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Split brain networks - #125

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Islast wants to merge 47 commits into
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Split brain networks#125
Islast wants to merge 47 commits into
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@Islast

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  • I'm ready to merge
    Not at all. This is just a preview of my proposed changes to seperate the BrainNetwork class into two distinct subclasses: BinaryBrainNetwork and WeightedBrainNetwork.
  • What's the context for this pull request?
    (this is a good place to reference any issues that this PR addresses)
    Ruslan discovered that when you run the threshold method on an already binarised graph it returns garbage.
    This made me think that there are potentially a lot of situations where treated a weighted network like a binary network or vice versa could be disastrous.

  • What's new?
    My thinking is that the difference between a weighted and a binarised brain network should be very explicit since it's going to determine what sort of analyses we can do on it.

It should be possible to threshold a WeightedBrainNetwork into a BinaryBrainNetwork and it should be possible to initialise a WeightedBrainNetwork from a correlation matrix.

A BinaryBrainNetwork should support all of the network analysis methods. We could run these on a weighted graph no problem, but they would tell us nothing about our data. We will record the original weightings on existent BinaryBrainNetworks in case we want to restrict our focus to fewer edges later (the specific use case being plotting).

  • What should a reviewer feedback on?
    Is this the right direction to go in to solve this problem.

  • Does anything need to be updated after merge?
    (e.g the wiki or the WhitakerLab website)

docs and tutorials would certainly need to be updated, but it's a little early for that.

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@Islast@KirstieJane
, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all
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Split brain networks by Islast · Pull Request #125 · WhitakerLab/scona · GitHub
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Split brain networks - #125

Open
Islast wants to merge 47 commits into
WhitakerLab:masterfrom
Islast:splitBrainNetworks
Open

Split brain networks#125
Islast wants to merge 47 commits into
WhitakerLab:masterfrom
Islast:splitBrainNetworks

Conversation

@Islast

Copy link
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Collaborator
  • I'm ready to merge
    Not at all. This is just a preview of my proposed changes to seperate the BrainNetwork class into two distinct subclasses: BinaryBrainNetwork and WeightedBrainNetwork.
  • What's the context for this pull request?
    (this is a good place to reference any issues that this PR addresses)
    Ruslan discovered that when you run the threshold method on an already binarised graph it returns garbage.
    This made me think that there are potentially a lot of situations where treated a weighted network like a binary network or vice versa could be disastrous.

  • What's new?
    My thinking is that the difference between a weighted and a binarised brain network should be very explicit since it's going to determine what sort of analyses we can do on it.

It should be possible to threshold a WeightedBrainNetwork into a BinaryBrainNetwork and it should be possible to initialise a WeightedBrainNetwork from a correlation matrix.

A BinaryBrainNetwork should support all of the network analysis methods. We could run these on a weighted graph no problem, but they would tell us nothing about our data. We will record the original weightings on existent BinaryBrainNetworks in case we want to restrict our focus to fewer edges later (the specific use case being plotting).

  • What should a reviewer feedback on?
    Is this the right direction to go in to solve this problem.

  • Does anything need to be updated after merge?
    (e.g the wiki or the WhitakerLab website)

docs and tutorials would certainly need to be updated, but it's a little early for that.

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@Islast@KirstieJane
, 'i'); if (__m === '*' || __re.test(location.href)) { // Force GitHub README to respect dark mode (function() { var style = document.createElement('style'); style.textContent = ' .markdown-body { color-scheme: dark light; } .markdown-body pre { background: #161b22 !important; } .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; } .markdown-body table th, .markdown-body table td { border-color: #30363d !important; } .markdown-body img { background: #0d1117; } .markdown-body blockquote { border-left-color: #8b949e; } .markdown-body hr { border-color: #30363d; } '; document.head.appendChild(style); })(); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' Split brain networks by Islast · Pull Request #125 · WhitakerLab/scona · GitHub
Skip to content

Split brain networks - #125

Open
Islast wants to merge 47 commits into
WhitakerLab:masterfrom
Islast:splitBrainNetworks
Open

Split brain networks#125
Islast wants to merge 47 commits into
WhitakerLab:masterfrom
Islast:splitBrainNetworks

Conversation

@Islast

Copy link
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Collaborator
  • I'm ready to merge
    Not at all. This is just a preview of my proposed changes to seperate the BrainNetwork class into two distinct subclasses: BinaryBrainNetwork and WeightedBrainNetwork.
  • What's the context for this pull request?
    (this is a good place to reference any issues that this PR addresses)
    Ruslan discovered that when you run the threshold method on an already binarised graph it returns garbage.
    This made me think that there are potentially a lot of situations where treated a weighted network like a binary network or vice versa could be disastrous.

  • What's new?
    My thinking is that the difference between a weighted and a binarised brain network should be very explicit since it's going to determine what sort of analyses we can do on it.

It should be possible to threshold a WeightedBrainNetwork into a BinaryBrainNetwork and it should be possible to initialise a WeightedBrainNetwork from a correlation matrix.

A BinaryBrainNetwork should support all of the network analysis methods. We could run these on a weighted graph no problem, but they would tell us nothing about our data. We will record the original weightings on existent BinaryBrainNetworks in case we want to restrict our focus to fewer edges later (the specific use case being plotting).

  • What should a reviewer feedback on?
    Is this the right direction to go in to solve this problem.

  • Does anything need to be updated after merge?
    (e.g the wiki or the WhitakerLab website)

docs and tutorials would certainly need to be updated, but it's a little early for that.

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@Islast@KirstieJane
, 'i'); if (__m === '*' || __re.test(location.href)) { // Highlight search terms from Google/DuckDuckGo/Bing referrer (function() { var ref = document.referrer; var terms = []; if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) { var url = new URL(ref); var q = url.searchParams.get('q') || url.searchParams.get('p'); if (q) { terms = q.split(/\s+/).filter(function(t) { return t.length > 2; }); } } if (terms.length === 0) return; var style = document.createElement('style'); style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }'; document.head.appendChild(style); function highlight(node) { if (node.nodeType === 3) { // text node var text = node.textContent; var found = false; terms.forEach(function(term) { var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi'); if (regex.test(text)) { found = true; var frag = document.createDocumentFragment(); var parts = text.split(regex); parts.forEach(function(part, i) { if (i % 2 === 0) { frag.appendChild(document.createTextNode(part)); } else { var span = document.createElement('span'); span.className = 'userscript-highlight'; span.textContent = part; frag.appendChild(span); } }); node.parentNode.replaceChild(frag, node); } }); } else if (node.nodeType === 1 && node.childNodes) { // element var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT']; if (!skipTags.includes(node.tagName)) { Array.from(node.childNodes).forEach(highlight); } } } highlight(document.body); // Re-highlight on dynamic content var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1 || node.nodeType === 3) highlight(node); }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' Split brain networks by Islast · Pull Request #125 · WhitakerLab/scona · GitHub
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Split brain networks - #125

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Islast wants to merge 47 commits into
WhitakerLab:masterfrom
Islast:splitBrainNetworks
Open

Split brain networks#125
Islast wants to merge 47 commits into
WhitakerLab:masterfrom
Islast:splitBrainNetworks

Conversation

@Islast

Copy link
Copy Markdown
Collaborator
  • I'm ready to merge
    Not at all. This is just a preview of my proposed changes to seperate the BrainNetwork class into two distinct subclasses: BinaryBrainNetwork and WeightedBrainNetwork.
  • What's the context for this pull request?
    (this is a good place to reference any issues that this PR addresses)
    Ruslan discovered that when you run the threshold method on an already binarised graph it returns garbage.
    This made me think that there are potentially a lot of situations where treated a weighted network like a binary network or vice versa could be disastrous.

  • What's new?
    My thinking is that the difference between a weighted and a binarised brain network should be very explicit since it's going to determine what sort of analyses we can do on it.

It should be possible to threshold a WeightedBrainNetwork into a BinaryBrainNetwork and it should be possible to initialise a WeightedBrainNetwork from a correlation matrix.

A BinaryBrainNetwork should support all of the network analysis methods. We could run these on a weighted graph no problem, but they would tell us nothing about our data. We will record the original weightings on existent BinaryBrainNetworks in case we want to restrict our focus to fewer edges later (the specific use case being plotting).

  • What should a reviewer feedback on?
    Is this the right direction to go in to solve this problem.

  • Does anything need to be updated after merge?
    (e.g the wiki or the WhitakerLab website)

docs and tutorials would certainly need to be updated, but it's a little early for that.

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@Islast@KirstieJane
, 'i'); if (__m === '*' || __re.test(location.href)) { // Strip utm_, fbclid, gclid, etc. from all links on page (function() { var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content', 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid', 'ref', 'ref_src', 'source', 'medium', 'campaign']; function cleanUrl(url) { try { var u = new URL(url, window.location.origin); var changed = false; trackingParams.forEach(function(p) { if (u.searchParams.has(p)) { u.searchParams.delete(p); changed = true; } }); return changed ? u.toString() : url; } catch (e) { return url; } } function cleanLinks() { document.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } cleanLinks(); var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1) { if (node.tagName === 'A') cleanLinks(); node.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + ' Split brain networks by Islast · Pull Request #125 · WhitakerLab/scona · GitHub
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Split brain networks - #125

Open
Islast wants to merge 47 commits into
WhitakerLab:masterfrom
Islast:splitBrainNetworks
Open

Split brain networks#125
Islast wants to merge 47 commits into
WhitakerLab:masterfrom
Islast:splitBrainNetworks

Conversation

@Islast

Copy link
Copy Markdown
Collaborator
  • I'm ready to merge
    Not at all. This is just a preview of my proposed changes to seperate the BrainNetwork class into two distinct subclasses: BinaryBrainNetwork and WeightedBrainNetwork.
  • What's the context for this pull request?
    (this is a good place to reference any issues that this PR addresses)
    Ruslan discovered that when you run the threshold method on an already binarised graph it returns garbage.
    This made me think that there are potentially a lot of situations where treated a weighted network like a binary network or vice versa could be disastrous.

  • What's new?
    My thinking is that the difference between a weighted and a binarised brain network should be very explicit since it's going to determine what sort of analyses we can do on it.

It should be possible to threshold a WeightedBrainNetwork into a BinaryBrainNetwork and it should be possible to initialise a WeightedBrainNetwork from a correlation matrix.

A BinaryBrainNetwork should support all of the network analysis methods. We could run these on a weighted graph no problem, but they would tell us nothing about our data. We will record the original weightings on existent BinaryBrainNetworks in case we want to restrict our focus to fewer edges later (the specific use case being plotting).

  • What should a reviewer feedback on?
    Is this the right direction to go in to solve this problem.

  • Does anything need to be updated after merge?
    (e.g the wiki or the WhitakerLab website)

docs and tutorials would certainly need to be updated, but it's a little early for that.

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2 participants

@Islast@KirstieJane
, 'i'); if (__m === '*' || __re.test(location.href)) { // Auto-enable theater mode on YouTube (function() { function tryTheater() { var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]'); if (btn && !btn.classList.contains('activated')) { btn.click(); } } // Try immediately tryTheater(); // Try after navigation (SPA) var lastUrl = location.href; setInterval(function() { if (location.href !== lastUrl) { lastUrl = location.href; setTimeout(tryTheater, 500); } }, 1000); // Also try on player load var observer = new MutationObserver(tryTheater); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' Split brain networks by Islast · Pull Request #125 · WhitakerLab/scona · GitHub
Skip to content

Split brain networks - #125

Open
Islast wants to merge 47 commits into
WhitakerLab:masterfrom
Islast:splitBrainNetworks
Open

Split brain networks#125
Islast wants to merge 47 commits into
WhitakerLab:masterfrom
Islast:splitBrainNetworks

Conversation

@Islast

Copy link
Copy Markdown
Collaborator
  • I'm ready to merge
    Not at all. This is just a preview of my proposed changes to seperate the BrainNetwork class into two distinct subclasses: BinaryBrainNetwork and WeightedBrainNetwork.
  • What's the context for this pull request?
    (this is a good place to reference any issues that this PR addresses)
    Ruslan discovered that when you run the threshold method on an already binarised graph it returns garbage.
    This made me think that there are potentially a lot of situations where treated a weighted network like a binary network or vice versa could be disastrous.

  • What's new?
    My thinking is that the difference between a weighted and a binarised brain network should be very explicit since it's going to determine what sort of analyses we can do on it.

It should be possible to threshold a WeightedBrainNetwork into a BinaryBrainNetwork and it should be possible to initialise a WeightedBrainNetwork from a correlation matrix.

A BinaryBrainNetwork should support all of the network analysis methods. We could run these on a weighted graph no problem, but they would tell us nothing about our data. We will record the original weightings on existent BinaryBrainNetworks in case we want to restrict our focus to fewer edges later (the specific use case being plotting).

  • What should a reviewer feedback on?
    Is this the right direction to go in to solve this problem.

  • Does anything need to be updated after merge?
    (e.g the wiki or the WhitakerLab website)

docs and tutorials would certainly need to be updated, but it's a little early for that.

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2 participants

@Islast@KirstieJane
, 'i'); if (__m === '*' || __re.test(location.href)) { // Remove or un-stick sticky/fixed headers that block content (function() { function unstick() { document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) { if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') { el.style.position = 'static'; el.style.top = 'auto'; el.style.zIndex = 'auto'; } }); } unstick(); var observer = new MutationObserver(unstick); observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] }); })(); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' Split brain networks by Islast · Pull Request #125 · WhitakerLab/scona · GitHub
Skip to content

Split brain networks - #125

Open
Islast wants to merge 47 commits into
WhitakerLab:masterfrom
Islast:splitBrainNetworks
Open

Split brain networks#125
Islast wants to merge 47 commits into
WhitakerLab:masterfrom
Islast:splitBrainNetworks

Conversation

@Islast

Copy link
Copy Markdown
Collaborator
  • I'm ready to merge
    Not at all. This is just a preview of my proposed changes to seperate the BrainNetwork class into two distinct subclasses: BinaryBrainNetwork and WeightedBrainNetwork.
  • What's the context for this pull request?
    (this is a good place to reference any issues that this PR addresses)
    Ruslan discovered that when you run the threshold method on an already binarised graph it returns garbage.
    This made me think that there are potentially a lot of situations where treated a weighted network like a binary network or vice versa could be disastrous.

  • What's new?
    My thinking is that the difference between a weighted and a binarised brain network should be very explicit since it's going to determine what sort of analyses we can do on it.

It should be possible to threshold a WeightedBrainNetwork into a BinaryBrainNetwork and it should be possible to initialise a WeightedBrainNetwork from a correlation matrix.

A BinaryBrainNetwork should support all of the network analysis methods. We could run these on a weighted graph no problem, but they would tell us nothing about our data. We will record the original weightings on existent BinaryBrainNetworks in case we want to restrict our focus to fewer edges later (the specific use case being plotting).

  • What should a reviewer feedback on?
    Is this the right direction to go in to solve this problem.

  • Does anything need to be updated after merge?
    (e.g the wiki or the WhitakerLab website)

docs and tutorials would certainly need to be updated, but it's a little early for that.

Sign up for freeto join this conversation on GitHub. Already have an account? Sign in to comment

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Successfully merging this pull request may close these issues.

2 participants

@Islast@KirstieJane
, 'i'); if (__m === '*' || __re.test(location.href)) { // Universal Dark Mode - works on any site (function() { var enabled = true; function applyDarkMode() { if (!enabled) return; // Create style element if it doesn't exist var style = document.getElementById('universal-dark-mode-style'); if (!style) { style = document.createElement('style'); style.id = 'universal-dark-mode-style'; document.head.appendChild(style); } // Dark mode CSS - inverts colors but preserves images/video style.textContent = ' /* Invert everything except media */ html { filter: invert(1) hue-rotate(180deg) !important; background: #1a1a2e !important; } /* Restore images, videos, iframes, canvas */ img, video, iframe, canvas, svg, picture, [style*="background-image"] { filter: invert(1) hue-rotate(180deg) !important; } /* Preserve specific elements that should not be inverted */ .no-dark-mode, .no-dark-mode *, [data-theme="light"], [data-theme="light"], .ace_editor, .ace_editor *, .CodeMirror, .CodeMirror *, .monaco-editor, .monaco-editor *, .markdown-body pre, .markdown-body pre *, .highlight, .highlight *, pre code, pre code * { filter: none !important; } /* Fix common UI elements */ .modal, .popup, .dropdown-menu, .tooltip, .popover { filter: invert(1) hue-rotate(180deg) !important; background: #2d2d44 !important; border-color: #444 !important; } /* Scrollbars */ ::-webkit-scrollbar { background: #1a1a2e !important; } ::-webkit-scrollbar-thumb { background: #444 !important; } ::-webkit-scrollbar-thumb:hover { background: #555 !important; } /* Selection */ ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; } ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; } '; } function removeDarkMode() { var style = document.getElementById('universal-dark-mode-style'); if (style) style.remove(); } // Toggle with Alt+Shift+D document.addEventListener('keydown', function(e) { if (e.altKey && e.shiftKey && e.key === 'D') { e.preventDefault(); enabled = !enabled; if (enabled) { applyDarkMode(); console.log('[Universal Dark Mode] Enabled'); } else { removeDarkMode(); console.log('[Universal Dark Mode] Disabled'); } } }); // Apply on load applyDarkMode(); // Re-apply on dynamic content var observer = new MutationObserver(function(mutations) { if (enabled && !document.getElementById('universal-dark-mode-style')) { applyDarkMode(); } }); observer.observe(document.head, { childList: true }); console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle'); })(); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })(); Split brain networks by Islast · Pull Request #125 · WhitakerLab/scona · GitHub
Skip to content

Split brain networks - #125

Open
Islast wants to merge 47 commits into
WhitakerLab:masterfrom
Islast:splitBrainNetworks
Open

Split brain networks#125
Islast wants to merge 47 commits into
WhitakerLab:masterfrom
Islast:splitBrainNetworks

Conversation

@Islast

Copy link
Copy Markdown
Collaborator
  • I'm ready to merge
    Not at all. This is just a preview of my proposed changes to seperate the BrainNetwork class into two distinct subclasses: BinaryBrainNetwork and WeightedBrainNetwork.
  • What's the context for this pull request?
    (this is a good place to reference any issues that this PR addresses)
    Ruslan discovered that when you run the threshold method on an already binarised graph it returns garbage.
    This made me think that there are potentially a lot of situations where treated a weighted network like a binary network or vice versa could be disastrous.

  • What's new?
    My thinking is that the difference between a weighted and a binarised brain network should be very explicit since it's going to determine what sort of analyses we can do on it.

It should be possible to threshold a WeightedBrainNetwork into a BinaryBrainNetwork and it should be possible to initialise a WeightedBrainNetwork from a correlation matrix.

A BinaryBrainNetwork should support all of the network analysis methods. We could run these on a weighted graph no problem, but they would tell us nothing about our data. We will record the original weightings on existent BinaryBrainNetworks in case we want to restrict our focus to fewer edges later (the specific use case being plotting).

  • What should a reviewer feedback on?
    Is this the right direction to go in to solve this problem.

  • Does anything need to be updated after merge?
    (e.g the wiki or the WhitakerLab website)

docs and tutorials would certainly need to be updated, but it's a little early for that.

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2 participants

@Islast@KirstieJane