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 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 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 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)
self.prosrs, self.geosrs = self.getSRSPair()
def getSRSPair(self):
"""获取投影坐标系和地理坐标系"""
prosrs = osr.SpatialReference()
prosrs.ImportFromWkt(self._dataset.GetProjection())
geosrs = prosrs.CloneGeogCS()
return prosrs, geosrs
def imagexy2pro(self):
"""将图上坐标转化为投影坐标"""
row, col = np.mgrid[0:self._shape[0], 0:self._shape[1]]
trans = self._dataset.GetGeoTransform()
px = trans[0] + col * trans[1] + row * trans[2]
py = trans[3] + col * trans[4] + row * trans[5]
return px, py
def pro2geo(self,x,y):
"""将投影坐标转化为地理坐标,仅仅适用于单点，非等经纬度"""
ct = osr.CoordinateTransformation(self.prosrs, self.geosrs)
coords = ct.TransformPoint(x,y)
return coords[:2]
'''
if __name__ == '__main__':
ectime = ecmwf.ecreptime()
dics = ['ECMWF_HR/RH/1000', 'ECMWF_HR/SKINT']
fh = list(range(12, 72, 3)) + list(range(72, 205, 6))
data = micapsdata(ectime, dics, fh)
with open('ectest.pkl', 'wb') as f:
f.dump()
