Dear Altruists, I am trying to convert NetCDF evaporation data to further processing, e.g. aggregate by month or calculating average value etc. However, I am struggling in the basic steps to unfold the multidimensional variables. When I am reading the data in NetCDF reader, it returned some attributes where I cannot see the lat/long or date file. But after using AttributeExposer to get the netcdf_subdataset_name; there I see lat, long and evaporation (apet) name but not sure how to get the values of these variables. I am given a python code in which the developer wrote a function to process the .nc data. Can anyone kindly tell me what FME transformers I need to use the replicate the python process as below:
def funt_1(fileName, points, var):
nc_fid = netCDF4.Dataset(fileName, 'r')
prbc = nc_fid.variables[var]
lats, lons = nc_fid.variables['lat'], nc_fid.variables['lon']
time = nc_fid.variables['time'][:]
# Convert time to Julian
Y = int(nc_fid.variables['time'].units.split(" ")[-1].split("-")[0])
M = int(nc_fid.variables['time'].units.split(" ")[-1].split("-")[1])
D = int(nc_fid.variables['time'].units.split(" ")[-1].split("-")[2])
DT = [dt.date(Y, M, D) + dt.timedelta(t) for t in time]
DT = pd.DataFrame(DT);
DT.columns = ['DateTime']
datecol = DT.apply(lambda x: x.DateTime.strftime('%Y/%m/%d'), axis=1)
rows=[]
for i in range(0, prbc.shape[0]):
if var == 'pr-bc':
nar_lats = lats[:][prbc[i, :, :].mask==False]
nar_lons = lons[:][prbc[i, :, :].mask==False]
prbc_um = prbc[i, :, :][prbc[i, :, :].mask==False]
narclim_swath = geometry.SwathDefinition(nar_lons, nar_lats)
else:
narclim_swath = geometry.GridDefinition(lons=lons[:], lats=lats[:])
prbc_um = prbc[i, :, :]
# Define some sample points
my_lons = np.array(points.Lon)
my_lats = np.array(points.Lat)
swath = geometry.SwathDefinition(lons=my_lons, lats=my_lats)
wf = lambda r: 1/r**2
result = pyresample.kd_tree.resample_custom(narclim_swath, prbc_um,
swath,
radius_of_influence=60000,
neighbours=3,
weight_funcs=wf,
fill_value=np.nan)
row = pd.DataFrame({'Date':datecol[i], 'Lon':points.Lon,
'Lat': points.Lat, '%s' % var : result})
rows.append(row)
return pd.concat(rows)
