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About 272 papers for “Construct (python library)”
Jauregui, Christian · Figshare · 2020
Python package designed to construct and replicate datasets from Ken French's online library (https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) by accessing WRDS remotely through its cloud server "wrds-cloud".<br>Efficient performance results from features such as the use of least recently used (LRU) caching.
Cited by 0Open access
Jauregui, Christian · Figshare · 2020
Python package designed to construct and replicate datasets from Ken French's online library (https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) by accessing WRDS remotely through its cloud server "wrds-cloud".<br>Efficient performance results from features such as the use of least recently used (LRU) caching.
Cited by 0Open access
Jauregui, Christian · Figshare · 2020
Python package designed to construct and replicate datasets from Ken French's online library (https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) by accessing WRDS remotely through its cloud server "wrds-cloud".<br>Efficient performance results from features such as the use of least recently used (LRU) caching.
Cited by 0Open access
Jauregui, Christian · Figshare · 2020
Python package designed to construct and replicate datasets from Ken French's online library (https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) by accessing WRDS remotely through its cloud server "wrds-cloud". Efficient performance results from features such as the use of least recently used (LRU) caching. The current release (as of April 20, 2020) is the initial release: Release 0.1.0
Cited by 0Open access
Jauregui, Christian · Figshare · 2020
Python package designed to construct and replicate datasets from Ken French's online library (https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) by accessing WRDS remotely through its cloud server "wrds-cloud". Efficient performance results from features such as the use of least recently used (LRU) caching. The current release (as of April 20, 2020) is the initial release: Release 0.1.0
Cited by 0Open access
Jauregui, Christian · Figshare · 2020
Python package designed to construct and replicate datasets from Ken French's online library (https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) by accessing WRDS remotely through its cloud server "wrds-cloud". Efficient performance results from features such as the use of least recently used (LRU) caching. The current release (as of April 20, 2020) is the initial release: Release 0.1.0
Cited by 0Open access
Ramskyi, Ihor · ErUCU (Electronic repository of the Ukrainian Catholic University) (Ukrainian Catholic University) · 2023
This thesis centers on the exploration of Electronic Data Interchange (EDI) data for- \nmats, with an emphasis on EDIFACT. Recognized for its complexity and arduous- \nness in utilization, the objective of this research is to construct an efficient and user- \nfriendly Python library for the facilitation of EDIFACT file manipulation. This li- \nbrary is designed with capabilities to parse, validate, and subsequently convert ED- \nIFACT files into contemporary data formats such as Extensible Markup Language \n(XML) and JavaScript Object Notation (JSON). Furthermore, this thesis provides a \ncomprehensive review of comparable open-source tools currently available.
Cited by 0
Association for Computational Linguistics 2024, Finlayson, Mark, Hummer, Jared et al. · Underline Science Inc. · 2024
pyTLEX is an implementation of the TimeLine EXtraction algorithm (TLEX) that enables users to work with TimeML annotations and perform advanced temporal analysis, offering a comprehensive suite of features. TimeML is a standardized markup language for temporal information in text. pyTLEX allows users to parse TimeML annotations, construct TimeML graphs, and execute the TLEX algorithm to effect complete timeline extraction. In contrast to previous implementations (i.e., jTLEX for Java), pyTLEX sets itself apart with a range of advanced features. It introduces a React-based visualization system, enhancing the exploration of temporal data and the comprehension of temporal connections within textual information. Furthermore, pyTLEX incorporates an algorithm for increasing connectivity in temporal graphs, which identifies graph disconnectivity and recommends links based on temporal reasoning, thus enhancing the coherence of the graph representation. Additionally, pyTLEX includes a built-in validation algorithm, ensuring compliance with TimeML annotation guidelines, which is essential for maintaining data quality and reliability. pyTLEX equips researchers and developers with an extensive toolkit for temporal analysis, and its testing across various datasets validates its accuracy and reliability.
Cited by 0Open access
Criado, Juan C. · Data Archiving and Networked Services (DANS) · 2018
MatchingTools is a Python library for doing symbolic calculations in effective field theory. It provides the tools to construct general models by defining their field content and their interaction Lagrangian. Once a model is given, the heavy particles can be integrated out at the tree level to obtain an effective Lagrangian in which only the light particles appear. After integration, some of the terms of the resulting Lagrangian might not be independent. MatchingTools contains functions for transforming these terms to rewrite them in terms of any chosen set of operators.
Cited by 0Open access
David Bieber, Kensen Shi, Petros Maniatis et al. · arXiv (Cornell University) · 2022
Graph representations of programs are commonly a central element of machine learning for code research. We introduce an open source Python library python_graphs that applies static analysis to construct graph representations of Python programs suitable for training machine learning models. Our library admits the construction of control-flow graphs, data-flow graphs, and composite ``program graphs'' that combine control-flow, data-flow, syntactic, and lexical information about a program. We present the capabilities and limitations of the library, perform a case study applying the library to millions of competitive programming submissions, and showcase the library's utility for machine learning research.
Cited by 1Open access
A. Renzini, A. Romero, C. Talbot et al. · The Astrophysical Journal · 2023
Abstract The collection of gravitational waves (GWs) that are either too weak or too numerous to be individually resolved is commonly referred to as the gravitational-wave background (GWB). A confident detection and model-driven characterization of such a signal will provide invaluable information about the evolution of the universe and the population of GW sources within it. We present a new, user-friendly, Python-based package for GW data analysis to search for an isotropic GWB in ground-based interferometer data. We employ cross-correlation spectra of GW detector pairs to construct an optimal estimator of the Gaussian and isotropic GWB, and Bayesian parameter estimation to constrain GWB models. The modularity and clarity of the code allow for both a shallow learning curve and flexibility in adjusting the analysis to one’s own needs. We describe the individual modules that make up pygwb , following the traditional steps of stochastic analyses carried out within the LIGO, Virgo, and KAGRA Collaboration. We then describe the built-in pipeline that combines the different modules and validate it with both mock data and real GW data from the O3 Advanced LIGO and Virgo observing run. We successfully recover all mock data injections and reproduce published results.
Cited by 27Open access
Halvani, Oren · Zenodo (CERN European Organization for Nuclear Research) · 2023
Constituent Treelib (CTL) allows you to easily construct a constituent tree representation of sentences, visualize them and export them into various file formats. Moreover, you can conveniently extract phrases according to their phrasal categories, which can be used, for example, as features for various NLP tasks. CTL is built on top of benepar (Berkeley Neural Parser) as well as the two well-known NLP frameworks spaCy and NLTK.
Cited by 0Open access
Pavlin G. Poličar, Martin Stražar, Blaž Zupan · bioRxiv (Cold Spring Harbor Laboratory) · 2019
Abstract Summary Point-based visualisations of large, multi-dimensional data from molecular biology can reveal meaningful clusters. One of the most popular techniques to construct such visualisations is t-distributed stochastic neighbor embedding (t-SNE), for which a number of extensions have recently been proposed to address issues of scalability and the quality of the resulting visualisations. We introduce openTSNE, a modular Python library that implements the core t-SNE algorithm and its extensions. The library is orders of magnitude faster than existing popular implementations, including those from scikit-learn. Unique to openTSNE is also the mapping of new data to existing embeddings, which can surprisingly assist in solving batch effects. Availability openTSNE is available at https://github.com/pavlin-policar/openTSNE . Contact pavlin.policar@fri.uni-lj.si , blaz.zupan@fri.uni-lj.si
Cited by 173Open access
Jihong Quan, Jiyue Quan · Applied Mathematics and Nonlinear Sciences · 2024
Abstract Nowadays, with the popularization of online education how to efficiently and accurately obtain the required educational resources from web pages is one of the key concerns in the teaching process. Based on the principle of maximum distance, the study improves the K-mean and establishes a learner group feature model based on the DM-K-mean clustering algorithm. In addition, for the problem that the importance of learning resources to learners changes with time, this paper integrates the time information into the neural collaborative filtering algorithm through the clustering classification algorithm. It proposes a deep learning-based recommendation algorithm for Korean language teaching resources. Python crawler technology is used to obtain relevant experimental data from the online teaching platform to verify the performance of the proposed model, so as to construct the Korean language teaching resource base. The learner group characteristic model classifies the sample students into three categories: excellent (0.489), good (0.307), and average (0.204) learning situations. The HR and NDCG values of the Improved NeuMF resource recommendation model have been improved by 3.6% and 2.2% compared to the NeuCF model, respectively, and the performance is optimal under various factors. The proposed system for teaching Korean language resources in this paper can recommend resources based on learner profiles to help learners access teaching resources and improve efficiency.
Cited by 2Open access
Huashan Lei, Guanping Xiao, Yepang Liu et al. · ArXiv.org · 2025
Python third-party libraries (TPLs) are essential in modern software development, but upgrades often cause compatibility issues, leading to system failures. These issues fall into two categories: version compatibility issues (VCIs) and code compatibility issues (CCIs). Existing tools mainly detect dependency conflicts but overlook code-level incompatibilities, with no solution fully automating the inference of compatible versions for both VCIs and CCIs. To fill this gap, we propose PCREQ, the first approach to automatically infer compatible requirements by combining version and code compatibility analysis. PCREQ integrates six modules: knowledge acquisition, version compatibility assessment, invoked APIs and modules extraction, code compatibility assessment, version change, and missing TPL completion. PCREQ collects candidate versions, checks for conflicts, identifies API usage, evaluates code compatibility, and iteratively adjusts versions to generate a compatible requirements.txt with a detailed repair report. To evaluate PCREQ, we construct REQBench, a large-scale benchmark with 2,095 upgrade test cases (including 406 unsolvable by pip). Results show PCREQ achieves a 94.03% inference success rate, outperforming PyEGo (37.02%), ReadPyE (37.16%), and LLM-based approaches (GPT-4o, DeepSeek V3/R1) by 18-20%. PCREQ processes each case from REQBench in 60.79s on average, demonstrating practical efficiency. PCREQ significantly reduces manual effort in troubleshooting upgrades, advancing Python dependency maintenance automation.
Cited by 0Open access
Matthew Tien, Dariya K. Sydykova, Austin G. Meyer et al. · 2013
We present a simple Python library to construct models of polypeptides from scratch. The intended use case is the generation of peptide models with pre-specified backbone angles. For example, using our library, one can generate a model of a set of amino acids in a specific conformation using just a few lines of python code. We do not provide any tools for energy minimization or rotamer packing, since powerful tools are available for these purposes. Instead, we provide a simple Python interface that enables one to add residues to a peptide chain in any desired conformation. Bond angles and bond lengths can be manipulated if so desired, and reasonable values are used by default.
Cited by 0Open access
David Bonet, M Cara, Míriam Barrabés et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2026
The increasing size and resolution of genomic and population genetic datasets offer unprecedented opportunities to study population structure and uncover the genetic basis of complex traits and diseases. The collection of existing analytical tools, however, is characterized by format incompatibilities, limited functionality, and computational inefficiencies, forcing researchers to construct fragile pipelines that chain together fragmented command-line utilities and ad hoc scripts. These are difficult to maintain, scale, and reproduce. To address such limitations, we present snputils, a Python library that unifies high-performance I/O, transformation, and analysis of genotype, ancestry, and phenotypic information within a single framework suitable for biobank-scale research. The library provides efficient tools for essential operations, including querying, cleaning, merging, and statistical analysis. In addition, it offers classical population genetic statistics with optional ancestry-specific masking. An identity-by-descent module supports reading of multiple formats, filtering and ancestry-restricted segment trimming for relatedness and demographic inference. snputils also incorporates ancestry-masking and multi-array functionalities for dimensionality reduction methods, as well as efficient implementations of admixture simulation, admixture mapping, and advanced visualization capabilities. With support for the most commonly used file formats, snputils integrates smoothly with existing tools and clinical databases. At the same time, its modular and optimized design reduces technical overhead, facilitating reproducible workflows that accelerate discoveries in population genetics, genomic research, and precision medicine. Benchmarking demonstrates a significant reduction in genotype data loading speed compared to existing Python libraries. The open-source library is available at https://github.com/AI-sandbox/snputils, with full documentation and tutorials at https://snputils.org.
Cited by 1Open access
Nikolaus Carl Awtrey, Oliver Beckstein · bioRxiv (Cold Spring Harbor Laboratory) · 2024
Kinetic diagrams are commonly used to represent biochemical systems in order to study phenomena such as free energy transduction and ion selectivity. While numerical methods are commonly used to analyze such kinetic networks, the diagram method by King, Altman and Hill makes it possible to construct exact algebraic expressions for steady-state observables in terms of the rate constants of the kinetic diagram. However, manually obtaining these expressions becomes infeasible for models of even modest complexity as the number of the required intermediate diagrams grows with the factorial of the number of states in the diagram. We developed <i>Kinetic Diagram Analysis</i> (KDA), a Python library that programmatically generates the relevant diagrams and expressions from a user-defined kinetic diagram. KDA outputs symbolic expressions for state probabilities and cycle fluxes at steady-state that can be symbolically manipulated and evaluated to quantify macroscopic system observables. We demonstrate the KDA approach for examples drawn from the biophysics of active secondary transmembrane transporters. For a generic 6-state antiporter model, we show how the introduction of a single leakage transition reduces transport efficiency by quantifying substrate turnover. We apply KDA to a real-world example, the 8-state free exchange model of the small multidrug resistance transporter EmrE of Hussey et al (<i>J General Physiology</i> <b>152</b> (2020), e201912437), where a change in transporter phenotype is achieved by biasing two different subsets of kinetic rates: alternating access and substrate unbinding rates. KDA is made available as open source software under the GNU General Public License version 3.
Cited by 0Open access
Dietmüller, Simone, Matthes, Sigrun, Dahlmann, Katrin et al. · tub.dok (Hamburg University of Technology) · 2023
Aviation aims to reduce its climate effect by adopting trajectories that avoid regions of the atmosphere where aviation emissions have a large impact. To that end, prototype algorithmic climate change functions (aCCFs) can be used, which provide spatially and temporally resolved information on aviation's climate effect in terms of future near-surface temperature change. These aCCFs can be calculated with meteorological input data obtained from, e.g., numerical weather prediction models. We present here the open-source Python library called CLIMaCCF, an easy-to-use and flexible tool which efficiently calculates both the individual aCCFs (i.e., aCCF of water vapor, nitrogen oxide (NOx)-induced ozone production and methane depletion, and contrail cirrus) and the merged non-CO2 aCCFs that combine all these individual contributions. To construct merged aCCFs all individual aCCFs are converted to the same physical unit. This unit conversion needs the technical specification of aircraft and engine parameters, i.e., NOx emission indices and flown distance per kilogram of burned fuel. These aircraft- and engine-specific values are provided within CLIMaCCF version V1.0 for a set of aggregated aircraft and engine classes (i.e., regional, single-aisle, wide-body). Moreover, CLIMaCCF allows the user to choose from a range of physical climate metrics (i.e., average temperature response for pulse or future scenario emissions over the time horizons of 20, 50, or 100 years). Finally, we demonstrate the abilities of CLIMaCCF through a series of example applications.
Cited by 0Open access
Dariya K. Sydykova, Austin G. Meyer, Claus O. Wilke · 2014
PeptideBuilder: A simple Python library to generate model peptides We present a simple Python library to construct models of polypeptides from scratch. The intended use case is the generation of peptide models with pre-specified backbone angles. For example, using our library, one can generate a model of a set of amino acids in a specific conformation using just a few Reviewing Manuscript lines of python code. We do not provide any tools for energy minimization or rotamer packing, since powerful tools are available for these purposes. Instead, we provide a simple Python interface that enables one to add residues to a peptide chain in any desired conformation. Bond angles and bond lengths can be manipulated if so desired, and reasonable values are used by default.
Cited by 0