feat(plotly): implement contour-density (#2567)

## Implementation: `contour-density` - plotly

Implements the **plotly** version of `contour-density`.

**File:** `plots/contour-density/implementations/plotly.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20593351187)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/contour-density/implementations/plotly.py

@@ -0,0 +1,81 @@
+""" pyplots.ai
+contour-density: Density Contour Plot
+Library: plotly 6.5.0 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import plotly.graph_objects as go
+
+
+# Data - bivariate distribution with multiple clusters
+np.random.seed(42)
+
+# Create three clusters to show density variations
+n_points = 500
+cluster1_x = np.random.normal(2, 0.8, n_points // 3)
+cluster1_y = np.random.normal(3, 0.6, n_points // 3)
+
+cluster2_x = np.random.normal(5, 1.0, n_points // 3)
+cluster2_y = np.random.normal(5, 0.8, n_points // 3)
+
+cluster3_x = np.random.normal(7, 0.5, n_points // 3)
+cluster3_y = np.random.normal(2.5, 0.7, n_points // 3)
+
+x = np.concatenate([cluster1_x, cluster2_x, cluster3_x])
+y = np.concatenate([cluster1_y, cluster2_y, cluster3_y])
+
+# Create figure with density contour
+fig = go.Figure()
+
+# Add density contour plot
+fig.add_trace(
+    go.Histogram2dContour(
+        x=x,
+        y=y,
+        colorscale=[[0, "rgba(255,255,255,0)"], [0.2, "#FFD43B"], [0.5, "#306998"], [1, "#1a3d5c"]],
+        contours=dict(showlabels=False, coloring="fill"),
+        ncontours=12,
+        showscale=True,
+        colorbar=dict(title=dict(text="Density", font=dict(size=20)), tickfont=dict(size=16), len=0.8),
+        line=dict(width=2, color="#306998"),
+    )
+)
+
+# Add scatter points for context (semi-transparent)
+fig.add_trace(
+    go.Scatter(
+        x=x,
+        y=y,
+        mode="markers",
+        marker=dict(size=8, color="#306998", opacity=0.3, line=dict(width=0)),
+        showlegend=False,
+    )
+)
+
+# Update layout
+fig.update_layout(
+    title=dict(text="contour-density · plotly · pyplots.ai", font=dict(size=32), x=0.5, xanchor="center"),
+    xaxis=dict(
+        title=dict(text="X Variable", font=dict(size=24)),
+        tickfont=dict(size=18),
+        showgrid=True,
+        gridcolor="rgba(0,0,0,0.1)",
+        gridwidth=1,
+    ),
+    yaxis=dict(
+        title=dict(text="Y Variable", font=dict(size=24)),
+        tickfont=dict(size=18),
+        showgrid=True,
+        gridcolor="rgba(0,0,0,0.1)",
+        gridwidth=1,
+    ),
+    template="plotly_white",
+    width=1600,
+    height=900,
+    margin=dict(l=100, r=120, t=100, b=100),
+)
+
+# Save as PNG and HTML
+fig.write_image("plot.png", width=1600, height=900, scale=3)
+fig.write_html("plot.html", include_plotlyjs="cdn")

plots/contour-density/metadata/plotly.yaml

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+library: plotly
+specification_id: contour-density
+created: '2025-12-30T09:31:21Z'
+updated: '2025-12-30T09:39:11Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20593351187
+issue: 0
+python_version: 3.13.11
+library_version: 6.5.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/contour-density/plotly/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/contour-density/plotly/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/contour-density/plotly/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent use of filled contours with custom colorscale transitioning from transparent
+    white to yellow to blue
+  - Smart combination of density contours with scatter point overlay provides context
+    as recommended in spec
+  - Three distinct clusters effectively demonstrate the plot purpose of finding density
+    patterns
+  - Clean code structure following KISS principles
+  - Proper font sizing for 4800x2700 output (scale=3)
+  weaknesses:
+  - Axis labels are generic (X Variable, Y Variable) rather than tied to a realistic
+    context
+  - Grid lines are too subtle (alpha 0.1) - consider alpha 0.2-0.3 for better visibility