#!/usr/bin/env python
# This Python file uses the following encoding: utf-8
# Copyright 2015 Tin Arm Engineering AB
# Copyright 2018 Google LLC
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Vehicle Routing Problem (VRP).

   This is a sample using the routing library python wrapper to solve a VRP
   problem.
   A description of the problem can be found here:
   http://en.wikipedia.org/wiki/Vehicle_routing_problem.

   Distances are in meters.
"""

from __future__ import print_function
from collections import namedtuple
from six.moves import xrange
from ortools.constraint_solver import pywrapcp
from ortools.constraint_solver import routing_enums_pb2

###########################
# Problem Data Definition #
###########################
# City block declaration
CityBlock = namedtuple('CityBlock', ['width', 'height'])

class DataProblem():
  """Stores the data for the problem"""
  def __init__(self):
    """Initializes the data for the problem"""
    # Locations in block unit
    locations = \
            [(4, 4), # depot
             (2, 0), (8, 0), # location to visit
             (0, 1), (1, 1),
             (5, 2), (7, 2),
             (3, 3), (6, 3),
             (5, 5), (8, 5),
             (1, 6), (2, 6),
             (3, 7), (6, 7),
             (0, 8), (7, 8)]
    # Compute locations in meters using the block dimension defined as follow
    # Manhattan average block: 750ft x 264ft -> 228m x 80m
    # here we use: 114m x 80m city block
    # src: https://nyti.ms/2GDoRIe "NY Times: Know Your distance"
    city_block = CityBlock(width=228/2, height=80)
    self._locations = [(loc[0] * city_block.width, loc[1] * city_block.height)
                       for loc in locations]

  @property
  def num_vehicles(self):
    """Gets number of vehicles"""
    return 4

  @property
  def locations(self):
    """Gets locations"""
    return self._locations

  @property
  def num_locations(self):
    """Gets number of locations"""
    return len(self.locations)

  @property
  def depot(self):
    """Gets depot location node index"""
    return 0

#######################
# Problem Constraints #
#######################
def manhattan_distance(position_1, position_2):
  """Computes the Manhattan distance between two points"""
  return (
      abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))

class CreateDistanceEvaluator(object):  # pylint: disable=too-few-public-methods
  """Creates callback to return distance between points."""

  def __init__(self, data):
    """Initializes the distance matrix."""
    self._distances = {}

    # precompute distance between location to have distance callback in O(1)
    for from_node in xrange(data.num_locations):
      self._distances[from_node] = {}
      for to_node in xrange(data.num_locations):
        if from_node == to_node:
          self._distances[from_node][to_node] = 0
        else:
          self._distances[from_node][to_node] = (
              manhattan_distance(data.locations[from_node],
                                 data.locations[to_node]))

  def distance_evaluator(self, from_node, to_node):
    """Returns the manhattan distance between the two nodes"""
    return self._distances[from_node][to_node]


def add_distance_dimension(routing, distance_evaluator):
  """Add Global Span constraint"""
  distance = 'Distance'
  routing.AddDimension(
      distance_evaluator,
      0,  # null slack
      3000,  # maximum distance per vehicle
      True,  # start cumul to zero
      distance)
  distance_dimension = routing.GetDimensionOrDie(distance)
  # Try to minimize the max distance among vehicles.
  # /!\ It doesn't mean the standard deviation is minimized
  distance_dimension.SetGlobalSpanCostCoefficient(100)

###########
# Printer #
###########
def print_solution(data, routing, assignment):
  """Prints assignment on console"""
  print('Objective: {}'.format(assignment.ObjectiveValue()))
  total_distance = 0
  for vehicle_id in xrange(data.num_vehicles):
    index = routing.Start(vehicle_id)
    plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
    distance = 0
    while not routing.IsEnd(index):
      plan_output += ' {} -> '.format(routing.IndexToNode(index))
      previous_index = index
      index = assignment.Value(routing.NextVar(index))
      distance += routing.GetArcCostForVehicle(previous_index, index, vehicle_id)
    plan_output += ' {}\n'.format(routing.IndexToNode(index))
    plan_output += 'Distance of the route: {}m\n'.format(distance)
    print(plan_output)
    total_distance += distance
  print('Total Distance of all routes: {}m'.format(total_distance))

########
# Main #
########
def main():
  """Entry point of the program"""
  # Instantiate the data problem.
  data = DataProblem()

  # Create Routing Model
  routing = pywrapcp.RoutingModel(data.num_locations, data.num_vehicles,
                                  data.depot)
  # Define weight of each edge
  distance_evaluator = CreateDistanceEvaluator(data).distance_evaluator
  routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator)
  add_distance_dimension(routing, distance_evaluator)

  # Setting first solution heuristic (cheapest addition).
  search_parameters = pywrapcp.RoutingModel.DefaultSearchParameters()
  search_parameters.first_solution_strategy = (
      routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC) # pylint: disable=no-member
  # Solve the problem.
  assignment = routing.SolveWithParameters(search_parameters)
  print_solution(data, routing, assignment)

if __name__ == '__main__':
  main()