def setUp(self):
self.lens_dist = kernel.dNdzMagLim(z_min=0.0, z_max=2.0,
a=2, z0=0.3, b=2)
self.source_dist = kernel.dNdzGaussian(z_min=0.0, z_max=2.0,
z0=1.0, sigma_z=0.2)
self.z_array = numpy.linspace(0.0, 2.0, 4)
self.lens_dist_list = [0.0, 0.00318532, 0.0, 0.0]
self.source_dist_list = [3.72665317e-06, 0.24935220,
0.24935220, 3.72665317e-06]
def correlation_unit_test():
import correlation
import hod
import kernel
print "\n******************************"
print "* *"
print "* Testing Correlation Module *"
print "* *"
print "******************************\n"
### Definining a correlation object requires first two window functions
### (these could in principle be the same window function), theta bounds to
### compute the correlation over, and optionaly and HOD object and a
### specification as to which power spectrum to use. Note: the different
### correlation classes have approprate default values.
### As in kernel_unit_test create galaxy distributions
lens_dist = kernel.dNdzMagLim(0.0, 2.0, 2, 0.3, 2)
source_dist = kernel.dNdzGaussian(0.0, 2.0, 1.0, 0.2)
### create appropreate window objects
lens_window = kernel.WindowFunctionGalaxy(lens_dist)
source_window = kernel.WindowFunctionConvergence(source_dist)
### define an hod (optional but needed in order to use power_gm or power_gg)
zheng = hod.HODZheng(10**13.0, 0.15, 10**13.0, 10**14.0, 1.0)
### Define the correlation objects. Note that each of these correlations
### is computed using the nonlinear dark matter power spectrum, power_mm.
### other options are linear_power which computes the correlation for the
### linear spectrum only, power_gm which is the galaxy-matter cross spectrum
### and power_gg which is the galaxy-galaxy power spectrum.
### Here we define the correlation function of galaxy clustering, note that
### it takes only one window function as the second is assumed identical
theta_array = numpy.logspace(-3, 0, 5)
auto = correlation.AutoCorrelation(theta_min=0.001 * degToRad,
theta_max=1.0 * degToRad,
window_function_galaxy=lens_window,
input_hod=zheng,
powSpec='power_mm')
print "Auto Correlation: (theta [deg], wtheta)"
for theta in theta_array:
print "\t", (theta, auto.correlation(theta * degToRad))
### Define the correlation for galaxy-galaxy magnification. Note it takes
### and WindowFunctionGalaxy object and a WindowFunctionConvergence Object
mag = correlation.MagCorrelation(0.001 * degToRad,
1.0 * degToRad,
lens_window,
source_window,
input_hod=zheng,
powSpec='power_mm')
print "Convergence Correlation: (theta [deg], wtheta)"
for theta in theta_array:
print "\t", (theta, mag.correlation(theta * degToRad))
print ""
def setUp(self):
lens_dist = kernel.dNdzMagLim(z_min=0.0, z_max=2.0,
a=1, z0=0.3, b=1)
source_dist = kernel.dNdzGaussian(z_min=0.0, z_max=2.0,
z0=1.0, sigma_z=0.2)
cosmo = cosmology.MultiEpoch(0.0, 5.0, cosmo_dict=c_dict)
self.lens_window = kernel.WindowFunctionGalaxy(
lens_dist, cosmo_multi_epoch=cosmo)
self.source_window = kernel.WindowFunctionConvergence(
source_dist, cosmo_multi_epoch=cosmo)
self.z_array = numpy.linspace(0.0, 2.0, 4)
def correlation_unit_test():
import correlation
import hod
import kernel
print "\n******************************"
print "* *"
print "* Testing Correlation Module *"
print "* *"
print "******************************\n"
### Definining a correlation object requires first two window functions
### (these could in principle be the same window function), theta bounds to
### compute the correlation over, and optionaly and HOD object and a
### specification as to which power spectrum to use. Note: the different
### correlation classes have approprate default values.
### As in kernel_unit_test create galaxy distributions
lens_dist = kernel.dNdzMagLim(0.0, 2.0, 2, 0.3, 2)
source_dist = kernel.dNdzGaussian(0.0, 2.0, 1.0, 0.2)
### create appropreate window objects
lens_window = kernel.WindowFunctionGalaxy(lens_dist)
source_window = kernel.WindowFunctionConvergence(source_dist)
### define an hod (optional but needed in order to use power_gm or power_gg)
zheng = hod.HODZheng(10**13.0, 0.15, 10**13.0, 10**14.0, 1.0)
### Define the correlation objects. Note that each of these correlations
### is computed using the nonlinear dark matter power spectrum, power_mm.
### other options are linear_power which computes the correlation for the
### linear spectrum only, power_gm which is the galaxy-matter cross spectrum
### and power_gg which is the galaxy-galaxy power spectrum.
### Here we define the correlation function of galaxy clustering, note that
### it takes only one window function as the second is assumed identical
theta_array = numpy.logspace(-3, 0, 5)
auto = correlation.AutoCorrelation(theta_min=0.001*degToRad,
theta_max=1.0*degToRad,
window_function_galaxy=lens_window,
input_hod=zheng,
powSpec='power_mm')
print "Auto Correlation: (theta [deg], wtheta)"
for theta in theta_array:
print "\t",(theta, auto.correlation(theta*degToRad))
### Define the correlation for galaxy-galaxy magnification. Note it takes
### and WindowFunctionGalaxy object and a WindowFunctionConvergence Object
mag = correlation.MagCorrelation(0.001*degToRad, 1.0*degToRad,
lens_window, source_window,
input_hod=zheng,
powSpec='power_mm')
print "Convergence Correlation: (theta [deg], wtheta)"
for theta in theta_array:
print "\t",(theta, mag.correlation(theta*degToRad))
print ""
def setUp(self):
cosmo = cosmology.MultiEpoch(0.0, 5.0, cosmo_dict=c_dict)
lens_dist = kernel.dNdzMagLim(z_min=0.0, z_max=2.0,
a=2, z0=0.3, b=2)
source_dist = kernel.dNdzGaussian(z_min=0.0, z_max=2.0,
z0=1.0, sigma_z=0.2)
lens_window = kernel.WindowFunctionGalaxy(
lens_dist, cosmo_multi_epoch=cosmo)
source_window = kernel.WindowFunctionConvergence(
source_dist, cosmo_multi_epoch=cosmo)
self.kern = kernel.Kernel(0.001*0.001*degToRad, 1.0*100.0*degToRad,
window_function_a=lens_window,
window_function_b=source_window,
cosmo_multi_epoch=cosmo)
self.ln_ktheta_array = numpy.linspace(-15, -1, 4)
def setUp(self):
cosmo_multi = cosmology.MultiEpoch(0.0, 5.0, cosmo_dict=c_dict)
lens_dist = kernel.dNdzMagLim(z_min=0.0, z_max=2.0,
a=2, z0=0.3, b=2)
source_dist = kernel.dNdzGaussian(z_min=0.0, z_max=2.0,
z0=1.0, sigma_z=0.2)
lens_window = kernel.WindowFunctionGalaxy(
lens_dist, cosmo_multi_epoch=cosmo_multi)
source_window = kernel.WindowFunctionConvergence(
source_dist, cosmo_multi_epoch=cosmo_multi)
kern = kernel.Kernel(0.001*0.001*deg_to_rad, 1.0*100.0*deg_to_rad,
window_function_a=lens_window,
window_function_b=source_window,
cosmo_multi_epoch=cosmo_multi)
zheng = hod.HODZheng(hod_dict)
cosmo_single = cosmology.SingleEpoch(0.0, cosmo_dict=c_dict)
h = halo.Halo(input_hod=zheng, cosmo_single_epoch=cosmo_single)
self.corr = correlation.Correlation(0.001, 1.0,
input_kernel=kern,
input_halo=h,
power_spec='power_mm')
self.theta_array = numpy.logspace(-3, 0, 4)*deg_to_rad
### Initialize the halo object with the mass function and single epoch
### cosmology implementation is from Seljak2000.
halo_model = halo.Halo(redshift=0.0,
input_hod=sdss_hod,
cosmo_single_epoch=cosmo_single)
### From this point we have fully defined our cosmology and halo model.
### The next step is defining the redshift distributions and appropriate
### window functions.
### Below we define our foreground lenses and background source distributions
### needed for projecting our power spectrum from halo onto the sky. We use
### the functional form of a magnitude limited sample for the lens and for the
### sources we use a Gaussian with mean z=1.0. Other options could be used here,
### see kernel.py for other implemented distributions.
lens_dist = kernel.dNdzMagLim(0.0, 2.0, 2.0, 0.3, 2.0)
source_dist = kernel.dNdzGaussian(0.0, 2.0, 1.0, 0.2)
### Now we need to create the appropriate window functions that will allow us
### to project the power spectrum from halo. Currently these come in two
### varieties. The first is WindowFunctionGalaxy which defines the dn/dchi
### distribution of galaxies. If one is interested in the clustering of two(one)
### galaxy populations this is correlation should be used. The second is
### WindowFunctionConvergence which defines the lensing kernel weighted
### distribution. Cosmic shear and magnification studies should use 2 of
### these. For the galaxy-galaxy magnification which we are trying to compute we
### need both a galaxy window function (for the lenses) and a convergence
### window function (for the sources).
lens_window = kernel.WindowFunctionGalaxy(lens_dist, cosmo_multi)
source_window = kernel.WindowFunctionConvergence(source_dist, cosmo_multi)
lens_window.write('test_lens_window.ascii')
def kernel_unit_test():
import cosmology
import kernel
print "\n*************************"
print "* *"
print "* Testing kernel Module *"
print "* *"
print "*************************\n"
print "Testing dNdz"
print "*************************"
### To define a Kernel object we need several things first. We need redshift
### distributions as well as the corresponding window functions.
### initilized to galaxy redshift distributions one as a magnitude limited
### sample, the other a Guassian with mean z=1.0
lens_dist = kernel.dNdzMagLim(z_min=0.0, z_max=2.0, a=2, z0=0.3, b=2)
source_dist = kernel.dNdzGaussian(z_min=0.0,
z_max=2.0,
z0=1.0,
sigma_z=0.2)
### normalize the distributions and create PDFs
lens_dist.normalize()
source_dist.normalize()
z_array = numpy.linspace(0.0, 2.0, 5)
print "Lens dNdz: (z, p(z)dz)"
for z in z_array:
print "\t", (z, lens_dist.dndz(z))
print "Source dNdz: (z, p(z)dz)"
for z in z_array:
print "\t", (z, source_dist.dndz(z))
print ""
print "Testing WindowFunction"
print "*************************"
cosmo = cosmology.MultiEpoch(0.0, 2.0)
### using the distributions defined above compute the distance weighted
### window functions for use in projecting a powerspectrum
### Define a galaxy window function
chi_array = cosmo.comoving_distance(z_array)
lens_window = kernel.WindowFunctionGalaxy(redshift_dist=lens_dist)
print "Lens Window: (chi [Mpc/h], window value [h/Mpc])"
for chi in chi_array:
print "\t", (chi, lens_window.window_function(chi))
### Backup write command if more information is needed
# lens_window.write('test_galaxy_window_function.ascii')
### Define a lensed population of galaxies
source_window = kernel.WindowFunctionConvergence(redshift_dist=source_dist)
print "Source Window: (chi [Mpc/h], window value [h/Mpc])"
for chi in chi_array:
print "\t", (chi, source_window.window_function(chi))
### Backup write command if more information is needed
# source_window.write('test_convergence_window_function.ascii')
print "Testing Kernel"
print "*************************"
### Initialize the kernel objects for projecting a power spectrum in z space
### Initilize the kernel for galaxy clustering
ln_ktheta_array = numpy.linspace(-15, -1, 5)
k_Auto = kernel.Kernel(ktheta_min=0.001 * degToRad * 0.001,
ktheta_max=1.0 * degToRad * 100.0,
window_function_a=lens_window,
window_function_b=lens_window)
print "Auto Kernel: (k*theta [h/Mpc*Radians], kernel value [(h/Mpc)^2])"
for ln_ktheta in ln_ktheta_array:
print "\t", (numpy.exp(ln_ktheta), k_Auto.kernel(ln_ktheta))
### Backup write command if more information is needed
# k_Auto.write('test_clustering_kernel.ascii')
### Kernel computing lensing convergence
k_Con = kernel.Kernel(0.001 * degToRad * 0.001, 1.0 * degToRad * 100.0,
lens_window, source_window)
print(
"Convergence Kernel: (k*theta [h/Mpc*Radians], "
"kernel value [(h/Mpc)^2])")
for ln_ktheta in ln_ktheta_array:
print "\t", (numpy.exp(ln_ktheta), k_Con.kernel(ln_ktheta))
print ""
### Backup write command if more information is needed
# k_Con.write("test_convergence_kernel.ascii")
### Print out the redshifts for which the kernel is maximaly sensitive
print "Peak Sensitivity at Redshifts:", k_Auto.z_bar, k_Con.z_bar
halo_model = halo.Halo(redshift=0.0, input_hod=sdss_hod,
cosmo_single_epoch=cosmo_single,
mass_func=mass,
extrapolate=True)
### From this point we have fully defined our cosmology and halo model.
### The next step is defining the redshift distributions and appropriate
### window functions.
### Below we define our foreground lenses and background source distributions
### needed for projecting our power spectrum from halo onto the sky. We use
### the functional form of a magnitude limited sample for the lens and for the
### sources we use a Gaussian with mean z=1.0. Other options could be used here,
### see kernel.py for other implemented distributions.
lens_dist = kernel.dNdzMagLim(0.001, 5.0, 2.0, 0.3, 2.0)
z_bar = 1.0
dz = 0.001
source_dist = kernel.dNdzGaussian(z_bar - 5.0*dz, z_bar + 5.0*dz, z_bar, dz)
### Now we need to create the appropriate window functions that will allow us
### to project the power spectrum from halo. Currently these come in two
### varieties. The first is WindowFunctionGalaxy which defines the dn/dchi
### distribution of galaxies. If one is interested in the clustering of two(one)
### galaxy populations this is correlation should be used. The second is
### WindowFunctionConvergence which defines the lensing kernel weighted
### distribution. Cosmic shear and magnification studies should use 2 of
### these. For the galaxy-galaxy magnification which we are trying to compute we
### need both a galaxy window function (for the lenses) and a convergence
### window function (for the sources).
lens_window = kernel.WindowFunctionGalaxy(lens_dist, cosmo_multi)
def kernel_unit_test():
import cosmology
import kernel
print "\n*************************"
print "* *"
print "* Testing kernel Module *"
print "* *"
print "*************************\n"
print "Testing dNdz"
print "*************************"
### To define a Kernel object we need several things first. We need redshift
### distributions as well as the corresponding window functions.
### initilized to galaxy redshift distributions one as a magnitude limited
### sample, the other a Guassian with mean z=1.0
lens_dist = kernel.dNdzMagLim(z_min=0.0, z_max=2.0, a=2, z0=0.3, b=2)
source_dist = kernel.dNdzGaussian(z_min=0.0, z_max=2.0, z0=1.0, sigma_z=0.2)
### normalize the distributions and create PDFs
lens_dist.normalize()
source_dist.normalize()
z_array = numpy.linspace(0.0, 2.0, 5)
print "Lens dNdz: (z, p(z)dz)"
for z in z_array:
print "\t",(z, lens_dist.dndz(z))
print "Source dNdz: (z, p(z)dz)"
for z in z_array:
print "\t",(z, source_dist.dndz(z))
print ""
print "Testing WindowFunction"
print "*************************"
cosmo = cosmology.MultiEpoch(0.0, 2.0)
### using the distributions defined above compute the distance weighted
### window functions for use in projecting a powerspectrum
### Define a galaxy window function
chi_array = cosmo.comoving_distance(z_array)
lens_window = kernel.WindowFunctionGalaxy(redshift_dist=lens_dist)
print "Lens Window: (chi [Mpc/h], window value [h/Mpc])"
for chi in chi_array:
print "\t",(chi, lens_window.window_function(chi))
### Backup write command if more information is needed
# lens_window.write('test_galaxy_window_function.ascii')
### Define a lensed population of galaxies
source_window = kernel.WindowFunctionConvergence(redshift_dist=source_dist)
print "Source Window: (chi [Mpc/h], window value [h/Mpc])"
for chi in chi_array:
print "\t",(chi, source_window.window_function(chi))
### Backup write command if more information is needed
# source_window.write('test_convergence_window_function.ascii')
print "Testing Kernel"
print "*************************"
### Initialize the kernel objects for projecting a power spectrum in z space
### Initilize the kernel for galaxy clustering
ln_ktheta_array = numpy.linspace(-15, -1, 5)
k_Auto = kernel.Kernel(ktheta_min=0.001*degToRad*0.001,
ktheta_max=1.0*degToRad*100.0,
window_function_a=lens_window,
window_function_b=lens_window)
print "Auto Kernel: (k*theta [h/Mpc*Radians], kernel value [(h/Mpc)^2])"
for ln_ktheta in ln_ktheta_array:
print "\t",(numpy.exp(ln_ktheta), k_Auto.kernel(ln_ktheta))
### Backup write command if more information is needed
# k_Auto.write('test_clustering_kernel.ascii')
### Kernel computing lensing convergence
k_Con = kernel.Kernel(0.001*degToRad*0.001, 1.0*degToRad*100.0,
lens_window, source_window)
print ("Convergence Kernel: (k*theta [h/Mpc*Radians], "
"kernel value [(h/Mpc)^2])")
for ln_ktheta in ln_ktheta_array:
print "\t",(numpy.exp(ln_ktheta), k_Con.kernel(ln_ktheta))
print ""
### Backup write command if more information is needed
# k_Con.write("test_convergence_kernel.ascii")
### Print out the redshifts for which the kernel is maximaly sensitive
print "Peak Sensitivity at Redshifts:", k_Auto.z_bar, k_Con.z_bar