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gas_profiles.py
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gas_profiles.py
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import numpy as np
from swiftsimio import load
from swiftsimio import mask
from velociraptor import load as load_catalogue
import unyt
import matplotlib.pyplot as plt
plt.style.use("mnras.mplstyle")
def make_paths(sim,
snap_id=36):
catalogue_path = "../hydro/L0300N0564_VR18_"+sim+"/stf/snap_"+str(snap_id).zfill(4)+"/"
catalogue_name = "snap_" + str(snap_id).zfill(4)
snapshot_path = "../hydro/L0300N0564_VR18_"+sim+"/snapshots/snap_"+str(snap_id).zfill(4)+".hdf5"
return catalogue_path, catalogue_name, snapshot_path
def get_redshift(sim, snap_id):
_, _, snapshot_path = make_paths(sim, snap_id=snap_id)
data = load(snapshot_path)
redshift = data.metadata.z
return redshift
def gas_profiles(sim, radii,
snap_id=36):
path, catalogue_name, snapshot_path = make_paths(sim, snap_id=snap_id)
catalogue = load_catalogue(path+catalogue_name+".properties")
# General properties for profiles
T_cut = 1e5
R200crit = catalogue.radii.r_200crit[0] / catalogue.scale_factor
M200crit = catalogue.masses.mass_200crit[0]
N_bins = len(radii) - 1
radii = radii * R200crit
# Get cluster centre
xc = catalogue.positions.xcmbp[0]
yc = catalogue.positions.ycmbp[0]
zc = catalogue.positions.zcmbp[0]
centre = [xc, yc, zc] / catalogue.scale_factor * unyt.Mpc
# Define region for swiftsimio to read in
max_region = radii[-1]
cluster_mask = mask(snapshot_path)
region = [[centre[0] - max_region, centre[0] + max_region],
[centre[1] - max_region, centre[1] + max_region],
[centre[2] - max_region, centre[2] + max_region]]
cluster_mask.constrain_spatial(region)
# Load data
data = load(snapshot_path, mask=cluster_mask)
# Get cosmology
Omega_m0 = data.metadata.cosmology.Om0
Omega_de0 = data.metadata.cosmology.Ode0
H0 = data.metadata.cosmology.H0
a = data.metadata.a
# Sort particle data
data.gas.coordinates = data.gas.coordinates - centre
dx = data.gas.coordinates[:,0]
dy = data.gas.coordinates[:,1]
dz = data.gas.coordinates[:,2]
particle_radii = np.sqrt(dx**2 + dy**2 + dz**2)
# Calculate hot gas profiles
density = np.zeros(N_bins)
temperature = np.zeros(N_bins)
for i in range(N_bins):
hot_gas_mask = np.where((particle_radii > radii[i]) &
(particle_radii <= radii[i+1]) &
(data.gas.temperatures > T_cut))[0]
volume = 4/3 * np.pi * (radii[i+1]**3 - radii[i]**3)
bin_mass = np.sum(data.gas.masses[hot_gas_mask])
temperature[i] = np.sum(data.gas.masses[hot_gas_mask]*data.gas.temperatures[hot_gas_mask]) / np.sum(data.gas.masses[hot_gas_mask])
density[i] = bin_mass / volume
#Calculate constants
H = np.sqrt(H0**2 * (Omega_m0 * a**-3 + Omega_de0)).value * unyt.km / unyt.s / unyt.Mpc
rho_crit = 3 * H**2 / (8*np.pi*unyt.G)
mu_e = 1.14 #mean atomic weight per free electron
mu = 0.59 #mean molecular weight
fb = 0.24 #universal baryon fraction #######change
T200 = unyt.G * M200crit * mu * unyt.mp / (2 * R200crit * a * unyt.kb)
P200 = 500 * fb * unyt.kb * T200 * rho_crit / (mu * unyt.mp)
K200 = unyt.kb * T200 / (500 * fb * (rho_crit / (mu_e * unyt.mp))**(2/3))
temperature = temperature * unyt.K
density = density * bin_mass.units / volume.units / 1e10
pressure = density/(mu*unyt.mp) * unyt.kb * temperature
entropy = unyt.kb * temperature / (density/(mu*unyt.mp))**(2/3)
return density/rho_crit, temperature/T200, pressure/P200, entropy/K200
def get_avg_profiles(sim, radii, snap_id):
N_bins = len(radii) - 1
if snap_id > 34: #edge case
snap_range = np.arange(34, 36+1) #range of snap_ids to average over
else:
snap_range = np.arange(snap_id-2, snap_id+2+1)
N_snap = len(snap_range)
densities = np.zeros(N_bins)
temperatures = np.zeros(N_bins)
pressures = np.zeros(N_bins)
entropies = np.zeros(N_bins)
z = np.array([])
for snap in snap_range:
rho, temp, pres, ent = gas_profiles(sim, radii, snap_id=snap)
densities = densities + rho
temperatures = temperatures + temp
pressures = pressures + pres
entropies = entropies + ent
z = np.append(z, get_redshift(sim, snap))
return densities/N_snap, temperatures/N_snap, pressures/N_snap, entropies/N_snap, z
def plot_compare_gas(comp_sim, *sims, snap_id=36):
N_sims = len(sims)
cm = plt.cm.viridis(np.linspace(0,1,N_sims))
N_bins = 30
log_radii = np.linspace(-1, np.log10(3), N_bins+1)
radii = 10 ** log_radii
rad_mid = 10**((log_radii[1:] + log_radii[:-1]) / 2)
rho_ref, T_ref, P_ref, K_ref = get_gas_profiles(comp_sim, radii, snap_id=snap_id)
fig, ax = plt.subplots(ncols=2, nrows=2,
gridspec_kw={'hspace' : 0.2, 'wspace' : 0.4})
for i, sim in enumerate(sims):
rho, T, P, K = get_gas_profiles(sim, radii, snap_id=snap_id)
ax[0,0].semilogx(rad_mid, rho/rho_ref,
color=cm[i],
label=sim)
ax[0,1].semilogx(rad_mid, T/T_ref, color=cm[i])
ax[1,0].semilogx(rad_mid, P/P_ref, color=cm[i])
ax[1,1].semilogx(rad_mid, K/K_ref, color=cm[i])
ax[0,0].set_ylabel("$\\rho / \\rho_{{\\rm{{{{{}}}}}}}$".format(comp_sim))
ax[0,1].set_ylabel("$T/T_{{\\rm{{{{{}}}}}}}$".format(comp_sim))
ax[1,0].set_ylabel("$P/P_{{\\rm{{{{{}}}}}}}$".format(comp_sim))
ax[1,1].set_ylabel("$K/K_{{\\rm{{{{{}}}}}}}$".format(comp_sim))
ax[0,0].legend()
plt.text(0.45, 0.05, "$r/R_{\\rm{200c}}$", transform=fig.transFigure)
plt.subplots_adjust(bottom=0.15, left=0.17)
plt.show()
def plot_all_gas(snap_id=36):
sims = np.array([["R1", "R2", "R3", "R4"],
["M4", "K2s", "K3s", "R4"],
["R1", "M2", "M3", "M4"]])
cmR = plt.cm.winter(np.linspace(0,1,4))
cmK = plt.cm.autumn(np.linspace(0,1,4))
cmM = plt.cm.summer(np.linspace(0,1,4))
N_bins = 30
log_radii = np.linspace(-1, np.log10(3), N_bins+1)
radii = 10 ** log_radii
rad_mid = 10**((log_radii[1:] + log_radii[:-1]) / 2)
fig, ax = plt.subplots(nrows=4, ncols=3, figsize=(6,7),
sharex="col", sharey="row",
gridspec_kw={'hspace' : 0, 'wspace' : 0})
for col in range(3):
if col == 0:
cm = cmR
elif col == 1:
cm = cmK
else:
cm = cmM
for sim in range(4):
rho, T, P, K, z_range = get_avg_profiles(sims[col,sim], radii, snap_id=snap_id)
ax[0,col].loglog(rad_mid, rho*rad_mid**2, color=cm[sim], label=sims[col,sim])
ax[1,col].semilogx(rad_mid, T, color=cm[sim])
ax[2,col].loglog(rad_mid, P*rad_mid**3, color=cm[sim])
ax[3,col].loglog(rad_mid, K, color=cm[sim])
ax[0,col].legend()
ax[0,0].set_ylabel("$\\rho / \\rho_{\\rm{crit}} (r/R_{\\rm{200c}})^2$")
ax[1,0].set_ylabel("$T/T_{200}$")
ax[2,0].set_ylabel("$P/P_{200} (r/R_{\\rm{200c}})^3$")
ax[3,0].set_ylabel("$K/K_{200}$")
plt.suptitle(str(np.round(z_range[-1],2)) + "< z <" + str(np.round(z_range[0],2)))
ax[3,1].set_xlabel("$r/R_{\\rm{200c}}$")
plt.subplots_adjust(bottom=0.15, left=0.17, right=0.99)
filename = "gas_profiles_z1.png"
plt.savefig(filename, dpi=300)
plt.show()
def plot_gas_properties(*sims, snap_id=36):
N_sims = len(sims)
cm = plt.cm.summer(np.linspace(0,1,N_sims))
N_bins = 30
log_radii = np.linspace(-1, np.log10(3), N_bins+1)
radii = 10 ** log_radii
rad_mid = 10**((log_radii[1:] + log_radii[:-1]) / 2)
fig, ax = plt.subplots(ncols=2, nrows=2,
gridspec_kw={'hspace' : 0.2, 'wspace' : 0.4})
for i, sim in enumerate(sims):
rho, T, P, K, z_range = get_avg_profiles(sim, radii, snap_id=snap_id)
ax[0,0].loglog(rad_mid, rho*rad_mid**2,
color=cm[i],
label=sim)
ax[0,1].semilogx(rad_mid, T, color=cm[i])
ax[1,0].loglog(rad_mid, P*rad_mid**3, color=cm[i])
ax[1,1].loglog(rad_mid, K, color=cm[i])
ax[0,0].set_ylabel("$\\rho / \\rho_{\\rm{crit}} (r/R_{\\rm{200c}})^2$")
ax[0,1].set_ylabel("$T/T_{200}$")
ax[1,0].set_ylabel("$P/P_{200}$")
ax[1,1].set_ylabel("$K/K_{200}$")
ax[0,0].legend()
plt.suptitle(str(np.round(z_range[-1],2)) + "< z <" + str(np.round(z_range[0],2)))
plt.text(0.45, 0.05, "$r/R_{\\rm{200c}}$", transform=fig.transFigure)
plt.subplots_adjust(bottom=0.15, left=0.17)
filename = "M_series_gas_profiles.png"
plt.savefig(filename, dpi=300)
plt.show()
#plot_gas_properties("R1", "M2", "M3", "M4", snap_id=36)
#plot_compare_gas("R1", "R2", "R3", snap_id=36)
plot_all_gas(snap_id=25)