diff --git a/bilby/core/utils/calculus.py b/bilby/core/utils/calculus.py
index 490195930..5fea6982a 100644
--- a/bilby/core/utils/calculus.py
+++ b/bilby/core/utils/calculus.py
@@ -1,8 +1,7 @@
 import math
 
-import array_api_compat as aac
 import numpy as np
-from scipy.interpolate import RectBivariateSpline, interp1d as _interp1d
+from scipy.interpolate import interp1d as _interp1d
 from scipy.special import logsumexp
 
 from .log import logger
@@ -218,64 +217,6 @@ def __call__(self, x):
             )
 
 
-class BoundedRectBivariateSpline(RectBivariateSpline):
-
-    def __init__(self, x, y, z, bbox=[None] * 4, kx=3, ky=3, s=0, fill_value=None):
-        self.x_min, self.x_max, self.y_min, self.y_max = bbox
-        if self.x_min is None:
-            self.x_min = min(x)
-        if self.x_max is None:
-            self.x_max = max(x)
-        if self.y_min is None:
-            self.y_min = min(y)
-        if self.y_max is None:
-            self.y_max = max(y)
-        self.fill_value = fill_value
-        self.x = x
-        self.y = y
-        self.z = z
-        super().__init__(x=x, y=y, z=z, bbox=bbox, kx=kx, ky=ky, s=s)
-
-    def __call__(self, x, y, dx=0, dy=0, grid=False):
-        xp = array_module([x, y])
-        if aac.is_numpy_namespace(xp):
-            return self._call_scipy(x, y, dx=dx, dy=dy, grid=grid)
-        elif aac.is_jax_namespace(xp):
-            return self._call_jax(x, y)
-        else:
-            raise NotImplementedError(
-                f"BoundedRectBivariateSpline not implemented for {xp.__name__} backend"
-            )
-
-    def _call_scipy(self, x, y, dx=0, dy=0, grid=False):
-        result = super().__call__(x=x, y=y, dx=dx, dy=dy, grid=grid)
-        out_of_bounds_x = (x < self.x_min) | (x > self.x_max)
-        out_of_bounds_y = (y < self.y_min) | (y > self.y_max)
-        bad = out_of_bounds_x | out_of_bounds_y
-        result[bad] = self.fill_value
-        if result.size == 1:
-            if bad:
-                return self.fill_value
-            else:
-                return result.item()
-        else:
-            return result
-
-    def _call_jax(self, x, y):
-        import jax.numpy as jnp
-        from interpax import interp2d
-
-        return interp2d(
-            jnp.asarray(x),
-            jnp.asarray(y),
-            jnp.asarray(self.x),
-            jnp.asarray(self.y),
-            jnp.asarray(self.z),
-            extrap=self.fill_value if self.fill_value is not None else False,
-            method="cubic2",
-        )
-
-
 class WrappedInterp1d(interp1d):
     """
     A wrapper around scipy interp1d which sets equality-by-instantiation and
diff --git a/bilby/gw/likelihood/base.py b/bilby/gw/likelihood/base.py
index 0030716ae..2e86a95bf 100644
--- a/bilby/gw/likelihood/base.py
+++ b/bilby/gw/likelihood/base.py
@@ -1,5 +1,4 @@
 
-import os
 import copy
 
 import array_api_compat as aac
@@ -8,11 +7,12 @@
 from scipy.special import logsumexp
 
 from ...core.likelihood import Likelihood
-from ...core.utils import logger, BoundedRectBivariateSpline, create_time_series
+from ...core.utils import logger, create_time_series
 from ...core.prior import Interped, Prior, Uniform, DeltaFunction
 from ..detector import InterferometerList, get_empty_interferometer, calibration
 from ..prior import BBHPriorDict, Cosmological
 from ..utils import noise_weighted_inner_product, zenith_azimuth_to_ra_dec, ln_i0
+from ...compat.utils import array_module
 
 
 class GravitationalWaveTransient(Likelihood):
@@ -57,13 +57,11 @@ class GravitationalWaveTransient(Likelihood):
         Warning: when using marginalisation the dict is overwritten which will change the
         the dict you are passing in. If this behaviour is undesired, pass `priors.copy()`.
     distance_marginalization_lookup_table: (dict, str), optional
-        If a dict, dictionary containing the lookup_table, distance_array,
-        (distance) prior_array, and reference_distance used to construct
-        the table.
-        If a string the name of a file containing these quantities.
-        The lookup table is stored after construction in either the
-        provided string or a default location:
-        '.distance_marginalization_lookup_dmin{}_dmax{}_n{}.npz'
+        Deprecated and will be removed in bilby v3.1.0: distance
+        marginalization is evaluated with :code:`distmarg` and no longer uses
+        a lookup table. If a string, it names the file to which a small
+        record of the tabulated distance prior is written (default
+        '.distance_marginalization_lookup.npz'); a dict is ignored.
     calibration_lookup_table: dict, optional
         If a dict, contains the arrays over which to marginalize for each interferometer or the filepaths of the
         calibration files.
@@ -207,13 +205,7 @@ def __init__(
         if self.distance_marginalization:
             self._lookup_table_filename = None
             self._check_marginalized_prior_is_set(key='luminosity_distance')
-            self._distance_array = np.linspace(
-                self.priors['luminosity_distance'].minimum,
-                self.priors['luminosity_distance'].maximum, int(1e4))
-            self.distance_prior_array = np.array(
-                [self.priors['luminosity_distance'].prob(distance)
-                 for distance in self._distance_array])
-            self._ref_dist = self.priors['luminosity_distance'].rescale(0.5)
+            self._ref_dist = float(self.priors['luminosity_distance'].rescale(0.5))
             self._setup_distance_marginalization(
                 distance_marginalization_lookup_table)
             for key in ['redshift', 'comoving_distance']:
@@ -684,25 +676,17 @@ def generate_distance_sample_from_marginalized_likelihood(
         d_inner_h, h_inner_h = self._calculate_inner_products(
             signal_polarizations, parameters=parameters
         )
-
-        d_inner_h_dist = (
-            d_inner_h * parameters['luminosity_distance'] / self._distance_array
-        )
-
-        h_inner_h_dist = (
-            h_inner_h * parameters['luminosity_distance']**2 / self._distance_array**2
-        )
-
-        if self.phase_marginalization:
-            distance_log_like = ln_i0(abs(d_inner_h_dist)) - h_inner_h_dist.real / 2
+        rho_mf, rho_opt = self._reference_snrs(
+            d_inner_h, h_inner_h, parameters=parameters)
+        rho_mf = float(np.asarray(rho_mf))
+        rho_opt = float(np.asarray(rho_opt))
+        if rho_opt > 0:
+            from ...core.utils import random
+            new_distance = float(self._distance_marginalizer.sample_distance(
+                rho_mf, rho_opt, self._ref_dist, rng=random.rng))
         else:
-            distance_log_like = (d_inner_h_dist.real - h_inner_h_dist.real / 2)
-
-        distance_post = (np.exp(distance_log_like - max(distance_log_like)) *
-                         self.distance_prior_array)
-
-        new_distance = Interped(
-            self._distance_array, distance_post).sample()
+            # zero template: the conditional posterior is the prior
+            new_distance = self.priors['luminosity_distance'].sample()
 
         self._rescale_signal(signal_polarizations, new_distance)
         return new_distance
@@ -773,15 +757,73 @@ def generate_phase_sample_from_marginalized_likelihood(
         return new_phase
 
     def distance_marginalized_likelihood(self, d_inner_h, h_inner_h, *, parameters):
+        """
+        Log likelihood ratio marginalized over luminosity distance.
+
+        Evaluated with :code:`distmarg` from the matched-filter and optimal
+        SNRs at the reference distance; the result follows the array
+        namespace of the inputs (numpy, JAX, ...).
+
+        Parameters
+        ==========
+        d_inner_h: complex or array-like
+            :math:`\\langle d | h \\rangle` at the current luminosity distance,
+            possibly with time-bin and/or calibration axes.
+        h_inner_h: float or array-like
+            :math:`\\langle h | h \\rangle` at the current luminosity distance.
+        parameters: dict
+            The current parameters.
+
+        Returns
+        =======
+        array-like
+            :math:`\\ln(L_D / Z_N)` with the shape of :code:`d_inner_h`;
+            zero where the template vanishes.
+        """
+        rho_mf, rho_opt = self._reference_snrs(
+            d_inner_h, h_inner_h, parameters=parameters)
+        xp = array_module(rho_mf)
+        valid = rho_opt > 0
+        ln_l = self._distance_marginalizer.ln_likelihood_ratio(
+            rho_mf, xp.where(valid, rho_opt, 1.0), self._ref_dist)
+        return xp.where(valid, ln_l, 0.0)
+
+    def _reference_snrs(self, d_inner_h, h_inner_h, *, parameters):
+        """
+        Convert inner products to the matched-filter and optimal SNRs at the
+        reference distance, as used by :code:`distmarg`.
+
+        Parameters
+        ==========
+        d_inner_h: complex or array-like
+            :math:`\\langle d | h \\rangle` at the current luminosity distance.
+        h_inner_h: float or array-like
+            :math:`\\langle h | h \\rangle` at the current luminosity distance.
+        parameters: dict
+            The current parameters.
+
+        Returns
+        =======
+        rho_mf, rho_opt: array-like
+            Matched-filter SNR (real part, or modulus with phase
+            marginalization) and optimal SNR at :code:`self._ref_dist`;
+            :code:`rho_opt` gains trailing axes to broadcast against
+            :code:`rho_mf`.
+        """
         d_inner_h_ref, h_inner_h_ref = self._setup_rho(
             d_inner_h, h_inner_h, parameters=parameters)
+        xp = array_module(d_inner_h_ref)
         if self.phase_marginalization:
             d_inner_h_ref = abs(d_inner_h_ref)
         else:
             d_inner_h_ref = d_inner_h_ref.real
-
-        return self._interp_dist_margd_loglikelihood(
-            d_inner_h_ref, h_inner_h_ref)
+        d_inner_h_ref = xp.asarray(d_inner_h_ref)
+        rho_opt = xp.sqrt(xp.asarray(h_inner_h_ref))
+        extra = d_inner_h_ref.ndim - rho_opt.ndim
+        if rho_opt.ndim > 0 and extra > 0:
+            rho_opt = xp.reshape(rho_opt, rho_opt.shape + (1,) * extra)
+        rho_mf = d_inner_h_ref / xp.where(rho_opt > 0, rho_opt, 1.0)
+        return rho_mf, rho_opt
 
     def phase_marginalized_likelihood(self, d_inner_h, h_inner_h):
         d_inner_h = ln_i0(abs(d_inner_h))
@@ -887,51 +929,57 @@ def _setup_rho(self, d_inner_h, optimal_snr_squared, parameters):
     def log_likelihood(self, parameters):
         return self.log_likelihood_ratio(parameters=parameters) + self.noise_log_likelihood()
 
-    @property
-    def _delta_distance(self):
-        return self._distance_array[1] - self._distance_array[0]
-
-    @property
-    def _dist_multiplier(self):
-        ''' Maximum value of ref_dist/dist_array '''
-        return self._ref_dist / self._distance_array[0]
+    def _setup_distance_marginalization(self, lookup_table=None):
+        """
+        Set up distance marginalization with :code:`distmarg`.
 
-    @property
-    def _optimal_snr_squared_ref_array(self):
-        """ Optimal filter snr at fiducial distance of ref_dist Mpc """
-        return np.logspace(-5, 10, self._dist_margd_loglikelihood_array.shape[0])
+        The luminosity-distance prior is tabulated (for
+        :code:`bilby.gw.prior.Cosmological` priors from the prior's own
+        cosmology, not its interpolated :code:`prob`) and fitted once; no
+        lookup table is built.
 
-    @property
-    def _d_inner_h_ref_array(self):
-        """ Matched filter snr at fiducial distance of ref_dist Mpc """
-        if self.phase_marginalization:
-            return np.logspace(-5, 10, self._dist_margd_loglikelihood_array.shape[1])
-        else:
-            n_negative = self._dist_margd_loglikelihood_array.shape[1] // 2
-            n_positive = self._dist_margd_loglikelihood_array.shape[1] - n_negative
-            return np.hstack((
-                -np.logspace(3, -3, n_negative), np.logspace(-3, 10, n_positive)
-            ))
-
-    def _setup_distance_marginalization(self, lookup_table=None):
+        Parameters
+        ==========
+        lookup_table: str, dict, optional
+            Deprecated, will be removed in bilby v3.1.0. A string sets the
+            file to which the tabulated prior is recorded (see
+            :code:`cache_lookup_table`); a dict, e.g. a table built by an
+            older version, is ignored.
+        """
+        try:
+            from distmarg import DistanceMarginalization
+            from distmarg.bilby import prior_to_arrays
+            from scipy.integrate import trapezoid
+        except ImportError as e:
+            raise ImportError(
+                "Distance marginalization requires the distmarg package, "
+                "install it with `pip install distmarg`."
+            ) from e
+        if lookup_table is not None:
+            import warnings
+
+            warnings.warn(
+                "The distance_marginalization_lookup_table argument is "
+                "deprecated and will be removed in bilby v3.1.0: distance "
+                "marginalization no longer uses a lookup table.",
+                DeprecationWarning,
+                stacklevel=3,
+            )
         if isinstance(lookup_table, str) or lookup_table is None:
             self.cached_lookup_table_filename = lookup_table
-            lookup_table = self.load_lookup_table(
-                self.cached_lookup_table_filename)
-        if isinstance(lookup_table, dict):
-            if self._test_cached_lookup_table(lookup_table):
-                self._dist_margd_loglikelihood_array = lookup_table[
-                    'lookup_table']
-            else:
-                self._create_lookup_table()
-        else:
-            self._create_lookup_table()
-        self._interp_dist_margd_loglikelihood = BoundedRectBivariateSpline(
-            self._d_inner_h_ref_array,
-            self._optimal_snr_squared_ref_array,
-            self._dist_margd_loglikelihood_array.T,
-            fill_value=-np.inf,
+
+        prior = self.priors['luminosity_distance']
+        distance, ln_prior = prior_to_arrays(prior)
+        self._distance_marginalizer = DistanceMarginalization(
+            distance, ln_prior, self.phase_marginalization)
+        self._distance_array = distance
+        density = np.exp(ln_prior - np.max(ln_prior))
+        self.distance_prior_array = density / trapezoid(density, distance)
+        logger.info(
+            f"Distance marginalization with distmarg ({self._distance_marginalizer.backend} backend), "
+            f"reference distance {self._ref_dist:.1f} Mpc"
         )
+        self.cache_lookup_table()
 
     @property
     def cached_lookup_table_filename(self):
@@ -947,76 +995,21 @@ def cached_lookup_table_filename(self, filename):
                 filename += '.npz'
         self._lookup_table_filename = filename
 
-    def load_lookup_table(self, filename):
-        if os.path.exists(filename):
-            try:
-                loaded_file = dict(np.load(filename))
-            except AttributeError as e:
-                logger.warning(e)
-                self._create_lookup_table()
-                return None
-            match, failure = self._test_cached_lookup_table(loaded_file)
-            if match:
-                logger.info('Loaded distance marginalisation lookup table from '
-                            '{}.'.format(filename))
-                return loaded_file
-            else:
-                logger.info('Loaded distance marginalisation lookup table does '
-                            'not match for {}.'.format(failure))
-        elif isinstance(filename, str):
-            logger.info('Distance marginalisation file {} does not '
-                        'exist'.format(filename))
-        return None
-
     def cache_lookup_table(self):
+        """
+        Record the tabulated distance prior used for marginalization.
+
+        There is no lookup table any more; this small file (a few tens of kB)
+        keeps workflows that read :code:`cached_lookup_table_filename`, such as
+        :code:`bilby_pipe`, working. Deprecated together with the lookup-table
+        argument and will be removed in bilby v3.1.0.
+        """
         np.savez(self.cached_lookup_table_filename,
                  distance_array=self._distance_array,
                  prior_array=self.distance_prior_array,
-                 lookup_table=self._dist_margd_loglikelihood_array,
                  reference_distance=self._ref_dist,
                  phase_marginalization=self.phase_marginalization)
 
-    def _test_cached_lookup_table(self, loaded_file):
-        pairs = dict(
-            distance_array=self._distance_array,
-            prior_array=self.distance_prior_array,
-            reference_distance=self._ref_dist,
-            phase_marginalization=self.phase_marginalization)
-        for key in pairs:
-            if key not in loaded_file:
-                return False, key
-            elif not np.allclose(np.atleast_1d(loaded_file[key]),
-                                 np.atleast_1d(pairs[key]),
-                                 rtol=1e-15):
-                return False, key
-        return True, None
-
-    def _create_lookup_table(self):
-        """ Make the lookup table """
-        from tqdm.auto import tqdm
-        logger.info('Building lookup table for distance marginalisation.')
-
-        self._dist_margd_loglikelihood_array = np.zeros((400, 800))
-        scaling = self._ref_dist / self._distance_array
-        d_inner_h_array_full = np.outer(self._d_inner_h_ref_array, scaling)
-        h_inner_h_array_full = np.outer(self._optimal_snr_squared_ref_array, scaling ** 2)
-        if self.phase_marginalization:
-            d_inner_h_array_full = ln_i0(abs(d_inner_h_array_full))
-        prior_term = self.distance_prior_array * self._delta_distance
-        for ii, optimal_snr_squared_array in tqdm(
-                enumerate(h_inner_h_array_full), total=len(self._optimal_snr_squared_ref_array)
-        ):
-            for jj, d_inner_h_array in enumerate(d_inner_h_array_full):
-                self._dist_margd_loglikelihood_array[ii][jj] = logsumexp(
-                    d_inner_h_array - optimal_snr_squared_array / 2,
-                    b=prior_term
-                )
-        log_norm = logsumexp(
-            0 / self._distance_array, b=self.distance_prior_array * self._delta_distance
-        )
-        self._dist_margd_loglikelihood_array -= log_norm
-        self.cache_lookup_table()
-
     def _setup_phase_marginalization(self, min_bound=-5, max_bound=10):
         logger.warning(
             "The _setup_phase_marginalization method is deprecated and will be removed, "
diff --git a/gw_requirements.txt b/gw_requirements.txt
index dded1efca..7c5bc194a 100644
--- a/gw_requirements.txt
+++ b/gw_requirements.txt
@@ -3,3 +3,4 @@ lalsuite
 gwpy
 pyfftw
 scikit-learn
+distmarg[rust]>=0.1.0
diff --git a/pixi.lock b/pixi.lock
index 451213db0..cf906ccd2 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -866,9 +866,11 @@ environments:
       - pypi: ./
       - pypi: https://files.pythonhosted.org/packages/09/dc/f3dfb7488b770f3f67e6545085bf2abea5172e88f57b8ad25ef860ca704c/myst_parser-5.1.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/23/cd/066e86230ae37ed0be70aae89aabf03ca8d9f39c8aea0dec8029455b5540/opt_einsum-3.4.0-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/28/f8/f73cf661fdb42b1fdb6186eeee9a7ede0af0e9259d96b260219a858adeb8/distmarg-0.1.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/31/ce/6c0dec3f7cc49efe66cbc611160496703e55961b1dd36fbfb558d300c558/nessai_bilby-0.3.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/34/c8/ea1a1084e0e6d92915bbe46da30940da4e231b3af440f348f7e144384b7c/nessai-0.15.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/36/ac/4ee28c65861605945223145fbcd3c9362ec2255ddf7d917574e205548c82/jaxlib-0.10.2-cp313-cp313-manylinux_2_27_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/3c/14/463e16d46d8c71a05744dab690240e6a34a1d60a91d580fec7604963dc66/distmarg_rs-0.1.0-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/40/ff/062a65c322e0dd4225a78627243b2925ebc902d5a6282871506d1d6767fd/bilby_cython-0.5.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/48/cb/99ee8021fa305d65b1fb369fabf96ccc65c60282407390e5355bb9e62f9f/zeus_mcmc-2.5.4-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/49/a6/cf084d85b8ce2b912337768d1181acf02a63264d18af674df1c84991392b/zeus_bilby-0.1.0-py3-none-any.whl
@@ -1323,9 +1325,11 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-ng-2.3.3-hed4e4f5_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda
       - pypi: ./
+      - pypi: https://files.pythonhosted.org/packages/00/fb/129607621b03fa3f0435275eb4741407e8bda7f94fc5e17bc73b16f5c5df/distmarg_rs-0.1.0-cp310-abi3-macosx_11_0_arm64.whl
       - pypi: https://files.pythonhosted.org/packages/05/7f/798705f5296a58ca505d600456748d1be48078eac8a7050d8a98bc9edb89/decorator-5.3.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/09/dc/f3dfb7488b770f3f67e6545085bf2abea5172e88f57b8ad25ef860ca704c/myst_parser-5.1.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/23/cd/066e86230ae37ed0be70aae89aabf03ca8d9f39c8aea0dec8029455b5540/opt_einsum-3.4.0-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/28/f8/f73cf661fdb42b1fdb6186eeee9a7ede0af0e9259d96b260219a858adeb8/distmarg-0.1.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/31/ce/6c0dec3f7cc49efe66cbc611160496703e55961b1dd36fbfb558d300c558/nessai_bilby-0.3.1-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/34/c8/ea1a1084e0e6d92915bbe46da30940da4e231b3af440f348f7e144384b7c/nessai-0.15.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/48/cb/99ee8021fa305d65b1fb369fabf96ccc65c60282407390e5355bb9e62f9f/zeus_mcmc-2.5.4-py3-none-any.whl
@@ -1788,7 +1792,9 @@ environments:
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+  name: distmarg-rs
+  version: 0.1.0
+  sha256: 2e7f2bcabe9747b2007e1a24fcdc3bf8328150fbac034752534754e4b7fef8cf
+  requires_dist:
+  - numpy>=1.24
+  requires_python: '>=3.10'
 - pypi: https://files.pythonhosted.org/packages/40/ff/062a65c322e0dd4225a78627243b2925ebc902d5a6282871506d1d6767fd/bilby_cython-0.5.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
   name: bilby-cython
   version: 0.5.4
diff --git a/pixi.toml b/pixi.toml
index 4234b3d85..848aeaa27 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -63,6 +63,7 @@ python-lalsimulation = "*"
 
 [feature.gw.pypi-dependencies]
 bilby-cython = ">=0.5.4"
+distmarg = { version = ">=0.1.0", extras = ["rust"] }
 
 [feature.jax.dependencies]
 numpy = "<2.5"
diff --git a/test/core/utils_test.py b/test/core/utils_test.py
index aee0c5437..bb83d92b2 100644
--- a/test/core/utils_test.py
+++ b/test/core/utils_test.py
@@ -381,49 +381,6 @@ def plot():
         self.assertTrue(os.path.isfile(self.filename))
 
 
-@pytest.mark.array_backend
-@pytest.mark.usefixtures("xp_class")
-class TestUnsortedInterp2d(unittest.TestCase):
-    def setUp(self):
-        if aac.is_torch_namespace(self.xp):
-            pytest.skip("Skipping Interp2d tests for torch backend")
-        self.xx = np.linspace(0, 1, 10)
-        self.yy = np.linspace(0, 1, 10)
-        self.zz = np.random.random((10, 10))
-        self.interpolant = bilby.core.utils.BoundedRectBivariateSpline(self.xx, self.yy, self.zz)
-
-    def tearDown(self):
-        pass
-
-    def test_returns_float_for_floats(self):
-        self.assertIsInstance(self.interpolant(0.5, 0.5), float)
-
-    def test_returns_none_for_floats_outside_range(self):
-        self.assertIsNone(self.interpolant(0.5, -0.5))
-        self.assertIsNone(self.interpolant(-0.5, 0.5))
-
-    def test_returns_float_for_float_and_array(self):
-        input_array = self.xp.asarray(np.random.random(10))
-        self.assertEqual(aac.get_namespace(self.interpolant(input_array, 0.5)), self.xp)
-        self.assertEqual(aac.get_namespace(
-            self.interpolant(input_array, input_array)), self.xp
-        )
-        self.assertEqual(aac.get_namespace(self.interpolant(0.5, input_array)), self.xp)
-
-    def test_raises_for_mismatched_arrays(self):
-        with self.assertRaises(ValueError):
-            self.interpolant(
-                self.xp.asarray(np.random.random(10)),
-                self.xp.asarray(np.random.random(20)),
-            )
-
-    def test_returns_fill_in_correct_place(self):
-        x_data = self.xp.asarray(np.random.random(10))
-        y_data = self.xp.asarray(np.random.random(10))
-        x_data = xpx.at(x_data, 3).set(-1)
-        self.assertTrue(self.xp.isnan(self.interpolant(x_data, y_data)[3]))
-
-
 @pytest.mark.array_backend
 @pytest.mark.usefixtures("xp_class")
 class TestTrapeziumRuleIntegration(unittest.TestCase):