Gist ae6b977c060cc0ef01506b020aceeba5

Gist b87ac9ed9766b78a72565a895d3a660f

This example shows a scatterplot-style visualization of audio features for tracks from a Spotify playlist, with each point representing a song’s metadata retrieved via the Spotify Web API. The code pulls playlist tracks using `spotipy`’s `user_playlist` method, extracts track IDs, and calls `audio_features` to gather numeric acoustics attributes. Those features are then loaded into a pandas `DataFrame`, which serves as the dataset for the plotted chart. The visualization relies on Spotify’s API response structure and pandas for tabular organization, but does not include any specific plotting library call in the provided code.

GGeorgeMcIntire

72% match

Gist 0878d3d41c2f75ff5bd436411a3b725c

This visualization maps a music library organized by decade, showing how songs are distributed across eras. The code likely uses D3.js to render an interactive tree or partition layout, with each branch representing a decade folder and leaves as individual tracks. Hovering or clicking reveals artist and song titles, while the structure emphasizes the collection’s chronological range. The rendering approach relies on hierarchical data binding, possibly with `d3.hierarchy` and `d3.treemap`, to visually encode the folder hierarchy. The data source is the file system itself, parsed into a nested JSON structure for D3 to consume.

WWill Carter

66% match

Projet 3

This visualization displays song recommendations as a directed network graph, where each node represents a musical artist and each edge connects an antecedent artist to a consequent artist, with edge thickness or weight reflecting the support count from the dataset. Using D3 version 4's force-directed layout via `d3.forceSimulation`, the graph arranges nodes spatially to reveal clusters of related musicians. The interface includes an interactive search box that filters and highlights specific artists from a predefined list, while a tooltip provides details on hover. The underlying data comes from a CSV file of association rules derived from user tagging patterns, rendered as an SVG with custom styling for nodes and links.

65% match

Projet_test2

This example shows a network of music artists and their associations, using a force-directed graph layout to reveal clusters of related performers. The visualization encodes connections through links and nodes, with the ability to hover and search for a specific artist. The code relies on `d3.forceSimulation` with `d3.forceLink` and `d3.forceManyBody` to compute the layout, and uses custom SVG elements for nodes and links. Data is loaded from two CSV files, `recommandations.csv` and `artists_tags_count.csv`, with node size reflecting artist popularity and a tooltip displaying tag information.

Projet_test3

This example visualizes a network of music artist recommendations, showing connections between artists based on association rule mining. An interactive force-directed graph places artists as nodes, with links representing the strength of their co-occurrence. Hovering over a node highlights it, and a tooltip appears; the code also implements a function to dynamically generate a list of five recommended artists for each selected node. The visualization uses D3.js v4 APIs including `d3.forceSimulation`, `d3.forceLink`, `d3.forceManyBody`, and `d3.forceCenter`, with data loaded from CSV files (`recommandations.csv` and `artists_tags_count.csv`). Artist images and top five tags are integrated from the latter source, enhancing node details.

BBrunoDumas

Podcasts

This visualization maps a curated list of podcasts from a GitHub gist, plotting each title as a node in a force-directed graph. The layout uses `d3.forceSimulation` with `d3.forceLink` to connect podcasts sharing a common theme or network (e.g., history or NPR), while node size reflects the number of linked episodes. The graph is rendered on a Canvas via `getImageData` for pixel-level interactivity, with hover states highlighting related podcasts. Data comes directly from the markdown file, parsed client-side. The background is a subtle grid, and the overall effect emphasizes the interconnected web of intellectual audio content.

DDylanFM

Projet 2

This visualization shows a force-directed network of music artists and their associations, mapping relationships derived from an association-rule mining dataset where nodes represent artists and links indicate co-occurrence strength. The graph uses `d3.forceSimulation` with collision and link forces to position nodes, with circle size encoding node degree and line opacity reflecting link weight. Hovering a node highlights its connections, and the code includes an autocomplete search feature that filters the network. Data is loaded from CSV files containing artist tags and recommendation pairs, rendered as SVG elements with tooltips for interactivity.

Projet final

This visualization shows a network of music artists, with nodes connected by association rules derived from item-set support values. It presents a force-directed graph where each circle represents an artist, and links indicate shared listener preferences or recommendations between them, with node size and link strength reflecting the support counts. The implementation uses D3 v4’s `d3.forceSimulation` for layout, rendering nodes and links as SVG circles and lines. Hovering highlights a node, dims unrelated connections, and displays a tooltip with the artist’s name and support value, based on data from CSV files containing artist tags and recommendation counts.