def map_M_kSZ_tSZ_3x1(num_halo,
redshift,
simulation_type,
nbins=100,
rfov=2,
output='save',
projection=0):
# Generate plot frame
plotpar.set_defaults_plot()
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(20, 9))
rendersz.render_M(axes[0],
num_halo,
redshift,
simulation_type,
projection=projection,
nbins=nbins,
rfov=rfov)
rendersz.render_kSZ(axes[1],
num_halo,
redshift,
simulation_type,
projection=projection,
nbins=nbins,
rfov=rfov)
rendersz.render_tSZ(axes[2],
num_halo,
redshift,
simulation_type,
projection=projection,
nbins=nbins,
rfov=rfov)
# Define output
if output == 'show':
plt.show()
elif output == 'save':
dir_name = 'Maps_M+kSZ+tSZ_3x1'
save_name = 'map_M+kSZ+tSZ_3x1' + '_halo' + str(num_halo) + '_z' + str(
redshift).replace(".", "") + '_rfov' + str(rfov) + '_nbins' + str(
nbins) + '_proj' + str(projection)
if not exists(dir_name): makedirs(dir_name)
plt.savefig(dir_name + '//' + save_name + '.pdf')
else:
print(
"[ERROR] The output type you are trying to select is not defined.")
exit(1)
def phasediag_1x2(redshift, output='show', nbins=600, max_halo=10):
plotpar.set_defaults_plot()
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(11.5, 7), sharey=True)
phase_diagram_master(axes[0], redshift, nbins=nbins, max_halo=max_halo, selection='all', bg='k')
phase_diagram_master(axes[1], redshift, nbins=nbins, max_halo=max_halo, selection='sub', bg='k')
# Define output
if output == 'show':
plt.show()
elif output == 'save':
save_name = 'phase-diagram_nHcgs_2x1_halo' + str(max_halo) + '_z' + str(redshift).replace(".", "") + '_nbins' + str(
nbins) + '.png'
dir_name = 'phase_diagrams_T-Rho_master390'
if not exists(dir_name): makedirs(dir_name)
plt.savefig(dir_name + '//' + save_name)
else:
print("[ERROR] The output type you are trying to select is not defined.")
exit(1)
def map_particles(num_halo, redshift, simulation_type = 'gas', output = 'show', title = True, save_name = 'Map_particles_gas', nbins = 400):
# Import data
path = extract.path_from_cluster_name(num_halo, simulation_type = simulation_type)
file = extract.file_name_hdf5(subject = 'groups', redshift = extract.redshift_floatTostr(redshift))
r200 = extract.group_r200(path, file)
group_CoP = extract.group_centre_of_potential(path, file)
file = extract.file_name_hdf5(subject = 'particledata', redshift = extract.redshift_floatTostr(redshift))
#print(r200)
h = extract.file_hubble_param(path, file)
# Gas particles
part_type = extract.particle_type('gas')
mass = extract.particle_masses(path, file, part_type)
coordinates = extract.particle_coordinates(path, file, part_type)
velocities = extract.particle_velocity(path, file, part_type)
group_number = extract.group_number(path, file, part_type)
subgroup_number = extract.subgroup_number(path, file, part_type)
tot_rest_frame, _ = profile.total_mass_rest_frame(path, file)
#gas_rest_frame, _ = profile.cluster_average_momentum(path, file, part_type)
# Retrieve coordinates & velocities
x = coordinates[:,0] - group_CoP[0]
y = coordinates[:,1] - group_CoP[1]
z = coordinates[:,2] - group_CoP[2]
vx = velocities[:,0] - tot_rest_frame[0]
vy = velocities[:,1] - tot_rest_frame[1]
vz = velocities[:,2] - tot_rest_frame[2]
# Rescale to comoving coordinates
x = profile.comoving_length(x, h, redshift)
y = profile.comoving_length(y, h, redshift)
z = profile.comoving_length(z, h, redshift)
r200 = profile.comoving_length(r200, h, redshift)
vx = profile.comoving_velocity(vx, h, redshift)
vy = profile.comoving_velocity(vy, h, redshift)
vz = profile.comoving_velocity(vz, h, redshift)
vx = profile.velocity_units(vx, unit_system = 'astro')
vy = profile.velocity_units(vy, unit_system = 'astro')
vz = profile.velocity_units(vz, unit_system = 'astro')
mass = profile.comoving_mass(mass, h, redshift)
mass = profile.mass_units(mass, unit_system = 'astro')
# Compute radial distance
r = np.sqrt(x**2+y**2+z**2)
# Select particles within 5*r200
index = np.where((r < 5*r200) & (group_number > -1) & (subgroup_number > -1))[0]
mass = mass[index]
x, y, z = x[index], y[index], z[index]
vx, vy, vz = vx[index], vy[index], vz[index]
# Generate plot
plotpar.set_defaults_plot()
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(20, 7))
# Bin data
#nbins = 250
cmap = 'terrain_r'
################################################################################################
x_Data = x
y_Data = y
x_bins = np.linspace(np.min(x_Data), np.max(x_Data), nbins)
y_bins = np.linspace(np.min(y_Data), np.max(y_Data), nbins)
Cx, Cy = mapgen.bins_meshify(x_Data, y_Data, x_bins, y_bins)
count = mapgen.bins_evaluate(x_Data, y_Data, x_bins, y_bins, weights = None)
img = axes[0].pcolor(Cx, Cy, np.log10(count), cmap=cmap)
axes[0].set_xlabel(r'$x\mathrm{/Mpc}$')
axes[0].set_ylabel(r'$y\mathrm{/Mpc}$')
axes[0].annotate(r'$\bigotimes z$', (0.03, 0.03), textcoords='axes fraction', size = 15)
# Colorbar adjustments
ax2_divider = make_axes_locatable(axes[0])
cax2 = ax2_divider.append_axes("right", size="3%", pad="2%")
cbar = plt.colorbar(img, cax=cax2, orientation='vertical')
#cbar.set_label(r'$\log_{10}(N_{particles})$', labelpad=17)
#cax2.xaxis.set_tick_labels(['0',' ','0.5',' ','1',' ', '1.5',' ','2'])
cax2.xaxis.set_ticks_position("top")
x_Data = y
y_Data = z
x_bins = np.linspace(np.min(x_Data), np.max(x_Data), nbins)
y_bins = np.linspace(np.min(y_Data), np.max(y_Data), nbins)
Cx, Cy = mapgen.bins_meshify(x_Data, y_Data, x_bins, y_bins)
count = mapgen.bins_evaluate(x_Data, y_Data, x_bins, y_bins, weights = None)
img = axes[1].pcolor(Cx, Cy, np.log10(count), cmap=cmap)
axes[1].set_xlabel(r'$y\mathrm{/Mpc}$')
axes[1].set_ylabel(r'$z\mathrm{/Mpc}$')
axes[1].annotate(r'$\bigotimes x$', (0.03, 0.03), textcoords='axes fraction', size = 15)
# Colorbar adjustments
ax2_divider = make_axes_locatable(axes[1])
cax2 = ax2_divider.append_axes("right", size="3%", pad="2%")
cbar = plt.colorbar(img, cax=cax2, orientation='vertical')
#cbar.set_label(r'$\log_{10}(N_{particles})$', labelpad=17)
#cax2.xaxis.set_tick_labels(['0',' ','0.5',' ','1',' ', '1.5',' ','2'])
cax2.xaxis.set_ticks_position("top")
x_Data = x
y_Data = z
x_bins = np.linspace(np.min(x_Data), np.max(x_Data), nbins)
y_bins = np.linspace(np.min(y_Data), np.max(y_Data), nbins)
Cx, Cy = mapgen.bins_meshify(x_Data, y_Data, x_bins, y_bins)
count = mapgen.bins_evaluate(x_Data, y_Data, x_bins, y_bins, weights = None)
img = axes[2].pcolor(Cx, Cy, np.log10(count), cmap=cmap)
axes[2].set_xlabel(r'$x\mathrm{/Mpc}$')
axes[2].set_ylabel(r'$z\mathrm{/Mpc}$')
axes[2].annotate(r'$\bigodot y$', (0.03, 0.03), textcoords='axes fraction', size = 15)
# Colorbar adjustments
ax2_divider = make_axes_locatable(axes[2])
cax2 = ax2_divider.append_axes("right", size="3%", pad="2%")
cbar = plt.colorbar(img, cax=cax2, orientation='vertical')
cbar.set_label(r'$\log_{10}(N_{particles})$', labelpad=17)
#cax2.xaxis.set_tick_labels(['0',' ','0.5',' ','1',' ', '1.5',' ','2'])
cax2.xaxis.set_ticks_position("top")
################################################################################################
# Plot r200 circle
for i in [0,1,2]:
axes[i].set_aspect('equal')
axes[i].add_artist(Circle((0,0), radius=r200, color = 'red', fill = False, linestyle = '--', label = r'$R_{200}$'))
axes[i].add_artist(Circle((0,0), radius=5*r200, color = 'black', fill = False, linewidth = 0.5,linestyle = '-', label = r'$R_{200}$'))
axes[i].set_xlim(-5.1*r200, 5.1*r200)
axes[i].set_ylim(-5.1*r200, 5.1*r200)
if title:
axes[i].set_title(r'$\mathrm{MACSIS\ halo\ } %3d \qquad z = %8.3f$' % (num_halo, redshift))
# Define output
if output == 'show':
plt.show()
elif output == 'save':
dir_name = 'Map particles'
if not exists(dir_name):
makedirs(dir_name)
plt.savefig(dir_name + '//'+save_name+'_partType'+part_type+'_halo'+str(num_halo)+'z'+str(redshift).replace(".", "")+'.pdf')
else:
print("[ERROR] The output type you are trying to select is not defined.")
exit(1)
def map_kSZ_intensity(num_halo, redshift, simulation_type, nbins, rfov):
# Import data
path = extract.path_from_cluster_name(num_halo,
simulation_type=simulation_type)
file = extract.file_name_hdf5(
subject='groups', redshift=extract.redshift_floatTostr(redshift))
r200 = extract.group_r200(path, file)
print(r200)
group_CoP = extract.group_centre_of_potential(path, file)
file = extract.file_name_hdf5(
subject='particledata', redshift=extract.redshift_floatTostr(redshift))
# Gas particles
part_type = extract.particle_type('gas')
mass = extract.particle_masses(path, file, part_type)
coordinates = extract.particle_coordinates(path, file, part_type)
velocities = extract.particle_velocity(path, file, part_type)
temperatures = extract.particle_temperature(path, file, part_type)
group_number = extract.group_number(path, file, part_type)
subgroup_number = extract.subgroup_number(path, file, part_type)
tot_rest_frame, _ = profile.total_mass_rest_frame(path, file)
#gas_rest_frame, _ = profile.cluster_average_momentum(path, file, part_type)
# Retrieve coordinates & velocities
x = coordinates[:, 0] - group_CoP[0]
y = coordinates[:, 1] - group_CoP[1]
z = coordinates[:, 2] - group_CoP[2]
vx = velocities[:, 0] - tot_rest_frame[0]
vy = velocities[:, 1] - tot_rest_frame[1]
vz = velocities[:, 2] - tot_rest_frame[2]
h = extract.file_hubble_param(path, file)
# Rescale to comoving coordinates
x = profile.comoving_length(x, h, redshift)
y = profile.comoving_length(y, h, redshift)
z = profile.comoving_length(z, h, redshift)
r200 = profile.comoving_length(r200, h, redshift)
vx = profile.comoving_velocity(vx, h, redshift)
vy = profile.comoving_velocity(vy, h, redshift)
vz = profile.comoving_velocity(vz, h, redshift)
vx = profile.velocity_units(vx, unit_system='astro')
vy = profile.velocity_units(vy, unit_system='astro')
vz = profile.velocity_units(vz, unit_system='astro')
mass = profile.comoving_mass(mass, h, redshift)
mass = profile.mass_units(mass, unit_system='astro')
T = temperatures
# Compute radial distance
r = np.sqrt(x**2 + y**2 + z**2)
# Particle selection
index = np.where((r < 5 * r200) & (group_number > -1)
& (subgroup_number > -1) & (T > 10**5))[0]
mass, T = mass[index], T[index]
x, y, z = x[index], y[index], z[index]
vx, vy, vz = vx[index], vy[index], vz[index]
# Generate plot
plotpar.set_defaults_plot()
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(20, 9))
# Convert to angular distances
#cosmo = {'omega_M_0' : 0.307, 'omega_lambda_0' : 0.693, 'h' : 0.6777}
#cosmo = cosmolopy.set_omega_k_0(cosmo)
redshift = extract.file_redshift(path, file)
#angular_distance = cosmolopy.angular_diameter_distance(redshift, z0 = 0, **cosmo)
cosmo = FlatLambdaCDM(H0=70, Om0=0.3)
angular_distance = cosmo.luminosity_distance(redshift)
print("angular_diameter_distance: ", angular_distance)
Mpc_to_arcmin = np.power(
np.pi, -1) * 180 * 60 / angular_distance * astropy.units.Mpc
x = x * Mpc_to_arcmin
y = y * Mpc_to_arcmin
z = z * Mpc_to_arcmin
r200 = r200 * Mpc_to_arcmin
# Bin data
cmap = [
plt.get_cmap('seismic'),
plt.get_cmap('seismic'),
plt.get_cmap('seismic_r')
]
#cmap = [mapgen.modified_spectral_cmap(Reversed = True), mapgen.modified_spectral_cmap(Reversed = True), mapgen.modified_spectral_cmap(Reversed = False)]
xlabel = [
r'$x\mathrm{/arcmin}$', r'$y\mathrm{/arcmin}$', r'$x\mathrm{/arcmin}$'
]
ylabel = [
r'$y\mathrm{/arcmin}$', r'$z\mathrm{/arcmin}$', r'$z\mathrm{/arcmin}$'
]
thirdAX = [r'$\bigotimes z$', r'$\bigotimes x$', r'$\bigodot y$']
cbarlabel = [
r'$\sum_{i} m_i v_{z, i}\ [\mathrm{M_\odot\ km\ s^{-1}}]$',
r'$\sum_{i} m_i v_{x, i}\ [\mathrm{M_\odot\ km\ s^{-1}}]$',
r'$\sum_{i} m_i v_{y, i}\ [\mathrm{M_\odot\ km\ s^{-1}}]$'
]
for i in [0, 1, 2]:
# Handle data
if i == 0:
x_Data = x
y_Data = y
weight = vz
elif i == 1:
x_Data = y
y_Data = z
weight = vx
elif i == 2:
x_Data = x
y_Data = z
weight = vy
x_bins = np.linspace(-rfov * r200, rfov * r200, nbins)
y_bins = np.linspace(-rfov * r200, rfov * r200, nbins)
Cx, Cy = mapgen.bins_meshify(x_Data, y_Data, x_bins, y_bins)
# line of sight momentum weights
count = mapgen.bins_evaluate(x_Data,
y_Data,
x_bins,
y_bins,
weights=mass * weight)
# convolution
kernel, _ = kernconv.nika2_kernel(x_bins, y_bins)
kernel = np.array(kernel)
kSZmap = convolve(count, kernel)
norm = mapgen.MidpointNormalize(vmin=kSZmap.min(),
vmax=kSZmap.max(),
midpoint=0)
img = axes[i].pcolor(Cx, Cy, kSZmap, cmap=cmap[i], norm=norm)
# Render elements in plots
axes[i].set_title(r'$\mathrm{MACSIS\ halo\ } %3d \qquad z = %8.3f$' %
(num_halo, redshift),
pad=94)
axes[i].set_aspect('equal')
axes[i].add_artist(
Circle((0, 0),
radius=r200,
color='black',
fill=False,
linestyle='--',
label=r'$R_{200}$'))
axes[i].add_artist(
Circle((0, 0),
radius=5 * r200,
color='black',
fill=False,
linewidth=0.5,
linestyle='-',
label=r'$R_{200}$'))
axes[i].set_xlim(-rfov * r200, rfov * r200)
axes[i].set_ylim(-rfov * r200, rfov * r200)
axes[i].set_xlabel(xlabel[i])
axes[i].set_ylabel(ylabel[i])
axes[i].annotate(thirdAX[i], (0.03, 0.03),
textcoords='axes fraction',
size=15)
#if title:
# axes[i].set_title(r'$\mathrm{MACSIS\ halo\ } %3d \qquad z = %8.3f$' % (num_halo, redshift))
# Colorbar adjustments
ax2_divider = make_axes_locatable(axes[i])
cax2 = ax2_divider.append_axes("top", size="5%", pad="2%")
cbar = plt.colorbar(img, cax=cax2, orientation='horizontal')
cbar.set_label(cbarlabel[i], labelpad=-70)
#cax2.xaxis.set_tick_labels(['0',' ','0.5',' ','1',' ', '1.5',' ','2'])
cax2.xaxis.set_ticks_position("top")
print("run completed:", i)
#outfilename = 'parallel-out//I_kSZ_halo' + str(num_halo) +'_z016_' + str(nbins) + 'bins_' + str(rfov) + 'rfov.pdf'
#plt.savefig(outfilename)
plt.show()
def map_weighted_velocity(num_halo,
redshift,
simulation_type='gas',
output='show',
title=True,
save_name='Map_particles_gas',
plot_groups='FoF',
nbins=400):
# Import data
path = extract.path_from_cluster_name(num_halo,
simulation_type=simulation_type)
file = extract.file_name_hdf5(
subject='groups', redshift=extract.redshift_floatTostr(redshift))
r200 = extract.group_r200(path, file)
group_CoP = extract.group_centre_of_potential(path, file)
file = extract.file_name_hdf5(
subject='particledata', redshift=extract.redshift_floatTostr(redshift))
# Gas particles
part_type = extract.particle_type('gas')
mass = extract.particle_masses(path, file, part_type)
coordinates = extract.particle_coordinates(path, file, part_type)
velocities = extract.particle_velocity(path, file, part_type)
group_number = extract.group_number(path, file, part_type)
subgroup_number = extract.subgroup_number(path, file, part_type)
tot_rest_frame, _ = profile.total_mass_rest_frame(path, file)
#gas_rest_frame, _ = profile.cluster_average_momentum(path, file, part_type)
h = extract.file_hubble_param(path, file)
# Retrieve coordinates & velocities
x = coordinates[:, 0] - group_CoP[0]
y = coordinates[:, 1] - group_CoP[1]
z = coordinates[:, 2] - group_CoP[2]
vx = velocities[:, 0] - tot_rest_frame[0]
vy = velocities[:, 1] - tot_rest_frame[1]
vz = velocities[:, 2] - tot_rest_frame[2]
# Rescale to comoving coordinates
x = profile.comoving_length(x, h, redshift)
y = profile.comoving_length(y, h, redshift)
z = profile.comoving_length(z, h, redshift)
r200 = profile.comoving_length(r200, h, redshift)
vx = profile.comoving_velocity(vx, h, redshift)
vy = profile.comoving_velocity(vy, h, redshift)
vz = profile.comoving_velocity(vz, h, redshift)
vx = profile.velocity_units(vx, unit_system='astro')
vy = profile.velocity_units(vy, unit_system='astro')
vz = profile.velocity_units(vz, unit_system='astro')
mass = profile.comoving_mass(mass, h, redshift)
mass = profile.mass_units(mass, unit_system='astro')
# Compute radial distance
r = np.sqrt(x**2 + y**2 + z**2)
# Select particles within 5*r200
if plot_groups == 'FoF':
index = np.where((r < 5 * r200) & (group_number > -1)
& (subgroup_number > -1))[0]
elif plot_groups == 'subgroups':
index = np.where((r < 5 * r200) & (group_number > -1)
& (subgroup_number > 0))[0]
else:
print(
"[ERROR] The (sub)groups you are trying to plot are not defined.")
exit(1)
mass = mass[index]
x, y, z = x[index], y[index], z[index]
vx, vy, vz = vx[index], vy[index], vz[index]
# Generate plot
plotpar.set_defaults_plot()
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(20, 7))
# Bin data
#nbins = 800
cmap = mapgen.modified_spectral_cmap(Reversed=True)
xlabel = [r'$x\mathrm{/Mpc}$', r'$y\mathrm{/Mpc}$', r'$x\mathrm{/Mpc}$']
ylabel = [r'$y\mathrm{/Mpc}$', r'$z\mathrm{/Mpc}$', r'$z\mathrm{/Mpc}$']
thirdAX = [r'$\bigotimes z$', r'$\bigotimes x$', r'$\bigodot y$']
cbarlabel = [
r'$\sum_{i} m_i v_{z, i} / \sum_{i} m_i \ [\mathrm{km\ s^{-1}}]$',
r'$\sum_{i} m_i v_{x, i} / \sum_{i} m_i \ [\mathrm{km\ s^{-1}}]$',
r'$\sum_{i} m_i v_{y, i} / \sum_{i} m_i \ [\mathrm{km\ s^{-1}}]$'
]
for i in [0, 1, 2]:
# Handle data
if i == 0:
x_Data = x
y_Data = y
weight = vz
elif i == 1:
x_Data = y
y_Data = z
weight = vx
elif i == 2:
x_Data = x
y_Data = z
weight = vy
cmap = mapgen.modified_spectral_cmap(Reversed=False)
x_bins = np.linspace(np.min(x_Data), np.max(x_Data), nbins)
y_bins = np.linspace(np.min(y_Data), np.max(y_Data), nbins)
Cx, Cy = mapgen.bins_meshify(x_Data, y_Data, x_bins, y_bins)
# line of sight momentum weights
count_mv = mapgen.bins_evaluate(x_Data,
y_Data,
x_bins,
y_bins,
weights=mass * weight)
# mass weights
count_m = mapgen.bins_evaluate(x_Data,
y_Data,
x_bins,
y_bins,
weights=mass)
# average mass weighted velocity
count_m[count_m == 0] = 1
count = np.divide(count_mv, count_m)
norm = mapgen.MidpointNormalize(vmin=count.min(),
vmax=count.max(),
midpoint=0)
img = axes[i].pcolor(Cx, Cy, count, cmap=cmap, norm=norm)
# Render elements in plots
axes[i].set_aspect('equal')
axes[i].add_artist(
Circle((0, 0),
radius=r200,
color='black',
fill=False,
linestyle='--',
label=r'$R_{200}$'))
axes[i].add_artist(
Circle((0, 0),
radius=5 * r200,
color='black',
fill=False,
linewidth=0.5,
linestyle='-',
label=r'$R_{200}$'))
axes[i].set_xlim(-5.1 * r200, 5.1 * r200)
axes[i].set_ylim(-5.1 * r200, 5.1 * r200)
axes[i].set_xlabel(xlabel[i])
axes[i].set_ylabel(ylabel[i])
axes[i].annotate(thirdAX[i], (0.03, 0.03),
textcoords='axes fraction',
size=15)
if title:
axes[i].set_title(
r'$\mathrm{MACSIS\ halo\ } %3d \qquad z = %8.3f$' %
(num_halo, redshift))
# Colorbar adjustments
ax2_divider = make_axes_locatable(axes[i])
cax2 = ax2_divider.append_axes("right", size="3%", pad="2%")
cbar = plt.colorbar(img, cax=cax2, orientation='vertical')
cbar.set_label(cbarlabel[i], labelpad=17)
#cax2.xaxis.set_tick_labels(['0',' ','0.5',' ','1',' ', '1.5',' ','2'])
cax2.xaxis.set_ticks_position("top")
# Define output
if output == 'show':
plt.show()
elif output == 'save':
dir_name = 'Map mass weighted velocity'
if not exists(dir_name):
makedirs(dir_name)
plt.savefig(dir_name + '//' + save_name + '_partType' + part_type +
'_halo' + str(num_halo) + 'z' +
str(redshift).replace(".", "") + '.pdf')
else:
print(
"[ERROR] The output type you are trying to select is not defined.")
exit(1)
def phase_diagram_master(redshift,
nbins=500,
output='save',
selection='all',
bg='w'):
master_density = []
master_temperature = []
for num_halo in np.arange(390):
print('Ímporting halo ' + str(num_halo))
# Import data
path = extract.path_from_cluster_name(num_halo, simulation_type='gas')
file = extract.file_name_hdf5(
subject='groups', redshift=extract.redshift_floatTostr(redshift))
r200 = extract.group_r200(path, file)
group_CoP = extract.group_centre_of_potential(path, file)
file = extract.file_name_hdf5(
subject='particledata',
redshift=extract.redshift_floatTostr(redshift))
# Gas particles
part_type = extract.particle_type('gas')
density = extract.particle_SPH_density(path, file, part_type)
coordinates = extract.particle_coordinates(path, file, part_type)
temperature = extract.particle_temperature(path, file, part_type)
group_number = extract.group_number(path, file, part_type)
subgroup_number = extract.subgroup_number(path, file, part_type)
# Retrieve coordinates
x = coordinates[:, 0] - group_CoP[0]
y = coordinates[:, 1] - group_CoP[1]
z = coordinates[:, 2] - group_CoP[2]
# Rescale to comoving coordinates
h = extract.file_hubble_param(path, file)
x = profile.comoving_length(x, h, redshift)
y = profile.comoving_length(y, h, redshift)
z = profile.comoving_length(z, h, redshift)
r200 = profile.comoving_length(r200, h, redshift)
density = profile.comoving_density(density, h, redshift)
density = profile.density_units(density, unit_system='nHcgs')
# Compute radial distance
r = np.sqrt(x**2 + y**2 + z**2)
index = 0
# Select particles within 5*r200
if selection.lower() == 'all':
index = np.where((r < 5 * r200) & (group_number == 1)
& (subgroup_number > -1))[0]
if selection.lower() == 'sub':
index = np.where((r < 5 * r200) & (group_number == 1)
& (subgroup_number > 0)
& (subgroup_number < 10000))[0]
if selection.lower() == 'icm':
index = np.where((r < 5 * r200) & (group_number == 1)
& (subgroup_number == 0))[0]
density = density[index]
temperature = temperature[index]
master_density.append(density)
master_temperature.append((temperature))
# Bin data
x_Data = np.concatenate(master_density)
y_Data = np.concatenate(master_temperature)
x_bins = np.logspace(np.min(np.log10(x_Data)), np.max(np.log10(x_Data)),
nbins)
y_bins = np.logspace(np.min(np.log10(y_Data)), np.max(np.log10(y_Data)),
nbins)
A_pix = (x_bins[1] - x_bins[0]) * (y_bins[1] - y_bins[0])
Cx, Cy = mapgen.bins_meshify(x_Data, y_Data, x_bins, y_bins)
count = mapgen.bins_evaluate(x_Data, y_Data, x_bins, y_bins,
weights=None) / A_pix
# Generate plot
plotpar.set_defaults_plot()
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(7, 7))
# Logarithmic normalization
norm = mpl.colors.LogNorm() # (vmin=10 ** -2, vmax=10 ** 1)
count2 = np.ma.masked_where(count == 0, count)
cmap = plt.get_cmap('CMRmap')
cmap.set_bad(color=bg, alpha=1)
img = axes.pcolormesh(Cx, Cy, count2, cmap=cmap, norm=norm)
axes.set_xscale('log')
axes.set_yscale('log')
axes.set_xlabel(r'$n_{\mathrm{H}}/\mathrm{cm}^{3}$')
#axes.set_xlabel(r'$\rho/(M_\odot\ kpc^{-3})$')
axes.set_ylabel(r'$T/\mathrm{K}$')
# Colorbar adjustments
ax2_divider = make_axes_locatable(axes)
cax2 = ax2_divider.append_axes("top", size="3%", pad="2%")
cbar = plt.colorbar(img, cax=cax2, orientation='horizontal')
cbar.set_label(
r'$N_{\mathrm{particles}} / (\mathrm{K}\ \mathrm{cm}^{-3})$',
labelpad=-70)
# cax2.xaxis.set_tick_labels(['0',' ','0.5',' ','1',' ', '1.5',' ','2'])
cax2.xaxis.set_ticks_position("top")
# Define output
if output == 'show':
plt.show()
elif output == 'save':
save_name = 'phase-diagram_nHcgs_halo' + str(10) + '_z' + str(
redshift).replace(
".", "") + '_nbins' + str(nbins) + '_' + selection + '.png'
dir_name = 'phase_diagrams_T-Rho_master390'
if not exists(dir_name): makedirs(dir_name)
plt.savefig(dir_name + '//' + save_name)
else:
print(
"[ERROR] The output type you are trying to select is not defined.")
exit(1)
def phase_diagram(num_halo,
redshift,
output='show',
title=True,
save_name='Central_group_all_part_halo_'):
# Import data
path = extract.path_from_cluster_name(num_halo,
simulation_type=simulation_type)
file = extract.file_name_hdf5(
subject='groups', redshift=extract.redshift_floatTostr(redshift))
r200 = extract.group_r200(path, file)
group_CoP = extract.group_centre_of_potential(path, file)
file = extract.file_name_hdf5(
subject='particledata', redshift=extract.redshift_floatTostr(redshift))
# Gas particles
part_type = extract.particle_type('gas')
density = extract.particle_SPH_density(path, file, part_type)
coordinates = extract.particle_coordinates(path, file, part_type)
temperature = extract.particle_temperature(path, file, part_type)
group_number = extract.group_number(path, file, part_type)
subgroup_number = extract.subgroup_number(path, file, part_type)
# Retrieve coordinates
x = coordinates[:, 0] - group_CoP[0]
y = coordinates[:, 1] - group_CoP[1]
z = coordinates[:, 2] - group_CoP[2]
# Rescale to comoving coordinates
x = profile.comoving_length(x, h, redshift)
y = profile.comoving_length(y, h, redshift)
z = profile.comoving_length(z, h, redshift)
r200 = profile.comoving_length(r200, h, redshift)
density = profile.comoving_density(density, h, redshift)
density = profile.density_units(density, unit_system='astro')
# Compute radial distance
r = np.sqrt(x**2 + y**2 + z**2)
# Select particles within 5*r200
index = np.where((r < 5 * r200) & (group_number > -1)
& (subgroup_number > 0))[0]
density = density[index]
temperature = temperature[index]
# Bin data
nbins = 600
x_Data = density
y_Data = temperature
x_bins = np.logspace(np.min(np.log10(x_Data)), np.max(np.log10(x_Data)),
nbins)
y_bins = np.logspace(np.min(np.log10(y_Data)), np.max(np.log10(y_Data)),
nbins)
Cx, Cy = mapgen.bins_meshify(x_Data, y_Data, x_bins, y_bins)
count = mapgen.bins_evaluate(x_Data, y_Data, x_bins, y_bins, weights=None)
# Generate plot
plotpar.set_defaults_plot()
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(7, 6))
img = axes.pcolor(Cx, Cy, np.log10(count + 1), cmap='viridis')
axes.set_xscale('log')
axes.set_yscale('log')
axes.set_xlabel(r'$\rho/(M_\odot\ pc^{-3})$')
axes.set_ylabel(r'$T/K$')
if title:
axes.set_title(r'$\mathrm{MACSIS\ halo\ } %3d \qquad z = %8.3f$' %
(num_halo, redshift))
# Colorbar adjustments
ax2_divider = make_axes_locatable(axes)
cax2 = ax2_divider.append_axes("right", size="3%", pad="2%")
cbar = plt.colorbar(img, cax=cax2, orientation='vertical')
cbar.set_label(r'$\log_{10}(N_{particles})$', labelpad=17)
#cax2.xaxis.set_tick_labels(['0',' ','0.5',' ','1',' ', '1.5',' ','2'])
cax2.xaxis.set_ticks_position("top")
# Define output
if output == 'show':
plt.show()
elif output == 'save':
dir_name = 'Temperature-Density phase diagrams'
if not exists(dir_name):
makedirs(dir_name)
plt.savefig(dir_name + '//' + save_name + str(num_halo) + 'z' +
str(redshift).replace(".", "") + '.pdf')
else:
print(
"[ERROR] The output type you are trying to select is not defined.")
exit(1)
def render_figure(halo_number, redshift, projection, output="Show"):
plotpar.set_defaults_plot()
nullfmt = NullFormatter()
fig = plt.figure(1, figsize=(10, 13))
# Now, create the gridspec structure, as required
gs = gridspec.GridSpec(ncols=3,
nrows=7,
height_ratios=[0.05, 1, 0.5, 0.7, 0.05, 1, 0.5],
width_ratios=[1, 1, 0.5])
# 3 rows, 4 columns, each with the required size ratios.
# Also make sure the margins and spacing are apropriate
gs.update(left=0.05,
right=0.95,
bottom=0.08,
top=0.93,
wspace=0.08,
hspace=0.08)
# Note: I set the margins to make it look good on my screen ...
# BUT: this is irrelevant for the saved image, if using bbox_inches='tight'in savefig !
# Note: Here, I use a little trick. I only have three vertical layers of plots :
# a scatter plot, a histogram, and a line plot. So, in principle, I could use a 3x3 structure.
# However, I want to have the histogram 'closer' from the scatter plot than the line plot.
# So, I insert a 4th layer between the histogram and line plot,
# keep it empty, and use its thickness (the 0.2 above) to adjust the space as required.
selection_color = 'coral'
# selection_color = 'lime'
colormap = 'YlGnBu_r'
alpha_select = 0.1
# LABELS
label_n = r'$n_{sub}$'
label_M = r'$M/M_\odot$'
label_R = r'$R/R_{200}$'
label_f = r'$f_{g}$'
label_v = r'$v_{z}/\mathrm{(km\ s^{-1})}$'
# GRIDS & NBINS
grid_on = False
# loop over plots
for j in [0, 4]:
for i in [0, 1]:
print('Block started')
if i == 0 and j == 0:
x = data_dict['R']
y = data_dict['Fg']
SELECT_x_min, SELECT_x_max = selec_dict[
'SELECT_R_MIN'], selec_dict['SELECT_R_MAX']
SELECT_y_min, SELECT_y_max = selec_dict[
'SELECT_Fg_MIN'], selec_dict['SELECT_Fg_MAX']
if i == 1 and j == 0:
x = data_dict['M']
y = data_dict['Fg']
SELECT_x_min, SELECT_x_max = selec_dict[
'SELECT_M_MIN'], selec_dict['SELECT_M_MAX']
SELECT_y_min, SELECT_y_max = selec_dict[
'SELECT_Fg_MIN'], selec_dict['SELECT_Fg_MAX']
if i == 0 and j == 4:
x = data_dict['R']
y = data_dict['Vr']
SELECT_x_min, SELECT_x_max = selec_dict[
'SELECT_R_MIN'], selec_dict['SELECT_R_MAX']
SELECT_y_min, SELECT_y_max = selec_dict[
'SELECT_Vr_MIN'], selec_dict['SELECT_Vr_MAX']
if i == 1 and j == 4:
x = data_dict['M']
y = data_dict['Vr']
SELECT_x_min, SELECT_x_max = selec_dict[
'SELECT_M_MIN'], selec_dict['SELECT_M_MAX']
SELECT_y_min, SELECT_y_max = selec_dict[
'SELECT_Vr_MIN'], selec_dict['SELECT_Vr_MAX']
x_min_LIN, x_max_LIN = np.min(x), np.max(x)
x_min_LOG, x_max_LOG = np.log10(x_min_LIN), np.log10(x_max_LIN)
y_min_LIN, y_max_LIN = np.min(y), np.max(y)
if j == 0: y_min_LIN, y_max_LIN = 0, 0.3
# First, the scatter plot
ax1 = fig.add_subplot(gs[j + 1, i])
print('\tComputing 2dhist \t\t (%1i, %1i)' % (j + 1, i))
# # Get the optimal number of bins based on knuth_bin_width
# N_xbins = int((np.max(x)-np.min(x))/knuth_bin_width(x)) + 1
# N_ybins = int((np.max(y)-np.min(y))/knuth_bin_width(y)) + 1
N_xbins = 50
N_ybins = N_xbins
bins_LOG = np.logspace(x_min_LOG, x_max_LOG, num=N_xbins)
bins_LIN = np.linspace(y_min_LIN, y_max_LIN, num=N_ybins)
Cx, Cy = mapgen.bins_meshify(x, y, bins_LOG, bins_LIN)
count = mapgen.bins_evaluate(x,
y,
bins_LOG,
bins_LIN,
weights=None)
norm = colors.LogNorm()
plt1 = ax1.pcolor(Cx, Cy, count, cmap=colormap, norm=norm)
ax1.grid(grid_on)
ax1.set_xlim([x_min_LIN, x_max_LIN])
ax1.set_ylim([y_min_LIN, y_max_LIN])
ax1.set_xscale('log')
ax1.set_yscale('linear')
ax1.set_xlabel(r' ') # Force this empty !
ax1.xaxis.set_major_formatter(nullfmt)
ax1.set_ylabel(label_f)
if j == 4:
ax1.axhspan(-SELECT_y_max,
-SELECT_y_min,
alpha=alpha_select,
color=selection_color)
rect2 = patches.Rectangle((SELECT_x_min, -SELECT_y_max),
SELECT_x_max - SELECT_x_min,
-SELECT_y_min + SELECT_y_max,
linewidth=1.5,
edgecolor='r',
facecolor='none')
ax1.add_patch(rect2)
ax1.axvspan(SELECT_x_min,
SELECT_x_max,
alpha=alpha_select,
color=selection_color)
ax1.axhspan(SELECT_y_min,
SELECT_y_max,
alpha=alpha_select,
color=selection_color)
rect = patches.Rectangle((SELECT_x_min, SELECT_y_min),
SELECT_x_max - SELECT_x_min,
SELECT_y_max - SELECT_y_min,
linewidth=1.5,
edgecolor='r',
facecolor='none')
ax1.add_patch(rect)
if j == 0:
ax1.set_ylabel(label_f)
elif j == 4:
ax1.set_ylabel(label_v)
# Colorbar
cbax = fig.add_subplot(gs[j, i])
print('\tComputing colorbar \t\t (%1i, %1i)' % (j, i))
cb = Colorbar(ax=cbax,
mappable=plt1,
orientation='horizontal',
ticklocation='top')
cb.set_label(label_n, labelpad=10)
trig_vertical_hist = 0
# VERTICAL HISTOGRAM
if i != 0:
ax1v = fig.add_subplot(gs[j + 1, i + 1])
print('\tComputing vert hist \t (%1i, %1i)' % (j + 1, i + 1))
ax1v.hist(y,
bins=bins_LIN,
orientation='horizontal',
color='k',
histtype='step')
ax1v.hist(y,
bins=bins_LIN,
orientation='horizontal',
color='red',
histtype='step',
cumulative=-1)
ax1v.set_yticks(ax1.get_yticks(
)) # Ensures we have the same ticks as the scatter plot !
ax1v.set_xlabel(label_n)
ax1v.tick_params(labelleft=False)
ax1v.set_ylim(ax1.get_ylim())
ax1v.set_xscale('log')
ax1v.set_yscale('linear')
ax1v.grid(grid_on)
ax1v.axhspan(SELECT_y_min,
SELECT_y_max,
alpha=alpha_select,
color=selection_color)
if j == 4:
ax1v.axhspan(-SELECT_y_max,
-SELECT_y_min,
alpha=alpha_select,
color=selection_color)
ax1.yaxis.set_major_formatter(nullfmt)
ax1.set_ylabel('')
trig_vertical_hist = 1
# Percentiles
percents = [15.9, 50, 84.1]
percent_str = [r'$16\%$', r'$50\%$', r'$84\%$']
clr = ['orange', 'blue', 'green']
percent_ticks = np.percentile(y, percents)
if trig_vertical_hist:
percent_str = np.flipud(percent_str)
clr = np.flipud(clr)
ax1v_TWIN = ax1v.twinx()
ax1v_TWIN.set_ylim(ax1.get_ylim())
ax1v_TWIN.tick_params(axis='y',
which='both',
labelleft='off',
labelright='on')
ax1v_TWIN.set_yticks(percent_ticks)
ax1v_TWIN.set_yticklabels(percent_str)
for percent_tick, c, tick in zip(
percent_ticks, clr, ax1v_TWIN.yaxis.get_major_ticks()):
tick.label1.set_color(c)
ax1v_TWIN.axhline(y=percent_tick, color=c, linestyle='--')
percent_str = np.flipud(percent_str)
clr = np.flipud(clr)
# HORIZONTAL HISTOGRAM
ax1h = fig.add_subplot(gs[j + 2, i])
print('\tComputing horiz hist \t (%1i, %1i)' % (j + 2, i))
ax1h.hist(x,
bins=bins_LOG,
orientation='vertical',
color='k',
histtype='step')
ax1h.hist(x,
bins=bins_LOG,
orientation='vertical',
color='red',
histtype='step',
cumulative=True)
ax1h.set_xticks(ax1.get_xticks(
)) # Ensures we have the same ticks as the scatter plot !
ax1h.set_xlim(ax1.get_xlim())
if i == 0:
ax1h.set_xlabel(label_R)
ax1h.set_ylabel(label_n)
elif i == 1:
ax1h.set_xlabel(label_M)
ax1h.tick_params(labelleft=False)
ax1h.set_ylabel('')
ax1h.set_xscale('log')
ax1h.set_yscale('log')
ax1h.grid(grid_on)
ax1h.axvspan(SELECT_x_min,
SELECT_x_max,
alpha=alpha_select,
color=selection_color)
percent_ticks = np.percentile(x, percents)
for i in range(len(percents)):
ax1h.axvline(x=percent_ticks[i], color=clr[i], linestyle='--')
print('Block completed\n')
if output.lower() == 'show':
fig.show()
elif output.lower() == 'save':
dir_name = 'Subfind-Selection'
if not exists(dir_name): makedirs(dir_name)
save_name = 'selection-phase-space_halo' + str(
halo_number) + '_z' + str(redshift).replace(
".", "") + '_proj' + str(projection) + '.pdf'
fig.savefig(dir_name + '//' + save_name,
dpi=None,
facecolor='w',
edgecolor='w',
orientation='portrait',
papertype=None,
format=None,
transparent=False,
bbox_inches='tight',
pad_inches=0.1,
frameon=None)
elif output.lower() == 'none':
pass
else:
print("Error: Invalid request")
def map_tSZ_intensity(num_halo,
redshift,
simulation_type,
nbins=100,
rfov=2,
output='show',
title=True,
plot_groups='FoF'):
# Import data
path = extract.path_from_cluster_name(num_halo,
simulation_type=simulation_type)
file = extract.file_name_hdf5(
subject='groups', redshift=extract.redshift_floatTostr(redshift))
r200 = extract.group_r200(path, file)
group_CoP = extract.group_centre_of_potential(path, file)
file = extract.file_name_hdf5(
subject='particledata', redshift=extract.redshift_floatTostr(redshift))
redshift_short = redshift
# Gas particles
part_type = extract.particle_type('gas')
mass = extract.particle_masses(path, file, part_type)
coordinates = extract.particle_coordinates(path, file, part_type)
velocities = extract.particle_velocity(path, file, part_type)
temperatures = extract.particle_temperature(path, file, part_type)
group_number = extract.group_number(path, file, part_type)
subgroup_number = extract.subgroup_number(path, file, part_type)
tot_rest_frame, _ = profile.total_mass_rest_frame(path, file)
#gas_rest_frame, _ = profile.cluster_average_momentum(path, file, part_type)
# Retrieve coordinates & velocities
x = coordinates[:, 0] - group_CoP[0]
y = coordinates[:, 1] - group_CoP[1]
z = coordinates[:, 2] - group_CoP[2]
vx = velocities[:, 0] - tot_rest_frame[0]
vy = velocities[:, 1] - tot_rest_frame[1]
vz = velocities[:, 2] - tot_rest_frame[2]
# Rescale to comoving coordinates
h = extract.file_hubble_param(path, file)
redshift = extract.file_redshift(path, file)
x = profile.comoving_length(x, h, redshift)
y = profile.comoving_length(y, h, redshift)
z = profile.comoving_length(z, h, redshift)
r200 = profile.comoving_length(r200, h, redshift)
vx = profile.comoving_velocity(vx, h, redshift)
vy = profile.comoving_velocity(vy, h, redshift)
vz = profile.comoving_velocity(vz, h, redshift)
vx = profile.velocity_units(vx, unit_system='SI')
vy = profile.velocity_units(vy, unit_system='SI')
vz = profile.velocity_units(vz, unit_system='SI')
mass = profile.comoving_mass(mass, h, redshift)
mass = profile.mass_units(mass, unit_system='SI')
T = temperatures
# Compute radial distance
r = np.sqrt(x**2 + y**2 + z**2)
# Particle selection
min_gn = 0
min_T = 10**5
max_r = 5
if plot_groups == 'FoF': min_sgn = 0
elif plot_groups == 'subgroups': min_sgn = 1
else:
print(
"[ERROR] The (sub)groups you are trying to plot are not defined.")
exit(1)
index = np.where((r < max_r * r200) & (group_number >= min_gn)
& (subgroup_number >= min_sgn) & (T > min_T))[0]
mass, T = mass[index], T[index]
x, y, z = x[index], y[index], z[index]
vx, vy, vz = vx[index], vy[index], vz[index]
# Generate plot frame
plotpar.set_defaults_plot()
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(20, 9))
# Convert to angular distances
angular_distance = cosmo.angular_diameter_D(redshift)
Mpc_to_arcmin = np.power(np.pi, -1) * 180 * 60 / angular_distance
x = x * Mpc_to_arcmin
y = y * Mpc_to_arcmin
z = z * Mpc_to_arcmin
r200 = r200 * Mpc_to_arcmin
# Bin data
cmap = ['Blues', 'Blues', 'Blues']
# cmap = [mapgen.modified_spectral_cmap(Reversed = True), mapgen.modified_spectral_cmap(Reversed = True), mapgen.modified_spectral_cmap(Reversed = False)]
xlabel = [
r'$x\mathrm{/arcmin}$', r'$y\mathrm{/arcmin}$', r'$x\mathrm{/arcmin}$'
]
ylabel = [
r'$y\mathrm{/arcmin}$', r'$z\mathrm{/arcmin}$', r'$z\mathrm{/arcmin}$'
]
thirdAX = [r'$\bigotimes z$', r'$\bigotimes x$', r'$\bigodot y$']
cbarlabel = [r'$y_{tSZ}$', r'$y_{tSZ}$', r'$y_{tSZ}$']
weight_function = r'$y_{tSZ} = - \frac{\sigma_T}{A_{pix} \mu_e m_H c} \sum_{i=0}^{N_{l.o.s.} m^{g}_i T^{g}_i}$'
for i in [0, 1, 2]:
# Handle data
if i == 0:
x_Data = x
y_Data = y
weight = T
elif i == 1:
x_Data = y
y_Data = z
weight = T
elif i == 2:
x_Data = x
y_Data = z
weight = T
# Compute angular bins
x_bins = np.linspace(-rfov * r200, rfov * r200, nbins)
y_bins = np.linspace(-rfov * r200, rfov * r200, nbins)
Cx, Cy = mapgen.bins_meshify(x_Data, y_Data, x_bins, y_bins)
from astropy.constants import c, sigma_T, k_B, m_e
m_H = 1.6737236 * 10**(-27) # Hydrogen atom mass in kg
A_pix = (x_bins[1] - x_bins[0]) * (y_bins[1] - y_bins[0]) * (
3.0856776 * 10**22 / Mpc_to_arcmin)**2
const = sigma_T.value * k_B.value / (m_e.value * c.value**2 * m_H *
1.16)
# line of sight momentum weights
mass = mass.astype(np.longdouble)
weight = weight.astype(np.longdouble)
count_mT = mapgen.bins_evaluate(x_Data,
y_Data,
x_bins,
y_bins,
weights=mass * weight)
# Compute tSZ
tSZ = count_mT * const / (A_pix)
# convolution
kernel_Type = 'gauss'
#kernel = Gaussian2DKernel(stddev=2)
kernel, fwhm = kernconv.nika2_kernel(x_bins,
y_bins,
kernel_Type=kernel_Type)
kernel = np.array(kernel)
tSZmap = convolve(tSZ, kernel)
# norm = mapgen.MidpointNormalize(vmin=tSZmap.min(), vmax=tSZmap.max(), midpoint=0)
c
# norm = colors.PowerNorm(gamma=0.2)
img = axes[i].pcolor(Cx, Cy, tSZmap, cmap=cmap[i], norm=norm)
# Render elements in plots
axes[i].set_aspect('equal')
axes[i].add_artist(
Circle((0, 0),
radius=r200,
color='black',
fill=False,
linestyle='--',
label=r'$R_{200}$'))
axes[i].add_artist(
Circle((0, 0),
radius=5 * r200,
color='black',
fill=False,
linewidth=0.5,
linestyle='-',
label=r'$R_{200}$'))
axes[i].set_xlim(-rfov * r200, rfov * r200)
axes[i].set_ylim(-rfov * r200, rfov * r200)
axes[i].set_xlabel(xlabel[i])
axes[i].set_ylabel(ylabel[i])
axes[i].annotate(thirdAX[i], (0.03, 0.03),
textcoords='axes fraction',
size=15)
if title and plot_groups == 'FoF':
axes[i].set_title(
r'$\mathrm{MACSIS\ halo\ } %3d \qquad z = %8.3f \qquad \mathrm{ICM + subhalos}$'
% (num_halo, redshift),
pad=94)
if title and plot_groups == 'subgroups':
axes[i].set_title(
r'$\mathrm{MACSIS\ halo\ } %3d \qquad z = %8.3f \qquad \mathrm{subhalos}$'
% (num_halo, redshift),
pad=94)
# Colorbar adjustments
ax2_divider = make_axes_locatable(axes[i])
cax2 = ax2_divider.append_axes("top", size="5%", pad="2%")
cbar = plt.colorbar(img, cax=cax2, orientation='horizontal')
cbar.set_label(cbarlabel[i], labelpad=-70)
#cax2.xaxis.set_tick_labels(['0',' ','0.5',' ','1',' ', '1.5',' ','2'])
cax2.xaxis.set_ticks_position("top")
print("Plot run completed:\t", i)
# Define output
if output == 'show':
plt.show()
elif output == 'save':
dir_name = 'tSZ maps'
if not exists(dir_name):
makedirs(dir_name)
save_name = 'tSZmap_' + plot_groups + '_halo' + str(
num_halo) + '_z' + str(redshift_short).replace(
".", "") + '_rfov' + str(rfov) + '_nbins' + str(nbins)
plt.savefig(dir_name + '//' + save_name + '.pdf')
# Generate metadata.txt
args = (num_halo, simulation_type, redshift, angular_distance, min_gn,
min_sgn, min_T, max_r, weight_function, nbins, rfov,
kernel_Type, fwhm, r200, r200 / Mpc_to_arcmin)
meta.metadata_file(args, dir_name + '//' + save_name)
else:
print(
"[ERROR] The output type you are trying to select is not defined.")
exit(1)
-------------------------------------------------------------------
"""
import numpy as np
from matplotlib import pyplot as plt
import sys
import os.path
sys.path.append(
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
from import_toolkit.cluster import Cluster
from visualisation.rendering import Map
from visualisation import map_plot_parameters as plotpar
plotpar.set_defaults_plot()
def group_centre_of_mass(cluster, out_allPartTypes=False, filter_radius=None):
"""
out_allPartTypes = (bool)
if True outputs the centre of mass and sum of masses of each
partType separately in arrays
if False outputs the overall CoM and sum of masses of the whole
cluster.
Returns the centre of mass of the cluster for a ALL particle types,
except for lowres_DM (2, 3).
"""
CoM_PartTypes = np.zeros((0, 3), dtype=np.float)
