pymoto.FigModule

class pymoto.FigModule(saveto: str = None, overwrite: bool = False, show: bool = True)

Abstract base class for any module which produces a figure

__init__(saveto: str = None, overwrite: bool = False, show: bool = True)

Initialize the abstract base-class for figure modules

Parameters:

saveto (str, optional) – Save images of each iteration to the specified location. Defaults to None.
overwrite (bool, optional) – Overwrite saved image every time the figure is updated, else prefix _0000 is added to the filename. Defaults to False.
show (bool, optional) – Show the figure on the screen. Defaults to True.

Methods

`__init__`([saveto, overwrite, show])	Initialize the abstract base-class for figure modules
`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