def load_daily_data_general(fname, varname, from_date, to_date,
slice_lon, slice_lat, level):
# the daily data is stored in yearly files
yr_start = from_date.year
yr_end = to_date.year
# load each NC dataset
gflist = []
Ndays = 0
for yr in range(yr_start, yr_end+1):
g = GeoField()
g.load(fname % yr, varname)
if level is not None:
g.slice_level(level)
g.slice_spatial(slice_lon, slice_lat)
g.slice_date_range(from_date, to_date)
Ndays += len(g.tm)
gflist.append(g)
# now append all of the records together
g = GeoField()
d = np.zeros((Ndays, len(gflist[0].lats), len(gflist[0].lons)))
tm = np.zeros((Ndays,))
n = 0
for g_i in gflist:
Ndays_i = len(g_i.tm)
d[n:Ndays_i + n, :, :] = g_i.d
tm[n:Ndays_i + n] = g_i.tm
n += Ndays_i
# load geo_fields and then append them together
g.use_existing(d, gflist[0].lons, gflist[0].lats, tm)
g.transform_to_anomalies()
g.normalize_variance()
return g
