From 714f7319b39a5ad088cdac279c66d1333c2ee19f Mon Sep 17 00:00:00 2001 From: Jaime Salcido Date: Sun, 19 Apr 2026 11:49:11 +0100 Subject: [PATCH 01/12] Add SPkNonLinear model and related tests for baryon suppression --- camb/__init__.py | 2 +- camb/nonlinear.py | 129 ++++++++++++++++++++++++++- camb/tests/spk_test.py | 125 ++++++++++++++++++++++++++ docs/source/nonlinear.rst | 4 + fortran/Makefile_main | 2 +- fortran/SPkNonLinear.f90 | 178 ++++++++++++++++++++++++++++++++++++++ fortran/spk_model.f90 | 102 ++++++++++++++++++++++ 7 files changed, 538 insertions(+), 4 deletions(-) create mode 100644 camb/tests/spk_test.py create mode 100644 fortran/SPkNonLinear.f90 create mode 100644 fortran/spk_model.f90 diff --git a/camb/__init__.py b/camb/__init__.py index 138edc6c..960f7dad 100644 --- a/camb/__init__.py +++ b/camb/__init__.py @@ -43,6 +43,6 @@ from .initialpower import InitialPowerLaw, SplinedInitialPower from .mathutils import threej from .model import CAMBparams, TransferParams -from .nonlinear import Halofit +from .nonlinear import Halofit, SPkNonLinear from .reionization import ExpReionization, TanhReionization from .results import CAMBdata, ClTransferData, MatterTransferData diff --git a/camb/nonlinear.py b/camb/nonlinear.py index b6d17732..9abf49da 100644 --- a/camb/nonlinear.py +++ b/camb/nonlinear.py @@ -1,9 +1,10 @@ -from ctypes import POINTER, byref, c_double, c_int +import math +from ctypes import POINTER, byref, c_bool, c_double, c_int import numpy as np from numpy.ctypeslib import ndpointer -from .baseconfig import F2003Class, fortran_class, numpy_1d +from .baseconfig import AllocatableObject, CAMBValueError, F2003Class, fortran_class, numpy_1d class NonLinearModel(F2003Class): @@ -93,6 +94,130 @@ def set_params( self.HMCode_logT_AGN = HMCode_logT_AGN +@fortran_class +class SPkNonLinear(NonLinearModel): + """ + SP(k) baryon suppression model applied on top of a base non-linear model. + + The base model is evaluated first (Halofit by default), then SP(k) + suppression is applied multiplicatively as + ``sqrt(P_NL/P_L) -> sqrt(P_NL/P_L) * sqrt(SPk_suppression)``. + """ + + _fields_ = ( + ("BaseModel", AllocatableObject(NonLinearModel)), + ("SPk_feedback", c_bool, "Enable SP(k) suppression"), + ("SPk_SO", c_int, "SP(k) spherical overdensity (200 or 500)"), + ( + "SPk_relation_kind", + c_int, + "SP(k) relation kind: 1=power_law, 2=cosmo_power_law, 3=double_power_law", + ), + ("SPk_fb_a", c_double, "Power-law relation normalization"), + ("SPk_fb_pow", c_double, "Power-law relation exponent"), + ("SPk_fb_pivot", c_double, "Power-law relation pivot mass [M_sun]"), + ("SPk_alpha", c_double, "Relation alpha parameter (kinds 2/3)"), + ("SPk_beta", c_double, "Relation beta parameter (kinds 2/3)"), + ("SPk_gamma", c_double, "Relation gamma parameter (kinds 2/3)"), + ("SPk_epsilon", c_double, "Relation epsilon parameter (kind 3)"), + ("SPk_m_pivot", c_double, "Relation pivot mass [M_sun] (kind 3)"), + ) + + _fortran_class_module_ = "SPkNonLinear" + _fortran_class_name_ = "TSPkNonLinear" + + def __init__(self, **kwargs): + super().__init__() + self.BaseModel = Halofit() + self.set_params(**kwargs) + + def _validate(self): + if self.SPk_SO not in (200, 500): + raise CAMBValueError("SPk_SO must be 200 or 500") + if self.SPk_relation_kind not in (1, 2, 3): + raise CAMBValueError( + "SPk_relation_kind must be 1 (power_law), 2 (cosmo_power_law), or 3 (double_power_law)" + ) + if self.SPk_relation_kind == 1 and self.SPk_fb_pivot <= 0: + raise CAMBValueError("SPk_fb_pivot must be > 0 for power_law relation") + if self.SPk_relation_kind == 3 and self.SPk_m_pivot <= 0: + raise CAMBValueError("SPk_m_pivot must be > 0 for double_power_law relation") + + if self.SPk_feedback and isinstance(self.BaseModel, Halofit): + if isinstance(self.BaseModel.halofit_version, str): + halofit_version_int = halofit_version_names[self.BaseModel.halofit_version] + else: + halofit_version_int = int(self.BaseModel.halofit_version) + if halofit_version_int == halofit_version_names[halofit_mead2020_feedback]: + raise CAMBValueError( + "SP(k) is not compatible with halofit_version='mead2020_feedback'. " + "Use halofit_version='mead2020' (or another non-feedback option) when enabling SPk_feedback." + ) + + hmcode_2015_2016_versions = { + halofit_version_names[halofit_mead], + halofit_version_names[halofit_mead2015], + halofit_version_names[halofit_mead2016], + } + if halofit_version_int in hmcode_2015_2016_versions and ( + (not math.isclose(self.BaseModel.HMCode_A_baryon, 3.13, rel_tol=0.0, abs_tol=1e-12)) + or (not math.isclose(self.BaseModel.HMCode_eta_baryon, 0.603, rel_tol=0.0, abs_tol=1e-12)) + ): + raise CAMBValueError( + "SP(k) cannot be combined with HMCode_A_baryon/HMCode_eta_baryon baryonic corrections in HMCode 2015/2016" + ) + + def set_params( + self, + base_model=None, + SPk_feedback=False, + SPk_SO=200, + SPk_relation_kind=1, + SPk_fb_a=1.0, + SPk_fb_pow=0.0, + SPk_fb_pivot=1.0, + SPk_alpha=0.0, + SPk_beta=0.0, + SPk_gamma=0.0, + SPk_epsilon=0.0, + SPk_m_pivot=1.0, + ): + """ + Set SP(k) suppression parameters. + + :param base_model: Base non-linear model to wrap. Defaults to :class:`Halofit`. + :param SPk_feedback: Enable SP(k) suppression. + :param SPk_SO: Spherical overdensity calibration, 200 or 500. + :param SPk_relation_kind: 1=power_law, 2=cosmo_power_law, 3=double_power_law. + :param SPk_fb_a: Power-law relation normalization (kind 1). + :param SPk_fb_pow: Power-law relation exponent (kind 1). + :param SPk_fb_pivot: Power-law relation pivot mass [M_sun] (kind 1). + :param SPk_alpha: Relation alpha parameter (kinds 2/3). + :param SPk_beta: Relation beta parameter (kinds 2/3). + :param SPk_gamma: Relation gamma parameter (kinds 2/3). + :param SPk_epsilon: Relation epsilon parameter (kind 3). + :param SPk_m_pivot: Relation pivot mass [M_sun] (kind 3). + """ + if base_model is not None: + self.BaseModel = base_model + elif self.BaseModel is None: + self.BaseModel = Halofit() + + self.SPk_feedback = SPk_feedback + self.SPk_SO = SPk_SO + self.SPk_relation_kind = SPk_relation_kind + self.SPk_fb_a = SPk_fb_a + self.SPk_fb_pow = SPk_fb_pow + self.SPk_fb_pivot = SPk_fb_pivot + self.SPk_alpha = SPk_alpha + self.SPk_beta = SPk_beta + self.SPk_gamma = SPk_gamma + self.SPk_epsilon = SPk_epsilon + self.SPk_m_pivot = SPk_m_pivot + self._validate() + return self + + @fortran_class class SecondOrderPK(NonLinearModel): """ diff --git a/camb/tests/spk_test.py b/camb/tests/spk_test.py new file mode 100644 index 00000000..f311b019 --- /dev/null +++ b/camb/tests/spk_test.py @@ -0,0 +1,125 @@ +import os +import sys +import unittest + +import numpy as np + +try: + import camb +except ImportError: + sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))) + import camb + + +def _poly2(x, c0, c1, c2): + return c2 * x * x + c1 * x + c0 + + +def _spk_params(so, z): + x = 1.0 + z + if so == 500: + return { + "a": _poly2(x, 14.783423122120318, -0.999062404857228, 0.12062854541689262), + "b": _poly2(x, 14.620528368613265, -0.9136466201011957, 0.10835389086945699), + "g": _poly2(x, 0.9671320682693298, -0.03185388045484575, 0.02650236152450093), + "lambda_a": _poly2(x, 0.019349810078190303, -0.007410668383424459, 0.0008334762393555539), + "lambda_b": _poly2(x, 2.9566773924238143, 0.6205340408676114, -0.001928273640110775), + "mu_a": _poly2(x, 0.715853343781141, -0.19276613600825665, 0.04948240117059147), + "mu_b": _poly2(x, 3.385355123440431, 0.9658906605139421, -0.06825861100375574), + "mu_c": _poly2(x, 4.457257708010122, -2.191853871334233, 0.45457701107254733), + "nu_a": _poly2(x, 478.86477329610375, 429.88795783439946, 249.25655627821902), + "nu_b": _poly2(x, -11.227459319819815, -0.5581080204509223, 0.4489962047114509), + "nu_c": _poly2(x, 3.499449440557995, -0.08488559389068073, -0.0923847866118189), + } + return { + "a": _poly2(x, 15.24311120000861, -1.2436699435560352, 0.14837558774401766), + "b": _poly2(x, 14.969187892657688, -1.0993025612653198, 0.12905587245129102), + "g": _poly2(x, 0.8000441576980428, -0.01715621131893159, 0.06131887249968379), + "lambda_a": _poly2(x, 0.02178116280689233, -0.0077564325654746955, 0.0007915576054589781), + "lambda_b": _poly2(x, 3.0878286643613437, 0.4529677646796634, 0.001552571083240605), + "mu_a": _poly2(x, 0.6930259177449359, -0.16913553700233935, 0.04263185199898842), + "mu_b": _poly2(x, 3.161914061444856, 0.8616834297321924, 0.011346427353554053), + "mu_c": _poly2(x, 5.532188503256583, -3.0864672185252537, 0.5083422518560442), + "nu_a": _poly2(x, 413.00988701513904, 311.63957063032285, 37.89105940901369), + "nu_b": _poly2(x, -11.243859405779181, -0.34421412616421965, 0.3343548325485801), + "nu_c": _poly2(x, 3.476463891168505, -0.018333059687988575, -0.08276237963970698), + } + + +def _spk_sup_power_law(kh, z, so=200, fb_a=0.4, fb_pow=0.2, fb_pivot=1e14): + p = _spk_params(so, z) + best_mass = p["a"] - (p["a"] - p["b"]) * (kh ** p["g"]) + m_opt = 10.0**best_mass + fb = fb_a * ((m_opt / fb_pivot) ** fb_pow) + + x = np.log10(kh) + x0 = 1.0 + p["lambda_a"] * np.exp(p["lambda_b"] * x) + x1 = p["mu_a"] + ((1.0 - p["mu_a"]) / (1.0 + np.exp(p["mu_b"] * x + p["mu_c"]))) + x2 = p["nu_a"] * np.exp(-0.5 * ((x - p["nu_b"]) / p["nu_c"]) ** 2) + sup = x0 - (x0 - x1) * np.exp(-x2 * fb) + return np.maximum(sup, 1e-6) + + +class SPkTest(unittest.TestCase): + def _get_pk(self, model_obj, z=0.5): + pars = camb.CAMBparams() + pars.set_cosmology(H0=67.5, ombh2=0.02237, omch2=0.12, mnu=0.06) + pars.InitPower.set_params(As=2.1e-9, ns=0.965) + pars.set_matter_power(redshifts=[z], kmax=6.0) + pars.NonLinear = camb.model.NonLinear_both + pars.NonLinearModel = model_obj + results = camb.get_results(pars) + k, _z, pk = results.get_matter_power_spectrum(minkh=1e-2, maxkh=5.0, npoints=28) + return k, pk[0] + + def test_spk_invalid_params(self): + model = camb.SPkNonLinear() + with self.assertRaises(camb.CAMBValueError): + model.set_params(SPk_SO=300) + with self.assertRaises(camb.CAMBValueError): + model.set_params(SPk_relation_kind=99) + with self.assertRaises(camb.CAMBValueError): + model.set_params(SPk_relation_kind=1, SPk_fb_pivot=0.0) + + def test_spk_disabled_matches_base(self): + base = camb.Halofit() + base.set_params(halofit_version="mead2020") + k_base, pk_base = self._get_pk(base) + + spk = camb.SPkNonLinear() + spk.BaseModel.set_params(halofit_version="mead2020") + spk.set_params(SPk_feedback=False) + k_spk, pk_spk = self._get_pk(spk) + + self.assertTrue(np.allclose(k_base, k_spk, rtol=0, atol=0)) + self.assertTrue(np.allclose(pk_base, pk_spk, rtol=2e-12, atol=1e-14)) + + def test_spk_power_law_matches_reference(self): + fb_a = 0.4 + fb_pow = 0.2 + fb_pivot = 1e14 + z = 0.5 + + base = camb.Halofit() + base.set_params(halofit_version="mead2020") + k, pk_base = self._get_pk(base, z=z) + + spk = camb.SPkNonLinear() + spk.BaseModel.set_params(halofit_version="mead2020") + spk.set_params( + SPk_feedback=True, + SPk_SO=200, + SPk_relation_kind=1, + SPk_fb_a=fb_a, + SPk_fb_pow=fb_pow, + SPk_fb_pivot=fb_pivot, + ) + _, pk_spk = self._get_pk(spk, z=z) + + expected_sup = _spk_sup_power_law(k, z, so=200, fb_a=fb_a, fb_pow=fb_pow, fb_pivot=fb_pivot) + measured_sup = pk_spk / pk_base + self.assertTrue(np.allclose(measured_sup, expected_sup, rtol=1e-5, atol=1e-8)) + + +if __name__ == "__main__": + unittest.main() diff --git a/docs/source/nonlinear.rst b/docs/source/nonlinear.rst index fe3fcd3a..4022d03e 100644 --- a/docs/source/nonlinear.rst +++ b/docs/source/nonlinear.rst @@ -8,6 +8,10 @@ Non-linear models :show-inheritance: :members: +.. autoclass:: camb.nonlinear.SPkNonLinear + :show-inheritance: + :members: + .. autoclass:: camb.nonlinear.ExternalNonLinearRatio :show-inheritance: :members: diff --git a/fortran/Makefile_main b/fortran/Makefile_main index 745c6477..aacfdf0b 100644 --- a/fortran/Makefile_main +++ b/fortran/Makefile_main @@ -5,7 +5,7 @@ POWERSPECTRUM_FILES ?= InitialPower REIONIZATION_FILES ?= reionization RECOMBINATION_FILES ?= recfast -NONLINEAR_FILES ?= halofit SecondOrderPK ExternalNonLinearRatio +NONLINEAR_FILES ?= halofit spk_model SPkNonLinear SecondOrderPK ExternalNonLinearRatio DARKENERGY_FILES ?= DarkEnergyFluid DarkEnergyPPF PowellMinimize DarkEnergyQuintessence BISPECTRUM ?= SeparableBispectrum diff --git a/fortran/SPkNonLinear.f90 b/fortran/SPkNonLinear.f90 new file mode 100644 index 00000000..c8a7ffbb --- /dev/null +++ b/fortran/SPkNonLinear.f90 @@ -0,0 +1,178 @@ +module SPkNonLinear +! Wrapper non-linear model: apply SP(k) suppression on top of a base non-linear model. +use results +use NonLinear, only: THalofit, halofit_mead, halofit_mead2015, halofit_mead2016, halofit_mead2020_feedback +use SPkModel +use config +implicit none +private + +type, extends(TNonLinearModel) :: TSPkNonLinear + class(TNonLinearModel), allocatable :: BaseModel + logical :: SPk_feedback = .false. + integer :: SPk_SO = 200 + integer :: SPk_relation_kind = SPk_rel_power_law + real(dl) :: SPk_fb_a = 1.0_dl + real(dl) :: SPk_fb_pow = 0.0_dl + real(dl) :: SPk_fb_pivot = 1.0_dl + real(dl) :: SPk_alpha = 0.0_dl + real(dl) :: SPk_beta = 0.0_dl + real(dl) :: SPk_gamma = 0.0_dl + real(dl) :: SPk_epsilon = 0.0_dl + real(dl) :: SPk_m_pivot = 1.0_dl +contains + procedure :: Init => TSPkNonLinear_Init + procedure :: ReadParams => TSPkNonLinear_ReadParams + procedure :: GetNonLinRatios => TSPkNonLinear_GetNonLinRatios + procedure, nopass :: PythonClass => TSPkNonLinear_PythonClass + procedure, nopass :: SelfPointer => TSPkNonLinear_SelfPointer + procedure, private :: ValidateConfig => TSPkNonLinear_ValidateConfig +end type TSPkNonLinear + +public TSPkNonLinear + +contains + +function TSPkNonLinear_PythonClass() +character(LEN=:), allocatable :: TSPkNonLinear_PythonClass + +TSPkNonLinear_PythonClass = 'SPkNonLinear' + +end function TSPkNonLinear_PythonClass + +subroutine TSPkNonLinear_SelfPointer(cptr, P) +use iso_c_binding +Type(c_ptr) :: cptr +Type(TSPkNonLinear), pointer :: PType +class(TPythonInterfacedClass), pointer :: P + +call c_f_pointer(cptr, PType) +P => PType + +end subroutine TSPkNonLinear_SelfPointer + +subroutine TSPkNonLinear_Init(this, State) +class(TSPkNonLinear) :: this +class(TCAMBdata), target :: State + +if (.not. allocated(this%BaseModel)) allocate(THalofit::this%BaseModel) +this%Min_kh_nonlinear = this%BaseModel%Min_kh_nonlinear +call this%BaseModel%Init(State) + +end subroutine TSPkNonLinear_Init + +subroutine TSPkNonLinear_ReadParams(this, Ini) +use IniObjects +class(TSPkNonLinear) :: this +class(TIniFile), intent(in) :: Ini + +if (.not. allocated(this%BaseModel)) allocate(THalofit::this%BaseModel) +call this%BaseModel%ReadParams(Ini) + +this%SPk_feedback = Ini%Read_Logical('SPk_feedback', .false.) +this%SPk_SO = Ini%Read_Int('SPk_SO', 200) +this%SPk_relation_kind = Ini%Read_Int('SPk_relation_kind', SPk_rel_power_law) +this%SPk_fb_a = Ini%Read_Double('SPk_fb_a', 1.0_dl) +this%SPk_fb_pow = Ini%Read_Double('SPk_fb_pow', 0.0_dl) +this%SPk_fb_pivot = Ini%Read_Double('SPk_fb_pivot', 1.0_dl) +this%SPk_alpha = Ini%Read_Double('SPk_alpha', 0.0_dl) +this%SPk_beta = Ini%Read_Double('SPk_beta', 0.0_dl) +this%SPk_gamma = Ini%Read_Double('SPk_gamma', 0.0_dl) +this%SPk_epsilon = Ini%Read_Double('SPk_epsilon', 0.0_dl) +this%SPk_m_pivot = Ini%Read_Double('SPk_m_pivot', 1.0_dl) + +end subroutine TSPkNonLinear_ReadParams + +subroutine TSPkNonLinear_ValidateConfig(this) +class(TSPkNonLinear), intent(in) :: this + +if (this%SPk_SO /= 200 .and. this%SPk_SO /= 500) then + call MpiStop('SP(k): SPk_SO must be 200 or 500') +end if +if (this%SPk_relation_kind < SPk_rel_power_law .or. this%SPk_relation_kind > SPk_rel_double_power_law) then + call MpiStop('SP(k): SPk_relation_kind must be 1 (power_law), 2 (cosmo_power_law), or 3 (double_power_law)') +end if + +if (this%SPk_relation_kind == SPk_rel_power_law) then + if (this%SPk_fb_pivot <= 0.0_dl) call MpiStop('SP(k): SPk_fb_pivot must be > 0 for power_law relation') +end if +if (this%SPk_relation_kind == SPk_rel_double_power_law) then + if (this%SPk_m_pivot <= 0.0_dl) call MpiStop('SP(k): SPk_m_pivot must be > 0 for double_power_law relation') +end if + +select type (base => this%BaseModel) +type is (THalofit) + if (this%SPk_feedback .and. base%halofit_version == halofit_mead2020_feedback) then + call MpiStop('SP(k) is not compatible with halofit_version=mead2020_feedback') + end if + if (this%SPk_feedback .and. & + (base%halofit_version == halofit_mead .or. base%halofit_version == halofit_mead2015 .or. base%halofit_version == halofit_mead2016) .and. & + (abs(base%HMcode_A_baryon - 3.13_dl) > 1e-12_dl .or. abs(base%HMcode_eta_baryon - 0.603_dl) > 1e-12_dl)) then + call MpiStop('SP(k) cannot be combined with HMCode_A_baryon/HMCode_eta_baryon baryonic corrections in HMCode 2015/2016') + end if +class default + continue +end select + +end subroutine TSPkNonLinear_ValidateConfig + +subroutine TSPkNonLinear_GetNonLinRatios(this, State, CAMB_Pk) +class(TSPkNonLinear) :: this +class(TCAMBdata) :: State +type(MatterPowerData), target :: CAMB_Pk +integer :: itf, i +real(dl) :: rk, spk_sup, spk_href, spk_eratio + +if (.not. allocated(this%BaseModel)) allocate(THalofit::this%BaseModel) + +call this%ValidateConfig() +call this%BaseModel%GetNonLinRatios(State, CAMB_Pk) + +if (.not. this%SPk_feedback) return + +select type (State) +class is (CAMBdata) + if (this%SPk_relation_kind == SPk_rel_cosmo_power_law .or. this%SPk_relation_kind == SPk_rel_double_power_law) then + spk_href = State%Hofz(0.3_dl) + else + spk_href = 1.0_dl + end if +class default + call MpiStop('SP(k): unsupported state type for Hofz evaluation') +end select + +do i = 1, CAMB_Pk%num_k + rk = exp(CAMB_Pk%log_kh(i)) + if (rk > this%Min_kh_nonlinear) then + if (rk < SPk_calibrated_k_min .or. rk > SPk_calibrated_k_max) then + call MpiStop('SP(k): k/h outside calibrated range [1e-12, 12.0]') + end if + end if +end do + +do itf = 1, CAMB_Pk%num_z + if (CAMB_Pk%redshifts(itf) < SPk_calibrated_z_min .or. CAMB_Pk%redshifts(itf) > SPk_calibrated_z_max) cycle + select type (State) + class is (CAMBdata) + if (this%SPk_relation_kind == SPk_rel_cosmo_power_law .or. this%SPk_relation_kind == SPk_rel_double_power_law) then + spk_eratio = State%Hofz(CAMB_Pk%redshifts(itf)) / spk_href + else + spk_eratio = 1.0_dl + end if + class default + call MpiStop('SP(k): unsupported state type for Hofz evaluation') + end select + do i = 1, CAMB_Pk%num_k + rk = exp(CAMB_Pk%log_kh(i)) + if (rk > this%Min_kh_nonlinear) then + spk_sup = SPk_Suppression(this%SPk_SO, rk, CAMB_Pk%redshifts(itf), this%SPk_relation_kind, & + this%SPk_fb_a, this%SPk_fb_pow, this%SPk_fb_pivot, this%SPk_alpha, this%SPk_beta, this%SPk_gamma, & + this%SPk_epsilon, this%SPk_m_pivot, spk_eratio) + CAMB_Pk%nonlin_ratio(i, itf) = CAMB_Pk%nonlin_ratio(i, itf) * sqrt(spk_sup) + end if + end do +end do + +end subroutine TSPkNonLinear_GetNonLinRatios + +end module SPkNonLinear diff --git a/fortran/spk_model.f90 b/fortran/spk_model.f90 new file mode 100644 index 00000000..193472db --- /dev/null +++ b/fortran/spk_model.f90 @@ -0,0 +1,102 @@ +module SPkModel +use precision +implicit none +private + +real(dl), parameter, public :: SPk_calibrated_z_min = 0.0_dl +real(dl), parameter, public :: SPk_calibrated_z_max = 3.0_dl +real(dl), parameter, public :: SPk_calibrated_k_min = 1e-12_dl +real(dl), parameter, public :: SPk_calibrated_k_max = 12.0_dl +real(dl), parameter, public :: SPk_min_suppression = 1e-6_dl + +integer, parameter, public :: SPk_rel_power_law = 1 +integer, parameter, public :: SPk_rel_cosmo_power_law = 2 +integer, parameter, public :: SPk_rel_double_power_law = 3 + +public :: SPk_Suppression + +contains + +pure function SPk_Poly2(x, c0, c1, c2) result(y) +real(dl), intent(in) :: x, c0, c1, c2 +real(dl) :: y + +y = c2 * x * x + c1 * x + c0 + +end function SPk_Poly2 + +pure subroutine SPk_GetParams(SO, z, spk_a_out, spk_b_out, spk_g_out, lambda_a, lambda_b, mu_a, mu_b, mu_c, nu_a, nu_b, nu_c) +integer, intent(in) :: SO +real(dl), intent(in) :: z +real(dl), intent(out) :: spk_a_out, spk_b_out, spk_g_out, lambda_a, lambda_b, mu_a, mu_b, mu_c, nu_a, nu_b, nu_c +real(dl) :: x + +x = 1.0_dl + z + +if (SO == 500) then + spk_a_out = SPk_Poly2(x, 14.783423122120318_dl, -0.999062404857228_dl, 0.12062854541689262_dl) + spk_b_out = SPk_Poly2(x, 14.620528368613265_dl, -0.9136466201011957_dl, 0.10835389086945699_dl) + spk_g_out = SPk_Poly2(x, 0.9671320682693298_dl, -0.03185388045484575_dl, 0.02650236152450093_dl) + lambda_a = SPk_Poly2(x, 0.019349810078190303_dl, -0.007410668383424459_dl, 0.0008334762393555539_dl) + lambda_b = SPk_Poly2(x, 2.9566773924238143_dl, 0.6205340408676114_dl, -0.001928273640110775_dl) + mu_a = SPk_Poly2(x, 0.715853343781141_dl, -0.19276613600825665_dl, 0.04948240117059147_dl) + mu_b = SPk_Poly2(x, 3.385355123440431_dl, 0.9658906605139421_dl, -0.06825861100375574_dl) + mu_c = SPk_Poly2(x, 4.457257708010122_dl, -2.191853871334233_dl, 0.45457701107254733_dl) + nu_a = SPk_Poly2(x, 478.86477329610375_dl, 429.88795783439946_dl, 249.25655627821902_dl) + nu_b = SPk_Poly2(x, -11.227459319819815_dl, -0.5581080204509223_dl, 0.4489962047114509_dl) + nu_c = SPk_Poly2(x, 3.499449440557995_dl, -0.08488559389068073_dl, -0.0923847866118189_dl) +else + spk_a_out = SPk_Poly2(x, 15.24311120000861_dl, -1.2436699435560352_dl, 0.14837558774401766_dl) + spk_b_out = SPk_Poly2(x, 14.969187892657688_dl, -1.0993025612653198_dl, 0.12905587245129102_dl) + spk_g_out = SPk_Poly2(x, 0.8000441576980428_dl, -0.01715621131893159_dl, 0.06131887249968379_dl) + lambda_a = SPk_Poly2(x, 0.02178116280689233_dl, -0.0077564325654746955_dl, 0.0007915576054589781_dl) + lambda_b = SPk_Poly2(x, 3.0878286643613437_dl, 0.4529677646796634_dl, 0.001552571083240605_dl) + mu_a = SPk_Poly2(x, 0.6930259177449359_dl, -0.16913553700233935_dl, 0.04263185199898842_dl) + mu_b = SPk_Poly2(x, 3.161914061444856_dl, 0.8616834297321924_dl, 0.011346427353554053_dl) + mu_c = SPk_Poly2(x, 5.532188503256583_dl, -3.0864672185252537_dl, 0.5083422518560442_dl) + nu_a = SPk_Poly2(x, 413.00988701513904_dl, 311.63957063032285_dl, 37.89105940901369_dl) + nu_b = SPk_Poly2(x, -11.243859405779181_dl, -0.34421412616421965_dl, 0.3343548325485801_dl) + nu_c = SPk_Poly2(x, 3.476463891168505_dl, -0.018333059687988575_dl, -0.08276237963970698_dl) +end if + +end subroutine SPk_GetParams + +pure function SPk_Suppression(SO, kh, z, relation_kind, fb_a, fb_pow, fb_pivot, rel_alpha, rel_beta, rel_gamma, & + rel_epsilon, rel_m_pivot, e_ratio) result(sup) +integer, intent(in) :: SO, relation_kind +real(dl), intent(in) :: kh, z, fb_a, fb_pow, fb_pivot +real(dl), intent(in) :: rel_alpha, rel_beta, rel_gamma, rel_epsilon, rel_m_pivot +real(dl), intent(in) :: e_ratio +real(dl) :: sup +real(dl) :: spk_a, spk_b, spk_g +real(dl) :: lambda_a, lambda_b, mu_a, mu_b, mu_c, nu_a, nu_b, nu_c +real(dl) :: best_mass, m_opt, fb, x, x0, x1, x2 +real(dl) :: e_ratio_eff + +call SPk_GetParams(SO, z, spk_a, spk_b, spk_g, lambda_a, lambda_b, mu_a, mu_b, mu_c, nu_a, nu_b, nu_c) + +best_mass = spk_a - (spk_a - spk_b) * (kh ** spk_g) +m_opt = 10.0_dl ** best_mass +e_ratio_eff = max(e_ratio, SPk_calibrated_k_min) + +select case (relation_kind) +case (SPk_rel_cosmo_power_law) + fb = (exp(rel_alpha) / 100.0_dl) * ((m_opt / 1.0e14_dl) ** (rel_beta - 1.0_dl)) * (e_ratio_eff ** rel_gamma) +case (SPk_rel_double_power_law) + fb = 0.5_dl * rel_epsilon * (((m_opt / rel_m_pivot) ** rel_alpha) + ((m_opt / rel_m_pivot) ** rel_beta)) * & + (e_ratio_eff ** rel_gamma) +case default + fb = fb_a * ((m_opt / fb_pivot) ** fb_pow) +end select + +x = log10(kh) +x0 = 1.0_dl + lambda_a * exp(lambda_b * x) +x1 = mu_a + ((1.0_dl - mu_a) / (1.0_dl + exp(mu_b * x + mu_c))) +x2 = nu_a * exp(-0.5_dl * ((x - nu_b) / nu_c) ** 2) + +sup = x0 - (x0 - x1) * exp(-x2 * fb) +sup = max(sup, SPk_min_suppression) + +end function SPk_Suppression + +end module SPkModel From 333e85459ac0bdb26fed3c9e447f3fe6aa23143d Mon Sep 17 00:00:00 2001 From: Jaime Salcido Date: Sun, 19 Apr 2026 11:56:42 +0100 Subject: [PATCH 02/12] Add tests for SPkNonLinear model and improve pyproject.toml formatting --- camb/tests/camb_test.py | 25 ++++++ camb/tests/spk_test.py | 176 ++++++++++++++++++++++++---------------- pyproject.toml | 46 ++++++++--- 3 files changed, 166 insertions(+), 81 deletions(-) diff --git a/camb/tests/camb_test.py b/camb/tests/camb_test.py index 2c58fb12..691cf96d 100644 --- a/camb/tests/camb_test.py +++ b/camb/tests/camb_test.py @@ -222,6 +222,31 @@ def testBackground(self): self.assertAlmostEqual(DA, bao[0][2], 3) self.assertAlmostEqual(H, bao[1][1], 3) + def testSPkNonLinearClassSelection(self): + pars = camb.CAMBparams() + pars.set_classes(non_linear_model="SPkNonLinear") + self.assertEqual(pars.NonLinearModel.__class__.__name__, "SPkNonLinear") + + pars.set_cosmology(H0=67.5, ombh2=0.02237, omch2=0.12, mnu=0.06) + pars.InitPower.set_params(As=2.1e-9, ns=0.965) + pars.set_matter_power(redshifts=[0.5], kmax=3.0) + pars.NonLinear = model.NonLinear_both + pars.NonLinearModel.BaseModel.set_params(halofit_version="mead2020") + pars.NonLinearModel.set_params( + SPk_feedback=True, + SPk_SO=200, + SPk_relation_kind=1, + SPk_fb_a=0.4, + SPk_fb_pow=0.2, + SPk_fb_pivot=1e14, + ) + + data = camb.get_results(pars) + k, z, pk = data.get_matter_power_spectrum(minkh=1e-2, maxkh=1.0, npoints=8) + self.assertEqual(len(z), 1) + self.assertTrue(np.all(np.isfinite(k))) + self.assertTrue(np.all(np.isfinite(pk))) + age2 = data.physical_time(0) self.assertAlmostEqual(age, age2, 4) diff --git a/camb/tests/spk_test.py b/camb/tests/spk_test.py index f311b019..9090c97a 100644 --- a/camb/tests/spk_test.py +++ b/camb/tests/spk_test.py @@ -10,54 +10,25 @@ sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))) import camb +try: + import pyspk +except ImportError: + pyspk = None -def _poly2(x, c0, c1, c2): - return c2 * x * x + c1 * x + c0 - - -def _spk_params(so, z): - x = 1.0 + z - if so == 500: - return { - "a": _poly2(x, 14.783423122120318, -0.999062404857228, 0.12062854541689262), - "b": _poly2(x, 14.620528368613265, -0.9136466201011957, 0.10835389086945699), - "g": _poly2(x, 0.9671320682693298, -0.03185388045484575, 0.02650236152450093), - "lambda_a": _poly2(x, 0.019349810078190303, -0.007410668383424459, 0.0008334762393555539), - "lambda_b": _poly2(x, 2.9566773924238143, 0.6205340408676114, -0.001928273640110775), - "mu_a": _poly2(x, 0.715853343781141, -0.19276613600825665, 0.04948240117059147), - "mu_b": _poly2(x, 3.385355123440431, 0.9658906605139421, -0.06825861100375574), - "mu_c": _poly2(x, 4.457257708010122, -2.191853871334233, 0.45457701107254733), - "nu_a": _poly2(x, 478.86477329610375, 429.88795783439946, 249.25655627821902), - "nu_b": _poly2(x, -11.227459319819815, -0.5581080204509223, 0.4489962047114509), - "nu_c": _poly2(x, 3.499449440557995, -0.08488559389068073, -0.0923847866118189), - } - return { - "a": _poly2(x, 15.24311120000861, -1.2436699435560352, 0.14837558774401766), - "b": _poly2(x, 14.969187892657688, -1.0993025612653198, 0.12905587245129102), - "g": _poly2(x, 0.8000441576980428, -0.01715621131893159, 0.06131887249968379), - "lambda_a": _poly2(x, 0.02178116280689233, -0.0077564325654746955, 0.0007915576054589781), - "lambda_b": _poly2(x, 3.0878286643613437, 0.4529677646796634, 0.001552571083240605), - "mu_a": _poly2(x, 0.6930259177449359, -0.16913553700233935, 0.04263185199898842), - "mu_b": _poly2(x, 3.161914061444856, 0.8616834297321924, 0.011346427353554053), - "mu_c": _poly2(x, 5.532188503256583, -3.0864672185252537, 0.5083422518560442), - "nu_a": _poly2(x, 413.00988701513904, 311.63957063032285, 37.89105940901369), - "nu_b": _poly2(x, -11.243859405779181, -0.34421412616421965, 0.3343548325485801), - "nu_c": _poly2(x, 3.476463891168505, -0.018333059687988575, -0.08276237963970698), - } - - -def _spk_sup_power_law(kh, z, so=200, fb_a=0.4, fb_pow=0.2, fb_pivot=1e14): - p = _spk_params(so, z) - best_mass = p["a"] - (p["a"] - p["b"]) * (kh ** p["g"]) - m_opt = 10.0**best_mass - fb = fb_a * ((m_opt / fb_pivot) ** fb_pow) - - x = np.log10(kh) - x0 = 1.0 + p["lambda_a"] * np.exp(p["lambda_b"] * x) - x1 = p["mu_a"] + ((1.0 - p["mu_a"]) / (1.0 + np.exp(p["mu_b"] * x + p["mu_c"]))) - x2 = p["nu_a"] * np.exp(-0.5 * ((x - p["nu_b"]) / p["nu_c"]) ** 2) - sup = x0 - (x0 - x1) * np.exp(-x2 * fb) - return np.maximum(sup, 1e-6) + +class _CambEFunc: + def __init__(self, results): + self._results = results + self._h0 = results.Params.H0 + + def efunc(self, z): + z_arr = np.asarray(z, dtype=float) + flat = z_arr.reshape(-1) + vals = np.array([self._results.hubble_parameter(float(zz)) / self._h0 for zz in flat], dtype=float) + vals = vals.reshape(z_arr.shape) + if np.isscalar(z): + return float(vals) + return vals class SPkTest(unittest.TestCase): @@ -70,7 +41,61 @@ def _get_pk(self, model_obj, z=0.5): pars.NonLinearModel = model_obj results = camb.get_results(pars) k, _z, pk = results.get_matter_power_spectrum(minkh=1e-2, maxkh=5.0, npoints=28) - return k, pk[0] + return k, pk[0], results + + def _assert_relation_match(self, relation_kind, z=0.5, so=200, **kwargs): + base = camb.Halofit() + base.set_params(halofit_version="mead2020") + k, pk_base, results = self._get_pk(base, z=z) + + spk = camb.SPkNonLinear() + spk.BaseModel.set_params(halofit_version="mead2020") + spk.set_params(SPk_feedback=True, SPk_SO=so, SPk_relation_kind=relation_kind, **kwargs) + _, pk_spk, _ = self._get_pk(spk, z=z) + + cosmo = _CambEFunc(results) + pyspk_kwargs = { + "SO": so, + "z": z, + "k_array": k, + "errors": False, + } + if relation_kind == 1: + pyspk_kwargs.update( + { + "fb_a": kwargs["SPk_fb_a"], + "fb_pow": kwargs["SPk_fb_pow"], + "fb_pivot": kwargs["SPk_fb_pivot"], + } + ) + elif relation_kind == 2: + pyspk_kwargs.update( + { + "alpha": kwargs["SPk_alpha"], + "beta": kwargs["SPk_beta"], + "gamma": kwargs["SPk_gamma"], + "cosmo": cosmo, + } + ) + elif relation_kind == 3: + pyspk_kwargs.update( + { + "epsilon": kwargs["SPk_epsilon"], + "alpha": kwargs["SPk_alpha"], + "beta": kwargs["SPk_beta"], + "gamma": kwargs["SPk_gamma"], + "m_pivot": kwargs["SPk_m_pivot"], + "cosmo": cosmo, + } + ) + else: + raise ValueError("Unknown relation kind") + + k_ref, sup_ref = pyspk.sup_model(**pyspk_kwargs) + measured_sup = pk_spk / pk_base + + self.assertTrue(np.allclose(k_ref, k, rtol=0, atol=0)) + self.assertTrue(np.allclose(measured_sup, sup_ref, rtol=1e-5, atol=1e-8)) def test_spk_invalid_params(self): model = camb.SPkNonLinear() @@ -84,41 +109,50 @@ def test_spk_invalid_params(self): def test_spk_disabled_matches_base(self): base = camb.Halofit() base.set_params(halofit_version="mead2020") - k_base, pk_base = self._get_pk(base) + k_base, pk_base, _ = self._get_pk(base) spk = camb.SPkNonLinear() spk.BaseModel.set_params(halofit_version="mead2020") spk.set_params(SPk_feedback=False) - k_spk, pk_spk = self._get_pk(spk) + k_spk, pk_spk, _ = self._get_pk(spk) self.assertTrue(np.allclose(k_base, k_spk, rtol=0, atol=0)) self.assertTrue(np.allclose(pk_base, pk_spk, rtol=2e-12, atol=1e-14)) + @unittest.skipIf(pyspk is None, "pyspk not installed") def test_spk_power_law_matches_reference(self): - fb_a = 0.4 - fb_pow = 0.2 - fb_pivot = 1e14 - z = 0.5 - - base = camb.Halofit() - base.set_params(halofit_version="mead2020") - k, pk_base = self._get_pk(base, z=z) + self._assert_relation_match( + relation_kind=1, + z=0.5, + so=200, + SPk_fb_a=0.4, + SPk_fb_pow=0.2, + SPk_fb_pivot=1e14, + ) - spk = camb.SPkNonLinear() - spk.BaseModel.set_params(halofit_version="mead2020") - spk.set_params( - SPk_feedback=True, - SPk_SO=200, - SPk_relation_kind=1, - SPk_fb_a=fb_a, - SPk_fb_pow=fb_pow, - SPk_fb_pivot=fb_pivot, + @unittest.skipIf(pyspk is None, "pyspk not installed") + def test_spk_cosmo_power_law_matches_reference(self): + self._assert_relation_match( + relation_kind=2, + z=1.0, + so=500, + SPk_alpha=3.4, + SPk_beta=1.0, + SPk_gamma=0.15, ) - _, pk_spk = self._get_pk(spk, z=z) - expected_sup = _spk_sup_power_law(k, z, so=200, fb_a=fb_a, fb_pow=fb_pow, fb_pivot=fb_pivot) - measured_sup = pk_spk / pk_base - self.assertTrue(np.allclose(measured_sup, expected_sup, rtol=1e-5, atol=1e-8)) + @unittest.skipIf(pyspk is None, "pyspk not installed") + def test_spk_double_power_law_matches_reference(self): + self._assert_relation_match( + relation_kind=3, + z=1.5, + so=200, + SPk_epsilon=0.55, + SPk_alpha=0.2, + SPk_beta=0.9, + SPk_gamma=0.1, + SPk_m_pivot=1e14, + ) if __name__ == "__main__": diff --git a/pyproject.toml b/pyproject.toml index f70afc38..9ea34d24 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,9 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "camb" -authors = [ - { name = "Antony Lewis" }, -] +authors = [{ name = "Antony Lewis" }] description = "Code for Anisotropies in the Microwave Background" keywords = ['cosmology', 'CAMB', 'CMB'] readme = "docs/README_pypi.rst" @@ -26,13 +24,17 @@ classifiers = [ "Programming Language :: Python :: 3.13", "Programming Language :: Python :: 3.14", ] -dependencies = [ - "numpy>=1.22", "scipy>=1.0", "sympy>=1.0", "packaging>=24.2" -] +dependencies = ["numpy>=1.22", "scipy>=1.0", "sympy>=1.0", "packaging>=24.2"] [project.optional-dependencies] -docs = ["sphinx>=4.0", "sphinx_rtd_theme>=1.0", "sphinxcontrib-jquery", "sphinx_markdown_builder", "jupytext"] -dev = ["ruff>=0.11.0", "pre-commit>=3.0.0"] +docs = [ + "sphinx>=4.0", + "sphinx_rtd_theme>=1.0", + "sphinxcontrib-jquery", + "sphinx_markdown_builder", + "jupytext", +] +dev = ["ruff>=0.11.0", "pre-commit>=3.0.0", "pyspk>=2.0.0"] [project.scripts] camb = "camb._command_line:run_command_line" @@ -70,6 +72,30 @@ docstring-code-format = true [tool.flake8] select = [ - "E713", "E704", "E703", "E714", "E741", "E10", "E11", "E20", "E22", "E23", "E25", "E27", - "E301", "E302", "E304", "E9", "F405", "F406", "F5", "F6", "F7", "F8", "W1", "W2", "W3", "W6" + "E713", + "E704", + "E703", + "E714", + "E741", + "E10", + "E11", + "E20", + "E22", + "E23", + "E25", + "E27", + "E301", + "E302", + "E304", + "E9", + "F405", + "F406", + "F5", + "F6", + "F7", + "F8", + "W1", + "W2", + "W3", + "W6", ] From aa15499eeb6d7cb523d0029cd6dae0e6f79245ef Mon Sep 17 00:00:00 2001 From: Jaime Salcido Date: Sun, 19 Apr 2026 11:59:47 +0100 Subject: [PATCH 03/12] Refactor SPk test assertions to use adjustable tolerances and reorganize test structure --- camb/tests/camb_test.py | 56 ++++++++++++++++++++--------------------- camb/tests/spk_test.py | 8 ++++-- 2 files changed, 34 insertions(+), 30 deletions(-) diff --git a/camb/tests/camb_test.py b/camb/tests/camb_test.py index 691cf96d..4439d315 100644 --- a/camb/tests/camb_test.py +++ b/camb/tests/camb_test.py @@ -222,31 +222,6 @@ def testBackground(self): self.assertAlmostEqual(DA, bao[0][2], 3) self.assertAlmostEqual(H, bao[1][1], 3) - def testSPkNonLinearClassSelection(self): - pars = camb.CAMBparams() - pars.set_classes(non_linear_model="SPkNonLinear") - self.assertEqual(pars.NonLinearModel.__class__.__name__, "SPkNonLinear") - - pars.set_cosmology(H0=67.5, ombh2=0.02237, omch2=0.12, mnu=0.06) - pars.InitPower.set_params(As=2.1e-9, ns=0.965) - pars.set_matter_power(redshifts=[0.5], kmax=3.0) - pars.NonLinear = model.NonLinear_both - pars.NonLinearModel.BaseModel.set_params(halofit_version="mead2020") - pars.NonLinearModel.set_params( - SPk_feedback=True, - SPk_SO=200, - SPk_relation_kind=1, - SPk_fb_a=0.4, - SPk_fb_pow=0.2, - SPk_fb_pivot=1e14, - ) - - data = camb.get_results(pars) - k, z, pk = data.get_matter_power_spectrum(minkh=1e-2, maxkh=1.0, npoints=8) - self.assertEqual(len(z), 1) - self.assertTrue(np.all(np.isfinite(k))) - self.assertTrue(np.all(np.isfinite(pk))) - age2 = data.physical_time(0) self.assertAlmostEqual(age, age2, 4) @@ -273,7 +248,7 @@ def testSPkNonLinearClassSelection(self): # Test BBN consistency, base_plikHM_TT_lowTEB best fit model pars.set_cosmology(H0=67.31, ombh2=0.022242, omch2=0.11977, mnu=0.06, omk=0) - self.assertAlmostEqual(pars.YHe, 0.2458, 5) + self.assertAlmostEqual(float(pars.YHe), 0.2458, 5) data.calc_background(pars) self.assertAlmostEqual(data.cosmomc_theta(), 0.0104090741, 7) self.assertAlmostEqual(data.get_derived_params()["kd"], 0.14055, 4) @@ -281,13 +256,13 @@ def testSPkNonLinearClassSelection(self): pars.set_cosmology( H0=67.31, ombh2=0.022242, omch2=0.11977, mnu=0.06, omk=0, bbn_predictor=bbn.BBN_table_interpolator() ) - self.assertAlmostEqual(pars.YHe, 0.2458, 5) + self.assertAlmostEqual(float(pars.YHe), 0.2458, 5) self.assertAlmostEqual(pars.get_Y_p(), bbn.BBN_table_interpolator().Y_p(0.022242, 0), 5) # test massive sterile models as in Planck papers pars.set_cosmology(H0=68.0, ombh2=0.022305, omch2=0.11873, mnu=0.06, nnu=3.073, omk=0, meffsterile=0.013) self.assertAlmostEqual(pars.omnuh2, 0.00078, 5) - self.assertAlmostEqual(pars.YHe, 0.246218, 5) + self.assertAlmostEqual(float(pars.YHe), 0.246218, 5) self.assertAlmostEqual(pars.N_eff, 3.073, 4) data.calc_background(pars) @@ -984,3 +959,28 @@ def test_quintessence(self): camb.get_background(pars) results = camb.get_results(pars) self.assertAlmostEqual(results.get_derived_params()["thetastar"], 1.044341764253, delta=1e-5) + + def testSPkNonLinearClassSelection(self): + pars = camb.CAMBparams() + pars.set_classes(non_linear_model="SPkNonLinear") + self.assertEqual(pars.NonLinearModel.__class__.__name__, "SPkNonLinear") + + pars.set_cosmology(H0=67.5, ombh2=0.02237, omch2=0.12, mnu=0.06) + pars.InitPower.set_params(As=2.1e-9, ns=0.965) + pars.set_matter_power(redshifts=[0.5], kmax=3.0) + pars.NonLinear = model.NonLinear_both + pars.NonLinearModel.BaseModel.set_params(halofit_version="mead2020") + pars.NonLinearModel.set_params( + SPk_feedback=True, + SPk_SO=200, + SPk_relation_kind=1, + SPk_fb_a=0.4, + SPk_fb_pow=0.2, + SPk_fb_pivot=1e14, + ) + + data = camb.get_results(pars) + k, z, pk = data.get_matter_power_spectrum(minkh=1e-2, maxkh=1.0, npoints=8) + self.assertEqual(len(z), 1) + self.assertTrue(np.all(np.isfinite(k))) + self.assertTrue(np.all(np.isfinite(pk))) diff --git a/camb/tests/spk_test.py b/camb/tests/spk_test.py index 9090c97a..46fec30b 100644 --- a/camb/tests/spk_test.py +++ b/camb/tests/spk_test.py @@ -43,7 +43,7 @@ def _get_pk(self, model_obj, z=0.5): k, _z, pk = results.get_matter_power_spectrum(minkh=1e-2, maxkh=5.0, npoints=28) return k, pk[0], results - def _assert_relation_match(self, relation_kind, z=0.5, so=200, **kwargs): + def _assert_relation_match(self, relation_kind, z=0.5, so=200, rtol=1e-5, atol=1e-8, **kwargs): base = camb.Halofit() base.set_params(halofit_version="mead2020") k, pk_base, results = self._get_pk(base, z=z) @@ -95,7 +95,7 @@ def _assert_relation_match(self, relation_kind, z=0.5, so=200, **kwargs): measured_sup = pk_spk / pk_base self.assertTrue(np.allclose(k_ref, k, rtol=0, atol=0)) - self.assertTrue(np.allclose(measured_sup, sup_ref, rtol=1e-5, atol=1e-8)) + self.assertTrue(np.allclose(measured_sup, sup_ref, rtol=rtol, atol=atol)) def test_spk_invalid_params(self): model = camb.SPkNonLinear() @@ -136,6 +136,8 @@ def test_spk_cosmo_power_law_matches_reference(self): relation_kind=2, z=1.0, so=500, + rtol=1e-3, + atol=1e-6, SPk_alpha=3.4, SPk_beta=1.0, SPk_gamma=0.15, @@ -147,6 +149,8 @@ def test_spk_double_power_law_matches_reference(self): relation_kind=3, z=1.5, so=200, + rtol=1e-3, + atol=1e-6, SPk_epsilon=0.55, SPk_alpha=0.2, SPk_beta=0.9, From fedfdb0d1e6dbc4a2f860817359ef5a1c0425846 Mon Sep 17 00:00:00 2001 From: Jaime Salcido Date: Sun, 19 Apr 2026 12:04:10 +0100 Subject: [PATCH 04/12] Refactor SPkNonLinear to streamline k/h calibration checks in suppression calculations --- fortran/SPkNonLinear.f90 | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/fortran/SPkNonLinear.f90 b/fortran/SPkNonLinear.f90 index c8a7ffbb..9facf47f 100644 --- a/fortran/SPkNonLinear.f90 +++ b/fortran/SPkNonLinear.f90 @@ -141,15 +141,6 @@ subroutine TSPkNonLinear_GetNonLinRatios(this, State, CAMB_Pk) call MpiStop('SP(k): unsupported state type for Hofz evaluation') end select -do i = 1, CAMB_Pk%num_k - rk = exp(CAMB_Pk%log_kh(i)) - if (rk > this%Min_kh_nonlinear) then - if (rk < SPk_calibrated_k_min .or. rk > SPk_calibrated_k_max) then - call MpiStop('SP(k): k/h outside calibrated range [1e-12, 12.0]') - end if - end if -end do - do itf = 1, CAMB_Pk%num_z if (CAMB_Pk%redshifts(itf) < SPk_calibrated_z_min .or. CAMB_Pk%redshifts(itf) > SPk_calibrated_z_max) cycle select type (State) @@ -165,6 +156,7 @@ subroutine TSPkNonLinear_GetNonLinRatios(this, State, CAMB_Pk) do i = 1, CAMB_Pk%num_k rk = exp(CAMB_Pk%log_kh(i)) if (rk > this%Min_kh_nonlinear) then + if (rk < SPk_calibrated_k_min .or. rk > SPk_calibrated_k_max) cycle spk_sup = SPk_Suppression(this%SPk_SO, rk, CAMB_Pk%redshifts(itf), this%SPk_relation_kind, & this%SPk_fb_a, this%SPk_fb_pow, this%SPk_fb_pivot, this%SPk_alpha, this%SPk_beta, this%SPk_gamma, & this%SPk_epsilon, this%SPk_m_pivot, spk_eratio) From 905dfa133bbeb3aaab85bd8c6f0fe359b992ac6a Mon Sep 17 00:00:00 2001 From: Jaime Salcido Date: Sun, 19 Apr 2026 12:39:15 +0100 Subject: [PATCH 05/12] Refactor SPk suppression calculations to improve handling of calibrated k limits --- fortran/SPkNonLinear.f90 | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/fortran/SPkNonLinear.f90 b/fortran/SPkNonLinear.f90 index 9facf47f..ae617798 100644 --- a/fortran/SPkNonLinear.f90 +++ b/fortran/SPkNonLinear.f90 @@ -156,9 +156,10 @@ subroutine TSPkNonLinear_GetNonLinRatios(this, State, CAMB_Pk) do i = 1, CAMB_Pk%num_k rk = exp(CAMB_Pk%log_kh(i)) if (rk > this%Min_kh_nonlinear) then - if (rk < SPk_calibrated_k_min .or. rk > SPk_calibrated_k_max) cycle - spk_sup = SPk_Suppression(this%SPk_SO, rk, CAMB_Pk%redshifts(itf), this%SPk_relation_kind, & - this%SPk_fb_a, this%SPk_fb_pow, this%SPk_fb_pivot, this%SPk_alpha, this%SPk_beta, this%SPk_gamma, & + if (rk < SPk_calibrated_k_min) cycle + spk_sup = SPk_Suppression(this%SPk_SO, min(rk, SPk_calibrated_k_max), CAMB_Pk%redshifts(itf), & + this%SPk_relation_kind, this%SPk_fb_a, this%SPk_fb_pow, this%SPk_fb_pivot, & + this%SPk_alpha, this%SPk_beta, this%SPk_gamma, & this%SPk_epsilon, this%SPk_m_pivot, spk_eratio) CAMB_Pk%nonlin_ratio(i, itf) = CAMB_Pk%nonlin_ratio(i, itf) * sqrt(spk_sup) end if From a4b791039b60efcca0cf82735befeade9b78e5eb Mon Sep 17 00:00:00 2001 From: Jaime Salcido Date: Sun, 19 Apr 2026 12:44:42 +0100 Subject: [PATCH 06/12] Add Fortran linter include paths for improved linting support --- .vscode/settings.json | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.vscode/settings.json b/.vscode/settings.json index efcf0467..8dd25021 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -116,4 +116,8 @@ }, "python-envs.defaultEnvManager": "ms-python.python:venv", "python-envs.defaultPackageManager": "ms-python.python:pip", + "fortran.linter.includePaths": [ + "${workspaceFolder}/fortran/Releaselib", + "${workspaceFolder}/forutils/Releaselib" + ] } From 8eb4ece4a04490f93db90bd33e835cdeb330c8a6 Mon Sep 17 00:00:00 2001 From: Jaime Salcido Date: Sun, 19 Apr 2026 13:09:30 +0100 Subject: [PATCH 07/12] feat(spk): track SP(k) demo notebook and tighten pyspk regression coverage unignore and add SPk_demo.ipynb to the repo add an end-to-end SP(k) demo comparing CAMB vs pyspk with relative-error diagnostics make SP(k) unit references more stable with dense transfer sampling (k_per_logint=100) tighten relation-match tolerances to 1e-6 / 1e-9 add a high-k boundary continuity regression test (k <= 12) to prevent cutoff artifacts --- .gitignore | 1 + camb/tests/spk_test.py | 56 +++++++- docs/SPk_demo.ipynb | 309 +++++++++++++++++++++++++++++++++++++++++ 3 files changed, 361 insertions(+), 5 deletions(-) create mode 100644 docs/SPk_demo.ipynb diff --git a/.gitignore b/.gitignore index e84f6796..a96d2ae3 100644 --- a/.gitignore +++ b/.gitignore @@ -42,6 +42,7 @@ testfile* *.ipynb !CAMBdemo.ipynb !ScalEqs.ipynb +!docs/SPk_demo.ipynb *.dll *.a *.o diff --git a/camb/tests/spk_test.py b/camb/tests/spk_test.py index 46fec30b..34967227 100644 --- a/camb/tests/spk_test.py +++ b/camb/tests/spk_test.py @@ -36,13 +36,25 @@ def _get_pk(self, model_obj, z=0.5): pars = camb.CAMBparams() pars.set_cosmology(H0=67.5, ombh2=0.02237, omch2=0.12, mnu=0.06) pars.InitPower.set_params(As=2.1e-9, ns=0.965) - pars.set_matter_power(redshifts=[z], kmax=6.0) + # Use dense transfer sampling to reduce interpolation noise in reference comparisons. + pars.set_matter_power(redshifts=[z], kmax=6.0, k_per_logint=100) pars.NonLinear = camb.model.NonLinear_both pars.NonLinearModel = model_obj results = camb.get_results(pars) k, _z, pk = results.get_matter_power_spectrum(minkh=1e-2, maxkh=5.0, npoints=28) return k, pk[0], results + def _get_pk_range(self, model_obj, z=0.125, kmax=12.0, npoints=400): + pars = camb.CAMBparams() + pars.set_cosmology(H0=67.5, ombh2=0.02237, omch2=0.12, mnu=0.06) + pars.InitPower.set_params(As=2.1e-9, ns=0.965) + pars.set_matter_power(redshifts=[z], kmax=kmax, k_per_logint=100) + pars.NonLinear = camb.model.NonLinear_both + pars.NonLinearModel = model_obj + results = camb.get_results(pars) + k, _z, pk = results.get_matter_power_spectrum(minkh=1e-4, maxkh=kmax, npoints=npoints) + return k, pk[0], results + def _assert_relation_match(self, relation_kind, z=0.5, so=200, rtol=1e-5, atol=1e-8, **kwargs): base = camb.Halofit() base.set_params(halofit_version="mead2020") @@ -125,6 +137,8 @@ def test_spk_power_law_matches_reference(self): relation_kind=1, z=0.5, so=200, + rtol=1e-6, + atol=1e-9, SPk_fb_a=0.4, SPk_fb_pow=0.2, SPk_fb_pivot=1e14, @@ -136,8 +150,8 @@ def test_spk_cosmo_power_law_matches_reference(self): relation_kind=2, z=1.0, so=500, - rtol=1e-3, - atol=1e-6, + rtol=1e-6, + atol=1e-9, SPk_alpha=3.4, SPk_beta=1.0, SPk_gamma=0.15, @@ -149,8 +163,8 @@ def test_spk_double_power_law_matches_reference(self): relation_kind=3, z=1.5, so=200, - rtol=1e-3, - atol=1e-6, + rtol=1e-6, + atol=1e-9, SPk_epsilon=0.55, SPk_alpha=0.2, SPk_beta=0.9, @@ -158,6 +172,38 @@ def test_spk_double_power_law_matches_reference(self): SPk_m_pivot=1e14, ) + @unittest.skipIf(pyspk is None, "pyspk not installed") + def test_spk_high_k_boundary_continuity(self): + base = camb.Halofit() + base.set_params(halofit_version="mead2020") + k, pk_base, _ = self._get_pk_range(base, z=0.125, kmax=12.0, npoints=400) + + spk = camb.SPkNonLinear() + spk.BaseModel.set_params(halofit_version="mead2020") + spk.set_params( + SPk_feedback=True, + SPk_SO=200, + SPk_relation_kind=1, + SPk_fb_a=0.4, + SPk_fb_pow=0.3, + SPk_fb_pivot=1e14, + ) + _, pk_spk, _ = self._get_pk_range(spk, z=0.125, kmax=12.0, npoints=400) + + _, sup_ref = pyspk.sup_model( + SO=200, + z=0.125, + fb_a=0.4, + fb_pow=0.3, + fb_pivot=1e14, + k_array=k, + errors=False, + ) + measured_sup = pk_spk / pk_base + rel = np.abs(measured_sup / sup_ref - 1.0) + + self.assertLess(float(np.max(rel[k <= 12.0])), 1e-3) + if __name__ == "__main__": unittest.main() diff --git a/docs/SPk_demo.ipynb b/docs/SPk_demo.ipynb new file mode 100644 index 00000000..f0d7fbac --- /dev/null +++ b/docs/SPk_demo.ipynb @@ -0,0 +1,309 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2c84399d", + "metadata": {}, + "source": [ + "# SP(k) Baryon Suppression Demo\n", + "\n", + "Compare the non-linear matter power spectrum with and without SP(k), then verify against `pyspk`.\n", + "\n", + "Note: SP(k) should not be combined with HMCode baryon-feedback models (e.g. `halofit_version='mead2020_feedback'`)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b82be437", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using CAMB 1.6.7 installed at /Users/jaimesalcido/CAMB/camb\n", + "Using pyspk 2.0.0\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'\n", + "\n", + "import os\n", + "import warnings\n", + "\n", + "import numpy as np\n", + "import pyspk\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import camb\n", + "from camb import model\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "print(f\"Using CAMB {camb.__version__} installed at {os.path.dirname(camb.__file__)}\")\n", + "print(f\"Using pyspk {pyspk.__version__}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e37d95d6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "redshifts: [0. 0.125 1. ]\n", + "k_per_logint: 100\n" + ] + } + ], + "source": [ + "# Cosmology + k/z sampling\n", + "minkh, maxkh, npoints = 1e-4, 12.0, 400\n", + "z_samples = [0.0, 0.125, 1.0]\n", + "\n", + "# Important for high-k SP(k) validation: dense transfer k sampling avoids interpolation artifacts.\n", + "k_per_logint = 100\n", + "\n", + "# Baseline non-linear model (HMCode, no SPk)\n", + "pars_base = camb.set_params(H0=67.5, ombh2=0.022, omch2=0.122, As=2e-9, ns=0.965)\n", + "pars_base.set_matter_power(redshifts=z_samples, kmax=maxkh, k_per_logint=k_per_logint, silent=True)\n", + "pars_base.NonLinear = model.NonLinear_both\n", + "pars_base.NonLinearModel = camb.Halofit()\n", + "pars_base.NonLinearModel.set_params(halofit_version='mead2020')\n", + "\n", + "results_base = camb.get_results(pars_base)\n", + "kh0, z0, pk0 = results_base.get_matter_power_spectrum(minkh=minkh, maxkh=maxkh, npoints=npoints)\n", + "\n", + "# SP(k) on top of the same base model\n", + "pars_spk = camb.set_params(H0=67.5, ombh2=0.022, omch2=0.122, As=2e-9, ns=0.965)\n", + "pars_spk.set_matter_power(redshifts=z_samples, kmax=maxkh, k_per_logint=k_per_logint, silent=True)\n", + "pars_spk.NonLinear = model.NonLinear_both\n", + "\n", + "spk_model = camb.SPkNonLinear()\n", + "spk_model.BaseModel.set_params(halofit_version='mead2020')\n", + "spk_model.set_params(\n", + " SPk_feedback=True,\n", + " SPk_SO=200,\n", + " SPk_relation_kind=1,\n", + " SPk_fb_a=0.4,\n", + " SPk_fb_pow=0.3,\n", + " SPk_fb_pivot=1e14,\n", + ")\n", + "pars_spk.NonLinearModel = spk_model\n", + "\n", + "results_spk = camb.get_results(pars_spk)\n", + "kh1, z1, pk1 = results_spk.get_matter_power_spectrum(minkh=minkh, maxkh=maxkh, npoints=npoints)\n", + "\n", + "assert np.allclose(kh0, kh1)\n", + "assert np.allclose(z0, z1)\n", + "z = np.array(z0)\n", + "print('redshifts:', z)\n", + "print('k_per_logint:', k_per_logint)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "942282a9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 385, + "width": 1189 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot non-linear vs non-linear+SP(k)\n", + "from matplotlib.lines import Line2D\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", + "linestyles = ['-', '--', ':', '-.']\n", + "\n", + "for i, redshift in enumerate(z):\n", + " ls = linestyles[i % len(linestyles)]\n", + " ax[0].loglog(kh0, pk0[i, :], color='C3', ls=ls)\n", + " ax[0].loglog(kh1, pk1[i, :], color='C0', ls=ls)\n", + "ax[0].set_xlabel('k/h Mpc')\n", + "ax[0].set_ylabel('P(k) [(Mpc/h)^3]')\n", + "ax[0].set_title('Non-linear matter power')\n", + "\n", + "model_handles = [\n", + " Line2D([], [], color='C3', lw=2, label='non-linear'),\n", + " Line2D([], [], color='C0', lw=2, label='non-linear + SP(k)'),\n", + "]\n", + "redshift_handles = [\n", + " Line2D([], [], color='k', lw=2, ls=linestyles[i % len(linestyles)], label=f'z={float(redshift):g}')\n", + " for i, redshift in enumerate(z)\n", + "]\n", + "leg_model = ax[0].legend(handles=model_handles, loc='lower left')\n", + "ax[0].add_artist(leg_model)\n", + "ax[0].legend(handles=redshift_handles, title='Redshift', loc='upper right')\n", + "\n", + "for i, redshift in enumerate(z):\n", + " ls = linestyles[i % len(linestyles)]\n", + " ax[1].semilogx(kh0, pk1[i, :] / pk0[i, :], ls=ls, label=f'z={float(redshift):g}')\n", + "ax[1].axhline(1.0, color='k', lw=1)\n", + "ax[1].set_xlabel('k/h Mpc')\n", + "ax[1].set_ylabel('P_nl(SPk) / P_nl')\n", + "ax[1].set_title('Suppression ratio')\n", + "ax[1].legend(loc='lower left')\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "69a98f59", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Max relative error between CAMB SP(k) and pyspk:\n", + "z=0: k<=8 -> 4.41e-06, k<=12 -> 4.32e-05\n", + "z=0.125: k<=8 -> 3.66e-06, k<=12 -> 3.48e-05\n", + "z=1: k<=8 -> 9.76e-07, k<=12 -> 9.76e-07\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 701, + "width": 875 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Verify suppression factor against pyspk and show implementation error\n", + "pyspk_sup_list = []\n", + "for redshift in z:\n", + " k_pyspk, sup_pyspk = pyspk.sup_model(\n", + " SO=200,\n", + " z=float(redshift),\n", + " fb_a=0.4,\n", + " fb_pow=0.3,\n", + " fb_pivot=1e14,\n", + " k_array=kh0,\n", + " errors=False,\n", + " verbose=False,\n", + " )\n", + " assert np.allclose(k_pyspk, kh0)\n", + " pyspk_sup_list.append(sup_pyspk)\n", + "\n", + "k_compare_max = 12.0\n", + "mask_plot = kh0 <= k_compare_max\n", + "\n", + "fig2, (ax2, ax3) = plt.subplots(\n", + " 2, 1, figsize=(10, 8), sharex=True,\n", + " gridspec_kw={\"height_ratios\": [3, 1], \"hspace\": 0.12},\n", + ")\n", + "colors = ['C0', 'C1', 'C2']\n", + "\n", + "for i, redshift in enumerate(z):\n", + " color = colors[i % len(colors)]\n", + " camb_sup = pk1[i, :] / pk0[i, :]\n", + " py_sup = pyspk_sup_list[i]\n", + " valid = mask_plot & np.isfinite(camb_sup) & np.isfinite(py_sup) & (np.abs(py_sup) > 1e-30)\n", + "\n", + " ax2.semilogx(\n", + " kh0[valid], camb_sup[valid], color=color, ls='-', lw=2,\n", + " label=f'CAMB SP(k), z={float(redshift):g}'\n", + " )\n", + " ax2.semilogx(\n", + " kh0[valid], py_sup[valid], color='k', ls=':', lw=1.5, alpha=0.8,\n", + " label='pyspk' if i == 0 else None\n", + " )\n", + "\n", + " ratio_err = camb_sup[valid] / py_sup[valid] - 1.0\n", + " ax3.semilogx(kh0[valid], ratio_err, color=color, ls='-', lw=1.8, label=f'z={float(redshift):g}')\n", + "\n", + "ax2.axhline(1.0, color='k', lw=1, alpha=0.4)\n", + "ax2.set_ylabel('Suppression factor')\n", + "ax2.set_title('SP(k) suppression: CAMB vs pyspk')\n", + "ax2.legend(loc='lower left', ncol=2, fontsize=9)\n", + "\n", + "ax3.axhline(0.0, color='k', lw=1, alpha=0.5)\n", + "ax3.set_xlabel('k/h Mpc')\n", + "ax3.set_ylabel('CAMB/pyspk - 1')\n", + "ax3.set_ylim(-0.01, 0.01)\n", + "ax3.set_title('Relative error (±1%)')\n", + "\n", + "# Reference marker: pyspk Nyquist limit\n", + "ax2.axvline(8.0, color='gray', lw=1, ls='--', alpha=0.6)\n", + "ax3.axvline(8.0, color='gray', lw=1, ls='--', alpha=0.6)\n", + "ax2.text(\n", + " 8.0 * 0.5, 0.98, 'pyspk k_Ny~8', color='gray', fontsize=9,\n", + " bbox=dict(facecolor='white', edgecolor='gray', alpha=0.8, boxstyle='round,pad=0.2')\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "\n", + "print('Max relative error between CAMB SP(k) and pyspk:')\n", + "for i, redshift in enumerate(z):\n", + " camb_sup = pk1[i, :] / pk0[i, :]\n", + " py_sup = pyspk_sup_list[i]\n", + " valid8 = (kh0 <= 8.0) & np.isfinite(camb_sup) & np.isfinite(py_sup) & (np.abs(py_sup) > 1e-30)\n", + " valid12 = (kh0 <= 12.0) & np.isfinite(camb_sup) & np.isfinite(py_sup) & (np.abs(py_sup) > 1e-30)\n", + " rel8 = np.max(np.abs(camb_sup[valid8] / py_sup[valid8] - 1.0))\n", + " rel12 = np.max(np.abs(camb_sup[valid12] / py_sup[valid12] - 1.0))\n", + " print(f'z={float(redshift):g}: k<=8 -> {rel8:.2e}, k<=12 -> {rel12:.2e}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6df1e08d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From dda7ef87bc849fb41737ae8baf22bd6a1b090add Mon Sep 17 00:00:00 2001 From: Jaime Salcido Date: Sun, 19 Apr 2026 17:20:20 +0100 Subject: [PATCH 08/12] Add warnings for SP(k) calculations outside calibrated ranges - Introduced warnings for redshift values outside the calibrated range, notifying users when SP(k) calculations are skipped. - Added clamping warnings for input k values exceeding the calibrated maximum, ensuring users are informed when values are adjusted. - Implemented logical flags to prevent repeated warnings during execution. --- camb/tests/spk_test.py | 70 ++++++++++++++++++++++++++++++++------ docs/SPk_demo.ipynb | 72 ++++++++++++++++++++++++++-------------- fortran/SPkNonLinear.f90 | 18 +++++++++- 3 files changed, 124 insertions(+), 36 deletions(-) diff --git a/camb/tests/spk_test.py b/camb/tests/spk_test.py index 34967227..802bf14d 100644 --- a/camb/tests/spk_test.py +++ b/camb/tests/spk_test.py @@ -1,6 +1,8 @@ import os import sys +import tempfile import unittest +import warnings import numpy as np @@ -32,6 +34,20 @@ def efunc(self, z): class SPkTest(unittest.TestCase): + def _capture_c_stdout(self, call): + saved_fd = os.dup(1) + try: + with tempfile.TemporaryFile(mode="w+b") as tmp: + os.dup2(tmp.fileno(), 1) + call() + tmp.flush() + tmp.seek(0) + out = tmp.read().decode("utf-8", errors="replace") + finally: + os.dup2(saved_fd, 1) + os.close(saved_fd) + return out + def _get_pk(self, model_obj, z=0.5): pars = camb.CAMBparams() pars.set_cosmology(H0=67.5, ombh2=0.02237, omch2=0.12, mnu=0.06) @@ -190,19 +206,53 @@ def test_spk_high_k_boundary_continuity(self): ) _, pk_spk, _ = self._get_pk_range(spk, z=0.125, kmax=12.0, npoints=400) - _, sup_ref = pyspk.sup_model( - SO=200, - z=0.125, - fb_a=0.4, - fb_pow=0.3, - fb_pivot=1e14, - k_array=k, - errors=False, - ) + with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", + message=r"Scales with k_max > k_ny = 8\.0 \[h/Mpc\] may not be accurately reproduced by the model\.", + category=UserWarning, + ) + _, sup_ref = pyspk.sup_model( + SO=200, + z=0.125, + fb_a=0.4, + fb_pow=0.3, + fb_pivot=1e14, + k_array=k, + errors=False, + ) measured_sup = pk_spk / pk_base rel = np.abs(measured_sup / sup_ref - 1.0) - self.assertLess(float(np.max(rel[k <= 12.0])), 1e-3) + self.assertLess(float(np.max(rel[k <= 12.0])), 1e-4) + + def test_spk_out_of_range_warnings(self): + pars = camb.CAMBparams() + pars.set_cosmology(H0=67.5, ombh2=0.02237, omch2=0.12, mnu=0.06) + pars.InitPower.set_params(As=2.1e-9, ns=0.965) + pars.set_matter_power(redshifts=[4.0, 1.0], kmax=20.0, k_per_logint=100) + pars.NonLinear = camb.model.NonLinear_both + + spk = camb.SPkNonLinear() + spk.BaseModel.set_params(halofit_version="mead2020") + spk.set_params( + SPk_feedback=True, + SPk_SO=200, + SPk_relation_kind=1, + SPk_fb_a=0.4, + SPk_fb_pow=0.3, + SPk_fb_pivot=1e14, + ) + pars.NonLinearModel = spk + + camb.set_feedback_level(1) + try: + output = self._capture_c_stdout(lambda: camb.get_results(pars)) + finally: + camb.set_feedback_level(0) + + self.assertIn("WARNING: SP(k) skipped outside calibrated redshift range.", output) + self.assertIn("WARNING: SP(k) input k exceeds calibrated range; clamping to k_max=", output) if __name__ == "__main__": diff --git a/docs/SPk_demo.ipynb b/docs/SPk_demo.ipynb index f0d7fbac..77febcb5 100644 --- a/docs/SPk_demo.ipynb +++ b/docs/SPk_demo.ipynb @@ -7,9 +7,15 @@ "source": [ "# SP(k) Baryon Suppression Demo\n", "\n", - "Compare the non-linear matter power spectrum with and without SP(k), then verify against `pyspk`.\n", + "This notebook compares non-linear matter power with and without SP(k), then validates the SP(k) suppression curve against `pyspk`.\n", "\n", - "Note: SP(k) should not be combined with HMCode baryon-feedback models (e.g. `halofit_version='mead2020_feedback'`)." + "## What this notebook does\n", + "1. Build a baseline non-linear model (`mead2020`) and an SP(k)-enabled model with the same cosmology.\n", + "2. Plot the full spectra and the suppression ratio.\n", + "3. Compare CAMB SP(k) vs `pyspk` suppression and show relative error.\n", + "\n", + "## Notes\n", + "- SP(k) should not be combined with HMCode baryon-feedback models (e.g. `halofit_version='mead2020_feedback'`)." ] }, { @@ -58,7 +64,7 @@ "output_type": "stream", "text": [ "redshifts: [0. 0.125 1. ]\n", - "k_per_logint: 100\n" + "k_per_logint: 50\n" ] } ], @@ -67,8 +73,7 @@ "minkh, maxkh, npoints = 1e-4, 12.0, 400\n", "z_samples = [0.0, 0.125, 1.0]\n", "\n", - "# Important for high-k SP(k) validation: dense transfer k sampling avoids interpolation artifacts.\n", - "k_per_logint = 100\n", + "k_per_logint = 50\n", "\n", "# Baseline non-linear model (HMCode, no SPk)\n", "pars_base = camb.set_params(H0=67.5, ombh2=0.022, omch2=0.122, As=2e-9, ns=0.965)\n", @@ -115,7 +120,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -168,6 +173,18 @@ "plt.tight_layout()" ] }, + { + "cell_type": "markdown", + "id": "b3b2d97c", + "metadata": {}, + "source": [ + "## Interpreting the validation plot\n", + "\n", + "- Top panel: suppression factor from CAMB SP(k) and `pyspk` (should overlap closely).\n", + "- Bottom panel: relative error, defined as `CAMB / pyspk - 1`, shown on a ±1% axis.\n", + "- Vertical dashed line at `k=8 h/Mpc`: approximate `pyspk` Nyquist reference." + ] + }, { "cell_type": "code", "execution_count": 4, @@ -179,14 +196,14 @@ "output_type": "stream", "text": [ "Max relative error between CAMB SP(k) and pyspk:\n", - "z=0: k<=8 -> 4.41e-06, k<=12 -> 4.32e-05\n", - "z=0.125: k<=8 -> 3.66e-06, k<=12 -> 3.48e-05\n", - "z=1: k<=8 -> 9.76e-07, k<=12 -> 9.76e-07\n" + "z=0: k<=8 -> 4.41e-06, k<=12 -> 1.05e-04\n", + "z=0.125: k<=8 -> 3.66e-06, k<=12 -> 8.42e-05\n", + "z=1: k<=8 -> 9.76e-07, k<=12 -> 1.52e-06\n" ] }, { "data": { - "image/png": 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" ] @@ -225,6 +242,8 @@ " gridspec_kw={\"height_ratios\": [3, 1], \"hspace\": 0.12},\n", ")\n", "colors = ['C0', 'C1', 'C2']\n", + "camb_handles = []\n", + "pyspk_handle = None\n", "\n", "for i, redshift in enumerate(z):\n", " color = colors[i % len(colors)]\n", @@ -232,14 +251,22 @@ " py_sup = pyspk_sup_list[i]\n", " valid = mask_plot & np.isfinite(camb_sup) & np.isfinite(py_sup) & (np.abs(py_sup) > 1e-30)\n", "\n", - " ax2.semilogx(\n", + " line_camb, = ax2.semilogx(\n", " kh0[valid], camb_sup[valid], color=color, ls='-', lw=2,\n", " label=f'CAMB SP(k), z={float(redshift):g}'\n", " )\n", - " ax2.semilogx(\n", - " kh0[valid], py_sup[valid], color='k', ls=':', lw=1.5, alpha=0.8,\n", - " label='pyspk' if i == 0 else None\n", - " )\n", + " camb_handles.append(line_camb)\n", + "\n", + " if pyspk_handle is None:\n", + " pyspk_handle, = ax2.semilogx(\n", + " kh0[valid], py_sup[valid], color='k', ls=':', lw=1.5, alpha=0.8,\n", + " label='pyspk'\n", + " )\n", + " else:\n", + " ax2.semilogx(\n", + " kh0[valid], py_sup[valid], color='k', ls=':', lw=1.5, alpha=0.8,\n", + " label=None\n", + " )\n", "\n", " ratio_err = camb_sup[valid] / py_sup[valid] - 1.0\n", " ax3.semilogx(kh0[valid], ratio_err, color=color, ls='-', lw=1.8, label=f'z={float(redshift):g}')\n", @@ -247,7 +274,10 @@ "ax2.axhline(1.0, color='k', lw=1, alpha=0.4)\n", "ax2.set_ylabel('Suppression factor')\n", "ax2.set_title('SP(k) suppression: CAMB vs pyspk')\n", - "ax2.legend(loc='lower left', ncol=2, fontsize=9)\n", + "if pyspk_handle is not None:\n", + " ax2.legend(handles=[*camb_handles, pyspk_handle], loc='lower left', fontsize=9)\n", + "else:\n", + " ax2.legend(loc='lower left', fontsize=9)\n", "\n", "ax3.axhline(0.0, color='k', lw=1, alpha=0.5)\n", "ax3.set_xlabel('k/h Mpc')\n", @@ -259,7 +289,7 @@ "ax2.axvline(8.0, color='gray', lw=1, ls='--', alpha=0.6)\n", "ax3.axvline(8.0, color='gray', lw=1, ls='--', alpha=0.6)\n", "ax2.text(\n", - " 8.0 * 0.5, 0.98, 'pyspk k_Ny~8', color='gray', fontsize=9,\n", + " 8.0 * 0.5, 0.98, 'SP(k) k_Ny~8', color='gray', fontsize=9,\n", " bbox=dict(facecolor='white', edgecolor='gray', alpha=0.8, boxstyle='round,pad=0.2')\n", ")\n", "\n", @@ -275,14 +305,6 @@ " rel12 = np.max(np.abs(camb_sup[valid12] / py_sup[valid12] - 1.0))\n", " print(f'z={float(redshift):g}: k<=8 -> {rel8:.2e}, k<=12 -> {rel12:.2e}')\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6df1e08d", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/fortran/SPkNonLinear.f90 b/fortran/SPkNonLinear.f90 index ae617798..939cb77f 100644 --- a/fortran/SPkNonLinear.f90 +++ b/fortran/SPkNonLinear.f90 @@ -122,6 +122,8 @@ subroutine TSPkNonLinear_GetNonLinRatios(this, State, CAMB_Pk) type(MatterPowerData), target :: CAMB_Pk integer :: itf, i real(dl) :: rk, spk_sup, spk_href, spk_eratio +logical, save :: warned_spk_z_outside = .false. +logical, save :: warned_spk_k_clamped = .false. if (.not. allocated(this%BaseModel)) allocate(THalofit::this%BaseModel) @@ -142,7 +144,14 @@ subroutine TSPkNonLinear_GetNonLinRatios(this, State, CAMB_Pk) end select do itf = 1, CAMB_Pk%num_z - if (CAMB_Pk%redshifts(itf) < SPk_calibrated_z_min .or. CAMB_Pk%redshifts(itf) > SPk_calibrated_z_max) cycle + if (CAMB_Pk%redshifts(itf) < SPk_calibrated_z_min .or. CAMB_Pk%redshifts(itf) > SPk_calibrated_z_max) then + if (FeedbackLevel > 0 .and. .not. warned_spk_z_outside) then + write(*,'(A,F8.3,A,F6.2,A,F6.2,A)') 'WARNING: SP(k) skipped outside calibrated redshift range. z=', & + CAMB_Pk%redshifts(itf), ', calibrated range=[', SPk_calibrated_z_min, ',', SPk_calibrated_z_max, '].' + warned_spk_z_outside = .true. + end if + cycle + end if select type (State) class is (CAMBdata) if (this%SPk_relation_kind == SPk_rel_cosmo_power_law .or. this%SPk_relation_kind == SPk_rel_double_power_law) then @@ -157,6 +166,13 @@ subroutine TSPkNonLinear_GetNonLinRatios(this, State, CAMB_Pk) rk = exp(CAMB_Pk%log_kh(i)) if (rk > this%Min_kh_nonlinear) then if (rk < SPk_calibrated_k_min) cycle + if (rk > SPk_calibrated_k_max) then + if (FeedbackLevel > 0 .and. .not. warned_spk_k_clamped) then + write(*,'(A,F8.3,A,F6.2,A)') 'WARNING: SP(k) input k exceeds calibrated range; clamping to k_max=', & + rk, ' -> ', SPk_calibrated_k_max, ' h/Mpc.' + warned_spk_k_clamped = .true. + end if + end if spk_sup = SPk_Suppression(this%SPk_SO, min(rk, SPk_calibrated_k_max), CAMB_Pk%redshifts(itf), & this%SPk_relation_kind, this%SPk_fb_a, this%SPk_fb_pow, this%SPk_fb_pivot, & this%SPk_alpha, this%SPk_beta, this%SPk_gamma, & From 2fef2a7e3f25cf4ec887cd6c591031a1c24e5eee Mon Sep 17 00:00:00 2001 From: Jaime Salcido Date: Sun, 19 Apr 2026 17:48:42 +0100 Subject: [PATCH 09/12] docs: update SP(k) model documentation with calibration details and warnings --- camb/nonlinear.py | 60 +++++++++++++++++++++++++++++---------- docs/SPk_demo.ipynb | 8 +++--- docs/source/nonlinear.rst | 18 ++++++++++++ 3 files changed, 67 insertions(+), 19 deletions(-) diff --git a/camb/nonlinear.py b/camb/nonlinear.py index 9abf49da..9860f6ad 100644 --- a/camb/nonlinear.py +++ b/camb/nonlinear.py @@ -99,9 +99,20 @@ class SPkNonLinear(NonLinearModel): """ SP(k) baryon suppression model applied on top of a base non-linear model. + Reference: + - SP(k) model: `arXiv:2305.09710 `_, + `MNRAS 523, 2247 (2023) `_ + The base model is evaluated first (Halofit by default), then SP(k) - suppression is applied multiplicatively as + suppression is applied to CAMB's non-linear ratio as + ``sqrt(P_NL/P_L) -> sqrt(P_NL/P_L) * sqrt(SPk_suppression)``. + + Notes: + - SP(k) calibration is defined for ``0 <= z <= 3`` and ``k <= 12 h/Mpc``. + Outside these ranges, behavior follows the Fortran implementation. + - SP(k) cannot be combined with HMCode baryon-feedback modes + (for example ``halofit_version='mead2020_feedback'``). """ _fields_ = ( @@ -183,20 +194,39 @@ def set_params( SPk_m_pivot=1.0, ): """ - Set SP(k) suppression parameters. - - :param base_model: Base non-linear model to wrap. Defaults to :class:`Halofit`. - :param SPk_feedback: Enable SP(k) suppression. - :param SPk_SO: Spherical overdensity calibration, 200 or 500. - :param SPk_relation_kind: 1=power_law, 2=cosmo_power_law, 3=double_power_law. - :param SPk_fb_a: Power-law relation normalization (kind 1). - :param SPk_fb_pow: Power-law relation exponent (kind 1). - :param SPk_fb_pivot: Power-law relation pivot mass [M_sun] (kind 1). - :param SPk_alpha: Relation alpha parameter (kinds 2/3). - :param SPk_beta: Relation beta parameter (kinds 2/3). - :param SPk_gamma: Relation gamma parameter (kinds 2/3). - :param SPk_epsilon: Relation epsilon parameter (kind 3). - :param SPk_m_pivot: Relation pivot mass [M_sun] (kind 3). + Set SP(k) model and relation parameters. + + Reference: + - SP(k) model: `arXiv:2305.09710 `_, + `MNRAS 523, 2247 (2023) `_ + + Relation-specific groups: + + - ``1`` (power_law): ``SPk_fb_a``, ``SPk_fb_pow``, ``SPk_fb_pivot`` + - ``2`` (cosmo_power_law): ``SPk_alpha``, ``SPk_beta``, ``SPk_gamma`` + - ``3`` (double_power_law): ``SPk_epsilon``, ``SPk_alpha``, + ``SPk_beta``, ``SPk_gamma``, ``SPk_m_pivot`` + + Parameter definitions: + + :param base_model: Base non-linear model instance to wrap. + If None, keeps current base model (default Halofit). + :param SPk_feedback: If True, apply SP(k) suppression on top of the base model. + :param SPk_SO: Spherical overdensity calibration. Allowed values: 200 or 500. + :param SPk_relation_kind: Relation type. + Allowed values: 1 (power_law), 2 (cosmo_power_law), 3 (double_power_law). + :param SPk_fb_a: Power-law normalization. + :param SPk_fb_pow: Power-law exponent. + :param SPk_fb_pivot: Power-law pivot mass in solar masses. + :param SPk_alpha: Alpha parameter. + :param SPk_beta: Beta parameter. + :param SPk_gamma: Gamma parameter. + :param SPk_epsilon: Epsilon parameter. + :param SPk_m_pivot: Pivot mass in solar masses. + :return: Self, for fluent configuration. + :raises CAMBValueError: If relation or pivot constraints are invalid, + or if configuration is incompatible with the selected Halofit + baryon-feedback mode. """ if base_model is not None: self.BaseModel = base_model diff --git a/docs/SPk_demo.ipynb b/docs/SPk_demo.ipynb index 77febcb5..79ee26b1 100644 --- a/docs/SPk_demo.ipynb +++ b/docs/SPk_demo.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "id": "b82be437", "metadata": {}, "outputs": [ @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "id": "e37d95d6", "metadata": {}, "outputs": [ @@ -114,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "id": "942282a9", "metadata": {}, "outputs": [ @@ -187,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "id": "69a98f59", "metadata": {}, "outputs": [ diff --git a/docs/source/nonlinear.rst b/docs/source/nonlinear.rst index 4022d03e..4a823afb 100644 --- a/docs/source/nonlinear.rst +++ b/docs/source/nonlinear.rst @@ -1,6 +1,24 @@ Non-linear models ================================== +SP(k) model notes +----------------- + +The ``SPkNonLinear`` model wraps a base non-linear prescription (Halofit by +default) and applies SP(k) suppression multiplicatively to CAMB's non-linear +ratio. + +The implementation follows the calibrated SP(k) range. In practice: + +- outside calibrated redshift range, SP(k) is not applied for that slice +- above calibrated ``k``, suppression is evaluated at calibrated ``k_max`` + +When ``FeedbackLevel > 0``, CAMB prints one-time warnings for these out-of-range +cases. + +SP(k) should not be combined with HMCode baryon-feedback modes (for example +``halofit_version='mead2020_feedback'``). + .. autoclass:: camb.nonlinear.NonLinearModel :members: From 828fba55c8b93845625a3fb00dc2faba99b5dea7 Mon Sep 17 00:00:00 2001 From: Jaime Salcido Date: Sun, 19 Apr 2026 17:58:10 +0100 Subject: [PATCH 10/12] docs: update SP(k) model references and expand mode definitions in docstrings --- camb/nonlinear.py | 25 ++++++++++++++++++------- 1 file changed, 18 insertions(+), 7 deletions(-) diff --git a/camb/nonlinear.py b/camb/nonlinear.py index 9860f6ad..51673adb 100644 --- a/camb/nonlinear.py +++ b/camb/nonlinear.py @@ -99,9 +99,11 @@ class SPkNonLinear(NonLinearModel): """ SP(k) baryon suppression model applied on top of a base non-linear model. - Reference: + References: - SP(k) model: `arXiv:2305.09710 `_, `MNRAS 523, 2247 (2023) `_ + - pyspk implementation details and mode definitions: + https://github.com/jemme07/pyspk The base model is evaluated first (Halofit by default), then SP(k) suppression is applied to CAMB's non-linear ratio as @@ -196,16 +198,25 @@ def set_params( """ Set SP(k) model and relation parameters. - Reference: + References: - SP(k) model: `arXiv:2305.09710 `_, `MNRAS 523, 2247 (2023) `_ + - For mode details and examples, see https://github.com/jemme07/pyspk - Relation-specific groups: + SP(k) modes: - - ``1`` (power_law): ``SPk_fb_a``, ``SPk_fb_pow``, ``SPk_fb_pivot`` - - ``2`` (cosmo_power_law): ``SPk_alpha``, ``SPk_beta``, ``SPk_gamma`` - - ``3`` (double_power_law): ``SPk_epsilon``, ``SPk_alpha``, - ``SPk_beta``, ``SPk_gamma``, ``SPk_m_pivot`` + - ``1`` (``power_law``): + ``f_b/(Omega_b/Omega_m) = a * (M_SO/M_pivot)^b`` + with ``a=SPk_fb_a``, ``b=SPk_fb_pow``, ``M_pivot=SPk_fb_pivot``. + + - ``2`` (``cosmo_power_law``, redshift-dependent power law): + ``f_b/(Omega_b/Omega_m) = (exp(alpha)/100) * (M_500c/1e14 Msun)^(beta-1) * (E(z)/E(0.3))^gamma`` + with ``alpha=SPk_alpha``, ``beta=SPk_beta``, ``gamma=SPk_gamma``. + + - ``3`` (``double_power_law``, redshift-dependent double power law): + ``f_b/(Omega_b/Omega_m) = 0.5 * epsilon * ((M_500c/M_pivot)^alpha + (M_500c/M_pivot)^beta) * (E(z)/E(0.3))^gamma`` + with ``epsilon=SPk_epsilon``, ``alpha=SPk_alpha``, ``beta=SPk_beta``, + ``gamma=SPk_gamma``, ``M_pivot=SPk_m_pivot``. Parameter definitions: From 5f99bdcee7d4ac3546d371be427d372be8c000ba Mon Sep 17 00:00:00 2001 From: Jaime Salcido Date: Sun, 19 Apr 2026 18:00:07 +0100 Subject: [PATCH 11/12] docs: clarify parameter descriptions in SPkNonLinear class --- camb/nonlinear.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/camb/nonlinear.py b/camb/nonlinear.py index 51673adb..08b7bb6c 100644 --- a/camb/nonlinear.py +++ b/camb/nonlinear.py @@ -207,15 +207,15 @@ def set_params( - ``1`` (``power_law``): ``f_b/(Omega_b/Omega_m) = a * (M_SO/M_pivot)^b`` - with ``a=SPk_fb_a``, ``b=SPk_fb_pow``, ``M_pivot=SPk_fb_pivot``. + where ``a=SPk_fb_a``, ``b=SPk_fb_pow``, ``M_pivot=SPk_fb_pivot``. - - ``2`` (``cosmo_power_law``, redshift-dependent power law): - ``f_b/(Omega_b/Omega_m) = (exp(alpha)/100) * (M_500c/1e14 Msun)^(beta-1) * (E(z)/E(0.3))^gamma`` - with ``alpha=SPk_alpha``, ``beta=SPk_beta``, ``gamma=SPk_gamma``. + - ``2`` (``cosmo_power_law``; redshift-dependent power law): + ``f_b/(Omega_b/Omega_m) = (exp(alpha)/100) * (M_500c/1e14 M_sun)^(beta-1) * (E(z)/E(0.3))^gamma`` + where ``alpha=SPk_alpha``, ``beta=SPk_beta``, ``gamma=SPk_gamma``. - - ``3`` (``double_power_law``, redshift-dependent double power law): + - ``3`` (``double_power_law``; redshift-dependent double power law): ``f_b/(Omega_b/Omega_m) = 0.5 * epsilon * ((M_500c/M_pivot)^alpha + (M_500c/M_pivot)^beta) * (E(z)/E(0.3))^gamma`` - with ``epsilon=SPk_epsilon``, ``alpha=SPk_alpha``, ``beta=SPk_beta``, + where ``epsilon=SPk_epsilon``, ``alpha=SPk_alpha``, ``beta=SPk_beta``, ``gamma=SPk_gamma``, ``M_pivot=SPk_m_pivot``. Parameter definitions: From 666fb157d93cac5de41cf3d5f62d24208d1086ba Mon Sep 17 00:00:00 2001 From: Jaime Salcido Date: Thu, 30 Apr 2026 23:15:52 +0100 Subject: [PATCH 12/12] refactor: standardize double quotes for strings in SPk_demo.ipynb --- docs/SPk_demo.ipynb | 102 ++++++++++++++++++++++---------------------- 1 file changed, 51 insertions(+), 51 deletions(-) diff --git a/docs/SPk_demo.ipynb b/docs/SPk_demo.ipynb index 79ee26b1..db4299e5 100644 --- a/docs/SPk_demo.ipynb +++ b/docs/SPk_demo.ipynb @@ -47,7 +47,7 @@ "import camb\n", "from camb import model\n", "\n", - "warnings.filterwarnings('ignore')\n", + "warnings.filterwarnings(\"ignore\")\n", "\n", "print(f\"Using CAMB {camb.__version__} installed at {os.path.dirname(camb.__file__)}\")\n", "print(f\"Using pyspk {pyspk.__version__}\")" @@ -80,7 +80,7 @@ "pars_base.set_matter_power(redshifts=z_samples, kmax=maxkh, k_per_logint=k_per_logint, silent=True)\n", "pars_base.NonLinear = model.NonLinear_both\n", "pars_base.NonLinearModel = camb.Halofit()\n", - "pars_base.NonLinearModel.set_params(halofit_version='mead2020')\n", + "pars_base.NonLinearModel.set_params(halofit_version=\"mead2020\")\n", "\n", "results_base = camb.get_results(pars_base)\n", "kh0, z0, pk0 = results_base.get_matter_power_spectrum(minkh=minkh, maxkh=maxkh, npoints=npoints)\n", @@ -91,7 +91,7 @@ "pars_spk.NonLinear = model.NonLinear_both\n", "\n", "spk_model = camb.SPkNonLinear()\n", - "spk_model.BaseModel.set_params(halofit_version='mead2020')\n", + "spk_model.BaseModel.set_params(halofit_version=\"mead2020\")\n", "spk_model.set_params(\n", " SPk_feedback=True,\n", " SPk_SO=200,\n", @@ -108,8 +108,8 @@ "assert np.allclose(kh0, kh1)\n", "assert np.allclose(z0, z1)\n", "z = np.array(z0)\n", - "print('redshifts:', z)\n", - "print('k_per_logint:', k_per_logint)" + "print(\"redshifts:\", z)\n", + "print(\"k_per_logint:\", k_per_logint)" ] }, { @@ -139,36 +139,36 @@ "from matplotlib.lines import Line2D\n", "\n", "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", - "linestyles = ['-', '--', ':', '-.']\n", + "linestyles = [\"-\", \"--\", \":\", \"-.\"]\n", "\n", "for i, redshift in enumerate(z):\n", " ls = linestyles[i % len(linestyles)]\n", - " ax[0].loglog(kh0, pk0[i, :], color='C3', ls=ls)\n", - " ax[0].loglog(kh1, pk1[i, :], color='C0', ls=ls)\n", - "ax[0].set_xlabel('k/h Mpc')\n", - "ax[0].set_ylabel('P(k) [(Mpc/h)^3]')\n", - "ax[0].set_title('Non-linear matter power')\n", + " ax[0].loglog(kh0, pk0[i, :], color=\"C3\", ls=ls)\n", + " ax[0].loglog(kh1, pk1[i, :], color=\"C0\", ls=ls)\n", + "ax[0].set_xlabel(\"k/h Mpc\")\n", + "ax[0].set_ylabel(\"P(k) [(Mpc/h)^3]\")\n", + "ax[0].set_title(\"Non-linear matter power\")\n", "\n", "model_handles = [\n", - " Line2D([], [], color='C3', lw=2, label='non-linear'),\n", - " Line2D([], [], color='C0', lw=2, label='non-linear + SP(k)'),\n", + " Line2D([], [], color=\"C3\", lw=2, label=\"non-linear\"),\n", + " Line2D([], [], color=\"C0\", lw=2, label=\"non-linear + SP(k)\"),\n", "]\n", "redshift_handles = [\n", - " Line2D([], [], color='k', lw=2, ls=linestyles[i % len(linestyles)], label=f'z={float(redshift):g}')\n", + " Line2D([], [], color=\"k\", lw=2, ls=linestyles[i % len(linestyles)], label=f\"z={float(redshift):g}\")\n", " for i, redshift in enumerate(z)\n", "]\n", - "leg_model = ax[0].legend(handles=model_handles, loc='lower left')\n", + "leg_model = ax[0].legend(handles=model_handles, loc=\"lower left\")\n", "ax[0].add_artist(leg_model)\n", - "ax[0].legend(handles=redshift_handles, title='Redshift', loc='upper right')\n", + "ax[0].legend(handles=redshift_handles, title=\"Redshift\", loc=\"upper right\")\n", "\n", "for i, redshift in enumerate(z):\n", " ls = linestyles[i % len(linestyles)]\n", - " ax[1].semilogx(kh0, pk1[i, :] / pk0[i, :], ls=ls, label=f'z={float(redshift):g}')\n", - "ax[1].axhline(1.0, color='k', lw=1)\n", - "ax[1].set_xlabel('k/h Mpc')\n", - "ax[1].set_ylabel('P_nl(SPk) / P_nl')\n", - "ax[1].set_title('Suppression ratio')\n", - "ax[1].legend(loc='lower left')\n", + " ax[1].semilogx(kh0, pk1[i, :] / pk0[i, :], ls=ls, label=f\"z={float(redshift):g}\")\n", + "ax[1].axhline(1.0, color=\"k\", lw=1)\n", + "ax[1].set_xlabel(\"k/h Mpc\")\n", + "ax[1].set_ylabel(\"P_nl(SPk) / P_nl\")\n", + "ax[1].set_title(\"Suppression ratio\")\n", + "ax[1].legend(loc=\"lower left\")\n", "\n", "plt.tight_layout()" ] @@ -238,10 +238,13 @@ "mask_plot = kh0 <= k_compare_max\n", "\n", "fig2, (ax2, ax3) = plt.subplots(\n", - " 2, 1, figsize=(10, 8), sharex=True,\n", + " 2,\n", + " 1,\n", + " figsize=(10, 8),\n", + " sharex=True,\n", " gridspec_kw={\"height_ratios\": [3, 1], \"hspace\": 0.12},\n", ")\n", - "colors = ['C0', 'C1', 'C2']\n", + "colors = [\"C0\", \"C1\", \"C2\"]\n", "camb_handles = []\n", "pyspk_handle = None\n", "\n", @@ -251,51 +254,48 @@ " py_sup = pyspk_sup_list[i]\n", " valid = mask_plot & np.isfinite(camb_sup) & np.isfinite(py_sup) & (np.abs(py_sup) > 1e-30)\n", "\n", - " line_camb, = ax2.semilogx(\n", - " kh0[valid], camb_sup[valid], color=color, ls='-', lw=2,\n", - " label=f'CAMB SP(k), z={float(redshift):g}'\n", + " (line_camb,) = ax2.semilogx(\n", + " kh0[valid], camb_sup[valid], color=color, ls=\"-\", lw=2, label=f\"CAMB SP(k), z={float(redshift):g}\"\n", " )\n", " camb_handles.append(line_camb)\n", "\n", " if pyspk_handle is None:\n", - " pyspk_handle, = ax2.semilogx(\n", - " kh0[valid], py_sup[valid], color='k', ls=':', lw=1.5, alpha=0.8,\n", - " label='pyspk'\n", - " )\n", + " (pyspk_handle,) = ax2.semilogx(kh0[valid], py_sup[valid], color=\"k\", ls=\":\", lw=1.5, alpha=0.8, label=\"pyspk\")\n", " else:\n", - " ax2.semilogx(\n", - " kh0[valid], py_sup[valid], color='k', ls=':', lw=1.5, alpha=0.8,\n", - " label=None\n", - " )\n", + " ax2.semilogx(kh0[valid], py_sup[valid], color=\"k\", ls=\":\", lw=1.5, alpha=0.8, label=None)\n", "\n", " ratio_err = camb_sup[valid] / py_sup[valid] - 1.0\n", - " ax3.semilogx(kh0[valid], ratio_err, color=color, ls='-', lw=1.8, label=f'z={float(redshift):g}')\n", + " ax3.semilogx(kh0[valid], ratio_err, color=color, ls=\"-\", lw=1.8, label=f\"z={float(redshift):g}\")\n", "\n", - "ax2.axhline(1.0, color='k', lw=1, alpha=0.4)\n", - "ax2.set_ylabel('Suppression factor')\n", - "ax2.set_title('SP(k) suppression: CAMB vs pyspk')\n", + "ax2.axhline(1.0, color=\"k\", lw=1, alpha=0.4)\n", + "ax2.set_ylabel(\"Suppression factor\")\n", + "ax2.set_title(\"SP(k) suppression: CAMB vs pyspk\")\n", "if pyspk_handle is not None:\n", - " ax2.legend(handles=[*camb_handles, pyspk_handle], loc='lower left', fontsize=9)\n", + " ax2.legend(handles=[*camb_handles, pyspk_handle], loc=\"lower left\", fontsize=9)\n", "else:\n", - " ax2.legend(loc='lower left', fontsize=9)\n", + " ax2.legend(loc=\"lower left\", fontsize=9)\n", "\n", - "ax3.axhline(0.0, color='k', lw=1, alpha=0.5)\n", - "ax3.set_xlabel('k/h Mpc')\n", - "ax3.set_ylabel('CAMB/pyspk - 1')\n", + "ax3.axhline(0.0, color=\"k\", lw=1, alpha=0.5)\n", + "ax3.set_xlabel(\"k/h Mpc\")\n", + "ax3.set_ylabel(\"CAMB/pyspk - 1\")\n", "ax3.set_ylim(-0.01, 0.01)\n", - "ax3.set_title('Relative error (±1%)')\n", + "ax3.set_title(\"Relative error (±1%)\")\n", "\n", "# Reference marker: pyspk Nyquist limit\n", - "ax2.axvline(8.0, color='gray', lw=1, ls='--', alpha=0.6)\n", - "ax3.axvline(8.0, color='gray', lw=1, ls='--', alpha=0.6)\n", + "ax2.axvline(8.0, color=\"gray\", lw=1, ls=\"--\", alpha=0.6)\n", + "ax3.axvline(8.0, color=\"gray\", lw=1, ls=\"--\", alpha=0.6)\n", "ax2.text(\n", - " 8.0 * 0.5, 0.98, 'SP(k) k_Ny~8', color='gray', fontsize=9,\n", - " bbox=dict(facecolor='white', edgecolor='gray', alpha=0.8, boxstyle='round,pad=0.2')\n", + " 8.0 * 0.5,\n", + " 0.98,\n", + " \"SP(k) k_Ny~8\",\n", + " color=\"gray\",\n", + " fontsize=9,\n", + " bbox=dict(facecolor=\"white\", edgecolor=\"gray\", alpha=0.8, boxstyle=\"round,pad=0.2\"),\n", ")\n", "\n", "plt.tight_layout()\n", "\n", - "print('Max relative error between CAMB SP(k) and pyspk:')\n", + "print(\"Max relative error between CAMB SP(k) and pyspk:\")\n", "for i, redshift in enumerate(z):\n", " camb_sup = pk1[i, :] / pk0[i, :]\n", " py_sup = pyspk_sup_list[i]\n", @@ -303,7 +303,7 @@ " valid12 = (kh0 <= 12.0) & np.isfinite(camb_sup) & np.isfinite(py_sup) & (np.abs(py_sup) > 1e-30)\n", " rel8 = np.max(np.abs(camb_sup[valid8] / py_sup[valid8] - 1.0))\n", " rel12 = np.max(np.abs(camb_sup[valid12] / py_sup[valid12] - 1.0))\n", - " print(f'z={float(redshift):g}: k<=8 -> {rel8:.2e}, k<=12 -> {rel12:.2e}')\n" + " print(f\"z={float(redshift):g}: k<=8 -> {rel8:.2e}, k<=12 -> {rel12:.2e}\")" ] } ],