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Significance Weighted Principal Component Analysis (SWPCA)

SWPCA is a technique (1) developed to parse out the influence of a categorical variable that introduces variability in a certain dataset. This was originally intended to remove acquisition site variance in neuroimaging databases.

Use

To use the script to remove, navigate to the download dir, load the library (import swpca) into your environment and execute this command using the current dataset and acquisition site variables:

importswpcadataset_rect,weights,A=swpca.swpca(dataset, site)

It will return the rectified dataset, to be used in subsequent analysis.

  1. Francisco Jesús Martinez-Murcia et al. On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis Human Brain Mapping, Access online. 2016. http://dx.doi.org/10.1002/hbm.23449

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Significance Weighted Principal Component Analysis (SWPCA)
SWPCA is a technique (1) developed to parse out the influence of a categorical variable that introduces variability in a certain dataset. This was originally intended to remove acquisition site variance in neuroimaging databases.
Use
To use the script to remove, navigate to the download dir, load the library (import swpca) into your environment and execute this command using the current dataset and acquisition site variables:
importswpcadataset_rect,weights,A=swpca.swpca(dataset, site)
It will return the rectified dataset, to be used in subsequent analysis.
  1. Francisco Jesús Martinez-Murcia et al. On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis Human Brain Mapping, Access online. 2016. http://dx.doi.org/10.1002/hbm.23449
About
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Significance Weighted Principal Component Analysis (SWPCA)
SWPCA is a technique (1) developed to parse out the influence of a categorical variable that introduces variability in a certain dataset. This was originally intended to remove acquisition site variance in neuroimaging databases.
Use
To use the script to remove, navigate to the download dir, load the library (import swpca) into your environment and execute this command using the current dataset and acquisition site variables:
importswpcadataset_rect,weights,A=swpca.swpca(dataset, site)
It will return the rectified dataset, to be used in subsequent analysis.
  1. Francisco Jesús Martinez-Murcia et al. On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis Human Brain Mapping, Access online. 2016. http://dx.doi.org/10.1002/hbm.23449
About
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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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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Significance Weighted Principal Component Analysis (SWPCA)
SWPCA is a technique (1) developed to parse out the influence of a categorical variable that introduces variability in a certain dataset. This was originally intended to remove acquisition site variance in neuroimaging databases.
Use
To use the script to remove, navigate to the download dir, load the library (import swpca) into your environment and execute this command using the current dataset and acquisition site variables:
importswpcadataset_rect,weights,A=swpca.swpca(dataset, site)
It will return the rectified dataset, to be used in subsequent analysis.
  1. Francisco Jesús Martinez-Murcia et al. On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis Human Brain Mapping, Access online. 2016. http://dx.doi.org/10.1002/hbm.23449
About
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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); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Significance Weighted Principal Component Analysis (SWPCA)
SWPCA is a technique (1) developed to parse out the influence of a categorical variable that introduces variability in a certain dataset. This was originally intended to remove acquisition site variance in neuroimaging databases.
Use
To use the script to remove, navigate to the download dir, load the library (import swpca) into your environment and execute this command using the current dataset and acquisition site variables:
importswpcadataset_rect,weights,A=swpca.swpca(dataset, site)
It will return the rectified dataset, to be used in subsequent analysis.
  1. Francisco Jesús Martinez-Murcia et al. On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis Human Brain Mapping, Access online. 2016. http://dx.doi.org/10.1002/hbm.23449
About
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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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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Significance Weighted Principal Component Analysis (SWPCA)
SWPCA is a technique (1) developed to parse out the influence of a categorical variable that introduces variability in a certain dataset. This was originally intended to remove acquisition site variance in neuroimaging databases.
Use
To use the script to remove, navigate to the download dir, load the library (import swpca) into your environment and execute this command using the current dataset and acquisition site variables:
importswpcadataset_rect,weights,A=swpca.swpca(dataset, site)
It will return the rectified dataset, to be used in subsequent analysis.
  1. Francisco Jesús Martinez-Murcia et al. On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis Human Brain Mapping, Access online. 2016. http://dx.doi.org/10.1002/hbm.23449
About
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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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} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Significance Weighted Principal Component Analysis (SWPCA)
SWPCA is a technique (1) developed to parse out the influence of a categorical variable that introduces variability in a certain dataset. This was originally intended to remove acquisition site variance in neuroimaging databases.
Use
To use the script to remove, navigate to the download dir, load the library (import swpca) into your environment and execute this command using the current dataset and acquisition site variables:
importswpcadataset_rect,weights,A=swpca.swpca(dataset, site)
It will return the rectified dataset, to be used in subsequent analysis.
  1. Francisco Jesús Martinez-Murcia et al. On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis Human Brain Mapping, Access online. 2016. http://dx.doi.org/10.1002/hbm.23449
About
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Significance Weighted Principal Component Analysis (SWPCA)
SWPCA is a technique (1) developed to parse out the influence of a categorical variable that introduces variability in a certain dataset. This was originally intended to remove acquisition site variance in neuroimaging databases.
Use
To use the script to remove, navigate to the download dir, load the library (import swpca) into your environment and execute this command using the current dataset and acquisition site variables:
importswpcadataset_rect,weights,A=swpca.swpca(dataset, site)
It will return the rectified dataset, to be used in subsequent analysis.
  1. Francisco Jesús Martinez-Murcia et al. On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis Human Brain Mapping, Access online. 2016. http://dx.doi.org/10.1002/hbm.23449
About
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