[Numpy-discussion] [ANN] mlabrap-1.0final: a high level python to matlab
I'm pleased to finally announce mlabwrap-1.0: Project website --- http://mlabwrap.sourceforge.net/ Description --- Mlabwrap-1.0 is a high-level python to matlab(tm) bridge that makes calling matlab functions from python almost as convenient as using a normal python library. It is available under a very liberal license (BSD/MIT) and should work on all major platforms and (non-ancient) python and matlab versions and either numpy or Numeric (Numeric support will be dropped in the future). Examples Creating a simple line plot: from mlabwrap import mlab; mlab.plot([1,2,3],'-o') Creating a surface plot: from mlabwrap import mlab; from numpy import * xx = arange(-2*pi, 2*pi, 0.2) mlab.surf(subtract.outer(sin(xx),cos(xx))) Creating a neural network and training it on the xor problem (requires netlab) net = mlab.mlp(2,3,1,'logistic') net = mlab.mlptrain(net, [[1,1], [0,0], [1,0], [0,1]], [0,0,1,1], 1000) What the future holds - Please note that mlabwrap-1.0 will be the last non-bugfix release with Numeric support. Future versions of mlabwrap will require numpy be a part of scipy's scikits infrastructure (so the package name will be ``scikits.mlabwrap``) and use setuptools rather than distutils so that it should be possible to automatically download and install via EasyInstall. The next major version of mlabwrap should also bring more powerful proxying and marshalling facilities, but the default conversion behavior might be changed to reflect the fact that matlab is becoming increasingly less ``double`` (-matrix) centric; although wrappers for old-style behavior will be provided if backwards-incompatible interface changes are introduced, for upwards compatibility it is recommended to explicitly pass in float64 arrays rather than e.g. lists of ints if the desired input type that matlab should see is a double array (i.e. use ``mlab.sin(array([1., 2., 3.])`` rather than ``mlab.sin([1,2,3])`` for production code in order to be on the safe side)). Please have a look at http://www.scipy.org/Mlabwrap if you're interested in the ongoing development of mlabwrap and planned features. Feedback and support The preferred formum for users to request help and offer feedback and keep informed about new releases is mlabwrap-user: https://lists.sourceforge.net/lists/listinfo/mlabwrap-user the list is low-volume and subscription is recommended. Discussion of mlabwrap development takes place on the scipy-dev (please mention mlabwrap in the subject line): http://projects.scipy.org/mailman/listinfo/scipy-dev cheers, Alexander Schmolck, mlabwrap author and maintainer ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] detecting shared data
Is there any way to detect whether one array is a view into another array? I'd like something like: arr = N.arange(5) subarr = arr[1:3] sharesdata(arr, subarr) True -- Matt ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] detecting shared data
On Wednesday 11 April 2007 18:12:16 Matthew Koichi Grimes wrote: Is there any way to detect whether one array is a view into another array? I'd like something like: arr = N.arange(5) subarr = arr[1:3] sharesdata(arr, subarr) Mmh, would arr.flags['OWNDATA'] would do the trick ? p. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] detecting shared data
On Wed, Apr 11, 2007 at 06:12:16PM -0400, Matthew Koichi Grimes wrote: Is there any way to detect whether one array is a view into another array? I'd like something like: arr = N.arange(5) subarr = arr[1:3] sharesdata(arr, subarr) True Your best bet is probably N.may_share_memory(arr,subarr) Cheers Stéfan ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] detecting shared data
It's better than nothing. I basically want some sanity-check assert code that can assert that some arrays are in fact sub-arrays of another array. Your OWNDATA suggestion meets me halfway by allowing me to check that these sub-arrays are at least sub-arrays of *someone*. Thanks, -- Matt Pierre GM wrote: On Wednesday 11 April 2007 18:12:16 Matthew Koichi Grimes wrote: Is there any way to detect whether one array is a view into another array? I'd like something like: arr = N.arange(5) subarr = arr[1:3] sharesdata(arr, subarr) Mmh, would arr.flags['OWNDATA'] would do the trick ? p. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] detecting shared data
On 4/12/07, Matthew Koichi Grimes [EMAIL PROTECTED] wrote: It's better than nothing. I basically want some sanity-check assert code that can assert that some arrays are in fact sub-arrays of another array. Your OWNDATA suggestion meets me halfway by allowing me to check that these sub-arrays are at least sub-arrays of *someone*. Thanks, -- Matt Pierre GM wrote: On Wednesday 11 April 2007 18:12:16 Matthew Koichi Grimes wrote: Is there any way to detect whether one array is a view into another array? I'd like something like: arr = N.arange(5) subarr = arr[1:3] sharesdata(arr, subarr) Mmh, would arr.flags['OWNDATA'] would do the trick ? p. I wrote this a while back that may do what you want: def same_array(a, b): Tries to figure out if a and b are sharing (some of) the same memory or not. This is sometimes useful for determining if a copy was made as a result of some operation, or for determining if modifying a might also modify b. A True result means that a and b both borrow memory from the same array object, but it does not necessarily mean the memory regions used overlap. For example: x = rand(4,4) a,b = x[:2], x[2:] same_array(a,b) True A False result means the arrays definitely do not overlap in memory. same_array(a, b) - Bool ab = a while ab.base is not None: ab = ab.base bb = b while bb.base is not None: bb = bb.base return ab is bb But maybe that's pretty much what may_share_memory does? --bb ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] detecting shared data
On 11/04/07, Bill Baxter [EMAIL PROTECTED] wrote: Must be pretty recent. I'm using 1.0.2.dev3520 (enthought egg) and the function's not there. It is. I've never been quite happy with it, though; I realize it's not very feasible to write one that efficiently checks all possible overlaps, but the current one claims (e.g.) a.real and a.imag may share memory, which irks me. I put in a quick fix. Also, may_share_memory did not have any tests (for shame!), so I put some of those in too. I wasn't sure how to express expected failure, and they're not the most thorough, but they should help. Anne M. Archibald share-patch Description: Binary data ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
