sup3r.preprocessing.batch_handling.ValidationDataSpatialDC
- class ValidationDataSpatialDC(data_handlers, batch_size=8, s_enhance=1, t_enhance=1, temporal_coarsening_method='subsample', hr_features_ind=None, smoothing=None, smoothing_ignore=None)[source]
Bases:
ValidationDataDC
Iterator for data-centric spatial validation data
- 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, min, max] 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
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
Methods
any
()Return True if any validation data exists
batch_next
(high_res)Assemble the next batch
Get random handler index based on handler weights
Attributes
N_SPACE_BINS
N_TIME_BINS
Get weights used to sample from different data handlers based on relative sizes
Shape of full validation dataset across all handlers
- 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