7.1. Example 1: Simple loss function.

In this example, a single loss function is defined as a 1:1 mapping of the input EDP. This means that the resulting loss distribution will be the same as the EDP distribution, allowing us to test and confirm that this is what happens.

from __future__ import annotations

import numpy as np
import pandas as pd

from pelicun import assessment, file_io

sample_size = 100000

# initialize a pelicun assessment
asmnt = assessment.Assessment({'PrintLog': False, 'Seed': 42})

#
# Demands
#

demands = pd.DataFrame(
    {
        'Theta_0': [0.50],
        'Theta_1': [0.90],
        'Family': ['lognormal'],
        'Units': ['mps2'],
    },
    index=pd.MultiIndex.from_tuples(
        [
            ('PFA', '0', '1'),
        ],
    ),
)

asmnt.demand.load_model({'marginals': demands})

asmnt.demand.generate_sample({'SampleSize': sample_size})

#
# Asset
#

asmnt.stories = 1

cmp_marginals = pd.read_csv('example_1/CMP_marginals.csv', index_col=0)
cmp_marginals['Blocks'] = cmp_marginals['Blocks']
asmnt.asset.load_cmp_model({'marginals': cmp_marginals})

asmnt.asset.generate_cmp_sample(sample_size)

#
# Damage
#

# nothing to do here.

#
# Losses
#

asmnt.loss.decision_variables = ('Cost',)

loss_map = pd.DataFrame(['cmp.A'], columns=['Repair'], index=['cmp.A'])
asmnt.loss.add_loss_map(loss_map)

loss_functions = file_io.load_data(
    'example_1/loss_functions.csv',
    reindex=False,
    unit_conversion_factors=asmnt.unit_conversion_factors,
)

asmnt.loss.load_model_parameters([loss_functions])
asmnt.loss.calculate()

loss, _ = asmnt.loss.aggregate_losses(future=True)