JupiterMag 1.0.2

Some magnetic field models for Jupiter

JupiterMag

Python wrapper for a collection of Jovian magnetic field models.

This is part of a community code project :

Magnetospheres of the Outer Planets Group Community Code

Authors

• Marty Brennan - NASA JPL

• Matt James - University of Leicester

• Gabby Provan - University of Leicester

• Marissa Vogt - Boston University

• Rob Wilson - LASP

Requirements

For the Python code to run (without rebuilding the C++ backend), the following Python packages would be required:

• NumPy

• Matplotlib

• DateTimeTools

• RecarrayTools

• PyFileIO

all of which would be installed automatically if using pip.

On some systems, the shared object files would need rebuilding before they can be loaded and accessed using Python. Upon the first import of the JupiterMag module, if the shared object/DLL fails to load then it will attempt to use a local C++ compiler to rebuild the binaries.

Linux

JupiterMag was built and tested primarily using Linux Mint 20.3 (based on Ubuntu 20.04/Debian). To rebuild the code, ensure that g++, make and ld are installed.

Windows

This has been tested on Windows 10 (64-bit), other versions may also work. Requires g++, make and ld to work (these can be provided by TDM-GCC).

MacOS

This module has been tested on MacOS 11 Big Sur. It requires g++, make and libtool to recompile (provided by Xcode).

Installation

Install using pip3:

pip3 install JupiterMag --user

Download the latest release (on the right -> if you're viewing this on GitHub), then from within the directory where it was saved:

pip3 install JupiterMag-1.0.0-py3-none-any.whl --user

Or using this repo (replace "1.0.0" with the current version number):

#pull this repo
git clone https://github.com/mattkjames7/JupiterMag.git
cd JupiterMag

#update the submodule
git submodule upate --init --recursive

#build the wheel file
python3 setup.py bdist_wheel
#the output of the previous command should give some indication of 
#the current version number. If it's not obvious then do
# $ls dist/ to see what the latest version is
pip3 install dist/JupiterMag-1.0.0-py3-none-any.whl --user

I recommend installing gcc >= 9.3 (that's what this is tested with, earlier versions may not support the required features of C++).

This module should now work with both Windows and MacOS

Usage

Internal Field

A number of internal field models are included (see here for more information) and can be accessed via the JupiterMag.Internal submodule, e.g.:

import JupiterMag as jm

#configure model to use VIP4 in polar coords (r,t,p)
jm.Internal.Config(Model="vip4",CartesianIn=False,CartesianOut=False)
Br,Bt,Bp = jm.Internal.Field(r,t,p)

#or use jrm33 in cartesian coordinates (x,y,z)
jm.Internal.Config(Model="jrm33",CartesianIn=True,CartesianOut=True)
Bx,By,Bz = jm.Internal.Field(x,y,z)

All coordinates are either in planetary radii (x,y,z,r) or radians (t,p). All Jovian models here use R_j=71,492 km.

External Field

Currently the only external field source included is the Con2020 field (see here), other models could be added in future.

This works in a similar way to the internal field, e.g.:

#configure model
jm.Con2020.Config(equation_type='analytic')
Bx,By,Bz = jm.Con2020.Field(x,y,z)

Tracing

Field line tracing can be done using the TraceField object, e.g.

import JupiterMag as jm

#configure external field model prior to tracing
#in this case using the analytic Con2020 model for speed
jm.Con2020.Config(equation_type='analytic')

#trace the field in both directions from a starting position
T = jm.TraceField(5.0,0.0,0.0,IntModel='jrm09',ExtModel='Con2020')

The above example will trace the field line from the Cartesian SIII position (5.0,0.0,0.0) (R_j) in both directions until it reaches the planet using the JRM09 internal field model with the Con2020 external field model. The object returned, T, is an instance of the TraceField class which contains the positions and magnetic field vectors at each step along the trace, along with some footprint coordinates and member functions which can be used for plotting.

A longer example below can be used to compare field traces using just an internal field model (JRM33) with both internal and external field models (JRM33 + Con2020):

import JupiterMag as jm
import numpy as np

#be sure to configure external field model prior to tracing
jm.Con2020.Config(equation_type='analytic')
#this may also become necessary with internal models in future, e.g.
#setting the model degree

#create some starting positions
n = 8
theta = (180.0 - np.linspace(22.5,35,n))*np.pi/180.0
r = np.ones(n)
x0 = r*np.sin(theta)
y0 = np.zeros(n)
z0 = r*np.cos(theta)

#create trace objects, pass starting position(s) x0,y0,z0
T0 = jm.TraceField(x0,y0,z0,Verbose=True,IntModel='jrm33',ExtModel='none')
T1 = jm.TraceField(x0,y0,z0,Verbose=True,IntModel='jrm33',ExtModel='Con2020')

#plot a trace
ax = T0.PlotRhoZ(label='JRM33',color='black')
ax = T1.PlotRhoZ(fig=ax,label='JRM33 + Con2020',color='red')

ax.set_xlim(-2.0,25.0)
ax.set_ylim(-10.0,10.0)

The resulting objects T0 and T1 store arrays of trace positions and magnetic field vectors along with a bunch of footprints.The above code produces a plot like this: