tidy3d.HeatSimulationData

class HeatSimulationData

Bases: AbstractSimulationData

Stores results of a heat simulation.

Parameters:

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

• data (Tuple[TemperatureData, ...]) – 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.

Example

>>> from tidy3d import Medium, SolidSpec, FluidSpec, UniformUnstructuredGrid, SpatialDataArray
>>> from tidy3d import Structure, Box, UniformUnstructuredGrid, UniformHeatSource
>>> from tidy3d import StructureBoundary, TemperatureBC, TemperatureMonitor, TemperatureData
>>> from tidy3d import HeatBoundarySpec
>>> import numpy as np
>>> temp_mnt = TemperatureMonitor(size=(1, 2, 3), name="sample")
>>> heat_sim = HeatSimulation(
...     size=(3.0, 3.0, 3.0),
...     structures=[
...         Structure(
...             geometry=Box(size=(1, 1, 1), center=(0, 0, 0)),
...             medium=Medium(
...                 permittivity=2.0, heat_spec=SolidSpec(
...                     conductivity=1,
...                     capacity=1,
...                 )
...             ),
...             name="box",
...         ),
...     ],
...     medium=Medium(permittivity=3.0, heat_spec=FluidSpec()),
...     grid_spec=UniformUnstructuredGrid(dl=0.1),
...     sources=[UniformHeatSource(rate=1, structures=["box"])],
...     boundary_spec=[
...         HeatBoundarySpec(
...             placement=StructureBoundary(structure="box"),
...             condition=TemperatureBC(temperature=500),
...         )
...     ],
...     monitors=[temp_mnt],
... )
>>> x = [1,2]
>>> y = [2,3,4]
>>> z = [3,4,5,6]
>>> coords = dict(x=x, y=y, z=z)
>>> temp_array = SpatialDataArray(300 * np.abs(np.random.random((2,3,4))), coords=coords)
>>> temp_mnt_data = TemperatureData(monitor=temp_mnt, temperature=temp_array)
>>> heat_sim_data = HeatSimulationData(
...     simulation=heat_sim, data=[temp_mnt_data],
... )

Attributes

data
log	Custom logger to avoid the complexities of the logging module

Methods

plot_field(monitor_name[, val, scale, ...])

Plot the data for a monitor with simulation plot overlaid.

Inherited Common Usage

simulation

data

plot_field(monitor_name, val='real', scale='lin', structures_alpha=0.2, robust=True, vmin=None, vmax=None, ax=None, **sel_kwargs)

Plot the data for a monitor with simulation plot overlaid.

Parameters:

• field_monitor_name (str) – Name of TemperatureMonitorData to plot.

• val (Literal['real', 'abs', 'abs^2'] = 'real') – Which part of the field to plot.

• scale (Literal['lin', 'log']) – Plot in linear or logarithmic scale.

• structures_alpha (float = 0.2) – Opacity of the structure permittivity. Must be between 0 and 1 (inclusive).

• robust (bool = True) – If True and vmin or vmax are absent, uses the 2nd and 98th percentiles of the data to compute the color limits. This helps in visualizing the field patterns especially in the presence of a source.

• vmin (float = None) – The lower bound of data range that the colormap covers. If None, they are inferred from the data and other keyword arguments.

• vmax (float = None) – The upper bound of data range that the colormap covers. If None, they are inferred from the data and other keyword arguments.

• ax (matplotlib.axes._subplots.Axes = None) – matplotlib axes to plot on, if not specified, one is created.

• sel_kwargs (keyword arguments used to perform .sel() selection in the monitor data.) – These kwargs can select over the spatial dimensions (x, y, z), or time dimension (t) if applicable. For the plotting to work appropriately, the resulting data after selection must contain only two coordinates with len > 1. Furthermore, these should be spatial coordinates (x, y, or z).

Returns:

The supplied or created matplotlib axes.

Return type:

matplotlib.axes._subplots.Axes

__hash__()

Hash method.