feat(plotly): implement network-weighted

plots/network-weighted/implementations/plotly.py

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+""" pyplots.ai
+network-weighted: Weighted Network Graph with Edge Thickness
+Library: plotly 6.5.1 | Python 3.13.11
+Quality: 92/100 | Created: 2026-01-08
+"""
+
+import numpy as np
+import plotly.graph_objects as go
+
+
+# Data - Trade network between countries (billions USD)
+np.random.seed(42)
+
+# Define nodes (countries)
+countries = [
+    "USA",
+    "China",
+    "Germany",
+    "Japan",
+    "UK",
+    "France",
+    "Canada",
+    "Mexico",
+    "Brazil",
+    "India",
+    "Australia",
+    "S. Korea",
+    "Netherlands",
+    "Italy",
+    "Spain",
+]
+n_nodes = len(countries)
+node_idx = {name: i for i, name in enumerate(countries)}
+
+# Create weighted edges (trade relationships)
+edges = [
+    # Major trade routes (high weight)
+    ("USA", "China", 580),
+    ("USA", "Canada", 620),
+    ("USA", "Mexico", 550),
+    ("China", "Japan", 320),
+    ("China", "S. Korea", 280),
+    ("China", "Germany", 190),
+    ("Germany", "France", 180),
+    ("Germany", "Netherlands", 210),
+    ("Germany", "Italy", 140),
+    ("UK", "Germany", 130),
+    ("UK", "USA", 140),
+    ("UK", "Netherlands", 90),
+    ("Japan", "USA", 200),
+    ("Japan", "S. Korea", 85),
+    # Medium trade routes
+    ("France", "Italy", 95),
+    ("France", "Spain", 110),
+    ("Spain", "Italy", 50),
+    ("Canada", "Mexico", 40),
+    ("Brazil", "USA", 75),
+    ("Brazil", "China", 100),
+    ("India", "USA", 90),
+    ("India", "China", 115),
+    ("India", "UK", 35),
+    ("Australia", "China", 145),
+    ("Australia", "Japan", 55),
+    ("Australia", "S. Korea", 45),
+    # Lower trade routes
+    ("Netherlands", "UK", 65),
+    ("S. Korea", "USA", 120),
+    ("Mexico", "China", 70),
+]
+
+# Compute force-directed layout (Fruchterman-Reingold algorithm)
+pos = np.random.rand(n_nodes, 2) * 2 - 1
+k = 0.5
+for _ in range(200):
+    displacement = np.zeros((n_nodes, 2))
+    # Repulsive forces
+    for i in range(n_nodes):
+        diff = pos[i] - pos
+        dist = np.sqrt((diff**2).sum(axis=1))
+        dist = np.where(dist < 0.01, 0.01, dist)
+        rep_force = k**2 / dist
+        rep_force[i] = 0
+        displacement[i] += (diff * rep_force[:, np.newaxis]).sum(axis=0)
+    # Attractive forces along edges
+    for source, target, weight in edges:
+        i, j = node_idx[source], node_idx[target]
+        diff = pos[j] - pos[i]
+        dist = np.sqrt((diff**2).sum())
+        if dist > 0.01:
+            attr_force = dist**2 / k * (1 + weight / 200)
+            displacement[i] += diff / dist * attr_force
+            displacement[j] -= diff / dist * attr_force
+    # Update positions
+    length = np.sqrt((displacement**2).sum(axis=1))
+    length = np.where(length < 0.01, 0.01, length)
+    pos += displacement / length[:, np.newaxis] * min(0.1, k)
+
+# Normalize positions with margin for labels and annotation
+pos = (pos - pos.min(axis=0)) / (pos.max(axis=0) - pos.min(axis=0))
+pos = pos * 1.6 - 0.8  # Scale to [-0.8, 0.8]
+# Center the layout
+pos = pos - pos.mean(axis=0)
+node_positions = {countries[i]: pos[i] for i in range(n_nodes)}
+
+# Calculate weighted degree for node sizing
+weighted_degree = dict.fromkeys(countries, 0)
+for source, target, weight in edges:
+    weighted_degree[source] += weight
+    weighted_degree[target] += weight
+
+node_sizes = [20 + (weighted_degree[node] / 40) for node in countries]
+
+# Create edge traces with varying thickness
+edge_traces = []
+min_weight = min(w for _, _, w in edges)
+max_weight = max(w for _, _, w in edges)
+
+for source, target, weight in edges:
+    x0, y0 = node_positions[source]
+    x1, y1 = node_positions[target]
+    # Scale width: 2 to 18 based on weight
+    normalized = (weight - min_weight) / (max_weight - min_weight)
+    line_width = 2 + normalized * 16
+    # Color alpha based on weight (darker = stronger)
+    alpha = 0.4 + normalized * 0.5
+    edge_traces.append(
+        go.Scatter(
+            x=[x0, x1, None],
+            y=[y0, y1, None],
+            mode="lines",
+            line={"width": line_width, "color": f"rgba(48, 105, 152, {alpha})"},
+            hoverinfo="text",
+            text=f"{source} ↔ {target}: ${weight}B",
+            showlegend=False,
+        )
+    )
+
+# Create node trace
+node_x = [node_positions[node][0] for node in countries]
+node_y = [node_positions[node][1] for node in countries]
+
+# Calculate smart label positions to avoid overlap with explicit handling
+# for known problematic pairs: Japan/S.Korea and Italy/France
+label_positions = []
+
+for i, node in enumerate(countries):
+    x, y = node_positions[node]
+    # Find nearby nodes and adjust position
+    nearby_above = 0
+    nearby_below = 0
+    nearby_left = 0
+    nearby_right = 0
+    for j, other in enumerate(countries):
+        if i != j:
+            ox, oy = node_positions[other]
+            dx, dy = x - ox, y - oy
+            dist = np.sqrt(dx**2 + dy**2)
+            if dist < 0.35:
+                if dy > 0:
+                    nearby_below += 1
+                else:
+                    nearby_above += 1
+                if dx > 0:
+                    nearby_left += 1
+                else:
+                    nearby_right += 1
+
+    # Handle specific known close pairs to avoid overlap
+    if node == "Japan":
+        pos_choice = "top right"
+    elif node == "S. Korea":
+        pos_choice = "bottom left"
+    elif node == "Italy":
+        pos_choice = "top left"
+    elif node == "France":
+        pos_choice = "bottom right"
+    elif nearby_above > nearby_below:
+        pos_choice = "bottom center"
+    elif nearby_left > nearby_right:
+        pos_choice = "middle right"
+    elif nearby_right > nearby_left:
+        pos_choice = "middle left"
+    else:
+        pos_choice = "top center"
+    label_positions.append(pos_choice)
+
+node_trace = go.Scatter(
+    x=node_x,
+    y=node_y,
+    mode="markers+text",
+    marker={"size": node_sizes, "color": "#FFD43B", "line": {"width": 2, "color": "#306998"}},
+    text=countries,
+    textposition=label_positions,
+    textfont={"size": 16, "color": "#333333"},
+    hoverinfo="text",
+    hovertext=[f"{c}<br>Trade Volume: ${weighted_degree[c]}B" for c in countries],
+    showlegend=False,
+)
+
+# Create figure
+fig = go.Figure()
+
+# Add edges first (behind nodes)
+for trace in edge_traces:
+    fig.add_trace(trace)
+
+# Add nodes
+fig.add_trace(node_trace)
+
+# Add weight scale annotation (positioned at top-left to avoid cutoff)
+fig.add_annotation(
+    x=0.01,
+    y=0.99,
+    xref="paper",
+    yref="paper",
+    text="Edge Thickness = Trade Volume<br>35B USD (thin) to 620B USD (thick)",
+    showarrow=False,
+    font={"size": 18, "color": "#333333", "family": "Arial"},
+    align="left",
+    xanchor="left",
+    yanchor="top",
+    bgcolor="rgba(255,255,255,0.95)",
+    bordercolor="#999999",
+    borderwidth=1,
+    borderpad=10,
+)
+
+# Update layout
+fig.update_layout(
+    title={
+        "text": "network-weighted · plotly · pyplots.ai", "font": {"size": 28, "color": "#333333"}, "x": 0.5, "xanchor": "center"
+    },
+    xaxis={"showgrid": False, "zeroline": False, "showticklabels": False, "title": ""},
+    yaxis={"showgrid": False, "zeroline": False, "showticklabels": False, "title": ""},
+    template="plotly_white",
+    showlegend=False,
+    margin={"l": 80, "r": 80, "t": 100, "b": 80},
+    plot_bgcolor="white",
+)
+
+# Save outputs
+fig.write_image("plot.png", width=1600, height=900, scale=3)
+fig.write_html("plot.html")