Common signal processing tasks in the numerical handling of experimental data include interpolation, smoothing, and propagation of uncertainty. A comparison of experimental results to a theoretical model further requires curve fitting, the plotting of functions and data, and a determination of the goodness of fit. These tasks often typically require an interactive, exploratory approach to the data, yet for the results to be reliable, the original data needs to be freely available and resulting analysis readily reproducible. In this article, we provide examples of how to use the Numerical Python (Numpy) and Scientific Python (SciPy) packages and interactive Jupyter Notebooks to accomplish these goals for data stored in a common plain text spreadsheet format. Sample Jupyter notebooks containing the Python code used to carry out these tasks are included and can be used as templates for the analysis of new data.
This document was originally posted July 25, 2013 at Uses This and is distributed under a CCBY-SA 4.0 license.Who are you, and what do you do?I'm a mathematical physicist who works at U.C. Riverside and also the Center of Quantum Technologies, in Singapore. I used to work on quantum gravity and the foundations of physics, but lately I've gotten interested in environmental issues, like what to do about global warming. I've been called the world's first blogger, which sounds great, or a "proto-blogger", which makes me sound like a caveman.
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