def plot_fsigma8_MG(self, zlist, fsigma8_datafile, a_B, a_M, a_trans, r):
"""
Plot fsigma8 vs. z, rel. dev. from LCDM vs. z,
and compute chi-squared from measurements
for Modified Gravity.
"""
# Get measured fsigma8 data
__, meas_zlist, meas_fsigma8, meas_fsigma8_error = self.get_fsigma8_data(fsigma8_datafile)
# LCDM fsigma8
lcdm_fsigma8 = Peturbations().fsigma8_GR(zlist)
# MG fsigma8 for our redshift range
mg_fsigma8 = Peturbations().fsigma8_MG(zlist, a_B, a_M, a_trans, r)
# MG fsigma8 for measured redshift range
mg_fsigma8_meas_z = Peturbations().fsigma8_MG(meas_zlist, a_B, a_M, a_trans, r)
# Compute chi squared
mg_chisqr = self.chisqr(mg_fsigma8_meas_z, meas_fsigma8, meas_fsigma8_error)
# Plot fsigma8
self.ax[0].plot(zlist, mg_fsigma8,
label=r'$\hat{{\alpha}}_\mathrm{{B}}={}, \hat{{\alpha}}_\mathrm{{M}}={}, a_t=1/(1+{}), r={}$'.format(
a_B, a_M, (1./a_trans)-1., r),
zorder=1)
# Plot rel. dev.
self.ax[1].plot(zlist, self.reldev(lcdm_fsigma8, mg_fsigma8),
label=r'$\chi^2={}$'.format(mg_chisqr),
zorder=1)
"""
def plot_fsigma8_GR(self, zlist, fsigma8_datafile):
"""
Plot fsigma8 vs. z, rel. dev. from LCDM vs. z,
and compute chi-squared from measurements
for General Relativity.
"""
# Get measured fsigma8 data
__, meas_zlist, meas_fsigma8, meas_fsigma8_error = self.get_fsigma8_data(fsigma8_datafile)
# fsigma8 for our redshift range
lcdm_fsigma8 = Peturbations().fsigma8_GR(zlist)
# fsigma8 for measured redshift range
lcdm_fsigma8_meas_z = Peturbations().fsigma8_GR(meas_zlist)
# Compute chi squared
lcdm_chisqr = self.chisqr(lcdm_fsigma8_meas_z, meas_fsigma8, meas_fsigma8_error)
# Plot fsigma8
self.ax[0].plot(zlist, lcdm_fsigma8,
dashes=[6,4], color='black',
label=r'$\Lambda\mathrm{CDM}$',
zorder=2)
# Plot rel. dev.
self.ax[1].plot(zlist, self.reldev(lcdm_fsigma8, lcdm_fsigma8),
dashes=[6,4], color='black',
label=r'$\chi^2={}$'.format(lcdm_chisqr),
zorder=2)
"""
def plot_fsigma8_MG(self, zlist, fsigma8_datafile, a_B, a_M, a_trans, r):
"""
Plot fsigma8 vs. z, rel. dev. from LCDM vs. z,
and compute chi-squared from measurements
for Modified Gravity.
"""
# Get measured fsigma8 data
__, meas_zlist, meas_fsigma8, meas_fsigma8_error = self.get_fsigma8_data(
fsigma8_datafile)
# LCDM fsigma8
lcdm_fsigma8 = Peturbations().fsigma8_GR(zlist)
# MG fsigma8 for our redshift range
mg_fsigma8 = Peturbations().fsigma8_MG(zlist, a_B, a_M, a_trans, r)
# MG fsigma8 for measured redshift range
mg_fsigma8_meas_z = Peturbations().fsigma8_MG(meas_zlist, a_B, a_M,
a_trans, r)
# Compute chi squared
mg_chisqr = self.chisqr(mg_fsigma8_meas_z, meas_fsigma8,
meas_fsigma8_error)
# Plot fsigma8
self.ax[0].plot(
zlist,
mg_fsigma8,
label=
r'$\hat{{\alpha}}_\mathrm{{B}}={}, \hat{{\alpha}}_\mathrm{{M}}={}, a_t=1/(1+{}), r={}$'
.format(a_B, a_M, (1. / a_trans) - 1., r),
zorder=1)
# Plot rel. dev.
self.ax[1].plot(zlist,
self.reldev(lcdm_fsigma8, mg_fsigma8),
label=r'$\chi^2={}$'.format(mg_chisqr),
zorder=1)
# Save fsigma8 output to .dat file
output = np.vstack((zlist, mg_fsigma8)).T
np.savetxt('fsigma8_output/fsigma8_MG_a_B_' + str(a_B) + '_a_M_' +
str(a_M) + '_a_trans_' + str(a_trans) + '_r_' + str(r) +
'.dat',
output,
delimiter=' ',
fmt='%0.4f',
header=str('Redshift z, fsigma8(z)'))
def plot_fsigma8_GR(self, zlist, fsigma8_datafile):
"""
Plot fsigma8 vs. z, rel. dev. from LCDM vs. z,
and compute chi-squared from measurements
for General Relativity.
"""
# Get measured fsigma8 data
__, meas_zlist, meas_fsigma8, meas_fsigma8_error = self.get_fsigma8_data(
fsigma8_datafile)
# fsigma8 for our redshift range
lcdm_fsigma8 = Peturbations().fsigma8_GR(zlist)
# fsigma8 for measured redshift range
lcdm_fsigma8_meas_z = Peturbations().fsigma8_GR(meas_zlist)
# Compute chi squared
lcdm_chisqr = self.chisqr(lcdm_fsigma8_meas_z, meas_fsigma8,
meas_fsigma8_error)
# Plot fsigma8
self.ax[0].plot(zlist,
lcdm_fsigma8,
dashes=[6, 4],
color='black',
label=r'$\Lambda\mathrm{CDM}$',
zorder=2)
# Plot rel. dev.
self.ax[1].plot(zlist,
self.reldev(lcdm_fsigma8, lcdm_fsigma8),
dashes=[6, 4],
color='black',
label=r'$\chi^2={}$'.format(lcdm_chisqr),
zorder=2)
# Save fsigma8 output to .dat file
output = np.vstack((zlist, lcdm_fsigma8)).T
np.savetxt('fsigma8_output/fsigma8_LCDM.dat',
output,
delimiter=' ',
fmt='%0.4f',
header=str('Redshift z, fsigma8(z)'))
def plot_Cl_TT_GR(self, zlist, llist):
"""
Plot C_ell^TT vs. ell for General Relativity
"""
# C_ell^TT
lcdm_Cl_TT = Peturbations().Cl_TT_GR(zlist, llist)
# Plot C_ell^TT
self.ax[0, 1].semilogx(llist, lcdm_Cl_TT,
dashes=[6,4], color='black',
zorder=2)
def plot_sigma8_GR(self, zlist):
"""
Plot sigma8 vs. z for General Relativity
"""
# sigma8 for our redshift range
lcdm_sigma8 = Peturbations().sigma8_GR(zlist)
# Plot sigma8
self.ax[1, 0].plot(zlist, lcdm_sigma8,
dashes=[6,4], color='black',
zorder=2)
def plot_Cl_kappa_kappa_GR(self, zlist, llist):
"""
Plot C_ell^kappakappa vs. ell for General Relativity
"""
# C_ell^kappakappa
lcdm_Cl_kap_kap = Peturbations().Cl_kappa_kappa_GR(zlist, llist)
# Plot C_ell^kappakappa
self.ax[1, 1].semilogx(llist, lcdm_Cl_kap_kap,
dashes=[6,4], color='black',
zorder=2)
def plot_fsigma8_GR(self, zlist):
"""
Plot fsigma8 vs. z for General Relativity
"""
# fsigma8 for our redshift range
lcdm_fsigma8 = Peturbations().fsigma8_GR(zlist)
# Plot fsigma8
self.ax[0, 0].plot(zlist, lcdm_fsigma8,
dashes=[6,4], color='black',
label=r'$\Lambda\mathrm{CDM}$',
zorder=2)
def plot_sigma8_MG(self, zlist, a_B, a_M, a_trans, r):
"""
Plot sigma8 vs. z for Modified Gravity.
"""
# LCDM sigma8
#lcdm_sigma8 = Peturbations().sigma8_GR(zlist)
# MG sigma8 for our redshift range
mg_sigma8 = Peturbations().sigma8_MG(zlist, a_B, a_M, a_trans, r)
# Plot sigma8
self.ax[1, 0].plot(zlist, mg_sigma8,
zorder=1)
def plot_Cl_kappa_kappa_MG(self, zlist, llist, a_B, a_M, a_trans, r):
"""
Plot C_ell^kappakappa vs. ell for Modified Gravity
"""
# LCDM C_ell^kappakappa
#lcdm_Cl_kap_kap = Peturbations().Cl_kappa_kappa_GR(zlist, llist)
# MG C_ell^kappakappa
mg_Cl_kap_kap = Peturbations().Cl_kappa_kappa_MG(zlist, llist, a_B, a_M, a_trans, r)
# Plot C_ell^kappakappa
self.ax[1, 1].semilogx(llist, mg_Cl_kap_kap,
zorder=1)
def plot_Cl_kappa_kappa_MG(self, llist, a_B, a_M, a_trans, r):
"""
s
"""
# LCDM Cl_kappa_kappa
lcdm_Cl_kap_kap = Peturbations().Cl_kappa_kappa_GR(llist)
# MG Cl_kappa_kappa
mg_Cl_kap_kap = Peturbations().Cl_kappa_kappa_MG(
llist, a_B, a_M, a_trans, r)
# Plot Cl_kappa_kappa
self.ax[0].loglog(
llist,
mg_Cl_kap_kap,
label=
r'$\hat{{\alpha}}_\mathrm{{B}}={}, \hat{{\alpha}}_\mathrm{{M}}={}, a_t=1/(1+{}), r={}$'
.format(a_B, a_M, (1. / a_trans) - 1., r),
zorder=1)
# Plot rel. dev.
self.ax[1].semilogx(llist,
self.reldev(lcdm_Cl_kap_kap, mg_Cl_kap_kap),
zorder=1)
def plot_fsigma8_MG(self, zlist, a_B, a_M, a_trans, r):
"""
Plot fsigma8 vs. z for Modified Gravity.
"""
# LCDM fsigma8
#lcdm_fsigma8 = Peturbations().fsigma8_GR(zlist)
# MG fsigma8 for our redshift range
mg_fsigma8 = Peturbations().fsigma8_MG(zlist, a_B, a_M, a_trans, r)
# Plot fsigma8
#self.ax[0, 0].plot(zlist, mg_fsigma8,
# label=r'$\hat{{\alpha}}_\mathrm{{B}}={}, \hat{{\alpha}}_\mathrm{{M}}={}, a_t=1/(1+{}), r={}$'.format(
# a_B, a_M, (1./a_trans)-1., r),
# zorder=1)
self.ax[0, 0].plot(zlist, mg_fsigma8,
label=r'$\hat{{\alpha}}_\mathrm{{B}}={}, \hat{{\alpha}}_\mathrm{{M}}={}$'.format(a_B, a_M),
zorder=1)
def plot_Cl_kappa_kappa_GR(self, llist):
"""
s
"""
# Cl_kappa_kappa
lcdm_Cl_kap_kap = Peturbations().Cl_kappa_kappa_GR(llist)
# Plot Cl_kappa_kappa
self.ax[0].loglog(llist,
lcdm_Cl_kap_kap,
dashes=[6, 4],
color='black',
label=r'$\Lambda\mathrm{CDM}$',
zorder=2)
# Plot rel. dev.
self.ax[1].semilogx(llist,
self.reldev(lcdm_Cl_kap_kap, lcdm_Cl_kap_kap),
dashes=[6, 4],
color='black',
zorder=2)
import numpy as np
from scipy.optimize import curve_fit
from peturbations import Peturbations
peturb = Peturbations()
"""
Current observational measurements on fsigma8
We read in current measurements between redshifts 0 and 2
from fsigma8_measurements.dat
Column format: Index_number, z, fs8, fs8_error
"""
data = np.loadtxt('fsigma8_measurements.dat')
meas_fsigma8 = data[:, 2]
meas_fsigma8_error = data[:, 3]
meas_zlist = data[:, 1]
"""
popt, pcov = curve_fit(peturb.fsigma8_MG_fit, meas_zlist, meas_fsigma8,
#p0=[0.1, 1./(1.+7.), 4.],
sigma=1./(meas_fsigma8_error*meas_fsigma8_error),
bounds=([0., 1./(1.+10.), 1.5], [0.15, 1./(1.+0.), 4.]))
#perr = np.sqrt(np.diag(pcov))
"""
"""
# Varying alpha_b only.
# We set alpha_m = 0.1, a_trans = 1./(1.+7.), and r = 4.
# Finds a_B = 0.00130177 which has chisqr = 3.45158072065
# This works -- it has a smaller chisqr than 0.00130176 and 0.00130178
popt, pcov = curve_fit(peturb.fsigma8_MG_fit, meas_zlist, meas_fsigma8,
p0=[0.],