def get_bfe_in_annual_max(varnames,
level_index_dict=None,
path_to_era_driven="",
path_to_gcm_driven="",
start_year=None,
end_year=None):
if level_index_dict is None:
level_index_dict = collections.defaultdict(lambda: 0)
finfo_to_season_to_diff = OrderedDict()
for vname in varnames:
finf = FieldInfo(vname, start_year, end_year)
era = analysis.get_annual_maxima(path_to_hdf_file=path_to_era_driven,
start_year=start_year,
end_year=end_year,
var_name=vname,
level=level_index_dict[vname])
gcm = analysis.get_annual_maxima(path_to_hdf_file=path_to_gcm_driven,
start_year=start_year,
end_year=end_year,
var_name=vname,
level=level_index_dict[vname])
finfo_to_season_to_diff[finf] = {
"Annual maximum":
np.mean(list(gcm.values()), axis=0) -
np.mean(list(era.values()), axis=0)
}
return finfo_to_season_to_diff
def get_bfe_in_annual_max(varnames, level_index_dict=None,
path_to_era_driven="", path_to_gcm_driven="",
start_year=None, end_year=None):
if level_index_dict is None:
level_index_dict = collections.defaultdict(lambda: 0)
finfo_to_season_to_diff = OrderedDict()
for vname in varnames:
finf = FieldInfo(vname, start_year, end_year)
era = analysis.get_annual_maxima(path_to_hdf_file=path_to_era_driven,
start_year=start_year, end_year=end_year,
var_name=vname, level=level_index_dict[vname])
gcm = analysis.get_annual_maxima(path_to_hdf_file=path_to_gcm_driven,
start_year=start_year, end_year=end_year,
var_name=vname, level=level_index_dict[vname])
finfo_to_season_to_diff[finf] = {
"Annual maximum": np.mean(list(gcm.values()), axis=0) - np.mean(list(era.values()), axis=0)
}
return finfo_to_season_to_diff
def do_calculations(data_path=None, lay_widths=None, start_year=None, end_year=None):
cache = get_cache_file(**locals())
if os.path.isfile(cache):
return pickle.load(open(cache, "rb"))
alt_data_manager = CRCMDataManager(layer_widths=lay_widths)
tsoil3d_field = None
for lev in range(len(lay_widths)):
field_dict = analysis.get_annual_maxima(path_to_hdf_file=data_path, var_name="I0", level=lev,
start_year=start_year, end_year=end_year)
field = np.max([fi for fi in field_dict.values()], axis=0)
if tsoil3d_field is None:
tsoil3d_field = np.zeros(field.shape + (len(lay_widths), ))
print("lev, min, max = {}, {}, {}".format(lev, field.min(), field.max()))
plt.figure()
img = plt.contourf(field)
plt.colorbar(img)
plt.show()
tsoil3d_field[:, :, lev] = field
alt_field = alt_data_manager.get_alt(tsoil3d_field)
with open(cache, "wb") as f:
pickle.dump(alt_field, f)
return alt_field