def plot_radius_density(data_dir, halo_id):
xfield = 'radius'
yfield = 'density'
filename = os.path.join(data_dir, "%s_%06d.h5" % (yfield, halo_id))
fontsize = 14
my_fig = GridFigure(1,
1,
figsize=(6, 4.5),
top_buffer=0.14,
bottom_buffer=0.13,
left_buffer=0.16,
right_buffer=0.19)
my_axes = my_fig[0]
my_axes.set_xscale('log')
my_axes.set_yscale('log')
my_cax = my_fig.add_cax(my_axes,
"right",
buffer=0.02,
length=0.95,
width=0.04)
plot_phase(filename,
'cell_mass',
'Msun',
my_axes,
my_cax=my_cax,
cmap=yt_colormaps['dusk'])
my_cax.yaxis.set_label_text("M [M$_{\\odot}$]")
xlim = (2e-6, 2e2)
tx = twin_unit_axes(my_axes, xlim, "r", "pc", top_units="AU")
ylim = (1e-25, 1e-10)
ymajor = np.logspace(-25, -10, 6)
yminor = np.logspace(-25, -10, 16)
ylabel = "$\\rho$ [g/cm$^{-3}$]"
mirror_yticks(my_axes, ylim, ymajor, yminor=yminor)
draw_major_grid(my_axes, 'y', ymajor)
my_axes.yaxis.set_label_text(ylabel)
output_filename = "figures/%s_%s.pdf" % \
(xfield, yfield)
pyplot.savefig(output_filename)
def plot_velocity_profiles(data_dir, file_prefix):
my_fig = GridFigure(1,
1,
figsize=(6, 4.5),
top_buffer=0.14,
bottom_buffer=0.12,
left_buffer=0.09,
right_buffer=0.02)
# http://www.colourlovers.com/palette/1329926/graphic_artist.
# colors = ["#B00029", "#90B004", "#19849C", "#851370", "#544D4D", "black"]
colors = ["red", "green", "blue", "purple", "#544D4D", "black"]
my_axes = my_fig[0]
my_axes.set_xscale('log')
fields = [
"velocity_magnitude", "tangential_velocity_magnitude",
"velocity_spherical_radius", "sound_speed"
]
for i, field in enumerate(fields):
filename = os.path.join(
data_dir, f"{file_prefix}_2D_profile_radius_{field}_None.h5")
plot_profile_distribution(my_axes,
filename,
'cell_mass',
x_units="pc",
y_units='km/s',
alpha_scale=0.7,
pkwargs=dict(color=colors[i], linewidth=1))
fn = os.path.join(data_dir,
f"{file_prefix}_1D_profile_radius_cell_mass.h5")
pds = yt.load(fn)
pradius = pds.profile.x.to("pc")
vsigma = pds.profile.standard_deviation['data',
'velocity_magnitude'].to("km/s")
my_axes.plot(pradius[vsigma > 0],
vsigma[vsigma > 0],
alpha=0.9,
linewidth=1,
color=colors[4],
zorder=998)
field = "matter_mass"
fn = os.path.join(data_dir, f"{file_prefix}_1D_profile_radius_None.h5")
mds = yt.load(fn)
radius = mds.profile.x.to("pc")
mass = mds.profile[field]
dfil = mass > 0
v_sp = np.sqrt(G * mass[dfil].cumsum() / radius[dfil]).to("km/s")
my_axes.plot(radius[dfil],
v_sp,
alpha=0.9,
linewidth=1,
color=colors[5],
zorder=997)
ylim = (-5, 13)
ymajor = np.arange(-5, 16, 5.)
yminor = np.arange(-5, 15, 1.)
my_axes.yaxis.set_label_text("v [km / s]")
my_axes.yaxis.labelpad = -3
draw_major_grid(my_axes,
'y',
ymajor,
color='black',
linestyle='-',
linewidth=1,
alpha=0.2)
ty = mirror_yticks(my_axes, ylim, ymajor, yminor=yminor)
xlim = (1e-1, 2e2)
tx = twin_unit_axes(my_axes, xlim, "r", "pc", top_units="AU")
labels = [
"|v|", "v$_{\\rm tan}$", "v$_{\\rm rad}$", "c$_{\\rm s}$", "$\\sigma$",
"v$_{\\rm c}$"
]
dist = [True] * 4 + [False] * 2
plot_items = list(zip(colors, labels, dist))
plot_profile_distribution_legend(my_axes, plot_items, alpha_scale=0.7)
pyplot.savefig("figures/velocity_profiles.pdf")
def plot_timescale_profiles(data_dir, file_prefix):
my_fig = GridFigure(1,
1,
figsize=(6, 4.5),
top_buffer=0.13,
bottom_buffer=0.12,
left_buffer=0.14,
right_buffer=0.02,
horizontal_buffer=0.05,
vertical_buffer=0)
my_axes = my_fig[0]
my_axes.set_xscale('log')
my_axes.set_yscale('log')
xlim = (5e-3, 3e2)
tx = twin_unit_axes(my_axes, xlim, "r", "pc", top_units="AU")
fields = [
"sound_crossing_time", "total_dynamical_time", "cooling_time",
"vortical_time"
]
units = ["yr", f"{np.sqrt(2)}*yr", "yr", f"{1/(2*np.pi)}*yr"]
labels = ["sound-crossing", "free-fall", "cooling", "mixing"]
colors = ["orange", "red", "green", "blue"]
filename = os.path.join(data_dir, "DD0295_1D_profile_radius_cell_mass.h5")
ds = yt.load(filename)
x_data = ds.profile.x.to("pc")
used = ds.profile.used
for field, unit, label, color in zip(fields, units, labels, colors):
if field == "sound_crossing_time":
cs = ds.profile["data", "sound_speed"]
vt = ds.profile.standard_deviation["data", "velocity_magnitude"]
v = np.sqrt(cs**2 + vt**2)
y_data = (2 * x_data / v).to(unit)
else:
y_data = ds.profile["data", field].to(unit)
my_axes.plot(x_data[used],
y_data[used],
color=color,
alpha=0.7,
linewidth=1.5,
label=label)
ylim = (1e4, 1e8)
ymajor = np.logspace(2, 10, 5)
yminor = np.logspace(1, 9, 5)
draw_major_grid(my_axes,
'y',
ymajor,
color='black',
linestyle='-',
linewidth=1,
alpha=0.2)
ty = mirror_yticks(my_axes, ylim, ymajor, yminor=yminor)
my_axes.yaxis.set_label_text("t [yr]")
my_axes.legend()
# my_axes.yaxis.labelpad = 4
pyplot.savefig("figures/timescale_profiles.pdf")
def plot_timescale_profiles(data_dir, halo_id):
my_fig = GridFigure([3, 1],
1,
figsize=(6, 4.5),
top_buffer=0.13,
bottom_buffer=0.12,
left_buffer=0.14,
right_buffer=0.02,
horizontal_buffer=0.05,
vertical_buffer=0)
colors = ["red", "green", "blue"]
for i, my_axes in enumerate(my_fig):
my_axes.set_xscale('log')
my_axes.set_yscale('log')
xlim = (2e-6, 3e2)
tx = twin_unit_axes(my_axes, xlim, "r", "pc", top_units="AU")
if i == 0:
fields = ["dynamical_time", "cooling_time", "vortical_time"]
units = ["%f*yr" % np.sqrt(2), "yr", "%f*yr" % (1 / (2 * np.pi))]
for i, field in enumerate(fields):
filename = os.path.join(data_dir,
"%s_%06d.h5" % (field, halo_id))
plot_profile_distribution(my_axes,
filename,
'cell_mass',
x_units="pc",
y_units=units[i],
alpha_scale=0.7,
pkwargs=dict(color=colors[i],
linewidth=1))
my_axes.xaxis.set_visible(False)
ylim = (10, 1e10)
ymajor = np.logspace(2, 10, 5)
yminor = np.logspace(1, 9, 5)
draw_major_grid(my_axes,
'y',
ymajor,
color='black',
linestyle='-',
linewidth=1,
alpha=0.2)
ty = mirror_yticks(my_axes, ylim, ymajor, yminor=yminor)
my_axes.yaxis.set_label_text("t [yr]")
# my_axes.yaxis.labelpad = 4
labels = ("free-fall", "cooling", "mixing")
dist = [True] * 3
plot_items = list(zip(colors, labels, dist))
plot_profile_distribution_legend(my_axes,
plot_items,
alpha_scale=0.7,
lheight=0.27,
label_rotation=315)
if i == 1:
fields = ["cooling_dynamical_ratio", "vortical_dynamical_ratio"]
units = [str(1 / np.sqrt(2)), str(1 / (2 * np.pi * np.sqrt(2)))]
for i, field in enumerate(fields):
filename = os.path.join(data_dir,
"%s_%06d.h5" % (field, halo_id))
plot_profile_distribution(my_axes,
filename,
'cell_mass',
x_units="pc",
y_units=units[i],
alpha_scale=0.7,
pkwargs=dict(color=colors[i + 1],
linewidth=1))
ds = yt.load(filename)
x_data = ds.profile.x.to("pc")
z_data = ds.profile['cell_mass'].transpose()
z_sum = z_data.sum(axis=0)
rfil = z_sum > 0
my_axes.plot(x_data[rfil],
np.ones(x_data[rfil].size),
alpha=0.9,
color=colors[0],
linewidth=1,
zorder=1)
tx.xaxis.set_visible(False)
ylim = (0.01, 100)
ymajor = np.logspace(-1, 1, 2)
yminor = np.logspace(-2, 2, 5)
draw_major_grid(my_axes,
'y',
ymajor,
color='black',
linestyle='-',
linewidth=1,
alpha=0.2)
ty = mirror_yticks(my_axes, ylim, ymajor, yminor=yminor)
my_axes.yaxis.set_label_text("t / t$_{\\rm ff}$")
my_axes.yaxis.labelpad = 0
pyplot.savefig("figures/timescale_profile_distributions.pdf")