def comp_lossr(v, ext, _ds):
cb = average_model_var(_ds,
f'cb_{v}',
area='Global',
dim=None,
minp=850.,
time_mask=None)
lr = average_model_var(_ds,
f'{v}{ext}',
area='Global',
dim=None,
minp=850.,
time_mask=None)
out = cb[f'cb_{v}'] / lr[f'{v}{ext}'] / (60 * 60 * 24)
if out < 0:
out = abs(out)
out.attrs['units'] = 'days'
return out
def get_avg_diff(case_oth, case_ctrl, case_dic, relative, var):
"""
:param case_oth:
:param case_ctrl:
:param case_dic:
:param relative:
:param var:
:return:
"""
# glob_avg_ctr = masked_average(xr1, weights=lat_wgt)
glob_avg_ctrl = average_model_var(case_dic[case_ctrl], var)[var].values
glob_avg_oth = average_model_var(case_dic[case_oth], var)[var].values
if relative:
_a = (glob_avg_oth - glob_avg_ctrl) / abs(glob_avg_ctrl) * 100
return ', $\mu$=%.2f %%' % _a
else:
_a = (glob_avg_oth - glob_avg_ctrl)
unit = sectional_v2.util.naming_conventions.var_info.get_fancy_unit_xr(
case_dic[case_ctrl][var], var)
return ', $\mu$=%.2f %s' % (_a, unit)
def get_average_map2(ds,
varlist,
case_name,
from_time,
to_time,
avg_over_lev=True,
pmin=850.,
p_level=1013.,
pressure_coord=True,
save_avg=True,
recalculate=False,
model_name='NorESM',
time_mask=None):
"""
:param ds:
:param varlist:
:param case_name:
:param from_time:
:param to_time:
:param avg_over_lev:
:param pmin:
:param p_level:
:param pressure_coord:
:param save_avg:
:param recalculate:
:param model_name:
:param time_mask:
:return:
"""
xr_out = ds.copy()
for var in varlist:
found_map_avg = False
# Look for file:
if not recalculate:
ds_copy, found_map_avg = load_average2map(model_name,
case_name,
var,
from_time,
to_time,
avg_over_lev,
pmin,
p_level,
pressure_coord,
time_mask=time_mask)
# if file not found:
if not found_map_avg:
# some fields require other fields for weighted average:
sub_varL = get_fields4weighted_avg(var)
ds_copy = ds.copy()
for svar in sub_varL:
if ('lev' in ds[svar].dims):
had_lev_coord = True
else:
had_lev_coord = False
if avg_over_lev:
ds_copy = average_model_var(
ds_copy,
svar,
area='Global',
dim=list(set(ds.dims) - {'lat', 'lon'}),
minp=pmin,
time_mask=time_mask)
else:
ds_copy = average_model_var(
ds_copy,
svar,
area='Global',
dim=list(set(ds.dims) - {'lat', 'lon', 'lev'}),
time_mask=time_mask)
if 'lev' in ds_copy[svar].dims:
ds_copy[svar] = ds_copy[svar].sel(lev=p_level,
method='nearest')
ds_copy = compute_weighted_averages(ds_copy, var, model_name)
ds_copy[var].attrs['Calc_weight_mean'] = str(
is_weighted_avg_var(var)) + ' map'
if save_avg:
filen = filename_map_avg(model_name,
case_name,
var,
from_time,
to_time,
avg_over_lev,
pmin,
p_level,
pressure_coord,
lev_was_coord=had_lev_coord,
time_mask=time_mask)
practical_functions.make_folders(filen)
practical_functions.save_dataset_to_netcdf(ds_copy, filen)
xr_out[var] = ds_copy[var]
return xr_out
# %%
dff_ = maps_dic[cases[1]][['DIR_Ghan']] - maps_dic[cases[0]][['DIR_Ghan'
]] #['DIR_Ghan']
print(cases[1])
# %%
dff2_ = maps_dic[cases[2]][['DIR_Ghan']] - maps_dic[cases[0]][[
'DIR_Ghan'
]] #['DIR_Ghan']
print(cases[2])
# %%
from sectional_v2.util.slice_average.avg_pkg import average_model_var
# %%
average_model_var(dff_, 'DIR_Ghan', area='Global')
# %%
average_model_var(dff2_, 'DIR_Ghan', area='Global')
# %%
varl = [
'LWDIR_Ghan', 'LWDIR_Ghan'
] #'LWDIR_Ghan']#, 'SO4_NAcondTend']#, 'leaveSecH2SO4','leaveSecSOA']#,'TGCLDCWP']
relative = False
#plot_diff(maps_dic, varl, cases[::-1],nr_cols=1, relative=relative)
load_and_plot_diff_mm(varl,
to_case,
from_cases,
def get_global_avg_map(ctrl_case, nested_cases, var):
glob_avg_ctrl = average_model_var(nested_cases[ctrl_case], var)[var]
return glob_avg_ctrl.values
def get_average_area(xr_ds: xr.Dataset, varList: list, case_name: str, area: str, from_time,
to_time, model_name: str, pmin: float,
avg_over_lev: bool, p_level: float, pressure_coords: bool, look_for_file: bool = True, ) -> xr.Dataset:
"""
:param from_time:
:param to_time:
:param xr_ds:
:param varList:
:param model_name:
:param pmin:
:param case_name:
:param avg_over_lev:
:param p_level:
:param pressure_coords:
:param look_for_file:
:return:
"""
from_time = from_time #xr_ds.attrs['startyear']
to_time = to_time #xr_ds.attrs['endyear']
xr_out = xr_ds.copy()
for var in varList:
print(var)
found_area_mean = False
ds_area_avgs, file_found = load_area_file_var(model_name, case_name, var, area, from_time, to_time, avg_over_lev,
pmin, p_level, pressure_coords)
if file_found:
xr_out[var] = ds_area_avgs[var]
if not found_area_mean or not look_for_file:
sub_varL = get_fields4weighted_avg(var)
dummy = xr_ds.copy()
for svar in sub_varL:
lev_was_coord = False
if 'lev' in xr_ds[svar].dims:
lev_was_coord=True
if avg_over_lev:
dummy = average_model_var(dummy, svar, area=area, minp=pmin, dim = list(xr_ds[svar].dims)) #\
else:
dummy = average_model_var(dummy,
svar, area=area,
dim = list(set(xr_ds[svar].dims)-{'lev'}))#.sel(lev=p_level, method='nearest')
print(dummy)
print(list(set(xr_ds[svar].dims)-{'lev'}))
if 'lev' in dummy.dims:
dummy = dummy.sel(lev=p_level, method='nearest')
dummy = compute_weighted_averages(dummy, var, model_name)
dummy[var].attrs['Calc_weight_mean']=str(is_weighted_avg_var(var)) + ' area: %s'%area
filen = filename_area_avg(var, model_name, case_name, area, from_time, to_time, avg_over_lev, pmin, p_level, pressure_coords,
lev_was_coord=lev_was_coord)
practical_functions.make_folders(filen)
if ds_area_avgs is None:
practical_functions.save_dataset_to_netcdf(dummy, filen)
else:
ds_area_avgs[var] = dummy[var]
practical_functions.save_dataset_to_netcdf(ds_area_avgs, filen)
xr_out[var] = dummy[var]
return xr_out