def build_geohash():
for lat in range(int(CHENGDU_LAT_LON["lat_min"] * 1000000),
int(CHENGDU_LAT_LON["lat_max"] * 1000000), 50000):
for lon in range(int(CHENGDU_LAT_LON["lon_min"] * 1000000),
int(CHENGDU_LAT_LON["lon_max"] * 1000000), 50000):
yield geohash.encode(lat / 1000000,
lon / 1000000), lat / 1000000, lon / 1000000
def geoEncoding(data, precision):
temp = data[['start_lat', 'start_lon']]
geohashList = []
for i in temp.values:
geohashList.append(geohash.encode(i[0], i[1], precision))
data['geohash{}'.format(precision)] = geohashList
return data
def ip_to_genhash(ip):
with geoip2.database.Reader(PATH + '/GeoIP2-City.mmdb') as reader:
print(ip)
response = reader.city(ip)
latitude = response.location.latitude
longitude = response.location.longitude
result = geohash.encode(latitude, longitude)
print(result)
return result
def closestCity(lat, lon):
if len(majorCityMap) == 0:
loadMajorCityMap()
gh = geohash.encode(lat, lon, 3)
if gh in majorCityMap:
(hash, name, country_code, admin2_code, admin1_code, latitude, longitude, population) = majorCityMap[gh]
else:
(name, country_code, admin2_code, admin1_code, latitude, longitude) = ("?","?","?","?","?","?")
return name, country_code, admin2_code, admin1_code, latitude, longitude
def create(self, validated_data):
latitude = validated_data.get("latitude")
if latitude == None or latitude == '':
raise serializers.ValidationError('未提供纬度')
longitude = validated_data.get("longitude")
if longitude == None or longitude == '':
raise serializers.ValidationError('未提供经度')
code = geohash.encode(float(latitude), float(longitude))
validated_data['geohash'] = code
addr = Address.objects.create(**validated_data)
return addr
def update(self, instance, validated_data):
instance.name = validated_data.get("name")
instance.content = validated_data.get("content")
instance.country = validated_data.get("country")
instance.province = validated_data.get("province")
instance.city = validated_data.get("city")
instance.city_zone = validated_data.get("city_zone")
latitude = validated_data.get("latitude")
if latitude == None or latitude == '':
raise serializers.ValidationError('未提供纬度')
instance.latitude = latitude
longitude = validated_data.get("longitude")
if longitude == None or longitude == '':
raise serializers.ValidationError('未提供经度')
instance.longitude = longitude
instance.geohash = geohash.encode(float(latitude), float(longitude))
instance.save()
return instance
def reverse(self, latitude, longitude):
addr = None
# try to get the address from the local cache, if we're using it
if self._using_cache():
ghash = geohash.encode(float(latitude), float(longitude), self._reverse_cache_geohash_length)
addr = self._reverse_cache.get(ghash)
# if we didn't get the address from the cache, or we're not using the cache
# then get it from Google
if not addr:
self._reverse_limiter.wait()
loc = self._geolocator.reverse((latitude, longitude), exactly_one=True)
addr = loc.address
# if we're using the cache, save the value we just got back
if addr and self._using_cache():
self._reverse_cache[ghash] = addr
return addr
def on_status(self, status):
if status.coordinates:
lon, lat = status.coordinates['coordinates']
author = status.author
hashtags = self.detect_hashtags(status.text)
payload = {
'author': {
'avatar_url': author.profile_image_url,
'name': author.name,
'handle': author.screen_name,
'profile_text_color': author.profile_text_color,
'profile_background_color': author.profile_background_color
},
'geo': geohash.encode(lat, lon, self.PRECISION),
'text': status.text,
'num_retweets': status.retweet_count,
'source_url': status.source_url,
'id': status.id_str,
'date': status.timestamp_ms,
'hashtags': hashtags
}
self.es.index(index='tweets', doc_type='tweets', body=payload)
def on_status(self, status):
if status.coordinates:
lon, lat = status.coordinates['coordinates']
author = status.author
hashtags = self.detect_hashtags(status.text)
payload = {
'author': {
'avatar_url': author.profile_image_url,
'name': author.name,
'handle': author.screen_name,
'profile_text_color': author.profile_text_color,
'profile_background_color': author.profile_background_color
},
'geo': geohash.encode(lat, lon, self.PRECISION),
'text': status.text,
'num_retweets': status.retweet_count,
'source_url': status.source_url,
'id': status.id_str,
'date': status.timestamp_ms,
'hashtags': hashtags
}
self.es.index(index='tweets', doc_type='tweets', body=payload)
def trajectory_set_grid(traj_set, precision, time=False):
extremums = np.array(map(get_extremum, traj_set))
p_bottom_left = [min(extremums[:, 0]), min(extremums[:, 1])]
p_top_right = [max(extremums[:, 2]), max(extremums[:, 3])]
p_ble = geoh.encode(p_bottom_left[1], p_bottom_left[0], precision)
p_tre = geoh.encode(p_top_right[1], p_top_right[0], precision)
lat_ble, lon_ble, dlat, dlon = geoh.decode_exactly(p_ble)
lat_tre, lon_tre, dlat, dlon = geoh.decode_exactly(p_tre)
lats_all = np.arange(lat_ble - dlat, lat_tre + (3 * dlat), dlat * 2)
lons_all = np.arange(lon_ble - dlon, lon_tre + 3 * dlon, dlon * 2)
lats_center_all = np.arange(lat_ble, lat_tre + 2 * dlat, dlat * 2)
lons_center_all = np.arange(lon_ble, lon_tre + 2 * dlon, dlon * 2)
cells_traj = []
for traj in traj_set:
p_start = traj[0]
cell_start_x = np.where(lons_all < p_start[0])[0][-1]
cell_start_y = np.where(lats_all < p_start[1])[0][-1]
cell_start = [cell_start_x, cell_start_y]
cells = []
for id_seg in range(len(traj) - 1):
start = traj[id_seg]
end = traj[id_seg + 1]
if time:
cell_start_time = start[2]
if abs(start[0] - end[0]) / dlon > abs(start[1] - end[1]) / dlat:
cell, cells_coord = linecell_lons_bigger_step(start, end, cell_start[:2], lons_all, lats_all,
lons_center_all,
lats_center_all)
else:
cell, cells_coord = linecell_lats_bigger_step(start, end, cell_start[:2], lons_all, lats_all,
lons_center_all,
lats_center_all)
if time:
if not cells:
cell_time = [cell[0] + [True, [cell_start_time]]]
else:
if cell[0] == cells[-1][:2]:
cells[-1][3].append(cell_start_time)
cell_time = []
else:
cell_time = [cell[0] + [True, [cell_start_time]]]
cell_time = cell_time + map(lambda x: x + [False, -1], cell[1:-1])
else:
if not cells:
cell_time = [cell[0] + [True]]
else:
if cell[0] == cells[-1][:2]:
cell_time = []
else:
cell_time = [cell[0] + [True]]
cell_time = cell_time + map(lambda x: x + [False], cell[1:-1])
cells.extend(cell_time)
cell_start = cell[-1]
if time:
cell_end_time = end[2]
if cell_start == cells[-1][:2]:
cells[-1][3].append(cell_end_time)
else:
cells.append(cell_start + [True, [cell_end_time]])
else:
if cell_start != cells[-1][:2]:
cells.append(cell_start + [True])
cells_traj.append(cells)
# cells_traj_=map(np.array,cells_traj)
return cells_traj, lons_all, lats_all, lons_center_all, lats_center_all
def get_tweet_feed():
"""
Tweet Feed just provides the 15 most recent tweets if no parameters are
passed in, otherwise provides locational tweets.
"""
hits = []
search_body = None
# Process the parameters
geo_params = parse_request_args(request.args)
# Enforce mandatory geographic information
if None not in geo_params:
lat, lon, precision_raw = map(float, geo_params)
precision = get_precision_from_raw(precision_raw)
geo = geohash.encode(lat, lon, precision)
# The sorting - root.sort
sorting_terms_array = [{
'_geo_distance': {
'geo': {
'lat': lat,
'lon': lon
},
'order': 'asc',
'unit': 'km',
'distance_type': 'plane'
}
}]
# The list of should matches - root.query.filtered.query.bool.should
# The array of OR'd hashtags to support multi-tag search
tag_should_array = []
# Add in any hashtags
hashtags_to_search, local_search_only = get_hashtags_args(request.args)
if len(hashtags_to_search) > 0:
# Append every hashtag into the should array
tag_should_array += [{
'match': {
'hashtags': tag
}
} for tag in hashtags_to_search]
# The query - root.query.filtered.query
# Used for querying hashtags.
body_query = {'bool': {'should': tag_should_array}}
# The filter - root.query.filtered.filter
# Filtering using geohash_cell
body_filter = {
'geohash_cell': {
'geo': {
'lat': lat,
'lon': lon
},
'precision': precision,
'neighbors': True
}
}
# Clear the body filter if we should only be parsing local searches
restrict_search = len(hashtags_to_search) > 0 and local_search_only or\
len(hashtags_to_search) == 0
if not restrict_search:
body_filter = {}
search_body = {
'sort': sorting_terms_array,
'query': {
'filtered': {
'filter': body_filter,
'query': body_query
}
}
}
# This really should be paginated using from_
result = g.es.search(index='tweets', size=250, body=search_body)
hits = [build_hit(hit) for hit in result['hits']['hits']]
return make_response({
'_count': len(hits),
'_took': result['took'],
'hits': hits
})
# if we have bad geo params, give back nothing
return make_response({'hits': [], '_took': 0})
def trajectory_set_grid(traj_set, precision, time=False):
extremums = np.array(map(get_extremum, traj_set))
p_bottom_left = [min(extremums[:, 0]), min(extremums[:, 1])]
p_top_right = [max(extremums[:, 2]), max(extremums[:, 3])]
p_ble = geoh.encode(p_bottom_left[1], p_bottom_left[0], precision)
p_tre = geoh.encode(p_top_right[1], p_top_right[0], precision)
lat_ble, lon_ble, dlat, dlon = geoh.decode_exactly(p_ble)
lat_tre, lon_tre, dlat, dlon = geoh.decode_exactly(p_tre)
lats_all = np.arange(lat_ble - dlat, lat_tre + (3 * dlat), dlat * 2)
lons_all = np.arange(lon_ble - dlon, lon_tre + 3 * dlon, dlon * 2)
lats_center_all = np.arange(lat_ble, lat_tre + 2 * dlat, dlat * 2)
lons_center_all = np.arange(lon_ble, lon_tre + 2 * dlon, dlon * 2)
cells_traj = []
for traj in traj_set:
p_start = traj[0]
cell_start_x = np.where(lons_all < p_start[0])[0][-1]
cell_start_y = np.where(lats_all < p_start[1])[0][-1]
cell_start = [cell_start_x, cell_start_y]
cells = []
for id_seg in range(len(traj) - 1):
start = traj[id_seg]
end = traj[id_seg + 1]
if time:
cell_start_time = start[2]
if abs(start[0] - end[0]) / dlon > abs(start[1] - end[1]) / dlat:
cell, cells_coord = linecell_lons_bigger_step(
start, end, cell_start[:2], lons_all, lats_all,
lons_center_all, lats_center_all)
else:
cell, cells_coord = linecell_lats_bigger_step(
start, end, cell_start[:2], lons_all, lats_all,
lons_center_all, lats_center_all)
if time:
if not cells:
cell_time = [cell[0] + [True, [cell_start_time]]]
else:
if cell[0] == cells[-1][:2]:
cells[-1][3].append(cell_start_time)
cell_time = []
else:
cell_time = [cell[0] + [True, [cell_start_time]]]
cell_time = cell_time + map(lambda x: x + [False, -1],
cell[1:-1])
else:
if not cells:
cell_time = [cell[0] + [True]]
else:
if cell[0] == cells[-1][:2]:
cell_time = []
else:
cell_time = [cell[0] + [True]]
cell_time = cell_time + map(lambda x: x + [False], cell[1:-1])
cells.extend(cell_time)
cell_start = cell[-1]
if time:
cell_end_time = end[2]
if cell_start == cells[-1][:2]:
cells[-1][3].append(cell_end_time)
else:
cells.append(cell_start + [True, [cell_end_time]])
else:
if cell_start != cells[-1][:2]:
cells.append(cell_start + [True])
cells_traj.append(cells)
# cells_traj_=map(np.array,cells_traj)
return cells_traj, lons_all, lats_all, lons_center_all, lats_center_all
def get_geohash(latitude, longitude):
return geohash.encode(latitude=latitude, longitude=longitude)
def generateCityMapping(level):
ghashes = {}
f = codecs.open("cities1000.txt", "r", "utf-8")
h = codecs.open("largestCityPerHash" + str(level) + ".tab", "w", "utf-8")
for line in f:
line = line.rstrip()
A = line.split(u'\t')
if len(A) == 19:
(geonameid ,
name ,
asciiname ,
alternatenames ,
latitude ,
longitude ,
feature_class ,
feature_code ,
country_code ,
cc2 ,
admin1_code ,
admin2_code ,
admin3_code ,
admin4_code ,
population ,
elevation ,
dem ,
timezone ,
modification_date ) = A
if feature_class == 'P':
gh = geohash.encode(float(latitude), float(longitude), level)
if gh not in ghashes:
ghashes[gh] = []
ghashes[gh].append( (long(population), name, country_code, admin2_code, admin1_code, latitude, longitude) )
# print >> sys.stderr, "cities1000.txt processed"
for gh in ghashes:
if len(ghashes[gh]) > 1:
ghashes[gh].sort(reverse=True)
ghashes[gh] = [ ghashes[gh][0] ]
f.close()
# print >> sys.stderr, "ghashes CAR(sort())"
# Now use airport list to see if we can fill any blanks:
g = codecs.open("airport-codes.csv", "r", "utf-8")
# print >> sys.stderr, "airport-codes.csv opened"
for line in g:
line = line.rstrip()
A = line.split(u',')
# print >> sys.stderr, str(len(A))
if len(A) == 13:
if A[0] == u"ident":
continue
(ident,type,name,latitude_deg,longitude_deg,elevation_ft,continent,iso_country,iso_region,municipality,gps_code,iata_code,local_code) = A
# print >> sys.stderr, iata_code
gh = geohash.encode(float(latitude_deg), float(longitude_deg), level)
if gh not in ghashes:
print >> sys.stderr, "Found airport for location:" + municipality + ", " + iso_country
if len(municipality) == 0:
municipality = name
ghashes[gh] = [ (0, municipality, iso_country, name, iata_code, latitude_deg, longitude_deg) ]
g.close()
for gh in ghashes:
print >> h, u"\t".join([ gh, ghashes[gh][0][1], ghashes[gh][0][2], ghashes[gh][0][3],ghashes[gh][0][4], str(ghashes[gh][0][5]),str(ghashes[gh][0][6]), str(ghashes[gh][0][0]) ])
h.close()
def get_tweet_feed():
"""
Tweet Feed just provides the 15 most recent tweets if no parameters are
passed in, otherwise provides locational tweets.
"""
hits = []
search_body = None
# Process the parameters
geo_params = parse_request_args(request.args)
# Enforce mandatory geographic information
if None not in geo_params:
lat, lon, precision_raw = map(float, geo_params)
precision = get_precision_from_raw(precision_raw)
geo = geohash.encode(lat, lon, precision)
# The sorting - root.sort
sorting_terms_array = [
{
'_geo_distance': {
'geo': {
'lat': lat,
'lon': lon
},
'order': 'asc',
'unit': 'km',
'distance_type': 'plane'
}
}
]
# The list of should matches - root.query.filtered.query.bool.should
# The array of OR'd hashtags to support multi-tag search
tag_should_array = []
# Add in any hashtags
hashtags_to_search, local_search_only = get_hashtags_args(request.args)
if len(hashtags_to_search) > 0:
# Append every hashtag into the should array
tag_should_array += [{'match': {'hashtags': tag}}
for tag in hashtags_to_search]
# The query - root.query.filtered.query
# Used for querying hashtags.
body_query = {
'bool': {
'should': tag_should_array
}
}
# The filter - root.query.filtered.filter
# Filtering using geohash_cell
body_filter = {
'geohash_cell': {
'geo': {
'lat': lat,
'lon': lon
},
'precision': precision,
'neighbors': True
}
}
# Clear the body filter if we should only be parsing local searches
restrict_search = len(hashtags_to_search) > 0 and local_search_only or\
len(hashtags_to_search) == 0
if not restrict_search:
body_filter = {}
search_body = {
'sort': sorting_terms_array,
'query': {
'filtered': {
'filter': body_filter,
'query': body_query
}
}
}
# This really should be paginated using from_
result = g.es.search(index='tweets', size=250, body=search_body)
hits = [build_hit(hit) for hit in result['hits']['hits']]
return make_response({'_count': len(hits), '_took': result['took'],
'hits': hits})
# if we have bad geo params, give back nothing
return make_response({'hits': [], '_took': 0})
