sup3r.utilities.pytest.helpers.SamplerTester

class SamplerTester(*args, **kwargs)

Bases: Sampler

Keep a record of sample indices for testing.

Parameters:

• data (Union[Sup3rX, Sup3rDataset],) – Object with data that will be sampled from. Usually the .data attribute of various Container objects. i.e. Loader, Rasterizer, Deriver, as long as the spatial dimensions are not flattened.

• sample_shape (tuple) – Size of arrays to sample from the contained data.

• batch_size (int) – Number of samples to get to build a single batch. A sample of (sample_shape[0], sample_shape[1], batch_size * sample_shape[2]) is first selected from underlying dataset and then reshaped into (batch_size, *sample_shape) to get a single batch. This is more efficient than getting N = batch_size samples and then stacking.

• feature_sets (Optional[dict]) – Optional dictionary describing how the full set of features is split between lr_only_features and hr_exo_features.

  featureslist | tuple

  List of full set of features to use for sampling. If no entry is provided then all data_vars from data will be used.

  lr_only_featureslist | tuple

  List of feature names or patt*erns that should only be included in the low-res training set and not the high-res observations.

  hr_exo_featureslist | tuple

  List of feature names or patt*erns that should be included in the high-resolution observation but not expected to be output from the generative model. An example is high-res topography that is to be injected mid-network.

Methods

get_sample_index(**kwargs)	Override get_sample_index to keep record of index accessible by batch handler.
post_init_log([args_dict])	Log additional arguments after initialization.
preflight()	Check if the sample_shape is larger than the requested raster size
wrap(data)	Return a Sup3rDataset object or tuple of such.

Attributes

data	Return underlying data.
hr_exo_features	Get a list of exogenous high-resolution features that are only used for training e.g., mid-network high-res topo injection.
hr_features	Get the high-resolution features corresponding to hr_features_ind
hr_features_ind	Get the high-resolution feature channel indices that should be included for training.
hr_out_features	Get a list of high-resolution features that are intended to be output by the GAN.
hr_sample_shape	Shape of the data sample to select when __next__() is called.
lr_only_features	List of feature names or patt*erns that should only be included in the low-res training set and not the high-res observations.
sample_shape	Shape of the data sample to select when __next__() is called.
shape	Get shape of underlying data.

property data

Return underlying data.

Returns:

Sup3rDataset

See also

wrap()

get_sample_index(**kwargs)

Override get_sample_index to keep record of index accessible by batch handler. We store the index with the time entry divided by the batch size, since we have multiplied by the batch size to get a continuous time sample for multiple observations.

property hr_exo_features

Get a list of exogenous high-resolution features that are only used for training e.g., mid-network high-res topo injection. These must come at the end of the high-res feature set. These can also be input to the model as low-res features.

property hr_features

Get the high-resolution features corresponding to hr_features_ind

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 high-resolution features that are intended to be output by the GAN. Does not include high-resolution exogenous features

property hr_sample_shape: Tuple

Shape of the data sample to select when __next__() is called. Same as sample_shape

property lr_only_features

List of feature names or patt*erns that should only be included in the low-res training set and not the high-res observations.

post_init_log(args_dict=None)

Log additional arguments after initialization.

preflight()

Check if the sample_shape is larger than the requested raster size

property sample_shape: Tuple

Shape of the data sample to select when __next__() is called.

property shape

Get shape of underlying data.

wrap(data)

Return a Sup3rDataset object or tuple of such. This is a tuple when the .data attribute belongs to a Collection object like BatchHandler. Otherwise this is Sup3rDataset object, which is either a wrapped 2-tuple or 1-tuple (e.g. len(data) == 2 or len(data) == 1). This is a 2-tuple when .data belongs to a dual container object like DualSampler and a 1-tuple otherwise.