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RTML Network Workshop Resources

Author: Dr. Rob Lyon

Email : robert.lyon@manchester.ac.uk

web : www.scienceguyrob.com

I've provided the following notebooks:

  • Machine Learning Basics.ipynb - This notebook explores introduces the basic concepts underpinning Machine Learning (ML) classification. The content presented here was originally written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
  • Pulsar Problem Intro.ipynb - This notebook introduces the basic concepts/process underpinning pulsar candidate classification. The content presented here was originally written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
  • Machine Learning Complications.ipynb - This notebook explores the main issues which reduce the accuracy of ML algorithms, used for candidate classification. It was written to support a talk delivered at IAU Symposium No. 337, Pulsar Astrophysics: The Next Fifty Years (2017).
  • Imbalanced Learning.ipynb - This notebook explores the Imbalanced Learning Problem, that reduces the accuracy of ML algorithms. It was written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.

These notebooks require Python 3.6, Numpy, Scipy, imbalanced-learn and Scikit-learn.

I have also provided a Dockerfile that you can use to build an environment that can run these examples. You can find this in the Docker directory.

I kindly request that if you make use of the resources, please cite them using the bibtex reference found at the top of each notebook.

License

The code and the contents of this notebook are released under the GNU GENERAL PUBLIC LICENSE, Version 3, 29 June 2007. We kindly request that if you make use of the notebook, you cite the work appropriately. The images are exempt from this, as some are used in publications I've written in the past (though I can use them here). If you'd like to use the images please let me know, and I'll sort something out.

Acknowledgements

The notebook's often utilise data obtained by the High Time Resolution Universe Collaboration using the Parkes Observatory, funded by the Commonwealth of Australia and managed by the CSIRO. The data was originally processed by Dr. Daniel Thornton & Dr. Samuel Bates, and I gratefully acknowledge their efforts.

Change log

Initial upload.

About

Resources for the first Radiotherapy Machine Learning (RTML) Network event.

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4 stars

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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" + '

Skip to content
BranchesTags
Repository files navigation
RTML Network Workshop Resources
Author: Dr. Rob Lyon
Email : robert.lyon@manchester.ac.uk
web : www.scienceguyrob.com
I've provided the following notebooks:
  • Machine Learning Basics.ipynb - This notebook explores introduces the basic concepts underpinning Machine Learning (ML) classification. The content presented here was originally written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
  • Pulsar Problem Intro.ipynb - This notebook introduces the basic concepts/process underpinning pulsar candidate classification. The content presented here was originally written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
  • Machine Learning Complications.ipynb - This notebook explores the main issues which reduce the accuracy of ML algorithms, used for candidate classification. It was written to support a talk delivered at IAU Symposium No. 337, Pulsar Astrophysics: The Next Fifty Years (2017).
  • Imbalanced Learning.ipynb - This notebook explores the Imbalanced Learning Problem, that reduces the accuracy of ML algorithms. It was written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
These notebooks require Python 3.6, Numpy, Scipy, imbalanced-learn and Scikit-learn.
I have also provided a Dockerfile that you can use to build an environment that can run these examples. You can find this in the Docker directory.
I kindly request that if you make use of the resources, please cite them using the bibtex reference found at the top of each notebook.
License
The code and the contents of this notebook are released under the GNU GENERAL PUBLIC LICENSE, Version 3, 29 June 2007. We kindly request that if you make use of the notebook, you cite the work appropriately. The images are exempt from this, as some are used in publications I've written in the past (though I can use them here). If you'd like to use the images please let me know, and I'll sort something out.
Acknowledgements
The notebook's often utilise data obtained by the High Time Resolution Universe Collaboration using the Parkes Observatory, funded by the Commonwealth of Australia and managed by the CSIRO. The data was originally processed by Dr. Daniel Thornton & Dr. Samuel Bates, and I gratefully acknowledge their efforts.
Change log
Initial upload.
About
Resources for the first Radiotherapy Machine Learning (RTML) Network event.
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

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

Skip to content
BranchesTags
Repository files navigation
RTML Network Workshop Resources
Author: Dr. Rob Lyon
Email : robert.lyon@manchester.ac.uk
web : www.scienceguyrob.com
I've provided the following notebooks:
  • Machine Learning Basics.ipynb - This notebook explores introduces the basic concepts underpinning Machine Learning (ML) classification. The content presented here was originally written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
  • Pulsar Problem Intro.ipynb - This notebook introduces the basic concepts/process underpinning pulsar candidate classification. The content presented here was originally written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
  • Machine Learning Complications.ipynb - This notebook explores the main issues which reduce the accuracy of ML algorithms, used for candidate classification. It was written to support a talk delivered at IAU Symposium No. 337, Pulsar Astrophysics: The Next Fifty Years (2017).
  • Imbalanced Learning.ipynb - This notebook explores the Imbalanced Learning Problem, that reduces the accuracy of ML algorithms. It was written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
These notebooks require Python 3.6, Numpy, Scipy, imbalanced-learn and Scikit-learn.
I have also provided a Dockerfile that you can use to build an environment that can run these examples. You can find this in the Docker directory.
I kindly request that if you make use of the resources, please cite them using the bibtex reference found at the top of each notebook.
License
The code and the contents of this notebook are released under the GNU GENERAL PUBLIC LICENSE, Version 3, 29 June 2007. We kindly request that if you make use of the notebook, you cite the work appropriately. The images are exempt from this, as some are used in publications I've written in the past (though I can use them here). If you'd like to use the images please let me know, and I'll sort something out.
Acknowledgements
The notebook's often utilise data obtained by the High Time Resolution Universe Collaboration using the Parkes Observatory, funded by the Commonwealth of Australia and managed by the CSIRO. The data was originally processed by Dr. Daniel Thornton & Dr. Samuel Bates, and I gratefully acknowledge their efforts.
Change log
Initial upload.
About
Resources for the first Radiotherapy Machine Learning (RTML) Network event.
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
RTML Network Workshop Resources
Author: Dr. Rob Lyon
Email : robert.lyon@manchester.ac.uk
web : www.scienceguyrob.com
I've provided the following notebooks:
  • Machine Learning Basics.ipynb - This notebook explores introduces the basic concepts underpinning Machine Learning (ML) classification. The content presented here was originally written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
  • Pulsar Problem Intro.ipynb - This notebook introduces the basic concepts/process underpinning pulsar candidate classification. The content presented here was originally written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
  • Machine Learning Complications.ipynb - This notebook explores the main issues which reduce the accuracy of ML algorithms, used for candidate classification. It was written to support a talk delivered at IAU Symposium No. 337, Pulsar Astrophysics: The Next Fifty Years (2017).
  • Imbalanced Learning.ipynb - This notebook explores the Imbalanced Learning Problem, that reduces the accuracy of ML algorithms. It was written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
These notebooks require Python 3.6, Numpy, Scipy, imbalanced-learn and Scikit-learn.
I have also provided a Dockerfile that you can use to build an environment that can run these examples. You can find this in the Docker directory.
I kindly request that if you make use of the resources, please cite them using the bibtex reference found at the top of each notebook.
License
The code and the contents of this notebook are released under the GNU GENERAL PUBLIC LICENSE, Version 3, 29 June 2007. We kindly request that if you make use of the notebook, you cite the work appropriately. The images are exempt from this, as some are used in publications I've written in the past (though I can use them here). If you'd like to use the images please let me know, and I'll sort something out.
Acknowledgements
The notebook's often utilise data obtained by the High Time Resolution Universe Collaboration using the Parkes Observatory, funded by the Commonwealth of Australia and managed by the CSIRO. The data was originally processed by Dr. Daniel Thornton & Dr. Samuel Bates, and I gratefully acknowledge their efforts.
Change log
Initial upload.
About
Resources for the first Radiotherapy Machine Learning (RTML) Network event.
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
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" + '

Skip to content
BranchesTags
Repository files navigation
RTML Network Workshop Resources
Author: Dr. Rob Lyon
Email : robert.lyon@manchester.ac.uk
web : www.scienceguyrob.com
I've provided the following notebooks:
  • Machine Learning Basics.ipynb - This notebook explores introduces the basic concepts underpinning Machine Learning (ML) classification. The content presented here was originally written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
  • Pulsar Problem Intro.ipynb - This notebook introduces the basic concepts/process underpinning pulsar candidate classification. The content presented here was originally written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
  • Machine Learning Complications.ipynb - This notebook explores the main issues which reduce the accuracy of ML algorithms, used for candidate classification. It was written to support a talk delivered at IAU Symposium No. 337, Pulsar Astrophysics: The Next Fifty Years (2017).
  • Imbalanced Learning.ipynb - This notebook explores the Imbalanced Learning Problem, that reduces the accuracy of ML algorithms. It was written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
These notebooks require Python 3.6, Numpy, Scipy, imbalanced-learn and Scikit-learn.
I have also provided a Dockerfile that you can use to build an environment that can run these examples. You can find this in the Docker directory.
I kindly request that if you make use of the resources, please cite them using the bibtex reference found at the top of each notebook.
License
The code and the contents of this notebook are released under the GNU GENERAL PUBLIC LICENSE, Version 3, 29 June 2007. We kindly request that if you make use of the notebook, you cite the work appropriately. The images are exempt from this, as some are used in publications I've written in the past (though I can use them here). If you'd like to use the images please let me know, and I'll sort something out.
Acknowledgements
The notebook's often utilise data obtained by the High Time Resolution Universe Collaboration using the Parkes Observatory, funded by the Commonwealth of Australia and managed by the CSIRO. The data was originally processed by Dr. Daniel Thornton & Dr. Samuel Bates, and I gratefully acknowledge their efforts.
Change log
Initial upload.
About
Resources for the first Radiotherapy Machine Learning (RTML) Network event.
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
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
Repository files navigation
RTML Network Workshop Resources
Author: Dr. Rob Lyon
Email : robert.lyon@manchester.ac.uk
web : www.scienceguyrob.com
I've provided the following notebooks:
  • Machine Learning Basics.ipynb - This notebook explores introduces the basic concepts underpinning Machine Learning (ML) classification. The content presented here was originally written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
  • Pulsar Problem Intro.ipynb - This notebook introduces the basic concepts/process underpinning pulsar candidate classification. The content presented here was originally written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
  • Machine Learning Complications.ipynb - This notebook explores the main issues which reduce the accuracy of ML algorithms, used for candidate classification. It was written to support a talk delivered at IAU Symposium No. 337, Pulsar Astrophysics: The Next Fifty Years (2017).
  • Imbalanced Learning.ipynb - This notebook explores the Imbalanced Learning Problem, that reduces the accuracy of ML algorithms. It was written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
These notebooks require Python 3.6, Numpy, Scipy, imbalanced-learn and Scikit-learn.
I have also provided a Dockerfile that you can use to build an environment that can run these examples. You can find this in the Docker directory.
I kindly request that if you make use of the resources, please cite them using the bibtex reference found at the top of each notebook.
License
The code and the contents of this notebook are released under the GNU GENERAL PUBLIC LICENSE, Version 3, 29 June 2007. We kindly request that if you make use of the notebook, you cite the work appropriately. The images are exempt from this, as some are used in publications I've written in the past (though I can use them here). If you'd like to use the images please let me know, and I'll sort something out.
Acknowledgements
The notebook's often utilise data obtained by the High Time Resolution Universe Collaboration using the Parkes Observatory, funded by the Commonwealth of Australia and managed by the CSIRO. The data was originally processed by Dr. Daniel Thornton & Dr. Samuel Bates, and I gratefully acknowledge their efforts.
Change log
Initial upload.
About
Resources for the first Radiotherapy Machine Learning (RTML) Network event.
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
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
Repository files navigation
RTML Network Workshop Resources
Author: Dr. Rob Lyon
Email : robert.lyon@manchester.ac.uk
web : www.scienceguyrob.com
I've provided the following notebooks:
  • Machine Learning Basics.ipynb - This notebook explores introduces the basic concepts underpinning Machine Learning (ML) classification. The content presented here was originally written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
  • Pulsar Problem Intro.ipynb - This notebook introduces the basic concepts/process underpinning pulsar candidate classification. The content presented here was originally written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
  • Machine Learning Complications.ipynb - This notebook explores the main issues which reduce the accuracy of ML algorithms, used for candidate classification. It was written to support a talk delivered at IAU Symposium No. 337, Pulsar Astrophysics: The Next Fifty Years (2017).
  • Imbalanced Learning.ipynb - This notebook explores the Imbalanced Learning Problem, that reduces the accuracy of ML algorithms. It was written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
These notebooks require Python 3.6, Numpy, Scipy, imbalanced-learn and Scikit-learn.
I have also provided a Dockerfile that you can use to build an environment that can run these examples. You can find this in the Docker directory.
I kindly request that if you make use of the resources, please cite them using the bibtex reference found at the top of each notebook.
License
The code and the contents of this notebook are released under the GNU GENERAL PUBLIC LICENSE, Version 3, 29 June 2007. We kindly request that if you make use of the notebook, you cite the work appropriately. The images are exempt from this, as some are used in publications I've written in the past (though I can use them here). If you'd like to use the images please let me know, and I'll sort something out.
Acknowledgements
The notebook's often utilise data obtained by the High Time Resolution Universe Collaboration using the Parkes Observatory, funded by the Commonwealth of Australia and managed by the CSIRO. The data was originally processed by Dr. Daniel Thornton & Dr. Samuel Bates, and I gratefully acknowledge their efforts.
Change log
Initial upload.
About
Resources for the first Radiotherapy Machine Learning (RTML) Network event.
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
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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RTML Network Workshop Resources
Author: Dr. Rob Lyon
Email : robert.lyon@manchester.ac.uk
web : www.scienceguyrob.com
I've provided the following notebooks:
  • Machine Learning Basics.ipynb - This notebook explores introduces the basic concepts underpinning Machine Learning (ML) classification. The content presented here was originally written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
  • Pulsar Problem Intro.ipynb - This notebook introduces the basic concepts/process underpinning pulsar candidate classification. The content presented here was originally written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
  • Machine Learning Complications.ipynb - This notebook explores the main issues which reduce the accuracy of ML algorithms, used for candidate classification. It was written to support a talk delivered at IAU Symposium No. 337, Pulsar Astrophysics: The Next Fifty Years (2017).
  • Imbalanced Learning.ipynb - This notebook explores the Imbalanced Learning Problem, that reduces the accuracy of ML algorithms. It was written to support a talk I delivered at European Week of Astronomy and Space Science (EWASS) meeting in 2018.
These notebooks require Python 3.6, Numpy, Scipy, imbalanced-learn and Scikit-learn.
I have also provided a Dockerfile that you can use to build an environment that can run these examples. You can find this in the Docker directory.
I kindly request that if you make use of the resources, please cite them using the bibtex reference found at the top of each notebook.
License
The code and the contents of this notebook are released under the GNU GENERAL PUBLIC LICENSE, Version 3, 29 June 2007. We kindly request that if you make use of the notebook, you cite the work appropriately. The images are exempt from this, as some are used in publications I've written in the past (though I can use them here). If you'd like to use the images please let me know, and I'll sort something out.
Acknowledgements
The notebook's often utilise data obtained by the High Time Resolution Universe Collaboration using the Parkes Observatory, funded by the Commonwealth of Australia and managed by the CSIRO. The data was originally processed by Dr. Daniel Thornton & Dr. Samuel Bates, and I gratefully acknowledge their efforts.
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Initial upload.
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Resources for the first Radiotherapy Machine Learning (RTML) Network event.
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