Checkroth

@Checkroth·6 public vizzes

Generated using https://github.com/bluemontag/igs-lsgp/, > Random structure generated in 1163.145503053 seconds. Generation method: Optimized Jacobson & Matthews generation method.

A 1000x1000 Latin square generated in 1163 seconds appears as a dense grid of colored cells, visualizing the statistical randomness and constraint satisfaction of the arrangement. The visualization uses d3 to render a canvas-based heatmap, where each cell’s color encodes a value from 0–999. The data comes from an optimized Jacobson and Matthews generation method, with the output shown as a single large matrix.

Mar 2, 2022

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Valheim Systemd File

This visualization renders a Valheim dedicated server’s systemd unit file as a structured diagram using D3.js force-directed layout. It encodes the document’s three `[Unit]`, `[Service]`, and `[Install]` sections as distinct node clusters, with edges connecting directives like `ExecStart` and `WantedBy` to their values via `d3.forceSimulation` and `d3.linkStrength`. Node positions are computed through iterative physics simulation, while text labels display the raw configuration strings. The underlying data originates from Checkroth’s gist, parsed into a graph that mirrors the service’s dependency chain and startup behavior.

Feb 18, 2021

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Gist e6556a05221bd9819d98cc5a5363a364

This example shows a live speech-to-text service that streams raw PCM audio from an HTTP request body into Microsoft Azure's Speech SDK for real-time transcription. The Python code implements a custom `PullAudioInputStream` callback that incrementally reads bytes from the Gunicorn request stream and writes them into a `BytesIO` buffer, while the falcon endpoint's `on_post` method triggers recognition. As Azure processes the audio, callback functions capture recognized and intermediate results, which are accumulated into lists along with timing and confidence data. The implementation uses the `speechsdk.audio` module, datetime for soft timeouts, and JSON parsing to extract transcription details from recognition events.

Apr 30, 2020

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Falcon API with Azure SR

This example shows a Falcon web API endpoint that transcribes speech from an uploaded audio file using Azure’s Speech SDK. It receives audio bytes in a POST request, streams them through a custom `BytesIOPullAudioInputStream` into `PullAudioInputStream`, and configures a `SpeechRecognizer` with credentials from environment variables. The handler connects event callbacks for session stop and recognition results, then returns the recognized JSON or error details as HTTP responses. The code relies on Falcon’s routing and request/response objects, alongside Azure’s `speechsdk` recognition and audio configuration classes.

Aug 19, 2019

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Gist 5ff2cc9f47a62f78fe295c76dd14eab2

This example demonstrates a multitenancy collision test in a Python datastore environment. It verifies that separate namespace assignments remain isolated across concurrent processes, ensuring that parallel operations do not interfere with each other’s namespace context. The code uses `multiprocessing.Process`, `multiprocessing.Manager`, and a shared dictionary for inter-process communication. It also employs `unittest` with setup/teardown fixtures and the `time` module to coordinate process timing. The test asserts that each process retrieves its assigned namespace correctly while the original namespace remains unchanged.

Oct 25, 2017

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Gist 03e850f4a387ea3d2a16648508705d10

This example shows how to test Django model relationships using pytest with randomized data generation. It defines two models, ModelA and ModelB, linked by a OneToOneField, and then uses the pytest_quickcheck library’s Generator classes (modelA and modelB) to create random instances with generated string fields. The test verifies that a repository’s `get_members` method returns the same list of saved models, using `pytest.mark.randomize` with `list_of` to generate up to 20 model instances, and fixtures (save_models) to persist them to the database before asserting equality.

Sep 13, 2017