How to use the easyvvuq.analysis.PCEAnalysis function in easyvvuq

delimiter='$',
        target_filename='cooling_in.json')
    decoder = uq.decoders.SimpleCSV(target_filename=output_filename,
                                    output_columns=output_columns,
                                    header=0)
    # Create a collation element for this campaign
    collater = uq.collate.AggregateSamples(average=False)
    # Create the sampler
    vary = {
        "kappa": cp.Uniform(0.025, 0.075),
        "t_env": cp.Uniform(15, 25)
    }
    sampler = uq.sampling.PCESampler(vary=vary,
                                     polynomial_order=3)
    actions = uq.actions.ExecuteLocal("tests/cooling/cooling_model.py cooling_in.json")
    stats = uq.analysis.PCEAnalysis(sampler=sampler,
                                    qoi_cols=output_columns)
    campaign(tmpdir, 'pce', 'pce', params, encoder, decoder, sampler,
             collater, actions, stats, vary, 0, 1)

sampler = uq.sampling.PCESampler(vary=vary, polynomial_order=4)
    my_campaign = uq.Campaign(name='gauss_vector', work_dir=tmpdir)
    my_campaign.add_app(name="gauss_vector",
                        params=params,
                        encoder=encoder,
                        decoder=decoder,
                        collater=collater)
    my_campaign.set_sampler(sampler)
    my_campaign.draw_samples()
    my_campaign.populate_runs_dir()
    my_campaign.apply_for_each_run_dir(actions)
    my_campaign.collate()

    data = my_campaign.get_collation_result()
    print("===== DATA:\n ", data)
    analysis = uq.analysis.PCEAnalysis(sampler=sampler, qoi_cols=["numbers"])
    my_campaign.apply_analysis(analysis)
    results = my_campaign.get_last_analysis()

encoder = uq.encoders.GenericEncoder(
        template_fname='tests/cooling/cooling.template',
        delimiter='$',
        target_filename='cooling_in.json')
    decoder = uq.decoders.SimpleCSV(target_filename=output_filename,
                                    output_columns=output_columns,
                                    header=0)
    collater = uq.collate.AggregateSamples(average=False)

    vary = {
        "kappa": cp.Uniform(0.025, 0.075),
        "t_env": cp.Uniform(15, 25)
    }
    cooling_sampler = uq.sampling.PCESampler(vary=vary, polynomial_order=3)
    cooling_action = uq.actions.ExecuteLocal("tests/cooling/cooling_model.py cooling_in.json")
    cooling_stats = uq.analysis.PCEAnalysis(sampler=cooling_sampler, qoi_cols=output_columns)

    return params, encoder, decoder, collater, cooling_sampler, cooling_action, cooling_stats

client.close()
    client.shutdown()
    
    time_end = time.time()
    print('Time for phase 4 = %.3f' % (time_end-time_start))
    time_start = time.time()

    my_campaign.collate()

    time_end = time.time()
    print('Time for phase 5 = %.3f' % (time_end-time_start))
    time_start = time.time()

    # Post-processing analysis
    my_campaign.apply_analysis(uq.analysis.PCEAnalysis(sampler=my_campaign.get_active_sampler(), qoi_cols=["te", "ne", "rho", "rho_norm"]))

    time_end = time.time()
    print('Time for phase 6 = %.3f' % (time_end-time_start))
    time_start = time.time()

    # Get Descriptive Statistics
    results = my_campaign.get_last_analysis()
    stats = results['statistical_moments']['te']
    per = results['percentiles']['te']
    sobols = results['sobols_first']['te']
    rho = results['statistical_moments']['rho']['mean']
    rho_norm = results['statistical_moments']['rho_norm']['mean']

    time_end = time.time()
    print('Time for phase 7 = %.3f' % (time_end-time_start))
    time_start = time.time()

cmd = f"{cwd}/fusion_model.py fusion_in.json"
my_campaign.apply_for_each_run_dir(uq.actions.ExecuteLocal(cmd, interpret='python3'))

time_end = time.time()
print('Time for phase 4 = %.3f' % (time_end-time_start))
time_start = time.time()

# Collate the results
my_campaign.collate()

time_end = time.time()
print('Time for phase 5 = %.3f' % (time_end-time_start))
time_start = time.time()

# Post-processing analysis
my_campaign.apply_analysis(uq.analysis.PCEAnalysis(sampler=my_campaign.get_active_sampler(), qoi_cols=["te", "ne", "rho", "rho_norm"]))

time_end = time.time()
print('Time for phase 6 = %.3f' % (time_end-time_start))
time_start = time.time()

# Get Descriptive Statistics
results = my_campaign.get_last_analysis()
stats = results['statistical_moments']['te']
per = results['percentiles']['te']
sobols = results['sobols_first']['te']
rho = results['statistical_moments']['rho']['mean']
rho_norm = results['statistical_moments']['rho_norm']['mean']

time_end = time.time()
print('Time for phase 7 = %.3f' % (time_end-time_start))
time_start = time.time()

polynomial_order=3)

# Associate the sampler with the campaign
my_campaign.set_sampler(my_sampler)

# Will draw all (of the finite set of samples)
my_campaign.draw_samples()

my_campaign.populate_runs_dir()
my_campaign.apply_for_each_run_dir(uq.actions.ExecuteLocal(
    "cooling_model.py cooling_in.json"))

my_campaign.collate()

# Post-processing analysis
my_analysis = uq.analysis.PCEAnalysis(sampler=my_sampler,
                                          qoi_cols=["te", "ti"])
my_campaign.apply_analysis(my_analysis)

# Get Descriptive Statistics
results = my_campaign.get_last_analysis()
stats = results['statistical_moments']['te']
per = results['percentiles']['te']
sobols = results['sobols_first']['te']

cluster = SLURMCluster(job_extra=['--cluster=mpp2'], queue='mpp2_batch',
                           cores=28, memory='1 GB')
    cluster.scale(1)
    print(cluster.job_script())
    client = Client(cluster)

    my_campaign.populate_runs_dir()

    cwd = os.getcwd()
    cmd = f"{cwd}/cooling_model.py cooling_in.json"
    my_campaign.apply_for_each_run_dir(uq.actions.ExecuteLocal(cmd, interpret='python3'), client)
    my_campaign.collate()

    # Post-processing analysis
    my_analysis = uq.analysis.PCEAnalysis(sampler=my_sampler,
                                          qoi_cols=["te"])
    my_campaign.apply_analysis(my_analysis)

    # Get Descriptive Statistics
    results = my_campaign.get_last_analysis()
    stats = results['statistical_moments']['te']
    per = results['percentiles']['te']
    sobols = results['sobols_first']['te']
    print(stats)
    print(per)
    print(sobols)