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

from collections import OrderedDict
from datetime import datetime, timezone
from pathlib import Path

import numpy as np

from ..._fiff.meas_info import create_info
from ..._fiff.utils import _mult_cal_one
from ...annotations import Annotations
from ...utils import _check_fname, fill_doc, logger, verbose, warn
from ..base import BaseRaw


def _ensure_path(fname):
    out = fname
    if not isinstance(out, Path):
        out = Path(out)
    return out


@fill_doc
def read_raw_nihon(fname, preload=False, verbose=None) -> "RawNihon":
    """Reader for an Nihon Kohden EEG file.

    Parameters
    ----------
    fname : path-like
        Path to the Nihon Kohden data file (``.EEG``).
    preload : bool
        If True, all data are loaded at initialization.
    %(verbose)s

    Returns
    -------
    raw : instance of RawNihon
        A Raw object containing Nihon Kohden data.
        See :class:`mne.io.Raw` for documentation of attributes and methods.

    See Also
    --------
    mne.io.Raw : Documentation of attributes and methods of RawNihon.
    """
    return RawNihon(fname, preload, verbose)


_valid_headers = [
    "EEG-1100A V01.00",
    "EEG-1100B V01.00",
    "EEG-1100C V01.00",
    "QI-403A   V01.00",
    "QI-403A   V02.00",
    "EEG-2100  V01.00",
    "EEG-2100  V02.00",
    "DAE-2100D V01.30",
    "DAE-2100D V02.00",
    # 'EEG-1200A V01.00',  # Not working for the moment.
]


def _read_nihon_metadata(fname):
    metadata = {}
    fname = _ensure_path(fname)
    pnt_fname = fname.with_suffix(".PNT")
    if not pnt_fname.exists():
        warn("No PNT file exists. Metadata will be blank")
        return metadata
    logger.info("Found PNT file, reading metadata.")
    with open(pnt_fname) as fid:
        version = np.fromfile(fid, "|S16", 1).astype("U16")[0]
        if version not in _valid_headers:
            raise ValueError(f"Not a valid Nihon Kohden PNT file ({version})")
        metadata["version"] = version

        # Read timestamp
        fid.seek(0x40)
        meas_str = np.fromfile(fid, "|S14", 1).astype("U14")[0]
        meas_date = datetime.strptime(meas_str, "%Y%m%d%H%M%S")
        meas_date = meas_date.replace(tzinfo=timezone.utc)
        metadata["meas_date"] = meas_date

    return metadata


_default_chan_labels = [
    "FP1",
    "FP2",
    "F3",
    "F4",
    "C3",
    "C4",
    "P3",
    "P4",
    "O1",
    "O2",
    "F7",
    "F8",
    "T3",
    "T4",
    "T5",
    "T6",
    "FZ",
    "CZ",
    "PZ",
    "E",
    "PG1",
    "PG2",
    "A1",
    "A2",
    "T1",
    "T2",
]
_default_chan_labels += [f"X{i}" for i in range(1, 12)]
_default_chan_labels += [f"NA{i}" for i in range(1, 6)]
_default_chan_labels += [f"DC{i:02}" for i in range(1, 33)]
_default_chan_labels += ["BN1", "BN2", "Mark1", "Mark2"]
_default_chan_labels += [f"NA{i}" for i in range(6, 28)]
_default_chan_labels += ["X12/BP1", "X13/BP2", "X14/BP3", "X15/BP4"]
_default_chan_labels += [f"X{i}" for i in range(16, 166)]
_default_chan_labels += ["NA28", "Z"]

_encodings = ("utf-8", "latin1")


def _read_21e_file(fname):
    fname = _ensure_path(fname)
    e_fname = fname.with_suffix(".21E")
    _chan_labels = [x for x in _default_chan_labels]
    if e_fname.exists():
        # Read the 21E file and update the labels accordingly.
        logger.info("Found 21E file, reading channel names.")
        for enc in _encodings:
            try:
                with open(e_fname, encoding=enc) as fid:
                    keep_parsing = False
                    for line in fid:
                        if line.startswith("["):
                            if "ELECTRODE" in line or "REFERENCE" in line:
                                keep_parsing = True
                            else:
                                keep_parsing = False
                        elif keep_parsing is True:
                            idx, name = line.split("=")
                            idx = int(idx)
                            if idx >= len(_chan_labels):
                                n = idx - len(_chan_labels) + 1
                                _chan_labels.extend(["UNK"] * n)
                            _chan_labels[idx] = name.strip()
            except UnicodeDecodeError:
                pass
            else:
                break
        else:
            warn(
                f"Could not decode 21E file as one of {_encodings}; "
                f"Default channel names are chosen."
            )

    return _chan_labels


def _read_nihon_header(fname):
    # Read the Nihon Kohden EEG file header
    fname = _ensure_path(fname)
    _chan_labels = _read_21e_file(fname)
    header = {}
    logger.info(f"Reading header from {fname}")
    with open(fname) as fid:
        version = np.fromfile(fid, "|S16", 1).astype("U16")[0]
        if version not in _valid_headers:
            raise ValueError(f"Not a valid Nihon Kohden EEG file ({version})")

        fid.seek(0x0081)
        control_block = np.fromfile(fid, "|S16", 1).astype("U16")[0]
        if control_block not in _valid_headers:
            raise ValueError(
                f"Not a valid Nihon Kohden EEG file (control block {version})"
            )

        fid.seek(0x17FE)
        waveform_sign = np.fromfile(fid, np.uint8, 1)[0]
        if waveform_sign != 1:
            raise ValueError("Not a valid Nihon Kohden EEG file (waveform block)")
        header["version"] = version

        fid.seek(0x0091)
        n_ctlblocks = np.fromfile(fid, np.uint8, 1)[0]
        header["n_ctlblocks"] = n_ctlblocks
        controlblocks = []
        for i_ctl_block in range(n_ctlblocks):
            t_controlblock = {}
            fid.seek(0x0092 + i_ctl_block * 20)
            t_ctl_address = np.fromfile(fid, np.uint32, 1)[0]
            t_controlblock["address"] = t_ctl_address
            fid.seek(t_ctl_address + 17)
            n_datablocks = np.fromfile(fid, np.uint8, 1)[0]
            t_controlblock["n_datablocks"] = n_datablocks
            t_controlblock["datablocks"] = []
            for i_data_block in range(n_datablocks):
                t_datablock = {}
                fid.seek(t_ctl_address + i_data_block * 20 + 18)
                t_data_address = np.fromfile(fid, np.uint32, 1)[0]
                t_datablock["address"] = t_data_address

                fid.seek(t_data_address + 0x26)
                t_n_channels = np.fromfile(fid, np.uint8, 1)[0].astype(np.int64)
                t_datablock["n_channels"] = t_n_channels

                t_channels = []
                for i_ch in range(t_n_channels):
                    fid.seek(t_data_address + 0x27 + (i_ch * 10))
                    t_idx = np.fromfile(fid, np.uint8, 1)[0]
                    t_channels.append(_chan_labels[t_idx])

                t_datablock["channels"] = t_channels

                fid.seek(t_data_address + 0x1C)
                t_record_duration = np.fromfile(fid, np.uint32, 1)[0].astype(np.int64)
                t_datablock["duration"] = t_record_duration

                fid.seek(t_data_address + 0x1A)
                sfreq = np.fromfile(fid, np.uint16, 1)[0] & 0x3FFF
                t_datablock["sfreq"] = sfreq.astype(np.int64)

                t_datablock["n_samples"] = np.int64(t_record_duration * sfreq // 10)
                t_controlblock["datablocks"].append(t_datablock)
            controlblocks.append(t_controlblock)
        header["controlblocks"] = controlblocks

    # Now check that every data block has the same channels and sfreq
    chans = []
    sfreqs = []
    nsamples = []
    for t_ctl in header["controlblocks"]:
        for t_dtb in t_ctl["datablocks"]:
            chans.append(t_dtb["channels"])
            sfreqs.append(t_dtb["sfreq"])
            nsamples.append(t_dtb["n_samples"])
    for i_elem in range(1, len(chans)):
        if chans[0] != chans[i_elem]:
            raise ValueError("Channel names in datablocks do not match")
        if sfreqs[0] != sfreqs[i_elem]:
            raise ValueError("Sample frequency in datablocks do not match")
    header["ch_names"] = chans[0]
    header["sfreq"] = sfreqs[0]
    header["n_samples"] = np.sum(nsamples)

    # TODO: Support more than one controlblock and more than one datablock
    if header["n_ctlblocks"] != 1:
        raise NotImplementedError(
            "I dont know how to read more than one "
            "control block for this type of file :("
        )
    if header["controlblocks"][0]["n_datablocks"] > 1:
        # Multiple blocks, check that they all have the same kind of data
        datablocks = header["controlblocks"][0]["datablocks"]
        block_0 = datablocks[0]
        for t_block in datablocks[1:]:
            if block_0["n_channels"] != t_block["n_channels"]:
                raise ValueError(
                    "Cannot read NK file with different number of channels "
                    "in each datablock"
                )
            if block_0["channels"] != t_block["channels"]:
                raise ValueError(
                    "Cannot read NK file with different channels in each datablock"
                )
            if block_0["sfreq"] != t_block["sfreq"]:
                raise ValueError(
                    "Cannot read NK file with different sfreq in each datablock"
                )

    return header


def _read_nihon_annotations(fname):
    fname = _ensure_path(fname)
    log_fname = fname.with_suffix(".LOG")
    if not log_fname.exists():
        warn("No LOG file exists. Annotations will not be read")
        return dict(onset=[], duration=[], description=[])
    logger.info("Found LOG file, reading events.")
    with open(log_fname) as fid:
        version = np.fromfile(fid, "|S16", 1).astype("U16")[0]
        if version not in _valid_headers:
            raise ValueError(f"Not a valid Nihon Kohden LOG file ({version})")

        fid.seek(0x91)
        n_logblocks = np.fromfile(fid, np.uint8, 1)[0]
        all_onsets = []
        all_descriptions = []
        for t_block in range(n_logblocks):
            fid.seek(0x92 + t_block * 20)
            t_blk_address = np.fromfile(fid, np.uint32, 1)[0]
            fid.seek(t_blk_address + 0x12)
            n_logs = np.fromfile(fid, np.uint8, 1)[0]
            fid.seek(t_blk_address + 0x14)
            t_logs = np.fromfile(fid, "|S45", n_logs)
            for t_log in t_logs:
                for enc in _encodings:
                    try:
                        t_log = t_log.decode(enc)
                    except UnicodeDecodeError:
                        pass
                    else:
                        break
                else:
                    warn(f"Could not decode log as one of {_encodings}")
                    continue
                t_desc = t_log[:20].strip("\x00")
                t_onset = datetime.strptime(t_log[20:26], "%H%M%S")
                t_onset = t_onset.hour * 3600 + t_onset.minute * 60 + t_onset.second
                all_onsets.append(t_onset)
                all_descriptions.append(t_desc)

        annots = dict(
            onset=all_onsets,
            duration=[0] * len(all_onsets),
            description=all_descriptions,
        )
    return annots


def _map_ch_to_type(ch_name):
    ch_type_pattern = OrderedDict(
        [("stim", ("Mark",)), ("misc", ("DC", "NA", "Z", "$")), ("bio", ("X",))]
    )
    for key, kinds in ch_type_pattern.items():
        if any(kind in ch_name for kind in kinds):
            return key
    return "eeg"


def _map_ch_to_specs(ch_name):
    unit_mult = 1e-3
    phys_min = -12002.9
    phys_max = 12002.56
    dig_min = -32768
    if ch_name.upper() in _default_chan_labels:
        idx = _default_chan_labels.index(ch_name.upper())
        if (idx < 42 or idx > 73) and idx not in [76, 77]:
            unit_mult = 1e-6
            phys_min = -3200
            phys_max = 3199.902
    t_range = phys_max - phys_min
    cal = t_range / 65535
    offset = phys_min - (dig_min * cal)

    out = dict(
        unit=unit_mult,
        phys_min=phys_min,
        phys_max=phys_max,
        dig_min=dig_min,
        cal=cal,
        offset=offset,
    )
    return out


@fill_doc
class RawNihon(BaseRaw):
    """Raw object from a Nihon Kohden EEG file.

    Parameters
    ----------
    fname : path-like
        Path to the Nihon Kohden data ``.eeg`` file.
    preload : bool
        If True, all data are loaded at initialization.
    %(verbose)s

    See Also
    --------
    mne.io.Raw : Documentation of attributes and methods.
    """

    @verbose
    def __init__(self, fname, preload=False, verbose=None):
        fname = _check_fname(fname, "read", True, "fname")
        data_name = fname.name
        logger.info(f"Loading {data_name}")

        header = _read_nihon_header(fname)
        metadata = _read_nihon_metadata(fname)

        # n_chan = len(header['ch_names']) + 1
        sfreq = header["sfreq"]
        # data are multiplexed int16
        ch_names = header["ch_names"]
        ch_types = [_map_ch_to_type(x) for x in ch_names]

        info = create_info(ch_names, sfreq, ch_types)
        n_samples = header["n_samples"]

        if "meas_date" in metadata:
            with info._unlock():
                info["meas_date"] = metadata["meas_date"]
        chs = {x: _map_ch_to_specs(x) for x in info["ch_names"]}

        cal = np.array([chs[x]["cal"] for x in info["ch_names"]], float)[:, np.newaxis]
        offsets = np.array([chs[x]["offset"] for x in info["ch_names"]], float)[
            :, np.newaxis
        ]
        gains = np.array([chs[x]["unit"] for x in info["ch_names"]], float)[
            :, np.newaxis
        ]

        raw_extras = dict(cal=cal, offsets=offsets, gains=gains, header=header)
        for i_ch, ch_name in enumerate(info["ch_names"]):
            t_range = chs[ch_name]["phys_max"] - chs[ch_name]["phys_min"]
            info["chs"][i_ch]["range"] = t_range
            info["chs"][i_ch]["cal"] = 1 / t_range

        super().__init__(
            info,
            preload=preload,
            last_samps=(n_samples - 1,),
            filenames=[fname.as_posix()],
            orig_format="short",
            raw_extras=[raw_extras],
        )

        # Get annotations from LOG file
        annots = _read_nihon_annotations(fname)

        # Annotate acquisition skips
        controlblock = header["controlblocks"][0]
        cur_sample = 0
        if controlblock["n_datablocks"] > 1:
            for i_block in range(controlblock["n_datablocks"] - 1):
                t_block = controlblock["datablocks"][i_block]
                cur_sample = cur_sample + t_block["n_samples"]
                cur_tpoint = (cur_sample - 0.5) / t_block["sfreq"]
                # Add annotations as in append raw
                annots["onset"].append(cur_tpoint)
                annots["duration"].append(0.0)
                annots["description"].append("BAD boundary")
                annots["onset"].append(cur_tpoint)
                annots["duration"].append(0.0)
                annots["description"].append("EDGE boundary")

        annotations = Annotations(**annots, orig_time=info["meas_date"])
        self.set_annotations(annotations)

    def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):
        """Read a chunk of raw data."""
        # For now we assume one control block
        header = self._raw_extras[fi]["header"]

        # Get the original cal, offsets and gains
        cal = self._raw_extras[fi]["cal"]
        offsets = self._raw_extras[fi]["offsets"]
        gains = self._raw_extras[fi]["gains"]

        # get the right datablock
        datablocks = header["controlblocks"][0]["datablocks"]
        ends = np.cumsum([t["n_samples"] for t in datablocks])

        start_block = np.where(start < ends)[0][0]
        stop_block = np.where(stop <= ends)[0][0]

        if start_block != stop_block:
            # Recursive call for each block independently
            new_start = start
            sample_start = 0
            for t_block_idx in range(start_block, stop_block + 1):
                t_block = datablocks[t_block_idx]
                if t_block == stop_block:
                    # If its the last block, we stop on the last sample to read
                    new_stop = stop
                else:
                    # Otherwise, stop on the last sample of the block
                    new_stop = t_block["n_samples"] + new_start
                samples_to_read = new_stop - new_start
                sample_stop = sample_start + samples_to_read

                self._read_segment_file(
                    data[:, sample_start:sample_stop],
                    idx,
                    fi,
                    new_start,
                    new_stop,
                    cals,
                    mult,
                )

                # Update variables for next loop
                sample_start = sample_stop
                new_start = new_stop
        else:
            datablock = datablocks[start_block]

            n_channels = datablock["n_channels"] + 1
            datastart = datablock["address"] + 0x27 + (datablock["n_channels"] * 10)

            # Compute start offset based on the beginning of the block
            rel_start = start
            if start_block != 0:
                rel_start = start - ends[start_block - 1]
            start_offset = datastart + rel_start * n_channels * 2

            with open(self.filenames[fi], "rb") as fid:
                to_read = (stop - start) * n_channels
                fid.seek(start_offset)
                block_data = np.fromfile(fid, "<u2", to_read) + 0x8000
                block_data = block_data.astype(np.int16)
                block_data = block_data.reshape(n_channels, -1, order="F")
                block_data = block_data[:-1] * cal  # cast to float64
                block_data += offsets
                block_data *= gains
                _mult_cal_one(data, block_data, idx, cals, mult)
