def test_ehnobao():
t = Transfer(transfer_model="EH")
tnobao = Transfer(transfer_model="EH_NoBAO")
assert np.isclose(t._unnormalised_lnT[0],
tnobao._unnormalised_lnT[0],
rtol=1e-5)
def test_updates():
t = Transfer()
t2 = Transfer()
for k, v in {
"z": 0.1,
"sigma_8": 0.82,
"n": 0.95,
"cosmo_params": {
"H0": 68.0
}
}.items():
yield check_update, t, t2, k, v
def hmf_halofit(): #https://github.com/steven-murray/hmf/blob/master/development/halofit_testing.ipynb from hmf.transfer import Transfer teh_nl_tk = Transfer(transfer_model="EH", takahashi=True, z=0.0) teh_nl_ntk = Transfer(transfer_model="EH", takahashi=False, z=0.0) teh_nl_tk.update(z=0) #plt.plot(teh_nl_tk.k, np.abs(teh_nl_ntk.nonlinear_power/teh_nl_tk.nonlinear_power -1)) ax0.loglog(teh_nl_ntk.k, teh_nl_ntk.nonlinear_power, 'k--') ax0.loglog(teh_nl_ntk.k, teh_nl_ntk.power, 'k-.')
def test_updates():
t = Transfer()
t2 = Transfer()
for k, v in {
"z": 0.1,
"wdm_mass": 10.0,
"transfer_options": {
"initial_mode": 2,
"lAccuracyBoost": 1.5,
"AccuracyBoost": 1.5
},
"sigma_8": 0.82,
"n": 0.95,
"H0": 68.0
}.iteritems():
yield check_update, t, t2, k, v
def test_updates():
t = Transfer()
t2 = Transfer()
for k, v in {
"z": 0.1,
"transfer_params": {
"lAccuracyBoost": 1.5,
"AccuracyBoost": 1.5
},
"sigma_8": 0.82,
"n": 0.95,
"cosmo_params": {
"H0": 68.0
}
}.iteritems():
yield check_update, t, t2, k, v
def test_data():
t = Transfer(cosmo_model=LambdaCDM(Om0=0.3, Ode0=0.7, H0=70.0, Ob0=0.05),
sigma_8=0.8,
n=1,
transfer_params={
"transfer__k_per_logint": 0,
"transfer__kmax": 100.0
},
lnk_min=np.log(1e-11),
lnk_max=np.log(1e11))
tdata = np.genfromtxt(LOCATION + "/data/transfer_for_hmf_tests.dat")
pdata = np.genfromtxt(LOCATION + "/data/power_for_hmf_tests.dat")
assert rms(t._unnormalised_lnT - np.log(tdata[:, 1])
) < 0.05 # Does better than 0.001 on my system...
diff = t.power - pdata[:, 1]
print t._unnormalised_lnT[400], t._unnormalised_power[400], t._power0[400]
assert rms(t.power - pdata[:, 1]) < 0.001
def test_data():
t = Transfer(omegab=0.05,
omegac=0.25,
omegav=0.7,
omegan=0.0,
H0=70.0,
sigma_8=0.8,
n=1,
transfer_options={
"transfer__k_per_logint": 0,
"transfer__kmax": 100.0
},
lnk_min=np.log(1e-11),
lnk_max=np.log(1e11))
tdata = np.genfromtxt(LOCATION + "/data/transfer_for_hmf_tests.dat")
pdata = np.genfromtxt(LOCATION + "/data/power_for_hmf_tests.dat")
assert rms(np.exp(t._unnormalised_lnT) -
tdata[:, 1]) < 0.05 # Does better than 0.001 on my system...
assert rms(np.exp(t.power) - pdata[:, 1]) < 0.001
plt.legend(loc = "lower left")
plt.show()
#plt.savefig('plots/hmf_OmegaM', bbox_inches='tight')
# Check if log scale is working properly
plt.figure(10)
plt.plot(Mass)
plt.plot(Mass[::10], 'o')
plt.yscale('log')
plt.show()
from hmf.transfer import Transfer
teh_nl_tk = Transfer(transfer_model="EH", takahashi=True, z= 8.0)
teh_nl_ntk = Transfer(transfer_model="EH", takahashi=False, z=8.0)
teh_nl_tk.update(z=0)
plt.plot(teh_nl_tk.k, np.abs(teh_nl_ntk.nonlinear_power/teh_nl_tk.nonlinear_power -1))
#plt.plot(teh_nl_ntk.k, teh_nl_ntk.nonlinear_power)
#plt.plot(teh_nl_ntk.k, teh_nl_ntk.power)
plt.xscale('log')
#plt.yscale('log')
plt.grid(True)
def test_halofit():
t = Transfer(lnk_min=-20, lnk_max=20, dlnk=0.05, transfer_fit="EH")
assert abs(t.power[0] - t.nonlinear_power[0]) < 1e-5
assert 5 + t.power[-1] < t.nonlinear_power[-1]
def test_halofit():
t = Transfer(lnk_min=-20, lnk_max=20, dlnk=0.05, transfer_model="EH")
print EH_BAO._defaults
print "in test_transfer, params are: ", t.transfer_params
assert np.isclose(t.power[0], t.nonlinear_power[0])
assert 5 * t.power[-1] < t.nonlinear_power[-1]
def test_bondefs():
t = Transfer(transfer_model="BondEfs")
print(np.exp(t._unnormalised_lnT))
assert np.isclose(np.exp(t._unnormalised_lnT[0]), 1, rtol=1e-5)