# Data Structure Conversion — DIMA 1.0.0 documentation

# Data Structure Conversion

utils.g5505_utils.augment_with_filenumber(df)

utils.g5505_utils.augment_with_filetype(df)

utils.g5505_utils.convert_attrdict_to_np_structured_array(attr_value: dict)

  Converts a dictionary of attributes into a numpy structured array for HDF5 compound type compatibility.

  Each dictionary key is mapped to a field in the structured array, with the data type (S) determined by the longest string representation of the values. If the dictionary is empty, the function returns ‘missing’.

  ## Parameters

  attr_valuedict

    Dictionary containing the attributes to be converted. Example: attr_value = {

    > ‘name’: ‘Temperature’, ‘unit’: ‘Celsius’, ‘value’: 23.5, ‘timestamp’: ‘2023-09-26 10:00’

    }

  ## Returns

  new_attr_valuendarray or str

    Numpy structured array with UTF-8 encoded fields. Returns ‘missing’ if the input dictionary is empty.

utils.g5505_utils.convert_dataframe_to_np_structured_array(df: DataFrame)

utils.g5505_utils.convert_string_to_bytes(input_list: list)

  Convert a list of strings into a numpy array with utf8-type entries.

  ## Parameters

  input_list (list) : list of string objects

  ## Returns

  input_array_bytes (ndarray): array of ut8-type entries.

utils.g5505_utils.copy_directory_with_contraints(input_dir_path, output_dir_path, select_dir_keywords=None, select_file_keywords=None, allowed_file_extensions=None, dry_run=False)

  Copies files from input_dir_path to output_dir_path based on specified constraints.

  ## Parameters

  > input_dir_path (str): Path to the input directory. output_dir_path (str): Path to the output directory. select_dir_keywords (list): optional, List of keywords for selecting directories. select_file_keywords (list): optional, List of keywords for selecting files. allowed_file_extensions (list): optional, List of allowed file extensions.

  ## Returns

  > path_to_files_dict (dict): dictionary mapping directory paths to lists of copied file names satisfying the constraints.

utils.g5505_utils.created_at(datetime_format='%Y-%m-%d %H:%M:%S')

utils.g5505_utils.group_by_df_column(df, column_name: str)

  df (pandas.DataFrame): column_name (str): column_name of df by which grouping operation will take place.

utils.g5505_utils.infer_units(column_name)

utils.g5505_utils.is_callable_list(x: list)

utils.g5505_utils.is_str_list(x: list)

utils.g5505_utils.is_structured_array(attr_val)

utils.g5505_utils.make_file_copy(source_file_path, output_folder_name: str = 'tmp_files')

utils.g5505_utils.progressBar(count_value, total, suffix='')

utils.g5505_utils.sanitize_dataframe(df: DataFrame) → DataFrame

utils.g5505_utils.setup_logging(log_dir, log_filename)

  Sets up logging to a specified directory and file.

  Parameters:

    log_dir (str): Directory to save the log file. log_filename (str): Name of the log file.

utils.g5505_utils.split_sample_col_into_sample_and_data_quality_cols(input_data: DataFrame)

utils.g5505_utils.to_serializable_dtype(value)

  Transform value’s dtype into YAML/JSON compatible dtype

  ## Parameters

  value_type_

    _description_

  ## Returns

  _type_

    _description_