liberata_metrics.utils package

Submodules

• liberata_metrics.utils.data_loading module
  • read_yaml_config()
  • save_sparse_npz()
• liberata_metrics.utils.data_wrangling module
  • coo_to_binned_array()
  • deserialize_upload_dates()
  • make_date_grid()
  • matrix_to_plot_array()
  • serialize_upload_dates()
• liberata_metrics.utils.load_supabase_data module
• liberata_metrics.utils.utils module
  • random_date()
  • sparse_divide()

Module contents

liberata_metrics.utils.coo_to_binned_array(coo: coo_matrix, max_side: int, agg_fn: Callable = <function sum>) → ndarray

utility function to convert COO to binned array

liberata_metrics.utils.deserialize_upload_dates(upload_dates_json: Dict[str, str]) → Dict[str, date]

convert ISO strings YYYY-MM-DD to date objects

liberata_metrics.utils.make_date_grid(start_date: date, end_date: date, time_step: timedelta) → List[date]

build a grid of dates from start_date to end_date with given time_step increments for time series data extraction

liberata_metrics.utils.matrix_to_plot_array(mat: spmatrix, max_side: int = 500, agg_fn: Callable = <function sum>) → ndarray

convert any COO matrix into appropriate array form for plotting (note: this is all ChatGPT)

liberata_metrics.utils.random_date(rng: RandomState, start: date, end: date) → date

draw a random date in given range

liberata_metrics.utils.read_yaml_config(config_path: str)

reads YAML config file and returns dictionary

liberata_metrics.utils.save_sparse_npz(path: str | Path, matrix: spmatrix, log: Logger | None = None) → None

save scipy.sparse matrix to .npz

liberata_metrics.utils.serialize_upload_dates(upload_dates: Dict[str, date]) → Dict[str, str]

convert date objects to ISO strings YYYY-MM-DD