"""
scratch/next_experiment_skeleton.py — segment-channel scoring analyzer.

This is the suggested NEXT EXPERIMENT for cracking the waveform body codec.
The goal is to figure out what segments 1+ contain, since segment 0 = Tran
is solved but multi-segment continuation diverges from truth at sample ~512.

────────────────────────────────────────────────────────────────────────────
The hypothesis to test
────────────────────────────────────────────────────────────────────────────

Segments rotate through channels:

    segment 0  →  Tran samples 0..509
    segment 1  →  Vert samples 0..507
    segment 2  →  Long samples 0..507
    segment 3  →  Mic  samples 0..507
    segment 4  →  Tran samples 510..N (continuation)
    ...

This would explain why segment 0 works perfectly (it's pure Tran) and why
applying segment 1's blocks as Tran continuation gives wrong values
(it's actually Vert).

────────────────────────────────────────────────────────────────────────────
What the analyzer should do
────────────────────────────────────────────────────────────────────────────

For each segment in each fixture event:

1. Run the segment-0 block-walker + RLE decode (the same algorithm that
   ``decode_tran_initial`` uses) over the segment's blocks.  Start from
   some anchor value and produce a cumulative trajectory of length =
   number-of-deltas-in-segment.

2. For each candidate channel C ∈ {Tran, Vert, Long, MicL}:
   For each candidate anchor location in the segment-header payload
   (try [0:2], [2:4], [4:6], [14:16], [16:18] as int16 BE):
       Compare the decoded trajectory against truth[C] starting from
       the segment's first sample index.
       Score = number of matches (or sum of squared errors).

3. Report the best (channel, anchor-location) combination per segment.

If the rotation hypothesis is correct, you'll see:
    segment 0  →  best score for (Tran, preamble bytes [3:5])    ✓ already known
    segment 1  →  best score for (Vert, <some-header-byte>)
    segment 2  →  best score for (Long, <some-header-byte>)
    segment 3  →  best score for (MicL, <some-header-byte>)
    segment 4  →  best score for (Tran, continuing from segment 0's end)

If the rotation hypothesis is NOT correct, the scorer will at least narrow
down what segment 1 actually carries.  Maybe channels interleave at finer
granularity, or maybe segments alternate by something other than channel.

────────────────────────────────────────────────────────────────────────────
Why this is a scoring analyzer, not a hand-written decoder
────────────────────────────────────────────────────────────────────────────

Direct hand-coding ("assume segment 1 is Vert with anchor at byte X") gets
stuck when the assumption is wrong because the failure mode is silent —
you get plausible-looking-but-wrong samples and have to manually diff
against truth to debug.

The scorer is brute-force but cheap: every fixture event × every segment ×
4 channels × 5 anchor-byte candidates is only ~hundreds of comparisons.
The winning combination jumps out by score.

────────────────────────────────────────────────────────────────────────────
Skeleton
────────────────────────────────────────────────────────────────────────────
"""
from __future__ import annotations

import os
import re
import sys
from dataclasses import dataclass
from typing import List, Optional, Tuple

sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))

from minimateplus.waveform_codec import walk_body, find_data_start, WaveformBlock


# ── Reusable pieces ──────────────────────────────────────────────────────────


CHANNELS = ("Tran", "Vert", "Long", "MicL")
LSB_INV = 200  # 1 in/s / 0.005 in/s/LSB; multiply BW-export floats by this
               # to get 16-count units (the body's native quantization).


@dataclass
class FixtureEvent:
    name: str           # e.g. "M529LL1A.SP0"
    bin_path: str
    txt_path: str
    body: bytes
    truth: dict         # {channel: list of int16-quantized samples}
    blocks: List[WaveformBlock]
    segment_starts: List[int]  # block indices of each 40 02 segment header
    segment_sample_starts: List[int]  # for each segment, the truth sample index it starts at


def s4(n: int) -> int:
    """4-bit signed nibble decode."""
    return n if n < 8 else n - 16


def i8(b: int) -> int:
    """int8 reinterpret of unsigned byte."""
    return b if b < 128 else b - 256


def load_fixture(name: str) -> FixtureEvent:
    """Load a fixture event with its truth values and parsed block stream."""
    # Find the fixture (search both subdirs of tests/fixtures/).
    base = os.path.join(os.path.dirname(__file__), "..", "tests", "fixtures")
    candidates = [
        os.path.join(base, "5-11-26", name),
        os.path.join(base, "decode-re-5-8-26", "event-a", name),  # not used directly
    ]
    bin_path = next((c for c in candidates if os.path.exists(c)), None)
    if bin_path is None:
        # Try a glob walk for the 5-8 fixtures (they're in subdirs).
        for root, _, files in os.walk(base):
            if name in files:
                bin_path = os.path.join(root, name)
                break
    if bin_path is None:
        raise FileNotFoundError(name)

    txt_path = bin_path + ".TXT"
    with open(bin_path, "rb") as f:
        raw = f.read()
    body = raw[43:-26]
    truth = _parse_txt(txt_path)
    blocks = walk_body(body, find_data_start(body))

    seg_idx = [i for i, b in enumerate(blocks) if b.tag_hi == 0x40]
    # Segment 0 starts at sample 0; subsequent segments start at the
    # cumulative sample count from previous segment(s).  Tran's segment 0
    # is N samples; if rotation hypothesis is correct, segment 1's data
    # starts at sample 0 for a *different* channel.  The analyzer should
    # try both "continues from previous segment" and "starts at sample 0
    # of a different channel."
    seg_sample_starts = _compute_segment_sample_starts(blocks, seg_idx)

    return FixtureEvent(
        name=name, bin_path=bin_path, txt_path=txt_path,
        body=body, truth=truth, blocks=blocks,
        segment_starts=seg_idx, segment_sample_starts=seg_sample_starts,
    )


def _parse_txt(path: str) -> dict:
    """Parse BW ASCII TXT export into {channel: [int_samples_in_16_count_units]}."""
    with open(path, "r", encoding="utf-8", errors="replace") as f:
        lines = f.read().splitlines()
    header_idx = next(
        (i for i, l in enumerate(lines)
         if all(c in l for c in CHANNELS)),
        None,
    )
    if header_idx is None:
        return {ch: [] for ch in CHANNELS}
    out = {ch: [] for ch in CHANNELS}
    for line in lines[header_idx + 1:]:
        parts = re.split(r"\s+", line.strip())
        if len(parts) < 4:
            continue
        try:
            vals = [float(p) for p in parts[:4]]
        except ValueError:
            continue
        for ch, v in zip(CHANNELS, vals):
            # Multiply by LSB_INV; geo channels are in in/s, MicL is in dB(L)
            # (which doesn't quantize the same way — leaving raw for MicL is fine,
            # the scorer should treat MicL specially).
            out[ch].append(round(v * LSB_INV) if ch != "MicL" else v)
    return out


def _compute_segment_sample_starts(
    blocks: List[WaveformBlock], seg_idx: List[int]
) -> List[int]:
    """Cumulative sample-count up to each segment header (if all blocks treated
    as Tran continuation).  Useful as one candidate for segment-1-Tran tests.

    The scorer should ALSO try "segment 1 starts at sample 0 of a new channel"
    as the rotation hypothesis predicts.
    """
    starts = []
    cum = 2  # T[0] + T[1] from preamble
    for i, b in enumerate(blocks):
        if i in seg_idx:
            starts.append(cum)
        if b.tag_hi == 0x10:
            cum += b.tag_lo
        elif b.tag_hi == 0x20:
            cum += b.tag_lo
        elif b.tag_hi == 0x00:
            cum += b.tag_lo
        # 30 NN and 40 02 don't contribute samples (for this hypothesis)
    return starts


# ── The core algorithm: decode a segment's blocks as deltas ─────────────────


def decode_segment_as_channel(
    blocks: List[WaveformBlock],
    seg_start_block_idx: int,
    seg_end_block_idx: int,
    anchor: int,
) -> List[int]:
    """Apply the segment-0 codec rules to a range of blocks, starting from *anchor*.

    Returns a list of cumulative sample values (one per delta).  Does NOT include
    the anchor itself in the output — the first returned value is anchor + first_delta.
    """
    out = []
    cur = anchor
    for bi in range(seg_start_block_idx, seg_end_block_idx):
        blk = blocks[bi]
        if blk.tag_hi == 0x10:
            for byte in blk.data:
                for nib in ((byte >> 4) & 0xF, byte & 0xF):
                    cur += s4(nib)
                    out.append(cur)
        elif blk.tag_hi == 0x20:
            for byte in blk.data:
                cur += i8(byte)
                out.append(cur)
        elif blk.tag_hi == 0x00:
            for _ in range(blk.tag_lo):
                out.append(cur)
        # 30 NN: skip (content unknown)
        # 40 02: shouldn't appear in segment data (it's the segment header)
    return out


def score_against_truth(
    decoded: List[int],
    truth: List[int],
    truth_start: int,
) -> Tuple[int, int]:
    """Compare *decoded* to truth[truth_start : truth_start + len(decoded)].

    Returns (n_matches, n_compared).
    """
    n = min(len(decoded), len(truth) - truth_start)
    if n <= 0:
        return (0, 0)
    matches = sum(1 for i in range(n) if decoded[i] == truth[truth_start + i])
    return (matches, n)


# ── TODO for the next pass ──────────────────────────────────────────────────


def score_segment_against_all_channels(
    event: FixtureEvent,
    segment_index: int,
) -> List[Tuple[str, int, int, int]]:
    """For segment *segment_index* of *event*, find the best (channel, start_sample)
    fit.

    For each candidate channel C and each candidate starting truth-sample index s,
    we pick the anchor that makes the FIRST decoded value match truth[C][s], then
    score the remaining decoded values against truth[C][s+1 : s+N].

    Returns rows of (channel_name, start_sample, n_matches, n_compared)
    sorted by match-count descending.
    """
    # Block range of this segment: from the segment header (inclusive) up to
    # the next segment header (exclusive), or end-of-blocks.
    seg_header_idx = event.segment_starts[segment_index]
    next_header_idx = (
        event.segment_starts[segment_index + 1]
        if segment_index + 1 < len(event.segment_starts)
        else len(event.blocks)
    )

    # Decode the segment's data blocks (skip the segment-header block itself).
    # Use anchor=0 — we'll re-anchor when scoring against each channel.
    deltas_trajectory = decode_segment_as_channel(
        event.blocks, seg_header_idx + 1, next_header_idx, anchor=0
    )
    if not deltas_trajectory:
        return []

    n = len(deltas_trajectory)
    results = []

    for ch in ("Tran", "Vert", "Long"):
        truth = event.truth.get(ch)
        if not truth or len(truth) < n + 1:
            continue
        # For each candidate starting sample s in truth, check if applying
        # the deltas starting from truth[s] reproduces truth[s+1:s+n+1].
        best = (0, -1)
        for s in range(len(truth) - n):
            anchor = truth[s]
            offset = anchor - deltas_trajectory[0] + truth[s + 1] - anchor
            # Recompute: trajectory[i] = anchor + cumulative_delta_through_i
            # but we already have deltas_trajectory computed from anchor=0,
            # so trajectory_relative[i] = anchor + deltas_trajectory[i].
            matches = 0
            for i in range(n):
                if truth[s + i + 1] == anchor + deltas_trajectory[i]:
                    matches += 1
                # Note: we could break early on first mismatch for "matches start",
                # but counting total matches gives a more robust score.
            if matches > best[0]:
                best = (matches, s)
        results.append((ch, best[1], best[0], n))

    results.sort(key=lambda r: -r[2])
    return results


# ── Driver ──────────────────────────────────────────────────────────────────


def main():
    """Run the analyzer on all loud-bundle events and print best scores."""
    events = ["M529LL1A.SP0", "M529LL1A.SS0", "M529LL1A.SV0",
              "M529LL1L.JQ0", "M529LL1L.V70"]
    for name in events:
        try:
            event = load_fixture(name)
        except FileNotFoundError:
            print(f"{name}: fixture not found")
            continue

        print(f"\n=== {name} ===")
        print(f"  body bytes: {len(event.body)}")
        print(f"  blocks: {len(event.blocks)}")
        print(f"  segments: {len(event.segment_starts)}")
        print(f"  segment sample-starts (if all blocks are 1 channel):")
        for si, sample_start in enumerate(event.segment_sample_starts):
            print(f"    seg {si}: sample {sample_start}")

        for si in range(len(event.segment_starts)):
            results = score_segment_against_all_channels(event, si)
            if not results:
                print(f"  seg {si}: (no scorable data)")
                continue
            tag = "✓" if results[0][2] / max(results[0][3], 1) > 0.9 else " "
            top = results[0]
            print(f"  seg {si}: best fit {tag} = {top[0]:<5} "
                  f"starting at sample {top[1]:>5}, {top[2]:>4}/{top[3]:<4} match"
                  + (f"  (next: {results[1][0]} @{results[1][1]} {results[1][2]}/{results[1][3]})"
                     if len(results) > 1 else ""))


if __name__ == "__main__":
    main()