def day_night_twin_run_no_obs_error(start, end, obs, f_name, its=1000):
d = dc.DalecDataTwin(start, end, obs, err_scale=0.0)
m = mc.DalecModel(d)
assim_results, xa = m.find_min_tnc_cvt(d.xb,
f_name + '_assim_res',
maxits=its)
d2 = dc.DalecDataTwin(start, 2013, obs)
# Plot 4dvar time series
ax, fig = p.plot_4dvar_twin('nee', d2, xa=xa)
fig.savefig(f_name + '_nee.pdf', bbox_inches='tight')
ax, fig = p.plot_4dvar_twin('nee_day', d2, xa=xa)
fig.savefig(f_name + '_need.pdf', bbox_inches='tight')
ax, fig = p.plot_4dvar_twin('nee_night', d2, xa=xa)
fig.savefig(f_name + '_neen.pdf', bbox_inches='tight')
ax, fig = p.plot_4dvar_twin('lai', d2, xa=xa)
fig.savefig(f_name + '_lai.pdf', bbox_inches='tight')
# Plot scatter plots of obs
ax, fig = p.plot_scatter_twin('nee', xa, d2, len(d.I), 'f')
fig.savefig(f_name + '_nee_scat.pdf', bbox_inches='tight')
ax, fig = p.plot_scatter_twin('nee_day', xa, d2, len(d.I), 'f')
fig.savefig(f_name + '_need_scat.pdf', bbox_inches='tight')
ax, fig = p.plot_scatter_twin('nee_night', xa, d2, len(d.I), 'f')
fig.savefig(f_name + '_neen_scat.pdf', bbox_inches='tight')
ax, fig = p.plot_scatter_twin('lai', xa, d2, len(d.I), 'f')
fig.savefig(f_name + '_lai_scat.pdf', bbox_inches='tight')
# Plot error in analysis and background
ax, fig = p.plottwinerr(d.x_truth, d.xb, xa)
fig.savefig(f_name + '_twin_err.pdf', bbox_inches='tight')
return 'all experimented'
def day_night_twin_run(start, end, obs, f_name, obs_loc):
d = dc.DalecDataTwin(start,
end,
obs,
nc_file='../../alice_holt_data/ah_data_daily_test.nc')
pik_obs = pickle.load(open(obs_loc, 'r'))
d.ob_dict = pik_obs
m = mc.DalecModel(d)
assim_results, xa = m.find_min_tnc_cvt(d.xb, f_name + '_assim_res')
d2 = dc.DalecDataTwin(start, 2013, obs)
# Plot 4dvar time series
ax, fig = p.plot_4dvar_twin('nee', d2, xa=xa)
fig.savefig(f_name + '_nee.pdf', bbox_inches='tight')
ax, fig = p.plot_4dvar_twin('nee_day', d2, xa=xa)
fig.savefig(f_name + '_need.pdf', bbox_inches='tight')
ax, fig = p.plot_4dvar_twin('nee_night', d2, xa=xa)
fig.savefig(f_name + '_neen.pdf', bbox_inches='tight')
ax, fig = p.plot_4dvar_twin('lai', d2, xa=xa)
fig.savefig(f_name + '_lai.pdf', bbox_inches='tight')
# Plot scatter plots of obs
ax, fig = p.plot_scatter_twin('nee', xa, d2, len(d.I), 'f')
fig.savefig(f_name + '_nee_scat.pdf', bbox_inches='tight')
ax, fig = p.plot_scatter_twin('nee_day', xa, d2, len(d.I), 'f')
fig.savefig(f_name + '_need_scat.pdf', bbox_inches='tight')
ax, fig = p.plot_scatter_twin('nee_night', xa, d2, len(d.I), 'f')
fig.savefig(f_name + '_neen_scat.pdf', bbox_inches='tight')
ax, fig = p.plot_scatter_twin('lai', xa, d2, len(d.I), 'f')
fig.savefig(f_name + '_lai_scat.pdf', bbox_inches='tight')
# Plot error in analysis and background
ax, fig = p.plottwinerr(d.x_truth, d.xb, xa)
fig.savefig(f_name + '_twin_err.pdf', bbox_inches='tight')
return 'all experimented'
def plotcostone_ens(vect=1, sizee=20):
"""Using test_cost plots convergance of cost fn gradient for decreasing
value of alpha.
"""
sns.set_context('poster',
font_scale=1.5,
rc={
'lines.linewidth': 1,
'lines.markersize': 6
})
fig, ax = plt.subplots(
nrows=1,
ncols=1,
) #figsize=(10,10))
sns.set_style('ticks')
power = np.arange(1, 14, 1)
xlist = [10**(-x) for x in power]
d = dC.DalecDataTwin(
1999,
2010,
'nee',
nc_file='../../alice_holt_data/ah_data_daily_test_nee3.nc',
scale_nee=0)
m = mc.DalecModel(d, size_ens=sizee)
tstlist = [test_cost_ens(m, x, vect) for x in xlist]
ax.semilogx(xlist, tstlist, 'k', marker='x', mew=1, ms=8)
#ax.semilogx(xlist, tstlist, 'k', 'x')
plt.xlabel(r'$\alpha$')
plt.ylabel(r'$f(\alpha)$')
#plt.title('test of the gradient of the cost function')
print tstlist
return ax, fig
def test_cost_cvt(alph=1e-8, vect=0):
"""Test for cost and gradcost functions.
"""
d = dC.DalecDataTwin(
1999,
2000,
'nee',
nc_file='../../alice_holt_data/ah_data_daily_test_nee3.nc',
scale_nee=1)
m = mc.DalecModel(d, size_ens=1)
pvals = d.edinburgh_mean
zvals = m.pvals2zvals(pvals)
gradj = m.gradcost_cvt(zvals)
print gradj.shape
if vect == 0:
h = zvals * (np.linalg.norm(zvals))**(-1)
elif vect == 1:
h = gradj * (np.linalg.norm(gradj))**(-1)
elif vect == 2:
h = np.ones(23) * (np.sqrt(23)**-1)
j = m.cost_cvt(zvals)
jalph = m.cost_cvt(zvals + alph * h)
print jalph - j
print np.dot(alph * h, gradj)
print(jalph - j) / (np.dot(alph * h, gradj))
return abs(jalph - j) / (np.dot(alph * h, gradj))
def twin_exps(exp, f_name):
if not os.path.exists(f_name):
os.makedirs(f_name)
if exp == 'a':
obs = 'nee_day_east, nee_night_east'
lab = '/twin_a'
ob_dic = pickle.load(open('twin_obs_a025'))
elif exp == 'b':
obs = 'nee_day_east, nee_night_east, lai_east, clma'
ob_dic = pickle.load(open('twin_obs_b025'))
lab = '/twin_b'
elif exp == 'c':
obs = 'nee_day_east, nee_night_east, lai_east, clma, c_woo_east'
ob_dic = pickle.load(open('twin_obs_c025'))
lab = '/twin_c'
d = dc.DalecDataTwin(
2015,
2016,
obs,
nc_file='../../alice_holt_data/ah_data_daily_test_nee2.nc',
scale_nee=1,
err_scale=0.25)
d.ob_dict = ob_dic
m = mc.DalecModel(d)
m.rmatrix = r_mat_corr(m.yerroblist,
m.ytimestep,
m.y_strlst,
m.rmatrix,
corr=0.3,
tau=2.)[1]
# run DA scheme
xa = m.find_min_tnc_cvt(d.xb, f_name + lab)
ax, fig = p.plottwinerr(d.x_truth, d.xb, xa[1])
fig.savefig(f_name + lab + 'twin_err.pdf')
ax, fig = p.plot_twinerr_red(d.x_truth, d.xb, xa[1])
fig.savefig(f_name + lab + 'twin_err_red.pdf')
ax, fig = p.plot_twin_err(d.x_truth, d.xb, xa[1])
fig.savefig(f_name + lab + 'twin_err2.pdf', bbox_inches='tight')
return xa