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aaa14fc880
* Add RAMSES2HDF5 conversion * Upload changes * Clean up * More clean up * updates * Little change * pep9 * Add basic SPH calculation for a snapshot * Add submit script * Remove echo * Little changes * Send off changes * Little formatting * Little updates * Add nthreads argument * Upload chagnes * Add nthreads arguemnts * Some local changes.. * Update scripts * Add submission script * Update script * Update params * Rename CSiBORGBox to CSiBORG1box * Rename CSiBORG1 reader * Move files * Rename folder again * Add basic plotting here * Add new skeletons * Move def * Update nbs * Edit directories * Rename files * Add units to converted snapshots * Fix empty dataset bug * Delete file * Edits to submission scripts * Edit paths * Update .gitignore * Fix attrs * Update weighting * Fix RA/dec bug * Add FORNAX cluster * Little edit * Remove boxes since will no longer need * Move func back * Edit to include sort by membership * Edit paths * Purge basic things * Start removing * Bring file back * Scratch * Update the rest * Improve the entire file * Remove old things * Remove old * Delete old things * Fully updates * Rename file * Edit submit script * Little things * Add print statement * Add here cols_to_structured * Edit halo cat * Remove old import * Add comment * Update paths manager * Move file * Remove file * Add chains
786 KiB
786 KiB
In [1]:
import numpy as np
import scipy.integrate
import symbolic_pofk.linear
import symbolic_pofk.syrenhalofit as syrenhalofit
In [26]:
def compute_sigma8_from_pk(k, pk):
"""Given a power spectrum P(k), compute sigma8."""
R = 8.0
x = k * R
W = np.zeros(x.shape)
m = x < 1.e-3
W[m] = 1.0
W[~m] =3.0 / x[~m]**3 * (np.sin(x[~m]) - x[~m] * np.cos(x[~m]))
y = pk * W**2 * k**3
sigma2 = scipy.integrate.simpson(y, x=np.log(x))
sigma = np.sqrt(sigma2 / (2.0 * np.pi**2))
return sigma
# Cosmological parameters
As = 2.105 # 10^9 A_s
h = 0.6766
Om = 0.3111
Ob = 0.02242 / h ** 2
ns = 0.9665
tau = 0.0561
# Define k integration range
def linear_sigma8(As, Om, Ob, h, ns):
"""Calculated from Deaglan's emulator."""
return symbolic_pofk.linear.As_to_sigma8(As, Om, Ob, h, ns)
# print('sigma8 from emulator', sigma8)
# # Test linear sigma8
# pk_lin = symbolic_pofk.linear.plin_emulated(k, sigma8, Om, Ob, h, ns,
# emulator='fiducial', extrapolate=True)
# new_sigma8 = compute_sigma8(k, pk_lin)
# print('sigma8 from integral:', new_sigma8)
# Get non-linear sigma8
def nonlinear_sigma8(As, Om0, Ob, h, ns, ks):
a = 1.
sigma8 = linear_sigma8(As, Om0, Ob, h, ns) # Linear sigma8
pk_nl = syrenhalofit.run_halofit(
ks, sigma8, Om, Ob, h, ns, a, emulator='fiducial', extrapolate=True,
which_params='Bartlett', add_correction=True)
return compute_sigma8_from_pk(ks, pk_nl)
# print('non-linear sigma8:', sigma8_nl)
# print('sigma8 non-linear bigger by a factor', sigma8_nl / sigma8)
In [18]:
kmin, kmax, nk = 1e-4, 1e1, 256
ks = np.logspace(np.log10(kmin), np.log10(kmax), nk) # Wavenumber
In [21]:
linear_sigma8(As, Om, Ob, h, ns)
Out[21]:
In [20]:
nonlinear_sigma8(As, Om, Ob, h, ns, ks)
Out[20]:
In [25]:
1e-10 * np.exp(3.0448)
Out[25]:
In [ ]: