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
CarND-LaneLines-P1 - Lane Finding Project for Self-Driving Car ND

Finding Lane Lines on the Road Overview When we drive, we use our eyes to decide where to go. The lines on the road that show us where the lanes are a

Udacity 769 Dec 27, 2022
Genetic Programming in Python, with a scikit-learn inspired API

Welcome to gplearn! gplearn implements Genetic Programming in Python, with a scikit-learn inspired and compatible API. While Genetic Programming (GP)

Trevor Stephens 1.3k Jan 03, 2023
Asynchronous Advantage Actor-Critic in PyTorch

Asynchronous Advantage Actor-Critic in PyTorch This is PyTorch implementation of A3C as described in Asynchronous Methods for Deep Reinforcement Learn

Reiji Hatsugai 38 Dec 12, 2022
Pytorch code for our paper "Feedback Network for Image Super-Resolution" (CVPR2019)

Feedback Network for Image Super-Resolution [arXiv] [CVF] [Poster] Update: Our proposed Gated Multiple Feedback Network (GMFN) will appear in BMVC2019

Zhen Li 539 Jan 06, 2023
PiRank: Learning to Rank via Differentiable Sorting

PiRank: Learning to Rank via Differentiable Sorting This repository provides a reference implementation for learning PiRank-based models as described

54 Dec 17, 2022
PyTorch inference for "Progressive Growing of GANs" with CelebA snapshot

Progressive Growing of GANs inference in PyTorch with CelebA training snapshot Description This is an inference sample written in PyTorch of the origi

320 Nov 21, 2022
Flaxformer: transformer architectures in JAX/Flax

Flaxformer is a transformer library for primarily NLP and multimodal research at Google.

Google 116 Jan 05, 2023
Revisting Open World Object Detection

Revisting Open World Object Detection Installation See INSTALL.md. Dataset Our n

58 Dec 23, 2022
Face Recognition and Emotion Detector Device

Face Recognition and Emotion Detector Device Orange PI 1 Python 3.10.0 + Django 3.2.9 Project's file explanation Django manage.py Django commands hand

BootyAss 2 Dec 21, 2021
Clean and readable code for Decision Transformer: Reinforcement Learning via Sequence Modeling

Minimal implementation of Decision Transformer: Reinforcement Learning via Sequence Modeling in PyTorch for mujoco control tasks in OpenAI gym

Nikhil Barhate 104 Jan 06, 2023
Local Attention - Flax module for Jax

Local Attention - Flax Autoregressive Local Attention - Flax module for Jax Install $ pip install local-attention-flax Usage from jax import random fr

Phil Wang 16 Jun 16, 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
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.

The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate. Website • Key Features • How To Use • Docs •

Pytorch Lightning 21.1k Jan 08, 2023
Mixed Transformer UNet for Medical Image Segmentation

MT-UNet Update 2022/01/05 By another round of training based on previous weights, our model also achieved a better performance on ACDC (91.61% DSC). W

dotman 92 Dec 25, 2022
NL-Augmenter 🦎 → 🐍 A Collaborative Repository of Natural Language Transformations

NL-Augmenter 🦎 → 🐍 The NL-Augmenter is a collaborative effort intended to add transformations of datasets dealing with natural language. Transformat

684 Jan 09, 2023
This is a collection of our NAS and Vision Transformer work.

AutoML - Neural Architecture Search This is a collection of our AutoML-NAS work iRPE (NEW): Rethinking and Improving Relative Position Encoding for Vi

Microsoft 832 Jan 08, 2023
An MQA (Studio, originalSampleRate) identifier for lossless flac files written in Python.

An MQA (Studio, originalSampleRate) identifier for "lossless" flac files written in Python.

Daniel 10 Oct 03, 2022
CUAD

Contract Understanding Atticus Dataset This repository contains code for the Contract Understanding Atticus Dataset (CUAD), a dataset for legal contra

The Atticus Project 273 Dec 17, 2022
🌎 The Modern Declarative Data Flow Framework for the AI Empowered Generation.

🌎 JSONClasses JSONClasses is a declarative data flow pipeline and data graph framework. Official Website: https://www.jsonclasses.com Official Docume

Fillmula Inc. 53 Dec 09, 2022
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

ONNX Runtime is a cross-platform inference and training machine-learning accelerator. ONNX Runtime inference can enable faster customer experiences an

Microsoft 8k Jan 04, 2023