torchsig.datasets.datasets.TorchSigDatasetConfig¶

class torchsig.datasets.datasets.TorchSigDatasetConfig(dataset_id: str, dataset_length: int, seed: int, impairment_level: int, output_representation: Literal['iq', 'spectrogram'], output_spectrogram_fft: int | None, signal_sampling_mode: Literal['per_signal', 'per_family'], dataset_metadata: dict[str, Any])[source]¶

Bases: object

Configuration dataclass for TorchSig datasets.

dataset_id¶

A unique identifier for the dataset.

Type:: str

dataset_length¶

The total number of samples in the dataset.

Type:: int

seed¶

A random seed for reproducibility.

Type:: int

impairment_level¶

The level of impairment to apply to the signals.

Type:: int

output_representation¶

The representation of the output data (e.g., “iq” or “spectrogram”).

Type:: Literal[‘iq’, ‘spectrogram’]

output_spectrogram_fft¶

The FFT size to use when generating spectrograms (if output_representation is “spectrogram”).

Type:: int | None

signal_sampling_mode¶

The mode for sampling signals, either “per_signal” or “per_family”.

Type:: Literal[‘per_signal’, ‘per_family’]

dataset_metadata¶

A dictionary containing additional metadata about the dataset.

Type:: dict[str, Any]

Methods

Attributes

`dataset_id`
`dataset_length`
`seed`
`impairment_level`
`output_representation`
`output_spectrogram_fft`
`signal_sampling_mode`
`dataset_metadata`

__init__(dataset_id: str, dataset_length: int, seed: int, impairment_level: int, output_representation: Literal['iq', 'spectrogram'], output_spectrogram_fft: int | None, signal_sampling_mode: Literal['per_signal', 'per_family'], dataset_metadata: dict[str, Any]) → None¶

__repr__()¶: Return repr(self).