"""Enhanced prediction analysis — sign-invariant modes and per-residue normalization."""
import numpy as np
import streamlit as st
import plotly.graph_objects as go
from plotly.subplots import make_subplots


def canonicalize_sign(modes: dict) -> dict:
    """Make eigenvectors sign-consistent.

    Eigenvectors are defined up to ±1 global sign. We canonicalize by choosing
    the sign such that the component with the largest absolute value is positive.
    This ensures consistent visualization across different runs/proteins.
    """
    canonical = {}
    for k, vecs in modes.items():
        # Flatten to (3N,), find component with max absolute value
        flat = vecs.flatten()
        max_idx = np.argmax(np.abs(flat))
        if flat[max_idx] < 0:
            canonical[k] = -vecs  # Flip sign
        else:
            canonical[k] = vecs.copy()
    return canonical


def per_residue_relative_norm(vecs: np.ndarray) -> np.ndarray:
    """Normalize displacement magnitudes to [0, 1] relative to max.

    Args:
        vecs: (N, 3) displacement vectors

    Returns:
        (N,) relative magnitudes in [0, 1]
    """
    mags = np.linalg.norm(vecs, axis=1)
    max_m = mags.max()
    return mags / max_m if max_m > 1e-12 else mags


def per_residue_direction(vecs: np.ndarray, ca_coords: np.ndarray) -> np.ndarray:
    """Compute relative direction of displacement vs protein backbone.

    Projects displacement onto local backbone direction (CA_i → CA_{i+1}).
    Returns signed projection: positive = along backbone, negative = against.

    Args:
        vecs: (N, 3) displacement vectors
        ca_coords: (N, 3) CA coordinates

    Returns:
        (N,) signed projections normalized by displacement magnitude
    """
    n = len(vecs)
    projections = np.zeros(n)

    for i in range(n):
        # Local backbone direction
        if i < n - 1:
            backbone = ca_coords[i + 1] - ca_coords[i]
        else:
            backbone = ca_coords[i] - ca_coords[i - 1]

        bb_norm = np.linalg.norm(backbone)
        if bb_norm < 1e-8:
            continue

        disp_mag = np.linalg.norm(vecs[i])
        if disp_mag < 1e-8:
            continue

        # Cosine angle between displacement and backbone direction
        projections[i] = np.dot(vecs[i], backbone) / (disp_mag * bb_norm)

    return projections


def render_prediction_analysis(
    modes: dict,
    seq: str,
    ca_coords: np.ndarray = None,
    coverage: np.ndarray = None,
    eigenvalues: np.ndarray = None,
    gt_modes: dict = None,
    protein_name: str = "",
):
    """Comprehensive prediction analysis panel.

    Shows:
    1. Normalized displacement heatmap (all modes × residues)
    2. Sign-canonical direction analysis
    3. Prediction vs ground truth comparison (if available)
    4. Per-residue statistics table
    """
    # Canonicalize signs
    modes_c = canonicalize_sign(modes)
    n_modes = len(modes_c)
    n_res = len(list(modes_c.values())[0])

    if coverage is None:
        coverage = np.ones(n_res)

    # ── Tab layout ──
    tab_norm, tab_dir, tab_compare, tab_table = st.tabs([
        "📊 Normalized Displacement", "🧭 Direction Analysis",
        "⚖️ Pred vs GT", "📋 Per-Residue Table"
    ])

    # ═══════════════════════════════════════════
    # Tab 1: Normalized displacement heatmap
    # ═══════════════════════════════════════════
    with tab_norm:
        # Compute relative norms for all modes
        rel_norms = np.zeros((n_modes, n_res))
        abs_mags = np.zeros((n_modes, n_res))
        for k in range(n_modes):
            abs_mags[k] = np.linalg.norm(modes_c[k], axis=1)
            rel_norms[k] = per_residue_relative_norm(modes_c[k])

        # Hover text with sequence
        hover = [[f"{seq[j] if j < len(seq) else '?'}{j+1}<br>"
                   f"Abs: {abs_mags[k][j]:.3f}Å<br>"
                   f"Rel: {rel_norms[k][j]:.2%}<br>"
                   f"Cov: {coverage[j]:.2f}"
                   for j in range(n_res)] for k in range(n_modes)]

        fig = make_subplots(rows=3, cols=1, row_heights=[0.4, 0.4, 0.2],
                            shared_xaxes=True, vertical_spacing=0.06,
                            subplot_titles=["Absolute Displacement (Å)",
                                            "Relative Displacement (0-1)",
                                            "Coverage"])

        # Absolute heatmap
        fig.add_trace(go.Heatmap(
            z=abs_mags, colorscale="YlOrRd",
            y=[f"Mode {k}" for k in range(n_modes)],
            text=hover, hovertemplate="%{text}<extra></extra>",
            colorbar=dict(title="Å", x=1.01, len=0.35, y=0.85),
        ), row=1, col=1)

        # Relative heatmap
        fig.add_trace(go.Heatmap(
            z=rel_norms, colorscale="Viridis", zmin=0, zmax=1,
            y=[f"Mode {k}" for k in range(n_modes)],
            text=hover, hovertemplate="%{text}<extra></extra>",
            colorbar=dict(title="Rel", x=1.08, len=0.35, y=0.5),
        ), row=2, col=1)

        # Coverage bar
        fig.add_trace(go.Bar(
            x=list(range(n_res)), y=coverage[:n_res],
            marker_color=["#10b981" if c > 0.5 else "#ef4444" for c in coverage[:n_res]],
            hovertemplate="Res %{x}<br>Coverage: %{y:.3f}<extra></extra>",
            showlegend=False,
        ), row=3, col=1)

        # Sequence ticks
        step = max(1, n_res // 50)
        tick_vals = list(range(0, n_res, step))
        tick_text = [f"{seq[i] if i < len(seq) else '?'}{i+1}" for i in tick_vals]
        fig.update_xaxes(tickvals=tick_vals, ticktext=tick_text, tickangle=45,
                          tickfont=dict(size=8), row=3, col=1)

        fig.update_layout(
            template="plotly_dark", height=550,
            paper_bgcolor="rgba(0,0,0,0)",
            plot_bgcolor="rgba(30,27,75,0.3)",
            margin=dict(l=60, r=80, t=30, b=50),
        )
        st.plotly_chart(fig, use_container_width=True)

        # Key insight
        for k in range(min(n_modes, 4)):
            top3 = np.argsort(abs_mags[k])[-3:][::-1]
            top_str = ", ".join([f"**{seq[i] if i<len(seq) else '?'}{i+1}** ({abs_mags[k][i]:.2f}Å)"
                                  for i in top3])
            st.markdown(f"Mode {k} hotspots: {top_str}")

    # ═══════════════════════════════════════════
    # Tab 2: Direction analysis
    # ═══════════════════════════════════════════
    with tab_dir:
        if ca_coords is not None and len(ca_coords) == n_res:
            st.markdown("""
            **Direction Analysis**: Projects displacement onto the local backbone direction (CA→CA).
            - 🔵 **Blue** = motion along backbone (stretching/compressing)
            - 🔴 **Red** = motion perpendicular to backbone (lateral/hinge)
            - Sign is arbitrary for eigenvectors → we show absolute cosine similarity
            """)

            fig_dir = go.Figure()
            colors = ["#6366f1", "#ef4444", "#10b981", "#f59e0b"]

            for k in range(min(n_modes, 4)):
                proj = per_residue_direction(modes_c[k], ca_coords)
                # Show absolute cosine (sign-invariant)
                abs_proj = np.abs(proj)

                _fill_map = {
                    "#6366f1": "rgba(99,102,241,0.12)",
                    "#ef4444": "rgba(239,68,68,0.12)",
                    "#10b981": "rgba(16,185,129,0.12)",
                    "#f59e0b": "rgba(245,158,11,0.12)",
                }
                fig_dir.add_trace(go.Scatter(
                    x=list(range(1, n_res + 1)), y=abs_proj,
                    mode="lines", name=f"Mode {k}",
                    line=dict(color=colors[k], width=1.5),
                    fill="tozeroy",
                    fillcolor=_fill_map.get(colors[k], "rgba(99,102,241,0.12)"),
                    hovertemplate="Res %{x}<br>|cos θ|: %{y:.3f}<extra>Mode " + str(k) + "</extra>",
                ))

            fig_dir.add_hline(y=0.5, line_dash="dash", line_color="#94a3b8",
                               annotation_text="isotropic threshold")

            fig_dir.update_layout(
                template="plotly_dark", height=350,
                paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(30,27,75,0.3)",
                xaxis_title="Residue", yaxis_title="|cos θ| (backbone projection)",
                yaxis_range=[0, 1.05],
                margin=dict(l=50, r=20, t=30, b=50),
            )
            st.plotly_chart(fig_dir, use_container_width=True)

            # Direction heatmap
            st.markdown("**Per-residue × mode direction matrix:**")
            dir_matrix = np.zeros((n_modes, n_res))
            for k in range(n_modes):
                dir_matrix[k] = np.abs(per_residue_direction(modes_c[k], ca_coords))

            fig_dh = go.Figure(go.Heatmap(
                z=dir_matrix, colorscale="RdBu_r", zmin=0, zmax=1,
                y=[f"Mode {k}" for k in range(n_modes)],
                colorbar=dict(title="|cos θ|"),
            ))
            fig_dh.update_layout(
                template="plotly_dark", height=200,
                paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(30,27,75,0.3)",
                margin=dict(l=60, r=60, t=10, b=30),
            )
            st.plotly_chart(fig_dh, use_container_width=True)
        else:
            st.info("Direction analysis requires CA coordinates (ground truth or PDB needed)")

    # ═══════════════════════════════════════════
    # Tab 3: Prediction vs Ground Truth
    # ═══════════════════════════════════════════
    with tab_compare:
        if gt_modes is not None and len(gt_modes) > 0:
            gt_c = canonicalize_sign(gt_modes)
            n_gt = len(gt_c)

            st.markdown("**Pred vs GT displacement profiles (sign-canonicalized):**")

            for k in range(min(n_modes, n_gt, 4)):
                pred_mag = np.linalg.norm(modes_c[k], axis=1)
                gt_mag = np.linalg.norm(gt_c[k], axis=1)

                # Normalize both to [0, 1]
                pred_rel = pred_mag / (pred_mag.max() + 1e-12)
                gt_rel = gt_mag / (gt_mag.max() + 1e-12)

                fig_cmp = go.Figure()
                fig_cmp.add_trace(go.Scatter(
                    x=list(range(1, n_res + 1)), y=gt_rel,
                    mode="lines", name="Ground Truth",
                    line=dict(color="#10b981", width=2),
                ))
                fig_cmp.add_trace(go.Scatter(
                    x=list(range(1, n_res + 1)), y=pred_rel,
                    mode="lines", name="Prediction",
                    line=dict(color="#6366f1", width=2, dash="dot"),
                ))

                # Correlation
                corr = np.corrcoef(pred_rel, gt_rel)[0, 1]
                rmse = np.sqrt(np.mean((pred_rel - gt_rel) ** 2))

                fig_cmp.update_layout(
                    template="plotly_dark", height=200,
                    title=f"Mode {k} — r={corr:.3f}, RMSE={rmse:.3f}",
                    paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(30,27,75,0.3)",
                    margin=dict(l=40, r=20, t=40, b=30),
                    legend=dict(orientation="h", y=1.15),
                )
                st.plotly_chart(fig_cmp, use_container_width=True)
        else:
            st.info("No ground truth available for comparison. "
                    "Ground truth is only available for proteins in the training database.")

    # ═══════════════════════════════════════════
    # Tab 4: Per-residue table
    # ═══════════════════════════════════════════
    with tab_table:
        import pandas as pd

        rows = []
        for i in range(n_res):
            row = {
                "Residue": i + 1,
                "AA": seq[i] if i < len(seq) else "?",
                "Coverage": f"{coverage[i]:.3f}" if i < len(coverage) else "—",
            }
            for k in range(min(n_modes, 4)):
                mag = np.linalg.norm(modes_c[k][i])
                rel = per_residue_relative_norm(modes_c[k])[i]
                row[f"M{k} (Å)"] = f"{mag:.3f}"
                row[f"M{k} rel"] = f"{rel:.2%}"
            rows.append(row)

        df = pd.DataFrame(rows)
        st.dataframe(df, use_container_width=True, height=500,
                      column_config={
                          "Residue": st.column_config.NumberColumn(width="small"),
                          "AA": st.column_config.TextColumn(width="small"),
                      })

        # Download CSV
        csv = df.to_csv(index=False)
        st.download_button("📥 Download CSV", csv,
                            f"{protein_name}_analysis.csv", "text/csv")