def icele():
# 获取ec数据
dics = ['ECMWF_HR\SKINT', 'ECMWF_HR\APCP', 'ECMWF_HR\SNOD']
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(此处可能存在问题，搞清12为5-8，还是8-11)
lonlatset, dataset = [], []
for dic in dics:
lon, lat, data = Datainterface.micapsdata(ectime, dic, fh)
lonlatset.append(lon, lat)
for i in range(data.shape[0] - 1):
# 此处参考233上的改动
newdata = []
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))
dataset.append(np.array(newdata)) # 保存插值后的数据集,data形状应为（57，X, X）
return dataset
def main():
snowpath, gpath, fpath, rainpath, savepath = Writefile.readxml(
glovar.forestpath, 0) # 积雪nc数据存放位置
snow = snowdepth(snowpath) # 积雪数据[10, 801, 1381]
data, *_ = Weatherdata(glovar.forestpath) # 森林火险数据
ldtype = landtype(gpath, fpath)
gindex, findex, mindex = firelevel(data, rainpath, snow, ldtype) # 最终生成指数
filetime = ecmwf.ecreptime()
fh = range(10)
fnames = ['_%03d' % i for i in fh]
Writefile.write_to_nc(savepath,
gindex,
filetime=filetime,
fnames=fnames,
lat=glovar.lat,
lon=glovar.lon,
name='green')
Writefile.write_to_nc(savepath,
findex,
filetime=filetime,
fnames=fnames,
lat=glovar.lat,
lon=glovar.lon,
name='forest')
Writefile.write_to_nc(savepath,
mindex,
filetime=filetime,
fnames=fnames,
lat=glovar.lat,
lon=glovar.lon,
name='meteo')
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 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 snowData():
# 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, 0)
*_, dics = Writefile.readxml(
r'/home/cqkj/LZD/Product/Product/config/Traffic.xml', 0)
dicslist = dics.split(',')[:-1]
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 write(path, data, name, lat=None, lon=None, type=0):
filetime = ecmwf.ecreptime()
fh = range(3, 169, 3)
fnames = ['_%03d' % i for i in fh]
if type == 0:
Writefile.write_to_nc(path, data, lat, lon, name, fnames, filetime)
else:
Writefile.write_to_csv(path, data, name, fnames, filetime)
def write(self, data, lat=None, lon=None, type=0):
filetime = ecmwf.ecreptime()
fh = range(3, 169, 3)
fnames = ['_%03d' % i for i in fh]
name = 'Snow'
if type == 0:
Writefile.write_to_nc(self.savepath, data, lat, lon, name, fnames,
filetime)
else:
Writefile.write_to_csv(self.indexpath, data, 'SnowIndex', fnames,
filetime)
def main():
# 计算出来灾害落区
# dataset 为获取的各气象要素等级预报
######读取nc要素文件#####
varname = ['Rain', 'Snow_depth', 'Wind', 'Roadic']
datasets = [
xr.open_mfdataset(path, concat_dim='time').values
for path, name in zip(filelist(), varname)
]
rain, snow, wind, roadic = datasets
roadic *= 8
snow *= 4
wind *= 2
rain *= 1
########################
disaster = roadic + snow + wind + rain
########################
configpath = r'../config/disaster.xml'
savepath = Writefile.readxml(configpath, 1)[-1]
filetime = ecmwf.ecreptime()
fh = range(3, 169, 3)
fnames = ['_%03d' % i for i in fh]
Writefile.write_to_nc(savepath, disaster, glovar.lat, glovar.lon,
'Disaster', fnames, filetime)
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(r'/home/cqkj/QHTraffic/Product/Traffic/ROADIC/config.xml', 0)).split(',')]
new_lon = np.arange(region[0], region[2], region[-1])
new_lat = np.arange(region[1], region[3], region[-1])
'''
dataset = icele()
icgrid = ice.icegrid(dataset, glovar.lat, glovar.lon)
# savepath, indexpath = Writefile.readxml(r'/home/cqkj/QHTraffic/Product/Traffic/ROADIC/config.xml', 1)[2:]
savepath, indexpath, cmpath, _ = Writefile.readxml(glovar.trafficpath,
4)[2:]
# write(savepath, icgrid, 'Roadic', new_lat, new_lon) # 先保存厚度网格数据
write(savepath, icgrid, 'Roadic', glovar.lat, glovar.lon)
# iceroad = ice.depth2onezero(icgrid, new_lat, new_lon)
iceroad = ice.depth2onezero(icgrid, glovar.lat, glovar.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(cmpath, delimiter=',')
icedays = RoadIceindex(cmissdata, iceroad)
roadicing = icedays.iceday()
write(indexpath, roadicing, 'RoadicIndex', type=1)
