diff --git a/pyccl/baryons/__init__.py b/pyccl/baryons/__init__.py
index 344b2bf61..5a99ccb88 100644
--- a/pyccl/baryons/__init__.py
+++ b/pyccl/baryons/__init__.py
@@ -2,3 +2,4 @@
 from .schneider15 import *
 from .baccoemu_baryons import *
 from .vandaalen19 import *
+from .fedeli14 import *
diff --git a/pyccl/baryons/fedeli14.py b/pyccl/baryons/fedeli14.py
new file mode 100644
index 000000000..46b1b08d5
--- /dev/null
+++ b/pyccl/baryons/fedeli14.py
@@ -0,0 +1,352 @@
+"""Fedeli14 baryon halo model."""
+
+__all__ = ("BaryonsFedeli14",)
+
+from typing import Any, Mapping
+import numpy as np
+from numpy.typing import NDArray
+
+from .. import Pk2D
+from . import Baryons
+
+from ..baryons.fedeli14_bhm.baryon_halo_model import BaryonHaloModel
+
+FloatArray = NDArray[np.floating[Any]]
+ComponentsDict = dict[str, Any]
+
+
+class BaryonsFedeli14(Baryons):
+    r"""Fedeli14 baryon halo model implemented as a multiplicative boost.
+
+    This model modifies an input matter power spectrum by multiplying it by a
+    scale- and redshift-dependent boost computed from the Fedeli (2014) baryon
+    halo model (BHM) <https://arxiv.org/abs/1401.2997>`.
+
+    The operation applied is:
+
+    .. math::
+        P_{\rm out}(k,a) = P_{\rm in}(k,a)\,f_{\rm fedeli}(k,a).
+
+    Notes:
+        - CCL ``Pk2D`` uses ``k`` in units of 1/Mpc, and this implementation
+          keeps ``k`` in 1/Mpc throughout (no k -> k/h conversion).
+        - If ``renormalize_large_scales=True``, the boost is rescaled (per
+          scale factor) so that its mean over ``k <= k_renorm_max`` is unity.
+        - For ``a < a_min``, the boost is forced to unity. The default
+          ``a_min=0.1`` avoids extrapolating the model to very early times.
+
+    See Also:
+        BaryonHaloModel: Lower-level wrapper that computes spectra and boosts.
+    """
+    name = "Fedeli14"
+
+    __repr_attrs__ = __eq_attrs__ = (
+        # diffuse mixing (physics)
+        "Fg", "bd",
+        # mass fractions (physics)
+        "rho_star", "m0_star", "sigma_star", "mmin_star", "mmax_star",
+        "m0_gas", "sigma_gas",
+        # gas profile (physics)
+        "gas_beta", "gas_r_co", "gas_r_ej",
+        # stellar profile (physics)
+        "star_x_delta", "star_alpha",
+        # optional DM knob (physics)
+        "concentration",
+        # reference used inside BHM.boost
+        "pk_ref",
+        # numerics/plumbing
+        "mass_def", "mass_function", "halo_bias",
+        "mass_ranges", "interpolation_grid",
+        "update_fftlog_precision", "fftlog_kwargs", "rgi_kwargs",
+        "n_m", "density_mmin", "density_mmax",
+        "a_min",
+    )
+
+    def __init__(
+        self,
+        *,
+        Fg: float = 0.9,
+        bd: float = 0.6,
+        rho_star: float | None = None,
+        m0_star: float = 5.0e12,
+        sigma_star: float = 1.2,
+        mmin_star: float = 1.0e10,
+        mmax_star: float = 1.0e15,
+        m0_gas: float = 1.0e12,
+        sigma_gas: float = 3.0,
+        gas_beta: float = 2.9,
+        gas_r_co: float = 0.1,
+        gas_r_ej: float = 4.5,
+        star_x_delta: float = 1.0 / 0.03,
+        star_alpha: float = 1.0,
+        concentration: Any = None,
+        pk_ref: str = "pk_dmo",
+        mass_def: Any = None,
+        mass_function: Any = None,
+        halo_bias: Any = None,
+        mass_ranges: Any = None,
+        interpolation_grid: Any = None,
+        update_fftlog_precision: bool = True,
+        fftlog_kwargs: Mapping[str, Any] | None = None,
+        rgi_kwargs: Mapping[str, Any] | None = None,
+        n_m: int = 512,
+        density_mmin: float = 1e6,
+        density_mmax: float = 1e16,
+        renormalize_large_scales: bool = True,
+        k_renorm_max: float = 1e-2,
+        a_min: float = 0.1,
+    ) -> None:
+        """Initialize Fedeli14 baryon halo model."""
+        self.Fg = float(Fg)
+        self.bd = float(bd)
+
+        self.rho_star = rho_star
+        self.m0_star = float(m0_star)
+        self.sigma_star = float(sigma_star)
+        self.mmin_star = float(mmin_star)
+        self.mmax_star = float(mmax_star)
+        self.m0_gas = float(m0_gas)
+        self.sigma_gas = float(sigma_gas)
+
+        self.gas_beta = float(gas_beta)
+        self.gas_r_co = float(gas_r_co)
+        self.gas_r_ej = float(gas_r_ej)
+
+        self.star_x_delta = float(star_x_delta)
+        self.star_alpha = float(star_alpha)
+
+        self.concentration = concentration
+        self.pk_ref = str(pk_ref)
+
+        self.mass_def = mass_def
+        self.mass_function = mass_function
+        self.halo_bias = halo_bias
+        self.mass_ranges = mass_ranges
+        self.interpolation_grid = interpolation_grid
+        self.update_fftlog_precision = bool(update_fftlog_precision)
+        self.fftlog_kwargs = dict(fftlog_kwargs or {})
+        self.rgi_kwargs = dict(rgi_kwargs or {})
+        self.n_m = int(n_m)
+        self.density_mmin = float(density_mmin)
+        self.density_mmax = float(density_mmax)
+
+        self.renormalize_large_scales = bool(renormalize_large_scales)
+        self.k_renorm_max = float(k_renorm_max)
+        self.a_min = float(a_min)
+
+    def update_parameters(self, **kwargs: Any) -> None:
+        """Update model parameters on this instance.
+
+        This is a convenience method for interactive work and testing.
+        Any keyword matching an existing attribute on this class will be
+        updated, with common numeric parameters cast to ``float`` or
+        ``int`` as appropriate.
+
+        Passing ``None`` for a parameter leaves the current value unchanged.
+
+        Args:
+            **kwargs: Parameter names and values to update.
+
+        Raises:
+            AttributeError: If a provided parameter name does not exist.
+        """
+        for key, val in kwargs.items():
+            if val is None:
+                continue
+            # cast common numeric params
+            if key in {
+                "Fg", "bd", "m0_star", "sigma_star", "mmin_star", "mmax_star",
+                "m0_gas", "sigma_gas", "gas_beta", "gas_r_co", "gas_r_ej",
+                "star_x_delta", "star_alpha", "density_mmin", "density_mmax",
+                "a_min",
+            }:
+                val = float(val)
+            elif key == "n_m":
+                val = int(val)
+            elif key in {"update_fftlog_precision",
+                         "renormalize_large_scales"}:
+                val = bool(val)
+            elif key == "pk_ref":
+                val = str(val)
+
+            if not hasattr(self, key):
+                raise AttributeError(f"Unknown parameter {key!r}")
+            setattr(self, key, val)
+
+    def _build_bhm(self, cosmo: Any) -> BaryonHaloModel:
+        """Build a ``BaryonHaloModel`` configured with the current parameters.
+
+        This creates the internal Fedeli14 baryon halo model wrapper used to
+        compute boosts and related spectra. The returned object is
+        configured using the physics and numerical settings stored on this
+        instance.
+
+        Args:
+            cosmo: Cosmology object passed through to the underlying model.
+
+        Returns:
+            A configured ``BaryonHaloModel`` instance.
+        """
+        return BaryonHaloModel(
+            cosmo=cosmo,
+            # plumbing
+            mass_def=self.mass_def,
+            mass_function=self.mass_function,
+            halo_bias=self.halo_bias,
+            concentration=self.concentration,
+            # mass fractions
+            rho_star=self.rho_star,
+            m0_star=self.m0_star,
+            sigma_star=self.sigma_star,
+            mmin_star=self.mmin_star,
+            mmax_star=self.mmax_star,
+            m0_gas=self.m0_gas,
+            sigma_gas=self.sigma_gas,
+            # profiles
+            gas_beta=self.gas_beta,
+            gas_r_co=self.gas_r_co,
+            gas_r_ej=self.gas_r_ej,
+            star_x_delta=self.star_x_delta,
+            star_alpha=self.star_alpha,
+            # diffuse mixing
+            Fg=self.Fg,
+            bd=self.bd,
+            # numerics
+            mass_ranges=self.mass_ranges,
+            interpolation_grid=self.interpolation_grid,
+            update_fftlog_precision=self.update_fftlog_precision,
+            fftlog_kwargs=self.fftlog_kwargs,
+            rgi_kwargs=self.rgi_kwargs,
+            n_m=self.n_m,
+            density_mmin=self.density_mmin,
+            density_mmax=self.density_mmax,
+        )
+
+    def boost_factor(
+        self,
+        cosmo: Any,
+        k: float | FloatArray,
+        a: float | FloatArray
+    ) -> float | FloatArray:
+        r"""Evaluate the Fedeli14 boost factor ``f(k,a)``.
+
+        The boost is defined as a ratio of the baryonic halo-model matter
+        spectrum to a chosen reference spectrum:
+
+        .. math::
+            f(k,a) = \frac{P_{\rm bar}^{\rm HM}(k,a)}{P_{\rm ref}(k,a)}.
+
+        The reference is controlled by ``self.pk_ref`` (e.g. "pk_dmo",
+        "pk_nlin", "pk_lin") and is interpreted by ``BaryonHaloModel.boost``.
+
+        Input arrays are broadcast to a 2D grid with shape (na, nk), and the
+        return value follows the input scalar/array structure:
+
+        - if both ``a`` and ``k`` are arrays: returns (na, nk)
+        - if ``a`` is scalar and ``k`` is array: returns (nk,)
+        - if ``a`` is array and ``k`` is scalar: returns (na,)
+        - if both are scalars: returns scalar
+
+        Notes:
+            - ``k`` is interpreted in units of 1/Mpc and is not rescaled by
+              ``h``.
+            - If ``renormalize_large_scales=True``, the boost is normalized so
+              that its mean over ``k <= k_renorm_max`` is unity for each
+              scale factor.
+
+        Args:
+            cosmo: CCL Cosmology object.
+            k: Wavenumber(s) in units of 1/Mpc.
+            a: Scale factor(s).
+
+        Returns:
+            Boost factor evaluated on the broadcasted (a, k) grid.
+        """
+        a_use, k_use = map(np.atleast_1d, [a, k])
+        a_use, k_use = a_use[:, None], k_use[None, :]  # (na,1), (1,nk)
+
+        # IMPORTANT: we do NOT convert k -> k/h here.
+        # We keep k in 1/Mpc everywhere so the pk_ref division uses the same k
+        # units.
+        k_1d = k_use.ravel()  # 1D array, 1/Mpc
+
+        bhm = self._build_bhm(cosmo)
+
+        out = np.empty((a_use.shape[0], k_use.shape[1]), dtype=float)
+        for i, aval in enumerate(a_use[:, 0]):
+            out[i, :] = bhm.boost(k=k_1d, a=float(aval), pk_ref=self.pk_ref)
+
+        # We need to enforce f(k->0)=1 by renormalizing on large scales.
+        # Otherwise, our boost will not behave at large scales.
+        if self.renormalize_large_scales:
+            k0 = self.k_renorm_max
+            m = k_1d <= k0
+            if np.any(m):
+                # Rescale the boost per-a so that its mean over a low-k band is
+                # unity. This is a pragmatic large-scale renormalization of
+                # the *ratio* to pk_ref: it removes small low-k offsets from
+                # numerics/pk_ref choice. It does not enforce B(k->0)=1
+                # pointwise, only that the average over k<=k_renorm_max is 1.
+                norm = np.mean(out[:, m], axis=1)
+                norm = np.where(np.isfinite(norm) & (norm > 0.0), norm, 1.0)
+
+                out = out / norm[:, None]
+                out = np.where(np.isfinite(out) & (out > 0.0), out, 1.0)
+                out = np.clip(out, 1e-6, 1e6)
+
+        if np.ndim(k) == 0:
+            out = np.squeeze(out, axis=-1)
+        if np.ndim(a) == 0:
+            out = np.squeeze(out, axis=0)
+        return out
+
+    def _include_baryonic_effects(
+        self,
+        cosmo: Any,
+        pk: Pk2D,
+    ) -> Pk2D:
+        r"""Return a new ``Pk2D`` with Fedeli14 baryonic effects applied.
+
+        The baryonic correction is multiplicative in the physical spectrum,
+
+        .. math::
+            P_{\rm out}(k,a) = P_{\rm in}(k,a)\,f(k,a).
+
+        If the output ``Pk2D`` is stored in log-space, this method converts the
+        physical input spectrum to ``log(P)`` before adding ``log(f)``."""
+        a_arr, lk_arr, pk_arr = pk.get_spline_arrays()
+        pk_arr = pk_arr.copy()
+        k_arr = np.exp(lk_arr)
+
+        bhm = self._build_bhm(cosmo)
+        k_model = bhm.interpolation_grid["dark_matter"]["k"]
+        kmin_model = k_model.min()
+        kmax_model = k_model.max()
+
+        rtol = 1e-12
+        fka = np.ones((a_arr.size, k_arr.size))
+
+        amask = a_arr >= self.a_min
+        kmask = (
+            (k_arr >= kmin_model * (1.0 - rtol))
+            & (k_arr <= kmax_model * (1.0 + rtol))
+        )
+
+        if np.any(amask) and np.any(kmask):
+            fka[np.ix_(amask, kmask)] = self.boost_factor(
+                cosmo, k_arr[kmask], a_arr[amask]
+            )
+
+        pk_arr *= fka
+
+        if pk.psp.is_log:
+            np.log(pk_arr, out=pk_arr)
+
+        return Pk2D(
+            a_arr=a_arr,
+            lk_arr=lk_arr,
+            pk_arr=pk_arr,
+            is_logp=pk.psp.is_log,
+            extrap_order_lok=pk.extrap_order_lok,
+            extrap_order_hik=pk.extrap_order_hik,
+        )
diff --git a/pyccl/baryons/fedeli14_bhm/__init__.py b/pyccl/baryons/fedeli14_bhm/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/pyccl/baryons/fedeli14_bhm/baryon_halo_model.py b/pyccl/baryons/fedeli14_bhm/baryon_halo_model.py
new file mode 100644
index 000000000..8f311e5ed
--- /dev/null
+++ b/pyccl/baryons/fedeli14_bhm/baryon_halo_model.py
@@ -0,0 +1,740 @@
+"""Baryon halo model by Fedeli (2014)."""
+
+from __future__ import annotations
+
+from typing import Any, Literal
+
+import numpy as np
+import pyccl as ccl
+
+from pyccl.baryons.fedeli14_bhm.mass_fractions import mass_fractions
+from pyccl.baryons.fedeli14_bhm.halo_profiles import (
+    GasHaloProfile,
+    StellarHaloProfile,
+    nfw_profile
+)
+from .profile_interpolation import build_profile_interpolators
+from .power_spectra import FedeliPkCalculator
+from .numerics import (
+    _require_a,
+    _require_k,
+)
+
+
+__all__ = ("BaryonHaloModel",)
+
+
+class BaryonHaloModel:
+    r"""Fedeli (2014) baryon halo model wrapper.
+
+    This class provides a high-level interface for computing halo-model power
+    spectra for a three-component matter field (dark matter, gas, stars). It
+    connects:
+
+    - a mass-fraction prescription that defines component fractions as
+      functions of halo mass and scale factor,
+    - halo density profiles for each component,
+    - Fourier-space profile interpolation for efficient evaluation of
+      :math:`y_X(M,k) = u_X(k|M)/M`,
+    - and a halo-model power spectrum calculator.
+
+    The main outputs are the baryonic halo-model total matter power spectrum
+    :math:`P_{\rm bar}^{\rm HM}(k,a)` and a boost factor relative to a chosen
+    reference spectrum:
+
+    .. math::
+        B(k,a) = \frac{P_{\rm bar}^{\rm HM}(k,a)}{P_{\rm ref}(k,a)}.
+
+    Notes
+    -----
+    - Mass fractions, profiles, and Fourier interpolators are cached per scale
+      factor ``a``.
+    - The default boost reference ``pk_ref="pk_dmo"`` is a halo-model DMO
+      baseline constructed consistently with the same machinery.
+    - This class also outputs the individual halo-model components via
+      ``pk_components`.
+    """
+    def __init__(
+        self,
+        *,
+        cosmo: Any,
+        # halo model plumbing
+        mass_def: Any | None = None,
+        mass_function: Any | None = None,
+        halo_bias: Any | None = None,
+        # DM profile knobs (optional)
+        concentration: Any | None = None,
+        # (1) MASS FRACTIONS
+        rho_star: float | None = None,
+        m0_star: float = 5.0e12,
+        sigma_star: float = 1.2,
+        mmin_star: float = 1.0e10,
+        mmax_star: float = 1.0e15,
+        m0_gas: float = 1.0e12,
+        sigma_gas: float = 3.0,
+        # (2) PROFILE SHAPES
+        gas_beta: float = 2.9,
+        gas_r_co: float = 0.1,
+        gas_r_ej: float = 4.5,
+        gas_x_min: float = 1.0e-3,
+        gas_x_max: float = 50.0,
+        gas_n_x: int = 500,
+        star_x_delta: float = 1.0 / 0.03,
+        star_alpha: float = 1.0,
+        # (3) DIFFUSE GAS MIXING
+        Fg: float = 0.9,
+        bd: float = 0.6,
+        # override profiles (optional)
+        profiles: dict[str, Any] | None = None,
+        # numerical knobs
+        mass_ranges: dict[str, dict[str, float]] | None = None,
+        interpolation_grid: dict[str, dict[str, np.ndarray]] | None = None,
+        update_fftlog_precision: bool = True,
+        fftlog_kwargs: dict[str, Any] | None = None,
+        rgi_kwargs: dict[str, Any] | None = None,
+        n_m: int = 512,
+        density_mmin: float = 1e6,
+        density_mmax: float = 1e16,
+    ):
+        r"""Initialize the Fedeli14 baryon halo model wrapper.
+
+        Sets the model plumbing (mass definition, mass function, halo bias),
+        the physical parameters for mass fractions and profiles, diffuse-gas
+        mixing parameters, and numerical grids for interpolation and integrals.
+
+        Args:
+            cosmo: Cosmology used for densities and reference power spectra.
+            mass_def: Halo mass definition. Defaults to
+                ``MassDef(200, "matter")``.
+            mass_function: Halo mass function (CCL convention).
+                Defaults to Tinker08.
+            halo_bias: Halo bias (CCL convention). Defaults to Tinker10.
+            concentration: Optional concentration-mass relation for
+                the NFW DM profile.
+            rho_star: Optional stellar density normalization used by the
+                mass-fraction model.
+            m0_star: Stellar mass-fraction parameter.
+            sigma_star: Stellar mass-fraction parameter.
+            mmin_star: Minimum mass used by the stellar mass-fraction model.
+            mmax_star: Maximum mass used by the stellar mass-fraction model.
+            m0_gas: Gas mass-fraction parameter.
+            sigma_gas: Gas mass-fraction parameter.
+            gas_beta: Gas profile shape parameter.
+            gas_r_co: Gas profile shape parameter.
+            gas_r_ej: Gas profile shape parameter.
+            gas_x_min: Minimum radius (in units of halo radius) used to
+                tabulate gas profiles.
+            gas_x_max: Maximum radius (in units of halo radius) used to
+                tabulate gas profiles.
+            gas_n_x: Number of radial samples used to tabulate gas profiles.
+            star_x_delta: Scale parameter for the stellar profile.
+            star_alpha: Slope parameter for the stellar profile.
+            Fg: Fraction of gas in the halo-tracing component.
+                Must satisfy 0 <= Fg <= 1.
+            bd: Bias-like coupling for the diffuse gas contribution.
+                Must be finite.
+            profiles: Optional mapping component -> profile object.
+                If provided, profiles
+                are not constructed internally.
+            mass_ranges: Component mass ranges for halo-model integrals,
+                mapping component -> {"min": ..., "max": ...}.
+            interpolation_grid: Interpolation grids for Fourier profiles,
+                mapping component -> {"mass": mass_grid, "k": k_grid}.
+            update_fftlog_precision: If True, update FFTLog precision on
+                profiles when supported.
+            fftlog_kwargs: Keyword arguments forwarded to FFTLog precision
+                updates.
+            rgi_kwargs: Keyword arguments forwarded to the interpolator
+                builder.
+            n_m: Number of mass samples used in halo-model integrals.
+            density_mmin: Minimum mass used to integrate mass fractions into
+                mean densities.
+            density_mmax: Maximum mass used to integrate mass fractions into
+                mean densities.
+
+        Raises:
+            ValueError: If Fg is outside [0, 1], if bd is non-finite, or if
+                the density integration limits do not satisfy
+                0 < density_mmin < density_mmax.
+        """
+        self.cosmo = cosmo
+
+        self.mass_def = mass_def or ccl.halos.MassDef(200, "matter")
+        self.mass_function = mass_function or ccl.halos.MassFuncTinker08(
+            mass_def=self.mass_def, mass_def_strict=False
+        )
+        self.halo_bias = halo_bias or ccl.halos.HaloBiasTinker10(
+            mass_def=self.mass_def, mass_def_strict=False
+        )
+
+        self.concentration = concentration
+
+        # store physical params
+        self._mass_frac_params = {
+            "rho_star": rho_star,
+            "m0_star": float(m0_star),
+            "sigma_star": float(sigma_star),
+            "mmin_star": float(mmin_star),
+            "mmax_star": float(mmax_star),
+            "m0_gas": float(m0_gas),
+            "sigma_gas": float(sigma_gas),
+        }
+        self._gas_profile_params = {
+            "beta": float(gas_beta),
+            "r_co": float(gas_r_co),
+            "r_ej": float(gas_r_ej),
+            "x_min": float(gas_x_min),
+            "x_max": float(gas_x_max),
+            "n_x": int(gas_n_x),
+        }
+        self._star_profile_params = {
+            "x_delta": float(star_x_delta),
+            "alpha": float(star_alpha),
+        }
+
+        self.Fg = float(Fg)
+        self.bd = float(bd)
+
+        # numerical knobs
+        self.update_fftlog_precision = bool(update_fftlog_precision)
+        self.fftlog_kwargs = dict(fftlog_kwargs or {})
+        self.rgi_kwargs = dict(rgi_kwargs or {})
+        self.n_m = int(n_m)
+        self.density_mmin = float(density_mmin)
+        self.density_mmax = float(density_mmax)
+
+        if not (0.0 <= self.Fg <= 1.0):
+            raise ValueError(f"Fg must be in [0, 1], got {self.Fg}.")
+        if not np.isfinite(self.bd):
+            raise ValueError("bd must be finite.")
+        if (
+                self.density_mmin <= 0
+                or self.density_mmax <= 0
+                or self.density_mmax <= self.density_mmin
+        ):
+            raise ValueError(
+                "density_mmin/density_mmax must satisfy 0 < min < max."
+            )
+
+        if mass_ranges is None:
+            mass_ranges = {
+                "dark_matter": {"min": 1e6, "max": 1e16},
+                "gas": {"min": 1e6, "max": 1e16},
+                "stars": {"min": 1e6, "max": 1e16},
+            }
+        self.mass_ranges = mass_ranges
+
+        if interpolation_grid is None:
+            mass_grid = np.logspace(6, 16, 128)
+            k_grid = np.logspace(-4, 2, 256)
+            interpolation_grid = {
+                "dark_matter": {"mass": mass_grid, "k": k_grid},
+                "gas": {"mass": mass_grid, "k": k_grid},
+                "stars": {"mass": mass_grid, "k": k_grid},
+            }
+        self.interpolation_grid = interpolation_grid
+        self._validate_ranges_vs_interp()
+
+        self._profiles_override = profiles
+        self._dmo_dm_interp_cache: dict[float, Any] = {}
+
+        # caches (per a)
+        self._frac_cache: dict[float, tuple[Any, Any, Any]] = {}
+        self._profile_cache: dict[float, dict[str, Any]] = {}
+        self._interp_cache: dict[float, dict[str, Any]] = {}
+
+    def _validate_ranges_vs_interp(self) -> None:
+        """Validate that halo-model mass ranges are contained in interpolation
+           grids.
+
+        Raises:
+            ValueError: If any component mass range extends beyond the
+                interpolation mass grid for that component.
+        """
+        for comp in ("dark_matter", "gas", "stars",):
+            r = self.mass_ranges[comp]
+            g = self.interpolation_grid[comp]
+            mmin, mmax = float(r["min"]), float(r["max"])
+            mgrid = np.asarray(g["mass"], dtype=float)
+            if mmin < mgrid.min() or mmax > mgrid.max():
+                raise ValueError(
+                    f"{comp}: mass_ranges [{mmin:.3e}, {mmax:.3e}] exceed "
+                    f"interpolation mass grid [{mgrid.min():.3e},"
+                    f" {mgrid.max():.3e}]."
+                )
+
+    def clear_cache(self) -> None:
+        """Clear cached mass fractions, profiles, and Fourier interpolators.
+
+        This forces regeneration of mass fractions, profile objects, and
+        interpolation objects on the next request at a given scale factor.
+        """
+        self._frac_cache.clear()
+        self._profile_cache.clear()
+        self._interp_cache.clear()
+        self._dmo_dm_interp_cache.clear()
+
+    def _mass_frac_kwargs(self) -> dict[str, Any]:
+        """Return mass-fraction model parameters that are explicitly set.
+
+        Only parameters with non-None values are forwarded to the mass-fraction
+        model constructor.
+        """
+        out = {}
+        for k, v in self._mass_frac_params.items():
+            if v is not None:
+                out[k] = v
+        return out
+
+    def _get_mass_fractions(self, a: float):
+        """Return component mass-fraction callables at scale factor a.
+
+        Returns callables (f_gas, f_star, f_dm) that map halo mass M to the
+        corresponding mass fraction at the given scale factor.
+        Results are cached per a.
+
+        Args:
+            a: Scale factor.
+
+        Returns:
+            Tuple of callables (f_gas, f_star, f_dm), each callable maps f(M)
+            to fraction.
+        """
+        a = float(a)
+        hit = self._frac_cache.get(a)
+        if hit is not None:
+            return hit
+
+        f_gas, f_star, f_dm = mass_fractions(
+            cosmo=self.cosmo,
+            a=a,
+            mass_function=self.mass_function,
+            **self._mass_frac_kwargs(),
+        )
+        self._frac_cache[a] = (f_gas, f_star, f_dm)
+        return f_gas, f_star, f_dm
+
+    def _get_profiles(self, a: float) -> dict[str, Any]:
+        """Return halo profile objects for dark matter, gas, and stars at scale
+           factor a.
+
+        If profile overrides were provided, they are returned directly.
+        Otherwise, profiles are constructed from the current mass-fraction
+        callables and cached per a.
+
+        Args:
+            a: Scale factor.
+
+        Returns:
+            Dict mapping component name -> profile object.
+        """
+        a = float(a)
+        if self._profiles_override is not None:
+            return self._profiles_override
+
+        hit = self._profile_cache.get(a)
+        if hit is not None:
+            return hit
+
+        f_gas, f_star, _ = self._get_mass_fractions(a)
+
+        prof_g = GasHaloProfile(
+            mass_def=self.mass_def, f_gas=f_gas, **self._gas_profile_params
+        )
+        prof_s = StellarHaloProfile(
+            mass_def=self.mass_def, f_star=f_star, **self._star_profile_params
+        )
+        prof_dm = nfw_profile(
+            mass_def=self.mass_def, concentration=self.concentration)
+
+        out = {
+            "dark_matter": prof_dm,
+            "gas": prof_g,
+            "stars": prof_s,
+        }
+        self._profile_cache[a] = out
+        return out
+
+    def _get_interpolators(self, a: float) -> dict[str, Any]:
+        r"""Return Fourier-space profile interpolators at scale factor a.
+
+        The returned mapping provides callables for each component that
+        evaluate y(M,k) = u(k|M)/M on demand. Interpolators are cached per a.
+
+        Args:
+            a: Scale factor.
+
+        Returns:
+            Dict mapping component name to interpolator callable y(M, k).
+        """
+        a = float(a)
+        hit = self._interp_cache.get(a)
+        if hit is not None:
+            return hit
+
+        profiles = self._get_profiles(a)
+        interps = build_profile_interpolators(
+            cosmo=self.cosmo,
+            a=a,
+            interpolation_grid=self.interpolation_grid,
+            profiles=profiles,
+            update_fftlog_precision=self.update_fftlog_precision,
+            fftlog_kwargs=self.fftlog_kwargs,
+            rgi_kwargs=self.rgi_kwargs,
+        )
+        self._interp_cache[a] = interps
+        return interps
+
+    def _get_dmo_dm_interpolator(self, a: float) -> Any:
+        """Return the baseline DMO dark-matter Fourier interpolator at scale
+           factor a.
+
+        This provides the reference dark-matter interpolator used for the DMO
+        halo-model baseline, independent of any baryonic modification to the
+        dark-matter profile. Cached per a.
+
+        Args:
+            a: Scale factor.
+
+        Returns:
+            Callable or interpolator object for the baseline DM y(M, k).
+        """
+        a = float(a)
+        hit = self._dmo_dm_interp_cache.get(a)
+        if hit is not None:
+            return hit
+
+        prof_dm_dmo = nfw_profile(mass_def=self.mass_def,
+                                  concentration=self.concentration)
+
+        # build only the DM interpolator on the DM grid
+        interps = build_profile_interpolators(
+            cosmo=self.cosmo,
+            a=a,
+            interpolation_grid={
+                "dark_matter": self.interpolation_grid["dark_matter"]},
+            profiles={"dark_matter": prof_dm_dmo},
+            components=("dark_matter",),
+            update_fftlog_precision=self.update_fftlog_precision,
+            fftlog_kwargs=self.fftlog_kwargs,
+            rgi_kwargs=self.rgi_kwargs,
+        )
+
+        self._dmo_dm_interp_cache[a] = interps["dark_matter"]
+        return interps["dark_matter"]
+
+    def pk_components(self, *, k: np.ndarray, a: float) -> dict[str, Any]:
+        """Compute halo-model spectra components on an input (k, a) grid.
+
+        Returns a nested packet containing:
+          - the evaluation grid,
+          - reference spectra (linear, nonlinear, and DMO halo-model baseline),
+          - component and pair power spectra,
+          - weights and metadata used to assemble the total matter spectrum.
+
+        Args:
+            k: Wavenumber grid in units of Mpc^{-1}.
+            a: Scale factor.
+
+        Returns:
+            Nested dict ("packet") with spectra arrays and metadata on the
+            input grid.
+        """
+        a = _require_a(a)
+        k = _require_k(k)
+
+        interpolators = self._get_interpolators(a)
+        f_gas, f_star, _ = self._get_mass_fractions(a)
+        dmo_dm = self._get_dmo_dm_interpolator(a)
+
+        calc = FedeliPkCalculator(
+            cosmo=self.cosmo,
+            a=a,
+            k=k,
+            profiles_u_over_m=interpolators,
+            mass_function=self.mass_function,
+            dmo_dm_u_over_m=dmo_dm,
+            halo_bias=self.halo_bias,
+            mass_ranges=self.mass_ranges,
+            densities={},  # filled below
+            gas_params={"Fg": self.Fg, "bd": self.bd},
+            n_m=self.n_m,
+        )
+        calc.ensure_densities(
+            f_gas=f_gas,
+            f_star=f_star,
+            mmin=self.density_mmin,
+            mmax=self.density_mmax,
+        )
+
+        # Packet schema: grid / pk_ref / pk / pairs / meta
+        return calc.pk_packet()
+
+    def pk_total(self, *, k: np.ndarray, a: float) -> np.ndarray:
+        r"""Return the total baryonic halo-model matter power spectrum.
+
+        This returns P_bar^HM(k,a).
+
+        Args:
+            k: Wavenumber grid in units of Mpc^{-1}.
+            a: Scale factor.
+
+        Returns:
+            Total baryonic halo-model matter power spectrum evaluated on k.
+        """
+        pk_tot = np.asarray(self.pk_components(k=k, a=a)["pk"]["total"],
+                            dtype=float)
+        return pk_tot
+
+    def boost(self, *, k: np.ndarray, a: float,
+              pk_ref: str = "pk_dmo") -> np.ndarray:
+        r"""Return the baryonic boost factor relative to a reference spectrum.
+
+        The boost is defined as:
+
+        .. math::
+            B(k,a) = \frac{P_{\rm bar}^{\rm HM}(k,a)}{P_{\rm ref}(k,a)}.
+
+        Args:
+            k: Wavenumber grid in units of Mpc^{-1}.
+            a: Scale factor.
+            pk_ref: Reference spectrum key provided by the packet.
+                Options are: "pk_dmo", "pk_nlin", "pk_lin".
+
+        Returns:
+            Boost factor evaluated on k.
+
+        Raises:
+            ValueError: If pk_ref is not available in the reference spectra
+            block.
+        """
+        a = _require_a(a)
+        k = _require_k(k)
+
+        out = self.pk_components(k=k, a=a)
+        pk_bar = np.asarray(out["pk"]["total"], dtype=float)
+
+        pkref_block = out.get("pk_ref", {})
+        if pk_ref not in pkref_block:
+            raise ValueError(
+                f"pk_ref must be one of {tuple(pkref_block.keys())},"
+                f" got {pk_ref!r}.")
+        pk_ref_arr = np.asarray(pkref_block[pk_ref], dtype=float)
+
+        return pk_bar / pk_ref_arr
+
+    def pk_total_dmo(self, *, k: np.ndarray, a: float) -> np.ndarray:
+        r"""Return the DMO halo-model baseline spectrum.
+
+        "DMO" here denotes a halo-model baseline constructed with the same
+        machinery, with all matter assigned to the collisionless (dark matter)
+        profile. The output corresponds to P_dmo^HM(k,a).
+
+        Args:
+            k: Wavenumber grid in units of Mpc^{-1}.
+            a: Scale factor.
+
+        Returns:
+            DMO halo-model baseline matter power spectrum evaluated on k.
+        """
+        a = _require_a(a)
+        k = _require_k(k)
+
+        interpolators = self._get_interpolators(a)
+        dmo_dm = self._get_dmo_dm_interpolator(a)
+
+        rho_m = float(ccl.rho_x(self.cosmo, a, "matter"))
+        calc = FedeliPkCalculator(
+            cosmo=self.cosmo,
+            a=a,
+            k=k,
+            profiles_u_over_m=interpolators,
+            dmo_dm_u_over_m=dmo_dm,
+            mass_function=self.mass_function,
+            halo_bias=self.halo_bias,
+            mass_ranges=self.mass_ranges,
+            densities={"matter": rho_m},
+            gas_params={"Fg": self.Fg, "bd": self.bd},
+            n_m=self.n_m,
+        )
+        return calc.pk_total_dmo()
+
+    def halo_profiles(
+        self,
+        *,
+        a: float,
+        space: str = "fourier"
+    ) -> dict[str, Any]:
+        """Return component halo profiles at scale factor a.
+
+        Args:
+            a: Scale factor.
+            space: Profile representation to return:
+                - "real": profile objects
+                  (e.g. NFW / GasHaloProfile / StellarHaloProfile)
+                - "fourier": Fourier-space interpolators callable as y(M, k)
+
+        Returns:
+            Dict mapping component name -> profile object (space="real") or
+            component name -> interpolator callable (space="fourier").
+
+        Raises:
+            ValueError: If space is not "real" or "fourier".
+        """
+        a = _require_a(a)
+        if space == "real":
+            return self._get_profiles(a)
+        if space == "fourier":
+            return self._get_interpolators(a)
+        raise ValueError("space must be 'real' or 'fourier'.")
+
+    def mass_fractions(self, *, a: float):
+        """Return Fedeli14 component mass-fraction callables at scale factor a.
+
+        Returns three callables (f_gas, f_star, f_dm) mapping halo mass M to
+        mass fractions. Results are cached per a.
+
+        Args:
+            a: Scale factor.
+
+        Returns:
+            Tuple (f_gas, f_star, f_dm) of callables f(M) mapping to fraction.
+        """
+        a = _require_a(a)
+        return self._get_mass_fractions(a)
+
+    def halo_radius(
+        self,
+        *,
+        M: float | np.ndarray,
+        a: float,
+        frame: Literal["physical", "comoving"] = "physical",
+    ) -> float | np.ndarray:
+        r"""Return the spherical-overdensity halo radius for the configured
+            mass definition.
+
+        Args:
+            M: Halo mass in the convention of the configured mass_def.
+            a: Scale factor.
+            frame: "physical" or "comoving". If "comoving", returns R_phys/a.
+
+        Returns:
+            Halo radius in Mpc (physical or comoving depending on frame).
+
+        Raises:
+            ValueError: If frame is not "physical" or "comoving".
+        """
+        a = _require_a(a)
+        cosmo = self.cosmo
+        # physical Mpc
+        radius = self.mass_def.get_radius(cosmo, M, a)
+        if frame == "comoving":
+            radius = radius / a
+        elif frame != "physical":
+            raise ValueError("frame must be 'physical' or 'comoving'.")
+        return radius
+
+    def _get_interpolators_with_concentration(
+        self,
+        a: float,
+        *,
+        concentration_override: Any
+    ) -> dict[str, Any]:
+        """Return Fourier interpolators using a dark matter concentration
+        override.
+
+        This is intended for diagnostics and plotting. Only the dark-matter
+        profile uses the overridden concentration; gas and stellar profiles
+        follow the baseline model settings at the same scale factor.
+
+        Args:
+            a: Scale factor.
+            concentration_override: Concentration-mass relation used for the DM
+                profile.
+
+        Returns:
+            Dict mapping component name -> interpolator callable y(M, k).
+        """
+        a = float(a)
+
+        # reuse gas/star fraction functions (same physics)
+        f_gas, f_star, _ = self._get_mass_fractions(a)
+
+        # build profiles with overridden DM concentration
+        prof_dm = nfw_profile(mass_def=self.mass_def,
+                              concentration=concentration_override)
+        prof_g = GasHaloProfile(mass_def=self.mass_def, f_gas=f_gas,
+                                **self._gas_profile_params)
+        prof_s = StellarHaloProfile(mass_def=self.mass_def, f_star=f_star,
+                                    **self._star_profile_params)
+        profiles = {"dark_matter": prof_dm, "gas": prof_g, "stars": prof_s}
+
+        # build interpolators
+        # (do NOT write into the main cache, keep it “diagnostic-local”)
+        interps = build_profile_interpolators(
+            cosmo=self.cosmo,
+            a=a,
+            interpolation_grid=self.interpolation_grid,
+            profiles=profiles,
+            update_fftlog_precision=self.update_fftlog_precision,
+            fftlog_kwargs=self.fftlog_kwargs,
+            rgi_kwargs=self.rgi_kwargs,
+        )
+        return interps
+
+    def pk_dmo_terms(
+        self,
+        *,
+        k: np.ndarray,
+        a: float,
+        concentration_override: Any,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        r"""Return DMO halo-model terms (1-halo, 2-halo, total) with a
+        concentration override.
+
+        This computes the DMO baseline using the halo-model decomposition:
+
+        .. math::
+            P_{\rm dmo}^{\rm HM}(k,a) = P^{1h}(k,a) + P^{2h}(k,a).
+
+        Args:
+            k: Wavenumber grid in units of Mpc^{-1}.
+            a: Scale factor.
+            concentration_override: Concentration-mass relation used for the
+                DM profile.
+
+        Returns:
+            Tuple (P_1h, P_2h, P_total) evaluated on k.
+        """
+        a = _require_a(a)
+        k = _require_k(k)
+
+        interps = self._get_interpolators_with_concentration(
+            a, concentration_override=concentration_override
+        )
+
+        rho_m = float(ccl.rho_x(self.cosmo, a, "matter"))
+        dmo_dm = interps["dark_matter"]
+
+        calc = FedeliPkCalculator(
+            cosmo=self.cosmo,
+            a=a,
+            k=k,
+            profiles_u_over_m=interps,
+            dmo_dm_u_over_m=dmo_dm,
+            mass_function=self.mass_function,
+            halo_bias=self.halo_bias,
+            mass_ranges=self.mass_ranges,
+            densities={"matter": rho_m},
+            gas_params={"Fg": self.Fg, "bd": self.bd},
+            n_m=self.n_m,
+        )
+
+        # DMO HM: use DM profile but normalize by rho_m
+        P1, P2 = calc.pk_halo_pair(
+            comp1="dark_matter",
+            comp2="dark_matter",
+            rho1=rho_m,
+            rho2=rho_m,
+        )
+        return P1, P2, (P1 + P2)
diff --git a/pyccl/baryons/fedeli14_bhm/examples/baryon_halo_model.ipynb b/pyccl/baryons/fedeli14_bhm/examples/baryon_halo_model.ipynb
new file mode 100644
index 000000000..95c2846f4
--- /dev/null
+++ b/pyccl/baryons/fedeli14_bhm/examples/baryon_halo_model.ipynb
@@ -0,0 +1,1408 @@
+{
+ "cells": [
+  {
+   "metadata": {
+    "collapsed": true
+   },
+   "cell_type": "markdown",
+   "source": [
+    "Baryonic halo model\n",
+    "===================\n",
+    "\n",
+    "This notebook explores the **[Fedeli (2014)](https://arxiv.org/abs/1401.2997)** baryonic halo model at the\n",
+    "*ingredient level*.\n",
+    "Unlike the high-level baryonic modules—which return only the final total matter power spectrum or an effective boost—here we expose and manipulate the **physical building blocks** of the model.\n",
+    "\n",
+    "The Fedeli model is a semi-analytic extension of the halo model in which **dark matter, hot gas, and stars are treated as separate components**, each with its own mass fraction and density profile inside halos. The total matter power spectrum is then assembled from the auto- and cross-spectra of these components.\n",
+    "\n",
+    "### What this notebook does\n",
+    "\n",
+    "We step through the model in the same logical order as the theory:\n",
+    "\n",
+    "1. **Mass fractions**\n",
+    "\n",
+    "   Specify how the halo mass is split between dark matter, hot gas, and stars as a function of halo mass (and implicitly redshift).\n",
+    "\n",
+    "\n",
+    "2. **Density profiles**\n",
+    "\n",
+    "   Define the spatial distribution of each component inside halos:\n",
+    "\n",
+    "   * Dark matter: NFW-like profiles,\n",
+    "   * Gas: cored profiles motivated by X-ray observations,\n",
+    "   * Stars: compact profiles tracing the stellar component.\n",
+    "\n",
+    "\n",
+    "3. **Power spectra and terms**\n",
+    "\n",
+    "   Construct the individual **1-halo and 2-halo contributions**, including all **auto- and cross-terms**, and inspect how each component contributes to the total matter power spectrum across scales.\n",
+    "\n",
+    "\n",
+    "4. **One-parameter sensitivity**\n",
+    "\n",
+    "    In addition, we perform controlled one-parameter sweeps around a\n",
+    "    fiducial configuration, varying individual baryonic parameters while\n",
+    "    holding the rest of the halo-model setu fixed. This allows us to isolate\n",
+    "     the impact of each physical ingredient on the matter power spectrum and\n",
+    "      to build intuition for which parameters drive suppression or enhancement on different scales.\n",
+    "\n",
+    "\n",
+    "### Why this level of access matters\n",
+    "\n",
+    "This low-level interface is designed for **model exploration and experimentation**:\n",
+    "\n",
+    "* testing alternative mass-fraction parameterizations,\n",
+    "* modifying profile shapes or concentrations,\n",
+    "* isolating specific halo-model terms,\n",
+    "* understanding which physical assumptions drive power suppression or enhancement at a given scale.\n",
+    "\n",
+    "All intermediate quantities (fractions, profiles, Fourier transforms, individual power-spectrum terms) are directly accessible, making this notebook a sandbox for developing, validating, and extending baryonic prescriptions beyond simple effective boosts.\n",
+    "\n"
+   ],
+   "id": "8143be87acc6ea60"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "# Setup: cosmology and halo-model grids\n",
+    "\n",
+    "We start by defining the cosmology and the halo-model ingredients used throughout the notebook:\n",
+    "a mass definition ($M_{200\\mathrm{m}}$), a halo mass function, and the $(M, k)$ grids on which we evaluate\n",
+    "mass fractions, density profiles, and power-spectrum terms. All masses here are in $\\mathrm{M_\\odot}/h$ and\n",
+    "$k$ is sampled over a wide range to cover both 2-halo (large-scale) and 1-halo (small-scale) regimes.\n",
+    "\n",
+    "Notice that we are calling the `baryon_halo_model` class directly, and not\n",
+    "the usual front-end `BaryonsFedeli14` module from `pyCCL`.\n"
+   ],
+   "id": "7d93dbc316da7b8"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-25T02:20:41.537981Z",
+     "start_time": "2026-01-25T02:20:40.996906Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pyccl as ccl\n",
+    "from pyccl.baryons.fedeli14_bhm.baryon_halo_model import BaryonHaloModel\n",
+    "from pyccl.baryons.fedeli14_bhm.examples.plot_style import (\n",
+    "    plot_style,\n",
+    "    get_colors,\n",
+    ")"
+   ],
+   "id": "b0834e821aefd93",
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-25T02:20:41.545428Z",
+     "start_time": "2026-01-25T02:20:41.538828Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# basic cosmology ingredients\n",
+    "cosmo = ccl.CosmologyVanillaLCDM()  # cosmology object\n",
+    "k_grid = np.logspace(-3, 2, 256)  # wavenumber k\n",
+    "scale_factor = 1.0  # scale factor a\n",
+    "h = cosmo[\"h\"]  # dimensionless hubble parameter\n",
+    "\n",
+    "# halo-model ingredients\n",
+    "mass_def = ccl.halos.MassDef(200, \"matter\")  # mass definition\n",
+    "hmf = ccl.halos.MassFuncTinker08(mass_def=mass_def, mass_def_strict=False)  # halo mass function\n",
+    "mass_grid = np.logspace(10, 16, 400)  # mass grid; in Msun/h\n",
+    "\n",
+    "# here we initialize the baryonic halo model\n",
+    "bhm = BaryonHaloModel(\n",
+    "    cosmo=cosmo,\n",
+    "    mass_def=mass_def,\n",
+    "    mass_function=hmf,\n",
+    "    concentration=None\n",
+    ")\n"
+   ],
+   "id": "ec2b18d0bf63eb03",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "tags": [
+     "mass_fractions"
+    ]
+   },
+   "cell_type": "markdown",
+   "source": [
+    "# Mass fractions as a function of halo mass\n",
+    "\n",
+    "Here we evaluate the **mass fractions** of the three matter components inside halos:\n",
+    "dark matter $f^{\\rm DM}(M)$, hot gas $f^{\\rm G}(M)$, and stars $f^S(M)$,\n",
+    " at the chosen scale factor\n",
+    "(here $a=1$, i.e. $z=0$). These functions encode how the model partitions the **equivalent halo mass**\n",
+    "among components before any spatial information (profiles) enters.\n",
+    "\n",
+    "We plot $\\log_{10} f_i(M)$ versus $\\log_{10} M$ across the full halo-mass range used in the notebook.\n",
+    "A small floor (`eps`) is applied to avoid taking $\\log_{10}(0)$ when a component becomes negligible at\n",
+    "some masses.\n",
+    "\n",
+    "For reference, the gray horizontal line marks the **universal baryon fraction**\n",
+    " $\\Omega_b/\\Omega_m$,\n",
+    "which the gas fraction approaches at high masses by construction in the Fedeli-style parameterization.\n",
+    "\n",
+    "This plot corresponds to Fig. 1 of Fedeli (2014).\n"
+   ],
+   "id": "53d7707efa27071d"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-25T02:20:42.027780Z",
+     "start_time": "2026-01-25T02:20:41.546017Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# We extract the mass fractions from the baryonic halo model\n",
+    "# for the setup we defined above\n",
+    "frac_gas, frac_star, frac_dm = bhm.mass_fractions(a=scale_factor)\n",
+    "\n",
+    "# avoid log10(0) screaming at you\n",
+    "eps = 1e-40\n",
+    "f_gas = np.maximum(frac_gas(mass_grid), eps)\n",
+    "f_star = np.maximum(frac_star(mass_grid), eps)\n",
+    "f_dm = np.maximum(frac_dm(mass_grid), eps)\n",
+    "\n",
+    "baryon_fraction = cosmo[\"Omega_b\"] / cosmo[\"Omega_m\"]\n",
+    "\n",
+    "with plot_style():\n",
+    "    fig, ax = plt.subplots()\n",
+    "\n",
+    "    cols = get_colors(3)\n",
+    "    ax.plot(np.log10(mass_grid), np.log10(f_dm), label=\"dark matter\", color=cols[0])\n",
+    "    ax.plot(np.log10(mass_grid), np.log10(f_gas), label=\"gas\", color=cols[1])\n",
+    "    ax.plot(np.log10(mass_grid), np.log10(f_star), label=\"stars\", color=cols[2])\n",
+    "\n",
+    "    ax.set_xlabel(r\"$\\log_{10}\\!\\left(M\\,[M_\\odot/h]\\right)$\")\n",
+    "    ax.set_ylabel(r\"$\\log_{10}\\!\\left[f_i(M,z)\\right]$\")\n",
+    "    ax.set_xlim(10, 16)\n",
+    "    ax.set_ylim(-2.5, 0.05)\n",
+    "    ax.legend(frameon=False, loc=\"best\")\n",
+    "\n",
+    "    # annotate at which redshift (a=1) we evaluate the model\n",
+    "    ax.text(0.05, 0.85, r\"at $z=0$\", transform=ax.transAxes, fontsize=15)\n",
+    "\n",
+    "    # horizontal line at log10(baryon_fraction)\n",
+    "    ax.axhline(np.log10(baryon_fraction), c=\"gainsboro\")\n",
+    "    ax.text(\n",
+    "        0.82,\n",
+    "        -0.65,\n",
+    "        r\"$\\Omega_b \\; / \\; \\Omega_m$\",\n",
+    "        transform=ax.get_yaxis_transform(),\n",
+    "        ha=\"left\",\n",
+    "        va=\"center\",\n",
+    "        fontsize=15\n",
+    "    )\n",
+    "\n",
+    "    # optionally save the figure\n",
+    "    # plt.savefig(\"mass_fractions.pdf\", bbox_inches=\"tight\", dpi=300)\n",
+    "\n",
+    "    plt.show()\n"
+   ],
+   "id": "3fa71f6094aae0b",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "tags": [
+     "halo_profiles"
+    ]
+   },
+   "cell_type": "markdown",
+   "source": [
+    "# Component density profiles in Fourier space\n",
+    "\n",
+    "Next we inspect the **normalized Fourier-space halo profiles** for each component,\n",
+    "$U_X(k\\,|\\,M)/M$, where $X\\in\\{\\mathrm{DM},\\,\\mathrm{G},\\,\\mathrm{S}\\}$\n",
+    "denotes dark matter, gas, and stars, respectively.\n",
+    "These profiles are the Fourier transforms of the real-space density profiles and determine how each\n",
+    "component contributes to the 1-halo and 2-halo terms once convolved with the halo mass function and bias.\n",
+    "\n",
+    "We evaluate the profiles at $z=0$ for two representative halo masses ($10^{13}$ and $10^{14}\\,M_\\odot/h$).\n",
+    "Following the presentation in Fedeli, the horizontal axis is shown as $\\log_{10}(kR_\\Delta)$, where\n",
+    "$R_\\Delta$ is the halo radius for the chosen mass definition. This rescales wavenumbers by the halo size\n",
+    "so the transition between large-scale ($kR_\\Delta \\ll 1$) and small-scale ($kR_\\Delta \\gtrsim 1$) behavior\n",
+    "is easy to compare across masses and components.\n",
+    "\n",
+    "Only positive values are shown (masking any non-positive numerical artifacts) so that $\\log_{10}$ is well-defined.\n",
+    "\n",
+    "This plot corresponds to Fig. 2 of Fedeli (2014).\n"
+   ],
+   "id": "ef6cfcb837a966e3"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-25T02:20:42.269728Z",
+     "start_time": "2026-01-25T02:20:42.037933Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "profiles_u = bhm.halo_profiles(a=scale_factor)\n",
+    "\n",
+    "# we define the halo masses for which we evaluate the profiles\n",
+    "masses = [1e13, 1e14]  # in Msun/h\n",
+    "\n",
+    "labels = [\"dark matter\", \"gas\", \"stars\"]\n",
+    "keys = [\"dark_matter\", \"gas\", \"stars\"]\n",
+    "\n",
+    "with plot_style():\n",
+    "    fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharex=True, sharey=True)\n",
+    "    cols = get_colors(len(keys))\n",
+    "\n",
+    "    for ax, mass in zip(axes, masses):\n",
+    "        radius = bhm.halo_radius(M=mass, a=scale_factor)  # Mpc\n",
+    "\n",
+    "        xlog = np.log10(k_grid * radius)\n",
+    "\n",
+    "        for key, lab, col in zip(keys, labels, cols):\n",
+    "            y = profiles_u[key](mass, k_grid)  # U_X(k|M)/M\n",
+    "            mask = y > 0\n",
+    "            ylog = np.log10(y[mask])\n",
+    "            ax.plot(xlog[mask], ylog, label=lab, color=col)\n",
+    "\n",
+    "        ax.text(\n",
+    "            0.05, 0.85,\n",
+    "            rf\"$M = 10^{{{int(np.log10(mass))}}}\\,M_\\odot\\,h^{{-1}}$\",\n",
+    "            fontsize=15,\n",
+    "            transform=ax.transAxes,\n",
+    "        )\n",
+    "        ax.text(0.05, 0.8, r\"$z = 0$\", fontsize=15, transform=ax.transAxes)\n",
+    "\n",
+    "        ax.set_xlim(-1, 2)\n",
+    "        ax.set_ylim(-2.5, 1.0)\n",
+    "\n",
+    "        ax.set_xlabel(r\"$\\log_{10}(k\\,R_\\Delta)$\")\n",
+    "\n",
+    "    axes[0].set_ylabel(r\"$\\log_{10}\\!\\left[U_X(k\\,|\\,M)/M\\right]$\")\n",
+    "    axes[0].legend(loc=\"best\", frameon=False)\n",
+    "\n",
+    "    # optionally save the figure\n",
+    "    # plt.savefig(\"halo_profiles.pdf\", bbox_inches=\"tight\", dpi=300)\n",
+    "\n",
+    "    plt.show()\n"
+   ],
+   "id": "1ce8c58876e1cf26",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "tags": [
+     "halo_radii"
+    ]
+   },
+   "cell_type": "markdown",
+   "source": [
+    "## Halo radius for the chosen mass definition\n",
+    "\n",
+    "Since the profile plots are shown as a function of the dimensionless\n",
+    "variable $kR_\\Delta$, we can\n",
+    "visualize the **halo radius** $R_\\Delta(M)$ implied by our mass definition (here $\\Delta=200$ with\n",
+    "respect to the mean matter density). We plot $R_\\Delta$ as a function of halo mass at the chosen\n",
+    "redshift, showing both the **physical** radius and the corresponding **comoving** radius\n",
+    "($R_{\\rm comov}=R_{\\rm phys}/a$) with respect to the scale factor $a$.\n",
+    "\n",
+    "The light gray vertical lines mark the specific halo masses used in the profile panels\n",
+    "(e.g. $10^{13}$ and $10^{14}\\,M_\\odot/h$), allowing us to read off the associated $R_\\Delta$ that enters\n",
+    "the rescaled wavenumber $kR_\\Delta$.\n"
+   ],
+   "id": "de5a9cb7b59f6979"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-25T02:20:42.414216Z",
+     "start_time": "2026-01-25T02:20:42.278979Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Halo radius vs mass (for the chosen mass definition)\n",
+    "r_phys = bhm.halo_radius(M=mass_grid, a=scale_factor, frame=\"physical\")  # in Mpc\n",
+    "r_comov = bhm.halo_radius(M=mass_grid, a=scale_factor, frame=\"comoving\")  # in Mpc\n",
+    "\n",
+    "with plot_style():\n",
+    "    fig, ax = plt.subplots(figsize=(6.5, 5))\n",
+    "\n",
+    "    colors = get_colors(4, cmap_range=(0.3, 0.7))\n",
+    "    ax.plot(np.log10(mass_grid), np.log10(r_phys), color=colors[1],\n",
+    "            label=r\"$R_\\Delta$ (physical)\")\n",
+    "    ax.plot(np.log10(mass_grid), np.log10(r_comov), color=colors[3],\n",
+    "            ls=\"--\", label=r\"$R_\\Delta$ (comoving)\")\n",
+    "\n",
+    "    ax.set_xlabel(r\"$\\log_{10}\\!\\left(M\\,[M_\\odot/h]\\right)$\")\n",
+    "    ax.set_ylabel(r\"$\\log_{10}\\!\\left(R_\\Delta\\,[{\\rm Mpc}]\\right)$\")\n",
+    "\n",
+    "    ax.text(0.05, 0.5, r\"at $z=0$\", transform=ax.transAxes, fontsize=15)\n",
+    "\n",
+    "    ax.tick_params(axis=\"both\", which=\"major\", length=7)\n",
+    "    ax.tick_params(axis=\"both\", which=\"minor\", length=4)\n",
+    "    ax.minorticks_on()\n",
+    "    for m in masses:\n",
+    "        x = np.log10(m)\n",
+    "        ax.axvline(x, color=colors[2], lw=2, ls=\"-\",)\n",
+    "\n",
+    "        # annotate what the line is\n",
+    "        ax.text(\n",
+    "            x, 0.05,\n",
+    "            rf\"$10^{{{int(np.log10(m))}}}\\,M_\\odot/h$\",\n",
+    "            transform=ax.get_xaxis_transform(),  # x in data, y in axes fraction\n",
+    "            ha=\"center\", va=\"bottom\",\n",
+    "            fontsize=15, color=colors[0],\n",
+    "            rotation=90,\n",
+    "        )\n",
+    "\n",
+    "    ax.legend(frameon=False, loc=\"best\")\n",
+    "\n",
+    "    # optionally save the figure\n",
+    "    # plt.savefig(\"halo_radius.pdf\", bbox_inches=\"tight\", dpi=300)\n",
+    "\n",
+    "    plt.show()\n"
+   ],
+   "id": "337cf2f9ac833f4b",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 650x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "# Power spectrum components and baryonic ratios\n",
+    "\n",
+    "We now examine the **halo-model power spectra** produced by the Fedeli (2014) baryonic prescription.\n",
+    "The curves shown here are functions of the comoving wavenumber $k$ and correspond to different\n",
+    "contributions to the total matter power spectrum.\n",
+    "\n",
+    "We present the results in two complementary ways. First, we show the absolute spectra\n",
+    "$\\log_{10} P (k)$ to illustrate how power is distributed across scales and how\n",
+    "individual components contribute to the total signal.\n",
+    "The total power spectrum is obtained as the sum of the individual halo-model\n",
+    " terms in linear space; this additivity is not visually apparent when displayed in logarithmic units. Second, we show ratios\n",
+    "$\\log_{10}\\!\\left[P(k)/P_{\\rm ref}(k)\\right]$ relative to a chosen reference spectrum\n",
+    "(e.g. the nonlinear power spectrum of the standard cosmology).\n",
+    "The ratio representation isolates **baryonic effects**, making deviations from the reference\n",
+    "particularly clear on small and intermediate scales where gas and stellar physics are most relevant.\n",
+    "\n",
+    "Throughout, all spectra are evaluated on the same $k$ grid and plotted in units of\n",
+    "$h \\, \\mathrm{Mpc^{-1}}$. A horizontal line at zero in the ratio plots\n",
+    "indicates exact agreement with\n",
+    "the reference model.\n",
+    "\n",
+    "These plots corresponds to Figs. 3, 4, 5, and 6 of Fedeli (2014).\n",
+    "\n",
+    "Note on the reference spectrum: In Fedeli (2014), Fig. 3 compares the\n",
+    "dark-matter-only halo-model\n",
+    "power spectrum to a version computed with halo concentrations artificially increased by a factor\n",
+    "of two. This is a diagnostic of concentration effects, not a baryon-versus-nonlinear comparison.\n",
+    "In contrast, our ratio plots use a fixed halo-model setup and compare baryonified spectra to a\n",
+    "chosen reference spectrum (by default the nonlinear matter power spectrum). As a result, the\n",
+    "large-$k$ behaviour need not match Fedeli’s Fig. 3. For completeness, this comparison can be reproduced as an exercise by altering the halo concentration–mass relation when constructing the halo-model ingredients via the `CCL` halo and concentration modules.\n",
+    "\n",
+    "\n"
+   ],
+   "id": "1d47da28b7d409ff"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-25T02:20:42.433217Z",
+     "start_time": "2026-01-25T02:20:42.426048Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# We first need to define some plotting functions we can call so we do not\n",
+    "# repeat the same code multiple times later on.\n",
+    "def plot_curves(\n",
+    "    pk_components,\n",
+    "    ys,\n",
+    "    *,\n",
+    "    labels,\n",
+    "    mode=\"log10\",  # \"log10\" or \"log10_ratio\"\n",
+    "    denom=None,  # used only for ratio; can be \"pk_nlin\" or \"pk_dmo\"\"\n",
+    "    h=None,\n",
+    "    xlabel=None,\n",
+    "    ylabel=None,\n",
+    "    xlim=None,\n",
+    "    ylim=None,\n",
+    "    figsize=(6, 5),\n",
+    "    legend_loc=\"best\",\n",
+    "    legend_ncol=1,\n",
+    "    frameon=True,\n",
+    "    style_params=None,\n",
+    "    lw=4,\n",
+    "):\n",
+    "    k = np.asarray(pk_components[\"grid\"][\"k\"], float)\n",
+    "    k = k / float(h)\n",
+    "    unit = r\"\\mathrm{h\\,Mpc^{-1}}\"\n",
+    "\n",
+    "    x = np.log10(k)\n",
+    "    xlab_default = rf\"$\\log_{{10}}\\!\\left(k\\,[{unit}]\\right)$\"\n",
+    "\n",
+    "    ys = [np.asarray(y, float) for y in ys]\n",
+    "    labels = list(labels)\n",
+    "\n",
+    "    if mode == \"log10_ratio\":\n",
+    "        if denom is None:\n",
+    "            den = np.asarray(pk_components[\"pk_ref\"][\"pk_nlin\"], float)\n",
+    "            den_name = \"pk_nlin\"\n",
+    "        elif isinstance(denom, str):\n",
+    "            # allow denom to live in either pk_ref or pk\n",
+    "            if denom in pk_components.get(\"pk_ref\", {}):\n",
+    "                den = np.asarray(pk_components[\"pk_ref\"][denom], float)\n",
+    "            else:\n",
+    "                den = np.asarray(pk_components[\"pk\"][denom], float)\n",
+    "            den_name = denom\n",
+    "\n",
+    "        yplot = [np.log10(y / den) for y in ys]\n",
+    "        if ylabel is None:\n",
+    "            ylabel = rf\"$\\log_{{10}}\\!\\left(P/P_{{\\rm {den_name}}}\\right)$\"\n",
+    "    else:\n",
+    "        yplot = [np.log10(y) for y in ys]\n",
+    "        if ylabel is None:\n",
+    "            ylabel = r\"$\\log_{10}\\,P(k)$\"\n",
+    "\n",
+    "    cols = get_colors(len(ys), cmap=\"cmr.pride\", cmap_range=(0.25, 0.75))\n",
+    "\n",
+    "    with plot_style(style_params):\n",
+    "        fig, ax = plt.subplots(1, 1, figsize=figsize, constrained_layout=True)\n",
+    "\n",
+    "        if mode == \"log10_ratio\":\n",
+    "            ax.axhline(0.0, color=\"gainsboro\", lw=3)\n",
+    "        for i, (lab, y) in enumerate(zip(labels, yplot)):\n",
+    "            ax.plot(x, y, label=lab, lw=lw, color=cols[i])\n",
+    "\n",
+    "        ax.set_xlabel(xlab_default if xlabel is None else xlabel)\n",
+    "        ax.set_ylabel(ylabel)\n",
+    "        ax.legend(frameon=frameon, loc=legend_loc, ncol=legend_ncol)\n",
+    "\n",
+    "        if xlim is not None:\n",
+    "            ax.set_xlim(*map(float, xlim))\n",
+    "        if ylim is not None:\n",
+    "            ax.set_ylim(*map(float, ylim))\n",
+    "\n",
+    "        plt.show()\n",
+    "\n",
+    "\n",
+    "def plot_spectra(pk_components, ys, *, labels, **kw):\n",
+    "    return plot_curves(pk_components, ys, labels=labels, mode=\"log10\", **kw)\n",
+    "\n",
+    "\n",
+    "def plot_spectra_ratio(pk_components, ys, *, labels, denom=None, **kw):\n",
+    "    return plot_curves(pk_components, ys, labels=labels, mode=\"log10_ratio\", denom=denom, **kw)\n"
+   ],
+   "id": "d8ffc150355812ef",
+   "outputs": [],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-25T02:20:42.524836Z",
+     "start_time": "2026-01-25T02:20:42.434208Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# We use a handy method from BaryonHaloModel called `BaryonHaloModel.pk_components`\n",
+    "# which returns all the relevant power-spectrum components in a dictionary.\n",
+    "# To see the structure of the dictionary simply call .keys() on the returned object.\n",
+    "pk_components = bhm.pk_components(k=k_grid, a=scale_factor)\n",
+    "halo_pairs = pk_components[\"halo_pairs\"]  # individual halo terms dict\n",
+    "pks = pk_components[\"pk\"]  # power spectra dict\n"
+   ],
+   "id": "c5e643b6cd4c0296",
+   "outputs": [],
+   "execution_count": 7
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Dark matter power spectra",
+   "id": "82f56b49dd7c4c6d"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-25T02:20:42.697801Z",
+     "start_time": "2026-01-25T02:20:42.525288Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "ys = [\n",
+    "    halo_pairs[\"dm_dm\"][\"terms\"][\"1h\"],\n",
+    "    halo_pairs[\"dm_dm\"][\"terms\"][\"2h\"],\n",
+    "    pks[\"dm\"],\n",
+    "]\n",
+    "labels = [\n",
+    "    r\"1-halo\",\n",
+    "    r\"2-halo\",\n",
+    "    r\"$P^{\\rm DM}(k)$\",\n",
+    "]\n",
+    "\n",
+    "plot_spectra(\n",
+    "    pk_components,\n",
+    "    ys,\n",
+    "    labels=labels,\n",
+    "    h=cosmo[\"h\"],\n",
+    "    ylabel=r\"$\\log_{10}\\!\\left(P(k)\\;[\\mathrm{Mpc}/h]^3\\right)$\",\n",
+    ")\n",
+    "\n",
+    "plot_spectra_ratio(\n",
+    "    pk_components,\n",
+    "    ys,\n",
+    "    labels=labels,\n",
+    "    h=cosmo[\"h\"],\n",
+    "    xlim=(-2, 2),\n",
+    "    ylim=(-2, 0.5),\n",
+    "    ylabel=r\"$\\log_{10}\\!\\left[P(k)/P_{\\rm ref}(k)\\right]$\",\n",
+    ")\n"
+   ],
+   "id": "f24fe56e15cea4f5",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Stellar power spectra",
+   "id": "f06e77d9d49cd57c"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-25T02:20:43.055090Z",
+     "start_time": "2026-01-25T02:20:42.706937Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "ys = [\n",
+    "    halo_pairs[\"stars_stars\"][\"terms\"][\"1h\"],\n",
+    "    halo_pairs[\"stars_stars\"][\"terms\"][\"2h\"],\n",
+    "    pks[\"stars\"],  # total P_starstar\n",
+    "]\n",
+    "labels = [\n",
+    "    r\"1-halo\",\n",
+    "    r\"2-halo\",\n",
+    "    r\"$P^{\\rm S}(k)$\",\n",
+    "]\n",
+    "\n",
+    "plot_spectra(\n",
+    "    pk_components,\n",
+    "    ys,\n",
+    "    labels=labels,\n",
+    "    h=cosmo[\"h\"],\n",
+    "    ylabel=r\"$\\log_{10}\\!\\left(P(k)\\;[\\mathrm{Mpc}/h]^3\\right)$\",\n",
+    ")\n",
+    "\n",
+    "plot_spectra_ratio(\n",
+    "    pk_components,\n",
+    "    ys,\n",
+    "    labels=labels,\n",
+    "    h=cosmo[\"h\"],\n",
+    "    xlim=(-2, 2),\n",
+    "    ylim=(-2, 3),\n",
+    "    ylabel=r\"$\\log_{10}\\!\\left[P(k)/P_{\\rm ref}(k)\\right]$\",\n",
+    ")\n"
+   ],
+   "id": "1114ae4c9b75fdcb",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 9
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Gas power spectra",
+   "id": "8df7e2eb9edc70c5"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-25T02:20:43.333575Z",
+     "start_time": "2026-01-25T02:20:43.056766Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "ys = [\n",
+    "    halo_pairs[\"gas_gas\"][\"terms\"][\"1h\"],\n",
+    "    halo_pairs[\"gas_gas\"][\"terms\"][\"2h\"],\n",
+    "    halo_pairs[\"gas_gas\"][\"terms\"][\"diffuse\"],\n",
+    "    halo_pairs[\"gas_gas\"][\"terms\"][\"diffuse_halo\"],\n",
+    "    pks[\"gas\"],  # total P_starstar\n",
+    "]\n",
+    "labels = [\n",
+    "    r\"1-halo\",\n",
+    "    r\"2-halo\",\n",
+    "    r\"diffuse\",\n",
+    "    r\"diffuse-halo\",\n",
+    "    r\"$P^{\\rm G}(k)$\",\n",
+    "]\n",
+    "\n",
+    "plot_spectra(\n",
+    "    pk_components,\n",
+    "    ys,\n",
+    "    labels=labels,\n",
+    "    h=cosmo[\"h\"],\n",
+    "    ylabel=r\"$\\log_{10}\\!\\left(P(k)\\;[\\mathrm{Mpc}/h]^3\\right)$\",\n",
+    ")\n",
+    "\n",
+    "plot_spectra_ratio(\n",
+    "    pk_components,\n",
+    "    ys,\n",
+    "    labels=labels,\n",
+    "    h=cosmo[\"h\"],\n",
+    "    xlim=(-2, 2.),\n",
+    "    ylim=(-2, 2),\n",
+    "    ylabel=r\"$\\log_{10}\\!\\left[P(k)/P_{\\rm ref}(k)\\right]$\",\n",
+    "    legend_ncol=2,\n",
+    ")\n"
+   ],
+   "id": "ce43c58d6813d96",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ],
+      "image/png": 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lhA4e1GGfUhUEQfBGbBTeAbTFzJiu67yy6DEUg5HkiDSSIupmvWJC4pHljlFEVdd1tLJDuPM3o+ZvwZ23BTV/C7qtrP2DMQWihCQhBcchB8UiB8fWfQ+KQwqKRQ6KQbKGI5lDkNr4/dOdNWg1xWjV+Whlh1BLD6CVHqxbA1d6oO7pTV9RzBiSR2JMOxNj2niUuAFt/vqgbqZs8+E1/Lr7B5S9OcRlFhCxqxS5lcWFA3t0J+HSS4g57xwMgYE+ilYQBKF9iJkxP2qrOmO1jmqspsAOMVOg6xpa6UHc+VvqZr3y6hIwnyYWDZGNyOGpKOGpyGHJyGEpKKHJyGHdkEOT6xKtDvA+NaZu5jC7LnnN33L8u15d6JPxJUsYhtSxGNPGY0w7EyUizSfjeuN0O1h34BfW7FuGVl5JzOYiorcWtXq2TAkMJO6CySRcdgkB3br5KFpBEIS2JZIxP+pI2yH5ilZdgDsnE3fOetw5maj5m9EdVW1+XSkoFiWyJ0pkT+SIHiiRPep+HdYNSe66i77Vimzch1fiPrIK15GVaKUHfDKuHJaCIWUMhvjBGOIHocT2r7sF62O1zhrW7f+ZzEMrsbtsmCodhB6qIPRQJSFHKrFUtLxAcdjI4URPmEDE6aeJJzMFQejQRDLmR509GdPddtz5W1FzNuA++qVVZLXpNaXAaJTofigx/TDE9EOJSUeJ7IVkDmrT63YWWlU+riOrcB/8GdfB5b7785AUlOi+daU04gaixGZgiM3w2fvudDvYlr2BLUfWkVf+W8zGGheBedUE5VUTlFdDUG41pprmb1RviY8ndMggQgcPInTIYKzJyZ1iRlQQhFODSMb8qDMlY8fXeeUem/XagJq/DbTmfzA2iTEAJbrv0YSrH0p0OkpMP788EdhZ1f2ZHcR18GdcB5fhPvQrut23e5DK4WkY4gagxA/CmDIWJX5Qq9eeldeUcKBoNwcKd3O4aB9O9YTZMV0nML+GqJ0lxGwuxFjbsr00TVGRRE+cSOzk8wjq20ckZoIg+JVIxvyoIydjusuGO3cj7qw1x5MvvbakTa4lWUJR4gbW3Q6LG4ghfiByuH8rzHdFuqai5m3GdXA5roPLcWet9XkyLZlDMKSMwdjjbEzpFyNbw1s1nqq5ySk7zIHCuuSsoCLnt2u5NSJ3lhC/IZ/gnOoWXyOgexpxk88ndvL5mCIjWhWvIAhCS4hkzI86UjKm1RTjzl6HO2sNrqw1qHmb22TWS7KGo8QNwhA/AEPcIJT4QchhKWJmwg90Zw2urNXHb2mqBdt9ewHFhLHXJMwDZmDseTaS0vrdGypqy9h4eBWZB1fiOGE/0MC8auLX5xO1vRhZbdk/VZKiED56FJFjxxBx+hixzkwQhHYjkjE/8lcyVnf76gDuI3WJlzt7LVrJPp9fRzIHoyQMxZAwpG6dUfyguicYReLVIWnVhbgO/Vp3S/PIqqM13nxDCo7HOvoOzEOuQTK1vvREraOGX/f8SObBFegn1KEz1LiI3VxI5M4SgvJbV7ctsGcPIsacRsSY0wgZkIEsqv8LgtBGRDLmR22RjC3espVvNmw46Zisu4l35ZLsOEg35yGSnYcI0lp+W8cTDYkiYzw55hRyTCnkmFMoMUR7vNXYlGTMUx9PZ3ns53H4pvWTPPbTMSsOzIoDi2I/+msnZsWORXFgUpwokooiq8e/y1Ldhtm6rqDpMrouoekyqq7g1Ew4VAsO1Xz0y4LNHUCVKxhVN7YgvsZfqyxJSJKEItd9lyUJWZLrvns5ZlZrCas9RHjNIcKrDxBac4AgW179oJpBs4TjHHAt7gFXY7SGYlQUDIpy0ndZbvrt6dyyIyzc9DlFVfn12kyVDkIO1z2VGZJVSUCJ3cMITaMEBhIxehTRZ00g4vQxovK/IAg+JZIxP2qLZOzDn3/hg8UL6CMVkC7n0U/Ko7dUiFlq2UJnb0r1AHbrsezRYtmtx7Jfj8GOsfETOxAJnQirndigGmKDagm32omw2gm3OOq+H/uyOFDk9vnxr7CbKK61UlxrpajGSm5VEEcqQjhSHkxedSCa7t91dFacpEgldJeK6S4VkyYVkyKVYJSat8VRpW5hjjqURVoGTk6ecTIZDFhNJsxGI1ajse77sd+bjv7eaMJiMmIx1n3Z1QMUV+1Ax3scxmonIVlVhO8rI3JXKYpTbdF7IFutRJ05luizJhI+cgSK1dqicQRBEI4RyZgf+ToZ03WdA/8eS2jlHmQf3gl06TL79Bh2HU2+9uixFBOE57mqjqQu2UoMriY2qJa4o0nXseQrJrAWk9KyfRL9wanK5FQGcag8hD3FEewsjmBvSTh2t39vn5lw0U/KZ5CczSApm55y0/cNLdYD+VQdwRKtLxqtSzSDLBoZqQ4ighr/M5WdKhG7S4ndWkzIoXKkFv7rJptNhA0fTuTY04kcezrmmOiWDSQIwilNJGN+1BYzY3tfHU9kResWYlfqZnbp8ezQ4tmpx7FPj8FNx9hKyROLwU1ySBVJoVUkhVSRFFJN8tFfB5p8OyPY0agaHC4PZVdxBNsLI9mYH0NBtX+3A0qgnPHKbsbLe4iVmlbwN0cP5RN1JCu0Hq1MynQSI930iHcRZGnaP1nmcjsBa0sI3V1GQmXrChQH9elD5JljiRx7OkF9eov1kYIgNIlIxvyoLTYKX/ve7fTMmtOsc/L0kKOJVzw7tThyCEfvgLNeZsVNalglaeEVx7+6hVYRHdi6LXS6mtyqQDbnRbMxP4ZNeTGU2PxzG01CZ6h0mBnKBvrKBU06p0APZpGawTKtN2W0JqnUiQ1TSYx0ExOq0pRlaE4XHNwFYfvL6V1UTI+SUixqy25lApiio4kcO4bIM8YSPnwYstnc4rEEQeiaxEbhHUBbzIyt+P6/9Fv3iNd2FZkcJY6Dhm4cMKRw0JBMlRxc1+jhj9jTH7qnHwWPPxxN6Of5x0ojNrCSpJASkoNLSQopJSm4hJjASp/efj1VHKkIZUtBApl58WwtiMHmVtB0HV3XUTWt7unaNv3rrZMh5TJD2cAgObtJZ6i6xF49hs16Mpu0JHbrsagtnJ01GXTiI9wkRboJDWz8FmZBmcLObBMOG3QrK6d3cQnpBYVE2lqe9MtWKxGjRxE1/kwixpyGsYPVFRQEwb/EzJgftUUyptkrKf9XT46nPaZADEkjMCaNwpA8CkPiECRTx9k6SNed6M696I7t6I4d6M4d6M69oLd8T8JWkyygRCEpkSd8j0CSg0AOBjkISQ4GOfjosUCQzCAZAOPR7wqSJKHrGuAGXQNU0J2gVaNr1aBVg1ZV92u1CN1diK4WwAnfoeUzM54ZkCxDka2nIVnHIJnTkSQF/Whyph39+n2idvzLy7Fjv3arKm5VxXXSd+34ry0FG0ja+SbBFbuaFbUdE3uVFLZL3cjUkjnoDsbpbv57E2zV6JXgJC684XN1HfLKFA7kG6msVUDXSaisYkB+AQPy8wmzt/znU5dljP3TSZp0NnETxot1ZoIgiGTMn9qqzljt4ieQQ5MwJI9EiUnvMBtl65oD3bm7LuFy7KhLwJx7gfZc12UAQxySIRHJEA+GBCRjIpIhAUmJBUM0SAEdYq2PrjvBdQTdeQDddfTLuQ/duQ+fvWdyKLJ1NJL19LoEzZjom3EboOs6rj3fYVv2D9Si5iVlx8gR3TEOvga936XUygFU2+1U2ex13+12qu02qmx2quw2qu324+3lNTWU1dRgMlYyIMWJxdT4P2vFlTL7ck2UVivHXgDxVVX0LSymb1ERia1cZ1YdH49x5HC6nTOJXkOHoDSjtIcgCF2DSMb8qCNV4Pc1XXeiO3ahO7YdTbp2oDv30y6Jl2RCMqSAKQ3JmHr0KxnJkABKDJLUcR9GaApds6M7d6E7tqDbt6I5toDbRxuCG1PqkjLzIGTzADCmttm2VLqm4tz2JbZf/oVWdrBFY0jmYCyjZ2IZdVuzism6VZX8siKW7vyanLK9TTonr1RhZ5YJu+vk9yPYbqdvUTF9jq4zM2otf0K3ODCQkl49CTxtNH3Hnk7/bslYTK3fuUAQhI5NJGN+1FWSMV3XwZ2L5tiMbt+C7tiM7tgBtNEm4sfIEUimXkimHkjGFCRjGpIxrW7mq5MnXM2luwvRbGvQ7WvQbGvB3bS1WY2SgpDM/ZEsA5BNGUjmdDAk+nTmUNdUXHu/x77uLdyHfsHLCsSGwwyOJ/DcWZj6XtC8a+s627IzWbzta+yu2savgwH07uSVGskuKaWwsvKkdqNbpUdpCX0L65KzYKezWfGcqNJsYndsLDXp/YgbPYrBPXuQkZxMoCg4KwhdjkjG/KizJmO6VoPu2Ipu31KXgDm2gNo2m4gDIFmRTD3rEi9jr6MJWG8kQ1TbXbOT0105aLbV6LZVaLaVoJX7bnA5BMnUD8ncD8mcjmxKB2OKTxJgtSIb165vcR1YhuvIKmhCgnQiY+/zCTh3Fkpo82632py1/LL7BzYeWommNz6z1Tsug/MGXYosmckpLSWruISskmKOFJdwpLiYg4WFOJxOEisq6VdYRL/CImJqWr49k92gsCs6mu3xcZgHD2JYn96M7NmTPgkJ4ramIHQBIhlrI2+++SZ//OMfCQwMpLra89ZDnSEZ03UN3bX/hBmvLUfXebXRj4MhEcmUjmzuU5dwmXqBIemUm+nyJV3X0J070W0r0WpXotsz8fmspWStS9Asg5ADJiBZBiNJrVurqKtO3NnrcB1YXreRee5GmvRzZwokYPyDmIffjCQ37+em0lbO+gO/sPHwapzuhhfpB5gCmdj/YjKShtabKdQ0jdyyMvYXFLA/v4D9BQWU7N1L5N599C0ooltFRbPiOpHdoLAzJoatcbEUJiUytFcvRvbsyciePYjuoP+OCILQMJGMtYGcnBz69+9PYGAgFRUVnSoZ0zX70VmvTDR7JrpjE2itW6DslSEZ2Zxe90SfqX/ddyWsba4lHKdrtej2DWi2lei1K9FdTVsz1SxyKHLAOOTA85ACzvBJMq3ZynDt/wnHxg9wH/610f5K/GCs4x/E2H1Cs2+r2l02Nh1azcq9S3C4G97TMjE8lXMGTCUuLKnRcWsdDvbk5bF7+w7KV67Eum0HiQWFGFr4z6vNYGBnTDRb42I5EBlB94QExvbtyxn9+tIzLq5DPIgiCELjRDLWBi666CIkSSIiIoI5c+Z06GRMV0vQ7RvrEi975tG1Xm2wyN6Ygmw6mniZ+9fNoiihvr+O0Gx1681WodtW1K03Uwt9ewFDIkrI5cjBlyIp4T4Z0p29ntqlT+E+sqrRvkp0X0wZl2JKn4YSntKs69Q6qlmy/Ru2ZW9opKfE4JRRjOt7HgHm5pWOKc7PZ9d3P1D+6wosu3ZjdLVs1rLWYGBnbN2M2YGIcGIiIhjbtw9j+/ZlUEoKBkXMLgtCRyWSMR/78MMPueOOO9ixYwePPPJIh0rGdF0H16HjiZfm2AiuQ76/kBKJZB6IbB6IZBmIZM6oq8sldAq6uwDdsRXNsfXo07DbfDM7KpmQg6ahhN+GZIhtfZy6jnPrF9Qufgy9tmlrFpXE4Zj7T8OUPgU5qOkx7MnbxqLNX1DrbHjdl8VoZVzf8xmSOrpFT6FqDgfFa9Zx4LvvqV2zFsXLvx2NqTYZ2RoXx6aEOHJCQggOCGBM796cNSCD4d27YzR0jHI3giDUEcmYDxUWFpKens5TTz3FHXfcwfXXX9+kZCwrK+ukN99sNmP2wdYpdeUldtbdkrJvRLdvBK201eOezHh0tmsQkmUgsnlQXe0ucXuky9B1DdxZaI4ddT9PR+vEtfihAMmMHPIHlLA/IikRrY5Pqy2hdvETOLd82owYZEzp07COf7DJs2U1jioWbZ7D3vzG935NjerF5MEzCA1o+UygrqpUbttO4dKfyP9xCVpJyx6SKQoMYHN8HJsS4im3WgmxWhmXns5ZAzIYnJoqHgAQBD9wOBw4HL+tS62srCQ5OVkkY74wffp08vLy+PXXX5EkqcnJ2O89/vjjPPHEE82+vq7r6Lafj858bUR3bPV9JXtDMrJlIJJ5YF0CZu6LJIk6SKcaXddBzUN37EA7lqDZNoDejJkcKQA59FqU0OuRlNbPDLsOraBm4b1opfubfpJsxHLaTKxn/BXJ0Ph/gHRdZ0/eNpZsn0+FrazBvmaDhbMzpjAgeXir/3OiaxqV23dQtHgJhUuW4ioqbtE4h8LD2BQfx7a4WOxGIxFBQUzo35+zBmSQkZws/hMlCO3kiSee4Mknn6x3XCRjrfTll19y5ZVXsnHjRtLT0wH8MjPmPDIJ3DktOrceyYRkHoBkGYJsHoJkGeSTmQyha9J1Z90sbM1PaDWLQc1v2olyCHLIlSihV7f650t327Gvewv7mtfRq5u2OTmAEtWHwItfxZAwpEn9XW4nq/cvY/Xepbi1htdY9ojtx3kDLyXEGtbkeBqiaxqVW7dRtHgpRUuX4ixu/oyZW5LYGRPNhqRE9kdGoEsSiRERnD94MOcNGUysh/8kCoLgO2JmrA1UV1fTs2dPrr76ah555LdNuu+44w7mz59PdnY2RqORwMCTK4S3xZoxd+H9aNXftOxkOawu8bIMRbIMrVtoL2a9hBbQdRW9dhlq5SfotpVNO0myIAdfihJ6LZIxuXXXdztw7vga57Y5uA7+DHoT9rGU5LpZsrH3Nrmaf0VtKUu2f8PuvK0N9jMZzExIv4AhKS1bS+aNrmlUbN5C0ZKlFC9dhrMFtzLLLBY2JCWQmZhApcWCJEkM796dyUOGcEa/vpiNRp/FKwiCZ2LNmA8cOnSItLS0BvtMmTKFr7766qRjbZGMqZWfohY/1bTOhm7HEy/ZMhSMaeI2heBzuvMAavl/0aq/pan16STLUOSAcUiWUUc3M2/5gnOtpgjnzm9wbp+LO2tN49cOisV6xr2YB8xoclJ2oHA3izZ/QaWtvMF+yZHduXDw5YQFRjZp3ObQVZXyjZsoXPQ9RUt/Qq1tXsFcDdgbFcn6pER2R0ehyTJBFgtnDcjgwqFD6ZvY9vuWCsKpSiRjPmC321m9enW9488++yzLly9n0aJFREVFkZGRcVJ7WyRjmnMv7uwpHlqUug81y9CjtxyHIBmifXJNQWgK3bkPd9n/odf80LwT5WAky3Bk66i65MzUq8WzS2p5FvaV/8ax8YNGZ8skSyjmwVdhHnYDSnhqo2PbXTYWb/uarVnrG+xnNlg4d+Cl9E9q2i3RllDtdkp++ZWCRd9TunoNqE2YGTxBlcnExsR4NiQmUhIYAEDfhASmjhzBWRkZYr9MQfAxkYy1oaauGfNlMqbrGq7DY0BXkSyDT7jlOABJDvDJNQShNTTHDtTSf6Pbfm7ZAHIYcsB45JDLkMyDWzSb6y7YTs03f0LNb/j2Yh0JU/oUrOMfQoloeAYc6spgfLflS2ocDZcDGZA8nEkDpmI2tO1ek87SMgp/XEzhd99TtWNns8/fGxnB6m7J7ImOQpckgiwWzh8ymKkjRtAtSmxJJgi+IJKxNuSPZAxAd2UdLS8hijwKHZdm34xa/gZ67dIWjyGZ0lEi/oRkPbPZSZmuurCv/De2X14ArQmFVmUjllG3YT3zPiSjtcGuNmctS7bPb3SWLDwwiinDriI+rHVr5Jqq9tBh8hcuomDBwmYv/C+zWliTnMSGxARsR2fGhqalMW3kSMb27SOKygpCK4hkzI86QgV+QfA3zbkXrfwttOqFtHT3B8kyDCXqUWRT72af6y7cQe2i+3Fn1V9q4IkcnkbghS9hTDm90b4HCnfz3ZY5VNR6L4MhSwrj+p3HqB7jfLq4vyG6203JylXkz/+GkhWrQGt8c/RjXLLMlvhYVndLJu/ov1txYWFMHz2KC4cOJdDStjN9gtAViWTMj0QyJgi/0dVStOpFaDU/oNs30fzNzJW6umXhdyDJTVt4f/zauo5r7/fYfvkXat7mJp1jHnod1vEPIQc0XI7D6Xbw49av2JK1rsF+qVG9uGjoFQRZ2vffAkdhEfkLF5L/9bfYc3Obde6h8DB+Te3G7uhodEkiwGzmomFDmT56NHFhYW0TsCB0QSIZ8yORjAmCZ7pmq9sz1bYa3b4G3bGduuf9mkCJwxD1IFLA2c2/danrqLmZ2Ne9hXPHV43evpTMwVjG/BnLyFuQjA2vydyRs5HvNn/Z4MbjVlMgFwy+nF5x6c2K2xd0TaN8QyZ5X39D8bLl6M3YI7MoIICVqd3YlBCPS1FQZJlx6elcPuY00pMa30BdEE51IhnzI5GMCULT6FoVum09mm1FXYkMrbLRcyTrmSjhM5EtA1p0Ta26ANuvL+LIfB8aKewqBcdhPeNvmAdfgSR7L8NRXlPC/MyPySk73OB4w9LGMjH9AgyKf2p8ucrLyZv/LblfzsWR3/TiuTVGI2uTk1jdLYmao4WrB6WkcO24MxnRo4conSMIXohkzI9EMiYIzadrDrTqeahlr4Na1Gh/yTwAOfA85MBzkIzNr5XlLtpFzbd3o+ZsaLSvHNmLgLOfwNTrHK99NE3l1z0/snLPEvQG6q7FhMQzZdhVRAXHNTtmX9FVlZJfV5IzZw7laxt+GOFELllmU0I8P6elUBZQN2PYLzGRa848k9P79EYW+2EKwklEMuZHIhkThJbTtVrU8v+glb9LUxf+S6Z05MBz6hIzU2ozrqXiWPcGtctmgavxYqrGvhcQeM4s5JB4r32OlOxn/oaPqbJXeO1jUIyc3f9iBqeM9vusUs3BQ+R+OZeCBYuaXFBWlSS2xsWyvHsqRUFBAHSPieGaM89kQkZ/sUm5IBwlkjE/Ovbm9+7dG0VRmDlzJjNnzvR3WILQqejOfbiL/45uX9us8yTr6Sjhf0K2DGzyOWrZYWoXP45r94LGO5uCCJj4COah1yPJnss+2Jy1LNr8RaPbKfWOy2Dy4BlYTf6vFeiuriH/m2/J/uxzHHlN239UA3bExrC8e+rxJzCTIiO5+owzOHfQQFEWQzhlzZ49m9mzZ6OqKnv27BHJmD+ImTFB8A1d19Gqv0Et/SeozaufJQdegBL1AJLS9C2KXNnrsC15sknbKynxgwg891kMScM9tuu6zqbDa1i8/WvcqvdF88GWUKYNv5bEiJQmx9mWdLebop+Wkf3xp80qJrs7KpJl3dPICg8DICkighsnTuCsjAxx+1I4ZYmZMT8SyZgg+JauVqKWv4FW+RnonosteySHokT+DTloapNvB9aVw/gB29KnUYt3N9rfNPByAiY+ihwU67G9uCqfrzd8RGFlnvcwJYVJA6YwJOU0v9+2PEbXdSo2bSb7408p+eVXaOJHxd7ICBb37EFOWChQd/vypokTOaNf3w7z2gShvYhkzI9EMiYIbUPXatCqv0Gr/h7dvo6mlsWQrKNRIu5DNvdrxrVUHJs/xrb0aXSb9+KuUFcKw3rWE5iHXOMx4XCrLn7asYD1B39tcJwBycM5d+ClGP30tKU3tUeOkPXhxxQsWITubto6vp3RUSzp2YP8kGAA+iQk8MezJjKyZ0+RlAmnDJGM+ZFIxgSh7elqCVrNUrSaH9Ftq2nKYn8pYCJKyGVI1rFN3lZMqymmdvHjOLd+3mhfQ+oZBF7wotcNyPcV7GDBxs+oddZ4HSM2JIFpI64jPLDpt1fbi72ggOwPPyHv66/RHM4mnbM1NoalPbsfX+g/sFs3bp00iYEp3doyVEHoEEQy5kciGROE9qWr5WiVH6GWvwu690TnOCUWOXgaSsjlSAbPtxd/z3VgOTWL7kMrO9hwR2NA3QL/4Td53Aap2l7JN5mfcKh4r9chLEYrFw+9kh6xTZ/Ja0/OklKyP/2M3DlfotbaGu2vAVvi41jaszulR0tijEtP57ZJZ5MU2fGSTkHwFZGM+ZFIxgTBP3R3Ee6SZ9BrfmjaCZIJOfhylPBbmrTQX3c7sK/5D7ZfX2y0FIYhaSSBF/0bJbJHvTZN1/hl1/es3LukoeAY22cSY3uf3W57WzaXq6KSnC/mkPPZ57grqxrt75Yk1iYn8VOPNGwmEwZFYdqIEVw3fhyhAf5/olQQfE0kY34kkjFB8C+tZinu4qdBbWKVeTkEJeJu5ODLmnT7UqvMo3bJEzi3z224o8GCddz9WEbd5rGC/568bXy78dMGt1LqFdefi4ZegdnQcTfqdldXk/3p52R/9EmTapXZDAaWd09ldbdk3IpCkMXCtWeeySWjRmI2dqz1coLQGiIZ8yORjAmC/+ladd0TmBUfgt74rTQAyZyBEvlok7dacu5fQu2Ce9EqcxrspyQMIfDCVzDE1L/tWFpdxNx171FU5b22V3RwHNNH3kBYB1xHdiJXRQVZH35Mzudz0OzeE8xjyi1mFvfqyeb4OHRJIj4sjFsnTWJiRn+xyF/oEkQy5kciGROEjkN3F6NWfoJWNbfJM2VS4DkoYbcjm/s0Pr6jitolT+HIfLfhjrIR6xn3YhnzZ6TfPS3pdDtYtHkOO3I2ej3dagrgkuHX0S2q/m3PjsZZUsqRDz4k98t56M7GF/rnBgfxXZ/eHIiMAGBIaip3XzCZ7rFNW88nCB2VSMb8SCRjgtDx6LqKbluBWjkHvXYJNLB/5DFSwFko4bcjm9Mb7es69Cs1396DVn6owX5KbP+6WbL4Qb+LT2f9wV9Zuv0bNN1zyQ5ZkjlnwCUMSR3daDwdgaOwiMPvvEve19+Aqjbaf3tMNN/16U1ZgBVFlrlk1EhunDCBIEvHvUUrCA0RyZgfiWRMEDo23bkfd9mrTV7oLwVMQAm/s9E6Zbqzhtpls3Cs/R8NJnuSgmXMn7CecS/S79aCZZUcZO6696h1ei9uOyztdM7ufzGyl+2YOpraw4c5MPs/lCz/udG+Llnml7QUfklLxaUohAcGcts5kzhv0CBRyV/odEQy5kdib0pB6By02pW4S54G1+Em9JaQgy9BCb8LyRDVYE9X1hpqvrkLrXR/g/3kqN4EXfhKvS2VKmpLmbP2XQorc72emxrVi6nDr+kQ+1o2VcWmzex/dTZV27Y32rfcYmFRn15sj40BSSIjOZm7L5hMn4SEdohUEFpH7E3ZAYiZMUHoPHTdiVb+Nmr5/0BvfNE5UiBK+G3IodcgSSbv47ps2H75J/ZVs8HLbce68WQso27DOu5+JONviZXT7eDbjZ82uNl4eGAUM0bdTERQw8lhR6LrOsVLl3HgtdexZzf84APA/ohwFvTrQ2FQEJIkccnIkdxy9lkEmM3tEK0gtI6YGfMjkYwJQueju4tRK95Fq/ykaU9fGuLrbl0GXdxgOQx37kZqvrkLtajhTbfl8DQCL3wZY8qY32LSNX7Z/SMr9vzo9TyrKZDpI28gKSK18Zg7EM3lInfuVxx+861Ga5SpksSqlGSW9OiBy6AQExrKXy68gNP7NP6AhSD4k0jG/EgkY4LQeelqaV1SVvEx6I3XzMLYHUPE3UgBZ3ktx6CrTmy/vox9xUugNbxtk3n4jQRMeBTJHHT82M6cTXy76TPcqsvjOYps4OKhV9A3YZDH9o7MVVHBwf/8j7x5Xze6GXm5xcL89L7sia6bCZyY0Z8/n38+kcHB7RGqIDSbSMb8SCRjgtD56Wr50aTsPdAdjfaXLMNRIh9o8MlLd8G2ulmy/C0NjiWHJhN40b8xpo49fiyvPJsv175Dlb3C63kT0y9kZI9xnbJGV9XuPez710tUbmn4vQHYFhvDgr59qLKYCbJYmHnuOVwwdGinfN1C1yaSMT8SyZggdB26Oxe15EW0moVN6C0hB01FibgbyRDteTzNjX3VbGw/Pw9qQzW4JCyn3Yl1/ANISt3atGp7JV+sfZv88myvZw1JPY1zMqZ2mictT6TrOoXf/8CBV2fjLC5psK/doPBDr56sS05ClySGpKZy35SLSRZ7XQodiEjG/EgkY4LQ9Wj2Daglz6E7tjXeWbKihN2CHHodkuy5RpZavIfqb+5CzVnf4FBK3ECCpv4HJaoXULewf37mx+zN9/5EYo+Yvkwdfg0mQ+dc5O6uqeHIu++T/fGn6O6Gb+tmhYbwVf9+FAQHYzYauW3S2VwycqQogyF0CCIZ8yORjAlC16TrOnrNj7jLXgHXwcZPMMSjRNyLHHi+x1touqbiWPcGtT/9A9wNPDRgsBIw6SnMQ69DkiQ0XWPJtvmsP/ir11NiQxOZMeomgiyd99+gmoOH2Pvs81Rs2txgP1WSWNY9jeXdU9FkmSGpqTwwbSoJ4eHtFKkgeCaSMT8SyZggdG267kar+hq17FVQCxvtL5mHoETej2wZ6LFdLT1Azbf34D6yssFxjL3OJfDCl5ED6xawrzvwC4u3zcdbgdmwgAguH31Lpyp98Xu6ppH/7QIOvDq70acuc4OD+XJAOgXBwVhNJu4871wuGjZMrCUT/EYkY34kkjFBODXoWi1q+VtoFe80qUaZHHQxSsRfkAwx9cfSNRxr/0ft0qcbXEsmBcUQeNGrmHpMBGBP3ja+zvzI65OWAaYgLh99M3FhSU18VR2Ts7SM/a+8SuF33zfYzy1JLOuRxs9pdbNkI3v25P4pFxMTGtpOkQrCb0Qy5kciGROEU4vuzkMtfRGtekHjnSXr0aKx13ksGusu2E7NV7ehFu1qcBjzyFsJmPgIksFCbtkRvljzttctlEwGM5eOuJ7U6F5Nej0dWematex9/l+NFozNCQlmbkZ/CoKDCLJYuGvy+Zw7aJCYJRPalUjG/EgkY4JwatLsm1FLnkV3NLzGCQBDMobI+5ECJtRLEHSXjdqlT+FY92aDQygx/Qm69E2UyJ6U15Tw2Zo3Ka0u8txXVrho6JX064S1yH5PtTs4/PY7ZH34cYMbkP9+lmxiRn/+etFFBFut7RitcCoTyZgfiWRMEE5duq6j1SxCLfkXqPmN9pesp2OIfADJ1KNem3Pfj9R8cxd6jecECwBjAIGT/4V5wGXUOmr4Yu1b5JYd8XY1zhkwlWFppzfx1XRsVTt3suvJv1N78FCD/Y6EhfLFgP6UBQQQFxbGo5deysCUbu0TpHBKE8mYH4mNwgVB0DU7WsW7qOVvNGF7JQU55EqU8JlIysn/YGs1RdR8cxeufd63RAIwDbqCwHNn4ZINzFv/PgcKd3vtO7b3JMb2OadL3LLTnE4Ovfk2WR98BJr3PUDtisK36X3ZFB+HLMtcP3481447E0WUwBDagNgovAMQM2OCIByjuwuOrif7pvHOcjhKxD3IwZcgSb8lCbqu48h8l9ofHwO39wcFlKg+BF76JlJkLxZs+ozt2Zle+w5NHcM5A6aedJ3OrHLHTnY/1fgs2Za4WOan98VuNDIoJYVHL72E2LCwdolROPWImTE/EsmYIAi/p9k3oZb8o0lFYyXzAJSoR5HNGScdV4t2Uz3vVtRC7wVfMVgJPPcfGAddwU87FrD2wM9eu2YkDeOCwTM6ZbV+TzSHo26W7MOPG5wlK7dY+GJgfw6HhxNksXD/lCmM7+99GytBaCmRjPmRSMYEQfBE1zW0qnmoZS+D2vB2PyAhB19Wt7WSEvbbGC4btT8+iiPzvQbPNmVMJ+C851mbtZ6fdnp/yrNP/ACmDLsKRTY0/YV0cJXbd7Dr8aewZWV57aMBy7un8VOPNDRZZuqIEfzp/PMwGbrO+yD4n0jG/EgkY4IgNETXqlDLXker+BBoeLsf5LCjty4vPemWomP7PGoW/AW8lLMAkCO6E3TJm+xw2li4eQ667nm2qHtMXy4ZcR1GxdiSl9MhqbW17HvpFfLnf9tgvyOhoXw2aAAVVgt9EhJ46vIZonK/4DMiGfMjkYwJgtAUuvMg7pJn0W2/NNpXMg9AiXwE2TLg+DG19ADV825BzWuglIZiJvC8ZzkSP4R56z9A1Twnf90iezB91A2YDZ730uysipb+xJ5ZzzVYvb/WYODLAf3ZHRNNkMXCw5dMY2zfvu0YpdBVtWsyVlNTw4oVK9i8eTP79++npKSEmpoajEYjQUFBJCcn06tXL8aMGUO/fv1ae7kOTyRjgiA0h1a7HHfxP8Dt/bZaHQk5eDpKxD3Hb13qbge1S57Ese6NBs80D76KomE3MyfzI1xeqvUnhKcwY9RNWE0BLXgVHZejoJBdT/2d8vUbGuz3S2oKP/bqgSbLXDn2dP541lkYlK6xnk7wjzZPxnRdZ86cObz77rssXrwYt9vNsaFkWSYoKAin04nd/tuTP5IkkZCQwOWXX87tt99Ojx716+p0NEuXLuXDDz9k5cqVZGVlERYWxvDhw3nssccYNmyYx3NEMiYIQnPpmgOt4q2jpTAcDXeWQ0+4dVmXLDh3L6Tmmz+j2yu8nqbEDaT6rCf5bOd3OLw8lRkbksAfTruFAHNQi19LR6RrGtkffcLB//wP3e391vCRsFA+G1h323JQSgpPzriMyODgdoxU6EraNBmbM2cODzzwAKqqMn78eIYOHcqQIUPo3r07ISEhBAX99pdYVVUqKiooLS1ly5YtbNy4kQ0bNrB8+XKmTp3K888/T2JiYsteZTu47LLLKCkp4bLLLiM9PZ2ioiJeeOEF1q9fz/fff8/EiRPrnSOSMUEQWkp3ZeMumYVe+1Ojfeueunwc2Vz3JKBafqTutmWO9xkgyRKGc9LTfJK3A5uz1mOfyKAYrhxzG0GWrvfvV9Wu3ex85PEGF/fXGo18PjCDfVGRRIeE8Pc/XE56Uufe21PwjzZJxsrLy7n55psJCgriuuuuY8KECS0OsKysjI8++oiPP/6YW265heuvv77FY7WlwsJCYmJO3tS3urqanj17kpGRweLFi+udI5IxQRBaq+m3LmXkkKtRIv6EJAeiqy5sS5/Gvub1Bs6R0EbeyqeaQpWXBwAigqK5asztXTIhc1fXsGfWcxQtXuK1jwYs7tWDn9NSMRmN3HvRhUweMqT9ghS6BJ8nYwcPHuTBBx/koYceYuDAgT4LVNM0XnnlFYqLi3nmmWd8Nm5bmzhxIjk5OezeXb/KtUjGBEHwhbpbl2+jlv+v8VuXSiyGyAeRAichSRLOHV9T/e1d4Kzxfk7KWL6MTKfY7XnsiMBorjz9NoItoa14FR2TruvkzfuafS+9gu50eu23PSaaLwf0x2kwcOmoUdx53rliHZnQZD5Pxvbv309aWhpyG20dkZeXR3Bw8Em3ODuqiooKUlJSmDhxInPnzq3XfuzNz8rKOunNN5vNmM3m9gxVEIQuQHfl1D11Wet9JucYyXomhqhHkIxJqMV7qZpzPVrxHu8nhCTyY8p4Dkie62uFB0Zx1ZjbCbZ2vYQMoHrPHnY8+Ci27GyvfQoDA/h4yCCKAwMZnJrKU5fPIDwwsB2jFDoLh8OBw/Hbf24qKytJTk5uNBlrcmbVo0ePNkvEAOLj4ztFIgYwc+ZMampqePjhhxvsl5ycTGho6PGvWbNmtVOEgiB0JZIxEWPcqxji/gOG5Ab76rafcWVfjFr2P+TIFEJv/B5T+lTvJ1TmMGnHFwyrLfTYXFZTzEcrX6fK5v3BgM4sqHdvhr73NtFn11//e0xMTS23rVpL38JCNh06xB//81925+a2Y5RCZzFr1qyTPveTkxv++3qMqDPWTI8++ih///vfefXVV7nzzjs99hEzY4IgtBVds6OWv4FW/ibguUTFccbuGKKeQLIMw7HuDWoXPw5e6owB7Isfxk9R6Wge9qsMD4ziyjG3EWINa90L6KB0XSf740858H+vNbiV0k/d01jasztGo5EHp03l7AEDvPYVTj0tnRnzWTJWVFREZGRkm86e+duTTz7JE088wTPPPMNDDz3ktZ9YMyYIQlvTnQdxFz+Fbl/TaF85aCpK5H24c/dQPfdm9Kp8r30LQ7uxKPE07B6Kv4YFRHLV6bd32YQMoGz9BnY+/Biu8nKvfXZFR/H5wAycBgM3T5zItePORJKk9gtS6DSamg80K3P6/PPPOfvsszn//PP58ccfAfjkk0+IjIwkLi6O6OhoXn+9oSd4Oq9jidgTTzzRYCImCILQHiRTGob4t1GinwMlssG+WvVXuLImI4dmE3LjjxhSTvfaN6biCNP3f0eErbReW3ltCR+teJ1KW3lrw++wwocPY+h7bxHUt4/XPn2LirllzTrCbDbeXLqUZ+bOw9lA7TJBaEyTZ8a+++47Jk+eTGJiImFhYezdu5dPP/2UGTNmcN555zFy5Eh++uknli1bxjfffMPkyZPbOvZ28/TTT/PYY4/xyCOP8PTTTzfaX8yMCYLQnnS1ArX0ZbSqz4GG/0mXLMNQIh7F/uvH2FfP9trPLRv5KWkMB8JS67WFB0Zx9el3dMmyF8eodgd7n/8XBQsWeu1TbTLy8eBBHAkPY1BKCs9c8QdCA7rW7gVC6/j8acqzzjoLi8XC/PnzURSFp59+mhdffJHJkyfz0UcfHe93/vnnA7Bo0aJWvoSO4YUXXuCvf/0r5513Ho8//ni99tGjR9c7JpIxQRD8QbNvRi1+At1Zv+TOyYwo4bfizo6kZsFfwUs1foCN0QNYFzcY/XfryKKCY7lqzO1drlL/iXRdJ2/uV+x78WWvVfvdksRX/fuxKTGBpMhInr/6KpIjG56pFE4dPk/GYmJieOONN5gyZQoA+fn5JCQk8M0333DBBRcc7/fZZ59x5513UlRU1MqX0DGMHz+e5cuXe2339PaJZEwQBH/RdTdaxUeoZf8G3dZgX8nYA127mtqvZ6FV5njtdzg4kaXdzsSpmE46HhOSwJVjbutye1n+XsXmLWy//yFcZWVe+yxPS2Vxrx4EBwTwzBV/YHBqavsFKHRYPl8zVlVVRXh4+PHfR0dHAxAXF3dSv/j4eCoqus4j0MuWLUPXda9fgiAIHYkkGVDCrsOY/C1SwKQG++qu/aA+ScC08zAkj/DaL6Uqh2l7FxD6u30vCytz+Wz1G9hdDSd9nV3ooIEMfedNAnv29Npn3MFDzNiyjdrqau55732+27S5HSMUOrsmJ2ORkZHk5//2BI4sy1x66aVE/m46tri4mNDQrlkcUBAEobOQDPEY417BEPsaGOIb7uyaj/mMakyDvCdvYc5Kpu1fSGLVyfW18sqz+GLNWzi9VPHvKizxcQx543Uizxjrtc+A/AKuX78Ro83OM3Pn8sHPP4v/tAtN0uRkLCMjg5UrVx7/vSRJfPHFF6T+bip2w4YN9O7d22cBCoIgCC0nB47HmDQfOfQ6GvonX6IYU/9NmMeOANlzNX6z6mTywcX0Kzl5TVp26SHmrH0bl9pI3bNOTgkIoP9z/yD5mqu89kktL+eWtesIr7Xxv8VLeHnBQtQG6pYJAjQjGXv88cc555xzGu13+PBhrrrK+w+qIAiC0L4kORBD5P0YEj9FMvVtsK+x2yGsZ0UjBQR7bJfROTNnNWNy1iLpvyUZh4v3M3fdu7jVrl3iQVIUut95B30eexjJaPTYJ7qmllvXrCOhopK5a9fy+Odf4HB17URVaB1Rgb8NiAX8giB0VLruQqt4H7VsNujen6LUalXsv7rRiiu99jkSnMiS3y3s7x2XwbTh1yDLXX8z7fLMjWz/24O4q6o8tjsVmc8GDmB3TDSDUlKYdeUVBFut7Ryl4E9tUvRVEARB6NwkyYgSdhPGpK+QrKd57ScHKFgnmjCk1K/Ef0y3qhym7FtIsPO3ZGRP/jYWbv4CXe/6t+bChg5h8Bv/wRwf57HdpGpctXEzI7Ky2Xz4MDPfepuiSu/JrXDqEslYGxoxYgTp6enMnu29sKIgCII/SMZuGOLeRIl+FuQwz30MEuYxQZgGeC9dEeGoYNreBcTVFBw/tjVrPUu3f3tKLF4PTEtlyJv/JaiP54r9MjBlxy7O3ruPgwUFzHzzLXJK6+9uIHQts2fPJj09nREjvD+lfKIW3aY8cuRIswNrTLdu3Xw+pr+I25SCIHQmulqKWvI8WvV8r31ch+04VleB6rldlWSWJ41hb3iP48fG9T2fMb3P8nW4HZJaW8uOhx+jdOUqr33WJCfxbb8+RISE8OK119A9NrYdIxT8wedFX08ky7JPN0WVJAl3F9rXSyRjgiB0RlrtCtzFT4I722O7WuzC/nMFut37x0ZmzADWxQ6Bo58R5w64hKFpY9ok3o5Gd7vZ+/wL5H3tPandEhfLlwP6ExgYyL+uvYZ+iYntGKHQ3tp8zVhDhVBb8iUIgiD4lxxwOsakr5BDbwTqL8BXooxYzw1HDvO+OH9o4VYmZv2CrNVNoX2/dR47cja2VcgdimQw0OvBv5F6+y1e+wzML+DKjZuxVVdz1zvvsvHgofYLUOiwWpyMjR07Fk3TWv11+umn+/L1CIIgCK0gyQEYIv+KIfEzJFP/eu1yoIJ1UjhKksnD2XV6lR/kgoM/YnY7AJ1vMj9hf+GuNoy645AkiZTrr6PP44+A4jlp7VNcwnXrN6LX1PDXDz5g1Z497Ryl0NGIBfyCIAhCPbI5HUPiJygR94JkPqlNMkpYzgjB2M97mYaEmgKm7F9IsKMKTdeYu+49sksOtnXYHUbc5PPp/9w/kEyek9bU8nJuWrcBY00ND378CUu2bmvnCIWOpEXJ2JlnnsmQIUN8EsDgwYM588wzfTKWIAiC4Dt1+1zehDFxHpJl2O/aJMxDgjCPCgYvS4jDHZVM3b+Q6Npi3KqLz9e8RWFlXjtE3jFEnTGWga+8iBLg+WnU+Kpq/rh2PcE1NTw5Zw7fbtjQzhEKHYUo+toGxAJ+QRC6Gl3X0Co/Qy19AfTak9rc+U7sv1SCy/PHiUtSWNLtTA6HdiPYEsq1Z/yJEGtYO0TdMVTt3MXWu+/FVV7usb3CbObtEUMpCQzk3osuZGoTyyEIHZ8o+ioIgiD4jCTJKKFXYEz6Gsl68mbZhjgT1klhSAGeP1KMusq5h3+if/FOquwVfLb6DWzOWo99u6Lgfn0Z/N/XMHspZRHqcHDz2g1EV1fzwjffMnfNmnaOUPA3kYwJgiAITSYZEzHE/Rcl+h8g//Y/fSXMgPWcMORwz5uMS8DY3LWclruO4sp8vlz3Lu4uvrH4iQJSUxj8v9ewdkv22B7sdHLTug3EVlXx0oKFzFm9up0jFPzJp8mYrutkZmby5Zdf8uOPP5KVleXL4QVBEIQOQJIklOCpGJO+QQqYdPy4HKBgPTsMJcH7k5YDi3cw6chycov28k3mJ6fEtknHWOLiGPzf1wnq09tje5DTxU3rMomvrOSVhYv4vIECskLX4rM1YwUFBUyZMoV169addDwiIoJhw4YxbNgwhg4dyrBhw0hNTfXFJTsssWZMEIRTiVb9Pe6Sv4NaAoCu6TjWV+Pe530j8tzAWL5PncjAnhM5O2OKTwuJd3Tu6mq23vNXKrds9dhuMxh4d9gQcsJCmXnuOfxBlIDqtNq0Ar8n119/Pe+//z6yLJOeno7L5WL//v3HK+uf+BctPDycoUOH8sMPP/ji0h3OsTe/d+/eKIrCzJkzmTlzpr/DEgRBaDO6Wo5a8hxa9dd1v9d1XDttODfVeD2nxBLOwrSzGDVoBqN7TmivUDsEd00N2/56PxWZngvi2hWF94YNISs8jJnnnssfTj81djHoKmbPns3s2bNRVZU9e/a0XzKWnJxMbm4uCxYs4LzzzgPA6XSydetWNmzYQGZmJhs2bGDbtm04HA4kSUJVvWxy1smJmTFBEE5VWu3PuIueADUfOLqn5aoq8HI3ssoYyMK0SZwx5nYykoa2X6AdgGq3s+2++ylfu95ju11ReHf4ULLDQrnngslcMmpUO0cotFa7z4wFBgaSkpLCjh07GuzndrvZtm0bmZmZ3Hjjjb64dIcjkjFBEE5lulaNWvoiWuWnAKiFLmw/V4DT88eNXTHxfdo5TDjrIVKje7VnqH6nORxsv/8hSld5XrBvMxh4Z/hQckNDuH/KxVw4bJjHfkLH1O6lLdLS0gjwUtjuRAaDgcGDB3fZREwQBOFUJ8lBGKIewxD/HhiSUWKMWM/2XvrCojq54MAi1i5+iqLK/HaO1r9ks5n+z88i8swzPLZb3W6uX1+3qP/5+d/w/ebN7Ryh0B58loxNnTqVnTt3Ult76tSOEQRBELyTrSPqNh4PuQYlzIh1UhhyqOf9Gg2aylkHvmP9t/dQY69q50j9SzaZSJ/1d6ImjPfYHuB2c/36jcRUVvGPufP4adv2do1PaHstSsY++eQTdu3axYl3OO+55x4CAgJ49tlnfRacIAiC0LlJshVD1IMY4t9DDk3FenYYcrSXWmQ6jDqwmB1zr8LpcrRzpP4lGwz0+/uTXmfIAl0ubli/gciqKp6cM4dfdp4aG6+fKlq0ZkyWZSRJwmq1MmDAAAYPHsyQIUOoqqri0Ucf5YEHHuCxxx5ri3g7BbFmTBAEoT5dq0UtfQm19EPsKypRc5xe+1am9SL5D8tQFO81y7oizelk+wMPU7pipcf2KpOJt0YMozw0hFlXXMHo3qfWGrvOpk0X8J9++uls2bKFmprfHln+fY2YPn36cNVVV3HWWWcxaNAgrFZrcy/TaYlkTBAEwTvNtg5XwcM4Vu3Evd97LTItJZKIyxYgW3q0Y3T+pzkcbPvbg5St9rwtUqXZxFsjhlMVGsLzV1/FsO7d2zlCoana/GlKXdfZvXs3mZmZbNy4kczMTDZt2kRZWdlvgx9N0GRZpk+fPscLvw4dOpQhQ4YQFBTUkkt3eCIZEwRBaJiu1eIueQH7ijdxbfO+1lhJtBB48ZMoEdcjSafODn6q3cG2+/7mtexFhcXMGyOH4wgN5ZUbrqdfYmI7Ryg0RbuXtjjm4MGDJyVoGzdupKCg4LcLnpCguVxdc18ykYwJgiA0jWZbS80vt+Bam+O1jxxjJOCc8RgTn0Myet7bsStS7Xa23vNXr4VhiwMCeGPkMAzh4cy+6UZSoqPbOUKhMX5LxjzJzc09npwdK/6anZ2NpnXNPclEMiYIgtB0ulZD+aob0Jf/5LU4rBxhwDohFkPCfcghV5wys2RqbS1b7r6Xys1bPLbnBQfx1ohhhERF8drNNxEbGtrOEQoN6VDJmCelpaVERET449JtTiRjgiAIzXd4zeME/fQ6ktvzx5IUomCdGIoScRqG6L8jGZPaOUL/cNfUsPWuv1C5dZvH9sNhobw7bCgJ8XHMvulGQptQ81NoH21e9HXUqFE899xz7N69u0Xnd9VETBAEQWiZlFFPcmjcw6hGz7XI9EoV24/lqEWrcGVPRa2cg5/mE9qVITCQAS/9iyAvT06mlFdw5abNZBcUcN8HH1LrOLXKgnQFLZ4ZO1beAuqenLzkkkuYOnUqw4cP92mAnZHYKFwQBKFldF1nyc8vkr76JSxeao1JFgnLhDCUcANSwDgMUU8hGbr+eilnaRmbbr0d25Esj+3bYmP4bNAAhvfsyXNXXYnR4Lmem9D22m2j8N27dzN37lzmzZvHhg0b0HUdSZJISEhg2rRpTJ06lfHjxyPLp8Z9/ROJ25SCIAgt51bdfP3TcwzLfJMQZ7XnTkYJ6/hQlGgjyCEoUY+hBE1u30D9wJ6fz6Zb7sBxwoNxJ1qXlMjX6X2ZOGAAj02/FOUU/AzuSNp1zVhOTg7z5s1j3rx5/PLLL7jdbiRJIjw8nIsuuoipU6dy7rnnYrFYWnupTkEkY4IgCK1T66jhsyXPMHbHF0Tayz13UsAyLhRDXF1hWDnwPJSox5CUsHaL0x9qjxxh0y234yor99i+pEd3furZnWkjR3DPBRfUqwMqtB+/LeAvLy9n/vz5zJs3jx9//JHa2trj1frPPfdcpk2bxoUXXkhYWJgvL9uhiGRMEASh9Yoq8/n0p39y9v5FxNYWee4kg+WMEAyJ5rrfK1EYop9GDhjXfoH6QdXuPWy+40+o1Z5nDr9K78f65ET+eNZZXDvuzHaOTjimQzxNabPZ+P7775k7dy4LFiygrKwMSZIwGAyMGzeOadOmcfHFF5PYxYrViWRMEATBN/bm7+CrVf/j3MNLSarO89xJBsvpIRiSzb8dCr4UJfJ+JLlrFhcHqNi8hS1/uhvNw4J9Dfh4yCB2xUTz8CWXcN7gQe0foNAxkrETqarK8uXLmTt3LvPnzyc7OxuoexDA7Xa3RwjtRiRjgiAIvrN6708s3z6fs478TPfKI547SWA+LRhj6gnLYQwJGKL/gWwd2T6B+kHJryvY9rcHQVXrtblkmbeHDyU3MoJ/XXM1w3ucWttKdQQdLhn7vXXr1jF37ly++uordu7c6Y8Q2oxIxgRBEHxH13W+3fgp27PWMS57JX3K9nvtax4VhLHHyXshyyHXokTcjSR3zXXLefO/Yc8zz3psqzUYeGPUCGoiI5h90430jItr5+hObW1eZ6y1RowYwaxZszpFIlZdXc3dd99NQkICFouFwYMH8+mnn/o7LEEQhFOCJEmcP2g68RFpLEs6nR0Rvb32daypxrXHdtIxrfJ9XDnT0exb2zpUv4i/+CJSb/2jx7YAt5vrNmSiVFRw3wcfUlBR0c7RCU3hs2QsKyuLf//73yxZsqTBfosXL+bf//43OTne9yHraC655BLee+89Hn/8cRYtWsSIESO44oor+Pjjj/0dmiAIwinBoBi5dMR1BFvD+CVxNFuj+nnt61hfjXPn7zYfdx3AnXsl7tJX0XVnG0fb/rrdcB0Jl07z2BZmd3Ddhk1Ul5Zy3wcfUmWzeewn+I/PkrE333yTe+65h9LS0gb7lZeXc8899/D222/76tJtauHChfz444+89tpr3HrrrUyYMIE33niDSZMmcd9996F6uE8vCIIg+F6QJYTpo27EaDCxMn4EG6MHeO3r3FiDc1vN746qaOWv4865As25t22DbWeSJNHz3nuIGu/5KdK46mqu3LiFI/n5PPLpZ7jFZ1eH4rNkbNGiRZhMJqZN85yZHzNlyhRMJhMLFy701aXb1Lx58wgKCuKyyy476fgNN9xAbm4ua9as8VNkgiAIp5640EQuHPIHkCTWxg9lbexgr32dW2pxbK6pt2WS7tyJO3s6avlb6HrXSUokRaHfU48T6uXJye5lZUzZvpPMAwd4acGCU2Irqc7CZ8nY4cOH6datG4ZGtl8wGo1069aNQ4cO+erSbWrbtm3069ev3usaOHDg8XZvKisrT/pyiP3CBEEQWq1vwiDG9DoLgI2xg1gVP8xrX9f2Wpyb6idk4EItfQF37nXoLi9PaHZCstlM/38+S0D3NI/tQ3PzGHfwEPPXb+DzVavaObquz+Fw1PvsbwqfJWMVFRVNLuQaFhZGWVmZry7dpkpKSjxuan7sWElJiddzk5OTCQ0NPf41a9asNotTEAThVHJG33PpHtMHgC3RGfyaMMprX9dOG86NnhIy0B2ZuLKnoVZ+1mVmiowhIQx8+UXMsbEe2yft3U9GfgGzv/+BFbt2t3N0XdusWbNO+txPTk5u0nk+S8aioqLYv9/748bH6LrO/v37CQ8P99Wl21xDW0k01JaVlUVFRcXxrwcffLAtwhMEQTjlyJLMxUOvIiwgEoDtUX1ZljQGb+mUa5f3hAzdhlr8JO7829HdXir9dzLm2BgyXvwnSkCAx/ZLt24nsaycJ+fMYV9+fjtH13U9+OCDJ33uZ2V53tT993yWjI0YMYKysjI+//zzBvvNmTOH0tJShg8f7qtLt6nIyEiPs1/HHlTwNGt2TEhIyElfZrPZa19BEASheaymAKaPvB6jUrc35e6IXixNPgMNz/9Jdu2y4cz0kpABuu1nXNlT0WoWt1nM7SmoZw/S//E0eNgs3KhpXL1xE5byCu7/6GNKqqr8EGHXYzab6332N4XPkrEbb7wRXde59dZb+fHHHz32Wbx4MbfccguSJHHjjTf66tJtasCAAezcubPeLgFbt9bVq8nIyPBHWIIgCAIQHRLPBUMuP/77feHdWdztTO8J2W4bzk2y91uSWhnugj/jLnwYXfO872NnEnHaaHree4/HtiCni6szN1FRXMKDH3+Cw+Vq5+iEY3xagX/69OnMnTsXSZIYPnw4p59+OmFhYZSXl7NixQrWr1+PrutMmTKFefPm+eqybWrRokVMnjyZTz/9lMsv/+0v/Pnnn8+WLVs4cuQIiqKcdI6owC8IgtC+ftqxgNX7fjr++7TyQ5x95GdkLzcuTQMGYczIaXCpCYZEDDHPIlu8PyDQWex78WVyPvvCc1tkBO8PHcz4gQN54rLpDb8nQrP4ZTskh8PBHXfcwbvvvouu6yf9gR77/fXXX8/s2bOxWDrPthTnnHMO69ev57nnnqNnz5588sknvPHGG3z44YdcddVV9fqLZEwQBKF9abrG56vf5GDRnuPH0ioOc9bh5SjeErIhF2Dsvx9J8/4gFkjIYTehhN+JJJl8HHX70VWVbX97kNJfV3hsX5eUyNfpfbl+wnhumjixfYPrwvy6N+X27duZM2cOW7dupbKykpCQEAYOHMill15K//79fX25NlddXc3DDz/M559/TmlpKX379uXBBx/kD3/4g8f+IhkTBEFofzZnLe/+/DLltb8VH28sITMPvxbjIBfYGl4nJpn6osQ8h2zq5dOY25NaW8umW++geo/ngrff9u3N6pRuPDVjBhMyOt9ndUfU4TcK78pEMiYIguAfhRW5vP/rq7jU39Y/NZ6Q3Yx5zCi0kmdA/33V/hNIJpSIvyCHXI0k+W1r51ZxFBSSeePNOIvrzwZqwHvDhpATH8frf7xZbCruAx1+o3BBEARB8LWY0ATOGzj9pGMHQ1NYnDIO1cuifsf6N3GsWoMhcR5SQ+vDdCdqybO4829Gd3fOchDm2Bgy/vU8soelQjJw+eatBJZX8NAnn1JRW1t/AKFNtMnM2IYNG1iwYAE7duygqqqK4OBg0tPTueCCCxg2rPMvhGyMmBkTBEHwr+82f8nGwydXmE+tOMzZDcyQWUbfgWXio+iV76GWvgK4PfYDQA5BiXoUJegCH0bdfoqX/8L2+x8EDylAYWAg/x01gv69e/HCtddg+N1DakLT+eU2ZWFhITfeeCOLFi0COOnR4WOL+c877zzeeust4rrw9KdIxgRBEPzLrbr44NfZ5Fdkn3Q8teIIkw4v8/qUpeX0ewiY8BCaYxdq4f3oroY3FJcDJ6NEPYqkhPos9vZy+N33OPT6/zy27YqO4qMhg5g+5jT+fP757RxZ19HuyVhlZSWjRo1iz5496LrOsGHDyMjIIC4ujoKCArZt28b69euRJIlevXqxdu3aLpuoiGRMEATB/8prS3ln+UvYXbaTjqdUHOGcI8uRdc3jedZxD2A94150zYFa9gpaxXvgtbY/oMRiiH4GOWCMD6Nve7qus/ORxylavMRj+89pKfzQuxcPXzKN8wYPbt/guoh2XzP2j3/8g927d5Oamsry5ctZt24d77zzDrNmzeLtt99m7dq1/Pzzz6SlpbF3717+8Y9/+OrSgiAIglBPWEAEFw29ot7xw6Hd+LHbODQvi/Bty5/FtvJVJNmMIfJvGOLfBqWBuzlqAe78m3EXz0LX7L4Kv81JkkSfRx8iqE8fj+1nHjzMoNw8/jn/G3ZkZ3vsI/iGz5KxOXPmIEkSX331FWeccYbHPmPHjmXevHnous6cOXN8dekOa8SIEaSnpzN79mx/hyIIgnBK6hmbzpheZ9U7fii0G0uSz0D3sqjftvQp7Gv/C4BsHYUx6SvkoIsavJZW+QHunMvQHDtaH3g7USwW+v9zFkYvW/tN3b6TiNIyHv3sc8prGnjSVDjJ7NmzSU9PZ8SIEU3q77PblBaLhR49erB9+/ZG+/bv358DBw5gs9ka7dsZiduUgiAIHYema3y66n8cLt5Xr61n2QEmZv2K5OU2ZMD5/8Qy7Prjv1erF6EWPwlaZQNXNKCE34kcdhOS1DkWv1ds3cbm2+9E97AlUqnVyuujR5LRry//vOZqFA97XQqetfttyoiICKxWa5P6Wq1WwsPDfXVpQRAEQfBKlmSmDLuKIEv9D8N94d1ZnTrB67m1i+7Dsemj479Xgs7HmPQ1krWh9WFu1LKXcedeh+7Kak3o7SZ0QAa977/PY1uEzcZlW7exft8+3vlpWfsGdorwWTI2fvx4tm/fTmlpaYP9SkpK2LZtGxMmeP/hFwRBEARfCjQHM23YNcge1oltCUlmR2/vtyBrvr0Hx9bfltZIhlgMcf9DiXwIJLPX83RHJq7sS1Crvva+MXkHEnfRBSTOuMxjW+/iEibuO8B7y5ezcvcej32ElvNZMvbYY4+hKArXXnst1dWed7qvqanhuuuuw2g08thjj/nq0oIgCILQqKTINCake64L9oslgoLB13o5U6dm/kycO74+fkSSZJTQqzEmzkEypXu/qF6DWvQgauG96GpFK6JvH93vupPQwYM8tk04cJC+hUX8fe5ccsvK2jmyrs1na8Z+/vln1qxZwyOPPEJQUBA333xzvdIWb775JtXV1fz9739n5MiRHsc588wzfRGOX4k1Y4IgCB2TruvMWfs2+wp21muTkLg+IAjT6v/zfLJsIOiy9zD1Oud3YzpRy15HK3+Duk2FvFDiMMTMQraOasUraHvOkhI2XHuDxy2T7AaF10ePIrJnD167+SbMRqMfIuw82r3OmCzLxwu76rp+/Ncn8nb8eDCShNvdQMXjTkIkY4IgCB1XrbOGt5e9SJW9/kxVsCWUa40G3Cte9HyywULwFZ9iTDm9XpNm34i78AFwN7ROTEIOvQEl4s9IkqmFr6DtVWzZWreg38NnckFQXYX+c0aN5P4pU/wQXefR7slYampqg4lWUx08eNAH0fiXSMYEQRA6tqySg3y08nV0D4Vfe8b05YLaAuwrX/Z8simIkKvnYUgYXK9J12pQS55Fq/qywetLpn4YYp5HMvVoQfTtI+eLL9n3L89J6Za4WD4fmMED06ZywdCh7RxZ5+GX7ZCEOiIZEwRB6PhW7lnC8l2LPLadlX4RGYeWYl/9msd2yRpO8LXzMUT39diu1fyIu+gx0BpYJyaZUSL+hhzyB59MZviaruvsfurvFCz8zmP7wj69WN+zB6//8WZ6x8e3c3SdQ7uXthAEQRCEzuS0XhNIje7tse2nnQspH3oj5iGeF/XrtjKqPpqOWnbIY7scOAlj0ldI1tO8B6A7UEuexl1wB7q7uLnhtzlJkuh1/98I6t3LY/u5e/YRX1TEo59+RlUXrRvaXkQyJgiCIJySJEnm4iFXEGgOrtem6SpfZ36EfPaTmNKneTxfry6g6qPpaJV5nsc3xGKIewMl4n7A+0J3vXY5ruypaLXLW/Q62pJiMZP+3D8whNR/jxRd5/LN2ygvKODvX85F0xp4eEFoUItvU+bm5rb64gkJCa0eoyMStykFQRA6j0NFe/lk1f/wtBl4v4TBXDx4BjVzrse170eP5ytRfQi+9mvkgEiv19Acu1EL70N31d8F4ERyyBUoEX9FkptWRL29lK5ew9a77wUPKcPeyAjeHzaEm88+m2vHdf6KCL7U5mvGTnx6siW6ypOTnhx783v37o2iKMycOZOZM2f6OyxBEATBi593fceKPYs9tl0w+HIGxGdQ9ekVuA+v8NhHiR9EyNXzkDzMsh2ja3bU0hfRKj9sOBhjdwwxzyObG6hf5geH336XQ/99w2Pb4p7d+blnD/59ww0MSk1p58g6ntmzZzN79mxUVWXPnj1tm4y1Vled0hQzY4IgCJ2Lpql8vOq/ZJUcqNdmUszcMO4ewo0WKj+6BDV3o8cxDN1OI/iKz5CMDc9qabW/4i56CNSG1okZUCLuQg69AcnDrgH+oGsaW+/5K2Wr19Rr04B3hg+lJi2Vd+64nZCAgPYPsANqt5mxfv36ceONN3LxxRdjbGbxt5SUrpk9i2RMEASh86myVfDmshewu2rrtcWHJXPN2DuR7BVUfTAFtWiXxzGMPc8m6LL3kJSGa4jpainuosfQa5c22E+yjMQQMwvJ0DGeVnSWlbHhmhtwFhXVa6symfi/MaMYMmgQ/7iiYz4h2t7aPBl76KGH+OCDD8jJyUGSJCIjI7n66qu58cYbycjIaHHgXYFIxgRBEDqn3XlbmbvuPY9tY3qdxbh+56NV5VP53kVo5Yc89jNlTCdwyuxGZ7R0XUermoNa8izoDTyNKIegRD2OEnR+U19Gm6rYtJlNd/wJVLVe2/6IcN4dPpQ/XzCZ6aNH+yG6jqXNS1v84x//4PDhw3z77bdMmzaNyspKXn75ZQYNGsTIkSP5z3/+Q0VFx9+HSxAEQRCO6RM/gMEpnpOIlXuXcqR4P3JwHMFXzUEKjvPYz7ltDrWLH290c3BJklBCLsOY+CWSuYFJDK0StfBe3IUPoGue935uT6GDB5F2+60e23qUljFh/wFe+/4H9uR5fspUqK9VN6JlWWby5MnMmTOHnJwcXnzxRTIyMli/fj0zZ84kPj6eq666isWLPS+KFARBEISO5qz+FxERFO2hRWd+5sfYnLUo4SmEXDkHyRrhcQzHmv9gX/Vqk64nmVIxJHyEHHYL4P3WnlY9H1f2JWh2z2vW2lPyVVcQcfoYj23j9x8kobiYxz//glqHo50j65x8tiowMjKSu+++m82bN7Nu3TpuvfVWLBYLn3zyCeeeey6pqan897//9dXlBEEQBKFNmAxmpgy7GllS6rVV2Sv4bvMcdF1Hie5D8JWfgynI4zi2pU/j2PRxk64pSUYMEXdjiH8PDA2UfXJn4869Bnfpq+i6q0ljtwVJlun7+COYY2PrtcnAZVu2UZyfz0sLFrR/cJ1QmzyiMWzYMF577TXy8vL48MMPSUlJISsriy+/bHivLkEQBEHoCOJCExnfz/MarV15W9iStQ4AQ/wggi97H7ws2K9Z8Bece75v8nVl63CMSfOQgy5soJeGVv467txr0F2Hmzy2rxlDQ0l/5ikkpX7SGmZ3MHX7Tr7buInvNm1q/+A6mTZ7XrakpIT//Oc/PP/88xw+fBhd131SDkMQBEEQ2sPIHmd63S7px61fUVJd90ShMe0Mgqa+jsdbjLpK9dybcWXVLwfhjSQHY4h5HiXmeZA8z7oB6I4tuLIvQa38stH1aW0lZEAGqbfd4rEto6CQ4dk5vPjtAo4Ud7ztnjoSn2ZHmqaxcOFCpk+fTmJiIn/5y1/YsmXL8Zmyzz77zJeXEwRBEIQ2I0kyFw65HKupfs0sl+pkfuZHqFrdE4WmfhcTcP5zngdy26n+7CrchTubdX0l6EKMSfOQLMO8d9JtqMWP4i64G10tb9b4vpJ89ZWEjRzusW3yrj0ElZbyxOdf4Oyihd59wSfJ2J49e3jggQdITk7moosuYu7cuYSGhnLXXXexdetW1q5dy2233UZoaKgvLicIgiAI7SLYEsoFgy/32JZfns3KE6r2W4bdgOWM+zz21e0VVH0yA7U8q1nXl4yJGOLfRQm/GzB47afX/nh0f8uVzRrfF+rWjz2KMSysXptJ07h88zYO5uTwxuIl7R5bZ9HiZKy6upq33nqL008/nX79+vH8889TWFjI5MmT+fLLL48/Xdm/f39fxisIgiAI7apXXH+GpJ7msW3F3iXklh05/nvrmfdhHnq9x756VT5Vn8xAqy1p1vUlSUEJvwVD4sdgTPXeUS3EnX8z7pLn0HVns67RWuaoKPo8+pDHtrjqas7ds49PV65k/f76OxwIrSj6GhQUhM1mQ9d1+vTpww033MC1115LXJznuiunElH0VRAEoWtxuZ288/PLlFQX1muLCIrmxjPvwWioW8Sva0fXie361uNYSsIQQq6ei+TlKcyG6FotaslzaFVfNNhPMvVBiXke2dSr2ddojX0vvEzO555j+2DIIEp79uDdU2i7pHbdDmnMGM+1RhoiSVKXLXUhNgoXBEHoevLKs3j/l1fR9Pr7Kg9LG8s5A6Ye/73utlP1yR+8bixu7Hk2QTM+QJK933psiFazBHfRY6CVee8kmVEi7kUOuardtibSHA4yb7qFmr376rXVGI3835jRDBs2hKdmzOjS2yX5ZaPwY29oU4aSJAld15EkCdXDVgpdgZgZEwRB6Jp+3f0Dv+z+wWPbH0bfQlrMb09f6o4qKt+/GLVgm8f+5iHXEDD5hRYnJbq7CHfRw+i2XxvsJ1nHYoh+BsngqZCt79UcPETmdTeieSj4emy7pAcumcbkIUPaJR5/avOZsRtuuKHFwR3zzjvvtHqMjkgkY4IgCF2Tpqm8/+v/kedhIX6wJZSbxt970tOXWnUBle9e6HUfS+u4B7CecW+L49F1Da3yI9TSF6ChdWJyOIbovyMHTmjxtZoj76v57Jnl+enSH3r1ZF3f3rx7x+0kRHjewaCraPNkTPBOJGOCIAhdV0l1IW8vfwm3Wr8Cfv/EoVw87MqTjqmlB6l87wL0miKP4wVe9CrmQX9oVUyacy9q4d/Qnbsb7CcHX44S+Tck2dqq6zVG13V2PPgIxT8tq9emShJvjBxO2IAMXr3xBgweisZ2FW2+UbggCIIgnIoig2KYkH6Bx7btOZnszN180jElIo3gyz8Go+dF6zUL7sF1YFmrYpJNvTAkfIocel2D/bSqz3DlTEdz7GjV9RojSRK9H7rf43ZJiq4zY8s29hw8yAc//9KmcXQWIhkTBEEQhGYaljrGa3X+7zZ/SbW98qRjhoTBBF3yBkgePnY1N1VzbsDtZW1ZU0myGUPk/Rji3gSlgfVhroO4c/6AWv4Wut52a7eNISH0e+px8LD7ToTNxnm79/Le8uXszs1tsxg6C5GMCYIgCEIzSZLMBYNnYDHWv91nd9WyYNPn9R5qM/U6h4Dz/+l5QGc1VZ9egVqR0+rY5IAxGJO+Qgo4u4FebtTSF3Dn3YTuzmv1Nb0JHTyIlBuv99g2MjuH7gWFPDN33ilfnb9FyVj37t35wx9ad3/7mBkzZtCjRw+fjOVrS5cu5cYbb6Rv374EBgaSmJjIlClT2LBhg79DEwRBEPwsxBrGOQOmeWw7ULiLLUfW1jtuGXotltPv8XiOXpVP9ad/QLNXtDo2SQnHEPsKStTTIHlfH6bb1+LKnoZavajV1/Qm5YbrCBk4wGPbtO07yM/O4e2lP7XZ9TuDFiVjhw4dItdH04p5eXkcOnTIJ2P52uuvv86hQ4e46667WLhwIa+88gqFhYWMHj2apUuX+js8QRAEwc/SE4fQL2GQx7Yl27+h0lZe77h1/IOYBszweI5atIvqL65Dd9cvC9FckiShhFyKMelLJLPnZAgArRK18F7chQ+ha9Wtvm69OAwG+j7+CLK1flIY4nBy4c5dfLJiBduONG+rqK6kRU9TyrLMiBEj+OKLhisAN8X06dPZsGFDh6w5VlhYSExMzEnHqqur6dmzJxkZGSxevNjjeeJpSkEQhFNHrbOGt356gWpHZb22tOg+XD765nq1xHTVSdUnV+A+9LPHMU39LyVw6mtIntaYtYCuu1DLXkMrfwOoX7T2OEMShpjnkS2DfXLdE+XOncfe5/7lse3TgRmUZ/Tnndtvw2Iy+fza/tKmpS2OVd/3pY6YjHkzceJEcnJy2L3b8yPEIhkTBEE4tewr2MEXa9722DZ50GUMShlV77hmr6Tq/YtQCz0/2WgZcxcBEx/xaZyabT3uogfA3dDdLQU57FaU8NuQpJbtEOCJrutsvfteylavqddWazTy79NHc964M7lr8mSfXdPf2ry0ha7rPvvqTCoqKsjMzGzSBuiVlZUnfTk8VCMWBEEQOr+esekMSB7usc3b7UrZEkLwHz5BDk7weJ595Ss4Nn3syzCRrcMxJs1DDrqwgV4qWvlruHOvQXcdaaBf80iSRJ+HH8QQElyvLcDlYtq2HcxZtZrMAwd9ds325nA46n32N0WLkjFN03z61ZlmxWbOnElNTQ0PP/xwo32Tk5MJDQ09/jVr1qx2iFAQBEHwh7P6X0yQpf7sh8NtZ+GmLzxOPsghCQRd8QmSuX6CAlCz8F5cB31bi0uSgzHEPI8S8zxI3jcr1x2bcWVfglo1z2cTJ+aYaHr91fOOA32KSxiencusr76itpNOXsyaNeukz/3k5OQmnXfKlLZYtmwZkiQ16WvTpk0ex3j00Uf56KOPeOmllxg2bFij18zKyqKiouL414MPPujjVyUIgiB0FFZTAOcPmu6x7WDRbrZkrfPYZohJJ2j6eyAb6zdqbqq/vAG1eK8vQwVACboQY9I8JMtQ7530WtSih1EL/4KulvvkutHnnE302RM9tp2/ew+O3Fz+77vvfXKt9vbggw+e9LmfldW0hxJOme2Q8vLyWLBgQZP6XnLJJUT8br+sJ598kieeeIJnnnmGhx56qMHzxZoxQRCEU9e3Gz9la9b6esfNBgs3T/grIdYwj+c5tnxGzfw7PbbJYamE3LAIOTDKl6ECoOsqWvmbqGWzgQbqfSmxGGKeRbbWX//WXK6KCtZfcQ3OkpJ6bYfCw3hrxDCev+YaRvfu1epr+ZPYm9KHjiViTzzxBI8//nij/UUyJgiCcOqyOWt586d/eXy6sntMH2aMqv905TG1y2Zh//VFj22GpBEEXz0XyWDxabzHaPatuIv+Bq7DDfSSkENvQIn4M5LUuqceS1asZNtf7vPYtqh3L3YPzOD9O2cS7KEkRmch9qb0kaeffponnniCRx55pEmJmCAIgnBqs5oCOM/L7coDhbvZ6uV2JYB13AOY0j0XknVnr6Pmm7va7ME32TIAY+Ic5GDPsdfR0Srexp1zBbpzf6uuF3n6GOKnXOyxbdLefci5eby8cGGrrtFZiGSsAS+88AKPPfYY5513HhdccAGrV68+6UsQBEEQPOkVl05Gkue1xYu3zff4dCXUPXEYePG/MSSN8Nju3D4X2/LnfBVm/evLgRiin8IQ+2+QQ7320507ceVchlr5aauSw+533Yklof7TpAZdZ/rW7SzeuInlO9p2U/OOQNymbMD48eNZvny513Zvb524TSkIgiA0dLuyR2w/Lht5o9fblVpNMZXvnIdW7vmWYeDF/4d54OU+jff3dHch7qKH0G0rG+wnBYzHEP00khLZouuUb9zE5tvvBA+fqYt7dmfTwAF8+Kc7CQ0IaNH4/iRuU/rAsmXLukx9NEEQBKF91d2uvNRj2/6CnezM3eT1XDkwiuA/fIxk9vwBXvPtPbgON5wktZZkiMEQ9z+UyAcAD096HqXXLsOVPRWt1vNuAo0JGzKYpCs973c9fv9BjAUF/N9337Vo7M5CJGOCIAiC0EZ6xfX3ervyx61fUeus8XquEtWboOnvguyhCr7monrO9ailrVu31RhJklFCr8WQ+DmSsaf3jmoJ7vzbcBf/HV2zN/s6abf+kYDuafWOG3SdS7bt4PuNm1izd1+zx+0sRDImCIIgCG3o7IwpBHoo6lrrrGHJtvkNnmtMO4PAyZ73c9RtZVR9eiWal/VnviSb+2BI/Bw55OoG+2mVH+POuQzNsauZ45vp8+jDINdPS5IrKjnt8BH+9c03nbYYbGPaLBmz2WyUlpa21fCCIAiC0ClYTQFMGjDVY9u27A0cKPS8z/Ex5sFXYRnzZ49tWukBqufejK41UB/MRyTZgiHqIQxx/4UG1ofprv24cy5HLX8XXW9gU/LfCUnvR9IVntfBnb13P87cXN5csrTZcXcGPknGnE4nX331FX/+858ZOHAgwcHBBAUFER0djdFoJD4+nosuuogXX3yRXbualy0LgiAIQmfXN34gveI872n83eY5ON0Nz/hYJzyMsd9FHtvcB5dT++OjrY6xqeSAMzAmfY0UMKGBXi7U0udx5/8R3V3Q5LFTb7kZa1JSveMmTWPqtp18uXo125tY1b4zadXTlEeOHGH27Nm88847lJSUNLqo/dhTI+PGjWPmzJlceqnnhY2dnXiaUhAEQfi9KlsFb/z0Txzu+muqRnQ/g7MzpjR4vu6yUfnBVNTcTI/tAef/C8uw63wSa1Pouo5W9TlqyXOgN7BOTA7FEPUkctA5TRq3fOMmNt8202Pb1+l9KR42lLduuxWjwcNaug6mTSvw19TU8Mwzz/Dyyy9jt9uRJInBgwdz2mmnMXr0aJKSkoiMjMRisVBaWkpJSQlbt25l1apVrFixgpKSkuPnvPLKK4wdO7ZVL7ajOfbm9+7dG0VRmDlzJjNnev7BEgRBEE4dmYdW8v2WuR5aJK49404Sw1MaPF+ryqfi7XPQq/LqN8oGgq/8AmNq+36m6s4DuAv/hu5suB6YHHwJSuSDSHJgo2Puff4Fcr+s/z7ZFYVXTz+NSyefxw0TGpqZ86/Zs2cze/ZsVFVlz549bZOMJSYmkpeXR1paGtdffz3XXnstKSkN/wAdo6oqCxcu5L333mP+/Pmoqsrrr7/OLbfc0twwOiwxMyYIgiB4ousaH614nazSg/XaooPjuGHc3Sienp48gTtvM5XvXQRuW702yRpOyA3fo0TUfzKxLem6E7X0/9Aq3gIaSCsM3TDEPI9sGdjgeO6aGtZfeQ2O/Pq3OHdHRfLJiGG8fcftpMXEtDLyttWmdcYMBgNvvPEGe/bs4dFHH21yIgagKAoXXXQRc+bMYfv27cyYMYPCwsKWhCEIgiAInYokyZw/+DKPCVdRVT6r9v7U6BiG+EEEXfyqxzbdVkbV51ejO6paHWtzSJIJQ+RfMMS/A0qc947uI7hzr0It+w+6rnrtZggMpPeD93ts61NcQv/sHJ776mtUrekPCHRkLZoZczqdmEyt2yC0LcfzNzEzJgiCIDRk5Z4lLN+1qN5xRVa4cdxfiAqObXQM28//xPbz8x7bjD3PJmjGh0iy0upYm0tXK1CLn0Krqf/6TiSZh2KIeQ7JmOi1z66n/k7Bgvrj1BqNvHL6aG6eNpXpo0e3Oua20qYzY54Sp7KyspYM5XU8QRAEQeiqRvUcT0xIfL3jqqayaPMXTSoJYTnjXkz9PG+07dq3GNvSp1odZ0tISihKzL9Qop8Fyfv6MN2RiSt7GmrVN1779Ljrz5gi65fRCHC5uGDXHv63eAn55eW+CNuvfFZn7KqrrvLVUIIgCILQpSmywuTBM5CovzdldukhNh9Z2+gYkiQTePGrKHGe11/ZV7+GY9PHrY61JSRJQgm+GGPSPCTzEO8d9WrUovtxF9yHrtbfw9MYGkKvv93r8dSB+QUk5+Tyz/nfdPotCn2WjH333Xf8/e9/b3J/RxetoisIgiAITREflsyIHmd6bPtpxwJqmrDuSzIGEDzjA6QgzwvZaxb+FVfWmlbF2RqSMQlDwnso4XcC3m+ZajULcOVMQ7Otr9cWNX4c0WdN9HjexTt2sWnXLr7fvMVXIfuFz5KxjIwMnnzySZYubbw6bklJCWeddZavLi0IgiAIndIZfc4hLCCi3nG7y8bS7d82aQw5JIHgy94HxVy/UXNR/cX1qBXZrQ21xSTJgBJ+B4aED8GQ7L2jOw933nW4S19G110nNfW89x4MwfW3lAqz2zlr335eXbSIshrv+3x2dD5Lxr744gusVitXXXUVeXke6p8cdeDAAU477TRWrVrlq0sLgiAIQqdkMpiZNGCax7Zt2Rs4VLS3SeMYEocReOHLHtv02mKq51yP7qpfCqM9yZZBGJPmIgdNbaCXjlb+P9y5V6E7Dx0/aoqMoPufPdfrPO1wFsH5+bz+ww8+jbc9+SwZ69OnD//5z38oKCjg8ssvR1XrP7K6Zs0aTjvtNPbt28ekSZN8dWlBEARB6LR6xvajb7zndV/fb5mLW23avpPmAdOxjLnLY5uat5maBff6fW2VJAdiiPkHhpgXQfb+dKHu2IYr51LUyi+Oxxx30YWEDq2//kwGpm7fyfcbMtl86HBbhd6mfLpR+JVXXskf//hHVqxYwf33n1wfZN68eUycOJGioiJuuOEGFixY4MtLC4IgCEKndfaAKZgM9W8zltYUsWpf0zfHtk54CGPv8z22Obd9gWPtf1scoy/JQedhTPoKyTLKeyfdhlr8OO6CP6GrZUiSRO8H/4bkoQJDfFU1Yw4f4YVvv8XtYTKoo/NpMgbw73//mwEDBvDSSy/x1VdfAfDSSy8xY8YMbDYbTz/9NG+99RaK0v61TwRBEAShIwq2hDKur+ckatXeJZRWFzVpHEmSCZryGkpUH4/ttYufwHXw5xbH6UuSIQ5D/FsoEfcC3ncd0GuX4sqegla7goBu3Ui5wfP+mxP3HaDi0CE+W9n5lkG1KBn79NNP2b17t8c2s9nMF198QWBgIDfccAM33ngj9957L4qi8MEHH/Dwww+3KuDOZMSIEaSnpzN79mx/hyIIgiB0cEPTxhAXmlTvuKqpfL9lbpNvMUrmIIJmvIdk9nAbUFepnvtH1LKOcTtPkmSUsJswJH4Kxu7eO6rFuPP/iLv4WZKumk5A9/rbPZk0jYt37OLdn37ye+2x2bNnk56ezogRI5rUv0UV+GVZRpIkAgMDGTRoEEOGDGHo0KEMHTqU/v37oygKn332GVdccQWSJBEaGsq8efMYN25cs19QZyQq8AuCIAgtkVeezXs/v4LuYX/Hi4ZeSUbS0CaP5dy3mOpPr8TTXpFKbH9CrluAZGp80+72oms21NJ/oVV+0mA/ydSbmsJb2HzHP8BDCvPZwAxCJ4xj1pVXtlWoTdamFfhPO+00AgICqK6uZsWKFfzf//0fN910E0OGDCEoKIgRI0awdOlSUlJSCA0NZdmyZadMIiYIgiAILRUflsSwtNM9ti3ZNh+bs7bJY5l6no11wkMe29SC7dR8e7ffF/SfSJKtGKIexRD7Gsj1y30cozv3EBD+EPEXpntsn7xrD+u2bufXXbvaKlSfa9HMGICu6+zevZvMzEw2btxIZmYmmzZtOmlbJEmqqyxsNBpJT09nyJAhx78GDRpEUFCQb15FByNmxgRBEISWcrjs/O+n56m2169IPyRlNOcNmt7ksXRdp3ruTbh2et5yyDrxMaxj/tTiWNuK7i7GXfQIus37+jZ3LWy+Lxpnaf2nTVd2S2bD6JF88Kc7sfpxy8Wm5gMtTsa8OXjw4EkJ2saNGykoKPjtgkcTNFmWcblc3obp1EQyJgiCILTGrtwtzFv/vse2a8f+icSIlCaPpTurqXz3AtTCHR5aJYKu+ARTj45XiF3XdbTKT1BL/wm65117SlYb2fNK/YkdDXj9tJFMmHw+t59zThtH6p3fkjFPcnNzjydnmZmZbNiwgezsbDSt8Y1QOyORjAmCIAitoes6X6x5i/2F9W+1xYUmct2ZdyFLTV9ppJYdovKtSej28nptkiWUkBt/QIloYAG9H+nOfbgL70N31n9wUNdh57NBVGwx1mvLCg3hrdNG8dYdt9M9NrY9Qq2nQyVjnpSWlhIR4f2ecGcmkjFBEAShtcprS3njp3/iVuvfRTp3wCUMTRvTrPFcB5ZR9cnloNefCFGi+xJywyIkU8dcPqTrTtTSV9Aq3qnXZsuT2Xx/CLqr/qbrX6X3xTX2dF698Ybjd+baU5su4PeFrpqICYIgCIIvhAVEMLa3591qlu9aRK2julnjGbuPxzrxMY9tatGuDreg/0SSZMIQeR+GuDdBOXlTdGu8RuLFdo/nnbNnH3v37mXRpk3tEGXLtSgZO3GRvi/4ejxBEARB6ApG9jiTiKDoesftLhs/7VzY7PEso+/A1P8Sj23OHV9jX/OfZo/ZnuSAMXWV+wNPXgeWcLEdc2z9yvsBbjfn7tnL6z/8SJXNv3tzNqRFyVj37t155plnqGnlDumrVq1iwoQJvPrqq60aRxAEQRC6IkU2cE6G543EtxxZS05p84q3SpJE4IUvocRmeGy3LXkS1+EVzY6zPUlKGIaYl1CingbJCoBigrTrPZf9GJaTR2h2Dm8t/ak9w2yWFiVjCQkJPProo6SkpPCnP/2JdevWNfncqqoq3nzzTcaOHcvYsWNZsWIFvXv3bkkYgiAIgtDlpcX09rqR+A9b56J5WAPWEMkYQNBl7yFZw+s3Hq3Qr1XmtSTUdiNJEkrIpRiT5iKZBwAQPthNxEinx/43Hsrkxw2/sC8/vz3DbLIWLeBXVZXZs2fz5JNPUlZWt3lnfHw8p512GiNHjiQxMZHIyEgsFgulpaWUlJSwbds2Vq9ezaZNm3C5XOi6zvnnn8+LL75Inz6e99DqrMQCfkEQBMGXKm3l/G/pc7h8tJgfwLX/p7oF/R4q9BuSRhB8zVdIiv9qdDWVrrtQy/6DVv5fHCU6m+4NRXPUX6wfPt3ND6kXcO/0R9ttMX+7PE1ZUVHBW2+9xeuvv87+/fvrBmzgBeq6jslkYvr06dx5552MHj26pZfu0EQyJgiCIPjaqr1LWeZhnZjFaOXWifcTYG7+k5C2X1/EtmyWxzbz8JsIPO/ZZo/pL5p9A+7CB8j9qpjDHwXUa5fNOgP+WUFhyDR69X0cSapfDsPX2r20xapVq1i6dCk///wzBw8epKioCJvNRlRUFHFxcQwbNowJEyYwadIkIiMjfXHJDuvYm9+7d28URWHmzJnMnDnT32EJgiAInZiquXnzpxcorSmq1zao20gmD57R7DF1XaP682tx7f3eY3vglNcwD7is2eP6i65V4cp/mk1//hlbllKvPWKkkz731KAZMzDH/RPJ2PTiuc0xe/ZsZs+ejaqq7Nmzp+PWGevKxMyYIAiC0BYOFO7ms9VveGxrbmX+YzR7BZVvnY1Wdqh+o8FKyA0LMXhZ8N9Rla76L1vv9ryDQd/7qwgf7AbJihL1MHLQtDa7bdmudcaOHDnC3r17W/10pSAIgiAI3nWP6UOf+AEe21qymB9AtoQSNP1dMFjrN7ptVH9xA5qtvNnj+lPEabcSe8EEj20H3wlAdQK6DbXoEdyF96Cr5e0a3++1Khn78ssv6datG2lpafTt25fw8HAmTZrUrKcrBUEQBEFourP6X4xRqb/eKb8ih02HVrdoTENsfwIveNFjm1Z+iJr5M9FbkOj5U/c/3YshOLjecUehQt63luO/12t+wJU9Dc3WsvfOF1qcjP3666/MmDGDnJwcdF1H13XcbjdLlizhjDPO4JtvPO8QLwiCIAhCy4UGhDOm99ke25bv+g6b03O9rcaYB0zHPOJmj22uvT9gX/1ai8b1F1N4OGkzb/fYlvO1BUfJCbcm1QLceTfiLvknul6/eGxba3Ey9sILL6DrOklJScyePZsFCxbw+uuvM2LECJxOJ9dffz1FRfUXGQqCIAiC0Doju48jItBTZf5aftn9Q4vHDTj7SQxJIzy22Zb+HdcR/80etUT8lIsI7p9e77jmlDw+cYm7AH/sFNniK65atQqDwcCPP/7I7bffzvnnn8+tt97KqlWruOyyyygvL+fNN9/0ZayCIAiCIAAGxcCkAVM9tmUeWklRZcuKm0qKiaBL30bykOihq1TP+yNaTXGLxvYHSZbp+de/gIcF+iWrTFTsMPx2wBCPEvWYXzYUb3EyVlJSQq9evepVz5dlmZdffhlJkvjuu+9aHWBH8uabbyJJEkFBHXNXe0EQBOHU0T2mD73i+tc7rusai7fPb/Gm33JwHEFT/wvUT0r0qnyqv74dXWv/W3ktFZLej7iLLvDYdug9K7oKOhKG6GeRFP9UQGhxMqaqqtd6YfHx8fTp04edO3e2OLCOJicnh7/+9a8kJCT4OxRBEARBAGBi+oXIUv16WoeK9rCvYEeLxzWmnYF13P0e29wHlmH/9aUWj+0PabffhhIYWO947REDOT9aWJ1/BrLV8+3Z9tBmN0bDw8OpqKhoq+Hb3W233caZZ57JpEmT/B2KIAiCIAAQERTNiO5neGxbsv0b3Kq7xWNbxt6Dsbvn8hC2n5/HdfDnFo/d3kwR4aT+8UaPbXs/CeK5b8PYnpXVzlH9xtB4l5aRJAm3u+U/BB3Jhx9+yPLly9mxYwePPPJIm13H5XKhqp1n6lcQfElRFIzGtt+eRBC6mtN7n83WrPXUOqtPOl5WU8z6g78wuqfnhKoxkiQTOOU1Kt6ciF71+43Ddaq/uo3Qm5ciB8e1MPL2lXDZdPK++obaQ4dOOm5xqozfc5BXFi7iP3+8GVlu/wX8rUrG1qxZw7Bhwxg6dChDhgxh6NChDBo0CKvVQ+G4TqqwsJC7776bZ599lqSkpGadW1lZedLvzWYzZrPZY7/i4mIcDkerYhWEzs5sNhMVFSV2rhCEZjAbLYxPn8zCTZ/Xa1uxZzEZScMIsrTs75QcGEXQJW9Q9f4U+F3JB72miOp5txB89Vwkuc3mdnxGNhjo8Ze72Prne+q1jcjKZt3OXfywZQvnDR7c4ms4HI6TPst/nwd406p3z+VysXHjRjZt2nT8mCzL9OrVi/z8uic51qxZw8CBAzttgnbHHXfQp08fbr/dc62ShiQnJ5/0+8cff5wnnnjipGOVlZXk5OQQFBREVFQURqPRL09yCII/6bqOy+WioqKCnJwcAJGQCUIzDEweTubBleRXZJ903Ol2sHzXd1zQgn0rjzEmj8I68VFsS56o1+Y+sgrbsmcJmNh2d418KWLUSCLHnUnJ8pNvscrAhbt2M2/NWs4dNKjFn8OzZs3iySefbPZ5Ld6bMisri8zMTDIzM9mwYQOZmZnHE7Djgx99MbIs07t375Nm0IYMGUJoaGhLLt0iy5YtY8KEpk3Vbty4kcGDB/Pll19y5ZVXsnHjRtLT6+qUXH/99cyZM4fq6mqv5x/biyorK+ukDxRPM2MHDhzAaDSSlJQkkjDhlKfrOtnZ2bhcLrp37+7vcAShU8kuOcgHK2Z7aJG4/sw/Ex+W7KGtaXRdp/qLa3Ht8VwlIejyjzD1OqfF47cnW04O6/5wFbrTVa+t5+OPkjj5vBaP7WlmLDk5uX03Cs/Pzz+emB1L0rKzf8vST0w22ntNWV5eHgsWLGhS30suuQSTyUTPnj25+uqrT1ondscddzB//nyys7MxGo0Eeng6o6kbg7pcLvbt20diYqKYBRCEo47NFvfs2VOsIROEZvp6w0fsyNlY73hieCrXjJ3Zqv/0a7ZyKt88C63iSL02yRJGyM1LUVqR8LWng//5H0feea/e8aC+fRj67ls+mxxpaj7g02TMk5KSEjZs2HBSknbw4EEkSerQi9UPHTpEWlpag32mTJnCV199Ve94U998u93OwYMHSU1N7bS3cQXB12w22/G/fxaLpfETBEE4rtJWzn+XPodbrT/rc/HQq+ifNKRV47tzN1H53gXU7bR9MiVxGCHXzkdSTK26RntQbTbWzrgSZ2EhAJKikHj5DFJuuh6DD2uJNjUfaPMVd5GRkZxzzjmcc85v05fl5eVkZv5/e/cdFcX19gH8O7SldxRQqiiISBOwC/aGBOyaRNCYmIRo1GgsSWwx0ajRGGOKpqjxp4k9ioJJVFAQpAgKIiJSBVRApHfm/YN3N667wALbgOdzDufIvbNznxmX5eHOLbcl3XSHGBsb49q1awLl27dvR1hYGIKDg2FoaCiWtujxJCH/oZ8HQtpPW00XQ2xGI1zIlkjXkoPQz3gAlJXanywpmTpDffwWVIasFahryI1D5ZUt0Jiwtd3nlxZFNTX0WRaI+59uhN6QwbBZ8SHULS1kFo9Mpj/o6upizJgxsmhaZKqqqvDy8hIoP3ToEBQVFYXWEUIIIbI2pI8X7mZHo7TqBV95WXUJotKuYaTdxA6dnzNoEeqzI1Gb/JdAXU30T1A2GwyV/tM61IY0GI0bCxUDA+i4OMv8j0DpL6ZBCCGEEIlRVlLBaHtvoXVRaddQUlncofMzDAONqd9AQb+P0PqKiyvQ8EJ2C6iKimEY6Lq6yDwRA9qQjF2+fBlHjhyRSBAFBQX47LPPUFZWJpHzi9OhQ4danElJCCGEyFp/Uyf01hcc91zfWI9ryaJNZmsJw9GE5oxfACXBcZ1sdQkqzi0BK2TcGhFO5GRs4sSmbk0fHx+xjfeqqqrCTz/9BH9/f7z77rvQ0tISy3lJ5xYXF4ft27dj+vTp6NWrFxiGafdA7kOHDoFhGIH13cRNWu0QQogoGIbBeIfXIGyz7/t5CXj8PLPDbSj1HACNSduF1tU/jkHV9R0dbqO7aNNjygULFmDbtm1YsWIFBg4ciK+//hrx8fGoqxM9+y0oKEBISAgWL14MCwsLPHnyBH/99Rd69erV5uBJ1/T5559j3bp1OHv2LPLy8mQdDiGEdErGur3hZO4htO7qvSCIYzEFFaf5UHGYJbSuOmIv6jJudLiN7qDNA/gHDBiAsLAwnD9/Hjt37sTq1auhoqICe3t7WFtbQ1tbm/dVW1uL0tJSlJaWoqioCImJicjPz4eGhgYWLFiA2NhYmJubS+K6SCc2dOhQODk5wd3dHe7u7jA27hz7nhFCiLzxtJuE+3kJqK3n324vtzgTD/Lvws7UqUPnZxgGGpN3oD43Fo3FGa/Usij/6z3ovB0KBQ3xrD7QVbV7NqWPjw98fHyQlpaGM2fO4MaNG7h16xby8vIEsm1VVVXY2Nhg8uTJGDduHKZNmwZ1dfUOB0+6pjVr1sg6BEII6RI0VLUw1GYMwlKCBequJV9CX+MBUOzgvpIMRxOa0w+i9LfJQCP/kzK2/CkqLiyF5pz/gWFozmBzOnxnbGxs8PHHH+PChQvIyclBVVUVcnNz8fDhQ2RkZKCgoACVlZW4e/cuDh48iDlz5lAiRmQiOzsb8+fPh5GREdTU1ODm5oYLFy4IHMeyLI4fP465c+eiX79+0NDQgJaWFjw8PPD999+jsbGxTe1WVlbi888/h4ODA9TU1KCjo4NRo0bhjz/+ENelEUJIs9z7jIKWquD2gy8qixCXcVMsbSiZOEFtzGdC6+rS/kVN9AGxtNNViX2dMRUVFZiYmIj7tJ2Su7s7FBUVERgYiMDAwDa99qu//kLG02cSikxyrHr2wJrXXpN1GAIyMzPh7u4OVVVVjBgxAk+fPkVkZCR8fX0RHBzMtyhxTU0N5s+fDz09Pdjb28PV1RWFhYWIjIxEYGAgoqOjcejQIZHaLSsrw+jRoxEXFwcjIyN4e3ujoqICV69exY0bNxAVFYVvvvlGMhdNCCEAlBWV4dl/CoLijwvURaT+g4FmblBT6XgniergJajPvI66tH8F6iqvbIGS+VAomXTssWhnsX//fuzfv1/knYZksuhrdxETE9PuPScznj7DvZf29SQdc/jwYSxduhS7d++GklLT237v3r1Yvnw5tm7dypeMKSkp4fTp0/D29oaKyn8rVRcUFGDKlCk4fPgwFi1ahFGjRrXa7vr16xEXF4dx48bh7Nmz0Pz/bTZSUlLg6emJvXv3YsKECZgyZYqYr5gQQv7j0NsFMenX8bQkl6+8uq4KNx9ewdgBHV+klWEUoDFtH0oOeoEtf8pf2ViH8jNvQ2fxVTAc8W03JK+4nTDc7ZBaQw9wSbdgbW2Nr7/+mpeIAU0/LHp6eoiKikJt7X/7rCkpKfE2i3+ZkZERtm3bBgD46y/BladfVVFRgV9++QUKCgr4/vvveYkYANjZ2fE2oP/22287dG2EENIahlFoNuGKywhHcUWRWNpR0DCE5ms/QNiSGo3FGagI/lgs7XQ1UusZu3//PpKSkvD8+XMAgL6+PhwcHNC/f39phUC6MS8vLygrK/OVKSkpwdraGnFxcSgqKhJ4vJ6QkIC///4bWVlZqKysBMuyvIWJHz582GqbcXFxqKqqwpAhQ9C3b1+B+jfffBPLli1DREQEWJaVi1WgCSFdl4WhDWx62iPtaTJfeUNjA8LuX4Kv25tiaUfZaiRUh3+I6ohvBOpqk06ixtoTHMc5Ymmrq5B4Mnb+/HmsXr0aaWlpArMsGYaBjY0Ndu3ahWnT5H8fKyJ/tm/fjpSUFL4yOzs7rF3Lv4lt7969hb6e21tVU/PftO/a2loEBATg+HHB8RVcouwWwV0jzdLSUmi9rq4udHR0UFJSInJXNiGEdMRo+6l49CwFLMs/Eel+3h24PR+J3vqWYmlHbdTHqM+KQP3jGIG6iuCPodTLDYoGwrdT6o4kmoydPXsWM2fOxLBhw7B69Wo4OjpCX18fAPD8+XPcvXsXR44cgZ+fH06fPo3X5HDgt6xY9ewh6xDaRdpxh4SEICwsjK/M09NTIBlrS6/T7t27cfz4cTg4OGDnzp1wdXWFnp4elJWVkZqaCltb2zYtlihK29QrRgiRBkOtnnC2GIz4zEiBuqv3gvDmiECxfB4xisrQ8P0RpT+PAVtdwl9ZV4nys29DOyAYjBKnw211BRJNxrZu3Yo333yz2ZlnHh4eWLx4MQICArBlyxZKxl4ijzMS5VFoaKjYz3n27FkA4CVkL0tPTxf5PKampgCAjIxXF0JsUlJSgpKSEt7SGYQQIg0jbSfg3uPbzSwEmwg7U0extKOoaw6NqXtQfnqRQF3Dk0RUXv0cGhO2iqWtzk6iA/iTk5MREBDQ6nEBAQG4f/++JEMhRGTFxcUAADMzM4G6EydOiHyeQYMGQU1NDdHR0ULHmB09ehQAMGLECOoZI4RIjQZHC0P7jhFady35Ihoa68XWlkr/aeC4+gutq4n+CbUP/xZbW52ZRJMxPT09ZGZmtnpcRkYGdHV1JRkKISLr168fAODHH3/kKz916hSOHDki8nk0NDSwaNEiNDY2IjAwEBUVFby61NRUbN3a9Bfh0qVLxRA1IYSIzt26+YVg4zOjxNqW+vjPoWhkJ7Su4sKHaCzvfGtqiptEk7EZM2Zg9erVOH/+fLPHXLhwAWvWrMHMmTMlGQrpRC5evIghQ4bwvoCmQfUvl128eFFi7X/88cdQVFTE2rVr4ebmhvnz58Pd3R2zZs3CihUr2nSubdu2YdCgQfjnn39gbW2N2bNnY+rUqXBycsKTJ0+wbNkyTJ06VUJXQgghwnEXghUmIvUf1NRXi60tRlkNGtMPAkpqAnVsZSEqgj4Uy6blnZlEx4xt27YN9+7dg6+vL3R1dWFvbw99fX0wDIOioiLcv38fL168gJeXF7788ktJhkI6kYKCAty6dYuvjGVZvrKCggKJtT9q1CiEh4fjk08+QXx8PFJTUzFw4ECcPn0arq6u2LVrl8jn0tLSQlhYGL7++mv8+eefOH/+PFRUVODm5ob3338f8+bNk9h1EEJISxx6uyA2/TqevLIQbGVtBaLTwjDSbqLY2lIysoP6hK2ovPSRQF1d2r+oifsNqm6CY8u6C4aVQjoaFBSEv/76C/fu3UNRUdPCcgYGBhgwYAD8/Py63Orj3GUKSkpKWlyBv7q6GhkZGbCysoKqqqoUIyREftHPBSHSk/EsFX9ECe4bqaLIwbvj1kKDI77JRSzLovz0QtSlCHmyoaQKncVXoGjYT2ztyQNR8wGpLPrq7e0Nb29vaTQlVzqyNyUhhBAiaVY9+sHSsC8yC/knGdU21CAi9QomDPQVW1sMw0BjytcoeRwruF1SfTXKz70H7YXBYBRVhJ+gE2nr3pS0HZIExcTEIDk5mRIxQgghcsvLXvjTqfjMSLFtk8SloG4AjWnCt4BreHIXVWFfibU9WQkMDERycjJiYgQXvRVGLpKx06dPQ1FRUdZhEEIIId2Oia4Z+ps6CZQ3sg24nhIi9vZU+owBx/1toXXVN/ehLuum2NuUd3KRjBFCCCFEdkbZTYICI5gSJOfG4+krA/zFQX3MZ80sd8Gi4q9ANL66an8XJ9ExY6KuySRqNx4hhBBCxE9f0whO5oMRnyW4TVLo/UuYM0R4T1Z7Mcpq0PD9AaW/TgQaavnqGksfozJkLTR9fxBrm/JMoslYQEAAGIYRaf0QWoGcEEIIkZ0RtuOR9DgWdQ11fOXpzx4gqzANFoY2Ym1PqacD1LzWo+rKJoG62qRTqLEZD47DdLG2Ka8kmozp6upi6tSpWLVqVYvH/fPPP1izZo0kQyGEEEJICzRVteFuPQo3H14RqLuWfBH+I5eJveNEdch7qEv7F/VZ4QJ1lcGroWTmAUWd3mJtUx5JNBlzdXVFfn4+nJwEBwa+LC0tTZJhEEIIIUQEg228cDszEtV1lXzl+S9yxLqJOBfDKEDD5zuUHvQE+8o4MbamFBV/BULrjTNgFLr2JD+JDuB3cXFBfHx8q8dpaGjA3NxckqEQQgghpBWqymoY1swm4mEpwWhsFG3drLZQ1OkF9ck7hdbVZ99E9a2uP3ZMosnYxo0bcfv27VaPmzRpEjIyMiQZCiGEEEJEMMhqOLTVdAXKn5cX4G6OZCbccQb4QWXgbKF1Vde+RP2TuxJpV15INBnT1NSEhYWFJJsghBBCiBgpKSpjpK3wfSnDH/yD+lcG+IuL+sRtUNAxE6xorEP5uffA1lVJpF15QOuMEUIIIYSPg9kgGGr1FCgvqy7B7UzB5S/EQUFVGxqv7QeErHfWWJiKyiubJdKuPKBkjBBCCCF8FBgFeNpNFloX+fAKautrJNKusvlQqA5bJrSuJvYX1GVcl0i7sia22ZRjxggf8CeMoqIitLW1YW1tDU9PT0yZMgUKCl0vL6SNwtuusrISf//9Ny5cuICYmBhkZmaioaEBNjY2mDFjBlauXAlNTU2Rz3fo0CEsXLgQGzduxKZNmyQWt7TaIYQQaelrPAAmumbIf5HDV15ZW4GY9BsY3m+cRNpVG7UadenX0JB/R6Cu4sKH0H4nDAqq2hJpW1zaulG42JKx0NBQAP8t3ipsoddX6xiGwe7du2FjY4M//vgDLi4u4gpHLsTExEBbW77fMPLm2LFjePvtppWeBwwYgEmTJqG0tBQ3b97Exo0bcfz4cYSFhaFHjx4yjpQQQro2hmHg2X8y/og8IFB3Ky0UrpbDoKaiLv52FVWg6fsjSg6OAer5x4k1lj5G5T+fQXPaXrG3K07cTpjS0lLo6Oi0erzYkrHffvsNWVlZ+PLLL8HhcODr6wsXFxdoaWmhrKwMCQkJOHfuHGpqarBu3ToYGBjg/v37OHHiBB4+fIiJEyfizp07MDExEVdIpBNSUVHBe++9hxUrVqBv37688vz8fEydOhXx8fFYvnw5jh07JsMoCSGke7A07Atzgz7ILnrEV15TX43oR2Hw7C/8UWZHKRrYQH3sZ6i8vF6grvbOMdTaTYVK3wkSaVsWxJaMTZgwAS4uLvDw8MDp06dhZGQkcExhYSGmT5+O77//HvHx8TAxMcEXX3wBb29v3Lx5E3v27MGOHTvEFRLphBYsWIAFCxYIlJuYmGD//v0YNmwYzpw5g9raWqioqMggQkII6T64vWO/h38nUBeTfgNu1iOgwdGSSNsct7dQm3JJ6Or8FRdXQOmdG1BQ15dI29ImtoFamzdvRnFxMf7880+hiRgAGBoa4s8//8Tz58+xceNGAICOjg5+/fVXsCyL4OBgcYVDuiDuTg41NTUoKipq8+uzs7Mxf/58GBkZQU1NDW5ubrhw4YLAcSzL4vjx45g7dy769esHDQ0NaGlpwcPDA99//z0aGxvb1G5lZSU+//xzODg4QE1NDTo6Ohg1ahT++OOPNl8DIYRIW299S/TpYSdQXtdQi8iHVyXWLsMoQGPaXkBFcJwwW/4MlZfXSaxtaRNbz9ilS5cwYMCAVh8zmpiYwMHBASEhIbyyvn37wsbGBpmZmeIKp9O7lHASBWVPZB1GmxlpGWOK8yyJnDs9PR0AoKysDH39tv01lJmZCXd3d6iqqmLEiBF4+vQpIiMj4evri+DgYEyY8F93d01NDebPnw89PT3Y29vD1dUVhYWFiIyMRGBgIKKjo3Ho0CGR2i0rK8Po0aMRFxcHIyMjeHt7o6KiAlevXsWNGzcQFRWFb775pk3XQggh0jaq/2Q8epYiUH47MxIefTyFLhIrDoq65lAfvwWVF1cK1NXeO4Na2ylQsX9NIm1Lk9iSsWfPnkFXV1ekYxsaGlBQUMBXpqenh5ycnGZe0f0UlD1BXnGWrMOQK3v3Ng3YnDRpEjgcTptee/jwYSxduhS7d++GkpIS73zLly/H1q1b+ZIxJSUlnD59Gt7e3nyPQgsKCjBlyhQcPnwYixYtwqhRo1ptd/369YiLi8O4ceNw9uxZ3kzQlJQUeHp6Yu/evZgwYQKmTJnSpushhBBpMtbpBTtTR6Tk8a+E39BYj4jUfzDZSTJ/hAMAx/kN1KVcRN0jwQ3MK4I/hpL5UChodu5JXWJ7TNmzZ0/cv3+f13vRnEePHuHevXsCs+EeP34MAwMDcYUjVuHh4ZgyZQr09PSgpqaGvn374vPPP5d1WN3KpUuX8Msvv0BZWbld997a2hpff/01LxEDmma76OnpISoqCrW1tbxyJSUlTJ8+XWBMmpGREbZt2wYA+Ouvv1pts6KiAr/88gsUFBTw/fff8y3JYWdnh08//RQA8O2337b5egghRNpG2U4CA0ag/E52DJ6XF0qsXYZhoOH9DRhVXYE6tuo5Ki59JHQFh85EbMmYj48PGhoa4Ofnh7S0NKHHPHr0CNOnTwfLsnjttf+6FQsKCpCfnw9ra2txhSM2x44dg6enJ3R0dHDkyBFcunQJa9as6fT/8Z3J/fv38cYbb4BlWezcuZM3dqwtvLy8oKyszFempKQEa2tr1NXVCR2DlpCQgB07diAwMBALFy5EQEAAfvihacPahw8fttpmXFwcqqqq4OHhwTczlOvNN98EAERERND7iRAi9wy0esDBbJBAOcs2IvzB3xJtW0HLGOqTtgutq0sNQW3iCYm2L2lie0y5ceNGnDt3DomJibC3t4enpyecnZ2hra2N0tJS3LlzB6Ghoaivr0fv3r2xYcMG3mu542/Gjx8vrnDEIjc3F++88w6WLFmC77//nlc+evRoGUbVvTx+/BiTJk1CcXExVq5ciQ8//JCvfvv27UhJ4R/HYGdnh7Vr1/KV9e7dW+j5ub1VNTX/rSZdW1uLgIAAHD9+vNm4ysrKWo09Ly8PAGBpaSm0XldXFzo6OigpKRF5LRpCCJGlEf3G497jeDSy/IuZ3suNx9C+o2GkLbnlqVQGTEdtShDqUoIE6iovr4eSxQgo6vSSWPuSJLZkzNDQEDdu3MDrr7+OyMhIXLlyBVev/jfLgvuX/7Bhw/D777/D0NCQVzdv3jz4+PigVy/5uok///wzKioqsGbNGqm3baRlLPU2xUGccRcWFmL8+PHIzs7GwoULsWvXLoFjQkJCEBYWxlfm6ekpkIxxFxwWxe7du3H8+HE4ODhg586dcHV1hZ6eHpSVlZGamgpbW9s29WSJ0nZb4iOEEFnR1TCAk4UH4gX2p2RxPeUyZngESKxthmGgMXknSrKjwFbyPxZla0pREbQcWvNPdMrPU7ElY0BTD0BERASuX7+O4OBgPHjwAGVlZdDS0oKtrS0mT54sdNBzc70Wsnb9+nXo6+sjJSUFr732GpKSkqCvr4/p06djx44dEl1dX1IzEjuLsrIyTJ48GSkpKZg+fToOHjwo9AeMu/ODOJ09exYAeAnZy1obE/kyU1NTAEBGRobQ+pKSEpSUlPCWziCEkM5geL9xSMyOQX1jPV956pMkPCnJhbEEe6cUNAyhMfVrlJ/0F6irzwhFze3DUB0UILH2JUWsyRjXqFGjRJppJu9yc3NRWVmJWbNmYd26dfjmm28QExODjRs3IikpCTdu3GgxAy8tLeX7nsPhtHkWYHdUU1OD1157DbGxsZg4cSKOHz8ORUVFqbVfXFwMADAzMxOoO3FC9HEJgwYNgpqaGqKjo/Hw4UOBcWNHjx4FAIwYMaJT/iVHCOmetFR1MMhqOG49ChOoC3/wN2Z6LJRo+yq2U6AycLbQcWKV/26EspUnFPWtJBpDc2pqaviGvbyaBzSn6+3O3YzQ0FAwDCPSV0JCAgCgsbER1dXVWL9+PdatWwcvLy+sXr0a27ZtQ0REBK5cEZxm+zIzMzPo6Ojwvrgz8UjzGhoaMG/ePFy7dg0jR47EmTNnpL7Sfr9+/QAAP/74I1/5qVOncOTIEZHPo6GhgUWLFqGxsRGBgYGoqKjg1aWmpmLr1q0AgKVLl4ohakIIkZ4hNmOgoijYufDwyT08KcmVePvqE74AoyVkfFpdJSouLAPbKNoG3eK2bds2vt/7wv6oF0YiPWNcNTU1KC0thba2tsx7hGxtbXHw4EGRjjU3NwcAGBgY8PbNfNnkyZOxfPly3L59G+PGNb9rfU5ODt+jTFnfg87gu+++4z0mNDQ0xPvvvy/0uF27dvGNOxSnjz/+GCEhIVi7di1OnjyJfv364eHDh4iNjcWqVauEjl1rzrZt2xAVFYV//vkH1tbW8PT05C36Wl1djWXLlmHq1KkSuQ5CCJEUdY4GBlkPF7oCvzR6xxTUdKHh/Q3Kj88RqKvPiUJ19AGoDXlPojEIs27dOqxc+d8CtaWlpSIlZGJPxrKysrBz505cvHgR2dnZvHILCwt4e3vjo48+goWFhbibbZWJiQkWL17cptc4OjoiKipKoJw7eFtBoeWORW1tbYmOK+uKuI8Igf/GbgmzadMmiSVjo0aNQnh4OD755BPEx8cjNTUVAwcOxOnTp+Hq6tqmZExLSwthYWH4+uuv8eeff+L8+fNQUVGBm5sb3n//fcybN08i10AIIZLmYe2JuPQI1DbU8JVze8ckOXYMAFT6jAHH1R81tw8L1FWFfgmVfhOgqN9HojG8qr3DkRhWjAscXbx4EfPnz0d5ebnQ2WYMw0BTUxPHjx/vFCuO//3335g4cSK++OILrF//387xe/bswcqVK3Hjxg2MGDFC4HXcZQpKSkpaTMaqq6uRkZEBKysrqKqqSuQaCOls6OeCkM4j9P4lob1jfY0HSLx3DADYmnKUHPRC4wvBHWuUzIZAa8FfYBjZjcgSNR8QW89Yeno6Zs+ejaqqKlhYWGDFihVwdHSEiYkJ8vPzkZiYiD179iAzMxOzZ8/G3bt35XKR15dNmDAB06ZNw5YtW9DY2IghQ4YgNjYWmzdvhre3t9BEjBBCCOkuZN07xnA0oTFtL8p+9wPA3wlUnxOFmrjfoOr2lkRjEAexpYs7duxAVVUVZs+ejYcPH2LZsmXw8vKCra0tvLy8sHTpUqSmpmLOnDmorKxs06MeWfrzzz+xfPlyHDhwAJMnT8YPP/yAFStW4NSpU7IOjRBCCJEp7tgxYSIe/COVGJQthoPjLjzhqryyBQ0vsoXWyROxPaa0sbFBfn4+8vPzW+yKKy0thYmJCUxMTJrdNqmzo8eUhLQf/VwQ0rlU1lTg+3+/QF1DrUDdQs8VEu8dAwC2thwlP3misUQw8VKy8oTW/JMyWUJI1HxAbD1jubm5sLe3b3XAura2Nuzt7ZGbK/mpr4QQQgiRLHWOBtyshA/bkVbvGKOiCQ3vPULr6jPCUJvwP6nE0V5iS8Y4HA7Ky8tFOra8vJyWeSCEEEK6CI8+nlBWFFwTkrsqvzQoW40Cx+VNoXWV/25AY2m+VOJoD7ElY3Z2dkhNTcXdu3dbPC4hIQEPHjyAnZ2duJomhBBCiAzJQ+8YAKiN3SR0MVi2pgwVwavatK+wNIktGZs9ezZYlsX06dNx8+ZNocfcvHkTM2bMAMMwmDNHcKE2QgghhHRO8tA7pqCqDY0pwicI1j38G7VJp6USR1uJbQB/TU0NBg8ejLt374JhGAwePBgDBw6EsbExnjx5gsTERNy6dQssy8LZ2RmRkZFd9lElDeAnpP3o54KQzis0+RIi0wTXHetn7IAZHgFSi6P8r0Che1cyanrQWRIOBc0eUolD6gP4ORwO/v77b4wbNw4syyIqKgo///wztm7dip9//hlRUVFgWRYTJkxASEhIl03EXubu7g57e3vs379f1qEQQgghEicPvWMAoD5hKxgNI4FytqoYFZfXSrz9/fv3w97eHu7u7iIdL9YV+LkiIyMRFBSElJQU3t6UdnZ28Pb2xtChQ8XdnNyhnjFC2o9+Lgjp3OSld6w2JQjlp4TvAqA54xeo9PeReAxSX4H/ZUOHDu0WSRchhBBC+Hn08URsRrjAumPc3jFprDsGACp23lDp74Pa++cF6ipC1kLJYgQU1PWlEktrZLdhEyGEEEK6HHmZWQkA6hO3gVETTLjYigJU/v2pVGNpSbt6xo4cOSKWxhcsWCCW8xBCCCFEfrTUO/a0JA89dUylEoeCZg+oT/wSFefeFairTTqJ2gG+UOk7QSqxtKRdyVhAQIBYthWgZIwQQgjperi9Y8LGjkU+vAJfN+GLs0qCyoDpqL13FnUPLwvUVVz6CEpLwqGgqiO1eIRpVzI2atQomezxREhAQAAOHz6Ma9euwcvLCwBgaWmJrKwsoYv5ffvtt/jpp5+QlpaG2tpaeHp6IjQ0FAAQFxeHdevWITo6GiUlJQCAjIwMWFpaSulqCCGk62qud+x+3l2MLH8GAyktL8EwDDQm70RJdiTYmlK+OrbsCar+3dTsVkrS0q5kjPvLjBB5dubMGXz44YfQ09ODj48PNDQ0eDs/lJWVwcfHB/n5+fDy8oKZmRkYhoGmpqaMoyaEkK5BnaMBF8uhiH4U9koNi8iHV+HtMldqsShom0B9/BZUBC0XqKtJOAoVhxlQthQ+zk0aJDKbkhBpunLlCurq6gTKz507BwA4deoUxowZw1cXExODvLw8vPnmm2IbA0kIIYTf4D6eiMuIQENjPV/5vce3MdJ2AnSkOJtRxWk+au6dQ31GqEBdxcWV0HknDIyymtTieRnNpiSdXp8+fYTudfr48WMAgLW1dZvqCCGEiIemqjYczQUXPm1kGxGVFirVWBiGgcbU3YCyumA8xRmouiF8GyVpoGSMyKXTp0/Dw8MDampq6NmzJxYsWIC8vDyhx1paWvKNYdy0aRMYhsG1a9cAAFZWVmAYBgzD4NChQ2AYBv7+/gCAzZs38+oCAgL4Xn/o0CGR2uO6desW/Pz8YGFhAQ6HA2NjY3h4eGDdunUoLy8XOD48PBx+fn7o0aMHOBwOLC0tsWzZMhQUFLTlVhFCiFwbYjMaDCOYbtzJjkZ5damQV0iOoq4Z1Ed/IrSuOnI/6p8mSTUeLnpMKafqCzaArX0o6zDajFHpCyWjLR06x3fffYelS5dCUVERnp6eMDQ0xL///oshQ4bAycmp1dc7OzvD398fISEhePr0KWbMmMEbC2ZjYwN/f3+kpaUhIiICTk5OcHZ2BgCMGNH+8QIXL16Ej48PGIbB8OHDMWzYMBQXFyM1NRXbt2/HkiVL+Majffvtt1i+fDkUFBTg4eGBXr16ISkpCfv27UNQUBAiIiJgYmLS7ngIIURe6Krrw6G3KxJzYvnKGxrrEf0oDGMGTJNqPBy3t1Bz7wwacuP4K9gGVAStgPaiv6U+SZGSMTnF1j4EW3NH1mFIXWZmJlatWgUOh4OQkBDejMnKykr4+voiKCio1XP4+vrC19cXXl5eePr0KXbt2sU3Q3LEiBE4dOgQIiIi4Ovri02bNnU47p07d4JlWURHR2PQoEF8ddHR0TAwMOB9HxUVhRUrVsDc3Bznz5+Ho6MjAIBlWWzduhUbNmzAsmXLcPLkyQ7HRQgh8mBo3zFIzIkDwD/r/XZmJIb0HQN1FQ2pxcIoKEJj6m6U/jwWeGksm4KeFdTHbpTJahH0mFKCaKPwtvv1119RU1ODBQsW8BIxAFBXV8e+ffvkdkmVZ8+eQUdHRyARAwAPDw9oaWnxvt++fTsaGxtx4MABXiIGNI1n+PTTT+Hi4oIzZ86gsLBQKrETQoikGWj2QH9TR4HyuoZaxKbfkHo8Sj3soTp0adM3jCJUhy2DzjthYptR2daNwikZk6CYmBgkJycjMDBQ1qF0GuHh4QCA2bNnC9TZ2trCxcVF2iGJZNCgQXjx4gXeeustJCU1P+agsbERV65cgZaWFsaOHStQz33M2djYiLi4OCFnIISQzmloX8HPPACIy4hATV21lKMB1EauhIq9H7Tf+gfqYz4T60zKwMBAJCcnIyYmRqTjKRkjcoU7SN/c3FxofXPlsvbll1/CyckJv/76KwYOHAgjIyO89tpr+O2331BTU8M7rqioCOXl5SgrK4OSkhJv8sDLX9999x0AUM8YIaRL6aljCpue/QXKq+uqcDvzptTjYZRUoTn9AJSMB0q97VfRmDE5xaj0lXUI7dLRuLmr6Mvr40igqXfrVWZmZoiNjcXVq1cRFBSEsLAwXLhwAefPn8eOHTtw8+ZN6OnpoaGhAQCgpaWF6dOnt9iOhYWFROInhBBZGdZ3HNKe3hcoj34UBjerEVBWUpFBVLJHyZic6uiMxM7K1NQUqampyMrKQt++golddna2xGNQUWn6MBC2HEVDQwOePHki9HVKSkqYMGECJkxo2nQ2OzsbCxcuxNWrV7F9+3Z89dVXMDQ0BIfDgbKycrNLZxBCSFfVS98CFoY2yCpM4yuvrK1AQvYtuFuPlFFkskWPKYlc4S4vIWwmYWpqKhISEiQeA3dJidTUVIG6q1evCl3tXxhzc3OsWbMGAJCYmAigKWHz8vLC8+fPcf36dTFFTAghncewZsaO3UoLFVipv7ugZIzIlYULF0JFRQVHjhzBjRv/zbCpqqrChx9+KPQRobh5enoCAI4ePYrMzExeeXp6OpYuXSr0NXv27MHTp08FykNCQgDwj3Vbv349FBQU4O/vz5uw8LK8vDyagUsI6bIsDG1gqic4DKOsuuT/l7/ofigZI3LF2toaX331FaqrqzF69GiMGzcOc+fOhY2NDZKSkuDt7S2VGBYsWIDi4mI4OzvDx8cHY8eOxcCBA+Ho6Ch0LNfmzZthamoKV1dXzJkzB7Nnz4adnR327NkDQ0NDrF69mnfsqFGjsHfvXuTk5GDkyJFwcnLCzJkz4e3tjYEDB8Lc3ByffCJ8hWhCCOnsGIZptncsKu0aGlnJ/9EtbygZI3Jn+fLlOHHiBJydnREeHo4rV67Ay8sLUVFRfIunStLBgwexdu1aaGtr4/Lly8jKysL69etx/Phxocfv27cPc+fORWVlJYKDgxESEgJFRUWsWrUKd+/eRZ8+ffiO/+CDD3Dr1i28/vrrKC4uxvnz5xEZGQkFBQW8++67+Ouvv6RxmYQQIhM2Pfujh7apQHlxRSFS8xNlEJFsMSx3+hoRm9LSUujo6KCkpATa2trNHlddXY2MjAxYWVlBVVVVihESIr/o54KQ7iE5NwF/xR0VKDfW6Y2AUR/K9ax6UYmaD1DPGCGEEEKkzs5kIHTVBZ92PCl5jMzCzrc3c0dQMkYIIYQQqVNQUMQQGy+hdVEPr0k3GBmjZEyCaG9KQgghpHkDzdygwdESKM8sfIj8FzkyiEg82ro3JY0ZkwAaM0ZI+9HPBSHdS+TDqwi9f0mg3M7EEX7uC2QQkfjQmDFCCCGEyD0Xy6HgKAn+4ZWSn4ii8gIZRCR9lIwRQgghRGZUldXgYjlUSA2LW2mh0g5HJigZI4QQQohMuVuPhKKC4HbZiTmxKKsqkUFE0kXJGCGEEEJkSlNVG45mbgLljWwDYtK7/j6+lIwRQgghROY8+niBgeBCr/GZUaiqrZRBRNJDyVgr4uPj4evrC1NTU6irq8POzg5btmxBZWXXfmMQQggh0qSvaQg7UyeB8tqGGiRkRckgIumhZKwFycnJGDZsGDIzM/HNN98gKCgIc+fOxZYtWzBv3jxZh0cIIYR0KUP6jhZaHpsejobGeilHIz2Co+UIz7Fjx1BdXY3Tp0/zNnoeM2YM8vPzceDAARQXF0NPT0/GURJCCCFdg7FOL1ga9hXYDqm8phT3HsfD0Vy0RVQ7G+oZa4GysjIAQEdHh69cV1cXCgoKUFFRkUVYhBBCSJc1uJktkqIfhaGrrlNPyVgL/P39oauri/feew/p6ekoKytDUFAQfvrpJwQGBkJDQ0PWIXY7AQEBYBgGoaGhvDJLS0swjOCgTwD49ttvMWDAAHA4HDAMAy8vL15dXFwcJkyYAF1dXTAMA4ZhkJmZKdkLkIDMzEyBa+vM7RBCujcro34w0jIWKC8oe4KMglQZRCR59JiyBZaWloiMjISfnx/vMSUALFu2DN98802rry8tLeX7nsPhgMPhiDtM0owzZ87gww8/hJ6eHnx8fKChoQE7OzsAQFlZGXx8fJCfnw8vLy+YmZmBYRhoamrKOGpCCOneGIaBRx9PXEz4U6DuVloorHvYyiAq0dTU1KCmpob3/at5QHO6TTIWGhqK0aOFDwx8VXx8PJydnZGZmYlp06ahZ8+eOHXqFIyMjHDr1i1s3boV5eXl+OWXX1o8j5mZGd/3GzduxKZNm9p7CaQZV65cQV1dnUD5uXPnAACnTp3CmDFj+OpiYmKQl5eHN998E0eOHJFGmIQQQkQ0oLcLwlKCUV7Nn8xkFj7E05Jc9NTpJaPIWrZt2zZs3ry5za/rNsmYra0tDh48KNKx5ubmAIC1a9eitLQUCQkJvEeSo0aNgqGhIRYtWoQFCxbA09Oz2fPk5OTwbQxKvWKS8XKv5cseP34MALC2tm5THSGEENlSVFCCm9UIoRuIRz+6jmmu8rmiwbp167By5Ure96WlpQIdM8J0mzFjJiYmWLx4sUhf+vr6AICEhATY29sLjA1zd2+azZGUlNRim9ra2nxflIyJ7vTp0/Dw8ICamhp69uyJBQsWIC8vT+ixr44Z27RpExiGwbVr1wAAVlZWvDFhhw4dAsMw8Pf3BwBs3ryZVxcQEMD3+kOHDonUHtetW7fg5+cHCwsLcDgcGBsbw8PDA+vWrUN5ebnA8eHh4fDz80OPHj3A4XBgaWmJZcuWoaCg/RvjVlVVYe3atbwYbGxs8NVXXwkd9Hrjxg188MEHcHR0hJ6eHtTU1GBnZ4e1a9fixYsXbW77999/x4gRI6CtrQ11dXU4Ojpi27ZtqK6ubvf1EEK6LxfLoVBRFPy9mZwbj9KqF9IPSAQcDkfgd78ouk3PWHuYmpoiKSkJ5eXlfGOJIiMjAQC9e/eWWNsVQStQX5AisfNLipKRHTS893ToHN999x2WLl0KRUVFeHp6wtDQEP/++y+GDBkCJyfBBQFf5ezsDH9/f4SEhODp06eYMWMG7//PxsYG/v7+SEtLQ0REBJycnODs7AwAGDFiRLtjvnjxInx8fMAwDIYPH45hw4ahuLgYqamp2L59O5YsWcL3Hvr222+xfPlyKCgowMPDA7169UJSUhL27duHoKAgREREwMTEpE0x1NbWYsKECbh37x48PDzQv39/hIWFYe3atSgrK8PWrVv5jl+9ejUSEhLg4OCAMWPGoKamBrdv38ZXX32FoKAgREVFiTyGbsmSJThw4ABUVVUxZswYqKurIzQ0FOvXr8eFCxdw5coVqKmptel6CCHdm6qyGpwsPBCTfoOvvJFtRGx6OMYM8JZRZOJHyVgLli9fDl9fX4wfPx4rVqyAoaEhoqKisG3bNtjb22Py5MkSa7u+IAUNubESO7+8yszMxKpVq8DhcBASEsKbuVdZWQlfX18EBQW1eg5fX1/4+vrCy8sLT58+xa5du2BpacmrHzFiBA4dOoSIiAj4+vqKZRzfzp07wbIsoqOjMWjQIL666OhoGBgY8L6PiorCihUrYG5ujvPnz8PR0REAwLIstm7dig0bNmDZsmU4efJkm2KIjIzEyJEjkZqaCkNDQwBAbGwshg4dij179mDt2rV8ydWGDRswdOhQvrXyampqsGzZMhw4cAC7d+/Ghg0bWm339OnTOHDgAHr16oXQ0FDY2NgAaOqenzp1KsLDw7Fx40bs2LGjTddDCCFu1iMRmxEBlm3kK0/IisLwfuPAUVaVUWTi1W0eU7aHj48Prly5Am1tbXz44Yfw9vbG4cOHsWTJEly/fp3WGZOAX3/9FTU1NViwYAHfEgrq6urYt29fs0tYyNqzZ8+go6MjkIgBgIeHB7S0tHjfb9++HY2NjThw4AAvEQOaZhB9+umncHFxwZkzZ1BYWNimGBQUFPDzzz/zEjEAcHNzw+TJk1FZWYnYWP7kfsqUKQKLFnM4HHzzzTdQUlLCX3/9JVK73377LQBgy5YtvEQMaHpM//3334NhGPz444+ora1t0/UQQoiuuj7sTB0Fymvqq3En+5YMIpIM6hlrxejRo0WehUk6Ljw8HAAwe/ZsgTpbW1u4uLjg9u3b0g6rVYMGDcLRo0fx1ltvYcWKFXBwcBB6XGNjI65cuQItLS2MHTtWoJ77mDM+Ph5xcXGYOHGiyDFYWlqiX79+AuXcsvz8fIG63NxcXLhwASkpKSgtLUVjY9NfnyoqKnj48KHA8a+qq6tDVFQUGIbB/PnzBeoHDhwIR0dH3LlzB3fu3OGNtySEEFEN7uOJ+7kJAuUx6TcwyGoEFBUUpR+UmFEyRuQKd5A+d0brq8zNzeUyGfvyyy+RmJiIX3/9Fb/++isMDQ0xbNgw+Pr6Yv78+bzJG0VFRbzB/EpKLf/4cXvGzp07x1um42WvTjBobgwj99Hky2vfAMDu3buxbt26DvVYFRUVoba2FsbGxlBVFf64wNLSEnfu3Gl2AgYhhLTERNcM5gZ9kF30iK+8tOoFUvLuYEBvVxlFJj6UjMkpJSM7WYfQLh2NmzvrT14fRwLg9R69zMzMDLGxsbh69SqCgoIQFhaGCxcu4Pz589ixYwdu3rwJPT09NDQ0AAC0tLQwffr0FtuxsLAA0DSr9/DhwwL1ryZjbblnUVFR+Oijj6Cjo4MDBw7Ay8sLxsbGvKTR1NRUaE9ac0RpW57/Twkh8m2wjadAMgYAtx6Fwb6XS6f/fKFkTE51dEZiZ2VqaorU1FRkZWWhb9++AvXZ2dkSj4E7FlDYchQNDQ148uSJ0NcpKSlhwoQJmDBhAoCmWBcuXIirV69i+/bt+Oqrr2BoaAgOhwNlZeVml8541aZNm8S+WPDZs2cBAFu3buUt88FVVVXV7DW+ysDAACoqKnjy5AmqqqqEzpjMysoCgDbPDiWEEK4+PexgoNkDReXP+MqfluQiqzANlkaCvy86ExrAT+QKd3kJYTMJU1NTkZCQIPEYuElDaqrgHmhXr14Vutq/MObm5lizZg0AIDExEUBTwubl5YXnz5/j+vXrYoq47YqLiwEI7hIBNN17UTfjVVZWxpAhQ8CyLI4fPy5Qn5SUhDt37kBLS0ukZUkIIUQYhlGARx/hi6zfehQm5WjEj5IxIlcWLlwIFRUVHDlyBDdu/Le2TFVVFT788EOhjwjFjburwtGjR/k2Dk9PT8fSpUuFvmbPnj14+vSpQHlISAgA/jFw69evh4KCAvz9/XkTFl6Wl5eH/fv3d+QSWsUd1P/LL7/wJZfJycm8BFJU3HuyceNGpKen88rLysrwwQcfgGVZLFmyhGYfE0I6xKG3K9RVBNc+TH+WgoJS0YdVyCNKxiTI3d0d9vb2Ev/F2pVYW1vjq6++QnV1NUaPHo1x48Zh7ty5sLGxQVJSEry9Jb/In7W1NRYsWIDi4mI4OzvDx8cHY8eO5c0M5I7letnmzZthamoKV1dXzJkzB7Nnz4adnR327NkDQ0NDrF69mnfsqFGjsHfvXuTk5GDkyJFwcnLCzJkz4e3tjYEDB8Lc3ByffPKJRK9x4cKFMDY2xoULF2Bra4s5c+Zg/PjxcHZ2xsiRI4VeY3NmzpyJd955B48fP4aDgwO8vb0xe/Zs9OnTB2FhYRgyZEi79mojhJCXKSkqw81a+OLc0Y9k96RBmP3798Pe3l7kGeSUjElQTEwMkpOTERgYKOtQOpXly5fjxIkTcHZ2Rnh4OK5cuQIvLy9ERUXxLZ4qSQcPHsTatWuhra2Ny5cvIysrC+vXrxf6KA4A9u3bh7lz56KyshLBwcEICQmBoqIiVq1ahbt37wrsn/nBBx/g1q1beP3111FcXIzz588jMjISCgoKePfdd0Ve46u9DAwMEBMTg/nz56O2thbnz59Hbm4utmzZ0uw1tuSnn37CkSNH4OLiwpu80KNHD3zxxRe4evUq1NXVJXAVhJDuxsVyKJQUlQXKkx7fRll1iQwiEi4wMBDJycmIiYkR6XiGFXVwCBFZaWkpdHR0UFJS0uK+VNXV1cjIyICVlVWzywIQ0t3QzwUhpCWX757B7cybAuXD+o2Dp90kGUTUPFHzAeoZI4QQQkin4dFnFADBpSziMyNR1yDaBCt5Q8kYIYQQQjoNPQ1D9DMeIFBeVVuB5Mfytyi4KCgZI4QQQkin4t5npNDymPQbIi/NI08oGSOEEEJIp2Kmb42eOr0EygvKniCrME0GEXUMJWOEEEII6VQYhoG7dfO9Y50NJWOEEEII6XT6mzpDg6MlUJ729D6elxfIIKL2o2SMEEIIIZ2OkqISXCyHCqlhEZsRIfV4OoKSMUIIIYR0Sq4WQ6GooChQnpgdg+q6KhlE1D6UjBFCCCGkU9JQ1YJ9LxeB8tqGGtzNFm31e3lAyRghhBBCOq3mBvLHZtxAI9so5Wjah5IxCaKNwgkhhBDJ6qnTC+YG1gLlJZXFePjkngwiavtG4UoSjqdbi4mJaXEvKkIIIYR0nLv1KGQXpQuUx6bfgK3JQKnHExgYiMDAQN7elK2hnjFCCCGEdGo2xvbQVdcXKM8uSseTklwZRNQ2lIwRQgghpFNTYBQwyGqE0LrYTrAILCVjhBBCCOn0HM3doaLIEShPzo1HeXWpDCISHSVjhBBCCOn0VJXV4GguOGC+obEB8ZmRMohIdJSMkS6lpqYGu3btgru7O7S0tKCpqYn+/fvjrbfeQlxcnKzDI4QQIkFNjyoZgfL4rCg0NNZLPyARUTJGuozy8nKMHDkSO3bsgL+/P65evYq7d+9i//79MDc3x4oVK2QdIiGEEAnS1zREX2N7gfKKmjLcz7srg4hEQ8kYkVsffPABGIbh+1JWVoa1tTVWrlyJ0lL+MQCffvopkpKScOvWLXzwwQdwd3eHtbU1xowZg40bNyIsLKzZtjZv3gxFRUUkJycL1AUHB4NhGLz//vstxpufnw81NTUEBga274IJIYR0mFszA/njMsKlHInoKBkjcuvOnTsAgKlTp8Lf3x/+/v4YO3YsHj9+jD179mDcuHFoaGgAADQ2NuL333/H66+/DisrK6HnYxjBrmsAePr0KXbt2oVZs2bB3l7wL6r4+HgAwKBBg1qM18TEBO+88w4OHDiABw8eiHydhBBCxMfC0AaGWj0FyvOKs5FXnC2DiFpHyRiRSyzL4u7dpi7lgwcP4tChQzh06BBCQkJw48YNKCkpISYmBsHBwQCAwsJCPH/+HP379+c7zwcffABNTU3eV3a24A/il19+ifLycqxbt05oLKImYwDw8ccfo7GxERs2bGjT9RJCCBEPhmEwyHK40Lq4jAgpRyMaSsaIXMrIyEBpaSn09fVhYmLCVzd48GCMGjUKwH+JEsuyQs+zceNGJCQk4KeffkJFRQUaG/n3KausrMThw4fh6OgIJycnoee4ffs2OBwOBgwY0GrcvXr1wujRo3H27Fk8ffq01eMJIYSIn4PZIHCUVAXK7+cloKKmTAYRtYySMQmivSnbj/uI0sHBQWi9hoYG3/dGRkbQ19cXGPNlZGQEGxsb9OrVS+h5Tp48iZKSErz++utC60tKSpCRkQFHR0coKyvz1R09ehTq6uowNTVFZOR/06bnz5+Puro6HDp0qMVrJIQQIhkqShw4mnsIlDc0NiAh65bE22/r3pSUjElQTEwMkpOTaUB3O7SWjD18+BAAeOPDFBQUMH/+fPzvf//Do0ePRG4nKCgIAODl5SW0PiEhASzL8j2irKurw7Jly/Dmm2/CyckJsbGxGDp0KK+ee66LFy+KHAchhBDxGmQ1DMKWubideRMNjQ0SbTswMBDJycmIiYkR6XjaKFxOPfhyOyoeCW56Ku80+ljDdv3aDp+npWTs8uXLSElJgZqaGiZOnMgr/+KLLxAeHo4hQ4bgs88+w9ChQ9GjRw88fvwYBw8eBAAoKirynSs8PBzKysrNPqLkPgZ1dXUF0DRjctasWYiIiMDixYuxf/9+qKio8L3G2toahoaGiI6ORk1NDTgcwRWhCSGESJaehiH69LTDo6f3+crLq0vxID8R9r2cZROYEJSMyamKR+koS7on6zBkJiEhAcB/yRjLssjJycGlS5d4A+03bNgAIyMj3mu0tbVx8+ZN7N27F4cPH8b69etRW1sLU1NTjBw5EuHh4TAzM+Md/+zZMzx58gR9+/ZtNmG6ffs2gKbB++Hh4Zg1axaKiorwww8/4N133202fltbW0RERODBgwdwdHTs0L0ghBDSPm5WIwSSMaBpmQtKxghpQWlpKTIzMwGAN1D/ZWpqavjqq6/w8ccfC61bu3Yt1q5tvXfu2bNnAAA9Pb1mj4mPj4eKigquXr2K9evXw8DAANeuXcPw4cJn6nDp6+sDAAoKClqNgxBCiGRYGfWFvqYRnpfzfxY/fp6JJyW5MNYRPp5Y2igZI3KH+4jSyMgIU6ZMAdD0eFFPTw+Ojo7w9vbmJTsdUVJSAgDQ0tISWl9dXY2UlBTU19dj9erV6NevH65evdrsZICXaWtr87VBCCFE+hhGAYOshuOfxHMCdXHp4ZjqMkf6QQlByRiRO9xkbNy4cRKdkaijowMAAiv5cyUmJqK+vh4TJ07EnTt3kJmZiczMTJGSMW4Sxm2DEEKIbAw0c0PY/WDU1tfwld/Ljcdoe2+oczSaeaX0dMtkrKysDJ9//jkSEhIQHx+PwsJCbNy4EZs2bRJ6/O3bt/Hxxx8jKioKSkpKGDNmDHbt2gVra2uJxajRR3LnliRxxM0dL+bs7Nzhc7WkR48eAIDnz58LreeOF5s8eTI2b94MT09P+Pn5ITo6GpaWli2eu7i4GAD4xrQRQgiRPo6SKhzN3BH7ynZIDY31uJsdjSF9R8sosv90y2SsqKgIBw4cgJOTE3x9ffHzzz83e2xKSgq8vLzg7OyMEydOoLq6Ghs2bMDIkSORkJAgsV+24piR2Flxe8akkYwZGxsjKysLVVVVUFNT46vnzqR0cnLC4MGD8fPPP+PNN9/EtGnTcPPmzWYfbwJN7xtVVVXY2tpK9BoIIYS0bpDVcIFkDADisyLhYeMJBUa2K311y3XGLCwsUFxcjLCwMGzbtq3FYzds2AAOh4OgoCBMmTIF06dPx8WLF1FQUIBdu3ZJKeLuo6GhAffuNc0ibW65CXEaOXIk6uvreYnXy7hl3NmQb7zxBtauXYukpCTMmzdPYDV/rkePHqGoqAgeHh60rAUhhMgBfU0jWBn1Eyh/Ufkc6c9SZBARv26ZjDEM0+ym0S+rr69HUFAQZsyYwRuQDTQlc9wtb4h4paamoqqqCsbGxujZU3CjV3GbOnUqAODatWt85Q0NDUhMTISZmRnfZIEvv/wSPj4+uHjxIlatWiX0nKGhoQDAm3xACCFE9gZZCZ8FfzvjppQjEdQtkzFRPXr0CFVVVULXiXJ0dERaWhqqq6tlEFnXJa1HlFyzZ8+Gjo4Ojh07xleekpKCqqoqgd45hmHwv//9DwMHDsSePXuEPuI+duwYlJWVERAQIMnQCSGEtEGfnv2hraYrUP7o2QMUVxRJP6CXUDLWgqKipv8cYcso6Ovrg2VZ3kBtYUpLS/m+ampqmj2WNJk7dy5YlkVwcLBU2lNTU8PChQuRnJzMG7APAAMGDADLsrhw4YLAazQ1NXH37l2wLIvFixfz1T1+/BihoaHw8/OTSs8eIYQQ0SgwCnCxGCqkhkV8ZqSQ8rarqakR+N0vUmxiaV2GQkNDeY8dW/viztJrq5YeabZUZ2ZmBh0dHd5Xa+PTiGysW7cOmpqa2L59e4fPtXPnTigoKGDLli1iiIwQQog4OVl4QIFRFCi/kx2Nuoa6Dp9/27ZtfL/3X971pSWdfjalra0tb9/B1pibm7fp3AYGBgD+6yF72fPnz8EwDHR1dZt9fU5ODt9YMxrMLZ969OiB1atXY/PmzUhOToa9vX27zpOfn48DBw7g7bffplmUhBAihzQ4WrAzdURyLv+kreq6StzPTYCjuXuHzr9u3TqsXLmS931paalICVmnT8ZMTEwEHhWJS58+faCmpobExESBusTERNjY2EBVVbXZ12tra/MlY0R+bdiwARs2bOjQOUxMTFBVVSWmiAghhEjCIMthAskYANzOvNnhZIzD4bSr46XTP6aUJCUlJUybNg1nzpxBWVkZrzw7OxvXrl3D9OnTZRgdIYQQQtqql74lemibCpTnv8hB/oscGUTUjZOx4OBgnDp1ijdAOzk5GadOncKpU6dQWVnJO27z5s2orKyEt7c3goODcfbsWUydOhWGhob46KOPZBU+IYQQQtqBYRi4Wg4TWierZS46/WPK9nrvvfeQlZXF+/7kyZM4efIkACAjI4O33Y2dnR1CQ0OxZs0azJw5k287JNrqhhBCCOl8BvR2wbXkINTUNy1PpaigBPteznC1Ep6kSVq3TcYyMzNFPnbQoEH4999/JRcMIYQQQqRGRYkDBzM3PHp6H66WQzHQ3B3qKrLbMLzbJmOEEEII6b68+k/GeAcfMDLelxKgZIwQQggh3ZCKkvwsNyX7dJCAZVlZh0CI3KCfB0JId0PJmAwpKjatAlxX1/FVfwnpKrg/D9yfD0II6eooGZMhZWVlcDgclJSUUG8AIWjqFSspKQGHw4GysrKswyGEEKmgMWMS5O7uDkVFRQQGBiIwMFDoMYaGhsjNzcXjx4+ho6MDZWXlFve7JKQrYlkWdXV1KCkpQXl5OXr16iXrkAghpN3279+P/fv3o6GhQaTjGZa6ZMSutLQUOjo6KCkpEWk7pNLSUhQWFqKmpkYK0REivzgcDgwNDWkbMUJIlyBqPkA9Y3KAu4dlXV2dyFk0IV2NoqIiPZokhHRLlIzJEWVlZfplRAghhHQzNICfEEIIIUSGKBkjhBBCCJEhSsakrKamBps2baLB+lJG91026L5LH91z2aD7Lhtd5b7TbEoJaGn2RFtnWhLxoPsuG3TfpY/uuWzQfZcNeb/vosZHPWOd3P79++XuXPIYk7h19Xslj/ddXq9PXs8lLvJ6ffL4MyhOXf1edfX73ubzsETsSkpKWABsSUlJm+rao3///mI5jzjPJY8xyet9l8d7Jc5zifO+y+P1yeO55PW9Lq/nksf3Ost27XslznPJ633nnkfU+GhpCwlg///Jb2lpqUAdt0xYXXs0NDTI3bnkMSZ5ve/yeK/EeS5x3nd5vD55PJe8vtfl9Vzy+F4Huva9Eue55PW+c8/DPRfbyogwGjMmAY8fP4aZmZmswyCEEEKIHMjJyUHv3r2bradkTAIaGxuRl5cHLS0t2meSEEII6aZYlkVZWRlMTU2hoND8MH1KxgghhBBCZIhmUxJCCCGEyBAlY4QQQgghMkTJGCGEEEKIDFEyJkMJCQmYOnUqzM3NoaamBn19fQwdOhRHjx6VdWhd2tWrV7Fo0SLY2dlBQ0MDvXr1wmuvvYa4uDhZh9allZWV4eOPP8aECRNgZGQEhmGwadMmWYfVZZSXl2P58uUwNTWFqqoqnJ2d8ccff8g6rC6P3tfS1xU/wykZk6EXL17AzMwMX375JS5duoQjR47A0tISb775JrZu3Srr8LqsH374AZmZmfjwww9x6dIl7N27F8+ePcOQIUNw9epVWYfXZRUVFeHAgQOoqamBr6+vrMPpcqZPn47Dhw9j48aNCA4Ohru7O+bNm4djx47JOrQujd7X0tcVP8NpNqUcGjJkCPLy8pCdnS3rULqkZ8+eoUePHnxl5eXlsLGxgYODA/79918ZRda1cT9qGIZBYWEhjIyMsHHjRupFEINLly5h6tSpOHbsGObNm8crnzBhAu7du4fs7GwoKirKMMKui97X0tcVP8OpZ0wOGRoaQkmJNkeQlFd/iAFAU1MT9vb2yMnJkUFE3QPDMLTunoScPXsWmpqamDVrFl/5woULkZeXh1u3bskosq6P3tfS1xU/wykZkwONjY2or69HQUEBvv/+e1y+fBlr1qyRdVjdSklJCW7fvo0BAwbIOhRC2iwpKQn9+/cX+CPO0dGRV09IV9bZP8Op+0UOvP/++/jpp58AACoqKvj222+xZMkSGUfVvQQGBqKiogKffPKJrEMhpM2KiopgbW0tUK6vr8+rJ6Qr6+yf4dQzJiahoaG87urWvhISEvheu379esTExODixYtYtGgRPvjgA+zatUs2F9LJdOS+c3322Wf43//+hz179mDQoEHSvYBOShz3nYhXS4/K6DEa6cq6wmc49YyJia2tLQ4ePCjSsebm5gLfc8umTJkCAFi3bh38/f1hZGQk3kC7mI7cdwDYvHkztm7dii+++AIffPCBuMPrsjp634l4GRgYCO39ev78OYD/esgI6Wq6ymc4JWNiYmJigsWLF4vlXB4eHvjxxx+Rnp5OyVgrOnLfN2/ejE2bNmHTpk1Yv369mCPr2sT5ficdN3DgQBw/fhz19fV848YSExMBAA4ODrIKjRCJ6Uqf4fSYUg5du3YNCgoKQseAEPH4/PPPsWnTJnz66afYuHGjrMMhpEP8/PxQXl6O06dP85UfPnwYpqamGDx4sIwiI0QyutpnOPWMydA777wDbW1teHh4oGfPnigsLMTJkyfx559/YvXq1dQrJiFff/01NmzYgEmTJmHq1KmIioriqx8yZIiMIuv6goODUVFRgbKyMgBAcnIyTp06BaDpEb26urosw+u0Jk+ejPHjx+O9995DaWkpbGxscPz4cYSEhODo0aO0xpiE0ftaurriZzgt+ipDv/32G3777Tfcv38fL168gKamJpycnLB48WK88cYbsg6vy/Ly8kJYWFiz9fQjITmWlpbIysoSWpeRkQFLS0vpBtSFlJeX45NPPsGJEyfw/Plz2NnZYd26dZg7d66sQ+vy6H0tXV3xM5ySMUIIIYQQGaIxY4QQQgghMkTJGCGEEEKIDFEyRgghhBAiQ5SMEUIIIYTIECVjhBBCCCEyRMkYIYQQQogMUTJGCCGEECJDlIwRQgghhMgQJWOEEEIIITJEyRghpEsICQmRdQikA8rKyrB69WqMHTsWenp6YBgGhw4davf50tLSkJ6eLr4ACZEgSsYIIZ3e4cOH8eTJE1mHQTqgqKgIP//8M1iWxZQpUzp8PisrK6xbtw7Pnz8XQ3SESBYlY4SQTu3evXs4efIkAgICZB0K6QALCwsUFxfj6tWrWL16dYfPp6ioiPXr1+Ptt98WQ3SESBYlY4R0MV5eXh1+xNOZvPfee9i4cWOLxwwfPhwMw+DYsWNtPj/DMHxflpaWQo+ztLQEwzAIDQ1tcxuk6T63Bfd9/vJXZmYm3zFOTk7Q0NDAyZMnxRgpIeJHyRghpNM6f/48ioqK4O7u3uwxjY2NuHPnDgDAxcWl3W25ublh+PDhLbYlDgEBAbzkQkNDA6WlpS0ev379er6E5PHjxxKNT14MHDgQw4cPx/Dhw1s8LiAgAGvXrkVDQ4OUIiOk7ZRkHQAhhLTXjh07Wn08+eDBA1RUVEBDQwO2trbtbuvkyZPN9opJSmVlJf78889mH7U1NDTgyJEjUo1JXuzbt4/375Z61UaPHo2GhgacOnUKc+bMkUZohLQZ9YwRQjqltLQ0REREYMyYMS0ed/v2bQBNj6wUFDrPR17//v0BAL/++muzx1y+fBm5ubm8Y+VBaGiowOPD5r6kMemCYRh4eXl1m8f2pHOinjFCSKd05swZKCsrw8HBocXj4uPjAXTsEaUsDBs2DI2NjYiKikJKSgrs7OwEjuEmaosWLRLLoHdxsLOzw2+//SbSsTo6OhKOpomrqys++ugjlJWVQUtLSyptEtIWnefPREJIh1VXV2P37t0YPHgwdHR0oKqqij59+mDJkiV49OhRi68tKCjA0qVLYW5uDg6HAzMzMyxZsgR5eXk4dOgQrwdCWq5fvw57e3twOJwWj+P2jLm6ugrUPXjwAI6OjmAYBpMmTUJRUZFYYsvJycHbb7+N3r178+7Vu+++i2fPnrXpPNxHsMJ6xwoLC3HhwgUYGBjAx8enxfO8PLng/v37mD9/PkxMTMDhcNCnTx+sWbMGJSUlLZ7jwoUL8PPzQ69evcDhcNCjRw+4u7tjw4YNyM7O5h1nbGyMgIAAkb7U1NTadD/ay8XFBfX19YiIiJBKe4S0FSVjhHQTT58+xZAhQ/DRRx8hOjoaxsbGGDBgAPLy8nDgwAEMHDgQQUFBQl+bkZEBV1dXfPfdd8jNzUXfvn1hYGCAX3/9FS4uLsjKypLy1QAJCQmwtrYW6ThAMBk7duwY3NzccO/ePWzatAmXLl2CgYFBh+O6e/cunJyccOTIERgZGcHc3Bx5eXn46aefMGLEiFYH5L/M398fioqK+P3331FfX89Xd/ToUdTW1uL111+HioqKSOeLjo6Gm5sbTp8+DRMTE1hbWyM9PR07duzA4MGDhT42rK6uxsyZM+Hj44Nz586hqqoKjo6O0NHRwd27d/H555/jzJkzIl+TLHDfJ9yJHITIHZYQ0qV4enqyANjffvuNr3z8+PEsANbS0pK9ffs2r7y4uJidNWsWC4DV1NRk09PTBc45bNgwFgA7YMAANjU1lVf++PFjdvjw4ayKigoLgPX09JTUZfGprq5mGYZh/f39Wzzu0aNHLABWRUWFra2tZVmWZauqqth33nmHBcAaGRmxf//9d4vnAMACYDMyMlo8zsLCggXAKisrs/PmzWOLiop4dXFxcWzPnj1ZAOymTZtaPI+/vz8LgH3rrbdYlmXZqVOnsgDYCxcu8B3n6OjIAmATEhLYjIwMXpw5OTktxjZp0iT22bNnvLqEhATWysqKBcBOnjxZ4LWLFi1iAbDa2trssWPH2IaGBl5dTU0N+8cff7R6D0V16dIl9uTJk+zOnTtZAGxgYCB78uRJ9uTJky2+rrX/o9LSUhYA+/bbb4slTkLEjZIxQroYYclYREQE7xfW9evXBV5TW1vL+4X93nvv8dWFhoayAFhFRUU2JSVF4LVPnz5ltbS0pJqM5ebmsgDYpUuXtnjcyZMnWQCsq6sry7Ism5qayjo5ObEA2GHDhrGPHz9uta22JmP9+vXjJX4v+/rrr/liac6rydjp06dZAKyfnx/vmJiYGL5ziZqM6erqsiUlJQL1YWFhvNdHR0fzyhMTE1mGYVgAbHBwcItxiwM3TmFfLWnt/6ixsZFVUFBgp0+fLoGoCek4ekxJSDfAffw4ePBgjBw5UqBeWVkZK1asAABcvHiRry44OBgA4OnpKXRpiB49esDX11fMEbessrISAKCtrd3icdzB+66urvjjjz8waNAg3LlzB8uXL0doaCh69eol9tiWLFkCZWVlgfKhQ4cCAB4+fNim802bNg1GRkYICgpCQUEBAP6B+22xePFiofds1KhRGDRoEAD+//9Tp06BZVm4ublh0qRJbWqrPTIzM8E2dRIIfHUEwzDQ1NRERUWFmCIlRLwoGSOkG0hJSQHQtFBmcxwdHQEA2dnZqKqq4pU/ePAAAODs7Nzsa5ubqdiWzZ/LysqwdOlSmJiYQFVVFa6urs2ORVJSapoIXltb22xMwH+D9//55x/MmzcPDMPg1KlT2LNnj9CESRyaW8usZ8+eAIDy8vI2nU9ZWRmvv/466urqcPToUVRXV+P48ePgcDiYP39+m87V0sxTbl1ycjKvLDExEQBaXVi1M6itrZXY/zkhHUXJGCHdAHfQuLGxcbPHmJiYCBwPNCVJQMu9UM0tFyDq5s8sy+K1117DsWPHsHXrVly6dAkODg6YOXMmzp07J3C8pqamQJzCcHvGXrx4AQDw8/PDjBkzWnxNR2loaAgt565x1p5enrfeegsA8Ntvv+H06dN48eIFfH19oaen16bztPT/z63j/n8D/91fXV3dNkYsX+rr61FdXc173xAib2idMUK6AW4i1dIim/n5+QLHA/8lWi0lPi//An8Zd/NnoGlWY3N7Q168eBHXrl3DmTNn4OfnB6Bp5fT09HSsXr1a4DGogYEBVFVVW4wpNzcXT58+hbKyMkJDQzF69GgcPnwYjo6OWLlyZbOvk0cODg5wc3NDbGwsPv30UwBtf0QJNM2oba3u5cSa+z7gJrOdFXfZDjMzMxlHQohw1DNGSDfAXTCU+9hJGG6dhYUF3/pP3MduLS0LwF0+4lWibv587tw56Ojo4LXXXuN7rb+/P9LS0nDv3j2B89rY2LS4Lhi3V8ze3h7Ozs44e/YsVFRUsHr1apw+fVqkuOQJN/nKzMyEmZkZxo0b1+ZzJCUltVpnb2/PK+M+uu7s63M9f/4cANC3b18ZR0KIcJSMEdINeHt7AwBu3bqF8PBwgfq6ujrs2bMHADB16lS+usmTJwNo2uZG2ODzwsJCoY8S2+LevXuwt7cX2K6ImwwISyIGDx7cYnLJHS/GHc/m5eWFX375BY2NjXjjjTcQGRnZoZilbd68eRg/fjzGjh2LdevWtWtrp59//lloL2Z4eDhiY2MB8P//z5gxAwzDIDY2Fn///Xf7g5exu3fvAmja1YAQeUTJGCHdwLBhwzBhwgQAwIIFC3i9RkDTI5wFCxYgMzMTmpqaWLVqFd9rPT09MWzYMDQ0NGDGjBlIS0vj1eXn52P69Omorq7uUHxFRUXQ19cXKOeWcXs2XjZu3Djk5+c3++jt1WQMAN544w1s2bIF1dXV8PHx4bsWeaerq4u///4b//77L9577712naO8vBxz585FYWEhrywxMZG30v/EiRPh7u7Oq3NwcODVzZ49GydOnEBjYyOvvra2FidPnsQ///zTrnikJT4+Hqampny9foTIE0rGCOkmjhw5AicnJ95q+nZ2dnBzc4OJiQn++OMPqKmp4dixY7CyshJ47e+//45evXohMTERtra2cHR0hIuLC8zNzZGSkoJ169YBABQVFdsdn6iPNLmmTZsGDQ0NxMXFCa0XlowBwGeffYaFCxeisLAQkydP5ktMurqtW7fylvQYNGgQ7O3t4ejoiEePHsHGxkbotkv79++Hj48PSkpKMGfOHBgZGcHDwwP9+vWDtrY2Zs+eLfAYWd7ExcVhzpw5bX6PESItlIwR0k307NkTkZGR2LVrF9zd3ZGXl4fExEQYGxvjnXfewd27dzFt2jShr7W2tsbt27cRGBgIU1NTPHjwAAUFBbxeNkNDQwCtr/vVHAMDA6Hjv7g9YsJ6zTQ0NLBgwQKcOnVKoK6oqAg5OTlgGAZOTk4C9QcOHMD48eORlpYGHx8fvqU8ujIPDw/ExsZi+vTpyM3NxaNHj2BlZYVVq1YhJiYGpqamAq9RU1PDuXPncOrUKUyZMgVKSkpISEhASUkJnJycsHHjRonPUO2I58+fIywsDO+++66sQyGkebJabZYQ0nW8//77LAB25cqVzR4THx8vdJsmlmXZt956i9XV1eXbaodlWfbgwYMsADYpKUnoOTMzM1kDAwO2vLy8Q/G3BCKuwC/PuCvbX7t2TdahSERL/0f79u1j586dK/2gCGkD6hkjhHTIixcvcOLECQBN48vaw8/PDy9evMCFCxf4yo8cOQIbGxsMGDBA6OssLCwQGBiI33//vV3ttsWsWbMwYsQIzJo1S+JtkdYtXboUI0aMwIgRI5o9pqGhAf/73/+wY8cOKUZGSNvROmOEkFbl5OTg+PHjCAgIQI8ePXjlDx8+xKJFi1BYWIg+ffoIXdg1ODgYFRUVyMzMBADExsbyFt+cOXMmAGDKlCkYPXo03nrrLRQVFcHKygqHDx9GeHh4s6vwc33yySfw8fGBn58fb5V7SeDONrSwsJBYG0R0iYmJrS65sXfvXrz99tu0vhiRewzLdnDTL0JIl5eSkoL+/fsDaFqpv3fv3iguLubNRtTX18fly5fh5uYm8FpLS0tkZWUJPe/LHz+lpaVYv349Tp06hRcvXqB///747LPPMH369Fbjy8nJwY8//ogvvviiPZfX5XH/D65duwYvLy9ZhyMVeXl52LdvH7Zt2ybrUAhpFSVjhJBWlZWVYffu3fjnn3+Qnp6OoqIiKCkpwdLSEpMmTcLKlSslsul2W5SUlEBHR0emMcir7piMlZaWQktLi2ZQkk6BkjFCCCGEEBmiAfyEEEIIITJEyRghhBBCiAxRMkYIIYQQIkOUjBFCCCGEyBAlY4QQQgghMkTJGCGEEEKIDFEyRgghhBAiQ5SMEUIIIYTIECVjhBBCCCEyRMkYIYQQQogMUTJGCCGEECJD/wfaB79b69NOXAAAAABJRU5ErkJggg=="
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 10
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Dark matter - stars cross-power spectra\n",
+   "id": "1f0b036f8d03e22a"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-25T02:20:43.670626Z",
+     "start_time": "2026-01-25T02:20:43.343371Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "ys = [\n",
+    "    pks[\"dm\"],\n",
+    "    pks[\"stars\"],\n",
+    "    pks[\"dm_stars\"],\n",
+    "]\n",
+    "labels = [\n",
+    "    r\"$P^{\\rm DM} (k)$\",\n",
+    "    r\"$P^{\\rm S} (k)$\",\n",
+    "    r\"$P^{\\rm DM \\times S} (k)$\",\n",
+    "\n",
+    "]\n",
+    "\n",
+    "plot_spectra(\n",
+    "    pk_components,\n",
+    "    ys,\n",
+    "    labels=labels,\n",
+    "    h=cosmo[\"h\"],\n",
+    "    ylabel=r\"$\\log_{10}\\!\\left(P(k)\\;[\\mathrm{Mpc}/h]^3\\right)$\",\n",
+    ")\n",
+    "\n",
+    "plot_spectra_ratio(\n",
+    "    pk_components,\n",
+    "    ys,\n",
+    "    labels=labels,\n",
+    "    h=cosmo[\"h\"],\n",
+    "    xlim=(-2, 2),\n",
+    "    ylim=(-1, 3),\n",
+    "    ylabel=r\"$\\log_{10}\\!\\left[P(k)/P_{\\rm ref}(k)\\right]$\",\n",
+    ")\n"
+   ],
+   "id": "4b43e60b540e28c5",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 11
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Dark matter - gas cross-power spectra",
+   "id": "6a35452a733d1088"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-25T02:20:44.036762Z",
+     "start_time": "2026-01-25T02:20:43.675549Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "ys = [\n",
+    "    pks[\"dm\"],\n",
+    "    pks[\"gas\"],\n",
+    "    pks[\"dm_gas\"],\n",
+    "]\n",
+    "labels = [\n",
+    "    r\"$P^{\\rm DM} (k)$\",\n",
+    "    r\"$P^{\\rm G} (k)$\",\n",
+    "    r\"$P^{\\rm DM \\times G} (k)$\",\n",
+    "\n",
+    "]\n",
+    "\n",
+    "plot_spectra(\n",
+    "    pk_components,\n",
+    "    ys,\n",
+    "    labels=labels,\n",
+    "    h=cosmo[\"h\"],\n",
+    "    ylabel=r\"$\\log_{10}\\!\\left(P(k)\\;[\\mathrm{Mpc}/h]^3\\right)$\",\n",
+    ")\n",
+    "\n",
+    "plot_spectra_ratio(\n",
+    "    pk_components,\n",
+    "    ys,\n",
+    "    labels=labels,\n",
+    "    h=cosmo[\"h\"],\n",
+    "    xlim=(-2, 2.),\n",
+    "    ylim=(-2, 2),\n",
+    "    ylabel=r\"$\\log_{10}\\!\\left[P(k)/P_{\\rm ref}(k)\\right]$\",\n",
+    ")\n"
+   ],
+   "id": "6497d24c5d509cce",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Gas - stars cross-power spectra",
+   "id": "308e39c707652a26"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-25T02:20:44.347264Z",
+     "start_time": "2026-01-25T02:20:44.045971Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "ys = [\n",
+    "    pks[\"gas\"],\n",
+    "    pks[\"stars\"],\n",
+    "    pks[\"gas_stars\"],\n",
+    "]\n",
+    "labels = [\n",
+    "    r\"$P^{\\rm G} (k)$\",\n",
+    "    r\"$P^{\\rm S} (k)$\",\n",
+    "    r\"$P^{\\rm G \\times S} (k)$\",\n",
+    "\n",
+    "]\n",
+    "\n",
+    "plot_spectra(\n",
+    "    pk_components,\n",
+    "    ys,\n",
+    "    labels=labels,\n",
+    "    h=cosmo[\"h\"],\n",
+    "    ylabel=r\"$\\log_{10}\\!\\left(P(k)\\;[\\mathrm{Mpc}/h]^3\\right)$\",\n",
+    ")\n",
+    "\n",
+    "plot_spectra_ratio(\n",
+    "    pk_components,\n",
+    "    ys,\n",
+    "    labels=labels,\n",
+    "    h=cosmo[\"h\"],\n",
+    "    xlim=(-2, 2.),\n",
+    "    ylim=(-2, 3),\n",
+    "    ylabel=r\"$\\log_{10}\\!\\left[P(k)/P_{\\rm ref}(k)\\right]$\",\n",
+    ")\n"
+   ],
+   "id": "aaabd50f20e944ee",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Total matter power spectra\n",
+    "\n",
+    "Here the individual curves correspond to the prefactor-weighted halo-model\n",
+    "contributions entering the baryonic power spectrum; the total spectrum $P^{\\rm BHM}$\n",
+    "is obtained as the sum of these terms in linear space, even though this additivity is not visually apparent when plotted in logarithmic units."
+   ],
+   "id": "d33c1209453d9958"
+  },
+  {
+   "metadata": {
+    "tags": [
+     "pk_total"
+    ],
+    "ExecuteTime": {
+     "end_time": "2026-01-25T02:20:44.745196Z",
+     "start_time": "2026-01-25T02:20:44.361638Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "ys = [\n",
+    "    pks[\"w_dm\"],\n",
+    "    pks[\"w_dm_gas\"],\n",
+    "    pks[\"w_gas\"],\n",
+    "    pks[\"w_dm_stars\"],\n",
+    "    pks[\"w_stars_gas\"],\n",
+    "    pks[\"w_stars\"],\n",
+    "    pks[\"total\"],\n",
+    "]\n",
+    "\n",
+    "labels = [\n",
+    "    r\"$w^{\\rm DM}\\,P^{\\rm DM\\times DM} (k)$\",\n",
+    "    r\"$w^{\\rm DM,G}\\,P^{\\rm DM\\times G} (k)$\",\n",
+    "    r\"$w^{\\rm G}\\,P^{\\rm G\\times G} (k)$\",\n",
+    "    r\"$w^{\\rm DM,S}\\,P^{\\rm DM\\times S} (k)$\",\n",
+    "    r\"$w^{\\rm G,S}\\,P^{\\rm G\\times S} (k)$\",\n",
+    "    r\"$w^{\\rm S}\\,P^{\\rm S\\times S} (k)$\",\n",
+    "    r\"$P^{\\rm BHM} (k)$\",\n",
+    "]\n",
+    "\n",
+    "plot_spectra(\n",
+    "    pk_components,\n",
+    "    ys,\n",
+    "    labels=labels,\n",
+    "    h=cosmo[\"h\"],\n",
+    "    ylabel=r\"$\\log_{10}\\!\\left(P(k)\\;[\\mathrm{Mpc}/h]^3\\right)$\",\n",
+    "    legend_ncol=2,\n",
+    ")\n",
+    "\n",
+    "plot_spectra_ratio(\n",
+    "    pk_components,\n",
+    "    ys,\n",
+    "    labels=labels,\n",
+    "    h=cosmo[\"h\"],\n",
+    "    ylabel=r\"$\\log_{10}\\!\\left[P(k)/P_{\\rm ref}(k)\\right]$\",\n",
+    "    legend_ncol=2,\n",
+    "    frameon=True,\n",
+    ")\n"
+   ],
+   "id": "7bc94119537f59b1",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 14
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "# One-parameter sensitivity: physical interpretation\n",
+    "\n",
+    "To build intuition for which physical ingredients drive the baryonic\n",
+    "correction, we vary **one model parameter at a time** while keeping the rest\n",
+    " of the halo-model setup fixed (mass definition, mass function, halo bias,\n",
+    " concentration, and numerical grids). For each variation we compute the\n",
+    " boost $B (k)$ relative to a dark-matter-only (DMO) halo-model baseline,\n",
+    "\n",
+    "$B(k) = \\frac{P_{\\rm bar}^{\\rm HM}(k)}{P_{\\rm dmo}^{\\rm HM}(k)}$,\n",
+    "\n",
+    "and plot the ratio $B(k) / B_{\\rm ref}(k)$ with respect to a fiducial baseline\n",
+    "configuration $B_{\\rm ref}$.\n",
+    "\n",
+    "Technically, this is implemented by rebuilding the halo model with a **single overridden parameter** while reusing all baseline settings. This “override” pattern is used here purely for diagnostic clarity: in practical applications with the `BaryonsFedeli14` class (via the `baryons` module), one would simply **set the desired parameter values directly** when constructing the model. The goal of this exercise is not to prescribe how the model should be configured in an analysis, but to isolate and visualize how each physical parameter impacts the matter power spectrum.\n",
+    "\n",
+    "Each panel therefore corresponds to a single physical “knob,” allowing us to assess whether changing that parameter primarily suppresses or enhances power, and on which scales the effect is most pronounced.\n",
+    "\n",
+    "#### Mass-fraction parameters\n",
+    "\n",
+    "* **$\\rho_{\\rm S}$** sets the overall normalization of the stellar mass\n",
+    "fraction. Increasing $\\rho_{\\rm S}$ enhances the contribution of compact stellar components, typically boosting power on small scales where baryons are most centrally concentrated.\n",
+    "\n",
+    "* **$m_{0,{\\rm S}}$** and **$\\sigma_{\\rm S}$** control the characteristic\n",
+    "halo\n",
+    "mass scale and width of the stellar mass-fraction transition. Shifting $m_{0,{\\rm S}}$ moves the mass range over which stars are most important, while $\\sigma_{\\rm S}$ controls how sharply this transition occurs.\n",
+    "\n",
+    "* **$m_{0,{\\rm G}}$** and **$\\sigma_{\\rm G}$** play an analogous role for\n",
+    "the gas\n",
+    "component, governing the halo masses where gas depletion or redistribution becomes important and how gradually this occurs across the halo population.\n",
+    "\n",
+    "#### Gas-profile shape parameters\n",
+    "\n",
+    "* **$\\beta$** controls the slope of the gas density profile. Larger values\n",
+    "generally produce a steeper fall-off, redistributing gas away from halo centers and suppressing small-scale power.\n",
+    "\n",
+    "* **$r_{\\rm co}$** sets the core radius of the gas profile, affecting the\n",
+    "inner flattening of the gas distribution.\n",
+    "\n",
+    "* **$r_{\\rm ej}$** controls the ejection scale of gas due to feedback\n",
+    "processes. Increasing $r_{\\rm ej}$ pushes gas to larger radii, leading to\n",
+    "stronger suppression of power on intermediate and small scales.\n",
+    "\n",
+    "#### Stellar-profile parameters\n",
+    "\n",
+    "* **$x_\\Delta$** sets the characteristic stellar scale relative to the halo\n",
+    "overdensity radius. Varying this parameter changes how concentrated the stellar component is within halos.\n",
+    "\n",
+    "* **$\\alpha$** controls the inner slope of the stellar profile, primarily\n",
+    "affecting the smallest scales.\n",
+    "\n",
+    "#### Diffuse gas mixing parameters\n",
+    "\n",
+    "* **$F_{\\rm G}$** regulates the fraction of gas retained in halos versus mixed\n",
+    "into\n",
+    "a diffuse component. Values closer to unity correspond to minimal gas ejection, while smaller values increase large-scale redistribution and suppress clustering power.\n",
+    "\n",
+    "* **$b_{\\rm d}$** parametrizes the bias of the diffuse gas component relative\n",
+    " to\n",
+    "the matter distribution, modulating how efficiently redistributed gas contributes back to large-scale power.\n",
+    "\n",
+    "Overall, these one-parameter sweeps provide a controlled way to understand\n",
+    "**which physical mechanisms are responsible for suppressing or enhancing the\n",
+    " matter power spectrum, and on which scales**. This intuition is essential when interpreting baryonic uncertainties in cosmological analyses, even though realistic applications will vary multiple parameters simultaneously.\n"
+   ],
+   "id": "44beea9d9f74b04a"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-25T02:20:44.793589Z",
+     "start_time": "2026-01-25T02:20:44.747637Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Sensitivity grid: B(k)/B_ref(k)\n",
+    "bhm0 = bhm\n",
+    "boost_ref = bhm0.boost(k=k_grid, a=scale_factor, pk_ref=\"pk_dmo\")\n",
+    "x = np.log10(k_grid)  # k in 1/Mpc\n",
+    "\n",
+    "def _make_bhm_with(**overrides):\n",
+    "    \"\"\"Rebuild BaryonHaloModel with baseline settings, overriding selected params.\"\"\"\n",
+    "    # baseline params pulled from bhm0 internals\n",
+    "    base_frac = bhm0._mass_frac_params\n",
+    "    base_gas = bhm0._gas_profile_params\n",
+    "    base_star = bhm0._star_profile_params\n",
+    "\n",
+    "    return BaryonHaloModel(\n",
+    "        cosmo=cosmo,\n",
+    "        mass_def=mass_def,\n",
+    "        mass_function=hmf,\n",
+    "        halo_bias=getattr(bhm0, \"halo_bias\", None),\n",
+    "        concentration=getattr(bhm0, \"concentration\", None),\n",
+    "\n",
+    "        # MASS FRACTIONS\n",
+    "        rho_star=overrides.get(\"rho_star\", base_frac.get(\"rho_star\", None)),\n",
+    "        m0_star=overrides.get(\"m0_star\", base_frac[\"m0_star\"]),\n",
+    "        sigma_star=overrides.get(\"sigma_star\", base_frac[\"sigma_star\"]),\n",
+    "        m0_gas=overrides.get(\"m0_gas\", base_frac[\"m0_gas\"]),\n",
+    "        sigma_gas=overrides.get(\"sigma_gas\", base_frac[\"sigma_gas\"]),\n",
+    "\n",
+    "        # PROFILE SHAPES\n",
+    "        gas_beta=overrides.get(\"gas_beta\", base_gas[\"beta\"]),\n",
+    "        gas_r_co=overrides.get(\"gas_r_co\", base_gas[\"r_co\"]),\n",
+    "        gas_r_ej=overrides.get(\"gas_r_ej\", base_gas[\"r_ej\"]),\n",
+    "        star_x_delta=overrides.get(\"star_x_delta\", base_star[\"x_delta\"]),\n",
+    "        star_alpha=overrides.get(\"star_alpha\", base_star[\"alpha\"]),\n",
+    "\n",
+    "        # DIFFUSE GAS MIXING\n",
+    "        Fg=overrides.get(\"Fg\", bhm0.Fg),\n",
+    "        bd=overrides.get(\"bd\", bhm0.bd),\n",
+    "    )\n",
+    "\n",
+    "def tex_sci(v: float, *, sig: int = 2) -> str:\n",
+    "    \"\"\"Return v as TeX scientific notation, e.g. 1.2×10^{-13}.\"\"\"\n",
+    "    if v == 0:\n",
+    "        return \"0\"\n",
+    "    m, e = f\"{v:.{sig}e}\".split(\"e\")\n",
+    "    return rf\"{m}\\times10^{{{int(e)}}}\"\n",
+    "\n",
+    "# We need to define rho_star_base here, since\n",
+    "# it gets cached in bhm0._mass_frac_params\n",
+    "rho_star_base = bhm0._mass_frac_params.get(\"rho_star\")\n",
+    "if rho_star_base is None:\n",
+    "    rho_star_base = 7.0e8 * float(cosmo[\"h\"])**2\n",
+    "\n",
+    "panels = [\n",
+    "    (r\"Mass fractions: $\\rho_{\\rm S}$\", \"rho_star\",\n",
+    "     [rho_star_base * f for f in [0.25, 0.35, 0.50, 0.70, 1.00, 1.40, 2.00, 2.80, 4.00]],\n",
+    "     lambda v: rf\"$\\rho_{{\\rm S}}={tex_sci(v, sig=1)}$\"),\n",
+    "\n",
+    "    (r\"Mass fractions: $m_{0,{\\rm S}}$\", \"m0_star\",\n",
+    "     [5.0e12 * f for f in [0.25, 0.35, 0.50, 0.70, 1.00, 1.40, 2.00, 2.80, 4.00]],\n",
+    "     lambda v: rf\"$m_{{0,\\rm S}}={tex_sci(v, sig=1)}$\"),\n",
+    "\n",
+    "    (r\"Mass fractions: $\\sigma_{{\\rm S}}$\", \"sigma_star\",\n",
+    "     [1.2 + d for d in [-0.6, -0.45, -0.30, -0.15, 0.0, 0.15, 0.30, 0.45, 0.60]],\n",
+    "     lambda v: rf\"$\\sigma_{{\\rm S}}={v:.2f}$\"),\n",
+    "\n",
+    "    (r\"Mass fractions: $m_{0,{\\rm G}}$\", \"m0_gas\",\n",
+    "     [1.0e12 * f for f in [0.25, 0.35, 0.50, 0.70, 1.00, 1.40, 2.00, 2.80, 4.00]],\n",
+    "     lambda v: rf\"$m_{{0,\\rm S}}={tex_sci(v, sig=1)}$\"),\n",
+    "\n",
+    "    (r\"Mass fractions: $\\sigma_{{\\rm G}}$\", \"sigma_gas\",\n",
+    "     [3.0 + d for d in [-1.5, -1.125, -0.75, -0.375, 0.0, 0.375, 0.75, 1.125, 1.5]],\n",
+    "     lambda v: rf\"$\\sigma_{{\\rm F}}={v:.2f}$\"),\n",
+    "\n",
+    "    (r\"Gas profile: $\\beta$\", \"gas_beta\",\n",
+    "     [2.9 + d for d in [-0.9, -0.675, -0.45, -0.225, 0.0, 0.225, 0.45, 0.675, 0.9]],\n",
+    "     lambda v: rf\"$\\beta={v:.2f}$\"),\n",
+    "\n",
+    "    (r\"Gas profile: $r_{\\rm co}$\", \"gas_r_co\",\n",
+    "     [0.1 * f for f in [0.25, 0.35, 0.50, 0.70, 1.00, 1.40, 2.00, 2.80, 4.00]],\n",
+    "     lambda v: rf\"$r_{{\\rm co}}={v:.3f}$\"),\n",
+    "\n",
+    "    (r\"Gas profile: $r_{\\rm ej}$\", \"gas_r_ej\",\n",
+    "     [4.5 * f for f in [0.25, 0.35, 0.50, 0.70, 1.00, 1.40, 2.00, 2.80, 4.00]],\n",
+    "     lambda v: rf\"$r_{{\\rm ej}}={v:.2f}$\"),\n",
+    "\n",
+    "    (r\"Stellar profile: $x_\\Delta$\", \"star_x_delta\",\n",
+    "     [(1.0/0.03) * f for f in [0.25, 0.35, 0.50, 0.70, 1.00, 1.40, 2.00, 2.80, 4.00]],\n",
+    "     lambda v: rf\"$x_\\Delta={v:.1f}$\"),\n",
+    "\n",
+    "    (r\"Stellar profile: $\\alpha$\", \"star_alpha\",\n",
+    "     [1.0 + d for d in [-0.4, -0.3, -0.2, -0.1, 0.0, 0.1, 0.2, 0.3, 0.4,]],\n",
+    "     lambda v: rf\"$\\alpha={v:.2f}$\"),\n",
+    "\n",
+    "    # Fg must be in [0, 1] -> symmetric around 0.9 but clipped manually\n",
+    "    (r\"Diffuse mixing: $F_{\\rm G}$\", \"Fg\",\n",
+    "     [0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.975, 0.99, 1.00],\n",
+    "     lambda v: rf\"$F_{{\\rm G}}={v:.2f}$\"),\n",
+    "\n",
+    "    (r\"Diffuse mixing: $b_{\\rm d}$\", \"bd\",\n",
+    "     [0.6 + d for d in [-0.45, -0.3375, -0.225, -0.1125, 0.0, 0.1125, 0.225, 0.3375, 0.45]],\n",
+    "     lambda v: rf\"$b_{{\\rm d}}={v:.2f}$\"),\n",
+    "]\n"
+   ],
+   "id": "4f7a5e961b84a1c7",
+   "outputs": [],
+   "execution_count": 15
+  },
+  {
+   "metadata": {
+    "tags": [
+     "boost_sensitivity"
+    ],
+    "ExecuteTime": {
+     "end_time": "2026-01-25T02:21:00.707923Z",
+     "start_time": "2026-01-25T02:20:44.800450Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "with plot_style():\n",
+    "    ncols = 2\n",
+    "    nrows = int(np.ceil(len(panels) / ncols))\n",
+    "\n",
+    "    fig, axes = plt.subplots(\n",
+    "        nrows, ncols,\n",
+    "        figsize=(17, 5 * nrows),\n",
+    "        #constrained_layout=True,\n",
+    "    )\n",
+    "    fig.subplots_adjust(wspace=0.25, hspace=6)\n",
+    "\n",
+    "    # axes is 2D if nrows>1, but could be 1D if nrows==1\n",
+    "    axes = np.atleast_1d(axes).ravel()\n",
+    "\n",
+    "    for ax, (title, key, vals, labfmt) in zip(axes, panels):\n",
+    "        cols = get_colors(len(vals))\n",
+    "\n",
+    "        ax.axhline(1.0, c=\"gainsboro\", lw=2)\n",
+    "\n",
+    "        for v, col in zip(vals, cols):\n",
+    "            bhm_i = _make_bhm_with(**{key: v})\n",
+    "            boost_i = bhm_i.boost(k=k_grid, a=scale_factor, pk_ref=\"pk_dmo\")\n",
+    "            ax.plot(x, boost_i / boost_ref, lw=2.5, color=col, label=labfmt(v))\n",
+    "\n",
+    "        ax.set_title(title, fontsize=17)\n",
+    "        ax.set_xlabel(r\"$\\log_{10}(k\\,[{\\rm Mpc}^{-1}])$\")\n",
+    "        ax.set_ylabel(r\"$B(k)/B_{\\rm ref}(k)$\")\n",
+    "        ax.tick_params(axis=\"both\", which=\"major\", length=7)\n",
+    "        ax.tick_params(axis=\"both\", which=\"minor\", length=4)\n",
+    "        ax.minorticks_on()\n",
+    "        ax.legend(frameon=True, loc=\"best\", fontsize=12, ncols=3)\n",
+    "\n",
+    "    # hide any unused axes (since nrows*ncols may exceed len(panels))\n",
+    "    for ax in axes[len(panels):]:\n",
+    "        ax.set_visible(False)\n",
+    "\n",
+    "    # optionally save the figure\n",
+    "    # plt.savefig(\"bhm_oneparam_sensitivity.pdf\", bbox_inches=\"tight\", dpi=300)\n",
+    "\n",
+    "    plt.show()"
+   ],
+   "id": "af6344701c78eeb9",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1700x3000 with 12 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 16
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (ccl3)",
+   "language": "python",
+   "name": "ccl3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pyccl/baryons/fedeli14_bhm/examples/baryons_module.ipynb b/pyccl/baryons/fedeli14_bhm/examples/baryons_module.ipynb
new file mode 100644
index 000000000..fc19ab030
--- /dev/null
+++ b/pyccl/baryons/fedeli14_bhm/examples/baryons_module.ipynb
@@ -0,0 +1,505 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2e09fdb6-2987-4e9f-82c3-2a408c5c65a6",
+   "metadata": {},
+   "source": [
+    "# Baryonic Effects on the Nonlinear Matter Power Spectrum\n",
+    "\n",
+    "Baryonic processes such as gas cooling, star formation, and feedback from supernovae and active galactic nuclei modify the clustering of matter on small and intermediate scales. These effects redistribute mass within halos and lead to characteristic scale-dependent deviations from a gravity-only prediction of the matter power spectrum.\n",
+    "\n",
+    "In this notebook, we compare several **baryonic prescriptions and\n",
+    "emulators** by examining their impact on the nonlinear matter power spectrum\n",
+    " at redshift $z=0$. All results are shown as ratios\n",
+    "\n",
+    "$F(k) = \\frac{P_{\\mathrm{model}}(k)}{P_{\\mathrm{nlin}}(k)}$,\n",
+    "\n",
+    "where $P_{\\mathrm{nlin}}(k)$ is the same nonlinear reference spectrum in\n",
+    "all cases. This normalization isolates baryonic effects and enables a direct comparison between different modeling approaches, independent of differences in the underlying nonlinear gravity treatment.\n",
+    "\n",
+    "The models considered span halo-model–based baryonification schemes, simulation-calibrated correction models, and emulators designed to reproduce hydrodynamical simulation results. The comparison highlights both the scale and magnitude of baryonic modifications predicted by each approach.\n",
+    "\n",
+    "\n",
+    "More generally, `pyCCL` provides a user-friendly and modular interface for baryonic\n",
+    "corrections, making it straightforward to experiment with different baryon models\n",
+    "and to generate baryon-boosted matter power spectra with minimal changes to the code.\n",
+    "This flexibility enables quick comparisons across modeling approaches and\n",
+    "parameter\n",
+    "choices within a consistent framework.\n",
+    "\n",
+    "\n",
+    "\n",
+    "This notebook serves as an example of how to use the\n",
+    "[baryonic models package](https://ccl.readthedocs.io/en/latest/api/pyccl.baryons.html),\n",
+    "as implemented in `pyCCL`.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31c25dbb-27df-4b30-8e50-bbf7fc5e4fac",
+   "metadata": {},
+   "source": [
+    "## Preliminary Definitions\n",
+    "\n"
+   ]
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-24T23:00:38.385145Z",
+     "start_time": "2026-01-24T23:00:38.047794Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import ScalarFormatter\n",
+    "import numpy as np\n",
+    "import cmasher as cmr\n",
+    "import pyccl as ccl\n",
+    "from pyccl.baryons.fedeli14 import BaryonsFedeli14\n",
+    "print(f\"I am using pyCCL version {ccl.__version__}\")\n"
+   ],
+   "id": "7b2f1bebdf153ec3",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I am using pyCCL version 3.2.2.dev12+gba7a7d4ab\n"
+     ]
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "We only need three ingredients to explore baryonic models in CCL:\n",
+    "- a cosmology,\n",
+    "- a wavenumber grid,\n",
+    "- and a scale factor.\n",
+    "\n",
+    "We compute the gravity-only nonlinear power spectrum here simply as a reference,\n",
+    "so baryonic effects can be shown as a suppression or boost relative to the no-baryons case."
+   ],
+   "id": "52d61bb275ede69c"
+  },
+  {
+   "cell_type": "code",
+   "id": "2b9dc02a-a995-46c6-80e3-922950266043",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-24T23:00:38.600705Z",
+     "start_time": "2026-01-24T23:00:38.385748Z"
+    }
+   },
+   "source": [
+    "cosmo = ccl.CosmologyVanillaLCDM()  # cosmology object\n",
+    "k_grid = np.logspace(-3, 1, 100)  # wavenumber\n",
+    "a = 1.  # scale factor a z=0\n",
+    "# construct the nonlinear power spectrum with no baryonic effects\n",
+    "pk_nlin = ccl.nonlin_matter_power(cosmo, k_grid, a)"
+   ],
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ffab7a3d-c13c-460d-bcb7-9c7a941c8b26",
+   "metadata": {},
+   "source": "## Power Spectra $P_k$ Computation"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Fedeli et al. (2014) baryonic halo model\n",
+    "\n",
+    "[Fedeli et al. (2014)](https://arxiv.org/abs/1407.3404) present a halo-model–based\n",
+    "prescription for baryonic effects, explicitly modeling the redistribution of mass\n",
+    "within halos due to gas and stellar components while preserving large-scale\n",
+    "consistency with a gravity-only prediction.\n",
+    "\n",
+    "The baryonic modification is implemented by recomputing the halo-model matter\n",
+    "power spectrum with baryonic halo profiles and taking the ratio with respect to\n",
+    "a dark-matter-only halo-model reference.\n",
+    "\n",
+    "To see the detailed implementation in `pyCCL`, call `help(ccl.BaryonsFedeli14)`.\n"
+   ],
+   "id": "228854bbc729325d"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-24T23:00:41.811991Z",
+     "start_time": "2026-01-24T23:00:38.601108Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# instantiate the Fedeli+14 baryonic halo-model correction\n",
+    "fed = ccl.baryons.BaryonsFedeli14()\n",
+    "\n",
+    "# wrap a baseline nonlinear P(k) with the Fedeli+14 baryonic modification\n",
+    "fed_bary = fed.include_baryonic_effects(\n",
+    "    cosmo=cosmo,\n",
+    "    pk=cosmo.get_nonlin_power(),\n",
+    ")\n",
+    "\n",
+    "# evaluate the baryon-corrected P(k,a) on our (k,a) grid\n",
+    "pk_fed = fed_bary(k_grid, a)\n"
+   ],
+   "id": "e56fb0a9ebf2d3cf",
+   "outputs": [],
+   "execution_count": 3
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Schneider et al. 2015: Baryon Correction Model\n",
+    "\n",
+    "The Baryon Correction Model (BCM) applies baryonic effects as a scale- and redshift-dependent\n",
+    "multiplicative correction to a gravity-only nonlinear matter power spectrum.\n",
+    "We compute the baryon-modified $P(k)$ using the Schneider et al. (2015) prescription\n",
+    "(see [Schneider et al. 2015](https://arxiv.org/abs/1510.06034)).\n",
+    "To see the detailed implementation in `pyCCL`, call `help(ccl.BaryonsSchneider15)`.\n"
+   ],
+   "id": "e17886a8ffdfbef6"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-24T23:00:41.849644Z",
+     "start_time": "2026-01-24T23:00:41.843815Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# instantiate the BCM (Schneider+15) baryon model\n",
+    "bcm = ccl.baryons.BaryonsSchneider15()\n",
+    "\n",
+    "# apply BCM to a baseline nonlinear P(k)\n",
+    "bcm_bary = bcm.include_baryonic_effects(\n",
+    "    cosmo=cosmo,\n",
+    "    pk=cosmo.get_nonlin_power()\n",
+    ")\n",
+    "\n",
+    "# evaluate the baryon-corrected P(k,a) on our (k,a) grid\n",
+    "pk_bcm = bcm_bary(k_grid, a)\n"
+   ],
+   "id": "2cfc2843fbe19265",
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Van Daalen et al. (2019) baryonic boost\n",
+    "\n",
+    "[Van Daalen et al. (2019)](https://arxiv.org/abs/1906.00968) provide a library of hydrodynamical vs.\n",
+    "collisionless (DMO) matter power spectra from `OWLS`, `cosmo-OWLS`, and\n",
+    "`BAHAMAS`, and an empirical prescription for the baryonic modification of the matter power spectrum.\n",
+    "\n",
+    "To see the detailed implementation in `pyCCL`, call `help(ccl.BaryonsVanDaalen19)`.\n"
+   ],
+   "id": "6bb855b4a91d14e"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-24T23:00:41.854281Z",
+     "start_time": "2026-01-24T23:00:41.849967Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# instantiate the van Daalen et al. (2019) baryon model\n",
+    "vd = ccl.baryons.BaryonsvanDaalen19()\n",
+    "\n",
+    "# apply the van Daalen baryonic correction to a baseline nonlinear P(k)\n",
+    "vd_bary = vd.include_baryonic_effects(\n",
+    "    cosmo=cosmo,\n",
+    "    pk=cosmo.get_nonlin_power()\n",
+    ")\n",
+    "\n",
+    "# evaluate the baryon-corrected P(k,a) on our (k,a) grid\n",
+    "pk_vd = vd_bary(k_grid, a)\n"
+   ],
+   "id": "bca11168fddb9b03",
+   "outputs": [],
+   "execution_count": 5
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Mead et al. (2020): HMCode\n",
+    "\n",
+    "HMCode provides a nonlinear matter power spectrum that incorporates baryonic\n",
+    "feedback through an effective halo-model prescription.\n",
+    "We compute the nonlinear $P(k)$ using HMCode as implemented in CAMB\n",
+    "(see [Mead et al. 2020](https://arxiv.org/abs/2009.01858)).\n",
+    "To see the detailed implementation in pyCCL, call `help(ccl.CosmologyVanillaLCDM)`.\n"
+   ],
+   "id": "d033c21ff8ed0fd4"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-24T23:00:42.178963Z",
+     "start_time": "2026-01-24T23:00:41.854550Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# build a ΛCDM cosmology that sources the nonlinear P(k) from CAMB\n",
+    "# (HMCode baryonic feedback is implemented on the CAMB side)\n",
+    "cosmo_hmcode = ccl.CosmologyVanillaLCDM(\n",
+    "    matter_power_spectrum=\"camb\",  # delegate P_nl(k,a) to CAMB\n",
+    "    extra_parameters={\n",
+    "        \"camb\": {\n",
+    "            \"kmax\": 20.0,  # CAMB internal k_max (1/Mpc) used for its P(k) tables\n",
+    "            \"halofit_version\": \"mead2020_feedback\",  # HMCode / Mead+20 feedback prescription\n",
+    "            \"HMCode_logT_AGN\": 7.8,  # AGN feedback strength parameter (higher = stronger feedback)\n",
+    "        }\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# evaluate the HMCode-modified nonlinear matter power spectrum on our (k,a) grid\n",
+    "pk_hmcode = cosmo_hmcode.nonlin_matter_power(k_grid, a)\n"
+   ],
+   "id": "35a5039b3f533b20",
+   "outputs": [],
+   "execution_count": 6
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Arico et al. (2020): BACCOemu\n",
+    "\n",
+    "BACCOemu provides fast, high-accuracy predictions for the nonlinear matter power spectrum using an emulator trained on suites of simulations. It builds on a baryonification approach, where particles in dark-matter-only simulations are displaced to mimic the impact of baryonic feedback, achieving percent-level agreement with hydrodynamical simulations across its validated range (see [Aricò+20](https://arxiv.org/abs/2011.15018)).\n",
+    "\n",
+    "Implementation note (CCL): `include_baryonic_effects` is not compatible with\n",
+    " BACCOemu in our setup because it attempts to apply the correction across\n",
+    " the full internal $(k, a)$ spline support of the reference `Pk2D`, while BACCOemu is only defined on a more limited $(k, a)$ domain. This range mismatch causes failures/invalid extrapolation, so BACCOemu must be queried directly on the $(k,a)$ grid we use.\n",
+    " To see the detailed implementation in `pyCCL`, call `help(ccl.BaccoemuBaryons)`.\n"
+   ],
+   "id": "fdddebd114a5eff8"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-24T23:00:45.946869Z",
+     "start_time": "2026-01-24T23:00:42.180444Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# instantiate the BACCOemu baryonification-based emulator (Aricò+20)\n",
+    "bacco = ccl.BaccoemuBaryons()\n",
+    "\n",
+    "# baseline ΛCDM cosmology used to generate the reference nonlinear P(k)\n",
+    "cosmo_bacco = ccl.CosmologyVanillaLCDM()\n",
+    "\n",
+    "# evaluate directly on our (k, a) grid because its valid domain is limited\n",
+    "bacco_boost = bacco.boost_factor(cosmo_bacco, k_grid, a)\n",
+    "\n",
+    "# compute the baseline nonlinear matter power spectrum on the same grid\n",
+    "pk_ref = cosmo_bacco.nonlin_matter_power(k_grid, a)\n",
+    "\n",
+    "# apply the BACCOemu boost on-grid to obtain the baryon-corrected P(k,a)\n",
+    "pk_bacco = pk_ref * bacco_boost\n"
+   ],
+   "id": "db0c39ac18beec71",
+   "outputs": [],
+   "execution_count": 7
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Moran et al. (2022): Mira–Titan DMO Emulator\n",
+    "\n",
+    "The Mira–Titan emulator provides a high-precision prediction for the dark-matter-only nonlinear matter power spectrum, calibrated on a large suite of N-body simulations.\n",
+    "We start by computing the dark-matter-only (DMO) $P(k)$ from the Mira–Titan\n",
+    "emulator.\n",
+    "\n",
+    "For more information about Mira–Titan, see [Heitmann+ 16](https://arxiv.org/abs/1601.00326) and [Moran+ 22](https://arxiv.org/abs/2203.05591). To\n",
+    "see the detailed implementation in pyCCL, call `help(ccl.CosmicemuMTIVPk)`."
+   ],
+   "id": "db441f8c64bff4fc"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-24T23:00:46.145773Z",
+     "start_time": "2026-01-24T23:00:45.956079Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Initialize the Mira–Titan IV emulator (DMO nonlinear P(k) model)\n",
+    "mt = ccl.CosmicemuMTIVPk(\"tot\")\n",
+    "\n",
+    "# Build a vanilla ΛCDM cosmology that uses the emulator for the nonlinear power spectrum\n",
+    "cosmo_mt = ccl.CosmologyVanillaLCDM(matter_power_spectrum=mt)\n",
+    "\n",
+    "# Evaluate the (DMO) nonlinear matter power spectrum on our k-grid at scale factor a\n",
+    "pk_mt = cosmo_mt.nonlin_matter_power(k_grid, a)\n"
+   ],
+   "id": "f69c7a34a0151085",
+   "outputs": [],
+   "execution_count": 8
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8a93a6d-33a8-47dc-b347-212210d029ec",
+   "metadata": {},
+   "source": [
+    "## Plot results\n",
+    "\n",
+    "We now compare a set of baryonic prescriptions by plotting the ratio\n",
+    "\n",
+    "$F(k)\\equiv \\frac{P_\\mathrm{model}(k)}{P_\\mathrm{nlin}(k)}$\n",
+    "\n",
+    "as a function of wavenumber.\n",
+    "The gray line at $F(k)=1$ marks the gravity-only nonlinear baseline,\n",
+    " so departures from unity directly show the scale-dependent suppression or\n",
+    " enhancement induced by each baryonic model.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "3607216c-3c55-463d-bec1-6e3c64336cef",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-24T23:00:46.326351Z",
+     "start_time": "2026-01-24T23:00:46.146345Z"
+    }
+   },
+   "source": [
+    "# List of pk data and their labels (first-author surname + year)\n",
+    "# This convention is consistent with ccl naming\n",
+    "pk_data = [\n",
+    "    (pk_fed/pk_nlin, \"Fedeli14\"),\n",
+    "    (pk_bcm/pk_nlin, \"Schneider15\"),\n",
+    "    (pk_vd/pk_nlin, \"VanDaalen19\"),\n",
+    "    (pk_hmcode/pk_nlin, \"Mead20\"),\n",
+    "    (pk_bacco/pk_nlin, \"Aricò21\"),\n",
+    "    (pk_mt/pk_nlin, \"Moran22\"),\n",
+    "]\n",
+    "\n",
+    "# take colors from the cmasher colormap\n",
+    "# note: you can also use any matplotlib colormap here\n",
+    "# e.g. pass \"viridis\" instead of \"cmr.pride\"\n",
+    "colors = cmr.take_cmap_colors(\"cmr.pride\",\n",
+    "                              len(pk_data),\n",
+    "                              cmap_range=(0.25, 0.75))\n",
+    "\n",
+    "plt.figure(figsize=(7, 5))\n",
+    "\n",
+    "# reference line for pk_baryons / pk_nonlin = 1\n",
+    "plt.semilogx(k_grid, [1]*len(k_grid), c=\"darkgray\", lw=3)\n",
+    "\n",
+    "# loop over the pk data and colors to plot them\n",
+    "for (pk, label), color in zip(pk_data, colors):\n",
+    "    plt.semilogx(k_grid, pk, c=color, label=label, lw=4)\n",
+    "\n",
+    "x_label = r\"$k \\; \\mathrm{[Mpc^{-1}]}$\"\n",
+    "y_label = r\"$F(k)\\equiv P_\\mathrm{model}(k) \\; / \\; P_\\mathrm{nlin}(k)$\"\n",
+    "\n",
+    "plt.xlabel(x_label, fontsize=15)\n",
+    "plt.ylabel(y_label, fontsize=15)\n",
+    "\n",
+    "plt.gca().tick_params(direction='in', which='both')\n",
+    "plt.gca().yaxis.set_major_formatter(ScalarFormatter(useOffset=False))\n",
+    "\n",
+    "plt.legend(frameon=False, fontsize=14, ncol=1, loc=3,\n",
+    "           title=\"Model:\", title_fontsize=14)\n",
+    "\n",
+    "plt.show()\n"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ],
+      "image/png": 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y9dVXk5uby+rVqxkyZAjbtm3jX//6F19//TWBgYEt0V9BEARBEIRTKt2+g4qMTK9yS+pATPHxrd8hP2rUNOtdd91FWloaaWlp9O7dG41GA0BsbCyff/455513HmPHjiUzMxOADz/8kBtvvLHFOy0IgiAIglCf3EXf+SyPnTihlXvif40K5l5//fWaOWaDwUC/fv1IS0sjNTWVtLQ0nnzySR544AFiY2P5+uuvGTx4sF86LQiCIAiCUBd3ZaXPRMGaoCAixo5p9f74W6OCuSVLlrB582Y2b97Mpk2bWL9+PevWrasJ8PR6PZIk0b9/f9asWUNlZSX9+vUjJCTEH30XBEEQBEHwkv/Lr7grKrzKo8ddgMZoaIMe+Zekqqra1BfbbDbS09NrgrvNmzeze/fumrxz1UFeQkICAwYM4Ouvv26RTreFhh52KwiCIAhC29o8/a/Ytmz1Kk+d+x7BPTtOSpKGxh7NSk1iNpsZPXo0o0ePrimz2+1s27atJrjbvHkz27ZtY9Ei3ztKBEEQBEEQWkrFoUM+A7nA7t0I6pHSBj3yv2YFc74YDAYGDRrEoEGDasoURWH37t0tfStBEARBEIRa6t74cOlplVvuRK1yKJksy/Tq1as1biUIgiAIwhlKcbnI/e57r3JJrydq3Lg26FHraFQw11Kja2KUThAEQRCElla0cjXOoiKv8ohzRqOznL7r3RsVzPXp04cbb7yR7du3N+lm6enpXHfddeJoL0EQBEEQWlzO19/4LI+deGkr96R1NSqYe+SRR1i0aBH9+/cnNTWV5557jg0bNuB0On22t9vtrFmzhqeeeoq+ffuSlpbGkiVLmDlzZqM6uWLFCiZMmECnTp2QJKlBu2KXL19OWloaRqORLl268Oabb9aqnzt3LpIkeT2qqqoa1TdBEARBENpeeUYmRatWe5UbYmMIGZTWBj1qPY3aADFr1iz++te/8sQTTzBv3jweeOABJElCp9ORlJREaGgowcHB2Gw2ioqKOHToEC6XC1VVsVgs3HPPPcyYMYPIyMhGdbK8vJz+/fszdepUrrrqqlO2z8jI4JJLLmHatGl89NFHrFy5kjvvvJPIyMharzebzezZs6fWa41GY6P6JgiCIAhC28v+9DOf5bETLkWSW2WLQJtp9G7WqKgoXnrpJf773//y2WefsXjxYlauXMnevXu92sbExDBq1CjGjx/Ptdde2+RA6eKLL+biiy9ucPs333yTzp078+KLLwLQs2dPNmzYwP/+979awZwkScTExDSpT4IgCIIgtA+O4mKOfe+98UE26Im98vLW71Ara3JqEpPJxOTJk5k8eTIA+fn55OXlYbVasVgsREVFNXoErqWsXr2aCy+8sFbZuHHjeO+993A6neh0OgDKyspITEzE7XYzYMAAHn/8cQYOHFjvtW02W62vDQYDBsPpl01aEARBEDqKnK++RrE7vMqjL74YfWhoG/SodbXYuGNkZCS9e/dmxIgR9O7du80COYDc3Fyio6NrlUVHR+NyuSgoKACgR48ezJ07l2+//Zb58+djNBoZOXIk+/btq/faCQkJWCyWmsdTTz3lt/chCIIgCEL9FLud7M+/9FkXf/2kVu5N22jxpMHtxcmJAatPLasuHzZsGMOGDaupHzlyJKmpqbzyyiu8/PLLdV43Kyur1pEaYlROEARBENrOsR9/wllc7FUeNnIEAUmJbdCj1uf3YM7pdHLs2DEURalV3rlzZ7/dMyYmhtzc3FpleXl5aLVawsPDfb5GlmUGDx58ypE5s9kszmYVBEEQhHZAVVWOzP/UZ138Dde1cm/ajt+CuYyMDG655RZ+//33mlEx8PzFS5KE2+32160ZPny411mwS5cuZdCgQTXr5U6mqirp6ekiB54gCIIgdBDFa9dRcTDDqzywezdC0lLboEdtw2/B3LRp0ygqKuKDDz4gLi6uWeehlZWVsX///pqvMzIySE9PJywsjM6dOzNjxgyys7OZN28eAHfccQevvvoq9913H9OmTWP16tW89957zJ8/v+Yajz76KMOGDaN79+7YbDZefvll0tPTee2115r+pgVBEARBaDVH5i/wWR5/w3Wn7TmsvvgtmFu7di2///47AwYMaPa1NmzYwNixY2u+vu+++wCYPHkyc+fOJScnh8OHD9fUJycns2TJEv7+97/z2muv0alTJ15++eVaaUlKSkqYPn06ubm5WCwWBg4cyIoVKxgyZEiz+ysIgiAIgn+VHzhI8Zq1XuX6iHCiLji/DXrUdiT1xDnQFtS1a1e+/vrr02ba0mazYbFYsFqtYs2cIAiCILSx3bMf59j3P3iVJ//1djpP+Usb9KjlNTT28FtK5JkzZzJr1izsdru/biEIgiAIwhmobP8Bjv3wo1e5bDQSe8Xlrd+hNua3ada5c+eyefNm4uLi6NGjB3q9vlb9r7/+6q9bC4IgCIJwGst4/U3wMbEYM/4SdJYzb/bMb8FcUlISSUlJ/rq8IAiCIAhnoJLN6RStXOVVLhv0p830amP5LZh7//33/XVpQRAEQRDOQKqqkvHaGz7r4q69BkNU250+1Zb8tmZOEARBEAShJRWu+B3btu1e5drgYBL+clMb9Kh9aNGRuXPPPZevvvqKkJAQxo4dW2+OF7FmThAEQRCEhlJdLjJef8tnXcLkm9GdwZkmWjSYS0xMRKPRAIj1coIgCIIgtJjcJd9TkZnpVa6PjCTumqtbv0PtSIsGcyeukxNr5gRBEARBaAnuKjuZb7/nsy5p+q1ojIZW7lH7ItbMCYIgCILQrh2Z/ymO/Hyv8oCkRGIuubgNetS+tPiauYYSa+YEQRAEQTiV8gMHOfSe79m+5DvvQNL6LTFHh9Hia+YEQRAEQRBagupysefxJ1CdTq86c98+hI8e1Qa9AkVR2JuTQ4+4uDa5/8n8tmZOEARBEAShOQ5/9DGlu3Z7V8gyXe+5u96sGf702eo1vL50KTeMHMlt552L9vjmz7bi97FJp9PJsWPHUBSlVnnnzp39fWtBEARBEDqo8gMHOfTOHJ918ddPwty3Tyv3yGPP0aO89fPPqKrKx3/8waaMDGZfczWdwsLapD/gxw0QGRkZjB07FpPJRGJiIsnJySQnJ5OUlERycrK/bisIgiAIQgenuFzsfuw/qC6XV11AUiJJ06e1Qa+gwm5n9udf4HK7a8p2ZWcz9Y03Wb5zZ5v0Cfw4Mjdt2jSKior44IMPiIuLa7OhUEEQBEEQOpasDz+mbPce7wpZJuWRh9osFcmLS5ZwpLDQq7zS4SDIaGyDHnn4LZhbu3Ytv//+OwMGDPDXLQRBEARBOM2U7dvPoXd9T68m3Hg95j69W7lHHj9t3cr3m9N91t006mzSunRp3Q6dwG/TrFFRUTWnQQiCIAiCIJyKu8rOrpmz65heTSJp2q1t0Cs4WlTE/xYt9lnXOz6eW8aObeUe1ea3YG7mzJnMmjULu93ur1sIgiAIgnAaOfjyq1QczPCukGVSZj6EbGj96VWX283sz7+gwkc8E2gwMPOaq0/f3axz585l8+bNxMXF0aNHD/R6fa16kTRYEARBEIRqBSt+5+iXX/msS7jpBsy9e7Vyjzze+/U3dmVn+6x7YOJEOoWGtnKPvPktmEtKSiIpKclflxcEQRAE4TRhz8tnz3+e8lkXlJJC0vTbWrlHHhsOHODjP/7wWTc+dSDntVF6lJM1OphTVZXvv/+ezZs3A5CcnMy4ceMIDw+v1U4kEBYEQRAE4VRURWH3o4/jslq96mSjkZ6Pz0LW6Vq9X8dKSpj9+ReoqupV1zkignsuuaTV+1SXRgVzpaWlXHDBBaxfv75WuclkYtasWTzwwAMt2jlBEARBEE5vWR99QsmGjT7rut3/dwLa4KhQh8vFIws+w1pR4VWn02iYdc3VmE5aPtaWGhXMzZ49m3Xr1qHVahk9ejSBgYHs27eP3bt3869//QtZlrn//vv91VdBEDogV1kZth07Kdu1G0dREe7yClzl5bjKy3CXV6ANDsYysD/hI0cS2K3rGZOT0lVayr5nnqPg9z8wRETQ9d67CT97ZFt3SxBalW3HTjLffNtnXeR55xIzYXwr98jjpSVL6lwnd8cFF3BWbGwr96h+kupr/LAOXbt2JTs7m99//53BgwfXlP/4449cf/31OBwOjhw5QkhIiD/62qZsNhsWiwWr1YrZbG7r7ghCu6QqChWHDmPbtg3btu3Ytu+gIiMTGvhjxhAVRdjI4YSffTZhw4cincbpjXb8898ULFv+Z4EsM+SLTzG1k4O7BcHfXKWlbLx5KlU5OV51hpho0j6ci64NPm+/27SJ/379jc+6c3r15PFJk1rtl86Gxh6NGpnLysri/PPPrxXIAYwbN44nn3ySO++8k8WLF3PTTTc1rdeCIHQo7ooKSnfvwbZ1G9atngDOZbM1+Xr2vDxyFn5DzsJvCEo5i34vv4CuDX45dJWWUrBsBaW7d6MJDKTTFZdjjI1pseuX7dtfO5ADUBSOffd9iy70tjudFJaWcsxqI6uwgMMFBRwuKCSroIDc4+uTdBoNOo0G7fFnt6LgUhRcbjcutxu3ohASGEi0xUJMSMjxh4W4sHCSIiMIDw4+Y0ZThZajqip7/vOUz0AOWabH7JltEsjtzs7m+cXf+azrHBHBjMsvb5ff740K5lwuFzExvn+gjR/vGQrdunVr83slCGcQVVXBXQ4uG6rLBq5ycFeiKpXgrgB3JShVoCqgulFVF6huQEKS9SDrQNKDrAeNEUkbAroQJJ3nGU1gi/zwURWFsr37sG3fQenOXZTu2kVF5iFQlGZf25eyPXvZNfNR+r74HJLst5SYNdwVFRT+vpK8n36maM1aVKezpu7Yd0tIff89DFGRLXKv7M+/8Fletv9Ag16vqiqZ+fnsys7GWl6BrbKS0spKSqsqsVZUUlhaSkFpKWVVVae8lsvtpvIUbfJtNvJtNrZnZXnVBRmNJEZEkBgZSUqnWNK6dKFzRES7/MAT2o+jX3zl/QvNcYlTJxMycEDrdggoKS/n4U8X4PCRsNik1/Of6yYR2IZHdtWnxVKTJCQkAFBcXNxSlxSEDkVVFXBaUZ2F4ChEdRaDowjVWXT8uRhcJajOEnD++YyrFPBPQASAbEAyxoEpAckYh2SMRzJ1Rgo6y/PQ1v3br6qqlG7fQf6vv5H/y2/Yjx3zXz99KF67jsNz55F4yxS/3UOx28l8dw7Zn32BUkfw4ygoJOPNt+kx86Fm389ptZL3w48+63wmSz2uyuFgU0Ymq/fuZc2+feSWlDS7Ly2hrKqKHUeOsOPIEZYcz3IQERzMoK5dSOvShcFduxIeHNzGvRTak9Ldezjw0is+6ywDB/j1/3td7E4n/57/Kcd87KgFmHH55SRHRbVyrxqu0cFcQUEBeXl5RNXxptxud7M7JQhtzROYlRwPxE4MzApPej5e7ygCZzF+DcqaSrGjVhyEioP4XLlmjEMKSkEO6oFkHgDm/lQccZO39Gfyf/kVe27LBnCagAA0gYFoAwORDXrKDxz0eXRPtcx33sPcry+hg9JatB/gCeS2P/AviteuO2XbYz/8SNJtt2Ds1LyFzznfLkaxO3zWVR45wv7Dh6lC4mhxMVmFBWQVFpJVUEhmfr7PEYP2qKC0lB/St/BD+hYkSWJAYiLn9e3DOb16ERIY2NbdE9qQq6ycnQ89Umvku5ouJISej81G0votBa5PiqLwxFcL2Xb4sM/660aOYGwbnQfbUI3+G/vuu++IjY0lLCyMPn361HoIQnujKq5ao2GeAM0KzmJPgOYsPl5XVBOQqc4iT1vfoc/ppyobtSqbigMrKNhqoWCrmcq8lplKMMbHYe7TG3OfPpj79Cawezfkk35Qu8rLKVm/gcI/VlGwbDmu0tLaF1EUdj0ym0EfzUV/Uj7L5lCcTnbMeLhBgRwAbjdZH31M9wf/0eR7WktLyZy/oO4Gqsq/n3+BnNNok5WqqmzOzGRzZiYvfLeEQV26cH6/fozt3QtDG+QOE9qOqqrsfeppqo743iXaY/YjLbaUoTHe+Oknftuxw2fdwKQkbj///FbuUeM1ajfrXXfdxdatW9m6dSu2ExY5n7g2IjIykvPPP58BAwYwYMAA+vfvT2Rk6//jtDSxm7X1qYoDXGXgKkN1l4KrFNVVCi4bOEuPry+z1TzjtKG6rJ6pzuPPuMva+m20W4pLoizbSOmhAKwHA7FlBoDa9HVOkk5HcI8UzP36YunfD3PfvujDGnfMjXXLVtL/ehf4GOG3pA6k/6svtcgOV8XlYue/H6Fw+YpGvU7S6Ri68HMMDfyZVulwsCXzEOsPHGDDgQMYtm3nxvT61xV/0bc36c0c/auPJSCAzhERJISHY9Bpcbg8Gx2cbhdOtxuNLKPTaNDIng0RKioFtlJyS0o4ZrVS6fA9qtiUflyalsrlgwcTcxpmQBC8ZX/xFfuffc5nXcJfbqLL3/7ayj2CL9eu5cXvlvisiwkJ4a3p0wgLCmrlXv2pobFHo4K5E2VkZLBly5aaR3p6OpmZmX9e+IQALyYmhoEDB7J48eKm3Kpd8Gcw9/PWbWg1GiKCg4kwBxMWFIS+lYeZm0JVVc/CfHcVKJWo1Qv1jy/aV92VnoX9bs9CftVdcfzr6j9XgKsc1V12fNF/Oaq7vCaAQ22ZD40zkeIG1SWjOCVcVRocpVqcZVqcpVocpVrKso2UZZtQXU3fWGAMdxGUHERwj66Y+48kOG08msCIZvc968OPOfjq6z7rOk+dTPId05t1fdXlYtesR8n/ue7zoU3x0chSLuVZ3sFt/A3X0fWeu2uVudxucktKyC4q4khREUcKCzmQe4ztWVk4TwhMb1m/kS5F9a8rXpGUyNKU7o18V6DVaDCbTAQbjYQGBRERHEx4cPVzMLEhoXSOCMccENDoa1dTVRVbZSXZRUUcyi/gUH4+hwryycwvILuoyGem/FORJYmRPVK4auhQUpOTxcaJ05R123a23PE3n0sqzP360v+NV71G7f3t9127eejTT31+3wYZjbwx7TaS2ngwyu/BnC+lpaW1grstW7awY8cOKisrkSSpQ6+n82cwN/HpZyguL69VZgkIICI4kKjgQCKCjIQGGAgNNBJqMmAJ0GEx6gk2aAg2aDHqJCTVBaoLFCcoDs+OR8XhCYiOl6E6URX78a/tnrLjD7Xm66rjz/bjZVU15ar7+NfuyuOvP/VOOeEEkg50oTW7TCVdKOgsoDV7NiFozUjaYNAGgSYASWMCTYDnIetB0iJJGlQ0OIqsVB4+SsXhLCoOHaEy6yiVWbk4SkpRHE5QWn6KWGNwE9qjlLBepZgTK9CaTl4fKCMF90IKGYIcOhQ5ZChSQOdG30dVFHY8+C8Kf1/pXSlJ9H76SSLOGd2k96C63ex+7D/k/bDUZ70cHETkHUPoHPg2jkKJLa928RqtdGo0vD/+ImxaLQ6XC4fLhasBP9uiS8u4e9WaU7bbHRnBR6kD6m0TGhjI2QnxDNYb6ZpyFpEpZ2HU6do0ELJWVLApI4ONBw6y8eBBjhQVNfoaveLjmTLmHIZ17y6CutOIo7CQjZNvwZFf4FWnNZtJ+2guxujoVu3T1kOHuW/ePOy+1u5pNDw/+S8MaAfny7dJMOeLoijs3buXLVu2MGnSpCZdY8WKFTz77LNs3LiRnJwcFi5cyOWXX17va5YvX859993Hjh076NSpEw8++CB33HFHrTZffvkljzzyCAcOHKBr16488cQTXHHFFT6v569grqpkNyU/n4dGUtBIKjIKcvWfxc+y9kkFFR2K1gJaC6omyPOsM6NqzJ5nOQhUI4qqR3VrUBUNKDJoNCBra54lufYzsgZkDaok47JWYM8rxnmsGMexQuy5+Thy8rEfzUO121vlrcp6hbAepYT1thHStRxZ17gfF6opGSXsHJTwc1FDBiPJBjzf1p4RQc/n9fE/IyFJIEkyLlsp2265w+fmC1mvp+9rL2Hp27dRH/ieQO6JOneSVmk1lE2ES/tvrynb93knCrdbvNou65LEz927NfjeAJft2MXgOtYKnajYaOS5c86u+dqg0xEfFkZCRDhdo6MZ1r07UdlH2fXwLM9ZlpJE/PWTvEYL21pOcTHLd+7i1+3b68ykX5cenToxZcwYRqScJYK6Dk5xudj6t//Dmr7FZ32f555p9ZNPdmRlcd+8D6mo4+forKuv5vx+fVu1T3VpN8FcS/j+++9ZuXIlqampXHXVVacM5jIyMujTpw/Tpk3j9ttvZ+XKldx5553Mnz+fq666CoDVq1czatQoHn/8ca644goWLlzIzJkz+eOPPxg6dKjXNf0VzB3c/hmm98URaGcaVQXFLeGq0uK0a3FWaXHaNZ7n4w9V8X9uNZ8klcA4O2HdrYQml6I1KUhaFUmL51kDyCqSDMiegExxglIu466QcVdIKBUy6kmzKSoSLlmHQ2vEqTWiyhKqLIEsocryCV/LKFoZR4GM7XPV9wbhQAn5vkiI0QFSra0q6vGRNPWEr1W3ijSvEHmT9zmLAKpBInFKIZ061Q4eK44Z2Pp6F6/2ikHmyP1dcBq0KIqEWwW3IuFyS7hUCbdbwqVION0SDpeMWuHm3A+2oXU37Mdt+fNPExIWQXx4GBHBwcgnBDSq283WybdQlXWk1mt6vPA/LKkDG3T91na0uJhlO3fx644dHCrwHp2pS7foaG4dO4ZBXbz/DYSO4dCrr5P7+Zc+6+L+chPxt05t1f7sycnhwU/m1xnI3Tp2DNcNH97o6xoMBr/84nFaBXMnkiTplMHcP//5T7799lt27dpVU3bHHXewZcsWVq9eDcCkSZOw2Wx8//33NW0uuugiQkNDmT9/vtc1/RXMbd3wCZHz/9li1xPaTnWA5nZqcLtk3A4NLqfs+fr4n10ODW67FpdDUxN0tAeSpGIIthMUVok5sgKtoRFLIiS1WRsn6lN8NIj8g2E+63RGJ3Gp+cgBEqpOg6LToGg1nj9rZRSdBlWrwS1rsP0q49hfx0300O3mo0R09p1fas/8eIp3e+dJkyea0VzcsPxp7p9KUb7yPhlDY3LjrvTe0KF5IBKSTKiKDkXRoipaVFWLouhQjzjQvei9icLRJ4myCeegKHrcbgOo7e8oNFVVOVpRwdbCIg7YbA3eL945KJAR0TFEmtpnwlbBN+OuPViW/OCzzp6USMkVE6EVEoJXy6+sZGFGJvY6Ep33DQvlnNjYJgVlEydOxOiHhMJ+Oc6ro1i9ejUXXnhhrbJx48bx3nvv4XQ60el0rF69mr///e9ebV588cV6r2076agig8GAwWBocl+tVQ5ce8Jxu2R0Rhd6owud0YXO5ERndLXm97lwElUFxSV7AjKHBtfxoMztlD1/rg7UnDJul4zikoH2E6DVR9YoGIPtmMx2TBY7xiAHsqaJv9f5MSgNiS3DUaHDmusdNDmrdORsDSehbx6yxnvdC3gOzcjZE4Gj0Peif0lWiDsrH3mvk+IM0/HDNFRkvYqkU5H0KpEpxT6DOeWXMuSxgUjG+v+TqqqK8nu5z7qEc/PJ/M7HqTo5TuQuepDdnBySuY+V+xys1O8/RFTsT0g6z7+HomhRXAbcbgOK24jbbcTtMuJ2m1BcRtwuE26XCUXR01rft5IkERcYSFxgIGVOJ9uLitleVETlKdYcHi4r53DZAXqEhDAsKopgvUhp0t5p8/IxL/3ZZ53LYsZ6yUWtGsgVVFXxdeahOgO5bmYzo5sYyLUHp2Uwl5ubS/RJiymjo6NxuVwUFBQQGxtbZ5vc3Nx6r1190kW1WbNmMXv27Cb3tVtMHAetBlwOX/8UKlq9G41OQdYoyBoVSaMgyyqSRvU8Vz8kFY1WQdYqNc81f9aodNDvzxZTHZhVB11ul1wThFWPpCknBmnHy/0ZqLQkjc6NvvoXAM0J3xeyiqxR0OoUNHo3Wr0brc6NrO0YA/KSBFFdi3E5NJQXeQdk9jID2TsjiepSjCGwdkBnr9CSnxFKRbHJ97Vlhbhe+QSE2EGVUB0SbgfgI+4KCKmkouSk65QrhL62A0tcOSCBBKrkeT6++A9VlrBXaMnO997lGxhZiUnn+yAtNdsB+E6uqx6sY71klYq6swqpv6efsuxC1rvQ+npDJ1AUDW5nIC5XAG5XAC5nIC5nUM3DXyN8QTodw6KjGBQZwY7iYjbmF1B+iqTIu0tK2Ge1khYRQVpkBFrx2267JJeVEbLwWyQf/56qVoN1wnjUVhxlza+s4pvMTKrq+KWhS3AwFybE11rO0NGclsEc4BVdV88mn1juq82povKsrKxaQ53NGZUDkFVdHYEcgITLocXV3AwdxwM9jc6NRqtUf+7U1AFodJ567fEPfI1OQZJUz3ojlZqg5sQAoTqolKTqgNIzXYdEs4JHVQVVkVAVCcUtodT8Wa4pq/765D8rbk+wduKf3ce/7iijZnWRNQo6k9MzemvyjODqTU70AU40HSQ4awpJgtiUQo5s11BV6v3/rdJq5NDmWALDKghLsKEqEsVHzJTXEcTB8UCudz4BloZtJAlLsHkHc0BFoYnQmDLqSzBdelTvszw4rBx3gQaNzu35xeEExs2FxMr7cQYbcQUZcQYff5hNOA/W/QNB2ViJ3L/u9+2LLLuRDTZ0Bu9pYFUFt8uEyxmM02HG6bDgcphxOsyoSsuMjmllmf7h4fQODWVncQkb8/Mpqyeoc6sq6/Lz2VVSwtkxMXQ1B3fY0ZTTktNJyMJFaMp85/i0nX8erujWOxYru7ycxYcO46hjRC4pOIiLEuLRdPDvodMymIuJifEaYcvLy0Or1RJ+PIN8XW1OHq07mdlsbtE1c3JFK/x2oh5fx+VsvTU0KvwZO538fApSx81g02yKUcIdJKMEy6jBMkqQhBokQZBnZ6mMCVlRkFUFSVVRJRlFklAkCVWSkVDRKAoaxX38oaBzu9C6XehOeGgVNxq3G9lPp1w4NRoq9Z7vbQ1udJILPS5kFM83h0LNLwmqQk1ZXd8kskalU698srZE46zyHUSUFwX4HL3zvpZCXO88TOaG/5YUYLFjCLJjL6sdTFbYDKgKns0gdfA9MqgSGOJJ7aMPcFJprf1/016hQ2N3obGXQcGfH4ouh8zB/Pg676VurUJ1qEj6lvlgkiTQ6irR6ioxBuTVqtPrIjAZEwkwJnkepkR02pBm3e9KwOFysSQ9nY//WElJhe8NKwClTiffZ2WRmpTEnRdeQGJE83McCs2jut3smzmb4rw8n/VRl09k6N/vabX+rNyzlze//hpnHYHc4C5dmH31VS2S17W5AzvN1ah30L17dy677DIuvfRSRo0ahaYFMrH7w/Dhw1m0aFGtsqVLlzJo0CB0x4+PGT58OD/99FOtdXNLly5lxIgRrdpXyep7rU9HJ8EJ2wnbsCPtSLlOR6nBgM1goNSor/lziclEsclIidGEU1vH/6lyfE7/NZrm+KP6S1VBjwuD6kKL4kmNg4oGFRkVnerGhIMA1YlJdRCA83hbNzrc6FQFHW6caMiRzeTIFo5KFoolk9fwrIzCyPCDXBWbTh9zjlfXPMGd5+F2SawsGcqa0mEYVTcBqgMTTgKjS4n+cReyvWkRf1MCuWqBoVVewZzqlqkq09d5PUWBCqv3D3ljkAONzvMBYwhwUmmt/Uud26HF7ZRr2lTzNTJZi13F+HMuyggLTov3v0FLcjgLcDgLsJZurCnT68IJDuxOcFAKwYFnEWhKRpYb90FpBK47+2wmDBrEJ3+sZMHq1T5zgVXblJnJ7e++x6QRw5kyZgwmve+RUMH/Drz0KsV/rPJZFzIojZR/3IfcSke4Ld64kWe/XYRSxx7PwV278tQN1582R8o1ajdr9+7dOXDgAJIkYbFYuOSSS5gwYQIXXXQRFot3LqaWUlZWxv79nq1oAwcO5Pnnn2fs2LGEhYXRuXNnZsyYQXZ2NvPmzQP+TE1y++23M23aNFavXs0dd9xRKzXJqlWrGD16NE888QSXXXYZ33zzDQ8//HCrpyYpWrOWw3PnUXkkG0d+fotdV/AvlyRRbtBTptdTrtdTqdNRodNSqdPVPMr0nvoyg54KnQ6lA6zvqT7KSafVotNoMGi16HU6DFotBp0OvVaLQavFqNdj1Okw6nQYjj8b9TpMej0GnQ6T7viz/oR6na7mWvqKrUiHXkXN+67+/nSejqbnU7Wm0Ww7d7H9vgdwFtd/ksLJtMGB9PzXHQTGh6FUleLc8SiKLQvVBapTQnFKqE4JVZOEWlWBUl7s2UFxXEWJgSPbvUfuwzuXEN7Ze4qyvteEJViJSPTsni3JDSRvv/eZs/F9j3lNA+dnWig+Uv/P2uCIcmJ7FGI3GLBFhVIeE4K9kxltoKbVc1fKko6gwG6Yg3pjCe5DcGB3ZLlxH555VitzfvuNJZvTT3nCRLTFwj2XXMKonj2a022hCY5+9TX7nn7WZ11AUiID3nkTXSschamqKh///gdv/ex78wV4Arknr78OYwcI/P2WmmTPnj188803LFq0iDVr1qAoClqtlrPPPpuJEydy6aWX0rVr12a/gRMtW7aMsWPHepVPnjyZuXPnMmXKFDIzM1m2bFlN3fLly/n73/9ekzT4n//8p1fS4C+++IKHH36YgwcP1iQNvvLKK332oTXOZnVXVVF19CiVWdlUHjlCVXY2VUdzcFVUoFRW4q6oxF3leVbsdtQOfKJGuyNJaM1mtCEWNGYzssWCxhyMHBKCbLEgWSxIIRYwm5EsZlSj0TMzqKgoquLZYKEqKKqKqqq4Fc8Qk+fr4zsaVRUVT72qgurJPuxFRUXyNd0onZBY9/jCR40kI0kSsiTVPMuyhCzJaI4/y7KEVqNBI8toZBnt8WedRoNG4zl/U3e8XtvKo+3unIW4dt4PzpI622i6/RNtt9rpexyFRWR99AlHv1qIUlX/SSSyQU/MpePpPHVyzZmqijUd5+pzvdpKwX3QjViOJEmoioJSUUTVb2NQbbm4yjVsndsF1V07KA+MN9NtUt/jiz3doKqoihsUNzmrssnf5P1LWudBOZjCAlCryqgs0ZC11XtHa1TXIkJia687ytoaRaWt/qUZkqzQdWi21+7kYwGhHAqLIzsylkpzICadm2C9SnyomVCTjMtVjMNZjD+H0mVJT3BQCpbgvoSaBxJg6tzg9W57c3J4YfF3bM/KOmXbkSkp3Dv+EnHmayspWL6CHTMe9nmmsi4khIFz3sYUF+f3fjhcLp5btJglmzfX2ebcPn14+Mor0HWAIzOhlfLMFRYW8t133/Htt9/y008/UVpaiiRJpKSkMHHiRCZMmMCIESNOi8WprRHMNZbqcqE4nSgOB+6qKlylZbhKS/982EpxWq04rVZcVhvOkhJcpaWoSvWCJU+QobpcOIs9dR2ZpNOhDQxEExiAJiDg+J8D0QYHozMHow0+/jAHo7NY0Fos6Cxmz5+DglrkAHeh8dSqozi3/Q21cHmdbTQ9nkKbdLtXubOkhCOffkb2Z1/gPulIPF1oCJ2uvopOV12BPjS09uu234Ny5EOv62l7PY+m85RaZa69T+A+6DkcfNe8BKwHah+6LWk0jPz5BzQ+zjzdOPkWynbvqf1eDG7SZloxnb8LRXFT8U0yG/+b4PXasN4hxPSswlVwCFx2VAX2r4lvUDLp2B75BEf43ikLYDUEsT8kngOhnckOikSr0TG4yyBGpwzHpHVQZc+l0p7jea46SmXVEZwu33n4mkOvCyPEPIBQSyohwX3RaOrfvKGqKj9u2cobS5dSVMcC+2oGnY6pY85h0ogRrf5LypmkcOUqdjw4w+eZq5JeT//XXsbSCqcpFNhsPPTpAnYeOVJnmyuHDuGeiy9G7gCzJNVaPWmw0+nk119/ZdGiRSxevJjDhw8jSRJhYWGMHz+eCRMmMG7cOIKCgk59sXaoPQZzLc1dZcdZVIS9sBBnSYknCJdlJI3G85AkT+BYWYm7qgqlqgp3lR3V6URxOlFdrpoA8/hw1PHAEVTV9wLU40NUf/4ZkI0GZKMRjdHoeTYYkE0nfG0yoTEa0JgCPH8OMCGbTK1+SLPQclRVwX3oLdx7HvWcJ+yDtt+baDpd67POVVrK0S8XUrhyNRqTkYgx5xB9ycVojN5rzFSnDceyXuA+aXG9Jgj92J1I2to/o5SyfTj/8Cy9OPpHGId/8p427fPC/wgfUTtrvKO4mNUXT6j5vq4W2qOUnv/XC13ap552ay5i4yNFOGy1px8tqQMZ8MarnhFC2zFK1ixj+6xXfb7/kwVFlNOpR2GD2tr0gewOT2JXeDIlAWGkJaVyYd/zsQTUns51Om1UVB2hoiqLispDlFdkUF55GFVtmXW/kqQlJLgfYSFDCAsZhE5b98/Z8qoq5i5fzuer1+CuY3F7tS5RUdw/YQL9Eht/TrBQv+J169l2/4OoDt//Z3s+NpuocRf4vR87so7w0KefUljPgMSt545l8jnndLjBpTY/AWLr1q188803LF68mA0bNqCqKnq9nldeeYVp06b545Z+dSYEc4LQ1tzHFuPaPAWfZ3hJGrQDP0ITNa559zj0Nq5d//Iqlzvfiq6X7zU/jtXno1o3UX7UyLa3kr3q42+4zuts1Lwff2LXzNlebZPG5xJ3/W1ou88AwLnzAXY8sRTr/tpBpNYSzIgfv6/58Dny6WcceOEl7875OIGjrqlW8MSW1pwgKm0G9AFOLDFlaPWev+9jAaHsCu/C/pgejB04geHdh6GR6x7VUlQXlZXZlFdmUFa+n9LyvZRXHqL5U7US5qCehIcMJTx0OHpdiM9WGXl5PLdoMVsOHTrlFS9NS+WvF1yA2ccIqtB4JZvT2XbPfSh1HIuVNP02ElvhqK4lmzfzv28X4axj2ZEkSdx36XguHzzY733xhzY/AaJfv37069ePRx55hNzcXBYtWsSiRYuwWlt+qF4QhNODJvpS6PMSru0+Do1X3bjSpyIN+QY5pGk/mFVVxX14ju97J9T9wSPHXoPbuomAmCq0Jheuyto/OovXb/B6TdHadT6vFdK1DMnc/89rB/chIGqRVzDnspbiLCpGH+45zsy2bbv3xSSV6EHFHFtf+8gzVZGpdCYQqDlcu1yFY/vCsOX9ea+yIhMJ/Y4hyxBdUUx0xUZGZqezK3Md87qP4vwxt5EQ7j0NDCBLWgIDEgkMSCQqfAwAbnclZRUHsJXtwVa2i9Ky3Sh1jLbWTcVWthNb2U4yjszFEtyHiNCRhIcMRav9M5lyclQUr9wylR/St/Dajz9irSeVyeKNm/hj9x7uvPBCLhrQv8ON0LQn1m3b2X7fP+oM5OKuvYbOt0zxax/Kq6p4/rvvWLrF+2i7agEGAzOvuoqRPVL82pf2oMOdzdpWxMicILQeV8ZruPc84rtSH4V+xC9IxsYvqFaKVuFcd6lXuRQ6DP3QJXW+TrXne6ZmVTd7F8RRtNP7Z8Dw7xejD/OszVNVlTUTLseRX/tQeUOogwH3HMAwdltN/xXrJrJfv4qDX3fyuma/V18idPAgANZMvBL7sWO16gNiquh6+VG2vel9EH3EmHNIefB27HtWYN/5C/Z9f2DL1pK7z3vnrK/NFtUOm2OoHHAZQy+6D5Oh8aNaiuKkrGI/1tIdWEu3UVq2F5Wmbd6SJC2h5lSiwscQYhmALP0ZVNsqKnjr55/5dsPGeq7g0T8xkfsuHU+XU+QVFbwVr1vPjn895LVGtVrsFZfR/Z8P+DVY3nb4MI9/+RU59exojw8L46kbbyDp+KanjqqhsUfHWQUoCMIZQ5v8NzRd7vNd6cjDuekm1JPXvDWAO/MNn+X1jcoBSIZIpJBhAFi6+P4QK9n4ZxBRcTDDK5ADsHQtRzJEgOHPwE0K6klAlO91Z+UHMwCwH8vzCuQAguIrCYixYwz3HiEpWr0aVRtK4IibCbttLiF/W0r+Ee+AEaDkaPDJS/tqdLblkrLiLTKeGEHOqo88O3UbQZZ1mIN6khB7NX3OepTB/d8jpcs/iA4/D70u7NQXOIGquiiyrmP3wWfYsO0OMo7MpbwiEwBzQAAPTJzI67fdesogbcuhQ9zyxpu89sOPVNQxuiR4y/l2Mdvuvb/OQC56/CV0f/AffgvkXG437//2G3fPeb/eQG5It268ffv0Dh/INYYI5gRBaJc03R9CriPIUm1bcG27+5R5x06kFPyK4iuvnS4cOWbiKV8vh58N1B3MnTjVWucUa7dyJHPtKT5JYyIgyfc0ZsWBgwDYtvuYYgWCO1cgSRDe23vht2J3sO2+f+AqL0dVVfY9+zLuSt+Bi6NSR6WP5MYnCi0vhC9ncOTJs6ncuBDVXf85qnXRagIIDxlC18TbSevzBv17PEtC7LUEmpIadR2Xy0ZO3hK27H6QLbseJDd/KS53BX07d+a9O27nzgsvxFhPQli3ovDpqlXc9Mqr/Lx1W6O+l840qqKQ8cZb7H3iqTpTYkVecD4pD/0LyU87RQ8cO8Zd781hzm/L6t30cv3IkTxz040Emxp3rF1HJ4I5QRDaJUmS0PZ6BjnS9244JXdhTcqQU1EVO66d//RZp4m/CUk+9VE8ctgoAAxhTvQW75G0kvV/jswV+wrmZBVzUjmyeYBXlTaiL4ZQ73Vl5QcPAGDdus1nn4LjPelHwnr7Tlpcun0H2+97gCMfz6d43XqfbarZbIn11tf0tfgIJZ/8H/nPnEvl5m+bFQRJkkRgQCIJsVfTv+czpPZ+laS4vxAc2Lg1TuWVmRzMepcN225n/6HXqaw8wHUjR/Dh3XcxMqX+a+XbbDz6xRf87b057Dl6tMnv5XSl2O3smvkoh+fOq7NNxJhz6DH7Eb+kd6p0OHhj6VJufeNNdtSTdiTIaOTRa6/hznEXoulAqUdaisjlIAhCuyVJGrT938G5+kLU8r1e9e59TyIFpqCJmVDvddwZr6BWHPCu0FrQJN3ZsL6EpIFsRFKqsHQpJ39zSK36qqNHqczOxhARgdVH0tKguEq0JgXJ3M/72uY+mCLXYC+unZG+/MABVFX1uflBG+jCEOYJKgOi7ZiTy7FlBHq1s6ZvwZq+5ZTvz3bEQfdHPsW5+1sqNnyB7K5/04K7IIOSj/6GbvnbBE94CEPX4fW2bwijIYpO0ZfSKfpS7I4CCopXUVD0B+WVmQ16vaLYyStcRl7hMgKMCURHXsh/rrucNfsO8+J3SzhWzwa8bYcPM+2ttxmfOpDp551H6AlptFSXC0dhEfa8POx5+djz8nBarZ6UTMfTMnlSMoE2OAit2YzObPbktAwNxZQQjyEqqsNtuqg4dIjdj/6H0h0762wTNe4CUh55yC+poVbt2csL331HbklJve36JybyyFVXEn0GJ4kWwZwgCO2apDWjS/0Ex5rzfZ4U4dr2VyRtMHLEGJ+vVysO4T7wvM867VmPIBkatq5Gkg1IoUNRC5f7DObAMzpniI1BsXsHQiHdPNOzsmWAV50c3JeAKDsle4NrlbsrqqjMOkLZHu9ANji+EknWgD4KyZ5D18uPsmNOEg5rE8+aVBTy/thGlzufxnLpv8hf8T6lv79HYJXvUb9qzqwtHHv+etwhgzCfP4XQURe1yAe7QR9BXPRE4qInUlGVTUHRH+QXrcDuaNixhxVVWWRkvceh7I+JDRvN29Mu4/O1GXy6ahWuuk7PURRW/f4H2T/9wvkhoSQ7HFQeOEjl0aOeg3abQRMQQEByEgFJSQQmJxHcpzfmXj2R2/iAdl9Ut5sjn35G5ltv+/xertb5likkTb+txYPUzPx83vrpZ/7YvbvedhpZ5paxY7hx1KgzcjTuRGI3awOJ3ayC0LaUwhU4N1zlOTLrZJIWbd/X0HS6xqvKufF6lPwfvV9iHohu+FIkqeFTQ64D/8O970kcZRo2PXuWV70ldSCSRkOJj1QlvW/LILiLCf25+70+/FR7PkdfGsz+L7136AalnOUzmEs4P4/48Z2RQ4fgPvQWAFWFOna8n4iztAEBnSx7BSi6kBCGfftVTYDhdFSyZuFsIrZ8S4jde7erqkDBoRCKs4Ph+BF0sl5D6LBhhI8aRdiwYRiiWm4Ruqoq2Mp2kle4jMKStShK4zYvBAf2QGscwVu/HmVjxiGC7HbirVbirTYSSmzE2awYXa13TKKk02Hu3QvLgP5YBg7A0r8fmjZe61Vx6BB7/vMUtjqm9sFz6slZ//4nMZeOb9F7e87hXcb3mzejnCI0iQsL45GrrqJ3QnyL9qG9adWkwU6nkz179pCfn4/VasVisRAZGUlKSgq6ehagdiQimBOEtuc+/B6unQ/UWa85azaa5LtrgiV33ve4Nt3oo6WEbvjPyJaBjbq/UrwG59pLANjyWjKVefWfk1rTL6ObQQ/uRY4ag37wVz7bFH3cj20vB/us86XXLZmEjp6CHD2+VrqVynw9O99PxFle9+hY7JWX47LZyP/5V6+6lFkPE3PJxShOJ/m//oZt23byjW4O2jcwsGg7YcdH6px2DTm7I6gqrX9kKbhnDzpdfSVRF16A3IIHm7vdlRSUrCGv4BdKfUzBn0xVVShwo+6zw35w73VCoe/NLG1F0umw9O9H6LChhA0bSmC3rq02NesoLib7sy848vEn9Y7GaYKC6P30k4QOSmuxe9sqKvjo9z/4cu1aHD6OBat1f1nmupEjmHLOORhb8PupvfJ70uD8/Hzmzp3Ld999x7p167D72N5tNBoZMmQI48ePZ/LkyUSeQduEBUFoeZrOt6KU7kTJet9nvXvvbNTKQ8jBvVDLD+DO/dpnOzlhSqMDOQDJkgqaAHBXYOlS3uBgztKlHEmDz80P1QK79kDWZ6E4Tj1dJMkqQZ2qkMKGI4UOBX0EODypUEyRDnpOPszOuUm4KryvZYzrRNe7/0bpnr0+g7mjn32BrNOR8ebbVB3JrilP0GjY070n9gQXvfIOULojELfr1KOapbt2s+fxJzn42pvEXX0lsVde7nVWblNoNCaiw8cSHT6W8srD5BX8Ql7RCtzuPwM0tVxB3VmFsqMKdY8dSpo3VepvqtNJyYaNlGzYSMarr6OPCCd0yGBChw4hdPDgmgTSLakyO5sjH39K7uLF9QZxAKaEBHo/+18Ck5Na5N45xcV8tno1323aTGUdR4KdqG/nzvxjwqUiP6APjR6Z27dvHzNnzmThwoU4jv/lR0REkJKSQlhYGGazGavVSnFxMbt376aw0HM+oF6v58orr+Sxxx6jW7duLf9O/EyMzAlC+6AqLlzb70Y5uqBpF9CFox+1DknftIDCsf4q1MLfKN4TxJ5PfKcUOVn3a48Q3rsU7YA5aGIu99nGtfc/HHzzI3JWeif1PVlgXCV9p2eiH7sHyRCJc/v/oRz5qFab8hwDuz7qhaus6s9CSaL/G68SMnAAqqqy8aYplO/f36D30FJkg57I888n8tyxhA5Oa9E1Yy5XFUc3fU3uiu+o2nQE9aCj+SeLNYCk0yHrtKgqKJWVfrtPYLduhA4ZjKV/X4J79kQfFdmkkTtHYRElGzdRsGw5+b8tO/V6QEki7tqrSb7zDjTGhv0CU589R48yf+VKlu3YecqzdQHMJhN/vfACLhk4EPkMWxvnl5G5u+++m7fffhu3283YsWO54YYbGDNmDMnJ3mcVVjt48CC//fYbn3zyCZ999hlffvkl06dP55VXXmnMrQVBEACQZC3avq/jNsTiznix0a/XpsxuciAHIIePwl34G+akCpBVUOr/MI3oZyWslycPXH0jc5K5D53Pz0NrcnP093Dc9rpHvYLjK5ECu9ds3pCjJ3gFc4GxdnreksfBxf0oP5iJLiSEbvf/HUvveJzb/w+1eC3RQ8I52LqxHIrdwbHvlnDsuyXIJhNhQ4cQcc5oQtJSGx2cqIpC+cEMSjZuomTjJqybN+Oy1X3YemM5ZJljwUEcCwqi2GTCZjRgNRqxGQ2UGgx0io7m6hHDubB//5opP8XpxGWz4bTacFqt2I8doyIjk/KMTCoyMqg8kt3kzRTl+/dTvn8/Rz6ZD4AuLIzgnj0ISjkLQ1QU+rAw9GGh6MLD0BhNuMpKcVltOEtLcZVYKd21i+ING6k4noy6IYzxcaQ8/G9CBg5oUp+rVdjt/LZjJ4s3bmR7VlaDXqPTaLhy6BBuGjWKkEDvndrCnxo1MhcQEMD06dN58MEH6dTJdybx+mRnZ/PMM8/w7rvvUl5HBun2SozMCUL74z70Dq5d/6Khwy9SyBB0Q5cgSU3/7V4pWY9zzTgADnwd63NXq8YkEd6ziIj+VsxJx0dqtBb05x2sM1hRyg/g/N1z5qyzQkPOyjBy14ahOL372nPKIUJHXYWuz0uAJ4+e47fe4CzyaitFTYAuL6MLCgTXMZzrLkOt8CQjdjskNj13Fu6qFhjtCFAJDS2lvNiIo6Jpa5k0gYEEJCUSmJyMKSkRbUAAyDKSLCHJGhSng8oj2VQeyabqyBEqj2TXeT5ooxklpEQ9UpIOqbMeOmnZ5Q5hdWYY+/MDUak7yDSbTExIS+OKoUOItljqvY1it1O6azcl6elYN2/BtnUb7nrOlG1LcddeQ/Kdtzd5U4aqquw8ks3iTRv5Zdv2Bk2lgif/4Lj+/bn13LHEnMHpRsBPGyByc3OJiYlpduda6jqtSQRzgtA+uXO/xbX1djjVzkZdCLqhS5CDejTrfqrixPFLV3CX4SzXkLkkmsIdZjQGibCzzyXywgsIqpqKrNY+bkgKG41+yNd1X1dVcCzrC/acmjJHmYajv4dzbEMoqssTcEUPLiZpfC66fq+jibuupq378BxcO//h89ravq8jh52NY91EOCln26EfoshZfeqp3frkdgtnywVn0bkylzGHNxBQWkV5kYmSnKAmB3Z+F6VB6m5A7qJHStZDtBZJ9h2w5ZfqWZ0RysYsC1XOukdMNbLM2T1SuGjAAIZ17462AUl0VZeLsn37KFq7juI167Bt3VbnKQutJXzUSDpPmYy5T+9Gv1ZRFHZlZ7Ns506W79xV77FbvoxMSWH6+eeJdXHHtcpu1rVr1zJ06NCmvrxDEcGcILRfStFqnFtuAftJ55fqI5ACuiJbBiAn3IIc1L1F7ufccC1Kwc81X6sKSBot+vMywFGIY8UAr9doku9Gm/Jovdd153yJa8t0Th5pdFXKlGWbMFicmCI9oxv60ZuQApL+7IOq4tx4LWrBL94X1gSBPgwqD3tVVRXqSH+lK6jegYwxNpbOt0ym8kg2ud8uxnnyB7NGJufCAWxKCYTjI45at4shOdsZlLsTWVGosBooyTZTXty2KTeMcZ0IHTqEkIEDMfaOp4gN5BX8isvtnXKlLg6XxJZsM2syQjlSUv/7sQQEcEG/vlw0YABnxcY2ePrYVVZOycZNFK9dS9HadbU2ofiTpNUSfdE44m+8nsAudS+d8qXS4WBL5iHW7t/Hip27yLPVn5vwZBpZ5oJ+fblu5Ei6iiCullYJ5uLj49m4cSPRZ8BfvgjmBKF9U11lKIXLQLEjBXRBCuiKpPPP/1VXxsu498z2KtcN+gLVVY4rfbJXnbb/O2hirzrltd3HFuPacjso9SykN8SiH7PdO19dVS6OlWf7nG6tz5FlERz57c9sA1qLhcSpk+l01RU16UQUp5OCZcvJXbyEioxMArt2ofMtU9CelcwrS1+nqLz2PSMqirno4EoiK0sAcFRoKckJprQgAHc9o1stRdJqsaQOIHzECMJGDseUkOD19+VWHBQU/UFu/g8NPmWi2pFiI2syQ9hyxILDXf80dXxYGCNSUhiRchb9ExMbNGJXrfLoUYrXrqd47TpKNmzEVdpyawIBDFFRRF54PvGTrm1wTkCX283enBw2HDjIhgMH2J6VhbMJo4kmvZ7LBg3i6uHDTjk9faZqlWAuMDCQgQMHsmzZMrSnyPg9Z84cbrnllqbeqs2JYE4QhGqKdTPO1ed5lWuS7wVJxn3Q+8QJ3aj1yIFdG3j9dJybbgB7rs96OeYKdAPe81nnzl3kM5isj6qAteQKSvP6E9glmchzx6I94TirU8m1HuO1n97A7qo91a1R3Jydu4PUo9s9NwFUFarK9JQXmigrMrXcNKwkEZRyFiFpqYSkpWIZ0B9tAxfNq6pKafkecvJ/oKh4LSoND0zsTpn0I2bWHgohu8QI9aytA88ZokO6dWNQ1y7069yZzhERDR61UxWFyqwsSnft9jx27qZs716UqqpTv/g4XWgoIYPSCB2URkhaKsb4uHrvr6oq+TYbO49ks+NIFjuzjrD76NFT5oOrT/eYGManpXJhv34Et3GS5PauVYK5jz76iL/85S9Mnz6dN998s852999/Py+++CLuNl4H0BwimBMEoZqquHD82hVcJ42S6CNBdXofO6YJQn9+ZqM2XqhV2Tg33oBa6p2JX9v7RTQJf6nztc5tf0PJnt/ge3kuakE/dheSpmmpJ3Zm7+KD3z9E9bEZpafbwSWH1qAUek/zOiq1VJXqcVSZUMx9sFudVGbXf3yWNjgYU0I8pvh4jPFxBPfogWVgf3Qt8LPZ4SzmWMEv5Bb8hNPZuPVe2SUG1h8KIf2IhcoGjj5aAgLo2zmBPgmd6R4bQ1JkJJFmc8MDPLcbZ0kJjsIiHEVFOIuKcRQVodjtaM3BaM1mtMHB6MxmdGGhGOuY8lUUhYLSUrKLisnMz+PgsTwy8vI4mJdHaQukWwkyGjm/b18uTUslpQkbKM9UrXYCxP/93//x2muv8eabbzJt2rRadeXl5Vx//fUsXryYvn37smXLqQ97bq9EMCcIwonqOibMFyl0JPqhixp9D9VVhmvr7Sh53/95rYAu6M5eiSTXnZ9NddlwrBztc42c5yJaUL1HVrT930MTe0Wj+1lt2a7lLNnyg8+6s5MGMvrQOirX1Z8f0DT4GoIvnYXDWobqdqO6FVAVzzOeaUGdxf8/gxXVRVHJenLzf8RWVvdB87643BI7c4PYcDiEfXmBKD7WI9Yn0GAgKTKSxMhIoi0WIszBhAcHE2k2ExYURIBej1GnO2XONVVVcbhcVNjtVDgc2CorKSoto7CslKKyMgpLy8gtKSGnuJickpJmjbb5YtLrGX7WWYzp1YvhZ3U/I05saGmtFsy5XC7GjBnDhg0bWLZsGcOGDQPg8OHDTJgwgW3btjFx4kQ+/vhjAjtwnhgRzAmCcCJXxmu49zzSoLaapL+h7fF4k+6jqgrKse9Q8n9EMnZCk/Q3JN2p1xd5jh4bz8mbKSRLKppu/8S1cZLXa+TIC9ClNTEZM57gYcHaz9mUudln/bQxtxJ/dBvWL/6N6uOs12raTr0JnfIW2vDEJvelJVVUHiG3YCn5hctx17eW0QdblYb0LAubj5g5aj31NGxjGHU6jHo9eq0WVM+YqKKqns0wbjcVdnuDkvK2JLPJxIiUszinVy8Gd+2K4TQ50rOttOrZrDk5OaSmpqLRaNiwYQMZGRlcccUV5OXl8a9//Ysnn3yyubdocyKYEwThRIptK85VYxrQUkJ39poW20nbGJ48fDMAzwe6FDoSXerHoA3G+fsQ1IoDJ71C9mysMDY9dZTT7eTNX94mq+iIV11IgIW/X3QvOlsOJR/+DeeReg5zN1kIueEljL281ya2Fbe7ioLileQW/ET58Vx9jZFXqif9iJnNWRaK2mvKlkbSaTT07dyZQV27MLhrV7rHxqI5w05p8Ce/BHN33XUXaWlppKWl0bt3bzQn7Mj5448/OO+88+jSpQuZmZkAvPvuu9x4o69DrjseEcwJgnAiVXXj+K0XOPLrbmSIRdv7f2iiLm69jp1EKdmIat0Mxk7IURfXrJdyHXge977/eLXXpMxGm/x/zbpnvi2fF398Bafb6VWXlpTKpGHXoLoclC75L+XL36n3WkEX3kfQBfcgtbMAoawig2MFv1BQ9HujR+sAsoqNbM02s+1oMMUdKLCzBATQKz6e3vHx9E6Ip09Cgpg+9SO/BHOyLNf8IDAYDPTr14+0tDRSU1NJS0vjl19+4YEHHiA2Npavv/6awYMHN/+dtBMimBME4WQ+E/VqzcjRl6KJvQopbBSS3KhTE1uNWnkEx/L+eE3DBqWgG7mqSWd+nmjl3lV8s8n3OsHJZ99M7/heAFRt+56S+ffVO+1q7Dcey3XPIxsCmtUnf3C7qygsWcOxgl8oLd/TpGscKTGyNTuY7UfNFJa3n8AoymKhS1QUXaOj6RIdTc/4OOLDwpr9vSE0nF+CuR9++IHNmzezefNmNm3aREZGBqqq1vzD6vV6HA4H48aN4+KLL6Z///7069ePkNPgOA4RzJ0+5s6dy9SpU3n//feZMmVKk68jSRLnnHMOy5Yta7G+CR2Lqqoo+T+gHP0SdBbkiHORI8+vd3NCe+JYfyVq4TKvct3wn5Etqc26tqIqvPvbHPbnnTyVC0GGIO67+B6CjJ70J678gxTPnY4rt+5gSNupN2G3vIcmNK5Z/fKnyqoc8ouWk1e4HIezsEnXqHKFkGWNZPNhE1uPuHG6/bfmTZYkQoOC6BQaSqfQUOLCQokNDSMuLJSkyEiRNqQdaJU1czabjfT09JrgbvPmzezevRvX8R0x1UFeQkICAwYM4Ouvv27qrdqcCOaaJzMzk+RkT1bxTp06cfjw4VrT9NW2bdtGv379AEhJSWH37t0t3hcRzAmCh/vo556j0E4iR12CJnEakikJjHFNHl0sLi/hhR9epMrpfdRa34Q+3Dzyz2U4ir0C6+f/pGrz13VeTw6KIHTKO+iTBzWpP61FVRWspdvJK1xGkXU9yqmOmquDTmshIKAvqtwNmyOOgjIn1ooKqhxOKp0OKh0OqhxOnG4XICFLEpIkIUmeUxUCDQYC9AYCDHoCDAaCjEbCgoJqHpaAALG+rZ1raOzRrPF/s9nM6NGjGT16dE2Z3W5n27ZtNcHd5s2b2bZtG4sWNX5bvnD60Wq1HD16lB9//JFLLrnEq/69995Dq9XW/EIgCIL/yNHjPUd9nXSklZK3BCVviecLSYsUdjbalMeQzX0adf3QwBAmDpzAZ+u+8KrblrWdPTl7SIlN8fTFEEDIjS9TkZiK7dtHQfHOS6qUFVD4xiQs1z5NwKCrG9WX1iRJMiHmfoSY++F2V1Fk3UBB0R+U2LY0KiGx02XFavsD+AOQSQ4+i9D4AYQE9ycwILlReQuF01uLL+YwGAwMGjSIQYP+/M1JURS/jLAIHc+IESPYsmULc+bM8QrmHA4HH3/8MZdccgnffvttG/VQEM4ckiYAOfYKlCMf1t1IdaEWLsO54Wr0I39HMjTsyKdqacmpbM/ewc7sXV51izZ/R7fobmhkzyi9JEkEjpqKNjaF4g9uR60o8b6g24F1/t9xF2YRdOG97X79lkZjJDLsbCLDzsbpslFYvIbCkjVYS3dw8nrF+imUlu+mtHw3h/kUrSYQc3BvLMF9CQnui9HQ8PNfhdNPq4T1sizTq1ev1riV0M6ZTCYmTZrEokWLKCgoqFX37bffUlBQwNSpU32+tqKigtmzZ9OjRw+MRiNhYWGMHz+eVatW+WxfVFTEHXfcQXR0NAEBAQwePJiFCxfW27+tW7dy3XXXERsbi16vJzExkbvvvpvCwqatfxGE9k4Td33DGjrycB14ttHXlySJqwZfQYDee/NCni2f1fvWeJUbuo0g4p5FaKPPqvO6ZUufx/rpfaguR6P71FZ0WjMxkRfSu/tMBvd9hy6dp2MJ7kdTPopd7nKKStaRkfUem3fey8btd7A340Vy85dSUXmEFsg6JnQgjfoOaqnRNTFKd2a75ZZbakbhTjRnzhyioqK49NJLvV5jt9s577zzePTRRwkMDOTee+/l8ssvZ9myZZxzzjl89dVXtdpXVFQwZswY3nrrLbp27co999xDSkoKkyZN4osvvKd8wBNMDhkyhEWLFjFmzBjuvfde+vbty6uvvsrw4cMpLj710T6ZmZlIkkRSUlLD/0IEoQ1JIUORwsc2qK2SNRel3HtDw6kEG4MZ1+9Cn3U/bf+Zcnu5V7k2Ionw//saQ6/z67xu5YYvKHrnZpRKa6P71NZ0OjMxEefTu/vDDO73Dt0S7yIsZChyEzfPOJzFFBSv4mDWu6Tvuo/1225j94FnyM79BlvZLtxKxwl62ztVsaMUr0NV288RpY0K5vr06cONN97I9u3bm3Sz9PR0rrvuOvr27duk1wunh6FDh9K7d2/mzJlTU5adnc3SpUu5+eab0Wq9Z/+feeYZ1qxZw4033siGDRv473//y5w5c1i9ejUajYbbbruN0tLSWu23bdvGtGnTWLVqFU899RQfffQRixcv5rvvvvO6fmFhITfffDORkZHs2rWL+fPn88wzz7B48WI++eQT9u3bx8yZM/3zFyIIbUiSJHT93kROmALaU2zuUl249z3RpPsM7TKY2BDvZMSVziqWbvvJ52tkYzChU98lcOxf67yuY/8qCl+5EpePJMUdhU4bTFT4aHp0uZ8h/d6jZ9d/ER1xPnp9RJOv6XKVUmTdwKGjH7N97yzWbZnM1t3/5mDWHPILV1BZdRRVbd3TIU4XauEfONdehOPXHji33ok792tUp61N+9SoYO6RRx5h0aJF9O/fn9TUVJ577jk2bNiA0+mdGBI8oylr1qzhqaeeom/fvqSlpbFkyZImfSi+/vrrJCcnYzQaSUtL4/fff6+3/WuvvUbPnj0xmUykpKQwb968WvVz5849vuun9qOqqqrRfRMab+rUqWzdupWNGzcCnn8Pt9vNLbfc4rP93Llz0el0/Pe//621LqRfv35MmTKF4uJivvnmm5ryefPmodfreeyxx2pd58ILL+S887wzys+bNw+bzcZTTz1F586da9Vdf/31pKam8umnn57yfcXFxbFr1y5++eWXU7YVhPZCMkSi6/08+vMy0J+7D92wn9D2edVzhutJlNyvUaybGn0PWZaZMNB71B1gzYF15JTk+O6brMF86b+xXPM0yL4Pr3cd20vhK5fhPNq4M1TbI1nWE2pJpWvn6aT1fo0BPZ8nMe5mLMF9kSTf778hVNVNWcV+cvN/YN+hV9m8817Wbb2VHfse51D2xxQUr6bKniumZxvAnb/U8wdnIcrRT3Gl34Lj124opW33/deoDRCzZs3ir3/9K0888QTz5s3jgQce8PxWp9ORlJREaGgowcHB2Gw2ioqKOHToEC6XC1VVsVgs3HPPPcyYMYPIyMYtoF2wYAH33nsvr7/+OiNHjuStt97i4osvZufOnV4fvABvvPEGM2bM4J133mHw4MGsW7eOadOmERoayoQJE2ramc1m9uypndfIaDQ2qm9C09x8883MmDGDOXPmkJaWxty5cxk6dKjPtZU2m42DBw/Ss2dP4uPjveqrp1PT09O56aabKC0tJSMjg169ehET4z0SMGrUKK9ga82aNTXP+/fv93pNVVUVBQUFFBQUEBFR92/LOp2OHj16nPL9C0J7JEkS6MOR9OEQkoZiS0c5/K5XO9ee2egGf9PoBffdorvSN74P247Unt1RVZVvNy1m+tjb6rxmwLAb0IR0onjeHag+pmUVWx6Fr11N6NR3MXQb0ah+tVeSJBFgiifAFE9c9ATc7ipsZbuxlm7FWrqd8srMZl3f7S7HWroNa+mfx6ppNAEEmpIJDEgk0JREoCkJkzEeuZ0mv25tntySP3pXaC1IQSmt36Hq2zf2BVFRUbz00kv897//5bPPPmPx4sWsXLmSvXv3erWNiYlh1KhRjB8/nmuvvbbJgdLzzz/Prbfeym233QbAiy++yI8//sgbb7zBU0895dX+ww8/5Pbbb2fSJM9B0l26dGHNmjU8/fTTtYI5SZJ8ftgL/hcVFcUll1zC/PnzmThxIvv37+cf//iHz7Y2m2f4Ojo62md99b+h1Wqt9RwVFeWzva/rFBUVAZ4R3fqUl5fXG8wJwulE2/UBHNmfeqUuUYv+QCn4GU3kBY2+5vgBF7Pr6G5cSu30QwfyDrL9yA76JtSd/sTQYwzhd31F0buTUay5XvVqVSlFb99MyA0vYhowwccVOjaNxkioZQChlgEAOJ02rGXbsZXuxFa2i4qqrGbfw+2uwFa2A1vZjpoySdJgMsYTYEwgwNSZAFNnAk2d0evCz7gdtGrZbqg87FUuR17QrJHT5mpyqG0ymZg8eTKTJ08GID8/n7y8PKxWKxaLhaioqEaPwPnicDjYuHEj//rXv2qVX3jhhXXuYrTb7V6Bo8lkYt26dTidTnQ6HQBlZWUkJibidrsZMGAAjz/+OAMHDqy3P9WBRTWDwYDB0DGyvbc3t9xyC9988w233norJpOJ66/3vauuOlHisWPHfNZXl1e3q37Oy8urt72ve2zbto0+fRqXS0sQTleSIRJN8l249//Xq869ZzZyxLmN/gALCwpjdI+z+XXnMq+679KX0LNTD7Sauj+adJ16EfF/31L07mRcOd7pTnA7KPnobyil+QSO8r1s43Sh05mJCB1BRKhnJNLpsmEr242tbCelZXspr8xokUX6quqmovIQFZWH4IR9YBrZhMl0PMgzxh//c/xpHeQp1VOsJ5GjfG/waS0tNm4aGRnZIsHbyQoKCnC73V6jKdHR0eTmev9mBjBu3DjeffddLr/8clJTU9m4cSNz5szB6XRSUFBAbGwsPXr0YO7cufTt2xebzcZLL73EyJEj2bJlC927d6+zPwkJCbW+njVrFrNnz272+zwTXXLJJcTExJCdnc1NN91UZ3Zrs9lMly5d2L9/P9nZ2cTF1T7OZ/ny5QAMGDCgpn1ycjL79+8nNzfXa/TV13rLoUOH8tVXX7F69WoRzAnCCTRJd+I+PAcctX85Ust2oRz9rOGpTU4wtucY1h/cSGlVaa3yovJi1mdsZHi3ofX3KSSW8Lu+pHjudBz7/vBuoKrYvp6F25ZH8CX/PG0Di5PptGbCQ4YQHjIEALfioLziIKVleygt30Np+X6crpIWu59bqaSsfB9l5ftqlcuyEZMxjgBjJ0zGOEzGeEyGThgN0ciyrsXu3xZ8TrFKWuTwc1u/MyfoMJPgJ/9nPPFM2JM98sgj5ObmMmzYMFRVJTo6milTpvDMM8/UHCE1bNgwhg0bVvOakSNHkpqayiuvvMLLL79cZz+ysrJqBR1iVK7ptFot3377LdnZ2aSm1n8O5OTJk5k1axYzZszggw8+qPm33759O++//z4Wi4XLL7+8pv3NN9/MY489xsyZM3n77bdrypcuXepzc8LUqVP5z3/+w0MPPcSIESPo3bt3rfqKigq2bt1a63vGF6fTyYEDB9DpdHTt2vVUfwWC0O5J2iC03R7EtdN7GYRr7+PIYaOQTN5rWetj0Bm4pP9FLFj7uVfdbzt/Y3ByWr2jc+DZ6Rp22weUfHofVZu/8dmm/NfXUMqLsFz9FFIdmydOZxpZjzmoB+YgzzpeVVVxOIsoK99PWcV+Ssv3U16Zgdtd0aL3VZQqyisOUF5xchobCaMhCpMhDqOxEyZDLCZjLEZDJ/S60HYfdKuOItTidV7lUugIJF3bHvPZ7oO5iIgINBqN1yhcXl5enWuoTCYTc+bM4a233uLYsWPExsby9ttvExwcXOd6J1mWGTx4MPv27fNZX81sNouzWVvQ4MGDGTx48CnbPfjgg3z33Xd8+OGH7Nq1i/POO4/8/HwWLFiA0+lk3rx5BAcH12r/1Vdf8c4777Bjxw5Gjx5NVlYWn332GePHj/dKTxIZGcn8+fO55ppr6N+/PxdddBE9evSgqqqKQ4cOsXz5ckaMGMEPP/xQbz+zs7Pp2bMniYmJZGZmNunvRBDaGzn+ZqTMN1ErTtocZM/FsW4i+iHfNjqgG5g0gN/3ruRo8dFa5SUVVtYf3MDw7vX/4gQgafWE3PAypeYoype/47NN5dr5qJVWQm58GUl7Zv/yLUkSBn04Bn044aGe0U9VVbE78iirOEh5RYbnUZmJ0+WP3H0qVfZjVNmPga32jmhZNmA0xGAyxGI0xGA0xh7/cyw6rbldBHpKwS+AdzqXtp5ihRYO5s49t+HDjL/++muD2un1etLS0vjpp5+44oorasp/+uknLrvssnpfq9PpanY/fvrpp1x66aXIdRwqrKoq6enpIgdeO2U0Gvn11195+umnWbBgAS+88AIBAQGMHj2af//735x99tm12gcGBrJ8+XJmzJjBwoUL2bRpE71792bBggVYrVafuebGjx/P5s2befbZZ/n555/56aefCAwMJD4+nqlTp3LTTTe11tsVhHZFknVoznoYV/oU78rKTBzrL0M/ZBGSsVODrylLMhf0OY8Pfvc+SuzXncsY3GXQKUfnPH2TMU+ciWyOpnTRf3y2qdq6hKLKUkKnvoNsCGxwH88EkiRhNERjNEQTETq8ptzhLKG8MpPyiszj6+UOe3LTNeJs2cZQFPuf6/JOopFNngCv+mGMwWSIwaiPRteKI3pKno8pVkCOHNcq96+PpLZgUpm6jmHy5f33329w2wULFnDzzTfz5ptvMnz4cN5+++2aEZfExERmzJhBdnZ2TS65vXv3sm7dOoYOHUpxcTHPP/88P/30Exs3bqzJzP/oo48ybNgwunfvjs1m4+WXX+bDDz9k5cqVDBkyxKsPNpsNi8WC1WoVI3OCIJxxVFXFueFK1MLlPuulgK7ohixCMjY8Q4Cqqry89FWyTxqdA7g8bSIjug/38aq6VW5aSMn8++CknbLVdJ0HEHbrB8hBYY26ruChKC4q7UepqDzseVQdobIqiyp7Ho07Z7blyLIBoz7qeKAXfULQF41BH9FiO0xVxYnj17PgpBFLKaAb+tHeU68tpaGxR4uOzDUmQGuMSZMmUVhYyGOPPUZOTg59+vRhyZIlJCYmApCTk8Phw39uFXa73Tz33HPs2bMHnU7H2LFjWbVqVa0jlkpKSpg+fTq5ublYLBYGDhzIihUrfAZygiAIZzpJktD1n4Nz/eWoJ+Qlq6ZWHMC5/jJ0Q75FMvheAuPrmhf0OZ+5v8/zqvvt+OicTtPwBfOm1CuQTBaKP7gdnN4J4J2H0yl84xrCbv8EjblhfRT+JMtaAo+nJTmRW3FQWZVNZWWWJ9irOkJl1VGq7Dl+P/JKUexUVGX5TMsiocFgiMCoj8FwfPSxJuDTR6HRNDxdmlq81iuQg/YxxQotPDJXl2PHjhEZGVnnFGdHIEbmBEEQPIvAPQGd72MdpeDe6IYtRdKYGnY9VeXlpa+RXZztVdeU0TkAR8Z6it6bilrHma2aiCTC7/gUTWicz3qhZSiqC7s9j8qqo1Taj3qeq7KptB/F5So99QX8TKe1nDCKd+LoXjQ6raXW9K1r9yO4M73zkOoGf4McPspvfWxo7OG3YM7lcjFr1ixeeeUVKioq2Lt3L126dOHBBx8kOTmZv/617rP22iMRzAmCIHiojkKc6y5DLfN9fJGcMBVd7+cafL2d2bt8js6ZTWb+eek/GjU6V815dBdFb9+EUuo736QmNI6w2+ejjUxu9LWF5nO6yqiy51BZlUPV8UCvyn6MSnsOitL2x2p6pm89gZ3BEE30vmfQ2E9aDqA1oz93H5If0620eTD3n//8h3fffZfHH3+c22+/ne3bt9OlSxcWLFjAiy++yOrVq/1xW78RwZwgCMKfVEcBznUTPRnxfdAOeB9NTP2b1Gqupaq88tNrHCnyHp27LHUCI89q2vFcroJMit6+EXehd8Z+ADk4irA7PkEX03bHMAm1qaqK02U9IdDLPf5ou0DP4KqgT5H3IQWlQWdh7XzL8aAvpmaHcEtq82AuJSWFl19+mXHjxhEcHMyWLVvo0qULO3fuZNSoURQWFvrjtn4jgjlBEITaVHseznUTUMt9pHTSmtGPWIEU4H1+ti+7ju7m/RUfeJU3Z3QOwG3NpeitG3Ad8512SgoIJfz2j9HFi0wG7d2fgV7u8RQnubWCPddJx861lKiKwySUeR9ZmmHuTZExFgC9LpxBfd9o8Xu3yQaIEx0+fNjngeNarZbKykp/3VYQBEFoJZIhCm3qxzhXjQV3ee1Klw3nltvQDf2uQdNQPWJTSAiLJ6voSK1yW6WN9ENbGNxlUJP6qLHEEHbn5xS9fROubO91fmpFMYVvXk/Y9I/Qdx7QpHsIrUOSJPS6EPS6kJpEyCdyucpq8thV2XOpchyr+drhbPoAksVe4FWmAlZ9eM3XRkPbnvPut2AuOTmZLVu21Ow4rbZ06VKfQZ4gCILQ8ciB3dD2ehbXtju96lTrBtz7/4v2rEdOeR1Jkji/z3k+R+dW7PmDQclpTc4npgkKJ/yvn1L0zmSchzZ697PSStFbNxA27UP0SWlNuofQ9rTaIIK0QQQFep++oygOqux5VDmOYbcfOyHoO0aVIw9Vdfq8pqy4CHYWe5WX6yy4ZX3N18YG7uD2F78Fc3/729/4v//7P7Razy12797N4sWLeeSRR3jxxRf9dVtBEAShlWnirkMpXI5ydIFXnfvgi8hhZyNHjD3ldXrEphAbEktOSU6t8mPWY+zN3UtKbNPXtskmC2G3f0zxnFtw7Pde/6RWlVL09o2E3fYB+i4tv/ZJaFuyrCfAFE+Aj5NKVFXB4Sw+HthVB3ueqVtDyWYkHzn0SvS1z6Jv62DOr6lJHn/8cZ5++mkqKjznvhmNRv7973/z8MMP++uWfiPWzAmCINRNdZXiXDUWteKgd6U+Av3wX5BMCae8zsaMTT7PbO0e3Y1pY29tfj+dlRR/cAf2Xb5PIZL0JkJv/QBDt8anRBFOP86tf0M5Ot+r/HD8TdhwY3cUACpnJd9LRGjTNurUp803QFSrrKxkx44dKIpC7969CQzsmEepiGBOEAShfop1C841F4KPKSspqBe6Yd8jaYN9vPJPLreL/y5+Flulzavu3nH/R6fQ2Gb3U3XZKZ53J/YdS3030BkJu2UOhrP8lz9MaP9UxYnjt57gLKpdYUxAf046kiShKC7sjnx0OgtaTUCL96GhsYffs/iaTCYGDRrEkCFDOmwgJwiCIJyabOmPJmW2zzq1bCeurXec8kQArUbLyDoSBf++5/fmdhEASWsg9C9vYOx7se8GziqK3puKfW/L3E/omNSi370DOUCOGlezflOWtZiMsX4J5BqjRYO55ORkunTp0qCHIAiCcPrRJN6BHOU7SFLyvse99/FTXmNot6HotXqv8vTDW7H6GLFrCkmrJ+Tm1zAOmOC7gcsuAroznDv3G5/lmug6vmfaUIsGc5MnT655XHfddRQWFhIeHs5ll13GZZddRmRkJIWFhVx//fUteVtBEAShnZAkCW2/N5CCevqsd2e8jPvIJ/VeI0BvYnCy965St+Jm1V7vzQtNJWl0hNzwMsbUK3w3qA7o9qxosXsKHYOqOFGOfeddoY9ECmv5tXHN1aLB3OzZs5k1axazZs3CarUyffp01q9fzwsvvMALL7zA2rVrueOOOzpcwmCh/ZMkiTFjxjTrGlOmTEGSJDIzM2vKMjMzkSSJKVOmNOvagnAmkbRmdKnzQR/hs9614+8oxWvqvcbZKWf7TEWyZv9a7E57i/QTQNJoCbn+BUyDrvbdwGWnaM4tIqA7w6hFf/ieYo2+FEnStEGP6ue3NXOffvop06dP9yq/9dZbWbDAe/u6cPqoDoDqe3R0Bw4cYPbs2UycOJG4uDgkSSIpKalR17jzzjtr/j5yc3P901FBaCNSQGd0A+eB5D1diurEuelGlFLfZ7sChAeF0Seut1d5pbOKDRneueKaQ5I1WCb9D9Pga3w3EAHdGafOKdYGHlHX2vyWZ87tdrNr1y66d+9eq3zXrl3+uqXQznTt2pWbbrqprbvRLHFxcezatQuLxVKr/Pfff+fRRx9Fo9HQs2fPRgdjv/zyC2+++SaBgYGUl5ef+gWC0AHJocPQ9nnRZ0JhnMU4N1yNfuh3SAG+D7sf3eNsth3xPrXh9z0rGd5tGLLccuMRkqzBcu2zAFSu906NUh3Qhd32AYbuI1vsvkL7oyoulGOLvSv0EUh+SD/SEvwWzF1//fXcdttt/Oc//2H48OFIksTKlSuZNWsW1113nb9uK7Qj3bp1Y/bs2W3djWbR6XQ+TywZPXo0q1evpn///phMJoxGY4OvWVpayq233soVV1xBYWEhy5cvb8kuC0K7oom7DrVsL+6MF70r7bk41l/pCeiMnbyqEyMSSQzvzKHCw7XKi8qL2J2zh15xvtflNVVDArriOVMJvW0ehq7DWvTeQvtR5y7W6EuRZL+FTc3it1699NJLGI1G7rnnHhwOB6qqotfrueOOO/jvf//rr9u2W09/8w0Zx/LauhsNkhwdxT8va52h5K1bt/Lkk0+yfPlyCgsLiY2NZeLEicyePZvw8HCv9u+++y4vvvgi+/fvJzIykuuvv57HHnuszuuXlpbyv//9jy+++IKDBw9iMBgYNmwYDz/8MGefffYp+5eZmUlycjKTJ09m7ty5NeXN2ZV9//33U1payuuvv86kSZOadA1B6Eg0Zz2MWrHf92hH5SGcG65CN2Qxkt77//zoHqP4cOXHXuWr9q1u8WAOTh3QqY5Kit+d7DnLNXlwi99faHt1T7Fe3rodaQS/BXN6vZ4XXniBJ554ggMHDqCqKt26dSMgoG1zsbSVjGN57Dhy5NQNzyDffvst1157LRqNhokTJ5KQkMDOnTt59dVX+fHHH1m7di2hoaE17R9//HFmzpxJdHQ006ZNQ6fTsWDBgjqn7ouKihg9ejQ7duxg1KhRjBs3DqvVyjfffMPYsWP5/PPPufzyy1vp3XosXbqUd955h3nz5hEd3bbHvwhCa5EkGW2/t3FunOQZ9TiJWrYH54Zr0Q1ZiKStnRi1d1wvQgJCKKkoqVW+N3cf+aUFRAb73mTRrP6eMqCroOidvxB2+8foE1Nb/P5C2/FMsfraxdp+p1jBj8FctYCAgJoRjDM1kDtT7d+/3+c060UXXUT37t25+eabiYyMZOXKlXTu3Lmmfv78+dxwww3MnDmTV155peZajz32GHFxcWzatImoqCjAs4N6yJAhPu9/9913s2PHDubMmcPUqVNryp988kkGDx7M9OnTueiiixo1RdocNpuN2267jUsuuYSbb765Ve4pCO2FpDGiS/0I5/orUK2bvOpV22ZPQJe2AEn35xpVWZYZ1m0oP2z90es1a/avZcLA8f7pb3VAp7ip3PiVd3/tZRS9fRNht3+CvvMAv/RBaH2eXazeGTfa8xQr+PkEiLlz59KlSxfMZjNms5kuXbrw/vvv+/OWQjty4MABHn30Ua/HmjVrmDdvHjabjaeeeqpWIAee9Zapqal8+umnNWWffPIJLpeL++67ryaQAzCbzT7P+i0oKGDBggWcd955tQI5gOjoaB544AHy8/P5+eefW/hd1+3ee+/FarXy1ltvtdo9BaE9kbTB6NI+RwryXocKoJasw7luAqq99pKUIV0GoZG900FsOLgBh8vhl75C9S7X5zAOmOizXq0qpejtm3AeFRv7ThcdbRdrNb+Fma+//jr33Xcff/3rXznnnHMAWLZsGXfeeScVFRX87W9/89ethXZi3Lhx/PDDDz7rqteKrVmzhv3793vVV1VVUVBQQEFBAREREWzZsgWAUaO8z0r0VbZ+/XrcbjdVVVU+Rwf37dsHwO7du7n00ksb/J6a6vvvv+f999/nzTffJD4+3u/3E4T2StKHohv0JY6146Ey06teLd2Oc+14dIO/QjIlABBkDKJ/Ql82HUqv1bbSWUX6oS0M6eq/tWuSRkvIDS9Rorip2uo9/aZWWil66wbC//YF2qiufuuH4H917mLVhSOFtu8dzH7dAPH8889z551/bkm//PLL6d69Oy+//PIZF8wlR0edulE70Rp9LSry7BR67bXX6m1XXl5OREQEVqsVoNaoXDVfa8+qr79y5UpWrlxZ7/X9raKigmnTpjF27FifuRcF4UwjGWPRD16IY+0lYM/xqlcrDuBYezG6QV8hB50FwPDuw72COYBV+9cwuMsgv+avlDRaQm56heJ5Luzbvad7lbICCt+8jvC/fYk2vLOPKwgdgVq8skNOsYIfg7nMzEzGjRvnVX7RRRdx//33++u27VZr7Q7tKMxmzyLnbdu20adPn1O2r87zlpeXR2JiYq26Y8eO1Xn9+++/n//973/N7W6z5OXlkZ2dTXZ2dp15sWJjYwHYvHkzAwYMaMXeCULbkAIS0Q3+GueGK6Eq27tB1VHPCF3afOSQQXQOTyAutBPZxUdrNTtafJTDhYdJjEj0vkZL9lejI/Tm1yn+4HbsO72XZyjWXIrevJ7wu75AY4n1a18E/+ioU6zgxzVzsbGxrF+/3qt83bp1YhefwNChQwFYvXp1g9r3798f8CTrPZmvssGDByNJUoOv70/BwcHceuutPh8xMTEA3HDDDdx6660+07EIwulKDuqOfuj3SAHdfDdwFuJceynuw+8BMKL7cJ/NVu2r/2iwliJp9YT+5Q303X2nNXIXHabwzRtwlxa0Sn+ElqMqdhRfwZwuHCns1Gms2prfgrnJkydz55138sorr7B161a2bt1aM706efJkf91W6CCmTp1KcHAwDz30EDt27PCqr6ioYM2aP39A33DDDWg0Gp5//nny8v5cHG2z2fjPf/7j9fqYmBiuvfZaVq1axbPPPouqql5t1q5dS0VFRQu9o7qFh4fz7rvv+nykpKQA8Nxzz/Huu++SkJDg9/4IQnsimeLRDf0OydzPdwPVgWvnA7i2Tqdfp66Y9CavJluztlFWVebnnnpIOiOhU99Dl+R7nZ47bz9Fb9+EUmltlf4ILUPJ+xGcxV7lcvT4dj/FCn6cZp05cyYul4sHH3ywJmmwwWDg73//O7NmzfLXbYUOIjIykvnz53PNNdfQv39/LrroInr06EFVVRWHDh1i+fLljBgxomYDRbdu3Zg5cyazZs2iX79+XHvttWi1Wr788kv69u3Lnj17vO7x+uuvs2fPHh588EE+/PBDhg8fjsViISsri40bN7Jv3z5ycnKalDKnoKCAf/zjHzVfO51OCgoKmDJlSk3ZiUmGBUGom2SIRDf4W5ybrkMt9j3KpuR8iWTbxjkJt/DDgYxadW7FzbqDGzi315hW6C3IhgDCbptL0VvX48za6lXvOrqDonenEDb9Y2SDSMnVESjZ832Wa+I6xolVfgvmNBoNTzzxBI888gj79+9HVVW6d+/eajm9hPZv/PjxbN68mWeffZaff/6Zn376icDAQOLj45k6darXua4zZ86kU6dOvPDCC7z11ltERUVx3XXX8dhjj/kMyMLCwli1ahWvvvoqCxYs4OOPP0ZRFGJiYujfvz+PPPIIERFNSzhaVlbGBx98UKusvLy8VpkI5gSh4SSdGd2gL3ClT0XJ/8lnG7V8LyMqH6VYn8o6Ry/UEyaX1uxfy5geo1v0vNb6yCYzYdM+ovD1a3Dlev8y6czcQPHcaYTdOgdJa2iVPglNo9qPoRT4SFMVkIwUMrT1O9QEkupr/knwYrPZsFgsWK3WmsX1giAIQstSFRfu/U/hPvhCve2yXREsqhxFtvvPHe5TRv3FL0d81cddmk/ha1fjzj/os97Y7xJCbnoNSdP+p+rOVK6MV3HvmelVrun+b7Rd/+HjFa2nobGH336FycvLY8qUKcTFxaHVatFoNLUegiAIgnAySdaiPesRtKnzQWups12ctoDpQQu51PQ7RskOwNoD61qrmzU0wZGE3/4Jckgnn/VVW5dg/fxBVEVp5Z4JDaGqah1TrBKaTh1jihX8OM06efJk9uzZw1133UVsbKxfcwAJgiAIpxdN1DjkEctwpk9FtaX7bCNLMNSwk966g6y092dDjpOS8hJCAkNat6+hcYTfMZ/CV69CKfPeyVq5/nMkoxnzZbPEZ2E7o9q2oJZ5n+AhhY9GMnWcBO9+m2YNDg5m2bJlpKWl+ePyrU5MswqCILQ+1V2Fa/fDKFlzTtm2UtGTZ7mU7oOfRtK3fpof59GdFL52DWqVzWd90MUPEHz+/7Vyr4T6OHf+E+XwO17l2n5voul0bRv0qLY2n2ZNTEwU06mCIAhCs0gaI7re/0M36Ku689EdZ5IdJJZ+hWNZf8+HtHWLz7RE/qLr1Iuw2z5A8pE+BaDs+2cpX/Vhq/VHqJ+q2FFyvvCu0AQhR41v/Q41g9+CuSeffJJ//etf5Ofn++sWgiAIwhlCjhiD7uzf0XT/N8inyIqgVKAcfgfn6rE4V56NK+Nl1CrvY8P8QZ88iNAp74JG77Pe9tVDVKYvapW+CPWrM7dc7OVI2sA26FHT+S2YO/fccwHPSRAJCQl06dKl1qOxXn/9dZKTkzEajaSlpfnM+n+i1157jZ49e2IymUhJSWHevHlebb788kt69eqFwWCgV69eLFy4sNH9EgRBEFqHJBvQdv0H+rNXIUec36DXqGW7cO+ZjWNZXxzrr8B9+H1Uu/cRgC3JkDKakJteAcnHR6yqUvLJPdh3L/NrH4RTqzu33A2t3JPm89sGiClTprBu3TpuvfXWZm+AWLBgAffeey+vv/46I0eO5K233uLiiy9m586ddO7sfajxG2+8wYwZM3jnnXcYPHgw69atY9q0aYSGhjJhwgTAc4zUpEmTePzxx7niiitYuHAh1157LX/88UfNUVOCIAhC+yMFJKFNW4C74Dcy1t9HZ/lwA16loBYux1W4HHb+AylkCHL0pWiiJyAFeH+ONJep3yWoVz+F9fN/ele6nRR/MJ2wOz5Fn5ja4vcWTu10yC13Ir9tgAgKCmLRokWMHTu22dcaOnQoqampvPHGGzVlPXv25PLLL+epp57yaj9ixAhGjhzJs88+W1N27733smHDBv744w8AJk2ahM1m4/vvv69pc9FFFxEaGsr8+d7RutgAIQiC0P78unMZ23fMY7QxnZ66zCZdQzIPRI65DE3MRKSApBbtX9mvr1P6nffnFIAUEEL4XV+hi+7eovcUTq0955Y7UZtvgIiMjKw5RLw5HA4HGzdu5MILL6xVfuGFF7Jq1Sqfr7Hb7V4nTZhMJtatW4fT6QQ8I3MnX3PcuHF1XrOazWar9bDb7Y19S4IgCEILGZycxlEllk/Kx/GK7Ro22lOwq7pGXUO1bca9dzaOFak4Vo31rLGzt8x678CxfyVwzO2+71tRQtHbN+EuPtoi9xIaRlVVlCO+NqJ0rNxyJ/JbMPfvf/+bxx9/vCZ4aqqCggLcbjfR0dG1yqOjo8nNzfX5mnHjxvHuu++yceNGVFVlw4YNzJkzp+b8TIDc3NxGXbNaQkICFoul5uFrZFAQBEFoHcGmYHrH9wIgTwnj68oxPG29mS/Kx1IeOAho3BIf1bbl+Bq7PjjTb0EpXI6qNj3hryRJBF/6EKbBvtNcKCVHKXrnZpQK74X4gn+oRStQy/d5lXe03HIn8tuauU8++YTNmzfTqVMnevTogU5X+zelX3/9tVHXO3nNnaqqda7De+SRR8jNzWXYsGGoqkp0dDRTpkzhmWeeqZUupTHXrJaVlVVrqNNgEGfuCYIgtKVhXYeyLWt7zddOdGxxnkWluzu3jnkf99EvUY59i2rd1PCLqk6U3K9Rcr9GCuiCnDAVTeepSBrvc6BPRZIkLNc8jVJRgn3HUq9617G9FL13C+G3f1JnWhOh5bgPv+ezXBN3k8/yjsBvI3NJSUlcccUVXHrppXTr1o3ExMRaj4aKiIhAo9F4jZjl5eV5jaxVM5lMzJkzh4qKCjIzMzl8+DBJSUkEBwfXHKweExPTqGtWM5vNtR4imBMEQWhbXaO7EB4U5lW+N3cfRS4T2i7/h374z+jP2Yq253+Rws6mMR9/asVB3HsewbFiEO7Dc1CVxs84SRotoTe/ir7LEJ/1zswNFM/7K6q7ebNZQv3UyiMox5Z4V+ijkGMmtH6HWojfNkC0pKFDh5KWlsbrr79eU9arVy8uu+yyBk9znnPOOcTFxfHJJ58Ang0QpaWlLFny5z/qxRdfTEhIiNgAIQiC0MEs27WcJVt+8Cof2/McLu5/kVe56ihAObYYd+63qEW/g+pu+M1MSWi7z0COvQrJV/qReiiVVgpfuxpXzm7flx58DZZJz4ljv/zEtfcJ3Aef8yrXdL0fbfeH2qBH9fPbBghVVVmyZAlPPPEETzzxBJ988gmFhYXN6uyp3Hfffbz77rvMmTOHXbt28fe//53Dhw9zxx13ADBjxgz+8pe/1LTfu3cvH330Efv27WPdunVcd911bN++nSeffLKmzT333MPSpUt5+umn2b17N08//TQ///wz9957r1/fi9A0mZmZSJLElClT2rorNWbPno0kSSxbtqzBr5EkiTFjxvitT4JwphqUnIZG9j51aP3BjbjcLq9ySR+BJmEK+sFfoR+7G22fl5DCz2nYzSozcW29HeeqMSjW9Eb1UzZZCJv2IZpQ32uzKtd/TtkPz/qsE5pHVey4j3jnnEXSoEmY0ur9aUmNCuZKS0sZPnw4EyZMYObMmcycOZObb76ZxMTEWmlAWtqkSZN48cUXeeyxxxgwYAArVqxgyZIlNdO1OTk5HD78Z54ht9vNc889R//+/bnggguoqqpi1apVJCUl1bQZMWIEn376Ke+//z79+vVj7ty5LFiwQOSYawEVFRU8+eSTpKamEhQUhNFoJD4+nlGjRjFjxgwOHDjQ1l08reXl5fHUU09x9dVXk5ycjCRJp/wtPykpqabdyY/qX5oEoT0LMgbRJ763V3mZvYyd2d4HqZ9I0oejib8Z/eCF6EdvQtPl76CPOuU91dLtONdciOvA/1AV74CxLhpLDGG3f4wc6D01DFD28yuUr/IRdAjNouQuAof3LmU56mIkY1wb9KjlNGqa9f777+eFF15Aq9UyevRoAgMD2bdvH7t370aSJJ555hnuv/9+f/a3zYhp1oYpLS3l7LPPZuvWrXTr1o3zzjuPkJAQsrKy2LFjB1u2bOGdd97htttua9R1MzMzSU5OZvLk/2/vvsOiuL4+gH9nd9mld2kWsGBXUBS7gl1jLzEmdtQYY4tRE38m1iQaY+8VsYsaW+wlCHal2VFRAaX3XrbM+wcvG9cZOiwsnM/z8CTMvXPnDCNwuHPLeHh4eJRP8MUUFxeHuLg41KlTB7q6RRsUzTAMunXrVqzevOK6efMmXF1dwTAM7O3t8fHjR2RkZBS4R6WdnR2SkpJ4e6bbtGmDAQMGlFu8hJSV4Oi32OW1h3O8gWV9THUt3s8cViGFIuYy5CHbwCY9KLQ+Y9QGopbbIdCrX+Rr5IQFImH7KLA5GTwNMjAZvxPaLfoVJ2xSgJz7fcEmPeQc12p7BgKzrhUQUeGKmnsUazbrmTNnIBaLcevWLbRt21Z5/MqVKxg9ejSWLFkCNzc3GBsblzhwotk2bNiAJ0+ewM3NDbt37+b0CL1//77KrM1nbm6unFBTmTRp0gTe3t5o1aoVDAwM0LhxY7x69arQ84yNjbF06dLyD5CQclLfoh7MDcwQl6o69Cc4+i3iUuNgblD071dGoAWh1UAILAdAEXcd8tcrwKY+y7c+m+wL6d1uEDX6DYLa44s05k1cxxEm43ciYe9E4POePZZF4qGZMPv2SL6TJkjRKVKe8iZyjF5DMKZdKiCislWsZO7Dhw/o2bOnSiIH5K7r9scff2D69Ok4f/48xozR3Om95eXkw1OISi54DbvKwsrICiOch5Xo3Hv37gEAZsyYwfvDrG7dupxjMTExWL16Nf755x+EhoZCV1cXDRs2xMiRI3l7et+9e4cFCxbgxo0byMnJQYcOHZSv1T+V91r9+fPn+PXXX+Hp6Ym4uDg0atQIixcvxogRIzht5+TkYMuWLTh06BBevXoFgUAAR0dHzJ8/H4MGDVKpu3TpUixbtgxeXl6ccXB79uzBhg0bEBwcjBo1amD06NFYvnx5vl+31NRUrFmzBidPnsS7d+8gkUjQvn17/PLLL+jcubNKXRcXF3h7eyMrK0s5bjU0NBSLFi3C0qVLYWlpWeisbEKqIoZh0K6+My4EXuKUPXj7CF84Fr+Xi2EYCGv0gsC8BxRRpyF/sxJsxjv+yvIMyF7MhSDZF6Jm68EICl+8WNLYBUaj/kLy0R+4hbJsJLhPyt0lwqphsWMn/5GHcXtsAUBYx61KTDYpVjInk8ny3dXhiy++AAA8efKk9FFVQVHJUQiL/1DRYZQ7U9PcMSDBwcFwdHQstP6bN2/g6uqK8PBwdO7cGUOGDEF6ejqePXuG33//nZPMhYSEoF27dmjatCkmTZqEt2/f4uzZs3B1dcXLly85SYxUKkXv3r2RkJCAYcOGISMjA8eOHcOXX36Jy5cvq+wCkp2djb59++LmzZto1aoV3NzcIJVKceHCBQwePBibN2/GjBkzCr2nFStWYPHixbC0tMSUKVOgpaUFT09PvHzJP24nISEBXbt2xfPnz9GlSxf06dMHycnJyvs6ceIEhgwZwjlv2LBhePz4Mfr06QNTU1PUq1ev0NgKkp2djf379yM8PBwmJibo2LEjJ0EmpLJrU9cJl59chVyhOjvV970f+rToBZGwZMurMowAQuvhEFgOgvzt2twZkfnMgFWEH4EsOwYiR3cwIv1C29ZtMwKK5GikXlzFKWMzk5GweyzMZ52B0Mi6RLFXd6w0CYqIk9wCoT4ENUepP6ByUGaLBteuXRsAkJhIq1hXZyNHjsThw4fh5uYGX19f9O7dG61atYKJiQlv/TFjxiA8PBy7du3ClClTVMo+fvzIqe/t7Y1Vq1bhp5/+27z6119/xW+//YZ9+/bh559/VqkfERGBtm3bwsvLC2KxGADw9ddfo2fPnli3bp1KMrd8+XLcvHkTS5cuxeLFi5V/raWmpqJ79+748ccfMWzYMNjY2OR7/8HBwVi+fDlq1qwJf39/WFjkDqJeunQpnJ35X5XMnDkTz58/h7u7OyZOnKg8/scff6Bt27aYOnUq+vbty9miLiIiAk+ePFEm0KUVFRXFmS3ct29fHDx4sFK+TiaEj55EDy1qNUdg2GOV4+nZ6Xj28TkcbUv3Bwoj0ILI/mcIavSC7Mk0sBn8E7oUcdchfTgIWk7HwEgKn0yh13065MlRyLjjwW0rKQIJu8fD7PuTEOjQmO3ikocfARSZnOMCmy/BiKrG17PYS5PExcUhJiYm33K5vBhr9ZAqZ/DgwVi9ejUUCgX+/PNP9OjRA6ampmjQoAFmzJiBN2/+20Ll0aNHePjwIbp27cpJ5ACgVi3u1P26deti/vz5Ksfc3NyU7fFZv369MpEDgB49esDW1lalvkKhwPbt29GgQQOVRA4ADAwMsHjxYuTk5ODUqVMF3v+RI0cgk8kwd+5cZSIH5C42/csvv3Dqx8XFwdPTEz169FBJ5IDc7eXmz5+P2NhYXL9+nXPusmXLyiyRmzRpEm7evInY2FikpKTg/v376NevHy5fvoxBgwYVOHmCkMqmXQP+P5zuv+WOmSopgbETtDrehKCOW7512JRA5NzvA0V6cKHtMQwDwyFLod3yC95yWeRLJO7/Fqwsp8QxV0esQgZ56C7eMmEBz07TFLtn7sKFC7C2toapqSmaN2+u8kEIAMyfPx/Tpk3D5cuXcffuXfj6+uLBgwfYunUr9u7dC09PTwwaNAgPH+b+YP20d6wwDg4OEAhU/wbJS/qSkpI49Y2NjXnH6dWqVUs5vg8AXr16hcTERNjY2GDZsmWc+rGxudPZg4L4F/rM8/hxbm9Aly7cAbV8xx49egS5XI6srCzeyQd5yW9QUBBnRml+PX0lsXjxYpXP27Vrh/Pnz6Nbt264ffs2Ll68qBxKQUhlV69GXVgY1kBMiuoyFO9i3iEmJRYWhjXK5DqMSA9aTf+C3LwnZI8nA/J0bqXMUEjv94VWm5MQGDkW3J5ACOOvNyAhLRY577iJZ86b20jynAfj0RvACMptA6cqRRF9DsgM4xxnTDpBYNCkAiIqH8VK5qZPn44nT57gyZMniI+Ph7e3N7y9vZW9GAzD4OLFixgzZgwcHR3h6OgIBwcH1KhRNt84mszKiH+sYWVUFrEaGBhg5MiRGDlyJAAgOTkZ//vf/7Bt2za4ubkhPDxcmXzVrFn09X2MjIw4x0Si3H/GfL3CfPXzzlEo/ts8OyEhAUDuZInnz5/ne/30dJ4f1p9ITk4GAJVeuTx8kxLyrnvnzh3cuXOnWNct70kOAoEAEydOxO3bt3Hnzh1K5ojGYBgGzvWdcT7gAqfswduHGNiqbP8tCy36gHH+B1K/UbzrmEGaAKnvCGi1uwiBfsETGRgtbZhM3Iv4LcMhi37NKc/yP41UI2sYDlhYVuFXWSzLQv5+M2+Z0O5bNUdTvoqVzG3ZskX5/+/fv8fjx4+VH4GBgQgJCUFMTAyOHDmisiWWlZUVWrVqhfPnz5dd5BqmpLNDqwojIyNs2bIFFy5cQGhoKJ4+fapcwiY8PLxigwOU6/cMHz4cJ0/yDJQtorzkMSYmhrMHcXR0dL7X/fHHH7FmzZpiXUsdM7DyxsplZPCsg0VIJdbGrjUuP74C2WdLfvi990Pflr2hJSx8pmlxCIwcIW5/BVLfkfzj6KQJkPoOg7jdZTA6/Ls/KNvSNYbplAOI2zQYihTuz410r20QGltDr/OEMoq+amITboFNecw5zug2gMCiaq3fV+J+2rp162LIkCFYsmQJTp06hXfv3iE5ORk+Pj7YvHkzJk2aBCcnJ2hrayMyMhKXLnGnipPqhWEYlcV1814TXr16taJCUmrSpAkMDQ3h6+sLqbTkG13nzf68desWp4zvWNu2bcEwjMor38rkwYPcxVI/3T2FEE2gK9FFy9rc4T8ZOZl4HFY+qy4wunbQan8ZjJETf4WsCEh9h4PNKXwLTKFJTZhOOQBGwj8bNuXMEmQ9u1KacKs82ftNvMeFdaeDYbhbv2myMn3pbmBggM6dO+P777/H7t278fDhQ6SmpuLFixfKDe5J1bZz5858JyKcOnUKQUFBMDY2RvPmzdG2bVs4OzvDx8cHu3fv5tRXZ4+dSCTCd999h9DQUMybN483oXv27FmBk3+A3JmyQqEQ69atU6mbkpKC3377jVPfysoKX375Je7evYu//vqLd6LBgwcPyrVn7MWLF7zjDW/fvo1169ZBIpFg2LDq3bNMNFO7BvzbM957c7/crsmIzaDlfBYC85685Wz6G0j9RoGVpRbalpZNU5hM2A3w9SKyCiQemoGc0IDShlwlKVKegY37l1sgrgGBzVfqD6icldnSJPkRCARo3LgxGjduXN6XIpXApUuXMG3aNDRo0ACdOnWCjY0N0tLSEBgYiFu3bkEgEGDbtm2QSCQAgEOHDsHFxQVTp07FwYMH0aFDB2RlZeH58+cICAhAfHzhf8GWlWXLlsHf3x+bNm3ChQsX0K1bN9SoUQPh4eF4+vQpHj9+jHv37vGOh8uTNxt2yZIlaNmyJb788kuIRCL8/fffaNGiBe9ODNu2bcOrV6+wYMEC5dfAyMgIHz58gJ+fH968eYPIyMgibxkGQGWJkcjISM6xNWvWKF+hHj9+HKtXr0aPHj1gZ2cHiUSCZ8+e4erVqxAIBNixYwfq1KlT5GsTUlnYmdvCysiKs2D7h4SP+BD/AbXNapfLdRmhLkSt9uf2wiVyE0c22R/SgHG5y5YIJAW2JWnYGcaj1iLpyCxuoTQLie4TYTbzDETmdmUUfdUgD9nCe1xo+y0YoTZvmSYr92SOVC9//vknOnXqhGvXrsHHx0eZSNSsWRPjx4/HzJkz4eT03ysIe3t7+Pv7Y+XKlfjnn3+wYcMG6Ovrw97enncpj/IkkUhw6dIl7N27FwcOHMDJkyeRnZ0NS0tLNG3aFNOmTUOLFi0KbWfx4sWwsbHB+vXrsXPnTlhYWOCrr77C8uXLeRMyU1NT3L17F1u2bIGnpycOHz4MhUIBKysrODg44Ndffy32Om/79+8v8NjSpUuVbeYtuOzv76/cWcLS0hKjRo3CDz/8UKazZglRJ4Zh0NG+PU75nuGU3X1zH6PKKZkDAEaoA63WRyF9OABsKndSFRvvDdmz2RC12F7o+Fcdp6GQJ0XwLiqsSItHwu5xMJ95BgL9slmqSNOxmR+hiPybWyDUg7DOJPUHpAYMSwtIFUlRN7slhBBSeWRLs/H7uVXIkmapHBcJRFg0+GfoSfTK9fpsdjRy7vcDMkN4y4WNV0JUhJmVLMsi5e//IePeId5yLbs2MJt2BIyWTmnCrRJkLxdBHrqdc1xoOw2iJn9UQEQlV9TcgxaqIYQQUmVJtCRoU7c157hMIcOjd77lfn1GYglx21OAhH8pIfmrX6BIyH9ZImU7DAPDoSsgadKDt1wa4oukIz+A/WTJpeqIlSZB/vEAt4ARQmg3Xf0BqQklc4QQQqq0Dg3a8x6/F/xAZb3J8sLo2kGrzUmAb+soVg5p4ESwmdztCzntCEUwHrsVWrX4h3tkPbnA+yq2OpGHufMu3iywGlbokjCarNTJnI+PD3x9c/+68fX1hY+Pj7Ls888JIYQQdathWAP2lg04xxPTExEUyZ2UVB4EBs0garkLAM/4uJw4SAMngJVnccs+b0eiBxM3DwhN+cf7pXttR8a9w6WMVjOxslTIQ7bxlgnrzlRzNOpV6mTOxcUF48ePBwCMHTsW3bt3V5Z9/jkhhBBSETrad+A9fi+4/JYp+ZzQojeE9vw7N7DJ/pC9mF+kfZCFhhYwnXwAjA7/DjfJpxYhO+hmaULVSPLQPYA0gXOcMXOFwLBqbzlaJq9ZP/3H9/k/RJpfQQghpKI1sWkMY11jzvFXka8RlxqntjiE9eZCYNGft0wRfhiKD9yZ6HxElg1gMjGfNegUciQe+A7SiJelCVWj5PbK8S9HIqo/V83RqB+NmSOEEFLlCQQCtM9vEeHgB2qLg2EEELXcBkbPnrdcFvQ/KFJfFKktSf0OMBrFvw0gm52GhD3jIf9sjb2qKrdXLpFznDHtAoFppwqISL0omSOEEFItONdrA6GAu42T7ztfZEuz1RYHIzKEqNVBQMizVZciC7LHU8DKM4vUlq7TMOj3+ZG3TJEciUT3SWBzitaWpmJlKfn3yjX4Sc3RVAxK5gghhFQL+tr6cKjNnQmaKc2C73s/tcYi0G8IUcsdvGVs2kvIXy0rclv6vWZDp80I3jLpx6dIOjqnSi9Zkn+vXFcITDtWQETqR8kcIYSQaqNjQ/6JELde3VbLMiWfElr2z3ftM3nYLshjrhapHYZhYDTyT4gb8CcuWU8uIu0K/+tYTVdwr9wCNUdTcSiZI4QQUm3UMasDW3NbzvGE9EQ8C+duu1XehA1/BWPAv26c7On3YLOKNuaNEYlhMn4nhBbcJVgAIO36ZmT48mxxpeHkobsBaRLneHXqlQMomSOEEFLNdGvchfe490sfta/AwAgkEDnsBgQ823BJ4yF9+j1Ytmg9hgJdY5i6uYPhmbULAMnHFyDn/aNSRFu5sNIUyEO28pZVl7FyeSiZI4QQUq00tWkCM30zzvEPCR8REheq9ngE+g3z3TOUjfeCPIS7z2h+ROZ1YTJhF/+SJfIcJHpMgSw+rKShViry0B38vXJm3SAw5X+dXlVRMkdIBZgwYQIYhkFISEhFh0JItSMQCNClEf9yFT5Bt9QcTS5BrXEQWA7gLZO/+Q2K1KKvGSep3wFGI1bylinS4pHoPgmKrLQSxVlZsNmxkL+v3jNYP0XJHCkzo0ePBsMwOHbsWIH14uPjIZFIYG5ujpycnHKL5+bNm2AYRuVDX18ftWvXRr9+/bBq1SpERESU2/UrIx8fH8ybNw+urq4wMjICwzCYMGFCged8/PgR3377LerUqQOxWAwbGxtMnDgRHz58UE/QhJSDNnWdoCvW5Rx/Ef4SsSmxao+HYRiImm0AJNbcQkU2ZE+ng1VIi9yervMo6LlM4y2TRb1C0pFZGj3DVfZ2NSDnJqSMWTcITPj34q3KKJkjZcbNzQ0AsG/fvgLrHTp0CDk5ORg7dizEYnG5x+Xk5IQlS5ZgyZIlmD59OlxdXfHmzRssXLgQ9evXx+bNm8s9hsrC3d0da9euxcOHD2FjY1No/bdv38LJyQm7du1C48aNMXv2bDg7O2P//v1o06YN3r59q4aoCSl7YpEYHey5v/RZsLj1+k4FRAQwYlNotdwBvv1b2ZTHkL8t3oxUgy9+hqRZL96y7OfXkHp5dUnCrHCKtDdQfPDgLRPZL1JvMJVEqZO51q1bo3nz3D3PmjVrhtatWyvLPv+cVG09evSAnZ0drl+/XmCvTV6yl5f8lbc2bdpg6dKlWLp0KVavXo0DBw4gODgYZ86cgZ6eHmbNmoW9e/eqJZaKNmPGDDx79gwpKSmFJt0AMHv2bMTExGDjxo24evUq/vrrL5w5cwaenp6IiYnB999/r4aoCSkfHe3bQyQQcY77vvdDWgW9hhSYdcl/uZJ366BI9i9yW4xACONvNkNk3YS3PP3GVmT6nS5RnBVJ/mYFwMo5xwVWQyAwblMBEVW8Uidzvr6+OH78OADg5MmTePTov5kyn39OqjaGYTBx4kQoFArs38+/v6Cfnx8eP34MZ2dnmJqaYsmSJWjfvj0sLCwgkUhgZ2eH6dOnIyYmhnPup+PMtm3bhiZNmkBbWxu2trZYtmxZsdeIGjx4ME6dOgUA+Pnnn5Genq4si4iIKFZsr1+/xoIFC9C6dWuYmZlBW1sbDRs2xM8//4y0tOL9UvDx8cHAgQNhbm4OiUQCe3t7/PLLL8jIyFCpl/caeenSpfD390efPn1gYGAAIyMjDB06lHc8Xps2bdCsWTMIhdxV8D+XlZWFK1euwNLSEjNnzlQpGzlyJBwdHXHlyhW8e/euWPdHSGVhoG2A1natOMdlcplat/j6nNB+ERj9RtwCVg7Zk++KvDsEAAgkejCZ5A4Bz4QPAEg6Ph85YQElDVXtFIn3oYg+zy1gtCBq+Kv6A6okuH+SkHIRHLoDGVmaMcZIV7s2Gtjyj7UozMSJE7Fs2TJ4eHhg0aJFYBjV1wWf9sr5+Phg7dq16NGjB9q1awctLS0EBARg+/btuHLlCvz9/WFkZMS5xvz583Hz5k0MGDAAvXv3xpkzZ7B06VLk5OTg999/L1a8Xbt2RdeuXeHj44N///0XAwcOBIBix3bq1Cns3bsXrq6ucHFxgUKhwP379/Hnn3/C29sbPj4+0NLimV32mR07dmD69OkwMTHBwIEDUaNGDTx69Ai///47vLy84OXlxXk17evri7/++gsuLi749ttvERAQgDNnzuDp06d49uwZtLW1i/U1yRMfHw+ZTAZbW1vOcwSAunXrIjAwEF5eXqhXr16JrkFIRevSqDMevuN2Otx9cw/dGneBWFT+Q0E+xwi1IWqxA9L7vQBWplLGpr+B/PVyiJrwT3DgIzKtBZMJuxG/fRQg/2zcnSwbie6TYT7nPITGPOP1KhGWZSF7tYS3TFhnEhjdumqOqPKgZE5NMrI+IC39TUWHUe5q166NXr164cqVK/Dx8UG3bt2UZdnZ2Thy5Ah0dXXx1VdfISsrC1FRUdDXV92f8MCBAxg/fjy2bNmCRYu44x/8/Pzw5MkTWFvn/uD59ddfYW9vj82bN2PJkiXFHofXrVs3+Pj44NGjR8pkrnv37sWKbezYsZg7dy7n2suXL8eSJUtw/PhxfPPNNwXG8eLFC8ycOROOjo64fv06TE1NlWWrVq3CwoULsXnzZvz4o+o+jBcuXMCxY8cwatQo5bFx48bh4MGDOHPmDL766qtifT3ymJiYQCgUIjQ0FCzLchK69+/fA8jtlSREU1kaWaCJTWO8jAhSOZ6enY67b+7BpUm3fM4sXwIjBwjrz4c8mJu0yUN3QmDRHwIz/vXy+IjrtoXR8D+QfHw+p0yRGoNEj8kw+/4kGC2e9e4qCUX0ebBJPG/7RAYQ1p+n1ljiUuPAAqhhYK7W6+ZHYyZAbNu2DXXr1oW2tjacnJxw61bB08cPHz4MBwcH6OrqwtraGhMnTkR8fLyy3MPDgzPTkWEYZGVllfetVHmTJk0CkDvY/lOnT59GYmIiRo4cCUNDQ1hYWHCSJSA3MTI0NMT169d52//111+ViRwAmJubY/DgwUhNTcWrV6+KHW/eRIC4uDjlseLGVrNmTd4kcsaMGQCQ7718aufOnZDJZNi0aZNKIgcACxYsQI0aNXD06FHOeV27dlVJ5ID/nkFphjno6uqiW7duiI6OxrZt21TKTp06hcDAQABAUlJSia9BSGXQNb9FhINuIVuareZo/iOs9wMYI/5x59Kn34OVphSrPd12X0Gv62T+9j48QfKJn9W+aHJRsQop5K+X85YJ680FI+Z/jVwusbAsTvmewbpLG3Ax8BKypBWfN2hEMufp6Yk5c+Zg0aJFCAgIQJcuXdCvXz+EhfEvfHj79m2MGzcObm5ueP78OU6cOIFHjx5h8mTVf8SGhoaIjIxU+SjpKynynyFDhsDMzAwnT55Eamqq8nhecpeXaAC5SUGfPn1Qo0YNiEQiMAwDgUCAlJSUfJcN4ZtUU6tWLQAlSyzy++FVnNhYloW7uzu6du0KU1NTCIVCMAwDM7PcHzBFWQLl/v37AIDLly8rJ2zkfSxfvhxaWloICgrinFfWX49PrVu3Dvr6+pgxYwb69u2LBQsWYNiwYRg5ciRatmwJAEUaf0dIZVavRl3UrWHHOZ7XO1dRGIEIohbbAAHP76Wsj5AF/a/YbRoMWARJYxfesky/U0j33lXsNtVB8WEf2Aye2fPaNSG0narWWF6Ev0Rw9FvIFXLcDPLBXxfWwfedHxRF3KmjPGjEa9Z169bBzc1NmYxt2LABV65cwfbt27FyJbcL+v79+7Czs8OsWbMA5I7t+fbbb7F6teo0bIZhYGVlVf43UM2IxWKMGTMGGzduxPHjx+Hm5oYPHz7gxo0bsLe3R9euXQEAa9euxbx581CjRg307t0btWrVgo5Obhf/hg0bkJ3N/xcx3zg6kSj3n7Jczp3hVJjIyEgAQI0aNZTHihvbrFmzsGXLFtSuXRuDBg2CtbU1JBIJAGDZsmX53sunEhISAKDY4/7K+uvxKQcHBzx69AhLlixRjtlr0KABdu7ciaSkJMyfP1/l60aIJmIYBr2a98Qurz2cMu8gH3Sw7wBtLUkFRJa7O4Sw4WLIeRI3RfgRyC36Q2jZv8jtMUIRjMdsRdzGQZDHcpOj1PN/QMuqUb4JX0Vgs6Mhe8O/Q4bIfhEYofpeDUvlUvwTcEHlWGpWKo4/PIksaRY657MYdXmr9MlcTk4O/Pz88PPPP6sc7927N+7evct7TseOHbFo0SJcvHgR/fr1Q0xMDE6ePIkvvvhCpV5aWhpsbW0hl8vh6OiIFStWoFUr7symT6WkqHZrSyQS5S/tguhq1y60TmVRFrG6ublh48aNcHd3h5ubGzw8PKBQKJS9cjKZDCtWrICNjQ0CAwNVEgKWZTmJd3m6efMmAKBt27Ylii0mJgZbt25Fy5Ytce/ePejq/rcQaVRUFJYtW1akOAwNDQHk/hszMDAozS2VqcaNG8PT05NzPG+x4TZtqudSAKRqaWBZH/Vq1MW72PcqxzNyMnH39V10b+ZaQZEBQtupUMRcApvAHV4kez4HAuO2YCRF/6NKoGMI00l7EbdxENisz17VsgokHpoB89n/QFSjckwokL1aAsi4r5QZg+YQ2IxUayy3X91BQnoC57iBtgHa1Ku4n4VlksxJpVK8evUKsbGxSE5OhpGREWrUqIFGjRoVaQZfQeLi4iCXy2Fpaaly3NLSElFRUbzndOzYEYcPH8aoUaOQlZUFmUyGQYMGqSwO27hxY3h4eKBFixZISUnBxo0b0alTJzx+/Bj29vb5xlO7tmqis2TJEixdurTQ+yjp7FBN1aJFC7Rt2xZ3795FUFAQPDw8IBQKMX78eAC5zzU5ORk9evTg9Oz4+voiM7PoU+9Lw9vbG7du3YKFhQW6d+9eotjevXsHlmXRs2dPlUQOQKFjOz/Vrl07+Pv74/79++jVi3+hz8oiNTUV//zzD0xNTSt9rIQUVa8WPbHz392c496vbqGDfQfoiCtmGA7DCKDVYgtybnfi7nqQEwfZix8hctzPO+s8PyKL+jAeswWJe8cDnw01YTOTkeA+CeazzkKgY1gWt1BiioQ7UEQc5y0TNV4BhlHfMI/kzBTceOHFW9bPoU+F9d4CpRgzFxsbq1wOwcjICA4ODujZsyeGDx+Onj17wsHBAcbGxnB1dcWaNWsQG1u67VE+/0fKN7suz4sXLzBr1iwsXrwYfn5+uHz5Mt6/f49p0/5LqNq3b48xY8bAwcEBXbp0wfHjx9GwYcNCdwP48OEDkpOTlR8LFy4s1X1VZXmLAk+ePBnv3r1D//79lRMXLCwsoKOjA39/f5X10xITEzlrmpWXc+fOYfjw4QCAP//8U5mIFTc2W1tbAMDdu3dV1rr7+PEjp0e5INOnT4dIJMLMmTN5F11OSkpCQIB614PKzMyETKa6NEJ2djbc3NyQkJCAJUuW0DhTUmXUt6iHBhb1OcczczJxp4J2hcjD6NSGqMkq3jJF9Pl8E56CaDdxhUF//t9h8phgJB2ZXaFbfrGKHMhe8M9SFVgPh8BMvTONLz++jBwZdwvK2qa1eNcrVKdi98y9efMGixcvxunTp5X7apqbm8PJyQmmpqYwNDREcnIyEhMTERQUBG9vb3h7e+OXX37BsGHDsHz5cjRo0KDI1zM3N4dQKOT0wsXExHB66/KsXLkSnTp1wvz5uVOwW7ZsCT09PXTp0gW//fabykzIPAKBAG3btsWbNwUvH2JoaKh8HUYKNnr0aMydOxd37uT+EPx0xweBQIDp06dj7dq1cHBwwMCBA5GSkoJLly7B1ta2SFtNFZWvr6+y9zQrKwuRkZG4c+cO3r59Cx0dHWzdulVlf9LixmZtbY3hw4fj77//Rps2bdCjRw9ER0fj/Pnz6N69e5EX1W3evDm2bduG7777Do0aNUL//v1Rv359pKSk4N27d/D29saECROwY8eOEn8tbt++jT17cscF5f2Bdfv2beX9N27cWCUB9fPzw7Bhw9CrVy/Url0bKSkpuHDhAsLCwjBlyhS1Jd6EqEuvFj0RfIM7luzWq9vo1LAjdMQVt3SHoOZoCGIuQhFzkVMme7kAAtNOYHRqFatNPddpkEa8QFbAGU5Z9ovrSLu6DgZ91bvsRx55yA6waTwrFAj1IWq0Qq2xhMWHwS+E/4/pQa0HQsBU7HzSYiVzM2fOxK5duyCXy+Hq6oqvv/4aLi4uqFs3//fq7969g5eXF44cOYLjx4/j77//xtSpU4u8H6ZYLIaTkxOuXbuGoUOHKo9fu3YNgwcP5j0nIyNDOQA8T96Mu/xmLrIsi8DAQLRo0aJIcZHCGRoaYsSIEThw4AAsLS05YxZXrlwJU1NTeHh4YNu2bbC0tMRXX32FZcuWKbeIKwt+fn7w8/MDkLvchqmpKZo1a4YpU6Zg3LhxvMl9cWPz8PCAnZ0d/v77b2zevBl16tTB3Llz8dNPPxVr3bspU6bA0dER69atg4+PD86dOwcjIyPUqVMHP/zwg/I1dUkFBwdzdud4+/atco/Vbt26qSRzderUgYuLC27duoXo6Gjo6uqidevWWLdunbJXk5CqpG4NO9hbNsCb6GCV45nSLNx+fQe9mvesoMhy31CJmq1HTtJDICdOtVCWCunT6dBqe7pYrx4ZhoHxqNWIj30L6cennPK0axshsmkGnZb9Sht+sbCZHyF/yz92Wmj/PzDa6pu8qGAVOOv3D29Za7tWsDWvo7ZY8sOwxVhURldXF1OnTsWCBQtK1HMSHh6O1atXY8+ePSpbJxXG09MTY8eOxY4dO9ChQwfs2rULu3fvxvPnz2Fra4uFCxciPDwcBw4cAJD7i3XKlCnYtGkT+vTpg8jISMyZMwcCgQAPHuRu0bJs2TK0b98e9vb2SElJwaZNm3Dw4EHcuXMHzs7OnBhSUlJgZGSE5ORk6pkjhJAqLCQuFNuuc3vAJSIJFnzxIwx0KnaCkjz6PGQB43jLhPa/QFR/bvHbTIpE3Pr+UKTFccoYsS7MZp2BVj57vJYHacA43m27GIPm0OrwLxiePXXLi997f3g+OME5LhaJMb//XBjpclcUKCtFzT2K1S/47t07bNiwocSvwGrWrImNGzcqewGKatSoUdiwYQOWL18OR0dH+Pj44OLFi8qxSpGRkSprzk2YMAHr1q3Dli1b0Lx5c4wcORKNGjVS7sMJ5I4/mjp1Kpo0aYLevXsjPDwcPj4+vIkcIYSQ6sPO3BYNrbgT4bJl2bj05EoFRKRKaDkAAhv+nV3kwSuhSHxY/DaNrWEyficg5E5aZHMykLhvMhQZicVutyTkMVf4918FIGq6Rq2JXJY0GxcfX+Yt697EpVwTueIoVs/c5x48eIB27dqVZTyVFvXMEUJI9REaF4at17fzls3sNR21zSp2uSlWmoKcO10Avj2/depA3NEHjFbxf1el3zuElJP8kyLEDbvAdPIBMMLyS6bYnHjk3OkMZEdzygS1xkCr+aZyuzaf84EX4RPEXZXAVM8EP/b/AVo8yW9ZKpeeuc8NHz4c0dHcLzghhBCiyWzN68ChTkvesrP+/1Toav8AwGgZQsthF8A3Pi4zDLLnc0u0NZdehzHQ7TCGtyzn9S2kXuBfvLcssCwL2fMfeRM5aJlA1HBJuV2bT2hcGG69us1bNqDVF+WeyBVHqZK5xMREDB8+nLN0AZ/P9+kkhBBCKrMvHPrx/sIOi/+AgJBA9Qf0GYFJOwjrL+AtU0SdgiKcu5dzURgOWQYtu7a8Zeneu5HxiDt+rCwoIk9AEX2Ot0zUaKla91+VyqQ4/uAkb0LcwKI+mtVsqrZYiqJUydzOnTtx9+5d5Wbi+fnxxx8xZcqU0lyKEEIIUStjPWO4NuFfy+zi48vIkha+TV95E9afC8akI2+Z7OVPUKQH85YVhBGJYTJ+BwRG/DNGk0/8jJxQ/2K3WxA28yNkL/gTU0GNXhDU5O8tLC9Xnl5DbCp3fVyhQIhBTgOLtUCzOpQqmRszZgxmzJiB3bt3Y/du7qrZ6enpGDRoENavX1+mS00QQggh6tCtcVeY6JlwjqdmpeLffHYDUCeGEUKr5Q5Ay5hbKE+HLGAcWFlqsdsVGlrAZMJuQMSzq4E8B4n7pkCeHFn8gHmwrALSpzN4t+yClilEzTaqNXl6HxuS7+vVXs17wsqIf43bilTqVe7WrVuHDh06YObMmbh//77yeFhYGDp27Ijz589j0KBB+e6jSgghhFRWWiItDHDk38j+1qvbiE3lLuWhboxOLYjymRjApgVB9uQ7sCUY4yeu4wjjL//iLVOkxiBx32Sw0tJvvSgP3QU2wYe3TNRsnVrXlMuR5eS+XgX39Wpt01ro1riL2mIpjlIncyKRCCdOnICJiQlGjBiBqKgo3Lt3D87Oznj69Cl+/vlnnDlzBnp6emURLyGEEKJWzWs1493mS66Q45z/PyWaaFDWhJYDIKg9kbdMEXMR8uCVJWpXx2ko9Fy/4y2TfniCpOM/ler+FakvIX+9nLdMYDMKQqtBJW67JC49uYL4tHjOcZFAhC/bjYBQoL69YIujWMncjBkzsG/fPjx58gRyuVx53NraGidOnEBsbCxcXV3RvXt3JCcn4+DBg/jjj/Kb+UIIIYSUN4ZhMKj1AN4tm15FvsbDd48qICouUePfwBjyz8CVv10LedSZErVr0P8nSJp05y3L8j+NdK+SbTHI5iRA6v8NoMjiFmrXhKjJnyVqt6SCo9/izmv+t4i9W/SCZSV8vZqnWOvMCQQC5XtriUSCli1bwsnJCa1bt4aTkxNu3LiB+fPnw9raGmfOnEHbtvyzYTQRrTNHCCHV21m/c7jz5h7nuFgkxg99ZsHMQH2zLfPDZn5Ezr3u3O2+AECoC612lyAwLP62lYrMFMRtHAR5LM+i/wwDk0n7oN20R9HjVEgh9R0ONoF/bJpW2zMQmHUtdpwlFZcahy3XtiEjh/vauI5ZbUzvMQ0Cgfr3Xy1q7lGsZO7y5csICAhAQEAA/P398f79e7Asq0zwxGIxcnJy0KdPH/Tr1w8ODg5o2bIljI2NS31DFY2SOUIIqd4ycjKx5uI6pGWlccrszG0xrfvUCvmF/zlFwj1IHw0GWJ5lw7RrQdzhBhhJjWK3K4t9h7gNA8FmcScqMBJ9mM0+By1L7s4ZfKTPf4Tiwz7eMqHtNIiaqO+tXkZOJrZe2847e1UkFGFOn1mwMCz+16sslEsyx3eRwMBAZXIXEBCAoKAg5bpzeUle7dq14ejoiDNnzpT0UhWOkjlCCCEvI4Kwz2c/b1m/ln3g2tRFvQHlQx7mAdkL/j1aGYNm0Gp7FozYtNjtZgfdRMKe8QDPhAqhuR3MZ5+DQJc7+1c1tr2QvZjPH5uxM7Scz4IR8MyiLQdyhRx7b+5DcAz/NqMDWn2Bro06qyUWPmpJ5vhkZ2fj6dOnyuQuICAAT58+RVZWlso4O01DyRwhhBAA+PvRaTx4y93/VCgQYmav6bAxKdn+5WVN+nweFB/4F+xnDFpAq+0ZMOKCEy8+ad67kHpuBW+Z2L4LTKfkv+WXIt4bUt8RAMuTD2jXgrjDdTASi2LHVBIsy+KU7xneZwkALWo1xzedRvOOlVQXtWznxUcikaBNmzaYOnUqtm/fjvv37yM1NRVPnz4t60sRUuaWLl0KhmFw8+bNig6FEFJJDXDsDzN9bq+WXCHHsfvHIZVLKyAqLlGTlfkuKMymPoXUdyjYnMRit6vXdQp02o7kLct5cwsp//zGW6ZIeQJp4ET+RE6oC63Wh9WWyAG5S8vkl8jVNKmJUe1HVmgiVxxqiVIgEKBp08q19QUpPyEhIWAYBgzDoGbNmvn2yD59+lRZr3HjxmqOsujCw8OxYcMG9O7dG3Xq1IFYLIaVlRWGDx+OBw8e5HteSkoK5s6dC1tbW0gkEtja2mLu3LlISeFZGJMQojEkWhKMav8l70K2UcnRuPj4cgVExcUItKDVygOMbj3ecjblSe4kBGlS8dplGBiNWAktWyfe8oxbe5Hx4JjKMUX8bUgfDADyuZaoxfYSTcwoqYCQQFwIvMRbZqRjiAldx0EsEqstntLSjJSTaCSRSISIiAhcuXKFt3zv3r0Qifi74iuTzZs344cffsC7d+/Qq1cv/Pjjj+jcuTPOnj2Ljh074vjx45xz0tPT0a1bN6xfvx6NGjXCDz/8gKZNm2L9+vXo1q0b0tPTK+BOCCFlxc7cFi6N+bf6uvP6Lh6+rRzLlTBic2i1PQPo2PGWsymBkPqOAJvNHfxfYLsiCUwm7ILAyJq3PPnv/yHnXW6vlzz6AqR+IwE5d+IIAAjt/weh1cBiXb80Hrx9iGP3j/MuDKwl1MKEruNhpKNZw6mKlcwFBQWVyUXLqh1SuXXs2BFGRkZwd+eO2cjJycHhw4fRvz//yuqVibOzM3x8fBAcHIy9e/di5cqVOHnyJLy8vCAUCvHdd98hO1t1j8bVq1cjMDAQCxYswNWrV7Fq1SpcunQJixcvRmBgIFavXl1Bd0MIKSu9mveAjTF/MnPK9wzeRr9Tc0T8GJ1aEDufA3Tq8Jazyf7IudsVivhbxWpXaGgB04l78tnyS4rE/VOR82IzZAHjAQX/PrYCq2EQ1vuxWNctjVuvbuPvR6d5EzkGDL7u8BVqVpIxj8VRrGSuefPm+Oabb/Ds2bMSXSwwMBBfffUVWrRQX1cqqTg6OjoYNWoU/vnnH8TFqa55dO7cOcTFxWHiRP4Vy1mWhbu7Ozp16gRDQ0Po6uqiTZs2vIlhREQElixZgvbt28PCwgISiQR2dnaYPn06YmJieNv/8OEDRo8eDVNTU+jr66Nbt27w8eHfTmbYsGHo0oW7hUuXLl3g6uqKhIQElTGhLMtiz5490NfXx+LFi1XOWbhwIUxMTLB3795KsWo8IaTkREIRvmr/JUQC7hsGBavAgTuHKsV2X8D/J3RtzwHatfkrZEdD+mgIZG9WgeUb05YPrdotYfzVWt4yRVo8ko6vBCvn30qMMe8OUYvNatl3lWVZ3HjuhX8CLuRbp59DXzSrpZlDwor1juvXX3/F2rVrcezYMTg4OOCbb75Bt27d4ODgAC0tLU797OxsBAQEwMvLC0eOHMGLFy+gp6fH+QVXHUifzQab+rKiwygSxqAJtJpvLJO2Jk2ahF27duHw4cOYPXu28ri7uzssLCwwYMAAzjksy2LMmDE4cuQIGjZsiK+//hpisRjXrl2Dm5sbXrx4gTVr1ijr+/j4YO3atejRowfatWsHLS0tBAQEYPv27bhy5Qr8/f1hZGSkrB8ZGYkOHTogPDwcffr0QevWrfHy5Uv06tULrq6uxbq/vH/3n74ufvPmDSIiItCnTx/ONnba2tro2rUrzp49i+DgYNjbF21NJkJI5WRlbIWRzsNx9L4npywzJxP7fPZjRq/p0BXrVEB0qhjdOhA7n0POw4FA1keeGizkb1dDkXgXWi13gtHm73X8nE6rwZBGBiH9xhZOmTxViLRnEui3zManOZvAejhELbaCEZT/uDSWZXH5yRV4vfTOt45rE5dKu+9qURQrmVuyZAm+++47/P777zhw4ADmz58PhmGgpaUFOzs7mJiYwMDAACkpKUhISEBoaChkMhlYloWRkRFmz56NhQsXokaNill8ryKxqS/BJvtWdBhq165dOzRr1gzu7u7KZC48PBxXr17FnDlzeMfM7dmzB0eOHIGbmxt27NihrJOTk4MRI0Zg7dq1GD16NJyccgffdu/eHVFRUdDX11dp58CBAxg/fjy2bNmCRYsWKY8vXLgQ4eHh+O2331SO79q1C99++22R7y0sLAzXr1+HlZWVSm/zmzdvACDfRC3v+Js3byiZI6QKaGXniJjUWNx4/i+nLC41DofuHIZbt4mVYl9PRtf2/xO6wUDWB946bMJt5NxyhqDmaAjrTIFAv/CfUwZ950MW/hjZQdxXtdIYEbLeK6BTL3eWr7DOVAib/AFGDTNFs6XZOPHwbzz5kP+KGn1b9Eb3ZsX7Q76yKfZX0sLCAhs3bkRERAT27duHYcOGwdTUFK9fv8aDBw9w/fp1PHz4EMHBwTAzM8OIESPg4eGByMhIrFu3rlomctXdxIkT8eTJE/j5+QEAPDw8IJfLMWnSJN76W7ZsgZ6eHrZs2aKS7InFYvz+++8AgKNHjyqPW1hYcBI5ABg7diwMDQ1x/fp15bGcnBx4enrCwsICP/6oOk5j8uTJaNiwYZHuSSqVYuzYscjOzsbq1ashFP73Qzo5ORkAVHoDP5W3VlBePUKI5uvVvAda1uYfQhQc/RaeD05Arqgca60yunYQd7wBgXnP/CvJ06EI2wPp7XbI8R0JecxVsNmxYD9ZLJhlWSjS3uQuUPxkKnRr+UCox/9KNfOtGDkxQggbLISwyUq1JHJxqXHYen17gYncoFYDND6RA4rZMxcWFoY6dXIHUOro6GD8+PEYP348ACA2NhYxMTFITk6GkZERLCwsKHEjAHKTqoULF8Ld3R1OTk7w8PBAu3bteJerycjIwNOnT2FjY4NVq1ZxyqXS3L/sPp9Ec+rUKezcuRP+/v5ITExUWQ4lIiJC+f+vXr1CVlYWunfvDm1tbZU2BAIBOnbsiNevXxd4PwqFApMmTYKPjw+mTJmCsWPHFv5FIIRUaQJGgC/bjUBCegI+JoRzygNDH0Mml+HrDl9BlM+CuurEiM0hcjoGechWyF+v4N/66/+xcTcgi7vx/yeKAIkFGIkV2KxwIDv6vzYZQN+RQcpDHbBS7ji49BeG0O45QC1j5F5FvsKRu8eQKc3iLWfAYHjboXCuXzX2kC/Wv6i6deti2rRp2Lp1K6esRo0alLwRXhYWFujfvz+OHj2KQYMGITg4GPPmzeOtm5iYCJZlER4ejmXLluXb5qdLe6xduxbz5s1DjRo10Lt3b9SqVQs6OrnjUzZs2KAy0zSvN8zCgn9hSktLywLvhWVZTJkyBYcOHcKYMWOwY8cOTp28Hrn8et7y1pnLr+eOEKKZxCIxJnQZh81XtyI5k7ue5LOPz7H/9kGM6zQGWiLuOHN1YxgBRHVnQmDSAdJAt3xfu6pgZUBWBNisCN5ioS4L/ZZZSPXXBljVpI2V5iDRfRLM5pyHUN+sLG6BQ6FQ4OZLb1x5eo13xiqQm3iPaj8SrWwdyyWGilCsZI5lWWRmZvKW3bhxA23atKFfUPlgDJpUdAhFVh6xTpo0CWfPnoWbmxt0dHQwevRo3np5ryCdnJzg61v4GEOZTIYVK1bAxsYGgYGBKn9QsCzLWQIk799nfrNco6OjeY8DuT8kJk+ejH379mH06NHw8PDg3VT70zFxfAobU0cI0VyGOoaY0HU8tl3fwbsTxKvI19jr44EJXcZBW0s9+48WRmDcBuJO3pA9nwdF1KlSt6dlqoBuoxxkBHHvT574EUn7v4Xpt0fAlPGivLEpsTj+8G+ExoXmW0dXrINvOn4Ne6sGZXrtilZmfb29evXCxIkTsXfv3rJqskopq9mhmqp///6wsrJCeHg4xowZk+8ecwYGBmjSpAlevnyJpKQkGBsbF9huXFwckpOT0aNHD07PsK+vL+ePj0aNGkFbWxu+vr7IyspSedWqUChw9+5d3ut8msiNGjUKBw8eVBkn9yl7e3vY2Njgzp07SE9PV5nRmpWVBR8fH9jY2KBBg6r1w4QQkqumiQ0mdZ2Afbf2I0eWwyl/F/MOe27uxcSu46En0eNpQf0YLWNoOe6BImUO5GG7oIg4CSj4X1EWhU67L8EaipD58G9OWc67B0g5vRiGI1aWyStXBavAndd3cenJFcjk+b8utja2xvjOY2DKsxWbpivTEYi0bhbJj0gkwrlz53D69GnlJIb8zJo1CxkZGZgyZQrvTgnv379HSEgIgNzXpTo6OvD390dGRoayTmJiImbOnMk5VywW48svv0RMTAzWrlVdG2nPnj284+UUCgXc3Nywb98+jBw5EocOHco3kQNyt7qZPHky0tLSsHz5cpWylStXIjExEZMnT1bLuBFCSMWob1kPU1wmQVtLm7c8LP4DNl7ZjNC4MDVHVjCBYXNoNd8EsctTCO1/BSRFW54EABj9xhDUmQKtjjchbrkFRsNXQ1yvHW/djPuHkXFnf6njjUuNw85/d+OfgAsFJnKOdRzwfc9pVTKRAwCGLUYGJhAIMGHCBN6FWwsqqwpSUlJgZGSE5OTkfHuVSK6QkBDUrVsXffr0weXLhe9RyDAMGjVqpJzUwLIsJk6ciP3798Pa2ho9e/aEjY0NoqOjERQUhAcPHuDIkSP46quvAADz5s3D2rVr0aBBAwwcOBApKSm4dOkSbG1tERoaCi0tLWXyB+SuM9e2bVuEh4ejb9++aNWqFV6+fImLFy/CxcUFV69ehZeXF1xcXAAAS5cuxbJly6Cvr4/Zs2fzLqcyZMgQODo6Kj9PT09H586dERgYiF69esHJyQmPHz/GpUuX4OjoiNu3b3PWoCOEVD3hiRHYc9Md6dn8W/gJGAH6O/ZDl4adKuUfeKxCCjbxAdj0YLDZkWCzo4HsaLDZMQAjBGPkCIFpJwhMOoKRcMfNy9PiEb9hAOSJPOvaCYQwnXoYEvtOxY4rIycT/z73wp03dwucJcwwDPo79EPXRp0r5de3MEXNPSp+Sg0hn2EYBh4eHujfvz92796N8+fPIy0tDRYWFrC3t8eaNWvQs+d/U+pXrlwJU1NTeHh4YNu2bbC0tMRXX32FZcuWoXnz5pz2ra2tcffuXSxYsABXrlyBj48PnJyccO3aNfz777+4evWqSv28RDAtLS3fXkU7OzuVZE5PTw83b97EsmXLcPLkSdy8eRNWVlb44YcfsGTJEkrkCKkmaprYYFr3KdjltRepWamccgWrwPmAC3gf8x4j242oFIsLf4oRaIEx6wyYdS7R+UJ9M5hMckf85iFgczJUCxVyJB6YBvPZ/0Bkblek9mRyGe4G38eN5/8iM4d/DH8eUz1TfNluBOpZ1C1R7Jqk2D1zAwcOxN69e2Fubs4po545QgghhCsuNQ67vPYiKSMp3zomusYY7DQITWwaa2QvUkGynl5GoscU3jKRZUOYzToDgbZBvufLFXIEhj7GtWc3kJCeUOj1Otp3QH+HvhCX8SQLdSu3nrnz58/D0tISpqamaNKkCZo2bcq7XhghhBBCcpkbmGNm7+k4etcTwTFveeskZiTB49YBNLJuiIGtBsDCsOos96Xdoi/0+/6ItMvcfVxl0a+RdHgWTCbuBfPZCgGZOZl48PYh7ry+y7vcy+dM9Eww0nk4GljWL7PYNUGxeuZmzJiBJ0+e4MmTJ8q1sgAo/4IQCASwt7eHo6MjWrVqpfwwMyuf9WTUiXrmCCGElJZCocD15//ixvN/810HDcgdS9e5YSf0aNYdOmL+SRSahmVZJB2cjqzH53nL9Xp8D8P+PwPIXWbkXvADPHz3iHdG8OcYhkGHBu3Qr2VfSCrJki9loai5R7GSuU+9f/8ejx8/Vn4EBgaqDDL/tIu4Zs2aaNWqFc6ePVuSS1UKlMwRQggpK2+ignH0nifSstMKrKcr1kG7+u3Q0b49jHQ1fx1XRXYG4rcMgyziOW95TI9ZuKWlh7D4Iixg/P+a2DRGf4e+sDQqeNF3TVTuyRyf1NRUleTu8ePHeP78OTIzM8EwjMoWS5qGkjlCCCFlKTkzBUfveeJdzLtC6woYARzqtETnRp1Q27SWGqIrP/LEcMRt+AKKtHhOmYwR4niT3ojWK/yNXk0TG3zh2L9Kv1KtkGSOj0KhwOvXr/H48WOMGjWqPC9VriiZI4QQUtYUrAJ+7/1x6fGVQnvp8tQ0qYmWtZujee3mqGFgXvgJlYiCVSAqKQohAf/A9uIfELAKTp1ULV0cbdoX6WJd3jYsDS3g2tQFjrYOEDBlulxupVNpkrmysm3bNvz111+IjIxEs2bNsGHDBnTp0iXf+ocPH8bq1avx5s0bGBkZoW/fvlizZo3K+L2///4bv/76K96+fYv69evj999/x9ChQ3nbo2SOEEJIecnMycL15zdw5/VdKHgSnPxYG1uhea3maGhlj5omNhAJK9eKY1KZFB8TPuJ9XChCYkMQGheKTGnuzhLNYoPRO+Q+73kReuY42bgX5IL/FmhvYFkfXRt1QSPrhlVutm9+qlQy5+npibFjx2Lbtm3o1KkTdu7ciT179uDFixeoU6cOp/7t27fRrVs3rF+/HgMHDkR4eDimTZsGe3t7nD59GgBw7949dOnSBStWrMDQoUNx+vRpLF68GLdv30a7dtwVqymZI4QQUt5iUmJwzv88Xkfx7+1cEJFAhFqmtWBnXgd1zOvAysgSJnomEAry37GmrMgVciRnJCMqORpRydGITIpEVHI0YlNiC0xOu4X5onV0EG/Zc/N6uFGvMxxsHdC1UWfYmNiUV/iVVpVK5tq1a4fWrVtj+/btymNNmjTBkCFDsHLlSk79NWvWYPv27Xj79r/p35s3b8bq1avx4UPuoMpRo0YpdwrI07dvX5iYmODo0aOcNimZI4QQoi4hsSG49eoOnoU/L9VWmUKBEGb6pjA3MIeZvhkMtPWhJ9GDnkQP+tp60BHrQiQQQSQQQijM/S8AyBRyyOUyyBQyyOQyZMmykZGdjvTsDKT//3+TM5KRmJ6IxPREJGemFKtHMQ/DKjD09b+wTYniLdfu/zNMenxf4vvXdFVmB4icnBz4+fnh559/Vjneu3fvfDdF79ixIxYtWoSLFy+iX79+iImJwcmTJ/HFF18o69y7dw8//PCDynl9+vTBhg0bCozn0yVZAEAikUAiqTrToAkhhFQ8uxp2sKthh4S0RNx5cxeP3j1CljS72O3IFXLEpMQiJiW2HKIsPZYR4EL9Lhj94jJMsrk7ZGRdWo2smk2h3di1AqLTHJV+5GBcXBzkcjksLVWnHFtaWiIqij+T79ixIw4fPoxRo0ZBLBbDysoKxsbG2Lx5s7JOVFRUsdrMU7t2bRgZGSk/+HoGCSGEkLJgqm+Cga2+wP8GLcSwNkNhb9mgygz6FwqEaGhljz7Ow2ExeT8Yvh0gWAWSDs6ALJ+FlkmuSt8zl+fzwY4sy+Y7APLFixeYNWsWFi9ejD59+iAyMhLz58/HtGnTsHfv3hK1mefDhw8qXZ3UK0cIIaS8aWtJ0L6BM9o3cEZGdgZehL/E04/P8DrqTYEbzVcmQoEQtU1r5fY6mtuinkU9aH+ywG/WmC1I3DsB+Oy1MpuVggT3STCffQ4CHc1fa688VPpkztzcHEKhkNNjFhMTw+lZy7Ny5Up06tQJ8+fPBwC0bNkSenp66NKlC3777TdYW1vDysqqWG3mMTQ0pDFzhBBCKoyuRBdt6jmhTT0n5Mhy8CH+I0LiQhESF4KwuDDlbNGKJBQIYWFoAWsjS1gZW8POvA5qmtaEllAr33O0m3SHQf+FSL3wB6dMHvsOSQe/h8nk/WDUMKFD01T6ZE4sFsPJyQnXrl1TWTbk2rVrGDx4MO85GRkZEIlUb00ozH34eQNJO3TogGvXrqmMm7t69So6duxY1rdAKoGHDx/C1dUVI0aMwP79+ys6HEIIKRNikRj1LeuhvmU9ALnruMWmxCI6JQZxKXGITY1FbGocYlPjkJmTWabXFgqEMNEzhomuCUz1TWGiZwJTPRNYGVuhhoF5iWbR6rlOgzTyJbL8T3PKsl95I/X87zActLgswq9SKn0yBwBz587F2LFj0aZNG3To0AG7du1CWFgYpk2bBgBYuHAhwsPDceDAAQDAwIEDMWXKFGzfvl35mnXOnDlwdnaGjU3u1ObZs2eja9eu+PPPPzF48GCcPXsW169fx+3btyvsPgk/FxcXeHt7l2pGl7OzMzw9PTFkyBB06tQJU6dOLcMICSGkchAwAlgaWfJubSWVSZGWnf7/s1HTkJaVjmxZdu7MVeXsVTkAFkKBCKL/n90qEoigJdL6/1mwutAV60JPogdtLe0yX++NYRgYf/kn4mPfQfrhMac83Xs3RFaNoev8ZZleV+OxGmLr1q2sra0tKxaL2datW7Pe3t7KsvHjx7PdunVTqb9p0ya2adOmrI6ODmttbc1+88037MePH1XqnDhxgm3UqBGrpaXFNm7cmP3777/zvX5ycjILgE1OTi7T+6rqxo4dywJgLS0tWalUWqI2unXrxpbVP9WdO3eyEomE9fPzy7fOrVu32Llz57KtW7dmTU1NWYlEwjZq1IhdsGABm5iYyHvOwYMH2alTp7JOTk6sWCxmAbD79u0rk5gJIaS6kSVFslFLW7MRc2txP+bXY7Pf+1Z0iGpR1NxDI9aZqwxonbniS0lJgbW1NTIzM8GyLM6cOZPvq/GChIWFISMjA40bNy6TuA4cOACWZTF+/HjecisrK8TFxaFz585o1aoVGIbBzZs3ERAQgPr16+Pu3buwsLBQOcfOzg6hoaEwNzeHnp4eQkNDsW/fPkyYMKFMYiaEkOomJywA8VtHAjLukiwCgxown3MBQmPrCohMfYqae1SN+c2kUjp69CgyMjLw448/gmEYlZnExVGnTp0yS+QAYNy4cfkmcgDwww8/4MOHD7h58ybWr1+PdevWwc/PD9999x3evn2LZcuWcc7Zs2cPQkJCEBsbq3z9TwghpOTEdVrB+Mu/eMsUqbFI3OcGtozHAWoqjRgzVxUkHV8AWdSrig6jSERWjWD85epSt7N3716IxWIsXLgQ9+/fx8WLFxEZGQlra9W/pBiGQbdu3XD48GEsWrQIly9fRkxMDP7991+4uLgUOGbu3Llz2Lp1K3x9fZGeng4rKyt06dIFP/30E5o3b66sFx8fj99++w1nzpxBREQEjIyM4OrqiiVLlqBp06Yqbf7000+c6zAMg19//RXbt2+Ht7c3p7xnz54l/TIRQgjJh47TUEgjXyLdazunTPrxKZI858F4zJZqs1drfiiZUxNZ1CtIQ/0rOgy1efr0KR49eoShQ4fC1NQU48aNw+3bt7F//37Obh5AbrLVoUMHmJqaYtSoUcjJySn0dfaCBQvw119/wdTUFEOGDIGFhQU+fPiA69evw8nJSZnMxcfHo3379ggODoaLiwu++uorhISE4OTJk7hw4QKuXbuGDh06FHpPWlq5U+o/nylNCCGk/Bj0/wmyqFfIfvkvpywr8BzSrRtDv+fMCois8qDfSqRc5L1SHTt2LADgyy+/xKxZs+Du7s6bzD179gwTJ07E7t27lcvIFOTixYv466+/0KJFC3h5ecHMzExZJpPJEB8fr/x8wYIFCA4OxsKFC/HHH/+tXzRhwgT07dsX48ePR1BQEASCgkcduLu7A8jdSo4QQoh6MAIhjL/ZjPjNQyCLfsMpT720GiKrhtBu3qcCoqscaMwcKXM5OTk4dOgQTExMlPvhGhkZYfDgwXjz5g18fHw454jFYqxevbpIiRwAbN26FQCwceNGlUQOyO05y1v8OScnB0ePHoWZmRl++eUXlXp9+vRBnz598ObNm3z3+c0TGBiIZcuWwcLCAgsWLChSjIQQQsqGQMcQJpP2gslnB4ikI7MhjQxSc1SVByVzpMydOXMG8fHxyr1x84wbNw7Afz1cn6pbty7Mzc2LfI2HDx9CIpGgW7duBdYLCgpCZmYmnJ2doauryyl3cXEBkJus5ef9+/cYMGAA5HI5jh07Vqw4CSGElA2ReV2YjNsO8CxGzGanI9HdDYr0xAqIrOLRa1Y1EVk1qugQiqy0seYla3mvWPP06dMHVlZWOHHiBDZt2qQyJq6wbdQ+l5SUhJo1axb6ajQlJaXA9q2srAAAycnJvOWhoaFwdXVFbGws/v77b7i6uhYrTkIIIWVH0rALDActRsqZJZwyeUIYEg9Mg+nUQ2AK2DasKqJkTk3KYnaoJvjw4QOuXbsGAOjUqVO+9Y4dO6ayC0NxZyIZGxsjKioKCoWiwIQuL2GMjo7mLc87zjfZIiQkBK6uroiIiMCJEycwYMCAYsVICCGk7Ol2nghpxEtkPjzGKcsJvouUM0thNPz3Cois4lAyR8rUvn37oFAo0LlzZzRqxO3hy8nJwcGDB7F3795Sbanl7OyMixcvwtvbu8DessaNG0NbWxuPHj1CRkYG51Vr3jIjjo6OKsdDQkLg4uKCiIgIeHp6lmixY0IIIWWPYRgYDf8Nspi3kIY84pRn3D0AkXVj6HUcy3N21UTJHCkzLMti3759YBgGBw4cQN26dXnrPXv2DA8fPsSzZ89U1oIrju+//x4XL17E7NmzcfPmTZiamirL8mazWlpaQiwWY/To0di3bx9WrlyJFStWKOtdv34dly5dQoMGDVR6EfMSufDwcHh6emLo0KElipEQQkj5YEQSmEzYibgNA6BIiuCUp5xeDJFlA0jqF77sVFVAyRwpMzdu3FC+mswvkQOAiRMnIiAgAHv37sX69etLdK3+/ftj3rx5WLNmDezt7TF06FBYWFggPDwcN27cwLx58zBnzhwAwJ9//glvb2/89ttvuHv3Ltq1a6dcZ05XVxf79u1TeVXr4uKC0NBQtG/fHk+ePMGTJ08411+6dKnK53v27MHt27cB5K6xl3fs5s2bAIAhQ4ZgyJAhJbpXQgghXEKDGjCd5I74LUO5O0EoZEj0+Bbmc85DZFanYgJUI9qbtYhob9bCjR49GseOHcPBgwcxZsyYfOvFx8fDxsYGhoaGCA8PV85KzUt8PlfQDhCnTp3Cli1b4O/vj6ysLFhbWyt3gGjWrJmyXlxcHFasWIGzZ88qd4BwcXHBkiVLOL2DRRm/93ksEyZMwP79+/Otv2TJEk4CSAghpPQyH59H0oHveMtE1o1hNuM0BNr6ao6qbBQ196BkrogomSOEEEIqp9TLa5F2bQNvmaR5H5iM3wWmkNUPKqOi5h6ad2eEEEIIIZ/Q7/0DJC368pZlP7uCtKvr1ByRelEyRwghhBCNxggEMB69ASLrJrzladc2IjPwHzVHpT6UzBFCCCFE4wkkejCZtBcCPVPe8qRjcyH9+FTNUakHJXOEEEIIqRJEprVhPH4nIOBZrEOahQR3N8hTY9UfWDmjZI4QQgghVYakfnsYDfuNt0yRHInEfVPAyrLVHFX5omSOEEIIIVWKbodvoNtpPG+ZNNQPySf/x7vclaaiZI4QQgghVY7h4CUQN+jIW5b56DgybrmrOaLyQ8kcIYQQQqocRqgFk3E7IDTl3wEi5dxyZL/yVnNU5YOSOUIIIYRUSQI9E5hM2gtGosctZBVIPPg9ZLHv1B9YGaNkjhBCCCFVlpZ1Yxh/vQng2aqRzUxGwt5JUGSmVEBkZYeSOUIIIYRUadrNe8Og3wLeMnnsWyQdmgFWIVdzVGWHkjlCCCGEVHl63b+HtuMg3rLsIC+kXlil5ojKDiVzhBBCCKnyGIaB8ag10KrVgrc8/eYOZPj+reaoygYlc4QQQgipFhixDkwm7oXAoAZvefKJn5ATGqDmqEqPkjlS5kJCQsAwDBiGQc2aNSGX849DePr0qbJe48aN1Rxl2QkPD8eGDRvQu3dv1KlTB2KxGFZWVhg+fDgePHjAqS+VSvH3339jwoQJaNKkCfT09GBgYIB27dph27Zt+X69CCGElJ7Q2BomE3YDQjG3UJaNRI/JkCdHqj+wUqBkjpQbkUiEiIgIXLlyhbd87969EIl49s/TMJs3b8YPP/yAd+/eoVevXvjxxx/RuXNnnD17Fh07dsTx48dV6r99+xYjRozAqVOn0KhRI8yYMQPffPMNPn78iO+//x5Dhw6tUiuTE0JIZSO2c4LRSP4xcoqUmNwtv6SZao6q5BiWfmsUSUpKCoyMjJCcnAxDQ8OKDqdSCwkJQd26ddG1a1c8fvwYPXv2xMmTJ1Xq5OTkoGbNmujYsSPOnTuHRo0aISgoqIIiLp1Tp06hRo0a6NKli8rxW7duoUePHjAwMEBERAQkEgmA3J68c+fOYfz48dDV1VXWT09Ph4uLC3x9fXH8+HGMHDlSrfdBCCHVTcq5FUj33sVbpt16KIy/3giGZ0kTdSlq7qH53SIa4tUfq5D+VjMWJtSrXw+N/vdzqdvR0dHBqFGj4OHhgbi4OJibmyvLzp07h7i4OEycOBHnzp3jnJuRkYHVq1fj2LFjCAkJga6uLjp06IBFixahY0fV7VmWLl2KZcuWwcvLC6Ghodi0aRNevnwJZ2dn3Lx5E8nJydixYwcuXryIN2/eKGPp2bMnlixZgvr16+fbXkxMDFatWoWXL1/C2NgYI0eOxJ9//gkdHR1l/WHDhvHef5cuXeDq6oqrV6/i6dOnaNOmDQCgZs2a+O677zj19fT0MHfuXHz99dfw9vamZI4QQsqZwYD/QRb9GtlBNzllWf6nkW7dGPrdp6s/sGKiZE5N0t++Q+qz5xUdhtpNmjQJu3btwuHDhzF79mzlcXd3d1hYWGDAgAGcc7Kzs9GjRw/cv38frVu3xpw5cxATEwNPT09cvXoVnp6evAnUX3/9BS8vLwwaNAi9evVSvsJ9+fIlFi9eDFdXVwwdOhR6enoICgrCkSNHcOHCBfj7+8PW1pbT3tatW3Hp0iUMHjwYLi4uuHz5MjZv3oz4+HgcPny4SPevpaUFAEV+nVzc+oQQQkqOEQhhPGYL4jYOgpxnJ4jUi6sgsmoE7aY9KiC6otOYMXPbtm1D3bp1oa2tDScnJ9y6dSvfuhMmTFAOrP/0o1mzZso6Hh4evHWysrLUcTvVRrt27dCsWTO4u/+3oXF4eDiuXr2KsWPH8iYtq1evxv379/HNN9/A19cXq1atgru7O+7duwehUIjJkycjNTWVc563tzcePHiAY8eOYdWqVfjtt98AAE2aNEFkZCQuX76MrVu3YvXq1Th37hyuXbuGpKQkZb3PXbt2DX5+fjh8+DDWrVsHPz8/NGzYEEePHkVERESh9x4WFobr16/DysoKLVrwT4X/XN7XqXfv3kWqTwghpHQEOkYwneQORpvnNSbLIunQDEij36g/sGLQiGTO09MTc+bMwaJFixAQEIAuXbqgX79+CAsL462/ceNGREZGKj8+fPgAU1NTzmsrQ0NDlXqRkZHQ1tZWxy1VKxMnTsSTJ0/g5+cHIDeRlsvlmDRpEm99Dw8PaGlpYdWqVSpjFVq2bIkJEyYgMTERZ8+e5Zw3depU3qTJyMgIpqamnOOurq5o1qwZrl+/zhvH7Nmz0ahRI+XnOjo6GD16NFiWVd5LfqRSKcaOHYvs7GysXr0aQqGwwPoAsGvXLly6dAndu3dH//79C61PCCGkbIgs6sNk7FaA4aZFbHYaEt0nQZGRWAGRFY1GJHPr1q2Dm5sbJk+ejCZNmmDDhg2oXbs2tm/fzlvfyMgIVlZWyg9fX18kJiZi4sSJKvUYhlGpZ2VlpY7bqXbGjh0LLS0tZa+Th4cH2rVrh6ZNm3LqpqSk4N27d2jQoAFq1arFKXdxcQEABAYGcsqcnZ3zjeHmzZsYMmQIrK2toaWlpeyJffr0ab69bK1bt+Ycy4spKSkp32spFApMmjQJPj4+mDJlCsaOHZtv3TwXLlzAjBkzYGtri0OHDhVanxBCSNmSNHaBwcBfeMvkcSFIPPA9WLlMzVEVTaUfmJOTkwM/Pz/8/LPqgPzevXvj7t27RWpj79696NmzJ2dcVFpaGmxtbSGXy+Ho6IgVK1agVatWBbaVkqK6Ga9EIlHOUiyIXv16RYq1MijrWC0sLNC/f38cPXoUgwYNQnBwMObNm8dbN+/ra2lpyVuel3AnJydzyvI758SJExg1ahT09fXRp08f2NnZQVdXFwzDwMPDA6GhobznGRkZcY7lvRbOby04lmUxZcoUHDp0CGPGjMGOHTt4633qypUrGD58OCwtLfHvv//C2tq60HMIIYSUPb2ukyGLfInMRyc4ZTlvbiHlnxUwGrKsAiIrWKVP5uLi4iCXyzm/qC0tLREVFVXo+ZGRkbh06RKOHDmicrxx48bw8PBAixYtkJKSgo0bN6JTp054/Pgx7O3t822vdu3aKp8vWbIES5cuLTSOspgdqskmTZqEs2fPws3NTfm6kk/e1Ovo6Gje8rzjfFO085s+vnTpUmhra8PPz4/zbI8dO1bkeyiMQqHA5MmTsW/fPowePRoeHh4QCAru/L58+TKGDh0Kc3NzeHl5oV49zUn6CSGkqmEYBkYjVkIW8w7SUO5wmoxb7tCyagzd9vy/wypKpU/m8nz+i5pl2SKt/eLh4QFjY2MMGTJE5Xj79u3Rvn175eedOnVC69atsXnzZmzatCnf9j58+KCSSBSlV44A/fv3h5WVFcLDwzFmzJh818sxNDREvXr1EBwcjPDwcNSsWVOl3NvbGwDg6OhY5Gu/ffsWzZo14yRyERERePv2bfFuJB+fJnKjRo3CwYMHCx0nd/nyZQwZMgSmpqbw8vJCgwYNyiQWQgghJceIJDCZsAtxGwZAwbMTRPKpRRBZ1Ie4Xv5De9St0o+ZMzc3h1Ao5PTCxcTE5PtaLQ/LsnB3d8fYsWMhFvNs2/EJgUCAtm3b4s2bgmesGBoaqnxQMlc0IpEI586dw+nTp/H7778XWHf8+PGQSqVYuHChyk4Iz549w759+2BkZMRJzgtia2uL4OBgld6+rKwsfPfdd5DJSj/+QaFQwM3NDfv27cPIkSNx6NChIidyJiYm8PLyKrA3mBBCiHoJDS1gOmkvoMUzKVIuReL+qZAnhqs/sHxU+p45sVgMJycnXLt2DUOHDlUev3btGgYPHlzgud7e3ggODoabm1uh12FZFoGBgUVeQoIUX9u2bdG2bdtC6y1YsAAXLlzAwYMH8fLlS/To0QOxsbHw9PSEVCrFgQMHYGBgUOTrzpw5EzNnzkSrVq0wYsQIyGQyXLt2DSzLwsHBAY8fPy7NbWH58uXw8PCAvr4+GjZsyLvUyZAhQ5S9iUFBQRgyZAiys7Ph4uKCo0ePcurb2dlhwoQJpYqLEEJIyWnVagHjUWuRdOh7TpkiLR4J7m4wm3EKAokuz9nqVemTOQCYO3cuxo4dizZt2qBDhw7YtWsXwsLCMG3aNADAwoULER4ejgMHDqict3fvXrRr1w7NmzfntLls2TK0b98e9vb2SElJwaZNmxAYGIitW7eq5Z5I/rS1tfHvv//izz//hKenJ9avXw9dXV107doV//vf/9C5c+ditff9999DS0sLmzdvxu7du2FsbIwvvvgCf/zxB7788stSxxsSEgIgd0JNfr2OdnZ2ymQuKioK2dnZAPIfs9etWzdK5gghpILptBoEWVQQ0q5v5pTJIp4j+dhcGI/dBqaQ8dHlTWP2Zt22bRtWr16NyMhING/eHOvXr0fXrl0B5C4SHBISgps3byrrJycnw9raGhs3bsSUKVM47f3www84deoUoqKiYGRkhFatWmHp0qXo0KED7/Vpb1ZCCCGk+mEVCiR6TEH286u85fp9foRB7znlcu2i5h4ak8xVNErmCCGEkOpJkZWG+M2DIYt6zVtuPH4XdFr2K/PrFjX3qPQTIAghhBBCKpJAWx8mk9zB6JrwlicfnQ1pxAs1R/UfSuYIIYQQQgohMrOFyfidgIA73YDNyUTOu4cVEFUuSuYIIYQQQopA0qADDIeq7gDBiHVhMmEX9DpPqJigQMkcIYQQQkiR6XUcB90OYwAAQtPaMJt1Btotyn68XHFoxNIkhBBCCCGVheHQ5WAketBz/Q5CfbOKDoeSOUIIIYSQ4mCEWjAc+EtFh6FEr1kJIYQQQjQYJXOEEEIIIRqMkjlCCCGEEA1GyRwhhBBCiAajZI4QQgghRINRMkcIIYQQosEomSOEEEII0WCUzFUi2dnZWLp0KbKzsys6FFLO6FlXH/Ssqxd63tVHZXrWDMuybEUHoQlSUlJgZGSE5ORkGBoaauw1SOVAz7r6oGddvdDzrj4qU15APXNlYOvWrcUuK+icyqa8Yi1Nu8U9t6j1C6tHz1r97WrSsy7O9SsaPevSl2vKswbKJ9bStlkZn7fGPmuWFElycjILgE1OTuaUNWnSJN/z8ivjO17QNSpSQfdXUe0W99yi1i+sHj1r9berSc+ar4yedfmdq45nXVB5df/eLm2blfF5V7ZnXdRr0N6sRcT+/9volJQUTplcLuc9XlAZ3/G8z/Nrq6IUdH8V1W5xzy1q/cLq0bNWf7ua9Kz5yuhZl9+56njWBZVX9+/t0rZZGZ93ZXvWeW2zhYyIozFzRfTx40fUrl27osMghBBCSDXz4cMH1KpVK99ySuaKSKFQICIiAgYGBmAYpqLDIYQQQkgVx7IsUlNTYWNjA4Eg/2kOlMwRQgghhGgwms1KCCGEEKLBKJkjhBBCCNFglMwRQgghhGgwSuYIIYQQQjQYJXMaKDU1FW3btoWjoyNatGiB3bt3V3RIpJx8+PABLi4uaNq0KVq2bIkTJ05UdEiknA0dOhQmJiYYMWJERYdCytj58+fRqFEj2NvbY8+ePRUdDilH6v4+ptmsGkgulyM7Oxu6urrIyMhA8+bN8ejRI5iZmVV0aKSMRUZGIjo6Go6OjoiJiUHr1q3x6tUr6OnpVXRopJx4eXkhLS0N+/fvx8mTJys6HFJGZDIZmjZtCi8vLxgaGqJ169Z48OABTE1NKzo0Ug7U/X1MPXMaSCgUQldXFwCQlZUFuVxe6OrQRDNZW1vD0dERAGBhYQFTU1MkJCRUbFCkXLm6usLAwKCiwyBl7OHDh2jWrBlq1qwJAwMD9O/fH1euXKnosEg5Uff3MSVz5cDHxwcDBw6EjY0NGIbBmTNnOHW2bduGunXrQltbG05OTrh161axrpGUlAQHBwfUqlULCxYsgLm5eRlFT4pDHc86j6+vLxQKBe1EUoHU+bxJ5VLaZx8REYGaNWsqP69VqxbCw8PVETopJk38Pqdkrhykp6fDwcEBW7Zs4S339PTEnDlzsGjRIgQEBKBLly7o168fwsLClHWcnJzQvHlzzkdERAQAwNjYGI8fP8b79+9x5MgRREdHq+XeiCp1PGsAiI+Px7hx47Br165yvyeSP3U9b1L5lPbZ8709od2EKqey+D5XO5aUKwDs6dOnVY45Ozuz06ZNUznWuHFj9ueffy7RNaZNm8YeP368pCGSMlJezzorK4vt0qULe+DAgbIIk5SR8vze9vLyYocPH17aEEk5Kcmzv3PnDjtkyBBl2axZs9jDhw+Xe6ykdErzfa7O72PqmVOznJwc+Pn5oXfv3irHe/fujbt37xapjejoaKSkpAAAUlJS4OPjg0aNGpV5rKR0yuJZsyyLCRMmoHv37hg7dmx5hEnKSFk8b6KZivLsnZ2d8ezZM4SHhyM1NRUXL15Enz59KiJcUgqV9ftcVGFXrqbi4uIgl8thaWmpctzS0hJRUVFFauPjx49wc3MDy7JgWRYzZsxAy5YtyyNcUgpl8azv3LkDT09PtGzZUjlu4+DBg2jRokVZh0tKqSyeNwD06dMH/v7+SE9PR61atXD69Gm0bdu2rMMlZagoz14kEmHt2rVwdXWFQqHAggULaAUCDVTU73N1fx9TMldBPh8rwbJskcdPODk5ITAwsByiIuWhNM+6c+fOUCgU5REWKSeled4AaIajBivs2Q8aNAiDBg1Sd1ikHBT2rNX9fUyvWdXM3NwcQqGQ85d6TEwMJ9Mnmo2edfVCz7v6omdffVTWZ03JnJqJxWI4OTnh2rVrKsevXbuGjh07VlBUpDzQs65e6HlXX/Tsq4/K+qzpNWs5SEtLQ3BwsPLz9+/fIzAwEKampqhTpw7mzp2LsWPHok2bNujQoQN27dqFsLAwTJs2rQKjJiVBz7p6oeddfdGzrz408lmrZc5sNePl5cUC4HyMHz9eWWfr1q2sra0tKxaL2datW7Pe3t4VFzApMXrW1Qs97+qLnn31oYnPmvZmJYQQQgjRYDRmjhBCCCFEg1EyRwghhBCiwSiZI4QQQgjRYJTMEUIIIYRoMErmCCGEEEI0GCVzhBBCCCEajJI5QgghhBANRskcIYQQQogGo2SOEEIIIUSDUTJHCCGEEKLBKJkjhBBCCNFglMwRQgghhGgwSuYIIaQa8PHxweDBg2FrawuGYbB06dKKDokQUkYomSOEkGogLS0NTZs2xerVq2FlZVXR4RBCyhAlc4QQjXLp0iUwDIPp06cX+9ybN2+CYRiVj6SkJJU6n5bdu3cv37aOHz+urGdnZ1fsWNStf//+WLlyJUaNGgWJRFJgXWNjY5Wvg4eHh3qCJISUiKiiAyCEkOIICAgAADg5OZW4jfr166Nz584AALFYnG+9w4cPo0OHDrxlhw4dKvH1K7uvv/4aGRkZCAwMxOPHjys6HEJIISiZI4RolLJI5jp37lxgb5NEIkH9+vXh6emJDRs2QCRS/VEZHx+Py5cvo3Xr1vD39y9xHKWRkJCAhISEAusYGhrCwsKi2G1v27YNALB06VJK5gjRAPSalRCiUfz9/SGRSNCsWbNyvc4333yDuLg4XLlyhVPm6ekJqVSKMWPGlGsMBdm0aRPs7e0L/FiwYEGFxUcIUR9K5gghGiM5ORnv379Hy5YtoaWlpVJ26NAh6OrqwsbGpsCxbkX1zTffgGEY3tephw4dgr6+PgYPHsx7bkhICBiGgYuLC1JSUjB79mzUrl0b2traaNKkCdavXw+FQpHvtcPCwjBjxgzY29tDW1sbZmZmcHZ2xh9//IHMzEwAwOLFiyGVSgv8cHd3L/XXgRBS+VEyRwjRGIGBgWBZVuUVq1QqxaxZszB27Fg4ODjA19c333FuxWFra4tOnTrh3LlzSEtLUx5///497t27h2HDhkFXV7fANrKzs9G9e3ccOHAAzs7O6NWrF0JDQzF37ly4ubnxnuPj44OWLVti69atUCgUGDx4MDp06IC4uDgsWrQI0dHRAACBQACRSFTgh0BAP+IJqQ5ozBwhRGPkjZdr3bo1ACAyMhIjR47EnTt3MHnyZGzdurXACQ3FNWbMGNy+fRunTp3CuHHjAPw38eGbb74p9Pz79++jZcuWePPmDczNzQEAb9++RdeuXeHh4YGhQ4di0KBByvqJiYkYMWIEkpOTsX79esyePRsMwyjLfXx8YGJiUqJ7SUtLQ3BwMAAgJycHUVFRCAwMhFgsRtOmTUvUJiGkcqA/2wghGiNvsoGTkxNu376N1q1b4+HDh9i+fTt2795dpokcAHz55ZcQi8U4fPiw8tjhw4dhZWWFHj16FKmNNWvWKBM5IHcm7a+//goA2Lp1q0rd3bt3IzY2FgMGDMCcOXNUEjkA6Nq1K4yMjEp0L76+vmjVqhVatWqFyMhI7Ny5E61atUL//v1L1B4hpPKgZI4QojECAgIgFovx77//onv37gAALy8vTJs2rVyuZ2Jigv79++PGjRuIiorCo0eP8OrVK4wePRpCobDQ801NTdGrVy/O8a+//hoAcPfuXbAsqzx+/fp1AMC3335bRnfwHxcXF7Asy/kICQkp82sRQtSLkjlCiEbIyspCUFAQcnJyMH/+fNStWxe+vr7o1KlTuV53zJgxkMvlOHbsmPIVa1Fnsdra2vIeNzQ0hLGxMdLS0pCSkqI8/uHDBwC5vXeEEFJUNGaOEKIRnj59CplMhj59+uDx48cICQlBSEgIatasWa7XHTBgAIyNjXHgwAFERESgSZMmyjF7pfFpj9znPn+9SgghBaGeOUKIRsgbL9evXz+cOXMGDMNg6NCh5f6aUCKRYMSIEQgICEB0dHSx1pYLCwvjPZ6SkoLk5GTo6enB0NBQebx27doAoJyoQAghRUHJHCFEI+TNZHVwcEC7du2wZ88exMbGYuDAgUhNTS3Xa48bNw5mZmYwNzcv0izWPPHx8cpxcJ86evQoAKBjx44qvXA9e/YEAOzatauUERNCqhNK5gghGiEvmWvZsiWA3HFrP//8M549e4bRo0cXuAhvaXXp0gVxcXGIjY3NdxxcfubPn4/4+Hjl5+/fv8eKFSsAANOnT1epO3nyZJibm+Off/7Bli1bOK9ib926heTk5BLeBSGkqqJkjhBS6cnlcjx9+hS1a9eGqamp8vgff/yBQYMG4cKFC5g3b14FRsivffv2EAgEsLe3x4gRIzBo0CA0b94c4eHhGDNmDIYMGaJS39TUFMePH4eBgQFmzpwJe3t7jBo1CgMHDkS9evXQtWtXJCYmVszNEEIqLUrmCCGVXlBQEDIzM+Hg4KBynGEYHD58GC1atMD69euxZ8+eCoqQn0Qiwb///ovRo0fj3r17uHLlCmrXro01a9bAw8OD9xxXV1cEBgZi6tSpkMlkOHPmDO7fvw8LCwusXLkSVlZW6r0JQkilR7NZCSGVXrNmzfKd/amvr48nT56U2bUKmmX6OSsrq0LrGxkZYevWrZwFggtSr1497Ny5s8j1CSHVGyVzhJBq5/bt25gwYQIAYNu2bYXusVrdTJ8+HRkZGQgMDKzoUAghRUDJHCGk2nn79i3evn0LANiwYQMlc585cuQITbQgRINQMkcIqTbytrQiBUtKSqroEAghxcCw9JONEEIIIURj0WxWQgghhBANRskcIYQQQogGo2SOEEIIIUSDUTJHCCGEEKLBKJkjhBBCCNFglMwRQgghhGgwSuYIIYQQQjQYJXOEEEIIIRqMkjlCCCGEEA1GyRwhhBBCiAajZI4QQgghRIP9H61HKt0YbnMcAAAAAElFTkSuQmCC"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 9
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python (ccl3)",
+   "language": "python",
+   "name": "ccl3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "165px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pyccl/baryons/fedeli14_bhm/examples/plot_style.py b/pyccl/baryons/fedeli14_bhm/examples/plot_style.py
new file mode 100644
index 000000000..be76e84de
--- /dev/null
+++ b/pyccl/baryons/fedeli14_bhm/examples/plot_style.py
@@ -0,0 +1,122 @@
+"""Plot style functions."""
+
+from __future__ import annotations
+
+from contextlib import contextmanager
+from typing import Any, Dict, Optional
+import warnings
+
+import matplotlib as mpl
+from matplotlib.axes import Axes
+from matplotlib.ticker import (
+    LogFormatterExponent,
+    LogLocator,
+    NullFormatter,
+)
+
+
+PLOTTING_PARAMS: Dict[str, Any] = {
+    "lines.linewidth": 4,
+    "xtick.labelsize": 12,
+    "ytick.labelsize": 12,
+    "axes.labelsize": 17,
+    "legend.fontsize": 15,
+    "legend.frameon": True,
+    "xtick.direction": "in",
+    "ytick.direction": "in",
+    "xtick.top": True,
+    "ytick.right": True,
+    "figure.autolayout": True,
+    "xtick.minor.visible": True,
+    "ytick.minor.visible": True,
+}
+
+
+def apply_plot_style(params: Optional[Dict[str, Any]] = None) -> None:
+    """Apply project plotting style globally via matplotlib rcParams."""
+    cfg = (
+        PLOTTING_PARAMS
+        if params is None
+        else {**PLOTTING_PARAMS, **params}
+    )
+    mpl.rcParams.update(cfg)
+
+
+@contextmanager
+def plot_style(params: Optional[Dict[str, Any]] = None):
+    """Temporarily apply plot style inside a `with` block (restores after)."""
+    style = PLOTTING_PARAMS if params is None else {**PLOTTING_PARAMS,
+                                                    **params}
+    with mpl.rc_context(style):
+        yield
+
+
+def set_log_scale_and_format(
+    ax: Axes,
+    *,
+    x_numticks: int = 10,
+    y_numticks: int = 5,
+    base: float = 10.0,
+    minor: bool = True,
+) -> None:
+    ax.set_xscale("log")
+    ax.set_yscale("log")
+
+    # --- majors ---
+    ax.xaxis.set_major_locator(LogLocator(base=base,
+                                          subs=(1.0,),
+                                          numticks=x_numticks))
+    ax.yaxis.set_major_locator(LogLocator(base=base,
+                                          subs=(1.0,),
+                                          numticks=y_numticks))
+    ax.xaxis.set_major_formatter(LogFormatterExponent(base=base))
+    ax.yaxis.set_major_formatter(LogFormatterExponent(base=base))
+
+    if not minor:
+        return
+
+    # --- minors ---
+    subs_minor = tuple(range(2, 10))
+    ax.xaxis.set_minor_locator(
+        LogLocator(base=base, subs=subs_minor, numticks=1000))
+    ax.yaxis.set_minor_locator(
+        LogLocator(base=base, subs=subs_minor, numticks=1000))
+    ax.xaxis.set_minor_formatter(NullFormatter())
+    ax.yaxis.set_minor_formatter(NullFormatter())
+
+    # force minor ticks to be enabled on each side (this is the key)
+    ax.tick_params(axis="x", which="minor", bottom=True, top=True)
+    ax.tick_params(axis="y", which="minor", left=True, right=True)
+
+
+def get_colors(
+    n: int,
+    *,
+    cmap: str = "cmr.pride",
+    cmap_range: tuple[float, float] = (0.25, 0.75),
+) -> list[str]:
+    """Returns n colors from a colormap in hex format.
+
+    Args:
+        n (int): Number of colors to return.
+        cmap (str): Name of the colormap to use (from cmasher).
+        cmap_range (tuple): Fractional range of the colormap to sample
+            the colors from (between 0 and 1).
+
+    Returns:
+        n_colors (list): List of n colors in hex format.
+    """
+    try:
+        import cmasher as cmr
+    except ModuleNotFoundError as err:
+        warnings.warn(
+            "cmasher is required for this plotting style.\n"
+            "Install it with:\n\n"
+            "    pip install cmasher\n",
+            category=ImportWarning,
+            stacklevel=2,
+        )
+        raise err
+
+    return cmr.take_cmap_colors(
+        cmap, n, cmap_range=cmap_range, return_fmt="hex")
diff --git a/pyccl/baryons/fedeli14_bhm/halo_profiles.py b/pyccl/baryons/fedeli14_bhm/halo_profiles.py
new file mode 100644
index 000000000..b857d460d
--- /dev/null
+++ b/pyccl/baryons/fedeli14_bhm/halo_profiles.py
@@ -0,0 +1,323 @@
+"""Halo profile models used by the Fedeli14 baryonification setup."""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+
+import numpy as np
+from ...halos.profiles import HaloProfile, HaloProfileNFW
+from ...halos.concentration import ConcentrationDuffy08
+from .numerics import _pos_float, _pos_int, _trapz_compat
+
+
+__all__ = (
+    "GasHaloProfile",
+    "StellarHaloProfile",
+    "nfw_profile",
+    "nfw_profile_dmo",
+)
+
+MassFracFn = Callable[[np.ndarray | float], np.ndarray | float]
+
+
+def nfw_profile(mass_def, concentration=None):
+    """Return a dark-matter-only NFW halo profile.
+
+    Args:
+        mass_def (:class:`~pyccl.halos.massdef.MassDef`): Halo mass definition.
+        concentration (:class:`~pyccl.halos.concentration.Concentration`
+            or :obj:`str` or callable or :obj:`None`): Concentration-mass
+            relation. If ``None``, uses
+            :class:`~pyccl.halos.concentration.ConcentrationDuffy08`
+            for the supplied ``mass_def``.
+
+    Returns:
+        :class:`~pyccl.halos.profiles.HaloProfileNFW`: NFW halo profile.
+    """
+    if mass_def is None:
+        raise TypeError("mass_def must be provided.")
+
+    if concentration is not None and not isinstance(concentration,
+                                                    str) and not callable(
+            concentration):
+        raise TypeError(
+            "concentration must be a CCL Concentration (callable) or"
+            " a callable (cosmo, M, a) -> c."
+        )
+
+    if concentration is None:
+        concentration = ConcentrationDuffy08(mass_def=mass_def)
+
+    return HaloProfileNFW(mass_def=mass_def, concentration=concentration)
+
+
+def nfw_profile_dmo(*, mass_def, concentration_dmo=None):
+    """Return the DMO baseline NFW profile.
+
+    This helper exists so the rest of the baryonification setup can keep a
+    stable dark-matter-only (DMO) reference profile even if the baryonic
+    ``dark_matter`` profile is modified elsewhere.
+
+    Args:
+        mass_def (:class:`~pyccl.halos.massdef.MassDef`): Halo mass definition.
+        concentration_dmo (:class:`~pyccl.halos.concentration.Concentration`
+            or :obj:`str` or callable or :obj:`None`): Concentration-mass
+            relation for the DMO profile. If ``None``, the default in
+            :func:`nfw_profile` is used.
+
+    Returns:
+        :class:`~pyccl.halos.profiles.HaloProfileNFW`: DMO NFW halo profile.
+    """
+    return nfw_profile(mass_def=mass_def, concentration=concentration_dmo)
+
+
+class GasHaloProfile(HaloProfile):
+    """Fedeli14 gas density profile.
+
+    Gas density profile used by the Fedeli14 baryonification model. The profile
+    is normalized so that the enclosed gas mass equals
+    :math:`f_{\\rm gas}(M) M` for a halo of mass :math:`M`.
+
+    Args:
+        mass_def (:class:`~pyccl.halos.massdef.MassDef`): Halo mass definition.
+        f_gas (callable): Gas mass fraction :math:`f_{\\rm gas}(M)`.
+        beta (:obj:`float`): Outer slope parameter.
+        r_co (:obj:`float`): Core radius in units of the halo radius.
+        r_ej (:obj:`float`): Ejection radius in units of the halo radius.
+        x_min (:obj:`float`): Minimum dimensionless radius for the internal
+            normalization grid.
+        x_max (:obj:`float`): Maximum dimensionless radius for the internal
+            normalization grid.
+        n_x (:obj:`int`): Number of samples in the internal normalization grid.
+
+    Notes:
+        The normalization is computed numerically on a fixed dimensionless
+        radius grid :math:`x=r/R`, where :math:`R` is the halo radius returned
+        by ``mass_def``. Radii are treated as comoving, following the CCL
+        convention that ``mass_def.get_radius`` returns a physical radius.
+
+    Raises:
+        TypeError: If ``f_gas`` is not callable.
+        ValueError: If any inputs are invalid, or if the normalization fails.
+    """
+    def __init__(
+        self,
+        *,
+        mass_def,
+        f_gas: MassFracFn,
+        beta: float = 2.9,
+        r_co: float = 0.1,
+        r_ej: float = 4.5,
+        x_min: float = 1.0e-3,
+        x_max: float = 50.0,
+        n_x: int = 500,
+    ):
+        """Initializes the gas profile class."""
+        if mass_def is None:
+            raise TypeError("mass_def must be provided.")
+        if not callable(f_gas):
+            raise TypeError("f_gas must be callable.")
+        beta = _pos_float(beta, "beta")
+        r_co = _pos_float(r_co, "r_co")
+        r_ej = _pos_float(r_ej, "r_ej")
+        x_min = _pos_float(x_min, "x_min")
+        x_max = _pos_float(x_max, "x_max")
+        if x_max <= x_min:
+            raise ValueError("x_max must be > x_min.")
+        n_x = _pos_int(n_x, "n_x")
+        if n_x < 2:
+            raise ValueError("n_x must be >= 2.")
+
+        super().__init__(mass_def=mass_def)
+        self._f_gas = f_gas
+        self._beta = beta
+        self._r_co = r_co
+        self._r_ej = r_ej
+
+        self._x = np.linspace(x_min, x_max, n_x)
+
+    def _rs(self, cosmo, M, a):
+        """Return the halo radius for each mass."""
+        a = _pos_float(a, "a")
+        # CCL convention: get_radius returns physical;
+        # divide by a for comoving.
+        R = self.mass_def.get_radius(cosmo, M, a) / a
+        if np.any(~np.isfinite(R)) or np.any(np.asarray(R) <= 0.0):
+            raise ValueError("mass_def.get_radius returned invalid radii.")
+        return R
+
+    def _norm(self, cosmo, M, a):
+        """Return the gas-density normalization for each halo mass."""
+        M_use = np.atleast_1d(M).astype(float)
+        if np.any(~np.isfinite(M_use)) or np.any(M_use <= 0.0):
+            raise ValueError("M must be finite and > 0.")
+
+        Rd = np.asarray(self._rs(cosmo, M_use, a), dtype=float)
+
+        x = self._x
+        beta = self._beta
+
+        u = x[None, :] / self._r_co
+        v = x[None, :] / self._r_ej
+        prof_shape = x[None, :] ** 2 / (
+            (1.0 + u) ** beta * (1.0 + v**2) ** ((7.0 - beta) / 2.0)
+        )
+
+        integ = _trapz_compat(prof_shape, x, axis=-1)
+
+        if np.any(~np.isfinite(integ)) or np.any(integ <= 0.0):
+            raise ValueError("Gas normalization integral is invalid"
+                             " (<=0 or non-finite).")
+
+        fgas = np.asarray(self._f_gas(M_use), dtype=float)
+        if fgas.shape != M_use.shape:
+            raise ValueError("f_gas(M) must return an array with the same"
+                             " shape as M."
+                             f"Got f_gas(M).shape={fgas.shape},"
+                             f" M.shape={M_use.shape}.")
+        if np.any(~np.isfinite(fgas)) or np.any(fgas < 0.0):
+            raise ValueError("f_gas(M) must be finite and >= 0.")
+
+        rho0 = fgas * M_use / (4.0 * np.pi * (Rd**3) * integ)
+        if np.any(~np.isfinite(rho0)) or np.any(rho0 < 0.0):
+            raise ValueError("Gas normalization produced invalid values.")
+
+        if np.ndim(M) == 0:
+            return float(rho0[0])
+        return rho0
+
+    def _real(self, cosmo, r, M, a):
+        """Return real-space gas density profile."""
+        r_use = np.atleast_1d(r).astype(float)
+        M_use = np.atleast_1d(M).astype(float)
+
+        if np.any(~np.isfinite(r_use)) or np.any(r_use < 0.0):
+            raise ValueError("r must be finite and >= 0.")
+        if np.any(~np.isfinite(M_use)) or np.any(M_use <= 0.0):
+            raise ValueError("M must be finite and > 0.")
+
+        Rd = np.asarray(self._rs(cosmo, M_use, a), dtype=float)
+        rho0 = np.asarray(self._norm(cosmo, M_use, a), dtype=float)
+
+        x = r_use[None, :] / Rd[:, None]  # dimensionless radius
+        beta = self._beta
+
+        prof = rho0[:, None] / (
+            (1.0 + x / self._r_co) ** beta
+            * (1.0 + (x / self._r_ej) ** 2) ** ((7.0 - beta) / 2.0)
+        )
+
+        if np.any(~np.isfinite(prof)) or np.any(prof < 0.0):
+            raise ValueError("Gas profile evaluation produced invalid values.")
+
+        r_is_scalar = np.ndim(r) == 0
+        M_is_scalar = np.ndim(M) == 0
+
+        if r_is_scalar and M_is_scalar:
+            return float(prof[0, 0])
+        if M_is_scalar:
+            return np.asarray(prof[0, :], dtype=float)  # (nr,)
+        if r_is_scalar:
+            return np.asarray(prof[:, 0], dtype=float)  # (nM,)
+        return np.asarray(prof, dtype=float)  # (nM, nr)
+
+
+class StellarHaloProfile(HaloProfile):
+    """Fedeli14 stellar density profile.
+
+    Stellar density profile used by the Fedeli14 baryonification model. The
+    profile is normalized so that the enclosed stellar mass equals
+    :math:`f_\\star(M) M` for a halo of mass :math:`M`.
+
+    Args:
+        mass_def (:class:`~pyccl.halos.massdef.MassDef`): Halo mass definition.
+        f_star (callable): Stellar mass fraction :math:`f_\\star(M)`.
+        x_delta (:obj:`float`): Dimensionless truncation parameter defining the
+            truncation radius :math:`r_t = R/x_\\Delta`, where :math:`R` is the
+            halo radius for ``mass_def``.
+        alpha (:obj:`float`): Exponential shape parameter.
+
+    Notes:
+        Radii are treated as comoving, following the CCL convention that
+        ``mass_def.get_radius`` returns a physical radius.
+
+    Raises:
+        TypeError: If ``f_star`` is not callable.
+        ValueError: If any inputs are invalid, or if the profile evaluation
+            produces non-finite values.
+    """
+    def __init__(
+        self,
+        *,
+        mass_def,
+        f_star: MassFracFn,
+        x_delta: float = 1.0 / 0.03,
+        alpha: float = 1.0,
+    ):
+        """Initializes the stellar profile class."""
+        if mass_def is None:
+            raise TypeError("mass_def must be provided.")
+        if not callable(f_star):
+            raise TypeError("f_star must be callable.")
+
+        x_delta = _pos_float(x_delta, "x_delta")
+        alpha = _pos_float(alpha, "alpha")
+
+        super().__init__(mass_def=mass_def)
+        self._f_star = f_star
+        self._x_delta = x_delta
+        self._alpha = alpha
+
+    def _rs(self, cosmo, M, a):
+        """Return the halo radius for each mass."""
+        a = _pos_float(a, "a")
+        R = self.mass_def.get_radius(cosmo, M, a) / a
+        if np.any(~np.isfinite(R)) or np.any(np.asarray(R) <= 0.0):
+            raise ValueError("mass_def.get_radius returned invalid radii.")
+        return R
+
+    def _real(self, cosmo, r, M, a):
+        """Return real-space stellar density profile."""
+        r_use = np.atleast_1d(r).astype(float)
+        M_use = np.atleast_1d(M).astype(float)
+
+        if np.any(~np.isfinite(r_use)) or np.any(r_use < 0.0):
+            raise ValueError("r must be finite and >= 0.")
+        if np.any(~np.isfinite(M_use)) or np.any(M_use <= 0.0):
+            raise ValueError("M must be finite and > 0.")
+
+        Rd = np.asarray(self._rs(cosmo, M_use, a), dtype=float)
+        r_t = Rd / self._x_delta
+
+        # Avoid division by zero at r=0.
+        # CCL never really needs r=0, but it's good to be safe.
+        x = r_use[None, :] / r_t[:, None]
+        x_safe = np.where(x == 0.0, np.finfo(float).tiny, x)
+
+        fstar = np.asarray(self._f_star(M_use), dtype=float)
+        if fstar.shape != M_use.shape:
+            raise ValueError("f_star(M) must return an array with the same"
+                             " shape as M."
+                             f"Got f_star(M).shape={fstar.shape}, M.shape"
+                             f"={M_use.shape}.")
+
+        if np.any(~np.isfinite(fstar)) or np.any(fstar < 0.0):
+            raise ValueError("f_star(M) must be finite and >= 0.")
+
+        rho_t = M_use * fstar / (4.0 * np.pi * r_t**3)
+        prof = rho_t[:, None] * np.exp(-(x_safe**self._alpha)) / x_safe
+
+        if np.any(~np.isfinite(prof)) or np.any(prof < 0.0):
+            raise ValueError("Stellar profile evaluation produced"
+                             " invalid values.")
+
+        r_is_scalar = np.ndim(r) == 0
+        M_is_scalar = np.ndim(M) == 0
+
+        if r_is_scalar and M_is_scalar:
+            return float(prof[0, 0])
+        if M_is_scalar:
+            return np.asarray(prof[0, :], dtype=float)  # (nr,)
+        if r_is_scalar:
+            return np.asarray(prof[:, 0], dtype=float)  # (nM,)
+        return np.asarray(prof, dtype=float)  # (nM, nr)
diff --git a/pyccl/baryons/fedeli14_bhm/mass_fractions.py b/pyccl/baryons/fedeli14_bhm/mass_fractions.py
new file mode 100644
index 000000000..132063688
--- /dev/null
+++ b/pyccl/baryons/fedeli14_bhm/mass_fractions.py
@@ -0,0 +1,264 @@
+"""Fedeli14 halo mass-fraction functions."""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from typing import Any
+
+import numpy as np
+from scipy import integrate
+from scipy.special import erf
+
+MassFracFn = Callable[[np.ndarray | float], np.ndarray | float]
+MassFunction = Callable[[Any, np.ndarray, float], np.ndarray]
+
+__all__ = ["mass_fractions"]
+
+
+def mass_fractions(
+    *,
+    cosmo: Any,
+    a: float,
+    mass_function: MassFunction,
+    rho_star: float | None = None,
+    m0_star: float = 5.0e12,  # in Msun/h
+    sigma_star: float = 1.2,
+    mmin_star: float = 1.0e10,
+    mmax_star: float = 1.0e15,
+    m0_gas: float = 1.0e12,
+    sigma_gas: float = 3.0,
+) -> tuple[MassFracFn, MassFracFn, MassFracFn]:
+    """Return Fedeli14 halo mass-fraction callables.
+
+    This function returns three callables ``(f_gas, f_star, f_dm)`` mapping
+    halo mass :math:`M` to gas, stellar, and dark-matter mass fractions.
+
+    The stellar fraction normalization is set by matching a target cosmic
+    stellar mass density :math:`\\rho_\\star` via
+
+    .. math::
+        \\rho_\\star = \\int d\\log_{10}M\\, M\\, f_\\star(M)\\,
+        \\frac{dn}{d\\log_{10}M}(M,a).
+
+    Args:
+        cosmo (:class:`~pyccl.cosmology.Cosmology`): Cosmological parameters.
+        a (:obj:`float`): Scale factor.
+        mass_function (callable): Halo mass function evaluated as
+            ``mass_function(cosmo, M, a)``. It is assumed to return
+            :math:`dn/d\\log_{10}M`.
+        rho_star (:obj:`float` or :obj:`None`): Target cosmic stellar mass
+            density used to normalize :math:`f_\\star`. If ``None``, a default
+            scaled by :math:`h^2` is used.
+        m0_star (:obj:`float`): Pivot mass for :math:`f_\\star` (default in
+            :math:`M_\\odot/h`).
+        sigma_star (:obj:`float`): Width of :math:`f_\\star` in
+            :math:`\\log_{10}(M/m0_\\star)`.
+        mmin_star (:obj:`float`): Lower integration bound for the stellar
+            normalization.
+        mmax_star (:obj:`float`): Upper integration bound for the stellar
+            normalization.
+        m0_gas (:obj:`float`): Characteristic mass for the gas transition.
+        sigma_gas (:obj:`float`): Width of the gas transition in
+            :math:`\\log_{10}(M/m0_{\\rm gas})`.
+
+    Returns:
+        tuple: Tuple ``(f_gas, f_star, f_dm)``. Each element is a callable
+        accepting :obj:`float` or `array` halo masses and returning the
+        corresponding mass fraction.
+
+    Raises:
+        TypeError: If ``mass_function`` is not callable.
+        KeyError: If ``cosmo`` does not provide ``h``, ``Omega_b``, or
+            ``Omega_m``.
+        ValueError: If inputs are non-finite or non-positive, if
+            :math:`\\Omega_b > \\Omega_m`, or if the stellar normalization
+            integral is non-finite or non-positive.
+    """
+    # Basic input sanity checks
+    if not np.isfinite(a) or a <= 0.0:
+        raise ValueError(f"mass_fractions: a must be finite"
+                         f" and > 0. Got a={a}.")
+
+    if not callable(mass_function):
+        raise TypeError("mass_fractions: mass_function must be callable.")
+
+    for key in ("h", "Omega_b", "Omega_m"):
+        try:
+            _ = cosmo[key]
+        except Exception as e:  # noqa: BLE001
+            raise KeyError(f"mass_fractions:"
+                           f" cosmo must provide '{key}'.") from e
+
+    h = float(cosmo["h"])
+    if not np.isfinite(h) or h <= 0.0:
+        raise ValueError(
+            f"mass_fractions: cosmo['h'] must be finite and > 0. Got h={h}.")
+
+    if rho_star is None:
+        rho_star = 7.0e8 * h ** 2
+    rho_star = float(rho_star)
+    if not np.isfinite(rho_star) or rho_star <= 0.0:
+        raise ValueError(
+            "mass_fractions: rho_star must be finite and > 0. "
+            f"Got rho_star={rho_star}."
+        )
+
+    omega_b = float(cosmo["Omega_b"])
+    omega_m = float(cosmo["Omega_m"])
+    if (not np.isfinite(omega_m)) or omega_m <= 0.0:
+        raise ValueError(
+            f"mass_fractions: cosmo['Omega_m'] must be finite "
+            f"and > 0. Got {omega_m}.")
+    if (not np.isfinite(omega_b)) or omega_b < 0.0:
+        raise ValueError(
+            f"mass_fractions: cosmo['Omega_b'] must be finite"
+            f" and >= 0. Got {omega_b}.")
+    if omega_b > omega_m:
+        raise ValueError(
+            "mass_fractions: require Omega_b <= Omega_m. "
+            f"Got Omega_b={omega_b}, Omega_m={omega_m}."
+        )
+
+    omega_ratio = float(omega_b / omega_m)
+
+    # Mass-scale parameter sanity checks (do not change conventions)
+    for name, val in (
+        ("m0_star", m0_star),
+        ("sigma_star", sigma_star),
+        ("mmin_star", mmin_star),
+        ("mmax_star", mmax_star),
+        ("m0_gas", m0_gas),
+        ("sigma_gas", sigma_gas),
+    ):
+        val = float(val)
+        if (not np.isfinite(val)) or val <= 0.0:
+            raise ValueError(f"mass_fractions: {name} must be finite"
+                             f" and > 0. Got {val}.")
+    if not (float(mmin_star) < float(mmax_star)):
+        raise ValueError(
+            "mass_fractions: require mmin_star < mmax_star. "
+            f"Got mmin_star={mmin_star}, mmax_star={mmax_star}."
+        )
+
+    # Detect what mass units the mass function expects.
+    # We test the mass function at a representative mass:
+    # if mf(M) is approx 0 but mf(M/h) is >0, then the MF likely expects
+    # Msun/h.
+    m_test = float(m0_star)
+    mf_test_msun = float(np.atleast_1d(mass_function(cosmo,
+                                                     m_test, a))[0])
+    mf_test_msunh = float(np.atleast_1d(mass_function(cosmo,
+                                                      m_test / h, a))[0])
+
+    if (not np.isfinite(mf_test_msun)) or (not np.isfinite(mf_test_msunh)):
+        raise ValueError(
+            "mass_fractions: mass_function returned non-finite values"
+            " at the test mass. Got mf(M0)={mf_test_msun},"
+            " mf(M0/h)={mf_test_msunh}."
+        )
+
+    # scale to apply to masses before calling mass_function
+    # (Msun -> Msun/h if needed)
+    if (mf_test_msun == 0.0) and (mf_test_msunh > 0.0):
+        mf_mass_scale = 1.0 / h
+    else:
+        mf_mass_scale = 1.0
+
+    A_cache: dict[str, float] = {}
+
+    def _compute_stellar_A() -> float:
+        """Return the cached stellar normalization factor."""
+        A = A_cache.get("A")
+        if A is not None:
+            return A
+
+        def integrand(m: float) -> float:
+            """Integrand for stellar normalization integral."""
+            # Use the same m for the Gaussian,
+            # but convert ONLY for the MF call.
+            m_mf = m * mf_mass_scale
+
+            # Here we assume mass_function returns dn/dlog10M (common in CCL).
+            # If that's true, then the integrand for rho_star is:
+            # rho_* = integral dlog10M [ M * f_*(M) * dn/dlog10M ]
+            # and since quad integrates over dm,
+            # we include dlog10M/dm = 1/(m ln 10)
+            dn_dlog10m = mass_function(cosmo, m_mf, a)
+            dn_dlog10m = float(np.atleast_1d(dn_dlog10m)[0])
+            if not np.isfinite(dn_dlog10m):
+                raise ValueError(
+                    "mass_fractions: mass_function returned non-finite"
+                    " dn/dlog10M at m={m}, m_mf={m_mf}, a={a}."
+                )
+            dn_dm = dn_dlog10m / (m * np.log(10.0))
+
+            numer = -(np.log10(m / m0_star)) ** 2
+            denom = 2.0 * sigma_star ** 2
+            fshape = np.exp(numer / denom)
+            return m * fshape * dn_dm
+
+        integral = integrate.quad(
+            integrand,
+            mmin_star,
+            mmax_star,
+            epsabs=0.0,
+            epsrel=1.0e-3,
+            limit=5000,
+        )[0]
+
+        if not np.isfinite(integral) or integral <= 0.0:
+            raise ValueError(
+                "mass_fractions: stellar normalization integral is "
+                f"{integral}. This usually means the halo mass function is ~0 "
+                "over the integration range (often a Msun vs Msun/h mismatch),"
+                " or the dn/dlog10M vs dn/dM Jacobian is wrong.\n"
+                f"Diagnostics: h={h}, a={a}, "
+                f"mf(M0)={mf_test_msun}, mf(M0/h)={mf_test_msunh}, "
+                f"mf_mass_scale={'1/h' if mf_mass_scale != 1.0 else '1'}, "
+                f"mmin_star={mmin_star:g}, mmax_star={mmax_star:g}, "
+                f"m0_star={m0_star:g}, sigma_star={sigma_star:g}."
+            )
+
+        A = float(rho_star / integral)
+        A_cache["A"] = A
+        return A
+
+    def f_star(M):
+        """Stellar mass fraction as a function of halo mass."""
+        scalar = np.ndim(M) == 0
+        M = np.atleast_1d(M).astype(float)
+
+        if np.any(~np.isfinite(M)) or np.any(M <= 0.0):
+            raise ValueError("mass_fractions: M must be finite and > 0.")
+
+        A = _compute_stellar_A()
+        out = A * np.exp(-(np.log10(M / m0_star)) ** 2 / (2.0 * sigma_star**2))
+        return float(out[0]) if scalar else out
+
+    def f_gas(M):
+        """Gas mass fraction as a function of halo mass."""
+        scalar = np.ndim(M) == 0
+        M = np.atleast_1d(M).astype(float)
+
+        if np.any(~np.isfinite(M)) or np.any(M <= 0.0):
+            raise ValueError("mass_fractions: M must be finite and > 0.")
+
+        out = np.zeros_like(M)
+        mask = M >= m0_gas
+        if np.any(mask):
+            M_o_m = M[mask] / m0_gas
+            out[mask] = omega_ratio * erf(np.log10(M_o_m) / sigma_gas)
+        return float(out[0]) if scalar else out
+
+    def f_dm(M):
+        """Dark-matter mass fraction as a function of halo mass."""
+        scalar = np.ndim(M) == 0
+        M = np.atleast_1d(M).astype(float)
+
+        if np.any(~np.isfinite(M)) or np.any(M <= 0.0):
+            raise ValueError("mass_fractions: M must be finite and > 0.")
+
+        out = np.full_like(M, 1.0 - omega_ratio)
+        return float(out[0]) if scalar else out
+
+    return f_gas, f_star, f_dm
diff --git a/pyccl/baryons/fedeli14_bhm/numerics.py b/pyccl/baryons/fedeli14_bhm/numerics.py
new file mode 100644
index 000000000..727ffee80
--- /dev/null
+++ b/pyccl/baryons/fedeli14_bhm/numerics.py
@@ -0,0 +1,244 @@
+"""Utility functions for numerical operations."""
+
+from __future__ import annotations
+
+from typing import Any, Callable, Mapping
+
+import numpy as np
+
+
+def _require_a(a: float) -> float:
+    """Check that a is finite and positive."""
+    a = float(a)
+    if not np.isfinite(a):
+        raise ValueError("a must be finite.")
+    if a <= 0.0:
+        raise ValueError("a must be > 0.")
+    return a
+
+
+def _require_k(k: np.ndarray) -> np.ndarray:
+    """Check that k is a non-empty 1D array of finite values strictly
+     increasing."""
+    k = np.asarray(k, dtype=float)
+    if k.ndim != 1 or k.size == 0:
+        raise ValueError("k must be a non-empty 1D array.")
+    if not np.all(np.isfinite(k)):
+        raise ValueError("k must contain only finite values.")
+    if np.any(k <= 0.0):
+        raise ValueError("k must be > 0.")
+    if not np.all(np.diff(k) > 0):
+        raise ValueError("k must be strictly increasing.")
+    return k
+
+
+def _require_callable(obj: Any, *, who: str) -> None:
+    """Check that an object is callable."""
+    if not callable(obj):
+        raise TypeError(f"{who} must be callable.")
+
+
+def _require_array_1d(name: str, x: np.ndarray) -> None:
+    """Check that an array is 1D and non-empty."""
+    if x.ndim != 1:
+        raise ValueError(f"{name} must be a 1D array.")
+    if x.size == 0:
+        raise ValueError(f"{name} must be non-empty.")
+
+
+def _require_finite_1d(name: str, x: np.ndarray) -> None:
+    """Check that a 1D array is finite."""
+    if not np.all(np.isfinite(x)):
+        raise ValueError(f"{name} must contain only finite values.")
+
+
+def _as_float(x, name: str) -> float:
+    """Return x as a finite float."""
+    try:
+        y = float(x)
+    except Exception as e:  # noqa: BLE001
+        raise TypeError(f"{name} must be a real number.") from e
+    if not np.isfinite(y):
+        raise ValueError(f"{name} must be finite.")
+    return y
+
+
+def _pos_float(x, name: str) -> float:
+    """Return x as a finite float > 0."""
+    y = _as_float(x, name)
+    if y <= 0.0:
+        raise ValueError(f"{name} must be > 0.")
+    return y
+
+
+def _pos_int(x, name: str) -> int:
+    """Return x as an int > 0."""
+    try:
+        y = int(x)
+    except Exception as e:  # noqa: BLE001
+        raise TypeError(f"{name} must be an int.") from e
+    if y <= 0:
+        raise ValueError(f"{name} must be > 0.")
+    return y
+
+
+def _require_attr(obj: Any, attr: str, *, who: str) -> None:
+    """Check that an object has a given attribute."""
+    if not hasattr(obj, attr):
+        raise TypeError(f"{who} must define attribute {attr!r}.")
+
+
+def _require_mapping(x: Any, *, who: str) -> None:
+    """Check that an object is a mapping (dict-like)."""
+    if not isinstance(x, Mapping):
+        raise TypeError(f"{who} must be a mapping (dict-like).")
+
+
+def _require_float(name: str, x: Any) -> None:
+    """Check that an object is convertible to float."""
+    try:
+        float(x)
+    except Exception as e:  # noqa: BLE001
+        raise TypeError(f"{name} must be a real number.") from e
+
+
+def _require_profiles_u_over_m(
+    profiles: Any,
+    *,
+    components: tuple[str, ...] = ("dark_matter", "gas", "stars"),
+) -> dict[str, Callable[[np.ndarray, float], np.ndarray]]:
+    """Check profiles_u_over_m is a dict with required callable components."""
+    _require_mapping(profiles, who="profiles_u_over_m")
+
+    out: dict[str, Callable[[np.ndarray, float], np.ndarray]] = {}
+    for comp in components:
+        if comp not in profiles:
+            raise KeyError(f"profiles_u_over_m missing component {comp!r}.")
+        fn = profiles[comp]
+        _require_callable(fn, who=f"profiles_u_over_m[{comp!r}]")
+        out[comp] = fn  # type: ignore[assignment]
+    return out
+
+
+def _require_mass_ranges(
+    mass_ranges: Any,
+    *,
+    components: tuple[str, ...] = ("dark_matter", "gas", "stars"),
+) -> dict[str, dict[str, float]]:
+    """Check mass_ranges[comp] has positive min/max with max>min
+     for each component."""
+    _require_mapping(mass_ranges, who="mass_ranges")
+
+    out: dict[str, dict[str, float]] = {}
+    for comp in components:
+        if comp not in mass_ranges:
+            raise KeyError(f"mass_ranges missing component {comp!r}.")
+
+        r = mass_ranges[comp]
+        _require_mapping(r, who=f"mass_ranges[{comp!r}]")
+
+        if "min" not in r or "max" not in r:
+            raise KeyError(f"mass_ranges[{comp!r}]"
+                           f" must have keys 'min' and 'max'.")
+
+        mmin = _pos_float(r["min"], f"mass_ranges[{comp!r}]['min']")
+        mmax = _pos_float(r["max"], f"mass_ranges[{comp!r}]['max']")
+        if mmax <= mmin:
+            raise ValueError(
+                f"mass_ranges[{comp!r}] must have max>min,"
+                f" got min={mmin}, max={mmax}."
+            )
+        out[comp] = {"min": mmin, "max": mmax}
+
+    return out
+
+
+def _require_densities(densities: Any) -> dict[str, float]:
+    """Check densities is a mapping and coerce values to float."""
+    _require_mapping(densities, who="densities")
+    return {str(k): float(v) for k, v in densities.items()}
+
+
+def _require_gas_params(gas_params: Any) -> tuple[float, float]:
+    """Check gas_params has finite Fg in [0,1] and finite bd parameter."""
+    _require_mapping(gas_params, who="gas_params")
+
+    if "Fg" not in gas_params or "bd" not in gas_params:
+        raise KeyError("gas_params must contain keys 'Fg' and 'bd'.")
+
+    Fg = _as_float(gas_params["Fg"], "gas_params['Fg']")
+    if not (0.0 <= Fg <= 1.0):
+        raise ValueError(f"Fg must be in [0, 1], got {Fg}.")
+
+    bd = _as_float(gas_params["bd"], "gas_params['bd']")
+    return Fg, bd
+
+
+def _add_pair_aliases(d: dict[str, Any], a: str, b: str) -> None:
+    """
+    Add a commutative alias for a two-component key.
+
+    If exactly one of ``"a_b"`` or ``"b_a"`` exists in the dictionary,
+    this function inserts the missing one so that both keys point to
+    the same object. No data are copied.
+
+    This allows downstream code to access pair terms without caring
+    about the ordering convention used when building the dictionary.
+    """
+    k1 = f"{a}_{b}"
+    k2 = f"{b}_{a}"
+    if k1 in d and k2 not in d:
+        d[k2] = d[k1]
+    elif k2 in d and k1 not in d:
+        d[k1] = d[k2]
+
+
+def _add_weighted_pair_aliases(d: dict[str, Any], a: str, b: str) -> None:
+    """
+    Add commutative aliases for weighted pair terms.
+
+    Ensures that both ``"w_a_b"`` and ``"w_b_a"`` exist in the dictionary
+    if either one is present, pointing to the same object. This mirrors
+    the behavior of :func:`_add_pair_aliases` but for weighted
+    contributions to the total power spectrum.
+
+    No arrays are copied; only additional dictionary keys are created.
+    """
+    k1 = f"w_{a}_{b}"
+    k2 = f"w_{b}_{a}"
+    if k1 in d and k2 not in d:
+        d[k2] = d[k1]
+    elif k2 in d and k1 not in d:
+        d[k1] = d[k2]
+
+
+def _require_component(
+    comp: str,
+    *,
+    allowed: Mapping[str, Any],
+    who: str = "component",
+) -> str:
+    """Check that `comp` is a known key in `allowed` and return it.
+
+    Raises:
+        KeyError: if `comp` is not present in `allowed`.
+    """
+    if comp not in allowed:
+        raise KeyError(f"Unknown component {comp!r}.")
+    return comp
+
+
+def _trapz_compat(
+    y: Any,
+    x: Any | None = None,
+    dx: float = 1.0,
+    axis: int = -1
+) -> Any:
+    """Trapezoidal integration compatible with old/new NumPy.
+
+    Uses np.trapezoid if available, else falls back to np.trapz.
+    """
+    trap = getattr(np, "trapezoid", None)
+    if trap is not None:
+        return trap(y, x=x, dx=dx, axis=axis)
+    return np.trapz(y, x=x, dx=dx, axis=axis)
diff --git a/pyccl/baryons/fedeli14_bhm/power_spectra.py b/pyccl/baryons/fedeli14_bhm/power_spectra.py
new file mode 100644
index 000000000..d5cc8d34b
--- /dev/null
+++ b/pyccl/baryons/fedeli14_bhm/power_spectra.py
@@ -0,0 +1,888 @@
+"""Power spectra calculator for baryon halo model by Fedeli (2014)."""
+
+from __future__ import annotations
+
+from typing import Any, Callable, TypeAlias
+
+import numpy as np
+import pyccl as ccl
+
+from pyccl.baryons.fedeli14_bhm.numerics import (
+    _require_a,
+    _require_k,
+    _require_callable,
+    _pos_int,
+    _require_profiles_u_over_m,
+    _require_mass_ranges,
+    _require_densities,
+    _require_gas_params,
+    _add_pair_aliases,
+    _add_weighted_pair_aliases,
+    _require_component,
+    _trapz_compat
+)
+
+_INV_LN10 = 1.0 / np.log(10.0)
+YCacheKey: TypeAlias = tuple[str, str, float, float, float, int, float, float]
+YCache: TypeAlias = dict[YCacheKey, np.ndarray]
+
+
+def _dndm_from_dndlog10m(dndlog10m: np.ndarray, m: np.ndarray) -> np.ndarray:
+    r"""Convert dn/dlog10M to dn/dM for a mass grid M:
+
+    .. math::
+        \frac{dn}{dM} = \frac{1}{M \ln 10}\,\frac{dn}{d\log_{10} M}.
+
+    Args:
+        dndlog10m: Mass function evaluated as dn/dlog10M on the grid ``m``.
+        m: Mass grid (must be positive).
+
+    Returns:
+        dn/dM evaluated on ``m``.
+    """
+    dndlog10m = np.asarray(dndlog10m, dtype=float)
+    m = np.asarray(m, dtype=float)
+    if dndlog10m.shape != m.shape:
+        raise ValueError("dndlog10m and m must have the same shape.")
+    if np.any(m <= 0.0):
+        raise ValueError("m must be > 0.")
+    return dndlog10m * _INV_LN10 / m
+
+
+def _k_cache_key(k: float, ndp: int = 12) -> float:
+    """Return a rounded float key for caching quantities as a function of k."""
+    return float(np.round(float(k), ndp))
+
+
+class FedeliPkCalculator:
+    r"""Halo-model matter power spectrum for the Fedeli14 baryonification
+    setup.
+
+    This class builds halo-model power spectra for the Fedeli14 components
+    (dark matter, gas, stars) using Fourier-space halo profiles u(k,M).
+
+    .. math::
+        u(k, M) \equiv
+        \int d^3r\,\rho(r|M)\,e^{i\mathbf{k}\cdot\mathbf{r}},
+
+        y(M,k) \equiv \frac{u(k,M)}{M}.
+
+    The halo-model cross-spectrum is written as
+    :math:`P_{ij}(k) = P_{ij}^{1h}(k) + P_{ij}^{2h}(k)`.
+
+    It provides:
+      - per-pair halo-model spectra (1-halo and 2-halo pieces),
+      - the gas auto-spectrum including a diffuse gas contribution,
+      - mixed cross-spectra between gas and (dark matter or stars),
+      - an assembled "packet" convenient for plotting and diagnostics,
+      - a dark-matter-only (DMO) halo-model baseline and the resulting boost.
+
+    The user supplies:
+      - a mass function dn/dlog10M(M,a),
+      - a halo bias b(M,a),
+      - per-component evaluators for u(k,M)/M on the requested k grid,
+      - component mass ranges and background densities.
+
+    Notes:
+        All returned spectra are functions of ``self.k`` at the scale factor
+        ``a``.
+    """
+    def __init__(
+        self,
+        *,
+        cosmo: Any,
+        a: float,
+        k: np.ndarray,
+        profiles_u_over_m: dict[str, Any],
+        dmo_dm_u_over_m: Any | None = None,
+        mass_function: Any,
+        halo_bias: Any,
+        mass_ranges: dict[str, dict[str, float]],
+        densities: dict[str, float],
+        gas_params: dict[str, float],
+        n_m: int = 512,
+    ):
+        """Initializes the power spectrum calculator."""
+        self.cosmo = cosmo
+        self.a = _require_a(a)
+        self.k = _require_k(k)
+
+        _require_callable(mass_function, who="mass_function")
+        _require_callable(halo_bias, who="halo_bias")
+        self.hmf = mass_function
+        self.hb = halo_bias
+
+        self.y = _require_profiles_u_over_m(profiles_u_over_m)
+        self.y_dmo_dm = (
+            dmo_dm_u_over_m
+            if dmo_dm_u_over_m is not None
+            else self.y["dark_matter"]
+        )
+        self.mass_ranges = _require_mass_ranges(mass_ranges)
+        self.rho = _require_densities(densities)
+        self.Fg, self.bd = _require_gas_params(gas_params)
+
+        self.n_m = _pos_int(n_m, "n_m")
+        if self.n_m < 2:
+            raise ValueError("n_m must be >= 2.")
+
+        self._init_caches()
+
+    def _init_caches(self) -> None:
+        """Initialize internal caches for expensive intermediate quantities.
+
+        Caches are used to avoid recomputing linear P(k), mass grids, profile
+        evaluations, and halo-model integrals across repeated calls.
+        """
+        self._linpk_cache: dict[float, float] = {}
+        self._P_lin: np.ndarray | None = None
+
+        self._m_grid_cache: dict[tuple[float, float], np.ndarray] = {}
+        self._dndm_cache: dict[tuple[float, float], np.ndarray] = {}
+        self._b_cache: dict[tuple[float, float], np.ndarray] = {}
+
+        # y(M,k) meshes and vector integrals
+        self._y_cache: YCache = {}
+        self._Ib_vec_cache: dict[
+            tuple[str, str, float, float], np.ndarray] = {}
+        self._I2_vec_cache: dict[
+            tuple[str, str, str, float, float], np.ndarray] = {}
+
+        # rho integrals from fractions
+        self._rho_cache: dict[tuple, float] = {}
+
+        # final assembled spectra caches
+        self._pk_dmo: np.ndarray | None = None
+        self._pk_packet_cache: dict[str, Any] | None = None
+
+    def _invalidate_final_caches(self) -> None:
+        """Clear cached outputs that depend on densities or gas mixing
+        parameters."""
+        self._pk_dmo = None
+        self._pk_packet_cache = None
+
+    def rho_from_fraction(
+            self,
+            *,
+            f_of_m: Callable[[np.ndarray], np.ndarray],
+            mmin: float,
+            mmax: float,
+            n_m: int | None = None,
+            cache_key: str | None = None,
+    ) -> float:
+        r"""Compute a mean density from a halo mass fraction model.
+
+        This evaluates the mass-weighted integral
+
+        .. math::
+            \rho_X = \int dM\,\frac{dn}{dM}\,M\,f_X(M).
+
+        Here :math:`f_X(M)` is the (dimensionless) halo mass fraction in
+        component X.
+
+        Args:
+            f_of_m: Function returning the fraction f_X(M) on an array of
+                masses.
+            mmin, mmax: Integration limits in mass.
+            n_m: Number of mass samples used in the integral (optional).
+            cache_key: Optional label used to cache the result for the given
+                limits.
+
+        Returns:
+            rho_X in the same mass-density units implied by the mass function.
+        """
+        if not callable(f_of_m):
+            raise TypeError("f_of_m must be callable.")
+
+        mmin = float(mmin)
+        mmax = float(mmax)
+        if mmin <= 0.0 or mmax <= 0.0 or mmax <= mmin:
+            raise ValueError(f"Invalid mass range: mmin={mmin}, mmax={mmax}")
+
+        if n_m is None:
+            n_m = max(1024, self.n_m)
+
+        key = (cache_key, mmin, mmax, int(n_m))
+        if cache_key is not None:
+            hit = self._rho_cache.get(key)
+            if hit is not None:
+                return hit
+
+        m = np.geomspace(mmin, mmax, int(n_m))
+        dndlog10m = np.asarray(self.hmf(self.cosmo, m, self.a), dtype=float)
+        dndm = _dndm_from_dndlog10m(dndlog10m, m)
+
+        fx = np.asarray(f_of_m(m), dtype=float)
+        if fx.shape != m.shape:
+            raise ValueError("f_of_m must return an array with the"
+                             " same shape as m.")
+        if not np.all(np.isfinite(fx)):
+            raise ValueError("f_of_m(m) must be finite.")
+
+        rho_x = float(_trapz_compat(dndm * m * fx, m))
+
+        if cache_key is not None:
+            self._rho_cache[key] = rho_x
+        return rho_x
+
+    def ensure_densities(
+        self,
+        *,
+        f_gas: Callable[[np.ndarray], np.ndarray],
+        f_star: Callable[[np.ndarray], np.ndarray],
+        mmin: float,
+        mmax: float,
+        n_m: int | None = None,
+    ) -> None:
+        """Ensure component mean densities required by the model are available.
+
+        This fills missing entries in ``self.rho``:
+          - 'matter' and 'dark_matter' from the cosmology,
+          - 'gas' and 'stars' from the supplied fraction functions via
+            :meth:`rho_from_fraction`.
+
+        Call this before requesting total spectra or packets if you did not
+        provide all densities at initialization.
+
+        Args:
+            f_gas: Function returning the fraction of gas mass in each halo.
+            f_star: Function returning the fraction of star mass in each halo.
+            mmin: Integration limit mininum mass in halo mass units.
+            mmax: Integration limit maximum mass in halo mass units.
+            n_m: Number of mass samples used in the integral (optional).
+
+        Raises:
+            KeyError: If any of the required cosmology parameters are missing.
+        """
+        for key in ("Omega_c", "Omega_m"):
+            try:
+                float(self.cosmo[key])
+            except Exception as e:  # noqa: BLE001
+                raise KeyError(
+                    f"cosmo must provide {key!r}"
+                    f" for dark_matter density.") from e
+
+        changed = False
+
+        if "matter" not in self.rho:
+            self.rho["matter"] = float(ccl.rho_x(
+                self.cosmo, self.a, "matter")
+            )
+            changed = True
+
+        if "dark_matter" not in self.rho:
+            rho_m = float(self.rho["matter"])
+            rho_m = float(self.rho["matter"])
+            omc = float(self.cosmo["Omega_c"])
+            omm = float(self.cosmo["Omega_m"])
+            self.rho["dark_matter"] = rho_m * (omc / omm)
+            changed = True
+
+        if "gas" not in self.rho:
+            self.rho["gas"] = self.rho_from_fraction(
+                f_of_m=f_gas, mmin=mmin, mmax=mmax, n_m=n_m, cache_key="gas"
+            )
+            changed = True
+
+        if "stars" not in self.rho:
+            self.rho["stars"] = self.rho_from_fraction(
+                f_of_m=f_star, mmin=mmin, mmax=mmax, n_m=n_m, cache_key="stars"
+            )
+            changed = True
+
+        if changed:
+            self._invalidate_final_caches()
+
+    def linpk(self, kval: float) -> float:
+        """Return the linear matter power spectrum :math:`P_L(k,a)`
+        at a single k."""
+        kval = float(kval)
+        key = _k_cache_key(kval, ndp=12)
+        pk = self._linpk_cache.get(key)
+        if pk is None:
+            pk = float(ccl.linear_matter_power(self.cosmo, kval, self.a))
+            self._linpk_cache[key] = pk
+        return pk
+
+    def P_lin(self) -> np.ndarray:
+        """Return :math:`P_L(k,a)` evaluated on the stored k grid."""
+        if self._P_lin is None:
+            try:
+                self._P_lin = np.asarray(
+                    ccl.linear_matter_power(self.cosmo, self.k, self.a),
+                    dtype=float,
+                )
+            except Exception:
+                self._P_lin = np.array(
+                    [self.linpk(float(kk)) for kk in self.k], dtype=float
+                )
+        return self._P_lin
+
+    def _mass_grid(self, mmin: float, mmax: float) -> np.ndarray:
+        """Return the internal log-spaced mass grid for an integration
+        range."""
+        key = (float(mmin), float(mmax))
+        m = self._m_grid_cache.get(key)
+        if m is None:
+            if mmin <= 0.0 or mmax <= 0.0 or mmax <= mmin:
+                raise ValueError(
+                    f"Invalid mass range: mmin={mmin}, mmax={mmax}"
+                )
+            m = np.logspace(np.log10(mmin), np.log10(mmax), self.n_m)
+            self._m_grid_cache[key] = m
+        return m
+
+    def _dndm(self, mmin: float, mmax: float) -> np.ndarray:
+        """Return dn/dM on the internal mass grid for an integration range."""
+        key = (float(mmin), float(mmax))
+        arr = self._dndm_cache.get(key)
+        if arr is None:
+            m = self._mass_grid(mmin, mmax)
+            dndlog10m = np.asarray(
+                self.hmf(self.cosmo, m, self.a), dtype=float)
+
+            if dndlog10m.shape != m.shape:
+                raise ValueError("mass_function must return an array with the"
+                                 " same shape as M.")
+            arr = _dndm_from_dndlog10m(dndlog10m, m)
+            self._dndm_cache[key] = arr
+        return arr
+
+    def _bias(self, mmin: float, mmax: float) -> np.ndarray:
+        """Return the halo bias b(M,a) on the internal mass grid for an
+        integration range."""
+        key = (float(mmin), float(mmax))
+        arr = self._b_cache.get(key)
+        if arr is None:
+            m = self._mass_grid(mmin, mmax)
+            arr = np.asarray(self.hb(self.cosmo, m, self.a), dtype=float)
+            if arr.shape != m.shape:
+                raise ValueError("halo_bias must return an array with the"
+                                 " same shape as M.")
+            self._b_cache[key] = arr
+        return arr
+
+    def _y_grid_mm(
+        self,
+        comp: str,
+        mmin: float,
+        mmax: float,
+        *,
+        y_fn: Callable[[np.ndarray, np.ndarray], np.ndarray] | None = None,
+        cache_tag: str = "",
+    ) -> np.ndarray:
+        """Return u(k,M)/M evaluated on the internal (M,k) mesh for one
+        component."""
+        k_sig = (int(self.k.size), float(self.k[0]), float(self.k[-1]))
+        key = (cache_tag, comp, float(mmin), float(mmax), float(self.a),
+               *k_sig)
+
+        # Cache is only used for the "default" evaluator or when the caller
+        # explicitly opts in by providing a cache_tag.
+        use_cache = (y_fn is None) or (cache_tag != "")
+
+        if use_cache:
+            hit = self._y_cache.get(key)
+            if hit is not None:
+                return hit
+
+        if y_fn is None:
+            y_fn = self.y[comp]
+
+        m = self._mass_grid(mmin, mmax)
+        y = np.asarray(y_fn(m[:, None], self.k[None, :]), dtype=float)
+
+        if y.shape != (m.size, self.k.size):
+            raise ValueError(
+                f"u_over_m({comp!r}) must return shape"
+                f" {(m.size, self.k.size)}, got {y.shape}."
+            )
+
+        if use_cache:
+            self._y_cache[key] = y
+
+        return y
+
+    def _Ib_vec(
+        self,
+        comp: str,
+        mmin: float,
+        mmax: float,
+        *,
+        y_fn=None,
+        cache_tag=""
+    ) -> np.ndarray:
+        r"""Return the 2-halo bias-weighted integral I_b(k) for one component.
+
+        .. math::
+            I_b(k) = \int dM\,\frac{dn}{dM}\,b(M)\,M\,y(M,k),
+
+        where :math:`y(M,k) = u(k,M)/M`.
+        """
+        _require_component(comp, allowed=self.y)
+        _require_component(comp, allowed=self.mass_ranges)
+        key = (cache_tag, comp, float(mmin), float(mmax))
+        hit = self._Ib_vec_cache.get(key)
+        if hit is not None:
+            return hit
+
+        m = self._mass_grid(mmin, mmax)
+        dndm = self._dndm(mmin, mmax)
+        b = self._bias(mmin, mmax)
+        y = self._y_grid_mm(comp, mmin, mmax, y_fn=y_fn, cache_tag=cache_tag)
+
+        Ib = _trapz_compat(
+            dndm[:, None] * (m[:, None] * b[:, None]) * y, m, axis=0)
+        Ib = np.asarray(Ib, dtype=float)
+        if Ib.shape != (self.k.size,):
+            raise ValueError("Internal error: Ib has wrong shape.")
+        self._Ib_vec_cache[key] = Ib
+        return Ib
+
+    def _I2_vec(
+        self,
+        comp1: str,
+        comp2: str,
+        mmin: float,
+        mmax: float,
+        *,
+        y1_fn=None,
+        y2_fn=None,
+        cache_tag=""
+    ) -> np.ndarray:
+        r"""Return the 1-halo profile integral I_2(k) for a component pair.
+
+        .. math::
+            I_2(k) = \int dM\,\frac{dn}{dM}\,M^2\,y_1(M,k)\,y_2(M,k),
+
+        where :math:`y_i(M,k) = u_i(k,M)/M`.
+        """
+        _require_component(comp1, allowed=self.y)
+        _require_component(comp2, allowed=self.y)
+        _require_component(comp1, allowed=self.mass_ranges)
+        _require_component(comp2, allowed=self.mass_ranges)
+
+        key = (cache_tag, comp1, comp2, float(mmin), float(mmax))
+        hit = self._I2_vec_cache.get(key)
+        if hit is not None:
+            return hit
+
+        m = self._mass_grid(mmin, mmax)
+        dndm = self._dndm(mmin, mmax)
+        y1 = self._y_grid_mm(comp1, mmin, mmax, y_fn=y1_fn,
+                             cache_tag=cache_tag)
+        y2 = self._y_grid_mm(comp2, mmin, mmax, y_fn=y2_fn,
+                             cache_tag=cache_tag)
+
+        I2 = _trapz_compat(
+            dndm[:, None] * (m[:, None] ** 2) * y1 * y2, m, axis=0)
+        I2 = np.asarray(I2, dtype=float)
+        if I2.shape != (self.k.size,):
+            raise ValueError("Internal error: I2 has wrong shape.")
+        self._I2_vec_cache[key] = I2
+        return I2
+
+    def pk_halo_pair(
+        self,
+        *,
+        comp1: str,
+        comp2: str,
+        rho1: float,
+        rho2: float,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        r"""Return the (1-halo, 2-halo) spectra for a component pair.
+
+        This builds the standard halo-model decomposition
+          ``P(k) = P_1h(k) + P_2h(k)`
+        for the cross-spectrum between two components, normalized by the
+        supplied mean densities rho1 and rho2
+
+        .. math::
+            P_{12}^{1h}(k) = \frac{I_2(k)}{\rho_1\,\rho_2},
+
+            P_{12}^{2h}(k) =
+            P_L(k)\,\frac{I_{b,1}(k)\,I_{b,2}(k)}{\rho_1\,\rho_2}.
+
+        Args:
+            comp1, comp2: Component names (must exist in ``profiles`` and
+                ``mass_ranges``).
+            rho1, rho2: Mean densities used to normalize the halo-model terms.
+
+        Returns:
+            Tuple (P_1h, P_2h), each an array on ``self.k``.
+        """
+        rho1 = float(rho1)
+        rho2 = float(rho2)
+        if rho1 <= 0.0 or rho2 <= 0.0:
+            raise ValueError("rho1 and rho2 must be > 0.")
+
+        for comp in (comp1, comp2):
+            if comp not in self.mass_ranges:
+                raise KeyError(f"Unknown component {comp!r} in mass_ranges.")
+            if comp not in self.y:
+                raise KeyError(f"Unknown component {comp!r} in profiles.")
+
+        mmin1 = float(self.mass_ranges[comp1]["min"])
+        mmax1 = float(self.mass_ranges[comp1]["max"])
+        mmin2 = float(self.mass_ranges[comp2]["min"])
+        mmax2 = float(self.mass_ranges[comp2]["max"])
+
+        mmin = max(mmin1, mmin2)
+        mmax = min(mmax1, mmax2)
+        if mmax <= mmin:
+            raise ValueError(f"No overlap mass range "
+                             f"for ({comp1},{comp2}): [{mmin},{mmax}]")
+
+        P_lin = self.P_lin()
+
+        I2 = self._I2_vec(comp1, comp2, mmin, mmax)
+        Ib1 = self._Ib_vec(comp1, mmin1, mmax1)
+        Ib2 = self._Ib_vec(comp2, mmin2, mmax2)
+
+        P1 = I2 / (rho1 * rho2)
+        P2 = P_lin * (Ib1 * Ib2) / (rho1 * rho2)
+        return P1, P2
+
+    def weights(self) -> dict[str, float]:
+        r"""Return dimensionless weights used to assemble the total matter
+        spectrum.
+
+        .. math::
+            w_{ii} = \left(\frac{\rho_i}{\rho_m}\right)^2, \qquad
+            w_{ij} = 2\,\frac{\rho_i\,\rho_j}{\rho_m^2}\quad (i \ne j).
+
+        The total matter spectrum is written as a weighted sum of auto- and
+        cross- spectra of the component fields. The weights are built from
+        the component mean densities in ``self.rho``.
+        """
+        rho_dm = float(self.rho["dark_matter"])
+        rho_g = float(self.rho["gas"])
+        rho_s = float(self.rho["stars"])
+        rho_m = float(self.rho["matter"])
+        return {
+            "w_dm": (rho_dm / rho_m) ** 2,
+            "w_g": (rho_g / rho_m) ** 2,
+            "w_s": (rho_s / rho_m) ** 2,
+            "w_dm_g": 2.0 * rho_dm * rho_g / (rho_m**2),
+            "w_g_s": 2.0 * rho_g * rho_s / (rho_m**2),
+            "w_dm_s": 2.0 * rho_dm * rho_s / (rho_m**2),
+        }
+
+    def pair_halo_packet(
+        self,
+        *,
+        comp1: str,
+        comp2: str,
+        rho1: float,
+        rho2: float
+    ) -> dict[str, Any]:
+        """Return a plot-friendly packet for a standard halo-model component
+         pair.
+
+        The packet includes the 1-halo and 2-halo pieces and their sum.
+        """
+        P1, P2 = self.pk_halo_pair(
+            comp1=comp1, comp2=comp2, rho1=rho1, rho2=rho2)
+
+        return {
+            "kind": "halo_pair",
+            "comp1": comp1,
+            "comp2": comp2,
+            "rho1": float(rho1),
+            "rho2": float(rho2),
+            "terms": {"1h": P1, "2h": P2},
+            "pk": P1 + P2,
+        }
+
+    def pair_gas_auto_packet(self) -> dict[str, Any]:
+        """Return the gas auto-spectrum packet including diffuse gas mixing.
+
+        In the Fedeli14 model, the gas field is a mixture of a halo-tracing
+        component and a diffuse component. This method returns the
+        decomposition used for the gas auto-spectrum, including the
+        halo-halo, diffuse-diffuse, and cross terms.
+        """
+        rho_g = float(self.rho["gas"])
+        P_lin = self.P_lin()
+
+        hh_1h, hh_2h = self.pk_halo_pair(
+            comp1="gas",
+            comp2="gas",
+            rho1=self.Fg * rho_g,
+            rho2=self.Fg * rho_g,
+        )
+
+        diffuse = (self.bd**2) * P_lin
+
+        mmin_g = float(self.mass_ranges["gas"]["min"])
+        mmax_g = float(self.mass_ranges["gas"]["max"])
+        Ib_g = self._Ib_vec("gas", mmin_g, mmax_g)
+        diffuse_halo = self.bd * P_lin * Ib_g / (self.Fg * rho_g)
+
+        pk = (
+            (self.Fg**2) * (hh_1h + hh_2h)
+            + (1.0 - self.Fg) ** 2 * diffuse
+            + 2.0 * self.Fg * (1.0 - self.Fg) * diffuse_halo
+        )
+
+        return {
+            "kind": "gas_auto",
+            "mix": {"Fg": float(self.Fg), "bd": float(self.bd)},
+            "rho_g": rho_g,
+            "terms": {
+                "1h": hh_1h,
+                "2h": hh_2h,
+                "diffuse": diffuse,
+                "diffuse_halo": diffuse_halo,
+            },
+            "pk": pk,
+        }
+
+    def pair_mixed_cross_packet(self, *, comp: str) -> dict[str, Any]:
+        """Return the cross-spectrum packet for (dark matter or stars) x gas.
+
+        The gas field is mixed, so the cross-spectrum includes a halo-tracing
+        piece and a diffuse-gas contribution controlled by the model mixing
+        parameters.
+        """
+        if comp not in {"dark_matter", "stars"}:
+            raise ValueError("comp must be 'dark_matter' or 'stars'.")
+
+        rho_c = float(self.rho[comp])
+        rho_g = float(self.rho["gas"])
+        P_lin = self.P_lin()
+
+        halo_1h, halo_2h = self.pk_halo_pair(
+            comp1=comp,
+            comp2="gas",
+            rho1=rho_c,
+            rho2=self.Fg * rho_g,
+        )
+
+        mmin = float(self.mass_ranges[comp]["min"])
+        mmax = float(self.mass_ranges[comp]["max"])
+        Ib = self._Ib_vec(comp, mmin, mmax)
+        diffuse = self.bd * P_lin * Ib / rho_c
+
+        pk = self.Fg * (halo_1h + halo_2h) + (1.0 - self.Fg) * diffuse
+
+        return {
+            "kind": "mixed_cross",
+            "comp": comp,
+            "mix": {"Fg": float(self.Fg), "bd": float(self.bd)},
+            "rho_comp": rho_c,
+            "rho_g": rho_g,
+            "terms": {
+                "Fg_halo_1h": self.Fg * halo_1h,
+                "Fg_halo_2h": self.Fg * halo_2h,
+                "1mFg_diffuse": (1.0 - self.Fg) * diffuse,
+            },
+            "pk": pk,
+        }
+
+    def pk_gas_auto(self) -> np.ndarray:
+        """Return the full gas auto power spectrum P_gg(k)
+        on the stored k grid."""
+        return np.asarray(self.pair_gas_auto_packet()["pk"], dtype=float)
+
+    def pk_dm_gas(self) -> np.ndarray:
+        """Return the full dark-matter x gas cross power spectrum on the
+         stored k grid."""
+        return np.asarray(
+            self.pair_mixed_cross_packet(comp="dark_matter")["pk"],
+            dtype=float)
+
+    def pk_star_gas(self) -> np.ndarray:
+        """Return the full stars x gas cross power spectrum on the stored
+         k grid."""
+        return np.asarray(
+            self.pair_mixed_cross_packet(comp="stars")["pk"],
+            dtype=float)
+
+    def pk_packet(self, *, use_cache: bool = True) -> dict[str, Any]:
+        """Return a dictionary of spectra and metadata.
+
+        The returned dictionary is intended for plotting and downstream
+        analysis.
+        It includes:
+          - the k grid and scale factor,
+          - reference spectra (linear, nonlinear, and a DMO halo-model
+            baseline),
+          - per-component and per-pair spectra,
+          - weighted contributions used to form the total matter spectrum,
+          - model metadata (mixing parameters, densities, weights).
+
+        Args:
+            use_cache: If True, reuse cached results when available.
+
+        Returns:
+            Dictionary containing spectra arrays on ``self.k`` and
+            accompanying metadata.
+
+        Raises:
+            KeyError: If required densities are missing.
+        """
+        if use_cache and (self._pk_packet_cache is not None):
+            return self._pk_packet_cache
+
+        # sanity: these must exist
+        for key in ("matter", "dark_matter", "gas", "stars"):
+            if key not in self.rho:
+                raise KeyError(
+                    f"Missing density {key!r}. Call ensure_densities() first.")
+
+        # reference spectra
+        pk_lin = np.asarray(self.P_lin(), dtype=float)
+        pk_nl = np.asarray(ccl.nonlin_matter_power(self.cosmo, self.k, self.a),
+                           dtype=float)
+        pk_dmo = np.asarray(self.pk_total_dmo(use_cache=use_cache),
+                            dtype=float)
+
+        # pair packets (decompositions)
+        pairs: dict[str, Any] = {
+            "dm_dm": self.pair_halo_packet(
+                comp1="dark_matter",
+                comp2="dark_matter",
+                rho1=float(self.rho["dark_matter"]),
+                rho2=float(self.rho["dark_matter"]),
+            ),
+            "stars_stars": self.pair_halo_packet(
+                comp1="stars",
+                comp2="stars",
+                rho1=float(self.rho["stars"]),
+                rho2=float(self.rho["stars"]),
+            ),
+            "dm_stars": self.pair_halo_packet(
+                comp1="dark_matter",
+                comp2="stars",
+                rho1=float(self.rho["dark_matter"]),
+                rho2=float(self.rho["stars"]),
+            ),
+            "gas_gas": self.pair_gas_auto_packet(),
+            "dm_gas": self.pair_mixed_cross_packet(comp="dark_matter"),
+            "stars_gas": self.pair_mixed_cross_packet(comp="stars"),
+        }
+
+        # weights + weighted contributions to total matter
+        w_full = self.weights()
+        weights = {
+            "dm_dm": float(w_full["w_dm"]),
+            "gas_gas": float(w_full["w_g"]),
+            "stars_stars": float(w_full["w_s"]),
+            "dm_gas": float(w_full["w_dm_g"]),
+            "stars_gas": float(w_full["w_g_s"]),
+            "dm_stars": float(w_full["w_dm_s"]),
+        }
+
+        contrib = {name: weights[name] * pairs[name]["pk"] for name in weights}
+
+        pk_total = (
+            contrib["dm_dm"]
+            + contrib["gas_gas"]
+            + contrib["stars_stars"]
+            + contrib["dm_gas"]
+            + contrib["stars_gas"]
+            + contrib["dm_stars"]
+        )
+
+        # spectra dictionary
+        pk = {
+            "total": pk_total,
+            "dm": pairs["dm_dm"]["pk"],
+            "gas": pairs["gas_gas"]["pk"],
+            "stars": pairs["stars_stars"]["pk"],
+            "dm_gas": pairs["dm_gas"]["pk"],
+            "stars_gas": pairs["stars_gas"]["pk"],
+            "dm_stars": pairs["dm_stars"]["pk"],
+            # If you want the *weighted* contributions too, keep these:
+            "w_dm": contrib["dm_dm"],
+            "w_gas": contrib["gas_gas"],
+            "w_stars": contrib["stars_stars"],
+            "w_dm_gas": contrib["dm_gas"],
+            "w_stars_gas": contrib["stars_gas"],
+            "w_dm_stars": contrib["dm_stars"],
+        }
+
+        # add commutative aliases (pk)
+        _add_pair_aliases(pk, "dm", "gas")
+        _add_pair_aliases(pk, "dm", "stars")
+        _add_pair_aliases(pk, "stars", "gas")
+
+        # add commutative aliases (weighted pair contributions)
+        _add_weighted_pair_aliases(pk, "dm", "gas")
+        _add_weighted_pair_aliases(pk, "dm", "stars")
+        _add_weighted_pair_aliases(pk, "stars", "gas")
+
+        out = {
+            "grid": {"k": self.k, "a": float(self.a)},
+            "pk_ref": {"pk_lin": pk_lin, "pk_nlin": pk_nl, "pk_dmo": pk_dmo},
+            "pk": pk,
+            "halo_pairs": pairs,
+            "meta": {
+                "mix": {"Fg": float(self.Fg), "bd": float(self.bd)},
+                "rho": dict(self.rho),
+                "weights": weights,
+            },
+        }
+
+        if use_cache:
+            self._pk_packet_cache = out
+        return out
+
+    def pk_total(self) -> np.ndarray:
+        """Return the total Fedeli14 matter Pk on the stored k grid."""
+        return np.asarray(self.pk_packet(use_cache=True)["pk"]["total"],
+                          dtype=float)
+
+    def pk_total_dmo(self, *, use_cache: bool = True) -> np.ndarray:
+        """Return a dark-matter-only halo-model baseline spectrum.
+
+        This uses a fixed DMO dark-matter profile evaluator (``y_dmo_dm``) so
+        that the baseline is stable even if the baryonic dark-matter profile
+         is modified.
+        """
+        if use_cache and (self._pk_dmo is not None):
+            return self._pk_dmo
+
+        if "matter" not in self.rho:
+            self.rho["matter"] = float(ccl.rho_x(self.cosmo,
+                                                 self.a,
+                                                 "matter"))
+        rho_m = float(self.rho["matter"])
+
+        # Use baseline DMO DM profile evaluator,
+        # NOT the (possibly modified) baryonic DM.
+        mmin = float(self.mass_ranges["dark_matter"]["min"])
+        mmax = float(self.mass_ranges["dark_matter"]["max"])
+
+        P_lin = self.P_lin()
+
+        I2 = self._I2_vec(
+            "dark_matter", "dark_matter", mmin, mmax,
+            y1_fn=self.y_dmo_dm, y2_fn=self.y_dmo_dm, cache_tag="dmo"
+        )
+        Ib = self._Ib_vec(
+            "dark_matter", mmin, mmax,
+            y_fn=self.y_dmo_dm, cache_tag="dmo"
+        )
+
+        P1 = I2 / (rho_m * rho_m)
+        P2 = P_lin * (Ib * Ib) / (rho_m * rho_m)
+        out = np.asarray(P1 + P2, dtype=float)
+
+        if use_cache:
+            self._pk_dmo = out
+        return out
+
+    def boost_hm_over_hm(self) -> np.ndarray:
+        """Return the halo-model boost B(k) = P_baryon_HM(k) / P_dmo_HM(k)."""
+        packet = self.pk_packet(use_cache=True)
+        pb = np.asarray(packet["pk"]["total"], dtype=float)
+        pdmo = np.asarray(packet["pk_ref"]["pk_dmo"], dtype=float)
+
+        if np.any(pdmo <= 0.0) or not np.all(np.isfinite(pdmo)):
+            raise ValueError(
+                "DMO halo-model power is non-positive or non-finite;"
+                " cannot form boost.")
+
+        return np.asarray(pb / pdmo, dtype=float)
diff --git a/pyccl/baryons/fedeli14_bhm/profile_interpolation.py b/pyccl/baryons/fedeli14_bhm/profile_interpolation.py
new file mode 100644
index 000000000..abfca43d3
--- /dev/null
+++ b/pyccl/baryons/fedeli14_bhm/profile_interpolation.py
@@ -0,0 +1,293 @@
+from __future__ import annotations
+
+from collections.abc import Callable, Mapping
+from typing import Any
+
+import numpy as np
+from scipy import interpolate
+
+from pyccl.baryons.fedeli14_bhm.numerics import (
+    _require_attr,
+    _require_float,
+    _require_mapping,
+)
+
+__all__ = [
+    "update_precision_fftlog",
+    "interpolate_profile_u_over_m",
+    "build_profile_interpolators",
+    "FEDELI_COMPONENTS",
+]
+
+ProfileEvalFn = Callable[[np.ndarray, np.ndarray], np.ndarray]
+
+FEDELI_COMPONENTS = ("dark_matter", "gas", "stars")
+
+
+def update_precision_fftlog(
+    profile: Any,
+    /,
+    *,
+    padding_hi_fftlog: float = 1.0e3,
+    padding_lo_fftlog: float = 1.0e-3,
+    n_per_decade: int = 1000,
+    plaw_fourier: float = -2.0,
+) -> Any:
+    """Update FFTLog settings on a halo profile, if supported.
+
+    If ``profile`` provides a ``update_precision_fftlog`` method, call it with
+    the provided keyword arguments and return ``profile``. Otherwise, return
+    ``profile`` unchanged.
+
+    Args:
+        profile: Halo profile-like object.
+        padding_hi_fftlog (:obj:`float`): High-k padding factor.
+        padding_lo_fftlog (:obj:`float`): Low-k padding factor.
+        n_per_decade (:obj:`int`): FFTLog sampling density.
+        plaw_fourier (:obj:`float`): FFTLog Fourier power-law tilt.
+
+    Returns:
+        The input ``profile`` (possibly modified in-place).
+
+    Raises:
+        TypeError: If ``update_precision_fftlog`` exists but is not callable.
+    """
+    upd = getattr(profile, "update_precision_fftlog", None)
+    if upd is None:
+        return profile
+    if not callable(upd):
+        raise TypeError(
+            "profile.update_precision_fftlog exists but is not callable.")
+
+    upd(
+        padding_hi_fftlog=padding_hi_fftlog,
+        padding_lo_fftlog=padding_lo_fftlog,
+        n_per_decade=n_per_decade,
+        plaw_fourier=plaw_fourier,
+    )
+    return profile
+
+
+def interpolate_profile_u_over_m(
+    *,
+    cosmo: Any,
+    a: float,
+    profile: Any,
+    mass: np.ndarray,
+    k: np.ndarray,
+    method: str = "linear",
+    bounds_error: bool = False,
+    fill_value: float | None = None,
+    log_axes: bool = True,
+    log_values: bool = False,
+    value_floor: float = 1.0e-300,
+) -> ProfileEvalFn:
+    """Return a callable for evaluating :math:`u(k,M)/M` by interpolation.
+
+    The returned function evaluates the Fourier-space halo profile
+    ``profile.fourier(cosmo, k, M, a)`` on a fixed mass and wavenumber grid,
+    converts it to :math:`u/M`, and interpolates it in 2D.
+
+    Args:
+        cosmo (:class:`~pyccl.cosmology.Cosmology`): Cosmological parameters.
+        a (:obj:`float`): Scale factor.
+        profile: Halo profile-like object implementing a ``fourier`` method.
+        mass (:obj:`numpy.ndarray`): 1D, strictly increasing mass grid.
+        k (:obj:`numpy.ndarray`): 1D, strictly increasing wavenumber grid.
+        method (:obj:`str`): Interpolation method passed to
+            :class:`scipy.interpolate.RegularGridInterpolator`.
+        bounds_error (:obj:`bool`): If True, raise an error for query points
+            outside the grid. If False, use ``fill_value`` or clamp behavior as
+            implemented by ``RegularGridInterpolator``.
+        fill_value (:obj:`float` or :obj:`None`): Fill value for out-of-bounds
+            queries when ``bounds_error=False``. If None, SciPy uses its
+            default behavior.
+        log_axes (:obj:`bool`): If True, interpolate over
+            :math:`(\\ln M, \\ln k)`.
+        log_values (:obj:`bool`): If True, interpolate :math:`\\ln(u/M)`
+            instead of :math:`u/M`.
+        value_floor (:obj:`float`): Floor applied to values before taking logs
+            when ``log_values=True``.
+
+    Returns:
+        callable: Function ``f(M, k)`` returning :math:`u(k|M)/M` evaluated at
+        the supplied ``M`` and ``k`` (broadcasting over inputs).
+
+    Raises:
+        TypeError: If ``profile.fourier`` is missing or not callable.
+        ValueError: If inputs are invalid, if grids are not 1D and strictly
+            increasing, if ``log_axes=True`` with non-positive grid values, or
+            if the Fourier grid has an unexpected shape.
+    """
+    mass = np.asarray(mass, dtype=float)
+    k = np.asarray(k, dtype=float)
+
+    _require_float("a", a)
+    a = float(a)
+    if not np.isfinite(a):
+        raise ValueError("a must be finite.")
+
+    if mass.size == 0 or k.size == 0:
+        raise ValueError("mass and k must be non-empty 1D arrays.")
+
+    if mass.ndim != 1 or k.ndim != 1:
+        raise ValueError("mass and k must be 1D grid vectors.")
+    if not (np.all(np.diff(mass) > 0) and np.all(np.diff(k) > 0)):
+        raise ValueError("mass and k grids must be strictly increasing.")
+    if log_axes and (np.any(mass <= 0.0) or np.any(k <= 0.0)):
+        raise ValueError("mass and k grids must be > 0 if log_axes=True.")
+
+    _require_attr(profile, "fourier", who="profile")
+    if not callable(profile.fourier):
+        raise TypeError("profile.fourier must be callable.")
+
+    fourier_raw = np.asarray(profile.fourier(cosmo, k, mass, a), dtype=float)
+
+    expected = (mass.size, k.size)
+    if fourier_raw.shape != expected:
+        raise ValueError(
+            "profile.fourier must return an array of shape (len(mass),"
+            " len(k)) = {expected}, got {fourier_raw.shape}."
+        )
+
+    values = fourier_raw / mass[:, None]  # u/M
+
+    if log_values:
+        if value_floor <= 0:
+            raise ValueError("value_floor must be > 0 when log_values=True.")
+
+    if log_values:
+        values = np.log(np.maximum(values, float(value_floor)))
+
+    xM = np.log(mass) if log_axes else mass
+    xk = np.log(k) if log_axes else k
+
+    rgi = interpolate.RegularGridInterpolator(
+        (xM, xk),
+        values,
+        method=method,
+        bounds_error=bounds_error,
+        fill_value=fill_value,
+    )
+
+    def eval_u_over_m(M: np.ndarray, kq: np.ndarray) -> np.ndarray:
+        """Evaluate u(k|M)/M at given M and k."""
+        M = np.asarray(M, dtype=float)
+        kq = np.asarray(kq, dtype=float)
+        M, kq = np.broadcast_arrays(M, kq)
+
+        if log_axes:
+            if np.any(M <= 0.0) or np.any(kq <= 0.0):
+                raise ValueError("M and k must be > 0 when log_axes=True.")
+            p0 = np.log(M.ravel())
+            p1 = np.log(kq.ravel())
+        else:
+            p0 = M.ravel()
+            p1 = kq.ravel()
+
+        pts = np.column_stack([p0, p1])
+        out = rgi(pts).reshape(M.shape)
+
+        if log_values:
+            out = np.exp(out)
+
+        return out
+
+    return eval_u_over_m
+
+
+def build_profile_interpolators(
+    *,
+    cosmo,
+    a: float,
+    interpolation_grid,
+    profiles,
+    components: tuple[str, ...] | None = None,
+    update_fftlog_precision: bool = True,
+    fftlog_kwargs=None,
+    rgi_kwargs=None,
+):
+    """Build per-component interpolators for :math:`u(k,M)/M`.
+
+    This is a convenience wrapper that constructs :math:`u/M` interpolators for
+    multiple halo-profile components (e.g. ``dark_matter``, ``gas``, ``stars``)
+    using per-component (mass, k) grids.
+
+    Args:
+        cosmo (:class:`~pyccl.cosmology.Cosmology`): Cosmological parameters.
+        a (:obj:`float`): Scale factor.
+        interpolation_grid (:obj:`dict`): Mapping from component name to a
+            mapping with keys ``'mass'`` and ``'k'`` defining the interpolation
+            grid vectors.
+        profiles (:obj:`dict`): Mapping from component name to halo
+            profile-like objects implementing ``fourier``.
+        components (:obj:`tuple` or :obj:`None`): Components to build. If None,
+            defaults to ``FEDELI_COMPONENTS``.
+        update_fftlog_precision (:obj:`bool`): If True, call
+            :func:`update_precision_fftlog` on each profile before sampling.
+        fftlog_kwargs (:obj:`dict` or :obj:`None`): Keyword arguments passed to
+            :func:`update_precision_fftlog`.
+        rgi_kwargs (:obj:`dict` or :obj:`None`): Keyword arguments forwarded to
+            :func:`interpolate_profile_u_over_m`.
+
+    Returns:
+        dict: Mapping from component name to an interpolator callable
+        ``f(M, k)`` returning :math:`u(k|M)/M`.
+
+    Raises:
+        TypeError: If ``profiles`` or ``interpolation_grid`` are not mappings,
+            or if ``fftlog_kwargs`` / ``rgi_kwargs`` are not mappings when
+            provided.
+        KeyError: If required components are missing from ``profiles`` or
+            ``interpolation_grid``.
+    """
+    _require_mapping(profiles, who="profiles")
+    _require_mapping(interpolation_grid, who="interpolation_grid")
+
+    if fftlog_kwargs is not None and not isinstance(fftlog_kwargs, Mapping):
+        raise TypeError("fftlog_kwargs must be a mapping (dict-like) or None.")
+    if rgi_kwargs is not None and not isinstance(rgi_kwargs, Mapping):
+        raise TypeError("rgi_kwargs must be a mapping (dict-like) or None.")
+
+    fftlog_kwargs = dict(fftlog_kwargs or {})
+    rgi_kwargs = dict(rgi_kwargs or {})
+
+    # NEW: allow subset builds (default = full Fedeli set)
+    if components is None:
+        components = FEDELI_COMPONENTS
+    components = tuple(components)
+
+    missing_p = [c for c in components if c not in profiles]
+    missing_g = [c for c in components if c not in interpolation_grid]
+    if missing_p or missing_g:
+        raise KeyError(
+            "Fedeli requires components "
+            f"{components}. Missing profiles={missing_p},"
+            f" missing grids={missing_g}"
+        )
+
+    for comp in components:
+        grid = interpolation_grid[comp]
+        if not isinstance(grid, Mapping):
+            raise TypeError(
+                f"interpolation_grid[{comp!r}] must be a mapping with keys"
+                f" 'mass' and 'k'.")
+        if "mass" not in grid or "k" not in grid:
+            raise KeyError(
+                f"interpolation_grid[{comp!r}] must contain keys"
+                f" 'mass' and 'k'.")
+
+    out = {}
+    for comp in components:
+        prof = profiles[comp]
+        grid = interpolation_grid[comp]
+        mass = np.asarray(grid["mass"], float)
+        k = np.asarray(grid["k"], float)
+
+        if update_fftlog_precision:
+            prof = update_precision_fftlog(prof, **fftlog_kwargs)
+
+        out[comp] = interpolate_profile_u_over_m(
+            cosmo=cosmo, a=a, profile=prof, mass=mass, k=k, **rgi_kwargs
+        )
+    return out
diff --git a/pyccl/tests/test_baryons_fedeli14.py b/pyccl/tests/test_baryons_fedeli14.py
new file mode 100644
index 000000000..ea36cc0bd
--- /dev/null
+++ b/pyccl/tests/test_baryons_fedeli14.py
@@ -0,0 +1,339 @@
+"""Unit tests for `pyccl.baryons.fedeli14`."""
+
+from __future__ import annotations
+
+import itertools
+
+import numpy as np
+import pytest
+
+import pyccl as ccl
+from pyccl import Pk2D
+
+from pyccl.baryons.fedeli14 import BaryonsFedeli14
+
+
+class DummyBHM:
+    """Mock BaryonHaloModel class."""
+
+    def __init__(
+        self,
+        *,
+        k_support: tuple[float, float] = (1e-6, 1e2),
+        boost_value: float | None = 1.0,
+        calls: list[tuple[float, np.ndarray, str]] | None = None,
+    ) -> None:
+        self.interpolation_grid = {
+            "dark_matter": {"k": np.array(k_support, dtype=float)}}
+        self.boost_value = boost_value
+        self.calls = calls
+
+    def boost(self, *, k: np.ndarray, a: float, pk_ref: str) -> np.ndarray:
+        """Tests that boost returns a configurable mock boost."""
+        k = np.asarray(k, float)
+
+        if self.calls is not None:
+            self.calls.append((float(a), k.copy(), str(pk_ref)))
+
+        if self.boost_value is None:
+            return np.where(k <= 1e-2, 2.0, 4.0)
+
+        return np.ones_like(k) * self.boost_value
+
+
+def _cosmo() -> ccl.Cosmology:
+    """Return a CCL cosmology."""
+    return ccl.Cosmology(
+        Omega_c=0.25, Omega_b=0.05, h=0.7, n_s=0.965, sigma8=0.8)
+
+
+def _make_pk2d(*, is_logp: bool) -> Pk2D:
+    """Make a small but spline-valid Pk2D with known behavior for testing."""
+    a_arr = np.array([0.05, 0.2, 0.5, 1.0], dtype=float)
+    k_arr = np.array([1e-4, 1e-2, 1e-1, 1.0], dtype=float)
+    lk_arr = np.log(k_arr)
+
+    pk_lin = np.ones((a_arr.size, k_arr.size), dtype=float) * 2.0
+    pk_arr = np.log(pk_lin) if is_logp else pk_lin
+
+    return Pk2D(a_arr=a_arr, lk_arr=lk_arr, pk_arr=pk_arr, is_logp=is_logp)
+
+
+@pytest.mark.parametrize(
+    "name,value,expected_type,expected_value",
+    [
+        ("Fg", None, float, None),
+        ("bd", None, float, None),
+        ("m0_star", None, float, None),
+        ("sigma_star", None, float, None),
+        ("mmin_star", None, float, None),
+        ("mmax_star", None, float, None),
+        ("m0_gas", None, float, None),
+        ("sigma_gas", None, float, None),
+        ("gas_beta", None, float, None),
+        ("gas_r_co", None, float, None),
+        ("gas_r_ej", None, float, None),
+        ("star_x_delta", None, float, None),
+        ("star_alpha", None, float, None),
+        ("density_mmin", None, float, None),
+        ("density_mmax", None, float, None),
+        ("a_min", None, float, None),
+        ("n_m", None, int, None),
+        ("Fg", 0.5, float, 0.5),
+        ("bd", 1, float, 1.0),
+        ("n_m", 16.0, int, 16),
+        ("pk_ref", 123, str, "123"),
+        ("renormalize_large_scales", 0, bool, False),
+        ("update_fftlog_precision", 1, bool, True),
+    ],
+)
+def test_update_parameters_casts_valid_values(
+    name: str,
+    value: object,
+    expected_type: type,
+    expected_value: object,
+) -> None:
+    """Tests that update_parameters casts valid values and ignores None."""
+    b = BaryonsFedeli14()
+
+    old_value = getattr(b, name)
+    b.update_parameters(**{name: value})
+
+    if value is None:
+        assert getattr(b, name) == old_value
+    else:
+        assert isinstance(getattr(b, name), expected_type)
+        assert getattr(b, name) == expected_value
+
+
+def test_update_parameters_accepts_parameter_combinations() -> None:
+    """Tests that update_parameters accepts valid parameter combinations."""
+    values = [
+        ("Fg", 0.5),
+        ("bd", 1),
+        ("n_m", 16),
+        ("pk_ref", "pk_lin"),
+        ("renormalize_large_scales", False),
+        ("a_min", None),
+    ]
+
+    for first, second in itertools.product(values, repeat=2):
+        b = BaryonsFedeli14()
+        updates = dict([first, second])
+        b.update_parameters(**updates)
+
+
+def test_update_parameters_rejects_unknown() -> None:
+    """Tests that update_parameters rejects unknown keys."""
+    b = BaryonsFedeli14()
+
+    with pytest.raises(AttributeError, match=r"Unknown parameter"):
+        b.update_parameters(nope=1)
+
+
+def test_boost_factor_broadcasts_and_calls_bhm_boost(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Tests that boost_factor broadcasts inputs and calls BHM per a."""
+    b = BaryonsFedeli14(renormalize_large_scales=False)
+
+    calls: list[tuple[float, np.ndarray, str]] = []
+    monkeypatch.setattr(
+        BaryonsFedeli14,
+        "_build_bhm",
+        lambda self, cosmo: DummyBHM(calls=calls),
+    )
+
+    k = np.array([1e-4, 1e-2, 1.0, 10.0], dtype=float)
+    a = np.array([0.5, 1.0], dtype=float)
+
+    out = b.boost_factor(_cosmo(), k, a)
+
+    assert out.shape == (a.size, k.size)
+    assert np.allclose(out, 1.0)
+    assert len(calls) == a.size
+
+    for _, kvals, pkref in calls:
+        assert pkref == b.pk_ref
+        assert np.allclose(kvals, k)
+
+
+def test_boost_factor_renormalizes_large_scales_to_unity(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Tests that boost_factor renormalizes large scales to unity."""
+    b = BaryonsFedeli14(renormalize_large_scales=True, k_renorm_max=1e-2)
+
+    monkeypatch.setattr(
+        BaryonsFedeli14,
+        "_build_bhm",
+        lambda self, cosmo: DummyBHM(boost_value=None),
+    )
+
+    k = np.array([1e-4, 1e-2, 1e-1, 1.0], dtype=float)
+    a = np.array([0.5, 1.0], dtype=float)
+
+    out = b.boost_factor(_cosmo(), k, a)
+
+    assert np.allclose(out[:, :2], 1.0)
+    assert np.allclose(out[:, 2:], 2.0)
+
+
+def test_incl_bary_eff_applies_unity_for_early_times_and_outside_k_support(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Tests that boost is applied only for a>=0.1 and supported k."""
+    b = BaryonsFedeli14(renormalize_large_scales=False)
+
+    monkeypatch.setattr(
+        BaryonsFedeli14,
+        "boost_factor",
+        lambda self, cosmo, k, a: np.ones((np.size(a), np.size(k))) * 10.0,
+    )
+    monkeypatch.setattr(
+        BaryonsFedeli14,
+        "_build_bhm",
+        lambda self, cosmo: DummyBHM(k_support=(1e-3, 1e-1)),
+    )
+
+    pk = _make_pk2d(is_logp=False)
+    out = b._include_baryonic_effects(_cosmo(), pk)
+
+    a_arr, lk_arr, pk_out = out.get_spline_arrays()
+    k_arr = np.exp(lk_arr)
+
+    assert np.allclose(pk_out[0, :], 2.0)
+
+    for i in range(1, a_arr.size):
+        assert np.allclose(pk_out[i, 0], 2.0)
+        assert np.allclose(pk_out[i, 1], 20.0)
+        assert np.allclose(pk_out[i, 2], 20.0)
+        assert np.allclose(pk_out[i, 3], 2.0)
+
+    a0, lk0, _ = pk.get_spline_arrays()
+    assert a_arr == pytest.approx(a0, rel=1e-5)
+    assert k_arr == pytest.approx(np.exp(lk0), rel=1e-5)
+
+
+def test_incl_bary_eff_respects_logp_representation(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Tests that log-space Pk2D inputs are returned in log-space form."""
+    b = BaryonsFedeli14(renormalize_large_scales=False)
+
+    monkeypatch.setattr(
+        BaryonsFedeli14,
+        "boost_factor",
+        lambda self, cosmo, k, a: np.ones((np.size(a), np.size(k))) * 10.0,
+    )
+    monkeypatch.setattr(
+        BaryonsFedeli14,
+        "_build_bhm",
+        lambda self, cosmo: DummyBHM(),
+    )
+
+    pk = _make_pk2d(is_logp=True)
+    out = b._include_baryonic_effects(_cosmo(), pk)
+
+    _, _, pk_out = out.get_spline_arrays()
+
+    assert np.allclose(pk_out[0, :], 2.0)
+    assert np.allclose(pk_out[1:, :], 20.0)
+
+
+def test_incl_bary_eff_preserves_extrap_orders_and_log_flag(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Tests that output Pk2D preserves extrap orders and log flag."""
+    b = BaryonsFedeli14(renormalize_large_scales=False)
+
+    monkeypatch.setattr(
+        BaryonsFedeli14,
+        "boost_factor",
+        lambda self, cosmo, k, a: np.ones((np.size(a), np.size(k))),
+    )
+    monkeypatch.setattr(
+        BaryonsFedeli14,
+        "_build_bhm",
+        lambda self, cosmo: DummyBHM(),
+    )
+
+    pk0 = _make_pk2d(is_logp=False)
+    out0 = b._include_baryonic_effects(_cosmo(), pk0)
+    assert out0.extrap_order_lok == pk0.extrap_order_lok
+    assert out0.extrap_order_hik == pk0.extrap_order_hik
+    assert bool(out0.psp.is_log) == bool(pk0.psp.is_log)
+
+    pk1 = _make_pk2d(is_logp=True)
+    out1 = b._include_baryonic_effects(_cosmo(), pk1)
+    assert out1.extrap_order_lok == pk1.extrap_order_lok
+    assert out1.extrap_order_hik == pk1.extrap_order_hik
+    assert bool(out1.psp.is_log) == bool(pk1.psp.is_log)
+
+
+def test_boost_factor_squeezes_for_scalar_k(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Tests that boost_factor returns a scalar when k is a scalar."""
+    b = BaryonsFedeli14(renormalize_large_scales=False)
+
+    monkeypatch.setattr(
+        BaryonsFedeli14,
+        "_build_bhm",
+        lambda self, cosmo: DummyBHM(boost_value=2.0),
+    )
+
+    out = b.boost_factor(_cosmo(), k=1e-2, a=np.array([0.5, 1.0]))
+
+    assert out.shape == (2,)
+    assert np.allclose(out, 2.0)
+
+
+def test_boost_factor_renorm_guard_replaces_bad_norm(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Tests that boost factor renormalization guard replaces bad norms."""
+    b = BaryonsFedeli14(renormalize_large_scales=True, k_renorm_max=1e-2)
+
+    class BadNormDummyBHM(DummyBHM):
+        """Mock BHM class with bad renormalization values."""
+
+        def boost(self, *, k: np.ndarray, a: float, pk_ref: str) -> np.ndarray:
+            """Tests that boost returns zero on renormalization scales."""
+            _, _ = a, pk_ref
+            k = np.asarray(k, float)
+            return np.where(k <= 1e-2, 0.0, 4.0)
+
+    monkeypatch.setattr(
+        BaryonsFedeli14,
+        "_build_bhm",
+        lambda self, cosmo: BadNormDummyBHM(),
+    )
+
+    k = np.array([1e-4, 1e-2, 1e-1], dtype=float)
+    a = np.array([0.5, 1.0], dtype=float)
+
+    out = b.boost_factor(_cosmo(), k, a)
+
+    assert np.all(np.isfinite(out))
+    assert np.all(out > 0.0)
+
+
+def test_boost_factor_matches_actual_fedeli14_bhm() -> None:
+    """Tests that boost_factor matches the actual Fedeli14 BHM boost."""
+    b = BaryonsFedeli14(
+        renormalize_large_scales=False,
+        n_m=16,
+    )
+
+    cosmo = _cosmo()
+    k = np.array([1e-3, 1e-2, 1e-1], dtype=float)
+    a = 0.5
+
+    bhm = b._build_bhm(cosmo)
+    expected = bhm.boost(k=k, a=a, pk_ref=b.pk_ref)
+    actual = b.boost_factor(cosmo, k=k, a=a)
+
+    assert np.all(np.isfinite(actual))
+    assert np.all(actual > 0.0)
+    assert np.allclose(actual, expected)
diff --git a/pyccl/tests/test_baryons_fedeli14_baryon_halo_model.py b/pyccl/tests/test_baryons_fedeli14_baryon_halo_model.py
new file mode 100644
index 000000000..89592843f
--- /dev/null
+++ b/pyccl/tests/test_baryons_fedeli14_baryon_halo_model.py
@@ -0,0 +1,646 @@
+"""Unit tests for `pyccl.baryons.fedeli14_bhm.baryon_halo_model`."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+import pytest
+
+import pyccl as ccl
+
+from pyccl.baryons.fedeli14_bhm.baryon_halo_model import BaryonHaloModel
+from pyccl.baryons.fedeli14_bhm.numerics import _require_a, _require_k
+
+
+def _cosmo() -> ccl.Cosmology:
+    """Return a simple, valid CCL cosmology."""
+    return ccl.Cosmology(
+        Omega_c=0.25, Omega_b=0.05, h=0.7, n_s=0.965, sigma8=0.8)
+
+
+def _kgrid() -> np.ndarray:
+    """Return a small, strictly increasing positive k grid."""
+    return np.array([0.1, 1.0, 10.0], dtype=float)
+
+
+def test_require_a_validates_finite_and_positive() -> None:
+    """Tests that _require_a rejects non-finite and non-positive values."""
+    assert _require_a(1.0) == 1.0
+
+    with pytest.raises(ValueError, match=r"a must be finite"):
+        _ = _require_a(float("nan"))
+
+    with pytest.raises(ValueError, match=r"a must be > 0"):
+        _ = _require_a(0.0)
+
+    with pytest.raises(ValueError, match=r"a must be > 0"):
+        _ = _require_a(-1.0)
+
+
+def test_require_k_validates_shape_finiteness_sign_and_monotonicity() -> None:
+    """Tests that _require_k enforces 1D, finite, positive, strictly
+    increasing grids."""
+    k = _kgrid()
+    out = _require_k(k)
+    assert isinstance(out, np.ndarray) and out.shape == k.shape
+    assert np.all(out == k)
+
+    with pytest.raises(ValueError, match=r"non-empty 1D array"):
+        _ = _require_k(np.ones((2, 2)))
+
+    with pytest.raises(ValueError, match=r"non-empty 1D array"):
+        _ = _require_k(np.array([], dtype=float))
+
+    with pytest.raises(ValueError, match=r"contain only finite values"):
+        _ = _require_k(np.array([0.1, np.nan, 1.0], dtype=float))
+
+    with pytest.raises(ValueError, match=r"k must be > 0"):
+        _ = _require_k(np.array([0.0, 0.1, 1.0], dtype=float))
+
+    with pytest.raises(ValueError, match=r"strictly increasing"):
+        _ = _require_k(np.array([0.1, 0.1, 1.0], dtype=float))
+
+
+def test_init_validates_Fg_bd_and_density_mass_range() -> None:
+    """Tests that BaryonHaloModel validates Fg, bd, and density integral
+    mass range."""
+    cosmo = _cosmo()
+
+    with pytest.raises(ValueError, match=r"Fg must be in \[0, 1\]"):
+        _ = BaryonHaloModel(cosmo=cosmo, Fg=1.5)
+
+    with pytest.raises(ValueError, match=r"bd must be finite"):
+        _ = BaryonHaloModel(cosmo=cosmo, bd=float("nan"))
+
+    with pytest.raises(
+            ValueError,
+            match=r"density_mmin/density_mmax must satisfy 0 < min < max"):
+        _ = BaryonHaloModel(cosmo=cosmo, density_mmin=0.0, density_mmax=1.0e6)
+
+    with pytest.raises(
+            ValueError,
+            match=r"density_mmin/density_mmax must satisfy 0 < min < max"):
+        _ = BaryonHaloModel(
+            cosmo=cosmo, density_mmin=1.0e6, density_mmax=1.0e6
+        )
+
+    with pytest.raises(
+            ValueError,
+            match=r"density_mmin/density_mmax must satisfy 0 < min < max"):
+        _ = BaryonHaloModel(
+            cosmo=cosmo, density_mmin=1.0e10, density_mmax=1.0e6
+        )
+
+
+def test_clear_cache_empties_all_internal_caches() -> None:
+    """Tests that clear_cache empties all per-a caches."""
+    bhm = BaryonHaloModel(cosmo=_cosmo())
+
+    def _y_dm(M, k):
+        """Helper function that returns a constant function of mass and k."""
+        return M * 0 + k
+
+    def _y_dmo(M, k):
+        """Helper function that returns a constant function of mass and k."""
+        return M * 0 + k
+
+    # seed caches directly (unit test: OK)
+    bhm._frac_cache[1.0] = (object(), object(), object())
+    bhm._profile_cache[1.0] = {"dark_matter": object()}
+    bhm._interp_cache[1.0] = {"dark_matter": _y_dm}
+    bhm._dmo_dm_interp_cache[1.0] = _y_dmo
+
+    bhm.clear_cache()
+
+    assert bhm._frac_cache == {}  # noqa: SLF001
+    assert bhm._profile_cache == {}  # noqa: SLF001
+    assert bhm._interp_cache == {}  # noqa: SLF001
+    assert bhm._dmo_dm_interp_cache == {}  # noqa: SLF001
+
+
+def test_mass_frac_kwargs_only_returns_non_none_keys() -> None:
+    """Tests that _mass_frac_kwargs drops None-valued entries
+    (rho_star by default)."""
+    bhm = BaryonHaloModel(cosmo=_cosmo(), rho_star=None)
+    kw = bhm._mass_frac_kwargs()  # noqa: SLF001
+    assert "rho_star" not in kw
+    assert "m0_star" in kw
+    assert "sigma_star" in kw
+
+    bhm2 = BaryonHaloModel(cosmo=_cosmo(), rho_star=123.0)
+    kw2 = bhm2._mass_frac_kwargs()  # noqa: SLF001
+    assert "rho_star" in kw2
+    assert float(kw2["rho_star"]) == 123.0
+
+
+def test_get_mass_fractions_is_cached(
+    monkeypatch: pytest.MonkeyPatch
+) -> None:
+    """Tests that _get_mass_fractions caches results per scale factor a."""
+    calls = {"n": 0}
+
+    def fake_mass_fractions(**kwargs: Any):
+        """Mock mass_fractions that returns constant functions."""
+        calls["n"] += 1
+
+        def f_g(m):
+            """Mock f_g that returns constant functions."""
+            return np.zeros_like(np.asarray(m, float)) + 0.1
+
+        def f_s(m):
+            """Mock f_s that returns constant functions."""
+            return np.zeros_like(np.asarray(m, float)) + 0.01
+
+        def f_d(m):
+            """Mock f_d that returns constant functions."""
+            return np.zeros_like(np.asarray(m, float)) + 0.89
+
+        return f_g, f_s, f_d
+
+    import pyccl.baryons.fedeli14_bhm.baryon_halo_model as mod
+
+    monkeypatch.setattr(mod, "mass_fractions", fake_mass_fractions)
+
+    bhm = BaryonHaloModel(cosmo=_cosmo())
+
+    fg1, fs1, fd1 = bhm._get_mass_fractions(1.0)  # noqa: SLF001
+    fg2, fs2, fd2 = bhm._get_mass_fractions(1.0)  # noqa: SLF001
+
+    assert calls["n"] == 1
+    assert fg1 is fg2
+    assert fs1 is fs2
+    assert fd1 is fd2
+
+
+def test_get_profiles_uses_override_and_does_not_cache() -> None:
+    """Tests that a profiles override is returned directly and bypasses
+    profile building."""
+    dummy = {"dark_matter": object(), "gas": object(), "stars": object()}
+    bhm = BaryonHaloModel(cosmo=_cosmo(), profiles=dummy)
+
+    out1 = bhm._get_profiles(1.0)  # noqa: SLF001
+    out2 = bhm._get_profiles(0.5)  # noqa: SLF001
+
+    assert out1 is dummy
+    assert out2 is dummy
+
+
+def test_get_interpolators_is_cached(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Tests that _get_interpolators caches interpolators per scale factor."""
+    calls = {"n": 0}
+
+    def fake_build_profile_interpolators(**kwargs: Any):
+        """Mock build_profile_interpolators that returns constant functions."""
+        calls["n"] += 1
+
+        def y_dm(M, k):
+            shape = np.broadcast(M, k).shape
+            return np.zeros(shape, dtype=float) + 1.0
+
+        def y_gas(M, k):
+            shape = np.broadcast(M, k).shape
+            return np.zeros(shape, dtype=float) + 2.0
+
+        def y_stars(M, k):
+            shape = np.broadcast(M, k).shape
+            return np.zeros(shape, dtype=float) + 3.0
+
+        return {
+            "dark_matter": y_dm,
+            "gas": y_gas,
+            "stars": y_stars,
+        }
+
+    import pyccl.baryons.fedeli14_bhm.baryon_halo_model as mod
+
+    monkeypatch.setattr(
+        mod,
+        "build_profile_interpolators",
+        fake_build_profile_interpolators)
+
+    bhm = BaryonHaloModel(cosmo=_cosmo())
+
+    i1 = bhm._get_interpolators(1.0)  # noqa: SLF001
+    i2 = bhm._get_interpolators(1.0)  # noqa: SLF001
+
+    assert calls["n"] == 1
+    assert i1 is i2
+    assert set(i1.keys()) == {"dark_matter", "gas", "stars"}
+
+
+def test_pk_components_validates_inputs_before_work() -> None:
+    """Tests that pk_components validates (k,a) and fails fast."""
+    bhm = BaryonHaloModel(cosmo=_cosmo())
+
+    with pytest.raises(ValueError, match=r"a must be > 0"):
+        _ = bhm.pk_components(k=_kgrid(), a=0.0)
+
+    with pytest.raises(ValueError, match=r"k must contain only finite values"):
+        _ = bhm.pk_components(k=np.array([0.1, np.nan, 1.0]), a=1.0)
+
+    with pytest.raises(ValueError, match=r"strictly increasing"):
+        _ = bhm.pk_components(k=np.array([0.1, 0.1, 1.0]), a=1.0)
+
+
+def test_pk_components_wires_calculator_and_returns_packet(
+    monkeypatch: pytest.MonkeyPatch
+) -> None:
+    """Tests that pk_components wires the calculator, calls ensure_densities,
+    and returns a packet."""
+    calls = {"ensure": 0}
+
+    def fake_get_interpolators(self: Any, a: float):
+        return {"dark_matter": object(), "gas": object(), "stars": object()}
+
+    def fake_get_mass_fractions(self: Any, a: float):
+        """Mock mass_fractions that returns constant functions."""
+        def f_g(m):
+            """Mock f_g that returns constant functions."""
+            return np.zeros_like(np.asarray(m, float)) + 0.1
+
+        def f_s(m):
+            """Mock f_s that returns constant functions."""
+            return np.zeros_like(np.asarray(m, float)) + 0.01
+
+        def f_d(m):
+            """Mock f_d that returns constant functions."""
+            return np.zeros_like(np.asarray(m, float)) + 0.89
+
+        return f_g, f_s, f_d
+
+    def fake_get_dmo_dm_interpolator(self: Any, a: float):
+        return object()
+
+    monkeypatch.setattr(
+        BaryonHaloModel,
+        "_get_interpolators",
+        fake_get_interpolators
+    )
+    monkeypatch.setattr(
+        BaryonHaloModel,
+        "_get_mass_fractions",
+        fake_get_mass_fractions
+    )
+    monkeypatch.setattr(
+        BaryonHaloModel,
+        "_get_dmo_dm_interpolator",
+        fake_get_dmo_dm_interpolator
+    )
+
+    class FakeCalc:
+        def __init__(self, **kwargs: Any) -> None:
+            self.k = np.asarray(kwargs["k"], float)
+
+        def ensure_densities(self, **kwargs: Any) -> None:
+            calls["ensure"] += 1
+
+        def pk_packet(self, **kwargs: Any) -> dict[str, Any]:
+            k = self.k
+            return {
+                "grid": {"k": k, "a": 1.0},
+                "pk_ref": {
+                    "pk_lin": np.ones_like(k) * 10.0,
+                    "pk_nlin": np.ones_like(k) * 20.0,
+                    "pk_dmo": np.ones_like(k) * 30.0,
+                },
+                "pk": {"total": np.ones_like(k) * 7.0},
+                "halo_pairs": {},
+                "meta": {},
+            }
+
+    import pyccl.baryons.fedeli14_bhm.baryon_halo_model as mod
+
+    monkeypatch.setattr(mod, "FedeliPkCalculator", FakeCalc)
+
+    bhm = BaryonHaloModel(cosmo=_cosmo(), n_m=8)
+
+    out = bhm.pk_components(k=_kgrid(), a=1.0)
+    assert isinstance(out, dict)
+    assert set(out.keys()) >= {"grid", "pk_ref", "pk"}
+    assert np.allclose(out["grid"]["k"], _kgrid())
+    assert np.allclose(out["pk"]["total"], 7.0)
+    assert calls["ensure"] == 1
+
+
+def test_pk_total_is_packet_total_view(
+    monkeypatch: pytest.MonkeyPatch
+) -> None:
+    """Tests that pk_total returns pk_components()['pk']['total']."""
+
+    def fake_pk_components(
+        self,
+        *,
+        k: np.ndarray,
+        a: float
+    ) -> dict[str, Any]:
+        """Mock pk_components that returns a packet with pk_ref=pref."""
+        k = np.asarray(k, float)
+        return {"grid": {"k": k, "a": float(a)},
+                "pk_ref": {}, "pk": {"total": k * 0.0 + 7.0}}
+
+    monkeypatch.setattr(
+        BaryonHaloModel, "pk_components", fake_pk_components)
+
+    bhm = BaryonHaloModel(cosmo=_cosmo())
+    out = bhm.pk_total(k=_kgrid(), a=1.0)
+    assert isinstance(out, np.ndarray)
+    assert out.shape == _kgrid().shape
+    assert np.all(out == 7.0)
+
+
+def test_pk_total_dmo_wires_calculator_and_returns_array(
+    monkeypatch: pytest.MonkeyPatch
+) -> None:
+    """Tests that pk_total_dmo wires the calculator and returns its DMO
+    halo-model baseline."""
+
+    def fake_get_interpolators(self: Any, a: float):
+        return {"dark_matter": object(), "gas": object(), "stars": object()}
+
+    def fake_get_dmo_dm_interpolator(self: Any, a: float):
+        return object()
+
+    monkeypatch.setattr(
+        BaryonHaloModel,
+        "_get_interpolators",
+        fake_get_interpolators
+    )
+    monkeypatch.setattr(
+        BaryonHaloModel,
+        "_get_dmo_dm_interpolator",
+        fake_get_dmo_dm_interpolator
+    )
+
+    class FakeCalc:
+        def __init__(self, **kwargs: Any) -> None:
+            self.k = np.asarray(kwargs["k"], float)
+
+        def pk_total_dmo(self, **kwargs: Any) -> np.ndarray:
+            return np.ones_like(self.k) * 2.0
+
+    import pyccl.baryons.fedeli14_bhm.baryon_halo_model as mod
+
+    monkeypatch.setattr(mod, "FedeliPkCalculator", FakeCalc)
+    monkeypatch.setattr(ccl, "rho_x", lambda cosmo, a, what: 123.0)
+
+    bhm = BaryonHaloModel(cosmo=_cosmo(), n_m=8)
+
+    out = bhm.pk_total_dmo(k=_kgrid(), a=1.0)
+    assert isinstance(out, np.ndarray)
+    assert out.shape == _kgrid().shape
+    assert np.all(out == 2.0)
+
+
+def test_boost_validates_pk_ref_and_returns_ratio(
+    monkeypatch: pytest.MonkeyPatch
+) -> None:
+    """Tests that boost validates pk_ref and returns pk_bar/pk_ref."""
+    k = _kgrid()
+    pb = np.array([2.0, 4.0, 6.0], dtype=float)
+    pref = np.array([1.0, 2.0, 3.0], dtype=float)
+
+    def fake_pk_components(
+        self,
+        *,
+        k: np.ndarray,
+        a: float
+    ) -> dict[str, Any]:
+        """Fake pk_components that returns a packet with pk_ref=pref."""
+        k = np.asarray(k, float)
+        return {
+            "grid": {"k": k, "a": float(a)},
+            "pk": {"total": pb},
+            "pk_ref": {"pk_dmo": pref, "pk_nlin": pref, "pk_lin": pref},
+        }
+
+    monkeypatch.setattr(
+        BaryonHaloModel, "pk_components", fake_pk_components)
+
+    bhm = BaryonHaloModel(cosmo=_cosmo())
+
+    b1 = bhm.boost(k=k, a=1.0, pk_ref="pk_dmo")
+    assert np.allclose(b1, pb / pref)
+
+    b2 = bhm.boost(k=k, a=1.0, pk_ref="pk_nlin")
+    assert np.allclose(b2, pb / pref)
+
+    b3 = bhm.boost(k=k, a=1.0, pk_ref="pk_lin")
+    assert np.allclose(b3, pb / pref)
+
+    with pytest.raises(ValueError, match=r"pk_ref must be one of"):
+        _ = bhm.boost(k=k, a=1.0, pk_ref="nope")
+
+
+def test_validate_ranges_vs_interp_raises_when_ranges_exceed_grid() -> None:
+    """Tests that validate_ranges_vs_interp raises when mass_ranges exceed the
+    interpolation grid."""
+    cosmo = _cosmo()
+
+    # interpolation mass grid only spans [1e10, 1e12]
+    mgrid = np.logspace(10, 12, 8)
+    kgrid = np.logspace(-3, 0, 4)
+
+    interp = {
+        "dark_matter": {"mass": mgrid, "k": kgrid},
+        "gas": {"mass": mgrid, "k": kgrid},
+        "stars": {"mass": mgrid, "k": kgrid},
+    }
+
+    # mass_ranges exceed the grid max -> should raise
+    ranges = {
+        "dark_matter": {"min": 1e10, "max": 1e13},
+        "gas": {"min": 1e10, "max": 1e12},
+        "stars": {"min": 1e10, "max": 1e12},
+    }
+
+    with pytest.raises(ValueError, match=r"exceed interpolation mass grid"):
+        _ = BaryonHaloModel(
+            cosmo=cosmo,
+            interpolation_grid=interp,
+            mass_ranges=ranges)
+
+
+def test_halo_profiles_space_real_fourier_and_invalid(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Tests that halo_profiles validates space and returns real or fourier."""
+    bhm = BaryonHaloModel(cosmo=_cosmo())
+
+    monkeypatch.setattr(
+        BaryonHaloModel,
+        "_get_profiles",
+        lambda self, a: {"x": "real"})
+    monkeypatch.setattr(
+        BaryonHaloModel,
+        "_get_interpolators",
+        lambda self, a: {"x": "fourier"})
+
+    out_real = bhm.halo_profiles(a=1.0, space="real")
+    assert out_real == {"x": "real"}
+
+    out_fourier = bhm.halo_profiles(a=1.0, space="fourier")
+    assert out_fourier == {"x": "fourier"}
+
+    with pytest.raises(ValueError, match=r"space must be"):
+        _ = bhm.halo_profiles(a=1.0, space="nope")
+
+
+def test_mass_fractions_wrapper_calls_get_mass_fractions_and_validates_a(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Tests that mass_fractions validates a and calls _get_mass_fractions."""
+    bhm = BaryonHaloModel(cosmo=_cosmo())
+
+    sentinel = (object(), object(), object())
+    monkeypatch.setattr(
+        BaryonHaloModel,
+        "_get_mass_fractions",
+        lambda self, a: sentinel)
+
+    out = bhm.mass_fractions(a=1.0)
+    assert out is sentinel
+
+    with pytest.raises(ValueError, match=r"a must be > 0"):
+        _ = bhm.mass_fractions(a=0.0)
+
+
+def test_halo_radius_physical_comoving_and_invalid_frame(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Tests that halo_radius validates frame and returns physical or
+    comoving."""
+    bhm = BaryonHaloModel(cosmo=_cosmo())
+
+    class _FakeMassDef:
+        def get_radius(self, cosmo, M, a):
+            return np.asarray(M, float) * 0 + (2.0 * float(a))
+
+    bhm.mass_def = _FakeMassDef()
+
+    r_phys = bhm.halo_radius(M=1e14, a=0.5, frame="physical")
+    assert np.allclose(r_phys, 1.0)  # 2*a = 1
+
+    r_com = bhm.halo_radius(M=np.array([1.0, 2.0]), a=0.5, frame="comoving")
+    assert np.allclose(r_com, 2.0)  # (2*a)/a = 2
+
+    with pytest.raises(ValueError, match=r"frame must be"):
+        _ = bhm.halo_radius(M=1e14, a=1.0, frame="nope")
+
+
+def test_get_dmo_dm_interpolator_is_cached_and_builds_dm_only(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Tests that _get_dmo_dm_interpolator caches interpolators per
+    scale factor."""
+    calls = {"n": 0, "components": None}
+
+    def fake_build_profile_interpolators(**kwargs: Any):
+        calls["n"] += 1
+        calls["components"] = kwargs.get("components", None)
+        # must return mapping containing "dark_matter"
+        return {"dark_matter": object()}
+
+    import pyccl.baryons.fedeli14_bhm.baryon_halo_model as mod
+    monkeypatch.setattr(mod,
+                        "build_profile_interpolators",
+                        fake_build_profile_interpolators)
+
+    # nfw_profile isn't important here; but keep it cheap/stable
+    monkeypatch.setattr(mod, "nfw_profile", lambda **kwargs: object())
+
+    bhm = BaryonHaloModel(cosmo=_cosmo())
+
+    y1 = bhm._get_dmo_dm_interpolator(1.0)  # noqa: SLF001
+    y2 = bhm._get_dmo_dm_interpolator(1.0)  # noqa: SLF001
+
+    assert y1 is y2
+    assert calls["n"] == 1
+    assert calls["components"] == ("dark_matter",)
+
+
+def test_get_interpolators_with_concentration_does_not_touch_main_cache(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Tests that _get_interpolators with concentration does not touch the
+    main cache and passes the override to nfw_profile."""
+    import pyccl.baryons.fedeli14_bhm.baryon_halo_model as mod
+
+    bhm = BaryonHaloModel(cosmo=_cosmo())
+    a = 1.0
+
+    bhm._interp_cache[a] = {"sentinel": True}  # noqa: SLF001
+
+    seen = {"conc": None}
+
+    def fake_nfw_profile(*, mass_def, concentration):
+        seen["conc"] = concentration
+        return object()
+
+    monkeypatch.setattr(mod,
+                        "nfw_profile",
+                        fake_nfw_profile)
+    monkeypatch.setattr(mod,
+                        "GasHaloProfile",
+                        lambda **kwargs: object())
+    monkeypatch.setattr(mod,
+                        "StellarHaloProfile",
+                        lambda **kwargs: object())
+    monkeypatch.setattr(mod,
+                        "build_profile_interpolators",
+                        lambda **kwargs: {"dark_matter": object(),
+                                          "gas": object(),
+                                          "stars": object()}
+                        )
+
+    conc_override = object()
+    interps = bhm._get_interpolators_with_concentration(  # noqa: SLF001
+        a, concentration_override=conc_override
+    )
+
+    assert set(interps.keys()) == {"dark_matter", "gas", "stars"}
+    assert seen["conc"] is conc_override
+    assert bhm._interp_cache[a] == {"sentinel": True}  # noqa: SLF001
+
+
+def test_pk_dmo_terms_wires_calc_pk_halo_pair_and_returns_total(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Tests that pk_dmo_terms wires the calculator and returns its DMO
+    halo-model baseline terms, and that the total is the sum of the 1- and
+    2-halo terms."""
+    import pyccl.baryons.fedeli14_bhm.baryon_halo_model as mod
+
+    k = _kgrid()
+    P1 = np.array([1.0, 2.0, 3.0])
+    P2 = np.array([10.0, 20.0, 30.0])
+
+    def fake_get_interps_with_conc(self, a, *, concentration_override):
+        return {"dark_matter": object(), "gas": object(), "stars": object()}
+
+    monkeypatch.setattr(
+        BaryonHaloModel,
+        "_get_interpolators_with_concentration",
+        fake_get_interps_with_conc,
+    )
+    monkeypatch.setattr(ccl, "rho_x", lambda cosmo, a, what: 5.0)
+
+    class FakeCalc:
+        def __init__(self, **kwargs: Any) -> None:
+            self.k = np.asarray(kwargs["k"], float)
+
+        def pk_halo_pair(self, *, comp1, comp2, rho1, rho2):
+            assert comp1 == "dark_matter"
+            assert comp2 == "dark_matter"
+            return P1, P2
+
+    monkeypatch.setattr(mod, "FedeliPkCalculator", FakeCalc)
+
+    bhm = BaryonHaloModel(cosmo=_cosmo(), n_m=8)
+
+    out1, out2, outt = bhm.pk_dmo_terms(k=k,
+                                        a=1.0,
+                                        concentration_override=object())
+    assert np.allclose(out1, P1)
+    assert np.allclose(out2, P2)
+    assert np.allclose(outt, P1 + P2)
diff --git a/pyccl/tests/test_baryons_fedeli14_halo_profiles.py b/pyccl/tests/test_baryons_fedeli14_halo_profiles.py
new file mode 100644
index 000000000..0374be9a4
--- /dev/null
+++ b/pyccl/tests/test_baryons_fedeli14_halo_profiles.py
@@ -0,0 +1,416 @@
+"""Unit tests for `pyccl.baryons.fedeli14_bhm.halo_profiles`."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+import pyccl as ccl
+
+from pyccl.baryons.fedeli14_bhm.halo_profiles import (
+    GasHaloProfile,
+    StellarHaloProfile,
+    nfw_profile,
+    nfw_profile_dmo,
+)
+
+
+def _cosmo() -> ccl.Cosmology:
+    """Return a simple, valid CCL cosmology."""
+    return ccl.Cosmology(
+        Omega_c=0.25, Omega_b=0.05, h=0.7, n_s=0.965, sigma8=0.8
+    )
+
+
+def _mass_def_200c() -> ccl.halos.MassDef:
+    """Mass definition used across halo-profile tests."""
+    return ccl.halos.MassDef(200, "critical")
+
+
+def _f_const(val: float):
+    """Return f(M)=const with shape preserved."""
+    v = float(val)
+
+    def f(M):
+        M = np.asarray(M, dtype=float)
+        return np.full_like(M, v, dtype=float)
+
+    return f
+
+
+def test_nfw_profile_requires_mass_def() -> None:
+    with pytest.raises(TypeError, match=r"mass_def must be provided"):
+        _ = nfw_profile(None)  # type: ignore[arg-type]
+
+
+def test_nfw_profile_rejects_invalid_concentration_type() -> None:
+    md = _mass_def_200c()
+    with pytest.raises(TypeError, match=r"concentration must be"):
+        _ = nfw_profile(md, concentration=123)  # type: ignore[arg-type]
+
+
+def test_nfw_profile_accepts_concentration_instance_and_returns_nfw() -> None:
+    md = _mass_def_200c()
+    conc = ccl.halos.ConcentrationDuffy08(mass_def=md)
+    prof = nfw_profile(md, concentration=conc)
+    assert isinstance(prof, ccl.halos.HaloProfileNFW)
+
+
+def test_nfw_profile_callable_concentration_errors_from_HaloProfileNFW(
+) -> None:
+    """Test that passing a callable concentration to nfw_profile raises an
+    error from HaloProfileNFW."""
+    md = _mass_def_200c()
+
+    def c_of_m(_cosmo, M, a):
+        M = np.asarray(M, dtype=float)
+        _ = a
+        return np.ones_like(M)
+
+    with pytest.raises(TypeError, match=r"Expected Concentration or str"):
+        _ = nfw_profile(md, concentration=c_of_m)
+
+
+def test_nfw_profile_uses_default_concentration_when_none() -> None:
+    """Test that passing None for concentration delegates to HaloProfileNFW,
+    and that the default concentration is used."""
+    md = _mass_def_200c()
+    prof = nfw_profile(md, concentration=None)
+    assert isinstance(prof, ccl.halos.HaloProfileNFW)
+
+
+def test_nfw_profile_dmo_delegates_to_nfw_profile_and_returns_nfw() -> None:
+    md = _mass_def_200c()
+    prof = nfw_profile_dmo(mass_def=md, concentration_dmo=None)
+    assert isinstance(prof, ccl.halos.HaloProfileNFW)
+
+
+def test_gas_profile_init_validates_inputs() -> None:
+    md = _mass_def_200c()
+
+    with pytest.raises(TypeError, match=r"mass_def must be provided"):
+        _ = GasHaloProfile(
+            mass_def=None,
+            f_gas=_f_const(0.1))  # type: ignore[arg-type]
+
+    with pytest.raises(TypeError, match=r"f_gas must be callable"):
+        _ = GasHaloProfile(mass_def=md, f_gas=123)  # type: ignore[arg-type]
+
+    with pytest.raises(ValueError, match=r"x_max must be > x_min"):
+        _ = GasHaloProfile(
+            mass_def=md,
+            f_gas=_f_const(0.1),
+            x_min=1.0,
+            x_max=1.0)
+
+    with pytest.raises(ValueError, match=r"n_x must be >= 2"):
+        _ = GasHaloProfile(mass_def=md, f_gas=_f_const(0.1), n_x=1)
+
+    with pytest.raises(ValueError, match=r"beta"):
+        _ = GasHaloProfile(mass_def=md, f_gas=_f_const(0.1), beta=-1.0)
+
+    with pytest.raises(ValueError, match=r"r_co"):
+        _ = GasHaloProfile(mass_def=md, f_gas=_f_const(0.1), r_co=0.0)
+
+    with pytest.raises(ValueError, match=r"r_ej"):
+        _ = GasHaloProfile(mass_def=md, f_gas=_f_const(0.1), r_ej=-1.0)
+
+    with pytest.raises(ValueError, match=r"x_min"):
+        _ = GasHaloProfile(mass_def=md, f_gas=_f_const(0.1), x_min=0.0)
+
+    with pytest.raises(ValueError, match=r"x_max"):
+        _ = GasHaloProfile(mass_def=md, f_gas=_f_const(0.1), x_max=0.0)
+
+    with pytest.raises(ValueError, match=r"n_x"):
+        _ = GasHaloProfile(mass_def=md, f_gas=_f_const(0.1), n_x=0)
+
+
+def test_gas_profile_rs_validates_a_and_radius_values(monkeypatch) -> None:
+    cosmo = _cosmo()
+    M = np.array([1.0e14, 2.0e14], dtype=float)
+
+    md = _mass_def_200c()
+    gp = GasHaloProfile(mass_def=md, f_gas=_f_const(0.1))
+
+    with pytest.raises(ValueError, match=r"\ba\b"):
+        _ = gp._rs(cosmo, M, a=0.0)  # noqa: SLF001
+
+    def bad_radius_nan(_cosmo, _M, _a):
+        _M = np.asarray(_M, dtype=float)
+        return np.full_like(_M, np.nan, dtype=float)
+
+    monkeypatch.setattr(md, "get_radius", bad_radius_nan, raising=True)
+    with pytest.raises(ValueError, match=r"get_radius returned invalid radii"):
+        _ = gp._rs(cosmo, M, a=1.0)  # noqa: SLF001
+
+
+def test_gas_profile_norm_validates_m_and_fgas_shape_and_values() -> None:
+    cosmo = _cosmo()
+    md = _mass_def_200c()
+    gp = GasHaloProfile(mass_def=md, f_gas=_f_const(0.1))
+    M = np.array([1.0e14, 2.0e14], dtype=float)
+
+    with pytest.raises(ValueError, match=r"M must be finite and > 0"):
+        _ = gp._norm(
+            cosmo,
+            np.array([1.0e14, -1.0], dtype=float),
+            a=1.0)  # noqa: SLF001
+
+    with pytest.raises(ValueError, match=r"M must be finite and > 0"):
+        _ = gp._norm(
+            cosmo,
+            np.array([np.nan, 1.0e14], dtype=float),
+            a=1.0)  # noqa: SLF001
+
+    def fgas_bad_shape(m):
+        m = np.asarray(m, dtype=float)
+        return np.ones(m.size - 1, dtype=float)
+
+    gp2 = GasHaloProfile(mass_def=md, f_gas=fgas_bad_shape)
+    with pytest.raises(
+            ValueError,
+            match=r"must return an array with the same"):
+        _ = gp2._norm(cosmo, M, a=1.0)  # noqa: SLF001
+
+    gp3 = GasHaloProfile(mass_def=md, f_gas=_f_const(-0.1))
+    with pytest.raises(ValueError, match=r"must be finite and >= 0"):
+        _ = gp3._norm(cosmo, M, a=1.0)  # noqa: SLF001
+
+    def fgas_nan(m):
+        m = np.asarray(m, dtype=float)
+        out = np.ones_like(m)
+        out[0] = np.nan
+        return out
+
+    gp4 = GasHaloProfile(mass_def=md, f_gas=fgas_nan)
+    with pytest.raises(ValueError, match=r"must be finite and >= 0"):
+        _ = gp4._norm(cosmo, M, a=1.0)  # noqa: SLF001
+
+
+def test_gas_profile_norm_returns_scalar_for_scalar_m() -> None:
+    cosmo = _cosmo()
+    gp = GasHaloProfile(mass_def=_mass_def_200c(), f_gas=_f_const(0.1))
+    rho0 = gp._norm(cosmo, 1.0e14, a=1.0)  # noqa: SLF001
+    assert isinstance(rho0, float)
+    assert np.isfinite(rho0)
+    assert rho0 >= 0.0
+
+
+def test_gas_profile_norm_raises_if_integral_invalid() -> None:
+    cosmo = _cosmo()
+    md = _mass_def_200c()
+    gp = GasHaloProfile(
+        mass_def=md,
+        f_gas=_f_const(0.1),
+        x_min=1.0e-3,
+        x_max=2.0e-3,
+        n_x=2)
+
+    # Force integral ~ 0 by zeroing x (x^2 factor kills integrand).
+    gp._x = np.zeros(2, dtype=float)  # noqa: SLF001
+
+    with pytest.raises(
+            ValueError,
+            match=r"Gas normalization integral is invalid"):
+        _ = gp._norm(
+            cosmo,
+            np.array([1.0e14, 2.0e14],
+                     dtype=float),
+            a=1.0)  # noqa: SLF001
+
+
+def test_gas_profile_real_validates_inputs_and_shapes() -> None:
+    cosmo = _cosmo()
+    gp = GasHaloProfile(mass_def=_mass_def_200c(), f_gas=_f_const(0.1))
+
+    with pytest.raises(ValueError, match=r"r must be finite and >= 0"):
+        _ = gp._real(cosmo,
+                     r=np.array([0.1, -1.0]),
+                     M=1.0e14, a=1.0)  # noqa: SLF001
+
+    with pytest.raises(ValueError, match=r"r must be finite and >= 0"):
+        _ = gp._real(
+            cosmo,
+            r=np.array([0.1, np.nan]),
+            M=1.0e14, a=1.0)  # noqa: SLF001
+
+    with pytest.raises(ValueError, match=r"M must be finite and > 0"):
+        _ = gp._real(
+            cosmo,
+            r=np.array([0.1, 1.0]),
+            M=np.array([1.0e14, 0.0]),
+            a=1.0)  # noqa: SLF001
+
+    r = np.array([0.01, 0.1, 1.0], dtype=float)
+    M = np.array([1.0e14, 2.0e14], dtype=float)
+
+    prof = gp._real(cosmo, r=r, M=M, a=1.0)  # noqa: SLF001
+    assert isinstance(prof, np.ndarray)
+    assert prof.shape == (M.size, r.size)
+    assert np.all(np.isfinite(prof))
+
+    prof2 = gp._real(cosmo, r=0.1, M=M, a=1.0)  # noqa: SLF001
+    assert isinstance(prof2, np.ndarray)
+    assert prof2.shape == (M.size,)
+    assert np.all(np.isfinite(prof2))
+
+    prof3 = gp._real(cosmo, r=r, M=1.0e14, a=1.0)  # noqa: SLF001
+    assert isinstance(prof3, np.ndarray)
+    assert prof3.shape == (r.size,)
+    assert np.all(np.isfinite(prof3))
+
+    prof4 = gp._real(cosmo, r=0.1, M=1.0e14, a=1.0)  # noqa: SLF001
+    assert np.isscalar(prof4)
+    assert np.isfinite(float(prof4))
+
+
+def test_gas_profile_real_raises_if_profile_invalid() -> None:
+    """Hit the final safety check in _real by forcing negative rho0."""
+    cosmo = _cosmo()
+    gp = GasHaloProfile(mass_def=_mass_def_200c(), f_gas=_f_const(0.1))
+
+    def bad_norm(_cosmo, M, a):
+        M = np.atleast_1d(M).astype(float)
+        _ = a
+        return -np.ones_like(M)
+
+    gp._norm = bad_norm  # type: ignore[method-assign]  # noqa: SLF001
+
+    with pytest.raises(
+            ValueError,
+            match=r"Gas profile evaluation produced invalid values"):
+        _ = gp._real(
+            cosmo,
+            r=np.array([0.1, 1.0]),
+            M=np.array([1.0e14, 2.0e14]),
+            a=1.0,
+        )  # noqa: SLF001
+
+
+def test_stellar_profile_init_validates_inputs() -> None:
+    md = _mass_def_200c()
+
+    with pytest.raises(TypeError, match=r"mass_def must be provided"):
+        _ = StellarHaloProfile(
+            mass_def=None,
+            f_star=_f_const(0.01))  # type: ignore[arg-type]
+
+    with pytest.raises(
+            TypeError,
+            match=r"f_star must be callable"):
+        _ = StellarHaloProfile(
+            mass_def=md,
+            f_star=123)  # type: ignore[arg-type]
+
+    with pytest.raises(ValueError, match=r"x_delta"):
+        _ = StellarHaloProfile(mass_def=md, f_star=_f_const(0.01), x_delta=0.0)
+
+    with pytest.raises(ValueError, match=r"alpha"):
+        _ = StellarHaloProfile(mass_def=md, f_star=_f_const(0.01), alpha=0.0)
+
+
+def test_stellar_profile_rs_validates_a_and_radius_values(monkeypatch) -> None:
+    cosmo = _cosmo()
+    M = np.array([1.0e14, 2.0e14], dtype=float)
+
+    md = _mass_def_200c()
+    sp = StellarHaloProfile(mass_def=md, f_star=_f_const(0.01))
+
+    with pytest.raises(ValueError, match=r"\ba\b"):
+        _ = sp._rs(cosmo, M, a=0.0)  # noqa: SLF001
+
+    def bad_radius_nan(_cosmo, _M, _a):
+        _M = np.asarray(_M, dtype=float)
+        return np.full_like(_M, np.nan, dtype=float)
+
+    monkeypatch.setattr(md, "get_radius", bad_radius_nan, raising=True)
+    with pytest.raises(ValueError, match=r"get_radius returned invalid radii"):
+        _ = sp._rs(cosmo, M, a=1.0)  # noqa: SLF001
+
+
+def test_stellar_profile_real_validates_inputs_fstar_and_shapes() -> None:
+    cosmo = _cosmo()
+    md = _mass_def_200c()
+
+    sp = StellarHaloProfile(
+        mass_def=md,
+        f_star=_f_const(1.0e-4),
+        x_delta=1.0 / 0.03,
+        alpha=1.0)
+
+    with pytest.raises(ValueError, match=r"r must be finite and >= 0"):
+        _ = sp._real(
+            cosmo,
+            r=np.array([0.1, -1.0]),
+            M=1.0e14,
+            a=1.0)  # noqa: SLF001
+
+    with pytest.raises(ValueError, match=r"M must be finite and > 0"):
+        _ = sp._real(
+            cosmo,
+            r=np.array([0.1, 1.0]),
+            M=np.array([1.0e14, 0.0]),
+            a=1.0)  # noqa: SLF001
+
+    def fstar_bad_shape(m):
+        m = np.asarray(m, dtype=float)
+        return np.ones(m.size - 1, dtype=float)
+
+    sp2 = StellarHaloProfile(mass_def=md, f_star=fstar_bad_shape)
+    with pytest.raises(
+            ValueError,
+            match=r"must return an array with the same shape"):
+        _ = sp2._real(
+            cosmo,
+            r=np.array([0.1, 1.0]),
+            M=np.array([1.0e14, 2.0e14]),
+            a=1.0)  # noqa: SLF001
+
+    sp3 = StellarHaloProfile(mass_def=md, f_star=_f_const(-0.01))
+    with pytest.raises(ValueError, match=r"must be finite and >= 0"):
+        _ = sp3._real(
+            cosmo,
+            r=np.array([0.1, 1.0]),
+            M=np.array([1.0e14, 2.0e14]),
+            a=1.0)  # noqa: SLF001
+
+    # Valid evaluation: avoid r=0 to prevent overflow (even with x_safe).
+    r = np.array([0.01, 0.1, 1.0], dtype=float)
+    M = np.array([1.0e14, 2.0e14], dtype=float)
+    prof = sp._real(cosmo, r=r, M=M, a=1.0)  # noqa: SLF001
+    assert isinstance(prof, np.ndarray)
+    assert prof.shape == (M.size, r.size)
+    assert np.all(np.isfinite(prof))
+
+    prof2 = sp._real(cosmo, r=0.1, M=M, a=1.0)  # noqa: SLF001
+    assert prof2.shape == (M.size,)
+
+    prof3 = sp._real(cosmo, r=r, M=1.0e14, a=1.0)  # noqa: SLF001
+    assert prof3.shape == (r.size,)
+
+    prof4 = sp._real(cosmo, r=0.1, M=1.0e14, a=1.0)  # noqa: SLF001
+    assert np.isscalar(prof4)
+    assert np.isfinite(float(prof4))
+
+
+def test_stellar_profile_real_raises_if_profile_invalid(monkeypatch) -> None:
+    """Force r_t -> 0 by making get_radius -> 0, which should trigger invalid
+     profile."""
+    cosmo = _cosmo()
+    md = _mass_def_200c()
+    sp = StellarHaloProfile(mass_def=md, f_star=_f_const(1.0e-4))
+
+    def zero_radius(_cosmo, _M, _a):
+        _M = np.asarray(_M, dtype=float)
+        return np.zeros_like(_M, dtype=float)
+
+    monkeypatch.setattr(md, "get_radius", zero_radius, raising=True)
+
+    with pytest.raises(ValueError,
+                       match=r"mass_def\.get_radius returned invalid radii"):
+        _ = sp._real(
+            cosmo,
+            r=np.array([0.1, 1.0]),
+            M=np.array([1.0e14, 2.0e14]),
+            a=1.0,
+        )  # noqa: SLF001# noqa: SLF001
diff --git a/pyccl/tests/test_baryons_fedeli14_mass_fractions.py b/pyccl/tests/test_baryons_fedeli14_mass_fractions.py
new file mode 100644
index 000000000..49f1d6e1f
--- /dev/null
+++ b/pyccl/tests/test_baryons_fedeli14_mass_fractions.py
@@ -0,0 +1,306 @@
+"""Unit tests for `pyccl.baryons.fedeli14_bhm.mass_fractions`."""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+import numpy as np
+import pytest
+
+from pyccl.baryons.fedeli14_bhm.mass_fractions import mass_fractions
+
+
+class DummyCosmo(dict):
+    """Mock cosmology class."""
+
+
+def _cosmo(
+    h: float = 0.7,
+    Omega_b: float = 0.05,
+    Omega_m: float = 0.3
+) -> DummyCosmo:
+    """Mock cosmology object."""
+    return DummyCosmo(h=h, Omega_b=Omega_b, Omega_m=Omega_m)
+
+
+def test_requires_positive_finite_a() -> None:
+    """Tests that mass_fractions rejects non-finite or non-positive scale
+    factor a."""
+    cosmo = _cosmo()
+
+    def mf(_cosmo: Any, M: np.ndarray, a: float) -> np.ndarray:
+        return np.ones_like(np.atleast_1d(M), dtype=float)
+
+    with pytest.raises(ValueError, match=r"a must be finite and > 0"):
+        mass_fractions(cosmo=cosmo, a=0.0, mass_function=mf)
+
+    with pytest.raises(ValueError, match=r"a must be finite and > 0"):
+        mass_fractions(cosmo=cosmo, a=float("nan"), mass_function=mf)
+
+
+def test_requires_callable_mass_function() -> None:
+    """Tests that mass_fractions requires a callable mass_function."""
+    cosmo = _cosmo()
+    with pytest.raises(TypeError, match="mass_function must be callable"):
+        mass_fractions(cosmo=cosmo, a=1.0, mass_function=123)
+
+
+def test_requires_cosmo_keys() -> None:
+    """Tests that mass_fractions requires cosmo to provide h, Omega_b, and
+    Omega_m."""
+    def mf(_cosmo: Any, M: np.ndarray, a: float) -> np.ndarray:
+        return np.ones_like(np.atleast_1d(M), dtype=float)
+
+    cosmo = DummyCosmo(h=0.7, Omega_b=0.05)  # missing Omega_m
+    with pytest.raises(KeyError, match="must provide 'Omega_m'"):
+        mass_fractions(cosmo=cosmo, a=1.0, mass_function=mf)
+
+
+def test_cosmo_param_ranges() -> None:
+    """Tests that mass_fractions enforces valid Omega_b/Omega_m ranges."""
+    def mf(_cosmo: Any, M: np.ndarray, a: float) -> np.ndarray:
+        return np.ones_like(np.atleast_1d(M), dtype=float)
+
+    with pytest.raises(ValueError, match=r"Omega_m.*> 0"):
+        mass_fractions(cosmo=_cosmo(Omega_m=0.0), a=1.0, mass_function=mf)
+
+    with pytest.raises(ValueError, match=r"Omega_b.*>= 0"):
+        mass_fractions(cosmo=_cosmo(Omega_b=-0.01), a=1.0, mass_function=mf)
+
+    with pytest.raises(ValueError, match=r"Omega_b <= Omega_m"):
+        mass_fractions(cosmo=_cosmo(Omega_b=0.4, Omega_m=0.3),
+                       a=1.0,
+                       mass_function=mf)
+
+
+def test_mass_param_ranges() -> None:
+    """Tests that mass_fractions validates mass-scale parameters and
+    integration bounds."""
+    cosmo = _cosmo()
+
+    def mf(_cosmo: Any, M: np.ndarray, a: float) -> np.ndarray:
+        return np.ones_like(np.atleast_1d(M), dtype=float)
+
+    with pytest.raises(ValueError, match=r"m0_star.*> 0"):
+        mass_fractions(cosmo=cosmo, a=1.0, mass_function=mf, m0_star=0.0)
+
+    with pytest.raises(ValueError, match=r"sigma_gas.*> 0"):
+        mass_fractions(cosmo=cosmo, a=1.0, mass_function=mf, sigma_gas=0.0)
+
+    with pytest.raises(ValueError, match=r"mmin_star < mmax_star"):
+        mass_fractions(cosmo=cosmo,
+                       a=1.0,
+                       mass_function=mf,
+                       mmin_star=1e15,
+                       mmax_star=1e10)
+
+
+def test_return_types_and_scalar_array_behavior() -> None:
+    """Tests that mass fraction callables return floats for scalars and arrays
+     for arrays."""
+    cosmo = _cosmo()
+
+    def mf(_cosmo: Any, M: np.ndarray, a: float) -> np.ndarray:
+        return np.ones_like(np.atleast_1d(M), dtype=float)
+
+    f_gas, f_star, f_dm = mass_fractions(cosmo=cosmo, a=1.0, mass_function=mf)
+
+    g0 = f_gas(1.0e12)
+    s0 = f_star(1.0e12)
+    d0 = f_dm(1.0e12)
+    assert isinstance(g0, float)
+    assert isinstance(s0, float)
+    assert isinstance(d0, float)
+
+    M = np.array([1.0e11, 1.0e12, 1.0e13])
+    g = f_gas(M)
+    s = f_star(M)
+    d = f_dm(M)
+    assert isinstance(g, np.ndarray) and g.shape == M.shape
+    assert isinstance(s, np.ndarray) and s.shape == M.shape
+    assert isinstance(d, np.ndarray) and d.shape == M.shape
+
+
+def test_component_basic_limits() -> None:
+    """Tests that mass fractions have sensible limiting behavior and
+    non-negativity."""
+    cosmo = _cosmo(Omega_b=0.05, Omega_m=0.30)
+    omega_ratio = cosmo["Omega_b"] / cosmo["Omega_m"]
+
+    def mf(_cosmo: Any, M: np.ndarray, a: float) -> np.ndarray:
+        return np.ones_like(np.atleast_1d(M), dtype=float)
+
+    m0_gas = 1.0e12
+    sigma_gas = 3.0
+    f_gas, f_star, f_dm = mass_fractions(
+        cosmo=cosmo,
+        a=1.0,
+        mass_function=mf,
+        m0_gas=m0_gas,
+        sigma_gas=sigma_gas
+    )
+
+    M = np.geomspace(1e10, 1e15, 10)
+    dm = f_dm(M)
+    assert np.allclose(dm, 1.0 - omega_ratio)
+
+    assert f_gas(m0_gas * 0.99) == 0.0
+    assert f_gas(m0_gas * 1.01) >= 0.0
+
+    high = f_gas(1.0e30)
+    assert high == pytest.approx(float(omega_ratio), rel=1e-6, abs=1e-12)
+
+    assert np.all(np.asarray(f_star(M)) >= 0.0)
+
+
+def test_mass_inputs_must_be_positive_finite() -> None:
+    """Tests that each returned mass-fraction callable rejects invalid halo
+    masses."""
+    cosmo = _cosmo()
+
+    def mf(_cosmo: Any, M: np.ndarray, a: float) -> np.ndarray:
+        return np.ones_like(np.atleast_1d(M), dtype=float)
+
+    f_gas, f_star, f_dm = mass_fractions(cosmo=cosmo, a=1.0, mass_function=mf)
+
+    for fn in (f_gas, f_star, f_dm):
+        with pytest.raises(ValueError, match=r"M must be finite and > 0"):
+            fn(0.0)
+        with pytest.raises(ValueError, match=r"M must be finite and > 0"):
+            fn(-1.0)
+        with pytest.raises(ValueError, match=r"M must be finite and > 0"):
+            fn(np.array([1.0, float("nan")]))
+
+
+def test_msunh_detection_path_works() -> None:
+    """Tests that the Msun vs Msun/h detection branch produces finite
+    results."""
+    cosmo = _cosmo(h=0.7)
+    m0_star = 5.0e12
+
+    def mf_triggers_detection(_cosmo: Any,
+                              M: np.ndarray,
+                              a: float) -> np.ndarray:
+        _ = a
+        M = np.atleast_1d(M).astype(float)
+        lo = m0_star * 1.0001
+        hi = (m0_star / float(_cosmo["h"])) * 0.9999
+        # Ensure lo < hi for h<1 and choose a threshold between them
+        thresh = 0.5 * (lo + hi)
+        return np.where(M >= thresh, 1.0, 0.0)
+
+    f_gas, f_star, f_dm = mass_fractions(
+        cosmo=cosmo,
+        a=1.0,
+        mass_function=mf_triggers_detection,
+        m0_star=m0_star
+    )
+
+    assert math.isfinite(float(f_star(m0_star)))
+    assert math.isfinite(float(f_gas(m0_star)))
+    assert math.isfinite(float(f_dm(m0_star)))
+
+
+def test_stellar_normalization_is_cached() -> None:
+    """Tests that the stellar normalization factor A is cached after first
+    evaluation."""
+    cosmo = _cosmo()
+    calls = {"n": 0}
+
+    def mf_counting(_cosmo: Any, M: np.ndarray, a: float) -> np.ndarray:
+        _ = a
+        calls["n"] += 1
+        return np.ones_like(np.atleast_1d(M), dtype=float)
+
+    _f_gas, f_star, _f_dm = mass_fractions(
+        cosmo=cosmo, a=1.0, mass_function=mf_counting)
+
+    _ = f_star(1.0e12)
+    n1 = calls["n"]
+    assert n1 > 0
+
+    _ = f_star(2.0e12)
+    n2 = calls["n"]
+    assert n2 == n1
+
+
+def test_rho_star_must_be_positive_finite() -> None:
+    """Tests that mass_fractions rejects non-finite or non-positive
+    rho_star."""
+    cosmo = _cosmo()
+
+    def mf(_cosmo: Any, M: np.ndarray, a: float) -> np.ndarray:
+        return np.ones_like(np.atleast_1d(M), dtype=float)
+
+    with pytest.raises(ValueError, match=r"rho_star must be finite and > 0"):
+        mass_fractions(cosmo=cosmo, a=1.0, mass_function=mf, rho_star=0.0)
+
+    with pytest.raises(ValueError, match=r"rho_star must be finite and > 0"):
+        mass_fractions(
+            cosmo=cosmo, a=1.0, mass_function=mf, rho_star=float("nan"))
+
+
+def test_rho_star_none_default_is_finite() -> None:
+    """Tests that mass_fractions with rho_star=None uses the default and
+    returns finite values."""
+    cosmo = _cosmo()
+
+    def mf(_cosmo: Any, M: np.ndarray, a: float) -> np.ndarray:
+        _ = a
+        return np.ones_like(np.atleast_1d(M), dtype=float)
+
+    f_gas, f_star, f_dm = mass_fractions(
+        cosmo=cosmo, a=1.0, mass_function=mf, rho_star=None)
+    assert math.isfinite(float(f_star(1.0e12)))
+    assert math.isfinite(float(f_gas(1.0e12)))
+    assert math.isfinite(float(f_dm(1.0e12)))
+
+
+def test_mass_function_nonfinite_in_unit_probe_raises() -> None:
+    """Tests that non-finite mass_function outputs in the unit-probe call
+    raise ValueError."""
+    cosmo = _cosmo()
+
+    def mf_nan(_cosmo: Any, M: np.ndarray, a: float) -> np.ndarray:
+        _, _ = M, a
+        return np.array([np.nan], dtype=float)
+
+    with pytest.raises(
+            ValueError, match=r"returned non-finite values at the test mass"):
+        mass_fractions(cosmo=cosmo, a=1.0, mass_function=mf_nan)
+
+
+def test_mass_function_nonfinite_inside_integrand_raises() -> None:
+    """Tests that non-finite dn/dlog10M during the stellar integral raises
+    ValueError."""
+    cosmo = _cosmo()
+    m0_star = 5.0e12
+    h = float(cosmo["h"])
+
+    def mf_nan_in_range(_cosmo: Any, M: np.ndarray, a: float) -> np.ndarray:
+        _ = a
+        M0 = float(np.atleast_1d(M)[0])
+        if np.isclose(M0, m0_star) or np.isclose(M0, m0_star / h):
+            return np.array([1.0], dtype=float)
+        return np.array([np.nan], dtype=float)
+
+    _f_gas, f_star, _f_dm = mass_fractions(
+        cosmo=cosmo, a=1.0, mass_function=mf_nan_in_range)
+    with pytest.raises(ValueError, match=r"returned non-finite dn/dlog10M"):
+        _ = f_star(1.0e12)
+
+
+def test_stellar_normalization_integral_nonpositive_raises() -> None:
+    """Tests that a non-positive stellar normalization integral raises
+    ValueError with diagnostics."""
+    cosmo = _cosmo()
+
+    def mf_zero(_cosmo: Any, M: np.ndarray, a: float) -> np.ndarray:
+        return np.zeros_like(np.atleast_1d(M), dtype=float)
+
+    _f_gas, f_star, _f_dm = mass_fractions(
+        cosmo=cosmo, a=1.0, mass_function=mf_zero
+    )
+    with pytest.raises(ValueError, match=r"stellar normalization integral is"):
+        _ = f_star(1.0e12)
diff --git a/pyccl/tests/test_baryons_fedeli14_numerics.py b/pyccl/tests/test_baryons_fedeli14_numerics.py
new file mode 100644
index 000000000..8c85548ad
--- /dev/null
+++ b/pyccl/tests/test_baryons_fedeli14_numerics.py
@@ -0,0 +1,248 @@
+"""Unit tests for `pyccl.baryons.fedeli14_bhm.numerics`."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+import pytest
+
+from pyccl.baryons.fedeli14_bhm.numerics import (
+    _add_pair_aliases,
+    _add_weighted_pair_aliases,
+    _pos_float,
+    _pos_int,
+    _require_a,
+    _require_gas_params,
+    _require_k,
+    _require_mass_ranges,
+    _require_profiles_u_over_m,
+    _require_component,
+    _trapz_compat,
+    _require_array_1d,
+    _require_finite_1d,
+    _require_float,
+    _as_float,
+    _require_attr,
+)
+
+
+def test_require_a_rejects_nonfinite_or_nonpositive() -> None:
+    """Tests that _require_a rejects non-finite and non-positive a."""
+    with pytest.raises(ValueError, match=r"a must be finite"):
+        _require_a(float("nan"))
+    with pytest.raises(ValueError, match=r"a must be > 0"):
+        _require_a(0.0)
+    assert _require_a(1.0) == 1.0
+
+
+def test_require_k_validates_shape_finiteness_and_order() -> None:
+    """Tests that _require_k enforces 1D, non-empty, finite, >0, strictly
+    increasing."""
+    with pytest.raises(ValueError, match=r"non-empty 1D"):
+        _require_k(np.array([]))
+    with pytest.raises(ValueError, match=r"non-empty 1D"):
+        _require_k(np.array([[1.0, 2.0]]))
+    with pytest.raises(ValueError, match=r"finite"):
+        _require_k(np.array([1.0, np.nan]))
+    with pytest.raises(ValueError, match=r"k must be > 0"):
+        _require_k(np.array([0.0, 1.0]))
+    with pytest.raises(ValueError, match=r"strictly increasing"):
+        _require_k(np.array([1.0, 1.0, 2.0]))
+    with pytest.raises(ValueError, match=r"strictly increasing"):
+        _require_k(np.array([2.0, 1.0]))
+
+    k = _require_k(np.array([1.0e-3, 1.0e-2, 1.0e-1]))
+    assert isinstance(k, np.ndarray) and k.ndim == 1 and k.size == 3
+
+
+def test_pos_float_and_pos_int_validation() -> None:
+    """Tests that _pos_float/_pos_int enforce type coercion and strict
+    positivity."""
+    assert _pos_float(2.5, "x") == 2.5
+    with pytest.raises(ValueError, match=r"x must be > 0"):
+        _pos_float(0.0, "x")
+    with pytest.raises(TypeError, match=r"x must be a real number"):
+        _pos_float(object(), "x")  # type: ignore[arg-type]
+
+    assert _pos_int(3, "n") == 3
+    with pytest.raises(ValueError, match=r"n must be > 0"):
+        _pos_int(0, "n")
+    with pytest.raises(TypeError, match=r"n must be an int"):
+        _pos_int("nope", "n")  # type: ignore[arg-type]
+
+
+def test_require_profiles_u_over_m_requires_components_and_callables() -> None:
+    """Tests that _require_profiles_u_over_m enforces required keys and
+    callables."""
+    def ok(_k: np.ndarray, _a: float) -> np.ndarray:
+        return np.ones_like(_k)
+
+    profiles = {"dark_matter": ok, "gas": ok, "stars": ok}
+    out = _require_profiles_u_over_m(profiles)
+    assert set(out.keys()) == {"dark_matter", "gas", "stars"}
+
+    with pytest.raises(KeyError, match=r"missing component 'gas'"):
+        _require_profiles_u_over_m({"dark_matter": ok, "stars": ok})
+
+    with pytest.raises(TypeError, match=r"must be callable"):
+        _require_profiles_u_over_m({"dark_matter": ok,
+                                    "gas": 123,
+                                    "stars": ok})
+
+
+def test_require_mass_ranges_validates_min_max_and_order() -> None:
+    """Tests that _require_mass_ranges requires min/max keys, positivity,
+    and max>min."""
+    mr = {
+        "dark_matter": {"min": 1.0e10, "max": 1.0e15},
+        "gas": {"min": 1.0e10, "max": 1.0e15},
+        "stars": {"min": 1.0e10, "max": 1.0e15},
+    }
+    out = _require_mass_ranges(mr)
+    assert out["gas"]["min"] == pytest.approx(1.0e10)
+    assert out["gas"]["max"] == pytest.approx(1.0e15)
+
+    with pytest.raises(KeyError, match=r"mass_ranges missing component 'gas'"):
+        _require_mass_ranges({"dark_matter": {"min": 1.0, "max": 2.0},
+                              "stars": {"min": 1.0, "max": 2.0}})
+
+    with pytest.raises(KeyError, match=r"must have keys 'min' and 'max'"):
+        _require_mass_ranges({**mr, "gas": {"min": 1.0}})
+
+    with pytest.raises(ValueError, match=r"max>min"):
+        _require_mass_ranges({**mr, "gas": {"min": 2.0, "max": 1.0}})
+
+
+def test_require_gas_params_validates_keys_and_range() -> None:
+    """Tests that _require_gas_params enforces required keys and Fg in
+    [0,1]."""
+    Fg, bd = _require_gas_params({"Fg": 0.7, "bd": 0.2})
+    assert Fg == pytest.approx(0.7)
+    assert bd == pytest.approx(0.2)
+
+    with pytest.raises(KeyError, match=r"must contain keys 'Fg' and 'bd'"):
+        _require_gas_params({"Fg": 0.7})
+
+    with pytest.raises(ValueError, match=r"Fg must be in \[0, 1\]"):
+        _require_gas_params({"Fg": 1.1, "bd": 0.2})
+
+
+def test_add_pair_aliases_adds_missing_key_only() -> None:
+    """Tests that _add_pair_aliases adds the missing commutative key without
+    overwriting."""
+    obj: Any = object()
+
+    d = {"a_b": obj}
+    _add_pair_aliases(d, "a", "b")
+    assert d["b_a"] is obj
+
+    d2 = {"b_a": obj}
+    _add_pair_aliases(d2, "a", "b")
+    assert d2["a_b"] is obj
+
+    d3 = {"a_b": "x", "b_a": "y"}
+    _add_pair_aliases(d3, "a", "b")
+    assert d3["a_b"] == "x" and d3["b_a"] == "y"
+
+
+def test_add_weighted_pair_aliases_adds_missing_key_only() -> None:
+    """Tests that _add_weighted_pair_aliases adds the missing weighted
+    commutative key."""
+    obj: Any = object()
+
+    d = {"w_a_b": obj}
+    _add_weighted_pair_aliases(d, "a", "b")
+    assert d["w_b_a"] is obj
+
+    d2 = {"w_b_a": obj}
+    _add_weighted_pair_aliases(d2, "a", "b")
+    assert d2["w_a_b"] is obj
+
+    d3 = {"w_a_b": "x", "w_b_a": "y"}
+    _add_weighted_pair_aliases(d3, "a", "b")
+    assert d3["w_a_b"] == "x" and d3["w_b_a"] == "y"
+
+
+def test_require_component_accepts_known_and_raises_for_unknown() -> None:
+    """Tests that _require_component returns comp for known keys and raises
+    for unknown."""
+    allowed = {"dark_matter": 1, "gas": 2}
+
+    assert _require_component("gas", allowed=allowed) == "gas"
+
+    with pytest.raises(KeyError, match=r"Unknown component"):
+        _require_component("stars", allowed=allowed)
+
+
+def test_trapz_compat_uses_trapezoid_or_falls_back(monkeypatch) -> None:
+    """Tests that _trapz_compat uses np.trapezoid when available and falls
+    back to np.trapz otherwise (simulated for NumPy >= 2.0)."""
+    y = np.array([0.0, 1.0, 2.0], dtype=float)
+    x = np.array([0.0, 1.0, 2.0], dtype=float)
+
+    # Branch 1: trapezoid available
+    out1 = _trapz_compat(y, x=x)
+    assert np.isfinite(out1)
+
+    # Branch 2: simulate old-NumPy fallback by *adding* np.trapz,
+    # then removing np.trapezoid.
+    trap = np.trapezoid
+    monkeypatch.setattr(np, "trapz", trap, raising=False)
+    monkeypatch.delattr(np, "trapezoid", raising=False)
+
+    out2 = _trapz_compat(y, x=x)
+    assert np.isfinite(out2)
+    assert out2 == pytest.approx(trap(y, x=x))
+
+
+def test_require_array_1d_rejects_non_1d_and_empty() -> None:
+    """Tests that _require_array_1d rejects non-1D arrays and empty arrays."""
+
+    with pytest.raises(ValueError, match=r"x must be a 1D array"):
+        _require_array_1d("x", np.ones((2, 2)))
+
+    with pytest.raises(ValueError, match=r"x must be non-empty"):
+        _require_array_1d("x", np.array([]))
+
+
+def test_require_finite_1d_rejects_nonfinite() -> None:
+    """Tests that _require_finite_1d rejects arrays containing non-finite
+     values."""
+
+    with pytest.raises(ValueError, match=r"x must contain only finite values"):
+        _require_finite_1d("x", np.array([1.0, np.nan]))
+
+
+def test_require_float_raises_typeerror_if_not_float_convertible() -> None:
+    """Tests that _require_float raises TypeError when float(x) fails."""
+
+    with pytest.raises(TypeError, match=r"x must be a real number"):
+        _require_float("x", object())
+
+
+def test_as_float_raises_typeerror_when_not_float_convertible() -> None:
+    """Tests that _as_float raises TypeError when float(x) fails."""
+    with pytest.raises(TypeError, match=r"x must be a real number"):
+        _as_float(object(), "x")
+
+
+def test_pos_float_raises_valueerror_for_nonpositive() -> None:
+    """Tests that _pos_float raises ValueError for y <= 0."""
+    with pytest.raises(ValueError, match=r"x must be > 0"):
+        _pos_float(0.0, "x")
+
+
+def test_pos_int_raises_valueerror_for_nonpositive() -> None:
+    """Tests that _pos_int raises ValueError for y <= 0."""
+    with pytest.raises(ValueError, match=r"n must be > 0"):
+        _pos_int(0, "n")
+
+
+def test_require_attr_raises_when_missing() -> None:
+    """Tests that _require_attr raises TypeError when attribute is missing."""
+    class Dummy:
+        pass
+
+    with pytest.raises(TypeError, match=r"obj must define attribute 'nope'"):
+        _require_attr(Dummy(), "nope", who="obj")
diff --git a/pyccl/tests/test_baryons_fedeli14_power_spectra.py b/pyccl/tests/test_baryons_fedeli14_power_spectra.py
new file mode 100644
index 000000000..47a979456
--- /dev/null
+++ b/pyccl/tests/test_baryons_fedeli14_power_spectra.py
@@ -0,0 +1,701 @@
+"""Unit tests for `pyccl.baryons.fedeli14_bhm.power_spectra`."""
+
+from __future__ import annotations
+
+from typing import Any, Callable
+
+import numpy as np
+import pytest
+
+import pyccl as ccl
+
+import pyccl.baryons.fedeli14_bhm.power_spectra as ps
+from pyccl.baryons.fedeli14_bhm.power_spectra import (
+    FedeliPkCalculator,
+    _dndm_from_dndlog10m,
+    _k_cache_key,
+)
+
+
+def _cosmo() -> ccl.Cosmology:
+    """Return a small valid CCL cosmology."""
+    return ccl.Cosmology(
+        Omega_c=0.25, Omega_b=0.05, h=0.7, n_s=0.965, sigma8=0.8)
+
+
+def _kgrid() -> np.ndarray:
+    """Return a tiny strictly increasing positive k grid."""
+    return np.array([0.1, 1.0, 10.0], dtype=float)
+
+
+def _mass_ranges(
+    mmin: float = 1.0e13, mmax: float = 1.0e15
+) -> dict[str, dict[str, float]]:
+    """Return valid per-component mass ranges."""
+    return {
+        "dark_matter": {"min": float(mmin), "max": float(mmax)},
+        "gas": {"min": float(mmin), "max": float(mmax)},
+        "stars": {"min": float(mmin), "max": float(mmax)},
+    }
+
+
+def _gas_params(Fg: float = 0.7, bd: float = 1.2) -> dict[str, float]:
+    """Return valid gas mixing parameters."""
+    return {"Fg": float(Fg), "bd": float(bd)}
+
+
+def _hmf_const(
+    val: float = 1.0
+) -> Callable[[Any, np.ndarray, float], np.ndarray]:
+    """Return dn/dlog10M = constant on the provided mass grid."""
+    v = float(val)
+
+    def hmf(_cosmo: Any, M: np.ndarray, a: float) -> np.ndarray:
+        """Halo mass function, constant on the provided mass grid."""
+        _ = a
+        M = np.asarray(M, dtype=float)
+        return np.full_like(M, v, dtype=float)
+
+    return hmf
+
+
+def _hb_const(
+    val: float = 1.0
+) -> Callable[[Any, np.ndarray, float], np.ndarray]:
+    """Return b(M) = constant on the provided mass grid."""
+    v = float(val)
+
+    def hb(_cosmo: Any, M: np.ndarray, a: float) -> np.ndarray:
+        _ = a
+        M = np.asarray(M, dtype=float)
+        return np.full_like(M, v, dtype=float)
+
+    return hb
+
+
+def _y_const(
+    val: float = 1.0
+) -> Callable[[np.ndarray, np.ndarray], np.ndarray]:
+    """Return y(M,k)=const with the correct broadcasted (nM,nk) shape."""
+    v = float(val)
+
+    def y(M: np.ndarray, k: np.ndarray) -> np.ndarray:
+        M = np.asarray(M, dtype=float)
+        k = np.asarray(k, dtype=float)
+
+        # The production code calls y(M[:,None], k[None,:]) i.e. both 2D.
+        # But keep this robust to 1D callers too.
+        if M.ndim == 1 and k.ndim == 1:
+            return np.full((M.size, k.size), v, dtype=float)
+
+        MM, KK = np.broadcast_arrays(M, k)
+        return np.full(MM.shape, v, dtype=float)
+
+    return y
+
+
+def _profiles() -> dict[str, Any]:
+    """Return a valid mapping of u_over_m evaluators."""
+    return {"dark_matter": _y_const(1.0),
+            "gas": _y_const(1.0),
+            "stars": _y_const(1.0)}
+
+
+def _densities_full(
+    *,
+    matter: float = 1.0,
+    dark_matter: float = 1.0,
+    gas: float = 1.0,
+    stars: float = 1.0,
+) -> dict[str, float]:
+    """Return a complete density dict for packet building."""
+    return {
+        "matter": float(matter),
+        "dark_matter": float(dark_matter),
+        "gas": float(gas),
+        "stars": float(stars),
+    }
+
+
+def _make_calc(
+    *,
+    cosmo: Any | None = None,
+    a: float = 1.0,
+    k: np.ndarray | None = None,
+    profiles_u_over_m: dict[str, Any] | None = None,
+    dmo_dm_u_over_m: Any | None = None,
+    mass_function: Any | None = None,
+    halo_bias: Any | None = None,
+    mass_ranges: dict[str, dict[str, float]] | None = None,
+    densities: dict[str, float] | None = None,
+    gas_params: dict[str, float] | None = None,
+    n_m: int = 16,
+) -> FedeliPkCalculator:
+    """Build a minimal valid FedeliPkCalculator."""
+    use_cosmo = _cosmo() if cosmo is None else cosmo
+    use_k = _kgrid() if k is None else k
+    use_prof = _profiles() if profiles_u_over_m is None else profiles_u_over_m
+    use_mf = _hmf_const(1.0) if mass_function is None else mass_function
+    use_hb = _hb_const(1.0) if halo_bias is None else halo_bias
+    use_ranges = _mass_ranges() if mass_ranges is None else mass_ranges
+    use_rho = _densities_full() if densities is None else densities
+    use_gas = _gas_params() if gas_params is None else gas_params
+
+    return FedeliPkCalculator(
+        cosmo=use_cosmo,
+        a=a,
+        k=use_k,
+        profiles_u_over_m=use_prof,
+        dmo_dm_u_over_m=dmo_dm_u_over_m,
+        mass_function=use_mf,
+        halo_bias=use_hb,
+        mass_ranges=use_ranges,
+        densities=use_rho,
+        gas_params=use_gas,
+        n_m=n_m,
+    )
+
+
+def test_dndm_from_dndlog10m_validates_shapes_and_positive_m() -> None:
+    """Tests dn/dM conversion checks shapes and enforces m > 0."""
+    m = np.array([1.0, 10.0, 100.0], dtype=float)
+    d = np.ones_like(m)
+
+    out = _dndm_from_dndlog10m(d, m)
+    assert out.shape == m.shape
+    assert np.all(np.isfinite(out))
+    assert np.all(out > 0.0)
+
+    with pytest.raises(ValueError):
+        _ = _dndm_from_dndlog10m(np.ones(2), m)
+
+    with pytest.raises(ValueError):
+        _ = _dndm_from_dndlog10m(d,
+                                 np.array([1.0, 0.0, 2.0],
+                                          dtype=float))
+
+
+def test_init_validates_k_grid_shape_monotonicity_and_sign() -> None:
+    """Tests that k must be 1D, finite, strictly increasing, and positive."""
+    with pytest.raises(Exception):
+        _ = _make_calc(k=np.ones((2, 2)))  # type: ignore[arg-type]
+
+    with pytest.raises(Exception):
+        _ = _make_calc(k=np.array([], dtype=float))
+
+    with pytest.raises(Exception):
+        _ = _make_calc(k=np.array([0.1, 0.1, 1.0], dtype=float))
+
+    with pytest.raises(Exception):
+        _ = _make_calc(k=np.array([0.1, np.nan, 1.0], dtype=float))
+
+    with pytest.raises(Exception):
+        _ = _make_calc(k=np.array([0.0, 0.1, 1.0], dtype=float))
+
+
+def test_init_validates_profiles_mapping_and_components() -> None:
+    """Tests that profiles_u_over_m must be a mapping with DM/gas/stars
+    callables."""
+    with pytest.raises(Exception):
+        _ = _make_calc(profiles_u_over_m=123)  # type: ignore[arg-type]
+
+    with pytest.raises(Exception):
+        _ = _make_calc(
+            profiles_u_over_m={"dark_matter": _y_const(1.0),
+                               "gas": _y_const(1.0)}
+        )
+
+    bad = {"dark_matter": _y_const(1.0), "gas": 123, "stars": _y_const(1.0)}
+    with pytest.raises(Exception):
+        _ = _make_calc(profiles_u_over_m=bad)
+
+
+def test_init_validates_mass_ranges_structure_and_values() -> None:
+    """Tests that mass_ranges must be present for all components with
+    valid min/max."""
+    with pytest.raises(Exception):
+        _ = _make_calc(mass_ranges=123)  # type: ignore[arg-type]
+
+    bad_missing = {"dark_matter": {"min": 1.0, "max": 2.0},
+                   "gas": {"min": 1.0, "max": 2.0}}
+    with pytest.raises(Exception):
+        _ = _make_calc(mass_ranges=bad_missing)  # type: ignore[arg-type]
+
+    bad_keys = _mass_ranges()
+    bad_keys["gas"] = {"min": 1.0}  # missing max
+    with pytest.raises(Exception):
+        _ = _make_calc(mass_ranges=bad_keys)
+
+    bad_vals = _mass_ranges()
+    bad_vals["stars"] = {"min": 1.0e15, "max": 1.0e14}
+    with pytest.raises(Exception):
+        _ = _make_calc(mass_ranges=bad_vals)
+
+
+def test_init_validates_gas_params_and_densities_types() -> None:
+    """Tests that gas_params has Fg/bd and densities is a mapping of floats."""
+    with pytest.raises(Exception):
+        _ = _make_calc(gas_params={"Fg": 0.5})  # type: ignore[arg-type]
+
+    with pytest.raises(Exception):
+        _ = _make_calc(gas_params=_gas_params(Fg=1.5, bd=1.0))
+
+    with pytest.raises(Exception):
+        _ = _make_calc(gas_params=_gas_params(Fg=0.5, bd=float("nan")))
+
+    with pytest.raises(Exception):
+        _ = _make_calc(densities=123)  # type: ignore[arg-type]
+
+
+def test_y_grid_mm_requires_u_over_m_return_shape() -> None:
+    """Tests that u_over_m must return (nM,nk) for the internal
+    (M[:,None],k[None,:]) call."""
+    calc = _make_calc(n_m=16)
+    mmin, mmax = 1.0e13, 1.0e14
+
+    y = calc._y_grid_mm("dark_matter", mmin, mmax)  # noqa: SLF001
+    assert y.shape == (calc.n_m, calc.k.size)
+    assert np.all(np.isfinite(y))
+
+    def y_bad(M: np.ndarray, k: np.ndarray) -> np.ndarray:
+        """Returns (nM,) instead of (nM,nk)."""
+        _ = k
+        M = np.asarray(M, dtype=float)
+        return np.ones((M.shape[0], 1), dtype=float)
+
+    with pytest.raises(ValueError, match=r"u_over_m"):
+        _ = calc._y_grid_mm(  # noqa: SLF001
+            "dark_matter", mmin, mmax, y_fn=y_bad, cache_tag="bad"
+        )
+
+
+def test_I2_and_Ib_validate_components_and_profile_shapes_and_cache() -> None:
+    """Tests that _I2_vec/_Ib_vec are finite, have shape (nk,), and cache hits
+    work."""
+    calc = _make_calc(n_m=16)
+    mmin, mmax = 1.0e13, 1.0e14
+
+    Ib1 = calc._Ib_vec("gas", mmin, mmax)  # noqa: SLF001
+    Ib2 = calc._Ib_vec("gas", mmin, mmax)  # noqa: SLF001
+    assert Ib1 is Ib2
+    assert Ib1.shape == (calc.k.size,)
+    assert np.all(np.isfinite(Ib1))
+
+    I21 = calc._I2_vec("dark_matter", "stars", mmin, mmax)
+    I22 = calc._I2_vec("dark_matter", "stars", mmin, mmax)
+    assert I21 is I22
+    assert I21.shape == (calc.k.size,)
+    assert np.all(np.isfinite(I21))
+
+
+def test_pk_gas_auto_dm_gas_star_gas_return_finite_arrays() -> None:
+    """Tests that the convenience spectra return finite arrays with shape
+    (nk,)."""
+    rho = _densities_full(matter=1.0, dark_matter=1.0, gas=1.0, stars=1.0)
+    calc = _make_calc(densities=rho,
+                      gas_params=_gas_params(Fg=0.7, bd=1.2),
+                      n_m=32)
+
+    Pg = calc.pk_gas_auto()
+    Pdg = calc.pk_dm_gas()
+    Psg = calc.pk_star_gas()
+
+    for arr in (Pg, Pdg, Psg):
+        assert isinstance(arr, np.ndarray)
+        assert arr.shape == (calc.k.size,)
+        assert np.all(np.isfinite(arr))
+
+
+def test_pk_total_dmo_adds_1h_and_2h_and_caches() -> None:
+    """Tests that pk_total_dmo returns a finite (nk,) array and caches when
+    requested."""
+    rho = _densities_full(matter=1.0, dark_matter=1.0, gas=1.0, stars=1.0)
+    calc = _make_calc(densities=rho, n_m=16)
+
+    p1 = calc.pk_total_dmo(use_cache=True)
+    p2 = calc.pk_total_dmo(use_cache=True)
+
+    assert p1.shape == (calc.k.size,)
+    assert np.all(np.isfinite(p1))
+    assert np.allclose(p1, p2)
+    assert calc._pk_dmo is not None  # noqa: SLF001
+
+
+def test_boost_hm_over_hm_is_ratio_of_total_to_dmo() -> None:
+    """Tests that boost_hm_over_hm equals pk_total / pk_total_dmo."""
+    rho = _densities_full(matter=1.0, dark_matter=1.0, gas=1.0, stars=1.0)
+    calc = _make_calc(densities=rho,
+                      gas_params=_gas_params(Fg=0.7, bd=1.2),
+                      n_m=16)
+
+    b = calc.boost_hm_over_hm()
+    assert b.shape == (calc.k.size,)
+    assert np.all(np.isfinite(b))
+
+    pb = calc.pk_total()
+    pd = calc.pk_total_dmo(use_cache=True)
+    assert np.allclose(b, pb / pd, rtol=0.0, atol=0.0)
+
+
+def test_pk_packet_adds_commutative_aliases_for_pairs_and_weighted_pairs(
+
+) -> None:
+    """Tests that pk_packet adds dm_gas==gas_dm etc. and weighted aliases."""
+    rho = _densities_full(matter=1.0, dark_matter=1.0, gas=1.0, stars=1.0)
+    calc = _make_calc(densities=rho,
+                      gas_params=_gas_params(Fg=0.7, bd=1.2),
+                      n_m=16)
+
+    pkt = calc.pk_packet(use_cache=False)
+    pk = pkt["pk"]
+
+    assert np.allclose(pk["dm_gas"], pk["gas_dm"])
+    assert np.allclose(pk["dm_stars"], pk["stars_dm"])
+    assert np.allclose(pk["stars_gas"], pk["gas_stars"])
+
+    assert np.allclose(pk["w_dm_gas"], pk["w_gas_dm"])
+    assert np.allclose(pk["w_dm_stars"], pk["w_stars_dm"])
+    assert np.allclose(pk["w_stars_gas"], pk["w_gas_stars"])
+
+
+def test_y_grid_mm_cache_depends_on_y_fn() -> None:
+    """Tests that _y_grid_mm cache keys include y_fn (or otherwise respect it),
+    so changing y_fn changes the returned cached result."""
+    calc = _make_calc(n_m=8)
+    mmin, mmax = 1e13, 1e14
+
+    y0 = calc._y_grid_mm("dark_matter", mmin, mmax)  # noqa: SLF001
+    assert y0.shape == (calc.n_m, calc.k.size)
+
+    def y_alt(M: np.ndarray, k: np.ndarray) -> np.ndarray:
+        return 2.0 * np.ones((M.shape[0], k.shape[1]), dtype=float)
+
+    y1 = calc._y_grid_mm("dark_matter", mmin, mmax, y_fn=y_alt)
+    assert y1.shape == (calc.n_m, calc.k.size)
+    assert np.allclose(y1, 2.0)
+
+    # Optional: calling again with the same y_fn should return the same values.
+    y1b = calc._y_grid_mm("dark_matter", mmin, mmax, y_fn=y_alt)
+    assert np.allclose(y1b, 2.0)
+
+
+def test_ensure_densities_invalidates_final_caches_when_changed() -> None:
+    """Tests that ensure_densities clears cached outputs when it fills missing
+    density entries."""
+    calc = _make_calc(n_m=16)
+
+    # Pre-fill caches as if we computed something already.
+    calc._pk_dmo = np.ones(calc.k.size)  # noqa: SLF001
+    calc._pk_packet_cache = {"sentinel": True}  # noqa: SLF001
+
+    # Remove gas/stars so ensure_densities must fill them.
+    calc.rho.pop("gas", None)
+    calc.rho.pop("stars", None)
+
+    calc.ensure_densities(
+        f_gas=lambda m: 0.1 * np.ones_like(m),
+        f_star=lambda m: 0.02 * np.ones_like(m),
+        mmin=1e10,
+        mmax=1e16,
+    )
+
+    assert calc._pk_dmo is None  # noqa: SLF001
+    assert calc._pk_packet_cache is None  # noqa: SLF001
+
+
+def test_pk_packet_adds_commutative_aliases() -> None:
+    """Tests that pk_packet adds commutative alias keys for cross-terms and
+    weighted cross-terms."""
+    calc = _make_calc(n_m=32)
+
+    packet = calc.pk_packet(use_cache=False)
+    pk = packet["pk"]
+
+    # unweighted aliases
+    assert np.allclose(pk["dm_gas"], pk["gas_dm"])
+    assert np.allclose(pk["dm_stars"], pk["stars_dm"])
+    assert np.allclose(pk["stars_gas"], pk["gas_stars"])
+
+    # weighted aliases
+    assert np.allclose(pk["w_dm_gas"], pk["w_gas_dm"])
+    assert np.allclose(pk["w_dm_stars"], pk["w_stars_dm"])
+    assert np.allclose(pk["w_stars_gas"], pk["w_gas_stars"])
+
+
+def test_pk_total_matches_packet_total() -> None:
+    """Tests that pk_total is the packet total spectrum and uses cached
+    packet."""
+    rho = _densities_full(matter=1.0, dark_matter=1.0, gas=1.0, stars=1.0)
+    calc = _make_calc(
+        densities=rho, gas_params=_gas_params(Fg=0.7, bd=1.2), n_m=32)
+
+    p0 = calc.pk_total()
+    p1 = calc.pk_packet(use_cache=True)["pk"]["total"]
+    assert np.allclose(p0, p1)
+
+
+def test_pk_halo_pair_raises_for_non_overlapping_mass_ranges() -> None:
+    """Tests that pk_halo_pair errors out when the two components have no
+    overlap in mass range."""
+    calc = _make_calc(n_m=16)
+
+    calc.mass_ranges["dark_matter"] = {"min": 1e10, "max": 1e11}
+    calc.mass_ranges["stars"] = {"min": 1e12, "max": 1e13}
+
+    with pytest.raises(ValueError, match=r"No overlap mass range"):
+        calc.pk_halo_pair(
+            comp1="dark_matter",
+            comp2="stars",
+            rho1=1.0,
+            rho2=1.0,
+        )
+
+
+def test_k_cache_key_rounding():
+    """Tests that k_cache_key rounds to the nearest 1e-3."""
+    assert _k_cache_key(1.23456789, ndp=3) == 1.235
+
+
+def test_fedeli_pk_calculator_requires_nm_ge_2() -> None:
+    """Tests that FedeliPkCalculator requires n_m >= 2."""
+    with pytest.raises(ValueError, match=r"n_m must be >= 2"):
+        _ = _make_calc(n_m=1)
+
+
+def test_fedeli_pk_calculator_requires_nm_ge_2_direct() -> None:
+    """Tests that FedeliPkCalculator requires n_m >= 2."""
+    with pytest.raises(ValueError, match=r"n_m must be >= 2"):
+        _ = FedeliPkCalculator(
+            cosmo=_cosmo(),
+            a=1.0,
+            k=_kgrid(),
+            profiles_u_over_m=_profiles(),
+            mass_function=_hmf_const(1.0),
+            halo_bias=_hb_const(1.0),
+            mass_ranges=_mass_ranges(),
+            densities=_densities_full(),
+            gas_params=_gas_params(),
+            n_m=1,
+        )
+
+
+def test_rho_from_fraction_validations_and_cache_hit() -> None:
+    """Tests that rho_from_fraction validates inputs and respects the cache."""
+    calc = _make_calc(n_m=8)
+
+    with pytest.raises(TypeError, match=r"callable"):
+        _ = calc.rho_from_fraction(  # type: ignore[arg-type]
+            f_of_m=123, mmin=1e13, mmax=1e14
+        )
+
+    with pytest.raises(ValueError, match=r"Invalid mass range"):
+        _ = calc.rho_from_fraction(
+            f_of_m=lambda m: np.ones_like(m), mmin=1e14, mmax=1e13
+        )
+
+    with pytest.raises(ValueError, match=r"same shape"):
+        _ = calc.rho_from_fraction(
+            f_of_m=lambda m: np.ones(m.size + 1), mmin=1e13, mmax=1e14, n_m=8
+        )
+
+    def f_nan(m: np.ndarray) -> np.ndarray:
+        out = np.ones_like(m, dtype=float)
+        out[0] = np.nan
+        return out
+
+    with pytest.raises(ValueError, match=r"must be finite"):
+        _ = calc.rho_from_fraction(f_of_m=f_nan, mmin=1e13, mmax=1e14, n_m=8)
+
+    key = ("gas", 1e13, 1e14, 8)
+    calc._rho_cache[key] = 123.0  # noqa: SLF001
+    out = calc.rho_from_fraction(
+        f_of_m=lambda m: np.ones_like(m),
+        mmin=1e13,
+        mmax=1e14,
+        n_m=8,
+        cache_key="gas",
+    )
+    assert out == 123.0
+
+
+def test_ensure_densities_requires_cosmo_keys_and_fills_matter_and_dm(
+    monkeypatch,
+) -> None:
+    """Tests that ensure_densities requires cosmo keys and fills matter and
+    dm."""
+    calc = _make_calc(cosmo={"Omega_m": 0.3}, densities={})
+    with pytest.raises(KeyError, match=r"cosmo must provide"):
+        calc.ensure_densities(
+            f_gas=lambda m: 0.1 * np.ones_like(m),
+            f_star=lambda m: 0.02 * np.ones_like(m),
+            mmin=1e13,
+            mmax=1e14,
+            n_m=8,
+        )
+
+    monkeypatch.setattr(ps.ccl, "rho_x", lambda cosmo, a, what: 10.0)
+
+    calc = _make_calc(cosmo={"Omega_c": 0.25, "Omega_m": 0.30}, densities={})
+    calc.ensure_densities(
+        f_gas=lambda m: 0.1 * np.ones_like(m),
+        f_star=lambda m: 0.02 * np.ones_like(m),
+        mmin=1e13,
+        mmax=1e14,
+        n_m=8,
+    )
+
+    assert calc.rho["matter"] == 10.0
+    assert np.isclose(calc.rho["dark_matter"], 10.0 * (0.25 / 0.30))
+    assert "gas" in calc.rho and "stars" in calc.rho
+
+
+def test_linpk_caches_single_k(monkeypatch) -> None:
+    """Tests that linpk caches the result for a single k."""
+    calc = _make_calc()
+
+    calls = {"n": 0}
+
+    def fake_linear(cosmo, k, a):
+        _, _, _ = cosmo, k, a
+        calls["n"] += 1
+        return 3.0
+
+    monkeypatch.setattr(ps.ccl, "linear_matter_power", fake_linear)
+
+    p1 = calc.linpk(0.123456)
+    p2 = calc.linpk(0.123456)  # this should hit cache
+
+    assert p1 == 3.0 and p2 == 3.0
+    assert calls["n"] == 1
+
+
+def test_P_lin_falls_back_to_scalar_loop(monkeypatch) -> None:
+    """Tests that P_lin falls back to scalar loop when linear_matter_power
+    fails on vector k input."""
+    calc = _make_calc()
+
+    def fake_linear(cosmo, k, a):
+        """Raise an error if k is a vector."""
+        _, _ = cosmo, a
+        if np.ndim(k) > 0:
+            raise RuntimeError("no vector")
+        return 5.0
+
+    monkeypatch.setattr(ps.ccl, "linear_matter_power", fake_linear)
+
+    out = calc.P_lin()
+    assert out.shape == (calc.k.size,)
+    assert np.allclose(out, 5.0)
+
+
+def test_mass_grid_raises_for_invalid_range() -> None:
+    """Tests that mass_grid raises for invalid mass ranges."""
+    calc = _make_calc()
+    with pytest.raises(ValueError, match=r"Invalid mass range"):
+        _ = calc._mass_grid(1e14, 1e13)  # noqa: SLF001
+
+
+def test_dndm_raises_if_mass_function_wrong_shape() -> None:
+    """Tests that _dndm raises if mass_function returns wrong shape."""
+    def hmf_bad(_cosmo: Any, M: np.ndarray, a: float) -> np.ndarray:
+        """Mass function that returns wrong shape."""
+        _ = a
+        return np.ones(M.size + 1, dtype=float)
+
+    calc = _make_calc(mass_function=hmf_bad)
+
+    with pytest.raises(ValueError, match=r"mass_function must return"):
+        _ = calc._dndm(1e13, 1e14)  # noqa: SLF001
+
+
+def test_bias_raises_if_halo_bias_wrong_shape() -> None:
+    """Tests that _bias raises if halo_bias returns wrong shape."""
+    def hb_bad(_cosmo: Any, M: np.ndarray, a: float) -> np.ndarray:
+        """Bias function that returns wrong shape."""
+        _ = a
+        return np.ones(M.size + 1, dtype=float)
+
+    calc = _make_calc(halo_bias=hb_bad)
+
+    with pytest.raises(ValueError, match=r"halo_bias must return"):
+        _ = calc._bias(1e13, 1e14)  # noqa: SLF001
+
+
+def test_Ib_and_I2_internal_shape_guards(monkeypatch) -> None:
+    """Tests that _Ib_vec and _I2_vec raise if their inputs have wrong
+    shape."""
+    calc = _make_calc()
+
+    monkeypatch.setattr(ps,
+                        "_trapz_compat",
+                        lambda y,
+                        x,
+                        axis=0: np.zeros(2))
+
+    with pytest.raises(ValueError, match=r"Ib has wrong shape"):
+        _ = calc._Ib_vec("gas", 1e13, 1e14)  # noqa: SLF001
+
+    with pytest.raises(ValueError, match=r"I2 has wrong shape"):
+        _ = calc._I2_vec("dark_matter", "stars", 1e13, 1e14)  # noqa: SLF001
+
+
+def test_pk_halo_pair_validates_rhos_and_components() -> None:
+    """Tests that pk_halo_pair validates rhos and components."""
+    calc = _make_calc()
+
+    with pytest.raises(ValueError, match=r"rho1 and rho2 must be > 0"):
+        calc.pk_halo_pair(comp1="gas", comp2="gas", rho1=0.0, rho2=1.0)
+
+    with pytest.raises(KeyError, match=r"mass_ranges"):
+        calc.pk_halo_pair(comp1="not_a_comp", comp2="gas", rho1=1.0, rho2=1.0)
+
+    calc2 = _make_calc()
+    calc2.y.pop("gas")
+    with pytest.raises(KeyError, match=r"profiles"):
+        calc2.pk_halo_pair(comp1="gas", comp2="stars", rho1=1.0, rho2=1.0)
+
+
+def test_pair_mixed_cross_packet_validates_comp() -> None:
+    """Tests that pair_mixed_cross_packet validates comp."""
+    calc = _make_calc()
+    with pytest.raises(ValueError, match=r"comp must be"):
+        _ = calc.pair_mixed_cross_packet(comp="gas")
+
+
+def test_pk_packet_requires_all_densities() -> None:
+    """Tests that pk_packet requires all densities."""
+    calc = _make_calc(densities={"matter": 1.0,
+                                 "dark_matter": 1.0,
+                                 "gas": 1.0})
+    with pytest.raises(KeyError, match=r"Missing density"):
+        _ = calc.pk_packet(use_cache=False)
+
+
+def test_pk_total_dmo_fills_matter_density_if_missing(monkeypatch) -> None:
+    """Tests that pk_total_dmo fills matter density if missing."""
+    rho = {"dark_matter": 1.0, "gas": 1.0, "stars": 1.0}
+    calc = _make_calc(densities=rho)
+
+    monkeypatch.setattr(ps.ccl, "rho_x", lambda cosmo, a, what: 7.0)
+
+    _ = calc.pk_total_dmo(use_cache=False)
+    assert "matter" in calc.rho
+    assert calc.rho["matter"] == 7.0
+
+
+def test_boost_hm_over_hm_raises_if_dmo_nonpositive(monkeypatch) -> None:
+    """Tests that boost_hm_over_hm raises if dmo is nonpositive."""
+    calc = _make_calc()
+
+    def fake_pk_packet(*, use_cache=True):
+        """Fake pk_packet that returns non-positive dmo."""
+        _ = use_cache
+        return {
+            "pk": {"total": np.ones(calc.k.size)},
+            "pk_ref": {"pk_dmo": np.zeros(calc.k.size)},  # non-positive
+        }
+
+    monkeypatch.setattr(calc, "pk_packet", fake_pk_packet)
+
+    with pytest.raises(ValueError, match=r"non-positive|non-finite"):
+        _ = calc.boost_hm_over_hm()
diff --git a/pyccl/tests/test_baryons_fedeli14_profile_interpolation.py b/pyccl/tests/test_baryons_fedeli14_profile_interpolation.py
new file mode 100644
index 000000000..8b27df163
--- /dev/null
+++ b/pyccl/tests/test_baryons_fedeli14_profile_interpolation.py
@@ -0,0 +1,441 @@
+"""Unit tests for `baryons.fedeli14_bhm.profile_interpolation`."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+import numpy as np
+import pytest
+
+import pyccl.baryons.fedeli14_bhm.profile_interpolation as pi
+
+
+@dataclass
+class _ProfileNoUpd:
+    """Profile without update_precision_fftlog."""
+    def fourier(
+        self,
+        cosmo: Any,
+        k: np.ndarray,
+        mass: np.ndarray,
+        a: float
+    ) -> np.ndarray:
+        """Fourier transform of mass profile."""
+        _ = cosmo, a
+        k = np.asarray(k, float)
+        mass = np.asarray(mass, float)
+        # expected shape: (len(mass), len(k))
+        return np.ones((mass.size, k.size), dtype=float) * mass[:, None]
+
+
+@dataclass
+class _ProfileBadUpd:
+    """Profile with non-callable update_precision_fftlog."""
+    update_precision_fftlog: Any = 123
+
+    def fourier(
+        self,
+        cosmo: Any,
+        k: np.ndarray,
+        mass: np.ndarray,
+        a: float
+    ) -> np.ndarray:
+        """Fourier transform of mass profile."""
+        _ = cosmo, a
+        k = np.asarray(k, float)
+        mass = np.asarray(mass, float)
+        return np.ones((mass.size, k.size), dtype=float) * mass[:, None]
+
+
+@dataclass
+class _ProfileUpdOK:
+    """Profile with callable update_precision_fftlog."""
+    called: dict[str, Any]
+
+    def update_precision_fftlog(self, **kwargs: Any) -> None:
+        self.called["kwargs"] = dict(kwargs)
+
+    def fourier(
+        self,
+        cosmo: Any,
+        k: np.ndarray,
+        mass: np.ndarray,
+        a: float
+    ) -> np.ndarray:
+        _ = cosmo, a
+        k = np.asarray(k, float)
+        mass = np.asarray(mass, float)
+        return np.ones((mass.size, k.size), dtype=float) * mass[:, None]
+
+
+@dataclass
+class _ProfileFourierBadShape:
+    """Profile with a fourier method that returns the wrong shape."""
+    def fourier(
+        self,
+        cosmo: Any,
+        k: np.ndarray,
+        mass: np.ndarray,
+        a: float
+    ) -> np.ndarray:
+        _ = cosmo, a
+        k = np.asarray(k, float)
+        mass = np.asarray(mass, float)
+        # wrong shape on purpose
+        return np.ones((mass.size, k.size + 1), dtype=float)
+
+
+@dataclass
+class _ProfileNoFourier:
+    """Profile missing fourier attribute."""
+    pass
+
+
+def _grid_mass_k() -> tuple[np.ndarray, np.ndarray]:
+    """Return a tiny strictly increasing positive mass and k grid."""
+    mass = np.array([1e13, 2e13, 5e13], dtype=float)
+    k = np.array([1e-2, 1e-1, 1.0], dtype=float)
+    return mass, k
+
+
+def test_update_precision_fftlog_no_method_returns_profile_unchanged() -> None:
+    """Tests that the profile is returned unchanged if it doesn't have an
+    update_precision_fftlog method."""
+    prof = _ProfileNoUpd()
+    out = pi.update_precision_fftlog(prof)
+    assert out is prof
+
+
+def test_update_precision_fftlog_noncallable_raises() -> None:
+    """Tests that an error is raised if the profile's
+    update_precision_fftlog attribute is not callable."""
+    prof = _ProfileBadUpd()
+    with pytest.raises(TypeError, match=r"not callable"):
+        _ = pi.update_precision_fftlog(prof)
+
+
+def test_update_precision_fftlog_callable_is_invoked_and_returns_profile(
+
+) -> None:
+    """Tests that the profile's update_precision_fftlog method is invoked and
+    that the profile is returned unchanged."""
+    called: dict[str, Any] = {}
+    prof = _ProfileUpdOK(called=called)
+
+    out = pi.update_precision_fftlog(
+        prof,
+        padding_hi_fftlog=9.0,
+        padding_lo_fftlog=8.0,
+        n_per_decade=7,
+        plaw_fourier=-1.5,
+    )
+    assert out is prof
+    assert "kwargs" in called
+    assert called["kwargs"]["padding_hi_fftlog"] == 9.0
+    assert called["kwargs"]["padding_lo_fftlog"] == 8.0
+    assert called["kwargs"]["n_per_decade"] == 7
+    assert called["kwargs"]["plaw_fourier"] == -1.5
+
+
+def test_interpolate_profile_u_over_m_validates_a_and_grids() -> None:
+    """Tests that invalid inputs to interpolate_profile_u_over_m raise
+    errors."""
+    mass, k = _grid_mass_k()
+    prof = _ProfileNoUpd()
+
+    with pytest.raises(ValueError, match=r"a must be finite"):
+        _ = pi.interpolate_profile_u_over_m(
+            cosmo={}, a=float("nan"), profile=prof, mass=mass, k=k
+        )
+
+    with pytest.raises(ValueError, match=r"non-empty"):
+        _ = pi.interpolate_profile_u_over_m(
+            cosmo={}, a=1.0, profile=prof, mass=np.array([]), k=k
+        )
+
+    with pytest.raises(ValueError, match=r"1D grid vectors"):
+        _ = pi.interpolate_profile_u_over_m(
+            cosmo={}, a=1.0, profile=prof, mass=mass.reshape(3, 1), k=k
+        )
+
+    with pytest.raises(ValueError, match=r"strictly increasing"):
+        _ = pi.interpolate_profile_u_over_m(
+            cosmo={}, a=1.0, profile=prof,
+            mass=np.array([1.0, 1.0, 2.0]), k=k
+        )
+
+    with pytest.raises(ValueError, match=r"log_axes=True"):
+        _ = pi.interpolate_profile_u_over_m(
+            cosmo={}, a=1.0, profile=prof,
+            mass=np.array([0.0, 1.0, 2.0]), k=k, log_axes=True
+        )
+
+
+def test_interpolate_profile_u_over_m_requires_fourier_and_callable() -> None:
+    """Tests that invalid inputs to interpolate_profile_u_over_m raise
+    errors."""
+    mass, k = _grid_mass_k()
+
+    with pytest.raises(TypeError):
+        _ = pi.interpolate_profile_u_over_m(
+            cosmo={}, a=1.0, profile=_ProfileNoFourier(), mass=mass, k=k
+        )
+
+    class _P:
+        fourier = 123
+
+    with pytest.raises(TypeError, match=r"must be callable"):
+        _ = pi.interpolate_profile_u_over_m(
+            cosmo={},
+            a=1.0,
+            profile=_P(),
+            mass=mass, k=k)
+
+
+def test_interpolate_profile_u_over_m_rejects_wrong_fourier_shape() -> None:
+    """Tests that invalid inputs to interpolate_profile_u_over_m raise
+    errors."""
+    mass, k = _grid_mass_k()
+    prof = _ProfileFourierBadShape()
+    with pytest.raises(ValueError, match=r"profile\.fourier must return"):
+        _ = pi.interpolate_profile_u_over_m(
+            cosmo={},
+            a=1.0,
+            profile=prof,
+            mass=mass,
+            k=k)
+
+
+def test_interpolate_profile_u_over_m_log_values_requires_positive_floor(
+
+) -> None:
+    """Tests that invalid inputs to interpolate_profile_u_over_m raise
+    errors."""
+    mass, k = _grid_mass_k()
+    prof = _ProfileNoUpd()
+
+    with pytest.raises(ValueError, match=r"value_floor must be > 0"):
+        _ = pi.interpolate_profile_u_over_m(
+            cosmo={}, a=1.0, profile=prof, mass=mass, k=k,
+            log_values=True, value_floor=0.0
+        )
+
+
+def test_interpolator_evaluates_and_broadcasts_log_axes_and_log_values(
+
+) -> None:
+    """Tests that the interpolator evaluates and broadcasts log_axes and
+    log_values."""
+    mass, k = _grid_mass_k()
+    prof = _ProfileNoUpd()
+
+    # prof.fourier returns u = mass[:,None], so u/M = 1 everywhere
+    f = pi.interpolate_profile_u_over_m(
+        cosmo={}, a=1.0, profile=prof, mass=mass, k=k,
+        log_axes=True, log_values=True
+    )
+
+    Mq = np.array([1e13, 3e13], dtype=float)[:, None]
+    kq = np.array([1e-2, 1e-1, 1.0], dtype=float)[None, :]
+    out = f(Mq, kq)
+
+    assert out.shape == (2, 3)
+    assert np.all(np.isfinite(out))
+    assert np.allclose(out, 1.0, rtol=0, atol=0)
+
+
+def test_interpolator_rejects_nonpositive_queries_when_log_axes_true() -> None:
+    """Tests that the interpolator rejects nonpositive queries when
+    log_axes=True."""
+    mass, k = _grid_mass_k()
+    prof = _ProfileNoUpd()
+
+    f = pi.interpolate_profile_u_over_m(
+        cosmo={},
+        a=1.0,
+        profile=prof,
+        mass=mass,
+        k=k,
+        log_axes=True)
+
+    with pytest.raises(ValueError, match=r"must be > 0"):
+        _ = f(np.array([0.0, 1.0]), np.array([0.1, 0.2]))
+
+    with pytest.raises(ValueError, match=r"must be > 0"):
+        _ = f(np.array([1.0, 2.0]), np.array([0.0, 0.2]))
+
+
+def test_interpolator_allows_nonpositive_queries_when_log_axes_false() -> None:
+    """Tests that the interpolator allows nonpositive queries when
+    log_axes=False."""
+    mass, k = _grid_mass_k()
+    prof = _ProfileNoUpd()
+
+    f = pi.interpolate_profile_u_over_m(
+        cosmo={},
+        a=1.0,
+        profile=prof,
+        mass=mass,
+        k=k,
+        log_axes=False)
+
+    out = f(np.array([-1.0, 2.0]), np.array([0.1, -0.2]))
+    assert out.shape == (2,)
+    assert np.all(np.isfinite(out))
+
+
+def test_interpolator_bounds_error_true_raises_out_of_bounds() -> None:
+    """Tests that the interpolator raises an error when bounds_error=True and
+    the query is out of bounds."""
+    mass, k = _grid_mass_k()
+    prof = _ProfileNoUpd()
+
+    f = pi.interpolate_profile_u_over_m(
+        cosmo={}, a=1.0, profile=prof, mass=mass, k=k,
+        bounds_error=True, log_axes=True
+    )
+
+    # out-of-bounds mass (below min mass)
+    with pytest.raises(ValueError):
+        _ = f(np.array([1e12]), np.array([1e-2]))
+
+
+def test_build_profile_interpolators_validates_mapping_types() -> None:
+    """Tests that invalid inputs to build_profile_interpolators raise
+    errors."""
+    mass, k = _grid_mass_k()
+    prof = _ProfileNoUpd()
+
+    grid = {
+        "dark_matter": {"mass": mass, "k": k},
+        "gas": {"mass": mass, "k": k},
+        "stars": {"mass": mass, "k": k},
+    }
+    profiles = {"dark_matter": prof, "gas": prof, "stars": prof}
+
+    with pytest.raises(TypeError, match=r"profiles"):
+        _ = pi.build_profile_interpolators(
+            cosmo={},
+            a=1.0,
+            interpolation_grid=grid,
+            profiles=123)  # type: ignore[arg-type]
+
+    with pytest.raises(TypeError, match=r"interpolation_grid"):
+        _ = pi.build_profile_interpolators(
+            cosmo={},
+            a=1.0,
+            interpolation_grid=123,
+            profiles=profiles)  # type: ignore[arg-type]
+
+    with pytest.raises(TypeError, match=r"fftlog_kwargs"):
+        _ = pi.build_profile_interpolators(
+            cosmo={}, a=1.0, interpolation_grid=grid, profiles=profiles,
+            fftlog_kwargs=123  # type: ignore[arg-type]
+        )
+
+    with pytest.raises(TypeError, match=r"rgi_kwargs"):
+        _ = pi.build_profile_interpolators(
+            cosmo={}, a=1.0, interpolation_grid=grid, profiles=profiles,
+            rgi_kwargs=123  # type: ignore[arg-type]
+        )
+
+
+def test_build_profile_interpolators_missing_components_raises_keyerror(
+
+) -> None:
+    """Tests that invalid inputs to build_profile_interpolators raise
+    errors."""
+    mass, k = _grid_mass_k()
+    prof = _ProfileNoUpd()
+
+    grid = {"dark_matter": {"mass": mass, "k": k}}  # missing gas/stars
+    profiles = {"dark_matter": prof, "gas": prof, "stars": prof}
+
+    with pytest.raises(
+            KeyError,
+            match=r"Missing profiles|missing grids|Fedeli requires"):
+        _ = pi.build_profile_interpolators(
+            cosmo={},
+            a=1.0,
+            interpolation_grid=grid,
+            profiles=profiles)
+
+
+def test_build_profile_interpolators_grid_schema_checks() -> None:
+    """Tests that invalid inputs to build_profile_interpolators raise
+    errors."""
+    mass, k = _grid_mass_k()
+    prof = _ProfileNoUpd()
+    profiles = {"dark_matter": prof, "gas": prof, "stars": prof}
+
+    grid_bad_type = {
+        "dark_matter": {"mass": mass, "k": k},
+        "gas": 123,  # not a mapping
+        "stars": {"mass": mass, "k": k},
+    }
+    with pytest.raises(TypeError, match=r"must be a mapping"):
+        _ = pi.build_profile_interpolators(
+            cosmo={},
+            a=1.0,
+            interpolation_grid=grid_bad_type,
+            profiles=profiles)
+
+    grid_missing_key = {
+        "dark_matter": {"mass": mass, "k": k},
+        "gas": {"mass": mass},  # missing 'k'
+        "stars": {"mass": mass, "k": k},
+    }
+    with pytest.raises(KeyError, match=r"must contain keys"):
+        _ = pi.build_profile_interpolators(
+            cosmo={},
+            a=1.0,
+            interpolation_grid=grid_missing_key,
+            profiles=profiles)
+
+
+def test_build_profile_interpolators_subset_components_and_update_toggle(
+) -> None:
+    """Tests that build_profile_interpolators supports subset builds and
+    FFTLog updates."""
+    mass, k = _grid_mass_k()
+    called: dict[str, Any] = {}
+    prof_upd = _ProfileUpdOK(called=called)
+
+    grid = {
+        "dark_matter": {"mass": mass, "k": k},
+        "gas": {"mass": mass, "k": k},
+        "stars": {"mass": mass, "k": k},
+    }
+    profiles = {"dark_matter": prof_upd, "gas": prof_upd, "stars": prof_upd}
+
+    # subset build: only gas
+    out = pi.build_profile_interpolators(
+        cosmo={},
+        a=1.0,
+        interpolation_grid=grid,
+        profiles=profiles,
+        components=("gas",),
+        update_fftlog_precision=False,  # ensure we hit the "no update" branch
+    )
+    assert set(out.keys()) == {"gas"}
+    assert callable(out["gas"])
+
+    # full build with update: ensure update_precision_fftlog gets invoked
+    called.clear()
+    out2 = pi.build_profile_interpolators(
+        cosmo={},
+        a=1.0,
+        interpolation_grid=grid,
+        profiles=profiles,
+        update_fftlog_precision=True,
+        fftlog_kwargs={"n_per_decade": 999},
+    )
+    assert set(out2.keys()) == set(pi.FEDELI_COMPONENTS)
+    assert "kwargs" in called
+    assert called["kwargs"]["n_per_decade"] == 999
+
+    # sanity check returned interpolator works
+    f = out2["dark_matter"]
+    val = f(np.array([2e13]), np.array([1e-1]))
+    assert np.all(np.isfinite(val))