def profiles_sub(areas, ax1, cases, linestd, linestd_nn, var, varl):
for case, ls in zip(cases, linests):
linestd[case] = ls
linestd_nn[get_nice_name_case(case)] = ls
ax = ax1 # plt.subplots(1, figsize=[6,8])
for area in areas:
prof_dic = get_averaged_fields.get_profiles(
cases,
varl,
startyear,
endyear,
area=area,
pressure_adjust=pressure_adjust)
for case in cases:
kwargs = dict(color=get_area_col(area), linestyle=linestd[case])
plot_profile(prof_dic[case][var],
ax=ax,
kwargs=kwargs,
xscale='linear',
label=case + ', ' + area,
ylim=[1000, 200]) # ,
ax.grid(False, which='both')
sns.despine(ax=ax)
#ax.set_yscale('log')
set_scalar_formatter(ax)
def plt_prof_map_together(var, areas, cases, asp_rat=1, width=6):
fig = plt.figure(figsize=[width, asp_rat * width])
gs = gridspec.GridSpec(2, 2, height_ratios=[1, 1.],
width_ratios=[5, 1]) #width_ratios=[2, 1])
ax1 = plt.subplot(gs[1, 0])
ax2 = plt.subplot(gs[1, 1])
ax3 = plt.subplot(gs[0, :], projection=ccrs.Robinson())
ax2.axis('off')
cmapd = get_cmap_dic(areas)
linestd = dict()
linestd_nn = dict()
for case, ls in zip(cases, linests):
linestd[case] = ls
linestd_nn[get_nice_name_case(case)] = ls
ax = ax1 # plt.subplots(1, figsize=[6,8])
for area in areas:
prof_dic = get_averaged_fields.get_profiles(
cases, [var],
startyear,
endyear,
area=area,
pressure_adjust=pressure_adjust)
for case in cases:
kwargs = dict(color=get_area_col(area), linestyle=linestd[case])
plot_profile(prof_dic[case][var],
ax=ax,
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)
cases_nn = [get_nice_name_case(case) for case in cases]
pressure_adjust=pressure_adjust)
for case in cases:
kwargs = dict(color=get_area_col(area), linestyle=linestd[case])
plot_profile(prof_dic[case][var],
ax=ax,
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()