Compare COCONUT with Tomographic Electron Density

Goal

Tomographic reconstructions provide electron density maps at several heliocentric distances. Comparing these with COCONUT helps validate the simulation and detect systematic offsets (e.g., current sheet latitude/longitude).

This page shows how to:

  1. Read a merged COCONUT VTU snapshot and a tomography ``.dat`` map.

  2. Interpolate onto a common grid and identify the current sheet.

  3. Produce a three-panel figure:

  • COCONUT density map at a given radius,

  • Tomography density map at the same radius,

  • Latitude of maximum density vs longitude (COCONUT vs tomography).

Script module

coconut_tools.tomography

Docstring overview:

  • Reads a .vtu simulation file and a tomography .dat file

  • Interpolates fields and locates the current sheet

  • Generates comparative plots

Recommended citations (when using images/data):

API entry point

def compare_tomography_with_simulation(vtufile: str, datfile: str, output: str) -> None:
    """
    Compare electron density between a COCONUT MHD simulation and a tomographic reconstruction.

    Args:
        vtufile (str): Path to the COCONUT output (.vtu).
        datfile (str): Path to the tomography .dat file.
        output (str): Path to save the output figure.

    Returns:
        None
    """

What the 3-panel plot contains

Internally, the function produces:

  • Panel 1 (COCONUT) — density map with contour overlays

  • Panel 2 (Tomography) — density map (same colormap/extent for comparability)

  • Panel 3 (Line comparison) — latitude (deg) of maximum density vs longitude (deg) for both datasets, to track the current sheet position

Illustrative snippet from the implementation:

im1 = ax1.imshow(dens_int, cmap='gist_stern', origin='upper', extent=extent)
fig.colorbar(im1, ax=ax1).set_label('Density [norm.]', fontsize=12)
ax1.contour(np.degrees(lon_tomo), np.degrees(clt_tomo - np.pi / 2.0), np.flip(dens_int, axis=0),
            levels=[0.2, 0.4, 0.6, 0.8], colors='k', linewidths=0.5, linestyles='dashed')
ax1.set_title(f'COCONUT at R={r_tomo:.2f}$R_\\odot$')
ax1.set_xlabel('Longitude [°]')
ax1.set_ylabel('Latitude [°]')

im2 = ax2.imshow(dens_tomo, cmap='gist_stern', origin='upper', extent=extent)
fig.colorbar(im2, ax=ax2).set_label('Density [norm.]', fontsize=12)
ax2.contour(np.degrees(lon_tomo), np.degrees(clt_tomo - np.pi / 2.0), np.flip(dens_tomo, axis=0),
            levels=[0.2, 0.4, 0.6, 0.8], colors='k', linewidths=0.5, linestyles='dashed')
ax2.set_title(f'Tomography at R={r_tomo:.2f}$R_\\odot$')
ax2.set_xlabel('Longitude [°]')
ax2.set_ylabel('Latitude [°]')

ax3.set_title(f'Maximum of density at R={r_tomo:.2f}$R_\\odot$')
ax3.plot(np.degrees(lon_tomo), np.degrees(listcolatitude_smooth), label='Tomography')
ax3.plot(np.degrees(lon_tomo), np.degrees(listcolatitude_coco_smooth), label='COCONUT')
ax3.set_xlabel('Longitude [°]')
ax3.set_ylabel('Latitude [°]')
ax3.legend()
ax3.set_xlim(extent[0], extent[1])

Usage example

Typical call pattern (with automatic download of the tomography file if missing):

import os
from coconut_tools.tomography import compare_tomography_with_simulation
from coconut_tools.download_tomo_file import download_tomography_file

date = "20190704"
altitude = "5-0"
output_dir = "C:/Users/luisl/Desktop/Tinatin"
datfile = os.path.join(output_dir, f"tomo_sta_cor2a_{date}_{altitude}.dat")

if not os.path.exists(datfile):
    download_tomography_file(date, altitude, output_dir)

vtufile = "C:/Users/luisl/Desktop/hmi/corona-mhd_0.vtu"
output  = os.path.join(output_dir, "tomo_3.pdf")

compare_tomography_with_simulation(vtufile, datfile, output)

Notes & guidance

  • Altitude consistency: choose a tomography altitude (e.g., "5-0") that matches the radius you extract/compare in COCONUT.

  • Merged VTU: if your COCONUT outputs are distributed per MPI rank, merge first (see Merge distributed .vtu files into a single file (ParaView)).

  • Color normalization: use consistent colormaps/ranges across both panels for fair visual comparison.

  • Current sheet: the third panel is very sensitive; apply reasonable smoothing to avoid over-interpreting small-scale numerical noise.

  • Attribution: cite the Morgan papers listed above when using tomography products.