From 4909a5585d336a71f3021283b0875f632fe2a309 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sun, 12 Apr 2026 18:10:21 +0000 Subject: [PATCH 1/5] feat(seaborn): implement scatter-lag --- plots/scatter-lag/implementations/seaborn.py | 95 ++++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 plots/scatter-lag/implementations/seaborn.py diff --git a/plots/scatter-lag/implementations/seaborn.py b/plots/scatter-lag/implementations/seaborn.py new file mode 100644 index 0000000000..9b0c56d2f7 --- /dev/null +++ b/plots/scatter-lag/implementations/seaborn.py @@ -0,0 +1,95 @@ +"""pyplots.ai +scatter-lag: Lag Plot for Time Series Autocorrelation Diagnosis +Library: seaborn | Python 3.13 +Quality: pending | Created: 2026-04-12 +""" + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns + + +# Data - synthetic AR(1) process with strong positive autocorrelation +np.random.seed(42) +n = 500 +phi = 0.85 +noise = np.random.normal(0, 1, n) + +values = np.zeros(n) +values[0] = noise[0] +for t in range(1, n): + values[t] = phi * values[t - 1] + noise[t] + +# Lag plot data (lag = 1) +lag = 1 +y_t = values[:-lag] +y_t_lag = values[lag:] +time_index = np.arange(len(y_t)) + +df = pd.DataFrame({"y(t)": y_t, "y(t + 1)": y_t_lag, "Time Index": time_index}) + +r = np.corrcoef(y_t, y_t_lag)[0, 1] + +# Plot +sns.set_theme( + style="ticks", + context="talk", + font_scale=1.1, + rc={"font.family": "sans-serif", "axes.edgecolor": "#888888", "axes.linewidth": 0.8}, +) + +fig, ax = plt.subplots(figsize=(16, 9)) + +scatter = ax.scatter( + y_t, y_t_lag, c=time_index, cmap="viridis", s=70, alpha=0.65, edgecolors="white", linewidths=0.4, zorder=3 +) + +# Diagonal reference line (y = x) +data_min = min(y_t.min(), y_t_lag.min()) +data_max = max(y_t.max(), y_t_lag.max()) +margin = (data_max - data_min) * 0.05 +ax.plot( + [data_min - margin, data_max + margin], + [data_min - margin, data_max + margin], + color="#c44e52", + linewidth=2, + linestyle="--", + alpha=0.6, + zorder=2, +) + +# Colorbar for temporal structure +cbar = plt.colorbar(scatter, ax=ax, pad=0.02, aspect=30) +cbar.set_label("Time Index", fontsize=18, color="#444444") +cbar.ax.tick_params(labelsize=14) + +# Correlation coefficient +ax.annotate( + f"r = {r:.2f}", + xy=(0.03, 0.96), + xycoords="axes fraction", + fontsize=18, + fontweight="bold", + color="#444444", + ha="left", + va="top", + bbox={"boxstyle": "round,pad=0.4", "facecolor": "white", "edgecolor": "#cccccc", "alpha": 0.9}, +) + +# Style +ax.set_title( + "AR(1) Autocorrelation · scatter-lag · seaborn · pyplots.ai", + fontsize=24, + fontweight="medium", + color="#333333", + pad=16, +) +ax.set_xlabel("y(t)", fontsize=20, color="#444444") +ax.set_ylabel("y(t + 1)", fontsize=20, color="#444444") +ax.tick_params(axis="both", labelsize=16, colors="#555555") +ax.grid(True, alpha=0.15, linewidth=0.6) +sns.despine(ax=ax) + +plt.tight_layout() +plt.savefig("plot.png", dpi=300, bbox_inches="tight") From bd7c39ea8244aa51460f08c737d2d359b799560b Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sun, 12 Apr 2026 18:10:34 +0000 Subject: [PATCH 2/5] chore(seaborn): add metadata for scatter-lag --- plots/scatter-lag/metadata/seaborn.yaml | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 plots/scatter-lag/metadata/seaborn.yaml diff --git a/plots/scatter-lag/metadata/seaborn.yaml b/plots/scatter-lag/metadata/seaborn.yaml new file mode 100644 index 0000000000..7fdb826843 --- /dev/null +++ b/plots/scatter-lag/metadata/seaborn.yaml @@ -0,0 +1,18 @@ +# Per-library metadata for seaborn implementation of scatter-lag +# Auto-generated by impl-generate.yml + +library: seaborn +specification_id: scatter-lag +created: '2026-04-12T18:10:33Z' +updated: '2026-04-12T18:10:33Z' +generated_by: claude-opus-4-5-20251101 +workflow_run: 24313009886 +issue: 5251 +python_version: 3.14.3 +library_version: 0.13.2 +preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-lag/seaborn/plot.png +preview_html: null +quality_score: null +review: + strengths: [] + weaknesses: [] From b6000b4ac440cad22ac37daf6cfbd18baade8f9b Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sun, 12 Apr 2026 18:15:40 +0000 Subject: [PATCH 3/5] chore(seaborn): update quality score 83 and review feedback for scatter-lag --- plots/scatter-lag/implementations/seaborn.py | 6 +- plots/scatter-lag/metadata/seaborn.yaml | 236 ++++++++++++++++++- 2 files changed, 232 insertions(+), 10 deletions(-) diff --git a/plots/scatter-lag/implementations/seaborn.py b/plots/scatter-lag/implementations/seaborn.py index 9b0c56d2f7..a63186de21 100644 --- a/plots/scatter-lag/implementations/seaborn.py +++ b/plots/scatter-lag/implementations/seaborn.py @@ -1,7 +1,7 @@ -"""pyplots.ai +""" pyplots.ai scatter-lag: Lag Plot for Time Series Autocorrelation Diagnosis -Library: seaborn | Python 3.13 -Quality: pending | Created: 2026-04-12 +Library: seaborn 0.13.2 | Python 3.14.3 +Quality: 83/100 | Created: 2026-04-12 """ import matplotlib.pyplot as plt diff --git a/plots/scatter-lag/metadata/seaborn.yaml b/plots/scatter-lag/metadata/seaborn.yaml index 7fdb826843..328e40e891 100644 --- a/plots/scatter-lag/metadata/seaborn.yaml +++ b/plots/scatter-lag/metadata/seaborn.yaml @@ -1,10 +1,7 @@ -# Per-library metadata for seaborn implementation of scatter-lag -# Auto-generated by impl-generate.yml - library: seaborn specification_id: scatter-lag created: '2026-04-12T18:10:33Z' -updated: '2026-04-12T18:10:33Z' +updated: '2026-04-12T18:15:40Z' generated_by: claude-opus-4-5-20251101 workflow_run: 24313009886 issue: 5251 @@ -12,7 +9,232 @@ python_version: 3.14.3 library_version: 0.13.2 preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-lag/seaborn/plot.png preview_html: null -quality_score: null +quality_score: 83 review: - strengths: [] - weaknesses: [] + strengths: + - 'Excellent data quality: AR(1) with phi=0.85 creates a textbook autocorrelation + example showing clear diagonal clustering' + - 'Perfect visual quality fundamentals: all font sizes explicitly set, viridis colormap + for colorblind-safe temporal coloring, clean layout' + - 'Strong spec compliance: all required features (diagonal reference line, temporal + coloring, r annotation, configurable lag) implemented correctly' + - Clean code structure with reproducibility seed and KISS architecture + weaknesses: + - Core scatter plot uses matplotlib ax.scatter() instead of seaborn sns.scatterplot(); + misses idiomatic seaborn approach for continuous hue coloring — use sns.scatterplot(data=df, + x=..., y=..., hue=..., palette=viridis, ax=ax) + - 'Marker size s=70 and opacity alpha=0.65 too large/opaque for 499 data points + (guidelines: s=20-50, alpha=0.3-0.5 for 300+); reduce to s=30-40, alpha=0.4-0.5' + - 'Title format deviates from required pattern: has extra prefix AR(1) Autocorrelation + before spec-id; fix to scatter-lag · seaborn · pyplots.ai' + image_description: The plot shows a scatter lag plot for an AR(1) time series, displaying + y(t) on the x-axis (range ~-4.5 to 5.5) vs y(t+1) on the y-axis (range ~-4.5 to + 6). Points are colored using the viridis colormap (purple/dark blue for early + time indices near 0, transitioning through teal/green to bright yellow for later + indices near 425), creating a clear temporal gradient visible in the scatter cloud. + A dashed diagonal reference line in salmon/reddish color runs from lower-left + to upper-right, representing the y=x (zero autocorrelation) baseline. A colorbar + on the right side is labeled "Time Index" with tick marks at 0, 100, 200, 300, + 400. In the upper-left corner, a rounded white annotation box with a light gray + border shows "r = 0.83" in bold dark text. The title reads "AR(1) Autocorrelation + · scatter-lag · seaborn · pyplots.ai" in medium-weight gray font. Both top and + right spines are removed (despined). A very subtle grid is visible at low alpha. + The scatter cloud clusters tightly along the diagonal reference line, clearly + demonstrating strong positive autocorrelation (r=0.83) expected for an AR(1) process + with phi=0.85. The figure dimensions appear to be 16x9 inches / 4800x2700px with + a clean white background. + criteria_checklist: + visual_quality: + score: 27 + max: 30 + items: + - id: VQ-01 + name: Text Legibility + score: 8 + max: 8 + passed: true + comment: 'All font sizes explicitly set: title 24pt, labels 20pt, ticks 16pt, + colorbar label 18pt' + - id: VQ-02 + name: No Overlap + score: 6 + max: 6 + passed: true + comment: No overlapping text elements + - id: VQ-03 + name: Element Visibility + score: 4 + max: 6 + passed: true + comment: 's=70 and alpha=0.65 too large/opaque for 499 points (guidelines: + s=20-50, alpha=0.3-0.5 for 300+); pattern visible but center is dense' + - id: VQ-04 + name: Color Accessibility + score: 4 + max: 4 + passed: true + comment: viridis is perceptually uniform and colorblind-safe + - id: VQ-05 + name: Layout & Canvas + score: 4 + max: 4 + passed: true + comment: Good proportions, colorbar well-positioned, nothing cut off + - id: VQ-06 + name: Axis Labels & Title + score: 1 + max: 2 + passed: true + comment: Labels y(t) and y(t+1) are descriptive but lack units + design_excellence: + score: 13 + max: 20 + items: + - id: DE-01 + name: Aesthetic Sophistication + score: 5 + max: 8 + passed: true + comment: 'Above defaults: custom annotation box, controlled typography colors, + intentional reference line color. Not FiveThirtyEight-level but clearly + above library defaults' + - id: DE-02 + name: Visual Refinement + score: 4 + max: 6 + passed: true + comment: sns.despine(), very subtle grid (alpha=0.15, linewidth=0.6), white + edge colors on markers. Good refinements + - id: DE-03 + name: Data Storytelling + score: 4 + max: 6 + passed: true + comment: Time coloring + r annotation + diagonal reference create autocorrelation + narrative. Visual hierarchy through temporal color gradient + spec_compliance: + score: 14 + max: 15 + items: + - id: SC-01 + name: Plot Type + score: 5 + max: 5 + passed: true + comment: 'Correct lag plot: y(t) vs y(t+1)' + - id: SC-02 + name: Required Features + score: 4 + max: 4 + passed: true + comment: Diagonal reference line, time-indexed coloring, correlation coefficient + annotation, configurable lag all present + - id: SC-03 + name: Data Mapping + score: 3 + max: 3 + passed: true + comment: y(t) on x-axis, y(t+1) on y-axis, all data visible + - id: SC-04 + name: Title & Legend + score: 2 + max: 3 + passed: false + comment: Title has extra prefix 'AR(1) Autocorrelation ·' before spec-id; + required format is {spec-id} · {library} · pyplots.ai + data_quality: + score: 15 + max: 15 + items: + - id: DQ-01 + name: Feature Coverage + score: 6 + max: 6 + passed: true + comment: 'Shows all aspects: autocorrelation pattern, diagonal baseline, temporal + structure via color, quantified correlation coefficient' + - id: DQ-02 + name: Realistic Context + score: 5 + max: 5 + passed: true + comment: Synthetic AR(1) process data is explicitly listed in spec as an example + domain; scientifically valid and neutral + - id: DQ-03 + name: Appropriate Scale + score: 4 + max: 4 + passed: true + comment: phi=0.85, noise N(0,1) produces realistic AR(1) values in range ~[-5, + 5] + code_quality: + score: 10 + max: 10 + items: + - id: CQ-01 + name: KISS Structure + score: 3 + max: 3 + passed: true + comment: 'Clean linear flow: imports → data → plot → save, no functions or + classes' + - id: CQ-02 + name: Reproducibility + score: 2 + max: 2 + passed: true + comment: np.random.seed(42) set + - id: CQ-03 + name: Clean Imports + score: 2 + max: 2 + passed: true + comment: All four imports (matplotlib, numpy, pandas, seaborn) are used + - id: CQ-04 + name: Code Elegance + score: 2 + max: 2 + passed: true + comment: Clean Pythonic code, appropriate complexity, no fake UI elements + - id: CQ-05 + name: Output & API + score: 1 + max: 1 + passed: true + comment: Saves as plot.png, current seaborn 0.14+ API used + library_mastery: + score: 4 + max: 10 + items: + - id: LM-01 + name: Idiomatic Usage + score: 3 + max: 5 + passed: false + comment: Uses sns.set_theme() and sns.despine() correctly but core scatter + uses ax.scatter() (matplotlib) instead of sns.scatterplot() with hue for + continuous data + - id: LM-02 + name: Distinctive Features + score: 1 + max: 5 + passed: false + comment: Only sns.set_theme() and sns.despine() represent seaborn-distinctive + usage; primary visualization is matplotlib with no seaborn statistical plotting + strengths + verdict: REJECTED +impl_tags: + dependencies: [] + techniques: + - annotations + - colorbar + patterns: + - data-generation + - matrix-construction + - explicit-figure + dataprep: [] + styling: + - alpha-blending + - custom-colormap + - edge-highlighting + - grid-styling From f427cdab931117e44da344ae3bdc02e5cb5ab61e Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sun, 12 Apr 2026 18:19:24 +0000 Subject: [PATCH 4/5] fix(seaborn): address review feedback for scatter-lag Attempt 1/3 - fixes based on AI review --- plots/scatter-lag/implementations/seaborn.py | 29 ++++++++++++-------- 1 file changed, 18 insertions(+), 11 deletions(-) diff --git a/plots/scatter-lag/implementations/seaborn.py b/plots/scatter-lag/implementations/seaborn.py index a63186de21..8837dadf92 100644 --- a/plots/scatter-lag/implementations/seaborn.py +++ b/plots/scatter-lag/implementations/seaborn.py @@ -1,4 +1,4 @@ -""" pyplots.ai +"""pyplots.ai scatter-lag: Lag Plot for Time Series Autocorrelation Diagnosis Library: seaborn 0.13.2 | Python 3.14.3 Quality: 83/100 | Created: 2026-04-12 @@ -41,8 +41,19 @@ fig, ax = plt.subplots(figsize=(16, 9)) -scatter = ax.scatter( - y_t, y_t_lag, c=time_index, cmap="viridis", s=70, alpha=0.65, edgecolors="white", linewidths=0.4, zorder=3 +sns.scatterplot( + data=df, + x="y(t)", + y="y(t + 1)", + hue="Time Index", + palette="viridis", + s=35, + alpha=0.45, + edgecolor="white", + linewidth=0.4, + legend=False, + ax=ax, + zorder=3, ) # Diagonal reference line (y = x) @@ -60,7 +71,9 @@ ) # Colorbar for temporal structure -cbar = plt.colorbar(scatter, ax=ax, pad=0.02, aspect=30) +norm = plt.Normalize(df["Time Index"].min(), df["Time Index"].max()) +sm = plt.cm.ScalarMappable(cmap="viridis", norm=norm) +cbar = plt.colorbar(sm, ax=ax, pad=0.02, aspect=30) cbar.set_label("Time Index", fontsize=18, color="#444444") cbar.ax.tick_params(labelsize=14) @@ -78,13 +91,7 @@ ) # Style -ax.set_title( - "AR(1) Autocorrelation · scatter-lag · seaborn · pyplots.ai", - fontsize=24, - fontweight="medium", - color="#333333", - pad=16, -) +ax.set_title("scatter-lag · seaborn · pyplots.ai", fontsize=24, fontweight="medium", color="#333333", pad=16) ax.set_xlabel("y(t)", fontsize=20, color="#444444") ax.set_ylabel("y(t + 1)", fontsize=20, color="#444444") ax.tick_params(axis="both", labelsize=16, colors="#555555") From 394ea490c8a333449821d2e13c7990bc170a3215 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sun, 12 Apr 2026 18:25:33 +0000 Subject: [PATCH 5/5] chore(seaborn): update quality score 89 and review feedback for scatter-lag --- plots/scatter-lag/implementations/seaborn.py | 4 +- plots/scatter-lag/metadata/seaborn.yaml | 176 ++++++++++--------- 2 files changed, 91 insertions(+), 89 deletions(-) diff --git a/plots/scatter-lag/implementations/seaborn.py b/plots/scatter-lag/implementations/seaborn.py index 8837dadf92..90590e94b0 100644 --- a/plots/scatter-lag/implementations/seaborn.py +++ b/plots/scatter-lag/implementations/seaborn.py @@ -1,7 +1,7 @@ -"""pyplots.ai +""" pyplots.ai scatter-lag: Lag Plot for Time Series Autocorrelation Diagnosis Library: seaborn 0.13.2 | Python 3.14.3 -Quality: 83/100 | Created: 2026-04-12 +Quality: 89/100 | Created: 2026-04-12 """ import matplotlib.pyplot as plt diff --git a/plots/scatter-lag/metadata/seaborn.yaml b/plots/scatter-lag/metadata/seaborn.yaml index 328e40e891..db673a9019 100644 --- a/plots/scatter-lag/metadata/seaborn.yaml +++ b/plots/scatter-lag/metadata/seaborn.yaml @@ -1,7 +1,7 @@ library: seaborn specification_id: scatter-lag created: '2026-04-12T18:10:33Z' -updated: '2026-04-12T18:15:40Z' +updated: '2026-04-12T18:25:33Z' generated_by: claude-opus-4-5-20251101 workflow_run: 24313009886 issue: 5251 @@ -9,43 +9,41 @@ python_version: 3.14.3 library_version: 0.13.2 preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-lag/seaborn/plot.png preview_html: null -quality_score: 83 +quality_score: 89 review: strengths: - - 'Excellent data quality: AR(1) with phi=0.85 creates a textbook autocorrelation - example showing clear diagonal clustering' - - 'Perfect visual quality fundamentals: all font sizes explicitly set, viridis colormap - for colorblind-safe temporal coloring, clean layout' - - 'Strong spec compliance: all required features (diagonal reference line, temporal - coloring, r annotation, configurable lag) implemented correctly' - - Clean code structure with reproducibility seed and KISS architecture + - 'Perfect spec compliance: diagonal reference line, time-index coloring, and r-value + annotation all implemented exactly as specified' + - 'Excellent visual quality: font sizes explicitly set to correct values, viridis + colormap is colorblind-safe and semantically meaningful for temporal ordering' + - Clean, reproducible code with appropriate marker size/alpha calibration for the + dataset density (s=35, alpha=0.45 for 499 points) weaknesses: - - Core scatter plot uses matplotlib ax.scatter() instead of seaborn sns.scatterplot(); - misses idiomatic seaborn approach for continuous hue coloring — use sns.scatterplot(data=df, - x=..., y=..., hue=..., palette=viridis, ax=ax) - - 'Marker size s=70 and opacity alpha=0.65 too large/opaque for 499 data points - (guidelines: s=20-50, alpha=0.3-0.5 for 300+); reduce to s=30-40, alpha=0.4-0.5' - - 'Title format deviates from required pattern: has extra prefix AR(1) Autocorrelation - before spec-id; fix to scatter-lag · seaborn · pyplots.ai' - image_description: The plot shows a scatter lag plot for an AR(1) time series, displaying - y(t) on the x-axis (range ~-4.5 to 5.5) vs y(t+1) on the y-axis (range ~-4.5 to - 6). Points are colored using the viridis colormap (purple/dark blue for early - time indices near 0, transitioning through teal/green to bright yellow for later - indices near 425), creating a clear temporal gradient visible in the scatter cloud. - A dashed diagonal reference line in salmon/reddish color runs from lower-left - to upper-right, representing the y=x (zero autocorrelation) baseline. A colorbar - on the right side is labeled "Time Index" with tick marks at 0, 100, 200, 300, - 400. In the upper-left corner, a rounded white annotation box with a light gray - border shows "r = 0.83" in bold dark text. The title reads "AR(1) Autocorrelation - · scatter-lag · seaborn · pyplots.ai" in medium-weight gray font. Both top and - right spines are removed (despined). A very subtle grid is visible at low alpha. - The scatter cloud clusters tightly along the diagonal reference line, clearly - demonstrating strong positive autocorrelation (r=0.83) expected for an AR(1) process - with phi=0.85. The figure dimensions appear to be 16x9 inches / 4800x2700px with - a clean white background. + - Design excellence is good but not exceptional — the aesthetic is polished but + not publication-ready; a more distinctive visual treatment would push DE-01 from + 5 to 6-7 + - Data storytelling only demonstrates one scenario (strong positive autocorrelation); + contrasting with what weak/no autocorrelation looks like would strengthen the + diagnostic narrative + image_description: The plot shows a scatter lag plot of a synthetic AR(1) time series + on a 16:9 canvas. The x-axis is labeled "y(t)" and the y-axis "y(t + 1)", both + in clean sans-serif font. Approximately 499 scatter points are rendered with the + viridis colormap (purple → teal → green → yellow) encoding the time index from + 0 to ~499. A red dashed diagonal reference line (y = x) runs from bottom-left + to top-right, visually anchoring the correlation pattern. A colorbar on the right + side of the plot is labeled "Time Index" with tick marks from 0 to 400. In the + top-left corner, a rounded annotation box displays "r = 0.83" in bold dark text + on a white background with a light grey border. The title reads "scatter-lag · + seaborn · pyplots.ai" at the top. The plot uses the seaborn "ticks" style with + top/right spines removed and a very faint grey grid. The x-axis ranges from approximately + -5 to 6 and the y-axis from approximately -4 to 6. The scatter shows a clear linear + pattern along the diagonal, indicating strong positive autocorrelation at lag + 1. The viridis color gradient shows no systematic temporal drift — early (purple) + and late (yellow) observations are mixed throughout, confirming the stationarity + of the process. Overall layout is clean and balanced. criteria_checklist: visual_quality: - score: 27 + score: 30 max: 30 items: - id: VQ-01 @@ -54,38 +52,41 @@ review: max: 8 passed: true comment: 'All font sizes explicitly set: title 24pt, labels 20pt, ticks 16pt, - colorbar label 18pt' + colorbar label 18pt, annotation 18pt' - id: VQ-02 name: No Overlap score: 6 max: 6 passed: true - comment: No overlapping text elements + comment: No text collisions; annotation box, colorbar, axis labels, and title + occupy distinct regions - id: VQ-03 name: Element Visibility - score: 4 + score: 6 max: 6 passed: true - comment: 's=70 and alpha=0.65 too large/opaque for 499 points (guidelines: - s=20-50, alpha=0.3-0.5 for 300+); pattern visible but center is dense' + comment: s=35, alpha=0.45 well-calibrated for 499 points; points clearly visible + without over-saturation - id: VQ-04 name: Color Accessibility score: 4 max: 4 passed: true - comment: viridis is perceptually uniform and colorblind-safe + comment: Viridis is perceptually uniform and colorblind-safe; red dashed line + provides clear contrast - id: VQ-05 name: Layout & Canvas score: 4 max: 4 passed: true - comment: Good proportions, colorbar well-positioned, nothing cut off + comment: Balanced margins, plot fills ~65-70% of canvas width; colorbar appropriately + sized - id: VQ-06 name: Axis Labels & Title - score: 1 + score: 2 max: 2 passed: true - comment: Labels y(t) and y(t+1) are descriptive but lack units + comment: y(t) / y(t+1) is standard lag plot notation; descriptive and domain-appropriate design_excellence: score: 13 max: 20 @@ -95,25 +96,26 @@ review: score: 5 max: 8 passed: true - comment: 'Above defaults: custom annotation box, controlled typography colors, - intentional reference line color. Not FiveThirtyEight-level but clearly - above library defaults' + comment: 'Above well-configured default: muted reference line color, viridis + for temporal ordering, styled annotation box. Intentional and cohesive, + but stops short of publication-ready polish' - id: DE-02 name: Visual Refinement score: 4 max: 6 passed: true - comment: sns.despine(), very subtle grid (alpha=0.15, linewidth=0.6), white - edge colors on markers. Good refinements + comment: Top/right spines removed via sns.despine; grid is very subtle (alpha=0.15, + linewidth=0.6); custom axis edge color. Good refinement above defaults - id: DE-03 name: Data Storytelling score: 4 max: 6 passed: true - comment: Time coloring + r annotation + diagonal reference create autocorrelation - narrative. Visual hierarchy through temporal color gradient + comment: 'Visual hierarchy works: viridis coloring reveals temporal uniformity; + r=0.83 gives immediate quantitative context; reference line anchors interpretation. + Only one autocorrelation scenario shown' spec_compliance: - score: 14 + score: 15 max: 15 items: - id: SC-01 @@ -121,52 +123,54 @@ review: score: 5 max: 5 passed: true - comment: 'Correct lag plot: y(t) vs y(t+1)' + comment: Correct scatter of y(t) vs y(t+k) for lag k=1 - id: SC-02 name: Required Features score: 4 max: 4 passed: true - comment: Diagonal reference line, time-indexed coloring, correlation coefficient - annotation, configurable lag all present + comment: Diagonal reference line, time-index coloring, correlation annotation, + configurable lag variable — all present - id: SC-03 name: Data Mapping score: 3 max: 3 passed: true - comment: y(t) on x-axis, y(t+1) on y-axis, all data visible + comment: x=y(t), y=y(t+1); time index mapped to viridis color; axes show full + data range - id: SC-04 name: Title & Legend - score: 2 + score: 3 max: 3 - passed: false - comment: Title has extra prefix 'AR(1) Autocorrelation ·' before spec-id; - required format is {spec-id} · {library} · pyplots.ai + passed: true + comment: Title format 'scatter-lag · seaborn · pyplots.ai' correct; colorbar + serves as legend for time index data_quality: - score: 15 + score: 14 max: 15 items: - id: DQ-01 name: Feature Coverage - score: 6 + score: 5 max: 6 passed: true - comment: 'Shows all aspects: autocorrelation pattern, diagonal baseline, temporal - structure via color, quantified correlation coefficient' + comment: 'AR(1) with phi=0.85 clearly shows strong positive autocorrelation. + Minor deduction: only one scenario shown; contrasting with random data would + strengthen diagnostic utility' - id: DQ-02 name: Realistic Context score: 5 max: 5 passed: true - comment: Synthetic AR(1) process data is explicitly listed in spec as an example - domain; scientifically valid and neutral + comment: Synthetic AR(1) process explicitly listed in spec as example data; + neutral and scientifically plausible - id: DQ-03 name: Appropriate Scale score: 4 max: 4 passed: true - comment: phi=0.85, noise N(0,1) produces realistic AR(1) values in range ~[-5, - 5] + comment: phi=0.85 with unit-variance noise yields values in [-4, 6], appropriate + for standardized financial/environmental time series code_quality: score: 10 max: 10 @@ -176,65 +180,63 @@ review: score: 3 max: 3 passed: true - comment: 'Clean linear flow: imports → data → plot → save, no functions or - classes' + comment: Clean Imports → Data → Plot → Save; no functions or classes - id: CQ-02 name: Reproducibility score: 2 max: 2 passed: true - comment: np.random.seed(42) set + comment: np.random.seed(42) - id: CQ-03 name: Clean Imports score: 2 max: 2 passed: true - comment: All four imports (matplotlib, numpy, pandas, seaborn) are used + comment: matplotlib.pyplot, numpy, pandas, seaborn — all used, none superfluous - id: CQ-04 name: Code Elegance score: 2 max: 2 passed: true - comment: Clean Pythonic code, appropriate complexity, no fake UI elements + comment: Clean, Pythonic; appropriate complexity; no fake UI elements - id: CQ-05 name: Output & API score: 1 max: 1 passed: true - comment: Saves as plot.png, current seaborn 0.14+ API used - library_mastery: - score: 4 + comment: plt.savefig('plot.png', dpi=300, bbox_inches='tight'); current seaborn + 0.13 API + library_features: + score: 7 max: 10 items: - id: LM-01 name: Idiomatic Usage - score: 3 + score: 4 max: 5 - passed: false - comment: Uses sns.set_theme() and sns.despine() correctly but core scatter - uses ax.scatter() (matplotlib) instead of sns.scatterplot() with hue for - continuous data + passed: true + comment: 'sns.set_theme with custom rc params, sns.scatterplot with continuous + hue, sns.despine — all idiomatic. Minor deduction: colorbar added via matplotlib + ScalarMappable (legend=False limitation)' - id: LM-02 name: Distinctive Features - score: 1 + score: 3 max: 5 - passed: false - comment: Only sns.set_theme() and sns.despine() represent seaborn-distinctive - usage; primary visualization is matplotlib with no seaborn statistical plotting - strengths + passed: true + comment: Seaborn's continuous hue encoding in scatterplot with viridis palette + is library-distinctive. Most visual decoration (colorbar, annotation, reference + line) handled through matplotlib directly verdict: REJECTED impl_tags: dependencies: [] techniques: - - annotations - colorbar + - annotations patterns: - data-generation - - matrix-construction - explicit-figure dataprep: [] styling: - alpha-blending - custom-colormap - edge-highlighting - - grid-styling