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Sensitivity Analysis Library (SALib)

Python implementations of commonly used sensitivity analysis methods. Useful in systems modeling to calculate the effects of model inputs or exogenous factors on outputs of interest.

Documentation:ReadTheDocs

Requirements:NumPy, SciPy, matplotlib, pandas, Python 3 (from SALib v1.2 onwards SALib does not officially support Python 2)

Installation:pip install SALib or python setup.py install or conda install SALib

Build Status:Build StatusTest Coverage:Coverage Status

SALib Paper:status

Herman, J., Usher, W., (2017), SALib: An open-source Python library for Sensitivity Analysis, Journal of Open Source Software, 2(9), 97, doi:10.21105/joss.00097

Methods included:

Contributing: see here

Quick Start

fromSALib.sampleimportsaltellifromSALib.analyzeimportsobolfromSALib.test_functionsimportIshigamiimportnumpyasnpproblem= {
'num_vars': 3,
'names': ['x1', 'x2', 'x3'],
'bounds': [[-np.pi, np.pi]]*3
}
# Generate samplesparam_values=saltelli.sample(problem, 1000)
# Run model (example)Y=Ishigami.evaluate(param_values)
# Perform analysisSi=sobol.analyze(problem, Y, print_to_console=True)
# Returns a dictionary with keys 'S1', 'S1_conf', 'ST', and 'ST_conf'# (first and total-order indices with bootstrap confidence intervals)

It's also possible to specify the parameter bounds in a file with 3 columns:

# name lower_bound upper_bound
P1 0.0 1.0
P2 0.0 5.0
...etc.

Then the problem dictionary above can be created from the read_param_file function:

fromSALib.utilimportread_param_fileproblem=read_param_file('/path/to/file.txt')
# ... same as above

Lots of other options are included for parameter files, as well as a command-line interface. See the advanced readme.

Also check out the examples for a full description of options for each method.

License

Copyright (C) 2012-2019 Jon Herman, Will Usher, and others. Versions v0.5 and later are released under the MIT license.

About

Sensitivity Analysis Library in Python (Numpy). Contains Sobol, Morris, Fractional Factorial and FAST methods.

SALib.github.io/SALib/

Resources

Readme
MIT license

Code of conduct

Code of conduct

Contributing

Contributing
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Stars

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Watchers

1 watching

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})();
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var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Sensitivity Analysis Library (SALib)
Python implementations of commonly used sensitivity analysis methods.
Useful in systems modeling to calculate the effects of model inputs or
exogenous factors on outputs of interest.
Documentation:ReadTheDocs
Requirements:NumPy,
SciPy,
matplotlib,
pandas,
Python 3 (from SALib v1.2 onwards SALib does not officially support Python 2)
Installation:pip install SALib or python setup.py install or conda install SALib
Build Status:Build StatusTest Coverage:Coverage Status
SALib Paper:status
Herman, J., Usher, W., (2017), SALib: An open-source Python library for Sensitivity Analysis, Journal of Open Source Software, 2(9), 97, doi:10.21105/joss.00097
Methods included:
Contributing: see here
Quick Start
fromSALib.sampleimportsaltellifromSALib.analyzeimportsobolfromSALib.test_functionsimportIshigamiimportnumpyasnpproblem= {
'num_vars': 3,
'names': ['x1', 'x2', 'x3'],
'bounds': [[-np.pi, np.pi]]*3
}
# Generate samplesparam_values=saltelli.sample(problem, 1000)
# Run model (example)Y=Ishigami.evaluate(param_values)
# Perform analysisSi=sobol.analyze(problem, Y, print_to_console=True)
# Returns a dictionary with keys 'S1', 'S1_conf', 'ST', and 'ST_conf'# (first and total-order indices with bootstrap confidence intervals)
It's also possible to specify the parameter bounds in a file with 3
columns:
# name lower_bound upper_bound
P1 0.0 1.0
P2 0.0 5.0
...etc.

Then the problem dictionary above can be created from the
read_param_file function:
fromSALib.utilimportread_param_fileproblem=read_param_file('/path/to/file.txt')
# ... same as above
Lots of other options are included for parameter files, as well as a
command-line interface. See the advanced
readme.
Also check out the
examples for a
full description of options for each method.
License
Copyright (C) 2012-2019 Jon Herman, Will Usher, and others. Versions v0.5 and
later are released under the MIT license.
About
Sensitivity Analysis Library in Python (Numpy). Contains Sobol, Morris, Fractional Factorial and FAST methods.
SALib.github.io/SALib/
Resources
Readme
MIT license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
0 stars
Watchers
1 watching
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0 forks
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}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
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var __re = new RegExp('^' + ".*" + '

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Sensitivity Analysis Library (SALib)
Python implementations of commonly used sensitivity analysis methods.
Useful in systems modeling to calculate the effects of model inputs or
exogenous factors on outputs of interest.
Documentation:ReadTheDocs
Requirements:NumPy,
SciPy,
matplotlib,
pandas,
Python 3 (from SALib v1.2 onwards SALib does not officially support Python 2)
Installation:pip install SALib or python setup.py install or conda install SALib
Build Status:Build StatusTest Coverage:Coverage Status
SALib Paper:status
Herman, J., Usher, W., (2017), SALib: An open-source Python library for Sensitivity Analysis, Journal of Open Source Software, 2(9), 97, doi:10.21105/joss.00097
Methods included:
Contributing: see here
Quick Start
fromSALib.sampleimportsaltellifromSALib.analyzeimportsobolfromSALib.test_functionsimportIshigamiimportnumpyasnpproblem= {
'num_vars': 3,
'names': ['x1', 'x2', 'x3'],
'bounds': [[-np.pi, np.pi]]*3
}
# Generate samplesparam_values=saltelli.sample(problem, 1000)
# Run model (example)Y=Ishigami.evaluate(param_values)
# Perform analysisSi=sobol.analyze(problem, Y, print_to_console=True)
# Returns a dictionary with keys 'S1', 'S1_conf', 'ST', and 'ST_conf'# (first and total-order indices with bootstrap confidence intervals)
It's also possible to specify the parameter bounds in a file with 3
columns:
# name lower_bound upper_bound
P1 0.0 1.0
P2 0.0 5.0
...etc.

Then the problem dictionary above can be created from the
read_param_file function:
fromSALib.utilimportread_param_fileproblem=read_param_file('/path/to/file.txt')
# ... same as above
Lots of other options are included for parameter files, as well as a
command-line interface. See the advanced
readme.
Also check out the
examples for a
full description of options for each method.
License
Copyright (C) 2012-2019 Jon Herman, Will Usher, and others. Versions v0.5 and
later are released under the MIT license.
About
Sensitivity Analysis Library in Python (Numpy). Contains Sobol, Morris, Fractional Factorial and FAST methods.
SALib.github.io/SALib/
Resources
Readme
MIT license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
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Packages
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Sensitivity Analysis Library (SALib)
Python implementations of commonly used sensitivity analysis methods.
Useful in systems modeling to calculate the effects of model inputs or
exogenous factors on outputs of interest.
Documentation:ReadTheDocs
Requirements:NumPy,
SciPy,
matplotlib,
pandas,
Python 3 (from SALib v1.2 onwards SALib does not officially support Python 2)
Installation:pip install SALib or python setup.py install or conda install SALib
Build Status:Build StatusTest Coverage:Coverage Status
SALib Paper:status
Herman, J., Usher, W., (2017), SALib: An open-source Python library for Sensitivity Analysis, Journal of Open Source Software, 2(9), 97, doi:10.21105/joss.00097
Methods included:
Contributing: see here
Quick Start
fromSALib.sampleimportsaltellifromSALib.analyzeimportsobolfromSALib.test_functionsimportIshigamiimportnumpyasnpproblem= {
'num_vars': 3,
'names': ['x1', 'x2', 'x3'],
'bounds': [[-np.pi, np.pi]]*3
}
# Generate samplesparam_values=saltelli.sample(problem, 1000)
# Run model (example)Y=Ishigami.evaluate(param_values)
# Perform analysisSi=sobol.analyze(problem, Y, print_to_console=True)
# Returns a dictionary with keys 'S1', 'S1_conf', 'ST', and 'ST_conf'# (first and total-order indices with bootstrap confidence intervals)
It's also possible to specify the parameter bounds in a file with 3
columns:
# name lower_bound upper_bound
P1 0.0 1.0
P2 0.0 5.0
...etc.

Then the problem dictionary above can be created from the
read_param_file function:
fromSALib.utilimportread_param_fileproblem=read_param_file('/path/to/file.txt')
# ... same as above
Lots of other options are included for parameter files, as well as a
command-line interface. See the advanced
readme.
Also check out the
examples for a
full description of options for each method.
License
Copyright (C) 2012-2019 Jon Herman, Will Usher, and others. Versions v0.5 and
later are released under the MIT license.
About
Sensitivity Analysis Library in Python (Numpy). Contains Sobol, Morris, Fractional Factorial and FAST methods.
SALib.github.io/SALib/
Resources
Readme
MIT license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
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Watchers
1 watching
Forks
0 forks
Report repository
Releases
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Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Sensitivity Analysis Library (SALib)
Python implementations of commonly used sensitivity analysis methods.
Useful in systems modeling to calculate the effects of model inputs or
exogenous factors on outputs of interest.
Documentation:ReadTheDocs
Requirements:NumPy,
SciPy,
matplotlib,
pandas,
Python 3 (from SALib v1.2 onwards SALib does not officially support Python 2)
Installation:pip install SALib or python setup.py install or conda install SALib
Build Status:Build StatusTest Coverage:Coverage Status
SALib Paper:status
Herman, J., Usher, W., (2017), SALib: An open-source Python library for Sensitivity Analysis, Journal of Open Source Software, 2(9), 97, doi:10.21105/joss.00097
Methods included:
Contributing: see here
Quick Start
fromSALib.sampleimportsaltellifromSALib.analyzeimportsobolfromSALib.test_functionsimportIshigamiimportnumpyasnpproblem= {
'num_vars': 3,
'names': ['x1', 'x2', 'x3'],
'bounds': [[-np.pi, np.pi]]*3
}
# Generate samplesparam_values=saltelli.sample(problem, 1000)
# Run model (example)Y=Ishigami.evaluate(param_values)
# Perform analysisSi=sobol.analyze(problem, Y, print_to_console=True)
# Returns a dictionary with keys 'S1', 'S1_conf', 'ST', and 'ST_conf'# (first and total-order indices with bootstrap confidence intervals)
It's also possible to specify the parameter bounds in a file with 3
columns:
# name lower_bound upper_bound
P1 0.0 1.0
P2 0.0 5.0
...etc.

Then the problem dictionary above can be created from the
read_param_file function:
fromSALib.utilimportread_param_fileproblem=read_param_file('/path/to/file.txt')
# ... same as above
Lots of other options are included for parameter files, as well as a
command-line interface. See the advanced
readme.
Also check out the
examples for a
full description of options for each method.
License
Copyright (C) 2012-2019 Jon Herman, Will Usher, and others. Versions v0.5 and
later are released under the MIT license.
About
Sensitivity Analysis Library in Python (Numpy). Contains Sobol, Morris, Fractional Factorial and FAST methods.
SALib.github.io/SALib/
Resources
Readme
MIT license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
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Watchers
1 watching
Forks
0 forks
Report repository
Releases
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Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Sensitivity Analysis Library (SALib)
Python implementations of commonly used sensitivity analysis methods.
Useful in systems modeling to calculate the effects of model inputs or
exogenous factors on outputs of interest.
Documentation:ReadTheDocs
Requirements:NumPy,
SciPy,
matplotlib,
pandas,
Python 3 (from SALib v1.2 onwards SALib does not officially support Python 2)
Installation:pip install SALib or python setup.py install or conda install SALib
Build Status:Build StatusTest Coverage:Coverage Status
SALib Paper:status
Herman, J., Usher, W., (2017), SALib: An open-source Python library for Sensitivity Analysis, Journal of Open Source Software, 2(9), 97, doi:10.21105/joss.00097
Methods included:
Contributing: see here
Quick Start
fromSALib.sampleimportsaltellifromSALib.analyzeimportsobolfromSALib.test_functionsimportIshigamiimportnumpyasnpproblem= {
'num_vars': 3,
'names': ['x1', 'x2', 'x3'],
'bounds': [[-np.pi, np.pi]]*3
}
# Generate samplesparam_values=saltelli.sample(problem, 1000)
# Run model (example)Y=Ishigami.evaluate(param_values)
# Perform analysisSi=sobol.analyze(problem, Y, print_to_console=True)
# Returns a dictionary with keys 'S1', 'S1_conf', 'ST', and 'ST_conf'# (first and total-order indices with bootstrap confidence intervals)
It's also possible to specify the parameter bounds in a file with 3
columns:
# name lower_bound upper_bound
P1 0.0 1.0
P2 0.0 5.0
...etc.

Then the problem dictionary above can be created from the
read_param_file function:
fromSALib.utilimportread_param_fileproblem=read_param_file('/path/to/file.txt')
# ... same as above
Lots of other options are included for parameter files, as well as a
command-line interface. See the advanced
readme.
Also check out the
examples for a
full description of options for each method.
License
Copyright (C) 2012-2019 Jon Herman, Will Usher, and others. Versions v0.5 and
later are released under the MIT license.
About
Sensitivity Analysis Library in Python (Numpy). Contains Sobol, Morris, Fractional Factorial and FAST methods.
SALib.github.io/SALib/
Resources
Readme
MIT license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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, 'i');
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injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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Sensitivity Analysis Library (SALib)
Python implementations of commonly used sensitivity analysis methods.
Useful in systems modeling to calculate the effects of model inputs or
exogenous factors on outputs of interest.
Documentation:ReadTheDocs
Requirements:NumPy,
SciPy,
matplotlib,
pandas,
Python 3 (from SALib v1.2 onwards SALib does not officially support Python 2)
Installation:pip install SALib or python setup.py install or conda install SALib
Build Status:Build StatusTest Coverage:Coverage Status
SALib Paper:status
Herman, J., Usher, W., (2017), SALib: An open-source Python library for Sensitivity Analysis, Journal of Open Source Software, 2(9), 97, doi:10.21105/joss.00097
Methods included:
Contributing: see here
Quick Start
fromSALib.sampleimportsaltellifromSALib.analyzeimportsobolfromSALib.test_functionsimportIshigamiimportnumpyasnpproblem= {
'num_vars': 3,
'names': ['x1', 'x2', 'x3'],
'bounds': [[-np.pi, np.pi]]*3
}
# Generate samplesparam_values=saltelli.sample(problem, 1000)
# Run model (example)Y=Ishigami.evaluate(param_values)
# Perform analysisSi=sobol.analyze(problem, Y, print_to_console=True)
# Returns a dictionary with keys 'S1', 'S1_conf', 'ST', and 'ST_conf'# (first and total-order indices with bootstrap confidence intervals)
It's also possible to specify the parameter bounds in a file with 3
columns:
# name lower_bound upper_bound
P1 0.0 1.0
P2 0.0 5.0
...etc.

Then the problem dictionary above can be created from the
read_param_file function:
fromSALib.utilimportread_param_fileproblem=read_param_file('/path/to/file.txt')
# ... same as above
Lots of other options are included for parameter files, as well as a
command-line interface. See the advanced
readme.
Also check out the
examples for a
full description of options for each method.
License
Copyright (C) 2012-2019 Jon Herman, Will Usher, and others. Versions v0.5 and
later are released under the MIT license.
About
Sensitivity Analysis Library in Python (Numpy). Contains Sobol, Morris, Fractional Factorial and FAST methods.
SALib.github.io/SALib/
Resources
Readme
MIT license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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Sensitivity Analysis Library (SALib)
Python implementations of commonly used sensitivity analysis methods.
Useful in systems modeling to calculate the effects of model inputs or
exogenous factors on outputs of interest.
Documentation:ReadTheDocs
Requirements:NumPy,
SciPy,
matplotlib,
pandas,
Python 3 (from SALib v1.2 onwards SALib does not officially support Python 2)
Installation:pip install SALib or python setup.py install or conda install SALib
Build Status:Build StatusTest Coverage:Coverage Status
SALib Paper:status
Herman, J., Usher, W., (2017), SALib: An open-source Python library for Sensitivity Analysis, Journal of Open Source Software, 2(9), 97, doi:10.21105/joss.00097
Methods included:
Contributing: see here
Quick Start
fromSALib.sampleimportsaltellifromSALib.analyzeimportsobolfromSALib.test_functionsimportIshigamiimportnumpyasnpproblem= {
'num_vars': 3,
'names': ['x1', 'x2', 'x3'],
'bounds': [[-np.pi, np.pi]]*3
}
# Generate samplesparam_values=saltelli.sample(problem, 1000)
# Run model (example)Y=Ishigami.evaluate(param_values)
# Perform analysisSi=sobol.analyze(problem, Y, print_to_console=True)
# Returns a dictionary with keys 'S1', 'S1_conf', 'ST', and 'ST_conf'# (first and total-order indices with bootstrap confidence intervals)
It's also possible to specify the parameter bounds in a file with 3
columns:
# name lower_bound upper_bound
P1 0.0 1.0
P2 0.0 5.0
...etc.

Then the problem dictionary above can be created from the
read_param_file function:
fromSALib.utilimportread_param_fileproblem=read_param_file('/path/to/file.txt')
# ... same as above
Lots of other options are included for parameter files, as well as a
command-line interface. See the advanced
readme.
Also check out the
examples for a
full description of options for each method.
License
Copyright (C) 2012-2019 Jon Herman, Will Usher, and others. Versions v0.5 and
later are released under the MIT license.
About
Sensitivity Analysis Library in Python (Numpy). Contains Sobol, Morris, Fractional Factorial and FAST methods.
SALib.github.io/SALib/
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MIT license
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