# -*- coding: utf-8 -*-

"""
***************************************************************************
    NearestNeighbourAnalysis.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'

# This will get replaced with a git SHA1 when you do a git archive

__revision__ = '$Format:%H$'

import math
from qgis.core import *
from processing.core.GeoAlgorithm import GeoAlgorithm
from processing.parameters.ParameterVector import ParameterVector
from processing.outputs.OutputHTML import OutputHTML
from processing.outputs.OutputNumber import OutputNumber
from processing.tools import dataobjects, vector


class NearestNeighbourAnalysis(GeoAlgorithm):

    POINTS = 'POINTS'

    OUTPUT = 'OUTPUT'

    OBSERVED_MD = 'OBSERVED_MD'
    EXPECTED_MD = 'EXPECTED_MD'
    NN_INDEX = 'NN_INDEX'
    POINT_COUNT = 'POINT_COUNT'
    Z_SCORE = 'Z_SCORE'

    # =========================================================================
    # def getIcon(self):
    #    return QIcon(os.path.dirname(__file__) + "/icons/neighbour.png")
    # =========================================================================

    def defineCharacteristics(self):
        self.name = 'Nearest neighbour analysis'
        self.group = 'Vector analysis tools'

        self.addParameter(ParameterVector(self.POINTS, 'Points',
                          [ParameterVector.VECTOR_TYPE_POINT]))

        self.addOutput(OutputHTML(self.OUTPUT, 'Result'))

        self.addOutput(OutputNumber(self.OBSERVED_MD, 'Observed mean distance'
                       ))
        self.addOutput(OutputNumber(self.EXPECTED_MD, 'Expected mean distance'
                       ))
        self.addOutput(OutputNumber(self.NN_INDEX, 'Nearest neighbour index'))
        self.addOutput(OutputNumber(self.POINT_COUNT, 'Number of points'))
        self.addOutput(OutputNumber(self.Z_SCORE, 'Z-Score'))

    def processAlgorithm(self, progress):
        layer = dataobjects.getObjectFromUri(
                self.getParameterValue(self.POINTS))
        output = self.getOutputValue(self.OUTPUT)

        spatialIndex = vector.spatialindex(layer)

        neighbour = QgsFeature()
        distance = QgsDistanceArea()

        sumDist = 0.00
        A = layer.extent()
        A = float(A.width() * A.height())

        current = 0
        features = vector.features(layer)
        count = len(features)
        total = 100.0 / float(len(features))
        for feat in features:
            neighbourID = spatialIndex.nearestNeighbor(
                    feat.geometry().asPoint(), 2)[1]
            request = QgsFeatureRequest().setFilterFid(neighbourID)
            neighbour = layer.getFeatures(request).next()
            sumDist += distance.measureLine(neighbour.geometry().asPoint(),
                    feat.geometry().asPoint())

            current += 1
            progress.setPercentage(int(current * total))

        do = float(sumDist) / count
        de = float(0.5 / math.sqrt(count / A))
        d = float(do / de)
        SE = float(0.26136 / math.sqrt(count ** 2 / A))
        zscore = float((do - de) / SE)

        data = []
        data.append('Observed mean distance: ' + unicode(do))
        data.append('Expected mean distance: ' + unicode(de))
        data.append('Nearest neighbour index: ' + unicode(d))
        data.append('Number of points: ' + unicode(count))
        data.append('Z-Score: ' + unicode(zscore))

        self.createHTML(output, data)

        self.setOutputValue(self.OBSERVED_MD, float(data[0].split(': ')[1]))
        self.setOutputValue(self.EXPECTED_MD, float(data[1].split(': ')[1]))
        self.setOutputValue(self.NN_INDEX, float(data[2].split(': ')[1]))
        self.setOutputValue(self.POINT_COUNT, float(data[3].split(': ')[1]))
        self.setOutputValue(self.Z_SCORE, float(data[4].split(': ')[1]))

    def createHTML(self, outputFile, algData):
        f = open(outputFile, 'w')
        for s in algData:
            f.write('<p>' + str(s) + '</p>')
        f.close()