def test_cutoff(self):
x = DataArray(np.linspace(-5, 5, 100))
self.assertTrue(np.all(self.kernels[1].pdf(x)[x.toarray(0) < 0] == 0))
x = DataArray.from_meshgrid(
*np.meshgrid(
np.linspace(-5, 5, 50),
np.linspace(-5, 5, 50)
)
)
res = self.kernels[2].pdf(x)
self.assertTrue(np.all(res[x.toarray(0) < 0] == 0))
self.assertTrue(np.all(res[x.toarray(0) >= 0] > 0.))
def create_network_with_crime_counts(
start_date=datetime.date(2011, 3, 1), domain_name='South'):
# load network, count crimes in 6mo and 12mo window, output shapefile
domains = chicago.get_chicago_side_polys()
domain = domains[domain_name]
end_date = start_date + datetime.timedelta(days=365)
crime_types = (
'THEFT',
'BURGLARY',
'HOMICIDE',
'BATTERY',
'ARSON',
'MOTOR VEHICLE THEFT',
'ASSAULT',
'ROBBERY',
)
time_window_filters = {
'6mo': lambda t: t <= 183,
'12mo': lambda t: t <= 365,
}
# get crime data
data, t0, cid = chicago.get_crimes_by_type(crime_type=crime_types,
start_date=start_date,
end_date=end_date,
domain=domain)
# get network
osm_file = os.path.join(
DATA_DIR, 'osm_chicago',
'%s_clipped.net' % consts.FILE_FRIENDLY_REGIONS[domain_name])
net = osm.OSMStreetNet.from_pickle(osm_file)
# snap crime data to network with maximum distance cutoff
netdata, failed = NetworkData.from_cartesian(net,
data[:, 1:],
radius=50,
return_failure_idx=True)
# get non-failed times
idx = sorted(set(range(data.shape[0])) - set(failed))
netdata = DataArray(data[idx, 0]).adddim(netdata,
type=NetworkSpaceTimeData)
# run over edges, count crimes in the two time windows
filters = {}
for filt_name, filt_func in time_window_filters.items():
filters[filt_name] = filt_func(netdata.toarray(0)).astype(int)
# add count attributes to all edges
for e in net.edges():
e.attrs['crimes_6mo'] = 0
e.attrs['crimes_12mo'] = 0
edge_counts = {}
for i, t in enumerate(netdata.space.toarray()):
t.edge.attrs['crimes_6mo'] += filters['6mo'][i]
t.edge.attrs['crimes_12mo'] += filters['12mo'][i]
net.save(consts.FILE_FRIENDLY_REGIONS[domain_name] +
'_network_crime_counts',
fmt='shp')
