[Numpy-discussion] Numerical Recipes (for Python)?
Wayne Watson
sierra_mtnview at sbcglobal.net
Fri Jun 4 23:00:57 EDT 2010
Got it. Thusly, <http://docs.scipy.org/doc/scipy/reference/>.
On 6/4/2010 11:50 AM, Anne Archibald wrote:
> On 4 June 2010 14:32, Wayne Watson<sierra_mtnview at sbcglobal.net> wrote:
>
>> At one point in my career I was very familiar, and that's an
>> understatement :-), with many of these methods (NR and beyond). I have
>> zero interest in implementing them.I do not need explanations of the
>> theory behind them. What I need to know is where some of these methods
>> exist in libraries? Optimization (linear, nonlinear), regression
>> (multiple, stepwise, and others), matrix inverse, eigenvalues, Fourier
>> transforms, ..., on and on I would expect to find a site that lists
>> all of them, and I can pick the ones I need. Python of course.
>>
> Read the scipy documentation.
>
> Anne
>
>
>> On 6/3/2010 11:09 PM, Anne Archibald wrote:
>>
>>> On 4 June 2010 00:24, Wayne Watson<sierra_mtnview at sbcglobal.net> wrote:
>>>
>>>
>>>> The link below leads me to http://numpy.scipy.org/, with or without the
>>>> whatever. IRAF is not mentioned on the home page.
>>>>
>>>>
>>> Um. I was not being specific. For a concrete example of what I mean,
>>> suppose you wanted to solve an ordinary differential equation. I would
>>> recommend you read the chapter on ODEs in Numerical Recipes in (say)
>>> C. This would talk about adaptive versus fixed step sizes, how to
>>> convert higher-order ODEs into first-order ODEs, how to formulate and
>>> solve boundary value problems, and so on. It would also describe in
>>> detail one particular adaptive integrator, a Runge-Kutta 4/5
>>> integrator. My recommendation would be to take that understanding of
>>> what integrators can and can't do and how they should be treated, and
>>> then use scipy.integrate.odeint or scipy.integrate.ode to solve your
>>> actual problem. These two packages contain careful thoroughly-tested
>>> implementations of adaptive integrators of the sort described in NR.
>>> They will correctly handle all sorts of awkward special cases, and are
>>> fairly hard to fool. If these are not sufficient (and I know their
>>> interface in scipy is not ideal) I'd recommend going to pydstool,
>>> which has a much more flexible interface, better performance, and more
>>> modern algorithms under the hood. Only in extremis would I consider
>>> implementing my own ODE solver: perhaps if I needed one with special
>>> features (a symplectic integrator, perhaps) and I couldn't find public
>>> well-tested code to do that.
>>>
>>> So: read Numerical Recipes, by all means, in any programming language
>>> you like; but use, if at all possible, existing libraries rather than
>>> implementing anything described in NR. Getting numerical code right is
>>> really hard. Let someone else do it for you. In the case of python,
>>> scipy itself is pretty much a library providing what's in NR.
>>>
>>> Anne
>>>
>>>
>>>
>>>
>>>> On 6/1/2010 9:04 PM, Anne Archibald wrote:
>>>>
>>>>
>>>>> On 2 June 2010 00:33, Wayne Watson<sierra_mtnview at sbcglobal.net> wrote:
>>>>>
>>>>>
>>>>>
>>>>>> Subject is a book title from some many years ago, I wonder if it ever
>>>>>> got to Python? I know there were C and Fortran versions.
>>>>>>
>>>>>>
>>>>>>
>>>>> There is no Numerical Recipes for python. The main reason there isn't
>>>>> a NR for python is that practically everything they discuss is already
>>>>> implemented as python libraries, and most of it is in numpy and/or
>>>>> scipy. (Their algorithms are also not suitable for pure-python
>>>>> implementation, but that's a whole other discussion.)
>>>>>
>>>>> I should also say that while NR is justifiably famous for its
>>>>> explanations of numerical issues, its code is not under a free license
>>>>> (so you may not use it without the authors' permission) and many
>>>>> people feel it has many bugs. The algorithms they discuss are also not
>>>>> always the best available.
>>>>>
>>>>> I generally recommend that people doing scientific programming read
>>>>> all or part of NR to understand the algorithms' limitations but then
>>>>> use the implementations available in
>>>>> numpy/scipy/scikits/IRAF/whatever.
>>>>>
>>>>> Anne
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>> --
>>>>>> Wayne Watson (Watson Adventures, Prop., Nevada City, CA)
>>>>>>
>>>>>> (121.015 Deg. W, 39.262 Deg. N) GMT-8 hr std. time)
>>>>>> Obz Site: 39° 15' 7" N, 121° 2' 32" W, 2700 feet
>>>>>>
>>>>>> "Science and democracy are based on the rejection
>>>>>> "of dogma." -- Dick Taverne, The March of Unreason
>>>>>>
>>>>>>
>>>>>> Web Page:<www.speckledwithstars.net/>
>>>>>>
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>>>>>>
>>>>>>
>>>>>>
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>>>>>
>>>>>
>>>> --
>>>> Wayne Watson (Watson Adventures, Prop., Nevada City, CA)
>>>>
>>>> (121.015 Deg. W, 39.262 Deg. N) GMT-8 hr std. time)
>>>> Obz Site: 39° 15' 7" N, 121° 2' 32" W, 2700 feet
>>>>
>>>> "Science and democracy are based on the rejection
>>>> "of dogma." -- Dick Taverne, The March of Unreason
>>>>
>>>>
>>>> Web Page:<www.speckledwithstars.net/>
>>>>
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>>>> NumPy-Discussion mailing list
>>>> NumPy-Discussion at scipy.org
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>>>>
>>>>
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>> --
>> Wayne Watson (Watson Adventures, Prop., Nevada City, CA)
>>
>> (121.015 Deg. W, 39.262 Deg. N) GMT-8 hr std. time)
>> Obz Site: 39° 15' 7" N, 121° 2' 32" W, 2700 feet
>>
>> "Science and democracy are based on the rejection
>> "of dogma." -- Dick Taverne, The March of Unreason
>>
>>
>> Web Page:<www.speckledwithstars.net/>
>>
>> _______________________________________________
>> NumPy-Discussion mailing list
>> NumPy-Discussion at scipy.org
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>>
>>
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--
Wayne Watson (Watson Adventures, Prop., Nevada City, CA)
(121.015 Deg. W, 39.262 Deg. N) GMT-8 hr std. time)
Obz Site: 39° 15' 7" N, 121° 2' 32" W, 2700 feet
"Science and democracy are based on the rejection
"of dogma." -- Dick Taverne, The March of Unreason
Web Page:<www.speckledwithstars.net/>
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