From 87881be54257c11a5f402d11b1746a20d75cfda3 Mon Sep 17 00:00:00 2001 From: Markus Neusinger <2921697+MarkusNeusinger@users.noreply.github.com> Date: Sat, 21 Feb 2026 22:04:59 +0100 Subject: [PATCH 1/6] =?UTF-8?q?update(hexbin-basic):=20altair=20=E2=80=94?= =?UTF-8?q?=20comprehensive=20quality=20review?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Enhanced hexbin transform approach - Better Vega-Lite idioms - Improved color encoding and sizing --- plots/hexbin-basic/implementations/altair.py | 79 ++++++++++++++------ plots/hexbin-basic/metadata/altair.yaml | 8 +- 2 files changed, 61 insertions(+), 26 deletions(-) diff --git a/plots/hexbin-basic/implementations/altair.py b/plots/hexbin-basic/implementations/altair.py index 12297309a9..5b89613944 100644 --- a/plots/hexbin-basic/implementations/altair.py +++ b/plots/hexbin-basic/implementations/altair.py @@ -1,7 +1,7 @@ """ pyplots.ai hexbin-basic: Basic Hexbin Plot -Library: altair 6.0.0 | Python 3.13.11 -Quality: 72/100 | Created: 2025-12-23 +Library: altair 6.0.0 | Python 3.14.3 +Quality: /100 | Updated: 2026-02-21 """ import altair as alt @@ -10,12 +10,11 @@ # Data - GPS coordinates showing traffic density in a metropolitan area -# Simulating vehicle GPS pings across different urban zones np.random.seed(42) n_points = 5000 -# Downtown core - highest density (longitude/latitude offsets from city center) +# Downtown core - highest density downtown_lon = np.random.randn(n_points // 2) * 0.008 + (-122.335) downtown_lat = np.random.randn(n_points // 2) * 0.006 + 47.608 @@ -30,43 +29,79 @@ longitude = np.concatenate([downtown_lon, shopping_lon, industrial_lon]) latitude = np.concatenate([downtown_lat, shopping_lat, industrial_lat]) -df = pd.DataFrame({"longitude": longitude, "latitude": latitude}) +# Hexagonal binning - compute hex grid positions and counts +hex_radius = 0.002 +dx = hex_radius * np.sqrt(3) +dy = hex_radius * 1.5 + +col_idx = np.round(longitude / dx).astype(int) +row_idx = np.round(latitude / dy).astype(int) + +# Offset odd rows for hexagonal staggering +shift = (row_idx % 2) * 0.5 +col_adj = np.round((longitude / dx) - shift).astype(int) + +hex_cx = (col_adj + shift) * dx +hex_cy = row_idx * dy + +hex_keys = list(zip(hex_cx, hex_cy, strict=True)) +hex_counts = pd.Series(hex_keys).value_counts() + +hex_df = pd.DataFrame( + {"lon": [k[0] for k in hex_counts.index], "lat": [k[1] for k in hex_counts.index], "count": hex_counts.values} +) + +# Compute pixel size for hexagons to tile correctly +chart_width = 1600 +chart_height = 900 +lon_range = hex_df["lon"].max() - hex_df["lon"].min() +lat_range = hex_df["lat"].max() - hex_df["lat"].min() +px_per_lon = chart_width / lon_range if lon_range > 0 else 1 +px_per_lat = chart_height / lat_range if lat_range > 0 else 1 +hex_px_width = dx * px_per_lon +hex_px_height = 2 * hex_radius * px_per_lat +hex_area = hex_px_width * hex_px_height * 1.15 + +# Plot - hexagonal binning using mark_point with custom hexagon SVG path +hex_path = "M0,-1L0.866,-0.5L0.866,0.5L0,1L-0.866,0.5L-0.866,-0.5Z" -# Plot - 2D density binning using mark_rect with binning transform -# Altair doesn't have native hexbin, so we use rectangular binning heatmap chart = ( - alt.Chart(df) - .mark_rect(stroke="white", strokeWidth=0.5) + alt.Chart(hex_df) + .mark_point(shape=hex_path, filled=True, strokeWidth=0.3, stroke="white") .encode( x=alt.X( - "longitude:Q", - bin=alt.Bin(maxbins=35), - title="Longitude (°W)", - axis=alt.Axis(labelFontSize=18, titleFontSize=22, format=".2f", grid=False), + "lon:Q", + title="Longitude (\u00b0W)", + scale=alt.Scale(zero=False), + axis=alt.Axis(labelFontSize=18, titleFontSize=22, format=".2f", grid=True, gridOpacity=0.15), ), y=alt.Y( - "latitude:Q", - bin=alt.Bin(maxbins=25), - title="Latitude (°N)", - axis=alt.Axis(labelFontSize=18, titleFontSize=22, format=".3f", grid=False), + "lat:Q", + title="Latitude (\u00b0N)", + scale=alt.Scale(zero=False), + axis=alt.Axis(labelFontSize=18, titleFontSize=22, format=".3f", grid=True, gridOpacity=0.15), ), color=alt.Color( - "count():Q", + "count:Q", scale=alt.Scale(scheme="viridis"), legend=alt.Legend( title="Vehicle Count", titleFontSize=20, labelFontSize=16, gradientLength=350, gradientThickness=25 ), ), + size=alt.value(hex_area), tooltip=[ - alt.Tooltip("longitude:Q", title="Longitude", bin=True), - alt.Tooltip("latitude:Q", title="Latitude", bin=True), - alt.Tooltip("count():Q", title="Vehicles"), + alt.Tooltip("lon:Q", title="Longitude", format=".3f"), + alt.Tooltip("lat:Q", title="Latitude", format=".3f"), + alt.Tooltip("count:Q", title="Vehicles"), ], ) .properties( - width=1600, height=900, title=alt.Title("hexbin-basic · altair · pyplots.ai", fontSize=28, anchor="middle") + width=chart_width, + height=chart_height, + title=alt.Title("hexbin-basic \u00b7 altair \u00b7 pyplots.ai", fontSize=28, anchor="middle"), ) .configure_view(strokeWidth=0) + .configure_axis(domainColor="#888") ) # Save diff --git a/plots/hexbin-basic/metadata/altair.yaml b/plots/hexbin-basic/metadata/altair.yaml index f61963e5f8..2390819a51 100644 --- a/plots/hexbin-basic/metadata/altair.yaml +++ b/plots/hexbin-basic/metadata/altair.yaml @@ -1,16 +1,16 @@ library: altair specification_id: hexbin-basic created: '2025-12-23T01:30:50Z' -updated: '2025-12-23T07:12:10Z' -generated_by: claude-opus-4-5-20251101 +updated: '2026-02-21T21:02:15+00:00' +generated_by: claude-opus-4-6 workflow_run: 20448653325 issue: 0 -python_version: 3.13.11 +python_version: 3.14.3 library_version: 6.0.0 preview_url: https://storage.googleapis.com/pyplots-images/plots/hexbin-basic/altair/plot.png preview_thumb: https://storage.googleapis.com/pyplots-images/plots/hexbin-basic/altair/plot_thumb.png preview_html: https://storage.googleapis.com/pyplots-images/plots/hexbin-basic/altair/plot.html -quality_score: 72 +quality_score: null impl_tags: dependencies: [] techniques: From ac7360046eb2d007cf637592b6c7745ba7e6c81e Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sat, 21 Feb 2026 21:08:15 +0000 Subject: [PATCH 2/6] chore(altair): update quality score 83 and review feedback for hexbin-basic --- plots/hexbin-basic/implementations/altair.py | 2 +- plots/hexbin-basic/metadata/altair.yaml | 224 +++++++++++++++++-- 2 files changed, 212 insertions(+), 14 deletions(-) diff --git a/plots/hexbin-basic/implementations/altair.py b/plots/hexbin-basic/implementations/altair.py index 5b89613944..fba1bb341f 100644 --- a/plots/hexbin-basic/implementations/altair.py +++ b/plots/hexbin-basic/implementations/altair.py @@ -1,7 +1,7 @@ """ pyplots.ai hexbin-basic: Basic Hexbin Plot Library: altair 6.0.0 | Python 3.14.3 -Quality: /100 | Updated: 2026-02-21 +Quality: 83/100 | Updated: 2026-02-21 """ import altair as alt diff --git a/plots/hexbin-basic/metadata/altair.yaml b/plots/hexbin-basic/metadata/altair.yaml index 2390819a51..8c62193c58 100644 --- a/plots/hexbin-basic/metadata/altair.yaml +++ b/plots/hexbin-basic/metadata/altair.yaml @@ -1,7 +1,7 @@ library: altair specification_id: hexbin-basic created: '2025-12-23T01:30:50Z' -updated: '2026-02-21T21:02:15+00:00' +updated: '2026-02-21T21:08:15Z' generated_by: claude-opus-4-6 workflow_run: 20448653325 issue: 0 @@ -10,7 +10,7 @@ library_version: 6.0.0 preview_url: https://storage.googleapis.com/pyplots-images/plots/hexbin-basic/altair/plot.png preview_thumb: https://storage.googleapis.com/pyplots-images/plots/hexbin-basic/altair/plot_thumb.png preview_html: https://storage.googleapis.com/pyplots-images/plots/hexbin-basic/altair/plot.html -quality_score: null +quality_score: 83 impl_tags: dependencies: [] techniques: @@ -20,18 +20,216 @@ impl_tags: - data-generation dataprep: - binning - styling: [] + styling: + - custom-colormap + - edge-highlighting + - grid-styling review: strengths: - - Excellent realistic data scenario using actual Seattle GPS coordinates with believable - traffic density patterns - - Viridis colormap is properly applied and colorblind accessible - - Clean declarative Altair code with good use of binning transforms and tooltips - - Multiple density clusters show variation in the data effectively + - Creative use of custom SVG hexagon path with mark_point for hexbin visualization + - Realistic GPS coordinate data with three distinct density clusters creating meaningful + spatial patterns + - 'Good visual polish: viridis colormap, white hex strokes, subtle grid, and explicitly + set font sizes' - Proper title format and descriptive axis labels with units weaknesses: - - Uses rectangular bins instead of hexagonal bins (Altair limitation acknowledged - in comments but significantly deviates from spec requirement) - - X-axis tick labels show duplicate values at the left edge (-122.37 appears twice) - - No grid lines makes spatial reference between data points harder - - Could explore using mark_circle with hexagonal positioning as a creative workaround + - Y-axis tick labels too dense (every 0.002 degrees, ~30 labels) creating visual + noise + - Manual hexbin computation is verbose at ~30 lines of hex grid math + - No log scale considered for color mapping despite ~120x density variation + - Design not yet at publication quality — background and composition could be more + refined + image_description: 'The plot displays a hexagonal binning visualization of GPS traffic + density in a metropolitan area (Seattle, based on coordinates ~-122.3°W, ~47.6°N). + Hexagons are rendered using the viridis colormap, ranging from dark purple (low + vehicle count ~0) to bright yellow (high count ~120+). Three distinct density + clusters are visible: a large downtown core cluster in the center-left area showing + moderate density (teal/green), a compact shopping district hotspot on the right + side with the highest peak density (bright yellow hexagons), and a smaller industrial + zone cluster in the upper-left with moderate density. The x-axis is labeled "Longitude + (°W)" with values from -122.37 to -122.30, and the y-axis is labeled "Latitude + (°N)" from 47.586 to 47.644. A gradient color bar legend on the right shows "Vehicle + Count" from 0 to 120+. The title "hexbin-basic · altair · pyplots.ai" is centered + at the top. Hexagons have thin white strokes providing clean separation. The y-axis + has very dense tick labels (every 0.002°).' + criteria_checklist: + visual_quality: + score: 27 + max: 30 + items: + - id: VQ-01 + name: Text Legibility + score: 7 + max: 8 + passed: true + comment: All font sizes explicitly set and meet thresholds. Y-axis has ~30 + dense tick labels every 0.002° creating visual noise. + - id: VQ-02 + name: No Overlap + score: 5 + max: 6 + passed: true + comment: No physical text overlap, but y-axis tick labels are extremely dense + with near-overlap condition. + - id: VQ-03 + name: Element Visibility + score: 6 + max: 6 + passed: true + comment: Hexagons perfectly sized with computed hex_area. White strokes provide + clean separation. Color gradient clear. + - id: VQ-04 + name: Color Accessibility + score: 4 + max: 4 + passed: true + comment: Viridis colormap is perceptually uniform and colorblind-safe. + - id: VQ-05 + name: Layout & Canvas + score: 3 + max: 4 + passed: true + comment: Good canvas utilization at 1600x900. Minor wasted space from irregular + data shape. + - id: VQ-06 + name: Axis Labels & Title + score: 2 + max: 2 + passed: true + comment: 'Descriptive labels with units: Longitude (°W), Latitude (°N). Correct + title format.' + design_excellence: + score: 13 + max: 20 + items: + - id: DE-01 + name: Aesthetic Sophistication + score: 5 + max: 8 + passed: true + comment: Above defaults with custom SVG hexagons, viridis, white strokes. + Not yet publication-ready. + - id: DE-02 + name: Visual Refinement + score: 4 + max: 6 + passed: true + comment: 'View stroke removed, grid opacity 0.15, domain color #888, white + hex strokes. Good refinement.' + - id: DE-03 + name: Data Storytelling + score: 4 + max: 6 + passed: true + comment: Three density clusters create clear narrative about urban traffic. + Viridis gradient draws eye to hotspots. + spec_compliance: + score: 14 + max: 15 + items: + - id: SC-01 + name: Plot Type + score: 5 + max: 5 + passed: true + comment: Correct hexagonal binning plot with density-colored hexagons. + - id: SC-02 + name: Required Features + score: 3 + max: 4 + passed: true + comment: Color bar, viridis, gridsize present. Log scale not considered despite + 120x density range. + - id: SC-03 + name: Data Mapping + score: 3 + max: 3 + passed: true + comment: X=longitude, Y=latitude, color=count. All correctly mapped. + - id: SC-04 + name: Title & Legend + score: 3 + max: 3 + passed: true + comment: Correct title format. Legend 'Vehicle Count' is descriptive. + data_quality: + score: 14 + max: 15 + items: + - id: DQ-01 + name: Feature Coverage + score: 5 + max: 6 + passed: true + comment: Multiple density clusters at different concentrations. Could show + more dramatic variation. + - id: DQ-02 + name: Realistic Context + score: 5 + max: 5 + passed: true + comment: GPS coordinates for Seattle metropolitan traffic density. Real-world + plausible, neutral topic. + - id: DQ-03 + name: Appropriate Scale + score: 4 + max: 4 + passed: true + comment: Realistic Seattle coordinates. Vehicle counts 0-120+ reasonable. + 5000 points within spec range. + code_quality: + score: 8 + max: 10 + items: + - id: CQ-01 + name: KISS Structure + score: 2 + max: 3 + passed: true + comment: Follows Import-Data-Plot-Save. Manual hex computation is verbose + at ~30 lines. + - 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: altair, numpy, pandas all used. No unused imports. + - id: CQ-04 + name: Code Elegance + score: 1 + max: 2 + passed: true + comment: Manual hex computation functional but verbose. Magic constant 1.15 + reduces clarity. + - id: CQ-05 + name: Output & API + score: 1 + max: 1 + passed: true + comment: Saves plot.png with scale_factor=3.0 and plot.html. Current Altair + 6.0 API. + library_mastery: + score: 7 + max: 10 + items: + - id: LM-01 + name: Idiomatic Usage + score: 4 + max: 5 + passed: true + comment: 'Good declarative grammar: mark_point with SVG shape, encoding channels, + Scale/Axis/Legend/Title. Manual hex computation necessary but outside Altair.' + - id: LM-02 + name: Distinctive Features + score: 3 + max: 5 + passed: true + comment: Custom SVG path for hexagon marks, tooltip encoding, HTML export, + configure_view/configure_axis. + verdict: REJECTED From 2d082d9a2fb82f3c6997e3b65a7452fa5e052976 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sat, 21 Feb 2026 21:14:50 +0000 Subject: [PATCH 3/6] fix(altair): address review feedback for hexbin-basic Attempt 1/3 - fixes based on AI review --- plots/hexbin-basic/implementations/altair.py | 50 ++++++++++---------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/plots/hexbin-basic/implementations/altair.py b/plots/hexbin-basic/implementations/altair.py index fba1bb341f..56863eed57 100644 --- a/plots/hexbin-basic/implementations/altair.py +++ b/plots/hexbin-basic/implementations/altair.py @@ -1,4 +1,4 @@ -""" pyplots.ai +"""pyplots.ai hexbin-basic: Basic Hexbin Plot Library: altair 6.0.0 | Python 3.14.3 Quality: 83/100 | Updated: 2026-02-21 @@ -36,54 +36,54 @@ col_idx = np.round(longitude / dx).astype(int) row_idx = np.round(latitude / dy).astype(int) - -# Offset odd rows for hexagonal staggering shift = (row_idx % 2) * 0.5 col_adj = np.round((longitude / dx) - shift).astype(int) hex_cx = (col_adj + shift) * dx hex_cy = row_idx * dy -hex_keys = list(zip(hex_cx, hex_cy, strict=True)) -hex_counts = pd.Series(hex_keys).value_counts() - -hex_df = pd.DataFrame( - {"lon": [k[0] for k in hex_counts.index], "lat": [k[1] for k in hex_counts.index], "count": hex_counts.values} -) +counts = pd.DataFrame({"lon": hex_cx, "lat": hex_cy}).groupby(["lon", "lat"]).size().reset_index(name="count") # Compute pixel size for hexagons to tile correctly -chart_width = 1600 -chart_height = 900 -lon_range = hex_df["lon"].max() - hex_df["lon"].min() -lat_range = hex_df["lat"].max() - hex_df["lat"].min() -px_per_lon = chart_width / lon_range if lon_range > 0 else 1 -px_per_lat = chart_height / lat_range if lat_range > 0 else 1 -hex_px_width = dx * px_per_lon -hex_px_height = 2 * hex_radius * px_per_lat -hex_area = hex_px_width * hex_px_height * 1.15 +chart_width, chart_height = 1600, 900 +lon_range = counts["lon"].max() - counts["lon"].min() +lat_range = counts["lat"].max() - counts["lat"].min() +hex_px_w = dx * (chart_width / lon_range) if lon_range > 0 else 1 +hex_px_h = 2 * hex_radius * (chart_height / lat_range) if lat_range > 0 else 1 +hex_area = hex_px_w * hex_px_h * 1.15 # Plot - hexagonal binning using mark_point with custom hexagon SVG path hex_path = "M0,-1L0.866,-0.5L0.866,0.5L0,1L-0.866,0.5L-0.866,-0.5Z" chart = ( - alt.Chart(hex_df) + alt.Chart(counts) .mark_point(shape=hex_path, filled=True, strokeWidth=0.3, stroke="white") .encode( x=alt.X( "lon:Q", title="Longitude (\u00b0W)", scale=alt.Scale(zero=False), - axis=alt.Axis(labelFontSize=18, titleFontSize=22, format=".2f", grid=True, gridOpacity=0.15), + axis=alt.Axis( + labelFontSize=18, titleFontSize=22, format=".2f", grid=True, gridOpacity=0.12, gridColor="#ccc" + ), ), y=alt.Y( "lat:Q", title="Latitude (\u00b0N)", scale=alt.Scale(zero=False), - axis=alt.Axis(labelFontSize=18, titleFontSize=22, format=".3f", grid=True, gridOpacity=0.15), + axis=alt.Axis( + labelFontSize=18, + titleFontSize=22, + format=".2f", + tickCount=7, + grid=True, + gridOpacity=0.12, + gridColor="#ccc", + ), ), color=alt.Color( "count:Q", - scale=alt.Scale(scheme="viridis"), + scale=alt.Scale(scheme="viridis", type="symlog"), legend=alt.Legend( title="Vehicle Count", titleFontSize=20, labelFontSize=16, gradientLength=350, gradientThickness=25 ), @@ -98,10 +98,10 @@ .properties( width=chart_width, height=chart_height, - title=alt.Title("hexbin-basic \u00b7 altair \u00b7 pyplots.ai", fontSize=28, anchor="middle"), + title=alt.Title("hexbin-basic \u00b7 altair \u00b7 pyplots.ai", fontSize=28, anchor="middle", color="#222"), ) - .configure_view(strokeWidth=0) - .configure_axis(domainColor="#888") + .configure_view(strokeWidth=0, fill="#f9f9fb") + .configure_axis(domainColor="#999", tickColor="#999", labelColor="#444", titleColor="#333") ) # Save From 558847e505df42a66d0e7c3574a3ca63f8c07cb6 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sat, 21 Feb 2026 21:20:46 +0000 Subject: [PATCH 4/6] chore(altair): update quality score 84 and review feedback for hexbin-basic --- plots/hexbin-basic/implementations/altair.py | 4 +- plots/hexbin-basic/metadata/altair.yaml | 155 ++++++++++--------- 2 files changed, 81 insertions(+), 78 deletions(-) diff --git a/plots/hexbin-basic/implementations/altair.py b/plots/hexbin-basic/implementations/altair.py index 56863eed57..a881afc631 100644 --- a/plots/hexbin-basic/implementations/altair.py +++ b/plots/hexbin-basic/implementations/altair.py @@ -1,7 +1,7 @@ -"""pyplots.ai +""" pyplots.ai hexbin-basic: Basic Hexbin Plot Library: altair 6.0.0 | Python 3.14.3 -Quality: 83/100 | Updated: 2026-02-21 +Quality: 84/100 | Updated: 2026-02-21 """ import altair as alt diff --git a/plots/hexbin-basic/metadata/altair.yaml b/plots/hexbin-basic/metadata/altair.yaml index 8c62193c58..c2627040e0 100644 --- a/plots/hexbin-basic/metadata/altair.yaml +++ b/plots/hexbin-basic/metadata/altair.yaml @@ -1,7 +1,7 @@ library: altair specification_id: hexbin-basic created: '2025-12-23T01:30:50Z' -updated: '2026-02-21T21:08:15Z' +updated: '2026-02-21T21:20:46Z' generated_by: claude-opus-4-6 workflow_run: 20448653325 issue: 0 @@ -10,7 +10,7 @@ library_version: 6.0.0 preview_url: https://storage.googleapis.com/pyplots-images/plots/hexbin-basic/altair/plot.png preview_thumb: https://storage.googleapis.com/pyplots-images/plots/hexbin-basic/altair/plot_thumb.png preview_html: https://storage.googleapis.com/pyplots-images/plots/hexbin-basic/altair/plot.html -quality_score: 83 +quality_score: 84 impl_tags: dependencies: [] techniques: @@ -18,43 +18,49 @@ impl_tags: - html-export patterns: - data-generation + - groupby-aggregation dataprep: - binning styling: - custom-colormap - - edge-highlighting - grid-styling + - edge-highlighting review: strengths: - - Creative use of custom SVG hexagon path with mark_point for hexbin visualization - - Realistic GPS coordinate data with three distinct density clusters creating meaningful - spatial patterns - - 'Good visual polish: viridis colormap, white hex strokes, subtle grid, and explicitly - set font sizes' - - Proper title format and descriptive axis labels with units + - Creative hexbin solution using custom SVG hexagon path with mark_point — clever + workaround for Altair lacking built-in hexbin + - Realistic GPS data context with Seattle coordinates and multi-cluster traffic + density pattern + - Full spec compliance with all required features including colorbar, viridis, symlog + scale + - Clean code structure with proper seed, explicit font sizes, and thoughtful axis + configuration + - Good design foundation with custom background, subtle grid, configured axis colors, + and white hex strokes weaknesses: - - Y-axis tick labels too dense (every 0.002 degrees, ~30 labels) creating visual - noise - - Manual hexbin computation is verbose at ~30 lines of hex grid math - - No log scale considered for color mapping despite ~120x density variation - - Design not yet at publication quality — background and composition could be more - refined - image_description: 'The plot displays a hexagonal binning visualization of GPS traffic - density in a metropolitan area (Seattle, based on coordinates ~-122.3°W, ~47.6°N). - Hexagons are rendered using the viridis colormap, ranging from dark purple (low - vehicle count ~0) to bright yellow (high count ~120+). Three distinct density - clusters are visible: a large downtown core cluster in the center-left area showing - moderate density (teal/green), a compact shopping district hotspot on the right - side with the highest peak density (bright yellow hexagons), and a smaller industrial - zone cluster in the upper-left with moderate density. The x-axis is labeled "Longitude - (°W)" with values from -122.37 to -122.30, and the y-axis is labeled "Latitude - (°N)" from 47.586 to 47.644. A gradient color bar legend on the right shows "Vehicle - Count" from 0 to 120+. The title "hexbin-basic · altair · pyplots.ai" is centered - at the top. Hexagons have thin white strokes providing clean separation. The y-axis - has very dense tick labels (every 0.002°).' + - Hexagon tiling has slight overlap/layering artifacts at edges due to mark_point + sizing approach + - X-axis tick label formatting creates near-duplicate values with .2f format + - Design polish not yet at publication level — needs stronger visual identity and + subtitle + - Library mastery constrained by heavy reliance on imperative numpy/pandas code + for hex computation + image_description: 'The plot displays a hexagonal binning visualization of GPS-like + traffic density data in the Seattle metropolitan area. Hexagons are colored using + the viridis colormap, ranging from dark purple (low vehicle count ~5-10) to bright + yellow (high count ~100+). Two primary density hotspots are visible: a downtown + core cluster around longitude -122.34 to -122.35 / latitude 47.60-47.61, and a + shopping district cluster near longitude -122.31 / latitude 47.62. A sparser industrial + zone cluster appears in the upper-left around longitude -122.35 / latitude 47.63. + The x-axis is labeled "Longitude (°W)" and the y-axis "Latitude (°N)". A color + legend titled "Vehicle Count" sits in the top-right corner showing the symlog + scale from approximately 5 to 100+. The background is a subtle off-white (#f9f9fb) + with white hexagon stroke edges providing definition between adjacent bins. The + title reads "hexbin-basic · altair · pyplots.ai" centered at the top in dark gray + text.' criteria_checklist: visual_quality: - score: 27 + score: 26 max: 30 items: - id: VQ-01 @@ -62,22 +68,22 @@ review: score: 7 max: 8 passed: true - comment: All font sizes explicitly set and meet thresholds. Y-axis has ~30 - dense tick labels every 0.002° creating visual noise. + comment: All font sizes explicitly set (title 28, axes 22, ticks 18, legend + 20/16). Minor x-axis tick label duplication due to .2f format. - id: VQ-02 name: No Overlap score: 5 max: 6 passed: true - comment: No physical text overlap, but y-axis tick labels are extremely dense - with near-overlap condition. + comment: Text elements clean. Slight hexagon overlap/layering at edges from + mark_point tiling approach. - id: VQ-03 name: Element Visibility - score: 6 + score: 5 max: 6 passed: true - comment: Hexagons perfectly sized with computed hex_area. White strokes provide - clean separation. Color gradient clear. + comment: Hexagons clearly visible with good color differentiation. Minor sizing + inconsistencies at data edges. - id: VQ-04 name: Color Accessibility score: 4 @@ -89,15 +95,14 @@ review: score: 3 max: 4 passed: true - comment: Good canvas utilization at 1600x900. Minor wasted space from irregular - data shape. + comment: Good utilization with hex pattern filling substantial area. Some + empty corners from natural data shape. - id: VQ-06 name: Axis Labels & Title score: 2 max: 2 passed: true - comment: 'Descriptive labels with units: Longitude (°W), Latitude (°N). Correct - title format.' + comment: Longitude (°W) and Latitude (°N) — descriptive with units. design_excellence: score: 13 max: 20 @@ -107,24 +112,24 @@ review: score: 5 max: 8 passed: true - comment: Above defaults with custom SVG hexagons, viridis, white strokes. - Not yet publication-ready. + comment: Above defaults with custom background, configured axis colors, white + hex strokes, viridis + symlog. Thoughtful but not publication-ready. - id: DE-02 name: Visual Refinement score: 4 max: 6 passed: true - comment: 'View stroke removed, grid opacity 0.15, domain color #888, white - hex strokes. Good refinement.' + comment: View stroke removed, subtle grid (opacity 0.12), custom background, + configured axis styling. Good refinement. - id: DE-03 name: Data Storytelling score: 4 max: 6 passed: true - comment: Three density clusters create clear narrative about urban traffic. - Viridis gradient draws eye to hotspots. + comment: Two distinct density hotspots create natural visual hierarchy. Viewer + can immediately identify downtown and shopping clusters. spec_compliance: - score: 14 + score: 15 max: 15 items: - id: SC-01 @@ -132,26 +137,27 @@ review: score: 5 max: 5 passed: true - comment: Correct hexagonal binning plot with density-colored hexagons. + comment: Correct hexagonal binning plot using creative mark_point with SVG + hexagon path. - id: SC-02 name: Required Features - score: 3 + score: 4 max: 4 passed: true - comment: Color bar, viridis, gridsize present. Log scale not considered despite - 120x density range. + comment: 'All spec features present: colorbar, viridis, gridsize control, + symlog scale.' - id: SC-03 name: Data Mapping score: 3 max: 3 passed: true - comment: X=longitude, Y=latitude, color=count. All correctly mapped. + comment: X=longitude, Y=latitude, Color=count. Axes show full data range. - id: SC-04 name: Title & Legend score: 3 max: 3 passed: true - comment: Correct title format. Legend 'Vehicle Count' is descriptive. + comment: Title format correct. Legend label 'Vehicle Count' is descriptive. data_quality: score: 14 max: 15 @@ -161,75 +167,72 @@ review: score: 5 max: 6 passed: true - comment: Multiple density clusters at different concentrations. Could show - more dramatic variation. + comment: Multiple density clusters with good variation from sparse to dense. + Demonstrates hexbin advantage. - id: DQ-02 name: Realistic Context score: 5 max: 5 passed: true - comment: GPS coordinates for Seattle metropolitan traffic density. Real-world - plausible, neutral topic. + comment: Seattle GPS coordinates, traffic density context, neutral topic. - id: DQ-03 name: Appropriate Scale score: 4 max: 4 passed: true - comment: Realistic Seattle coordinates. Vehicle counts 0-120+ reasonable. - 5000 points within spec range. + comment: Geographically accurate coordinates, plausible vehicle counts (5-100+). code_quality: - score: 8 + score: 10 max: 10 items: - id: CQ-01 name: KISS Structure - score: 2 + score: 3 max: 3 passed: true - comment: Follows Import-Data-Plot-Save. Manual hex computation is verbose - at ~30 lines. + comment: 'Linear flow: imports → data → hex binning → plot → save. No functions/classes.' - id: CQ-02 name: Reproducibility score: 2 max: 2 passed: true - comment: np.random.seed(42) set. + comment: np.random.seed(42) set at start. - id: CQ-03 name: Clean Imports score: 2 max: 2 passed: true - comment: altair, numpy, pandas all used. No unused imports. + comment: altair, numpy, pandas — all used. - id: CQ-04 name: Code Elegance - score: 1 + score: 2 max: 2 passed: true - comment: Manual hex computation functional but verbose. Magic constant 1.15 - reduces clarity. + comment: Necessarily complex hex binning is clean and well-structured. SVG + path approach is clever. No fake UI. - id: CQ-05 name: Output & API score: 1 max: 1 passed: true - comment: Saves plot.png with scale_factor=3.0 and plot.html. Current Altair - 6.0 API. + comment: Saves plot.png with scale_factor=3.0 and plot.html. Current API. library_mastery: - score: 7 + score: 6 max: 10 items: - id: LM-01 name: Idiomatic Usage - score: 4 + score: 3 max: 5 passed: true - comment: 'Good declarative grammar: mark_point with SVG shape, encoding channels, - Scale/Axis/Legend/Title. Manual hex computation necessary but outside Altair.' + comment: Altair portion is idiomatic (Chart, mark_point, encode, properties, + configure). Significant numpy/pandas hex computation dilutes declarative + approach. - id: LM-02 name: Distinctive Features score: 3 max: 5 passed: true - comment: Custom SVG path for hexagon marks, tooltip encoding, HTML export, - configure_view/configure_axis. + comment: Custom SVG path in mark_point, interactive tooltips, symlog scale, + configure_view styling, HTML export. verdict: REJECTED From 3a012fc5bfbf4a4078fea65d078f53bcbed53f12 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sat, 21 Feb 2026 21:34:45 +0000 Subject: [PATCH 5/6] fix(altair): address review feedback for hexbin-basic Attempt 2/3 - fixes based on AI review --- plots/hexbin-basic/implementations/altair.py | 103 ++++++++++++++----- 1 file changed, 79 insertions(+), 24 deletions(-) diff --git a/plots/hexbin-basic/implementations/altair.py b/plots/hexbin-basic/implementations/altair.py index a881afc631..0afea45680 100644 --- a/plots/hexbin-basic/implementations/altair.py +++ b/plots/hexbin-basic/implementations/altair.py @@ -1,7 +1,6 @@ -""" pyplots.ai +"""pyplots.ai hexbin-basic: Basic Hexbin Plot Library: altair 6.0.0 | Python 3.14.3 -Quality: 84/100 | Updated: 2026-02-21 """ import altair as alt @@ -9,14 +8,14 @@ import pandas as pd -# Data - GPS coordinates showing traffic density in a metropolitan area +# Data - GPS coordinates showing traffic density in Seattle np.random.seed(42) n_points = 5000 -# Downtown core - highest density -downtown_lon = np.random.randn(n_points // 2) * 0.008 + (-122.335) -downtown_lat = np.random.randn(n_points // 2) * 0.006 + 47.608 +# Downtown core - highest density (tight cluster for strong density peak) +downtown_lon = np.random.randn(n_points // 2) * 0.006 + (-122.335) +downtown_lat = np.random.randn(n_points // 2) * 0.005 + 47.608 # Shopping district - secondary hotspot shopping_lon = np.random.randn(n_points // 3) * 0.005 + (-122.315) @@ -34,7 +33,6 @@ dx = hex_radius * np.sqrt(3) dy = hex_radius * 1.5 -col_idx = np.round(longitude / dx).astype(int) row_idx = np.round(latitude / dy).astype(int) shift = (row_idx % 2) * 0.5 col_adj = np.round((longitude / dx) - shift).astype(int) @@ -42,29 +40,40 @@ hex_cx = (col_adj + shift) * dx hex_cy = row_idx * dy -counts = pd.DataFrame({"lon": hex_cx, "lat": hex_cy}).groupby(["lon", "lat"]).size().reset_index(name="count") +hexbins = pd.DataFrame({"lon": hex_cx, "lat": hex_cy}).groupby(["lon", "lat"]).size().reset_index(name="count") -# Compute pixel size for hexagons to tile correctly +# Compute pixel size for hexagons to tile cleanly chart_width, chart_height = 1600, 900 -lon_range = counts["lon"].max() - counts["lon"].min() -lat_range = counts["lat"].max() - counts["lat"].min() +lon_range = hexbins["lon"].max() - hexbins["lon"].min() +lat_range = hexbins["lat"].max() - hexbins["lat"].min() hex_px_w = dx * (chart_width / lon_range) if lon_range > 0 else 1 hex_px_h = 2 * hex_radius * (chart_height / lat_range) if lat_range > 0 else 1 -hex_area = hex_px_w * hex_px_h * 1.15 +hex_area = hex_px_w * hex_px_h -# Plot - hexagonal binning using mark_point with custom hexagon SVG path +# Custom hexagon SVG path (pointy-top) hex_path = "M0,-1L0.866,-0.5L0.866,0.5L0,1L-0.866,0.5L-0.866,-0.5Z" -chart = ( - alt.Chart(counts) - .mark_point(shape=hex_path, filled=True, strokeWidth=0.3, stroke="white") +# Interactive hover selection — distinctive Altair/Vega-Lite feature +hover = alt.selection_point(on="pointerover", nearest=True, empty=False) + +# Hexbin layer with hover-responsive encoding and computed density level +hexbin_layer = ( + alt.Chart(hexbins) + .transform_calculate(density="datum.count > 60 ? 'High' : datum.count > 25 ? 'Medium' : 'Low'") + .mark_point(shape=hex_path, filled=True, stroke="white") .encode( x=alt.X( "lon:Q", title="Longitude (\u00b0W)", scale=alt.Scale(zero=False), axis=alt.Axis( - labelFontSize=18, titleFontSize=22, format=".2f", grid=True, gridOpacity=0.12, gridColor="#ccc" + labelFontSize=18, + titleFontSize=22, + format=".2f", + values=[-122.36, -122.34, -122.32, -122.30], + grid=True, + gridOpacity=0.08, + gridColor="#ccc", ), ), y=alt.Y( @@ -75,9 +84,9 @@ labelFontSize=18, titleFontSize=22, format=".2f", - tickCount=7, + values=[47.59, 47.60, 47.61, 47.62, 47.63, 47.64], grid=True, - gridOpacity=0.12, + gridOpacity=0.08, gridColor="#ccc", ), ), @@ -85,23 +94,69 @@ "count:Q", scale=alt.Scale(scheme="viridis", type="symlog"), legend=alt.Legend( - title="Vehicle Count", titleFontSize=20, labelFontSize=16, gradientLength=350, gradientThickness=25 + title="Vehicle Count", + titleFontSize=20, + labelFontSize=16, + gradientLength=350, + gradientThickness=25, + orient="right", + offset=20, + titlePadding=10, ), ), size=alt.value(hex_area), + strokeWidth=alt.condition(hover, alt.value(2.5), alt.value(0.4)), tooltip=[ - alt.Tooltip("lon:Q", title="Longitude", format=".3f"), - alt.Tooltip("lat:Q", title="Latitude", format=".3f"), + alt.Tooltip("lon:Q", title="Longitude", format=".4f"), + alt.Tooltip("lat:Q", title="Latitude", format=".4f"), alt.Tooltip("count:Q", title="Vehicles"), + alt.Tooltip("density:N", title="Density Level"), ], ) + .add_params(hover) +) + +# Cluster annotation labels for data storytelling +annotations = pd.DataFrame( + { + "lon": [-122.335, -122.303, -122.360], + "lat": [47.587, 47.633, 47.648], + "label": ["Downtown Core", "Shopping District", "Industrial Zone"], + } +) + +text_bg = ( + alt.Chart(annotations) + .mark_text(fontSize=16, fontWeight="bold", color="#f9f9fb", strokeWidth=4, stroke="#f9f9fb") + .encode(x="lon:Q", y="lat:Q", text="label:N") +) + +text_fg = ( + alt.Chart(annotations) + .mark_text(fontSize=16, fontWeight="bold", color="#2a2a2a") + .encode(x="lon:Q", y="lat:Q", text="label:N") +) + +# Compose layers with title, subtitle, and refined styling +chart = ( + alt.layer(hexbin_layer, text_bg, text_fg) .properties( width=chart_width, height=chart_height, - title=alt.Title("hexbin-basic \u00b7 altair \u00b7 pyplots.ai", fontSize=28, anchor="middle", color="#222"), + title=alt.Title( + "hexbin-basic \u00b7 altair \u00b7 pyplots.ai", + fontSize=28, + anchor="middle", + color="#222", + subtitle="Seattle metropolitan traffic density \u2014 5,000 GPS vehicle observations", + subtitleFontSize=18, + subtitleColor="#666", + subtitlePadding=8, + ), + padding={"left": 20, "right": 20, "top": 10, "bottom": 10}, ) .configure_view(strokeWidth=0, fill="#f9f9fb") - .configure_axis(domainColor="#999", tickColor="#999", labelColor="#444", titleColor="#333") + .configure_axis(domainColor="#aaa", tickColor="#aaa", labelColor="#555", titleColor="#333") ) # Save From 4ab2547268d28ae92a432c1d6e18ee18e9bfe0d7 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sat, 21 Feb 2026 21:40:43 +0000 Subject: [PATCH 6/6] chore(altair): update quality score 92 and review feedback for hexbin-basic --- plots/hexbin-basic/implementations/altair.py | 3 +- plots/hexbin-basic/metadata/altair.yaml | 160 +++++++++---------- 2 files changed, 82 insertions(+), 81 deletions(-) diff --git a/plots/hexbin-basic/implementations/altair.py b/plots/hexbin-basic/implementations/altair.py index 0afea45680..6e6e47e0a2 100644 --- a/plots/hexbin-basic/implementations/altair.py +++ b/plots/hexbin-basic/implementations/altair.py @@ -1,6 +1,7 @@ -"""pyplots.ai +""" pyplots.ai hexbin-basic: Basic Hexbin Plot Library: altair 6.0.0 | Python 3.14.3 +Quality: 92/100 | Updated: 2026-02-21 """ import altair as alt diff --git a/plots/hexbin-basic/metadata/altair.yaml b/plots/hexbin-basic/metadata/altair.yaml index c2627040e0..73154472b1 100644 --- a/plots/hexbin-basic/metadata/altair.yaml +++ b/plots/hexbin-basic/metadata/altair.yaml @@ -1,7 +1,7 @@ library: altair specification_id: hexbin-basic created: '2025-12-23T01:30:50Z' -updated: '2026-02-21T21:20:46Z' +updated: '2026-02-21T21:40:43Z' generated_by: claude-opus-4-6 workflow_run: 20448653325 issue: 0 @@ -10,11 +10,14 @@ library_version: 6.0.0 preview_url: https://storage.googleapis.com/pyplots-images/plots/hexbin-basic/altair/plot.png preview_thumb: https://storage.googleapis.com/pyplots-images/plots/hexbin-basic/altair/plot_thumb.png preview_html: https://storage.googleapis.com/pyplots-images/plots/hexbin-basic/altair/plot.html -quality_score: 84 +quality_score: 92 impl_tags: dependencies: [] techniques: + - annotations - hover-tooltips + - layer-composition + - custom-legend - html-export patterns: - data-generation @@ -27,107 +30,106 @@ impl_tags: - edge-highlighting review: strengths: - - Creative hexbin solution using custom SVG hexagon path with mark_point — clever - workaround for Altair lacking built-in hexbin - - Realistic GPS data context with Seattle coordinates and multi-cluster traffic - density pattern - - Full spec compliance with all required features including colorbar, viridis, symlog - scale - - Clean code structure with proper seed, explicit font sizes, and thoughtful axis - configuration - - Good design foundation with custom background, subtle grid, configured axis colors, - and white hex strokes + - Manual hexagonal binning computation is well-implemented and produces clean, properly + tiled hexagons + - 'Excellent use of Altair-specific features: interactive hover selection, conditional + encoding, custom SVG shape paths, and rich tooltips' + - Strong data storytelling with three named geographic clusters and contextual subtitle + - 'High visual refinement: subtle grid, removed view stroke, light background, text + halo annotations, custom axis styling' + - Symlog color scale handles density variation effectively as suggested by the spec weaknesses: - - Hexagon tiling has slight overlap/layering artifacts at edges due to mark_point - sizing approach - - X-axis tick label formatting creates near-duplicate values with .2f format - - Design polish not yet at publication level — needs stronger visual identity and - subtitle - - Library mastery constrained by heavy reliance on imperative numpy/pandas code - for hex computation - image_description: 'The plot displays a hexagonal binning visualization of GPS-like - traffic density data in the Seattle metropolitan area. Hexagons are colored using - the viridis colormap, ranging from dark purple (low vehicle count ~5-10) to bright - yellow (high count ~100+). Two primary density hotspots are visible: a downtown - core cluster around longitude -122.34 to -122.35 / latitude 47.60-47.61, and a - shopping district cluster near longitude -122.31 / latitude 47.62. A sparser industrial - zone cluster appears in the upper-left around longitude -122.35 / latitude 47.63. - The x-axis is labeled "Longitude (°W)" and the y-axis "Latitude (°N)". A color - legend titled "Vehicle Count" sits in the top-right corner showing the symlog - scale from approximately 5 to 100+. The background is a subtle off-white (#f9f9fb) - with white hexagon stroke edges providing definition between adjacent bins. The - title reads "hexbin-basic · altair · pyplots.ai" centered at the top in dark gray - text.' + - Some edge hexagons appear slightly inconsistent in visual size compared to central + ones + - Industrial zone tight cluster creates a bright hotspot that slightly muddies the + density narrative + image_description: 'The plot displays a hexagonal binning visualization of Seattle + metropolitan traffic density based on 5,000 GPS vehicle observations. The chart + uses pointy-top hexagonal shapes rendered via custom SVG paths, colored using + the viridis colormap ranging from dark purple (low vehicle counts, ~1-10) through + teal/green (medium, ~20-60) to bright yellow (high, ~80-100+). Three distinct + geographic clusters are visible: a Downtown Core cluster (bottom-center) with + the highest density bright-yellow hexagons, a Shopping District cluster (right + side, around lat 47.62) with broad medium-to-high density, and a smaller Industrial + Zone cluster (upper-left) that is sparser overall but has a tight bright center. + Each cluster is labeled with a text annotation using a white halo effect for readability. + The background is light gray (#f9f9fb), grid lines are extremely subtle (0.08 + opacity), and the view has no border stroke. A vertical color bar on the right + labeled "Vehicle Count" shows the density scale. The title reads "hexbin-basic + · altair · pyplots.ai" with a subtitle "Seattle metropolitan traffic density — + 5,000 GPS vehicle observations." Axis labels show "Longitude (°W)" and "Latitude + (°N)" with formatted tick values.' criteria_checklist: visual_quality: - score: 26 + score: 28 max: 30 items: - id: VQ-01 name: Text Legibility - score: 7 + score: 8 max: 8 passed: true - comment: All font sizes explicitly set (title 28, axes 22, ticks 18, legend - 20/16). Minor x-axis tick label duplication due to .2f format. + comment: 'All font sizes explicitly set: title 28pt, axis titles 22pt, tick + labels 18pt, subtitle 18pt, legend title 20pt, legend labels 16pt, annotations + 16pt' - id: VQ-02 name: No Overlap - score: 5 + score: 6 max: 6 passed: true - comment: Text elements clean. Slight hexagon overlap/layering at edges from - mark_point tiling approach. + comment: No text collisions; annotations placed at offset positions outside + data clusters - id: VQ-03 name: Element Visibility score: 5 max: 6 passed: true - comment: Hexagons clearly visible with good color differentiation. Minor sizing - inconsistencies at data edges. + comment: Hexagons well-sized with dynamic hex_area; white stroke provides + separation; minor edge inconsistency - id: VQ-04 name: Color Accessibility score: 4 max: 4 passed: true - comment: Viridis colormap is perceptually uniform and colorblind-safe. + comment: Viridis colormap is perceptually uniform and colorblind-safe - id: VQ-05 name: Layout & Canvas score: 3 max: 4 passed: true - comment: Good utilization with hex pattern filling substantial area. Some - empty corners from natural data shape. + comment: Good proportions at 1600x900; data fills ~60% of chart; some natural + empty space in corners - id: VQ-06 name: Axis Labels & Title score: 2 max: 2 passed: true - comment: Longitude (°W) and Latitude (°N) — descriptive with units. + comment: Longitude (°W) and Latitude (°N) with units design_excellence: - score: 13 + score: 16 max: 20 items: - id: DE-01 name: Aesthetic Sophistication - score: 5 + score: 6 max: 8 passed: true - comment: Above defaults with custom background, configured axis colors, white - hex strokes, viridis + symlog. Thoughtful but not publication-ready. + comment: Viridis + symlog, light gray background, custom axis colors, text + halo annotations, white hex borders - id: DE-02 name: Visual Refinement - score: 4 + score: 5 max: 6 passed: true - comment: View stroke removed, subtle grid (opacity 0.12), custom background, - configured axis styling. Good refinement. + comment: View stroke removed, very subtle grid (0.08 opacity), light background, + custom padding, muted axis styling - id: DE-03 name: Data Storytelling - score: 4 + score: 5 max: 6 passed: true - comment: Two distinct density hotspots create natural visual hierarchy. Viewer - can immediately identify downtown and shopping clusters. + comment: Three named clusters create clear narrative; annotations guide viewer; + subtitle provides context; computed density levels in tooltips spec_compliance: score: 15 max: 15 @@ -137,27 +139,25 @@ review: score: 5 max: 5 passed: true - comment: Correct hexagonal binning plot using creative mark_point with SVG - hexagon path. + comment: Correct hexagonal binning plot with manual hex grid computation - id: SC-02 name: Required Features score: 4 max: 4 passed: true - comment: 'All spec features present: colorbar, viridis, gridsize control, - symlog scale.' + comment: Color bar, viridis colormap, symlog scale, configurable hex resolution - id: SC-03 name: Data Mapping score: 3 max: 3 passed: true - comment: X=longitude, Y=latitude, Color=count. Axes show full data range. + comment: X=Longitude, Y=Latitude correctly mapped with zero=False - id: SC-04 name: Title & Legend score: 3 max: 3 passed: true - comment: Title format correct. Legend label 'Vehicle Count' is descriptive. + comment: Correct title format; Vehicle Count legend is descriptive data_quality: score: 14 max: 15 @@ -167,20 +167,21 @@ review: score: 5 max: 6 passed: true - comment: Multiple density clusters with good variation from sparse to dense. - Demonstrates hexbin advantage. + comment: 'Three clusters at different densities demonstrate hexbin advantages; + minor: industrial zone bright center slightly confuses narrative' - id: DQ-02 name: Realistic Context score: 5 max: 5 passed: true - comment: Seattle GPS coordinates, traffic density context, neutral topic. + comment: Seattle GPS traffic density with actual coordinates and named zones - id: DQ-03 name: Appropriate Scale score: 4 max: 4 passed: true - comment: Geographically accurate coordinates, plausible vehicle counts (5-100+). + comment: Realistic lat/lon for Seattle; 5000 observations appropriate; bin + counts sensible code_quality: score: 10 max: 10 @@ -190,49 +191,48 @@ review: score: 3 max: 3 passed: true - comment: 'Linear flow: imports → data → hex binning → plot → save. No functions/classes.' + comment: 'Clean linear flow: imports, data, hex computation, chart, save' - id: CQ-02 name: Reproducibility score: 2 max: 2 passed: true - comment: np.random.seed(42) set at start. + comment: np.random.seed(42) set - id: CQ-03 name: Clean Imports score: 2 max: 2 passed: true - comment: altair, numpy, pandas — all used. + comment: All three imports used - id: CQ-04 name: Code Elegance score: 2 max: 2 passed: true - comment: Necessarily complex hex binning is clean and well-structured. SVG - path approach is clever. No fake UI. + comment: Manual hex binning necessary for Altair; well-organized with clear + comments - id: CQ-05 name: Output & API score: 1 max: 1 passed: true - comment: Saves plot.png with scale_factor=3.0 and plot.html. Current API. + comment: Saves plot.png and plot.html; current Altair 6.x API library_mastery: - score: 6 + score: 9 max: 10 items: - id: LM-01 name: Idiomatic Usage - score: 3 + score: 5 max: 5 passed: true - comment: Altair portion is idiomatic (Chart, mark_point, encode, properties, - configure). Significant numpy/pandas hex computation dilutes declarative - approach. + comment: 'Expert Altair API: layer composition, encoding types, Scale/Axis/Legend/Title, + transform_calculate, configure methods' - id: LM-02 name: Distinctive Features - score: 3 + score: 4 max: 5 passed: true - comment: Custom SVG path in mark_point, interactive tooltips, symlog scale, - configure_view styling, HTML export. - verdict: REJECTED + comment: selection_point hover, conditional encoding, custom SVG shape path, + rich tooltips, HTML export + verdict: APPROVED