diff --git a/clmm/__init__.py b/clmm/__init__.py
index f1a42f5ef..79f815a57 100644
--- a/clmm/__init__.py
+++ b/clmm/__init__.py
@@ -12,6 +12,7 @@
     compute_convergence,
     compute_excess_surface_density,
     compute_excess_surface_density_2h,
+    compute_excess_surface_density_triaxial,
     compute_magnification,
     compute_magnification_bias,
     compute_magnification_bias_from_magnification,
diff --git a/clmm/clusterensemble.py b/clmm/clusterensemble.py
index d37591545..4690a2ff9 100644
--- a/clmm/clusterensemble.py
+++ b/clmm/clusterensemble.py
@@ -31,14 +31,20 @@ class ClusterEnsemble:
 
         * "tan_sc" : tangential component computed with sample covariance
         * "cross_sc" : cross component computed with sample covariance
+        * "quad_4theta_sc" : quadrupole 4theta component computed with sample covariance
+        * "quad_const_sc" : quadrupole constant component computed with sample covariance
         * "tan_bs" : tangential component computed with bootstrap
         * "cross_bs" : cross component computed with bootstrap
+        * "quad_4theta_bs" : quadrupole 4theta component computed with bootstrap
+        * "quad_const_bs" : quadrupole constant component computed with bootstrap
         * "tan_jk" : tangential component computed with jackknife
         * "cross_jk" : cross component computed with jackknife
+        * "quad_4theta_jk" : quadrupole 4theta component computed with jackknife
+        * "quad_const_jk" : quadrupole constant component computed with jackknife
 
     """
 
-    def __init__(self, unique_id, gc_list=None, **kwargs):
+    def __init__(self, unique_id, gc_list=None, include_quadrupole=False, **kwargs):
         """Initializes a ClusterEnsemble object
         Parameters
         ----------
@@ -51,6 +57,7 @@ def __init__(self, unique_id, gc_list=None, **kwargs):
             raise TypeError(f"unique_id incorrect type: {type(unique_id)}")
         self.unique_id = unique_id
         self.data = GCData(meta={"bin_units": None, "radius_min": None, "radius_max": None})
+        self.include_quadrupole = include_quadrupole
         if gc_list is not None:
             self._add_values(gc_list, **kwargs)
             weights_out = kwargs.get("weights_out", "W_l")
@@ -63,10 +70,16 @@ def __init__(self, unique_id, gc_list=None, **kwargs):
         self.cov = {
             "tan_sc": None,
             "cross_sc": None,
+            "quad_4theta_sc": None,
+            "quad_const_sc": None,
             "tan_bs": None,
             "cross_bs": None,
+            "quad_4theta_bs": None,
+            "quad_const_bs": None,
             "tan_jk": None,
             "cross_jk": None,
+            "quad_4theta_jk": None,
+            "quad_const_jk": None,
         }
 
     def _add_values(self, gc_list, **kwargs):
@@ -90,6 +103,7 @@ def __len__(self):
         """Returns length of ClusterEnsemble"""
         return len(self.data)
 
+    # pylint: disable=R0914
     def make_individual_radial_profile(
         self,
         galaxycluster,
@@ -99,10 +113,16 @@ def make_individual_radial_profile(
         cosmo=None,
         tan_component_in="et",
         cross_component_in="ex",
+        quad_4theta_component_in="e_quad_4theta",
+        quad_const_component_in="e_quad_const",
         tan_component_out="gt",
         cross_component_out="gx",
+        quad_4theta_component_out="g_quad_4theta",
+        quad_const_component_out="g_quad_const",
         tan_component_in_err=None,
         cross_component_in_err=None,
+        quad_4theta_component_in_err=None,
+        quad_const_component_in_err=None,
         use_weights=True,
         weights_in="w_ls",
         weights_out="W_l",
@@ -137,18 +157,38 @@ def make_individual_radial_profile(
         cross_component_in: string, optional
             Name of the cross component column in `galcat` to be binned.
             Default: 'ex'
+        quad_4theta_component_in: string, optional
+            Name of the quadrupole 4theta component column in `galcat` to be binned.
+            Default: 'e_quad_4theta'
+        quad_const_component_in: string, optional
+            Name of the quadrupole constant component column in `galcat` to be binned.
+            Default: 'e_quad_const'
         tan_component_out: string, optional
             Name of the tangetial component binned column to be added in profile table.
             Default: 'gt'
         cross_component_out: string, optional
             Name of the cross component binned profile column to be added in profile table.
             Default: 'gx'
+        quad_4theta_component_out: string, optional
+            Name of the quadrupole 4theta component binned profile column to be added
+            in profile table.
+            Default: 'g_quad_4theta'
+        quad_const_component_out: string, optional
+            Name of the quadrupole constant component binned profile column to be added
+            in profile table.
+            Default: 'g_quad_const'
         tan_component_in_err: string, None, optional
             Name of the tangential component error column in `galcat` to be binned.
             Default: None
         cross_component_in_err: string, None, optional
             Name of the cross component error column in `galcat` to be binned.
             Default: None
+        quad_4theta_component_in_err: string, None, optional
+            Name of the quadrupole 4theta component error column in `galcat` to be binned.
+            Default: None
+        quad_const_component_in_err: string, None, optional
+            Name of the quadrupole constant component error column in `galcat` to be binned.
+            Default: None
         weights_in : str, None
             Name of the weight column in `galcat` to be considered in binning.
         weights_out : str, None
@@ -170,13 +210,14 @@ def make_individual_radial_profile(
         profile_table = galaxycluster.make_radial_profile(
             include_empty_bins=True, gal_ids_in_bins=False, add=False, **tb_kwargs
         )
-
         self.add_individual_radial_profile(
             galaxycluster,
             profile_table,
-            tan_component_out,
-            cross_component_out,
-            weights_out,
+            tan_component=tan_component_out,
+            cross_component=cross_component_out,
+            quad_4theta_component=quad_4theta_component_out,
+            quad_const_component=quad_const_component_out,
+            weights=weights_out,
         )
 
     def add_individual_radial_profile(
@@ -185,6 +226,8 @@ def add_individual_radial_profile(
         profile_table,
         tan_component="gt",
         cross_component="gx",
+        quad_4theta_component="g_quad_4theta",
+        quad_const_component="g_quad_const",
         weights="W_l",
     ):
         """Compute the individual shear profile from a single GalaxyCluster object
@@ -203,6 +246,12 @@ def add_individual_radial_profile(
         cross_component: string, optional
             Name of the cross component binned profile column in the profile table.
             Default: 'gx'
+        quad_4theta_component: string, optional
+            Name of the quadrupole 4theta component binned profile column in the profile table.
+            Default: 'g_quad_4theta'
+        quad_const_component: string, optional
+            Name of the quadrupole constant component binned profile column in the profile table.
+            Default: 'g_quad_const'
         weights : str, None
             Name of the weight binned column in the profile table.
         """
@@ -215,7 +264,11 @@ def add_individual_radial_profile(
         cl_cosmo = profile_table.meta.get("cosmo", None)
         self.data.update_info_ext_valid("cosmo", self.data, cl_cosmo, overwrite=False)
 
-        tbcols = ("radius", tan_component, cross_component, weights)
+        _quad_cols = ()
+        if self.include_quadrupole:
+            _quad_cols = (quad_4theta_component, quad_const_component)
+        tbcols = ("radius", tan_component, cross_component, *_quad_cols, weights)
+
         data_to_save = [
             galaxycluster.unique_id,
             galaxycluster.ra,
@@ -238,7 +291,14 @@ def _check_empty_data(self):
                 "for each cluster in your catalog"
             )
 
-    def make_stacked_radial_profile(self, tan_component="gt", cross_component="gx", weights="W_l"):
+    def make_stacked_radial_profile(
+        self,
+        tan_component="gt",
+        cross_component="gx",
+        quad_4theta_component="g_quad_4theta",
+        quad_const_component="g_quad_const",
+        weights="W_l",
+    ):
         """Computes stacked profile and mean separation distances and add it internally
         to `stacked_data`.
 
@@ -250,15 +310,25 @@ def make_stacked_radial_profile(self, tan_component="gt", cross_component="gx",
         cross_component : string, optional
             Name of the cross component column in `data`.
             Default: 'gx'
+        quad_4theta_component : string, optional
+            Name of the quadrupole 4theta component column in `data`.
+            Default: 'g_quad_4theta'
+        quad_const_component : string, optional
+            Name of the quadrupole constant component column in `data`.
+            Default: 'g_quad_const'
         weights : str
             Name of the weights column in `data`.
         """
         self._check_empty_data()
 
+        _col_names = [tan_component, cross_component]
+        if self.include_quadrupole:
+            _col_names += [quad_4theta_component, quad_const_component]
+
         radius, components = make_stacked_radial_profile(
             self.data["radius"],
             self.data[weights],
-            [self.data[tan_component], self.data[cross_component]],
+            [self.data[_col] for _col in _col_names],
         )
         self.stacked_data = GCData(
             [
@@ -268,10 +338,16 @@ def make_stacked_radial_profile(self, tan_component="gt", cross_component="gx",
                 *components,
             ],
             meta={k: v for k, v in self.data.meta.items() if k not in ("radius_min", "radius_max")},
-            names=("radius_min", "radius_max", "radius", tan_component, cross_component),
+            names=("radius_min", "radius_max", "radius", *_col_names),
         )
 
-    def compute_sample_covariance(self, tan_component="gt", cross_component="gx"):
+    def compute_sample_covariance(
+        self,
+        tan_component="gt",
+        cross_component="gx",
+        quad_4theta_component="g_quad_4theta",
+        quad_const_component="g_quad_const",
+    ):
         """Compute Sample covariance matrix for cross and tangential and cross
         stacked profiles and updates .cov dict (`tan_sc`, `cross_sc`).
 
@@ -283,15 +359,33 @@ def compute_sample_covariance(self, tan_component="gt", cross_component="gx"):
         cross_component : string, optional
             Name of the cross component column in `data`.
             Default: 'gx'
+        quad_4theta_component : string, optional
+            Name of the quadrupole 4theta component column in `data`.
+            Default: 'g_quad_4theta'
+        quad_const_component : string, optional
+            Name of the quadrupole constant component column in `data`.
+            Default: 'g_quad_const'
         """
         self._check_empty_data()
 
+        _components = [("tan_sc", tan_component), ("cross_sc", cross_component)]
+        if self.include_quadrupole:
+            _components += [
+                ("quad_4theta_sc", quad_4theta_component),
+                ("quad_const_sc", quad_const_component),
+            ]
+
         n_catalogs = len(self.data)
-        self.cov["tan_sc"] = np.cov(self.data[tan_component].T, bias=False) / n_catalogs
-        self.cov["cross_sc"] = np.cov(self.data[cross_component].T, bias=False) / n_catalogs
+        for _cov_name_out, _col_name_in in _components:
+            self.cov[_cov_name_out] = np.cov(self.data[_col_name_in].T, bias=False) / n_catalogs
 
     def compute_bootstrap_covariance(
-        self, tan_component="gt", cross_component="gx", n_bootstrap=10
+        self,
+        tan_component="gt",
+        cross_component="gx",
+        quad_4theta_component="g_quad_4theta",
+        quad_const_component="g_quad_const",
+        n_bootstrap=10,
     ):
         """Compute the bootstrap covariance matrix, add boostrap covariance matrix for
         tangential and cross stacked profiles and updates .cov dict (`tan_jk`, `cross_bs`).
@@ -304,6 +398,12 @@ def compute_bootstrap_covariance(
         cross_component : string, optional
             Name of the cross component column in `data`.
             Default: 'gx'
+        quad_4theta_component : string, optional
+            Name of the quadrupole 4theta component column in `data`.
+            Default: 'g_quad_4theta'
+        quad_const_component : string, optional
+            Name of the quadrupole constant component column in `data`.
+            Default: 'g_quad_const'
         n_bootstrap : int
             number of bootstrap resamplings
         """
@@ -314,21 +414,35 @@ def compute_bootstrap_covariance(
         cluster_index_bootstrap = [
             np.random.choice(cluster_index, n_catalogs) for n_boot in range(n_bootstrap)
         ]
+        _shear_components = [tan_component, cross_component]
+        if self.include_quadrupole:
+            _shear_components += [quad_4theta_component, quad_const_component]
 
-        gt_boot, gx_boot = make_stacked_radial_profile(
+        g_boot_components = make_stacked_radial_profile(
             self["radius"][None, cluster_index_bootstrap][0].transpose(1, 2, 0),
             self["W_l"][None, cluster_index_bootstrap][0].transpose(1, 2, 0),
             [
-                self[tan_component][None, cluster_index_bootstrap][0].transpose(1, 2, 0),
-                self[cross_component][None, cluster_index_bootstrap][0].transpose(1, 2, 0),
+                self[_component][None, cluster_index_bootstrap][0].transpose(1, 2, 0)
+                for _component in _shear_components
             ],
         )[1]
 
         coeff = (n_catalogs / (n_catalogs - 1)) ** 2
-        self.cov["tan_bs"] = coeff * np.cov(np.array(gt_boot), bias=False, ddof=0)
-        self.cov["cross_bs"] = coeff * np.cov(np.array(gx_boot), bias=False)
-
-    def compute_jackknife_covariance(self, tan_component="gt", cross_component="gx", n_side=16):
+        self.cov["tan_bs"] = coeff * np.cov(np.array(g_boot_components[0]), bias=False, ddof=0)
+        self.cov["cross_bs"] = coeff * np.cov(np.array(g_boot_components[1]), bias=False)
+        if self.include_quadrupole:
+            _, _, g4theta_boot, gconst_boot = g_boot_components
+            self.cov["quad_4theta_bs"] = coeff * np.cov(np.array(g4theta_boot), bias=False, ddof=0)
+            self.cov["quad_const_bs"] = coeff * np.cov(np.array(gconst_boot), bias=False, ddof=0)
+
+    def compute_jackknife_covariance(
+        self,
+        tan_component="gt",
+        cross_component="gx",
+        quad_4theta_component="g_quad_4theta",
+        quad_const_component="g_quad_const",
+        n_side=16,
+    ):
         """Compute the jackknife covariance matrix, add boostrap covariance matrix for
         tangential and cross stacked profiles and updates .cov dict (`tan_jk`, `cross_jk`).
 
@@ -342,6 +456,12 @@ def compute_jackknife_covariance(self, tan_component="gt", cross_component="gx",
         cross_component : string, optional
             Name of the cross component column in `data`.
             Default: 'gx'
+        quad_4theta_component : string, optional
+            Name of the quadrupole 4theta component column in `data`.
+            Default: 'g_quad_4theta'
+        quad_const_component : string, optional
+            Name of the quadrupole constant component column in `data`.
+            Default: 'g_quad_const'
         n_side : int
             healpix sky area division parameter (number of sky area : 12*n_side^2)
         """
@@ -352,19 +472,44 @@ def compute_jackknife_covariance(self, tan_component="gt", cross_component="gx",
         pixels = healpy.ang2pix(n_side, self.data["ra"], self.data["dec"], nest=True, lonlat=True)
         pixels_list_unique = np.unique(pixels)
         gt_jack, gx_jack = [], []
+        if self.include_quadrupole:
+            g4theta_jack, gconst_jack = [], []
         for hp_list_delete in pixels_list_unique:
             mask = ~np.isin(pixels, hp_list_delete)
-            gt, gx = make_stacked_radial_profile(
-                self["radius"][mask],
-                self["W_l"][mask],
-                [self[tan_component][mask], self[cross_component][mask]],
-            )[1]
-            gt_jack.append(gt)
-            gx_jack.append(gx)
+            if self.include_quadrupole:
+                g_components = make_stacked_radial_profile(
+                    self["radius"][mask],
+                    self["W_l"][mask],
+                    [
+                        self[tan_component][mask],
+                        self[cross_component][mask],
+                        self[quad_4theta_component][mask],
+                        self[quad_const_component][mask],
+                    ],
+                )[1]
+                gt_jack.append(g_components[0])
+                gx_jack.append(g_components[1])
+                g4theta_jack.append(g_components[2])
+                gconst_jack.append(g_components[3])
+            else:
+                g_components = make_stacked_radial_profile(
+                    self["radius"][mask],
+                    self["W_l"][mask],
+                    [self[tan_component][mask], self[cross_component][mask]],
+                )[1]
+                gt_jack.append(g_components[0])
+                gx_jack.append(g_components[1])
         n_jack = pixels_list_unique.size
         coeff = (n_jack - 1) ** 2 / (n_jack)
         self.cov["tan_jk"] = coeff * np.cov(np.transpose(gt_jack), bias=False, ddof=0)
         self.cov["cross_jk"] = coeff * np.cov(np.transpose(gx_jack), bias=False, ddof=0)
+        if self.include_quadrupole:
+            self.cov["quad_4theta_jk"] = coeff * np.cov(
+                np.transpose(g4theta_jack), bias=False, ddof=0
+            )
+            self.cov["quad_const_jk"] = coeff * np.cov(
+                np.transpose(gconst_jack), bias=False, ddof=0
+            )
 
     def save(self, filename, **kwargs):
         """Saves GalaxyCluster object to filename using Pickle"""
diff --git a/clmm/dataops/__init__.py b/clmm/dataops/__init__.py
index 6af06c4c2..769b857b6 100644
--- a/clmm/dataops/__init__.py
+++ b/clmm/dataops/__init__.py
@@ -11,6 +11,7 @@
     _integ_pzfuncs,
     _validate_coordinate_system,
     _validate_dec,
+    _validate_include_quadrupole_phi_major,
     _validate_is_deltasigma_sigma_c,
     _validate_ra,
     arguments_consistency,
@@ -32,6 +33,9 @@ def compute_tangential_and_cross_components(
     geometry="curve",
     is_deltasigma=False,
     sigma_c=None,
+    include_quadrupole=False,
+    phi_major=None,
+    info_mem=None,
     validate_input=True,
 ):
     r"""Computes tangential- and cross- components for shear or ellipticity
@@ -71,6 +75,14 @@ def compute_tangential_and_cross_components(
         g_t =& -\left( g_1\cos\left(2\phi\right)+g_2\sin\left(2\phi\right)\right)\\
         g_x =& g_1 \sin\left(2\phi\right)-g_2\cos\left(2\phi\right)
 
+    The quadrupole ellipticity/shear components, :math:`g_4theta` and :math:`g_const`, 
+    are also calculated using the two ellipticity/shear components :math:`g_1` and :math:`g_2` 
+    of the source galaxies, following Eq.31 and Eq.34 in Shin et al. (2018), arXiv:1705.11167. 
+
+    .. math::
+        g_4theta =& \left( g_1\cos\left(4\phi\right)+g_2\sin\left(4\phi\right)\right)\\
+        g_const =& g_1
+
     Finally, if  the critical surface density (:math:`\Sigma_\text{crit}`) is provided, an estimate
     of the excess surface density :math:`\widehat{\Delta\Sigma}` is obtained from
 
@@ -96,9 +108,9 @@ def compute_tangential_and_cross_components(
     shear2: array
         The measured shear (or reduced shear or ellipticity) of the source galaxies
     coordinate_system: str, optional
-        Coordinate system of the ellipticity components. Must be either 'celestial' or
-        euclidean'. See https://doi.org/10.48550/arXiv.1407.7676 section 5.1 for more details.
-        If not set, defaults to 'euclidean'.
+        Coordinate system of the ellipticity components. Must be either `celestial` or
+        `euclidean`. See https://doi.org/10.48550/arXiv.1407.7676 section 5.1 for more details.
+        Default is `euclidean`.
     geometry: str, optional
         Sky geometry to compute angular separation.
         Options are curve (uses astropy) or flat.
@@ -108,6 +120,21 @@ def compute_tangential_and_cross_components(
     sigma_c : None, array_like
         Critical (effective) surface density in units of :math:`M_\odot\ Mpc^{-2}`.
         Used only when is_deltasigma=True.
+    include_quadrupole: bool
+        If `True`, the quadrupole shear components (g_4theta, g_const; Shin+2018) are calculated
+    phi_major : float, optional
+        the direction of the major axis of the input cluster in the unit of radian. 
+        only needed when `include_quadrupole` is `True`.
+        This quantity is always in Euclidean coordinates, for celestial coordinates only set
+        the coordinate_system=`celestial`.
+        Users could choose to provide ra_mem, dec_mem and weight_mem instead of this quantity.
+    info_mem : list of array, optional
+        RAs, DECs and weights of the member galaxies of the input cluster,
+        to calcualte the direction of the major axis of the cluster.
+        Only needed when `include_quadrupole` is `True` and `phi_major` is not provided.
+        The shape must be in `[ra_mem,dec_mem,weight_mem]`, where each element is an array.
+        The weights could be `1` (no weights) or 
+        `membership probability (p_mem)` or any user choice. 
     validate_input: bool
         Validade each input argument
 
@@ -119,8 +146,13 @@ def compute_tangential_and_cross_components(
         Tangential shear (or assimilated quantity) for each source galaxy
     cross_component: array_like
         Cross shear (or assimilated quantity) for each source galaxy
+    IF include_quadrupole:
+        4theta_component: array_like
+            4theta shear component (or assimilated quantity) for each source galaxy
+        const_component: array_like
+            constant shear component (or assimilated quantity) for each source galaxy
     """
-    # pylint: disable-msg=too-many-locals
+    # pylint: disable-msg=too-many-locals,too-many-branches
     # Note: we make these quantities to be np.array so that a name is not passed from astropy
     # columns
     if coordinate_system is None:
@@ -135,6 +167,8 @@ def compute_tangential_and_cross_components(
         validate_argument(locals(), "shear1", "float_array")
         validate_argument(locals(), "shear2", "float_array")
         validate_argument(locals(), "geometry", str)
+        validate_argument(locals(), "is_deltasigma", bool)
+        validate_argument(locals(), "include_quadrupole", bool)
         validate_argument(locals(), "sigma_c", "float_array", none_ok=True)
         _validate_coordinate_system(locals(), "coordinate_system")
         ra_source_, dec_source_, shear1_, shear2_ = arguments_consistency(
@@ -143,6 +177,9 @@ def compute_tangential_and_cross_components(
             prefix="Tangential- and Cross- shape components sources",
         )
         _validate_is_deltasigma_sigma_c(is_deltasigma, sigma_c)
+        _validate_include_quadrupole_phi_major(include_quadrupole, phi_major, info_mem)
+        validate_argument(locals(), "phi_major", float, none_ok=True)
+        validate_argument(locals(), "info_mem", list, none_ok=True)
     elif np.iterable(ra_source):
         ra_source_, dec_source_, shear1_, shear2_ = (
             np.array(col) for col in [ra_source, dec_source, shear1, shear2]
@@ -168,12 +205,31 @@ def compute_tangential_and_cross_components(
     # Compute the tangential and cross shears
     tangential_comp = _compute_tangential_shear(shear1_, shear2_, phi)
     cross_comp = _compute_cross_shear(shear1_, shear2_, phi)
-
+    # If the is_deltasigma flag is True, multiply the results by Sigma_crit.
     if sigma_c is not None:
         _sigma_c_arr = np.array(sigma_c)
         tangential_comp *= _sigma_c_arr
         cross_comp *= _sigma_c_arr
 
+    if include_quadrupole:
+        if (phi_major is None) and (info_mem is None):
+            raise ValueError("Either phi_major or (ra_mem, dec_mem, weight_mem) should be provided")
+        if phi_major is None:
+            # info_mem=[ra_mem,dec_mem,weight_mem]
+            phi_major = calculate_major_axis(
+                ra_lens, dec_lens, info_mem[0], info_mem[1], info_mem[2]
+            )
+        if coordinate_system == "celestial":
+            phi_major = np.pi - phi_major
+        rotated_shear1, rotated_shear2 = _rotate_shear(shear1_, shear2_, phi_major)
+        # Compute the quadrupole shear components
+        four_theta_comp = _compute_4theta_shear(rotated_shear1, rotated_shear2, phi - phi_major)
+        const_comp = rotated_shear1
+        # If the is_deltasigma flag is True, multiply the results by Sigma_crit.
+        if sigma_c is not None:
+            four_theta_comp *= _sigma_c_arr
+            const_comp *= _sigma_c_arr
+        return angsep, tangential_comp, cross_comp, four_theta_comp, const_comp
     return angsep, tangential_comp, cross_comp
 
 
@@ -446,6 +502,51 @@ def _compute_lensing_angles_astropy(
     return angsep, phi
 
 
+def calculate_major_axis(ra_lens_, dec_lens_, ra_mem_, dec_mem_, weight_mem_):
+    r"""Compute the major axis of a given cluster from the distribution of
+    its member galaxies using the position second moments.
+
+    The computation is done according to Eq. 5-10 of Shin et al. 2018, arXiv:1705.11167
+
+    Current implementation assumes the +RA direction is aligned with +x direction.
+
+    For extended descriptions of parameters, see `compute_shear()` documentation.
+    """
+    ind_bcg = (np.array(ra_mem_) == ra_lens_) * (np.array(dec_mem_) == dec_lens_)
+    sk_lens = SkyCoord(ra_lens_ * u.deg, dec_lens_ * u.deg, frame="icrs")
+    sk_mem = SkyCoord(
+        np.array(ra_mem_)[~ind_bcg] * u.deg, np.array(dec_mem_)[~ind_bcg] * u.deg, frame="icrs"
+    )
+    position_angle_mem = np.array(sk_lens.position_angle(sk_mem).radian + np.pi / 2.0)
+    separation_mem = np.array(sk_lens.separation(sk_mem).degree)
+    x_mem = separation_mem * np.cos(position_angle_mem)
+    y_mem = separation_mem * np.sin(position_angle_mem)
+    distance_weight_mem = 1.0 / (separation_mem**2)
+    weight_total_mem = np.array(weight_mem_) * distance_weight_mem
+    sum_weight_total_mem = np.sum(weight_total_mem)
+    # Calcualte second moments of the member galaxies
+    ixx = np.sum(x_mem**2 * weight_total_mem) / sum_weight_total_mem
+    iyy = np.sum(y_mem**2 * weight_total_mem) / sum_weight_total_mem
+    ixy = np.sum(x_mem * y_mem * weight_total_mem) / sum_weight_total_mem
+    # Transformation of the second moments to the direction of the major axis
+    phi_major = np.arctan2(2.0 * ixy, ixx - iyy) / 2.0
+    return phi_major
+
+
+def _rotate_shear(shear1, shear2, phi_major):
+    r"""Rotate shear components into the coordinate where +x axis is aligned with
+    the cluster major axis.
+
+    .. math::
+        g_rotated = g * \exp\left(-2i\phi\right)
+
+    For extended descriptions of parameters, see `compute_shear()` documentation.
+    """
+    shear_vec = shear1 + shear2 * 1.0j
+    rotated_shear_vec = shear_vec * np.exp(-2.0j * phi_major)
+    return np.real(rotated_shear_vec), np.imag(rotated_shear_vec)
+
+
 def _compute_tangential_shear(shear1, shear2, phi):
     r"""Compute the tangential shear given the two shears and azimuthal positions for
     a single source or list of sources.
@@ -475,6 +576,25 @@ def _compute_cross_shear(shear1, shear2, phi):
     return shear1 * np.sin(2.0 * phi) - shear2 * np.cos(2.0 * phi)
 
 
+def _compute_4theta_shear(shear1, shear2, phi):
+    r"""Compute the 4theta component of the quadrupole shear given the two shear components and
+    azimuthal positions for a single source or list of sources.
+
+    We compute the tangential shear following Eq. 31 of Shin et al. 2018, arXiv:1705.11167
+
+    .. math::
+        g_4theta = g_1\cos\left(4\phi\right)+g_2\sin\left(4\phi\right)
+
+    Note that here `\phi` is the position angle of the source galaxies
+    with respect to the major axis of the cluster.
+    Also, `shear1` and `shear2` are measured in the coordinate where
+    the +x axis is aligned with the major axis of the cluster.
+
+    For extended descriptions of parameters, see `compute_shear()' documentation.
+    """
+    return shear1 * np.cos(4.0 * phi) + shear2 * np.sin(4.0 * phi)
+
+
 def make_radial_profile(
     components,
     angsep,
@@ -656,7 +776,7 @@ def make_stacked_radial_profile(angsep, weights, components):
     Parameters
     ----------
     angsep: 2d array
-        Transvesal distances corresponding to each object with shape `n_obj, n_rad_bins`.
+        Transversal distances corresponding to each object with shape `n_obj, n_rad_bins`.
     weights: 2d array
         Weights corresponding to each objects with shape `n_obj, n_rad_bins`.
     components: list of 2d arrays
diff --git a/clmm/galaxycluster.py b/clmm/galaxycluster.py
index a162804fe..2cf77dc22 100644
--- a/clmm/galaxycluster.py
+++ b/clmm/galaxycluster.py
@@ -39,15 +39,21 @@ class GalaxyCluster:
         Table of background galaxy data containing at least galaxy_id, ra, dec, e1, e2, z
     validate_input: bool
         Validade each input argument
+    include_quadrupole: bool
+        If quadrupole WL be calculated.
     """
 
-    def __init__(self, *args, validate_input=True, **kwargs):
+    # pylint: disable=too-many-instance-attributes
+    # Eight is reasonable in this case.
+
+    def __init__(self, *args, validate_input=True, include_quadrupole=False, **kwargs):
         self.unique_id = None
         self.ra = None
         self.dec = None
         self.z = None
         self.galcat = None
         self.validate_input = validate_input
+        self.include_quadrupole = include_quadrupole
         if len(args) > 0 or len(kwargs) > 0:
             self._add_values(*args, **kwargs)
             self._check_types()
@@ -217,11 +223,15 @@ def compute_tangential_and_cross_components(
         shape_component2="e2",
         tan_component="et",
         cross_component="ex",
+        quad_4theta_component="e_quad_4theta",
+        quad_const_component="e_quad_const",
         geometry="curve",
         is_deltasigma=False,
         use_pdz=False,
         cosmo=None,
         add=True,
+        phi_major=None,
+        info_mem=None,
     ):
         r"""Adds a tangential- and cross- components for shear or ellipticity to self
 
@@ -235,6 +245,9 @@ def compute_tangential_and_cross_components(
         geometry: `input` geometry
         is_deltasigma: `input` is_deltasigma
         coordinate_system: `galcat` coordinate_system
+        include_quadrupole: `input` include_quadrupole
+        phi_major: `cluster` major axis direction (in radian with respect to +x)
+        info_mem: `cluster` [RAs, DECs, weights] of member galaxies as a list of array
 
         Parameters
         ----------
@@ -252,6 +265,16 @@ def compute_tangential_and_cross_components(
             Name of the column to be added to the `galcat` astropy table that will contain the
             cross component computed from columns `shape_component1` and `shape_component2`.
             Default: `ex`
+        quad_4theta_component: string, optional
+            Name of the column to be added to the `galcat` astropy table that will contain the
+            4theta quarupole component computed from columns `shape_component1`
+            and `shape_component2`.
+            Default: `e_quad_4theta`
+        quad_const_component: string, optional
+            Name of the column to be added to the `galcat` astropy table that will contain the
+            constant quarupole component computed from columns `shape_component1`
+            and `shape_component2`.
+            Default: `e_quad_const`
         geometry: str, optional
             Sky geometry to compute angular separation.
             Options are curve (uses astropy) or flat.
@@ -259,9 +282,16 @@ def compute_tangential_and_cross_components(
             If `True`, the tangential and cross components returned are multiplied by Sigma_crit.
             Results in units of :math:`M_\odot\ Mpc^{-2}`
         cosmo: astropy cosmology object
-            Specifying a cosmology is required if `is_deltasigma` is True
+            Specifying a cosmology is required if `is_deltasigma` is `True`
         add: bool
             If `True`, adds the computed shears to the `galcat`
+        phi_major: string, optional
+            `cluster` major axis direction (in radian with respect to +x).
+            If include_quadrupole is `True`, either phi_major or info_mem needs to be supplied.
+        info_mem: string, optional
+            `cluster` [RAs, DECs, weights] of member galaxies as a list of array,
+            for calculating major axis of a given cluster.
+            If include_quadrupole is `True`, either phi_major or info_mem needs to be supplied.
 
         Returns
         -------
@@ -271,6 +301,12 @@ def compute_tangential_and_cross_components(
             Tangential shear (or assimilated quantity) for each source galaxy
         cross_component: array_like
             Cross shear (or assimilated quantity) for each source galaxy
+        quad_4theta_component: array_like
+            4theta quadrupole shear (or assimilated quantity) for each source galaxy
+            Returned only if include_quadrupole is `True`.
+        quad_const_component: array_like
+            constatnt quadrupole shear (or assimilated quantity) for each source galaxy
+            Returned only if include_quadrupole is `True`.
         """
         # Check is all the required data is available
         col_dict = {
@@ -285,7 +321,40 @@ def compute_tangential_and_cross_components(
         cols = self._get_input_galdata(col_dict)
 
         # compute shears
-        angsep, tangential_comp, cross_comp = compute_tangential_and_cross_components(
+        if self.include_quadrupole:
+            angsep_and_components = compute_tangential_and_cross_components(
+                is_deltasigma=is_deltasigma,
+                ra_lens=self.ra,
+                dec_lens=self.dec,
+                geometry=geometry,
+                validate_input=self.validate_input,
+                include_quadrupole=self.include_quadrupole,
+                phi_major=phi_major,
+                info_mem=info_mem,
+                coordinate_system=self.galcat.meta["coordinate_system"],
+                **cols,
+            )
+            if add:
+                self.galcat["theta"] = angsep_and_components[0]
+                self.galcat[tan_component] = angsep_and_components[1]
+                self.galcat[cross_component] = angsep_and_components[2]
+                self.galcat[quad_4theta_component] = angsep_and_components[3]
+                self.galcat[quad_const_component] = angsep_and_components[4]
+                if is_deltasigma:
+                    sigmac_type = "effective" if use_pdz else "standard"
+                    self.galcat.meta[f"{tan_component}_sigmac_type"] = sigmac_type
+                    self.galcat.meta[f"{cross_component}_sigmac_type"] = sigmac_type
+                    self.galcat.meta[f"{quad_4theta_component}_sigmac_type"] = sigmac_type
+                    self.galcat.meta[f"{quad_const_component}_sigmac_type"] = sigmac_type
+            return (
+                angsep_and_components[0],
+                angsep_and_components[1],
+                angsep_and_components[2],
+                angsep_and_components[3],
+                angsep_and_components[4],
+            )
+
+        angsep_and_components = compute_tangential_and_cross_components(
             is_deltasigma=is_deltasigma,
             ra_lens=self.ra,
             dec_lens=self.dec,
@@ -295,14 +364,14 @@ def compute_tangential_and_cross_components(
             **cols,
         )
         if add:
-            self.galcat["theta"] = angsep
-            self.galcat[tan_component] = tangential_comp
-            self.galcat[cross_component] = cross_comp
+            self.galcat["theta"] = angsep_and_components[0]
+            self.galcat[tan_component] = angsep_and_components[1]
+            self.galcat[cross_component] = angsep_and_components[2]
             if is_deltasigma:
                 sigmac_type = "effective" if use_pdz else "standard"
                 self.galcat.meta[f"{tan_component}_sigmac_type"] = sigmac_type
                 self.galcat.meta[f"{cross_component}_sigmac_type"] = sigmac_type
-        return angsep, tangential_comp, cross_comp
+        return angsep_and_components[0], angsep_and_components[1], angsep_and_components[2]
 
     def compute_background_probability(
         self, use_pdz=False, add=True, p_background_name="p_background"
@@ -479,10 +548,16 @@ def make_radial_profile(
         cosmo=None,
         tan_component_in="et",
         cross_component_in="ex",
+        quad_4theta_component_in="e_quad_4theta",
+        quad_const_component_in="e_quad_const",
         tan_component_out="gt",
         cross_component_out="gx",
+        quad_4theta_component_out="g_quad_4theta",
+        quad_const_component_out="g_quad_const",
         tan_component_in_err=None,
         cross_component_in_err=None,
+        quad_4theta_component_in_err=None,
+        quad_const_component_in_err=None,
         include_empty_bins=False,
         gal_ids_in_bins=False,
         add=True,
@@ -498,7 +573,8 @@ def make_radial_profile(
         tangential shears or ellipticities and angular separation of the source galaxies
 
         Calls `clmm.dataops.make_radial_profile` with the following arguments:
-        components: `galcat` components (tan_component_in, cross_component_in, z)
+        components: `galcat` components (tan_component_in, cross_component_in, z) OR
+        (quad_4theta_component_in, quad_const_component_in, z) IF include_quadrupole
         angsep: `galcat` theta
         angsep_units: 'radians'
         bin_units: `input` bin_units
@@ -532,18 +608,37 @@ def make_radial_profile(
         cross_component_in: string, optional
             Name of the cross component column in `galcat` to be binned.
             Default: 'ex'
+        quad_4theta_component_in: string, optional
+            Name of the 4theta quadrupole component column in `galcat` to be binned.
+            Default: 'e_quad_4theta'
+        quad_const_component_in: string, optional
+            Name of the constant quadrupole component column in `galcat` to be binned.
+            Default: 'e_quad_const'
         tan_component_out: string, optional
             Name of the tangetial component binned column to be added in profile table.
             Default: 'gt'
         cross_component_out: string, optional
             Name of the cross component binned profile column to be added in profile table.
             Default: 'gx'
+        quad_4theta_component_out: string, optional
+            Name of the 4theta quadrupole component binned column to be added in profile table.
+            Default: 'g_quad_4theta'
+        quad_const_component_out: string, optional
+            Name of the constant quadrupole component binned profile column
+            to be added in profile table.
+            Default: 'g_quad_const'
         tan_component_in_err: string, None, optional
             Name of the tangential component error column in `galcat` to be binned.
             Default: None
         cross_component_in_err: string, None, optional
             Name of the cross component error column in `galcat` to be binned.
             Default: None
+        quad_4theta_component_in_err: string, None, optional
+            Name of the 4theta quadrupole component error column in `galcat` to be binned.
+            Default: None
+        quad_const_component_in_err: string, None, optional
+            Name of the constant quadrupole component error column in `galcat` to be binned.
+            Default: None
         include_empty_bins: bool, optional
             Also include empty bins in the returned table
         gal_ids_in_bins: bool, optional
@@ -572,10 +667,40 @@ def make_radial_profile(
         """
         # Too many local variables (19/15)
         # pylint: disable=R0914
-
-        if not all(
-            t_ in self.galcat.columns for t_ in (tan_component_in, cross_component_in, "theta")
-        ):
+        input_var_plus_theta = (tan_component_in, cross_component_in, "theta")
+        input_var_plus_z = (tan_component_in, cross_component_in, "z")
+        input_err = (tan_component_in_err, cross_component_in_err, None)
+        output_var_plus_z = [tan_component_out, cross_component_out, "z"]
+        if self.include_quadrupole:
+            input_var_plus_theta = (
+                tan_component_in,
+                cross_component_in,
+                quad_4theta_component_in,
+                quad_const_component_in,
+                "theta",
+            )
+            input_var_plus_z = (
+                tan_component_in,
+                cross_component_in,
+                quad_4theta_component_in,
+                quad_const_component_in,
+                "z",
+            )
+            input_err = (
+                tan_component_in_err,
+                cross_component_in_err,
+                quad_4theta_component_in_err,
+                quad_const_component_in_err,
+                None,
+            )
+            output_var_plus_z = [
+                tan_component_out,
+                cross_component_out,
+                quad_4theta_component_out,
+                quad_const_component_out,
+                "z",
+            ]
+        if not all(t_ in self.galcat.columns for t_ in input_var_plus_theta):
             raise TypeError(
                 "Shear or ellipticity information is missing. Galaxy catalog must have tangential"
                 "and cross shears (gt, gx) or ellipticities (et, ex). "
@@ -585,7 +710,7 @@ def make_radial_profile(
             raise TypeError("Missing galaxy redshifts!")
         # Compute the binned averages and associated errors
         profile_table, binnumber = make_radial_profile(
-            [self.galcat[n].data for n in (tan_component_in, cross_component_in, "z")],
+            [self.galcat[n].data for n in input_var_plus_z],
             angsep=self.galcat["theta"],
             angsep_units="radians",
             bin_units=bin_units,
@@ -596,15 +721,12 @@ def make_radial_profile(
             cosmo=cosmo,
             z_lens=self.z,
             validate_input=self.validate_input,
-            components_error=[
-                None if n is None else self.galcat[n].data
-                for n in (tan_component_in_err, cross_component_in_err, None)
-            ],
+            components_error=[None if n is None else self.galcat[n].data for n in input_err],
             weights=self.galcat[weights_in].data if use_weights else None,
             coordinate_system=self.galcat.meta["coordinate_system"],
         )
         # Reaname table columns
-        for i, name in enumerate([tan_component_out, cross_component_out, "z"]):
+        for i, name in enumerate(output_var_plus_z):
             profile_table.rename_column(f"p_{i}", name)
             profile_table.rename_column(f"p_{i}_err", f"{name}_err")
         # Reaname weights columns
@@ -638,6 +760,10 @@ def plot_profiles(
         tangential_component_error="gt_err",
         cross_component="gx",
         cross_component_error="gx_err",
+        quad_4theta_component="g_quad_4theta",
+        quad_4theta_component_error="g_quad_4theta_err",
+        quad_const_component="g_quad_const",
+        quad_const_component_error="g_quad_const_err",
         table_name="profile",
         xscale="linear",
         yscale="linear",
@@ -658,6 +784,19 @@ def plot_profiles(
         cross_component_error: str, optional
             Name of the column in the galcat Table corresponding to the uncertainty in the cross
             component of the shear or reduced shear. Default: 'gx_err'
+        quad_4theta_component: str, optional
+            Name of the column in the galcat Table corresponding to the 4theta quadrupole component
+            of the shear or reduced shear (Delta Sigma not yet implemented).
+            Default: 'g_quad_4theta'
+        quad_4theta_component_error: str, optional
+            Name of the column in the galcat Table corresponding to the uncertainty in
+            4theta quadrupole component of the shear or reduced shear. Default: 'g_quad_4theta_err'
+        quad_const_component: str, optional
+            Name of the column in the galcat Table corresponding to the constant quadrupole
+            component of the shear or reduced shear. Default: 'gconst'
+        quad_const_component_error: str, optional
+            Name of the column in the galcat Table corresponding to the uncertainty in the
+            constant quadrupole component of the shear or reduced shear. Default: 'gconst_er'
         table_name: str, optional
             Name of the GalaxyCluster() `.profile` attribute. Default: 'profile'
         xscale:
@@ -675,6 +814,66 @@ def plot_profiles(
         if not hasattr(self, table_name):
             raise ValueError(f"GalaxyClusters does not have a '{table_name}' table.")
         profile = getattr(self, table_name)
+        if self.include_quadrupole:
+            for col in (
+                tangential_component,
+                cross_component,
+                quad_4theta_component,
+                quad_const_component,
+            ):
+                if col not in profile.columns:
+                    raise ValueError(f"Column for plotting '{col}' does not exist.")
+            for col in (
+                tangential_component_error,
+                cross_component_error,
+                quad_4theta_component_error,
+                quad_const_component_error,
+            ):
+                if col not in profile.columns:
+                    warnings.warn(f"Column for plotting '{col}' does not exist.")
+            return (
+                plot_profiles(
+                    rbins=profile["radius"],
+                    r_units=profile.meta["bin_units"],
+                    tangential_component=profile[tangential_component],
+                    tangential_component_error=(
+                        profile[tangential_component_error]
+                        if tangential_component_error in profile.columns
+                        else None
+                    ),
+                    cross_component=profile[cross_component],
+                    cross_component_error=(
+                        profile[cross_component_error]
+                        if cross_component_error in profile.columns
+                        else None
+                    ),
+                    xscale=xscale,
+                    yscale=yscale,
+                    tangential_component_label=tangential_component,
+                    cross_component_label=cross_component,
+                ),
+                plot_profiles(
+                    rbins=profile["radius"],
+                    r_units=profile.meta["bin_units"],
+                    tangential_component=profile[quad_4theta_component],
+                    tangential_component_error=(
+                        profile[quad_4theta_component_error]
+                        if quad_4theta_component_error in profile.columns
+                        else None
+                    ),
+                    cross_component=profile[quad_const_component],
+                    cross_component_error=(
+                        profile[quad_const_component_error]
+                        if quad_const_component_error in profile.columns
+                        else None
+                    ),
+                    xscale=xscale,
+                    yscale=yscale,
+                    tangential_component_label=quad_4theta_component,
+                    cross_component_label=quad_const_component,
+                ),
+            )
+
         for col in (tangential_component, cross_component):
             if col not in profile.columns:
                 raise ValueError(f"Column for plotting '{col}' does not exist.")
diff --git a/clmm/theory/__init__.py b/clmm/theory/__init__.py
index 10420f6fc..a037bd0d9 100644
--- a/clmm/theory/__init__.py
+++ b/clmm/theory/__init__.py
@@ -15,6 +15,7 @@
     compute_critical_surface_density_eff,
     compute_excess_surface_density,
     compute_excess_surface_density_2h,
+    compute_excess_surface_density_triaxial,
     compute_magnification,
     compute_magnification_bias,
     compute_mean_surface_density,
diff --git a/clmm/theory/func_layer.py b/clmm/theory/func_layer.py
index 5d570cbed..230dc2550 100644
--- a/clmm/theory/func_layer.py
+++ b/clmm/theory/func_layer.py
@@ -9,6 +9,7 @@
 # "_modeling_object".
 
 import numpy as np
+from scipy.interpolate import InterpolatedUnivariateSpline
 
 if "_modeling_object" not in globals():
     _modeling_object = None
@@ -1331,3 +1332,101 @@ def compute_magnification_bias(
 
     _modeling_object.validate_input = True
     return magnification_bias
+
+
+def compute_excess_surface_density_triaxial(
+    r_proj, mdelta, cdelta, z_cl, ell, cosmo, term, n_grid=10000, **kwargs
+):
+    r"""Compute the excess surface density lensing profile for the monopole, 4theta quadrupole,
+    or constant quadrupole component given in Shin et al. 2018
+    (https://doi.org/10.1093/mnras/stx3366)
+
+    Parameters
+    ----------
+    r_source : array
+        Radial distance of each source galaxy
+    mdelta : float
+        Mass of lens cluster
+    cdelta : array
+        Concentration of lens cluster
+    z_cluster : float
+        Redshift of lens cluster
+    ell : float
+        Ellipticity of halo defined by e = (1-q)/(1+q), q is the axis ratio.
+        q=b/a (Ratio of major axis to the minor axis lengths)
+    cosmo : clmm.cosmology.Cosmology object
+        CLMM Cosmology object
+    term : str
+        The component of the Taylor expansion to return as described in Shin et al. 2018.
+        Must be in : 'mono', 'quad_4theta', 'quad_const'
+        - 'mono' : the ellipticity corrected monopole term
+        - 'quad_4theta' : the 4theta component of the quadrupole term
+        - 'quad_const' : the constant component of the quadrupole term
+    n_grid : int
+        Grid resolution for functions to be computed on.
+        Too low n_grid can lead to large deviations.
+
+    Returns
+    -------
+    dsmono : array
+        Component of delta sigma excess for the elliptical halo specified
+    """
+    grid = np.logspace(-3, np.log10(3 * np.max(r_proj)), n_grid)
+    sigma0_grid = compute_surface_density(
+        grid,
+        mdelta,
+        cdelta,
+        z_cl,
+        cosmo,
+        **kwargs,
+    )
+    sigma0 = compute_surface_density(
+        r_proj,
+        mdelta,
+        cdelta,
+        z_cl,
+        cosmo,
+        **kwargs,
+    )
+    eta_grid = grid * np.gradient(np.log(sigma0_grid), grid)
+    eta = InterpolatedUnivariateSpline(grid, eta_grid, k=5)(r_proj)
+
+    if term == "mono":
+        deta_dlnr_grid = grid * np.gradient(eta_grid, grid)
+        deta_dlnr = InterpolatedUnivariateSpline(grid, deta_dlnr_grid, k=5)(r_proj)
+        sigma_correction_grid = sigma0_grid * (
+            0.5 * ell**2 * (eta_grid + 0.5 * eta_grid**2 + 0.5 * deta_dlnr_grid)
+        )
+        sigma_correction = sigma0 * (0.5 * ell**2 * (eta + 0.5 * eta**2 + 0.5 * deta_dlnr))
+        integral_vec = np.vectorize(
+            InterpolatedUnivariateSpline(grid, grid * sigma_correction_grid, k=5).integral
+        )
+        delta_sigma = (
+            compute_excess_surface_density(
+                r_proj,
+                mdelta,
+                cdelta,
+                z_cl,
+                cosmo,
+                **kwargs,
+            )
+            + (2 / r_proj**2) * integral_vec(0, r_proj)
+            - sigma_correction
+        )
+
+    elif term == "quad_4theta":
+        integral_vec = np.vectorize(
+            InterpolatedUnivariateSpline(grid, grid**3 * sigma0_grid * eta_grid, k=5).integral
+        )
+        delta_sigma = 0.5 * ell * (2 * (3 / r_proj**4 * integral_vec(0, r_proj)) - sigma0 * eta)
+
+    elif term == "quad_const":
+        integral_vec = np.vectorize(
+            InterpolatedUnivariateSpline(grid, sigma0_grid * eta_grid / grid, k=5).integral
+        )
+        delta_sigma = 0.5 * ell * (2 * integral_vec(r_proj, np.inf) - sigma0 * eta)
+
+    else:
+        raise ValueError(f"Unsupported term (='{term}')")
+
+    return delta_sigma
diff --git a/clmm/theory/parent_class.py b/clmm/theory/parent_class.py
index a2870b12d..9c53cc40b 100644
--- a/clmm/theory/parent_class.py
+++ b/clmm/theory/parent_class.py
@@ -9,7 +9,7 @@
 
 # functions for the 2h term
 from scipy.integrate import quad, simpson
-from scipy.interpolate import splev, splrep
+from scipy.interpolate import InterpolatedUnivariateSpline, splev, splrep
 from scipy.special import gamma, gammainc, jv
 
 from ..utils import (
@@ -278,6 +278,88 @@ def _eval_excess_surface_density_2h(
             2, r_proj, z_cl, halobias, logkbounds, ksteps, loglbounds, lsteps
         )
 
+    def _eval_excess_surface_density_triaxial_mono(self, r_proj, z_cl, ell, n_grid=10000):
+        """eval individual terms of  excess surface density"""
+
+        grid = np.logspace(-3, np.log10(3 * np.max(r_proj)), n_grid)
+
+        sigma0_grid = self._eval_surface_density(grid, z_cl)
+        sigma0 = self._eval_surface_density(r_proj, z_cl)
+
+        eta_grid = grid * np.gradient(np.log(sigma0_grid), grid)
+        eta = InterpolatedUnivariateSpline(grid, eta_grid, k=5)(r_proj)
+
+        deta_dlnr_grid = grid * np.gradient(eta_grid, grid)
+        deta_dlnr = InterpolatedUnivariateSpline(grid, deta_dlnr_grid, k=5)(r_proj)
+
+        sigma_correction_grid = sigma0_grid * (
+            0.5 * ell**2 * (eta_grid + 0.5 * eta_grid**2 + 0.5 * deta_dlnr_grid)
+        )
+        sigma_correction = sigma0 * (0.5 * ell**2 * (eta + 0.5 * eta**2 + 0.5 * deta_dlnr))
+
+        integral_vec = np.vectorize(
+            InterpolatedUnivariateSpline(grid, grid * sigma_correction_grid, k=5).integral
+        )
+        integral = integral_vec(0, r_proj)
+
+        correction = (2 / r_proj**2) * integral - sigma_correction
+
+        return self._eval_excess_surface_density(r_proj, z_cl) + correction
+
+    def _eval_excess_surface_density_triaxial_quad_4theta(self, r_proj, z_cl, ell, n_grid=10000):
+        """eval individual terms of  excess surface density"""
+
+        grid = np.logspace(-3, np.log10(3 * np.max(r_proj)), n_grid)
+
+        sigma0_grid = self._eval_surface_density(grid, z_cl)
+        sigma0 = self._eval_surface_density(r_proj, z_cl)
+
+        eta_grid = grid * np.gradient(np.log(sigma0_grid), grid)
+        eta = InterpolatedUnivariateSpline(grid, eta_grid, k=5)(r_proj)
+
+        integral_vec = np.vectorize(
+            InterpolatedUnivariateSpline(grid, grid**3 * sigma0_grid * eta_grid, k=5).integral
+        )
+        integral = 3 / r_proj**4 * integral_vec(0, r_proj)
+
+        return 0.5 * ell * (2 * integral - sigma0 * eta)
+
+    def _eval_excess_surface_density_triaxial_quad_const(self, r_proj, z_cl, ell, n_grid=10000):
+        """eval individual terms of  excess surface density"""
+
+        grid = np.logspace(-3, np.log10(3 * np.max(r_proj)), n_grid)
+
+        sigma0_grid = self._eval_surface_density(grid, z_cl)
+        sigma0 = self._eval_surface_density(r_proj, z_cl)
+
+        eta_grid = grid * np.gradient(np.log(sigma0_grid), grid)
+        eta = InterpolatedUnivariateSpline(grid, eta_grid, k=5)(r_proj)
+
+        integral_vec = np.vectorize(
+            InterpolatedUnivariateSpline(grid, sigma0_grid * eta_grid / grid, k=5).integral
+        )
+        integral = integral_vec(r_proj, np.inf)
+
+        return 0.5 * ell * (2 * integral - sigma0 * eta)
+
+    def _eval_excess_surface_density_triaxial(self, r_proj, z_cl, ell, term, n_grid=10000):
+        """eval individual terms of  excess surface density"""
+
+        if term == "mono":
+            delta_sigma = self._eval_excess_surface_density_triaxial_mono(r_proj, z_cl, ell, n_grid)
+        elif term == "quad_4theta":
+            delta_sigma = self._eval_excess_surface_density_triaxial_quad_4theta(
+                r_proj, z_cl, ell, n_grid
+            )
+        elif term == "quad_const":
+            delta_sigma = self._eval_excess_surface_density_triaxial_quad_const(
+                r_proj, z_cl, ell, n_grid
+            )
+        else:
+            raise ValueError(f"Unsupported term (='{term}')")
+
+        return delta_sigma
+
     def _eval_rdelta(self, z_cl):
         delta_mdef = self._get_delta_mdef(z_cl)
         return compute_rdelta(self.mdelta, z_cl, self.cosmo, self.massdef, delta_mdef)
@@ -885,6 +967,48 @@ def eval_surface_density_2h(
             r_proj, z_cl, halobias, logkbounds, ksteps, loglbounds, lsteps
         )
 
+    def eval_excess_surface_density_triaxial(self, r_proj, z_cl, ell, term, n_grid=10000):
+        r"""Compute the individual terms in the quadrupole expansion of the excess surface density.
+
+        Parameters
+        ----------
+        r_proj: array
+            Projected radial position from the cluster center in :math:`M\!pc`.
+        z_cl: float
+            Redshift of lens cluster
+        ell: float
+            ellipticity of halo defined by e = (1-q)/(1+q), q is the axis ratio.
+            q=b/a (Ratio of major axis to the minor axis lengths)
+        term: str
+            The expansion term wanted.
+                * 'mono': The monopole term with the ellipticity corrections applied. This will
+                    give the usual excess surface density but for a triaxial halo.
+                * 'quad_4theta': The 4theta component of the quadrupole term.
+                * 'quad_const': The constant component of the quadrupole term.
+        n_grid: int
+            Grid steps for gradient calculations.
+
+        Returns
+        -------
+        numpy.ndarry, float
+            Requested component of the excess surface density in units of :math:`M_\odot\ Mpc^{-2}`.
+        """
+
+        if self.validate_input:
+            validate_argument(locals(), "r_proj", "float_array", argmin=0)
+            validate_argument(locals(), "z_cl", float, argmin=0)
+            validate_argument(locals(), "ell", float, argmin=0, argmax=1)
+            validate_argument(locals(), "term", str)
+            validate_argument(locals(), "n_grid", int, argmin=2)
+
+        if self.backend not in ("ccl", "nc"):
+            raise NotImplementedError(
+                f"Triaxial-4theta term not currently supported with the {self.backend} backend. "
+                "Use the CCL or NumCosmo backend instead"
+            )
+
+        return self._eval_excess_surface_density_triaxial(r_proj, z_cl, ell, term, n_grid)
+
     def eval_tangential_shear(self, r_proj, z_cl, z_src, z_src_info="discrete", verbose=False):
         r"""Computes the tangential shear
 
diff --git a/clmm/utils/__init__.py b/clmm/utils/__init__.py
index 3ca66453b..327c6419a 100644
--- a/clmm/utils/__init__.py
+++ b/clmm/utils/__init__.py
@@ -31,12 +31,15 @@
     gaussian,
     make_bins,
 )
-from .units import convert_units
+from .units import (
+    convert_units,
+)
 from .validation import (
     DiffArray,
     _patch_rho_crit_to_cd2018,
     _validate_coordinate_system,
     _validate_dec,
+    _validate_include_quadrupole_phi_major,
     _validate_is_deltasigma_sigma_c,
     _validate_ra,
     arguments_consistency,
diff --git a/clmm/utils/validation.py b/clmm/utils/validation.py
index e30d6c4a9..e39313547 100644
--- a/clmm/utils/validation.py
+++ b/clmm/utils/validation.py
@@ -212,7 +212,7 @@ def _validate_dec(loc, dec_name, is_array):
 
 
 def _validate_is_deltasigma_sigma_c(is_deltasigma, sigma_c):
-    r""" "Validate the compatibility between is_deltasigma and sigma_c arguments.
+    r"""Validate the compatibility between is_deltasigma and sigma_c arguments.
 
 
     Parameters
@@ -228,6 +228,25 @@ def _validate_is_deltasigma_sigma_c(is_deltasigma, sigma_c):
         raise TypeError(f"sigma_c (={sigma_c}) must be None when is_deltasigma=False")
 
 
+def _validate_include_quadrupole_phi_major(include_quadrupole, phi_major, info_mem):
+    r"""Validate the compatibility between include_quadrupole and [phi_major or info_mem].
+
+    Parameters
+    ----------
+    include_quadrupole: bool
+        If `True`, include quadrupole lensing signal computed,
+        else, only tangential and cross shears.
+    phi_major: float
+        The direction of the cluster major axis in radian (+x // -RA)
+    info_mem: list of arrays
+        [ra, dec, weight] of the cluster member galaxies for calculating major axis
+    """
+    if include_quadrupole and (phi_major is None and info_mem is None):
+        raise TypeError(
+            "either phi_major or info_mem should be provided when include_quadrupole is True"
+        )
+
+
 def _validate_coordinate_system(loc, argname):
     r"""Validate the coordinate system.
 
diff --git a/examples/demo_boost_factors.ipynb b/examples/demo_boost_factors.ipynb
index d7f8b320e..de00de1e7 100644
--- a/examples/demo_boost_factors.ipynb
+++ b/examples/demo_boost_factors.ipynb
@@ -444,7 +444,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "wrk",
+   "display_name": "Python 3",
    "language": "python",
    "name": "wrk"
   },
@@ -458,7 +458,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/examples/demo_mock_ensemble.ipynb b/examples/demo_mock_ensemble.ipynb
index 35c2246ba..dd95ed4ae 100644
--- a/examples/demo_mock_ensemble.ipynb
+++ b/examples/demo_mock_ensemble.ipynb
@@ -719,7 +719,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/examples/triaxiality/CLMM triaxiality example notebook.ipynb b/examples/triaxiality/CLMM triaxiality example notebook.ipynb
new file mode 100644
index 000000000..77b09645a
--- /dev/null
+++ b/examples/triaxiality/CLMM triaxiality example notebook.ipynb	
@@ -0,0 +1,646 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "69e5eb57-ab3f-415c-ba11-b6db63cdc9f8",
+   "metadata": {},
+   "source": [
+    "# CLMM Triaxiality - From Quadrupole Shears to Mass and Ellipticity\n",
+    "### This example showcases fitting a Mock Catalog"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "184deae3-95f7-45a1-ba79-2404c2784275",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import Python Modules\n",
+    "\n",
+    "#CLMM:\n",
+    "import clmm\n",
+    "from clmm import Cosmology\n",
+    "from clmm.dataops import _compute_tangential_shear, _compute_4theta_shear, make_radial_profile\n",
+    "\n",
+    "#General:\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.pyplot import cm\n",
+    "\n",
+    "#MCMC:\n",
+    "from tqdm import tqdm\n",
+    "import time as pytime\n",
+    "import h5py \n",
+    "import corner\n",
+    "import emcee\n",
+    "from multiprocessing import Pool\n",
+    "\n",
+    "#Fiducial Cosmology:\n",
+    "cosmo = Cosmology(H0=70.0, Omega_dm0=0.2248, Omega_b0=0.3 - 0.2248, Omega_k0=0.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d47d856-6b19-4521-9303-6febcc594220",
+   "metadata": {},
+   "source": [
+    "## Load Mock Catalog "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1abbfb8-8bae-4a8d-b9aa-10c979e11636",
+   "metadata": {},
+   "source": [
+    "We have used an elliptical NFW model to generate a mock catalog with weak lensing shears."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "130f9c90-4b5c-4a06-88ef-ec4164c5942d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1300x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Loading mock catalog:\n",
+    "q_true=0.7\n",
+    "dir_loc_mcmc = \"Elliptical_lenses_data/catalog_horizontal_rescale_q\"+str(q_true)+\"/\"\n",
+    "f=dir_loc_mcmc + \"MCMC\"\n",
+    "\n",
+    "z_cluster=0.47\n",
+    "z_source=0.8\n",
+    "sigma_crit = cosmo.eval_sigma_crit(z_cluster,z_source)\n",
+    "\n",
+    "gamma1 = np.load(dir_loc_mcmc+\"gamma1.npy\").flatten()\n",
+    "gamma2 = np.load(dir_loc_mcmc+\"gamma2.npy\").flatten()\n",
+    "x_arcsec = np.load(dir_loc_mcmc+\"x_arcsec.npy\").flatten()\n",
+    "y_arcsec = np.load(dir_loc_mcmc+\"y_arcsec.npy\").flatten()\n",
+    "r = np.sqrt((x_arcsec**2 + y_arcsec**2))                 #In arcsecs\n",
+    "r_rad = r*np.pi/180.0 * 1/3600                           #In radians\n",
+    "theta = np.arctan2(y_arcsec, x_arcsec)                   #In radians\n",
+    "\n",
+    "# Computing the Monopole and Quadrupole lensing profiles using in-built CLMM functions:\n",
+    "DSmono = _compute_tangential_shear(gamma1, gamma2, theta)*sigma_crit\n",
+    "DS4theta = _compute_4theta_shear(gamma1, gamma2, theta)*sigma_crit\n",
+    "DSconst = gamma1*sigma_crit\n",
+    "\n",
+    "\n",
+    "# Binning the Monopole and Quadrupole profiles using CLMM wrapper:\n",
+    "N_bins=15\n",
+    "r_min = 0.3\n",
+    "r_max = 2.5\n",
+    "bin_edges = np.logspace(np.log10(r_min), np.log10(r_max), N_bins)                       \n",
+    "binned_profile = make_radial_profile([DSmono,DS4theta,DSconst],angsep=r_rad,angsep_units='radians',bin_units='Mpc', bins=bin_edges, \n",
+    "                                     error_model='std',cosmo=cosmo,z_lens=z_cluster)\n",
+    "#Monopole Bins:\n",
+    "r_mono = binned_profile['radius']\n",
+    "r_quad = binned_profile['radius']\n",
+    "\n",
+    "ds_mono = binned_profile['p_0']\n",
+    "ds_mono_err = binned_profile['p_0_err']/np.sqrt(binned_profile['n_src'])\n",
+    "ds_quad_4theta = binned_profile['p_1']\n",
+    "ds_quad_4theta_err = binned_profile['p_1_err']/np.sqrt(binned_profile['n_src'])\n",
+    "ds_quad_const = binned_profile['p_2']\n",
+    "ds_quad_const_err = binned_profile['p_2_err']/np.sqrt(binned_profile['n_src'])\n",
+    "\n",
+    "global ds_mono,ds_mono_err,r_mono,ds_quad_4theta,ds_quad_4theta_err,ds_quad_const,ds_quad_const_err,r_quad                                                                                                                               \n",
+    "\n",
+    "# Visualizing the Profiles:\n",
+    "fig,ax = plt.subplots(1,3, figsize=[13,5])\n",
+    "ax[0].errorbar(r_mono, ds_mono/1E12, yerr=ds_mono_err/1E12, color='orange', label='Monopole')\n",
+    "ax[0].set_ylabel(r'$\\Delta\\Sigma \\,[M_{\\odot} h/\\rm{pc}^{2}]$', fontsize=15)\n",
+    "ax[0].set_xlabel('r [Mpc]', fontsize=15)\n",
+    "ax[0].loglog()\n",
+    "ax[0].legend(fontsize=15)\n",
+    "ax[0].tick_params(axis='both', which='both', labelsize=14)\n",
+    "ax[0].set_ylabel(r'$\\Delta\\Sigma \\,[M_{\\odot} h/\\rm{pc}^{2}]$', fontsize=18)\n",
+    "ax[0].set_title(r'$\\Delta\\Sigma^{mono}$', fontsize=18)\n",
+    "ax[0].yaxis.set_minor_formatter(matplotlib.ticker.NullFormatter())\n",
+    "ax[0].xaxis.set_minor_formatter(matplotlib.ticker.NullFormatter())\n",
+    "\n",
+    "ax[1].errorbar(r_quad, ds_quad_const/1E12, yerr=ds_quad_const_err/1E12, color='orange', label='const')\n",
+    "ax[1].legend(fontsize=15)\n",
+    "ax[1].set_xlabel('r [Mpc]', fontsize=15)\n",
+    "ax[1].set_xscale('log')\n",
+    "ax[1].tick_params(axis='both', which='both', labelsize=14)\n",
+    "ax[1].xaxis.set_minor_formatter(matplotlib.ticker.NullFormatter())\n",
+    "ax[1].set_title(r'$\\Delta\\Sigma^{quad}_{const}$', fontsize=18)\n",
+    "\n",
+    "ax[2].errorbar(r_quad, ds_quad_4theta/1E12, yerr=ds_quad_4theta_err/1E12, color='orange', label='4theta')\n",
+    "ax[2].set_xscale('log')\n",
+    "ax[2].set_xlabel('r [Mpc]', fontsize=15)\n",
+    "ax[2].tick_params(axis='both', which='both', labelsize=14)\n",
+    "ax[2].set_title(r'$\\Delta\\Sigma^{quad}_{4\\Theta}$', fontsize=18)\n",
+    "ax[2].xaxis.set_minor_formatter(matplotlib.ticker.NullFormatter())\n",
+    "ax[2].legend(fontsize=15)\n",
+    "plt.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b527c05-6115-4da8-93d6-924134263894",
+   "metadata": {},
+   "source": [
+    "## Set up the Prior, Likelihood and Posterior functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42b1d708-4fe7-4303-badd-0a296e777ef6",
+   "metadata": {},
+   "source": [
+    "We use a guassian likelihood with flat priors on the Mass, concentration and ellipticity parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2105bffa-79b2-4018-b842-e1aa0d2b476a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gaussian_log_likelihood(data,error,model):\n",
+    "    '''\n",
+    "    A gaussian log likelihood function.\n",
+    "    \n",
+    "    INPUTS:\n",
+    "        data: array\n",
+    "            Array of data that is described by a guassian likelihood\n",
+    "        error: array\n",
+    "            1-Sigma error on the data point. Should be of the same dimension as data\n",
+    "        model: array\n",
+    "            Model prediction for each data point. Should also be of the same dimension as data\n",
+    "    OUTPUTS:\n",
+    "        result: float\n",
+    "            The log likelihood computed given the model and the data.\n",
+    "    '''\n",
+    "    sigma = error\n",
+    "    term1 = -0.5*np.sum(np.log(2*np.pi*(sigma**2)))\n",
+    "    chi2 = ((data - model)**2 / (sigma)**2)\n",
+    "    term2 = -0.5*np.sum(chi2)\n",
+    "    result = term1+term2\n",
+    "    return result\n",
+    "\n",
+    "def priors(params, limits):\n",
+    "    '''\n",
+    "    Computes log priors for each parameter. We use flat priors on all the parameters.\n",
+    "    INPUTS:\n",
+    "        params: array\n",
+    "            A set of parameter values of length ndim.\n",
+    "        limits: array\n",
+    "            Min and Max bounds for the flat prior. The shape should be (2,ndim)\n",
+    "    OUTPUTS:\n",
+    "        prior: float\n",
+    "            The log prior evaluated for the given set of parameter values.\n",
+    "    '''\n",
+    "    a,b,c = params\n",
+    "    if (a < limits[0][0] or a > limits[0][1]):        #mdelta\n",
+    "        return -np.inf\n",
+    "    elif (b < limits[1][0] or b > limits[1][1]):      #cdelta\n",
+    "        return -np.inf\n",
+    "    elif (c < limits[2][0] or c > limits[2][1]):      #ell\n",
+    "        return -np.inf\n",
+    "\n",
+    "    else:\n",
+    "        return 0.0\n",
+    "    \n",
+    "\n",
+    "def log_posterior(params):\n",
+    "    '''\n",
+    "    Log of posterior calculated by the Bayes rule, a sum of the log of likelihood and log prior.\n",
+    "    INPUTS:\n",
+    "        params: array\n",
+    "            An array of parameter values where the posterior is to be computed.\n",
+    "    OUTPUTS:\n",
+    "        log_posterior: float\n",
+    "            The sum of log likelihood and log prior.\n",
+    "    '''\n",
+    "    mdelta,cdelta,ell = params\n",
+    "    \n",
+    "    #Evaluate the prior:\n",
+    "    prior=priors(params, limits)\n",
+    "    \n",
+    "    #Avoid likelihood evaluation when prior is -infinity:\n",
+    "    if prior == -np.inf:\n",
+    "        lnP = -np.inf\n",
+    "    else:\n",
+    "        #Evaluating the model:\n",
+    "        ds_mono_model = clmm.compute_excess_surface_density_triaxial(r_proj=r_mono, mdelta=mdelta, cdelta=cdelta, ell=ell, z_cl=z_cl, cosmo=cosmo, halo_profile_model='nfw',\n",
+    "                                                                n_grid=10000, delta_mdef=200, massdef='critical', term='mono')\n",
+    "        ds_quad_4theta_model = clmm.compute_excess_surface_density_triaxial(r_proj=r_quad, mdelta=mdelta, cdelta=cdelta, ell=ell, z_cl=z_cl, cosmo=cosmo, halo_profile_model='nfw',\n",
+    "                                                                n_grid=10000, delta_mdef=200, massdef='critical', term='quad_4theta')\n",
+    "        ds_quad_const_model = clmm.compute_excess_surface_density_triaxial(r_proj=r_quad, mdelta=mdelta, cdelta=cdelta, ell=ell, z_cl=z_cl, cosmo=cosmo, halo_profile_model='nfw',\n",
+    "                                                                n_grid=10000, delta_mdef=200, massdef='critical', term='quad_const')\n",
+    "        \n",
+    "        #Evaluating the likelihood:\n",
+    "        lnP = prior \n",
+    "        lnP += gaussian_log_likelihood(ds_mono, ds_mono_err, ds_mono_model)\n",
+    "        lnP += gaussian_log_likelihood(ds_quad_4theta, ds_quad_4theta_err, ds_quad_4theta_model)\n",
+    "        lnP += gaussian_log_likelihood(ds_quad_const, ds_quad_const_err, ds_quad_const_model)\n",
+    "\n",
+    "    # safeguard against NaNs:\n",
+    "    if np.isnan(lnP):\n",
+    "        return -np.inf\n",
+    "    else:\n",
+    "        return lnP\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b49bb69-2148-41fd-bc8b-e50d56749611",
+   "metadata": {},
+   "source": [
+    "## Defining a wrapper for MCMC sampling with emcee and fitting the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b5086221-fc85-4db8-8d17-85605d72cbcb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def emcee_wrapper(params_key, limits, true_value,\n",
+    "                 ndim, nwalkers, nsteps=1000, nburn=350, filename=None):  \n",
+    "    '''\n",
+    "    A wrapper for the emcee function, a corner plot and a chain plot.\n",
+    "    INPUTS:\n",
+    "        params_key: array of length ndim\n",
+    "            List of parameter names. Should be of length ndim.\n",
+    "        limits: array\n",
+    "            Min and Max values for a flat prior for each parameter. Its of the shape (2, ndim)\n",
+    "        true_value: array of length ndim\n",
+    "            Truth for each free parameter\n",
+    "        ndim: float\n",
+    "            Number of free parameters in the model\n",
+    "        nwalkers:\n",
+    "            Number of MCMC walkers/chains\n",
+    "        nsteps: float, optional\n",
+    "            Number of steps to be traversed by each MCMC walker\n",
+    "        nburn: float, optional\n",
+    "            Number of initial steps per walker to be burned or removed. \n",
+    "        filename: string\n",
+    "            Location and name of the file where the walkers are to be stored.\n",
+    "    OUTPUTS:\n",
+    "        post: array\n",
+    "            Walkers post-burn\n",
+    "        mean: array\n",
+    "            Mean for each parameter\n",
+    "        err_plus: array\n",
+    "            1-sigma error on each parameter above the mean\n",
+    "        err_minus: array\n",
+    "            1-sigma error on each parameter below the mean\n",
+    "    '''\n",
+    "    \n",
+    "    starting_guess_t=[]\n",
+    "    for i in np.arange(len(params_key)):\n",
+    "        guess=[]\n",
+    "        for j in np.arange(nwalkers):\n",
+    "            guess.append((limits[i][1]-limits[i][0])*np.random.random()+limits[i][0])\n",
+    "        starting_guess_t.append(guess)\n",
+    "    starting_guesses=np.column_stack(starting_guess_t)\n",
+    "    print('Starting guesses for ',nwalkers,' chains:')\n",
+    "    print(starting_guesses)\n",
+    "            \n",
+    "    start=pytime.time()\n",
+    "    if filename!=None:\n",
+    "        ## STORING THE CHAINS TO FILE\n",
+    "        backend = emcee.backends.HDFBackend(filename[:-4]+'.h5')\n",
+    "        backend.reset(nwalkers, ndim) ## Comment this line out to append steps to a previous run stored in the file\n",
+    "        with Pool() as pool:\n",
+    "            sampler = emcee.EnsembleSampler(nwalkers, ndim, log_posterior, args=None,\n",
+    "                                             vectorize=False, backend=backend, pool=pool)\n",
+    "            pos, prob, state = sampler.run_mcmc(starting_guesses, nsteps, progress=True)\n",
+    "            \n",
+    "    if filename==None:   \n",
+    "        ## RETURNING THE CHAINS WITHOUT STORING TO FILE\n",
+    "        with Pool() as pool:\n",
+    "            sampler = emcee.EnsembleSampler(nwalkers, ndim, log_posterior, args=[gamma1, gamma2, x_arcsec, y_arcsec],\n",
+    "                                             vectorize=False, pool=pool)\n",
+    "            pos, prob, state = sampler.run_mcmc(starting_guesses, nsteps, progress=True)\n",
+    "\n",
+    "    end=pytime.time()\n",
+    "    print(\"Time taken:{0:.1f} seconds\".format(end-start))\n",
+    "            \n",
+    "    post = np.concatenate(sampler.chain[:, nburn: , :])\n",
+    "        \n",
+    "    #Corner plot:\n",
+    "\n",
+    "    fig = plt.figure(figsize=[10,10])\n",
+    "    figure = corner.corner(post,labels=params_key, quantiles=[0.16, 0.5, 0.84], \n",
+    "                           show_titles=True, title_fmt='.2e', fig=fig)\n",
+    "    figure.patch.set_facecolor('white')\n",
+    "    # Extract the axes\n",
+    "    axes = np.array(figure.axes).reshape((ndim, ndim))\n",
+    "    \n",
+    "    if filename!=None:\n",
+    "        plt.savefig(filename[:-4]+\"_corner_plot.png\",facecolor=\"white\", dpi=\"figure\", format=\"png\")  \n",
+    "        \n",
+    "    #Chain plot:\n",
+    "    color=iter(cm.rainbow(np.linspace(0,1,nwalkers)))\n",
+    "    \n",
+    "    # set up a plot with two windows, one for each parameter:\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    for i in range(len(params_key)):\n",
+    "        plt.subplot(100*len(params_key)+10+i+1)\n",
+    "        plt.ylabel(params_key[i], fontsize=10)\n",
+    "    for j in range(nwalkers):\n",
+    "        c=next(color)\n",
+    "        for i in range(len(params_key)):\n",
+    "            plt.subplot(100*len(params_key)+10+i+1)\n",
+    "            plt.plot(sampler.chain[j,nburn:,i], ',', color=c)\n",
+    "            \n",
+    "    if filename!= None:\n",
+    "        plt.savefig(filename[:-4]+\"_chain_plot.png\",facecolor=\"white\", dpi=\"figure\", format=\"png\")\n",
+    "        \n",
+    "    mean=[]\n",
+    "    err_minus=[]\n",
+    "    err_plus=[]\n",
+    "    for i in range(len(params_key)):\n",
+    "        mean.append(np.quantile((post[:,i].flatten()), 0.5))\n",
+    "        err_minus.append(np.quantile((post[:,i].flatten()), 0.16))\n",
+    "        err_plus.append(np.quantile((post[:,i].flatten()), 0.84))\n",
+    "    mean = np.array(mean)\n",
+    "    err_plus = np.array(err_plus) - mean\n",
+    "    err_minus = mean - np.array(err_minus)\n",
+    "\n",
+    "    return post, mean, err_plus, err_minus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "09f4ebfd-0f89-4537-b770-328e84062ef5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting guesses for  10  chains:\n",
+      "[[2.74857938e+14 1.58345008e+01 9.68834297e-01]\n",
+      " [3.57879494e+14 1.05778984e+01 7.91187063e-01]\n",
+      " [3.01778925e+14 1.13608912e+01 4.56918421e-01]\n",
+      " [2.72896708e+14 1.85119328e+01 7.72745832e-01]\n",
+      " [2.12403745e+14 1.42072116e+00 1.17179854e-01]\n",
+      " [3.23301162e+14 1.74258599e+00 6.33557819e-01]\n",
+      " [2.19356018e+14 4.04367949e-01 1.42005419e-01]\n",
+      " [4.45994727e+14 1.66523969e+01 9.35227761e-01]\n",
+      " [4.81867717e+14 1.55631350e+01 5.16677654e-01]\n",
+      " [1.92337318e+14 1.74002430e+01 4.10573854e-01]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 3000/3000 [05:16<00:00,  9.48it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time taken:317.3 seconds\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# CALL EMCEE\n",
+    "\n",
+    "#MCMC parameters:\n",
+    "params_key=[\"M200c\",\"c200c\",\"e\"]\n",
+    "ndim = len(params_key)\n",
+    "nwalkers = ndim*2+4\n",
+    "nsteps = 3000\n",
+    "nburns = 1000\n",
+    "np.random.seed(0)\n",
+    "\n",
+    "limits=[[1.0E12,5.0E14],  # mdelta\n",
+    "       [0.0,20.0],     # cdelta\n",
+    "       [0.0001,0.99]]   # e\n",
+    "\n",
+    "z_cl=0.47\n",
+    "z_gal=0.8\n",
+    "ell_true= 1*(1-q_true)/(1+q_true)\n",
+    "true_value=[2.0E14, 3.89055, ell_true]\n",
+    "\n",
+    "post, mean, err_plus, err_minus = emcee_wrapper(params_key=params_key,limits=limits,true_value=true_value, ndim=ndim,nwalkers=nwalkers,\n",
+    "                                                nsteps=nsteps, nburn=nburns, filename=f)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "395c8fc3-76ab-4ef9-9765-ef765c879a52",
+   "metadata": {},
+   "source": [
+    "## Fit results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "834ec40d-1923-4d71-922a-7f5e9c26e6ee",
+   "metadata": {},
+   "source": [
+    "We can check the monopole and quadrupole simultaneous fits by overplotting it on the data-points. The shaded regions represent 1-$\\sigma$ uncertainty on the fit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "4e68b917-32bc-4d8f-8ec2-276e26e76850",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mdelta_fit= 201077345290408.97 cdelta_fit= 3.8314249882920413 ell_fit =  0.1762525289115617\n",
+      "Computed and Plotted 1-sigma intervals for Mono...\n",
+      "Computed and Plotted 1-sigma intervals for Const...\n",
+      "Computed and Plotted 1-sigma intervals for 4theta...\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1300x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mdef = 'critical'\n",
+    "reader = emcee.backends.HDFBackend(f)\n",
+    "nburns=500\n",
+    "post = reader.get_chain(discard=nburns, flat=True)\n",
+    "\n",
+    "mean=[]\n",
+    "err_minus=[]\n",
+    "err_plus=[]\n",
+    "for i in range(len(params_key)):\n",
+    "    mean.append(np.quantile((post[:,i].flatten()), 0.5))\n",
+    "    err_minus.append(np.quantile((post[:,i].flatten()), 0.16))\n",
+    "    err_plus.append(np.quantile((post[:,i].flatten()), 0.84))\n",
+    "mean = np.array(mean)\n",
+    "err_plus = np.array(err_plus) - mean\n",
+    "err_minus = mean - np.array(err_minus)\n",
+    "\n",
+    "mdelta_fit = mean[0]\n",
+    "cdelta_fit = mean[1]\n",
+    "ell_fit = mean[2]\n",
+    "print(\"mdelta_fit=\",mdelta_fit,\"cdelta_fit=\",cdelta_fit, \"ell_fit = \",ell_fit)\n",
+    "cosmo = Cosmology(H0=70.0, Omega_dm0=0.2248, Omega_b0=0.3 - 0.2248, Omega_k0=0.0)\n",
+    "\n",
+    "fig,ax = plt.subplots(1,3, figsize=[13,5])\n",
+    "ax[0].errorbar(r_mono, ds_mono/1E12, yerr=np.abs(ds_mono_err)/1E12, label='data', color='orange')\n",
+    "r=np.linspace(np.min(r_mono), np.max(r_mono), 100)\n",
+    "ds_model = clmm.compute_excess_surface_density_triaxial(ell=ell_fit, r_proj=r, mdelta=mdelta_fit, cdelta=cdelta_fit, z_cl=z_cl, cosmo=cosmo, halo_profile_model='nfw', \n",
+    "                                             n_grid=10000, delta_mdef=200, massdef=mdef, term='mono')\n",
+    "\n",
+    "ax[0].plot(r, ds_model/1E12, label='Model Fit', color='darkblue', lw=2)\n",
+    "ax[0].set_xlabel('r [Mpc]', fontsize=18)\n",
+    "ax[0].set_yscale('log')\n",
+    "ax[0].set_xscale('log')\n",
+    "ax[0].set_title(r'$\\Delta\\Sigma^{mono}$', fontsize=18)\n",
+    "ax[0].tick_params(axis='both', which='both', labelsize=14)\n",
+    "ax[0].set_ylabel(r'$\\Delta\\Sigma \\,[M_{\\odot} h/\\rm{pc}^{2}]$', fontsize=18)\n",
+    "ax[0].yaxis.set_minor_formatter(matplotlib.ticker.NullFormatter())\n",
+    "ax[0].xaxis.set_minor_formatter(matplotlib.ticker.NullFormatter())\n",
+    "ax[0].legend()\n",
+    "#plt.xlim([1.75,2.25])\n",
+    "\n",
+    "\n",
+    "r=np.linspace(np.min(r_quad), np.max(r_quad), 100)\n",
+    "ds_model = clmm.compute_excess_surface_density_triaxial(ell=ell_fit, r_proj=r, mdelta=mdelta_fit, cdelta=cdelta_fit, z_cl=z_cl, cosmo=cosmo, halo_profile_model='nfw', \n",
+    "                                             n_grid=10000, delta_mdef=200, massdef=mdef, term='quad_const')\n",
+    "ax[1].plot(r, ds_model/1E12, label='Model Fit', color='darkblue')\n",
+    "ax[1].errorbar(r_quad, ds_quad_const/1E12,  yerr=ds_quad_const_err/1E12, label='data', color='orange')\n",
+    "ax[1].set_yscale('linear')\n",
+    "ax[1].set_xscale('log')\n",
+    "ax[1].set_title(r'$\\Delta\\Sigma^{quad}_{const}$', fontsize=18)\n",
+    "ax[1].set_xlabel('r [Mpc]', fontsize=18)\n",
+    "ax[1].tick_params(axis='both', which='both', labelsize=14)\n",
+    "ax[1].xaxis.set_minor_formatter(matplotlib.ticker.NullFormatter())\n",
+    "ax[1].legend()\n",
+    "\n",
+    "\n",
+    "ds_model = clmm.compute_excess_surface_density_triaxial(ell=ell_fit, r_proj=r, mdelta=mdelta_fit, cdelta=cdelta_fit, z_cl=z_cl, cosmo=cosmo, halo_profile_model='nfw', \n",
+    "                                             n_grid=10000, delta_mdef=200, massdef=mdef, term='quad_4theta')\n",
+    "\n",
+    "ax[2].plot(r, ds_model/1E12, label='Model Fit', color='darkblue')\n",
+    "ax[2].errorbar(r_quad, ds_quad_4theta/1E12,  yerr=ds_quad_4theta_err/1E12, label='data', color='orange')\n",
+    "ax[2].set_yscale('linear')\n",
+    "ax[2].set_xscale('log')\n",
+    "ax[2].set_title(r'$\\Delta\\Sigma^{quad}_{4\\Theta}$', fontsize=18)\n",
+    "ax[2].set_xlabel('r [Mpc]', fontsize=18)\n",
+    "ax[2].tick_params(axis='both', which='both', labelsize=14)\n",
+    "ax[2].xaxis.set_minor_formatter(matplotlib.ticker.NullFormatter())\n",
+    "ax[2].legend()\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "\n",
+    "\n",
+    "plot_confidence_bands = True\n",
+    "if plot_confidence_bands == True:\n",
+    "    post_s=post[0:500]\n",
+    "    ds_model = np.zeros([len(post_s),len(r)])\n",
+    "    for i,p in enumerate(post_s):\n",
+    "        ds_model[i,:] = clmm.compute_excess_surface_density_triaxial(ell=p[2], r_proj=r, mdelta=p[0], cdelta=p[1], z_cl=z_cl, cosmo=cosmo, halo_profile_model='nfw', \n",
+    "                                                 n_grid=10000, delta_mdef=200, massdef=mdef, term='mono')\n",
+    "    model_minus = np.zeros(len(r))\n",
+    "    model_plus = np.zeros(len(r))\n",
+    "    for i,r_i in enumerate(r):\n",
+    "        model_minus[i] = np.quantile(ds_model[:,i],0.16)\n",
+    "        model_plus[i] = np.quantile(ds_model[:,i],0.84)\n",
+    "    ax[0].fill_between(r, model_minus/1E12, model_plus/1E12, color='blue', alpha=0.2)\n",
+    "    print('Computed and Plotted 1-sigma intervals for Mono...')\n",
+    "    \n",
+    "\n",
+    "    ds_model = np.zeros([len(post_s),len(r)])\n",
+    "    for i,p in enumerate(post_s):\n",
+    "        ds_model[i,:] = clmm.compute_excess_surface_density_triaxial(ell=p[2], r_proj=r, mdelta=p[0], cdelta=p[1], z_cl=z_cl, cosmo=cosmo, halo_profile_model='nfw', \n",
+    "                                                 n_grid=10000, delta_mdef=200, massdef=mdef, term='quad_const')\n",
+    "    model_minus = np.zeros(len(r))\n",
+    "    model_plus = np.zeros(len(r))\n",
+    "    for i,r_i in enumerate(r):\n",
+    "        model_minus[i] = np.quantile(ds_model[:,i],0.16)\n",
+    "        model_plus[i] = np.quantile(ds_model[:,i],0.84)\n",
+    "    ax[1].fill_between(r, model_minus/1E12, model_plus/1E12, color='blue', alpha=0.2)\n",
+    "    print('Computed and Plotted 1-sigma intervals for Const...')\n",
+    "    \n",
+    "\n",
+    "    ds_model = np.zeros([len(post_s),len(r)])\n",
+    "    for i,p in enumerate(post_s):\n",
+    "        ds_model[i,:] = clmm.compute_excess_surface_density_triaxial(ell=p[2], r_proj=r, mdelta=p[0], cdelta=p[1], z_cl=z_cl, cosmo=cosmo, halo_profile_model='nfw', \n",
+    "                                                 n_grid=10000, delta_mdef=200, massdef=mdef, term='quad_4theta')\n",
+    "    model_minus = np.zeros(len(r))\n",
+    "    model_plus = np.zeros(len(r))\n",
+    "    for i,r_i in enumerate(r):\n",
+    "        model_minus[i] = np.quantile(ds_model[:,i],0.16)\n",
+    "        model_plus[i] = np.quantile(ds_model[:,i],0.84)\n",
+    "    ax[2].fill_between(r, model_minus/1E12, model_plus/1E12, color='blue', alpha=0.2)\n",
+    "    print('Computed and Plotted 1-sigma intervals for 4theta...')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base3",
+   "language": "python",
+   "name": "base3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/triaxiality/catalog_horizontal_rescale.zip b/examples/triaxiality/catalog_horizontal_rescale.zip
new file mode 100644
index 000000000..3fb88ce51
Binary files /dev/null and b/examples/triaxiality/catalog_horizontal_rescale.zip differ
diff --git a/examples/triaxiality/clmm_triax_test_cc.ipynb b/examples/triaxiality/clmm_triax_test_cc.ipynb
new file mode 100644
index 000000000..8b759da53
--- /dev/null
+++ b/examples/triaxiality/clmm_triax_test_cc.ipynb
@@ -0,0 +1,364 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9346610c-7a7f-4761-a60a-ab9d6afb5e1f",
+   "metadata": {},
+   "source": [
+    "# Try out CLMM quadrupole measurement "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "068d55c1-f45a-458d-ac0f-062c1652a9a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.12.5'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "import sys\n",
+    "import os\n",
+    "import numpy as np\n",
+    "from astropy.table import Table\n",
+    "from numpy import random\n",
+    "import scipy\n",
+    "import matplotlib.pyplot as plt\n",
+    "import clmm\n",
+    "from clmm import GalaxyCluster, ClusterEnsemble, GCData\n",
+    "from clmm import Cosmology\n",
+    "from clmm.support import mock_data as mock\n",
+    "\n",
+    "clmm.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5bb057d3-847e-4ab4-ac9e-56db85d77000",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cosmo = Cosmology(H0=71.0, Omega_dm0=0.265 - 0.0448, Omega_b0=0.0448, Omega_k0=0.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dafceb01-ba1b-4c4a-bb75-7141051025fc",
+   "metadata": {},
+   "source": [
+    "### Read in mock catalogs and create galaca"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a2646ede-597e-45ee-abf0-3a1f8fb1d929",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi = np.pi/2  ## rotation angle (don't know what it should be)\n",
+    "\n",
+    "indir = 'axis_ratio_2_3/'\n",
+    "gamma1_ = np.load(f'catalogs/{indir}gamma1.npy')\n",
+    "gamma2_ = np.load(f'catalogs/{indir}gamma2.npy')\n",
+    "\n",
+    "x_arcsec_ = np.load(f'catalogs/{indir}x_arcsec.npy')\n",
+    "y_arcsec_ = np.load(f'catalogs/{indir}y_arcsec.npy')\n",
+    "\n",
+    "ra = x_arcsec_ / 3600.\n",
+    "dec = y_arcsec_ / 3600."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "52686635-5731-486a-9db5-7e3f508d613a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "galcat = GCData()\n",
+    "galcat['ra'] = ra.flatten()\n",
+    "galcat['dec'] = dec.flatten()\n",
+    "galcat['e1'] = gamma1_.flatten()\n",
+    "galcat['e2'] = gamma2_.flatten()\n",
+    "galcat['z'] = np.zeros(len(ra.flatten())) + 0.8\n",
+    "\n",
+    "galcat2 = GCData(np.copy(galcat))\n",
+    "galcat3 = GCData(np.copy(galcat))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4bca9c6c-c136-4e55-be5c-c9105cdccc77",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<b>GCData</b>\n",
+       "<br> <b>defined by:</b> cosmo=None\n",
+       "<br> <b>with columns:</b> ra, dec, e1, e2, z\n",
+       "<br> 3 objects\n",
+       "<br> \n",
+       "<table id=\"table135548200950224\" class=\"table-striped table-bordered table-condensed\">\n",
+       "<thead><tr><th>ra</th><th>dec</th><th>e1</th><th>e2</th><th>z</th></tr></thead>\n",
+       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
+       "<tr><td>-0.1375</td><td>0.1375</td><td>-0.0001238742439844591</td><td>0.0015250221728086307</td><td>0.8</td></tr>\n",
+       "<tr><td>-0.13472222222222222</td><td>0.1375</td><td>-9.485376092024044e-05</td><td>0.0015532780665121148</td><td>0.8</td></tr>\n",
+       "<tr><td>-0.13194444444444445</td><td>0.1375</td><td>-6.406121842896439e-05</td><td>0.0015815081966413323</td><td>0.8</td></tr>\n",
+       "</table></div>"
+      ],
+      "text/plain": [
+       "GCData(cosmo=None, columns: ra, dec, e1, e2, z)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "galcat[0:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d390287b-71a9-4d93-858e-c51112b88adb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster_id = \"Awesome Cluster\"\n",
+    "\n",
+    "cluster_ra = 0\n",
+    "cluster_dec = 0\n",
+    "\n",
+    "mdelta = 2e14\n",
+    "delta_mdef = 200\n",
+    "cluster_z = 0.47"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37ce7a97-4eb4-49db-bd89-9fcd98c94dc2",
+   "metadata": {},
+   "source": [
+    "### Create 3 GC objects and compute the radial profile\n",
+    "- `include_quadrupole = False` and `coordinate_system='celestial'`\n",
+    "- `include_quadrupole = True` and `coordinate_system='celestial'`\n",
+    "- `include_quadrupole = False` and `coordinate_system='euclidean'`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6a58cfae-7c8b-422d-a27c-446f60bdecb3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "gc_object1 = clmm.GalaxyCluster(cluster_id, cluster_ra, cluster_dec, cluster_z, galcat, \n",
+    "                               include_quadrupole=False,\n",
+    "                               coordinate_system='celestial')\n",
+    "\n",
+    "gc_object2 = clmm.GalaxyCluster(cluster_id, cluster_ra, cluster_dec, cluster_z, galcat2, \n",
+    "                               include_quadrupole=True,\n",
+    "                               coordinate_system='celestial')\n",
+    "\n",
+    "gc_object3 = clmm.GalaxyCluster(cluster_id, cluster_ra, cluster_dec, cluster_z, galcat3, \n",
+    "                               include_quadrupole=False,\n",
+    "                               coordinate_system='euclidean')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f3eb5995-02f1-4e16-8750-b957aa30b89a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.00339387, 0.00335976, 0.00332601, ..., 0.00332601, 0.00335976,\n",
+       "        0.00339387]),\n",
+       " array([5.77079148e+12, 5.86916550e+12, 5.96946029e+12, ...,\n",
+       "        5.96946029e+12, 5.86916550e+12, 5.77079148e+12]),\n",
+       " array([-4.68765605e+11, -4.78816222e+11, -4.88903209e+11, ...,\n",
+       "        -4.88903209e+11, -4.78816222e+11, -4.68765605e+11]))"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gc_object1.compute_tangential_and_cross_components(add=True, cosmo=cosmo, is_deltasigma=True, phi_major=phi)\n",
+    "gc_object2.compute_tangential_and_cross_components(add=True, cosmo=cosmo, is_deltasigma=True, phi_major=phi)\n",
+    "gc_object3.compute_tangential_and_cross_components(add=True, cosmo=cosmo, is_deltasigma=True, phi_major=phi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c3813693-9a8e-4237-9cb4-1ec4a10676b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bins = clmm.utils.make_bins(0.05,5,nbins=10, method='evenlog10width')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0e902d39-2913-4332-99e3-2e11fee4f46a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/media/taeshin/research/CLMM/clmm/utils/statistic.py:97: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  err_y = np.sqrt(stat_yerr2 + data_yerr2)\n",
+      "/media/taeshin/research/CLMM/clmm/utils/statistic.py:97: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  err_y = np.sqrt(stat_yerr2 + data_yerr2)\n",
+      "/media/taeshin/research/CLMM/clmm/utils/statistic.py:97: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  err_y = np.sqrt(stat_yerr2 + data_yerr2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "gc_object1.make_radial_profile(bins=bins, bin_units='Mpc', cosmo=cosmo, add=True );\n",
+    "gc_object2.make_radial_profile(bins=bins, bin_units='Mpc', cosmo=cosmo, add=True );\n",
+    "gc_object3.make_radial_profile(bins=bins, bin_units='Mpc', cosmo=cosmo, add=True );"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01175051-f88d-48d3-a6b3-b68bce521c57",
+   "metadata": {},
+   "source": [
+    "### Results and where the problem is...\n",
+    "\n",
+    "- When not resquesting the computation of the quadrupole components, the `celestial` (blue) coordinates give the expected results for the monopole, while the `euclidean` (orange) is the wrong coordinate description\n",
+    "\n",
+    "- When turning on the `include_quadrupole` option, and specifying `celestial` (green), the monopole is zero and exactly the same as with the `euclidean` option before --> the coordinate option may not be taken into account properly in the quadrupole case?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7aa33d97-4ca7-4767-9ed3-61e64e878068",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.errorbar(0.98*gc_object1.profile['radius'], gc_object1.profile['gt'], gc_object1.profile['gt_err'], \n",
+    "             ls='', marker='.', label=\"include_quadrupole=False, coord = celestial\")\n",
+    "plt.errorbar(1.00*gc_object3.profile['radius'], gc_object3.profile['gt'], gc_object3.profile['gt_err'], \n",
+    "             ls='', marker='x', label=\"include_quadrupole=False, coord = euclidean\", markersize=10)\n",
+    "plt.errorbar(1.02*gc_object2.profile['radius'], gc_object2.profile['gt'], gc_object2.profile['gt_err'], \n",
+    "             ls='', marker='.', label=\"include_quadrupole=True, coord = celestial\")\n",
+    "plt.legend()\n",
+    "plt.xscale('log')\n",
+    "#plt.yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82886b5c-86ba-4143-9aa1-2a6fa8bac9ea",
+   "metadata": {},
+   "source": [
+    "### Looking at quadrupole values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "7c3cf61c-3585-497a-8580-c5dcc97791e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.errorbar(0.99*gc_object2.profile['radius'], gc_object2.profile['gconst'], gc_object2.profile['gconst_err'],\n",
+    "            label= 'const')\n",
+    "plt.errorbar(1.01*gc_object2.profile['radius'], gc_object2.profile['g4theta'],gc_object2.profile['g4theta_err'],\n",
+    "            label=r'4$\\theta$')\n",
+    "plt.legend()\n",
+    "plt.xscale('log')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91e1d24a-85ef-4f30-ad43-3fe02d6f56fb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd897eb1-1e52-47fe-b735-58109dcc4ec5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cluster",
+   "language": "python",
+   "name": "cluster"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tests/conftest.py b/tests/conftest.py
index 52a9fd1d3..d06a92797 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -14,7 +14,7 @@
         {
             "nick": "ccl",
             "cosmo_reltol": 8.0e-8,
-            "dataops_reltol": 3.0e-8,
+            "dataops_reltol": 1.0e-7,
             "theory_reltol": 2.0e-6,
             "theory_reltol_num": 5.0e-5,
             "ps_reltol": 5.0e-3,
@@ -22,7 +22,7 @@
         {
             "nick": "nc",
             "cosmo_reltol": 1.0e-8,
-            "dataops_reltol": 1.0e-8,
+            "dataops_reltol": 1.0e-7,
             "theory_reltol": 1.0e-8,
             "theory_reltol_num": 1.0e-8,
             "ps_reltol": 1.0e-5,
@@ -38,7 +38,7 @@
         {
             "nick": "notabackend",
             "cosmo_reltol": 8.0e-8,
-            "dataops_reltol": 3.0e-8,
+            "dataops_reltol": 1.0e-7,
             "theory_reltol": 2.0e-6,
             "ps_reltol": 5.0e-3,
         },
diff --git a/tests/test_clusterensemble.py b/tests/test_clusterensemble.py
index 63c3d3958..815055d4d 100644
--- a/tests/test_clusterensemble.py
+++ b/tests/test_clusterensemble.py
@@ -39,9 +39,17 @@ def test_cluster_ensemble():
     cluster = clmm.GalaxyCluster(
         unique_id="test", ra=ra_lens, dec=dec_lens, z=z_lens, galcat=galcat
     )
+    cluster_quad = clmm.GalaxyCluster(
+        unique_id="test", ra=ra_lens, dec=dec_lens, z=z_lens, galcat=galcat, include_quadrupole=True
+    )
     cluster.compute_tangential_and_cross_components()
+    cluster_quad.compute_tangential_and_cross_components(
+        phi_major=0.0,
+        info_mem=[np.array([119.9, 120.1]), np.array([41.9, 42.1]), np.array([1.0, 1.0])],
+    )
     bins = bins_radians
     gc_list = [cluster]
+    gc_list_quad = [cluster_quad]
     # check empty cluster list
     ce_empty = ClusterEnsemble(
         "cluster_ensemble",
@@ -52,7 +60,20 @@ def test_cluster_ensemble():
         bin_units="radians",
         cosmo=cosmo,
     )
+    ce_empty_quad = ClusterEnsemble(
+        "cluster_ensemble",
+        tan_component_in="et",
+        cross_component_in="ex",
+        quad_4theta_component_in="e_quad_4theta",
+        quad_const_component_in="e_quad_const",
+        weights_in="w_ls",
+        bins=bins,
+        bin_units="radians",
+        cosmo=cosmo,
+        include_quadrupole=True,
+    )
     assert_raises(ValueError, ce_empty.make_stacked_radial_profile)
+    assert_raises(ValueError, ce_empty_quad.make_stacked_radial_profile)
     ce_empty.make_individual_radial_profile(
         cluster,
         tan_component_in="et",
@@ -62,9 +83,21 @@ def test_cluster_ensemble():
         bin_units="radians",
         cosmo=cosmo,
     )
+    ce_empty_quad.make_individual_radial_profile(
+        cluster_quad,
+        tan_component_in="et",
+        cross_component_in="ex",
+        quad_4theta_component_in="e_quad_4theta",
+        quad_const_component_in="e_quad_const",
+        weights_in="w_ls",
+        bins=bins,
+        bin_units="radians",
+        cosmo=cosmo,
+    )
 
     # check bad id
     assert_raises(TypeError, ClusterEnsemble, 1.3, gc_list)
+    assert_raises(TypeError, ClusterEnsemble, 1.3, gc_list_quad)
 
     # test without kwargs, args
     ce = ClusterEnsemble(
@@ -77,9 +110,23 @@ def test_cluster_ensemble():
         bin_units="radians",
         cosmo=cosmo,
     )
+    ce_quad = ClusterEnsemble(
+        "cluster_ensemble",
+        gc_list_quad,
+        tan_component_in="et",
+        cross_component_in="ex",
+        quad_4theta_component_in="e_quad_4theta",
+        quad_const_component_in="e_quad_const",
+        weights_in="w_ls",
+        bins=bins,
+        bin_units="radians",
+        cosmo=cosmo,
+        include_quadrupole=True,
+    )
 
     # test the lenght of the clusterensemble data attribute
     assert_equal(ce.__len__(), 1)
+    assert_equal(ce_quad.__len__(), 1)
 
     # test the lenght of the clusterensemble data attribute (after doubling the number of individual cluster)
     ce._add_values(
@@ -91,10 +138,24 @@ def test_cluster_ensemble():
         bin_units="radians",
         cosmo=cosmo,
     )
+    ce_quad._add_values(
+        [cluster_quad],
+        tan_component_in="et",
+        cross_component_in="ex",
+        quad_4theta_component_in="e_quad_4theta",
+        quad_const_component_in="e_quad_const",
+        weights_in="w_ls",
+        bins=bins,
+        bin_units="radians",
+        cosmo=cosmo,
+    )
     assert_equal(ce.__len__(), 2)
+    assert_equal(ce_quad.__len__(), 2)
     # test if the len of averaged profile has the lenght of binning axis
     assert_equal(len(ce.data["W_l"][0]), len(bins_radians) - 1)
     assert_equal(ce.__getitem__("gt"), ce.data["gt"])
+    assert_equal(len(ce_quad.data["W_l"][0]), len(bins_radians) - 1)
+    assert_equal(ce_quad.__getitem__("g_quad_const"), ce_quad.data["g_quad_const"])
 
 
 def test_covariance():
@@ -107,6 +168,7 @@ def test_covariance():
     sindec = [-0.1, 0, 0.1]
     cluster_dec = np.arcsin(sindec) * 180 / np.pi  # from -90 to 90 deg
     gclist = []
+    gclist_quad = []
     Rmin, Rmax = 0.3, 5  # Mpc
     thetamin, thetamax = Rmin / dz, Rmax / dz  # radians
     phi = np.pi
@@ -120,16 +182,33 @@ def test_covariance():
         bin_units="radians",
         cosmo=cosmo,
     )
+    ce_empty_quad = ClusterEnsemble(
+        "2",
+        tan_component_in="et",
+        cross_component_in="ex",
+        quad_4theta_component_in="e_quad_4theta",
+        quad_const_component_in="e_quad_const",
+        weights_in="w_ls",
+        bins=bins,
+        bin_units="radians",
+        cosmo=cosmo,
+        include_quadrupole=True,
+    )
 
     # check empty cluster list
     assert_raises(ValueError, ce_empty.compute_sample_covariance)
     assert_raises(ValueError, ce_empty.compute_bootstrap_covariance)
     assert_raises(ValueError, ce_empty.compute_jackknife_covariance)
+    assert_raises(ValueError, ce_empty_quad.compute_sample_covariance)
+    assert_raises(ValueError, ce_empty_quad.compute_bootstrap_covariance)
+    assert_raises(ValueError, ce_empty_quad.compute_jackknife_covariance)
 
     for i in range(n_catalogs):
         # generate random catalog
         e1, e2 = np.random.randn(ngals) * 0.001, np.random.randn(ngals) * 0.001
         et, ex = da._compute_tangential_shear(e1, e2, phi), da._compute_cross_shear(e1, e2, phi)
+        eft = da._compute_4theta_shear(e1, e2, phi)
+        ecn = e1
         z_gal = np.random.random(ngals) * (3 - 1.1) + 1.1
         id_gal = np.arange(ngals)
         theta_gal = np.linspace(0, 1, ngals) * (thetamax - thetamin) + thetamin
@@ -142,10 +221,21 @@ def test_covariance():
             "e2": e2,
             "et": et,
             "ex": ex,
+            "e_quad_4theta": eft,
+            "e_quad_const": ecn,
             "w_ls": w_ls,
         }
         cl = clmm.GalaxyCluster("mock_cluster", cluster_ra[i], cluster_dec[i], 1.0, GCData(data))
+        cl_quad = clmm.GalaxyCluster(
+            "mock cluster",
+            cluster_ra[i],
+            cluster_dec[i],
+            1.0,
+            GCData(data),
+            include_quadrupole=True,
+        )
         gclist.append(cl)
+        gclist_quad.append(cl_quad)
         ce_empty.make_individual_radial_profile(
             galaxycluster=cl,
             tan_component_in="et",
@@ -155,13 +245,26 @@ def test_covariance():
             bin_units="Mpc",
             cosmo=cosmo,
         )
+        ce_empty_quad.make_individual_radial_profile(
+            galaxycluster=cl_quad,
+            tan_component_in="et",
+            cross_component_in="ex",
+            quad_4theta_component_in="e_quad_4theta",
+            quad_const_component_in="e_quad_const",
+            weights_in="w_ls",
+            bins=bins,
+            bin_units="Mpc",
+            cosmo=cosmo,
+        )
 
     ensemble_id = 1
-    names = ["id", "ra", "dec", "z", "radius", "gt", "gx", "W_l"]
+    names = ["id", "ra", "dec", "z", "radius", "gt", "gx", "g_quad_4theta", "g_quad_const", "W_l"]
 
     # test without args, kwargs
     ce = ClusterEnsemble(ensemble_id)
+    ce_quad = ClusterEnsemble(ensemble_id, include_quadrupole=True)
     assert_raises(ValueError, ce.make_stacked_radial_profile)
+    assert_raises(ValueError, ce_quad.make_stacked_radial_profile)
 
     # test with args, kwargs
     ce = ClusterEnsemble(
@@ -174,50 +277,101 @@ def test_covariance():
         bin_units="Mpc",
         cosmo=cosmo,
     )
+    ce_quad = ClusterEnsemble(
+        ensemble_id,
+        gclist_quad,
+        tan_component_in="et",
+        cross_component_in="ex",
+        quad_4theta_component_in="e_quad_4theta",
+        quad_const_component_in="e_quad_const",
+        weights_in="w_ls",
+        bins=bins,
+        bin_units="Mpc",
+        cosmo=cosmo,
+        include_quadrupole=True,
+    )
 
     ce.make_stacked_radial_profile()
+    ce_quad.make_stacked_radial_profile()
 
     assert_raises(ValueError, ce.make_individual_radial_profile, gclist[0], bin_units="radians")
+    assert_raises(
+        ValueError, ce_quad.make_individual_radial_profile, gclist_quad[0], bin_units="radians"
+    )
+
     # test if te list object matches the calculation from the object with manually added clusters
     ce_empty.make_stacked_radial_profile()
+    ce_empty_quad.make_stacked_radial_profile()
     assert_array_equal(ce_empty.stacked_data, ce.stacked_data)
+    assert_array_equal(ce_empty_quad.stacked_data, ce_quad.stacked_data)
 
     # comparing brut force calculation for cross and tangential component
     gt_individual, gx_individual = ce.data["gt"], ce.data["gx"]
+    gft_individual, gcn_individual = ce_quad.data["g_quad_4theta"], ce_quad.data["g_quad_const"]
     Wl_individual = ce.data["W_l"]
     gt_stack = np.average(gt_individual, weights=Wl_individual, axis=0)
     gx_stack = np.average(gx_individual, weights=Wl_individual, axis=0)
+    gft_stack = np.average(gft_individual, weights=Wl_individual, axis=0)
+    gcn_stack = np.average(gcn_individual, weights=Wl_individual, axis=0)
     gt_stack_method = ce.stacked_data["gt"]
     gx_stack_method = ce.stacked_data["gx"]
+    gft_stack_method = ce_quad.stacked_data["g_quad_4theta"]
+    gcn_stack_method = ce_quad.stacked_data["g_quad_const"]
     assert_equal(gt_stack, gt_stack_method)
     assert_equal(gx_stack, gx_stack_method)
+    assert_equal(gft_stack, gft_stack_method)
+    assert_equal(gcn_stack, gcn_stack_method)
     ce.compute_sample_covariance()
+    ce_quad.compute_sample_covariance()
     ce.compute_bootstrap_covariance(n_bootstrap=3)
+    ce_quad.compute_bootstrap_covariance(n_bootstrap=3)
     ce.compute_jackknife_covariance(n_side=2)
+    ce_quad.compute_jackknife_covariance(n_side=2)
 
     # cross vs tangential covariances within a method -> shapes
     assert_equal(ce.cov["tan_sc"].shape, ce.cov["cross_sc"].shape)
     assert_equal(ce.cov["tan_bs"].shape, ce.cov["cross_bs"].shape)
     assert_equal(ce.cov["tan_jk"].shape, ce.cov["cross_jk"].shape)
+    # 4theta vs constant covariances within a method -> shapes
+    assert_equal(ce_quad.cov["quad_4theta_sc"].shape, ce_quad.cov["quad_const_sc"].shape)
+    assert_equal(ce_quad.cov["quad_4theta_bs"].shape, ce_quad.cov["quad_const_bs"].shape)
+    assert_equal(ce_quad.cov["quad_4theta_jk"].shape, ce_quad.cov["quad_const_jk"].shape)
 
     # comparing covariance estimation methods -> shapes
     assert_equal(ce.cov["tan_sc"].shape, ce.cov["tan_bs"].shape)
     assert_equal(ce.cov["tan_bs"].shape, ce.cov["tan_jk"].shape)
     assert_equal(ce.cov["tan_jk"].shape, ce.cov["tan_sc"].shape)
+    # comparing covariance estimation methods -> shapes
+    assert_equal(ce_quad.cov["quad_4theta_sc"].shape, ce_quad.cov["quad_4theta_bs"].shape)
+    assert_equal(ce_quad.cov["quad_4theta_bs"].shape, ce_quad.cov["quad_4theta_jk"].shape)
+    assert_equal(ce_quad.cov["quad_4theta_jk"].shape, ce_quad.cov["quad_4theta_sc"].shape)
 
-    # comparing brut force calculation for sample variance
+    # comparing brute force calculation for sample variance
     std_gt_stack = np.std(gt_individual, axis=0) / np.sqrt(n_catalogs - 1)
     assert_allclose(ce.cov["tan_sc"].diagonal() ** 0.5, std_gt_stack, 1e-6)
+    std_gft_stack = np.std(gft_individual, axis=0) / np.sqrt(n_catalogs - 1)
+    assert_allclose(ce_quad.cov["quad_4theta_sc"].diagonal() ** 0.5, std_gft_stack, 1e-6)
 
     # test save/load
     ce.save("ce.test.pkl")
+    ce_quad.save("ce_quad.test.pkl")
 
     ce2 = ClusterEnsemble.load("ce.test.pkl")
+    ce_quad2 = ClusterEnsemble.load("ce_quad.test.pkl")
     os.system("rm ce.test.pkl")
+    os.system("rm ce_quad.test.pkl")
+
     assert_array_equal(ce.stacked_data, ce2.stacked_data)
+    assert_array_equal(ce_quad.stacked_data, ce_quad2.stacked_data)
     assert_equal(ce.cov["tan_sc"].shape, ce2.cov["tan_sc"].shape)
     assert_equal(ce.cov["tan_bs"].shape, ce2.cov["tan_bs"].shape)
     assert_equal(ce.cov["tan_jk"].shape, ce2.cov["tan_jk"].shape)
     assert_equal(ce.cov["cross_sc"].shape, ce2.cov["cross_sc"].shape)
     assert_equal(ce.cov["cross_bs"].shape, ce2.cov["cross_bs"].shape)
     assert_equal(ce.cov["cross_jk"].shape, ce2.cov["cross_jk"].shape)
+    assert_equal(ce_quad.cov["quad_4theta_sc"].shape, ce_quad2.cov["quad_4theta_sc"].shape)
+    assert_equal(ce_quad.cov["quad_4theta_bs"].shape, ce_quad2.cov["quad_4theta_bs"].shape)
+    assert_equal(ce_quad.cov["quad_4theta_jk"].shape, ce_quad2.cov["quad_4theta_jk"].shape)
+    assert_equal(ce_quad.cov["quad_const_sc"].shape, ce_quad2.cov["quad_const_sc"].shape)
+    assert_equal(ce_quad.cov["quad_const_bs"].shape, ce_quad2.cov["quad_const_bs"].shape)
+    assert_equal(ce_quad.cov["quad_const_jk"].shape, ce_quad2.cov["quad_const_jk"].shape)
diff --git a/tests/test_dataops.py b/tests/test_dataops.py
index 9031005b8..dfb3368c5 100644
--- a/tests/test_dataops.py
+++ b/tests/test_dataops.py
@@ -55,6 +55,84 @@ def test_compute_tangential_shear():
     assert_allclose(da._compute_tangential_shear(0.0, 0.0, 0.3), 0.0, **TOLERANCE)
 
 
+def test_compute_4theta_shear():
+    """test compute quadrupole 4theta shear"""
+    shear1, shear2, phi = 0.15, 0.08, 0.52
+    expected_4theta_shear = -0.003271676989552594
+    four_theta_shear = da._compute_4theta_shear(shear1, shear2, phi)
+    assert_allclose(four_theta_shear, expected_4theta_shear)
+
+    shear1 = np.array([0.15, 0.40])
+    shear2 = np.array([0.08, 0.30])
+    phi = np.array([0.52, 1.23])
+    expected_4theta_shear = [-0.003271676989552594, -0.2111087147687582]
+    four_theta_shear = da._compute_4theta_shear(shear1, shear2, phi)
+    assert_allclose(four_theta_shear, expected_4theta_shear)
+
+    # test for reasonable values
+    assert_allclose(da._compute_4theta_shear(100.0, 0.0, 0.0), 100.0, **TOLERANCE)
+    assert_allclose(da._compute_4theta_shear(0.0, 100.0, np.pi / 8.0), 100.0, **TOLERANCE)
+    assert_allclose(da._compute_4theta_shear(0.0, 0.0, 0.3), 0.0, **TOLERANCE)
+
+
+def test_calculate_major_axis():
+    """test calculate major axis"""
+    ra_lens, dec_lens = 180.0, 0.0
+
+    ra_mem, dec_mem, weight_mem = [180.0, 180.0], [-0.5, 0.5], [1.0, 1.0]
+    expected_major_axis = np.pi / 2.0
+    assert_allclose(
+        da.calculate_major_axis(ra_lens, dec_lens, ra_mem, dec_mem, weight_mem),
+        expected_major_axis,
+        **TOLERANCE,
+    )
+
+    ra_mem, dec_mem, weight_mem = [179.5, 180.5], [0.0, 0.0], [1.0, 1.0]
+    expected_major_axis = 0.0
+    assert_allclose(
+        da.calculate_major_axis(ra_lens, dec_lens, ra_mem, dec_mem, weight_mem),
+        expected_major_axis,
+        **TOLERANCE,
+    )
+
+    ra_mem, dec_mem, weight_mem = [179.99, 180.01], [-0.01, 0.01], [1.0, 1.0]
+    expected_major_axis = -np.pi / 4.0
+    assert_allclose(
+        da.calculate_major_axis(ra_lens, dec_lens, ra_mem, dec_mem, weight_mem),
+        expected_major_axis,
+        **TOLERANCE,
+    )
+
+    ra_mem, dec_mem, weight_mem = [179.99, 180.01], [0.01, -0.01], [1.0, 1.0]
+    expected_major_axis = np.pi / 4.0
+    assert_allclose(
+        da.calculate_major_axis(ra_lens, dec_lens, ra_mem, dec_mem, weight_mem),
+        expected_major_axis,
+        **TOLERANCE,
+    )
+
+
+def test_rotate_shear():
+    """test rotate shear components"""
+    shear1, shear2, phi = 0.15, 0.08, 0.52
+    phi_major_45 = np.pi / 4.0
+    phi_major_90 = np.pi / 2.0
+    phi_major_180 = np.pi
+    expected_shear1_45, expected_shear2_45 = 0.08, -0.15
+    expected_shear1_90, expected_shear2_90 = -0.15, -0.08
+    expected_shear1_180, expected_shear2_180 = 0.15, 0.08
+
+    shear1_45, shear2_45 = da._rotate_shear(shear1, shear2, phi_major_45)
+    shear1_90, shear2_90 = da._rotate_shear(shear1, shear2, phi_major_90)
+    shear1_180, shear2_180 = da._rotate_shear(shear1, shear2, phi_major_180)
+
+    assert_allclose([shear1_45, shear2_45], [expected_shear1_45, expected_shear2_45], **TOLERANCE)
+    assert_allclose([shear1_90, shear2_90], [expected_shear1_90, expected_shear2_90], **TOLERANCE)
+    assert_allclose(
+        [shear1_180, shear2_180], [expected_shear1_180, expected_shear2_180], **TOLERANCE
+    )
+
+
 def test_compute_lensing_angles_flatsky():
     """test compute lensing angles flatsky"""
     ra_l, dec_l = 161.0, 65.0
@@ -200,6 +278,7 @@ def test_compute_tangential_and_cross_components(modeling_data):
     # Input values
     reltol = modeling_data["dataops_reltol"]
     ra_lens, dec_lens, z_lens = 120.0, 42.0, 0.5
+    phi_major = 0.0
     gals = GCData(
         {
             "ra": np.array([120.1, 119.9]),
@@ -215,20 +294,30 @@ def test_compute_tangential_and_cross_components(modeling_data):
         "angsep": np.array([0.0021745039090962414, 0.0037238407383072053]),
         "cross_shear": np.array([0.2780316984090899, 0.6398792901134982]),
         "tangential_shear": np.array([-0.22956126563459447, -0.02354769805831558]),
+        "four_theta_shear": np.array([-0.3324295, -0.43566851]),
+        "const_shear": np.array([0.2, 0.4]),
         # DeltaSigma expected values for clmm.Cosmology(H0=70.0, Omega_dm0=0.275, Omega_b0=0.025)
         "cross_DS": np.array([8.58093068e14, 1.33131522e15]),
         # [1224.3326297393244, 1899.6061989365176])*0.7*1.0e12*1.0002565513832675
         "tangential_DS": np.array([-7.08498103e14, -4.89926917e13]),
         # [-1010.889584349285, -69.9059242788237])*0.7*1.0e12*1.0002565513832675
+        "four_theta_DS": np.array([-1.02598173e15, -9.06439876e14]),
+        "const_DS": np.array([6.17262745e14, 8.32228959e14]),
     }
+
     expected_curve = {
         "angsep": np.array([0.002175111279323424171, 0.003723129781247932167]),
         "cross_shear": np.array([0.277590689496438781, 0.639929479722048944]),
         "tangential_shear": np.array([-0.23009434826803484841, -0.02214183783401518779]),
+        "four_theta_shear": np.array([-0.33189127, -0.43360245]),
+        "const_shear": np.array([0.2, 0.4]),
         # DeltaSigma expected values for clmm.Cosmology(H0=70.0, Omega_dm0=0.275, Omega_b0=0.025)
         "cross_DS": np.array([8.56731976e14, 1.33141964e15]),
         "tangential_DS": np.array([-7.10143363e14, -4.60676976e13]),
+        "four_theta_DS": np.array([-1.02432059e15, -9.02141297e14]),
+        "const_DS": np.array([6.17262745e14, 8.32228959e14]),
     }
+
     # Geometries to test
     geo_tests = [("flat", expected_flat), ("curve", expected_curve)]
     # Test domains on inputs
@@ -363,20 +452,96 @@ def test_compute_tangential_and_cross_components(modeling_data):
             angsep,
             expected["angsep"],
             **TOLERANCE,
-            err_msg="Angular Separation not correct when passing lists",
+            err_msg="Angular Separation not correct when passing arrays",
         )
         assert_allclose(
             tshear,
             expected["tangential_shear"],
             **TOLERANCE,
-            err_msg="Tangential Shear not correct when passing lists",
+            err_msg="Tangential Shear not correct when passing arrays",
         )
         assert_allclose(
             xshear,
             expected["cross_shear"],
             **TOLERANCE,
-            err_msg="Cross Shear not correct when passing lists",
+            err_msg="Cross Shear not correct when passing arrays",
         )
+
+        ## Turn on quadrupole option
+        angsep, _, _, ftshear, cnshear = da.compute_tangential_and_cross_components(
+            ra_lens=ra_lens,
+            dec_lens=dec_lens,
+            ra_source=gals["ra"],
+            dec_source=gals["dec"],
+            shear1=gals["e1"],
+            shear2=gals["e2"],
+            geometry=geometry,
+            include_quadrupole=True,
+            phi_major=0.0,
+        )
+        assert_allclose(
+            ftshear,
+            expected["four_theta_shear"],
+            **TOLERANCE,
+            err_msg="4theta Shear not correct when passing arrays and phi_major",
+        )
+        assert_allclose(
+            cnshear,
+            expected["const_shear"],
+            **TOLERANCE,
+            err_msg="Constant Shear not correct when passing arrays and phi_major",
+        )
+        ## Turn on quadrupole option with Celestial coordinates
+        angsep, _, _, ftshear, cnshear = da.compute_tangential_and_cross_components(
+            ra_lens=ra_lens,
+            dec_lens=dec_lens,
+            ra_source=gals["ra"],
+            dec_source=gals["dec"],
+            shear1=gals["e1"],
+            shear2=-gals["e2"],
+            geometry=geometry,
+            include_quadrupole=True,
+            phi_major=0.0,
+            coordinate_system="celestial",
+        )
+        assert_allclose(
+            ftshear,
+            expected["four_theta_shear"],
+            **TOLERANCE,
+            err_msg="4theta Shear not correct when passing phi_major for celestial coord.",
+        )
+        assert_allclose(
+            cnshear,
+            expected["const_shear"],
+            **TOLERANCE,
+            err_msg="Constant Shear not correct when passing phi_major for celestial coord.",
+        )
+
+        ## Pass members info instead of major axis directly
+        angsep, _, _, ftshear, cnshear = da.compute_tangential_and_cross_components(
+            ra_lens=ra_lens,
+            dec_lens=dec_lens,
+            ra_source=gals["ra"],
+            dec_source=gals["dec"],
+            shear1=gals["e1"],
+            shear2=gals["e2"],
+            geometry=geometry,
+            include_quadrupole=True,
+            info_mem=[np.array([119.99, 120.01]), np.array([42.0, 42.0]), np.array([1.0, 1.0])],
+        )
+        assert_allclose(
+            ftshear,
+            expected["four_theta_shear"],
+            **TOLERANCE,
+            err_msg="4theta Shear not correct when passing arrays and mem_info",
+        )
+        assert_allclose(
+            cnshear,
+            expected["const_shear"],
+            **TOLERANCE,
+            err_msg="Constant Shear not correct when passing arrays and mem_info",
+        )
+
         # Pass LISTS into function
         angsep, tshear, xshear = da.compute_tangential_and_cross_components(
             ra_lens=ra_lens,
@@ -405,6 +570,57 @@ def test_compute_tangential_and_cross_components(modeling_data):
             **TOLERANCE,
             err_msg="Cross Shear not correct when passing lists",
         )
+
+        ## Turn on quadrupole option
+        angsep, _, _, ftshear, cnshear = da.compute_tangential_and_cross_components(
+            ra_lens=ra_lens,
+            dec_lens=dec_lens,
+            ra_source=list(gals["ra"]),
+            dec_source=list(gals["dec"]),
+            shear1=list(gals["e1"]),
+            shear2=list(gals["e2"]),
+            geometry=geometry,
+            include_quadrupole=True,
+            phi_major=0.0,
+        )
+        assert_allclose(
+            ftshear,
+            expected["four_theta_shear"],
+            **TOLERANCE,
+            err_msg="4theta Shear not correct when passing lists and phi_major",
+        )
+        assert_allclose(
+            cnshear,
+            expected["const_shear"],
+            **TOLERANCE,
+            err_msg="Constant Shear not correct when passing listss and phi_major",
+        )
+
+        ## Pass members info instead of major axis directly
+        angsep, _, _, ftshear, cnshear = da.compute_tangential_and_cross_components(
+            ra_lens=ra_lens,
+            dec_lens=dec_lens,
+            ra_source=list(gals["ra"]),
+            dec_source=list(gals["dec"]),
+            shear1=list(gals["e1"]),
+            shear2=list(gals["e2"]),
+            geometry=geometry,
+            include_quadrupole=True,
+            info_mem=[np.array([119.99, 120.01]), np.array([42.0, 42.0]), np.array([1.0, 1.0])],
+        )
+        assert_allclose(
+            ftshear,
+            expected["four_theta_shear"],
+            **TOLERANCE,
+            err_msg="4theta Shear not correct when passing lists and mem_info",
+        )
+        assert_allclose(
+            cnshear,
+            expected["const_shear"],
+            **TOLERANCE,
+            err_msg="Constant Shear not correct when passing lists and mem_info",
+        )
+
         # Test without validation
         angsep, tshear, xshear = da.compute_tangential_and_cross_components(
             ra_lens=ra_lens,
@@ -434,6 +650,74 @@ def test_compute_tangential_and_cross_components(modeling_data):
             **TOLERANCE,
             err_msg="Cross Shear not correct when passing lists",
         )
+
+        ## Turn on quadrupole option
+        angsep, _, _, ftshear, cnshear = da.compute_tangential_and_cross_components(
+            ra_lens=ra_lens,
+            dec_lens=dec_lens,
+            ra_source=list(gals["ra"]),
+            dec_source=list(gals["dec"]),
+            shear1=list(gals["e1"]),
+            shear2=list(gals["e2"]),
+            geometry=geometry,
+            include_quadrupole=True,
+            phi_major=0.0,
+            validate_input=False,
+        )
+        assert_allclose(
+            ftshear,
+            expected["four_theta_shear"],
+            **TOLERANCE,
+            err_msg="4theta Shear not correct when passing lists and phi_major",
+        )
+        assert_allclose(
+            cnshear,
+            expected["const_shear"],
+            **TOLERANCE,
+            err_msg="Constant Shear not correct when passing listss and phi_major",
+        )
+
+        ## Pass members info instead of major axis directly
+        angsep, _, _, ftshear, cnshear = da.compute_tangential_and_cross_components(
+            ra_lens=ra_lens,
+            dec_lens=dec_lens,
+            ra_source=list(gals["ra"]),
+            dec_source=list(gals["dec"]),
+            shear1=list(gals["e1"]),
+            shear2=list(gals["e2"]),
+            geometry=geometry,
+            include_quadrupole=True,
+            info_mem=[np.array([119.99, 120.01]), np.array([42.0, 42.0]), np.array([1.0, 1.0])],
+            validate_input=False,
+        )
+        assert_allclose(
+            ftshear,
+            expected["four_theta_shear"],
+            **TOLERANCE,
+            err_msg="4theta Shear not correct when passing lists and mem_info",
+        )
+        assert_allclose(
+            cnshear,
+            expected["const_shear"],
+            **TOLERANCE,
+            err_msg="Constant Shear not correct when passing lists and mem_info",
+        )
+
+        # Test for ValueError if neither phi_major or mem positions are given with quadrupole
+        assert_raises(
+            ValueError,
+            da.compute_tangential_and_cross_components,
+            ra_lens=ra_lens,
+            dec_lens=dec_lens,
+            ra_source=list(gals["ra"]),
+            dec_source=list(gals["dec"]),
+            shear1=list(gals["e1"]),
+            shear2=list(gals["e2"]),
+            geometry=geometry,
+            include_quadrupole=True,
+            validate_input=False,
+        )
+
         # Test without validation and float arguments
         angsep, tshear, xshear = da.compute_tangential_and_cross_components(
             ra_lens=ra_lens,
@@ -491,6 +775,14 @@ def test_compute_tangential_and_cross_components(modeling_data):
     cluster = clmm.GalaxyCluster(
         unique_id="blah", ra=ra_lens, dec=dec_lens, z=z_lens, galcat=gals["ra", "dec", "e1", "e2"]
     )
+    cluster_quad = clmm.GalaxyCluster(
+        unique_id="blah",
+        ra=ra_lens,
+        dec=dec_lens,
+        z=z_lens,
+        galcat=gals["ra", "dec", "e1", "e2"],
+        include_quadrupole=True,
+    )
     # Test error with bad name/missing column
     assert_raises(
         TypeError, cluster.compute_tangential_and_cross_components, shape_component1="crazy name"
@@ -518,6 +810,39 @@ def test_compute_tangential_and_cross_components(modeling_data):
             **TOLERANCE,
             err_msg="Cross Shear not correct when using cluster method",
         )
+        # include_quadrupole=True, with phi_major input
+        _, _, _, ftshear2, cnshear2 = cluster_quad.compute_tangential_and_cross_components(
+            geometry=geometry, phi_major=0.0
+        )
+        assert_allclose(
+            ftshear2,
+            expected["four_theta_shear"],
+            **TOLERANCE,
+            err_msg="4theta Shear not correct when using cluster method w/ phi_major",
+        )
+        assert_allclose(
+            cnshear2,
+            expected["const_shear"],
+            **TOLERANCE,
+            err_msg="Constant Shear not correct when using cluster method w/ phi_major",
+        )
+        # include_quadrupole=True, with info_mem instead of phi_major input
+        _, _, _, ftshear3, cnshear3 = cluster_quad.compute_tangential_and_cross_components(
+            geometry=geometry,
+            info_mem=[np.array([119.99, 120.01]), np.array([42.0, 42.0]), np.array([1.0, 1.0])],
+        )
+        assert_allclose(
+            ftshear3,
+            expected["four_theta_shear"],
+            **TOLERANCE,
+            err_msg="4theta Shear not correct when using cluster method w/ info_mem",
+        )
+        assert_allclose(
+            cnshear3,
+            expected["const_shear"],
+            **TOLERANCE,
+            err_msg="Constant Shear not correct when using cluster method w/ info_mem",
+        )
     # Check behaviour for the deltasigma option.
     cosmo = clmm.Cosmology(H0=70.0, Omega_dm0=0.275, Omega_b0=0.025)
 
@@ -548,6 +873,18 @@ def test_compute_tangential_and_cross_components(modeling_data):
         is_deltasigma=True,
         sigma_c=None,
     )
+    # check validation between include_quadrupole and {phi_major|info_mem}
+    assert_raises(
+        TypeError,
+        da.compute_tangential_and_cross_components,
+        ra_lens=ra_lens,
+        dec_lens=dec_lens,
+        ra_source=gals["ra"],
+        dec_source=gals["dec"],
+        shear1=gals["e1"],
+        shear2=gals["e2"],
+        include_quadrupole=True,
+    )
     # test values
     for geometry, expected in geo_tests:
         angsep_DS, tDS, xDS = da.compute_tangential_and_cross_components(
@@ -568,6 +905,38 @@ def test_compute_tangential_and_cross_components(modeling_data):
             tDS, expected["tangential_DS"], reltol, err_msg="Tangential Shear not correct"
         )
         assert_allclose(xDS, expected["cross_DS"], reltol, err_msg="Cross Shear not correct")
+        ## Turn on include_quadrupole w/ phi_major
+        _, _, _, ftDS, cnDS = da.compute_tangential_and_cross_components(
+            ra_lens=ra_lens,
+            dec_lens=dec_lens,
+            ra_source=gals["ra"],
+            dec_source=gals["dec"],
+            shear1=gals["e1"],
+            shear2=gals["e2"],
+            is_deltasigma=True,
+            sigma_c=sigma_c,
+            geometry=geometry,
+            include_quadrupole=True,
+            phi_major=0.0,
+        )
+        assert_allclose(ftDS, expected["four_theta_DS"], reltol, err_msg="4theta shear not correct")
+        assert_allclose(cnDS, expected["const_DS"], reltol, err_msg="constant shear not correct")
+        ## Turn on include_quadrupole w/ info_mem
+        _, _, _, ftDS, cnDS = da.compute_tangential_and_cross_components(
+            ra_lens=ra_lens,
+            dec_lens=dec_lens,
+            ra_source=gals["ra"],
+            dec_source=gals["dec"],
+            shear1=gals["e1"],
+            shear2=gals["e2"],
+            is_deltasigma=True,
+            sigma_c=sigma_c,
+            geometry=geometry,
+            include_quadrupole=True,
+            info_mem=[np.array([119.99, 120.01]), np.array([42.0, 42.0]), np.array([1.0, 1.0])],
+        )
+        assert_allclose(ftDS, expected["four_theta_DS"], reltol, err_msg="4theta shear not correct")
+        assert_allclose(cnDS, expected["const_DS"], reltol, err_msg="constant shear not correct")
     # Tests with the cluster object
     # cluster object missing source redshift, and function call missing cosmology
     cluster = clmm.GalaxyCluster(
@@ -582,6 +951,14 @@ def test_compute_tangential_and_cross_components(modeling_data):
         z=z_lens,
         galcat=gals["ra", "dec", "e1", "e2", "z"],
     )
+    cluster_quad = clmm.GalaxyCluster(
+        unique_id="blah",
+        ra=ra_lens,
+        dec=dec_lens,
+        z=z_lens,
+        galcat=gals["ra", "dec", "e1", "e2", "z"],
+        include_quadrupole=True,
+    )
     assert_raises(TypeError, cluster.compute_tangential_and_cross_components, is_deltasigma=True)
     # check values for DeltaSigma
     for geometry, expected in geo_tests:
@@ -606,6 +983,41 @@ def test_compute_tangential_and_cross_components(modeling_data):
             reltol,
             err_msg="Cross Shear not correct when using cluster method",
         )
+        # Turn on include_quadrupole w/ phi_major
+        _, _, _, ftDS, cnDS = cluster_quad.compute_tangential_and_cross_components(
+            cosmo=cosmo, is_deltasigma=True, geometry=geometry, phi_major=0.0
+        )
+        assert_allclose(
+            ftDS,
+            expected["four_theta_DS"],
+            reltol,
+            err_msg="4theta Shear not correct when using cluster method w/ phi_major",
+        )
+        assert_allclose(
+            cnDS,
+            expected["const_DS"],
+            reltol,
+            err_msg="Constant Shear not correct when using cluster method w/ phi_major",
+        )
+        # Turn on include_quadrupole w/ info_mem
+        _, _, _, ftDS, cnDS = cluster_quad.compute_tangential_and_cross_components(
+            cosmo=cosmo,
+            is_deltasigma=True,
+            geometry=geometry,
+            info_mem=[np.array([119.99, 120.01]), np.array([42.0, 42.0]), np.array([1.0, 1.0])],
+        )
+        assert_allclose(
+            ftDS,
+            expected["four_theta_DS"],
+            reltol,
+            err_msg="4theta Shear not correct when using cluster method w/ info_mem",
+        )
+        assert_allclose(
+            cnDS,
+            expected["const_DS"],
+            reltol,
+            err_msg="Constant Shear not correct when using cluster method w/ info_mem",
+        )
 
     # test basic weights functionality
     cluster.compute_galaxy_weights()
diff --git a/tests/test_galaxycluster.py b/tests/test_galaxycluster.py
index 0102cc7f0..3b8d09d16 100644
--- a/tests/test_galaxycluster.py
+++ b/tests/test_galaxycluster.py
@@ -26,11 +26,13 @@ def test_initialization():
         "galcat": GCData(),
     }
     cl1 = clmm.GalaxyCluster(**testdict1)
+    cl2 = clmm.GalaxyCluster(**testdict1, include_quadrupole=True)
 
     assert_equal(testdict1["unique_id"], cl1.unique_id)
     assert_equal(testdict1["ra"], cl1.ra)
     assert_equal(testdict1["dec"], cl1.dec)
     assert_equal(testdict1["z"], cl1.z)
+    assert_equal(testdict1["z"], cl2.z)
     assert isinstance(cl1.galcat, GCData)
 
 
@@ -259,18 +261,35 @@ def test_integrity_of_lensfuncs():
     # Check metadata addition
     pzbins = np.linspace(0.0001, 5, 100)
     cluster = clmm.GalaxyCluster(unique_id="1", ra=161.3, dec=34.0, z=0.3, galcat=galcat)
+    cluster_quad = clmm.GalaxyCluster(
+        unique_id="1", ra=161.3, dec=34.0, z=0.3, galcat=galcat, include_quadrupole=True
+    )
     cluster.galcat.pzpdf_info["zbins"] = pzbins
     cluster.galcat["pzbins"] = [pzbins for i in range(len(z_src))]
     cluster.galcat["pzpdf"] = [multivariate_normal.pdf(pzbins, mean=z, cov=0.3) for z in z_src]
+    cluster_quad.galcat.pzpdf_info["zbins"] = pzbins
+    cluster_quad.galcat["pzbins"] = [pzbins for i in range(len(z_src))]
+    cluster_quad.galcat["pzpdf"] = [multivariate_normal.pdf(pzbins, mean=z, cov=0.3) for z in z_src]
 
     for pztype in ("individual_bins", "shared_bins"):
         cluster.galcat.pzpdf_info["type"] = pztype
+        cluster_quad.galcat.pzpdf_info["type"] = pztype
 
         cluster.compute_tangential_and_cross_components(
             is_deltasigma=True, use_pdz=True, cosmo=cosmo, add=True
         )
+        cluster_quad.compute_tangential_and_cross_components(
+            is_deltasigma=True,
+            use_pdz=True,
+            cosmo=cosmo,
+            add=True,
+            phi_major=0.0,
+            info_mem=[np.array([161.2, 161.4]), np.array([33.9, 34.1]), np.array([1.0, 1.0])],
+        )
         for comp_name in ("et", "ex"):
             assert_equal(cluster.galcat.meta[f"{comp_name}_sigmac_type"], "effective")
+        for comp_name in ("e_quad_4theta", "e_quad_const"):
+            assert_equal(cluster_quad.galcat.meta[f"{comp_name}_sigmac_type"], "effective")
 
 
 def test_integrity_of_probfuncs():
@@ -465,16 +484,35 @@ def test_plot_profiles():
             [ra_source, dec_source, shear1, shear2, z_src], names=("ra", "dec", "e1", "e2", "z")
         ),
     )
+    cluster_quad = clmm.GalaxyCluster(
+        unique_id="test",
+        ra=ra_lens,
+        dec=dec_lens,
+        z=z_lens,
+        galcat=GCData(
+            [ra_source, dec_source, shear1, shear2, z_src], names=("ra", "dec", "e1", "e2", "z")
+        ),
+        include_quadrupole=True,
+    )
     cluster.compute_tangential_and_cross_components()
+    cluster_quad.compute_tangential_and_cross_components(
+        phi_major=0.0,
+        info_mem=[np.array([119.9, 120, 1]), np.array([41.9, 42.1]), np.array([1.0, 1.0])],
+    )
     cluster.make_radial_profile(bin_units, bins=bins_radians, include_empty_bins=True)
+    cluster_quad.make_radial_profile(bin_units, bins=bins_radians, include_empty_bins=True)
     # missing profile name
     assert_raises(ValueError, cluster.plot_profiles, table_name="made_up_table")
+    assert_raises(ValueError, cluster_quad.plot_profiles, table_name="made_up_table")
     # missing shear component
     assert_raises(ValueError, cluster.plot_profiles, cross_component="made_up_component")
+    assert_raises(ValueError, cluster_quad.plot_profiles, quad_4theta_component="made_up_component")
     # check basic plot is working
     cluster.plot_profiles()
+    cluster_quad.plot_profiles()
     # check it passes missing a component error
     cluster.plot_profiles(cross_component_error="made_up_component")
+    cluster_quad.plot_profiles(quad_4theta_component_error="made_up_component")
 
 
 def test_coordinate_system():
diff --git a/tests/test_mockdata.py b/tests/test_mockdata.py
index 637defe75..f9e3d7599 100644
--- a/tests/test_mockdata.py
+++ b/tests/test_mockdata.py
@@ -259,13 +259,17 @@ def test_shapenoise():
 
     # Verify that the shape noise is Gaussian around 0 (for the very small shear here)
     sigma = 0.25
-    data = mock.generate_galaxy_catalog(10**12.0, 0.3, 4, cosmo, 0.8, ngals=50000, shapenoise=sigma)
+    data = mock.generate_galaxy_catalog(
+        10**12.0, 0.3, 4, cosmo, 0.8, ngals=50000, shapenoise=sigma
+    )
     # Check that there are no galaxies with |e|>1
     assert_equal(np.count_nonzero((data["e1"] > 1) | (data["e1"] < -1)), 0)
     assert_equal(np.count_nonzero((data["e2"] > 1) | (data["e2"] < -1)), 0)
     # Check that shape noise is Guassian with correct std dev
     bins = np.arange(-1, 1.1, 0.1)
-    gauss = 5000 * np.exp(-0.5 * (bins[:-1] + 0.05) ** 2 / sigma**2) / (sigma * np.sqrt(2 * np.pi))
+    gauss = (
+        5000 * np.exp(-0.5 * (bins[:-1] + 0.05) ** 2 / sigma**2) / (sigma * np.sqrt(2 * np.pi))
+    )
     assert_allclose(np.histogram(data["e1"], bins=bins)[0], gauss, atol=50, rtol=0.05)
     assert_allclose(np.histogram(data["e2"], bins=bins)[0], gauss, atol=50, rtol=0.05)
 
diff --git a/tests/test_theory.py b/tests/test_theory.py
index 9b9d82565..45fe03e9b 100644
--- a/tests/test_theory.py
+++ b/tests/test_theory.py
@@ -556,6 +556,136 @@ def test_profiles(modeling_data, profile_init):
                 assert_raises(NotImplementedError, mod.set_projected_quad, True)
 
 
+def test_triaxial(modeling_data):
+    cfg = load_validation_config()
+    cosmo = cfg["cosmo"]
+
+    # Object Oriented tests
+    mod = theo.Modeling()
+    mod.set_cosmo(cosmo)
+    mod.set_concentration(cfg["SIGMA_PARAMS"]["cdelta"])
+    mod.set_mass(cfg["SIGMA_PARAMS"]["mdelta"])
+
+    if mod.backend not in ["ccl", "nc"]:
+        assert_raises(
+            NotImplementedError,
+            mod.eval_excess_surface_density_triaxial,
+            1.0,
+            cfg["SIGMA_PARAMS"]["z_cl"],
+            0.1,
+            "mono",
+            100,
+        )
+    else:
+        # Just checking that it runs and returns array of the right length
+        # To be updated with proper comparison to benchmark when available
+        assert_equal(
+            len(
+                mod.eval_excess_surface_density_triaxial(
+                    cfg["SIGMA_PARAMS"]["r_proj"], cfg["SIGMA_PARAMS"]["z_cl"], 0.1, "mono", 100
+                )
+            ),
+            len(cfg["SIGMA_PARAMS"]["r_proj"]),
+        )
+        assert_equal(
+            len(
+                mod.eval_excess_surface_density_triaxial(
+                    cfg["SIGMA_PARAMS"]["r_proj"],
+                    cfg["SIGMA_PARAMS"]["z_cl"],
+                    0.1,
+                    "quad_4theta",
+                    100,
+                )
+            ),
+            len(cfg["SIGMA_PARAMS"]["r_proj"]),
+        )
+        assert_equal(
+            len(
+                mod.eval_excess_surface_density_triaxial(
+                    cfg["SIGMA_PARAMS"]["r_proj"],
+                    cfg["SIGMA_PARAMS"]["z_cl"],
+                    0.1,
+                    "quad_const",
+                    100,
+                )
+            ),
+            len(cfg["SIGMA_PARAMS"]["r_proj"]),
+        )
+
+        # Check that wrong term value is caught in OO-oriented and functional interface.
+        assert_raises(
+            ValueError,
+            theo.compute_excess_surface_density_triaxial,
+            cfg["SIGMA_PARAMS"]["r_proj"],
+            cfg["SIGMA_PARAMS"]["mdelta"],
+            cfg["SIGMA_PARAMS"]["cdelta"],
+            cfg["SIGMA_PARAMS"]["z_cl"],
+            0.1,
+            cosmo,
+            term="bleh",
+            n_grid=500,
+        )
+        assert_raises(
+            ValueError,
+            mod.eval_excess_surface_density_triaxial,
+            cfg["SIGMA_PARAMS"]["r_proj"],
+            cfg["SIGMA_PARAMS"]["z_cl"],
+            0.1,
+            "bleh",
+            500,
+        )
+
+        # Checks that OO-oriented and functional interface give the same results
+        assert_allclose(
+            theo.compute_excess_surface_density_triaxial(
+                cfg["SIGMA_PARAMS"]["r_proj"],
+                cfg["SIGMA_PARAMS"]["mdelta"],
+                cfg["SIGMA_PARAMS"]["cdelta"],
+                cfg["SIGMA_PARAMS"]["z_cl"],
+                0.1,
+                cosmo,
+                term="quad_4theta",
+                n_grid=500,
+            ),
+            mod.eval_excess_surface_density_triaxial(
+                cfg["SIGMA_PARAMS"]["r_proj"], cfg["SIGMA_PARAMS"]["z_cl"], 0.1, "quad_4theta", 500
+            ),
+            **TOLERANCE,
+        )
+        assert_allclose(
+            theo.compute_excess_surface_density_triaxial(
+                cfg["SIGMA_PARAMS"]["r_proj"],
+                cfg["SIGMA_PARAMS"]["mdelta"],
+                cfg["SIGMA_PARAMS"]["cdelta"],
+                cfg["SIGMA_PARAMS"]["z_cl"],
+                0.1,
+                cosmo,
+                term="quad_const",
+                n_grid=500,
+            ),
+            mod.eval_excess_surface_density_triaxial(
+                cfg["SIGMA_PARAMS"]["r_proj"], cfg["SIGMA_PARAMS"]["z_cl"], 0.1, "quad_const", 500
+            ),
+            **TOLERANCE,
+        )
+        assert_allclose(
+            theo.compute_excess_surface_density_triaxial(
+                cfg["SIGMA_PARAMS"]["r_proj"],
+                cfg["SIGMA_PARAMS"]["mdelta"],
+                cfg["SIGMA_PARAMS"]["cdelta"],
+                cfg["SIGMA_PARAMS"]["z_cl"],
+                0.1,
+                cosmo,
+                term="mono",
+                n_grid=500,
+            ),
+            mod.eval_excess_surface_density_triaxial(
+                cfg["SIGMA_PARAMS"]["r_proj"], cfg["SIGMA_PARAMS"]["z_cl"], 0.1, "mono", 500
+            ),
+            **TOLERANCE,
+        )
+
+
 def test_2halo_term(modeling_data):
     cfg = load_validation_config()
     cosmo = cfg["cosmo"]
@@ -1304,7 +1434,7 @@ def test_mass_conversion(modeling_data, profile_init):
 def test_delta_mdef_virial(modeling_data):
     if modeling_data["nick"] in ["nc", "ccl"]:
         cfg = load_validation_config()
-        cosmo = cfg['cosmo']
+        cosmo = cfg["cosmo"]
         mod = theo.Modeling(massdef="virial")
         mod.set_cosmo(cosmo)
         assert_equal(mod._get_delta_mdef(0.1), 111)