Re: [Numpy-discussion] Issue Tracking
On 2/13/12 2:56 PM, Matthew Brett wrote: I have the impression that the Cython / SAGE team are happy with their Jenkins configuration. I'm not aware of a Jenkins buildbot system for Sage, though I think Cython uses such a system: https://sage.math.washington.edu:8091/hudson/ We do have a number of systems we build and test Sage on, though I don't think we have continuous integration yet. I've CCd Jeroen Demeyer, who is the current release manager for Sage. Jeroen, do we have an automatic buildbot system for Sage? Thanks, Jason -- Jason Grout ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy performance testing
On 12/29/11 10:37 PM, Jaidev Deshpande wrote: Hi! Along with test coverage, have any of you considered any systematic monitoring of NumPy performance? I'm mildly obsessed with performance and benchmarking of NumPy. I used to use a lot of MATLAB until a year back and I tend to compare Python performance with it all the time. I generally don't feel happy until I'm convinced that I've extracted the last bit of speed out of my Python code. I think the generalization of this idea is more or less equivalent to performance benchmarking. Of course, I know there's a lot more than 'MATLAB vs Python' to it. I'd be more than happy to be involved. GSoC or otherwise. Where do I start? We've recently had a discussion about more intelligent timeit commands and timing objects in Python/Sage. People here might find the discussion interesting, and it might also be interesting to collaborate on code. The basic idea was a much smarter timeit command that uses more intelligent statistics and presents a much more comprehensive look at the timing information. Here is the discussion: https://groups.google.com/forum/#!topic/sage-devel/8lq3twm9Olc Here is our ticket tracking the issue: http://trac.sagemath.org/sage_trac/ticket/12168 Here are some examples of the analysis: http://sagenb.org/home/pub/3857/ I've CCd the sage-devel list as well, which is where our discussion happened. Thanks, Jason ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Reduced row echelon form
Anne Archibald wrote: 2008/11/18 Robert Young [EMAIL PROTECTED]: Is there a method in NumPy that reduces a matrix to it's reduced row echelon form? I'm brand new to both NumPy and linear algebra, and I'm not quite sure where to look. Unfortunately, reduced row-echelon form doesn't really work when using approximate values: any construction of the reduced row-echelon form forces you many times to ask is this number exactly zero or just very small?; if it's zero you do one thing, but if it's very small you do something totally different - usually divide by it. With floating-point numbers, every calculation is approximate, and such a method will blow up completely. If you really need reduced row echelon form, you have to start with exact numbers and use a package that does exact computations (I think SymPy might be a place to start). I also recommend Sage here (http://www.sagemath.org). For example, here is a session in which we calculate the reduced echelon form of a matrix over the rationals (QQ): sage: a=matrix(QQ,[[1,2,3],[4,5,6],[7,8,9]]) sage: a.echelon_form() [ 1 0 -1] [ 0 1 2] [ 0 0 0] Sage has quite a bit of advanced functionality for exact linear algebra. (It also uses numpy as a backend to provide some nice functionality for approximate linear algebra). Here is a short tutorial on constructions in linear algebra: http://sagemath.org/doc/const/node28.html Jason ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] (Late) summary of PEP-225 discussion at Scipy
Fernando Perez wrote: Hi everyone, thanks for all the feedback. Last call on this one. If nobody objects to the language that's written here: https://cirl.berkeley.edu/fperez/static/numpy-pep225/ one small typo: in the Why just go one step? section, you have the phrase: and it is thus wortwhile solving the general problem wortwhile should be worthwhile. I've posted a message about this to sage-devel, as they are likely interested in this topic as well. Thanks, Jason ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy.complex* functions do not call the __complex__ method
In creating an array of type numpy.complex128, I'm having problems passing in Sage types that should be considered complex numbers since they implement the standard __complex__ method. However, numpy doesn't recognize that. Here's a minimal example: In [1]: class MyNum: ...: def __complex__(self): ...: return complex(1,3) ...: ...: In [2]: import numpy In [3]: a=MyNum() In [4]: complex(a) Out[4]: (1+3j) In [5]: numpy.complex128(a) --- type 'exceptions.AttributeError'Traceback (most recent call last) /home/jason/download/numpy/numpy-1.1.0/src/numpy/ipython console in module() type 'exceptions.AttributeError': MyNum instance has no attribute '__float__' This is with numpy 1.1. Is this a bug, or is this by design? Thanks, Jason ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy.complex* functions do not call the __complex__ method
Neal Becker wrote: [EMAIL PROTECTED] wrote: In creating an array of type numpy.complex128, I'm having problems passing in Sage types that should be considered complex numbers since they implement the standard __complex__ method. However, numpy doesn't recognize that. Here's a minimal example: I had tried to add my own complex_int class (using c++ code). Although partly successful, I was not able to get x.real to work. The reason is that PyArray_ISCOMPLEX is used in various places, and this is a hard-coded macro. There is no way to extend numpy's complex behavior to support user added types. I wish there was. Is there any possibility of redefining the macro to first call the Python function that converts to a python complex? (I don't know much about the python C API, just throwing an idea out). My example works if I do: numpy.complex128(complex(my_complex_number)), but not if I do numpy.complex128(my_complex_number). Can we make the call to python's complex (or maybe numpy's copy of it) automatic? Thanks, Jason ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] PyArray_SETITEM macro ends in semicolon
Dag Sverre Seljebotn wrote: [EMAIL PROTECTED] wrote: I'm working on getting the Sage matrices for real/complex doubles to use numpy as a backend. In this, I'm using the PyArray_SETITEM macro from within Cython. However, Cython wraps the macro in a function call to convert the output to a Python value: __pyx_1 = PyInt_FromLong(PyArray_SETITEM(__pyx_v_self-_matrix_numpy, PyArray_GETPTR2(__pyx_v_self-_matrix_numpy, __pyx_v_i, __pyx_v_j), __pyx_v_value)); However, after preprocessing, because of the semicolon at the end of the SETITEM macro, we get: PyInt_FromLong(((PyArrayObject *)(__pyx_v_self-_matrix_numpy))-descr-f-setitem((PyObject *)(__pyx_v_value), (char *)(((void *)PyArrayObject *)(__pyx_v_self-_matrix_numpy))-data) + (__pyx_v_i)*(((PyArrayObject *)(__pyx_v_self-_matrix_numpy))-strides)[0] + (__pyx_v_j)*(((PyArrayObject *)(__pyx_v_self-_matrix_numpy))-strides)[1]))), (PyArrayObject *)(__pyx_v_self-_matrix_numpy));); Note that at the end, we have a ););. The file refuses to compile. Presumably, since SETITEM returns a value, wrapping the return value in a function call seems to be a reasonable thing to do. Would there be a problem in eliminating the semicolon and instead wrapping the entire function body in parenthesis? I noticed that GETITEM also ended in a semicolon, though I didn't have the same problem as above since Cython didn't automatically wrap it in a function call (I'm not even sure if it returns something). On a side note, is the above the best way (i.e., fastest way given an arbitrary numpy array) to set/get an element? If I get to where I want to be with Cython/NumPy integration, there definitely shouldn't be a need to call PyArray_SETITEM from Cython. As you already use Cython, and if you know that your array is real/complex doubles (as indicated by your post), and also know that you have two dimensions (as indicated by your code), then there are definitely faster methods. Have you looked at the Cython numpy array support, as explained in http://wiki.cython.org/tutorials/numpy ? (This was mentored by Robert Bradshaw of Sage too). Your usecase looks like a place where it can be used. I am actively developing this, so if you wish you can send me proposals for changes to the Cython NumPy support which fixes whatever issues you have with it. (Support for access to complex numbers is an obvious one and that is worked on now.) Yes, I'm excited about this. In fact, I held off making the transition to numpy for a while thinking that the buffer interface would be in Sage's Cython any day. However, I realized that the biggest thing this would affect would be the getter and setter function for an array entry (most things are just offloaded to numpy/scipy functions), so I decided to make a push for it and convert the double and complex double matrices over to numpy arrays anyway. I'm really looking forward to seeing the cython buffer access, though. Thanks for all your work; I've been lurking on the mailing list archives and Cython website to track the progress on occasion during the summer. (As an aside, the int returned from PyArray_SETITEM is likely an error return value (though I'm not 100% sure). Converting it to a Python long is really inefficient, type the variable that is assigned to as an int. But that really belongs on the Cython list.) That's a really good point. Thanks. It is an error value; I should type it to prevent it from being converted to a python long. Thanks, Jason ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] PyArray_SETITEM macro ends in semicolon
I'm working on getting the Sage matrices for real/complex doubles to use numpy as a backend. In this, I'm using the PyArray_SETITEM macro from within Cython. However, Cython wraps the macro in a function call to convert the output to a Python value: __pyx_1 = PyInt_FromLong(PyArray_SETITEM(__pyx_v_self-_matrix_numpy, PyArray_GETPTR2(__pyx_v_self-_matrix_numpy, __pyx_v_i, __pyx_v_j), __pyx_v_value)); However, after preprocessing, because of the semicolon at the end of the SETITEM macro, we get: PyInt_FromLong(((PyArrayObject *)(__pyx_v_self-_matrix_numpy))-descr-f-setitem((PyObject *)(__pyx_v_value), (char *)(((void *)PyArrayObject *)(__pyx_v_self-_matrix_numpy))-data) + (__pyx_v_i)*(((PyArrayObject *)(__pyx_v_self-_matrix_numpy))-strides)[0] + (__pyx_v_j)*(((PyArrayObject *)(__pyx_v_self-_matrix_numpy))-strides)[1]))), (PyArrayObject *)(__pyx_v_self-_matrix_numpy));); Note that at the end, we have a ););. The file refuses to compile. Presumably, since SETITEM returns a value, wrapping the return value in a function call seems to be a reasonable thing to do. Would there be a problem in eliminating the semicolon and instead wrapping the entire function body in parenthesis? I noticed that GETITEM also ended in a semicolon, though I didn't have the same problem as above since Cython didn't automatically wrap it in a function call (I'm not even sure if it returns something). On a side note, is the above the best way (i.e., fastest way given an arbitrary numpy array) to set/get an element? Thanks, Jason ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
