"""Tests for AI-powered tools (401-420) and pseudo-quantum tools (421-440)."""
import math
import pytest
from geoprompt import GeoPromptFrame


@pytest.fixture()
def sample_frame():
    return GeoPromptFrame.from_records([
        {"geometry": {"type": "Point", "coordinates": [0, 0]}, "val": 10, "cat": "A", "f1": 1.0, "f2": 5.0, "f3": 2.0, "time": 1},
        {"geometry": {"type": "Point", "coordinates": [1, 0]}, "val": 20, "cat": "B", "f1": 2.0, "f2": 3.0, "f3": 4.0, "time": 2},
        {"geometry": {"type": "Point", "coordinates": [0, 1]}, "val": 15, "cat": "A", "f1": 1.5, "f2": 4.0, "f3": 3.0, "time": 3},
        {"geometry": {"type": "Point", "coordinates": [1, 1]}, "val": 30, "cat": "B", "f1": 3.0, "f2": 2.0, "f3": 5.0, "time": 4},
        {"geometry": {"type": "Point", "coordinates": [0.5, 0.5]}, "val": 25, "cat": "A", "f1": 2.5, "f2": 3.5, "f3": 3.5, "time": 5},
    ], crs="EPSG:4326")


@pytest.fixture()
def larger_frame():
    """Frame with more points for algorithms that need larger datasets."""
    import random
    rng = random.Random(42)
    recs = []
    for i in range(30):
        recs.append({
            "geometry": {"type": "Point", "coordinates": [rng.uniform(-1, 1), rng.uniform(-1, 1)]},
            "val": rng.uniform(0, 100),
            "cat": rng.choice(["A", "B", "C"]),
            "f1": rng.uniform(0, 10),
            "f2": rng.uniform(0, 10),
            "f3": rng.uniform(0, 10),
            "time": i,
        })
    return GeoPromptFrame.from_records(recs, crs="EPSG:4326")


# ==================================================================
# AI-Powered Tools (401-420)
# ==================================================================

class TestNeuralSpatialInterpolation:
    """Tool 401"""
    def test_basic(self, sample_frame):
        result = sample_frame.neural_spatial_interpolation("val", grid_resolution=3, epochs=10, seed=42)
        recs = result.to_records()
        assert len(recs) == 9  # 3x3 grid
        assert "predicted_nsi" in recs[0]
        assert "grid_row_nsi" in recs[0]

    def test_custom_params(self, sample_frame):
        result = sample_frame.neural_spatial_interpolation("val", grid_resolution=2, hidden_size=4, epochs=5, seed=1)
        recs = result.to_records()
        assert len(recs) == 4
        for r in recs:
            assert isinstance(r["predicted_nsi"], (int, float))


class TestSpatialAutoencoder:
    """Tool 402"""
    def test_basic(self, sample_frame):
        result = sample_frame.spatial_autoencoder(["f1", "f2", "f3"], encoding_dim=2, epochs=10, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "enc_0_sae" in recs[0]
        assert "enc_1_sae" in recs[0]
        assert "recon_error_sae" in recs[0]

    def test_single_dim(self, sample_frame):
        result = sample_frame.spatial_autoencoder(["f1", "f2"], encoding_dim=1, epochs=5, seed=1)
        recs = result.to_records()
        assert "enc_0_sae" in recs[0]


class TestAISpatialClassifier:
    """Tool 403"""
    def test_basic(self, sample_frame):
        result = sample_frame.ai_spatial_classifier(["f1", "f2", "f3"], "cat", epochs=20, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "predicted_aicls" in recs[0]
        assert "confidence_aicls" in recs[0]
        assert recs[0]["predicted_aicls"] in ("A", "B")

    def test_correct_field(self, sample_frame):
        result = sample_frame.ai_spatial_classifier(["f1", "f2"], "cat", epochs=10, seed=1)
        recs = result.to_records()
        assert "correct_aicls" in recs[0]
        assert recs[0]["correct_aicls"] in (0, 1)


class TestGradientBoostedSpatialRegression:
    """Tool 404"""
    def test_basic(self, sample_frame):
        result = sample_frame.gradient_boosted_spatial_regression(["f1", "f2", "f3"], "val", n_trees=5, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "predicted_gbr" in recs[0]
        assert "residual_gbr" in recs[0]

    def test_predictions_reasonable(self, sample_frame):
        result = sample_frame.gradient_boosted_spatial_regression(["f1", "f2"], "val", n_trees=10, seed=1)
        recs = result.to_records()
        for r in recs:
            assert isinstance(r["predicted_gbr"], (int, float))


class TestRandomForestSpatialClassifier:
    """Tool 405"""
    def test_basic(self, sample_frame):
        result = sample_frame.random_forest_spatial_classifier(["f1", "f2", "f3"], "cat", n_trees=5, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "predicted_rfc" in recs[0]
        assert "votes_rfc" in recs[0]
        assert recs[0]["predicted_rfc"] in ("A", "B")

    def test_all_fields(self, sample_frame):
        result = sample_frame.random_forest_spatial_classifier(["f1", "f2"], "cat", n_trees=3, seed=1)
        recs = result.to_records()
        assert "actual_rfc" in recs[0]
        assert "correct_rfc" in recs[0]


class TestSpatialAnomalyDetector:
    """Tool 406"""
    def test_basic(self, larger_frame):
        result = larger_frame.spatial_anomaly_detector(["f1", "f2", "f3"], contamination=0.1, n_trees=10, seed=42)
        recs = result.to_records()
        assert len(recs) == 30
        assert "anomaly_score_anom" in recs[0]
        assert "is_anomaly_anom" in recs[0]

    def test_anomaly_scores_bounded(self, larger_frame):
        result = larger_frame.spatial_anomaly_detector(["f1", "f2"], n_trees=5, seed=1)
        recs = result.to_records()
        for r in recs:
            assert 0 <= r["anomaly_score_anom"] <= 1.5  # scores near 1.0


class TestSelfOrganizingMapSpatial:
    """Tool 407"""
    def test_basic(self, sample_frame):
        result = sample_frame.self_organizing_map_spatial(["f1", "f2", "f3"], grid_rows=2, grid_cols=2, epochs=10, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "bmu_row_som" in recs[0]
        assert "bmu_col_som" in recs[0]
        assert "bmu_cluster_som" in recs[0]

    def test_cluster_range(self, sample_frame):
        result = sample_frame.self_organizing_map_spatial(["f1", "f2"], grid_rows=3, grid_cols=3, epochs=5, seed=1)
        recs = result.to_records()
        for r in recs:
            assert 0 <= r["bmu_cluster_som"] < 9


class TestSpatialReinforcementLearner:
    """Tool 408"""
    def test_basic(self, sample_frame):
        result = sample_frame.spatial_reinforcement_learner("val", n_episodes=10, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "max_q_srl" in recs[0]
        assert "reward_srl" in recs[0]

    def test_q_values(self, sample_frame):
        result = sample_frame.spatial_reinforcement_learner("val", n_episodes=20, seed=1)
        recs = result.to_records()
        for r in recs:
            assert isinstance(r["max_q_srl"], (int, float))


class TestEvolutionaryFeatureSelection:
    """Tool 409"""
    def test_basic(self, sample_frame):
        result = sample_frame.evolutionary_feature_selection(["f1", "f2", "f3"], "val", pop_size=5, generations=5, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "selected_features_efs" in recs[0]
        assert "n_selected_efs" in recs[0]
        assert "fitness_efs" in recs[0]

    def test_selection_valid(self, sample_frame):
        result = sample_frame.evolutionary_feature_selection(["f1", "f2"], "val", pop_size=4, generations=3, seed=1)
        recs = result.to_records()
        sel = recs[0]["selected_features_efs"]
        for feat in sel.split(","):
            if feat != "none":
                assert feat in ("f1", "f2")


class TestSpatialAttentionMechanism:
    """Tool 410"""
    def test_basic(self, sample_frame):
        result = sample_frame.spatial_attention_mechanism("val", n_heads=2, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "attention_value_attn" in recs[0]
        assert "original_attn" in recs[0]

    def test_head_count(self, sample_frame):
        result = sample_frame.spatial_attention_mechanism("val", n_heads=4, seed=1)
        recs = result.to_records()
        assert recs[0]["n_heads_attn"] == 4


class TestSpatialBayesianOptimizer:
    """Tool 411"""
    def test_basic(self, sample_frame):
        result = sample_frame.spatial_bayesian_optimizer("val", n_iterations=5, seed=42)
        recs = result.to_records()
        assert len(recs) == 1
        assert "optimal_x_sbo" in recs[0]
        assert "optimal_y_sbo" in recs[0]
        assert "optimal_value_sbo" in recs[0]

    def test_within_bounds(self, sample_frame):
        result = sample_frame.spatial_bayesian_optimizer("val", n_iterations=3, seed=1)
        recs = result.to_records()
        b = sample_frame.bounds()
        assert b.min_x <= recs[0]["optimal_x_sbo"] <= b.max_x
        assert b.min_y <= recs[0]["optimal_y_sbo"] <= b.max_y


class TestSpatialGANAugmentor:
    """Tool 412"""
    def test_basic(self, sample_frame):
        result = sample_frame.spatial_gan_augmentor(n_synthetic=5, epochs=10, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "synthetic_id_gan" in recs[0]
        assert "source_gan" in recs[0]

    def test_count(self, sample_frame):
        result = sample_frame.spatial_gan_augmentor(n_synthetic=10, epochs=5, seed=1)
        assert len(result.to_records()) == 10


class TestSpatialEmbedding:
    """Tool 413"""
    def test_basic(self, sample_frame):
        result = sample_frame.spatial_embedding(["f1", "f2", "f3"], embedding_dim=2, n_iterations=10, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "embed_0_emb" in recs[0]
        assert "embed_1_emb" in recs[0]

    def test_3d(self, sample_frame):
        result = sample_frame.spatial_embedding(["f1", "f2"], embedding_dim=3, n_iterations=5, seed=1)
        recs = result.to_records()
        assert "embed_2_emb" in recs[0]


class TestFuzzySpatialClassifier:
    """Tool 414"""
    def test_basic(self, sample_frame):
        result = sample_frame.fuzzy_spatial_classifier("val", n_classes=2, max_iterations=10, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "class_fzy" in recs[0]
        assert "max_membership_fzy" in recs[0]
        assert "membership_0_fzy" in recs[0]

    def test_membership_sum(self, sample_frame):
        result = sample_frame.fuzzy_spatial_classifier("val", n_classes=3, max_iterations=5, seed=1)
        recs = result.to_records()
        for r in recs:
            total = sum(r[f"membership_{c}_fzy"] for c in range(3))
            assert abs(total - 1.0) < 0.05


class TestSpatialDecisionTree:
    """Tool 415"""
    def test_basic(self, sample_frame):
        result = sample_frame.spatial_decision_tree(["f1", "f2", "f3"], "val", max_depth=3)
        recs = result.to_records()
        assert len(recs) == 5
        assert "predicted_sdt" in recs[0]
        assert "residual_sdt" in recs[0]

    def test_predictions(self, sample_frame):
        result = sample_frame.spatial_decision_tree(["f1", "f2"], "val", max_depth=5)
        recs = result.to_records()
        for r in recs:
            assert isinstance(r["predicted_sdt"], (int, float))


class TestSpatialKNNImputer:
    """Tool 416"""
    def test_basic(self):
        frame = GeoPromptFrame.from_records([
            {"geometry": {"type": "Point", "coordinates": [0, 0]}, "val": 10},
            {"geometry": {"type": "Point", "coordinates": [1, 0]}, "val": None},
            {"geometry": {"type": "Point", "coordinates": [0, 1]}, "val": 20},
            {"geometry": {"type": "Point", "coordinates": [1, 1]}, "val": 30},
        ], crs="EPSG:4326")
        result = frame.spatial_knn_imputer("val", k=2)
        recs = result.to_records()
        assert len(recs) == 4
        assert recs[1]["imputed_knn"] == 1
        assert recs[0]["imputed_knn"] == 0
        assert recs[1]["val_knn"] is not None

    def test_no_missing(self, sample_frame):
        result = sample_frame.spatial_knn_imputer("val", k=3)
        recs = result.to_records()
        for r in recs:
            assert r["imputed_knn"] == 0


class TestSpatialNaiveBayes:
    """Tool 417"""
    def test_basic(self, sample_frame):
        result = sample_frame.spatial_naive_bayes(["f1", "f2", "f3"], "cat")
        recs = result.to_records()
        assert len(recs) == 5
        assert "predicted_snb" in recs[0]
        assert "confidence_snb" in recs[0]
        assert recs[0]["predicted_snb"] in ("A", "B")

    def test_confidence_range(self, sample_frame):
        result = sample_frame.spatial_naive_bayes(["f1", "f2"], "cat")
        recs = result.to_records()
        for r in recs:
            assert 0.0 <= r["confidence_snb"] <= 1.0


class TestSpatialSVMClassifier:
    """Tool 418"""
    def test_basic(self, sample_frame):
        result = sample_frame.spatial_svm_classifier(["f1", "f2", "f3"], "cat", epochs=20)
        recs = result.to_records()
        assert len(recs) == 5
        assert "predicted_svm" in recs[0]
        assert "margin_svm" in recs[0]
        assert recs[0]["predicted_svm"] in ("A", "B")

    def test_correct_field(self, sample_frame):
        result = sample_frame.spatial_svm_classifier(["f1", "f2"], "cat", epochs=10)
        recs = result.to_records()
        assert "correct_svm" in recs[0]


class TestSpatialLSTMSequence:
    """Tool 419"""
    def test_basic(self, sample_frame):
        result = sample_frame.spatial_lstm_sequence("val", "time", hidden_size=4, epochs=5, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "predicted_lstm" in recs[0]
        assert "actual_lstm" in recs[0]
        assert "time_lstm" in recs[0]

    def test_sorted_output(self, sample_frame):
        result = sample_frame.spatial_lstm_sequence("val", "time", epochs=3, seed=1)
        recs = result.to_records()
        times = [r["time_lstm"] for r in recs]
        assert times == sorted(times)


class TestAILandUseClassifier:
    """Tool 420"""
    def test_basic(self, sample_frame):
        result = sample_frame.ai_land_use_classifier(["f1", "f2", "f3"], n_classes=2, epochs=10, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "class_luc" in recs[0]
        assert "dist_to_centre_luc" in recs[0]

    def test_class_range(self, sample_frame):
        result = sample_frame.ai_land_use_classifier(["f1", "f2"], n_classes=3, epochs=5, seed=1)
        recs = result.to_records()
        for r in recs:
            assert 0 <= r["class_luc"] < 3


# ==================================================================
# Pseudo-Quantum Tools (421-440)
# ==================================================================

class TestQuantumSpatialAnnealer:
    """Tool 421"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_spatial_annealer("val", n_sweeps=10, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "spin_qsa" in recs[0]
        assert "cluster_qsa" in recs[0]
        assert recs[0]["spin_qsa"] in (-1, 1)

    def test_clusters_binary(self, sample_frame):
        result = sample_frame.quantum_spatial_annealer("val", n_sweeps=5, seed=1)
        recs = result.to_records()
        for r in recs:
            assert r["cluster_qsa"] in (0, 1)


class TestQuantumWalkClustering:
    """Tool 422"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_walk_clustering(n_steps=10, n_clusters=2, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "cluster_qwc" in recs[0]
        assert "amplitude_qwc" in recs[0]
        assert "phase_qwc" in recs[0]

    def test_cluster_range(self, sample_frame):
        result = sample_frame.quantum_walk_clustering(n_steps=5, n_clusters=3, seed=1)
        recs = result.to_records()
        for r in recs:
            assert 0 <= r["cluster_qwc"] < 3


class TestQuantumFeatureMap:
    """Tool 423"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_feature_map(["f1", "f2", "f3"], n_layers=2)
        recs = result.to_records()
        assert len(recs) == 5
        assert "qfeat_0_qfm" in recs[0]
        assert "phase_0_qfm" in recs[0]

    def test_all_features(self, sample_frame):
        result = sample_frame.quantum_feature_map(["f1", "f2"])
        recs = result.to_records()
        assert "qfeat_1_qfm" in recs[0]


class TestQuantumApproximateCounting:
    """Tool 424"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_approximate_counting("val", threshold=15.0, seed=42)
        recs = result.to_records()
        assert len(recs) == 1
        assert "actual_count_qac" in recs[0]
        assert "quantum_estimate_qac" in recs[0]
        assert recs[0]["actual_count_qac"] == 3  # 20, 25, 30 > 15

    def test_estimation_reasonable(self, sample_frame):
        result = sample_frame.quantum_approximate_counting("val", threshold=10.0, seed=1)
        recs = result.to_records()
        actual = recs[0]["actual_count_qac"]
        estimate = recs[0]["quantum_estimate_qac"]
        assert abs(estimate - actual) < actual + 2  # within reasonable range


class TestQuantumPhaseClustering:
    """Tool 425"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_phase_clustering(["f1", "f2", "f3"], n_clusters=2, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "cluster_qpc" in recs[0]
        assert "phase_dist_qpc" in recs[0]

    def test_cluster_range(self, sample_frame):
        result = sample_frame.quantum_phase_clustering(["f1", "f2"], n_clusters=3, seed=1)
        recs = result.to_records()
        for r in recs:
            assert 0 <= r["cluster_qpc"] < 3


class TestQuantumEntanglementGraph:
    """Tool 426"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_entanglement_graph("val")
        recs = result.to_records()
        assert len(recs) == 5
        assert "entanglement_degree_qeg" in recs[0]
        assert "strongest_partner_qeg" in recs[0]
        assert "max_entanglement_qeg" in recs[0]

    def test_degree_range(self, sample_frame):
        result = sample_frame.quantum_entanglement_graph("val", entanglement_threshold=0.3)
        recs = result.to_records()
        for r in recs:
            assert r["entanglement_degree_qeg"] >= 0


class TestQuantumTeleportationInterpolation:
    """Tool 427"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_teleportation_interpolation("val", grid_resolution=3)
        recs = result.to_records()
        assert len(recs) == 9
        assert "predicted_qti" in recs[0]

    def test_grid_count(self, sample_frame):
        result = sample_frame.quantum_teleportation_interpolation("val", grid_resolution=4)
        assert len(result.to_records()) == 16


class TestQuantumSuperpositionQuery:
    """Tool 428"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_superposition_query("val")
        recs = result.to_records()
        assert len(recs) == 5
        assert "combined_qsq" in recs[0]
        assert "value_qsq" in recs[0]

    def test_custom_conditions(self, sample_frame):
        result = sample_frame.quantum_superposition_query("val", conditions=[("big", 20.0), ("small", 10.0)])
        recs = result.to_records()
        assert "prob_big_qsq" in recs[0]
        assert "prob_small_qsq" in recs[0]


class TestQuantumGroverSearch:
    """Tool 429"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_grover_search("val", target_value=25.0, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "search_prob_qgs" in recs[0]
        assert "is_match_qgs" in recs[0]

    def test_target_max(self, sample_frame):
        result = sample_frame.quantum_grover_search("val", seed=1)  # default: target = max
        recs = result.to_records()
        probs = [r["search_prob_qgs"] for r in recs]
        assert abs(sum(probs) - 1.0) < 0.01


class TestQuantumVariationalOptimizer:
    """Tool 430"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_variational_optimizer("val", n_iterations=5, seed=42)
        recs = result.to_records()
        assert len(recs) == 1
        assert "optimal_x_qvo" in recs[0]
        assert "energy_qvo" in recs[0]

    def test_positive_energy(self, sample_frame):
        result = sample_frame.quantum_variational_optimizer("val", n_iterations=3, seed=1)
        recs = result.to_records()
        assert recs[0]["energy_qvo"] > 0


class TestQuantumErrorCorrectedInterpolation:
    """Tool 431"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_error_corrected_interpolation("val", grid_resolution=3)
        recs = result.to_records()
        assert len(recs) == 9
        assert "predicted_qec" in recs[0]
        assert "spread_qec" in recs[0]

    def test_spread_nonneg(self, sample_frame):
        result = sample_frame.quantum_error_corrected_interpolation("val", grid_resolution=2, n_redundancy=5)
        recs = result.to_records()
        for r in recs:
            assert r["spread_qec"] >= 0


class TestQuantumTunnelingOptimizer:
    """Tool 432"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_tunneling_optimizer("val", n_particles=3, n_iterations=10, seed=42)
        recs = result.to_records()
        assert len(recs) == 1
        assert "optimal_value_qto" in recs[0]

    def test_within_bounds(self, sample_frame):
        result = sample_frame.quantum_tunneling_optimizer("val", n_particles=5, n_iterations=5, seed=1)
        recs = result.to_records()
        b = sample_frame.bounds()
        assert b.min_x <= recs[0]["optimal_x_qto"] <= b.max_x
        assert b.min_y <= recs[0]["optimal_y_qto"] <= b.max_y


class TestQuantumBornRuleDensity:
    """Tool 433"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_born_rule_density(grid_resolution=3)
        recs = result.to_records()
        assert len(recs) == 9
        assert "density_qbd" in recs[0]

    def test_density_nonneg(self, sample_frame):
        result = sample_frame.quantum_born_rule_density(grid_resolution=2)
        recs = result.to_records()
        for r in recs:
            assert r["density_qbd"] >= 0


class TestQuantumDecoherenceSmoothing:
    """Tool 434"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_decoherence_smoothing("val", n_steps=3)
        recs = result.to_records()
        assert len(recs) == 5
        assert "smoothed_qds" in recs[0]
        assert "original_qds" in recs[0]
        assert "delta_qds" in recs[0]

    def test_smoothing_effect(self, sample_frame):
        result = sample_frame.quantum_decoherence_smoothing("val", decoherence_rate=1.0, n_steps=20)
        recs = result.to_records()
        # High decoherence should make values converge
        smoothed = [r["smoothed_qds"] for r in recs]
        originals = [r["original_qds"] for r in recs]
        assert max(smoothed) - min(smoothed) <= max(originals) - min(originals)


class TestQuantumInterferencePattern:
    """Tool 435"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_interference_pattern(grid_resolution=3)
        recs = result.to_records()
        assert len(recs) == 9
        assert "intensity_qip" in recs[0]
        assert "constructive_qip" in recs[0]

    def test_intensity_nonneg(self, sample_frame):
        result = sample_frame.quantum_interference_pattern(grid_resolution=2)
        recs = result.to_records()
        for r in recs:
            assert r["intensity_qip"] >= 0


class TestQuantumBoltzmannSampler:
    """Tool 436"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_boltzmann_sampler("val", n_samples=5, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "sample_id_qbs" in recs[0]
        assert "probability_qbs" in recs[0]

    def test_sample_count(self, sample_frame):
        result = sample_frame.quantum_boltzmann_sampler("val", n_samples=10, seed=1)
        assert len(result.to_records()) == 10


class TestQubitSpatialEncoder:
    """Tool 437"""
    def test_basic(self, sample_frame):
        result = sample_frame.qubit_spatial_encoder(["f1", "f2", "f3"])
        recs = result.to_records()
        assert len(recs) == 5
        assert "qubit_0_p0_qse" in recs[0]
        assert "qubit_0_p1_qse" in recs[0]

    def test_probabilities_sum_to_one(self, sample_frame):
        result = sample_frame.qubit_spatial_encoder(["f1", "f2"])
        recs = result.to_records()
        for r in recs:
            assert abs(r["qubit_0_p0_qse"] + r["qubit_0_p1_qse"] - 1.0) < 1e-6


class TestQuantumFourierSpatialAnalysis:
    """Tool 438"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_fourier_spatial_analysis("val", n_harmonics=3)
        recs = result.to_records()
        assert len(recs) == 5
        assert "value_qft" in recs[0]
        assert "reconstructed_qft" in recs[0]
        assert "harmonic_0_amp_qft" in recs[0]

    def test_harmonic_count(self, sample_frame):
        result = sample_frame.quantum_fourier_spatial_analysis("val", n_harmonics=2)
        recs = result.to_records()
        assert "harmonic_1_amp_qft" in recs[0]


class TestQuantumMeasurementClassifier:
    """Tool 439"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_measurement_classifier(["f1", "f2", "f3"], n_classes=2, n_shots=20, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "class_qmc" in recs[0]
        assert "confidence_qmc" in recs[0]

    def test_confidence_range(self, sample_frame):
        result = sample_frame.quantum_measurement_classifier(["f1", "f2"], n_classes=3, n_shots=50, seed=1)
        recs = result.to_records()
        for r in recs:
            assert 0.0 <= r["confidence_qmc"] <= 1.0
            assert 0 <= r["class_qmc"] < 3


class TestQuantumAdiabaticSolver:
    """Tool 440"""
    def test_basic(self, sample_frame):
        result = sample_frame.quantum_adiabatic_solver("val", n_steps=10, seed=42)
        recs = result.to_records()
        assert len(recs) == 5
        assert "group_qas" in recs[0]
        assert "spin_qas" in recs[0]

    def test_groups_binary(self, sample_frame):
        result = sample_frame.quantum_adiabatic_solver("val", n_steps=5, seed=1)
        recs = result.to_records()
        for r in recs:
            assert r["group_qas"] in (0, 1)
            assert r["spin_qas"] in (-1, 1)
