Python Library for Signal/Image Data Analysis with Transport Methods

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/

You might also like...
 Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport
Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport

Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport This GitHub page provides code for reproducing the results i

Official implementation of NLOS-OT: Passive Non-Line-of-Sight Imaging Using Optimal Transport (IEEE TIP, accepted)
Official implementation of NLOS-OT: Passive Non-Line-of-Sight Imaging Using Optimal Transport (IEEE TIP, accepted)

NLOS-OT Official implementation of NLOS-OT: Passive Non-Line-of-Sight Imaging Using Optimal Transport (IEEE TIP, accepted) Description In this reposit

Universal Probability Distributions with Optimal Transport and Convex Optimization

Sylvester normalizing flows for variational inference Pytorch implementation of Sylvester normalizing flows, based on our paper: Sylvester normalizing

A general and strong 3D object detection codebase that supports more methods, datasets and tools (debugging, recording and analysis).

ALLINONE-Det ALLINONE-Det is a general and strong 3D object detection codebase built on OpenPCDet, which supports more methods, datasets and tools (de

Deep learning (neural network) based remote photoplethysmography: how to extract pulse signal from video using deep learning tools

Deep-rPPG: Camera-based pulse estimation using deep learning tools Deep learning (neural network) based remote photoplethysmography: how to extract pu

The source code of the paper "Understanding Graph Neural Networks from Graph Signal Denoising Perspectives"

GSDN-F and GSDN-EF This repository provides a reference implementation of GSDN-F and GSDN-EF as described in the paper "Understanding Graph Neural Net

Code release for the ICML 2021 paper "PixelTransformer: Sample Conditioned Signal Generation".

PixelTransformer Code release for the ICML 2021 paper "PixelTransformer: Sample Conditioned Signal Generation". Project Page Installation Please insta

Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

Deep-Learning-based-Spectrum-Sensing Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectru

A Simple LSTM-Based Solution for "Heartbeat Signal Classification and Prediction" in Tianchi

LSTM-Time-Series-Prediction A Simple LSTM-Based Solution for "Heartbeat Signal Classification and Prediction" in Tianchi Contest. The Link of the Cont

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
Releases(0.1)
Python Implementation of Chess Playing AI with variable difficulty

Chess AI with variable difficulty level implemented using the MiniMax AB-Pruning Algorithm

Ali Imran 7 Feb 20, 2022
Compact Bilinear Pooling for PyTorch

Compact Bilinear Pooling for PyTorch. This repository has a pure Python implementation of Compact Bilinear Pooling and Count Sketch for PyTorch. This

Grégoire Payen de La Garanderie 234 Dec 07, 2022
PyTorch code of my WACV 2022 paper Improving Model Generalization by Agreement of Learned Representations from Data Augmentation

Improving Model Generalization by Agreement of Learned Representations from Data Augmentation (WACV 2022) Paper ArXiv Why it matters? When data augmen

Rowel Atienza 5 Mar 04, 2022
PyTorch Implementation for AAAI'21 "Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection"

UMS for Multi-turn Response Selection Implements the model described in the following paper Do Response Selection Models Really Know What's Next? Utte

Taesun Whang 47 Nov 22, 2022
SemiNAS: Semi-Supervised Neural Architecture Search

SemiNAS: Semi-Supervised Neural Architecture Search This repository contains the code used for Semi-Supervised Neural Architecture Search, by Renqian

Renqian Luo 21 Aug 31, 2022
alfred-py: A deep learning utility library for **human**

Alfred Alfred is command line tool for deep-learning usage. if you want split an video into image frames or combine frames into a single video, then a

JinTian 800 Jan 03, 2023
Hippocampal segmentation using the UNet network for each axis

Hipposeg Hippocampal segmentation using the UNet network for each axis, inspired by https://github.com/MICLab-Unicamp/e2dhipseg Red: False Positive Gr

Juan Carlos Aguirre Arango 0 Sep 02, 2021
GNN-based Recommendation Benchma

GRecX A Fair Benchmark for GNN-based Recommendation Preliminary Comparison DiffNet-Yelp dataset (featureless) Algo 73 Oct 17, 2022

Repositorio de los Laboratorios de Análisis Numérico / Análisis Numérico I de FAMAF, UNC.

Repositorio de los Laboratorios de Análisis Numérico / Análisis Numérico I de FAMAF, UNC. Para los Laboratorios de la materia, vamos a utilizar el len

Luis Biedma 18 Dec 12, 2022
Myia prototyping

Myia Myia is a new differentiable programming language. It aims to support large scale high performance computations (e.g. linear algebra) and their g

Mila 456 Nov 07, 2022
AttGAN: Facial Attribute Editing by Only Changing What You Want (IEEE TIP 2019)

News 11 Jan 2020: We clean up the code to make it more readable! The old version is here: v1. AttGAN TIP Nov. 2019, arXiv Nov. 2017 TensorFlow impleme

Zhenliang He 568 Dec 14, 2022
Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (CVPR 2018).

Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (CVPR2018) By Zilong Huang, Xinggang Wang, Jiasi Wang, Wenyu Liu and J

Zilong Huang 245 Dec 13, 2022
Generating Fractals on Starknet with Cairo

StarknetFractals Generating the mandelbrot set on Starknet Current Implementation generates 1 pixel of the fractal per call(). It takes a few minutes

Orland0x 10 Jul 16, 2022
Photographic Image Synthesis with Cascaded Refinement Networks - Pytorch Implementation

Photographic Image Synthesis with Cascaded Refinement Networks-Pytorch (https://arxiv.org/abs/1707.09405) This is a Pytorch implementation of cascaded

Soumya Tripathy 63 Mar 27, 2022
AutoML library for deep learning

Official Website: autokeras.com AutoKeras: An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras

Keras 8.7k Jan 08, 2023
Digan - Official PyTorch implementation of Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks

DIGAN (ICLR 2022) Official PyTorch implementation of "Generating Videos with Dyn

Sihyun Yu 147 Dec 31, 2022
For AILAB: Cross Lingual Retrieval on Yelp Search Engine

Cross-lingual Information Retrieval Model for Document Search Train Phase CUDA_VISIBLE_DEVICES="0,1,2,3" \ python -m torch.distributed.launch --nproc_

Chilia Waterhouse 104 Nov 12, 2022
Generate indoor scenes with Transformers

SceneFormer: Indoor Scene Generation with Transformers Initial code release for the Sceneformer paper, contains models, train and test scripts for the

Chandan Yeshwanth 110 Dec 06, 2022
Training RNNs as Fast as CNNs

News SRU++, a new SRU variant, is released. [tech report] [blog] The experimental code and SRU++ implementation are available on the dev branch which

ASAPP Research 2.1k Jan 01, 2023
FinGAT: A Financial Graph Attention Networkto Recommend Top-K Profitable Stocks

FinGAT: A Financial Graph Attention Networkto Recommend Top-K Profitable Stocks This is our implementation for the paper: FinGAT: A Financial Graph At

Yu-Che Tsai 64 Dec 13, 2022