This is code implements the example given in pages 11-15 of An Introduction to the Kalman Filter by Greg Welch and Gary Bishop, University of North Carolina at Chapel Hill, Department of Computer Science.
mat files contain data saved in Matlab's proprietary format. How to read these files in Python depends on the version of Matlab used to save them, up to 7.1 or greater. Here are exemples of how to read two variables lat and lon from a mat file called "test.mat".
Installing numpy and scipy is easy; unfortunately, both packages depend on other softwares which themselves can be tricky to install, or are often distributed with bugs by major distributions. Hopefully, you can install numpy and scipy without any softwar
This automator installation package will install recent SVN builds of Numpy (1.1) and Scipy (0.7), as well as Matplotlib (0.98), iPython (0.8.3) and PyMC (2.0 beta) for OS X 10.5 (Leopard) on Intel Macintosh.
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.
Pyoptic is an optics and physics simulation based on python and uses scipy as a calculation engine, matplotlib and tvtk for visualisation. It is mainly intended for simple simulations of optical engineering, imaging systems, laser systems, laser transportation and applications, lens design etc.
PyTables is a package for managing hierarchical datasets and designed to efficiently and easily cope with extremely large amounts of data.
PyTables is built on top of the HDF5 library, using the Python language and the NumPy package.
This page provides downloads of 32- and 64-bit Windows binaries of many open-source extension packages for the Python programming language. The files are unofficial (meaning: informal, unrecognized, personal, unsupported) and made available for testing and evaluation purposes only. Consider using the Python(x,y) or Enthought distributions if you are new to Python or need support. Most binaries are built from source code found in the projects public revision control systems. Source code changes, if any, have been submitted to the project maintainers or are included in the packages.