from math import ceil
from typing import Dict, Any, List, Tuple

import numpy as np
from osgeo import gdal
from qgis._core import (QgsProcessingContext, QgsProcessingFeedback, QgsRasterLayer, QgsProcessingParameterField,
                        QgsCategorizedSymbolRenderer, QgsProcessingException)

from enmapboxprocessing.algorithm.prepareclassificationdatasetfromcategorizedrasteralgorithm import \
    PrepareClassificationDatasetFromCategorizedRasterAlgorithm
from enmapboxprocessing.algorithm.rasterizecategorizedvectoralgorithm import RasterizeCategorizedVectorAlgorithm
from enmapboxprocessing.enmapalgorithm import EnMAPProcessingAlgorithm, Group
from enmapboxprocessing.rasterreader import RasterReader
from enmapboxprocessing.typing import SampleX, SampleY, Categories, checkSampleShape, ClassifierDump
from enmapboxprocessing.utils import Utils
from typeguard import typechecked


@typechecked
class PrepareClassificationDatasetFromCategorizedVectorAlgorithm(EnMAPProcessingAlgorithm):
    P_CATEGORIZED_VECTOR, _CATEGORIZED_VECTOR = 'categorizedVector', 'Categorized vector layer'
    P_FEATURE_RASTER, _FEATURE_RASTER = 'featureRaster', 'Raster layer with features'
    P_CATEGORY_FIELD, _CATEGORY_FIELD = 'categoryField', 'Field with class values'
    P_COVERAGE, _COVERAGE = 'coverage', 'Minimum pixel coverage'
    P_MAJORITY_VOTING, _MAJORITY_VOTING = 'majorityVoting', 'Majority voting'
    P_OUTPUT_DATASET, _OUTPUT_DATASET = 'outputClassificationDataset', 'Output dataset'

    @classmethod
    def displayName(cls) -> str:
        return 'Create classification dataset (from categorized vector layer and feature raster)'

    def shortDescription(self) -> str:
        return 'Create a classification dataset by sampling data for pixels that match the given categories ' \
               'and store the result as a pickle file.\n' \
               'If the layer is not categorized, or the field with class values is selected manually, ' \
               'categories are derived from the sampled target data y. ' \
               'To be more precise: ' \
               'i) category values are derived from unique attribute values (after excluding no data or zero data values), ' \
               'ii) category names are set equal to the category values, ' \
               'and iii) category colors are picked randomly.'

    def helpParameters(self) -> List[Tuple[str, str]]:
        return [
            (self._CATEGORIZED_VECTOR,
             f'Categorized vector layer specifying sample locations and target data y. '
             f'If required, the layer is reprojected and rasterized internally to match the feature raster grid.'),
            (self._FEATURE_RASTER, 'Raster layer used for sampling feature data X.'),
            (self._CATEGORY_FIELD, 'Field with class values used as target data y. '
                                   'If not selected, the field defined by the renderer is used. '
                                   'If that is also not specified, an error is raised.'),
            (self._COVERAGE, 'Exclude all pixel where (polygon) coverage is smaller than given threshold.'),
            (self._MAJORITY_VOTING, 'Whether to use majority voting. '
                                    'Turn off to use simple nearest neighbour resampling, which is much faster, '
                                    'but may result in highly inaccurate class decisions.'),
            (self._OUTPUT_DATASET, self.PickleFileDestination)
        ]

    def group(self):
        return Group.Test.value + Group.DatasetCreation.value

    def initAlgorithm(self, configuration: Dict[str, Any] = None):
        self.addParameterVectorLayer(self.P_CATEGORIZED_VECTOR, self._CATEGORIZED_VECTOR)
        self.addParameterRasterLayer(self.P_FEATURE_RASTER, self._FEATURE_RASTER)
        self.addParameterField(
            self.P_CATEGORY_FIELD, self._CATEGORY_FIELD, None, self.P_CATEGORIZED_VECTOR,
            QgsProcessingParameterField.Any, False, True, False, True
        )
        self.addParameterInt(self.P_COVERAGE, self._COVERAGE, 50, False, 0, 100, advanced=True)
        self.addParameterBoolean(self.P_MAJORITY_VOTING, self._MAJORITY_VOTING, True, False, True)
        self.addParameterFileDestination(self.P_OUTPUT_DATASET, self._OUTPUT_DATASET, self.PickleFileFilter)

    def processAlgorithm(
            self, parameters: Dict[str, Any], context: QgsProcessingContext, feedback: QgsProcessingFeedback
    ) -> Dict[str, Any]:
        classification = self.parameterAsVectorLayer(parameters, self.P_CATEGORIZED_VECTOR, context)
        raster = self.parameterAsRasterLayer(parameters, self.P_FEATURE_RASTER, context)
        classField = self.parameterAsField(parameters, self.P_CATEGORY_FIELD, context)
        minCoverage = self.parameterAsInt(parameters, self.P_COVERAGE, context)
        majorityVoting = self.parameterAsBoolean(parameters, self.P_MAJORITY_VOTING, context)
        filename = self.parameterAsFileOutput(parameters, self.P_OUTPUT_DATASET, context)

        with open(filename + '.log', 'w') as logfile:
            feedback, feedback2 = self.createLoggingFeedback(feedback, logfile)
            self.tic(feedback, parameters, context)

            # derive classification scheme
            renderer = classification.renderer()
            if classField is None:
                if isinstance(renderer, QgsCategorizedSymbolRenderer):
                    categories = Utils.categoriesFromCategorizedSymbolRenderer(renderer)
                    classField = renderer.classAttribute()
                    feedback.pushInfo(f'Use categories from style: {categories}')
                else:
                    message = 'Select either a categorited vector layer, or a field with class values.'
                    feedback.reportError(message, fatalError=True)
                    raise QgsProcessingException(message)
            else:
                categories = Utils.categoriesFromVectorField(classification, classField)
                feedback.pushInfo(f'Derive categories from selected field: {categories}')

            # set derived renderer
            classification = classification.clone()  # make a copy to not overwrite the current renderer
            renderer = Utils.categorizedSymbolRendererFromCategories(classField, categories)
            classification.setRenderer(renderer)

            feedback.pushInfo('Rasterize categorized vector layer')
            alg = RasterizeCategorizedVectorAlgorithm()
            alg.initAlgorithm()
            parameters = {
                alg.P_CATEGORIZED_VECTOR: classification,
                alg.P_GRID: raster,
                alg.P_COVERAGE: minCoverage,
                alg.P_MAJORITY_VOTING: majorityVoting,
                alg.P_OUTPUT_CATEGORIZED_RASTER: Utils.tmpFilename(filename, 'classification.tif')
            }
            result = self.runAlg(alg, parameters, None, feedback2, context, True)
            classification = QgsRasterLayer(result[alg.P_OUTPUT_CATEGORIZED_RASTER])
            categories = Utils.categoriesFromPalettedRasterRenderer(classification.renderer())
            feedback.pushInfo('Sample data')
            X, y = PrepareClassificationDatasetFromCategorizedRasterAlgorithm.sampleData(
                raster, classification, 1, categories, feedback2
            )

            features = [RasterReader(raster).bandName(i + 1) for i in range(raster.bandCount())]
            feedback.pushInfo(f'Sampled data: X=array{list(X.shape)} y=array{list(y.shape)}')

            dump = ClassifierDump(categories=categories, features=features, X=X, y=y)
            dumpDict = dump.__dict__

            Utils.pickleDump(dumpDict, filename)

            result = {self.P_OUTPUT_DATASET: filename}
            self.toc(feedback, result)
        return result

    @classmethod
    def sampleData(
            cls, raster: QgsRasterLayer, classification: QgsRasterLayer, classBandNo: int, categories: Categories,
            feedback: QgsProcessingFeedback = None
    ) -> Tuple[SampleX, SampleY]:
        assert raster.crs() == classification.crs()
        assert raster.extent() == classification.extent()
        assert (raster.width(), raster.height()) == (classification.width(), classification.height())

        maximumMemoryUsage = gdal.GetCacheMax()
        rasterReader = RasterReader(raster)
        classificationReader = RasterReader(classification)
        lineMemoryUsage = rasterReader.lineMemoryUsage(1)
        lineMemoryUsage += classificationReader.width() * classificationReader.dataTypeSize()
        blockSizeY = min(raster.height(), ceil(maximumMemoryUsage / lineMemoryUsage))
        blockSizeX = raster.width()

        X = list()
        y = list()
        for block in rasterReader.walkGrid(blockSizeX, blockSizeY, feedback):
            blockClassification = classificationReader.arrayFromBlock(block, [classBandNo])[0]
            labeled = np.full_like(blockClassification, False, bool)
            for c in categories:
                np.logical_or(labeled, blockClassification == c.value, out=labeled)
            blockY = blockClassification[labeled]
            blockX = list()
            for bandNo in range(1, rasterReader.bandCount() + 1):
                blockBand = rasterReader.arrayFromBlock(block, [bandNo])[0]
                blockX.append(blockBand[labeled])
            X.append(blockX)
            y.append(blockY)
        X = np.concatenate(X, axis=1).T
        y = np.expand_dims(np.concatenate(y), 1)
        checkSampleShape(X, y)
        return X, y
