Mailman 3 April 2009 - SciPy-Dev

SLSQP Constrained Optimizer Status
by Rob Falck Nov. 24, 2013

Nov. 24, 2013

I'm currently implementing the Sequential Least Squares Quadratic Programming (SLSQP) optimizer, by Dieter Kraft, for use in Scipy. The Fortran code being wrapped with F2PY is here: http://www.netlib.org/toms/733 (its use within Scipy has been cleared) SLSQP provides for bounds on the independent variables, as well as equality and inequality constraint functions, which is a capability that doesn't exist in scipy.optimize. Currently the code works, although the constraint normals are being generated by approximation. I'm working on a way to pass these in. I think the most elegant way will be a single function that returns the matrix of constraint normals. For a demonstration of what the code can do, here is an optimization of f(x,y) = 2xy + 2x - x**2 - 2y**2 Example 14.2 in Chapra & Canale gives the maximum as x=2.0, y=1.0. The unbounded optimization tests find this solution. As expected, its faster when derivatives are provided rather than approximated. Unbounded optimization. Derivatives approximated. Elapsed time: 1.45792961121 ms Results [[1.9999999515712266, 0.99999996181577444], -1.9999999999999984, 4, 0, 'Optimization terminated successfully.'] Unbounded optimization. Derivatives provided. Elapsed time: 1.03211402893 ms Results [[1.9999999515712266, 0.99999996181577444], -1.9999999999999984, 4, 0, 'Optimization terminated successfully.'] The following example uses an equality constraint to find the optimal when x=y. Bound optimization. Derivatives approximated. Elapsed time: 1.384973526 ms Results [[0.99999996845920858, 0.99999996845920858], -0.99999999999999889, 4, 0, 'Optimization terminated successfully.'] I've tried to conform to the syntax used by the other optimizers in scipy.optimize. The function definition and doc string are below. If anyone is interested in testing it out, let me know. def fmin_slsqp( func, x0 , eqcons=[], f_eqcons=None, ieqcons=[], f_ieqcons=None, bounds = [], fprime = None, fprime_cons=None,args = (), iter = 100, acc = 1.0E-6, iprint = 1, full_output = 0, epsilon = _epsilon ): """ Minimize a function using Sequential Least SQuares Programming Description: Python interface function for the SLSQP Optimization subroutine originally implemented by Dieter Kraft. Inputs: func - Objective function (in the form func(x, *args)) x0 - Initial guess for the independent variable(s). eqcons - A list of functions of length n such that eqcons[j](x0,*args) == 0.0 in a successfully optimized problem f_eqcons - A function of the form f_eqcons(x, *args) that returns an array in which each element must equal 0.0 in a successfully optimized problem. If f_eqcons is specified, eqcons is ignored. ieqcons - A list of functions of length n such that ieqcons[j](x0,*args) >= 0.0 in a successfully optimized problem f_ieqcons - A function of the form f_ieqcons(x0, *args) that returns an array in which each element must be greater or equal to 0.0 in a successfully optimized problem. If f_ieqcons is specified, ieqcons is ignored. bounds - A list of tuples specifying the lower and upper bound for each independent variable [ (xl0, xu0), (xl1, xu1), ...] fprime - A function that evaluates the partial derivatives of func fprime_cons - A function of the form f(x, *args) that returns the m by n array of constraint normals. If not provided, the normals will be approximated. Equality constraint normals precede inequality constraint normals. args - A sequence of additional arguments passed to func and fprime iter - The maximum number of iterations (int) acc - Requested accuracy (float) iprint - The verbosity of fmin_slsqp. iprint <= 0 : Silent operation iprint == 1 : Print summary upon completion (default) iprint >= 2 : Print status of each iterate and summary full_output - If 0, return only the minimizer of func (default). Otherwise, output final objective function and summary information. epsilon - The step size for finite-difference derivative estimates. Outputs: ( x, { fx, gnorm, its, imode, smode }) x - The final minimizer of func. fx - The final value of the objective function. its - The number of iterations. imode - The exit mode from the optimizer, as an integer. smode - A string describing the exit mode from the optimizer. Exit modes are defined as follows: -1 : Gradient evaluation required (g & a) 0 : Optimization terminated successfully. 1 : Function evaluation required (f & c) 2 : Number of equality constraints larger than number of independent variables 3 : More than 3*n iterations in LSQ subproblem 4 : Inequality constraints incompatible 5 : Singular matrix E in LSQ subproblem 6 : Singular matrix C in LSQ subproblem 7 : Rank-deficient equality constraint subproblem HFTI 8 : Positive directional derivative for linesearch 9 : Iteration limit exceeded -- - Rob Falck

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Continuous Wavelet Transform for SciPy
by Sean Arms Nov. 12, 2009

Nov. 12, 2009

Greetings! My name is Sean Arms and I'm a graduate student at the University of Oklahoma in the School of Meteorology. As part of my PhD research, I'm studying coherent structures in atmospheric boundary layer turbulence, primarily using in-situ observations and, secondarily, Direct Numerical Simulation (DNS) output. One common approach for detecting coherent structures in observational datasets relies on the use of the global wavelet power spectrum as estimated from a continuous wavelet transform (CWT). I know SciPy has a DWT impementation, and I've already been in contact with Filip. He recommeded that I post my code in hopes that it would add some momentum to the python-cwt development and create some feedback (I'm currently looking for a good place to post my code). I've implemented the CWT using pure python (that is, I did not write any C extension code myself - nothing to build), along with one mother wavelet (second derivitive of a Gaussian, or the Mexican Hat) - I'll be adding more Mother wavelets as I go along. I've made it a point to (try to) design my MotherWavelet class to be easily extendable. I'm working on documentation and a few tests at the moment, but so far my code compares well with other wavelet routines. The point of this email is to introduce myself and let the SciPy dev community know that I am willing to help develop CWT support for SciPy - I'll already be doing the work for my research, so I might as well put in the extra effort to make is usable by the larger community! Cheers! Sean Arms Graduate Research Assistant School of Meteorology University of Oklahoma

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Porting SciPy to Py3k GSOC project
by ross smith July 20, 2009

July 20, 2009

Hello everyone, I am interested in porting SciPy/NumPy to Py3k. I've been working this past school year to port an existing code base to py3k for a research group on campus. A lot of the code relies on SciPy and NumPy but the scope of my project didn't let me work on porting either project, to my dismay. I'd love the opportunity to port a project I use heavily in my own code and gain a better understanding of how it works. We are supposed to contact the group we would be working with, to flesh out the details of our application. I've looked at the application and the only thing I know I'll need significant help with is the Milestones portion. Of course, Any and all suggestions are welcome! thank you, Ross Smith (Gaurdro on Freenode)

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rgb_to_hsv in scipy.misc ? (was: [Numpy-discussion] Optimizing speed for large-array inter-element algorithms (specifically, color space conversion))
by Nicolas Pinto July 8, 2009

July 8, 2009

Hello, Would it be possible to include the following rgb to hsv conversion code in scipy (probably in misc along with misc.imread, etc.) ? What do you think? Thanks in advance. Best regards, -- Nicolas Pinto Ph.D. Candidate, Brain & Computer Sciences Massachusetts Institute of Technology, USA http://web.mit.edu/pinto # ------------------------------------------------------------------------------ import numpy as np def rgb_to_hsv_arr(arr): """ fast rgb_to_hsv using numpy array """ # adapted from Arnar Flatberg # http://www.mail-archive.com/numpy-discussion@scipy.org/msg06147.html # it now handles NaN properly and mimics colorsys.rgb_to_hsv output arr = arr/255. out = np.empty_like(arr) arr_max = arr.max(-1) delta = arr.ptp(-1) s = delta / arr_max s[delta==0] = 0 # red is max idx = (arr[:,:,0] == arr_max) out[idx, 0] = (arr[idx, 1] - arr[idx, 2]) / delta[idx] # green is max idx = (arr[:,:,1] == arr_max) out[idx, 0] = 2. + (arr[idx, 2] - arr[idx, 0] ) / delta[idx] # blue is max idx = (arr[:,:,2] == arr_max) out[idx, 0] = 4. + (arr[idx, 0] - arr[idx, 1] ) / delta[idx] out[:,:,0] = (out[:,:,0]/6.0) % 1.0 out[:,:,1] = s out[:,:,2] = arr_max # rescale back to [0, 255] out *= 255. # remove NaN out[np.isnan(out)] = 0 return out

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Should ndimage.measurements.* should return lists if index is a list?
by Thouis (Ray) Jones May 15, 2009

May 15, 2009

Current behavior: >>> from scipy import ndimage >>> ndimage.maximum([[1,2], [3,4]], [[1,0],[0,2]], index=[1,2]) [1.0, 4.0] >>> ndimage.maximum([[1,2], [3,4]], [[1,0],[0,2]], index=[1]) 1.0 >>> ndimage.maximum([[1,2], [3,4]], [[1,0],[0,2]], index=1) 1.0 I think the second result should be [1.0]. We're using the ndimage code for image processing, and have to wrap the measurement functions to make the code general for the case where there is only one object in the image. Less important, though it might make sense, is to have this code return [] rather than raising an exception: >>> ndimage.maximum([[1,2], [3,4]], [[1,0],[0,2]], index=[]) Ray Jones

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How to handle code contribution with GSoC students ?
by David Cournapeau May 5, 2009

May 5, 2009

Hi, I was wondering about the best way to handle code by students for the upcoming GSoC. The year I participated, I already had write access, so the question did not come up. I think I would prefer starting with code reviews, before giving them svn write access later. Do people have better suggestions ? cheers, David

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Matlab Control System Toolbox for Python / SciPy
by Luke May 1, 2009

May 1, 2009

Does anybody know of any projects that aim to implement much of the functionality of the control system toolbox in Matlab? There is a control system toolbox for Octave, with what looks like a large amount of similar tools, but I haven't really found anything for Python. Is anybody out there interested in using and/or developing such a set of tools? ~Luke

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Re: [SciPy-dev] [Enthought-dev] EPD 4.2.30201 released
by Gael Varoquaux May 1, 2009

May 1, 2009

On Thu, Apr 30, 2009 at 02:55:37PM -0500, Ilan Schnell wrote: > Hello, > I am pleased to announce that EPD (Enthought Python Distribution) version > 4.2.30201 has been released. You may find more information about EPD, as > well as download a 30 day free trial here: Congratulations to all the team! Gaël

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EPD 4.2.30201 released
by Ilan Schnell May 1, 2009

May 1, 2009

Hello, I am pleased to announce that EPD (Enthought Python Distribution) version 4.2.30201 has been released. You may find more information about EPD, as well as download a 30 day free trial here: http://www.enthought.com/products/epd.php You can check out the release notes here: https://svn.enthought.com/epd/wiki/Py25/4.2.30201/RelNotes About EPD --------- The Enthought Python Distribution (EPD) is a "kitchen-sink-included" distribution of the Python Programming Language, including over 80 additional tools and libraries. The EPD bundle includes NumPy, SciPy, IPython, 2D and 3D visualization, database adapters, and a lot of other tools right out of the box. http://www.enthought.com/products/epdlibraries.php It is currently available as a single-click installer for Windows XP (x86), Mac OS X (a universal binary for OS X 10.4 and above), RedHat 3, 4 and 5 (x86 and amd64), as well as Solaris 10 (x86). EPD is free for academic use. An annual subscription including installation support is available for individual and commercial use. Additional support options, including customization, bug fixes and training classes are also available: http://www.enthought.com/products/support_level_table.php - Ilan

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scikits odes example planarpendulum_ida.py
by Nils Wagner April 29, 2009

April 29, 2009

Hi Benny, I just run the new example planarpendulum_ida.py Can you reproduce the AttributeErrors (see below) ? python docs/src/examples/planarpendulum_ida.py /data/home/nwagner/local/lib/python2.5/site-packages/matplotlib/__init__.py:371: UserWarning: matplotlibrc text.usetex can not be used with *Agg backend unless dvipng-1.5 or later is installed on your system warnings.warn( 'matplotlibrc text.usetex can not be used with *Agg ' started from z0 = [ 0.86575984 -0.50045969 0. 0. 0.1 ] initial condition calculated, [z,zprime] = [ [0.86575983949234436, -0.50045968900820581, 0.0, 0.0, 0.50045968901834781] [-2.9416572526260959e-07, 1.700449485844178e-07, -0.43327790003400429, -0.74954009965926327, -1.0] ] last sol [-0.06675332 -0.99777846 0.99507358 -0.06657502 1.99234658] [ 0.99507565 -0.06657258 0.13298731 0.98792826 0.19969072] has residual: [ -2.06966270e-06 -2.43986652e-06 8.42570737e-06 -7.77048074e-06 -2.55736431e-08] Exception exceptions.AttributeError: "'NoneType' object has no attribute 'c_void_p'" in <bound method IdaMemObj.__del__ of <pysundials.ida.IdaMemObj object at 0x2a9c8b6f90>> ignored Exception exceptions.AttributeError: "'NoneType' object has no attribute 'c_void_p'" in <bound method IdaMemObj.__del__ of <pysundials.ida.IdaMemObj object at 0x2a9c8b6e90>> ignored Exception exceptions.AttributeError: "'NoneType' object has no attribute 'c_void_p'" in <bound method IdaMemObj.__del__ of <pysundials.ida.IdaMemObj object at 0x2a9c8b65d0>> ignored Exception exceptions.AttributeError: "'NoneType' object has no attribute 'c_void_p'" in <bound method IdaMemObj.__del__ of <pysundials.ida.IdaMemObj object at 0x2a9c8b62d0>> ignored Nils

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