sup3r.preprocessing.batch_handling.SpatialBatchHandlerCC

class SpatialBatchHandlerCC(data_handlers, batch_size=8, s_enhance=1, t_enhance=1, means=None, stds=None, norm=True, n_batches=10, temporal_coarsening_method='subsample', stdevs_file=None, means_file=None, overwrite_stats=False, smoothing=None, smoothing_ignore=None, worker_kwargs=None)

Bases: BatchHandler

Batch handling class for climate change data with daily averages as the coarse dataset with only spatial samples, e.g. the batch tensor shape is (n_obs, spatial_1, spatial_2, features)

Parameters:

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

• batch_size (int) – Number of observations in a batch

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

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

• means (dict | none) – Dictionary of means for all features with keys: feature names and values: mean values. if None, this will be calculated. if norm is true these will be used for data normalization

• stds (dict | none) – dictionary of standard deviation values for all features with keys: feature names and values: standard deviations. if None, this will be calculated. if norm is true these will be used for data normalization

• norm (bool) – Whether to normalize the data or not

• n_batches (int) – Number of batches in an epoch, this sets the iteration limit for this object.

• 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

• stdevs_file (str | None) – Optional .json path to stdevs data or where to save data after calling get_stats

• means_file (str | None) – Optional .json path to means data or where to save data after calling get_stats

• overwrite_stats (bool) – Whether to overwrite stats cache files.

• 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

• worker_kwargs (dict | None) – Dictionary of worker values. Can include max_workers, norm_workers, stats_workers, and load_workers. Each argument needs to be an integer or None.

  Providing a value for max workers will be used to set the value of all other worker arguments. If max_workers == 1 then all processes will be serialized. If None then other workers arguments will use their own provided values.

  load_workers is the max number of workers to use for loading data handlers. norm_workers is the max number of workers to use for normalizing data handlers. stats_workers is the max number of workers to use for computing stats across data handlers.

Methods

cache_stats()	Saved stdevs and means to cache files if files are not None
check_cached_stats()	Get standard deviations and means for all data features from cache files if available.
get_handler_index()	Get random handler index based on handler weights
get_rand_handler()	Get random handler based on handler weights
get_stats()	Get standard deviations and means for all data features
load_handler_data()	Load data handler data in parallel or serial
normalize([means, stds])	Compute means and stds for each feature across all datasets and normalize each data handler dataset.

Attributes

DATA_HANDLER_CLASS
feature_mem	Get memory used by each feature in data handlers
features	Get the ordered list of feature names held in this object's data handlers
handler_weights	Get weights used to sample from different data handlers based on relative sizes
hr_exo_features	Get a list of high-resolution features that are only used for training e.g., mid-network high-res topo injection.
hr_features_ind	Get the high-resolution feature channel indices that should be included for training.
hr_out_features	Get a list of low-resolution features that are intended to be output by the GAN.
load_workers	Get max workers for loading data handler based on memory usage
lr_features	Get a list of low-resolution features.
norm_workers	Get max workers used for calculating and normalization across features
shape	Shape of full dataset across all handlers
stats_workers	Get max workers for calculating stats based on memory usage

VAL_CLASS

alias of ValidationData

BATCH_CLASS

alias of Batch

cache_stats()

Saved stdevs and means to cache files if files are not None

check_cached_stats()

Get standard deviations and means for all data features from cache files if available.

Returns:

• means (dict | none) – Dictionary of means for all features with keys: feature names and values: mean values. if None, this will be calculated. if norm is true these will be used for data normalization

• stds (dict | none) – dictionary of standard deviation values for all features with keys: feature names and values: standard deviations. if None, this will be calculated. if norm is true these will be used for data normalization

property feature_mem

Get memory used by each feature in data handlers

property features

Get the ordered list of feature names held in this object’s data handlers

get_handler_index()

Get random handler index based on handler weights

get_rand_handler()

Get random handler based on handler weights

get_stats()

Get standard deviations and means for all data features

property handler_weights

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

property hr_exo_features

Get a list of high-resolution features that are only used for training e.g., mid-network high-res topo injection.

property hr_features_ind

Get the high-resolution feature channel indices that should be included for training. Any high-resolution features that are only included in the data handler to be coarsened for the low-res input are removed

property hr_out_features

Get a list of low-resolution features that are intended to be output by the GAN.

load_handler_data()

Load data handler data in parallel or serial

property load_workers

Get max workers for loading data handler based on memory usage

property lr_features

Get a list of low-resolution features. All low-resolution features are used for training.

property norm_workers

Get max workers used for calculating and normalization across features

normalize(means=None, stds=None)

Compute means and stds for each feature across all datasets and normalize each data handler dataset. Checks if input means and stds are different from stored means and stds and renormalizes if they are

Parameters:

• means (dict | none) – Dictionary of means for all features with keys: feature names and values: mean values. if None, this will be calculated. if norm is true these will be used for data normalization

• stds (dict | none) – dictionary of standard deviation values for all features with keys: feature names and values: standard deviations. if None, this will be calculated. if norm is true these will be used for data normalization

• features (list | None) – Optional list of features used to index data array during normalization. If this is None self.features will be used.

property shape

Shape of full dataset across all handlers

Returns:

shape (tuple) – (spatial_1, spatial_2, temporal, features) With spatiotemporal extent equal to the sum across all data handler dimensions

property stats_workers

Get max workers for calculating stats based on memory usage