def east_west_joint_run(xb, f_name):
de = dc.DalecData(
2015,
2016,
'nee_day_east, c_roo_east, c_woo_east, clma, lai_east',
nc_file='../../alice_holt_data/ah_data_daily_test_nee.nc')
de.ob_err_dict['clma'] = (1. / 3.) * de.ob_err_dict['clma']
de.ob_err_dict['lai'] = (1. / 3.) * de.ob_err_dict['lai']
B = pickle.load(open('b_edc.p', 'r'))
B = np.delete(B, 10, axis=0)
B = 0.8 * np.delete(B, 10, axis=1)
de.B = B
#de.B = np.concatenate((de.edinburgh_std[:10], de.edinburgh_std[11:]))**2*np.eye(22)
#A = pickle.load(open('A_D.p', 'r'))
#A = np.delete(A, (10), axis=0)
#A = np.delete(A, (10), axis=1)
#de.B = 1.2*A
dw = dc.DalecData(
2015,
2016,
'nee_day_west, c_roo_west, c_woo_west, clma, lai_west',
nc_file='../../alice_holt_data/ah_data_daily_test_nee.nc')
dw.ob_err_dict['clma'] = (1. / 3.) * dw.ob_err_dict['clma']
dw.ob_err_dict['lai'] = (1. / 3.) * dw.ob_err_dict['lai']
dw.B = B
#dw.B = np.concatenate((de.edinburgh_std[:10], de.edinburgh_std[11:]))**2*np.eye(22)
#dw.B = A
me = mc_p.DalecModel(de)
mw = mc_p.DalecModel(dw)
xa_e = me.find_min_tnc_cvt(xb, f_name + 'east_assim')
xa_w = mw.find_min_tnc_cvt(xb, f_name + 'west_assim')
xbb = np.array(
me.create_ordered_lst(np.array(xb.tolist()[0], dtype=np.float)))
save_plots(f_name, xbb, xa_e[2], xa_w[2], de, dw)
return 'done'
def east_west_joint_run_nee_err_no_r(xb,
f_name,
nee_scale=0,
clma_er=1.,
lai_er=1.,
need_er=1.,
neen_er=1.,
cr_er=1.,
cw_er=1.):
f_name += 'clmaer%r_laier%r_needer%r_neener%r_crer%r_cwer%r' % (
clma_er, lai_er, need_er, neen_er, cr_er, cw_er)
# Construct B
b_cor = pickle.load(open('b_edc_cor.p', 'r'))
b_std = np.sqrt(np.diag(pickle.load(open('b_edc.p', 'r'))))
b_std[10] = 0.25 * b_std[10]
b_std[1] = 0.25 * b_std[1]
b_std[0:17] = 0.5 * b_std[0:17]
D = np.zeros_like(b_cor)
np.fill_diagonal(D, b_std)
b = 0.6 * np.dot(np.dot(D, b_cor), D)
# east data
de = dc.DalecData(
2015,
2016,
'nee_day_east, nee_night_east, c_roo_east, c_woo_east, clma, lai_east',
nc_file='../../alice_holt_data/ah_data_daily_test_nee.nc',
scale_nee=nee_scale)
de.B = b
# obs err scaling
de.ob_err_dict['clma'] = clma_er * de.ob_err_dict['clma']
de.ob_err_dict['lai'] = lai_er * de.ob_err_dict['lai']
de.ob_err_dict['nee_day'] = need_er * de.ob_err_dict['nee_day']
de.ob_err_dict['nee_night'] = neen_er * de.ob_err_dict['nee_night']
de.ob_err_dict['c_roo'] = cr_er * de.ob_err_dict['c_roo']
de.ob_err_dict['c_woo'] = cw_er * de.ob_err_dict['c_woo']
# west data
dw = dc.DalecData(
2015,
2016,
'nee_day_west, nee_night_west, c_roo_west, c_woo_west, clma, lai_west',
nc_file='../../alice_holt_data/ah_data_daily_test_nee.nc',
scale_nee=nee_scale)
dw.B = b
# obs err scaling
dw.ob_err_dict['clma'] = clma_er * dw.ob_err_dict['clma']
dw.ob_err_dict['lai'] = lai_er * dw.ob_err_dict['lai']
dw.ob_err_dict['nee_day'] = need_er * dw.ob_err_dict['nee_day']
dw.ob_err_dict['nee_night'] = neen_er * dw.ob_err_dict['nee_night']
dw.ob_err_dict['c_roo'] = cr_er * dw.ob_err_dict['c_roo']
dw.ob_err_dict['c_woo'] = cw_er * dw.ob_err_dict['c_woo']
# setup model
me = mc.DalecModel(de)
mw = mc.DalecModel(dw)
# run DA scheme
xa_e = me.find_min_tnc_cvt(xb, f_name + 'east_assim')
xa_w = mw.find_min_tnc_cvt(xb, f_name + 'west_assim')
# save plots
save_plots(f_name, xb, xa_e[1], xa_w[1], de, dw, me, mw)
return 'done'
def east_west_joint_run_prior(xb,
f_name,
obs_str,
b_mat,
rm='None',
end_yr=2014,
start_yr=2012):
if not os.path.exists(f_name):
os.makedirs(f_name)
# east data
obs_east = ob_str_east_west(obs_str, 'east', rm_obs=rm)
de = dc.DalecData(
start_yr,
end_yr,
obs_east,
nc_file='../../alice_holt_data/ah_data_daily_test_nee3.nc',
scale_nee=0)
de.B = b_mat
# obs err scaling
# west data
obs_west = ob_str_east_west(obs_str, 'west', rm_obs=rm)
dw = dc.DalecData(
start_yr,
end_yr,
obs_west,
nc_file='../../alice_holt_data/ah_data_daily_test_nee3.nc',
scale_nee=0)
dw.B = b_mat
# obs err scaling
# setup model
me = mc.DalecModel(de)
me.rmatrix = r_mat_corr(me.yerroblist,
me.ytimestep,
me.y_strlst,
me.rmatrix,
corr=0.3,
tau=2.)[1]
mw = mc.DalecModel(dw)
mw.rmatrix = r_mat_corr(mw.yerroblist,
mw.ytimestep,
mw.y_strlst,
mw.rmatrix,
corr=0.3,
tau=2.)[1]
# run DA scheme
xa_e = me.find_min_tnc_cvt(xb, f_name + 'east_assim')
xa_w = mw.find_min_tnc_cvt(xb, f_name + 'west_assim')
# save plots
save_plots(f_name, xb, xa_e[1], xa_w[1], de, dw, me, mw)
return 'done'
def calc_exp_vals(exp_name):
east = pickle.load(open(exp_name + 'east_assim', 'r'))
west = pickle.load(open(exp_name + 'west_assim', 'r'))
b = 1.5 * east['b_mat']
# east data
de = dc.DalecData(
2015,
2016,
'clma',
nc_file='../../alice_holt_data/ah_data_daily_test_nee2.nc',
scale_nee=1)
de.B = b
de.ob_dict = east['obs']
de.ob_err_dict = east['obs_err']
# obs err scaling
# west data
dw = dc.DalecData(
2015,
2016,
'clma',
nc_file='../../alice_holt_data/ah_data_daily_test_nee2.nc',
scale_nee=1)
dw.B = b
dw.ob_dict = west['obs']
dw.ob_err_dict = west['obs_err']
# obs err scaling
# setup model
me = mc.DalecModel(de)
me.rmatrix = r_mat_corr(me.yerroblist,
me.ytimestep,
me.y_strlst,
me.rmatrix,
corr=0.3,
tau=2.)[1]
mw = mc.DalecModel(dw)
mw.rmatrix = r_mat_corr(mw.yerroblist,
mw.ytimestep,
mw.y_strlst,
mw.rmatrix,
corr=0.3,
tau=2.)[1]
# a_east = pickle.load(open('a_east.p', 'r'))
# a_west = pickle.load(open('a_west.p', 'r'))
a_east = me.acovmat(east['xa'])
a_west = mw.acovmat(west['xa'])
e_ens = p.create_ensemble(de, a_east, east['xa'])
w_ens = p.create_ensemble(de, a_west, west['xa'])
p_e_ens = p.plist_ens(de, e_ens)
p_w_ens = p.plist_ens(dw, w_ens)
return east, west, de, dw, p_e_ens, p_w_ens
def east_west_joint_run_nee_err_r_errs(xb, f_name):
# Construct B
b_cor = pickle.load(open('b_edc_cor.p', 'r'))
b_std = np.sqrt(np.diag(pickle.load(open('b_edc.p', 'r'))))
b_std[10] = 0.25 * b_std[10]
b_std[1] = 0.25 * b_std[1]
#b_std[2] = 0.1*b_std[2]
b_std[0:17] = b_std[0:17] * 0.5
D = np.zeros_like(b_cor)
np.fill_diagonal(D, b_std)
b = 0.6 * np.dot(np.dot(D, b_cor), D) #*0.6
# east data
de = dc.DalecData(
2015,
2016,
'nee_east, nee_night_east, c_roo_east, c_woo_east, clma, lai_east',
nc_file='../../alice_holt_data/ah_data_daily_test_nee2.nc',
scale_nee=1)
de.B = b
# obs err scaling
# west data
dw = dc.DalecData(
2015,
2016,
'nee_west, nee_night_west, c_roo_west, c_woo_west, clma, lai_west',
nc_file='../../alice_holt_data/ah_data_daily_test_nee2.nc',
scale_nee=1)
dw.B = b
# obs err scaling
# setup model
me = mc.DalecModel(de)
me.rmatrix = r_mat_corr(me.yerroblist,
me.ytimestep,
me.y_strlst,
me.rmatrix,
corr=0.3,
tau=2.)[1]
mw = mc.DalecModel(dw)
mw.rmatrix = r_mat_corr(mw.yerroblist,
mw.ytimestep,
mw.y_strlst,
mw.rmatrix,
corr=0.3,
tau=2.)[1]
# run DA scheme
xa_e = me.find_min_tnc_cvt(xb, f_name + 'east_assim')
xa_w = mw.find_min_tnc_cvt(xb, f_name + 'west_assim')
# save plots
save_plots(f_name, xb, xa_e[1], xa_w[1], de, dw, me, mw)
return 'done'
def east_west_joint_run_ceff_a(xb, f_name):
de = dc.DalecData(
2015,
2016,
'nee_day_east, nee_night_east, c_roo_east, c_woo_east, clma, lai_east',
nc_file='../../alice_holt_data/ah_data_daily_test_nee.nc',
scale_nee=1)
de.ob_err_dict['clma'] = 0.33 * de.ob_err_dict['clma']
de.ob_err_dict['lai'] = 0.33 * de.ob_err_dict['lai']
B = pickle.load(open('a_cov.p', 'r'))
B = np.delete(B, 10, axis=0)
B = np.delete(B, 10, axis=1)
#de.B = np.concatenate((de.edinburgh_std[:10], de.edinburgh_std[11:]))**2*np.eye(22)
#A = pickle.load(open('A_D.p', 'r'))
#A = np.delete(A, (10), axis=0)
#A = np.delete(A, (10), axis=1)
de.B = B
dw = dc.DalecData(
2015,
2016,
'nee_day_west, nee_night_west, c_roo_west, c_woo_west, clma, lai_west',
nc_file='../../alice_holt_data/ah_data_daily_test_nee.nc',
scale_nee=1)
dw.ob_err_dict['clma'] = 0.33 * dw.ob_err_dict['clma']
dw.ob_err_dict['lai'] = 0.33 * dw.ob_err_dict['lai']
dw.B = B
#dw.B = np.concatenate((de.edinburgh_std[:10], de.edinburgh_std[11:]))**2*np.eye(22)
#dw.B = A
me = mc_p.DalecModel(de)
me.rmatrix = r_mat_corr(me.yerroblist,
me.ytimestep,
me.y_strlst,
me.rmatrix,
corr=0.3,
tau=2.)[1]
mw = mc_p.DalecModel(dw)
mw.rmatrix = r_mat_corr(mw.yerroblist,
mw.ytimestep,
mw.y_strlst,
mw.rmatrix,
corr=0.3,
tau=2.)[1]
xa_e = me.find_min_tnc_cvt(xb, f_name + 'east_assim')
xa_w = mw.find_min_tnc_cvt(xb, f_name + 'west_assim')
xbb = np.array(
me.create_ordered_lst(np.array(xb.tolist()[0], dtype=np.float)))
save_plots(f_name, xbb, xa_e[2], xa_w[2], de, dw, me, mw)
return 'done'
def test_costfn(alph=1e-9):
"""Test for cost and gradcost functions.
"""
d = dC.DalecData(50, 'nee')
m = mc.DalecModel(d)
gradj = m.gradcost(d.pvals)
h = gradj * (np.linalg.norm(gradj))**(-1)
j = m.cost(d.pvals)
jalph = m.cost(d.pvals + alph * h)
print(jalph - j) / (np.dot(alph * h, gradj))
assert (jalph - j) / (np.dot(alph * h, gradj)) < 1.0001
def run_4dvar_desroziers(pvals, east_west):
d = dc.DalecData(2015, 2016, 'nee_day_'+east_west+', nee_night_'+east_west+', c_roo_'+east_west+','
' c_woo_'+east_west+', clma, lai_'+east_west,
nc_file='../../alice_holt_data/ah_data_daily_test_nee.nc')
d.B = b
d.ob_err_dict['clma'] = 0.33 * d.ob_err_dict['clma']
d.ob_err_dict['lai'] = 0.33 * d.ob_err_dict['lai']
m = mc.DalecModel(d)
m.yoblist = perturb_obs(m.yoblist, m.yerroblist)
out = m.find_min_tnc_cvt(pvals, dispp=0)
return m.yoblist, pvals, out
def r_estimate(yoblist, pvals, out, east_west):
d = dc.DalecData(2015, 2016, 'nee_day_'+east_west+', nee_night_'+east_west+', c_roo_'+east_west+','
' c_woo_'+east_west+', clma, lai_'+east_west,
nc_file='../../alice_holt_data/ah_data_daily_test_nee.nc')
d.B = b
m = mc.DalecModel(d)
m.yoblist = yoblist
pvallistxb = m.mod_list(pvals)
pvallistxa = m.mod_list(out)
yhxb = m.yoblist - m.hxcost(pvallistxb)
yhxa = m.yoblist - m.hxcost(pvallistxa)
r_estimate = np.dot(np.matrix(yhxa).T, np.matrix(yhxb))
return r_estimate
def test_linmod(gamma=1e1):
""" Test from TLM to check it converges.
"""
d = dC.DalecData(731, 'nee')
pvals = d.pvals
m = mc.DalecModel(d)
cx, matlist = m.linmod_list(pvals)
pvals2 = pvals * (1 + 0.3 * gamma)
cxdx = m.mod_list(pvals2)[-1]
pvals3 = pvals * (0.3 * gamma)
dxl = np.linalg.norm(np.dot(m.mfac(matlist, 730), pvals3.T))
dxn = np.linalg.norm(cxdx - cx - dxl)
return dxl / dxn
def east_west_run_b(f_name, east_west, net_file="None"):
if east_west == 'east':
obs = 'nee_day_east, nee_night_east, clma, lai_east, c_woo_east, c_roo_east'
elif east_west == 'west':
obs = 'nee_day_west, nee_night_west, clma, lai_west, c_woo_west, c_roo_west'
if net_file != "None":
d = dc.DalecData(2015, 2016, obs, nc_file=net_file)
else:
d = dc.DalecData(2015, 2016, obs)
m = mc.DalecModel(d)
assim_results, xa = m.find_min_tnc_cvt(d.edinburgh_mean,
f_name + '_assim_res')
# Plot 4dvar time series
ax, fig = p.plot_4dvar('nee', d, xb=d.edinburgh_mean, xa=xa)
fig.savefig(f_name + '_nee.png', bbox_inches='tight')
ax, fig = p.plot_4dvar('nee_day', d, xb=d.edinburgh_mean, xa=xa)
fig.savefig(f_name + '_need.png', bbox_inches='tight')
ax, fig = p.plot_4dvar('nee_night', d, xb=d.edinburgh_mean, xa=xa)
fig.savefig(f_name + '_neen.png', bbox_inches='tight')
ax, fig = p.plot_4dvar('lai', d, xb=d.edinburgh_mean, xa=xa)
fig.savefig(f_name + '_lai.png', bbox_inches='tight')
ax, fig = p.plot_4dvar('c_woo', d, xb=d.edinburgh_mean, xa=xa)
fig.savefig(f_name + '_cwoo.png', bbox_inches='tight')
ax, fig = p.plot_4dvar('c_roo', d, xb=d.edinburgh_mean, xa=xa)
fig.savefig(f_name + '_croo.png', bbox_inches='tight')
# Plot scatter plots of obs
ax, fig = p.plot_scatter('nee_day', xa, d, len(d.I), 'a')
fig.savefig(f_name + '_need_scat.png', bbox_inches='tight')
ax, fig = p.plot_scatter('nee_night', xa, d, len(d.I), 'a')
fig.savefig(f_name + '_neen_scat.png', bbox_inches='tight')
# Plot error in analysis and background
ax, fig = p.plot_a_inc(d.edinburgh_mean, xa, east_west)
fig.savefig(f_name + '_xa_inc.png', bbox_inches='tight')
return 'all experimented'
def test_cost(alph=1e-8, vect=0):
"""Test for cost and gradcost functions.
"""
d = dC.DalecData(365, 'nee')
m = mc.DalecModel(d)
pvals = d.edinburghmean
gradj = m.gradcost2(pvals)
if vect == True:
h = pvals * (np.linalg.norm(pvals))**(-1)
else:
h = gradj * (np.linalg.norm(gradj))**(-1)
j = m.cost(pvals)
jalph = m.cost(pvals + alph * h)
print jalph - j
print np.dot(alph * h, gradj)
return (jalph - j) / (np.dot(alph * h, gradj))
def run_assimilation(yr):
d = ahd.DalecData(
yr,
yr + 1,
'nee, nee_day, nee_night',
nc_file='../../alice_holt_data/ah_data_daily_test_nee3.nc')
m = mc.DalecModel(d)
find_min, xa = m.find_min_tnc_cvt(d.xb_ew_lai_hi)
result_dic = {}
result_dic['time'] = [t.strftime('%m/%d/%Y') for t in d.dates]
result_dic['incident_radiation'] = d.I.tolist()
result_dic['t_mean'] = d.t_mean.tolist()
result_dic['doy'] = d.D.tolist()
mod_lst_xb = m.mod_list(d.xb_ew_lai_hi)
result_dic['nee_day_xb'] = m.oblist('nee_day', mod_lst_xb).tolist()
result_dic['nee_xb'] = m.oblist('nee', mod_lst_xb).tolist()
mod_lst_xa = m.mod_list(xa)
result_dic['nee_day_xa'] = m.oblist('nee_day', mod_lst_xa).tolist()
result_dic['nee_xa'] = m.oblist('nee', mod_lst_xa).tolist()
return result_dic
def return_a_mat(pick_dict):
b = pick_dict['b_mat'] #1.5*pick_dict['b_mat']
# east data
d = dc.DalecData(
2015,
2016,
'clma',
nc_file='../../alice_holt_data/ah_data_daily_test_nee2.nc',
scale_nee=1)
d.B = b
d.ob_dict = pick_dict['obs']
d.ob_err_dict = pick_dict['obs_err']
# obs err scaling
# west data
# obs err scaling
# setup model
m = mc.DalecModel(d)
m.rmatrix = pick_dict['rmat']
a_cov = m.acovmat(pick_dict['xa'])
return a_cov
def east_west_run(f_name, ob_list, east_west):
ob_str = ''
for ob in ob_list:
if ob == 'clma':
ob_str += ob + ','
else:
ob_str += ob + '_' + east_west + ','
d = dc.DalecData(2015, 2016, ob_str)
d.B = d.make_b(d.edinburgh_std)
m = mc.DalecModel(d)
assim_results, xa = m.find_min_tnc_cvt(d.edinburgh_mean,
f_name + '_assim_res')
# Plot 4dvar time series
ax, fig = p.plot_4dvar('nee', d, xb=d.edinburgh_mean, xa=xa)
fig.savefig(f_name + '_nee.png', bbox_inches='tight')
ax, fig = p.plot_4dvar('nee_day', d, xb=d.edinburgh_mean, xa=xa)
fig.savefig(f_name + '_need.png', bbox_inches='tight')
ax, fig = p.plot_4dvar('nee_night', d, xb=d.edinburgh_mean, xa=xa)
fig.savefig(f_name + '_neen.png', bbox_inches='tight')
ax, fig = p.plot_4dvar('lai', d, xb=d.edinburgh_mean, xa=xa)
fig.savefig(f_name + '_lai.png', bbox_inches='tight')
ax, fig = p.plot_4dvar('c_woo', d, xb=d.edinburgh_mean, xa=xa)
fig.savefig(f_name + '_cwoo.png', bbox_inches='tight')
ax, fig = p.plot_4dvar('c_roo', d, xb=d.edinburgh_mean, xa=xa)
fig.savefig(f_name + '_croo.png', bbox_inches='tight')
# Plot scatter plots of obs
ax, fig = p.plot_scatter('nee_day', xa, d, len(d.I), 'a')
fig.savefig(f_name + '_need_scat.png', bbox_inches='tight')
ax, fig = p.plot_scatter('nee_night', xa, d, len(d.I), 'a')
fig.savefig(f_name + '_neen_scat.png', bbox_inches='tight')
# Plot error in analysis and background
ax, fig = p.plot_a_inc(d.edinburgh_mean, xa, east_west)
fig.savefig(f_name + '_xa_inc.png', bbox_inches='tight')
return 'all experimented'
def plot_cum_nee(f_name, exp_name, f_typ='pdf', exp='a'):
sns.set_context('poster',
font_scale=1.,
rc={
'lines.linewidth': 1.5,
'lines.markersize': 1.
})
sns.set_style('whitegrid')
if not os.path.exists(f_name):
os.makedirs(f_name)
east = pickle.load(open(exp_name + 'east_assim', 'r'))
west = pickle.load(open(exp_name + 'west_assim', 'r'))
b = 1.5 * east['b_mat']
# east data
de = dc.DalecData(
2015,
2016,
'clma',
nc_file='../../alice_holt_data/ah_data_daily_test_nee2.nc',
scale_nee=1)
de.B = b
de.ob_dict = east['obs']
de.ob_err_dict = east['obs_err']
# obs err scaling
# west data
dw = dc.DalecData(
2015,
2016,
'clma',
nc_file='../../alice_holt_data/ah_data_daily_test_nee2.nc',
scale_nee=1)
dw.B = b
dw.ob_dict = west['obs']
dw.ob_err_dict = west['obs_err']
# obs err scaling
# setup model
me = mc.DalecModel(de)
me.rmatrix = r_mat_corr(me.yerroblist,
me.ytimestep,
me.y_strlst,
me.rmatrix,
corr=0.3,
tau=2.)[1]
mw = mc.DalecModel(dw)
mw.rmatrix = r_mat_corr(mw.yerroblist,
mw.ytimestep,
mw.y_strlst,
mw.rmatrix,
corr=0.3,
tau=2.)[1]
# a_east = pickle.load(open('a_east.p', 'r'))
# a_west = pickle.load(open('a_west.p', 'r'))
a_east = me.acovmat(east['xa'])
a_west = mw.acovmat(west['xa'])
e_ens = p.create_ensemble(de, a_east, east['xa'])
w_ens = p.create_ensemble(de, a_west, west['xa'])
p_e_ens = p.plist_ens(de, e_ens)
p_w_ens = p.plist_ens(dw, w_ens)
plot, ob_std_e, ob_std_w = p.plot_east_west_paper_cum2(
'nee',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label='Cumulative NEE (g C m$^{-2}$)')
plot[1].savefig(f_name + 'nee_cum.' + f_typ, bbox_inches='tight')
return east['xa'], west['xa'], de, dw, ob_std_e, ob_std_w
def save_paper_plots(f_name, exp_name):
east = pickle.load(open(exp_name + 'east_assim', 'r'))
west = pickle.load(open(exp_name + 'west_assim', 'r'))
#b_cor = pickle.load(open('b_edc_cor.p', 'r'))
#b_std = np.sqrt(np.diag(pickle.load(open('b_edc.p', 'r'))))
#b_std[10] = 0.2*b_std[10]
#b_std[1] = 0.2*b_std[1]
#b_std[2] = 0.1*b_std[2]
#b_std[0:17] = b_std[0:17]*0.5
#D = np.zeros_like(b_cor)
#np.fill_diagonal(D, b_std)
#b = 0.6*np.dot(np.dot(D, b_cor), D) #*0.6
b = east['b_mat']
# east data
de = dc.DalecData(
2015,
2016,
'clma',
nc_file='../../alice_holt_data/ah_data_daily_test_nee2.nc',
scale_nee=1)
de.B = b
de.ob_dict = east['obs']
de.ob_err_dict = east['obs_err']
# obs err scaling
# west data
dw = dc.DalecData(
2015,
2016,
'clma',
nc_file='../../alice_holt_data/ah_data_daily_test_nee2.nc',
scale_nee=1)
dw.B = b
dw.ob_dict = west['obs']
dw.ob_err_dict = west['obs_err']
# obs err scaling
# setup model
me = mc.DalecModel(de)
me.rmatrix = r_mat_corr(me.yerroblist,
me.ytimestep,
me.y_strlst,
me.rmatrix,
corr=0.3,
tau=2.)[1]
mw = mc.DalecModel(dw)
mw.rmatrix = r_mat_corr(mw.yerroblist,
mw.ytimestep,
mw.y_strlst,
mw.rmatrix,
corr=0.3,
tau=2.)[1]
# a_east = pickle.load(open('a_east.p', 'r'))
# a_west = pickle.load(open('a_west.p', 'r'))
a_east = me.acovmat(east['xa'])
a_west = mw.acovmat(west['xa'])
ax, fig = p.plot_east_west_paper(
'rh',
east['xa'],
west['xa'],
de,
dw,
a_east,
a_west,
y_label=r'Heterotrophic respiration (g C m$^{-2}$ day$^{-1}$)')
fig.savefig(f_name + 'rh.pdf', bbox_inches='tight')
ax, fig = p.plot_east_west_paper(
'nee_day',
east['xa'],
west['xa'],
de,
dw,
a_east,
a_west,
y_label=r'NEE$_{day}$ (g C m$^{-2}$ day$^{-1}$)',
y_lim=[-15, 5])
fig.savefig(f_name + 'nee_day.pdf', bbox_inches='tight')
ax, fig = p.plot_east_west_paper_cum(
'nee_day',
east['xa'],
west['xa'],
de,
dw,
a_east,
a_west,
y_label='Cumulative NEE (g C m$^{-2}$ day$^{-1}$)')
fig.savefig(f_name + 'nee_day_cum.pdf', bbox_inches='tight')
ax, fig = p.plot_east_west_paper_cum(
'nee',
east['xa'],
west['xa'],
de,
dw,
a_east,
a_west,
y_label='Cumulative NEE (g C m$^{-2}$ day$^{-1}$)')
fig.savefig(f_name + 'nee_cum.pdf', bbox_inches='tight')
ax, fig = p.plot_east_west_paper(
'nee_night',
east['xa'],
west['xa'],
de,
dw,
a_east,
a_west,
y_label=r'NEE$_{night}$ (g C m$^{-2}$ day$^{-1}$)')
fig.savefig(f_name + 'nee_night.pdf', bbox_inches='tight')
ax, fig = p.plot_east_west_paper(
'gpp',
east['xa'],
west['xa'],
de,
dw,
a_east,
a_west,
y_label=r'Gross primary production (g C m$^{-2}$ day$^{-1}$)')
fig.savefig(f_name + 'gpp.pdf', bbox_inches='tight')
ax, fig = p.plot_east_west_paper_cum(
'gpp',
east['xa'],
west['xa'],
de,
dw,
a_east,
a_west,
y_label='Cumulative GPP (g C m$^{-2}$ day$^{-1}$)')
fig.savefig(f_name + 'gpp_cum.pdf', bbox_inches='tight')
ax, fig = p.plot_east_west_paper('lai',
east['xa'],
west['xa'],
de,
dw,
a_east,
a_west,
y_label=r'Leaf area index')
fig.savefig(f_name + 'lai.pdf', bbox_inches='tight')
ax, fig = p.plot_east_west_paper(
'c_woo',
east['xa'],
west['xa'],
de,
dw,
a_east,
a_west,
y_label=r'Woody biomass and coarse root carbon (g C m$^{-2}$)',
y_lim=[9000, 14500])
fig.savefig(f_name + 'c_woo.pdf', bbox_inches='tight')
ax, fig = p.plot_east_west_paper(
'ra',
east['xa'],
west['xa'],
de,
dw,
a_east,
a_west,
y_label=r'Autotrophic respiration (g C m$^{-2}$ day$^{-1}$)')
fig.savefig(f_name + 'ra.pdf', bbox_inches='tight')
ax, fig = p.plot_east_west_paper(
'rt',
east['xa'],
west['xa'],
de,
dw,
a_east,
a_west,
y_label=r'Total ecosystem respiration (g C m$^{-2}$ day$^{-1}$)')
fig.savefig(f_name + 'rt.pdf', bbox_inches='tight')
ax, fig = p.plot_east_west_paper_cum(
'rt',
east['xa'],
west['xa'],
de,
dw,
a_east,
a_west,
y_label='Cumulative ecosystem respiration (g C m$^{-2}$ day$^{-1}$)')
fig.savefig(f_name + 'rt_cum.pdf', bbox_inches='tight')
ax, fig = p.plot_inc_east_west(east['xb'], east['xa'], west['xa'])
fig.savefig(f_name + 'xa_inc.pdf', bbox_inches='tight')
return 'done!'
import data_class as dc
import mod_class as mc
import numpy as np
import sympy as smp
import plot as p
import re
import os
import pickle
import matplotlib.pyplot as plt
import seaborn as sns
d = dc.DalecData(2015,
2016,
'clma',
nc_file='../../alice_holt_data/ah_data_daily_test_nee2.nc',
scale_nee=0)
def experiment_bmat(f_name):
# Construct B
b_cor = pickle.load(open('b_edc_cor.p', 'r'))
b_std = np.sqrt(np.diag(pickle.load(open('b_edc.p', 'r'))))
b_std[0:17] = b_std[0:17] * 0.5
D = np.zeros_like(b_cor)
np.fill_diagonal(D, b_std)
b = 0.8 * np.dot(np.dot(D, b_cor), D) #*0.6
experiment(f_name, b)
return 'done!'
def experiment_bmat_ceff(f_name):
def east_west_joint_run_nee_err_a(xb,
f_name,
nee_scale=0,
clma_er=1.,
lai_er=1.,
need_er=1.,
neen_er=1.,
cr_er=1.,
cw_er=1.):
f_name += 'clmaer%r_laier%r_needer%r_neener%r_crer%r_cwer%r' % (
clma_er, lai_er, need_er, neen_er, cr_er, cw_er)
# Construct B
b = 0.6 * pickle.load(open('a_cov.p', 'r'))
# b = pickle.load(open('b_edc.p', 'r'))
# east data
de = dc.DalecData(
2015,
2016,
'nee_day_east, nee_night_east, c_woo_east, clma, lai_east',
nc_file='../../alice_holt_data/ah_data_daily_test_nee.nc',
scale_nee=nee_scale)
de.B = b
# obs err scaling
de.ob_err_dict['clma'] = clma_er * de.ob_err_dict['clma']
de.ob_err_dict['lai'] = lai_er * de.ob_err_dict['lai']
de.ob_err_dict['nee_day'] = need_er * de.ob_err_dict['nee_day']
de.ob_err_dict['nee_night'] = neen_er * de.ob_err_dict['nee_night']
# de.ob_err_dict['c_roo'] = cr_er * de.ob_err_dict['c_roo']
de.ob_err_dict['c_woo'] = cw_er * de.ob_err_dict['c_woo']
# west data
dw = dc.DalecData(
2015,
2016,
'nee_day_west, nee_night_west, c_woo_west, clma, lai_west',
nc_file='../../alice_holt_data/ah_data_daily_test_nee.nc',
scale_nee=nee_scale)
dw.B = b
# obs err scaling
dw.ob_err_dict['clma'] = clma_er * dw.ob_err_dict['clma']
dw.ob_err_dict['lai'] = lai_er * dw.ob_err_dict['lai']
dw.ob_err_dict['nee_day'] = need_er * dw.ob_err_dict['nee_day']
dw.ob_err_dict['nee_night'] = neen_er * dw.ob_err_dict['nee_night']
# dw.ob_err_dict['c_roo'] = cr_er * dw.ob_err_dict['c_roo']
dw.ob_err_dict['c_woo'] = cw_er * dw.ob_err_dict['c_woo']
# setup model
me = mc.DalecModel(de)
me.rmatrix = r_mat_corr(me.yerroblist,
me.ytimestep,
me.y_strlst,
me.rmatrix,
corr=0.3,
tau=2.)[1]
mw = mc.DalecModel(dw)
mw.rmatrix = r_mat_corr(mw.yerroblist,
mw.ytimestep,
mw.y_strlst,
mw.rmatrix,
corr=0.3,
tau=2.)[1]
# run DA scheme
xa_e = me.find_min_tnc_cvt(xb, f_name + 'east_assim')
xa_w = mw.find_min_tnc_cvt(xb, f_name + 'west_assim')
# save plots
save_plots(f_name, xb, xa_e[1], xa_w[1], de, dw, me, mw)
return 'done'
def save_paper_plots(f_name, exp_name, f_typ='pdf', exp='a'):
if not os.path.exists(f_name):
os.makedirs(f_name)
east = pickle.load(open(exp_name + 'east_assim', 'r'))
west = pickle.load(open(exp_name + 'west_assim', 'r'))
b = 1.5 * east['b_mat']
# east data
de = dc.DalecData(
2015,
2016,
'clma',
nc_file='../../alice_holt_data/ah_data_daily_test_nee2.nc',
scale_nee=1)
de.B = b
de.ob_dict = east['obs']
de.ob_err_dict = east['obs_err']
# obs err scaling
# west data
dw = dc.DalecData(
2015,
2016,
'clma',
nc_file='../../alice_holt_data/ah_data_daily_test_nee2.nc',
scale_nee=1)
dw.B = b
dw.ob_dict = west['obs']
dw.ob_err_dict = west['obs_err']
# obs err scaling
# setup model
me = mc.DalecModel(de)
me.rmatrix = r_mat_corr(me.yerroblist,
me.ytimestep,
me.y_strlst,
me.rmatrix,
corr=0.3,
tau=2.)[1]
mw = mc.DalecModel(dw)
mw.rmatrix = r_mat_corr(mw.yerroblist,
mw.ytimestep,
mw.y_strlst,
mw.rmatrix,
corr=0.3,
tau=2.)[1]
# a_east = pickle.load(open('a_east.p', 'r'))
# a_west = pickle.load(open('a_west.p', 'r'))
a_east = me.acovmat(east['xa'])
a_west = mw.acovmat(west['xa'])
e_ens = p.create_ensemble(de, a_east, east['xa'])
w_ens = p.create_ensemble(de, a_west, west['xa'])
p_e_ens = p.plist_ens(de, e_ens)
p_w_ens = p.plist_ens(dw, w_ens)
annual_flux_lst = []
if exp == 'a':
cwoo_ylim = [0, 16000]
else:
cwoo_ylim = [9000, 14500]
# set context
sns.set_context('poster',
font_scale=1.,
rc={
'lines.linewidth': .8,
'lines.markersize': 1.
})
sns.set_style('whitegrid')
# joint plots
ax, fig = p.plot_east_west_paper_part(
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label=r'Cumulative fluxes (g C m$^{-2}$)')
fig.savefig(f_name + 'cum_flux.' + f_typ, bbox_inches='tight')
#ax, fig = p.plot_obs_east_west_part(east['xa'], de)
#fig.savefig(f_name+'resp_part_e.'+f_typ, bbox_inches='tight')
#ax, fig = p.plot_obs_east_west_part(west['xa'], de)
#fig.savefig(f_name+'resp_part_w.'+f_typ, bbox_inches='tight')
fig = p.part_plot(east['xa'], west['xa'], de, dw)
fig.savefig(f_name + 'resp_part.' + f_typ, bbox_inches='tight')
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
# Observed values
p.plot_east_west_paper('nee_day',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label=r'NEE$_{day}$ (g C m$^{-2}$ day$^{-1}$)',
y_lim=[-15, 5],
axes=ax1)
ax1.set_title(r'a) Daytime NEE') #, y=1.06)
p.plot_east_west_paper('nee_night',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label=r'NEE$_{night}$ (g C m$^{-2}$ day$^{-1}$)',
axes=ax2)
ax2.set_title(r'b) Nighttime NEE') #, y=1.06)
p.plot_east_west_paper('lai',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label=r'Leaf area index',
axes=ax3)
ax3.set_title(r'c) Leaf area index') #, y=1.06)
p.plot_east_west_paper('c_woo',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label=r'C$_{woo}$ (g C m$^{-2}$)',
y_lim=cwoo_ylim,
axes=ax4) # [9000, 14500][3000, 14500]
ax4.set_title(r'd) Woody and coarse root carbon') #, y=1.06)
f.tight_layout()
#f.subplots_adjust(hspace=.5)
f.savefig(f_name + 'obs_comp.' + f_typ) #, bbox_inches='tight')
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
# Fluxes
p.plot_east_west_paper('gpp',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label=r'GPP (g C m$^{-2}$ day$^{-1}$)',
axes=ax1)
ax1.set_title(r'a) Gross primary productivity') #, y=1.06)
p.plot_east_west_paper('rt',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label=r'R$_{eco}$ (g C m$^{-2}$ day$^{-1}$)',
axes=ax2)
ax2.set_title(r'b) Total ecosystem respiration') #, y=1.06)
p.plot_east_west_paper('ra',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label=r'R$_{a}$ (g C m$^{-2}$ day$^{-1}$)',
axes=ax3)
ax3.set_title(r'c) Autotrophic respiration') #, y=1.06)
p.plot_east_west_paper('rh',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label=r'R$_{h}$ (g C m$^{-2}$ day$^{-1}$)',
axes=ax4)
ax4.set_title(r'd) Heterotrophic respiration') #, y=1.06)
f.tight_layout()
#f.subplots_adjust(hspace=.5)
f.savefig(f_name + 'flux_comp.' + f_typ)
# std dev reduction
ax, fig = p.plot_var_red_east_west(b, a_east, a_west)
fig.savefig(f_name + 'var_red.' + f_typ, bbox_inches='tight')
sns.set_style('whitegrid')
ax, fig = p.plot_east_west_paper_part(
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label='Cumulative NEE partitioning (g C m$^{-2}$)')
fig.savefig(f_name + 'nee_cum_part.' + f_typ, bbox_inches='tight')
ax, fig = p.plot_east_west_paper(
'rh',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label=r'Heterotrophic respiration (g C m$^{-2}$ day$^{-1}$)')
fig.savefig(f_name + 'rh.' + f_typ, bbox_inches='tight')
ax, fig = p.plot_east_west_paper(
'nee_day',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label=r'NEE$_{day}$ (g C m$^{-2}$ day$^{-1}$)',
y_lim=[-15, 5])
fig.savefig(f_name + 'nee_day.' + f_typ, bbox_inches='tight')
# ax, fig, cum_east, cum_west = p.plot_east_west_paper_cum('nee_day', east['xa'], west['xa'], de, dw, p_e_ens, p_w_ens,
# y_label='Cumulative NEE (g C m$^{-2}$ day$^{-1}$)')
# fig.savefig(f_name+'nee_day_cum.png', bbox_inches='tight')
ax, fig, cum_east, cum_west = p.plot_east_west_paper_cum(
'nee',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label='Cumulative NEE (g C m$^{-2}$)')
fig.savefig(f_name + 'nee_cum.' + f_typ, bbox_inches='tight')
annual_flux_lst.append(cum_east)
annual_flux_lst.append(cum_west)
ax, fig = p.plot_east_west_paper(
'nee_night',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label=r'NEE$_{night}$ (g C m$^{-2}$ day$^{-1}$)')
fig.savefig(f_name + 'nee_night.' + f_typ, bbox_inches='tight')
ax, fig = p.plot_east_west_paper(
'gpp',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label=r'Gross primary production (g C m$^{-2}$ day$^{-1}$)')
fig.savefig(f_name + 'gpp.' + f_typ, bbox_inches='tight')
ax, fig, cum_east, cum_west = p.plot_east_west_paper_cum(
'gpp',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label='Cumulative GPP (g C m$^{-2}$)')
fig.savefig(f_name + 'gpp_cum.' + f_typ, bbox_inches='tight')
annual_flux_lst.append(cum_east)
annual_flux_lst.append(cum_west)
ax, fig = p.plot_east_west_paper('lai',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label=r'Leaf area index')
fig.savefig(f_name + 'lai.' + f_typ, bbox_inches='tight')
ax, fig = p.plot_east_west_paper(
'c_woo',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label=r'Woody biomass and coarse root carbon (g C m$^{-2}$)',
y_lim=cwoo_ylim)
fig.savefig(f_name + 'c_woo.' + f_typ, bbox_inches='tight')
ax, fig = p.plot_east_west_paper(
'ra',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label=r'Autotrophic respiration (g C m$^{-2}$ day$^{-1}$)')
fig.savefig(f_name + 'ra.' + f_typ, bbox_inches='tight')
ax, fig = p.plot_east_west_paper(
'rt',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label=r'Total ecosystem respiration (g C m$^{-2}$ day$^{-1}$)')
fig.savefig(f_name + 'rt.' + f_typ, bbox_inches='tight')
ax, fig, cum_east, cum_west = p.plot_east_west_paper_cum(
'rt',
east['xa'],
west['xa'],
de,
dw,
p_e_ens,
p_w_ens,
y_label='Cumulative ecosystem respiration (g C m$^{-2}$)')
fig.savefig(f_name + 'rt_cum.' + f_typ, bbox_inches='tight')
annual_flux_lst.append(cum_east)
annual_flux_lst.append(cum_west)
ax, fig = p.plot_inc_east_west(east['xb'], east['xa'], west['xa'])
fig.savefig(f_name + 'xa_inc.' + f_typ, bbox_inches='tight')
ax, fig = p.plot_rmat(p.cov2cor(east['rmat']))
fig.savefig(f_name + 'rmat_east.pdf', bbox_inches='tight')
ax, fig = p.plot_rmat(p.cov2cor(west['rmat']))
fig.savefig(f_name + 'rmat_west.pdf', bbox_inches='tight')
f = open(f_name + 'annual_fluxes.txt', 'w')
for item in annual_flux_lst:
f.write("%s\n" % item)
f.close()
return 'done!'
def save_paper_plots_test(f_name, exp_name):
if not os.path.exists(f_name):
os.makedirs(f_name)
east = pickle.load(open(exp_name + 'east_assim', 'r'))
west = pickle.load(open(exp_name + 'west_assim', 'r'))
b = east['b_mat']
# east data
de = dc.DalecData(
2015,
2016,
'clma',
nc_file='../../alice_holt_data/ah_data_daily_test_nee3.nc',
scale_nee=1)
de_all = dc.DalecData(
2015,
2016,
'clma, nee_day_east, nee_night_east, lai_east, c_woo_east',
nc_file='../../alice_holt_data/ah_data_daily_test_nee3.nc',
scale_nee=1)
de.B = b
de.ob_dict = east['obs']
de.ob_err_dict = east['obs_err']
# obs err scaling
# west data
dw = dc.DalecData(
2015,
2016,
'clma',
nc_file='../../alice_holt_data/ah_data_daily_test_nee3.nc',
scale_nee=1)
dw_all = dc.DalecData(
2015,
2016,
'clma, nee_day_west, nee_night_west, lai_west, c_woo_west',
nc_file='../../alice_holt_data/ah_data_daily_test_nee3.nc',
scale_nee=1)
dw.B = b
dw.ob_dict = west['obs']
dw.ob_err_dict = west['obs_err']
# obs err scaling
# setup model
me = mc.DalecModel(de)
me.rmatrix = r_mat_corr(me.yerroblist,
me.ytimestep,
me.y_strlst,
me.rmatrix,
corr=0.3,
tau=2.)[1]
mw = mc.DalecModel(dw)
mw.rmatrix = r_mat_corr(mw.yerroblist,
mw.ytimestep,
mw.y_strlst,
mw.rmatrix,
corr=0.3,
tau=2.)[1]
# a_east = pickle.load(open('a_east.p', 'r'))
# a_west = pickle.load(open('a_west.p', 'r'))
a_east = me.acovmat(east['xa'])
a_west = mw.acovmat(west['xa'])
e_ens = p.create_ensemble(de, a_east, east['xa'])
w_ens = p.create_ensemble(de, a_west, west['xa'])
p_e_ens = p.plist_ens(de, e_ens)
p_w_ens = p.plist_ens(dw, w_ens)
ax, fig = p.plot_var_red_east_west(b, a_east, a_west)
ax.set_ylim([0, 100])
fig.savefig(f_name + 'var_red.png', bbox_inches='tight')
sns.set_context('poster',
font_scale=1.,
rc={
'lines.linewidth': .8,
'lines.markersize': 1.
})
sns.set_style('whitegrid')
#de.ob_err_dict['lai']=np.sqrt(de.ob_err_dict['lai'])
#dw.ob_err_dict['lai']=np.sqrt(dw.ob_err_dict['lai'])
#ax, fig = p.plot_east_west_paper_part(east['xa'], west['xa'], de, dw, p_e_ens, p_w_ens,
# y_label='Cumulative NEE partitioning (g C m$^{-2}$)')
#fig.savefig(f_name+'nee_cum_part.png', bbox_inches='tight')
#f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
# Observed values
#p.plot_east_west_paper('nee_day', east['xa'], west['xa'], de, dw, p_e_ens, p_w_ens,
# y_label=r'NEE$_{day}$ (g C m$^{-2}$ day$^{-1}$)', y_lim=[-14, 6], axes=ax1,
# de_obdic=de_all,dw_obdic=dw_all)
#ax1.set_ylim([-14, 6])
#ax1.set_title(r'a) Daytime NEE')#, y=1.06)
#p.plot_east_west_paper('nee_night', east['xa'], west['xa'], de, dw, p_e_ens, p_w_ens,
# y_label=r'NEE$_{night}$ (g C m$^{-2}$ day$^{-1}$)', y_lim=[-14, 6],
# axes=ax2, de_obdic=de_all,
# dw_obdic=dw_all)
#ax2.set_ylim([-14, 6])
#ax2.set_title(r'b) Nighttime NEE')#, y=1.06)
#p.plot_east_west_paper('lai', east['xa'], west['xa'], de, dw, p_e_ens, p_w_ens,
# y_label=r'Leaf area index', y_lim=[0, 5], axes=ax3, de_obdic=de_all,
# dw_obdic=dw_all)
#ax3.set_ylim([0, 5])
#ax3.set_title(r'c) Leaf area index')#, y=1.06)
#p.plot_east_west_paper('c_woo', east['xa'], west['xa'], de, dw, p_e_ens, p_w_ens,
# y_label=r'C$_{woo}$ (g C m$^{-2}$)',
# y_lim=[9000, 14500], axes=ax4, de_obdic=de_all,
# dw_obdic=dw_all)
#ax4.set_ylim([0, 18000])
#ax4.set_title(r'd) Woody and coarse root carbon')#, y=1.06)
#f.tight_layout()
#f.subplots_adjust(hspace=.5)
#f.savefig(f_name+'obs_comp.pdf') #, bbox_inches='tight')
#f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
# Fluxes
#p.plot_east_west_paper('gpp', east['xa'], west['xa'], de, dw, p_e_ens, p_w_ens,
# y_label=r'GPP (g C m$^{-2}$ day$^{-1}$)', axes=ax1)
#ax1.set_title(r'a) Gross primary productivity')#, y=1.06)
#p.plot_east_west_paper('rt', east['xa'], west['xa'], de, dw, p_e_ens, p_w_ens,
# y_label=r'R$_{eco}$ (g C m$^{-2}$ day$^{-1}$)', axes=ax2)
#ax2.set_title(r'b) Total ecosystem respiration')#, y=1.06)
#p.plot_east_west_paper('ra', east['xa'], west['xa'], de, dw, p_e_ens, p_w_ens,
# y_label=r'R$_{a}$ (g C m$^{-2}$ day$^{-1}$)', axes=ax3)
#ax3.set_title(r'c) Autotrophic respiration')#, y=1.06)
#p.plot_east_west_paper('rh', east['xa'], west['xa'], de, dw, p_e_ens, p_w_ens,
# y_label=r'R$_{h}$ (g C m$^{-2}$ day$^{-1}$)', axes=ax4)
#ax4.set_title(r'd) Heterotrophic respiration')#, y=1.06)
#f.tight_layout()
#f.subplots_adjust(hspace=.5)
#f.savefig(f_name+'flux_comp.pdf') #, bbox_inches='tight')
return 'done!'