feat(letsplot): implement box-horizontal (#2575)

## Implementation: `box-horizontal` - letsplot

Implements the **letsplot** version of `box-horizontal`.

**File:** `plots/box-horizontal/implementations/letsplot.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20593352038)*

---------

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

Author: github-actions[bot]

plots/box-horizontal/implementations/letsplot.py

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+""" pyplots.ai
+box-horizontal: Horizontal Box Plot
+Library: letsplot 4.8.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import *
+
+
+LetsPlot.setup_html()
+
+# Data - Response times by service type (realistic scenario with varied distributions)
+np.random.seed(42)
+
+services = ["API Gateway", "Database Query", "Authentication", "File Storage", "Cache Lookup", "Email Service"]
+
+data = []
+# Different distributions to showcase boxplot features (medians, spreads, outliers)
+distributions = {
+    "API Gateway": (120, 40, 3),  # Medium response, moderate spread, some outliers
+    "Database Query": (250, 100, 5),  # Slower, high variability, more outliers
+    "Authentication": (80, 25, 2),  # Fast, consistent
+    "File Storage": (300, 80, 4),  # Slowest, variable
+    "Cache Lookup": (15, 8, 2),  # Very fast, tight distribution
+    "Email Service": (180, 60, 6),  # Medium, some outliers
+}
+
+for service in services:
+    mean, std, n_outliers = distributions[service]
+    # Main distribution
+    values = np.random.normal(mean, std, 80)
+    # Add some outliers
+    outliers = np.random.normal(mean + 4 * std, std / 2, n_outliers)
+    all_values = np.concatenate([values, outliers])
+    all_values = np.maximum(all_values, 5)  # Ensure positive values
+
+    for val in all_values:
+        data.append({"Service": service, "Response Time (ms)": val})
+
+df = pd.DataFrame(data)
+
+# Create horizontal box plot
+plot = (
+    ggplot(df, aes(x="Response Time (ms)", y="Service"))
+    + geom_boxplot(
+        fill="#306998", color="#1a3a4f", alpha=0.7, outlier_color="#FFD43B", outlier_size=4, outlier_alpha=0.8, size=1.2
+    )
+    + labs(x="Response Time (ms)", y="Service Type", title="box-horizontal · letsplot · pyplots.ai")
+    + theme_minimal()
+    + theme(
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        axis_text_y=element_text(size=16),
+        plot_title=element_text(size=24),
+        panel_grid_major_y=element_blank(),
+        panel_grid_minor=element_blank(),
+    )
+    + ggsize(1600, 900)
+)
+
+# Save as PNG (scale 3x to get 4800 x 2700 px)
+ggsave(plot, "plot.png", path=".", scale=3)
+
+# Save interactive HTML version
+ggsave(plot, "plot.html", path=".")

plots/box-horizontal/metadata/letsplot.yaml

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+library: letsplot
+specification_id: box-horizontal
+created: '2025-12-30T09:31:53Z'
+updated: '2025-12-30T09:42:25Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20593352038
+issue: 0
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/box-horizontal/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/box-horizontal/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/box-horizontal/letsplot/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent data design with varied distributions showcasing boxplot features (different
+    medians, spreads, outliers)
+  - Clean horizontal orientation with readable service type labels
+  - Good color contrast between blue boxes and yellow outliers
+  - Realistic tech scenario with appropriate response time values
+  - Proper title format and axis labels with units
+  weaknesses:
+  - Grid lines could be more subtle (alpha reduced) for better visual balance
+  - Does not leverage lets-plot distinctive features like interactive tooltips or
+    hover information
+  - Minor empty space on right side of plot due to outlier distribution