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Fix inconsistent use of tabs and spaces in indentation #403

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79cfe36
Fix inconsistent use of tabs and spaces in indentation
Zhi-ChaoZhao Aug 7, 2025
6ab243d
Fix inconsistent use of tabs and spaces in indentation
Zhi-ChaoZhao Aug 7, 2025
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212 changes: 106 additions & 106 deletions montepython/likelihoods/ska1_lensing/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,14 +37,14 @@ def __init__(self, path, data, command_line):
if (self.use_halofit):
self.need_cosmo_arguments(data, {'non linear':'halofit'})

# Warn if theoretical error and linear cutoff are requested
if (self.use_lmax_lincut and self.theoretical_error!=0):
warnings.warn("A lmax cutoff infered from kmax and a theoretical error are requested. This combination is not implemented and in most cases not necessary as the theoretical error should induce a cutoff by itself.")
# Warn if theoretical error and linear cutoff are requested
if (self.use_lmax_lincut and self.theoretical_error!=0):
warnings.warn("A lmax cutoff infered from kmax and a theoretical error are requested. This combination is not implemented and in most cases not necessary as the theoretical error should induce a cutoff by itself.")

# Define array of l values, and initialize them
# It is a logspace
# find nlmax in order to reach lmax with logarithmic steps dlnl
self.nlmax = np.int(np.log(self.lmax/self.lmin)/self.dlnl)+1
self.nlmax = int(np.log(self.lmax/self.lmin)/self.dlnl)+1
# redefine slightly dlnl so that the last point is always exactly lmax
self.dlnl = np.log(self.lmax/self.lmin)/(self.nlmax-1)
self.l = self.lmin*np.exp(self.dlnl*np.arange(self.nlmax))
Expand All @@ -69,7 +69,7 @@ def __init__(self, path, data, command_line):
# first value is already correctly set to 0.
self.z_bin_edge = np.zeros(self.nbin+1, 'float64')

total_count = 0.
total_count = 0.
for Bin in xrange(self.nbin-1):
bin_count = 0.
z = self.z_bin_edge[Bin]
Expand All @@ -79,23 +79,23 @@ def __init__(self, path, data, command_line):
bin_count += 0.5*(gd_1+gd_2)*self.dz
z += self.dz
self.z_bin_edge[Bin+1] = z
total_count += bin_count
total_count += bin_count
self.z_bin_edge[self.nbin] = self.zmax

# Fill array of discrete z values
self.z = np.linspace(0, self.zmax, num=self.nzmax)

# check normalizations:
# check normalizations:
#print("n_tot = %s" % (n_tot))
#int_nz = self.nzmax
#int_z = np.linspace(0, self.zmax, num=int_nz)
#DDD = np.zeros(int_nz, 'float64')
#for nz in xrange(self.nzmax):
# #for nz2 in xrange(int_nz):
# # DDD[nz2] = self.photo_z_distribution(self.z[nz], int_z[nz2],False)
#int_nz = self.nzmax
#int_z = np.linspace(0, self.zmax, num=int_nz)
#DDD = np.zeros(int_nz, 'float64')
#for nz in xrange(self.nzmax):
# #for nz2 in xrange(int_nz):
# # DDD[nz2] = self.photo_z_distribution(self.z[nz], int_z[nz2],False)
# DDD = self.photo_z_distribution(self.z[nz], int_z)
# D_tot = scipy.integrate.trapz(DDD,int_z)
# print("%s\t%s\t%s" % (self.z[nz], self.galaxy_distribution(self.z[nz]), D_tot ))
# print("%s\t%s\t%s" % (self.z[nz], self.galaxy_distribution(self.z[nz]), D_tot ))

# Fill distribution for each bin (convolving with photo_z distribution)
self.eta_z = np.zeros((self.nzmax, self.nbin), 'float64')
Expand Down Expand Up @@ -157,7 +157,7 @@ def galaxy_distribution(self, z, array=False):
If the array flag is set to True, z is then interpretated as an array,
and not as a single value.

Replaced by Tim Sprenger due to switch from euclid to ska
Replaced by Tim Sprenger due to switch from euclid to ska
"""

z0 = self.par_zm/self.par_a
Expand All @@ -170,58 +170,58 @@ def galaxy_distribution(self, z, array=False):
return galaxy_dist

def photo_z_distribution(self, z, zph, array=True):
"""
Photo z distribution

If the array flag is set to True, zph is then interpretated as an array,
and not as a single value.

Replaced by Tim Sprenger due to switch from euclid to ska
"""

# Note: you must normalize it yourself to one if you want to get nice
# plots of the galaxy distribution function in each bin (otherwise, the
# spectra will remain correct, but each D_i(x) will loot strangely
# normalized when compared to the original D(z)
if not array:
if (z<=self.z_spec):
photo_z_dist = (1.-self.f_spec)*math.exp(
-0.5*((z-zph)/self.sigma_phot/(1.+z))**2)/self.sigma_phot/(
1.+z)/math.sqrt(2.*math.pi)
if (z==zph):
photo_z_dist += self.f_spec/self.zmax*(self.nzmax-1.)
if (z==0.):
photo_z_dist += self.f_spec/self.zmax*(self.nzmax-1.)
elif (z<=self.z_phot):
photo_z_dist = math.exp(
-0.5*((z-zph)/self.sigma_phot/(1.+z))**2)/self.sigma_phot/(
1.+z)/math.sqrt(2.*math.pi)
else:
photo_z_dist = math.exp(
-0.5*((z-zph)/self.sigma_noz/(1.+z))**2)/self.sigma_noz/(
1.+z)/math.sqrt(2.*math.pi)
else:
if (z<=self.z_spec):
photo_z_dist = (1.-self.f_spec)*np.exp(
-0.5*((z-zph)/self.sigma_phot/(1.+z))**2)/self.sigma_phot/(
1.+z)/math.sqrt(2.*math.pi)
for index_z in xrange(len(zph)):
if (z==zph[index_z]):
photo_z_dist[index_z] += self.f_spec/self.zmax*(self.nzmax-1.)
if (z==0.):
photo_z_dist[index_z] += self.f_spec/self.zmax*(self.nzmax-1.)
break
elif (z<=self.z_phot):
photo_z_dist = np.exp(
-0.5*((z-zph)/self.sigma_phot/(1.+z))**2)/self.sigma_phot/(
1.+z)/math.sqrt(2.*math.pi)
else:
photo_z_dist = np.exp(
-0.5*((z-zph)/self.sigma_noz/(1.+z))**2)/self.sigma_noz/(
1.+z)/math.sqrt(2.*math.pi)


return photo_z_dist
"""
Photo z distribution

If the array flag is set to True, zph is then interpretated as an array,
and not as a single value.

Replaced by Tim Sprenger due to switch from euclid to ska
"""

# Note: you must normalize it yourself to one if you want to get nice
# plots of the galaxy distribution function in each bin (otherwise, the
# spectra will remain correct, but each D_i(x) will loot strangely
# normalized when compared to the original D(z)
if not array:
if (z<=self.z_spec):
photo_z_dist = (1.-self.f_spec)*math.exp(
-0.5*((z-zph)/self.sigma_phot/(1.+z))**2)/self.sigma_phot/(
1.+z)/math.sqrt(2.*math.pi)
if (z==zph):
photo_z_dist += self.f_spec/self.zmax*(self.nzmax-1.)
if (z==0.):
photo_z_dist += self.f_spec/self.zmax*(self.nzmax-1.)
elif (z<=self.z_phot):
photo_z_dist = math.exp(
-0.5*((z-zph)/self.sigma_phot/(1.+z))**2)/self.sigma_phot/(
1.+z)/math.sqrt(2.*math.pi)
else:
photo_z_dist = math.exp(
-0.5*((z-zph)/self.sigma_noz/(1.+z))**2)/self.sigma_noz/(
1.+z)/math.sqrt(2.*math.pi)
else:
if (z<=self.z_spec):
photo_z_dist = (1.-self.f_spec)*np.exp(
-0.5*((z-zph)/self.sigma_phot/(1.+z))**2)/self.sigma_phot/(
1.+z)/math.sqrt(2.*math.pi)
for index_z in xrange(len(zph)):
if (z==zph[index_z]):
photo_z_dist[index_z] += self.f_spec/self.zmax*(self.nzmax-1.)
if (z==0.):
photo_z_dist[index_z] += self.f_spec/self.zmax*(self.nzmax-1.)
break
elif (z<=self.z_phot):
photo_z_dist = np.exp(
-0.5*((z-zph)/self.sigma_phot/(1.+z))**2)/self.sigma_phot/(
1.+z)/math.sqrt(2.*math.pi)
else:
photo_z_dist = np.exp(
-0.5*((z-zph)/self.sigma_noz/(1.+z))**2)/self.sigma_noz/(
1.+z)/math.sqrt(2.*math.pi)


return photo_z_dist

def loglkl(self, cosmo, data):

Expand Down Expand Up @@ -251,9 +251,9 @@ def loglkl(self, cosmo, data):
fun[1:]+fun[:-1])*(self.r[nr+1:]-self.r[nr:-1]))
g[nr, Bin] *= 2.*self.r[nr]*(1.+self.z[nr])

# compute the maximum l where most contributions are linear
# as a function of the lower bin number
if self.use_lmax_lincut:
# compute the maximum l where most contributions are linear
# as a function of the lower bin number
if self.use_lmax_lincut:
lintegrand_lincut_o = np.zeros((self.nzmax, self.nbin, self.nbin), 'float64')
lintegrand_lincut_u = np.zeros((self.nzmax, self.nbin, self.nbin), 'float64')
l_lincut = np.zeros((self.nbin, self.nbin), 'float64')
Expand All @@ -276,26 +276,26 @@ def loglkl(self, cosmo, data):
lintegrand_lincut_u[1:, Bin1, Bin2] +
lintegrand_lincut_u[:-1, Bin1, Bin2])*(
self.r[1:]-self.r[:-1]))
z_peak = np.zeros((self.nbin, self.nbin), 'float64')
z_peak = np.zeros((self.nbin, self.nbin), 'float64')
for Bin1 in xrange(self.nbin):
for Bin2 in xrange(Bin1,self.nbin):
z_peak[Bin1, Bin2] = self.zmax
z_peak[Bin1, Bin2] = self.zmax
for index_z in xrange(self.nzmax):
if (self.r[index_z]>l_lincut[Bin1, Bin2]):
z_peak[Bin1, Bin2] = self.z[index_z]
break
if (self.r[index_z]>l_lincut[Bin1, Bin2]):
z_peak[Bin1, Bin2] = self.z[index_z]
break
if self.use_zscaling:
l_lincut[Bin1, Bin2] *= self.kmax_hMpc*cosmo.h()*pow(1.+z_peak[Bin1, Bin2],2./(2.+cosmo.n_s()))
l_lincut[Bin1, Bin2] *= self.kmax_hMpc*cosmo.h()*pow(1.+z_peak[Bin1, Bin2],2./(2.+cosmo.n_s()))
else:
l_lincut[Bin1, Bin2] *= self.kmax_hMpc*cosmo.h()
l_lincut_mean[Bin1] = np.sum(l_lincut[Bin1, :])/(self.nbin-Bin1)
l_lincut[Bin1, Bin2] *= self.kmax_hMpc*cosmo.h()
l_lincut_mean[Bin1] = np.sum(l_lincut[Bin1, :])/(self.nbin-Bin1)

#for Bin1 in xrange(self.nbin):
#for Bin2 in xrange(Bin1,self.nbin):
#print("%s\t%s\t%s\t%s" % (Bin1, Bin2, l_lincut[Bin1, Bin2], l_lincut_mean[Bin1]))
#for Bin1 in xrange(self.nbin):
#for Bin2 in xrange(Bin1,self.nbin):
#print("%s\t%s\t%s\t%s" % (Bin1, Bin2, l_lincut[Bin1, Bin2], l_lincut_mean[Bin1]))

#for nr in xrange(1, self.nzmax-1):
# print("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" % (self.z[nr], g[nr, 0], g[nr, 1], g[nr, 2], g[nr, 3], g[nr, 4], g[nr, 5], g[nr, 6], g[nr, 7], g[nr, 8], g[nr, 9]))
#for nr in xrange(1, self.nzmax-1):
# print("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" % (self.z[nr], g[nr, 0], g[nr, 1], g[nr, 2], g[nr, 3], g[nr, 4], g[nr, 5], g[nr, 6], g[nr, 7], g[nr, 8], g[nr, 9]))

# Get power spectrum P(k=l/r,z(r)) from cosmological module
kmin_in_inv_Mpc = self.k_min_h_by_Mpc * cosmo.h()
Expand All @@ -318,7 +318,7 @@ def loglkl(self, cosmo, data):
else:
pk[index_l, index_z] = cosmo.pk(self.l[index_l]/self.r[index_z], self.z[index_z])

#print("%s\t%s\t%s" %(self.l[index_l], self.z[index_z], pk[index_l, index_z]))
#print("%s\t%s\t%s" %(self.l[index_l], self.z[index_z], pk[index_l, index_z]))

# Recover the non_linear scale computed by halofit. If no scale was
# affected, set the scale to one, and make sure that the nuisance
Expand All @@ -329,15 +329,15 @@ def loglkl(self, cosmo, data):
else:
k_sigma = cosmo.nonlinear_scale(self.z, self.nzmax)

if not (cosmo.nonlinear_method == 0):
k_sigma_problem = False
for index_z in xrange(self.nzmax-1):
if (k_sigma[index_z+1]<k_sigma[index_z]) or (k_sigma[index_z+1]>2.5):
k_sigma[index_z+1] = 2.5
k_sigma_problem = True
#print("%s\t%s" % (k_sigma[index_z], self.z[index_z]))
if k_sigma_problem:
warnings.warn("There were unphysical (decreasing in redshift or exploding) values of k_sigma (=cosmo.nonlinear_scale(...)). To proceed they were set to 2.5, the highest scale that seems to be stable.")
if not (cosmo.nonlinear_method == 0):
k_sigma_problem = False
for index_z in xrange(self.nzmax-1):
if (k_sigma[index_z+1]<k_sigma[index_z]) or (k_sigma[index_z+1]>2.5):
k_sigma[index_z+1] = 2.5
k_sigma_problem = True
#print("%s\t%s" % (k_sigma[index_z], self.z[index_z]))
if k_sigma_problem:
warnings.warn("There were unphysical (decreasing in redshift or exploding) values of k_sigma (=cosmo.nonlinear_scale(...)). To proceed they were set to 2.5, the highest scale that seems to be stable.")

# Define the alpha function, that will characterize the theoretical
# uncertainty. Chosen to be 0.001 at low k, raise between 0.1 and 0.2
Expand Down Expand Up @@ -504,7 +504,7 @@ def loglkl(self, cosmo, data):
# TODO parallelize this
for index, ell in enumerate(ells):

if self.use_lmax_lincut:
if self.use_lmax_lincut:
CutBin = -1
for zBin in xrange(self.nbin):
if (ell<l_lincut_mean[zBin]):
Expand All @@ -514,7 +514,7 @@ def loglkl(self, cosmo, data):
break
if (CutBin==-1):
break
else:
else:
det_theory = np.linalg.det(Cov_theory[index, :, :])
det_observ = np.linalg.det(Cov_observ[index, :, :])

Expand Down Expand Up @@ -549,14 +549,14 @@ def loglkl(self, cosmo, data):
newCov[:, i] = Cov_observ[index,:, i]#MArchi#newCov[:, i] = Cov_observ[:, i]
det_theory_plus_error_cross_obs += np.linalg.det(
newCov)
try:
function_vector[k] = (2.*ell+1.)*self.fsky*(det_theory_plus_error_cross_obs/det_theory_plus_error + math.log(det_theory_plus_error/det_observ) - self.nbin ) + dof*vector[k]**2
except ValueError:
warnings.warn("ska1_lensing: Could not evaluate chi2 including theoretical error with the current parameters. The corresponding chi2 is now set to nan!")
break
break
break
chi2 = np.nan
try:
function_vector[k] = (2.*ell+1.)*self.fsky*(det_theory_plus_error_cross_obs/det_theory_plus_error + math.log(det_theory_plus_error/det_observ) - self.nbin ) + dof*vector[k]**2
except ValueError:
warnings.warn("ska1_lensing: Could not evaluate chi2 including theoretical error with the current parameters. The corresponding chi2 is now set to nan!")
break
break
break
chi2 = np.nan


# Computing first
Expand All @@ -582,20 +582,20 @@ def loglkl(self, cosmo, data):


else:
if self.use_lmax_lincut:
if self.use_lmax_lincut:
det_cross = 0.
for i in xrange(0,self.nbin-CutBin):
newCov = np.copy(Cov_theory[index, CutBin:, CutBin:])
newCov[:, i] = Cov_observ[index, CutBin:, CutBin+i]
det_cross += np.linalg.det(newCov)
else:
else:
det_cross = 0.
for i in xrange(self.nbin):
newCov = np.copy(Cov_theory[index, :, :])
newCov[:, i] = Cov_observ[index, :, i]
det_cross += np.linalg.det(newCov)

if self.use_lmax_lincut:
if self.use_lmax_lincut:
chi2 += (2.*ell+1.)*self.fsky*(det_cross/det_theory + math.log(det_theory/det_observ) - self.nbin+CutBin)
else:
chi2 += (2.*ell+1.)*self.fsky*(det_cross/det_theory + math.log(det_theory/det_observ) - self.nbin)
Expand Down
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