19384398576

Committed 15 Nov 2025 04:24AM UTC coverage: 79.66% (-0.1%) from 79.76%

Build # 19384398576

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push

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avivajpeyi

Commit Message

fix scaling and one sided psd

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70.1

/src/log_psplines/plotting/diagnostics.py

import os
from dataclasses import dataclass
from typing import Optional

import arviz as az
import matplotlib.pyplot as plt
import numpy as np

from ..logger import logger
from .base import PlotConfig, safe_plot, setup_plot_style

# Setup consistent styling for diagnostics plots
setup_plot_style()


@dataclass
class DiagnosticsConfig:
    """Configuration for diagnostics plotting parameters."""

    figsize: tuple = (12, 8)
    dpi: int = 150
    ess_threshold: int = 400
    rhat_threshold: float = 1.01
    fontsize: int = 11
    labelsize: int = 12
    titlesize: int = 12


def plot_trace(idata: az.InferenceData, compact=True) -> plt.Figure:
    groups = {
        "delta": [
            v for v in idata.posterior.data_vars if v.startswith("delta")
        ],
        "phi": [v for v in idata.posterior.data_vars if v.startswith("phi")],
        "weights": [
            v for v in idata.posterior.data_vars if v.startswith("weights")
        ],
    }

    if compact:
        nrows = 3
    else:
        nrows = len(groups)
    fig, axes = plt.subplots(nrows, 2, figsize=(7, 3 * nrows))

    for row, (group_name, vars) in enumerate(groups.items()):

        # if vars are more than 1, and compact, then we need to repeat the axes
        if compact:
            group_axes = axes[row, :].reshape(1, 2)
            group_axes = np.repeat(group_axes, len(vars), axis=0)
        else:
            group_axes = axes[row, :]

        group_axes[0, 0].set_title(
            f"{group_name.capitalize()} Parameters", fontsize=14
        )

        for i, var in enumerate(vars):
            data = idata.posterior[
                var
            ].values  # shape is (nchain, nsamples, ndim) if ndim>1 else (nchain, nsamples)
            if data.ndim == 3:
                data = data[0].T  # shape is now (ndim, nsamples)

            ax_trace = group_axes[i, 0] if compact else group_axes[0]
            ax_hist = group_axes[i, 1] if compact else group_axes[1]
            ax_trace.set_ylabel(group_name, fontsize=8)
            ax_trace.set_xlabel("MCMC Step", fontsize=8)
            ax_hist.set_xlabel(group_name, fontsize=8)
            # place ylabel on right side of hist
            ax_hist.yaxis.set_label_position("right")
            ax_hist.set_ylabel("Density", fontsize=8, rotation=270, labelpad=0)

            # remove axes yspine for hist
            ax_hist.spines["left"].set_visible(False)
            ax_hist.spines["right"].set_visible(False)
            ax_hist.spines["top"].set_visible(False)
            ax_hist.set_yticks([])  # remove y ticks
            ax_hist.yaxis.set_ticks_position("none")

            ax_trace.spines["right"].set_visible(False)
            ax_trace.spines["top"].set_visible(False)

            color = f"C{i}"
            label = f"{var}"
            if group_name in ["phi", "delta"]:
                ax_trace.set_yscale("log")
                ax_hist.set_xscale("log")

            for p in data:
                ax_trace.plot(p, color=color, alpha=0.7, label=label)

                # if phi or delta, use log scale for hist-x, log for trace y
                if group_name in ["phi", "delta"]:
                    bins = np.logspace(
                        np.log10(np.min(p)), np.log10(np.max(p)), 30
                    )
                    logp = np.log(p)
                    log_grid, log_pdf = az.kde(logp)
                    grid = np.exp(log_grid)
                    pdf = log_pdf / grid  # change of variables
                else:
                    bins = 30
                    grid, pdf = az.kde(p)
                ax_hist.plot(grid, pdf, color=color, label=label)
                ax_hist.hist(
                    p, bins=bins, density=True, color=color, alpha=0.3
                )

                # KDE plot instead of histogram

    plt.suptitle("Parameter Traces", fontsize=16)
    plt.tight_layout()
    return fig


def plot_diagnostics(
    idata: az.InferenceData,
    outdir: str,
    n_channels: Optional[int] = None,
    n_freq: Optional[int] = None,
    runtime: Optional[float] = None,
    config: Optional[DiagnosticsConfig] = None,
) -> None:
    """
    Create essential MCMC diagnostics in organized subdirectories.
    """
    if outdir is None:
        return

    if config is None:
        config = DiagnosticsConfig()

    # Create diagnostics subdirectory
    diag_dir = os.path.join(outdir, "diagnostics")
    os.makedirs(diag_dir, exist_ok=True)

    logger.info("Generating MCMC diagnostics...")

    # Generate summary report
    generate_diagnostics_summary(idata, diag_dir)
    _create_diagnostic_plots(
        idata, diag_dir, config, n_channels, n_freq, runtime
    )


def _create_diagnostic_plots(
    idata, diag_dir, config, n_channels, n_freq, runtime
):
    """Create only the essential diagnostic plots."""
    logger.debug("Generating diagnostic plots...")

    # 1. ArviZ trace plots
    @safe_plot(f"{diag_dir}/trace_plots.png", config.dpi)
    def create_trace_plots():
        return plot_trace(idata)

    create_trace_plots()

    # 2. Summary dashboard with key convergence metrics
    @safe_plot(f"{diag_dir}/summary_dashboard.png", config.dpi)
    def plot_summary():
        _plot_summary_dashboard(idata, config, n_channels, n_freq, runtime)

    plot_summary()

    # 3. Log posterior diagnostics
    @safe_plot(f"{diag_dir}/log_posterior.png", config.dpi)
    def plot_lp():
        _plot_log_posterior(idata, config)

    plot_lp()

    # 4. Acceptance rate diagnostics
    @safe_plot(f"{diag_dir}/acceptance_diagnostics.png", config.dpi)
    def plot_acceptance():
        _plot_acceptance_diagnostics_blockaware(idata, config)

    plot_acceptance()

    # 5. Sampler-specific diagnostics
    _create_sampler_diagnostics(idata, diag_dir, config)

    # 6. Divergences diagnostics (for NUTS only)
    _create_divergences_diagnostics(idata, diag_dir, config)


def _plot_summary_dashboard(idata, config, n_channels, n_freq, runtime):

    # Create 2x2 layout
    fig, axes = plt.subplots(
        2, 2, figsize=(config.figsize[0] * 0.8, config.figsize[1])
    )
    ess_ax = axes[0, 0]
    meta_ax = axes[0, 1]
    param_ax = axes[1, 0]
    status_ax = axes[1, 1]

    # Get ESS values once
    ess_values = None
    try:
        ess = idata.attrs.get("ess")
        ess_values = ess[~np.isnan(ess)]
    except Exception:
        pass

    # 1. ESS Distribution
    _plot_ess_histogram(ess_ax, ess_values, config)

    # 2. Analysis Metadata
    _plot_metadata(meta_ax, idata, n_channels, n_freq, runtime)

    # 3. Parameter Summary
    _plot_parameter_summary(param_ax, idata)

    # 4. Convergence Status
    _plot_convergence_status(status_ax, ess_values, config)

    plt.tight_layout()


def _plot_ess_histogram(ax, ess_values, config):
    """Plot ESS distribution with quality thresholds."""
    if ess_values is None or len(ess_values) == 0:
        ax.text(0.5, 0.5, "ESS unavailable", ha="center", va="center")
        ax.set_title("ESS Distribution")
        return

    # Histogram
    ax.hist(ess_values, bins=30, alpha=0.7, edgecolor="black")

    # Reference lines
    thresholds = [
        (400, "red", "--", "Minimum reliable"),
        (1000, "orange", "--", "Good"),
        (np.max(ess_values), "green", ":", f"Max = {np.max(ess_values):.0f}"),
    ]

    for threshold, color, style, label in thresholds:
        ax.axvline(
            x=threshold,
            color=color,
            linestyle=style,
            linewidth=2 if threshold < np.max(ess_values) else 1,
            alpha=0.8,
            label=label,
        )

    ax.set_xlabel("ESS")
    ax.set_ylabel("Count")
    ax.set_title("ESS Distribution")
    ax.legend(loc="upper right", fontsize="x-small")
    ax.grid(True, alpha=0.3)

    # Summary stats
    pct_good = (ess_values >= config.ess_threshold).mean() * 100
    stats_text = f"Min: {ess_values.min():.0f}\nMean: {ess_values.mean():.0f}\n≥{config.ess_threshold}: {pct_good:.1f}%"
    ax.text(
        0.02,
        0.98,
        stats_text,
        transform=ax.transAxes,
        fontsize=10,
        verticalalignment="top",
        bbox=dict(boxstyle="round", facecolor="lightblue", alpha=0.7),
    )


def _plot_metadata(ax, idata, n_channels, n_freq, runtime):
    """Display analysis metadata."""
    try:
        n_samples = idata.posterior.sizes.get("draw", 0)
        n_chains = idata.posterior.sizes.get("chain", 1)
        n_params = len(list(idata.posterior.data_vars))
        sampler_type = idata.attrs["sampler_type"]

        metadata_lines = [
            f"Sampler: {sampler_type}",
            f"Samples: {n_samples} × {n_chains} chains",
            f"Parameters: {n_params}",
        ]
        if n_channels is not None:
            metadata_lines.append(f"Channels: {n_channels}")
        if n_freq is not None:
            metadata_lines.append(f"Frequencies: {n_freq}")
        if runtime is not None:
            metadata_lines.append(f"Runtime: {runtime:.2f}s")

        ax.text(
            0.05,
            0.95,
            "\n".join(metadata_lines),
            transform=ax.transAxes,
            fontsize=12,
            verticalalignment="top",
            fontfamily="monospace",
        )
    except Exception:
        ax.text(0.5, 0.5, "Metadata unavailable", ha="center", va="center")

    ax.set_title("Analysis Summary")
    ax.axis("off")


def _plot_parameter_summary(ax, idata):
    """Display parameter count summary."""
    try:
        param_groups = _group_parameters_simple(idata)
        if param_groups:
            summary_text = "Parameter Summary:\n"
            for group_name, params in param_groups.items():
                if params:
                    summary_text += f"{group_name}: {len(params)}\n"
            ax.text(
                0.05,
                0.95,
                summary_text.strip(),
                transform=ax.transAxes,
                fontsize=11,
                verticalalignment="top",
                fontfamily="monospace",
            )
    except Exception:
        ax.text(
            0.5,
            0.5,
            "Parameter summary\nunavailable",
            ha="center",
            va="center",
        )

    ax.set_title("Parameter Summary")
    ax.axis("off")


def _plot_convergence_status(ax, ess_values, config):
    """Display convergence status based on ESS only."""
    try:
        status_lines = ["Convergence Status:"]

        if ess_values is not None and len(ess_values) > 0:
            ess_good = (ess_values >= config.ess_threshold).mean() * 100
            status_lines.append(
                f"ESS ≥ {config.ess_threshold}: {ess_good:.0f}%"
            )
            status_lines.append("")
            status_lines.append("Overall Status:")

            if ess_good >= 90:
                status_lines.append("✓ EXCELLENT")
                color = "green"
            elif ess_good >= 75:
                status_lines.append("✓ ADEQUATE")
                color = "orange"
            else:
                status_lines.append("⚠ NEEDS ATTENTION")
                color = "red"
        else:
            status_lines.append("? UNABLE TO ASSESS")
            color = "gray"

        ax.text(
            0.05,
            0.95,
            "\n".join(status_lines),
            transform=ax.transAxes,
            fontsize=11,
            verticalalignment="top",
            fontfamily="monospace",
            color=color,
        )
    except Exception:
        ax.text(0.5, 0.5, "Status unavailable", ha="center", va="center")

    ax.set_title("Convergence Status")
    ax.axis("off")


def _plot_log_posterior(idata, config):
    """Log posterior diagnostics."""
    fig, axes = plt.subplots(2, 2, figsize=config.figsize)

    # Check for lp first, then log_likelihood
    if "lp" in idata.sample_stats:
        lp_values = idata.sample_stats["lp"].values.flatten()
        var_name = "lp"
        title_prefix = "Log Posterior"
    elif "log_likelihood" in idata.sample_stats:
        lp_values = idata.sample_stats["log_likelihood"].values.flatten()
        var_name = "log_likelihood"
        title_prefix = "Log Likelihood"
    else:
        # Create a fallback layout when no posterior data available
        fig, axes = plt.subplots(1, 1, figsize=config.figsize)
        axes.text(
            0.5,
            0.5,
            "No log posterior\nor log likelihood\navailable",
            ha="center",
            va="center",
            fontsize=14,
        )
        axes.set_title("Log Posterior Diagnostics")
        axes.axis("off")
        plt.tight_layout()
        return

    # Trace plot with running mean overlaid
    axes[0, 0].plot(
        lp_values, alpha=0.7, linewidth=1, color="blue", label="Trace"
    )

    # Add running mean on the same plot
    window_size = max(10, len(lp_values) // 100)
    if len(lp_values) > window_size:
        running_mean = np.convolve(
            lp_values, np.ones(window_size) / window_size, mode="valid"
        )
        axes[0, 0].plot(
            range(window_size // 2, window_size // 2 + len(running_mean)),
            running_mean,
            alpha=0.9,
            linewidth=3,
            color="red",
            label=f"Running mean (w={window_size})",
        )

    axes[0, 0].set_xlabel("Iteration")
    axes[0, 0].set_ylabel(title_prefix)
    axes[0, 0].set_title(f"{title_prefix} Trace with Running Mean")
    axes[0, 0].legend(loc="best", fontsize="small")
    axes[0, 0].grid(True, alpha=0.3)

    # Distribution
    axes[0, 1].hist(
        lp_values, bins=50, alpha=0.7, density=True, edgecolor="black"
    )
    axes[0, 1].axvline(
        np.mean(lp_values),
        color="red",
        linestyle="--",
        linewidth=2,
        label=f"Mean: {np.mean(lp_values):.1f}",
    )
    axes[0, 1].set_xlabel(title_prefix)
    axes[0, 1].set_ylabel("Density")
    axes[0, 1].set_title(f"{title_prefix} Distribution")
    axes[0, 1].legend(loc="best", fontsize="small")
    axes[0, 1].grid(True, alpha=0.3)

    # Step-to-step changes
    lp_diff = np.diff(lp_values)
    axes[1, 0].plot(lp_diff, alpha=0.5, linewidth=1)
    axes[1, 0].axhline(0, color="red", linestyle="--", alpha=0.7)
    axes[1, 0].axhline(
        np.mean(lp_diff),
        color="blue",
        linestyle="--",
        alpha=0.7,
        label=f"Mean change: {np.mean(lp_diff):.1f}",
    )
    axes[1, 0].set_xlabel("Iteration")
    axes[1, 0].set_ylabel(f"{title_prefix} Difference")
    axes[1, 0].set_title("Step-to-Step Changes")
    axes[1, 0].legend(loc="best", fontsize="small")
    axes[1, 0].grid(True, alpha=0.3)

    # Summary statistics
    stats_lines = [
        f"Mean: {np.mean(lp_values):.2f}",
        f"Std: {np.std(lp_values):.2f}",
        f"Min: {np.min(lp_values):.2f}",
        f"Max: {np.max(lp_values):.2f}",
        f"Range: {np.max(lp_values) - np.min(lp_values):.2f}",
        "",
        "Stability:",
        f"Final variation: {np.std(lp_values[-len(lp_values)//4:]):.2f}",
    ]

    axes[1, 1].text(
        0.05,
        0.95,
        "\n".join(stats_lines),
        transform=axes[1, 1].transAxes,
        fontsize=10,
        verticalalignment="top",
        fontfamily="monospace",
        bbox=dict(boxstyle="round", facecolor="lightblue", alpha=0.8),
    )
    axes[1, 1].set_title("Posterior Statistics")
    axes[1, 1].axis("off")

    plt.tight_layout()


def _plot_acceptance_diagnostics(idata, config):
    """Acceptance rate diagnostics."""
    accept_key = None
    if "accept_prob" in idata.sample_stats:
        accept_key = "accept_prob"
    elif "acceptance_rate" in idata.sample_stats:
        accept_key = "acceptance_rate"

    if accept_key is None:
        fig, ax = plt.subplots(figsize=config.figsize)
        ax.text(
            0.5,
            0.5,
            "Acceptance rate data unavailable",
            ha="center",
            va="center",
        )
        ax.set_title("Acceptance Rate Diagnostics")
        return

    fig, axes = plt.subplots(2, 2, figsize=config.figsize)

    accept_rates = idata.sample_stats[accept_key].values.flatten()
    target_rate = getattr(idata.attrs, "target_accept_rate", 0.44)
    sampler_type = (
        idata.attrs["sampler_type"].lower()
        if "sampler_type" in idata.attrs
        else "unknown"
    )
    sampler_type = "NUTS" if "nuts" in sampler_type else "MH"

    # Define good ranges based on sampler
    if target_rate > 0.5:  # NUTS
        good_range = (0.7, 0.9)
        low_range = (0.0, 0.6)
        high_range = (0.9, 1.0)
        concerning_range = (0.6, 0.7)
    else:  # MH
        good_range = (0.2, 0.5)
        low_range = (0.0, 0.2)
        high_range = (0.5, 1.0)
        concerning_range = (0.1, 0.2)  # MH can be lower than NUTS

    # Trace plot with color zones
    # Add background zones
    axes[0, 0].axhspan(
        good_range[0],
        good_range[1],
        alpha=0.1,
        color="green",
        label=f"Good ({good_range[0]:.1f}-{good_range[1]:.1f})",
    )
    axes[0, 0].axhspan(
        low_range[0], low_range[1], alpha=0.1, color="red", label="Too low"
    )
    axes[0, 0].axhspan(
        high_range[0],
        high_range[1],
        alpha=0.1,
        color="orange",
        label="Too high",
    )
    if concerning_range[1] > concerning_range[0]:
        axes[0, 0].axhspan(
            concerning_range[0],
            concerning_range[1],
            alpha=0.1,
            color="yellow",
            label="Concerning",
        )

    # Main trace plot
    axes[0, 0].plot(
        accept_rates, alpha=0.8, linewidth=1, color="blue", label="Trace"
    )
    axes[0, 0].axhline(
        target_rate,
        color="red",
        linestyle="--",
        linewidth=2,
        label=f"Target ({target_rate})",
    )

    # Add running average on the same plot
    window_size = max(10, len(accept_rates) // 50)
    if len(accept_rates) > window_size:
        running_mean = np.convolve(
            accept_rates, np.ones(window_size) / window_size, mode="valid"
        )
        axes[0, 0].plot(
            range(window_size // 2, window_size // 2 + len(running_mean)),
            running_mean,
            alpha=0.9,
            linewidth=3,
            color="purple",
            label=f"Running mean (w={window_size})",
        )

    axes[0, 0].set_xlabel("Iteration")
    axes[0, 0].set_ylabel("Acceptance Rate")
    axes[0, 0].set_title(f"{sampler_type} Acceptance Rate Trace")
    axes[0, 0].legend(loc="best", fontsize="small")
    axes[0, 0].grid(True, alpha=0.3)

    # Add interpretation text
    interpretation = f"{sampler_type} aims for {target_rate:.2f}."
    if target_rate > 0.5:
        interpretation += " Green: efficient sampling."
    else:
        interpretation += " MH adapts to find optimal rate."
    axes[0, 0].text(
        0.02,
        0.02,
        interpretation,
        transform=axes[0, 0].transAxes,
        fontsize=9,
        bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.7),
    )

    # Distribution
    axes[0, 1].hist(
        accept_rates, bins=30, alpha=0.7, density=True, edgecolor="black"
    )
    axes[0, 1].axvline(
        target_rate,
        color="red",
        linestyle="--",
        linewidth=2,
        label=f"Target ({target_rate})",
    )
    axes[0, 1].set_xlabel("Acceptance Rate")
    axes[0, 1].set_ylabel("Density")
    axes[0, 1].set_title("Acceptance Rate Distribution")
    axes[0, 1].legend()
    axes[0, 1].grid(True, alpha=0.3)

    # Since running means are already overlaid on the main plot, use the bottom row for additional info

    # Additional acceptance analysis - evolution over time
    if len(accept_rates) > 10:
        # Show moving standard deviation or coefficient of variation
        window_std = np.array(
            [
                np.std(accept_rates[max(0, i - 20) : i + 1])
                for i in range(len(accept_rates))
            ]
        )
        axes[1, 0].plot(window_std, alpha=0.7, color="green")
        axes[1, 0].set_xlabel("Iteration")
        axes[1, 0].set_ylabel("Rolling Std")
        axes[1, 0].set_title("Rolling Standard Deviation")
        axes[1, 0].grid(True, alpha=0.3)
    else:
        axes[1, 0].text(
            0.5,
            0.5,
            "Acceptance variability\nanalysis unavailable",
            ha="center",
            va="center",
        )
        axes[1, 0].set_title("Acceptance Stability")

    # Summary statistics (expanded)
    stats_text = [
        f"Sampler: {sampler_type}",
        f"Target: {target_rate:.3f}",
        f"Mean: {np.mean(accept_rates):.3f}",
        f"Std: {np.std(accept_rates):.3f}",
        f"CV: {np.std(accept_rates)/np.mean(accept_rates):.3f}",
        f"Min: {np.min(accept_rates):.3f}",
        f"Max: {np.max(accept_rates):.3f}",
        "",
        "Stability:",
        f"Final std: {np.std(accept_rates[-len(accept_rates)//4:]):.3f}",
    ]

    axes[1, 1].text(
        0.05,
        0.95,
        "\n".join(stats_text),
        transform=axes[1, 1].transAxes,
        fontsize=9,
        verticalalignment="top",
        fontfamily="monospace",
    )
    axes[1, 1].set_title("Acceptance Analysis")
    axes[1, 1].axis("off")

    plt.tight_layout()


def _plot_acceptance_diagnostics_blockaware(idata, config):
    """Acceptance diagnostics that also handle per‑channel series from blocked NUTS.

    If keys like ``accept_prob_channel_0`` are found in ``idata.sample_stats``,
    they are overlaid on the overall trace and included in the summary.
    """
    # Detect overall series
    accept_key = None
    if "accept_prob" in idata.sample_stats:
        accept_key = "accept_prob"
    elif "acceptance_rate" in idata.sample_stats:
        accept_key = "acceptance_rate"

    # Collect per-channel series
    channel_series = {}
    for key in idata.sample_stats:
        if isinstance(key, str) and key.startswith("accept_prob_channel_"):
            try:
                ch = int(key.rsplit("_", 1)[-1])
                channel_series[ch] = idata.sample_stats[key].values.flatten()
            except Exception:
                pass

    if accept_key is None and not channel_series:
        fig, ax = plt.subplots(figsize=config.figsize)
        ax.text(
            0.5,
            0.5,
            "Acceptance rate data unavailable",
            ha="center",
            va="center",
        )
        ax.set_title("Acceptance Rate Diagnostics")
        return

    fig, axes = plt.subplots(2, 2, figsize=config.figsize)

    # Overall or concatenated series
    if accept_key is not None:
        accept_rates = idata.sample_stats[accept_key].values.flatten()
    else:
        accept_rates = np.concatenate(list(channel_series.values()))

    sampler_type_attr = idata.attrs.get("sampler_type", "").lower()
    is_nuts = "nuts" in sampler_type_attr
    target_rate = idata.attrs.get(
        "target_accept_rate",
        idata.attrs.get("target_accept_prob", 0.8 if is_nuts else 0.44),
    )
    sampler_type = "NUTS" if is_nuts else "MH"

    # Ranges
    if is_nuts:
        good_range = (0.7, 0.9)
        low_range = (0.0, 0.6)
        high_range = (0.9, 1.0)
        concerning_range = (0.6, 0.7)
    else:
        good_range = (0.2, 0.5)
        low_range = (0.0, 0.2)
        high_range = (0.5, 1.0)
        concerning_range = (0.1, 0.2)

    # Background zones
    axes[0, 0].axhspan(good_range[0], good_range[1], alpha=0.1, color="green")
    axes[0, 0].axhspan(low_range[0], low_range[1], alpha=0.1, color="red")
    axes[0, 0].axhspan(high_range[0], high_range[1], alpha=0.1, color="orange")
    if concerning_range[1] > concerning_range[0]:
        axes[0, 0].axhspan(
            concerning_range[0], concerning_range[1], alpha=0.1, color="yellow"
        )

    # Plot traces: overall + per-channel overlays
    if accept_key is not None:
        axes[0, 0].plot(
            accept_rates, alpha=0.8, linewidth=1, color="blue", label="overall"
        )
    for ch in sorted(channel_series):
        axes[0, 0].plot(
            channel_series[ch], alpha=0.6, linewidth=1, label=f"ch {ch}"
        )
    axes[0, 0].axhline(
        target_rate,
        color="red",
        linestyle="--",
        linewidth=2,
        label=f"Target ({target_rate})",
    )

    # Running mean for overall
    window_size = max(10, len(accept_rates) // 50)
    if len(accept_rates) > window_size:
        running_mean = np.convolve(
            accept_rates, np.ones(window_size) / window_size, mode="valid"
        )
        axes[0, 0].plot(
            range(window_size // 2, window_size // 2 + len(running_mean)),
            running_mean,
            alpha=0.9,
            linewidth=3,
            color="purple",
            label=f"Running mean (w={window_size})",
        )

    axes[0, 0].set_xlabel("Iteration")
    axes[0, 0].set_ylabel("Acceptance Rate")
    axes[0, 0].set_title(f"{sampler_type} Acceptance Rate Trace")
    axes[0, 0].legend(loc="best", fontsize="small")
    axes[0, 0].grid(True, alpha=0.3)

    # Histogram and summary
    axes[0, 1].hist(
        accept_rates, bins=30, alpha=0.7, density=True, edgecolor="black"
    )
    axes[0, 1].axvline(
        target_rate,
        color="red",
        linestyle="--",
        linewidth=2,
        label=f"Target ({target_rate})",
    )
    axes[0, 1].set_xlabel("Acceptance Rate")
    axes[0, 1].set_ylabel("Density")
    axes[0, 1].set_title("Acceptance Rate Distribution")
    axes[0, 1].legend()
    axes[0, 1].grid(True, alpha=0.3)

    if len(accept_rates) > 10:
        window_std = np.array(
            [
                np.std(accept_rates[max(0, i - 20) : i + 1])
                for i in range(len(accept_rates))
            ]
        )
        axes[1, 0].plot(window_std, alpha=0.7, color="green")
        axes[1, 0].set_xlabel("Iteration")
        axes[1, 0].set_ylabel("Rolling Std")
        axes[1, 0].set_title("Rolling Standard Deviation")
        axes[1, 0].grid(True, alpha=0.3)
    else:
        axes[1, 0].text(
            0.5,
            0.5,
            "Acceptance variability\nanalysis unavailable",
            ha="center",
            va="center",
        )
        axes[1, 0].set_title("Acceptance Stability")

    # Summary text
    stats_text = [
        f"Sampler: {sampler_type}",
        f"Target: {target_rate:.3f}",
        f"Mean: {np.mean(accept_rates):.3f}",
        f"Std: {np.std(accept_rates):.3f}",
        f"CV: {np.std(accept_rates)/np.mean(accept_rates):.3f}",
        f"Min: {np.min(accept_rates):.3f}",
        f"Max: {np.max(accept_rates):.3f}",
    ]
    if channel_series:
        stats_text.append("")
        stats_text.append("Per-channel means:")
        for ch in sorted(channel_series):
            stats_text.append(f"  ch {ch}: {np.mean(channel_series[ch]):.3f}")

    axes[1, 1].text(
        0.05,
        0.95,
        "\n".join(stats_text),
        transform=axes[1, 1].transAxes,
        fontsize=9,
        va="top",
        family="monospace",
    )
    axes[1, 1].set_title("Acceptance Analysis")
    axes[1, 1].axis("off")

    plt.tight_layout()


def _plot_nuts_diagnostics_blockaware(idata, config):
    """NUTS diagnostics supporting per‑channel (blocked) diagnostics fields.

    Overlays per‑channel series when keys like ``energy_channel_{j}`` or
    ``num_steps_channel_{j}`` are present.
    """
    # Presence of overall arrays
    has_energy = "energy" in idata.sample_stats
    has_potential = "potential_energy" in idata.sample_stats
    has_steps = "num_steps" in idata.sample_stats
    has_accept = "accept_prob" in idata.sample_stats

    # Collect per-channel data
    def _collect(base):
        out = {}
        prefix = f"{base}_channel_"
        for key in idata.sample_stats:
            if isinstance(key, str) and key.startswith(prefix):
                try:
                    ch = int(key.replace(prefix, ""))
                    out[ch] = idata.sample_stats[key].values.flatten()
                except Exception:
                    pass
        return out

    energy_ch = _collect("energy")
    potential_ch = _collect("potential_energy")
    steps_ch = _collect("num_steps")
    accept_ch = _collect("accept_prob")

    fig, axes = plt.subplots(2, 2, figsize=config.figsize)

    # Energy / potential
    ax = axes[0, 0]
    plotted = False
    if has_energy:
        ax.plot(
            idata.sample_stats.energy.values.flatten(),
            alpha=0.7,
            lw=1,
            label="H",
        )
        plotted = True
    if has_potential:
        ax.plot(
            idata.sample_stats.potential_energy.values.flatten(),
            alpha=0.7,
            lw=1,
            label="P",
        )
        plotted = True
    for ch in sorted(energy_ch):
        ax.plot(energy_ch[ch], alpha=0.5, lw=1, label=f"H ch {ch}")
        plotted = True
    for ch in sorted(potential_ch):
        ax.plot(potential_ch[ch], alpha=0.5, lw=1, label=f"P ch {ch}")
        plotted = True
    if not plotted:
        ax.text(0.5, 0.5, "Energy data\nunavailable", ha="center", va="center")
    ax.set_title("Energy Diagnostics")
    ax.set_xlabel("Iteration")
    ax.set_ylabel("Energy")
    ax.grid(True, alpha=0.3)
    if plotted:
        ax.legend(loc="best", fontsize="small")

    # Steps histogram
    ax = axes[0, 1]
    if has_steps:
        vals = idata.sample_stats.num_steps.values.flatten()
    else:
        vals = (
            np.concatenate(list(steps_ch.values()))
            if steps_ch
            else np.array([])
        )
    if vals.size:
        ax.hist(vals, bins=20, alpha=0.7, edgecolor="black")
        ax.set_title("Leapfrog Steps Distribution")
        ax.set_xlabel("Steps")
        ax.set_ylabel("Trajectories")
        ax.grid(True, alpha=0.3)
    else:
        ax.text(0.5, 0.5, "Steps data\nunavailable", ha="center", va="center")

    # Acceptance (overlay per-channel)
    ax = axes[1, 0]
    plotted = False
    if has_accept:
        ax.plot(
            idata.sample_stats.accept_prob.values.flatten(),
            alpha=0.8,
            lw=1,
            label="overall",
        )
        plotted = True
    for ch in sorted(accept_ch):
        ax.plot(accept_ch[ch], alpha=0.6, lw=1, label=f"ch {ch}")
        plotted = True
    if not plotted:
        ax.text(
            0.5, 0.5, "Acceptance data\nunavailable", ha="center", va="center"
        )
    ax.set_title("Acceptance Trace")
    ax.set_xlabel("Iteration")
    ax.set_ylabel("accept_prob")
    ax.grid(True, alpha=0.3)
    if plotted:
        ax.legend(loc="best", fontsize="small")

    # Summary text
    ax = axes[1, 1]
    lines = []
    if has_steps or steps_ch:
        lines.append("Steps summary:")
        if has_steps:
            s = idata.sample_stats.num_steps.values.flatten()
            lines.append(f"  overall μ={np.mean(s):.1f}, max={np.max(s):.0f}")
        for ch in sorted(steps_ch):
            s = steps_ch[ch]
            lines.append(f"  ch {ch} μ={np.mean(s):.1f}, max={np.max(s):.0f}")
        lines.append("")
    if has_accept or accept_ch:
        lines.append("Acceptance summary:")
        if has_accept:
            a = idata.sample_stats.accept_prob.values.flatten()
            lines.append(f"  overall μ={np.mean(a):.3f}")
        for ch in sorted(accept_ch):
            a = accept_ch[ch]
            lines.append(f"  ch {ch} μ={np.mean(a):.3f}")
    if lines:
        ax.text(
            0.05,
            0.95,
            "\n".join(lines),
            transform=ax.transAxes,
            va="top",
            family="monospace",
        )
    ax.set_title("NUTS Diagnostics Summary")
    ax.axis("off")

    plt.tight_layout()


def _create_sampler_diagnostics(idata, diag_dir, config):
    """Create sampler-specific diagnostics."""

    # Better sampler detection - check sampler type first
    sampler_type = (
        idata.attrs["sampler_type"].lower()
        if "sampler_type" in idata.attrs
        else "unknown"
    )

    # Check for NUTS-specific fields that MH definitely doesn't have
    nuts_specific_fields = [
        "energy",
        "num_steps",
        "tree_depth",
        "diverging",
        "energy_error",
    ]

    has_nuts = (
        any(field in idata.sample_stats for field in nuts_specific_fields)
        or "nuts" in sampler_type
    )

    # Check for MH-specific fields (exclude anything NUTS might have)
    has_mh = "step_size_mean" in idata.sample_stats and not has_nuts

    if has_nuts:

        @safe_plot(f"{diag_dir}/nuts_diagnostics.png", config.dpi)
        def plot_nuts():
            _plot_nuts_diagnostics_blockaware(idata, config)

        plot_nuts()
    elif has_mh:

        @safe_plot(f"{diag_dir}/mh_step_sizes.png", config.dpi)
        def plot_mh():
            _plot_mh_step_sizes(idata, config)

        plot_mh()


def _plot_nuts_diagnostics(idata, config):
    """NUTS diagnostics with enhanced information."""
    # Determine available data to decide layout
    has_energy = "energy" in idata.sample_stats
    has_potential = "potential_energy" in idata.sample_stats
    has_steps = "num_steps" in idata.sample_stats
    has_accept = "accept_prob" in idata.sample_stats
    has_divergences = "diverging" in idata.sample_stats
    has_tree_depth = "tree_depth" in idata.sample_stats
    has_energy_error = "energy_error" in idata.sample_stats

    # Create a 2x2 layout, potentially combining energy and potential on same plot
    fig, axes = plt.subplots(2, 2, figsize=config.figsize)

    # Top-left: Energy diagnostics (combine Hamiltonian and Potential if both available)
    energy_ax = axes[0, 0]

    if has_energy and has_potential:
        # Both available - plot them together on one plot
        energy = idata.sample_stats.energy.values.flatten()
        potential = idata.sample_stats.potential_energy.values.flatten()

        # Plot both energies on same axis
        energy_ax.plot(
            energy, alpha=0.7, linewidth=1, color="blue", label="Hamiltonian"
        )
        energy_ax.plot(
            potential,
            alpha=0.7,
            linewidth=1,
            color="orange",
            label="Potential",
        )

        # Add difference (which relates to kinetic energy)
        energy_diff = energy - potential
        # Create second y-axis for difference
        ax2 = energy_ax.twinx()
        ax2.plot(
            energy_diff,
            alpha=0.5,
            linewidth=1,
            color="red",
            label="H - Potential (Kinetic)",
            linestyle="--",
        )
        ax2.set_ylabel("Energy Difference", color="red")
        ax2.tick_params(axis="y", labelcolor="red")

        energy_ax.set_xlabel("Iteration")
        energy_ax.set_ylabel("Energy", color="blue")
        energy_ax.tick_params(axis="y", labelcolor="blue")
        energy_ax.set_title("Hamiltonian & Potential Energy")
        energy_ax.legend(loc="best", fontsize="small")
        energy_ax.grid(True, alpha=0.3)

        # Add statistics
        energy_ax.text(
            0.02,
            0.98,
            f"H: μ={np.mean(energy):.1f}, σ={np.std(energy):.1f}\nP: μ={np.mean(potential):.1f}, σ={np.std(potential):.1f}",
            transform=energy_ax.transAxes,
            fontsize=8,
            bbox=dict(boxstyle="round", facecolor="lightblue", alpha=0.8),
            verticalalignment="top",
        )

    elif has_energy:
        # Only Hamiltonian energy
        energy = idata.sample_stats.energy.values.flatten()
        energy_ax.plot(energy, alpha=0.7, linewidth=1, color="blue")
        energy_ax.set_xlabel("Iteration")
        energy_ax.set_ylabel("Hamiltonian Energy")
        energy_ax.set_title("Hamiltonian Energy Trace")
        energy_ax.grid(True, alpha=0.3)

    elif has_potential:
        # Only potential energy
        potential = idata.sample_stats.potential_energy.values.flatten()
        energy_ax.plot(potential, alpha=0.7, linewidth=1, color="orange")
        energy_ax.set_xlabel("Iteration")
        energy_ax.set_ylabel("Potential Energy")
        energy_ax.set_title("Potential Energy Trace")
        energy_ax.grid(True, alpha=0.3)

    else:
        energy_ax.text(
            0.5,
            0.5,
            "Energy data\nunavailable",
            ha="center",
            va="center",
            transform=energy_ax.transAxes,
        )
        energy_ax.set_title("Energy Diagnostics")

    # Top-right: Sampling efficiency diagnostics
    if has_steps:
        steps_ax = axes[0, 1]
        num_steps = idata.sample_stats.num_steps.values.flatten()

        # Show histogram with color zones for step efficiency
        n, bins, edges = steps_ax.hist(
            num_steps, bins=20, alpha=0.7, edgecolor="black"
        )

        # Add shaded regions for different efficiency levels
        # Green: efficient (tree depth ≤5, ~32 steps)
        # Yellow: moderate (tree depth 6-8, ~64-256 steps)
        # Red: inefficient (tree depth >8, >256 steps)
        steps_ax.axvspan(
            0, 64, alpha=0.1, color="green", label="Efficient (≤64)"
        )
        steps_ax.axvspan(
            64, 256, alpha=0.1, color="yellow", label="Moderate (65-256)"
        )
        steps_ax.axvspan(
            256,
            np.max(num_steps),
            alpha=0.1,
            color="red",
            label="Inefficient (>256)",
        )

        # Add reference lines for different tree depths
        for depth in [5, 7, 10]:  # Common tree depths
            max_steps = 2**depth
            steps_ax.axvline(
                x=max_steps,
                color="gray",
                linestyle=":",
                alpha=0.7,
                linewidth=1,
                label=f"2^{depth} ({max_steps})",
            )

        steps_ax.set_xlabel("Leapfrog Steps")
        steps_ax.set_ylabel("Trajectories")
        steps_ax.set_title("Leapfrog Steps Distribution")
        steps_ax.legend(loc="best", fontsize="small")
        steps_ax.grid(True, alpha=0.3)

        # Add efficiency statistics
        pct_inefficient = (num_steps > 256).mean() * 100
        pct_moderate = ((num_steps > 64) & (num_steps <= 256)).mean() * 100
        pct_efficient = (num_steps <= 64).mean() * 100
        steps_ax.text(
            0.02,
            0.98,
            f"Efficient: {pct_efficient:.1f}%\nModerate: {pct_moderate:.1f}%\nInefficient: {pct_inefficient:.1f}%\nMean steps: {np.mean(num_steps):.1f}",
            transform=steps_ax.transAxes,
            fontsize=7,
            bbox=dict(boxstyle="round", facecolor="lightblue", alpha=0.8),
            verticalalignment="top",
        )

    else:
        axes[0, 1].text(
            0.5, 0.5, "Steps data\nunavailable", ha="center", va="center"
        )
        axes[0, 1].set_title("Sampling Steps")

    # Bottom-left: Acceptance and NS divergence diagnostics
    accept_ax = axes[1, 0]

    if has_accept:
        accept_prob = idata.sample_stats.accept_prob.values.flatten()

        # Plot acceptance probability with guidance zones
        accept_ax.fill_between(
            range(len(accept_prob)),
            0.7,
            0.9,
            alpha=0.1,
            color="green",
            label="Good (0.7-0.9)",
        )
        accept_ax.fill_between(
            range(len(accept_prob)),
            0,
            0.6,
            alpha=0.1,
            color="red",
            label="Too low",
        )
        accept_ax.fill_between(
            range(len(accept_prob)),
            0.9,
            1.0,
            alpha=0.1,
            color="orange",
            label="Too high",
        )

        accept_ax.plot(
            accept_prob,
            alpha=0.8,
            linewidth=1,
            color="blue",
            label="Acceptance prob",
        )
        accept_ax.axhline(
            0.8,
            color="red",
            linestyle="--",
            linewidth=2,
            label="NUTS target (0.8)",
        )
        accept_ax.set_xlabel("Iteration")
        accept_ax.set_ylabel("Acceptance Probability")
        accept_ax.set_title("NUTS Acceptance Diagnostic")
        accept_ax.legend(loc="best", fontsize="small")
        accept_ax.set_ylim(0, 1)
        accept_ax.grid(True, alpha=0.3)

    else:
        accept_ax.text(
            0.5, 0.5, "Acceptance data\nunavailable", ha="center", va="center"
        )
        accept_ax.set_title("Acceptance Diagnostic")

    # Bottom-right: Summary statistics and additional diagnostics
    summary_ax = axes[1, 1]

    # Collect available statistics
    stats_lines = []

    if has_energy:
        energy = idata.sample_stats.energy.values.flatten()
        stats_lines.append(
            f"Energy: μ={np.mean(energy):.1f}, σ={np.std(energy):.1f}"
        )

    if has_potential:
        potential = idata.sample_stats.potential_energy.values.flatten()
        stats_lines.append(
            f"Potential: μ={np.mean(potential):.1f}, σ={np.std(potential):.1f}"
        )

    if has_steps:
        num_steps = idata.sample_stats.num_steps.values.flatten()
        stats_lines.append(
            f"Steps: μ={np.mean(num_steps):.1f}, max={np.max(num_steps):.0f}"
        )
        stats_lines.append("")

    if has_tree_depth:
        tree_depth = idata.sample_stats.tree_depth.values.flatten()
        stats_lines.append(f"Tree depth: μ={np.mean(tree_depth):.1f}")
        pct_max_depth = (tree_depth >= 10).mean() * 100
        stats_lines.append(f"Max depth (≥10): {pct_max_depth:.1f}%")

    if has_divergences:
        divergences = idata.sample_stats.diverging.values.flatten()
        n_divergences = np.sum(divergences)
        pct_divergent = n_divergences / len(divergences) * 100
        stats_lines.append(
            f"Divergent: {n_divergences}/{len(divergences)} ({pct_divergent:.2f}%)"
        )

    if has_energy_error:
        energy_error = idata.sample_stats.energy_error.values.flatten()
        stats_lines.append(
            f"Energy error: |μ|={np.mean(np.abs(energy_error)):.3f}"
        )

    if not stats_lines:
        summary_ax.text(
            0.5,
            0.5,
            "No diagnostics\ndata available",
            ha="center",
            va="center",
            transform=summary_ax.transAxes,
        )
        summary_ax.set_title("NUTS Statistics")
        summary_ax.axis("off")
    else:
        summary_text = "\n".join(["NUTS Diagnostics:"] + [""] + stats_lines)
        summary_ax.text(
            0.05,
            0.95,
            summary_text,
            transform=summary_ax.transAxes,
            fontsize=10,
            verticalalignment="top",
            fontfamily="monospace",
            bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.9),
        )
        summary_ax.set_title("NUTS Summary Statistics")
        summary_ax.axis("off")

    plt.tight_layout()


def _plot_mh_step_sizes(idata, config):
    """MH step size diagnostics."""
    fig, axes = plt.subplots(2, 2, figsize=config.figsize)

    step_means = idata.sample_stats.step_size_mean.values.flatten()
    step_stds = idata.sample_stats.step_size_std.values.flatten()

    # Step size evolution
    axes[0, 0].plot(
        step_means, alpha=0.7, linewidth=1, label="Mean", color="blue"
    )
    axes[0, 0].plot(
        step_stds, alpha=0.7, linewidth=1, label="Std", color="orange"
    )
    axes[0, 0].set_xlabel("Iteration")
    axes[0, 0].set_ylabel("Step Size")
    axes[0, 0].set_title("Step Size Evolution")
    axes[0, 0].legend()
    axes[0, 0].grid(True, alpha=0.3)

    # Step size distributions
    axes[0, 1].hist(step_means, bins=30, alpha=0.5, label="Mean", color="blue")
    axes[0, 1].hist(step_stds, bins=30, alpha=0.5, label="Std", color="orange")
    axes[0, 1].set_xlabel("Step Size")
    axes[0, 1].set_ylabel("Count")
    axes[0, 1].set_title("Step Size Distributions")
    axes[0, 1].legend()
    axes[0, 1].grid(True, alpha=0.3)

    # Step size adaptation quality
    axes[1, 0].plot(step_means / step_stds, alpha=0.7, linewidth=1)
    axes[1, 0].set_xlabel("Iteration")
    axes[1, 0].set_ylabel("Mean / Std")
    axes[1, 0].set_title("Step Size Consistency")
    axes[1, 0].grid(True, alpha=0.3)

    # Summary statistics
    summary_lines = [
        "Step Size Summary:",
        f"Final mean: {step_means[-1]:.4f}",
        f"Final std: {step_stds[-1]:.4f}",
        f"Mean of means: {np.mean(step_means):.4f}",
        f"Mean of stds: {np.mean(step_stds):.4f}",
        "",
        "Adaptation Quality:",
        f"CV of means: {np.std(step_means)/np.mean(step_means):.3f}",
        f"CV of stds: {np.std(step_stds)/np.mean(step_stds):.3f}",
    ]

    axes[1, 1].text(
        0.05,
        0.95,
        "\n".join(summary_lines),
        transform=axes[1, 1].transAxes,
        fontsize=10,
        verticalalignment="top",
        fontfamily="monospace",
    )
    axes[1, 1].set_title("Step Size Statistics")
    axes[1, 1].axis("off")

    plt.tight_layout()


def _create_divergences_diagnostics(idata, diag_dir, config):
    """Create divergences diagnostics for NUTS samplers."""
    # Check if divergences data exists
    has_divergences = "diverging" in idata.sample_stats
    has_channel_divergences = any(
        key.startswith("diverging_channel_") for key in idata.sample_stats
    )

    if not has_divergences and not has_channel_divergences:
        return  # Nothing to plot

    @safe_plot(f"{diag_dir}/divergences.png", config.dpi)
    def plot_divergences():
        _plot_divergences(idata, config)

    plot_divergences()


def _plot_divergences(idata, config):
    """Plot divergences diagnostics."""
    # Collect all divergence data
    divergences_data = {}

    # Check for main divergences (single chain NUTS)
    if "diverging" in idata.sample_stats:
        divergences_data["main"] = (
            idata.sample_stats.diverging.values.flatten()
        )

    # Check for channel-specific divergences (blocked NUTS)
    channel_divergences = {}
    for key in idata.sample_stats:
        if key.startswith("diverging_channel_"):
            channel_idx = key.replace("diverging_channel_", "")
            channel_divergences[int(channel_idx)] = idata.sample_stats[
                key
            ].values.flatten()

    if channel_divergences:
        divergences_data.update(channel_divergences)

    if not divergences_data:
        fig, ax = plt.subplots(figsize=config.figsize)
        ax.text(
            0.5, 0.5, "No divergence data available", ha="center", va="center"
        )
        ax.set_title("Divergences Diagnostics")
        return

    # Create subplot layout
    n_plots = len(divergences_data)
    if n_plots == 1:
        fig, axes = plt.subplots(1, 2, figsize=config.figsize)
        trace_ax, summary_ax = axes
    else:
        # Multiple plots - arrange in grid
        cols = 2
        rows = (n_plots + 1) // cols  # Ceiling division
        fig, axes = plt.subplots(rows, cols, figsize=config.figsize)
        if rows == 1:
            axes = axes.reshape(1, -1)
        axes = axes.flatten()

        # Last plot goes in summary_ax if odd number
        if n_plots % 2 == 1:
            trace_axes = axes[:-1]
            summary_ax = axes[-1]
        else:
            trace_axes = axes
            summary_ax = None

    # Plot divergences traces
    total_divergences = 0
    total_iterations = 0

    plot_idx = 0
    for label, div_values in divergences_data.items():
        if label == "main":
            title = "NUTS Divergences"
            ax = trace_axes[plot_idx] if n_plots > 1 else axes[0]
        else:
            title = f"Channel {label} Divergences"
            ax = trace_axes[plot_idx] if n_plots > 1 else axes[0]
            plot_idx += 1

        # Plot divergence indicators (where divergences occur)
        div_indices = np.where(div_values)[0]
        ax.scatter(
            div_indices,
            np.ones_like(div_indices),
            color="red",
            marker="x",
            s=50,
            linewidth=2,
            label="Divergent",
            alpha=0.8,
        )

        # Add background shading for divergent regions
        if len(div_indices) > 0:
            for idx in div_indices:
                ax.axvspan(idx - 0.5, idx + 0.5, alpha=0.2, color="red")

        ax.set_xlabel("Iteration")
        ax.set_ylabel("Divergence Indicator")
        ax.set_title(title)
        ax.set_yticks([0, 1])
        ax.set_yticklabels(["No", "Yes"])
        ax.grid(True, alpha=0.3)

        # Add statistics
        n_divergent = np.sum(div_values)
        pct_divergent = n_divergent / len(div_values) * 100
        stats_text = f"{n_divergent}/{len(div_values)} ({pct_divergent:.2f}%)"
        ax.text(
            0.02,
            0.98,
            stats_text,
            transform=ax.transAxes,
            fontsize=10,
            bbox=dict(boxstyle="round", facecolor="lightcoral", alpha=0.8),
            verticalalignment="top",
        )

        total_divergences += n_divergent
        total_iterations += len(div_values)

        # Legend only if there are divergences
        if n_divergent > 0:
            ax.legend(loc="upper right", fontsize="small")

    # Summary plot
    if summary_ax is not None and n_plots > 1:
        summary_ax.text(
            0.05,
            0.95,
            _get_divergences_summary(divergences_data),
            transform=summary_ax.transAxes,
            fontsize=12,
            verticalalignment="top",
            fontfamily="monospace",
            bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.9),
        )
        summary_ax.set_title("Divergences Summary")
        summary_ax.axis("off")
    elif n_plots == 1:
        axes[1].text(
            0.05,
            0.95,
            _get_divergences_summary(divergences_data),
            transform=axes[1].transAxes,
            fontsize=12,
            verticalalignment="top",
            fontfamily="monospace",
            bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.9),
        )
        axes[1].set_title("Divergences Summary")
        axes[1].axis("off")

    # Overall title
    overall_pct = (
        total_divergences / total_iterations * 100
        if total_iterations > 0
        else 0
    )
    fig.suptitle(f"Overall Divergences: {overall_pct:.2f}%")

    plt.tight_layout()


def _get_divergences_summary(divergences_data):
    """Generate text summary of divergences."""
    lines = ["Divergences Summary:", ""]

    total_divergences = 0
    total_iterations = 0

    for label, div_values in divergences_data.items():
        n_divergent = np.sum(div_values)
        pct_divergent = n_divergent / len(div_values) * 100

        if label == "main":
            lines.append(
                f"NUTS: {n_divergent}/{len(div_values)} ({pct_divergent:.2f}%)"
            )
        else:
            lines.append(
                f"Channel {label}: {n_divergent}/{len(div_values)} ({pct_divergent:.2f}%)"
            )

        total_divergences += n_divergent
        total_iterations += len(div_values)

    lines.append("")
    overall_pct = (
        total_divergences / total_iterations * 100
        if total_iterations > 0
        else 0
    )
    lines.append(
        f"Total: {total_divergences}/{total_iterations} ({overall_pct:.2f}%)"
    )

    lines.append("")
    lines.append("Interpretation:")
    if overall_pct == 0:
        lines.append("  ✓ No divergences detected")
        lines.append("    Sampling appears well-behaved")
    elif overall_pct < 0.1:
        lines.append("  ~ Few divergences")
        lines.append("    Generally good, but monitor")
    elif overall_pct < 1.0:
        lines.append("  ⚠ Some divergences detected")
        lines.append("    May indicate sampling issues")
    else:
        lines.append("  ✗ Many divergences!")
        lines.append("    Significant sampling problems")
        lines.append("    Consider model reparameterization")

    return "\n".join(lines)


def _group_parameters_simple(idata):
    """Simple parameter grouping for counting."""
    param_groups = {"phi": [], "delta": [], "weights": [], "other": []}

    for param in idata.posterior.data_vars:
        if param.startswith("phi"):
            param_groups["phi"].append(param)
        elif param.startswith("delta"):
            param_groups["delta"].append(param)
        elif param.startswith("weights"):
            param_groups["weights"].append(param)
        else:
            param_groups["other"].append(param)

    return {k: v for k, v in param_groups.items() if v}


def generate_diagnostics_summary(idata, outdir):
    """Generate comprehensive text summary."""
    summary = []
    summary.append("=== MCMC Diagnostics Summary ===\n")

    # Basic info
    attrs = getattr(idata, "attrs", {}) or {}
    if not hasattr(attrs, "get"):
        attrs = dict(attrs)

    n_samples = idata.posterior.sizes.get("draw", 0)
    n_chains = idata.posterior.sizes.get("chain", 1)
    n_params = len(list(idata.posterior.data_vars))
    sampler_type = attrs.get("sampler_type", "Unknown")

    summary.append(f"Sampler: {sampler_type}")
    summary.append(
        f"Samples: {n_samples} per chain × {n_chains} chains = {n_samples * n_chains} total"
    )
    summary.append(f"Parameters: {n_params}")

    # Parameter breakdown
    param_groups = _group_parameters_simple(idata)
    if param_groups:
        param_summary = ", ".join(
            [f"{k}: {len(v)}" for k, v in param_groups.items()]
        )
        summary.append(f"Parameter groups: {param_summary}")

    # ESS
    try:
        ess = idata.attrs.get("ess")
        ess_values = ess[~np.isnan(ess)]

        if len(ess_values) > 0:
            summary.append(
                f"\nESS: min={ess_values.min():.0f}, mean={ess_values.mean():.0f}, max={ess_values.max():.0f}"
            )
            summary.append(f"ESS ≥ 400: {(ess_values >= 400).mean()*100:.1f}%")
    except Exception as e:
        summary.append(f"\nESS: unavailable")

    # Acceptance
    accept_key = None
    if "accept_prob" in idata.sample_stats:
        accept_key = "accept_prob"
    elif "acceptance_rate" in idata.sample_stats:
        accept_key = "acceptance_rate"

    if accept_key is not None:
        accept_rate = idata.sample_stats[accept_key].values.mean()
        target_rate = attrs.get(
            "target_accept_rate", attrs.get("target_accept_prob", 0.44)
        )
        summary.append(
            f"Acceptance rate: {accept_rate:.3f} (target: {target_rate:.3f})"
        )
    else:
        # Blocked NUTS: compute a combined mean from per‑channel keys if present
        channel_means = []
        for key in idata.sample_stats:
            if isinstance(key, str) and key.startswith("accept_prob_channel_"):
                try:
                    channel_means.append(
                        float(idata.sample_stats[key].values.mean())
                    )
                except Exception:
                    pass
        if channel_means:
            target_rate = attrs.get(
                "target_accept_rate", attrs.get("target_accept_prob", 0.8)
            )
            summary.append(
                f"Acceptance rate (per-channel mean): {np.mean(channel_means):.3f} (target: {target_rate:.3f})"
            )

    # PSD accuracy diagnostics (requires true_psd in attrs)
    has_true_psd = "true_psd" in attrs

    if has_true_psd:
        coverage_level = attrs.get("coverage_level")
        coverage_label = (
            f"{int(round(coverage_level * 100))}% interval coverage"
            if coverage_level is not None
            else "Interval coverage"
        )

        def _format_riae_line(value, errorbars, prefix="  "):
            line = f"{prefix}RIAE: {value:.3f}"
            if errorbars:
                q05, q25, median, q75, q95 = errorbars
                line += f" (median {median:.3f}, 5-95% [{q05:.3f}, {q95:.3f}])"
            summary.append(line)

        def _format_coverage_line(value, prefix="  "):
            if value is None:
                return
            summary.append(f"{prefix}{coverage_label}: {value * 100:.1f}%")

        summary.append("\nPSD accuracy diagnostics:")

        if "riae" in attrs:
            _format_riae_line(attrs["riae"], attrs.get("riae_errorbars"))
        if "coverage" in attrs:
            _format_coverage_line(attrs["coverage"])

        channel_indices = sorted(
            int(key.replace("riae_ch", ""))
            for key in attrs.keys()
            if key.startswith("riae_ch")
        )

        for idx in channel_indices:
            metrics = []
            riae_key = f"riae_ch{idx}"
            cov_key = f"coverage_ch{idx}"
            error_key = f"riae_errorbars_ch{idx}"

            if riae_key in attrs:
                riae_line = f"RIAE {attrs[riae_key]:.3f}"
                errorbars = attrs.get(error_key)
                if errorbars:
                    q05, _, median, _, q95 = errorbars
                    riae_line += (
                        f" (median {median:.3f}, 5-95% [{q05:.3f}, {q95:.3f}])"
                    )
                metrics.append(riae_line)

            if cov_key in attrs:
                metrics.append(f"{coverage_label} {attrs[cov_key] * 100:.1f}%")

            if metrics:
                summary.append(f"  Channel {idx}: " + "; ".join(metrics))

    # Overall assessment
    try:
        # rhat_values = []
        # rhat_good = (rhat_values <= 1.01).mean() * 100
        if len(ess_values) > 0:
            ess_good = (ess_values >= 400).mean() * 100
            summary.append(f"\nOverall Convergence Assessment:")
            if ess_good >= 90:  # and rhat_good >= 90:
                summary.append("  Status: EXCELLENT ✓")
            elif ess_good >= 75:  # and rhat_good >= 75:
                summary.append("  Status: GOOD ✓")
            else:
                summary.append("  Status: NEEDS ATTENTION ⚠")
    except:
        pass

    summary_text = "\n".join(summary)

    if outdir:
        with open(f"{outdir}/diagnostics_summary.txt", "w") as f:
            f.write(summary_text)

    logger.info(f"\n{summary_text}\n")
    return summary_text

nz-gravity / LogPSplinePSD / 19384398576

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