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Fix numpy API deprecations np.trapz -> np.trapezoid #81

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Fix numpy API deprecations np.trapz -> np.trapezoid #81

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f03687d
Fix numpy API deprecations np.trapz -> np.trapezoid
xgarrido Jul 5, 2024
52136eb
possibility to call trapezoid for numpy >= 2.0
sgiardie Jul 5, 2024
f7be1c4
np.trapz -> trapz
sgiardie Jul 5, 2024
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2 changes: 1 addition & 1 deletion mflike/mflike.py
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Original file line number Diff line number Diff line change
Expand Up @@ -165,7 +165,7 @@ def logp(self, **params_values):
return self.loglike(cl, **params_values_nocosmo)

def loglike(self, cl, **params_values_nocosmo):
"""
r"""
Computes the gaussian log-likelihood

:param cl: the dictionary of theory + foregrounds :math:`D_{\ell}`
Expand Down
17 changes: 12 additions & 5 deletions mflike/theoryforge.py
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Original file line number Diff line number Diff line change
Expand Up @@ -72,6 +72,11 @@
from itertools import product

import numpy as np
if np.__version__ >= "2.0.0":
from numpy import trapezoid as trapz
else:
from numpy import trapz

from cobaya.log import LoggedError


Expand Down Expand Up @@ -214,7 +219,7 @@ def _bandpass_construction(self, **params):
tranb = _cmb2bb(nub)
# normalization integral to be evaluated at the shifted freqs
# in order to have cmb component calibrated to 1
tranb_norm = np.trapz(_cmb2bb(nub), nub)
tranb_norm = trapz(_cmb2bb(nub), nub)
self.bandint_freqs.append([nub, tranb / tranb_norm])
# in case we don't want to do band integration, e.g. when we have multifreq bandpass in sacc file
if self.bandint_nsteps == 1:
Expand All @@ -229,7 +234,7 @@ def _bandpass_construction(self, **params):
data_are_monofreq = True
self.bandint_freqs.append(nub[0])
else:
trans_norm = np.trapz(bp * _cmb2bb(nub), nub)
trans_norm = trapz(bp * _cmb2bb(nub), nub)
trans = bp / trans_norm * _cmb2bb(nub)
self.bandint_freqs.append([nub, trans])

Expand Down Expand Up @@ -336,15 +341,17 @@ def _init_foreground_model(self):
self.ksz = fgc.FactorizedCrossSpectrum(fgf.ConstantSED(), fgp.kSZ_bat())
self.cibp = fgc.FactorizedCrossSpectrum(fgf.ModifiedBlackBody(), fgp.PowerLaw())
self.radio = fgc.FactorizedCrossSpectrum(fgf.PowerLaw(), fgp.PowerLaw())
self.tsz = fgc.FactorizedCrossSpectrum(fgf.ThermalSZ(), fgp.PowerLawRescaledTemplate(tsz_file))
self.tsz = fgc.FactorizedCrossSpectrum(
fgf.ThermalSZ(), fgp.PowerLawRescaledTemplate(tsz_file)
)
self.cibc = fgc.FactorizedCrossSpectrum(fgf.CIB(), fgp.PowerSpectrumFromFile(cibc_file))
self.dust = fgc.FactorizedCrossSpectrum(fgf.ModifiedBlackBody(), fgp.PowerLaw())

tsz_cib_sed = fgf.Join(fgf.ThermalSZ(), fgf.CIB())
tsz_cib_power_spectra = [
fgp.PowerLawRescaledTemplate(tsz_file),
fgp.PowerSpectrumFromFile(cibc_file),
fgp.PowerSpectrumFromFile(cibxtsz_file)
fgp.PowerSpectrumFromFile(cibxtsz_file),
]
tsz_cib_cl = fgp.PowerSpectraAndCovariance(*tsz_cib_power_spectra)

Expand Down Expand Up @@ -439,7 +446,7 @@ def _get_foreground_model(self, ell=None, freqs_order=None, **fg_params):
"ell": ell,
"ell_0": ell_0,
"amp": fg_params["a_tSZ"],
"alpha": fg_params["alpha_tSZ"]
"alpha": fg_params["alpha_tSZ"],
},
{"ell": ell, "ell_0": ell_0, "amp": fg_params["a_c"]},
{
Expand Down