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Add technical and development notes to README
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Hugo Locurcio
63
README.md
63
README.md
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# Godot GitHub issue statistics
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This repository gathers hardware and software information reported by users on the
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[issue tracker of the main Godot repository](https://github.com/godotengine/godot/issues).
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This information is then displayed on a [website](https://godotengine.github.io/issue-stats/).
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As such, these statistics are <em>not</em> representative of the entire Godot community,
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but they allow seeing what kind of hardware and software is popular among issue reporters.
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## How it works
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### Backend
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- Using GitHub's GraphQL API, 30 requests are performed to fetch the
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description, author and creation date of the 3,000 latest issues.
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- In the resulting data, the `System information` field of the issue is parsed
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in a case-sensitive, punctuation-insensitive manner.
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- The operating system, CPU and GPU is detected using this information
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provided by the user. All other information (such as the number of physical
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cores or amount of video memory) is inferred from the model names reported
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by the user.[^1]
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- If there's no valid `System information` section or it contains no usable
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information, the issue is ignored.
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- A dictionary of `set()` values is created with all possible values that users
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may be counted in. Each detected value is added to a set of users who have
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reported this information. This ensures that a user may only increment each
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statistic once, even if they've reported several issues with the same hardware
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and software configuration. If a single user has reported several issues with
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*different* hardware and software configurations, then all configurations are
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counted from this user.
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- The dictionary is written to a JSON file, with the `set()` value replaced by
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the number of users who have been detected to be using the hardware/software
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in question.
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- The resulting JSON file + the frontend is deployed to GitHub Pages using
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GitHub Actions every day.
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[^1]: In rare scenarios, this can be inaccurate. In this case, the script errs
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on the side of the most popular variant. For instance, the GeForce GTX 1060
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is considered to always have 6 GB of VRAM, even though it also exists in a
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less popular 3 GB variant. See comments in [`build.py`](/build.py)'s
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hardware detection routine for details.
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### Frontend
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- The frontend is a single [`index.html`](/index.html) page, plus the
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third-party dependencies mentioned below. No frontend building is required.
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- [Frappe Charts](https://frappe.io/charts) is used to display charts.
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- [Ky](https://github.com/sindresorhus/ky) is used to make an HTTP request to the JSON file.
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- [Water.css](https://watercss.kognise.dev/) is used for styling the page.
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## Development
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Follow these instructions to set up this site locally for development purposes:
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- Make sure you have [Python](https://python.org) 3.7 or later, pip and virtualenv.
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- Create a virtualenv and activate it: `virtualenv venv && . venv/bin/activate`.
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- Within the virtualenv, install dependencies by running `pip install -r requirements.txt`.
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- Copy [`.env.example`](/.env.example) to `.env` and fill in the GitHub API key
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with a personal access token. You can generate one
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[here](https://github.com/settings/tokens) (it **must** have the `public_repo` scope).
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- Run `python build.py` to fetch issue data from the GitHub API.
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- Start a local web server in the root directory then browse `index.html`.
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## License
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Copyright © 2023-present Hugo Locurcio and contributors
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42
index.html
42
index.html
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"dedicated_gcn2.0": "#991b1b",
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"dedicated_gcn1.0": "#7f1d1d",
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dedicated_vliw4: "#450a0a",
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integrated_rdna3: "#92400e",
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integrated_rdna2: "#78350f",
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integrated_rdna3: "#92400e",
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integrated_rdna2: "#78350f",
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"integrated_gcn5.0": "#451a03",
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// Intel GPU generation.
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@@ -273,12 +273,19 @@
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function getRemappedString(label) {
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// Capitalize string (like_this => Like This) if not found in the remaps table.
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// Replace "Dedicated" and "Integrated" with shorter forms if not using the remaps table (useful for NVIDIA GPUs).
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// Replace "Dedicated" and "Integrated" with shorter forms if not using the remaps table
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// (useful for NVIDIA GPUs).
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// Replace " Gb" for VRAM sizes.
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return capitalizeStringRemaps[label] ?? label.replaceAll("_", " ").replace(/\b\w/g, l => l.toUpperCase()).replace("Dedicated", "Dedi.").replace("Integrated", "IGP").replace(" Gb", " GB").replace("Macos", "macOS").replace("Ios", "iOS");
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return capitalizeStringRemaps[label] ?? label.replaceAll("_", " ").replace(/\b\w/g, l => l.toUpperCase())
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.replace("Dedicated", "Dedi.")
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.replace("Integrated", "IGP")
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.replace(" Gb", " GB")
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.replace("Macos", "macOS")
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.replace("Ios", "iOS");
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}
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function getChartColor(label) {
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// Use a random color as a fallback if the color for the given label can't be found in the list.
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return chartColors[label] ?? '#' + (0x1000000 + (Math.random()) * 0xffffff).toString(16).substr(1, 6);
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}
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@@ -290,9 +297,14 @@
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new frappe.Chart(id, {
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data: {
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labels: Object.keys(dataset).map(label => getRemappedString(label)),
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datasets: [{ values: chartDatatype == ChartDatatype.INDIVIDUAL ? Object.values(dataset) : Object.values(dataset).map(subObject => Object.values(subObject).reduce(sum, 0)) }],
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datasets: [{
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values: chartDatatype == ChartDatatype.INDIVIDUAL
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? Object.values(dataset)
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: Object.values(dataset).map(subObject => Object.values(subObject).reduce(sum, 0)),
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}],
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},
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height: 145, // Make charts slightly more compact (default height is 168 in our case).
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// Make charts slightly more compact (default height is 168 in our case).
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height: 145,
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type: "percentage",
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colors: Object.keys(dataset).map(label => getChartColor(label)),
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});
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@@ -544,13 +556,19 @@
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Scores are sourced from <a href="https://www.videocardbenchmark.net/">PassMark</a>.<br>
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For reference, here are some example GPUs that roughly match each score threshold:
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<ul>
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<li><strong>2,500 points:</strong> GeForce GT 1030, GeForce GTX 850M, Radeon HD 8950, Ryzen 5 5600G IGP, Intel Iris Xe</li>
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<li><strong>5,000 points:</strong> GeForce GTX 1050, Quadro P2000 with Max-Q Design, Radeon HD 7970, Radeon R9 M295X</li>
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<li><strong>10,000 points:</strong> GeForce RTX 1060, GeForce RTX 3050 Ti Laptop GPU, Radeon R9 Fury X</li>
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<li><strong>15,000 points:</strong> GeForce GTX 1080, GeForce RTX 2080 Mobile, Radeon RX 6600, Radeon RX7600S</li>
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<li><strong>20,000 points:</strong> GeForce RTX 3060 Ti, GeForce RTX 3080 Ti Laptop GPU, Radeon RX 6700XT</li>
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<li><strong>2,500 points:</strong> GeForce GT 1030, GeForce GTX 850M, Radeon HD 8950, Ryzen 5 5600G IGP,
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Intel Iris Xe</li>
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<li><strong>5,000 points:</strong> GeForce GTX 1050, Quadro P2000 with Max-Q Design, Radeon HD 7970,
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Radeon R9 M295X</li>
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<li><strong>10,000 points:</strong> GeForce RTX 1060, GeForce RTX 3050 Ti Laptop GPU, Radeon R9 Fury X
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</li>
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<li><strong>15,000 points:</strong> GeForce GTX 1080, GeForce RTX 2080 Mobile, Radeon RX 6600, Radeon
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RX7600S</li>
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<li><strong>20,000 points:</strong> GeForce RTX 3060 Ti, GeForce RTX 3080 Ti Laptop GPU, Radeon RX
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6700XT</li>
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<li><strong>25,000 points:</strong> GeForce RTX 3080, Radeon RX 6800 XT</li>
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<li><strong>30,000 points:</strong> GeForce RTX 3090 Ti, GeForce RTX 4090 Laptop GPU, Radeon RX 7900 XTX</li>
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<li><strong>30,000 points:</strong> GeForce RTX 3090 Ti, GeForce RTX 4090 Laptop GPU, Radeon RX 7900 XTX
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</li>
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</ul>
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</div>
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</details>
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