Hardware parameters of optimal implementations. by Xuzhen He (14676114)

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Locality of reference in optimal implementations. by Xuzhen He (14676114)

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Poster: PyGMT: Accessing the Generic Mapping Tools from Python by Leonardo Uieda (97471)

“…Since version 5, GMT has provided a C language Application Programming Interface (API) that allows other programs to access its core functionality. We are using this bridge to develop PyGMT (<a href="http://www.pygmt.org/" rel="nofollow">www.pygmt.org</a>; formerly GMT/Python), an open-source library that allows users of the Python programming language to leverage the almost thirty years of continuous GMT development. …”

Implementation of the 1D heat model with array programming. by Xuzhen He (14676114)

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Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements by Pascal Wang (10130612)

“…<div>This directory contains Python 3 scripts implementing the Trajectory Adaptive Multilevel Sampling algorithm (TAMS), a variant of Adaptive Multilevel Splitting (AMS), for the study of rare events. …”

Core concepts implemented in the MetDataModel package. by Joshua M. Mitchell (14703178)

Python script implementation of butanol and butanol producer models. by Laura de Arroyo Garcia (9226096)

System Hardware ID Generator Script: A Cross-Platform Hardware Identification Tool by Pavel Izosimov (20096259)

“…</p><ul><li>For advanced <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank">Python code protection tools</a>, consider using the <a href="https://xn--mxac.net/local-python-code-protector.html" target="_blank">Local Python Code Protector Script</a>. …”

Tools used in implementing the RNN models. by Fahmi H. Quradaa (17896527)

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GraSPy: an Open Source Python Package for Statistical Connectomics by Benjamin Pedigo (6580352)

“…We developed GraSPy, an open-source Python toolkit for statistical inference on graphs. …”

Core data preparation features. by Bjørn André Bredesen-Aa (13767126)

Runtimes of analytical methods implemented in scedar with different number of CPU cores for parallel computation. by Yuanchao Zhang (229875)

a)Growth rate, b) peak percentage prevalence, c) cumulative percentage prevalence, d) percentage of households with at least one case during the simulation period as a function of... by Joe Hilton (7003265)

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Peak and b) cumulative prevalence among the clinically vulnerable population as a percentage of the baseline values when (OOHI) is not implemented, and c) peak and d) cumulative pr... by Joe Hilton (7003265)

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a)Growth rate, b) peak percentage prevalence, c) cumulative percentage prevalence, d) percentage of households with at least one case during the simulation period as a function of... by Joe Hilton (7003265)

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Expected secondary attack ratios by size. by Joe Hilton (7003265)

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Fig 5 - by Joe Hilton (7003265)

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Parameter notation, choices, and sources for the age- and household structured infection model. by Joe Hilton (7003265)

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Estimated age cohort populations and numbers shielding from ONS data [52, 54], estimated age distribution of shielding population, and proportion of each age group shielding. by Joe Hilton (7003265)

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Convergence of model from initial conditions to exponential growth regime. by Joe Hilton (7003265)

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