gvec.coils#

class gvec.coils.Coil(coil_points: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], coil_current: float)#

Bases: object

eval_A(pos: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes])#

Evaluate the vector potential due to currents flowing in the coil at given positions.

Parameters:

pos (ArrayLike) – Cartesian coordinates of the evaluation points with shape (3,n_positions).

Returns:

Dataset containing the vector potential at the evaluation positions.

Return type:

xr.Dataset

eval_B(pos: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes])#

Evaluate the magnetic field due to currents flowing in the coil at given positions.

Parameters:

pos (ArrayLike) – Cartesian coordinates of the evaluation points with shape (3,n_positions).

Returns:

Dataset containing the magnetic field at the evaluation positions.

Return type:

xr.Dataset

eval_mod_A(pos: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes])#

Evaluate the modulus of the vector potential due to currents flowing in the coil at given positions.

Parameters:

pos (ArrayLike) – Cartesian coordinates of the evaluation points with shape (3,n_positions).

Returns:

ds – Dataset containing the vector potential and its modulus at the evaluation positions.

Return type:

xr.Dataset

eval_mod_B(pos: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes])#

Evaluate the modulus of the magnetic field due to currents flowing in the coil at given positions.

Parameters:

pos (ArrayLike) – Cartesian coordinates of the evaluation points with shape (3,n_positions).

Returns:

ds – Dataset containing the magnetic field and its modulus at the evaluation positions.

Return type:

xr.Dataset

classmethod from_simsopt(coil)#

Initialize a gvec Coil from a simsopt Coil object.

Parameters:

coil (simsopt.field.coil.Coil) – simsopt coil object.

Returns:

GVEC coil object.

Return type:

Coil

get_as_dataobject()#

Return the coil as an xarray data-set.

Returns:

Dataset containing the coil-points and the current.

Return type:

Dataset

classmethod load(path: str | Path, **kwargs)#
plot(ax=None, show=True, name: str | None = None, **kwargs)#

Visualize the coil in 3D via plotly.

Parameters:

  • ax (plotly.graph_objects.Figure, optional) – Existing plotly figure to add the coil to, by default None

  • show (bool, optional) – Whether to show the plot, by default True

  • name (str, optional) – Label for the coil.

Returns:

3D plotly figure.

Return type:

plotly.graph_objects.Figure

save(path: str | Path, **kwargs)#

Safe the coils discretization points and current in a netcdf file.

Parameters:

path (str | Path) – Defines where to save te file.

class gvec.coils.CoilSet(coils: list[Coil], coil_names: list[str] | None = None)#

Bases: Coil, Mapping

eval_A(pos: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes])#

Evaluate the vector potential due to currents flowing in the coil at given positions.

Parameters:

pos (ArrayLike) – Evaluation positions in xyz with shape (3,n_positions).

Returns:

Vector potential at the evaluation positions.

Return type:

xr.Dataset

eval_B(pos: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes])#

Evaluate the magnetic field due to currents flowing in the coil at given positions.

Parameters:

pos (ArrayLike) – Evaluation positions in xyz with shape (3,n_positions). If a DataArray is provided, “xyz” is an expected dimensions.

Returns:

Magnetic field at the evaluation positions.

Return type:

xr.Dataset

classmethod from_simsopt(coilset)#

Initialize a gvec CoilSet from a simsopt CoilSet object.

Parameters:

coil (simsopt.field.coilset.CoilSet) – simsopt coilset object.

Returns:

GVEC coilset object.

Return type:

CoilSet

get_as_dataobject()#

Return the coil as an xarray data-set.

Returns:

Dataset containing the coil-points and the current.

Return type:

Dataset

classmethod load(path: str | Path, **kwargs)#
plot(ax=None, show=True, **kwargs)#

Visualize the coil set in 3D via plotly.

Parameters:

  • ax (plotly.graph_objects.Figure, optional) – Existing plotly figure to add the coils to, by default None

  • show (bool, optional) – Whether to show the plot, by default True

Returns:

3D plotly figure.

Return type:

plotly.graph_objects.Figure

class gvec.coils.IntersectionPlane(surf_normal: ndarray, surf_point: ndarray, xlim: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes] = inf, ylim: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes] = inf, zlim: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes] = inf, e_q1: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes] | None = None, e_q2: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes] | None = None)#

Bases: object

transform_events_to_X1_X2(events)#
gvec.coils.check_plane_factory(surf_normal: ndarray, surf_point: ndarray)#

Function factory for checking intersection with a plane defined by a normal vector. Used for the poincare plots in fieldline tracing.

Parameters:

surf_normal (np.ndarray) – Normal vector of the plane.

gvec.coils.get_phi_edge_from_coils(state: State, coil_set: CoilSet | Coil)#

Calculate the total toroidal flux from the geometry of a state object and the magnetic field of a coil-set.

Parameters:

  • state (State) – State object used for evaluating the geometry.

  • coil_set (CoilSet | Coil) – Coil-set used for evaluating the magnetic field.

Returns:

Averaged total toroidal flux.

Return type:

float

gvec.coils.intersection_planes_from_state(state: State, zetas: int | float | DataArray | ndarray | Sequence = 3, min_box_size: float = 0.001)#

Create IntersectionPlane objects from a State object at specified zeta positions.

Parameters:

  • state (State) – GVEC state object to construct the planes from.

  • zetas (CoordinateSpec, optional) – Zeta positions of the desired planes. If an integer is given linearly spaced zetas will be used. By default 3

  • min_box_size (float, optional) – Minimum length of the bounding box, by default 1e-3.

Returns:

List of IntersectionPlane objects.

Return type:

List[IntersectionPlane]

gvec.coils.trace_fieldlines(starts: ndarray | Dataset, coils: CoilSet, time: float, surf_normals: list[ndarray] | None = None, surf_points: list[ndarray] | None = None, n_jobs: int = 1, return_solves: bool = False, verbosity: Literal['WARNING', 'INFO', 'DEBUG'] = 'INFO', **kwargs)#

Trace magnetic field lines of a coil field.

Parameters:

  • starts (np.ndarray | xr.Dataset) – Initial positions of the magnetic field lines in Carthesian coordinates. Expected shape (3,n_fieldlines).

  • coils (CoilSet) – Coils set used for evaluating the magnetic field.

  • time (float) – Time for which to trace the field lines so that t_span = [0,t].

  • surf_normals (list[np.ndarray], optional) – List of normal vectors of the planes for which intersections of the field lines should be checked. This can then be used for Poincare plots. The default is None

  • surf_points (list[np.ndarray], optional) – List of points on the planes for which intersections of the field lines should be checked. If None, the origin is used. However, this does not work for the G-frame.

  • n_jobs (int, optional) – Number of jobs for parallelization over fieldlines, by default 1.

  • return_solves (bool, optional) – Whether to return a list of solve_ivp objects or an xarray datatree.

  • verbosity (str) – Level of the logger.