Open Planck, CSFD, and Lenz2017 maps with memory mapping - #69

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hombit:healpix-fits-mmap
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Open Planck, CSFD, and Lenz2017 maps with memory mapping#69
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hombit:healpix-fits-mmap

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I've found that in multiple pipelines the RAM pressure is significantly high, especially if running stuff in parallel. See, e.g., SNAD Viewer, which we run on a virtual machine with just 8GB of RAM, or LightCurveLynx, which has to duplicate dust maps when using multiprocessing.

This may be solved by using memory mapping instead of loading the data into memory. When I was looking for low-hanging fruits for those changes, I found that CSFD is really easy to change for using memory mapping: just changing .flatten() to .raven() prevents memory copying.

Similar changes are applied to HEALPixFITSQuery. I've also moved dtype casting and scaling from the constructor to .query(), so no memory copy happening for Planck and Lenz2017. I've run benchmarks and found no difference in .query() performance for both scalar and 10,000-element input.

@JulienPeloton

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Hello -- Any update on this one? It seems a super useful addition :-)

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
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Open Planck, CSFD, and Lenz2017 maps with memory mapping - #69

Open
hombit wants to merge 1 commit into
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hombit:healpix-fits-mmap
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Open Planck, CSFD, and Lenz2017 maps with memory mapping#69
hombit wants to merge 1 commit into
gregreen:masterfrom
hombit:healpix-fits-mmap

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I've found that in multiple pipelines the RAM pressure is significantly high, especially if running stuff in parallel. See, e.g., SNAD Viewer, which we run on a virtual machine with just 8GB of RAM, or LightCurveLynx, which has to duplicate dust maps when using multiprocessing.

This may be solved by using memory mapping instead of loading the data into memory. When I was looking for low-hanging fruits for those changes, I found that CSFD is really easy to change for using memory mapping: just changing .flatten() to .raven() prevents memory copying.

Similar changes are applied to HEALPixFITSQuery. I've also moved dtype casting and scaling from the constructor to .query(), so no memory copy happening for Planck and Lenz2017. I've run benchmarks and found no difference in .query() performance for both scalar and 10,000-element input.

@JulienPeloton

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Hello -- Any update on this one? It seems a super useful addition :-)

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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"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Open Planck, CSFD, and Lenz2017 maps with memory mapping - #69

Open
hombit wants to merge 1 commit into
gregreen:masterfrom
hombit:healpix-fits-mmap
Open

Open Planck, CSFD, and Lenz2017 maps with memory mapping#69
hombit wants to merge 1 commit into
gregreen:masterfrom
hombit:healpix-fits-mmap

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@hombit

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I've found that in multiple pipelines the RAM pressure is significantly high, especially if running stuff in parallel. See, e.g., SNAD Viewer, which we run on a virtual machine with just 8GB of RAM, or LightCurveLynx, which has to duplicate dust maps when using multiprocessing.

This may be solved by using memory mapping instead of loading the data into memory. When I was looking for low-hanging fruits for those changes, I found that CSFD is really easy to change for using memory mapping: just changing .flatten() to .raven() prevents memory copying.

Similar changes are applied to HEALPixFITSQuery. I've also moved dtype casting and scaling from the constructor to .query(), so no memory copy happening for Planck and Lenz2017. I've run benchmarks and found no difference in .query() performance for both scalar and 10,000-element input.

@JulienPeloton

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Hello -- Any update on this one? It seems a super useful addition :-)

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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); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Open Planck, CSFD, and Lenz2017 maps with memory mapping - #69

Open
hombit wants to merge 1 commit into
gregreen:masterfrom
hombit:healpix-fits-mmap
Open

Open Planck, CSFD, and Lenz2017 maps with memory mapping#69
hombit wants to merge 1 commit into
gregreen:masterfrom
hombit:healpix-fits-mmap

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@hombit

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I've found that in multiple pipelines the RAM pressure is significantly high, especially if running stuff in parallel. See, e.g., SNAD Viewer, which we run on a virtual machine with just 8GB of RAM, or LightCurveLynx, which has to duplicate dust maps when using multiprocessing.

This may be solved by using memory mapping instead of loading the data into memory. When I was looking for low-hanging fruits for those changes, I found that CSFD is really easy to change for using memory mapping: just changing .flatten() to .raven() prevents memory copying.

Similar changes are applied to HEALPixFITSQuery. I've also moved dtype casting and scaling from the constructor to .query(), so no memory copy happening for Planck and Lenz2017. I've run benchmarks and found no difference in .query() performance for both scalar and 10,000-element input.

@JulienPeloton

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Hello -- Any update on this one? It seems a super useful addition :-)

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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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Open Planck, CSFD, and Lenz2017 maps with memory mapping - #69

Open
hombit wants to merge 1 commit into
gregreen:masterfrom
hombit:healpix-fits-mmap
Open

Open Planck, CSFD, and Lenz2017 maps with memory mapping#69
hombit wants to merge 1 commit into
gregreen:masterfrom
hombit:healpix-fits-mmap

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@hombit

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I've found that in multiple pipelines the RAM pressure is significantly high, especially if running stuff in parallel. See, e.g., SNAD Viewer, which we run on a virtual machine with just 8GB of RAM, or LightCurveLynx, which has to duplicate dust maps when using multiprocessing.

This may be solved by using memory mapping instead of loading the data into memory. When I was looking for low-hanging fruits for those changes, I found that CSFD is really easy to change for using memory mapping: just changing .flatten() to .raven() prevents memory copying.

Similar changes are applied to HEALPixFITSQuery. I've also moved dtype casting and scaling from the constructor to .query(), so no memory copy happening for Planck and Lenz2017. I've run benchmarks and found no difference in .query() performance for both scalar and 10,000-element input.

@JulienPeloton

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Hello -- Any update on this one? It seems a super useful addition :-)

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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); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Open Planck, CSFD, and Lenz2017 maps with memory mapping - #69

Open
hombit wants to merge 1 commit into
gregreen:masterfrom
hombit:healpix-fits-mmap
Open

Open Planck, CSFD, and Lenz2017 maps with memory mapping#69
hombit wants to merge 1 commit into
gregreen:masterfrom
hombit:healpix-fits-mmap

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@hombit

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I've found that in multiple pipelines the RAM pressure is significantly high, especially if running stuff in parallel. See, e.g., SNAD Viewer, which we run on a virtual machine with just 8GB of RAM, or LightCurveLynx, which has to duplicate dust maps when using multiprocessing.

This may be solved by using memory mapping instead of loading the data into memory. When I was looking for low-hanging fruits for those changes, I found that CSFD is really easy to change for using memory mapping: just changing .flatten() to .raven() prevents memory copying.

Similar changes are applied to HEALPixFITSQuery. I've also moved dtype casting and scaling from the constructor to .query(), so no memory copy happening for Planck and Lenz2017. I've run benchmarks and found no difference in .query() performance for both scalar and 10,000-element input.

@JulienPeloton

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Hello -- Any update on this one? It seems a super useful addition :-)

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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"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Open Planck, CSFD, and Lenz2017 maps with memory mapping - #69

Open
hombit wants to merge 1 commit into
gregreen:masterfrom
hombit:healpix-fits-mmap
Open

Open Planck, CSFD, and Lenz2017 maps with memory mapping#69
hombit wants to merge 1 commit into
gregreen:masterfrom
hombit:healpix-fits-mmap

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@hombit

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I've found that in multiple pipelines the RAM pressure is significantly high, especially if running stuff in parallel. See, e.g., SNAD Viewer, which we run on a virtual machine with just 8GB of RAM, or LightCurveLynx, which has to duplicate dust maps when using multiprocessing.

This may be solved by using memory mapping instead of loading the data into memory. When I was looking for low-hanging fruits for those changes, I found that CSFD is really easy to change for using memory mapping: just changing .flatten() to .raven() prevents memory copying.

Similar changes are applied to HEALPixFITSQuery. I've also moved dtype casting and scaling from the constructor to .query(), so no memory copy happening for Planck and Lenz2017. I've run benchmarks and found no difference in .query() performance for both scalar and 10,000-element input.

@JulienPeloton

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Hello -- Any update on this one? It seems a super useful addition :-)

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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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Open Planck, CSFD, and Lenz2017 maps with memory mapping - #69

Open
hombit wants to merge 1 commit into
gregreen:masterfrom
hombit:healpix-fits-mmap
Open

Open Planck, CSFD, and Lenz2017 maps with memory mapping#69
hombit wants to merge 1 commit into
gregreen:masterfrom
hombit:healpix-fits-mmap

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@hombit

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I've found that in multiple pipelines the RAM pressure is significantly high, especially if running stuff in parallel. See, e.g., SNAD Viewer, which we run on a virtual machine with just 8GB of RAM, or LightCurveLynx, which has to duplicate dust maps when using multiprocessing.

This may be solved by using memory mapping instead of loading the data into memory. When I was looking for low-hanging fruits for those changes, I found that CSFD is really easy to change for using memory mapping: just changing .flatten() to .raven() prevents memory copying.

Similar changes are applied to HEALPixFITSQuery. I've also moved dtype casting and scaling from the constructor to .query(), so no memory copy happening for Planck and Lenz2017. I've run benchmarks and found no difference in .query() performance for both scalar and 10,000-element input.

@JulienPeloton

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Hello -- Any update on this one? It seems a super useful addition :-)

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