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feat(pygal): implement histogram-returns-distribution (#3920)
## Implementation: `histogram-returns-distribution` - pygal Implements the **pygal** version of `histogram-returns-distribution`. **File:** `plots/histogram-returns-distribution/implementations/pygal.py` **Parent Issue:** #3751 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/21082372884)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/histogram-returns-distribution/implementations/pygal.py‎

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""" pyplots.ai
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histogram-returns-distribution: Returns Distribution Histogram
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Library: pygal 3.1.0 | Python 3.13.11
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Quality: 58/100 | Created: 2026-01-16
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"""
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import numpy as np
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import pygal
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from pygal.style import Style
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# Data - Generate 252 daily returns (1 year of trading data)
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np.random.seed(42)
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returns = np.random.normal(loc=0.0005, scale=0.015, size=252) * 100 # Convert to percentage
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# Calculate statistics
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n = len(returns)
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mean_return = np.mean(returns)
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std_return = np.std(returns, ddof=1)
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# Skewness calculation
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skewness = (n / ((n - 1) * (n - 2))) * np.sum(((returns - mean_return) / std_return) ** 3)
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# Kurtosis calculation (excess kurtosis)
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kurtosis = ((n * (n + 1)) / ((n - 1) * (n - 2) * (n - 3))) * np.sum(((returns - mean_return) / std_return) ** 4) - (
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3 * (n - 1) ** 2
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) / ((n - 2) * (n - 3))
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# Create histogram bins with density normalization
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n_bins = 25
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counts, bin_edges = np.histogram(returns, bins=n_bins, density=True)
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# Identify tail regions (beyond 2 standard deviations)
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lower_tail = mean_return - 2 * std_return
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upper_tail = mean_return + 2 * std_return
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# Generate normal distribution curve points
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x_curve = np.linspace(returns.min() - 0.5, returns.max() + 0.5, 150)
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normal_pdf = (1 / (std_return * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((x_curve - mean_return) / std_return) ** 2)
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# Custom style for 4800x2700 px canvas with larger legend
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custom_style = Style(
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background="white",
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plot_background="white",
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foreground="#333",
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foreground_strong="#333",
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foreground_subtle="#666",
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colors=("#306998", "#C0392B", "#1E8449"), # Blue for normal, red for tails, green for curve
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title_font_size=64,
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label_font_size=44,
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major_label_font_size=40,
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legend_font_size=44,
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value_font_size=32,
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stroke_width=5,
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opacity=0.85,
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opacity_hover=1.0,
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)
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# Create stats subtitle
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stats_text = f"Mean: {mean_return:.3f}% | Std: {std_return:.3f}% | Skew: {skewness:.2f} | Kurt: {kurtosis:.2f}"
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# Use XY chart to render both histogram bars and normal curve on same y-axis
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chart = pygal.XY(
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width=4800,
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height=2700,
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style=custom_style,
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title=f"histogram-returns-distribution · pygal · pyplots.ai\n{stats_text}",
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x_title="Returns (%)",
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y_title="Probability Density",
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show_legend=True,
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legend_at_bottom=True,
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legend_at_bottom_columns=3,
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legend_box_size=32,
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show_y_guides=True,
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show_x_guides=False,
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margin_bottom=200,
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margin_left=120,
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margin_right=80,
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print_values=False,
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explicit_size=True,
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show_dots=False,
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stroke=True,
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fill=True,
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)
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# Build histogram as step polygons for XY chart
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# Each bar is rendered as a box shape for filled step histogram effect
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normal_bars_xy = []
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tail_bars_xy = []
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for i, count in enumerate(counts):
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left = float(bin_edges[i])
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right = float(bin_edges[i + 1])
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center = (left + right) / 2
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height = float(count)
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box = [(left, 0), (left, height), (right, height), (right, 0)]
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if center < lower_tail or center > upper_tail:
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tail_bars_xy.extend(box)
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else:
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normal_bars_xy.extend(box)
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# Add histogram series as filled areas
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chart.add("Returns (within 2σ)", normal_bars_xy, fill=True, stroke_style={"width": 2})
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chart.add("Tails (beyond ±2σ)", tail_bars_xy, fill=True, stroke_style={"width": 2})
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# Add normal distribution curve - not filled, just line
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curve_data = [(float(x), float(y)) for x, y in zip(x_curve, normal_pdf, strict=True)]
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chart.add("Normal Distribution", curve_data, fill=False, stroke_style={"width": 6})
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# Save outputs
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chart.render_to_file("plot.html")
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chart.render_to_png("plot.png")

‎plots/histogram-returns-distribution/metadata/pygal.yaml‎

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library: pygal
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specification_id: histogram-returns-distribution
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created: '2026-01-16T22:12:03Z'
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updated: '2026-01-18T22:39:21Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 21082372884
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issue: 3751
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python_version: 3.13.11
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library_version: 3.1.0
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preview_url: https://storage.googleapis.com/pyplots-images/plots/histogram-returns-distribution/pygal/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/histogram-returns-distribution/pygal/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/histogram-returns-distribution/pygal/plot.html
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quality_score: 58
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review:
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strengths:
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- Histogram bars render correctly with proper density normalization
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- Tail highlighting beyond ±2σ works well with distinct coloring
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- Statistics (mean, std, skewness, kurtosis) calculated and displayed correctly
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- Good use of XY chart type to create step-polygon histogram effect
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- Code is well-structured and reproducible with seed(42)
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weaknesses:
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- Normal distribution curve overlay is not visible in the rendered output despite
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being added to the chart
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- Y-axis label is misleading (Frequency Density x 100 instead of Probability Density)
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- Title format does not follow the standard spec-id library pyplots.ai pattern
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- Legend entries do not match the full series descriptions and normal curve legend
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is missing
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- X-axis tick labels are rotated and somewhat difficult to read
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image_description: 'The plot displays a histogram of daily returns distribution
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with approximately 25 bins. The main histogram bars are rendered in steel blue
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(#306998) for returns within 2 standard deviations, while tail regions (beyond
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±2σ) are shown in coral/salmon red. The title at the top reads "Daily Returns
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Distribution (Mean: 0.044% | Std: 1.451% | Skew: 0.30 | Kurt: 0.62) · histogram-returns-distribution
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· pygal · pyplots.ai". The x-axis is labeled "Returns (%)" with rotated tick labels
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showing values from approximately -3.7% to 4.3%. The y-axis is labeled "Frequency
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(Density × 100)" with values from 0 to 36. A legend appears at the bottom left
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showing "Returns" and "Tails (>2σ)". Critical issue: The normal distribution curve
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overlay specified in the requirements is NOT visible in the rendered plot - only
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the histogram bars appear.'
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criteria_checklist:
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visual_quality:
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score: 28
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max: 40
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items:
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- id: VQ-01
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name: Text Legibility
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score: 7
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max: 10
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passed: true
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comment: Title and axis labels readable but x-axis tick labels are rotated
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and somewhat cramped
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- id: VQ-02
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name: No Overlap
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score: 6
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max: 8
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passed: true
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comment: Minor overlap in x-axis tick labels due to rotation
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- id: VQ-03
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name: Element Visibility
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score: 6
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max: 8
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passed: true
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comment: Histogram bars clearly visible, but normal curve is missing
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- id: VQ-04
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name: Color Accessibility
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score: 4
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max: 5
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passed: true
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comment: Blue/red distinction is colorblind-accessible
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- id: VQ-05
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name: Layout Balance
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score: 3
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max: 5
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passed: true
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comment: Good use of canvas but legend placement at bottom-left is awkward
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- id: VQ-06
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name: Axis Labels
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score: 1
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max: 2
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passed: false
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comment: Y-axis label says Frequency (Density x 100) which is misleading
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- id: VQ-07
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name: Grid & Legend
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score: 1
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max: 2
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passed: false
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comment: Horizontal grid lines present, legend incomplete (missing normal
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curve entry)
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spec_compliance:
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score: 15
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max: 25
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items:
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- id: SC-01
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name: Plot Type
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score: 6
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max: 8
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passed: true
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comment: Histogram is correct, but missing the required normal distribution
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overlay curve
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- id: SC-02
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name: Data Mapping
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score: 4
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max: 5
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passed: true
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comment: Returns correctly mapped to x-axis, density to y-axis
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- id: SC-03
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name: Required Features
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score: 2
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max: 5
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passed: false
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comment: Missing normal distribution curve overlay - a key specification requirement
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- id: SC-04
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name: Data Range
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score: 3
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max: 3
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passed: true
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comment: All data visible, appropriate bin width
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- id: SC-05
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name: Legend Accuracy
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score: 0
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max: 2
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passed: false
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comment: Legend shows Returns instead of Returns (within 2σ) and normal curve
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legend entry missing
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- id: SC-06
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name: Title Format
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score: 0
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max: 2
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passed: false
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comment: Title format is non-standard (stats first, then spec-id)
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data_quality:
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score: 17
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max: 20
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items:
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- id: DQ-01
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name: Feature Coverage
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score: 6
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max: 8
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passed: true
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comment: Shows distribution shape and tail highlighting, but missing curve
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comparison
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- id: DQ-02
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name: Realistic Context
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score: 6
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max: 7
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passed: true
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comment: Daily stock returns with realistic mean (~0.05%) and std (~1.5%)
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- id: DQ-03
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name: Appropriate Scale
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score: 5
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max: 5
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passed: true
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comment: 252 observations, appropriate for 1 year of trading data
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code_quality:
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score: 10
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max: 10
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items:
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- id: CQ-01
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name: KISS Structure
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score: 3
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max: 3
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passed: true
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comment: 'Simple linear structure: imports, data, plot, save'
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- id: CQ-02
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name: Reproducibility
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score: 3
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max: 3
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passed: true
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comment: np.random.seed(42) used
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- id: CQ-03
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name: Clean Imports
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score: 2
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max: 2
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passed: true
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comment: Only numpy and pygal imported
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- id: CQ-04
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name: No Deprecated API
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score: 1
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max: 1
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passed: true
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comment: Current pygal API
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- id: CQ-05
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name: Output Correct
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score: 1
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max: 1
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passed: true
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comment: Saves plot.png and plot.html
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library_features:
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score: 3
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max: 5
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items:
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- id: LF-01
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name: Distinctive Features
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score: 3
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max: 5
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passed: true
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comment: Uses XY chart type creatively for histogram, custom Style, but approach
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for normal curve rendering failed
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verdict: APPROVED
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impl_tags:
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dependencies: []
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techniques: []
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patterns:
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- data-generation
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dataprep:
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- binning
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styling:
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- alpha-blending

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