"""Analyze and visualize DENIM capability-extension v4 without model claims."""

from __future__ import annotations

import json
from collections import defaultdict
from pathlib import Path

import h5py
import matplotlib.pyplot as plt
import numpy as np

from src.generate_t2_denim_capability_v4 import (
    ARTIFACT_DIR,
    DATA_PATH,
    INDEX_PATH,
    MANIFEST_PATH,
    _write_json,
)


ROOT = Path(__file__).resolve().parents[1]
SUMMARY_PATH = ARTIFACT_DIR / "capability_summary.json"
PREVIEW_PATH = ARTIFACT_DIR / "capability_extension_preview.png"


def _rmse(left: np.ndarray, right: np.ndarray) -> float:
    return float(np.sqrt(np.mean(np.square(np.asarray(left) - np.asarray(right)))))


def analyze() -> dict[str, object]:
    rows = [json.loads(line) for line in INDEX_PATH.read_text(encoding="utf-8").splitlines() if line]
    manifest = json.loads(MANIFEST_PATH.read_text(encoding="utf-8"))
    by_group: dict[str, list[tuple[int, dict[str, object]]]] = defaultdict(list)
    for index, row in enumerate(rows):
        by_group[str(row["group_id"])].append((index, row))

    resolution_errors: dict[int, list[float]] = defaultdict(list)
    active_components = []
    observation_keep = []
    strain_noise = []
    stress_noise_mpa = []
    structure_metrics: dict[str, dict[str, float | int]] = {}
    long_metrics: dict[str, list[float]] = defaultdict(list)

    with h5py.File(DATA_PATH, "r") as h5:
        for group_id, members in by_group.items():
            if not group_id.startswith("matched-resolution-"):
                continue
            reference_index, _ = next((item for item in members if int(item[1]["points"]) == 481))
            reference = h5[f"{reference_index:05d}"]
            reference_time = reference["time_coordinate"][...]
            reference_stress = reference["stress_pa"][...]
            for index, row in members:
                points = int(row["points"])
                group = h5[f"{index:05d}"]
                time = group["time_coordinate"][...]
                stress = group["stress_pa"][...]
                interpolated = np.column_stack(
                    [np.interp(time, reference_time, reference_stress[:, component]) for component in range(6)]
                )
                resolution_errors[points].append(_rmse(stress, interpolated) / 1.0e6)
                mask = group["observation_mask"][...].astype(bool)
                observation_keep.append(float(mask.mean()))
                strain_noise.append(
                    _rmse(group["observed_strain"][...], group["strain"][...])
                    / max(float(np.max(np.abs(group["strain"][...]))), 1.0e-15)
                )
                stress_noise_mpa.append(
                    _rmse(group["observed_stress_pa"][...], group["stress_pa"][...]) / 1.0e6
                )

        for index, row in enumerate(rows):
            group = h5[f"{index:05d}"]
            cohort = str(row["cohort"])
            if cohort == "full_3d_nonproportional":
                strain = group["strain"][...]
                maxima = np.max(np.abs(strain), axis=0)
                threshold = 0.01 * max(float(maxima.max()), 1.0e-15)
                active_components.append(int(np.count_nonzero(maxima > threshold)))
            elif cohort == "structure_derived":
                entry = structure_metrics.setdefault(
                    str(row["group_id"]),
                    {"trajectory_count": 0, "maximum_peeq": 0.0, "maximum_absolute_stress_mpa": 0.0},
                )
                entry["trajectory_count"] = int(entry["trajectory_count"]) + 1
                entry["maximum_peeq"] = max(float(entry["maximum_peeq"]), float(np.max(group["peeq"][...])))
                entry["maximum_absolute_stress_mpa"] = max(
                    float(entry["maximum_absolute_stress_mpa"]),
                    float(np.max(np.abs(group["stress_pa"][...]))) / 1.0e6,
                )
            elif cohort == "long_cycle":
                long_metrics[str(row["split"])].append(float(group["peeq"][-1]))

    summary: dict[str, object] = {
        "schema": "agentfem.physics-data.denim-capability-extension.analysis",
        "sample_count": len(rows),
        "cohort_counts": manifest["cohort_counts"],
        "quality": manifest["quality"],
        "long_cycle_final_peeq": {
            split: {
                "count": len(values),
                "median": float(np.median(values)),
                "maximum": float(np.max(values)),
            }
            for split, values in sorted(long_metrics.items())
        },
        "full_3d_component_coverage": {
            "minimum_active_voigt_components": int(min(active_components)),
            "median_active_voigt_components": float(np.median(active_components)),
            "all_six_components_active_fraction": float(np.mean(np.asarray(active_components) == 6)),
        },
        "structure_derived": structure_metrics,
        "matched_resolution_stress_rmse_mpa_against_481": {
            str(points): {
                "median": float(np.median(values)),
                "maximum": float(np.max(values)),
            }
            for points, values in sorted(resolution_errors.items())
        },
        "observation_protocol": {
            "retained_fraction_minimum": float(np.min(observation_keep)),
            "retained_fraction_median": float(np.median(observation_keep)),
            "retained_fraction_maximum": float(np.max(observation_keep)),
            "relative_strain_noise_rmse_median": float(np.median(strain_noise)),
            "stress_noise_rmse_mpa_median": float(np.median(stress_noise_mpa)),
        },
    }
    _write_json(SUMMARY_PATH, summary)
    return summary


def plot() -> Path:
    ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
    with h5py.File(DATA_PATH, "r") as h5:
        long = h5["00299"]
        full3d = h5["00300"]
        structural = h5["00600"]
        observed = h5["00801"]

        figure, axes = plt.subplots(2, 2, figsize=(13.0, 8.2), constrained_layout=True)

        axes[0, 0].plot(long["time_coordinate"][...], long["stress_pa"][:, 0] / 1.0e6, lw=1.0, color="#1f6feb")
        axes[0, 0].set_title("100-cycle long-horizon response")
        axes[0, 0].set_xlabel("normalized history coordinate")
        axes[0, 0].set_ylabel(r"$\sigma_{xx}$ (MPa)")

        strain = full3d["strain"][...]
        labels = ("xx", "yy", "zz", "xy", "yz", "xz")
        for component, label in enumerate(labels):
            axes[0, 1].plot(full3d["time_coordinate"][...], 100.0 * strain[:, component], lw=1.1, label=label)
        axes[0, 1].set_title("Full six-component non-proportional path")
        axes[0, 1].set_xlabel("normalized history coordinate")
        axes[0, 1].set_ylabel("tensor strain (%)")
        axes[0, 1].legend(ncol=3, fontsize=8, frameon=False)

        axes[1, 0].plot(100.0 * structural["strain"][:, 0], structural["stress_pa"][:, 0] / 1.0e6, lw=1.5, color="#d97706")
        axes[1, 0].set_title("AgentFEM-derived local structural path")
        axes[1, 0].set_xlabel(r"$\varepsilon_{xx}$ (%)")
        axes[1, 0].set_ylabel(r"$\sigma_{xx}$ (MPa)")

        time = observed["time_coordinate"][...]
        mask = observed["observation_mask"][...].astype(bool)
        axes[1, 1].plot(time, observed["stress_pa"][:, 0] / 1.0e6, lw=1.5, color="#22c55e", label="clean truth")
        axes[1, 1].scatter(
            time[mask], observed["observed_stress_pa"][mask, 0] / 1.0e6,
            s=10, alpha=0.65, color="#ef4444", label="noisy sparse observation",
        )
        axes[1, 1].set_title("Paired observation robustness protocol")
        axes[1, 1].set_xlabel("normalized history coordinate")
        axes[1, 1].set_ylabel(r"$\sigma_{xx}$ (MPa)")
        axes[1, 1].legend(frameon=False, fontsize=8)

        for axis in axes.ravel():
            axis.grid(alpha=0.20)
        figure.suptitle("AgentFEM DENIM capability extension v4", fontsize=16, fontweight="bold")
        figure.savefig(PREVIEW_PATH, dpi=180)
        plt.close(figure)
    return PREVIEW_PATH


if __name__ == "__main__":
    print(json.dumps(analyze(), indent=2, sort_keys=True))
    print(plot())