pyAMNESIA
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Getting started

  • Installation
    • Install pyAMNESIA
    • Install CICADA
  • How to use
    • pyAMNESIA as a standalone tool
      • Use the CLI
      • Use pyAMNESIA’s modules
    • pyAMNESIA as a CICADA module
      • Setup
      • Use

Overview

  • Introduction
    • Motivation
    • Functionalities
  • Skeleton
    • Methods
      • Sequence denoising
      • Image skeletonization
      • In-depth skeleton analysis
      • Branch uniformity study
    • Parameter selection
      • Sequence denoising
      • Image skeletonization
      • Projection method
      • Trace processing
      • Branch validation
  • Clustering
    • Methods
      • Dimensionality reduction
      • Clustering
    • Parameter selection
      • Dimensionality reduction
      • Clustering
  • Factorization
    • Methods
      • Dimensionality reduction
      • Source separation
      • Skewness selection
      • Clipping
    • Parameter selection
      • Selecting factorization_method
      • Selecting nb_comp_intermed
      • Selecting nb_comp_final
      • Selecting skewness_threshold
  • Results and computation
    • Results
    • Computational performance

Miscellaneous

  • Configuration templates
    • Clustering templates
      • Skeleton pixels clustering
      • Skeleton branches clustering
      • Active pixels clustering
    • Factorization templates
      • NMF
      • PCA and ICA
  • Robustness
    • Parameter tuning
    • Consecutive clusterings
    • Skip skeletonization
  • Support
    • Contributing
    • License
    • Contact
pyAMNESIA
  • Docs »
  • Welcome to pyAMNESIA’s documentation!
  • Edit on GitLab

Welcome to pyAMNESIA’s documentation!¶

pyAMNESIA is a python pipeline for analysing the Activity and Morphology of NEurons using Skeletonization and other Image Analysis techniques.

Getting started

  • Installation
  • How to use

Overview

  • Introduction
    • Motivation
    • Functionalities
  • Skeleton
    • Methods
    • Parameter selection
  • Clustering
    • Methods
    • Parameter selection
  • Factorization
    • Methods
    • Parameter selection
  • Results and computation
    • Results
    • Computational performance

Miscellaneous

  • Configuration templates
    • Clustering templates
    • Factorization templates
  • Robustness
    • Parameter tuning
    • Consecutive clusterings
    • Skip skeletonization
  • Support
    • Contributing
    • License
    • Contact
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© Copyright 2020, Théo Dumont, Tom Szwagier Revision e2246c70.

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