def PartProjAni(folder):
yt.funcs.mylog.setLevel(50)
ts = yt.load("/u/yali/" + folder + "/test/output/snapshot_*",
unit_base=unit_base,
bounding_box=bbox)
#sorted(ds.field_list)
#print(dir(ds.fields.PartType0))
#plot = yt.ProjectionPlot(ts[0], 'x', 'density')
plot = yt.ParticleProjectionPlot(ts[0], 'x', fields="Masses")
#plot.set_zlim('density', 8e-29, 3e-26)
fig = plot.plots['Masses'].figure
def animate(i):
ds = ts[i]
plot._switch_ds(ds)
plot.annotate_timestamp(corner='upper_left',
redshift=False,
draw_inset_box=True)
plot.annotate_scale(corner='upper_right')
plot.set_buff_size((200, 200))
ani = FuncAnimation(fig, animate, frames=len(ts))
return ani
def calcProjectionExtrema(Param_Dict, ds, field):
"""Creates dummy projection plots to then calculate the extrema in those.
Returns a ytArray of these extrema"""
try:
if Param_Dict["ParticlePlot"]:
projPlot = yt.ParticleProjectionPlot(ds, Param_Dict["NAxis"],
field)
else:
projPlot = yt.ProjectionPlot(ds, Param_Dict["NAxis"], field)
except yt.utilities.exceptions.YTFieldNotFound:
GUILogger.warning(f"Couldn't find {field} in the available fields")
return
ZMin = projPlot.frb[field].min()
ZMax = projPlot.frb[field].max()
return yt.YTArray([ZMin, ZMax])
def sphere_particle_projection(halo, fields, axes="xyz", output_dir=".",
sphere=None):
if sphere is None:
sphere = getattr(halo, 'data_object')
if sphere is None:
raise RuntimeError('No sphere provided.')
yt.mylog.info(f"Projecting halo {int(halo.quantities['particle_identifier']):d}.")
for axis in axes:
plot = yt.ParticleProjectionPlot(halo.halo_catalog.data_ds, axis, fields,
data_source=sphere,
center=sphere.center,
width=(2*sphere.radius))
plot.set_axes_unit("pc")
plot.annotate_title(f"M = {halo.quantities['particle_mass'].in_units('Msun')}.")
plot.save(os.path.join(output_dir, f"halo_{int(halo.quantities['particle_identifier']):06d}"))
def plot_particle_density(self, projection_plane, savename):
"""
Inputs:
------
projection_plane: int or str
0,1,2 or 'x', 'y', 'z' to set which cross section of the 3D data is plotts
savename: str
String to save file name. Leave off the file extension
Output:
------
A 2D particle plot showing the mass density of the cut box
"""
bbox = 1.1 * np.array([[np.min(self.x), np.max(self.x)],
[np.min(self.y), np.max(self.y)],
[np.min(self.z), np.max(self.z)]])
ds = yt.load_particles(self.data,
length_unit=kpc,
mass_unit=1e10,
bbox=bbox,
n_ref=4)
center = self.com
p = yt.ParticleProjectionPlot(
ds, projection_plane,
['particle_mass'
]) #, center=center)#, width=self.box_size, depth=self.box_size)
p.set_colorbar_label('particle_mass', 'Msun')
#p.zoom(2)
p.annotate_text((0.7, 0.85),
'%s Gyr' % round(self.header_time(), 2),
coord_system='figure',
text_args={'color': 'black'})
p.save('%s' % savename)
return
print('')
print('-------------------------------------------------------------------\n')
idx_start = args.idx_start
idx_end = args.idx_end
didx = args.didx
prefix = args.prefix
colormap = 'arbre'
dpi = 150
field = 'particle_mass'
yt.enable_parallelism()
ts = yt.load([
prefix + '/Data_%06d' % idx for idx in range(idx_start, idx_end + 1, didx)
])
for ds in ts.piter():
par = yt.ParticleProjectionPlot(ds, 2, field, center='c')
par.set_background_color(field)
par.set_zlim(field, 6.0e-6, 3.0e-4, dynamic_range=None)
par.set_cmap(field, colormap)
par.set_font({'size': 16})
par.set_axes_unit('code_length')
par.set_unit(field, 'code_mass')
par.annotate_timestamp(time_unit='code_time',
corner='upper_right',
text_args={'color': 'k'})
par.save(mpl_kwargs={"dpi": dpi})
ds.add_particle_filter("stars_old")
# What are the total masses of different ages of star in the whole simulation
# volume?
ad = ds.all_data()
mass_young = ad["stars_young", "particle_mass"].in_units("Msun").sum()
mass_medium = ad["stars_medium", "particle_mass"].in_units("Msun").sum()
mass_old = ad["stars_old", "particle_mass"].in_units("Msun").sum()
print("Mass of young stars = %g Msun" % mass_young)
print("Mass of medium stars = %g Msun" % mass_medium)
print("Mass of old stars = %g Msun" % mass_old)
# Generate 4 projections: gas density, young stars, medium stars, old stars
fields = [
("stars_young", "particle_mass"),
("stars_medium", "particle_mass"),
("stars_old", "particle_mass"),
]
prj1 = yt.ProjectionPlot(ds,
"z", ("gas", "density"),
center="max",
width=(100, "kpc"))
prj1.save()
prj2 = yt.ParticleProjectionPlot(ds,
"z",
fields,
center="max",
width=(100, "kpc"))
prj2.save()
def make_figure(figdir,
DD,
cen_name,
simdir,
haloname,
simname,
wd=30,
wdd=30,
wd2=300,
wdd2=300):
DDname = 'DD%.4i' % DD
ds = yt.load('%s/%s/%s/%s/%s' %
(simdir, haloname, simname, DDname, DDname))
def _stars(pfilter, data):
return data[(pfilter.filtered_type, "particle_type")] == 2
yt.add_particle_filter("stars",
function=_stars,
filtered_type='all',
requires=["particle_type"])
ds.add_particle_filter('stars')
cen_fits = fits.open(
'/nobackupp2/rcsimons/foggie_momentum/anchor_files/%s/%s_DD%.4i_anchorprops.fits'
% (cen_name, simname, DD))
central_xyz_fit = np.load(
'/nobackupp2/rcsimons/foggie_momentum/catalogs/center_natural.npy')[()]
xf = central_xyz_fit['x']
yf = central_xyz_fit['y']
zf = central_xyz_fit['z']
central_x = xf[0] * DD**4. + xf[1] * DD**3. + xf[2] * DD**2. + xf[
3] * DD + xf[4]
central_y = yf[0] * DD**4. + yf[1] * DD**3. + yf[2] * DD**2. + yf[
3] * DD + yf[4]
central_z = zf[0] * DD**4. + zf[1] * DD**3. + zf[2] * DD**2. + zf[
3] * DD + zf[4]
cen_central = yt.YTArray([central_x, central_y, central_z], 'kpc')
W = yt.YTArray([wd, wd, wd], 'kpc')
W2 = yt.YTArray([wd2, wd2, wd2], 'kpc')
for sat_n in arange(12):
if len(cen_fits['SAT_%.2i' % sat_n].data['box_avg']) > 0:
cenx = cen_fits['SAT_%.2i' % sat_n].data['box_avg'][0]
ceny = cen_fits['SAT_%.2i' % sat_n].data['box_avg'][1]
cenz = cen_fits['SAT_%.2i' % sat_n].data['box_avg'][2]
cen_g = yt.YTArray([cenx, ceny, cenz], 'kpc')
for axis in ['x', 'y', 'z']:
figname_zoomin = '%s_%.4i_%.2i_%s_zoomin.png' % (cen_name, DD,
sat_n, axis)
figname_zoomout = '%s_%.4i_%.2i_%s_zoomout.png' % (
cen_name, DD, sat_n, axis)
if not os.path.exists('%s/%s' % (figdir, figname_zoomin)):
if axis == 'x':
box = ds.r[cen_g[0] - 0.5 * yt.YTArray(wdd, 'kpc'): cen_g[0] + 0.5 * yt.YTArray(wdd, 'kpc'), \
cen_g[1] - 0.5 * yt.YTArray(3*wd, 'kpc'): cen_g[1] + 0.5 * yt.YTArray(3*wd, 'kpc'), \
cen_g[2] - 0.5 * yt.YTArray(3*wd, 'kpc'): cen_g[2] + 0.5 * yt.YTArray(3*wd, 'kpc')]
box2 = ds.r[cen_central[0] - 0.5 * yt.YTArray(wdd2, 'kpc'): cen_central[0] + 0.5 * yt.YTArray(wdd2, 'kpc'), \
cen_central[1] - 0.5 * yt.YTArray(3*wd2, 'kpc'): cen_central[1] + 0.5 * yt.YTArray(3*wd2, 'kpc'), \
cen_central[2] - 0.5 * yt.YTArray(3*wd2, 'kpc'): cen_central[2] + 0.5 * yt.YTArray(3*wd2, 'kpc')]
p_1 = cen_g[1] - cen_central[1]
p_2 = cen_g[2] - cen_central[2]
elif axis == 'y':
box = ds.r[cen_g[0] - 0.5 * yt.YTArray(3*wd, 'kpc'): cen_g[0] + 0.5 * yt.YTArray(3*wd, 'kpc'), \
cen_g[1] - 0.5 * yt.YTArray(wdd, 'kpc'): cen_g[1] + 0.5 * yt.YTArray(wdd, 'kpc'), \
cen_g[2] - 0.5 * yt.YTArray(3*wd, 'kpc'): cen_g[2] + 0.5 * yt.YTArray(3*wd, 'kpc')]
box2 = ds.r[cen_central[0] - 0.5 * yt.YTArray(3*wd2, 'kpc') : cen_central[0] + 0.5 * yt.YTArray(3*wd2, 'kpc'), \
cen_central[1] - 0.5 * yt.YTArray(wdd2, 'kpc') : cen_central[1] + 0.5 * yt.YTArray(wdd2, 'kpc'), \
cen_central[2] - 0.5 * yt.YTArray(3*wd2, 'kpc'): cen_central[2] + 0.5 * yt.YTArray(3*wd2, 'kpc')]
p_1 = cen_g[0] - cen_central[0]
p_2 = cen_g[2] - cen_central[2]
elif axis == 'z':
box = ds.r[cen_g[0] - 0.5 * yt.YTArray(3*wd, 'kpc'): cen_g[0] + 0.5 * yt.YTArray(3*wd, 'kpc'), \
cen_g[1] - 0.5 * yt.YTArray(3*wd, 'kpc'): cen_g[1] + 0.5 * yt.YTArray(3*wd, 'kpc'), \
cen_g[2] - 0.5 * yt.YTArray(wdd, 'kpc'): cen_g[2] + 0.5 * yt.YTArray(wdd, 'kpc')]
box2 = ds.r[cen_central[0] - 0.5 * yt.YTArray(3*wd2, 'kpc'): cen_central[0] + 0.5 * yt.YTArray(3*wd2, 'kpc'), \
cen_central[1] - 0.5 * yt.YTArray(3*wd2, 'kpc'): cen_central[1] + 0.5 * yt.YTArray(3*wd2, 'kpc'), \
cen_central[2] - 0.5 * yt.YTArray(wdd2, 'kpc'): cen_central[2] + 0.5 * yt.YTArray(wdd2, 'kpc')]
p_1 = cen_g[0] - cen_central[0]
p_2 = cen_g[1] - cen_central[1]
fig = plt.figure(sat_n)
grid = AxesGrid(fig, (0.0, 0.0, 1.0, 1.0),
nrows_ncols=(1, 2),
axes_pad=0.0,
label_mode="1",
share_all=False,
cbar_mode=None,
aspect=False)
p = yt.ProjectionPlot(ds,
axis, ("gas", "density"),
center=cen_g,
data_source=box,
width=W)
p.set_unit(('gas', 'density'), 'Msun/pc**2')
p.set_zlim(('gas', 'density'),
zmin=density_proj_min * 0.1,
zmax=density_proj_max)
p.set_cmap(('gas', 'density'), density_color_map)
p.annotate_timestamp(corner='upper_left',
redshift=True,
draw_inset_box=True)
p.hide_axes()
p.annotate_timestamp(corner='upper_left',
redshift=True,
draw_inset_box=True)
p.annotate_scale(size_bar_args={'color': 'white'})
plot = p.plots[("gas", "density")]
plot.figure = fig
plot.axes = grid[0].axes
p._setup_plots()
p = yt.ParticleProjectionPlot(ds,
axis,
('stars', 'particle_mass'),
center=cen_g,
data_source=box,
width=W)
cmp = plt.cm.Greys_r
cmp.set_bad('k')
p.set_cmap(field=('stars', 'particle_mass'), cmap=cmp)
p.hide_axes()
p.annotate_scale(size_bar_args={'color': 'white'})
p.set_zlim(field=('stars', 'particle_mass'),
zmin=2.e35 * 0.3,
zmax=1.e42 * 0.9)
plot = p.plots[('stars', 'particle_mass')]
plot.figure = fig
plot.axes = grid[1].axes
p._setup_plots()
fig.set_size_inches(12, 6)
fig.savefig('%s/%s' % (figdir, figname_zoomin))
plt.close(fig)
fig = plt.figure(sat_n)
grid = AxesGrid(fig, (0.0, 0.0, 1.0, 1.0),
nrows_ncols=(1, 2),
axes_pad=0.0,
label_mode="1",
share_all=False,
cbar_mode=None,
aspect=False)
p = yt.ProjectionPlot(ds,
axis, ("gas", "density"),
center=cen_central,
data_source=box2,
width=W2)
p.set_unit(('gas', 'density'), 'Msun/pc**2')
p.set_zlim(('gas', 'density'),
zmin=density_proj_min * 0.1,
zmax=density_proj_max)
p.set_cmap(('gas', 'density'), density_color_map)
p.annotate_timestamp(corner='upper_left',
redshift=True,
draw_inset_box=True)
p.hide_axes()
p.annotate_timestamp(corner='upper_left',
redshift=True,
draw_inset_box=True)
p.annotate_scale(size_bar_args={'color': 'white'})
plot = p.plots[("gas", "density")]
plot.figure = fig
plot.axes = grid[0].axes
p._setup_plots()
if (abs(p_1) < W2 / 2.) & (abs(p_2) < W2 / 2.):
plot.axes.scatter(p_1,
p_2,
marker='o',
facecolor="none",
edgecolor='red',
lw=2,
s=800)
p = yt.ParticleProjectionPlot(ds,
axis,
('stars', 'particle_mass'),
center=cen_central,
data_source=box2,
width=W2)
cmp = plt.cm.Greys_r
cmp.set_bad('k')
p.set_cmap(field=('stars', 'particle_mass'), cmap=cmp)
p.hide_axes()
p.annotate_scale(size_bar_args={'color': 'white'})
p.set_zlim(field=('stars', 'particle_mass'),
zmin=2.e35 * 0.3,
zmax=1.e42 * 0.9)
plot = p.plots[('stars', 'particle_mass')]
plot.figure = fig
plot.axes = grid[1].axes
p._setup_plots()
if (abs(p_1) < W2 / 2.) & (abs(p_2) < W2 / 2.):
plot.axes.scatter(p_1,
p_2,
marker='o',
facecolor="none",
edgecolor='red',
lw=2,
s=800)
fig.set_size_inches(12, 6)
fig.savefig('%s/%s' % (figdir, figname_zoomout))
plt.close(fig)
ds.index.clear_all_data()
print( '-------------------------------------------------------------------' )
for t in range( len(sys.argv) ):
print str(sys.argv[t]),
print( '' )
print( '-------------------------------------------------------------------\n' )
idx_start = args.idx_start
idx_end = args.idx_end
didx = args.didx
prefix_in = args.prefix_in
prefix_out = args.prefix_out
yt.enable_parallelism()
ts = yt.load( [ prefix_in+'/Data_%06d'%idx for idx in range(idx_start, idx_end+1, didx) ] )
#ts = yt.load( 'Data_??????' )
for ds in ts.piter():
# plot
p = yt.ParticleProjectionPlot( ds, proj_axis, fields=field, center=center, width=(width_x,width_y), depth=width_z )
# p.set_unit( field, 'Msun' )
# p.set_zlim( field, 1.0e6, 1.0e8 )
p.set_cmap( field, colormap )
p.annotate_timestamp( time_unit='code_time', corner='upper_right', text_args={'color':'k'} )
# save the image
p.save( prefix_out+'_'+ds.basename+'.png', mpl_kwargs={'dpi':dpi} )
def ProjectionPlot(Param_Dict, worker):
"""Takes a DataSet object loaded with yt and performs a projectionPlot on
it.
Parameters:
Param_Dict: Dict with Parameters
"""
ds = Param_Dict["CurrentDataSet"]
field = Param_Dict["ZAxis"]
if Param_Dict["DomainDiv"]:
# in case the user wants to divide everything by the domain_height,
# we define a new field which is just the old field divided by height
# and then do a projectionPlot for that.
height = Param_Dict["FieldMaxs"]["DomainHeight"] - Param_Dict["FieldMins"]["DomainHeight"]
field = "Normed " + field
unit = yt.units.unit_object.Unit(Param_Dict["ZUnit"] + "/cm")
realHeight = height.to_value("au") # Important! Bugs occur if we just used "height"
def _NormField(field, data):
return data[Param_Dict["ZAxis"]]/realHeight/yt.units.au
if Param_Dict["ParticlePlot"]:
ds.add_field(("io", field), function=_NormField,
units="auto", dimensions=unit.dimensions,
force_override=True, particle_type=True)
else:
ds.add_field(("gas", field), function=_NormField,
units="auto", dimensions=unit.dimensions,
force_override=True)
gridUnit = Param_Dict["GridUnit"]
c0 = yt.YTQuantity(Param_Dict["XCenter"], gridUnit)
c1 = yt.YTQuantity(Param_Dict["YCenter"], gridUnit)
if Param_Dict["Geometry"] == "cartesian":
c2 = yt.YTQuantity(Param_Dict["ZCenter"], gridUnit)
else:
c2 = Param_Dict["ZCenter"]
width = (Param_Dict["HorWidth"], gridUnit)
height = (Param_Dict["VerWidth"], gridUnit)
if Param_Dict["ParticlePlot"]:
plot = yt.ParticleProjectionPlot(ds, Param_Dict["NAxis"], field,
axes_unit=Param_Dict["GridUnit"],
weight_field=Param_Dict["WeightField"],
fontsize=14, center=[c0, c1, c2],
width=(width, height))
else:
if Param_Dict["NormVecMode"] == "Axis-Aligned":
plot = yt.ProjectionPlot(ds, Param_Dict["NAxis"], field,
axes_unit=Param_Dict["GridUnit"],
weight_field=Param_Dict["WeightField"],
fontsize=14, center=[c0, c1, c2],
width=(width, height))
else:
normVec = [Param_Dict[axis + "NormDir"] for axis in ["X", "Y", "Z"]]
northVec = [Param_Dict[axis + "NormNorth"] for axis in ["X", "Y", "Z"]]
plot = yt.OffAxisProjectionPlot(ds, normVec, field,
north_vector=northVec,
weight_field=Param_Dict["WeightField"],
axes_unit=Param_Dict["GridUnit"],
fontsize=14, center=[c0, c1, c2],
width=(width, height))
emitStatus(worker, "Setting projection plot modifications")
# Set min, max, unit log and color scheme:
setAxisSettings(plot, Param_Dict, "Z")
plot.zoom(Param_Dict["Zoom"])
emitStatus(worker, "Annotating the projection plot")
annotatePlot(Param_Dict, plot)
finallyDrawPlot(plot, Param_Dict, worker)
function=new_star,
filtered_type="all",
requires=["ParCreTime"])
AllPar = ('all', 'particle_mass')
NewPar = ('new_star', 'particle_mass')
for ds in ts.piter():
# add the particle filter
ds.add_particle_filter("new_star")
# face-on (all particles)
pz = yt.ParticleProjectionPlot(ds,
'z',
AllPar,
center=center_mode,
width=(width_kpc, 'kpc'),
depth=(depth_kpc, 'kpc'))
pz.set_unit(AllPar, 'Msun')
pz.set_zlim(AllPar, 1.0e6, 1.0e8)
pz.set_cmap(AllPar, colormap)
pz.annotate_timestamp(time_unit='Myr',
corner='upper_right',
text_args={'color': 'k'})
pz.save(name=ds.basename + '_AllPar', mpl_kwargs={"dpi": dpi})
# face-on (new particles)
pz = yt.ParticleProjectionPlot(ds,
'z',
NewPar,
center=center_mode,
p.annotate_timestamp(corner='upper_left',
redshift=True,
draw_inset_box=True)
p.hide_axes()
p.annotate_timestamp(corner='upper_left',
redshift=True,
draw_inset_box=True)
p.annotate_scale(size_bar_args={'color': 'white'})
plot = p.plots[("gas", "density")]
plot.figure = fig
plot.axes = grid[0].axes
p._setup_plots()
p = yt.ParticleProjectionPlot(ds,
'y', ('stars', 'particle_mass'),
center=cen_g,
data_source=box,
width=W)
cmp = plt.cm.Greys_r
cmp.set_bad('k')
p.set_cmap(field=('stars', 'particle_mass'), cmap=cmp)
p.hide_axes()
p.annotate_scale(size_bar_args={'color': 'white'})
p.set_zlim(field=('stars', 'particle_mass'),
zmin=2.e35,
zmax=1.e42)
plot = p.plots[('stars', 'particle_mass')]
plot.figure = fig
plot.axes = grid[1].axes
p._setup_plots()
plt.legend()
plt.xlim(t1, t2)
plt.yscale('log')
plt.title(r'$z={:.2f}$'.format(z))
plt.tight_layout()
plt.savefig(rep + 'tt_dis_' + str(sn) + '_' + str(ind) + '.pdf')
plt.close()
ad = ds.box(le, re)
#"""
prj = yt.ParticleProjectionPlot(ds,
2, ('PartType0', 'Metallicity_00'),
center=cen,
width=((ext, 'kpccm/h'), (ext / asp,
'kpccm/h')),
depth=thk,
weight_field=('PartType0',
'Masses')) #, data_source=ad)
prj.set_zlim('all', 1e-6 * 0.02, 0.2)
prj.set_buff_size((xb, yb))
prj.annotate_title('$z={:.2f}$'.format(z))
prj.save(rep + 'metaldis_' + str(sn) + '.png')
prj = yt.ParticleProjectionPlot(ds,
2, ('PartType1', 'Masses'),
center=cen,
width=((ext, 'kpccm/h'), (ext / asp,
'kpccm/h')),
depth=thk,
density=True) #, data_source=ad)
def make_figure(figdir,
DD,
cen_name,
simdir,
haloname,
simname,
wd=100.,
wdd=100.,
wd2=150.,
wdd2=150.,
wd3=1000.,
wdd3=1000.):
#figname_zoomoutfar = '%s_%.4i_%.2i_%s_zoomoutfar.png'%(cen_name, DD, 6, 'x')
#figname_check = '%s/%s/%s/%s'%(figdir,'x', 'zoomoutfar', figname_zoomoutfar)
#if os.path.isfile(figname_check): return
DDname = 'DD%.4i' % DD
ds = yt.load('%s/%s/%s/%s/%s' %
(simdir, haloname, simname, DDname, DDname))
def _stars(pfilter, data):
return data[(pfilter.filtered_type, "particle_type")] == 2
yt.add_particle_filter("stars",
function=_stars,
filtered_type='all',
requires=["particle_type"])
ds.add_particle_filter('stars')
cen_fits = np.load(
'/nobackupp2/rcsimons/foggie_momentum/catalogs/sat_interpolations/%s_interpolations_DD0150_new.npy'
% cen_name,
allow_pickle=True)[()]
'''
#encoding='latin1' is needed for loading python 2 pickles in python 3
if 'natural' in cen_name:
central_xyz_fit = np.load('/nobackupp2/rcsimons/foggie_momentum/catalogs/center_natural.npy', allow_pickle=True, encoding='latin1')[()]
elif ('nref11n_nref10f' in cen_name) | ('nref11c_nref9f' in cen_name):
central_xyz_fit = np.load('/nobackupp2/rcsimons/foggie_momentum/catalogs/center_nref11n_nref10f.npy', allow_pickle=True, encoding='latin1')[()]
'''
#central_x = cen_fits['CENTRAL']['fxe'](DD)
#central_y = cen_fits['CENTRAL']['fye'](DD)
#central_z = cen_fits['CENTRAL']['fze'](DD)
central_x = cen_fits['CENTRAL']['fxe'](DD)
central_y = cen_fits['CENTRAL']['fye'](DD)
central_z = cen_fits['CENTRAL']['fze'](DD)
print(central_x, central_y, central_z)
'''
xf = central_xyz_fit['x']
yf = central_xyz_fit['y']
zf = central_xyz_fit['z']
'''
#central_x = xf[0] * DD**4. + xf[1] * DD**3. + xf[2] * DD**2. + xf[3] * DD + xf[4]
#central_y = yf[0] * DD**4. + yf[1] * DD**3. + yf[2] * DD**2. + yf[3] * DD + yf[4]
#central_z = zf[0] * DD**4. + zf[1] * DD**3. + zf[2] * DD**2. + zf[3] * DD + zf[4]
cen_central = yt.YTArray([central_x, central_y, central_z], 'kpc')
W = yt.YTArray([wd, wd, wd], 'kpc')
W2 = yt.YTArray([wd2, wd2, wd2], 'kpc')
W3 = yt.YTArray([wd3, wd3, wd3], 'kpc')
for axis in ['x']: #@, 'y', 'z']:
if axis == 'x':
box2 = ds.r[cen_central[0] - 0.5 * yt.YTArray(wdd2, 'kpc'): cen_central[0] + 0.5 * yt.YTArray(wdd2, 'kpc'), \
cen_central[1] - 0.5 * yt.YTArray(3*wd2, 'kpc'): cen_central[1] + 0.5 * yt.YTArray(3*wd2, 'kpc'), \
cen_central[2] - 0.5 * yt.YTArray(3*wd2, 'kpc'): cen_central[2] + 0.5 * yt.YTArray(3*wd2, 'kpc')]
box3 = ds.r[cen_central[0] - 0.5 * yt.YTArray(wdd3, 'kpc'): cen_central[0] + 0.5 * yt.YTArray(wdd3, 'kpc'), \
cen_central[1] - 0.5 * yt.YTArray(3*wd3, 'kpc'): cen_central[1] + 0.5 * yt.YTArray(3*wd3, 'kpc'), \
cen_central[2] - 0.5 * yt.YTArray(3*wd3, 'kpc'): cen_central[2] + 0.5 * yt.YTArray(3*wd3, 'kpc')]
if axis == 'y':
box2 = ds.r[cen_central[0] - 0.5 * yt.YTArray(3*wd2, 'kpc') : cen_central[0] + 0.5 * yt.YTArray(3*wd2, 'kpc'), \
cen_central[1] - 0.5 * yt.YTArray(wdd2, 'kpc') : cen_central[1] + 0.5 * yt.YTArray(wdd2, 'kpc'), \
cen_central[2] - 0.5 * yt.YTArray(3*wd2, 'kpc'): cen_central[2] + 0.5 * yt.YTArray(3*wd2, 'kpc')]
box3 = ds.r[cen_central[0] - 0.5 * yt.YTArray(3*wd3, 'kpc') : cen_central[0] + 0.5 * yt.YTArray(3*wd3, 'kpc'), \
cen_central[1] - 0.5 * yt.YTArray(wdd3, 'kpc') : cen_central[1] + 0.5 * yt.YTArray(wdd3, 'kpc'), \
cen_central[2] - 0.5 * yt.YTArray(3*wd3, 'kpc'): cen_central[2] + 0.5 * yt.YTArray(3*wd3, 'kpc')]
if axis == 'z':
box2 = ds.r[cen_central[0] - 0.5 * yt.YTArray(3*wd2, 'kpc'): cen_central[0] + 0.5 * yt.YTArray(3*wd2, 'kpc'), \
cen_central[1] - 0.5 * yt.YTArray(3*wd2, 'kpc'): cen_central[1] + 0.5 * yt.YTArray(3*wd2, 'kpc'), \
cen_central[2] - 0.5 * yt.YTArray(wdd2, 'kpc'): cen_central[2] + 0.5 * yt.YTArray(wdd2, 'kpc')]
box3 = ds.r[cen_central[0] - 0.5 * yt.YTArray(3*wd3, 'kpc'): cen_central[0] + 0.5 * yt.YTArray(3*wd3, 'kpc'), \
cen_central[1] - 0.5 * yt.YTArray(3*wd3, 'kpc'): cen_central[1] + 0.5 * yt.YTArray(3*wd3, 'kpc'), \
cen_central[2] - 0.5 * yt.YTArray(wdd3, 'kpc'): cen_central[2] + 0.5 * yt.YTArray(wdd3, 'kpc')]
'''
a = time.time()
p_wd2_g= yt.ProjectionPlot(ds, axis, ("gas","density"), center = cen_central, data_source=box2, width=W2)
b = time.time()
print ('p_wd2_g', b-a)
p_wd2_g.set_unit(('gas','density'), 'Msun/pc**2')
p_wd2_g.set_zlim(('gas', 'density'), zmin = density_proj_min, zmax = density_proj_max)
p_wd2_g.set_cmap(('gas', 'density'), density_color_map)
p_wd2_g.annotate_timestamp(corner='upper_left', redshift=True, draw_inset_box=True)
p_wd2_g.hide_axes()
p_wd2_g.annotate_timestamp(corner='upper_left', redshift=True, draw_inset_box=True)
p_wd2_g.annotate_scale(size_bar_args={'color':'white'})
a = time.time()
p_wd2_s = yt.ParticleProjectionPlot(ds, axis, ('stars', 'particle_mass'), center = cen_central, data_source=box2, width = W2)
b = time.time()
print ('p_wd2_s', b-a)
cmp = plt.cm.Greys_r
cmp.set_bad('k')
p_wd2_s.set_cmap(field = ('stars','particle_mass'), cmap = cmp)
p_wd2_s.hide_axes()
p_wd2_s.annotate_scale(size_bar_args={'color':'white'})
p_wd2_s.set_zlim(field = ('stars','particle_mass'), zmin = 2.e35 * 0.3, zmax = 1.e42*0.9)
a = time.time()
p_wd3_g= yt.ProjectionPlot(ds, axis, ("gas","density"), center = cen_central, data_source=box3, width=W3)
b = time.time()
print ('p_wd3_g', b-a)
p_wd3_g.set_unit(('gas','density'), 'Msun/pc**2')
p_wd3_g.set_zlim(('gas', 'density'), zmin = density_proj_min, zmax = density_proj_max)
p_wd3_g.set_cmap(('gas', 'density'), density_color_map)
p_wd3_g.annotate_timestamp(corner='upper_left', redshift=True, draw_inset_box=True)
p_wd3_g.hide_axes()
p_wd3_g.annotate_timestamp(corner='upper_left', redshift=True, draw_inset_box=True)
p_wd3_g.annotate_scale(size_bar_args={'color':'white'})
a = time.time()
p_wd3_s = yt.ParticleProjectionPlot(ds, axis, ('stars', 'particle_mass'), center = cen_central, data_source=box3, width = W3)
b = time.time()
print ('p_wd3_s', b-a)
cmp = plt.cm.Greys_r
cmp.set_bad('k')
p_wd3_s.set_cmap(field = ('stars','particle_mass'), cmap = cmp)
p_wd3_s.hide_axes()
p_wd3_s.annotate_scale(size_bar_args={'color':'white'})
p_wd3_s.set_zlim(field = ('stars','particle_mass'), zmin = 2.e35 * 0.3, zmax = 1.e42*0.9)
'''
for sat_n in arange(6, 7):
if sat_n < 6:
cenx = cen_fits['SAT_%.2i' % sat_n]['fxe'](DD)
ceny = cen_fits['SAT_%.2i' % sat_n]['fye'](DD)
cenz = cen_fits['SAT_%.2i' % sat_n]['fze'](DD)
if sat_n == 6:
#cenx = cen_fits['CENTRAL']['fxe'](DD)
#ceny = cen_fits['CENTRAL']['fye'](DD)
#cenz = cen_fits['CENTRAL']['fze'](DD)
cenx = cen_fits['CENTRAL']['fxe'](DD)
ceny = cen_fits['CENTRAL']['fye'](DD)
cenz = cen_fits['CENTRAL']['fze'](DD)
cen_g = yt.YTArray([cenx, ceny, cenz], 'kpc')
print('satellite center: ', cen_g)
print('central center: ', cen_central)
figname_zoomin = '%s_%.4i_%.2i_%s_zoomin_100kpc.png' % (
cen_name, DD, sat_n, axis)
figname_zoomout = '%s_%.4i_%.2i_%s_zoomout.png' % (cen_name, DD,
sat_n, axis)
figname_zoomoutfar = '%s_%.4i_%.2i_%s_zoomoutfar.png' % (
cen_name, DD, sat_n, axis)
if axis == 'x':
box = ds.r[cen_g[0] - 0.5 * yt.YTArray(wdd, 'kpc'): cen_g[0] + 0.5 * yt.YTArray(wdd, 'kpc'), \
cen_g[1] - 0.5 * yt.YTArray(3*wd, 'kpc'): cen_g[1] + 0.5 * yt.YTArray(3*wd, 'kpc'), \
cen_g[2] - 0.5 * yt.YTArray(3*wd, 'kpc'): cen_g[2] + 0.5 * yt.YTArray(3*wd, 'kpc')]
p_1 = cen_g[1] - cen_central[1]
p_2 = cen_g[2] - cen_central[2]
p_3 = cen_g[0] - cen_central[0]
elif axis == 'y':
box = ds.r[cen_g[0] - 0.5 * yt.YTArray(3*wd, 'kpc'): cen_g[0] + 0.5 * yt.YTArray(3*wd, 'kpc'), \
cen_g[1] - 0.5 * yt.YTArray(wdd, 'kpc'): cen_g[1] + 0.5 * yt.YTArray(wdd, 'kpc'), \
cen_g[2] - 0.5 * yt.YTArray(3*wd, 'kpc'): cen_g[2] + 0.5 * yt.YTArray(3*wd, 'kpc')]
p_1 = cen_g[2] - cen_central[2]
p_2 = cen_g[0] - cen_central[0]
p_3 = cen_g[1] - cen_central[1]
elif axis == 'z':
box = ds.r[cen_g[0] - 0.5 * yt.YTArray(3*wd, 'kpc'): cen_g[0] + 0.5 * yt.YTArray(3*wd, 'kpc'), \
cen_g[1] - 0.5 * yt.YTArray(3*wd, 'kpc'): cen_g[1] + 0.5 * yt.YTArray(3*wd, 'kpc'), \
cen_g[2] - 0.5 * yt.YTArray(wdd, 'kpc'): cen_g[2] + 0.5 * yt.YTArray(wdd, 'kpc')]
p_1 = cen_g[0] - cen_central[0]
p_2 = cen_g[1] - cen_central[1]
p_3 = cen_g[2] - cen_central[2]
fig = plt.figure(sat_n)
grid = AxesGrid(fig, (0.0, 0.0, 1.0, 1.0),
nrows_ncols=(1, 2),
axes_pad=0.0,
label_mode="1",
share_all=False,
cbar_mode=None,
aspect=False)
p = yt.ProjectionPlot(ds,
axis, ("gas", "density"),
center=cen_g,
data_source=box,
width=W)
p.set_unit(('gas', 'density'), 'Msun/pc**2')
p.set_zlim(('gas', 'density'),
zmin=density_proj_min,
zmax=density_proj_max)
p.set_cmap(('gas', 'density'), density_color_map)
p.annotate_timestamp(corner='upper_left',
redshift=True,
draw_inset_box=True)
p.hide_axes()
p.annotate_timestamp(corner='upper_left',
redshift=True,
draw_inset_box=True)
p.annotate_scale(size_bar_args={'color': 'white'})
plot = p.plots[("gas", "density")]
plot.figure = fig
plot.axes = grid[0].axes
p._setup_plots()
p = yt.ParticleProjectionPlot(ds,
axis, ('stars', 'particle_mass'),
center=cen_g,
data_source=box,
width=W)
cmp = plt.cm.Greys_r
cmp.set_bad('k')
p.set_cmap(field=('stars', 'particle_mass'), cmap=cmp)
p.hide_axes()
p.annotate_scale(size_bar_args={'color': 'white'})
p.set_zlim(field=('stars', 'particle_mass'),
zmin=2.e35 * 0.3,
zmax=1.e42 * 0.9)
plot = p.plots[('stars', 'particle_mass')]
plot.figure = fig
plot.axes = grid[1].axes
p._setup_plots()
fig.set_size_inches(12, 6)
fig.savefig('%s/%s/%s/%s' %
(figdir, axis, 'zoomin', figname_zoomin))
plt.close(fig)
'''
fig = plt.figure(sat_n)
grid = AxesGrid(fig, (0.0,0.0,1.0,1.0),
nrows_ncols = (1, 2),
axes_pad = 0.0, label_mode = "1",
share_all = False, cbar_mode=None,
aspect = False)
p = copy.copy(p_wd2_g)
plot = p.plots[("gas","density")]
plot.figure = fig
plot.axes = grid[0].axes
p._setup_plots()
print (abs(p_1), abs(p_2), W2)
if (abs(p_1) < wd2/2.) & (abs(p_2) < wd2/2.) & (abs(p_3) < wd2/2.): plot.axes.scatter(p_1, p_2, marker = 'o', facecolor = "none", edgecolor='red', lw = 2, s = 800)
p = copy.copy(p_wd2_s)
plot = p.plots[('stars','particle_mass')]
plot.figure = fig
plot.axes = grid[1].axes
p._setup_plots()
#if (abs(p_1) < wd2/2.) & (abs(p_2) < wd2/2.) & (abs(p_3) < wd2/2.): plot.axes.scatter(p_1, p_2, marker = 'o', facecolor = "none", edgecolor='red', lw = 2, s = 800)
fig.set_size_inches(12, 6)
fig.savefig('%s/%s/%s/%s'%(figdir,axis, 'zoomout', figname_zoomout))
plt.close(fig)
fig = plt.figure(sat_n)
grid = AxesGrid(fig, (0.0,0.0,1.0,1.0),
nrows_ncols = (1, 2),
axes_pad = 0.0, label_mode = "1",
share_all = False, cbar_mode=None,
aspect = False)
p = copy.copy(p_wd3_g)
plot = p.plots[("gas","density")]
plot.figure = fig
plot.axes = grid[0].axes
p._setup_plots()
print (abs(p_1), abs(p_2), W2)
#if (abs(p_1) < wd3/2.) & (abs(p_2) < wd3/2.) & (abs(p_3) < wdd3/2.): plot.axes.scatter(p_1, p_2, marker = 'o', facecolor = "none", edgecolor='red', lw = 2, s = 800)
p = copy.copy(p_wd3_s)
plot = p.plots[('stars','particle_mass')]
plot.figure = fig
plot.axes = grid[1].axes
p._setup_plots()
#if (abs(p_1) < wd3/2.) & (abs(p_2) < wd3/2.) & (abs(p_3) < wdd3/2.): plot.axes.scatter(p_1, p_2, marker = 'o', facecolor = "none", edgecolor='red', lw = 2, s = 800)
fig.set_size_inches(12, 6)
fig.savefig('%s/%s/%s/%s'%(figdir,axis, 'zoomoutfar', figname_zoomoutfar))
plt.close(fig)
'''
ds.index.clear_all_data()
ds = yt.load(filename)
ds.add_particle_filter('stars_young')
ds.add_particle_filter('stars_medium')
ds.add_particle_filter('stars_old')
# What are the total masses of different ages of star in the whole simulation
# volume?
ad = ds.all_data()
mass_young = ad['stars_young', 'particle_mass'].in_units('Msun').sum()
mass_medium = ad['stars_medium', 'particle_mass'].in_units('Msun').sum()
mass_old = ad['stars_old', 'particle_mass'].in_units('Msun').sum()
print("Mass of young stars = %g Msun" % mass_young)
print("Mass of medium stars = %g Msun" % mass_medium)
print("Mass of old stars = %g Msun" % mass_old)
# Generate 4 projections: gas density, young stars, medium stars, old stars
fields = [('stars_young', 'particle_mass'), ('stars_medium', 'particle_mass'),
('stars_old', 'particle_mass')]
prj1 = yt.ProjectionPlot(ds,
'z', ("gas", "density"),
center="max",
width=(100, "kpc"))
prj1.save()
prj2 = yt.ParticleProjectionPlot(ds,
'z',
fields,
center="max",
width=(100, 'kpc'))
prj2.save()
y = data[2]
z = data[3]
data_dic = { 'particle_mass' : m,
'particle_position_x': x,
'particle_position_y': y,
'particle_position_z': z }
bbox_width = [ max(x)-min(x), max(y)-min(y), max(z)-min(z) ]
bbox = np.array( [ [ min(x)-bext*bbox_width[0], max(x)+bext*bbox_width[0] ],
[ min(y)-bext*bbox_width[1], max(y)+bext*bbox_width[1] ],
[ min(z)-bext*bbox_width[2], max(z)+bext*bbox_width[2] ] ] )
ds = yt.load_particles( data_dic, length_unit=unit_l, mass_unit=unit_m, time_unit=unit_t,
n_ref=nref, bbox=bbox, sim_time=time, periodicity=(False,False,False) )
# plot
p = yt.ParticleProjectionPlot( ds, proj_axis, fields=field, center=plot_center,
width=(plot_width[0],plot_width[1]), depth=plot_width[2] )
# p = yt.ParticlePlot( ds, 'particle_position_x', 'particle_position_y', 'particle_mass' )
p.set_unit( field, 'Msun' )
p.set_unit( 'particle_position_x', 'Mpc' )
p.set_unit( 'particle_position_y', 'Mpc' )
p.set_zlim( field, 1.0e0, 6.0e0 )
p.set_cmap( field, colormap )
p.annotate_timestamp( time_unit='Myr', corner='upper_right', text_args={'color':'k'} )
p.save( prefix_out+'_%06d'%idx+'.png', mpl_kwargs={'dpi':dpi} )