From ccc5b527bc59c8299584f43b5ce4ff466a29bfbf Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Fri, 15 May 2026 09:23:40 +0000 Subject: [PATCH 1/2] chore(plotly): add metadata for circos-basic --- .../implementations/python/plotly.py | 176 +++++++------- .../circos-basic/metadata/python/plotly.yaml | 225 ++---------------- 2 files changed, 107 insertions(+), 294 deletions(-) diff --git a/plots/circos-basic/implementations/python/plotly.py b/plots/circos-basic/implementations/python/plotly.py index d717ee63df..4eb352033e 100644 --- a/plots/circos-basic/implementations/python/plotly.py +++ b/plots/circos-basic/implementations/python/plotly.py @@ -1,91 +1,103 @@ -""" pyplots.ai +"""anyplot.ai circos-basic: Circos Plot -Library: plotly 6.5.0 | Python 3.13.11 -Quality: 90/100 | Created: 2025-12-31 +Library: plotly | Python 3.13 +Quality: pending | Created: 2025-12-31 """ +import os +import sys + + +# Prioritize venv's site-packages over current directory +if sys.prefix not in sys.path: + import site + + site_packages = site.getsitepackages() + if isinstance(site_packages, list): + sys.path = site_packages + sys.path + else: + sys.path.insert(0, site_packages) + import numpy as np import plotly.graph_objects as go -# Data: Trade flows between regions (as example for circos visualization) +# Theme tokens +THEME = os.getenv("ANYPLOT_THEME", "light") +PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17" +ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420" +INK = "#1A1A17" if THEME == "light" else "#F0EFE8" +INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0" + +# Okabe-Ito palette with first series as brand +OKABE_ITO = [ + "#009E73", # bluish green (brand) + "#D55E00", # vermillion + "#0072B2", # blue + "#CC79A7", # reddish purple + "#E69F00", # orange + "#56B4E9", # sky blue + "#F0E442", # yellow + "#1A1A1A" if THEME == "light" else "#E8E8E0", # neutral +] + +# Data: Trade flows between regions np.random.seed(42) -# Define 8 segments (regions) for the circular layout segments = ["North America", "Europe", "East Asia", "South America", "Africa", "Middle East", "South Asia", "Oceania"] n_segments = len(segments) # Segment sizes (proportional to economic importance) segment_sizes = np.array([25, 30, 28, 10, 8, 12, 15, 6]) -segment_sizes = segment_sizes / segment_sizes.sum() * 360 # Normalize to 360 degrees +segment_sizes = segment_sizes / segment_sizes.sum() * 360 # Connection matrix (trade flow values) -# Random but symmetric-ish values for bilateral trade connections = np.array( [ - [0, 45, 60, 15, 5, 10, 8, 12], # North America - [40, 0, 35, 12, 18, 25, 15, 8], # Europe - [55, 38, 0, 10, 12, 20, 30, 18], # East Asia - [12, 10, 8, 0, 8, 3, 4, 5], # South America - [6, 20, 10, 10, 0, 15, 6, 2], # Africa - [12, 28, 22, 4, 12, 0, 18, 5], # Middle East - [10, 18, 35, 5, 8, 22, 0, 8], # South Asia - [15, 10, 22, 6, 3, 6, 10, 0], # Oceania + [0, 45, 60, 15, 5, 10, 8, 12], + [40, 0, 35, 12, 18, 25, 15, 8], + [55, 38, 0, 10, 12, 20, 30, 18], + [12, 10, 8, 0, 8, 3, 4, 5], + [6, 20, 10, 10, 0, 15, 6, 2], + [12, 28, 22, 4, 12, 0, 18, 5], + [10, 18, 35, 5, 8, 22, 0, 8], + [15, 10, 22, 6, 3, 6, 10, 0], ] ) -# Colors for each segment -colors = ["#306998", "#FFD43B", "#E34234", "#2ECC71", "#9B59B6", "#E67E22", "#1ABC9C", "#3498DB"] - - -# Helper to blend two hex colors -def blend_colors(c1, c2, ratio=0.5): - """Blend two hex colors. ratio=0 gives c1, ratio=1 gives c2.""" - r1, g1, b1 = int(c1[1:3], 16), int(c1[3:5], 16), int(c1[5:7], 16) - r2, g2, b2 = int(c2[1:3], 16), int(c2[3:5], 16), int(c2[5:7], 16) - r = int(r1 * (1 - ratio) + r2 * ratio) - g = int(g1 * (1 - ratio) + g2 * ratio) - b = int(b1 * (1 - ratio) + b2 * ratio) - return f"#{r:02x}{g:02x}{b:02x}" - - -# Calculate segment positions on the circle -gap = 2 # Gap between segments in degrees +# Calculate segment positions on circle +gap = 2 total_gap = gap * n_segments available = 360 - total_gap segment_angles = segment_sizes / segment_sizes.sum() * available -# Starting angles for each segment start_angles = np.zeros(n_segments) for i in range(1, n_segments): start_angles[i] = start_angles[i - 1] + segment_angles[i - 1] + gap -# Create figure fig = go.Figure() -# Outer ring radius +# Radii for visualization layers outer_r = 1.0 inner_r = 0.85 ribbon_inner = 0.80 +track_r_outer = 0.78 +track_r_inner = 0.60 # Draw outer segments (arcs) for i in range(n_segments): theta_start = start_angles[i] theta_end = theta_start + segment_angles[i] - # Create arc points theta = np.linspace(np.radians(theta_start), np.radians(theta_end), 50) theta_rev = theta[::-1] - # Outer arc x_outer = outer_r * np.cos(theta) y_outer = outer_r * np.sin(theta) - # Inner arc (for the segment) x_inner = inner_r * np.cos(theta_rev) y_inner = inner_r * np.sin(theta_rev) - # Combine to make a filled arc x_arc = np.concatenate([x_outer, x_inner, [x_outer[0]]]) y_arc = np.concatenate([y_outer, y_inner, [y_outer[0]]]) @@ -94,26 +106,24 @@ def blend_colors(c1, c2, ratio=0.5): x=x_arc, y=y_arc, fill="toself", - fillcolor=colors[i], - line=dict(color="white", width=1), + fillcolor=OKABE_ITO[i], + line={"color": INK_SOFT, "width": 1}, name=segments[i], hoverinfo="name", showlegend=True, ) ) - # Add label for segment + # Add segment labels mid_angle = np.radians((theta_start + theta_end) / 2) label_r = outer_r + 0.12 label_x = label_r * np.cos(mid_angle) label_y = label_r * np.sin(mid_angle) - # Rotate text based on position for better readability text_angle = (theta_start + theta_end) / 2 if 90 < text_angle < 270: text_angle = text_angle - 180 - # Adjust text anchor based on position for less cramping mid_deg = (theta_start + theta_end) / 2 if 45 < mid_deg < 135: xanchor = "center" @@ -133,77 +143,68 @@ def blend_colors(c1, c2, ratio=0.5): y=label_y, text=segments[i], showarrow=False, - font=dict(size=16, color="#333333"), + font={"size": 20, "color": INK}, textangle=-text_angle, xanchor=xanchor, yanchor=yanchor, ) # Draw ribbons (connections between segments) -# Get midpoint angles for each segment mid_angles = start_angles + segment_angles / 2 - -# Track positions within each segment for ribbon placement segment_positions = np.zeros(n_segments) -# Draw connections as curved ribbons for i in range(n_segments): for j in range(i + 1, n_segments): - if connections[i, j] > 5: # Only show significant connections - # Normalize ribbon width + if connections[i, j] > 5: max_conn = connections.max() width_i = (connections[i, j] / max_conn) * segment_angles[i] * 0.3 width_j = (connections[i, j] / max_conn) * segment_angles[j] * 0.3 - # Source positions theta_i_start = start_angles[i] + segment_positions[i] theta_i_end = theta_i_start + width_i segment_positions[i] += width_i + 1 - # Target positions theta_j_start = start_angles[j] + segment_positions[j] theta_j_end = theta_j_start + width_j segment_positions[j] += width_j + 1 - # Create bezier-like ribbon using multiple points n_points = 30 - # Source arc points theta_src = np.linspace(np.radians(theta_i_start), np.radians(theta_i_end), 10) x_src = ribbon_inner * np.cos(theta_src) y_src = ribbon_inner * np.sin(theta_src) - # Target arc points theta_tgt = np.linspace(np.radians(theta_j_start), np.radians(theta_j_end), 10) x_tgt = ribbon_inner * np.cos(theta_tgt) y_tgt = ribbon_inner * np.sin(theta_tgt) - # Create curved path through center - # Bezier-like curve from source to target t = np.linspace(0, 1, n_points) - # Control points - curve through center with some offset cp1_x, cp1_y = 0.2 * x_src[-1], 0.2 * y_src[-1] cp2_x, cp2_y = 0.2 * x_tgt[0], 0.2 * y_tgt[0] - # Quadratic bezier for top edge curve1_x = (1 - t) ** 2 * x_src[-1] + 2 * (1 - t) * t * cp1_x + t**2 * x_tgt[0] curve1_y = (1 - t) ** 2 * y_src[-1] + 2 * (1 - t) * t * cp1_y + t**2 * y_tgt[0] - # Control points for bottom edge cp3_x, cp3_y = 0.2 * x_tgt[-1], 0.2 * y_tgt[-1] cp4_x, cp4_y = 0.2 * x_src[0], 0.2 * y_src[0] - # Quadratic bezier for bottom edge (reversed) curve2_x = (1 - t) ** 2 * x_tgt[-1] + 2 * (1 - t) * t * cp3_x + t**2 * x_src[0] curve2_y = (1 - t) ** 2 * y_tgt[-1] + 2 * (1 - t) * t * cp3_y + t**2 * y_src[0] - # Combine all points to form ribbon shape x_ribbon = np.concatenate([x_src, curve1_x, x_tgt, curve2_x, [x_src[0]]]) y_ribbon = np.concatenate([y_src, curve1_y, y_tgt, curve2_y, [y_src[0]]]) - # Blend colors from source and target segments for better visual connection - ribbon_color = blend_colors(colors[i], colors[j], 0.5) + # Blend colors inline + c1 = OKABE_ITO[i] + c2 = OKABE_ITO[j] + r1, g1, b1 = int(c1[1:3], 16), int(c1[3:5], 16), int(c1[5:7], 16) + r2, g2, b2 = int(c2[1:3], 16), int(c2[3:5], 16), int(c2[5:7], 16) + r = int(r1 * 0.5 + r2 * 0.5) + g = int(g1 * 0.5 + g2 * 0.5) + b = int(b1 * 0.5 + b2 * 0.5) + ribbon_color = f"#{r:02x}{g:02x}{b:02x}" + fig.add_trace( go.Scatter( x=x_ribbon, @@ -211,16 +212,14 @@ def blend_colors(c1, c2, ratio=0.5): fill="toself", fillcolor=ribbon_color, opacity=0.5, - line=dict(color="white", width=0.5), + line={"color": INK_SOFT, "width": 0.5}, hoverinfo="text", hovertext=f"{segments[i]} ↔ {segments[j]}: {connections[i, j]}", showlegend=False, ) ) -# Add inner track (simulated data - e.g., GDP values as bar heights) -track_r_outer = 0.78 -track_r_inner = 0.60 +# Draw inner track (data bars) track_values = np.array([0.8, 0.95, 0.9, 0.4, 0.25, 0.5, 0.55, 0.3]) for i in range(n_segments): @@ -230,7 +229,6 @@ def blend_colors(c1, c2, ratio=0.5): theta = np.linspace(np.radians(theta_start), np.radians(theta_end), 30) theta_rev = theta[::-1] - # Height based on track value height = track_r_inner + (track_r_outer - track_r_inner) * track_values[i] x_outer = height * np.cos(theta) @@ -246,29 +244,37 @@ def blend_colors(c1, c2, ratio=0.5): x=x_bar, y=y_bar, fill="toself", - fillcolor=colors[i], + fillcolor=OKABE_ITO[i], opacity=0.6, - line=dict(color="white", width=0.5), + line={"color": INK_SOFT, "width": 0.5}, hoverinfo="text", hovertext=f"{segments[i]} GDP Index: {track_values[i]:.2f}", showlegend=False, ) ) -# Update layout +# Update layout with theme-adaptive colors fig.update_layout( - title=dict(text="circos-basic · plotly · pyplots.ai", font=dict(size=28, color="#333333"), x=0.5, xanchor="center"), + title={"text": "circos-basic · plotly · anyplot.ai", "font": {"size": 28, "color": INK}, "x": 0.5, "xanchor": "center"}, showlegend=True, - legend=dict(orientation="h", yanchor="bottom", y=-0.15, xanchor="center", x=0.5, font=dict(size=14)), - xaxis=dict(showgrid=False, zeroline=False, showticklabels=False, range=[-1.5, 1.5], scaleanchor="y", scaleratio=1), - yaxis=dict(showgrid=False, zeroline=False, showticklabels=False, range=[-1.5, 1.5]), - plot_bgcolor="white", - paper_bgcolor="white", - margin=dict(l=50, r=50, t=100, b=120), + legend={ + "orientation": "h", + "yanchor": "bottom", + "y": -0.15, + "xanchor": "center", + "x": 0.5, + "font": {"size": 18, "color": INK_SOFT}, + "bgcolor": ELEVATED_BG, + "bordercolor": INK_SOFT, + "borderwidth": 1, + }, + xaxis={"showgrid": False, "zeroline": False, "showticklabels": False, "range": [-1.5, 1.5], "scaleanchor": "y", "scaleratio": 1}, + yaxis={"showgrid": False, "zeroline": False, "showticklabels": False, "range": [-1.5, 1.5]}, + plot_bgcolor=PAGE_BG, + paper_bgcolor=PAGE_BG, + margin={"l": 50, "r": 50, "t": 100, "b": 120}, ) -# Save as PNG (4800x2700 equivalent via scale) -fig.write_image("plot.png", width=1600, height=900, scale=3) - -# Save interactive HTML version -fig.write_html("plot.html", include_plotlyjs=True, full_html=True) +script_dir = os.path.dirname(os.path.abspath(__file__)) +fig.write_image(os.path.join(script_dir, f"plot-{THEME}.png"), width=1600, height=900, scale=3) +fig.write_html(os.path.join(script_dir, f"plot-{THEME}.html"), include_plotlyjs="cdn") diff --git a/plots/circos-basic/metadata/python/plotly.yaml b/plots/circos-basic/metadata/python/plotly.yaml index b7093cc960..4762705f0d 100644 --- a/plots/circos-basic/metadata/python/plotly.yaml +++ b/plots/circos-basic/metadata/python/plotly.yaml @@ -1,214 +1,21 @@ +# Per-library metadata for plotly implementation of circos-basic +# Auto-generated by impl-generate.yml + library: plotly +language: python specification_id: circos-basic created: '2025-12-31T11:06:47Z' -updated: '2025-12-31T11:28:17Z' -generated_by: claude-opus-4-5-20251101 -workflow_run: 20617614742 +updated: '2026-05-15T09:23:40Z' +generated_by: claude-haiku +workflow_run: 25910268508 issue: 3005 -python_version: 3.13.11 -library_version: 6.5.0 -preview_url: https://storage.googleapis.com/anyplot-images/plots/circos-basic/plotly/plot.png -preview_html: https://storage.googleapis.com/anyplot-images/plots/circos-basic/plotly/plot.html -quality_score: 90 -impl_tags: - dependencies: [] - techniques: - - bezier-curves - - html-export - patterns: - - data-generation - - matrix-construction - - iteration-over-groups - dataprep: [] - styling: - - alpha-blending +python_version: 3.13.13 +library_version: 6.7.0 +preview_url_light: https://storage.googleapis.com/anyplot-images/plots/circos-basic/python/plotly/plot-light.png +preview_url_dark: https://storage.googleapis.com/anyplot-images/plots/circos-basic/python/plotly/plot-dark.png +preview_html_light: https://storage.googleapis.com/anyplot-images/plots/circos-basic/python/plotly/plot-light.html +preview_html_dark: https://storage.googleapis.com/anyplot-images/plots/circos-basic/python/plotly/plot-dark.html +quality_score: null review: - strengths: - - Excellent implementation of Circos visualization using Plotly Scatter with fill=toself - for complex shapes - - Ribbon colors now properly blend source and target segment colors (addressed previous - feedback) - - Includes inner track for additional data layer (GDP index) as specified - - Good interactive HTML output alongside PNG export - - Well-chosen color palette with 8 distinct, accessible colors - - Proper proportional sizing of segments based on economic importance - - Adaptive label positioning based on angle to minimize overlap - weaknesses: - - 'Title format still includes extra descriptor: should be circos-basic · plotly - · pyplots.ai not Regional Trade Flows · circos-basic · plotly · pyplots.ai' - - Helper function blend_colors() violates strict KISS principle (imports → data - → plot → save) - image_description: The plot displays a Circos visualization showing regional trade - flows between 8 world regions. The outer ring contains colored arc segments for - North America (dark blue), Europe (golden yellow), East Asia (red), South America - (green), Africa (purple), Middle East (orange), South Asia (teal), and Oceania - (light blue). Each segment is proportionally sized based on economic importance. - Curved ribbon connections pass through the center, using blended colors from source - and target segments (addressing the previous feedback). An inner track displays - GDP index values as bar heights using the same color scheme. Labels are positioned - around the outer edge with adaptive text anchoring. A horizontal legend at the - bottom identifies all regions. The title reads "Regional Trade Flows · circos-basic - · plotly · pyplots.ai" - note this still includes an extra descriptor before the - spec-id. - criteria_checklist: - visual_quality: - score: 36 - max: 40 - items: - - id: VQ-01 - name: Text Legibility - score: 9 - max: 10 - passed: true - comment: Title and labels readable, font size adequate at 16pt for segment - labels - - id: VQ-02 - name: No Overlap - score: 7 - max: 8 - passed: true - comment: Minor cramping on "Middle East" and "Africa" labels due to proximity, - but still readable - - id: VQ-03 - name: Element Visibility - score: 8 - max: 8 - passed: true - comment: Segments, ribbons, and inner track all clearly visible with good - sizing - - id: VQ-04 - name: Color Accessibility - score: 5 - max: 5 - passed: true - comment: 8 distinct colors, well differentiated, no red-green confusion - - id: VQ-05 - name: Layout Balance - score: 5 - max: 5 - passed: true - comment: Good canvas utilization, plot fills substantial area, balanced margins - - id: VQ-06 - name: Axis Labels - score: 0 - max: 2 - passed: true - comment: N/A for circular plot, no axis labels needed (deducting 0 as circular - plots don't have axes) - - id: VQ-07 - name: Grid & Legend - score: 2 - max: 2 - passed: true - comment: Legend well positioned horizontally at bottom, clean without grid - spec_compliance: - score: 24 - max: 25 - items: - - id: SC-01 - name: Plot Type - score: 8 - max: 8 - passed: true - comment: Correct Circos plot with segments, ribbons, and inner track - - id: SC-02 - name: Data Mapping - score: 5 - max: 5 - passed: true - comment: Source/target connections correctly mapped as ribbons, values determine - width - - id: SC-03 - name: Required Features - score: 5 - max: 5 - passed: true - comment: Has segments, ribbons, inner track, gaps, proportional sizing - - id: SC-04 - name: Data Range - score: 3 - max: 3 - passed: true - comment: All 8 segments visible with all connections shown - - id: SC-05 - name: Legend Accuracy - score: 2 - max: 2 - passed: true - comment: All 8 regions correctly listed with matching colors - - id: SC-06 - name: Title Format - score: 1 - max: 2 - passed: true - comment: Title includes extra "Regional Trade Flows" prefix, should be just - "circos-basic · plotly · pyplots.ai" - data_quality: - score: 18 - max: 20 - items: - - id: DQ-01 - name: Feature Coverage - score: 7 - max: 8 - passed: true - comment: Shows segment sizing, ribbons, inner track; ribbon widths vary appropriately - - id: DQ-02 - name: Realistic Context - score: 7 - max: 7 - passed: true - comment: Trade flows between world regions is excellent real-world application - - id: DQ-03 - name: Appropriate Scale - score: 4 - max: 5 - passed: true - comment: Trade values reasonable; segment sizes proportional to economic importance - code_quality: - score: 8 - max: 10 - items: - - id: CQ-01 - name: KISS Structure - score: 1 - max: 3 - passed: false - comment: Has helper function blend_colors() which deviates from pure KISS - style - - id: CQ-02 - name: Reproducibility - score: 3 - max: 3 - passed: true - comment: np.random.seed(42) set properly - - id: CQ-03 - name: Clean Imports - score: 2 - max: 2 - passed: true - comment: Only numpy and plotly.graph_objects imported - - id: CQ-04 - name: No Deprecated API - score: 1 - max: 1 - passed: true - comment: Uses current Plotly API - - id: CQ-05 - name: Output Correct - score: 1 - max: 1 - passed: true - comment: Saves as plot.png and plot.html - library_features: - score: 4 - max: 5 - items: - - id: LF-01 - name: Uses distinctive library features - score: 4 - max: 5 - passed: true - comment: Good use of Scatter with fill='toself' for shapes, interactive HTML - output, but could leverage more Plotly-specific features - verdict: APPROVED + strengths: [] + weaknesses: [] From 18926bf89aa42c5c39b171dd3405d3258ab1bd03 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Fri, 15 May 2026 09:26:44 +0000 Subject: [PATCH 2/2] chore(plotly): update quality score 91 and review feedback for circos-basic --- .../implementations/python/plotly.py | 6 +- .../circos-basic/metadata/python/plotly.yaml | 228 +++++++++++++++++- 2 files changed, 224 insertions(+), 10 deletions(-) diff --git a/plots/circos-basic/implementations/python/plotly.py b/plots/circos-basic/implementations/python/plotly.py index 4eb352033e..88898d9004 100644 --- a/plots/circos-basic/implementations/python/plotly.py +++ b/plots/circos-basic/implementations/python/plotly.py @@ -1,7 +1,7 @@ -"""anyplot.ai +""" anyplot.ai circos-basic: Circos Plot -Library: plotly | Python 3.13 -Quality: pending | Created: 2025-12-31 +Library: plotly 6.7.0 | Python 3.13.13 +Quality: 91/100 | Updated: 2026-05-15 """ import os diff --git a/plots/circos-basic/metadata/python/plotly.yaml b/plots/circos-basic/metadata/python/plotly.yaml index 4762705f0d..d6838eafe1 100644 --- a/plots/circos-basic/metadata/python/plotly.yaml +++ b/plots/circos-basic/metadata/python/plotly.yaml @@ -1,11 +1,8 @@ -# Per-library metadata for plotly implementation of circos-basic -# Auto-generated by impl-generate.yml - library: plotly language: python specification_id: circos-basic created: '2025-12-31T11:06:47Z' -updated: '2026-05-15T09:23:40Z' +updated: '2026-05-15T09:26:44Z' generated_by: claude-haiku workflow_run: 25910268508 issue: 3005 @@ -15,7 +12,224 @@ preview_url_light: https://storage.googleapis.com/anyplot-images/plots/circos-ba preview_url_dark: https://storage.googleapis.com/anyplot-images/plots/circos-basic/python/plotly/plot-dark.png preview_html_light: https://storage.googleapis.com/anyplot-images/plots/circos-basic/python/plotly/plot-light.html preview_html_dark: https://storage.googleapis.com/anyplot-images/plots/circos-basic/python/plotly/plot-dark.html -quality_score: null +quality_score: 91 review: - strengths: [] - weaknesses: [] + strengths: + - Excellent theme adaptation with perfectly readable text in both light and dark + renders + - Sophisticated Bezier ribbon construction with color blending creates visual interest + and polish + - Smart annotation positioning handles all quadrants correctly with no overlaps + - Complete spec compliance—all required circos features fully realized + - Clean, reproducible code with deterministic data and proper KISS structure + weaknesses: + - Design Excellence could emphasize visual hierarchy more—consider custom color + palette or striking emphasis on key patterns like largest trade flows + image_description: |- + Light render (plot-light.png): + Background: Warm off-white (#FAF8F1) - correct and appropriate + Chrome: Title, segment labels, legend all in dark INK (#1A1A17) - excellent readability on light surface + Data: 8 segments in Okabe-Ito colors (starting with brand green #009E73); semi-transparent blended ribbons show trade flows; inner track bars show GDP indices + Legibility verdict: PASS - all text perfectly readable with strong contrast + + Dark render (plot-dark.png): + Background: Warm near-black (#1A1A17) - correct and appropriate + Chrome: Title, labels, legend now in light INK (#F0EFE8) and INK_SOFT (#B8B7B0) - excellent readability on dark surface; no dark-on-dark failures + Data: Segment colors identical to light render (only chrome adapted); brand green (#009E73) vibrant and visible; ribbon blending and track bars render clearly + Legibility verdict: PASS - all text perfectly readable with strong contrast; theme adaptation flawless + criteria_checklist: + visual_quality: + score: 30 + max: 30 + items: + - id: VQ-01 + name: Text Legibility + score: 8 + max: 8 + passed: true + comment: All fonts explicitly set (title 28px, labels 20px, legend 18px); + perfectly readable in both themes + - id: VQ-02 + name: No Overlap + score: 6 + max: 6 + passed: true + comment: Smart anchor positioning prevents all overlaps; labels well-spaced + around circle + - id: VQ-03 + name: Element Visibility + score: 6 + max: 6 + passed: true + comment: All elements (segments, ribbons, inner track) optimally visible and + adapted to circular layout + - id: VQ-04 + name: Color Accessibility + score: 2 + max: 2 + passed: true + comment: Okabe-Ito palette; good contrast; opacity (0.5-0.6) supports colorblind + safety + - id: VQ-05 + name: Layout & Canvas + score: 4 + max: 4 + passed: true + comment: Plot fills 60-70% of canvas with balanced margins (50/100/120); well-utilized + space + - id: VQ-06 + name: Axis Labels & Title + score: 2 + max: 2 + passed: true + comment: Descriptive title format; segment labels serve as categorical identifiers + - id: VQ-07 + name: Palette Compliance + score: 2 + max: 2 + passed: true + comment: 'First series #009E73 ✓; Okabe-Ito order maintained; backgrounds + correct; theme-adaptive text in both renders' + design_excellence: + score: 14 + max: 20 + items: + - id: DE-01 + name: Aesthetic Sophistication + score: 5 + max: 8 + passed: false + comment: Thoughtful Bezier ribbons and color blending, but relies on defaults + rather than custom design innovations + - id: DE-02 + name: Visual Refinement + score: 5 + max: 6 + passed: true + comment: Clean removal of spines/grid; generous whitespace; segment gaps add + visual separation + - id: DE-03 + name: Data Storytelling + score: 4 + max: 6 + passed: true + comment: Good visual hierarchy (outer ring → ribbons → inner track); could + emphasize key patterns more + spec_compliance: + score: 15 + max: 15 + items: + - id: SC-01 + name: Plot Type + score: 5 + max: 5 + passed: true + comment: Correct circos with segments, ribbons, and inner track + - id: SC-02 + name: Required Features + score: 4 + max: 4 + passed: true + comment: 'All spec features present: segments, proportional ribbons, color + coding, inner data track' + - id: SC-03 + name: Data Mapping + score: 3 + max: 3 + passed: true + comment: Circular coordinates correct; segment angles, ribbon width, track + height proportional + - id: SC-04 + name: Title & Legend + score: 3 + max: 3 + passed: true + comment: Title format correct; legend matches data perfectly + data_quality: + score: 15 + max: 15 + items: + - id: DQ-01 + name: Feature Coverage + score: 6 + max: 6 + passed: true + comment: 'Shows all circos aspects: varying segment sizes, connection strengths + (5-60), inner track variation' + - id: DQ-02 + name: Realistic Context + score: 5 + max: 5 + passed: true + comment: Trade flows between geographic regions—real-world plausible and neutral + - id: DQ-03 + name: Appropriate Scale + score: 4 + max: 4 + passed: true + comment: Segment sizes reflect economic importance; trade values and GDP indices + proportionally sensible + code_quality: + score: 10 + max: 10 + items: + - id: CQ-01 + name: KISS Structure + score: 3 + max: 3 + passed: true + comment: Linear flow with no unnecessary functions/classes + - id: CQ-02 + name: Reproducibility + score: 2 + max: 2 + passed: true + comment: seed(42) set; all data deterministic + - id: CQ-03 + name: Clean Imports + score: 2 + max: 2 + passed: true + comment: 'All imports actually used: os, sys, numpy, plotly' + - id: CQ-04 + name: Code Elegance + score: 2 + max: 2 + passed: true + comment: Pythonic; Bezier and color blending well-executed + - id: CQ-05 + name: Output & API + score: 1 + max: 1 + passed: true + comment: 'Correct output: plot-{THEME}.png + plot-{THEME}.html' + library_mastery: + score: 7 + max: 10 + items: + - id: LM-01 + name: Idiomatic Usage + score: 4 + max: 5 + passed: true + comment: Proper go.Figure(), add_trace(), update_layout(); standard patterns + - id: LM-02 + name: Distinctive Features + score: 3 + max: 5 + passed: true + comment: Bezier ribbon construction and advanced annotation positioning show + sophistication + verdict: APPROVED +impl_tags: + dependencies: [] + techniques: + - annotations + - bezier-curves + patterns: + - data-generation + - matrix-construction + - iteration-over-groups + dataprep: [] + styling: + - alpha-blending