def do_fapar(save_dir, f_retval, n_site=1, year=2001, lad=1):
# Write files which will be used by Fortran for calculation of fAPAR
dir_params = save_dir
# dir_fapar_mean = save_dir+'output_fapar_mean/'
# f_fapar_mean = save_dir + 'output_fapar_mean/fapar_in_Ne%d_%d.dat' % (n_site, year)
dir_fapar_sd = save_dir + "output_fapar_sd/"
if not os.path.exists(save_dir + "output_fapar_nadim_mean"):
os.makedirs(save_dir + "output_fapar_nadim_mean")
if not os.path.exists(save_dir + "output_fapar_nadim_sd"):
os.makedirs(save_dir + "output_fapar_nadim_sd")
prior_orig = pickle.load(open(save_dir + "prior_%d_%d.pkl" % (year, n_site), "rb"))
try:
(f_fapar_mean, f_fapar_sd) = write_fapar_in(
f_retval, year, dir_params, dir_fapar_sd, n_train=50, n_site=n_site, lad=lad, prior=prior_orig
)
except ValueError:
print "Something wrong with write_fapar_in()..."
# compile fortran code using the system call
os.system("f2py -c -m fapar_py nadimtools.f nadimbrf.f fapar_py.f90")
# Call fortran functions for fapar
out_fapar_mean = save_dir + "output_fapar_nadim_mean/fapar_out_Ne%d_%d.dat" % (n_site, year)
fapar_py.fapar_mean(f_fapar_mean, out_fapar_mean)
out_fapar_sd = []
# print 'dir_fapar_sd=', dir_fapar_sd
# print 'dir_fapar_sd[-1]=', dir_fapar_sd[-1]
for day_sd in range(1, 366):
out_fapar_sd = np.append(
out_fapar_sd, save_dir + "output_fapar_nadim_sd/fapar_out_Ne%d_%d_%d.dat" % (n_site, year, day_sd)
)
# print 'out_fapar_sd=', out_fapar_sd
fapar_py.fapar_mean(dir_fapar_sd + "fapar_in_Ne%d_%d_%d.dat" % (n_site, year, day_sd), out_fapar_sd[-1])
return f_fapar_mean, f_fapar_sd, out_fapar_mean, out_fapar_sd
os.system("f2py -c -m fapar_py nadimtools.f nadimbrf.f fapar_py_2.f90")
for n_site in [3]:
for year in [2007]:
f_fapar_mean = save_dir + 'output_fapar_mean/fapar_in_Ne%d_%d_mean.dat' % (n_site, year)
dir_fapar_sd = save_dir+'output_fapar_sd/'
f_retval = save_dir+'misr_etm_all_Ne%d_%d_ang7.pkl'%(n_site, year)
prior_orig = pickle.load(open(save_dir+'prior_%d_%d.pkl' % (year, n_site), "rb"))
write_fapar_in(f_retval, year, dir_params, f_fapar_mean, dir_fapar_sd, n_train=50,\
n_site=n_site, lad=lad, prior=prior_orig)
# Call fortran functiooutput_fapar_nadim_meanns for fapar
fapar_py.fapar_mean(f_fapar_mean, save_dir_nad +\
'output_fapar_nadim_mean/fapar_out_Ne%d_%d_mean.dat' %\
(n_site, year))
for day_sd in range(1, 366):
fapar_py_2.fapar_sd(dir_fapar_sd +\
'fapar_in_Ne%d_%d_%d_ang7.dat'%(n_site, year, day_sd),\
save_dir_nad +\
'output_fapar_nadim_sd/fapar_out_Ne%d_%d_%d.dat' %\
(n_site, year, day_sd))
# fapar = np.loadtxt(save_dir + 'output_fapar_nadim_sd/fapar_out_Ne%d_%d_%d.dat' % (n_site, year, day_sd))
# plot retrieved FAPAR
# plt.plot(-2.*np.log(prior.mu['xlai']), label='prior')
# plt.title('year %d' % year)
# plt.plot(fapar, label='retrieved year %d' % year)
# plt.xlim(1, 366)
# plt.legend()
