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# coding: utf-8
# Copyright 2020-2022 IFREMER (Brest, FRANCE), all rights reserved.
# contact -- mailto:nicolas.raillard@ifremer.fr
# This file is part of Resourcecode.
# Resourcecode is free software: you can redistribute it and/or modify it under
# the terms of the GNU General Public License as published by the Free
# Software Foundation, either version 3.0 of the License, or any later version.
# Resourcecode is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# You should have received a copy of the GNU General Public License along
# with Resourcecode. If not, see <https://www.gnu.org/licenses/>.
from typing import Tuple, Any
from functools import partial
from pathlib import Path
import datetime
import pandas as pd
from resourcecode.utils import haversine
DATA_DIR = Path(__file__).parent
get_coastline = partial(pd.read_feather, DATA_DIR / "coastline.feather")
get_grid_field = partial(pd.read_feather, DATA_DIR / "grid_FIELD.feather")
get_grid_spec = partial(pd.read_feather, DATA_DIR / "grid_SPEC.feather")
get_islands = partial(pd.read_feather, DATA_DIR / "islands.feather")
get_triangles = partial(pd.read_feather, DATA_DIR / "triangles.feather")
get_variables = partial(pd.read_feather, DATA_DIR / "variables.feather")
# those parameters are from the feather.read_feather function
COMMON_PARAMETERS = """\
columns : sequence, optional
    Only read a specific set of columns. If not provided, all columns are
use_threads: bool, default True
    Whether to parallelize reading using multiple threads.
memory_map : boolean, default True
    Use memory mapping when opening file on disk
df : pandas.DataFrame
get_coastline.__doc__ = f"""\
Return a pandas dataframe describing the costline nodes.
The default returned columns are: longitude, latitude, depth.
{COMMON_PARAMETERS}
get_grid_field.__doc__ = f"""\
Return a pandas dataframe describing the grid field.
The default returned columns are: node, longitude, latitude, depth, d50
{COMMON_PARAMETERS}
get_grid_spec.__doc__ = f"""\
Return a pandas dataframe describing the grid specifications.
The default returned columns are: longitude, latitude, name, depth, d50
{COMMON_PARAMETERS}
get_islands.__doc__ = f"""\
Return a pandas dataframe describing the islands.
The default returned columns are: longitude, latitude, depth, ID
{COMMON_PARAMETERS}
get_triangles.__doc__ = f"""\
Return a pandas dataframe describing the mesh triangles.
The default returned columns are: Corner 1, Corner 2, Corner 3.
{COMMON_PARAMETERS}
get_variables.__doc__ = f"""\
Return a pandas dataframe describing the variables available in the Cassandra
The default returned columns are: name, longname, unit
{COMMON_PARAMETERS}
def _get_closest(
    dataset: pd.DataFrame, latitude: float, longitude: float, returned_attribute: str
) -> Tuple[Any, float]:
    distances = haversine(
        dataset.latitude,
        dataset.longitude,
        latitude,
        longitude,
    min_idx = distances.idxmin()
    return dataset.loc[min_idx, returned_attribute], distances[min_idx].round(2)
def get_closest_point(latitude: float, longitude: float) -> Tuple[int, float]:
    """Get the closest point in the mesh, from the given position
    Parameters
    ----------
    latitude
        the latitude in decimal degrees
    longitude
        the latitude in decimal degrees
    (pointId, distance)
        the corresponding point id, and its distance in meters, to the requested
        coordinates
    return _get_closest(get_grid_field(), latitude, longitude, "node")
def get_closest_station(latitude: float, longitude: float) -> Tuple[str, float]:
    """Get the closest station name from the given position
    Parameters
    ----------
    latitude
        the latitude in decimal degrees
    longitude
        the latitude in decimal degrees
    (station name, distance)
        the corresponding station name, and its distance in meters, to the
        requested coordinates
    return _get_closest(get_grid_spec(), latitude, longitude, "name")
def get_covered_period() -> dict:
    """Get the closest station name from the given position
    Parameters
    ----------
    (start, end)
       a datetime dict with the starting and ending dates covered by the hindcast
    return {
        "start": datetime.datetime(1990, 1, 1, 0, 0, 0),
        "end": datetime.datetime(2020, 12, 31, 23, 0, 0),
__all__ = [
    "get_coastline",
    "get_grid_field",
    "get_grid_spec",
    "get_islands",
    "get_triangles",
    "get_variables",
    "get_closest_point",
    "get_closest_station",
    "get_covered_period",
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