#! /usr/bin/env python
''' Traffic Engineering Tools and Examples'''

import prediction

from math import ceil


#########################
# Simulation
#########################

def generateTimeHeadways(meanTimeHeadway, simulationTime):
    '''Generates the time headways between arrivals 
    given the meanTimeHeadway and the negative exponential distribution
    over a time interval of length simulationTime (assumed to be in same time unit as headway'''
    from random import expovariate
    headways = []
    totalTime = 0
    flow = 1/meanTimeHeadway
    while totalTime < simulationTime:
        h = expovariate(flow)
        headways.append(h)
        totalTime += h
    return headways

class RoadUser(object):
    '''Simple example of inheritance to plot different road users '''
    def __init__(self, position, velocity):
        'Both fields are 2D numpy arrays'
        self.position = position.astype(float)        
        self.velocity = velocity.astype(float)

    def move(self, deltaT):
        self.position += deltaT*self.velocity

    def draw(self, init = False):
        from matplotlib.pyplot import plot
        if init:
            self.plotLine = plot(self.position[0], self.position[1], self.getDescriptor())[0]
        else:
            self.plotLine.set_data(self.position[0], self.position[1])


class PassengerVehicle(RoadUser):
    def getDescriptor(self):
        return 'dr'

class Pedestrian(RoadUser):
    def getDescriptor(self):
        return 'xb'

class Cyclist(RoadUser):
    def getDescriptor(self):
        return 'og'

#########################
# queueing models
#########################

class CapacityReduction(object):
    def __init__(self, beta, reductionDuration, demandCapacityRatio = None, demand = None, capacity = None):
        '''reduction duration should be positive
        demandCapacityRatio is demand/capacity (q/s)'''
        if demandCapacityRatio is None and demand is None and capacity is None:
            print('Missing too much information (demand, capacity and ratio)')
            import sys
            sys.exit()
        if 0 <= beta < 1:
            self.beta = beta
            self.reductionDuration = reductionDuration

            if demandCapacityRatio is not None:
                self.demandCapacityRatio = demandCapacityRatio
            if demand is not None:
                self.demand = demand
            if capacity is not None:
                self.capacity = capacity
            if capacity is not None and demand is not None:
                self.demandCapacityRatio = float(self.demand)/self.capacity
                if demand <= beta*capacity:
                    print('There is no queueing as the demand {} is inferior to the reduced capacity {}'.format(demand, beta*capacity))
        else:
            print('reduction coefficient (beta={}) is not in [0, 1['.format(beta))

    def queueingDuration(self):
        return self.reductionDuration*(1-self.beta)/(1-self.demandCapacityRatio)

    def nArrived(self, t):
        if self.demand is None:
            print('Missing demand field')
            return None
        return self.demand*t

    def nServed(self, t):
        if self.capacity is None:
            print('Missing capacity field')
            return None
        if 0<=t<=self.reductionDuration:
            return self.beta*self.capacity*t
        elif self.reductionDuration < t <= self.queueingDuration():
            return self.beta*self.capacity*self.reductionDuration+self.capacity*(t-self.reductionDuration)

    def nQueued(self, t):
        return self.nArrived(t)-self.nServed(t)

    def maxNQueued(self):
        return self.nQueued(self.reductionDuration)

    def totalDelay(self):
        if self.capacity is None:
            print('Missing capacity field')
            return None
        return self.capacity*self.reductionDuration**2*(1-self.beta)*(self.demandCapacityRatio-self.beta)/(2*(1-self.demandCapacityRatio))
    
    def averageDelay(self):
        return self.reductionDuration*(self.demandCapacityRatio-self.beta)/(2*self.demandCapacityRatio)

    def averageNQueued(self):
        return self.totalDelay()/self.queueingDuration()


#########################
# fundamental diagram
#########################

class FundamentalDiagram(object):
    ''' '''
    def __init__(self, name):
        self.name = name

    def q(self, k):
        return k*self.v(k)

    @staticmethod
    def meanHeadway(k):
        return 1/k
    
    @staticmethod
    def meanSpacing(q):
        return 1/q

    def plotVK(self, language='fr', units={}):
        from numpy import arange
        from matplotlib.pyplot import figure,plot,xlabel,ylabel
        densities = [k for k in arange(1, self.kj+1)]
        figure()
        plot(densities, [self.v(k) for k in densities])
        xlabel('Densite (veh/km)') # todo other languages and adapt to units
        ylabel('Vitesse (km/h)')

    def plotQK(self, language='fr', units={}):
        from numpy import arange
        from matplotlib.pyplot import figure,plot,xlabel,ylabel
        densities = [k for k in arange(1, self.kj+1)]
        figure()
        plot(densities, [self.q(k) for k in densities])
        xlabel('Densite (veh/km)') # todo other languages and adapt to units
        ylabel('Debit (km/h)')

class GreenbergFD(FundamentalDiagram):
    '''Speed is the logarithm of density'''
    def __init__(self, vc, kj):
        FundamentalDiagram.__init__(self,'Greenberg')
        self.vc=vc
        self.kj=kj
    
    def v(self,k):
        from numpy import log
        return self.vc*log(self.kj/k)

    def criticalDensity(self): 
        from numpy import e
        self.kc = self.kj/e
        return self.kc

    def capacity(self):
        self.qmax = self.kc*self.vc
        return self.qmax

#########################
# intersection
#########################

class FourWayIntersection(object):
    '''Simple class for simple intersection outline'''
    def __init__(self, dimension, coordX, coordY):
        self.dimension = dimension
        self.coordX = coordX
        self.coordY = coordY

    def plot(self, options = 'k'):
        from matplotlib.pyplot import plot, axis
    
        minX = min(self.dimension[0])
        maxX = max(self.dimension[0])
        minY = min(self.dimension[1])
        maxY = max(self.dimension[1])
        
        plot([minX, self.coordX[0], self.coordX[0]], [self.coordY[0], self.coordY[0], minY],options)
        plot([self.coordX[1], self.coordX[1], maxX], [minY, self.coordY[0], self.coordY[0]],options)
        plot([minX, self.coordX[0], self.coordX[0]], [self.coordY[1], self.coordY[1], maxY],options)
        plot([self.coordX[1], self.coordX[1], maxX], [maxY, self.coordY[1], self.coordY[1]],options)
        axis('equal')

#########################
# traffic signals
#########################

class Volume(object):
    '''Class to represent volumes with varied vehicule types '''
    def __init__(self, volume, types = ['pc'], proportions = [1], equivalents = [1], nLanes = 1):
        '''mvtEquivalent is the equivalent if the movement is right of left turn'''

        # check the sizes of the lists
        if sum(proportions) == 1:
            self.volume = volume
            self.types = types
            self.proportions = proportions
            self.equivalents = equivalents
            self.nLanes = nLanes
        else:
            print('Proportions do not sum to 1')
            pass

    def checkProtected(self, opposedThroughMvt):
        '''Checks if this left movement should be protected,
        ie if one of the main two conditions on left turn is verified'''
        return self.volume >= 200 or self.volume*opposedThroughMvt.volume/opposedThroughMvt.nLanes > 50000

    def getPCUVolume(self):
        '''Returns the passenger-car equivalent for the input volume'''
        v = 0
        for p, e in zip(self.proportions, self.equivalents):
            v += p*e
        return v*self.volume

class IntersectionMovement(object):
    '''Represents an intersection movement
    with a volume, a type (through, left or right)
    and an equivalent for movement type'''
    def __init__(self, volume, mvtEquivalent = 1):
        self.volume = volume
        self.mvtEquivalent = mvtEquivalent

    def getTVUVolume(self):
        return self.mvtEquivalent*self.volume.getPCUVolume()    

class LaneGroup(object):
    '''Class that represents a group of mouvements'''

    def __init__(self, movements, nLanes):
        self.movements = movements
        self.nLanes = nLanes

    def getTVUVolume(self):
        return sum([mvt.getTVUVolume() for mvt in self.movements])

    def getCharge(self, saturationVolume):
        return self.getTVUVolume()/(self.nLanes*saturationVolume)

def optimalCycle(lostTime, criticalCharge):
    return (1.5*lostTime+5)/(1-criticalCharge)

def minimumCycle(lostTime, criticalCharge, degreeSaturation=1.):
    'degree of saturation can be used as the peak hour factor too'
    return lostTime/(1-criticalCharge/degreeSaturation)

class Cycle(object):
    '''Class to compute optimal cycle and the split of effective green times'''
    def __init__(self, phases, lostTime, saturationVolume):
        '''phases is a list of phases
        a phase is a list of lanegroups'''
        self.phases = phases
        self.lostTime = lostTime
        self.saturationVolume = saturationVolume

    def computeCriticalCharges(self):
        self.criticalCharges = [max([lg.getCharge(self.saturationVolume) for lg in phase]) for phase in self.phases]
        self.criticalCharge = sum(self.criticalCharges)
        
    def computeOptimalCycle(self):
        self.computeCriticalCharges()
        self.C = optimalCycle(self.lostTime, self.criticalCharge)
        return self.C

    def computeMinimumCycle(self, degreeSaturation=1.):
        self.computeCriticalCharges()
        self.C = minimumCycle(self.lostTime, self.criticalCharge, degreeSaturation)
        return self.C

    def computeEffectiveGreen(self):
        #from numpy import round
        #self.computeCycle() # in case it was not done before
        effectiveGreenTime = self.C-self.lostTime
        self.effectiveGreens = [round(c*effectiveGreenTime/self.criticalCharge,1) for c in self.criticalCharges]
        return self.effectiveGreens


def computeInterGreen(perceptionReactionTime, initialSpeed, intersectionLength, vehicleAverageLength = 6, deceleration = 3):
    '''Computes the intergreen time (yellow/amber plus all red time)
    Deceleration is positive
    All variables should be in the same units'''
    if deceleration > 0:
        return [perceptionReactionTime+float(initialSpeed)/(2*deceleration), float(intersectionLength+vehicleAverageLength)/initialSpeed]
    else:
        print 'Issue deceleration should be strictly positive'
        return None

def uniformDelay(cycleLength, effectiveGreen, saturationDegree):
    '''Computes the uniform delay'''
    return 0.5*cycleLength*(1-float(effectiveGreen)/cycleLength)**2/(1-float(effectiveGreen*saturationDegree)/cycleLength)

def randomDelay(volume, saturationDegree):
    '''Computes the random delay = queueing time for M/D/1'''
    return saturationDegree**2/(2*volume*(1-saturationDegree))

def incrementalDelay(T, X, c, k=0.5, I=1):
    '''Computes the incremental delay (HCM)
    T in hours
    c capacity of the lane group
    k default for fixed time signal
    I=1 for isolated intersection (Poisson arrival)'''
    from math import sqrt
    return 900*T*(X - 1 + sqrt((X - 1)**2 + 8*k*I*X/(c*T)))

#########################
# misc
#########################

def timeChangingSpeed(v0, vf, a, TPR):
    'for decelerations, a < 0'
    return TPR-(vf-v0)/a

def distanceChangingSpeed(v0, vf, a, TPR):
    'for decelerations, a < 0'
    return TPR*v0-(vf**2-v0**2)/(2*a)
