def search_cell_lac(session, data):
radio = RADIO_TYPE.get(data['radio'], -1)
lacs = []
for cell in data['cell']:
cell = normalized_cell_dict(cell, default_radio=radio)
if not cell:
continue
cell['cid'] = CELLID_LAC
key = to_cellkey(cell)
query = session.query(Cell.lat, Cell.lon, Cell.range).filter(
*join_cellkey(Cell, key)).filter(
Cell.lat.isnot(None)).filter(
Cell.lon.isnot(None)
)
result = query.first()
if result is not None:
lacs.append(Network(key, *result))
if not lacs:
return
# take the smallest LAC of any the user is inside
lac = sorted(lacs, key=operator.attrgetter('range'))[0]
return {
'lat': quantize(lac.lat),
'lon': quantize(lac.lon),
'accuracy': max(LAC_MIN_ACCURACY, lac.range),
}
def search_cell(session, data):
sql_null = None # avoid pep8 warning
radio = RADIO_TYPE.get(data['radio'], -1)
cells = []
for cell in data['cell']:
if cell.get('radio'):
radio = RADIO_TYPE.get(cell['radio'], -1)
query = session.query(Cell.lat, Cell.lon).filter(
Cell.radio == radio).filter(Cell.mcc == cell['mcc']).filter(
Cell.mnc == cell['mnc']).filter(
Cell.lac == cell['lac']).filter(
Cell.cid == cell['cid']).filter(
Cell.lat != sql_null).filter(Cell.lon != sql_null)
result = query.first()
if result is not None:
cells.append(result)
if not cells:
return
length = len(cells)
avg_lat = sum([c[0] for c in cells]) / length
avg_lon = sum([c[1] for c in cells]) / length
return {
'lat': quantize(avg_lat),
'lon': quantize(avg_lon),
'accuracy': 35000,
}
def search_cell(session, data):
radio = RADIO_TYPE.get(data['radio'], -1)
cells = []
for cell in data['cell']:
cell = normalized_cell_dict(cell, default_radio=radio)
if not cell:
continue
key = to_cellkey(cell)
query = session.query(Cell.lat, Cell.lon, Cell.range).filter(
*join_cellkey(Cell, key)).filter(
Cell.lat.isnot(None)).filter(
Cell.lon.isnot(None)
)
result = query.first()
if result is not None:
cells.append(Network(key, *result))
if not cells:
return
length = len(cells)
avg_lat = sum([c.lat for c in cells]) / length
avg_lon = sum([c.lon for c in cells]) / length
return {
'lat': quantize(avg_lat),
'lon': quantize(avg_lon),
'accuracy': estimate_accuracy(avg_lat, avg_lon,
cells, CELL_MIN_ACCURACY),
}
def search_wifi(session, data):
wifi_data = data['wifi']
wifi_keys = set([normalize_wifi_key(w['key']) for w in wifi_data])
if not any(wifi_keys):
# no valid normalized keys
return None
if len(wifi_keys) < 2:
# we didn't even get two keys, bail out
return None
sql_null = None # avoid pep8 warning
query = session.query(Wifi.lat, Wifi.lon).filter(
Wifi.key.in_(wifi_keys)).filter(Wifi.lat != sql_null).filter(
Wifi.lon != sql_null)
wifis = query.all()
if len(wifis) < 2:
# we got fewer than two actual matches
return None
length = len(wifis)
avg_lat = sum([w[0] for w in wifis]) / length
avg_lon = sum([w[1] for w in wifis]) / length
return {
'lat': quantize(avg_lat),
'lon': quantize(avg_lon),
'accuracy': 500,
}
def search_cell(session, data):
sql_null = None # avoid pep8 warning
radio = RADIO_TYPE.get(data['radio'], -1)
cells = []
for cell in data['cell']:
if cell.get('radio'):
radio = RADIO_TYPE.get(cell['radio'], -1)
query = session.query(Cell.lat, Cell.lon).filter(
Cell.radio == radio).filter(
Cell.mcc == cell['mcc']).filter(
Cell.mnc == cell['mnc']).filter(
Cell.lac == cell['lac']).filter(
Cell.cid == cell['cid']).filter(
Cell.lat != sql_null).filter(
Cell.lon != sql_null
)
result = query.first()
if result is not None:
cells.append(result)
if not cells:
return
length = len(cells)
avg_lat = sum([c[0] for c in cells]) / length
avg_lon = sum([c[1] for c in cells]) / length
return {
'lat': quantize(avg_lat),
'lon': quantize(avg_lon),
'accuracy': 35000,
}
def search_wifi(session, data):
wifi_data = data['wifi']
wifi_keys = set([normalize_wifi_key(w['key']) for w in wifi_data])
if not any(wifi_keys):
# no valid normalized keys
return None
if len(wifi_keys) < 2:
# we didn't even get two keys, bail out
return None
sql_null = None # avoid pep8 warning
query = session.query(Wifi.lat, Wifi.lon).filter(
Wifi.key.in_(wifi_keys)).filter(
Wifi.lat != sql_null).filter(
Wifi.lon != sql_null)
wifis = query.all()
if len(wifis) < 2:
# we got fewer than two actual matches
return None
length = len(wifis)
avg_lat = sum([w[0] for w in wifis]) / length
avg_lon = sum([w[1] for w in wifis]) / length
return {
'lat': quantize(avg_lat),
'lon': quantize(avg_lon),
'accuracy': 500,
}
def search_cell(session, data):
radio = RADIO_TYPE.get(data['radio'], -1)
cell = data['cell'][0]
if cell.get('radio'):
radio = RADIO_TYPE.get(cell['radio'], -1)
mcc = cell['mcc']
mnc = cell['mnc']
lac = cell['lac']
cid = cell['cid']
query = session.query(Cell)
query = query.filter(Cell.radio == radio)
query = query.filter(Cell.mcc == mcc)
query = query.filter(Cell.mnc == mnc)
query = query.filter(Cell.cid == cid)
if lac >= 0:
query = query.filter(Cell.lac == lac)
result = query.first()
if result is None:
return
return {
'lat': quantize(result.lat),
'lon': quantize(result.lon),
'accuracy': 35000,
}
def search_cell_lac(session, data):
sql_null = None # avoid pep8 warning
radio = RADIO_TYPE.get(data['radio'], -1)
lacs = []
for cell in data['cell']:
if cell['mcc'] < 1 or cell['mnc'] < 0 or \
cell['lac'] < 0 or cell['cid'] < 0:
# Skip over invalid values
continue
if cell.get('radio'):
radio = RADIO_TYPE.get(cell['radio'], -1)
query = session.query(Cell).filter(
Cell.radio == radio).filter(
Cell.mcc == cell['mcc']).filter(
Cell.mnc == cell['mnc']).filter(
Cell.lac == cell['lac']).filter(
Cell.cid == CELLID_LAC).filter(
Cell.lat != sql_null).filter(
Cell.lon != sql_null
)
result = query.first()
if result is not None:
lacs.append(result)
if not lacs:
return None
# take the smallest LAC of any the user is inside
lac = sorted(lacs, key=operator.attrgetter('range'))[0]
return {
'lat': quantize(lac.lat),
'lon': quantize(lac.lon),
'accuracy': lac.range,
}
def search_wifi(session, data):
wifi_data = data['wifi']
wifi_keys = set([normalize_wifi_key(w['key']) for w in wifi_data])
if not any(wifi_keys):
# no valid normalized keys
return None
if len(wifi_keys) < 3:
# we didn't even get three keys, bail out
return None
sql_null = None # avoid pep8 warning
query = session.query(Wifi.lat, Wifi.lon).filter(
Wifi.key.in_(wifi_keys)).filter(
Wifi.lat != sql_null).filter(
Wifi.lon != sql_null)
wifis = query.all()
if len(wifis) < 3:
# we got fewer than three actual matches
return None
length = len(wifis)
avg_lat = sum([w[0] for w in wifis]) / length
avg_lon = sum([w[1] for w in wifis]) / length
# check to make sure all wifi AP's are close by
# we might later relax this to allow some outliers
latitudes = [w[0] for w in wifis]
longitudes = [w[1] for w in wifis]
lat_diff = abs(max(latitudes) - min(latitudes))
lon_diff = abs(max(longitudes) - min(longitudes))
if lat_diff >= MAX_DIFF or lon_diff >= MAX_DIFF:
return None
return {
'lat': quantize(avg_lat),
'lon': quantize(avg_lon),
'accuracy': 500,
}
def search_cell(session, data):
radio = RADIO_TYPE.get(data['radio'], -1)
cells = []
for cell in data['cell']:
if cell['mcc'] < 1 or cell['mnc'] < 0 or \
cell['lac'] < 0 or cell['cid'] < 0:
# Skip over invalid values
continue
if cell.get('radio'):
radio = RADIO_TYPE.get(cell['radio'], -1)
query = session.query(Cell.lat, Cell.lon).filter(
Cell.radio == radio).filter(
Cell.mcc == cell['mcc']).filter(
Cell.mnc == cell['mnc']).filter(
Cell.lac == cell['lac']).filter(
Cell.cid == cell['cid']).filter(
Cell.lat.isnot(None)).filter(
Cell.lon.isnot(None)
)
result = query.first()
if result is not None:
cells.append(result)
if not cells:
return
length = len(cells)
avg_lat = sum([c[0] for c in cells]) / length
avg_lon = sum([c[1] for c in cells]) / length
return {
'lat': quantize(avg_lat),
'lon': quantize(avg_lon),
'accuracy': 35000,
}
def search_wifi(session, data):
# Estimate signal strength at -100 dBm if none is provided,
# which is worse than the 99th percentile of wifi dBms we
# see in practice (-98).
def signal_strength(w):
if 'signal' in w:
return int(w['signal'])
else:
return -100
wifi_signals = dict([(normalized_wifi_key(w['key']),
signal_strength(w))
for w in data['wifi']])
wifi_keys = set(wifi_signals.keys())
if not any(wifi_keys):
# No valid normalized keys.
return None
if len(wifi_keys) < MIN_WIFIS_IN_QUERY:
# We didn't get enough keys.
return None
query = session.query(Wifi.key, Wifi.lat, Wifi.lon, Wifi.range).filter(
Wifi.key.in_(wifi_keys)).filter(
Wifi.lat.isnot(None)).filter(
Wifi.lon.isnot(None))
wifis = query.all()
if len(wifis) < MIN_WIFIS_IN_QUERY:
# We didn't get enough matches.
return None
wifis = [Network(normalized_wifi_key(w[0]), w[1], w[2], w[3])
for w in wifis]
# Sort networks by signal strengths in query.
wifis.sort(lambda a, b: cmp(wifi_signals[b.key],
wifi_signals[a.key]))
clusters = []
# The first loop forms a set of clusters by distance,
# preferring the cluster with the stronger signal strength
# if there's a tie.
for w in wifis:
# Try to assign w to a cluster (but at most one).
for c in clusters:
for n in c:
if distance(quantize(n.lat),
quantize(n.lon),
quantize(w.lat),
quantize(w.lon)) <= MAX_WIFI_CLUSTER_KM:
c.append(w)
w = None
break
if w is None:
break
# If w didn't adhere to any cluster, make a new one.
if w is not None:
clusters.append([w])
# The second loop selects a cluster and estimates the position of that
# cluster. The selected cluster is the one with the most points, larger
# than MIN_WIFIS_IN_CLUSTER; its position is estimated taking up-to
# MAX_WIFIS_IN_CLUSTER worth of points from the cluster, which is
# pre-sorted in signal-strength order due to the way we built the
# clusters.
#
# The reasoning here is that if we have >1 cluster at all, we probably
# have some bad data -- likely an AP or set of APs associated with a
# single antenna that moved -- since a user shouldn't be able to hear
# multiple groups 500m apart.
#
# So we're trying to select a cluster that's most-likely good data,
# which we assume to be the one with the most points in it.
#
# The reason we take a subset of those points when estimating location
# is that we're doing a (non-weighted) centroid calculation, which is
# itself unbalanced by distant elements. Even if we did a weighted
# centroid here, using radio intensity as a proxy for distance has an
# error that increases significantly with distance, so we'd have to
# underweight pretty heavily.
clusters = [c for c in clusters if len(c) > MIN_WIFIS_IN_CLUSTER]
if len(clusters) == 0:
return None
clusters.sort(lambda a, b: cmp(len(b), len(a)))
cluster = clusters[0]
sample = cluster[:min(len(cluster), MAX_WIFIS_IN_CLUSTER)]
length = len(sample)
avg_lat = sum([n.lat for n in sample]) / length
avg_lon = sum([n.lon for n in sample]) / length
return {
'lat': quantize(avg_lat),
'lon': quantize(avg_lon),
'accuracy': estimate_accuracy(avg_lat, avg_lon,
sample, WIFI_MIN_ACCURACY),
}
def search_wifi(session, data):
# estimate signal strength at -100 dBm if none is provided,
# which is worse than the 99th percentile of wifi dBms we
# see in practice (-98).
def signal_strength(w):
if 'signal' in w:
return int(w['signal'])
else:
return -100
wifi_signals = dict([(normalize_wifi_key(w['key']),
signal_strength(w))
for w in data['wifi']])
wifi_keys = set(wifi_signals.keys())
if not any(wifi_keys):
# no valid normalized keys
return None
if len(wifi_keys) < 3:
# we didn't even get three keys, bail out
return None
sql_null = None # avoid pep8 warning
query = session.query(Wifi.key, Wifi.lat, Wifi.lon).filter(
Wifi.key.in_(wifi_keys)).filter(
Wifi.lat != sql_null).filter(
Wifi.lon != sql_null)
wifis = query.all()
if len(wifis) < 3:
# we got fewer than three actual matches
return None
wifis = [Network(normalize_wifi_key(w[0]), w[1], w[2]) for w in wifis]
# sort networks by signal strengths in query
wifis.sort(lambda a, b: cmp(wifi_signals[b.key],
wifi_signals[a.key]))
clusters = []
for w in wifis:
# try to assign w to a cluster (but at most one)
for c in clusters:
for n in c:
if distance(quantize(n.lat), quantize(n.lon),
quantize(w.lat), quantize(w.lon)) <= MAX_DIST:
c.append(w)
w = None
break
if len(c) >= 3:
# if we have a cluster with more than 3
# networks in it, return its centroid.
length = len(c)
avg_lat = sum([n.lat for n in c]) / length
avg_lon = sum([n.lon for n in c]) / length
return {
'lat': quantize(avg_lat),
'lon': quantize(avg_lon),
'accuracy': 500,
}
if w is None:
break
# if w didn't adhere to any cluster, make a new one
if w is not None:
clusters.append([w])
# if we didn't get any clusters with >3 networks,
# the query is a bunch of outliers; give up and
# let the next location method try.
return None
