<gh_stars>1-10
# -*- coding: utf-8 -*-
"""Created on Fri Apr  2 15:08:50 2021.

@author: oebbe
"""
import logging

import numpy as np
import xarray as xr
from scipy import interpolate
from scipy.interpolate import griddata

from . import mgrid

logger = logging.getLogger(__name__)


def resample_dataarray2d_to_vertex_grid(da_in, gridprops=None,
                                        xyi=None, cid=None,
                                        method='nearest',
                                        **kwargs):
    """resample a 2d dataarray (xarray) from a structured grid to a new
    dataaraay of a vertex grid.

    Parameters
    ----------
    da_in : xarray.DataArray
        data array with dimensions (y, x). y and x are from the original
        grid
    gridprops : dictionary, optional
        dictionary with grid properties output from gridgen.
    xyi : numpy.ndarray, optional
        array with x and y coördinates of cell centers, shape(len(cid), 2). If
        xyi is None xyi is calculated from the gridproperties.
    cid : list or numpy.ndarray, optional
        list with cellids. If  cid is None cid is calculated from the
        gridproperties.
    method : str, optional
        type of interpolation used to resample. The default is 'nearest'.

    Returns
    -------
    da_out : xarray.DataArray
        data array with dimension (cid).
    """
    if (xyi is None) or (cid is None):
        xyi, cid = mgrid.get_xyi_cid(gridprops)

    # get x and y values of all cells in dataarray
    mg = np.meshgrid(da_in.x.data, da_in.y.data)
    points = np.vstack((mg[0].ravel(), mg[1].ravel())).T

    # regrid
    arr_out = griddata(points, da_in.data.flatten(), xyi, method=method,
                       **kwargs)

    # new dataset
    da_out = xr.DataArray(arr_out, dims=('cid'),
                          coords={'cid': cid})

    return da_out


def resample_dataarray3d_to_vertex_grid(da_in, gridprops=None,
                                        xyi=None, cid=None,
                                        method='nearest'):
    """resample a dataarray (xarray) from a structured grid to a new dataaraay
    of a vertex grid.

    Parameters
    ----------
    da_in : xarray.DataArray
        data array with dimensions (layer, y, x). y and x are from the original
        grid
    gridprops : dictionary, optional
        dictionary with grid properties output from gridgen.
    xyi : numpy.ndarray, optional
        array with x and y coördinates of cell centers, shape(len(cid), 2). If
        xyi is None xyi is calculated from the gridproperties.
    cid : list or numpy.ndarray, optional
        list with cellids. If  cid is None cid is calculated from the
        gridproperties.
    method : str, optional
        type of interpolation used to resample. The default is 'nearest'.

    Returns
    -------
    da_out : xarray.DataArray
        data array with dimensions (layer,cid).
    """
    if (xyi is None) or (cid is None):
        xyi, cid = mgrid.get_xyi_cid(gridprops=gridprops)

    # get x and y values of all cells in dataarray
    mg = np.meshgrid(da_in.x.data, da_in.y.data)
    points = np.vstack((mg[0].ravel(), mg[1].ravel())).T

    layers = da_in.layer.data
    arr_out = np.zeros((len(layers), len(xyi)))
    for i, lay in enumerate(layers):

        ds_lay = da_in.sel(layer=lay)

        # regrid
        arr_out[i] = griddata(
            points, ds_lay.data.flatten(), xyi, method=method)

    # new dataset
    da_out = xr.DataArray(arr_out, dims=('layer', 'cid'),
                          coords={'cid': cid,
                                  'layer': layers})

    return da_out


def resample_dataset_to_vertex_grid(ds_in, gridprops,
                                    method='nearest'):
    """resample a dataset (xarray) from an structured grid to a new dataset
    from a vertex grid.

    Parameters
    ----------
    ds_in : xarray.Dataset
        dataset with dimensions (layer, y, x). y and x are from the original
        structured grid
    gridprops : dictionary
        dictionary with grid properties output from gridgen.
    method : str, optional
        type of interpolation used to resample. The default is 'nearest'.

    Returns
    -------
    ds_out : xarray.Dataset
        dataset with dimensions (layer, cid), cid are cell id's from the new
        grid.
    """

    assert isinstance(ds_in, xr.core.dataset.Dataset)

    xyi, cid = mgrid.get_xyi_cid(gridprops)

    ds_out = xr.Dataset(coords={'cid': cid,
                                'layer': ds_in.layer.data})

    # add x and y coordinates
    ds_out['x'] = xr.DataArray(xyi[:, 0], dims=('cid'),
                               coords={'cid': cid})
    ds_out['y'] = xr.DataArray(xyi[:, 0], dims=('cid'),
                               coords={'cid': cid})

    # add other variables
    for data_var in ds_in.data_vars:
        if ds_in[data_var].dims == ('layer', 'y', 'x'):
            data_arr = resample_dataarray3d_to_vertex_grid(ds_in[data_var],
                                                           xyi=xyi, cid=cid,
                                                           method=method)
        elif ds_in[data_var].dims == ('y', 'x'):
            data_arr = resample_dataarray2d_to_vertex_grid(ds_in[data_var],
                                                           xyi=xyi, cid=cid,
                                                           method=method)

        elif ds_in[data_var].dims == ('layer'):
            data_arr = ds_in[data_var]

        else:
            logger.warning(
                f'did not resample data array {data_var} because conversion with dimensions {ds_in[data_var].dims} is not (yet) supported')
            continue

        ds_out[data_var] = data_arr

    return ds_out


def resample_dataarray2d_to_structured_grid(da_in, extent=None,
                                            delr=None, delc=None,
                                            xmid=None, ymid=None,
                                            kind='linear', nan_factor=0.01,
                                            **kwargs):
    """resample a dataarray (xarray) from a structured grid to a new dataaraay
    from a different structured grid.

    Parameters
    ----------
    da_in : xarray.DataArray
        data array with dimensions (y, x). y and x are from the original
        grid
    extent : list, tuple or np.array, optional
        extent (xmin, xmax, ymin, ymax) of the desired grid, if not defined
        xmid and ymid are used
    delr : int or float, optional
        cell size along rows of the desired grid, if not defined xmid and
        ymid are used
    delc : int or float, optional
        cell size along columns of the desired grid, if not defined xmid and
        ymid are used
    xmid : np.array, optional
        x coördinates of the cell centers of the desired grid shape(ncol), if
        not defined xmid and ymid are calculated from the extent, delr and delc.
    ymid : np.array, optional
        y coördinates of the cell centers of the desired grid shape(nrow), if
        not defined xmid and ymid are calculated from the extent, delr and delc.
    kind : str, optional
        type of interpolation used to resample. The default is 'linear'.
    nan_factor : float, optional
        the nan values in the original raster are filled with zeros before
        interpolation because the interp2d function cannot handle nan values
        very well. Therefore an extra interpolation is done to determine how
        much these nan values have influenced the new raster values. If the
        the interpolated value is influenced more than this factor by a nan
        value. The value in the interpolated raster is set to nan.
        See also: https://stackoverflow.com/questions/51474792/2d-interpolation-with-nan-values-in-python

    Returns
    -------
    ds_out : xarray.DataArray
        data array with dimensions (y, x). y and x are from the new grid.
    """

    assert isinstance(
        da_in, xr.core.dataarray.DataArray), f'expected type xr.core.dataarray.DataArray got {type(da_in)} instead'

    if xmid is None:
        xmid, ymid = mgrid.get_xy_mid_structured(extent, delr, delc)

    # check if ymid is in descending order
    assert np.array_equal(ymid, np.sort(
        ymid)[::-1]), 'ymid should be in descending order'

    # check for nan values
    if (da_in.isnull().sum() > 0) and (kind == "linear"):
        arr_out = resample_2d_struc_da_nan_linear(da_in, xmid, ymid,
                                                  nan_factor, **kwargs)
    # faster for linear
    elif kind in ["linear", 'cubic']:
        # no need to fill nan values
        f = interpolate.interp2d(da_in.x.data, da_in.y.data,
                                 da_in.data, kind='linear', **kwargs)
        # for some reason interp2d flips the y-values
        arr_out = f(xmid, ymid)[::-1]
    elif kind == 'nearest':
        xydata = np.vstack([v.ravel() for v in
                            np.meshgrid(da_in.x.data, da_in.y.data)]).T
        xyi = np.vstack([v.ravel() for v in np.meshgrid(xmid, ymid)]).T
        fi = griddata(xydata, da_in.data.ravel(), xyi, method=kind,
                      **kwargs)
        arr_out = fi.reshape(ymid.shape[0], xmid.shape[0])
    else:
        raise ValueError(f'unexpected value for "kind": {kind}')

    # new dataset
    da_out = xr.DataArray(arr_out, dims=('y', 'x'),
                          coords={'x': xmid,
                                  'y': ymid})

    return da_out


def resample_dataarray3d_to_structured_grid(da_in, extent=None,
                                            delr=None, delc=None,
                                            xmid=None, ymid=None,
                                            kind='linear', nan_factor=0.01,
                                            **kwargs):
    """resample a dataarray (xarray) from a structured grid to a new dataaraay
    from a different structured grid.

    Parameters
    ----------
    da_in : xarray.DataArray
        data array with dimensions (layer, y, x). y and x are from the original
        grid
    extent : list, tuple or np.array, optional
        extent (xmin, xmax, ymin, ymax) of the desired grid, if not defined
        xmid and ymid are used
    delr : int or float, optional
        cell size along rows of the desired grid, if not defined xmid and
        ymid are used
    delc : int or float, optional
        cell size along columns of the desired grid, if not defined xmid and
        ymid are used
    xmid : np.array, optional
        x coördinates of the cell centers of the desired grid shape(ncol), if
        not defined xmid and ymid are calculated from the extent, delr and delc.
    ymid : np.array, optional
        y coördinates of the cell centers of the desired grid shape(nrow), if
        not defined xmid and ymid are calculated from the extent, delr and delc.
    kind : str, optional
        type of interpolation used to resample. The default is 'linear'.
    nan_factor : float, optional
        the nan values in the original raster are filled with zeros before
        interpolation because the interp2d function cannot handle nan values
        very well. Therefore an extra interpolation is done to determine how
        much these nan values have influenced the new raster values. If the
        the interpolated value is influenced more than this factor by a nan
        value. The value in the interpolated raster is set to nan.
        See also: https://stackoverflow.com/questions/51474792/2d-interpolation-with-nan-values-in-python

    Returns
    -------
    ds_out : xarray.DataArray
        data array with dimensions (layer, y, x). y and x are from the new
        grid.
    """

    assert isinstance(
        da_in, xr.core.dataarray.DataArray), f'expected type xr.core.dataarray.DataArray got {type(da_in)} instead'

    # check if ymid is in descending order
    assert np.array_equal(ymid, np.sort(
        ymid)[::-1]), 'ymid should be in descending order'

    if xmid is None:
        xmid, ymid = mgrid.get_xy_mid_structured(extent, delr, delc)

    layers = da_in.layer.data
    arr_out = np.zeros((len(layers), len(ymid), len(xmid)))
    for i, lay in enumerate(layers):

        ds_lay = da_in.sel(layer=lay)
        # check for nan values
        if (ds_lay.isnull().sum() > 0) and (kind == "linear"):
            arr_out[i] = resample_2d_struc_da_nan_linear(ds_lay, xmid, ymid,
                                                         nan_factor, **kwargs)
        # faster for linear
        elif kind in ['linear', 'cubic']:
            # no need to fill nan values
            f = interpolate.interp2d(ds_lay.x.data, ds_lay.y.data,
                                     ds_lay.data, kind='linear', **kwargs)
            # for some reason interp2d flips the y-values
            arr_out[i] = f(xmid, ymid)[::-1]
        elif kind == 'nearest':
            xydata = np.vstack([v.ravel() for v in
                                np.meshgrid(ds_lay.x.data, ds_lay.y.data)]).T
            xyi = np.vstack([v.ravel() for v in np.meshgrid(xmid, ymid)]).T
            fi = griddata(xydata, ds_lay.data.ravel(), xyi, method=kind,
                          **kwargs)
            arr_out[i] = fi.reshape(ymid.shape[0], xmid.shape[0])
        else:
            raise ValueError(f'unexpected value for "kind": {kind}')

    # new dataset
    da_out = xr.DataArray(arr_out, dims=('layer', 'y', 'x'),
                          coords={'x': xmid,
                                  'y': ymid,
                                  'layer': layers})

    return da_out


def resample_2d_struc_da_nan_linear(da_in, new_x, new_y,
                                    nan_factor=0.01, **kwargs):
    """resample a structured, 2d data-array with nan values onto a new grid.

    Parameters
    ----------
    da_in : xarray DataArray
        dataset you want to project on a new grid
    new_x : numpy array
        x coördinates of the new grid
    new_y : numpy array
        y coördaintes of the new grid
    nan_factor : float, optional
        the nan values in the original raster are filled with zeros before
        interpolation because the interp2d function cannot handle nan values
        very well. Therefore an extra interpolation is done to determine how
        much these nan values have influenced the new raster values. If the
        the interpolated value is influenced more than this factor by a nan
        value. The value in the interpolated raster is set to nan.
        See also: https://stackoverflow.com/questions/51474792/2d-interpolation-with-nan-values-in-python

    Returns
    -------
    arr_out : numpy array
        resampled array
    """
    nan_map = np.where(da_in.isnull().data, 1, 0)
    fill_map = np.where(da_in.isnull().data, 0, da_in.data)
    f = interpolate.interp2d(da_in.x.data, da_in.y.data,
                             fill_map, kind='linear', **kwargs)
    f_nan = interpolate.interp2d(da_in.x.data, da_in.y.data,
                                 nan_map, kind='linear')
    arr_out_raw = f(new_x, new_y)
    nan_new = f_nan(new_x, new_y)
    arr_out_raw[nan_new > nan_factor] = np.nan

    # for some reason interp2d flips the y-values
    arr_out = arr_out_raw[::-1]

    return arr_out


def resample_dataset_to_structured_grid(ds_in, extent, delr, delc, kind='linear'):
    """Resample a dataset (xarray) from a structured grid to a new dataset from
    a different structured grid.

    Parameters
    ----------
    ds_in : xarray.Dataset
        dataset with dimensions (layer, y, x). y and x are from the original
        grid
    extent : list, tuple or np.array
        extent (xmin, xmax, ymin, ymax) of the desired grid.
    delr : int or float
        cell size along rows of the desired grid (dx).
    delc : int or float
        cell size along columns of the desired grid (dy).
    kind : str, optional
        type of interpolation used to resample. The default is 'linear'.

    Returns
    -------
    ds_out : xarray.Dataset
        dataset with dimensions (layer, y, x). y and x are from the new
        grid.
    """

    assert isinstance(ds_in, xr.core.dataset.Dataset)

    xmid, ymid = mgrid.get_xy_mid_structured(extent, delr, delc)

    ds_out = xr.Dataset(coords={'y': ymid,
                                'x': xmid,
                                'layer': ds_in.layer.data})
    for data_var in ds_in.data_vars:
        data_arr = resample_dataarray3d_to_structured_grid(ds_in[data_var],
                                                           xmid=xmid,
                                                           ymid=ymid,
                                                           kind=kind)
        ds_out[data_var] = data_arr

    return ds_out


def get_resampled_ml_layer_ds_vertex(raw_ds=None,
                                     extent=None,
                                     gridprops=None):
    """Project model layer dataset on a vertex model grid.

    Parameters
    ----------
    raw_ds : xr.Dataset, optional
        raw model layer dataset. The default is None.
    extent : list, tuple or np.array
        extent (xmin, xmax, ymin, ymax) of the desired grid.
    gridprops : dictionary, optional
        dictionary with grid properties output from gridgen. Used as the
        definition of the vertex grid.

    Returns
    -------
    ml_layer_ds : xr.dataset
        model layer dataset projected onto the modelgrid.
    """

    logger.info('resample model layer data to vertex modelgrid')
    ml_layer_ds = resample_dataset_to_vertex_grid(
        raw_ds, gridprops)
    ml_layer_ds['x'] = xr.DataArray([r[1] for r in gridprops['cell2d']],
                                    dims=('cid'),
                                    coords={'cid': ml_layer_ds.cid.data})

    ml_layer_ds['y'] = xr.DataArray([r[2] for r in gridprops['cell2d']],
                                    dims=('cid'),
                                    coords={'cid': ml_layer_ds.cid.data})
    ml_layer_ds.attrs['gridtype'] = 'vertex'
    ml_layer_ds.attrs['delr'] = raw_ds.delr
    ml_layer_ds.attrs['delc'] = raw_ds.delc
    ml_layer_ds.attrs['extent'] = extent

    return ml_layer_ds


def fillnan_dataarray_structured_grid(xar_in):
    """fill not-a-number values in a structured grid, DataArray.

    The fill values are determined using the 'nearest' method of the
    scipy.interpolate.griddata function


    Parameters
    ----------
    xar_in : xarray DataArray
        DataArray with nan values. DataArray should have 2 dimensions
        (y and x).

    Returns
    -------
    xar_out : xarray DataArray
        DataArray without nan values. DataArray has 2 dimensions
        (y and x)

    Notes
    -----
    can be slow if the xar_in is a large raster
    """
    # check dimensions
    if xar_in.dims != ('y', 'x'):
        raise ValueError(
            f"expected dataarray with dimensions ('y' and 'x'), got dimensions -> {xar_in.dims}")

    # get list of coordinates from all points in raster
    mg = np.meshgrid(xar_in.x.data, xar_in.y.data)
    points_all = np.vstack((mg[0].ravel(), mg[1].ravel())).T

    # get all values in DataArray
    values_all = xar_in.data.flatten()

    # get 1d arrays with only values where DataArray is not nan
    mask1 = ~np.isnan(values_all)
    points_in = points_all[np.where(mask1)[0]]
    values_in = values_all[np.where(mask1)[0]]

    # get nearest value for all nan values
    values_out = griddata(points_in, values_in, points_all, method='nearest')
    arr_out = values_out.reshape(xar_in.shape)

    # create DataArray without nan values
    xar_out = xr.DataArray(arr_out, dims=('y', 'x'),
                           coords={'x': xar_in.x.data,
                                   'y': xar_in.y.data})

    return xar_out


def fillnan_dataarray_vertex_grid(xar_in, gridprops=None,
                                  xyi=None, cid=None):
    """fill not-a-number values in a vertex grid, DataArray.

    The fill values are determined using the 'nearest' method of the
    scipy.interpolate.griddata function

    Parameters
    ----------
    xar_in : xr.DataArray
        data array with nan values. Shape is (cid)
    gridprops : dictionary, optional
        dictionary with grid properties output from gridgen.
    xyi : numpy.ndarray
        array with x and y coördinates of cell centers, shape(len(cid), 2).
    cid : list
        list with cellids.

    Returns
    -------
    xar_out : xr.DataArray
        data array with nan values. Shape is (cid)

    Notes
    -----
    can be slow if the xar_in is a large raster
    """

    # get list of coordinates from all points in raster
    if (xyi is None) or (cid is None):
        xyi, cid = mgrid.get_xyi_cid(gridprops)

    # fill nan values in DataArray
    values_all = xar_in.data

    # get 1d arrays with only values where DataArray is not nan
    mask1 = ~np.isnan(values_all)
    xyi_in = xyi[mask1]
    values_in = values_all[mask1]

    # get nearest value for all nan values
    values_out = griddata(xyi_in, values_in, xyi, method='nearest')

    # create DataArray without nan values
    xar_out = xr.DataArray(values_out, dims=('cid'),
                           coords={'cid': xar_in.cid.data})

    return xar_out


def resample_vertex_2d_da_to_struc_2d_da(da_in, model_ds=None,
                                         xmid=None, ymid=None,
                                         cellsize=25,
                                         method='nearest'):
    """resample a 2d dataarray (xarray) from a vertex grid to a new dataaraay
    from a structured grid.

    Parameters
    ----------
    da_in : xarray.DataArray
        data array with dimensions ('cid').
    model_ds : xarray.DataArray
        model dataset with 'x' and 'y' data variables.
    cellsize : int or float, optional
        required cell size of structured grid. The default is 25.
    method : str, optional
        method used for resampling. The default is 'nearest'.

    Returns
    -------
    da_out : xarray.DataArray
        data array with dimensions ('y', 'x').
    """
    if xmid is None or ymid is None:
        xmid = model_ds.x.values
        ymid = model_ds.y.values

    points_vertex = np.array([xmid, ymid]).T
    modelgrid_x = np.arange(xmid.min(),
                            xmid.max(),
                            cellsize)
    modelgrid_y = np.arange(ymid.max(),
                            ymid.min() - cellsize,
                            -cellsize)
    mg = np.meshgrid(modelgrid_x, modelgrid_y)
    points = np.vstack((mg[0].ravel(), mg[1].ravel())).T

    arr_out_1d = griddata(points_vertex, da_in.values, points, method=method)
    arr_out2d = arr_out_1d.reshape(len(modelgrid_y),
                                   len(modelgrid_x))

    da_out = xr.DataArray(arr_out2d,
                          dims=('y', 'x'),
                          coords={'y': modelgrid_y,
                                  'x': modelgrid_x})

    return da_out

# -*- coding: utf-8 -*-
"""Created on Fri Apr  2 15:08:50 2021.

@author: oebbe
"""
import logging

import numpy as np
import xarray as xr
from scipy import interpolate
from scipy.interpolate import griddata

from . import mgrid

logger = logging.getLogger(__name__)


def resample_dataarray2d_to_vertex_grid(da_in, gridprops=None,
                                        xyi=None, cid=None,
                                        method='nearest',
                                        **kwargs):
    """resample a 2d dataarray (xarray) from a structured grid to a new
    dataaraay of a vertex grid.

    Parameters
    ----------
    da_in : xarray.DataArray
        data array with dimensions (y, x). y and x are from the original
        grid
    gridprops : dictionary, optional
        dictionary with grid properties output from gridgen.
    xyi : numpy.ndarray, optional
        array with x and y coördinates of cell centers, shape(len(cid), 2). If
        xyi is None xyi is calculated from the gridproperties.
    cid : list or numpy.ndarray, optional
        list with cellids. If  cid is None cid is calculated from the
        gridproperties.
    method : str, optional
        type of interpolation used to resample. The default is 'nearest'.

    Returns
    -------
    da_out : xarray.DataArray
        data array with dimension (cid).
    """
    if (xyi is None) or (cid is None):
        xyi, cid = mgrid.get_xyi_cid(gridprops)

    # get x and y values of all cells in dataarray
    mg = np.meshgrid(da_in.x.data, da_in.y.data)
    points = np.vstack((mg[0].ravel(), mg[1].ravel())).T

    # regrid
    arr_out = griddata(points, da_in.data.flatten(), xyi, method=method,
                       **kwargs)

    # new dataset
    da_out = xr.DataArray(arr_out, dims=('cid'),
                          coords={'cid': cid})

    return da_out


def resample_dataarray3d_to_vertex_grid(da_in, gridprops=None,
                                        xyi=None, cid=None,
                                        method='nearest'):
    """resample a dataarray (xarray) from a structured grid to a new dataaraay
    of a vertex grid.

    Parameters
    ----------
    da_in : xarray.DataArray
        data array with dimensions (layer, y, x). y and x are from the original
        grid
    gridprops : dictionary, optional
        dictionary with grid properties output from gridgen.
    xyi : numpy.ndarray, optional
        array with x and y coördinates of cell centers, shape(len(cid), 2). If
        xyi is None xyi is calculated from the gridproperties.
    cid : list or numpy.ndarray, optional
        list with cellids. If  cid is None cid is calculated from the
        gridproperties.
    method : str, optional
        type of interpolation used to resample. The default is 'nearest'.

    Returns
    -------
    da_out : xarray.DataArray
        data array with dimensions (layer,cid).
    """
    if (xyi is None) or (cid is None):
        xyi, cid = mgrid.get_xyi_cid(gridprops=gridprops)

    # get x and y values of all cells in dataarray
    mg = np.meshgrid(da_in.x.data, da_in.y.data)
    points = np.vstack((mg[0].ravel(), mg[1].ravel())).T

    layers = da_in.layer.data
    arr_out = np.zeros((len(layers), len(xyi)))
    for i, lay in enumerate(layers):

        ds_lay = da_in.sel(layer=lay)

        # regrid
        arr_out[i] = griddata(
            points, ds_lay.data.flatten(), xyi, method=method)

    # new dataset
    da_out = xr.DataArray(arr_out, dims=('layer', 'cid'),
                          coords={'cid': cid,
                                  'layer': layers})

    return da_out


def resample_dataset_to_vertex_grid(ds_in, gridprops,
                                    method='nearest'):
    """resample a dataset (xarray) from an structured grid to a new dataset
    from a vertex grid.

    Parameters
    ----------
    ds_in : xarray.Dataset
        dataset with dimensions (layer, y, x). y and x are from the original
        structured grid
    gridprops : dictionary
        dictionary with grid properties output from gridgen.
    method : str, optional
        type of interpolation used to resample. The default is 'nearest'.

    Returns
    -------
    ds_out : xarray.Dataset
        dataset with dimensions (layer, cid), cid are cell id's from the new
        grid.
    """

    assert isinstance(ds_in, xr.core.dataset.Dataset)

    xyi, cid = mgrid.get_xyi_cid(gridprops)

    ds_out = xr.Dataset(coords={'cid': cid,
                                'layer': ds_in.layer.data})

    # add x and y coordinates
    ds_out['x'] = xr.DataArray(xyi[:, 0], dims=('cid'),
                               coords={'cid': cid})
    ds_out['y'] = xr.DataArray(xyi[:, 0], dims=('cid'),
                               coords={'cid': cid})

    # add other variables
    for data_var in ds_in.data_vars:
        if ds_in[data_var].dims == ('layer', 'y', 'x'):
            data_arr = resample_dataarray3d_to_vertex_grid(ds_in[data_var],
                                                           xyi=xyi, cid=cid,
                                                           method=method)
        elif ds_in[data_var].dims == ('y', 'x'):
            data_arr = resample_dataarray2d_to_vertex_grid(ds_in[data_var],
                                                           xyi=xyi, cid=cid,
                                                           method=method)

        elif ds_in[data_var].dims == ('layer'):
            data_arr = ds_in[data_var]

        else:
            logger.warning(
                f'did not resample data array {data_var} because conversion with dimensions {ds_in[data_var].dims} is not (yet) supported')
            continue

        ds_out[data_var] = data_arr

    return ds_out


def resample_dataarray2d_to_structured_grid(da_in, extent=None,
                                            delr=None, delc=None,
                                            xmid=None, ymid=None,
                                            kind='linear', nan_factor=0.01,
                                            **kwargs):
    """resample a dataarray (xarray) from a structured grid to a new dataaraay
    from a different structured grid.

    Parameters
    ----------
    da_in : xarray.DataArray
        data array with dimensions (y, x). y and x are from the original
        grid
    extent : list, tuple or np.array, optional
        extent (xmin, xmax, ymin, ymax) of the desired grid, if not defined
        xmid and ymid are used
    delr : int or float, optional
        cell size along rows of the desired grid, if not defined xmid and
        ymid are used
    delc : int or float, optional
        cell size along columns of the desired grid, if not defined xmid and
        ymid are used
    xmid : np.array, optional
        x coördinates of the cell centers of the desired grid shape(ncol), if
        not defined xmid and ymid are calculated from the extent, delr and delc.
    ymid : np.array, optional
        y coördinates of the cell centers of the desired grid shape(nrow), if
        not defined xmid and ymid are calculated from the extent, delr and delc.
    kind : str, optional
        type of interpolation used to resample. The default is 'linear'.
    nan_factor : float, optional
        the nan values in the original raster are filled with zeros before
        interpolation because the interp2d function cannot handle nan values
        very well. Therefore an extra interpolation is done to determine how
        much these nan values have influenced the new raster values. If the
        the interpolated value is influenced more than this factor by a nan
        value. The value in the interpolated raster is set to nan.
        See also: https://stackoverflow.com/questions/51474792/2d-interpolation-with-nan-values-in-python

    Returns
    -------
    ds_out : xarray.DataArray
        data array with dimensions (y, x). y and x are from the new grid.
    """

    assert isinstance(
        da_in, xr.core.dataarray.DataArray), f'expected type xr.core.dataarray.DataArray got {type(da_in)} instead'

    if xmid is None:
        xmid, ymid = mgrid.get_xy_mid_structured(extent, delr, delc)

    # check if ymid is in descending order
    assert np.array_equal(ymid, np.sort(
        ymid)[::-1]), 'ymid should be in descending order'

    # check for nan values
    if (da_in.isnull().sum() > 0) and (kind == "linear"):
        arr_out = resample_2d_struc_da_nan_linear(da_in, xmid, ymid,
                                                  nan_factor, **kwargs)
    # faster for linear
    elif kind in ["linear", 'cubic']:
        # no need to fill nan values
        f = interpolate.interp2d(da_in.x.data, da_in.y.data,
                                 da_in.data, kind='linear', **kwargs)
        # for some reason interp2d flips the y-values
        arr_out = f(xmid, ymid)[::-1]
    elif kind == 'nearest':
        xydata = np.vstack([v.ravel() for v in
                            np.meshgrid(da_in.x.data, da_in.y.data)]).T
        xyi = np.vstack([v.ravel() for v in np.meshgrid(xmid, ymid)]).T
        fi = griddata(xydata, da_in.data.ravel(), xyi, method=kind,
                      **kwargs)
        arr_out = fi.reshape(ymid.shape[0], xmid.shape[0])
    else:
        raise ValueError(f'unexpected value for "kind": {kind}')

    # new dataset
    da_out = xr.DataArray(arr_out, dims=('y', 'x'),
                          coords={'x': xmid,
                                  'y': ymid})

    return da_out


def resample_dataarray3d_to_structured_grid(da_in, extent=None,
                                            delr=None, delc=None,
                                            xmid=None, ymid=None,
                                            kind='linear', nan_factor=0.01,
                                            **kwargs):
    """resample a dataarray (xarray) from a structured grid to a new dataaraay
    from a different structured grid.

    Parameters
    ----------
    da_in : xarray.DataArray
        data array with dimensions (layer, y, x). y and x are from the original
        grid
    extent : list, tuple or np.array, optional
        extent (xmin, xmax, ymin, ymax) of the desired grid, if not defined
        xmid and ymid are used
    delr : int or float, optional
        cell size along rows of the desired grid, if not defined xmid and
        ymid are used
    delc : int or float, optional
        cell size along columns of the desired grid, if not defined xmid and
        ymid are used
    xmid : np.array, optional
        x coördinates of the cell centers of the desired grid shape(ncol), if
        not defined xmid and ymid are calculated from the extent, delr and delc.
    ymid : np.array, optional
        y coördinates of the cell centers of the desired grid shape(nrow), if
        not defined xmid and ymid are calculated from the extent, delr and delc.
    kind : str, optional
        type of interpolation used to resample. The default is 'linear'.
    nan_factor : float, optional
        the nan values in the original raster are filled with zeros before
        interpolation because the interp2d function cannot handle nan values
        very well. Therefore an extra interpolation is done to determine how
        much these nan values have influenced the new raster values. If the
        the interpolated value is influenced more than this factor by a nan
        value. The value in the interpolated raster is set to nan.
        See also: https://stackoverflow.com/questions/51474792/2d-interpolation-with-nan-values-in-python

    Returns
    -------
    ds_out : xarray.DataArray
        data array with dimensions (layer, y, x). y and x are from the new
        grid.
    """

    assert isinstance(
        da_in, xr.core.dataarray.DataArray), f'expected type xr.core.dataarray.DataArray got {type(da_in)} instead'

    # check if ymid is in descending order
    assert np.array_equal(ymid, np.sort(
        ymid)[::-1]), 'ymid should be in descending order'

    if xmid is None:
        xmid, ymid = mgrid.get_xy_mid_structured(extent, delr, delc)

    layers = da_in.layer.data
    arr_out = np.zeros((len(layers), len(ymid), len(xmid)))
    for i, lay in enumerate(layers):

        ds_lay = da_in.sel(layer=lay)
        # check for nan values
        if (ds_lay.isnull().sum() > 0) and (kind == "linear"):
            arr_out[i] = resample_2d_struc_da_nan_linear(ds_lay, xmid, ymid,
                                                         nan_factor, **kwargs)
        # faster for linear
        elif kind in ['linear', 'cubic']:
            # no need to fill nan values
            f = interpolate.interp2d(ds_lay.x.data, ds_lay.y.data,
                                     ds_lay.data, kind='linear', **kwargs)
            # for some reason interp2d flips the y-values
            arr_out[i] = f(xmid, ymid)[::-1]
        elif kind == 'nearest':
            xydata = np.vstack([v.ravel() for v in
                                np.meshgrid(ds_lay.x.data, ds_lay.y.data)]).T
            xyi = np.vstack([v.ravel() for v in np.meshgrid(xmid, ymid)]).T
            fi = griddata(xydata, ds_lay.data.ravel(), xyi, method=kind,
                          **kwargs)
            arr_out[i] = fi.reshape(ymid.shape[0], xmid.shape[0])
        else:
            raise ValueError(f'unexpected value for "kind": {kind}')

    # new dataset
    da_out = xr.DataArray(arr_out, dims=('layer', 'y', 'x'),
                          coords={'x': xmid,
                                  'y': ymid,
                                  'layer': layers})

    return da_out


def resample_2d_struc_da_nan_linear(da_in, new_x, new_y,
                                    nan_factor=0.01, **kwargs):
    """resample a structured, 2d data-array with nan values onto a new grid.

    Parameters
    ----------
    da_in : xarray DataArray
        dataset you want to project on a new grid
    new_x : numpy array
        x coördinates of the new grid
    new_y : numpy array
        y coördaintes of the new grid
    nan_factor : float, optional
        the nan values in the original raster are filled with zeros before
        interpolation because the interp2d function cannot handle nan values
        very well. Therefore an extra interpolation is done to determine how
        much these nan values have influenced the new raster values. If the
        the interpolated value is influenced more than this factor by a nan
        value. The value in the interpolated raster is set to nan.
        See also: https://stackoverflow.com/questions/51474792/2d-interpolation-with-nan-values-in-python

    Returns
    -------
    arr_out : numpy array
        resampled array
    """
    nan_map = np.where(da_in.isnull().data, 1, 0)
    fill_map = np.where(da_in.isnull().data, 0, da_in.data)
    f = interpolate.interp2d(da_in.x.data, da_in.y.data,
                             fill_map, kind='linear', **kwargs)
    f_nan = interpolate.interp2d(da_in.x.data, da_in.y.data,
                                 nan_map, kind='linear')
    arr_out_raw = f(new_x, new_y)
    nan_new = f_nan(new_x, new_y)
    arr_out_raw[nan_new > nan_factor] = np.nan

    # for some reason interp2d flips the y-values
    arr_out = arr_out_raw[::-1]

    return arr_out


def resample_dataset_to_structured_grid(ds_in, extent, delr, delc, kind='linear'):
    """Resample a dataset (xarray) from a structured grid to a new dataset from
    a different structured grid.

    Parameters
    ----------
    ds_in : xarray.Dataset
        dataset with dimensions (layer, y, x). y and x are from the original
        grid
    extent : list, tuple or np.array
        extent (xmin, xmax, ymin, ymax) of the desired grid.
    delr : int or float
        cell size along rows of the desired grid (dx).
    delc : int or float
        cell size along columns of the desired grid (dy).
    kind : str, optional
        type of interpolation used to resample. The default is 'linear'.

    Returns
    -------
    ds_out : xarray.Dataset
        dataset with dimensions (layer, y, x). y and x are from the new
        grid.
    """

    assert isinstance(ds_in, xr.core.dataset.Dataset)

    xmid, ymid = mgrid.get_xy_mid_structured(extent, delr, delc)

    ds_out = xr.Dataset(coords={'y': ymid,
                                'x': xmid,
                                'layer': ds_in.layer.data})
    for data_var in ds_in.data_vars:
        data_arr = resample_dataarray3d_to_structured_grid(ds_in[data_var],
                                                           xmid=xmid,
                                                           ymid=ymid,
                                                           kind=kind)
        ds_out[data_var] = data_arr

    return ds_out


def get_resampled_ml_layer_ds_vertex(raw_ds=None,
                                     extent=None,
                                     gridprops=None):
    """Project model layer dataset on a vertex model grid.

    Parameters
    ----------
    raw_ds : xr.Dataset, optional
        raw model layer dataset. The default is None.
    extent : list, tuple or np.array
        extent (xmin, xmax, ymin, ymax) of the desired grid.
    gridprops : dictionary, optional
        dictionary with grid properties output from gridgen. Used as the
        definition of the vertex grid.

    Returns
    -------
    ml_layer_ds : xr.dataset
        model layer dataset projected onto the modelgrid.
    """

    logger.info('resample model layer data to vertex modelgrid')
    ml_layer_ds = resample_dataset_to_vertex_grid(
        raw_ds, gridprops)
    ml_layer_ds['x'] = xr.DataArray([r[1] for r in gridprops['cell2d']],
                                    dims=('cid'),
                                    coords={'cid': ml_layer_ds.cid.data})

    ml_layer_ds['y'] = xr.DataArray([r[2] for r in gridprops['cell2d']],
                                    dims=('cid'),
                                    coords={'cid': ml_layer_ds.cid.data})
    ml_layer_ds.attrs['gridtype'] = 'vertex'
    ml_layer_ds.attrs['delr'] = raw_ds.delr
    ml_layer_ds.attrs['delc'] = raw_ds.delc
    ml_layer_ds.attrs['extent'] = extent

    return ml_layer_ds


def fillnan_dataarray_structured_grid(xar_in):
    """fill not-a-number values in a structured grid, DataArray.

    The fill values are determined using the 'nearest' method of the
    scipy.interpolate.griddata function


    Parameters
    ----------
    xar_in : xarray DataArray
        DataArray with nan values. DataArray should have 2 dimensions
        (y and x).

    Returns
    -------
    xar_out : xarray DataArray
        DataArray without nan values. DataArray has 2 dimensions
        (y and x)

    Notes
    -----
    can be slow if the xar_in is a large raster
    """
    # check dimensions
    if xar_in.dims != ('y', 'x'):
        raise ValueError(
            f"expected dataarray with dimensions ('y' and 'x'), got dimensions -> {xar_in.dims}")

    # get list of coordinates from all points in raster
    mg = np.meshgrid(xar_in.x.data, xar_in.y.data)
    points_all = np.vstack((mg[0].ravel(), mg[1].ravel())).T

    # get all values in DataArray
    values_all = xar_in.data.flatten()

    # get 1d arrays with only values where DataArray is not nan
    mask1 = ~np.isnan(values_all)
    points_in = points_all[np.where(mask1)[0]]
    values_in = values_all[np.where(mask1)[0]]

    # get nearest value for all nan values
    values_out = griddata(points_in, values_in, points_all, method='nearest')
    arr_out = values_out.reshape(xar_in.shape)

    # create DataArray without nan values
    xar_out = xr.DataArray(arr_out, dims=('y', 'x'),
                           coords={'x': xar_in.x.data,
                                   'y': xar_in.y.data})

    return xar_out


def fillnan_dataarray_vertex_grid(xar_in, gridprops=None,
                                  xyi=None, cid=None):
    """fill not-a-number values in a vertex grid, DataArray.

    The fill values are determined using the 'nearest' method of the
    scipy.interpolate.griddata function

    Parameters
    ----------
    xar_in : xr.DataArray
        data array with nan values. Shape is (cid)
    gridprops : dictionary, optional
        dictionary with grid properties output from gridgen.
    xyi : numpy.ndarray
        array with x and y coördinates of cell centers, shape(len(cid), 2).
    cid : list
        list with cellids.

    Returns
    -------
    xar_out : xr.DataArray
        data array with nan values. Shape is (cid)

    Notes
    -----
    can be slow if the xar_in is a large raster
    """

    # get list of coordinates from all points in raster
    if (xyi is None) or (cid is None):
        xyi, cid = mgrid.get_xyi_cid(gridprops)

    # fill nan values in DataArray
    values_all = xar_in.data

    # get 1d arrays with only values where DataArray is not nan
    mask1 = ~np.isnan(values_all)
    xyi_in = xyi[mask1]
    values_in = values_all[mask1]

    # get nearest value for all nan values
    values_out = griddata(xyi_in, values_in, xyi, method='nearest')

    # create DataArray without nan values
    xar_out = xr.DataArray(values_out, dims=('cid'),
                           coords={'cid': xar_in.cid.data})

    return xar_out


def resample_vertex_2d_da_to_struc_2d_da(da_in, model_ds=None,
                                         xmid=None, ymid=None,
                                         cellsize=25,
                                         method='nearest'):
    """resample a 2d dataarray (xarray) from a vertex grid to a new dataaraay
    from a structured grid.

    Parameters
    ----------
    da_in : xarray.DataArray
        data array with dimensions ('cid').
    model_ds : xarray.DataArray
        model dataset with 'x' and 'y' data variables.
    cellsize : int or float, optional
        required cell size of structured grid. The default is 25.
    method : str, optional
        method used for resampling. The default is 'nearest'.

    Returns
    -------
    da_out : xarray.DataArray
        data array with dimensions ('y', 'x').
    """
    if xmid is None or ymid is None:
        xmid = model_ds.x.values
        ymid = model_ds.y.values

    points_vertex = np.array([xmid, ymid]).T
    modelgrid_x = np.arange(xmid.min(),
                            xmid.max(),
                            cellsize)
    modelgrid_y = np.arange(ymid.max(),
                            ymid.min() - cellsize,
                            -cellsize)
    mg = np.meshgrid(modelgrid_x, modelgrid_y)
    points = np.vstack((mg[0].ravel(), mg[1].ravel())).T

    arr_out_1d = griddata(points_vertex, da_in.values, points, method=method)
    arr_out2d = arr_out_1d.reshape(len(modelgrid_y),
                                   len(modelgrid_x))

    da_out = xr.DataArray(arr_out2d,
                          dims=('y', 'x'),
                          coords={'y': modelgrid_y,
                                  'x': modelgrid_x})

    return da_out
# -*- coding: utf-8 -*- Created on Fri Apr  2 15:08:50 2021.  @author: oebbe resample a 2d dataarray (xarray) from a structured grid to a new     dataaraay of a vertex grid.      Parameters     ----------     da_in : xarray.DataArray         data array with dimensions (y, x). y and x are from the original         grid     gridprops : dictionary, optional         dictionary with grid properties output from gridgen.     xyi : numpy.ndarray, optional         array with x and y coördinates of cell centers, shape(len(cid), 2). If         xyi is None xyi is calculated from the gridproperties.     cid : list or numpy.ndarray, optional         list with cellids. If  cid is None cid is calculated from the         gridproperties.     method : str, optional         type of interpolation used to resample. The default is 'nearest'.      Returns     -------     da_out : xarray.DataArray         data array with dimension (cid). # get x and y values of all cells in dataarray # regrid # new dataset resample a dataarray (xarray) from a structured grid to a new dataaraay     of a vertex grid.      Parameters     ----------     da_in : xarray.DataArray         data array with dimensions (layer, y, x). y and x are from the original         grid     gridprops : dictionary, optional         dictionary with grid properties output from gridgen.     xyi : numpy.ndarray, optional         array with x and y coördinates of cell centers, shape(len(cid), 2). If         xyi is None xyi is calculated from the gridproperties.     cid : list or numpy.ndarray, optional         list with cellids. If  cid is None cid is calculated from the         gridproperties.     method : str, optional         type of interpolation used to resample. The default is 'nearest'.      Returns     -------     da_out : xarray.DataArray         data array with dimensions (layer,cid). # get x and y values of all cells in dataarray # regrid # new dataset resample a dataset (xarray) from an structured grid to a new dataset     from a vertex grid.      Parameters     ----------     ds_in : xarray.Dataset         dataset with dimensions (layer, y, x). y and x are from the original         structured grid     gridprops : dictionary         dictionary with grid properties output from gridgen.     method : str, optional         type of interpolation used to resample. The default is 'nearest'.      Returns     -------     ds_out : xarray.Dataset         dataset with dimensions (layer, cid), cid are cell id's from the new         grid. # add x and y coordinates # add other variables resample a dataarray (xarray) from a structured grid to a new dataaraay     from a different structured grid.      Parameters     ----------     da_in : xarray.DataArray         data array with dimensions (y, x). y and x are from the original         grid     extent : list, tuple or np.array, optional         extent (xmin, xmax, ymin, ymax) of the desired grid, if not defined         xmid and ymid are used     delr : int or float, optional         cell size along rows of the desired grid, if not defined xmid and         ymid are used     delc : int or float, optional         cell size along columns of the desired grid, if not defined xmid and         ymid are used     xmid : np.array, optional         x coördinates of the cell centers of the desired grid shape(ncol), if         not defined xmid and ymid are calculated from the extent, delr and delc.     ymid : np.array, optional         y coördinates of the cell centers of the desired grid shape(nrow), if         not defined xmid and ymid are calculated from the extent, delr and delc.     kind : str, optional         type of interpolation used to resample. The default is 'linear'.     nan_factor : float, optional         the nan values in the original raster are filled with zeros before         interpolation because the interp2d function cannot handle nan values         very well. Therefore an extra interpolation is done to determine how         much these nan values have influenced the new raster values. If the         the interpolated value is influenced more than this factor by a nan         value. The value in the interpolated raster is set to nan.         See also: https://stackoverflow.com/questions/51474792/2d-interpolation-with-nan-values-in-python      Returns     -------     ds_out : xarray.DataArray         data array with dimensions (y, x). y and x are from the new grid. # check if ymid is in descending order # check for nan values # faster for linear # no need to fill nan values # for some reason interp2d flips the y-values # new dataset resample a dataarray (xarray) from a structured grid to a new dataaraay     from a different structured grid.      Parameters     ----------     da_in : xarray.DataArray         data array with dimensions (layer, y, x). y and x are from the original         grid     extent : list, tuple or np.array, optional         extent (xmin, xmax, ymin, ymax) of the desired grid, if not defined         xmid and ymid are used     delr : int or float, optional         cell size along rows of the desired grid, if not defined xmid and         ymid are used     delc : int or float, optional         cell size along columns of the desired grid, if not defined xmid and         ymid are used     xmid : np.array, optional         x coördinates of the cell centers of the desired grid shape(ncol), if         not defined xmid and ymid are calculated from the extent, delr and delc.     ymid : np.array, optional         y coördinates of the cell centers of the desired grid shape(nrow), if         not defined xmid and ymid are calculated from the extent, delr and delc.     kind : str, optional         type of interpolation used to resample. The default is 'linear'.     nan_factor : float, optional         the nan values in the original raster are filled with zeros before         interpolation because the interp2d function cannot handle nan values         very well. Therefore an extra interpolation is done to determine how         much these nan values have influenced the new raster values. If the         the interpolated value is influenced more than this factor by a nan         value. The value in the interpolated raster is set to nan.         See also: https://stackoverflow.com/questions/51474792/2d-interpolation-with-nan-values-in-python      Returns     -------     ds_out : xarray.DataArray         data array with dimensions (layer, y, x). y and x are from the new         grid. # check if ymid is in descending order # check for nan values # faster for linear # no need to fill nan values # for some reason interp2d flips the y-values # new dataset resample a structured, 2d data-array with nan values onto a new grid.      Parameters     ----------     da_in : xarray DataArray         dataset you want to project on a new grid     new_x : numpy array         x coördinates of the new grid     new_y : numpy array         y coördaintes of the new grid     nan_factor : float, optional         the nan values in the original raster are filled with zeros before         interpolation because the interp2d function cannot handle nan values         very well. Therefore an extra interpolation is done to determine how         much these nan values have influenced the new raster values. If the         the interpolated value is influenced more than this factor by a nan         value. The value in the interpolated raster is set to nan.         See also: https://stackoverflow.com/questions/51474792/2d-interpolation-with-nan-values-in-python      Returns     -------     arr_out : numpy array         resampled array # for some reason interp2d flips the y-values Resample a dataset (xarray) from a structured grid to a new dataset from     a different structured grid.      Parameters     ----------     ds_in : xarray.Dataset         dataset with dimensions (layer, y, x). y and x are from the original         grid     extent : list, tuple or np.array         extent (xmin, xmax, ymin, ymax) of the desired grid.     delr : int or float         cell size along rows of the desired grid (dx).     delc : int or float         cell size along columns of the desired grid (dy).     kind : str, optional         type of interpolation used to resample. The default is 'linear'.      Returns     -------     ds_out : xarray.Dataset         dataset with dimensions (layer, y, x). y and x are from the new         grid. Project model layer dataset on a vertex model grid.      Parameters     ----------     raw_ds : xr.Dataset, optional         raw model layer dataset. The default is None.     extent : list, tuple or np.array         extent (xmin, xmax, ymin, ymax) of the desired grid.     gridprops : dictionary, optional         dictionary with grid properties output from gridgen. Used as the         definition of the vertex grid.      Returns     -------     ml_layer_ds : xr.dataset         model layer dataset projected onto the modelgrid. fill not-a-number values in a structured grid, DataArray.      The fill values are determined using the 'nearest' method of the     scipy.interpolate.griddata function       Parameters     ----------     xar_in : xarray DataArray         DataArray with nan values. DataArray should have 2 dimensions         (y and x).      Returns     -------     xar_out : xarray DataArray         DataArray without nan values. DataArray has 2 dimensions         (y and x)      Notes     -----     can be slow if the xar_in is a large raster # check dimensions # get list of coordinates from all points in raster # get all values in DataArray # get 1d arrays with only values where DataArray is not nan # get nearest value for all nan values # create DataArray without nan values fill not-a-number values in a vertex grid, DataArray.      The fill values are determined using the 'nearest' method of the     scipy.interpolate.griddata function      Parameters     ----------     xar_in : xr.DataArray         data array with nan values. Shape is (cid)     gridprops : dictionary, optional         dictionary with grid properties output from gridgen.     xyi : numpy.ndarray         array with x and y coördinates of cell centers, shape(len(cid), 2).     cid : list         list with cellids.      Returns     -------     xar_out : xr.DataArray         data array with nan values. Shape is (cid)      Notes     -----     can be slow if the xar_in is a large raster # get list of coordinates from all points in raster # fill nan values in DataArray # get 1d arrays with only values where DataArray is not nan # get nearest value for all nan values # create DataArray without nan values resample a 2d dataarray (xarray) from a vertex grid to a new dataaraay     from a structured grid.      Parameters     ----------     da_in : xarray.DataArray         data array with dimensions ('cid').     model_ds : xarray.DataArray         model dataset with 'x' and 'y' data variables.     cellsize : int or float, optional         required cell size of structured grid. The default is 25.     method : str, optional         method used for resampling. The default is 'nearest'.      Returns     -------     da_out : xarray.DataArray         data array with dimensions ('y', 'x').