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Trendify

trendify: Efficient Plotting and Table Building

PyPI versionPython versionsTests & Release StatusCoverageRuffPydantic v2LicenseDocumentation

Installation

Install the package via your preferred manager:

uv add trendify

or with pip:

pip install trendify

Core Features

  • Built to scale: process thousands of runs without holding every run's data in memory at once. Throughput stays flat whether you have dozens of runs or tens of thousands.
  • Typed records, no migrations: points, traces, tables, and histograms are validated Pydantic models. Add your own record types anytime without writing a schema migration.
  • Parallelizable: trendify generate can fan your processing function out across multiple CPU cores with --n-procs, with multiprocessing-safe logging that funnels every worker's output through a single queue.
  • Static assets or a live dashboard: render tagged data straight to Matplotlib images and CSV tables with trendify render, or launch an interactive FastAPI dashboard with trendify viewer to browse tags, tables, and interactive plots in the browser.

Why use trendify?

  • Scalable. Throughput stays flat whether you're processing dozens of runs or tens of thousands.
  • Memory efficient. Each run is processed once and cached on disk, nothing needs to stay open or held in memory for the whole sweep. This is critical for processing large amounts of data with less memory than is available when batch processing.
  • Flexible output. Render static Matplotlib images and CSV tables for a report, or browse the same data interactively in a live dashboard.

About

No description, website, or topics provided.

Resources

Readme
MIT license
Activity

Stars

1 star

Watchers

1 watching

Forks

0 forks
Report repository

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Packages

Used by

Contributors

Languages

, '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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Trendify
trendify: Efficient Plotting and Table Building
PyPI versionPython versionsTests & Release StatusCoverageRuffPydantic v2LicenseDocumentation
Installation
Install the package via your preferred manager:
uv add trendify
or with pip:
pip install trendify
Core Features
  • Built to scale: process thousands of runs without holding every run's data in memory at once. Throughput stays flat whether you have dozens of runs or tens of thousands.
  • Typed records, no migrations: points, traces, tables, and histograms are validated Pydantic models. Add your own record types anytime without writing a schema migration.
  • Parallelizable: trendify generate can fan your processing function out across multiple CPU cores with --n-procs, with multiprocessing-safe logging that funnels every worker's output through a single queue.
  • Static assets or a live dashboard: render tagged data straight to Matplotlib images and CSV tables with trendify render, or launch an interactive FastAPI dashboard with trendify viewer to browse tags, tables, and interactive plots in the browser.
Why use trendify?
  • Scalable. Throughput stays flat whether you're processing dozens of runs or tens of thousands.
  • Memory efficient. Each run is processed once and cached on disk, nothing needs to stay open or held in memory for the whole sweep. This is critical for processing large amounts of data with less memory than is available when batch processing.
  • Flexible output. Render static Matplotlib images and CSV tables for a report, or browse the same data interactively in a live dashboard.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
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Used by
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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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Trendify
trendify: Efficient Plotting and Table Building
PyPI versionPython versionsTests & Release StatusCoverageRuffPydantic v2LicenseDocumentation
Installation
Install the package via your preferred manager:
uv add trendify
or with pip:
pip install trendify
Core Features
  • Built to scale: process thousands of runs without holding every run's data in memory at once. Throughput stays flat whether you have dozens of runs or tens of thousands.
  • Typed records, no migrations: points, traces, tables, and histograms are validated Pydantic models. Add your own record types anytime without writing a schema migration.
  • Parallelizable: trendify generate can fan your processing function out across multiple CPU cores with --n-procs, with multiprocessing-safe logging that funnels every worker's output through a single queue.
  • Static assets or a live dashboard: render tagged data straight to Matplotlib images and CSV tables with trendify render, or launch an interactive FastAPI dashboard with trendify viewer to browse tags, tables, and interactive plots in the browser.
Why use trendify?
  • Scalable. Throughput stays flat whether you're processing dozens of runs or tens of thousands.
  • Memory efficient. Each run is processed once and cached on disk, nothing needs to stay open or held in memory for the whole sweep. This is critical for processing large amounts of data with less memory than is available when batch processing.
  • Flexible output. Render static Matplotlib images and CSV tables for a report, or browse the same data interactively in a live dashboard.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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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Trendify
trendify: Efficient Plotting and Table Building
PyPI versionPython versionsTests & Release StatusCoverageRuffPydantic v2LicenseDocumentation
Installation
Install the package via your preferred manager:
uv add trendify
or with pip:
pip install trendify
Core Features
  • Built to scale: process thousands of runs without holding every run's data in memory at once. Throughput stays flat whether you have dozens of runs or tens of thousands.
  • Typed records, no migrations: points, traces, tables, and histograms are validated Pydantic models. Add your own record types anytime without writing a schema migration.
  • Parallelizable: trendify generate can fan your processing function out across multiple CPU cores with --n-procs, with multiprocessing-safe logging that funnels every worker's output through a single queue.
  • Static assets or a live dashboard: render tagged data straight to Matplotlib images and CSV tables with trendify render, or launch an interactive FastAPI dashboard with trendify viewer to browse tags, tables, and interactive plots in the browser.
Why use trendify?
  • Scalable. Throughput stays flat whether you're processing dozens of runs or tens of thousands.
  • Memory efficient. Each run is processed once and cached on disk, nothing needs to stay open or held in memory for the whole sweep. This is critical for processing large amounts of data with less memory than is available when batch processing.
  • Flexible output. Render static Matplotlib images and CSV tables for a report, or browse the same data interactively in a live dashboard.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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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Trendify
trendify: Efficient Plotting and Table Building
PyPI versionPython versionsTests & Release StatusCoverageRuffPydantic v2LicenseDocumentation
Installation
Install the package via your preferred manager:
uv add trendify
or with pip:
pip install trendify
Core Features
  • Built to scale: process thousands of runs without holding every run's data in memory at once. Throughput stays flat whether you have dozens of runs or tens of thousands.
  • Typed records, no migrations: points, traces, tables, and histograms are validated Pydantic models. Add your own record types anytime without writing a schema migration.
  • Parallelizable: trendify generate can fan your processing function out across multiple CPU cores with --n-procs, with multiprocessing-safe logging that funnels every worker's output through a single queue.
  • Static assets or a live dashboard: render tagged data straight to Matplotlib images and CSV tables with trendify render, or launch an interactive FastAPI dashboard with trendify viewer to browse tags, tables, and interactive plots in the browser.
Why use trendify?
  • Scalable. Throughput stays flat whether you're processing dozens of runs or tens of thousands.
  • Memory efficient. Each run is processed once and cached on disk, nothing needs to stay open or held in memory for the whole sweep. This is critical for processing large amounts of data with less memory than is available when batch processing.
  • Flexible output. Render static Matplotlib images and CSV tables for a report, or browse the same data interactively in a live dashboard.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
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Trendify
trendify: Efficient Plotting and Table Building
PyPI versionPython versionsTests & Release StatusCoverageRuffPydantic v2LicenseDocumentation
Installation
Install the package via your preferred manager:
uv add trendify
or with pip:
pip install trendify
Core Features
  • Built to scale: process thousands of runs without holding every run's data in memory at once. Throughput stays flat whether you have dozens of runs or tens of thousands.
  • Typed records, no migrations: points, traces, tables, and histograms are validated Pydantic models. Add your own record types anytime without writing a schema migration.
  • Parallelizable: trendify generate can fan your processing function out across multiple CPU cores with --n-procs, with multiprocessing-safe logging that funnels every worker's output through a single queue.
  • Static assets or a live dashboard: render tagged data straight to Matplotlib images and CSV tables with trendify render, or launch an interactive FastAPI dashboard with trendify viewer to browse tags, tables, and interactive plots in the browser.
Why use trendify?
  • Scalable. Throughput stays flat whether you're processing dozens of runs or tens of thousands.
  • Memory efficient. Each run is processed once and cached on disk, nothing needs to stay open or held in memory for the whole sweep. This is critical for processing large amounts of data with less memory than is available when batch processing.
  • Flexible output. Render static Matplotlib images and CSV tables for a report, or browse the same data interactively in a live dashboard.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
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Trendify
trendify: Efficient Plotting and Table Building
PyPI versionPython versionsTests & Release StatusCoverageRuffPydantic v2LicenseDocumentation
Installation
Install the package via your preferred manager:
uv add trendify
or with pip:
pip install trendify
Core Features
  • Built to scale: process thousands of runs without holding every run's data in memory at once. Throughput stays flat whether you have dozens of runs or tens of thousands.
  • Typed records, no migrations: points, traces, tables, and histograms are validated Pydantic models. Add your own record types anytime without writing a schema migration.
  • Parallelizable: trendify generate can fan your processing function out across multiple CPU cores with --n-procs, with multiprocessing-safe logging that funnels every worker's output through a single queue.
  • Static assets or a live dashboard: render tagged data straight to Matplotlib images and CSV tables with trendify render, or launch an interactive FastAPI dashboard with trendify viewer to browse tags, tables, and interactive plots in the browser.
Why use trendify?
  • Scalable. Throughput stays flat whether you're processing dozens of runs or tens of thousands.
  • Memory efficient. Each run is processed once and cached on disk, nothing needs to stay open or held in memory for the whole sweep. This is critical for processing large amounts of data with less memory than is available when batch processing.
  • Flexible output. Render static Matplotlib images and CSV tables for a report, or browse the same data interactively in a live dashboard.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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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Trendify
trendify: Efficient Plotting and Table Building
PyPI versionPython versionsTests & Release StatusCoverageRuffPydantic v2LicenseDocumentation
Installation
Install the package via your preferred manager:
uv add trendify
or with pip:
pip install trendify
Core Features
  • Built to scale: process thousands of runs without holding every run's data in memory at once. Throughput stays flat whether you have dozens of runs or tens of thousands.
  • Typed records, no migrations: points, traces, tables, and histograms are validated Pydantic models. Add your own record types anytime without writing a schema migration.
  • Parallelizable: trendify generate can fan your processing function out across multiple CPU cores with --n-procs, with multiprocessing-safe logging that funnels every worker's output through a single queue.
  • Static assets or a live dashboard: render tagged data straight to Matplotlib images and CSV tables with trendify render, or launch an interactive FastAPI dashboard with trendify viewer to browse tags, tables, and interactive plots in the browser.
Why use trendify?
  • Scalable. Throughput stays flat whether you're processing dozens of runs or tens of thousands.
  • Memory efficient. Each run is processed once and cached on disk, nothing needs to stay open or held in memory for the whole sweep. This is critical for processing large amounts of data with less memory than is available when batch processing.
  • Flexible output. Render static Matplotlib images and CSV tables for a report, or browse the same data interactively in a live dashboard.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages