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The code includes both deep-learning-based models (PDL and DLLCN) and learning strategies to optically characterize printers w.r.t. color and translucency. Measurements for the Stratasys and Mimaki printers are included to reproduce the results presented in the paper. It is written in Python and uses additional libraries. Please unpack the ZIP-file and read the Readme.txt file for more information.

Step by step description of aGWA analyses. This uses the output from ChromosomePainter and associates them with phenotypes in a GLM framework. To compute p-values an empirical p distribution is generated using permutations.

Linear Assignment Problem solver (LAPJV/LAPMOD).

Analysis Utilities (or analysisUtils for short) is a small set of Python source code files that provide a number of analysis and plotting utilities. The primary file is analysisUtils.py (114k lines as of January 2023). The utilities were developed (and continue to be developed) for ALMA commissioning and data reduction and are, in many cases, also useful for EVLA data reduction. The files included in this tarball is a small subset of the AIV package of ALMA. Most of the functions have fairly complete inline help. Once installed, functions can be searched for by name via minimum match string with the au.help function (where comma denotes a logical AND): e.g. au.help('antenna') or au.help('spw,caltable'). The documentation for the two script generators almaqa2csg.py and almaqa2isg.py, as well as how to provide feedback or get support for their usage, can be found at this link. Be advised that analysisUtils is a commissioning tool, so the level of support available is not as extensive as for CASA, but we do welcome feedback.

A custom Monte Carlo sampler for the (gravitational) two-body problem

FLOCK is designed to unravel genetic structure within a collection of genotypes, whether pure or admixed. It is assumed that pure, “source”, samples are not available. The program may be used to solve the “number (K) of populations” problem. Since FLOCK is a non-model algorithm, the populations may originate from sexual as well as clonal reproduction. When K is already known, it may be used to separate pure and admixed specimens into K groups. FLOCK is a non- Bayesian, non MCMC, method and therefore differs substantially from previous clustering algorithms and processing time is much shorter (20 sweeps). Its working principle is repeated re-allocation of all collected specimens (total sample) to k subsamples. The methods to map genetic admixture on a set of samples (K known) and to estimate the number of populations K were described respectively in: Duchesne P, Turgeon J (2009) FLOCK: a method for quick mapping of admixture without source samples. Molecular Ecology Resources 9: 1333-1344 Duchesne P, Turgeon J (2012) FLOCK Provides Reliable Solutions to the "Number of Populations" Problem Journal of Heredity 2012; doi: 10.1093/jhered/ess038 Three versions of FLOCK are available, one for microsatellite, one for AFLP and one for SNP markers. The basic algorithm, input and output formats are the same in the three versions.

Resonance Control GUI for LCLS-II ResCtl System is a Qt5-based, python GUI used for interacting with an LCLS-II resonance control system for an 8-cavity cryomodule. The code is written in Python, and can use auxiliary software Qt5, pyqtgraph, numpy, and scipy. The code is operating system-agnostic, has no specific hardware requirements, and has no embedded source code.

Templates and code for rendering the Solar Forecast Arbiter dashboard.

Find-Object project