def post_proc(state_file, work_dir):
#Reload the campaign
my_campaign = uq.Campaign(state_file = state_file, work_dir = work_dir)
print('========================================================')
print('Reloaded campaign', my_campaign.campaign_dir.split('/')[-1])
print('========================================================')
#get sampler and output columns from my_campaign object
my_sampler = my_campaign._active_sampler
output_columns = my_campaign._active_app_decoder.output_columns
#fetch the results from the (remote) host via FabSim3
#get_UQ_results(my_campaign.campaign_dir, machine='eagle_vecma')
fab.get_uq_samples(my_campaign.campaign_dir, machine='localhost')
#collate output
my_campaign.collate()
# Post-processing analysis
sc_analysis = uq.analysis.SCAnalysis(sampler=my_sampler, qoi_cols=output_columns)
my_campaign.apply_analysis(sc_analysis)
results = my_campaign.get_last_analysis()
results['n_samples'] = sc_analysis._number_of_samples
# #store data
# store_uq_results(my_campaign.campaign_dir, results)
return results, sc_analysis, my_sampler, my_campaign
###############################################
fab.run_uq_ensemble(CONFIG, campaign.campaign_dir, script='ade',
machine=MACHINE, PJ=PILOT_JOB)
# wait for job to complete
fab.wait(machine=MACHINE)
# check if all output files are retrieved from the remote machine, returns a Boolean flag
all_good = fab.verify(CONFIG, campaign.campaign_dir,
TARGET_FILENAME,
machine=MACHINE)
if all_good:
# copy the results from the FabSim results dir to the EasyVVUQ results dir
fab.get_uq_samples(CONFIG, campaign.campaign_dir, sampler.n_samples, machine=MACHINE)
else:
print("Not all samples executed correctly")
import sys
sys.exit()
#############################################
# All output files are present, decode them #
#############################################
output_columns = ["u"]
decoder = uq.decoders.SimpleCSV(
target_filename=TARGET_FILENAME,
output_columns=output_columns)
actions = uq.actions.Actions(
# work_dir = home + "/VECMA/Campaigns/"
work_dir = '/tmp'
#Reload the campaign
my_campaign = uq.Campaign(state_file = 'campaign_state.json', work_dir = work_dir)
print('========================================================')
print('Reloaded campaign', my_campaign.campaign_dir.split('/')[-1])
print('========================================================')
#get sampler and output columns from my_campaign object
my_sampler = my_campaign.get_active_sampler()
output_columns = my_campaign._active_app_decoder.output_columns
#fetch the results from the (remote) host via FabSim3
fab.get_uq_samples(my_campaign.campaign_dir, machine='localhost')
#collate output
my_campaign.collate()
# Post-processing analysis
sc_analysis = uq.analysis.SCAnalysis(sampler=my_sampler, qoi_cols=output_columns)
my_campaign.apply_analysis(sc_analysis)
results = my_campaign.get_last_analysis()
results['n_samples'] = sc_analysis._number_of_samples
mu_S = results['statistical_moments']['S']['mean']
std_S = results['statistical_moments']['S']['std']
mu_I = results['statistical_moments']['I']['mean']
std_I = results['statistical_moments']['I']['std']
mu_R = results['statistical_moments']['R']['mean']
# print(sampler._samples.shape)
output_columns = campaign._active_app_decoder.output_columns
#Manually specify a subset of the output QoIs, is faster
#output_columns = ["IC_prev_avg_max", "IC_ex_max"]
fab.verify(config,
campaign.campaign_dir,
campaign._active_app_decoder.target_filename,
machine=machine,
PJ=True)
fab.get_uq_samples(config,
campaign.campaign_dir,
sampler.n_samples(),
skip=0,
machine=machine)
# collate output
campaign.collate()
# get full dataset of data
data = campaign.get_collation_result()
# print(data)
# Post-processing analysis
qmc_analysis = uq.analysis.QMCAnalysis(sampler=sampler,
qoi_cols=output_columns)
campaign.apply_analysis(qmc_analysis)
#manually execute analyse, such that we can supply output_index=-1, only using the last entry
def run_campaign(poly_order, work_dir='/tmp'):
# Set up a fresh campaign called "sc"
my_campaign = uq.Campaign(name='sc', work_dir=work_dir)
# Define parameter space
params = {
"x1": {
"type": "float",
"min": 0.0,
"max": 1.0,
"default": 0.5
},
"x2": {
"type": "float",
"min": 0.0,
"max": 1.0,
"default": 0.5
},
"out_file": {
"type": "string",
"default": "output.csv"
}
}
output_filename = params["out_file"]["default"]
output_columns = ["f"]
# Create an encoder, decoder and collation element
encoder = uq.encoders.GenericEncoder(template_fname=HOME +
'/sc/sobol.template',
delimiter='$',
target_filename='sobol_in.json')
decoder = uq.decoders.SimpleCSV(target_filename=output_filename,
output_columns=output_columns,
header=0)
collater = uq.collate.AggregateSamples(average=False)
# Add the SC app (automatically set as current app)
my_campaign.add_app(name="sc",
params=params,
encoder=encoder,
decoder=decoder,
collater=collater)
# Create the sampler
vary = {"x1": cp.Uniform(0.0, 1.0), "x2": cp.Uniform(0.0, 1.0)}
"""
SPARSE GRID PARAMETERS
----------------------
- sparse = True: use a Smolyak sparse grid
- growth = True: use an exponential rule for the growth of the number
of 1D collocation points per level. Used to make e.g. clenshaw-curtis
quadrature nested.
"""
my_sampler = uq.sampling.SCSampler(vary=vary,
polynomial_order=poly_order,
quadrature_rule="G",
sparse=False,
growth=False)
# Associate the sampler with the campaign
my_campaign.set_sampler(my_sampler)
print('Number of samples:', my_sampler.xi_d.shape[0])
# Will draw all (of the finite set of samples)
my_campaign.draw_samples()
my_campaign.populate_runs_dir()
# Use this instead to run the samples using EasyVVUQ on the localhost
# my_campaign.apply_for_each_run_dir(uq.actions.ExecuteLocal(
# "sc/sobol_model.py sobol_in.json"))
#Run execution using Fabsim
fab.run_uq_ensemble(my_campaign.campaign_dir,
'sobol_test',
machine='localhost')
fab.get_uq_samples(my_campaign.campaign_dir, machine='localhost')
my_campaign.collate()
# Post-processing analysis
analysis = uq.analysis.SCAnalysis(sampler=my_sampler,
qoi_cols=output_columns)
my_campaign.apply_analysis(analysis)
results = my_campaign.get_last_analysis()
#the unique ID of this Campaign
ID = my_campaign.campaign_dir.split('/')[-1]
#store the sobol indices of each campaign to the same results directory
results_dir = work_dir + '/sobols/' + ID
if os.path.exists(results_dir) == False:
os.makedirs(results_dir)
#store the 1st order sobols indices to a CSV file
sobols = pd.DataFrame(results['sobols_first']['f'])
sobols.to_csv(results_dir + '/sobols.csv')
return results, ID
