pymoto.ScalarToFile

class pymoto.ScalarToFile(saveto: str, fmt: str = '.10e', separator: str = '\t')

Writes iteration data to a log file

This function can also handle small vectors of scalars, i.e. eigenfrequencies or multiple constraints.

Input Signals:

*args (Numeric or np.ndarray): Values to write to file. The signal tags are used as name.

__init__(saveto: str, fmt: str = '.10e', separator: str = '\t')

Initialize scalar to file module

Parameters:

saveto (str) – Location to save the log file, supports .txt or .csv
fmt (str, optional) – Value format (e.g. ‘e’, ‘f’, ‘.3e’, ‘.5g’, ‘.3f’). Defaults to “.10e”.
separator (str, optional) – Value separator, .csv files will automatically use a comma. Defaults to “ “.

Methods

`__init__`(saveto[, fmt, separator])	Initialize scalar to file 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