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():
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 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 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 depth2onezero(self, icegrid, lat, lon):
with open(self.roadpath, 'rb') as f:
roadmes = pickle.load(f)
station_lat, station_lon = roadmes.lat, roadmes.lon
icegrid = np.where(icegrid > 0.05, 1, 0)
iceindex = []
for i in range(56):
iceindex.append(
interp.interpolateGridData(icegrid[i],
lat,
lon,
station_lat,
station_lon,
isGrid=False))
iceroad = np.concatenate(iceindex, axis=1)
return iceroad
def clcindex(self, sdgrid, lat, lon):
"""
积雪指数计算
:param path:道路文件路径
:return: 道路指数数据
"""
with open(self.roadpath, 'rb') as f:
roadmes = pickle.load(f)
station_lat, station_lon = roadmes.lat, roadmes.lon
self.sdgindex = np.piecewise(sdgrid, [
sdgrid <= 0, (sdgrid > 0) & (sdgrid <= 5),
(sdgrid > 5) & (sdgrid <= 10), sdgrid > 10
], [0, 1, 2, 3])
self.sdgindex = self.sdgindex.astype('int32')
for sdgindex in self.sdgindex:
sdgindex = interp.interpolateGridData(sdgindex,
lat,
lon,
newlat=station_lat,
newlon=station_lon,
isGrid=False)
self.sdindex.append(sdgindex)
return self.sdindex
def index(sdgrid):
with open(glovar.roadpath, 'rb') as f:
roadmes = pickle.load(f)
station_lat, station_lon = roadmes.lat, roadmes.lon
sdgindex = np.piecewise(sdgrid, [
sdgrid <= 0, (sdgrid > 0) & (sdgrid <= 5),
(sdgrid > 5) & (sdgrid <= 10), sdgrid > 10
], [0, 1, 2, 3])
sdgindex = sdgindex.astype('int32')
'''
for sdgindex in sdgindex:
sdgindex = interp.interpolateGridData(sdgindex, lat, lon, newlat=station_lat, newlon=station_lon, isGrid=False)
sdindex.append(sdgindex)
'''
sdindex = [
interp.interpolateGridData(sdg,
glovar.lat,
glovar.lon,
newlat=station_lat,
newlon=station_lon,
isGrid=False) for sdg in sdgindex
]
return sdindex
def firelevel(data, path, snow, landtype):
"""
????????????
:param data: ??????????????????
:return: greenfirelevel
"""
############################################################
# ?????????§Õ›¥???????????????????????????›¥?pickle
with open(path, 'rb') as f:
predq = pickle.load(f)
preres = np.argmax(np.array(predq)[::-1], axis=0)
tem = np.add.reduce(predq)
preres[tem == 0] = 8 # ?????????
##############################################################
# ??????????????????????????????????????§ß?? 0?? ????1??
*eda, erh, tmp, er = data # eda?U??V??????????UV??????¡Â?????tmp???????????????????????
refertest = []
for i in range(len(er)):
if i == 0:
test = np.piecewise(er[i], [er[i] < 0.1, er[i] >= 0.1], [1, 0]) # ????????????????
# np.piecewise(test, [test == 0, test > 0], [0, test+preres])
test = np.where(test>0, test+preres, 0)
else:
test = np.argmax(er[:i + 1][::-1], axis=0)
refer = np.add.reduce(er[:i + 1])
test[refer == 0] = i + 1
# np.piecewise(test, [test < i+1, test >= i+1], [test, lambda x:x+preres])
test = np.where(test>=i+1, test+preres, test)
refertest.append(test)
#############################################################
# ????????????????????????????
eda = np.sqrt(eda[0]**2 + eda[1]**2) # ????uv????¡Â????§³
edaindex = np.piecewise(eda, [(0<=eda)&(eda<1.6), (eda>=1.6)&(eda<3.5), (eda>=3.5)&
(eda<5.6), (eda>=5.6)&(eda<8.1), (eda>=8.1)&(eda<10.9),
(eda >=10.9)&(eda<14),(eda>14)&(eda<=17.2), eda>17.2],
[3.846, 7.692, 11.538, 15.382, 19.236, 23.076, 26.923, 30.9])
tmp -= 273.15 # ???????????
tmpindex = np.piecewise(tmp, [tmp<5, (tmp>=5)&(tmp<11), (tmp>=11)&
(tmp<16), (tmp>=16)&(tmp<21), (tmp>=21)&(tmp<=25),
tmp>25], [0, 4.61, 6.1, 9.23, 12.5, 15.384])
erhindex = np.piecewise(erh, [erh>70, (erh>=60)&(erh<=70), (erh>=50)&
(erh<60), (erh>=40)&(erh<50), (erh>=30)&(erh<=40),
erh<30], [0, 3.076, 6.153, 9.23, 12.307, 15.384])
refertest = np.array(refertest)
mindex = np.piecewise(refertest, [refertest==0, refertest==1, refertest==2, refertest==3,
refertest==4, refertest==5, refertest==6, refertest==7,
refertest>=8], [0, 7.692, 11.538, 19.23, 23.076, 26.923,
30.7, 34.615, 38])
u = edaindex + tmpindex + erhindex + mindex
###################################################################################
# ??????????????????????????????????????, ??????????????????
rain = np.piecewise(er, [er<0.1, er>=0.1], [0, 1])
rain = [interp.interpolateGridData(r, glovar.latt, glovar.lonn, glovar.lat, glovar.lon) for r in rain]
rain = np.nan_to_num(np.array(rain))
u = [interp.interpolateGridData(u_, glovar.latt, glovar.lonn, glovar.lat, glovar.lon) for u_ in u]
u = np.nan_to_num(np.array(u))
green = u*landtype[0]*rain*snow # ?????????
forest = u*landtype[1]*rain*snow # ????????
###################################################################################
# ????????????????????
gindex = np.piecewise(green, [green<=25, (green>25)&(green<51), (green>=51)&(green<73), (green>=73)&(green<91), green>=91], [1,2,3,4,5])
findex = np.piecewise(forest, [forest <= 25, (forest > 25) & (forest < 51), (forest >= 51) & (forest < 73),
(forest >= 73) & (forest < 91), forest >= 91], [1, 2, 3, 4, 5])
mindex = np.maximum(gindex, findex)
return gindex, findex, mindex
def firelevel(data, path, snow, landtype):
"""
计算草原火险等级
:param data: 气象要素外的其它条件
:return: greenfirelevel
"""
############################################################
# 采用双端队列存储实况降水数据数据量为八天左右，存储为pickle
with open(path, 'rb') as f:
predq = pickle.load(f)
preres = np.argmax(np.array(predq)[::-1], axis=0)
tem = np.add.reduce(predq)
preres[tem == 0] = 8 # 八天干旱情况
##############################################################
# 计算连续无降水日数代码：假定已完成数据编码处理（有降水 0， 无降水1）
*eda, erh, tmp, er = data # eda为U、V风速，需根据UV风速求得风速此处tmp为开氏温度，需转化成摄氏温度
refertest = []
for i in range(len(er)):
if i == 0:
test = np.piecewise(er[i], [er[i] < 0.1, er[i] >= 0.1],
[1, 0]) # 结果为连续无降水日数
# np.piecewise(test, [test == 0, test > 0], [0, test+preres])
test = np.where(test > 0, test + preres, 0)
else:
test = np.argmax(er[:i + 1][::-1], axis=0)
refer = np.add.reduce(er[:i + 1])
test[refer == 0] = i + 1
# np.piecewise(test, [test < i+1, test >= i+1], [test, lambda x:x+preres])
test = np.where(test >= i + 1, test + preres, test)
refertest.append(test)
#############################################################
# 此处根据火险气象因子查对应火险指数
eda = np.sqrt(eda[0]**2 + eda[1]**2) # 根据uv风求得风速大小
edaindex = np.piecewise(
eda, [(0 <= eda) & (eda < 1.6), (eda >= 1.6) & (eda < 3.5),
(eda >= 3.5) & (eda < 5.6), (eda >= 5.6) & (eda < 8.1),
(eda >= 8.1) & (eda < 10.9), (eda >= 10.9) & (eda < 14),
(eda > 14) & (eda <= 17.2), eda > 17.2],
[3.846, 7.692, 11.538, 15.382, 19.236, 23.076, 26.923, 30.9])
tmp -= 273.15 # 转变为摄氏温度
tmpindex = np.piecewise(tmp, [
tmp < 5, (tmp >= 5) & (tmp < 11), (tmp >= 11) & (tmp < 16),
(tmp >= 16) & (tmp < 21), (tmp >= 21) & (tmp <= 25), tmp > 25
], [0, 4.61, 6.1, 9.23, 12.5, 15.384])
erhindex = np.piecewise(erh, [
erh > 70, (erh >= 60) & (erh <= 70), (erh >= 50) & (erh < 60),
(erh >= 40) & (erh < 50), (erh >= 30) & (erh <= 40), erh < 30
], [0, 3.076, 6.153, 9.23, 12.307, 15.384])
refertest = np.array(refertest)
mindex = np.piecewise(refertest, [
refertest == 0, refertest == 1, refertest == 2, refertest == 3,
refertest == 4, refertest == 5, refertest == 6, refertest == 7,
refertest >= 8
], [0, 7.692, 11.538, 19.23, 23.076, 26.923, 30.7, 34.615, 38])
u = edaindex + tmpindex + erhindex + mindex
###################################################################################
# 订正部分计算（需要积雪深度矩阵、降水矩阵、地表状况矩阵, 先不使用积雪深度矩阵订正）
rain = np.piecewise(er, [er < 0.1, er >= 0.1], [0, 1])
rain = [
interp.interpolateGridData(r, glovar.latt, glovar.lonn, glovar.lat,
glovar.lon) for r in rain
]
rain = np.nan_to_num(np.array(rain))
u = [
interp.interpolateGridData(u_, glovar.latt, glovar.lonn, glovar.lat,
glovar.lon) for u_ in u
]
u = np.nan_to_num(np.array(u))
green = u * landtype[0] * rain * snow # 草原火险产品
forest = u * landtype[1] * rain * snow # 森林火险产品
###################################################################################
# 进行森林火险气象等级划分
gindex = np.piecewise(green, [
green <= 25, (green > 25) & (green < 51), (green >= 51) & (green < 73),
(green >= 73) & (green < 91), green >= 91
], [1, 2, 3, 4, 5])
findex = np.piecewise(forest, [
forest <= 25,
(forest > 25) & (forest < 51), (forest >= 51) & (forest < 73),
(forest >= 73) & (forest < 91), forest >= 91
], [1, 2, 3, 4, 5])
mindex = np.maximum(gindex, findex)
return gindex, findex, mindex