import os
import warnings
from qgis.PyQt.QtCore import QCoreApplication
from qgis.core import (
    QgsProcessing,
    QgsProcessingAlgorithm,
    QgsProcessingParameterString,
    QgsProcessingParameterFile,
    QgsProcessingParameterVectorLayer,
    QgsProcessingParameterField,
    QgsProcessingParameterFolderDestination,
    QgsProcessingParameterBoolean,
    QgsProcessingParameterNumber,
    QgsProcessingException,
    QgsProcessingParameterEnum,
    QgsProcessingParameterDefinition,
    QgsProcessingFeedback,
    QgsProcessingParameterBand
)

from Bathymetrix_AI.infrastructure.logging import append_log
from Bathymetrix_AI.core.temporal.data_scanner import TemporalDataScanner
from Bathymetrix_AI.core.temporal.spatiotemporal_runner import SpatiotemporalSDBRunner
from Bathymetrix_AI.qgis_interface.algorithms.SDB_MasterFlow import SDBMasterOrchestrator

warnings.filterwarnings("ignore")

class SDBSpatiotemporalFlow(SDBMasterOrchestrator):
    """
    Bathymetrix-AI Spatiotemporal SDB Master Flow
    """

    INPUT_IMAGE_ROOT = "INPUT_IMAGE_ROOT"
    OUTPUT_MASTER_FOLDER = "OUTPUT_MASTER_FOLDER"
    FIELD_YEAR_ICESAT = "FIELD_YEAR_ICESAT"

    def tr(self, string):
        return QCoreApplication.translate("Processing", string)

    def createInstance(self):
        return SDBSpatiotemporalFlow()

    def name(self):
        return "sdb_spatiotemporal_flow"

    def displayName(self):
        return self.tr("4. SDB Spatiotemporal Masterflow")

    def group(self):
        return ""

    def groupId(self):
        return ""

    def shortHelpString(self):
        return """
        <div style="font-family: 'Segoe UI', Arial, sans-serif; line-height: 1.5; color: #2C3E50;">
            <h2 style="margin-bottom: 5px; color: #2E86C1;">🪐 Bathymetrix-AI: Spatiotemporal Masterflow</h2>
            <p style="margin-top: 0; margin-bottom: 15px; font-size: 13px;">
                An advanced <b>Spatiotemporal Auto-ML Pipeline</b> for multi-year Satellite-Derived Bathymetry (SDB). 
                Unlike single-year analysis, this module autonomously iterates through multi-temporal datasets, 
                extracts environmental features, and injects <b>"Time/Year"</b> into a unified global learning matrix. 
                It trains a single, highly robust <b>Global AI Model</b> (XGBoost, Random Forest, CatBoost, LightGBM) to achieve seamless 
                temporal depth consistency across all years, then applies Adaptive Spatial Residual Refinement (Phase 04) per year.
            </p>

            <h3 style="color: #117A65; margin-bottom: 5px; border-bottom: 2px solid #117A65; padding-bottom: 3px;">📂 Required Workspace Structure</h3>
            <pre style="background: #F8F9F9; padding: 10px; border-left: 4px solid #2E86C1; font-family: Consolas, monospace; font-size: 12px; margin-top: 5px; border-radius: 4px;">
  📁 Spatiotemporal_Inputs/
     ├── 📁 2019/
     │      ├── Image_2019.tif              (Satellite GeoTIFF - REQUIRED)
     │      ├── Training_2019.shp           (Yearly Training Points - OPTIONAL if global layer is provided)
     │      ├── Control_Points_2019.shp     (Yearly Control Points for Phase 04 - OPTIONAL)
     │      └── Validation_2019.shp         (Yearly Validation Points for Phase 05 - OPTIONAL)
     │
     ├── 📁 2020/
     │      ├── Image_2020.tif              (Satellite GeoTIFF - REQUIRED)
     │      └── Control_Points_2020.shp     (OPTIONAL)
     │
     └── 📁 2024/
            └── Image_2024.tif              (Satellite GeoTIFF - REQUIRED)
            </pre>
            <p style="margin-top: 10px; font-size: 12px;">
                <b>Notes & Flexibilities:</b><br>
                • <b>Image Files:</b> Place each year's satellite image inside its corresponding 4-digit year folder (e.g. <code>2019/</code>, <code>2020/</code>, <code>2024/</code>). Any GeoTIFF file (e.g., <code>Image_2019.tif</code>, <code>2019.tif</code>, etc.) is automatically detected.<br>
                • <b>Training Altimetry:</b> You can either provide a <b>single global multi-year ICESat-2 layer</b> (with a year attribute field) or separate training shapefiles inside each year folder.
            </p>
            
            <h3 style="color: #D35400; margin-top: 20px; margin-bottom: 5px; border-bottom: 2px solid #D35400; padding-bottom: 3px;">⚙️ Pipeline Phases</h3>
            <ul style="font-size: 12px; margin-top: 5px; padding-left: 20px;">
                <li><b>Phase 01:</b> Automated sun-glint correction, water masking, and Log-Ratio feature engineering per year.</li>
                <li><b>Phase 02:</b> ICESat-2 spatial filtering, duplicate collision handling, and RANSAC / LS / Huber outlier rejection per year.</li>
                <li><b>Phase 03 — Global Spatiotemporal AI:</b> Combines all yearly features and injects <b>Year</b> as a predictive feature into a unified Auto-ML model to ensure consistent temporal physics.</li>
                <li><b>Phase 04 — Spatial Residual Correction:</b> Localized zero-mean spatial residual modeling and 95% spatial uncertainty estimation applied independently to each year's depth map.</li>
                <li><b>Phase 05 — Multi-Year Validation & Dashboards:</b> Generates automated interactive HTML dashboards, temporal accuracy comparisons, and scientific reports for every year.</li>
            </ul>
            <br><b>Developer:</b> Mohamed Aly Nasef
        </div>
        """

    def helpString(self):
        return self.shortHelpString()

    def initAlgorithm(self, config=None):

        # -------------------------------------------------------------------
        # [0] General Settings
        # -------------------------------------------------------------------
        self.addParameter(
            QgsProcessingParameterFile(self.INPUT_IMAGE_ROOT, "📁 [0.1] Spatiotemporal Input Workspace (Contains Year Folders)", behavior=QgsProcessingParameterFile.Folder)
        )

        self.addParameter(
            QgsProcessingParameterFolderDestination(self.OUTPUT_MASTER_FOLDER, "📁 [0.3] Spatiotemporal Master Output Workspace")
        )
        self.addParameter(
            QgsProcessingParameterNumber(
                self.NUM_THREADS,
                "⚙️ [0] Processing Threads",
                type=QgsProcessingParameterNumber.Integer,
                defaultValue=4,
            )
        )

        # -------------------------------------------------------------------
        # [1] Phase 01: Advanced Pre-processing
        # -------------------------------------------------------------------
        self.addParameter(
            QgsProcessingParameterBoolean(
                self.ENABLE_PREPROCESSING,
                "⚙️ [1] Enable Phase 01 Pre-processing",
                defaultValue=True,
            )
        )
        self.addParameter(
            QgsProcessingParameterBand(
                self.COASTAL_BAND,
                "📡 [1.1] Coastal Band",
                
                defaultValue=1,
            )
        )
        self.addParameter(
            QgsProcessingParameterBand(
                self.BLUE_BAND,
                "📡 [1.1] Blue Band",
                
                defaultValue=2,
            )
        )
        self.addParameter(
            QgsProcessingParameterBand(
                self.GREEN_BAND,
                "📡 [1.1] Green Band",
                
                defaultValue=3,
            )
        )
        self.addParameter(
            QgsProcessingParameterBand(
                self.RED_BAND,
                "📡 [1.1] Red Band",
                
                defaultValue=4,
            )
        )
        self.addParameter(
            QgsProcessingParameterBand(
                self.NIR_BAND,
                "🌍 [1] NIR Band",
                
                defaultValue=8,
            )
        )
        self.addParameter(
            QgsProcessingParameterBand(
                self.SWIR_BAND,
                "🌍 [1] SWIR Band (For 3 Indices Mask)",
                
                defaultValue=11,
            )
        )

        self.addParameter(
            QgsProcessingParameterBoolean(
                self.APPLY_SUNGLINT,
                "☀️ [1.2] Apply Sunglint Correction",
                defaultValue=True,
            )
        )

        self.addParameter(
            QgsProcessingParameterNumber(
                self.SUNGLINT_PERCENTILE,
                "☀️ [1.2] Sunglint Deep Water %",
                type=QgsProcessingParameterNumber.Double,
                defaultValue=1.0,
            )
        )

        # -------------------------------------------------------------------
        # [1] Phase 01: Advanced Pre-processing (Masking & Features)
        # -------------------------------------------------------------------
        self.addParameter(
            QgsProcessingParameterVectorLayer(
                self.WATER_MASK_POLY,
                "🗺️ [1.3] Ready-made Water Mask Polygon",
                types=[QgsProcessing.TypeVectorPolygon],
                optional=True,
            )
        )
        self.addParameter(
            QgsProcessingParameterNumber(
                self.SHRINK_EDGE_DIST,
                "🗺️ [1.3] Water Edge Shrink (Map Units, e.g. -10)",
                type=QgsProcessingParameterNumber.Double,
                defaultValue=0.0,
            )
        )
        self.addParameter(
            QgsProcessingParameterBoolean(
                self.ENABLE_MASKING,
                "🏖️ [1.3] Enable Automated Water Masking",
                defaultValue=True,
            )
        )
        self.addParameter(
            QgsProcessingParameterEnum(
                self.MASKING_METHOD,
                "🏖️ [1.3] Water Masking Method",
                options=self.MASK_METHODS_NAMES,
                defaultValue=0,
            )
        )
        self.addParameter(
            QgsProcessingParameterNumber(
                self.MANUAL_THRESHOLD,
                "🏖️ [1.3] Manual Threshold",
                type=QgsProcessingParameterNumber.Double,
                defaultValue=0.0,
                optional=True,
            )
        )
        self.addParameter(
            QgsProcessingParameterNumber(
                self.OTSU_ADJUSTMENT,
                "🏖️ [1.3] Otsu Threshold Adjustment",
                type=QgsProcessingParameterNumber.Double,
                defaultValue=0.0,
            )
        )
        self.addParameter(
            QgsProcessingParameterNumber(
                self.MASK_KERNEL_SIZE,
                "🏖️ [1.3] Mask Cleanup Kernel Size",
                type=QgsProcessingParameterNumber.Integer,
                defaultValue=3,
            )
        )

        default_feats = list(range(1, len(self.FEATURE_OPTIONS_NAMES)))
        self.addParameter(
            QgsProcessingParameterEnum(
                self.FEATURE_SELECTION,
                "📊 [1.4] Output Feature Stack",
                options=self.FEATURE_OPTIONS_NAMES,
                allowMultiple=True,
                defaultValue=default_feats,
            )
        )
        self.addParameter(
            QgsProcessingParameterBoolean(
                self.ENABLE_BAND_CALC,
                "🧮 [1.4] Enable Custom Band Math",
                defaultValue=False,
            )
        )
        self.addParameter(
            QgsProcessingParameterString(
                self.BAND_MATH_FORMULA,
                "🧮 [1.4] Band Math Formula",
                defaultValue="",
                optional=True,
            )
        )

        # -------------------------------------------------------------------
        # [1] Phase 01: Advanced Pre-processing (OSW Filter)
        # -------------------------------------------------------------------
        self.addParameter(
            QgsProcessingParameterBoolean(
                self.APPLY_DEEPWATER,
                "🌊 [1.5] Apply Deep Water Filter",
                defaultValue=True,
            )
        )
        self.addParameter(
            QgsProcessingParameterEnum(
                self.DEEPWATER_METHOD,
                "🌊 [1.5] Deep Water Definition Method",
                options=self.OSW_METHODS_NAMES,
                defaultValue=2,
            )
        )
        self.addParameter(
            QgsProcessingParameterVectorLayer(
                self.DEEPWATER_ROI,
                "🌊 [1.5] Deep Water ROI (Polygon) [Optional if Manual Mode]",
                types=[QgsProcessing.TypeVectorPolygon],
                optional=True,
            )
        )
        self.addParameter(
            QgsProcessingParameterNumber(
                self.NIR_PERCENTILE_OSW,
                "🌊 [1.5] NIR Percentile for Deep Water (e.g. 10%)",
                type=QgsProcessingParameterNumber.Double,
                defaultValue=10.0,
            )
        )
        self.addParameter(
            QgsProcessingParameterNumber(
                self.OSW_MEDIAN_SIZE,
                "🌊 [1.5] OSW Mask Median Filter Size",
                type=QgsProcessingParameterNumber.Integer,
                defaultValue=3,
            )
        )
        p_fill = QgsProcessingParameterBoolean(
            self.FILL_INTERNAL_HOLES,
            "🌊 [1.5] Fill All Internal Holes in OSW Mask",
            defaultValue=True,
        )
        p_fill.setFlags(p_fill.flags() | QgsProcessingParameterDefinition.FlagHidden)
        self.addParameter(p_fill)

        p_extract = QgsProcessingParameterBoolean(
            self.EXTRACT_POLYGON,
            "🌊 [1.5] Extract OSW Mask as Polygon",
            defaultValue=True,
        )
        p_extract.setFlags(p_extract.flags() | QgsProcessingParameterDefinition.FlagHidden)
        self.addParameter(p_extract)

        # -------------------------------------------------------------------
        # [2] Phase 02: Robust Filtering
        # -------------------------------------------------------------------
        self.addParameter(
            QgsProcessingParameterVectorLayer(
                self.INPUT_TRAIN, "📍 [2.1] Main Training Points"
            )
        )
        self.addParameter(
            QgsProcessingParameterField(
                self.FIELD_YEAR_ICESAT,
                "📅 [2.1] Year Field (Optional for Global ICESat)",
                defaultValue="year",
                parentLayerParameterName=self.INPUT_TRAIN,
                optional=True,
                type=QgsProcessingParameterField.Numeric
            )
        )

        self.addParameter(
            QgsProcessingParameterField(
                self.FIELD_DEPTH,
                "📏 [2.1] Depth Field",
                defaultValue="ortho_h",
                parentLayerParameterName=self.INPUT_TRAIN,
            )
        )
        self.addParameter(
            QgsProcessingParameterField(
                self.FIELD_WEIGHT,
                "⚖️ [2.1] Weight Field",
                defaultValue="confidence",
                parentLayerParameterName=self.INPUT_TRAIN,
                optional=True,
            )
        )
        self.addParameter(
            QgsProcessingParameterNumber(
                self.MAX_DEPTH_THRESHOLD,
                "🛑 [2.1] Maximum Depth Threshold (e.g. -30)",
                type=QgsProcessingParameterNumber.Double,
                defaultValue=-30.0,
            )
        )

        self.addParameter(
            QgsProcessingParameterBoolean(
                self.ENABLE_RANSAC,
                "🧹 [2.2] Enable Data Filtering (Noise Removal)",
                defaultValue=True,
            )
        )
        self.addParameter(
            QgsProcessingParameterEnum(
                self.FILTER_MODE,
                "🧹 [2.2] Filtering Strategy",
                options=self.FILTER_MODES_NAMES,
                defaultValue=2,
            )
        )
        self.addParameter(
            QgsProcessingParameterBand(
                self.FILTER_NUMERATOR_BAND,
                "🧹 [2.2] Log-Ratio Numerator Band",
                
                defaultValue=2,
            )
        )
        self.addParameter(
            QgsProcessingParameterBand(
                self.FILTER_DENOMINATOR_BAND,
                "🧹 [2.2] Log-Ratio Denominator Band",
                
                defaultValue=3,
            )
        )
        self.addParameter(
            QgsProcessingParameterNumber(
                self.RANSAC_THRESHOLD,
                "🧹 [2.2] Threshold / Sigma Multiplier",
                type=QgsProcessingParameterNumber.Double,
                defaultValue=3.0,
            )
        )
        self.addParameter(
            QgsProcessingParameterNumber(
                self.RANSAC_MAX_TRIALS,
                "🧹 [2.2] RANSAC Trials",
                type=QgsProcessingParameterNumber.Integer,
                defaultValue=100,
            )
        )

        # -------------------------------------------------------------------
        # [3] Phase 03: Global Auto-ML & Feature Analysis
        # -------------------------------------------------------------------
        self.addParameter(
            QgsProcessingParameterEnum(
                self.SELECTED_ALGOS,
                "🤖 [3] Algorithms to Benchmark",
                options=self.MODEL_LIST_NAMES,
                allowMultiple=True,
                defaultValue=[3, 12, 13, 14], # Extra Trees, XGBoost, LightGBM, CatBoost
            )
        )
        self.addParameter(
            QgsProcessingParameterEnum(
                self.OPTIMIZER_METHOD,
                "🤖 [3] Optimizer Method",
                options=self.OPTIMIZER_LIST_NAMES,
                defaultValue=0,
            )
        )
        self.addParameter(
            QgsProcessingParameterEnum(
                self.COLLISION_HANDLING,
                "🤖 [3] Collision Handling",
                options=self.COLLISION_LIST_NAMES,
                defaultValue=0,
            )
        )
        self.addParameter(
            QgsProcessingParameterNumber(
                self.N_ITERATIONS,
                "🤖 [3] Optimization Iterations",
                type=QgsProcessingParameterNumber.Integer,
                defaultValue=20,
            )
        )
        self.addParameter(
            QgsProcessingParameterBoolean(
                self.ENABLE_ENSEMBLE,
                "⚙️ [3] Enable Ensemble of Top Models",
                defaultValue=False,
            )
        )
        
        self.addParameter(
            QgsProcessingParameterEnum(
                self.ENSEMBLE_METHOD,
                "📊 [3] Ensemble Blending Method",
                options=["Average", "Median", "Stacking", "Uncertainty-Weighted Fusion"],
                defaultValue=0,
            )
        )
        p_ens_size = QgsProcessingParameterNumber(
            self.ENSEMBLE_SIZE,
            "📊 [Phase 03] Ensemble Size (Top N Models to blend)",
            type=QgsProcessingParameterNumber.Integer,
            defaultValue=3,
            minValue=2,
            maxValue=5,
        )
        p_ens_size.setFlags(p_ens_size.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_ens_size)
        p_sp_p3 = QgsProcessingParameterBoolean(
            self.SPATIAL_CV_P3,
            "🌍 [Phase 03] Enable Spatial Block Cross-Validation",
            defaultValue=False,
        )
        p_sp_p3.setFlags(p_sp_p3.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_sp_p3)
        self.addParameter(
            QgsProcessingParameterNumber(
                self.MEDIAN_SIZE,
                "🤖 [3] Output Median Filter Size",
                type=QgsProcessingParameterNumber.Integer,
                defaultValue=5,
            )
        )
        self.addParameter(
            QgsProcessingParameterEnum(
                self.FEATURE_CORR_METHOD,
                "🤖 [3] Feature Correlation Method",
                options=["Disabled", "Pearson (Linear)", "Spearman (Rank)", "Automatic-RANSAC", "Automatic-Random Forest"],
                defaultValue=3,
            )
        )
        self.addParameter(
            QgsProcessingParameterEnum(
                self.FEATURE_CORR_THRESHOLD,
                "🤖 [3] Feature Correlation Threshold",
                options=self.FEATURE_CORR_THRESHOLDS,
                defaultValue=2,
            )
        )

        p_var_corr = QgsProcessingParameterBoolean(
            self.ENABLE_DEPTH_VARIANCE_CORR,
            "🤖 [Phase 03] Enable Depth Variance Correction",
            defaultValue=False,
        )
        if self.__class__.__name__ == "SDBMasterOrchestrator":
            p_var_corr.setFlags(p_var_corr.flags() | QgsProcessingParameterDefinition.FlagAdvanced | QgsProcessingParameterDefinition.FlagHidden)
        else:
            p_var_corr.setFlags(p_var_corr.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_var_corr)

        p_cv = QgsProcessingParameterNumber(
            self.CV_FOLDS, "🎛️ [Phase 03] ML Cross-Validation Folds", type=QgsProcessingParameterNumber.Integer, defaultValue=5
        )
        p_cv.setFlags(p_cv.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_cv)

        p_uncert = QgsProcessingParameterNumber(
            self.UNCERT_TREES, "🎛️ [Phase 03] Uncertainty Model Estimators (Trees)", type=QgsProcessingParameterNumber.Integer, defaultValue=200
        )
        p_uncert.setFlags(p_uncert.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_uncert)

        p_split = QgsProcessingParameterNumber(
            self.TRAIN_TEST_SPLIT, "🎛️ [Phase 03 & 04] Training Data Ratio (e.g., 0.8 for 80%)", type=QgsProcessingParameterNumber.Double, defaultValue=0.8
        )
        p_split.setFlags(p_split.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_split)

        p_gpr = QgsProcessingParameterNumber(
            self.MAX_GPR_SAMPLES, "📍 [Phase 04] Max GPR Training Samples", type=QgsProcessingParameterNumber.Integer, defaultValue=1500
        )
        p_gpr.setFlags(p_gpr.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_gpr)

        # Hyperparameters and ML Settings
        p_rs = QgsProcessingParameterNumber(
            self.RANDOM_STATE, "⚙️ [General] Random State for ML Split", type=QgsProcessingParameterNumber.Integer, defaultValue=42
        )
        p_rs.setFlags(p_rs.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_rs)

        p_fmt = QgsProcessingParameterEnum(
            self.OUTPUT_FORMAT, "⚙️ [General] Output Raster Format (Bit Depth)", options=["float32", "float64", "uint16"], defaultValue=0
        )
        p_fmt.setFlags(p_fmt.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_fmt)

        p_rf = QgsProcessingParameterString(self.PARAM_RF, "🎛️ [General ML] Random Forest Hyperparameters", defaultValue="'n_estimators':[100, 500], 'max_depth':[10, 30]")
        p_rf.setFlags(p_rf.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_rf)

        p_gb = QgsProcessingParameterString(self.PARAM_GB, "🎛️ [General ML] Gradient Boosting Hyperparameters", defaultValue="'n_estimators':[100, 300], 'learning_rate':[0.05, 0.1]")
        p_gb.setFlags(p_gb.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_gb)

        p_et = QgsProcessingParameterString(self.PARAM_ET, "🎛️ [General ML] Extra Trees Hyperparameters", defaultValue="'n_estimators':[100, 500], 'max_depth':[10, 30]")
        p_et.setFlags(p_et.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_et)

        p_svr = QgsProcessingParameterString(self.PARAM_SVR, "🎛️ [General ML] SVR Hyperparameters", defaultValue="'C':[1, 10, 100], 'kernel':['rbf'], 'cache_size':[1000], 'max_iter':[20000]")
        p_svr.setFlags(p_svr.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_svr)

        p_mlp = QgsProcessingParameterString(self.PARAM_MLP, "🎛️ [General ML] MLP Hyperparameters", defaultValue="'hidden_layer_sizes':[(100,), (50, 50)], 'max_iter':[500]")
        p_mlp.setFlags(p_mlp.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_mlp)

        p_ridge = QgsProcessingParameterString(self.PARAM_RIDGE, "🎛️ [General ML] Ridge Hyperparameters", defaultValue="'alpha':[0.1, 1.0]", optional=True)
        p_ridge.setFlags(p_ridge.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_ridge)

        p_lasso = QgsProcessingParameterString(self.PARAM_LASSO, "🎛️ [General ML] Lasso Hyperparameters", defaultValue="'alpha':[0.01, 0.1]", optional=True)
        p_lasso.setFlags(p_lasso.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_lasso)

        p_en = QgsProcessingParameterString(self.PARAM_ELASTICNET, "🎛️ [General ML] ElasticNet Hyperparameters", defaultValue="'l1_ratio':[0.5]", optional=True)
        p_en.setFlags(p_en.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_en)

        p_knn = QgsProcessingParameterString(self.PARAM_KNN, "🎛️ [General ML] KNN Hyperparameters", defaultValue="'n_neighbors':[5, 10]", optional=True)
        p_knn.setFlags(p_knn.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_knn)

        p_dt = QgsProcessingParameterString(self.PARAM_DT, "🎛️ [General ML] Decision Tree Hyperparameters", defaultValue="'max_depth':[5, 10]", optional=True)
        p_dt.setFlags(p_dt.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_dt)

        p_huber = QgsProcessingParameterString(self.PARAM_HUBER, "🎛️ [General ML] Huber Hyperparameters", defaultValue="'epsilon':[1.1, 1.35, 1.5]", optional=True)
        p_huber.setFlags(p_huber.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_huber)

        p_xgb = QgsProcessingParameterString(self.PARAM_XGB, "🎛️ [General ML] XGBoost Hyperparameters", defaultValue="'n_estimators':[100, 200], 'max_depth':[4, 6], 'learning_rate':[0.05, 0.1]", optional=True)
        p_xgb.setFlags(p_xgb.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_xgb)

        p_lgbm = QgsProcessingParameterString(self.PARAM_LGBM, "🎛️ [General ML] LightGBM Hyperparameters", defaultValue="'n_estimators':[100, 200], 'max_depth':[4, 6], 'learning_rate':[0.05, 0.1]", optional=True)
        p_lgbm.setFlags(p_lgbm.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_lgbm)

        p_cat = QgsProcessingParameterString(self.PARAM_CATBOOST, "🎛️ [General ML] CatBoost Hyperparameters", defaultValue="'iterations':[100, 200], 'depth':[4, 6], 'learning_rate':[0.05, 0.1]", optional=True)
        p_cat.setFlags(p_cat.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_cat)

        self.addParameter(
            QgsProcessingParameterBoolean(
                self.ENABLE_ADAPTIVE,
                "🎯 [4] Enable Phase 04 Adaptive Refinement",
                defaultValue=False,
            )
        )
        self.addParameter(
            QgsProcessingParameterEnum(
                self.STACK_COMPONENTS_P4,
                "🎯 [4] Features for Retraining",
                options=["Phase 03 Depth Map", "Residual Error Grid"],
                allowMultiple=True,
                defaultValue=[0, 1],
            )
        )
        self.addParameter(
            QgsProcessingParameterEnum(
                self.FEATURE_CORR_METHOD_P4,
                "🤖 [4] Feature Correlation Method",
                options=["Disabled", "Pearson (Linear)", "Spearman (Rank)", "Automatic-RANSAC", "Automatic-Random Forest"],
                defaultValue=3,
            )
        )
        self.addParameter(
            QgsProcessingParameterEnum(
                self.FEATURE_CORR_THRESHOLD_P4,
                "🤖 [4] Feature Correlation Threshold",
                options=self.FEATURE_CORR_THRESHOLDS_P4,
                defaultValue=0,
            )
        )
        self.addParameter(
            QgsProcessingParameterEnum(
                self.RESIDUAL_INTERP_METHOD,
                "📍 [4] Spatial Residual Interpolation Method",
                options=["Standard KNN", "Robust KNN (Huber Weights)", "Gaussian Process / Kriging"],
                defaultValue=0,
            )
        )
        p_knn = QgsProcessingParameterNumber(
            self.KNN_NEIGHBORS,
            "📍 [Phase 04] KNN Nearest Neighbors (K) for Residuals",
            type=QgsProcessingParameterNumber.Integer,
            defaultValue=15,
            minValue=1,
            maxValue=100,
        )
        p_knn.setFlags(p_knn.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_knn)

        p_ens_p4 = QgsProcessingParameterBoolean(
            self.ENABLE_ENSEMBLE_P4,
            "⚙️ [Phase 04] Enable Ensemble of Top Models",
            defaultValue=False,
        )
        p_ens_p4.setFlags(p_ens_p4.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_ens_p4)

        p_ens_meth_p4 = QgsProcessingParameterEnum(
            self.ENSEMBLE_METHOD_P4,
            "📊 [Phase 04] Ensemble Blending Method",
            options=["Average", "Median", "Stacking", "Uncertainty-Weighted Fusion"],
            defaultValue=0,
        )
        p_ens_meth_p4.setFlags(p_ens_meth_p4.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_ens_meth_p4)

        p_ens_size_p4 = QgsProcessingParameterNumber(
            self.ENSEMBLE_SIZE_P4,
            "📊 [Phase 04] Ensemble Size (Top N Models to blend)",
            type=QgsProcessingParameterNumber.Integer,
            defaultValue=3,
            minValue=2,
            maxValue=5,
        )
        p_ens_size_p4.setFlags(p_ens_size_p4.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_ens_size_p4)

        p_sp_p4 = QgsProcessingParameterBoolean(
            self.SPATIAL_CV_P4,
            "🌍 [Phase 04] Enable Spatial Block Cross-Validation",
            defaultValue=False,
        )
        p_sp_p4.setFlags(p_sp_p4.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(p_sp_p4)
        # Note: Phase 04 (Adaptive) and Phase 05 (Validation) shapefiles are 
        # auto-discovered by the TemporalDataScanner inside the year folders.
        self.addParameter(
            QgsProcessingParameterString(
                self.FIELD_ADAPTIVE_DEPTH,
                "🎯 [4] Adaptive Depth Field (For Auto-discovered Control Points)",
                defaultValue="field_3"
            )
        )

        # -------------------------------------------------------------------
        # [5] Phase 05: Validation & Reporting
        # -------------------------------------------------------------------
        self.addParameter(
            QgsProcessingParameterBoolean(
                self.ENABLE_VALIDATION,
                "📉 [5] Enable Phase 05 Validation & Reporting",
                defaultValue=False,
            )
        )
        self.addParameter(
            QgsProcessingParameterString(
                self.FIELD_TEST_DEPTH,
                "📉 [5] Validation Depth Field (For Auto-discovered Validation Points)",
                defaultValue="field_3"
            )
        )

        self.addParameter(
            QgsProcessingParameterBoolean(
                self.REMOVE_POSITIVES,
                "🧽 [Cleanup] Remove Positive Depths (>= 0)",
                defaultValue=True,
            )
        )
        self.addParameter(
            QgsProcessingParameterBoolean(
                self.ENABLE_SLOPE_FILTER,
                "🧽 [Cleanup] Apply Slope Filter (Remove sharp jumps)",
                defaultValue=True,
            )
        )
        self.addParameter(
            QgsProcessingParameterNumber(
                self.SLOPE_THRESHOLD,
                "🧽 [Cleanup] Slope Filter Threshold (Degrees)",
                type=QgsProcessingParameterNumber.Double,
                defaultValue=35.0,
            )
        )

    def processAlgorithm(self, parameters, context, feedback):
        image_root = self.parameterAsString(parameters, self.INPUT_IMAGE_ROOT, context)
        global_train_layer = self.parameterAsVectorLayer(parameters, self.INPUT_TRAIN, context)
        depth_fld = self.parameterAsString(parameters, self.FIELD_DEPTH, context)
        out_folder = self.parameterAsString(parameters, self.OUTPUT_MASTER_FOLDER, context)
        
        os.makedirs(out_folder, exist_ok=True)
        log_file_path = os.path.join(out_folder, "Spatiotemporal_Master_Log.txt")
        
        class FileLoggingFeedback(QgsProcessingFeedback):
            def __init__(self, inner, log_path):
                super().__init__()
                self.inner = inner
                self.log_path = log_path

            def setProgressText(self, text):
                append_log(text, self.log_path, None)
                self.inner.setProgressText(text)

            def pushInfo(self, info):
                append_log(info, self.log_path, None)
                self.inner.pushInfo(info)

            def pushCommandInfo(self, info):
                append_log(info, self.log_path, None)
                self.inner.pushCommandInfo(info)

            def pushDebugInfo(self, info):
                append_log(info, self.log_path, None)
                self.inner.pushDebugInfo(info)

            def pushConsoleInfo(self, info):
                append_log(info, self.log_path, None)
                self.inner.pushConsoleInfo(info)

            def reportError(self, error, fatalError=False):
                append_log(f"ERROR: {error}", self.log_path, None)
                self.inner.reportError(error, fatalError)

            def isCanceled(self):
                return self.inner.isCanceled()

        custom_feedback = FileLoggingFeedback(feedback, log_file_path)

        icesat_year_field = self.parameterAsString(parameters, self.FIELD_YEAR_ICESAT, context)
        append_log("Starting Spatiotemporal Data Scanner...", log_file_path, None)
        self.inner = feedback
        self.inner.pushInfo("Starting Spatiotemporal Data Scanner...")
        scanner = TemporalDataScanner(image_root)
        yearly_datasets = scanner.scan_yearly_datasets(global_train_layer, icesat_year_field)
        
        if not yearly_datasets:
            raise QgsProcessingException("No valid image folders found in the input directory!")

        masterflow_params = {}
        for k, v in parameters.items():
            masterflow_params[k] = v

        runner = SpatiotemporalSDBRunner(out_folder)
        results = runner.run_spatiotemporal_flow(yearly_datasets, masterflow_params, self, context, custom_feedback)
        
        return results

