Software Listing of Author : "Fred Sigworth"

Hidden Markov models are used to describe the "stepping" behavior of molecular motors, as measured by single-molecule fluorescence techniques. Our HMM implementation is more versatile than the one described by Milescu, et al. Biophys. J. 91:3135-3150 and can be used as a "blind" step detector or to find rate constants and step sizes directly from a measured time course of positions. A manuscript is in preparation (write to me for a copy at fred.sigworth@yale.edu). Meanwhile a readme file and example script demonstrate the use of the functions.

  • Platform: Matlab, Scripts

This directory contains m-functions for reading and writing files used in electron microscopy and 3D reconstruction. The file formats those used by the IMAGIC software package (Image Science GmbH; EMAN and Frealign are public-domain programs that also use this format), the MRC program library, and the Digital Micrograph (Gatan, Inc.) file format. These functions were written on what published information we could find, and work for our limited purposes; they are not supported by the software authors at all. The functions generally assume that little-endian files are being read by a little-endian machine. However, some functions might still work with big-endian machines such as PowerPC, as they were originally written on a Mac computer. Fred Sigworth, Liguo Wang Yale University ReadImagic Loads part or all of the data in an Imagic...

  • Platform: Matlab, Scripts

Wanting to acquire analog data from a National Instruments USB multifunction device using my Mac, I wrote this set of functions to allow "on demand" acquisition from multiple analog input channels. The routines provide analog input only, and assume that you are using only one device on your computer. Although tested only with the NI USB-6009, presumably it will work with other NI hardware too.

  • Platform: Matlab, Scripts

This is a single M-file that implements a Nelder-Mead simplex minimizer. It makes use of MATLAB's persistent variables to create a "state machine" implementation. This allows entire minimization programs to be written as MATLAB scripts, as it does not require a function to be defined and passed to the minimization routine. In my opinion, this makes the fitting of functions to data much more fun and educational! The traditional approach to optimization is to pass a function to an optimizer, which then takes control of the program until it is done. For example, the function 'fun' is minimized by a call to x = fminsearch(fun,x0,options). The disadvantage of this approach is that 'fun' must be defined in an M-file, and inserting code to observe the progress of the minimization is made difficult by the different scopes of the calling...

  • Platform: Matlab, Scripts
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