#! /usr/bin/env python
'''Libraries for events
Interactions, pedestrian crossing...'''

import moving, prediction, indicators, utils, cvutils
from base import VideoFilenameAddable

import numpy as np

import multiprocessing
import itertools


def findRoute(prototypes,objects,i,j,noiseEntryNums,noiseExitNums,minSimilarity= 0.3, spatialThreshold=1.0, delta=180):
    if i[0] not in noiseEntryNums: 
        prototypesRoutes= [ x for x in sorted(prototypes.keys()) if i[0]==x[0]]
    elif i[1] not in noiseExitNums:
        prototypesRoutes=[ x for x in sorted(prototypes.keys()) if i[1]==x[1]]
    else:
        prototypesRoutes=[x for x in sorted(prototypes.keys())]
    routeSim={}
    lcss = utils.LCSS(similarityFunc=lambda x,y: (distanceForLCSS(x,y) <= spatialThreshold),delta=delta)
    for y in prototypesRoutes: 
        if y in prototypes.keys():
            prototypesIDs=prototypes[y]
            similarity=[]
            for x in prototypesIDs:
                s=lcss.computeNormalized(objects[j].positions, objects[x].positions)
                similarity.append(s)
            routeSim[y]=max(similarity)
    route=max(routeSim, key=routeSim.get)
    if routeSim[route]>=minSimilarity:
        return route
    else:
        return i

def getRoute(obj,prototypes,objects,noiseEntryNums,noiseExitNums,useDestination=True):
    route=(obj.startRouteID,obj.endRouteID)
    if useDestination:
        if route not in prototypes.keys():
            route= findRoute(prototypes,objects,route,obj.getNum(),noiseEntryNums,noiseExitNums)
    return route

class Interaction(moving.STObject, VideoFilenameAddable):
    '''Class for an interaction between two road users 
    or a road user and an obstacle
    
    link to the moving objects
    contains the indicators in a dictionary with the names as keys
    '''

    categories = {'Head On': 0,
                  'rearend': 1,
                  'side': 2,
                  'parallel': 3}

    indicatorNames = ['Collision Course Dot Product',
                      'Collision Course Angle',
                      'Distance',
                      'Minimum Distance',
                      'Velocity Angle',
                      'Speed Differential',
                      'Collision Probability',
                      'Time to Collision', # 7
                      'Probability of Successful Evasive Action',
                      'predicted Post Encroachment Time']

    indicatorNameToIndices = utils.inverseEnumeration(indicatorNames)

    indicatorShortNames = ['CCDP',
                           'CCA',
                           'Dist',
                           'MinDist',
                           'VA',
                           'SD',
                           'PoC',
                           'TTC',
                           'P(SEA)',
                           'pPET']

    indicatorUnits = ['',
                      'rad',
                      'm',
                      'm',
                      'rad',
                      'm/s',
                      '',
                      's',
                      '',
                      '']

    timeIndicators = ['Time to Collision', 'predicted Post Encroachment Time']

    def __init__(self, num = None, timeInterval = None, roaduserNum1 = None, roaduserNum2 = None, roadUser1 = None, roadUser2 = None, categoryNum = None):
        moving.STObject.__init__(self, num, timeInterval)
        if timeInterval is None and roadUser1 is not None and roadUser2 is not None:
            self.timeInterval = roadUser1.commonTimeInterval(roadUser2)
        self.roadUser1 = roadUser1
        self.roadUser2 = roadUser2
        if roaduserNum1 is not None and roaduserNum2 is not None:
            self.roadUserNumbers = set([roaduserNum1, roaduserNum2])
        elif roadUser1 is not None and roadUser2 is not None:
            self.roadUserNumbers = set([roadUser1.getNum(), roadUser2.getNum()])
        else:
            self.roadUserNumbers = None
        self.categoryNum = categoryNum
        self.indicators = {}
        self.interactionInterval = None
         # list for collison points and crossing zones
        self.collisionPoints = None
        self.crossingZones = None

    def getRoadUserNumbers(self):
        return self.roadUserNumbers

    def setRoadUsers(self, objects):
        nums = sorted(list(self.getRoadUserNumbers()))
        if nums[0]<len(objects) and objects[nums[0]].getNum() == nums[0]:
            self.roadUser1 = objects[nums[0]]
        if nums[1]<len(objects) and objects[nums[1]].getNum() == nums[1]:
            self.roadUser2 = objects[nums[1]]

        if self.roadUser1 is None or self.roadUser2 is None:
            self.roadUser1 = None
            self.roadUser2 = None
            i = 0
            while i < len(objects) and self.roadUser2 is None:
                if objects[i].getNum() in nums:
                    if self.roadUser1 is None:
                        self.roadUser1 = objects[i]
                    else:
                        self.roadUser2 = objects[i]
                i += 1

    def getIndicator(self, indicatorName):
        return self.indicators.get(indicatorName, None)

    def addIndicator(self, indicator):
        if indicator is not None:
            self.indicators[indicator.name] = indicator

    def getIndicatorValueAtInstant(self, indicatorName, instant):
        indicator = self.getIndicator(indicatorName)
        if indicator is not None:
            return indicator[instant]
        else:
            return None

    def getIndicatorValuesAtInstant(self, instant):
        '''Returns list of indicator values at instant
        as dict (with keys from indicators dict)'''
        values = {}
        for k, indicator in self.indicators.iteritems():
            values[k] = indicator[instant]
        return values
        
    def plot(self, options = '', withOrigin = False, timeStep = 1, withFeatures = False, **kwargs):
        self.roadUser1.plot(options, withOrigin, timeStep, withFeatures, **kwargs)
        self.roadUser2.plot(options, withOrigin, timeStep, withFeatures, **kwargs)

    def plotOnWorldImage(self, nPixelsPerUnitDistance, options = '', withOrigin = False, timeStep = 1, **kwargs):
        self.roadUser1.plotOnWorldImage(nPixelsPerUnitDistance, options, withOrigin, timeStep, **kwargs)
        self.roadUser2.plotOnWorldImage(nPixelsPerUnitDistance, options, withOrigin, timeStep, **kwargs)

    def play(self, videoFilename, homography = None, undistort = False, intrinsicCameraMatrix = None, distortionCoefficients = None, undistortedImageMultiplication = 1.):
        if self.roadUser1 is not None and self.roadUser2 is not None:
            cvutils.displayTrajectories(videoFilename, [self.roadUser1, self.roadUser2], homography = homography, firstFrameNum = self.getFirstInstant(), lastFrameNumArg = self.getLastInstant(), undistort = undistort, intrinsicCameraMatrix = intrinsicCameraMatrix, distortionCoefficients = distortionCoefficients, undistortedImageMultiplication = undistortedImageMultiplication)
        else:
            print('Please set the interaction road user attributes roadUser1 and roadUser1 through the method setRoadUsers')

    def computeIndicators(self):
        '''Computes the collision course cosine only if the cosine is positive'''
        collisionCourseDotProducts = {}#[0]*int(self.timeInterval.length())
        collisionCourseAngles = {}
        velocityAngles = {}
        distances = {}#[0]*int(self.timeInterval.length())
        speedDifferentials = {}
        interactionInstants = []
        for instant in self.timeInterval:
            deltap = self.roadUser1.getPositionAtInstant(instant)-self.roadUser2.getPositionAtInstant(instant)
            v1 = self.roadUser1.getVelocityAtInstant(instant)
            v2 = self.roadUser2.getVelocityAtInstant(instant)
            deltav = v2-v1
            velocityAngles[instant] = np.arccos(moving.Point.dot(v1, v2)/(v1.norm2()*v2.norm2()))
            collisionCourseDotProducts[instant] = moving.Point.dot(deltap, deltav)
            distances[instant] = deltap.norm2()
            speedDifferentials[instant] = deltav.norm2()
            if collisionCourseDotProducts[instant] > 0:
                interactionInstants.append(instant)
            if distances[instant] != 0 and speedDifferentials[instant] != 0:
                collisionCourseAngles[instant] = np.arccos(collisionCourseDotProducts[instant]/(distances[instant]*speedDifferentials[instant]))

        if len(interactionInstants) >= 2:
            self.interactionInterval = moving.TimeInterval(interactionInstants[0], interactionInstants[-1])
        else:
            self.interactionInterval = moving.TimeInterval()
        self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[0], collisionCourseDotProducts))
        self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[1], collisionCourseAngles))
        self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[2], distances, mostSevereIsMax = False))
        self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[4], velocityAngles))
        self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[5], speedDifferentials))

        # if we have features, compute other indicators
        if self.roadUser1.hasFeatures() and self.roadUser2.hasFeatures():
            minDistances={}
            for instant in self.timeInterval:
                minDistances[instant] = moving.MovingObject.minDistance(self.roadUser1, self.roadUser2, instant)
            self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[3], minDistances, mostSevereIsMax = False))

    def computeCrossingsCollisions(self, predictionParameters, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None, nProcesses = 1, usePrototypes=False, route1= (-1,-1), route2=(-1,-1), prototypes={}, secondStepPrototypes={}, nMatching={}, objects=[], noiseEntryNums=[], noiseExitNums=[], minSimilarity=0.1, mostMatched=None, useDestination=True, useSpeedPrototype=True, acceptPartialLength=30, step=1):
        '''Computes all crossing and collision points at each common instant for two road users. '''
        TTCs = {}
        if usePrototypes:
            route1= getRoute(self.roadUser1,prototypes,objects,noiseEntryNums,noiseExitNums,useDestination)
            route2= getRoute(self.roadUser2,prototypes,objects,noiseEntryNums,noiseExitNums,useDestination)

        if timeInterval is not None:
            commonTimeInterval = timeInterval
        else:
            commonTimeInterval = self.timeInterval
        self.collisionPoints, crossingZones = predictionParameters.computeCrossingsCollisions(self.roadUser1, self.roadUser2, collisionDistanceThreshold, timeHorizon, computeCZ, debug, commonTimeInterval, nProcesses,usePrototypes,route1,route2,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype,acceptPartialLength, step)
        for i, cp in self.collisionPoints.iteritems():
            TTCs[i] = prediction.SafetyPoint.computeExpectedIndicator(cp)
        if len(TTCs) > 0:
            self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[7], TTCs, mostSevereIsMax=False))
        
        # crossing zones and pPET
        if computeCZ:
            self.crossingZones = crossingZones
            pPETs = {}
            for i, cz in self.crossingZones.iteritems():
                pPETs[i] = prediction.SafetyPoint.computeExpectedIndicator(cz)
            self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[9], pPETs, mostSevereIsMax=False))
        # TODO add probability of collision, and probability of successful evasive action

    def computePET(self, collisionDistanceThreshold):
        # TODO add crossing zone
        self.pet = moving.MovingObject.computePET(self.roadUser1, self.roadUser2, collisionDistanceThreshold)

    def setCollision(self, collision):
        '''indicates if it is a collision: argument should be boolean'''
        self.collision = collision

    def isCollision(self):
        if hasattr(self, 'collision'):
            return self.collision
        else:
            return None

    def getCollisionPoints(self):
        return self.collisionPoints

    def getCrossingZones(self):
        return self.crossingZones

def createInteractions(objects, _others = None):
    '''Create all interactions of two co-existing road users'''
    if _others is not None:
        others = _others

    interactions = []
    num = 0
    for i in xrange(len(objects)):
        if _others is None:
            others = objects[:i]
        for j in xrange(len(others)):
            commonTimeInterval = objects[i].commonTimeInterval(others[j])
            if not commonTimeInterval.empty():
                interactions.append(Interaction(num, commonTimeInterval, objects[i].num, others[j].num, objects[i], others[j]))
                num += 1
    return interactions

def findInteraction(interactions, roadUserNum1, roadUserNum2):
    'Returns the right interaction in the set'
    i=0
    while i<len(interactions) and set([roadUserNum1, roadUserNum2]) != interactions[i].getRoadUserNumbers():
        i+=1
    if i<len(interactions):
        return interactions[i]
    else:
        return None

def aggregateSafetyPoints(interactions, pointType = 'collision'):
    '''Put all collision points or crossing zones in a list for display'''
    allPoints = []
    if pointType == 'collision':
        for i in interactions:
            for points in i.collisionPoints.values():
                allPoints += points
    elif pointType == 'crossing':
        for i in interactions:
            for points in i.crossingZones.values():
                allPoints += points
    else:
        print('unknown type of point: '+pointType)
    return allPoints

def prototypeCluster(interactions, similarityMatrix, alignmentMatrix, indicatorName, minSimilarity, minClusterSize = None):
    '''Finds exemplar indicator time series for all interactions
    Returns the prototype indices (in the interaction list) and the label of each indicator (interaction)

    if an indicator profile (time series) is different enough (<minSimilarity), 
    it will become a new prototype. 
    Non-prototype interactions will be assigned to an existing prototype
    if minClusterSize is not None, the clusters will be refined by removing iteratively the smallest clusters
    and reassigning all elements in the cluster until no cluster is smaller than minClusterSize'''

    # sort indicators based on length
    indices = range(similarityMatrix.shape[0])
    def compare(i, j):
        if len(interactions[i].getIndicator(indicatorName)) > len(interactions[j].getIndicator(indicatorName)):
            return -1
        elif len(interactions[i].getIndicator(indicatorName)) == len(interactions[j].getIndicator(indicatorName)):
            return 0
        else:
            return 1
    indices.sort(compare)
    # go through all indicators
    prototypeIndices = [indices[0]]
    for i in indices[1:]:
        if similarityMatrix[i][prototypeIndices].max() < minSimilarity:
             prototypeIndices.append(i)

    # assignment
    indices = [i for i in range(similarityMatrix.shape[0]) if i not in prototypeIndices]
    assign = True
    while assign:
        labels = [-1]*similarityMatrix.shape[0]
        for i in prototypeIndices:
            labels[i] = i
        for i in indices:
            prototypeIndex = similarityMatrix[i][prototypeIndices].argmax()
            labels[i] = prototypeIndices[prototypeIndex]
        clusterSizes = {i: sum(np.array(labels) == i) for i in prototypeIndices}
        smallestClusterIndex = min(clusterSizes, key = clusterSizes.get) 
        assign = (clusterSizes[smallestClusterIndex] < minClusterSize)
        print prototypeIndices, smallestClusterIndex, clusterSizes[smallestClusterIndex]
        if assign:
            prototypeIndices.remove(smallestClusterIndex)
            indices.append(smallestClusterIndex)

    return prototypeIndices, labels

def prototypeMultivariateCluster(interactions, similarityMatrics, indicatorNames, minSimilarities, minClusterSize):
    '''Finds exmaple indicator time series (several indicators) for all interactions

    if any interaction indicator time series is different enough (<minSimilarity),
    it will become a new prototype. 
    Non-prototype interactions will be assigned to an existing prototype if all indicators are similar enough'''
    pass


class Crossing(moving.STObject):
    '''Class for the event of a street crossing

    TODO: detecter passage sur la chaussee
    identifier origines et destination (ou uniquement chaussee dans FOV)
    carac traversee
    detecter proximite veh (retirer si trop similaire simultanement
    carac interaction'''
    
    def __init__(self, roaduserNum = None, num = None, timeInterval = None):
        moving.STObject.__init__(self, num, timeInterval)
        self.roaduserNum = roaduserNum

    

if __name__ == "__main__":
    import doctest
    import unittest
    suite = doctest.DocFileSuite('tests/events.txt')
    #suite = doctest.DocTestSuite()
    unittest.TextTestRunner().run(suite)