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 corr_plt_latlon(var_c, varl_to, cases_ctrl=cases_orig, case_oth=cases_sec[0], cmap='RdBu_r', pmin=850.):
# %%
cbar_orientation = 'vertical'
cases_ctrl = cases_orig
case_oth = cases_sec[0]
ncol = len(cases_ctrl)
nrow = len(varl_to)
subfig_size = 2.6
asp_ratio = 1.6
figsize = [subfig_size * ncol * asp_ratio, subfig_size * nrow]
# noinspection PyTypeChecker
fig, axs = subplots_map(nrow, ncol, figsize=figsize, sharex=True, sharey=True)
norm_dic = dict(
NCONC01=colors.SymLogNorm(vmin=-1e3, vmax=1e3, linthresh=10),
NMR01=colors.SymLogNorm(vmin=-10, vmax=10, linthresh=.1),
AWNC_incld=colors.SymLogNorm(vmin=-20, vmax=20, linthresh=.1),
AREL_incld=colors.SymLogNorm(vmin=-5, vmax=5, linthresh=.1)
)
for j, var in enumerate(varl_to):
saxs = axs[j, :]
for i, case in enumerate(cases_ctrl):
ax = saxs[i]
_vars = [var, var_c]
_ds = cases_dic[case_oth][_vars]- cases_dic[case][_vars]
_ds = _ds.sel(lev=slice(pmin,None))
_da_corr = corr(_ds[var],_ds[var_c], dim=['time','lev'])
nn_ctrl = get_nice_name_case(case)
nn_oth = get_nice_name_case(case_oth)
title = f'Correlation $\Delta V = V_x - V_y$),\n x={nn_oth}, y={nn_ctrl}'
_da_corr.load()
label = f'corr($\Delta${get_fancy_var_name(var)},$\Delta${get_fancy_var_name(var_c)})'#)
plt_kwargs ={}
plt_kwargs = make_cbar_kwargs(label, plt_kwargs, cbar_orientation)
plt_map(_da_corr, ax=ax, cmap=cmap,
# cbar_orientation='vertical',
# #ax=ax,
# #norm=norm_dic[var],
# #relative=False,
# #ylim=[1e3, 200],
**plt_kwargs)
ax.set_title(title)
for ax in axs.flatten():
ax.set_ylabel('')
ax.set_xlabel('')
#for ax in axs[:, 0]:
# ax.set_ylabel('Pressure [hPa]')
for ax in axs[-1, :]:
ax.set_xlabel('Latitude [$^\circ$N]')
fig.tight_layout()
#fn = filen_base + f'corr_NMR_N_clouds_{case_oth}' + '_'.join(cases_ctrl) + f'{startyear}-{endyear}'
#plt.savefig(fn + '.pdf')
#plt.savefig(fn + '.png')
plt.show()
# %%
return _da_corr
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 plot_diff(maps_dic,
varl,
cases,
nr_cols=2,
relative=False,
width=5.,
axs=None):
#fig, axs = subplots_map(int(np.ceil(len(varl)/2)), 2, figsize=[10,4*len(varl)])
if axs is None:
nr_rows = int(np.ceil(len(varl) / nr_cols))
print(nr_rows)
fig, axs = subplots_map(nr_rows,
nr_cols,
figsize=[width * nr_cols, 2.5 * nr_rows
]) #7*nr_cols,3*nr_rows])
for var, ax in zip(varl, axs.flatten()):
plot_map_diff_2case(var,
cases[0],
cases[1],
maps_dic,
relative=relative,
ax=ax,
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.):
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
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,
kwargs_diff={},
axs=saxs,
cmap_diff='RdBu_r',
cbar_loc='side',
tight_layout=False,
inverse_diff=True)
#for from_case,i in zip(from_cases,range(nr_cols)):
def plot_stuff():
# %%
var = 'AWNC_incld'
case1 = cases[0]
case2 = cases[2]
avg_over_lev = True
groupby = None
dims = ('lon', )
area = 'Global'
ci = .95
avg_dim = 'time'
endyear = '2010-12'
startyear = '2008-01'
# %%
T, t, sig_map, data4comp = get_significance_map_paired_monthly(
var,
case1,
case2,
startyear,
endyear,
pmin=pmin,
pressure_adjust=pressure_adjust,
avg_over_lev=avg_over_lev,
ci=ci,
groupby=groupby,
dims=dims,
area=area,
avg_dim=avg_dim)
# %%
da1 = data4comp[case1]
da2 = data4comp[case2]
mean_D = data4comp[f'{case1}-{case2}'].mean(avg_dim)
# %%
fig, axs = plt.subplots(3, figsize=[4, 7])
t.where((t > T) | (t < -T)).plot(ax=axs[0], robust=True)
(mean_D / da2.mean(avg_dim) * 100).where((t > T) | (t < -T)).plot(
ax=axs[1], vmax=10)
((mean_D / da2.mean(avg_dim) * 100)).plot(ax=axs[2], robust=True, vmax=10)
plt.suptitle(endyear)
for ax in axs.flatten():
ax.set_ylim([1e3, 100])
ax.set_yscale('log')
plt.show()
# %%
fig, axs = subplots_map(3, figsize=[4, 7])
for ax in axs.flatten():
fix_axis4map_plot(ax)
plt_map(t.where((t > T) | (t < -T)), ax=axs[0], robust=True)
plt_map((mean_D / da2.mean(avg_dim) * 100).where((t > T) | (t < -T)),
ax=axs[1],
vmax=10)
plt_map((mean_D / da2.mean(avg_dim) * 100),
ax=axs[2],
robust=True,
vmax=10)
plt.suptitle(endyear)
plt.show()
# %%
return
axs = load_and_plot('N_AER', cases, startyear, endyear, avg_over_lev, pmin=pmin, pressure_adjust=pressure_adjust)
# %%
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])
axs = load_and_plot(var,
cases3,
startyear,
endyear,
avg_over_lev,
pmin=pmin,
pressure_adjust=pressure_adjust,
relative=True)
# %% [markdown]
# # SECTIONAL PARTICLES
# %%
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,