Hi all, This email is intended to clarify the status of various bits of code I've committed/not committed so I don't leave too much of a mess after me. I'm about to start writing my PhD thesis, so I'll be too busy to work on scipy until September. 1. sandbox.spline stuff The module here was supposed to be a tidy up of scipy.interpolate. All of the dierkx functionality has been moved to f2py wrappers, which I think makes maintenance easier. However, it does not add any new functionality, and in retrospect appears to have been a waste of my time. So I'll leave to you to decide if you want to integrate it into scipy.interpolate or not; though it was originaly planned to be a seperate module to clear up the ambiguity between interpolation and smoothing spline functionality. However I have added 20 unit tests to the code (most of which simply check the wrapper against the pure fortran output, but still useful I think) which could easily be moved over to the current scipy.interpolate module. 2. Radial basis function module in sandbox (rbf) This code has recently had attention from Robert Hetland and is quite improved. I'm not sure if I will ever go into scipy though?? If not, I will put it into a scikit at some point. Related to this, the wiki page has been updated, however, it is at http://www.scipy.org/RadialBasisFunctions but should probably be in the Cookbook. I don't know how to move it over, so some pointers would be helpful. In addition there are four unused attachments I uploaded that could be deleted. 3. Boundary value ODE solver (BVP_SOLVER) I have fairly functional code interfacing to some very good BVP solver code (http://cs.smu.ca/~muir/BVP_SOLVER_Webpage.shtml). The license is good for scipy (as confirmed by the authors), but the code if fortran 95 - therefore cannot apparently go into scipy. I'll make a scikit when I get time (probably September). 4. An interface to the pikaia genetic algorithm routine I have a python interface to pikaia fortran code for genetic algorithm optimisation of real valued functions (http://www.hao.ucar.edu/Public/models/pikaia/pikaia.html). The fortran code itself is robust and highly optimised for numerical work, though it is nowhere near as general as the sandbox.ga module. I think it is useful to go in scipy.optimization alongside the annealing module. If people disagree I'll make it into a scikit. I hope this clears a few things up. Best regards, John Travers
John Travers wrote:
Related to this, the wiki page has been updated, however, it is at http://www.scipy.org/RadialBasisFunctions but should probably be in the Cookbook. I don't know how to move it over, so some pointers would be helpful. In addition there are four unused attachments I uploaded that could be deleted.
I added "JohnTravers" to the EditorsGroup page, so you should be able to do the following: Once you're logged into the wiki, select from the "More Actions:" menu on the left "Rename Page". Also you can then recreate a new page at the original location that has contents just "#redirect Cookbook/MyNewLocation" and both locations will work. To delete attachments, click into the "Attachments" page and go from there. -Andrew
On 10/06/07, Andrew Straw <strawman@astraw.com> wrote:
John Travers wrote:
Related to this, the wiki page has been updated, however, it is at http://www.scipy.org/RadialBasisFunctions but should probably be in the Cookbook. I don't know how to move it over, so some pointers would be helpful. In addition there are four unused attachments I uploaded that could be deleted.
I added "JohnTravers" to the EditorsGroup page, so you should be able to do the following:
Thanks very much. I have now made the changes I wanted to. Cheers, John
Hi, 2. Radial basis function module in sandbox (rbf)
This code has recently had attention from Robert Hetland and is quite improved. I'm not sure if I will ever go into scipy though?? If not, I will put it into a scikit at some point. Related to this, the wiki page has been updated, however, it is at http://www.scipy.org/RadialBasisFunctions but should probably be in the Cookbook. I don't know how to move it over, so some pointers would be helpful. In addition there are four unused attachments I uploaded that could be deleted.
What are the kernels that can be used with this RBF ? (This is related to the current work on SVM and/or KPCA for machine learning) Is it possible to give the samples in a single array ? Is there an optimization in the computation of the value of the RBF (i.e. if the point is "far" from the RBF, the kernel value is low or zero, so computing the value is not useful) ? I'd like to use this for probabilities (RBF fields), if possible ;) Matthieu
On Sun, Jun 10, 2007 at 9:55 AM, John Travers <jtravs@gmail.com> wrote: <snip>
1. sandbox.spline stuff The module here was supposed to be a tidy up of scipy.interpolate. All of the dierkx functionality has been moved to f2py wrappers, which I think makes maintenance easier. However, it does not add any new functionality, and in retrospect appears to have been a waste of my time. So I'll leave to you to decide if you want to integrate it into scipy.interpolate or not; though it was originaly planned to be a seperate module to clear up the ambiguity between interpolation and smoothing spline functionality. However I have added 20 unit tests to the code (most of which simply check the wrapper against the pure fortran output, but still useful I think) which could easily be moved over to the current scipy.interpolate module.
Hey John, I am hosting a NumPy/SciPy sprint this week at UC Berkeley and among other things am hoping to make some progress on cleaning up the various interpolation/spline code in SciPy as well as continue removing the scipy sandbox. Do you have any more plans for your sandbox.spline code? Any thoughts on where it should go or what still needs to be done? Thanks, -- Jarrod Millman Computational Infrastructure for Research Labs 10 Giannini Hall, UC Berkeley phone: 510.643.4014 http://cirl.berkeley.edu/
participants (4)
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Andrew Straw -
Jarrod Millman -
John Travers -
Matthieu Brucher