pymoto.PlotIter

class pymoto.PlotIter(ylim=None, log_scale=False, **kwargs)

Plot iteration history of one or more variables

Input Signals:

Keyword Arguments:

saveto (str) – Save images of each iteration to the specified location. (default = None)
overwrite (bool) – Overwrite saved image every time the figure is updated, else prefix _0000 is added to the filename (default = False)
show (bool) – Show the figure on the screen
ylim – Provide y-axis limits for the plot

__init__(ylim=None, log_scale=False, **kwargs)

Initialize iteration plot module

Parameters:

ylim ([float, float], optional) – Provide y-axis limits for the plot. Defaults to automatic scaling.
log_scale (bool, optional) – Use logarithmic scale for the y-axis. Defaults to False.
**kwargs – Additional keyword arguments for pymoto.FigModule

Methods

`__init__`([ylim, log_scale])	Initialize iteration 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