""" Monitor Level Data, store the DataArrays associated with a single monitor."""
from __future__ import annotations
from abc import ABC
from typing import Union, Tuple, Callable, Dict, List, Any
import warnings
import xarray as xr
import numpy as np
import pydantic.v1 as pd
from pandas import DataFrame
from .data_array import FluxTimeDataArray, FluxDataArray
from .data_array import MixedModeDataArray, ModeAmpsDataArray
from .data_array import GroupIndexDataArray, ModeDispersionDataArray
from .data_array import FieldProjectionAngleDataArray, FieldProjectionCartesianDataArray
from .data_array import FieldProjectionKSpaceDataArray
from .data_array import DataArray, DiffractionDataArray
from .data_array import ScalarFieldDataArray, ScalarFieldTimeDataArray
from .data_array import FreqDataArray, TimeDataArray, FreqModeDataArray
from .dataset import Dataset, AbstractFieldDataset, ElectromagneticFieldDataset
from .dataset import FieldDataset, FieldTimeDataset, ModeSolverDataset, PermittivityDataset
from ..base import TYPE_TAG_STR, cached_property, skip_if_fields_missing
from ..types import Coordinate, Symmetry, ArrayFloat1D, ArrayFloat2D, Size, Numpy, TrackFreq
from ..types import EpsSpecType, Literal
from ..grid.grid import Grid, Coords
from ..validators import enforce_monitor_fields_present, required_if_symmetry_present
from ..monitor import MonitorType, FieldMonitor, FieldTimeMonitor, ModeSolverMonitor
from ..monitor import ModeMonitor, FluxMonitor, FluxTimeMonitor, PermittivityMonitor
from ..monitor import FieldProjectionAngleMonitor, FieldProjectionCartesianMonitor
from ..monitor import FieldProjectionKSpaceMonitor, FieldProjectionSurface
from ..monitor import DiffractionMonitor
from ..source import SourceTimeType, CustomFieldSource
from ..medium import Medium, MediumType
from ...exceptions import SetupError, DataError, Tidy3dNotImplementedError, ValidationError
from ...constants import ETA_0, C_0, MICROMETER
from ...log import log
from ..base_sim.data.monitor_data import AbstractMonitorData
Coords1D = ArrayFloat1D
[docs]
class MonitorData(AbstractMonitorData, ABC):
"""
Abstract base class of objects that store data pertaining to a single :class:`.monitor`.
"""
monitor: MonitorType = pd.Field(
...,
title="Monitor",
description="Monitor associated with the data.",
discriminator=TYPE_TAG_STR,
)
@property
def symmetry_expanded(self) -> MonitorData:
"""Return self with symmetry applied."""
return self
[docs]
def normalize(self, source_spectrum_fn: Callable[[float], complex]) -> Dataset:
"""Return copy of self after normalization is applied using source spectrum function."""
return self.copy()
def _updated(self, update: Dict) -> MonitorData:
"""Similar to ``updated_copy``, but does not actually copy components, for speed.
Note
----
This does **not** produce a copy of mutable objects, so e.g. if some of the data arrays
are not updated, they will point to the values in the original data. This method should
thus be used carefully.
"""
data_dict = self.dict()
data_dict.update(update)
return type(self).parse_obj(data_dict)
class AbstractFieldData(MonitorData, AbstractFieldDataset, ABC):
"""Collection of scalar fields with some symmetry properties."""
monitor: Union[FieldMonitor, FieldTimeMonitor, PermittivityMonitor, ModeMonitor]
symmetry: Tuple[Symmetry, Symmetry, Symmetry] = pd.Field(
(0, 0, 0),
title="Symmetry",
description="Symmetry eigenvalues of the original simulation in x, y, and z.",
)
symmetry_center: Coordinate = pd.Field(
None,
title="Symmetry Center",
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: Grid = pd.Field(
None,
title="Expanded Grid",
description=":class:`.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.",
)
@pd.validator("grid_expanded", always=True)
def warn_missing_grid_expanded(cls, val, values):
"""If ``grid_expanded`` not provided and fields data is present, warn that some methods
will break."""
field_comps = ["Ex", "Ey", "Ez", "Hx", "Hy", "Hz"]
if val is None and any(values[comp] is not None for comp in field_comps):
log.warning(
"Monitor data requires 'grid_expanded' to be defined to compute values like "
"flux, Poynting and dot product with other data."
)
return val
_require_sym_center = required_if_symmetry_present("symmetry_center")
_require_grid_expanded = required_if_symmetry_present("grid_expanded")
def _expanded_grid_field_coords(self, field_name: str) -> Coords:
"""Coordinates in the expanded grid corresponding to a given field component."""
return self.grid_expanded[self.grid_locations[field_name]]
@property
def symmetry_expanded(self):
"""Return the :class:`.AbstractFieldData` with fields expanded based on symmetry. If
any symmetry is nonzero (i.e. expanded), the interpolation implicitly creates a copy of the
data array. However, if symmetry is not expanded, the returned array contains a view of
the data, not a copy.
Returns
-------
:class:`AbstractFieldData`
A data object with the symmetry expanded fields.
"""
if all(sym == 0 for sym in self.symmetry):
return self
return self._updated(self._symmetry_update_dict)
@property
def symmetry_expanded_copy(self) -> AbstractFieldData:
"""Create a copy of the :class:`.AbstractFieldData` with fields expanded based on symmetry.
Returns
-------
:class:`AbstractFieldData`
A data object with the symmetry expanded fields.
"""
if all(sym == 0 for sym in self.symmetry):
return self.copy()
return self.copy(update=self._symmetry_update_dict)
@property
def _symmetry_update_dict(self) -> Dict:
"""Dictionary of data fields to create data with expanded symmetry."""
update_dict = {}
for field_name, scalar_data in self.field_components.items():
eigenval_fn = self.symmetry_eigenvalues[field_name]
# get grid locations for this field component on the expanded grid
field_coords = self._expanded_grid_field_coords(field_name)
for sym_dim, (sym_val, sym_loc) in enumerate(zip(self.symmetry, self.symmetry_center)):
dim_name = "xyz"[sym_dim]
# Continue if no symmetry along this dimension
if sym_val == 0:
continue
# Get coordinates for this field component on the expanded grid
coords = field_coords.to_list[sym_dim]
coords = self.monitor.downsample(coords, axis=sym_dim)
# Get indexes of coords that lie on the left of the symmetry center
flip_inds = np.where(coords < sym_loc)[0]
# Get the symmetric coordinates on the right
coords_interp = np.copy(coords)
coords_interp[flip_inds] = 2 * sym_loc - coords[flip_inds]
# Interpolate. There generally shouldn't be values out of bounds except potentially
# when handling modes, in which case they should be at the boundary and close to 0.
scalar_data = scalar_data.sel(**{dim_name: coords_interp}, method="nearest")
scalar_data = scalar_data.assign_coords({dim_name: coords})
# apply the symmetry eigenvalue (if defined) to the flipped values
if eigenval_fn is not None:
sym_eigenvalue = eigenval_fn(sym_dim)
scalar_data = scalar_data.multiply_at(
value=sym_val * sym_eigenvalue, coord_name=dim_name, indices=flip_inds
)
# assign the final scalar data to the update_dict
update_dict[field_name] = scalar_data
update_dict.update({"symmetry": (0, 0, 0), "symmetry_center": None})
return update_dict
def at_coords(self, coords: Coords) -> xr.Dataset:
"""Colocate data to some supplied coordinates. This is a convenience method that wraps
``colocate``, and skips dimensions for which the data has a single data point only
(``colocate`` will error in that case.) If the coords are out of bounds for the data
otherwise, an error will still be produced.
Parameters
----------
coords : :class:`Coords`
Coordinates in x, y and z to colocate to.
Returns
-------
xarray.Dataset
Dataset containing all of the fields in the data interpolated to boundary locations on
the Yee grid.
"""
# pass coords if each of the scalar field data have more than one coordinate along a dim
xyz_kwargs = {}
for dim, coords_dim in zip("xyz", (coords.x, coords.y, coords.z)):
scalar_data = list(self.field_components.values())
coord_lens = [len(data.coords[dim]) for data in scalar_data]
if all(ncoords > 1 for ncoords in coord_lens):
xyz_kwargs[dim] = coords_dim
return self.colocate(**xyz_kwargs)
[docs]
class ElectromagneticFieldData(AbstractFieldData, ElectromagneticFieldDataset, ABC):
"""Collection of electromagnetic fields."""
grid_primal_correction: Union[
float, FreqDataArray, TimeDataArray, FreqModeDataArray
] = pd.Field(
1.0,
title="Field correction factor",
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: Union[float, FreqDataArray, TimeDataArray, FreqModeDataArray] = pd.Field(
1.0,
title="Field correction factor",
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.",
)
def _expanded_grid_field_coords(self, field_name: str):
"""Coordinates in the expanded grid corresponding to a given field component."""
if self.monitor.colocate:
bounds_dict = self.grid_expanded.boundaries.to_dict
return Coords(**{key: val[:-1] for key, val in bounds_dict.items()})
return self.grid_expanded[self.grid_locations[field_name]]
@property
def _grid_correction_dict(self):
"""Return the primal and dual finite grid correction factors as a dictionary."""
return {
"grid_primal_correction": self.grid_primal_correction,
"grid_dual_correction": self.grid_dual_correction,
}
@property
def _tangential_dims(self) -> List[str]:
"""For a 2D monitor data, return the names of the tangential dimensions. Raise if cannot
confirm that the associated monitor is 2D."""
if len(self.monitor.zero_dims) != 1:
raise DataError("Data must be 2D to get tangential dimensions.")
tangential_dims = ["x", "y", "z"]
tangential_dims.pop(self.monitor.zero_dims[0])
return tangential_dims
@property
def colocation_boundaries(self) -> Coords:
"""Coordinates to be used for colocation of the data to grid boundaries."""
if not self.grid_expanded:
raise DataError(
"Monitor data requires 'grid_expanded' to be defined in order to "
"compute colocation coordinates."
)
# Get boundaries from the expanded grid
grid_bounds = self.grid_expanded.boundaries.to_dict
# Non-colocating monitors can only colocate starting from the first boundary
# (unless there's a single data point, in which case data has already been snapped).
# Regardless of colocation, we also drop the last boundary.
colocate_bounds = {}
for dim, bounds in grid_bounds.items():
cbs = bounds[:-1]
if not self.monitor.colocate and cbs.size > 1:
cbs = cbs[1:]
colocate_bounds[dim] = cbs
return Coords(**colocate_bounds)
@property
def colocation_centers(self) -> Coords:
"""Coordinates to be used for colocation of the data to grid centers."""
colocate_centers = {}
for dim, coords in self.colocation_boundaries.to_dict.items():
colocate_centers[dim] = (coords[1:] + coords[:-1]) / 2
return Coords(**colocate_centers)
@property
def _plane_grid_boundaries(self) -> Tuple[Coords1D, Coords1D]:
"""For a 2D monitor data, return the boundaries of the in-plane grid to be used to compute
differential area and to colocate fields if needed."""
if np.any(np.array(self.monitor.interval_space) > 1):
raise Tidy3dNotImplementedError(
"Cannot determine grid boundaries corresponding to "
"down-sampled monitor data ('interval_space' > 1 along a direction)."
)
dim1, dim2 = self._tangential_dims
bounds_dict = self.colocation_boundaries.to_dict
return (bounds_dict[dim1], bounds_dict[dim2])
@property
def _plane_grid_centers(self) -> Tuple[Coords1D, Coords1D]:
"""For 2D monitor data, return the centers of the in-plane grid"""
return [(bs[1:] + bs[:-1]) / 2 for bs in self._plane_grid_boundaries]
@property
def _diff_area(self) -> xr.DataArray:
"""For a 2D monitor data, return the area of each cell in the plane, for use in numerical
integrations. This assumes that data is colocated to grid boundaries, and uses the
difference in the surrounding grid centers to compute the area.
"""
# Monitor values are interpolated to bounds
bounds = self._plane_grid_boundaries
# Coords to compute cell sizes around the interpolation locations
coords = [bs.copy() for bs in self._plane_grid_centers]
# Append the first and last boundary
_, plane_inds = self.monitor.pop_axis([0, 1, 2], self.monitor.size.index(0.0))
coords[0] = np.array([bounds[0][0]] + coords[0].tolist() + [bounds[0][-1]])
coords[1] = np.array([bounds[1][0]] + coords[1].tolist() + [bounds[1][-1]])
"""Truncate coords to monitor boundaries. This implicitly makes extra pixels which may be
present have size 0 and so won't be included in the integration. For pixels intersected
by the monitor edge, the size is truncated to the part covered by the monitor. When using
the differential area sizes defined in this way together with integrand values
defined at cell boundaries, the integration is equivalent to trapezoidal rule with the first
and last values interpolated to the exact monitor start/end location, if the integrand
is zero outside of the monitor geometry. This should usually be the case for flux and dot
computations"""
mnt_bounds = np.array(self.monitor.bounds)
mnt_bounds = mnt_bounds[:, plane_inds].T
coords[0][np.argwhere(coords[0] < mnt_bounds[0, 0])] = mnt_bounds[0, 0]
coords[0][np.argwhere(coords[0] > mnt_bounds[0, 1])] = mnt_bounds[0, 1]
coords[1][np.argwhere(coords[1] < mnt_bounds[1, 0])] = mnt_bounds[1, 0]
coords[1][np.argwhere(coords[1] > mnt_bounds[1, 1])] = mnt_bounds[1, 1]
# Do not apply the spurious dl along a dimension where the simulation is 2D.
# Instead, we just set the boundaries such that the cell size along the zero dimension is 1,
# such that quantities like flux will come out in units of W / um.
sizes_dim0 = coords[0][1:] - coords[0][:-1] if bounds[0].size > 1 else [1.0]
sizes_dim1 = coords[1][1:] - coords[1][:-1] if bounds[1].size > 1 else [1.0]
return xr.DataArray(np.outer(sizes_dim0, sizes_dim1), dims=self._tangential_dims)
def _tangential_corrected(self, fields: Dict[str, DataArray]) -> Dict[str, DataArray]:
"""For a 2D monitor data, extract the tangential components from fields and orient them
such that the third component would be the normal axis. This just means that the H field
gets an extra minus sign if the normal axis is ``"y"``. Raise if any of the tangential
field components is missing.
The finite grid correction is also applied, so the intended use of these fields is in
poynting, flux, and dot-like methods. The normal coordinate is dropped from the field data.
"""
if len(self.monitor.zero_dims) != 1:
raise DataError("Data must be 2D to get tangential fields.")
# Tangential field components
tan_dims = self._tangential_dims
components = [fname + dim for fname in "EH" for dim in tan_dims]
normal_dim = "xyz"[self.monitor.size.index(0)]
tan_fields = {}
for component in components:
if component not in fields:
raise DataError(f"Tangential field component '{component}' missing in field data.")
correction = 1
# sign correction to H
if normal_dim == "y" and component[0] == "H":
correction *= -1
# finite grid correction to all fields
eig_val = self.symmetry_eigenvalues[component](normal_dim)
if eig_val < 0:
correction *= self.grid_dual_correction
else:
correction *= self.grid_primal_correction
field_squeezed = fields[component].squeeze(dim=normal_dim, drop=True)
tan_fields[component] = field_squeezed * correction
return tan_fields
@property
def _tangential_fields(self) -> Dict[str, DataArray]:
"""For a 2D monitor data, get the tangential E and H fields in the 2D plane grid. Fields
are oriented such that the third component would be the normal axis. This just means that
the H field gets an extra minus sign if the normal axis is ``"y"``.
Note
----
The finite grid correction factors are applied and symmetry is expanded.
"""
return self._tangential_corrected(self.symmetry_expanded.field_components)
@property
def _colocated_fields(self) -> Dict[str, DataArray]:
"""For a 2D monitor data, get all E and H fields colocated to the cell boundaries in the 2D
plane grid, with symmetries expanded.
"""
field_components = self.symmetry_expanded.field_components
if self.monitor.colocate:
return field_components
# Interpolate field components to cell boundaries
interp_dict = {"assume_sorted": True}
for dim, bounds in zip(self._tangential_dims, self._plane_grid_boundaries):
if bounds.size > 1:
interp_dict[dim] = bounds
colocated_fields = {key: val.interp(**interp_dict) for key, val in field_components.items()}
return colocated_fields
@property
def _colocated_tangential_fields(self) -> Dict[str, DataArray]:
"""For a 2D monitor data, get the tangential E and H fields colocated to the cell boundaries
in the 2D plane grid. Fields are oriented such that the third component would be the normal
axis. This just means that the H field gets an extra minus sign if the normal axis is
``"y"``. Raise if any of the tangential field components is missing.
Note
----
The finite grid correction factors are applied and symmetry is expanded.
"""
return self._tangential_corrected(self._colocated_fields)
@property
def grid_corrected_copy(self) -> ElectromagneticFieldData:
"""Return a copy of self with grid correction factors applied (if necessary) and symmetry
expanded."""
field_data = self.symmetry_expanded_copy
if len(self.monitor.zero_dims) != 1:
return field_data
normal_dim = "xyz"[self.monitor.zero_dims[0]]
update = {"grid_primal_correction": 1.0, "grid_dual_correction": 1.0}
for field_name, field in field_data.field_components.items():
eig_val = self.symmetry_eigenvalues[field_name](normal_dim)
if eig_val < 0:
update[field_name] = field * self.grid_dual_correction
else:
update[field_name] = field * self.grid_primal_correction
return field_data.copy(update=update)
@property
def intensity(self) -> ScalarFieldDataArray:
"""Return the sum of the squared absolute electric field components."""
normal_dim = "xyz"[self.monitor.size.index(0)]
fields = self._colocated_fields
components = ("Ex", "Ey", "Ez")
if any(cmp not in fields for cmp in components):
raise KeyError("Can't compute intensity, all E field components must be present.")
intensity = sum(fields[cmp].abs ** 2 for cmp in components)
return intensity.squeeze(dim=normal_dim, drop=True)
@property
def poynting(self) -> ScalarFieldDataArray:
"""Time-averaged Poynting vector for frequency-domain data associated to a 2D monitor,
projected to the direction normal to the monitor plane."""
# Tangential fields are ordered as E1, E2, H1, H2
tan_fields = self._colocated_tangential_fields
dim1, dim2 = self._tangential_dims
e_x_h_star = tan_fields["E" + dim1] * tan_fields["H" + dim2].conj()
e_x_h_star -= tan_fields["E" + dim2] * tan_fields["H" + dim1].conj()
poynting = 0.5 * np.real(e_x_h_star)
return poynting
[docs]
def package_flux_results(self, flux_values: xr.DataArray) -> Any:
"""How to package flux"""
return FluxDataArray(flux_values)
@cached_property
def flux(self) -> FluxDataArray:
"""Flux for data corresponding to a 2D monitor."""
# Compute flux by integrating Poynting vector in-plane
d_area = self._diff_area
poynting = self.poynting
flux_values = poynting * d_area
flux_values = flux_values.sum(dim=d_area.dims)
return self.package_flux_results(flux_values)
@cached_property
def mode_area(self) -> FreqModeDataArray:
r"""Effective mode area corresponding to a 2D monitor.
.. math:
\frac{\left(\int |E|^2 \, {\rm d}S\right)^2}{\int |E|^4 \, {\rm d}S}
"""
intensity = self.intensity
# integrate over the plane
d_area = self._diff_area
num = (intensity * d_area).sum(dim=d_area.dims) ** 2
den = (intensity**2 * d_area).sum(dim=d_area.dims)
area = num / den
if hasattr(self.monitor, "mode_spec"):
area *= np.cos(self.monitor.mode_spec.angle_theta)
return FreqModeDataArray(area)
[docs]
def dot(
self, field_data: Union[FieldData, ModeData, ModeSolverData], conjugate: bool = True
) -> ModeAmpsDataArray:
r"""Dot product (modal overlap) with another :class:`.FieldData` object. Both datasets have
to be frequency-domain data associated with a 2D monitor. Along the tangential directions,
the datasets have to have the same discretization. Along the normal direction, the monitor
position may differ and is ignored. Other coordinates (``frequency``, ``mode_index``) have
to be either identical or broadcastable. Broadcasting is also supported in the case in
which the other ``field_data`` has a dimension of size 1 whose coordinate is not in the list
of coordinates in the ``self`` dataset along the corresponding dimension. In that case, the
coordinates of the ``self`` dataset are used in the output.
The dot product is defined as:
.. math:
\frac{1}{4} \int \left( E_0 \times H_1^* + H_0^* \times E_1 \) \, {\rm d}S
Parameters
----------
field_data : :class:`ElectromagneticFieldData`
A data instance to compute the dot product with.
conjugate : bool, optional
If ``True`` (default), the dot product is defined as above. If ``False``, the definition
is similar, but without the complex conjugation of the $H$ fields.
Note
----
The dot product with and without conjugation is equivalent (up to a phase) for
modes in lossless waveguides but differs for modes in lossy materials. In that case,
the conjugated dot product can be interpreted as the fraction of the power of the first
mode carried by the second, but modes are not orthogonal with respect to that product
and the sum of carried power fractions may be different from the total flux.
In the non-conjugated definition, modes are orthogonal, but the interpretation of the
dot product power carried by a given mode is no longer valid.
"""
# Tangential fields for current and other field data
fields_self = self._colocated_tangential_fields
fields_other = field_data._colocated_tangential_fields
if conjugate:
fields_self = {key: field.conj() for key, field in fields_self.items()}
# Drop size-1 dimensions in the other data
fields_other = {key: field.squeeze(drop=True) for key, field in fields_other.items()}
# Cross products of fields
dim1, dim2 = self._tangential_dims
e_self_x_h_other = fields_self["E" + dim1] * fields_other["H" + dim2]
e_self_x_h_other -= fields_self["E" + dim2] * fields_other["H" + dim1]
h_self_x_e_other = fields_self["H" + dim1] * fields_other["E" + dim2]
h_self_x_e_other -= fields_self["H" + dim2] * fields_other["E" + dim1]
# Integrate over plane
d_area = self._diff_area
integrand = (e_self_x_h_other - h_self_x_e_other) * d_area
return ModeAmpsDataArray(0.25 * integrand.sum(dim=d_area.dims))
def _interpolated_tangential_fields(self, coords: ArrayFloat2D) -> Dict[str, DataArray]:
"""For 2D monitors, interpolate this fields to given coords in the tangential plane.
Parameters
----------
coords : ArrayFloat2D
Interpolation coords in the monitor's tangential plane.
Return
------
Dictionary with interpolated fields.
"""
fields = self._tangential_fields
interp_dict = {"assume_sorted": True}
for dim, cents in zip(self._tangential_dims, coords):
if cents.size > 0:
interp_dict[dim] = cents
kwargs = {"bounds_error": False, "fill_value": 0.0}
for component, field in fields.items():
fields[component] = field.interp(kwargs=kwargs, **interp_dict)
return fields
[docs]
def outer_dot(
self, field_data: Union[FieldData, ModeData], conjugate: bool = True
) -> MixedModeDataArray:
r"""Dot product (modal overlap) with another :class:`.FieldData` object.
The tangential fields from ``field_data`` are interpolated to this object's grid, so the
data arrays don't need to have the same discretization. The calculation is performed for
all common frequencies between data arrays. In the output, ``mode_index_0`` and
``mode_index_1`` are the mode indices from this object and ``field_data``, respectively, if
they are instances of ``ModeData``.
The dot product is defined as:
.. math:
\frac{1}{4} \int \left( E_0 \times H_1^* + H_0^* \times E_1 \) \, {\rm d}S
Parameters
----------
field_data : :class:`ElectromagneticFieldData`
A data instance to compute the dot product with.
conjugate : bool = True
If ``True`` (default), the dot product is defined as above. If ``False``, the definition
is similar, but without the complex conjugation of the $H$ fields.
Returns
-------
:class:`xarray.DataArray`
Data array with the complex-valued modal overlaps between the two mode data.
See also
--------
:member:`dot`
"""
tan_dims = self._tangential_dims
if not all(a == b for a, b in zip(tan_dims, field_data._tangential_dims)):
raise DataError("Tangential dimensions must match between the two monitors.")
# Tangential fields for current
fields_self = self._colocated_tangential_fields
if conjugate:
fields_self = {component: field.conj() for component, field in fields_self.items()}
# Tangential fields for other data
fields_other = field_data._interpolated_tangential_fields(self._plane_grid_boundaries)
# Tangential field component names
dim1, dim2 = tan_dims
e_1 = "E" + dim1
e_2 = "E" + dim2
h_1 = "H" + dim1
h_2 = "H" + dim2
# Prepare array with proper dimensions for the dot product data
arrays = (fields_self[e_1], fields_other[e_1])
coords = (arrays[0].coords, arrays[1].coords)
# Common frequencies to both data arrays
f = np.array(sorted(set(coords[0]["f"].values).intersection(coords[1]["f"].values)))
isel1 = [list(coords[0]["f"].values).index(freq) for freq in f]
isel2 = [list(coords[1]["f"].values).index(freq) for freq in f]
# Mode indices, if available
modes_in_self = "mode_index" in coords[0]
coords[0]["mode_index"].values if modes_in_self else np.zeros(1, dtype=int)
modes_in_other = "mode_index" in coords[1]
coords[1]["mode_index"].values if modes_in_other else np.zeros(1, dtype=int)
keys = (e_1, e_2, h_1, h_2)
for key in keys:
fields_self[key] = fields_self[key].isel(f=isel1)
if modes_in_self:
fields_self[key] = fields_self[key].rename(mode_index="mode_index_0")
else:
fields_self[key] = fields_self[key].expand_dims(
dim={"mode_index_0": [0]}, axis=len(fields_self[key].shape)
)
fields_other[key] = fields_other[key].isel(f=isel2)
if modes_in_other:
fields_other[key] = fields_other[key].rename(mode_index="mode_index_1")
else:
fields_other[key] = fields_other[key].expand_dims(
dim={"mode_index_1": [0]}, axis=len(fields_other[key].shape)
)
d_area = self._diff_area.expand_dims(dim={"f": f}, axis=2).to_numpy()
# function to apply at each pair of mode indices before integrating
def fn(fields_1, fields_2):
e_self_1 = fields_1[e_1]
e_self_2 = fields_1[e_2]
h_self_1 = fields_1[h_1]
h_self_2 = fields_1[h_2]
e_other_1 = fields_2[e_1]
e_other_2 = fields_2[e_2]
h_other_1 = fields_2[h_1]
h_other_2 = fields_2[h_2]
# Cross products of fields
e_self_x_h_other = e_self_1 * h_other_2 - e_self_2 * h_other_1
h_self_x_e_other = h_self_1 * e_other_2 - h_self_2 * e_other_1
summand = 0.25 * (e_self_x_h_other - h_self_x_e_other) * d_area
return summand
result = self._outer_fn_summation(
fields_1=fields_self,
fields_2=fields_other,
outer_dim_1="mode_index_0",
outer_dim_2="mode_index_1",
sum_dims=tan_dims,
fn=fn,
)
# Remove mode index coordinate if the input did not have it
if not modes_in_self:
result = result.isel(mode_index_0=0, drop=True)
if not modes_in_other:
result = result.isel(mode_index_1=0, drop=True)
return result
@staticmethod
def _outer_fn_summation(
fields_1: Dict[str, xr.DataArray],
fields_2: Dict[str, xr.DataArray],
outer_dim_1: str,
outer_dim_2: str,
sum_dims: List[str],
fn: Callable,
) -> xr.DataArray:
"""
Loop over ``outer_dim_1`` and ``outer_dim_2``, apply ``fn`` to ``fields_1`` and ``fields_2``, and sum over ``sum_dims``.
The resulting ``xr.DataArray`` has has dimensions any dimensions in the fields which are not contained in sum_dims.
This can be more memory efficient than vectorizing over the ``outer_dims``, which can involve broadcasting and reshaping data.
It also converts to numpy arrays outside the loops to minimize xarray overhead.
"""
# first, convert to numpy outside the loop to reduce xarray overhead
fields_1_numpy = {key: val.to_numpy() for key, val in fields_1.items()}
fields_2_numpy = {key: val.to_numpy() for key, val in fields_2.items()}
# get one of the data arrays to look at for indexing
# assuming all data arrays have the same structure
data_array_temp_1 = list(fields_1.values())[0]
data_array_temp_2 = list(fields_2.values())[0]
numpy_temp_1 = data_array_temp_1.to_numpy()
numpy_temp_2 = data_array_temp_2.to_numpy()
# find the numpy axes associated with the provided dimensions
outer_axis_1 = data_array_temp_1.get_axis_num(outer_dim_1)
outer_axis_2 = data_array_temp_2.get_axis_num(outer_dim_2)
sum_axes = [data_array_temp_1.get_axis_num(dim) for dim in sum_dims]
# coords and array for result of calculation
coords = {key: val.to_numpy() for key, val in data_array_temp_1.coords.items()}
for dim in sum_dims:
coords.pop(dim)
# last two inds are the outer_dims
coords.pop(outer_dim_1)
coords[outer_dim_1] = data_array_temp_1.coords[outer_dim_1].to_numpy()
coords[outer_dim_2] = data_array_temp_2.coords[outer_dim_2].to_numpy()
# drop scalar non-indexing dimensions
coords = {key: val for key, val in coords.items() if len(val.shape) != 0}
shape = [len(val) for val in coords.values()]
dtype = np.promote_types(numpy_temp_1.dtype, numpy_temp_2.dtype)
data = np.zeros(shape, dtype=dtype)
# indexing tuples
idx_1 = [slice(None)] * numpy_temp_1.ndim
idx_2 = [slice(None)] * numpy_temp_2.ndim
idx_data = [slice(None)] * data.ndim
# calculate the sums of products
for outer_1 in range(numpy_temp_1.shape[outer_axis_1]):
for outer_2 in range(numpy_temp_2.shape[outer_axis_2]):
idx_1[outer_axis_1] = outer_1
idx_2[outer_axis_2] = outer_2
idx_data[-2] = outer_1
idx_data[-1] = outer_2
fields_1_curr = {key: val[tuple(idx_1)] for key, val in fields_1_numpy.items()}
fields_2_curr = {key: val[tuple(idx_2)] for key, val in fields_2_numpy.items()}
summand_curr = fn(fields_1_curr, fields_2_curr)
data_curr = np.sum(summand_curr, axis=tuple(sum_axes))
data[tuple(idx_data)] = data_curr
return xr.DataArray(data, coords=coords)
@property
def time_reversed_copy(self) -> FieldData:
"""Make a copy of the data with time-reversed fields."""
# Time reversal for frequency-domain fields; overwritten in :class:`FieldTimeData`,
# :class:`ModeData`, and :class:`ModeSolverData`.
new_data = {}
for comp, field in self.field_components.items():
if comp[0] == "H":
new_data[comp] = -np.conj(field)
else:
new_data[comp] = np.conj(field)
return self.copy(update=new_data)
[docs]
class FieldData(FieldDataset, ElectromagneticFieldData):
"""
Data associated with a :class:`.FieldMonitor`: scalar components of E and H fields.
Notes
-----
The data is stored as a `DataArray <https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html>`_
object using the `xarray <https://docs.xarray.dev/en/stable/index.html>`_ package.
This dataset can contain all electric and magnetic field components: ``Ex``, ``Ey``, ``Ez``, ``Hx``, ``Hy``,
and ``Hz``.
Example
-------
>>> from tidy3d import ScalarFieldDataArray
>>> x = [-1,1,3]
>>> y = [-2,0,2,4]
>>> z = [-3,-1,1,3,5]
>>> f = [2e14, 3e14]
>>> coords = dict(x=x[:-1], y=y[:-1], z=z[:-1], f=f)
>>> grid = Grid(boundaries=Coords(x=x, y=y, z=z))
>>> scalar_field = ScalarFieldDataArray((1+1j) * np.random.random((2,3,4,2)), coords=coords)
>>> monitor = FieldMonitor(
... size=(2,4,6), freqs=[2e14, 3e14], name='field', fields=['Ex', 'Hz'], colocate=True
... )
>>> data = FieldData(monitor=monitor, Ex=scalar_field, Hz=scalar_field, grid_expanded=grid)
.. TODO sort out standalone data example.
See Also
--------
**Notebooks:**
* `Quickstart <../../notebooks/StartHere.html>`_: Usage in a basic simulation flow.
* `Performing visualization of simulation data <../../notebooks/VizData.html>`_
* `Advanced monitor data manipulation and visualization <../../notebooks/XarrayTutorial.html>`_
"""
monitor: FieldMonitor = pd.Field(
..., title="Monitor", description="Frequency-domain field monitor associated with the data."
)
_contains_monitor_fields = enforce_monitor_fields_present()
[docs]
def normalize(self, source_spectrum_fn: Callable[[float], complex]) -> FieldDataset:
"""Return copy of self after normalization is applied using source spectrum function."""
fields_norm = {}
for field_name, field_data in self.field_components.items():
src_amps = source_spectrum_fn(field_data.f)
fields_norm[field_name] = (field_data / src_amps).astype(field_data.dtype)
return self.copy(update=fields_norm)
[docs]
def to_source(
self, source_time: SourceTimeType, center: Coordinate, size: Size = None, **kwargs
) -> CustomFieldSource:
"""Create a :class:`.CustomFieldSource` from the fields stored in the :class:`.FieldData`.
Parameters
----------
source_time: :class:`.SourceTime`
Specification of the source time-dependence.
center: Tuple[float, float, float]
Source center in x, y and z.
size: Tuple[float, float, float]
Source size in x, y, and z. If not provided, the size of the monitor associated to the
data is used.
**kwargs
Extra keyword arguments passed to :class:`.CustomFieldSource`.
Returns
-------
:class:`.CustomFieldSource`
Source injecting the fields stored in the :class:`.FieldData`, with other settings as
provided in the input arguments.
"""
if not size:
size = self.monitor.size
fields = {}
for name, field in self.symmetry_expanded_copy.field_components.items():
fields[name] = field.copy()
for dim, dim_name in enumerate("xyz"):
coords_shift = field.coords[dim_name] - self.monitor.center[dim]
fields[name].coords[dim_name] = coords_shift
dataset = FieldDataset(**fields)
return CustomFieldSource(
field_dataset=dataset, source_time=source_time, center=center, size=size, **kwargs
)
[docs]
class FieldTimeData(FieldTimeDataset, ElectromagneticFieldData):
"""
Data associated with a :class:`.FieldTimeMonitor`: scalar components of E and H fields.
Notes
-----
The data is stored as a `DataArray <https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html>`_
object using the `xarray <https://docs.xarray.dev/en/stable/index.html>`_ package.
Example
-------
>>> from tidy3d import ScalarFieldTimeDataArray
>>> x = [-1,1,3]
>>> y = [-2,0,2,4]
>>> z = [-3,-1,1,3,5]
>>> t = [0, 1e-12, 2e-12]
>>> coords = dict(x=x[:-1], y=y[:-1], z=z[:-1], t=t)
>>> grid = Grid(boundaries=Coords(x=x, y=y, z=z))
>>> scalar_field = ScalarFieldTimeDataArray(np.random.random((2,3,4,3)), coords=coords)
>>> monitor = FieldTimeMonitor(
... size=(2,4,6), interval=100, name='field', fields=['Ex', 'Hz'], colocate=True
... )
>>> data = FieldTimeData(monitor=monitor, Ex=scalar_field, Hz=scalar_field, grid_expanded=grid)
"""
monitor: FieldTimeMonitor = pd.Field(
..., title="Monitor", description="Time-domain field monitor associated with the data."
)
_contains_monitor_fields = enforce_monitor_fields_present()
@property
def poynting(self) -> ScalarFieldTimeDataArray:
"""Instantaneous Poynting vector for time-domain data associated to a 2D monitor, projected
to the direction normal to the monitor plane."""
# Tangential fields are ordered as E1, E2, H1, H2
tan_fields = self._colocated_tangential_fields
dim1, dim2 = self._tangential_dims
e_x_h = np.real(tan_fields["E" + dim1]) * np.real(tan_fields["H" + dim2])
e_x_h -= np.real(tan_fields["E" + dim2]) * np.real(tan_fields["H" + dim1])
return e_x_h
@cached_property
def flux(self) -> FluxTimeDataArray:
"""Flux for data corresponding to a 2D monitor."""
# Compute flux by integrating Poynting vector in-plane
d_area = self._diff_area
return FluxTimeDataArray((self.poynting * d_area).sum(dim=d_area.dims))
[docs]
def dot(self, field_data: ElectromagneticFieldData, conjugate: bool = True) -> xr.DataArray:
"""Inner product is not defined for time-domain data."""
raise DataError("Inner product is not defined for time-domain data.")
@property
def time_reversed_copy(self) -> FieldTimeData:
"""Make a copy of the data with time-reversed fields. The sign of the magnetic fields is
flipped, and the data is reversed along the ``t`` dimension, such that for a given field,
``field[t_beg + t] -> field[t_end - t]``, where ``t_beg`` and ``t_end`` are the first and
last coordinates along the ``t`` dimension.
"""
new_data = {}
for comp, field in self.field_components.items():
if comp[0] == "H":
new_data[comp] = -field
else:
new_data[comp] = field
# Reverse time coordinates
new_data[comp] = new_data[comp].assign_coords({"t": field.t[::-1]}).sortby("t")
return self.copy(update=new_data)
[docs]
class PermittivityData(PermittivityDataset, AbstractFieldData):
"""Data for a :class:`.PermittivityMonitor`: diagonal components of the permittivity tensor.
Notes
-----
The data is stored as a `DataArray <https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html>`_
object using the `xarray <https://docs.xarray.dev/en/stable/index.html>`_ package.
Example
-------
>>> from tidy3d import ScalarFieldDataArray
>>> x = [-1,1,3]
>>> y = [-2,0,2,4]
>>> z = [-3,-1,1,3,5]
>>> f = [2e14, 3e14]
>>> coords = dict(x=x[:-1], y=y[:-1], z=z[:-1], f=f)
>>> grid = Grid(boundaries=Coords(x=x, y=y, z=z))
>>> sclr_fld = ScalarFieldDataArray((1+1j) * np.random.random((2,3,4,2)), coords=coords)
>>> monitor = PermittivityMonitor(size=(2,4,6), freqs=[2e14, 3e14], name='eps')
>>> data = PermittivityData(
... monitor=monitor, eps_xx=sclr_fld, eps_yy=sclr_fld, eps_zz=sclr_fld, grid_expanded=grid
... )
"""
monitor: PermittivityMonitor = pd.Field(
..., title="Monitor", description="Permittivity monitor associated with the data."
)
[docs]
class ModeData(ModeSolverDataset, ElectromagneticFieldData):
"""
Data associated with a :class:`.ModeMonitor`: modal amplitudes, propagation indices and mode profiles.
Notes
-----
The data is stored as a `DataArray <https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html>`_
object using the `xarray <https://docs.xarray.dev/en/stable/index.html>`_ 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)
"""
monitor: ModeMonitor = pd.Field(
..., title="Monitor", description="Mode monitor associated with the data."
)
amps: ModeAmpsDataArray = pd.Field(
..., title="Amplitudes", description="Complex-valued amplitudes associated with the mode."
)
eps_spec: List[EpsSpecType] = pd.Field(
None,
title="Permettivity Specification",
description="Characterization of the permittivity profile on the plane where modes are "
"computed. Possible values are 'diagonal', 'tensorial_real', 'tensorial_complex'.",
)
[docs]
@pd.validator("eps_spec", always=True)
@skip_if_fields_missing(["monitor"])
def eps_spec_match_mode_spec(cls, val, values):
"""Raise validation error if frequencies in eps_spec does not match frequency list"""
if val:
mode_data_freqs = values["monitor"].freqs
if len(val) != len(mode_data_freqs):
raise ValidationError(
"eps_spec must be provided at the same frequencies as mode solver data."
)
return val
[docs]
def normalize(self, source_spectrum_fn) -> ModeData:
"""Return copy of self after normalization is applied using source spectrum function."""
source_freq_amps = source_spectrum_fn(self.amps.f)[None, :, None]
new_amps = (self.amps / source_freq_amps).astype(self.amps.dtype)
return self.copy(update=dict(amps=new_amps))
[docs]
def overlap_sort(
self,
track_freq: TrackFreq,
overlap_thresh: float = 0.9,
) -> ModeData:
"""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.
"""
if len(self.field_components) == 0:
return self.copy()
num_freqs = len(self.monitor.freqs)
num_modes = self.monitor.mode_spec.num_modes
if track_freq == "lowest":
f0_ind = 0
elif track_freq == "highest":
f0_ind = num_freqs - 1
elif track_freq == "central":
f0_ind = num_freqs // 2
# Compute sorting order and overlaps with neighboring frequencies
sorting = -np.ones((num_freqs, num_modes), dtype=int)
overlap = np.zeros((num_freqs, num_modes))
phase = np.zeros((num_freqs, num_modes))
sorting[f0_ind, :] = np.arange(num_modes) # base frequency won't change
overlap[f0_ind, :] = np.ones(num_modes)
# Sort in two directions from the base frequency
for step, last_ind in zip([-1, 1], [-1, num_freqs]):
# Start with the base frequency
data_template = self._isel(f=[f0_ind])
# March to lower/higher frequencies
for freq_id in range(f0_ind + step, last_ind, step):
# Get next frequency to sort
data_to_sort = self._isel(f=[freq_id])
# Assign to the base frequency so that outer_dot will compare them
data_to_sort = data_to_sort._assign_coords(f=[self.monitor.freqs[f0_ind]])
# Compute "sorting w.r.t. to neighbor" and overlap values
sorting_one_mode, amps_one_mode = data_template._find_ordering_one_freq(
data_to_sort, overlap_thresh
)
# Transform "sorting w.r.t. neighbor" to "sorting w.r.t. to f0_ind"
sorting[freq_id, :] = sorting_one_mode[sorting[freq_id - step, :]]
overlap[freq_id, :] = np.abs(amps_one_mode[sorting[freq_id - step, :]])
phase[freq_id, :] = phase[freq_id - step, :] + np.angle(
amps_one_mode[sorting[freq_id - step, :]]
)
# Check for discontinuities and show warning if any
for mode_ind in list(np.nonzero(overlap[freq_id, :] < overlap_thresh)[0]):
log.warning(
f"Mode '{mode_ind}' appears to undergo a discontinuous change "
f"between frequencies '{self.monitor.freqs[freq_id]}' "
f"and '{self.monitor.freqs[freq_id - step]}' "
f"(overlap: '{overlap[freq_id, mode_ind]:.2f}')."
)
# Reassign for the next iteration
data_template = data_to_sort
# Rearrange modes using computed sorting values
mode_data_sorted = self._reorder_modes(
sorting=sorting,
phase=phase,
track_freq=track_freq,
)
return mode_data_sorted
def _isel(self, **isel_kwargs):
"""Wraps ``xarray.DataArray.isel`` for all data fields that are defined over frequency and
mode index. Used in ``overlap_sort`` but not officially supported since for example
``self.monitor.mode_spec`` and ``self.monitor.freqs`` will no longer be matching the
newly created data."""
update_dict = dict(self._grid_correction_dict, **self.field_components)
update_dict = {key: field.isel(**isel_kwargs) for key, field in update_dict.items()}
return self._updated(update=update_dict)
def _assign_coords(self, **assign_coords_kwargs):
"""Wraps ``xarray.DataArray.assign_coords`` for all data fields that are defined over frequency and
mode index. Used in ``overlap_sort`` but not officially supported since for example
``self.monitor.mode_spec`` and ``self.monitor.freqs`` will no longer be matching the
newly created data."""
update_dict = dict(self._grid_correction_dict, **self.field_components)
update_dict = {
key: field.assign_coords(**assign_coords_kwargs) for key, field in update_dict.items()
}
return self._updated(update=update_dict)
def _find_ordering_one_freq(
self,
data_to_sort: ModeData,
overlap_thresh: float,
) -> Tuple[Numpy, Numpy]:
"""Find new ordering of modes in data_to_sort based on their similarity to own modes."""
num_modes = self.n_complex.sizes["mode_index"]
# Current pairs and their overlaps
pairs = np.arange(num_modes)
complex_amps = self.dot(data_to_sort).data.ravel()
if self.monitor.store_fields_direction == "-":
complex_amps *= -1
# Check whether modes already match
modes_to_sort = np.where(np.abs(complex_amps) < overlap_thresh)[0]
num_modes_to_sort = len(modes_to_sort)
if num_modes_to_sort <= 1:
return pairs, complex_amps
# Extract all modes of interest from template data
data_template_reduced = self._isel(mode_index=modes_to_sort)
amps_reduced = data_template_reduced.outer_dot(
data_to_sort._isel(mode_index=modes_to_sort)
).to_numpy()[0, :, :]
if self.monitor.store_fields_direction == "-":
amps_reduced *= -1
# Find the most similar modes and corresponding overlap values
pairs_reduced, amps_reduced = self._find_closest_pairs(amps_reduced)
# Insert new sorting and overlap values into arrays with all data
complex_amps[modes_to_sort] = amps_reduced
pairs[modes_to_sort] = modes_to_sort[pairs_reduced]
return pairs, complex_amps
@staticmethod
def _find_closest_pairs(arr: Numpy) -> Tuple[Numpy, Numpy]:
"""Given a complex overlap matrix pair row and column entries."""
n, k = np.shape(arr)
if n != k:
raise DataError("Overlap matrix must be square.")
arr_abs = np.abs(arr)
pairs = -np.ones(n, dtype=int)
values = np.zeros(n, dtype=np.complex128)
for _ in range(n):
imax, jmax = np.unravel_index(np.argmax(arr_abs, axis=None), (n, k))
pairs[imax] = jmax
values[imax] = arr[imax, jmax]
arr_abs[imax, :] = -1
arr_abs[:, jmax] = -1
return pairs, values
def _reorder_modes(
self,
sorting: Numpy,
phase: Numpy,
track_freq: TrackFreq,
) -> ModeData:
"""Rearrange modes for the i-th frequency according to sorting[i, :] and apply phase
shifts."""
num_freqs, _ = np.shape(sorting)
# Create new dict with rearranged field components
update_dict = {}
for field_name, field in self.field_components.items():
field_sorted = field.copy()
# Rearrange modes
for freq_id in range(num_freqs):
field_sorted.data[..., freq_id, :] = field_sorted.data[
..., freq_id, sorting[freq_id, :]
]
# Apply phase shift
phase_fact = np.exp(-1j * phase[None, None, None, :, :]).astype(field_sorted.data.dtype)
field_sorted.data = field_sorted.data * phase_fact
update_dict[field_name] = field_sorted
# Rearrange data over f and mode_index
data_dict = dict(**self._grid_correction_dict, n_complex=self.n_complex)
for key, data in data_dict.items():
update_dict[key] = data.copy()
for freq_id in range(num_freqs):
update_dict[key].data[freq_id, :] = update_dict[key].data[
freq_id, sorting[freq_id, :]
]
# Update mode_spec in the monitor
mode_spec = self.monitor.mode_spec.copy(update=dict(track_freq=track_freq))
update_dict["monitor"] = self.monitor.copy(update=dict(mode_spec=mode_spec))
return self.copy(update=update_dict)
def _group_index_post_process(self, frequency_step: float) -> ModeData:
"""Calculate group index and remove added frequencies used only for this calculation.
Parameters
----------
frequency_step: float
Fractional frequency step used to calculate the group index.
Returns
-------
:class:`.ModeData`
Filtered data with calculated group index.
"""
freqs = self.n_complex.coords["f"].values
num_freqs = freqs.size
back = slice(0, num_freqs, 3)
center = slice(1, num_freqs, 3)
fwd = slice(2, num_freqs, 3)
freqs = freqs[center]
# calculate group index
n_center = self.n_eff.isel(f=center).values
n_backward = self.n_eff.isel(f=back).values
n_forward = self.n_eff.isel(f=fwd).values
inv_step = 1 / frequency_step
# n_g = n + f * df/dn
# dn/df = (n+ - n-) / (2 f df)
n_group_data = n_center + (n_forward - n_backward) * inv_step * 0.5
# D = -2 * pi * c / lda^2 * d(v_g^-1)/dw = -(f / c)^2 * (2 * dn/df + f * d2n/df2)
# d2n/df2 = (n+ - 2n + n-) / (f df)^2
# The '1e18' factor converts from s/um^2 to ps/(nm km)
dispersion_data = (
(n_forward * (inv_step + 1) + n_backward * (inv_step - 1) - n_center * inv_step * 2)
* freqs.reshape((-1, 1))
* (-1e18 * inv_step / C_0**2)
)
mode_index = list(self.n_complex.coords["mode_index"].values)
f = list(freqs)
n_group = GroupIndexDataArray(
n_group_data,
coords={"f": f, "mode_index": mode_index},
)
dispersion = ModeDispersionDataArray(
dispersion_data,
coords={"f": f, "mode_index": mode_index},
)
# remove data corresponding to frequencies used only for group index calculation
update_dict = {
"n_complex": self.n_complex.isel(f=center),
"n_group_raw": n_group,
"dispersion_raw": dispersion,
}
for key, field in self.field_components.items():
update_dict[key] = field.isel(f=center)
for key, data in self._grid_correction_dict.items():
update_dict[key] = data.isel(f=center)
if self.eps_spec:
update_dict["eps_spec"] = self.eps_spec[center]
update_dict["monitor"] = self.monitor.updated_copy(freqs=freqs)
return self.copy(update=update_dict)
@property
def time_reversed_copy(self) -> FieldData:
"""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."""
# Time reversal
new_data = {}
for comp, field in self.field_components.items():
if comp[0] == "H":
new_data[comp] = -np.conj(field)
else:
new_data[comp] = np.conj(field)
# switch direction in the monitor
mnt = self.monitor
new_dir = "+" if mnt.store_fields_direction == "-" else "-"
new_data["monitor"] = mnt.updated_copy(store_fields_direction=new_dir)
return self.copy(update=new_data)
def _colocated_propagation_axes_field(self, field_name: Literal["E", "H"]) -> xr.DataArray:
"""Collect a field DataArray containing all 3 field components and rotate from frame
with normal axis along z to frame with propagation axis along z.
"""
tan_dims = self._tangential_dims
normal_dim = "xyz"[self.monitor.zero_dims[0]]
fields = self._colocated_fields
fields = {key: val.squeeze(dim=normal_dim, drop=True) for key, val in fields.items()}
mode_spec = self.monitor.mode_spec
# fields as a (3, ...) numpy array ordered as [tangential1, tagential2, normal]
field = [fields[field_name + dim].values for dim in tan_dims]
field = np.array(field + [fields[field_name + normal_dim].values])
# rotate axes
if mode_spec.angle_phi != 0:
field = self.monitor.rotate_points(field, [0, 0, 1], -mode_spec.angle_phi)
if mode_spec.angle_theta != 0:
field = self.monitor.rotate_points(field, [0, 1, 0], -mode_spec.angle_theta)
# new coords for the (3, ...) array
coords = {"component": [0, 1, 2]}
# fields are colocated, so all components should have the same coords
for dim in fields["Ex"].dims:
coords.update({dim: fields["Ex"].coords[dim]})
return xr.DataArray(data=field, coords=coords)
@cached_property
def pol_fraction(self) -> xr.Dataset:
r"""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:
.. math::
\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.
"""
if len(self.field_components) == 0:
raise DataError(
"Field data not included in this ModeData onbject. Set "
"'ModeMonitor.store_fields_direction' to the desired propagation direction to "
"include the mode field profiles in the corresponding 'ModeData'."
)
tan_dims = self._tangential_dims
e_field = self._colocated_propagation_axes_field("E")
diff_area = self._diff_area
tm_int = (diff_area * np.abs(e_field.sel(component=1, drop=True)) ** 2).sum(dim=tan_dims)
te_int = (diff_area * np.abs(e_field.sel(component=0, drop=True)) ** 2).sum(dim=tan_dims)
te_frac = te_int / (te_int + tm_int)
return xr.Dataset(data_vars={"te": te_frac, "tm": 1 - te_frac})
@cached_property
def pol_fraction_waveguide(self) -> xr.Dataset:
r"""Compute the TE and TM polarization fraction using the waveguide definition. If $n$ is
the propagation direction, the TE fraction is defined as:
.. math::
1 - \frac{\int |E \cdot n|^2 \, {\rm d}S}{\int |E|^2 \, {\rm d}S}
and the TM fraction is defined as
.. math::
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.
"""
if len(self.field_components) == 0:
raise DataError(
"Field data not included in this ModeData onbject. Set "
"'ModeMonitor.store_fields_direction' to the desired propagation direction to "
"include the mode field profiles in the corresponding 'ModeData'."
)
tan_dims = self._tangential_dims
e_field = self._colocated_propagation_axes_field("E")
h_field = self._colocated_propagation_axes_field("H")
diff_area = self._diff_area
# te fraction
field_int = [np.abs(e_field.sel(component=ind, drop=True)) ** 2 for ind in range(3)]
norm_int = (diff_area * field_int[2]).sum(dim=tan_dims)
tot_int = norm_int + (diff_area * (field_int[0] + field_int[1])).sum(dim=tan_dims)
te_frac = 1 - norm_int / tot_int
# tm fraction
field_int = [np.abs(h_field.sel(component=ind, drop=True)) ** 2 for ind in range(3)]
norm_int = (diff_area * field_int[2]).sum(dim=tan_dims)
tot_int = norm_int + (diff_area * (field_int[0] + field_int[1])).sum(dim=tan_dims)
tm_frac = 1 - norm_int / tot_int
return xr.Dataset(data_vars={"te": te_frac, "tm": tm_frac})
@property
def modes_info(self) -> xr.Dataset:
"""Dataset collecting various properties of the stored modes."""
lambda_cm = C_0 / self.k_eff.f / 1e4
loss_db_cm = 20 * 2 * np.pi * np.log10(np.e) * self.k_eff / lambda_cm
info = {
"wavelength": C_0 / self.n_eff.f,
"n eff": self.n_eff,
"k eff": self.k_eff,
"loss (dB/cm)": loss_db_cm,
f"TE (E{self._tangential_dims[0]}) fraction": self.pol_fraction["te"],
"wg TE fraction": self.pol_fraction_waveguide["te"],
"wg TM fraction": self.pol_fraction_waveguide["tm"],
"mode area": self.mode_area,
"group index": self.n_group_raw, # Use raw field to avoid issuing a warning
"dispersion (ps/(nm km))": self.dispersion_raw, # Use raw field to avoid issuing a warning
}
if self.n_group_raw is not None:
info["group index"] = self.n_group_raw
if len(self.field_components) == 6:
info["mode area"] = self.mode_area
info[f"TE (E{self._tangential_dims[0]}) fraction"] = self.pol_fraction["te"]
info["wg TE fraction"] = self.pol_fraction_waveguide["te"]
info["wg TM fraction"] = self.pol_fraction_waveguide["tm"]
return xr.Dataset(data_vars=info)
[docs]
def to_dataframe(self) -> DataFrame:
"""xarray-like method to export the ``modes_info`` into a pandas dataframe which is e.g.
simple to visualize as a table."""
dataset = self.modes_info
drop = []
if not np.any(dataset["group index"].values):
drop.append("group index")
if not np.any(dataset["dispersion (ps/(nm km))"].values):
drop.append("dispersion (ps/(nm km))")
if np.all(dataset["loss (dB/cm)"] == 0):
drop.append("loss (dB/cm)")
return dataset.drop_vars(drop).to_dataframe()
[docs]
class ModeSolverData(ModeData):
"""
Data associated with a :class:`.ModeSolverMonitor`: scalar components of E and H fields.
Notes
-----
The data is stored as a `DataArray <https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html>`_
object using the `xarray <https://docs.xarray.dev/en/stable/index.html>`_ 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
... )
"""
monitor: ModeSolverMonitor = pd.Field(
..., title="Monitor", description="Mode solver monitor associated with the data."
)
amps: ModeAmpsDataArray = pd.Field(
None, title="Amplitudes", description="Unused for ModeSolverData."
)
[docs]
def normalize(self, source_spectrum_fn: Callable[[float], complex]) -> ModeSolverData:
"""Return copy of self after normalization is applied using source spectrum function."""
return self.copy()
@property
def time_reversed_copy(self) -> FieldData:
"""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."""
# Time reversal
new_data = {}
for comp, field in self.field_components.items():
if comp[0] == "H":
new_data[comp] = -np.conj(field)
else:
new_data[comp] = np.conj(field)
# switch direction in the monitor
mnt = self.monitor
new_dir = "+" if mnt.store_fields_direction == "-" else "-"
new_data["monitor"] = mnt.updated_copy(direction=new_dir, store_fields_direction=new_dir)
return self.copy(update=new_data)
[docs]
class FluxData(MonitorData):
"""
Data associated with a :class:`.FluxMonitor`: flux data in the frequency-domain.
Notes
-----
The data is stored as a `DataArray <https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html>`_
object using the `xarray <https://docs.xarray.dev/en/stable/index.html>`_ package.
We can access the data for each monitor by indexing into the :class:`SimulationData` with the monitor
``.name``. For the flux monitor data, we can access the raw flux data as a function of frequency with
``.flux``. As most data are multidimensional, it’s often very helpful to print out the data and directly
inspect its structure.
Example
-------
>>> from tidy3d import FluxDataArray
>>> f = [2e14, 3e14]
>>> coords = dict(f=f)
>>> flux_data = FluxDataArray(np.random.random(2), coords=coords)
>>> monitor = FluxMonitor(size=(2,0,6), freqs=[2e14, 3e14], name='flux')
>>> data = FluxData(monitor=monitor, flux=flux_data)
See Also
--------
**Notebooks:**
* `Advanced monitor data manipulation and visualization <../../notebooks/XarrayTutorial.html>`_
"""
monitor: FluxMonitor = pd.Field(
..., title="Monitor", description="Frequency-domain flux monitor associated with the data."
)
flux: FluxDataArray = pd.Field(
..., title="Flux", description="Flux values in the frequency-domain."
)
[docs]
def normalize(self, source_spectrum_fn) -> FluxData:
"""Return copy of self after normalization is applied using source spectrum function."""
source_freq_amps = source_spectrum_fn(self.flux.f)
source_power = abs(source_freq_amps) ** 2
new_flux = (self.flux / source_power).astype(self.flux.dtype)
return self.copy(update=dict(flux=new_flux))
[docs]
class FluxTimeData(MonitorData):
"""
Data associated with a :class:`.FluxTimeMonitor`: flux data in the time-domain.
Notes
-----
The data is stored as a `DataArray <https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html>`_
object using the `xarray <https://docs.xarray.dev/en/stable/index.html>`_ package.
Example
-------
>>> from tidy3d import FluxTimeDataArray
>>> t = [0, 1e-12, 2e-12]
>>> coords = dict(t=t)
>>> flux_data = FluxTimeDataArray(np.random.random(3), coords=coords)
>>> monitor = FluxTimeMonitor(size=(2,0,6), interval=100, name='flux_time')
>>> data = FluxTimeData(monitor=monitor, flux=flux_data)
"""
monitor: FluxTimeMonitor = pd.Field(
..., title="Monitor", description="Time-domain flux monitor associated with the data."
)
flux: FluxTimeDataArray = pd.Field(
..., title="Flux", description="Flux values in the time-domain."
)
ProjFieldType = Union[
FieldProjectionAngleDataArray,
FieldProjectionCartesianDataArray,
FieldProjectionKSpaceDataArray,
DiffractionDataArray,
]
ProjMonitorType = Union[
FieldProjectionAngleMonitor,
FieldProjectionCartesianMonitor,
FieldProjectionKSpaceMonitor,
DiffractionMonitor,
]
[docs]
class AbstractFieldProjectionData(MonitorData):
"""Collection of projected fields in spherical coordinates in the frequency domain."""
monitor: ProjMonitorType = pd.Field(
...,
title="Projection monitor",
description="Field projection monitor.",
discriminator=TYPE_TAG_STR,
)
Er: ProjFieldType = pd.Field(
...,
title="Ephi",
description="Spatial distribution of r-component of the electric field.",
)
Etheta: ProjFieldType = pd.Field(
...,
title="Etheta",
description="Spatial distribution of the theta-component of the electric field.",
)
Ephi: ProjFieldType = pd.Field(
...,
title="Ephi",
description="Spatial distribution of phi-component of the electric field.",
)
Hr: ProjFieldType = pd.Field(
...,
title="Hphi",
description="Spatial distribution of r-component of the magnetic field.",
)
Htheta: ProjFieldType = pd.Field(
...,
title="Htheta",
description="Spatial distribution of theta-component of the magnetic field.",
)
Hphi: ProjFieldType = pd.Field(
...,
title="Hphi",
description="Spatial distribution of phi-component of the magnetic field.",
)
medium: MediumType = pd.Field(
Medium(),
title="Background Medium",
description="Background medium through which to project fields.",
discriminator=TYPE_TAG_STR,
)
@property
def field_components(self) -> Dict[str, DataArray]:
"""Maps the field components to their associated data."""
return dict(
Er=self.Er,
Etheta=self.Etheta,
Ephi=self.Ephi,
Hr=self.Hr,
Htheta=self.Htheta,
Hphi=self.Hphi,
)
@property
def f(self) -> np.ndarray:
"""Frequencies."""
return np.array(self.Etheta.coords["f"])
@property
def coords(self) -> Dict[str, np.ndarray]:
"""Coordinates of the fields contained."""
return self.Etheta.coords
@property
def coords_spherical(self) -> Dict[str, np.ndarray]:
"""Coordinates grid for the fields in the spherical system."""
if "theta" in self.coords.keys():
r, theta, phi = np.meshgrid(
self.coords["r"].values,
self.coords["theta"].values,
self.coords["phi"].values,
indexing="ij",
)
elif "z" in self.coords.keys():
xs, ys, zs = np.meshgrid(
self.coords["x"].values,
self.coords["y"].values,
self.coords["z"].values,
indexing="ij",
)
r, theta, phi = self.monitor.car_2_sph(xs, ys, zs)
else:
uxs, uys, r = np.meshgrid(
self.coords["ux"].values,
self.coords["uy"].values,
self.coords["r"].values,
indexing="ij",
)
theta, phi = self.monitor.kspace_2_sph(uxs, uys, self.monitor.proj_axis)
return {"r": r, "theta": theta, "phi": phi}
@property
def dims(self) -> Tuple[str, ...]:
"""Dimensions of the radiation vectors contained."""
return self.Etheta.dims
[docs]
def make_data_array(self, data: np.ndarray) -> xr.DataArray:
"""Make an xr.DataArray with data and same coords and dims as fields of self."""
return xr.DataArray(data=data, coords=self.coords, dims=self.dims)
[docs]
def make_dataset(self, keys: Tuple[str, ...], vals: Tuple[np.ndarray, ...]) -> xr.Dataset:
"""Make an xr.Dataset with keys and data with same coords and dims as fields."""
data_arrays = tuple(map(self.make_data_array, vals))
return xr.Dataset(dict(zip(keys, data_arrays)))
[docs]
def make_renormalized_data(
self, phase: np.ndarray, proj_distance: float
) -> AbstractFieldProjectionData:
"""Helper to apply the re-projection phase to a copied dataset."""
new_data = self.copy()
for field in new_data.field_components.values():
field.values *= phase
if "r" in self.coords.keys():
field["r"] = np.atleast_1d(proj_distance)
return new_data
[docs]
def normalize(
self, source_spectrum_fn: Callable[[float], complex]
) -> AbstractFieldProjectionData:
"""Return copy of self after normalization is applied using source spectrum function."""
fields_norm = {}
for field_name, field_data in self.field_components.items():
src_amps = source_spectrum_fn(field_data.f)
fields_norm[field_name] = (field_data / src_amps).astype(field_data.dtype)
return self.copy(update=fields_norm)
[docs]
@staticmethod
def wavenumber(medium: MediumType, frequency: float) -> complex:
"""Complex valued wavenumber associated with a frequency."""
index_n, index_k = medium.nk_model(frequency=frequency)
return (2 * np.pi * frequency / C_0) * (index_n + 1j * index_k)
@property
def nk(self) -> Tuple[float, float]:
"""Returns the real and imaginary parts of the background medium's refractive index."""
return self.medium.nk_model(frequency=self.f)
@property
def k(self) -> complex:
"""Returns the complex wave number associated with the background medium."""
return self.wavenumber(medium=self.medium, frequency=self.f)
@property
def eta(self) -> complex:
"""Returns the complex wave impedance associated with the background medium."""
eps_complex = self.medium.eps_model(frequency=self.f)
return ETA_0 / np.sqrt(eps_complex)
[docs]
@staticmethod
def propagation_phase(dist: Union[float, None], k: complex) -> complex:
"""Phase associated with propagation of a distance with a given wavenumber."""
if dist is None:
return 1.0
return -1j * k * np.exp(1j * k * dist) / (4 * np.pi * dist)
@property
def fields_spherical(self) -> xr.Dataset:
"""Get all field components in spherical coordinates relative to the monitor's
local origin for all projection grid points and frequencies specified in the
:class:`AbstractFieldProjectionMonitor`.
Returns
-------
``xarray.Dataset``
xarray dataset containing
(``Er``, ``Etheta``, ``Ephi``, ``Hr``, ``Htheta``, ``Hphi``)
in spherical coordinates.
"""
return self.make_dataset(
keys=self.field_components.keys(), vals=self.field_components.values()
)
@property
def fields_cartesian(self) -> xr.Dataset:
"""Get all field components in Cartesian coordinates relative to the monitor's
local origin for all projection grid points and frequencies specified in the
:class:`AbstractFieldProjectionMonitor`.
Returns
-------
``xarray.Dataset``
xarray dataset containing (``Ex``, ``Ey``, ``Ez``, ``Hx``, ``Hy``, ``Hz``)
in Cartesian coordinates.
"""
# convert the field components to the Cartesian coordinate system
coords_sph = self.coords_spherical
e_data = self.monitor.sph_2_car_field(
self.Er.values,
self.Etheta.values,
self.Ephi.values,
coords_sph["theta"][..., None],
coords_sph["phi"][..., None],
)
h_data = self.monitor.sph_2_car_field(
self.Hr.values,
self.Htheta.values,
self.Hphi.values,
coords_sph["theta"][..., None],
coords_sph["phi"][..., None],
)
# package into dataset
keys = ("Ex", "Ey", "Ez", "Hx", "Hy", "Hz")
field_components = np.concatenate((e_data, h_data), axis=0)
return self.make_dataset(keys=keys, vals=field_components)
@property
def power(self) -> xr.DataArray:
"""Get power measured on the projection grid relative to the monitor's local origin.
Returns
-------
``xarray.DataArray``
Power at points relative to the local origin.
"""
power_theta = 0.5 * np.real(self.Etheta.values * np.conj(self.Hphi.values))
power_phi = 0.5 * np.real(-self.Ephi.values * np.conj(self.Htheta.values))
power = power_theta + power_phi
return self.make_data_array(data=power)
@property
def radar_cross_section(self) -> xr.DataArray:
"""Radar cross section in units of incident power."""
_, index_k = self.nk
if not np.all(index_k == 0):
raise SetupError("Can't compute RCS for a lossy background medium.")
k = self.k[None, None, None, ...]
eta = self.eta[None, None, None, ...]
constant = k**2 / (8 * np.pi * eta)
# normalize fields by the distance-based phase factor
coords_sph = self.coords_spherical
if coords_sph["r"] is None:
phase = 1.0
else:
phase = self.propagation_phase(dist=coords_sph["r"][..., None], k=k)
Etheta = self.Etheta.values / phase
Ephi = self.Ephi.values / phase
rcs_data = constant * (np.abs(Etheta) ** 2 + np.abs(Ephi) ** 2)
return self.make_data_array(data=rcs_data)
[docs]
class FieldProjectionAngleData(AbstractFieldProjectionData):
"""Data associated with a :class:`.FieldProjectionAngleMonitor`: components of projected fields.
Example
-------
>>> from tidy3d import FieldProjectionAngleDataArray
>>> f = np.linspace(1e14, 2e14, 10)
>>> r = np.atleast_1d(5)
>>> theta = np.linspace(0, np.pi, 10)
>>> phi = np.linspace(0, 2*np.pi, 20)
>>> coords = dict(r=r, theta=theta, phi=phi, f=f)
>>> values = (1+1j) * np.random.random((len(r), len(theta), len(phi), len(f)))
>>> scalar_field = FieldProjectionAngleDataArray(values, coords=coords)
>>> monitor = FieldProjectionAngleMonitor(
... center=(1,2,3), size=(2,2,2), freqs=f, name='n2f_monitor', phi=phi, theta=theta
... )
>>> data = FieldProjectionAngleData(
... 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,
... )
"""
monitor: FieldProjectionAngleMonitor = pd.Field(
...,
title="Projection monitor",
description="Field projection monitor with an angle-based projection grid.",
)
projection_surfaces: Tuple[FieldProjectionSurface, ...] = pd.Field(
...,
title="Projection surfaces",
description="Surfaces of the monitor where near fields were recorded for projection",
)
Er: FieldProjectionAngleDataArray = pd.Field(
...,
title="Er",
description="Spatial distribution of r-component of the electric field.",
)
Etheta: FieldProjectionAngleDataArray = pd.Field(
...,
title="Etheta",
description="Spatial distribution of the theta-component of the electric field.",
)
Ephi: FieldProjectionAngleDataArray = pd.Field(
...,
title="Ephi",
description="Spatial distribution of phi-component of the electric field.",
)
Hr: FieldProjectionAngleDataArray = pd.Field(
...,
title="Hr",
description="Spatial distribution of r-component of the magnetic field.",
)
Htheta: FieldProjectionAngleDataArray = pd.Field(
...,
title="Htheta",
description="Spatial distribution of theta-component of the magnetic field.",
)
Hphi: FieldProjectionAngleDataArray = pd.Field(
...,
title="Hphi",
description="Spatial distribution of phi-component of the magnetic field.",
)
@property
def r(self) -> np.ndarray:
"""Radial distance."""
return self.Etheta.r.values
@property
def theta(self) -> np.ndarray:
"""Polar angles."""
return self.Etheta.theta.values
@property
def phi(self) -> np.ndarray:
"""Azimuthal angles."""
return self.Etheta.phi.values
[docs]
def renormalize_fields(self, proj_distance: float) -> FieldProjectionAngleData:
"""Return a :class:`.FieldProjectionAngleData` 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 radial distance relative to the monitor's local origin.
Returns
-------
:class:`.FieldProjectionAngleData`
Copy of this :class:`.FieldProjectionAngleData` with fields re-projected
to ``proj_distance``.
"""
if self.monitor and not self.monitor.far_field_approx:
raise DataError(
"Fields projected without invoking the far field approximation "
"cannot be re-projected to a new distance."
)
# the phase factor associated with the old distance must be removed
r = self.coords_spherical["r"][..., None]
old_phase = self.propagation_phase(dist=r, k=self.k[None, None, None, :])
# the phase factor associated with the new distance must be applied
new_phase = self.propagation_phase(dist=proj_distance, k=self.k)
# net phase
phase = new_phase[None, None, None, :] / old_phase
# compute updated fields and their coordinates
return self.make_renormalized_data(phase, proj_distance)
[docs]
class FieldProjectionCartesianData(AbstractFieldProjectionData):
"""Data associated with a :class:`.FieldProjectionCartesianMonitor`: components of
projected fields.
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,
... )
"""
monitor: FieldProjectionCartesianMonitor = pd.Field(
...,
title="Projection monitor",
description="Field projection monitor with a Cartesian projection grid.",
)
projection_surfaces: Tuple[FieldProjectionSurface, ...] = pd.Field(
...,
title="Projection surfaces",
description="Surfaces of the monitor where near fields were recorded for projection",
)
Er: FieldProjectionCartesianDataArray = pd.Field(
...,
title="Er",
description="Spatial distribution of r-component of the electric field.",
)
Etheta: FieldProjectionCartesianDataArray = pd.Field(
...,
title="Etheta",
description="Spatial distribution of the theta-component of the electric field.",
)
Ephi: FieldProjectionCartesianDataArray = pd.Field(
...,
title="Ephi",
description="Spatial distribution of phi-component of the electric field.",
)
Hr: FieldProjectionCartesianDataArray = pd.Field(
...,
title="Hr",
description="Spatial distribution of r-component of the magnetic field.",
)
Htheta: FieldProjectionCartesianDataArray = pd.Field(
...,
title="Htheta",
description="Spatial distribution of theta-component of the magnetic field.",
)
Hphi: FieldProjectionCartesianDataArray = pd.Field(
...,
title="Hphi",
description="Spatial distribution of phi-component of the magnetic field.",
)
@property
def x(self) -> np.ndarray:
"""X positions."""
return self.Etheta.x.values
@property
def y(self) -> np.ndarray:
"""Y positions."""
return self.Etheta.y.values
@property
def z(self) -> np.ndarray:
"""Z positions."""
return self.Etheta.z.values
[docs]
def renormalize_fields(self, proj_distance: float) -> FieldProjectionCartesianData:
"""Return a :class:`.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
-------
:class:`.FieldProjectionCartesianData`
Copy of this :class:`.FieldProjectionCartesianData` with fields re-projected
to ``proj_distance``.
"""
if not self.monitor.far_field_approx:
raise DataError(
"Fields projected without invoking the far field approximation "
"cannot be re-projected to a new distance."
)
# the phase factor associated with the old distance must be removed
k = self.k[None, None, None, :]
r = self.coords_spherical["r"][..., None]
old_phase = self.propagation_phase(dist=r, k=k)
# update the field components' projection distance
norm_dir, _ = self.monitor.pop_axis(["x", "y", "z"], axis=self.monitor.proj_axis)
for field in self.field_components.values():
field[norm_dir] = np.atleast_1d(proj_distance)
# the phase factor associated with the new distance must be applied
r = self.coords_spherical["r"][..., None]
new_phase = self.propagation_phase(dist=r, k=k)
# net phase
phase = new_phase / old_phase
# compute updated fields and their coordinates
return self.make_renormalized_data(phase, proj_distance)
[docs]
class FieldProjectionKSpaceData(AbstractFieldProjectionData):
"""Data associated with a :class:`.FieldProjectionKSpaceMonitor`: components of
projected fields.
Example
-------
>>> from tidy3d import FieldProjectionKSpaceDataArray
>>> f = np.linspace(1e14, 2e14, 10)
>>> ux = np.linspace(0, 0.4, 10)
>>> uy = np.linspace(0, 0.6, 20)
>>> r = np.atleast_1d(5)
>>> coords = dict(ux=ux, uy=uy, r=r, f=f)
>>> values = (1+1j) * np.random.random((len(ux), len(uy), len(r), len(f)))
>>> scalar_field = FieldProjectionKSpaceDataArray(values, coords=coords)
>>> monitor = FieldProjectionKSpaceMonitor(
... center=(1,2,3), size=(2,2,2), freqs=f, name='n2f_monitor', ux=ux, uy=uy, proj_axis=2
... )
>>> data = FieldProjectionKSpaceData(
... 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,
... )
"""
monitor: FieldProjectionKSpaceMonitor = pd.Field(
...,
title="Projection monitor",
description="Field projection monitor with a projection grid defined in k-space.",
)
projection_surfaces: Tuple[FieldProjectionSurface, ...] = pd.Field(
...,
title="Projection surfaces",
description="Surfaces of the monitor where near fields were recorded for projection",
)
Er: FieldProjectionKSpaceDataArray = pd.Field(
...,
title="Er",
description="Spatial distribution of r-component of the electric field.",
)
Etheta: FieldProjectionKSpaceDataArray = pd.Field(
...,
title="Etheta",
description="Spatial distribution of the theta-component of the electric field.",
)
Ephi: FieldProjectionKSpaceDataArray = pd.Field(
...,
title="Ephi",
description="Spatial distribution of phi-component of the electric field.",
)
Hr: FieldProjectionKSpaceDataArray = pd.Field(
...,
title="Hr",
description="Spatial distribution of r-component of the magnetic field.",
)
Htheta: FieldProjectionKSpaceDataArray = pd.Field(
...,
title="Htheta",
description="Spatial distribution of theta-component of the magnetic field.",
)
Hphi: FieldProjectionKSpaceDataArray = pd.Field(
...,
title="Hphi",
description="Spatial distribution of phi-component of the magnetic field.",
)
@property
def ux(self) -> np.ndarray:
"""Reciprocal X positions."""
return self.Etheta.ux.values
@property
def uy(self) -> np.ndarray:
"""Reciprocal Y positions."""
return self.Etheta.uy.values
@property
def r(self) -> np.ndarray:
"""Radial distance."""
return self.Etheta.r.values
[docs]
def renormalize_fields(self, proj_distance: float) -> FieldProjectionKSpaceData:
"""Return a :class:`.FieldProjectionKSpaceData` 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 radial distance relative to the monitor's local origin.
Returns
-------
:class:`.FieldProjectionKSpaceData`
Copy of this :class:`.FieldProjectionKSpaceData` with fields re-projected
to ``proj_distance``.
"""
if self.monitor and not self.monitor.far_field_approx:
raise DataError(
"Fields projected without invoking the far field approximation "
"cannot be re-projected to a new distance."
)
# the phase factor associated with the old distance must be removed
r = self.coords_spherical["r"][..., None]
old_phase = self.propagation_phase(dist=r, k=self.k[None, None, None, :])
# the phase factor associated with the new distance must be applied
new_phase = self.propagation_phase(dist=proj_distance, k=self.k)
# net phase
phase = new_phase[None, None, None, :] / old_phase
# compute updated fields and their coordinates
return self.make_renormalized_data(phase, proj_distance)
[docs]
class DiffractionData(AbstractFieldProjectionData):
"""Data for a :class:`.DiffractionMonitor`: complex components of diffracted far fields.
Example
-------
>>> from tidy3d import DiffractionDataArray
>>> f = np.linspace(1e14, 2e14, 10)
>>> orders_x = list(range(-4, 5))
>>> orders_y = list(range(-6, 7))
>>> pol = ["s", "p"]
>>> coords = dict(orders_x=orders_x, orders_y=orders_y, f=f)
>>> values = (1+1j) * np.random.random((len(orders_x), len(orders_y), len(f)))
>>> field = DiffractionDataArray(values, coords=coords)
>>> monitor = DiffractionMonitor(
... center=(1,2,3), size=(np.inf,np.inf,0), freqs=f, name='diffraction'
... )
>>> data = DiffractionData(
... monitor=monitor, sim_size=[1,1], bloch_vecs=[1,2],
... Etheta=field, Ephi=field, Er=field,
... Htheta=field, Hphi=field, Hr=field,
... )
"""
monitor: DiffractionMonitor = pd.Field(
..., title="Monitor", description="Diffraction monitor associated with the data."
)
Er: DiffractionDataArray = pd.Field(
...,
title="Er",
description="Spatial distribution of r-component of the electric field.",
)
Etheta: DiffractionDataArray = pd.Field(
...,
title="Etheta",
description="Spatial distribution of the theta-component of the electric field.",
)
Ephi: DiffractionDataArray = pd.Field(
...,
title="Ephi",
description="Spatial distribution of phi-component of the electric field.",
)
Hr: DiffractionDataArray = pd.Field(
...,
title="Hr",
description="Spatial distribution of r-component of the magnetic field.",
)
Htheta: DiffractionDataArray = pd.Field(
...,
title="Htheta",
description="Spatial distribution of theta-component of the magnetic field.",
)
Hphi: DiffractionDataArray = pd.Field(
...,
title="Hphi",
description="Spatial distribution of phi-component of the magnetic field.",
)
sim_size: Tuple[float, float] = pd.Field(
...,
title="Domain size",
description="Size of the near field in the local x and y directions.",
units=MICROMETER,
)
bloch_vecs: Tuple[float, float] = pd.Field(
...,
title="Bloch vectors",
description="Bloch vectors along the local x and y directions in units of "
"``2 * pi / (simulation size along the respective dimension)``.",
)
[docs]
@staticmethod
def shifted_orders(orders: Tuple[int, ...], bloch_vec: float) -> np.ndarray:
"""Diffraction orders shifted by the Bloch vector."""
return bloch_vec + np.atleast_1d(orders)
[docs]
@staticmethod
def reciprocal_coords(
orders: np.ndarray, size: float, bloch_vec: float, f: float, medium: MediumType
) -> np.ndarray:
"""Get the normalized "u" reciprocal coords for a vector of orders, size, and bloch vec."""
if size == 0:
return np.atleast_2d(0)
epsilon = medium.eps_model(f)
bloch_array = DiffractionData.shifted_orders(orders, bloch_vec)
return bloch_array[:, None] / size * C_0 / f / np.real(np.sqrt(epsilon))
[docs]
@staticmethod
def compute_angles(
reciprocal_vectors: Tuple[np.ndarray, np.ndarray],
) -> Tuple[np.ndarray, np.ndarray]:
"""Compute the polar and azimuth angles associated with the given reciprocal vectors."""
# some wave number pairs are outside the light cone, leading to warnings from numpy.arcsin
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", message="invalid value encountered in arcsin", category=RuntimeWarning
)
ux, uy = reciprocal_vectors
thetas, phis = DiffractionMonitor.kspace_2_sph(ux[:, None, :], uy[None, :, :], axis=2)
return (thetas, phis)
@property
def coords_spherical(self) -> Dict[str, np.ndarray]:
"""Coordinates grid for the fields in the spherical system."""
theta, phi = self.angles
return {"r": None, "theta": theta, "phi": phi}
@property
def orders_x(self) -> np.ndarray:
"""Allowed orders along x."""
return np.atleast_1d(np.array(self.Etheta.coords["orders_x"]))
@property
def orders_y(self) -> np.ndarray:
"""Allowed orders along y."""
return np.atleast_1d(np.array(self.Etheta.coords["orders_y"]))
@property
def reciprocal_vectors(self) -> Tuple[np.ndarray, np.ndarray]:
"""Get the normalized "ux" and "uy" reciprocal vectors."""
return (self.ux, self.uy)
@property
def ux(self) -> np.ndarray:
"""Normalized wave vector along x relative to ``local_origin`` and oriented
with respect to ``monitor.normal_dir``, normalized by the wave number in the
projection medium."""
return self.reciprocal_coords(
orders=self.orders_x,
size=self.sim_size[0],
bloch_vec=self.bloch_vecs[0],
f=self.f,
medium=self.medium,
)
@property
def uy(self) -> np.ndarray:
"""Normalized wave vector along y relative to ``local_origin`` and oriented
with respect to ``monitor.normal_dir``, normalized by the wave number in the
projection medium."""
return self.reciprocal_coords(
orders=self.orders_y,
size=self.sim_size[1],
bloch_vec=self.bloch_vecs[1],
f=self.f,
medium=self.medium,
)
@property
def angles(self) -> Tuple[xr.DataArray]:
"""The (theta, phi) angles corresponding to each allowed pair of diffraction
orders storeds as data arrays. Disallowed angles are set to ``np.nan``.
"""
thetas, phis = self.compute_angles(self.reciprocal_vectors)
theta_data = xr.DataArray(thetas, coords=self.coords)
phi_data = xr.DataArray(phis, coords=self.coords)
return theta_data, phi_data
@property
def amps(self) -> xr.DataArray:
"""Complex power amplitude in each order for 's' and 'p' polarizations, normalized so that
the power carried by the wave of that order and polarization equals ``abs(amps)^2``.
"""
cos_theta = np.cos(np.nan_to_num(self.angles[0]))
# will set amplitudes to 0 for glancing or negative angles
cos_theta[cos_theta <= 0] = np.inf
norm = 1.0 / np.sqrt(2.0 * self.eta) / np.sqrt(cos_theta)
amp_theta = self.Etheta.values * norm
amp_phi = self.Ephi.values * norm
# stack the amplitudes in s- and p-components along a new polarization axis
coords = {}
coords["orders_x"] = np.atleast_1d(self.orders_x)
coords["orders_y"] = np.atleast_1d(self.orders_y)
coords["f"] = np.atleast_1d(self.f)
coords["polarization"] = ["s", "p"]
return xr.DataArray(np.stack([amp_phi, amp_theta], axis=3), coords=coords)
@property
def power(self) -> xr.DataArray:
"""Total power in each order, summed over both polarizations."""
return (np.abs(self.amps) ** 2).sum(dim="polarization")
@property
def fields_spherical(self) -> xr.Dataset:
"""Get all field components in spherical coordinates relative to the monitor's
local origin for all allowed diffraction orders and frequencies specified in the
:class:`DiffractionMonitor`.
Returns
-------
``xarray.Dataset``
xarray dataset containing
(``Er``, ``Etheta``, ``Ephi``, ``Hr``, ``Htheta``, ``Hphi``)
in spherical coordinates.
"""
fields = [field.values for field in self.field_components.values()]
keys = ["Er", "Etheta", "Ephi", "Hr", "Htheta", "Hphi"]
return self._make_dataset(fields, keys)
@property
def fields_cartesian(self) -> xr.Dataset:
"""Get all field components in Cartesian coordinates relative to the monitor's
local origin for all allowed diffraction orders and frequencies specified in the
:class:`DiffractionMonitor`.
Returns
-------
``xarray.Dataset``
xarray dataset containing (``Ex``, ``Ey``, ``Ez``, ``Hx``, ``Hy``, ``Hz``)
in Cartesian coordinates.
"""
theta, phi = self.angles
theta = theta.values
phi = phi.values
e_x, e_y, e_z = self.monitor.sph_2_car_field(
0, self.Etheta.values, self.Ephi.values, theta, phi
)
h_x, h_y, h_z = self.monitor.sph_2_car_field(
0, self.Htheta.values, self.Hphi.values, theta, phi
)
e_x, e_y, e_z, h_x, h_y, h_z = (
np.nan_to_num(fld) for fld in [e_x, e_y, e_z, h_x, h_y, h_z]
)
fields = [e_x, e_y, e_z, h_x, h_y, h_z]
keys = ["Ex", "Ey", "Ez", "Hx", "Hy", "Hz"]
return self._make_dataset(fields, keys)
def _make_dataset(self, fields: Tuple[np.ndarray, ...], keys: Tuple[str, ...]) -> xr.Dataset:
"""Make an xr.Dataset for fields with given field names."""
data_arrays = []
for field in fields:
data_arrays.append(xr.DataArray(data=field, coords=self.coords, dims=self.dims))
return xr.Dataset(dict(zip(keys, data_arrays)))
MonitorDataTypes = (
FieldData,
FieldTimeData,
PermittivityData,
ModeSolverData,
ModeData,
FluxData,
FluxTimeData,
FieldProjectionKSpaceData,
FieldProjectionCartesianData,
FieldProjectionAngleData,
DiffractionData,
)
MonitorDataType = Union[MonitorDataTypes]
