import numpy as np
import xarray as xr

from xarray.core import utils

from .. import helpers
from .plot import _Transect_PlotMethods
from .orcbase import ORCBase


@xr.register_dataset_accessor("transect")
class Transect(ORCBase):
    """Transect functionalities that can be applied on ``xarray.Dataset``"""

    def __init__(self, xarray_obj):
        """
        Initialize a transect ``xarray.Dataset`` containing cross sectional velocities in [time, points] dimensions

        Parameters
        ----------
        xarray_obj: xr.Dataset
            transect data fields (from ``pyorc.Velocimetry.get_transect``)
        """

        super(Transect, self).__init__(xarray_obj)

    def vector_to_scalar(self, v_x="v_x", v_y="v_y"):
        """Set "v_eff" and "v_dir" variables as effective velocities over cross-section, and its angle

        Parameters
        ----------
        v_x : str, optional
            name of variable containing x-directional velocities. (default: "v_x")
        v_y :
            name of variable containing y-directional velocities. (default: "v_y")

        Returns
        -------
        da : xr.DataArray
            velocities perpendicular to cross section [time, points]

        """
        xs = self._obj["x"].values
        ys = self._obj["y"].values
        # find points that are located on the area of interest
        idx = np.isfinite(xs)
        xs = xs[idx]
        ys = ys[idx]
        # compute per velocity vector in the dataset, what its angle is
        v_angle = np.arctan2(self._obj[v_x], self._obj[v_y])
        # compute the scalar value of velocity
        v_scalar = (self._obj[v_x] ** 2 + self._obj[v_y] ** 2) ** 0.5

        # compute difference in angle between velocity and perpendicular of cross section
        flow_dir = self._obj["v_dir"]
        angle_diff = v_angle - flow_dir
        # compute effective velocity in the flow direction (i.e. perpendicular to cross section)
        v_eff = np.cos(angle_diff) * v_scalar
        v_eff.attrs = {
            "standard_name": "velocity",
            "long_name": "velocity in perpendicular direction of cross section, measured by angle in radians, "
                         "measured from up-direction",
            "units": "m s-1",
        }
        # set name
        v_eff.name = "v_eff_nofill"  # there still may be gaps in this series
        self._obj["v_eff_nofill"] = v_eff

    def get_xyz_perspective(self, M=None, xs=None, ys=None, mask_outside=True):
        """Get camera-perspective column, row coordinates from cross-section locations.

        Parameters
        ----------
        M : np.ndarray, optional
             perspective transform matrix (Default value = None)
        xs : np.array, optional
            x-coordinates to transform, derived from self.x if not provided (Default value = None)
        ys :
            y-coordinates to transform, derived from self.y if not provided (Default value = None)
        mask_outside :
            values not fitting in the original camera frame are set to NaN (Default value = True)

        Returns
        -------
        cols : list of ints
            columns of locations in original camera perspective
        rows : list of ints
            rows of locations in original camera perspective


        """
        if xs is None:
            xs = self._obj.x.values
        if ys is None:
            ys = self._obj.y.values
        # compute bathymetry as measured in local height reference (such as staff gauge)
        if self.camera_config.gcps["h_ref"] is None:
            h_ref = 0.
        else:
            h_ref = self.camera_config.gcps["h_ref"]
        hs = self.camera_config.z_to_h(self._obj.zcoords).values
        # zs = (self._obj.zcoords - self.camera_config.gcps["z_0"] + h_ref).values
        if M is None:
            Ms = [self.camera_config.get_M(h, reverse=True, to_bbox_grid=True) for h in hs]
        else:
            # use user defined M instead
            Ms = [M for _ in hs]
        # compute row and column position of vectors in original reprojected background image col/row coordinates
        cols, rows = zip(*[
            helpers.xy_to_perspective(
                x,
                y,
                self.camera_config.resolution,
                M,
                reverse_y=self.camera_config.shape[0]
            ) for x, y, M in zip(xs, ys, Ms)
        ])

        # ensure y coordinates start at the top in the right orientation
        shape_y, shape_x = self.camera_shape
        rows = shape_y - np.array(rows)
        cols = np.array(cols)
        if mask_outside:
            # remove values that do not fit in the frames
            cols[np.any([cols < 0, cols > self.camera_shape[1]], axis=0)] = np.nan
            rows[np.any([rows < 0, rows > self.camera_shape[0]], axis=0)] = np.nan

        return cols, rows


    def get_river_flow(self, q_name="q", Q_name="river_flow"):
        """Integrate time series of depth averaged velocities [m2 s-1] into cross-section integrated flow [m3 s-1]
        estimating one or several quantiles over the time dimension. Depth average velocities must first have been
        estimated using get_q. A variable "Q" will be added to Dataset, with only "quantiles" as dimension.

        Parameters
        ----------
        q_name : str, optional
             name of variable where depth integrated velocities [m2 s-1] are stored (Default value = "q")
        Q_name : str, optional
             name of variable where resulting width integrated river flow estimates must be stored
             (Default value = "river_flow")

        """
        if "q" not in self._obj:
            raise ValueError(f'Dataset must contain variable "{q_name}", which is the depth-integrated velocity [m2 s-1], perpendicular to cross-section. Create this with ds.transect.get_q')
        # integrate over the distance coordinates (s-coord)
        Q = self._obj[q_name].fillna(0.0).integrate(coord="scoords")
        Q.attrs = {
            "standard_name": "river_discharge",
            "long_name": "River Flow",
            "units": "m3 s-1",
        }
        # set name
        Q.name = "Q"
        self._obj[Q_name] = Q

    def get_q(self, v_corr=0.9, fill_method="zeros"):
        """Depth integrated velocity for quantiles of time series using a correction v_corr between surface velocity and
        depth-average velocity.

        Parameters
        ----------
        v_corr : float, optional
            correction factor (default: 0.9)
        fill_method : str, optional
            method to fill missing values. "zeros" fills NaNS with zeros, "interpolate" interpolates values
            from nearest neighbour, "log_interp" interpolates values linearly with velocities scaled by the log of
            depth over a roughness length, "log_fit" fits a 4-parameter logarithmic profile with depth and with
            changing velocities towards banks on known velocities, and fills missing with the fitted relationship
            (experimental) (Default value = "zeros").

        Returns
        -------
        ds : xr.Dataset
            Transect for selected quantiles in time, with "q_nofill" containing the integrated values, and "q" the
            integrated values, filled with chosen fill method.

        """
        # aggregate to a limited set of quantiles
        assert(fill_method in ["zeros", "log_fit", "log_interp", "interpolate"]),\
            f'fill_method must be "zeros", "log_fit", "log_interp", or "interpolate", instead "{fill_method}" given'
        ds = self._obj
        x = ds["xcoords"].values
        y = ds["ycoords"].values
        z = ds["zcoords"].values
        # add filled surface velocities with a logarithmic profile curve fit
        depth = self.camera_config.get_depth(z, self.h_a)
        # make velocities zero where depth is zero
        ds["v_eff_nofill"][:, depth<=0] = 0.
        if fill_method == "zeros":
            ds["v_eff"] = ds["v_eff_nofill"].fillna(0.)
        elif fill_method == "log_fit":
            dist_shore = self.camera_config.get_dist_shore(x, y, z, self.h_a)
            ds["v_eff"] = helpers.velocity_log_fit(ds["v_eff_nofill"], depth, dist_shore, dim="quantile")
        elif fill_method == "log_interp":
            dist_wall = self.camera_config.get_dist_wall(x, y, z, self.h_a)
            ds["v_eff"] = helpers.velocity_log_interp(ds["v_eff_nofill"], dist_wall, dim="quantile")
        elif fill_method == "interpolate":
            depth = ds.zcoords*0 + self.camera_config.get_depth(ds.zcoords, self.h_a)
            # interpolate gaps in between known values
            ds["v_eff"] = ds["v_eff_nofill"].interpolate_na(dim="points")
            # anywhere where depth == 0, remove values
            ds["v_eff"] = ds["v_eff"].where(depth > 0)
            # fill NAs with zeros
            ds["v_eff"] = ds["v_eff"].fillna(0.)
        # compute q for both non-filled and filled velocities
        ds["q_nofill"] = helpers.depth_integrate(depth, ds["v_eff_nofill"], v_corr=v_corr, name="q_nofill")
        ds["q"] = helpers.depth_integrate(depth, ds["v_eff"], v_corr=v_corr, name="q")
        return ds

    plot = utils.UncachedAccessor(_Transect_PlotMethods)
