Vectorize boozer transform over multiple surfaces

desc/compute/_omnigenity.py

@@ -20,30 +20,38 @@
 @register_compute_fun(
     name="B_theta_mn",
     label="B_{\\theta, m, n}",
-    units="T \\cdot m}",
+    units="T \\cdot m",
     units_long="Tesla * meters",
     description="Fourier coefficients for covariant poloidal component of "
     "magnetic field.",
     dim=1,
     params=[],
-    transforms={"B": [[0, 0, 0]]},
+    transforms={"B": [[0, 0, 0]], "grid": []},
     profiles=[],
     coordinates="rtz",
     data=["B_theta"],
+    resolution_requirement="tz",
+    grid_requirement={"is_meshgrid": True},
     M_booz="int: Maximum poloidal mode number for Boozer harmonics. Default 2*eq.M",
     N_booz="int: Maximum toroidal mode number for Boozer harmonics. Default 2*eq.N",
-    resolution_requirement="tz",
 )
 def _B_theta_mn(params, transforms, profiles, data, **kwargs):
-    data["B_theta_mn"] = transforms["B"].fit(data["B_theta"])
+    B_theta = transforms["grid"].meshgrid_reshape(data["B_theta"], "rtz")
+
+    def fitfun(x):
+        return transforms["B"].fit(x.flatten(order="F"))
+
+    B_theta_mn = vmap(fitfun)(B_theta)
+    # modes stored as shape(rho, mn) flattened
+    data["B_theta_mn"] = B_theta_mn.flatten()
     return data
 
 
 # TODO: do math to change definition of nu so that we can just use B_zeta_mn here
 @register_compute_fun(
     name="B_phi_mn",
     label="B_{\\phi, m, n}",
-    units="T \\cdot m}",
+    units="T \\cdot m",
     units_long="Tesla * meters",
     description="Fourier coefficients for covariant toroidal component of "
     "magnetic field in (ρ,θ,ϕ) coordinates.",
@@ -53,13 +61,21 @@ def _B_theta_mn(params, transforms, profiles, data, **kwargs):
     profiles=[],
     coordinates="rtz",
     data=["B_phi|r,t"],
-    M_booz="int: Maximum poloidal mode number for Boozer harmonics. Default 2*eq.M",
-    N_booz="int: Maximum toroidal mode number for Boozer harmonics. Default 2*eq.N",
     resolution_requirement="tz",
+    grid_requirement={"is_meshgrid": True},
     aliases="B_zeta_mn",  # TODO: remove when phi != zeta
+    M_booz="int: Maximum poloidal mode number for Boozer harmonics. Default 2*eq.M",
+    N_booz="int: Maximum toroidal mode number for Boozer harmonics. Default 2*eq.N",
 )
 def _B_phi_mn(params, transforms, profiles, data, **kwargs):
-    data["B_phi_mn"] = transforms["B"].fit(data["B_phi|r,t"])
+    B_phi = transforms["grid"].meshgrid_reshape(data["B_phi|r,t"], "rtz")
+
+    def fitfun(x):
+        return transforms["B"].fit(x.flatten(order="F"))
+
+    B_zeta_mn = vmap(fitfun)(B_phi)
+    # modes stored as shape(rho, mn) flattened
+    data["B_phi_mn"] = B_zeta_mn.flatten()
     return data
 
 
@@ -72,15 +88,16 @@ def _B_phi_mn(params, transforms, profiles, data, **kwargs):
     + "Boozer Coordinates'",
     dim=1,
     params=[],
-    transforms={"w": [[0, 0, 0]], "B": [[0, 0, 0]]},
+    transforms={"w": [[0, 0, 0]], "B": [[0, 0, 0]], "grid": []},
     profiles=[],
     coordinates="rtz",
     data=["B_theta_mn", "B_phi_mn"],
+    grid_requirement={"is_meshgrid": True},
     M_booz="int: Maximum poloidal mode number for Boozer harmonics. Default 2*eq.M",
     N_booz="int: Maximum toroidal mode number for Boozer harmonics. Default 2*eq.N",
 )
 def _w_mn(params, transforms, profiles, data, **kwargs):
-    w_mn = jnp.zeros((transforms["w"].basis.num_modes,))
+    w_mn = jnp.zeros((transforms["grid"].num_rho, transforms["w"].basis.num_modes))
     Bm = transforms["B"].basis.modes[:, 1]
     Bn = transforms["B"].basis.modes[:, 2]
     wm = transforms["w"].basis.modes[:, 1]
@@ -89,15 +106,19 @@ def _w_mn(params, transforms, profiles, data, **kwargs):
     mask_t = (Bm[:, None] == -wm) & (Bn[:, None] == wn) & (wm != 0)
     mask_z = (Bm[:, None] == wm) & (Bn[:, None] == -wn) & (wm == 0) & (wn != 0)
 
-    num_t = (mask_t @ sign(wn)) * data["B_theta_mn"]
+    num_t = (mask_t @ sign(wn)) * data["B_theta_mn"].reshape(
+        (transforms["grid"].num_rho, -1)
+    )
     den_t = mask_t @ jnp.abs(wm)
-    num_z = (mask_z @ sign(wm)) * data["B_phi_mn"]
+    num_z = (mask_z @ sign(wm)) * data["B_phi_mn"].reshape(
+        (transforms["grid"].num_rho, -1)
+    )
     den_z = mask_z @ jnp.abs(NFP * wn)
 
-    w_mn = jnp.where(mask_t.any(axis=0), mask_t.T @ safediv(num_t, den_t), w_mn)
-    w_mn = jnp.where(mask_z.any(axis=0), mask_z.T @ safediv(num_z, den_z), w_mn)
+    w_mn = jnp.where(mask_t.any(axis=0), (mask_t.T @ safediv(num_t, den_t).T).T, w_mn)
+    w_mn = jnp.where(mask_z.any(axis=0), (mask_z.T @ safediv(num_z, den_z).T).T, w_mn)
 
-    data["w_Boozer_mn"] = w_mn
+    data["w_Boozer_mn"] = w_mn.flatten()
     return data
 
 
@@ -110,16 +131,22 @@ def _w_mn(params, transforms, profiles, data, **kwargs):
     + "'Transformation from VMEC to Boozer Coordinates'",
     dim=1,
     params=[],
-    transforms={"w": [[0, 0, 0]]},
+    transforms={"w": [[0, 0, 0]], "grid": []},
     profiles=[],
     coordinates="rtz",
     data=["w_Boozer_mn"],
     resolution_requirement="tz",
+    grid_requirement={"is_meshgrid": True},
     M_booz="int: Maximum poloidal mode number for Boozer harmonics. Default 2*eq.M",
     N_booz="int: Maximum toroidal mode number for Boozer harmonics. Default 2*eq.N",
 )
 def _w(params, transforms, profiles, data, **kwargs):
-    data["w_Boozer"] = transforms["w"].transform(data["w_Boozer_mn"])
+    grid = transforms["grid"]
+    w_mn = data["w_Boozer_mn"].reshape((grid.num_rho, -1))
+    w = vmap(transforms["w"].transform)(w_mn)  # shape(rho, theta*zeta)
+    w = w.reshape((grid.num_rho, grid.num_theta, grid.num_zeta), order="F")
+    w = jnp.moveaxis(w, 0, 1)
+    data["w_Boozer"] = w.flatten(order="F")
     return data
 
 
@@ -132,16 +159,24 @@ def _w(params, transforms, profiles, data, **kwargs):
     + "'Transformation from VMEC to Boozer Coordinates', poloidal derivative",
     dim=1,
     params=[],
-    transforms={"w": [[0, 1, 0]]},
+    transforms={"w": [[0, 1, 0]], "grid": []},
     profiles=[],
     coordinates="rtz",
     data=["w_Boozer_mn"],
     resolution_requirement="tz",
+    grid_requirement={"is_meshgrid": True},
     M_booz="int: Maximum poloidal mode number for Boozer harmonics. Default 2*eq.M",
     N_booz="int: Maximum toroidal mode number for Boozer harmonics. Default 2*eq.N",
 )
 def _w_t(params, transforms, profiles, data, **kwargs):
-    data["w_Boozer_t"] = transforms["w"].transform(data["w_Boozer_mn"], dt=1)
+    grid = transforms["grid"]
+    w_mn = data["w_Boozer_mn"].reshape((grid.num_rho, -1))
+    # need to close over dt which can't be vmapped
+    fun = lambda x: transforms["w"].transform(x, dt=1)
+    w_t = vmap(fun)(w_mn)  # shape(rho, theta*zeta)
+    w_t = w_t.reshape((grid.num_rho, grid.num_theta, grid.num_zeta), order="F")
+    w_t = jnp.moveaxis(w_t, 0, 1)
+    data["w_Boozer_t"] = w_t.flatten(order="F")
     return data
 
 
@@ -154,16 +189,24 @@ def _w_t(params, transforms, profiles, data, **kwargs):
     + "'Transformation from VMEC to Boozer Coordinates', toroidal derivative",
     dim=1,
     params=[],
-    transforms={"w": [[0, 0, 1]]},
+    transforms={"w": [[0, 0, 1]], "grid": []},
     profiles=[],
     coordinates="rtz",
     data=["w_Boozer_mn"],
     resolution_requirement="tz",
+    grid_requirement={"is_meshgrid": True},
     M_booz="int: Maximum poloidal mode number for Boozer harmonics. Default 2*eq.M",
     N_booz="int: Maximum toroidal mode number for Boozer harmonics. Default 2*eq.N",
 )
 def _w_z(params, transforms, profiles, data, **kwargs):
-    data["w_Boozer_z"] = transforms["w"].transform(data["w_Boozer_mn"], dz=1)
+    grid = transforms["grid"]
+    w_mn = data["w_Boozer_mn"].reshape((grid.num_rho, -1))
+    # need to close over dz which can't be vmapped
+    fun = lambda x: transforms["w"].transform(x, dz=1)
+    w_z = vmap(fun)(w_mn)  # shape(rho, theta*zeta)
+    w_z = w_z.reshape((grid.num_rho, grid.num_theta, grid.num_zeta), order="F")
+    w_z = jnp.moveaxis(w_z, 0, 1)
+    data["w_Boozer_z"] = w_z.flatten(order="F")
     return data
 
 
@@ -290,21 +333,38 @@ def _sqrtg_B(params, transforms, profiles, data, **kwargs):
     description="Boozer harmonics of magnetic field",
     dim=1,
     params=[],
-    transforms={"B": [[0, 0, 0]]},
+    transforms={"B": [[0, 0, 0]], "grid": []},
     profiles=[],
     coordinates="rtz",
     data=["sqrt(g)_B", "|B|", "rho", "theta_B", "zeta_B"],
+    resolution_requirement="tz",
+    grid_requirement={"is_meshgrid": True},
     M_booz="int: Maximum poloidal mode number for Boozer harmonics. Default 2*eq.M",
     N_booz="int: Maximum toroidal mode number for Boozer harmonics. Default 2*eq.N",
 )
 def _B_mn(params, transforms, profiles, data, **kwargs):
-    nodes = jnp.array([data["rho"], data["theta_B"], data["zeta_B"]]).T
     norm = 2 ** (3 - jnp.sum((transforms["B"].basis.modes == 0), axis=1))
-    data["|B|_mn"] = (
-        norm  # 1 if m=n=0, 2 if m=0 or n=0, 4 if m!=0 and n!=0
-        * (transforms["B"].basis.evaluate(nodes).T @ (data["sqrt(g)_B"] * data["|B|"]))
-        / transforms["B"].grid.num_nodes
+    grid = transforms["grid"]
+
+    def fun(rho, theta_B, zeta_B, sqrtg_B, B):
+        # this fits Boozer modes on a single surface
+        nodes = jnp.array([rho, theta_B, zeta_B]).T
+        B_mn = (
+            norm  # 1 if m=n=0, 2 if m=0 or n=0, 4 if m!=0 and n!=0
+            * (transforms["B"].basis.evaluate(nodes).T @ (sqrtg_B * B))
+            / transforms["B"].grid.num_nodes
+        )
+        return B_mn
+
+    def reshape(x):
+        return grid.meshgrid_reshape(x, "rtz").reshape((grid.num_rho, -1))
+
+    rho, theta_B, zeta_B, sqrtg_B, B = map(
+        reshape,
+        (data["rho"], data["theta_B"], data["zeta_B"], data["sqrt(g)_B"], data["|B|"]),
     )
+    B_mn = vmap(fun)(rho, theta_B, zeta_B, sqrtg_B, B)
+    data["|B|_mn"] = B_mn.flatten()
     return data
 
 

desc/compute/data_index.py

@@ -63,6 +63,7 @@ def register_compute_fun(  # noqa: C901
     aliases=None,
     parameterization="desc.equilibrium.equilibrium.Equilibrium",
     resolution_requirement="",
+    grid_requirement=None,
     source_grid_requirement=None,
     **kwargs,
 ):
@@ -110,6 +111,11 @@ def register_compute_fun(  # noqa: C901
         If the computation simply performs pointwise operations, instead of a
         reduction (such as integration) over a coordinate, then an empty string may
         be used to indicate no requirements.
+    grid_requirement : dict
+        Attributes of the grid that the compute function requires.
+        Also assumes dependencies were computed on such a grid.
+        As an example, quantities that require tensor product grids over 2 or more
+        coordinates may specify ``grid_requirement={"is_meshgrid": True}``.
     source_grid_requirement : dict
         Attributes of the source grid that the compute function requires.
         Also assumes dependencies were computed on such a grid.
@@ -130,6 +136,8 @@ def register_compute_fun(  # noqa: C901
         aliases = []
     if source_grid_requirement is None:
         source_grid_requirement = {}
+    if grid_requirement is None:
+        grid_requirement = {}
     if not isinstance(parameterization, (tuple, list)):
         parameterization = [parameterization]
     if not isinstance(aliases, (tuple, list)):
@@ -168,6 +176,7 @@ def _decorator(func):
             "dependencies": deps,
             "aliases": aliases,
             "resolution_requirement": resolution_requirement,
+            "grid_requirement": grid_requirement,
             "source_grid_requirement": source_grid_requirement,
         }
         for p in parameterization:

desc/compute/utils.py

@@ -33,7 +33,9 @@ def _parse_parameterization(p):
     return module + "." + klass.__qualname__
 
 
-def compute(parameterization, names, params, transforms, profiles, data=None, **kwargs):
+def compute(  # noqa: C901
+    parameterization, names, params, transforms, profiles, data=None, **kwargs
+):
     """Compute the quantity given by name on grid.
 
     Parameters
@@ -88,6 +90,15 @@ def compute(parameterization, names, params, transforms, profiles, data=None, **
     if "grid" in transforms:
 
         def check_fun(name):
+            reqs = data_index[p][name]["grid_requirement"]
+            for req in reqs:
+                errorif(
+                    not hasattr(transforms["grid"], req)
+                    or reqs[req] != getattr(transforms["grid"], req),
+                    AttributeError,
+                    f"Expected grid with '{req}:{reqs[req]}' to compute {name}.",
+                )
+
             reqs = data_index[p][name]["source_grid_requirement"]
             errorif(
                 reqs and not hasattr(transforms["grid"], "source_grid"),
@@ -517,6 +528,7 @@ def get_transforms(
 
     """
     from desc.basis import DoubleFourierSeries
+    from desc.grid import LinearGrid
     from desc.transform import Transform
 
     method = "jitable" if jitable or kwargs.get("method") == "jitable" else "auto"
@@ -556,8 +568,15 @@ def get_transforms(
                 )
             transforms[c] = c_transform
         elif c == "B":  # used for Boozer transform
+            # assume grid is a meshgrid but only care about a single surface
+            if grid.num_rho > 1:
+                theta = grid.nodes[grid.unique_theta_idx, 1]
+                zeta = grid.nodes[grid.unique_zeta_idx, 2]
+                grid_B = LinearGrid(theta=theta, zeta=zeta, NFP=grid.NFP, sym=grid.sym)
+            else:
+                grid_B = grid
             transforms["B"] = Transform(
-                grid,
+                grid_B,
                 DoubleFourierSeries(
                     M=kwargs.get("M_booz", 2 * obj.M),
                     N=kwargs.get("N_booz", 2 * obj.N),
@@ -570,8 +589,15 @@ def get_transforms(
                 method=method,
             )
         elif c == "w":  # used for Boozer transform
+            # assume grid is a meshgrid but only care about a single surface
+            if grid.num_rho > 1:
+                theta = grid.nodes[grid.unique_theta_idx, 1]
+                zeta = grid.nodes[grid.unique_zeta_idx, 2]
+                grid_w = LinearGrid(theta=theta, zeta=zeta, NFP=grid.NFP, sym=grid.sym)
+            else:
+                grid_w = grid
             transforms["w"] = Transform(
-                grid,
+                grid_w,
                 DoubleFourierSeries(
                     M=kwargs.get("M_booz", 2 * obj.M),
                     N=kwargs.get("N_booz", 2 * obj.N),

desc/objectives/_omnigenity.py

@@ -47,7 +47,7 @@ class QuasisymmetryBoozer(_Objective):
         reverse mode and forward over reverse mode respectively.
     grid : Grid, optional
         Collocation grid containing the nodes to evaluate at.
-        Must be a LinearGrid with a single flux surface and sym=False.
+        Must be a LinearGrid with sym=False.
         Defaults to ``LinearGrid(M=M_booz, N=N_booz)``.
     helicity : tuple, optional
         Type of quasi-symmetry (M, N). Default = quasi-axisymmetry (1, 0).
@@ -122,12 +122,6 @@ def build(self, use_jit=True, verbose=1):
             grid = self._grid
 
         errorif(grid.sym, ValueError, "QuasisymmetryBoozer grid must be non-symmetric")
-        errorif(
-            grid.num_rho != 1,
-            ValueError,
-            "QuasisymmetryBoozer grid must be on a single surface. "
-            "To target multiple surfaces, use multiple objectives.",
-        )
         warnif(
             grid.num_theta < 2 * eq.M,
             RuntimeWarning,
@@ -195,7 +189,7 @@ def compute(self, params, constants=None):
         Returns
         -------
         f : ndarray
-            Quasi-symmetry flux function error at each node (T^3).
+            Symmetry breaking harmonics of B (T).
 
         """
         if constants is None:
@@ -207,8 +201,11 @@ def compute(self, params, constants=None):
             transforms=constants["transforms"],
             profiles=constants["profiles"],
         )
-        B_mn = constants["matrix"] @ data["|B|_mn"]
-        return B_mn[constants["idx"]]
+        B_mn = data["|B|_mn"].reshape((constants["transforms"]["grid"].num_rho, -1))
+        B_mn = constants["matrix"] @ B_mn.T
+        # output order = (rho, mn).flatten(), ie all the surfaces concatenated
+        # one after the other
+        return B_mn[constants["idx"]].T.flatten()
 
     @property
     def helicity(self):