Python package for the analysis and visualisation of finite-difference fields.

Overview

discretisedfield

Marijan Beg1,2, Martin Lang2, Samuel Holt3, Ryan A. Pepper4, Hans Fangohr2,5,6

1 Department of Earth Science and Engineering, Imperial College London, London SW7 2AZ, UK
2 Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK
3 Department of Physics, University of Warwick, Coventry CV4 7AL, UK
4 Research Software Group, University of Birmingham, Birmingham B15 2TT, UK
5 Max Planck Institute for the Structure and Dynamics of Matter, Luruper Chaussee 149, 22761 Hamburg, Germany
6 Center for Free-Electron Laser Science, Luruper Chaussee 149, 22761 Hamburg, Germany

Description Badge
Tests Build status
conda
Releases PyPI version
Anaconda-Server Badge
Coverage codecov
Documentation Documentation
YouTube YouTube
Binder Binder
Platforms Platforms
Downloads Downloads
License License
DOI DOI

About

discretisedfield is a Python package, integrated with Jupyter, providing:

  • definition of finite-difference regions, meshes, lines, and fields,

  • analysis of finite-difference fields,

  • visualisation using matplotlib and k3d, and

  • manipulation of different file types (OVF, VTK, and HDF5).

It is available on Windows, MacOS, and Linux. It requires Python 3.8+.

Documentation

APIs and tutorials are available in the documentation. To access the documentation, use the badge in the table above.

Installation, testing, and upgrade

We recommend installation using conda package manager. Instructions can be found in the documentation.

Binder

This package can be used in the cloud via Binder. To access Binder, use the badge in the table above.

YouTube

YouTube video tutorials are available on the Ubermag channel.

Support

If you require support, have questions, want to report a bug, or want to suggest an improvement, please raise an issue in ubermag/help repository.

Contributions

All contributions are welcome, however small they are. If you would like to contribute, please fork the repository and create a pull request. If you are not sure how to contribute, please contact us by raising an issue in ubermag/help repository, and we are going to help you get started and assist you on the way.

Contributors:

License

Licensed under the BSD 3-Clause "New" or "Revised" License. For details, please refer to the LICENSE file.

How to cite

  1. M. Beg, M. Lang, and H. Fangohr. Ubermag: Towards more effective micromagnetic workflows. IEEE Transactions on Magnetics (2021).

  2. M. Beg, R. A. Pepper, and H. Fangohr. User interfaces for computational science: A domain specific language for OOMMF embedded in Python. AIP Advances 7, 56025 (2017).

  3. Marijan Beg, Martin Lang, Samuel Holt, Ryan A. Pepper, Hans Fangohr. discretisedfield: Python package for the analysis and visualisation of finite-difference fields. DOI: 10.5281/zenodo.3539461 (2021).

Acknowledgements

  • OpenDreamKit – Horizon 2020 European Research Infrastructure project (676541)

  • EPSRC Programme Grant on Skyrmionics (EP/N032128/1)

Comments
  • Add to_xarray as field method

    Add to_xarray as field method

    Adding to_xarray method for Field object which returns field value as an xarray.DataArray.

    1. Typically, the returned DataArray has four dimensions, namely x, y, z, and comp. The first three corresponds to geometry while the fourth dimension corresponds to the components of field. comp dimension has field.components as co-ordinates.
    2. If the Field object is a scalar field, the comp dimension is 'squeezed' and the DataArray has only three dimensions corresponding to the geometry.
    3. Instead of giving fourth dimension (i.e. comp) the name of the field, the name is assigned to the DataArray itself. The default name is 'field', but it can be changed with name parameter.
    4. The units of the field can be set with units parameter. Units of geometry dimensions are set to mesh.attributes['unit'] if the attribute exists, otherwise set to 'm' (meter).
    opened by swapneelap 16
  • Rewrite ovf reading routine

    Rewrite ovf reading routine

    Execution times for 1M cells:

    • Reading
    mode   old    new   speedup
    ==== ======= ====== =======
    bin4 1730 ms  21 ms   82
    bin8 1860 ms  36 ms   52
    text 4920 ms 401 ms   12
    
    • Writing
    mode    old    new   speedup
    ==== ======== ====== =======
    bin4 63000 ms  56 ms   1125
    bin8 64000 ms  84 ms    762
    text 69000 ms 4510 ms    15
    

    Filesizes are

    • 2.9M for bin4
    • 5.8M for bin8
    • 15M for txt
    opened by lang-m 13
  • New initialisation method when passin a `df.Field` using the new xarray functionality.

    New initialisation method when passin a `df.Field` using the new xarray functionality.

    Test

    • field with mesh.n = (100, 100, 10)
    • creation of a new field with mesh.n = (10, 10, 10) and passing the old field to value

    Performance improvement:

    • old implementation (using that the field is callable): ~ 6.5 s
    • new implementation (interpolation done by xarray): ~ 5 ms

    @swapneelap This is probably the first use case for your new method.

    opened by lang-m 10
  • Refactor region

    Refactor region

    @lang-m I've made the initial critical changes we discussed and added tests.

    There are two main issues that I have at the moment that it would be good for you to look at if you have time:

    • [x] Typesystem - as I am not familiar with the type system I've not too sure how to do the correct types for things like units and pmin etc. this is causing some errors in the tests. e.g. allowing complex numbers
    • [x] html representation - this is causing some tests to fail now that I have changed the repr to include pmin and pmax rather than p1 and p2. I don't know all the html code off of the top of my head so I'm hoping it will be a quicker fix for you but let me know if not.
    opened by samjrholt 8
  • On the value of discretisedfield

    On the value of discretisedfield

    Dear Ubermag team, I encountered a problem in simulating a multilayer structure. I don't know what it means to define a three-dimensional value in field. I don't know if I rely on this value and norm=Ms to determine the initial magnetic moment distribution of this material. There is a non-magnetic layer in the multilayer structure that I simulate. How should I define its value for this non-magnetic region? I define the value of field through the following figure function, but when I am creating field, the program reports an error. How can I solve this problem to achieve the desired results? image image

    opened by code-whale 8
  • Mumax view

    Mumax view

    Hi there, Have you considered integrating mumax-view as a tool to visualize 3D fields ? Compared to k3d, it is faster as it is compiled to wasm and has better shaders out of the box. I'm not sure if it's possible to embed it as a notebook widget in a similar way to k3d. Also it would require a few changes to be called through python. I doubt this would be an easy task but k3d can be insanely slow even on top-end workstations ( single-threaded js can only go so far ). I also think mumax-view is a beautifully written software that deserves more attention and it might shine as part of the Ubermag framework.

    opened by MathieuMoalic 7
  • Representation strings.

    Representation strings.

    After merging #86 we have to review __repr__ for Region, Mesh, Field, and FieldRotator. Representation strings are difficult to read because of bad formatting and relatively much information.

    opened by lang-m 5
  • Method returning vector field component as a scalar field

    Method returning vector field component as a scalar field

    Probably the most convenient way for accessing individual components of a vector field is to have a method which returns a scalar field. The question is how this method should be named and called. Maybe

    field.component("x")
    

    Also, we can allow the argument to be 0, 1, 2

    enhancement question 
    opened by marijanbeg 5
  • Copy method in Field class

    Copy method in Field class

    Should Field class be able to set its value with another field like:

    f1 = Field(mesh, dim=3, value=(0, 0, 1))
    f2 = Field(mesh, dim=3)
    
    f2.value = f1
    

    Or f2.value = f1.value is already sufficient?

    enhancement question interface 
    opened by marijanbeg 5
  • Refactor field: vdims

    Refactor field: vdims

    • renamed: dim -> nvdim
    • renamed: components -> vdims
    • removed: coordinate_field
    • removed: typesystem decorator

    Todo

    • [x] check doctests
    • [x] update docstrings
    opened by lang-m 4
  • Automatically determine dtype for array_like inputs.

    Automatically determine dtype for array_like inputs.

    Simplifies array creation and mathematical operations with fields.

    This had been implemented previously but I had initially removed it when rewriting the as_array function.

    Determining the array for callable or dict for value is more complex and therefore not anymore done automatically. Instead, the numpy default (np.float64) is used by default. Furthermore, these types of value only occur during creation by the user where a different dtype can be easily specified (so we don't have any issues with wrong data types during operations on fields).

    opened by lang-m 4
  • Refactor `mesh.allclose` method

    Refactor `mesh.allclose` method

    At present the use of atol and rtol in the method is a bit confusing. Moreover, the method fails if one of the points in the two meshes that are equal to or close to 0.0. We can refactor it to take into account the exponent of the cell dimensions, for example.

    opened by swapneelap 0
Releases(0.65.0)
Owner
ubermag
Computational magnetism tools
ubermag
Customizing Visual Styles in Plotly

Customizing Visual Styles in Plotly Code for a workshop originally developed for an Unconference session during the Outlier Conference hosted by Data

Data Design Dimension 9 Aug 03, 2022
Python scripts for plotting audiograms and related data from Interacoustics Equinox audiometer and Otoaccess software.

audiometry Python scripts for plotting audiograms and related data from Interacoustics Equinox 2.0 audiometer and Otoaccess software. Maybe similar sc

Hamilton Lab at UT Austin 2 Jun 15, 2022
Learn Data Science with focus on adding value with the most efficient tech stack.

DataScienceWithPython Get started with Data Science with Python An engaging journey to become a Data Scientist with Python TL;DR Download all Jupyter

Learn Python with Rune 110 Dec 22, 2022
View part of your screen in grayscale or simulated color vision deficiency.

monolens View part of your screen in grayscale or filtered to simulate color vision deficiency. Watch the demo on YouTube. Install with pip install mo

Hans Dembinski 31 Oct 11, 2022
FairLens is an open source Python library for automatically discovering bias and measuring fairness in data

FairLens FairLens is an open source Python library for automatically discovering bias and measuring fairness in data. The package can be used to quick

Synthesized 69 Dec 15, 2022
A pandas extension that solves all problems of Jalai/Iraninan/Shamsi dates

Jalali Pandas Extentsion A pandas extension that solves all problems of Jalai/Iraninan/Shamsi dates Features Series Extenstion Convert string to Jalal

51 Jan 02, 2023
ScisorWiz: Differential Isoform Visualizer for Long-Read RNA Sequencing Data

ScisorWiz: Vizualizer for Differential Isoform Expression README ScisorWiz is a linux-based R-package for visualizing differential isoform expression

Alexander Stein 6 Oct 04, 2022
Python library that makes it easy for data scientists to create charts.

Chartify Chartify is a Python library that makes it easy for data scientists to create charts. Why use Chartify? Consistent input data format: Spend l

Spotify 3.2k Jan 01, 2023
A Python function that makes flower plots.

Flower plot A Python 3.9+ function that makes flower plots. Installation This package requires at least Python 3.9. pip install

Thomas Roder 4 Jun 12, 2022
基于python爬虫爬取COVID-19爆发开始至今全球疫情数据并利用Echarts对数据进行分析与多样化展示。

COVID-19-Epidemic-Map 基于python爬虫爬取COVID-19爆发开始至今全球疫情数据并利用Echarts对数据进行分析与多样化展示。 觉得项目还不错的话欢迎给一个star! 项目的源码可以正常运行,各个库的版本、数据库的建表语句、运行过程中遇到的坑以及解决方式在笔记.md中都

31 Dec 15, 2022
Generate graphs with NetworkX, natively visualize with D3.js and pywebview

webview_d3 This is some PoC code to render graphs created with NetworkX natively using D3.js and pywebview. The main benifit of this approac

byt3bl33d3r 68 Aug 18, 2022
https://there.oughta.be/a/macro-keyboard

inkkeys Details and instructions can be found on https://there.oughta.be/a/macro-keyboard In contrast to most of my other projects, I decided to put t

Sebastian Staacks 209 Dec 21, 2022
Minimal Ethereum fee data viewer for the terminal, contained in a single python script.

Minimal Ethereum fee data viewer for the terminal, contained in a single python script. Connects to your node and displays some metrics in real-time.

48 Dec 05, 2022
The windML framework provides an easy-to-use access to wind data sources within the Python world, building upon numpy, scipy, sklearn, and matplotlib. Renewable Wind Energy, Forecasting, Prediction

windml Build status : The importance of wind in smart grids with a large number of renewable energy resources is increasing. With the growing infrastr

Computational Intelligence Group 125 Dec 24, 2022
Interactive Dashboard for Visualizing OSM Data Change

Dashboard and intuitive data downloader for more interactive experience with interpreting osm change data.

1 Feb 20, 2022
Investment and risk technologies maintained by Fortitudo Technologies.

Fortitudo Technologies Open Source This package allows you to freely explore open-source implementations of some of our fundamental technologies under

Fortitudo Technologies 11 Dec 14, 2022
ipyvizzu - Jupyter notebook integration of Vizzu

ipyvizzu - Jupyter notebook integration of Vizzu. Tutorial · Examples · Repository About The Project ipyvizzu is the Jupyter Notebook integration of V

Vizzu 729 Jan 08, 2023
Fast data visualization and GUI tools for scientific / engineering applications

PyQtGraph A pure-Python graphics library for PyQt5/PyQt6/PySide2/PySide6 Copyright 2020 Luke Campagnola, University of North Carolina at Chapel Hill h

pyqtgraph 3.1k Jan 08, 2023
Pretty Confusion Matrix

Pretty Confusion Matrix Why pretty confusion matrix? We can make confusion matrix by using matplotlib. However it is not so pretty. I want to make con

Junseo Ko 5 Nov 22, 2022
:bowtie: Create a dashboard with python!

Installation | Documentation | Gitter Chat | Google Group Bowtie Introduction Bowtie is a library for writing dashboards in Python. No need to know we

Jacques Kvam 753 Dec 22, 2022