feat(altair): implement violin-box (#2662)

## Implementation: `violin-box` - altair

Implements the **altair** version of `violin-box`.

**File:** `plots/violin-box/implementations/altair.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20595338513)*

---------

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

Author: github-actions[bot]

plots/violin-box/implementations/altair.py

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+""" pyplots.ai
+violin-box: Violin Plot with Embedded Box Plot
+Library: altair 6.0.0 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import altair as alt
+import numpy as np
+import pandas as pd
+
+
+# Data - Generate realistic response time data for different server tiers
+np.random.seed(42)
+
+groups = ["Basic", "Standard", "Premium", "Enterprise"]
+n_per_group = 80
+
+data = []
+# Basic tier - higher latency, more variance
+data.extend([(np.random.exponential(120) + 80, "Basic") for _ in range(n_per_group)])
+# Standard tier - moderate latency
+data.extend([(np.random.normal(100, 25), "Standard") for _ in range(n_per_group)])
+# Premium tier - lower latency, tighter distribution
+data.extend([(np.random.normal(60, 15), "Premium") for _ in range(n_per_group)])
+# Enterprise tier - lowest latency, bimodal (some cached, some not)
+enterprise_cached = np.random.normal(25, 8, n_per_group // 2)
+enterprise_uncached = np.random.normal(55, 12, n_per_group // 2)
+data.extend([(v, "Enterprise") for v in np.concatenate([enterprise_cached, enterprise_uncached])])
+
+df = pd.DataFrame(data, columns=["Response Time (ms)", "Server Tier"])
+# Ensure positive values for response times
+df["Response Time (ms)"] = df["Response Time (ms)"].clip(lower=5)
+
+# Create violin plot layer using transform_density
+violin = (
+    alt.Chart(df)
+    .transform_density("Response Time (ms)", as_=["Response Time (ms)", "density"], groupby=["Server Tier"])
+    .mark_area(orient="horizontal", opacity=0.6)
+    .encode(
+        y=alt.Y("Response Time (ms):Q"),
+        x=alt.X(
+            "density:Q",
+            stack="center",
+            impute=None,
+            title=None,
+            axis=alt.Axis(labels=False, values=[0], grid=False, ticks=False),
+        ),
+        color=alt.Color(
+            "Server Tier:N",
+            scale=alt.Scale(
+                domain=["Basic", "Standard", "Premium", "Enterprise"],
+                range=["#306998", "#FFD43B", "#4B8BBE", "#8BC34A"],
+            ),
+        ),
+    )
+)
+
+# Create box plot layer
+boxplot = (
+    alt.Chart(df)
+    .mark_boxplot(
+        extent="min-max",
+        size=25,
+        median={"stroke": "white", "strokeWidth": 2},
+        box={"fill": "#333333", "fillOpacity": 0.7},
+        outliers={"size": 60, "strokeWidth": 2},
+    )
+    .encode(y=alt.Y("Response Time (ms):Q", title="Response Time (ms)"), x=alt.value(0), color=alt.value("#333333"))
+)
+
+# Layer violin and box plots first, then facet
+layered = alt.layer(violin, boxplot).properties(width=280, height=600)
+
+# Apply faceting after layering
+chart = (
+    layered.facet(
+        column=alt.Column(
+            "Server Tier:N",
+            header=alt.Header(titleFontSize=20, labelFontSize=18, labelOrient="bottom"),
+            title=None,
+            sort=["Basic", "Standard", "Premium", "Enterprise"],
+        )
+    )
+    .properties(title=alt.Title("violin-box · altair · pyplots.ai", fontSize=28, anchor="middle", offset=20))
+    .configure_axis(labelFontSize=16, titleFontSize=20, gridOpacity=0.3)
+    .configure_view(stroke=None)
+    .configure_legend(titleFontSize=18, labelFontSize=16, symbolSize=200, orient="right")
+    .resolve_scale(x="independent")
+)
+
+# Save outputs
+chart.save("plot.png", scale_factor=3.0)
+chart.save("plot.html")

plots/violin-box/metadata/altair.yaml

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+library: altair
+specification_id: violin-box
+created: '2025-12-30T11:22:21Z'
+updated: '2025-12-30T11:29:59Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20595338513
+issue: 0
+python_version: 3.13.11
+library_version: 6.0.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/violin-box/altair/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/violin-box/altair/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/violin-box/altair/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent use of Altair declarative layering to combine violin (mark_area with
+    transform_density) and box plot layers
+  - Realistic and varied data showing different distribution shapes (exponential,
+    normal, tight normal, bimodal)
+  - Clean faceted layout with independent x-scales for proper violin centering
+  - Good color palette that is colorblind-accessible
+  - Proper title format and clear axis labeling with units
+  weaknesses:
+  - Box plot median line could have higher contrast against the gray box for better
+    visibility
+  - Legend placement in top-right leaves some empty space; could be integrated more
+    tightly