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                    ython
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                    xtracto
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                    ): an open source,
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                    package to monitor and post-process numerical relativity simulations

Abstract Advances in hardware automation in synthetic biology laboratories are not yet fully matched by those of their software counterparts. Such automated laboratories, now commonly called biofoundries, require software solutions that would help with many specialized tasks such as batch DNA design, sample and data tracking, and data analysis, among others. Typically, many of the challenges facing biofoundries are shared, yet there is frequent wheel-reinvention where many labs develop similar software solutions in parallel. In this article, we present the first attempt at creating a standardized, open-source Python package. A number of tools will be integrated and developed that we envisage will become the obvious starting point for software development projects within biofoundries globally. Specifically, we describe the current state of available software, present usage scenarios and case studies for common problems, and finally describe plans for future development. SynBiopython is publicly available at the following address: http://synbiopython.org.

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QuakeMigrate: a Modular, Open-Source Python Package for Automatic Earthquake Detection and Location

10.1002/essoar.10505850.1 ◽

2021 ◽

Author(s):

Tom Winder ◽

Conor Bacon ◽

Jonathan Smith ◽

Thomas Hudson ◽

Tim Greenfield ◽

...

Keyword(s):

Open Source ◽

Earthquake Detection ◽

Python Package

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AQUAMI: An open source Python package and GUI for the automatic quantitative analysis of morphologically complex multiphase materials

Computational Materials Science ◽

10.1016/j.commatsci.2017.08.012 ◽

2017 ◽

Vol 139 ◽

pp. 320-329 ◽

Cited By ~ 22

Author(s):

Joshua Stuckner ◽

Katherine Frei ◽

Ian McCue ◽

Michael J. Demkowicz ◽

Mitsuhiro Murayama

Keyword(s):

Quantitative Analysis ◽

Open Source ◽

Multiphase Materials ◽

Python Package

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DEBoost: A Python Library for Weighted Distance Ensembling in Machine Learning

10.20944/preprints202005.0354.v1 ◽

2020 ◽

Author(s):

Wei Hao Khoong

Keyword(s):

Machine Learning ◽

Open Source ◽

Data Preprocessing ◽

Weighted Distance ◽

Open Source License ◽

Classification Tasks ◽

Python Package

In this paper, we introduce deboost, a Python library devoted to weighted distance ensembling of predictions for regression and classification tasks. Its backbone resides on the scikit-learn library for default models and data preprocessing functions. It offers flexible choices of models for the ensemble as long as they contain the predict method, like the models available from scikit-learn. deboost is released under the MIT open-source license and can be downloaded from the Python Package Index (PyPI) at https://pypi.org/project/deboost. The source scripts are also available on a GitHub repository at https://github.com/weihao94/DEBoost.

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MoVE: A Mobility Virtual Environment for Autonomous Vehicle Testing

Volume 4: Dynamics, Vibration, and Control ◽

10.1115/imece2019-10936 ◽

2019 ◽

Author(s):

Marc Compere ◽

Garrett Holden ◽

Otto Legon ◽

Roberto Martinez Cruz

Keyword(s):

Open Source ◽

Virtual Environment ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Time History ◽

Experimental Tests ◽

Coordinate Frame ◽

Medical Evacuation ◽

Post Process ◽

Common Framework

Abstract Autonomous vehicle researchers need a common framework in which to test autonomous vehicles and algorithms along a realism spectrum from simulation-only to real vehicles and real people. The community needs an open-source, publicly available framework, with source code, in which to develop, simulate, execute, and post-process multi-vehicle tests. This paper presents a Mobility Virtual Environment (MoVE) for testing autonomous system algorithms, vehicles, and their interactions with real and simulated vehicles and pedestrians. The result is a network-centric framework designed to represent multiple real and multiple virtual vehicles interacting and possibly communicating with each other in a common coordinate frame with a common timestamp. This paper presents a literature review of comparable autonomous vehicle softwares, presents MoVE concepts and architecture, and presents three experimental tests with multiple virtual and real vehicles, with real pedestrians. The first scenario is a traffic wave simulation using a real lead vehicle and 3 real follower vehicles. The second scenario is a medical evacuation scenario with 2 real pedestrians and 1 real vehicles. Real pedestrians are represented using live-GPS-followers streaming GPS position from mobile phones over the cellular network. Time-history and spatial plots of real and virtual vehicles are presented with vehicle-to-vehicle distance calculations indicating where and when potential collisions were detected and avoided. The third scenario highlights the avoid() behavior successfully avoiding other virtual vehicles and 1 real pedestrian in a small outdoor area. The MoVE set of concepts and interfaces are implemented as open-source software available for use and customization within the autonomous vehicle community. MoVE is freely available under the GPLv3 open-source license at gitlab.com/comperem/move.

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PySeqLab: an open source Python package for sequence labeling and segmentation

Bioinformatics ◽

10.1093/bioinformatics/btx451 ◽

2017 ◽

Vol 33 (21) ◽

pp. 3497-3499 ◽

Cited By ~ 1

Author(s):

Ahmed Allam ◽

Michael Krauthammer

Keyword(s):

Open Source ◽

Sequence Labeling ◽

Python Package

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Geophysical tutorial: Euler deconvolution of potential-field data

The Leading Edge ◽

10.1190/tle33040448.1 ◽

2014 ◽

Vol 33 (4) ◽

pp. 448-450 ◽

Cited By ~ 6

Author(s):

Leonardo Uieda ◽

Vanderlei C. Oliveira ◽

Valéria C. F. Barbosa

Keyword(s):

Open Source ◽

Potential Field ◽

Field Data ◽

Synthetic Data ◽

Euler Deconvolution ◽

Potential Field Data ◽

Python Package

In this tutorial, we will talk about a widely used method of interpretation for potential-field data called Euler de-convolution. Our goal is to demonstrate its usefulness and, most important, to call attention to some pitfalls encountered in interpretation of the results. The code and synthetic data required to reproduce our results and figures can be found in the accompanying IPython notebooks ( ipython.org/notebook ) at dx.doi.org/10.6084/m9.figshare.923450 or github.com/pinga-lab/paper-tle-euler-tutorial . The note-books also expand the analysis presented here. We encourage you to download the data and try them on your software of choice. For this tutorial, we will use the implementation in the open-source Python package Fatiando a Terra ( fatiando.org ).

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HFTools - An open source python package for microwave engineering

83rd ARFTG Microwave Measurement Conference ◽

10.1109/arftg.2014.6899531 ◽

2014 ◽

Author(s):

J. Stenarson

Keyword(s):

Open Source ◽

Microwave Engineering ◽

Python Package

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AN INTRODUCTION TO THE EINSTEIN TOOLKIT

International Journal of Modern Physics A ◽

10.1142/s0217751x13400149 ◽

2013 ◽

Vol 28 (22n23) ◽

pp. 1340014 ◽

Cited By ~ 23

Author(s):

MIGUEL ZILHÃO ◽

FRANK LÖFFLER

Keyword(s):

Open Source ◽

Numerical Relativity ◽

Target Group ◽

Continuous Development ◽

The World ◽

Lecture Notes ◽

Scientific Papers

We give an introduction to the Einstein Toolkit, a mature, open-source computational infrastructure for numerical relativity based on the Cactus Framework, for the target group of new users. This toolkit is composed of several different modules, is developed by researchers from different institutions throughout the world and is in active continuous development. Documentation for the toolkit and its several modules is often scattered across different locations, a difficulty new users may at times have to struggle with. Scientific papers exist describing the toolkit and its methods in detail, but they might be overwhelming at first. With these lecture notes we hope to provide an initial overview for new users. We cover how to obtain, compile and run the toolkit, and give an overview of some of the tools and modules provided with it.

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