Python Library for Signal/Image Data Analysis with Transport Methods

Related tags

Deep LearningPyTransKit
Overview

PyTransKit

Python Transport Based Signal Processing Toolkit

Website and documentation: https://pytranskit.readthedocs.io/

Installation

The library could be installed through pip

pip install pytranskit

Alternately, you could clone/download the repository and add the pytranskit directory to your Python path

import sys
sys.path.append('path/to/pytranskit')

from pytranskit.optrans.continuous.cdt import CDT

Low Level Functions

CDT, SCDT

R-CDT

CLOT

  • Continuous Linear Optimal Transport Transform (CLOT) tutorial [notebook] [nbviewer]

Classification Examples

  • CDT Nearest Subspace (CDT-NS) classifier for 1D data [notebook] [nbviewer]
  • SCDT Nearest Subspace (SCDT-NS) classifier for 1D data [8] [notebook] [nbviewer]
  • Radon-CDT Nearest Subspace (RCDT-NS) classifier for 2D data [4] [notebook] [nbviewer]
  • 3D Radon-CDT Nearest Subspace (3D-RCDT-NS) classifier for 3D data [notebook] [nbviewer]

Estimation Examples

Transport-based Morphometry

  • Transport-based Morphometry to detect and visualize cell phenotype differences [7] [notebook] [nbviewer]

References

  1. The cumulative distribution transform and linear pattern classification, Applied and Computational Harmonic Analysis, November 2018
  2. The Radon Cumulative Distribution Transform and Its Application to Image Classification, IEEE Transactions on Image Processing, December 2015
  3. A continuous linear optimal transport approach for pattern analysis in image datasets, Pattern Recognition, March 2016
  4. Radon cumulative distribution transform subspace modeling for image classification, Journal of Mathematical Imaging and Vision, 2021
  5. Parametric Signal Estimation Using the Cumulative Distribution Transform, IEEE Transactions on Signal Processing, May 2020
  6. The Signed Cumulative Distribution Transform for 1-D Signal Analysis and Classification, ArXiv 2021
  7. Detecting and visualizing cell phenotype differences from microscopy images using transport-based morphometry, PNAS 2014
  8. Nearest Subspace Search in the Signed Cumulative Distribution Transform Space for 1D Signal Classification, ArXiv 2021

Resources

External website http://imagedatascience.com/transport/

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Comments
  • Problem installing `bluepy` from the repo.

    Problem installing `bluepy` from the repo.

    Problem: for my machine (machine spec mentioned below), installing requirements on this repo, as given in requirements.txt throws the following error.

    error: legacy-install-failure

× Encountered error while trying to install package.
╰─> bluepy

note: This is an issue with the package mentioned above, not pip.
hint: See above for output from the failure.

    This error is in context with mention of bluepy in requirements.txt.

    Machine Specs:

    1. miniconda venv for python 3.9.12 running on MacOS Monterey; CPU: Apple M2.
    2. miniconda venv for python 3.10.4 running on Ubuntu Jammy Jellyfish; CPU: AMD Ryzen.

    Interesting Note: I don't find bluepy being directly imported in the code on the master or the CDT-app-gui branch.

    Proposed Solution:

    1. Remove bluepy from requirements.txt

    Note: This is not a problem with installing PyTranskit itself. It installs pretty gracefully through pip.

    opened by Ujjawal-K-Panchal 1
  • Changed filter to filter_name

    Changed filter to filter_name

    In the radoncdt.py file passing the option filter was not working since scikit-image expects the key filter_name.

    Tutorial 2 was failing for this reason.

    opened by giovastabile 0
  • Create a

    Create a "NS" classifier

    Create a "NS" classifier, as an standalone implementation of the nearest subspace classification method. The "RCDT_NS" and "CDT-NS" classifier can be a subclass of this classifier.

    opened by xuwangyin 0
  • Issue when setting forward('rm_edge = True')

    Issue when setting forward('rm_edge = True')

    This possibly just needs an edit to reduce the size of the reference signal array alongside the reduction in size of the signal with removed edges.

    File "\RCDT_Basic_Tests.py", line 115, in <module>
    Irev = rcdt.inverse(Ihat, temp, nlims)

  File "\pytranskit\optrans\continuous\radoncdt.py", line 123, in inverse
    return self.apply_inverse_map(transport_map, sig0, x1_range)

  File "\pytranskit\optrans\continuous\radoncdt.py", line 235, in apply_inverse_map
    sig1_recon = match_shape2d(sig0, sig1_recon)

  File "\pytranskit\optrans\utils\data_utils.py", line 81, in match_shape2d
    raise ValueError("A is bigger than B: "

ValueError: A is bigger than B: (250, 250) vs (248, 248)
    opened by TobiasLong 0
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