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DOILicense: GPL v3GitHub release (latest by date)

Clustering

Script and data from: Population cluster data to assess the urban-rural split and electrification in Sub-Saharan Africa by Babak Khavari, Alexandros Korkovelos, Andeas Sahlberg, Francesco Fuso-Nerini and Mark Howells. Datasets produced using the method described in the paper are available at: https://data.mendeley.com/datasets/z9zfhzk8cr.

Content

This repository contains:

  • An environment .yml file needed for generating a fully functioning python 3.7 environment necessary for the clustering algorithm.
  • The clustering code and related functions. These files also contain necessary steps in order to reproduce results.
  • An example case for Benin.

Installing and running the clustering notebook

Requirements

The clustering module (as well as all supporting scripts in this repo) have been developed in Python 3. We recommend installing Anaconda's free distribution as suited for your operating system.

Install the clustering repository from GitHub

After installing Anaconda you can download the repository directly or clone it to your designated local directory using:

> conda install git
> git clone https://github.com/OnSSET/Clustering.git

Once installed, open anaconda prompt and move to your local "clustering" directory using:

> cd ..\Clustering

In order to be able to run the clustering tool (main.ipynb and funcs.ipynb) you have to install all necessary packages. "full_project.yml" contains all of these and can be easily set up by creating a new virtual environment using:

conda env create --name clustering --file full_project.yml

This might take some time. When complete, activate the virtual environment using:

conda activate clustering

With the environment activated, you can now move to the clustering directory and start a "jupyter notebook" session by simply typing:

..\Clustering> jupyter notebook

Changelog

5-April-2020: Original code base published

Resources

Original dataset can be found here: https://data.mendeley.com/datasets/z9zfhzk8cr

Journal article can be found here:

Credits

Conceptualization:Babak Khavari & Francesco Fuso-Nerini
Methodology:Babak Khavari
Software:Babak Khavari
Validation:Babak Khavari, Alexandros Konrkovelos & Andreas Sahlberg
Supervision and Advisory support:Francesco Fuso-Nerini & Mark Howells

About

Script and data from: "Assessing the urban-rural split and electrification in Sub-Saharan Africa - a cluster-based methodology" by Babak Khavari, Alexandros Korkovelos, Andeas Sahlberg, Francesco Fuso-Nerini and Mark Howells.

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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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DOILicense: GPL v3GitHub release (latest by date)
Clustering
Script and data from: Population cluster data to assess the urban-rural split and electrification in Sub-Saharan Africa by Babak Khavari, Alexandros Korkovelos, Andeas Sahlberg, Francesco Fuso-Nerini and Mark Howells. Datasets produced using the method described in the paper are available at: https://data.mendeley.com/datasets/z9zfhzk8cr.
Content
This repository contains:
  • An environment .yml file needed for generating a fully functioning python 3.7 environment necessary for the clustering algorithm.
  • The clustering code and related functions. These files also contain necessary steps in order to reproduce results.
  • An example case for Benin.
Installing and running the clustering notebook
Requirements
The clustering module (as well as all supporting scripts in this repo) have been developed in Python 3. We recommend installing Anaconda's free distribution as suited for your operating system.
Install the clustering repository from GitHub
After installing Anaconda you can download the repository directly or clone it to your designated local directory using:
> conda install git
> git clone https://github.com/OnSSET/Clustering.git

Once installed, open anaconda prompt and move to your local "clustering" directory using:
> cd ..\Clustering

In order to be able to run the clustering tool (main.ipynb and funcs.ipynb) you have to install all necessary packages. "full_project.yml" contains all of these and can be easily set up by creating a new virtual environment using:
conda env create --name clustering --file full_project.yml

This might take some time. When complete, activate the virtual environment using:
conda activate clustering

With the environment activated, you can now move to the clustering directory and start a "jupyter notebook" session by simply typing:
..\Clustering> jupyter notebook 
Changelog
5-April-2020: Original code base published
Resources
Original dataset can be found here: https://data.mendeley.com/datasets/z9zfhzk8cr
Journal article can be found here:
Credits
Conceptualization:Babak Khavari & Francesco Fuso-Nerini
Methodology:Babak Khavari
Software:Babak Khavari
Validation:Babak Khavari, Alexandros Konrkovelos & Andreas Sahlberg
Supervision and Advisory support:Francesco Fuso-Nerini & Mark Howells
About
Script and data from: "Assessing the urban-rural split and electrification in Sub-Saharan Africa - a cluster-based methodology" by Babak Khavari, Alexandros Korkovelos, Andeas Sahlberg, Francesco Fuso-Nerini and Mark Howells. 
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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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DOILicense: GPL v3GitHub release (latest by date)
Clustering
Script and data from: Population cluster data to assess the urban-rural split and electrification in Sub-Saharan Africa by Babak Khavari, Alexandros Korkovelos, Andeas Sahlberg, Francesco Fuso-Nerini and Mark Howells. Datasets produced using the method described in the paper are available at: https://data.mendeley.com/datasets/z9zfhzk8cr.
Content
This repository contains:
  • An environment .yml file needed for generating a fully functioning python 3.7 environment necessary for the clustering algorithm.
  • The clustering code and related functions. These files also contain necessary steps in order to reproduce results.
  • An example case for Benin.
Installing and running the clustering notebook
Requirements
The clustering module (as well as all supporting scripts in this repo) have been developed in Python 3. We recommend installing Anaconda's free distribution as suited for your operating system.
Install the clustering repository from GitHub
After installing Anaconda you can download the repository directly or clone it to your designated local directory using:
> conda install git
> git clone https://github.com/OnSSET/Clustering.git

Once installed, open anaconda prompt and move to your local "clustering" directory using:
> cd ..\Clustering

In order to be able to run the clustering tool (main.ipynb and funcs.ipynb) you have to install all necessary packages. "full_project.yml" contains all of these and can be easily set up by creating a new virtual environment using:
conda env create --name clustering --file full_project.yml

This might take some time. When complete, activate the virtual environment using:
conda activate clustering

With the environment activated, you can now move to the clustering directory and start a "jupyter notebook" session by simply typing:
..\Clustering> jupyter notebook 
Changelog
5-April-2020: Original code base published
Resources
Original dataset can be found here: https://data.mendeley.com/datasets/z9zfhzk8cr
Journal article can be found here:
Credits
Conceptualization:Babak Khavari & Francesco Fuso-Nerini
Methodology:Babak Khavari
Software:Babak Khavari
Validation:Babak Khavari, Alexandros Konrkovelos & Andreas Sahlberg
Supervision and Advisory support:Francesco Fuso-Nerini & Mark Howells
About
Script and data from: "Assessing the urban-rural split and electrification in Sub-Saharan Africa - a cluster-based methodology" by Babak Khavari, Alexandros Korkovelos, Andeas Sahlberg, Francesco Fuso-Nerini and Mark Howells. 
Resources
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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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DOILicense: GPL v3GitHub release (latest by date)
Clustering
Script and data from: Population cluster data to assess the urban-rural split and electrification in Sub-Saharan Africa by Babak Khavari, Alexandros Korkovelos, Andeas Sahlberg, Francesco Fuso-Nerini and Mark Howells. Datasets produced using the method described in the paper are available at: https://data.mendeley.com/datasets/z9zfhzk8cr.
Content
This repository contains:
  • An environment .yml file needed for generating a fully functioning python 3.7 environment necessary for the clustering algorithm.
  • The clustering code and related functions. These files also contain necessary steps in order to reproduce results.
  • An example case for Benin.
Installing and running the clustering notebook
Requirements
The clustering module (as well as all supporting scripts in this repo) have been developed in Python 3. We recommend installing Anaconda's free distribution as suited for your operating system.
Install the clustering repository from GitHub
After installing Anaconda you can download the repository directly or clone it to your designated local directory using:
> conda install git
> git clone https://github.com/OnSSET/Clustering.git

Once installed, open anaconda prompt and move to your local "clustering" directory using:
> cd ..\Clustering

In order to be able to run the clustering tool (main.ipynb and funcs.ipynb) you have to install all necessary packages. "full_project.yml" contains all of these and can be easily set up by creating a new virtual environment using:
conda env create --name clustering --file full_project.yml

This might take some time. When complete, activate the virtual environment using:
conda activate clustering

With the environment activated, you can now move to the clustering directory and start a "jupyter notebook" session by simply typing:
..\Clustering> jupyter notebook 
Changelog
5-April-2020: Original code base published
Resources
Original dataset can be found here: https://data.mendeley.com/datasets/z9zfhzk8cr
Journal article can be found here:
Credits
Conceptualization:Babak Khavari & Francesco Fuso-Nerini
Methodology:Babak Khavari
Software:Babak Khavari
Validation:Babak Khavari, Alexandros Konrkovelos & Andreas Sahlberg
Supervision and Advisory support:Francesco Fuso-Nerini & Mark Howells
About
Script and data from: "Assessing the urban-rural split and electrification in Sub-Saharan Africa - a cluster-based methodology" by Babak Khavari, Alexandros Korkovelos, Andeas Sahlberg, Francesco Fuso-Nerini and Mark Howells. 
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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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DOILicense: GPL v3GitHub release (latest by date)
Clustering
Script and data from: Population cluster data to assess the urban-rural split and electrification in Sub-Saharan Africa by Babak Khavari, Alexandros Korkovelos, Andeas Sahlberg, Francesco Fuso-Nerini and Mark Howells. Datasets produced using the method described in the paper are available at: https://data.mendeley.com/datasets/z9zfhzk8cr.
Content
This repository contains:
  • An environment .yml file needed for generating a fully functioning python 3.7 environment necessary for the clustering algorithm.
  • The clustering code and related functions. These files also contain necessary steps in order to reproduce results.
  • An example case for Benin.
Installing and running the clustering notebook
Requirements
The clustering module (as well as all supporting scripts in this repo) have been developed in Python 3. We recommend installing Anaconda's free distribution as suited for your operating system.
Install the clustering repository from GitHub
After installing Anaconda you can download the repository directly or clone it to your designated local directory using:
> conda install git
> git clone https://github.com/OnSSET/Clustering.git

Once installed, open anaconda prompt and move to your local "clustering" directory using:
> cd ..\Clustering

In order to be able to run the clustering tool (main.ipynb and funcs.ipynb) you have to install all necessary packages. "full_project.yml" contains all of these and can be easily set up by creating a new virtual environment using:
conda env create --name clustering --file full_project.yml

This might take some time. When complete, activate the virtual environment using:
conda activate clustering

With the environment activated, you can now move to the clustering directory and start a "jupyter notebook" session by simply typing:
..\Clustering> jupyter notebook 
Changelog
5-April-2020: Original code base published
Resources
Original dataset can be found here: https://data.mendeley.com/datasets/z9zfhzk8cr
Journal article can be found here:
Credits
Conceptualization:Babak Khavari & Francesco Fuso-Nerini
Methodology:Babak Khavari
Software:Babak Khavari
Validation:Babak Khavari, Alexandros Konrkovelos & Andreas Sahlberg
Supervision and Advisory support:Francesco Fuso-Nerini & Mark Howells
About
Script and data from: "Assessing the urban-rural split and electrification in Sub-Saharan Africa - a cluster-based methodology" by Babak Khavari, Alexandros Korkovelos, Andeas Sahlberg, Francesco Fuso-Nerini and Mark Howells. 
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GPL-3.0 license
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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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DOILicense: GPL v3GitHub release (latest by date)
Clustering
Script and data from: Population cluster data to assess the urban-rural split and electrification in Sub-Saharan Africa by Babak Khavari, Alexandros Korkovelos, Andeas Sahlberg, Francesco Fuso-Nerini and Mark Howells. Datasets produced using the method described in the paper are available at: https://data.mendeley.com/datasets/z9zfhzk8cr.
Content
This repository contains:
  • An environment .yml file needed for generating a fully functioning python 3.7 environment necessary for the clustering algorithm.
  • The clustering code and related functions. These files also contain necessary steps in order to reproduce results.
  • An example case for Benin.
Installing and running the clustering notebook
Requirements
The clustering module (as well as all supporting scripts in this repo) have been developed in Python 3. We recommend installing Anaconda's free distribution as suited for your operating system.
Install the clustering repository from GitHub
After installing Anaconda you can download the repository directly or clone it to your designated local directory using:
> conda install git
> git clone https://github.com/OnSSET/Clustering.git

Once installed, open anaconda prompt and move to your local "clustering" directory using:
> cd ..\Clustering

In order to be able to run the clustering tool (main.ipynb and funcs.ipynb) you have to install all necessary packages. "full_project.yml" contains all of these and can be easily set up by creating a new virtual environment using:
conda env create --name clustering --file full_project.yml

This might take some time. When complete, activate the virtual environment using:
conda activate clustering

With the environment activated, you can now move to the clustering directory and start a "jupyter notebook" session by simply typing:
..\Clustering> jupyter notebook 
Changelog
5-April-2020: Original code base published
Resources
Original dataset can be found here: https://data.mendeley.com/datasets/z9zfhzk8cr
Journal article can be found here:
Credits
Conceptualization:Babak Khavari & Francesco Fuso-Nerini
Methodology:Babak Khavari
Software:Babak Khavari
Validation:Babak Khavari, Alexandros Konrkovelos & Andreas Sahlberg
Supervision and Advisory support:Francesco Fuso-Nerini & Mark Howells
About
Script and data from: "Assessing the urban-rural split and electrification in Sub-Saharan Africa - a cluster-based methodology" by Babak Khavari, Alexandros Korkovelos, Andeas Sahlberg, Francesco Fuso-Nerini and Mark Howells. 
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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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DOILicense: GPL v3GitHub release (latest by date)
Clustering
Script and data from: Population cluster data to assess the urban-rural split and electrification in Sub-Saharan Africa by Babak Khavari, Alexandros Korkovelos, Andeas Sahlberg, Francesco Fuso-Nerini and Mark Howells. Datasets produced using the method described in the paper are available at: https://data.mendeley.com/datasets/z9zfhzk8cr.
Content
This repository contains:
  • An environment .yml file needed for generating a fully functioning python 3.7 environment necessary for the clustering algorithm.
  • The clustering code and related functions. These files also contain necessary steps in order to reproduce results.
  • An example case for Benin.
Installing and running the clustering notebook
Requirements
The clustering module (as well as all supporting scripts in this repo) have been developed in Python 3. We recommend installing Anaconda's free distribution as suited for your operating system.
Install the clustering repository from GitHub
After installing Anaconda you can download the repository directly or clone it to your designated local directory using:
> conda install git
> git clone https://github.com/OnSSET/Clustering.git

Once installed, open anaconda prompt and move to your local "clustering" directory using:
> cd ..\Clustering

In order to be able to run the clustering tool (main.ipynb and funcs.ipynb) you have to install all necessary packages. "full_project.yml" contains all of these and can be easily set up by creating a new virtual environment using:
conda env create --name clustering --file full_project.yml

This might take some time. When complete, activate the virtual environment using:
conda activate clustering

With the environment activated, you can now move to the clustering directory and start a "jupyter notebook" session by simply typing:
..\Clustering> jupyter notebook 
Changelog
5-April-2020: Original code base published
Resources
Original dataset can be found here: https://data.mendeley.com/datasets/z9zfhzk8cr
Journal article can be found here:
Credits
Conceptualization:Babak Khavari & Francesco Fuso-Nerini
Methodology:Babak Khavari
Software:Babak Khavari
Validation:Babak Khavari, Alexandros Konrkovelos & Andreas Sahlberg
Supervision and Advisory support:Francesco Fuso-Nerini & Mark Howells
About
Script and data from: "Assessing the urban-rural split and electrification in Sub-Saharan Africa - a cluster-based methodology" by Babak Khavari, Alexandros Korkovelos, Andeas Sahlberg, Francesco Fuso-Nerini and Mark Howells. 
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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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DOILicense: GPL v3GitHub release (latest by date)
Clustering
Script and data from: Population cluster data to assess the urban-rural split and electrification in Sub-Saharan Africa by Babak Khavari, Alexandros Korkovelos, Andeas Sahlberg, Francesco Fuso-Nerini and Mark Howells. Datasets produced using the method described in the paper are available at: https://data.mendeley.com/datasets/z9zfhzk8cr.
Content
This repository contains:
  • An environment .yml file needed for generating a fully functioning python 3.7 environment necessary for the clustering algorithm.
  • The clustering code and related functions. These files also contain necessary steps in order to reproduce results.
  • An example case for Benin.
Installing and running the clustering notebook
Requirements
The clustering module (as well as all supporting scripts in this repo) have been developed in Python 3. We recommend installing Anaconda's free distribution as suited for your operating system.
Install the clustering repository from GitHub
After installing Anaconda you can download the repository directly or clone it to your designated local directory using:
> conda install git
> git clone https://github.com/OnSSET/Clustering.git

Once installed, open anaconda prompt and move to your local "clustering" directory using:
> cd ..\Clustering

In order to be able to run the clustering tool (main.ipynb and funcs.ipynb) you have to install all necessary packages. "full_project.yml" contains all of these and can be easily set up by creating a new virtual environment using:
conda env create --name clustering --file full_project.yml

This might take some time. When complete, activate the virtual environment using:
conda activate clustering

With the environment activated, you can now move to the clustering directory and start a "jupyter notebook" session by simply typing:
..\Clustering> jupyter notebook 
Changelog
5-April-2020: Original code base published
Resources
Original dataset can be found here: https://data.mendeley.com/datasets/z9zfhzk8cr
Journal article can be found here:
Credits
Conceptualization:Babak Khavari & Francesco Fuso-Nerini
Methodology:Babak Khavari
Software:Babak Khavari
Validation:Babak Khavari, Alexandros Konrkovelos & Andreas Sahlberg
Supervision and Advisory support:Francesco Fuso-Nerini & Mark Howells
About
Script and data from: "Assessing the urban-rural split and electrification in Sub-Saharan Africa - a cluster-based methodology" by Babak Khavari, Alexandros Korkovelos, Andeas Sahlberg, Francesco Fuso-Nerini and Mark Howells. 
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages