def dealWin(tamp, param):
print(len(tamp), param)
list2 = []
dicts = {}
users = {}
com = {}
for i in tamp:
code = mzgeohash.encode((float(i[0]), float(i[1])), 7)
user = i[2]
coms = code + user
tuples = (i[2], code, i[3])
list2.append(tuples)
dicts[code] = dicts.get(code, 0) + 1
users[user] = users.get(user, 0) + 1
com[coms] = com.get(coms, 0) + 1
print(users)
userss = sorted(com.items(), key=lambda x: x[1], reverse=True)
print(dicts)
print(com)
print(userss)
print(len(dicts.keys()), len(users.keys()), len(com.keys()))
# print(dicts.keys())
iindiff = {}
for i in users.keys():
for j in com.keys():
if i == j[7:]:
iindiff[i] = iindiff.get(i, 0) + 1
# print(iindiff)
iindiff = sorted(iindiff.items(), key=lambda x: x[1], reverse=True)
print(iindiff)
print(
"============================================================================"
)
for i in users.keys():
ii = []
T = True
for j in tamp:
if j[2] == i:
ii.append(mzgeohash.encode((float(j[0]), float(j[1])), 7))
if len(ii) >= 2:
co = ii[0]
for jj in range(1, len(ii)):
# ju = neigh(co,ii[jj])
if neigh(co, ii[jj]) == 0:
T = False
break
co = ii[jj]
print(i, T, end=" ")
def city_point(city):
"""
:param city:需要查询的城市
:return:城市内的符合要求的等距坐标
"""
list_p = []
list_q = []
# 根据接口查询城市边界的坐标
url = city_url.format(0, GAODE_API_KEY, city)
response = requests.get(url=url, headers=headers)
data = response.json()
# 获得城市边界坐标
poly = data['districts'][0]['polyline']
# 不匹配岛屿
# poly = re.sub('\|.*\|',';',poly)
# poly = poly.split("|")
poly = re.sub('\|', ';', poly)
# print(len(poly))
# 将坐标进行处理，获得四个角落的坐标，构造一个矩形网络
poly_list1 = poly.split(';')
poly_list2 = list(zip(*[a.split(',') for a in poly_list1]))
max_lng = max([int(a.replace('.', '')) for a in poly_list2[0]])
min_lng = min([int(a.replace('.', '')) for a in poly_list2[0]])
max_lat = max([int(a.replace('.', '')) for a in poly_list2[1]])
min_lat = min([int(a.replace('.', '')) for a in poly_list2[1]])
# 纬度1°为111.3195km，经度每度为111.3195cos(纬度)
g = int((max_lng - min_lng) * 102.3022 / 4242640)
g1 = [round(min_lng + i * 4242640 / 102.3022) for i in range(1, g)]
h = int((max_lat - min_lat) * 111.3195 / 4242640)
h1 = [round(min_lat + i * 4242640 / 111.3195) for i in range(1, h)]
# 获得矩形网络内距离为4.2km的所有坐标
while g1:
g11 = g1.pop()
# print(g11)
for j in h1:
list_p.append((g11, j))
k = [a.split(',') for a in poly_list1]
coords = [
tuple(map(int, (i.replace('.', ''), j.replace('.', '')))) for i, j in k
]
poly = MultiPoint(coords).convex_hull
# 根据判断所有属于城市内的坐标并返回值
for p in list_p:
if poly.contains(Point(p)):
list_q.append(p)
# print(len(list_p))
t_list = []
for a, b in list_q:
t_geohash = mzgeohash.encode((a / 1000000, b / 1000000), length=12)
t_list.append((a / 1000000, b / 1000000, t_geohash))
# print(t_list)
return t_list
def winOperation(list1, id):
list2 = []
dicts = {}
for i in list1:
code = mzgeohash.encode((float(i[0]), float(i[1])), 5)
tuples = (i[2], code, i[3])
list2.append(tuples)
dicts[code] = dicts.get(code, 0) + 1
print("id : " + id, end=" ")
print(dicts)
def calculatorZone(list1):
# print(mzgeohash.encode((116.40957280303152, 39.9190363799492),5))
dicts = {}
for i in list1:
t = (float(i[0]), float(i[1]))
word = mzgeohash.encode(t, 5)
print(word)
dicts[word] = dicts.get(word, 0) + 1
# print(type(dicts))
dicts = sorted(dicts.items(), key=lambda x: x[1], reverse=True)
print(dicts)
print(str(len(dicts)))
def get_videoswithneighbour(self, longitude, latitude, level, page):
if not Rules.rule_geo(latitude=latitude, longitude=longitude, level=level):
raise RuntimeError(errors.GEOINFORMATION_ERROR)
longitude=round(float(longitude), 10)
latitude=round(float(latitude), 10)
level = int(level)
geohash = mzgeohash.encode((longitude, latitude), level)
neighbours = mzgeohash.neighbors(geohash).values()
getvideos_SQL = SQL_refector.videos_getwithgeo(neighbours, level, page)
getvideos_res = yield self.db.query_all(getvideos_SQL)
if not getvideos_res:
raise gen.Return([])
else:
datas = []
for data in getvideos_res:
datas.append(Jsonify.videojson(data))
raise gen.Return(datas)
del stores['air_area_name_0']
del stores['air_genre_name_0']
# geohash 替代聚类处理
import mzgeohash
train_loc = zip(stores['longitude'], stores['latitude'])
# test_loc = zip(test['longitude'],test['latitude'])
list_store_code_1 = []
list_store_code_2 = []
# list_test_code = []
print 'geohash feature'
for i in train_loc:
list_store_code_1.append(mzgeohash.encode(i, length=6))
list_store_code_2.append(mzgeohash.encode(i, length=8))
lbe = preprocessing.LabelEncoder()
stores['geohash'] = list_store_code_1
stores['geohash'] = lbe.fit_transform(stores['geohash'])
stores['geohash_sm'] = list_store_code_2
stores['geohash_sm'] = lbe.fit_transform(stores['geohash_sm'])
stores_count = stores.groupby(['geohash'],
as_index=False).count_observations.sum()
stores_count.rename(columns={'count_observations': 'geohash_visitors_sum'},
inplace=True)
stores = pd.merge(stores, stores_count, how='left', on=['geohash'])
def get_geohash(x):
return mzgeohash.encode((float(x[0]), float(x[1])))
def geohash(self):
"""Return 10 characters of geohash."""
return mzgeohash.encode(self.point())
if gps[1] < mid:
lat_bs += "0"
lat_range[1] = mid
lat_range_tag[1] = 0
else:
lat_bs += "1"
lat_range[0] = mid
final_bs = "".join([lng_bs[i] + lat_bs[i] for i in range(len(lng_bs))])
#converse 2 bit to 32 bit
return "".join([
converse_table[sum(
[int(final_bs[x + 4 - k]) * pow(2, k) for k in range(5)])]
for x in range(0, len(final_bs), 5)
])
lat_range = [18 * 1000, 54 * 1000]
lng_range = [70 * 1000, 136 * 1000]
geo_blocks = []
for i in range(lng_range[0], lng_range[1]):
if i % 10000 == 0:
print(lng_range[1] - lng_range[0]) / 1000
for j in range(lat_range[0], lat_range[1]):
geo_block = geohash.encode((i * 1.0 / 100000, j * 1.0 / 100000))
if geo_block not in geo_blocks:
geo_blocks.append(geo_block)
print len(geo_blocks)
from TDrive import ReadFromDirectory
import matplotlib.pyplot as plt
import mzgeohash
from TDrive import Updatesample
# t = mzgeohash.encode((116.4036519233,39.9238241903),4)
# print(t)
print(mzgeohash.decode("wx4g"))
t = mzgeohash.encode((116.4406325205,39.9543963732),6)
print(t)
zone =[]
for i in mzgeohash.neighbors(mzgeohash.encode((116.4406325205,39.9543963732),6)).values():
zone.append(i)
print(zone)
print(mzgeohash.neighbors(mzgeohash.encode((116.4406325205,39.9543963732),6)))
datas = ReadFromDirectory.FromDireGetData("J:\\DataSetForExp\\release\\test")
transDatas = ReadFromDirectory.CleanDataSet(datas)
transDatas.sort(key = lambda x:x[3],reverse = False)
transData = []
for ii in transDatas:
# if(float(ii[0]) > 116.2075579685 and float(ii[0]) < 116.5632212466 and float(ii[1]) > 39.8333472137 and float(ii[1]) < 40.0326836503):
transData.append(ii)
print(len(transData))
dicts = {}
target = ""
target = "wx4g3"