saftig.evaluation.dataset

A representation of a dataset for the evaluation of noise mitigation methods.

Attributes

NDArrayF

Classes

EvaluationDataset

A representation of a dataset for the evaluation of noise mitigation methods.

Module Contents

saftig.evaluation.dataset.NDArrayF
class saftig.evaluation.dataset.EvaluationDataset(sample_rate, witness_conditioning, target_conditioning, witness_evaluation, target_evaluation, signal_conditioning=None, signal_evaluation=None, name='Unnamed', target_unit='1')

A representation of a dataset for the evaluation of noise mitigation methods.

Provided sequences will be stored as immutable float64 numpy arrays.

Parameters:

  • sample_rate (float) – Sample rate in Hz

  • witness_conditioning (collections.abc.Sequence[collections.abc.Sequence[NDArrayF]]) – witness channel data for the conditioning format: witness_conditioning[sequence_idx][channel_idx][sample_idx]

  • target_conditioning (collections.abc.Sequence[NDArrayF]) – target channel data for the conditioning format: witness_conditioning[sequence_idx][sample_idx]

  • witness_evaluation (collections.abc.Sequence[collections.abc.Sequence[NDArrayF]]) – witness channel data for the evaluation

  • target_evaluation (collections.abc.Sequence[NDArrayF]) – target channel data for the evaluation

  • signal_conditioning (collections.abc.Sequence[NDArrayF] | None) – (Optional) A signal that can be subtracted from the target for performance metrics

  • signal_evaluation (collections.abc.Sequence[NDArrayF] | None) – (Optional) A signal that can be subtracted from the target for performance metrics

  • name (str) – (Optional) a string describing the dataset

  • target_unit (str) –

sample_rate: float
witness_conditioning: collections.abc.Sequence[collections.abc.Sequence[NDArrayF]]
target_conditioning: collections.abc.Sequence[NDArrayF]
witness_evaluation: collections.abc.Sequence[collections.abc.Sequence[NDArrayF]]
target_evaluation: collections.abc.Sequence[NDArrayF]
signal_conditioning: collections.abc.Sequence[NDArrayF] | None
signal_evaluation: collections.abc.Sequence[NDArrayF] | None
name: str
target_unit: str
static _prepare_dataset(witness_inp, target_inp, signal_inp=None)

Convert input to immutable np.float64 arrays and check shape

Parameters:

  • witness_inp (collections.abc.Sequence[collections.abc.Sequence[NDArrayF]]) –

  • target_inp (collections.abc.Sequence[NDArrayF]) –

  • signal_inp (collections.abc.Sequence[NDArrayF] | None) –

Return type:

tuple[collections.abc.Sequence[collections.abc.Sequence[NDArrayF]], collections.abc.Sequence[NDArrayF], collections.abc.Sequence[NDArrayF] | None]

get_min_sequence_len(separate=False)

Get the length of the shortest sequence in the dataset

Parameters:

separate (bool) – If True, returns the minimum separately for conditioning and evaluation data.

Return type:

int | tuple[int, int]

static _hash_wts_data(witness, target, signal=None)

Calculate a hash value for a set of witness, target, signal data

Parameters:

  • witness (collections.abc.Sequence[collections.abc.Sequence[NDArrayF]]) –

  • target (collections.abc.Sequence[NDArrayF]) –

  • signal (collections.abc.Sequence[NDArrayF] | None) –

hash_bytes()

return a hash over the dataset data as a bytes object

Return type:

bytes

__hash__()
Return type:

int