leaf_engine.adapt.adapt_actions
===============================

.. py:module:: leaf_engine.adapt.adapt_actions


Classes
-------

.. autoapisummary::

   leaf_engine.adapt.adapt_actions.Action


Functions
---------

.. autoapisummary::

   leaf_engine.adapt.adapt_actions.eval_point
   leaf_engine.adapt.adapt_actions.get_distances
   leaf_engine.adapt.adapt_actions.get_synthetic_rate_mean
   leaf_engine.adapt.adapt_actions.internal_substitute_candidates
   leaf_engine.adapt.adapt_actions.lane_balance_adjusted
   leaf_engine.adapt.adapt_actions.leaf_flex_actions
   leaf_engine.adapt.adapt_actions.pipeline


Module Contents
---------------

.. py:class:: Action(leaf_day_rate_max: dict[str, float], leaf_day_rate_min: dict[str, float], leaf_lsp_fee, leaf_shipper_fee, shipper_trigger_margin, min_shipments, lane_od_market, dat_market_balance_adj, patterns, lane_annualized_loads, lane_rpm)

   .. py:method:: add_market_balance(df, legs=None)


   .. py:method:: add_pattern_annualization_rpm(df: pandas.DataFrame, legs=None)


   .. py:method:: add_shorty_prices(df)


   .. py:method:: apply_network_pricing(df, legs, circuit, lspf=None, sf=None, stm=None)


   .. py:method:: get_leaf_day_rate_equip(equips: set[str])


   .. py:method:: network_day_rate(days: float, equips: set[str], scale_width: int = 4, accuracy: int = 5) -> float


   .. py:attribute:: dat_market_balance_adj


   .. py:attribute:: lane_annualized_loads


   .. py:attribute:: lane_od_market


   .. py:attribute:: lane_rpm


   .. py:attribute:: leaf_day_rate_max


   .. py:attribute:: leaf_day_rate_min


   .. py:attribute:: leaf_lsp_fee


   .. py:attribute:: leaf_shipper_fee


   .. py:attribute:: min_shipments


   .. py:attribute:: patterns


   .. py:attribute:: shipper_trigger_margin


.. py:function:: eval_point(x)

.. py:function:: get_distances(df: pandas.DataFrame) -> List

   Utility for measuring distances between clusters.

   Given a dataframe with four columns (x ID, x geometry, y ID, y geometry),
   returns a pd.Series with calculated distances between x and y.
   Memoizes calculated distances so calculations aren't repeated,
   even if x and y appear in different leg positions (l0, l1, etc.).
   Note: ID column can be any unique identifier (e.g., WKT, database ID).


.. py:function:: get_synthetic_rate_mean(_market_rates: pandas.DataFrame, _run_date: str = 'today', _day_window: int = 90, _ceiling: bool = True) -> pandas.DataFrame

   Creates a mean market rate from a window of synthetic market data.

   Synthetic market rate columns are expected to have the format est_lh_*_YYYY-MM-DD.

   :param _market_rates: The AdaptSyntheticRateContext "market_rates" dataframe.
   :param _run_date: Start date to work backwards from.
   :param _day_window: The number of days into the past from _run_date to include
                       in the mean calculation.
   :param _ceiling: If True, will select at least _day_window days in the mean calculation.
                    If False, may select less or more days than _day_window, whichever is closer.

   :returns:

             A dataframe containing the original "o_market" and "d_market"
                 columns as well as the mean fuel price in "est_lh_rate_ex_fuel".
   :rtype: pd.DataFrame


.. py:function:: internal_substitute_candidates(_lanes, _lane_class_annualized_loads, _internal_cm_priced)

.. py:function:: lane_balance_adjusted(_market_rates, _lanes)

.. py:function:: leaf_flex_actions(flex_patterns_, daily_patterns_, dow_patterns_, lanes_, class_ships_, dat_rates_, lane_balance_adj_, leaf_day_rate_, leaf_lsp_fee_, leaf_shipper_fee_, shipper_trigger_margin_, window_days_, flex_ow_discount_=0.1, flex_ow_min_=700, dat_markup_=0.2)

.. py:function:: pipeline(df: pandas.DataFrame, funcs: list) -> pandas.DataFrame

   Pipes dataframe df through a list of functions invoke as df.pipe(pipeline,
   funcs=[ ...

   ])