Using Mathematica for Simultaneous Instrument Control, Data Archiving, and Analysis

W. V. Meyer, C. Saltiel, and A. E. Smart

NCSER/NASA GRC and Scattering Solutions, Inc.

Abstract

We have designed and written Mathematica programs to run hardware, archive data, retrieve data files, and perform data analysis. For surface light scattering we generate a preview of what a data set will look like based on the instrument settings. This allows us to optimize experiment settings. For volume light scattering, we have written a more elaborate Mathematica program that has a "Standard User Mode" and an "Advanced User Mode" to exploit the power of Mathematica’s sophisticated constrained nonlinear regression routines and plotting capabilities.

Our requirements are to improve the speed and accuracy of complex instrument control and real-time data processing. Our Surface Light Scattering Spectroscopy (SLSS) instrument measures the thermally induced ‘ripplons’ at a fluid interface to determine surface tension, viscosity and more sophisticated tensor properties of interfacial membranes, with applications ranging from the biophysics of lipid bilayers to oil recovery from tar sands. Analogously, our Dynamic Light Scattering (DLS) instrument measures the hydrodynamic radius of nano- to micron-scale particles suspended in a transparent liquid.

Figure 1 (on-line) shows a window of our Surface Response Function modeling program. Figures 2-5 (on-line) show windows of the Correlation Data Program, with its Data Summarized tab selected. A plot of the raw data is shown as it accumulates, with instrument control settings for the graphical control panel also visible. Data may be retrieved from any of several sources, including a real time raw data stream from the experimental, compressed or post-processed data from hardware or software correlators, data synthesized with Mathematica, and previously archived data. Data from any of these sources can be analyzed by proprietary algorithms, nonlinear regression fitting routines, and/or combinations of other high-level functions available in Mathematica 7. We have created and refined graphical tools to allow the optimization of external constraints by easily visualized real-time representations of their effects. For advanced analyses, comparisons can be made by simultaneous comparison of different fitting routines, input parameter settings, and constraints. Data sets from different sources, including the current data set (taken in real-time), may also be simultaneously compared.

Additional capabilities will be demonstrated during the presentation, with emphasis on coding techniques for the many levels and components that work together efficiently in this program.

We wish to thank and acknowledge the Wolfram Partnerships Group, Technical Support Staff, and Developers at Wolfram Research, whose support has made this development possible.

Surface Response Function Program - Previewing Experimental Results [Figure 1]

Correlation Data Program - Data Summarized (Standard User Mode) [Figure 2]

Correlation Data Program - Changing Experiment Parameters (Standard User Mode) [Figure 3]

Correlation Data Program - Data Summarized (Advanced User Mode) [Figure 4]

Correlation Data Program - Changing Experiment Parameters (Advanced User Mode) [Figure 5]