#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2010-2021 Pyresample developers
#
# This program is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the Free
# Software Foundation, either version 3 of the License, or (at your option) any
# later version.
#
# This program 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 Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License along
# with this program.  If not, see <http://www.gnu.org/licenses/>.
"""Multiprocessing versions of KDTree and Proj classes."""

from __future__ import absolute_import

import ctypes
import multiprocessing as mp

import numpy as np
import pyproj
from pyproj import CRS
from pyproj.enums import TransformDirection

try:
    import numexpr as ne
except ImportError:
    ne = None

from ._multi_proc import Scheduler, shmem_as_ndarray
from .utils.proj4 import get_geodetic_crs_with_no_datum_shift

# Earth radius
R = 6370997.0


class cKDTree_MP(object):
    """Multiprocessing cKDTree subclass, shared memory."""

    def __init__(self, data, leafsize=10, nprocs=2, chunk=None,
                 schedule='guided'):
        """Prepare shared memory for KDTree operations.

        Same as cKDTree.__init__ except that an internal copy of data to shared memory is made.

        Extra keyword arguments:
        chunk : Minimum chunk size for the load balancer.
        schedule: Strategy for balancing work load
        ('static', 'dynamic' or 'guided').
        """
        self.n, self.m = data.shape
        # Allocate shared memory for data
        self.shmem_data = mp.RawArray(ctypes.c_double, self.n * self.m)

        # View shared memory as ndarray, and copy over the data.
        # The RawArray objects have information about the dtype and
        # buffer size.
        _data = shmem_as_ndarray(self.shmem_data).reshape((self.n, self.m))
        _data[:, :] = data

        # Initialize parent, we must do this last because
        # cKDTree stores a reference to the data array. We pass in
        # the copy in shared memory rather than the origial data.
        self.leafsize = leafsize
        self._nprocs = nprocs
        self._chunk = chunk
        self._schedule = schedule

    def query(self, x, k=1, eps=0, p=2, distance_upper_bound=np.inf):
        """Query for points at index 'x' parallelized with multiple processes and shared memory."""
        # allocate shared memory for x and result
        nx = x.shape[0]
        shmem_x = mp.RawArray(ctypes.c_double, nx * self.m)
        shmem_d = mp.RawArray(ctypes.c_double, nx * k)
        shmem_i = mp.RawArray(ctypes.c_int, nx * k)

        # view shared memory as ndarrays
        _x = shmem_as_ndarray(shmem_x).reshape((nx, self.m))
        if k == 1:
            _d = shmem_as_ndarray(shmem_d)
            _i = shmem_as_ndarray(shmem_i)
        else:
            _d = shmem_as_ndarray(shmem_d).reshape((nx, k))
            _i = shmem_as_ndarray(shmem_i).reshape((nx, k))

        # copy x to shared memory
        _x[:] = x

        # set up a scheduler to load balance the query
        scheduler = Scheduler(nx, self._nprocs, chunk=self._chunk,
                              schedule=self._schedule)

        # query with multiple processes
        query_args = [scheduler, self.shmem_data, self.n, self.m,
                      self.leafsize, shmem_x, nx, shmem_d, shmem_i,
                      k, eps, p, distance_upper_bound]

        _run_jobs(_parallel_query, query_args, self._nprocs)
        # return results (private memory)
        return _d.copy(), _i.copy()


class Proj_MP:
    """Multi-processing version of the pyproj Proj class."""

    def __init__(self, *args, **kwargs):
        self._args = args
        self._kwargs = kwargs

    def __call__(self, data1, data2, inverse=False, radians=False,
                 errcheck=False, nprocs=2, chunk=None, schedule='guided'):
        """Transform coordinates to coordinates in the current coordinate system."""
        grid_shape = data1.shape
        n = data1.size

        # Create shared memory
        shmem_data1 = mp.RawArray(ctypes.c_double, n)
        shmem_data2 = mp.RawArray(ctypes.c_double, n)
        shmem_res1 = mp.RawArray(ctypes.c_double, n)
        shmem_res2 = mp.RawArray(ctypes.c_double, n)

        # view shared memory as ndarrays
        _data1 = shmem_as_ndarray(shmem_data1)
        _data2 = shmem_as_ndarray(shmem_data2)
        _res1 = shmem_as_ndarray(shmem_res1)
        _res2 = shmem_as_ndarray(shmem_res2)

        # copy input data to shared memory
        _data1[:] = data1.ravel()
        _data2[:] = data2.ravel()

        # set up a scheduler to load balance the query
        scheduler = Scheduler(n, nprocs, chunk=chunk, schedule=schedule)

        # Projection with multiple processes
        proj_call_args = [scheduler, shmem_data1, shmem_data2, shmem_res1,
                          shmem_res2, self._args, self._kwargs, inverse,
                          radians, errcheck]

        _run_jobs(_parallel_proj, proj_call_args, nprocs)
        return _res1.copy().reshape(grid_shape), _res2.copy().reshape(grid_shape)


class Cartesian(object):
    """Cartesian coordinates."""

    def __init__(self, *args, **kwargs):
        pass

    def transform_lonlats(self, lons, lats):
        """Transform longitudes and latitues to cartesian coordinates."""
        if np.issubdtype(lons.dtype, np.integer):
            lons = lons.astype(np.float64)
        coords = np.zeros((lons.size, 3), dtype=lons.dtype)
        if ne:
            deg2rad = np.pi / 180  # noqa: F841
            coords[:, 0] = ne.evaluate("R*cos(lats*deg2rad)*cos(lons*deg2rad)")
            coords[:, 1] = ne.evaluate("R*cos(lats*deg2rad)*sin(lons*deg2rad)")
            coords[:, 2] = ne.evaluate("R*sin(lats*deg2rad)")
        else:
            coords[:, 0] = R * np.cos(np.deg2rad(lats)) * np.cos(np.deg2rad(lons))
            coords[:, 1] = R * np.cos(np.deg2rad(lats)) * np.sin(np.deg2rad(lons))
            coords[:, 2] = R * np.sin(np.deg2rad(lats))
        return coords


Cartesian_MP = Cartesian


def _run_jobs(target, args, nprocs):
    """Run process pool."""
    # return status in shared memory
    # access to these values are serialized automatically
    ierr = mp.Value(ctypes.c_int, 0)
    warn_msg = mp.Array(ctypes.c_char, 1024)

    args.extend((ierr, warn_msg))

    pool = [mp.Process(target=target, args=args) for n in range(nprocs)]
    for p in pool:
        p.start()
    for p in pool:
        p.join()
    if ierr.value != 0:
        raise RuntimeError('%d errors in worker processes. Last one reported:\n%s' %
                           (ierr.value, warn_msg.value._object_hook()))

# This is executed in an external process:


def _parallel_query(scheduler,  # scheduler for load balancing
                    # data needed to reconstruct the kd-tree
                    data, ndata, ndim, leafsize,
                    x, nx, d, i,  # query data and results
                    k, eps, p, dub,  # auxillary query parameters
                    ierr, warn_msg):  # return values (0 on success)

    try:
        # View shared memory as ndarrays.
        _data = shmem_as_ndarray(data).reshape((ndata, ndim))
        _x = shmem_as_ndarray(x).reshape((nx, ndim))
        if k == 1:
            _d = shmem_as_ndarray(d)
            _i = shmem_as_ndarray(i)
        else:
            _d = shmem_as_ndarray(d).reshape((nx, k))
            _i = shmem_as_ndarray(i).reshape((nx, k))

        # Reconstruct the kd-tree from the data.
        import scipy.spatial as sp
        kdtree = sp.cKDTree(_data, leafsize=leafsize)

        # Query for nearest neighbours, using slice ranges,
        # from the load balancer.
        for s in scheduler:
            if k == 1:
                _d[s], _i[s] = kdtree.query(_x[s, :], k=1, eps=eps, p=p,
                                            distance_upper_bound=dub)
            else:
                _d[s, :], _i[s, :] = kdtree.query(_x[s, :], k=k, eps=eps, p=p,
                                                  distance_upper_bound=dub)
    # An error occured, increment the return value ierr.
    # Access to ierr is serialized by multiprocessing.
    except Exception as e:
        ierr.value += 1
        warn_msg.value = str(e).encode()


def _parallel_proj(scheduler, data1, data2, res1, res2, proj_args, proj_kwargs,
                   inverse, radians, errcheck, ierr, warn_msg):
    try:
        # View shared memory as ndarrays.
        _data1 = shmem_as_ndarray(data1)
        _data2 = shmem_as_ndarray(data2)
        _res1 = shmem_as_ndarray(res1)
        _res2 = shmem_as_ndarray(res2)

        # Initialise pyproj
        proj_def = proj_args[0] if proj_args else proj_kwargs
        crs = CRS.from_user_input(proj_def)
        gcrs = get_geodetic_crs_with_no_datum_shift(crs)
        transformer = pyproj.Transformer.from_crs(gcrs, crs, always_xy=True)
        trans_kwargs = {"radians": radians, "errcheck": errcheck,
                        "direction": TransformDirection.INVERSE if inverse else TransformDirection.FORWARD}

        # Reproject data segment
        for s in scheduler:
            _res1[s], _res2[s] = transformer.transform(_data1[s], _data2[s], **trans_kwargs)

    # An error occured, increment the return value ierr.
    # Access to ierr is serialized by multiprocessing.
    except Exception as e:
        ierr.value += 1
        warn_msg.value = str(e).encode()


def _parallel_transform(scheduler, lons, lats, n, coords, ierr, warn_msg):
    try:
        # View shared memory as ndarrays.
        _lons = shmem_as_ndarray(lons)
        _lats = shmem_as_ndarray(lats)
        _coords = shmem_as_ndarray(coords).reshape((n, 3))

        # Transform to cartesian coordinates
        for s in scheduler:
            _coords[s, 0] = R * \
                np.cos(np.radians(_lats[s])) * np.cos(np.radians(_lons[s]))
            _coords[s, 1] = R * \
                np.cos(np.radians(_lats[s])) * np.sin(np.radians(_lons[s]))
            _coords[s, 2] = R * np.sin(np.radians(_lats[s]))
    # An error occured, increment the return value ierr.
    # Access to ierr is serialized by multiprocessing.
    except Exception as e:
        ierr.value += 1
        warn_msg.value = str(e).encode()