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 plot_seasonal_surface_loc_sizedistributions(cases_sec, cases_orig, from_t, to_t,
variables=['dNdlogD'],
minDiameter=5., maxDiameter=39.6, resolution='month',
history_field='.h0.',
figsize= [12,6], locations=constants.collocate_locations.keys()):
cl = Sizedistribution
s_list = []
for case in cases_sec:
s1 = cl(case, from_t, to_t,
[minDiameter, maxDiameter], True, resolution,
history_field=history_field)
s_list.append(s1)
for case in cases_orig:
s1 = cl(case, from_t, to_t,
[minDiameter, maxDiameter], False, resolution,
history_field=history_field)
s_list.append(s1)
cmap_dic = get_cmap_dic(cases_sec+cases_orig)
for loc in locations:
fig, axs = plt.subplots(2, 2, figsize=figsize)
axs = axs.flatten()
for s in s_list:
ls = variables
#if s.isSectional:
# ls = ls + ['dNdlogD_sec']
s.plot_location(variables=ls,
c=cmap_dic[s.case_name],
axs=axs,
loc=loc,
ylim=[10,1e4])
fig.tight_layout()
savepath = constants.paths_plotsave['sizedist'] +'/season/'
_cas = '_'.join(cases_sec) + '_'+'_'.join(cases_orig)
savepath = savepath +loc+ _cas + 'mean_%s-%s_%s.png'%(from_t, to_t, resolution)
make_folders(savepath)
plt.savefig(savepath, dpi=200)
plt.show()
set_scalar_formatter(ax)
ax.grid(False, which='both')
subp_insert_abc(np.array(axs_all[:-1]))
plt.tight_layout()
vars_n = '_'.join(varl)
fn_figure = '%s%s_%s-%s.'%(fn_base,vars_n,startyear, endyear)
print(fn_figure)
#axs_prof[0].set_xlim([1e-13,5e-11])
#axs_prof[1].set_xlim([1e-13,5e-11])
#plt.savefig(fn_figure + 'png')
plt.savefig(fn_figure + 'pdf', dpi=300)
plt.show()
# %%
from matplotlib.colors import LogNorm
varl = ['nrSEC_tot','N_secmod']
vmin=1e2
vmax=1e4
norm=LogNorm(vmin=vmin, vmax=vmax)
map_kwargs=dict(norm=norm)
fig, axs_maps, axs_prof, axs_all = plt_prof_map_together_ls(*varl, areas, [case_sec], asp_rat=.6, width=5.5, map_kwargs=map_kwargs)
plt.tight_layout()
vars_n = '_'.join(varl)
fn_figure = '%s%s_%s-%s.'%(fn_base,vars_n,startyear, endyear)
print(fn_figure)
def corr_plt(var_c=None, varl_to=None,cases_ctrl=None,
case_oth = None):
# %%
if var_c is None:
var_c = 'AWNC_incld'
if varl_to is None:
varl_to = ['NCONC01','NMR01']
cbar_orientation = 'vertical'
if cases_ctrl is None:
cases_ctrl = cases_orig
if case_oth is None:
case_oth = cases_sec[0]
ncol = len(cases_ctrl)
nrow = len(varl_to)
subfig_size = 2.5
asp_ratio = 1.6
figsize = [subfig_size * ncol * asp_ratio, subfig_size * nrow]
# noinspection PyTypeChecker
fig, axs = plt.subplots(nrow, ncol, figsize=figsize, sharex=True, sharey=True)
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]
_da_corr = corr(_ds[var],_ds[var_c], dim=['time','lon'])
nn_ctrl = get_nice_name_case(case)
nn_oth = get_nice_name_case(case_oth)
title =f'x={nn_oth}, y={nn_ctrl}'# 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)
plot_levlat(ax, 'RdBu_r', _da_corr, title, [1e3,200],
# cbar_orientation='vertical',
# #ax=ax,
# #norm=norm_dic[var],
# #relative=False,
# #ylim=[1e3, 200],
yscale='log', **plt_kwargs)
ax.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.0f'))
ax.yaxis.set_minor_formatter(mtick.FormatStrFormatter('%.0f'))
title = f'Correlations $\Delta V = V_x - V_y$'#' x={nn_oth}, y={nn_ctrl}'
stit = plt.suptitle(title,y = 1.03)
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_{var_c}{case_oth}' + '_'.join(cases_ctrl) + f'{startyear}-{endyear}'
subp_insert_abc(axs, pos_x=1.13,pos_y=1.01)
print(fn)
plt.savefig(fn + '.pdf', bbox_extra_artists=(stit,),bbox_inches='tight')
plt.savefig(fn + '.png',bbox_extra_artists=(stit,),bbox_inches='tight')
plt.show()
