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FlickrVsFoursquare.py
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FlickrVsFoursquare.py
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#! /usr/bin/python2
# vim: set fileencoding=utf-8
"""Compute discrepancy between tweets and photos."""
import scipy.io as sio
import numpy as np
import more_query as mq
from explore import Entity
import spatial_scan as sps
import matplotlib as mpl
import os
FREQUENCE_FILE = 'freq_{}_{}_{}.mat'
def compute_frequency(client, city, entity, k=200):
"""Splits `city` in k^2 rectangles and save the frequency of `entity` in
each of them as a matrix."""
assert entity in [Entity.photo, Entity.checkin]
freq_name = FREQUENCE_FILE.format(entity.name, city, k)
try:
mat_count = sio.loadmat(freq_name)['c']
return mat_count
except IOError:
pass
count = (k*k+1)*[0, ]
get_data = {Entity.photo: get_photos, Entity.checkin: get_checkins}[entity]
coords = get_data(client, city)
bbox = (cities.US+cities.EU)[cities.INDEX[city]]
rectangles, rect_to_index, _ = mq.k_split_bbox(bbox, k)
# count[0] is for potential points that do not fall in any region (it
# must only happens because of rounding inprecision)
count = (len(rectangles)+1)*[0, ]
for loc in coords:
count[rect_to_index(loc)+1] += 1
mat_count = np.array(count[1:])
sio.savemat(freq_name, {'c': mat_count}, do_compression=True)
return mat_count
def get_photos(client, city):
"""Return a list of [lng, lat] of all photos in `city`."""
photos = client.world.photos.find({'hint': city}, {'loc': 1})
return [p['loc']['coordinates'] for p in photos]
def get_checkins(client, city):
"""Return a list of [lng, lat] of all checkins in `city`."""
checkins = client.foursquare.checkin.find({'city': city}, {'loc': 1})
return [p['loc']['coordinates'] for p in checkins]
# lratio = np.log(np.zeros(p.shape))
# nz = np.log(ratio)
# lratio[both] = nz
def full_disc_json(lratio, nz, city='paris'):
it = np.nditer(lratio, flags=['f_index'])
colormap = mpl.cm.ScalarMappable(sps.mcolor.Normalize(nz.min(), nz.max()),
'coolwarm')
schema = {'geometry': 'Polygon', 'properties': [('ratio', 'float'),
('color', 'str')]}
get_color = lambda v: sps.mcolor.rgb2hex(colormap.to_rgba(v))
polys = []
box = lambda i: sps.shape(sps.bbox_to_polygon(sps.index_to_rect(i), False))
while not it.finished:
idx, val = it.index, it[0]
if not np.isinf(val):
val = float(val)
polys.append({'geometry': sps.mapping(box(idx)), 'properties':
{'ratio': val, 'color': get_color(val)}})
it.iternext()
print(polys[0])
name = 'maps/'+city+'_full_d.json'
write_collection(polys, name, schema)
def write_collection(polys, name, schema):
"""Write JSON array `polys` in the file `name` using `schema`."""
try:
os.remove(name)
except OSError:
pass
print(name)
with sps.fiona.collection(name, "w", "GeoJSON", schema) as f:
f.writerecords(polys)
def output_json(regions, options):
"""Write a GeoJSON collection of `regions` with their discrepancy."""
discrepancies = [v[0] for v in regions]
colormap = mpl.cm.ScalarMappable(sps.mcolor.Normalize(min(discrepancies),
max(discrepancies)),
'YlOrBr')
schema = {'geometry': 'Polygon', 'properties': [('discrepancy', 'float'),
('color', 'str'),
('photos', 'float'),
('checkins', 'float')]}
get_color = lambda v: sps.mcolor.rgb2hex(colormap.to_rgba(v))
city = options['city']
photos_as_background = options['photos_background']
prefix = '_only' if options['only'] else ''
ratio = options.pop('ratio', 0)
if photos_as_background:
photos_idx, checkins_idx = 2, 3
name = city+prefix+'_checkins_d.json'
options['photos_ratio'] = ratio
else:
photos_idx, checkins_idx = 3, 2
name = city+prefix+'_photos_d.json'
options['checkins_ratio'] = ratio
polys = [{'geometry': sps.mapping(r[1]), 'properties':
{'discrepancy': r[0], 'color': get_color(r[0]),
'photos': r[photos_idx], 'checkins': r[checkins_idx]}}
for r in regions]
name = os.path.join('maps', name)
write_collection(polys, name, schema)
options['city'] = '"{}"'.format(options['city'])
with open(os.path.join('maps', city+'.js'), 'a') as f:
f.write('\n'.join(['var {} = {};'.format(var, str(val).lower())
for var, val in options.iteritems()]))
options['city'] = city
def compute_ratio(background, measured):
"""Compute the mean ratio of non extrem values between `background` and
`measured` where both occured."""
both = np.logical_and(background > 0, measured > 0)
background = background.astype(np.float32, copy=False)
measured = measured.astype(np.float32, copy=False)
ratio = background[both]/measured[both]
lower, upper = np.percentile(ratio, [5, 95])
return np.mean(ratio[np.logical_and(ratio >= lower, ratio <= upper)])
def load_frequency(client, city, k, photos_as_background=True):
"""Set background and measured array in the right order"""
photos = compute_frequency(client, city, Entity.photo, k)
checkins = compute_frequency(client, city, Entity.checkin, k)
if photos_as_background:
return photos, checkins
return checkins, photos
def do_scan(client, city, k, photos_as_background=True):
"""Perform discrepancy scan on `city` with grid_size."""
background, measured = load_frequency(client, city, k,
photos_as_background)
total_b = np.sum(background)
total_m = np.sum(measured)
if not total_m > 0:
return
if 0 < total_m <= 500:
support = 20
if 500 < total_m <= 2000:
support = 40
if 2000 < total_m:
support = sps.MAX_SUPPORT
discrepancy = sps.get_discrepancy_function(total_m, total_b, support)
grid_dim = (k, k)
info = u'g={}, s={}, k={}, w={}, h={}, max={}'
print(info.format(k, support, sps.TOP_K, sps.MIN_WIDTH, sps.MIN_HEIGHT,
sps.MAX_SIZE))
top_loc = sps.exact_grid(np.reshape(measured, grid_dim),
np.reshape(background, grid_dim),
discrepancy, sps.TOP_K, k/sps.MAX_SIZE)
return top_loc, compute_ratio(background, measured)
def stand_alone(client, city, k, photos_as_background=True, p=99.5):
"""Return the top list of cells with only one kind of entity."""
background, measured = load_frequency(client, city, k,
photos_as_background)
alone = np.logical_and(measured > 0, background < 1)
threshold = np.percentile(measured[alone], p)
high_alone = np.logical_and(alone, measured > threshold)
print(photos_as_background)
print(np.sum(measured))
print(threshold)
print(np.sum(high_alone))
cells = np.argwhere(high_alone)[:, 1]
count = measured[0, cells]
max_values = []
for idx, val in zip(cells, count):
max_values = sps.add_maybe([val, [idx, idx], 0, val], max_values, 500)
return sorted(max_values, key=lambda x: x[0], reverse=True)
if __name__ == '__main__':
#pylint: disable=C0103
import CommonMongo as cm
import arguments
import cities
import sys
sys.exit()
args = arguments.city_parser().parse_args()
city = args.city
_, client = cm.connect_to_db('foursquare', args.host, args.port)
# client = None
photos_in_background = True
k = 100
sps.GRID_SIZE = k
sps.MAX_SUPPORT = 200
bbox = (cities.US+cities.EU)[cities.INDEX[city]]
sps.BBOX = bbox
_, _, sps.index_to_rect = sps.k_split_bbox(bbox, k)
options = {'city': city, 'photos_background': True,
'bbox': cities.bbox_to_polygon(bbox), 'only': False}
top_loc, ratio = do_scan(client, city, k, options['photos_background'])
options['ratio'] = ratio
output_json(sps.merge_regions(top_loc), options)
options['photos_background'] = False
top_loc, ratio = do_scan(client, city, k, options['photos_background'])
options['ratio'] = ratio
output_json(sps.merge_regions(top_loc), options)
# options['only'] = True
# for pb in [True, False]:
# options['photos_background'] = pb
# top_loc = stand_alone(client, city, 100,
# options['photos_background'])
# output_json(sps.merge_regions(top_loc, use_mean=False), options)