def prova(cco2, alt1, alt2, use_model=1):
diffnlte = []
hras = []
hrbs = []
temps = []
tempsgrad = []
for atm, col in zip(allatms, npl.color_set(6)):
diffnlte.append(all_coeffs_nlte[(atm, cco2, 'hr_ref')][alt1:alt2] -
all_coeffs_nlte[(atm, cco2, 'hr_lte')][alt1:alt2])
hra, hrb = npl.hr_from_ab_diagnondiag(all_coeffs[(atm, cco2,
'acoeff')],
all_coeffs[(atm, cco2,
'bcoeff')],
all_coeffs[(atm, cco2, 'asurf')],
all_coeffs[(atm, cco2, 'bsurf')],
atm_pt[(atm, 'temp')],
atm_pt[(atm, 'surf_temp')],
max_alts=66)
hras.append(hra[alt1:alt2])
hrbs.append(hrb[alt1:alt2])
temps.append(atm_pt[(atm, 'temp')][alt1:alt2])
tempsgrad.append(np.gradient(atm_pt[(atm, 'temp')])[alt1:alt2])
hras = np.concatenate(hras, axis=1)
hrbs = np.concatenate(hrbs, axis=1)
temps = np.concatenate(temps, axis=1)
tempsgrad = np.concatenate(tempsgrad, axis=1)
diffnlte = np.concatenate(diffnlte, axis=1)
# Mod 1: uso solo hra e hrb
X = np.stack([hras, hrbs]).T
Y = np.stack(diffnlte)
# STANDARDIZZO LE FEATURES
#scaler = StandardScaler().fit(X)
#X = scaler.transform(X)
scores = []
model1 = LinearRegression().fit(X, Y)
print(model1.score(X, Y))
scores.append(model1.score(X, Y))
# # Mod 2: butto dentro anche temperature
# X = np.stack([hras, hrbs, temps]).T
# model2 = LinearRegression().fit(X, Y)
# print(model2.score(X, Y))
# scores.append(model2.score(X, Y))
##################################################################
return model1.intercept_, model1.coef_
####################################################################################
ialt_strano = 41
atm = 'mle'
interp_coeffs = dict()
for nam in ['acoeff', 'bcoeff', 'asurf', 'bsurf', 'alpha', 'Lesc']:
int_fun, signc = npl.interp_coeff_logco2(coeffs[nam], coeffs['co2profs'])
interp_coeffs[(nam, 'int_fun')] = int_fun
interp_coeffs[(nam, 'signc')] = signc
print('Coeffs from interpolation!')
mults = np.arange(0.25, 8.1, 0.25)
#for nam in ['acoeff', 'bcoeff', 'asurf', 'bsurf', 'alpha', 'Lesc']:
colors = npl.color_set(n_alts_trlo)
for nam in ['acoeff', 'bcoeff']:
figs = []
axes = []
namsurf = nam[0] + 'surf'
coefff = []
for co2mult in mults:
co2vmr = co2mult * atm_pt[(atm, 2, 'co2')]
int_fun = interp_coeffs[(namsurf, 'int_fun')]
sc = interp_coeffs[(namsurf, 'signc')]
interplog = [
int_fun[ialt](co2vmr[ialt]) for ialt in range(n_alts_trlo)
]
#print(co2mult, interplog)
alt_fomi, hr_fomi = npl.old_param(alts, temp, pres, co2vmr)
oldco = spline(alt_fomi, hr_fomi)
hr_fomi = oldco(alts)
tit = 'co2: {} - atm: {}'.format(cco2, atm)
xlab = 'CR (K/day)'
ylab = 'Alt (km)'
# labels = ['nlte_ref', 'new_param', 'np_wutop', 'np_all_wutop', 'np_aw_extended', 'np_noalpha', 'old param']
# hrs = [hr_ref, hr_calc, hr_calc_wutop, hr_calc_all, hr_calc_extended, hr_calc_alpha1, hr_fomi]
labels = [
'nlte_ref', 'np_all_wutop', 'np_aw_extended', 'np_w_lfomi',
'old param'
]
hrs = [hr_ref, hr_calc_all, hr_calc_extended, hr_calc_fom, hr_fomi]
colors = np.array(npl.color_set(6))[np.arange(6) != 4]
fig, a0, a1 = npl.manuel_plot(
alts,
hrs,
labels,
xlabel=xlab,
ylabel=ylab,
title=tit,
xlimdiff=(-15, 15),
xlim=(-70, 10),
linestyles=['-', '--', '-', '--', '-', ':', ':'],
colors=colors,
orizlines=[70., alts[n_alts_trlo], alts[n_alts_trhi]])
figs.append(fig)
a0s.append(a0)
sys.exit()
cco2 = 4
fig = plt.figure()
fig, axes = plt.subplots(nrows=2, ncols=3, sharex=True, sharey=True)
axes = np.squeeze(np.reshape(axes, (1, 6)))
for ii, atm in enumerate(allatms):
ax = axes[ii]
hr_nlte_fun = all_coeffs_nlte[(atm, cco2, 'hr_nlte_fb')] + all_coeffs_nlte[
(atm, cco2, 'hr_nlte_iso')]
hr_nlte_hot = all_coeffs_nlte[(atm, cco2, 'hr_nlte_hot')]
hr_nlte = all_coeffs_nlte[(atm, cco2, 'hr_nlte')]
hr_lte_fun, hr_lte_hot = npl.hr_LTE_FB_vs_ob(atm, cco2)
hr_lte = all_coeffs_nlte[(atm, cco2, 'hr_lte')]
hr_ref = all_coeffs[(atm, cco2, 'hr_ref')]
cols = npl.color_set(5)
if ii == 0:
ax.plot(hr_lte_fun, all_alts, label='LTE fun', color=cols[0])
ax.plot(hr_lte_hot, all_alts, label='LTE hot', color=cols[2])
ax.plot(hr_lte, all_alts, label='LTE', color=cols[4])
ax.plot(hr_nlte_fun,
all_alts,
label='NLTE fun',
color=cols[0],
linestyle='--')
ax.plot(hr_nlte_hot,
all_alts,
label='NLTE hot',
color=cols[2],
linestyle='--')
ax.plot(hr_nlte, all_alts, label='NLTE', color=cols[4], linestyle='--')
Oprof=ovmr,
O2prof=o2vmr,
N2prof=n2vmr)
oldco = spline(alt_fomi, hr_fomi)
hr_fomi = oldco(alts)
### FIGURE
tit = 'co2: {} - atm: {}'.format(cco2, atm)
xlab = 'CR (K/day)'
ylab = 'Alt (km)'
# labels = ['nlte_ref', 'new_param', 'np_wutop', 'np_all_wutop', 'np_aw_extended', 'np_noalpha', 'old param']
# hrs = [hr_ref, hr_calc, hr_calc_wutop, hr_calc_all, hr_calc_extended, hr_calc_alpha1, hr_fomi]
labels = ['nlte_ref', 'new_param', 'old_param']
hrs = [hr_ref, hr_calc, hr_fomi]
colors = np.array(npl.color_set(3))
fig, a0, a1 = npl.manuel_plot(alts,
hrs,
labels,
xlabel=xlab,
ylabel=ylab,
title=tit,
xlimdiff=(-15, 15),
xlim=(-70, 10),
linestyles=['-', '-', '--'],
colors=colors,
orizlines=[70., alts[n_alts_trlo], alts[n_top]])
fig.savefig(cart_out_4 + 'test_calchr.pdf')
#ok.
x0 = surfanom # per i cosi surface uso la anomaly di surface temperature
acos = acos[:, :n_alts]
corrco = np.empty_like(acos[0])
for i in range(acos[0].shape[0]):
corrco[i] = np.corrcoef(x0, acos[:, i])[1, 0]
cico, regrco, _, _ = npl.linearregre_coeff(x0, acos)
regrcoef[(conam, 'R')] = corrco
regrcoef[(conam, 'c')] = cico
regrcoef[(conam, 'm')] = regrco
for conam in ['acoeff', 'bcoeff']:
fig = plt.figure()
for ialt, col in zip(range(n_alts), npl.color_set(n_alts)):
plt.plot(coefsolv[conam].eofs()[0][:, ialt], alts, color=col)
plt.xlim(-0.02, 0.02)
plt.title(conam + ' - eof 0')
fig.savefig(cartou + '{}_eof0.pdf'.format(conam))
for conam in ['acoeff', 'bcoeff']:
fig = plt.figure()
for ialt, col in zip(range(n_alts), npl.color_set(n_alts)):
plt.plot(np.abs(regrcoef[(conam, 'R')][:, ialt]), alts, color=col)
#plt.xlim(-0.02, 0.02)
plt.title(conam + ' - rcorr')
fig.savefig(cartou + '{}_rcorr.pdf'.format(conam))
fig = plt.figure()
plt.plot(coefsolv['asurf'].eofs()[0], alts, label='asurf')
# # Mod 2: butto dentro anche temperature
# X = np.stack([hras, hrbs, temps]).T
# model2 = LinearRegression().fit(X, Y)
# print(model2.score(X, Y))
# scores.append(model2.score(X, Y))
##################################################################
return model1.intercept_, model1.coef_
alt1 = 40
alt2 = 53
cco2 = 7
fig2, ax2 = plt.subplots()
for atm, col in zip(allatms, npl.color_set(6)):
diff = all_coeffs_nlte[(atm, cco2, 'hr_ref')][alt1:alt2] - all_coeffs_nlte[
(atm, cco2, 'hr_lte')][alt1:alt2]
hra, hrb = npl.hr_from_ab_diagnondiag(all_coeffs[(atm, cco2, 'acoeff')],
all_coeffs[(atm, cco2, 'bcoeff')],
all_coeffs[(atm, cco2, 'asurf')],
all_coeffs[(atm, cco2, 'bsurf')],
atm_pt[(atm, 'temp')],
atm_pt[(atm, 'surf_temp')],
max_alts=66)
ax2.plot(diff, np.arange(alt1, alt2), color=col, label=atm)
ax2.plot(hra[alt1:alt2], np.arange(alt1, alt2), color=col, linestyle='--')
ax2.plot(hrb[alt1:alt2], np.arange(alt1, alt2), color=col, linestyle=':')
ax2.grid()
ax2.legend()
npl.adjust_ax_scale(a0s)
npl.adjust_ax_scale(a1s)
npl.plot_pdfpages(
cart_out + 'check_newparam_LTE_final_{}_correcto.pdf'.format(tip),
figs)
#pickle.dump(interp_coeffs, open(cart_out + 'interp_coeffs_v1_LTE.p', 'w'))
for tip in ['unifit', 'varfit']:
var_cosi = np.linspace(0., 1., 20)
co2profs = [
atm_pt[('mle', 1, 'co2')] * (1 - cos) + atm_pt[('mle', 6, 'co2')] * cos
for cos in var_cosi
]
colors = npl.color_set(20)
###########################################################################
figs = []
axes = []
for atm in allatms:
temp = atm_pt[(atm, 'temp')]
surf_temp = atm_pt[(atm, 'surf_temp')]
hrs_interp = []
for co2pr in co2profs:
# i coeffs universali:
coeffs = []
for nam in ['acoeff', 'bcoeff', 'asurf', 'bsurf']:
int_fun = interp_coeffs[(tip, nam, 'int_fun')]
# print('Atm {}. Unstable ialt {}'.format(atm, ialt))
# axes[0].plot(np.mean([coeff[:n_alts, ialt-1][:-2], coeff[:n_alts, ialt+1][2:]], axis = 0), alts[1:-1], color = col, linestyle = '--')
# if np.abs(mco[ialt, ialt])/np.abs(np.mean([mco[ialt-1, ialt-1], mco[ialt+1, ialt+1]])) > 1.5:
# print('Atm {}. Unstable ialt {}'.format(atm, ialt))
# axes[1].plot(np.mean([mco[:n_alts, ialt-1][:-2], mco[:n_alts, ialt+1][2:]], axis = 0), alts[1:-1], color = col, linestyle = '--')
# axes[0].set_title('c')
# axes[1].set_title('m')
# plt.suptitle(conam)
# figs.append(fig)
# npl.plot_pdfpages(cartou + 'regrcoeff.pdf', figs)
# ### OK! now I use the dotprods and the regrcoef to reconstruct the a and b coeffs, and compute the hr and check wrt the reference and the varfit5.
cart_out = '/home/fabiano/Research/lavori/CO2_cooling/new_param/LTE/'
tot_coeff_co2 = pickle.load(open(cart_out + 'tot_coeffs_co2_v2_LTE.p', 'rb'))
colors = npl.color_set(5)
colors = colors[:3] + [colors[4]]
figs = []
a0s = []
a1s = []
for cco2 in range(1, 8):
for atm, dp, dp1, sa in zip(allatms, dotprods, dotprods1, surfanom):
temp = atm_pt[(atm, 'temp')]
surf_temp = atm_pt[(atm, 'surf_temp')]
coeffs = dict()
for conam in ['acoeff', 'bcoeff', 'asurf', 'bsurf']:
if 'surf' in conam:
coeffs[conam] = regrcoef[(cco2, conam, 'c')] + regrcoef[
(cco2, conam, 'm')] * sa
def prova(cco2, alt1, alt2, use_model=1):
diffnlte = []
hras = []
hrbs = []
temps = []
tempsgrad = []
for atm, col in zip(allatms, npl.color_set(6)):
diffnlte.append(all_coeffs_nlte[(atm, cco2, 'hr_ref')][alt1:alt2] -
all_coeffs_nlte[(atm, cco2, 'hr_lte')][alt1:alt2])
hra, hrb = npl.hr_from_ab_decomposed(all_coeffs[(atm, cco2, 'acoeff')],
all_coeffs[(atm, cco2, 'bcoeff')],
all_coeffs[(atm, cco2, 'asurf')],
all_coeffs[(atm, cco2, 'bsurf')],
atm_pt[(atm, 'temp')],
atm_pt[(atm, 'surf_temp')],
max_alts=66)
hras.append(hra[alt1:alt2])
hrbs.append(hrb[alt1:alt2])
temps.append(atm_pt[(atm, 'temp')][alt1:alt2])
tempsgrad.append(np.gradient(atm_pt[(atm, 'temp')])[alt1:alt2])
hras = np.concatenate(hras)
hrbs = np.concatenate(hrbs)
temps = np.concatenate(temps)
tempsgrad = np.concatenate(tempsgrad)
diffnlte = np.concatenate(diffnlte)
# Mod 1: uso solo hra e hrb
X = np.stack([hras, hrbs]).T
Y = np.stack(diffnlte)
# STANDARDIZZO LE FEATURES
#scaler = StandardScaler().fit(X)
#X = scaler.transform(X)
scores = []
model1 = LinearRegression().fit(X, Y)
print(model1.score(X, Y))
scores.append(model1.score(X, Y))
# Mod 2: butto dentro anche temperature
X = np.stack([hras, hrbs, temps]).T
model2 = LinearRegression().fit(X, Y)
print(model2.score(X, Y))
scores.append(model2.score(X, Y))
# Mod 3: solo temp e tempgrad
X = np.stack([hras, hrbs, temps, tempsgrad]).T
model3 = LinearRegression().fit(X, Y)
print(model3.score(X, Y))
scores.append(model3.score(X, Y))
# Mod 4: butto dentro temps
X = np.stack([hras, temps]).T
model4 = LinearRegression().fit(X, Y)
print(model4.score(X, Y))
scores.append(model4.score(X, Y))
# Mod 5: solo temp e tempgrad
X = np.stack([temps, tempsgrad]).T
model5 = LinearRegression().fit(X, Y)
print(model5.score(X, Y))
scores.append(model5.score(X, Y))
models = [model1, model2, model3, model4, model5]
##################################################################
return scores
