tidy3d.FieldProjectionCartesianData

class FieldProjectionCartesianData

Bases: AbstractFieldProjectionData

Data associated with a FieldProjectionCartesianMonitor: components of projected fields.

Parameters:

• monitor (FieldProjectionCartesianMonitor) – Field projection monitor with a Cartesian projection grid.

• Er (FieldProjectionCartesianDataArray) – Spatial distribution of r-component of the electric field.

• Etheta (FieldProjectionCartesianDataArray) – Spatial distribution of the theta-component of the electric field.

• Ephi (FieldProjectionCartesianDataArray) – Spatial distribution of phi-component of the electric field.

• Hr (FieldProjectionCartesianDataArray) – Spatial distribution of r-component of the magnetic field.

• Htheta (FieldProjectionCartesianDataArray) – Spatial distribution of theta-component of the magnetic field.

• Hphi (FieldProjectionCartesianDataArray) – Spatial distribution of phi-component of the magnetic field.

• medium (Union[Medium, AnisotropicMedium, PECMedium, PoleResidue, Sellmeier, Lorentz, Debye, Drude, FullyAnisotropicMedium, CustomMedium, CustomPoleResidue, CustomSellmeier, CustomLorentz, CustomDebye, CustomDrude, CustomAnisotropicMedium, PerturbationMedium, PerturbationPoleResidue, Medium2D] = Medium(name=None, frequency_range=None, allow_gain=False, nonlinear_spec=None, modulation_spec=None, heat_spec=None, type='Medium', permittivity=1.0, conductivity=0.0)) – Background medium through which to project fields.

• projection_surfaces (Tuple[FieldProjectionSurface, ...]) – Surfaces of the monitor where near fields were recorded for projection

Example

>>> from tidy3d import FieldProjectionCartesianDataArray
>>> f = np.linspace(1e14, 2e14, 10)
>>> x = np.linspace(0, 5, 10)
>>> y = np.linspace(0, 10, 20)
>>> z = np.atleast_1d(5)
>>> coords = dict(x=x, y=y, z=z, f=f)
>>> values = (1+1j) * np.random.random((len(x), len(y), len(z), len(f)))
>>> scalar_field = FieldProjectionCartesianDataArray(values, coords=coords)
>>> monitor = FieldProjectionCartesianMonitor(
...     center=(1,2,3), size=(2,2,2), freqs=f, name='n2f_monitor', x=x, y=y,
...     proj_axis=2, proj_distance=50
...     )
>>> data = FieldProjectionCartesianData(
...     monitor=monitor, Er=scalar_field, Etheta=scalar_field, Ephi=scalar_field,
...     Hr=scalar_field, Htheta=scalar_field, Hphi=scalar_field,
...     projection_surfaces=monitor.projection_surfaces,
...     )

Attributes

x	X positions.
y	Y positions.
z	Z positions.

Methods

renormalize_fields(proj_distance)

Return a FieldProjectionCartesianData with fields re-normalized to a new projection distance, by applying a phase factor based on proj_distance.

monitor

projection_surfaces

Er

Etheta

Ephi

Hr

Htheta

Hphi

property x

X positions.

property y

Y positions.

property z

Z positions.

renormalize_fields(proj_distance)

Return a FieldProjectionCartesianData with fields re-normalized to a new projection distance, by applying a phase factor based on proj_distance.

Parameters:

proj_distance (float = None) – (micron) new plane distance relative to the monitor’s local origin.

Returns:

Copy of this FieldProjectionCartesianData with fields re-projected to proj_distance.

Return type:

FieldProjectionCartesianData

__hash__()

Hash method.