def check_Bessel_spectrum(l, k=None, r0=100):
# Initialise survey and spectra
cosmo = cosmicpy.cosmology()
surv = cosmicpy.survey(nzparams={
'type': 'gaussian',
'cosmology': cosmo,
'r0': r0
},
zmax=5,
fsky=1.0)
spectra = cosmicpy.spectra(cosmo, surv)
survey_volume = pi**(3.0 / 2.0) * r0**3
if k is None:
k = np.logspace(log(0.0001) / log(10),
log(0.25) / log(10),
512,
endpoint=False)
kw, w = spectra.W(l, k, evol=False)
kw = kw.squeeze()
w = w.squeeze()
w_exact = np.zeros_like(w)
for k1 in range(len(k)):
w_exact[k1, :] = gaussian_bessel_window(l, r0, k[k1],
kw[k1, :]) / survey_volume
pk = cosmo.pk_lin(kw)
integrand = np.square(kw * w_exact) * pk
res = (2.0 / pi)**2 * trapz(integrand, x=kw)
cl = spectra.cl_sfb(l, k)
return k, cl, res
def check_Bessel_spectrum(l, k=None, r0=100):
# Initialise survey and spectra
cosmo = cosmicpy.cosmology()
surv = cosmicpy.survey(nzparams={'type': 'gaussian',
'cosmology': cosmo,
'r0': r0},
zmax=5,
fsky=1.0)
spectra = cosmicpy.spectra(cosmo, surv)
survey_volume = pi**(3.0 / 2.0) * r0**3
if k is None:
k = np.logspace(log(0.0001) / log(10), log(0.25) / log(10), 512,
endpoint=False)
kw, w = spectra.W(l, k, evol=False)
kw = kw.squeeze()
w = w.squeeze()
w_exact = np.zeros_like(w)
for k1 in range(len(k)):
w_exact[k1, :] = gaussian_bessel_window(l,r0, k[k1], kw[k1,:])/ survey_volume
pk = cosmo.pk_lin(kw)
integrand = np.square(kw * w_exact) * pk
res = (2.0 / pi)**2 * trapz(integrand, x=kw)
cl = spectra.cl_sfb(l, k)
return k, cl, res
def check_Bessel_window(l, k=None, r0=100):
r""" Checks the computation of the bessel window compared to the exact
solution
for Gaussian selection functions.
Parameters
----------
l : int
Order of the multipole.
r0 : real, optional
Radius parameter for the survey Gaussian selection function in Mpc/h
Returns
-------
(k, W, Wexact) : tuple of ndarrays
Window compute the discrete spherical bessel transform and the exact
window computed from theory
"""
# Initialise survey and spectra
cosmo = cosmicpy.cosmology()
surv = cosmicpy.survey(nzparams={
'type': 'gaussian',
'cosmology': cosmo,
'r0': r0
},
zmax=0.5,
fsky=1.0)
spectra = cosmicpy.spectra(cosmo, surv)
survey_volume = pi**(3.0 / 2.0) * r0**3
if k is None:
k = np.logspace(log(0.001) / log(10),
log(0.20) / log(10),
512,
endpoint=False)
W = spectra.W(l, k, k, evol=False, kmax=0.5)
W = W.squeeze()
Wexact = np.zeros_like(W)
for k1 in range(len(k)):
Wexact[k1, :] = (gaussian_bessel_window(l, r0, k[k1], k) /
survey_volume)
print("Maximum absolute error vs maximum value: " +
str(abs(W - Wexact).max()) + " " + str(Wexact.max()))
return (k, W, Wexact)
def check_Bessel_window(l, k=None, r0=100):
r""" Checks the computation of the bessel window compared to the exact
solution
for Gaussian selection functions.
Parameters
----------
l : int
Order of the multipole.
r0 : real, optional
Radius parameter for the survey Gaussian selection function in Mpc/h
Returns
-------
(k, W, Wexact) : tuple of ndarrays
Window compute the discrete spherical bessel transform and the exact
window computed from theory
"""
# Initialise survey and spectra
cosmo = cosmicpy.cosmology()
surv = cosmicpy.survey(nzparams={'type': 'gaussian',
'cosmology': cosmo,
'r0': r0},
zmax=0.5,
fsky=1.0)
spectra = cosmicpy.spectra(cosmo, surv)
survey_volume = pi**(3.0 / 2.0) * r0**3
if k is None:
k = np.logspace(log(0.001) / log(10), log(0.20) / log(10), 512,
endpoint=False)
W = spectra.W(l, k, k, evol=False, kmax=0.5)
W = W.squeeze()
Wexact = np.zeros_like(W)
for k1 in range(len(k)):
Wexact[k1, :] = (gaussian_bessel_window(l, r0, k[k1], k) /
survey_volume)
print("Maximum absolute error vs maximum value: " +
str(abs(W - Wexact).max()) +
" " +
str(Wexact.max()))
return (k, W, Wexact)
def import_survey(filename, structure_name="fid", expt_name="sv1"):
r""" Loads an icosmo survey from a fiducial structure stored in an
idl save file into a cosmicpy survey.
Parameters
----------
filename : str
Name of the idl save from which to load the cosmology.
structure_name : str, optional
Name of the icosmo fiducial structure stored in the save file.
Returns
-------
surv : survey
cosmicpy survey corresponding to the icosmo input.
"""
icosmo_file = readsav(filename)
icosmo = icosmo_file.get(structure_name)['expt'][0]
nzbins = icosmo[expt_name][0]['n_zbin'][0]
zerror = icosmo[expt_name][0]['zerror'][0]
ng = icosmo[expt_name][0]['ng'][0]
a_survey = icosmo[expt_name][0]['a_survey'][0]
dndztype = icosmo[expt_name][0]['dndztype'][0]
if dndztype != b"smail":
print("unsupported galaxy distribution")
return None
a = icosmo[expt_name][0]['dndzp'][0][0]
b = icosmo[expt_name][0]['dndzp'][0][1]
zmed = icosmo[expt_name][0]['z_med'][0]
biastype = icosmo[expt_name][0]['biastype'][0]
if biastype == b'bias0':
btype = 'constant'
elif biastype == b'bias1':
btype = 'sqrt'
else:
print("unsupported bias type")
return None
# Compute fsky
fsky = a_survey/(180./pi)**2/(4.0*pi)
# find z0 corresponding to zmedian
def med_smail(z0):
smail = lambda z: z**a * exp(-(z/z0)**b)
smail_norm = romberg(smail, 0, 5)
smailn = lambda z: (z**a * exp(-(z/z0)**b)) / smail_norm
f = lambda x: romberg(smailn, 0, x) - 0.5
return brentq(f, 0.01, 5) - zmed
z0 = brentq(med_smail, 0.01, 3)
surv = cosmicpy.survey(nzbins=nzbins,
ngal=ng,
zphot_sig=zerror,
fsky=fsky,
biastype=btype,
nzparams={'type': 'smail',
'a': a,
'b': b,
'z0': z0})
return surv
def import_survey(filename, structure_name="fid", expt_name="sv1"):
r""" Loads an icosmo survey from a fiducial structure stored in an
idl save file into a cosmicpy survey.
Parameters
----------
filename : str
Name of the idl save from which to load the cosmology.
structure_name : str, optional
Name of the icosmo fiducial structure stored in the save file.
Returns
-------
surv : survey
cosmicpy survey corresponding to the icosmo input.
"""
icosmo_file = readsav(filename)
icosmo = icosmo_file.get(structure_name)['expt'][0]
nzbins = icosmo[expt_name][0]['n_zbin'][0]
zerror = icosmo[expt_name][0]['zerror'][0]
ng = icosmo[expt_name][0]['ng'][0]
a_survey = icosmo[expt_name][0]['a_survey'][0]
dndztype = icosmo[expt_name][0]['dndztype'][0]
if dndztype != b"smail":
print("unsupported galaxy distribution")
return None
a = icosmo[expt_name][0]['dndzp'][0][0]
b = icosmo[expt_name][0]['dndzp'][0][1]
zmed = icosmo[expt_name][0]['z_med'][0]
biastype = icosmo[expt_name][0]['biastype'][0]
if biastype == b'bias0':
btype = 'constant'
elif biastype == b'bias1':
btype = 'sqrt'
else:
print("unsupported bias type")
return None
# Compute fsky
fsky = a_survey / (180. / pi)**2 / (4.0 * pi)
# find z0 corresponding to zmedian
def med_smail(z0):
smail = lambda z: z**a * exp(-(z / z0)**b)
smail_norm = romberg(smail, 0, 5)
smailn = lambda z: (z**a * exp(-(z / z0)**b)) / smail_norm
f = lambda x: romberg(smailn, 0, x) - 0.5
return brentq(f, 0.01, 5) - zmed
z0 = brentq(med_smail, 0.01, 3)
surv = cosmicpy.survey(nzbins=nzbins,
ngal=ng,
zphot_sig=zerror,
fsky=fsky,
biastype=btype,
nzparams={
'type': 'smail',
'a': a,
'b': b,
'z0': z0
})
return surv
