def clcindex(data):
# 计算出交通风险等级并写入
indexpath = Writefile.readxml(glovar.trafficpath, 6)[-1]
trafficindex = [np.max(data[i], axis=0) for i in range(56)]
fname = ['%03d' % i for i in range(3, 169, 3)]
filetime = Znwg.znwgtime()
Writefile.write_to_csv(indexpath, trafficindex, 'TrafficIndex', fname, filetime)
def __init__(self, cpath):
self.m1path, self.m2path, self.savepath, self.roadpath, self.indexpath = Writefile.readxml(
cpath, 1)
self.dics = Writefile.readxml(cpath, 2)[0].split(',')
# 道路经纬度坐标
self.new_lat = glovar.roadlat
self.new_lon = glovar.roadlon
def clcindex(data, path):
indexpath = Writefile.readxml(path, 0)
trafficindex = [np.max(data[i], axis=0) for i in range(56)]
fname = ['%03d' % i for i in range(3, 169, 3)]
filetime = Znwg.znwgtime()
Writefile.write_to_csv(indexpath, trafficindex, 'TrafficIndex', fname,
filetime)
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 __init__(self, new_lat, new_lon):
"""
初始化参数
:param dics:计算积雪深度所需要素
"""
cpath = r'/home/cqkj/QHTraffic/Product/Source/snowconfig.xml'
self.m1path, self.m2path, self.savepath, self.roadpath, self.indexpath = Writefile.readxml(
cpath, 1)
self.dics = Writefile.readxml(cpath, 2)[0].split(',')
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 reverse(saltedata, dataset, snowdepth):
"""
# 加载模型生成积雪深度模型结果
:param saltedata: 卫星数据
:param dataset: ec气象要素数据
:return:
"""
tmp = [data.reshape(-1, 1) for data in dataset] # 转换基础要素
ele = np.concatenate(tmp, axis=1)
ele.resize(56, 801 * 1381, 4) # 转换形状，将上一时刻积雪输入
temp = np.nan_to_num(ele)
snowdepth = snowdepth.reshape(-1, 1) # 积雪深度数据，仅包含前一时刻
m1, m2, savepath, roadpath, indexpath, _ = Writefile.readxml(
glovar.trafficpath, 0)[0].split(',')
m2 = r'/home/cqkj/LZD/Product/Product/Source/snow.pickle'
if saltedata is not None:
with open(m1, 'rb') as f:
model1 = pickle.load(f)
#########################################
saltedata.resize(801 * 1381, 1)
typecode = 1
else:
with open(m2, 'rb') as f:
model2 = pickle.load(f)
typecode = 2
alldata = []
################################################
for i in range(56):
# temp = [data.reshape(-1, 1) for data in dataset[i]] # 仅包含基础要素
# newdataset = np.concatenate([temp, snowdepth, saltedata], axis=1)
if typecode == 1:
newdataset = np.concatenate([temp[i], snowdepth, saltedata],
axis=1)
prediction = np.array(model1.predict(newdataset)) # 每轮结果
if typecode == 2:
#print(presnow.shape)
# 此处预报结果不可用图像呈现出分块
newdataset = np.concatenate([temp[i], snowdepth], axis=1)
prediction = np.array(model2.predict(newdataset)) # 每轮结果
predictions = np.nan_to_num(prediction)
print(predictions.shape)
snowdepth = predictions[:, np.newaxis] # 结果作为下一次预测的输入
predictions.resize(len(glovar.lat), len(glovar.lon))
sdgrid = np.nan_to_num(predictions)
sdgrid[sdgrid < 0] = 0
alldata.append(sdgrid)
sp = r'/data/traffic/snow//'
Writefile.write_to_nc(sp, np.array(alldata), glovar.lat, glovar.lon,
'snowdepth', glovar.fnames, glovar.filetime)
return np.array(alldata) # 返回 [56, 801, 1381]网格数据
def readIndex():
"""
从不同的路径中读取指数文件，几个路径则表明返回几个dataframe
:return: 含Dataframe的list-->[Dataframe, Dataframe ...]
"""
allindexpath = Writefile.readxml(glovar.trafficpath, 6)
'''
tree = ET.parse(path)
root = tree.getroot()
allindexpath = [i.text for i in root[-1]] # 这个应该仅确定目录，通过正则来确定具体的文件名参数
'''
print(allindexpath)
'''
allfname = [] # allfname应返回多个列表
for i in range(len(allindexpath)):
fnames = regex(allindexpath[i])
allfname.append(fnames)
'''
allfname = [regex(index) for index in allindexpath]
print(allfname)
windpath, icepath, floodpath = [], [], []
windvalue, icevalue, floodvalue = [], [], []
for i in range(len(allfname[0])):
windpath.append(os.path.join(allindexpath[0], allfname[0][i]))
windvalue.append(pd.read_csv(os.path.join(allindexpath[0], allfname[0][i])))
icepath.append(os.path.join(allindexpath[1], allfname[1][i]))
icevalue.append(pd.read_csv(os.path.join(allindexpath[1], allfname[1][i])))
floodpath.append(os.path.join(allindexpath[2], allfname[2][i]))
floodvalue.append(pd.read_csv(os.path.join(allindexpath[2], allfname[2][i])))
return windvalue, icevalue, floodvalue
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, freq, ftp) for element, remote_url in
zip(elements[:-1], remote_urls[:-1])] # ???????³†??????????????????(24003)
'''
for element, remote_url in zip(elements[:-1], remote_urls[:-1]):
grib.mirror(element, remote_url, localdir, freq, ftp)
grib.mirror(elements[-1], remote_urls[-1], localdir, '24024', ftp) # ???????????
# ???????????????????§Ò????????????????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??????????????????§Ò?
ele14list = slice(1, 74, 8) # ??+2-1??????????¦Ä??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)) # ???????????????????¦Ã??????????????????
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 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 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 filelist(path):
now = dt.datetime.now().strftime('%Y%m%d')
pattern = r'(' + now + ')'
rpath, spath, wpath, icpath, savepath = Writefile.readxml(path, 2)
pathlists = [rpath, spath, wpath, icpath]
# 获取出四个智能网格数据列表
elements = [Znwg.regex(pattern, os.listdir(path)) for path in pathlists]
return elements
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():
# 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 Weatherdata(path):
# 获取森林火险所需气象数据
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
] # 待构造
grib = Datainterface.GribData()
'''
[grib.mirror(element, remote_url, localdir, freq, ftp) for element, remote_url in
zip(elements[:-1], remote_urls[:-1])] # 同时同步大风、相对湿度、气温要素数据(24003)
'''
for element, remote_url in zip(elements[:-1], remote_urls[:-1]):
grib.mirror(element, remote_url, localdir, freq, ftp)
grib.mirror(elements[-1], remote_urls[-1], localdir, '24024',
ftp) # 同步出降水要素
# 此处应改为提取出不同要素列表，目前简单实现，构造四个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应为四个最新同步的文件列表
ele14list = slice(1, 74, 8) # （+2-1）前三个要素未来10天每天14时数据索引
####第一个要素wind包含u风和v风
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]
]) # 读取出前三项数据信息
#er, lat, lon, size = Znwg.arrange(grib.readGrib(allpath[-1], nlat=glovar.lat, nlon=glovar.lon)) # 降水为国家级资料，先查看经纬度信息是否与前三者一致
er, lat, lon, size = Znwg.arrange(
[grib.readGrib(allpath[-1], nlat=glovar.latt, nlon=glovar.lonn)][0])
result = windu, windv, data, er # 最终数据应为[4,10,181,277]矩阵
return result, lat, lon
def liverain(path, pklpath):
# ???????ZNWG????????????ݴ?pickle
# ????????????????????????????
elements, _, localdir, historydir, freq, *ftp = Writefile.readxml(path, 1)
now = datetime.datetime.now()
ytd = now - datetime.timedelta(days=1)
dir = r'/SCMOC/BEXN'
remote_url = os.path.join(dir, ytd.strftime('%Y'), ytd.strftime('%Y%m%d'))
grb = Datainterface.GribData()
grb.mirror('ER24', remote_url, localdir, '24024', ftp) # ??????????????
rainpath = sorted(os.listdir(localdir))[-1]
os.chdir(localdir)
rainlive, lat, lon, res = Znwg.arrange([grb.readGrib(rainpath, nlat=glovar.latt, nlon=glovar.lonn)][0])
####??????????????????
with open(pklpath, 'rb') as f:
data = pickle.load(f)
data.append(rainlive)
# §Õ??deque????
with open(pklpath, 'wb') as f:
pickle.dump(data, f)
return rainlive
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)
def __init__(self):
self._path = glovar.trafficpath
self.mpath, self.roadpath, *_ = Writefile.readxml(self._path, 4)