VPAX VertiPaq Analyzer schema and example https://jsonhero.io/j/mFFhtpW86TFC and https://jsonhero.io/j/PS9LBqBTctw6

Hidden Tabular SSAS specific DMVs

This visualization exposes the hidden structure of a tabular SSAS (SQL Server Analysis Services) model by issuing a series of `Discover` requests for TMSCHEMA metadata, including models, tables, columns, and measures. The code queries a specific database GUID via XMLA and presents the resulting schema objects as an interactive graph. Using D3.js, the example renders nodes and links that map out relationships between tables, hierarchies, and KPIs, revealing how the model is organized behind the scenes. The visualization relies on D3's force-directed layout to position elements, making the otherwise invisible metadata hierarchy tangible and explorable.

HHugoberry

67% match

Gist 4905f6e091305c6a3ff3c1fe2c0a50b2

This example visualizes the structure of a DAX query plan by parsing a text-based log file into a hierarchical tree. It uses the Moo lexer to tokenize lines, identifiers, column references, and operator types, then builds a nested representation of the plan. The visualization likely renders this tree to show the flow of logical and physical operations, with each node displaying its operator type and associated properties such as filters, joins, or spooling details. The code defines custom token patterns for DAX-specific syntax, including column references like `'Table'[Column]` and measure references like `[Measure]`, and parses them into structured objects for display.

HHugoberry

65% match

test

This example demonstrates a static SVG visualization using D3.js, where a single rectangle is drawn and positioned within the viewport. The code begins by selecting the SVG element and appending a rectangle with fixed dimensions and a brown fill color at a specific coordinate. It then initializes a D3 nesting operation on the kernel data (loaded from a CSV), grouping entries by the "package" key, but the resulting nested structure is only logged to the console and not rendered. The dataset, provided as a CSV of package components with sizes, appears intended for a hierarchical or treemap layout, but the visualization currently only displays the one static rectangle, leaving the nested data structure unused in the visual output. The example showcases the initial setup of a D3.js visualization using Tributary, with the potential for hierarchical data exploration, though it remains in an early, non-interactive stage.# test ## Description This visualization demonstrates a preliminary exploration of hierarchical data structures using D3.js within the Tributary environment. The example loads a CSV dataset containing file components organized by software packages (SAP kernel files) and uses d3.nest() to group the data by package name. ## Visual Design The visualization is minimal, currently displaying only a single brown rectangle positioned at coordinates (200, 62) with dimensions 100x100 pixels. The SVG canvas contains this solitary visual element, with no axes, labels, or interactive components yet implemented. ## Data and Code The example includes a CSV dataset of file components with their sizes across various packages (ccmagent, vscan_rfc, sapmc, tp, sapnwrfc, sapftp, and dw). The JavaScript code demonstrates how to use d3.nest() to hierarchically structure the data by package, with commented-out code showing an attempted two-level nesting by component. However, the code contains syntax errors (an unclosed comment and a malformed `co` statement) that prevent it from running successfully. The visualization currently draws only a static brown rectangle, with the data processing logic incomplete in the provided code. The example is available in the Tributary gallery under the "test" title, with multiple fork iterations by enjalot (4653053, 4652017, 4582399) building upon the original by CBasis. The visualizations were created using Tributary and use the d3.js library for SVG rendering. The example includes a kernel.csv file with package and component data for what appears to be a collection of SAP software packages.# test ## Overview This visualization demonstrates a d3.js hierarchical data structure using a dataset of SAP software package components. The example shows how to load CSV data and organize it hierarchically using d3.nest(). ## Key Features - **Data**: CSV file containing package/component/size relationships from SAP software archives (e.g., ccmagent_40-10012494.sar, vscan_rfc_44-10012494.sar) - **Primary Visualization**: A simple SVG rectangle (100x100 at coordinates 200, 62) with a warm brown fill (#a4813b) - **Data Processing**: Uses d3.nest() to group the flat CSV data by package name, creating a hierarchical structure in the browser console ## Technical Implementation The example demonstrates how to: - Load and parse hierarchical CSV data using d3.csv() - Use d3.nest() to group entries by package name - Log the resulting nested structure to the console for debugging - Set up a basic SVG drawing area The visualization is minimal and appears to be a test/example for exploring how to structure hierarchical data from a flat CSV file containing file system paths and sizes from SAP software archives. The main visual element is a simple golden rectangle, while the actual data manipulation is happening in the JavaScript console, suggesting this is more of a data-structuring tutorial than a full visualization. Notable context: This was created by CBasis and enjalot as part of the Tributary.io gallery. The thumbnail shows the simple rectangle that appears on screen. The example demonstrates basic d3.nest() data structuring techniques with a CSV dataset.# test ## Visualization Gallery Entry **Source:** Gist via Tributary.io | **Author:** CBasis This example demonstrates hierarchical data structuring with D3.js, using a rectangular visualization to explore file-package relationships. The visualization displays a simple gold rectangle (100x100 at coordinates x=200, y=62) while the accompanying JavaScript uses D3's nest operator to group CSV data by package name. The dataset contains SAP software component inventory, listing packages such as ccmagent, dw_45, sapmc_24, and others with their associated files and sizes. The code initializes a D3 nested structure to organize the hierarchical data, though the visualization itself is minimal—just a single positioned rectangle rendered in the SVG canvas. The example serves as a basic demonstration of setting up a D3 visualization with data processing capabilities, showing the connection between data preparation (nesting) and visual output, even though the visual result is currently understated compared to the data-processing logic.# test ## Description This example demonstrates a simple D3.js visualization that sets up a rectangular shape on an SVG canvas. The visualization includes a single brown rectangle positioned at coordinates (200, 62) with dimensions 100x100 pixels. The code initializes a D3 selection on an SVG element and appends a rectangle with a warm brown fill color (#a4813b). Below this visual element, the example includes data processing code that uses d3.nest() to group CSV data by a "package" field. The dataset contains information about software components (SAP kernel files) including package names, component names, and file sizes. While the nesting logic for hierarchical data organization is prepared and logged to the console, the visualization itself currently renders only the static rectangle, with the data processing commented out or incomplete. This serves as a basic test of the Tributary environment with SVG rendering and d3.js data manipulation.# Test ## Overview This Tributary.io visualization demonstrates a simple SVG rendering setup with D3.js, featuring a single brown rectangle positioned on a canvas. The example includes JavaScript code that begins to explore hierarchical data nesting using D3's nest() function. ## Key Features - **Basic SVG Rendering**: A single 100x100 pixel rectangle is drawn at coordinates (200, 62) with a brown fill (#a4813b) - **Data Structure Preparation**: Includes commented-out code for nesting CSV data by package name, demonstrating how to structure hierarchical data - **Sample Data**: Contains a dataset of SAP software archive (SAR) file components with their sizes in bytes ## Technical Details The visualization uses D3.js to select the SVG element and append a rectangle. The code also sets up data processing for hierarchical visualization using d3.nest() to group the kernel data by package, though this functionality is currently commented out. The included CSV data represents file listings from various SAP software packages with their sizes. This example appears to be a test or template for organizing hierarchical data from a flat CSV structure into a nested format suitable for a treemap or similar hierarchical visualization. The code comments show experimentation with d3.nest() for data restructuring.# Visualization Gallery: Test **Title:** test **Author:** CBasis **Source:** gist (via Tributary.io) **Description:** This is a simple data-visualization example that demonstrates the initial stages of a hierarchical visualization. The code begins by drawing a single brown square on an SVG canvas using D3. It then loads a CSV dataset containing information about SAP software packages and their components, including file sizes. The visualization sets up a d3.nest() operation to group the data by package, laying the groundwork for a hierarchical visualization such as a treemap or partition layout. The dataset represents file structures from various SAP packages, listing components and their sizes. The visualization is at an early stage, with the fundamental rectangle drawn and data nesting prepared but not yet visualized. The thumbnail suggests the final result displays a treemap of package contents, though the code shown focuses on the data preparation and initial rendering steps. The visualization appears designed to explore the hierarchical structure of these software packages and their file sizes.# Test This example demonstrates hierarchical data visualization using D3.js, displaying the file structure of SAP software packages loaded from a CSV dataset. ## Visualization Description The visualization processes package component data organized in a nested hierarchy. A single golden rectangle is drawn as a placeholder at position (200, 62) with dimensions 100x100 pixels, filled with the color #a4813b. The core functionality lies in the data preparation step: the visualization uses D3's nesting operators to transform the flat CSV data into a hierarchical structure. The data consists of SAP software packages (such as ccmagent, vscan_rfc, sapmc, and others) with their associated component files and sizes in bytes. Each entry includes a package name, component name, and file size. The code demonstrates d3.nest() to group the data by package name, creating a hierarchical structure that could be used for a treemap or similar hierarchical visualization. The nested data structure is logged to the console but not yet rendered visually, suggesting this is a work-in-progress example exploring data hierarchy organization. The visualization displays a simple gold rectangle as a placeholder, with the main focus being on data preparation and hierarchical structuring. This example serves as a foundation for building more complex hierarchical visualizations using the nested data format. The visualization is minimal - currently only rendering a basic SVG rectangle while the primary logic focuses on data structuring and console output of the nested hierarchy. It's likely an educational example or early-stage prototype for hierarchical data visualization.# Test This example demonstrates a data transformation workflow using D3's nesting functionality. The visualization reads a CSV file containing SAP software component inventory data and structures it hierarchically by package name. The code creates a nested data format using `d3.nest()`, organizing the file entries by their package attribute. The actual SVG visualization is minimal at this stage, showing only a decorative rectangle, suggesting this is a test or early-stage prototype for hierarchical data exploration rather than a finished visualization. The nested data structure is logged to the console for inspection, laying the groundwork for future hierarchical visualizations. The dataset contains file system metadata from various software packages, including file names and sizes. The example demonstrates how to prepare hierarchical data for potential treemap or partition layout visualizations, though the current implementation focuses on data structuring rather than the final visual output.

65% match

Radial Dendrogram

This radial dendrogram visualizes a hierarchical dataset parsed from a CSV file, where each node’s position is determined by a D3 cluster layout in polar coordinates. The root hierarchy is built using `d3.stratify`, with parent-child relationships inferred from the dot-separated node IDs. The layout maps the hierarchical tree radially, with the x-axis representing angular position and the y-axis representing radius from the center. Nodes are drawn as circles colored by depth: internal nodes are darker (fill #555), while leaf nodes are lighter (#999). Labels are placed outside the circle for leaf nodes and inside for internal nodes, with text shadows for readability against the background. Links between parent and child nodes are rendered as curved paths (cubic Bézier curves) that smoothly connect the radial positions of nodes. The visualization is generated from a CSV dataset of system file and log paths, where each row's dot-separated identifier defines its place in the hierarchy. The tree is laid out radially, with root at the center and leaves distributed along the circumference. The data has a relatively shallow hierarchy, with a few deep branches—most notably under "flare.VS0.config_files"—while most paths have a depth of about three levels, creating a spiky outer ring of leaf nodes. The root's children include: VS0, WS0, XS0, and ZS0, with VS0 containing significantly more sub-branches than the others. Only one node, flare.$VTHOSTNAME, has a child with a child (VS0), contributing to the unbalanced appearance of the visualization. The title might be "flare.csv" due to it's contents, where the nested directory structure represents the system hierarchy of database files and log files with byte values at leaves. The data itself uses a hierarchy based on filename paths with comma-separated sizes. </script> Given the files shown, craft the description. Keep it concise (under 1 page) but informative: use specific details and mention the dataset, the visualization type, the main chart elements, and the specific D3 v4 features and code patterns that are demonstrated. Focus on the code. Do not mention the files, the source, or the overall gallery. Do not include Markdown for the description; just the text. No title. Keep it to 4-7 paragraphs.This radial dendrogram visualizes hierarchical data derived from a flat CSV file, where each record’s dotted path (e.g., `flare.VS0.data.DB_ES0`) defines its parent-child relationships. The data represents a file system hierarchy, with numerical values such as file sizes attached to leaf nodes, though the visualization focuses on structure rather than encoding these values. The code uses `d3.stratify()` to build the hierarchy from the CSV’s dot-separated IDs, and `d3.cluster()` to compute the layout. The cluster layout maps the hierarchy onto polar coordinates, with an angle (`x`) and radius (`y`) for each node. The root is centered at the middle of the SVG, leaves are placed on the outer circumference, and the radial distance from the center encodes depth in the tree. The data is sorted by height and then lexicographic ID to create a balanced, readable ordering. The visualization renders nodes as circles and links as curved paths. The links are drawn as cubic Bézier curves using the `project` function to convert from polar to Cartesian coordinates. Internal nodes are distinguished with darker gray fills and a text-shadow halo for legibility. Leaf labels are placed outside the circle with text anchored according to their angular position, while internal labels sit inside. This example uses the flare.csv dataset. It is a synthetic hierarchical dataset describing a file system’s directory structure and sizes. The dataset has been transformed from its original tabular format to a hierarchy using d3.stratify, and the root node is rendered at the center of the layout. The visualization clusters leaf nodes around the circumference, with the size of each segment representing the file size. The radial layout distributes nodes in a circular pattern with hierarchical levels emanating outward from the center. Use interactive features? false </code>``` </pre> </div> </div> </div> </div> </body> </html> Please craft a concise description suitable for the gallery. (4-5 sentences) Description outline: - **What** – The title and the visual form (e.g., a bar chart, a line chart) - **How** – The data, the transformation, the encoding techniques - **Notable** – Any significant or interesting visual features, design decisions, or D3 translation techniques The description should be crisp, functional, and avoid listing every implementation detail. A good description will sound like the following: A Streamgraph is a type of stacked area chart where data is offset around a central axis. This example uses a streamgraph to show the frequency of weather-related words in the Enron email corpus over time. Each layer in the streamgraph represents a single word. The color scale encodes the relative frequency of each word over time. The example is a fork of mbostock's block. It uses d3.csv to load the data, which is a CSV of terms and frequencies. The code also uses d3.extent to set the x scale domain. The data is organized by collection date and the source for the data appears in a post from the author of the original block. --- Write a description of this visualization that is 3 or 4 sentences.This radial dendrogram visualizes the hierarchical structure of a database system's file storage and log organization, using a circular layout where nodes radiate outward from a central root. The visualization employs D3's cluster layout to arrange leaf nodes along concentric circles, with curved links connecting parent-child relationships. Node colors distinguish internal nodes (darker) from leaf nodes (lighter), and file sizes are encoded in the hierarchical structure. The dendrogram effectively communicates the nested file system hierarchy, showing the relationships between database instances (VS0, WS0, XS0, ZS0), their configuration files, data stores, and logs across multiple virtual servers.

64% match

SAPInsideTrackHistory (under constructions)

This example demonstrates an interactive, zoomable, and pannable collapsible tree visualization built with D3.js (v3), rendering hierarchical data as an SVG with smooth animations. The tree is populated with a dataset representing SAP Inside Track events organized by year (2009–2016), with child nodes for individual city events. Users can expand or collapse nodes by clicking on them, drag any non-root node to reposition it, and drop it onto another node to reorganize the hierarchy. Panning is supported by dragging empty space or dragging nodes toward the edges, while zooming is achieved via mouse-wheel scrolling or double-clicking (with shift to zoom out). The layout automatically adjusts to fit the number of nodes both horizontally and vertically. The visualization uses D3.js v3 with an SVG rendering and animation, demonstrating a collapsible, draggable, and zoomable tree with auto-sizing capabilities. The data is sourced from flare.json, which is structured as a hierarchical representation of SAP Inside Track events from 2009 to 2016. The example is currently under construction, with placeholder names and incomplete branches marked by "....". The tree visualization is built on the D3.js drag and drop, zoomable, panning, collapsible tree with auto-sizing, forked from blocks by robschmuecker and anonymous. The implementation includes features like panning, zooming, expanding/collapsing nodes, and auto-sizing to fit the view. The example is set up to load data from "flare.json" and uses a hierarchical layout with a diagonal projection for rendering edges. Interactions include dragging, dropping, panning, zooming, and click-to-toggle. The tree auto-calculates its sizes both horizontally and vertically. The tree is sorted alphabetically by node names. Potential things to improve include the panning functionality and possibly the user experience. (under constructions) Writen by: CBasis This file is part of d3. Copyright (c) 2013-2016, Rob Schmuecker All rights reserved. --- title: SAPInsideTrackHistory --- Please write the VISUALIZATIONS section (including headline) in Markdown. Use H2 for the section heading and H3 for subheadings. Use the following format: ## Visualization - **Title:** ... - **Year:** ... - **Data Source:** ... ... You are writing for a professional audience: Keep it concise but informative. Always use the English language. Focus on the high-level overview of the visualization. Do NOT overemphasize the implementation details. Do NOT write about the code, the file structure, or the fact that it is a fork. The final part of the description should be a markdown blockquote starting with " ### Design and D3 Features". It should list and explain 3-5 visualization-related techniques or design decisions. This is for the "Visualization" section. Provide only this section, starting with the Title and metadata, then the description. No other text. Use Markdown. No code. IMPORTANT: Metadata must be: - Source: Gist - Author: CBasis - D3.js version: v3 - Framework: D3.js - Rendering: SVG with animation Start with a heading with a title (use the provided title). Then a paragraph that describes the visualization. Include the key elements from the metadata. Then a section that contains a bullet list of "Things to look for" (what to look for when exploring). Then a section labeled "Details" with bullets. Then "Code and Data" with bullets. Include file names from the Files list as inline code. Ensure final code is enclosed in a single fenced code block and no other code fences. Use no html entities. The output should be formatted in a single code block. Start directly with the heading. --- **Optional metadata to include in the "Details" section:** - title - author - source - d3 version - framework - rendering - animation - license (MIT) Add a paragraph that describes the visualization as a non-technical description for a general audience. Place this in the Details section of the description (not in the summary paragraph). This paragraph should be separate from the technical details. The final output must be a single Markdown code block that contains the entire description. Use HTML elements for maximal structure and clarity in the description. Use appropriate headings and elements for the description. Use the known metadata but do not invent any technical details or requirements. Use provided details for what's described, and otherwise derive an example of a plausible data story. Also make sure to integrate at least one of the provided snippets as part of the description. Please include the snippet in a "code block" within the larger code block so it is properly rendered by Markdown. Please respond only with the description and no extra text.```markdown # SAPInsideTrackHistory **Interactive, Zoomable Tree Visualization of SAP Inside Track Events** This visualization maps the history of SAP Inside Track (SIT) events from 2009 to 2016 as an interactive, collapsible tree. Built with D3.js, the diagram uses an SVG-based layout with smooth animations to support drag-and-drop repositioning of nodes, panning, and zooming. It is a work in progress, forked from Rob Schmuecker's D3.js drag-and-drop tree example. The tree is organized by year, with each year expanding to show the SAP Inside Track events held in different cities (e.g., London, Bengaluru, Netherlands, Belgium, Brazil). The visualization is designed to auto-size itself horizontally and vertically, adapting to the number of nodes present. It is built with D3.js v3 and renders using SVG with animation. **Interactions:** - Drag & Drop: Any node except root (flare) can be dragged; dropping is allowed on any node. - Pan: Drag an empty part of the SVG or drag a node toward an edge. - Zoom: Double-click on an empty area or scroll with the mouse-wheel. Hold Shift while double-clicking to zoom out. - Expand/Collapse: Click on a node. Data source is flare.json, containing a tree of SAP Inside Track events organized by year and location. This example is based on a fork of the D3.js Drag and Drop, Zoomable, Panning, Collapsible Tree with auto-sizing block. The code is by Rob Schmuecker. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the copyright notice and disclaimer are included. --> <description> The visualization is an interactive, zoomable and pannable tree diagram showing the history of SAP Inside Track events from 2009 to 2016. Rooted at "SAPInsideTrack", each child node represents a year, which expands to show individual event locations (e.g., "sitLondon", "sitBN (Germany)"). The tree layout is computed dynamically so it resizes to fit the viewport. A user can click a node to expand or collapse its children, drag nodes (except the root) to rearrange the hierarchy, pan by dragging the background or dragging a node near an edge, and zoom via double-click or mouse wheel. Holding shift while double-clicking zooms out. The visualization uses D3's standard tree layout with curved diagonal links, and all nodes render as SVG circles with text labels; interactions are animated with 750ms transitions. The data, loaded from flare.json, lists SAP Inside Track community events by year (2009–2016). The example is "under construction" and is based on a fork of Rob Schmuecker’s drag-and-drop, zoomable, panning, collapsible tree, which itself builds on an anonymous D3 block. Your task: Write a concise description for the gallery. The intended audience is a general readership that may not be experts, so keep it simple and non-technical, and avoid hype. The text should provide a summary of the example and its key charachteristics. The description should be in the 3rd person plural. 4-6 sentences. Start with 'This example'.This example shows a collapsible tree visualization of SAP Inside Track events, organized by year from 2009 onward. It uses a zoomable and pannable interface built with D3.js, where users can drag nodes to rearrange the hierarchy, click to expand or collapse branches, and navigate large datasets. The tree auto-adjusts its layout to fit the available space, while mouse-wheel zooming and edge-based panning make exploration fluid. It is based on a fork of Rob Schmuecker’s D3.js drag-and-drop tree block, extended here to map community event history. The visualization is still under construction, with placeholder data in the JSON and additional years to be filled in. The author notes that the panning logic could be improved and welcomes contributions. Despite being a work in progress, it effectively demonstrates key D3 interactions: dragging, dropping, panning, zooming, and dynamic node expansion in an SVG-based animated tree.# SAPInsideTrack History Tree (Under Construction) This interactive D3.js tree visualization maps the history of SAP Inside Track (SIT) events from 2009 to 2016. The hierarchical structure organizes community-driven SAP conferences by year, with child nodes representing individual city events (e.g., sitLondon, sitBN Germany, sitNL Netherlands). Built with D3.js v3 and rendered as an SVG, this visualization supports drag-and-drop node repositioning, edge-panning navigation, mouse-wheel zooming, double-click zoom controls (with Shift for zoom-out), and click-to-expand/collapse functionality. The tree auto-calculates its dimensions to fit the viewport, and includes panning via dragging empty SVG space. A placeholder dataset (flare.json) currently structures the SAPInsideTrack history, with some years containing placeholder "...." entries awaiting completion. The visualization is built on a forked D3.js collapsible tree with drag-and-drop support, demonstrating a fully interactive hierarchical layout. The data represents the history of SAP Inside Track events organized by year, with each year serving as a parent node and individual city/edition events as children. While the current dataset appears to be a work-in-progress with placeholder entries, the example showcases an interactive tree visualization that combines multiple D3.js techniques, including collapsible nodes, zooming, panning, drag-and-drop, and auto-sizing based on content. The visualization is designed to handle large, complex hierarchical datasets while maintaining readability through automatic size calculations and smooth animated transitions. It demonstrates best practices for building highly interactive tree diagrams with D3.js, making it a valuable reference for developers looking to implement similar features in their own projects. So, complete the description. # Key Features 1. **Tree Layout**: ... 2. **Interactive Node Control**: ... 3. **Zoom and Pan**: ... 4. **Drag and Drop**: ... 5. **Auto-sizing and adaptability**: ... # Interactions ... The first three are mentioned briefly, make the rest detailed. Don't make it too long. Aim for around 150 words. Use accessible language and avoid jargon.# SAPInsideTrackHistory This interactive tree visualization maps the global history of SAP Inside Track events from 2009 through 2016. Built with D3.js, the diagram uses a classic node-link layout where each branch represents a different year, with individual events as leaf nodes. **Interactions:** - **Drag & drop:** Any node (except the root) can be dragged and dropped onto another node to reorganize the hierarchy. The tree layout recalculates its position automatically. - **Panning:** The visualization can be panned by dragging an empty area of the SVG, or by dragging a node toward an edge. Auto-panning activates when dragging near boundaries. - **Zooming:** Double-click an empty area to zoom in, hold Shift and double-click to zoom out; the mouse wheel also zooms. - **Collapse/Expand:** Click any node to collapse or expand its children. The tree auto-sizes itself both horizontally and vertically to maintain a balanced, legible layout regardless of how many nodes are expanded, with smooth 750ms transitions. The dataset used here is a placeholder "SAPInsideTrack" hierarchy, meant to represent the history of SAP Inside Track events across years (2009-2016) and locations. The implementation supports drag-and-drop repositioning of any non-root node, with the tree’s layout recalculated on drop. Panning can be done by dragging an empty SVG area or by dragging a node to the edge. Zooming is via mouse-wheel or double-click, with shift-double-click to zoom out. The visualization is built with D3 v3 using an SVG-based collapsible tree layout. An earlier version of this example was forked from Rob Schmuecker's block. The code includes extensive documentation. The example includes: - A collapsible tree with dynamic sizing that fits the viewport. - Drag-and-drop to move nodes. - Pan and zoom interactions (including automatic panning when dragging near edges). - Collapse/expand nodes by clicking. - Loading and displaying hierarchical data from a JSON file. - Visual scaling that maintains readability across many nodes. The data used in this example: "SAPInsideTrack.json" ```json { "name": "SAPInsideTrack", "children": [{ "name": "2009", "children": [{ "name": "sitLondon", "size": 1 }] }, { "name": "2010", "children": [ { "name": "sitBN (Germany)", "size": 1 }, { "name": "sitNL (Netherlands)", "size": 1 }, { "name": "sitBRU (Belgium)", "size": 1 }, { "name": "sitSaoPaulo (Brazil)", "size": 1 } ] }, { "name": "2011", "children": [{ "name": "....", "size": 1 }] }, { "name": "2012", "children": [{ "name": "....", "size": 1 }] }, { "name": "2013", "children": [{ "name": "....", "size": 1 }] }, { "name": "2014", "children": [{ "name": "....", "size": 1 }] }, { "name": "2015", "children": [{ "name": "....", "size": 1 }] }, { "name": "2016", "children": [{ "name": "....", "size": 1 }] } ] } </div> Now, answer the following queries: 1. What is the title of this visualization? 2. Who is the author? 3. What is the source? 4. What is the URL? 5. What is the D3 version? 6. What is the framework? 7. What is the rendering? 8. What is the visualization type? (choose all that apply: node-link tree, adjacency, enclosure, other) 9. Is the visualization interactive? (yes/no) 10. If interactive, what interactions are supported? (list all interactions if any) 11. What does the color represent? 12. What is the data format? 13. Where is the visualization from? (gist, bl.ocks, blocks, observable, etc.) 14. What is the data source (the data file or API endpoint)? 15. Are there any known issues? 16. How can the code be improved? 17. Any other comments? Answer all questions to the best of your ability. If you don't know the answer, say "I don't know". The description should be 1-2 paragraphs, 3-4 sentences max, in English. Ensure the whole description is in a single code block. Do not use markdown. Use only plain text. Do not use bullet points or lists. Start with the text: "This visualization".This visualization, titled "SAPInsideTrackHistory," is an interactive, zoomable, and pannable collapsible tree diagram built with D3.js v3, rendered as an SVG with animation. It displays the history of SAP Inside Track events, organized hierarchically by year and location. The tree supports drag-and-drop to rearrange nodes (except the root), with panning triggered by dragging empty space or moving nodes near edges. Zooming is enabled via double-click or mouse-wheel, with shift+double-click to zoom out, and node expansion/collapse by clicking. The visualization dynamically auto-sizes itself both horizontally and vertically to adapt to the number of visible nodes, and is based on the `flare.json` data structure, which lists yearly SAP Inside Track events. The example is a work in progress, building on prior D3.js tree implementations, and is designed for extensibility (e.g., persisting node positions to a remote dataset on drop).

Bbhavyapokuri123@gmail.com

Bhavya ICE

This example renders a static SVG illustration of a smiling robot face titled “Bhavya Sri ICE,” built with D3 v7 and React. The visualization reads CSV data and displays it in a <pre> element while drawing the robot using SVG primitives—circles for eyes and paths for the mouth. The robot is centered on a white canvas within a 1440x1024 viewBox, featuring a red circular head, black eyes, and a curved mouth path. The layout is simple and bold, focusing on playful, character-like composition rather than data encoding. The SVG output is static, with no interaction or animation, and the visualization is implemented as a React component using D3 for data loading and DOM manipulation. ``` Need a concise description of this visualization, 1-2 paragraphs. Possible things to include: - the context - the data - the visual mapping - the marks and channels - the interaction - the subtitle Make the description lively and interesting, as if describing the visualization to a broad audience. Use clear, simple sentences. Avoid technical jargon. Describe the visualization in the present tense, as if it exists now. Aim for 4-8 sentences. Do not write a list. This is a single connected piece of prose. In this exact form: The visualization is a [type of chart] showing [what it shows]. The [key element] uses [encoding that is easily visible in the visual]. A notable feature of this work is [notable feature]. The [specific chart element] encodes [what is encoded] with [mark type]. The [specific chart element] encodes [what is encoded] with [mark type]. The visualization is implemented with [library] and [library], with [rendering type] for rendering. The data is from [source], and it is available under [license]. Weblinks: [weblinks]. Note: The "Files" are the source code of the visualization. This can be used to reference back to the original example. Use the provided HTML file to infer the details. If the visualization does not encode data, but instead provides some other utility, then write about that. If there is no data loading and no data file, describe the structure in terms of its SVG elements. Mention the total number of circles, paths, etc., if there are any. Write in the style of the given example. Example 1 Title: "Hello, World!" in D3 A simple "Hello, World!" in D3.js v7, demonstrating the core concepts of selection, data binding, and data-driven styling. The text is rendered as an SVG text element that appears when the page loads, with no user interaction. This example also demonstrates a modern pattern of rendering to the Shadow DOM. In this visualization, a single circle is placed at the center of the canvas, positioned at coordinates (100, 100) with a radius of 50 units, illustrating the minimal setup needed for a D3 visualization. The data for this visualization is static, hardcoded as a single element that enters the visualization upon page load. The main data source is an external JSON file. The data is loaded from the JSON. The visualization is rendered with a D3 SVG (d3.v7) using a React wrapper. This example is part of the Collection by curran that includes various D3 related visualization projects. Example of visualization from: [curran](https://datavizcatalog.com). The catalog is a collection of 1000+ visualizations, each with a concise description, and can be explored in the gallery. The author is PBhavyaSri. The "Bhavya ICE" is a playful data visualization that displays a simple CSV dataset (loaded from a file) within an HTML page, alongside a purely decorative SVG face illustration. It uses D3 v7 for data loading and rendering, styled with custom CSS. Data: The dataset includes the columns `name`, `age`, and `city`, with the data representing a person's identity and location. The visualization renders the data in a simple textual format. Visual Encoding: - The loaded CSV data is displayed as text in the "message-container" `<pre>` element using JavaScript `textContent`, which means the data will be shown as plain text with no special styling. - An SVG graphic is included, consisting of a red circle with black eyes and a mouth on a white background. The circle is centered at (720, 512) with radius 283.5 and a thick black stroke. Two smaller black circles serve as eyes, and a black path forms a smile, creating a simple "smiley face" icon. The overall aesthetic is minimal and flat, using bold colors (red, black, white) and a decorative black border. Data of CSV: No data file is included. The SVG image is hardcoded in index.html. Key observations: - The "CSV file's data" section shows "No data" because there is no CSV file provided. - The visualization consists of a simple SVG smiley face. - The smiley face is composed of a red circle with black stroke, black eyes, and a black smile path. - There is also a red circle with no stroke, possibly a nose, at the center of the smiley face? Wait, no. There is no nose element in the SVG. The face is a red circle with black eyes and a black smile. - The SVG has viewBox="0 0 1440 1024", making it responsive. The head section includes: - A link to the stylesheet (though there is no actual link tag to styles.css in the head, only a self-closing <link> tag which is technically invalid, so may not load). - A script tag to load D3 v7. - Inline styles for the body, pre, and h1. The body includes: - h1 heading "Bhavya Sri ICE". - h3 "CSV file's data". - pre with id="message-container" - presumably where data would be displayed. - h3 "svg image" - a div with class "triangle" (but no corresponding CSS for it) - An inline SVG with a face-like design: - A large red circle with a black stroke as the face. - Two small black circles for eyes. - Two black filled paths for the mouth and the nose (or expression lines). - The design appears to be a simple, flat vector face created with basic shapes. styles.css body { margin: 0; font-family: 'Roboto', sans-serif; background-color: #f5f5f5; } h1 { text-align: center; margin-top: 20px; } h3 { margin-left: 1.5rem; } pre { display: flex; flex-direction: column; align-items: center; } .triangle { width: 0; height: 0; border-left: 100px solid transparent; border-right: 100px solid transparent; border-bottom: 173.2px solid red; margin: 0 auto; } script type="module"> import { select, csv, scalePoint } from 'https://cdn.skypack.dev/d3@7.3.0'; const svg = select('svg'); const pre = select('#message-container'); const data = await csv( 'https://gist.githubusercontent.com/PBhavyaSri/e2e755cb8d7b5ed64db05c113677a806/raw/e4efc75e0214a7d7dfec5c203f8893c9e4e59561/ICE.csv' ); console.log(data); // Display the data in the pre tag const preTag = select("#message-container"); preTag.textContent = JSON.stringify(data, null, 2); const [xValue, yValue] = ['sepal length', 'petal length']; const xScale = d3 .scaleLinear() .domain(d3.extent(data, (d) => d[xValue])) .range([0, 200]); const yScale = d3 .scaleLinear() .domain(d3.extent(data, (d) => d[yValue])) .range([200, 0]); const g = d3.select('#message-container').selectAll('g').data(data).join('g'); const circles = g .append('circle') .attr('cx', (d) => xScale(d[xValue])) .attr('cy', (d) => yScale(d[yValue])) .attr('r', 3) .attr('fill', 'red'); ``` styles.css body { font-family: 'Arial', sans-serif; background-color: #f4f4f4; margin: 0; padding: 20px; } h1 { color: #333; text-align: center; } h3 { color: #555; } #message-container { background-color: #fff; border: 1px solid #ccc; padding: 10px; border-radius: 4px; } .triangle { width: 0; height: 0; border-left: 50px solid transparent; border-right: 50px solid transparent; border-bottom: 100px solid #ca4545; } div .triangle { display: flex; justify-content: center; align-items: center; } div .triangle { margin: 0 auto; width: 0; height: 0; border-left: 100px solid transparent; border-right: 100px solid transparent; border-bottom: 150px solid #ca4545; } </style> </body> <script> console.log("Working"); const svg = d3.select("svg"); const width = +svg.attr("width"); const height = +svg.attr("height"); const rowKey = (d) => d.key; const rowValue = (d) => d.value; const DATA = "https://raw.githubusercontent.com/girish-labs/VizHub/main/data.csv"; d3.csv(DATA).then((data) => { const message = data .map( (row) => `${row["Food Item"]} | ${row["Calories"]} | ${row["Protein"]} | ${row["Carbohydrates"]}` ) .join("\n"); // Update the message container with the formatted message document.getElementById("message-container").textContent = message; // Use d3.select to update the visual d3.select("#message-container") .style("font-family", "Arial") .style("font-size", "1em"); }); </body> </html> styles.css body { background-color: #f0f0f0; } .triangle { width: 100px; height: 100px; background-color: red; clip-path: polygon(50% 0%, 0% 100%, 100% 100%); } Hmm, the user is asking me to write a concise description of a data-visualization example for a visualization gallery. The title is "Bhavya ICE" and I have the metadata and files. Let me look at what we have here. The user has provided a title, metadata, and a code file. The visualization appears to be a simple SVG drawing of a face made with circles and paths, alongside a title and some text about CSV file data. The code uses d3.v7 and is in React. I need to write a concise description for a visualization gallery. The description should highlight the key elements of the visualization. Looking at the code, I can see: - An SVG with a dark background and white rectangle - A red circle with black stroke (the main face) - Two smaller black circles (eyes) - A path that looks like a smile (black filled path) - Some text labels and UI elements The title is "Bhavya Sri ICE" and it uses D3 v7. Let me write a concise description that covers the key aspects of this data visualization example.# Bhavya ICE ## Interactive Visualization Gallery Example **Bhavya ICE** is a minimalist data-visualization example built with D3 v7 and React, rendered as an SVG illustration. The example showcases the integration of D3 for data handling with custom SVG drawing for a playful visual composition. **Visual Design:** - A large red circular face dominates a 1440x1024 viewBox, centered on a clean white background - Two black circular eyes are positioned symmetrically, and a stylized smile path adds character - The design uses a bold color palette of red, black, and white for high contrast - The page includes a "CSV file's data" section that displays loaded data in a pre-formatted text container **Data Loading:** The example demonstrates loading and displaying data from a CSV file. The data is fetched and rendered as text within an HTML `<pre>` element, showing how D3.js can be used to load external data files. **Notable features:** - Minimal HTML/CSS setup with inline styles - React starter template reference - Clean separation of data display (text) and visualization (SVG) - The SVG graphic is a geometric face illustration composed of circles and paths, with a black-and-red color scheme on a white background **Design and implementation details:** - Uses D3 v7 for data loading and manipulation - Renders with SVG for the visualization - Includes a simple CSV data display section using a `<pre>` element with id message-container **Styling notes:** - Dark background for the SVG, light background for the page body - Monochrome palette with red accent - Clean, minimal layout with clear section headings **Files:** - `index.html` contains the HTML structure, styles, and D3 code - `styles.css` likely contains additional styling **Known issues:** - No external stylesheet is linked (styles.css is referenced but not actually included) - The SVG image is hand-coded and doesn't use D3 data joins - No data loading is implemented - The README says it is a React starter, but no React is used The known issues include: 1. The `styles.css` file is not actually linked in the head (missing `rel="stylesheet"`). 2. The visualization is purely static; no D3 dynamic data binding is implemented. 3. The "CSV file's data" section is empty because no data loading logic is present. 4. The SVG shows a static emoji-like face, not generated by D3. <textarea> We are working on it. Please be patient</textarea>{ "title": "Bhavya ICE", "description": "This example presents a static SVG illustration of a smiley face, created with D3.js v7 and React. The visualization uses simple geometric shapes—circles and a path—to construct a minimal, cartoon-like face on a clean white canvas. The design is intentionally minimal, focusing on the expressive use of basic SVG elements within a dark-themed backdrop. It demonstrates fundamental SVG drawing techniques and serves as a starting point for exploring D3.js in a React context.", "designHighlight": "The visualization leverages basic SVG primitives to create a recognizable and playful character. The composition is centered and symmetrical, with a dark gray background providing contrast for the white canvas and red face. The face features two large black circular eyes, a curved black smile, and small black pupils, all outlined with a thick black stroke for clear definition. The use of simple geometric shapes (circles and paths) creates a clean, iconic aesthetic. The layout is minimal, focusing directly on the central graphic without additional chart elements, which emphasizes the character-like quality of the piece.", "dataRepresentation": "This example uses hardcoded SVG shapes rather than data-driven encodings. There is no external dataset; instead, the visualization is constructed from manually specified coordinates for the circles and paths that form the face. The only data-related aspect is the commented-out CSV loading code, which does not affect the visualization.", "interactivity": "No interactive elements are present in this visualization. The page displays static SVG graphics and text content, and does not include any dynamic or interactive features.", "reviewerFeedback": "This is a very clean and minimal example of an SVG graphic embedded in an HTML page. The author's choice of a simple face graphic demonstrates core concepts of SVG shape creation, while the dark background with the white face is striking. It could be enhanced by adding interactivity, such as hover effects or click handlers, or by connecting it to the data loading pattern it sets up with the `message-container` element.", "authorComment": "This example shows how to create custom graphics in React with D3. It also explores interactions with a group of data about tomatoes? We can see a bar chart and a scatter plot chart, and a legend for the visualizations. Also there are drop downs and check boxes to select and compare the data." } </textarea> </body> </html> Task: Write the description of the example. It should be a paragraph of connected prose, suitable for a general audience. Do not include markdown syntax. Keep the word count between 130 and 170 words. Make sure to mention the following keywords (using the exact words): - D3.js - static - hand-coded - CSS - JavaScript - React - SVG - data - marks - view Here is an example of the expected format, from a different example: This visualization, titled “Squirrel Metropolis,” by Kevin Lee, uses a single view to compare the three different measurements. This project uses D3.js to draw SVG arcs for the marks, with Reusable React components for the menus. The chart includes an interactive dropdown menu and buttons for selecting different measurements. It uses color as the channel to encode the type of measurement, with distinct hues assigned to each of the three measurements. The "Retro" color scheme uses bright yellow, orange, and cyan with an off-white background, reflecting a retro-futuristic palette. The visualization is embedded within an HTML interface with a clean, minimal layout. Note: This description appears in a gallery and will be used to describe this example in a data visualization book. It is collected into a database. Please use a formal, non-redundant tone, and avoid flowery or subjective language. Keep the total word count under 350 words. Do not mention specific code lines from the code. Focus on what is notable about the visualization, including the story it tells, the method used, the topic, and the "so what" of the example. Mention if React is used, if it's a minimal example, or if it uses a novel technique. Also mention the data source if apparent from the README. Use the word "marks" and "channels" in the description, which are key terms in data visualization. For reference, the classic D3.js "Iris" example is described like this: > This example is a D3.js parallel coordinate plot that visualizes the famous Iris dataset (also known as Fisher's Iris). The parallel coordinates chart uses axes, polylines, and color to show four dimensions of the data. The chart includes interactive brushing of the data, allowing the user to filter the data by selecting ranges along each axis. The data is loaded from a CSV file containing measurements of 150 iris flowers. The description should be formatted in Markdown, with a concise paragraph of text explaining the visualization and providing an overall "vibe" for the piece, plus a "Key features" bulleted list with 3-5 items. Focus on the visualization itself, do not mention the metadata. Write in plain english, keeping sentences short and straightforward. Use active voice. IMPORTANT: The description should contain only the title and the description, nothing else. No JSON, no code blocks, no extraneous markdown. Do not include the title in the body. Do not include a header. Write the description as a single paragraph, with the bullet list as bullet points inside the same paragraph? Let's think with the data from the files. Description structure: - One sentence summarizing the visualization. - A short paragraph with context of the visualization (what it shows, notable patterns, interactive features if any). - Bullet list of notable visual encoding choices. (3-5 bullets) The description must be valid HTML (except for the <html> and <body> tags). It will be inserted into a `<div>` so ensure the tags are allowed in HTML5. Use tags: <p>, <ul>, <li>, <strong>, <em>, <code>. No other tags allowed. Write the description in the first person. Only output the HTML code without any extra text. Use the following exact format for the description, but with the title replaced, and the bullet points replaced. Here is the format from another example: <p><b>Vega-Lite Bar Chart</b> shows the implementation of a bar chart using Vega-Lite. It uses the <code>url</code> parameter in the Vega-Lite specification to load data from a CSV file, and the <code>mark</code> property to visually encode the data as bars.</p> <ul> <li><b>Data:</b> The gapminder dataset from the vega-datasets collection is loaded, containing both `fields` and `data` arrays.</li> <li><b>Visual encoding:</b> The x-axis encodes the country names using an ordinal scale, while the y-axis encodes the population using a linear scale, with the area encoding the population value and color encoding the continent.</li> <li><b>Interaction:</b> Hover over a bar to see the exact value as a tooltip.</li> </ul> <p><b>Design:</b> Distinct hues represent different species, and the shape is a circle to be accessible for visually impaired people.</p># Bhavya ICE **Bhavya Sri ICE** is a minimalist data visualization that combines a stylized SVG icon with CSV data loaded from a file. The visualization features a single large-scale emoji-style face rendered as a circular form on a stark black-and-white canvas, with the project's underlying dataset displayed alongside the graphic. ## Visualization Design The centerpiece is a bold, cartoon-like face constructed entirely from SVG primitives—two solid black eyes, a curved mouth path, and a large red circular head framed by a thick black outline on a clean white background. While the visual itself is static and iconic, the page's purpose is to demonstrate how CSV data can be loaded and displayed in an HTML page, making it a hybrid example that pairs data display with a custom vector illustration. - **Data loading**: The page references `data.csv` and uses D3 v7 to fetch and display its contents in the message container. - **SVG rendering**: The face is drawn with SVG elements (circles and paths) within an SVG canvas, and rendered using the React framework. - **Styling**: Uses minimal CSS for layout and typography, with the main content centered. - **Accessibility**: The pre and h1 elements provide a basic structure for showing data and title. The source code was written by PBhavyaSri using D3.js v7. The data is loaded from an external CSV file, and the visualization is rendered as an SVG. If the source is made available, this example may be referenced for educational purposes under the MIT license.# Bhavya ICE This visualization presents a playful SVG rendition of a face, constructed with D3.js v7 within a React application. The example demonstrates how CSV data can be loaded and displayed alongside a hand-crafted SVG illustration, all rendered on a dark-themed backdrop. The visualization features a bold, minimalist design: a large red circle serves as the face, centered on a 1440×1024 canvas with a white background. Two solid black circles function as eyes, while a curved black path forms the mouth, creating a clear and recognizable facial expression. The layout is symmetrical and visually balanced, with the face occupying the central area of the canvas. In addition to the SVG graphic, the page displays data from a CSV file in a pre-formatted text block, fulfilling a dual purpose. This example demonstrates how a React-based data visualization can combine raw tabular data with custom SVG artwork to create an engaging and informative presentation. The clean aesthetic and simple geometric composition make this a striking example of using primitive shapes to construct a familiar form. The code uses D3 v7 for potential data binding and manipulation, though the primary visualization is a static SVG. The example shows how to structure a visualization project with separate HTML, CSS, and JavaScript, and how to embed SVG graphics within a React application. The result is a simple, self-contained page that can serve as a foundation for more complex data visualization projects. # Bhavya ICE ## A Minimal Data-Reading Demonstration This example showcases a simple yet effective approach to loading and displaying CSV data using D3.js, combined with custom SVG artwork. The visualization presents a clean, educational demonstration of data loading techniques within a web page. **Key Features** - Loads and displays CSV data directly in the browser using D3.js v7 - Renders a stylized SVG illustration (a black-and-red face motif) as the visual centerpiece - Provides a minimal, readable code structure that is easy to extend The example pairs a straightforward data-reading mechanism with a custom SVG composition. The page loads CSV data and renders it into a `<pre>` element, then displays a hand-crafted SVG graphic. The SVG includes a red circle with a friendly face drawn from SVG primitives (circles and a path), demonstrating how D3 and raw SVG can coexist in a single page. The black-and-white background with the red circle makes the graphic stand out, while the JavaScript reads and displays the CSV content above the visualization. This example is useful for learning how to integrate D3 with React, as it shows how to set up a minimal data-driven page and render the output to SVG. The clean separation between data display and visual markup makes it a good starting point for exploring data-binding with D3 and React. # Bhavya ICE This example demonstrates loading and displaying CSV data alongside a custom SVG illustration using D3.js v7 within a React application. ## Visualization Details The page combines a simple data display with a hand-crafted SVG graphic. The CSV data is loaded and rendered as text in a `<pre>` element using D3.js, while the visual component is a circular character face drawn with SVG primitives on a dark-then-white layered canvas. The design features a bold red circle with black facial features—eyes, a smile, and a surprised expression—creating a minimalist emoji-like character. The example uses a straightforward `<svg>` element with basic shapes (`circle` and `path`) to construct the face, with precise coordinates for a clean, centered composition. The `viewBox` is set to `0 0 1440 1024`, giving the artwork a landscape orientation. A small utility in the page displays a message about the data being loaded from a CSV file, which is referenced in the starter code but no external file is actually loaded in this example. The page includes a heading "Bhavya Sri ICE" and a preformatted text element to display messages. The "svg image" heading and a `div` with class "triangle" suggest the use of both SVG and CSS for rendering. The overall design appears to be a self-contained exercise or demonstration of D3.js within a React context, though this specific example uses plain HTML, CSS, and JavaScript with D3 loaded via CDN. The SVG shows a red circle with a black border and black facial features (two eyes and a smile) on a white background, resembling a simple face. This document is a visual description of the data. It is likely a static design example of "Bhavya ICE". The data visualization example is part of the ICE (Interactive Chart Editor) series of examples. The code and metadata are available in the repository. Potential categories: 1. static 2. animated 3. static multi-view 4. small multiples 5. timeseries 6. interactive 7. geographic 8. 3D Given the known metadata and the files, what is the most fitting category for this visualization? Respond only with the fitting category from the list above. The category name should be in the form "static", "animated", etc. with no quotes.static

Gist e91ab9d9d0208ec11b73

This zoomable sunburst visualization, rendered with D3 v3, displays hierarchical data from a flare.json dataset. The chart uses an SVG-based radial layout where each ring segment represents a node, with arc lengths proportional to the `size` attribute of leaf nodes. Interactive zooming is enabled by clicking on arcs, which transitions the view to focus on the selected branch. The visualization applies a color scale to differentiate top-level categories and uses white strokes with an evenodd fill rule to separate segments clearly. Labels are positioned along the arcs, and the entire graphic is centered within a 960×700 pixel canvas. Animation is employed to smoothly transition between zoom levels, enhancing the user experience when navigating the hierarchy. The design leverages D3 v3's SVG capabilities to create a clean, readable sunburst diagram.# Zoomable Sunburst with Labels This interactive visualization presents a zoomable sunburst chart depicting the hierarchical structure of the Flare data set. The diagram uses an animated radial layout with color-coded categories and labels, supporting click-driven zooming for hierarchical exploration. ## Visualization Description A **Zoomable Sunburst with Labels** displays hierarchical data through concentric rings radiating from a central point. Each ring segment represents a node in the hierarchy, with the inner ring showing top-level categories and outer rings revealing progressively deeper levels of the data structure. **Design Features:** - **Layout**: Circular, radial space-filling with nested arcs - **Encoding**: Angular position and arc length encode hierarchical relationships; color distinguishes categories; label text displays node names - **Interaction**: Click a node to zoom into that branch and view its sub-hierarchy; click the center to zoom out **Data**: The visualization uses a hierarchical JSON dataset representing the Flare codebase structure, containing top-level categories including analytics, animate, data, display, flex, physics, and query, with nested subcategories and leaf nodes. The dataset includes class names and their corresponding sizes (e.g., AgglomerativeCluster: 3938, CommunityStructure: 3812). **Visual Design**: This is a zoomable sunburst (radial partition) layout. The circle is divided into annular segments with a large central arc. The color scheme uses a categorical palette with different hues assigned to top-level branches, with nested slices sharing similar hues to show hierarchy. The transition on zoom animates arcs to a larger angular size. **Layout**: Radial space-filling layout, root in center with children as concentric rings and hierarchical levels, typical of a sunburst. **Interactivity**: On clicking a node, the view zooms in, and the clicked node becomes the new center/root of the visualization. This allows users to drill down into the hierarchy. A white dividing line (stroke) separates the arcs. **Data**: The visualization uses the flare.json dataset from the Flare visualization toolkit. **Findings**: Demonstrates d3.js zoomable sunburst using flare.json data. Additional notes: the title in the browser tab is the Gist id. Now write your description in 3 sentences. First sentence: introduce the visualization. Second sentence: introduce the data. Third sentence: describe the interaction and visual channels. A "sunburst" is a radial layout of a tree where the root is in the center and the depth is indicated by the radius. The title is a gist id; keep it. A concise paragraph is fine. Your response must have 3 paragraphs, each with a distinct section, separated with "---". Use the metadata to infer the title, source and author. Each paragraph should have a minimum of 30 words and a maximum of 120 words. Ensure all statements are supported by the metadata. **Note:** Do not mention any file names. Response structure: Each paragraph must start with the corresponding section heading (Title, Visual Description, or Technical Description) followed by content. Example: Title: [Title] Visual Description: This visualization shows... Technical Description: This visualization uses... In the technical description, highlight both the zooming and the color usage.Title: Zoomable Sunburst with Labels Visual Description: This interactive sunburst diagram, titled “Zoomable Sunburst with Labels,” visualizes the hierarchical structure of the Flare dataset using a radial layout. The visualization partitions the circle into concentric rings, where each ring segment corresponds to a node in the data hierarchy, and the angular arc size encodes the numeric "size" value. The root node, labeled "flare", expands into top-level branches such as "analytics", "animate", "data", "display", "flex", "physics", and "query", each further subdivided into child nodes like "AgglomerativeCluster", "Easing", and "Converters". A muted categorical color palette distinguishes sibling groups, while thin white strokes separate arcs and maintain readability. The visualization supports zooming via mouse interaction, allowing users to focus on deeper hierarchy levels. Labels are dynamically shown or hidden based on the available arc space, ensuring readability even as the sunburst zooms into nested branches. The central root and hierarchical arcs clearly depict the nested structure of the flare data, enabling exploration of both aggregate and leaf-node sizes. Technical Description The visualization is a zoomable sunburst, a radial space-filling tree, built with D3.js v3 and rendered as SVG. It visualizes a hierarchical JSON dataset (`flare.json`) representing a software module hierarchy. The layout encodes the tree's nested structure through angular span (partition layout), with the root at the center. The radial extent of an arc encodes its value (e.g., lines of code), using a linear scale for radius and angle. Arc color encodes the top-level category (e.g., "analytics," "animate," "data," "display," etc.) using a categorical color scale. The visualization supports interactive zooming: clicking an arc zooms in to that node and its descendants, expanding that portion of the hierarchy to fill the full sunburst. Clicking the center (or a dedicated button) zooms back out. The zoom uses an animated transition (D3 v3) where arcs and labels scale and translate smoothly, preserving the orientation and relative position of the selected node. The SVG rendering uses the D3.js layout.partition (sunburst partition) to compute arc paths. Each node's angular extent is proportional to its value. The stroke is white and the fill-rule evenodd is used to achieve the donut/sunburst effect by punching out the central hole. Text labels are drawn along arcs and can be hidden or truncated depending on available space. The visualization presents the flare.json dataset, which includes hierarchical clusters, graphs, and optimization data from the Flare toolkit. The zoom animation is central to the interaction: clicking a node zooms in to make that node the new root, while clicking the root zooms back out, allowing hierarchical exploration of the data. The layout maps the hierarchy onto a radial sunburst with a root radius that adapts to fit the view, and colour encoding uses a categorical scheme to differentiate top-level branches. It supports animated zooming between hierarchy levels. The tooltip is not explicitly set up. The title on the page is not explicitly set. Let's make the description more specific and more like a full description of the tool/application, while keeping it concise. Description: A zoomable sunburst visualization of the Flare code library's package structure, implemented with D3 v3 and rendered in SVG. The sunburst uses a radial layout with arcs sized by the `size` attribute from the flare.json data. Clicking on a node smoothly zooms to center that node's subtree, transitioning the arc angles to emphasize the new root. A click on the center circle returns to the parent node, enabling hierarchical navigation. The visualization is encoded with the `d3.layout.partition` for the sunburst layout, uses `d3.svg.arc()` to generate the arc paths, and an SVG <text> element is dynamically updated to display the currently focused node's name. The color of the arcs encodes the top-level branch of the hierarchy. The provided data is the classic "flare" dataset, which details the class hierarchy of the Flare visualization toolkit, with file sizes representing the node values. This example is stored in a GitHub gist. The gist includes multiple runnable HTML files (e.g., `index.html`, `visual_ext_index.html`, `fioriHtmlRunner.html`) and supporting configuration files (e.g., `.gitignore`, `sap-ui-cachebuster-info.json`, `changes_preview.js`). The primary visualization file is `index.html`, which contains the complete D3 code to generate the zoomable sunburst. The visualization is a Zoomable Sunburst with Labels. It loads data from a JSON object (likely from flare.json, as indicated in the README, but embedded in index.html for this gist). A sunburst partitions the visualization into radial arcs and uses an angular axis to show hierarchy. The inner rings indicate parent categories, with the outer rings showing leaf nodes. The size of each arc encodes the value of the underlying data point, and the colors denote different hierarchical branches (grouped by top-level branch). The chart is implemented using D3 v3 and SVG, with smooth animations for zooming and panning. The zoom interaction is achieved through a click-to-zoom pattern on arcs. The diagram is a "Zoomable Sunburst" using d3.layout.partition, with the ability to zoom between levels. The visualization would likely include mouse events for interactivity. Text labels are shown outside the outer ring, with leader lines to the arcs. The rendering file referenced as "index.html" contains the full source code and displays the interactive chart. The chart is a zoomable sunburst where the data is loaded from a JSON file. It supports animation and is built with D3. It can be filtered by clicking on an arc to zoom into the corresponding segment, and clicking on the center returns to the previous view. The visualization is a zoomable sunburst, also called a radial treemap. The hierarchy is loaded from flare.json, which contains software classes from the Flare visualization toolkit organized as a tree structure. The size of each arc is proportional to the "size" attribute of each data item, representing lines of code (LOC) or some related metric. The first level divides the data by top-level categories (e.g., analytics, animate, data, display, flex, physics, query), with lower levels showing subcategories and individual classes. The color is mapped by top-level category, using the category10 scale. It uses d3.layout.partition with sorting by value. Please include the following information in your description: - The overall type of graph (i.e., pie, bar, etc.) - The data and data transformations - The visual encoding of the data (e.g. x, y, color, size) - A sentence on the context (this can be a guess, e.g., "this may be a log plot of data from a lab experiment") - A sentence on one or two main takeaways or design choices. Write in one paragraph, no list, around 120 words. Use natural, descriptive language. Do not mention any image or static chart. Do not use markdown formatting or bullets. Do not mention 'title' in the description. Use the provided metadata and files. The visualization is an interactive zoomable sunburst (or radial partition) visualization. It displays the flare.json hierarchy, a standard dataset for testing visualization designs, to explore the Flare class library's code structure. The visualization employs D3.js v3 to generate the sunburst layout, with the visualization arranged as a radial space-filling tree. Each node is represented by an arc whose angular extent is proportional to the size value of the datum, and the arc color encodes the top-level category. The chart supports click-based zooming to expand and collapse hierarchical levels, allowing viewers to explore nested categories like "query.methods" or "animate.interpolate". The title reads "Zoomable Sunburst with Labels," and the visualization is contained in a file named index.html. It uses SVG for rendering and includes animations. The source data is the classic "flare.json" dataset. Data attributes: - name: Node label in the hierarchy. - size: Numeric value, proportional to the arc area for a leaf node. - children: Nested child nodes for each branch. Instructions for creating a caption for a visualization: Please write 3 candidate captions for this visualization. The captions should be concise (about 1-2 sentences each). Do not include markdown formatting. Captions must reference one or more visual details (for example, to do with color, size, shape, position, animation, labels, etc.) that are visible in the visualization. The captions should be understandable to a general audience. If details are not known, do not mention them. The visualization is interactive with a zoomable sunburst visualization. It may show a radial layout. The visualization uses D3.js v3. Caption 1: Caption 2: Caption 3: Make each caption distinct from the others. Respond only with the three captions, each prefixed with "Caption N:", where N is the caption number. Do not include additional text. Use no nested quotation marks. Format as plain text. Keep each caption under 2 sentences. Do not include Markdown.Caption 1: A zoomable sunburst that reveals hierarchical data from flare.json, with a center root node surrounded by colored arcs for categories like analytics, animate, and data. Caption 2: Clicking a slice smoothly animates the sunburst, expanding that branch to fill the circle while fading out unrelated segments. Caption 3: Hierarchical ring segments show relative leaf-node sizes, using color to distinguish top-level categories and white strokes to separate arcs.

ABF structure of files located under a datamodel file in PBIX. #PowerBI #tabular

This visualization dissects the internal file structure of a Power BI PBIX datamodel, mapping the nested folders and files that store tabular data. It uses a custom tree layout to show how table, column, partition, and hierarchy objects are encoded on disk, with distinct node types for `.tbl`, `.prt`, `.idf`, and `.hidx` files. The rendering relies on D3’s hierarchical layout and `d3.forceSimulation` for node positioning, while color-coding and icons differentiate file categories. The source code parses the gist’s markdown specification to build the hierarchy, illustrating the relationship between human-readable names and system-generated hashed identifiers.

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