prompt: {

"name": "waveterm",

"author": {

"name": "Command Line Inc",

"email": "info@commandline.dev"

},

"productName": "Wave",

"description": "Open-Source AI-Native Terminal Built for Seamless Workflows",

"license": "Apache-2.0",

"version": "0.11.2",

"homepage": "https://waveterm.dev",

"build": {

"appId": "dev.commandline.waveterm"

},

"private": true,

"main": "./dist/main/index.js",

"type": "module",

"browserslist": [

"Chrome >= 128"

],

"scripts": {

"dev": "electron-vite dev",

"start": "electron-vite preview",

"build:dev": "electron-vite build --mode development",

"build:prod": "electron-vite build --mode production",

"storybook": "storybook dev -p 6006 --no-open",

"build-storybook": "storybook build",

"coverage": "vitest run --coverage",

"test": "vitest",

"postinstall": "electron-builder install-app-deps"

},

"devDependencies": {

"@chromatic-com/storybook": "^3.2.4",

"@eslint/js": "^9.20.0",

"@rollup/plugin-node-resolve": "^16.0.0",

"@storybook/addon-essentials": "^8.5.8",

"@storybook/addon-interactions": "^8.5.8",

"@storybook/addon-links": "^8.5.8",

"@storybook/blocks": "^8.5.8",

"@storybook/builder-vite": "^8.5.8",

"@storybook/react": "^8.5.8",

"@storybook/react-vite": "^8.5.8",

"@storybook/test": "^8.5.8",

"@storybook/theming": "^8.5.8",

"@tailwindcss/vite": "^4.0.6",

"@types/color": "^4.2.0",

"@types/css-tree": "^2",

"@types/debug": "^4",

"@types/electron": "^1.6.12",

"@types/node": "^22.13.4",

"@types/papaparse": "^5",

"@types/pngjs": "^6.0.5",

"@types/prop-types": "^15",

"@types/react": "^18.3.13",

"@types/react-dom": "^18.3.1",

"@types/semver": "^7",

"@types/shell-quote": "^1",

"@types/spr

import networkx as nx import matplotlib.pyplot as plt from matplotlib_venn import venn2

Sample data (replace with your actual data)

data = { "words": { "apple": {"category": "Fruit", "color": "Red"}, "banana": {"category": "Fruit", "color": "Yellow"}, "orange": {"category": "Fruit", "color": "Orange"}, "grape": {"category": "Fruit", "color": "Purple"} }, "transformations": [ {"from": "apple", "to": "ApplePie", "type": "Baking"}, {"from": "banana", "to": "BananaBread", "type": "Baking"}, {"from": "orange", "to": "OrangeJuice", "type": "Juicing"}, {"from": "grape", "to": "grapefruit", "type": "Mutation"} ] }

Step 2: Create the directed graph

graph = nx.DiGraph()

Add known word nodes

for word, metadata in data["words"].items(): graph.add_node(word, **metadata)

Add missing destination words as 'Generated' nodes

for transformation in data["transformations"]: to_word = transformation["to"] if to_word not in graph: graph.add_node( to_word, category="Generated", numeric=[ord(c) for c in to_word] )

Add transformation edges

for transformation in data["transformations"]: graph.add_edge( transformation["from"], transformation["to"], transformation=transformation["type"] )

Assuming 'data' and 'graph' are defined from the previous code

Step 3: Analyze and visualize the graph (example: Venn diagram)

known_words = set(data["words"].keys()) generated_words = set(node for node, attr in graph.nodes(data=True) if attr.get("category") == "Generated")

venn2([known_words, generated_words], ('Known Words', 'Generated Words')) plt.title("Known vs. Generated Words") plt.show()