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requairments.txt

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R(i,j) Matrix Min-Max Range Checker

This project provides a Python script that processes a numerical dataset, computes an R(i,j) matrix, and checks if the values of a randomly generated object fall within the min and max range of each column in the matrix. If any value does not lie within the specified range, it will return the name of the columns where the mismatch occurs. Additionally, a visualization is provided for mismatched columns.

Features

  • Dataset Handling: Loads an Excel file (.xlsx) and selects numerical columns.
  • R(i,j) Matrix Calculation: Computes an R(i,j) matrix based on relationships between column values.
  • Min-Max Range Checking: Generates a random object and checks if the R(i,j) values for this object fall within the precomputed min and max ranges for each column.
  • Visualization: Displays a bar chart showing the min and max values for columns where the random object's values did not fit within the expected range.

Requirements

To run the script, you need Python 3.x and the following dependencies:

  • pandas
  • numpy
  • matplotlib
  • openpyxl (for reading .xlsx files)

Installation

To install the required dependencies, run the following command in your terminal:

pip install pandas numpy matplotlib openpyxl

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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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R(i,j) Matrix Min-Max Range Checker
This project provides a Python script that processes a numerical dataset, computes an R(i,j) matrix, and checks if the values of a randomly generated object fall within the min and max range of each column in the matrix. If any value does not lie within the specified range, it will return the name of the columns where the mismatch occurs. Additionally, a visualization is provided for mismatched columns.
Features
  • Dataset Handling: Loads an Excel file (.xlsx) and selects numerical columns.
  • R(i,j) Matrix Calculation: Computes an R(i,j) matrix based on relationships between column values.
  • Min-Max Range Checking: Generates a random object and checks if the R(i,j) values for this object fall within the precomputed min and max ranges for each column.
  • Visualization: Displays a bar chart showing the min and max values for columns where the random object's values did not fit within the expected range.
Requirements
To run the script, you need Python 3.x and the following dependencies:
  • pandas
  • numpy
  • matplotlib
  • openpyxl (for reading .xlsx files)
Installation
To install the required dependencies, run the following command in your terminal:
pip install pandas numpy matplotlib openpyxl
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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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R(i,j) Matrix Min-Max Range Checker
This project provides a Python script that processes a numerical dataset, computes an R(i,j) matrix, and checks if the values of a randomly generated object fall within the min and max range of each column in the matrix. If any value does not lie within the specified range, it will return the name of the columns where the mismatch occurs. Additionally, a visualization is provided for mismatched columns.
Features
  • Dataset Handling: Loads an Excel file (.xlsx) and selects numerical columns.
  • R(i,j) Matrix Calculation: Computes an R(i,j) matrix based on relationships between column values.
  • Min-Max Range Checking: Generates a random object and checks if the R(i,j) values for this object fall within the precomputed min and max ranges for each column.
  • Visualization: Displays a bar chart showing the min and max values for columns where the random object's values did not fit within the expected range.
Requirements
To run the script, you need Python 3.x and the following dependencies:
  • pandas
  • numpy
  • matplotlib
  • openpyxl (for reading .xlsx files)
Installation
To install the required dependencies, run the following command in your terminal:
pip install pandas numpy matplotlib openpyxl
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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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Repository files navigation
R(i,j) Matrix Min-Max Range Checker
This project provides a Python script that processes a numerical dataset, computes an R(i,j) matrix, and checks if the values of a randomly generated object fall within the min and max range of each column in the matrix. If any value does not lie within the specified range, it will return the name of the columns where the mismatch occurs. Additionally, a visualization is provided for mismatched columns.
Features
  • Dataset Handling: Loads an Excel file (.xlsx) and selects numerical columns.
  • R(i,j) Matrix Calculation: Computes an R(i,j) matrix based on relationships between column values.
  • Min-Max Range Checking: Generates a random object and checks if the R(i,j) values for this object fall within the precomputed min and max ranges for each column.
  • Visualization: Displays a bar chart showing the min and max values for columns where the random object's values did not fit within the expected range.
Requirements
To run the script, you need Python 3.x and the following dependencies:
  • pandas
  • numpy
  • matplotlib
  • openpyxl (for reading .xlsx files)
Installation
To install the required dependencies, run the following command in your terminal:
pip install pandas numpy matplotlib openpyxl
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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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Repository files navigation
R(i,j) Matrix Min-Max Range Checker
This project provides a Python script that processes a numerical dataset, computes an R(i,j) matrix, and checks if the values of a randomly generated object fall within the min and max range of each column in the matrix. If any value does not lie within the specified range, it will return the name of the columns where the mismatch occurs. Additionally, a visualization is provided for mismatched columns.
Features
  • Dataset Handling: Loads an Excel file (.xlsx) and selects numerical columns.
  • R(i,j) Matrix Calculation: Computes an R(i,j) matrix based on relationships between column values.
  • Min-Max Range Checking: Generates a random object and checks if the R(i,j) values for this object fall within the precomputed min and max ranges for each column.
  • Visualization: Displays a bar chart showing the min and max values for columns where the random object's values did not fit within the expected range.
Requirements
To run the script, you need Python 3.x and the following dependencies:
  • pandas
  • numpy
  • matplotlib
  • openpyxl (for reading .xlsx files)
Installation
To install the required dependencies, run the following command in your terminal:
pip install pandas numpy matplotlib openpyxl
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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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Repository files navigation
R(i,j) Matrix Min-Max Range Checker
This project provides a Python script that processes a numerical dataset, computes an R(i,j) matrix, and checks if the values of a randomly generated object fall within the min and max range of each column in the matrix. If any value does not lie within the specified range, it will return the name of the columns where the mismatch occurs. Additionally, a visualization is provided for mismatched columns.
Features
  • Dataset Handling: Loads an Excel file (.xlsx) and selects numerical columns.
  • R(i,j) Matrix Calculation: Computes an R(i,j) matrix based on relationships between column values.
  • Min-Max Range Checking: Generates a random object and checks if the R(i,j) values for this object fall within the precomputed min and max ranges for each column.
  • Visualization: Displays a bar chart showing the min and max values for columns where the random object's values did not fit within the expected range.
Requirements
To run the script, you need Python 3.x and the following dependencies:
  • pandas
  • numpy
  • matplotlib
  • openpyxl (for reading .xlsx files)
Installation
To install the required dependencies, run the following command in your terminal:
pip install pandas numpy matplotlib openpyxl
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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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Repository files navigation
R(i,j) Matrix Min-Max Range Checker
This project provides a Python script that processes a numerical dataset, computes an R(i,j) matrix, and checks if the values of a randomly generated object fall within the min and max range of each column in the matrix. If any value does not lie within the specified range, it will return the name of the columns where the mismatch occurs. Additionally, a visualization is provided for mismatched columns.
Features
  • Dataset Handling: Loads an Excel file (.xlsx) and selects numerical columns.
  • R(i,j) Matrix Calculation: Computes an R(i,j) matrix based on relationships between column values.
  • Min-Max Range Checking: Generates a random object and checks if the R(i,j) values for this object fall within the precomputed min and max ranges for each column.
  • Visualization: Displays a bar chart showing the min and max values for columns where the random object's values did not fit within the expected range.
Requirements
To run the script, you need Python 3.x and the following dependencies:
  • pandas
  • numpy
  • matplotlib
  • openpyxl (for reading .xlsx files)
Installation
To install the required dependencies, run the following command in your terminal:
pip install pandas numpy matplotlib openpyxl
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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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R(i,j) Matrix Min-Max Range Checker
This project provides a Python script that processes a numerical dataset, computes an R(i,j) matrix, and checks if the values of a randomly generated object fall within the min and max range of each column in the matrix. If any value does not lie within the specified range, it will return the name of the columns where the mismatch occurs. Additionally, a visualization is provided for mismatched columns.
Features
  • Dataset Handling: Loads an Excel file (.xlsx) and selects numerical columns.
  • R(i,j) Matrix Calculation: Computes an R(i,j) matrix based on relationships between column values.
  • Min-Max Range Checking: Generates a random object and checks if the R(i,j) values for this object fall within the precomputed min and max ranges for each column.
  • Visualization: Displays a bar chart showing the min and max values for columns where the random object's values did not fit within the expected range.
Requirements
To run the script, you need Python 3.x and the following dependencies:
  • pandas
  • numpy
  • matplotlib
  • openpyxl (for reading .xlsx files)
Installation
To install the required dependencies, run the following command in your terminal:
pip install pandas numpy matplotlib openpyxl
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