Skip to content

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

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

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

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

Changes from 1 commit
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
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
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
24 changes: 21 additions & 3 deletions petab/v1/simulate.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -241,20 +241,38 @@ 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
)
# 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)
elif observable_transformation == petab.C.LOG10:
simulated_value = np.log10(simulated_value)

# 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
)

if observable_transformation == petab.C.LOG:
simulated_value_with_noise = np.exp(simulated_value_with_noise)
elif observable_transformation == petab.C.LOG10:
simulated_value_with_noise = np.power(10, simulated_value_with_noise)

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