#!/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/>.
"""Resample image from one projection to another using nearest neighbour method."""

from __future__ import absolute_import

import numpy as np

from pyresample import _spatial_mp, geometry


def get_image_from_linesample(row_indices, col_indices, source_image,
                              fill_value=0):
    """Sample from image based on index arrays.

    Parameters
    ----------
    row_indices : numpy array
        Row indices. Dimensions must match col_indices
    col_indices : numpy array
        Col indices. Dimensions must match row_indices
    source_image : numpy array
        Source image
    fill_value : int or None, optional
            Set undetermined pixels to this value.
            If fill_value is None a masked array is returned
            with undetermined pixels masked

    Returns
    -------
    image_data : numpy array
        Resampled image
    """
    # mask out non valid row and col indices
    row_mask = (row_indices >= 0) * (row_indices < source_image.shape[0])
    col_mask = (col_indices >= 0) * (col_indices < source_image.shape[1])
    valid_rows = row_indices * row_mask
    valid_cols = col_indices * col_mask

    # free memory
    del row_indices
    del col_indices

    # get valid part of image
    target_image = source_image[valid_rows, valid_cols]

    # free memory
    del valid_rows
    del valid_cols

    # create mask for valid data points
    valid_data = row_mask * col_mask
    if valid_data.ndim != target_image.ndim:
        for _ in range(target_image.ndim - valid_data.ndim):
            valid_data = np.expand_dims(valid_data, axis=valid_data.ndim)

    # free memory
    del row_mask
    del col_mask

    # fill the non valid part of the image
    if fill_value is not None:
        target_filled = (target_image * valid_data +
                         (1 - valid_data) * fill_value)
    else:
        if np.ma.is_masked(target_image):
            mask = ((1 - valid_data) | target_image.mask)
        else:
            mask = (1 - valid_data)
        target_filled = np.ma.array(target_image, mask=mask)

    return target_filled.astype(target_image.dtype)


def get_linesample(lons, lats, source_area_def, nprocs=1):
    """Return index row and col arrays for resampling.

    Parameters
    ----------
    lons : numpy array
        Lons. Dimensions must match lats
    lats : numpy array
        Lats. Dimensions must match lons
    source_area_def : object
        Source definition as AreaDefinition object
    nprocs : int, optional
        Number of processor cores to be used

    Returns
    -------
    (row_indices, col_indices) : tuple of numpy arrays
        Arrays for resampling area by array indexing
    """
    # Proj.4 definition of source area projection
    if nprocs > 1:
        source_proj = _spatial_mp.Proj_MP(**source_area_def.proj_dict)
    else:
        source_proj = _spatial_mp.Proj(**source_area_def.proj_dict)

    # get cartesian projection values from longitude and latitude
    source_x, source_y = source_proj(lons, lats, nprocs=nprocs)

    # Find corresponding pixels (element by element conversion of ndarrays)
    source_pixel_x = (source_area_def.pixel_offset_x +
                      source_x / source_area_def.pixel_size_x).astype(np.int32)

    source_pixel_y = (source_area_def.pixel_offset_y -
                      source_y / source_area_def.pixel_size_y).astype(np.int32)

    return source_pixel_y, source_pixel_x


def get_image_from_lonlats(lons, lats, source_area_def, source_image_data,
                           fill_value=0, nprocs=1):
    """Sample from image based on lon lat arrays using nearest neighbour method in cartesian projection coordinates.

    Parameters
    ----------
    lons : numpy array
        Lons. Dimensions must match lats
    lats : numpy array
        Lats. Dimensions must match lons
    source_area_def : object
        Source definition as AreaDefinition object
    source_image_data : numpy array
        Source image data
    fill_value : int or None, optional
            Set undetermined pixels to this value.
            If fill_value is None a masked array is returned
            with undetermined pixels masked
    nprocs : int, optional
        Number of processor cores to be used

    Returns
    -------
    image_data : numpy array
        Resampled image data
    """
    source_pixel_y, source_pixel_x = get_linesample(lons, lats,
                                                    source_area_def,
                                                    nprocs=nprocs)

    # Return target image
    return get_image_from_linesample(source_pixel_y, source_pixel_x,
                                     source_image_data, fill_value)


def get_resampled_image(target_area_def, source_area_def, source_image_data,
                        fill_value=0, nprocs=1, segments=None):
    """Resample image using nearest neighbour method in cartesian projection coordinate systems.

    Parameters
    ----------
    target_area_def : object
        Target definition as AreaDefinition object
    source_area_def : object
        Source definition as AreaDefinition object
    source_image_data : numpy array
        Source image data
    fill_value : {int, None} optional
        Set undetermined pixels to this value.
        If fill_value is None a masked array is returned
        with undetermined pixels masked
    nprocs : int, optional
        Number of processor cores to be used
    segments : {int, None} optional
        Number of segments to use when resampling.
        If set to None an estimate will be calculated.

    Returns
    -------
    image_data : numpy array
        Resampled image data
    """
    if not isinstance(target_area_def, geometry.AreaDefinition):
        raise TypeError('target_area_def must be of type AreaDefinition')
    if not isinstance(source_area_def, geometry.AreaDefinition):
        raise TypeError('source_area_def must be of type AreaDefinition')
    if not isinstance(source_image_data, (np.ndarray,
                                          np.ma.core.MaskedArray)):
        raise TypeError('source_image must be of type ndarray'
                        ' or a masked array.')

    # Calculate number of segments if needed
    if segments is None:
        rows = target_area_def.height
        cut_off = 500
        if rows > cut_off:
            segments = int(rows / cut_off)
        else:
            segments = 1

    if segments > 1:
        # Iterate through segments
        for i, target_slice in enumerate(geometry._get_slice(segments,
                                                             target_area_def.shape)):

            # Select data from segment with slice
            lons, lats = target_area_def.get_lonlats(
                nprocs=nprocs, data_slice=target_slice)

            # Calculate partial result
            next_result = get_image_from_lonlats(lons, lats, source_area_def,
                                                 source_image_data,
                                                 fill_value, nprocs)

            # Build result iteratively
            if i == 0:
                # First iteration
                result = next_result
            else:
                if isinstance(next_result, np.ma.core.MaskedArray):
                    stack = np.ma.row_stack
                else:
                    stack = np.row_stack
                result = stack((result, next_result))

        return result
    else:
        # Get lon lat arrays of target area
        lons, lats = target_area_def.get_lonlats(nprocs)
        # Get target image
        return get_image_from_lonlats(lons, lats, source_area_def,
                                      source_image_data, fill_value, nprocs)
