salib

library1.5.2pypypi✓ verified 84d ago

SALib is an open-source Python library providing implementations of various global sensitivity analysis methods, including Sobol', Morris, FAST, DGSM, PAWN, HDMR, Moment Independent, and fractional factorial. It is currently at version 1.5.2 and maintains an active development status with somewhat regular releases.

data ai-ml

pip install SALib

github ↗package ↗docs ↗

INSTALL

IMPORT

SIG · SALIB

salib

datapythonv1.5.2

Install

16.1s avg

Import

1107ms

Disk

403MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.5.2 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.920 runs

installs and imports cleanly · install 0.0s · import 1.138s · 399.8MB

glibc

py 3.10–3.920 runs

installs and imports cleanly · install 16.1s · import 1.076s · 383MB

403MB installed

● package 403MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

sample_method

✓ from SALib.sample import saltelli

Sampling methods are imported from the `SALib.sample` submodule.

analyze_method

✓ from SALib.analyze import sobol

Analysis methods are imported from the `SALib.analyze` submodule.

ProblemSpec

✓ from SALib import ProblemSpec

For the object-oriented interface, ProblemSpec is imported directly from the top-level `SALib` package.

This quickstart demonstrates a Sobol' sensitivity analysis using the Ishigami test function. It covers defining problem parameters, generating samples using the Saltelli sampler, evaluating a model, and analyzing the results to compute sensitivity indices.

import numpy as np from SALib.sample import saltelli from SALib.analyze import sobol from SALib.test_functions import Ishigami # 1. Define the model inputs problem = { 'num_vars': 3, 'names': ['x1', 'x2', 'x3'], 'bounds': [[-np.pi, np.pi]] * 3 } # 2. Generate samples param_values = saltelli.sample(problem, 1024) # 3. Run model (example: Ishigami function) Y = Ishigami.evaluate(param_values) # 4. Perform analysis Si = sobol.analyze(problem, Y, print_to_console=True) print(f"First-order sensitivity indices: {Si['S1']}") print(f"Total-order sensitivity indices: {Si['ST']}")

Debug

Known issues

breakingSALib version 1.2 and onwards officially dropped support for Python 2. Users must use Python 3 or later versions.

fix

Ensure your Python environment is running Python 3.x (>=3.9 recommended for 1.5.2).

affects: >=1.2

breakingStarting with SALib 1.0.0, the Python API for sampling and analysis methods expects NumPy matrices directly as input for model runs and results, rather than file paths. The command-line interface remains unchanged.

fix

Update your code to pass NumPy arrays (e.g., `param_values`, `Y`) directly to sampling and analysis functions like `saltelli.sample()` and `sobol.analyze()`.

affects: >=1.0.0

breakingSALib 1.5.2 introduced an updated NumPy dependency, requiring NumPy version 2.0 or higher.

fix

Upgrade NumPy in your environment to version 2.0 or newer using `pip install --upgrade numpy`.

affects: >=1.5.2

gotchaMismatching sampling and analysis methods can lead to incorrect sensitivity indices. For example, Sobol' analysis requires samples generated by a Sobol' sampler (like Saltelli's).

fix

Always use the appropriate sampling method for the chosen analysis method, as specified in the SALib documentation. E.g., `SALib.sample.saltelli` for `SALib.analyze.sobol`.

affects: All

gotchaSobol' index estimates can be biased if model outputs are not centered. SALib normalizes outputs by default to mitigate this. If using non-normalized outputs, larger sample sizes may be required for convergence.

fix

Be aware of the output normalization behavior if comparing results with other tools, and consider increasing sample size for non-centered outputs if normalization is bypassed or if convergence issues are observed.

affects: All

Errors

Common errors & fixes

ValueError: The number of samples in Y does not match the expected number of samples.

Y array length must exactly match the sample count from SALib.sample

fix

Ensure len(Y) == X.shape[0] where X is the sample matrix from saltelli.sample()

ImportError: cannot import name 'saltelli' from 'SALib.sample'

Old SALib version — saltelli was reorganized in SALib 1.4+

fix

pip install --upgrade salib  # saltelli is in SALib.sample since 1.4

ModuleNotFoundError: No module named 'SALib'

SALib not installed or wrong case — pip package is salib (lowercase)

fix

pip install salib

AttributeError: module 'SALib' has no attribute 'Problem'

problem is a plain dict, not a SALib class

fix

problem = {'num_vars': 3, 'names': ['x1','x2','x3'], 'bounds': [[0,1],[0,1],[0,1]]}

KeyError: 'num_vars'

num_vars key missing from problem dict

fix

Always include 'num_vars' in the problem dict

Upgrade

Version history

1.5.2latest on PyPI · released Oct 12, 2025

Audit

Dependencies

numpyrequiredCore numerical operations; requires >=2.0 since SALib 1.5.2.

scipyrequiredMathematical and scientific computations.

matplotlibrequiredPlotting and visualization of results.

pandasrequiredData structures and analysis, especially for results handling.

Agent activity

49 hits · last 30 days

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Bingbot

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Resources

githubhttps://github.com/SALib/SALib ↗

packagehttps://pypi.org/project/salib/ ↗

docshttps://salib.readthedocs.io ↗