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feat(pygal): implement forest-basic (#2417)
## Implementation: `forest-basic` - pygal Implements the **pygal** version of `forest-basic`. **File:** `plots/forest-basic/implementations/pygal.py` --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20543283240)* --------- 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>
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‎plots/forest-basic/implementations/pygal.py‎

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""" pyplots.ai
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forest-basic: Meta-Analysis Forest Plot
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Library: pygal 3.1.0 | Python 3.13.11
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Quality: 75/100 | Created: 2025-12-27
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"""
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import pygal
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from pygal.style import Style
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# Data: Meta-analysis of treatment effect (mean difference)
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# Format: (study_name, effect_size, ci_lower, ci_upper, weight)
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studies = [
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("Anderson 2023", -0.44, -0.82, -0.06, 12.8),
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("Taylor 2022", -0.61, -1.08, -0.14, 7.6),
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("Moore 2022", -0.38, -0.71, -0.05, 13.8),
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("Wilson 2021", -0.55, -0.98, -0.12, 9.1),
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("Miller 2021", -0.29, -0.65, 0.07, 11.7),
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("Davis 2020", -0.41, -0.78, -0.04, 14.2),
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("Brown 2020", -0.67, -1.15, -0.19, 10.3),
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("Williams 2019", -0.18, -0.58, 0.22, 9.8),
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("Johnson 2019", -0.52, -0.95, -0.09, 12.5),
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("Smith 2018", -0.35, -0.72, 0.02, 8.2),
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]
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# Pooled estimate (diamond in traditional forest plots)
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pooled_effect = -0.43
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pooled_ci_lower = -0.58
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pooled_ci_upper = -0.28
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# Inline weight normalization (min=7.6, max=14.2, range=6.6)
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min_weight = 7.6
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max_weight = 14.2
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weight_range = 6.6
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# Custom style for large canvas (4800 x 2700)
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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="#333333",
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foreground_strong="#333333",
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foreground_subtle="#666666",
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colors=(
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"#306998", # Study markers (Python Blue)
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"#FFD43B", # Pooled diamond (Yellow for contrast)
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),
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title_font_size=56,
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label_font_size=32,
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major_label_font_size=28,
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legend_font_size=28,
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value_font_size=24,
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tooltip_font_size=28,
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stroke_width=6,
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font_family="Arial",
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)
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# Create XY chart for forest plot
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chart = pygal.XY(
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width=4800,
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height=2700,
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title="forest-basic · pygal · pyplots.ai",
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x_title="Mean Difference (95% CI)",
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style=custom_style,
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show_legend=False,
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dots_size=16,
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stroke=False,
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show_y_guides=False,
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show_x_guides=True,
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x_label_rotation=0,
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range=(-1.3, 0.4),
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margin=100,
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)
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# Add vertical reference line at x=0 (null effect) first (background layer)
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chart.add(
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None,
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[(0, -0.5), (0, len(studies) + 0.5)],
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stroke=True,
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show_dots=False,
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stroke_style={"width": 3, "dasharray": "12, 6"},
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)
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# Add CI whiskers (thicker lines for better visibility at this canvas size)
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for i, (_study, _effect, ci_low, ci_high, _weight) in enumerate(studies):
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y_pos = len(studies) - i
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chart.add(None, [(ci_low, y_pos), (ci_high, y_pos)], stroke=True, show_dots=False, stroke_style={"width": 6})
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# Add each study point with weight-proportional size (inline calculation)
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for i, (_study, effect, _ci_low, _ci_high, weight) in enumerate(studies):
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y_pos = len(studies) - i
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dot_size = int(12 + ((weight - min_weight) / weight_range) * 16)
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chart.add(None, [(effect, y_pos)], dots_size=dot_size, stroke=False)
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# Add pooled CI whisker (thicker for emphasis)
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chart.add(None, [(pooled_ci_lower, 0), (pooled_ci_upper, 0)], stroke=True, show_dots=False, stroke_style={"width": 7})
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# Add pooled estimate as a diamond shape using filled polygon
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# Draw diamond with 4 lines forming a closed shape (traditional forest plot diamond)
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diamond_half_height = 0.4
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# Top-left edge
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chart.add(
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None,
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[(pooled_ci_lower, 0), (pooled_effect, diamond_half_height)],
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stroke=True,
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show_dots=False,
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stroke_style={"width": 4},
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)
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# Top-right edge
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chart.add(
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None,
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[(pooled_effect, diamond_half_height), (pooled_ci_upper, 0)],
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stroke=True,
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show_dots=False,
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stroke_style={"width": 4},
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)
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# Bottom-right edge
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chart.add(
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None,
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[(pooled_ci_upper, 0), (pooled_effect, -diamond_half_height)],
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stroke=True,
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show_dots=False,
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stroke_style={"width": 4},
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)
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# Bottom-left edge
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chart.add(
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None,
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[(pooled_effect, -diamond_half_height), (pooled_ci_lower, 0)],
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stroke=True,
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show_dots=False,
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stroke_style={"width": 4},
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)
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# Add center point for the diamond (filled dot at center for visual emphasis)
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chart.add(None, [(pooled_effect, 0)], dots_size=28, stroke=False)
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# Y-axis labels with study names and CIs
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y_labels = []
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for i, (study, _effect, ci_low, ci_high, _weight) in enumerate(studies):
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y_labels.append({"value": len(studies) - i, "label": f"{study} [{ci_low:.2f}, {ci_high:.2f}]"})
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y_labels.append({"value": 0, "label": f"Pooled [{pooled_ci_lower:.2f}, {pooled_ci_upper:.2f}]"})
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chart.y_labels = y_labels
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# Save outputs
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chart.render_to_png("plot.png")
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chart.render_to_file("plot.html")

‎plots/forest-basic/metadata/pygal.yaml‎

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library: pygal
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specification_id: forest-basic
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created: '2025-12-27T19:25:07Z'
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updated: '2025-12-27T19:52:15Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20543283240
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issue: 0
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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/forest-basic/pygal/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/forest-basic/pygal/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/forest-basic/pygal/plot.html
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quality_score: 75
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review:
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strengths:
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- Clean data representation with study names and CI bounds displayed on Y-axis labels
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- Good use of color contrast between study markers (blue) and pooled estimate (yellow)
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- Proper null reference line at x=0 as dashed vertical line
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- Weight-proportional marker sizing implemented correctly
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- Appropriate font sizes for the 4800x2700 canvas
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weaknesses:
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- Pooled estimate diamond is rendered as a circle rather than the traditional diamond
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shape specified in the requirements
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- CI whiskers extend beyond the visible plot area on the right side, creating visual
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artifacts
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- Legend at bottom is redundant and cluttered; forest plots typically do not need
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a legend as the visual encoding is standard
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- The diamond outline attempt using 4 line segments is not visible/effective

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