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]
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 = SizedistributionSurface
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()
if axs is None:
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())
axs=[ax1,ax2,ax3]
ax2.axis('off')
cases_nn = [get_nice_name_case(case) for case in cases]
set_legend_area_profs(ax2, areas,cases_nn, linestd_nn)
ax1 = axs[0]
ax3 = axs[1]
cmapd = get_cmap_dic(areas)
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='log',
label=case+', '+ area,
ylim=[1000,200])#,
from sectional_v2.util.eusaar_data.histc_vars import load_var_as_dtframe
from sectional_v2.util.eusaar_data import distc_var, histc_vars, histsc_hists # import load_var_as_dtframe
import matplotlib.pyplot as plt
from useful_scit.plot import get_cmap_dic
# %% [markdown]
# ## distc_var
# %%
a = distc_var.get_distc_xarray_all()
# %%
sts = a['station'].values[:4]
year='BOTH'
subs='TOT'
cma = get_cmap_dic(sts)
da = a.sel(year=year,subset=subs)
for st in sts:
_da50 = da.sel(station=st, percentile='50th percentile')#
_da50.plot(yscale='log',xscale='log', label='50th perc, %s'%st, color = cma[st])
_da95 = da.sel(station=st, percentile='95th percentile')
_da5 = da.sel(station=st, percentile='5th percentile')
plt.fill_between(da.diameter, _da5,_da95, alpha=.2 , color = cma[st])
plt.ylim(1,3e4)
plt.xlim(5,1e3)
plt.legend()
plt.title('Test sizedistributions')
# %% [markdown]
# ### Conversion dN/dlog10D to dN/dlnD:
# $$ ln(x) = ln(10)\cdot log10(x)$$
def plot_grid(dic_finish,
subs='TOT',
st_ls=None,
name='all_stations',
ylim=[5, 8.8e3],
yscale='linear',
plot_sec=True,
nr_col=4,
figsize=None,
ylim_ZEP=[0, 500]):
colors_source = get_cmap_dic(dic_finish.keys())
colors_source['EUSAAR'] = 'k'
dic_ds = dic_finish
t_cs = dic_ds[list(dic_ds.keys())[0]]
st_ls = list(get_ordered_stations())
# st_ls = list(loc_tr[loc_tr['Region']==reg].index)
print(list(st_ls))
if len(st_ls) > nr_col:
nr_row = int(np.ceil(len(st_ls) / nr_col))
else:
nr_row = 1
nr_col = len(st_ls)
if figsize is None:
figsize = [10 / 4 * nr_col, 10 / 6 * nr_row]
fig, axs = plt.subplots(nr_row,
nr_col,
sharex=True,
sharey=True,
figsize=figsize)
axs_nf = axs
if nr_row > 1: axs = axs.flatten()
for station, ax in zip(st_ls, axs):
lines = []
labels = []
for key in dic_finish.keys():
_ds = dic_finish[key]
#if 'dNdlog10dp_sec' in _ds:
# plt_perc(dic_finish[key]['dNdlog10dp_sec'], station, key,
# color=get_case_col(key),
# ax=ax,
# subs=subs, percs=[16,84], yscale=yscale, ylim=ylim)
## plt_perc(dic_finish[key]['dNdlog10dp_mod'], station, key,
# color=get_case_col(key),
# ax=ax,
# subs=subs, percs=[16,84], yscale=yscale, ylim=ylim)
#else:
plt_perc(dic_finish[key]['dNdlog10dp'],
station,
key,
color=get_case_col(key),
ax=ax,
subs=subs,
percs=[16, 84],
yscale=yscale,
ylim=ylim)
for key in dic_finish.keys():
_ds = dic_finish[key]
#if 'dNdlog10dp_sec' in _ds:
# line =plt_median(dic_finish[key]['dNdlog10dp_sec'], station, key,
# color=get_case_col(key),
# ax=ax,
# subs=subs, percs=[16,84], yscale=yscale, ylim=ylim)
# line =plt_median(dic_finish[key]['dNdlog10dp_mod'], station, key,
# color=get_case_col(key),
# ax=ax,
# subs=subs, percs=[16,84], yscale=yscale, ylim=ylim)
#else:
if 'dNdlog10dp_sec' in _ds:
line = plt_median(dic_finish[key]['dNdlog10dp_sec'].where(
dic_finish[key]['dNdlog10dp_sec'] > 0),
station,
key,
color=get_case_col(key),
ax=ax,
subs=subs,
percs=[16, 84],
yscale=yscale,
ylim=ylim,
plt_kwargs={'linestyle': 'dashed'})
line = plt_median(dic_finish[key]['dNdlog10dp'],
station,
key,
color=get_case_col(key),
ax=ax,
subs=subs,
percs=[16, 84],
yscale=yscale,
ylim=ylim)
lines = lines + line
labels.append(get_nice_name_case(key))
if station == 'ZEP':
axins = insert_ZEP(ax)
for key in dic_finish.keys():
plt_perc(dic_finish[key]['dNdlog10dp'],
station,
key,
color=get_case_col(key),
ax=axins,
subs=subs,
percs=[16, 84],
yscale=yscale,
ylim=ylim_ZEP)
for key in dic_finish.keys():
plt_median(dic_finish[key]['dNdlog10dp'],
station,
key,
color=get_case_col(key),
ax=axins,
subs=subs,
percs=[16, 84],
yscale=yscale,
ylim=ylim_ZEP)
if 'dNdlog10dp_sec' in _ds:
plt_median(dic_finish[key]['dNdlog10dp_sec'],
station,
key,
color=get_case_col(key),
ax=axins,
subs=subs,
percs=[16, 84],
yscale=yscale,
ylim=ylim_ZEP)
ax.indicate_inset_zoom(
axins,
edgecolor='r',
)
fix_ins(axins)
if subs == 'TOT':
cat = coll_ltr.loc[station, dall_c]
ax.set_title(station) #+' '+cat)
else:
cat = coll_ltr.loc[station, dall_c]
ax.set_title(station + ' ' + subs) #+', '+cat)
ax.grid(True)
ax.spines['left'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.tick_params(axis=u'y', which=u'both', length=0)
if nr_row > 1:
for ii in range(nr_row):
for jj in range(nr_col):
if ii != nr_row - 1:
axs_nf[ii, jj].set_xlabel('')
if jj != 0:
axs_nf[ii, jj].set_ylabel('')
else:
axs_nf[ii, jj].set_ylabel('dN/dlog$_{10}$D [cm$^{-3}$]')
fig.tight_layout()
lgn = fig.legend(
lines,
labels,
bbox_to_anchor=(0, 1., 1, 0.5), # (0, -0.04, 1., .1),
loc='lower center',
ncol=4,
# mode="expand",
borderaxespad=0.,
fontsize=11,
frameon=False) # bbox_to_anchor=(0, 1., 1, 0.5))
#fig.legend(lines, labels, bbox_to_anchor=(0,1.,1,0.5),#(0, -0.04, 1., .1),
# loc='lower center', ncol=4,
# #mode="expand",
# borderaxespad=0., fontsize=11, frameon=False)# bbox_to_anchor=(0, 1., 1, 0.5))
fn = paths_plotsave[
'eusaar'] + '/sizedist/%s_overview_yscale_%s_sec%s_%s.' % (
name.replace(' ', '-'), yscale, plot_sec, subs)
print(fn)
make_folders(fn)
plt.savefig(fn + 'png', bbox_extra_artists=(lgn, ), bbox_inches='tight')
plt.savefig(fn + 'pdf', bbox_extra_artists=(lgn, ), bbox_inches='tight')
plt.show()
# %%
plot_grid(dic_finish, subs='SUM', ylim=[0, 1e4])
# %%
plot_grid(dic_finish, subs='WIN', ylim=[0, 8e3])
# %%
plot_grid(dic_finish, subs='SPR', ylim=[0, 8e3])
# %%
plot_grid(dic_finish, subs='AUT', ylim=[0, 8e3])
# %%
from sectional_v2.constants import collocate_locations
station = 'ASP'
colors_source = get_cmap_dic(dic_finish.keys())
colors_source['EUSAAR'] = 'k'
for station in dic_finish[cases[0]].coords['station'].values:
fig, axs = plt.subplots(2, 2)
seasons = ['SPR', 'SUM', 'AUT', 'WIN']
for seas, ax in zip(seasons, axs.flatten()):
for key in dic_finish.keys():
plt_median_perc(dic_clean[key]['dNdlog10dp'],
station,
key,
color=get_case_col(key),
ax=ax,
subs=seas,
percs=[16, 84],
yscale='log',
ylim=[10, 1e4])
s_list.append(s1)
#s1.compute_sizedist_mod_tot()
#s.compute_sizedist_sec_tot()
#a1 = s1.get_collocated_dataset()
case = cases_sec[1]
s2 = cl(case,
from_t,
to_t, [minDiameter, maxDiameter],
True,
'month',
history_field='.h0.')
#a2 = s1.get_collocated_dataset()
from useful_scit.plot import get_cmap_dic
cmap_dic = get_cmap_dic(cases_sec + cases_orig)
for loc in constants.collocate_locations.keys():
print(loc, '***********************************************')
fig, axs = plt.subplots(2, 2)
axs = axs.flatten()
for s in s_list:
ls = ['dNdlogD_mode01']
if s.isSectional:
ls = ls + ['dNdlogD_sec']
a = s.get_collocated_dataset(variables=ls)
print(a)
b = a['dNdlogD_mode01'] # + a['dNdlogD_sec']
c = b.mean('lev').groupby('time.season').mean('time')