Gist 4eaba7d2be548a560bf0730b6aabf8e7

100%

Gist 4eaba7d2be548a560bf0730b6aabf8e7

✓ Published0🌍 Public

Last edited Oct 9, 2020

Created on Aug 18, 2020

This example downloads and plays a sequence of Twitch clips, using the Twitch Kraken API to fetch the top clips based on the specified period and mode. It constructs a URL with the environment-specified client ID and query parameters, then parses the JSON response to collect clip URLs while limiting the total duration to under two minutes. The `requests` library downloads each MP4 file to a temporary directory for playback. The visualization demonstrates a straightforward data-fetching pipeline, where the clip list is assembled from the API response and stored locally, without any graphical rendering or charting library involved.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist de36ebb099ca71e1ac63f67335f7e71e

This example shows an automated pipeline that fetches the most popular Twitch clips of the week, concatenates them into a single video, and uploads the result to Streamable and Reddit. It uses the Twitch Kraken API to retrieve clip metadata and download the actual video files, then MoviePy to resize and concatenate the clips with a fixed intro. The script relies on the `requests` library for HTTP calls, `praw` for Reddit posting, and the `pystreamable` API for uploads. Environment variables supply authentication keys and configuration, while the final video is saved with a timestamped filename before being shared.

CConorAspell

79% match

Loading thumbnail…

Gist 82c5f73258108bef4df748b8e77ae770

This example shows an AWS Lambda function that automatically downloads trending Twitch clips for a specified game and stores them in an S3 bucket, organized by date. It first queries the Twitch Kraken API for top clips, extracts the MP4 URLs from the thumbnail paths, then downloads each video file to the Lambda’s temporary storage before uploading it to S3. The code uses the `requests` library for HTTP calls, `boto3` for S3 operations, and reads configuration from environment variables. The visualization highlights how serverless functions can automate media archiving pipelines.

CConorAspell

79% match

Loading thumbnail…

Gist af4d272b0b59fb00bf383b183eb0cc52

This example shows an automated pipeline that fetches and reposts top Twitch clips to a subreddit, using a Streamable link as the video source. The code uploads a local video file to Streamable via the `pystreamable` API, polls until the upload reaches 100% processing, and then submits the resulting URL as a Reddit post titled "Top Twitch Clips for This Week." It relies on the `praw` library for Reddit interactions, `requests` and `json` for API calls, `datetime` for timestamps, and `fake_useragent` to rotate user-agent strings. Environment variables supply all credentials, and the script exits if the upload does not complete within six polling attempts.

CConorAspell

74% match

Loading thumbnail…

Gist a803adce92b47f80c229d8375adfa195

This example demonstrates an automated pipeline that fetches Twitch clips, edits them into a compilation video, and uploads the result to Streamable, with Reddit integration for sharing. The workflow is orchestrated through a series of Python scripts (`get_videos.py`, `edit_videos.py`, `upload_video.py`, `main.py`) running inside a Docker container, using environment variables for configuration like `twitch_id` and `streamable_username`. The code relies on the `requests` library for API calls to Twitch and Streamable, while video editing is performed programmatically, though the specific editing library is not visible in the provided files. The container sets up a Python 3 environment and installs dependencies from `requirements.txt`, with all credentials and settings passed via `env.list`.

CConorAspell

69% match

Loading thumbnail…

Gist 77c69eeb802c8bd617a1f30df0f71e93

This example shows an automated video-pipeline system that fetches video content, edits it, and distributes the result to multiple platforms. The code’s main script orchestrates three distinct stages: retrieving source videos via `get_videos`, processing them through `edit_video`, and finally uploading the finished output—first to Streamable through `upload_to_streamable` and then to Reddit via `upload_to_reddit`. No visualization libraries are used; instead, the concrete APIs are the custom modules `get_videos`, `edit_videos`, and `upload_video`, which handle data acquisition, editing logic, and remote uploads. The gist demonstrates a file-based workflow, with no graphical rendering or interactive charts.

CConorAspell

66% match

Loading thumbnail…

Gist 2602cf85d001d560e28f586a6330e4f0

This example shows a system that automatically generates chess-themed videos from Reddit content, using a containerized Python application deployed on AWS Fargate. The visualization demonstrates how raw Reddit posts are transformed into video files, with the code specifying environment variables for Reddit API credentials, Twitch ID, and Streamable upload credentials. The task configuration uses AWS ECS task roles and execution roles, with the container image hosted on ECR. The pipeline fetches chess-related discussions from the chess subreddit, processes them through the `reddit-video-maker` image, and outputs weekly video compilations, as indicated by the `period` and `number_of_videos` parameters.

CConorAspell

61% match

Loading thumbnail…

Loading twitch data

This example demonstrates the process of loading and parsing a Twitch streaming dataset for D3.js visualization. The visualization initially displays a raw JSON representation of the data, which is fetched from a local CSV file using the `fetch` API. The code parses the CSV string with `csvParse` and converts numeric columns to numbers. The data includes streamer metrics like watch time, peak viewers, and followers. The example uses `select`, `selectAll`, and `.join` from D3 v7 to render the data as a text preview, highlighting the loading workflow.

IIshaChid76

59% match

Loading thumbnail…

Gist 57367d54143770b7c1d534cbfe002cd9

This example shows a Python script that combines multiple video clips into a single montage, resizing each to a fixed 460×720 resolution and concatenating them in sequence. The code uses MoviePy’s `VideoFileClip`, `concatenate_videoclips`, and `write_videofile` functions, along with its `vfx` module for potential filters. It saves the final output with a timestamped filename and writes it as an MP4 using the H.264 codec. The rendering approach relies on MoviePy’s FFmpeg backend, processing clips in sequential order without additional data sources.

CConorAspell

59% match