sup3r.preprocessing.batch_handling.BatchHandlerSpatialDC
- class BatchHandlerSpatialDC(*args, **kwargs)[source]
Bases:
BatchHandler
Data-centric batch handler
- Parameters:
*args (list) – Same positional args as BatchHandler
**kwargs (dict) – Same keyword args as BatchHandler
Methods
Saved stdevs and means to cache files if files are not None
Get standard deviations and means for all data features from cache files if available.
Get random handler index based on handler weights
Get random handler based on handler weights
Get standard deviations and means for all data features
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.
Keep track of number of observations from each temporal bin
Attributes
Get memory used by each feature in data handlers
Get the ordered list of feature names held in this object's data handlers
Get weights used to sample from different data handlers based on relative sizes
Get a list of high-resolution features that are only used for training e.g., mid-network high-res topo injection.
Get the high-resolution feature channel indices that should be included for training.
Get a list of low-resolution features that are intended to be output by the GAN.
Get max workers for loading data handler based on memory usage
Get a list of low-resolution features.
Get max workers used for calculating and normalization across features
Shape of full dataset across all handlers
Get max workers for calculating stats based on memory usage
- VAL_CLASS
alias of
ValidationDataSpatialDC
- DATA_HANDLER_CLASS
alias of
DataHandlerDCforH5
- 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