ArticleFrontiers in neuroinformatics2016
pypet: A Python Toolkit for Data Management of Parameter Explorations.
Article in Frontiers in neuroinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework.PLoS computational biology · 2024Article
- Error Function Optimization to Compare Neural Activity and Train Blended Rhythmic Networks.Brain sciences · 2024Article
- Intra-ripple frequency accommodation in an inhibitory network model for hippocampal ripple oscillations.PLoS computational biology · 2024Article
- A Spiking Neural Network Builder for Systematic Data-to-Model Workflow.Frontiers in neuroinformatics · 2022Article
- A geno-clinical decision model for the diagnosis of myelodysplastic syndromes.Blood advances · 2021Article
- Spatially structured inhibition defined by polarized parvalbumin interneuron axons promotes head direction tuning.Science advances · 2021Article
- Article
- A Serverless Tool for Platform Agnostic Computational Experiment Management.Frontiers in neuroinformatics · 2019Article
- Coding Prony's method in MATLAB and applying it to biomedical signal filtering.BMC bioinformatics · 2018Article
- Enhanced Nucleocytoplasmic Transport due to Competition for Elastic Binding Sites.Biophysical journal · 2018Article
- Arkheia: Data Management and Communication for Open Computational Neuroscience.Frontiers in neuroinformatics · 2018Article
- The Influence of Mexican Hat Recurrent Connectivity on Noise Correlations and Stimulus Encoding.Frontiers in computational neuroscience · 2017Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
pypet (Python parameter exploration toolkit) is a new multi-platform Python toolkit for managing numerical simulations. Sampling the space of model parameters is a key aspect of simulations and numerical experiments. pypet is designed to allow easy and arbitrary sampling of trajectories through a parameter space beyond simple grid searches. pypet collects and stores both simulation parameters and results in a single HDF5 file. This collective storage allows fast and convenient loading of data for further analyses. pypet provides various additional features such as multiprocessing and parallelization of simulations, dynamic loading of data, integration of git version control, and supervision of experiments via the electronic lab notebook Sumatra. pypet supports a rich set of data formats, including native Python types, Numpy and Scipy data, Pandas DataFrames, and BRIAN(2) quantities. Besides these formats, users can easily extend the toolkit to allow customized data types. pypet is a flexible tool suited for both short Python scripts and large scale projects. pypet's various features, especially the tight link between parameters and results, promote reproducible research in computational neuroscience and simulation-based disciplines.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.