"""
***************************************************************************
    RandomSelection.py
    ---------------------
    Date                 : August 2012
    Copyright            : (C) 2012 by Victor Olaya
    Email                : volayaf at gmail dot com
***************************************************************************
*                                                                         *
*   This program is free software; you can redistribute it and/or modify  *
*   it under the terms of the GNU General Public License as published by  *
*   the Free Software Foundation; either version 2 of the License, or     *
*   (at your option) any later version.                                   *
*                                                                         *
***************************************************************************
"""

__author__ = "Victor Olaya"
__date__ = "August 2012"
__copyright__ = "(C) 2012, Victor Olaya"

import os
import random

from qgis.PyQt.QtGui import QIcon
from qgis.core import (
    QgsApplication,
    QgsFeatureSink,
    QgsProcessingException,
    QgsProcessingUtils,
    QgsProcessingAlgorithm,
    QgsProcessingParameterVectorLayer,
    QgsProcessingParameterEnum,
    QgsProcessingParameterNumber,
    QgsProcessingParameterFeatureSink,
    QgsProcessingOutputVectorLayer,
)

from processing.algs.qgis.QgisAlgorithm import QgisAlgorithm

pluginPath = os.path.split(os.path.split(os.path.dirname(__file__))[0])[0]


class RandomSelection(QgisAlgorithm):
    INPUT = "INPUT"
    OUTPUT = "OUTPUT"
    METHOD = "METHOD"
    NUMBER = "NUMBER"

    def icon(self):
        return QgsApplication.getThemeIcon("/algorithms/mAlgorithmSelectRandom.svg")

    def svgIconPath(self):
        return QgsApplication.iconPath("/algorithms/mAlgorithmSelectRandom.svg")

    def group(self):
        return self.tr("Vector selection")

    def groupId(self):
        return "vectorselection"

    def flags(self):
        return (
            super().flags()
            | QgsProcessingAlgorithm.Flag.FlagNoThreading
            | QgsProcessingAlgorithm.Flag.FlagNotAvailableInStandaloneTool
        )

    def __init__(self):
        super().__init__()

    def initAlgorithm(self, config=None):
        self.methods = [
            self.tr("Number of selected features"),
            self.tr("Percentage of selected features"),
        ]

        self.addParameter(
            QgsProcessingParameterVectorLayer(self.INPUT, self.tr("Input layer"))
        )
        self.addParameter(
            QgsProcessingParameterEnum(
                self.METHOD, self.tr("Method"), self.methods, False, 0
            )
        )
        self.addParameter(
            QgsProcessingParameterNumber(
                self.NUMBER,
                self.tr("Number/percentage of selected features"),
                QgsProcessingParameterNumber.Type.Integer,
                10,
                False,
                0.0,
            )
        )
        self.addOutput(
            QgsProcessingOutputVectorLayer(self.OUTPUT, self.tr("Selected (random)"))
        )

    def name(self):
        return "randomselection"

    def displayName(self):
        return self.tr("Random selection")

    def processAlgorithm(self, parameters, context, feedback):
        layer = self.parameterAsVectorLayer(parameters, self.INPUT, context)
        method = self.parameterAsEnum(parameters, self.METHOD, context)

        ids = layer.allFeatureIds()
        value = self.parameterAsInt(parameters, self.NUMBER, context)

        if method == 0:
            if value > len(ids):
                raise QgsProcessingException(
                    self.tr(
                        "Selected number is greater than feature count. "
                        "Choose a lower value and try again."
                    )
                )
        else:
            if value > 100:
                raise QgsProcessingException(
                    self.tr(
                        "Percentage can't be greater than 100. Set a "
                        "different value and try again."
                    )
                )
            value = int(round(value / 100.0, 4) * len(ids))

        selran = random.sample(ids, value)

        layer.selectByIds(selran)
        return {self.OUTPUT: parameters[self.INPUT]}
