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1. .editorconfig Summary (Solid Foundations + Perks)

You’ve locked in:

• root = true (no cascading ambiguity)

• UTF-8 + LF + newline enforcement

• Scoped formatting for web-focused files

You’ve enhanced with:

• Special rules for docs (.md, .html), scripts (.sh), and structured data files (.json, .yml)

• Optional project-scoped overrides like [frontend/**.ts]

Would you like me to generate a complete .editorconfig that includes frontend/backend sections and GitHub workflow file handling?

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2. Data Graph Venn Diagram Snippet

This part of your code (assuming data contains a dictionary with known words, and graph is a NetworkX graph of word relationships):

from matplotlib_venn import venn2

import matplotlib.pyplot as plt

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()

Gives you a quick semantic overlap diagnostic. This is great for:

• Identifying novel symbols not yet validated

• Spotting duplicates or coverage gaps

• Understanding generation breadth

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Optional Enhancements

Here are a few ideas if you’re interested in evolving this diagram further:

1. Venn3 for Deeper Comparison

If you introduce a third category (e.g., Validated Words), you can expand:

from matplotlib_venn import venn3

validated_words = {w for w in known_words if data["words"][w].get("validated")}

venn3([known_words, generated_words, validated_words], ('Known', 'Generated', 'Validated'))

2. Interactive Venn Exploration (e.g., Plotly)

Turn the static into dynamic using Plotly or Bokeh.

3. Logging Missing Crossovers

Print or log the words in generated_words - known_words

import networkx as nx import matplotlib.pyplot as plt from matplotlib_venn import venn3

Sample data (replace with your actual data)

data = { "words": { "apple": {"category": "Fruit", "color": "Red", "validated": True}, "banana": {"category": "Fruit", "color": "Yellow", "validated": True}, "orange": {"category": "Fruit", "color": "Orange", "validated": False}, "grape": {"category": "Fruit", "color": "Purple", "validated": True} }, "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 (unchanged)

graph = nx.DiGraph() for word, metadata in data["words"].items(): graph.add_node(word, **metadata) 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]) graph.add_edge(transformation["from"], transformation["to"], transformation=transformation["type"])

Step 3: Enhanced Venn Diagram with Validated Words

known_words = set(data["words"].keys()) generated_words = set(node for node, attr in graph.nodes(data=True) if attr.get("category") == "Generated") validated_words = {w for w in known_words if data["words"][w].get("validated", False)} # Handle missing "validated" key

venn3([known_words, generated_words, validated_words], ('Known', 'Generated', 'Validated')) plt.title("Known vs. Generated vs. Validated Words") plt.show()

Logging missing crossovers

missing_words = generated_words - known_words print("Generated words not in known words:", missing_words)

Name		Name	Last commit message	Last commit date
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.github		.github
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
README.md		README.md
event_bus.py		event_bus.py

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