wolfhece.hydrology.climate_data
===============================

.. py:module:: wolfhece.hydrology.climate_data






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

.. py:data:: DATADIR

.. py:function:: transform_latlon_to_lambert72_list(lat_list: list[float], lon_list: list[float]) -> list[tuple[float, float]]

   Transform lists of EPSG:4258 coordinates to Lambert 72 coordinates.

   Coordinates from IRM are in EPSG:4258, and we want to convert them to Lambert 72 (EPSG:31370).


.. py:function:: read_pixel_positions(data_dir: pathlib.Path = DATADIR) -> tuple[list[int], list[tuple[float, float]]]

   Read pixel positions from the metadata file.


.. py:function:: convert_pixels_to_squares(pixels: list[tuple[float, float]]) -> tuple[list[tuple[tuple[float, float], Ellipsis]], scipy.spatial.KDTree]

   From pixels coordinates, define squares around each pixel center.

   Corners are defined as the average of the pixel center and its neighbors.


.. py:function:: read_historical_year_month(year: int, month: int, data_dir: pathlib.Path = DATADIR) -> pandas.DataFrame

   Read a specific year and month from the climate data.

   Available variables are :
   - day
   - temp_max
   - temp_min
   - temp_avg
   - precip_quantity
   - humidity_relative
   - pressure
   - sun_duration
   - short_wave_from_sky
   - evapotrans_ref

   From IRM's Metadata description:
   - TEMP_MAX          °C              daily maximum temperature from 08:00LT on DATE_BEGIN to 08:00LT on DATE_END+1
   - TEMP_MIN          °C              daily minimum temperature from 08:00LT on DATE_BEGIN-1 to 08:00LT on DATE_END
   - TEMP_AVG          °C              average temperature (average of TEMP_MAX and TEMP_MIN)
   - PRECIP_QUANTITY           mm              precipitation quantity from 08:00LT on DATE_BEGIN to 08:00LT on DATE_END+1
   - HUMIDITY_RELATIVE percentage      average relative humidity
   - PRESSURE          hPa             sea level pressure
   - SUN_DURATION                              average daily sunshine duration (hours/day)
   - SHORT_WAVE_FROM_SKY                       average daily global solar radiation (kWh/m2/day)
   - EVAPOTRANS_REF            mm              reference evapotranspiration ET0

   :param year: Year to read
   :type year: int
   :param month: Month to read
   :type month: int
   :param variable: Variable to read (e.g., 'temperature', 'precipitation')
   :type variable: str
   :param data_dir: Directory where the data is stored
   :type data_dir: Path
   :return: DataFrame containing the data for the specified year and month


.. py:function:: scan_climate_files(data_dir: pathlib.Path = DATADIR) -> list[pathlib.Path]

   Scan the directory for climate data files.

   :param data_dir: Directory where the data is stored
   :type data_dir: Path
   :return: List of paths to climate data files


.. py:function:: find_first_available_year_month(data_dir: pathlib.Path = DATADIR) -> int

   Find the first available year in the climate data files.

   :param data_dir: Directory where the data is stored
   :type data_dir: Path
   :return: First available year as an integer


.. py:function:: find_last_available_year_month(data_dir: pathlib.Path = DATADIR) -> int

   Find the last available year in the climate data files.

   :param data_dir: Directory where the data is stored
   :type data_dir: Path
   :return: Last available year as an integer


.. py:function:: read_between(data_dir: pathlib.Path = DATADIR, start_year: int = 1961, start_month: int = 1, end_year: int = 2025, end_month: int = 6) -> pandas.DataFrame

   Read climate data files into a single DataFrame.

   :param data_dir: Directory where the data is stored
   :type data_dir: Path
   :return: DataFrame containing all climate data


.. py:function:: read_all_data(data_dir: pathlib.Path = DATADIR) -> pandas.DataFrame

   Read all climate data files into a single DataFrame.

   :param data_dir: Directory where the data is stored
   :type data_dir: Path
   :return: DataFrame containing all climate data


.. py:data:: data
   :value: None