diff --git a/desc/coils.py b/desc/coils.py
index f184365918..d8a84bbe1a 100644
--- a/desc/coils.py
+++ b/desc/coils.py
@@ -1,7 +1,7 @@
 """Classes for magnetic field coils."""
 
 import numbers
-from abc import ABC
+from abc import ABC, abstractmethod
 from collections.abc import MutableSequence
 
 import numpy as np
@@ -16,10 +16,12 @@
     tree_unstack,
     vmap,
 )
+from desc.basis import FourierSeries
 from desc.compute import get_params, rpz2xyz, rpz2xyz_vec, xyz2rpz, xyz2rpz_vec
 from desc.compute.geom_utils import reflection_matrix
 from desc.compute.utils import _compute as compute_fun
 from desc.compute.utils import safenorm
+from desc.compute.utils import dot
 from desc.geometry import (
     FourierPlanarCurve,
     FourierRZCurve,
@@ -29,7 +31,7 @@
 from desc.grid import LinearGrid
 from desc.magnetic_fields import _MagneticField
 from desc.optimizable import Optimizable, OptimizableCollection, optimizable_parameter
-from desc.utils import equals, errorif, flatten_list, warnif
+from desc.utils import copy_coeffs, equals, errorif, flatten_list, warnif
 
 
 @jit
@@ -123,6 +125,172 @@ def biot_savart_quad(eval_pts, coil_pts, tangents, current):
     return B
 
 
+@jit
+def finite_build_regularization_rect(a, b):
+    """Computes the regularization term for a rectangular finite-build coil.
+
+    The regularization term is used in the denominator of the regularized Biot-Savart
+    integral for the finite-build coil. This term ensures that the singularity at the coil
+    filament centerline is integrated correctly. The finite-build self-field is computed by
+    the procedure outlined in [1].
+
+    Parameters
+    ----------
+    a : float
+        Coil cross section dimension 1 (Order does not matter)
+    b : float
+        Coil cross section dimension 2
+
+    Returns
+    -------
+    float
+       Regularization term for the finite build Biot Savart denominator
+
+    [1] Landreman et. al, "Efficient calculation of self magnetic field, self-force,
+    and self-inductance for electromagnetic coils. II. Rectangular cross-section" (2023)
+    """
+
+    k = -(a**4 - 6 * a**2 * b**2 + b**4) / (6 * a**2 * b**2) * jnp.log(a / b + b / a)
+    +b * b / (6 * a * a) * jnp.log(b / a)
+    +a * a / (6 * b * b) * jnp.log(a / b)
+    +(4.0 * b) / (3 * a) * jnp.arctan(a / b)
+    +(4.0 * a) / (3 * b) * jnp.arctan(b / a)
+
+    delta = jnp.exp(-(25.0 / 6) + k)
+
+    return a * b * delta
+
+
+@jit
+def biot_savart_quad_regularized(eval_pts, coil_pts, tangents, current, regularization):
+    """Regularized Biot-Savart law for filamentary coil using numerical quadrature.
+
+    Parameters
+    ----------
+    eval_pts : array-like shape(n,3)
+        Evaluation points in cartesian coordinates
+    coil_pts : array-like shape(m,3)
+        Points in cartesian space defining coil
+    tangents : array-like, shape(m,3)
+        Tangent vectors to the coil at coil_pts. If the curve is given
+        by x(s) with curve parameter s, coil_pts = x, tangents = dx/ds*ds where
+        ds is the spacing between points.
+    current : float
+        Current through the coil (in Amps).
+    regularization : float
+        Regularization term for the Biot Savart denominator
+
+    Returns
+    -------
+    B : ndarray, shape(n,3)
+        magnetic field in cartesian components at specified points
+    """
+
+    dl = tangents
+    R_vec = eval_pts[jnp.newaxis, :] - coil_pts[:, jnp.newaxis, :]
+    R_mag = jnp.linalg.norm(R_vec, axis=-1)
+
+    vec = jnp.cross(dl[:, jnp.newaxis, :], R_vec, axis=-1)
+    denom = (R_mag**2 + regularization) ** (3 / 2)
+
+    # 1e-7 == mu_0/(4 pi)
+    B = jnp.sum(1.0e-7 * current * vec / denom[:, :, None], axis=0)
+    return B
+
+
+@jit
+def biot_savart_quad_regularized_singularity_sub(
+    ds,
+    eval_pts,
+    coil_pts,
+    eval_angles,
+    coil_angles,
+    eval_prime,
+    coil_prime,
+    eval_prime_prime,
+    curvatures,
+    binormals,
+    current,
+    regularization,
+):
+    """Regularized Biot-Savart law for filamentary coil using numerical quadrature.
+    Uses singularity subtraction method for coils outlined in [1]. Requires that the
+    evaluation points are on the coil centerline -- usually this means that eval_points = coil_points
+    and eval_angles = coil_angles, etc.
+
+    Parameters
+    ----------
+    ds : array-like shape(m,)
+        Spacing between points in the coil discretization
+    eval_pts : array-like shape(n,3)
+        Evaluation points in cartesian coordinates
+    coil_pts : array-like shape(m,3)
+        Points in cartesian space defining coil
+    eval_angles : array-like shape(n,)
+        Angles of the evaluation points (curve parameter s), in radians with a periodicity of 2*pi
+    coil_angles : array-like shape(m,)
+        Angles of the coil points (curve parameter s), in radians with a periodicity of 2*pi
+    eval_prime : array-like shape(n,3)
+        Derivative of the evaluation points with respect to the curve parameter, dx/ds for curve x(s)
+    coil_prime : array-like shape(m,3)
+        Derivative of the coil points with respect to the curve parameter, dx/ds for curve x(s)
+    eval_prime_prime : array-like shape(n,3)
+        Second derivative of the evaluation points with respect to the curve parameter, d^2x/ds^2 for curve x(s)
+    curvatures : array-like shape(n,)
+        Curvature of the coil centerline at the evaluation points
+    binormals : array-like shape(n,3)
+        Frenet-Serret binormal vectors at the evaluation points
+    current : float
+        Current through the coil (in Amps).
+    regularization : float
+        Regularization term for the Biot Savart denominator
+
+    Returns
+    -------
+    B : ndarray, shape(n,3)
+        magnetic field in cartesian components at specified points
+
+    [1] Landreman et. al, "Efficient calculation of self magnetic field, self-force,
+    and self-inductance for electromagnetic coils. II. Rectangular cross-section" (2023)
+    """
+
+    R_vec = eval_pts[jnp.newaxis, :] - coil_pts[:, jnp.newaxis, :]
+    R_mag = safenorm(R_vec, axis=-1) #used because of potential singularity at R=0 if eval and coil points are the same
+    ds = ds[:, jnp.newaxis]
+
+    vec = jnp.cross(coil_prime[:, jnp.newaxis, :], R_vec, axis=-1)
+    denom = (R_mag**2 + regularization) ** (3 / 2) / ds
+
+    phi_diff = coil_angles[:, jnp.newaxis] - eval_angles[jnp.newaxis, :]
+
+    eval_prime_squared = safenorm(eval_prime, axis=-1) ** 2
+
+    vec2 = (
+        -jnp.cross(eval_prime, eval_prime_prime, axis=-1)[jnp.newaxis, :, :]
+        * (1 - jnp.cos(phi_diff))[:, :, jnp.newaxis]
+    )
+    denom2 = (
+        (2 - 2 * jnp.cos(phi_diff)) * eval_prime_squared[jnp.newaxis, :]
+        + regularization
+    ) ** (3 / 2) / ds
+
+    B = jnp.sum(
+        vec / denom[:, :, jnp.newaxis] + vec2 / denom2[:, :, jnp.newaxis], axis=0
+    )
+
+    B += (
+        0.5
+        * curvatures[:, jnp.newaxis]
+        * binormals
+        * (-2 + jnp.log(64 * eval_prime_squared / regularization))[:, jnp.newaxis]
+    )
+
+    # 1e-7 == mu_0/(4 pi)
+    B = 1.0e-7 * current * B
+
+    return B
+
+
 class _Coil(_MagneticField, Optimizable, ABC):
     """Base class representing a magnetic field coil.
 
@@ -920,6 +1088,7 @@ def _check_type(coil0, coil):
         FourierXYZCoil: ["X_basis", "Y_basis", "Z_basis"],
         FourierPlanarCoil: ["r_basis"],
         SplineXYZCoil: ["method", "N", "knots"],
+        FourierPlanarFiniteBuildCoil: ["r_basis"],
     }
 
     for attr in attrs[coil0.__class__]:
@@ -936,6 +1105,395 @@ def _check_type(coil0, coil):
         )
 
 
+class _FramedCoil(_Coil, Optimizable, ABC):
+    """Base class representing a magnetic field coil with a frame representing winding angle.
+
+    Parameters
+    ----------
+    alpha_n : array-like
+        Fourier coefficients for the winding angle alpha as a function of toroidal angle phi.
+    modes : array-like
+        Mode numbers associated with alpha_n. If not given defaults to [-n:n].
+    """
+
+    _io_attrs_ = _Coil._io_attrs_ + ["_alpha_n", "_alpha_basis"]
+
+    def __init__(self, alpha_n=[0, 0, 0], modes=None, *args, **kwargs):
+
+        alpha_n = np.atleast_1d(alpha_n)
+        if modes is None:
+            modes = np.arange(-(alpha_n.size // 2), alpha_n.size // 2 + 1)
+        else:
+            modes = np.asarray(modes)
+
+        assert issubclass(modes.dtype.type, np.integer)
+
+        assert alpha_n.size == modes.size, "alpha_n and modes must be the same size"
+
+        N = np.max(abs(modes))
+        self._alpha_basis = FourierSeries(N, NFP=1, sym=False)
+        self._alpha_n = copy_coeffs(alpha_n, modes, self.alpha_basis.modes[:, 2])
+
+        super().__init__(*args, **kwargs)
+
+    @optimizable_parameter
+    @property
+    def alpha_n(self):
+        """ndarray: Twist angle of the coil, represented as a fourier series."""
+        return self._alpha_n
+
+    @alpha_n.setter
+    def alpha_n(self, new):
+        if len(new) == self.alpha_basis.num_modes:
+            self._alpha_n = jnp.asarray(new)
+        else:
+            raise ValueError(
+                f"alpha_n should have the same size as the basis, got {len(new)} for "
+                + f"basis with {self.alpha_basis.num_modes} modes."
+            )
+
+    @property
+    def alpha_basis(self):
+        """Spectral basis for alpha Fourier series."""
+        return self._alpha_basis
+
+    @property
+    def alpha_N(self):
+        """Maximum mode number."""
+        return max(self._alpha_basis.N)
+
+
+class _FiniteBuildCoil(_FramedCoil, Optimizable, ABC):
+    """Base class representing a magnetic field coil with finite build dimensions. Implements the compute_self_field method.
+
+    Subclasses should inherit from this class as well as a subclass of Coil.
+
+    Parameters
+    ----------
+    cross_section_dims : array-like
+        Dimensions of the coil cross section, with 1 or 2 dimensions depending on the cross section shape (circular or rectangular).\
+        For circular cross sections, this should be a single value representing the radius.\
+        For rectangular cross sections, this should be a 2-element array representing the dimensions in the p and q directions, respectively.\
+        Refer to figure 3 in https://arxiv.org/pdf/2310.12087 for the p and q directions.
+
+    cross_section_shape : str
+        Shape of the coil cross section, either 'circular' or 'rectangular'.
+    """
+
+    _io_attrs_ = (
+        _FramedCoil._io_attrs_ + ["_cross_section_shape"] + ["_cross_section_dims"]
+    )
+
+    def __init__(self, cross_section_dims, cross_section_shape, *args, **kwargs):
+        if cross_section_shape == "circular":
+            assert (
+                len(cross_section_dims) == 1
+            ), "Circular cross section coils can only have one radius dimension"
+        elif cross_section_shape == "rectangular":
+            assert (
+                len(cross_section_dims) == 2
+            ), "Rectangular cross section coils can only have exactly two dimensions"
+        else:
+            raise ValueError(
+                "cross_section_shape must be 'circular' or 'rectangular', got "
+                f"{cross_section_shape}"
+            )
+        self._cross_section_shape = cross_section_shape
+        self._cross_section_dims = cross_section_dims
+        super().__init__(*args, **kwargs)
+
+    @optimizable_parameter
+    @property
+    def cross_section_dims(self):
+        """ndarray: Geometry of the coil, with 1 or 2 cross section dimensions depending on shape."""
+        return self._cross_section_dims
+
+    @cross_section_dims.setter
+    def cross_section_dims(self, new):
+        if self._cross_section_shape == "circular":
+            assert (
+                len(new) == 1
+            ), "Circular cross section coils can only have one radius dimension"
+        elif self._cross_section_shape == "rectangular":
+            assert (
+                len(new) == 2
+            ), "Rectangular cross section coils can only have exactly two dimensions"
+        self._cross_section_dims = new
+
+    @property
+    def cross_section_shape(self):
+        """str: Shape of the coil cross section, either 'circular' or 'rectangular'."""
+        return self._cross_section_shape
+
+    def compute_self_field(
+        self, xsection_grid, centerline_grid=None, coil_frame=False, params=None
+    ):
+        """Compute the magnetic field from the coil on the coil itself, with a rectangular cross section.
+        A mask is returned to indicate which points are on the coil centerline, to allow for separate handling of the field on the centerline.
+
+        Parameters
+        ----------
+        xsection_grid : LinearGrid, int or None
+            Grid used to evaluate the field on the coil cross section. If an integer, uses that many equally spaced points in each dimension of the cross section.
+            If provided, must be a 2D grid with L and M dimensions corresponding to the cross section spacing desired with endpoint = True.
+        centerline_grid : LinearGrid or int, optional
+            Grid used to evaluate the coil centerline. If an integer, uses 2x that many equally spaced points in each dimension.
+            If provided, must be a 1D grid with N dimension corresponding to the centerline spacing desired.
+        coil_frame : bool, optional
+            Whether to compute the field in the t, p ,q coil frame. Default is False, which computes the field in the lab frame.
+        params : dict, optional
+            Parameters to pass to the coil object.
+
+        Returns
+        -------
+        field : ndarray, shape(n,3)
+            magnetic field at specified points, at a combination of the coil centerline and cross section grids.
+        positions : ndarray, shape(n,3)
+            positions of the field points in the lab frame, currently in X,Y,Z coordinates.
+        centerline_mask : ndarray, shape(n,)
+            boolean mask indicating whether the field point is on the coil centerline or not.
+        """
+
+        if self.cross_section_shape == "circular":
+            return self.compute_self_field_circ(
+                xsection_grid, centerline_grid, coil_frame, params
+            )
+        if self.cross_section_shape == "rectangular":
+            return self.compute_self_field_rect(
+                xsection_grid, centerline_grid, coil_frame, params
+            )
+
+    def compute_self_field_rect(
+        self, xsection_grid, centerline_grid=None, coil_frame=False, params=None
+    ):
+        """
+        # Compute the magnetic field from the coil on the coil itself, with a rectangular cross section.
+        #"""
+
+        if params is None:
+            current = self.current
+            cross_section_dims = self.cross_section_dims
+        else:
+            current = params.get("current", self.current)
+            cross_section_dims = params.get(
+                "cross_section_dims", self.cross_section_dims
+            )
+
+        # set cross section grid if not provided for u,v cross sectional coordinates
+        # L has a doubled grid size to be consistent with how M and N work
+        if xsection_grid is None:
+            xsection_grid = LinearGrid(L=4, M=2, endpoint=True)
+        elif isinstance(xsection_grid, numbers.Integral):
+            xsection_grid = LinearGrid(
+                L=xsection_grid * 2, M=xsection_grid, endpoint=True
+            )
+        elif isinstance(xsection_grid, LinearGrid) and not xsection_grid.endpoint:
+            raise ValueError(
+                "The cross section grid must have endpoint=True to compute the self field"
+            )
+        else:
+            xsection_grid = (
+                xsection_grid.copy()
+            )  # is this memory efficient? Don't want to mutate the input grid
+
+        # set centerline grid if not provided for phi cross sectional coordinates
+        if centerline_grid is None:
+            centerline_grid = LinearGrid(N=50)
+        elif isinstance(centerline_grid, numbers.Integral):
+            centerline_grid = LinearGrid(N=centerline_grid)
+
+        # validate the grid dimensions
+        if xsection_grid.N != 1:
+            raise ValueError("The cross section grid must have dimension N=1")
+        if centerline_grid.L != 1 or centerline_grid.M != 1:
+            raise ValueError("The centerline grid must have dimension L=1 and M=1")
+
+        # expand the xsection grid to include the centerline grid dimensions
+        L = xsection_grid.L
+        M = xsection_grid.M
+        N = centerline_grid.N
+        xsection_grid.change_resolution(L, M, N)
+
+        abdelta = finite_build_regularization_rect(
+            cross_section_dims[0], cross_section_dims[1]
+        )
+
+        # B_b and B_reg are only computed on the centerline
+        B_b_fb = self.compute("B_b_fb", grid=centerline_grid, params=params)["B_b_fb"]
+
+        # compute regularized self field integral
+        data = self.compute(
+            ["x", "x_s", "x_ss", "ds", "s", "curvature", "frenet_binormal"],
+            grid=centerline_grid,
+            params=params,
+            basis="xyz",
+        )
+        B_reg_fb = biot_savart_quad_regularized_singularity_sub(
+            data["ds"],
+            data["x"],
+            data["x"],
+            data["s"],
+            data["s"],
+            data["x_s"],
+            data["x_s"],
+            data["x_ss"],
+            data["curvature"],
+            data["frenet_binormal"],
+            current,
+            abdelta,
+        )
+
+        # get vector series along the coil centerlines
+        p_frame = self.compute("p_frame", grid=centerline_grid, params=params)[
+            "p_frame"
+        ]
+        q_frame = self.compute("q_frame", grid=centerline_grid, params=params)[
+            "q_frame"
+        ]
+
+        # also need curvature parameters
+        curv1_frame = self.compute("curv1_frame", grid=centerline_grid, params=params)[
+            "curv1_frame"
+        ]
+        curv2_frame = self.compute("curv2_frame", grid=centerline_grid, params=params)[
+            "curv2_frame"
+        ]
+
+        # expand the vector series to include the cross section dimensions
+        p_frame = xsection_grid.expand(p_frame, "zeta")
+        q_frame = xsection_grid.expand(q_frame, "zeta")
+        curv1_frame = xsection_grid.expand(curv1_frame, "zeta")
+        curv2_frame = xsection_grid.expand(curv2_frame, "zeta")
+        x_centerline = xsection_grid.expand(
+            data["x"], "zeta"
+        )  # needed to get lab frame coordinates of the coil points
+        B_b_fb = xsection_grid.expand(B_b_fb, "zeta")
+        B_reg_fb = xsection_grid.expand(B_reg_fb, "zeta")
+
+        # pack the parameters for the self field computation
+        B_fb_params = {
+            "p_frame": p_frame,
+            "q_frame": q_frame,
+            "current": current,
+            "cross_section_dims": cross_section_dims,
+            "curv1_frame": curv1_frame,
+            "curv2_frame": curv2_frame,
+            "x_centerline": x_centerline,
+        }
+        B_fb_params = params | B_fb_params if params is not None else B_fb_params
+
+        B_0_fb = compute_fun(
+            self,
+            "B_0_fb",
+            transforms={"grid": xsection_grid},
+            params=B_fb_params,
+            profiles={},
+        )["B_0_fb"]
+        B_kappa_fb = compute_fun(
+            self,
+            "B_kappa_fb",
+            transforms={"grid": xsection_grid},
+            params=B_fb_params,
+            profiles={},
+        )["B_kappa_fb"]
+
+        B_self = B_0_fb + B_kappa_fb + B_b_fb + B_reg_fb
+
+        # get position of field measurement points in lab frame
+        x_fb = compute_fun(
+            self,
+            "x_fb",
+            transforms={"grid": xsection_grid},
+            params=B_fb_params,
+            profiles={},
+        )["x_fb"]
+
+        if coil_frame:  # reproject the field to the t,p,q frame
+            t_frame = self.compute(
+                "centroid_tangent", grid=centerline_grid, params=params
+            )["centroid_tangent"]
+            t_frame = xsection_grid.expand(t_frame, "zeta")
+
+            B_t = dot(B_self, t_frame)
+            B_p = dot(B_self, p_frame)
+            B_q = dot(B_self, q_frame)
+            B_self = jnp.stack((B_t, B_p, B_q), axis=-1)
+
+        # find mask for the grid axis to prevent downstream singularities with biot savart
+        centerline_mask = jnp.zeros(len(xsection_grid.nodes), dtype=bool)
+        centerline_mask = centerline_mask.at[(L + 1) * (2 * M + 1) // 2 :: (L + 1) * (2 * M + 1)].set(True)
+
+        return B_self, x_fb, centerline_mask
+
+    def compute_self_field_circ(
+        self, xsection_grid, centerline_grid=None, coil_frame=False, params=None
+    ):
+        return NotImplementedError(
+            "Circular cross section self field not implemented yet"
+        )
+
+
+class FourierPlanarFiniteBuildCoil(_FiniteBuildCoil, FourierPlanarCoil):
+    """Coil that lies in a plane, with a finite cross section.
+
+    Refer to FourierPlanarCoil for a description of the parameterization for the planar coil centerline.
+    In the case of rectangular cross sections, the coil cross section is assumed to remain aligned
+    with respect to the coil plane. The first dimension of the rectangular cross section is within the coil plane,
+    and the second dimension is in the coil plane normal direction. No twist angle is assumed for this coil.
+
+    Parameters
+    ----------
+    cross_section_dims : array-like
+        Dimensions of the coil cross section, with 1 or 2 dimensions depending on the cross section shape (circular or rectangular).
+    cross_section_shape : str
+        Shape of the coil cross section, either 'circular' or 'rectangular'.
+    current : float
+        Current through the coil, in Amperes.
+    center : array-like, shape(3,)
+        Coordinates of center of curve, in system determined by basis.
+    normal : array-like, shape(3,)
+        Components of normal vector to planar surface, in system determined by basis.
+    r_n : array-like
+        Fourier coefficients for radius from center as function of polar angle
+    modes : array-like
+        mode numbers associated with r_n
+    basis : {'xyz', 'rpz'}
+        Coordinate system for center and normal vectors. Default = 'xyz'.
+    name : str
+        Name for this coil.
+    """
+
+    _io_attrs_ = _FiniteBuildCoil._io_attrs_ + FourierPlanarCoil._io_attrs_
+
+    def __init__(
+        self,
+        cross_section_dims=[0.1],
+        cross_section_shape="circular",
+        current=1,
+        center=[10, 0, 0],
+        normal=[0, 1, 0],
+        r_n=2,
+        modes=None,
+        basis="xyz",
+        name="",
+    ):
+        alpha_n = [0, 0, 0]  # by default, this coil has no twist
+        alpha_modes = None
+        super().__init__(
+            cross_section_dims,
+            cross_section_shape,
+            alpha_n,
+            alpha_modes,
+            current,
+            center,
+            normal,
+            r_n,
+            modes,
+            basis,
+            name,
+        )
+
+
 class CoilSet(OptimizableCollection, _Coil, MutableSequence):
     """Set of coils of different geometry but shared parameterization and resolution.
 
diff --git a/desc/compute/_curve.py b/desc/compute/_curve.py
index 818da18092..d22b8119a3 100644
--- a/desc/compute/_curve.py
+++ b/desc/compute/_curve.py
@@ -1028,3 +1028,72 @@ def _length_SplineXYZCurve(params, transforms, profiles, data, **kwargs):
         # but also works if grid.endpoint is False
         data["length"] = jnp.sum(T * data["ds"])
     return data
+
+
+@register_compute_fun(
+    name="centroid_tangent",
+    label="\\mathbf{T}_{\\mathrm{Centroid}}",
+    units="~",
+    units_long="None",
+    description="Tangent unit vector to curve in centroid frame (see Singh et al. 2020, section 3.1)",
+    dim=3,
+    params=[],
+    transforms={},
+    profiles=[],
+    coordinates="s",
+    data=["x_s"],
+    parameterization="desc.geometry.core.Curve",
+)
+def _centroid_tangent(params, transforms, profiles, data, **kwargs):
+    # this is defined identically to the Frenet-Serret tangent
+    data["centroid_tangent"] = (
+        data["x_s"] / jnp.linalg.norm(data["x_s"], axis=-1)[:, None]
+    )
+    return data
+
+
+@register_compute_fun(
+    name="centroid_normal",
+    label="\\mathbf{N}_{\\mathrm{Centroid}}",
+    units="~",
+    units_long="None",
+    description="Normal unit vector to curve in centroid frame (see Singh et al. 2020, section 3.1)",
+    dim=3,
+    params=[],
+    transforms={},
+    profiles=[],
+    coordinates="s",
+    data=["x", "center", "centroid_tangent"],
+    parameterization="desc.geometry.core.Curve",
+)
+def _centroid_normal(params, transforms, profiles, data, **kwargs):
+    delta = data["x"] - data["center"]  # do I need to convert this to xyz?
+    delta_orth = (
+        delta - dot(delta, data["centroid_tangent"])[:, None] * data["centroid_tangent"]
+    )
+    delta_orth = delta_orth / jnp.linalg.norm(delta_orth, axis=-1)[:, None]
+    data["centroid_normal"] = delta_orth
+
+    return data
+
+
+@register_compute_fun(
+    name="centroid_binormal",
+    label="\\mathbf{B}_{\\mathrm{Centroid}}",
+    units="~",
+    units_long="None",
+    description="Binormal unit vector to curve in centroid frame (see Singh et al. 2020, section 3.1)",
+    dim=3,
+    params=[],
+    transforms={},
+    profiles=[],
+    coordinates="s",
+    data=["centroid_tangent", "centroid_normal"],
+    parameterization="desc.geometry.core.Curve",
+)
+def _centroid_binormal(params, transforms, profiles, data, **kwargs):
+    data["centroid_binormal"] = cross(
+        data["centroid_tangent"], data["centroid_normal"]
+    )  # TODO: figure out if rotation is necessary for these unit vecs
+
+    return data
diff --git a/desc/compute/_field.py b/desc/compute/_field.py
index eef1354e3d..26282f51a7 100644
--- a/desc/compute/_field.py
+++ b/desc/compute/_field.py
@@ -3516,3 +3516,323 @@ def _L_grad_B(params, transforms, profiles, data, **kwargs):
 def _K_vc(params, transforms, profiles, data, **kwargs):
     data["K_vc"] = cross(data["n_rho"], data["B"]) / mu_0
     return data
+
+
+@register_compute_fun(
+    name="p_frame",
+    label="\\mathbf{p}_{\\mathrm{Frame}}",
+    units="~",
+    units_long="None",
+    description="P unit vector for framed coil with arbitrary rotation fourier series",
+    dim=3,
+    params=["alpha_n"],
+    transforms={"alpha": [[0, 0, 0]]},
+    profiles=[],
+    coordinates="s",
+    data=["centroid_normal", "centroid_binormal"],
+    parameterization="desc.coils._FramedCoil",
+)
+def _p_frame(params, transforms, profiles, data, **kwargs):
+    alpha = transforms["alpha"].transform(
+        params["alpha_n"], dz=0
+    )  # TODO: check if this is right
+
+    p_frame = (
+        data["centroid_normal"] * jnp.cos(alpha)[:, jnp.newaxis]
+        + data["centroid_binormal"] * jnp.sin(alpha)[:, jnp.newaxis]
+    )
+
+    data["p_frame"] = p_frame
+    return data
+
+
+@register_compute_fun(
+    name="q_frame",
+    label="\\mathbf{q}_{\\mathrm{Frame}}",
+    units="~",
+    units_long="None",
+    description="Q unit vector for framed coil with arbitrary rotation fourier series",
+    dim=3,
+    params=["alpha_n"],
+    transforms={"alpha": [[0, 0, 0]]},
+    profiles=[],
+    coordinates="s",
+    data=["centroid_normal", "centroid_binormal"],
+    parameterization="desc.coils._FramedCoil",
+)
+def _q_frame(params, transforms, profiles, data, **kwargs):
+    alpha = transforms["alpha"].transform(params["alpha_n"], dz=0)
+
+    q_frame = (
+        -data["centroid_normal"] * jnp.sin(alpha)[:, jnp.newaxis]
+        + data["centroid_binormal"] * jnp.cos(alpha)[:, jnp.newaxis]
+    )
+
+    data["q_frame"] = q_frame
+    return data
+
+
+@register_compute_fun(
+    name="curv1_frame",
+    label="\\kappa_{1}}_{\\mathrm{Frame}",
+    units="~",
+    units_long="None",
+    description="Curvature component in the p-vector direction for a rectangular cross section coil",
+    dim=1,
+    params=[],
+    transforms={},
+    profiles=[],
+    coordinates="s",
+    data=["curvature", "frenet_normal", "p_frame"],
+    parameterization="desc.coils._FramedCoil",
+)
+def _curv1_frame(params, transforms, profiles, data, **kwargs):
+    data["curv1_frame"] = data["curvature"] * dot(
+        data["frenet_normal"], data["p_frame"]
+    )
+
+    return data
+
+
+@register_compute_fun(
+    name="curv2_frame",
+    label="\\kappa_{2}}_{\\mathrm{Frame}",
+    units="~",
+    units_long="None",
+    description="Curvature component in the q-vector direction for a rectangular cross section coil",
+    dim=1,
+    params=[],
+    transforms={},
+    profiles=[],
+    coordinates="s",
+    data=["curvature", "frenet_normal", "q_frame"],
+    parameterization="desc.coils._FramedCoil",
+)
+def _curv2_frame(params, transforms, profiles, data, **kwargs):
+    data["curv2_frame"] = data["curvature"] * dot(
+        data["frenet_normal"], data["q_frame"]
+    )
+
+    return data
+
+
+@register_compute_fun(
+    name="u_fb",
+    label="u_{FB}",
+    units="",
+    units_long="",
+    description="U coordinate for finite build expansion",
+    dim=1,
+    params=[],
+    transforms={"grid": []},
+    profiles=[],
+    coordinates="rt",  # just two degrees of freedom
+    data=[],
+    parameterization="desc.coils._FiniteBuildCoil",
+)
+def _u_fb(params, transforms, profiles, data, **kwargs):
+    grid = transforms["grid"]
+    # TODO: check that grid is LinearGrid?
+    data["u_fb"] = (
+        (grid.nodes[:, 0] - 0.5) / (0.5) * 0.999
+    )  # rescale to [-0.999,0.999], as the finite build term is singular at the edge
+    return data
+
+
+@register_compute_fun(
+    name="v_fb",
+    label="v_{FB}",
+    units="",
+    units_long="",
+    description="V coordinate for finite build expansion",
+    dim=1,
+    params=[],
+    transforms={"grid": []},
+    profiles=[],
+    coordinates="rt",
+    data=[],
+    parameterization="desc.coils._FiniteBuildCoil", 
+)
+def _v_fb(params, transforms, profiles, data, **kwargs):
+    grid = transforms["grid"]
+    # TODO: check that grid is LinearGrid?
+    data["v_fb"] = (
+        (grid.nodes[:, 1] - jnp.pi) / (jnp.pi) * 0.999
+    )  # rescale to [-0.999,0.999]
+    return data
+
+
+@register_compute_fun(
+    name="B_0_fb",
+    label="B_{0,FB}",
+    units="T",
+    units_long="Tesla",
+    description="B_0 term in finite build expansion",
+    dim=3,
+    params=["p_frame", "q_frame", "current", "cross_section_dims"],
+    transforms={},
+    profiles=[],
+    coordinates="rtz",
+    data=["u_fb", "v_fb"],
+    parameterization="desc.coils._FiniteBuildCoil",
+)
+def _B_0_fb(params, transforms, profiles, data, **kwargs):
+    # fed in externally
+    p_frame = params["p_frame"]
+    q_frame = params["q_frame"]
+
+    # scalars
+    current = params["current"]
+    a = params["cross_section_dims"][0]
+    b = params["cross_section_dims"][1]
+
+    # u and v are computed
+    u = data["u_fb"]
+    v = data["v_fb"]
+
+    # add dimension to u and v to accomodate vectorized operations
+    u = u[:, jnp.newaxis]
+    v = v[:, jnp.newaxis]
+
+    def G(x, y):
+        return y * jnp.arctan(x / y) + x / 2 * jnp.log(1 + (y / x) ** 2)
+
+    data["B_0_fb"] = mu_0 * current/(4*jnp.pi*a*b) * ( (q_frame * G(b*(v+1), a*(u+1)) - p_frame * G(a*(u+1), b*(v+1))) + 
+                                (-1) * (q_frame * G(b*(v+1), a*(u-1)) - p_frame * G(a*(u-1), b*(v+1))) + 
+                                (-1) * (q_frame * G(b*(v-1), a*(u+1)) - p_frame * G(a*(u+1), b*(v-1))) + 
+                                (q_frame * G(b*(v-1), a*(u-1)) - p_frame * G(a*(u-1), b*(v-1))))
+
+    return data
+
+
+@register_compute_fun(
+    name="B_kappa_fb",
+    label="B_{\\kappa,FB}",
+    units="T",
+    units_long="Tesla",
+    description="B_kappa term in finite build expansion",
+    dim=3,
+    params=[
+        "p_frame",
+        "q_frame",
+        "current",
+        "cross_section_dims",
+        "curv1_frame",
+        "curv2_frame",
+    ],
+    transforms={},
+    profiles=[],
+    coordinates="rtz",
+    data=["u_fb", "v_fb"],
+    parameterization="desc.coils._FiniteBuildCoil", 
+)
+def _B_kappa_fb(params, transforms, profiles, data, **kwargs):
+    # fed in externally
+    p_frame = params["p_frame"]
+    q_frame = params["q_frame"]
+    curv1_frame = params["curv1_frame"]
+    curv2_frame = params["curv2_frame"]
+
+    # scalars
+    current = params["current"]
+    a = params["cross_section_dims"][0]
+    b = params["cross_section_dims"][1]
+
+    # u and v are computed
+    u = data["u_fb"]
+    v = data["v_fb"]
+
+    # add dimension to scalars to accomodate vectorized operations
+    u = u[:, jnp.newaxis]
+    v = v[:, jnp.newaxis]
+    curv1_frame = curv1_frame[:, jnp.newaxis]
+    curv2_frame = curv2_frame[:, jnp.newaxis]
+
+    def K(U,V):
+        return ((-2*U*V * (curv1_frame*q_frame-curv2_frame*p_frame) * jnp.log(a/b*U**2 + b/a*V**2) + 
+                (curv2_frame*q_frame-curv1_frame*p_frame) * (a/b*U**2 + b/a*V**2) * jnp.log(a/b*U**2 + b/a*V**2) + 
+                4*a/b*curv2_frame*p_frame*U**2 * jnp.arctan(b*V/(a*U)) -
+                4*b/a*curv1_frame*q_frame*V**2 * jnp.arctan(a*U/(b*V))) )
+
+    data["B_kappa_fb"] = mu_0 * current/(64*jnp.pi) * ( K(u+1, v+1) + K(u-1, v-1) +
+                                  (-1) * K(u-1, v+1) + (-1) * K(u+1, v-1))
+
+    return data
+
+
+@register_compute_fun(
+    name="B_b_fb",
+    label="B_{b,FB}",
+    units="T",
+    units_long="Tesla",
+    description="B_b term in finite build expansion",
+    dim=3,
+    params=["current", "cross_section_dims"],
+    transforms={},
+    profiles=[],
+    coordinates="r",
+    data=["curvature", "frenet_binormal"],
+    parameterization="desc.coils._FiniteBuildCoil",  
+)
+def _B_b_fb(params, transforms, profiles, data, **kwargs):
+    # scalars, external
+    current = params["current"]
+    a = params["cross_section_dims"][0]
+    b = params["cross_section_dims"][1]
+
+    # u and v are computed
+    curvature = data["curvature"][:, jnp.newaxis]
+    binormal = data["frenet_binormal"]
+
+    k = -(a**4 - 6 * a**2 * b**2 + b**4) / (6 * a**2 * b**2) * jnp.log(a / b + b / a)
+    +b * b / (6 * a * a) * jnp.log(b / a)
+    +a * a / (6 * b * b) * jnp.log(a / b)
+    +(4.0 * b) / (3 * a) * jnp.arctan(a / b)
+    +(4.0 * a) / (3 * b) * jnp.arctan(b / a)
+
+    delta = jnp.exp(-(25.0 / 6) + k)
+
+    data["B_b_fb"] = mu_0 * current/(8*jnp.pi) * curvature * binormal * (4 + 2*jnp.log(2) + jnp.log(delta))
+
+    return data
+
+@register_compute_fun(
+    name="x_fb",
+    label="x_{FB}",
+    units="m",
+    units_long="meters",
+    description="Position vector in lab frame for finite build coil cross section",
+    dim=3,
+    params=[
+        "p_frame",
+        "q_frame",
+        "cross_section_dims",
+        "x_centerline"
+    ],
+    transforms={},
+    profiles=[],
+    coordinates="rtz",
+    data=["u_fb", "v_fb"],
+    parameterization="desc.coils._FiniteBuildCoil", 
+)
+def _x_fb(params, transforms, profiles, data, **kwargs):
+    # fed in externally
+    p_frame = params["p_frame"]
+    q_frame = params["q_frame"]
+    x_centerline = params["x_centerline"]
+
+    # scalars
+    a = params["cross_section_dims"][0]
+    b = params["cross_section_dims"][1]
+
+    # u and v are computed
+    u = data["u_fb"]
+    v = data["v_fb"]
+
+    # add dimension to scalars to accomodate vectorized operations
+    u = u[:, jnp.newaxis]
+    v = v[:, jnp.newaxis]
+
+    data["x_fb"] = x_centerline + a * u * p_frame + b * v * q_frame
+
+    return data
diff --git a/desc/compute/data_index.py b/desc/compute/data_index.py
index cf0a32714e..8af82333d0 100644
--- a/desc/compute/data_index.py
+++ b/desc/compute/data_index.py
@@ -246,6 +246,13 @@ def _decorator(func):
         "desc.geometry.curve.FourierPlanarCurve",
         "desc.geometry.core.Curve",
     ],
+    "desc.coils.FourierPlanarFiniteBuildCoil": [
+        "desc.coils._FramedCoil",
+        "desc.coils._FiniteBuildCoil",
+        "desc.coils.FourierPlanarCoil",
+        "desc.geometry.curve.FourierPlanarCurve",
+        "desc.geometry.core.Curve",
+    ],
     "desc.magnetic_fields._current_potential.CurrentPotentialField": [
         "desc.geometry.surface.FourierRZToroidalSurface",
         "desc.geometry.core.Surface",
diff --git a/desc/objectives/__init__.py b/desc/objectives/__init__.py
index 3ae2e59af0..0a0d2d5bac 100644
--- a/desc/objectives/__init__.py
+++ b/desc/objectives/__init__.py
@@ -10,6 +10,7 @@
     PlasmaCoilSetMinDistance,
     QuadraticFlux,
     ToroidalFlux,
+    CoilSetMaxNormB,
 )
 from ._equilibrium import (
     CurrentDensity,
diff --git a/desc/objectives/_coils.py b/desc/objectives/_coils.py
index 62ef664622..fb9f613ab2 100644
--- a/desc/objectives/_coils.py
+++ b/desc/objectives/_coils.py
@@ -9,6 +9,8 @@
     tree_leaves,
     tree_map,
     tree_unflatten,
+    scan,
+    tree_stack,
 )
 from desc.compute import get_profiles, get_transforms, rpz2xyz
 from desc.compute.utils import _compute as compute_fun
@@ -849,6 +851,200 @@ def body(k):
         return min_dist_per_coil
 
 
+class CoilSetMaxNormB(_Objective):
+    """Target the maximum magnetic field magnetic field on coil for a given coilset.
+    Self field on the coil is computed with the finite build method, and field from other
+    coils on the coil is computed with the filamentary method.
+
+    Will yield one value for each unique coil in the coilset, which is the maximum magnitude
+    of magnetic field on that coil. All coils in the coilset must inherit from _FiniteBuildCoil.
+
+    Parameters
+    ----------
+    coil : CoilSet
+        Coil(s) that are to be optimized.
+    target : float, ndarray, optional
+        Target value(s) of the objective. Only used if bounds is None.
+        Must be broadcastable to Objective.dim_f. If array, it has to
+        be flattened according to the number of inputs.
+    bounds : tuple of float, ndarray, optional
+        Lower and upper bounds on the objective. Overrides target.
+        Both bounds must be broadcastable to to Objective.dim_f
+    weight : float, ndarray, optional
+        Weighting to apply to the Objective, relative to other Objectives.
+        Must be broadcastable to to Objective.dim_f
+    normalize : bool, optional
+        Whether to compute the error in physical units or non-dimensionalize.
+    normalize_target : bool, optional
+        Whether target and bounds should be normalized before comparing to computed
+        values. If `normalize` is `True` and the target is in physical units,
+        this should also be set to True.
+        be set to True.
+    loss_function : {None, 'mean', 'min', 'max'}, optional
+        Loss function to apply to the objective values once computed. This loss function
+        is called on the raw compute value, before any shifting, scaling, or
+        normalization. Operates over all coils, not each individial coil.
+    deriv_mode : {"auto", "fwd", "rev"}
+        Specify how to compute jacobian matrix, either forward mode or reverse mode AD.
+        "auto" selects forward or reverse mode based on the size of the input and output
+        of the objective. Has no effect on self.grad or self.hess which always use
+        reverse mode and forward over reverse mode respectively.
+    xsection_grid : Grid, list, optional
+        Collocation grid used to discretize each coil cross section. Defaults to the default grid
+        for the given coil-type, see ``coils.py`` for more details. If a list, must have the
+        same structure as coils.
+    centerline_grid : Grid, list, optional
+        Collocation grid used to discretize each coil centerline. Defaults to the default grid
+        for the given coil-type, see ``coils.py`` and ``curve.py`` for more details.
+        If a list, must have the same structure as coils.
+    name : str, optional
+        Name of the objective function.
+
+    """
+
+    _scalar = False
+    _units = "(T)"
+    _print_value_fmt = "Maximum field magnitude on coil: {:10.3e} "
+
+    def __init__(
+        self,
+        coil,
+        target=None,
+        bounds=None,
+        weight=1,
+        normalize=True,
+        normalize_target=True,
+        loss_function=None,
+        deriv_mode="auto",
+        xsection_grid=None,
+        centerline_grid=None,
+        name="field magnitude on coil distance",
+    ):
+        from desc.coils import CoilSet, _FiniteBuildCoil
+
+        if target is None and bounds is None:
+            bounds = (-0.0, 20.0)
+
+        self._xsection_grid = xsection_grid
+        self._centerline_grid = centerline_grid
+
+        errorif(
+            not type(coil) is CoilSet,
+            ValueError,
+            "coil must be of type CoilSet, not an individual Coil. Mixed coil sets are not supported.",
+        )
+        errorif(
+            not all([isinstance(c, _FiniteBuildCoil) for c in coil.coils]),
+            ValueError,
+            "All coils in the CoilSet must inherit from _FiniteBuildCoil",
+        )
+        super().__init__(
+            things=coil,
+            target=target,
+            bounds=bounds,
+            weight=weight,
+            normalize=normalize,
+            normalize_target=normalize_target,
+            loss_function=loss_function,
+            deriv_mode=deriv_mode,
+            name=name,
+        )
+
+    def build(self, use_jit=True, verbose=1):
+        """Build constant arrays.
+
+        Parameters
+        ----------
+        use_jit : bool, optional
+            Whether to just-in-time compile the objective and derivatives.
+        verbose : int, optional
+            Level of output.
+
+        """
+        coilset = self.things[0]
+        xsection_grid = self._xsection_grid or None
+        centerline_grid = self._centerline_grid or None
+
+        self._dim_f = coilset.num_coils
+        self._constants = {
+            "coilset": coilset,
+            "xsection_grid": xsection_grid,
+            "centerline_grid": centerline_grid,
+            "quad_weights": 1.0,
+        }
+
+        if self._normalize:
+            coils = tree_leaves(coilset, is_leaf=lambda x: not hasattr(x, "__len__"))
+            scales = [compute_scaling_factors(coil)["B"] for coil in coils]
+            self._normalization = np.mean(
+                scales
+            )  # mean centerline field strength of coils
+
+        super().build(use_jit=use_jit, verbose=verbose)
+
+    def compute(self, params, constants=None):
+        """Compute maximum field magnitude on each coil.
+
+        Parameters
+        ----------
+        params : dict
+            Dictionary of coilset degrees of freedom, eg CoilSet.params_dict
+        constants : dict
+            Dictionary of constant data, eg transforms, profiles etc.
+            Defaults to self._constants.
+
+        Returns
+        -------
+        f : array of floats
+            Maximum field on coil for each coil in the coilset.
+
+        """
+        if constants is None:
+            constants = self.constants
+
+        def body(dummy, x):
+            # compute self field and other coil field to find maximum field magnitude on the coil k
+            coil = constants["coilset"][0]
+            self_field, quad_points, centerline_mask = coil.compute_self_field(
+                xsection_grid=constants["xsection_grid"],
+                centerline_grid=constants["centerline_grid"],
+                params=x,  # just this coil's parameters
+            )
+
+            quad_points = jnp.where(
+                centerline_mask[:, None], 100, quad_points
+            )  # TODO: better way to do this
+
+            # compute field from other coils and subtract the coil computation to prevent double counting
+            other_field = constants["coilset"].compute_magnetic_field(
+                quad_points,
+                params=params,  # all coils' parameters
+                basis="xyz",
+                source_grid=constants["centerline_grid"],
+            )
+            other_field -= coil.compute_magnetic_field(
+                quad_points,
+                params=x,
+                basis="xyz",
+                source_grid=constants["centerline_grid"],
+            )
+
+            total_field = self_field + other_field
+            total_field = jnp.where(centerline_mask[:, None], 0, total_field)
+
+            field_mag = safenorm(total_field, axis=-1)
+
+            # return maximum field magnitude on the coil. NaNs could be generated on the coil axis where the filament has a singularity
+            # in effect, the coil centerline points are not used in the field computation, which is ok since the field is not maximal on the coil axis
+            max_mag = jnp.max(field_mag, initial=0)
+
+            return 0, max_mag
+
+        _, max_field_per_coil = scan(body, 0, tree_stack(params))
+
+        return max_field_per_coil
+
+
 class PlasmaCoilSetMinDistance(_Objective):
     """Target the minimum distance between the plasma and coilset.
 
diff --git a/desc/objectives/normalization.py b/desc/objectives/normalization.py
index 255eb346f4..17086ad9e7 100644
--- a/desc/objectives/normalization.py
+++ b/desc/objectives/normalization.py
@@ -11,6 +11,7 @@ def compute_scaling_factors(thing):
     # local import to avoid circular import
     from desc.equilibrium import Equilibrium
     from desc.geometry import FourierRZToroidalSurface
+    from desc.coils import _Coil
 
     scales = {}
 
@@ -69,6 +70,9 @@ def get_lowest_mode(basis, coeffs):
 
     elif isinstance(thing, Curve):
         scales["a"] = thing.compute("length")["length"] / (2 * np.pi)
+        
+        if isinstance(thing, _Coil):
+            scales["B"] = mu_0 * thing.current / (2 * scales["a"]) # field at center of equivalent loop
 
     # replace 0 scales to avoid normalizing by zero
     for scale in scales.keys():
diff --git a/docs/api.rst b/docs/api.rst
index 3173f8b418..2f07140b4f 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -31,6 +31,7 @@ Coils
     desc.coils.FourierPlanarCoil
     desc.coils.MixedCoilSet
     desc.coils.SplineXYZCoil
+    desc.coils.FourierPlanarFiniteBuildCoil
 
 Compatibility
 *************
diff --git a/docs/api_fields.rst b/docs/api_fields.rst
index 32b5cc572b..69294060a6 100644
--- a/docs/api_fields.rst
+++ b/docs/api_fields.rst
@@ -88,6 +88,7 @@ classes, can also use the same ``Curve`` conversion methods to convert between c
     desc.coils.FourierXYZCoil
     desc.coils.FourierPlanarCoil
     desc.coils.SplineXYZCoil
+    desc.coils.FourierPlanarFiniteBuildCoil
 
 There are also objects for holding a collection of coils with efficient methods for
 evaluating the combined field. A ``CoilSet`` must consist of members with the same
diff --git a/finitebuildtest.ipynb b/finitebuildtest.ipynb
new file mode 100644
index 0000000000..630547e9dc
--- /dev/null
+++ b/finitebuildtest.ipynb
@@ -0,0 +1,4764 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DESC version 0+untagged.9359.g6e2db0e.dirty,using JAX backend, jax version=0.4.31, jaxlib version=0.4.31, dtype=float64\n",
+      "Using device: CPU, with 11.36 GB available memory\n"
+     ]
+    }
+   ],
+   "source": [
+    "from desc.coils import FourierPlanarFiniteBuildCoil\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coil = FourierPlanarFiniteBuildCoil(center = [10,0,0], cross_section_dims=[0.1,0.2], r_n = 0.5, modes = [0], cross_section_shape= \"rectangular\", current = 1e6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Array([[-2.53479632e-38, -7.87612863e-05, -4.82273786e-21]], dtype=float64)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coil.compute_magnetic_field([0,0,0], basis = \"xyz\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.957678999877364"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "field_array, field_positions = coil.compute_self_field(xsection_grid=1, coil_frame=True)\n",
+    "max_field = np.max(np.linalg.norm(field_array, axis=1))\n",
+    "max_field"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.392891658173041"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max_perp = np.max(np.abs(field_array[:,1]))\n",
+    "max_perp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.957678999877364"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max_para = np.max(np.abs(field_array[:,2]))\n",
+    "max_para"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
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Field Distribution\"},\"scene\":{\"xaxis\":{\"title\":{\"text\":\"X\"}},\"yaxis\":{\"title\":{\"text\":\"Y\"}},\"zaxis\":{\"title\":{\"text\":\"Z\"}}}},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('3c5add39-0321-4fb4-a12c-0ae8572e8545');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.graph_objects as go\n",
+    "\n",
+    "# Extract x, y, z coordinates from field_positions\n",
+    "x = field_positions[:, 0]\n",
+    "y = field_positions[:, 1]\n",
+    "z = field_positions[:, 2]\n",
+    "\n",
+    "# Extract the field values\n",
+    "field_values = field_array[:, 1]\n",
+    "\n",
+    "# Create a 3D scatter plot\n",
+    "fig = go.Figure(data=[go.Scatter3d(\n",
+    "    x=x,\n",
+    "    y=y,\n",
+    "    z=z,\n",
+    "    mode='markers',\n",
+    "    marker=dict(\n",
+    "        size=5,\n",
+    "        color=field_values,\n",
+    "        colorscale='Viridis',\n",
+    "        colorbar=dict(title='Field Value')\n",
+    "    )\n",
+    ")])\n",
+    "\n",
+    "# Set plot title and axis labels\n",
+    "fig.update_layout(\n",
+    "    title='Magnetic Field Distribution',\n",
+    "    scene=dict(\n",
+    "        xaxis_title='X',\n",
+    "        yaxis_title='Y',\n",
+    "        zaxis_title='Z'\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "fig.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "desc-env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}