from __future__ import annotations

import warnings
from typing import TYPE_CHECKING, Any, Callable, Hashable, Iterable, Sequence

import numpy as np

from ._aggregations import DataArrayResampleAggregations, DatasetResampleAggregations
from .groupby import DataArrayGroupByBase, DatasetGroupByBase, GroupBy
from .types import Dims, InterpOptions, T_Xarray

if TYPE_CHECKING:
    from .dataarray import DataArray
    from .dataset import Dataset

RESAMPLE_DIM = "__resample_dim__"


class Resample(GroupBy[T_Xarray]):
    """An object that extends the `GroupBy` object with additional logic
    for handling specialized re-sampling operations.

    You should create a `Resample` object by using the `DataArray.resample` or
    `Dataset.resample` methods. The dimension along re-sampling

    See Also
    --------
    DataArray.resample
    Dataset.resample

    """

    def __init__(
        self,
        *args,
        dim: Hashable | None = None,
        resample_dim: Hashable | None = None,
        **kwargs,
    ) -> None:

        if dim == resample_dim:
            raise ValueError(
                f"Proxy resampling dimension ('{resample_dim}') "
                f"cannot have the same name as actual dimension ('{dim}')!"
            )
        self._dim = dim

        super().__init__(*args, **kwargs)

    def _flox_reduce(
        self,
        dim: Dims,
        keep_attrs: bool | None = None,
        **kwargs,
    ) -> T_Xarray:

        from .dataarray import DataArray

        kwargs.setdefault("method", "cohorts")

        # now create a label DataArray since resample doesn't do that somehow
        repeats = []
        for slicer in self._group_indices:
            assert isinstance(slicer, slice)
            stop = (
                slicer.stop
                if slicer.stop is not None
                else self._obj.sizes[self._group_dim]
            )
            repeats.append(stop - slicer.start)
        labels = np.repeat(self._unique_coord.data, repeats)
        group = DataArray(labels, dims=(self._group_dim,), name=self._unique_coord.name)

        result = super()._flox_reduce(
            dim=dim, group=group, keep_attrs=keep_attrs, **kwargs
        )
        result = self._maybe_restore_empty_groups(result)
        result = result.rename({RESAMPLE_DIM: self._group_dim})
        return result

    def _drop_coords(self) -> T_Xarray:
        """Drop non-dimension coordinates along the resampled dimension."""
        obj = self._obj
        for k, v in obj.coords.items():
            if k != self._dim and self._dim in v.dims:
                obj = obj.drop_vars(k)
        return obj

    def pad(self, tolerance: float | Iterable[float] | None = None) -> T_Xarray:
        """Forward fill new values at up-sampled frequency.

        Parameters
        ----------
        tolerance : float | Iterable[float] | None, default: None
            Maximum distance between original and new labels to limit
            the up-sampling method.
            Up-sampled data with indices that satisfy the equation
            ``abs(index[indexer] - target) <= tolerance`` are filled by
            new values. Data with indices that are outside the given
            tolerance are filled with ``NaN`` s.

        Returns
        -------
        padded : DataArray or Dataset
        """
        obj = self._drop_coords()
        return obj.reindex(
            {self._dim: self._full_index}, method="pad", tolerance=tolerance
        )

    ffill = pad

    def backfill(self, tolerance: float | Iterable[float] | None = None) -> T_Xarray:
        """Backward fill new values at up-sampled frequency.

        Parameters
        ----------
        tolerance : float | Iterable[float] | None, default: None
            Maximum distance between original and new labels to limit
            the up-sampling method.
            Up-sampled data with indices that satisfy the equation
            ``abs(index[indexer] - target) <= tolerance`` are filled by
            new values. Data with indices that are outside the given
            tolerance are filled with ``NaN`` s.

        Returns
        -------
        backfilled : DataArray or Dataset
        """
        obj = self._drop_coords()
        return obj.reindex(
            {self._dim: self._full_index}, method="backfill", tolerance=tolerance
        )

    bfill = backfill

    def nearest(self, tolerance: float | Iterable[float] | None = None) -> T_Xarray:
        """Take new values from nearest original coordinate to up-sampled
        frequency coordinates.

        Parameters
        ----------
        tolerance : float | Iterable[float] | None, default: None
            Maximum distance between original and new labels to limit
            the up-sampling method.
            Up-sampled data with indices that satisfy the equation
            ``abs(index[indexer] - target) <= tolerance`` are filled by
            new values. Data with indices that are outside the given
            tolerance are filled with ``NaN`` s.

        Returns
        -------
        upsampled : DataArray or Dataset
        """
        obj = self._drop_coords()
        return obj.reindex(
            {self._dim: self._full_index}, method="nearest", tolerance=tolerance
        )

    def interpolate(self, kind: InterpOptions = "linear") -> T_Xarray:
        """Interpolate up-sampled data using the original data as knots.

        Parameters
        ----------
        kind : {"linear", "nearest", "zero", "slinear", \
                "quadratic", "cubic", "polynomial"}, default: "linear"
            The method used to interpolate. The method should be supported by
            the scipy interpolator:

            - ``interp1d``: {"linear", "nearest", "zero", "slinear",
              "quadratic", "cubic", "polynomial"}
            - ``interpn``: {"linear", "nearest"}

            If ``"polynomial"`` is passed, the ``order`` keyword argument must
            also be provided.

        Returns
        -------
        interpolated : DataArray or Dataset

        See Also
        --------
        DataArray.interp
        Dataset.interp
        scipy.interpolate.interp1d

        """
        return self._interpolate(kind=kind)

    def _interpolate(self, kind="linear") -> T_Xarray:
        """Apply scipy.interpolate.interp1d along resampling dimension."""
        obj = self._drop_coords()
        return obj.interp(
            coords={self._dim: self._full_index},
            assume_sorted=True,
            method=kind,
            kwargs={"bounds_error": False},
        )


# https://github.com/python/mypy/issues/9031
class DataArrayResample(Resample["DataArray"], DataArrayGroupByBase, DataArrayResampleAggregations):  # type: ignore[misc]
    """DataArrayGroupBy object specialized to time resampling operations over a
    specified dimension
    """

    def map(
        self,
        func: Callable[..., Any],
        args: tuple[Any, ...] = (),
        shortcut: bool | None = False,
        **kwargs: Any,
    ) -> DataArray:
        """Apply a function to each array in the group and concatenate them
        together into a new array.

        `func` is called like `func(ar, *args, **kwargs)` for each array `ar`
        in this group.

        Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how
        to stack together the array. The rule is:

        1. If the dimension along which the group coordinate is defined is
           still in the first grouped array after applying `func`, then stack
           over this dimension.
        2. Otherwise, stack over the new dimension given by name of this
           grouping (the argument to the `groupby` function).

        Parameters
        ----------
        func : callable
            Callable to apply to each array.
        shortcut : bool, optional
            Whether or not to shortcut evaluation under the assumptions that:

            (1) The action of `func` does not depend on any of the array
                metadata (attributes or coordinates) but only on the data and
                dimensions.
            (2) The action of `func` creates arrays with homogeneous metadata,
                that is, with the same dimensions and attributes.

            If these conditions are satisfied `shortcut` provides significant
            speedup. This should be the case for many common groupby operations
            (e.g., applying numpy ufuncs).
        args : tuple, optional
            Positional arguments passed on to `func`.
        **kwargs
            Used to call `func(ar, **kwargs)` for each array `ar`.

        Returns
        -------
        applied : DataArray
            The result of splitting, applying and combining this array.
        """
        # TODO: the argument order for Resample doesn't match that for its parent,
        # GroupBy
        combined = super().map(func, shortcut=shortcut, args=args, **kwargs)

        # If the aggregation function didn't drop the original resampling
        # dimension, then we need to do so before we can rename the proxy
        # dimension we used.
        if self._dim in combined.coords:
            combined = combined.drop_vars(self._dim)

        if RESAMPLE_DIM in combined.dims:
            combined = combined.rename({RESAMPLE_DIM: self._dim})

        return combined

    def apply(self, func, args=(), shortcut=None, **kwargs):
        """
        Backward compatible implementation of ``map``

        See Also
        --------
        DataArrayResample.map
        """
        warnings.warn(
            "Resample.apply may be deprecated in the future. Using Resample.map is encouraged",
            PendingDeprecationWarning,
            stacklevel=2,
        )
        return self.map(func=func, shortcut=shortcut, args=args, **kwargs)

    def asfreq(self) -> DataArray:
        """Return values of original object at the new up-sampling frequency;
        essentially a re-index with new times set to NaN.

        Returns
        -------
        resampled : DataArray
        """
        self._obj = self._drop_coords()
        return self.mean(None if self._dim is None else [self._dim])


# https://github.com/python/mypy/issues/9031
class DatasetResample(Resample["Dataset"], DatasetGroupByBase, DatasetResampleAggregations):  # type: ignore[misc]
    """DatasetGroupBy object specialized to resampling a specified dimension"""

    def map(
        self,
        func: Callable[..., Any],
        args: tuple[Any, ...] = (),
        shortcut: bool | None = None,
        **kwargs: Any,
    ) -> Dataset:
        """Apply a function over each Dataset in the groups generated for
        resampling and concatenate them together into a new Dataset.

        `func` is called like `func(ds, *args, **kwargs)` for each dataset `ds`
        in this group.

        Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how
        to stack together the datasets. The rule is:

        1. If the dimension along which the group coordinate is defined is
           still in the first grouped item after applying `func`, then stack
           over this dimension.
        2. Otherwise, stack over the new dimension given by name of this
           grouping (the argument to the `groupby` function).

        Parameters
        ----------
        func : callable
            Callable to apply to each sub-dataset.
        args : tuple, optional
            Positional arguments passed on to `func`.
        **kwargs
            Used to call `func(ds, **kwargs)` for each sub-dataset `ar`.

        Returns
        -------
        applied : Dataset
            The result of splitting, applying and combining this dataset.
        """
        # ignore shortcut if set (for now)
        applied = (func(ds, *args, **kwargs) for ds in self._iter_grouped())
        combined = self._combine(applied)

        # If the aggregation function didn't drop the original resampling
        # dimension, then we need to do so before we can rename the proxy
        # dimension we used.
        if self._dim in combined.coords:
            combined = combined.drop_vars(self._dim)

        if RESAMPLE_DIM in combined.dims:
            combined = combined.rename({RESAMPLE_DIM: self._dim})

        return combined

    def apply(self, func, args=(), shortcut=None, **kwargs):
        """
        Backward compatible implementation of ``map``

        See Also
        --------
        DataSetResample.map
        """

        warnings.warn(
            "Resample.apply may be deprecated in the future. Using Resample.map is encouraged",
            PendingDeprecationWarning,
            stacklevel=2,
        )
        return self.map(func=func, shortcut=shortcut, args=args, **kwargs)

    def reduce(
        self,
        func: Callable[..., Any],
        dim: Dims = None,
        *,
        axis: int | Sequence[int] | None = None,
        keep_attrs: bool | None = None,
        keepdims: bool = False,
        shortcut: bool = True,
        **kwargs: Any,
    ) -> Dataset:
        """Reduce the items in this group by applying `func` along the
        pre-defined resampling dimension.

        Parameters
        ----------
        func : callable
            Function which can be called in the form
            `func(x, axis=axis, **kwargs)` to return the result of collapsing
            an np.ndarray over an integer valued axis.
        dim : "...", str, Iterable of Hashable or None, optional
            Dimension(s) over which to apply `func`.
        keep_attrs : bool, optional
            If True, the datasets's attributes (`attrs`) will be copied from
            the original object to the new one.  If False (default), the new
            object will be returned without attributes.
        **kwargs : dict
            Additional keyword arguments passed on to `func`.

        Returns
        -------
        reduced : Dataset
            Array with summarized data and the indicated dimension(s)
            removed.
        """
        return super().reduce(
            func=func,
            dim=dim,
            axis=axis,
            keep_attrs=keep_attrs,
            keepdims=keepdims,
            shortcut=shortcut,
            **kwargs,
        )

    def asfreq(self) -> Dataset:
        """Return values of original object at the new up-sampling frequency;
        essentially a re-index with new times set to NaN.

        Returns
        -------
        resampled : Dataset
        """
        self._obj = self._drop_coords()
        return self.mean(None if self._dim is None else [self._dim])
