feat: flux and poynting properties for FieldProjectionCartesianData

CHANGELOG.md

@@ -13,6 +13,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - More convenient mesh importing from another simulation through `grid_spec = GridSpec.from_grid(sim.grid)`.
 - `autograd` gradient calculations can be performed on the server by passing `local_gradient = False` into `web.run()` or `web.run_async()`.
 - Automatic differentiation with `autograd` supports multiple frequencies through single, broadband adjoint simulation when the objective function can be formulated as depending on a single dataset in the output `SimulationData` with frequency dependence only.
+- Added `flux` and `poynting` properties to `FieldProjectionCartesianData`.
 
 ### Changed
 - Error if field projection monitors found in 2D simulations, except `FieldProjectionAngleMonitor` with `far_field_approx = True`. Support for other monitors and for exact field projection will be coming in a subsequent Tidy3D version.

tests/test_components/test_field_projection.py

@@ -344,6 +344,8 @@ def test_proj_clientside():
     far_fields_cartesian.fields_cartesian
     far_fields_cartesian.radar_cross_section
     far_fields_cartesian.power
+    far_fields_cartesian.poynting
+    far_fields_cartesian.flux
     for val in far_fields_cartesian.field_components.values():
         val.sel(f=f0)
     far_fields_cartesian.renormalize_fields(proj_distance=5e6)
@@ -366,6 +368,8 @@ def test_proj_clientside():
     exact_fields_cartesian.fields_cartesian
     exact_fields_cartesian.radar_cross_section
     exact_fields_cartesian.power
+    exact_fields_cartesian.poynting
+    exact_fields_cartesian.flux
     for val in exact_fields_cartesian.field_components.values():
         val.sel(f=f0)
     with pytest.raises(DataError):

tidy3d/components/data/monitor_data.py

@@ -2465,6 +2465,35 @@ def z(self) -> np.ndarray:
         """Z positions."""
         return self.Etheta.z.values
 
+    @property
+    def tangential_dims(self):
+        tangential_dims = ["x", "y", "z"]
+        tangential_dims.pop(self.monitor.proj_axis)
+        return tangential_dims
+
+    @property
+    def poynting(self) -> xr.DataArray:
+        """Time-averaged Poynting vector for field data associated to a Cartesian field projection monitor."""
+        fc = self.fields_cartesian
+        dim1, dim2 = self.tangential_dims
+
+        e1 = fc["E" + dim1]
+        e2 = fc["E" + dim2]
+        h1 = fc["H" + dim1]
+        h2 = fc["H" + dim2]
+
+        e1_h2 = e1 * h2.conj()
+        e2_h1 = e2 * h1.conj()
+
+        e_x_h_star = e1_h2 - e2_h1
+        return 0.5 * np.real(e_x_h_star)
+
+    @cached_property
+    def flux(self) -> FluxDataArray:
+        """Flux for projecteded field data corresponding to a Cartesian field projection monitor."""
+        flux = self.poynting.integrate(self.tangential_dims)
+        return FluxDataArray(flux)
+
     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``.