#!/usr/bin/env bash
# Re-create the SPE-1 Arrow dataset from scratch via CRCNS download.
#
# Usage (from the dataset root, e.g. systems/datasets/spe1/):
#     CRCNS_USERNAME=your_user CRCNS_PASSWORD=your_pass \
#         bash scripts/prepare.sh
#
# If CRCNS_PASSWORD is unset you will be prompted interactively.
#
# Environment overrides:
#   CRCNS_USERNAME   CRCNS account username                 (REQUIRED)
#   CRCNS_PASSWORD   CRCNS account password                 (prompted if unset)
#   SPE1_RAW_DIR     Where to store the downloaded raw data (default: ./.raw)
#   SPE1_OUT_DIR     Where to write Arrow datasets          (default: .)
#   SPE1_CELLS       Space-separated cell IDs               (default: all 12)
#   SPE1_DURATION    Recording seconds per cell             (default: 300)
#   PYTHON           Python interpreter to use              (default: python3)
#
# Required Python packages:
#     pip install requests scipy pandas openpyxl spikeinterface datasets numpy

set -euo pipefail

SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
DATASET_ROOT="$(cd "${SCRIPT_DIR}/.." && pwd)"

PYTHON="${PYTHON:-python3}"
SPE1_OUT_DIR="${SPE1_OUT_DIR:-${DATASET_ROOT}}"
SPE1_RAW_DIR="${SPE1_RAW_DIR:-${SPE1_OUT_DIR}/.raw}"
SPE1_DURATION="${SPE1_DURATION:-300}"

# Default: 11 Zhao et al. 2026 cells + c5 (longest WC-IC, used for V(t) plots).
DEFAULT_CELLS="c5 c14 c15 c16 c19 c24 c26 c28 c29 c37 c45 c46"
SPE1_CELLS="${SPE1_CELLS:-${DEFAULT_CELLS}}"

CONVERTER="${SCRIPT_DIR}/convert_to_arrow.py"
LOG_DIR="${SPE1_OUT_DIR}/.logs"
DATA_DIR="${SPE1_RAW_DIR}/Recordings"
CHAN_MAP="${SPE1_RAW_DIR}/chanMap.mat"
SUMMARY_XLS="${SPE1_RAW_DIR}/Data Summary.xlsx"

# CRCNS dataset paths.
CRCNS_DATA_PREFIX="spe-1/data"
CRCNS_ANCILLARY=(
    "spe-1/chanMap.mat"
    "spe-1/Data Summary.xlsx"
)

mkdir -p "${SPE1_OUT_DIR}" "${LOG_DIR}" "${SPE1_RAW_DIR}" "${DATA_DIR}"

log() { echo "[$(date '+%H:%M:%S')] $*"; }
die() { echo "ERROR: $*" >&2; exit 1; }

if [[ -z "${CRCNS_USERNAME:-}" ]]; then
    die "CRCNS_USERNAME is required.  Register a free account at
    https://crcns.org/register
and re-run with:
    CRCNS_USERNAME=<user> CRCNS_PASSWORD=<pass> bash $0"
fi
if [[ -z "${CRCNS_PASSWORD:-}" ]]; then
    read -r -s -p "CRCNS password for ${CRCNS_USERNAME}: " CRCNS_PASSWORD
    echo
    export CRCNS_PASSWORD
fi

log "SPE-1 prepare pipeline (CRCNS)"
log "  Output:   ${SPE1_OUT_DIR}"
log "  Raw data: ${SPE1_RAW_DIR}"
log "  Cells:    ${SPE1_CELLS}"
log "  Duration: ${SPE1_DURATION}s per cell"

# ---------------------------------------------------------------------------
# Validate Python environment
# ---------------------------------------------------------------------------
"${PYTHON}" - <<'PYCHECK'
import sys
missing = []
for pkg in ["requests", "scipy", "spikeinterface", "datasets", "numpy", "pandas"]:
    try:
        __import__(pkg)
    except ImportError:
        missing.append(pkg)
if missing:
    sys.exit("Missing packages: " + ", ".join(missing) +
             "\nInstall with:  pip install " + " ".join(missing))
print("  All required packages present.")
PYCHECK

crcns_download() {
    # crcns_download <relative_path_on_portal> <local_output_path>
    local fn="$1"
    local out="$2"
    if [[ -s "${out}" ]]; then
        return 0
    fi
    mkdir -p "$(dirname "${out}")"
    CRCNS_FN="${fn}" CRCNS_OUT="${out}" \
    "${PYTHON}" - <<'PYDL'
import os, sys, requests
URL = "https://portal.nersc.gov/project/crcns/download/index.php"
fn  = os.environ["CRCNS_FN"]
out = os.environ["CRCNS_OUT"]
data = dict(username=os.environ["CRCNS_USERNAME"],
            password=os.environ["CRCNS_PASSWORD"],
            fn=fn, submit="Login")
tmp = out + ".part"
total = 0
with requests.Session() as s, requests.post(
    URL, data=data, stream=True, timeout=60
) as r:
    r.raise_for_status()
    if r.headers.get("Content-Type", "").startswith("text/html"):
        sys.exit("CRCNS returned HTML (likely auth failure): "
                 + r.text[:300].replace("\n", " "))
    with open(tmp, "wb") as f:
        for chunk in r.iter_content(chunk_size=1 << 20):
            if chunk:
                f.write(chunk)
                total += len(chunk)
                if total % (256 << 20) < (1 << 20):
                    print(f"  ... {total / (1<<20):.0f} MiB", flush=True)
os.replace(tmp, out)
print(f"  Done: {total / (1<<20):.1f} MiB -> {out}")
PYDL
}

# ---------------------------------------------------------------------------
# Step 1 - download ancillary files (chanMap + Data Summary)
# ---------------------------------------------------------------------------
for src in "${CRCNS_ANCILLARY[@]}"; do
    fname="$(basename "${src}")"
    target="${SPE1_RAW_DIR}/${fname}"
    if [[ ! -s "${target}" ]]; then
        log "Downloading ${fname} from CRCNS …"
        crcns_download "${src}" "${target}"
    fi
done

# Mirror the small ancillary files at the dataset root so consumers don't
# need .raw/ to load probe geometry or ground-truth electrode assignments.
cp -u "${CHAN_MAP}"     "${SPE1_OUT_DIR}/chanMap.mat"
cp -u "${SUMMARY_XLS}"  "${SPE1_OUT_DIR}/Data Summary.xlsx"

# ---------------------------------------------------------------------------
# Step 2 - download + extract per-cell tar.gz archives
# ---------------------------------------------------------------------------
for cell in ${SPE1_CELLS}; do
    if [[ -n "$(find "${DATA_DIR}/${cell}" -maxdepth 1 -name '*npx_raw.bin' 2>/dev/null | head -1)" ]]; then
        log "  ${cell}: already extracted — skipping"
        continue
    fi

    archive="${SPE1_RAW_DIR}/${cell}.tar.gz"
    if [[ ! -s "${archive}" ]]; then
        log "Downloading ${cell}.tar.gz from CRCNS …"
        crcns_download "${CRCNS_DATA_PREFIX}/${cell}.tar.gz" "${archive}" \
            2>&1 | tee "${LOG_DIR}/download_${cell}.log"
        if [[ ! -s "${archive}" ]]; then
            log "  ERROR: download failed for ${cell}.tar.gz"
            rm -f "${archive}" "${archive}.part"
            continue
        fi
    fi

    log "Extracting ${cell}.tar.gz into ${DATA_DIR}/ …"
    tar -xzf "${archive}" -C "${DATA_DIR}" \
        || { log "  ERROR: extraction failed for ${cell}.tar.gz"; continue; }
done

# ---------------------------------------------------------------------------
# Step 3 - convert each cell to Arrow
# ---------------------------------------------------------------------------
log "Converting cells to Arrow (one cell per subprocess to bound memory) …"
CONVERT_ERRORS=0
SKIPPED_CELLS=""
for cell in ${SPE1_CELLS}; do
    npx_bin=$(find "${DATA_DIR}/${cell}" -maxdepth 1 -name "*npx_raw.bin" 2>/dev/null | head -1)
    if [[ -z "${npx_bin}" ]]; then
        log "  Skipping ${cell}: npx_raw.bin not present"
        SKIPPED_CELLS="${SKIPPED_CELLS} ${cell}"
        continue
    fi

    log "  Converting ${cell} …"
    "${PYTHON}" "${CONVERTER}" \
        --data-dir  "${DATA_DIR}" \
        --chan-map  "${CHAN_MAP}" \
        --summary   "${SUMMARY_XLS}" \
        --output    "${SPE1_OUT_DIR}" \
        --cells     "${cell}" \
        --duration  "${SPE1_DURATION}" \
        2>&1 | tee "${LOG_DIR}/convert_${cell}.log"
    if [[ ${PIPESTATUS[0]} -ne 0 ]]; then
        log "  ERROR: conversion failed for ${cell}"
        CONVERT_ERRORS=$((CONVERT_ERRORS + 1))
    fi
done

if [[ -n "${SKIPPED_CELLS}" ]]; then
    log "Skipped (npx_raw.bin missing):${SKIPPED_CELLS}"
fi
if [[ ${CONVERT_ERRORS} -gt 0 ]]; then
    die "${CONVERT_ERRORS} cell(s) failed to convert.  Check ${LOG_DIR}/"
fi
log "Pipeline complete.  Datasets at: ${SPE1_OUT_DIR}"