feat(seaborn): implement network-directed (#2889)

## Implementation: `network-directed` - seaborn

Implements the **seaborn** version of `network-directed`.

**File:** `plots/network-directed/implementations/seaborn.py`

**Parent Issue:** #2858

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20608483455)*

---------

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

Author: github-actions[bot]

plots/network-directed/implementations/seaborn.py

@@ -0,0 +1,163 @@
+""" pyplots.ai
+network-directed: Directed Network Graph
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+# Set seaborn style
+sns.set_theme(style="white")
+
+np.random.seed(42)
+
+# Software module dependencies data with improved circular-hierarchical layout
+# Positions organized in concentric layers for better visual balance
+modules = {
+    "app": {"group": "core", "pos": (0.5, 0.88)},
+    "config": {"group": "core", "pos": (0.18, 0.70)},
+    "api": {"group": "services", "pos": (0.50, 0.68)},
+    "auth": {"group": "services", "pos": (0.82, 0.70)},
+    "db": {"group": "data", "pos": (0.28, 0.45)},
+    "cache": {"group": "data", "pos": (0.72, 0.45)},
+    "log": {"group": "utils", "pos": (0.15, 0.22)},
+    "valid": {"group": "utils", "pos": (0.50, 0.22)},
+    "router": {"group": "services", "pos": (0.85, 0.22)},
+    "model": {"group": "data", "pos": (0.32, 0.05)},
+    "mware": {"group": "services", "pos": (0.68, 0.05)},
+}
+
+# Directed edges with weights (source → target, weight)
+# Weight represents dependency strength/frequency
+edges = [
+    ("app", "config", 3),
+    ("app", "db", 5),
+    ("app", "api", 5),
+    ("app", "auth", 4),
+    ("api", "valid", 4),
+    ("api", "router", 3),
+    ("api", "log", 2),
+    ("auth", "cache", 4),
+    ("auth", "db", 3),
+    ("auth", "log", 2),
+    ("db", "config", 3),
+    ("db", "log", 1),
+    ("cache", "config", 2),
+    ("router", "valid", 3),
+    ("router", "mware", 2),
+    ("model", "db", 4),
+    ("model", "valid", 3),
+    ("mware", "auth", 3),
+    ("mware", "log", 1),
+]
+
+# Color palette using seaborn
+groups = ["core", "data", "services", "utils"]
+palette = sns.color_palette(["#306998", "#FFD43B", "#4CAF50", "#E57373"], n_colors=4)
+group_colors = dict(zip(groups, palette, strict=True))
+
+# Prepare node data as DataFrame for seaborn
+node_data = []
+for name, data in modules.items():
+    node_data.append({"name": name, "x": data["pos"][0], "y": data["pos"][1], "group": data["group"]})
+nodes_df = pd.DataFrame(node_data)
+
+# Create figure
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Draw all edges with directed arrows - thickness based on weight
+for source, target, weight in edges:
+    start = modules[source]["pos"]
+    end = modules[target]["pos"]
+
+    # Calculate direction for shortening arrows
+    dx = end[0] - start[0]
+    dy = end[1] - start[1]
+    length = np.sqrt(dx**2 + dy**2)
+    dx_norm = dx / length if length > 0 else 0
+    dy_norm = dy / length if length > 0 else 0
+
+    # Shorten to avoid overlapping nodes
+    shrink = 0.055
+    start_adj = (start[0] + dx_norm * shrink, start[1] + dy_norm * shrink)
+    end_adj = (end[0] - dx_norm * shrink, end[1] - dy_norm * shrink)
+
+    # Line width based on edge weight (1-5 scale to 1.5-4.0 linewidth)
+    line_width = 1.0 + weight * 0.6
+
+    # Draw arrow using FancyArrowPatch with weight-based thickness
+    arrow = mpatches.FancyArrowPatch(
+        start_adj,
+        end_adj,
+        connectionstyle="arc3,rad=0.1",
+        arrowstyle="->,head_length=8,head_width=5",
+        color="#555555",
+        alpha=0.5 + weight * 0.08,
+        linewidth=line_width,
+        zorder=1,
+    )
+    ax.add_patch(arrow)
+
+# Draw nodes using seaborn's scatterplot
+sns.scatterplot(
+    data=nodes_df,
+    x="x",
+    y="y",
+    hue="group",
+    palette=group_colors,
+    s=3000,
+    edgecolor="white",
+    linewidth=3,
+    legend=False,
+    ax=ax,
+    zorder=2,
+)
+
+# Add node labels with larger font size (14-16pt per feedback)
+for name, data in modules.items():
+    ax.text(
+        data["pos"][0],
+        data["pos"][1],
+        name,
+        ha="center",
+        va="center",
+        fontsize=14,
+        fontweight="bold",
+        color="white" if data["group"] != "data" else "#333333",
+        zorder=3,
+    )
+
+# Create legend with edge weight indicator
+legend_handles = [
+    plt.scatter([], [], c=[group_colors[g]], s=400, label=g.capitalize(), edgecolors="white", linewidths=2)
+    for g in groups
+]
+# Add edge weight legend entries
+legend_handles.append(plt.Line2D([0], [0], color="#555555", linewidth=1.6, label="Weak (1)", alpha=0.6))
+legend_handles.append(plt.Line2D([0], [0], color="#555555", linewidth=4.0, label="Strong (5)", alpha=0.9))
+
+ax.legend(
+    handles=legend_handles,
+    loc="upper left",
+    fontsize=14,
+    title="Module Type / Edge Weight",
+    title_fontsize=16,
+    framealpha=0.95,
+    markerscale=1.0,
+    borderpad=1,
+)
+
+# Styling
+ax.set_title("network-directed · seaborn · pyplots.ai", fontsize=24, fontweight="bold", pad=20)
+ax.set_xlim(-0.02, 1.02)
+ax.set_ylim(-0.08, 1.02)
+ax.axis("off")
+ax.set_facecolor("#fafafa")
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight", facecolor="white")

plots/network-directed/metadata/seaborn.yaml

@@ -0,0 +1,28 @@
+library: seaborn
+specification_id: network-directed
+created: '2025-12-30T23:56:22Z'
+updated: '2025-12-31T00:12:11Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20608483455
+issue: 2858
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/network-directed/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/network-directed/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent visual clarity with well-sized nodes and clearly visible directed arrows
+  - Smart use of curved edges (arc3,rad=0.1) to prevent arrow overlap on bidirectional-style
+    edges
+  - Edge weight visualization through both line thickness and alpha transparency creates
+    good visual hierarchy
+  - Proper title format and professional layout with light background
+  - Realistic software module dependency scenario with meaningful groupings
+  - Clean code structure following KISS principles
+  weaknesses:
+  - Legend shows module types but edge weight legend (Weak/Strong) mentioned in code
+    may not be rendering in final image
+  - Node label font size at 14pt is acceptable but 16pt would improve readability
+    at full resolution