#! /usr/bin/env python3

import sys, argparse

import numpy as np

from trafficintelligence import ml, utils, storage, moving

parser = argparse.ArgumentParser(description='The program learns prototypes for the motion patterns') #, epilog = ''
#parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file')
parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True)
parser.add_argument('-o', dest = 'outputPrototypeDatabaseFilename', help = 'name of the Sqlite database file to save prototypes')
parser.add_argument('-i', dest = 'inputPrototypeDatabaseFilename', help = 'name of the Sqlite database file for prototypes to start the algorithm with')
parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to learn from', choices = ['objectfeatures', 'feature', 'object'], default = 'objectfeatures')
parser.add_argument('--max-nobjectfeatures', dest = 'maxNObjectFeatures', help = 'maximum number of features per object to load', type = int, default = 1)
parser.add_argument('-n', dest = 'nTrajectories', help = 'number of the object or feature trajectories to load', type = int, default = None)
parser.add_argument('-e', dest = 'epsilon', help = 'distance for the similarity of trajectory points', type = float, required = True)
parser.add_argument('--metric', dest = 'metric', help = 'metric for the similarity of trajectory points', default = 'cityblock') # default is manhattan distance
parser.add_argument('-s', dest = 'minSimilarity', help = 'minimum similarity to put a trajectory in a cluster', type = float, required = True)
parser.add_argument('-c', dest = 'minClusterSize', help = 'minimum cluster size', type = int, default = 0)
parser.add_argument('--learn', dest = 'learn', help = 'learn', action = 'store_true')
parser.add_argument('--optimize', dest = 'optimizeCentroid', help = 'recompute centroid at each assignment', action = 'store_true')
parser.add_argument('--random', dest = 'randomInitialization', help = 'random initialization of clustering algorithm', action = 'store_true')
parser.add_argument('--subsample', dest = 'positionSubsamplingRate', help = 'rate of position subsampling (1 every n positions)', type = int)
parser.add_argument('--display', dest = 'display', help = 'display trajectories', action = 'store_true')
parser.add_argument('--save-similarities', dest = 'saveSimilarities', help = 'save computed similarities (in addition to prototypes)', action = 'store_true')
parser.add_argument('--save-matches', dest = 'saveMatches', help = 'saves the assignments of the objects (not for features) to the prototypes', action = 'store_true')
parser.add_argument('--assign', dest = 'assign', help = 'assigns the objects to the prototypes and saves the assignments', action = 'store_true')

args = parser.parse_args()

# use cases
# 1. learn proto from one file, save in same or another
# 2. load proto, load objects, update proto, save proto
# 3. assign objects from one db to proto
# 4. load objects from several files, save in another -> see metadata: site with view and times
# 5. keep prototypes, with positions/velocities, in separate db (keep link to original data through filename, type and index)

# TODO add possibility to cluster with velocities
# TODO add possibilite to load all trajectories and use minclustersize
# save the objects that match the prototypes
# write an assignment function for objects

# load trajectories to cluster or assign
if args.trajectoryType == 'objectfeatures':
    trajectoryType = 'feature'
    objectFeatureNumbers = storage.loadObjectFeatureFrameNumbers(args.databaseFilename, objectNumbers = args.nTrajectories)
    featureNumbers = []
    for numbers in objectFeatureNumbers.values():
        featureNumbers += numbers[:min(len(numbers), args.maxNObjectFeatures)]
    objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, 'feature', objectNumbers = featureNumbers, timeStep = args.positionSubsamplingRate)
else:
    trajectoryType = args.trajectoryType
    objects = storage.loadTrajectoriesFromSqlite(args.databaseFilename, trajectoryType, objectNumbers = args.nTrajectories, timeStep = args.positionSubsamplingRate)

trajectories = [o.getPositions().asArray().T for o in objects]

# load initial prototypes, if any    
if args.inputPrototypeDatabaseFilename is not None:
    initialPrototypes = storage.loadPrototypesFromSqlite(args.inputPrototypeDatabaseFilename, True)
    trajectories = [p.getMovingObject().getPositions().asArray().T for p in initialPrototypes]+trajectories
    if len(initialPrototypes) > 0:
        initialPrototypeIndices = list(range(len(initialPrototypes)))
    else:
        initialPrototypeIndices = None
else:
    initialPrototypes = []
    initialPrototypeIndices = None

lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon)
nTrajectories = len(trajectories)

similarities = -np.ones((nTrajectories, nTrajectories))
similarityFunc = lambda x,y : lcss.computeNormalized(x, y)
# the next line can be called again without reinitializing similarities
if args.learn:
    prototypeIndices = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, similarityFunc, args.optimizeCentroid, args.randomInitialization, initialPrototypeIndices)
else:
    prototypeIndices = initialPrototypeIndices

if args.assign: # TODO don't touch initial prototypes if not from same db as trajectories
    #if not args.learn and args.minClusterSize >= 1: # allow only 
    #   print('Warning: you did not learn the prototypes and you are using minimum cluster size of {}, which may lead to removing prototypes and assigning them to others'.format(args.minClusterSize))
    # if args.minClusterSize >= 1:
    #     if initialPrototypeIndices is None:
    #         prototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc, args.minClusterSize)
    #     else:
    #         print('Not assigning with non-zero minimum cluster size and initial prototypes (would remove initial prototypes based on other trajectories')
    # else:
    #     prototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc)
    prototypeIndices, labels = ml.assignToPrototypeClusters(trajectories, prototypeIndices, similarities, args.minSimilarity, similarityFunc)
    clusterSizes = ml.computeClusterSizes(labels, prototypeIndices, -1)
    print(clusterSizes)

if args.learn and not args.assign:
    prototypes = []
    for i in prototypeIndices:
        if i<len(initialPrototypes):
            prototypes.append(initialPrototypes[i])
        else:
            prototypes.append(moving.Prototype(args.databaseFilename, objects[i-len(initialPrototypes)].getNum(), trajectoryType))

    if args.outputPrototypeDatabaseFilename is None:
        outputPrototypeDatabaseFilename = args.databaseFilename
    else:
        outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
        if args.inputPrototypeDatabaseFilename == args.outputPrototypeDatabaseFilename:
            storage.deleteFromSqlite(args.outputPrototypeDatabaseFilename, 'prototype')
    storage.savePrototypesToSqlite(outputPrototypeDatabaseFilename, prototypes)

if not args.learn and args.assign: # no new prototypes # not save assignments of past prototypes if removes with minClusterSize
    prototypes = []
    for i in prototypeIndices:
        nMatchings = clusterSizes[i]-1
        if initialPrototypes[i].nMatchings is None:
            initialPrototypes[i].nMatchings = nMatchings
        else:
            initialPrototypes[i].nMatchings += nMatchings
        prototypes.append(initialPrototypes[i])
    if args.outputPrototypeDatabaseFilename is None:
        outputPrototypeDatabaseFilename = args.databaseFilename
    else:
        outputPrototypeDatabaseFilename = args.outputPrototypeDatabaseFilename
    storage.setPrototypeMatchingsInSqlite(outputPrototypeDatabaseFilename, prototypes)

    labelsToProtoIndices = {protoId: i for i, protoId in enumerate(prototypeIndices)}
    if args.saveMatches:
        storage.savePrototypeAssignmentsToSqlite(args.databaseFilename, objects, trajectoryType, [labelsToProtoIndices[l] for l in labels], prototypes)

if (args.learn or args.assign) and args.saveSimilarities:
    np.savetxt(utils.removeExtension(args.databaseFilename)+'-prototype-similarities.txt.gz', similarities, '%.4f')

if args.display and args.assign:
    from matplotlib.pyplot import figure, show, axis
    figure()
    for i,o in enumerate(objects):
        if i not in prototypeIndices:
            if labels[i] < 0:
                o.plot('kx')
            else:
                o.plot(utils.colors[labels[i]])
    for i in prototypeIndices:
            objects[i].plot(utils.colors[i]+'o')
    axis('equal')
    show()