Mercurial > hg > GlobalNeighbors

annotate globalneighbors/distance.py @ 15:21095c9006e5

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city page is now functional + linky
author Jeff Hammel <k0scist@gmail.com>
date Sun, 25 Jun 2017 14:55:53 -0700
parents 27925261c137
children 811adc9736eb
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1 """
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2 distance functionality
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3 """
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4
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5 import argparse
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6 import bisect
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7 import json
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8 import sys
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9 import time
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10 from math import asin, sin, cos, sqrt, pi, fabs
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11 from .cli import CitiesParser
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12 from .constants import Rearth
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13 from .locations import locations
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14 from .read import read_cities
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15 from .schema import fields
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16
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17 DEGREESTORADIANS = pi/180.
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18
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19
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20 def haversine(lat1, lon1, lat2, lon2, r=1):
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21 """
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22 see
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23 https://en.wikipedia.org/wiki/Haversine_formula
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24 Coordinates in radians
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25 """
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26 return 2*r*asin(
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27 sqrt(sin(0.5*(lat2-lat1))**2
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28 +cos(lat1)*cos(lat2)*(sin(0.5*(lon2-lon1))**2)
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29 ))
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30
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31
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32 def deg_to_rad(degrees):
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33 return degrees*DEGREESTORADIANS
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34
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35
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36 def insert_distance(distances, i, new_distance, k):
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37 """
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38 insert `(i, new_distance)` into `distances`
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39 keeping distances in order with maximum of size `k`
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40 """
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41
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42 if len(distances) == k and new_distance >= distances[-1][-1]:
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43 return
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44
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45 # TODO: Binary Search Tree
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46 for _index, (geoid, old_distance) in enumerate(distances):
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47 if new_distance < old_distance:
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48 distances.insert(_index, (i, new_distance))
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49 if len(distances) == k+1:
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50 distances.pop()
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51 break
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52 else:
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53 distances.append((i, new_distance))
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54
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55
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56 class KeyWrapper(object):
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57 """wrapper for python's `bisect` methods"""
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58 def __init__(self, iterable, key):
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59 self.it = iterable
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60 self.key = key
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61
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62 def __getitem__(self, i):
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63 return self.key(self.it[i])
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64
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65 def __len__(self):
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66 return len(self.it)
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69 def insert_distance_bisect(distances, i, new_distance, k):
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70
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71 if not distances:
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72 distances.append((i, new_distance))
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73 return
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74 if new_distance >= distances[-1][-1]:
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75 if len(distances) < k:
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76 distances.append((i, new_distance))
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77 return
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78
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79 point = bisect.bisect_left(KeyWrapper(distances,
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80 key=lambda x: x[-1]),
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81 new_distance)
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82 distances.insert(point, (i, new_distance))
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83 if len(distances) == k+1:
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84 distances.pop()
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85
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86 def calculate_distances(locations, r=Rearth):
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87 """
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88 WARNING! This is an N-squared approach
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89 """
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90
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91 # convert to rad
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92 rad_locations = [(location, tuple([deg_to_rad(i)
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93 for i in latlon]))
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94 for location, latlon
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95 in locations.items()]
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96
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97 # use haversince function on N-body problem
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98 for index, loc1 in enumerate(rad_locations):
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99 id1, (lat1, lon1) = loc1
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100 for loc2 in rad_locations[index+1:]:
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101 id2, (lat2, lon2) = loc2
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102 key = (id1, id2) if id2 > id1 else (id2, id1)
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103 yield (key,
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104 haversine(lat1, lon1, lat2, lon2, r=r))
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105
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106
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107 def calculate_neighbors(locations,
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108 k=10,
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109 lat_tol=1.,
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110 lon_tol=1.,
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111 output=None,
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112 neighbors=None):
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113 """
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114 calculate `k` nearest neighbors for each location
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115
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116 locations -- dict of `geoid: (lat, lon)`
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117 """
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118 neighbors = neighbors or {}
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119 items = locations.items() # copy
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120 index = 0
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121 n_items = len(items)
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122 start = int(time.time())
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123 while items:
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124 index += 1
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125 if output and not index % output:
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126 # output status counter
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127 now = int(time.time())
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128 duration = now - start
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129 start = now
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130 print ('{},{},{},{}'.format(index,
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131 len(items),
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132 n_items,
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133 duration))
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134 id1, (lat1, lon1) = items.pop()
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135 for loc2 in items:
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136 id2, (lat2, lon2) = loc2
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137
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138 # filter out locations based on latlon boxing
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139 if fabs(lat2 - lat1) > lat_tol:
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140 continue
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141 if fabs(lon2 - lon1) > lon_tol:
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142 continue
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143
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144 # determine distance
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145 args = [deg_to_rad(i) for i in
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146 (lat1, lon1, lat2, lon2)]
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147 new_distance = haversine(*args, r=Rearth)
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148
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149 # insert in order
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150 ids = (id1, id2)
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151 for i in (0, 1):
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152 distances = neighbors.setdefault(ids[i], [])
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153 insert_distance_bisect(distances,
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154 ids[i-1],
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155 new_distance,
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156 k)
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158 return neighbors
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159
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160
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161 def main(args=sys.argv[1:]):
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162 """CLI"""
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163
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164 # parse command line arguments
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165 description = """write nearest neighborfiles"""
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166 parser = CitiesParser(description=description)
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167 parser.add_argument('output', type=argparse.FileType('w'),
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168 help="output file to dump JSON to")
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169 parser.add_argument('--latlon', dest='latlon', type=float,
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170 nargs=2, metavar=("LAT", "LON"),
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171 default=(1., 1.),
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172 help="tolerance of latitude and longitude in degrees [DEFAULT: %(default)s]")
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173 parser.add_argument('--counter', '--output-counter',
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174 dest='output_counter',
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175 type=int, default=100,
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176 help="how often to output progress updates [DEFAULT: %(default)s]")
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177 parser.add_argument('-k', dest='k',
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178 type=int, default=50,
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179 help="number of neighbors to determine [DEFAULT: %(default)s]")
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180 options = parser.parse_args(args)
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181
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182 # parse cities
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183 cities = list(read_cities(options.cities, fields=fields))
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184
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185 # get locations
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186 city_locations = locations(cities)
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187
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188 # calculate neighbors
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189 neighbors = calculate_neighbors(city_locations,
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190 k=options.k,
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191 lat_tol=options.latlon[0],
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192 lon_tol=options.latlon[-1],
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193 output=options.output_counter)
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194
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195 # output
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196 options.output.write(json.dumps(neighbors))
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197
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198 if __name__ == '__main__':
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199 main()