CS计算机代考程序代写 python “””Assignment 1 – Domain classes (Task 2)

“””Assignment 1 – Domain classes (Task 2)

CSC148, Winter 2021

This code is provided solely for the personal and private use of
students taking the CSC148 course at the University of Toronto.
Copying for purposes other than this use is expressly prohibited.
All forms of distribution of this code, whether as given or with
any changes, are expressly prohibited.

Authors: Diane Horton, Ian Berlott-Atwell, Jonathan Calver,
Sophia Huynh, Maryam Majedi, and Jaisie Sin.

All of the files in this directory and all subdirectories are:
Copyright (c) 2021 Diane Horton, Ian Berlott-Atwell, Jonathan Calver,
Sophia Huynh, Maryam Majedi, and Jaisie Sin.

===== Module Description =====

This module contains the classes required to represent the entities
in the simulation: Parcel, Truck and Fleet.
“””
from typing import List, Dict, Tuple
from distance_map import DistanceMap

class Parcel:
“””A map of distance data.

=== Instance Attributes ===
parcel_id: The unique id of a parcel.
source:
distance1: The distance from city1 to city2.
distance2: The distance from city2 to city1.

“””
city1: str
city2: str
distance1: int
distance2: int

class Truck:
# TODO: Implement this class!
# It must be consistent with the Fleet class docstring examples below.
pass

class Fleet:
“”” A fleet of trucks for making deliveries.

===== Public Attributes =====
trucks:
List of all Truck objects in this fleet.
“””
trucks: List[Truck]

def __init__(self) -> None:
“””Create a Fleet with no trucks.

>>> f = Fleet()
>>> f.num_trucks()
0
“””
# TODO: Complete this method.
pass

def add_truck(self, truck: Truck) -> None:
“””Add to this fleet.

Precondition: No truck with the same ID as has already been
added to this Fleet.

>>> f = Fleet()
>>> t = Truck(1423, 1000, ‘Toronto’)
>>> f.add_truck(t)
>>> f.num_trucks()
1
“””
# TODO: Complete this method.
pass

# We will not test the format of the string that you return — it is up
# to you.
def __str__(self) -> str:
“””Produce a string representation of this fleet
“””
# TODO: Complete this method.
pass

def num_trucks(self) -> int:
“””Return the number of trucks in this fleet.

>>> f = Fleet()
>>> t1 = Truck(1423, 10, ‘Toronto’)
>>> f.add_truck(t1)
>>> f.num_trucks()
1
“””
# TODO: Complete this method.
pass

def num_nonempty_trucks(self) -> int:
“””Return the number of non-empty trucks in this fleet.

>>> f = Fleet()
>>> t1 = Truck(1423, 10, ‘Toronto’)
>>> f.add_truck(t1)
>>> p1 = Parcel(1, 5, ‘Buffalo’, ‘Hamilton’)
>>> t1.pack(p1)
True
>>> p2 = Parcel(2, 4, ‘Toronto’, ‘Montreal’)
>>> t1.pack(p2)
True
>>> t1.fullness()
90.0
>>> t2 = Truck(5912, 20, ‘Toronto’)
>>> f.add_truck(t2)
>>> p3 = Parcel(3, 2, ‘New York’, ‘Windsor’)
>>> t2.pack(p3)
True
>>> t2.fullness()
10.0
>>> t3 = Truck(1111, 50, ‘Toronto’)
>>> f.add_truck(t3)
>>> f.num_nonempty_trucks()
2
“””
# TODO: Complete this method.
pass

def parcel_allocations(self) -> Dict[int, List[int]]:
“””Return a dictionary in which each key is the ID of a truck in this
fleet and its value is a list of the IDs of the parcels packed onto it,
in the order in which they were packed.

>>> f = Fleet()
>>> t1 = Truck(1423, 10, ‘Toronto’)
>>> p1 = Parcel(27, 5, ‘Toronto’, ‘Hamilton’)
>>> p2 = Parcel(12, 5, ‘Toronto’, ‘Hamilton’)
>>> t1.pack(p1)
True
>>> t1.pack(p2)
True
>>> t2 = Truck(1333, 10, ‘Toronto’)
>>> p3 = Parcel(28, 5, ‘Toronto’, ‘Hamilton’)
>>> t2.pack(p3)
True
>>> f.add_truck(t1)
>>> f.add_truck(t2)
>>> f.parcel_allocations() == {1423: [27, 12], 1333: [28]}
True
“””
# TODO: Complete this method.
pass

def total_unused_space(self) -> int:
“””Return the total unused space, summed over all non-empty trucks in
the fleet.
If there are no non-empty trucks in the fleet, return 0.

>>> f = Fleet()
>>> f.total_unused_space()
0
>>> t = Truck(1423, 1000, ‘Toronto’)
>>> p = Parcel(1, 5, ‘Buffalo’, ‘Hamilton’)
>>> t.pack(p)
True
>>> f.add_truck(t)
>>> f.total_unused_space()
995
“””
# TODO: Complete this method.
pass

def _total_fullness(self) -> float:
“””Return the sum of truck.fullness() for each non-empty truck in the
fleet. If there are no non-empty trucks, return 0.

>>> f = Fleet()
>>> f._total_fullness() == 0.0
True
>>> t = Truck(1423, 10, ‘Toronto’)
>>> f.add_truck(t)
>>> f._total_fullness() == 0.0
True
>>> p = Parcel(1, 5, ‘Buffalo’, ‘Hamilton’)
>>> t.pack(p)
True
>>> f._total_fullness()
50.0
“””
# TODO: Complete this method.
pass

def average_fullness(self) -> float:
“””Return the average percent fullness of all non-empty trucks in the
fleet.

Precondition: At least one truck is non-empty.

>>> f = Fleet()
>>> t = Truck(1423, 10, ‘Toronto’)
>>> p = Parcel(1, 5, ‘Buffalo’, ‘Hamilton’)
>>> t.pack(p)
True
>>> f.add_truck(t)
>>> f.average_fullness()
50.0
“””
# TODO: Complete this method.
pass

def total_distance_travelled(self, dmap: DistanceMap) -> int:
“””Return the total distance travelled by the trucks in this fleet,
according to the distances in .

Precondition: contains all distances required to compute the
average distance travelled.

>>> f = Fleet()
>>> t1 = Truck(1423, 10, ‘Toronto’)
>>> p1 = Parcel(1, 5, ‘Toronto’, ‘Hamilton’)
>>> t1.pack(p1)
True
>>> t2 = Truck(1333, 10, ‘Toronto’)
>>> p2 = Parcel(2, 5, ‘Toronto’, ‘Hamilton’)
>>> t2.pack(p2)
True
>>> from distance_map import DistanceMap
>>> m = DistanceMap()
>>> m.add_distance(‘Toronto’, ‘Hamilton’, 9)
>>> f.add_truck(t1)
>>> f.add_truck(t2)
>>> f.total_distance_travelled(m)
36
“””
# TODO: Complete this method.
pass

def average_distance_travelled(self, dmap: DistanceMap) -> float:
“””Return the average distance travelled by the trucks in this fleet,
according to the distances in .

Include in the average only trucks that have actually travelled some
non-zero distance.

Preconditions:
– contains all distances required to compute the average
distance travelled.
– At least one truck has travelled a non-zero distance.

>>> f = Fleet()
>>> t1 = Truck(1423, 10, ‘Toronto’)
>>> p1 = Parcel(1, 5, ‘Toronto’, ‘Hamilton’)
>>> t1.pack(p1)
True
>>> t2 = Truck(1333, 10, ‘Toronto’)
>>> p2 = Parcel(2, 5, ‘Toronto’, ‘Hamilton’)
>>> t2.pack(p2)
True
>>> from distance_map import DistanceMap
>>> m = DistanceMap()
>>> m.add_distance(‘Toronto’, ‘Hamilton’, 9)
>>> f.add_truck(t1)
>>> f.add_truck(t2)
>>> f.average_distance_travelled(m)
18.0
“””
# TODO: Complete this method.
pass

if __name__ == ‘__main__’:
import python_ta
python_ta.check_all(config={
‘allowed-import-modules’: [‘doctest’, ‘python_ta’, ‘typing’,
‘distance_map’],
‘disable’: [‘E1136’],
‘max-attributes’: 15,
})
import doctest
doctest.testmod()