Conversion between units used in magnetism

Last update: Jul 15, 2021

Overview

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:

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).
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.

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

86 Dec 25, 2022

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

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

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

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.

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

81 Dec 15, 2022

Conversion between units used in magnetism

Related tags

Overview

convmag

Installation:

Pip

Usage options:

You might also like...

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

Official implementation of "One-Shot Voice Conversion with Weight Adaptive Instance Normalization".

An evaluation toolkit for voice conversion models.

Phonetic PosteriorGram (PPG)-Based Voice Conversion (VC)

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

Implementation for "Manga Filling Style Conversion with Screentone Variational Autoencoder" (SIGGRAPH ASIA 2020 issue)

StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion

[ICCV2021] IICNet: A Generic Framework for Reversible Image Conversion

Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

Releases(v0.0.3)

v0.0.3(Jul 13, 2021)

v0.0.2(Jul 13, 2021)

v0.0.1(Jul 9, 2021)

Owner

NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-based Simulation (ACL-IJCNLP 2021)

Code Release for the paper "TriBERT: Full-body Human-centric Audio-visual Representation Learning for Visual Sound Separation"

End-to-End Object Detection with Fully Convolutional Network

Pytorch implementations of the paper Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients

4th place solution for the SIGIR 2021 challenge.

Official PyTorch implementation of the paper "TEMOS: Generating diverse human motions from textual descriptions"

An official reimplementation of the method described in the INTERSPEECH 2021 paper - Speech Resynthesis from Discrete Disentangled Self-Supervised Representations.

Help you understand Manual and w/ Clutch point while driving.

[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data

Pytorch version of SfmLearner from Tinghui Zhou et al.

PyTorch implementation of SampleRNN: An Unconditional End-to-End Neural Audio Generation Model

This is the official implementation of the paper "Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation".

Implementation of "JOKR: Joint Keypoint Representation for Unsupervised Cross-Domain Motion Retargeting"

TorchXRayVision: A library of chest X-ray datasets and models.

Optimal Camera Position for a Practical Application of Gaze Estimation on Edge Devices,

Pytorch implementation of the paper Progressive Growing of Points with Tree-structured Generators (BMVC 2021)

Implementation of Uniformer, a simple attention and 3d convolutional net that achieved SOTA in a number of video classification tasks

The codes and models in 'Gaze Estimation using Transformer'.

Real-time analysis of intracranial neurophysiology recordings.

本步态识别系统主要基于GaitSet模型进行实现