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 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 floodsk(predata, data):
# 计算前一时刻积水深度, data为sk降水， predata为实况前一时刻降水, 返回各点数据
dr = np.zeros_like(predata) # predata前一时刻降水
pre = FloodModel.cal2(predata[np.newaxis, ], dr)
datas = FloodModel.cal2(data[np.newaxis, ], pre)
return datas