def iceData():
# 获取ec数据信息(气温、降水、地温、湿度、积雪深度)
ectime = ecmwf.ecreptime()
fh = [i for i in range(12, 181, 3)] # 20点的预报获取今天8:00的ec预报
*_, dics = Writefile.readxml(glovar.trafficpath, 4)
dicslist = dics.split(',')
lonlatset, dataset = [], []
for dic in dicslist:
newdata = []
lon, lat, data = Datainterface.micapsdata(ectime, dic, fh)
lonlatset.append((lon, lat))
for i in range(data.shape[0] - 1):
if (np.isnan(data[i]).all() == True) and (i + 1 <= data.shape[0]):
data[i] = data[i + 1] / 2
data[i + 1] = data[i + 1] / 2
newdata.append(
interp.interpolateGridData(data[i], lat, lon, glovar.lat,
glovar.lon))
else:
newdata.append(
interp.interpolateGridData(data[i], lat, lon, glovar.lat,
glovar.lon))
newdata = np.array(newdata)
# newdata[newdata<0] = 0 # 保证数据正确性
dataset.append(newdata) # 保存插值后的数据集
return np.array(dataset)
def presnow():
# 得到前一时刻积雪深度
ectime = ecmwf.ecreptime()
fh = [0]
dic = 'ECMWF_HR/SNOD'
lon, lat, data = Datainterface.micapsdata(ectime, dic, fh)
return interp.interpolateGridData(data, lat, lon, glovar.lat, glovar.lon)
def mirrorSkint(hours):
# 6.14.16测试
# 从cimiss中获取所有站点地温插值,hours控制选取的时间间隔
interfaceId = 'getSurfEleInRegionByTimeRange'
elements = "Station_Id_C,Lat,Lon,GST,Year,Mon,Day,Hour"
# 设定每天8：00、 20:00运行
lastime = datetime.datetime.now().replace(
minute=0, second=0) - datetime.timedelta(hours=8)
firstime = lastime - datetime.timedelta(hours=hours)
temp = ('[', firstime.strftime('%Y%m%d%H%M%S'), ',',
lastime.strftime('%Y%m%d%H%M%S'), ')')
Time = ''.join(temp)
params = {
'dataCode': "SURF_CHN_MUL_HOR", # 需更改为地面逐小时资料
'elements': elements,
'timeRange': Time,
'adminCodes': "630000"
}
initData = Datainterface.cimissdata(interfaceId, elements,
**params) # 获取出cimiss初始数据
initData.Time = pd.to_datetime(initData.Time)
# 需按时间进行划分插值，得到 地温网格数据
timeList = pd.to_datetime(initData.Time.unique()).strftime(
'%Y%m%d%H%M%S') # 此处可能存在问题，目的仅按顺序取出来时间
initData.set_index(initData.Time, inplace=True)
oneHourgrid = [
np.nan_to_num(
interp.interpolateGridData(
initData.loc[tm].GST.values.astype('float32'),
initData.loc[tm].Lat.values.astype('float32'),
initData.loc[tm].Lon.values.astype('float32'), glovar.lat,
glovar.lon)) for tm in timeList
]
return oneHourgrid # oneHourgrid 格点数据形状应为[12, newlat, newlon]
def main():
ice = Roadic()
rep = ecmwf.ecreptime()
fh = [i for i in range(12, 181, 3)]
region = [float(i) for i in ','.join(Writefile.readxml(glovar.trafficpath, 0)).split(',')]
new_lon = np.arange(region[0], region[2], region[-1])
new_lat = np.arange(region[1], region[3], region[-1])
lonlatset, dataset = [], []
# 提取数据及经纬度(双重循环，看能否改进)
for dic in ice.dics:
lon, lat, data = Datainterface.micapsdata(rep, dic, fh)
lonlatset.append((lon, lat))
for i in range(data.shape[0] - 1):
if (np.isnan(data[i]).all() == True) and (i + 1 <= data.shape[0]):
data[i] = data[i + 1] / 2
data[i+1] = data[i + 1] / 2
interp.interpolateGridData(data,lat,lon,new_lat, new_lon)
else:
interp.interpolateGridData(data, lat, lon,new_lat, new_lon)
dataset.append(data) # 保存插值后的数据集
icgrid = ice.icegrid(dataset, new_lat, new_lon)
savepath, indexpath = Writefile.readxml(glovar.trafficpath, 1)[2:]
write(savepath, icgrid, 'Roadic', new_lat, new_lon) # 先保存厚度网格数据
iceroad = ice.depth2onezero(icgrid, new_lat, new_lon)
################################################################################
# 获取cimiss数据集,此处仅为读取，实况数据获取及保存由另一程序实现
cmissdata = np.loadtxt('/home/cqkj/QHTraffic/qhroadic/cmsk.csv', delimiter=',')
icedays = RoadIceindex(cmissdata, iceroad)
roadicing = icedays.iceday()
write(indexpath, roadicing, 'RoadicIndex', type=1)
def main():
saltedata = saltedata(path)
snowpre = np.random.randint(0, 1, size=(801 * 1381, 1))
snow = SnowDepth()
rep = ecmwf.ecreptime()
fh = [i for i in range(12, 181, 3)]
region = [
float(i) for i in ','.join(
Writefile.readxml(
r'/home/cqkj/QHTraffic/Product/Traffic/SNOD/config.xml',
0)).split(',')
]
new_lon = np.arange(region[0], region[2], region[-1])
new_lat = np.arange(region[1], region[3], region[-1])
lonlatset, dataset = [], []
# 提取数据及经纬度(双重循环，看能否改进)
for dic in snow.dics:
lon, lat, data = Datainterface.micapsdata(rep, dic, fh)
lonlatset.append((lon, lat))
for i in range(data.shape[0] - 1):
if (np.isnan(data[i]).all() == True) and (i + 1 <= data.shape[0]):
data[i] = data[i + 1] / 2
data[i + 1] = data[i + 1] / 2
interp.interpolateGridData(data, lat, lon, new_lat, new_lon)
else:
interp.interpolateGridData(data, lat, lon, new_lat, new_lon)
dataset.append(data) # 保存插值后的数据集
depthgrid = snow.clcsd(dataset, new_lat, new_lon, saltedata, snowpre)
snow.write(depthgrid, new_lat, new_lon)
dangerindex = snow.clcindex(depthgrid, new_lat, new_lon)
snow.write(dangerindex, type=1)
def rainData():
# 同步降雨智能网格文件并解析
now = datetime.datetime.now()
*_, elements, ftp = Writefile.readxml(glovar.trafficpath, 1)
#*_, elements, ftp = Writefile.readxml(r'/home/cqkj/LZD/Product/Product/config/Traffic.xml', 5)
element = elements.split(',')
ftp = ftp.split(',')
grib = Datainterface.GribData()
remote_url = os.path.join(r'\\SPCC\\BEXN', now.strftime('%Y'), now.strftime('%Y%m%d'))
grib.mirror(element[0], remote_url, element[1], ftp, element[2])
rname = sorted(os.listdir(element[1]))[-1]
rpath = element[1] + rname
dataset, lat, lon, _ = Znwg.arrange((grib.readGrib(rpath))) # result包含data,lat,lon,size
return [interp.interpolateGridData(data, lat, lon, glovar.lat, glovar.lon) for data in dataset[:56]]
def mirrorGrib(path):
# 6.14.16测试
# 用来同步8-20/20-8的实况格点场数据气温(开式)、降水（累计）、湿度， 20时同步今天8时数据， 8时同步昨日20时数据
grid = Datainterface.GribData()
now = datetime.datetime.now()
elements, subdirs, localdirs, _, freq, *ftp = Writefile.readxml(
path, 0) # freq应为None
elements = elements.split(',')
subdirs = subdirs.split(',')
localdirs = localdirs.split(',') # 为三个文件夹目录
remote_urls = [
os.path.join(subdir, now.strftime('%Y'), now.strftime('%Y%m%d'))
for subdir in subdirs
] # 构造三个路径
for localdir, element, remote_url in zip(localdirs, elements, remote_urls):
grid.mirror(element, remote_url, localdir,
ftp) # 同步至每个文件夹，此处待测试，需保证范围在08-20或次日20-当日08时
# 查看各文件夹里数据信息,此处默认TEM, RAIN, RH 为08-20时的文件名列表
RAINs, RHs, TEMs = [
sorted(os.listdir(localdir)) for localdir in localdirs
] # 零时开始至今
e2tTems = [tem for tem in TEMs if int(tem[-7:-5]) in range(8, 21)]
e2tRains = [rain for rain in RAINs if int(rain[-7:-5]) in range(8, 21)]
e2tRhs = [rh for rh in RHs if int(rh[-7:-5]) in range(8, 21)]
# 认为形状为同一分辨率下的[12, lat * lon]
tem = [
Znwg.arrange(grid.readGrib(os.path.join(localdirs[2], TEM)))
for TEM in e2tTems
] # temdata 包含四个要素（data, lat, lon, size）, 全国范围，需插值到青海
lat, lon = tem[0][1], tem[0][2]
temdata = np.array([
np.nan_to_num(
interp.interpolateGridData(t[0] - 273.15, lat, lon, glovar.lat,
glovar.lon)) for t in tem
])
raindata = np.array([
np.nan_to_num(
interp.interpolateGridData(
Znwg.arrange(grid.readGrib(os.path.join(localdirs[0],
RAIN)))[0], lat, lon,
glovar.lat, glovar.lon)) for RAIN in e2tRains
])
rhdata = np.array([
np.nan_to_num(
interp.interpolateGridData(
Znwg.arrange(grid.readGrib(os.path.join(localdirs[1], RH)))[0],
lat, lon, glovar.lat, glovar.lon)) for RH in e2tRhs
])
return temdata, raindata, rhdata
def upSnowele(gbpath, sktpath):
# 处理积雪历史要素文件, gbpath为格点要素pkl存放路径, sktpath为cimiss插值pkl路径
grid = Datainterface.GribData()
gbNlist, sktList = sorted(os.listdir(gbpath))[-5:], sorted(
os.listdir(sktpath))[-5:] #使用正则优化
# 获取每天11时到17时， 23时到05时的数据，考虑取最后
eleData = [
Znwg.arrange(grid.readGrib(os.path.join(gbpath, gblist)))
for gblist in gbNlist
]
sktData = [
Znwg.arrange(grid.readGrib(os.path.join(gbpath, sktlist)))
for sktlist in sktList
]
return np.array(eleData), np.array(sktData)
def windData(path):
# 获取数据信息
*_, elements, ftp = Writefile.readxml(path, 2)
element = elements.split(',')
ftp = ftp.split(',')
grib = Datainterface.GribData()
remote_url = os.path.join(r'\\SPCC\\BEXN', glovar.now.strftime('%Y'),
glovar.now.strftime('%Y%m%d'))
grib.mirror(element[0], remote_url, element[1], ftp, element[2])
rname = sorted(os.listdir(element[1]))[-1]
rpath = element[1] + rname
dataset, lat, lon, _ = Znwg.arrange(
(grib.readGrib(rpath))) # result包含data,lat,lon,size
return [
interp.interpolateGridData(data, lat, lon, glovar.lat, glovar.lon)
for data in dataset
] # 返回插值后列表格式数据
def predepth():
# 前一时刻积水深度,此处需在服务器端测试优化
dr = np.zeros(shape=(801, 1381)) # 目前默认前一时刻积水深度为0
now = datetime.datetime.now()
znwgtm = Znwg.znwgtime()
*_, ftp = Writefile.readxml(glovar.trafficpath, 1)
grib = Datainterface.GribData()
remote_url = os.path.join(r'\\ANALYSIS\\CMPA', now.strftime('%Y'), now.strftime('%Y%m%d'))
localdir = r'/home/cqkj/QHTraffic/Product/Product/mirror/rainlive'
grib.mirror('FRT_CHN_0P05_3HOR', remote_url, localdir, ftp)
rname = sorted(os.listdir(localdir))[-1]
rpath = localdir + rname
data, lat, lon, _ = Znwg.arrange((grib.readGrib(rpath)))
data = interp.interpolateGridData(data, lat, lon, glovar.lat, glovar.lon)
dataset = data[np.newaxis, ] # 符合形状要求
res = FloodModel.cal2(dataset, dr)
return res[0]
def mirrorskgrib(path):
# 6月15待测试
# 还需写一个同步实况小时所需数据的代码（包括三网格), 滞后15分钟，可使用同一个config文件
grid = Datainterface.GribData()
now = datetime.datetime.now()
elements, subdirs, localdirs, _, freq, *ftp = Writefile.readxml(
path, 0) # freq应为None
elements = elements.split(',')
subdirs = subdirs.split(',')
localdirs = localdirs.split(',') # 为三个文件夹目录
remote_urls = [
os.path.join(subdir, now.strftime('%Y'), now.strftime('%Y%m%d'))
for subdir in subdirs
] # 构造三个路径
for localdir, element, remote_url in zip(localdirs, elements, remote_urls):
grid.mirror(element, remote_url, localdir,
ftp) # 同步至每个文件夹，此处待测试，需保证范围在08-20或次日20-当日08时
# 查看各文件夹里数据信息,此处默认TEM, RAIN, RH 为08-20时的文件名列表
RAIN, RH, TEM = [
sorted(os.listdir(localdir))[-1] for localdir in localdirs
] # 零时开始至今
tem = Znwg.arrange(grid.readGrib(os.path.join(localdirs[2], TEM)))
lat, lon = tem[1], tem[2]
temdata = np.array(
np.nan_to_num(
interp.interpolateGridData(tem[0] - 273.15, lat, lon, glovar.lat,
glovar.lon)))
raindata = np.array(
np.nan_to_num(
interp.interpolateGridData(
Znwg.arrange(grid.readGrib(os.path.join(localdirs[0],
RAIN)))[0], lat, lon,
glovar.lat, glovar.lon)))
rhdata = np.array(
np.nan_to_num(
interp.interpolateGridData(
Znwg.arrange(grid.readGrib(os.path.join(localdirs[1], RH)))[0],
lat, lon, glovar.lat, glovar.lon)))
Time = datetime.datetime.now().strftime('%Y%m%d%H')
savepath = ''.join(r'/home/cqkj/QHTraffic/tmp/ele', Time, r'.pkl')
# 存储每个时刻的降水、湿度、温度
with open(savepath, 'wb') as f: # 文件名称用时间区分，精确到小时
pickle.dump([temdata, raindata, rhdata], f)
return temdata, raindata, rhdata
'''
def Weatherdata(path):
# ?????????????????????
elements, subdirs, localdir, _, freq, *ftp = Writefile.readxml(path, 1)
now = datetime.datetime.now()
elements = elements.split(',')
subdirs = subdirs.split(',')
remote_urls = [
os.path.join(subdir, now.strftime('%Y'), now.strftime('%Y%m%d'))
for subdir in subdirs
] # ??????
grib = Datainterface.GribData()
'''
[grib.mirror(element, remote_url, localdir, ftp, freq=freq) for element, remote_url in
zip(elements[:-1], remote_urls[:-1])] # ???????????????????????????(24003)
'''
for element, remote_url in zip(elements[:-1], remote_urls[:-1]):
grib.mirror(element, remote_url, localdir, ftp, freq=freq)
grib.mirror(elements[-1], remote_urls[-1], localdir, ftp,
freq='24024') # ???????????
# ?????????????????????????????????????pattern
strings = ','.join(os.listdir(localdir))
patterns = [
r'(\w+.EDA.*?.GRB2)', r'(\w+.ERH.*?.GRB2)', r'(\w+.TMP.*?.GRB2)',
r'(\w+.ER24.*?.GRB2)'
]
allpath = [
localdir + sorted(Znwg.regex(pattern, strings), key=str.lower)[-1]
for pattern in patterns
] # allpath?????????????????????
ele14list = slice(1, 74, 8) # ??+2-1??????????????10?????14?????????
####????????wind????u???v??
wind = grib.readGrib(allpath[0])[0]
windu_v = np.array([v for _, v in wind.items()])
windu, windv = windu_v[::2][ele14list], windu_v[1::2][ele14list]
data = np.array([
Znwg.arrange(grib.readGrib(path))[0][ele14list]
for path in allpath[1:-1]
]) # ?????????????????
#er, lat, lon, size = Znwg.arrange(grib.readGrib(allpath[-1], nlat=glovar.lat, nlon=glovar.lon)) # ???????????????????????????????????????
er, lat, lon, size = Znwg.arrange(
[grib.readGrib(allpath[-1], nlat=glovar.latt, nlon=glovar.lonn)][0])
result = windu, windv, *data, er # ??????????[4,10,181,277]????
return result, lat, lon
def mirrorskskt(newlat, newlon, hours=1):
# 6.14.16测试
# 从cimiss中获取所有站点地温插值,hours控制选取的时间间隔
interfaceId = 'getSurfEleInRegionByTimeRange'
elements = "Station_Id_C,Lat,Lon,GST,Year,Mon,Day,Hour"
# 设定每天8：00、 20:00运行
lastime = datetime.datetime.now().replace(
minute=0, second=0) - datetime.timedelta(hours=8)
firstime = lastime - datetime.timedelta(hours=hours)
temp = ('[', firstime.strftime('%Y%m%d%H%M%S'), ',',
lastime.strftime('%Y%m%d%H%M%S'), ']')
Time = ''.join(temp)
params = {
'dataCode': "SURF_CHN_MUL_HOR", # 需更改为地面逐小时资料
'elements': elements,
'timeRange': Time,
'adminCodes': "630000"
}
initData = Datainterface.cimissdata(interfaceId, elements,
**params) # 获取出cimiss初始数据
initData.Time = pd.to_datetime(initData.Time)
# 需按时间进行划分插值，得到 地温网格数据
timeList = pd.to_datetime(initData.Time.unique()).strftime(
'%Y%m%d%H%M%S') # 此处可能存在问题，目的仅按顺序取出来时间
initData.set_index(initData.Time, inplace=True)
oneHourgrid = [
np.nan_to_num(
interp.interpolateGridData(
initData.loc[tm].GST.values.astype('float32'),
initData.loc[tm].Lat.values.astype('float32'),
initData.loc[tm].Lon.values.astype('float32'), glovar.lat,
glovar.lon)) for tm in timeList
]
Time = datetime.datetime.now().strftime('%Y%m%d%H')
savepath = ''.join(r'/home/cqkj/QHTraffic/tmp/skt', Time, r'.pkl')
with open(savepath, 'rb') as f: # 文件名用时间区分精确到小时，供updateSnow使用
pickle.dump(oneHourgrid, f)
return oneHourgrid
def liverain(path, pklpath):
# ???????ZNWG???????????????pickle
# ????????????????????????????
elements, _, localdir, historydir, freq, *ftp = Writefile.readxml(path, 1)
now = datetime.datetime.now()
ytd = now - datetime.timedelta(days=1)
dir = r'//ANALYSIS//CMPA//0P05'
remote_url = os.path.join(dir, now.strftime('%Y'), now.strftime('%Y%m%d'))
grb = Datainterface.GribData()
grb.mirror('FAST_CHN_0P05_DAY-PRE', remote_url, localdir, ftp,
freq=None) # ??????????????
rainpaths = sorted(os.listdir(localdir))[-1]
os.chdir(localdir)
rainlive, lat, lon, res = Znwg.arrange(
[grb.readGrib(rainpaths, nlat=glovar.latt, nlon=glovar.lonn)][0])
####??????????????????
with open(pklpath, 'rb') as f:
data = pickle.load(f)
data.append(rainlive)
# ????deque????
with open(pklpath, 'wb') as f:
pickle.dump(rainlive, f)
return rainlive
