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

A curated list of programmatic weak supervision papers and resources

InsCLR: Improving Instance Retrieval with Self-Supervision

This repository introduces a short project about Transfer Learning for Classification of MRI Images.

Official implementation for the paper: "Multi-label Classification with Partial Annotations using Class-aware Selective Loss"

A PyTorch implementation of the baseline method in Panoptic Narrative Grounding (ICCV 2021 Oral)

This repository is a basic Machine Learning train & validation Template (Using PyTorch)

Face Mesh is a face geometry solution that estimates 468 3D face landmarks in real-time even on mobile devices

The reference baseline of final exam for XMU machine learning course

STEM: An approach to Multi-source Domain Adaptation with Guarantees

Codes and models for the paper "Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction".

I have created this Virtual Paint Program, in this you can paint(draw) on your screen using hand gestures, created in Python-3 using OpenCV and Mediapipe library. Gestures :- Index Finger for drawing and Index+Middle Finger for changing position and objects.

Simple tools for logging and visualizing, loading and training

Image Classification - A research on image classification and auto insurance claim prediction, a systematic experiments on modeling techniques and approaches

Pytorch implementation of the paper DocEnTr: An End-to-End Document Image Enhancement Transformer.

Combinatorial model of ligand-receptor binding

Tracking Pipeline helps you to solve the tracking problem more easily

这是一个yolox-pytorch的源码，可以用于训练自己的模型。

Implementation of "Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency"

Self Driving RC Car Code

Code for the AI lab course 2021/2022 of the University of Verona