#!/usr/bin/env python

# -*- coding: utf8 -*-

'''

gpx_reduce v1.8: removes points from gpx-files to reduce filesize and

tries to keep introduced distortions to the track at a minimum.

Copyright (C) 2011,2012,2013,2015,2016,2017 travelling_salesman on OpenStreetMap

changelog: v1.2: clarity refractoring + speedup for identical points

           v1.3: new track weighting functions, progress display

           v1.4: new track weighting function, restructuring for memory saving

           v1.5: algorithm speedup by roughly a factor of 2 by eliminating some cases.

           v1.6: presets for train etc.

           v1.7: introduced weighting function for elevation errors

           v1.8: speed-dependent distance limit

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 3 of the License, or

(at your option) any later version.

This program is distributed in the hope that it will be useful,

but WITHOUT ANY WARRANTY; without even the implied warranty of

MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the

GNU General Public License for more details.

You should have received a copy of the GNU General Public License

along with this program.  If not, see <http://www.gnu.org/licenses/>.

'''

import datetime

import sys

import time

import numpy as np

import numpy.linalg as la

from math import *

import xml.etree.ElementTree as etree

from optparse import OptionParser

parser = OptionParser('usage: %prog [options] input-file.gpx')

parser.add_option('-v', '--verbose', action='store', type='int',

    dest='verbose', default=1, help='verbose=[0,1]')

parser.add_option('-p', '--plot', action='store_true', dest='plot',

    default=False, help='Show a plot of the result at the end.')

parser.add_option('-d', '--dist', action='store', type='float', dest='max_dist',

    default=0.5, help='Maximum distance of line from original points in meters')

parser.add_option('-o', '--out', action='store', type='string',

    dest='ofname', default=None, help='Output file name')

parser.add_option('-m', '--maxsep', action='store', type='float', dest='max_sep',

    default=200.0, help='Absolute maximum separation of points. No points will be deleted where the resulting distance would become greater than maxsep. Standard JOSM settings will not display points spaced more than 200m. -1 means no limit.')

parser.add_option('-n', '--maxsep0', action='store', type='float', dest='max_sep0',

    default=4.0, help='Maximum separation of points at zero speed.')

parser.add_option('-t', '--maxseptime', action='store', type='float', dest='max_sep_time',

    default=3.0, help='Maximum time separation of points, which will be added to maxsep0. Maximum allowed point separation will be min(m, n + t*v) where v is the speed in m/s.')

parser.add_option('-e', '--ele-weight', action='store', type='float',

    dest='ele_weight', default=0.0,

    help='Weighting of elevation errors vs. horizontal errors. Default is 0.')

parser.add_option('-b', '--bend', action='store', type='float', dest='bend',

    default=1.0, help='Penalty value for large bending angles at each trackpoint. Larger values (1 or more) make the track smoother.')

parser.add_option('-w', '--weighting', action='store', type='string',

    dest='weighting', default='exp',

    help='''Weighting function to be minimized for track reduction:

pnum (number of points),

sqrdistsum (number of points plus sum of squared distances to leftout points),

sqrdistmax (number of points plus sum of squared distances to each maximally separated leftout point per new line segment),

sqrlength (number of points plus sum of squared new line segment lengths normalized by maxsep),

mix (number of points plus sum of squared distances to each maximally separated leftout point per new line segment weighted with corresponding segment length),

exp (number of points plus sum of squared distances to leftout points with exponential weighting of 1/2, 1/4, 1/8... from furthest to closest point). exp=standard''')

parser.add_option ('-r', dest='remove', default='extensions,hdop', help='remove tags T1,T2,..,Tn from every trackpoint', metavar='T1 T2 Tn')

options, args = parser.parse_args()

if len(args) < 1:

    parser.print_usage()

    exit(2)

# use the WGS-84 ellipsoid

rE = 6356752.314245 # earth's radius

a = 6378137.0

b = 6356752.314245179

timeformat = '%Y-%m-%dT%H:%M:%SZ'

def distance(p1_, pm_, p2_, ele_weight=1.0):

    # returns distance of pm from line between p1 and p2

    p1, pm, p2 = np.array(p1_), np.array(pm_), np.array(p2_)

    h1, hm, h2 = la.norm(p1), la.norm(pm), la.norm(p2)

    if ele_weight != 1.0 and min(h1, hm, h2) > 0.0:

        hmean = (h1 + hm + h2) / 3.0

        p1 *= (ele_weight + (1.0 - ele_weight) * hmean / h1)

        pm *= (ele_weight + (1.0 - ele_weight) * hmean / hm)

        p2 *= (ele_weight + (1.0 - ele_weight) * hmean / h2)

    line = p2 - p1

    linel = la.norm(line)

    vm = pm - p1

    if linel == 0.0:

        return la.norm(vm)

    linem = line / linel

    position = np.dot(vm, linem) / linel

    if position < 0.0:

        return la.norm(vm)

    elif position > 1.0:

        return la.norm(pm - p2)

    else:

        return la.norm(vm - line * position)

def latlonele_to_xyz(lat, lon, ele):

    s = sin(radians(lat))

    c = cos(radians(lat))

    r = ele + a * b / la.norm([s*a, c*b])

    lon = radians(lon)

    return r * c * sin(lon), r * c * (-cos(lon)), r * s

def reduced_track_indices(coordinate_list, timesteps=None):

    # returns a list of indices of trackpoints that constitute the reduced track

    # takes a list of kartesian coordinate tuples

    m = len(coordinate_list)

    if (m == 0): return []

    if timesteps != None and len(timesteps) != len(coordinate_list):

        timesteps = None

    # number of dimensions

    d = len(coordinate_list[0])

    # remove identical entries (can speed up algorithm considerably)

    original_indices = [0]

    points = [{'p': coordinate_list[0], 'weight':1}]

    if timesteps != None: points[0]['t'] = timesteps[0]

    for i in range(1, m):

        if False in [coordinate_list[i-1][j] == coordinate_list[i][j] for j in range(d)]:

            original_indices.append(i)

            points.append({'p': coordinate_list[i], 'weight':1})

            if timesteps != None: points[-1]['t'] = timesteps[i]

        else:

            points[-1]['weight'] += 1

    n = len(points)

    # progress printing initialisations

    progress_printed = False

    progress = None

    tprint = time.time()

    # execute Dijkstra-like algorithm on points

    points[0]['cost'] = 1.0

    points[0]['prev'] = -1

    for i2 in range(1, n):

        penalties = {}

        imin = None

        costmin = float('inf')

        for i1 in reversed(range(i2)):

            p1 = np.array(points[i1]['p'])

            p2 = np.array(points[i2]['p'])

            seglength = la.norm(p2 - p1)

            # estimate speed between p1 and p2

            if timesteps != None:

                dt = (points[i2]['t'] - points[i1]['t']).total_seconds()

                v = seglength / max(0.1, dt)

            else:

                v = seglength / float(i2 - i1) # assume 1s time spacing

            max_sep = options.max_sep0 + v * options.max_sep_time

            if options.max_dist >= 0:

                max_sep = min(max_sep, options.max_sep)

            if (seglength >= max_sep and i1 != i2 - 1):

                # point separation is too far

                # but always accept direct predecessor i1 = i2 - 1

                if (seglength >= max_sep + options.max_dist):

                    # no chance to find a valid earlier predecessor point

                    break

                else:

                    continue

            if points[i1]['cost'] + 1.0 > costmin:

                # the possible predecessor i1 is already too bad.

                continue

            i1_i2_segment_valid = True

            lower_i1_possible = True

            distance_squaremax = 0.0

            distance_squaresum = 0.0

            distances_squared = []

            # iterate all medium points between i1 and i2

            for im in range(i1+1, i2):

                pm = np.array(points[im]['p'])

                d = distance(p1, pm, p2, options.ele_weight)

                if (d <= options.max_dist):

                    d_sq = (d / options.max_dist) ** 2

                    distance_squaremax = max(distance_squaremax, d_sq)

                    distance_squaresum += points[im]['weight'] * d_sq

                    distances_squared.append(d_sq)

                else:

                    i1_i2_segment_valid = False

                    # check if connection to any further point i1 is impossible

                    d1 = np.dot(p1 - p2, p1 - p2)

                    d2 = np.dot(pm - p2, pm - p2)

                    dd = options.max_dist ** 2

                    d1d2 = np.dot(p1 - p2, pm - p2)

                    # formula from cosines of point separation angle and cone-opening angles around points

                    if (d1 > dd and d2 > dd and (d1d2 + dd)**2 < (d2 - dd) * (d1 - dd)):

                        lower_i1_possible = False

                        break

            if (lower_i1_possible == False):

                break

            if i1_i2_segment_valid:

                if options.weighting == 'sqrdistmax':

                    penalties[i1] = distance_squaremax

                elif options.weighting == 'sqrdistsum':

                    penalties[i1] = distance_squaresum

                elif options.weighting == 'sqrlength':

                    penalties[i1] = (seglength / max_sep) ** 2

                elif options.weighting == 'mix':

                    penalties[i1] = (distance_squaremax * (1.0 + seglength / max_sep))

                elif options.weighting == 'exp':

                    penalties[i1] = 0.5 * sum([0.5**i * d for i, d in

                        enumerate(sorted(distances_squared, reverse=True))])

                else:

                    penalties[i1] = 0.0

                # add a penalty for kinks

                if options.bend > 0.:

                    if points[i1]['prev'] != -1:

                        p0 = np.array(points[points[i1]['prev']]['p'])

                        v0 = p1 - p0

                        v1 = p2 - p1

                        if la.norm(v0) > 0. and la.norm(v1) > 0.:

                            v0 /= la.norm(v0)

                            v1 /= la.norm(v1)

                            kink = (1.0 - np.dot(v0, v1)) / 2.0

                            penalties[i1] += options.bend * kink

        # find best predecessor

        imin = None

        costmin = float('inf')

        for prev, penalty in penalties.iteritems():

            # cost function is sum of points used (1.0) plus penalties

            cost = points[prev]['cost'] + 1.0 + penalty

            if cost < costmin:

                imin = prev

                costmin = cost

        points[i2]['cost'] = costmin

        points[i2]['prev'] = imin

        # print progess

        if options.verbose == 1 and (100 * i2) / n > progress and time.time() >= tprint + 1:

            tprint = time.time()

            progress = (100 * i2) / n

            print '\r', progress, '%',

            sys.stdout.flush()

            progress_printed = True

    if progress_printed:

        print '\r',

    # trace route backwards to collect final points

    final_pnums = []

    i = n-1

    while i >= 0:

        final_pnums = [i] + final_pnums

        i = points[i]['prev']

    return [original_indices[i] for i in final_pnums]

############################## main function #################################

for fname in args:

    # initialisations

    # import xml data from files

    print 'opening file', fname

    infile = open(fname)

    tree = etree.parse(infile)

    infile.close()

    gpx = tree.getroot()

    nsurl = gpx.tag.split ('}')[0][1:]  # == 'http://www.topografix.com/GPX/1/1'

    etree.register_namespace('', nsurl) # default namespace -> xmlns:.... in the output

    nsmap = '{' + nsurl + '}'           # prefix for all tags in the tree

    # extract data from xml

        trkpts = trkseg.findall(nsmap + 'trkpt')

        n = len(trkpts)

        # extract coordinate values

        lats = [float(trkpt.get('lat')) for trkpt in trkpts]

        lons = [float(trkpt.get('lon')) for trkpt in trkpts]

        eles = [float(trkpt.find(nsmap + 'ele').text) for trkpt in trkpts]

        try:

            times = [datetime.datetime.strptime(trkpt.find(nsmap + 'time'

                                       ).text, timeformat) for trkpt in trkpts]

        except Exception as e:

            times = None

        # calculate projected points to work on

        coords = []

        for i in range(n):

            coords.append((x, y, z))

        # execute the reduction algorithm

        final_pnums = reduced_track_indices(coords, times)

        # delete certain points from original data

        delete_pnums = [i for i in range(n) if i not in final_pnums]

        for i in reversed(delete_pnums):

            trkseg.remove (trkpts[i])   # remove from the xml-tree

            del trkpts [i]              # also remove from the list

        # remove certain sub-elements from remaining points

        options.remove = options.remove.replace ('+','extensions,hdop,')

        taglist = options.remove.split (',')

        if taglist: print 'remove %s subelements from points' % ', '.join (taglist)

        for trkpnt in trkpts:

            for tag in taglist:

                e = trkpnt.find (nsmap + tag)

                if e != None: trkpnt.remove (e)

    # export data to file

    if options.ofname != None:

        ofname = options.ofname

    elif fname.endswith('.gpx'):

        ofname = fname[:-4] + '_reduced.gpx'

    else:

        ofname = fname + '_reduced.gpx'

    outfile = open(ofname, 'w')

    tree.write (outfile, encoding="utf-8", xml_declaration=True, default_namespace=None, method="xml")

    outfile.close()

    print 'modified copy written to', ofname