Conversion between units used in magnetism

Related tags

Deep Learningpythonconversionunitsmagnetismmagnetic
Overview

PyPI Version Supported Python Versions

convmag

Conversion between various units used in magnetism

The conversions between base units available are:

         T  <->  G         :    1e4
         T  <->  Oe        :    1e4
       A/m  <->  T         :    MU_0
       A/m  <->  G         :    1e4 * MU_0
         G  <->  Oe        :    1
       A/m  <->  Oe        :    1e4 * MU_0
  emu/cm^3  <->  T         :    1e3 * MU_0
erg/Oecm^3  <->  A/m       :    1e3
     emu/g  <->  Am^2/kg   :    1
     J/m^3  <->  GOe       :    1e8 * MU_0
     J/m^3  <->  erg/cm^3  :    1e1
  erg/cm^3  <->  GOe       :    1e7 * MU_0
      Am^2  <->  emu       :    1e3
      Am^2  <->  erg/G     :    1e3
      Am^2  <->  erg/Oe    :    1e3
       emu  <->  erg/G     :    1
       muB  <->  Am^2      :    MU_B
       muB  <->  emu       :    1e3 * MU_B
    muB/fu  <->  T         :    requires user input of lattice parameters

(the factors given above are for the forward conversion)

  • permeability of free space, MU_0 = 4 * 3.14159 * 1e-7 H/m (== Vs/Am)

  • Bohr magneton, MU_B = 9.274015e-24 Am^2 (muB is the unit string for conversions with Bohr magnetons)

The prefactors available for any base unit are: M (1e6), k (1e3), m (1e-3), µ (1e-6)

You can combine prefactors and base units to give e.g. MA/m or kJ/m^3


Installation:

Pip

You can install the current release (0.0.3) with pip:

    pip install convmag

Usage options:

  1. a console script is provided and should be located in the Scripts directory of your Python distribution after installation. If you have this directory in your Path (environment variable on Windows) you can start the program by typing "convmag" in the console. In this case only single values can be converted (at one time).

  2. the package can be imported into python and then you can pass numpy arrays into the function convert_unit(), making sure to keep the default verbose=False. That way many values can be converted at once. The converted values are returned as a numpy array for further processing.

    >>> import numpy as np
    >>> import convmag as cm
    
    >>> vals_in_T = np.arange(0,130,20)
    
    >>> vals_in_T
    array([  0,  20,  40,  60,  80, 100, 120])
   
    >>> vals_in_Oe = cm.convert_unit(vals_in_T, "T", "Oe", verbose=False)
    
    >>> vals_in_Oe
    array([      0.,  200000.,  400000.,  600000.,  800000., 1000000., 1200000.])

Pure python, no other dependencies.

Requires Python >= 3.6 because f-strings are used

You might also like...
Implementation of Kaneko et al.'s MaskCycleGAN-VC model for non-parallel voice conversion.
Implementation of Kaneko et al.'s MaskCycleGAN-VC model for non-parallel voice conversion.

MaskCycleGAN-VC Unofficial PyTorch implementation of Kaneko et al.'s MaskCycleGAN-VC (2021) for non-parallel voice conversion. MaskCycleGAN-VC is the

null 86 Dec 25, 2022
Official implementation of
Official implementation of "One-Shot Voice Conversion with Weight Adaptive Instance Normalization".

One-Shot Voice Conversion with Weight Adaptive Instance Normalization By Shengjie Huang, Yanyan Xu*, Dengfeng Ke*, Mingjie Chen, Thomas Hain. This rep

null 31 Dec 7, 2022
An evaluation toolkit for voice conversion models.

Voice-conversion-evaluation An evaluation toolkit for voice conversion models. Sample test pair Generate the metadata for evaluating models. The direc

null 30 Aug 29, 2022
Phonetic PosteriorGram (PPG)-Based Voice Conversion (VC)

ppg-vc Phonetic PosteriorGram (PPG)-Based Voice Conversion (VC) This repo implements different kinds of PPG-based VC models. Pretrained models. More m

Liu Songxiang 227 Dec 28, 2022
Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations.
Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations.

S2VC Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations. In thi

null 81 Dec 15, 2022
Implementation for "Manga Filling Style Conversion with Screentone Variational Autoencoder" (SIGGRAPH ASIA 2020 issue)

Manga Filling with ScreenVAE SIGGRAPH ASIA 2020 | Project Website | BibTex This repository is for ScreenVAE introduced in the following paper "Manga F

null 30 Dec 24, 2022
StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion

StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion Yinghao Aaron Li, Ali Zare, Nima Mesgarani We pres

Aaron (Yinghao) Li 282 Jan 1, 2023
[ICCV2021] IICNet: A Generic Framework for Reversible Image Conversion
[ICCV2021] IICNet: A Generic Framework for Reversible Image Conversion

IICNet - Invertible Image Conversion Net Official PyTorch Implementation for IICNet: A Generic Framework for Reversible Image Conversion (ICCV2021). D

felixcheng97 55 Dec 6, 2022
Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

Core ML Tools Use coremltools to convert machine learning models from third-party libraries to the Core ML format. The Python package contains the sup

Apple 3k Jan 8, 2023
Owner
GitHub Repository
Data, notebooks, and articles associated with the RSNA AI Deep Learning Lab at RSNA 2021

RSNA AI Deep Learning Lab 2021 Intro Welcome Deep Learners! This document provides all the information you need to participate in the RSNA AI Deep Lea

RSNA 65 Dec 16, 2022
Object Detection using YOLO from PyImageSearch

Object Detection using YOLO from PyImageSearch By applying object detection, you’ll not only be able to determine what is in an image, but also where

Mohamed NIANG 1 Feb 09, 2022
Refactoring dalle-pytorch and taming-transformers for TPU VM

Text-to-Image Translation (DALL-E) for TPU in Pytorch Refactoring Taming Transformers and DALLE-pytorch for TPU VM with Pytorch Lightning Requirements

Kim, Taehoon 61 Nov 07, 2022
Official PyTorch implementation of "Improving Face Recognition with Large AgeGaps by Learning to Distinguish Children" (BMVC 2021)

Inter-Prototype (BMVC 2021): Official Project Webpage This repository provides the official PyTorch implementation of the following paper: Improving F

Jungsoo Lee 16 Jun 30, 2022
Pre-Trained Image Processing Transformer (IPT)

Pre-Trained Image Processing Transformer (IPT) By Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Cha

HUAWEI Noah's Ark Lab 332 Dec 18, 2022
A library for graph deep learning research

Documentation | Paper [JMLR] | Tutorials | Benchmarks | Examples DIG: Dive into Graphs is a turnkey library for graph deep learning research. Why DIG?

DIVE Lab, Texas A&M University 1.3k Jan 01, 2023
Repo for "Physion: Evaluating Physical Prediction from Vision in Humans and Machines" submission to NeurIPS 2021 (Datasets & Benchmarks track)

Physion: Evaluating Physical Prediction from Vision in Humans and Machines This repo contains code and data to reproduce the results in our paper, Phy

Cognitive Tools Lab 38 Jan 06, 2023
Unofficial implementation (replicates paper results!) of MINER: Multiscale Implicit Neural Representations in pytorch-lightning

MINER_pl Unofficial implementation of MINER: Multiscale Implicit Neural Representations in pytorch-lightning. 📖 Ref readings Laplacian pyramid explan

AI葵 51 Nov 28, 2022
Implementation of Convolutional LSTM in PyTorch.

ConvLSTM_pytorch This file contains the implementation of Convolutional LSTM in PyTorch made by me and DavideA. We started from this implementation an

Andrea Palazzi 1.3k Dec 29, 2022
[SDM 2022] Towards Similarity-Aware Time-Series Classification

SimTSC This is the PyTorch implementation of SDM2022 paper Towards Similarity-Aware Time-Series Classification. We propose Similarity-Aware Time-Serie

Daochen Zha 49 Dec 27, 2022
Code of the paper "Multi-Task Meta-Learning Modification with Stochastic Approximation".

Multi-Task Meta-Learning Modification with Stochastic Approximation This repository contains the code for the paper "Multi-Task Meta-Learning Modifica

Andrew 3 Jan 05, 2022
Disentangled Cycle Consistency for Highly-realistic Virtual Try-On, CVPR 2021

Disentangled Cycle Consistency for Highly-realistic Virtual Try-On, CVPR 2021 [WIP] The code for CVPR 2021 paper 'Disentangled Cycle Consistency for H

ChongjianGE 94 Dec 11, 2022
PyTorch implementation of the Flow Gaussian Mixture Model (FlowGMM) model from our paper

Flow Gaussian Mixture Model (FlowGMM) This repository contains a PyTorch implementation of the Flow Gaussian Mixture Model (FlowGMM) model from our pa

Pavel Izmailov 124 Nov 06, 2022
A library of extension and helper modules for Python's data analysis and machine learning libraries.

Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks. Sebastian Raschka 2014-2020 Links Doc

Sebastian Raschka 4.2k Jan 02, 2023
Genetic feature selection module for scikit-learn

sklearn-genetic Genetic feature selection module for scikit-learn Genetic algorithms mimic the process of natural selection to search for optimal valu

Manuel Calzolari 260 Dec 14, 2022
Baselines for TrajNet++

TrajNet++ : The Trajectory Forecasting Framework PyTorch implementation of Human Trajectory Forecasting in Crowds: A Deep Learning Perspective TrajNet

VITA lab at EPFL 183 Jan 05, 2023
Code for ICLR 2020 paper "VL-BERT: Pre-training of Generic Visual-Linguistic Representations".

VL-BERT By Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, Jifeng Dai. This repository is an official implementation of the paper VL-BERT:

Weijie Su 698 Dec 18, 2022
Official repository for "On Improving Adversarial Transferability of Vision Transformers" (2021)

Improving-Adversarial-Transferability-of-Vision-Transformers Muzammal Naseer, Kanchana Ranasinghe, Salman Khan, Fahad Khan, Fatih Porikli arxiv link A

Muzammal Naseer 47 Dec 02, 2022
Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation.

vid2vid Project | YouTube(short) | YouTube(full) | arXiv | Paper(full) Pytorch implementation for high-resolution (e.g., 2048x1024) photorealistic vid

NVIDIA Corporation 8.1k Jan 01, 2023
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Master status: Development status: Package information: TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assista

Epistasis Lab at UPenn 8.9k Dec 30, 2022