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analytic_profiles.py
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import glob
import numpy as np
import h5py
from scipy import integrate, interpolate
from astropy import units as un, constants as cons
from astropy.cosmology import Planck15 as cosmo
import colossus, colossus.cosmology.cosmology
colossus.cosmology.cosmology.setCosmology('planck15')
from colossus.halo import profile_dk14
import matplotlib
matplotlib.rcParams['xtick.direction'] = 'in'
matplotlib.rcParams['ytick.direction'] = 'in'
matplotlib.rcParams['xtick.top'] = True
matplotlib.rcParams['ytick.right'] = True
matplotlib.rcParams['xtick.minor.visible'] = True
matplotlib.rcParams['ytick.minor.visible'] = True
matplotlib.rcParams['lines.dash_capstyle'] = "round"
matplotlib.rcParams['lines.solid_capstyle'] = "round"
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from matplotlib import cm
from matplotlib.colors import ListedColormap
gamma = 5/3.
kb = 1.3806488e-16
mp = 1.67373522381e-24
km = 1e5
s = 1
yr = 3.1536e7
Myr = 3.1536e13
Gyr = 3.1536e16
pc = 3.086e18
kpc = 1.0e3 * pc
Mpc = 1.0e6 * pc
H0 = 70*km/s/Mpc
G = 6.673e-8
Msun = 2.e33
OL = 0.73
Om = 0.27
fb = 0.158
keV = 1.60218e-9
mu = 0.62
metallicity = 10**-0.5
muH = 1/0.75
redshift=0.0
"""
Cooling curve as a function of density, temperature, metallicity, redshift
"""
file = glob.glob('./data/Cooling_Tables/Lambda_tab.npz')
if len(file) > 0:
data = np.load(file[0])
Lambda_tab = data['Lambda_tab']
redshifts = data['redshifts']
Zs = data['Zs']
log_Tbins = data['log_Tbins']
log_nHbins = data['log_nHbins']
Lambda = interpolate.RegularGridInterpolator((log_nHbins,log_Tbins,Zs,redshifts), Lambda_tab, bounds_error=False, fill_value=0)
else:
files = np.sort(glob.glob('./data/Cooling_Tables/z_*hdf5'))
redshifts = np.array([float(f[-10:-5]) for f in files])
HHeCooling = {}
ZCooling = {}
TE_T_n = {}
for i in range(len(files)):
f = h5py.File(files[i], 'r')
i_X_He = -3
Metal_free = f.get('Metal_free')
Total_Metals = f.get('Total_Metals')
log_Tbins = np.array(np.log10(Metal_free['Temperature_bins']))
log_nHbins = np.array(np.log10(Metal_free['Hydrogen_density_bins']))
Cooling_Metal_free = np.array(Metal_free['Net_Cooling'])[i_X_He] ##### what Helium_mass_fraction to use Total_Metals = f.get('Total_Metals')
Cooling_Total_Metals = np.array(Total_Metals['Net_cooling'])
HHeCooling[redshifts[i]] = interpolate.RectBivariateSpline(log_Tbins,log_nHbins, Cooling_Metal_free)
ZCooling[redshifts[i]] = interpolate.RectBivariateSpline(log_Tbins,log_nHbins, Cooling_Total_Metals)
f.close()
Lambda_tab = np.array([[[[HHeCooling[zz].ev(lT,ln)+Z*ZCooling[zz].ev(lT,ln) for zz in redshifts] for Z in Zs] for lT in log_Tbins] for ln in log_nHbins])
np.savez('./data/Cooling_Tables/Lambda_tab.npz', Lambda_tab=Lambda_tab, redshifts=redshifts, Zs=Zs, log_Tbins=log_Tbins, log_nHbins=log_nHbins)
Lambda = interpolate.RegularGridInterpolator((log_nHbins,log_Tbins,Zs,redshifts), Lambda_tab, bounds_error=False, fill_value=0)
print("interpolated lambda")
def c_DuttonMaccio14(lMhalo, z=0): #table 3 appropriate for Mvir
c_z0 = lambda lMhalo: 10.**(1.025 - 0.097*(lMhalo-np.log10(0.7**-1*1e12)))
c_z05 = lambda lMhalo: 10.**(0.884 - 0.085*(lMhalo-np.log10(0.7**-1*1e12)))
c_z1 = lambda lMhalo: 10.**(0.775 - 0.073*(lMhalo-np.log10(0.7**-1*1e12)))
c_z2 = lambda lMhalo: 10.**(0.643 - 0.051*(lMhalo-np.log10(0.7**-1*1e12)))
zs = np.array([0.,0.5,1.,2.])
cs = np.array([c_func(lMhalo) for c_func in (c_z0,c_z05,c_z1,c_z2)])
return np.interp(z, zs, cs)
def Behroozi_params(z, parameter_file='./data/smhm_true_med_cen_params.txt'):
param_file = open(parameter_file, "r")
param_list = []
allparams = []
for line in param_file:
param_list.append(float((line.split(" "))[1]))
allparams.append(line.split(" "))
if (len(param_list) != 20):
print("Parameter file not correct length. (Expected 20 lines, got %d)." % len(param_list))
quit()
names = "EFF_0 EFF_0_A EFF_0_A2 EFF_0_Z M_1 M_1_A M_1_A2 M_1_Z ALPHA ALPHA_A ALPHA_A2 ALPHA_Z BETA BETA_A BETA_Z DELTA GAMMA GAMMA_A GAMMA_Z CHI2".split(" ");
params = dict(zip(names, param_list))
#Print SMHM relation
a = 1.0/(1.0+z)
a1 = a - 1.0
lna = np.log(a)
zparams = {}
zparams['m_1'] = params['M_1'] + a1*params['M_1_A'] - lna*params['M_1_A2'] + z*params['M_1_Z']
zparams['sm_0'] = zparams['m_1'] + params['EFF_0'] + a1*params['EFF_0_A'] - lna*params['EFF_0_A2'] + z*params['EFF_0_Z']
zparams['alpha'] = params['ALPHA'] + a1*params['ALPHA_A'] - lna*params['ALPHA_A2'] + z*params['ALPHA_Z']
zparams['beta'] = params['BETA'] + a1*params['BETA_A'] + z*params['BETA_Z']
zparams['delta'] = params['DELTA']
zparams['gamma'] = 10**(params['GAMMA'] + a1*params['GAMMA_A'] + z*params['GAMMA_Z'])
smhm_max = 14.5-0.35*z
if (params['CHI2']>200):
print('#Warning: chi^2 > 200 implies that not all features are well fit. Comparison with the raw data (in data/smhm/median_raw/) is crucial.')
ms = 0.05 * np.arange(int(10.5*20),int(smhm_max*20+1),1)
dms = ms - zparams['m_1']
dm2s = dms/zparams['delta']
sms = zparams['sm_0'] - np.log10(10**(-zparams['alpha']*dms) + 10**(-zparams['beta']*dms)) + zparams['gamma']*np.e**(-0.5*(dm2s*dm2s))
return ms,sms
def MgalaxyBehroozi(lMhalo, z, parameter_file='./data/smhm_true_med_cen_params.txt'):
ms,sms = Behroozi_params(z,parameter_file)
lMstar = interpolate.interp1d(ms, sms, fill_value='extrapolate')(lMhalo)
return 10.**lMstar*un.Msun
class DK14_with_Galaxy:
mu=0.6
X=0.75
gamma = 5/3.
def __init__(self,Mgalaxy,half_mass_radius=None,dk14=None,**kwargs):
if dk14!=None:
self.dk14=dk14
else:
self.dk14=colossus.halo.profile_dk14.getDK14ProfileWithOuterTerms(**kwargs)
self.Mgalaxy = Mgalaxy
self.z = kwargs['z']
if half_mass_radius==None: self.half_mass_radius = 0.015 * self.RDelta('200c') #Kravtsov 2013
else: self.half_mass_radius = half_mass_radius
self._Rs = 10.**np.arange(-4.,1.5,0.01) * self.rvir().to('kpc').value
self._Ms = self.dk14.enclosedMass(self._Rs*cosmo.h) / cosmo.h #only DM mass
self._Ms += self.enclosedMass_galaxy(self._Rs*un.kpc).to('Msun').value
drs = (self._Rs[2:]-self._Rs[:-2])/2.
drs = np.pad(drs,1,mode='edge')
self._phis = ((-self.g(self._Rs*un.kpc)[::-1].to('km**2*s**-2*kpc**-1').value * drs[::-1]).cumsum())[::-1]
def enclosedMass_galaxy(self,r):
return self.Mgalaxy * r/(r+self.half_mass_radius)
def enclosedMass(self,r):
return np.interp(r.to('kpc').value, self._Rs, self._Ms)*un.Msun
def enclosedMassInner(self,r):
return self.dk14.enclosedMassInner(r.to('kpc').value*cosmo.h)*un.Msun / cosmo.h #only DM mass
def enclosedMassOuter(self,r):
return self.dk14.enclosedMassOuter(r.to('kpc').value*cosmo.h)*un.Msun / cosmo.h #only DM mass
def g(self, r):
return cons.G*self.enclosedMass(r) / r**2
def rvir(self):
return self.dk14.RDelta(self.z,'vir') * un.kpc/cosmo.h
def RDelta(self,mdef):
return self.dk14.RDelta(self.z,mdef) * un.kpc/cosmo.h
def Tvir(self):
return (self.mu * cons.m_p * self.vc(self.rvir())**2 / (2*cons.k_B)).to('K')
def vc(self,r):
return ((cons.G*self.enclosedMass(r) / r)**0.5).to('km/s')
def Tc(self,r):
return (self.mu*cons.m_p * self.vc(r)**2 / (self.gamma*cons.k_B)).to('K')
def Mhalo(self):
return self.dk14.MDelta(self.z,'vir')*un.Msun/cosmo.h
def phi(self,rs,r0):
phis = np.interp(rs.to('kpc').value, self._Rs, self._phis)
phi0 = np.interp(r0.to('kpc').value, self._Rs, self._phis)
return (phis - phi0) * un.km**2/un.s**2
def rho(self,r):
return (self.dk14.density(r.to('kpc').value*cosmo.h) * un.Msun * cosmo.h**2 / un.kpc**3).to('g*cm**-3')
def tff(self,r):
return (2**0.5 * r/ self.vc(r)).to('Gyr')
def rho_b(self,r):
return self.rho(r) * cosmo.Ob0 / cosmo.Om0
#### Fit to Diemer+14 nu vs logMhalo ---- THIS ONLY works for z = 0
nus = np.array([0.720000,0.7777,0.845000,1.200000,1.845400,3.140000])
logMhalos = np.array([11.5,11.75,12.,13.,14.,15.])
lMhalo=12.
nu = np.interp(lMhalo, logMhalos, nus)
nu = 0.845
f_cgm = 0.01
"""
gamma P / rho = cs^2
vc^2 / cs^2 = fcs
"""
Mhalo = 10**lMhalo * Msun
z = 0.
Mgalaxy=MgalaxyBehroozi(lMhalo, z)
dk14 = DK14_with_Galaxy(Mgalaxy=Mgalaxy,z=z,M = 10.**lMhalo*cosmo.h, c = c_DuttonMaccio14(lMhalo,z), mdef = 'vir')
cnfw = c_DuttonMaccio14(lMhalo,z)
rvir = dk14.rvir().value
r200m = dk14.RDelta('200m').value
rgal = dk14.half_mass_radius.value
print ('Mhalo = %e' % 10**lMhalo)
print ('cnfw = %f' % cnfw)
print ('rvir = %f' % rvir)
print ('r200m = %f' % r200m)
print ('Mgal = %e' % Mgalaxy.value)
print ('Rgal = %f' % rgal)
H0 = 67.74*km/s/Mpc
Om = 0.3075
rhom = (3 * H0**2 * Om * (1.+z)**3) / (8*np.pi*G)
rhoc = (3 * H0**2) / (8*np.pi*G)
rs = rvir/cnfw * kpc
rho0 = Mhalo / (4 * np.pi * rs**3 * ( np.log(1.+cnfw) - cnfw/(1.+cnfw) ))
rt = (1.9-0.18*nu)*r200m * kpc
a = 5. * cnfw * r200m / rvir
b = rs/rt
def grav_acc(r):
# 1/r**2 d/dr( r**2 g ) = 4 pi G rho
# g(r) = integral ( 4 pi G rho_NFW r**2 ) / r**2
# g_NFW(r) = integral ( 4 pi G rhos rs**2 x**2/(x*(1+x)**2)) / (rs**2 x**2 )
# g_NFW(r) = 4 pi G rhos (1/(x+1) + log(x+1))|_0**x /x**2
# g_NFW(r) = 4 pi G rhos (log(x+1)-x/(x+1)) /x**2
# rho_DM = rhos / (x * (1+x)**2 ) / (1.0 + (rs/rt)**4 * x**4)**2
# + rhom * ( (rs/5*rvir)**-1.5 * x**-1.5 + 1. )
# rho_DM = rhos / (x * (1+x)**2 ) / (1.0 + b**4 * x**4)**2
# + rhom * ( bb**-1.5 * x**-1.5 + 1. )
x = r/rs
g = 4. * np.pi * G * rs
g *= ((64.*a**1.5*rhom*x**1.5 + 32.*rhom*x**3. + (96.*rho0)/((1. + b**4.)**2.*(1. + x)) -
(24.*rho0*(-1. + b**4.*(3. + x*(-4. + x*(3. - 2.*x + b**4.*(-1. + 2.*x))))))/((1. + b**4.)**2.*(1. + b**4.*x**4.)) +
(12.*b*(-5.*np.sqrt(2.) + b*(18. - 14.*np.sqrt(2.)*b + 12.*np.sqrt(2.)*b**3. - 16.*b**4. + 2.*np.sqrt(2.)*b**5. + np.sqrt(2.)*b**7. - 2.*b**8.))*rho0*
np.arctan(1. - np.sqrt(2.)*b*x))/(1. + b**4.)**3. +
(6*rho0*(4.*(1. + b**4.)*(-5. + 3.*b**4.) + 2.*b**2.*(-9 + 8.*b**4. + b**8.)*np.pi -
2.*b*(-5.*np.sqrt(2.) + b*(-18. - 14.*np.sqrt(2.)*b + 12.*np.sqrt(2.)*b**3. + 16.*b**4. + 2.*np.sqrt(2.)*b**5. + np.sqrt(2.)*b**7. + 2.*b**8.))*
np.arctan(1. + np.sqrt(2.)*b*x) + 16.*(1. - 7.*b**4.)*np.log(1. + x) + 4.*(-1. + 7.*b**4.)*np.log(1. + b**4.*x**4.) -
np.sqrt(2.)*b*(-5. + 14.*b**2. + 12.*b**4. - 2.*b**6 + b**8.)*(np.log(1. + b*x*(-np.sqrt(2.) + b*x)) - np.log(1. + b*x*(np.sqrt(2.) + b*x)))))/
(1. + b**4.)**3.)/(96.*x**2.))
g += G*Mgalaxy.value*Msun/(r*(r+rgal*kpc))
return g
def vc(r):
return np.sqrt(grav_acc(r)*r)
r_inner = 0.1*rvir*kpc
r_outer = rvir*kpc
radii = np.linspace(r_inner,r_outer,100)
vc_outer = np.sqrt(r_outer*grav_acc(r_outer))
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class HSE:
def __init__(self, f_cs_HSE = 2.0, f_cgm=0.1):
self.f_cs_HSE = f_cs_HSE
self.f_cgm = f_cgm
def find_rho_ta_HSE(Mass,r_inner, r_outer):
rho = lambda r: np.square(vc(r_outer) / vc(r)) * (r/r_outer)**(-gamma*self.f_cs_HSE)
m_shell = lambda r: 4*np.pi*r**2 * rho(r)
M_cgm = integrate.quad(m_shell,r_inner, r_outer)[0]
return Mass/M_cgm
self.rho_ta_HSE = find_rho_ta_HSE(self.f_cgm*Mhalo, 0.1*rvir*kpc, rvir*kpc)
def rho(self,r):
return self.rho_ta_HSE*np.square(vc(rvir*kpc) / vc(r)) * (r/(rvir*kpc))**(-gamma*self.f_cs_HSE)
def n(self,r):
return self.rho(r)/mu/mp
def cs(self,r):
return vc(r)/np.sqrt(self.f_cs_HSE)
def T(self,r):
return mu*mp*self.cs(r)**2/kb/gamma
def P(self,r):
return self.n(r) * self.T(r)
def K(self,r):
return self.T(r) * self.n(r)**(-2/3.)
def tcool_P(self,T, P, metallicity):
return 1.5 * (muH/mu)**2 * kb * T / ( P/T * Lambda((np.log10(P/T*(mu/muH)),np.log10(T), metallicity, redshift)))
def M_enc(self,r):
m_shell = lambda radius: 4*np.pi*radius**2 * self.rho(radius)
M_cgm = integrate.quad(m_shell,0.1*rvir*kpc,r)[0]
return M_cgm
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class HSE_turb:
"""
Mach = dv_turb/cs
"""
def __init__(self, f_cs_HSE_turb = 2.0, f_cgm=0.1, Mach=0.5):
self.f_cs_HSE_turb = f_cs_HSE_turb
self.f_cgm = f_cgm
self.Mach = Mach
def find_rho_ta_HSE_turb(Mass,r_inner, r_outer):
rho = lambda r: np.square(vc(r_outer) / vc(r)) * (r/r_outer)**(-gamma*self.f_cs_HSE_turb / (1.0 + self.Mach**2))
m_shell = lambda r: 4*np.pi*r**2 * rho(r)
M_cgm = integrate.quad(m_shell,r_inner, r_outer)[0]
return Mass/M_cgm
self.rho_ta_HSE_turb = find_rho_ta_HSE_turb(self.f_cgm*Mhalo, 0.1*rvir*kpc, rvir*kpc)
def rho(self,r):
return self.rho_ta_HSE_turb*np.square(vc(rvir*kpc) / vc(r)) * (r/(rvir*kpc))**(-gamma*self.f_cs_HSE_turb/ (1.0 + self.Mach**2))
def n(self,r):
return self.rho(r)/mu/mp
def cs(self,r):
return vc(r)/np.sqrt(self.f_cs_HSE_turb)
def T(self,r):
return mu*mp*self.cs(r)**2/kb/gamma
def P(self,r):
return self.n(r) * self.T(r)
def K(self,r):
return self.T(r) * self.n(r)**(-2/3.)
def tcool_P(self,T, P, metallicity):
return 1.5 * (muH/mu)**2 * kb * T / ( P/T * Lambda((np.log10(P/T*(mu/muH)),np.log10(T), metallicity, redshift)))
def M_enc(self,r):
m_shell = lambda radius: 4*np.pi*radius**2 * self.rho(radius)
M_cgm = integrate.quad(m_shell,0.1*rvir*kpc,r)[0]
return M_cgm
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class HSE_rot:
def __init__(self, f_cs_HSE_rot = 2.0, f_cgm=0.1, lam=0.05):
self.f_cs_HSE_rot = f_cs_HSE_rot
self.f_cgm = f_cgm
self.lam = lam
self.r_circ = self.lam*rvir*kpc
Nr = 1000
Ntheta=500
r_edges = np.linspace(r_inner,r_outer,Nr+1)
theta_edges = np.arccos(np.linspace(-0.99,0.99,Ntheta+1))
r_centers = r_edges[:-1] + 0.5*np.diff(r_edges)
thetas = theta_edges[:-1] + 0.5*np.diff(theta_edges)
gamma = 5/3.
self.r0 = rvir*kpc
def rho_outer(r,theta):
rho = 1
rho*= (vc(r)/vc(self.r0))**-2
rho*= (r/self.r0)**(-gamma*self.f_cs_HSE_rot)
rho*= np.exp(-gamma*self.f_cs_HSE_rot*self.lam**2*(rvir*kpc)**2 / (2*np.sin(theta)**2 * r**2))
return rho
def rho_inner(r,theta):
rho = (self.r0/self.r_circ)**(gamma*1.0) * np.exp(-gamma*self.f_cs_HSE_rot*self.lam**2*(rvir*kpc)**2 / (2*self.r_circ**2))
rho*= (vc(r)/vc(self.r0))**-2
rho*= (r/self.r0)**(-gamma*(self.f_cs_HSE_rot-1.0))
rho*= np.sin(theta)**(gamma*1.0)
return rho
def rho_HSE_rot(r,theta):
R = r * np.sin(theta)
rho = mp * 0.62
if R > self.r_circ:
rho*= (vc(r)/vc(self.r0))**-2
rho*= (r/self.r0)**(-gamma*self.f_cs_HSE_rot)
rho*= np.exp(-gamma*self.f_cs_HSE_rot*self.lam**2*(rvir*kpc)**2 / (2*np.sin(theta)**2 * r**2))
return rho
else:
rho*= (self.r0/self.r_circ)**(gamma*1.0) * np.exp(-gamma*self.f_cs_HSE_rot*self.lam**2*(rvir*kpc)**2 / (2*self.r_circ**2))
rho*= (vc(r)/vc(self.r0))**-2
rho*= (r/self.r0)**(-gamma*(self.f_cs_HSE_rot-1.0))
rho*= np.sin(theta)**(gamma*1.0)
return rho
rho_HSE_rot = np.vectorize(rho_HSE_rot)
def integral_argument(r,costheta):
"""
2 pi r^2 rho(r,theta) dr dcostheta
"""
return 2*np.pi*r**2 * rho_HSE_rot(r,np.arccos(costheta))
Nr = 100
Ntheta = 50
r_edges = np.linspace(0.1*rvir*kpc,rvir*kpc,Nr+1)
theta_edges = np.arccos(np.linspace(0.99,-0.99,Ntheta+1))
r_centers = r_edges[:-1] + 0.5*np.diff(r_edges)
thetas = theta_edges[:-1] + 0.5*np.diff(theta_edges)
mass = np.sum(np.sum(np.array([integral_argument(r_centers, np.cos(t))*np.diff(r_edges) for t in thetas]),axis=1)*np.diff(theta_edges))
self.n_ta=(self.f_cgm*10**lMhalo * Msun)/mass
self.rho_ta = self.n_ta * mu*mp
# def integral_argument(r,theta):
# """
# 2 pi r^2 rho(r,theta) dr dcostheta
# """
# return 2*np.pi*r**2 * rho_HSE_rot(r,theta)
# mass = integrate.dblquad(integral_argument, 0.1*rvir*kpc, 1.0*rvir*kpc, lambda x: -0.99, lambda x: 0.99)
# self.n_ta=(self.f_cgm*10**lMhalo * Msun)/mass
# self.rho_ta = self.n_ta * mu*mp
# Nr = 100
# Ntheta = 50
# r_edges = np.linspace(0.1*rvir*kpc,rvir*kpc,Nr+1)
# theta_edges = np.arccos(np.linspace(0.99,-0.99,Ntheta+1))
# r_centers = r_edges[:-1] + 0.5*np.diff(r_edges)
# thetas = theta_edges[:-1] + 0.5*np.diff(theta_edges)
# mass = np.array([integral_argument(r_centers, t) for t in thetas])
# self.n_ta=(self.f_cgm*10**lMhalo * Msun)/(np.sum(mass)*np.diff(thetas)[0]*np.diff(r_centers)[0])
# self.rho_ta = self.n_ta * mu*mp
def rho(self,r,theta):
R = r * np.sin(theta)
rho = self.rho_ta
if R > self.r_circ:
rho*= (vc(r)/vc(self.r0))**-2
rho*= (r/self.r0)**(-gamma*self.f_cs_HSE_rot)
rho*= np.exp(-gamma*self.f_cs_HSE_rot*self.lam**2*(rvir*kpc)**2 / (2*np.sin(theta)**2 * r**2))
return rho
else:
rho*= (self.r0/self.r_circ)**(gamma*1.0) * np.exp(-gamma*self.f_cs_HSE_rot*self.lam**2*(rvir*kpc)**2 / (2*self.r_circ**2))
rho*= (vc(r)/vc(self.r0))**-2
rho*= (r/self.r0)**(-gamma*(self.f_cs_HSE_rot-1.0))
rho*= np.sin(theta)**(gamma*1.0)
return rho
# self.rho = np.vectorize(self.rho)
def average_rho(self,r):
return np.array([ np.mean(np.array([self.rho(R,THETA) for THETA in np.arccos(np.linspace(0.99,-0.99,100)) ])) for R in r])
def n(self,r):
return self.average_rho(r)/mu/mp
def cs(self,r):
return vc(r)/np.sqrt(self.f_cs_HSE_rot)
def T(self,r):
return mu*mp*self.cs(r)**2/kb/gamma
def P(self,r):
return self.n(r) * self.T(r)
def K(self,r):
return self.T(r) * self.n(r)**(-2/3.)
def tcool_P(self,T, P, metallicity):
return 1.5 * (muH/mu)**2 * kb * T / ( P/T * Lambda((np.log10(P/T*(mu/muH)),np.log10(T), metallicity, redshift)))
def v_phi(self,r,theta):
R = r * np.sin(theta)
if R > self.r_circ:
return vc(r) * self.r_circ/R
else:
return vc(r)
def average_v_phi(self,r):
return np.array([ np.mean(np.array([self.v_phi(R,THETA) for THETA in np.arccos(np.linspace(-0.99,0.99,100))]))for R in r ])
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"""
Cooling Flow
"""
class cooling_flow:
def __init__(self, f_cs_CF = 2.0, Mdot = -3.0 * Msun/yr):
self.f_cs_CF = f_cs_CF
self.Mdot = Mdot
TT = lambda r: mu*mp*vc(r)**2 / self.f_cs_CF / gamma /kb
def dlogrhodlogr(logrho,logr):
r = np.exp(logr)
rho = np.exp(logrho)
vr = self.Mdot/(4*np.pi*r**2*rho)
return (self.f_cs_CF - (2/3.)*(r*Lambda((np.log10(rho/muH/mp), np.log10(TT(r)), metallicity, 0.0))*rho) / ( vr * vc(r)**2 * (mu*mp)**2 ) - 2.0) / ( (vr/vc(r))**2 * self.f_cs_CF - 1.0 ) - 2.0
def rho_coolingflow(r_inner=0.05*rvir*kpc, r_outer=2.0*rvir*kpc, rho_inner = Mhalo/(rvir*kpc)**3):
logr0 = np.log(r_inner)
logrmax = np.log(r_outer)
logrho0 = np.log(rho_inner)
logrs=[logr0]
logrhos=[logrho0]
dlogr = (logrmax - logr0)/1e3
while logrs[-1] <= logrmax:
logrhos.append(logrhos[-1]+dlogr*dlogrhodlogr(logrhos[-1],logrs[-1]))
logrs.append(logrs[-1]+dlogr)
return np.exp(logrhos), np.exp(logrs)
self.rho_cf, self.r_cf = rho_coolingflow(r_inner=0.05*rvir*kpc, r_outer=2.0*rvir*kpc, rho_inner = -1.0*self.Mdot/(4*np.pi*(0.02*rvir*kpc)**2*0.99*np.sqrt(kb*TT(0.02*rvir*kpc)/(mu*mp))))#2*Mhalo/(rvir*kpc)**3)
def T(self,r):
return mu*mp*vc(r)**2 / self.f_cs_CF / gamma /kb
def rho(self,r):
return np.interp(r,self.r_cf,self.rho_cf)
def n(self,r):
return np.interp(r,self.r_cf,self.rho_cf)/(mu*mp)
def vr(self,r):
return self.Mdot/(4.*np.pi*r**2*self.rho(r))
def K(self,r):
return self.T(r) * (self.n(r))**(-2/3.)
def P(self,r):
return self.T(r) * self.n(r)
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################################################################################################################################################################
"""
Precipitation
"""
class precipitate:
def __init__(self, tcooltff=10.0, T_outer=0.25*mu*mp*vc(rvir*kpc)**2/kb):
self.tcooltff = tcooltff
self.T_outer = T_outer
logrs = np.linspace(np.log(0.001*rvir*kpc), np.log(2.1*rvir*kpc),500)
vc2 = (np.max(vc(np.exp(logrs))))**2
logTs = np.log(np.logspace(4.2,8,100))
cooling_slope = interpolate.interp1d(logTs, np.gradient(np.log10(Lambda((-1, np.log10(np.exp(logTs)), metallicity, 0.0))))/np.gradient(logTs) )
vc_slope = interpolate.interp1d( logrs , np.gradient(np.log10(vc(np.exp(logrs)))) / np.gradient(logrs))
def dlogTdlogr(logT,logr):
return (1.0 - vc_slope(logr) - mu * mp * vc(np.exp(logr))**2 / (kb*np.exp(logT)))/(2.0 - cooling_slope(logT))
def T_precip(r_inner=0.01*rvir*kpc, r_outer=2.0*rvir*kpc, T_outer = self.T_outer):
logr0 = np.log(r_outer)
logrmin = np.log(r_inner)
logT0 = np.log(T_outer)
logrs=[logr0]
logTs=[logT0]
dlogr = -(np.log(r_outer) - np.log(r_inner))/1e3
while logrs[-1] >= logrmin:
logTs.append(logTs[-1]+dlogr*dlogTdlogr(logTs[-1],logrs[-1]))
logrs.append(logrs[-1]+dlogr)
return np.exp(logTs), np.exp(logrs)
self.T_precip, self.r_precip = T_precip(r_inner=0.01*rvir*kpc, r_outer=2.0*rvir*kpc, T_outer = self.T_outer)
def T(self,r):
return np.interp(r,self.r_precip[::-1], self.T_precip[::-1])
def rho(self,r):
return 1.5*mu*mp*kb*self.T(r)*vc(r)/(Lambda((-1,np.log10(self.T(r)),metallicity,0.0))* self.tcooltff * r )
def n(self,r):
return self.rho(r)/(mu*mp)
def K(self,r):
return self.T(r) * (self.n(r))**(-2/3.)
def P(self,r):
return self.T(r) * self.n(r)
################################################################################################################################################################
################################################################################################################################################################
################################################################################################################################################################
line_colors_VW = ['k', "#003366", "#003300", "#3333cc", "#339900", "#66a61e"]
# fn="50_M12subhalos_snap099_TNG100"
# fn="M12_s529643_snap099_TNG100"
files = glob.glob('./data/simulations/*npz')
for fn in files:
fn = fn[19:-4]
print(fn)
data = np.load('./data/simulations/'+fn+'.npz')
HSE_halo = HSE(2.0,0.05) # f_cs_HSE = 2.0, f_cgm=0.1):
HSE_turb_halo = HSE_turb(2.0,0.05,0.5) # f_cs_HSE_turb = 2.0, f_cgm=0.1, Mach=0.5):
HSE_rot_halo = HSE_rot(2.0,0.05,0.05) # f_cs_HSE_rot = 2.0, f_cgm=0.1, lam=0.05):
cooling_flow_halo = cooling_flow(1.5,-4.0*Msun/yr) # f_cs_CF = 2.0, Mdot = -3.0 * Msun/yr):
precipitate_halo = precipitate(10.0,0.05*mu*mp*vc(rvir*kpc)**2/kb) # tcooltff=10.0, T_outer=0.25*mu*mp*vc(rvir*kpc)**2/kb):
r_inner = 0.05*rvir*kpc
r_outer = 2.0*rvir*kpc
radii = np.linspace(r_inner,r_outer,100)
vc_outer = np.sqrt(r_outer*grav_acc(r_outer))
dlogT = np.diff(np.log10(data['temperature_bins']))[0]
dlogn = np.diff(np.log10(data['number_density_bins']))[0]
dlogP = np.diff(np.log10(data['pressure_bins']))[0]
dlogK = np.diff(np.log10(data['entropy_bins']))[0]
dlogr = np.diff(np.log10(data['r_r200m_profile']))[0]
dvr = np.diff(data['radial_velocity_bins'])
dvphi = np.diff(data['azimuthal_velocity_bins'])
fig,ax = plt.subplots(1,1)
plot=ax.pcolormesh(data['r_r200m_profile'], data['temperature_bins'],
(data['temperature_Volume']/np.sum(data['temperature_Volume']))/dlogT/dlogr,
norm=colors.LogNorm(vmin=1e-2, vmax =3), cmap='plasma')
ax.plot(radii/(rvir*kpc), HSE_halo.T(radii), color=cm.viridis(0.0), lw=2.5, label='HSE')
ax.plot(radii/(rvir*kpc), cooling_flow_halo.T(radii), dashes=[1,2], color=cm.viridis(0.2), lw=2.5, label='Cooling Flow'),
ax.plot(radii/(rvir*kpc), HSE_turb_halo.T(radii), dashes=[4,2], color=cm.viridis(0.4), lw=2.5, label='HSE w/'+r'$\mathcal{M}=0.5$'+' turb.')
ax.plot(radii/(rvir*kpc), HSE_rot_halo.T(radii), dashes=[4,2,1,2], color=cm.viridis(0.6), lw=2.5, label='HSE w/ rot. '+r'$\lambda=0.05$')
ax.plot(radii/(rvir*kpc), precipitate_halo.T(radii), dashes=[4,2,1,2,1,2], color=cm.viridis(0.8), lw=2.5, label='HSE w/ ' + r'$\frac{t_{\rm cool}}{t_{\rm ff}} = 10$'+' precip.')
cb = fig.colorbar(plot)
cb.set_label(r'$d^2 (V/V_{\rm tot}) / d \log T \, d \log r$',rotation=270,fontsize=12,labelpad=15)
ax.set_xlim(5e-2, 2)
ax.set_ylim(3e3,4e7)
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylabel(r'$T\,[\mathrm{K}]$')
ax.set_xlabel(r'$r/r_{\rm vir}$')
ax.legend(loc='upper right', fontsize=8,ncol=2,columnspacing=-3, handlelength=3.0)
ax.set_title(r'$t='+str(np.round(data['time'],2))+r'\,\mathrm{Gyr}$')
ax.grid(color='grey',linestyle=':', alpha=0.5, linewidth=1.0)
fig.set_size_inches(5,3)
plt.savefig('./plots/temperature_Volume_'+fn+'.png',bbox_inches='tight',dpi=200)
plt.clf()
fig,ax = plt.subplots(1,1)
plot=ax.pcolormesh(data['r_r200m_profile'], data['temperature_bins'],
(data['temperature_Mass']/(fb*Mhalo/Msun)/dlogT/dlogr),
norm=colors.LogNorm(vmin=1e-2, vmax=3), cmap='viridis')
ax.plot(radii/(rvir*kpc), HSE_halo.T(radii), color=cm.plasma(0.0), lw=2.5, label='HSE')
ax.plot(radii/(rvir*kpc), cooling_flow_halo.T(radii), dashes=[1,2], color=cm.plasma(0.2), lw=2.5, label='Cooling Flow'),
ax.plot(radii/(rvir*kpc), HSE_turb_halo.T(radii), dashes=[4,2], color=cm.plasma(0.4), lw=2.5, label='HSE w/'+r'$\mathcal{M}=0.5$'+' turb.')
ax.plot(radii/(rvir*kpc), HSE_rot_halo.T(radii), dashes=[4,2,1,2], color=cm.plasma(0.6), lw=2.5, label='HSE w/ rot. '+r'$\lambda=0.05$')
ax.plot(radii/(rvir*kpc), precipitate_halo.T(radii), dashes=[4,2,1,2,1,2], color=cm.plasma(0.8), lw=2.5, label='HSE w/ ' + r'$\frac{t_{\rm cool}}{t_{\rm ff}} = 10$'+' precip.')
cb = fig.colorbar(plot)
cb.set_label(r'$d^2 (M/ f_b M_{\rm halo}) / d \log T \, d \log r$',rotation=270,fontsize=12,labelpad=15)
ax.set_xlim(5e-2, 2)
ax.set_ylim(3e3,4e7)
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylabel(r'$T\,[\mathrm{K}]$')
ax.set_xlabel(r'$r/r_{\rm vir}$')
ax.legend(loc='upper right', fontsize=8,ncol=2,columnspacing=-3, handlelength=3.0)
ax.set_title(r'$t='+str(np.round(data['time'],2))+r'\,\mathrm{Gyr}$')
ax.grid(color='grey',linestyle=':', alpha=0.5, linewidth=1.0)
fig.set_size_inches(5,3)
plt.savefig('./plots/temperature_Mass_'+fn+'.png',bbox_inches='tight',dpi=200)
plt.clf()
plt.close('all')
fig,ax = plt.subplots(1,1)
plot=ax.pcolormesh(data['r_r200m_profile'], data['number_density_bins'],
(data['number_density_Volume']/np.sum(data['number_density_Volume']))/dlogn/dlogr,
norm=colors.LogNorm(vmin=1e-2, vmax =3), cmap='plasma')
ax.plot(radii/(rvir*kpc), HSE_halo.n(radii), color=cm.viridis(0.0), lw=2.5, label='HSE')
ax.plot(radii/(rvir*kpc), cooling_flow_halo.n(radii), dashes=[1,2], color=cm.viridis(0.2), lw=2.5, label='Cooling Flow'),
ax.plot(radii/(rvir*kpc), HSE_turb_halo.n(radii), dashes=[4,2], color=cm.viridis(0.4), lw=2.5, label='HSE w/'+r'$\mathcal{M}=0.5$'+' turb.')
ax.plot(radii/(rvir*kpc), HSE_rot_halo.n(radii), dashes=[4,2,1,2], color=cm.viridis(0.6), lw=2.5, label='HSE w/ rot. '+r'$\lambda=0.05$')
ax.plot(radii/(rvir*kpc), precipitate_halo.n(radii), dashes=[4,2,1,2,1,2], color=cm.viridis(0.8), lw=2.5, label='HSE w/ ' + r'$\frac{t_{\rm cool}}{t_{\rm ff}} = 10$'+' precip.')
cb = fig.colorbar(plot)
cb.set_label(r'$d^2 (V/V_{\rm tot}) / d \log n \, d \log r$',rotation=270,fontsize=12,labelpad=15)
ax.set_xlim(5e-2, 2)
ax.set_ylim(5e-6,3e0)
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylabel(r'$n\,[\mathrm{cm}^{-3}]$')
ax.set_xlabel(r'$r/r_{\rm vir}$')
ax.legend(loc='upper right', fontsize=8,ncol=2,columnspacing=-3, handlelength=3.0)
ax.set_title(r'$t='+str(np.round(data['time'],2))+r'\,\mathrm{Gyr}$')
ax.grid(color='grey',linestyle=':', alpha=0.5, linewidth=1.0)
fig.set_size_inches(5,3)
plt.savefig('./plots/number_density_Volume_'+fn+'.png',bbox_inches='tight',dpi=200)
plt.clf()
fig,ax = plt.subplots(1,1)
plot=ax.pcolormesh(data['r_r200m_profile'], data['number_density_bins'],
(data['number_density_Mass']/(fb*Mhalo/Msun)/dlogn/dlogr),
norm=colors.LogNorm(vmin=1e-2, vmax=3), cmap='viridis')
ax.plot(radii/(rvir*kpc), HSE_halo.n(radii), color=cm.plasma(0.0), lw=2.5, label='HSE')
ax.plot(radii/(rvir*kpc), cooling_flow_halo.n(radii), dashes=[1,2], color=cm.plasma(0.2), lw=2.5, label='Cooling Flow'),
ax.plot(radii/(rvir*kpc), HSE_turb_halo.n(radii), dashes=[4,2], color=cm.plasma(0.4), lw=2.5, label='HSE w/'+r'$\mathcal{M}=0.5$'+' turb.')
ax.plot(radii/(rvir*kpc), HSE_rot_halo.n(radii), dashes=[4,2,1,2], color=cm.plasma(0.6), lw=2.5, label='HSE w/ rot. '+r'$\lambda=0.05$')
ax.plot(radii/(rvir*kpc), precipitate_halo.n(radii), dashes=[4,2,1,2,1,2], color=cm.plasma(0.8), lw=2.5, label='HSE w/ ' + r'$\frac{t_{\rm cool}}{t_{\rm ff}} = 10$'+' precip.')
cb = fig.colorbar(plot)
cb.set_label(r'$d^2 (M/ f_b M_{\rm halo}) / d \log n \, d \log r$',rotation=270,fontsize=12,labelpad=15)
ax.set_xlim(5e-2, 2)
ax.set_ylim(5e-6,3e0)
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylabel(r'$n\,[\mathrm{cm}^{-3}]$')
ax.set_xlabel(r'$r/r_{\rm vir}$')
ax.legend(loc='upper right', fontsize=8,ncol=2,columnspacing=-3, handlelength=3.0)
ax.set_title(r'$t='+str(np.round(data['time'],2))+r'\,\mathrm{Gyr}$')
ax.grid(color='grey',linestyle=':', alpha=0.5, linewidth=1.0)
fig.set_size_inches(5,3)
plt.savefig('./plots/number_density_Mass_'+fn+'.png',bbox_inches='tight',dpi=200)
plt.clf()
plt.close('all')
fig,ax = plt.subplots(1,1)
plot=ax.pcolormesh(data['r_r200m_profile'], data['pressure_bins'],
(data['pressure_Volume']/np.sum(data['pressure_Volume']))/dlogP/dlogr,
norm=colors.LogNorm(vmin=1e-2, vmax =3), cmap='plasma')
ax.plot(radii/(rvir*kpc), HSE_halo.P(radii), color=cm.viridis(0.0), lw=2.5, label='HSE')
ax.plot(radii/(rvir*kpc), cooling_flow_halo.P(radii), dashes=[1,2], color=cm.viridis(0.2), lw=2.5, label='Cooling Flow'),
ax.plot(radii/(rvir*kpc), HSE_turb_halo.P(radii), dashes=[4,2], color=cm.viridis(0.4), lw=2.5, label='HSE w/'+r'$\mathcal{M}=0.5$'+' turb.')
ax.plot(radii/(rvir*kpc), HSE_rot_halo.P(radii), dashes=[4,2,1,2], color=cm.viridis(0.6), lw=2.5, label='HSE w/ rot. '+r'$\lambda=0.05$')
ax.plot(radii/(rvir*kpc), precipitate_halo.P(radii), dashes=[4,2,1,2,1,2], color=cm.viridis(0.8), lw=2.5, label='HSE w/ ' + r'$\frac{t_{\rm cool}}{t_{\rm ff}} = 10$'+' precip.')
cb = fig.colorbar(plot)
cb.set_label(r'$d^2 (V/V_{\rm tot}) / d \log P \, d \log r$',rotation=270,fontsize=12,labelpad=15)
ax.set_xlim(5e-2, 2)
ax.set_ylim(1e-1,1e6)
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylabel(r'$P\,[\mathrm{K\,cm}^{-3}]$')
ax.set_xlabel(r'$r/r_{\rm vir}$')
ax.legend(loc='upper right', fontsize=8,ncol=2,columnspacing=-3, handlelength=3.0)
ax.set_title(r'$t='+str(np.round(data['time'],2))+r'\,\mathrm{Gyr}$')
ax.grid(color='grey',linestyle=':', alpha=0.5, linewidth=1.0)
fig.set_size_inches(5,3)
plt.savefig('./plots/pressure_Volume_'+fn+'.png',bbox_inches='tight',dpi=200)
plt.clf()
fig,ax = plt.subplots(1,1)
plot=ax.pcolormesh(data['r_r200m_profile'], data['pressure_bins'],
(data['pressure_Mass']/(fb*Mhalo/Msun)/dlogP/dlogr),
norm=colors.LogNorm(vmin=1e-2, vmax=3), cmap='viridis')
ax.plot(radii/(rvir*kpc), HSE_halo.P(radii), color=cm.plasma(0.0), lw=2.5, label='HSE')
ax.plot(radii/(rvir*kpc), cooling_flow_halo.P(radii), dashes=[1,2], color=cm.plasma(0.2), lw=2.5, label='Cooling Flow'),
ax.plot(radii/(rvir*kpc), HSE_turb_halo.P(radii), dashes=[4,2], color=cm.plasma(0.4), lw=2.5, label='HSE w/'+r'$\mathcal{M}=0.5$'+' turb.')
ax.plot(radii/(rvir*kpc), HSE_rot_halo.P(radii), dashes=[4,2,1,2], color=cm.plasma(0.6), lw=2.5, label='HSE w/ rot. '+r'$\lambda=0.05$')
ax.plot(radii/(rvir*kpc), precipitate_halo.P(radii), dashes=[4,2,1,2,1,2], color=cm.plasma(0.8), lw=2.5, label='HSE w/ ' + r'$\frac{t_{\rm cool}}{t_{\rm ff}} = 10$'+' precip.')
cb = fig.colorbar(plot)
cb.set_label(r'$d^2 (M/ f_b M_{\rm halo}) / d \log P \, d \log r$',rotation=270,fontsize=12,labelpad=15)
ax.set_xlim(5e-2, 2)
ax.set_ylim(1e-1,1e6)
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylabel(r'$P\,[\mathrm{K\,cm}^{-3}]$')
ax.set_xlabel(r'$r/r_{\rm vir}$')
ax.legend(loc='upper right', fontsize=8,ncol=2,columnspacing=-3, handlelength=3.0)
ax.set_title(r'$t='+str(np.round(data['time'],2))+r'\,\mathrm{Gyr}$')
ax.grid(color='grey',linestyle=':', alpha=0.5, linewidth=1.0)
fig.set_size_inches(5,3)
plt.savefig('./plots/pressure_Mass_'+fn+'.png',bbox_inches='tight',dpi=200)
plt.clf()
plt.close('all')
fig,ax = plt.subplots(1,1)
plot=ax.pcolormesh(data['r_r200m_profile'], data['entropy_bins'],
(data['entropy_Volume']/np.sum(data['entropy_Volume']))/dlogK/dlogr,
norm=colors.LogNorm(vmin=1e-2, vmax =3), cmap='plasma')
ax.plot(radii/(rvir*kpc), HSE_halo.K(radii), color=cm.viridis(0.0), lw=2.5, label='HSE')
ax.plot(radii/(rvir*kpc), cooling_flow_halo.K(radii), dashes=[1,2], color=cm.viridis(0.2), lw=2.5, label='Cooling Flow'),
ax.plot(radii/(rvir*kpc), HSE_turb_halo.K(radii), dashes=[4,2], color=cm.viridis(0.4), lw=2.5, label='HSE w/'+r'$\mathcal{M}=0.5$'+' turb.')
ax.plot(radii/(rvir*kpc), HSE_rot_halo.K(radii), dashes=[4,2,1,2], color=cm.viridis(0.6), lw=2.5, label='HSE w/ rot. '+r'$\lambda=0.05$')
ax.plot(radii/(rvir*kpc), precipitate_halo.K(radii), dashes=[4,2,1,2,1,2], color=cm.viridis(0.8), lw=2.5, label='HSE w/ ' + r'$\frac{t_{\rm cool}}{t_{\rm ff}} = 10$'+' precip.')
cb = fig.colorbar(plot)
cb.set_label(r'$d^2 (V/V_{\rm tot}) / d \log K \, d \log r$',rotation=270,fontsize=12,labelpad=15)
ax.set_xlim(5e-2, 2)
# ax.set_ylim(1e-1,1e6)
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylabel(r'$K\,[\mathrm{K\,cm}^{2}]$')
ax.set_xlabel(r'$r/r_{\rm vir}$')
ax.legend(loc='lower right', fontsize=8,ncol=1, handlelength=3.0)
ax.set_title(r'$t='+str(np.round(data['time'],2))+r'\,\mathrm{Gyr}$')
ax.grid(color='grey',linestyle=':', alpha=0.5, linewidth=1.0)
fig.set_size_inches(5,3)
plt.savefig('./plots/entropy_Volume_'+fn+'.png',bbox_inches='tight',dpi=200)
plt.clf()
fig,ax = plt.subplots(1,1)
plot=ax.pcolormesh(data['r_r200m_profile'], data['entropy_bins'],
(data['entropy_Mass']/(fb*Mhalo/Msun)/dlogK/dlogr),
norm=colors.LogNorm(vmin=1e-2, vmax=3), cmap='viridis')
ax.plot(radii/(rvir*kpc), HSE_halo.K(radii), color=cm.plasma(0.0), lw=2.5, label='HSE')
ax.plot(radii/(rvir*kpc), cooling_flow_halo.K(radii), dashes=[1,2], color=cm.plasma(0.2), lw=2.5, label='Cooling Flow'),
ax.plot(radii/(rvir*kpc), HSE_turb_halo.K(radii), dashes=[4,2], color=cm.plasma(0.4), lw=2.5, label='HSE w/'+r'$\mathcal{M}=0.5$'+' turb.')
ax.plot(radii/(rvir*kpc), HSE_rot_halo.K(radii), dashes=[4,2,1,2], color=cm.plasma(0.6), lw=2.5, label='HSE w/ rot. '+r'$\lambda=0.05$')
ax.plot(radii/(rvir*kpc), precipitate_halo.K(radii), dashes=[4,2,1,2,1,2], color=cm.plasma(0.8), lw=2.5, label='HSE w/ ' + r'$\frac{t_{\rm cool}}{t_{\rm ff}} = 10$'+' precip.')
cb = fig.colorbar(plot)
cb.set_label(r'$d^2 (M/ f_b M_{\rm halo}) / d \log K \, d \log r$',rotation=270,fontsize=12,labelpad=15)
ax.set_xlim(5e-2, 2)
# ax.set_ylim(1e-1,1e6)
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylabel(r'$K\,[\mathrm{K\,cm}^{2}]$')
ax.set_xlabel(r'$r/r_{\rm vir}$')
ax.legend(loc='lower right', fontsize=8,ncol=1, handlelength=3.0)
ax.set_title(r'$t='+str(np.round(data['time'],2))+r'\,\mathrm{Gyr}$')
ax.grid(color='grey',linestyle=':', alpha=0.5, linewidth=1.0)
fig.set_size_inches(5,3)
plt.savefig('./plots/entropy_Mass_'+fn+'.png',bbox_inches='tight',dpi=200)
plt.clf()
plt.close('all')
fig,ax = plt.subplots(1,1)
plot=ax.pcolormesh(data['r_r200m_profile'], data['radial_velocity_bins'],
(((data['radial_velocity_Volume']/np.sum(data['radial_velocity_Volume']))).T/dvr).T /dlogr,
norm=colors.LogNorm(vmin=1e-3,vmax=3), cmap='plasma')
ax.plot(radii/(rvir*kpc), cooling_flow_halo.vr(radii)/1e5, dashes=[1,2], color=cm.viridis(0.2), lw=2.5, label='Cooling Flow'),
cb = fig.colorbar(plot)
cb.set_label(r'$d^2 (V/V_{\rm tot}) / d v_r \, d \log r$',rotation=270,fontsize=12,labelpad=15)
ax.set_xlim(5e-2, 2)
# ax.set_ylim(1e-1,1e6)
# ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylabel(r'$v_r\,[\mathrm{km/s}]$')
ax.set_xlabel(r'$r/r_{\rm vir}$')
ax.legend(loc='lower right', fontsize=8,ncol=1, handlelength=3.0)
ax.set_title(r'$t='+str(np.round(data['time'],2))+r'\,\mathrm{Gyr}$')
ax.grid(color='grey',linestyle=':', alpha=0.5, linewidth=1.0)
fig.set_size_inches(5,3)
plt.savefig('./plots/radial_velocity_Volume_'+fn+'.png',bbox_inches='tight',dpi=200)
plt.clf()
fig,ax = plt.subplots(1,1)
plot=ax.pcolormesh(data['r_r200m_profile'], data['radial_velocity_bins'],
(data['radial_velocity_Mass'].T/(fb*Mhalo/Msun)/dvr /dlogr).T,
norm=colors.LogNorm(vmin=3e-4, vmax=3e-1), cmap='viridis')
ax.plot(radii/(rvir*kpc), cooling_flow_halo.vr(radii)/1e5, dashes=[1,2], color=cm.plasma(0.2), lw=2.5, label='Cooling Flow'),
cb = fig.colorbar(plot)
cb.set_label(r'$d^2 (M/ f_b M_{\rm halo}) / d v_r \, d \log r$',rotation=270,fontsize=12,labelpad=15)
ax.set_xlim(5e-2, 2)
# ax.set_ylim(1e-1,1e6)
# ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylabel(r'$v_r\,[\mathrm{km/s}]$')
ax.set_xlabel(r'$r/r_{\rm vir}$')
ax.legend(loc='lower right', fontsize=8,ncol=1, handlelength=3.0)
ax.set_title(r'$t='+str(np.round(data['time'],2))+r'\,\mathrm{Gyr}$')
ax.grid(color='grey',linestyle=':', alpha=0.5, linewidth=1.0)
fig.set_size_inches(5,3)
plt.savefig('./plots/radial_velocity_Mass_'+fn+'.png',bbox_inches='tight',dpi=200)
plt.clf()
plt.close('all')
fig,ax = plt.subplots(1,1)
plot=ax.pcolormesh(data['r_r200m_profile'], data['azimuthal_velocity_bins'],
(((data['azimuthal_velocity_Volume']/np.sum(data['azimuthal_velocity_Volume']))).T/dvphi).T /dlogr,
norm=colors.LogNorm(vmin=1e-3,vmax=3), cmap='plasma')
ax.plot(radii/(rvir*kpc), HSE_rot_halo.average_v_phi(radii)/1e5, dashes=[4,2,1,2], color=cm.viridis(0.6), lw=2.5, label='HSE w/ rot. '+r'$\lambda=0.05$')
cb = fig.colorbar(plot)
cb.set_label(r'$d^2 (V/V_{\rm tot}) / d v_\phi \, d \log r$',rotation=270,fontsize=12,labelpad=15)
ax.set_xlim(5e-2, 2)
# ax.set_ylim(1e-1,1e6)
# ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylabel(r'$v_\phi\,[\mathrm{km/s}]$')
ax.set_xlabel(r'$r/r_{\rm vir}$')
ax.legend(loc='lower right', fontsize=8,ncol=1, handlelength=3.0)
ax.set_title(r'$t='+str(np.round(data['time'],2))+r'\,\mathrm{Gyr}$')
ax.grid(color='grey',linestyle=':', alpha=0.5, linewidth=1.0)
fig.set_size_inches(5,3)
plt.savefig('./plots/azimuthal_velocity_Volume_'+fn+'.png',bbox_inches='tight',dpi=200)
plt.clf()
fig,ax = plt.subplots(1,1)
plot=ax.pcolormesh(data['r_r200m_profile'], data['azimuthal_velocity_bins'],
(data['azimuthal_velocity_Mass'].T/(fb*Mhalo/Msun)/dvphi /dlogr).T,
norm=colors.LogNorm(vmin=3e-4, vmax=3e-1), cmap='viridis')
ax.plot(radii/(rvir*kpc), HSE_rot_halo.average_v_phi(radii)/1e5, dashes=[4,2,1,2], color=cm.plasma(0.6), lw=2.5, label='HSE w/ rot. '+r'$\lambda=0.05$')
cb = fig.colorbar(plot)
cb.set_label(r'$d^2 (M/ f_b M_{\rm halo}) / d v_\phi \, d \log r$',rotation=270,fontsize=12,labelpad=15)
ax.set_xlim(5e-2, 2)
# ax.set_ylim(1e-1,1e6)
# ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylabel(r'$v_\phi\,[\mathrm{km/s}]$')
ax.set_xlabel(r'$r/r_{\rm vir}$')
ax.legend(loc='lower right', fontsize=8,ncol=1, handlelength=3.0)
ax.set_title(r'$t='+str(np.round(data['time'],2))+r'\,\mathrm{Gyr}$')
ax.grid(color='grey',linestyle=':', alpha=0.5, linewidth=1.0)
fig.set_size_inches(5,3)
plt.savefig('./plots/azimuthal_velocity_Mass_'+fn+'.png',bbox_inches='tight',dpi=200)
plt.clf()
plt.close('all')
ir = 5
fig,ax = plt.subplots(1,1)
plot=ax.pcolormesh(data['pressure_bins'], data['entropy_bins'],
data['pressure_entropy_Volume'][...,ir]/np.sum(data['pressure_entropy_Volume'][...,ir]),
norm=colors.LogNorm(vmin=1e-5,vmax=1e-1), cmap='plasma')