def load_and_plot(var,
cases,
startyear,
endyear,
period=None,
avg_over_lev=avg_over_lev,
pmin=pmin,
pressure_adjust=pressure_adjust,
p_level=None,
relative=False):
maps_dic = get_averaged_fields.get_maps_cases(
cases, [var],
startyear,
endyear,
avg_over_lev=avg_over_lev,
pmin=pmin,
time_mask=period,
pressure_adjust=pressure_adjust,
p_level=p_level)
clear_output()
return plot_map_abs_abs_diff(var,
cases,
maps_dic,
relative=relative,
figsize=[18, 3],
cbar_equal=True,
kwargs_abs={},
kwargs_diff={},
axs=None,
cmap_abs='Reds',
cmap_diff='RdBu_r')
def load_and_plot_diff_mm(varl,to_case,from_cases,startyear, endyear,
avg_over_lev=avg_over_lev,
pmin=pmin,nr_cols=2,
pressure_adjust=pressure_adjust,
p_level=None,
relative=False,
width=6., height=2.3):
cases = [to_case] + from_cases
maps_dic = get_averaged_fields.get_maps_cases(cases,varl,startyear, endyear,
avg_over_lev=avg_over_lev,
pmin=pmin,
pressure_adjust=pressure_adjust, p_level=p_level)
nr_rows = int(np.ceil(len(varl)/nr_cols))
nr_cols = len(from_cases)
fig, axs = subplots_map(nr_rows, nr_cols, figsize=[width*nr_cols,height*nr_rows])
for i, var in enumerate(varl):
if len(varl) == 1: saxs = axs
else: saxs = axs[i,:]
plot_map_diff_only(var, [to_case,*from_cases], maps_dic, relative=relative, cbar_equal=True,
cbar_loc='side', tight_layout=False, inverse_diff=True, axs=saxs)
#for from_case,i in zip(from_cases,range(nr_cols)):
# sax = axs[:,i]
# plot_diff(maps_dic, varl, [from_case,to_case],nr_cols=nr_cols, relative=relative, width=width, axs=sax)
subp_insert_abc(axs, pos_y=0.1)
return axs
def load_and_plot_diff(varl,
cases,
startyear,
endyear,
avg_over_lev=avg_over_lev,
pmin=pmin,
nr_cols=2,
pressure_adjust=pressure_adjust,
p_level=None,
period=None,
relative=False,
width=5.):
maps_dic = get_averaged_fields.get_maps_cases(
cases,
varl,
startyear,
endyear,
avg_over_lev=avg_over_lev,
time_mask=period,
pmin=pmin,
pressure_adjust=pressure_adjust,
p_level=p_level)
plot_diff(maps_dic,
varl,
cases,
nr_cols=nr_cols,
relative=relative,
width=width,
period=None)
return
def map_sub(ax3, cases, relative, var, var_map, varl, kwargs_diff=None):
if var_map is not None:
var = var_map
maps_dic = get_averaged_fields.get_maps_cases(
cases,
varl,
startyear,
endyear,
avg_over_lev=avg_over_lev,
pmin=pmin,
pressure_adjust=pressure_adjust)
plot_map_diff_only(
var,
cases,
maps_dic,
relative=relative,
cbar_equal=True,
kwargs_diff=kwargs_diff,
axs=ax3, #cmap_diff='RdBu_r',
cbar_loc='under',
tight_layout=False)
def load_and_plot_rows(varl,
cases,
startyear,
endyear,
period=None,
avg_over_lev=avg_over_lev,
pmin=pmin,
pressure_adjust=pressure_adjust,
p_level=None,
relative=False):
maps_dic = get_averaged_fields.get_maps_cases(
cases,
varl,
startyear,
endyear,
avg_over_lev=avg_over_lev,
time_mask=period,
pmin=pmin,
pressure_adjust=pressure_adjust,
p_level=p_level)
fig, axs = subplots_map(len(varl), 3, figsize=[18, 3 * len(varl)])
ii = 0
for var in varl:
axss = axs[ii, :]
ii += 1
plot_map_abs_abs_diff(var,
cases,
maps_dic,
relative=relative,
figsize=[18, 3],
cbar_equal=True,
kwargs_abs={},
axs=axss,
kwargs_diff={},
cmap_abs='Reds',
cmap_diff='RdBu_r')
return axs
def load_and_plot_diff_mm(varl,
to_case,
from_cases,
startyear,
endyear,
avg_over_lev=avg_over_lev,
pmin=pmin,
nr_cols=2,
pressure_adjust=pressure_adjust,
p_level=None,
relative=False,
width=6.):
cases = [to_case] + from_cases
maps_dic = get_averaged_fields.get_maps_cases(
cases,
varl,
startyear,
endyear,
avg_over_lev=avg_over_lev,
pmin=pmin,
pressure_adjust=pressure_adjust,
p_level=p_level)
nr_rows = int(np.ceil(len(varl) / nr_cols))
nr_cols = len(from_cases)
fig, axs = subplots_map(nr_rows,
nr_cols,
figsize=[width * nr_cols, 2.5 * nr_rows])
for from_case, i in zip(from_cases, range(nr_cols)):
sax = axs[:, i]
plot_diff(maps_dic,
varl, [from_case, to_case],
nr_cols=nr_cols,
relative=relative,
width=width,
axs=sax)
return
kwargs=kwargs,
xscale='log',
label=case+', '+ area,
ylim=[1000,200])#,
ax.grid(False, which='both')
sns.despine(ax=ax)
ax.set_yscale('log')
set_scalar_formatter(ax)
# maps:
if var_map is not None:
var=var_map
maps_dic = get_averaged_fields.get_maps_cases(cases,[var],startyear, endyear,
avg_over_lev=avg_over_lev,
pmin=pmin,
pressure_adjust=pressure_adjust)
plot_map(var, cases[0], maps_dic, figsize=None,
ax=ax3, cmap_abs='Reds', cbar_orientation='horizontal', kwargs_abs=map_kwargs)
#plt.tight_layout()
return
def plt_prof_map_together_ls(var1,var2, areas, cases, asp_rat=1, width=5.5, varl_map=None, map_kwargs={},height_rat = 1.3):
nvars = 2
fig = plt.figure(figsize = [width*nvars,asp_rat*width*nvars])
gs = gridspec.GridSpec(2, nvars+1,height_ratios=[1,height_rat], width_ratios=[5,5,1])#width_ratios=[2, 1])
axs_prof = []
varl_abs = ['NCFT_Ghan'] #,'TGCLDCWP']
varl = varl_rel + varl_abs
#varl=['ACTNL_incld', 'ACTREL_incld','TGCLDCWP']#,'TGCLDCWP']
period = 'JJA'
width = 4.7
asp_rat = 0.48
relative = True
cases = [to_case] + from_cases
maps_dic = get_averaged_fields.get_maps_cases(cases,
varl,
startyear,
endyear,
avg_over_lev=avg_over_lev,
pmin=pmin,
time_mask=period,
pressure_adjust=pressure_adjust,
p_level=p_level)
nr_cols = len(from_cases)
nr_rows = int(np.ceil(len(varl)))
fig, axs = subplots_map(nr_rows,
nr_cols,
figsize=[width * nr_cols, asp_rat * width * nr_rows])
for i, var in enumerate(varl):
saxs = axs[i, :]
plot_map_diff_only(var, [to_case, *from_cases],
maps_dic,
relative=(var in varl_rel),
cbar_equal=True,
axs = load_and_plot('N_AER', cases, startyear, endyear, avg_over_lev, pmin=pmin,relative=True, pressure_adjust=pressure_adjust)
# %% [markdown]
# ### Particle number from nucleation:
# %% [markdown]
# # In sectional scheme:
#
# %%
from sectional_v2.util.plot.plot_maps import subplots_map
varl= ['nrSOA_SEC_tot', 'nrSO4_SEC_tot','nrSEC_tot' ]
fig, axs = subplots_map(1, len(varl), figsize=[15,3])
for var, ax in zip(varl,axs):
maps_dic = get_averaged_fields.get_maps_cases(cases_sec,[var],startyear, endyear,
avg_over_lev=avg_over_lev,
pmin=pmin,
pressure_adjust=pressure_adjust)
plot_map(var, cases_sec[0], maps_dic, figsize=[6, 3],
kwargs_abs={}, ax=ax, cmap_abs='Reds')
plt.show()
# %%
from sectional_v2.util.plot.plot_maps import subplots_map
varl= ['cb_SOA_SEC01', 'cb_SOA_SEC02','cb_SOA_SEC03' ]
fig, axs = subplots_map(1, len(varl), figsize=[15,3])
for var, ax in zip(varl,axs):
maps_dic = get_averaged_fields.get_maps_cases(cases_sec,[var],startyear, endyear,
avg_over_lev=avg_over_lev,
pmin=pmin,
v = 'SO4_NA'
#varl=[]
for v in ['SOA_NA', 'SO4_NA']: #, 'SOA_NA_OCW','SO4_NA_OCW']:
varl = [f'{v}coagTend', f'{v}clcoagTend', f'{v}condTend'] + varl
# f"{v}SFSIC",f"{v}SFSBC",f"{v}SFSIS",f"{v}SFSBS", f"{v}_mixnuc1",
"""
for v in [ 'SOA_NA_OCW','SO4_NA_OCW']:
varl=varl+ [f'cb_{v}']#'LWDIR_Ghan']#, 'SO4_NAcondTend']#, 'leaveSecH2SO4','leaveSecSOA']#,'TGCLDCWP']
varl = [f"{v}DDF",f"{v}SFWET"]+ varl
"""
maps_dic = get_averaged_fields.get_maps_cases(
cases_all,
varl,
startyear,
endyear,
avg_over_lev=avg_over_lev,
pmin=pmin,
pressure_adjust=pressure_adjust) #, p_level=p_level)
# %%
def calc_tot_LR(ds, v):
return (-ds[f'{v}DDF'] + ds[f'{v}SFWET'] + ds[f'{v}coagTend'] +
ds[f'{v}clcoagTend'])
def LR_dd_wd(ds, v):
return (-ds[f'{v}DDF'] + ds[f'{v}SFWET']
) # + ds[f'{v}coagTend'] + ds[f'{v}clcoagTend'])
for case in cases:
get_field_fixed(
case,
varl,
startyear,
endyear, #raw_data_path=constants.get_input_datapath(),
pressure_adjust=True,
model='NorESM',
history_fld='.h0.',
comp='atm',
chunks=None)
maps_dic = get_averaged_fields.get_maps_cases(cases,
varl,
startyear,
endyear,
avg_over_lev=avg_over_lev,
pmin=pmin,
pressure_adjust=pressure_adjust,
p_level=p_level)
maps_dic = get_averaged_fields.get_maps_cases(cases_sec,
varl_sec,
startyear,
endyear,
avg_over_lev=avg_over_lev,
pmin=pmin,
pressure_adjust=pressure_adjust,
p_level=p_level)
for period in ['JJA', 'DJF']:
maps_dic = get_averaged_fields.get_maps_cases(
cases,
