# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import numpy as np
from scipy.signal import get_window

from .utils import _ensure_int, _validate_type, logger, verbose

###############################################################################
# Class for interpolation between adjacent points


class _Interp2:
    r"""Interpolate between two points.

    Parameters
    ----------
    control_points : array, shape (n_changes,)
        The control points (indices) to use.
    values : callable | array, shape (n_changes, ...)
        Callable that takes the control point and returns a list of
        arrays that must be interpolated.
    interp : str
        Can be 'zero', 'linear', 'hann', or 'cos2' (same as hann).

    Notes
    -----
    This will process data using overlapping windows of potentially
    different sizes to achieve a constant output value using different
    2-point interpolation schemes. For example, for linear interpolation,
    and window sizes of 6 and 17, this would look like::

        1 _     _
          |\   / '-.           .-'
          | \ /     '-.     .-'
          |  x         |-.-|
          | / \     .-'     '-.
          |/   \_.-'           '-.
        0 +----|----|----|----|---
          0    5   10   15   20   25

    """

    def __init__(self, control_points, values, interp="hann", *, name="Interp2"):
        # set up interpolation
        self.control_points = np.array(control_points, int).ravel()
        if not np.array_equal(np.unique(self.control_points), self.control_points):
            raise ValueError("Control points must be sorted and unique")
        if len(self.control_points) == 0:
            raise ValueError("Must be at least one control point")
        if not (self.control_points >= 0).all():
            raise ValueError(
                f"All control points must be positive (got {self.control_points[:3]})"
            )
        if isinstance(values, np.ndarray):
            values = [values]
        if isinstance(values, list | tuple):
            for v in values:
                if not (v is None or isinstance(v, np.ndarray)):
                    raise TypeError(
                        'All entries in "values" must be ndarray or None, got '
                        f"{type(v)}"
                    )
                if v is not None and v.shape[0] != len(self.control_points):
                    raise ValueError(
                        "Values, if provided, must be the same length as the number of "
                        f"control points ({len(self.control_points)}), got {v.shape[0]}"
                    )
            use_values = values

            def val(pt):
                idx = np.where(control_points == pt)[0][0]
                return [v[idx] if v is not None else None for v in use_values]

            values = val
        self.values = values
        self.n_last = None
        self._position = 0  # start at zero
        self._left_idx = 0
        self._left = self._right = self._use_interp = None
        self.name = name
        known_types = ("cos2", "linear", "zero", "hann")
        if interp not in known_types:
            raise ValueError(f'interp must be one of {known_types}, got "{interp}"')
        self._interp = interp

    def feed_generator(self, n_pts):
        """Feed data and get interpolators as a generator."""
        self.n_last = 0
        n_pts = _ensure_int(n_pts, "n_pts")
        original_position = self._position
        stop = self._position + n_pts
        logger.debug(f"    ~ {self.name} Feed {n_pts} ({self._position}-{stop})")
        used = np.zeros(n_pts, bool)
        if self._left is None:  # first one
            logger.debug(f"    ~   {self.name} Eval @ 0 ({self.control_points[0]})")
            self._left = self.values(self.control_points[0])
            if len(self.control_points) == 1:
                self._right = self._left
        n_used = 0

        # Left zero-order hold condition
        if self._position < self.control_points[self._left_idx]:
            n_use = min(self.control_points[self._left_idx] - self._position, n_pts)
            logger.debug(f"    ~   {self.name} Left ZOH {n_use}")
            this_sl = slice(None, n_use)
            assert used[this_sl].size == n_use
            assert not used[this_sl].any()
            used[this_sl] = True
            yield [this_sl, self._left, None, None]
            self._position += n_use
            n_used += n_use
            self.n_last += 1

        # Standard interpolation condition
        stop_right_idx = np.where(self.control_points >= stop)[0]
        if len(stop_right_idx) == 0:
            stop_right_idx = [len(self.control_points) - 1]
        stop_right_idx = stop_right_idx[0]
        left_idxs = np.arange(self._left_idx, stop_right_idx)
        self.n_last += max(len(left_idxs) - 1, 0)
        for bi, left_idx in enumerate(left_idxs):
            if left_idx != self._left_idx or self._right is None:
                if self._right is not None:
                    assert left_idx == self._left_idx + 1
                    self._left = self._right
                    self._left_idx += 1
                    self._use_interp = None  # need to recreate it
                eval_pt = self.control_points[self._left_idx + 1]
                logger.debug(
                    f"    ~   {self.name} Eval @ {self._left_idx + 1} ({eval_pt})"
                )
                self._right = self.values(eval_pt)
            assert self._right is not None
            left_point = self.control_points[self._left_idx]
            right_point = self.control_points[self._left_idx + 1]
            if self._use_interp is None:
                interp_span = right_point - left_point
                if self._interp == "zero":
                    self._use_interp = None
                elif self._interp == "linear":
                    self._use_interp = np.linspace(
                        1.0, 0.0, interp_span, endpoint=False
                    )
                else:  # self._interp in ('cos2', 'hann'):
                    self._use_interp = np.cos(
                        np.linspace(0, np.pi / 2.0, interp_span, endpoint=False)
                    )
                    self._use_interp *= self._use_interp
            n_use = min(stop, right_point) - self._position
            if n_use > 0:
                logger.debug(
                    f"    ~   {self.name} Interp {self._interp} {n_use} "
                    f"({left_point}-{right_point})"
                )
                interp_start = self._position - left_point
                assert interp_start >= 0
                if self._use_interp is None:
                    this_interp = None
                else:
                    this_interp = self._use_interp[interp_start : interp_start + n_use]
                    assert this_interp.size == n_use
                this_sl = slice(n_used, n_used + n_use)
                assert used[this_sl].size == n_use
                assert not used[this_sl].any()
                used[this_sl] = True
                yield [this_sl, self._left, self._right, this_interp]
                self._position += n_use
                n_used += n_use

        # Right zero-order hold condition
        if self.control_points[self._left_idx] <= self._position:
            n_use = stop - self._position
            if n_use > 0:
                logger.debug(f"    ~   {self.name} Right ZOH %s" % n_use)
                this_sl = slice(n_pts - n_use, None)
                assert not used[this_sl].any()
                used[this_sl] = True
                assert self._right is not None
                yield [this_sl, self._right, None, None]
                self._position += n_use
                n_used += n_use
                self.n_last += 1
        assert self._position == stop
        assert n_used == n_pts
        assert used.all()
        assert self._position == original_position + n_pts

    def feed(self, n_pts):
        """Feed data and get interpolated values."""
        # Convenience function for assembly
        out_arrays = None
        for o in self.feed_generator(n_pts):
            if out_arrays is None:
                out_arrays = [
                    np.empty(v.shape + (n_pts,)) if v is not None else None
                    for v in o[1]
                ]
            for ai, arr in enumerate(out_arrays):
                if arr is not None:
                    if o[3] is None:
                        arr[..., o[0]] = o[1][ai][..., np.newaxis]
                    else:
                        arr[..., o[0]] = o[1][ai][..., np.newaxis] * o[3] + o[2][ai][
                            ..., np.newaxis
                        ] * (1.0 - o[3])
        assert out_arrays is not None
        return out_arrays


###############################################################################
# Constant overlap-add processing class


def _check_store(store):
    _validate_type(store, (np.ndarray, list, tuple, _Storer), "store")
    if isinstance(store, np.ndarray):
        store = [store]
    if not isinstance(store, _Storer):
        if not all(isinstance(s, np.ndarray) for s in store):
            raise TypeError("All instances must be ndarrays")
        store = _Storer(*store)
    return store


class _COLA:
    r"""Constant overlap-add processing helper.

    Parameters
    ----------
    process : callable
        A function that takes a chunk of input data with shape
        ``(n_channels, n_samples)`` and processes it.
    store : ndarray | list of ndarray | _Storer
        The output data in which to store the results.
    n_total : int
        The total number of samples.
    n_samples : int
        The number of samples per window.
    n_overlap : int
        The overlap between windows.
    window : str
        The window to use. Default is "hann".
    tol : float
        The tolerance for COLA checking.

    Notes
    -----
    This will process data using overlapping windows to achieve a constant
    output value. For example, for ``n_total=27``, ``n_samples=10``,
    ``n_overlap=5`` and ``window='triang'``::

        1 _____               _______
          |    \   /\   /\   /
          |     \ /  \ /  \ /
          |      x    x    x
          |     / \  / \  / \
          |    /   \/   \/   \
        0 +----|----|----|----|----|-
          0    5   10   15   20   25

    This produces four windows: the first three are the requested length
    (10 samples) and the last one is longer (12 samples). The first and last
    window are asymmetric.
    """

    @verbose
    def __init__(
        self,
        process,
        store,
        n_total,
        n_samples,
        n_overlap,
        sfreq,
        window="hann",
        tol=1e-10,
        *,
        name="COLA",
        verbose=None,
    ):
        n_samples = _ensure_int(n_samples, "n_samples")
        n_overlap = _ensure_int(n_overlap, "n_overlap")
        n_total = _ensure_int(n_total, "n_total")
        if n_samples <= 0:
            raise ValueError(f"n_samples must be > 0, got {n_samples}")
        if n_overlap < 0:
            raise ValueError(f"n_overlap must be >= 0, got {n_overlap}")
        if n_total < 0:
            raise ValueError(f"n_total must be >= 0, got {n_total}")
        self._n_samples = int(n_samples)
        self._n_overlap = int(n_overlap)
        del n_samples, n_overlap
        if n_total < self._n_samples:
            raise ValueError(
                f"Number of samples per window ({self._n_samples}) must be at "
                f"most the total number of samples ({n_total})"
            )
        if not callable(process):
            raise TypeError(f"process must be callable, got type {type(process)}")
        self._process = process
        self._step = self._n_samples - self._n_overlap
        self._store = _check_store(store)
        self._idx = 0
        self._in_buffers = self._out_buffers = None
        self.name = name

        # Create our window boundaries
        window_name = window if isinstance(window, str) else "custom"
        self._window = get_window(
            window, self._n_samples, fftbins=bool((self._n_samples - 1) % 2)
        )
        self._window /= _check_cola(
            self._window, self._n_samples, self._step, window_name, tol=tol
        )
        self.starts = np.arange(0, n_total - self._n_samples + 1, self._step)
        self.stops = self.starts + self._n_samples
        delta = n_total - self.stops[-1]
        self.stops[-1] = n_total
        sfreq = float(sfreq)
        pl = "s" if len(self.starts) != 1 else ""
        logger.info(
            f"    Processing {len(self.starts):4d} data chunk{pl} of (at least) "
            f"{self._n_samples / sfreq:0.1f} s with "
            f"{self._n_overlap / sfreq:0.1f} s overlap and {window_name} windowing"
        )
        del window, window_name
        if delta > 0:
            logger.info(
                f"    The final {delta / sfreq} s will be lumped into the final window"
            )

    @property
    def _in_offset(self):
        """Compute from current processing window start and buffer len."""
        return self.starts[self._idx] + self._in_buffers[0].shape[-1]

    @verbose
    def feed(self, *datas, verbose=None, **kwargs):
        """Pass in a chunk of data."""
        # Append to our input buffer
        if self._in_buffers is None:
            self._in_buffers = [None] * len(datas)
        if len(datas) != len(self._in_buffers):
            raise ValueError(
                f"Got {len(datas)} array(s), needed {len(self._in_buffers)}"
            )
        current_offset = 0  # should be updated below
        for di, data in enumerate(datas):
            if not isinstance(data, np.ndarray) or data.ndim < 1:
                raise TypeError(
                    f"data entry {di} must be an 2D ndarray, got {type(data)}"
                )
            if self._in_buffers[di] is None:
                # In practice, users can give large chunks, so we use
                # dynamic allocation of the in buffer. We could save some
                # memory allocation by only ever processing max_len at once,
                # but this would increase code complexity.
                self._in_buffers[di] = np.empty(data.shape[:-1] + (0,), data.dtype)
            if (
                data.shape[:-1] != self._in_buffers[di].shape[:-1]
                or self._in_buffers[di].dtype != data.dtype
            ):
                raise TypeError(
                    f"data must dtype {self._in_buffers[di].dtype} and "
                    f"shape[:-1]=={self._in_buffers[di].shape[:-1]}, got dtype "
                    f"{data.dtype} shape[:-1]={data.shape[:-1]}"
                )
            # This gets updated on first iteration, so store it before it updates
            if di == 0:
                current_offset = self._in_offset
            logger.debug(
                f"    + {self.name}[{di}] Appending  "
                f"{current_offset}:{current_offset + data.shape[-1]}"
            )
            self._in_buffers[di] = np.concatenate([self._in_buffers[di], data], -1)
            if self._in_offset > self.stops[-1]:
                raise ValueError(
                    f"data (shape {data.shape}) exceeded expected total buffer size ("
                    f"{self._in_offset} > {self.stops[-1]})"
                )
        # Check to see if we can process the next chunk and dump outputs
        while self._idx < len(self.starts) and self._in_offset >= self.stops[self._idx]:
            start, stop = self.starts[self._idx], self.stops[self._idx]
            this_len = stop - start
            this_window = self._window.copy()
            if self._idx == len(self.starts) - 1:
                this_window = np.pad(
                    self._window, (0, this_len - len(this_window)), "constant"
                )
                for offset in range(self._step, len(this_window), self._step):
                    n_use = len(this_window) - offset
                    this_window[offset:] += self._window[:n_use]
            if self._idx == 0:
                for offset in range(self._n_samples - self._step, 0, -self._step):
                    this_window[:offset] += self._window[-offset:]
            this_proc = [in_[..., :this_len].copy() for in_ in self._in_buffers]
            logger.debug(
                f"    * {self.name}[:] Processing {start}:{stop} "
                f"(e.g., {this_proc[0].flat[[0, -1]]})"
            )
            if not all(
                proc.shape[-1] == this_len == this_window.size for proc in this_proc
            ):
                raise RuntimeError("internal indexing error")
            start = self._store.idx
            stop = self._store.idx + this_len
            outs = self._process(*this_proc, start=start, stop=stop, **kwargs)
            if self._out_buffers is None:
                max_len = np.max(self.stops - self.starts)
                self._out_buffers = [
                    np.zeros(o.shape[:-1] + (max_len,), o.dtype) for o in outs
                ]
            for oi, out in enumerate(outs):
                out *= this_window
                self._out_buffers[oi][..., : stop - start] += out
            self._idx += 1
            if self._idx < len(self.starts):
                next_start = self.starts[self._idx]
            else:
                next_start = self.stops[-1]
            delta = next_start - self.starts[self._idx - 1]
            logger.debug(
                f"    + {self.name}[:] Shifting input and output buffers by "
                f"{delta} samples (storing {start}:{stop})"
            )
            for di in range(len(self._in_buffers)):
                self._in_buffers[di] = self._in_buffers[di][..., delta:]
            self._store(*[o[..., :delta] for o in self._out_buffers])
            for ob in self._out_buffers:
                ob[..., :-delta] = ob[..., delta:]
                ob[..., -delta:] = 0.0


def _check_cola(win, nperseg, step, window_name, tol=1e-10):
    """Check whether the Constant OverLap Add (COLA) constraint is met."""
    # adapted from SciPy
    binsums = np.sum(
        [win[ii * step : (ii + 1) * step] for ii in range(nperseg // step)], axis=0
    )
    if nperseg % step != 0:
        binsums[: nperseg % step] += win[-(nperseg % step) :]
    const = np.median(binsums)
    deviation = np.max(np.abs(binsums - const))
    if deviation > tol:
        raise ValueError(
            f"segment length {nperseg} with step {step} for {window_name} "
            "window type does not provide a constant output "
            f"({100 * deviation / const:g}% deviation)"
        )
    return const


class _Storer:
    """Store data in chunks."""

    def __init__(self, *outs, picks=None):
        for oi, out in enumerate(outs):
            if not isinstance(out, np.ndarray) or out.ndim < 1:
                raise TypeError(f"outs[oi] must be >= 1D ndarray, got {out}")
        self.outs = outs
        self.idx = 0
        self.picks = picks

    def __call__(self, *outs):
        if len(outs) != len(self.outs) or not all(
            out.shape[-1] == outs[0].shape[-1] for out in outs
        ):
            raise ValueError("Bad outs")
        idx = (Ellipsis,)
        if self.picks is not None:
            idx += (self.picks,)
        stop = self.idx + outs[0].shape[-1]
        idx += (slice(self.idx, stop),)
        for o1, o2 in zip(self.outs, outs):
            o1[idx] = o2
        self.idx = stop
