def main():
cur_dir = os.getcwd() + '/'
csv_name = sys.argv[1] # name of csv is input by user
cbf_dir = '../../../../../../scratch/users/cfamigli/cardamom/files/cbf_longadapted/' + csv_name[:
3] + '/'
plot_dir = '../../../../../../scratch/users/cfamigli/cardamom/plots/'
to_plot = read_csv(cur_dir + '../../misc/' + csv_name,
header=None) # read csv data
to_plot = to_plot[(to_plot.iloc[:, 0] < 5000) & (
to_plot.iloc[:, 1] > -9999
)] # ensure that first column only contains real pixel values (nothing numbered over 5000, which can happen due to read error)
# plot map
# first column of csv contains pixel numbers
# second column of csv contains values to plot
pixels_to_plot = to_plot.iloc[:, 0].tolist()
values_to_plot = to_plot.iloc[:, 1].tolist()
autil.plot_map(nrows=46,
ncols=73,
land_pixel_list=[
file[-8:-4]
for file in glob.glob(cur_dir + cbf_dir + '*.cbf')
],
pixel_value_list=list(map(str, pixels_to_plot)),
value_list=values_to_plot,
savepath=cur_dir + plot_dir + 'maps/',
savename=csv_name[:-4])
return
def main():
# set run information to read
model_id = sys.argv[1]
mcmc_id = sys.argv[2] # 119 for normal, 3 for DEMCMC
n_iter = sys.argv[3]
nbe_optimization = sys.argv[4] # 'OFF' or 'ON'
ens_size = 500
assim_type = sys.argv[5]
# set directories
cur_dir = os.getcwd() + '/'
misc_dir = cur_dir + '../../misc/'
cbf_dir = cur_dir + '../../../../../../scratch/users/cfamigli/cardamom/files/cbf' + assim_type + '/' + model_id + '/'
cbr_opt_dir = cur_dir + '../../../../../../scratch/users/cfamigli/cardamom/files/cbr' + assim_type + '/' + model_id + '/'
cbr_ef_dir = cur_dir + '../../../../../../scratch/users/cfamigli/cardamom/files/cbr' + assim_type + '_ef/' + model_id + '/'
cbr_pft_dir = cur_dir + '../../../../../../scratch/users/cfamigli/cardamom/files/cbr' + assim_type + '_pft/' + model_id + '/'
output_opt_dir = cur_dir + '../../../../../../scratch/users/cfamigli/cardamom/files/output' + assim_type + '/' + model_id + '/'
output_ef_dir = cur_dir + '../../../../../../scratch/users/cfamigli/cardamom/files/output' + assim_type + '_ef/' + model_id + '/'
output_pft_dir = cur_dir + '../../../../../../scratch/users/cfamigli/cardamom/files/output' + assim_type + '_pft/' + model_id + '/'
plot_dir = cur_dir + '../../../../../../../scratch/users/cfamigli/cardamom/plots/'
parnames = autil.get_parnames('../../misc/', model_id)
# get list of cbfs
os.chdir(cbf_dir)
cbf_files = glob.glob('*.cbf')
cbf_files.sort()
pixel_lst = []
os.chdir(cur_dir + '/../')
# initialize lists for error maps
card_unc, opt_obs_err, pft_obs_err, ef_obs_err, obs_std = np.zeros(
len(cbf_files)) * np.nan, np.zeros(len(cbf_files)) * np.nan, np.zeros(
len(cbf_files)) * np.nan, np.zeros(
len(cbf_files)) * np.nan, np.zeros(len(cbf_files)) * np.nan
opt_pft_trend, opt_ef_trend, opt_pft_seas, opt_ef_seas, opt_mean, pft_mean, ef_mean = np.zeros(
len(cbf_files)) * np.nan, np.zeros(len(cbf_files)) * np.nan, np.zeros(
len(cbf_files)) * np.nan, np.zeros(
len(cbf_files)) * np.nan, np.zeros(
len(cbf_files)) * np.nan, np.zeros(
len(cbf_files)) * np.nan, np.zeros(
len(cbf_files)) * np.nan
pft_mean_within_opt_unc, ef_mean_within_opt_unc = np.zeros(
len(cbf_files)) * np.nan, np.zeros(len(cbf_files)) * np.nan
################################################## iterate through pixels ##################################################
############################################################################################################################
include_ef = True
include_pft = True
include_opt = True
write_txt_sh_pft_rerun = True
# initialize
n_fluxes = autil.get_nofluxes_nopools_lma(model_id)[0]
n_pools = autil.get_nofluxes_nopools_lma(model_id)[1]
# load list of globcover labels
gl_lbls = list(
read_csv(misc_dir + 'Globcover2009_Legend.csv')['Value'].values)
n_classes = len(gl_lbls)
# load globcover csv for av_fracs determination
gl_fracs = read_csv(misc_dir + 'globcover_fracs.csv', header=0)
# load bestchains for cbr_files
conv_chains = read_pickle(cbr_opt_dir + model_id + assim_type + '_ALL' +
'_MCMC' + mcmc_id + '_' + n_iter +
'_best_subset.pkl')
conv_chains.columns = ['pixel', 'bestchains',
'conv'] #rename columns for easier access
# create csv to track pft reruns
pft_rerun_filename = 'pft_rerun_' + model_id + assim_type + '_MCMC' + mcmc_id + '_' + n_iter + '.csv'
pft_rerun = open(misc_dir + pft_rerun_filename, 'w')
w = csv.writer(pft_rerun)
# run through all pixels
for cbf_file in cbf_files:
ind = cbf_files.index(cbf_file)
pixel = cbf_file[-8:-4]
pixel_lst.append(pixel)
print(pixel)
# read in fracs and types for pixel
if int(pixel) in gl_fracs['pixel'].values:
fracs_at_pixel = gl_fracs.loc[gl_fracs['pixel'] == int(
pixel)].values[0][1:]
types_at_pixel = get_types_at_pixel(gl_fracs, pixel)
else:
fracs_at_pixel = np.zeros(len(gl_lbls))
types_at_pixel = []
# read in cbf
cbf_pixel = rwb.read_cbf_file(cbf_dir + cbf_file)
nsteps = cbf_pixel['nodays']
################################################## get PFT forward runs ##################################################
##########################################################################################################################
can_plot_pft = False
if include_pft:
pixel_rerun = []
pft_spec = '5rp_'
# initialize matrices to hold weighted average of fluxes and pools
flux_pft_pixel = np.zeros((1, nsteps, n_fluxes))
pool_pft_pixel = np.zeros((1, nsteps + 1, n_pools))
#flux_pft_pixel = np.zeros((ens_size, nsteps, n_fluxes))
#pool_pft_pixel = np.zeros((ens_size, nsteps+1, n_pools))
# read all forward runs (each pft's run) for a given pixel
print(types_at_pixel)
for pft in types_at_pixel:
suffix = cbf_file[:-9] + '_MCMC' + mcmc_id + '_' + n_iter + '_PFT' + str(
int(pft)
) + '_forward_' + pixel + '.bin' #cbf_file[:-8]+'MCMC'+mcmc_id+'_'+n_iter+'_PFT'+str(int(pft))+'_'+pixel+'.bin'
if (len(glob.glob(output_pft_dir + 'fluxfile_' + suffix)) > 0
) & (len(glob.glob(output_pft_dir + 'poolfile_' + suffix))
> 0):
print(str(int(pft)))
flux_pft = rwb.readbinarymat(
output_pft_dir + 'fluxfile_' + suffix,
[nsteps, n_fluxes])
pool_pft = rwb.readbinarymat(
output_pft_dir + 'poolfile_' + suffix,
[nsteps + 1, n_pools])
#autil.plot_general_timeseries(autil.get_output('NBE', model_id, flux_pft, pool_pft, cbr_data=[], lma_ind=autil.get_nofluxes_nopools_lma(model_id)[2]), 'NBE', cbf_pixel, plot_dir+'timeseries/pft/', model_id + '_MCMC'+mcmc_id + '_'+n_iter + '_' + pixel + '_'+str(int(pft))+'.png')
# add each flux and pool matrix (corresponding to each pft) according to pft fractions, as weighted average
flux_pft[np.isnan(flux_pft)] = 0.
pool_pft[np.isnan(pool_pft)] = 0.
if (flux_pft.shape[0] > 0) & (pool_pft.shape[0] > 0):
lbl_ind = gl_lbls.index(int(pft))
flux_pft_pixel += flux_pft * fracs_at_pixel[lbl_ind]
pool_pft_pixel += pool_pft * fracs_at_pixel[lbl_ind]
can_plot_pft = True
else:
pixel_rerun.append(pft)
else:
pixel_rerun.append(pft)
if len(pixel_rerun) > 0:
w.writerow([pixel] + pixel_rerun)
################################################ get optimal forward runs ################################################
##########################################################################################################################
can_plot_opt = False
if include_opt:
# get pixel's convergent chain numbers
if pixel in conv_chains['pixel'].values:
best_chains = conv_chains.loc[
conv_chains['pixel'] == pixel]['bestchains'].values[0][1:]
flux_opt, pool_opt = [], []
# aggregate best chain outputs into one list
for chain in best_chains:
suffix = cbf_file[:
-8] + 'MCMC' + mcmc_id + '_' + n_iter + '_' + pixel + '_' + chain + '.bin'
if (len(glob.glob(output_opt_dir + 'fluxfile_' + suffix)) >
0) & (len(
glob.glob(output_opt_dir + 'poolfile_' +
suffix)) > 0):
flux_opt.append(
rwb.readbinarymat(
output_opt_dir + 'fluxfile_' + suffix,
[nsteps, n_fluxes]))
pool_opt.append(
rwb.readbinarymat(
output_opt_dir + 'poolfile_' + suffix,
[nsteps + 1, n_pools]))
can_plot_opt = True
# stack list elements for plotting
flux_opt = np.vstack(flux_opt)
pool_opt = np.vstack(pool_opt)
################################################### get EF forward runs ###################################################
###########################################################################################################################
can_plot_ef = False
if include_ef:
ef_spec = 'clipped_PLS_soilgrids_poolobs_rescaled_forward_'
# if 'wpolys' in ef_spec: use '_MCMC'
# else: use 'MCMC'
suffix = cbf_file[:
-9] + '_MCMC' + mcmc_id + '_' + n_iter + '_' + ef_spec + pixel + '.bin' #cbf_file[:-8]+'MCMC'+mcmc_id+'_'+n_iter+'_EF_'+pixel+'.bin'
if (len(glob.glob(output_ef_dir + 'fluxfile_' + suffix)) > 0) & (
len(glob.glob(output_ef_dir + 'poolfile_' + suffix)) > 0):
flux_ef = rwb.readbinarymat(
output_ef_dir + 'fluxfile_' + suffix, [nsteps, n_fluxes])
pool_ef = rwb.readbinarymat(
output_ef_dir + 'poolfile_' + suffix,
[nsteps + 1, n_pools])
can_plot_ef = True
##################################################### plot and compare ####################################################
###########################################################################################################################
can_decompose = True if (can_plot_opt) & (can_plot_pft) & (
can_plot_ef) else False
# plot optimal and pft predictions together
output_opt = autil.get_output(
'NBE',
model_id,
flux_opt,
pool_opt,
cbr_data=[],
lma_ind=autil.get_nofluxes_nopools_lma(model_id)[2]) if (
include_opt) & (can_plot_opt) else np.ones(nsteps) * np.nan
output_pft = autil.get_output(
'NBE',
model_id,
flux_pft_pixel,
pool_pft_pixel,
cbr_data=[],
lma_ind=autil.get_nofluxes_nopools_lma(model_id)[2]) if (
include_pft) & (can_plot_pft) else np.ones(nsteps) * np.nan
output_ef = autil.get_output(
'NBE',
model_id,
flux_ef,
pool_ef,
cbr_data=[],
lma_ind=autil.get_nofluxes_nopools_lma(model_id)
[2]) if (include_ef) & (can_plot_ef) else np.ones(nsteps) * np.nan
card_unc[ind], opt_obs_err[ind], pft_obs_err[ind], ef_obs_err[
ind], obs_std[ind] = autil.plot_opt_pft_ef_timeseries(
output_opt,
output_pft,
output_ef,
'NBE',
pixel,
autil.rowcol_to_latlon([pixel]),
cbf_pixel,
err_v_obs=False,
savepath=plot_dir + 'forward_compare/timeseries/' + model_id +
'/',
title=model_id + '_MCMC' + mcmc_id + '_' + n_iter + '_' +
pft_spec + ef_spec + pixel + '.png')
if can_decompose:
opt_pft_trend[ind], opt_ef_trend[ind], opt_pft_seas[
ind], opt_ef_seas[ind], opt_mean[ind], pft_mean[ind], ef_mean[
ind], pft_mean_within_opt_unc[ind], ef_mean_within_opt_unc[
ind] = timeseries_decompose(
output_opt,
output_pft,
output_ef,
pixel,
savepath=plot_dir + 'forward_compare/decomp/' +
model_id + '/',
savename=model_id + '_MCMC' + mcmc_id + '_' +
n_iter + '_' + pft_spec + ef_spec + pixel)
# close csv for rerun tracking
pft_rerun.close()
# plot decomposition results
plot_decomposed(
[opt_pft_trend, opt_ef_trend], [opt_pft_seas, opt_ef_seas],
[opt_mean, pft_mean, ef_mean],
[pft_mean_within_opt_unc, ef_mean_within_opt_unc],
savepath=plot_dir + 'forward_compare/decomp/' + model_id + '/',
savename=model_id + '_MCMC' + mcmc_id + '_' + n_iter + '_' + pft_spec +
ef_spec)
# plot error maps
for data, plot_title, vmin, vmax in zip(
[
card_unc, opt_obs_err, pft_obs_err, ef_obs_err, obs_std,
opt_obs_err / obs_std, pft_obs_err / obs_std, ef_obs_err / obs_std,
pft_obs_err / obs_std - opt_obs_err / obs_std,
ef_obs_err / obs_std - opt_obs_err / obs_std,
pft_obs_err / obs_std - ef_obs_err / obs_std
], [
'opt_unc', 'opt_err', 'pft_err', 'ef_err', 'obs_std',
'norm_opt_err', 'norm_pft_err', 'norm_ef_err',
'norm_pft_minus_norm_opt_err', 'norm_ef_minus_norm_opt_err',
'norm_pft_minus_norm_ef_err'
], [0., 0., 0., 0., 0., 0., 0., 0., -1., -1., -1.],
[0.7, 0.7, 0.7, 0.7, 0., 2., 2., 2., 1., 1., 1.]):
data_nonan, pixel_lst_nonan = remove_nan(data, pixel_lst)
stipple = card_unc if (plot_title == 'ef_err') | (
plot_title == 'pft_err') else None
autil.plot_map(
nrows=46,
ncols=73,
land_pixel_list=[file[-8:-4] for file in cbf_files],
pixel_value_list=pixel_lst_nonan,
value_list=data_nonan,
vmin=vmin,
vmax=vmax,
cmap='bwr',
savepath=plot_dir + 'forward_compare/maps/' + model_id + '/',
savename=model_id + '_MCMC' + mcmc_id + '_' + n_iter + '_' +
pft_spec + ef_spec + plot_title,
stipple=stipple) #vmax=np.nanpercentile(data_nonan, 90)
# save errors for comparison analysis
DataFrame(list(zip(pixel_lst, list(ef_obs_err / obs_std))),
columns=[
'pixels', 'norm_mae'
]).to_pickle(misc_dir + 'mae_pkls/' + model_id + '_MCMC' +
mcmc_id + '_' + n_iter + '_' + ef_spec + '.pkl')
DataFrame(list(zip(pixel_lst, list(pft_obs_err / obs_std))),
columns=[
'pixels', 'norm_mae'
]).to_pickle(misc_dir + 'mae_pkls/' + model_id + '_MCMC' +
mcmc_id + '_' + n_iter + '_' + pft_spec + '.pkl')
# plot discrete map showing best parameterization (lowest error) for each pixel
'''best_param_nonan, pixel_lst_nonan = best_param_nonancol([opt_obs_err, pft_obs_err, ef_obs_err], pixel_lst)
autil.plot_map(nrows=46, ncols=73, land_pixel_list=[file[-8:-4] for file in cbf_files], pixel_value_list=pixel_lst_nonan, value_list=best_param_nonan, cmap=LinearSegmentedColormap.from_list('mycmap', [(0, 'dodgerblue'), (0.5, 'orangered'), (1., 'limegreen')]),savepath=plot_dir+'forward_compare/maps/'+model_id+'/', savename=model_id+'_MCMC'+mcmc_id+'_'+n_iter+'_'+ef_spec+'best_param')'''
best_param_nonan, pixel_lst_nonan = best_param_nonancol(
[pft_obs_err, ef_obs_err], pixel_lst)
autil.plot_map(
nrows=46,
ncols=73,
land_pixel_list=[file[-8:-4] for file in cbf_files],
pixel_value_list=pixel_lst_nonan,
value_list=best_param_nonan,
cmap=LinearSegmentedColormap.from_list('mycmap', [(0, 'orangered'),
(1., 'limegreen')]),
savepath=plot_dir + 'forward_compare/maps/' + model_id + '/',
savename=model_id + '_MCMC' + mcmc_id + '_' + n_iter + '_' + pft_spec +
ef_spec + 'best_param')
rgb_triplets = err_rgb_triplets([opt_obs_err, pft_obs_err, ef_obs_err],
pixel_lst)
autil.plot_map_rgb(
nrows=46,
ncols=73,
land_pixel_list=[file[-8:-4] for file in cbf_files],
pixel_value_list=pixel_lst,
value_list=rgb_triplets,
savepath=plot_dir + 'forward_compare/maps/' + model_id + '/',
savename=model_id + '_MCMC' + mcmc_id + '_' + n_iter + '_' + pft_spec +
ef_spec + 'rgb')
############################################### create resubmission for pft ###############################################
###########################################################################################################################
if write_txt_sh_pft_rerun:
# set additional directories
mdf_dir = '../code/CARDAMOM_2.1.6c/C/projects/CARDAMOM_MDF/' if nbe_optimization == 'OFF' else '../code/CARDAMOM_Uma_2.1.6c-master/C/projects/CARDAMOM_MDF/'
runmodel_dir = '../code/CARDAMOM_2.1.6c/C/projects/CARDAMOM_GENERAL/' if nbe_optimization == 'OFF' else '../code/CARDAMOM_Uma_2.1.6c-master/C/projects/CARDAMOM_GENERAL/'
cbf_dir = '../../../../../../scratch/users/cfamigli/cardamom/files/cbf' + assim_type + '/' + model_id + '/'
cbf_pft_ic_dir = '../../../../../../scratch/users/cfamigli/cardamom/files/cbf' + assim_type + '_pft_ic/' + model_id + '/'
cbr_pft_dir = '../../../../../scratch/users/cfamigli/cardamom/files/cbr' + assim_type + '_pft/' + model_id + '/'
output_dir = '../../../../../scratch/users/cfamigli/cardamom/files/output' + assim_type + '/' + model_id + '/'
output_pft_dir = '../../../../../scratch/users/cfamigli/cardamom/files/output' + assim_type + '_pft/' + model_id + '/'
if mcmc_id == '119':
frac_save_out = str(int(int(n_iter) / 500))
elif mcmc_id == '3':
frac_save_out = str(
int(int(n_iter) / 500 * 100)
) # n_iterations/ frac_save_out * 100 will be ensemble size
# set up which files to rerun
pft_rerun = read_csv(misc_dir + pft_rerun_filename,
header=None,
sep=',',
names=['pixel'] + gl_lbls)
txt_filename = 'pft_ic_combined_list_' + model_id + assim_type + '_MCMC' + mcmc_id + '_' + n_iter + '_rerun.txt'
txt_file = open(txt_filename, 'w')
cl_count, row_count = 1, 0
for cbf_file in cbf_files:
pixel = cbf_file[-8:-4]
if int(pixel) in pft_rerun['pixel'].values:
pixel_classes = pft_rerun.loc[pft_rerun['pixel'] == int(
pixel)].values[0][1:]
for cl in pixel_classes:
if ~np.isnan(cl):
f = cbf_file[:-9] + '_PFT' + str(int(cl)) + '_' + pixel
txt_file.write(
'%sCARDAMOM_MDF.exe %s%s %s%s %s 0 %s 0.001 %s 1000'
% (mdf_dir, cbf_pft_ic_dir[3:], f + '.cbf',
cbr_pft_dir, f + '.cbr', n_iter, frac_save_out,
mcmc_id))
txt_file.write(
' && %sCARDAMOM_RUN_MODEL.exe %s%s %s%s %s%s %s%s %s%s %s%s'
% (runmodel_dir, cbf_pft_ic_dir[3:], f + '.cbf',
cbr_pft_dir, f + '.cbr', output_pft_dir,
'fluxfile_' + f + '.bin', output_pft_dir,
'poolfile_' + f + '.bin', output_pft_dir,
'edcdfile_' + f + '.bin', output_pft_dir,
'probfile_' + f + '.bin'))
cl_count += 1
if np.mod(cl_count, 5) == 0:
txt_file.write('\n')
row_count += 1
else:
txt_file.write(' && ')
txt_file.close()
sh_file = open(txt_filename[:-3] + 'sh', 'w')
autil.fill_in_sh(sh_file,
array_size=row_count,
n_hours=10,
txt_file=txt_filename,
combined=True)
return
def main():
### set specifications
model_id = sys.argv[1]
run_type = 'ALL'
mcmc_id = '119'
n_iter = '40000000'
ens_size = 500
assim_type = '_longadapted'
### set directories
cur_dir = os.getcwd() + '/'
cbf_dir = cur_dir + '../../../../../../scratch/users/cfamigli/cardamom/files/cbf'+assim_type+'/' + model_id + '/'
cbr_dir = cur_dir + '../../../../../../scratch/users/cfamigli/cardamom/files/cbr'+assim_type+'/' + model_id + '/'
output_dir = cur_dir + '../../../../../../scratch/users/cfamigli/cardamom/files/output'+assim_type+'/' + model_id + '/'
plot_dir = cur_dir + '../../../../../../scratch/users/cfamigli/cardamom/plots/'
parnames = autil.get_parnames(cur_dir + '../../misc/', model_id)
# get list of cbfs
os.chdir(cbf_dir)
cbf_files = glob.glob('*.cbf')
cbf_files.sort()
os.chdir(cur_dir)
# initialize lists of pixel names and rmses
pixels_plot = []
nbe_rmse, lai_rmse = [], []
for cbf_file in cbf_files:
print(cbf_file, cbf_files.index(cbf_file))
cbf_pixel = rwb.read_cbf_file(cbf_dir + cbf_file)
pixel = cbf_file[-8:-4]
cbr_files = glob.glob(cbr_dir + '*MCMC'+mcmc_id+'_'+n_iter+'_' + pixel + '_*.cbr')
cbr_files = sorted(cbr_files, key=lambda x: int(x.partition(pixel+'_')[-1].partition('.cbr')[0]))
# get all possible XX member combinations of cbr files
n_chains_to_converge = 4
cbr_files_all_subsets = [list(i) for i in itertools.combinations(cbr_files, n_chains_to_converge)]
continue_check = True
for subset in cbr_files_all_subsets:
if continue_check:
# read parameters and compute gelman rubin
cbr_chain_list = []
for cbr_file in subset:
cbr_chain = rwb.read_cbr_file(cbr_file, {'nopars': len(parnames)})
cbr_chain = autil.modulus_Bday_Fday(cbr_chain, parnames)
if np.shape(cbr_chain)[0]==ens_size:
cbr_chain_list.append(cbr_chain)
if len(cbr_chain_list)>1:
gr = autil.gelman_rubin(cbr_chain_list)
if sum(gr<1.2)/len(parnames)>=0.9:
continue_check = False
cbr_agg = np.vstack(cbr_chain_list)
pixels_plot.append(pixel)
best_subset = subset.copy()
else:
gr = np.nan
# if there is a convergent subset, read fluxes and pools
if not continue_check:
convergent_chain_nums = [el.partition('.cbr')[0].partition(pixel)[-1][1:] for el in best_subset]
convergent_files = [el.partition('.cbr')[0].partition(model_id+'/')[-1] for el in best_subset]
flux_pixel = []
pool_pixel = []
for filename in convergent_files:
flux_chain = rwb.readbinarymat(output_dir + 'fluxfile_' + filename+'.bin', [cbf_pixel['nodays'], autil.get_nofluxes_nopools_lma(model_id)[0]])
pool_chain = rwb.readbinarymat(output_dir + 'poolfile_' + filename+'.bin', [cbf_pixel['nodays']+1, autil.get_nofluxes_nopools_lma(model_id)[1]])
if (flux_chain.shape[0]==ens_size) & (pool_chain.shape[0]==ens_size):
flux_pixel.append(flux_chain)
pool_pixel.append(pool_chain)
nbe_pred = autil.get_output('NBE', model_id, np.vstack(flux_pixel), np.vstack(pool_pixel), cbr_agg, autil.get_nofluxes_nopools_lma(model_id)[2])
lai_pred = autil.get_output('LAI', model_id, np.vstack(flux_pixel), np.vstack(pool_pixel), cbr_agg, autil.get_nofluxes_nopools_lma(model_id)[2])
nbe_obs, lai_obs = cbf_pixel['OBS']['NBE'], cbf_pixel['OBS']['LAI']
nbe_rmse.append(rmse_real_numbers_only(nbe_pred, nbe_obs))
lai_rmse.append(rmse_real_numbers_only(lai_pred, lai_obs))
print(rmse_real_numbers_only(nbe_pred, nbe_obs), rmse_real_numbers_only(lai_pred, lai_obs))
autil.plot_map(nrows=46, ncols=73, land_pixel_list=[file[-8:-4] for file in glob.glob(cbf_dir + '*.cbf')], pixel_value_list=pixels_plot, value_list=nbe_rmse, savepath=plot_dir+'maps/', savename='rmse_nbe_' + model_id + assim_type+ '_MCMC' + mcmc_id + '_' + n_iter)
autil.plot_map(nrows=46, ncols=73, land_pixel_list=[file[-8:-4] for file in glob.glob(cbf_dir + '*.cbf')], pixel_value_list=pixels_plot, value_list=lai_rmse, savepath=plot_dir+'maps/', savename='rmse_lai_' + model_id + assim_type+ '_MCMC' + mcmc_id + '_' + n_iter)
rmse_df = DataFrame(list(zip(pixels_plot, nbe_rmse, lai_rmse)))
rmse_df.columns = ['pixel','nbe_rmse','lai_rmse']
rmse_df.to_pickle(cur_dir + '../../misc/rmse_' + model_id + assim_type+ '_MCMC' + mcmc_id + '_' + n_iter + '.pkl')
#################################################################################################################################################################
# analyze regionally
'''region_mask = Dataset(cur_dir + '../../misc/fourregion_maskarrays.nc')
region_mask.set_auto_mask(False)
regionmat, lat, lon = region_mask['4region'][:], region_mask['lat'][:], region_mask['lon'][:]
lat[0] = -90
lat[-1] = 90
model_ids = ['811', '911']
rmse_dfs = []
for model_id in model_ids:
rmse_df = read_pickle(cur_dir + '../../misc/rmse_' + model_id + assim_type+ '_MCMC' + mcmc_id + '_' + n_iter + '.pkl')
rmse_df.columns = ['pixel','nbe_rmse','lai_rmse']
regions = []
for pixel in rmse_df[rmse_df.columns[0]].tolist():
pixlat, pixlon = rwb.rowcol_to_latlon(pixel)
regions.append(regionmat[np.argwhere(lat==pixlat)[0][0], np.argwhere(lon==pixlon)[0][0]])
rmse_df.insert(loc=1, column='region', value=regions)
rmse_dfs.append(rmse_df)
print(rmse_dfs[0].groupby('region')['nbe_rmse'].mean(), rmse_dfs[0].groupby('region')['lai_rmse'].mean())
print(rmse_dfs[1].groupby('region')['nbe_rmse'].mean(), rmse_dfs[1].groupby('region')['lai_rmse'].mean())'''
return
def main():
model_id = sys.argv[1]
run_type = sys.argv[2] # ALL or SUBSET
mcmc_id = sys.argv[3] # 119 for normal, 3 for DEMCMC
nbe_optimization = sys.argv[4] # 'OFF' or 'ON'
assim_type = '_p25adapted'
cur_dir = os.getcwd() + '/'
mdf_dir = '../code/CARDAMOM_2.1.6c/C/projects/CARDAMOM_MDF/' if nbe_optimization == 'OFF' else '../code/CARDAMOM_Uma_2.1.6c-master/C/projects/CARDAMOM_MDF/'
runmodel_dir = '../code/CARDAMOM_2.1.6c/C/projects/CARDAMOM_GENERAL/' if nbe_optimization == 'OFF' else '../code/CARDAMOM_Uma_2.1.6c-master/C/projects/CARDAMOM_GENERAL/'
cbf_dir = '../../../../../../scratch/users/cfamigli/cardamom/files/cbf' + assim_type + '/' + model_id + '/'
cbr_dir = '../../../../../scratch/users/cfamigli/cardamom/files/cbr' + assim_type + '/' + model_id + '/'
output_dir = '../../../../../scratch/users/cfamigli/cardamom/files/output' + assim_type + '/' + model_id + '/'
plot_dir = '../../../../../../scratch/users/cfamigli/cardamom/plots/'
parnames = autil.get_parnames('../../misc/', model_id)
n_iterations = sys.argv[5]
runtime_assim = int(sys.argv[6])
resubmit_num = sys.argv[7]
n_chains_resubmit = 4
ens_size = 500
if mcmc_id == '119':
frac_save_out = str(int(int(n_iterations) / 500))
elif mcmc_id == '3':
frac_save_out = str(int(
int(n_iterations) / 500 *
100)) # n_iterations/ frac_save_out * 100 will be ensemble size
# select which pixels to submit
os.chdir(cbf_dir)
if run_type == 'ALL':
cbf_files = glob.glob('*.cbf')
elif run_type == 'SUBSET_RANDOM':
cbf_files = sample(glob.glob('*.cbf'), 10)
elif run_type == 'SUBSET_INPUT':
cbf_files = select_cbf_files(glob.glob('*.cbf'), [
'3809', '3524', '2224', '4170', '1945', '3813', '4054', '3264',
'1271', '3457'
])
os.chdir(cur_dir + '/../')
cbf_files.sort()
# create one combined submission file with all assimilation and forward commands for each pixel's chain on one line
txt_filename = 'combined_assim_forward_list_' + model_id + '_' + run_type + assim_type + '_MCMC' + mcmc_id + '_' + n_iterations + '_resubmit' + resubmit_num + '.txt'
txt_file = open(txt_filename, 'w')
resubmit_count = 0
gr_pixels = np.zeros(
len(cbf_files)) * np.nan # list of GR for each pixel, for mapping
pixels = []
best_subset = []
conv_bool_lst = []
for cbf_file in cbf_files:
best_subset_pixel = []
resubmit = False
print(cbf_file, cbf_files.index(cbf_file))
cbf_pixel = rwb.read_cbf_file(cur_dir + cbf_dir + cbf_file)
pixel = cbf_file[-8:-4]
cbr_files = glob.glob(cur_dir + '../' + cbr_dir + '*MCMC' + mcmc_id +
'_' + n_iterations + '_' + pixel + '_*.cbr')
cbr_files = sorted(
cbr_files,
key=lambda x: int(
x.partition(pixel + '_')[-1].partition('.cbr')[0]))
if len(cbr_files) >= n_chains_resubmit: pixels.append(pixel)
#cbr_files = cbr_files[:16] ############ TEMP
if len(cbr_files) > 0:
end_chain = int(
cbr_files[-1].partition(pixel + '_')[-1].partition('.cbr')[0])
#print('ENDCHAIN: '+str(end_chain))
else:
end_chain = 0
resubmit = True
# get all possible XX member combinations of cbr files
n_chains_to_converge = n_chains_resubmit
cbr_files_all_subsets = [
list(i)
for i in itertools.combinations(cbr_files, n_chains_to_converge)
]
continue_check = True
for subset in cbr_files_all_subsets:
if continue_check:
# read parameters and compute gelman rubin
cbr_chain_list = []
chain_nums = ['0']
for cbr_file in subset:
#print(cbr_file[-10:-4])
cbr_chain = rwb.read_cbr_file(cbr_file,
{'nopars': len(parnames)})
cbr_chain = autil.modulus_Bday_Fday(cbr_chain, parnames)
chain_nums.append(
cbr_file.partition('.cbr')[0].partition(pixel + '_')
[-1]) # append chain number
if np.shape(cbr_chain)[0] == ens_size:
cbr_chain_list.append(cbr_chain)
#print(np.shape(cbr_chain))
else:
print('incorrect ensemble size)')
resubmit = True
if len(cbr_chain_list) > 1:
gr = autil.gelman_rubin(cbr_chain_list)
#print(gr)
print(
'%i/%i' % (sum(gr < 1.2), len(parnames))
) #print('%i of %i parameters converged' % (sum(gr<1.2), len(parnames)))
if (np.isnan(gr_pixels[cbf_files.index(cbf_file)])):
gr_pixels[cbf_files.index(cbf_file)] = sum(
gr < 1.2) / len(parnames)
#if len(cbr_files_all_subsets)==1: best_subset_pixel.append(chain_nums)
if sum(gr < 1.2) / len(parnames) < 0.9:
#print('gr too low')
resubmit = True
if (sum(gr < 1.2) / len(parnames) >=
gr_pixels[cbf_files.index(cbf_file)]):
gr_pixels[cbf_files.index(cbf_file)] = sum(
gr < 1.2) / len(parnames)
best_subset_pixel.append(chain_nums)
conv_bool = 0
else:
resubmit = False
continue_check = False
gr_pixels[cbf_files.index(cbf_file)] = sum(
gr < 1.2) / len(parnames)
best_subset_pixel.append(chain_nums)
conv_bool = 1
else:
gr = np.nan
print('gr undefined')
best_subset_pixel.append(chain_nums)
conv_bool = 0
resubmit = True
if len(best_subset_pixel) > 0:
best_subset.append(best_subset_pixel[-1])
conv_bool_lst.append(conv_bool)
# write into text file if pixel needs to be resubmitted
if resubmit:
first_resubmit_chain = end_chain + 1
last_resubmit_chain = end_chain + n_chains_resubmit
for chain in range(first_resubmit_chain, last_resubmit_chain + 1):
c = '_' + str(chain)
txt_file.write(
'%sCARDAMOM_MDF.exe %s%s %s%s %s 0 %s 0.001 %s 1000' %
(mdf_dir, cbf_dir[3:], cbf_file, cbr_dir,
cbf_file[:-8] + 'MCMC' + mcmc_id + '_' + n_iterations +
'_' + cbf_file[-8:-4] + c + '.cbr', n_iterations,
frac_save_out, mcmc_id))
txt_file.write(
' && %sCARDAMOM_RUN_MODEL.exe %s%s %s%s %s%s %s%s %s%s %s%s'
% (runmodel_dir, cbf_dir[3:], cbf_file, cbr_dir,
cbf_file[:-8] + 'MCMC' + mcmc_id + '_' + n_iterations +
'_' + cbf_file[-8:-4] + c + '.cbr', output_dir,
'fluxfile_' + cbf_file[:-8] + 'MCMC' + mcmc_id + '_' +
n_iterations + '_' + cbf_file[-8:-4] + c + '.bin',
output_dir, 'poolfile_' + cbf_file[:-8] + 'MCMC' +
mcmc_id + '_' + n_iterations + '_' + cbf_file[-8:-4] +
c + '.bin', output_dir, 'edcdfile_' + cbf_file[:-8] +
'MCMC' + mcmc_id + '_' + n_iterations + '_' +
cbf_file[-8:-4] + c + '.bin', output_dir,
'probfile_' + cbf_file[:-8] + 'MCMC' + mcmc_id + '_' +
n_iterations + '_' + cbf_file[-8:-4] + c + '.bin'))
txt_file.write(
' && ') if chain < last_resubmit_chain else txt_file.write(
'\n')
resubmit_count += 1
txt_file.close()
sh_file = open(txt_filename[:-3] + 'sh', 'w')
autil.fill_in_sh(sh_file,
array_size=resubmit_count,
n_hours=runtime_assim,
txt_file=txt_filename,
combined=True)
autil.plot_map(nrows=46,
ncols=73,
land_pixel_list=pixels,
pixel_value_list=pixels,
value_list=gr_pixels * 100,
savepath=cur_dir + plot_dir + 'maps/',
savename='gr_' + model_id + assim_type + '_' + run_type +
'_MCMC' + mcmc_id + '_' + n_iterations + '_resubmit' +
resubmit_num)
#print(pixels, best_subset, conv_bool_lst)
print(len(pixels), len(best_subset), len(conv_bool_lst))
DataFrame(list(
zip(pixels, best_subset,
conv_bool_lst))).to_pickle(cur_dir + '../' + cbr_dir + model_id +
assim_type + '_' + run_type + '_MCMC' +
mcmc_id + '_' + n_iterations +
'_best_subset.pkl')
return
def main():
cur_dir = os.getcwd() + '/'
plot_dir = '../../../../../../scratch/users/cfamigli/cardamom/plots/'
os.chdir(plot_dir+'dists/')
# get list of model ids
models = list(set([el.split('_')[0] for el in glob.glob('*.png')]))
# remove 101, temporary until 102-->101
models.remove('102')
os.chdir(cur_dir)
# set lists of variables and pixels
vrs = ['NBE','cumNBE','LAI','GPP','Reco','Rauto','Rhet','lit','root','som','wood']
pixels = ['3809','3524','2224','4170','1945','3813','4054','3264','1271','3457']
# set MCMC ID
mcmc_id = sys.argv[1]
n_iter = sys.argv[2]
assim_type = '_longadapted'
# dataframe will hold model structural uncertainty (Ms) and model parametric uncertainty (Mp) for each pixel-var combination
# n is number of models that make up the suite
partitioning = DataFrame(columns={'Ms','Mp','n'})
df_dir = '../../../../../../scratch/users/cfamigli/cardamom/files/processed_df/'
for var in vrs:
print('Variable: ' + var)
Mp_pixels = np.zeros(len(pixels))*np.nan # list of Mp for each pixel, for mapping
for pixel in pixels:
print('Pixel: ' + pixel)
nsteps = 228 if assim_type=='_longadapted' else 240
meds, ub, lb = np.zeros((len(models), nsteps))*np.nan, np.zeros((len(models), nsteps))*np.nan, np.zeros((len(models), nsteps))*np.nan # medians, upper bounds, lower bounds of prediction through time
Mp, n = 0, 0
for model in models:
print(model)
cbf_dir = '../../../../../../scratch/users/cfamigli/cardamom/files/cbf'+assim_type+'/' + model + '/'
cbr_dir = '../../../../../../scratch/users/cfamigli/cardamom/files/cbr'+assim_type+'/' + model + '/'
output_dir = '../../../../../../scratch/users/cfamigli/cardamom/files/output'+assim_type+'/' + model + '/'
parnames = autil.get_parnames(cur_dir+'../../misc/', model)
os.chdir(cur_dir+cbr_dir)
#files = set(glob.glob('*.cbr')) - set(glob.glob('*MCMC'+mcmc_id+'*.cbr'))
#files = glob.glob('*MCMC'+mcmc_id+'*.cbr')
files = set(glob.glob('*MCMC'+mcmc_id+'_'+n_iter+'_*.cbr'))
pixel_chains = autil.find_all_chains(files, pixel) # list of files corresponding to each chain at that pixel, e.g. 2224_1, 2224_2, 2224_3, 2222_4
pixel_chains.sort()
n_chains = len(pixel_chains)
if n_chains>0:
cbf_pixel = rwb.read_cbf_file(cur_dir + cbf_dir + pixel_chains[0].partition('_MCMC')[0]+'_'+pixel+'.cbf')
cbr_chain_list = []
for pixel_chain in pixel_chains:
print(pixel_chain)
cbr_chain = rwb.read_cbr_file(pixel_chain, {'nopars': len(parnames)}) # cbr file for one chain
cbr_chain_list.append(cbr_chain) # list of separate cbrs for each chain, use for gelman rubin
cbr_pixel = np.copy(cbr_chain) if pixel_chains.index(pixel_chain)==0 else np.concatenate((cbr_pixel, cbr_chain), axis=0) # concatenate all chain cbrs
#autil.plot_par_histograms(cbr_chain, parnames=parnames, savepath=cur_dir+plot_dir+'dists/', title=model+'_'+pixel_chain[:-3]+'png')
flux_chain = rwb.readbinarymat(cur_dir + output_dir + 'fluxfile_' + pixel_chain[:-3]+'bin', [cbf_pixel['nodays'], autil.get_nofluxes_nopools_lma(model)[0]])
pool_chain = rwb.readbinarymat(cur_dir + output_dir + 'poolfile_' + pixel_chain[:-3]+'bin', [cbf_pixel['nodays']+1, autil.get_nofluxes_nopools_lma(model)[1]])
#autil.plot_flux_pool_timeseries(cbf_pixel, cbr_chain, flux_chain, pool_chain, autil.get_nofluxes_nopools_lma(model)[2], savepath=cur_dir+plot_dir+'timeseries/', title=model+'_'+pixel_chain[:-3]+'png')
flux_pixel = np.copy(flux_chain) if pixel_chains.index(pixel_chain)==0 else np.concatenate((flux_pixel, flux_chain), axis=0) # concatenate all chain flux outputs
pool_pixel = np.copy(pool_chain) if pixel_chains.index(pixel_chain)==0 else np.concatenate((pool_pixel, pool_chain), axis=0) # concatenate all chain pool outputs
gr = autil.gelman_rubin(cbr_chain_list) # gelman rubin function from matt
gr_thresh = 1.2 # below this value parameters are assumed to be convergent
print('%i of %i parameters converged with GR<%.1f' % (sum(gr<gr_thresh), len(parnames), gr_thresh))
#autil.plot_par_histograms(cbr_pixel, parnames=parnames, savepath=cur_dir+plot_dir+'dists/', title=model+'_'+pixel_chain[:-6]+'.png')
#autil.plot_flux_pool_timeseries(cbf_pixel, cbr_pixel, flux_pixel, pool_pixel, autil.get_nofluxes_nopools_lma(model)[2], savepath=cur_dir+plot_dir+'timeseries/', title=model+'_'+pixel_chain[:-6]+'.png')
if (sum(gr<gr_thresh)/len(parnames)<.9): # don't include nonconvergent runs in analysis
continue
else:
fwd_data = autil.get_output(var, model, flux_pixel, pool_pixel, cbr_pixel, autil.get_nofluxes_nopools_lma(model)[2]) # get forward data for var
if len(fwd_data)>0:
if fwd_data.shape[1]>nsteps:
fwd_data = fwd_data[:,:-1]
fwd_data = autil.remove_outliers(fwd_data)
# fill medians, upper bounds, and lower bounds
meds[models.index(model), :] = np.nanmedian(fwd_data, axis=0)
ub[models.index(model), :] = np.nanpercentile(fwd_data, 75, axis=0)
lb[models.index(model), :] = np.nanpercentile(fwd_data, 25, axis=0)
fwd_data = autil.remove_below_25_above_75(fwd_data) # set values outside of 25th-75th range to nan
Mp += np.nanvar(fwd_data, axis=0) # sum of intra-ensemble variance, only compute on 25th-75th
n += 1
Ms = np.nanvar(meds, axis=0) # inter-median variance
Mp = Mp/n if n!=0 else float('nan')
Ms_div_sum = Ms/(Ms+Mp)
#Ms_div_sum[np.isnan(Ms_div_sum)] = 0
Mp_div_sum = Mp/(Ms+Mp)
#Mp_div_sum[np.isnan(Mp_div_sum)] = 1
partitioning.loc[pixel+'_'+var] = {'Ms': np.nanmean(Ms_div_sum), 'Mp': np.nanmean(Mp_div_sum), 'n': n}
autil.plot_anova_ts(meds, ub, lb, Ms_div_sum, Mp_div_sum, models, pixel=pixel, var=var, assim_type=assim_type + '_MCMC'+mcmc_id, savepath=cur_dir+plot_dir+'anova_ts')
Mp_pixels[pixels.index(pixel)] = np.nanmean(Mp_div_sum)
autil.plot_map(nrows=46, ncols=73, land_pixel_list=[file[-8:-4] for file in glob.glob(cur_dir + cbf_dir + '*.cbf')], pixel_value_list=pixels, value_list=Mp_pixels, vmin=0, vmax=1, cbar_label='Fraction of variance due to parameters', savepath=cur_dir+plot_dir+'maps/', title=var + assim_type + '_MCMC'+mcmc_id)
print(partitioning.to_string())
partitioning.sort_index(axis=1).to_pickle(cur_dir+df_dir+'summary' + assim_type + '_MCMC'+mcmc_id + '_' + date.today().strftime("%m%d%y")+'.pkl')
autil.plot_partitioning_grouped(partitioning, pixels, savepath=cur_dir+plot_dir+'anova_summary', savename='pixels' + assim_type + '_MCMC'+mcmc_id)
autil.plot_partitioning_grouped(partitioning, vrs, savepath=cur_dir+plot_dir+'anova_summary', savename='vars' + assim_type + '_MCMC'+mcmc_id)
return
def main():
model_id = sys.argv[1]
run_type = sys.argv[2] # ALL or SUBSET
mcmc_id = sys.argv[3] # 119 for normal, 3 for DEMCMC
n_iter = sys.argv[4]
ens_size = 500
assim_type = '_p25adapted'
use_bestchains_pkl = False
cur_dir = os.getcwd() + '/'
cbf_dir = '../../../../../../scratch/users/cfamigli/cardamom/files/cbf'+assim_type+'_ef_ic/' + model_id + '/'
cbr_dir = '../../../../../../scratch/users/cfamigli/cardamom/files/cbr'+assim_type+'_ef/' + model_id + '/'
output_dir = '../../../../../../scratch/users/cfamigli/cardamom/files/output'+assim_type+'_ef/' + model_id + '/'
plot_dir = '../../../../../../scratch/users/cfamigli/cardamom/plots/'
parnames = autil.get_parnames('../../misc/', model_id)
# load list of land pixels
pixels = list(set([file[-8:-4] for file in glob.glob(cbf_dir + '*.cbf')])) if run_type=='ALL' else ['3809','3524','2224','4170','1945','3813','4054','3264','1271','3457']
pixels.sort()
# load list of cbrs
cbr_files = glob.glob(cbr_dir+'*MCMC'+mcmc_id+'_'+n_iter+'_*PLS*forward*.cbr')
# for loop over pixels
gr_pixels = np.zeros(len(pixels))*np.nan # list of GR for each pixel, for mapping
par_pixels = np.zeros((len(pixels), len(parnames)))*np.nan
for pixel in pixels:
print(pixel, pixels.index(pixel))
pixel_chains = autil.find_all_chains(cbr_files, pixel)
pixel_chains.sort() # filenames
if use_bestchains_pkl:
conv_chains_pkl = read_pickle(glob.glob(cbr_dir + model_id + assim_type + '*_MCMC'+mcmc_id + '_'+n_iter+'_best_subset.pkl')[0])
conv_chains_pkl.columns = ['pixel','bestchains','conv'] #rename columns for easier access
if pixel in conv_chains_pkl['pixel'].values:
bestchains = conv_chains_pkl.loc[conv_chains_pkl['pixel']==pixel]['bestchains'].values[0][1:]
print(bestchains)
pixel_chains = [pixel_chain for pixel_chain in pixel_chains if pixel_chain.partition(pixel+'_')[-1][:-4] in bestchains]
else:
continue
#cbf_pixel = rwb.read_cbf_file(cur_dir + cbf_dir + pixel_chains[0].partition('_MCMC')[0]+'_'+pixel+'.cbf')
cbf_filename = glob.glob(cur_dir + cbf_dir + '*'+pixel+'.cbf')[0]
cbf_pixel = rwb.read_cbf_file(cbf_filename)
cbr_chain_list = []
for pixel_chain in pixel_chains:
print(pixel_chain)
cbr_chain = rwb.read_cbr_file(pixel_chain, {'nopars': len(parnames)})
cbr_chain = autil.modulus_Bday_Fday(cbr_chain, parnames)
cbr_pixel = np.copy(cbr_chain) if pixel_chains.index(pixel_chain)==0 else np.concatenate((cbr_pixel, cbr_chain), axis=0)
#autil.plot_par_histograms(cbr_chain, parnames=parnames, savepath=cur_dir+plot_dir+'dists/', title=model_id+'_'+pixel_chain[:-3]+'png')
try:
flux_chain = rwb.readbinarymat(cur_dir + output_dir + 'fluxfile_' + pixel_chain.partition(cbr_dir)[-1][:-3]+'bin', [cbf_pixel['nodays'], autil.get_nofluxes_nopools_lma(model_id)[0]])
pool_chain = rwb.readbinarymat(cur_dir + output_dir + 'poolfile_' + pixel_chain.partition(cbr_dir)[-1][:-3]+'bin', [cbf_pixel['nodays']+1, autil.get_nofluxes_nopools_lma(model_id)[1]])
#autil.plot_flux_pool_timeseries(cbf_pixel, cbr_chain, flux_chain, pool_chain, autil.get_nofluxes_nopools_lma(model_id)[2], savepath=cur_dir+plot_dir+'timeseries/', title=model_id+'_'+pixel_chain[:-3]+'png')
flux_pixel = np.copy(flux_chain) if pixel_chains.index(pixel_chain)==0 else np.concatenate((flux_pixel, flux_chain), axis=0)
pool_pixel = np.copy(pool_chain) if pixel_chains.index(pixel_chain)==0 else np.concatenate((pool_pixel, pool_chain), axis=0)
except Exception as e:
pass
if np.shape(cbr_chain)[0]==ens_size:
cbr_chain_list.append(cbr_chain)
#print(np.shape(cbr_chain))
if len(cbr_chain_list)>1:
gr = autil.gelman_rubin(cbr_chain_list)
#print(gr)
print('%i of %i parameters converged' % (sum(gr<1.2), len(parnames)))
gr_pixels[pixels.index(pixel)] = sum(gr<1.2)/len(parnames)
else:
gr = np.nan
par_pixels[pixels.index(pixel),:] = np.nanmedian(cbr_pixel, axis=0)
#autil.plot_par_histograms(cbr_pixel, parnames=parnames, savepath=cur_dir+plot_dir+'dists/', title=model_id+assim_type+'_MCMC'+mcmc_id+'_'+cbf_filename.partition(cbf_dir)[-1][:-4]+'.png')
#autil.plot_flux_pool_timeseries(cbf_pixel, cbr_pixel, flux_pixel, pool_pixel, autil.get_nofluxes_nopools_lma(model_id)[2], savepath=cur_dir+plot_dir+'timeseries/', title=model_id+assim_type+'_MCMC'+mcmc_id+'_'+cbf_filename.partition(cbf_dir)[-1][:-4]+'.png')
#vmax = [None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,210,200,215,6600,195,24000,None,None,None,900,None,None,None,None,None,None,None] #np.nanpercentile(par_pixels[:,par], 90)
for par in range(len(parnames)): autil.plot_map(nrows=46, ncols=73, land_pixel_list=[file[-8:-4] for file in glob.glob(cur_dir + cbf_dir + '*.cbf')], pixel_value_list=pixels, value_list=par_pixels[:,par], vmax=np.nanpercentile(par_pixels[:,par], 90), savepath=cur_dir+plot_dir+'maps/', savename='par'+str(par)+'_' + model_id +assim_type+ '_MCMC' + mcmc_id +'_'+ n_iter+'_EF_clipped_PLS_soilgrids_poolobs_rescaled_forward')
#autil.plot_map(nrows=46, ncols=73, land_pixel_list=[file[-8:-4] for file in glob.glob(cur_dir + cbf_dir + '*.cbf')], pixel_value_list=pixels, value_list=np.ones(len(pixels)), savepath=cur_dir+plot_dir+'maps/', title='test_pixels.png')
#autil.plot_map(nrows=46, ncols=73, land_pixel_list=[file[-8:-4] for file in glob.glob(cur_dir + cbf_dir + '*.cbf')], pixel_value_list=pixels, value_list=gr_pixels*100, savepath=cur_dir+plot_dir+'maps/', savename='gr_' + model_id + assim_type+ '_' +run_type+ '_MCMC' + mcmc_id + '_' + n_iter)
return