cvvidproc is a small library with utilities for processing OpenCV videos. It was developed to support Andy Ylitalo's bubble-tracking project with faster implementations of video processing than can be achieved in pure Python.

This repository contains all code for the background fringe visibility subtraction of DHM data as referenced in the PhD Thesis of Manuel Bedrossian. Please refer to the thesis text and appendices for more information on the code as well as instructions on its use.

The data was obtained from the National Renewable Energy Laboratory's (NREL's) National Solar Radiation Database (NSRDB), with a focus on the continental United States. we consider solar irradiance data from the NSRDB in terms of direct normal irradiance (DNI) and diffused horizontal irradiance (DHI) at an approximately 4-km x 1/2-hr spatiotempo...

The data was obtained from the National Renewable Energy Laboratory's (NREL's) Wind Integration National Database (WIND) Toolkit, with a focus on the continental United States. Wind velocity data is comprised of westward (ua) and southward (va) wind components, calculated from wind speeds and directions 100-km from Earth's surface. The WIND Tool...

Python code for tracking objects in a video using the CvVidProc library, which speeds up image segmentation by performing computations with OpenCV in C++ and parallelizing computations (thanks to UkoeHB!). While the code can be used to track any objects, it was created for high-speed microscopy of objects in a microfluidic stream traveling upwa...

A number of research efforts have investigated extracting and analyzing textual information contained in software artifacts. However, source code files can contain meaningless text, such as random text used as markers or test cases, and code extraction methods can also sometimes make mistakes and produce garbled text. When used in processing pip...

Raw data along with all necessary MATLAB and R scripts to regenerate and analyze all raw data, and plot all figures in the manuscript (main text and supplement). README file explains how files are organized.

This repository contains all code for the quantification of the signal to noise ratio SNR of DHM data as referenced in the PhD Thesis of Manuel Bedrossian. Please refer to the thesis text and appendices for more information on the code as well as instructions on its use.

This is the additional supplemental information for "Tuning transcriptional regulation through signaling: A predictive theory for allosteric induction". In this document, we take a more in-depth dissection of the properties, applications, and nuances of our statistical mechanical model of allosteric transcriptional regulation.

This release only corrects inconsistent statements about the license terms of the software. There are no functional or other changes in this release.