update(box-basic): highcharts — comprehensive quality review and improvement

plots/box-basic/implementations/highcharts.py

@@ -1,9 +1,10 @@
 """ pyplots.ai
 box-basic: Basic Box Plot
-Library: highcharts unknown | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-23
+Library: highcharts 1.10.3 | Python 3.14
+Quality: 92/100 | Created: 2025-12-23
 """
 
+import json
 import tempfile
 import time
 import urllib.request
@@ -13,132 +14,277 @@
 from highcharts_core.chart import Chart
 from highcharts_core.options import HighchartsOptions
 from highcharts_core.options.series.boxplot import BoxPlotSeries
+from highcharts_core.options.series.data.boxplot import BoxPlotData
 from highcharts_core.options.series.scatter import ScatterSeries
 from selenium import webdriver
 from selenium.webdriver.chrome.options import Options
 
 
-# Data - generate sample data for 5 categories with different distributions
+# Data - employee performance scores across 5 departments
 np.random.seed(42)
-categories = ["Group A", "Group B", "Group C", "Group D", "Group E"]
+departments = ["Engineering", "Marketing", "Sales", "Design", "Finance"]
 colors = ["#306998", "#FFD43B", "#9467BD", "#17BECF", "#8C564B"]
+colors_fill = [
+    "rgba(48, 105, 152, 0.72)",
+    "rgba(255, 212, 59, 0.72)",
+    "rgba(148, 103, 189, 0.72)",
+    "rgba(23, 190, 207, 0.72)",
+    "rgba(140, 86, 75, 0.72)",
+]
 
-# Generate raw data (100 points each with different means and spreads)
-raw_data = [
-    np.random.normal(50, 10, 100),  # Group A: moderate mean, moderate spread
-    np.random.normal(65, 15, 100),  # Group B: higher mean, larger spread
-    np.random.normal(45, 8, 100),  # Group C: lower mean, tighter spread
-    np.random.normal(70, 12, 100),  # Group D: highest mean
-    np.random.normal(55, 20, 100),  # Group E: moderate mean, widest spread
+scores = [
+    np.random.normal(78, 8, 80),  # Engineering: high, tight
+    np.random.normal(72, 14, 60),  # Marketing: moderate, wide spread
+    np.random.normal(68, 9, 90),  # Sales: lower mean, moderate
+    np.random.normal(82, 7, 50),  # Design: highest, tight
+    np.random.normal(75, 18, 70),  # Finance: moderate, widest spread
 ]
 
-# Calculate box plot statistics (inline, no functions)
-box_data = []
+# Calculate box plot statistics
+box_stats = []
 outlier_data = []
 
-for i, data in enumerate(raw_data):
+for i, data in enumerate(scores):
+    data = np.clip(data, 0, 100)
     q1 = float(np.percentile(data, 25))
     median = float(np.percentile(data, 50))
     q3 = float(np.percentile(data, 75))
     iqr = q3 - q1
-    whisker_low = max(float(data.min()), q1 - 1.5 * iqr)
-    whisker_high = min(float(data.max()), q3 + 1.5 * iqr)
+    whisker_low = float(max(data[data >= q1 - 1.5 * iqr].min(), data.min()))
+    whisker_high = float(min(data[data <= q3 + 1.5 * iqr].max(), data.max()))
 
-    # Box data: [low, q1, median, q3, high]
-    box_data.append(
-        {"low": whisker_low, "q1": q1, "median": median, "q3": q3, "high": whisker_high, "color": colors[i]}
+    box_stats.append(
+        {
+            "low": round(whisker_low, 1),
+            "q1": round(q1, 1),
+            "median": round(median, 1),
+            "q3": round(q3, 1),
+            "high": round(whisker_high, 1),
+        }
     )
 
-    # Find and add outliers
-    outliers = data[(data < whisker_low) | (data > whisker_high)]
-    for outlier in outliers:
-        outlier_data.append([i, float(outlier)])
+    outliers = data[(data < q1 - 1.5 * iqr) | (data > q3 + 1.5 * iqr)]
+    for val in outliers:
+        outlier_data.append([i, round(float(val), 1)])
+
+# Identify key insights for annotations
+medians = [s["median"] for s in box_stats]
+spreads = [s["q3"] - s["q1"] for s in box_stats]
+best_dept_idx = int(np.argmax(medians))
+widest_dept_idx = int(np.argmax(spreads))
 
-# Create chart
+# Build BoxPlotData objects via highcharts-core API
+box_data = [
+    BoxPlotData(
+        low=box_stats[i]["low"],
+        q1=box_stats[i]["q1"],
+        median=box_stats[i]["median"],
+        q3=box_stats[i]["q3"],
+        high=box_stats[i]["high"],
+        color=colors[i],
+    )
+    for i in range(len(departments))
+]
+
+# Per-point data with fillColor (not exposed by BoxPlotData API, injected post-generation)
+box_data_api_js = "[" + ",\n".join(d.to_js_literal() for d in box_data) + "]"
+box_data_with_fill = json.dumps(
+    [
+        {
+            "low": box_stats[i]["low"],
+            "q1": box_stats[i]["q1"],
+            "median": box_stats[i]["median"],
+            "q3": box_stats[i]["q3"],
+            "high": box_stats[i]["high"],
+            "color": colors[i],
+            "fillColor": colors_fill[i],
+        }
+        for i in range(len(departments))
+    ]
+)
+
+# Build chart using highcharts-core Python API
 chart = Chart(container="container")
 chart.options = HighchartsOptions()
 
-# Chart configuration
 chart.options.chart = {
     "type": "boxplot",
     "width": 4800,
     "height": 2700,
-    "backgroundColor": "#ffffff",
-    "marginBottom": 280,
-    "spacingBottom": 80,
+    "backgroundColor": "#fafafa",
+    "marginBottom": 220,
+    "marginLeft": 240,
+    "marginRight": 120,
+    "spacingTop": 40,
+    "style": {"fontFamily": "'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif"},
+    "animation": False,
 }
 
-# Title
 chart.options.title = {
-    "text": "box-basic · highcharts · pyplots.ai",
-    "style": {"fontSize": "72px", "fontWeight": "bold"},
+    "text": "box-basic \u00b7 highcharts \u00b7 pyplots.ai",
+    "style": {"fontSize": "64px", "fontWeight": "700", "color": "#1a1a2e", "letterSpacing": "0.5px"},
+    "margin": 50,
+}
+
+chart.options.subtitle = {
+    "text": "Annual Performance Review Scores by Department",
+    "style": {"fontSize": "42px", "color": "#636e72", "fontWeight": "300"},
 }
 
-# X-axis
 chart.options.x_axis = {
-    "categories": categories,
-    "title": {"text": "Category", "style": {"fontSize": "48px"}},
-    "labels": {"style": {"fontSize": "40px"}},
+    "categories": departments,
+    "title": {
+        "text": "Department",
+        "style": {"fontSize": "44px", "color": "#2d3436", "fontWeight": "600"},
+        "margin": 24,
+    },
+    "labels": {"style": {"fontSize": "38px", "color": "#2d3436", "fontWeight": "500"}},
+    "lineWidth": 0,
+    "tickWidth": 0,
+    "gridLineWidth": 0,
 }
 
-# Y-axis
 chart.options.y_axis = {
-    "title": {"text": "Value", "style": {"fontSize": "48px"}},
-    "labels": {"style": {"fontSize": "36px"}},
+    "title": {
+        "text": "Score (out of 100)",
+        "style": {"fontSize": "44px", "color": "#2d3436", "fontWeight": "600"},
+        "margin": 20,
+    },
+    "labels": {"style": {"fontSize": "34px", "color": "#636e72"}},
     "gridLineWidth": 1,
-    "gridLineColor": "rgba(0, 0, 0, 0.1)",
+    "gridLineColor": "rgba(0, 0, 0, 0.06)",
+    "gridLineDashStyle": "Dot",
+    "tickInterval": 5,
+    "lineWidth": 0,
 }
 
-# Legend
-chart.options.legend = {"enabled": True, "itemStyle": {"fontSize": "36px"}}
+chart.options.legend = {"enabled": False}
+chart.options.credits = {"enabled": False}
+chart.options.tooltip = {"enabled": False}
 
-# Plot options for box styling
 chart.options.plot_options = {
     "boxplot": {
-        "lineWidth": 4,
+        "pointWidth": 480,
+        "lineWidth": 3,
         "medianWidth": 6,
-        "medianColor": "#1a1a1a",
+        "medianColor": "#1a1a2e",
+        "stemColor": "#555555",
         "stemWidth": 3,
+        "stemDashStyle": "Solid",
         "whiskerWidth": 4,
         "whiskerLength": "50%",
-        "colorByPoint": True,
-    }
+        "whiskerColor": "#555555",
+    },
+    "series": {"animation": False},
 }
 
-# Box plot series with individual colors per box
-box_series = BoxPlotSeries()
-box_series.name = "Distribution"
-box_series.data = box_data
-box_series.colors = colors
-
-chart.add_series(box_series)
+# Create BoxPlotSeries and ScatterSeries via Python API
+box_series = BoxPlotSeries(name="Department Scores", data=box_data)
 
-# Outliers as scatter series
-if outlier_data:
-    outlier_series = ScatterSeries()
-    outlier_series.name = "Outliers"
-    outlier_series.data = outlier_data
-    outlier_series.marker = {
-        "fillColor": "#E74C3C",
+outlier_series = ScatterSeries(
+    name="Outliers",
+    data=outlier_data,
+    marker={
+        "fillColor": "rgba(231, 76, 60, 0.75)",
         "lineWidth": 2,
-        "lineColor": "#C0392B",
-        "radius": 12,
+        "lineColor": "#c0392b",
+        "radius": 14,
         "symbol": "circle",
-    }
-    chart.add_series(outlier_series)
+    },
+    z_index=10,
+    show_in_legend=False,
+)
+
+chart.add_series(box_series)
+chart.add_series(outlier_series)
+
+# Generate JS config from the Python API
+chart_js = chart.to_js_literal(event_listener_enabled=False)
+
+# Inject fillColor into box data (BoxPlotData doesn't expose fillColor property)
+chart_js = chart_js.replace(box_data_api_js, box_data_with_fill)
 
 # Download Highcharts JS files (required for headless Chrome)
 highcharts_url = "https://code.highcharts.com/highcharts.js"
 with urllib.request.urlopen(highcharts_url, timeout=30) as response:
     highcharts_js = response.read().decode("utf-8")
 
-# BoxPlot requires highcharts-more.js
 highcharts_more_url = "https://code.highcharts.com/highcharts-more.js"
 with urllib.request.urlopen(highcharts_more_url, timeout=30) as response:
     highcharts_more_js = response.read().decode("utf-8")
 
+# Annotation JS for data storytelling — uses Highcharts renderer API (no Python equivalent)
+best_dept = departments[best_dept_idx]
+best_median = medians[best_dept_idx]
+widest_dept = departments[widest_dept_idx]
+widest_iqr = spreads[widest_dept_idx]
+n_outliers = len(outlier_data)
+outlier_s = "s" if n_outliers != 1 else ""
+
+annotation_js = f"""
+setTimeout(function() {{
+    var chart = Highcharts.charts[0];
+    if (!chart) return;
+
+    // Top Performer annotation — positioned at top-left of plot area
+    chart.renderer.label(
+        '<span style="font-size:30px;color:#1a6b3c;font-weight:700;">\\u25B2 Top Performer</span>' +
+        '<br><span style="font-size:26px;color:#555;">{best_dept} \\u2014 Median: {best_median:.0f}</span>' +
+        '<br><span style="font-size:24px;color:#777;">Highest scores, consistent results</span>',
+        chart.plotLeft + 20,
+        chart.plotTop + 15
+    )
+    .attr({{
+        fill: 'rgba(255,255,255,0.95)',
+        stroke: '#27ae60',
+        'stroke-width': 2.5,
+        r: 12,
+        padding: 18,
+        zIndex: 20
+    }})
+    .css({{ lineHeight: '38px' }})
+    .add();
+
+    // Widest Spread annotation — positioned at top-right of plot area
+    chart.renderer.label(
+        '<span style="font-size:30px;color:#b45309;font-weight:700;">\\u25CF Widest Spread</span>' +
+        '<br><span style="font-size:26px;color:#555;">{widest_dept} \\u2014 IQR: {widest_iqr:.0f} pts</span>' +
+        '<br><span style="font-size:24px;color:#777;">Highly variable performance</span>',
+        chart.plotLeft + chart.plotWidth - 620,
+        chart.plotTop + 15
+    )
+    .attr({{
+        fill: 'rgba(255,255,255,0.95)',
+        stroke: '#e67e22',
+        'stroke-width': 2.5,
+        r: 12,
+        padding: 18,
+        zIndex: 20
+    }})
+    .css({{ lineHeight: '38px' }})
+    .add();
+
+    // Outlier count annotation — bottom-left of plot area
+    chart.renderer.label(
+        '<span style="font-size:28px;color:#c0392b;font-weight:600;">\\u25CF {n_outliers} outlier{outlier_s} detected</span>' +
+        '<br><span style="font-size:24px;color:#777;">Scores beyond 1.5\\u00d7IQR from quartiles</span>',
+        chart.plotLeft + 20,
+        chart.plotTop + chart.plotHeight - 120
+    )
+    .attr({{
+        fill: 'rgba(255,255,255,0.95)',
+        stroke: '#e74c3c',
+        'stroke-width': 2,
+        r: 12,
+        padding: 16,
+        zIndex: 20
+    }})
+    .css({{ lineHeight: '36px' }})
+    .add();
+}}, 500);
+"""
+
 # Generate HTML with inline scripts
-html_str = chart.to_js_literal()
 html_content = f"""<!DOCTYPE html>
 <html>
 <head>
@@ -148,7 +294,8 @@
 </head>
 <body style="margin:0;">
     <div id="container" style="width: 4800px; height: 2700px;"></div>
-    <script>{html_str}</script>
+    <script>{chart_js}</script>
+    <script>{annotation_js}</script>
 </body>
 </html>"""
 
@@ -157,23 +304,21 @@
     f.write(html_content)
     temp_path = f.name
 
-# Save HTML file for interactive viewing
-with open("plot.html", "w", encoding="utf-8") as f:
-    f.write(html_content)
-
 # Take screenshot with Selenium
 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=4800,2700")
+chrome_options.add_argument("--window-size=5000,3000")
 
 driver = webdriver.Chrome(options=chrome_options)
 driver.get(f"file://{temp_path}")
-time.sleep(5)  # Wait for chart to render
-driver.save_screenshot("plot.png")
+time.sleep(6)
+
+container = driver.find_element("id", "container")
+container.screenshot("plot.png")
 driver.quit()
 
-# Clean up temp file
+# Clean up
 Path(temp_path).unlink()