tidy3d.SimulationData

class SimulationData

Bases: AbstractYeeGridSimulationData

Stores data from a collection of Monitor objects in a Simulation.

Parameters:

• simulation (Simulation) – Original Simulation associated with the data.

• data (tuple[Union[FieldData, FieldTimeData, PermittivityData, MediumData, ModeSolverData, ModeData, FluxData, FluxTimeData, AuxFieldTimeData, FieldProjectionKSpaceData, FieldProjectionCartesianData, FieldProjectionAngleData, DiffractionData, DirectivityData, FieldOverlapData, MicrowaveModeData, MicrowaveModeSolverData, SurfaceFieldData, SurfaceFieldTimeData], …]) – List of MonitorData instances associated with the monitors of the original Simulation.

• log (Optional[str] = None) – A string containing the log information from the simulation run.

• diverged (bool = False) – A boolean flag denoting whether the simulation run diverged.

Notes

The SimulationData objects store a copy of the original Simulation:, so it can be recovered if the SimulationData is loaded in a new session and the Simulation is no longer in memory.

More importantly, the SimulationData contains a reference to the data for each of the monitors within the original Simulation. This data can be accessed directly using the name given to the monitors initially.

Examples

Standalone example:

>>> import tidy3d as td
>>> num_modes = 5
>>> 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 = td.Grid(boundaries=td.Coords(x=x, y=y, z=z))
>>> scalar_field = td.ScalarFieldDataArray((1+1j) * np.random.random((2,3,4,2)), coords=coords)
>>> field_monitor = td.FieldMonitor(
...     size=(2,4,6),
...     freqs=[2e14, 3e14],
...     name='field',
...     fields=['Ex'],
...     colocate=True,
... )
>>> sim = td.Simulation(
...     size=(2, 4, 6),
...     grid_spec=td.GridSpec(wavelength=1.0),
...     monitors=[field_monitor],
...     run_time=2e-12,
...     sources=[
...         td.UniformCurrentSource(
...             size=(0, 0, 0),
...             center=(0, 0.5, 0),
...             polarization="Hx",
...             source_time=td.GaussianPulse(
...                 freq0=2e14,
...                 fwidth=4e13,
...             ),
...             current_amplitude_definition="total",
...         )
...     ],
... )
>>> field_data = td.FieldData(monitor=field_monitor, Ex=scalar_field, grid_expanded=grid)
>>> sim_data = td.SimulationData(simulation=sim, data=(field_data,))

To save and load the SimulationData object.

sim_data.to_file(fname='path/to/file.hdf5') # Save a SimulationData object to a HDF5 file
sim_data = SimulationData.from_file(fname='path/to/file.hdf5') # Load a SimulationData object from a HDF5 file.

Optionally, the simulation data can be loaded in a lazy mode, which only holds a reference until a field is accessed or a method is applied. This is useful to save I/O operations and memory.

sim_data = SimulationData.from_file(fname='path/to/file.hdf5', lazy=True) # Does not contain data until accessed.

See also

Notebooks:

• Quickstart: Usage in a basic simulation flow.

• Performing visualization of simulation data

• Advanced monitor data manipulation and visualization

Attributes

field_decay	Returns a TimeDataArray of field decay values over time steps.
final_decay_value	Returns value of the field decay at the final time step.
simulation
data
diverged
log

Methods

renormalize(normalize_index)	Return a copy of the SimulationData with a different source used for the normalization.
source_spectrum(source_index)	Get a spectrum normalization function for a given source index.

simulation

data

diverged

property field_decay

Returns a TimeDataArray of field decay values over time steps.

property final_decay_value

Returns value of the field decay at the final time step.

source_spectrum(source_index)

Get a spectrum normalization function for a given source index.

renormalize(normalize_index)

Return a copy of the SimulationData with a different source used for the normalization.