"""Bubble chart template — flow family.
Substitution note: d3-graph-gallery.com's "circular packing" pages
(https://d3-graph-gallery.com/graph/circularpacking_basic.html and
siblings, confirmed by re-checking https://d3-graph-gallery.com/
circularpacking.html's link list) are all `d3.forceSimulation()` bubble
demos with flat dummy data, not `d3.pack()`/`d3.hierarchy()` layouts — the
same substitution note hier_circle_pack.py already made for its own nested
version of this problem. `flow_bubble` needs a FLAT, already-packed bubble
chart (not the nested tree hier_circle_pack.py uses), so this is adapted
instead from a different, real, verified flat `d3.pack()` bubble-chart
source: https://multimedia.report/classes/coding/2018/exercises/basicbubblepackchart/
— which already builds its hierarchy from a flat list exactly the way this
template needs (`d3.hierarchy({children: data}).sum(d => d.value)` then
`d3.pack().size([...]).padding(...)`, one circle + centered label per leaf),
so no nested-to-flat adaptation was even needed here. Field names are wired
to this template's own flat `BubbleData` schema (`label`/`value` instead of
the fetched source's CSV `Fruit`/`Amount` columns); circle fill color and
the radius-proportional label truncation follow the same conventions
already used by hier_circle_pack.py for its own leaf circles.
"""
from __future__ import annotations
from pydantic import BaseModel
from app.agents.d3.registry import D3Template, register
class Bubble(BaseModel):
label: str
value: float
class BubbleData(BaseModel):
title: str = ""
data: list[Bubble]
_HTML = """
"""
golden_sample = BubbleData(
title="Demo",
data=[
Bubble(label="A", value=10),
Bubble(label="B", value=20),
Bubble(label="C", value=5),
],
)
register(
D3Template(
id="flow_bubble",
family="flow",
title="Bubble chart",
when_to_use="Show relative size of a flat set of named quantities as packed circles (not a hierarchy).",
data_requirements="A flat list of labeled values (no nesting).",
schema=BubbleData,
html_template=_HTML,
golden_sample=golden_sample,
)
)