Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.
About 10,000 papers for “Python library”
Islam, Mohayeminul, Nadi, Sarah, Akhmetov, Ildar et al. · Figshare · 2024
Artifacts for the paper <i>Characterizing Python Library Migrations</i>.This is the archival data linked to the paper. Please visit our GitHub repository for any future updates.
Cited by 0Open access
Schrangl, Lukas · Zenodo (CERN European Organization for Nuclear Research) · 2025
Cited by 0Open access
Peixoto, Tiago P. · Figshare · 2015
Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library.
Cited by 0Open access
Tiago P. Peixoto · Figshare · 2014
Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library.
Cited by 188Open access
Peixoto, Tiago P. · Figshare · 2015
Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library.
Cited by 0Open access
Peixoto, Tiago P. · Figshare · 2014
Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library.
Cited by 0Open access
Peixoto, Tiago P. · Figshare · 2017
Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library.
Cited by 30Open access
Peixoto, Tiago P. · Figshare · 2014
Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library.
Cited by 0Open access
Giménez de Castro Marciani, Manuel · Zenodo (CERN European Organization for Nuclear Research) · 2024
This is a module to Read, Manage, and Write using Standard Workflow Format. The file is a copy of the repository as in the wrapper-impact-paper tag.
Cited by 0Open access
Peixoto, Tiago P. · Figshare · 2014
Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library.
Cited by 0Open access
Islam, Mohayeminul, Nadi, Sarah, Akhmetov, Ildar et al. · Figshare · 2024
Artifacts for the paper <i>Characterizing Python Library Migrations</i>.This is the archival data linked to the paper. Please visit our GitHub repository for any future updates.
Cited by 1Open access
Alves, Kaike Sa Teles Rocha · Zenodo (CERN European Organization for Nuclear Research) · 2025
The evolvingfuzzysystems library is a Python package that provides implementations of various Evolving Fuzzy Systems (eFS) models. These models are a class of machine learning algorithms capable of adaptively updating their structure in response to data dynamics while maintaining interpretability. The library aims to address the limited public availability of eFS model implementations, thereby fostering broader accessibility and adoption in research and practical applications. Key features and capabilities of evolvingfuzzysystems include: Implemented eFS Models: The library offers several well-established eFS models, such as ePL-KRLS-DISCO, ePL+, eMG, ePL, exTS, SimpleTS, and eTS. Adaptive Learning: eFS models can update their internal structure without requiring retraining, which is a significant advantage over traditional machine learning models in dynamic environments. They can autonomously develop their structure, capture data stream dynamics, and produce accurate results even with nonlinear data. Interpretability: eFS models offer interpretability, combining accuracy, flexibility, and simplicity. Performance Evaluation Tools: The library includes built-in tools for training, visualization, and performance assessment, facilitating model evaluation and comparison. Computational Efficiency: Models within the library implement adaptive filters like Recursive Least Squares (RLS) and Weighted Recursive Least Squares (wRLS) for estimating consequent parameters. Visualization: The library provides functions to visualize fuzzy rules and their evolution during the training phase, enhancing the interpretability of the models. This includes: show_rules(), plot_rules(), plot_gaussians(), plot_rules_evolution(), and plot_2d_projections(). Installation: The package can be easily installed using pip with the command: pip install evolvingfuzzysystems. The library evaluates its models using the California housing dataset, measuring performance with metrics like normalized root-mean-square error (NRMSE), non-dimensional error index (NDEI), and mean absolute percentage error (MAPE). Computational complexity is also analyzed by measuring execution times and rule evolution during training and testing phases. Notably, the ePL model demonstrates a balance between accuracy and computational cost, making it suitable for real-world applications.
Cited by 0Open access
Giménez de Castro Marciani, Manuel · Zenodo (CERN European Organization for Nuclear Research) · 2024
This is a module to Read, Manage, and Write using Standard Workflow Format. The file is a copy of the repository as in the wrapper-impact-paper tag.
Cited by 0Open access
Wei Wan, Zhao Lan, Jaewoo Shin et al. · 2015
Cited by 0
Simon Sez IT · 2022
The Espresso Tutorials SAP eBook Library provides access to more than 220 SAP English and German-language books and video tutorials and is updated as new books are released. Get up-to-date SAP information anytime, anywhere. Library users have access to all Espresso Tutorials SAP books and videos
Cited by 0
OSF Preprints (OSF Preprints) · 2017
Python code to measure data usually obtain with Sound Analysis Pro. The goal of this library is to allow cross-plateform, jupyter notebook compatible analysis utilities
Cited by 0
Peixoto, Tiago P. · Figshare · 2015
Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library.
Cited by 0Open access
Schrangl, Lukas · Zenodo (CERN European Organization for Nuclear Research) · 2025
Cited by 0Open access
Schrangl, Lukas · Zenodo (CERN European Organization for Nuclear Research) · 2026
Cited by 0Open access
Schrangl, Lukas · Zenodo (CERN European Organization for Nuclear Research) · 2026
sdt-python is a collection of tools for analysis of fluorescence microscopy data. It contains algorithms for localization of fluorescent features in images methods for evaluation of tracking data functions to evaluate brightness data as well as multi-color data support for automated determination and correction of chromatic aberrations methods for reading and writing single molecule data in various formats handling of ROIs (both rectangular and described by arbitrary paths) methods for simulation of fluorescence microscopy images much more.
Cited by 2Open access