euromod_linking.methods.lma_labour_alignment
============================================

.. py:module:: euromod_linking.methods.lma_labour_alignment

.. autoapi-nested-parse::

   lma_labour_alignment — align EUROMOD microdata to external labour-market
   targets and drive the LMA add-on.

   Works over any population cells the shock table defines, on full-population
   input, deterministically. Methodology is fixed here; scenarios can only set the
   declared params (period, tolerance_pct).



Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/euromod_linking/methods/lma_labour_alignment/hierarchy/index
   /autoapi/euromod_linking/methods/lma_labour_alignment/imputation/index
   /autoapi/euromod_linking/methods/lma_labour_alignment/scoring/index
   /autoapi/euromod_linking/methods/lma_labour_alignment/states/index
   /autoapi/euromod_linking/methods/lma_labour_alignment/targets/index
   /autoapi/euromod_linking/methods/lma_labour_alignment/weights/index


Attributes
----------

.. autoapisummary::

   euromod_linking.methods.lma_labour_alignment.DEFAULT_FULLTIME_HOURS_PER_WEEK
   euromod_linking.methods.lma_labour_alignment.FULLTIME_HOURS_BOUNDS
   euromod_linking.methods.lma_labour_alignment.INJECTED
   euromod_linking.methods.lma_labour_alignment.MONTHS_PER_YEAR
   euromod_linking.methods.lma_labour_alignment.WEEKS_PER_MONTH


Classes
-------

.. autoapisummary::

   euromod_linking.methods.lma_labour_alignment.LmaLabourAlignment


Functions
---------

.. autoapisummary::

   euromod_linking.methods.lma_labour_alignment.fulltime_hours


Package Contents
----------------

.. py:class:: LmaLabourAlignment

   See module docstring. Instances are stateless between calls.


   .. py:method:: apply(data: pandas.DataFrame, shocks: pandas.DataFrame, params: dict, ctx: euromod_linking.methods.base.MethodContext) -> euromod_linking.methods.base.MethodResult


   .. py:method:: baseline_transform(data: pandas.DataFrame, ctx: euromod_linking.methods.base.MethodContext) -> pandas.DataFrame

      Neutral LMA columns: identical for every shock table/period, so the
      baseline simulation is shared via the cache.

      The baseline runs WITH the LMA add-on active but lma=0 for everyone
      (no transitions), rather than without the add-on: both runs then go
      through an identical policy configuration, so the baseline/
      counterfactual delta isolates the labour-market transitions and cannot
      pick up incidental differences from toggling the add-on itself.



   .. py:method:: check_dataset(columns, shocks: pandas.DataFrame) -> list[str]


   .. py:method:: preview(data: pandas.DataFrame, shocks: pandas.DataFrame, params: dict, ctx: euromod_linking.methods.base.MethodContext) -> dict

      What apply() would target, without fitting or aligning anything.

      Cheap enough to run at validation time, which is where a mis-sized shock
      has to be caught: after the run there is nothing left to do but discard
      two simulations.



.. py:function:: fulltime_hours(country_code: str, system_name: str) -> tuple[float | None, str]

   The country's standard full-time week, from the model's own $lhw constant.

   Full-time differs by country (35 in FR, 38 in DK, 42 in AT), so the hardcoded
   40 above would misstate entrants' earnings. Returns (hours, note); hours is
   None when $lhw is absent (IE, LV, NL) or implausible, and the default applies.


.. py:data:: DEFAULT_FULLTIME_HOURS_PER_WEEK
   :value: 40.0


.. py:data:: FULLTIME_HOURS_BOUNDS
   :value: (10.0, 60.0)


.. py:data:: INJECTED
   :value: ('lma', 'yem_a', 'yemmy_a', 'lhw_a')


.. py:data:: MONTHS_PER_YEAR
   :value: 12


.. py:data:: WEEKS_PER_MONTH
   :value: 4.3333