Skip to main content

Python module for the Connerney 2020 model.

Project description

con2020

Python implementation of the Connerney et al., 1981 and Connerney et al., 2020 Jovian magnetodisc model. This model provides the magnetic field due to a "washer-shaped" current near to Jupiter's magnetic equator. By default, this model code uses either analytical equations from Edwards et al., 2001 or the numerical integration of the Connerney et al., 1981 equations to provide the magnetodisc field, depending upon proximity to the disc along z and the inner edge of the disc, r0.

Installation

Install the module using pip3:

pip3 install --user con2020

Or using this repo:

#clone the repo
git clone https://github.com/gabbyprovan/con2020
cd con2020

#EITHER create a wheel and install (X.X.X is the current version number)
python3 setup.py bdist_wheel
pip3 install --user dist/con2020-X.X.X-py3-none-any.whl

#or directly install using setup.py
python3 setup.py insall --user

Usage

To call the model, an object must be created first using con2020.Model(), where the default model parameters, model equations used or coordinate systems of input and output can be altered using keywords, e.g:

import con2020

#initialize a model object with default parameters
def_model = con2020.Model()

#initialize a model which uses spherical polar coordinates for input and output
sph_model = con2020.Model(CartesianIn=False,CartesianOut=False)

#initialize a model with custom parameters (longhand)
cust_model0 = con2020.Model(mu_i_div2__current_density_nT=150.0,
                           	r0__inner_rj=9.5,
                           	d__cs_half_thickness_rj=3.1)

#equivalently, a custom parameter model (shorthand)
cust_model1 = con2020.Model(mu_i=150.0,r0=9.5,d=3.1)

Once a model object is initialized, the model field can be obtained by calling the member function Field() and supplying input coordinates as three scalars, or three arrays (all of which are in right-handed System III), e.g.:

#Example 1: the model at a single Cartesian position (all in Rj)
x = 5.0
y = 10.0
z = 6.0
Bcart = def_model.Field(x,y,z)

#Example 2: the model at an array of positions of spherical polar coordinates
r = np.array([10.0,20.0,30.0])					#radial distance in Rj
theta = np.array([30.0,35.0,40.0])*np.pi/180.0	#colatitude in radians 
phi = np.array([90.0,95.0,100.0])*np.pi/180.0	#east longitude in radians
Bpol = sph_model.Field(r,theta,phi)

The output will be a numpy.ndarray with a shape (n,3), where n is the number of input coordinates, B[:,0] corresponds to either Bx or Br; B[:,1] corresponds to By or Btheta; and B[:,2] corresponds to either Bz or Bphi. A full list of model keywords is shown below:

Keyword (long) Keyword (short) Default Value Description
mu_i_div2__current_density_nT mu_i 139.6* Current sheet current density in nT.
i_rho__azimuthal_current_density_nT i_rho 16.7* Azimuthal current density in nT from Connerney et al 2020.
r0__inner_rj r0 7.8 Inner edge of the current sheet in Rj.
r1__outer_rj r1 51.4 Outer edge of the current sheet in Rj.
d__cs_half_thickness_rj d 3.6 Current sheet half thickness in Rj.
xt__cs_tilt_degs xt 9.3 Tilt angle of the current sheet away from the SIII z-axis in degrees.
xp__cs_rhs_azimuthal_angle_of_tilt_degs xp -24.2 (Right-Handed) Longitude towards which the current sheet is tilted in degrees.
equation_type 'hybrid' Which method to use, can be:
'analytic' - use only the analytical equations
'integral' - numerically integrate the equations
'hybrid' - a combination of analytical and integration (default)
error_check True Check errors on inputs the the Field() member function - set to False at your own risk for a slight speedup.
CartesianIn True If True (default) then the input coordinates are expected to be in Cartesian right-handed SIII coordinates. If False then right-handed spherical polar SIII coordinates will be expected.
CartesianOut True If True the output magnetic field components will be in right-handed Cartesian SIII coordinates. If False then the output will be such that it has radial, meridional and azimuthal components.
Edwards True If True (default) then the Edwards et al 2001 divergence-free equations are used for the analytical model. If False then the original equations of Connerney et al 1981 will be used.

*Default current densities used here are averages provided in Connerney et al., 2020 (see Figure 6), but can vary from one pass to the next. Table 2 of Connerney et al., 2020 provides a list of both current densities for 23 out of the first 24 perijoves of Juno.

The con2020.Test() function should produce the following:

References

  • Connerney, J. E. P., Timmins, S., Herceg, M., & Joergensen, J. L. (2020). A Jovian magnetodisc model for the Juno era. Journal of Geophysical Research: Space Physics, 125, e2020JA028138. https://doi.org/10.1029/2020JA028138
  • Connerney, J. E. P., Acuña, M. H., and Ness, N. F. (1981), Modeling the Jovian current sheet and inner magnetosphere, J. Geophys. Res., 86( A10), 8370– 8384, doi:10.1029/JA086iA10p08370.
  • Edwards T.M., Bunce E.J., Cowley S.W.H. (2001), A note on the vector potential of Connerney et al.'s model of the equatorial current sheet in Jupiter's magnetosphere, *Planetary and Space Science,*49, 1115-1123,https://doi.org/10.1016/S0032-0633(00)00164-1.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

con2020-1.0.0-py3-none-any.whl (25.3 kB view details)

Uploaded Python 3

File details

Details for the file con2020-1.0.0-py3-none-any.whl.

File metadata

  • Download URL: con2020-1.0.0-py3-none-any.whl
  • Upload date:
  • Size: 25.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.19.0 setuptools/40.6.2 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.6.4

File hashes

Hashes for con2020-1.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 ccc0a2946dc0cf7b5345d88aa62569d955e0979350df6a5c6ab937e0f4e1b793
MD5 a6cce08f198a7bec19616b3eeb1a5e08
BLAKE2b-256 5a0e8900505567b56dc584bd1bf8a0c1039f0f52fc99a1a710b61d544a466a2f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page