tidy3d.ModeSolverData

class ModeSolverData

Bases: ModeSolverDataset, ElectromagneticFieldData

Data associated with a ModeSolverMonitor: scalar components of E and H fields.

Parameters:

• Ex (Attribute: Ex) –

  Type	ScalarModeFieldDataArray
  Default
  Description	Spatial distribution of the x-component of the electric field of the mode.

• Ey (Attribute: Ey) –

  Type	ScalarModeFieldDataArray
  Default
  Description	Spatial distribution of the y-component of the electric field of the mode.

• Ez (Attribute: Ez) –

  Type	ScalarModeFieldDataArray
  Default
  Description	Spatial distribution of the z-component of the electric field of the mode.

• Hx (Attribute: Hx) –

  Type	ScalarModeFieldDataArray
  Default
  Description	Spatial distribution of the x-component of the magnetic field of the mode.

• Hy (Attribute: Hy) –

  Type	ScalarModeFieldDataArray
  Default
  Description	Spatial distribution of the y-component of the magnetic field of the mode.

• Hz (Attribute: Hz) –

  Type	ScalarModeFieldDataArray
  Default
  Description	Spatial distribution of the z-component of the magnetic field of the mode.

• monitor (Attribute: monitor) –

  Type	ModeSolverMonitor
  Default
  Description	Mode solver monitor associated with the data.

• symmetry (Attribute: symmetry) –

  Type	Tuple[Literal[0, -1, 1], Literal[0, -1, 1], Literal[0, -1, 1]]
  Default	= (0, 0, 0)
  Description	Symmetry eigenvalues of the original simulation in x, y, and z.

• symmetry_center (Attribute: symmetry_center) –

  Type	Optional[Tuple[float, float, float]]
  Default	= None
  Description	Center of the symmetry planes of the original simulation in x, y, and z. Required only if any of the symmetry field are non-zero.

• grid_expanded (Attribute: grid_expanded) –

  Type	Optional[Grid]
  Default	= None
  Description	Grid discretization of the associated monitor in the simulation which created the data. Required if symmetries are present, as well as in order to use some functionalities like getting poynting and flux.

• grid_primal_correction (Attribute: grid_primal_correction) –

  Type	Union[float, FreqDataArray, TimeDataArray, FreqModeDataArray]
  Default	= 1.0
  Description	Correction factor that needs to be applied for data corresponding to a 2D monitor to take into account the finite grid in the normal direction in the simulation in which the data was computed. The factor is applied to fields defined on the primal grid locations along the normal direction.

• grid_dual_correction (Attribute: grid_dual_correction) –

  Type	Union[float, FreqDataArray, TimeDataArray, FreqModeDataArray]
  Default	= 1.0
  Description	Correction factor that needs to be applied for data corresponding to a 2D monitor to take into account the finite grid in the normal direction in the simulation in which the data was computed. The factor is applied to fields defined on the dual grid locations along the normal direction.

• n_complex (Attribute: n_complex) –

  Type	ModeIndexDataArray
  Default
  Description	Complex-valued effective propagation constants associated with the mode.

• n_group_raw (Attribute: n_group_raw) –

  Type	Optional[GroupIndexDataArray]
  Default	= None
  Description	Index associated with group velocity of the mode.

• dispersion_raw (Attribute: dispersion_raw) –

  Type	Optional[ModeDispersionDataArray]
  Default	= None
  Units	ps/(nm km)
  Description	Dispersion parameter for the mode.

• eps_spec (Attribute: eps_spec) –

  Type	Optional[List[Literal[‘diagonal’, ‘tensorial_real’, ‘tensorial_complex’]]]
  Default	= None
  Description	Characterization of the permittivity profile on the plane where modes are computed. Possible values are ‘diagonal’, ‘tensorial_real’, ‘tensorial_complex’.

Notes

The data is stored as a DataArray object using the xarray package.

Example

>>> from tidy3d import ModeSpec
>>> from tidy3d import ScalarModeFieldDataArray, ModeIndexDataArray
>>> x = [-1,1,3]
>>> y = [-2,0]
>>> z = [-3,-1,1,3,5]
>>> f = [2e14, 3e14]
>>> mode_index = np.arange(5)
>>> grid = Grid(boundaries=Coords(x=x, y=y, z=z))
>>> field_coords = dict(x=x[:-1], y=y[:-1], z=z[:-1], f=f, mode_index=mode_index)
>>> field = ScalarModeFieldDataArray((1+1j)*np.random.random((2,1,4,2,5)), coords=field_coords)
>>> index_coords = dict(f=f, mode_index=mode_index)
>>> index_data = ModeIndexDataArray((1+1j) * np.random.random((2,5)), coords=index_coords)
>>> monitor = ModeSolverMonitor(
...    size=(2,0,6),
...    freqs=[2e14, 3e14],
...    mode_spec=ModeSpec(num_modes=5),
...    name='mode_solver',
... )
>>> data = ModeSolverData(
...     monitor=monitor,
...     Ex=field,
...     Ey=field,
...     Ez=field,
...     Hx=field,
...     Hy=field,
...     Hz=field,
...     n_complex=index_data,
...     grid_expanded=grid
... )

Attributes

modes_info	Dataset collecting various properties of the stored modes.
pol_fraction	Compute the TE and TM polarization fraction defined as the field intensity along the first or the second of the two tangential axes.
pol_fraction_waveguide	Compute the TE and TM polarization fraction using the waveguide definition.
time_reversed_copy	Make a copy of the data with direction-reversed fields.

Methods

eps_spec_match_mode_spec(val, values)	Raise validation error if frequencies in eps_spec does not match frequency list
overlap_sort(track_freq[, overlap_thresh])	Starting from the base frequency defined by parameter track_freq, sort modes at each frequency according to their overlap values with the modes at the previous frequency.
to_dataframe()	xarray-like method to export the modes_info into a pandas dataframe which is e.g.

monitor

eps_spec

classmethod eps_spec_match_mode_spec(val, values)

Raise validation error if frequencies in eps_spec does not match frequency list

overlap_sort(track_freq, overlap_thresh=0.9)

Starting from the base frequency defined by parameter track_freq, sort modes at each frequency according to their overlap values with the modes at the previous frequency. That is, it attempts to rearrange modes in such a way that a given mode_index corresponds to physically the same mode at all frequencies. Modes with overlap values over overlap_tresh are considered matching and not rearranged.

Parameters:

• track_freq (Literal["central", "lowest", "highest"]) – Parameter that specifies which frequency will serve as a starting point in the reordering process.

• overlap_thresh (float = 0.9) – Modal overlap threshold above which two modes are considered to be the same and are not rearranged. If after the sorting procedure the overlap value between two corresponding modes is less than this threshold, a warning about a possible discontinuity is displayed.

property time_reversed_copy

Make a copy of the data with direction-reversed fields. In lossy or gyrotropic systems, the time-reversed fields will not be the same as the backward-propagating modes.

property pol_fraction

Compute the TE and TM polarization fraction defined as the field intensity along the first or the second of the two tangential axes. More precisely, if E1 and E2 are the electric field components along the two tangential axes, the TE fraction is defined as integrate(E1.abs**2) / integrate(E1.abs**2 + E2.abs**2), and the TM fraction is equal to one minus the TE fraction. The tangential axes are defined by popping the normal axis from the list of x, y, z, so e.g. x and z for propagation in the y direction.

property pol_fraction_waveguide

Compute the TE and TM polarization fraction using the waveguide definition. If E1 and E2 are the electric field components along the two tangential axes and En is the component along the propagation direction, the TE fraction is defined as 1 - integrate(En.abs**2) / integrate(E1.abs**2 + E2.abs**2 + En.abs**2), and the TM fraction is defined as 1 - integrate(Hn.abs**2) / integrate(H1.abs**2 + H2.abs**2 + Hn.abs**2), with H denoting the magnetic field components.

Note

The waveguide TE and TM fractions do not sum to one. For example, TEM modes that are completely transverse (zero electric and magnetic field in the propagation direction) have TE fraction and TM fraction both equal to one.

property modes_info

Dataset collecting various properties of the stored modes.

to_dataframe()

xarray-like method to export the modes_info into a pandas dataframe which is e.g. simple to visualize as a table.

__hash__()

Hash method.