def __init__(self, lon, lat, t):
self.lon = lon
self.lat = lat
x, y = geo.project((self.lon, self.lat))
self.x = (x - MIN_X) / (MAX_X - MIN_X)
self.y = (y - MIN_Y) / (MAX_Y - MIN_Y)
dt = timeutil.t_to_dt(t, tz="America/New_York")
self.period = ((dt.hour * 60) + (dt.minute)) // PERIOD_SIZE
def __init__(self, lon, lat, t):
self.lon = lon
self.lat = lat
x, y = geo.project((self.lon, self.lat))
self.x = (x - MIN_X) / (MAX_X - MIN_X)
self.y = (y - MIN_Y) / (MAX_Y - MIN_Y)
self.t = t
self.geohash = geo.geohash_encode((self.lon, self.lat),
precision=LOCATION_SIZE)
self.cluster = None
self.location = None
dt = timeutil.t_to_dt(self.t,
tz="America/New_York") # not sure why, honestly.
self.period = ((dt.hour * 60) + (dt.minute)) // PERIOD_SIZE
self.address = None
self.display_time = None
def get(self, page):
if page == "stations":
stations = model.fetch_stations()
s = {}
for station_id, station in stations.items():
s[station_id] = geo.project((station['lon'], station['lat']))
max_x = s[max(s, key=lambda d: s[d][0])][0]
min_x = s[min(s, key=lambda d: s[d][0])][0]
max_y = s[max(s, key=lambda d: s[d][1])][1]
min_y = s[min(s, key=lambda d: s[d][1])][1]
for station_id, value in s.items():
x, y = value
s[station_id] = util.scale(x, min_x, max_x), util.scale(y, min_y, max_y)
return self.json(s)
return self.render("home.html")
if len(sequence) < MIN_STEPS:
continue
walks.append(walk)
LON = 0
LAT = 1
X = 2
Y = 3
all_points = []
for walk in walks:
points = model.fetch_geo(walk['id'])
points = np.array([(point['lng'], point['lat'], None, None) for point in points])
for point in points:
point[X], point[Y] = geo.project((point[LON], point[LAT]))
walk['points'] = points
all_points.extend(points)
all_points = np.array(all_points)
max_x = np.max(all_points[:,X])
min_x = np.min(all_points[:,X])
max_y = np.max(all_points[:,Y])
min_y = np.min(all_points[:,Y])
width = float(abs(max_x - min_x))
height = float(abs(max_y - min_y))
ratio = width / height
print(ratio)
}
})
log.info("POINTS %s" % results.count())
users = len(results.distinct('user_id'))
log.info("USERS %s" % users)
points = np.array([(result['location']['coordinates'][0],
result['location']['coordinates'][1], result['user_id'])
for result in results])
min_lon, max_lon = (np.min(points[:, 0]), np.max(points[:, 0]))
min_lat, max_lat = (np.min(points[:, 1]), np.max(points[:, 1]))
log.debug("%f %f %f %f" % (min_lon, max_lon, min_lat, max_lat))
min_x, max_y = geo.project((min_lon, max_lat))
max_x, min_y = geo.project((max_lon, min_lat))
ratio = (max_x - min_x) / (max_y - min_y)
ctx = drawing.Context(1000,
int(1000 / ratio),
relative=True,
flip=True,
hsv=True)
log.info("Drawing %d %d..." % (ctx.width, ctx.height))
for point in points:
x, y = geo.project((point[0], point[1]))
#!/usr/bin/env python3
import random, datetime, json, math, requests, json
import numpy as np
from housepy import geo, config, log, util, timeutil
from sklearn.cluster import Birch
from mongo import db
import drawer
PERIOD_SIZE = config['period_size']
PERIODS = int(1440 / PERIOD_SIZE)
LON_1, LAT_1 = config['bounds']['NW']
LON_2, LAT_2 = config['bounds']['SE']
MIN_X, MAX_Y = geo.project((LON_1, LAT_1))
MAX_X, MIN_Y = geo.project((LON_2, LAT_2))
# generator for retrieving user points from mongo
def get_user_points(user_ids):
location = {'$geoWithin': {'$geometry': {'type': "Polygon", 'coordinates': [[ [LON_1, LAT_1], [LON_2, LAT_1], [LON_2, LAT_2], [LON_1, LAT_2], [LON_1, LAT_1] ]]}}}
for u, user_id in enumerate(user_ids):
log.info("USER %s..." % user_id)
cursor = db.entries.find({'user_id': user_id, 'location': location, 't': {'$gt': timeutil.timestamp(timeutil.string_to_dt(config['start_date'], tz="America/New_York")), '$lt': timeutil.timestamp(timeutil.string_to_dt(config['stop_date'], tz="America/New_York"))}}).sort('t')
points = [Point(point['location']['coordinates'][0], point['location']['coordinates'][1], point['t']) for point in cursor]
log.info("--> %d points" % len(points))
yield user_id, points
yield (None, None)
class Point():
