Example #1
0
    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
Example #2
0
 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
Example #3
0
 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
Example #4
0
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)
Example #5
0
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)
Example #6
0
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)
Example #7
0
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)
Example #8
0
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)
Example #9
0
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)
Example #10
0
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)
Example #11
0
    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')
Example #12
0
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)
Example #13
0
#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,
Example #14
0
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,
Example #15
0
# -*- 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>


Example #16
0
# -*- 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>
Example #17
0
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
Example #18
0
'''
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
Example #19
0
'''
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()
Example #20
0

#===============================================================================
# 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])
Example #21
0
    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: