daout = []
#sfc
u = data[var_sfc[0]].values
v = data[var_sfc[1]].values
attrs_u = data[var_sfc[0]].attrs
attrs_v = data[var_sfc[1]].attrs
#missing values need to be searched
dfu = data[var_sfc[0]].to_pandas()
dfv = data[var_sfc[1]].to_pandas()
if (dfu.isnull().values.sum() != 0 or dfv.isnull().values.sum() != 0):
print("missing value exists in input")
else:
lon2d = lon.reshape(1, -1)
lat2d = lat.reshape(-1, 1)
xd, yd, zd = librotate.uv2xyzd(u, v, lon2d, lat2d)
da_xd = xr.DataArray(xd.reshape(1,len(lat),len(lon)),\
[('time',pd.date_range(date,periods=1)),('latitude',lat),('longitude',lon)],\
name='xd')
da_yd = xr.DataArray(yd.reshape(1,len(lat),len(lon)),\
[('time',pd.date_range(date,periods=1)),('latitude',lat),('longitude',lon)],\
name='yd')
da_zd = xr.DataArray(zd.reshape(1,len(lat),len(lon)),\
[('time',pd.date_range(date,periods=1)),('latitude',lat),('longitude',lon)],\
name='zd')
xd_interp = da_xd.interp(longitude=newlon, latitude=newlat)
yd_interp = da_yd.interp(longitude=newlon, latitude=newlat)
zd_interp = da_zd.interp(longitude=newlon, latitude=newlat)
#missing value
daout = []
#sfc
u = data[var_sfc[0]].values
v = data[var_sfc[1]].values
attrs_u = data[var_sfc[0]].attrs
attrs_v = data[var_sfc[1]].attrs
#missing values need to be searched
dfu = data[var_sfc[0]].to_pandas()
dfv = data[var_sfc[1]].to_pandas()
if (dfu.isnull().values.sum() != 0 or dfv.isnull().values.sum() != 0):
logging.warning("missing value exists in input")
continue
lon2d = lon.reshape(1, -1)
lat2d = lat.reshape(-1, 1)
xd, yd, zd = librotate.uv2xyzd(u, v, lon2d, lat2d)
#debug
ud, vd = librotate.xyzd2uv(xd, yd, zd, lon2d, lat2d)
logging.debug(f"uv2xyzd u {np.max(np.abs(u-ud))}")
logging.debug(f"uv2xyzd v {np.max(np.abs(v-vd))}")
da_xd = xr.DataArray(xd.reshape(1,len(lat),len(lon)),\
[('time',pd.date_range(date,periods=1)),('latitude',lat),('longitude',lon)],\
name='xd')
da_yd = xr.DataArray(yd.reshape(1,len(lat),len(lon)),\
[('time',pd.date_range(date,periods=1)),('latitude',lat),('longitude',lon)],\
name='yd')
da_zd = xr.DataArray(zd.reshape(1,len(lat),len(lon)),\
[('time',pd.date_range(date,periods=1)),('latitude',lat),('longitude',lon)],\
name='zd')
('lonlat1d',np.arange(len(lattc)))],\
attrs=attrs,name='msl')
logging.debug(dataout)
daout.append(dataout)
#exit()
#u, v
attrs_u = data['u'].attrs
attrs_v = data['v'].attrs
#missing values need to be searched
if (np.isnan(u).sum() != 0 or np.isnan(v).sum() != 0):
logging.warning("missing value exists in input")
continue
lon3d = lonin[None, None, :]
lat3d = latin[None, :, None]
xd, yd, zd = librotate.uv2xyzd(u, v, lon3d, lat3d)
#debug
ud, vd = librotate.xyzd2uv(xd, yd, zd, lon3d, lat3d)
logging.debug(f"uv2xyzd u {np.max(np.abs(u-ud))}")
logging.debug(f"uv2xyzd v {np.max(np.abs(v-vd))}")
xb, yb, zb = librotate.uv2xyzd(ud, vd, lon3d, lat3d)
logging.debug(f"xyzd2uv x {np.max(np.abs(xd-xb))}")
logging.debug(f"xyzd2uv y {np.max(np.abs(yd-yb))}")
logging.debug(f"xyzd2uv z {np.max(np.abs(zd-zb))}")
da_xd = xr.DataArray(xd[None,:],\
[('time',pd.date_range(date,periods=1)),\
('level',level),\
('latitude',latin),('longitude',lonin)],\
name='xd')
da_yd = xr.DataArray(yd[None,:],\
