update(arc-basic): seaborn — comprehensive quality review

plots/arc-basic/implementations/seaborn.py

@@ -1,120 +1,151 @@
 """ pyplots.ai
 arc-basic: Basic Arc Diagram
-Library: seaborn 0.13.2 | Python 3.13.11
-Quality: 88/100 | Created: 2025-12-23
+Library: seaborn 0.13.2 | Python 3.14.3
+Quality: 91/100 | Updated: 2026-02-23
 """
 
-import matplotlib.patches as patches
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import seaborn as sns
-from matplotlib.lines import Line2D
 
 
-# Data: Character interactions in a story (12 characters for readability)
-np.random.seed(42)
+# Data: Character interactions in a story (12 characters)
 nodes = ["Alice", "Bob", "Carol", "Dave", "Eve", "Frank", "Grace", "Henry", "Ivy", "Jack", "Kate", "Leo"]
 n_nodes = len(nodes)
 
-# Create edges with weights (character interaction strength)
+# Edges: (source_index, target_index, interaction_weight)
 edges = [
-    (0, 1, 5),  # Alice - Bob (close friends)
-    (0, 3, 2),  # Alice - Dave
-    (1, 2, 4),  # Bob - Carol
-    (1, 4, 3),  # Bob - Eve
-    (2, 5, 2),  # Carol - Frank
-    (3, 4, 5),  # Dave - Eve (close)
-    (3, 6, 3),  # Dave - Grace
-    (4, 7, 4),  # Eve - Henry
-    (5, 6, 2),  # Frank - Grace
-    (0, 11, 1),  # Alice - Leo (distant, long arc)
-    (2, 6, 3),  # Carol - Grace
-    (1, 5, 2),  # Bob - Frank
-    (7, 8, 4),  # Henry - Ivy
-    (8, 9, 3),  # Ivy - Jack
-    (9, 10, 5),  # Jack - Kate (close)
-    (10, 11, 2),  # Kate - Leo
-    (6, 9, 2),  # Grace - Jack
-    (5, 10, 1),  # Frank - Kate (distant)
+    (0, 1, 5),  # Alice – Bob
+    (0, 3, 2),  # Alice – Dave
+    (1, 2, 4),  # Bob – Carol
+    (1, 4, 3),  # Bob – Eve
+    (2, 5, 2),  # Carol – Frank
+    (3, 4, 5),  # Dave – Eve
+    (3, 6, 3),  # Dave – Grace
+    (4, 7, 4),  # Eve – Henry
+    (5, 6, 2),  # Frank – Grace
+    (0, 11, 1),  # Alice – Leo (long-range)
+    (2, 6, 3),  # Carol – Grace
+    (1, 5, 2),  # Bob – Frank
+    (7, 8, 4),  # Henry – Ivy
+    (8, 9, 3),  # Ivy – Jack
+    (9, 10, 5),  # Jack – Kate
+    (10, 11, 2),  # Kate – Leo
+    (6, 9, 2),  # Grace – Jack
+    (5, 10, 1),  # Frank – Kate (long-range)
 ]
 
-# Node positions along x-axis
 x_positions = np.arange(n_nodes)
 
-# Create figure with seaborn styling - use whitegrid then disable grid
+# Build long-form DataFrame of arc coordinates for seaborn lineplot
+arc_rows = []
+n_pts = 80
+for eid, (src, tgt, w) in enumerate(edges):
+    x1, x2 = x_positions[src], x_positions[tgt]
+    dist = abs(x2 - x1)
+    h = dist * 0.4
+    t = np.linspace(0, np.pi, n_pts)
+    cx, rx = (x1 + x2) / 2, dist / 2
+    arc_x = cx + rx * np.cos(np.pi - t)
+    arc_y = h * np.sin(t)
+    for xi, yi in zip(arc_x, arc_y, strict=True):
+        arc_rows.append({"x": xi, "y": yi, "weight": w, "edge_id": eid})
+
+arc_df = pd.DataFrame(arc_rows)
+
+# Categorize weights for seaborn hue encoding
+strength_names = {1: "1 · Weak", 2: "2 · Light", 3: "3 · Moderate", 4: "4 · Strong", 5: "5 · Intense"}
+cat_order = [strength_names[k] for k in sorted(strength_names)]
+arc_df["strength"] = pd.Categorical(arc_df["weight"].map(strength_names), categories=cat_order, ordered=True)
+
+# Theme
 sns.set_theme(style="white", context="talk", font_scale=1.1)
 fig, ax = plt.subplots(figsize=(16, 9))
 
-# Explicitly disable grid for arc diagram (abstract visualization)
-ax.grid(False)
-
-# Plot nodes as points using seaborn
-node_data = pd.DataFrame({"x": x_positions, "y": np.zeros(n_nodes), "node": nodes})
-sns.scatterplot(data=node_data, x="x", y="y", s=600, color="#306998", zorder=5, ax=ax, legend=False)
-
-# Draw arcs for each edge
-for start, end, weight in edges:
-    x1, x2 = x_positions[start], x_positions[end]
-    # Arc height proportional to distance between nodes
-    distance = abs(x2 - x1)
-    height = distance * 0.4
-
-    # Arc thickness based on weight
-    linewidth = weight * 0.8 + 0.5
-
-    # Create arc using matplotlib patches
-    center_x = (x1 + x2) / 2
-    width = abs(x2 - x1)
-
-    arc = patches.Arc(
-        (center_x, 0),
-        width,
-        height * 2,
-        angle=0,
-        theta1=0,
-        theta2=180,
-        color="#FFD43B",
-        linewidth=linewidth,
-        alpha=0.6,
-        zorder=2,
-    )
-    ax.add_patch(arc)
-
-# Add node labels below the axis
-for i, name in enumerate(nodes):
-    ax.text(x_positions[i], -0.15, name, ha="center", va="top", fontsize=16, fontweight="bold", color="#306998")
-
-# Styling - adjust limits for 12 nodes
-ax.set_xlim(-0.8, n_nodes - 0.2)
-ax.set_ylim(-0.8, 5.0)  # More vertical space for longer arcs
-ax.set_title("arc-basic · seaborn · pyplots.ai", fontsize=24, pad=20)
-
-# Remove all axis elements for abstract visualization
-for spine in ax.spines.values():
-    spine.set_visible(False)
-ax.set_xticks([])
-ax.set_yticks([])
-
-# Add a subtle horizontal baseline
-ax.axhline(y=0, color="#306998", linewidth=2, alpha=0.3, zorder=1)
+# Viridis palette (reversed so stronger connections = darker/more prominent)
+viridis = sns.color_palette("viridis", as_cmap=True)
+palette = [viridis(v) for v in [0.82, 0.66, 0.48, 0.30, 0.12]]
+
+# Draw arcs via seaborn lineplot (hue=color by strength, size=thickness by weight)
+sns.lineplot(
+    data=arc_df,
+    x="x",
+    y="y",
+    hue="strength",
+    size="weight",
+    units="edge_id",
+    estimator=None,
+    palette=palette,
+    sizes=(2.0, 6.0),
+    alpha=0.7,
+    ax=ax,
+    sort=False,
+)
 
-# Add legend for arc thickness (interaction strength)
-legend_elements = [
-    Line2D([0], [0], color="#FFD43B", linewidth=1.3, alpha=0.6, label="Weak (1)"),
-    Line2D([0], [0], color="#FFD43B", linewidth=2.9, alpha=0.6, label="Medium (3)"),
-    Line2D([0], [0], color="#FFD43B", linewidth=4.5, alpha=0.6, label="Strong (5)"),
-]
-legend = ax.legend(
-    handles=legend_elements,
+# Keep only color legend entries (remove redundant size entries)
+handles, labels = ax.get_legend_handles_labels()
+cat_set = set(cat_order)
+filtered = [(h, lab) for h, lab in zip(handles, labels, strict=True) if lab in cat_set]
+ax.legend(
+    [h for h, _ in filtered],
+    [lab for _, lab in filtered],
     title="Interaction Strength",
+    title_fontsize=20,
+    fontsize=16,
     loc="upper right",
-    fontsize=14,
-    title_fontsize=16,
     frameon=True,
     fancybox=True,
     framealpha=0.9,
+    edgecolor="#cccccc",
+)
+
+# Draw nodes with seaborn scatterplot
+node_df = pd.DataFrame({"x": x_positions, "y": np.zeros(n_nodes)})
+sns.scatterplot(
+    data=node_df, x="x", y="y", s=600, color="#306998", zorder=5, ax=ax, legend=False, edgecolor="white", linewidth=1.5
+)
+
+# Node labels below the baseline
+for i, name in enumerate(nodes):
+    ax.text(x_positions[i], -0.22, name, ha="center", va="top", fontsize=16, fontweight="medium", color="#306998")
+
+# Storytelling: highlight the contrast between arc distance and weight
+# The tallest arc (Alice–Leo) is the weakest connection
+ax.annotate(
+    "Weakest link, longest reach",
+    xy=(5.5, 4.2),
+    fontsize=13,
+    fontstyle="italic",
+    color="#555555",
+    ha="center",
+    xytext=(2.0, 4.9),
+    arrowprops={"arrowstyle": "->", "color": "#888888", "lw": 1.0},
+)
+# Three strongest bonds are all between nearest neighbors
+ax.annotate(
+    "Strongest local bonds",
+    xy=(3.5, 0.42),
+    fontsize=13,
+    fontstyle="italic",
+    color="#555555",
+    ha="center",
+    xytext=(6.0, 2.0),
+    arrowprops={"arrowstyle": "->", "color": "#888888", "lw": 1.0},
 )
 
+# Axis styling
+ax.set_xlim(-0.8, n_nodes - 0.2)
+ax.set_ylim(-0.45, 5.6)
+ax.set_title("arc-basic \u00b7 seaborn \u00b7 pyplots.ai", fontsize=24, fontweight="medium", pad=20)
+ax.set_xlabel("")
+ax.set_ylabel("")
+sns.despine(ax=ax, left=True, bottom=True)
+ax.set_xticks([])
+ax.set_yticks([])
+
+# Subtle horizontal baseline
+ax.axhline(y=0, color="#306998", linewidth=2, alpha=0.3, zorder=1)
+
 plt.tight_layout()
 plt.savefig("plot.png", dpi=300, bbox_inches="tight")