def test_fcolls(self):
pert = Perturbations(M=np.linspace(10, 15, 1301))
fits = ['PS', 'Peacock']
for fit in fits:
pert.update(mf_fit=fit)
yield self.check_fcoll, pert, fit
def test_sigmas(self):
pert = Perturbations(M=np.linspace(7, 15, 801), omegab=0.05, omegac=0.25, omegav=0.7, sigma_8=0.8, n=1, H0=70.0,
k_bounds=[np.exp(-21), np.exp(21)], transfer__kmax=10, transfer__k_per_logint=50, mf_fit='ST',
z=0.0)
for redshift in [0.0, 2.0]:
pert.update(z=redshift)
for origin in ['camb', 'hmf']:
for col in ['sigma', 'lnsigma', 'n_eff']:
yield self.check_col, pert, "ST", redshift, origin, col
def test_fits(self):
pert = Perturbations(M=np.linspace(7, 15, 801), omegab=0.05, omegac=0.25, omegav=0.7, sigma_8=0.8, n=1, H0=70.0,
k_bounds=[np.exp(-21), np.exp(21)], transfer__kmax=10, transfer__k_per_logint=50, mf_fit='ST',
z=0.0)
for redshift in [0.0, 2.0]:
pert.update(z=redshift)
for fit in ["ST", "PS", "Reed03", "Warren", "Jenkins", "Reed07"]:
pert.update(mf_fit=fit)
for origin in ['camb', 'hmf']:
for col in ['dndlog10m', 'ngtm', 'fsigma']:
yield self.check_col, pert, fit, redshift, origin, col
def test_kbounds():
M = np.linspace(8, 15, 70)
pert = Perturbations(M, k_bounds=kbounds[0])
assert check_k(pert.k)
assert check_mf(pert.MassFunction())
assert check_vfv(pert.vfv)
for bounds in kbounds[1:]:
pert.update(k_bounds=bounds)
assert check_k(pert.k)
assert check_mf(pert.MassFunction())
assert check_vfv(pert.vfv)
def test_wdm():
M = np.linspace(8, 15, 70)
pert = Perturbations(M, wdm=wdms[0])
assert check_k(pert.k)
assert check_mf(pert.MassFunction())
assert check_vfv(pert.vfv)
for wdm in wdms[1:]:
pert.update(wdm=wdm)
assert check_k(pert.k)
assert check_mf(pert.MassFunction())
assert check_vfv(pert.vfv)
def test_z():
M = np.linspace(8, 15, 70)
pert = Perturbations(M, z=redshifts[0])
assert check_k(pert.k)
assert check_mf(pert.MassFunction())
assert check_vfv(pert.vfv)
for z in redshifts[1:]:
pert.update(z=z)
assert check_k(pert.k)
assert check_mf(pert.MassFunction())
assert check_vfv(pert.vfv)
def test_on():
M = np.linspace(8, 15, 70)
pert = Perturbations(M, omegan=omegans[0])
assert check_k(pert.k)
assert check_mf(pert.MassFunction())
assert check_vfv(pert.vfv)
for on in omegans[1:]:
pert.update(omegan=on)
assert check_k(pert.k)
assert check_mf(pert.MassFunction())
assert check_vfv(pert.vfv)
def test_H0():
M = np.linspace(8, 15, 70)
pert = Perturbations(M, H0=H0s[0])
assert check_k(pert.k)
assert check_mf(pert.MassFunction())
assert check_vfv(pert.vfv)
for h in H0s[1:]:
pert.update(H0=h)
assert check_k(pert.k)
assert check_mf(pert.MassFunction())
assert check_vfv(pert.vfv)
def test_sigma_8():
M = np.linspace(8, 15, 70)
pert = Perturbations(M, sigma_8=sigma_8s[0])
assert check_k(pert.k)
assert check_mf(pert.MassFunction())
assert check_vfv(pert.vfv)
for s8 in sigma_8s[1:]:
pert.update(sigma_8=s8)
assert check_k(pert.k)
assert check_mf(pert.MassFunction())
assert check_vfv(pert.vfv)
def test_n():
M = np.linspace(8, 15, 70)
pert = Perturbations(M, n=ns[0])
assert check_k(pert.k)
assert check_mf(pert.MassFunction())
assert check_vfv(pert.vfv)
for n in ns[1:]:
pert.update(n=n)
assert check_k(pert.k)
assert check_mf(pert.MassFunction())
assert check_vfv(pert.vfv)
def __init__(self):
# Make a pert class
self.pert = Perturbations(omegab=0.05, omegac=0.25, omegav=0.7, sigma_8=0.8, n=1, H0=70.0,
k_bounds=[np.exp(-21), np.exp(21)], transfer__kmax=10, transfer__k_per_logint=50)
#Get the camb transfer
self.camb_transfer = np.genfromtxt("data/camb_transfer")
#Get the camb power spec
self.camb_power = np.genfromtxt('data/camb_power')
#Get the genmf power (ie. camb power normalised by genmf)
self.genmf_power = np.genfromtxt('data/genmf_power')
class TestPower(object):
def __init__(self):
# Make a pert class
self.pert = Perturbations(omegab=0.05, omegac=0.25, omegav=0.7, sigma_8=0.8, n=1, H0=70.0,
k_bounds=[np.exp(-21), np.exp(21)], transfer__kmax=10, transfer__k_per_logint=50)
#Get the camb transfer
self.camb_transfer = np.genfromtxt("data/camb_transfer")
#Get the camb power spec
self.camb_power = np.genfromtxt('data/camb_power')
#Get the genmf power (ie. camb power normalised by genmf)
self.genmf_power = np.genfromtxt('data/genmf_power')
def test_transfer(self):
""" Testing whether the transfer function calculated by pycamb is the same as that of camb (via CLI)"""
my_T_vec = self.pert._transfer_function_callable(np.log(self.camb_transfer[:, 0]))
assert rms_diff(my_T_vec, np.log(self.camb_transfer[:, 1]), 1E-3)
#plt.savefig(directory + 'sigma.eps')
#===============================================================================
# fsigma and fsigma (Compare) one on the other
#===============================================================================
plt.clf()
M = np.linspace(10, 15, 501)
fig = plt.figure(figsize=(12 * 1.2, 7.0 * 1.2))
gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1])
ax = [plt.subplot(gs[1])]
ax.insert(0, plt.subplot(gs[0], sharex=ax[0]))
ax[0].grid(True)
ax[1].grid(True)
pert = Perturbations(M)
vfv_ST = pert.fsigma
approaches = [
'PS', 'ST', 'Jenkins', 'Reed03', 'Warren', 'Reed07', 'Tinker', 'Crocce',
'Courtin', 'Bhattacharya', 'Angulo', 'Angulo_Bound', 'Watson_FoF',
'Watson', "Peacock", "Behroozi"
]
lines = ["-", "--", "-.", ":"]
ax[1].set_xlabel(xlab)
ax[0].set_ylabel(r'Fraction of Mass Collapsed, $f(\sigma)$')
ax[1].set_ylabel(r'$f(\sigma)/f_{\rm ST}(\sigma)$')
for i, approach in enumerate(approaches):
pert.update(mf_fit=approach)
ax[0].plot(10**M,
import time
redshifts = [0.0, 1.0]
fits = ["ST", "PS"]
s8s = [0.8, 0.9]
omegavs = [0.7, 0.6]
#===============================================================================
# Do hmf first
#===============================================================================
# First do a "fair" comparison of a single calculation
start = time.time()
pert = Perturbations(M=np.linspace(3, 17, 1401),
k_bounds=np.exp([-21, 21]),
omegav=0.7,
omegab=0.05,
omegac=0.25,
H0=70.0,
n=1.0,
sigma_8=0.8)
#pert.dndlnm
pert.ngtm
time_hmf_1 = time.time() - start
#Now do a comparison of 2 redshifts, 2 fitting functions, 2 sigma_8s and 2 cosmos
start = time.time()
pert = Perturbations(M=np.linspace(3, 17, 1401),
k_bounds=np.exp([-21, 21]),
omegav=0.7,
omegab=0.05,
# -*- coding: utf-8 -*- # <nbformat>3.0</nbformat> # <codecell> from hmf import Perturbations import numpy as np M = np.arange(10,15,0.01) pert_object = Perturbations(M,z=6.0) mass_func = pert_object.dndlnm # <codecell> %pylab inline plot (M, log10(mass_func)) # <codecell>
# -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <codecell>
from hmf import Perturbations
import numpy as np
M = np.arange(10,15,0.01)
pert_object = Perturbations(M,z=0.0)
"""
PLANCK PARAMETERS (mar-2013)
Angular size of sound horizon at recombination
theta_* = (1.0414)*10^-2
Baryons density:
Omega_b h^2 = 0.0220
Omega_b = 0.0486
Dark cold matter density:
Omega_c h^2 = 0.1199
Omega_c = 0.2647
Spectral index
n_s = 0.9693
Hubble constant:
H_0 = 67.3 km s^-1 Mpc ^-1
h = 0.67.3
Matter density:
Omega_m = 0.315
Optical depth
tau = 0.089
"""
# <codecell>
import sys
from hmf import Perturbations
import time
redshifts = [0.0, 1.0]
fits = ["ST", "PS"]
s8s = [0.8, 0.9]
omegavs = [0.7, 0.6]
#===============================================================================
# Do hmf first
#===============================================================================
# First do a "fair" comparison of a single calculation
start = time.time()
pert = Perturbations(M=np.linspace(3, 17, 1401), k_bounds=np.exp([-21, 21]),
omegav=0.7, omegab=0.05, omegac=0.25, H0=70.0, n=1.0,
sigma_8=0.8)
#pert.dndlnm
pert.ngtm
time_hmf_1 = time.time() - start
#Now do a comparison of 2 redshifts, 2 fitting functions, 2 sigma_8s and 2 cosmos
start = time.time()
pert = Perturbations(M=np.linspace(3, 17, 1401), k_bounds=np.exp([-21, 21]),
omegav=0.7, omegab=0.05, omegac=0.25, H0=70.0, n=1.0,
sigma_8=0.8)
for z in redshifts:
pert.update(z=z)
pert.ngtm
'''
This script explores the projected differences between using an EH and CAMB
transfer function.
'''
from hmf import Perturbations
import time
import numpy as np
omegab = [0.02, 0.05, 0.1]
omegac = [0.2, 0.25, 0.5]
H0 = [50, 70, 90]
n = [0.8, 0.9, 1.0]
pert_camb = Perturbations(transfer_fit="CAMB")
pert_EH = Perturbations(transfer_fit="EH")
camb_time = 0.0
eh_time = 0.0
for ob in omegab:
for oc in omegac:
for h in H0:
for nn in n:
pert_camb.update(omegab=ob, omegac=oc, H0=h, n=nn)
pert_EH.update(omegab=ob, omegac=oc, H0=h, n=nn)
start = time.time()
camb = pert_camb.dndm
camb_time += time.time() - start
start = time.time()
eh = pert_EH.dndm
'''
This script explores the projected differences between using an EH and CAMB
transfer function.
'''
from hmf import Perturbations
import time
import numpy as np
omegab = [0.02, 0.05, 0.1]
omegac = [0.2, 0.25, 0.5]
H0 = [50, 70, 90]
n = [0.8, 0.9, 1.0]
pert_camb = Perturbations(transfer_fit="CAMB")
pert_EH = Perturbations(transfer_fit="EH")
camb_time = 0.0
eh_time = 0.0
for ob in omegab:
for oc in omegac:
for h in H0:
for nn in n:
pert_camb.update(omegab=ob, omegac=oc, H0=h, n=nn)
pert_EH.update(omegab=ob, omegac=oc, H0=h, n=nn)
start = time.time()
camb = pert_camb.dndm
camb_time += time.time() - start
start = time.time()
#===============================================================================
# fsigma and fsigma (Compare) one on the other
#===============================================================================
plt.clf()
M = np.linspace(10, 15, 501)
fig = plt.figure(figsize=(12 * 1.2, 7.0 * 1.2))
gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1])
ax = [plt.subplot(gs[1])]
ax.insert(0, plt.subplot(gs[0], sharex=ax[0]))
ax[0].grid(True)
ax[1].grid(True)
pert = Perturbations(M)
vfv_ST = pert.fsigma
approaches = ['PS', 'ST', 'Jenkins', 'Reed03', 'Warren', 'Reed07', 'Tinker', 'Crocce',
'Courtin', 'Bhattacharya', 'Angulo', 'Angulo_Bound', 'Watson_FoF', 'Watson',
"Peacock", "Behroozi"]
lines = ["-", "--", "-.", ":"]
ax[1].set_xlabel(xlab)
ax[0].set_ylabel(r'Fraction of Mass Collapsed, $f(\sigma)$')
ax[1].set_ylabel(r'$f(\sigma)/f_{\rm ST}(\sigma)$')
for i, approach in enumerate(approaches):
pert.update(mf_fit=approach)
ax[0].plot(10 ** M, pert.fsigma, label=approach.replace("_", " "), linestyle=lines[(i / 7) % 4])
ax[1].plot(10 ** M, pert.fsigma / vfv_ST, linestyle=lines[(i / 7) % 4])
Omega_b h^2 = 0.0220
Dark cold matter density:
Omega_c h^2 = 0.1199
Spectral index
n_s = 0.9693
Hubble constant:
H_0 = 67.3 km s^-1 Mpc ^-1
Matter density:
Omega_m = 0.315
Optical depth
tau = 0.089
"""
# <codecell>
pert_object = Perturbations(M,z=1.0)
pert_object.update(omegab = 0.05)
print "Transfer Cosmo"
print pert_object._transfer_cosmo
print "Extra Cosmo"
print pert_object._extra_cosmo
print "Cosmo Params"
print pert_object.cosmo_params
# <codecell>
#kwargs = {'sigma_8':0.9}
#kwargs = {'z':6.0, 'sigma_8'}
#pert_object.update(**kwargs)
# -*- coding: utf-8 -*- # <nbformat>3.0</nbformat> # <codecell> from hmf import Perturbations import numpy as np M = np.arange(10,15,0.01) pert_object = Perturbations(M,z=6.0) object_mass_func = pert_object.dndlnm #Output: #WARNING: Unrecognized parameters: #transfer__kmax #transfer__k_per_logint # <codecell> #""" #PLANCK PARAMETERS (mar-2013) #Angular size of sound horizon at recombination # theta_* = (1.0414)*10^-2 #Baryons density: # Omega_b h^2 = 0.0220 # Omega_b = 0.0486 #Dark cold matter density: # Omega_c h^2 = 0.1199 # Omega_c = 0.2647 #Spectral index # n_s = 0.9693 #Hubble constant: