IDL format output loader

  • Read data
file = "1d_bin.out";
bd = load(file);
bd = load(file, verbose=true);
bd = load(file, npict=1);
  • 3D structured spherical coordinates
file = "3d_structured.out";
bd = load(file, verbose=false);
  • log file
logfilename = "shocktube.log";
head, data = readlogdata(logfilename)

Data Extraction

  • Raw variables
ρ = getvar(bd, "rho")
  • Derived variables
v = getvars(bd, ["Bx", "By", "Bz"])
Bmag = bd["Bmag"]

Here is a full list of predefined derived quantities:

Derived variable nameMeaningRequired variable
B2magnetic field magnitude squaredBx, By, Bz
E2electric field magnitude squaredEx, Ey, Ez
U2velocity magnitude squaredUx, Uy, Uz
Bmagmagnetic field magnitudeBx, By, Bz
Emagelectric field magnitudeEx, Ey, Ez
Umagvelocity magnitudeUx, Uy, Uz
Bmagnetic field vectorBx, By, Bz
Eelectric field vectorEx, Ey, Ez
Uvelocity vectorUx, Uy, Uz

Output format conversion

We can convert 2D/3D BATSRUS outputs *.dat to VTK formats. It uses the VTK XML format writer writeVTK to generate files for Paraview and Tecplot. The default converted filename is out.vtu.

ASCII Tecplot file (supports both tec and tcp) and binary Tecplot file (set DOSAVETECBINARY=TRUE in BATSRUS PARAM.in):

file = "x=0_mhd_1_n00000050.dat"
head, data, connectivity = readtecdata(file)
convertTECtoVTU(head, data, connectivity)

3D structured IDL file (gridType=1 returns rectilinear vtr file, gridType=2 returns structured vts file):

file = "3d_structured.out"
convertIDLtoVTK(file, gridType=1)

3D unstructured IDL file together with header and tree file:

filetag = "3d_var_1_n00002500"

The file suffix should not be provided for this to work correctly!

Multiple files:

using Batsrus, Glob
filenamesIn = "3d*.dat"
dir = "."
filenames = Vector{String}(undef, 0)
filesfound = glob(filenamesIn, dir)
filenames = vcat(filenames, filesfound)
tec = readtecdata.(filenames) # head, data, connectivity
for (i, outname) in enumerate(filenames)
   convertTECtoVTU(tec[i][1], tec[i][2], tec[i][3], outname[1:end-4])

If each individual file size is large, consider using:

using Batsrus, Glob
filenamesIn = "3d*.dat"
dir = "."
filenames = Vector{String}(undef, 0)
filesfound = glob(filenamesIn, dir)
filenames = vcat(filenames, filesfound)
for (i, outname) in enumerate(filenames)
   head, data, connectivity = readtecdata(outname)
   convertTECtoVTU(head, data, connectivity, outname[1:end-4])

Multiple files in parallel:

using Distributed
@everywhere using Batsrus, Glob

filenamesIn = "cut*.dat"
dir = "."
filenames = Vector{String}(undef, 0)
filesfound = glob(filenamesIn, dir)
filenames = vcat(filenames, filesfound)

@sync @distributed for outname in filenames
   head, data, connectivity = readtecdata(outname)
   convertTECtoVTU(head, data, connectivity, outname[1:end-4])

More examples can be found in examples.

HDF format output loader

filename = "3d__var_1_n00006288.h5"
file = BatsrusHDF5Uniform(filename)

Field extraction

Variables within a box region can be extracted as following:

out, (xl_new, yl_new, zl_new), (xu_new, yu_new, zu_new) =
   extract_field(file, "bx"; xmin, xmax, ymin, ymax, zmin, zmax)

Data visualization

We provide plot recipes for Plots.jl, Makie.jl, and wrappers for PyPlot.jl.

The recipes for Plots.jl and Makie.jl will work on all kinds of plots given the correct dimensions, e.g.

using Plots
plot(bd, "p")
contourf(bd, "Mx", xlabel="x")

See the official documentation for Plots.jl for more information.

On the other hand, most common 1D and 2D plotting functions are wrapped over their Matplotlib equivalences through PyPlot.jl. To trigger the wrapper, using PyPlot. Check out the documentation for more details.

Quick exploration of data

A general plotdata function is provided for quick visualizations using Matplotlib.

  • 1D binary
plotdata(bd, "p", plotmode="line")
plotdata(bd, "p", plotmode="linegrid")
  • 2D Cartesian (structured)
plotdata(bd, "p bx;by", plotmode="contbar streamover")
plotdata(bd, "p bx;by", plotmode="contbar quiverover")
plotdata(bd, "p bx;by", plotmode="contbar streamover", density=2.0)
plotdata(bd, "p", plotmode="grid")
plotdata(bd, "p", plotmode="contbar", plotrange=[-50., 50., -1., 1.])
plotdata(bd, "p", plotmode="contbar")
plotdata(bd, "p", plotmode="contbarlog")
plotdata(bd, "p", plotmode="surfbar")
  • 2D unstructured
plotdata(bd, "rho", plotmode="contbar")
plotdata(bd, "rho", plotmode="trimesh")
plotdata(bd, "rho", plotmode="tricont")
  • 2D structured spherical coordinates
plotdata(bd, "rho", plotmode="contbar")
  • 3D box
plotdata(bd, "bx", plotmode="contbar", dir="y", sequence=1, level=20)
plotdata(bd, "bx", plotmode="contbar", dir="y", plotrange=[-1.4,-1.1,0.70,0.78])
using PyPlot

cutplot(bd, "Ex"; dir="y", sequence=128, plotrange)

Finding indexes

To get the index of a certain quantity, e.g. electron number density

ρe_= findfirst(x->x=="rhoS0", bd.head.wnames)

Multiple dispatch for Matplotlib functions

Using the same plotting functions as in Matplotlib is allowed, and actually recommended. Some plotting functions can be directly called as shown below, which allows for more control from the user. using PyPlot to import the full capability of the package, etc. adding colorbar, changing line colors, setting colorbar range with clim.

  • line plot
plot(bd, "p", linewidth=2, color="green")
c = plot(bd, "p")
plt.setp(c, linestyle="--", linewidth=2);
  • scatter plot
scatter(bd, "p")
  • contour
# 2D contour
contour(bd, "p")
  • filled contour
contourf(bd, "p")
contourf(bd, "p", levels, plotrange=[-10,10,-Inf,Inf], plotinterval=0.1)
  • surface plot
plot_surface(bd, "p")
  • triangle surface plot
plot_trisurf(bd, "p")
  • triangle filled contour plot
tricontourf(bd, "p")
  • streamline
streamplot(bd, "bx;bz")
streamplot(bd, "bx;bz", density=2.0, color="k", plotinterval=1.0, plotrange=[-10,10,-Inf,Inf])
  • quiver (currently only for Cartesian grid)
quiver(bd, "ux;uy", stride=50)
  • streamline + contourf
using Batsrus, PyPlot

file = "y.out"
bd = load(file)

DN = matplotlib.colors.DivergingNorm

contourf(bd, "uxS0", 50, plotrange=[-3,3,-3,3], plotinterval=0.05, norm=DN(0))
streamplot(bd, "uxS0;uzS0", density=2.0, color="g", plotrange=[-3,3,-3,3])
xlabel("x"); ylabel("y"); title("Ux [km/s]")

contourf(bd,"uxS0", 50, plotinterval=0.05, norm=DN(0))
xlabel("x"); ylabel("y"); title("uxS0")


The built-in streamplot function in Matplotlib is not satisfactory for accurately tracing. Instead we recommend FieldTracer.jl for tracing fieldlines and streamlines.

An example of tracing in a 2D cut and plot the field lines over contour:

using Batsrus, PyPlot

file = "test/y=0_var_1_t00000000_n00000000.out"
bd = load(file)

bx = bd.w[:,:,5]
bz = bd.w[:,:,7]
x  = bd.x[:,1,1]
z  = bd.x[1,:,2]

seeds = select_seeds(x, z; nSeed=100) # randomly select the seeding points

for i = 1:size(seeds)[2]
   xs = seeds[1,i]
   zs = seeds[2,i]
   # Tracing in both direction. Check the document for more options.
   x1, z1 = trace2d_eul(bx, bz, xs, zs, x, z, ds=0.1, maxstep=1000, gridType="ndgrid")

Currently the select_seeds function uses pseudo random number generator that produces the same seeds every time.