def stagnant(savepath, predepth, zdata):
# 得到道路积水深度数据
data = zdata
rdata = np.nan_to_num(data[:56])
mes = pd.read_csv(glovar.roadpath, index_col=0)
stationlon, stationlat = mes['Lon'].values, mes['Lat'].values # 提取出道路坐标
datas = FloodModel.cal2(rdata, predepth)
#data = FloodModel.cal2(data, dr)
# 此处需要加快速度，插值到道路点的速度过慢
roadpoint = [
interp.grid_interp_to_station([glovar.longrid, glovar.latgrid, data],
stationlon, stationlat) for data in datas
]
result = np.nan_to_num(roadpoint)
result[result < 0] = 0
flooding_index = np.piecewise(result, [
result <= 0.4,
(result > 0.4) & (result <= 1.0), (result > 1.0) & (result <= 1.5),
(result > 1.5) & (result <= 2.0), result > 2.0
], [1, 2, 3, 4, 5])
Writefile.write_to_csv(savepath, flooding_index, 'floodindex',
glovar.fnames, glovar.filetime)
return roadpoint
def clcindex(data, path):
indexpath = Writefile.readxml(path, 6)
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 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 press(savepath, datau, datav):
# 计算出道路点风压大小
mes = pd.read_csv(glovar.windpath, index_col=0)
lon, lat = mes['Lon'], mes['Lat'] # 提取出道路坐标
length = np.linalg.norm(mes.iloc[:, -2:], axis=1)
ur = np.divide(mes['ur'], length)
vr = np.divide(mes['vr'], length)
uv = [[i, j] for i, j in zip(ur, vr)]
latt, lonn = np.linspace(31.4, 39.4, 801), np.linspace(89.3, 103.1, 1381)
for u, v in zip(datau, datav):
uvalue = interp.interpolateGridData(u,
latt,
lonn,
lat,
lon,
isGrid=False)
vvalue = interp.interpolateGridData(v,
latt,
lonn,
lat,
lon,
isGrid=False)
UVvalue = [[i, j] for i, j in zip(uvalue, vvalue)]
windcross = []
for i, value in enumerate(UVvalue):
datas = np.cross(uv, np.array(value).T)
w = 1 / 2 * 1.29 * (np.square(datas)) * 1000
w = np.piecewise(w, [
w < 83, (w >= 83) & (w < 134), (w >= 134) & (w < 602),
(w >= 602) & (w < 920), w >= 920
], [1, 2, 3, 4, 5])
windcross.append(w[np.newaxis, :])
Writefile.write_to_csv(savepath, windcross, 'windindex', glovar.fnames,
glovar.filetime)
def revise(message):
# 订正解析结果，并插值到1km分辨率
mdpath, gcpath, savepath, *_ = Writefile.readxml(glovar.trafficpath, 1)
net = torch.load(mdpath)
dem = pd.read_csv(gcpath, index_col=0).values
arrays = np.array(
[np.nan_to_num([data, dem]) for data in message[:, :801, :1381]])
inputs = torch.from_numpy(arrays)
# torch.no_grad()
outputs = [net(it[np.newaxis, :]).detach().numpy() for it in inputs]
outputs = np.nan_to_num(outputs)
outputs[outputs < 0] = 0
print(outputs.shape)
output = np.squeeze(outputs)
lat = np.linspace(31.4, 39.4, 801)
lon = np.linspace(89.3, 103.1, 1381)
raingb = np.array([
np.nan_to_num(
interp.interpolateGridData(op, lat, lon, glovar.lat, glovar.lon))
for op in output
])
Writefile.write_to_nc(savepath, raingb, glovar.lat, glovar.lon, 'Rain',
glovar.fnames, glovar.filetime)
return outputs
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, 901 * 1401, 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)
# 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(901 * 1401, 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(
np.nan_to_num(newdataset))) # 每轮结果
# predictions = np.nan_to_num(model2.predict(np.nan_to_num(newdataset)))
predictions = np.nan_to_num(prediction)
# predictions[predictions < 0] = 0
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'/home/cqkj/QHTraffic/Data//'
Writefile.write_to_nc(sp, np.array(alldata), glovar.lat, glovar.lon,
'SnowDepth', glovar.fnames, glovar.filetime)
return np.array(alldata) # 返回 [56, 901, 1401]网格数据
def clcRoadic(snowdepth, skint, rain):
# 计算道路结冰厚度数据
now = datetime.datetime.now().strftime('%Y%m%d%H')
# #########
with open(r'道路结冰模型', 'rb') as f:
model = pickle.load(f)
Roadic = model.predict(
[snowdepth.reshape[-1, 1], skint.reshape[-1, 1], rain.reshape[-1, 1]])
Roadic.resize(1, glovar.lat, glovar.lon)
path = r'填入网格文件的保存路径'
Writefile.write_to_nc(path, Roadic, glovar.lat, glovar.lon, 'iceDepth', '',
now)
return Roadic
def revise(message):
# 订正解析结果，并插值到1km分辨率
mdpath, gcpath, savepath, *_ = Writefile.readxml(glovar.trafficpath, 1)
net = torch.load(mdpath)
dem = pd.read_csv(gcpath, index_col=0).values
arrays = np.nan_to_num([np.array([data, dem]) for data in message])
inputs = torch.from_numpy(arrays)
# torch.no_grad()
outputs = [net(it[np.newaxis, :]).detach().numpy() for it in inputs]
outputs = np.nan_to_num(outputs)
outputs[outputs < 0] = 0
print(outputs.shape)
output = np.squeeze(outputs)
Writefile.write_to_nc(savepath, output, glovar.lat, glovar.lon, 'rain',
glovar.fnames, glovar.filetime)
return outputs
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 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 revise(path, message):
# 订正解析结果，并插值到1km分辨率
mdpath, _, gcpath, savepath, indexpath, *_ = Writefile.readxml(path, 2)
data = [message[::2, :, :][:56], message[1::2, :, :][:56]]
# data = [data[::2, :, :][:56], data[1::2, :, :][:56]] # 分别取出来U风V风
net = torch.load(mdpath)
net.eval()
dem = pd.read_csv(gcpath, index_col=0).values
arrays = np.nan_to_num(
[np.array([i, j, dem]) for i, j in zip(data[0], data[1])])
inputs = torch.from_numpy(arrays)
torch.no_grad()
outputs = [net(it[np.newaxis, :]).detach().numpy() for it in inputs]
output = np.squeeze(np.nan_to_num(outputs))
datau, datav = output[:, 0], output[:, 1]
Writefile.write_to_nc(savepath, datau, glovar.lat, glovar.lon, 'U',
glovar.fnames, glovar.filetime)
Writefile.write_to_nc(savepath, datav, glovar.lat, glovar.lon, 'V',
glovar.fnames, glovar.filetime)
return indexpath, datau, datav
def revise(path, message):
# 订正解析结果，并插值到1km分辨率
mdpath, _, gcpath, savepath, indexpath, *_ = Writefile.readxml(path, 2)
data = np.array(message)
data = [
np.nan_to_num(data[::2, :, :][:56]),
np.nan_to_num(data[1::2, :, :][:56])
]
net = torch.load(mdpath)
net.eval()
dem = pd.read_csv(gcpath, index_col=0).values
arrays = np.array([
np.array([i, j, dem])
for i, j in zip(data[0][:, :801, :1381], data[1][:, :801, :1381])
])
inputs = torch.from_numpy(arrays)
torch.no_grad()
outputs = [
np.nan_to_num(net(it[np.newaxis, :]).detach().numpy()) for it in inputs
]
datau, datav = np.squeeze(outputs)[:, 0, ...], np.squeeze(outputs)[:, 1,
...]
# 统一格式
lat = np.linspace(31.4, 39.4, 801)
lon = np.linspace(89.3, 103.1, 1381)
uwind = [
np.nan_to_num(
interp.interpolateGridData(u, lat, lon, glovar.lat, glovar.lon))
for u in datau
]
vwind = [
np.nan_to_num(
interp.interpolateGridData(v, lat, lon, glovar.lat, glovar.lon))
for v in datav
]
Writefile.write_to_nc(savepath, np.array(uwind), glovar.lat, glovar.lon,
'U', glovar.fnames, glovar.filetime)
Writefile.write_to_nc(savepath, np.array(vwind), glovar.lat, glovar.lon,
'V', glovar.fnames, glovar.filetime)
return indexpath, datau, datav
def reverse(dataset):
# 此函数用来生成地表最高、 最低温度产品，对现有模型进行整体改动
maxpath, minpath, *_ = Writefile.readxml(glovar.trafficpath, 3)
with open(maxpath, 'rb') as f:
maxmodel = pickle.load(f)
with open(minpath, 'rb') as f:
minmodel = pickle.load(f)
# dataset.resize(56, 801 * 1381, 4)
################################################
temp = [data.reshape(-1, 1) for data in dataset] # 数据量可能过大
allele = np.concatenate(temp, axis=1) # 训练用元素
# 先采用一个进行测试
maxvalue = maxmodel.predict(allele).reshape(56, 901, 1401)
minvalue = minmodel.predict(allele).reshape(56, 901, 1401)
# 路径
savepath = r'/home/cqkj/QHtraffic/Data/skint//'
Writefile.write_to_nc(savepath, maxvalue, glovar.lat, glovar.lon,
'maxskint', glovar.fnames, glovar.filetime)
Writefile.write_to_nc(savepath, minvalue, glovar.lat, glovar.lon,
'minskint', glovar.fnames, glovar.filetime)
return maxvalue, minvalue # 返回地表最高、最低温度
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 main():
ice = Roadic()
dataset = iceData()
icgrid = ice.icegrid(dataset, glovar.lat, glovar.lon)
savepath, indexpath, _ = Writefile.readxml(glovar.trafficpath, 4)[2:]
write(savepath, icgrid, 'IceDepth', glovar.lat, glovar.lon) # 先保存厚度网格数据
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(
'/home/cqkj/project/Product/Product/source/cmsk.csv', delimiter=',')
icedays = RoadIceindex(cmissdata, iceroad)
roadicing = icedays.iceday()
write(indexpath, roadicing, 'icingindex', type=1)
def main():
dataset = iceData()
ice = Roadic()
icgrid = ice.icegrid(dataset, glovar.lat, glovar.lon)
savepath, indexpath = Writefile.readxml(glovar.trafficpath, 4)[1:3] # 此处需进行修改，根据路径
write(savepath, icgrid, 'IceDepth', glovar.lat, glovar.lon) # 先保存厚度网格数据
iceroad = ice.depth2onezero(icgrid, glovar.lat, glovar.lon)
################################################################################
# 获取cimiss数据集
cmissdata = np.loadtxt('/home/cqkj/project/industry/Product/Product/Source/cmsk.csv', delimiter=',')
icedays = RoadIceindex(cmissdata, iceroad)
roadicing = icedays.iceday()
res = roadicing.T
write(indexpath, res, 'icigindex', type=1)
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 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 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
def main():
snowpath, gpath, fpath, rainpath, fspath, gspath, mspath = Writefile.readxml(
glovar.forestpath, 0) # ???nc??????????
snow = snowdepth(snowpath) # ???????[10, 801, 1381]
data, *_ = Weatherdata(glovar.forestpath) # ??????????
ldtype = landtype(gpath, fpath)
gindex, findex, mindex = firelevel(data, rainpath, snow,
ldtype) # ???????????
filetime = glovar.filetime
fh = range(10)
fnames = ['_%03d' % i for i in fh]
Writefile.write_to_nc_fire(gspath,
gindex,
name='greenfire',
lat=glovar.lat,
lon=glovar.lon,
filetime=filetime,
fnames=fnames,
elename=None,
nums=1)
Writefile.write_to_nc_fire(fspath,
findex,
name='forestfire',
filetime=filetime,
fnames=fnames,
lat=glovar.lat,
lon=glovar.lon,
elename=None,
nums=1)
Writefile.write_to_nc_fire(mspath,
mindex,
filetime=filetime,
fnames=fnames,
lat=glovar.lat,
lon=glovar.lon,
name='meteorological',
elename='risk',
nums=1)
def __init__(self):
self._path = glovar.trafficpath
self.mpath, self.roadpath = Writefile.readxml(self._path, 1)[:2]
def __init__(self):
self._path = glovar.trafficpath
self.mpath, self.roadpath = Writefile.readxml(self._path, 1)[:2]
self.dics = Writefile.readxml(self._path, 2)[0].split(',')
