Message-ID: <CAN06oV-NNmhwM7N8KSioAKsKpwtW7TW8=--eHC3KGX1uPcW6LQ@mail.gmail.com>

I don't think this is correct:
shaase at sebs-macbookpro:~: python-32
Python 2.7.2 (v2.7.2:8527427914a2, Jun 11 2011, 15:22:34)
[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import sys
>>> sys.maxint
2147483647


Are you talking maybe about the default python installation .... ?

Cheers,
Sebastian Haase


On Tue, Mar 19, 2013 at 8:02 AM, Jerry <lanceboyle at qwest.net> wrote:
>
> On Mar 18, 2013, at 3:56 AM, Almar Klein wrote:
>
>
>> Pyzo crashed on OS X 10.7.5 in under three minutes while trying to run a
>> hello world program.
>
>
> I just found: "Mac OS X 10.7 "Lion" was the first version of OS X to drop
> support for 32-bit Intel processors and run exclusively on 64-bit Intel
> CPUs."
> So, I'm sorry, but you'll have to wait for the next release.
>
> The reason that the binaries are 32 bit is that I forgot to make the VM
> 64bit when I created it. Woops. I only found out much later (after all the
> hard work of setting up the machine was done). So the blame's on me.
>
> - Almar
>
>
> Thanks for looking into this.
> Jerry
>
>
>
>
>>
>>
>> Dear all,
>>
>> We're pleased to announce release 2013b of Pyzo distro, a Python
>> distribution for scientific computing based on Python 3. For more
>> information, and to give it a try, visit http://www.pyzo.org.
>>
>> The most notable changes since the last release:
>>
>> Pyzo is also build for Mac.
>> Python 3.3 is now used.
>> Added an executable to launch IPython notebook.
>> New versions of several packages, including the IDE.
>>
>> More information:
>>
>> Pyzo is available for Windows, Mac and Linux. The distribution is
>> portable, thus providing a way to install a scientific Python stack on
>> computers without the need for admin rights.
>>
>> Naturally, Pyzo distro is complient with the scipy-stack, and comes with
>> additional packages such as scikit-image and scikit-learn.
>>
>> With Pyzo we want to make scientific computing in Python easier
>> accessible. We especially hope to make it easier for newcomers (such as
>> Matlab converts) to join our awesome community. Pyzo uses  IEP as the
>> default front-end IDE, and IPython (with notebook) is also available.
>>
>> Happy coding,
>>   Almar
>>
>> _______________________________________________
>> 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
>>
>
>
>
> --
> Almar Klein, PhD
> Science Applied
> phone: +31 6 19268652
> e-mail: a.klein at science-applied.nl
> _______________________________________________
> 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 seb.haase at gmail.com  Tue Mar 19 03:59:38 2013
From: seb.haase at gmail.com (Sebastian Haase)
Date: Tue, 19 Mar 2013 08:59:38 +0100
Subject: [SciPy-User] ANN: Pyzo distro 2013b
In-Reply-To: <CAN06oV-NNmhwM7N8KSioAKsKpwtW7TW8=--eHC3KGX1uPcW6LQ@mail.gmail.com>
References: <CAB_T6=kYOCF38uoRin3CjmwF=ckWSW+cFyDyFNC=ZONYd23Kqw@mail.gmail.com>
	<152D9C59-D775-4217-9A98-79F3CD6E71AE@qwest.net>
	<CAB_T6=mbbtVj5R5+Bby+GVosL4NVunZgVbnzZNwW1vWztqMyhQ@mail.gmail.com>
	<0242EE41-9844-4EAB-9F3D-1FEEA4DBF124@qwest.net>
	<CAN06oV-NNmhwM7N8KSioAKsKpwtW7TW8=--eHC3KGX1uPcW6LQ@mail.gmail.com>
Message-ID: <CAN06oV-QOQFb2mrqj8akn=56xYbS=7YhdJmJtFF4Z7BCpzxEcA@mail.gmail.com>

shaase at sebs-macbookpro:~: file  /usr/bin/python
/usr/bin/python: Mach-O universal binary with 2 architectures
/usr/bin/python (for architecture x86_64):	Mach-O 64-bit executable x86_64
/usr/bin/python (for architecture i386):	Mach-O executable i386

shaase at sebs-macbookpro:~: uname -a
Darwin xxxxxxxx.fu-berlin.de 11.4.2 Darwin Kernel Version 11.4.2: Thu
Aug 23 16:25:48 PDT 2012; root:xnu-1699.32.7~1/RELEASE_X86_64 x86_64

OS-X: 10.7.5


-S.

On Tue, Mar 19, 2013 at 8:57 AM, Sebastian Haase <seb.haase at gmail.com> wrote:
> I don't think this is correct:
> shaase at sebs-macbookpro:~: python-32
> Python 2.7.2 (v2.7.2:8527427914a2, Jun 11 2011, 15:22:34)
> [GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin
> Type "help", "copyright", "credits" or "license" for more information.
>>>> import sys
>>>> sys.maxint
> 2147483647
>
>
> Are you talking maybe about the default python installation .... ?
>
> Cheers,
> Sebastian Haase
>
>
> On Tue, Mar 19, 2013 at 8:02 AM, Jerry <lanceboyle at qwest.net> wrote:
>>
>> On Mar 18, 2013, at 3:56 AM, Almar Klein wrote:
>>
>>
>>> Pyzo crashed on OS X 10.7.5 in under three minutes while trying to run a
>>> hello world program.
>>
>>
>> I just found: "Mac OS X 10.7 "Lion" was the first version of OS X to drop
>> support for 32-bit Intel processors and run exclusively on 64-bit Intel
>> CPUs."
>> So, I'm sorry, but you'll have to wait for the next release.
>>
>> The reason that the binaries are 32 bit is that I forgot to make the VM
>> 64bit when I created it. Woops. I only found out much later (after all the
>> hard work of setting up the machine was done). So the blame's on me.
>>
>> - Almar
>>
>>
>> Thanks for looking into this.
>> Jerry
>>
>>
>>
>>
>>>
>>>
>>> Dear all,
>>>
>>> We're pleased to announce release 2013b of Pyzo distro, a Python
>>> distribution for scientific computing based on Python 3. For more
>>> information, and to give it a try, visit http://www.pyzo.org.
>>>
>>> The most notable changes since the last release:
>>>
>>> Pyzo is also build for Mac.
>>> Python 3.3 is now used.
>>> Added an executable to launch IPython notebook.
>>> New versions of several packages, including the IDE.
>>>
>>> More information:
>>>
>>> Pyzo is available for Windows, Mac and Linux. The distribution is
>>> portable, thus providing a way to install a scientific Python stack on
>>> computers without the need for admin rights.
>>>
>>> Naturally, Pyzo distro is complient with the scipy-stack, and comes with
>>> additional packages such as scikit-image and scikit-learn.
>>>
>>> With Pyzo we want to make scientific computing in Python easier
>>> accessible. We especially hope to make it easier for newcomers (such as
>>> Matlab converts) to join our awesome community. Pyzo uses  IEP as the
>>> default front-end IDE, and IPython (with notebook) is also available.
>>>
>>> Happy coding,
>>>   Almar
>>>
>>> _______________________________________________
>>> 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
>>>
>>
>>
>>
>> --
>> Almar Klein, PhD
>> Science Applied
>> phone: +31 6 19268652
>> e-mail: a.klein at science-applied.nl
>> _______________________________________________
>> 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 a.klein at science-applied.nl  Tue Mar 19 05:48:43 2013
From: a.klein at science-applied.nl (Almar Klein)
Date: Tue, 19 Mar 2013 10:48:43 +0100
Subject: [SciPy-User] ANN: Pyzo distro 2013b
In-Reply-To: <CAN06oV-QOQFb2mrqj8akn=56xYbS=7YhdJmJtFF4Z7BCpzxEcA@mail.gmail.com>
References: <CAB_T6=kYOCF38uoRin3CjmwF=ckWSW+cFyDyFNC=ZONYd23Kqw@mail.gmail.com>
	<152D9C59-D775-4217-9A98-79F3CD6E71AE@qwest.net>
	<CAB_T6=mbbtVj5R5+Bby+GVosL4NVunZgVbnzZNwW1vWztqMyhQ@mail.gmail.com>
	<0242EE41-9844-4EAB-9F3D-1FEEA4DBF124@qwest.net>
	<CAN06oV-NNmhwM7N8KSioAKsKpwtW7TW8=--eHC3KGX1uPcW6LQ@mail.gmail.com>
	<CAN06oV-QOQFb2mrqj8akn=56xYbS=7YhdJmJtFF4Z7BCpzxEcA@mail.gmail.com>
Message-ID: <CAB_T6=mVFHa9VBPCD-XNg751h4kXbC-bNBGXHFkfDCZykVYxCA@mail.gmail.com>

Mmm, you're right. It indeed says it dropped support for 32bit intel CPU's;
not necesarily 32bit libraries.

- Almar


On 19 March 2013 08:59, Sebastian Haase <seb.haase at gmail.com> wrote:

> shaase at sebs-macbookpro:~: file  /usr/bin/python
> /usr/bin/python: Mach-O universal binary with 2 architectures
> /usr/bin/python (for architecture x86_64):      Mach-O 64-bit executable
> x86_64
> /usr/bin/python (for architecture i386):        Mach-O executable i386
>
> shaase at sebs-macbookpro:~: uname -a
> Darwin xxxxxxxx.fu-berlin.de 11.4.2 Darwin Kernel Version 11.4.2: Thu
> Aug 23 16:25:48 PDT 2012; root:xnu-1699.32.7~1/RELEASE_X86_64 x86_64
>
> OS-X: 10.7.5
>
>
> -S.
>
> On Tue, Mar 19, 2013 at 8:57 AM, Sebastian Haase <seb.haase at gmail.com>
> wrote:
> > I don't think this is correct:
> > shaase at sebs-macbookpro:~: python-32
> > Python 2.7.2 (v2.7.2:8527427914a2, Jun 11 2011, 15:22:34)
> > [GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin
> > Type "help", "copyright", "credits" or "license" for more information.
> >>>> import sys
> >>>> sys.maxint
> > 2147483647
> >
> >
> > Are you talking maybe about the default python installation .... ?
> >
> > Cheers,
> > Sebastian Haase
> >
> >
> > On Tue, Mar 19, 2013 at 8:02 AM, Jerry <lanceboyle at qwest.net> wrote:
> >>
> >> On Mar 18, 2013, at 3:56 AM, Almar Klein wrote:
> >>
> >>
> >>> Pyzo crashed on OS X 10.7.5 in under three minutes while trying to run
> a
> >>> hello world program.
> >>
> >>
> >> I just found: "Mac OS X 10.7 "Lion" was the first version of OS X to
> drop
> >> support for 32-bit Intel processors and run exclusively on 64-bit Intel
> >> CPUs."
> >> So, I'm sorry, but you'll have to wait for the next release.
> >>
> >> The reason that the binaries are 32 bit is that I forgot to make the VM
> >> 64bit when I created it. Woops. I only found out much later (after all
> the
> >> hard work of setting up the machine was done). So the blame's on me.
> >>
> >> - Almar
> >>
> >>
> >> Thanks for looking into this.
> >> Jerry
> >>
> >>
> >>
> >>
> >>>
> >>>
> >>> Dear all,
> >>>
> >>> We're pleased to announce release 2013b of Pyzo distro, a Python
> >>> distribution for scientific computing based on Python 3. For more
> >>> information, and to give it a try, visit http://www.pyzo.org.
> >>>
> >>> The most notable changes since the last release:
> >>>
> >>> Pyzo is also build for Mac.
> >>> Python 3.3 is now used.
> >>> Added an executable to launch IPython notebook.
> >>> New versions of several packages, including the IDE.
> >>>
> >>> More information:
> >>>
> >>> Pyzo is available for Windows, Mac and Linux. The distribution is
> >>> portable, thus providing a way to install a scientific Python stack on
> >>> computers without the need for admin rights.
> >>>
> >>> Naturally, Pyzo distro is complient with the scipy-stack, and comes
> with
> >>> additional packages such as scikit-image and scikit-learn.
> >>>
> >>> With Pyzo we want to make scientific computing in Python easier
> >>> accessible. We especially hope to make it easier for newcomers (such as
> >>> Matlab converts) to join our awesome community. Pyzo uses  IEP as the
> >>> default front-end IDE, and IPython (with notebook) is also available.
> >>>
> >>> Happy coding,
> >>>   Almar
> >>>
> >>> _______________________________________________
> >>> 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
> >>>
> >>
> >>
> >>
> >> --
> >> Almar Klein, PhD
> >> Science Applied
> >> phone: +31 6 19268652
> >> e-mail: a.klein at science-applied.nl
> >> _______________________________________________
> >> 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
>



-- 
Almar Klein, PhD
Science Applied
phone: +31 6 19268652
e-mail: a.klein at science-applied.nl
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From pmhobson at gmail.com  Tue Mar 19 15:47:02 2013
From: pmhobson at gmail.com (Paul Hobson)
Date: Tue, 19 Mar 2013 12:47:02 -0700
Subject: [SciPy-User] Problems with optimize.leastsq and curve_fit
Message-ID: <CADT3MEDTM+x_3qWZjETsKM8yBF816t5USVTFWO3bP5EQH-Q6jA@mail.gmail.com>

I guess I don't quite understand how to use the functions properly since
they're not returning meaningful results on what should be a pretty trivial
linear fit. My initial thought is that they're being tripped up by my `x`
variable not being uniformly spaced.

Consider the following (overly) simplified example:

## start
import numpy as np
import scipy.optimize as opt
x = np.array([  0. ,   0.5,   1. ,   2. ,   3. ,   3.5,   5. ,   5.5,   6. ,
                6.5,   7. ,   7.5,   8. ,   8.5,   9. ,   9.5,  10. ,  11. ,
               11.5,  12.5], dtype=np.float32)

y = np.array([  2.7,   8.6,   9.  ,  13.8,  16.8,  17.8,  23.24,  25.2,
               26.3,  27.1,  27.68,  30.7,    32.9,  32.9,  37.07,  39.5,
               40.5,  45.7,  46.76,  50.2], dtype=np.float32)

def lsq_line(params, x, y):
    return y - (params[0]*x + params[1])

def cf_line(x, m, b):
    return m*x + b

np.polyfit(x, y, 1)  # works great
# Out[6]: array([ 3.54965901,  5.09342384], dtype=float32)

opt.leastsq(lsq_line, [3,5], args=(x,y))  # bad
# Out[9]: (array([ 3.,  5.]), 4)

opt.curve_fit(cf_line, x, y, p0=[3,5]) # bad
# Out[10]: (array([ 3.,  5.]), inf)
## stop

Any thoughts or advice on how to better use these functions would be much
appreciated.

Thanks,
-p
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From pav at iki.fi  Tue Mar 19 15:56:36 2013
From: pav at iki.fi (Pauli Virtanen)
Date: Tue, 19 Mar 2013 21:56:36 +0200
Subject: [SciPy-User] Problems with optimize.leastsq and curve_fit
In-Reply-To: <CADT3MEDTM+x_3qWZjETsKM8yBF816t5USVTFWO3bP5EQH-Q6jA@mail.gmail.com>
References: <CADT3MEDTM+x_3qWZjETsKM8yBF816t5USVTFWO3bP5EQH-Q6jA@mail.gmail.com>
Message-ID: <kiaftg$4bb$1@ger.gmane.org>

19.03.2013 21:47, Paul Hobson kirjoitti:
> I guess I don't quite understand how to use the functions properly since
> they're not returning meaningful results on what should be a pretty
> trivial linear fit. My initial thought is that they're being tripped up
> by my `x` variable not being uniformly spaced.

The problem is the use of float32 rather than float64. This is a bug,
which was fixed in 0.12.0.

-- 
Pauli Virtanen



From pmhobson at gmail.com  Tue Mar 19 17:39:01 2013
From: pmhobson at gmail.com (Paul Hobson)
Date: Tue, 19 Mar 2013 14:39:01 -0700
Subject: [SciPy-User] Problems with optimize.leastsq and curve_fit
In-Reply-To: <kiaftg$4bb$1@ger.gmane.org>
References: <CADT3MEDTM+x_3qWZjETsKM8yBF816t5USVTFWO3bP5EQH-Q6jA@mail.gmail.com>
	<kiaftg$4bb$1@ger.gmane.org>
Message-ID: <CADT3MEBVYtsKbzcxZbucudHk51SD3cAHx6ZvRk3_qUY=Ta7JPA@mail.gmail.com>

On Tue, Mar 19, 2013 at 12:56 PM, Pauli Virtanen <pav at iki.fi> wrote:

> 19.03.2013 21:47, Paul Hobson kirjoitti:
> > I guess I don't quite understand how to use the functions properly since
> > they're not returning meaningful results on what should be a pretty
> > trivial linear fit. My initial thought is that they're being tripped up
> > by my `x` variable not being uniformly spaced.
>
> The problem is the use of float32 rather than float64. This is a bug,
> which was fixed in 0.12.0.
>
>
Thanks for the info and the prompt reply. This is the info I need.
-pauil
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From indiajoe at gmail.com  Thu Mar 21 16:10:30 2013
From: indiajoe at gmail.com (Joe Philip Ninan)
Date: Fri, 22 Mar 2013 01:40:30 +0530
Subject: [SciPy-User] optimize.leastsq error estimation
Message-ID: <CAJ_T+b6jexyqPuDZuZGEy+orqSemMQXMOwAY7RbGO3vSA=_h+g@mail.gmail.com>

Hi,
I want to confirm whether i understood the documentation of
optimize.leastsq correctly.
(
http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.leastsq.html)
For estimating the error in the fitted parameter.
The documentation says :
"This matrix (cov_x) must be multiplied by the residual variance to get the
covariance of the parameter estimates"
i.e. Multiply the output cov_x with the "reduced chi square". Right? and
take sqrt of corresponding diagonal element from covariance matrix.
Where "residual chi square" is the sum of {[(fitted function -
data)/sigma]**2} /N
and where N is degrees of freedom [= len(data)-number of parameters]
And sigma is the error in each data point.

I mainly want to confirm whether the divide by sigma is required or not.
(I couldn't find the definition of "residual variance" in documentation, so
i assumed it is reduced chi square).

Thanking you,
Joe


-- 
/---------------------------------------------------------------
"GNU/Linux: because a PC is a terrible thing to waste" -  GNU Generation
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From newville at cars.uchicago.edu  Thu Mar 21 22:38:25 2013
From: newville at cars.uchicago.edu (Matt Newville)
Date: Thu, 21 Mar 2013 21:38:25 -0500
Subject: [SciPy-User] optimize.leastsq error estimation
In-Reply-To: <CAJ_T+b6jexyqPuDZuZGEy+orqSemMQXMOwAY7RbGO3vSA=_h+g@mail.gmail.com>
References: <CAJ_T+b6jexyqPuDZuZGEy+orqSemMQXMOwAY7RbGO3vSA=_h+g@mail.gmail.com>
Message-ID: <CA+7ESboKdNf8tvnQHqpT_uhuigmC8HBoydp9yE0Jv3dQS5Mn7A@mail.gmail.com>

Hi Joe,

On Thu, Mar 21, 2013 at 3:10 PM, Joe Philip Ninan <indiajoe at gmail.com> wrote:
> Hi,
> I want to confirm whether i understood the documentation of optimize.leastsq
> correctly.
> (
> http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.leastsq.html
> )
> For estimating the error in the fitted parameter.
> The documentation says :
> "This matrix (cov_x) must be multiplied by the residual variance to get the
> covariance of the parameter estimates"
> i.e. Multiply the output cov_x with the "reduced chi square". Right? and
> take sqrt of corresponding diagonal element from covariance matrix.
> Where "residual chi square" is the sum of {[(fitted function -
> data)/sigma]**2} /N
> and where N is degrees of freedom [= len(data)-number of parameters]
> And sigma is the error in each data point.
>
> I mainly want to confirm whether the divide by sigma is required or not.
> (I couldn't find the definition of "residual variance" in documentation, so
> i assumed it is reduced chi square).
>
> Thanking you,
> Joe

I think you're correct, but there may be some confusion in
terminology.  The standard errors are the square-root of the diagonal
elements of (covar * reduced_chi_square), where reduced_chi_square is

   (((data-model)/uncertainty)**2).sum() / (len(ydata)- len(variables))

In (untested!) code,  it might look like:

    bestvals, covar, info, errmsg, ier  = leastsq(objectivefunc, initvals,

  args=args, full_output=1, **kws)

    residual = objectivefunc(bestvals, *args)
    reduced_chi_square = (residual**2).sum() / (len(data) - len(initvals))

    covar = covar *  reduced_chi_square

    std_error = array([sqrt(covar[i, i]) for i in range(len(bestvals))])

Here, objectivefunc is expected to return the scaled misfit array,
that is  "(data - model)/uncertainty".

Cheers,

--Matt Newville <newville at cars.uchicago.edu> 630-252-0431


From indiajoe at gmail.com  Fri Mar 22 04:58:06 2013
From: indiajoe at gmail.com (Joe Philip Ninan)
Date: Fri, 22 Mar 2013 14:28:06 +0530
Subject: [SciPy-User] optimize.leastsq error estimation
In-Reply-To: <CA+7ESboKdNf8tvnQHqpT_uhuigmC8HBoydp9yE0Jv3dQS5Mn7A@mail.gmail.com>
References: <CAJ_T+b6jexyqPuDZuZGEy+orqSemMQXMOwAY7RbGO3vSA=_h+g@mail.gmail.com>
	<CA+7ESboKdNf8tvnQHqpT_uhuigmC8HBoydp9yE0Jv3dQS5Mn7A@mail.gmail.com>
Message-ID: <CAJ_T+b4eegEaT=8CS+EUoG_H-L3oygf5oMGNkecwt=Be82uwpQ@mail.gmail.com>

Hi Matt,
Thanks for clarifying and also for providing an example.
I think it will be nice if we add the term "reduced chi square" also along
with "reduced variance" in the scipy documentation.
And also an example to show how to estimate the error in the documentation
page.
Thanking you,
Joe


On 22 March 2013 08:08, Matt Newville <newville at cars.uchicago.edu> wrote:

> Hi Joe,
>
> On Thu, Mar 21, 2013 at 3:10 PM, Joe Philip Ninan <indiajoe at gmail.com>
> wrote:
> > Hi,
> > I want to confirm whether i understood the documentation of
> optimize.leastsq
> > correctly.
> > (
> >
> http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.leastsq.html
> > )
> > For estimating the error in the fitted parameter.
> > The documentation says :
> > "This matrix (cov_x) must be multiplied by the residual variance to get
> the
> > covariance of the parameter estimates"
> > i.e. Multiply the output cov_x with the "reduced chi square". Right? and
> > take sqrt of corresponding diagonal element from covariance matrix.
> > Where "residual chi square" is the sum of {[(fitted function -
> > data)/sigma]**2} /N
> > and where N is degrees of freedom [= len(data)-number of parameters]
> > And sigma is the error in each data point.
> >
> > I mainly want to confirm whether the divide by sigma is required or not.
> > (I couldn't find the definition of "residual variance" in documentation,
> so
> > i assumed it is reduced chi square).
> >
> > Thanking you,
> > Joe
>
> I think you're correct, but there may be some confusion in
> terminology.  The standard errors are the square-root of the diagonal
> elements of (covar * reduced_chi_square), where reduced_chi_square is
>
>    (((data-model)/uncertainty)**2).sum() / (len(ydata)- len(variables))
>
> In (untested!) code,  it might look like:
>
>     bestvals, covar, info, errmsg, ier  = leastsq(objectivefunc, initvals,
>
>   args=args, full_output=1, **kws)
>
>     residual = objectivefunc(bestvals, *args)
>     reduced_chi_square = (residual**2).sum() / (len(data) - len(initvals))
>
>     covar = covar *  reduced_chi_square
>
>     std_error = array([sqrt(covar[i, i]) for i in range(len(bestvals))])
>
> Here, objectivefunc is expected to return the scaled misfit array,
> that is  "(data - model)/uncertainty".
>
> Cheers,
>
> --Matt Newville <newville at cars.uchicago.edu> 630-252-0431
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>



-- 
/---------------------------------------------------------------
"GNU/Linux: because a PC is a terrible thing to waste" -  GNU Generation
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From simonb at google.com  Fri Mar 22 08:43:42 2013
From: simonb at google.com (Simon Baldwin)
Date: Fri, 22 Mar 2013 12:43:42 +0000
Subject: [SciPy-User] int32 overflow and sqrt of -ve number in
	scipy.stats.wilcoxon
Message-ID: <CAPTY64pBwYEF+0L4b0fBFV2QkGzAsXkEMZhsvzSsUdTKoh7mRw@mail.gmail.com>

I have run into two distinct but possibly connected failure modes in
scipy.stats.wilcoxon.  Before bug-reporting, I'd appreciate any
thoughts on these.

The first is an int32 overflow that occurs where a count of data ties
exceeds 46341.  This occurs in my real data set, comprising around
0.7M samples.  The cause is 32-bit counts returned by dfreps() in
futilmodule.c.  Minimal failing demonstration:

import numpy
import scipy.stats
numpy.seterr(all='raise')
# Raises: FloatingPointError: overflow encountered in int_scalars,
morestats.py, line 1242
scipy.stats.wilcoxon([0.1] * 46341)  # 46341^2 > 2^31-1

The second arises from an attempt to take sqrt of a negative number.
Again, this is in wilcoxon data tie handling and occurs in real data.
Demonstration:

import numpy
import scipy.stats
numpy.seterr(all='raise')
# Raises: FloatingPointError: invalid value encountered in sqrt,
morestats.py, line 1244
scipy.stats.wilcoxon([0.1] * 10)

Both on scipy version 0.9, the latest I have access to.  I've taken a
quick look at the 0.11 code for wilcoxon, and didn't notice any
visible functional change.

Thanks.

--
Google UK Limited | Registered Office: Belgrave House, 76 Buckingham
Palace Road, London SW1W 9TQ | Registered in England Number: 3977902


From josef.pktd at gmail.com  Fri Mar 22 10:59:47 2013
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Fri, 22 Mar 2013 10:59:47 -0400
Subject: [SciPy-User] int32 overflow and sqrt of -ve number in
	scipy.stats.wilcoxon
In-Reply-To: <CAPTY64pBwYEF+0L4b0fBFV2QkGzAsXkEMZhsvzSsUdTKoh7mRw@mail.gmail.com>
References: <CAPTY64pBwYEF+0L4b0fBFV2QkGzAsXkEMZhsvzSsUdTKoh7mRw@mail.gmail.com>
Message-ID: <CAMMTP+A=mL-sF9Gx6zA2-ud0h-YDRUg1Pzj3Nt3rH6X7ms9h8g@mail.gmail.com>

On Fri, Mar 22, 2013 at 8:43 AM, Simon Baldwin <simonb at google.com> wrote:
> I have run into two distinct but possibly connected failure modes in
> scipy.stats.wilcoxon.  Before bug-reporting, I'd appreciate any
> thoughts on these.
>
> The first is an int32 overflow that occurs where a count of data ties
> exceeds 46341.  This occurs in my real data set, comprising around
> 0.7M samples.  The cause is 32-bit counts returned by dfreps() in
> futilmodule.c.  Minimal failing demonstration:
>
> import numpy
> import scipy.stats
> numpy.seterr(all='raise')
> # Raises: FloatingPointError: overflow encountered in int_scalars,
> morestats.py, line 1242
> scipy.stats.wilcoxon([0.1] * 46341)  # 46341^2 > 2^31-1

this sounds like the same problem that rankdata had and Warren recently fixed.

My guess is that we can implement wilcoxon using the new rankdata
(maybe, if we can get the tie counts).
Otherwise, it would need a change to the c code.

(old code that wasn't designed for modern computers ?)


>
> The second arises from an attempt to take sqrt of a negative number.
> Again, this is in wilcoxon data tie handling and occurs in real data.
> Demonstration:
>
> import numpy
> import scipy.stats
> numpy.seterr(all='raise')
> # Raises: FloatingPointError: invalid value encountered in sqrt,
> morestats.py, line 1244
> scipy.stats.wilcoxon([0.1] * 10)

That might be a numerical precision issue in a corner case (-0) that should be
explicitly handled.

Please open a ticket.

Josef

>
> Both on scipy version 0.9, the latest I have access to.  I've taken a
> quick look at the 0.11 code for wilcoxon, and didn't notice any
> visible functional change.
>
> Thanks.
>
> --
> Google UK Limited | Registered Office: Belgrave House, 76 Buckingham
> Palace Road, London SW1W 9TQ | Registered in England Number: 3977902
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user


From simonb at google.com  Fri Mar 22 11:57:35 2013
From: simonb at google.com (Simon Baldwin)
Date: Fri, 22 Mar 2013 15:57:35 +0000 (UTC)
Subject: [SciPy-User]
	=?utf-8?q?int32_overflow_and_sqrt_of_-ve_number_in?=
	=?utf-8?q?=09scipy=2Estats=2Ewilcoxon?=
References: <CAPTY64pBwYEF+0L4b0fBFV2QkGzAsXkEMZhsvzSsUdTKoh7mRw@mail.gmail.com>
	<CAMMTP+A=mL-sF9Gx6zA2-ud0h-YDRUg1Pzj3Nt3rH6X7ms9h8g@mail.gmail.com>
Message-ID: <loom.20130322T164420-93@post.gmane.org>

 <josef.pktd <at> gmail.com> writes:
> 
> On Fri, Mar 22, 2013 at 8:43 AM, Simon Baldwin <simonb <at> google.com> wrote:
> > I have run into two distinct but possibly connected failure modes in
> > scipy.stats.wilcoxon.  Before bug-reporting, I'd appreciate any
> > thoughts on these.
> ...
> 
> this sounds like the same problem that rankdata had and Warren recently fixed.
> 
> My guess is that we can implement wilcoxon using the new rankdata
> (maybe, if we can get the tie counts).
> Otherwise, it would need a change to the c code.
> 
> (old code that wasn't designed for modern computers ?)

Thanks for the note.  The offending computation is

  corr += 0.5*si*(si*si-1.0)

so I think just converting si from int32 to int64 or float before use would 
solve the majority of cases.


> 
> ...
> 
> That might be a numerical precision issue in a corner case (-0) that should be
> explicitly handled.

I think it may be a more egregious error than that.  In this example the value 
being passed to sqrt is -443.05555555555554.

So far I haven't found an easy workround, not least because I don't yet 
understand the ties loop in scipy.stats.wilcoxon.


> 
> Please open a ticket.

Will do.  Thanks.

--
Google UK Limited | Registered Office: Belgrave House, 76 Buckingham
Palace Road, London SW1W 9TQ | Registered in England Number: 3977902




From jsseabold at gmail.com  Fri Mar 22 16:50:13 2013
From: jsseabold at gmail.com (Skipper Seabold)
Date: Fri, 22 Mar 2013 16:50:13 -0400
Subject: [SciPy-User] Using LU Decomposition from UMFPACK?
Message-ID: <CAKF=DjsRPTKc6ULtyGqJ-QBgYgXa9EVM_rLhG9kn32AaVCMkXA@mail.gmail.com>

I thought I built scipy with UMFPACK support but I can't seem to get this
to work. I would like to do a sparse LU decomposition and recover the U
matrix.

I built SuiteSparse so long ago, so might there have been a build issue I
haven't noticed until now?

scipy.test() does not show any errors, though there are 28 skipped tests.

Does this work for anyone?

[~/]
[1]: from scipy import version

[~/]
[2]: version.full_version
[2]: '0.13.0.dev-61f05fe'

[~/]
[3]: from scipy.sparse.linalg.dsolve import umfpack

[~/]
[4]: umf = umfpack.UmfpackContext()
Exception AttributeError: "'UmfpackContext' object has no attribute
'_symbolic'" in <bound method UmfpackContext.__del__ of
<scipy.sparse.linalg.dsolve.umfpack.umfpack.UmfpackContext object at
0x56c0b10>> ignored
---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
<ipython-input-3-2e28850f247d> in <module>()
----> 1 umf = umfpack.UmfpackContext("di")

/usr/local/lib/python2.7/dist-packages/scipy/sparse/linalg/dsolve/umfpack/umfpack.pyc
in __init__(self, family, **kwargs)
    279                    a warning is issued (default: 1e12)"""
    280         if _um is None:
--> 281             raise ImportError('Scipy was built without UMFPACK
support. '
    282                               'You need to install the UMFPACK
library and '
    283                               'header files before building scipy.')

ImportError: Scipy was built without UMFPACK support. You need to install
the UMFPACK library and header files before building scipy.

[~/]
[5]: from scipy import show_config

[~/]
[6]: show_config()
amd_info:
    libraries = ['amd']
    library_dirs = ['/home/skipper/atlas_build2/lib']
    define_macros = [('SCIPY_AMD_H', None)]
    swig_opts = ['-I/home/skipper/atlas_build2/include']
    include_dirs = ['/home/skipper/atlas_build2/include']
umfpack_info:
    libraries = ['umfpack', 'amd']
    library_dirs = ['/home/skipper/atlas_build2/lib']

    define_macros = [('SCIPY_UMFPACK_H', None), ('SCIPY_AMD_H', None)]

    swig_opts = ['-I/home/skipper/atlas_build2/include',
'-I/home/skipper/atlas_build2/include']

    include_dirs = ['/home/skipper/atlas_build2/include']
atlas_threads_info:
    libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
    library_dirs = ['/home/skipper/atlas_build2/lib']
    define_macros = [('NO_ATLAS_INFO', -1)]
    language = f77
    include_dirs = ['/home/skipper/atlas_build2/include']
blas_opt_info:
    libraries = ['ptf77blas', 'ptcblas', 'atlas']
    library_dirs = ['/home/skipper/atlas_build2/lib']
    define_macros = [('NO_ATLAS_INFO', -1)]
    language = c
    include_dirs = ['/home/skipper/atlas_build2/include']
atlas_blas_threads_info:
    libraries = ['ptf77blas', 'ptcblas', 'atlas']
    library_dirs = ['/home/skipper/atlas_build2/lib']
    define_macros = [('NO_ATLAS_INFO', -1)]
    language = c
    include_dirs = ['/home/skipper/atlas_build2/include']
lapack_opt_info:
    libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
    library_dirs = ['/home/skipper/atlas_build2/lib']
    define_macros = [('NO_ATLAS_INFO', -1)]
    language = f77
    include_dirs = ['/home/skipper/atlas_build2/include']
lapack_mkl_info:
  NOT AVAILABLE
blas_mkl_info:
  NOT AVAILABLE
mkl_info:
  NOT AVAILABLE

Skipper
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From jsseabold at gmail.com  Fri Mar 22 17:04:33 2013
From: jsseabold at gmail.com (Skipper Seabold)
Date: Fri, 22 Mar 2013 17:04:33 -0400
Subject: [SciPy-User] Using LU Decomposition from UMFPACK?
In-Reply-To: <CAKF=DjsRPTKc6ULtyGqJ-QBgYgXa9EVM_rLhG9kn32AaVCMkXA@mail.gmail.com>
References: <CAKF=DjsRPTKc6ULtyGqJ-QBgYgXa9EVM_rLhG9kn32AaVCMkXA@mail.gmail.com>
Message-ID: <CAKF=Djt13m3qnQ42PBhQ4o8d9nk3zBOLmSQ=URKzoYKG57dL+w@mail.gmail.com>

On Fri, Mar 22, 2013 at 4:50 PM, Skipper Seabold <jsseabold at gmail.com>wrote:

> I thought I built scipy with UMFPACK support but I can't seem to get this
> to work. I would like to do a sparse LU decomposition and recover the U
> matrix.
>
> I built SuiteSparse so long ago, so might there have been a build issue I
> haven't noticed until now?
>
> scipy.test() does not show any errors, though there are 28 skipped tests.
>
> Does this work for anyone?
>
> [~/]
> [1]: from scipy import version
>
> [~/]
> [2]: version.full_version
> [2]: '0.13.0.dev-61f05fe'
>
> [~/]
> [3]: from scipy.sparse.linalg.dsolve import umfpack
>
> [~/]
> [4]: umf = umfpack.UmfpackContext()
> Exception AttributeError: "'UmfpackContext' object has no attribute
> '_symbolic'" in <bound method UmfpackContext.__del__ of
> <scipy.sparse.linalg.dsolve.umfpack.umfpack.UmfpackContext object at
> 0x56c0b10>> ignored
> ---------------------------------------------------------------------------
> ImportError                               Traceback (most recent call last)
> <ipython-input-3-2e28850f247d> in <module>()
> ----> 1 umf = umfpack.UmfpackContext("di")
>
> /usr/local/lib/python2.7/dist-packages/scipy/sparse/linalg/dsolve/umfpack/umfpack.pyc
> in __init__(self, family, **kwargs)
>     279                    a warning is issued (default: 1e12)"""
>     280         if _um is None:
> --> 281             raise ImportError('Scipy was built without UMFPACK
> support. '
>     282                               'You need to install the UMFPACK
> library and '
>     283                               'header files before building
> scipy.')
>
> ImportError: Scipy was built without UMFPACK support. You need to install
> the UMFPACK library and header files before building scipy.
>
> [~/]
> [5]: from scipy import show_config
>
> [~/]
> [6]: show_config()
> amd_info:
>     libraries = ['amd']
>     library_dirs = ['/home/skipper/atlas_build2/lib']
>     define_macros = [('SCIPY_AMD_H', None)]
>     swig_opts = ['-I/home/skipper/atlas_build2/include']
>     include_dirs = ['/home/skipper/atlas_build2/include']
> umfpack_info:
>     libraries = ['umfpack', 'amd']
>     library_dirs = ['/home/skipper/atlas_build2/lib']
>
>     define_macros = [('SCIPY_UMFPACK_H', None), ('SCIPY_AMD_H', None)]
>
>     swig_opts = ['-I/home/skipper/atlas_build2/include',
> '-I/home/skipper/atlas_build2/include']
>
>     include_dirs = ['/home/skipper/atlas_build2/include']
> atlas_threads_info:
>     libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
>     library_dirs = ['/home/skipper/atlas_build2/lib']
>     define_macros = [('NO_ATLAS_INFO', -1)]
>     language = f77
>     include_dirs = ['/home/skipper/atlas_build2/include']
> blas_opt_info:
>     libraries = ['ptf77blas', 'ptcblas', 'atlas']
>     library_dirs = ['/home/skipper/atlas_build2/lib']
>     define_macros = [('NO_ATLAS_INFO', -1)]
>     language = c
>     include_dirs = ['/home/skipper/atlas_build2/include']
> atlas_blas_threads_info:
>     libraries = ['ptf77blas', 'ptcblas', 'atlas']
>     library_dirs = ['/home/skipper/atlas_build2/lib']
>     define_macros = [('NO_ATLAS_INFO', -1)]
>     language = c
>     include_dirs = ['/home/skipper/atlas_build2/include']
> lapack_opt_info:
>     libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
>     library_dirs = ['/home/skipper/atlas_build2/lib']
>     define_macros = [('NO_ATLAS_INFO', -1)]
>     language = f77
>     include_dirs = ['/home/skipper/atlas_build2/include']
> lapack_mkl_info:
>   NOT AVAILABLE
> blas_mkl_info:
>   NOT AVAILABLE
> mkl_info:
>   NOT AVAILABLE
>
> Skipper
>

Ah, might it be this? From SuiteSparse/AMD/Doc/ChangeLog

Jun 1, 2012: version 2.3.0

    * changed from UFconfig to SuiteSparse_config

Skipper
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From jsseabold at gmail.com  Fri Mar 22 17:39:53 2013
From: jsseabold at gmail.com (Skipper Seabold)
Date: Fri, 22 Mar 2013 17:39:53 -0400
Subject: [SciPy-User] Using LU Decomposition from UMFPACK?
In-Reply-To: <CAKF=Djt13m3qnQ42PBhQ4o8d9nk3zBOLmSQ=URKzoYKG57dL+w@mail.gmail.com>
References: <CAKF=DjsRPTKc6ULtyGqJ-QBgYgXa9EVM_rLhG9kn32AaVCMkXA@mail.gmail.com>
	<CAKF=Djt13m3qnQ42PBhQ4o8d9nk3zBOLmSQ=URKzoYKG57dL+w@mail.gmail.com>
Message-ID: <CAKF=DjtaMF0+yHqRBfyTxPKhNvDvE1GCJb7p=3bL=cH5ZO1VeA@mail.gmail.com>

On Fri, Mar 22, 2013 at 5:04 PM, Skipper Seabold <jsseabold at gmail.com>wrote:

> On Fri, Mar 22, 2013 at 4:50 PM, Skipper Seabold <jsseabold at gmail.com>wrote:
>
>> I thought I built scipy with UMFPACK support but I can't seem to get this
>> to work. I would like to do a sparse LU decomposition and recover the U
>> matrix.
>>
>> I built SuiteSparse so long ago, so might there have been a build issue I
>> haven't noticed until now?
>>
>> scipy.test() does not show any errors, though there are 28 skipped tests.
>>
>> Does this work for anyone?
>>
>> [~/]
>> [1]: from scipy import version
>>
>> [~/]
>> [2]: version.full_version
>> [2]: '0.13.0.dev-61f05fe'
>>
>> [~/]
>> [3]: from scipy.sparse.linalg.dsolve import umfpack
>>
>> [~/]
>> [4]: umf = umfpack.UmfpackContext()
>> Exception AttributeError: "'UmfpackContext' object has no attribute
>> '_symbolic'" in <bound method UmfpackContext.__del__ of
>> <scipy.sparse.linalg.dsolve.umfpack.umfpack.UmfpackContext object at
>> 0x56c0b10>> ignored
>>
>> ---------------------------------------------------------------------------
>> ImportError                               Traceback (most recent call
>> last)
>> <ipython-input-3-2e28850f247d> in <module>()
>> ----> 1 umf = umfpack.UmfpackContext("di")
>>
>> /usr/local/lib/python2.7/dist-packages/scipy/sparse/linalg/dsolve/umfpack/umfpack.pyc
>> in __init__(self, family, **kwargs)
>>     279                    a warning is issued (default: 1e12)"""
>>     280         if _um is None:
>> --> 281             raise ImportError('Scipy was built without UMFPACK
>> support. '
>>     282                               'You need to install the UMFPACK
>> library and '
>>     283                               'header files before building
>> scipy.')
>>
>> ImportError: Scipy was built without UMFPACK support. You need to install
>> the UMFPACK library and header files before building scipy.
>>
>> [~/]
>> [5]: from scipy import show_config
>>
>> [~/]
>> [6]: show_config()
>> amd_info:
>>     libraries = ['amd']
>>     library_dirs = ['/home/skipper/atlas_build2/lib']
>>     define_macros = [('SCIPY_AMD_H', None)]
>>     swig_opts = ['-I/home/skipper/atlas_build2/include']
>>     include_dirs = ['/home/skipper/atlas_build2/include']
>> umfpack_info:
>>     libraries = ['umfpack', 'amd']
>>     library_dirs = ['/home/skipper/atlas_build2/lib']
>>
>>     define_macros = [('SCIPY_UMFPACK_H', None), ('SCIPY_AMD_H', None)]
>>
>>     swig_opts = ['-I/home/skipper/atlas_build2/include',
>> '-I/home/skipper/atlas_build2/include']
>>
>>     include_dirs = ['/home/skipper/atlas_build2/include']
>> atlas_threads_info:
>>     libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
>>     library_dirs = ['/home/skipper/atlas_build2/lib']
>>     define_macros = [('NO_ATLAS_INFO', -1)]
>>     language = f77
>>     include_dirs = ['/home/skipper/atlas_build2/include']
>> blas_opt_info:
>>     libraries = ['ptf77blas', 'ptcblas', 'atlas']
>>     library_dirs = ['/home/skipper/atlas_build2/lib']
>>     define_macros = [('NO_ATLAS_INFO', -1)]
>>     language = c
>>     include_dirs = ['/home/skipper/atlas_build2/include']
>> atlas_blas_threads_info:
>>     libraries = ['ptf77blas', 'ptcblas', 'atlas']
>>     library_dirs = ['/home/skipper/atlas_build2/lib']
>>     define_macros = [('NO_ATLAS_INFO', -1)]
>>     language = c
>>     include_dirs = ['/home/skipper/atlas_build2/include']
>> lapack_opt_info:
>>     libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
>>     library_dirs = ['/home/skipper/atlas_build2/lib']
>>     define_macros = [('NO_ATLAS_INFO', -1)]
>>     language = f77
>>     include_dirs = ['/home/skipper/atlas_build2/include']
>> lapack_mkl_info:
>>   NOT AVAILABLE
>> blas_mkl_info:
>>   NOT AVAILABLE
>> mkl_info:
>>   NOT AVAILABLE
>>
>> Skipper
>>
>
> Ah, might it be this? From SuiteSparse/AMD/Doc/ChangeLog
>
> Jun 1, 2012: version 2.3.0
>
>     * changed from UFconfig to SuiteSparse_config
>

I suppose not. Also, does this look right? Should they be shared objects?
(I'm just about out of my depth)

[~/src/SuiteSparse]
|27 $ ls ~/atlas_build2/lib/
libamd.a    libcholmod.a     liblapack.a     libptlapack.a  libtstatlas.a
libatlas.a  libf77blas.a     libptcblas.a    libsatlas.so   libumfpack.a
libcblas.a  libf77refblas.a  libptf77blas.a  libtatlas.so

I have the -fPIC flag set when building SuiteSparse.

Skipper
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From josef.pktd at gmail.com  Sat Mar 23 12:17:55 2013
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sat, 23 Mar 2013 12:17:55 -0400
Subject: [SciPy-User] expanding optimize.brentq
In-Reply-To: <CAMMTP+AQY1dCfLeKjyW-1hy-grrZauu03LoRhdqTafEai7BFmg@mail.gmail.com>
References: <CAMMTP+C0uNsAsi2TgQj+M=o99+0Y+LC456YfR+gn-apyADY+Lg@mail.gmail.com>
	<CAB6mnxJt+pZOnsEUo=kTRRqzos+Eu3CF25p3GPj=KvmNBjGU1w@mail.gmail.com>
	<CAMMTP+AtKC-Qz2-bUOxP+ap0Yn5c0dJpp5KB_66vWiBkyCa7Pw@mail.gmail.com>
	<CAMMTP+BjxYT=nyOKBiLraVQnVSFk0_9bRimRYLjmAOF32pxgrg@mail.gmail.com>
	<CAMMTP+AQY1dCfLeKjyW-1hy-grrZauu03LoRhdqTafEai7BFmg@mail.gmail.com>
Message-ID: <CAMMTP+AKnyd30CzMT5-4XSCNFfP9r63_kSmthy9vOrKzPcOcNg@mail.gmail.com>

In case someone else is interested, I'm pretty much done with brentq_expanding.

https://github.com/statsmodels/statsmodels/pull/723#diff-2

It doesn't work as well without specifying anything as I hoped.
But specifying soft bounds, works pretty well in my test cases.

Josef


From jsseabold at gmail.com  Sat Mar 23 13:20:31 2013
From: jsseabold at gmail.com (Skipper Seabold)
Date: Sat, 23 Mar 2013 13:20:31 -0400
Subject: [SciPy-User] Using LU Decomposition from UMFPACK?
In-Reply-To: <CAKF=DjtaMF0+yHqRBfyTxPKhNvDvE1GCJb7p=3bL=cH5ZO1VeA@mail.gmail.com>
References: <CAKF=DjsRPTKc6ULtyGqJ-QBgYgXa9EVM_rLhG9kn32AaVCMkXA@mail.gmail.com>
	<CAKF=Djt13m3qnQ42PBhQ4o8d9nk3zBOLmSQ=URKzoYKG57dL+w@mail.gmail.com>
	<CAKF=DjtaMF0+yHqRBfyTxPKhNvDvE1GCJb7p=3bL=cH5ZO1VeA@mail.gmail.com>
Message-ID: <CAKF=DjtD7BcAUqOSpRp4YixWYK5rtV2fG--UDun-ZvqmA1-8kw@mail.gmail.com>

On Fri, Mar 22, 2013 at 5:39 PM, Skipper Seabold <jsseabold at gmail.com>wrote:

> On Fri, Mar 22, 2013 at 5:04 PM, Skipper Seabold <jsseabold at gmail.com>wrote:
>
>> On Fri, Mar 22, 2013 at 4:50 PM, Skipper Seabold <jsseabold at gmail.com>wrote:
>>
>>> I thought I built scipy with UMFPACK support but I can't seem to get
>>> this to work. I would like to do a sparse LU decomposition and recover the
>>> U matrix.
>>>
>>> I built SuiteSparse so long ago, so might there have been a build issue
>>> I haven't noticed until now?
>>>
>>> scipy.test() does not show any errors, though there are 28 skipped tests.
>>>
>>> Does this work for anyone?
>>>
>>> [~/]
>>> [1]: from scipy import version
>>>
>>> [~/]
>>> [2]: version.full_version
>>> [2]: '0.13.0.dev-61f05fe'
>>>
>>> [~/]
>>> [3]: from scipy.sparse.linalg.dsolve import umfpack
>>>
>>> [~/]
>>> [4]: umf = umfpack.UmfpackContext()
>>> Exception AttributeError: "'UmfpackContext' object has no attribute
>>> '_symbolic'" in <bound method UmfpackContext.__del__ of
>>> <scipy.sparse.linalg.dsolve.umfpack.umfpack.UmfpackContext object at
>>> 0x56c0b10>> ignored
>>>
>>> ---------------------------------------------------------------------------
>>> ImportError                               Traceback (most recent call
>>> last)
>>> <ipython-input-3-2e28850f247d> in <module>()
>>> ----> 1 umf = umfpack.UmfpackContext("di")
>>>
>>> /usr/local/lib/python2.7/dist-packages/scipy/sparse/linalg/dsolve/umfpack/umfpack.pyc
>>> in __init__(self, family, **kwargs)
>>>     279                    a warning is issued (default: 1e12)"""
>>>     280         if _um is None:
>>> --> 281             raise ImportError('Scipy was built without UMFPACK
>>> support. '
>>>     282                               'You need to install the UMFPACK
>>> library and '
>>>     283                               'header files before building
>>> scipy.')
>>>
>>> ImportError: Scipy was built without UMFPACK support. You need to
>>> install the UMFPACK library and header files before building scipy.
>>>
>>> [~/]
>>> [5]: from scipy import show_config
>>>
>>> [~/]
>>> [6]: show_config()
>>> amd_info:
>>>     libraries = ['amd']
>>>     library_dirs = ['/home/skipper/atlas_build2/lib']
>>>     define_macros = [('SCIPY_AMD_H', None)]
>>>     swig_opts = ['-I/home/skipper/atlas_build2/include']
>>>     include_dirs = ['/home/skipper/atlas_build2/include']
>>> umfpack_info:
>>>     libraries = ['umfpack', 'amd']
>>>     library_dirs = ['/home/skipper/atlas_build2/lib']
>>>
>>>     define_macros = [('SCIPY_UMFPACK_H', None), ('SCIPY_AMD_H', None)]
>>>
>>>     swig_opts = ['-I/home/skipper/atlas_build2/include',
>>> '-I/home/skipper/atlas_build2/include']
>>>
>>>     include_dirs = ['/home/skipper/atlas_build2/include']
>>> atlas_threads_info:
>>>     libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
>>>     library_dirs = ['/home/skipper/atlas_build2/lib']
>>>     define_macros = [('NO_ATLAS_INFO', -1)]
>>>     language = f77
>>>     include_dirs = ['/home/skipper/atlas_build2/include']
>>> blas_opt_info:
>>>     libraries = ['ptf77blas', 'ptcblas', 'atlas']
>>>     library_dirs = ['/home/skipper/atlas_build2/lib']
>>>     define_macros = [('NO_ATLAS_INFO', -1)]
>>>     language = c
>>>     include_dirs = ['/home/skipper/atlas_build2/include']
>>> atlas_blas_threads_info:
>>>     libraries = ['ptf77blas', 'ptcblas', 'atlas']
>>>     library_dirs = ['/home/skipper/atlas_build2/lib']
>>>     define_macros = [('NO_ATLAS_INFO', -1)]
>>>     language = c
>>>     include_dirs = ['/home/skipper/atlas_build2/include']
>>> lapack_opt_info:
>>>     libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
>>>     library_dirs = ['/home/skipper/atlas_build2/lib']
>>>     define_macros = [('NO_ATLAS_INFO', -1)]
>>>     language = f77
>>>     include_dirs = ['/home/skipper/atlas_build2/include']
>>> lapack_mkl_info:
>>>   NOT AVAILABLE
>>> blas_mkl_info:
>>>   NOT AVAILABLE
>>> mkl_info:
>>>   NOT AVAILABLE
>>>
>>> Skipper
>>>
>>
>> Ah, might it be this? From SuiteSparse/AMD/Doc/ChangeLog
>>
>> Jun 1, 2012: version 2.3.0
>>
>>     * changed from UFconfig to SuiteSparse_config
>>
>
> I suppose not. Also, does this look right? Should they be shared objects?
> (I'm just about out of my depth)
>
> [~/src/SuiteSparse]
> |27 $ ls ~/atlas_build2/lib/
> libamd.a    libcholmod.a     liblapack.a     libptlapack.a  libtstatlas.a
> libatlas.a  libf77blas.a     libptcblas.a    libsatlas.so   libumfpack.a
> libcblas.a  libf77refblas.a  libptf77blas.a  libtatlas.so
>
> I have the -fPIC flag set when building SuiteSparse.
>

The issue was that there was a problem in the building of SuiteSparse. I
had not correctly linked against blas and never knew it.

Skipper
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From sergio_r at mail.com  Sat Mar 23 17:37:23 2013
From: sergio_r at mail.com (Sergio Rojas)
Date: Sat, 23 Mar 2013 17:37:23 -0400
Subject: [SciPy-User] scipy 0.12.0b1  errors on python 323
Message-ID: <20130323213724.287210@gmx.com>

Hello all,

 I just installed Python 3.2.3 along with
numpy 1.7.0 and scypi 0.12.0b1.

While numpy.test('full') finished errors=0 and failures=0,
scipy.test('full') finished with errors=1 failures=1.

======================================================================
ERROR: Failure: ImportError (scipy.weave only supports Python 2.x)
----------------------------------------------------------------------
======================================================================
FAIL: test_mathieu_modsem2 (test_basic.TestCephes)
----------------------------------------------------------------------

My concern is whether these tests are critical or important for
numerical computing and if there is a way to fix them.

Thanks in advance

Sergio
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From ralf.gommers at gmail.com  Sat Mar 23 17:47:45 2013
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Sat, 23 Mar 2013 22:47:45 +0100
Subject: [SciPy-User] scipy 0.12.0b1 errors on python 323
In-Reply-To: <20130323213724.287210@gmx.com>
References: <20130323213724.287210@gmx.com>
Message-ID: <CABL7CQhiVvP55H9Knz6F=Ohk1-5KWwHDN=+Ei4QvfFPnj-1v4A@mail.gmail.com>

On Sat, Mar 23, 2013 at 10:37 PM, Sergio Rojas <sergio_r at mail.com> wrote:

> Hello all,
>
>   I just installed Python 3.2.3 along with
> numpy 1.7.0 and scypi 0.12.0b1.
>
> While numpy.test('full') finished errors=0 and failures=0,
> scipy.test('full') finished with errors=1 failures=1.
>
> ======================================================================
> ERROR: Failure: ImportError (scipy.weave only supports Python 2.x)
> ----------------------------------------------------------------------
> ======================================================================
> FAIL: test_mathieu_modsem2 (test_basic.TestCephes)
> ----------------------------------------------------------------------
>
> My concern is whether these tests are critical or important  for
> numerical computing and if there is a way to fix them.
>

Hi Sergio, the error is expected (scipy.weave was never ported to Python
3.x), the failure will be fixed for the final 0.12.0 release. It shouldn't
affect any other functionality, so unless you use
scipy.special.mathieu_modsem2 you can ignore the failure.

Ralf
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From jsseabold at gmail.com  Sat Mar 23 22:04:16 2013
From: jsseabold at gmail.com (Skipper Seabold)
Date: Sat, 23 Mar 2013 22:04:16 -0400
Subject: [SciPy-User] Using LU Decomposition from UMFPACK?
In-Reply-To: <CAKF=DjtD7BcAUqOSpRp4YixWYK5rtV2fG--UDun-ZvqmA1-8kw@mail.gmail.com>
References: <CAKF=DjsRPTKc6ULtyGqJ-QBgYgXa9EVM_rLhG9kn32AaVCMkXA@mail.gmail.com>
	<CAKF=Djt13m3qnQ42PBhQ4o8d9nk3zBOLmSQ=URKzoYKG57dL+w@mail.gmail.com>
	<CAKF=DjtaMF0+yHqRBfyTxPKhNvDvE1GCJb7p=3bL=cH5ZO1VeA@mail.gmail.com>
	<CAKF=DjtD7BcAUqOSpRp4YixWYK5rtV2fG--UDun-ZvqmA1-8kw@mail.gmail.com>
Message-ID: <CAKF=Djtx71eyHJkLLAttmEhhOSuDD+tDKetxHQELA63z+FAohg@mail.gmail.com>

On Sat, Mar 23, 2013 at 1:20 PM, Skipper Seabold <jsseabold at gmail.com>wrote:

> On Fri, Mar 22, 2013 at 5:39 PM, Skipper Seabold <jsseabold at gmail.com>wrote:
>
>> On Fri, Mar 22, 2013 at 5:04 PM, Skipper Seabold <jsseabold at gmail.com>wrote:
>>
>>> On Fri, Mar 22, 2013 at 4:50 PM, Skipper Seabold <jsseabold at gmail.com>wrote:
>>>
>>>> I thought I built scipy with UMFPACK support but I can't seem to get
>>>> this to work. I would like to do a sparse LU decomposition and recover the
>>>> U matrix.
>>>>
>>>> I built SuiteSparse so long ago, so might there have been a build issue
>>>> I haven't noticed until now?
>>>>
>>>> scipy.test() does not show any errors, though there are 28 skipped
>>>> tests.
>>>>
>>>> Does this work for anyone?
>>>>
>>>> [~/]
>>>> [1]: from scipy import version
>>>>
>>>> [~/]
>>>> [2]: version.full_version
>>>> [2]: '0.13.0.dev-61f05fe'
>>>>
>>>> [~/]
>>>> [3]: from scipy.sparse.linalg.dsolve import umfpack
>>>>
>>>> [~/]
>>>> [4]: umf = umfpack.UmfpackContext()
>>>> Exception AttributeError: "'UmfpackContext' object has no attribute
>>>> '_symbolic'" in <bound method UmfpackContext.__del__ of
>>>> <scipy.sparse.linalg.dsolve.umfpack.umfpack.UmfpackContext object at
>>>> 0x56c0b10>> ignored
>>>>
>>>> ---------------------------------------------------------------------------
>>>> ImportError                               Traceback (most recent call
>>>> last)
>>>> <ipython-input-3-2e28850f247d> in <module>()
>>>> ----> 1 umf = umfpack.UmfpackContext("di")
>>>>
>>>> /usr/local/lib/python2.7/dist-packages/scipy/sparse/linalg/dsolve/umfpack/umfpack.pyc
>>>> in __init__(self, family, **kwargs)
>>>>     279                    a warning is issued (default: 1e12)"""
>>>>     280         if _um is None:
>>>> --> 281             raise ImportError('Scipy was built without UMFPACK
>>>> support. '
>>>>     282                               'You need to install the UMFPACK
>>>> library and '
>>>>     283                               'header files before building
>>>> scipy.')
>>>>
>>>> ImportError: Scipy was built without UMFPACK support. You need to
>>>> install the UMFPACK library and header files before building scipy.
>>>>
>>>> [~/]
>>>> [5]: from scipy import show_config
>>>>
>>>> [~/]
>>>> [6]: show_config()
>>>> amd_info:
>>>>     libraries = ['amd']
>>>>     library_dirs = ['/home/skipper/atlas_build2/lib']
>>>>     define_macros = [('SCIPY_AMD_H', None)]
>>>>     swig_opts = ['-I/home/skipper/atlas_build2/include']
>>>>     include_dirs = ['/home/skipper/atlas_build2/include']
>>>> umfpack_info:
>>>>     libraries = ['umfpack', 'amd']
>>>>     library_dirs = ['/home/skipper/atlas_build2/lib']
>>>>
>>>>     define_macros = [('SCIPY_UMFPACK_H', None), ('SCIPY_AMD_H', None)]
>>>>
>>>>     swig_opts = ['-I/home/skipper/atlas_build2/include',
>>>> '-I/home/skipper/atlas_build2/include']
>>>>
>>>>     include_dirs = ['/home/skipper/atlas_build2/include']
>>>> atlas_threads_info:
>>>>     libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
>>>>     library_dirs = ['/home/skipper/atlas_build2/lib']
>>>>     define_macros = [('NO_ATLAS_INFO', -1)]
>>>>     language = f77
>>>>     include_dirs = ['/home/skipper/atlas_build2/include']
>>>> blas_opt_info:
>>>>     libraries = ['ptf77blas', 'ptcblas', 'atlas']
>>>>     library_dirs = ['/home/skipper/atlas_build2/lib']
>>>>     define_macros = [('NO_ATLAS_INFO', -1)]
>>>>     language = c
>>>>     include_dirs = ['/home/skipper/atlas_build2/include']
>>>> atlas_blas_threads_info:
>>>>     libraries = ['ptf77blas', 'ptcblas', 'atlas']
>>>>     library_dirs = ['/home/skipper/atlas_build2/lib']
>>>>     define_macros = [('NO_ATLAS_INFO', -1)]
>>>>     language = c
>>>>     include_dirs = ['/home/skipper/atlas_build2/include']
>>>> lapack_opt_info:
>>>>     libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
>>>>     library_dirs = ['/home/skipper/atlas_build2/lib']
>>>>     define_macros = [('NO_ATLAS_INFO', -1)]
>>>>     language = f77
>>>>     include_dirs = ['/home/skipper/atlas_build2/include']
>>>> lapack_mkl_info:
>>>>   NOT AVAILABLE
>>>> blas_mkl_info:
>>>>   NOT AVAILABLE
>>>> mkl_info:
>>>>   NOT AVAILABLE
>>>>
>>>> Skipper
>>>>
>>>
>>> Ah, might it be this? From SuiteSparse/AMD/Doc/ChangeLog
>>>
>>> Jun 1, 2012: version 2.3.0
>>>
>>>     * changed from UFconfig to SuiteSparse_config
>>>
>>
>> I suppose not. Also, does this look right? Should they be shared objects?
>> (I'm just about out of my depth)
>>
>> [~/src/SuiteSparse]
>> |27 $ ls ~/atlas_build2/lib/
>> libamd.a    libcholmod.a     liblapack.a     libptlapack.a  libtstatlas.a
>> libatlas.a  libf77blas.a     libptcblas.a    libsatlas.so   libumfpack.a
>> libcblas.a  libf77refblas.a  libptf77blas.a  libtatlas.so
>>
>> I have the -fPIC flag set when building SuiteSparse.
>>
>
> The issue was that there was a problem in the building of SuiteSparse. I
> had not correctly linked against blas and never knew it.
>

Ok, I figured it out after some more fumbling around in the dark. This
thread [1] indicates that you need to change the default [umfpack] section
in site.cfg to read

[umfpack]
umfpack_libs = umfpack, cholmod,  colamd, amd

I didn't have any luck with my old ATLAS/LAPACK, but I was able to link
SuiteSparse (0.4.2) against OpenBlas (current master) and add the following
to site.cfg.

[umfpack]
umfpack_libs = umfpack, cholmod,  colamd, amd, suitesparseconfig, spqr, rt

You only need rt if you didn't disable in in SuiteSparse_config.mk, and
make sure to include the correct library directory for it. It's likely
system-wide if the others aren't. Now I can do my sparse LU decompositions,
which is a great relief.

from scipy import sparse
umf = sparse.linalg.umfpack.UmfpackContext("di")
L, U, P, Q, R, recip = umf.lu(W)

Skipper

[1] http://mail.scipy.org/pipermail/scipy-dev/2011-August/016459.html
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From sergio_r at mail.com  Sun Mar 24 09:00:53 2013
From: sergio_r at mail.com (Sergio Rojas)
Date: Sun, 24 Mar 2013 09:00:53 -0400
Subject: [SciPy-User] Build of scipy-0.12.0b1 under python3.3.0 fails
Message-ID: <20130324130053.287200@gmx.com>

When trying to install scipy-0.12.0b1 under python3.3.0
the following error ends the building of scipy:

python3 setup.py config_fc --fcompiler=gnu95 build
...
error: Command "g++ -pthread -Wno-unused-result -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -Iscipy/interpolate/src -I/home/myPROG/Python330GNU/Linux64b/lib/python3.3/site-packages/numpy/core/include -I/home/myPROG/Python330GNU/Linux64b/include/python3.3m -c scipy/interpolate/src/_interpolate.cpp -o build/temp.linux-x86_64-3.3/scipy/interpolate/src/_interpolate.o" failed with exit status 1


Since I was able to install and test scipy-0.12.0b1 under
python3.2.3 in the same environment,
 could some body point out whether this might be a bug associated
to python3.3.0?

Sergio
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From pav at iki.fi  Sun Mar 24 09:08:27 2013
From: pav at iki.fi (Pauli Virtanen)
Date: Sun, 24 Mar 2013 15:08:27 +0200
Subject: [SciPy-User] Build of scipy-0.12.0b1 under python3.3.0 fails
In-Reply-To: <20130324130053.287200@gmx.com>
References: <20130324130053.287200@gmx.com>
Message-ID: <kimts8$n8m$1@ger.gmane.org>

24.03.2013 15:00, Sergio Rojas kirjoitti:
> When trying to install scipy-0.12.0b1 under python3.3.0
> the following error ends the building of scipy:
> 
> python3 setup.py config_fc --fcompiler=gnu95 build
> ...
> error: Command "g++ -pthread -Wno-unused-result -DNDEBUG -g -fwrapv -O3
> -Wall -fPIC -Iscipy/interpolate/src
> -I/home/myPROG/Python330GNU/Linux64b/lib/python3.3/site-packages/numpy/core/include
> -I/home/myPROG/Python330GNU/Linux64b/include/python3.3m -c
> scipy/interpolate/src/_interpolate.cpp -o
> build/temp.linux-x86_64-3.3/scipy/interpolate/src/_interpolate.o" failed
> with exit status 1

Please put the *whole* build log to gist.github.com and paste a link to
it here.

Python 3.3.0 works for me.

-- 
Pauli Virtanen



From sergio_r at mail.com  Sun Mar 24 19:07:24 2013
From: sergio_r at mail.com (Sergio Rojas)
Date: Sun, 24 Mar 2013 19:07:24 -0400
Subject: [SciPy-User] Build of scipy-0.12.0b1 under python3.3.0 fails
Message-ID: <20130324230724.287200@gmx.com>

Thanks for replying Pauli. The output attempting to build scipy-0.12.0b1 under python3.3.0 fails is at: https://gist.github.com/anonymous/5233970 Sergio ---------------------------------------------------------------------- Message: 1 Date: Sun, 24 Mar 2013 09:00:53 -0400 From: "Sergio Rojas" <sergio_r at mail.com> Subject: [SciPy-User] Build of scipy-0.12.0b1 under python3.3.0 fails To: scipy-user at scipy.org Message-ID: <20130324130053.287200 at gmx.com> Content-Type: text/plain; charset="utf-8" When trying to install scipy-0.12.0b1 under python3.3.0 the following error ends the building of scipy: python3 setup.py config_fc --fcompiler=gnu95 build ... error: Command "g++ -pthread -Wno-unused-result -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -Iscipy/interpolate/src -I/home/myPROG/Python330GNU/Linux64b/lib/python3.3/site-packages/numpy/core/include -I/home/myPROG/Python330GNU/Linux64b/include/python3.3m -c scipy/interpolate/src/_interpolate.cpp -o build/temp.linux-x86_64-3.3/scipy/interpolate/src/_interpolate.o" failed with exit status 1 Since I was able to install and test scipy-0.12.0b1 under python3.2.3 in the same environment, could some body point out whether this might be a bug associated to python3.3.0? Sergio -------------- next part -------------- An HTML attachment was scrubbed... URL: http://mail.scipy.org/pipermail/scipy-user/attachments/20130324/4d2871a9/attachment-0001.html ------------------------------ Message: 2 Date: Sun, 24 Mar 2013 15:08:27 +0200 From: Pauli Virtanen <pav at iki.fi> Subject: Re: [SciPy-User] Build of scipy-0.12.0b1 under python3.3.0 fails To: scipy-user at scipy.org Message-ID: <kimts8$n8m$1 at ger.gmane.org> Content-Type: text/plain; charset=ISO-8859-1 24.03.2013 15:00, Sergio Rojas kirjoitti: > When trying to install scipy-0.12.0b1 under python3.3.0 > the following error ends the building of scipy: > > python3 setup.py config_fc --fcompiler=gnu95 build > ... > error: Command "g++ -pthread -Wno-unused-result -DNDEBUG -g -fwrapv -O3 > -Wall -fPIC -Iscipy/interpolate/src > -I/home/myPROG/Python330GNU/Linux64b/lib/python3.3/site-packages/numpy/core/include > -I/home/myPROG/Python330GNU/Linux64b/include/python3.3m -c > scipy/interpolate/src/_interpolate.cpp -o > build/temp.linux-x86_64-3.3/scipy/interpolate/src/_interpolate.o" failed > with exit status 1 Please put the *whole* build log to gist.github.com and paste a link to it here. Python 3.3.0 works for me. -- Pauli Virtanen ------------------------------ _______________________________________________ SciPy-User mailing list SciPy-User at scipy.org http://mail.scipy.org/mailman/listinfo/scipy-user End of SciPy-User Digest, Vol 115, Issue 29 *******************************************
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From pav at iki.fi  Sun Mar 24 19:31:15 2013
From: pav at iki.fi (Pauli Virtanen)
Date: Mon, 25 Mar 2013 01:31:15 +0200
Subject: [SciPy-User] Build of scipy-0.12.0b1 under python3.3.0 fails
In-Reply-To: <20130324230724.287200@gmx.com>
References: <20130324230724.287200@gmx.com>
Message-ID: <kio2bv$ev7$1@ger.gmane.org>

Hi,

25.03.2013 01:07, Sergio Rojas kirjoitti:
> Thanks for replying Pauli. 
> 
> The output attempting to build scipy-0.12.0b1 under python3.3.0 fails 
>  is at: 
> 
>     https://gist.github.com/anonymous/5233970 

The issue seems to that something is wrong with the combination of your
Python installation and your C++ compiler:

/home/myPROG/Python330GNU/Linux64b/include/python3.3m/longintrepr.h:47:2: error:
#error "30-bit long digits requested, but the necessary types are not
available on this platform"

It's probably nothing Scipy-specific, just that your setup is not able
to compile Python extension modules using C++.

This is not a typical failure, and I don't know how to fix it. Does your
g++ compiler version match your gcc version? OTOH, I don't have better
suggestions than trying to google for the error message.

-- 
Pauli Virtanen



From djpine at gmail.com  Sun Mar 24 19:53:35 2013
From: djpine at gmail.com (David Pine)
Date: Sun, 24 Mar 2013 19:53:35 -0400
Subject: [SciPy-User] a routine for fitting a straight line to data
Message-ID: <E75C0FC4-EF32-4247-8D00-CF64C101DFE7@gmail.com>

I would like to submit a routine to scipy for performing least squares fitting of a straight line f(x) = ax + b to an (x,y) data set.  There are a number of ways of doing this currently using scipy or numpy but all have serious drawbacks.  Here is what is currently available, as far as I can tell, and what seem to me to be their drawbacks.

1. numpy.polyfit : 
    a.  It is slower than it needs to be.  polyfit uses matrix methods that are needed to find best fits to general polynomials (quadratic, cubic, quartic, and higher orders), but matrix methods are overkill when you just want to fit a straight line f(x)= ax + b to data set.  A direct approach can yield fits significantly faster.
    b.  polyfit currently does not allow using absolute error estimates for weighting the data; only relative error estimates are currently possible.  This can be fixed, but for the moment it's a problem.
    c.  New or inexperienced uses are unlikely to look for a routine to fit straight lines in a routine that is advertised as being for polynomials.  This is a more important point than it may seem.  Fitting data to a straight line is probably the most common curve fitting task performed, and the only one that many users will ever use.  It makes sense to cater to such users by providing them with a routine that does what they want in as clear and straightforward a manner as possible.  I am a physics professor and have seen the confusion first hand with a wide spectrum of students who are new to Python.  It should not be this hard for them.

2. scipy.linalg.lstsq
    a.  Using linalg.lstsq to fit a straight line is clunky and very slow (on the order of 10 times slower than polyfit, which is already slower than it needs to be).
    b.  While linalg.lstsq can be used to fit data with error estimates (i.e. using weighting), how to do this is far from obvious.  It's unlikely that anyone but an expert would figure out how to do it.
    c.  linalg.lstsq requires the use of matrices, which will be unfamiliar to some users.  Moreover, it should not be necessary to use matrices when the task at hand only involves one-dimensional arrays.

3. scipy.curve_fit
    a.  This is a nonlinear fitting routine.  As such, it searches for the global minimum in the objective function (chi-squared) rather than just calculating where the global minimum is using the analytical expressions for the best fits.  It's the wrong method for the problem, although it will work.

Questions:  What do others in the scientific Python community think about the need for such a routine?   Where should routine to fit data to a straight line go?  It would seem to me that it should go in the scipy.optimize package, but I wonder what others think.

David Pine

From josef.pktd at gmail.com  Sun Mar 24 20:19:20 2013
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Sun, 24 Mar 2013 20:19:20 -0400
Subject: [SciPy-User] a routine for fitting a straight line to data
In-Reply-To: <E75C0FC4-EF32-4247-8D00-CF64C101DFE7@gmail.com>
References: <E75C0FC4-EF32-4247-8D00-CF64C101DFE7@gmail.com>
Message-ID: <CAMMTP+CGA=ig43KhqX0Os-Z9+_T9knYFCCn6cwL_SoqahPKOgw@mail.gmail.com>

On Sun, Mar 24, 2013 at 7:53 PM, David Pine <djpine at gmail.com> wrote:
> I would like to submit a routine to scipy for performing least squares fitting of a straight line f(x) = ax + b to an (x,y) data set.  There are a number of ways of doing this currently using scipy or numpy but all have serious drawbacks.  Here is what is currently available, as far as I can tell, and what seem to me to be their drawbacks.
>
> 1. numpy.polyfit :
>     a.  It is slower than it needs to be.  polyfit uses matrix methods that are needed to find best fits to general polynomials (quadratic, cubic, quartic, and higher orders), but matrix methods are overkill when you just want to fit a straight line f(x)= ax + b to data set.  A direct approach can yield fits significantly faster.
>     b.  polyfit currently does not allow using absolute error estimates for weighting the data; only relative error estimates are currently possible.  This can be fixed, but for the moment it's a problem.
>     c.  New or inexperienced uses are unlikely to look for a routine to fit straight lines in a routine that is advertised as being for polynomials.  This is a more important point than it may seem.  Fitting data to a straight line is probably the most common curve fitting task performed, and the only one that many users will ever use.  It makes sense to cater to such users by providing them with a routine that does what they want in as clear and straightforward a manner as possible.  I am a physics professor and have seen the confusion first hand with a wide spectrum of students who are new to Python.  It should not be this hard for them.
>
> 2. scipy.linalg.lstsq
>     a.  Using linalg.lstsq to fit a straight line is clunky and very slow (on the order of 10 times slower than polyfit, which is already slower than it needs to be).
>     b.  While linalg.lstsq can be used to fit data with error estimates (i.e. using weighting), how to do this is far from obvious.  It's unlikely that anyone but an expert would figure out how to do it.
>     c.  linalg.lstsq requires the use of matrices, which will be unfamiliar to some users.  Moreover, it should not be necessary to use matrices when the task at hand only involves one-dimensional arrays.
>
> 3. scipy.curve_fit
>     a.  This is a nonlinear fitting routine.  As such, it searches for the global minimum in the objective function (chi-squared) rather than just calculating where the global minimum is using the analytical expressions for the best fits.  It's the wrong method for the problem, although it will work.
>
> Questions:  What do others in the scientific Python community think about the need for such a routine?   Where should routine to fit data to a straight line go?  It would seem to me that it should go in the scipy.optimize package, but I wonder what others think.

scipy.stats.linregress if there is only one x

Josef


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


From warren.weckesser at gmail.com  Sun Mar 24 20:22:55 2013
From: warren.weckesser at gmail.com (Warren Weckesser)
Date: Sun, 24 Mar 2013 20:22:55 -0400
Subject: [SciPy-User] a routine for fitting a straight line to data
In-Reply-To: <E75C0FC4-EF32-4247-8D00-CF64C101DFE7@gmail.com>
References: <E75C0FC4-EF32-4247-8D00-CF64C101DFE7@gmail.com>
Message-ID: <CAGzF1ufNmsegyBtjpLcj4gQ1N_92c=1h6OjC8tMzGg43VPfZbQ@mail.gmail.com>

On 3/24/13, David Pine <djpine at gmail.com> wrote:
> I would like to submit a routine to scipy for performing least squares
> fitting of a straight line f(x) = ax + b to an (x,y) data set.  There are a
> number of ways of doing this currently using scipy or numpy but all have
> serious drawbacks.  Here is what is currently available, as far as I can
> tell, and what seem to me to be their drawbacks.
>
> 1. numpy.polyfit :
>     a.  It is slower than it needs to be.  polyfit uses matrix methods that
> are needed to find best fits to general polynomials (quadratic, cubic,
> quartic, and higher orders), but matrix methods are overkill when you just
> want to fit a straight line f(x)= ax + b to data set.  A direct approach can
> yield fits significantly faster.
>     b.  polyfit currently does not allow using absolute error estimates for
> weighting the data; only relative error estimates are currently possible.
> This can be fixed, but for the moment it's a problem.
>     c.  New or inexperienced uses are unlikely to look for a routine to fit
> straight lines in a routine that is advertised as being for polynomials.
> This is a more important point than it may seem.  Fitting data to a straight
> line is probably the most common curve fitting task performed, and the only
> one that many users will ever use.  It makes sense to cater to such users by
> providing them with a routine that does what they want in as clear and
> straightforward a manner as possible.  I am a physics professor and have
> seen the confusion first hand with a wide spectrum of students who are new
> to Python.  It should not be this hard for them.
>
> 2. scipy.linalg.lstsq
>     a.  Using linalg.lstsq to fit a straight line is clunky and very slow
> (on the order of 10 times slower than polyfit, which is already slower than
> it needs to be).
>     b.  While linalg.lstsq can be used to fit data with error estimates
> (i.e. using weighting), how to do this is far from obvious.  It's unlikely
> that anyone but an expert would figure out how to do it.
>     c.  linalg.lstsq requires the use of matrices, which will be unfamiliar
> to some users.  Moreover, it should not be necessary to use matrices when
> the task at hand only involves one-dimensional arrays.
>
> 3. scipy.curve_fit
>     a.  This is a nonlinear fitting routine.  As such, it searches for the
> global minimum in the objective function (chi-squared) rather than just
> calculating where the global minimum is using the analytical expressions for
> the best fits.  It's the wrong method for the problem, although it will
> work.
>
> Questions:  What do others in the scientific Python community think about
> the need for such a routine?   Where should routine to fit data to a
> straight line go?  It would seem to me that it should go in the
> scipy.optimize package, but I wonder what others think.
>
> David Pine


David,

There is also scipy.stats.linregress, which is a basic 1-D (ie. x and
y are 1-D vectors) linear regression.

Warren

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


From djpine at gmail.com  Sun Mar 24 21:27:09 2013
From: djpine at gmail.com (David Pine)
Date: Sun, 24 Mar 2013 21:27:09 -0400
Subject: [SciPy-User] a routine for fitting a straight line to data
In-Reply-To: <CAMMTP+CGA=ig43KhqX0Os-Z9+_T9knYFCCn6cwL_SoqahPKOgw@mail.gmail.com>
References: <E75C0FC4-EF32-4247-8D00-CF64C101DFE7@gmail.com>
	<CAMMTP+CGA=ig43KhqX0Os-Z9+_T9knYFCCn6cwL_SoqahPKOgw@mail.gmail.com>
Message-ID: <41FE3C6C-5337-4B4C-A742-812B9A9340C3@gmail.com>

scipy.stats.linregress does not do the job.  First, it does not allow for weighting of the data, relative, absolute, or otherwise.  It does not return the covariance matrix, which provides estimates of the uncertainties in the fitting parameters, nor does it return chi-squared, which is the standard measure of the quality of the fit in the physical sciences.

David


On Mar 24, 2013, at 8:19 PM, josef.pktd at gmail.com wrote:

> On Sun, Mar 24, 2013 at 7:53 PM, David Pine <djpine at gmail.com> wrote:
>> I would like to submit a routine to scipy for performing least squares fitting of a straight line f(x) = ax + b to an (x,y) data set.  There are a number of ways of doing this currently using scipy or numpy but all have serious drawbacks.  Here is what is currently available, as far as I can tell, and what seem to me to be their drawbacks.
>> 
>> 1. numpy.polyfit :
>>    a.  It is slower than it needs to be.  polyfit uses matrix methods that are needed to find best fits to general polynomials (quadratic, cubic, quartic, and higher orders), but matrix methods are overkill when you just want to fit a straight line f(x)= ax + b to data set.  A direct approach can yield fits significantly faster.
>>    b.  polyfit currently does not allow using absolute error estimates for weighting the data; only relative error estimates are currently possible.  This can be fixed, but for the moment it's a problem.
>>    c.  New or inexperienced uses are unlikely to look for a routine to fit straight lines in a routine that is advertised as being for polynomials.  This is a more important point than it may seem.  Fitting data to a straight line is probably the most common curve fitting task performed, and the only one that many users will ever use.  It makes sense to cater to such users by providing them with a routine that does what they want in as clear and straightforward a manner as possible.  I am a physics professor and have seen the confusion first hand with a wide spectrum of students who are new to Python.  It should not be this hard for them.
>> 
>> 2. scipy.linalg.lstsq
>>    a.  Using linalg.lstsq to fit a straight line is clunky and very slow (on the order of 10 times slower than polyfit, which is already slower than it needs to be).
>>    b.  While linalg.lstsq can be used to fit data with error estimates (i.e. using weighting), how to do this is far from obvious.  It's unlikely that anyone but an expert would figure out how to do it.
>>    c.  linalg.lstsq requires the use of matrices, which will be unfamiliar to some users.  Moreover, it should not be necessary to use matrices when the task at hand only involves one-dimensional arrays.
>> 
>> 3. scipy.curve_fit
>>    a.  This is a nonlinear fitting routine.  As such, it searches for the global minimum in the objective function (chi-squared) rather than just calculating where the global minimum is using the analytical expressions for the best fits.  It's the wrong method for the problem, although it will work.
>> 
>> Questions:  What do others in the scientific Python community think about the need for such a routine?   Where should routine to fit data to a straight line go?  It would seem to me that it should go in the scipy.optimize package, but I wonder what others think.
> 
> scipy.stats.linregress if there is only one x
> 
> Josef
> 
> 
>> David Pine
>> _______________________________________________
>> 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 Jerome.Kieffer at esrf.fr  Mon Mar 25 02:28:52 2013
From: Jerome.Kieffer at esrf.fr (Jerome Kieffer)
Date: Mon, 25 Mar 2013 07:28:52 +0100
Subject: [SciPy-User] a routine for fitting a straight line to data
In-Reply-To: <41FE3C6C-5337-4B4C-A742-812B9A9340C3@gmail.com>
References: <E75C0FC4-EF32-4247-8D00-CF64C101DFE7@gmail.com>
	<CAMMTP+CGA=ig43KhqX0Os-Z9+_T9knYFCCn6cwL_SoqahPKOgw@mail.gmail.com>
	<41FE3C6C-5337-4B4C-A742-812B9A9340C3@gmail.com>
Message-ID: <20130325072852.d2c97ffd.Jerome.Kieffer@esrf.fr>

On Sun, 24 Mar 2013 21:27:09 -0400
David Pine <djpine at gmail.com> wrote:

> scipy.stats.linregress does not do the job.  First, it does not allow for weighting of the data, relative, absolute, or otherwise.  It does not return the covariance matrix, which provides estimates of the uncertainties in the fitting parameters, nor does it return chi-squared, which is the standard measure of the quality of the fit in the physical sciences.

I implemented a linear regression according to what I found in stats textbooks (and various pages of wikipedia) ... 

(arrays are 2D, considered as m datasets of length n

Sx = (w * x).sum(axis= -1)
Sy = (w * y).sum(axis= -1)
Sxx = (w * x * x).sum(axis= -1)
Sxy = (w * y * x).sum(axis= -1)
Syy = (w * y * y).sum(axis= -1)
Sw = w.sum(axis= -1)
slope = (Sw * Sxy - Sx * Sy) / (Sw * Sxx - Sx * Sx)
intercept = (Sy - Sx * slope) / Sw
df = n - 2
r_num = ssxym = (Sw * Sxy) - (Sx * Sy)
ssxm = Sw * Sxx - Sx * Sx
ssym = Sw * Syy - Sy * Sy
r_den = numpy.sqrt(ssxm * ssym)
correlationR = r_num / r_den
correlationR[r_den == 0] = 0.0
correlationR[correlationR > 1.0] = 1.0  # Numerical errors
correlationR[correlationR < -1.0] = -1.0  # Numerical errors
sterrest = numpy.sqrt((1.0 - correlationR * correlationR) * ssym / ssxm / df)

Hope this helps

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


From sloan.lindsey at gmail.com  Mon Mar 25 07:55:30 2013
From: sloan.lindsey at gmail.com (Sloan Lindsey)
Date: Mon, 25 Mar 2013 12:55:30 +0100
Subject: [SciPy-User] cobyla
In-Reply-To: <CABL7CQjO5m_ibhKHe1Lkh48Nd1FTv6m-YMLTY8q-en2hn6ZPoQ@mail.gmail.com>
References: <mailman.13.1349024403.12109.scipy-user@scipy.org>
	<1349039373.72572.YahooMailNeo@web31805.mail.mud.yahoo.com>
	<CABL7CQjO5m_ibhKHe1Lkh48Nd1FTv6m-YMLTY8q-en2hn6ZPoQ@mail.gmail.com>
Message-ID: <CAB5qdEHVnq5LPr=+e+SDU25vGm6Kepu0cUv5u02tbzqFSDNbbg@mail.gmail.com>

How does one parse the doc string? I'm interested in having my script
know when MAXFUN is reached. I see it coming out as a print line ( I
have to use 0.7.2 because of an older centOS install), but I'd really
like my script to know that it has happened so I can tag that solution
as suspect. Is there any cute way to redirect that output into
something I can parse?

-Sloan

On Mon, Oct 1, 2012 at 9:58 PM, Ralf Gommers <ralf.gommers at gmail.com> wrote:
>
>
> On Sun, Sep 30, 2012 at 11:09 PM, The Helmbolds <helmrp at yahoo.com> wrote:
>>
>> On my system (Windows 7, Python 2.7.x and IDLE, latest SciPy), I observe
>> the following behavior with fmin_cobyla and minimize's COBYLA method.
>>
>> Case 1: When run either in the IDLE interactive shell or within an
>> enclosing Python program:
>>     1.1. The fmin_cobyla function never returns the Results dictionary,
>> and never displays it to Python's stdout. This is true regardless of the
>> function call's disp setting.
>
>
> Correct. The fmin_cobyla docstring clearly says what it returns. Result
> objects are only returned by the new interfaces in the 0.11.0 release
> (minimize, minimize_scalar, root).
>
>>     1.2. The 'minimize' function always returns the Results dictionary but
>> never displays it to Python's stdout. Again, this is true regardless of the
>> function call's disp setting.
>
>
> `disp` doesn't print the Results objects. For me it works as advertized (in
> IPython), it prints something like:
>
>    Normal return from subroutine COBYLA
>
>    NFVALS =   37   F = 8.000000E-01    MAXCV = 0.000000E+00
>    X = 1.400113E+00   1.700056E+00
>
> Ralf
>
>>
>> Case 2: When run interactively in Window's Command Prompt box:
>>     2.1 The fmin_cobyla function never returns the Result dictionary,
>> regardless of the function call's disp setting. Setting disp to True or
>> False either displays the Results dictionary in the command box or not
>> (respectively). I don't think the Results dictionary gets to the command box
>> via stdout.
>>     2.2 The 'minimize' function always returns the Result dictionary,
>> regardless of the function call's disp setting.  Setting disp to True or
>> False either displays the Results dictionary in the command box or not
>> (respectively). I don't think the Results dictionary gets to the command box
>> via stdout.
>>
>> My thanks to all who helped clarify this situation.
>>
>> Bob H
>> _______________________________________________
>> 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 pierrega at consortech.com  Mon Mar 25 05:51:42 2013
From: pierrega at consortech.com (Pierre Gauthier)
Date: Mon, 25 Mar 2013 05:51:42 -0400
Subject: [SciPy-User] scipy.spatial.Delaunay qhull_option
Message-ID: <C92469F581C1E642B4F8C732216131EEBEF5D4F4C3@DC.consortech.com>

Hi,

What is the syntax when I want to set the "En" qhull_option?

This is not working:
d = scipy.spatial.Delaunay(nd, qhull_options="Qz Qt QJ En 0.0001")

Thanks,
Pierre

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From sergio_r at mail.com  Mon Mar 25 09:21:21 2013
From: sergio_r at mail.com (Sergio Rojas)
Date: Mon, 25 Mar 2013 09:21:21 -0400
Subject: [SciPy-User] Build of scipy-0.12.0b1 under python3.3.0 fails
Message-ID: <20130325132121.287210@gmx.com>

Thanks Pauli. Your hint

>
> The issue seems to that something is wrong with the combination of your
> Python installation and your C++ compiler:
>

send me to revise my python3.3.0 installation setup. In building python3.3.0
I passed to the configure script the option "--enable-big-digits"
which I was thinking to be the replacement of the option
"--with-wide-unicode" that I used when building python3.2.3.
(the configuration script of python3.3.0 does not recognize the option
"--with-wide-unicode").

So, I took out the option "--enable-big-digits" in building python3.3.0
and then the build of scipy-0.12.0b1 finalized without errors.

This time, however, I am getting a failing in the "test_mio.test_save_dict"
which did not happened in the building of scipy-0.12.0b1
under python 3.2.3. What might be the consequences of that failure
in using scipy? (Ralf has already shed some light on the consequences
of using scipy in spite of the other two test failures)

What follows is a summary of "scipy.test('full', verbose=2)" under both
 both python3.3.0 and python3.2.3:

Scipy scipy-0.12.0b1
NumPy version 1.7.0
Python version 3.3.0 (default, Mar 25 2013, 06:25:03) [GCC 4.6.1]
======================================================================
ERROR: Failure: ImportError (scipy.weave only supports Python 2.x)
----------------------------------------------------------------------
======================================================================
FAIL: test_mio.test_save_dict
----------------------------------------------------------------------
======================================================================
FAIL: test_mathieu_modsem2 (test_basic.TestCephes)
----------------------------------------------------------------------
----------------------------------------------------------------------
Ran 6426 tests in 277.767s
FAILED (KNOWNFAIL=15, SKIP=47, errors=1, failures=2)
nose version 1.2.1
<nose.result.TextTestResult run=6426 errors=1 failures=2>
****************

Scipy scipy-0.12.0b1
NumPy version 1.7.0
Python version 3.2.3 (default, Mar 23 2013, 07:28:36) [GCC 4.6.1]
======================================================================
ERROR: Failure: ImportError (scipy.weave only supports Python 2.x)
----------------------------------------------------------------------
======================================================================
FAIL: test_mathieu_modsem2 (test_basic.TestCephes)
----------------------------------------------------------------------
Ran 6426 tests in 277.812s
FAILED (KNOWNFAIL=15, SKIP=47, errors=1, failures=1)
nose version 1.2.1
<nose.result.TextTestResult run=6426 errors=1 failures=1>

Thanks again for your help,

Sergio


 ------------------------------ Message: 2 Date: Mon, 25 Mar 2013 01:31:15 +0200 From: Pauli Virtanen <pav at iki.fi> Subject: Re: [SciPy-User] Build of scipy-0.12.0b1 under python3.3.0 fails To: scipy-user at scipy.org Message-ID: <kio2bv$ev7$1 at ger.gmane.org> Content-Type: text/plain; charset=ISO-8859-1 Hi, 25.03.2013 01:07, Sergio Rojas kirjoitti: > Thanks for replying Pauli. > > The output attempting to build scipy-0.12.0b1 under python3.3.0 fails > is at: > > https://gist.github.com/anonymous/5233970 The issue seems to that something is wrong with the combination of your Python installation and your C++ compiler: /home/myPROG/Python330GNU/Linux64b/include/python3.3m/longintrepr.h:47:2: error: #error "30-bit long digits requested, but the necessary types are not available on this platform" It's probably nothing Scipy-specific, just that your setup is not able to compile Python extension modules using C++. This is not a typical failure, and I don't know how to fix it. Does your g++ compiler version match your gcc version? OTOH, I don't have better suggestions than trying to google for the error message. -- Pauli Virtanen ------------------------------
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From takowl at gmail.com  Mon Mar 25 09:28:27 2013
From: takowl at gmail.com (Thomas Kluyver)
Date: Mon, 25 Mar 2013 13:28:27 +0000
Subject: [SciPy-User] Build of scipy-0.12.0b1 under python3.3.0 fails
In-Reply-To: <20130325132121.287210@gmx.com>
References: <20130325132121.287210@gmx.com>
Message-ID: <CAOvn4qhow0kZdfhk7Fdfvcf6H7m=hDLi1PHG3-5u2JegHrpEEg@mail.gmail.com>

On 25 March 2013 13:21, Sergio Rojas <sergio_r at mail.com> wrote:

> (the configuration script of python3.3.0 does not recognize the option
> "--with-wide-unicode").


'Wide unicode' is no longer necessary - all builds of 3.3 will behave
mostly like wide unicode builds, but with less memory use. For details, see:
http://docs.python.org/3/whatsnew/3.3.html#pep-393-flexible-string-representation

>From a quick search, 'big digits' seems to relate to how large integers are
stored.

Thomas
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From pav at iki.fi  Mon Mar 25 13:20:49 2013
From: pav at iki.fi (Pauli Virtanen)
Date: Mon, 25 Mar 2013 19:20:49 +0200
Subject: [SciPy-User] scipy.spatial.Delaunay qhull_option
In-Reply-To: <C92469F581C1E642B4F8C732216131EEBEF5D4F4C3@DC.consortech.com>
References: <C92469F581C1E642B4F8C732216131EEBEF5D4F4C3@DC.consortech.com>
Message-ID: <kiq11d$kjm$1@ger.gmane.org>

25.03.2013 11:51, Pierre Gauthier kirjoitti:
[clip]
> What is the syntax when I want to set the ?En? qhull_option?
> 
> This is not working:
> 
> d = scipy.spatial.Delaunay(nd, qhull_options="Qz Qt QJ En 0.0001")

It should be the same as on qhull command line:

	"Qz Qt QJ E0.0001"

-- 
Pauli Virtanen



From ralf.gommers at gmail.com  Mon Mar 25 15:38:40 2013
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Mon, 25 Mar 2013 20:38:40 +0100
Subject: [SciPy-User] Build of scipy-0.12.0b1 under python3.3.0 fails
In-Reply-To: <20130325132121.287210@gmx.com>
References: <20130325132121.287210@gmx.com>
Message-ID: <CABL7CQhs-pP_3BAfYEkcFUa0WBbrNN4e5rnUntWZ3VQsUS4XSw@mail.gmail.com>

On Mon, Mar 25, 2013 at 2:21 PM, Sergio Rojas <sergio_r at mail.com> wrote:

> Thanks Pauli. Your hint
>
>
> >
> > The issue seems to that something is wrong with the combination of your
> > Python installation and your C++ compiler:
> >
>
> send me to revise my python3.3.0 installation setup. In building
> python3.3.0
> I passed to the configure script the option "--enable-big-digits"
> which I was thinking to be the replacement of the option
> "--with-wide-unicode" that I used when building python3.2.3.
> (the configuration script of python3.3.0 does not recognize the option
> "--with-wide-unicode").
>
> So, I took out the option "--enable-big-digits" in building python3.3.0
> and then the build of scipy-0.12.0b1 finalized without errors.
>
> This time, however, I am getting a failing in the "test_mio.test_save_dict"
> which did not happened in the building of scipy-0.12.0b1
> under python 3.2.3. What might be the consequences of that failure
> in using scipy? (Ralf has already shed some light on the consequences
> of using scipy in spite of the other two test failures)
>

I'm seeing that failure as well on 3.2, will investigate for 0.12. It looks
like saving Matlab files is partially broken on 3.x by this. No further
impact.

Ralf


>
> What follows is a summary of "scipy.test('full', verbose=2)" under both
>  both python3.3.0 and python3.2.3:
>
> Scipy scipy-0.12.0b1
> NumPy version 1.7.0
> Python version 3.3.0 (default, Mar 25 2013, 06:25:03) [GCC 4.6.1]
> ======================================================================
> ERROR: Failure: ImportError (scipy.weave only supports Python 2.x)
> ----------------------------------------------------------------------
> ======================================================================
> FAIL: test_mio.test_save_dict
> ----------------------------------------------------------------------
> ======================================================================
> FAIL: test_mathieu_modsem2 (test_basic.TestCephes)
> ----------------------------------------------------------------------
> ----------------------------------------------------------------------
> Ran 6426 tests in 277.767s
> FAILED (KNOWNFAIL=15, SKIP=47, errors=1, failures=2)
> nose version 1.2.1
> <nose.result.TextTestResult run=6426 errors=1 failures=2>
> ****************
>
> Scipy scipy-0.12.0b1
> NumPy version 1.7.0
> Python version 3.2.3 (default, Mar 23 2013, 07:28:36) [GCC 4.6.1]
> ======================================================================
> ERROR: Failure: ImportError (scipy.weave only supports Python 2.x)
> ----------------------------------------------------------------------
> ======================================================================
> FAIL: test_mathieu_modsem2 (test_basic.TestCephes)
> ----------------------------------------------------------------------
> Ran 6426 tests in 277.812s
> FAILED (KNOWNFAIL=15, SKIP=47, errors=1, failures=1)
> nose version 1.2.1
> <nose.result.TextTestResult run=6426 errors=1 failures=1>
>
> Thanks again for your help,
>
> Sergio
>
>
>
>
>
>
>
>
>
> ------------------------------
>
> Message: 2
> Date: Mon, 25 Mar 2013 01:31:15 +0200
> From: Pauli Virtanen <pav at iki.fi>
> Subject: Re: [SciPy-User] Build of scipy-0.12.0b1 under python3.3.0
> 	fails
> To: scipy-user at scipy.org
> Message-ID: <kio2bv$ev7$1 at ger.gmane.org>
> Content-Type: text/plain; charset=ISO-8859-1
>
> Hi,
>
> 25.03.2013 01:07, Sergio Rojas kirjoitti:
> > Thanks for replying Pauli.
> >
> > The output attempting to build scipy-0.12.0b1 under python3.3.0 fails
> >  is at:
> >
> >     https://gist.github.com/anonymous/5233970
>
> The issue seems to that something is wrong with the combination of your
> Python installation and your C++ compiler:
>
> /home/myPROG/Python330GNU/Linux64b/include/python3.3m/longintrepr.h:47:2: error:
> #error "30-bit long digits requested, but the necessary types are not
> available on this platform"
>
> It's probably nothing Scipy-specific, just that your setup is not able
> to compile Python extension modules using C++.
>
> This is not a typical failure, and I don't know how to fix it. Does your
> g++ compiler version match your gcc version? OTOH, I don't have better
> suggestions than trying to google for the error message.
>
> --
> Pauli Virtanen
>
>
>
> ------------------------------
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
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From ralf.gommers at gmail.com  Mon Mar 25 16:19:03 2013
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Mon, 25 Mar 2013 21:19:03 +0100
Subject: [SciPy-User] cobyla
In-Reply-To: <CAB5qdEHVnq5LPr=+e+SDU25vGm6Kepu0cUv5u02tbzqFSDNbbg@mail.gmail.com>
References: <mailman.13.1349024403.12109.scipy-user@scipy.org>
	<1349039373.72572.YahooMailNeo@web31805.mail.mud.yahoo.com>
	<CABL7CQjO5m_ibhKHe1Lkh48Nd1FTv6m-YMLTY8q-en2hn6ZPoQ@mail.gmail.com>
	<CAB5qdEHVnq5LPr=+e+SDU25vGm6Kepu0cUv5u02tbzqFSDNbbg@mail.gmail.com>
Message-ID: <CABL7CQhFUpWn=pKDnRQkMK=LHvnG2+dhWi05vqpj605BNT4qog@mail.gmail.com>

On Mon, Mar 25, 2013 at 12:55 PM, Sloan Lindsey <sloan.lindsey at gmail.com>wrote:

> How does one parse the doc string? I'm interested in having my script
> know when MAXFUN is reached. I see it coming out as a print line ( I
> have to use 0.7.2 because of an older centOS install), but I'd really
> like my script to know that it has happened so I can tag that solution
> as suspect. Is there any cute way to redirect that output into
> something I can parse?
>

I don't think there's a good way to get at it. The output comes from some
Fortran code, so redirecting sys.stdout won't help you.

Ralf



>
> -Sloan
>
> On Mon, Oct 1, 2012 at 9:58 PM, Ralf Gommers <ralf.gommers at gmail.com>
> wrote:
> >
> >
> > On Sun, Sep 30, 2012 at 11:09 PM, The Helmbolds <helmrp at yahoo.com>
> wrote:
> >>
> >> On my system (Windows 7, Python 2.7.x and IDLE, latest SciPy), I observe
> >> the following behavior with fmin_cobyla and minimize's COBYLA method.
> >>
> >> Case 1: When run either in the IDLE interactive shell or within an
> >> enclosing Python program:
> >>     1.1. The fmin_cobyla function never returns the Results dictionary,
> >> and never displays it to Python's stdout. This is true regardless of the
> >> function call's disp setting.
> >
> >
> > Correct. The fmin_cobyla docstring clearly says what it returns. Result
> > objects are only returned by the new interfaces in the 0.11.0 release
> > (minimize, minimize_scalar, root).
> >
> >>     1.2. The 'minimize' function always returns the Results dictionary
> but
> >> never displays it to Python's stdout. Again, this is true regardless of
> the
> >> function call's disp setting.
> >
> >
> > `disp` doesn't print the Results objects. For me it works as advertized
> (in
> > IPython), it prints something like:
> >
> >    Normal return from subroutine COBYLA
> >
> >    NFVALS =   37   F = 8.000000E-01    MAXCV = 0.000000E+00
> >    X = 1.400113E+00   1.700056E+00
> >
> > Ralf
> >
> >>
> >> Case 2: When run interactively in Window's Command Prompt box:
> >>     2.1 The fmin_cobyla function never returns the Result dictionary,
> >> regardless of the function call's disp setting. Setting disp to True or
> >> False either displays the Results dictionary in the command box or not
> >> (respectively). I don't think the Results dictionary gets to the
> command box
> >> via stdout.
> >>     2.2 The 'minimize' function always returns the Result dictionary,
> >> regardless of the function call's disp setting.  Setting disp to True or
> >> False either displays the Results dictionary in the command box or not
> >> (respectively). I don't think the Results dictionary gets to the
> command box
> >> via stdout.
> >>
> >> My thanks to all who helped clarify this situation.
> >>
> >> Bob H
> >> _______________________________________________
> >> 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 ralf.gommers at gmail.com  Mon Mar 25 17:36:35 2013
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Mon, 25 Mar 2013 22:36:35 +0100
Subject: [SciPy-User] Build of scipy-0.12.0b1 under python3.3.0 fails
In-Reply-To: <CABL7CQhs-pP_3BAfYEkcFUa0WBbrNN4e5rnUntWZ3VQsUS4XSw@mail.gmail.com>
References: <20130325132121.287210@gmx.com>
	<CABL7CQhs-pP_3BAfYEkcFUa0WBbrNN4e5rnUntWZ3VQsUS4XSw@mail.gmail.com>
Message-ID: <CABL7CQgFCyrQqoj6+jGfA8O=-j-ozJQAeOkD5FMszacoM7Y8Sg@mail.gmail.com>

On Mon, Mar 25, 2013 at 8:38 PM, Ralf Gommers <ralf.gommers at gmail.com>wrote:

>
>
>
> On Mon, Mar 25, 2013 at 2:21 PM, Sergio Rojas <sergio_r at mail.com> wrote:
>
>> Thanks Pauli. Your hint
>>
>>
>> >
>> > The issue seems to that something is wrong with the combination of your
>> > Python installation and your C++ compiler:
>> >
>>
>> send me to revise my python3.3.0 installation setup. In building
>> python3.3.0
>> I passed to the configure script the option "--enable-big-digits"
>> which I was thinking to be the replacement of the option
>> "--with-wide-unicode" that I used when building python3.2.3.
>> (the configuration script of python3.3.0 does not recognize the option
>> "--with-wide-unicode").
>>
>> So, I took out the option "--enable-big-digits" in building python3.3.0
>> and then the build of scipy-0.12.0b1 finalized without errors.
>>
>> This time, however, I am getting a failing in the
>> "test_mio.test_save_dict"
>> which did not happened in the building of scipy-0.12.0b1
>> under python 3.2.3. What might be the consequences of that failure
>> in using scipy? (Ralf has already shed some light on the consequences
>> of using scipy in spite of the other two test failures)
>>
>
> I'm seeing that failure as well on 3.2, will investigate for 0.12. It
> looks like saving Matlab files is partially broken on 3.x by this. No
> further impact.
>

fixed in commit 31db7fc


>
> Ralf
>
>
>>
>> What follows is a summary of "scipy.test('full', verbose=2)" under both
>>  both python3.3.0 and python3.2.3:
>>
>> Scipy scipy-0.12.0b1
>> NumPy version 1.7.0
>> Python version 3.3.0 (default, Mar 25 2013, 06:25:03) [GCC 4.6.1]
>> ======================================================================
>> ERROR: Failure: ImportError (scipy.weave only supports Python 2.x)
>> ----------------------------------------------------------------------
>> ======================================================================
>> FAIL: test_mio.test_save_dict
>> ----------------------------------------------------------------------
>> ======================================================================
>> FAIL: test_mathieu_modsem2 (test_basic.TestCephes)
>> ----------------------------------------------------------------------
>> ----------------------------------------------------------------------
>> Ran 6426 tests in 277.767s
>> FAILED (KNOWNFAIL=15, SKIP=47, errors=1, failures=2)
>> nose version 1.2.1
>> <nose.result.TextTestResult run=6426 errors=1 failures=2>
>> ****************
>>
>> Scipy scipy-0.12.0b1
>> NumPy version 1.7.0
>> Python version 3.2.3 (default, Mar 23 2013, 07:28:36) [GCC 4.6.1]
>> ======================================================================
>> ERROR: Failure: ImportError (scipy.weave only supports Python 2.x)
>> ----------------------------------------------------------------------
>> ======================================================================
>> FAIL: test_mathieu_modsem2 (test_basic.TestCephes)
>> ----------------------------------------------------------------------
>> Ran 6426 tests in 277.812s
>> FAILED (KNOWNFAIL=15, SKIP=47, errors=1, failures=1)
>> nose version 1.2.1
>> <nose.result.TextTestResult run=6426 errors=1 failures=1>
>>
>> Thanks again for your help,
>>
>> Sergio
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> ------------------------------
>>
>> Message: 2
>> Date: Mon, 25 Mar 2013 01:31:15 +0200
>> From: Pauli Virtanen <pav at iki.fi>
>> Subject: Re: [SciPy-User] Build of scipy-0.12.0b1 under python3.3.0
>> 	fails
>> To: scipy-user at scipy.org
>> Message-ID: <kio2bv$ev7$1 at ger.gmane.org>
>> Content-Type: text/plain; charset=ISO-8859-1
>>
>> Hi,
>> 25.03.2013 01:07, Sergio Rojas kirjoitti:
>> > Thanks for replying Pauli.
>> >
>> > The output attempting to build scipy-0.12.0b1 under python3.3.0 fails
>> >  is at:
>> >
>> >     https://gist.github.com/anonymous/5233970
>>
>> The issue seems to that something is wrong with the combination of your
>> Python installation and your C++ compiler:
>>
>> /home/myPROG/Python330GNU/Linux64b/include/python3.3m/longintrepr.h:47:2: error:
>> #error "30-bit long digits requested, but the necessary types are not
>> available on this platform"
>>
>> It's probably nothing Scipy-specific, just that your setup is not able
>> to compile Python extension modules using C++.
>>
>> This is not a typical failure, and I don't know how to fix it. Does your
>> g++ compiler version match your gcc version? OTOH, I don't have better
>> suggestions than trying to google for the error message.
>>
>> --
>> Pauli Virtanen
>>
>>
>>
>> ------------------------------
>>
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>>
>
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From sloan.lindsey at gmail.com  Tue Mar 26 07:09:19 2013
From: sloan.lindsey at gmail.com (Sloan Lindsey)
Date: Tue, 26 Mar 2013 12:09:19 +0100
Subject: [SciPy-User] cobyla
In-Reply-To: <CABL7CQhFUpWn=pKDnRQkMK=LHvnG2+dhWi05vqpj605BNT4qog@mail.gmail.com>
References: <mailman.13.1349024403.12109.scipy-user@scipy.org>
	<1349039373.72572.YahooMailNeo@web31805.mail.mud.yahoo.com>
	<CABL7CQjO5m_ibhKHe1Lkh48Nd1FTv6m-YMLTY8q-en2hn6ZPoQ@mail.gmail.com>
	<CAB5qdEHVnq5LPr=+e+SDU25vGm6Kepu0cUv5u02tbzqFSDNbbg@mail.gmail.com>
	<CABL7CQhFUpWn=pKDnRQkMK=LHvnG2+dhWi05vqpj605BNT4qog@mail.gmail.com>
Message-ID: <CAB5qdEGH+LTD82z+U8Xz+k8wn1KNOZxZGZJF_dzg9n90SsPVVQ@mail.gmail.com>

That's a bummer, it would be nice to see it in a future version.

For anyone who is interested in doing something similar, I ended up
passing a counter variable in the args that is incremented every time
the the fn to be minimized is called. I'm still debugging so I'll let
everyone know if it doesn't work.
-Sloan

On Mon, Mar 25, 2013 at 9:19 PM, Ralf Gommers <ralf.gommers at gmail.com> wrote:
>
>
>
> On Mon, Mar 25, 2013 at 12:55 PM, Sloan Lindsey <sloan.lindsey at gmail.com>
> wrote:
>>
>> How does one parse the doc string? I'm interested in having my script
>> know when MAXFUN is reached. I see it coming out as a print line ( I
>> have to use 0.7.2 because of an older centOS install), but I'd really
>> like my script to know that it has happened so I can tag that solution
>> as suspect. Is there any cute way to redirect that output into
>> something I can parse?
>
>
> I don't think there's a good way to get at it. The output comes from some
> Fortran code, so redirecting sys.stdout won't help you.
>
> Ralf
>
>
>>
>>
>> -Sloan
>>
>> On Mon, Oct 1, 2012 at 9:58 PM, Ralf Gommers <ralf.gommers at gmail.com>
>> wrote:
>> >
>> >
>> > On Sun, Sep 30, 2012 at 11:09 PM, The Helmbolds <helmrp at yahoo.com>
>> > wrote:
>> >>
>> >> On my system (Windows 7, Python 2.7.x and IDLE, latest SciPy), I
>> >> observe
>> >> the following behavior with fmin_cobyla and minimize's COBYLA method.
>> >>
>> >> Case 1: When run either in the IDLE interactive shell or within an
>> >> enclosing Python program:
>> >>     1.1. The fmin_cobyla function never returns the Results dictionary,
>> >> and never displays it to Python's stdout. This is true regardless of
>> >> the
>> >> function call's disp setting.
>> >
>> >
>> > Correct. The fmin_cobyla docstring clearly says what it returns. Result
>> > objects are only returned by the new interfaces in the 0.11.0 release
>> > (minimize, minimize_scalar, root).
>> >
>> >>     1.2. The 'minimize' function always returns the Results dictionary
>> >> but
>> >> never displays it to Python's stdout. Again, this is true regardless of
>> >> the
>> >> function call's disp setting.
>> >
>> >
>> > `disp` doesn't print the Results objects. For me it works as advertized
>> > (in
>> > IPython), it prints something like:
>> >
>> >    Normal return from subroutine COBYLA
>> >
>> >    NFVALS =   37   F = 8.000000E-01    MAXCV = 0.000000E+00
>> >    X = 1.400113E+00   1.700056E+00
>> >
>> > Ralf
>> >
>> >>
>> >> Case 2: When run interactively in Window's Command Prompt box:
>> >>     2.1 The fmin_cobyla function never returns the Result dictionary,
>> >> regardless of the function call's disp setting. Setting disp to True or
>> >> False either displays the Results dictionary in the command box or not
>> >> (respectively). I don't think the Results dictionary gets to the
>> >> command box
>> >> via stdout.
>> >>     2.2 The 'minimize' function always returns the Result dictionary,
>> >> regardless of the function call's disp setting.  Setting disp to True
>> >> or
>> >> False either displays the Results dictionary in the command box or not
>> >> (respectively). I don't think the Results dictionary gets to the
>> >> command box
>> >> via stdout.
>> >>
>> >> My thanks to all who helped clarify this situation.
>> >>
>> >> Bob H
>> >> _______________________________________________
>> >> 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 lists at hilboll.de  Tue Mar 26 07:36:23 2013
From: lists at hilboll.de (Andreas Hilboll)
Date: Tue, 26 Mar 2013 12:36:23 +0100
Subject: [SciPy-User] constrained optimization: question about Jacobian
Message-ID: <51518837.3050701@hilboll.de>

I want to perform constrained optimization of the function

def F(X, data):
    # x1, x2, x3, t2 are scalars, data is a constant np.array
    x1, x2, x3, t2 = X
    T = np.arange(X.size)
    t1, t3 = T[0], T[-1]
    Xbefore = x1 + (T - t1) * (x2 - x1) / (t2 - t1)
    Xafter = x2 + (T - t2) * (x3 - x2) / (t3 - t2)
    Xbreak = np.where(T <= t2, Xbefore, Xafter)
    return ((Xbreak - data)**2).sum()

where the parameter value t2 must be within 0 <= t2 < data.size.

According to the tutorial
(http://docs.scipy.org/doc/scipy/reference/tutorial/optimize.html#constrained-minimization-of-multivariate-scalar-functions-minimize),
I need to define the Jacobian. However, I'm unsure of how to define the
derivative with regards to t2.

Any ideas are greatly appreciated :)

Cheers, A.


From denis at laxalde.org  Tue Mar 26 07:49:41 2013
From: denis at laxalde.org (Denis Laxalde)
Date: Tue, 26 Mar 2013 12:49:41 +0100
Subject: [SciPy-User] constrained optimization: question about Jacobian
In-Reply-To: <51518837.3050701@hilboll.de>
References: <51518837.3050701@hilboll.de>
Message-ID: <51518B55.9050309@laxalde.org>

Andreas Hilboll a ?crit :
> According to the tutorial
> (http://docs.scipy.org/doc/scipy/reference/tutorial/optimize.html#constrained-minimization-of-multivariate-scalar-functions-minimize),
> I need to define the Jacobian. However, I'm unsure of how to define the
> derivative with regards to t2.

The Jacobian is optional for most if not all solvers, so you don't 
*need* to provide it.


From lists at hilboll.de  Tue Mar 26 09:15:37 2013
From: lists at hilboll.de (Andreas Hilboll)
Date: Tue, 26 Mar 2013 14:15:37 +0100
Subject: [SciPy-User] constrained optimization: question about Jacobian
In-Reply-To: <51518B55.9050309@laxalde.org>
References: <51518837.3050701@hilboll.de> <51518B55.9050309@laxalde.org>
Message-ID: <51519F79.8000003@hilboll.de>

>> According to the tutorial
>> (http://docs.scipy.org/doc/scipy/reference/tutorial/optimize.html#constrained-minimization-of-multivariate-scalar-functions-minimize),
>> I need to define the Jacobian. However, I'm unsure of how to define the
>> derivative with regards to t2.
> 
> The Jacobian is optional for most if not all solvers, so you don't 
> *need* to provide it.

Merci, Denis, I wasn't aware of that. Just followed the tutorial.[*]
Now everything's working fine.

Cheers, Andreas.


[*] I just submitted a PR to mention this:
https://github.com/scipy/scipy/pull/481



From doanviettrung at gmail.com  Tue Mar 26 19:37:02 2013
From: doanviettrung at gmail.com (DoanVietTrungAtGmail)
Date: Wed, 27 Mar 2013 10:37:02 +1100
Subject: [SciPy-User] Size of sparse boolean coo_matrix unexpectedly tiny
Message-ID: <CAFZXNMHZHpOFrg247KAW878wztOLV6Gds49VjUt_AG=goxiuFA@mail.gmail.com>

I created an NxN (N = 10^6) sparse boolean coo_matrix A and randomly filled
it with 200,000 Trues. sys.getsizeof(A) said A took 32 bytes - a thousand
times smaller than I expected. I am new to Scipy, please tell me what's
going on, thanks.

>>> rows = [random.randint(0,999999) for j in xrange(200000)]
>>> cols = [random.randint(0,999999) for j in xrange(200000)]
>>> vals = ones_like(rows)
>>> A = sparse.coo_matrix((vals,(rows,cols)), shape = (int(1E6), int(1E6)),
dtype = bool)
>>> len(vals)
200000
>>> print sys.getsizeof(A)
32
>>>
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From elsuizo37 at gmail.com  Tue Mar 26 23:11:32 2013
From: elsuizo37 at gmail.com (El suisse)
Date: Wed, 27 Mar 2013 00:11:32 -0300
Subject: [SciPy-User] Sequence not work
Message-ID: <CADKcdOF5wvZsHDBSTjNWj4QzgwTx_Vr40UHvhkEjuR2FBVvnbQ@mail.gmail.com>

Hi
I have to represent the following sequence:

[image: z[2]=2]
[image: z[n+1]=2^{n-1/2}\sqrt{1-\sqrt{1-4^{1-n}z[n]^{2}}]  [image:
n=2,3,4....]


and my code is as follows:


#!/usr/bin/env python


import matplotlib.pyplot as plt

import numpy as np


num_muestras = 100

z = np.zeros(num_muestras)



z[2] = 2.0  #En Python se cuenta desde el cero

for n in range(2,num_muestras-1):


    z[n+1] = np.power(2,n-(1/2)) * (np.sqrt(1- np.sqrt(1-np.power(4,1-n) *
np.power(z[n],2))) )



fig1 = plt.figure()

ax = plt.gca()
plt.ylabel('$z[n+1]=2^{n-1/2}\sqrt{1-\sqrt{1-4^{1-n}z[n]^{2}}}$')
plt.xlabel('$n$')
plt.grid()
ax.plot(z)

plt.show()




but does not work, z should tend to pi. might be wrong?

thanks in advance
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From robert.kern at gmail.com  Wed Mar 27 07:14:04 2013
From: robert.kern at gmail.com (Robert Kern)
Date: Wed, 27 Mar 2013 11:14:04 +0000
Subject: [SciPy-User] Sequence not work
In-Reply-To: <CADKcdOF5wvZsHDBSTjNWj4QzgwTx_Vr40UHvhkEjuR2FBVvnbQ@mail.gmail.com>
References: <CADKcdOF5wvZsHDBSTjNWj4QzgwTx_Vr40UHvhkEjuR2FBVvnbQ@mail.gmail.com>
Message-ID: <CAF6FJivHd+=+oVoPLUkH8pDy455SY8Dff0VGZBDVrL2Ri9Wi2g@mail.gmail.com>

On Wed, Mar 27, 2013 at 3:11 AM, El suisse <elsuizo37 at gmail.com> wrote:

> Hi
> I have to represent the following sequence:
>
> [image: z[2]=2]
> [image: z[n+1]=2^{n-1/2}\sqrt{1-\sqrt{1-4^{1-n}z[n]^{2}}]  [image:
> n=2,3,4....]
>
>
> and my code is as follows:
>
>
> #!/usr/bin/env python
>
>
> import matplotlib.pyplot as plt
>
> import numpy as np
>
>
> num_muestras = 100
>
> z = np.zeros(num_muestras)
>
>
>
> z[2] = 2.0  #En Python se cuenta desde el cero
>
> for n in range(2,num_muestras-1):
>
>
>     z[n+1] = np.power(2,n-(1/2)) * (np.sqrt(1- np.sqrt(1-np.power(4,1-n) *
> np.power(z[n],2))) )
>

(1/2) should be 0.5 . (1/2) gives 0, not 0.5. The division of integers in
Python 2.x defaults to returning an integer, not a floating point number
(for a variety of reasons that you can Google for if you want an
explanation).

-- 
Robert Kern
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From warren.weckesser at gmail.com  Wed Mar 27 08:12:48 2013
From: warren.weckesser at gmail.com (Warren Weckesser)
Date: Wed, 27 Mar 2013 08:12:48 -0400
Subject: [SciPy-User] Sequence not work
In-Reply-To: <CAF6FJivHd+=+oVoPLUkH8pDy455SY8Dff0VGZBDVrL2Ri9Wi2g@mail.gmail.com>
References: <CADKcdOF5wvZsHDBSTjNWj4QzgwTx_Vr40UHvhkEjuR2FBVvnbQ@mail.gmail.com>
	<CAF6FJivHd+=+oVoPLUkH8pDy455SY8Dff0VGZBDVrL2Ri9Wi2g@mail.gmail.com>
Message-ID: <CAGzF1ufw9pRNy0uNjHW7bLey9=StsiY0xm9QTEQYcBsAB7tsZA@mail.gmail.com>

On 3/27/13, Robert Kern <robert.kern at gmail.com> wrote:
> On Wed, Mar 27, 2013 at 3:11 AM, El suisse <elsuizo37 at gmail.com> wrote:
>
>> Hi
>> I have to represent the following sequence:
>>
>> [image: z[2]=2]
>> [image: z[n+1]=2^{n-1/2}\sqrt{1-\sqrt{1-4^{1-n}z[n]^{2}}]  [image:
>> n=2,3,4....]
>>
>>
>> and my code is as follows:
>>
>>
>> #!/usr/bin/env python
>>
>>
>> import matplotlib.pyplot as plt
>>
>> import numpy as np
>>
>>
>> num_muestras = 100
>>
>> z = np.zeros(num_muestras)
>>
>>
>>
>> z[2] = 2.0  #En Python se cuenta desde el cero
>>
>> for n in range(2,num_muestras-1):
>>
>>
>>     z[n+1] = np.power(2,n-(1/2)) * (np.sqrt(1- np.sqrt(1-np.power(4,1-n)
>> *
>> np.power(z[n],2))) )
>>
>
> (1/2) should be 0.5 . (1/2) gives 0, not 0.5. The division of integers in
> Python 2.x defaults to returning an integer, not a floating point number
> (for a variety of reasons that you can Google for if you want an
> explanation).


Also, in numpy, an integer to a negative power gives 0:

In [21]: np.power(4, -3)
Out[21]: 0

so change np.power(4, 1-n) to np.power(4.0, 1-n), or even 4**(1-n),
since there is really no need to use a numpy function there.

And then only run your series out to about num_muestras = 30; you'll
see that it converges to pi, but then blows up.

Warren

>
> --
> Robert Kern
>


From mailinglists at xgm.de  Wed Mar 27 12:45:06 2013
From: mailinglists at xgm.de (Florian Lindner)
Date: Wed, 27 Mar 2013 17:45:06 +0100
Subject: [SciPy-User] Best way to feed data to Gnuplot?
Message-ID: <7072300.23VAXm2rWG@horus>

Hello,

I want to utilise gnuplot for my plots. I know about matplotlib, but I don't 
like it (interface, appearance).

What is the best way to feed data from a numpy array to gnuplot? At the moment 
I try to use it like gnuplot -e "plot '-' ...." and write the data to stdin:

1,2,3
2,6,5
e
10,20,30

Is there a better way? What is the best to print the numpy data in the correct 
format?

There is a Gnuplot.py package, however that seems very outdated, last version 
is from 2008. According to the docs it still depends on Numeric.


Thanks!

Florian


From afraser at lanl.gov  Wed Mar 27 13:26:01 2013
From: afraser at lanl.gov (Andrew Fraser)
Date: Wed, 27 Mar 2013 11:26:01 -0600
Subject: [SciPy-User] Best way to feed data to Gnuplot?
In-Reply-To: <7072300.23VAXm2rWG@horus>
References: <7072300.23VAXm2rWG@horus>
Message-ID: <51532BA9.7030903@lanl.gov>

I switched from gnuplot to matplotlib in 2008.  The new code is a little
easier to read but the plots from gnuplot looked better.

To get access to all of the features of gnuplot, I fed custom commands
to it through a pipe.  I figured out how to do it by looking at the
source for the Gnuplot.py package that you mention.  Since I wanted many
features not provided by the package, I just used the pipe rather than
Gnuplot.py.  I wrote data to files and used the pipe to issue commands
to a gnuplot process.


On 03/27/2013 10:45 AM, Florian Lindner wrote:
> Hello,
>
> I want to utilise gnuplot for my plots. I know about matplotlib, but I don't 
> like it (interface, appearance).
>
> What is the best way to feed data from a numpy array to gnuplot? At the moment 
> I try to use it like gnuplot -e "plot '-' ...." and write the data to stdin:
>
> 1,2,3
> 2,6,5
> e
> 10,20,30
>
> Is there a better way? What is the best to print the numpy data in the correct 
> format?
>
> There is a Gnuplot.py package, however that seems very outdated, last version 
> is from 2008. According to the docs it still depends on Numeric.
>
>
> Thanks!
>
> Florian
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user



From beamesleach at gmail.com  Wed Mar 27 13:39:43 2013
From: beamesleach at gmail.com (Alex Leach)
Date: Wed, 27 Mar 2013 17:39:43 -0000
Subject: [SciPy-User] Best way to feed data to Gnuplot?
In-Reply-To: <7072300.23VAXm2rWG@horus>
References: <7072300.23VAXm2rWG@horus>
Message-ID: <op.wul8ohof8s54ea@ns1.alexleach.org.uk>

On Wed, 27 Mar 2013 17:26:01 -0000, Andrew Fraser <afraser at lanl.gov> wrote:

> I switched from gnuplot to matplotlib in 2008.  The new code is a little
> easier to read but the plots from gnuplot looked better.

You beat me to it! +1 for matplotlib. I only used gnuplot for about 3  
months, also in 2008, before switching to matplotlib. True, the graphs  
don't look as nice, but my code does; I haven't looked back...

On Wed, 27 Mar 2013 16:45:06 -0000, Florian Lindner <mailinglists at xgm.de>  
wrote:

> Is there a better way? What is the best to print the numpy data in the  
> correct
> format?

Have you considered using matplotlib[1]? It interfaces with numpy arrays  
really well.

In days long gone, I wrote formatted strings to gnuplot-compatible text  
files, and then called gnuplot with Python's os.system. It wasn't a very  
elegant solution at all..

matplotlib is at first overwhelmingly large, as it provides both an Object  
Oriented API[2], and also tries to emulate a very similar interface to  
matlab (aka matplotlib.pyplot[3]). When used in conjunction with numpy,  
scipy (and ipython), it goes by the name pylab[4].

Cheers,
Alex

[1] http://matplotlib.org/
[2] http://matplotlib.org/1.2.1/api/index.html
[3] http://matplotlib.org/1.2.1/api/pyplot_summary.html
[4] http://www.scipy.org/PyLab


From pav at iki.fi  Wed Mar 27 15:50:29 2013
From: pav at iki.fi (Pauli Virtanen)
Date: Wed, 27 Mar 2013 21:50:29 +0200
Subject: [SciPy-User] Best way to feed data to Gnuplot?
In-Reply-To: <51532BA9.7030903@lanl.gov>
References: <7072300.23VAXm2rWG@horus> <51532BA9.7030903@lanl.gov>
Message-ID: <kivii1$uvv$1@ger.gmane.org>

27.03.2013 19:26, Andrew Fraser kirjoitti:
> I switched from gnuplot to matplotlib in 2008.  The new code is a little
> easier to read but the plots from gnuplot looked better.

Interestingly, I've had the opposite experience --- matplotlib plots
look as good or better than gnuplot ones, fully publication quality.

Here's my matplotlibrc (you'll probably need to grab the stix math
fonts, though):

# -- Output settings
path.simplify: True
savefig.dpi: 300
ps.papersize: a4
ps.usedistiller: xpdf

# -- Font settings
text.usetex: false

font.size: 10.0
font.family: serif
font.serif: STIXGeneral
font.sans-serif: Arial
font.cursive: Zapf Chancery
font.monospace: Courier New

mathtext.fontset: stix

axes.labelsize: medium
axes.titlesize: medium
legend.fontsize: medium
xtick.labelsize: medium
ytick.labelsize: medium


# -- Figure size default settings (interactive use)
figure.figsize: 6, 4
figure.subplot.left : 0.2
figure.subplot.bottom: 0.2
figure.subplot.right : 0.95
figure.subplot.top: 0.9


-- 
Pauli Virtanen




From vanforeest at gmail.com  Wed Mar 27 15:55:38 2013
From: vanforeest at gmail.com (nicky van foreest)
Date: Wed, 27 Mar 2013 20:55:38 +0100
Subject: [SciPy-User] Best way to feed data to Gnuplot?
In-Reply-To: <kivii1$uvv$1@ger.gmane.org>
References: <7072300.23VAXm2rWG@horus> <51532BA9.7030903@lanl.gov>
	<kivii1$uvv$1@ger.gmane.org>
Message-ID: <CAK_GA2GOyO2gzaMU4kBC8U2hhd8ChwydaTkFAY5wgpYD1w46xw@mail.gmail.com>

Hi,

I use matplotlib while testing, but use gnuplot for making the final plots,
as I find gnuplot easier to configure. The book 'gnuplot in action' by
janert is very nice to help you tune your graphs the way you want, and what
data format is ideal for gnuplot. I don't use gnuplot.py anymore, because
matplotlib is way easier to use.

Nicky


On 27 March 2013 20:50, Pauli Virtanen <pav at iki.fi> wrote:

> 27.03.2013 19:26, Andrew Fraser kirjoitti:
> > I switched from gnuplot to matplotlib in 2008.  The new code is a little
> > easier to read but the plots from gnuplot looked better.
>
> Interestingly, I've had the opposite experience --- matplotlib plots
> look as good or better than gnuplot ones, fully publication quality.
>
> Here's my matplotlibrc (you'll probably need to grab the stix math
> fonts, though):
>
> # -- Output settings
> path.simplify: True
> savefig.dpi: 300
> ps.papersize: a4
> ps.usedistiller: xpdf
>
> # -- Font settings
> text.usetex: false
>
> font.size: 10.0
> font.family: serif
> font.serif: STIXGeneral
> font.sans-serif: Arial
> font.cursive: Zapf Chancery
> font.monospace: Courier New
>
> mathtext.fontset: stix
>
> axes.labelsize: medium
> axes.titlesize: medium
> legend.fontsize: medium
> xtick.labelsize: medium
> ytick.labelsize: medium
>
>
> # -- Figure size default settings (interactive use)
> figure.figsize: 6, 4
> figure.subplot.left : 0.2
> figure.subplot.bottom: 0.2
> figure.subplot.right : 0.95
> figure.subplot.top: 0.9
>
>
> --
> Pauli Virtanen
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From nils106 at googlemail.com  Wed Mar 27 15:23:31 2013
From: nils106 at googlemail.com (Nils Wagner)
Date: Wed, 27 Mar 2013 19:23:31 +0000
Subject: [SciPy-User] Strange behaviour of scipy.test()
Message-ID: <CAE7vhgMkJB_jTJyMNJm6rXe5Dih5itUxGSWpwQPiR1pUwBSRjg@mail.gmail.com>

Hi all,

I am confused by the current behavior of scipy.test()
The number of tests is 0. Any idea ?

Nils

nwagner at linux-itfv:~> python
Python 2.7.2 (default, Aug 19 2011, 20:41:43) [GCC] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import scipy
>>> scipy.test()
Running unit tests for scipy
NumPy version 1.8.0.dev-1a816c7
NumPy is installed in
/home/nwagner/local/lib64/python2.7/site-packages/numpy
SciPy version 0.13.0.dev-9ba2fda
SciPy is installed in
/home/nwagner/local/lib64/python2.7/site-packages/scipy-0.13.0.dev_9ba2fda-py2.7-linux-x86_64.egg/scipy
Python version 2.7.2 (default, Aug 19 2011, 20:41:43) [GCC]
nose version 1.1.2

----------------------------------------------------------------------
Ran 0 tests in 0.055s

OK
<nose.result.TextTestResult run=0 errors=0 failures=0>
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From warren.weckesser at gmail.com  Thu Mar 28 10:52:44 2013
From: warren.weckesser at gmail.com (Warren Weckesser)
Date: Thu, 28 Mar 2013 10:52:44 -0400
Subject: [SciPy-User] Strange behaviour of scipy.test()
In-Reply-To: <CAE7vhgMkJB_jTJyMNJm6rXe5Dih5itUxGSWpwQPiR1pUwBSRjg@mail.gmail.com>
References: <CAE7vhgMkJB_jTJyMNJm6rXe5Dih5itUxGSWpwQPiR1pUwBSRjg@mail.gmail.com>
Message-ID: <CAGzF1uec0dGrUDVaLD_6sKyUKcMePxmUSHFV=88g45MSd8jGEA@mail.gmail.com>

On 3/27/13, Nils Wagner <nils106 at googlemail.com> wrote:
> Hi all,
>
> I am confused by the current behavior of scipy.test()
> The number of tests is 0. Any idea ?
>
> Nils
>
> nwagner at linux-itfv:~> python
> Python 2.7.2 (default, Aug 19 2011, 20:41:43) [GCC] on linux2
> Type "help", "copyright", "credits" or "license" for more information.
>>>> import scipy
>>>> scipy.test()
> Running unit tests for scipy
> NumPy version 1.8.0.dev-1a816c7
> NumPy is installed in
> /home/nwagner/local/lib64/python2.7/site-packages/numpy
> SciPy version 0.13.0.dev-9ba2fda
> SciPy is installed in
> /home/nwagner/local/lib64/python2.7/site-packages/scipy-0.13.0.dev_9ba2fda-py2.7-linux-x86_64.egg/scipy
> Python version 2.7.2 (default, Aug 19 2011, 20:41:43) [GCC]
> nose version 1.1.2
>
> ----------------------------------------------------------------------
> Ran 0 tests in 0.055s
>
> OK
> <nose.result.TextTestResult run=0 errors=0 failures=0>
>


Tests?  We don't need no stinkin' tests!

Well, actually, a recently merged pull request introduced the problem.
 See the discussion here:https://github.com/scipy/scipy/pull/454

Warren


From mutantturkey at gmail.com  Thu Mar 28 15:46:39 2013
From: mutantturkey at gmail.com (Calvin Morrison)
Date: Thu, 28 Mar 2013 15:46:39 -0400
Subject: [SciPy-User] Sparse Matricies and NNLS
Message-ID: <CAJFUHyWk-jfYRSU_c4sLQwELeqA+-Yx+rJL4SKZAtdt-B=arzg@mail.gmail.com>

Hi!

I have a very large matrix that I am using with the
scipy.optimize.nnls function, however the matrix is so large that it
takes 30Gb of memory to load with python!

I was thinking about using a sparse matrix since it is relatively
sparse, but the problem is that even if I was to use the sparse
matricies, the nnls function only accepts a ndarray, not a sparse
matrix. (when I try and throw a sparse matrix at it I get an error.)

So of course I'd need to convert it to a dense array before passing it
into nnls, but that would totally void the whole point of a sparse
array, because then python would still have to allocate the full dense
matrix.

Does anyone have an idea about how to efficiently pass a matrix and
store it without blowing up the memory usage?

Thank you,

Calvin


From mwmorrison93 at gmail.com  Thu Mar 28 16:05:35 2013
From: mwmorrison93 at gmail.com (Michael Morrison)
Date: Thu, 28 Mar 2013 13:05:35 -0700
Subject: [SciPy-User] Sparse Matricies and NNLS
In-Reply-To: <CAJFUHyWk-jfYRSU_c4sLQwELeqA+-Yx+rJL4SKZAtdt-B=arzg@mail.gmail.com>
References: <CAJFUHyWk-jfYRSU_c4sLQwELeqA+-Yx+rJL4SKZAtdt-B=arzg@mail.gmail.com>
Message-ID: <CANGs2yuPryJTCUqANCkmOehPRktB1kVUHUDwpeN_ezSCuc869Q@mail.gmail.com>

Hey Calvin, I was just looking into this same issue recently. Here's the
solution someone recommended on stackoverflow:

http://stackoverflow.com/questions/1053928/python-numpy-very-large-matrices

I haven't actually used it yet, but it seems Pytables is the way to go.

Good Luck,
Mike


On Thu, Mar 28, 2013 at 12:46 PM, Calvin Morrison <mutantturkey at gmail.com>wrote:

> Hi!
>
> I have a very large matrix that I am using with the
> scipy.optimize.nnls function, however the matrix is so large that it
> takes 30Gb of memory to load with python!
>
> I was thinking about using a sparse matrix since it is relatively
> sparse, but the problem is that even if I was to use the sparse
> matricies, the nnls function only accepts a ndarray, not a sparse
> matrix. (when I try and throw a sparse matrix at it I get an error.)
>
> So of course I'd need to convert it to a dense array before passing it
> into nnls, but that would totally void the whole point of a sparse
> array, because then python would still have to allocate the full dense
> matrix.
>
> Does anyone have an idea about how to efficiently pass a matrix and
> store it without blowing up the memory usage?
>
> Thank you,
>
> Calvin
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From pav at iki.fi  Thu Mar 28 16:48:26 2013
From: pav at iki.fi (Pauli Virtanen)
Date: Thu, 28 Mar 2013 22:48:26 +0200
Subject: [SciPy-User] Sparse Matricies and NNLS
In-Reply-To: <CAJFUHyWk-jfYRSU_c4sLQwELeqA+-Yx+rJL4SKZAtdt-B=arzg@mail.gmail.com>
References: <CAJFUHyWk-jfYRSU_c4sLQwELeqA+-Yx+rJL4SKZAtdt-B=arzg@mail.gmail.com>
Message-ID: <kj2aam$8qf$1@ger.gmane.org>

28.03.2013 21:46, Calvin Morrison kirjoitti:
[clip]
> I was thinking about using a sparse matrix since it is relatively
> sparse, but the problem is that even if I was to use the sparse
> matricies, the nnls function only accepts a ndarray, not a sparse
> matrix. (when I try and throw a sparse matrix at it I get an error.)

The nnls algorithm in Scipy relies on dense matrix algebra, and is
moreover written in Fortran. So, there is no way to tell it to use
sparse matrices.

You'll need to find an implementation of NNLS algorithm that either is
matrix-free or works for sparse problems. If you find such code, be sure
to reply to this list --- it might be useful to include it in Scipy,
provided the license is compatible.

-- 
Pauli Virtanen



From mutantturkey at gmail.com  Thu Mar 28 17:33:07 2013
From: mutantturkey at gmail.com (Calvin Morrison)
Date: Thu, 28 Mar 2013 17:33:07 -0400
Subject: [SciPy-User] Sparse Matricies and NNLS
In-Reply-To: <kj2aam$8qf$1@ger.gmane.org>
References: <CAJFUHyWk-jfYRSU_c4sLQwELeqA+-Yx+rJL4SKZAtdt-B=arzg@mail.gmail.com>
	<kj2aam$8qf$1@ger.gmane.org>
Message-ID: <CAJFUHyX29mF2VZfjf8dGHNv5O9v2cmNVFja4-yOMwsRcrUO=_w@mail.gmail.com>

Paul,

It seems nobody wants to touch the nnls algorithm because the only
implementation that is floating around is the one from the original
publication or automatic conversions of it.

Calvin
On Mar 28, 2013 4:48 PM, "Pauli Virtanen" <pav at iki.fi> wrote:

> 28.03.2013 21:46, Calvin Morrison kirjoitti:
> [clip]
> > I was thinking about using a sparse matrix since it is relatively
> > sparse, but the problem is that even if I was to use the sparse
> > matricies, the nnls function only accepts a ndarray, not a sparse
> > matrix. (when I try and throw a sparse matrix at it I get an error.)
>
> The nnls algorithm in Scipy relies on dense matrix algebra, and is
> moreover written in Fortran. So, there is no way to tell it to use
> sparse matrices.
>
> You'll need to find an implementation of NNLS algorithm that either is
> matrix-free or works for sparse problems. If you find such code, be sure
> to reply to this list --- it might be useful to include it in Scipy,
> provided the license is compatible.
>
> --
> Pauli Virtanen
>
> _______________________________________________
> 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  Thu Mar 28 22:58:14 2013
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Thu, 28 Mar 2013 22:58:14 -0400
Subject: [SciPy-User] special's hidden treasures: rootfinding for
	distribution cdfs
Message-ID: <CAMMTP+BAcayxz7aJqk1rRoRCnbryRjTRVxcyrSp=NBhCSGtXfA@mail.gmail.com>

The fortran code for the distribution functions also has rootfinding build in.
We can solve for any value given the other values.

For example, solve for the non-centrality parameter for the
non-central t-distribution.
I never thought I would ever need that.

Is this the right function for special.nctdtrinc and friends? I didn't
manage to follow the trail.

\scipy\special\cdflib\cdftnc.f

C      SUBROUTINE CDFTNC( WHICH, P, Q, T, DF, PNONC, STATUS, BOUND )
C               Cumulative Distribution Function
C                  Non-Central T distribution
C
C                               Function
C
C     Calculates any one parameter of the noncentral t distribution give
C     values for the others.
C
C                               Arguments
C
C     WHICH --> Integer indicating which  argument
C               values is to be calculated from the others.
C               Legal range: 1..3
C               iwhich = 1 : Calculate P and Q from T,DF,PNONC
C               iwhich = 2 : Calculate T from P,Q,DF,PNONC
C               iwhich = 3 : Calculate DF from P,Q,T
C               iwhich = 4 : Calculate PNONC from P,Q,DF,T


I need iwhich=4

solving for the non-centrality parameter
>>> t = stats.nct._ppf(0.1, 9, 10)
>>> special.nctdtrinc(9, 0.1, t)
10.000000055648481

confidence interval for non-centrality parameter (AFAIU)

>>> special.nctdtrinc(24, [0.975, 0.025], 25)
array([ 17.68259,  32.26892])

upper value differs slightly from an R package (which has it's own
implementation)

cross check
>>> nc = special.nctdtrinc(24, 0.025, 25)
>>> stats.nct.cdf(25, 24, nc)
0.024999999999859579

a paper refers to a SAS function which seems to do the same
''''
following SAS syntax:
LowNC_CV = TNONCT(tobs., ?, 1 - ? / 2),
'''

Background: If I understand this part of some papers correctly, then I can get
confidence intervals for effect sizes for statistical tests that are
based on the
t-distributions with non-central t-distribution under the alternative, with
essentially just a call to scipy.special.

------------------
It's nice to find the answer in scipy special when we need to look for
something we never knew that it existed.



**Scipy special needs your help.**
==========================

\scipy\special\add_newdocs.py      (maybe my checkout is a bit outdated)

<...>
add_newdoc("scipy.special", "ncfdtridfd",
    """
    """)

add_newdoc("scipy.special", "ncfdtridfn",
    """
    """)

add_newdoc("scipy.special", "ncfdtrinc",
    """
    """)

add_newdoc("scipy.special", "nctdtr",
    """
    """)

add_newdoc("scipy.special", "nctdtridf",
    """
    """)

add_newdoc("scipy.special", "nctdtrinc",
    """
    """)

add_newdoc("scipy.special", "nctdtrit",
    """
    """)
<...>

The information is available in the fortran docstrings. A pull request
that fills in some blanks is an easy way to become a scipy
contributer.


Josef


From mailinglists at xgm.de  Fri Mar 29 10:13:31 2013
From: mailinglists at xgm.de (Florian Lindner)
Date: Fri, 29 Mar 2013 15:13:31 +0100
Subject: [SciPy-User] Best way to feed data to Gnuplot?
In-Reply-To: <7072300.23VAXm2rWG@horus>
References: <7072300.23VAXm2rWG@horus>
Message-ID: <29040443.uxYWd0ZOeR@horus>

Am Mittwoch, 27. M?rz 2013, 17:45:06 schrieb Florian Lindner:
> Hello,
> 
> I want to utilise gnuplot for my plots. I know about matplotlib, but I don't
> like it (interface, appearance).

Thanks for your replies! I think I'll reevaluate matplotlib and give it 
another try...

Florian


From mailinglists at xgm.de  Fri Mar 29 10:27:56 2013
From: mailinglists at xgm.de (Florian Lindner)
Date: Fri, 29 Mar 2013 15:27:56 +0100
Subject: [SciPy-User] Find and interpolate zero value
Message-ID: <6045024.F2qzHeQDIF@horus>

Hello,

I have an array like:

array([[-1. ,  2. ],
       [-0.5,  4. ],
       [ 0.5,  6. ]])

first row are coordinates, second row is data.

Is there a scipy/numpy way to find zero from the first row and interpolate a 
data value from the second row?

Here I the result would be 5 ( 4 + (6-4)/( 0.5 - (-0.5)) * 0.5 )

How would this best be done with scipy/numpy?

Thanks,

Florian


From pav at iki.fi  Fri Mar 29 12:42:45 2013
From: pav at iki.fi (Pauli Virtanen)
Date: Fri, 29 Mar 2013 18:42:45 +0200
Subject: [SciPy-User] Find and interpolate zero value
In-Reply-To: <6045024.F2qzHeQDIF@horus>
References: <6045024.F2qzHeQDIF@horus>
Message-ID: <kj4ga1$ljv$1@ger.gmane.org>

29.03.2013 16:27, Florian Lindner kirjoitti:
> Hello,
> 
> I have an array like:
> 
> array([[-1. ,  2. ],
>        [-0.5,  4. ],
>        [ 0.5,  6. ]])
> 
> first row are coordinates, second row is data.
> 
> Is there a scipy/numpy way to find zero from the first row and interpolate a 
> data value from the second row?

These may be helpful:

>>> numpy.lookfor('interpolate')
>>> numpy.lookfor('interpolate 1D', module='scipy')

http://docs.scipy.org/doc/numpy/reference/generated/numpy.interp.html
http://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html

To find zeros, interpolate y-vs-x instead of x-vs-y

-- 
Pauli Virtanen



From pav at iki.fi  Fri Mar 29 12:50:16 2013
From: pav at iki.fi (Pauli Virtanen)
Date: Fri, 29 Mar 2013 18:50:16 +0200
Subject: [SciPy-User] Find and interpolate zero value
In-Reply-To: <kj4ga1$ljv$1@ger.gmane.org>
References: <6045024.F2qzHeQDIF@horus> <kj4ga1$ljv$1@ger.gmane.org>
Message-ID: <kj4go4$r0g$1@ger.gmane.org>

29.03.2013 18:42, Pauli Virtanen kirjoitti:
[clip]
> To find zeros, interpolate y-vs-x instead of x-vs-y

If there are multiple roots, you can use splines:

http://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.InterpolatedUnivariateSpline.html

See the roots() method.

-- 
Pauli Virtanen



From ralf.gommers at gmail.com  Fri Mar 29 16:34:49 2013
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Fri, 29 Mar 2013 21:34:49 +0100
Subject: [SciPy-User] special's hidden treasures: rootfinding for
 distribution cdfs
In-Reply-To: <CAMMTP+BAcayxz7aJqk1rRoRCnbryRjTRVxcyrSp=NBhCSGtXfA@mail.gmail.com>
References: <CAMMTP+BAcayxz7aJqk1rRoRCnbryRjTRVxcyrSp=NBhCSGtXfA@mail.gmail.com>
Message-ID: <CABL7CQgWj+f2Fmr-wy7yj9nhXYNtHJK+5JseyhhOMGTrpbXp=A@mail.gmail.com>

On Fri, Mar 29, 2013 at 3:58 AM, <josef.pktd at gmail.com> wrote:

> The fortran code for the distribution functions also has rootfinding build
> in.
> We can solve for any value given the other values.
>
> For example, solve for the non-centrality parameter for the
> non-central t-distribution.
> I never thought I would ever need that.
>
> Is this the right function for special.nctdtrinc and friends? I didn't
> manage to follow the trail.
>
> \scipy\special\cdflib\cdftnc.f
>
> C      SUBROUTINE CDFTNC( WHICH, P, Q, T, DF, PNONC, STATUS, BOUND )
> C               Cumulative Distribution Function
> C                  Non-Central T distribution
> C
> C                               Function
> C
> C     Calculates any one parameter of the noncentral t distribution give
> C     values for the others.
> C
> C                               Arguments
> C
> C     WHICH --> Integer indicating which  argument
> C               values is to be calculated from the others.
> C               Legal range: 1..3
> C               iwhich = 1 : Calculate P and Q from T,DF,PNONC
> C               iwhich = 2 : Calculate T from P,Q,DF,PNONC
> C               iwhich = 3 : Calculate DF from P,Q,T
> C               iwhich = 4 : Calculate PNONC from P,Q,DF,T
>
>
> I need iwhich=4
>
> solving for the non-centrality parameter
> >>> t = stats.nct._ppf(0.1, 9, 10)
> >>> special.nctdtrinc(9, 0.1, t)
> 10.000000055648481
>
> confidence interval for non-centrality parameter (AFAIU)
>
> >>> special.nctdtrinc(24, [0.975, 0.025], 25)
> array([ 17.68259,  32.26892])
>
> upper value differs slightly from an R package (which has it's own
> implementation)
>
> cross check
> >>> nc = special.nctdtrinc(24, 0.025, 25)
> >>> stats.nct.cdf(25, 24, nc)
> 0.024999999999859579
>
> a paper refers to a SAS function which seems to do the same
> ''''
> following SAS syntax:
> LowNC_CV = TNONCT(tobs., ?, 1 - ? / 2),
> '''
>
> Background: If I understand this part of some papers correctly, then I can
> get
> confidence intervals for effect sizes for statistical tests that are
> based on the
> t-distributions with non-central t-distribution under the alternative, with
> essentially just a call to scipy.special.
>
> ------------------
> It's nice to find the answer in scipy special when we need to look for
> something we never knew that it existed.
>
>
>
> **Scipy special needs your help.**
> ==========================
>
> \scipy\special\add_newdocs.py      (maybe my checkout is a bit outdated)
>
> <...>
> add_newdoc("scipy.special", "ncfdtridfd",
>     """
>     """)
>
> add_newdoc("scipy.special", "ncfdtridfn",
>     """
>     """)
>
> add_newdoc("scipy.special", "ncfdtrinc",
>     """
>     """)
>
> add_newdoc("scipy.special", "nctdtr",
>     """
>     """)
>
> add_newdoc("scipy.special", "nctdtridf",
>     """
>     """)
>
> add_newdoc("scipy.special", "nctdtrinc",
>     """
>     """)
>
> add_newdoc("scipy.special", "nctdtrit",
>     """
>     """)
> <...>
>
> The information is available in the fortran docstrings. A pull request
> that fills in some blanks is an easy way to become a scipy
> contributer.
>

Are you sure that works? Last time I checked we couldn't add docstrings to
f2py-wrapped functions with add_newdoc.

Ralf
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From pav at iki.fi  Fri Mar 29 16:49:12 2013
From: pav at iki.fi (Pauli Virtanen)
Date: Fri, 29 Mar 2013 22:49:12 +0200
Subject: [SciPy-User] special's hidden treasures: rootfinding for
	distribution cdfs
In-Reply-To: <CABL7CQgWj+f2Fmr-wy7yj9nhXYNtHJK+5JseyhhOMGTrpbXp=A@mail.gmail.com>
References: <CAMMTP+BAcayxz7aJqk1rRoRCnbryRjTRVxcyrSp=NBhCSGtXfA@mail.gmail.com>
	<CABL7CQgWj+f2Fmr-wy7yj9nhXYNtHJK+5JseyhhOMGTrpbXp=A@mail.gmail.com>
Message-ID: <kj4uo4$5hk$1@ger.gmane.org>

29.03.2013 22:34, Ralf Gommers kirjoitti:
[clip]
> Are you sure that works? Last time I checked we couldn't add docstrings
> to f2py-wrapped functions with add_newdoc.

It works, it's not really add_newdoc.

-- 
Pauli Virtanen



From josef.pktd at gmail.com  Fri Mar 29 16:55:31 2013
From: josef.pktd at gmail.com (josef.pktd at gmail.com)
Date: Fri, 29 Mar 2013 16:55:31 -0400
Subject: [SciPy-User] special's hidden treasures: rootfinding for
 distribution cdfs
In-Reply-To: <CABL7CQgWj+f2Fmr-wy7yj9nhXYNtHJK+5JseyhhOMGTrpbXp=A@mail.gmail.com>
References: <CAMMTP+BAcayxz7aJqk1rRoRCnbryRjTRVxcyrSp=NBhCSGtXfA@mail.gmail.com>
	<CABL7CQgWj+f2Fmr-wy7yj9nhXYNtHJK+5JseyhhOMGTrpbXp=A@mail.gmail.com>
Message-ID: <CAMMTP+BRPKbtgmJRkHKRh4+sWEbjgkGpzxbhySzy=ygcXA7fEg@mail.gmail.com>

On Fri, Mar 29, 2013 at 4:34 PM, Ralf Gommers <ralf.gommers at gmail.com> wrote:
>
>
>
>
> On Fri, Mar 29, 2013 at 3:58 AM, <josef.pktd at gmail.com> wrote:
>>
>> The fortran code for the distribution functions also has rootfinding build in.
>> We can solve for any value given the other values.
>>
>> For example, solve for the non-centrality parameter for the
>> non-central t-distribution.
>> I never thought I would ever need that.
>>
>> Is this the right function for special.nctdtrinc and friends? I didn't
>> manage to follow the trail.
>>
>> \scipy\special\cdflib\cdftnc.f
>>
>> C      SUBROUTINE CDFTNC( WHICH, P, Q, T, DF, PNONC, STATUS, BOUND )
>> C               Cumulative Distribution Function
>> C                  Non-Central T distribution
>> C
>> C                               Function
>> C
>> C     Calculates any one parameter of the noncentral t distribution give
>> C     values for the others.
>> C
>> C                               Arguments
>> C
>> C     WHICH --> Integer indicating which  argument
>> C               values is to be calculated from the others.
>> C               Legal range: 1..3
>> C               iwhich = 1 : Calculate P and Q from T,DF,PNONC
>> C               iwhich = 2 : Calculate T from P,Q,DF,PNONC
>> C               iwhich = 3 : Calculate DF from P,Q,T
>> C               iwhich = 4 : Calculate PNONC from P,Q,DF,T
>>
>>
>> I need iwhich=4
>>
>> solving for the non-centrality parameter
>> >>> t = stats.nct._ppf(0.1, 9, 10)
>> >>> special.nctdtrinc(9, 0.1, t)
>> 10.000000055648481
>>
>> confidence interval for non-centrality parameter (AFAIU)
>>
>> >>> special.nctdtrinc(24, [0.975, 0.025], 25)
>> array([ 17.68259,  32.26892])
>>
>> upper value differs slightly from an R package (which has it's own
>> implementation)
>>
>> cross check
>> >>> nc = special.nctdtrinc(24, 0.025, 25)
>> >>> stats.nct.cdf(25, 24, nc)
>> 0.024999999999859579
>>
>> a paper refers to a SAS function which seems to do the same
>> ''''
>> following SAS syntax:
>> LowNC_CV = TNONCT(tobs., ?, 1 - ? / 2),
>> '''
>>
>> Background: If I understand this part of some papers correctly, then I can get
>> confidence intervals for effect sizes for statistical tests that are
>> based on the
>> t-distributions with non-central t-distribution under the alternative, with
>> essentially just a call to scipy.special.
>>
>> ------------------
>> It's nice to find the answer in scipy special when we need to look for
>> something we never knew that it existed.
>>
>>
>>
>> **Scipy special needs your help.**
>> ==========================
>>
>> \scipy\special\add_newdocs.py      (maybe my checkout is a bit outdated)
>>
>> <...>
>> add_newdoc("scipy.special", "ncfdtridfd",
>>     """
>>     """)
>>
>> add_newdoc("scipy.special", "ncfdtridfn",
>>     """
>>     """)
>>
>> add_newdoc("scipy.special", "ncfdtrinc",
>>     """
>>     """)
>>
>> add_newdoc("scipy.special", "nctdtr",
>>     """
>>     """)
>>
>> add_newdoc("scipy.special", "nctdtridf",
>>     """
>>     """)
>>
>> add_newdoc("scipy.special", "nctdtrinc",
>>     """
>>     """)
>>
>> add_newdoc("scipy.special", "nctdtrit",
>>     """
>>     """)
>> <...>
>>
>> The information is available in the fortran docstrings. A pull request
>> that fills in some blanks is an easy way to become a scipy
>> contributer.
>
>
> Are you sure that works? Last time I checked we couldn't add docstrings to f2py-wrapped functions with add_newdoc.
>


Now I'm not sure, and I never tried.

I was just jumping to conclusions based on superficially following
https://github.com/scipy/scipy/pull/459

Josef



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


From ralf.gommers at gmail.com  Sat Mar 30 08:31:36 2013
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Sat, 30 Mar 2013 13:31:36 +0100
Subject: [SciPy-User] ANN: SciPy 0.12.0 release candidate 1
Message-ID: <CABL7CQhPai-Wj1grjkeBqw6oJs4HWYHbA-ChYtPOxHwOkWL5-g@mail.gmail.com>

Hi,

I am pleased to announce the availability of the first release candidate of
SciPy 0.12.0. This is shaping up to be another solid release, with some
cool new features (see highlights below) and a large amount of bug fixes
and maintenance work under the hood. The number of contributors also keeps
rising steadily, we're at 74 so far for this release.

Sources and binaries can be found at
http://sourceforge.net/projects/scipy/files/scipy/0.12.0rc1/, release notes
are copied below.

Please try this release and report any problems on the mailing list. If no
issues are found, the final release will be in one week.

Cheers,
Ralf


==========================
SciPy 0.12.0 Release Notes
==========================

.. note:: Scipy 0.12.0 is not released yet!

.. contents::

SciPy 0.12.0 is the culmination of 7 months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and
better documentation.  There have been a number of deprecations and
API changes in this release, which are documented below.  All users
are encouraged to upgrade to this release, as there are a large number
of bug-fixes and optimizations.  Moreover, our development attention
will now shift to bug-fix releases on the 0.12.x branch, and on adding
new features on the master branch.

Some of the highlights of this release are:

  - Completed QHull wrappers in scipy.spatial.
  - cKDTree now a drop-in replacement for KDTree.
  - A new global optimizer, basinhopping.
  - Support for Python 2 and Python 3 from the same code base (no more
2to3).

This release requires Python 2.6, 2.7 or 3.1-3.3 and NumPy 1.5.1 or greater.
Support for Python 2.4 and 2.5 has been dropped as of this release.


New features
============

``scipy.spatial`` improvements
------------------------------

cKDTree feature-complete
^^^^^^^^^^^^^^^^^^^^^^^^
Cython version of KDTree, cKDTree, is now feature-complete.  Most operations
(construction, query, query_ball_point, query_pairs, count_neighbors and
sparse_distance_matrix) are between 200 and 1000 times faster in cKDTree
than
in KDTree.  With very minor caveats, cKDTree has exactly the same interface
as
KDTree, and can be used as a drop-in replacement.

Voronoi diagrams and convex hulls
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
`scipy.spatial` now contains functionality for computing Voronoi
diagrams and convex hulls using the Qhull library. (Delaunay
triangulation was available since Scipy 0.9.0.)

Delaunay improvements
^^^^^^^^^^^^^^^^^^^^^
It's now possible to pass in custom Qhull options in Delaunay
triangulation. Coplanar points are now also recorded, if present.
Incremental construction of Delaunay triangulations is now also
possible.

Spectral estimators (``scipy.signal``)
--------------------------------------
The functions ``scipy.signal.periodogram`` and ``scipy.signal.welch`` were
added, providing DFT-based spectral estimators.


``scipy.optimize`` improvements
-------------------------------

Callback functions in L-BFGS-B and TNC
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
A callback mechanism was added to L-BFGS-B and TNC minimization solvers.

Basin hopping global optimization (``scipy.optimize.basinhopping``)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
A new global optimization algorithm.  Basinhopping is designed to
efficiently
find the global minimum of a smooth function.


``scipy.special`` improvements
------------------------------

Revised complex error functions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The computation of special functions related to the error function now uses
a
new `Faddeeva library from MIT <http://ab-initio.mit.edu/Faddeeva>`__ which
increases their numerical precision. The scaled and imaginary error
functions
``erfcx`` and ``erfi`` were also added, and the Dawson integral ``dawsn``
can
now be evaluated for a complex argument.

Faster orthogonal polynomials
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Evaluation of orthogonal polynomials (the ``eval_*`` routines) in now
faster in ``scipy.special``, and their ``out=`` argument functions
properly.


``scipy.sparse.linalg`` features
--------------------------------
- In ``scipy.sparse.linalg.spsolve``, the ``b`` argument can now be either
  a vector or a matrix.
- ``scipy.sparse.linalg.inv`` was added.  This uses ``spsolve`` to compute
  a sparse matrix inverse.
- ``scipy.sparse.linalg.expm`` was added.  This computes the exponential of
  a sparse matrix using a similar algorithm to the existing dense array
  implementation in ``scipy.linalg.expm``.


Listing Matlab(R) file contents in ``scipy.io``
-----------------------------------------------
A new function ``whosmat`` is available in ``scipy.io`` for inspecting
contents
of MAT files without reading them to memory.


Documented BLAS and LAPACK low-level interfaces (``scipy.linalg``)
------------------------------------------------------------------
The modules `scipy.linalg.blas` and `scipy.linalg.lapack` can be used
to access low-level BLAS and LAPACK functions.


Polynomial interpolation improvements (``scipy.interpolate``)
-------------------------------------------------------------
The barycentric, Krogh, piecewise and pchip polynomial interpolators in
``scipy.interpolate`` accept now an ``axis`` argument.


Deprecated features
===================

`scipy.lib.lapack`
------------------
The module `scipy.lib.lapack` is deprecated. You can use
`scipy.linalg.lapack`
instead. The module `scipy.lib.blas` was deprecated earlier in Scipy 0.10.0.


`fblas` and `cblas`
-------------------
Accessing the modules `scipy.linalg.fblas`, `cblas`, `flapack`, `clapack` is
deprecated. Instead, use the modules `scipy.linalg.lapack` and
`scipy.linalg.blas`.


Backwards incompatible changes
==============================

Removal of ``scipy.io.save_as_module``
--------------------------------------
The function ``scipy.io.save_as_module`` was deprecated in Scipy 0.11.0,
and is
now removed.

Its private support modules ``scipy.io.dumbdbm_patched`` and
``scipy.io.dumb_shelve`` are also removed.


Other changes
=============


Authors
=======
* Anton Akhmerov +
* Alexander Ebersp?cher +
* Anne Archibald
* Jisk Attema +
* K.-Michael Aye +
* bemasc +
* Sebastian Berg +
* Fran?ois Boulogne +
* Matthew Brett
* Lars Buitinck
* Steven Byrnes +
* Tim Cera +
* Christian +
* Keith Clawson +
* David Cournapeau
* Nathan Crock +
* endolith
* Bradley M. Froehle +
* Matthew R Goodman
* Christoph Gohlke
* Ralf Gommers
* Robert David Grant +
* Yaroslav Halchenko
* Charles Harris
* Jonathan Helmus
* Andreas Hilboll
* Hugo +
* Oleksandr Huziy
* Jeroen Demeyer +
* Johannes Sch?nberger +
* Steven G. Johnson +
* Chris Jordan-Squire
* Jonathan Taylor +
* Niklas Kroeger +
* Jerome Kieffer +
* kingson +
* Josh Lawrence
* Denis Laxalde
* Alex Leach +
* Lorenzo Luengo +
* Stephen McQuay +
* MinRK
* Sturla Molden +
* Eric Moore +
* mszep +
* Matt Newville +
* Vlad Niculae
* Travis Oliphant
* David Parker +
* Fabian Pedregosa
* Josef Perktold
* Zach Ploskey +
* Alex Reinhart +
* Richard Lindsley +
* Gilles Rochefort +
* Ciro Duran Santillli +
* Jan Schlueter +
* Jonathan Scholz +
* Anthony Scopatz
* Skipper Seabold
* Fabrice Silva +
* Scott Sinclair
* Jacob Stevenson +
* Sturla Molden +
* Julian Taylor +
* thorstenkranz +
* John Travers +
* True Price +
* Nicky van Foreest
* Jacob Vanderplas
* Patrick Varilly
* Daniel Velkov +
* Pauli Virtanen
* Stefan van der Walt
* Warren Weckesser

A total of 74 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
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From ralf.gommers at gmail.com  Sun Mar 31 07:08:03 2013
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Sun, 31 Mar 2013 13:08:03 +0200
Subject: [SciPy-User] a routine for fitting a straight line to data
In-Reply-To: <41FE3C6C-5337-4B4C-A742-812B9A9340C3@gmail.com>
References: <E75C0FC4-EF32-4247-8D00-CF64C101DFE7@gmail.com>
	<CAMMTP+CGA=ig43KhqX0Os-Z9+_T9knYFCCn6cwL_SoqahPKOgw@mail.gmail.com>
	<41FE3C6C-5337-4B4C-A742-812B9A9340C3@gmail.com>
Message-ID: <CABL7CQiAgnsq7HkW5QOyvoTGQrGP7EWzK9s3HWy48KRMzN8=7A@mail.gmail.com>

On Mon, Mar 25, 2013 at 2:27 AM, David Pine <djpine at gmail.com> wrote:

> scipy.stats.linregress does not do the job.  First, it does not allow for
> weighting of the data, relative, absolute, or otherwise.


Weighting could be easily added to linregress, that would be useful.


> It does not return the covariance matrix, which provides estimates of the
> uncertainties in the fitting parameters, nor does it return chi-squared,
> which is the standard measure of the quality of the fit in the physical
> sciences.


These are a little trickier to add, would require adding a
full_output=False input because just adding a return value would break
backwards compatibility. But it's also possible. Would you like to have a
go at adding your suggestions to linregress?

There's still the issue that if you enhance stats.linregress it will be
hard to find. Fitting tools are scattered all over, and it's not clear what
the best solution is. A doc section giving an overview of all fit
functionality would be a good start. See
https://github.com/scipy/scipy/pull/448 for a recent discussion on that
topic.

Ralf



> David
>
>
> On Mar 24, 2013, at 8:19 PM, josef.pktd at gmail.com wrote:
>
> > On Sun, Mar 24, 2013 at 7:53 PM, David Pine <djpine at gmail.com> wrote:
> >> I would like to submit a routine to scipy for performing least squares
> fitting of a straight line f(x) = ax + b to an (x,y) data set.  There are a
> number of ways of doing this currently using scipy or numpy but all have
> serious drawbacks.  Here is what is currently available, as far as I can
> tell, and what seem to me to be their drawbacks.
> >>
> >> 1. numpy.polyfit :
> >>    a.  It is slower than it needs to be.  polyfit uses matrix methods
> that are needed to find best fits to general polynomials (quadratic, cubic,
> quartic, and higher orders), but matrix methods are overkill when you just
> want to fit a straight line f(x)= ax + b to data set.  A direct approach
> can yield fits significantly faster.
> >>    b.  polyfit currently does not allow using absolute error estimates
> for weighting the data; only relative error estimates are currently
> possible.  This can be fixed, but for the moment it's a problem.
> >>    c.  New or inexperienced uses are unlikely to look for a routine to
> fit straight lines in a routine that is advertised as being for
> polynomials.  This is a more important point than it may seem.  Fitting
> data to a straight line is probably the most common curve fitting task
> performed, and the only one that many users will ever use.  It makes sense
> to cater to such users by providing them with a routine that does what they
> want in as clear and straightforward a manner as possible.  I am a physics
> professor and have seen the confusion first hand with a wide spectrum of
> students who are new to Python.  It should not be this hard for them.
> >>
> >> 2. scipy.linalg.lstsq
> >>    a.  Using linalg.lstsq to fit a straight line is clunky and very
> slow (on the order of 10 times slower than polyfit, which is already slower
> than it needs to be).
> >>    b.  While linalg.lstsq can be used to fit data with error estimates
> (i.e. using weighting), how to do this is far from obvious.  It's unlikely
> that anyone but an expert would figure out how to do it.
> >>    c.  linalg.lstsq requires the use of matrices, which will be
> unfamiliar to some users.  Moreover, it should not be necessary to use
> matrices when the task at hand only involves one-dimensional arrays.
> >>
> >> 3. scipy.curve_fit
> >>    a.  This is a nonlinear fitting routine.  As such, it searches for
> the global minimum in the objective function (chi-squared) rather than just
> calculating where the global minimum is using the analytical expressions
> for the best fits.  It's the wrong method for the problem, although it will
> work.
> >>
> >> Questions:  What do others in the scientific Python community think
> about the need for such a routine?   Where should routine to fit data to a
> straight line go?  It would seem to me that it should go in the
> scipy.optimize package, but I wonder what others think.
> >
> > scipy.stats.linregress if there is only one x
> >
> > Josef
> >
> >
> >> David Pine
> >> _______________________________________________
> >> 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 ndbecker2 at gmail.com  Sun Mar 31 07:59:21 2013
From: ndbecker2 at gmail.com (Neal Becker)
Date: Sun, 31 Mar 2013 07:59:21 -0400
Subject: [SciPy-User] assignment optimization problem
Message-ID: <kj98er$e56$1@ger.gmane.org>

Are there python tools for addressing problems like assignment?  At this point, 
I don't fully understand my problem, but I believe it is a mixture of discrete 
assignment together with some continuous variables.  My son suggests coding it 
by hand using some kind of simple hill climbing, but maybe I could leverage 
existing code for this?



From robert.kern at gmail.com  Sun Mar 31 08:51:11 2013
From: robert.kern at gmail.com (Robert Kern)
Date: Sun, 31 Mar 2013 13:51:11 +0100
Subject: [SciPy-User] assignment optimization problem
In-Reply-To: <kj98er$e56$1@ger.gmane.org>
References: <kj98er$e56$1@ger.gmane.org>
Message-ID: <CAF6FJitcFAgUZDURsa6M3wSD8pRhjARN+C-jOVyCzoQU-J0zpw@mail.gmail.com>

On Sun, Mar 31, 2013 at 12:59 PM, Neal Becker <ndbecker2 at gmail.com> wrote:
> Are there python tools for addressing problems like assignment?  At this point,
> I don't fully understand my problem, but I believe it is a mixture of discrete
> assignment together with some continuous variables.  My son suggests coding it
> by hand using some kind of simple hill climbing, but maybe I could leverage
> existing code for this?

There are some tools for simple linear assignment.

  https://pypi.python.org/pypi/munkres/
  https://pypi.python.org/pypi/hungarian/
  https://pypi.python.org/pypi/pyLAPJV/

None of them will help if you need to do continuous optimization as
well. You may be able to get a satisficing answer by alternating
linear assignment and continuous optimization, but I'm pretty sure
there are no algorithmic guarantees with that approach.

You may be able to cast your problem as a mixed integer programming
problem. Check out the tools provided by Coopr and COIN-OR:

  https://software.sandia.gov/trac/coopr
  http://www.coin-or.org/

--
Robert Kern


From e.antero.tammi at gmail.com  Sun Mar 31 09:08:08 2013
From: e.antero.tammi at gmail.com (eat)
Date: Sun, 31 Mar 2013 16:08:08 +0300
Subject: [SciPy-User] assignment optimization problem
In-Reply-To: <kj98er$e56$1@ger.gmane.org>
References: <kj98er$e56$1@ger.gmane.org>
Message-ID: <CAKa=AYST_fx7CHO2GSZVO1WnPiMVmksoBtxfojzcyDkpvCFGoQ@mail.gmail.com>

Hi,

On Sun, Mar 31, 2013 at 2:59 PM, Neal Becker <ndbecker2 at gmail.com> wrote:

> Are there python tools for addressing problems like assignment?  At this
> point,
> I don't fully understand my problem, but I believe it is a mixture of
> discrete
> assignment together with some continuous variables.  My son suggests
> coding it
> by hand using some kind of simple hill climbing, but maybe I could leverage
> existing code for this?
>
FWIW, Ihave  implemented a pure python assignment solver few years back,
based on quite comprehensive book "Assignment Problems" by Rainer Burkard,
Mauro Dell'Amico, and Silvano Martello (ISBN: 978-0-898716-63-4),
http://www.assignmentproblems.com/

I can mail the code (less than 100 lines) to you, but the code is
slightly awkward, since it follows very closely the algorithm described in
the book (so you might need to have access for it).

BTW, what is typical size of your cost matrix you are aiming to handle?


Regards,
-eat



> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
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From freebluewater at gmail.com  Sat Mar 30 18:26:32 2013
From: freebluewater at gmail.com (freeblue)
Date: Sat, 30 Mar 2013 15:26:32 -0700 (PDT)
Subject: [SciPy-User] How to use Scipy to solve a Two-Point Boundary Value
 problem, of a nth system Nonlinear Second-Order Differential Equation
Message-ID: <eba8109f-f80e-4b47-bf20-f1e3902f71a1@googlegroups.com>

Hello to everyone here, 

I am trying to find the root of the next equation using Newton method:
http://www.wolframalpha.com/input/?i=d%2Fdx%28du%2Fdx%29+%3D+%28-3%2F%28k1%29[x]%29*%28k4[x]-%28k2[x]%2Bk3[x]%29*u
^
%281%2F3%29%29+*%28%28du%2Fdx%29^%282%2F3%29%29
which has 2 boundary conditions (u(x=0)=0, u(x=n(max) = m (constant)) ...
k1, k2, k3, k4 are arrays, calculated on each x gridpoint

Please, do someone knows if it is possible to solve this nth nonlinear 
second-order differential equation using scipy.integrate.odeint? (nonlinear 
ODE BVP 1-D.
I tried to use Central differencing to "simplify" it but the power number 
(2/3) makes its hard to use later Newton method.

Could be possible then to use scipy?  Do you know or see somewhere any 
relative example ? I am trying to find and write a solution on this the 
last 3 weeks, so any help will be more than welcome!!!!!

thank you in advance,
Kas

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