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feat(pygal): implement scatter-lag (#5273)
## Implementation: `scatter-lag` - pygal Implements the **pygal** version of `scatter-lag`. **File:** `plots/scatter-lag/implementations/pygal.py` **Parent Issue:** #5251 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/24313009875)* --------- 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/scatter-lag/implementations/pygal.py‎

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""" pyplots.ai
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scatter-lag: Lag Plot for Time Series Autocorrelation Diagnosis
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Library: pygal 3.1.0 | Python 3.14.3
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Quality: 87/100 | Created: 2026-04-12
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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 — synthetic AR(1) process with moderate positive autocorrelation
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np.random.seed(42)
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n = 400
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phi = 0.78
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noise = np.random.normal(0, 1.0, n)
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temperature = np.zeros(n)
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temperature[0] = 20.0
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for i in range(1, n):
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temperature[i] = 20.0 + phi * (temperature[i - 1] - 20.0) + noise[i]
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lag = 1
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y_t = temperature[:-lag]
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y_t_lag = temperature[lag:]
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# Temporal quartile masks
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time_idx = np.arange(len(y_t))
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q_bounds = np.percentile(time_idx, [25, 50, 75])
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# Build scatter series with interactive tooltips (pygal dict format)
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early = [
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{"value": (float(y_t[i]), float(y_t_lag[i])), "label": f"Day {i + 1}"}
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for i in range(len(y_t))
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if time_idx[i] < q_bounds[0]
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]
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mid_early = [
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{"value": (float(y_t[i]), float(y_t_lag[i])), "label": f"Day {i + 1}"}
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for i in range(len(y_t))
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if q_bounds[0] <= time_idx[i] < q_bounds[1]
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]
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mid_late = [
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{"value": (float(y_t[i]), float(y_t_lag[i])), "label": f"Day {i + 1}"}
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for i in range(len(y_t))
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if q_bounds[1] <= time_idx[i] < q_bounds[2]
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]
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late = [
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{"value": (float(y_t[i]), float(y_t_lag[i])), "label": f"Day {i + 1}"}
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for i in range(len(y_t))
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if time_idx[i] >= q_bounds[2]
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]
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# Correlation coefficient
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r = np.corrcoef(y_t, y_t_lag)[0, 1]
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# Reference geometry
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data_min = float(min(y_t.min(), y_t_lag.min()))
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data_max = float(max(y_t.max(), y_t_lag.max()))
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pad = (data_max - data_min) * 0.05
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ref_start = data_min - pad
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ref_end = data_max + pad
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ref_line = [(ref_start, ref_start), (ref_end, ref_end)]
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# ±1σ envelope around y=x to visualise autocorrelation spread
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sigma = float(np.std(y_t_lag - y_t))
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upper_env = [(ref_start, ref_start + sigma), (ref_end, ref_end + sigma)]
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lower_env = [(ref_start, ref_start - sigma), (ref_end, ref_end - sigma)]
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# Warm-to-cool temporal palette: terracotta → amber → teal → navy
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font = "DejaVu Sans, Helvetica, Arial, sans-serif"
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custom_style = Style(
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background="white",
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plot_background="#f8f7f5",
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foreground="#2a2a2a",
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foreground_strong="#1a1a1a",
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foreground_subtle="#d5d5d3",
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guide_stroke_color="#e0dfdd",
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colors=(
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"#c25a3c", # Q1 — terracotta
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"#d4a028", # Q2 — warm amber
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"#2a9d8f", # Q3 — teal
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"#264653", # Q4 — deep navy
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"#c0bebb", # +1σ envelope
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"#c0bebb", # −1σ envelope
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"#888886", # y = x reference
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),
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font_family=font,
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title_font_family=font,
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title_font_size=56,
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label_font_size=42,
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major_label_font_size=38,
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legend_font_size=32,
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legend_font_family=font,
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value_font_size=28,
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tooltip_font_size=28,
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tooltip_font_family=font,
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opacity=0.60,
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opacity_hover=0.95,
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stroke_opacity=0.7,
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stroke_opacity_hover=1,
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)
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# Chart — interactivity enabled for SVG hover tooltips
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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"Lag Plot (k={lag}, r={r:.2f}) \u00b7 scatter-lag \u00b7 pygal \u00b7 pyplots.ai",
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x_title="y(t)",
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y_title=f"y(t+{lag})",
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show_legend=True,
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legend_at_bottom=True,
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legend_at_bottom_columns=5,
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legend_box_size=24,
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stroke=False,
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dots_size=8,
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show_x_guides=True,
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show_y_guides=True,
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x_value_formatter=lambda x: f"{x:.1f}",
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value_formatter=lambda y: f"{y:.1f}",
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margin_bottom=100,
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margin_left=80,
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margin_right=30,
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margin_top=40,
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range=(ref_start, ref_end),
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xrange=(ref_start, ref_end),
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x_labels_major_count=8,
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y_labels_major_count=8,
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print_values=False,
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print_zeroes=False,
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truncate_legend=40,
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)
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# Temporal quartile scatter series
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chart.add("Days 1\u2013100", early, stroke=False, dots_size=9)
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chart.add("Days 101\u2013200", mid_early, stroke=False)
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chart.add("Days 201\u2013300", mid_late, stroke=False)
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chart.add("Days 301\u2013399", late, stroke=False, dots_size=9)
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# ±1σ envelope (no legend entry)
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env_style = {"width": 3, "dasharray": "6, 8", "linecap": "round"}
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chart.add(None, upper_env, stroke=True, show_dots=False, stroke_style=env_style)
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chart.add(None, lower_env, stroke=True, show_dots=False, stroke_style=env_style)
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# Diagonal reference line y = x
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chart.add(
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"y = x (\u00b11\u03c3)",
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ref_line,
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stroke=True,
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show_dots=False,
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stroke_style={"width": 6, "dasharray": "24, 12", "linecap": "round"},
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)
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# Save
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chart.render_to_png("plot.png")
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chart.render_to_file("plot.html")

‎plots/scatter-lag/metadata/pygal.yaml‎

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library: pygal
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specification_id: scatter-lag
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created: '2026-04-12T18:12:36Z'
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updated: '2026-04-12T18:32:40Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 24313009875
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issue: 5251
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python_version: 3.14.3
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library_version: 3.1.0
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preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-lag/pygal/plot.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-lag/pygal/plot.html
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quality_score: 87
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review:
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strengths:
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- Intentional warm-to-cool temporal palette (terracotta → amber → teal → navy) creates
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a compelling visual narrative of time progression within the autocorrelation structure
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- All required spec features fully implemented plus value-adding ±1σ envelope around
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the diagonal reference line
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- Comprehensive explicit font sizing throughout; perfect spec compliance (15/15)
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and code quality (10/10)
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weaknesses:
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- dots_size=8–9 causes overplotting for 400 data points; smaller dots (5–6) with
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lower opacity (~0.45) would reduce central cluster overlap
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- Terracotta (#c25a3c) and amber (#d4a028) risk confusion for deuteranopic (red-green
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colorblind) viewers; palette shift needed for full accessibility
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image_description: 'The plot shows a lag scatter plot on a light beige (#f8f7f5)
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background. The title reads "Lag Plot (k=1, r=0.75) · scatter-lag · pygal · pyplots.ai"
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centered at the top. The X-axis is labeled "y(t)" and the Y-axis "y(t+1)". Four
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temporal quartile series are plotted: Days 1–100 in terracotta (brick-red), Days
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101–200 in amber/golden-yellow, Days 201–300 in teal/seafoam, and Days 301–399
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in deep navy. A dashed gray diagonal reference line (y = x ±1σ) runs from lower-left
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to upper-right. Subtle dotted grid lines are visible across the plot. The legend
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sits at the bottom with five labeled entries. A clear positive linear autocorrelation
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pattern is evident — data points cluster diagonally, confirming the AR(1) process
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(r=0.75).'
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criteria_checklist:
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visual_quality:
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score: 25
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max: 30
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items:
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- id: VQ-01
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name: Text Legibility
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score: 8
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max: 8
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passed: true
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comment: 'All font sizes explicitly set: title=56, labels=42, major=38, legend=32,
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value=28; fully readable at 4800x2700 px'
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- id: VQ-02
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name: No Overlap
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score: 5
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max: 6
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passed: true
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comment: Text elements clear; minor dot overlap in dense central cluster expected
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for 400 points
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- id: VQ-03
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name: Element Visibility
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score: 4
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max: 6
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passed: false
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comment: dots_size=8-9 for 400 points causes noticeable overplotting; guidelines
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suggest size 20-50 with alpha 0.3-0.5 for 300+ points
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- id: VQ-04
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name: Color Accessibility
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score: 3
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max: 4
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passed: false
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comment: Terracotta (#c25a3c) and amber (#d4a028) may be confused by deuteranopic
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users; rest of palette is good
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- id: VQ-05
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name: Layout & Canvas
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score: 3
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max: 4
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passed: true
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comment: Good proportions; margins tuned; legend well-anchored at bottom
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- id: VQ-06
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name: Axis Labels & Title
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score: 2
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max: 2
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passed: true
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comment: y(t) / y(t+1) are standard lag-plot notation; title is descriptive
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and complete
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design_excellence:
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score: 15
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max: 20
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items:
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- id: DE-01
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name: Aesthetic Sophistication
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score: 6
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max: 8
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passed: true
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comment: Intentional warm-to-cool temporal palette with custom beige background;
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clearly above defaults, but not quite publication-ready
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- id: DE-02
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name: Visual Refinement
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score: 4
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max: 6
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passed: true
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comment: Subtle guide_stroke_color, beige plot background, legend columns,
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margin tuning; limited further by pygal API (no spine removal)
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- id: DE-03
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name: Data Storytelling
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score: 5
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max: 6
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passed: true
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comment: Temporal color gradient is an effective storytelling device; correlation
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coefficient in title; ±1σ envelope contextualises autocorrelation spread
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spec_compliance:
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score: 15
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max: 15
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items:
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- id: SC-01
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name: Plot Type
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score: 5
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max: 5
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passed: true
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comment: Correct XY scatter for lag plot
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- id: SC-02
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name: Required Features
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score: 4
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max: 4
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passed: true
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comment: Diagonal y=x reference line, temporal point coloring, r-value annotation
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in title, configurable lag; bonus ±1σ envelope
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- id: SC-03
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name: Data Mapping
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score: 3
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max: 3
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passed: true
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comment: y(t) on X, y(t+lag) on Y, all data visible
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- id: SC-04
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name: Title & Legend
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score: 3
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max: 3
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passed: true
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comment: Title includes spec-id, library, pyplots.ai, plus informative k and
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r values; legend labels match temporal quartiles
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data_quality:
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score: 14
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max: 15
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items:
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- id: DQ-01
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name: Feature Coverage
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score: 5
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max: 6
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passed: true
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comment: Clear positive autocorrelation pattern demonstrated; temporal coloring
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reveals consistent structure; could include stronger/weaker autocorrelation
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variation
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- id: DQ-02
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name: Realistic Context
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score: 5
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max: 5
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passed: true
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comment: Daily temperature readings with AR(1) mean reversion (mu=20C) is
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natural and neutral
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- id: DQ-03
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name: Appropriate Scale
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score: 4
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max: 4
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passed: true
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comment: Values 16-25C and phi=0.78 are realistic for daily temperatures
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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: Clean imports -> data -> plot -> save; no functions or classes
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- id: CQ-02
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name: Reproducibility
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score: 2
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max: 2
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passed: true
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comment: np.random.seed(42) set
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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: numpy, pygal, Style; all used
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- id: CQ-04
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name: Code Elegance
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score: 2
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max: 2
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passed: true
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comment: Four separate list comprehensions for quartiles are verbose but clear;
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no fake functionality
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- id: CQ-05
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name: Output & API
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score: 1
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max: 1
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passed: true
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comment: render_to_png('plot.png') correct; HTML export is a pygal bonus
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library_mastery:
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score: 8
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max: 10
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items:
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- id: LM-01
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name: Idiomatic Usage
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score: 5
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max: 5
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passed: true
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comment: Dict-format data points with label keys for SVG tooltips, stroke_style
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for line customisation, legend_at_bottom_columns — all idiomatic pygal
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- id: LM-02
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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: Pygal-specific dict tooltip data and stroke_style dasharray are library-distinctive;
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could leverage pygal interactive SVG more explicitly
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verdict: REJECTED
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impl_tags:
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dependencies: []
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techniques:
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- hover-tooltips
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- html-export
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patterns:
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- data-generation
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- iteration-over-groups
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dataprep:
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- time-series
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styling:
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- alpha-blending

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