feat(letsplot): implement histogram-stepwise (#2681)

## Implementation: `histogram-stepwise` - letsplot

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20595340386)*

---------

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-stepwise/implementations/letsplot.py

@@ -0,0 +1,64 @@
+""" pyplots.ai
+histogram-stepwise: Step Histogram
+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 *  # noqa: F403, F401
+
+
+LetsPlot.setup_html()
+
+# Data - Generate two distributions for comparison (temperature readings)
+np.random.seed(42)
+morning_temps = np.random.normal(loc=18, scale=4, size=500)  # Morning temperatures (°C)
+afternoon_temps = np.random.normal(loc=26, scale=5, size=500)  # Afternoon temperatures (°C)
+
+# Compute histogram bins manually for step representation
+bins = 30
+all_data = np.concatenate([morning_temps, afternoon_temps])
+bin_edges = np.linspace(all_data.min(), all_data.max(), bins + 1)
+
+# Calculate histogram counts for each distribution
+morning_counts, _ = np.histogram(morning_temps, bins=bin_edges)
+afternoon_counts, _ = np.histogram(afternoon_temps, bins=bin_edges)
+
+# Create step data: duplicate each point for step appearance
+# For step histogram, we need x values at bin edges and corresponding y values
+step_data = []
+for counts, label in [(morning_counts, "Morning"), (afternoon_counts, "Afternoon")]:
+    for i in range(len(counts)):
+        # Left edge of bin at count level
+        step_data.append({"x": bin_edges[i], "y": counts[i], "period": label})
+        # Right edge of bin at count level
+        step_data.append({"x": bin_edges[i + 1], "y": counts[i], "period": label})
+
+df_step = pd.DataFrame(step_data)
+
+# Plot - Step histogram using geom_line (outline only, no fill)
+plot = (
+    ggplot(df_step, aes(x="x", y="y", color="period"))
+    + geom_line(size=2.5)
+    + scale_color_manual(values=["#306998", "#FFD43B"])
+    + labs(x="Temperature (°C)", y="Frequency", title="histogram-stepwise · letsplot · pyplots.ai", color="Time Period")
+    + theme_minimal()
+    + theme(
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        plot_title=element_text(size=24),
+        legend_title=element_text(size=18),
+        legend_text=element_text(size=16),
+        legend_position="right",
+        panel_grid_major=element_line(color="#CCCCCC", size=0.5),
+        panel_grid_minor=element_blank(),
+    )
+    + ggsize(1600, 900)
+)
+
+# Save PNG (scale=3 gives 4800x2700)
+ggsave(plot, "plot.png", path=".", scale=3)
+
+# Save HTML for interactivity
+ggsave(plot, "plot.html", path=".")

plots/histogram-stepwise/metadata/letsplot.yaml

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+library: letsplot
+specification_id: histogram-stepwise
+created: '2025-12-30T11:24:54Z'
+updated: '2025-12-30T11:36:25Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20595340386
+issue: 0
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/histogram-stepwise/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/histogram-stepwise/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/histogram-stepwise/letsplot/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent implementation of step histogram concept using manual bin calculation
+    and geom_line
+  - Two overlaid distributions clearly demonstrate the key use case of comparing distributions
+    without visual overlap
+  - Strong color contrast between blue and yellow that is colorblind-accessible
+  - Proper use of ggplot grammar with clean, readable code structure
+  - Realistic temperature comparison scenario that makes immediate sense
+  - Text sizing and layout are publication-quality
+  weaknesses:
+  - Could leverage lets-plot interactive tooltip features for the HTML output
+  - Legend title is slightly generic compared to a more descriptive label