tidy3d.components.base_sim.monitor.AbstractMonitor

class AbstractMonitor

Bases: Box, ABC

Abstract base class for steady-state monitors.

Parameters:

• center (Attribute: center) –

  Type	Tuple[float, float, float]
  Default	= (0.0, 0.0, 0.0)
  Units	um
  Description	Center of object in x, y, and z.

• size (Attribute: size) –

  Type	Tuple[NonNegativeFloat, NonNegativeFloat, NonNegativeFloat]
  Default
  Units	um
  Description	Size in x, y, and z directions.

• name (Attribute: name) –

  Type	ConstrainedStrValue
  Default
  Description	Unique name for monitor.

Attributes

geometry	Box representation of monitor.
plot_params	Default parameters for plotting a Monitor object.

Methods

downsample(arr, axis)	Downsample a 1D array making sure to keep the first and last entries, based on the spatial interval defined for the axis.
downsampled_num_cells(num_cells)	Given a tuple of the number of cells spanned by the monitor along each dimension, return the number of cells one would have after downsampling based on interval_space.
storage_size(num_cells, tmesh)	Size of monitor storage given the number of points after discretization.

name

property plot_params

Default parameters for plotting a Monitor object.

property geometry

Box representation of monitor.

Returns:

Representation of the monitor geometry as a Box.

Return type:

Box

abstract storage_size(num_cells, tmesh)

Size of monitor storage given the number of points after discretization.

Parameters:

• num_cells (int) – Number of grid cells within the monitor after discretization by a Simulation.

• tmesh (Array) – The discretized time mesh of a Simulation.

Returns:

Number of bytes to be stored in monitor.

Return type:

int

downsample(arr, axis)

Downsample a 1D array making sure to keep the first and last entries, based on the spatial interval defined for the axis.

Parameters:

• arr (Numpy) – A 1D array of arbitrary type.

• axis (Axis) – Axis for which to select the interval_space defined for the monitor.

Returns:

Downsampled array.

Return type:

Numpy

downsampled_num_cells(num_cells)

Given a tuple of the number of cells spanned by the monitor along each dimension, return the number of cells one would have after downsampling based on interval_space.

__hash__()

Hash method.