# Call different data sets here, possibly create loop.
ds = yt.load("~/bigdata/Fiducial00/data.0100.3d.hdf5")
#Loading all data into one object.
ad = ds.all_data()
#Printing Derived Variables to be able to use (comment in if needed).
for i in sorted(ds.derived_field_list):
print(i)
# Basic Projection Plots
#Density Projection along z
yt.ProjectionPlot(ds, "z", "density").save("z_mass_density_plot.png")
#Angular Momentum (x,y) Projected along z
z_projected_angular_momentum_y = yt.ProjectionPlot(ds,
"z",
"angular_momentum_y",
data_source=ad)
z_projected_angular_momentum_y.set_cmap(field="angular_momentum_y", cmap='bwr')
z_projected_angular_momentum_y.save("z_projected_angular_momentum_y.png")
z_projected_angular_momentum_x = yt.ProjectionPlot(ds,
"z",
"angular_momentum_x",
data_source=ad)
z_projected_angular_momentum_x.set_cmap(field="angular_momentum_x", cmap='bwr')
z_projected_angular_momentum_x.save("z_projected_angular_momentum_x.png")
import yt
import matplotlib.cm as cm
# Load the dataset.
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
# Create projections using each colormap available.
p = yt.ProjectionPlot(ds, "z", "density", weight_field="density", width=0.4)
for cmap in cm.datad:
if cmap.startswith("idl"):
continue
p.set_cmap(field="density", cmap=cmap)
p.annotate_title(cmap)
p.save("Projection_%s.png" % cmap.replace(" ", "_"))
zmin = -250
zmax = 250
unit = 'km/s'
return field, cmap, weight_field, zmin, zmax, unit
#if you pass these into the command line parser, it will override these
haloname, output = ('halo_008508', 'DD1000')
#Read in command-line arguments
args = parse_args(haloname, output)
ds, refine_box = load_sim(args)
field, cmap, weight_field, zmin, zmax, unit = retrieve_projection_parameters(
args)
prj = yt.ProjectionPlot(ds, axs, field, center = center, data_source = refine_box,\
width=x_width, weight_field = weight_field)
prj.set_unit(field, unit)
prj.set_zlim(field, zmin=zmin, zmax=zmax)
prj.set_cmap(field, cmap)
if False:
#These are all optional
prj.annotate_scale(size_bar_args={'color': 'white'})
prj.annotate_timestamp(corner='upper_right',
redshift=True,
draw_inset_box=True)
prj.annotate_text((0.05, 0.9), haloname, coord_system='axis', \
text_args = {'fontsize': 500, 'color': 'white'}, inset_box_args = {})
prj.hide_axes()
prj.hide_colorbar()
def _Bxz(field, data): #define a new field in x-z plane
return np.sqrt(data["X-magnfield"]**2 + data["Z-magnfield"]**2)
fn = "data.0510.3d.hdf5"
ds = yt.load(fn) #load dataset
ds.add_field(("gas", "Bxy"), function=_Bxy, units="G",
force_override=True) #add the new field
ds.add_field(("gas", "Byz"), function=_Byz, units="G", force_override=True)
ds.add_field(("gas", "Bxz"), function=_Bxz, units="G", force_override=True)
#Make projection plots of the components of the field. Turn these into 2D arrays
mppx = yt.ProjectionPlot(
ds, "z", "X-magnfield",
weight_field="density") #Project X-component of B-field from z-direction
#mppx.save("projected-Bx.png") #save
imgx = mpimg.imread("projected-Bx.png") #imread turns an image into a 2D array
#print imgx
mppy = yt.ProjectionPlot(
ds, "z", "Y-magnfield",
weight_field="density") #Project Y-component of B-field from z-direction
#mppy.save("projected-By.png")
imgy = mpimg.imread("projected-By.png")
#print imgy
#mppz=yt.ProjectionPlot(ds, "x", "Z-magnfield", weight_field="density")
#mppz.save("projected-Bz.png")
#imgz=mpimg.imread("projected-Bz.png")
