5 MiB
5 MiB
In [39]:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import astropy.constants
import astropy.units as apu
import astropy.cosmology
from astropy.coordinates import SkyCoord
import borg_velocity.poisson_process as poisson_process
from astropy.cosmology import LambdaCDM, z_at_value
In [29]:
columns = ['ID', 'RAdeg', 'DEdeg', 'cz2mrs', 'Kmag', 'Hmag', 'Jmag', 'e_Kmag', 'e_Hmah', 'e_Jmag', 'WHIc', 'e_WHIc']
fname = '/data101/bartlett/fsigma8/PV_data/2MASS/table1.dat'
df = pd.read_csv(fname, sep='\s+', names=columns)
eta = np.log10(df['WHIc']) - 2.5
bins = np.linspace(eta.min(), eta.max(), 30)
plt.hist(eta, bins=bins, density=True)
mu = np.median(eta)
sigma = (np.percentile(eta, 84) - np.percentile(eta, 16)) / 2
g = np.exp(- (bins - mu) ** 2 / 2 / sigma**2) / np.sqrt(2 * np.pi) / sigma
plt.plot(bins, g)
sigma_eta = df['e_WHIc'] / df['WHIc'] / np.log(10)
print(sigma_eta.min(), sigma_eta.max(), sigma_eta.mean(), sigma_eta.median())
print(df['cz2mrs'].min(), df['cz2mrs'].max())
z = df['cz2mrs'] / astropy.constants.c.to('km/s').value
cosmo = astropy.cosmology.Planck18
dL = cosmo.luminosity_distance(z).to(apu.Mpc).value # Mpc
mu = 5 * np.log10(dL) + 25
plt.figure()
plt.plot(eta, mu, '.')
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In [64]:
def estimate_data_parameters():
"""
ID 2MASS XSC ID name (HHMMSSss+DDMMSSs)
RAdeg Right ascension (J2000)
DEdeg Declination (J2000)
cz2mrs Heliocentric redshift from the 2MRS (km/s)
Kmag NIR magnitudes in the K band from the 2MRS (mag)
Hmag NIR magnitudes in the H band from the 2MRS (mag)
Jmag NIR magnitudes in the J band from the 2MRS (mag)
e_Kmag Error of the NIR magnitudes in K band from the (mag)
e_Hmag Error of the NIR magnitudes in H band from the (mag)
e_Jmag Error of the NIR magnitudes in J band from the (mag)
WHIc Corrected HI width (km/s)
e_WHIc Error of corrected HI width (km/s)
"""
columns = ['ID', 'RAdeg', 'DEdeg', 'cz2mrs', 'Kmag', 'Hmag', 'Jmag', 'e_Kmag', 'e_Hmah', 'e_Jmag', 'WHIc', 'e_WHIc']
fname = '/data101/bartlett/fsigma8/PV_data/2MASS/table1.dat'
df = pd.read_csv(fname, sep='\s+', names=columns)
sigma_m = np.median(df['e_Kmag'])
eta = np.log10(df['WHIc']) - 2.5
sigma_eta = np.median(df['e_WHIc'] / df['WHIc'] / np.log(10))
hyper_eta_mu = np.median(eta)
hyper_eta_sigma = (np.percentile(eta, 84) - np.percentile(eta, 16)) / 2
return sigma_m, sigma_eta, hyper_eta_mu, hyper_eta_sigma
columns = ['ID', 'RAdeg', 'DEdeg', 'cz2mrs', 'Kmag', 'Hmag', 'Jmag', 'e_Kmag', 'e_Hmah', 'e_Jmag', 'WHIc', 'e_WHIc']
fname = '/data101/bartlett/fsigma8/PV_data/2MASS/table1.dat'
df = pd.read_csv(fname, sep='\s+', names=columns)
# Get some parameters from the data
sigma_m, sigma_eta, hyper_eta_mu, hyper_eta_sigma = estimate_data_parameters()
# cosmo = astropy.cosmology.Planck18
cosmo = LambdaCDM(H0 = 80, Om0 = 0.3, Ode0 = 0.7)
# Other parameters to use
L = 200.0
N = 64
xmin = -L/2
Rmax = 100
Nt = 2062
alpha = 1.4
mthresh = 11.25
a_TFR = -23
b_TFR = -8.2
sigma_TFR = 0.3
bias_epsilon = 1e-7
dens = np.load('dens.npy')
vel = np.load('vel.npy')
N = dens.shape[0]
phi = (1. + dens + bias_epsilon) ** alpha
# Generate positions (comoving)
xtrue = poisson_process.sample_3d(phi, Nt, L, (xmin, xmin, xmin))
# Convert to RA, Dec, Distance (comoving)
rtrue = np.sqrt(np.sum(xtrue** 2, axis=0)) # Mpc/h
c = SkyCoord(x=xtrue[0], y=xtrue[1], z=xtrue[2], representation_type='cartesian')
RA = c.spherical.lon.degree
Dec = c.spherical.lat.degree
r_hat = np.array(SkyCoord(ra=RA*apu.deg, dec=Dec*apu.deg).cartesian.xyz)
# Compute cosmological redshift
zcosmo = z_at_value(cosmo.comoving_distance, rtrue * apu.Mpc / cosmo.h).value
print('zcosmo', zcosmo.min(), zcosmo.max())
# Compute luminosity distance
# DO I NEED TO DO /h???
# dL = (1 + zcosmo) * rtrue # Mpc/h
dL = cosmo.luminosity_distance(zcosmo).to(apu.Mpc).value # Mpc
# Compute true distance modulus
mutrue = 5 * np.log10(dL) + 25
# Sample true linewidth (eta) from its prior
etatrue = hyper_eta_mu + hyper_eta_sigma * np.random.randn(Nt)
print('etatrue:', etatrue.min(), etatrue.max())
# Obtain muTFR from mutrue using the intrinsic scatter
muTFR = mutrue + sigma_TFR * np.random.randn(Nt)
print('muTFR', muTFR.min(), muTFR.max())
print('mutrue', mutrue.min(), mutrue.max())
print('sigma_TFR', sigma_TFR)
# Obtain apparent magnitude from the TFR
mtrue = muTFR + (a_TFR + b_TFR * etatrue)
print('mtrue', mtrue.min(), mtrue.max())
# Scatter true observed apparent magnitudes and linewidths
mobs = mtrue + sigma_m * np.random.randn(Nt)
etaobs = etatrue + sigma_eta * np.random.randn(Nt)
# Apply apparement magnitude cut
m = mobs <= mthresh
mobs = mobs[m]
etaobs = etaobs[m]
xtrue = xtrue[:,m]
data_eta = np.log10(df['WHIc']) - 2.5
plt.hist(etaobs, label='synthetic', histtype='step', density=True, bins=20)
plt.hist(data_eta, label='data', histtype='step', density=True, bins=20)
plt.legend()
plt.title('eta')
plt.figure()
data_m = df['Kmag']
plt.hist(mobs, label='synthetic', histtype='step', density=True, bins=20)
plt.hist(data_m, label='data', histtype='step', density=True, bins=20)
plt.legend()
plt.title('m')
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In [72]:
L = 500
xmin = - 250
Rmax = 100
N = 64
x = np.linspace(xmin, xmin + L, N+1)
i0 = np.argmin(np.abs(x + Rmax))
i1 = np.argmin(np.abs(x - Rmax))
Lsmall = x[i1] - x[i0]
dens_small = dens[i0:i1, i0:i1, i0:i1]
xmin_small = x[i0]
print(Lsmall)
print(dens_small.shape)
print(xmin_small)
print(x[1] - x[0])
print(L / N)
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