supplychainpy.reporting package

Submodules

supplychainpy.reporting.config module

supplychainpy.reporting.extensions module

supplychainpy.reporting.forms module

supplychainpy.reporting.load module

supplychainpy.reporting.load.batch(analysis, n)

Yield n-sized batches from analysis. :param analysis: :param n:

supplychainpy.reporting.load.build_results_pickle(ses_forecast_results: dict)
supplychainpy.reporting.load.cleanup_pickled_files()
supplychainpy.reporting.load.currency_codes() → dict

Retrives HTML Entity (decimal) for currency symbol.

Returns:Currency Symbols.
Return type:dict
supplychainpy.reporting.load.load(file_path: str, location: str = None)

Loads analysis and forecast into local database for reporting suite.

Parameters:
  • file_path (str) – File path to source file containing data for analysis.
  • location (str) – Location of database to populate.
supplychainpy.reporting.load.load_currency(fx_codes: {'AED': ('United Arab Emirates Dirham', '&#92;&#117;&#48;&#54;&#50;&#102;&#46;'), 'ANG': ('Netherlands Antilles Guilder', '&#402'), 'GBP': ('United Kingdom Pound', '&#163;'), 'USD': ('United States Dollar', '&#36;'), 'EUR': ('Euro Member Countries', '&#8364;')}, ctx: <SQLAlchemy engine=None>)

Loads Currency Symbols

supplychainpy.reporting.load.load_profile_recommendations(analysed_order, forecast, transaction_log_id)
supplychainpy.reporting.load.load_recommendations(summary, forecast, analysed_order)
supplychainpy.reporting.load.parallelise_htc(batched_analysis: list, core_count: int)

Execute the Holts’ trend corrected smoothing forecast in parallel.

Parameters:
  • batched_analysis – Uncertain demand objects batched for appropriate number of cores available on the host machine
  • core_count – Number of cores available on the host machine, minus one.
Returns:

dict

supplychainpy.reporting.load.parallelise_ses(pickled_ses_batch_files: list, core_count: int) → dict

Execute the exponential smoothing forecast in parallel.

Parameters:
  • pickled_ses_batch_files – Uncertain demand objects batched for appropriate number of cores available on the host machine
  • core_count – Number of cores available on the host machine, minus one.
Returns:

dict

supplychainpy.reporting.load.pickle_ses_forecast(batched_analysis: list) → list
supplychainpy.reporting.load.read_pickle(batch_path: str)

Read pickled data from file

supplychainpy.reporting.load.remove_pickle(path: str)
supplychainpy.reporting.load.retrieve_results_pickle()
supplychainpy.reporting.load.write_pickle(**kwargs) → str

pickle data to file

supplychainpy.reporting.manage module

supplychainpy.reporting.migrate module

supplychainpy.reporting.views module

Module contents