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Handle observableTransformation in petab.v1.simulate.sample_noise #383

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Merged
dweindl merged 4 commits into from
Apr 28, 2025
Merged

Handle observableTransformation in petab.v1.simulate.sample_noise #383

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1fb6b39
Handle `observableTransformation` in `petab.v1.simulate.sample_noise`
dweindl Apr 28, 2025
75f5f7f
Update petab/v1/simulate.py
dweindl Apr 28, 2025
6f57b37
Merge branch 'develop' into fix_382
dweindl Apr 28, 2025
3cc79bd
noqa
dweindl Apr 28, 2025
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25 changes: 22 additions & 3 deletions petab/v1/simulate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -241,20 +241,39 @@ def sample_noise(
simulated_value,
)

# default noise distribution is petab.C.NORMAL
noise_distribution = petab_problem.observable_df.loc[
observable_row = petab_problem.observable_df.loc[
measurement_row[petab.C.OBSERVABLE_ID]
].get(petab.C.NOISE_DISTRIBUTION, petab.C.NORMAL)
]
# default noise distribution is petab.C.NORMAL
noise_distribution = observable_row.get(
petab.C.NOISE_DISTRIBUTION, petab.C.NORMAL
)
# an empty noise distribution column in an observables table can result in
# `noise_distribution == float('nan')`
if pd.isna(noise_distribution):
noise_distribution = petab.C.NORMAL

observable_transformation = observable_row.get(
petab.C.OBSERVABLE_TRANSFORMATION, petab.C.LIN
)
transform = lambda x: x # noqa: E731
# observableTransformation=log -> the log of the simulated value is
# distributed according to `noise_distribution`
if observable_transformation == petab.C.LOG:
simulated_value = np.log(simulated_value)
transform = np.exp
elif observable_transformation == petab.C.LOG10:
simulated_value = np.log10(simulated_value)
transform = lambda x: np.power(10, x) # noqa: E731

# below is e.g.: `np.random.normal(loc=simulation, scale=noise_value)`
simulated_value_with_noise = getattr(rng, noise_distribution)(
loc=simulated_value, scale=noise_value * noise_scaling_factor
)

# apply observable transformation, ensure `float` type
simulated_value_with_noise = float(transform(simulated_value_with_noise))

if zero_bounded and np.sign(simulated_value) != np.sign(
simulated_value_with_noise
):
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