Frequently Asked Questions ========================== Where do the headers get installed? ----------------------------------- Its easiest to find this location using this Python code snippet:: from imp import find_module file, pathname, descr = find_module("pyublas") from os.path import join return join(pathname, "..", "include") Since PyUblas requires numpy, you'll need a similar snippet for that, too:: from imp import find_module file, pathname, descr = find_module("numpy") from os.path import join return join(pathname, "core", "include") What about 0-dimensional arrays? -------------------------------- 0-dimensional arrays are supported by PyUblas, they can be converted to :ctype:`numpy_vector` instances of length 1. .. _faq-overload-failure: My wrapped function is not found by Boost.Python. Help! ------------------------------------------------------- Even if you heed all the advice in :ref:`frompython`, Boost.Python will sometimes still complain that no valid overload can be found. Common reasons include: * You expect to be handling a float array, but you actually got an int (or different dtype) array. To be consistent, PyUblas will not copy-and-cast in this situation. If necessary, use :func:`numpy.asarray` in your Python code. (I'm debating whether to add C++ syntax to say "copying is ok here". If you have input, let me know.) * If converting a matrix, the row/column-major setting may not match what Ublas is expecting. By default, :ctype:`numpy_matrix` is row-major, as is :class:`numpy.array` when creating a 2D array. The function :func:`pyublas.why_not` can help you debug these cases. .. _speed-faq: Gaah! Why is this garbage so slow? ---------------------------------- Ok, let's substantiate this discussion somehwat. I've obtained the following numbers using :file:`test/strided_speed.py` on my 1.7GHz Pentium M. You can run that file yourself for comparison. PyUblas with Boost.Ublas in its default configuration obtains the following speeds:: test_ublas_speed: 0.023819s test_unstrided_speed: 0.152608s test_strided_speed: 0.234522s All these tests measure a certain number of large in-place vector-scalar multiplications. The assumption is that performance for most other vector-vector operations will be very similar. ``test_ublas_speed`` measures the performance of that operation for :ctype:`boost::numeric::ublas::vector`, ``test_unstrided_speed`` for :ctype:`numpy_vector`, and ``test_strided_speed`` for :ctype:`numpy_strided_vector`. Now, you'll say, that's scary, because, unlike what's promised :ref:`here `, the unstrided :ctype:`numpy_vector` actually is about an order of magnitude slower than native Ublas. This is due to the fact that Ublas uses indexed access for dense vector/matrix operations by default. This default can be changed, however, by defining :cmacro:`BOOST_UBLAS_USE_ITERATING`, in which case the timings are pretty much as promised:: test_ublas_speed: 0.031008s test_unstrided_speed: 0.034083s test_strided_speed: 0.205794s If you configure PyUblas with :option:`--use-iterators`, it will define :cmacro:`BOOST_UBLAS_USE_ITERATING` while it is being compiled. Note however that you still need to define this macro when compiling your own code. .. note:: Unfortunately, Boost 1.35 shipped with code that breaks when :cmacro:`BOOST_UBLAS_USE_ITERATING` is defined. I have submitted a `patch `_ to the Ublas folks to fix this. The final question is, then, why ``test_strided_speed`` is still about an order of magnitude slower than the other two. The answer is that Ublas will always use indexing access in :ctype:`boost::numeric::vector_slice`, from which :ctype:`numpy_strided_vector` is derived. User-visible Changes ==================== PyUblas 0.93 ------------ * Negative strides are supported. Slice handling was cleaned up and should be correct now. * :class:`invalid_ok` was added. * :class:`numpy_strided_vector` was added as another way of transparently dealing with non-contiguous slices. * :cfunc:`numpy_vector::min_stride` is gone. It was ill-specified and not capable of doing what it promised to do. * :mod:`numpy` forces every C/C++ module that uses its functionality to call :cfunc:`import_array`. PyUblas has a clever mechanism that does this for you. This mechanism was not correct previously, it would often fail when a particular piece of code was not inlined. Acknowledgements ================ PyUblas lives through contributions from users like you! The following people have been kind enough to contribute their changes back to PyUblas: * Neal Becker provided lots of feedback and a good bit of code. * Bryan Silverthorn added :ctype:`strided_vector` to-Python conversion. * Joshua Napoli made PyUblas compatible with MSVC and made PyUblas buildable with Boost.Build. Thanks to all of you! (Any omission here? If so, please let me know.) Licensing ========= PyUblas is licensed to you under the MIT/X Consortium license: Copyright (c) 2008 Andreas Klöckner Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.