MarkusNeusinger
diff --git a/‎plots/bullet-basic/implementations/pygal.py‎
Lines changed: 108 additions & 116 deletions b/‎plots/bullet-basic/implementations/pygal.py‎
Lines changed: 108 additions & 116 deletions
@@ -1,152 +1,144 @@
 """ pyplots.ai
 bullet-basic: Basic Bullet Chart
-Library: pygal 3.1.0 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-23
+Library: pygal 3.1.0 | Python 3.14.3
+Quality: 85/100 | Updated: 2026-02-22
 """
 
+import xml.etree.ElementTree as ET
+
 import cairosvg
 import pygal
 from pygal.style import Style
 
 
 # Data - Sales KPIs showing actual vs target with qualitative ranges
 metrics = [
-    {"label": "Revenue", "actual": 275, "target": 250, "max": 300, "unit": "$K"},
-    {"label": "Profit", "actual": 85, "target": 100, "max": 120, "unit": "$K"},
-    {"label": "New Orders", "actual": 320, "target": 350, "max": 400, "unit": ""},
-    {"label": "Customers", "actual": 1450, "target": 1400, "max": 1600, "unit": ""},
-    {"label": "Satisfaction", "actual": 4.2, "target": 4.5, "max": 5.0, "unit": "/5"},
+    {"label": "Revenue", "actual": 275, "target": 250, "max": 300, "fmt": "${}K"},
+    {"label": "Profit", "actual": 85, "target": 100, "max": 120, "fmt": "${}K"},
+    {"label": "New Orders", "actual": 320, "target": 350, "max": 400, "fmt": "{}"},
+    {"label": "Customers", "actual": 1450, "target": 1400, "max": 1600, "fmt": "{}"},
+    {"label": "Satisfaction", "actual": 4.2, "target": 4.5, "max": 5.0, "fmt": "{}/5"},
+    {"label": "Avg Deal Size", "actual": 42, "target": 50, "max": 60, "fmt": "${}K"},
+    {"label": "Retention", "actual": 92, "target": 85, "max": 100, "fmt": "{}%"},
 ]
 
-# Qualitative range thresholds as percentage of max
 POOR_PCT = 50
-SATISFACTORY_PCT = 75
+SAT_PCT = 75
+
+# Normalize to percentages and classify performance vs target
+actual_pcts = [round((m["actual"] / m["max"]) * 100, 1) for m in metrics]
+target_pcts = [round((m["target"] / m["max"]) * 100, 1) for m in metrics]
+above_target = [m["actual"] >= m["target"] for m in metrics]
+labels = [f"{m['label']} ({m['fmt'].format(m['actual'])})" for m in metrics]
+
+# Performance-coded colors for data storytelling (colorblind-safe teal vs amber)
+COLOR_ABOVE = "#2A9D8F"
+COLOR_BELOW = "#D4770B"
+COLOR_TARGET = "#1a1a1a"
 
-# Custom style for bullet chart with grayscale bands
+# Style: grayscale range bands + performance-coded bars + black target
 custom_style = Style(
     background="white",
     plot_background="white",
     foreground="#333333",
     foreground_strong="#333333",
-    foreground_subtle="#666666",
-    colors=(
-        "#E0E0E0",  # Poor range (lightest)
-        "#B8B8B8",  # Satisfactory range
-        "#909090",  # Good range (darkest)
-        "#306998",  # Actual value (Python blue)
-        "#1a1a1a",  # Target marker (black)
-    ),
-    title_font_size=72,
-    label_font_size=42,
-    major_label_font_size=40,
-    legend_font_size=36,
-    value_font_size=32,
-    tooltip_font_size=32,
+    foreground_subtle="#999999",
+    font_family="DejaVu Sans, Helvetica, Arial, sans-serif",
+    colors=("#E0E0E0", "#BFBFBF", "#969696", COLOR_ABOVE, COLOR_BELOW, COLOR_TARGET),
+    title_font_size=64,
+    label_font_size=40,
+    major_label_font_size=36,
+    legend_font_size=34,
+    value_font_size=30,
+    tooltip_font_size=30,
 )
 
-# Create horizontal stacked bar chart for range bands
 chart = pygal.HorizontalStackedBar(
     width=4800,
     height=2700,
-    title="bullet-basic · pygal · pyplots.ai",
+    title="bullet-basic \u00b7 pygal \u00b7 pyplots.ai",
     style=custom_style,
     show_legend=True,
     legend_at_bottom=True,
-    legend_box_size=28,
+    legend_box_size=26,
     print_values=False,
+    print_zeroes=False,
     show_y_guides=False,
     show_x_guides=True,
-    x_label_rotation=0,
-    margin=80,
-    spacing=40,
+    margin=40,
+    spacing=0,
+    rounded_bars=2,
+    truncate_label=-1,
     x_title="Performance (% of Maximum)",
     range=(0, 100),
 )
-
-# Build labels and range data
-labels = []
-poor_vals = []
-satisfactory_vals = []
-good_vals = []
-actual_pcts = []
-target_pcts = []
-
-for m in metrics:
-    actual_pct = (m["actual"] / m["max"]) * 100
-    target_pct = (m["target"] / m["max"]) * 100
-    labels.append(f"{m['label']} ({m['actual']}{m['unit']})")
-    # Stacked segments for qualitative ranges
-    poor_vals.append(POOR_PCT)
-    satisfactory_vals.append(SATISFACTORY_PCT - POOR_PCT)
-    good_vals.append(100 - SATISFACTORY_PCT)
-    actual_pcts.append(actual_pct)
-    target_pcts.append(target_pct)
-
 chart.x_labels = labels
-chart.add("Poor (0-50%)", poor_vals)
-chart.add("Satisfactory (50-75%)", satisfactory_vals)
-chart.add("Good (75-100%)", good_vals)
-
-# Render base chart to SVG, then inject actual bars and target markers
-svg_string = chart.render().decode("utf-8")
-
-# Pygal plot coordinates (from SVG analysis):
-# Plot area: transform="translate(624, 192)", width=4096, height=2104
-# Bars start at x=78.77 within plot (total from origin: 624 + 78.77 = 702.77)
-# X-axis 0 maps to ~78.77, 100 maps to ~4017.23 (range of ~3938.46 pixels)
-PLOT_OFFSET_X = 624
-PLOT_OFFSET_Y = 192
-BAR_START_X = 78.77  # Within plot coordinates
-X_SCALE = 39.3846  # pixels per percentage point (3938.46 / 100)
-
-# Row Y positions (from SVG: y values in <desc> tags) - relative to plot origin
-ROW_Y_CENTERS = [1861.23, 1456.62, 1052.0, 647.38, 242.77]
-
-# Build custom SVG elements for actual bars and target markers
-custom_elements = []
-for i, (actual_pct, target_pct) in enumerate(zip(actual_pcts, target_pcts, strict=True)):
-    # Calculate positions in plot coordinates
-    y_center = PLOT_OFFSET_Y + ROW_Y_CENTERS[i]
-    x_start = PLOT_OFFSET_X + BAR_START_X
-
-    # Actual value bar - narrower bar centered on row
-    actual_width = actual_pct * X_SCALE
-    bar_height = 100  # Thinner than the range bands
-    custom_elements.append(
-        f'<rect x="{x_start}" y="{y_center - bar_height / 2}" '
-        f'width="{actual_width}" height="{bar_height}" fill="#306998"/>'
-    )
-
-    # Target marker - thin vertical line extending beyond the bar
-    target_x = x_start + target_pct * X_SCALE
-    marker_height = 180
-    marker_width = 12
-    custom_elements.append(
-        f'<rect x="{target_x - marker_width / 2}" y="{y_center - marker_height / 2}" '
-        f'width="{marker_width}" height="{marker_height}" fill="#1a1a1a"/>'
-    )
-
-# Add legend entries for Actual and Target (positioned after existing legend items)
-legend_y = 2574  # Same line as existing legend
-legend_x_actual = 3200
-legend_x_target = 3700
-custom_elements.append(f'<rect x="{legend_x_actual}" y="{legend_y}" width="28" height="28" fill="#306998"/>')
-custom_elements.append(
-    f'<text x="{legend_x_actual + 40}" y="{legend_y + 24}" '
-    f'font-family="Consolas, monospace" font-size="36" fill="#333">Actual</text>'
-)
-custom_elements.append(f'<rect x="{legend_x_target}" y="{legend_y}" width="28" height="28" fill="#1a1a1a"/>')
-custom_elements.append(
-    f'<text x="{legend_x_target + 40}" y="{legend_y + 24}" '
-    f'font-family="Consolas, monospace" font-size="36" fill="#333">Target</text>'
-)
-
-# Inject custom elements before closing </svg>
-injection = "\n".join(custom_elements)
-svg_output = svg_string.replace("</svg>", f"{injection}\n</svg>")
-
-# Save SVG and PNG
-with open("plot.html", "w") as f:
-    f.write(svg_output)
 
-cairosvg.svg2png(bytestring=svg_output.encode(), write_to="plot.png")
+# Qualitative range bands as stacked series with per-value config dicts
+chart.add("Poor (0-50%)", [{"value": POOR_PCT, "label": labels[i]} for i in range(len(metrics))])
+chart.add("Satisfactory (50-75%)", [{"value": SAT_PCT - POOR_PCT, "label": labels[i]} for i in range(len(metrics))])
+chart.add("Good (75-100%)", [{"value": 100 - SAT_PCT, "label": labels[i]} for i in range(len(metrics))])
+
+# Legend-only series for performance-coded actual bars and target marker
+chart.add("Above Target", [None] * len(metrics))
+chart.add("Below Target", [None] * len(metrics))
+chart.add("Target", [None] * len(metrics))
+
+# Render SVG and parse for programmatic element injection
+ET.register_namespace("", "http://www.w3.org/2000/svg")
+ET.register_namespace("xlink", "http://www.w3.org/1999/xlink")
+svg_bytes = chart.render()
+root = ET.fromstring(svg_bytes)
+NS = "http://www.w3.org/2000/svg"
+
+parent_map = {child: parent for parent in root.iter() for child in parent}
+
+# Remove dashed leader lines for cleaner appearance
+for line in list(root.iter(f"{{{NS}}}line")):
+    if line.get("stroke-dasharray"):
+        p = parent_map.get(line)
+        if p is not None:
+            p.remove(line)
+
+# Locate serie-0 (Poor range) bars as coordinate reference
+serie_0 = next((g for g in root.iter(f"{{{NS}}}g") if "serie-0" in g.get("class", "")), None)
+
+# Extract bar positions from Poor range (x, y, width, height per metric row)
+poor_bars = []
+if serie_0 is not None:
+    for rect in serie_0.iter(f"{{{NS}}}rect"):
+        w, h = float(rect.get("width", "0")), float(rect.get("height", "0"))
+        if w > 1 and h > 1:
+            poor_bars.append((float(rect.get("x")), float(rect.get("y")), w, h))
+
+# Inject actual bars and target markers into the plot coordinate space
+inject_parent = parent_map.get(serie_0, root)
+for i, (bx, by, bw, bh) in enumerate(poor_bars):
+    # Convert percentage to pixel width using Poor band as scale reference
+    px_per_pct = bw / POOR_PCT
+    cy = by + bh / 2  # vertical center of this metric row
+
+    # Actual value bar (42% of band height for classic bullet chart layering)
+    actual_w = actual_pcts[i] * px_per_pct
+    bar_h = bh * 0.42
+    bar_color = COLOR_ABOVE if above_target[i] else COLOR_BELOW
+    a = ET.SubElement(inject_parent, f"{{{NS}}}rect")
+    a.set("x", f"{bx:.1f}")
+    a.set("y", f"{cy - bar_h / 2:.1f}")
+    a.set("width", f"{actual_w:.1f}")
+    a.set("height", f"{bar_h:.1f}")
+    a.set("fill", bar_color)
+    a.set("rx", "2")
+
+    # Target marker (prominent vertical line at target percentage)
+    tx = bx + target_pcts[i] * px_per_pct
+    marker_h = bh * 0.75
+    t = ET.SubElement(inject_parent, f"{{{NS}}}rect")
+    t.set("x", f"{tx - 6:.1f}")
+    t.set("y", f"{cy - marker_h / 2:.1f}")
+    t.set("width", "12")
+    t.set("height", f"{marker_h:.1f}")
+    t.set("fill", COLOR_TARGET)
+
+# Save as PNG at native 4800×2700 resolution
+cairosvg.svg2png(bytestring=ET.tostring(root, encoding="utf-8"), write_to="plot.png")