MATLAB® and NumPy/SciPy have a lot in common. But there are many differences. NumPy and SciPy were created to do numerical and scientific computing in the most natural way with Python, not to be MATLAB® clones. This page is intended to be a place to collect wisdom about the differences, mostly for the purpose of helping proficient MATLAB® users become proficient NumPy and SciPy users. NumPyProConPage is another page for curious people who are thinking of adopting Python with NumPy and SciPy instead of MATLAB® and want to see a list of pros and cons.
PyFacebook is currently best-tested with Django, and if you are just starting out with Python web development, the author highly recommends this combination :-). If you'd rather use another framework, there are also Pylons and other WSGI helpers in PyFacebook as well.
Cython is a language that makes writing C extensions for the Python language as easy as Python itself. Cython is based on the well-known Pyrex, but supports more cutting edge functionality and optimizations.
From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures.
Mat estimateRigidTransform(const Mat& srcpt, const Mat& dstpt, bool fullAffine)¶ Computes optimal affine transformation between two 2D point sets Parameters: * srcpt – The first input 2D point set * dst – The second input 2D point set of the same size and the same type as A * fullAffine – If true, the function finds the optimal affine transformation with no any additional resrictions (i.e. there are 6 degrees of freedom); otherwise, the class of transformations to choose from is limited to combinations of translation, rotation and uniform scaling (i.e. there are 5 degrees of freedom) The function finds the optimal affine transform [A|b] (a 2 \times 3 floating-point matrix) that approximates best the transformation from \texttt{srcpt}_i to \texttt{dstpt}_i : [A^*|b^*] = arg \min _{[A|b]} \sum _i \| \texttt{dstpt} _i - A { \texttt{srcpt} _i}^T - b \| ^2 where [A|b] can be either arbitrary (when fullAffine=true ) or have form
The purpose of NIPY is to make it easier to do better brain imaging research. We believe that neuroscience ideas and analysis ideas develop together. Good ideas come from understanding; understanding comes from clarity, and clarity must come from well-designed teaching materials and well-designed software. The software must be designed as a natural extension of the underlying ideas.
This documentation describes the profiler functionality provided in the modules cProfile, profile and pstats. This profiler provides deterministic profiling of Python programs. It also provides a series of report generation tools to allow users to rapidly examine the results of a profile operation.