def main():
# Monthly means from diagnostics
model_data_path = Path("/RECH2/huziy/BC-MH/bc_mh_044deg/Diagnostics")
swe_obs_manager = SweDataManager(var_name="SWE")
swe_obs_manager.get_daily_clim_fields_interpolated_to()
pass
def get_seasonal_clim_obs_data(rconfig=None,
vname="TT",
bmp_info=None,
season_to_months=None,
obs_path=None):
"""
:param rconfig:
:param vname: Corresponding model variable name i.e either TT or PR
"""
assert isinstance(rconfig, RunConfig)
if season_to_months is None:
season_to_months = DEFAULT_SEASON_TO_MONTHS
if bmp_info is None:
bmp_info = analysis.get_basemap_info_from_hdf(
file_path=rconfig.data_path)
obs_query_params = dict(start_year=rconfig.start_year,
end_year=rconfig.end_year,
lons_target=bmp_info.lons,
lats_target=bmp_info.lats)
# Calculate daily mean temperatures as T = (T(min) + T(max)) * 0.5
if vname == "TT":
tmax_obs_manager = AnuSplinManager(variable="stmx",
folder_path=obs_path)
tmin_obs_manager = AnuSplinManager(variable="stmn",
folder_path=obs_path)
dates, vals_max = tmax_obs_manager.get_daily_clim_fields_interpolated_to(
**obs_query_params)
_, vals_min = tmin_obs_manager.get_daily_clim_fields_interpolated_to(
**obs_query_params)
daily_obs = (dates, (vals_min + vals_max) * 0.5)
elif vname == "PR":
pcp_obs_manager = AnuSplinManager(variable="pcp", folder_path=obs_path)
daily_obs = pcp_obs_manager.get_daily_clim_fields_interpolated_to(
**obs_query_params)
# SWE
elif vname == "I5":
swe_manager = SweDataManager(var_name="SWE", path=obs_path)
daily_obs = swe_manager.get_daily_clim_fields_interpolated_to(
**obs_query_params)
else:
raise Exception("Unknown variable: {}".format(vname))
season_to_obs_data = OrderedDict()
for season, months in season_to_months.items():
season_to_obs_data[season] = np.mean(
[f for d, f in zip(*daily_obs) if d.month in months], axis=0)
return season_to_obs_data
def get_seasonal_clim_obs_data(rconfig=None, vname="TT", bmp_info=None, season_to_months=None, obs_path=None):
"""
:param rconfig:
:param vname: Corresponding model variable name i.e either TT or PR
"""
assert isinstance(rconfig, RunConfig)
if season_to_months is None:
season_to_months = DEFAULT_SEASON_TO_MONTHS
if bmp_info is None:
bmp_info = analysis.get_basemap_info_from_hdf(file_path=rconfig.data_path)
obs_query_params = dict(
start_year=rconfig.start_year,
end_year=rconfig.end_year,
lons_target=bmp_info.lons,
lats_target=bmp_info.lats
)
# Calculate daily mean temperatures as T = (T(min) + T(max)) * 0.5
if vname == "TT":
tmax_obs_manager = AnuSplinManager(variable="stmx", folder_path=obs_path)
tmin_obs_manager = AnuSplinManager(variable="stmn", folder_path=obs_path)
dates, vals_max = tmax_obs_manager.get_daily_clim_fields_interpolated_to(**obs_query_params)
_, vals_min = tmin_obs_manager.get_daily_clim_fields_interpolated_to(**obs_query_params)
daily_obs = (dates, (vals_min + vals_max) * 0.5)
elif vname == "PR":
pcp_obs_manager = AnuSplinManager(variable="pcp", folder_path=obs_path)
daily_obs = pcp_obs_manager.get_daily_clim_fields_interpolated_to(**obs_query_params)
# SWE
elif vname == "I5":
swe_manager = SweDataManager(var_name="SWE", path=obs_path)
daily_obs = swe_manager.get_daily_clim_fields_interpolated_to(**obs_query_params)
else:
raise Exception("Unknown variable: {}".format(vname))
season_to_obs_data = OrderedDict()
for season, months in season_to_months.items():
season_to_obs_data[season] = np.mean([f for d, f in zip(*daily_obs) if d.month in months], axis=0)
return season_to_obs_data
