Project_Carmignac/repair_challenge/carmignac_analysis.py

"""
Carmignac Data Challenge — Pipeline Results Analysis
=====================================================
Analyses the CSV outputs produced by carmignac_repair.py:
  - carmignac_scores.csv      (post-surgery score history)
  - carmignac_mapping.csv     (reg_id mapping history)
  - carmignac_surgery_log.csv (surgery operations)

Produces a self-contained HTML report with interactive charts.

Usage:
    python carmignac_analysis.py
    python carmignac_analysis.py --scores path/to/scores.csv \
                                  --mapping path/to/mapping.csv \
                                  --surgery path/to/surgery_log.csv \
                                  --out report.html
"""

import argparse
import json
import os
import sys
import numpy as np
import pandas as pd

# ─────────────────────────────────────────────────────────────
# 1. LOAD & VALIDATE
# ─────────────────────────────────────────────────────────────

def load_outputs(scores_path, mapping_path, surgery_path,
                 err_isin_path=None, err_agg_path=None):
    scores  = pd.read_csv(scores_path,  parse_dates=["date"])
    mapping = pd.read_csv(mapping_path, parse_dates=["date"])
    surgery = pd.read_csv(surgery_path, parse_dates=["date"])

    # Normalise dtypes
    scores["reg_id"]       = scores["reg_id"].astype(str)
    mapping["reg_orig"]    = mapping["reg_orig"].astype(str)
    mapping["reg_used"]    = mapping["reg_used"].astype(str)
    mapping["changed"]     = mapping["changed"].astype(bool)
    surgery["reg_orig"]    = surgery["reg_orig"].astype(str)
    surgery["reg_from"]    = surgery["reg_from"].astype(str)
    surgery["reg_to"]      = surgery["reg_to"].astype(str)
    if "lookback_months" not in surgery.columns:
        surgery["lookback_months"] = 1  # backwards compat

    # Error account (optional)
    err_isin = None
    err_agg  = None
    if err_isin_path and os.path.exists(err_isin_path):
        err_isin = pd.read_csv(err_isin_path, parse_dates=["date"])
        err_isin["isin"] = err_isin["isin"].astype(str)
    if err_agg_path and os.path.exists(err_agg_path):
        err_agg = pd.read_csv(err_agg_path, parse_dates=["date"])

    return scores, mapping, surgery, err_isin, err_agg



# ─────────────────────────────────────────────────────────────
# 1b. LOAD ERROR ACCOUNT (optional)
# ─────────────────────────────────────────────────────────────

def load_error_account(isin_path, agg_path):
    """
    Loads the error account CSVs produced by carmignac_diagnostics.py.
    Returns (df_err_isin, df_err_agg) or (None, None) if files not found.
    """
    if not isin_path or not agg_path:
        return None, None
    try:
        ei = pd.read_csv(isin_path, parse_dates=["date"])
        ea = pd.read_csv(agg_path,  parse_dates=["date"])
        ei["isin"] = ei["isin"].astype(str)
        print(f"[Load] error account (ISIN) : {len(ei)} rows, "
              f"{ei['isin'].nunique()} ISINs")
        print(f"[Load] error account (agg)  : {len(ea)} rows")
        return ei, ea
    except Exception as e:
        print(f"[WARN] Could not load error account: {e}")
        return None, None

# ─────────────────────────────────────────────────────────────
# 2. COMPUTE ANALYTICS
# ─────────────────────────────────────────────────────────────

def compute_analytics(scores, mapping, surgery):
    dates = sorted(scores["date"].unique())

    # ── 2.1  Sum of scores per date (post-surgery) ──────────────
    sum_post = (scores.groupby("date")["score"]
                      .sum()
                      .reindex(dates)
                      .rename("sum_post"))

    # ── 2.2  Reconstruct pre-surgery (counterfactual) ───────────
    # Without surgery, every reg_id that had a hard break would score 0
    # from that date backwards.  We propagate the surgery "gain" as a
    # cumulative deficit going back in time.
    gain_by_date = surgery.groupby("date")["gain_vs_no_surgery"].sum()
    # cumulative deficit = sum of gains for all surgeries at or after date t
    cumulative_deficit = pd.Series(0.0, index=dates)
    for d in dates:
        cumulative_deficit[d] = gain_by_date[gain_by_date.index >= d].sum()
    sum_pre = (sum_post - cumulative_deficit).clip(lower=0).rename("sum_pre")

    timeline = pd.DataFrame({"sum_post": sum_post, "sum_pre": sum_pre})
    timeline.index = pd.to_datetime(timeline.index)
    timeline["recovery_pct"] = np.where(
        sum_pre < sum_post,
        (sum_post - sum_pre) / sum_post.clip(lower=1e-9) * 100,
        0.0,
    )

    # ── 2.3  Per-date surgery stats ─────────────────────────────
    surgery_stats = (
        surgery.groupby("date")
               .agg(
                   n_surgeries        = ("reg_orig",            "count"),
                   total_gain         = ("gain_vs_no_surgery",  "sum"),
                   avg_gain           = ("gain_vs_no_surgery",  "mean"),
                   avg_jaccard        = ("jaccard_composite",   "mean"),
                   avg_score_before   = ("score_before",        "mean"),
                   avg_score_after    = ("score_after",         "mean"),
               )
               .reindex(dates, fill_value=0)
    )

    # ── 2.4  Score distribution over time ───────────────────────
    # Wide format: rows=dates, cols=reg_ids
    pivot = scores.pivot_table(index="date", columns="reg_id",
                               values="score", aggfunc="last")
    pivot = pivot.reindex(dates)
    pivot.index = pd.to_datetime(pivot.index)

    # ── 2.5  Mapping churn ──────────────────────────────────────
    # For each date, how many reg_ids are remapped (not using their original code)?
    churn = (mapping.groupby("date")["changed"]
                    .sum()
                    .reindex(dates, fill_value=0)
                    .rename("n_remapped"))

    # ── 2.6  Score entropy (distribution spread) ────────────────
    def entropy(row):
        p = row.dropna()
        p = p[p > 0]
        if len(p) == 0:
            return np.nan
        p = p / p.sum()
        return -(p * np.log(p)).sum()

    timeline["entropy"] = pivot.apply(entropy, axis=1).values

    # ── 2.7  Individual score trajectories ──────────────────────
    # Identify which reg_ids were ever remapped
    ever_remapped = set(mapping.loc[mapping["changed"], "reg_orig"].unique())

    # ── 2.8  Surgery detail table ───────────────────────────────
    surgery_detail = surgery.copy()
    surgery_detail["gain_pct_of_score"] = (
        surgery_detail["gain_vs_no_surgery"]
        / surgery_detail["score_before"].clip(lower=1e-9) * 100
    ).round(2)

    return {
        "timeline":       timeline,
        "surgery_stats":  surgery_stats,
        "pivot":          pivot,
        "churn":          churn,
        "ever_remapped":  ever_remapped,
        "surgery_detail": surgery_detail,
        "dates":          [d.strftime("%Y-%m-%d") for d in dates],
    }


# ─────────────────────────────────────────────────────────────
# 3. PRINT CONSOLE SUMMARY
# ─────────────────────────────────────────────────────────────

def print_summary(analytics, surgery):
    tl = analytics["timeline"]
    ss = analytics["surgery_stats"]

    print("\n" + "=" * 65)
    print("  CARMIGNAC PIPELINE — RESULTS SUMMARY")
    print("=" * 65)

    print(f"\n  Date range   : {tl.index.min().date()} → {tl.index.max().date()}")
    print(f"  Total months : {len(tl)}")
    print(f"  Reg IDs      : {analytics['pivot'].shape[1]}")

    print(f"\n  ── Score (Σ) ──────────────────────────────────────────")
    print(f"  At t_ref (latest)  : {tl['sum_post'].iloc[-1]:.6f}")
    print(f"  At t_min (earliest): {tl['sum_post'].iloc[0]:.6f}")
    print(f"  Min (post-surgery) : {tl['sum_post'].min():.6f}  "
          f"({tl['sum_post'].idxmin().date()})")
    print(f"  Min (pre-surgery)  : {tl['sum_pre'].min():.6f}  "
          f"({tl['sum_pre'].idxmin().date()})")
    print(f"  Max recovery (pct) : {tl['recovery_pct'].max():.2f}%")

    print(f"\n  ── Surgeries ─────────────────────────────────────────")
    if len(surgery) == 0:
        print("  No surgeries performed.")
    else:
        print(f"  Total operations   : {len(surgery)}")
        print(f"  Total score gained : {surgery['gain_vs_no_surgery'].sum():.6f}")
        print(f"  Avg Jaccard        : {surgery['jaccard_composite'].mean():.4f}")
        print(f"  Avg gain / surgery : {surgery['gain_vs_no_surgery'].mean():.6f}")
        print()
        print(f"  {'Date':12s} {'Reg orig':12s} {'From':15s} {'To':15s} "
              f"{'Jaccard':>8s} {'Gain':>10s}")
        print("  " + "-" * 78)
        for _, row in surgery.sort_values("date").iterrows():
            print(f"  {str(row['date'].date()):12s} {row['reg_orig']:12s} "
                  f"{row['reg_from']:15s} {row['reg_to']:15s} "
                  f"{row['jaccard_composite']:8.4f} {row['gain_vs_no_surgery']:10.6f}")

    print(f"\n  ── Mapping churn ─────────────────────────────────────")
    ch = analytics["churn"]
    print(f"  Max remapped at one date  : {int(ch.max())}  ({ch.idxmax().date() if ch.max()>0 else 'N/A'})")
    print(f"  Reg IDs ever remapped     : {len(analytics['ever_remapped'])}")

    print(f"\n  ── Score entropy (distribution spread) ───────────────")
    ent = analytics["timeline"]["entropy"]
    print(f"  Mean entropy : {ent.mean():.4f}")
    print(f"  Std  entropy : {ent.std():.4f}")
    print()


# ─────────────────────────────────────────────────────────────
# 4. BUILD HTML REPORT
# ─────────────────────────────────────────────────────────────

def build_html(analytics, surgery, scores, mapping, df_err_isin=None, df_err_agg=None):
    tl  = analytics["timeline"]
    ss  = analytics["surgery_stats"]
    piv = analytics["pivot"]
    ch  = analytics["churn"]
    dates_str = analytics["dates"]

    # ── helpers to serialise for JS ─────────────────────────────
    def jf(arr, decimals=6):
        return json.dumps([round(float(v), decimals) if not np.isnan(v) else None
                           for v in arr])

    def js(arr):
        return json.dumps(list(arr))

    # ── colour palette ───────────────────────────────────────────
    REG_COLORS = [
        "#2563eb","#16a34a","#dc2626","#d97706","#7c3aed",
        "#0891b2","#db2777","#65a30d","#ea580c","#6366f1",
        "#059669","#b45309","#9333ea","#0284c7","#e11d48",
    ]

    # ── 4.1  Surgery sparkline data ──────────────────────────────
    surg_dates  = [d.strftime("%Y-%m-%d") for d in ss.index]
    n_surg      = jf(ss["n_surgeries"].values, 0)
    total_gain  = jf(ss["total_gain"].values)
    avg_gain    = jf(ss["avg_gain"].values)
    avg_jaccard = jf(ss["avg_jaccard"].values)

    # ── 4.2  Individual trajectories ────────────────────────────
    reg_ids = list(piv.columns)
    traj_datasets = []
    # Surgery lookup: reg_orig -> list of {date, from, to, composite}
    surg_by_reg = {}
    for _, row in surgery.iterrows():
        surg_by_reg.setdefault(row["reg_orig"], []).append({
            "date":      row["date"].strftime("%Y-%m-%d"),
            "reg_from":  str(row["reg_from"]),
            "reg_to":    str(row["reg_to"]),
            "composite": round(float(row["jaccard_composite"]), 4),
            "gain":      round(float(row["gain_vs_no_surgery"]), 6),
        })

    for idx, rid in enumerate(reg_ids):
        remapped = rid in analytics["ever_remapped"]
        traj_datasets.append({
            "label":           rid,
            "data":            [round(float(v), 6) if not np.isnan(v) else None
                                for v in piv[rid].values],
            "borderColor":     REG_COLORS[idx % len(REG_COLORS)],
            "backgroundColor": REG_COLORS[idx % len(REG_COLORS)] + "22",
            "borderWidth":     2,
            "borderDash":      [6, 3] if remapped else [],
            "pointRadius":     0,
            "tension":         0.3,
            "fill":            False,
            "remapped":        remapped,
            "surgeries":       surg_by_reg.get(rid, []),
        })

    traj_json = json.dumps(traj_datasets)

    # ── 4.2b  Error account data (optional) ────────────────────
    has_error = df_err_isin is not None and df_err_agg is not None

    if has_error:
        err_dates    = [d.strftime("%Y-%m-%d") for d in pd.to_datetime(df_err_agg["date"])]
        err_agg_stock = [round(float(v), 3) if not pd.isna(v) else None
                         for v in df_err_agg["stock_error_agg"].values]
        err_agg_res   = [round(float(v), 3) if not pd.isna(v) else None
                         for v in df_err_agg["residual_agg"].values]
        err_agg_pct   = [round(float(v), 4) if not pd.isna(v) else None
                         for v in df_err_agg["stock_error_agg_pct"].values]

        # Top 5 ISINs by max |stock error|
        top_err = (df_err_isin.groupby("isin")["stock_error"]
                               .apply(lambda x: x.abs().max())
                               .nlargest(5).index.tolist())
        all_err_dates = sorted(df_err_isin["date"].unique())
        ERR_COLORS = ["#ef4444","#f59e0b","#8b5cf6","#06b6d4","#10b981"]
        err_isin_ds = []
        for idx, isin in enumerate(top_err):
            sub = (df_err_isin[df_err_isin["isin"] == isin]
                   .set_index("date")["stock_error"]
                   .reindex(all_err_dates))
            err_isin_ds.append({
                "label": isin,
                "data":  [round(float(v), 3) if not pd.isna(v) else None for v in sub.values],
                "borderColor": ERR_COLORS[idx % len(ERR_COLORS)],
                "backgroundColor": ERR_COLORS[idx % len(ERR_COLORS)] + "22",
                "borderWidth": 1.5, "pointRadius": 0, "tension": 0.3, "fill": False,
            })

        max_err_stock = float(df_err_agg["stock_error_agg"].abs().max())
        max_err_pct   = float(df_err_agg["stock_error_agg_pct"].max())
        agg_std  = float(df_err_agg["stock_error_agg"].std())
        agg_mean = float(df_err_agg["stock_error_agg"].abs().mean())
        stationarity = round(agg_std / max(agg_mean, 1e-9), 3)

        err_dates_js     = json.dumps(err_dates)
        err_agg_stock_js = json.dumps(err_agg_stock)
        err_agg_res_js   = json.dumps(err_agg_res)
        err_agg_pct_js   = json.dumps(err_agg_pct)
        err_isin_ds_js   = json.dumps(err_isin_ds)
        err_isin_dates_js = json.dumps([d.strftime("%Y-%m-%d") if hasattr(d, "strftime")
                                         else str(d)[:10] for d in all_err_dates])

        # ISIN detail table (top 100 worst)
        err_rows = []
        for _, r in (df_err_isin.assign(abs_s=df_err_isin["stock_error"].abs())
                                 .sort_values("abs_s", ascending=False)
                                 .head(100).iterrows()):
            ds  = r["date"].strftime("%Y-%m-%d") if hasattr(r["date"], "strftime") else str(r["date"])[:10]
            sc  = "color:var(--danger)" if r["stock_error"] < 0 else "color:var(--warn)"
            rc  = "color:var(--danger)" if r["residual"]    < 0 else "color:var(--warn)"
            pch = "color:var(--danger);font-weight:600" if r["stock_error_pct"] > 5 else                   ("color:var(--warn)" if r["stock_error_pct"] > 1 else "")
            err_rows.append(
                f'<tr><td>{ds}</td>'
                f'<td style="font-family:var(--mono)">{r["isin"]}</td>'
                f'<td style="text-align:right;font-family:var(--mono);{rc}">{r["residual"]:+,.2f}</td>'
                f'<td style="text-align:right;font-family:var(--mono);{sc}">{r["stock_error"]:+,.2f}</td>'
                f'<td style="text-align:right;font-family:var(--mono);{pch}">{r["stock_error_pct"]:.3f}%</td>'
                f'</tr>'
            )
        err_isin_detail = "".join(err_rows) if err_rows else (
            '<tr><td colspan="5" style="padding:24px;text-align:center;color:var(--accent2)'
            ';font-family:var(--mono)">✓ Error account is flat</td></tr>'
        )

        # HTML block for error account section
        err_section_html = f"""
  <div class="section-label">06 · Error Account</div>

  <div class="card">
    <div class="card-header">
      <span class="card-title">Aggregate error account stock</span>
      <span class="card-desc">
        Stock_error(t_ref) = 0. The stock absorbs unreconciled residuals going backwards.
        A flat signal near zero = clean data. A drift = structural gap.
      </span>
    </div>
    <div class="card-body" style="padding-bottom:8px">
      <div style="display:grid;grid-template-columns:repeat(3,1fr);gap:1px;background:var(--border);margin-bottom:20px">
        <div style="background:var(--surface);padding:14px 20px">
          <div style="font-family:var(--mono);font-size:.68rem;letter-spacing:.1em;text-transform:uppercase;color:var(--muted)">Max |error stock|</div>
          <div style="font-family:var(--mono);font-size:1.35rem;font-weight:700;color:var(--danger)">{max_err_stock:,.1f} shares</div>
        </div>
        <div style="background:var(--surface);padding:14px 20px">
          <div style="font-family:var(--mono);font-size:.68rem;letter-spacing:.1em;text-transform:uppercase;color:var(--muted)">Max % of total AUM</div>
          <div style="font-family:var(--mono);font-size:1.35rem;font-weight:700;color:{'var(--danger)' if max_err_pct > 5 else 'var(--warn)'}">{max_err_pct:.3f}%</div>
        </div>
        <div style="background:var(--surface);padding:14px 20px">
          <div style="font-family:var(--mono);font-size:.68rem;letter-spacing:.1em;text-transform:uppercase;color:var(--muted)">Stationarity σ/μ</div>
          <div style="font-family:var(--mono);font-size:1.35rem;font-weight:700;color:{'var(--accent2)' if stationarity < 1 else 'var(--warn)'}">{stationarity:.3f}</div>
          <div style="font-size:.7rem;color:var(--muted);font-family:var(--mono)">lower = more stationary</div>
        </div>
      </div>
      <div class="chart-wrap-tall"><canvas id="chartErrStock"></canvas></div>
    </div>
  </div>

  <div class="grid-2">
    <div class="card">
      <div class="card-header">
        <span class="card-title">Monthly aggregate residual</span>
        <span class="card-desc">ΔQ_total − F_total per month</span>
      </div>
      <div class="card-body"><div class="chart-wrap"><canvas id="chartErrRes"></canvas></div></div>
    </div>
    <div class="card">
      <div class="card-header">
        <span class="card-title">Error stock — top 5 ISINs</span>
        <span class="card-desc">Cumulative error stock per ISIN</span>
      </div>
      <div class="card-body"><div class="chart-wrap"><canvas id="chartErrIsin"></canvas></div></div>
    </div>
  </div>

  <div class="card">
    <div class="card-header">
      <span class="card-title">Error account detail — worst (ISIN, month) pairs</span>
    </div>
    <div class="card-body" style="padding:0">
      <table>
        <thead><tr>
          <th>Date</th><th>ISIN</th>
          <th style="text-align:right">Monthly residual</th>
          <th style="text-align:right">Cumul. stock</th>
          <th style="text-align:right">% of max AUM</th>
        </tr></thead>
        <tbody>{err_isin_detail}</tbody>
      </table>
    </div>
  </div>"""

        # JS block for error account charts
        err_js_block = f"""
// ── 8. Error account charts ──────────────────────────────────
const ERR_DATES      = {err_dates_js};
const ERR_AGG_STOCK  = {err_agg_stock_js};
const ERR_AGG_RES    = {err_agg_res_js};
const ERR_ISIN_TS    = {err_isin_ds_js};
const ERR_ISIN_DATES = {err_isin_dates_js};

new Chart(document.getElementById('chartErrStock'), {{
  type: 'line',
  data: {{ labels: ERR_DATES, datasets: [{{
    label: 'Aggregate error stock', data: ERR_AGG_STOCK,
    borderColor: '#ef4444', backgroundColor: '#ef444415',
    borderWidth: 2, pointRadius: 0, tension: 0.3, fill: true
  }}] }},
  options: {{
    responsive: true, maintainAspectRatio: false,
    interaction: {{mode:'index', intersect:false}},
    plugins: {{ legend: {{display:false}}, tooltip: tooltip() }},
    scales: {{ x: timeAxis(), y: {{
      ...yAxis('Shares'),
      grid: {{ color: ctx => ctx.tick.value === 0 ? '#ffffff55' : '#1a2030',
               lineWidth: ctx => ctx.tick.value === 0 ? 1.5 : 1 }}
    }} }}
  }}
}});

new Chart(document.getElementById('chartErrRes'), {{
  type: 'bar',
  data: {{ labels: ERR_DATES, datasets: [{{
    label: 'Monthly residual', data: ERR_AGG_RES,
    backgroundColor: ERR_AGG_RES.map(v => v != null && v < 0 ? '#ef444488' : '#f59e0b88'),
    borderColor:     ERR_AGG_RES.map(v => v != null && v < 0 ? '#ef4444'   : '#f59e0b'),
    borderWidth: 1, borderRadius: 2
  }}] }},
  options: {{
    responsive: true, maintainAspectRatio: false,
    plugins: {{ legend: {{display:false}}, tooltip: tooltip() }},
    scales: {{ x: timeAxis(), y: yAxis('Shares') }}
  }}
}});

new Chart(document.getElementById('chartErrIsin'), {{
  type: 'line',
  data: {{ labels: ERR_ISIN_DATES, datasets: ERR_ISIN_TS }},
  options: {{
    responsive: true, maintainAspectRatio: false,
    interaction: {{mode:'index', intersect:false}},
    plugins: {{
      legend: {{position:'right', labels:{{boxWidth:10, padding:8, font:{{size:10}}}}}},
      tooltip: tooltip()
    }},
    scales: {{ x: timeAxis(), y: yAxis('Error stock (shares)') }}
  }}
}});"""

    else:
        err_section_html = ""
        err_js_block     = ""

    # ── 4.3  Surgery detail table rows ──────────────────────────
    sd = analytics["surgery_detail"].sort_values("date")
    surg_rows_html = ""
    if len(sd) == 0:
        surg_rows_html = "<tr><td colspan='9' style='text-align:center;color:#888'>No surgeries performed</td></tr>"
    else:
        for _, r in sd.iterrows():
            gain_class = "gain-high" if r["gain_vs_no_surgery"] > 0.05 else "gain-low"
            lb = int(r.get("lookback_months", 1))
            lb_cell = (f'<span style="font-family:var(--mono);font-size:.65rem;padding:1px 5px;'
                       f'border-radius:3px;background:#7c3aed22;border:1px solid #7c3aed55;'
                       f'color:#a78bfa">{lb}m</span>' if lb > 1 else "—")
            surg_rows_html += f"""
            <tr>
                <td>{r['date'].date()}</td>
                <td><span class="reg-badge">{r['reg_orig']}</span></td>
                <td class="code-cell">{r['reg_from']}</td>
                <td>→</td>
                <td class="code-cell">{r['reg_to']}</td>
                <td>{r['jaccard_composite']:.4f}</td>
                <td class="{gain_class}">+{r['gain_vs_no_surgery']:.6f}</td>
                <td>{r['gain_pct_of_score']:.1f}%</td>
                <td>{lb_cell}</td>
            </tr>"""

    # ── 4.4  Top accounts table ──────────────────────────────────
    last_date = piv.index.max()
    top_accounts = piv.loc[last_date].dropna().sort_values(ascending=False)
    top_rows_html = ""
    for rank, (rid, sc) in enumerate(top_accounts.items(), 1):
        remapped = "✓" if rid in analytics["ever_remapped"] else ""
        bar_w    = int(sc / top_accounts.max() * 100)
        color    = REG_COLORS[(rank - 1) % len(REG_COLORS)]
        top_rows_html += f"""
        <tr>
            <td class="rank">#{rank}</td>
            <td><span class="reg-badge" style="background:{color}22;border-color:{color}">{rid}</span></td>
            <td class="score-val">{sc:.6f}</td>
            <td class="bar-cell">
                <div class="score-bar" style="width:{bar_w}%;background:{color}"></div>
            </td>
            <td class="center">{remapped}</td>
        </tr>"""

    # ─────────────────────────────────────────────────────────────
    # HTML TEMPLATE
    # ─────────────────────────────────────────────────────────────
    html = f"""<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width,initial-scale=1">
<title>Carmignac Pipeline — Analysis Report</title>
<script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"></script>
<style>
  @import url('https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;600&family=IBM+Plex+Sans:wght@300;400;600;700&display=swap');

  :root {{
    --bg:       #0d0f12;
    --surface:  #151820;
    --border:   #252a35;
    --accent:   #3b82f6;
    --accent2:  #10b981;
    --warn:     #f59e0b;
    --danger:   #ef4444;
    --text:     #e2e8f0;
    --muted:    #64748b;
    --mono:     'IBM Plex Mono', monospace;
    --sans:     'IBM Plex Sans', sans-serif;
  }}

  *, *::before, *::after {{ box-sizing: border-box; margin: 0; padding: 0; }}

  body {{
    font-family: var(--sans);
    background: var(--bg);
    color: var(--text);
    min-height: 100vh;
    padding: 0 0 60px;
  }}

  /* ── Header ── */
  .header {{
    background: linear-gradient(135deg, #0d1117 0%, #111827 50%, #0d1f3c 100%);
    border-bottom: 1px solid var(--border);
    padding: 40px 48px 36px;
    position: relative;
    overflow: hidden;
  }}
  .header::before {{
    content: '';
    position: absolute;
    inset: 0;
    background: radial-gradient(ellipse 70% 80% at 80% 50%, #1e40af18, transparent);
    pointer-events: none;
  }}
  .header-eyebrow {{
    font-family: var(--mono);
    font-size: 11px;
    letter-spacing: 0.15em;
    color: var(--accent);
    text-transform: uppercase;
    margin-bottom: 10px;
  }}
  .header h1 {{
    font-size: 2rem;
    font-weight: 700;
    letter-spacing: -0.02em;
    line-height: 1.1;
    margin-bottom: 8px;
  }}
  .header-sub {{
    font-size: 0.85rem;
    color: var(--muted);
    font-family: var(--mono);
  }}

  /* ── KPI strip ── */
  .kpi-strip {{
    display: grid;
    grid-template-columns: repeat(auto-fit, minmax(160px, 1fr));
    gap: 1px;
    background: var(--border);
    border-bottom: 1px solid var(--border);
  }}
  .kpi {{
    background: var(--surface);
    padding: 22px 28px;
    display: flex;
    flex-direction: column;
    gap: 4px;
  }}
  .kpi-label {{
    font-size: 0.7rem;
    letter-spacing: 0.1em;
    text-transform: uppercase;
    color: var(--muted);
    font-family: var(--mono);
  }}
  .kpi-value {{
    font-size: 1.6rem;
    font-weight: 700;
    font-family: var(--mono);
    color: var(--text);
    line-height: 1;
  }}
  .kpi-value.accent  {{ color: var(--accent); }}
  .kpi-value.success {{ color: var(--accent2); }}
  .kpi-value.warn    {{ color: var(--warn); }}
  .kpi-sub {{
    font-size: 0.7rem;
    color: var(--muted);
    font-family: var(--mono);
  }}

  /* ── Main layout ── */
  .main {{
    max-width: 1400px;
    margin: 0 auto;
    padding: 36px 48px;
    display: flex;
    flex-direction: column;
    gap: 32px;
  }}

  /* ── Cards ── */
  .card {{
    background: var(--surface);
    border: 1px solid var(--border);
    border-radius: 8px;
    overflow: hidden;
  }}
  .card-header {{
    padding: 18px 24px 14px;
    border-bottom: 1px solid var(--border);
    display: flex;
    align-items: baseline;
    gap: 12px;
  }}
  .card-title {{
    font-size: 0.8rem;
    font-weight: 600;
    letter-spacing: 0.1em;
    text-transform: uppercase;
    color: var(--muted);
    font-family: var(--mono);
  }}
  .card-desc {{
    font-size: 0.78rem;
    color: #475569;
  }}
  .card-body {{
    padding: 24px;
  }}
  .chart-wrap {{
    position: relative;
    height: 280px;
  }}
  .chart-wrap-tall {{
    position: relative;
    height: 340px;
  }}

  /* ── Two-column grid ── */
  .grid-2 {{
    display: grid;
    grid-template-columns: 1fr 1fr;
    gap: 24px;
  }}
  @media (max-width: 900px) {{
    .grid-2 {{ grid-template-columns: 1fr; }}
    .main {{ padding: 24px 20px; }}
  }}

  /* ── Section label ── */
  .section-label {{
    font-family: var(--mono);
    font-size: 0.68rem;
    letter-spacing: 0.15em;
    text-transform: uppercase;
    color: var(--muted);
    padding: 0 4px;
    border-left: 3px solid var(--accent);
    padding-left: 10px;
    margin-bottom: -8px;
  }}

  /* ── Tables ── */
  table {{
    width: 100%;
    border-collapse: collapse;
    font-size: 0.82rem;
  }}
  th {{
    font-family: var(--mono);
    font-size: 0.68rem;
    letter-spacing: 0.08em;
    text-transform: uppercase;
    color: var(--muted);
    padding: 10px 14px;
    text-align: left;
    border-bottom: 1px solid var(--border);
    background: #0f1218;
  }}
  td {{
    padding: 10px 14px;
    border-bottom: 1px solid #1a1f2a;
    vertical-align: middle;
  }}
  tr:last-child td {{ border-bottom: none; }}
  tr:hover td {{ background: #181e2b; }}

  .rank {{ color: var(--muted); font-family: var(--mono); font-size: 0.75rem; }}
  .score-val {{ font-family: var(--mono); color: var(--accent2); }}
  .code-cell {{ font-family: var(--mono); font-size: 0.78rem; color: #94a3b8; }}
  .center {{ text-align: center; color: var(--accent2); }}
  .gain-high {{ font-family: var(--mono); color: var(--accent2); font-weight: 600; }}
  .gain-low  {{ font-family: var(--mono); color: var(--warn); }}

  .bar-cell {{ width: 120px; }}
  .score-bar {{
    height: 6px;
    border-radius: 3px;
    min-width: 2px;
    transition: width 0.3s;
  }}

  .reg-badge {{
    display: inline-block;
    padding: 2px 8px;
    border-radius: 4px;
    background: #1e2a3a;
    border: 1px solid #2d3f54;
    font-family: var(--mono);
    font-size: 0.75rem;
    color: var(--accent);
    white-space: nowrap;
  }}

  /* ── Legend patch ── */
  .legend-patch {{
    display: inline-block;
    width: 12px; height: 12px;
    border-radius: 2px;
    margin-right: 4px;
    vertical-align: middle;
  }}

  /* ── No-surgery notice ── */
  .no-surg {{
    padding: 32px;
    text-align: center;
    color: var(--muted);
    font-family: var(--mono);
    font-size: 0.82rem;
  }}

  /* ── Trajectory explorer ── */
  .badge-surgery {{
    font-family: var(--mono);
    font-size: 0.68rem;
    color: var(--warn);
    background: #f59e0b18;
    border: 1px solid #f59e0b44;
    border-radius: 3px;
    padding: 1px 6px;
    margin-left: 4px;
  }}
  .traj-selector {{
    display: flex;
    flex-wrap: wrap;
    gap: 6px;
    padding: 16px 20px;
    border-bottom: 1px solid var(--border);
    background: #0f1218;
  }}
  .traj-btn {{
    font-family: var(--mono);
    font-size: 0.72rem;
    padding: 5px 12px;
    border-radius: 4px;
    border: 1px solid var(--border);
    background: var(--surface);
    color: var(--muted);
    cursor: pointer;
    transition: all 0.15s;
  }}
  .traj-btn:hover {{ border-color: var(--acc, var(--accent)); color: var(--text); }}
  .traj-btn.active {{
    background: color-mix(in srgb, var(--acc, var(--accent)) 15%, transparent);
    border-color: var(--acc, var(--accent));
    color: var(--acc, var(--accent));
  }}
  .traj-btn-badge {{
    font-size: 0.6rem;
    background: #f59e0b33;
    color: var(--warn);
    border-radius: 3px;
    padding: 0 4px;
    margin-left: 4px;
  }}
  .traj-focus-wrap {{ padding: 16px 24px 8px; }}
  .traj-account-meta {{
    display: flex;
    align-items: center;
    flex-wrap: wrap;
    gap: 8px;
    margin-bottom: 12px;
    min-height: 28px;
  }}
  .meta-id {{
    font-family: var(--mono);
    font-size: 0.85rem;
    font-weight: 600;
    padding: 3px 10px;
    border-radius: 4px;
    border: 1.5px solid;
  }}
  .meta-surgs {{ display: flex; flex-wrap: wrap; gap: 6px; }}
  .surg-chip {{
    font-family: var(--mono);
    font-size: 0.7rem;
    color: var(--warn);
    background: #f59e0b18;
    border: 1px solid #f59e0b44;
    border-radius: 4px;
    padding: 2px 8px;
  }}
  .spark-section-label {{
    font-family: var(--mono);
    font-size: 0.65rem;
    letter-spacing: 0.12em;
    text-transform: uppercase;
    color: var(--muted);
    padding: 14px 24px 6px;
    border-top: 1px solid var(--border);
    margin-top: 8px;
  }}
  .spark-grid {{
    display: grid;
    grid-template-columns: repeat(auto-fill, minmax(140px, 1fr));
    gap: 1px;
    background: var(--border);
    border-top: 1px solid var(--border);
  }}
  .spark-cell {{
    background: var(--surface);
    padding: 10px 12px 8px;
    cursor: pointer;
    transition: background 0.12s;
  }}
  .spark-cell:hover {{ background: #1a2030; }}
  .spark-cell.active {{ background: #131b2a; outline: 1px solid #3b82f644; }}
  .spark-label {{
    font-family: var(--mono);
    font-size: 0.68rem;
    color: var(--muted);
    margin-bottom: 4px;
    white-space: nowrap;
    overflow: hidden;
    text-overflow: ellipsis;
  }}
  .spark-badge {{
    font-size: 0.58rem;
    color: var(--warn);
    background: #f59e0b22;
    border-radius: 2px;
    padding: 0 3px;
  }}

  /* ── Footer ── */
  .footer {{
    text-align: center;
    font-family: var(--mono);
    font-size: 0.68rem;
    color: #334155;
    margin-top: 16px;
    letter-spacing: 0.05em;
  }}
</style>
</head>
<body>

<!-- ═══════════════════════════════════════════ HEADER -->
<div class="header">
  <div class="header-eyebrow">Carmignac × ENSAE · Data Challenge 2025</div>
  <h1>Pipeline Results — Analysis Report</h1>
  <div class="header-sub">Registrar ID repair · Score propagation · Surgery audit</div>
</div>

<!-- ═══════════════════════════════════════════ KPI STRIP -->
<div class="kpi-strip">
  <div class="kpi">
    <span class="kpi-label">Σ score at t_ref</span>
    <span class="kpi-value success">{tl['sum_post'].iloc[-1]:.4f}</span>
    <span class="kpi-sub">post-surgery</span>
  </div>
  <div class="kpi">
    <span class="kpi-label">Σ score at t_min</span>
    <span class="kpi-value accent">{tl['sum_post'].iloc[0]:.4f}</span>
    <span class="kpi-sub">post-surgery</span>
  </div>
  <div class="kpi">
    <span class="kpi-label">Max recovery</span>
    <span class="kpi-value warn">{tl['recovery_pct'].max():.1f}%</span>
    <span class="kpi-sub">score rescued by surgery</span>
  </div>
  <div class="kpi">
    <span class="kpi-label">Total surgeries</span>
    <span class="kpi-value">{len(surgery)}</span>
    <span class="kpi-sub">operations performed</span>
  </div>
  <div class="kpi">
    <span class="kpi-label">Reg IDs universe</span>
    <span class="kpi-value">{piv.shape[1]}</span>
    <span class="kpi-sub">at reference date</span>
  </div>
  <div class="kpi">
    <span class="kpi-label">Ever remapped</span>
    <span class="kpi-value warn">{len(analytics['ever_remapped'])}</span>
    <span class="kpi-sub">reg IDs w/ code change</span>
  </div>
</div>

<!-- ═══════════════════════════════════════════ MAIN -->
<div class="main">

  <div class="section-label">01 · Score Integrity Over Time</div>

  <!-- Chart 1: Σ score with vs without surgery -->
  <div class="card">
    <div class="card-header">
      <span class="card-title">Sum of scores — pre vs post surgery</span>
      <span class="card-desc">
        Post-surgery (solid) shows the corrected score after code repairs.
        Pre-surgery (dashed) is the counterfactual without any remapping.
        Gap = score rescued.
      </span>
    </div>
    <div class="card-body">
      <div class="chart-wrap-tall">
        <canvas id="chartSigma"></canvas>
      </div>
    </div>
  </div>

  <!-- Chart 2: Score drop (pre) -->
  <div class="grid-2">
    <div class="card">
      <div class="card-header">
        <span class="card-title">Score recovered by surgery</span>
        <span class="card-desc">Difference post − pre at each date</span>
      </div>
      <div class="card-body">
        <div class="chart-wrap">
          <canvas id="chartRecovery"></canvas>
        </div>
      </div>
    </div>

    <div class="card">
      <div class="card-header">
        <span class="card-title">Portfolio concentration (entropy)</span>
        <span class="card-desc">Shannon entropy of score distribution — higher = more spread</span>
      </div>
      <div class="card-body">
        <div class="chart-wrap">
          <canvas id="chartEntropy"></canvas>
        </div>
      </div>
    </div>
  </div>

  <div class="section-label">02 · Individual Score Trajectories</div>

  <div class="card">
    <div class="card-header">
      <span class="card-title">Score explorer — per Registrar Account</span>
      <span class="card-desc">
        Click an account to inspect its full history.
        <span class="badge-surgery">◆ remapped</span> = surgery was applied.
      </span>
    </div>
    <div class="card-body" style="padding:0">

      <!-- Selector pill bar -->
      <div class="traj-selector" id="trajSelector"></div>

      <!-- Focused chart + metadata -->
      <div class="traj-focus-wrap">
        <div class="traj-account-meta" id="trajMeta"></div>
        <div style="position:relative;height:300px">
          <canvas id="chartTrajFocus"></canvas>
        </div>
      </div>

      <!-- Sparkline overview grid -->
      <div class="spark-section-label">All accounts — overview</div>
      <div class="spark-grid" id="sparkGrid"></div>

    </div>
  </div>

  <div class="section-label">03 · Surgery Operations</div>

  <div class="grid-2">
    <div class="card">
      <div class="card-header">
        <span class="card-title">Surgeries per time step</span>
        <span class="card-desc">Number of code remappings performed at each month</span>
      </div>
      <div class="card-body">
        <div class="chart-wrap">
          <canvas id="chartNSurg"></canvas>
        </div>
      </div>
    </div>

    <div class="card">
      <div class="card-header">
        <span class="card-title">Score gain per surgery</span>
        <span class="card-desc">Average gain in Σ score from surgery at each month</span>
      </div>
      <div class="card-body">
        <div class="chart-wrap">
          <canvas id="chartGain"></canvas>
        </div>
      </div>
    </div>
  </div>

  <div class="card">
    <div class="card-header">
      <span class="card-title">Jaccard similarity of surgery matches</span>
      <span class="card-desc">
        Composite Jaccard score of the matched code pair — closer to 1.0 = stronger portfolio overlap.
        Low values may indicate uncertain matches.
      </span>
    </div>
    <div class="card-body">
      <div class="chart-wrap">
        <canvas id="chartJaccard"></canvas>
      </div>
    </div>
  </div>

  <div class="section-label">04 · Surgery Detail Log</div>

  <div class="card">
    <div class="card-header">
      <span class="card-title">All surgery operations</span>
    </div>
    <div class="card-body" style="padding:0">
      {'<div class="no-surg">No surgeries were performed on this dataset.</div>' if len(surgery) == 0 else f"""
      <table>
        <thead>
          <tr>
            <th>Date</th>
            <th>Reg orig</th>
            <th>Code from</th>
            <th></th>
            <th>Code to</th>
            <th>Jaccard</th>
            <th>Score gain</th>
            <th>% of score</th>
            <th>Lookback</th>
          </tr>
        </thead>
        <tbody>{surg_rows_html}</tbody>
      </table>"""}
    </div>
  </div>

  <div class="section-label">05 · Score Ranking at t_ref</div>

  <div class="card">
    <div class="card-header">
      <span class="card-title">Accounts ranked by weight at reference date</span>
      <span class="card-desc">✓ in last column = account was remapped at some point in history</span>
    </div>
    <div class="card-body" style="padding:0">
      <table>
        <thead>
          <tr>
            <th>Rank</th>
            <th>Registrar ID</th>
            <th>Score (weight)</th>
            <th style="width:140px">Relative size</th>
            <th>Remapped</th>
          </tr>
        </thead>
        <tbody>{top_rows_html}</tbody>
      </table>
    </div>
  </div>

  {err_section_html}


</div><!-- /main -->

<div class="footer">Generated by carmignac_analysis.py · Carmignac × ENSAE Data Challenge 2025</div>

<!-- ═══════════════════════════════════════════ CHARTS JS -->
<script>
Chart.defaults.color = '#64748b';
Chart.defaults.borderColor = '#1e2535';
Chart.defaults.font.family = "'IBM Plex Mono', monospace";
Chart.defaults.font.size = 11;

const DATES   = {js(dates_str)};
const SUM_POST = {jf(tl['sum_post'].values)};
const SUM_PRE  = {jf(tl['sum_pre'].values)};
const RECOVERY = {jf(tl['recovery_pct'].values, 4)};
const ENTROPY  = {jf(tl['entropy'].values, 4)};
const SURG_DATES   = {js(surg_dates)};
const N_SURG       = {n_surg};
const TOTAL_GAIN   = {total_gain};
const AVG_GAIN     = {avg_gain};
const AVG_JACCARD  = {avg_jaccard};
const TRAJ         = {traj_json};

// ── Shared options helpers ────────────────────────────────────
function timeAxis(label) {{
  return {{
    type: 'category',
    ticks: {{ maxTicksLimit: 10, maxRotation: 0 }},
    grid: {{ color: '#1a2030' }},
    title: {{ display: !!label, text: label, color: '#475569' }},
  }};
}}
function yAxis(label, opts={{}}) {{
  return {{
    grid: {{ color: '#1a2030' }},
    title: {{ display: !!label, text: label, color: '#475569' }},
    ...opts,
  }};
}}
function tooltip() {{
  return {{
    backgroundColor: '#0d1117',
    borderColor: '#252a35',
    borderWidth: 1,
    titleFont: {{ family: "'IBM Plex Mono'" }},
    bodyFont:  {{ family: "'IBM Plex Mono'" }},
    padding: 10,
  }};
}}

// ── 1. Sigma pre/post ─────────────────────────────────────────
new Chart(document.getElementById('chartSigma'), {{
  type: 'line',
  data: {{
    labels: DATES,
    datasets: [
      {{
        label: 'Σ score (post-surgery)',
        data: SUM_POST,
        borderColor: '#10b981',
        backgroundColor: '#10b98115',
        borderWidth: 2.5,
        pointRadius: 0,
        fill: false,
        tension: 0.2,
      }},
      {{
        label: 'Σ score (pre-surgery / counterfactual)',
        data: SUM_PRE,
        borderColor: '#ef4444',
        borderDash: [6, 4],
        borderWidth: 1.5,
        pointRadius: 0,
        fill: false,
        tension: 0.2,
        backgroundColor: 'transparent',
      }},
    ],
  }},
  options: {{
    responsive: true, maintainAspectRatio: false,
    interaction: {{ mode: 'index', intersect: false }},
    plugins: {{
      legend: {{ position: 'top', labels: {{ boxWidth: 12, padding: 16 }} }},
      tooltip: tooltip(),
    }},
    scales: {{
      x: timeAxis(),
      y: yAxis('Σ scores', {{ min: 0, max: 1.05, ticks: {{ stepSize: 0.1 }} }}),
    }},
  }},
}});

// ── 2. Recovery ───────────────────────────────────────────────
new Chart(document.getElementById('chartRecovery'), {{
  type: 'bar',
  data: {{
    labels: DATES,
    datasets: [{{
      label: 'Score recovered (%)',
      data: RECOVERY,
      backgroundColor: '#3b82f6aa',
      borderColor: '#3b82f6',
      borderWidth: 1,
      borderRadius: 2,
    }}],
  }},
  options: {{
    responsive: true, maintainAspectRatio: false,
    plugins: {{ legend: {{ display: false }}, tooltip: tooltip() }},
    scales: {{
      x: timeAxis(),
      y: yAxis('Recovery (% of Σ)', {{ min: 0 }}),
    }},
  }},
}});

// ── 3. Entropy ────────────────────────────────────────────────
new Chart(document.getElementById('chartEntropy'), {{
  type: 'line',
  data: {{
    labels: DATES,
    datasets: [{{
      label: 'Shannon entropy',
      data: ENTROPY,
      borderColor: '#d97706',
      backgroundColor: '#d9770622',
      borderWidth: 2,
      pointRadius: 0,
      fill: true,
      tension: 0.3,
    }}],
  }},
  options: {{
    responsive: true, maintainAspectRatio: false,
    plugins: {{ legend: {{ display: false }}, tooltip: tooltip() }},
    scales: {{ x: timeAxis(), y: yAxis('Entropy (nats)') }},
  }},
}});

// ── 4. Trajectory explorer ───────────────────────────────────
(function() {{
  let focusChart = null;
  let sparkCharts = [];
  let activeIdx = 0;

  // Surgery annotation plugin (vertical line + label)
  const surgeryPlugin = {{
    id: 'surgeryLines',
    afterDraw(chart) {{
      const surgeries = chart._surgeries || [];
      if (!surgeries.length) return;
      const {{ctx, chartArea, scales}} = chart;
      surgeries.forEach(op => {{
        const xIdx = DATES.indexOf(op.date);
        if (xIdx < 0) return;
        const x = scales.x.getPixelForValue(xIdx);
        ctx.save();
        ctx.strokeStyle = '#f59e0bcc';
        ctx.lineWidth = 1.5;
        ctx.setLineDash([4, 3]);
        ctx.beginPath();
        ctx.moveTo(x, chartArea.top);
        ctx.lineTo(x, chartArea.bottom);
        ctx.stroke();
        // Label
        ctx.font = "10px 'IBM Plex Mono'";
        ctx.fillStyle = '#f59e0b';
        ctx.textAlign = 'center';
        ctx.fillText('✦', x, chartArea.top + 10);
        ctx.restore();
      }});
    }}
  }};

  function buildFocusChart(idx) {{
    const d = TRAJ[idx];
    if (focusChart) focusChart.destroy();

    const ctx = document.getElementById('chartTrajFocus').getContext('2d');
    focusChart = new Chart(ctx, {{
      type: 'line',
      data: {{
        labels: DATES,
        datasets: [{{
          label: d.label,
          data: d.data,
          borderColor: d.borderColor,
          backgroundColor: d.borderColor + '18',
          borderWidth: 2.5,
          borderDash: d.borderDash,
          pointRadius: d.data.map((_, i) => {{
            return d.surgeries.some(s => DATES[i] === s.date) ? 6 : 0;
          }}),
          pointBackgroundColor: '#f59e0b',
          pointBorderColor: '#f59e0b',
          tension: 0.3,
          fill: true,
        }}],
      }},
      options: {{
        responsive: true, maintainAspectRatio: false,
        interaction: {{ mode: 'index', intersect: false }},
        plugins: {{
          legend: {{ display: false }},
          tooltip: {{
            ...tooltip(),
            callbacks: {{
              afterBody(items) {{
                const i = items[0].dataIndex;
                const s = d.surgeries.find(x => DATES[i] === x.date);
                if (!s) return [];
                return [
                  '',
                  '✦ Surgery applied',
                  'From : ' + s.reg_from,
                  'To   : ' + s.reg_to,
                  'Jaccard: ' + s.composite,
                  'Gain: +' + s.gain,
                ];
              }}
            }}
          }},
        }},
        scales: {{
          x: timeAxis(),
          y: yAxis('Score (weight)', {{ min: 0 }}),
        }},
      }},
      plugins: [surgeryPlugin],
    }});
    focusChart._surgeries = d.surgeries;

    // Update meta panel
    const meta = document.getElementById('trajMeta');
    const surgHTML = d.surgeries.length
      ? d.surgeries.map(s =>
          `<span class="surg-chip">✦ ${{s.date}} &nbsp;${{s.reg_from}} → ${{s.reg_to}}</span>`
        ).join('')
      : '<span style="color:#475569;font-size:0.75rem">No surgery applied</span>';
    meta.innerHTML = `
      <span class="meta-id" style="border-color:${{d.borderColor}};color:${{d.borderColor}}">${{d.label}}</span>
      <div class="meta-surgs">${{surgHTML}}</div>
    `;
  }}

  function buildSparkGrid() {{
    const grid = document.getElementById('sparkGrid');
    grid.innerHTML = '';
    TRAJ.forEach((d, idx) => {{
      const wrap = document.createElement('div');
      wrap.className = 'spark-cell' + (idx === activeIdx ? ' active' : '');
      wrap.dataset.idx = idx;

      const label = document.createElement('div');
      label.className = 'spark-label';
      label.innerHTML = d.label + (d.remapped
        ? ' <span class="spark-badge">R</span>' : '');

      const canvasWrap = document.createElement('div');
      canvasWrap.style.cssText = 'position:relative;height:52px';
      const cv = document.createElement('canvas');
      canvasWrap.appendChild(cv);

      wrap.appendChild(label);
      wrap.appendChild(canvasWrap);
      grid.appendChild(wrap);

      const sc = new Chart(cv, {{
        type: 'line',
        data: {{
          labels: DATES,
          datasets: [{{
            data: d.data,
            borderColor: d.borderColor,
            borderWidth: 1.5,
            pointRadius: 0,
            tension: 0.3,
            fill: false,
          }}],
        }},
        options: {{
          responsive: true, maintainAspectRatio: false,
          animation: false,
          plugins: {{ legend: {{ display: false }}, tooltip: {{ enabled: false }} }},
          scales: {{
            x: {{ display: false }},
            y: {{ display: false, min: 0 }},
          }},
        }},
      }});
      sparkCharts.push(sc);

      wrap.addEventListener('click', () => {{
        document.querySelectorAll('.spark-cell').forEach(c => c.classList.remove('active'));
        document.querySelectorAll('.traj-btn').forEach(b => b.classList.remove('active'));
        wrap.classList.add('active');
        document.querySelector(`.traj-btn[data-idx="${{idx}}"]`).classList.add('active');
        activeIdx = idx;
        buildFocusChart(idx);
      }});
    }});
  }}

  function buildSelector() {{
    const sel = document.getElementById('trajSelector');
    sel.innerHTML = '';
    TRAJ.forEach((d, idx) => {{
      const btn = document.createElement('button');
      btn.className = 'traj-btn' + (idx === 0 ? ' active' : '');
      btn.dataset.idx = idx;
      btn.style.setProperty('--acc', d.borderColor);
      btn.innerHTML = d.label + (d.remapped ? ' <span class="traj-btn-badge">R</span>' : '');
      btn.addEventListener('click', () => {{
        document.querySelectorAll('.traj-btn').forEach(b => b.classList.remove('active'));
        document.querySelectorAll('.spark-cell').forEach(c => c.classList.remove('active'));
        btn.classList.add('active');
        const cell = document.querySelector(`.spark-cell[data-idx="${{idx}}"]`);
        if (cell) cell.classList.add('active');
        activeIdx = idx;
        buildFocusChart(idx);
      }});
      sel.appendChild(btn);
    }});
  }}

  buildSelector();
  buildFocusChart(0);
  buildSparkGrid();
}})();

// ── 5. N surgeries ────────────────────────────────────────────
new Chart(document.getElementById('chartNSurg'), {{
  type: 'bar',
  data: {{
    labels: SURG_DATES,
    datasets: [{{
      label: 'Surgeries',
      data: N_SURG,
      backgroundColor: '#7c3aed99',
      borderColor: '#7c3aed',
      borderWidth: 1,
      borderRadius: 3,
    }}],
  }},
  options: {{
    responsive: true, maintainAspectRatio: false,
    plugins: {{ legend: {{ display: false }}, tooltip: tooltip() }},
    scales: {{
      x: timeAxis('Month'),
      y: yAxis('# operations', {{ min: 0, ticks: {{ stepSize: 1 }} }}),
    }},
  }},
}});

// ── 6. Avg gain ───────────────────────────────────────────────
new Chart(document.getElementById('chartGain'), {{
  type: 'bar',
  data: {{
    labels: SURG_DATES,
    datasets: [
      {{
        label: 'Total gain',
        data: TOTAL_GAIN,
        backgroundColor: '#10b98199',
        borderColor: '#10b981',
        borderWidth: 1,
        borderRadius: 3,
      }},
      {{
        label: 'Avg gain / surgery',
        data: AVG_GAIN,
        backgroundColor: '#06b6d455',
        borderColor: '#06b6d4',
        borderWidth: 1,
        borderRadius: 3,
      }},
    ],
  }},
  options: {{
    responsive: true, maintainAspectRatio: false,
    interaction: {{ mode: 'index', intersect: false }},
    plugins: {{ legend: {{ position: 'top', labels: {{ boxWidth: 10 }} }}, tooltip: tooltip() }},
    scales: {{ x: timeAxis('Month'), y: yAxis('Score gain') }},
  }},
}});

// ── 7. Jaccard ────────────────────────────────────────────────
new Chart(document.getElementById('chartJaccard'), {{
  type: 'bar',
  data: {{
    labels: SURG_DATES,
    datasets: [{{
      label: 'Avg Jaccard composite',
      data: AVG_JACCARD,
      backgroundColor: '#f59e0b88',
      borderColor: '#f59e0b',
      borderWidth: 1,
      borderRadius: 3,
    }}],
  }},
  options: {{
    responsive: true, maintainAspectRatio: false,
    plugins: {{ legend: {{ display: false }}, tooltip: tooltip() }},
    scales: {{
      x: timeAxis('Month'),
      y: yAxis('Jaccard composite', {{ min: 0, max: 1.05 }}),
    }},
  }},
}});
{err_js_block}
</script>
</body>
</html>"""

    return html


# ─────────────────────────────────────────────────────────────
# 5. MAIN
# ─────────────────────────────────────────────────────────────

def main():
    parser = argparse.ArgumentParser(description="Carmignac pipeline results analyser")
    parser.add_argument("--scores",  default="repair_results/carmignac_scores.csv")
    parser.add_argument("--mapping", default="repair_results/carmignac_mapping.csv")
    parser.add_argument("--surgery", default="repair_results/carmignac_surgery_log.csv")
    parser.add_argument("--out",     default="repair_results/carmignac_report.html")
    parser.add_argument("--error-account-isin", default=None,
                        dest="error_isin",
                        help="Path to carmignac_error_account.csv (optional)")
    parser.add_argument("--error-account-agg", default=None,
                        dest="error_agg",
                        help="Path to carmignac_error_account_agg.csv (optional)")
    args = parser.parse_args()

    # Resolve paths relative to this script's directory if files not found
    base = os.path.dirname(os.path.abspath(__file__))
    def resolve(p, required=True):
        if p is None:
            return None
        if os.path.exists(p):
            return p
        alt = os.path.join(base, p)
        if os.path.exists(alt):
            return alt
        if required:
            sys.exit(f"[ERROR] File not found: {p}")
        print(f"[WARN] Optional file not found: {p}")
        return None

    scores_path  = resolve(args.scores)
    mapping_path = resolve(args.mapping)
    surgery_path = resolve(args.surgery)
    error_isin_path = resolve(args.error_isin, required=False)
    error_agg_path  = resolve(args.error_agg,  required=False)

    print(f"[Load] scores  : {scores_path}")
    print(f"[Load] mapping : {mapping_path}")
    print(f"[Load] surgery : {surgery_path}")

    scores, mapping, surgery, df_err_isin, df_err_agg = load_outputs(
        scores_path, mapping_path, surgery_path,
        err_isin_path=error_isin_path, err_agg_path=error_agg_path
    )
    analytics = compute_analytics(scores, mapping, surgery)

    print_summary(analytics, surgery)

    html = build_html(analytics, surgery, scores, mapping,
                      df_err_isin=df_err_isin, df_err_agg=df_err_agg)

    out_path = args.out
    with open(out_path, "w", encoding="utf-8") as f:
        f.write(html)
    print(f"\n[Report] Written to → {out_path}")


if __name__ == "__main__":
    main()