feat(altair): implement network-force-directed

plots/network-force-directed/implementations/python/altair.py

@@ -1,103 +1,88 @@
-""" pyplots.ai
+""" anyplot.ai
 network-force-directed: Force-Directed Graph
-Library: altair 6.0.0 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-23
+Library: altair 6.1.0 | Python 3.14.4
+Quality: 85/100 | Updated: 2026-04-26
 """
 
+import os
+
 import altair as alt
 import numpy as np
 import pandas as pd
 
 
-# Set seed for reproducibility
-np.random.seed(42)
+# Theme tokens
+THEME = os.getenv("ANYPLOT_THEME", "light")
+PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
+ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
+INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
+INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
+EDGE_COLOR = "#6B6A63" if THEME == "light" else "#A8A79F"
 
-# Data: A social network with 50 nodes in 3 communities
-# Demonstrates force-directed layout with clear community structure
-nodes = []
-edges = []
+# Okabe-Ito categorical palette (first series is always #009E73)
+OKABE_ITO = ["#009E73", "#D55E00", "#0072B2"]
+
+# Data: a 50-node organisational network with three communities
+np.random.seed(42)
 
-# Create 3 communities
-community_sizes = [18, 17, 15]  # Total: 50 nodes
+community_sizes = [18, 17, 15]
 community_names = ["Engineering", "Marketing", "Sales"]
-node_id = 0
 
+nodes = []
+node_id = 0
 for comm_idx, size in enumerate(community_sizes):
     for _ in range(size):
         nodes.append({"id": node_id, "community": community_names[comm_idx]})
         node_id += 1
 
-# Intra-community edges (dense connections within communities)
-# Engineering: nodes 0-17
-for i in range(18):
-    for j in range(i + 1, 18):
-        if np.random.random() < 0.3:
-            edges.append((i, j))
-
-# Marketing: nodes 18-34
-for i in range(18, 35):
-    for j in range(i + 1, 35):
-        if np.random.random() < 0.3:
-            edges.append((i, j))
-
-# Sales: nodes 35-49
-for i in range(35, 50):
-    for j in range(i + 1, 50):
-        if np.random.random() < 0.3:
-            edges.append((i, j))
-
-# Inter-community edges (sparse bridges between communities)
-bridge_edges = [(0, 18), (5, 20), (10, 25), (18, 35), (22, 40), (30, 45), (8, 38), (15, 48)]
-edges.extend(bridge_edges)
-
-# Force-directed layout algorithm (Fruchterman-Reingold)
-n = len(nodes)
-positions = np.random.rand(n, 2) * 2 - 1  # Initial random positions
+edges = []
+# Intra-community edges (dense)
+for start, end in [(0, 18), (18, 35), (35, 50)]:
+    for i in range(start, end):
+        for j in range(i + 1, end):
+            if np.random.random() < 0.3:
+                edges.append((i, j))
 
-# Optimal distance parameter
+# Inter-community bridges (sparse)
+edges.extend([(0, 18), (5, 20), (10, 25), (18, 35), (22, 40), (30, 45), (8, 38), (15, 48)])
+
+# Fruchterman-Reingold force-directed layout
+n = len(nodes)
+positions = np.random.rand(n, 2) * 2 - 1
 k = 0.5
 iterations = 200
 
 for iteration in range(iterations):
     displacement = np.zeros((n, 2))
-
-    # Repulsive forces between all node pairs (nodes push apart)
     for i in range(n):
         for j in range(i + 1, n):
             diff = positions[i] - positions[j]
             dist = max(np.linalg.norm(diff), 0.01)
-            repulsive_force = (k * k / dist) * (diff / dist)
-            displacement[i] += repulsive_force
-            displacement[j] -= repulsive_force
-
-    # Attractive forces along edges (connected nodes pull together)
+            repulsive = (k * k / dist) * (diff / dist)
+            displacement[i] += repulsive
+            displacement[j] -= repulsive
     for src, tgt in edges:
         diff = positions[src] - positions[tgt]
         dist = max(np.linalg.norm(diff), 0.01)
-        attractive_force = (dist * dist / k) * (diff / dist)
-        displacement[src] -= attractive_force
-        displacement[tgt] += attractive_force
-
-    # Apply displacement with cooling (decreasing temperature)
+        attractive = (dist * dist / k) * (diff / dist)
+        displacement[src] -= attractive
+        displacement[tgt] += attractive
     temperature = 1 - iteration / iterations
     for i in range(n):
         disp_norm = np.linalg.norm(displacement[i])
         if disp_norm > 0:
-            # Limit movement by temperature
             positions[i] += (displacement[i] / disp_norm) * min(disp_norm, 0.15 * temperature)
 
-# Normalize positions to [0.05, 0.95] range
 pos_min = positions.min(axis=0)
 pos_max = positions.max(axis=0)
 positions = (positions - pos_min) / (pos_max - pos_min + 1e-6) * 0.9 + 0.05
 
-# Calculate node degrees (number of connections)
+# Node-level summary
 degrees = {node["id"]: 0 for node in nodes}
 for src, tgt in edges:
     degrees[src] += 1
     degrees[tgt] += 1
 
-# Create node dataframe with positions and attributes
 node_df = pd.DataFrame(
     {
         "id": [node["id"] for node in nodes],
@@ -107,67 +92,71 @@
         "degree": [degrees[node["id"]] for node in nodes],
     }
 )
-
-# Scale node size by degree for visualization
 node_df["size"] = node_df["degree"] * 30 + 200
 
-# Create edge dataframe for line segments
+# Edge segments (long-form, two rows per edge)
 edge_data = []
 for src, tgt in edges:
     edge_data.append({"edge_id": f"{src}-{tgt}", "x": positions[src][0], "y": positions[src][1], "order": 0})
     edge_data.append({"edge_id": f"{src}-{tgt}", "x": positions[tgt][0], "y": positions[tgt][1], "order": 1})
 edge_df = pd.DataFrame(edge_data)
 
-# Community color mapping (Python Blue, Python Yellow, and colorblind-safe coral)
-community_colors = ["#306998", "#FFD43B", "#FF6B6B"]
+# Label only the four most-connected nodes to avoid clutter
+hub_df = node_df.nlargest(4, "degree").copy()
+hub_df["label"] = "Hub " + hub_df["id"].astype(str)
 
-# Create edges layer
+# Edges layer
 edges_chart = (
     alt.Chart(edge_df)
-    .mark_line(strokeWidth=1.5, opacity=0.4)
+    .mark_line(strokeWidth=1.4, opacity=0.55)
     .encode(
         x=alt.X("x:Q", axis=None),
         y=alt.Y("y:Q", axis=None),
         detail="edge_id:N",
         order="order:O",
-        color=alt.value("#AAAAAA"),
+        color=alt.value(EDGE_COLOR),
     )
 )
 
-# Create nodes layer
+# Nodes layer
 nodes_chart = (
     alt.Chart(node_df)
-    .mark_circle(stroke="#333333", strokeWidth=1.5, opacity=0.85)
+    .mark_circle(stroke=PAGE_BG, strokeWidth=1.5, opacity=0.95)
     .encode(
         x=alt.X("x:Q", axis=None),
         y=alt.Y("y:Q", axis=None),
-        size=alt.Size("size:Q", legend=None, scale=alt.Scale(range=[200, 800])),
+        size=alt.Size("size:Q", legend=None, scale=alt.Scale(range=[200, 900])),
         color=alt.Color(
             "community:N",
-            scale=alt.Scale(domain=["Engineering", "Marketing", "Sales"], range=community_colors),
-            legend=alt.Legend(title="Teams", titleFontSize=18, labelFontSize=16, symbolSize=400),
+            scale=alt.Scale(domain=community_names, range=OKABE_ITO),
+            legend=alt.Legend(title="Team", titleFontSize=18, labelFontSize=16, symbolSize=400),
         ),
-        tooltip=["community:N", "degree:Q"],
+        tooltip=[alt.Tooltip("community:N", title="Team"), alt.Tooltip("degree:Q", title="Connections")],
     )
 )
 
-# Label high-degree nodes (hubs)
-hub_df = node_df[node_df["degree"] >= 7].copy()
-hub_df["label"] = "Hub"
-
+# Hub labels
 hub_labels = (
     alt.Chart(hub_df)
-    .mark_text(fontSize=14, fontWeight="bold", color="#333333", dy=-15)
+    .mark_text(fontSize=15, fontWeight="bold", color=INK, dy=-22)
     .encode(x=alt.X("x:Q", axis=None), y=alt.Y("y:Q", axis=None), text="label:N")
 )
 
-# Combine all layers
 chart = (
     (edges_chart + nodes_chart + hub_labels)
-    .properties(width=1600, height=900, title=alt.Title("network-force-directed · altair · pyplots.ai", fontSize=28))
-    .configure_view(strokeWidth=0)
+    .properties(
+        width=1600,
+        height=900,
+        background=PAGE_BG,
+        title=alt.Title(
+            "network-force-directed · altair · anyplot.ai", fontSize=28, color=INK, anchor="start", offset=20
+        ),
+    )
+    .configure_view(fill=PAGE_BG, strokeWidth=0)
+    .configure_legend(
+        fillColor=ELEVATED_BG, strokeColor=INK_SOFT, labelColor=INK_SOFT, titleColor=INK, padding=12, cornerRadius=4
+    )
 )
 
-# Save as PNG (4800x2700 at scale_factor=3) and HTML
-chart.save("plot.png", scale_factor=3.0)
-chart.save("plot.html")
+chart.save(f"plot-{THEME}.png", scale_factor=3.0)
+chart.save(f"plot-{THEME}.html")