update(pie-basic): pygal — comprehensive quality review

plots/pie-basic/implementations/pygal.py

@@ -1,51 +1,71 @@
 """ pyplots.ai
 pie-basic: Basic Pie Chart
-Library: pygal 3.1.0 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-23
+Library: pygal 3.1.0 | Python 3.14.0
+Quality: 90/100 | Created: 2025-12-23
 """
 
 import pygal
 from pygal.style import Style
 
 
-# Data - Market share distribution
-categories = ["Product A", "Product B", "Product C", "Product D", "Product E"]
-values = [35, 25, 20, 12, 8]
+# Data - Global smartphone market share (2024)
+categories = [("Apple", 23.3), ("Samsung", 19.4), ("Xiaomi", 14.1), ("OPPO", 8.7), ("vivo", 7.5), ("Others", 27.0)]
+
+# Saturated, high-contrast palette — colorblind-safe (no red-green ambiguity)
+palette = ("#2563EB", "#D97706", "#0D9488", "#E11D48", "#7C3AED", "#64748B")
 
-# Custom style for 3600x3600 px (square format for pie chart)
 custom_style = Style(
     background="white",
     plot_background="white",
-    foreground="#333",
-    foreground_strong="#333",
-    foreground_subtle="#666",
-    colors=("#306998", "#FFD43B", "#4ECDC4", "#FF6B6B", "#95E1D3"),
-    title_font_size=72,
+    foreground="#1E293B",
+    foreground_strong="#0F172A",
+    foreground_subtle="#94A3B8",
+    colors=palette,
+    title_font_size=76,
     label_font_size=48,
     major_label_font_size=48,
-    legend_font_size=42,
-    value_font_size=42,
+    value_label_font_size=44,
+    legend_font_size=60,
+    value_font_size=56,
     tooltip_font_size=36,
+    value_colors=("#FFFFFF", "#FFFFFF", "#FFFFFF", "#FFFFFF", "#FFFFFF", "#FFFFFF"),
+    stroke_opacity=1,
 )
 
-# Create pie chart
 chart = pygal.Pie(
     width=3600,
     height=3600,
     style=custom_style,
-    title="pie-basic · pygal · pyplots.ai",
+    title="Smartphone Market Share · pie-basic · pygal · pyplots.ai",
     inner_radius=0,
     legend_at_bottom=True,
-    legend_at_bottom_columns=5,
+    legend_at_bottom_columns=3,
+    legend_box_size=40,
     print_values=True,
+    print_labels=True,
     print_values_position="center",
-    value_formatter=lambda x: f"{x}%",
+    value_formatter=lambda x: f"{x:.1f}%",
+    margin=60,
+    margin_bottom=100,
+    truncate_legend=-1,
 )
 
-# Add data slices
-for category, value in zip(categories, values, strict=True):
-    chart.add(category, value)
+# Storytelling: narrative legend names convey insight; on-slice labels add context
+legend_names = {"Others": "Others (largest segment)", "Apple": "Apple (#1 brand)", "Samsung": "Samsung (#2 worldwide)"}
+
+for company, value in categories:
+    slice_css = "stroke: white; stroke-width: 5"
+    slice_data = {"value": value, "style": slice_css}
+
+    # Explode the largest slice ("Others") with a subtle fixed offset for emphasis
+    if company == "Others":
+        slice_data["node"] = {"transform": "translate(0, -18)"}
+        slice_data["label"] = "Largest"
+    elif company == "Apple":
+        slice_data["label"] = "#1 brand"
+
+    name = legend_names.get(company, company)
+    chart.add(name, [slice_data])
 
-# Save as PNG and HTML
 chart.render_to_png("plot.png")
 chart.render_to_file("plot.html")