feat(plotnine): implement circlepacking-basic (#2545)

## Implementation: `circlepacking-basic` - plotnine

Implements the **plotnine** version of `circlepacking-basic`.

**File:** `plots/circlepacking-basic/implementations/plotnine.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20585401342)*

---------

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Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/circlepacking-basic/implementations/plotnine.py

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+""" pyplots.ai
+circlepacking-basic: Circle Packing Chart
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    coord_fixed,
+    element_text,
+    geom_polygon,
+    geom_text,
+    ggplot,
+    guide_legend,
+    guides,
+    labs,
+    scale_fill_manual,
+    scale_size_identity,
+    theme,
+    theme_void,
+)
+
+
+np.random.seed(42)
+
+# Hierarchical data - Company organizational structure
+# Format: (id, label, parent_id, value)
+nodes = [
+    ("root", "Company", None, None),
+    ("eng", "Engineering", "root", 50),
+    ("ops", "Operations", "root", 35),
+    ("prod", "Product", "root", 30),
+    ("be", "Backend", "eng", 20),
+    ("fe", "Frontend", "eng", 18),
+    ("dops", "DevOps", "eng", 12),
+    ("fin", "Finance", "ops", 15),
+    ("leg", "Legal", "ops", 10),
+    ("hr", "HR", "ops", 10),
+    ("des", "Design", "prod", 12),
+    ("pm", "PM", "prod", 10),
+    ("res", "Research", "prod", 8),
+]
+
+# Circle positions and radii - manually laid out for proper size differentiation
+# Root circle
+root_x, root_y, root_r = 0.0, 0.0, 1.0
+
+# Calculate department radii based on their total values (area encoding: r ∝ sqrt(value))
+dept_values = {"eng": 50, "ops": 35, "prod": 30}
+dept_total = sum(dept_values.values())
+dept_scale = 0.48  # Larger scale factor for department circles to fill space better
+
+eng_r = np.sqrt(dept_values["eng"] / dept_total) * dept_scale
+ops_r = np.sqrt(dept_values["ops"] / dept_total) * dept_scale
+prod_r = np.sqrt(dept_values["prod"] / dept_total) * dept_scale
+
+# Position departments more centrally - place them in a tighter triangular arrangement
+eng_x, eng_y = 0.0, 0.30  # Engineering at top center
+ops_x, ops_y = -0.28, -0.12  # Operations bottom left, moved up
+prod_x, prod_y = 0.28, -0.12  # Product bottom right, moved up
+
+# Calculate team radii with more variation to show size differences clearly
+# Teams within Engineering (values: 20, 18, 12) - wider value range
+eng_team_values = {"be": 20, "fe": 18, "dops": 12}
+eng_team_total = sum(eng_team_values.values())
+team_scale_eng = eng_r * 0.75
+
+be_r = np.sqrt(eng_team_values["be"] / eng_team_total) * team_scale_eng
+fe_r = np.sqrt(eng_team_values["fe"] / eng_team_total) * team_scale_eng
+dops_r = np.sqrt(eng_team_values["dops"] / eng_team_total) * team_scale_eng
+
+# Teams within Operations (values: 15, 10, 10)
+ops_team_values = {"fin": 15, "leg": 10, "hr": 10}
+ops_team_total = sum(ops_team_values.values())
+team_scale_ops = ops_r * 0.75
+
+fin_r = np.sqrt(ops_team_values["fin"] / ops_team_total) * team_scale_ops
+leg_r = np.sqrt(ops_team_values["leg"] / ops_team_total) * team_scale_ops
+hr_r = np.sqrt(ops_team_values["hr"] / ops_team_total) * team_scale_ops
+
+# Teams within Product (values: 12, 10, 8)
+prod_team_values = {"des": 12, "pm": 10, "res": 8}
+prod_team_total = sum(prod_team_values.values())
+team_scale_prod = prod_r * 0.75
+
+des_r = np.sqrt(prod_team_values["des"] / prod_team_total) * team_scale_prod
+pm_r = np.sqrt(prod_team_values["pm"] / prod_team_total) * team_scale_prod
+res_r = np.sqrt(prod_team_values["res"] / prod_team_total) * team_scale_prod
+
+# Position teams within their parent departments - tighter arrangement
+# Engineering teams - arranged in a triangle
+be_x, be_y = eng_x - 0.12, eng_y + 0.0
+fe_x, fe_y = eng_x + 0.12, eng_y + 0.0
+dops_x, dops_y = eng_x + 0.0, eng_y - 0.14
+
+# Operations teams - arranged in a triangle
+fin_x, fin_y = ops_x + 0.0, ops_y + 0.10
+leg_x, leg_y = ops_x - 0.10, ops_y - 0.05
+hr_x, hr_y = ops_x + 0.10, ops_y - 0.05
+
+# Product teams - arranged in a triangle
+des_x, des_y = prod_x + 0.0, prod_y + 0.10
+pm_x, pm_y = prod_x - 0.10, prod_y - 0.05
+res_x, res_y = prod_x + 0.10, prod_y - 0.05
+
+# All circles data: (id, label, cx, cy, r, depth)
+circles_data = [
+    ("root", "Company", root_x, root_y, root_r, 0),
+    ("eng", "Engineering", eng_x, eng_y, eng_r, 1),
+    ("ops", "Operations", ops_x, ops_y, ops_r, 1),
+    ("prod", "Product", prod_x, prod_y, prod_r, 1),
+    ("be", "Backend", be_x, be_y, be_r, 2),
+    ("fe", "Frontend", fe_x, fe_y, fe_r, 2),
+    ("dops", "DevOps", dops_x, dops_y, dops_r, 2),
+    ("fin", "Finance", fin_x, fin_y, fin_r, 2),
+    ("leg", "Legal", leg_x, leg_y, leg_r, 2),
+    ("hr", "HR", hr_x, hr_y, hr_r, 2),
+    ("des", "Design", des_x, des_y, des_r, 2),
+    ("pm", "PM", pm_x, pm_y, pm_r, 2),
+    ("res", "Research", res_x, res_y, res_r, 2),
+]
+
+# Sort by depth for proper layering (draw root first, teams last/on top)
+circles_data = sorted(circles_data, key=lambda c: c[5])
+
+# Build polygon dataframe for drawing circles
+polygon_rows = []
+n_points = 64
+
+for circle_id, _label, cx, cy, r, depth in circles_data:
+    angles = np.linspace(0, 2 * np.pi, n_points)
+    xs = cx + r * np.cos(angles)
+    ys = cy + r * np.sin(angles)
+    for j, (x, y) in enumerate(zip(xs, ys, strict=True)):
+        polygon_rows.append({"circle_id": circle_id, "x": x, "y": y, "order": j, "depth": depth})
+
+df_circles = pd.DataFrame(polygon_rows)
+
+# Build labels dataframe - include ALL labels (departments AND teams)
+label_rows = []
+for _circle_id, label, cx, cy, r, depth in circles_data:
+    if depth == 0:
+        continue  # Skip root label
+    if depth == 1:
+        # Department labels: position at top edge of circle
+        label_y = cy + r * 0.60
+        text_size = 12
+    else:
+        # Team labels: centered in circle, larger size for visibility
+        label_y = cy
+        text_size = 10
+    label_rows.append({"x": cx, "y": label_y, "label": label, "text_size": text_size, "depth": depth})
+
+df_labels = pd.DataFrame(label_rows)
+
+# Create the plot
+plot = (
+    ggplot()
+    + geom_polygon(
+        df_circles, aes(x="x", y="y", group="circle_id", fill="factor(depth)"), color="#333333", size=0.5, alpha=0.92
+    )
+    + geom_text(
+        df_labels,
+        aes(x="x", y="y", label="label", size="text_size"),
+        color="#222222",
+        fontweight="bold",
+        show_legend=False,
+    )
+    + scale_fill_manual(
+        values=["#E0E0E0", "#306998", "#FFD43B"], labels=["Root", "Departments", "Teams"], name="Hierarchy Level"
+    )
+    + scale_size_identity()
+    + coord_fixed(ratio=1)
+    + labs(title="circlepacking-basic · plotnine · pyplots.ai")
+    + theme_void()
+    + theme(
+        figure_size=(12, 12),
+        plot_title=element_text(size=28, ha="center", weight="bold", margin={"b": 20}),
+        legend_text=element_text(size=14),
+        legend_title=element_text(size=16, weight="bold"),
+        legend_position="bottom",
+        legend_direction="horizontal",
+    )
+    + guides(fill=guide_legend(override_aes={"size": 0.5}))
+)
+
+plot.save("plot.png", dpi=300, width=12, height=12, verbose=False)

plots/circlepacking-basic/metadata/plotnine.yaml

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+library: plotnine
+specification_id: circlepacking-basic
+created: '2025-12-30T00:08:30Z'
+updated: '2025-12-30T00:55:11Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20585401342
+issue: 0
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/circlepacking-basic/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/circlepacking-basic/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent visual hierarchy with clear color differentiation between levels (gray
+    root, blue departments, yellow teams)
+  - Creative implementation using geom_polygon to draw circles since plotnine lacks
+    native circle packing
+  - Proper area encoding (r ∝ sqrt(value)) for accurate visual perception of sizes
+  - Clean, readable code with well-documented manual layout calculations
+  - Realistic company organizational data that effectively demonstrates the visualization
+    concept
+  - All labels readable with appropriate sizing and bold text for emphasis
+  weaknesses:
+  - Manual circle positioning rather than algorithmic packing means circles do not
+    pack as tightly as possible
+  - Slight empty space at bottom of the root circle could be better utilized