torchsig.utils.printing.dataset_metadata_str

torchsig.utils.printing.dataset_metadata_str(dataset_metadata, max_width: int = 100, first_col_width: int = 29, array_width_indent_offset: int = 2) str[source]

Custom string representation for the class.

This method returns a formatted string that provides a detailed summary of the object’s key attributes, including signal parameters, dataset configuration, and transform details. It uses textwrap.fill to format long attributes such as lists or arrays into a neatly wrapped format for easier readability.

The string includes information on the dataset’s configuration, signal characteristics, transformations, and other attributes in a human-readable way. The result is intended to provide a concise yet comprehensive overview of the object’s state, useful for debugging, logging, or displaying object details.

Parameters:
  • dataset_metadata (Any) – The dataset metadata object to generate a string for.

  • max_width (int, optional) – Maximum width of the output string. Defaults to 100.

  • first_col_width (int, optional) – Width of the first column in the output string. Defaults to 29.

  • array_width_indent_offset (int, optional) – Indentation offset for array-like attributes. Defaults to 2.

Returns:

A formatted string that represents the object’s attributes in a readable format.

Return type:

str

Example Output:

` MyClass ---------------------------------------------------------------------------------------------------- num_iq_samples_dataset            1000 fft_size                          512 sample_rate                       1000.0 num_signals_min                   1 num_signals_max                   5 num_signals_distribution          [0.2, 0.3, 0.5] snr_db_min                        5.0 snr_db_max                        30.0 signal_duration_min               0.001 signal_duration_max               0.01 signal_bandwidth_min              10 signal_bandwidth_max              100 signal_center_freq_min            -10 signal_center_freq_max            10 class_list                        [Class1, Class2, Class3] class_distribution                [0.3, 0.4, 0.3] seed                               42 `