tidy3d.ModeData#

class ModeData[source]#

Bases: ModeSolverDataset, ElectromagneticFieldData

Data associated with a ModeMonitor: modal amplitudes, propagation indices and mode profiles.

Parameters:

Ex (Optional[ScalarModeFieldDataArray] = None) – Spatial distribution of the x-component of the electric field of the mode.
Ey (Optional[ScalarModeFieldDataArray] = None) – Spatial distribution of the y-component of the electric field of the mode.
Ez (Optional[ScalarModeFieldDataArray] = None) – Spatial distribution of the z-component of the electric field of the mode.
Hx (Optional[ScalarModeFieldDataArray] = None) – Spatial distribution of the x-component of the magnetic field of the mode.
Hy (Optional[ScalarModeFieldDataArray] = None) – Spatial distribution of the y-component of the magnetic field of the mode.
Hz (Optional[ScalarModeFieldDataArray] = None) – Spatial distribution of the z-component of the magnetic field of the mode.
monitor (ModeMonitor) – Mode monitor associated with the data.
symmetry (Tuple[Literal[0, -1, 1], Literal[0, -1, 1], Literal[0, -1, 1]] = (0, 0, 0)) – Symmetry eigenvalues of the original simulation in x, y, and z.
symmetry_center (Optional[Tuple[float, float, float]] = None) – 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 (Optional[Grid] = None) – 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 vector and flux.
grid_primal_correction (Union[float, FreqDataArray, TimeDataArray, FreqModeDataArray] = 1.0) – 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 (Union[float, FreqDataArray, TimeDataArray, FreqModeDataArray] = 1.0) – 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 (ModeIndexDataArray) – Complex-valued effective propagation constants associated with the mode.
n_group_raw (Optional[GroupIndexDataArray] = None) – Index associated with group velocity of the mode.
dispersion_raw (Optional[ModeDispersionDataArray] = None) – [units = ps/(nm km)]. Dispersion parameter for the mode.
amps (ModeAmpsDataArray) – Complex-valued amplitudes associated with the mode.
eps_spec (Optional[List[Literal['diagonal', 'tensorial_real', 'tensorial_complex']]] = None) – 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.

The mode monitor data contains the complex effective indices and the complex mode amplitudes at the monitor position calculated by mode decomposition. The data structure of the complex effective indices :attr`n_complex` contains two coordinates: f and mode_index, both of which are specified when defining the :class:ModeMonitor in the simulation.

Besides the effective index, :class:ModeMonitor is primarily used to calculate the transmission of certain modes in certain directions. We can extract the complex amplitude and square it to compute the mode transmission power.

Example

>>> from tidy3d import ModeSpec
>>> from tidy3d import ModeAmpsDataArray, ModeIndexDataArray
>>> direction = ["+", "-"]
>>> f = [1e14, 2e14, 3e14]
>>> mode_index = np.arange(5)
>>> index_coords = dict(f=f, mode_index=mode_index)
>>> index_data = ModeIndexDataArray((1+1j) * np.random.random((3, 5)), coords=index_coords)
>>> amp_coords = dict(direction=direction, f=f, mode_index=mode_index)
>>> amp_data = ModeAmpsDataArray((1+1j) * np.random.random((2, 3, 5)), coords=amp_coords)
>>> monitor = ModeMonitor(
...    size=(2,0,6),
...    freqs=[2e14, 3e14],
...    mode_spec=ModeSpec(num_modes=5),
...    name='mode',
... )
>>> data = ModeData(monitor=monitor, amps=amp_data, n_complex=index_data)

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
`normalize`(source_spectrum_fn)	Return copy of self after normalization is applied using source spectrum function.
`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. simple to visualize as a table.

Inherited Common Usage

monitor#

amps#

eps_spec#

classmethod eps_spec_match_mode_spec(val, values)[source]#: Raise validation error if frequencies in eps_spec does not match frequency list

normalize(source_spectrum_fn)[source]#: Return copy of self after normalization is applied using source spectrum function.

overlap_sort(track_freq, overlap_thresh=0.9)[source]#

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 $E_1$ and $E_2$ are the electric field components along the two tangential axes, the TE fraction is defined as:

\[\frac{\int |E_1|^2 \, {\rm d}S}{\int \left(|E_1|^2 + |E_2|^2\right) \, {\rm d}S}\]

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 $n$ is the propagation direction, the TE fraction is defined as:

\[1 - \frac{\int |E \cdot n|^2 \, {\rm d}S}{\int |E|^2 \, {\rm d}S}\]

and the TM fraction is defined as

\[1 - \frac{\int |H \cdot n|^2 \, {\rm d}S}{\int |H|^2 \, {\rm d}S}\]

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()[source]#: 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.

tidy3d.ModeData

Contents

tidy3d.ModeData#