windwhisper.health_impacts
==========================

.. py:module:: windwhisper.health_impacts


Attributes
----------

.. autoapisummary::

   windwhisper.health_impacts.geod


Classes
-------

.. autoapisummary::

   windwhisper.health_impacts.HumanHealth


Functions
---------

.. autoapisummary::

   windwhisper.health_impacts.load_human_health_parameters
   windwhisper.health_impacts.load_disease_data
   windwhisper.health_impacts.guess_country
   windwhisper.health_impacts.approximate_grid_cell_areas


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

.. py:function:: load_human_health_parameters()

   Load DALY parameter curves from the packaged JSON file.

   :returns: Mapping of health impact identifiers to parameter sets.
   :rtype: dict


.. py:function:: load_disease_data(country)

   Return disease burden statistics for the requested country.

   :param country: ISO alpha-2 code identifying the country of interest.
   :type country: str
   :returns: DataFrame limited to the requested country or the European
       baseline when no data is available.
   :rtype: pandas.DataFrame


.. py:function:: guess_country(bbox_geom)

   Infer the country overlapping the analysis bounding box.

   :param bbox_geom: Bounding box geometry in EPSG:4326 coordinates.
   :type bbox_geom: shapely.geometry.base.BaseGeometry
   :returns: Tuple containing the ISO alpha-2 code and the estimated
       population of the matching country.
   :rtype: tuple[str, float]


.. py:data:: geod

.. py:function:: approximate_grid_cell_areas(lat, lon)

   Approximate cell areas for a rectilinear latitude/longitude grid.

   :param lat: One-dimensional latitude coordinates.
   :type lat: numpy.ndarray
   :param lon: One-dimensional longitude coordinates.
   :type lon: numpy.ndarray
   :returns: Two-dimensional array containing the area of each grid cell in
       square metres.
   :rtype: numpy.ndarray


.. py:class:: HumanHealth(noiseanalysis, lifetime = 20)

   Evaluate human health impacts resulting from noise exposure.


   .. py:attribute:: lifetime
      :value: 20



   .. py:attribute:: load_factor


   .. py:attribute:: electricity_production


   .. py:attribute:: population


   .. py:attribute:: noiseanalysis


   .. py:attribute:: disease_data


   .. py:attribute:: human_health_parameters


   .. py:attribute:: population_rate


   .. py:attribute:: human_health_wo_turbines


   .. py:attribute:: human_health_per_kWh_wo_turbines


   .. py:attribute:: human_health


   .. py:attribute:: human_health_per_kWh


   .. py:method:: get_disease_totals(disease)

      Return YLD and YLL totals for the active country.

      :param disease: Disease identifier (e.g. ``"ischemic_heart_disease"``).
      :type disease: str
      :returns: Tuple containing the YLD and YLL values.
      :rtype: tuple[float, float]
      :raises ValueError: If no statistics are available for the disease.



   .. py:method:: calculate_highly_annoyed_dalys(lden, noise_type_ha)

      Calculate DALYs for the highly annoyed indicator.

      :param lden: Day-evening-night noise level raster.
      :type lden: xarray.DataArray
      :param noise_type_ha: Human health parameter set identifier.
      :type noise_type_ha: str
      :returns: DALY raster for the highly annoyed population.
      :rtype: xarray.DataArray



   .. py:method:: calculate_high_sleep_disorder_dalys(lnight, noise_type_hsd)

      Calculate DALYs for the high sleep disorder indicator.

      :param lnight: Night noise level raster.
      :type lnight: xarray.DataArray
      :param noise_type_hsd: Human health parameter set identifier.
      :type noise_type_hsd: str
      :returns: DALY raster for the high sleep disorder population.
      :rtype: xarray.DataArray



   .. py:method:: calculate_ihd_dalys(lden, noise_type)

      Calculate DALYs attributable to ischemic heart disease.

      :param lden: Day-evening-night noise level raster.
      :type lden: xarray.DataArray
      :param noise_type: Human health parameter set identifier.
      :type noise_type: str
      :returns: DALY raster for ischemic heart disease.
      :rtype: xarray.DataArray



   .. py:method:: calculate_diabetes_dalys(lden, noise_type)

      Calculate DALYs attributable to diabetes.

      :param lden: Day-evening-night noise level raster.
      :type lden: xarray.DataArray
      :param noise_type: Human health parameter set identifier.
      :type noise_type: str
      :returns: DALY raster for diabetes.
      :rtype: xarray.DataArray



   .. py:method:: calculate_stroke_dalys(lden, noise_type)

      Calculate DALYs attributable to stroke.

      :param lden: Day-evening-night noise level raster.
      :type lden: xarray.DataArray
      :param noise_type: Human health parameter set identifier.
      :type noise_type: str
      :returns: DALY raster for stroke.
      :rtype: xarray.DataArray



   .. py:method:: calculate_total_dalys(lden, lnight, noise_type_ha = 'road_without_alpinestudies', noise_type_hsd = 'combined', noise_type = 'road middle')

      Aggregate DALY layers across all health indicators.

      :param lden: Day-evening-night noise level raster.
      :type lden: xarray.DataArray
      :param lnight: Night noise level raster.
      :type lnight: xarray.DataArray
      :param noise_type_ha: Parameter set for the highly annoyed metric.
      :type noise_type_ha: str
      :param noise_type_hsd: Parameter set for the sleep disorder metric.
      :type noise_type_hsd: str
      :param noise_type: Parameter set for chronic disease metrics.
      :type noise_type: str
      :returns: Dataset containing the DALY layers for each impact.
      :rtype: xarray.Dataset



   .. py:method:: export_to_excel(filepath = 'human_health_results.xlsx')

      Export human health inputs and results to an Excel workbook.

      :param filepath: Destination path for the generated workbook.
      :type filepath: str