diff --git a/plots/violin-basic/implementations/highcharts.py b/plots/violin-basic/implementations/highcharts.py index 23874223ce..9a2a99f63b 100644 --- a/plots/violin-basic/implementations/highcharts.py +++ b/plots/violin-basic/implementations/highcharts.py @@ -1,7 +1,7 @@ """ pyplots.ai violin-basic: Basic Violin Plot -Library: highcharts unknown | Python 3.13.11 -Quality: 91/100 | Created: 2025-12-23 +Library: highcharts 1.10.3 | Python 3.14.3 +Quality: 92/100 | Updated: 2026-02-21 """ import tempfile @@ -13,145 +13,228 @@ from highcharts_core.chart import Chart from highcharts_core.options import HighchartsOptions from highcharts_core.options.series.polygon import PolygonSeries -from highcharts_core.options.series.scatter import ScatterSeries from scipy.stats import gaussian_kde from selenium import webdriver from selenium.webdriver.chrome.options import Options -# Data - generate sample distributions for 4 categories +# Data - test scores across 4 study groups with distinct distributions np.random.seed(42) -categories = ["Group A", "Group B", "Group C", "Group D"] -colors = ["#306998", "#FFD43B", "#9467BD", "#17BECF"] +categories = ["Control", "Tutorial", "Self-Study", "Intensive"] +colors = ["#306998", "#E5AB00", "#9467BD", "#17BECF"] -# Generate data with different distributions raw_data = { - "Group A": np.random.normal(50, 12, 200), - "Group B": np.concatenate([np.random.normal(40, 8, 100), np.random.normal(65, 8, 100)]), # Bimodal - "Group C": np.random.normal(60, 10, 200), - "Group D": np.random.exponential(15, 200) + 30, # Skewed + "Control": np.random.normal(50, 12, 200), + "Tutorial": np.concatenate([np.random.normal(40, 8, 100), np.random.normal(65, 8, 100)]), + "Self-Study": np.random.normal(60, 10, 200), + "Intensive": np.clip(np.random.exponential(15, 200) + 30, 0, 100), } +# RGB values for gradient fills +colors_rgb = ["48,105,152", "229,171,0", "148,103,189", "23,190,207"] + +# Overall mean for reference line +all_scores = np.concatenate(list(raw_data.values())) +overall_mean = float(np.mean(all_scores)) + # Calculate KDE and statistics for each category -violin_width = 0.35 # Half-width of violin in category units +violin_width = 0.35 violin_data = [] for i, cat in enumerate(categories): data = raw_data[cat] - - # Compute KDE using scipy - y_min, y_max = data.min() - 5, data.max() + 5 + y_min, y_max = data.min() - 3, data.max() + 3 y_grid = np.linspace(y_min, y_max, 100) kde_func = gaussian_kde(data) density = kde_func(y_grid) - - # Normalize density to fit within violin width density_norm = density / density.max() * violin_width - # Statistics for markers - q1 = np.percentile(data, 25) - median = np.percentile(data, 50) - q3 = np.percentile(data, 75) - violin_data.append( { "category": cat, "index": i, "y_grid": y_grid, "density": density_norm, - "q1": q1, - "median": median, - "q3": q3, + "q1": float(np.percentile(data, 25)), + "median": float(np.percentile(data, 50)), + "q3": float(np.percentile(data, 75)), + "mean": float(np.mean(data)), + "std": float(np.std(data)), + "n": len(data), "color": colors[i], + "rgb": colors_rgb[i], } ) -# Create chart +# Chart chart = Chart(container="container") chart.options = HighchartsOptions() -# Chart configuration chart.options.chart = { "type": "scatter", "width": 4800, "height": 2700, "backgroundColor": "#ffffff", - "marginBottom": 200, - "marginLeft": 250, - "marginRight": 100, + "plotBorderWidth": 0, + "marginBottom": 180, + "marginLeft": 240, + "marginRight": 80, + "marginTop": 200, + "animation": {"duration": 1000}, } -# Title chart.options.title = { - "text": "violin-basic · highcharts · pyplots.ai", - "style": {"fontSize": "84px", "fontWeight": "bold"}, + "text": "violin-basic \u00b7 highcharts \u00b7 pyplots.ai", + "style": {"fontSize": "72px", "fontWeight": "bold", "color": "#333333"}, +} + +chart.options.subtitle = { + "text": "Distribution of scores across 200 students per group", + "style": {"fontSize": "44px", "fontWeight": "normal", "color": "#777777"}, } -# X-axis (categories) chart.options.x_axis = { - "title": {"text": "Study Group", "style": {"fontSize": "56px"}}, - "labels": {"style": {"fontSize": "44px"}, "format": "{value}"}, + "title": {"text": "Study Group", "style": {"fontSize": "52px", "color": "#555555"}}, + "labels": {"style": {"fontSize": "44px", "color": "#555555"}}, "min": -0.5, "max": 3.5, "tickPositions": [0, 1, 2, 3], "categories": categories, - "lineWidth": 2, + "lineWidth": 0, + "tickLength": 0, + "crosshair": {"width": 2, "color": "rgba(0, 0, 0, 0.15)", "dashStyle": "Dash"}, } -# Y-axis (values) chart.options.y_axis = { - "title": {"text": "Test Score (points)", "style": {"fontSize": "56px"}}, - "labels": {"style": {"fontSize": "44px"}}, + "title": {"text": "Test Score (points)", "style": {"fontSize": "52px", "color": "#555555"}}, + "labels": {"style": {"fontSize": "44px", "color": "#555555"}}, "gridLineWidth": 1, - "gridLineColor": "rgba(0, 0, 0, 0.15)", + "gridLineColor": "rgba(0, 0, 0, 0.08)", + "lineWidth": 0, + "min": 0, + "max": 105, + "tickInterval": 10, + "crosshair": {"width": 1, "color": "rgba(0, 0, 0, 0.12)", "dashStyle": "Dot"}, + "plotLines": [ + { + "value": overall_mean, + "color": "rgba(0, 0, 0, 0.22)", + "dashStyle": "LongDash", + "width": 3, + "zIndex": 3, + "label": { + "text": f"Overall Mean ({overall_mean:.0f})", + "style": {"fontSize": "32px", "color": "rgba(0, 0, 0, 0.40)", "fontStyle": "italic"}, + "align": "right", + "x": -15, + "y": -10, + }, + } + ], +} + +chart.options.legend = { + "enabled": True, + "itemStyle": {"fontSize": "40px", "color": "#555555"}, + "verticalAlign": "top", + "align": "right", + "layout": "vertical", + "x": -20, + "y": 80, + "floating": True, } -# Legend -chart.options.legend = {"enabled": True, "itemStyle": {"fontSize": "44px"}} +chart.options.credits = {"enabled": False} + +chart.options.tooltip = { + "enabled": True, + "shared": False, + "useHTML": True, + "style": {"fontSize": "28px"}, + "headerFormat": "", + "backgroundColor": "rgba(255, 255, 255, 0.95)", + "borderColor": "#cccccc", + "borderRadius": 8, + "shadow": {"color": "rgba(0,0,0,0.15)", "offsetX": 2, "offsetY": 2, "width": 4}, +} -# Plot options chart.options.plot_options = { - "polygon": {"lineWidth": 3, "fillOpacity": 0.6, "enableMouseTracking": True}, - "scatter": {"marker": {"radius": 20, "symbol": "circle"}, "zIndex": 10}, + "polygon": { + "lineWidth": 2, + "fillOpacity": 1.0, + "enableMouseTracking": True, + "animation": True, + "states": {"hover": {"lineWidth": 3, "brightness": 0.1}, "inactive": {"opacity": 0.4}}, + }, + "scatter": {"marker": {"radius": 18, "symbol": "circle"}, "zIndex": 10, "enableMouseTracking": True}, + "series": {"animation": {"duration": 1200, "easing": "easeOutBounce"}}, } -# Add violin shapes as polygon series +# Violin shapes as polygon series with tooltip showing statistics for v in violin_data: - # Create polygon points for the violin shape - # Go up the right side, then down the left side to form a closed shape polygon_points = [] - - # Right side (positive x offset from center) for y_val, dens in zip(v["y_grid"], v["density"], strict=True): polygon_points.append([float(v["index"] + dens), float(y_val)]) - - # Left side (negative x offset from center) - reversed to close the polygon for j in range(len(v["y_grid"]) - 1, -1, -1): - y_val = v["y_grid"][j] - dens = v["density"][j] - polygon_points.append([float(v["index"] - dens), float(y_val)]) + polygon_points.append([float(v["index"] - v["density"][j]), float(v["y_grid"][j])]) + + is_featured = v["category"] == "Tutorial" + center_alpha = "0.70" if is_featured else "0.55" + edge_alpha = "0.20" if is_featured else "0.12" series = PolygonSeries() series.data = polygon_points series.name = v["category"] series.color = v["color"] - series.fill_color = v["color"] - series.fill_opacity = 0.6 + series.fill_color = { + "linearGradient": {"x1": 0, "y1": 0, "x2": 1, "y2": 0}, + "stops": [ + [0, f"rgba({v['rgb']},{edge_alpha})"], + [0.5, f"rgba({v['rgb']},{center_alpha})"], + [1, f"rgba({v['rgb']},{edge_alpha})"], + ], + } + series.fill_opacity = 1.0 + series.line_width = 3 if is_featured else 2 + series.tooltip = { + "pointFormat": ( + f'' + f"{v['category']}
" + f"n = {v['n']}
" + f"Mean: {v['mean']:.1f}
" + f"Median: {v['median']:.1f}
" + f"Q1: {v['q1']:.1f} | Q3: {v['q3']:.1f}
" + f"Std Dev: {v['std']:.1f}" + ) + } chart.add_series(series) -# Add median markers as scatter points -med_series = ScatterSeries() -med_series.data = [[float(v["index"]), float(v["median"])] for v in violin_data] -med_series.name = "Median" -med_series.color = "#FF0000" -med_series.marker = {"fillColor": "#FF0000", "lineColor": "#000000", "lineWidth": 5, "radius": 22, "symbol": "diamond"} -med_series.z_index = 20 -chart.add_series(med_series) - -# Add quartile box indicators (thin rectangles for IQR) +# Median lines (horizontal lines across each violin at the median position) for v in violin_data: - # Create a thin box for IQR - box_width = 0.05 + # Find density at median to determine line width + kde_func = gaussian_kde(raw_data[v["category"]]) + med_density = kde_func(v["median"])[0] + max_density = max(kde_func(v["y_grid"])) + line_half_width = (med_density / max_density) * violin_width * 0.85 + + med_line = PolygonSeries() + med_line.data = [ + [float(v["index"] - line_half_width), float(v["median"])], + [float(v["index"] + line_half_width), float(v["median"])], + ] + med_line.name = "Median" if v["index"] == 0 else f"Median {v['category']}" + med_line.show_in_legend = v["index"] == 0 + med_line.color = "#ffffff" + med_line.line_width = 8 + med_line.fill_opacity = 0 + med_line.z_index = 15 + med_line.enable_mouse_tracking = False + med_line.marker = {"enabled": False} + chart.add_series(med_line) + +# IQR boxes (thin rectangles for interquartile range) +for v in violin_data: + box_width = 0.10 box_points = [ [float(v["index"] - box_width), float(v["q1"])], [float(v["index"] + box_width), float(v["q1"])], @@ -163,71 +246,71 @@ box_series.data = box_points box_series.name = f"{v['category']} IQR" box_series.show_in_legend = False - box_series.color = "#000000" - box_series.fill_color = "#000000" - box_series.fill_opacity = 0.8 + box_series.color = "#333333" + box_series.fill_color = "#333333" + box_series.fill_opacity = 0.85 + box_series.enable_mouse_tracking = False chart.add_series(box_series) -# Download Highcharts JS files -highcharts_url = "https://code.highcharts.com/highcharts.js" +# Export +highcharts_url = "https://cdn.jsdelivr.net/npm/highcharts@11/highcharts.js" with urllib.request.urlopen(highcharts_url, timeout=30) as response: highcharts_js = response.read().decode("utf-8") -# Polygon requires highcharts-more.js -highcharts_more_url = "https://code.highcharts.com/highcharts-more.js" +highcharts_more_url = "https://cdn.jsdelivr.net/npm/highcharts@11/highcharts-more.js" with urllib.request.urlopen(highcharts_more_url, timeout=30) as response: highcharts_more_js = response.read().decode("utf-8") -# Generate HTML with inline scripts html_str = chart.to_js_literal() -html_content = f""" + +# plot.html for interactive viewing (CDN links for browser) +with open("plot.html", "w", encoding="utf-8") as f: + standalone_html = f""" - - + + -
+
""" + f.write(standalone_html) -# Write temp HTML file -with tempfile.NamedTemporaryFile(mode="w", suffix=".html", delete=False, encoding="utf-8") as f: - f.write(html_content) - temp_path = f.name - -# Save HTML for interactive viewing -with open("plot.html", "w", encoding="utf-8") as f: - standalone_html = f""" +# Temp HTML for screenshot (inline JS for headless Chrome) +html_content = f""" - - + + -
+
""" - f.write(standalone_html) -# Take screenshot with Selenium +with tempfile.NamedTemporaryFile(mode="w", suffix=".html", delete=False, encoding="utf-8") as f: + f.write(html_content) + temp_path = f.name + +# Screenshot chrome_options = Options() chrome_options.add_argument("--headless") chrome_options.add_argument("--no-sandbox") chrome_options.add_argument("--disable-dev-shm-usage") chrome_options.add_argument("--disable-gpu") -chrome_options.add_argument("--window-size=5000,3000") +chrome_options.add_argument("--window-size=4900,2800") driver = webdriver.Chrome(options=chrome_options) driver.get(f"file://{temp_path}") -time.sleep(5) # Wait for chart to render +time.sleep(5) container = driver.find_element("id", "container") container.screenshot("plot.png") driver.quit() -Path(temp_path).unlink() # Clean up temp file +Path(temp_path).unlink() diff --git a/plots/violin-basic/metadata/highcharts.yaml b/plots/violin-basic/metadata/highcharts.yaml index 33fba10d6f..2ec103063d 100644 --- a/plots/violin-basic/metadata/highcharts.yaml +++ b/plots/violin-basic/metadata/highcharts.yaml @@ -1,224 +1,242 @@ library: highcharts specification_id: violin-basic created: '2025-12-23T00:36:59Z' -updated: '2025-12-23T07:20:01Z' -generated_by: claude-opus-4-5-20251101 +updated: '2026-02-21T23:13:37Z' +generated_by: claude-opus-4-6 workflow_run: 20447783582 issue: 0 -python_version: 3.13.11 -library_version: unknown +python_version: 3.14.3 +library_version: 1.10.3 preview_url: https://storage.googleapis.com/pyplots-images/plots/violin-basic/highcharts/plot.png preview_thumb: https://storage.googleapis.com/pyplots-images/plots/violin-basic/highcharts/plot_thumb.png preview_html: https://storage.googleapis.com/pyplots-images/plots/violin-basic/highcharts/plot.html -quality_score: 91 +quality_score: 92 impl_tags: dependencies: - - scipy - - selenium + - scipy + - selenium techniques: - - html-export + - hover-tooltips + - html-export + - custom-legend patterns: - - data-generation - dataprep: [] - styling: [] + - data-generation + - iteration-over-groups + dataprep: + - kde + styling: + - gradient-fill + - alpha-blending + - grid-styling review: strengths: - - Excellent distribution variety demonstrating violin plot capabilities (normal, - bimodal, skewed) - - Clear colorblind-safe color palette with good contrast - - Proper use of Highcharts PolygonSeries to create violin shapes with KDE - - Includes median markers and IQR boxes as spec requires - - Realistic test score scenario with appropriate data ranges - - Both PNG and interactive HTML outputs provided - - Clean separation of violin shapes with no overlap + - Excellent data choices with four distinct distribution shapes (normal, bimodal, + shifted normal, skewed) showcasing violin plot capabilities + - Gradient fills with center-to-edge transparency create a polished professional + look + - All font sizes explicitly set and perfectly readable at 4800x2700 + - 'Clean visual refinement: removed axis lines, removed ticks, subtle grid, generous + margins' + - Rich HTML tooltips with formatted statistics (n, mean, median, Q1/Q3, std dev) + - Overall mean reference line provides useful context for cross-group comparison weaknesses: - - Legend positioning at bottom overlaps slightly with x-axis title - could be better - placed - - The IQR boxes are very thin (box_width=0.05) and could be slightly wider for better - visibility - - Grid lines could be even more subtle (currently alpha=0.15, could use 0.1) - image_description: 'The plot displays four violin shapes representing Study Groups - A, B, C, and D on a white background. Each violin uses a distinct colorblind-safe - color: blue (#306998) for Group A, yellow (#FFD43B) for Group B, purple (#9467BD) - for Group C, and cyan (#17BECF) for Group D. Each violin features a centered black - IQR (interquartile range) box and a red diamond marker indicating the median. - The title "violin-basic · highcharts · pyplots.ai" appears at the top in bold. - The Y-axis labeled "Test Score (points)" ranges from 0-130, and the X-axis labeled - "Study Group" shows the four category labels. A legend at the bottom identifies - each group color and the median marker. The violins effectively demonstrate different - distribution shapes: Group A shows a symmetric normal distribution, Group B displays - clear bimodality with two bulges, Group C is normally distributed, and Group D - exhibits right-skewed exponential distribution with a long upper tail reaching - ~120.' + - Blue and cyan colors could be slightly similar for some forms of color vision + deficiency + - IQR boxes are plain dark rectangles — more refined styling could improve polish + image_description: The plot displays four violin shapes representing test score + distributions across study groups (Control, Tutorial, Self-Study, Intensive). + Each violin is rendered as a PolygonSeries with horizontal gradient fills — center + opaque fading to transparent edges. Colors are steel blue (#306998), golden yellow + (#E5AB00), medium purple (#9467BD), and cyan (#17BECF). Dark IQR boxes sit inside + each violin spanning Q1–Q3, with white horizontal median lines crossing each at + the median position. A dashed "Overall Mean (51)" reference line spans the full + width. The y-axis reads "Test Score (points)" from 0–110 with subtle gridlines + every 10 points. The x-axis reads "Study Group" with category labels. A floating + legend in the upper right shows all four categories plus "Median". The title "violin-basic + · highcharts · pyplots.ai" appears at the top with subtitle "Distribution of scores + across 200 students per group." The Control violin is bell-shaped centered ~50, + Tutorial is distinctly bimodal (wide body spanning ~25–85), Self-Study is a tighter + bell centered ~60, and Intensive is right-skewed with a long thin tail reaching + ~103. The layout is clean with no axis lines, no tick marks, and generous margins. criteria_checklist: visual_quality: - score: 36 - max: 40 + score: 29 + max: 30 items: - id: VQ-01 name: Text Legibility - score: 9 - max: 10 + score: 8 + max: 8 passed: true - comment: Title, axis labels, and tick marks are all clearly readable at full - size. Font sizes are appropriately scaled for 4800x2700 resolution. + comment: All font sizes explicitly set (title 72px, subtitle 44px, axis titles + 52px, axis labels 44px, legend 40px). All text perfectly readable. - id: VQ-02 name: No Overlap - score: 8 - max: 8 + score: 6 + max: 6 passed: true - comment: No overlapping text elements. Category labels, axis titles, and legend - are all clearly separated. + comment: No overlapping text elements. Category labels well-spaced, legend + floats cleanly. - id: VQ-03 name: Element Visibility - score: 7 - max: 8 + score: 6 + max: 6 passed: true - comment: Violin shapes are clearly visible with good fill opacity. IQR boxes - and median markers are well-sized and prominent. + comment: Violin shapes clearly visible with appropriate sizing. IQR boxes + and median lines have strong contrast. - id: VQ-04 name: Color Accessibility - score: 5 - max: 5 + score: 3 + max: 4 passed: true - comment: Uses colorblind-safe palette (blue, yellow, purple, cyan) - no red-green - conflicts. + comment: Four distinct hue families work for most CVD types. Blue and cyan + could be slightly similar for tritanopia. - id: VQ-05 - name: Layout Balance + name: Layout & Canvas score: 4 - max: 5 + max: 4 passed: true - comment: Good overall layout with appropriate margins. Slight excess whitespace - at top of chart above Group D's extended distribution. + comment: Plot fills ~60% of canvas. Explicit margins create balanced layout. + Nothing cut off. - id: VQ-06 - name: Axis Labels + name: Axis Labels & Title score: 2 max: 2 passed: true - comment: 'Descriptive labels with units: "Test Score (points)" and "Study - Group".' - - id: VQ-07 - name: Grid & Legend - score: 1 - max: 2 + comment: Y-axis 'Test Score (points)' with units. X-axis 'Study Group' descriptive + for categorical data. + design_excellence: + score: 15 + max: 20 + items: + - id: DE-01 + name: Aesthetic Sophistication + score: 6 + max: 8 + passed: true + comment: Custom palette, gradient fills, featured violin, overall mean reference + line. Professional and polished, clearly above defaults. + - id: DE-02 + name: Visual Refinement + score: 5 + max: 6 passed: true - comment: Grid is subtle. Legend is positioned at bottom but could be placed - more optimally. + comment: Axis lines removed, tick marks removed, subtle grid (alpha 0.08), + styled crosshairs, generous whitespace. + - id: DE-03 + name: Data Storytelling + score: 4 + max: 6 + passed: true + comment: Featured Tutorial violin with higher opacity, overall mean reference + line, distinct distribution shapes create natural comparison. spec_compliance: - score: 24 - max: 25 + score: 15 + max: 15 items: - id: SC-01 name: Plot Type - score: 8 - max: 8 - passed: true - comment: Correct violin plot type with kernel density estimation shown via - polygon shapes. - - id: SC-02 - name: Data Mapping score: 5 max: 5 passed: true - comment: Categories on X-axis, values on Y-axis correctly assigned. - - id: SC-03 + comment: Correct violin plot combining KDE density shapes with box-plot-style + statistics. + - id: SC-02 name: Required Features score: 4 - max: 5 + max: 4 passed: true - comment: Shows quartile markers (IQR box), median line (diamond marker), mirrored - density on both sides. Split violin comparison not shown but spec says "consider" - not required. - - id: SC-04 - name: Data Range + comment: Quartile markers (IQR boxes), mirrored density, median line all present. + - id: SC-03 + name: Data Mapping score: 3 max: 3 passed: true - comment: All data visible within the axis ranges (0-130 appropriately covers - all distributions). - - id: SC-05 - name: Legend Accuracy - score: 2 - max: 2 - passed: true - comment: Legend correctly identifies all groups and median marker. - - id: SC-06 - name: Title Format - score: 2 - max: 2 + comment: Categories on X-axis, numerical values on Y-axis. 4 categories with + 200 points each. + - id: SC-04 + name: Title & Legend + score: 3 + max: 3 passed: true - comment: 'Correct format: "violin-basic · highcharts · pyplots.ai"' + comment: Title follows exact format. Legend labels match category names. data_quality: - score: 19 - max: 20 + score: 15 + max: 15 items: - id: DQ-01 name: Feature Coverage - score: 7 - max: 8 + score: 6 + max: 6 passed: true - comment: Excellent variety showing normal (A, C), bimodal (B), and skewed - (D) distributions. Demonstrates the plot type's ability to reveal distribution - shapes. + comment: 'Four distinct distributions: normal, bimodal, shifted normal, right-skewed + exponential.' - id: DQ-02 name: Realistic Context - score: 7 - max: 7 + score: 5 + max: 5 passed: true - comment: Test scores by study group is a plausible, real-world scenario that - makes sense for comparing distributions. + comment: Test scores across study groups — plausible educational research + scenario, neutral topic. - id: DQ-03 name: Appropriate Scale - score: 5 - max: 5 + score: 4 + max: 4 passed: true - comment: Test scores in 0-130 range with most values in 20-100 range are realistic - and sensible. + comment: Scores 0-100, 200 students per group — realistic for educational + assessment. 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 imports → data → plot → save structure, but code is more - complex than strictly necessary due to manual polygon construction for violins. + comment: 'Linear script: imports, data, chart setup, series, export. No functions + or classes.' - id: CQ-02 name: Reproducibility - score: 3 - max: 3 + score: 2 + max: 2 passed: true - comment: Uses np.random.seed(42) for reproducibility. + comment: np.random.seed(42) set at start. - id: CQ-03 name: Clean Imports score: 2 max: 2 passed: true - comment: All imports are used. + comment: All imports used. - id: CQ-04 - name: No Deprecated API - score: 1 - max: 1 + name: Code Elegance + score: 2 + max: 2 passed: true - comment: Uses current API. + comment: Clean dictionary-based data structure. Loop-based series construction + is appropriate. - id: CQ-05 - name: Output Correct - score: 0 + name: Output & API + score: 1 max: 1 passed: true - comment: Saves both plot.png and plot.html correctly. - library_features: - score: 4 - max: 5 + comment: Saves as plot.png via Selenium. Uses current highcharts_core API. + library_mastery: + score: 8 + max: 10 items: - - id: LF-01 - name: Uses distinctive library features + - id: LM-01 + name: Idiomatic Usage + score: 4 + max: 5 + passed: true + comment: Correct use of PolygonSeries for custom shapes. Chart options, tooltip, + crosshair, and series config follow highcharts_core patterns. + - id: LM-02 + name: Distinctive Features score: 4 max: 5 passed: true - comment: Good use of PolygonSeries and ScatterSeries to construct custom violin - shapes. Uses highcharts-more.js for polygon support. Interactive HTML output - included. + comment: Linear gradient fills, HTML tooltips, hover states, crosshairs, animation + easing — distinctive Highcharts features. verdict: APPROVED