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paper_plotter_final.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
from matplotlib import gridspec
import palettable
from scipy.ndimage.filters import gaussian_filter
import scipy.interpolate
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))
def averager(files):
for i,fn in enumerate(files):
fn = fn[19:-4]
print(fn)
data = np.load('./data/simulations/'+fn+'.npz')
if i == 0 :
azimuthal_velocity_Mass = data['azimuthal_velocity_Mass']
azimuthal_velocity_entropy_Mass = data['azimuthal_velocity_entropy_Mass']
azimuthal_velocity_temperature_Mass = data['azimuthal_velocity_temperature_Mass']
density_temperature_Mass = data['density_temperature_Mass']
entropy_Mass = data['entropy_Mass']
number_density_Mass = data['number_density_Mass']
polar_velocity_Mass = data['polar_velocity_Mass']
pressure_Mass = data['pressure_Mass']
pressure_entropy_Mass = data['pressure_entropy_Mass']
radial_velocity_Mass = data['radial_velocity_Mass']
radial_velocity_entropy_Mass = data['radial_velocity_entropy_Mass']
radial_velocity_temperature_Mass = data['radial_velocity_temperature_Mass']
specific_angular_momentum_x_Mass = data['specific_angular_momentum_x_Mass']
specific_angular_momentum_y_Mass = data['specific_angular_momentum_y_Mass']
specific_angular_momentum_z_Mass = data['specific_angular_momentum_z_Mass']
# tcool_Mass = data['tcool_Mass']
temperature_Mass = data['temperature_Mass']
azimuthal_velocity_Volume = data['azimuthal_velocity_Volume']
azimuthal_velocity_entropy_Volume = data['azimuthal_velocity_entropy_Volume']
azimuthal_velocity_temperature_Volume = data['azimuthal_velocity_temperature_Volume']
density_temperature_Volume = data['density_temperature_Volume']
entropy_Volume = data['entropy_Volume']
number_density_Volume = data['number_density_Volume']
polar_velocity_Volume = data['polar_velocity_Volume']
pressure_Volume = data['pressure_Volume']
pressure_entropy_Volume = data['pressure_entropy_Volume']
radial_velocity_Volume = data['radial_velocity_Volume']
radial_velocity_entropy_Volume = data['radial_velocity_entropy_Volume']
radial_velocity_temperature_Volume = data['radial_velocity_temperature_Volume']
specific_angular_momentum_x_Volume = data['specific_angular_momentum_x_Volume']
specific_angular_momentum_y_Volume = data['specific_angular_momentum_y_Volume']
specific_angular_momentum_z_Volume = data['specific_angular_momentum_z_Volume']
# tcool_Volume = data['tcool_Volume']
temperature_Volume = data['temperature_Volume']
azimuthal_velocity_Mass += data['azimuthal_velocity_Mass']
azimuthal_velocity_entropy_Mass += data['azimuthal_velocity_entropy_Mass']
azimuthal_velocity_temperature_Mass += data['azimuthal_velocity_temperature_Mass']
density_temperature_Mass += data['density_temperature_Mass']
entropy_Mass += data['entropy_Mass']
number_density_Mass += data['number_density_Mass']
polar_velocity_Mass += data['polar_velocity_Mass']
pressure_Mass += data['pressure_Mass']
pressure_entropy_Mass += data['pressure_entropy_Mass']
radial_velocity_Mass += data['radial_velocity_Mass']
radial_velocity_entropy_Mass += data['radial_velocity_entropy_Mass']
radial_velocity_temperature_Mass += data['radial_velocity_temperature_Mass']
specific_angular_momentum_x_Mass += data['specific_angular_momentum_x_Mass']
specific_angular_momentum_y_Mass += data['specific_angular_momentum_y_Mass']
specific_angular_momentum_z_Mass += data['specific_angular_momentum_z_Mass']
# tcool_Mass += data['tcool_Mass']
temperature_Mass += data['temperature_Mass']
azimuthal_velocity_Volume += data['azimuthal_velocity_Volume']
azimuthal_velocity_entropy_Volume += data['azimuthal_velocity_entropy_Volume']
azimuthal_velocity_temperature_Volume += data['azimuthal_velocity_temperature_Volume']
density_temperature_Volume += data['density_temperature_Volume']
entropy_Volume += data['entropy_Volume']
number_density_Volume += data['number_density_Volume']
polar_velocity_Volume += data['polar_velocity_Volume']
pressure_Volume += data['pressure_Volume']
pressure_entropy_Volume += data['pressure_entropy_Volume']
radial_velocity_Volume += data['radial_velocity_Volume']
radial_velocity_entropy_Volume += data['radial_velocity_entropy_Volume']
radial_velocity_temperature_Volume += data['radial_velocity_temperature_Volume']
specific_angular_momentum_x_Volume += data['specific_angular_momentum_x_Volume']
specific_angular_momentum_y_Volume += data['specific_angular_momentum_y_Volume']
specific_angular_momentum_z_Volume += data['specific_angular_momentum_z_Volume']
# tcool_Volume += data['tcool_Volume']
temperature_Volume += data['temperature_Volume']
r_r200m_phase = data['r_r200m_phase']
r_r200m_profile = data['r_r200m_profile']
halo_mass = data['halo_mass']
time = data['time']
r200m = data['r200m']
azimuthal_velocity_bins = data['azimuthal_velocity_bins']
entropy_bins = data['entropy_bins']
number_density_bins = data['number_density_bins']
polar_velocity_bins = data['polar_velocity_bins']
pressure_bins = data['pressure_bins']
radial_velocity_bins = data['radial_velocity_bins']
specific_angular_momentum_x_bins = data['specific_angular_momentum_x_bins']
specific_angular_momentum_y_bins = data['specific_angular_momentum_y_bins']
specific_angular_momentum_z_bins = data['specific_angular_momentum_z_bins']
temperature_bins = data['temperature_bins']
if i == len(files)-1:
data={}
data['azimuthal_velocity_Mass'] = azimuthal_velocity_Mass/len(files)
data['azimuthal_velocity_entropy_Mass'] = azimuthal_velocity_entropy_Mass/len(files)
data['azimuthal_velocity_temperature_Mass'] = azimuthal_velocity_temperature_Mass/len(files)
data['density_temperature_Mass'] = density_temperature_Mass/len(files)
data['entropy_Mass'] = entropy_Mass/len(files)
data['number_density_Mass'] = number_density_Mass/len(files)
data['polar_velocity_Mass'] = polar_velocity_Mass/len(files)
data['pressure_Mass'] = pressure_Mass/len(files)
data['pressure_entropy_Mass'] = pressure_entropy_Mass/len(files)
data['radial_velocity_Mass'] = radial_velocity_Mass/len(files)
data['radial_velocity_entropy_Mass'] = radial_velocity_entropy_Mass/len(files)
data['radial_velocity_temperature_Mass'] = radial_velocity_temperature_Mass/len(files)
data['specific_angular_momentum_x_Mass'] = specific_angular_momentum_x_Mass/len(files)
data['specific_angular_momentum_y_Mass'] = specific_angular_momentum_y_Mass/len(files)
data['specific_angular_momentum_z_Mass'] = specific_angular_momentum_z_Mass/len(files)
# data['tcool_Mass'] = tcool_Mass/len(files)
data['temperature_Mass'] = temperature_Mass/len(files)
data['azimuthal_velocity_Volume'] = azimuthal_velocity_Volume/len(files)
data['azimuthal_velocity_entropy_Volume'] = azimuthal_velocity_entropy_Volume/len(files)
data['azimuthal_velocity_temperature_Volume'] = azimuthal_velocity_temperature_Volume/len(files)
data['density_temperature_Volume'] = density_temperature_Volume/len(files)
data['entropy_Volume'] = entropy_Volume/len(files)
data['number_density_Volume'] = number_density_Volume/len(files)
data['polar_velocity_Volume'] = polar_velocity_Volume/len(files)
data['pressure_Volume'] = pressure_Volume/len(files)
data['pressure_entropy_Volume'] = pressure_entropy_Volume/len(files)
data['radial_velocity_Volume'] = radial_velocity_Volume/len(files)
data['radial_velocity_entropy_Volume'] = radial_velocity_entropy_Volume/len(files)
data['radial_velocity_temperature_Volume'] = radial_velocity_temperature_Volume/len(files)
data['specific_angular_momentum_x_Volume'] = specific_angular_momentum_x_Volume/len(files)
data['specific_angular_momentum_y_Volume'] = specific_angular_momentum_y_Volume/len(files)
data['specific_angular_momentum_z_Volume'] = specific_angular_momentum_z_Volume/len(files)
# data['tcool_Volume'] = tcool_Volume/len(files)
data['temperature_Volume'] = temperature_Volume/len(files)
data['r_r200m_phase'] = r_r200m_phase
data['r_r200m_profile'] = r_r200m_profile
data['halo_mass'] = halo_mass
data['time'] = time
data['r200m'] = r200m
data['azimuthal_velocity_bins'] = azimuthal_velocity_bins
data['entropy_bins'] = entropy_bins
data['number_density_bins'] = number_density_bins
data['polar_velocity_bins'] = polar_velocity_bins
data['pressure_bins'] = pressure_bins
data['radial_velocity_bins'] = radial_velocity_bins
data['specific_angular_momentum_x_bins'] = specific_angular_momentum_x_bins
data['specific_angular_momentum_y_bins'] = specific_angular_momentum_y_bins
data['specific_angular_momentum_z_bins'] = specific_angular_momentum_z_bins
data['temperature_bins'] = temperature_bins
return data
def get_medians(data,fn):
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'])
if len(data['r_r200m_profile']) > 200:
r_r200m_profile_centers = 10**(np.log10(data['r_r200m_profile'])[:-1] + 0.5*np.diff(np.log10(data['r_r200m_profile'])))
else :
r_r200m_profile_centers = data['r_r200m_profile']
temperature_bin_centers = 10**(np.log10(data['temperature_bins'])[:-1] + 0.5*np.diff(np.log10(data['temperature_bins'])))
number_density_bin_centers = 10**(np.log10(data['number_density_bins'])[:-1] + 0.5*np.diff(np.log10(data['number_density_bins'])))
entropy_bin_centers = 10**(np.log10(data['entropy_bins'])[:-1] + 0.5*np.diff(np.log10(data['entropy_bins'])))
pressure_bin_centers = 10**(np.log10(data['pressure_bins'])[:-1] + 0.5*np.diff(np.log10(data['pressure_bins'])))
radial_velocity_bin_centers = data['radial_velocity_bins'][:-1] + 0.5*np.diff(data['radial_velocity_bins'])
azimuthal_velocity_bin_centers = data['azimuthal_velocity_bins'][:-1] + 0.5*np.diff(data['azimuthal_velocity_bins'])
polar_velocity_bin_centers = azimuthal_velocity_bin_centers#data['polar_velocity_bins'][:-1] + 0.5*np.diff(data['polar_velocity_bins'])
Nradii = len(r_r200m_profile_centers)
sigma = 0.75
temperature_profile_Volume = np.zeros(Nradii)*np.nan
temperature_profile_Volume_quartiles = np.zeros((2,Nradii))*np.nan
temperature_profile_Mass = np.zeros(Nradii)*np.nan
temperature_profile_Mass_quartiles = np.zeros((2,Nradii))*np.nan
for i in range(Nradii):
if np.sum(data['temperature_Volume'][:,i]) > 0:
f_interp = scipy.interpolate.interp1d(np.cumsum(data['temperature_Volume'][:,i])/np.sum(data['temperature_Volume'][:,i]),temperature_bin_centers, bounds_error=False, fill_value=0.0)
temperature_profile_Volume[i] = f_interp(0.5)
temperature_profile_Volume_quartiles[0,i] = f_interp( 0.25)
temperature_profile_Volume_quartiles[1,i] = f_interp( 0.75)
if np.sum(data['temperature_Mass'][:,i]) > 0:
f_interp = scipy.interpolate.interp1d(np.cumsum(data['temperature_Mass'][:,i])/np.sum(data['temperature_Mass'][:,i]),temperature_bin_centers, bounds_error=False, fill_value=0.0)
temperature_profile_Mass[i] = f_interp(0.5)
temperature_profile_Mass_quartiles[0,i] = f_interp( 0.25)
temperature_profile_Mass_quartiles[1,i] = f_interp( 0.75)
number_density_profile_Volume = np.zeros(Nradii)*np.nan
number_density_profile_Volume_quartiles = np.zeros((2,Nradii))*np.nan
number_density_profile_Mass = np.zeros(Nradii)*np.nan
number_density_profile_Mass_quartiles = np.zeros((2,Nradii))*np.nan
for i in range(Nradii):
if np.sum(data['number_density_Volume'][:,i]) > 0:
f_interp = scipy.interpolate.interp1d(np.cumsum(data['number_density_Volume'][:,i])/np.sum(data['number_density_Volume'][:,i]),number_density_bin_centers, bounds_error=False, fill_value=0.0)
number_density_profile_Volume[i] = f_interp(0.5)
number_density_profile_Volume_quartiles[0,i] = f_interp( 0.25)
number_density_profile_Volume_quartiles[1,i] = f_interp( 0.75)
if np.sum(data['number_density_Mass'][:,i]) > 0:
f_interp = scipy.interpolate.interp1d(np.cumsum(data['number_density_Mass'][:,i])/np.sum(data['number_density_Mass'][:,i]),number_density_bin_centers, bounds_error=False, fill_value=0.0)
number_density_profile_Mass[i] = f_interp(0.5)
number_density_profile_Mass_quartiles[0,i] = f_interp( 0.25)
number_density_profile_Mass_quartiles[1,i] = f_interp( 0.75)
entropy_profile_Volume = np.zeros(Nradii)*np.nan
entropy_profile_Volume_quartiles = np.zeros((2,Nradii))*np.nan
entropy_profile_Mass = np.zeros(Nradii)*np.nan
entropy_profile_Mass_quartiles = np.zeros((2,Nradii))*np.nan
for i in range(Nradii):
if np.sum(data['entropy_Volume'][:,i]) > 0:
f_interp = scipy.interpolate.interp1d(np.cumsum(data['entropy_Volume'][:,i])/np.sum(data['entropy_Volume'][:,i]),entropy_bin_centers, bounds_error=False, fill_value=0.0)
entropy_profile_Volume[i] = f_interp(0.5)
entropy_profile_Volume_quartiles[0,i] = f_interp( 0.25)
entropy_profile_Volume_quartiles[1,i] = f_interp( 0.75)
if np.sum(data['entropy_Mass'][:,i]) > 0:
f_interp = scipy.interpolate.interp1d(np.cumsum(data['entropy_Mass'][:,i])/np.sum(data['entropy_Mass'][:,i]),entropy_bin_centers, bounds_error=False, fill_value=0.0)
entropy_profile_Mass[i] = f_interp(0.5)
entropy_profile_Mass_quartiles[0,i] = f_interp( 0.25)
entropy_profile_Mass_quartiles[1,i] = f_interp( 0.75)
pressure_profile_Volume = np.zeros(Nradii)*np.nan
pressure_profile_Volume_quartiles = np.zeros((2,Nradii))*np.nan
pressure_profile_Mass = np.zeros(Nradii)*np.nan
pressure_profile_Mass_quartiles = np.zeros((2,Nradii))*np.nan
for i in range(Nradii):
if np.sum(data['pressure_Volume'][:,i]) > 0:
f_interp = scipy.interpolate.interp1d(np.cumsum(data['pressure_Volume'][:,i])/np.sum(data['pressure_Volume'][:,i]),pressure_bin_centers, bounds_error=False, fill_value=0.0)
pressure_profile_Volume[i] = f_interp(0.5)
pressure_profile_Volume_quartiles[0,i] = f_interp( 0.25)
pressure_profile_Volume_quartiles[1,i] = f_interp( 0.75)
if np.sum(data['pressure_Mass'][:,i]) > 0:
f_interp = scipy.interpolate.interp1d(np.cumsum(data['pressure_Mass'][:,i])/np.sum(data['pressure_Mass'][:,i]),pressure_bin_centers, bounds_error=False, fill_value=0.0)
pressure_profile_Mass[i] = f_interp(0.5)
pressure_profile_Mass_quartiles[0,i] = f_interp( 0.25)
pressure_profile_Mass_quartiles[1,i] = f_interp( 0.75)
radial_velocity_profile_Volume = np.zeros(Nradii)*np.nan
radial_velocity_profile_Volume_quartiles = np.zeros((2,Nradii))*np.nan
radial_velocity_profile_Mass = np.zeros(Nradii)*np.nan
radial_velocity_profile_Mass_quartiles = np.zeros((2,Nradii))*np.nan
for i in range(Nradii):
if np.sum(data['radial_velocity_Volume'][:,i]) > 0:
f_interp = scipy.interpolate.interp1d(np.cumsum(data['radial_velocity_Volume'][:,i])/np.sum(data['radial_velocity_Volume'][:,i]),radial_velocity_bin_centers, bounds_error=False, fill_value=0.0)
radial_velocity_profile_Volume[i] = f_interp(0.5)
radial_velocity_profile_Volume_quartiles[0,i] = f_interp( 0.25)
radial_velocity_profile_Volume_quartiles[1,i] = f_interp( 0.75)
if np.sum(data['radial_velocity_Mass'][:,i]) > 0:
f_interp = scipy.interpolate.interp1d(np.cumsum(data['radial_velocity_Mass'][:,i])/np.sum(data['radial_velocity_Mass'][:,i]),radial_velocity_bin_centers, bounds_error=False, fill_value=0.0)
radial_velocity_profile_Mass[i] = f_interp(0.5)
radial_velocity_profile_Mass_quartiles[0,i] = f_interp( 0.25)
radial_velocity_profile_Mass_quartiles[1,i] = f_interp( 0.75)
azimuthal_velocity_profile_Volume = np.zeros(Nradii)*np.nan
azimuthal_velocity_profile_Volume_quartiles = np.zeros((2,Nradii))*np.nan
azimuthal_velocity_profile_Mass = np.zeros(Nradii)*np.nan
azimuthal_velocity_profile_Mass_quartiles = np.zeros((2,Nradii))*np.nan
for i in range(Nradii):
if np.sum(data['azimuthal_velocity_Volume'][:,i]) > 0:
f_interp = scipy.interpolate.interp1d(np.cumsum(data['azimuthal_velocity_Volume'][:,i])/np.sum(data['azimuthal_velocity_Volume'][:,i]),azimuthal_velocity_bin_centers, bounds_error=False, fill_value=0.0)
azimuthal_velocity_profile_Volume[i] = f_interp(0.5)
azimuthal_velocity_profile_Volume_quartiles[0,i] = f_interp( 0.25)
azimuthal_velocity_profile_Volume_quartiles[1,i] = f_interp( 0.75)
if np.sum(data['azimuthal_velocity_Mass'][:,i]) > 0:
f_interp = scipy.interpolate.interp1d(np.cumsum(data['azimuthal_velocity_Mass'][:,i])/np.sum(data['azimuthal_velocity_Mass'][:,i]),azimuthal_velocity_bin_centers, bounds_error=False, fill_value=0.0)
azimuthal_velocity_profile_Mass[i] = f_interp(0.5)
azimuthal_velocity_profile_Mass_quartiles[0,i] = f_interp( 0.25)
azimuthal_velocity_profile_Mass_quartiles[1,i] = f_interp( 0.75)
medians={}
medians["temperature_profile_Volume"] = temperature_profile_Volume
medians["temperature_profile_Volume_quartiles"] = temperature_profile_Volume_quartiles
medians["temperature_profile_Mass"] = temperature_profile_Mass
medians["temperature_profile_Mass_quartiles"] = temperature_profile_Mass_quartiles
medians["number_density_profile_Volume"] = number_density_profile_Volume
medians["number_density_profile_Volume_quartiles"] = number_density_profile_Volume_quartiles
medians["number_density_profile_Mass"] = number_density_profile_Mass
medians["number_density_profile_Mass_quartiles"] = number_density_profile_Mass_quartiles
medians["entropy_profile_Volume"] = entropy_profile_Volume
medians["entropy_profile_Volume_quartiles"] = entropy_profile_Volume_quartiles
medians["entropy_profile_Mass"] = entropy_profile_Mass
medians["entropy_profile_Mass_quartiles"] = entropy_profile_Mass_quartiles
medians["pressure_profile_Volume"] = pressure_profile_Volume
medians["pressure_profile_Volume_quartiles"] = pressure_profile_Volume_quartiles
medians["pressure_profile_Mass"] = pressure_profile_Mass
medians["pressure_profile_Mass_quartiles"] = pressure_profile_Mass_quartiles
medians["radial_velocity_profile_Volume"] = radial_velocity_profile_Volume
medians["radial_velocity_profile_Volume_quartiles"] = radial_velocity_profile_Volume_quartiles
medians["radial_velocity_profile_Mass"] = radial_velocity_profile_Mass
medians["radial_velocity_profile_Mass_quartiles"] = radial_velocity_profile_Mass_quartiles
medians["azimuthal_velocity_profile_Volume"] = azimuthal_velocity_profile_Volume
medians["azimuthal_velocity_profile_Volume_quartiles"] = azimuthal_velocity_profile_Volume_quartiles
medians["azimuthal_velocity_profile_Mass"] = azimuthal_velocity_profile_Mass
medians["azimuthal_velocity_profile_Mass_quartiles"] = azimuthal_velocity_profile_Mass_quartiles
medians["dMdlogK_02r04"] = gaussian_filter(np.sum(data['pressure_entropy_Mass'][...,2:4],axis=(1,2))/np.sum(data['pressure_entropy_Mass'][...,2:4]),sigma)/dlogK
medians["dMdlogK_06r08"] = gaussian_filter(np.sum(data['pressure_entropy_Mass'][...,6:8],axis=(1,2))/np.sum(data['pressure_entropy_Mass'][...,6:8]),sigma)/dlogK
medians["dMdlogP_02r04"] = gaussian_filter(np.sum(data['pressure_entropy_Mass'][...,2:4],axis=(0,2))/np.sum(data['pressure_entropy_Mass'][...,2:4]),sigma)/dlogP
medians["dMdlogP_06r08"] = gaussian_filter(np.sum(data['pressure_entropy_Mass'][...,6:8],axis=(0,2))/np.sum(data['pressure_entropy_Mass'][...,6:8]),sigma)/dlogP
medians["dMdlogn_02r04"] = gaussian_filter(np.sum(data['density_temperature_Mass'][...,2:4],axis=(0,2))/np.sum(data['density_temperature_Mass'][...,2:4]),sigma)/dlogn
medians["dMdlogn_06r08"] = gaussian_filter(np.sum(data['density_temperature_Mass'][...,6:8],axis=(0,2))/np.sum(data['density_temperature_Mass'][...,6:8]),sigma)/dlogn
medians["dMdlogT_02r04"] = gaussian_filter(np.sum(data['density_temperature_Mass'][...,2:4],axis=(1,2))/np.sum(data['density_temperature_Mass'][...,2:4]),sigma)/dlogT
medians["dMdlogT_06r08"] = gaussian_filter(np.sum(data['density_temperature_Mass'][...,6:8],axis=(1,2))/np.sum(data['density_temperature_Mass'][...,6:8]),sigma)/dlogT
medians["dMdvr_02r04"] = gaussian_filter(np.sum(data['radial_velocity_temperature_Mass'][...,2:4],axis=(0,2))/np.sum(data['radial_velocity_temperature_Mass'][...,2:4]),sigma)
medians["dMdvr_06r08"] = gaussian_filter(np.sum(data['radial_velocity_temperature_Mass'][...,6:8],axis=(0,2))/np.sum(data['radial_velocity_temperature_Mass'][...,6:8]),sigma)
medians["Mdot_cold"] = np.array([ np.sum(np.sum(data['radial_velocity_temperature_Mass'][:20,:,iradius],axis=0) * radial_velocity_bin_centers*1e5 / (np.gradient(data['r_r200m_phase']*data['r200m'])[iradius] * kpc) * 3.15e7) for iradius in range(len(data['r_r200m_phase']))])
medians["Mdot_warm"] = np.array([ np.sum(np.sum(data['radial_velocity_temperature_Mass'][20:30,:,iradius],axis=0) * radial_velocity_bin_centers*1e5 / (np.gradient(data['r_r200m_phase']*data['r200m'])[iradius] * kpc) * 3.15e7) for iradius in range(len(data['r_r200m_phase']))])
medians["Mdot_hot"] = np.array([ np.sum(np.sum(data['radial_velocity_temperature_Mass'][30:,:,iradius],axis=0) * radial_velocity_bin_centers*1e5 / (np.gradient(data['r_r200m_phase']*data['r200m'])[iradius] * kpc) * 3.15e7) for iradius in range(len(data['r_r200m_phase']))])
medians["Mdot_radius"] = data['r_r200m_phase']
medians["entropy_bin_centers"] = entropy_bin_centers
medians["pressure_bin_centers"] = pressure_bin_centers
medians["temperature_bin_centers"] = temperature_bin_centers
medians["number_density_bin_centers"] = number_density_bin_centers
medians["radial_velocity_bin_centers"] = radial_velocity_bin_centers
radial_velocity_mean_MW = np.zeros(Nradii)*np.nan
radial_velocity_std_MW = np.zeros(Nradii)*np.nan
for i in range(Nradii):
if np.sum(data['radial_velocity_Mass'][:,i]) > 0:
radial_velocity_mean_MW[i] = np.average(radial_velocity_bin_centers, weights = data['radial_velocity_Mass'][:,i])
radial_velocity_std_MW[i] = np.sqrt(np.average( np.square(radial_velocity_bin_centers - radial_velocity_mean_MW[i]), weights = data['radial_velocity_Mass'][:,i]))
azimuthal_velocity_mean_MW = np.zeros(Nradii)*np.nan
azimuthal_velocity_std_MW = np.zeros(Nradii)*np.nan
for i in range(Nradii):
if np.sum(data['azimuthal_velocity_Mass'][:,i]) > 0:
azimuthal_velocity_mean_MW[i] = np.average(azimuthal_velocity_bin_centers, weights = data['azimuthal_velocity_Mass'][:,i])
azimuthal_velocity_std_MW[i] = np.sqrt(np.average( np.square(azimuthal_velocity_bin_centers - azimuthal_velocity_mean_MW[i]), weights = data['azimuthal_velocity_Mass'][:,i]))
polar_velocity_mean_MW = np.zeros(Nradii)*np.nan
polar_velocity_std_MW = np.zeros(Nradii)*np.nan
for i in range(Nradii):
if np.sum(data['polar_velocity_Mass'][:,i]) > 0:
polar_velocity_mean_MW[i] = np.average(polar_velocity_bin_centers, weights = data['polar_velocity_Mass'][:,i])
polar_velocity_std_MW[i] = np.sqrt(np.average( np.square(polar_velocity_bin_centers - polar_velocity_mean_MW[i]), weights = data['polar_velocity_Mass'][:,i]))
medians["radial_velocity_std_MW"] = radial_velocity_std_MW
medians["azimuthal_velocity_std_MW"] = azimuthal_velocity_std_MW
medians["polar_velocity_std_MW"] = polar_velocity_std_MW
medians["velocity_std_MW"] = np.sqrt( radial_velocity_std_MW**2 + azimuthal_velocity_std_MW**2 + polar_velocity_std_MW**2 )
medians["r_r200m_profile_centers"] = r_r200m_profile_centers
if type(data) == dict:
if "r200m" in data.keys():
if data['halo_mass'] == 1e9:
halo_mass = 1e12
medians["r200m"] = data['r200m']
medians["halo_mass"] = halo_mass
nvir = halo_mass*2e33/(4*np.pi/3. * (data['r200m']*kpc)**3 * mu * mp)
Tvir = 0.5*mu*mp*G*(halo_mass*2e33) / (data['r200m']*kpc) / kb
medians["nvir"] = nvir
medians["Pvir"] = nvir*Tvir
medians["Tvir"] = Tvir
medians["Kvir"] = nvir**(-2/3.) * Tvir
else:
medians["r200m"] = r200m
medians["halo_mass"] = Mhalo/2e33
nvir = halo_mass*2e33/(4*np.pi/3. * (r200m*kpc)**3 * mu * mp)
Tvir = 0.5*mu*mp*G*(Mhalo) / (r200m*kpc) / kb
medians["nvir"] = nvir
medians["Pvir"] = nvir*Tvir
medians["Tvir"] = Tvir
medians["Kvir"] = nvir**(-2/3.) * Tvir
else:
if "r200m" in data.files:
if data['halo_mass'] == 1e9:
halo_mass = 1e12
else:
halo_mass = data['halo_mass']
medians["r200m"] = data['r200m']
medians["halo_mass"] = halo_mass
nvir = halo_mass*2e33/(4*np.pi/3. * (data['r200m']*kpc)**3 * mu * mp)
Tvir = 0.5*mu*mp*G*(halo_mass*2e33) / (data['r200m']*kpc) / kb
medians["nvir"] = nvir
medians["Pvir"] = nvir*Tvir
medians["Tvir"] = Tvir
medians["Kvir"] = nvir**(-2/3.) * Tvir
else:
if data['halo_mass'] == 1e9:
halo_mass = 1e12
medians["r200m"] = r200m
medians["halo_mass"] = Mhalo/2e33
nvir = halo_mass*2e33/(4*np.pi/3. * (r200m*kpc)**3 * mu * mp)
Tvir = 0.5*mu*mp*G*(Mhalo) / (r200m*kpc) / kb
medians["nvir"] = nvir
medians["Pvir"] = nvir*Tvir
medians["Tvir"] = Tvir
medians["Kvir"] = nvir**(-2/3.) * Tvir
print("done")
return medians
files = np.sort(glob.glob("./data/simulations/ksu/average/*npz"))
ksu_medians={}
for fn in files:
data = np.load(fn)
ksu_medians[fn.split('/')[-1][:-4]] = get_medians(data,fn.split('/')[-1][:-4])
files = np.sort(glob.glob("./data/simulations/daniel_M12_TNG100_starforming/Sub*npz"))
M12_TNG100_starforming={}
for i,fn in enumerate(files):
data = np.load(fn)
M12_TNG100_starforming[fn.split('/')[-1].split('_')[0][7:]] = get_medians(data,fn.split('/')[-1][:-4]+"_starforming")
print(fn.split('/')[-1].split('_')[0][7:])
files = np.sort(glob.glob("./data/simulations/daniel_M12_TNG100_quenched/Sub*npz"))
M12_TNG100_quenched={}
for i,fn in enumerate(files):
data = np.load(fn)
M12_TNG100_quenched[fn.split('/')[-1].split('_')[0][7:]]=get_medians(data,fn.split('/')[-1][:-4]+"_quenched")
print(fn.split('/')[-1].split('_')[0][7:])
drummond_M12_var_palet = palettable.cubehelix.Cubehelix.make( start=0.75+(2/6.), rotation=0.15, reverse=True, max_light=1.0, min_light=0.1, n=64).mpl_colormap
files = np.sort(glob.glob('./data/simulations/drummond/*drummond*var*npz'))
data = averager(files)
drummond_M12_var_time_averaged_medians = get_medians(data, "drummond_M12_var_time_averaged")
MLi_SFR3_palet = palettable.cubehelix.Cubehelix.make( start=0.75+(3/6.), rotation=0.15, reverse=True, max_light=1.0, min_light=0.1, n=64).mpl_colormap
files = np.sort(glob.glob('./data/simulations/MLi/*MLi*_SFR3*npz'))
data = averager(files)
MLi_SFR3_time_averaged_medians = get_medians(data, "MLi_SFR3_time_averaged")
drummond_M12_ref_palet = palettable.cubehelix.Cubehelix.make( start=2.75+(2/6.)-3.0, rotation=0.15, reverse=True, max_light=1.0, min_light=0.1, n=64).mpl_colormap
files = np.sort(glob.glob('./data/simulations/drummond/*drummond*ref*npz'))
data = averager(files)
drummond_M12_ref_time_averaged_medians = get_medians(data, "drummond_M12_ref_time_averaged")
MLi_SFR10_palet = palettable.cubehelix.Cubehelix.make( start=2.75+(3/6.)-3.0, rotation=0., reverse=True, max_light=1.0, min_light=0.1, n=64).mpl_colormap
files = np.sort(glob.glob('./data/simulations/MLi/*MLi*_SFR10*npz'))
data = averager(files)
MLi_SFR10_time_averaged_medians = get_medians(data, "MLi_SFR10_time_averaged")
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fig,((ax1,ax2),(ax3,ax4),(ax5,ax6)) = plt.subplots(3,2, sharex=True)
sigma_profiles=3.0
line_styles=['-','--',':']
ax1.plot(drummond_M12_var_time_averaged_medians['r_r200m_profile_centers'],
drummond_M12_var_time_averaged_medians['temperature_profile_Mass']/drummond_M12_var_time_averaged_medians['Tvir'],
label = r"DF low eta",
color = '0.0', lw=2, alpha=1.0)
ax1.plot(drummond_M12_ref_time_averaged_medians['r_r200m_profile_centers'],
drummond_M12_ref_time_averaged_medians['temperature_profile_Mass']/drummond_M12_ref_time_averaged_medians['Tvir'],
label = r"DF high eta",
color = '0.0',ls='--', lw=2, alpha=1.0)
ax1.plot(np.nan,np.nan,label = r' ',color='none')
ax1.plot(MLi_SFR3_time_averaged_medians['r_r200m_profile_centers'],
MLi_SFR3_time_averaged_medians['temperature_profile_Mass']/MLi_SFR3_time_averaged_medians['Tvir'],
label = r"MLi SFR3",
color = '0.66', lw=2, alpha=1.0)
ax1.plot(MLi_SFR10_time_averaged_medians['r_r200m_profile_centers'],
MLi_SFR10_time_averaged_medians['temperature_profile_Mass']/MLi_SFR10_time_averaged_medians['Tvir'],
label = r"MLi SFR10",
color = '0.66',ls='--', lw=2, alpha=1.0)
ax1.plot(np.nan,np.nan,label = r' ',color='none')
for i,key in enumerate(ksu_medians.keys()):
ax1.plot(ksu_medians[key]['r_r200m_profile_centers'],
ksu_medians[key]['temperature_profile_Mass']/ksu_medians[key]['Tvir'],
label = r"ksu "+key,
color = '0.33',ls=line_styles[i], lw=2, alpha=1.0)
temperature_profile_M12_TNG100_starforming_unshifted = np.zeros((len(M12_TNG100_starforming.keys()), len(M12_TNG100_starforming['478037']['r_r200m_profile_centers'])))
N_M12_TNG100_starforming = 0
for II,i in enumerate(M12_TNG100_starforming.keys()):
# if np.isnan( np.max(M12_TNG100_starforming[i]['temperature_profile_Mass']/M12_TNG100_starforming[i]['Tvir'])):
# continue
r_T = np.ma.masked_where(np.isnan(M12_TNG100_starforming[i]['temperature_profile_Mass']), M12_TNG100_starforming[i]['r_r200m_profile_centers'])
T_Tvir = np.ma.masked_where(np.isnan(M12_TNG100_starforming[i]['temperature_profile_Mass']),M12_TNG100_starforming[i]['temperature_profile_Mass']) / M12_TNG100_starforming[i]['Tvir']
ax1.plot(r_T ,gaussian_filter(T_Tvir, sigma_profiles),
color = palettable.cartocolors.qualitative.Bold_10.hex_colors[2], lw=0.25, alpha=0.25)
for j in range(len(r_T)):
if not T_Tvir.mask[j]:
temperature_profile_M12_TNG100_starforming_unshifted[II,j] += T_Tvir[j]
N_M12_TNG100_starforming += 1
temperature_profile_M12_TNG100_starforming_unshifted_median = np.zeros(len(M12_TNG100_starforming['478037']['r_r200m_profile_centers']))
for k in range(len(M12_TNG100_starforming['478037']['r_r200m_profile_centers'])):
temperature_profile_M12_TNG100_starforming_unshifted_median[k] = np.median(temperature_profile_M12_TNG100_starforming_unshifted[:,k][temperature_profile_M12_TNG100_starforming_unshifted[:,k]!=0.])
ax1.plot(M12_TNG100_starforming[i]['r_r200m_profile_centers'],
gaussian_filter(temperature_profile_M12_TNG100_starforming_unshifted_median, sigma_profiles),
label = r'TNG SF average',
color = palettable.cartocolors.qualitative.Bold_10.hex_colors[2], lw=2, alpha=1.0)
temperature_profile_M12_TNG100_quenched_unshifted = np.zeros((len(M12_TNG100_quenched.keys()), len(M12_TNG100_quenched["482814"]['r_r200m_profile_centers'])))
N_M12_TNG100_quenched = 0
for II,i in enumerate(M12_TNG100_quenched.keys()):
# if np.isnan( np.max(M12_TNG100_quenched[i]['temperature_profile_Mass']/M12_TNG100_quenched[i]['Tvir'])):
# continue
r_T = np.ma.masked_where(np.isnan(M12_TNG100_quenched[i]['temperature_profile_Mass']), M12_TNG100_quenched[i]['r_r200m_profile_centers'])
T_Tvir = np.ma.masked_where(np.isnan(M12_TNG100_quenched[i]['temperature_profile_Mass']),M12_TNG100_quenched[i]['temperature_profile_Mass']) / M12_TNG100_quenched[i]['Tvir']
ax1.plot(r_T ,gaussian_filter(T_Tvir, sigma_profiles),
color = palettable.cartocolors.qualitative.Bold_10.hex_colors[4], lw=0.25, alpha=0.25)
for j in range(len(r_T)):
if not T_Tvir.mask[j]:
temperature_profile_M12_TNG100_quenched_unshifted[II,j] += T_Tvir[j]
N_M12_TNG100_quenched += 1
temperature_profile_M12_TNG100_quenched_unshifted_median = np.zeros(len(M12_TNG100_quenched["482814"]['r_r200m_profile_centers']))
for k in range(len(M12_TNG100_quenched["482814"]['r_r200m_profile_centers'])):
temperature_profile_M12_TNG100_quenched_unshifted_median[k] = np.median(temperature_profile_M12_TNG100_quenched_unshifted[:,k][temperature_profile_M12_TNG100_quenched_unshifted[:,k]!=0.])
ax1.plot(M12_TNG100_quenched[i]['r_r200m_profile_centers'],
gaussian_filter(temperature_profile_M12_TNG100_quenched_unshifted_median, sigma_profiles),
label = r'TNG Q average',
color = palettable.cartocolors.qualitative.Bold_10.hex_colors[4], lw=2, alpha=1.0)
ax1.plot(np.nan,np.nan,label = r' ',color='none')
ax2.plot(drummond_M12_var_time_averaged_medians['r_r200m_profile_centers'],
drummond_M12_var_time_averaged_medians['number_density_profile_Mass']/(drummond_M12_var_time_averaged_medians['nvir']*fb),
label = r"DF low eta",
color = '0.0', lw=2, alpha=1.0)
ax2.plot(drummond_M12_ref_time_averaged_medians['r_r200m_profile_centers'],
drummond_M12_ref_time_averaged_medians['number_density_profile_Mass']/(drummond_M12_ref_time_averaged_medians['nvir']*fb),
label = r"DF high eta",
color = '0.0',ls='--', lw=2, alpha=1.0)
ax2.plot(MLi_SFR3_time_averaged_medians['r_r200m_profile_centers'],
MLi_SFR3_time_averaged_medians['number_density_profile_Mass']/(MLi_SFR3_time_averaged_medians['nvir']*fb),
label = r"MLi SFR3",
color = '0.66', lw=2, alpha=1.0)
ax2.plot(MLi_SFR10_time_averaged_medians['r_r200m_profile_centers'],
MLi_SFR10_time_averaged_medians['number_density_profile_Mass']/(MLi_SFR10_time_averaged_medians['nvir']*fb),
label = r"MLi SFR10",
color = '0.66',ls='--', lw=2, alpha=1.0)
for i,key in enumerate(ksu_medians.keys()):
ax2.plot(ksu_medians[key]['r_r200m_profile_centers'],
ksu_medians[key]['number_density_profile_Mass']/(ksu_medians[key]['nvir']*fb),
label = r"ksu "+key,
color = '0.33',ls=line_styles[i], lw=2, alpha=1.0)
number_density_profile_M12_TNG100_starforming_unshifted = np.zeros((len(M12_TNG100_starforming.keys()), len(M12_TNG100_starforming['478037']['r_r200m_profile_centers'])))
N_M12_TNG100_starforming = 0
for II,i in enumerate(M12_TNG100_starforming.keys()):
# if np.isnan( np.max(M12_TNG100_starforming[i]['number_density_profile_Mass']/M12_TNG100_starforming)):
# continue
r_T = np.ma.masked_where(np.isnan(M12_TNG100_starforming[i]['number_density_profile_Mass']), M12_TNG100_starforming[i]['r_r200m_profile_centers'])
T_Tvir = np.ma.masked_where(np.isnan(M12_TNG100_starforming[i]['number_density_profile_Mass']),M12_TNG100_starforming[i]['number_density_profile_Mass']/(M12_TNG100_starforming[i]['nvir']*fb) )
ax2.plot(r_T ,gaussian_filter(T_Tvir, sigma_profiles),
color = palettable.cartocolors.qualitative.Bold_10.hex_colors[2], lw=0.25, alpha=0.25)
if hasattr(T_Tvir.mask, '__len__'):
for j in range(len(r_T)):
if not T_Tvir.mask[j]:
number_density_profile_M12_TNG100_starforming_unshifted[II,j] += T_Tvir[j]
else:
number_density_profile_M12_TNG100_starforming_unshifted[II] += T_Tvir
N_M12_TNG100_starforming += 1
number_density_profile_M12_TNG100_starforming_unshifted_median = np.zeros(len(M12_TNG100_starforming['478037']['r_r200m_profile_centers']))
for k in range(len(M12_TNG100_starforming['478037']['r_r200m_profile_centers'])):
number_density_profile_M12_TNG100_starforming_unshifted_median[k] = np.median(number_density_profile_M12_TNG100_starforming_unshifted[:,k][number_density_profile_M12_TNG100_starforming_unshifted[:,k]!=0.])
ax2.plot(M12_TNG100_starforming[i]['r_r200m_profile_centers'],
gaussian_filter(number_density_profile_M12_TNG100_starforming_unshifted_median, sigma_profiles),
label = r'TNG SF average',
color = palettable.cartocolors.qualitative.Bold_10.hex_colors[2], lw=2, alpha=1.0)
number_density_profile_M12_TNG100_quenched_unshifted = np.zeros((len(M12_TNG100_quenched.keys()), len(M12_TNG100_quenched["482814"]['r_r200m_profile_centers'])))
N_M12_TNG100_quenched = 0
for II,i in enumerate(M12_TNG100_quenched.keys()):
# if np.isnan( np.max(M12_TNG100_quenched[i]['number_density_profile_Mass']/M12_TNG100_quenched)):
# continue
r_T = np.ma.masked_where(np.isnan(M12_TNG100_quenched[i]['number_density_profile_Mass']), M12_TNG100_quenched[i]['r_r200m_profile_centers'])
T_Tvir = np.ma.masked_where(np.isnan(M12_TNG100_quenched[i]['number_density_profile_Mass']),M12_TNG100_quenched[i]['number_density_profile_Mass']/(M12_TNG100_quenched[i]['nvir']*fb))
ax2.plot(r_T ,gaussian_filter(T_Tvir, sigma_profiles),
color = palettable.cartocolors.qualitative.Bold_10.hex_colors[4], lw=0.25, alpha=0.25)
for j in range(len(r_T)):
if not T_Tvir.mask[j]:
number_density_profile_M12_TNG100_quenched_unshifted[II,j] += T_Tvir[j]
N_M12_TNG100_quenched += 1
number_density_profile_M12_TNG100_quenched_unshifted_median = np.zeros(len(M12_TNG100_quenched["482814"]['r_r200m_profile_centers']))
for k in range(len(M12_TNG100_quenched["482814"]['r_r200m_profile_centers'])):
number_density_profile_M12_TNG100_quenched_unshifted_median[k] = np.median(number_density_profile_M12_TNG100_quenched_unshifted[:,k][number_density_profile_M12_TNG100_quenched_unshifted[:,k]!=0.])
ax2.plot(M12_TNG100_quenched[i]['r_r200m_profile_centers'],
gaussian_filter(number_density_profile_M12_TNG100_quenched_unshifted_median, sigma_profiles),
label = r'TNG Q average',
color = palettable.cartocolors.qualitative.Bold_10.hex_colors[4], lw=2, alpha=1.0)
ax3.plot(drummond_M12_var_time_averaged_medians['r_r200m_profile_centers'],
drummond_M12_var_time_averaged_medians['pressure_profile_Mass']/drummond_M12_var_time_averaged_medians['Pvir'],
label = r"DF low eta",
color = '0.0', lw=2, alpha=1.0)
ax3.plot(drummond_M12_ref_time_averaged_medians['r_r200m_profile_centers'],
drummond_M12_ref_time_averaged_medians['pressure_profile_Mass']/drummond_M12_ref_time_averaged_medians['Pvir'],
label = r"DF high eta",
color = '0.0',ls='--', lw=2, alpha=1.0)
ax3.plot(MLi_SFR3_time_averaged_medians['r_r200m_profile_centers'],
MLi_SFR3_time_averaged_medians['pressure_profile_Mass']/MLi_SFR3_time_averaged_medians['Pvir'],
label = r"MLi SFR3",
color = '0.66', lw=2, alpha=1.0)
ax3.plot(MLi_SFR10_time_averaged_medians['r_r200m_profile_centers'],
MLi_SFR10_time_averaged_medians['pressure_profile_Mass']/MLi_SFR10_time_averaged_medians['Pvir'],
label = r"MLi SFR10",
color = '0.66',ls='--', lw=2, alpha=1.0)
for i,key in enumerate(ksu_medians.keys()):
ax3.plot(ksu_medians[key]['r_r200m_profile_centers'],
ksu_medians[key]['pressure_profile_Mass']/ksu_medians[key]['Pvir'],
label = r"ksu "+key,
color = '0.33',ls=line_styles[i], lw=2, alpha=1.0)
pressure_profile_M12_TNG100_starforming_unshifted = np.zeros((len(M12_TNG100_starforming.keys()), len(M12_TNG100_starforming['478037']['r_r200m_profile_centers'])))
N_M12_TNG100_starforming = 0
for II,i in enumerate(M12_TNG100_starforming.keys()):
# if np.isnan( np.max(M12_TNG100_starforming[i]['pressure_profile_Mass']/M12_TNG100_starforming)):
# continue
r_T = np.ma.masked_where(np.isnan(M12_TNG100_starforming[i]['pressure_profile_Mass']), M12_TNG100_starforming[i]['r_r200m_profile_centers'])
T_Tvir = np.ma.masked_where(np.isnan(M12_TNG100_starforming[i]['pressure_profile_Mass']),M12_TNG100_starforming[i]['pressure_profile_Mass']) /M12_TNG100_starforming[i]['Pvir']
ax3.plot(r_T ,gaussian_filter(T_Tvir, sigma_profiles),
color = palettable.cartocolors.qualitative.Bold_10.hex_colors[2], lw=0.25, alpha=0.25)
if hasattr(T_Tvir.mask, '__len__'):
for j in range(len(r_T)):
if not T_Tvir.mask[j]:
pressure_profile_M12_TNG100_starforming_unshifted[II,j] += T_Tvir[j]
else:
pressure_profile_M12_TNG100_starforming_unshifted[II] += T_Tvir
N_M12_TNG100_starforming += 1
pressure_profile_M12_TNG100_starforming_unshifted_median = np.zeros(len(M12_TNG100_starforming['478037']['r_r200m_profile_centers']))
for k in range(len(M12_TNG100_starforming['478037']['r_r200m_profile_centers'])):
pressure_profile_M12_TNG100_starforming_unshifted_median[k] = np.median(pressure_profile_M12_TNG100_starforming_unshifted[:,k][pressure_profile_M12_TNG100_starforming_unshifted[:,k]!=0.])
ax3.plot(M12_TNG100_starforming[i]['r_r200m_profile_centers'],
gaussian_filter(pressure_profile_M12_TNG100_starforming_unshifted_median, sigma_profiles),
label = r'TNG SF average',
color = palettable.cartocolors.qualitative.Bold_10.hex_colors[2], lw=2, alpha=1.0)
pressure_profile_M12_TNG100_quenched_unshifted = np.zeros((len(M12_TNG100_quenched.keys()), len(M12_TNG100_quenched["482814"]['r_r200m_profile_centers'])))
N_M12_TNG100_quenched = 0
for II,i in enumerate(M12_TNG100_quenched.keys()):
# if np.isnan( np.max(M12_TNG100_quenched[i]['pressure_profile_Mass']/M12_TNG100_quenched)):