magpylib 1.0.2b0

A simple, user friendly Python 3 toolbox for calculating magnetic fields from permanent magnets and current distributions.

magpylib - A simple and user friendly magnetic toolbox for Python 3

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What is magpylib ?

• Python package for calculating magnetic fields of magnets, currents and moments (sources).
• It provides convenient methods to generate, geometrically manipulate, group and vizualize assemblies of sources.
• The magnetic fields are determined from underlying (semi-analytical) solutions which results in fast computation times (sub-millisecond) and requires little computation power.

Dependencies:

Python3.6+, Numpy, Matplotlib

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Guides & Installation:

Please check out our documentation for getting started and more info!

(Installation methods are still WIP.)

Installing this project using pip:

• run the following in your Python environment terminal:

  pip install magpylib
  

Installing this project locally:

• Clone this repository to your machine.
• In the directory, run pip install . in your conda terminal.

Example:

• Two permanent magnets with axial magnetization are created and geometrically manipulated. They are grouped in a single collection and the system geometry is displayed using a supplied method.
• The total magnetic field that is generated by the collection is calculated on a grid in the xz-plane and is displayed using matplotlib.

Program output:

Code:

# imports
import numpy as np
import matplotlib.pyplot as plt
import magpylib as magpy

# create magnets
magnet1 = magpy.source.magnet.Box(mag=[0,0,600],dim=[3,3,3],pos=[-4,0,3])
magnet2 = magpy.source.magnet.Cylinder(mag=[0,0,500], dim=[3,5], pos=[0,0,0])

# manipulate magnets
magnet1.rotate(45,[0,1,0],anchor=[0,0,0])
magnet2.move([5,0,-4])

# collect magnets
pmc = magpy.Collection(magnet1,magnet2)

# display system geometry
pmc.displaySystem()

# calculate B-fields on a grid
xs = np.linspace(-10,10,20)
zs = np.linspace(-10,10,20)
Bs = np.array([[pmc.getB([x,0,z]) for x in xs] for z in zs])

# display fields using matplotlib
fig, ax = plt.subplots()
X,Y = np.meshgrid(xs,zs)
U,V = Bs[:,:,0], Bs[:,:,2]
ax.streamplot(X, Y, U, V, color=np.log(U**2+V**2), density=1.5)
plt.show()