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

2017 ◽  
Vol 33 (21) ◽  
pp. 3497-3499 ◽  
Author(s):  
Ahmed Allam ◽  
Michael Krauthammer
Keyword(s):  
Open Source ◽  
Sequence Labeling ◽  
Python Package

scholarly journals SynBiopython: an open-source software library for Synthetic Biology

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Jing Wui Yeoh ◽  
Neil Swainston ◽  
Peter Vegh ◽  
Valentin Zulkower ◽  
Pablo Carbonell ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Open Source ◽  
Open Source Software ◽  
Development Projects ◽  
Software Library ◽  
Current State ◽  
Starting Point ◽  
Common Problems ◽  
Data Tracking ◽  
Python Package

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.

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

2021 ◽  
Author(s):  
Tom Winder ◽  
Conor Bacon ◽  
Jonathan Smith ◽  
Thomas Hudson ◽  
Tim Greenfield ◽  
...  
Keyword(s):  
Open Source ◽  
Earthquake Detection ◽  
Python Package

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

2017 ◽  
Vol 139 ◽  
pp. 320-329 ◽  
Author(s):  
Joshua Stuckner ◽  
Katherine Frei ◽  
Ian McCue ◽  
Michael J. Demkowicz ◽  
Mitsuhiro Murayama
Keyword(s):  
Quantitative Analysis ◽  
Open Source ◽  
Multiphase Materials ◽  
Python Package

scholarly journals Associating Natural Language Comment and Source Code Entities

2020 ◽  
Vol 34 (05) ◽  
pp. 8592-8599
Author(s):  
Sheena Panthaplackel ◽  
Milos Gligoric ◽  
Raymond J. Mooney ◽  
Junyi Jessy Li
Keyword(s):  
Software Development ◽  
Natural Language ◽  
Open Source ◽  
Source Code ◽  
Initial Step ◽  
Binary Classifier ◽  
Sequence Labeling ◽  
Evaluation Dataset ◽  
Revision Histories

Comments are an integral part of software development; they are natural language descriptions associated with source code elements. Understanding explicit associations can be useful in improving code comprehensibility and maintaining the consistency between code and comments. As an initial step towards this larger goal, we address the task of associating entities in Javadoc comments with elements in Java source code. We propose an approach for automatically extracting supervised data using revision histories of open source projects and present a manually annotated evaluation dataset for this task. We develop a binary classifier and a sequence labeling model by crafting a rich feature set which encompasses various aspects of code, comments, and the relationships between them. Experiments show that our systems outperform several baselines learning from the proposed supervision.

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

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.

scholarly journals Geophysical tutorial: Euler deconvolution of potential-field data

2014 ◽  
Vol 33 (4) ◽  
pp. 448-450 ◽  
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 ).

scholarly journals scikit-rf: An Open Source Python Package for Microwave Network Creation, Analysis, and Calibration [Speaker’s Corner]

2022 ◽  
Vol 23 (1) ◽  
pp. 98-105
Author(s):  
Alexander Arsenovic ◽  
Julien Hillairet ◽  
Jackson Anderson ◽  
Henrik Forsten ◽  
Vincent Ries ◽  
...  
Keyword(s):  
Open Source ◽  
Python Package

scholarly journals Assocplots: a python package for static and interactive visualization of multiple-group GWAS results

2016 ◽  
Author(s):  
Ekaterina A Khramtsova ◽  
Barbara E. Stranger
Keyword(s):  
Open Source ◽  
Data Visualization ◽  
Genetic Variants ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Multiple Group ◽  
Genome Wide ◽  
Quantile Plots ◽  
Python Package

AbstractSummaryOver the last decade, genome-wide association studies (GWAS) have generated vast amounts of analysis results, requiring development of novel tools for data visualization. Quantile-quantile plots and Manhattan plots are classical tools which have been utilized to visually summarize GWAS results and identify genetic variants significantly associated with traits of interest. However, static visualizations are limiting in the information that can be shown. Here we present Assocplots, a python package for viewing and exploring GWAS results not only using classic static Manhattan and quantile-quantile plots, but also through a dynamic extension which allows to visualize data interactively, and to visualize the relationships between GWAS results from multiple cohorts or studies.AvailabilityThe Assocplots package is open source and distributed under the MIT license via GitHub (https://github.com/khramts/assocplots) along with examples, documentation and installation [email protected], [email protected]

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