Search 250 million research papers

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”

Python - Zeichnen mit der Library Turtle

Dixer · TUGraz OPEN Library (Graz University of Technology) · 2025

Dies ist eine Präsentation über die ersten Schritte mit der Library Turtle in Python.

Cited by 0Open access

Abydos NLP/IR library for Python

Little, Christopher C. · Zenodo (CERN European Organization for Nuclear Research) · 2018

Abydos is a Python library of phonetic algorithms, string distance measures & metrics, stemmers, and string fingerprinters such as Soundex, (Double) Metaphone, NYSIIS, Alpha-SIS, Beider-Morse Phonetic Matching, Levenshtein(-Damerau) distance, Jaro(-Winkler) distance, Jaccard similarity, and Pollock & Zomora's skeleton & omission keys.

Cited by 0Open access

NumCu: Numerical CUDA-based Python library

da Costa-Luis, Casper O. · Zenodo (CERN European Organization for Nuclear Research) · 2023

Numerical CUDA-based Python library built on CuVec

Cited by 0Open access

Formulate: a python library for formulation

Brunet Luc · 2021

Formulate is a library to build and manipulate formulations. It can be use for materials, cosmetics or any activities involving mixing of components. This version computes oxygen balance, eutectic points, equilibrium temperature by deep learning. The purpose of this library is allowing a way to build deep learning datasets for materials and formulations.

Cited by 0

Python Parameter Value Language (PVL) Library

R. A. Beyer · 2021

Cited by 0

NumCu: Numerical CUDA-based Python library

da Costa-Luis, Casper O. · Zenodo (CERN European Organization for Nuclear Research) · 2025

Numerical CUDA-based Python library built on CuVec

Cited by 0Open access

PyTensor: A Python based Tensor Library

Ji Oh Yoo, Aravind Ramanathan, Christopher J. Langmead · Figshare · 2018

Multi-dimensional (or multi-way) analysis have evinced considerable interest within the datamining community. In this technical report, we describe the development and implementation of a python based library to perform multi-way analysis. Our results show that the python implementation runs comparable to the MATLAB implementation. The implementation is available from http://pytensor.googlecode.com/.

Cited by 6Open access

pymnet: A Python Library for MultilayerNetworks

Tarmo Nurmi, Arash Badie-Modiri, Corinna Coupette et al. · The Journal of Open Source Software · 2024

Many complex systems can be readily modeled as networks and represented as graphs. Such systems include social interactions, transport infrastructures, biological pathways, brains, ecosystems, and many more. A major advantage of representing complex systems as graphs is that the same graph tools and methods can be applied in a wide variety of domains. However, the graph representation has its limitations: many systems contain nodes with multidimensional features, interactions of various types, different levels of hierarchy, or multiple modalities, which deserve to be modeled but cannot be described by simple graphs. Multilayer networks (Kivelä et al., 2014) generalize graphs to capture the rich network data often associated with complex systems, allowing us to study a broad range of phenomena using the same representations, tools, and methods. With pymnet, we introduce a Python package that provides the essential data structures and computational tools for multilayer-network analysis. As highlights, the library offers efficient and scalable implementations for sparse multilayer networks and multiplex networks, integration with bliss to analyze multilayernetwork isomorphisms and automorphisms, and versatile methods for multilayer-network visualization.

Cited by 4Open access

Online Python Library for Impedance Processing

Garg, Rishabh · Figshare · 2024

The present method consists of using impedance.py python library for fitting the circuit directly to the lab data. Accuracy metrics are yet to be improvised by adjusting the circuit model.

Cited by 0Open access

Pupil-Lib Trial Extraction Library - Python

Mierzwinski, W. Gregory · Zenodo (CERN European Organization for Nuclear Research) · 2019

This is a python library for performing trial extraction on data obtained from the Pupil Labs [1] eye trackers in combination with Lab Streaming Layer (LSL) [2] for high-precision event-based research experiments. The extracted trials have millisecond precision as the errors in synchronization between markers and data are based entirely on LSL's precision. See https://github.com/gmierz/pupil-lib-python for more info on usage and development. See this page for the Matlab version (no longer updated): https://github.com/gmierz/pupil-lib-python v1.2.0: See release information here, Pupil LSL Relay v2.0 is now supported: https://github.com/gmierz/pupil-lib/releases/tag/v1.2.0 v1.1.0: See release information here, main update is the new MATLAB output support: https://github.com/gmierz/pupil-lib/releases/tag/v1.1.0 v1.0.0: Contains various bug fixes, stronger CSV exporting, and a better command-line tool. It also simplifies processing trials with the pre/post-processor functions. [1]: https://pupil-labs.com/ [2]: https://github.com/sccn/labstreaminglayer

Cited by 0Open access

formulate A python library for formulations

Brunet, Luc Emile · 2021

Cited by 0Open access

DMP library for Matlab and Python

Gašpar, Timotej, Pahič, Rok, Deniša, Miha · Zenodo (CERN European Organization for Nuclear Research) · 2020

Joint and Cartesian space Dynamic Movement Primitives (DMP) library for Matlab and Python

Cited by 0Open access

OHBA Software Library in Python (OSL)

Quinn, Andrew J., van Es, M.W.J., Gohil, Chetan et al. · Zenodo (CERN European Organization for Nuclear Research) · 2022

The OHBA Software Library (OSL) is created by the OHBA Analysis Group, OHBA, University of Oxford, UK. OSL is a Python package for running M/EEG analysis, and it is the successor of the MATLAB OSL package. OSL in Python integrates well-known software packages like MNE-Python and FSL into its own analysis functions and design philosophy. At the core of OSL sits a configuration user interface, which allows the user to set up a pipeline for processing the data in the most concise way, without losing any flexibility. The config structure is typically on the size of one to two dozen lines of code that capture an entire (pre-) processing pipeline, and can easily be shared for reproducibility and replicability. Moreover, it contains tools for batch and parallel processing, as well as a set of reporting tools for quality control of processed (batch) data. OSL is under constant development to incorporate new functionalities. The most recent version of OSL can always be found on GitHub; this is also where issue reporting and feature requests take place. Version 0.1.1 of OSL has the following features: Interface to ELEKTA's Maxfilter software Preprocessing of M/EEG data FSL-based surface extraction and coregistration of M/EEG, headshape, and sMRI data Volumetric source reconstruction, including parcellation and spatial leakage reduction via orthogonalisation Batch processing using functions from OSL, MNE-Python, and custom written functions Visualizing data and creating reports of processed data Utilities for parallel processing, file handling, etc.

Cited by 11Open access

ART (Actually Robust Training) Python Library

Chwilczyński, Sebastian, Trębacz, Kacper, Cyganik, Karol et al. · Zenodo (CERN European Organization for Nuclear Research) · 2024

This Zenodo entry contains the code of the official release of ART, a Python library that teaches good practices when training deep neural networks with PyTorch. ART encourages the user to train DNNs through a series of steps that ensure the correctness and robustness of their training pipeline. The steps implemented using ART can be viewed not only as guidance for early adepts of deep learning but also as a project template and checklist for more advanced users.

Cited by 0Open access

Pantry: A Macro Library for Python

Pang, Derek · San José State University ScholarWorks (San Jose State University) · 2018

Python lacks a simple way to create custom syntax and constructs that goes outside of its own syntax rules. A paradigm that allows for these possibilities to exist within languages is macros. Macros allow for a shorter set of syntax to expand into a longer set of instructions at compile-time. This gives the capability to evolve the language to fit personal needs.\nPantry, implements a hygienic text-substitution macro system for Python. Pantry achieves this through the introduction of an additional preparsing step that utilizes parsing and lexing of the source code. Pantry proposes a way to simply declare a pattern to be recognized, articulate instructions that replace the pattern, and replace the pattern in the source code. This form of meta-programming allows its users to be able to more concisely write their Python code and present the language in a more natural and intuitive manner.\nWe validate Pantry’s utility through use cases inspired by Python Enhancement Proposals (PEPs) and go through five of them. These are requests from the Python community for features to be implemented into Python. Pantry fulfills these desires through the composition of macros that that performs the new feature.

Cited by 1Open access

frictionless: Python library for Data Packages

Karev, Evgeny, Camilleri, Pierre, Baptista, Vitor et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023

Data management framework for Python that provides functionality to describe, extract, validate, and transform tabular data

Cited by 0Open access

pyEQL: A python library for water chemistry

Kingsbury, Ryan · Zenodo (CERN European Organization for Nuclear Research) · 2024

Version 1.2.0 See CHANGELOG for a detailed explanation of changes.  What's Changed 🐛 Bug Fixes database fixes by @rkingsbury in https://github.com/KingsburyLab/pyEQL/pull/190 🤷‍♂️ Other Changes pymatgen>=2024.9.10 updated after bugfix and conflict resolve by @abhardwaj73 in https://github.com/KingsburyLab/pyEQL/pull/188 Full Changelog: https://github.com/KingsburyLab/pyEQL/compare/v1.1.6...v1.2.0

Cited by 0Open access

Supplementary files for the dingo Python library

Apostolos Chalkis, Vissarion Fisikopoulos, Elias Tsigaridas et al. · Zenodo (CERN European Organization for Nuclear Research) · 2022

We compare the Multiphase Monte Carlo flux Sampling (MMCS) feature of the dingo library against the combined method of PolyRound (for rounding) followed by hopsy (for sampling) on a set of 7 models with a ranging dimension (ext_data.zip). The simpl_transf_polytopes.zip contains the polytopes retrieved after the simplify() and transform() functions of the PolyRound library. These polytopes were used as input for the dingo implementation of the MMCS algorithm asking for an ESS of 1000. Under the dingo_samples_on_simpl_transf_polytopes.zip the resulting samples from dingo can be found. Similarly, polyrounded_polytopes.zip contains the polytopes retrieved after applying simplify(), transform() and round() functions of the PolyRound library. These polytopes were used as input for the hopsy library, again, asking for an ESS of 1000. The hopsy_samples.zip folder contains the resulting samples from hopsy library, using a thinning of 100d ; only in the case of Recon3D a thinning of 200d was used as suggested by the authors. Under the hopsy_samples_ess_1000.zip folder, we provide the hopsy samples with an ESS of 1000. Further, the sars_samples.zip file contains dingo samples from the solution space of the SARS-CoV-2 integrated model of Renz et al (2020) for the following cases: unbiased; where the zero vector has been used as the objective function of the model after maximising for the human biomass after maximising for the virus biomass objective function (VBOF) The following Python scripts to perform these experiments are included: polyround_preproces.py : runs the PolyRound functions and builds the simplified and transformed polytopes that dingo will use as well as the simplified, transformed and rounded polytopes hopsy uses hopsy_on_polyrounded_polytopes.py : performs sampling with hopsy dingo_on_simpl_transf_polytopes.py : performs sampling with dingo binary_search.py : a function to return the index in the chain where ESS becomes 1000 compute_ess.py: based on a model's hopsy samples (under the hopsy_samples.zip folder) it retrieves the samples with an ESS of 1000 and the corresponding required time for hopsy to build them. The script requires the total time of the hopsy experiment recorded in the model's corresponding .txt file (you can find this under the hopsy_samples.zip) A notebook is available for how the integrated model was sampled.

Cited by 0Open access

Supplementary files for the dingo Python library

Apostolos Chalkis, Vissarion Fisikopoulos, Elias Tsigaridas et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023

We compare the Multiphase Monte Carlo flux Sampling (MMCS) feature of the dingo library against the combined method of PolyRound (for rounding) followed by hopsy (for sampling) on a set of 7 models with a ranging dimension (ext_data.zip). The simpl_transf_polytopes.zip contains the polytopes retrieved after the simplify() and transform() functions of the PolyRound library. These polytopes were used as input for the dingo implementation of the MMCS algorithm asking for an ESS of 1000. Under the dingo_samples_on_simpl_transf_polytopes.zip the resulting samples from dingo can be found using the MMCS algorithm. Similarly, polyrounded_polytopes.zip contains the polytopes retrieved after applying simplify(), transform() and round() functions of the PolyRound library. These polytopes were used as input for the hopsy library, again, asking for an ESS of 1000. The hopsy_samples.zip folder contains the resulting samples from hopsy library, using a thinning of 100d ; only in the case of Recon3D a thinning of 200d was used as suggested by the authors. Under the hopsy_samples_ess_1000.zip folder, we provide the hopsy samples with an ESS of 1000. Last, the samples produced using the efficient Billiard Walk implementation of dingo can be found under the BWRsamples.zip file. In this case, 20000 points were sampled for each model. Further, the sars_samples.zip file contains dingo samples from the solution space of the SARS-CoV-2 integrated model of Renz et al (2020) for the following cases: unbiased; where the zero vector has been used as the objective function of the model after maximising for the human biomass after maximising for the virus biomass objective function (VBOF) The following Python scripts to perform these experiments are included: polyround_preproces.py : runs the PolyRound functions and builds the simplified and transformed polytopes that dingo will use as well as the simplified, transformed and rounded polytopes hopsy uses hopsy_on_polyrounded_polytopes.py : performs sampling with hopsy dingo_on_simpl_transf_polytopes.py : performs sampling with dingo run_bwr_exp.py computes samples using the efficient Billiard Walk of dingo binary_search.py : a function to return the index in the chain where ESS becomes 1000 compute_ess.py: based on a model's hopsy samples (under the hopsy_samples.zip folder) it retrieves the samples with an ESS of 1000 and the corresponding required time for hopsy to build them. The script requires the total time of the hopsy experiment recorded in the model's corresponding .txt file (you can find this under the hopsy_samples.zip) compute_ess_psrf_per_phase.py computes ESS and PSRF in specific indices which correspond to those when MMCS switches from a phase a to next one A notebook is available for how the integrated model was sampled.

Cited by 0Open access

pymnet: A Python Library for Multilayer Networks

Nurmi, Tarmo, Badie-Modiri, Arash, Coupette, Corinna et al. · Zenodo (CERN European Organization for Nuclear Research) · 2024

This repository includes the manuscript submission archival version of pymnet, a Python library for multilayer networks.

Cited by 0Open access