pymoto.WriteToVTI

class pymoto.WriteToVTI(domain: VoxelDomain, saveto: str, overwrite: bool = False, scale=1.0, interval=1)

Writes vectors to a Paraview VTI file

See also: VoxelDomain.write_to_vti()

The size of the vectors should be a multiple of nel or nnodes. 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 block-vectors of multiple dimensions (e.g. (2, 3*nnodes)) are accepted, which get the suffixed as _00.

Input Signals:

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

__init__(domain: VoxelDomain, saveto: str, overwrite: bool = False, scale=1.0, interval=1)

Initialize VTI writer module

Parameters:

domain (pymoto.VoxelDomain) – The finite element domain layout
saveto (str) – Location to save the VTI file
overwrite (bool, optional) – Overwrite the VTI file for each iteration. Defaults to False.
scale (float, optional) – Scaling factor for the domain. Defaults to 1.0.
interval (int, optional) – Interval at which to write the VTI file, defaults to 1 (every iteration). Defaults to 1.

Methods

`__init__`(domain, saveto[, overwrite, scale, ...])	Initialize VTI writer 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

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