pymoto.PlotDomain

class pymoto.PlotDomain(domain: VoxelDomain, *args, clim=None, cmap='gray_r', **kwargs)

Plots the densities of a domain (2D or 3D)

Input Signal:

__init__(domain: VoxelDomain, *args, clim=None, cmap='gray_r', **kwargs)

Initialize domain plot module

Parameters:

domain (pymoto.VoxelDomain) – The domain layout
*args – Additional arguments for pymoto.FigModule
clim ([float, float] or float, optional) – In 2D [cmin, cmax]: the values of minimum and maximum color. In 3D clipval: the value below which elements are clipped.
cmap (str, optional) – Colormap (only for 2D). Defaults to “gray_r”.
**kwargs – Additional keyword arguments for pymoto.FigModule

Methods

`__init__`(domain, *args[, clim, cmap])	Initialize domain plot module
`connect`(sig_in[, sig_out])	Connect without automatic adding to a function network
`get_input_sensitivities`([as_list])
`get_input_states`([as_list])
`get_output_sensitivities`([as_list])
`get_output_states`([as_list])
`reset`()	Reset the state of the sensitivities (they are set to zero or to None)
`response`()	Calculate the response from sig_in and output this to sig_out
`sensitivity`()	Calculate sensitivities using backpropagation

Attributes

`n_in`	Get the number of input signals
`n_out`	Get the number of output signals
`sig_in`
`sig_out`

connect(sig_in: Signal | Iterable[Signal], sig_out: Signal | Iterable[Signal] = None): Connect without automatic adding to a function network

property n_out: int

Get the number of output signals

Note: Cannot be used in the initial __call__()

reset(): Reset the state of the sensitivities (they are set to zero or to None)

sensitivity()

Calculate sensitivities using backpropagation

Based on the sensitivity we get from sig_out, reverse the process and output the new sensitivities to sig_in