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"""
tracktable.data_generators.heatmap point - Generating heatmap points around n largest cities.
"""
import csv
import datetime
import logging
from tracktable.data_generators import point
from tracktable.domain import terrestrial
from tracktable.feature import interleave_points
from tracktable.info import cities
logger = logging.getLogger(__name__)
[docs]
def n_largest_cities(howmany):
"""
n_largest_cities(howmany: int) -> list of CityInfo objects
Retrieve a list of the N largest cities in the world (by
population) sorted in descending order.
"""
return cities.largest_cities_in_bbox(count=howmany)
# ----------------------------------------------------------------------
[docs]
def point_radius_for_city(city):
"""point_radius_for_city(city: tracktable.info.cities.CityInfo) -> float(km)
Return a radius proportional to a city's population. Arbitrarily,
a city with a population of 1 million will get a radius of 50 km.
This has no particular real-world meaning. It's just a way to
scatter points around the city center.
"""
return (city.population / 1000000) * 50.0
# ----------------------------------------------------------------------
[docs]
def write_points_to_file(point_source, outfile):
outfile.write('# object_id timestamp longitude latitude\n')
writer = csv.writer(outfile, delimiter=',')
for point in point_source:
row = [ point.object_id, '2014-01-01 00:00:00', point[0], point[1] ]
writer.writerow(row)
# ----------------------------------------------------------------------
[docs]
def points_near_city(city, num_points):
center = terrestrial.BasePoint()
center[0] = city.longitude
center[1] = city.latitude
center.timestamp = datetime.datetime.now()
center.object_id = 'ANON'
max_radius = point_radius_for_city(city)
return point.random_circle_linear_falloff(center, num_points, max_radius)
# ----------------------------------------------------------------------
[docs]
def generate_heatmap_points(**kwargs):
logger.info("Generating {} points around each of the {} largest cities in the world.".format(kwargs['num_points_per_city'], kwargs['num_cities']))
heatmap_points = [ points_near_city(city, kwargs['num_points_per_city']) for city in n_largest_cities(kwargs['num_cities']) ]
combined_point_source = interleave_points.interleave_points_by_timestamp(*heatmap_points)
if kwargs['write_file']:
with open(kwargs ['outfilename'], 'w') as outfile:
write_points_to_file(combined_point_source, outfile)
return heatmap_points