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NM Test

Questions & Answers

Please make note of the major choices you make (tools, techniques, designs & assumptions)

Tools:

  • Using jupyter notebook to explore the dataset
  • Using library h5py to read/write metadata from/to the h5 files
  • Using matplotlib to visualise the generated/aggregated marks

Assumptions:

  • Assuming we are aggregating the attributes by location and date, rather than aggregate by location. Since aggregate by location may not make sense as building may change overtime at the same location, e.g roof may be different at different times.
  • Assuming we are training a CNN model to label Roof and Solar panel, we are only interest the labels marked as 'present' and 'not present', hence in the code, I have normalised all values >= 1 to just 1, everything else is 0.
  • At the moment, the 1s are just a single pixel in the center of a grid, I assume when training the CNN model, you would fill all the pixels in the grids so that the 1s will cover the entire roof/solar panel.

If you have used frameworks then write a brief note to describe how your solution will be deployed to the cloud.

  • This solution didn't use any framework, to scale up to a bigger dataset, I think we could use AWS S3 bucket to host the dataset, hook a AWS Lambda function to the s3 bucket so that when new jobs added to the bucket, the aggregation function will be triggered, and the aggregation function in term could archive the result to another s3 bucket.
  • If processing a large amount of data in a short time is critical, some sort of parallelism will be needed - potentially using Spark/Kubernetes to distribute the data processing may be a better solution, however I have very limited experience working with Spark/Kubernetes at the moment, more research is needed to confirm the hypothesis.

Installation

> git clone https://github.com/mondor/nmtest.git
> cd nmtest
> conda env create -n nmtest -f requirements.yml
> conda activate nmtest

Unzip the Test Dataset "New_Data", move it into the "data" folder, such that the project has the following structure:

.
│ ├── nmtest
├── tests
├── main.py ├── data │ ├── New_Data
│ │ ├── 1011
│ │ │ ├── 1_1011_2013-06-29.tar
│ │ │ └── 2_1011_2013-06-29.tar
│ │ │ └── ...
│ │ ├── 1012
│ │ ├── 1029
│ │ ├── ...
│ │ ├── attribute_manifest.csv

Execute the program

> cd nmtest
> conda activate nmtest
> python main.py

Run Tests

> cd nmtest
> conda activate nmtest
> PYTHONPATH=. pytest tests/

Type checks

> cd nmtest
> conda activate nmtest
> stubgen nmtest
> python -m mypy main.py

Visualise the aggregated marks

> cd nmtest
> conda activate nmtest
> jupyter notebook
> # Open notebook Visualise.ipynb on your browser
> # Execute all the ceils > # Or Open Visualise.html to see my output

About

nmtest

Resources

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Activity

Stars

1 star

Watchers

1 watching

Forks

0 forks
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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
NM Test
Questions & Answers
Please make note of the major choices you make (tools, techniques, designs & assumptions)
Tools:
  • Using jupyter notebook to explore the dataset
  • Using library h5py to read/write metadata from/to the h5 files
  • Using matplotlib to visualise the generated/aggregated marks
Assumptions:
  • Assuming we are aggregating the attributes by location and date, rather than aggregate by location. Since aggregate by location may not make sense as building may change overtime at the same location, e.g roof may be different at different times.
  • Assuming we are training a CNN model to label Roof and Solar panel, we are only interest the labels marked as 'present' and 'not present',
hence in the code, I have normalised all values >= 1 to just 1, everything else is 0.
  • At the moment, the 1s are just a single pixel in the center of a grid, I assume when training the CNN model, you would fill all the pixels in the grids so that the 1s will cover the entire roof/solar panel.
If you have used frameworks then write a brief note to describe how your solution will be deployed to the cloud.
  • This solution didn't use any framework, to scale up to a bigger dataset, I think we could use AWS S3 bucket to host the dataset, hook a AWS Lambda function to the s3 bucket so that when new jobs added to the bucket, the aggregation function will be triggered, and the aggregation function in term could archive the result to another s3 bucket.
  • If processing a large amount of data in a short time is critical, some sort of parallelism will be needed - potentially using Spark/Kubernetes to distribute the data processing may be a better solution, however I have very limited experience working with Spark/Kubernetes at the moment, more research is needed to confirm the hypothesis.
Installation
> git clone https://github.com/mondor/nmtest.git
> cd nmtest
> conda env create -n nmtest -f requirements.yml
> conda activate nmtest 
Unzip the Test Dataset "New_Data", move it into the "data" folder, such that the project
has the following structure:
.
│ ├── nmtest
├── tests
├── main.py ├── data │ ├── New_Data
│ │ ├── 1011
│ │ │ ├── 1_1011_2013-06-29.tar
│ │ │ └── 2_1011_2013-06-29.tar
│ │ │ └── ...
│ │ ├── 1012
│ │ ├── 1029
│ │ ├── ...
│ │ ├── attribute_manifest.csv

Execute the program
> cd nmtest
> conda activate nmtest
> python main.py

Run Tests
> cd nmtest
> conda activate nmtest
> PYTHONPATH=. pytest tests/

Type checks
> cd nmtest
> conda activate nmtest
> stubgen nmtest
> python -m mypy main.py 
Visualise the aggregated marks
> cd nmtest
> conda activate nmtest
> jupyter notebook
> # Open notebook Visualise.ipynb on your browser
> # Execute all the ceils > # Or Open Visualise.html to see my output

About
nmtest
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
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
NM Test
Questions & Answers
Please make note of the major choices you make (tools, techniques, designs & assumptions)
Tools:
  • Using jupyter notebook to explore the dataset
  • Using library h5py to read/write metadata from/to the h5 files
  • Using matplotlib to visualise the generated/aggregated marks
Assumptions:
  • Assuming we are aggregating the attributes by location and date, rather than aggregate by location. Since aggregate by location may not make sense as building may change overtime at the same location, e.g roof may be different at different times.
  • Assuming we are training a CNN model to label Roof and Solar panel, we are only interest the labels marked as 'present' and 'not present',
hence in the code, I have normalised all values >= 1 to just 1, everything else is 0.
  • At the moment, the 1s are just a single pixel in the center of a grid, I assume when training the CNN model, you would fill all the pixels in the grids so that the 1s will cover the entire roof/solar panel.
If you have used frameworks then write a brief note to describe how your solution will be deployed to the cloud.
  • This solution didn't use any framework, to scale up to a bigger dataset, I think we could use AWS S3 bucket to host the dataset, hook a AWS Lambda function to the s3 bucket so that when new jobs added to the bucket, the aggregation function will be triggered, and the aggregation function in term could archive the result to another s3 bucket.
  • If processing a large amount of data in a short time is critical, some sort of parallelism will be needed - potentially using Spark/Kubernetes to distribute the data processing may be a better solution, however I have very limited experience working with Spark/Kubernetes at the moment, more research is needed to confirm the hypothesis.
Installation
> git clone https://github.com/mondor/nmtest.git
> cd nmtest
> conda env create -n nmtest -f requirements.yml
> conda activate nmtest 
Unzip the Test Dataset "New_Data", move it into the "data" folder, such that the project
has the following structure:
.
│ ├── nmtest
├── tests
├── main.py ├── data │ ├── New_Data
│ │ ├── 1011
│ │ │ ├── 1_1011_2013-06-29.tar
│ │ │ └── 2_1011_2013-06-29.tar
│ │ │ └── ...
│ │ ├── 1012
│ │ ├── 1029
│ │ ├── ...
│ │ ├── attribute_manifest.csv

Execute the program
> cd nmtest
> conda activate nmtest
> python main.py

Run Tests
> cd nmtest
> conda activate nmtest
> PYTHONPATH=. pytest tests/

Type checks
> cd nmtest
> conda activate nmtest
> stubgen nmtest
> python -m mypy main.py 
Visualise the aggregated marks
> cd nmtest
> conda activate nmtest
> jupyter notebook
> # Open notebook Visualise.ipynb on your browser
> # Execute all the ceils > # Or Open Visualise.html to see my output

About
nmtest
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
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
NM Test
Questions & Answers
Please make note of the major choices you make (tools, techniques, designs & assumptions)
Tools:
  • Using jupyter notebook to explore the dataset
  • Using library h5py to read/write metadata from/to the h5 files
  • Using matplotlib to visualise the generated/aggregated marks
Assumptions:
  • Assuming we are aggregating the attributes by location and date, rather than aggregate by location. Since aggregate by location may not make sense as building may change overtime at the same location, e.g roof may be different at different times.
  • Assuming we are training a CNN model to label Roof and Solar panel, we are only interest the labels marked as 'present' and 'not present',
hence in the code, I have normalised all values >= 1 to just 1, everything else is 0.
  • At the moment, the 1s are just a single pixel in the center of a grid, I assume when training the CNN model, you would fill all the pixels in the grids so that the 1s will cover the entire roof/solar panel.
If you have used frameworks then write a brief note to describe how your solution will be deployed to the cloud.
  • This solution didn't use any framework, to scale up to a bigger dataset, I think we could use AWS S3 bucket to host the dataset, hook a AWS Lambda function to the s3 bucket so that when new jobs added to the bucket, the aggregation function will be triggered, and the aggregation function in term could archive the result to another s3 bucket.
  • If processing a large amount of data in a short time is critical, some sort of parallelism will be needed - potentially using Spark/Kubernetes to distribute the data processing may be a better solution, however I have very limited experience working with Spark/Kubernetes at the moment, more research is needed to confirm the hypothesis.
Installation
> git clone https://github.com/mondor/nmtest.git
> cd nmtest
> conda env create -n nmtest -f requirements.yml
> conda activate nmtest 
Unzip the Test Dataset "New_Data", move it into the "data" folder, such that the project
has the following structure:
.
│ ├── nmtest
├── tests
├── main.py ├── data │ ├── New_Data
│ │ ├── 1011
│ │ │ ├── 1_1011_2013-06-29.tar
│ │ │ └── 2_1011_2013-06-29.tar
│ │ │ └── ...
│ │ ├── 1012
│ │ ├── 1029
│ │ ├── ...
│ │ ├── attribute_manifest.csv

Execute the program
> cd nmtest
> conda activate nmtest
> python main.py

Run Tests
> cd nmtest
> conda activate nmtest
> PYTHONPATH=. pytest tests/

Type checks
> cd nmtest
> conda activate nmtest
> stubgen nmtest
> python -m mypy main.py 
Visualise the aggregated marks
> cd nmtest
> conda activate nmtest
> jupyter notebook
> # Open notebook Visualise.ipynb on your browser
> # Execute all the ceils > # Or Open Visualise.html to see my output

About
nmtest
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
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
NM Test
Questions & Answers
Please make note of the major choices you make (tools, techniques, designs & assumptions)
Tools:
  • Using jupyter notebook to explore the dataset
  • Using library h5py to read/write metadata from/to the h5 files
  • Using matplotlib to visualise the generated/aggregated marks
Assumptions:
  • Assuming we are aggregating the attributes by location and date, rather than aggregate by location. Since aggregate by location may not make sense as building may change overtime at the same location, e.g roof may be different at different times.
  • Assuming we are training a CNN model to label Roof and Solar panel, we are only interest the labels marked as 'present' and 'not present',
hence in the code, I have normalised all values >= 1 to just 1, everything else is 0.
  • At the moment, the 1s are just a single pixel in the center of a grid, I assume when training the CNN model, you would fill all the pixels in the grids so that the 1s will cover the entire roof/solar panel.
If you have used frameworks then write a brief note to describe how your solution will be deployed to the cloud.
  • This solution didn't use any framework, to scale up to a bigger dataset, I think we could use AWS S3 bucket to host the dataset, hook a AWS Lambda function to the s3 bucket so that when new jobs added to the bucket, the aggregation function will be triggered, and the aggregation function in term could archive the result to another s3 bucket.
  • If processing a large amount of data in a short time is critical, some sort of parallelism will be needed - potentially using Spark/Kubernetes to distribute the data processing may be a better solution, however I have very limited experience working with Spark/Kubernetes at the moment, more research is needed to confirm the hypothesis.
Installation
> git clone https://github.com/mondor/nmtest.git
> cd nmtest
> conda env create -n nmtest -f requirements.yml
> conda activate nmtest 
Unzip the Test Dataset "New_Data", move it into the "data" folder, such that the project
has the following structure:
.
│ ├── nmtest
├── tests
├── main.py ├── data │ ├── New_Data
│ │ ├── 1011
│ │ │ ├── 1_1011_2013-06-29.tar
│ │ │ └── 2_1011_2013-06-29.tar
│ │ │ └── ...
│ │ ├── 1012
│ │ ├── 1029
│ │ ├── ...
│ │ ├── attribute_manifest.csv

Execute the program
> cd nmtest
> conda activate nmtest
> python main.py

Run Tests
> cd nmtest
> conda activate nmtest
> PYTHONPATH=. pytest tests/

Type checks
> cd nmtest
> conda activate nmtest
> stubgen nmtest
> python -m mypy main.py 
Visualise the aggregated marks
> cd nmtest
> conda activate nmtest
> jupyter notebook
> # Open notebook Visualise.ipynb on your browser
> # Execute all the ceils > # Or Open Visualise.html to see my output

About
nmtest
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
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
NM Test
Questions & Answers
Please make note of the major choices you make (tools, techniques, designs & assumptions)
Tools:
  • Using jupyter notebook to explore the dataset
  • Using library h5py to read/write metadata from/to the h5 files
  • Using matplotlib to visualise the generated/aggregated marks
Assumptions:
  • Assuming we are aggregating the attributes by location and date, rather than aggregate by location. Since aggregate by location may not make sense as building may change overtime at the same location, e.g roof may be different at different times.
  • Assuming we are training a CNN model to label Roof and Solar panel, we are only interest the labels marked as 'present' and 'not present',
hence in the code, I have normalised all values >= 1 to just 1, everything else is 0.
  • At the moment, the 1s are just a single pixel in the center of a grid, I assume when training the CNN model, you would fill all the pixels in the grids so that the 1s will cover the entire roof/solar panel.
If you have used frameworks then write a brief note to describe how your solution will be deployed to the cloud.
  • This solution didn't use any framework, to scale up to a bigger dataset, I think we could use AWS S3 bucket to host the dataset, hook a AWS Lambda function to the s3 bucket so that when new jobs added to the bucket, the aggregation function will be triggered, and the aggregation function in term could archive the result to another s3 bucket.
  • If processing a large amount of data in a short time is critical, some sort of parallelism will be needed - potentially using Spark/Kubernetes to distribute the data processing may be a better solution, however I have very limited experience working with Spark/Kubernetes at the moment, more research is needed to confirm the hypothesis.
Installation
> git clone https://github.com/mondor/nmtest.git
> cd nmtest
> conda env create -n nmtest -f requirements.yml
> conda activate nmtest 
Unzip the Test Dataset "New_Data", move it into the "data" folder, such that the project
has the following structure:
.
│ ├── nmtest
├── tests
├── main.py ├── data │ ├── New_Data
│ │ ├── 1011
│ │ │ ├── 1_1011_2013-06-29.tar
│ │ │ └── 2_1011_2013-06-29.tar
│ │ │ └── ...
│ │ ├── 1012
│ │ ├── 1029
│ │ ├── ...
│ │ ├── attribute_manifest.csv

Execute the program
> cd nmtest
> conda activate nmtest
> python main.py

Run Tests
> cd nmtest
> conda activate nmtest
> PYTHONPATH=. pytest tests/

Type checks
> cd nmtest
> conda activate nmtest
> stubgen nmtest
> python -m mypy main.py 
Visualise the aggregated marks
> cd nmtest
> conda activate nmtest
> jupyter notebook
> # Open notebook Visualise.ipynb on your browser
> # Execute all the ceils > # Or Open Visualise.html to see my output

About
nmtest
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
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
NM Test
Questions & Answers
Please make note of the major choices you make (tools, techniques, designs & assumptions)
Tools:
  • Using jupyter notebook to explore the dataset
  • Using library h5py to read/write metadata from/to the h5 files
  • Using matplotlib to visualise the generated/aggregated marks
Assumptions:
  • Assuming we are aggregating the attributes by location and date, rather than aggregate by location. Since aggregate by location may not make sense as building may change overtime at the same location, e.g roof may be different at different times.
  • Assuming we are training a CNN model to label Roof and Solar panel, we are only interest the labels marked as 'present' and 'not present',
hence in the code, I have normalised all values >= 1 to just 1, everything else is 0.
  • At the moment, the 1s are just a single pixel in the center of a grid, I assume when training the CNN model, you would fill all the pixels in the grids so that the 1s will cover the entire roof/solar panel.
If you have used frameworks then write a brief note to describe how your solution will be deployed to the cloud.
  • This solution didn't use any framework, to scale up to a bigger dataset, I think we could use AWS S3 bucket to host the dataset, hook a AWS Lambda function to the s3 bucket so that when new jobs added to the bucket, the aggregation function will be triggered, and the aggregation function in term could archive the result to another s3 bucket.
  • If processing a large amount of data in a short time is critical, some sort of parallelism will be needed - potentially using Spark/Kubernetes to distribute the data processing may be a better solution, however I have very limited experience working with Spark/Kubernetes at the moment, more research is needed to confirm the hypothesis.
Installation
> git clone https://github.com/mondor/nmtest.git
> cd nmtest
> conda env create -n nmtest -f requirements.yml
> conda activate nmtest 
Unzip the Test Dataset "New_Data", move it into the "data" folder, such that the project
has the following structure:
.
│ ├── nmtest
├── tests
├── main.py ├── data │ ├── New_Data
│ │ ├── 1011
│ │ │ ├── 1_1011_2013-06-29.tar
│ │ │ └── 2_1011_2013-06-29.tar
│ │ │ └── ...
│ │ ├── 1012
│ │ ├── 1029
│ │ ├── ...
│ │ ├── attribute_manifest.csv

Execute the program
> cd nmtest
> conda activate nmtest
> python main.py

Run Tests
> cd nmtest
> conda activate nmtest
> PYTHONPATH=. pytest tests/

Type checks
> cd nmtest
> conda activate nmtest
> stubgen nmtest
> python -m mypy main.py 
Visualise the aggregated marks
> cd nmtest
> conda activate nmtest
> jupyter notebook
> # Open notebook Visualise.ipynb on your browser
> # Execute all the ceils > # Or Open Visualise.html to see my output

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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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NM Test
Questions & Answers
Please make note of the major choices you make (tools, techniques, designs & assumptions)
Tools:
  • Using jupyter notebook to explore the dataset
  • Using library h5py to read/write metadata from/to the h5 files
  • Using matplotlib to visualise the generated/aggregated marks
Assumptions:
  • Assuming we are aggregating the attributes by location and date, rather than aggregate by location. Since aggregate by location may not make sense as building may change overtime at the same location, e.g roof may be different at different times.
  • Assuming we are training a CNN model to label Roof and Solar panel, we are only interest the labels marked as 'present' and 'not present',
hence in the code, I have normalised all values >= 1 to just 1, everything else is 0.
  • At the moment, the 1s are just a single pixel in the center of a grid, I assume when training the CNN model, you would fill all the pixels in the grids so that the 1s will cover the entire roof/solar panel.
If you have used frameworks then write a brief note to describe how your solution will be deployed to the cloud.
  • This solution didn't use any framework, to scale up to a bigger dataset, I think we could use AWS S3 bucket to host the dataset, hook a AWS Lambda function to the s3 bucket so that when new jobs added to the bucket, the aggregation function will be triggered, and the aggregation function in term could archive the result to another s3 bucket.
  • If processing a large amount of data in a short time is critical, some sort of parallelism will be needed - potentially using Spark/Kubernetes to distribute the data processing may be a better solution, however I have very limited experience working with Spark/Kubernetes at the moment, more research is needed to confirm the hypothesis.
Installation
> git clone https://github.com/mondor/nmtest.git
> cd nmtest
> conda env create -n nmtest -f requirements.yml
> conda activate nmtest 
Unzip the Test Dataset "New_Data", move it into the "data" folder, such that the project
has the following structure:
.
│ ├── nmtest
├── tests
├── main.py ├── data │ ├── New_Data
│ │ ├── 1011
│ │ │ ├── 1_1011_2013-06-29.tar
│ │ │ └── 2_1011_2013-06-29.tar
│ │ │ └── ...
│ │ ├── 1012
│ │ ├── 1029
│ │ ├── ...
│ │ ├── attribute_manifest.csv

Execute the program
> cd nmtest
> conda activate nmtest
> python main.py

Run Tests
> cd nmtest
> conda activate nmtest
> PYTHONPATH=. pytest tests/

Type checks
> cd nmtest
> conda activate nmtest
> stubgen nmtest
> python -m mypy main.py 
Visualise the aggregated marks
> cd nmtest
> conda activate nmtest
> jupyter notebook
> # Open notebook Visualise.ipynb on your browser
> # Execute all the ceils > # Or Open Visualise.html to see my output

About
nmtest
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages