def __init__(self):
"""
Container class to compute the Cl due to high-z contributions; we first define the window functions
"""
# Set constants
self.fromHztoeV = 6.58e-16
self.gramstoeV = 1 / (1.78 * 1e-33)
self.mtoev = 1 / (1.97 * 1e-7)
self.H0 = cosmo.H(0).value * 1e3 / (1e3 * const.kpc.value
) #expressed in 1/s
self.rhocritical = cosmo.critical_density(0).value * self.gramstoeV / (
1e-2)**3 # eV/m**3
self.Om0 = cosmo.Om0 #total matter
self.OLambda0 = cosmo.Ode0 # cosmological constant
self.DM0 = self.Om0 - cosmo.Ob0 # dark matter
self.evtonJoule = 1.60218 * 1e-10 # from eV to nJ
self.evtoJoule = 1.60218 * 1e-19 # from eV to J
self.h = 0.6766
PS1h = np.loadtxt(
"/Users/andreacaputo/Desktop/Phd/AxionDecayCrossCorr/Codes/NIRB_PS/PS_DM_1h.dat"
)
PS2h = np.loadtxt(
"/Users/andreacaputo/Desktop/Phd/AxionDecayCrossCorr/Codes/NIRB_PS/PS_DM_2h.dat"
)
self.Mpctometer = 3.086e+22 # from Mpc to meters
self.Mpc = 1e3 * const.kpc.value
self.zmin = 0.001
self.zmax = 30.001
self.zbins = 301
self.h = cosmo.h
self.TotPower = PS1h + PS2h
self.z_vect = np.linspace(self.zmin, self.zmax, self.zbins)
self.k_vect = PS1h[:, 0] * self.h
self.Power = self.TotPower[:, 1:] / self.h**3
self.Praw_prova = interp2d(self.k_vect, self.z_vect,
np.transpose(self.Power))
self.Msun = const.M_sun.value #Kg
self.Lsun = const.L_sun.value # in W
self.Sb = const.sigma_sb.value # Stefan-Boltzmann constant in W/m**2/K**4
self.Rsun = const.R_sun.value # in meters
self.hplanck = const.h.value # Js
self.cc = const.c.value #m/s
self.kb = const.k_B.value # kb constant J/K
self.mp = const.m_p.value # proton mass in Kg
self.Hseconds = cosmo.H(0).value / (1e6 * u.parsec.to(u.km)) # in 1/s
self.nHnebula = 1e4 / (
1e-2)**3 # in m**-3, is also equal to the electron number density
self.eVtoK = 1.16e4 # from eV to K
self.year = 3.154e+7 # from year to seconds
self.Ry = 1.1e7 #1/m Rydberg constant
self.ergtoJoule = 1e-7 # from erg to Joule
self.fromJouletoeV = 6.242e+18 # from Joule to eV
self.Mpc = 1e3 * const.kpc.value
self.RyK = 13.6057 * self.eVtoK
self.Tgas = 3e4 # Kelvin, below Eq.9 in the paper, said to be used to calculate alphaB
self.nuly = 2.4663495e15 # Lyman alpha frequency in Hz, which corresponds to a photon energy of
def __init__(self):
"""
Container class to compute window functions due to resolution and volume effects. See for example https://arxiv.org/pdf/1907.10067.pdf
"""
# Set constants
self.fromHztoeV = 6.58e-16
self.gramstoeV = 1 / (1.78 * 1e-33)
self.mtoev = 1 / (1.97 * 1e-7)
self.H0 = cosmo.H(0).value * 1e3 / (1e3 * const.kpc.value
) #expressed in 1/s
self.rhocritical = cosmo.critical_density(0).value * self.gramstoeV / (
1e-2)**3 # eV/m**3
self.Om0 = cosmo.Om0 #total matter
self.OLambda0 = cosmo.Ode0 # cosmological constant
self.DM0 = self.Om0 - cosmo.Ob0 # dark matter
self.evtonJoule = 1.60218 * 1e-10 # from eV to nJ
self.evtoJoule = 1.60218 * 1e-19 # from eV to J
self.h = 0.6766
self.Mpc = 1e3 * const.kpc.value
self.zmin = 0.001
self.zmax = 30.001
self.zbins = 301
self.h = cosmo.h
def z_distort(gals, z, L):
# Convert z-axis to redshift space
gals['zn_z'] += gals['zn_velZ'] * (1 + z) / Planck13.H(z).value
# fix z coordinate positions if they fall outside the box
gals.loc[gals['zn_z'] < 0, 'zn_z'] = gals.loc[gals['zn_z'] < 0, 'zn_z'] + L
gals.loc[gals['zn_z'] > L, 'zn_z'] = gals.loc[gals['zn_z'] > L, 'zn_z'] - L
return gals
def M_to_sigma(M, z=0, mode='3D'):
if mode == '3D':
ndim_scale = np.sqrt(3)
elif mode == '1D':
ndim_scale = 1.
# return prefac * .00989 * U.km / U.s \
# * np.power(M.to(U.Msun).value, 1 / 3)
# Note: Delta_vir returns the overdensity in units of background,
# which is Delta_c * Om in B&N98 notation.
return 0.016742 * U.km * U.s**-1 * np.power(
np.power(M.to(U.Msun).value, 2) * Delta_vir(z) / Delta_vir(0) *
cosmo.Om(z) / cosmo.Om0 * np.power(cosmo.H(z) / cosmo.H0, 2),
1 / 6) / ndim_scale
def __init__(self):
"""
Container class to compute various quantities for low-z Galaxy as studied here --> https://arxiv.org/pdf/1201.4398.pdf
"""
# Set constants
self.fromHztoeV = 6.58e-16
self.gramstoeV = 1 / (1.78 * 1e-33)
self.mtoev = 1 / (1.97 * 1e-7)
self.H0 = cosmo.H(0).value * 1e3 / (1e3 * const.kpc.value
) #expressed in 1/s
self.rhocritical = cosmo.critical_density(0).value * self.gramstoeV / (
1e-2)**3 # eV/m**3
self.Om0 = cosmo.Om0 #total matter
self.OLambda0 = cosmo.Ode0 # cosmological constant
self.DM0 = self.Om0 - cosmo.Ob0 # dark matter
self.evtonJoule = 1.60218 * 1e-10 # from eV to nJ
self.evtoJoule = 1.60218 * 1e-19 # from eV to J
self.h = 0.6766
self.Mpc = 1e3 * const.kpc.value
self.zmin = 0.001
self.zmax = 30.001
self.zbins = 301
self.h = cosmo.h
self.lambda_eff = np.array([
0.15, 0.36, 0.45, 0.55, 0.65, 0.79, 0.91, 1.27, 1.63, 2.20, 3.60,
4.50
])
self.Mstar0_vect = np.array([
-19.62, -20.20, -21.35, -22.13, -22.40, -22.80, -22.86, -23.04,
-23.41, -22.97, -22.40, -21.84
])
self.q_vect = np.array(
[1.1, 1.0, 0.6, 0.5, 0.5, 0.4, 0.4, 0.4, 0.5, 0.4, 0.2, 0.3])
self.phistar0_vect = np.array([
2.43, 5.46, 3.41, 2.42, 2.25, 2.05, 2.55, 2.21, 1.91, 2.74, 3.29,
3.29
])
self.p_vect = np.array(
[0.2, 0.5, 0.4, 0.5, 0.5, 0.4, 0.4, 0.6, 0.8, 0.8, 0.8, 0.8])
self.alpha0_vect = np.array(
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.])
self.r_vect = np.array([
0.086, 0.076, 0.055, 0.060, 0.070, 0.070, 0.060, 0.035, 0.035,
0.035, 0.035, 0.035
])
self.zmax_vect = np.array(
[8.0, 4.5, 4.5, 3.6, 3.0, 3.0, 2.9, 3.2, 3.2, 3.8, 0.7, 0.7])
def __init__(self):
"""
Container class to compute the Power spectrum and the Cl
"""
# Set constants
self.fromHztoeV = 6.58e-16
self.gramstoeV = 1 / (1.78 * 1e-33)
self.mtoev = 1 / (1.97 * 1e-7)
self.H0 = cosmo.H(0).value * 1e3 / (1e3 * const.kpc.value
) #expressed in 1/s
self.rhocritical = cosmo.critical_density(0).value * self.gramstoeV / (
1e-2)**3 # eV/m**3
self.Om0 = cosmo.Om0 #total matter
self.OLambda0 = cosmo.Ode0 # cosmological constant
self.DM0 = self.Om0 - cosmo.Ob0 # dark matter
self.evtonJoule = 1.60218 * 1e-10 # from eV to nJ
self.evtoJoule = 1.60218 * 1e-19 # from eV to J
PSgal1h = np.loadtxt(
"/Users/andreacaputo/Desktop/Phd/AxionDecayCrossCorr/Codes/NIRB_PS/PS_GALl_1h.dat"
)
PSgal2h = np.loadtxt(
"/Users/andreacaputo/Desktop/Phd/AxionDecayCrossCorr/Codes/NIRB_PS/PS_GALl_2h.dat"
)
self.Mpc = 1e3 * const.kpc.value
self.zmin = 0.001
self.zmax = 30.001
self.zbins = 301
self.h = cosmo.h
self.z_vect = np.linspace(self.zmin, self.zmax, self.zbins)
self.k_vect = PSgal1h[:, 0] * self.h
self.Power1h = PSgal1h[:, 1:] / (self.h**3)
self.Power2h = PSgal2h[:, 1:] / (self.h**3)
self.Power = self.Power1h + self.Power2h
self.Praw_prova1h = interp2d(self.k_vect, self.z_vect,
np.transpose(self.Power1h))
self.Praw_prova2h = interp2d(self.k_vect, self.z_vect,
np.transpose(self.Power2h))
self.Praw_prova = interp2d(self.k_vect, self.z_vect,
np.transpose(self.Power))
freqs = f['data'].attrs['nu'] # MHz
ras = f['data'].attrs['ra'] # degree
decs = f['data'].attrs['dec'] # degree
ras = np.radians(ras) # radians
decs = np.radians(decs) # radians
nf = freqs.shape[0]
nra = ras.shape[0]
ndec = decs.shape[0]
freq0 = 1420.4 # MHz
freqc = freqs[nf / 2] # central frequency, MHz
zc = freq0 / freqc - 1.0 # central redshifts
rc = cosmo.comoving_distance(zc).value # Mpc
rcp = const.c * (1 + zc)**2 / (1.0e6 * freq0 * 1.0e3 * cosmo.H(zc).value
) # Mpc/Hz, cosmo.H(z) in unit km/s/Mpc
Omega = (ras[-1] - ras[0]) * (
decs[-1] - decs[0]) # this is true approximately for a small flat sky area
# zs = freq0 / freqs - 1.0 # redshifts
# # get comoving distance
# cd = cosmo.comoving_distance(zs).value # Mpc
# # print cosmo.h
# cd /= cosmo.h # Mpc / h
# # get k_parallel by approximate cd as uniform
# k_paras = np.fft.fftshift(2*np.pi * np.fft.fftfreq(nf, d=(cd[0]-cd[-1])/nf)) # h Mpc^-1
# k_paras = k_paras[nf/2:] # get only positive k_paras
data_fft2 = np.zeros((nf, nra, ndec), dtype=np.complex128)
def rcrit200(M, z):
H = cosmo.H(z).value * converts_H_to_SI
return ((G * M) / (200 * H**2))**(1 / 3.)
def XLT(nu, z):
return (const.c**3 * (1 + z)**2 /
(8 * np.pi * const.k_B * nu**3 * cosmo.H(z))).to(u.K * u.Mpc**3 /
u.W)
sigma = Tsys * omega / np.sqrt(npol * nbl * dfreq * dt)
print sigma
# generate noise
np.random.seed(0)
noise = np.random.normal(loc=0.0, scale=sigma, size=(nf, nra, ndec)) # K
data = fg_data + HI_data + noise
freq0 = 1420.4 # MHz
freqc = freqs[nf/2] # central frequency, MHz
zc = freq0 / freqc - 1.0 # central redshifts
rc = cosmo.comoving_distance(zc).value # Mpc
rcp = const.c * (1 + zc)**2 / (1.0e6*freq0 * 1.0e3*cosmo.H(zc).value) # Mpc/Hz, cosmo.H(z) in unit km/s/Mpc
Omega = (ras[-1] - ras[0]) * (decs[-1] - decs[0]) # this is true approximately for a small flat sky area
data_fft2 = np.zeros((nf, nra, ndec), dtype=np.complex128)
for fi in range(nf):
data_fft2[fi] = Omega * np.fft.fftshift(np.fft.ifft2(data[fi])) # K^2
# # for check
# print np.allclose(np.fft.fft2(np.fft.ifftshift(data_fft2[fi])).real / Omega, data[fi])
Ux = np.fft.fftshift(np.fft.fftfreq(nra, d=ras[1] - ras[0]))
Uy = np.fft.fftshift(np.fft.fftfreq(ndec, d=decs[1] - decs[0]))
lmodes = defaultdict(list)
for xi in range(nra):
for yi in range(ndec):
from astropy.cosmology import Planck13 as cosmo
from astropy.cosmology import z_at_value
from astropy.constants import c
import numpy as np
import pandas as pd
t0 = cosmo.age(0)
t = np.linspace(0, t0, 100)[1:-1]
z = [z_at_value(cosmo.age, i) for i in t]
a = cosmo.scale_factor(z)
a_dot = np.gradient(a, t[1] - t[0]) # derivative of a with respect to t
h_calculated = (a_dot / a).to('km / (Mpc s)')
H = cosmo.H(z)
df = pd.DataFrame({
't': t,
'a': a,
'a_dot': a_dot,
'h calculé': h_calculated,
'H': H
})
print(df)
fig, ax = plt.subplots()
ax.scatter(t, a, s=1, c='b') # a
ax.scatter(t, a_dot, s=1, c='r') # derivative of a
plt.xlabel('t')
def tointegrate(self, lobs, mlim, k, z):
lowz = LowGal()
return (1e-3 * const.c.value / cosmo.H(z).value) * self.PSgal(
k, z) * (lowz.WindowGal(
lobs, mlim, z))**2 / (cosmo.comoving_distance(z).value)**2
def WindowGal(self, lobs, mlim, z): # nW/m^2/sr/Mpc ---> H/c dF/dz
c_km = 1e-3 * const.c.value
return cosmo.H(z).value / c_km * self.dFdz(lobs, mlim, z)
def dVdz(self, z):
dH0 = (1e-3 * const.c.value / cosmo.H(0).value)
return cosmo.comoving_distance(z).value**2 * dH0 / self.E(z)
def single_frame(num, max_pixel, nframes):
snap = "%04d" % num
# Define path
path = '/cosma/home/dp004/dc-rope1/cosma7/SWIFT/DMO_1380_data/ani_hydro_' + snap + ".hdf5"
snap = "%05d" % num
data = load(path)
meta = data.metadata
boxsize = meta.boxsize[0]
z = meta.redshift
print(boxsize, z)
# Define centre
cent = np.array([boxsize / 2, boxsize / 2, boxsize / 2])
# Define targets
targets = [
[0, 0, 0],
]
# Define anchors dict for camera parameters
anchors = {}
anchors['sim_times'] = [
0.0, 'same', 'same', 'same', 'same', 'same', 'same', 'same'
]
anchors['id_frames'] = np.linspace(0, nframes, 8, dtype=int)
anchors['id_targets'] = [
0, 'same', 'same', 'same', 'same', 'same', 'same', 'same'
]
anchors['r'] = [
0.1, 'same', 'same', 'same', 'same', 'same', 'same', 'same'
]
anchors['t'] = [5, 'same', 'same', 'same', 'same', 'same', 'same', 'same']
anchors['p'] = [
-50, 'same', 'same', 'same', 'same', 'same', 'same', 'same'
]
anchors['zoom'] = [
1., 'same', 'same', 'same', 'same', 'same', 'same', 'same'
]
anchors['extent'] = [
10, 'same', 'same', 'same', 'same', 'same', 'same', 'same'
]
# Define the camera trajectory
cam_data = camera_tools.get_camera_trajectory(targets, anchors)
poss = data.dark_matter.coordinates.value
hsmls = data.dark_matter.softenings.value / (1 + z)
print(hsmls)
poss -= cent
poss[np.where(poss > boxsize.value / 2)] -= boxsize.value
poss[np.where(poss < -boxsize.value / 2)] += boxsize.value
poss /= (1 + z)
tree = cKDTree(poss)
dists, _ = tree.query(np.array([0, 0, 0]), k=poss.shape[0])
v = cosmo.H(z) * dists
print(cosmo.H(z))
print(v)
# Get images
rgb_DM, ang_extent = getimage(cam_data, poss, hsmls, num, z, v)
i = cam_data[num]
extent = ang_extent
print(ang_extent, extent)
dpi = rgb_DM.shape[0]
print(dpi, rgb_DM.shape)
fig = plt.figure(figsize=(1, 1.77777777778), dpi=dpi)
ax = fig.add_subplot(111)
ax.imshow(rgb_DM, extent=ang_extent, origin='lower')
ax.tick_params(axis='both',
left=False,
top=False,
right=False,
bottom=False,
labelleft=False,
labeltop=False,
labelright=False,
labelbottom=False)
ax.text(0.975,
0.05,
"$t=$%.1f Gyr" % cosmo.age(z).value,
transform=ax.transAxes,
verticalalignment="top",
horizontalalignment='right',
fontsize=1,
color="w")
ax.plot([0.05, 0.15], [0.025, 0.025],
lw=0.1,
color='w',
clip_on=False,
transform=ax.transAxes)
ax.plot([0.05, 0.05], [0.022, 0.027],
lw=0.15,
color='w',
clip_on=False,
transform=ax.transAxes)
ax.plot([0.15, 0.15], [0.022, 0.027],
lw=0.15,
color='w',
clip_on=False,
transform=ax.transAxes)
# ax.plot([0.05, 0.15], [0.105, 0.105], lw=0.1, color='w', clip_on=False,
# transform=ax.transAxes)
#
# ax.plot([0.05, 0.05], [0.102, 0.107], lw=0.15, color='w', clip_on=False,
# transform=ax.transAxes)
# ax.plot([0.15, 0.15], [0.102, 0.107], lw=0.15, color='w', clip_on=False,
# transform=ax.transAxes)
axis_to_data = ax.transAxes + ax.transData.inverted()
left = axis_to_data.transform((0.05, 0.075))
right = axis_to_data.transform((0.15, 0.075))
dist = extent[1] * (right[0] - left[0]) / (ang_extent[1] - ang_extent[0])
print(left, right, (right[0] - left[0]) / (ang_extent[1] - ang_extent[0]),
dist)
if dist > 0.1:
ax.text(0.1,
0.065,
'%.1f $^\circ$' % dist,
transform=ax.transAxes,
verticalalignment="top",
horizontalalignment='center',
fontsize=1,
color="w")
# elif 100 > dist * 10**3 > 1:
# ax.text(0.1, 0.065, "%.1f pkpc" % dist * 10**3,
# transform=ax.transAxes, verticalalignment="top",
# horizontalalignment='center', fontsize=1, color="w")
# else:
# ax.text(0.1, 0.065, "%.1f pkpc" % dist * 10**6,
# transform=ax.transAxes, verticalalignment="top",
# horizontalalignment='center', fontsize=1, color="w")
plt.margins(0, 0)
fig.savefig('plots/Ani/Recession/DM_recession_animation_' + snap + '.png',
bbox_inches='tight',
pad_inches=0)
plt.close(fig)
def sigma_par(dnu, nuObs, z):
# cutoff for line-of-sight modes
return (const.c * dnu * (1 + z) / (cosmo.H(z) * nuObs)).to(u.Mpc) # in Mpc
def E(self, z):
return cosmo.H(z).value / cosmo.H(0).value
3) The third method is using astropy's '.lookback_distance()' method.
"""
from astropy.cosmology import Planck13 as cosmo
from astropy.constants import G, c
import numpy as np
import matplotlib.pyplot as plt
Or0 = cosmo.Ogamma(0) + cosmo.Onu(0)
Om0 = cosmo.Om(0)
Ode0 = cosmo.Ode(0)
# deceleration parameter (equation 6.11)
q0 = Or0 + 0.5 * Om0 - Ode0
H0 = cosmo.H(0).to('1 / s')
z = np.linspace(0, 2, 100)
# current proper distance in terms of z (equation 6.12)
dp0_612 = c * z / H0
dp0_612 = dp0_612.to('Mpc')
# current proper distance in terms of z (equation 6.19)
dp0_619 = (z * c / H0) * (1 - (z * (1 + q0) / 2))
dp0_619 = dp0_619.to('Mpc')
# current proper distance in terms of z (equation 6.19)
fig, ax = plt.subplots()
ax.scatter(z, dp0_612, s=1, label='d=cz/H0')
def to_integrateCooray(self, nuobs, k, z, T, fesc):
factor = (1e-3 * const.c.value / cosmo.H(z).value) * (PSgal(
k, z)) / (cosmo.comoving_distance(z).value)**2
return factor * self.WindowHighzCooray(T, z, fesc, nuobs)**2
Mstar_30 = np.concatenate(Mstar_30) * 1e10
sfr_30 = np.concatenate(sfr_30)
ok = np.where(lum_to_M(L_FUV) < low)[0]
L_FUV, L_NUV, L_FUV_int, Mstar_30 = L_FUV[ok], L_NUV[ok], L_FUV_int[
ok], Mstar_30[ok]
sfr_30 = sfr_30[ok] / Mstar_30
beta = np.log10(L_FUV / L_NUV) / np.log10(1500. / 2500.) - 2.0
att = -2.5 * np.log10(L_FUV / L_FUV_int)
zpos = -17.5
if tag == '010_z005p000':
from astropy.cosmology import Planck13
from astropy import units as u
tmp = 1 / Planck13.H(5).decompose()
print((1.0 / (3 * tmp.to(u.yr))).value)
check = np.logical_and(Mstar_30 > 1e10, att < 0.4)
print(sfr_30[check])
print(beta[check])
if input == plt_options[0]:
x, y, z, w = lum_to_M(L_FUV), att, beta, ws[ok]
gridsize = (50, 21)
extent = [*[low, -24.5], *ylims]
add = ''
savename = F'att_lfuv_beta_z5_10'
xlabel = r'M$_{1500}$'
elif input == plt_options[1]:
x, y, z, w = lum_to_M(L_FUV_int), att, beta, ws[ok]
gridsize = (55, 21)
# page 156 pdf import numpy as np from astropy.cosmology import Planck13 as cosmo import matplotlib.pyplot as plt import seaborn as sns sns.set() Or0 = cosmo.Ogamma(0) + cosmo.Onu(0) Om0 = cosmo.Om(0) Ode0 = cosmo.Ode(0) H0 = cosmo.H(0) t0 = cosmo.age(0) q0 = Or0 + 0.5*Om0 - Ode0 #equation 6.11 t = np.linspace(0, t0, 1000) a = 1 + H0*(t-t0) - 0.5*q0*(H0**2)*(t-t0)**2 fig, ax = plt.subplots() ax.scatter(t, a, s=1) plt.show()
