pymoto.SeriesToVTI

class pymoto.SeriesToVTI(domain: VoxelDomain, saveto: str, delta_t: float = 1.0, interval: int = 10, scale: float = 1.0)

Writes transient response vectors to a Paraview VTI file

This module utilizes a series file to properly associate the correct time with each timestep: https://gitlab.kitware.com/paraview/paraview/blob/v5.5.0/Documentation/release/ParaView-5.5.0.md#json-based-new-meta-file-format-for-series-added

See also: VoxelDomain.write_to_vti()

The size of the vectors should be a multiple of nel or nnodes on its first dimension. Based on their size they are marked as cell-data or point-data in the VTI file. For 2D data (size is equal to 2*nnodes), the z-dimension is padded with zeros to have 3-dimensional data. Also, non-transient block-vectors of multiple dimensions (e.g. (2, 3*nnodes)) are accepted, which get the suffixed as _00. The time dimension is the last dimension of the vector.

Input Signals:

*args (numpy.ndarray): Vectors to write to VTI. The signal tags are used as name.

__init__(domain: VoxelDomain, saveto: str, delta_t: float = 1.0, interval: int = 10, scale: float = 1.0): Initialize Series VTI writer module :param - domain: The domain layout :param - saveto: Location to save folders with transient responses for specific iterations :type - saveto: str :param - delta_t: Length of timestep :type - delta_t: float :param - interval: Iteration interval for saving data :type - interval: int, optional :param - scale: Scaling factor for the domain :type - scale: float, optional

Methods

`__init__`(domain, saveto[, delta_t, ...])	Initialize Series VTI writer module :param - domain: The domain layout :param - saveto: Location to save folders with transient responses for specific iterations :type - saveto: str :param - delta_t: Length of timestep :type - delta_t: float :param - interval: Iteration interval for saving data :type - interval: int, optional :param - scale: Scaling factor for the domain :type - scale: float, optional
`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

get_input_sensitivities(as_list=False)

get_input_states(as_list=False)

get_output_sensitivities(as_list=False)

get_output_states(as_list=False)

property n_in: int: Get the number of input signals

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)

response(): Calculate the response from sig_in and output this to sig_out

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

sig_in: List = None

sig_out: List = None