sup3r.preprocessing.conditional_moment_batch_handling.ValidationDataMom2SF

class ValidationDataMom2SF(data_handlers, batch_size=8, s_enhance=3, t_enhance=1, temporal_coarsening_method='subsample', temporal_enhancing_method='constant', hr_features_ind=None, smoothing=None, smoothing_ignore=None, model_mom1=None, s_padding=None, t_padding=None, end_t_padding=False)

Bases: ValidationDataMom1

Iterator for validation data for second conditional moment of subfilter velocity

Parameters:

• data_handlers (list[DataHandler]) – List of DataHandler instances

• batch_size (int) – Size of validation data batches

• s_enhance (int) – Factor by which to coarsen spatial dimensions of the high resolution data

• t_enhance (int) – Factor by which to coarsen temporal dimension of the high resolution data

• temporal_coarsening_method (str) – [subsample, average, total] Subsample will take every t_enhance-th time step, average will average over t_enhance time steps, total will sum over t_enhance time steps

• temporal_enhancing_method (str) – [constant, linear] Method to enhance temporally when constructing subfilter. At every temporal location, a low-res temporal data is substracted from the high-res temporal data predicted. constant will assume that the low-res temporal data is constant between landmarks. linear will linearly interpolate between landmarks to generate the low-res data to remove from the high-res.

• hr_features_ind (list | np.ndarray | None) – List/array of feature channel indices that are used for generative output, without any feature indices used only for training.

• smoothing (float | None) – Standard deviation to use for gaussian filtering of the coarse data. This can be tuned by matching the kinetic energy of a low resolution simulation with the kinetic energy of a coarsened and smoothed high resolution simulation. If None no smoothing is performed.

• smoothing_ignore (list | None) – List of features to ignore for the smoothing filter. None will smooth all features if smoothing kwarg is not None

• model_mom1 (Sup3rCondMom | None) – model that predicts the first conditional moments. Useful to prepare data for learning second conditional moment.

• s_padding (int | None) – Width of spatial padding to predict only middle part. If None, no padding is used

• t_padding (int | None) – Width of temporal padding to predict only middle part. If None, no padding is used

• end_t_padding (bool | False) – Zero pad the end of temporal space. Ensures that loss is calculated only if snapshot is surrounded by temporal landmarks. False by default

Methods

any()	Return True if any validation data exists
batch_next(high_res)	Assemble the next batch
get_handler_index()	Get random handler index based on handler weights

Attributes

handler_weights	Get weights used to sample from different data handlers based on relative sizes
shape	Shape of full validation dataset across all handlers

BATCH_CLASS

alias of BatchMom2SF

any()

Return True if any validation data exists

batch_next(high_res)

Assemble the next batch

Parameters:

high_res (np.ndarray) – 4D | 5D array (batch_size, spatial_1, spatial_2, features) (batch_size, spatial_1, spatial_2, temporal, features)

Returns:

batch (Batch)

get_handler_index()

Get random handler index based on handler weights

property handler_weights

Get weights used to sample from different data handlers based on relative sizes

property shape

Shape of full validation dataset across all handlers

Returns:

shape (tuple) – (spatial_1, spatial_2, temporal, features) With temporal extent equal to the sum across all data handlers time dimension