iupitermag 0.1.0

Python package with Rust bindings to calculate the magnetic field in Jupiter's magnetosphere.

iupitermag

Introduction

iupitermag is a Python package written in Rust to model Jupiter's magnetic field.

Jupiter's internal magnetic field intensity at the 1-bar "surface".

Many other public codes do this or something similar,

• JupiterMag - A Python package that uses the libjupitermag C++ library (both written by Matt James).
• jovian_jrm09_internal, con_2020 - Codes written for MATLAB and IDL by Marrisa Vogt.
• con2020 - Python code by Gabby Provan.

More details on Jupiter magnetic field models and these codes can be found in this paper - Wilson, R.J., Vogt, M.F., Provan, G. et al. Internal and External Jovian Magnetic Fields: Community Code to Serve the Magnetospheres of the Outer Planets Community. Space Sci Rev 219, 15 (2023). https://doi.org/10.1007/s11214-023-00961-3

Installation

Installing from source using uv

If you are using uv as your Python package manager, you can run the following commands after cloning and changing into this directory.

$ uv pip install .

Usage

Calculating the internal and current sheet fields at a single point.

All positions should be in the IAU_JUPITER coordinate system.

import iupitermag as im

internal_field = im.InternalField("JRM33")
cs_field = im.CurrentSheetField("CON2020")

r = 10.
theta = 0.
phi = 0.
b_int_rtp = internal_field.calc_field(r, theta, phi)
b_ext_rtp = cs_field.calc_field(r, theta, phi)

# Or, you can the cartesian form.  This calls the spherical version internally.
x = 10.
y = 5.
z = 1.
b_int_xyz = internal_field.calc_field_xyz(x, y, z)
b_ext_xyz = cs_field.calc_field_xyz(x, y, z)

Calculating the internal and current sheet fields for a collection of points.

If you have a collection of points stored as a single numpy array of shape (N, 3), you can use map_calc_field or parmap_calc_field (or their corresponding cartesian versions map_calc_field_xyz and parmap_calc_field_xyz).

points = np.zeros((10000, 3))
points[:, 0] = np.random.random_sample((10000,)) * 10 + 5

b_int = internal_field.map_calc_field(points)
b_ext = cs_field.map_calc_field(points)

b_int = internal_field.parmap_calc_field(points)
b_ext = cs_field.parmap_calc_field(points)

points_xyz = points * 1.

b_int_xyz = internal_field.map_calc_field_xyz(points_xyz)
b_ext_xyz = cs_field.map_calc_field_xyz(point_xyz)

b_int_xyz = internal_field.parmap_calc_field_xyz(points_xyz)
b_ext_xyz = cs_field.parmap_calc_field_xyz(points_xyz)

Based on some benchmarks, you should find parmap_calc_field nearly an order of magnitude faster than map_calc_field or a Python loop over all points calling calc_field repeatedly. parmap_* uses Rayon for parallelizing the calculation. Here are the results of benchmarking different methods to calculate the JRM33 field for different number of points.

As you can see, parmap_ is usually the better option if you have more than 20 or so points. Note that the Python loop version still calls the Rust code internally to calculate the JRM33 field, so even that is faster than a pure-Python implementation (not shown above).

Tracing magnetic field lines

iupitermag can trace magnetic field lines to Jupiter using trace_field_to_planet, which takes as input a collection of starting points (which each result in a separate trace). The coordinates for these starting points should be cartesian.

internal_field = im.InternalField("JRM33")
cs_field = im.CurrentSheetField("CON2020")

starting_positions_xyz = np.array([
    [-10., 0., 0.],
    [-15., 0., 0.],
    [-20., 0., 0.],
    [-25., 0., 0.],
    [10., 0., 0.],
    [15., 0., 0.],
    [20., 0., 0.],
    [25., 0., 0.],
])

trace = im.trace_field_to_planet(starting_positions_xyz, internal_field, cs_field)