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feat(plotnine): implement scatter-lag (#5275)
## Implementation: `scatter-lag` - plotnine Implements the **plotnine** version of `scatter-lag`. **File:** `plots/scatter-lag/implementations/plotnine.py` **Parent Issue:** #5251 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/24313009899)* --------- 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/plotnine.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: plotnine 0.15.3 | Python 3.14.3
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Quality: 88/100 | Created: 2026-04-12
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"""
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import numpy as np
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import pandas as pd
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from plotnine import (
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aes,
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element_blank,
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element_line,
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element_text,
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facet_wrap,
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geom_abline,
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geom_point,
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ggplot,
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labs,
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scale_color_gradient,
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theme,
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theme_minimal,
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)
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# Data - Daily temperature with natural autocorrelation (AR(1) process)
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np.random.seed(42)
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n = 300
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phi = 0.85
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noise = np.random.normal(0, 2.5, n)
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temperatures = np.zeros(n)
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temperatures[0] = 20 + noise[0]
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for i in range(1, n):
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temperatures[i] = (1 - phi) * 20 + phi * temperatures[i - 1] + noise[i]
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# Create lag data for multiple lag values
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lags = [1, 3, 7]
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rows = []
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for lag in lags:
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for i in range(n - lag):
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rows.append(
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{
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"temp_t": temperatures[i],
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"temp_t_lag": temperatures[i + lag],
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"day": i,
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"lag": f"Lag = {lag} day{'s' if lag > 1 else ''}",
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}
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)
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df = pd.DataFrame(rows)
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df["lag"] = pd.Categorical(df["lag"], categories=[f"Lag = {k} day{'s' if k > 1 else ''}" for k in lags], ordered=True)
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# Plot
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plot = (
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ggplot(df, aes(x="temp_t", y="temp_t_lag", color="day"))
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+ geom_abline(intercept=0, slope=1, color="#BBBBBB", linetype="dashed", size=0.7)
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+ geom_point(size=3, alpha=0.65)
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+ facet_wrap("lag", ncol=3)
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+ scale_color_gradient(low="#306998", high="#E8A838", name="Day")
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+ labs(
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x="Temperature at Day t (°C)",
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y="Temperature at Day t+k (°C)",
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title="Daily Temperature · scatter-lag · plotnine · pyplots.ai",
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)
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+ theme_minimal()
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+ theme(
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figure_size=(16, 9),
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plot_title=element_text(size=24, weight="bold"),
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axis_title=element_text(size=20),
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axis_text=element_text(size=16),
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strip_text=element_text(size=18, weight="bold"),
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legend_title=element_text(size=16),
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legend_text=element_text(size=14),
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panel_grid_major=element_line(color="#E8E8E8", size=0.5),
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panel_grid_minor=element_blank(),
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axis_ticks=element_blank(),
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)
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)
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# Save
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plot.save("plot.png", dpi=300)

‎plots/scatter-lag/metadata/plotnine.yaml‎

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library: plotnine
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specification_id: scatter-lag
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created: '2026-04-12T18:13:17Z'
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updated: '2026-04-12T18:17:42Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 24313009899
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issue: 5251
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python_version: 3.14.3
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library_version: 0.15.3
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preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-lag/plotnine/plot.png
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preview_html: null
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quality_score: 88
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review:
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strengths:
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- 'Perfect code quality: KISS structure, reproducible seed, all imports used, clean
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Pythonic code throughout'
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- 'Full spec compliance: all three required features (configurable lags, diagonal
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reference line, temporal color coding) correctly implemented'
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- 'Excellent data quality: AR(1) daily temperature is a realistic neutral scenario;
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scale and autocorrelation strength are appropriate'
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- Explicit font size configuration at all levels meets the 24/20/16pt requirements
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weaknesses:
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- Title format starts with 'Daily Temperature' prefix instead of spec-id; should
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start with 'scatter-lag · plotnine · pyplots.ai'
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- 'Color gradient (#306998 to #E8A838) is not a formally colorblind-optimized palette;
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consider viridis or plasma'
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- Design could reach publication quality with explicit spine removal, coord_fixed()
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for equal axis scales, and stronger visual identity
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image_description: Three faceted scatter plots arranged side by side, titled "Daily
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Temperature · scatter-lag · plotnine · pyplots.ai". Each panel shows daily temperature
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at day t (°C) on the x-axis vs temperature at day t+k (°C) on the y-axis, for
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lags of 1, 3, and 7 days respectively. Points are colored on a blue-to-gold gradient
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representing the day index (0–200+), with a "Day" colorbar legend on the right.
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A dashed gray diagonal reference line (y=x) runs through each panel. The Lag=1
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panel shows tight clustering along the diagonal (strong autocorrelation); Lag=3
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shows moderate spread; Lag=7 shows noticeably more scatter — telling a clear visual
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story of autocorrelation decay. The layout is clean on a white background using
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theme_minimal with a subtle light gray grid. Temperature values range roughly
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from 10–30°C across all panels.
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criteria_checklist:
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visual_quality:
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score: 28
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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 24pt bold, axis titles 20pt,
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tick labels 16pt, strip text 18pt bold, legend 14-16pt'
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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: 6
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passed: true
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comment: No overlapping text or elements across all three facets
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- id: VQ-03
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name: Element Visibility
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score: 5
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max: 6
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passed: true
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comment: Points well-sized for ~100 per facet; alpha=0.65 adequate but slightly
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high for density
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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: true
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comment: Blue-to-gold gradient not red-green but not formally colorblind-optimized
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(no viridis)
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- id: VQ-05
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name: Layout & Canvas
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score: 4
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max: 4
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passed: true
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comment: Three facets fill canvas well; legend positioned cleanly; balanced
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margins
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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: 'Descriptive labels with units: Temperature at Day t (°C) and Temperature
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at Day t+k (°C)'
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design_excellence:
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score: 13
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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: 5
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max: 8
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passed: true
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comment: Thoughtful color choice (Python Blue to amber), bold strip labels,
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clean minimal theme; above defaults but short of 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 gray grid, minor grid removed, tick marks removed; good refinement
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but no explicit spine removal beyond theme_minimal
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- id: DE-03
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name: Data Storytelling
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score: 4
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max: 6
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passed: true
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comment: Multiple lags tell visual story of autocorrelation decay; temporal
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color coding reveals structure; viewer reads insight clearly
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spec_compliance:
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score: 14
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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 lag plot: scatter of y(t) vs y(t+k)'
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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: Configurable lags (1,3,7), diagonal reference line, color by time
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index all present
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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: X=y(t), Y=y(t+k) as specified; all data visible
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- id: SC-04
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name: Title & Legend
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score: 2
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max: 3
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passed: false
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comment: Title has extra prefix 'Daily Temperature' before spec-id; should
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start with 'scatter-lag · plotnine · pyplots.ai'
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data_quality:
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score: 15
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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: 6
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max: 6
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passed: true
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comment: 'Shows all aspects: multiple lags demonstrating autocorrelation decay,
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temporal color structure, diagonal reference, realistic data spread'
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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 AR(1) process is a classic, neutral, real-world
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time series scenario
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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: 10-30 degrees C range realistic; phi=0.85 produces natural AR(1)
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behavior
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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: All 12 imported names are 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: Pythonic, appropriate complexity; AR(1) loop is necessary, not over-engineered
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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: plot.save('plot.png', dpi=300) correct
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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: 'Expert ggplot grammar: layered geoms, facet_wrap, scale_color_gradient,
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labs, theme overrides — all idiomatic plotnine'
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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: facet_wrap for small multiples and scale_color_gradient are distinctively
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ggplot-idiomatic; could push with coord_fixed() or stat_smooth()
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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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- faceting
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- layer-composition
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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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- custom-colormap
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- alpha-blending

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