def load_and_plot_diffs(varl, case_ctrl, case_other, start_time, end_time,
pressure_coords=True,
relative=False,
cbar_orient='vertical',
asp_ratio=2, subfig_size=3,
ncol=None,
ylim=None,
yscale='log',
norm=None
):
if ylim is None:
ylim = [1e3, 100]
cases_dict = get_averaged_fields.get_levlat_cases(cases, varl, start_time, end_time,
pressure_adjust=pressure_coords)
_nv = len(varl)
if ncol is None:
if _nv > 3:
ncol = 2
else:
ncol = 1
# noinspection PyUnresolvedReferences
nrow = int(np.ceil(_nv / ncol))
figsize = [subfig_size * ncol * asp_ratio, subfig_size * nrow]
# noinspection PyTypeChecker
fig, axs = plt.subplots(nrow, ncol, figsize=figsize, sharex=True, sharey=True)
for ax, var in zip(axs.flat, varl):
plot_levlat_diff(var, case_ctrl, case_other, cases_dict,
cbar_orientation=cbar_orient,
relative=relative,
ylim=ylim,
yscale=yscale,
ax=ax,
norm=norm)
return axs
def load_and_plot_diffs_more_cases(varl, cases, case_oth, startyear, endyear,
pressure_adjust=pressure_adjust,
relative=False,
cbar_orientation='vertical',
asp_ratio=2, subfig_size=3,
ylim=None,
yscale='log',
cbar_eq=True,
norm=None):
if ylim is None:
ylim = [1e3, 100]
imp_cases = list(set(cases).union({case_oth}))
print(imp_cases)
cases_dic = get_averaged_fields.get_levlat_cases(imp_cases, varl, startyear, endyear,
pressure_adjust=pressure_adjust)
ncol = len(cases)
nrow = len(varl)
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):
saxs = axs[j, :]
levlat_more_cases_var(var, case_oth, cases, cases_dic, cbar_eq, cbar_orientation, saxs, norm, relative, ylim,
yscale)
return axs
def _plt_2dhist(ds_diff,
xvar,
yvar,
nr_bins=40,
yscale='symlog',
xscale='log',
xlim=[1e-6, 1e-2],
ylim=[5., 1e3],
ax=None):
"""
xvar = f'NUCLRATE_{case_orig}'
yvar='NCONC01'
xlim = [1e-6,1e-2]
ylim=[1,1e3]
nr_bins = 40
yscale='symlog'
xscale='log'
"""
print(xlim)
print(ylim)
varList = [
xvar, yvar
] #f'NUCLRATE_{case_orig}',f'logNUCLRATE_{case_orig}',f'logSOA_LV_{case_orig}',f'logH2SO4_{case_orig}',f'logNCONC01_{case_orig}',f'logN_AER_{case_orig}',f'N_AER_{case_orig}',f'H2SO4_{case_orig}','NCONC01']
dims = tuple(ds_diff[varList].dims)
_ds_s = ds_diff[varList].stack(ind=dims) #('lat','lon','lev','time'))
ybins = mk_bins(ylim[0], vmax=ylim[1], nr_bins=nr_bins, scale=yscale)
xbins = mk_bins(xlim[0], vmax=xlim[1], nr_bins=nr_bins, scale=xscale)
data = _ds_s.to_dataframe()
lim = 0
#data = -data[(data['NCONC01']<lim)]# | (data['NCONC01']>=lim)]
x = data[xvar] #f'NUCLRATE_{case_orig}']
y = data[yvar] #'NCONC01']
if ax is None:
fig, ax = plt.subplots(1)
h = ax.hist2d(x, y, bins=[xbins, ybins],
cmap='Reds') #,extent=[-3,3,-300,20],yscale='symlog')
plt.colorbar(h[3], ax=ax, format=OOMFormatter(4, mathText=False))
#cb = fig.colorbar(c, ax=ax)
if yscale == 'symlog':
ax.set_yscale('symlog',
linthreshy=ylim[0],
linscaley=ylim[0] / 10,
subsy=[2, 3, 4, 5, 6, 7, 8, 9])
yt = ax.get_yticks()
ml = np.abs(yt[yt != 0]).min()
ytl = yt
ytl[(yt == ml) | (yt == -ml)] = None
ax.set_yticks(ticks=yt) #[y for y in yt if y!=0])#,
ax.set_yticklabels(ytl) #[-1e2,-1e1,-1e0,1e0,1e1,1e2])
elif yscale == 'log':
print('set log scale')
ax.set_yscale(
'log'
) #, linthreshy=ylim[0], linscaley=ylim[0]/10,subsy=[2,3,4,5,6,7,8,9])
#ax.set_yticks([y for y in yt if y!=0])#[-1e2,-1e1,-1e0,1e0,1e1,1e2])
ax.set_xscale('log')
return ax
def corr_plt():
# %%
var_c = 'AWNC_incld'
varl_to = ['NCONC01','NMR01']
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 = plt.subplots(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]
_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'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)
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}'
subp_insert_abc(axs, pos_x=1.1,pos_y=1.1)
plt.savefig(fn + '.pdf')
plt.savefig(fn + '.png')
plt.show()
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()
ax=ax,
norm=norm, **cba_kwargs)
# %%
varlist = ['NCONC01', 'NMR01', 'AWNC_incld', 'AREL_incld']
cbar_orientation = 'vertical'
cases_ctrl = cases_orig
case_oth = cases_sec[0]
ncol = len(cases_ctrl)
nrow = len(varlist)
subfig_size = 2.6
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)
norm_dic = dict(
NCONC01=colors.SymLogNorm(vmin=-1e3, vmax=1e3, linthresh=10),
NMR01=colors.SymLogNorm(vmin=-10, vmax=10, linthresh=1),# linscale=.5),
AWNC_incld=colors.SymLogNorm(vmin=-20, vmax=20, linthresh=1),
AREL_incld=colors.SymLogNorm(vmin=-5, vmax=5, linthresh=.1)
)
cases_dic = get_averaged_fields.get_levlat_cases(cases, varlist, startyear, endyear,
pressure_adjust=pressure_adjust)
for j, var in enumerate(varlist):
saxs = axs[j, :]
levlat_more_cases_var(var, case_oth, cases_ctrl, cases_dic, cbar_eq=True,
cbar_orientation='vertical',
axs=saxs,
# %%
import seaborn as sns
# %%
import importlib as il
from useful_scit.util import conditional_stats
il.reload(conditional_stats)
varList = ['H2SO4', 'SOA_LV', 'NCONC01']
varList = ['N_AER', 'GR', 'NCONC01']
sec_sub = cases_dic[case_sec][varList].stack(ind=('lat', 'lon', 'lev',
'time')) #
#sec_sub = sec_sub
nosec_sub = cases_dic[case_orig][varList].stack(ind=('lat', 'lon', 'lev',
'time'))
fig, axs = plt.subplots(3, 2, figsize=[10, 10])
axs = axs.flatten()
df, pl_xr_ns, kwargs, xedges, yedges = conditional_stats.plot_cond_statistic(
nosec_sub,
varList,
axs[0],
quant=0.1,
scale='log',
stat='mean',
plt_title='Mean Non sectional',
cscale='log')
df, pl_xr_s, kwargs, xedges, yedges = conditional_stats.plot_cond_statistic(
sec_sub,
varList,
axs[1],
quant=0.1,
suptit = f'{fvar_diff}$(m_1)-${fvar_diff}$(m_2)$ vs. $X(m_2)$ \n for $m_1=${get_nice_name_case(case_sec)}, $m_2=${get_nice_name_case(case_base)}'
fig.subplots_adjust(hspace=.5, wspace=0.1) #,top=0.8, )
stit = fig.suptitle(suptit, fontsize=12, y=.98)
return stit
# %% [markdown]
# # Difference:
#
# %% [markdown]
# ## Main plot: below 100 hPa:
# %%
fig, axs = plt.subplots(3, 2, figsize=[6.4, 7.1],
sharey=True) #, constrained_layout=True)
var_xl = ['NUCLRATE', 'GR', 'H2SO4', 'SOA_LV', 'NCONC01', 'COAGNUCL']
lev_min = 100.
_ds = ds_diff.sel(lev=slice(lev_min, None))
var_diff = 'NCONC01'
xlims = {
'NUCLRATE': [1.e-6, 10],
'H2SO4': [1.e-3, 1],
'SOA_LV': [1.e-5, 1],
'GR': [1.e-3, 1],
'N_AER': [1e0, 1e4],
'NCONC01': [5e-1, 1e3],
'COAGNUCL': [1e-5, .1],
}
stit = _plt_tmp(_ds, axs, var_xl, var_diff, xlims)
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()
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')
for seas, ax in zip(c['season'].values, axs):
_da = c.sel(season=seas, location=loc)
_da.plot(xscale='log',
