import csv import math from typing import List, TextIO

ID_INDEX = 0 NAME_INDEX = 1 HIGHWAY_INDEX = 2 LAT_INDEX = 3 LON_INDEX = 4 YEAR_INDEX = 5 LAST_MAJOR_INDEX = 6 LAST_MINOR_INDEX = 7 NUM_SPANS_INDEX = 8 SPAN_LENGTH_INDEX = 9 LENGTH_INDEX = 10 LAST_INSPECTED_INDEX = 11 BCIS_INDEX = 12

MAX_BCIS = 5 HIGH_PRIORITY_BCI = 60 LOW_PRIORITY_BCI = 100

HIGH_PRIORITY_RADIUS = 500 LOW_PRIORITY_RADIUS = 100

EARTH_RADIUS = 6371

### SAMPLE DATA TO USE IN DOCSTRING EXAMPLES ####

THREE_BRIDGES_UNCLEANED = [ ['1 - 32/', 'Highway 24 Underpass at Highway 403', '403', '43.167233', '-80.275567', '1965', '2014', '2009', '4', 'Total=64 (1)=12;(2)=19;(3)=21;(4)=12;', '65', '04/13/2012', '72.3', '', '72.3', '', '69.5', '', '70', '', '70.3', '', '70.5', '', '70.7', '72.9', ''], ['1 - 43/', 'WEST STREET UNDERPASS', '403', '43.164531', '-80.251582', '1963', '2014', '2007', '4', 'Total=60.4 (1)=12.2;(2)=18;(3)=18;(4)=12.2;', '61', '04/13/2012', '71.5', '', '71.5', '', '68.1', '', '69', '', '69.4', '', '69.4', '', '70.3', '73.3', ''], ['2 - 4/', 'STOKES RIVER BRIDGE', '6', '45.036739', '-81.33579', '1958', '2013', '', '1', 'Total=16 (1)=16;', '18.4', '08/28/2013', '85.1', '85.1', '', '67.8', '', '67.4', '', '69.2', '70', '70.5', '', '75.1', '', '90.1', ''] ]

THREE_BRIDGES = [[1, 'Highway 24 Underpass at Highway 403', '403', 43.167233, -80.275567, '1965', '2014', '2009', 4, [12.0, 19.0, 21.0, 12.0], 65.0, '04/13/2012', [72.3, 69.5, 70.0, 70.3, 70.5, 70.7, 72.9]], [2, 'WEST STREET UNDERPASS', '403', 43.164531, -80.251582, '1963', '2014', '2007', 4, [12.2, 18.0, 18.0, 12.2], 61.0, '04/13/2012', [71.5, 68.1, 69.0, 69.4, 69.4, 70.3, 73.3]], [3, 'STOKES RIVER BRIDGE', '6', 45.036739, -81.33579, '1958', '2013', '', 1, [16.0], 18.4, '08/28/2013', [85.1, 67.8, 67.4, 69.2, 70.0, 70.5, 75.1, 90.1]] ]

####### BEGIN HELPER FUNCTIONS ####################

def read_data(csv_file: TextIO) -> List[List[str]]: """Read and return the contents of the open CSV file csv_file as a list of lists, where each inner list contains the values from one line of csv_file.

Docstring examples not given since results depend on csv_file. """

data = [] lines = csv.reader(csv_file) for line in lines: data.append(line) data = data[2:] return data

def calculate_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> float: """Return the distance in kilometers between the two locations defined by (lat1, lon1) and (lat2, lon2), rounded to the nearest meter. >>> calculate_distance(43.659777, -79.397383, 43.657129, -79.399439) 0.338 >>> calculate_distance(43.42, -79.24, 53.32, -113.30) 2713.226 """

# This function uses the haversine function to find the # distance between two locations. You do NOT need to understand why it # works. You will just need to call on the function and work with what it # returns. # Based on code at goo.gl/JrPG4j

# convert decimal degrees to radians lon1, lat1, lon2, lat2 = (math.radians(lon1), math.radians(lat1), math.radians(lon2), math.radians(lat2))

# haversine formula t lon_diff = lon2 - lon1 lat_diff = lat2 - lat1 a = (math.sin(lat_diff / 2) ** 2 + math.cos(lat1) * math.cos(lat2) * math.sin(lon_diff / 2) ** 2) c = 2 * math.asin(math.sqrt(a)) return round(c * EARTH_RADIUS, 3)

THE FUNCTION NEED HELP WITH

def get_distance_to_inspector(bridge1: list, inspector: list) -> float: """Return the distance in kilometres, rounded to the nearest metre (i.e., 3 decimal places), between a bridge bridge1 and the inspector. >>> get_distance_to_inspector(get_bridge(THREE_BRIDGES, 1), [43.10, -80.15]) 12.638 """

AND

def find_closest_inspector(bridge_data: List[list], bridge_id: int, inspectors: List[list]) -> int: """Return the index of the inspector in the list of inspectors that has the shortest distance to the bridge with id bridge_id from bridge_data. Precondition: a bridge with bridge_id is in bridge_data, and there is at least one inspector in the inspectors list. >>> find_closest_inspector(THREE_BRIDGES, 2, [[43.20, -80.35], \ [43.10, -80.15]]) 0 """