feat(letsplot): implement histogram-cumulative (#2596)

## Implementation: `histogram-cumulative` - letsplot

Implements the **letsplot** version of `histogram-cumulative`.

**File:** `plots/histogram-cumulative/implementations/letsplot.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20593353906)*

---------

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/histogram-cumulative/implementations/letsplot.py

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+""" pyplots.ai
+histogram-cumulative: Cumulative Histogram
+Library: letsplot 4.8.2 | Python 3.13.11
+Quality: 99/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import *
+
+
+LetsPlot.setup_html()
+
+# Data - Response times in milliseconds (realistic API monitoring scenario)
+np.random.seed(42)
+response_times = np.concatenate(
+    [
+        np.random.exponential(scale=50, size=400),  # Normal requests
+        np.random.exponential(scale=150, size=80),  # Slower requests
+        np.random.uniform(300, 500, size=20),  # Occasional slow outliers
+    ]
+)
+
+# Compute cumulative histogram data
+n_bins = 25
+counts, bin_edges = np.histogram(response_times, bins=n_bins)
+cumulative_counts = np.cumsum(counts)
+
+df = pd.DataFrame({"xmin": bin_edges[:-1], "xmax": bin_edges[1:], "ymin": 0, "ymax": cumulative_counts})
+
+# Plot - Cumulative histogram using geom_rect for precise bin widths
+plot = (
+    ggplot(df)
+    + geom_rect(
+        aes(xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax"), fill="#306998", color="#1e4a6e", alpha=0.85, size=0.5
+    )
+    + labs(x="Response Time (ms)", y="Cumulative Count", title="histogram-cumulative · letsplot · pyplots.ai")
+    + scale_x_continuous(expand=[0.02, 0])
+    + scale_y_continuous(expand=[0, 0, 0.05, 0])
+    + theme_minimal()
+    + theme(
+        plot_title=element_text(size=24, face="bold"),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        panel_grid_major=element_line(color="#cccccc", size=0.3),
+        panel_grid_minor=element_blank(),
+    )
+    + ggsize(1600, 900)
+)
+
+# Save as PNG and HTML
+ggsave(plot, "plot.png", scale=3)
+ggsave(plot, "plot.html")

plots/histogram-cumulative/metadata/letsplot.yaml

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+library: letsplot
+specification_id: histogram-cumulative
+created: '2025-12-30T09:37:49Z'
+updated: '2025-12-30T09:47:30Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20593353906
+issue: 0
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/histogram-cumulative/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/histogram-cumulative/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/histogram-cumulative/letsplot/plot.html
+quality_score: 99
+review:
+  strengths:
+  - Excellent visual quality with proper text sizing and canvas utilization
+  - Correct implementation of cumulative histogram using geom_rect for precise bin
+    control
+  - Realistic API response time scenario with appropriate data distribution
+  - Clean KISS code structure with proper reproducibility seed
+  - Proper title format following spec guidelines
+  weaknesses:
+  - Could use geom_histogram with cumulative parameter if available, though geom_rect
+    approach is valid