zensvi.metadata.MLYMetadata
===========================

.. py:class:: zensvi.metadata.MLYMetadata(path_input: Union[str, pathlib.Path], log_path: Union[str, pathlib.Path] = None)

   A class to compute metadata for the MLY dataset.

   :param path_input: path to the input CSV file (e.g.,
                      "mly_pids.csv"). The CSV file should contain the following
                      columns: "id", "lat", "lon", "captured_at", "compass_angle",
                      "creator_id", "sequence_id", "organization_id", "is_pano".
   :type path_input: Union[str, Path]


   .. py:method:: compute_metadata(unit: str = 'image', grid_resolution: int = 7, coverage_buffer: int = 50, indicator_list: str = 'all', path_output: Union[str, pathlib.Path] = None, max_distance: int = 50)

      Compute metadata for the dataset.

      :param unit: The unit of analysis. Defaults to "image".
      :type unit: str
      :param grid_resolution: The resolution of the H3 grid.
                              Defaults to 7.
      :type grid_resolution: int
      :param indicator_list: List of indicators to compute metadata
                             for. Use space- separated string of indicators or "all".
                             Options for image-level metadata: "year", "month",
                             "day", "hour", "day_of_week", "relative_angle", "h3_id",
                             "speed_kmh". Options for grid-level metadata:
                             "coverage", "count", "days_elapsed", "most_recent_date",
                             "oldest_date", "number_of_years", "number_of_months",
                             "number_of_days", "number_of_hours",
                             "number_of_days_of_week", "number_of_daytime",
                             "number_of_nighttime", "number_of_spring",
                             "number_of_summer", "number_of_autumn",
                             "number_of_winter", "average_compass_angle",
                             "average_relative_angle", "average_is_pano",
                             "number_of_users", "number_of_sequences",
                             "number_of_organizations", "average_speed_kmh". Defaults
                             to "all".
      :type indicator_list: str
      :param path_output: Path to save the output
                          metadata. Defaults to None.
      :type path_output: Union[str, Path]
      :param max_distance: The maximum distance to search for the
                           nearest street segment. Defaults to 50.
      :type max_distance: int

      :returns: A DataFrame containing the computed metadata.
      :rtype: pd.DataFrame