feat(plotnine): implement histogram-stepwise (#2660)

## Implementation: `histogram-stepwise` - plotnine

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20595336206)*

---------

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

Author: github-actions[bot]

plots/histogram-stepwise/implementations/plotnine.py

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+""" pyplots.ai
+histogram-stepwise: Step Histogram
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import aes, element_line, element_text, geom_step, ggplot, labs, scale_color_manual, theme, theme_minimal
+
+
+# Data - Response times (ms) for two server configurations
+np.random.seed(42)
+
+# Configuration A: Standard server setup
+config_a = np.random.normal(loc=250, scale=60, size=400)
+
+# Configuration B: Optimized server setup (faster, tighter distribution)
+config_b = np.random.normal(loc=180, scale=40, size=400)
+
+# Calculate histograms with shared bins for comparison
+n_bins = 30
+all_data = np.concatenate([config_a, config_b])
+bin_edges = np.linspace(all_data.min() - 10, all_data.max() + 10, n_bins + 1)
+
+# Compute histogram counts for each configuration
+counts_a, _ = np.histogram(config_a, bins=bin_edges)
+counts_b, _ = np.histogram(config_b, bins=bin_edges)
+
+# Create step data: prepend zero at start, append zero at end for clean closure
+# For step histogram: use bin edges (not centers) for x values
+step_x_a = np.repeat(bin_edges, 2)[1:-1]
+step_y_a = np.repeat(counts_a, 2)
+
+step_x_b = np.repeat(bin_edges, 2)[1:-1]
+step_y_b = np.repeat(counts_b, 2)
+
+# Create DataFrames
+df_a = pd.DataFrame({"x": step_x_a, "count": step_y_a, "config": "Standard Setup"})
+df_b = pd.DataFrame({"x": step_x_b, "count": step_y_b, "config": "Optimized Setup"})
+df = pd.concat([df_a, df_b], ignore_index=True)
+
+# Plot
+plot = (
+    ggplot(df, aes(x="x", y="count", color="config"))
+    + geom_step(size=2, alpha=0.9)
+    + labs(
+        x="Response Time (ms)", y="Frequency", title="histogram-stepwise · plotnine · pyplots.ai", color="Configuration"
+    )
+    + scale_color_manual(values=["#306998", "#FFD43B"])
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 9),
+        text=element_text(size=14),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        plot_title=element_text(size=24),
+        legend_text=element_text(size=16),
+        legend_title=element_text(size=18),
+        panel_grid_major=element_line(color="#cccccc", size=0.5, alpha=0.3),
+        panel_grid_minor=element_line(alpha=0.2),
+    )
+)
+
+# Save
+plot.save("plot.png", dpi=300, verbose=False)

plots/histogram-stepwise/metadata/plotnine.yaml

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+library: plotnine
+specification_id: histogram-stepwise
+created: '2025-12-30T11:22:17Z'
+updated: '2025-12-30T11:29:32Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20595336206
+issue: 0
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/histogram-stepwise/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/histogram-stepwise/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent implementation of step histogram using geom_step with manually computed
+    bin edges
+  - Perfect demonstration of overlaying multiple distributions for comparison (key
+    application from spec)
+  - Clean, readable code following KISS principles
+  - Proper figure sizing (16x9) and text scaling for 4800x2700 output
+  - Good color contrast between the two distributions
+  - Realistic and neutral data scenario (server performance comparison)
+  weaknesses:
+  - Legend placement inside the plot area could potentially overlap with data in some
+    cases; consider placing outside
+  - Manual histogram computation instead of exploring plotnine native histogram capabilities