feat(pygal): implement parallel-categories-basic (#2853)

## Implementation: `parallel-categories-basic` - pygal

Implements the **pygal** version of `parallel-categories-basic`.

**File:** `plots/parallel-categories-basic/implementations/pygal.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20606635585)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/parallel-categories-basic/implementations/pygal.py

@@ -0,0 +1,371 @@
+""" pyplots.ai
+parallel-categories-basic: Basic Parallel Categories Plot
+Library: pygal 3.1.0 | Python 3.13.11
+Quality: 90/100 | Created: 2025-12-30
+"""
+
+import cairosvg
+import numpy as np
+import pygal
+from pygal.style import Style
+
+
+# Set seed for reproducibility
+np.random.seed(42)
+
+# Data: Product journey from category through channel to outcome
+# This shows customer flow through a purchase funnel
+categories = ["Category", "Channel", "Payment", "Outcome"]
+
+# Define values for each dimension
+dimension_values = {
+    "Category": ["Electronics", "Clothing", "Home & Garden", "Sports"],
+    "Channel": ["Online", "Store", "Mobile App"],
+    "Payment": ["Credit Card", "Debit Card", "Digital Wallet"],
+    "Outcome": ["Completed", "Returned", "Cancelled"],
+}
+
+
+# Generate flow data - counts of observations for each path
+# Structure: (dim1_value, dim2_value, dim3_value, dim4_value): count
+np.random.seed(42)
+flows = {}
+
+# Generate realistic shopping journey data
+base_counts = {
+    # Electronics patterns - high online, good completion
+    ("Electronics", "Online", "Credit Card", "Completed"): 450,
+    ("Electronics", "Online", "Credit Card", "Returned"): 85,
+    ("Electronics", "Online", "Digital Wallet", "Completed"): 280,
+    ("Electronics", "Online", "Digital Wallet", "Returned"): 45,
+    ("Electronics", "Store", "Credit Card", "Completed"): 320,
+    ("Electronics", "Store", "Debit Card", "Completed"): 180,
+    ("Electronics", "Mobile App", "Digital Wallet", "Completed"): 220,
+    ("Electronics", "Mobile App", "Digital Wallet", "Cancelled"): 75,
+    ("Electronics", "Online", "Credit Card", "Cancelled"): 40,
+    # Clothing patterns - balanced channels, higher returns
+    ("Clothing", "Online", "Credit Card", "Completed"): 380,
+    ("Clothing", "Online", "Credit Card", "Returned"): 120,
+    ("Clothing", "Online", "Debit Card", "Completed"): 190,
+    ("Clothing", "Online", "Debit Card", "Returned"): 65,
+    ("Clothing", "Store", "Credit Card", "Completed"): 410,
+    ("Clothing", "Store", "Debit Card", "Completed"): 250,
+    ("Clothing", "Store", "Debit Card", "Returned"): 40,
+    ("Clothing", "Mobile App", "Digital Wallet", "Completed"): 175,
+    ("Clothing", "Mobile App", "Credit Card", "Completed"): 130,
+    ("Clothing", "Online", "Digital Wallet", "Cancelled"): 45,
+    # Home & Garden - more store visits
+    ("Home & Garden", "Store", "Credit Card", "Completed"): 380,
+    ("Home & Garden", "Store", "Debit Card", "Completed"): 290,
+    ("Home & Garden", "Store", "Debit Card", "Returned"): 55,
+    ("Home & Garden", "Online", "Credit Card", "Completed"): 210,
+    ("Home & Garden", "Online", "Credit Card", "Returned"): 40,
+    ("Home & Garden", "Online", "Digital Wallet", "Completed"): 145,
+    ("Home & Garden", "Mobile App", "Digital Wallet", "Completed"): 95,
+    # Sports - mobile-friendly, good completion
+    ("Sports", "Mobile App", "Digital Wallet", "Completed"): 260,
+    ("Sports", "Mobile App", "Credit Card", "Completed"): 185,
+    ("Sports", "Online", "Credit Card", "Completed"): 295,
+    ("Sports", "Online", "Debit Card", "Completed"): 175,
+    ("Sports", "Store", "Credit Card", "Completed"): 220,
+    ("Sports", "Store", "Debit Card", "Completed"): 165,
+    ("Sports", "Store", "Debit Card", "Returned"): 30,
+}
+
+# Colors for first dimension (Category) - colorblind-safe
+category_colors = {
+    "Electronics": "#306998",  # Python Blue
+    "Clothing": "#FFD43B",  # Python Yellow
+    "Home & Garden": "#4ECDC4",  # Teal
+    "Sports": "#E17055",  # Coral
+}
+
+# Secondary colors for middle dimensions - distinct from category colors
+dimension_colors = {
+    "Channel": {"Online": "#7B68EE", "Store": "#20B2AA", "Mobile App": "#FF69B4"},
+    "Payment": {"Credit Card": "#9370DB", "Debit Card": "#3CB371", "Digital Wallet": "#FF6347"},
+    "Outcome": {"Completed": "#32CD32", "Returned": "#FFA500", "Cancelled": "#DC143C"},
+}
+
+# Custom style for pygal
+custom_style = Style(
+    background="white",
+    plot_background="white",
+    foreground="#333333",
+    foreground_strong="#333333",
+    foreground_subtle="#666666",
+    title_font_size=72,
+)
+
+# Create minimal chart for title rendering
+chart = pygal.XY(
+    width=4800,
+    height=2700,
+    style=custom_style,
+    title="parallel-categories-basic · pygal · pyplots.ai",
+    show_legend=False,
+    show_x_guides=False,
+    show_y_guides=False,
+    show_x_labels=False,
+    show_y_labels=False,
+    dots_size=0,
+    stroke=False,
+    range=(0, 100),
+    xrange=(0, 100),
+)
+
+# Add empty data to avoid "No data" message
+chart.add("", [(50, 50)])
+
+# Render base SVG
+base_svg = chart.render().decode("utf-8")
+
+# SVG coordinate mapping
+margin_left = 450
+margin_right = 350
+margin_top = 350
+margin_bottom = 250
+chart_width = 4800 - margin_left - margin_right
+chart_height = 2700 - margin_top - margin_bottom
+
+# Calculate positions for each dimension axis
+n_dims = len(categories)
+x_positions = [margin_left + i * chart_width / (n_dims - 1) for i in range(n_dims)]
+bar_width = 120
+gap_ratio = 0.05  # Gap between categories on each axis
+
+# Calculate totals for each category in each dimension
+dim_totals = {}
+for dim_idx, dim_name in enumerate(categories):
+    dim_totals[dim_idx] = {}
+    for cat in dimension_values[dim_name]:
+        total = 0
+        for path, count in base_counts.items():
+            if path[dim_idx] == cat:
+                total += count
+        dim_totals[dim_idx][cat] = total
+
+# Calculate node positions
+node_positions = {}  # {(dim_idx, category): (y_top, y_bottom, x)}
+
+for dim_idx, dim_name in enumerate(categories):
+    x = x_positions[dim_idx]
+    dim_total = sum(dim_totals[dim_idx].values())
+    total_gap = gap_ratio * chart_height
+    available_height = chart_height - total_gap
+    n_cats = len(dimension_values[dim_name])
+    gap_size = total_gap / max(1, n_cats - 1) if n_cats > 1 else 0
+
+    y_top = margin_top
+    for _cat_idx, cat in enumerate(dimension_values[dim_name]):
+        height = (dim_totals[dim_idx][cat] / dim_total) * available_height if dim_total > 0 else 0
+        y_bottom = y_top + height
+        node_positions[(dim_idx, cat)] = (y_top, y_bottom, x)
+        y_top = y_bottom + gap_size
+
+# Build SVG elements
+parallel_svg = '<g id="parallel-categories">'
+
+# Draw nodes (category bars) for each dimension
+for dim_idx, dim_name in enumerate(categories):
+    x = x_positions[dim_idx]
+
+    for cat in dimension_values[dim_name]:
+        y_top, y_bottom, _ = node_positions[(dim_idx, cat)]
+        height = y_bottom - y_top
+
+        if height < 1:
+            continue
+
+        # Color based on dimension - use category colors for first dim, dimension colors for others
+        if dim_idx == 0:
+            fill_color = category_colors[cat]
+        else:
+            fill_color = dimension_colors[dim_name][cat]
+
+        parallel_svg += f'''
+    <rect x="{x - bar_width / 2:.0f}" y="{y_top:.0f}" width="{bar_width:.0f}" height="{height:.0f}"
+          fill="{fill_color}" stroke="white" stroke-width="2" opacity="0.9"/>'''
+
+    # Add dimension label at top (escape & for Home & Garden)
+    dim_name_escaped = dim_name.replace("&", "&amp;")
+    parallel_svg += f'''
+    <text x="{x:.0f}" y="{margin_top - 60:.0f}" text-anchor="middle"
+          font-size="48" font-weight="bold" font-family="DejaVu Sans, sans-serif"
+          fill="#333333">{dim_name_escaped}</text>'''
+
+# Add category labels for each dimension
+for dim_idx, dim_name in enumerate(categories):
+    x = x_positions[dim_idx]
+    for cat in dimension_values[dim_name]:
+        y_top, y_bottom, _ = node_positions[(dim_idx, cat)]
+        y_center = (y_top + y_bottom) / 2
+        height = y_bottom - y_top
+
+        # Position label based on dimension
+        if dim_idx == 0:  # Left side - outside bar
+            label_x = x - bar_width / 2 - 20
+            anchor = "end"
+        elif dim_idx == n_dims - 1:  # Right side - outside bar
+            label_x = x + bar_width / 2 + 20
+            anchor = "start"
+        else:  # Middle dimensions - below the bar
+            label_x = x
+            anchor = "middle"
+
+        # Use consistent readable font size (minimum 28px for all labels)
+        font_size = max(28, min(36, height * 0.35))
+
+        # Escape special characters
+        cat_escaped = cat.replace("&", "&amp;")
+
+        if dim_idx in [0, n_dims - 1]:
+            # Side labels - next to bars
+            parallel_svg += f'''
+    <text x="{label_x:.0f}" y="{y_center:.0f}" text-anchor="{anchor}"
+          font-size="{font_size:.0f}" font-family="DejaVu Sans, sans-serif"
+          fill="#333333" dominant-baseline="middle">{cat_escaped}</text>'''
+        else:
+            # Middle dimension labels - below each bar segment
+            label_y = y_bottom + 35
+            parallel_svg += f'''
+    <text x="{label_x:.0f}" y="{label_y:.0f}" text-anchor="{anchor}"
+          font-size="{font_size:.0f}" font-family="DejaVu Sans, sans-serif"
+          fill="#333333">{cat_escaped}</text>'''
+
+# Calculate flow offsets for drawing ribbons
+# Track cumulative position for each (dim_idx, category, direction)
+source_offsets = {}  # For outgoing flows
+target_offsets = {}  # For incoming flows
+
+for dim_idx in range(n_dims):
+    for cat in dimension_values[categories[dim_idx]]:
+        y_top, y_bottom, _ = node_positions[(dim_idx, cat)]
+        source_offsets[(dim_idx, cat)] = y_top
+        target_offsets[(dim_idx, cat)] = y_top
+
+# Draw flows between consecutive dimensions
+for dim_idx in range(n_dims - 1):
+    dim1_name = categories[dim_idx]
+    dim2_name = categories[dim_idx + 1]
+    x0 = x_positions[dim_idx]
+    x1 = x_positions[dim_idx + 1]
+
+    # Calculate total for normalization at each dimension
+    dim1_total = sum(dim_totals[dim_idx].values())
+    dim2_total = sum(dim_totals[dim_idx + 1].values())
+
+    # Aggregate flows between consecutive dimensions
+    flow_aggregates = {}
+    for path, count in base_counts.items():
+        key = (path[dim_idx], path[dim_idx + 1], path[0])  # Include first category for color
+        if key not in flow_aggregates:
+            flow_aggregates[key] = 0
+        flow_aggregates[key] += count
+
+    # Sort flows for consistent drawing (by source category order)
+    sorted_flows = sorted(
+        flow_aggregates.items(),
+        key=lambda x: (dimension_values[dim1_name].index(x[0][0]), dimension_values[dim2_name].index(x[0][1])),
+    )
+
+    # Draw each flow
+    for (source_cat, target_cat, first_cat), flow_value in sorted_flows:
+        if flow_value <= 0:
+            continue
+
+        source_y_top, source_y_bottom, _ = node_positions[(dim_idx, source_cat)]
+        target_y_top, target_y_bottom, _ = node_positions[(dim_idx + 1, target_cat)]
+
+        source_dim_total = dim_totals[dim_idx][source_cat]
+        target_dim_total = dim_totals[dim_idx + 1][target_cat]
+
+        source_height = (
+            (flow_value / source_dim_total) * (source_y_bottom - source_y_top) if source_dim_total > 0 else 0
+        )
+        target_height = (
+            (flow_value / target_dim_total) * (target_y_bottom - target_y_top) if target_dim_total > 0 else 0
+        )
+
+        # Get current positions
+        y0_top = source_offsets[(dim_idx, source_cat)]
+        y0_bottom = y0_top + source_height
+        y1_top = target_offsets[(dim_idx + 1, target_cat)]
+        y1_bottom = y1_top + target_height
+
+        # Bezier curve control points
+        band_x0 = x0 + bar_width / 2
+        band_x1 = x1 - bar_width / 2
+        cx0 = band_x0 + 0.4 * (band_x1 - band_x0)
+        cx1 = band_x0 + 0.6 * (band_x1 - band_x0)
+
+        # Create path for the curved ribbon
+        path_d = (
+            f"M {band_x0:.0f},{y0_top:.0f} "
+            f"C {cx0:.0f},{y0_top:.0f} {cx1:.0f},{y1_top:.0f} {band_x1:.0f},{y1_top:.0f} "
+            f"L {band_x1:.0f},{y1_bottom:.0f} "
+            f"C {cx1:.0f},{y1_bottom:.0f} {cx0:.0f},{y0_bottom:.0f} {band_x0:.0f},{y0_bottom:.0f} "
+            f"Z"
+        )
+
+        # Color by first category
+        ribbon_color = category_colors[first_cat]
+
+        parallel_svg += f'''
+    <path d="{path_d}" fill="{ribbon_color}" fill-opacity="0.4" stroke="none"/>'''
+
+        # Update offsets
+        source_offsets[(dim_idx, source_cat)] = y0_bottom
+        target_offsets[(dim_idx + 1, target_cat)] = y1_bottom
+
+# Add legend for categories
+legend_x = margin_left
+legend_y = chart_height + margin_top + 100
+legend_spacing = 400
+
+for idx, (cat, color) in enumerate(category_colors.items()):
+    lx = legend_x + idx * legend_spacing
+    cat_escaped = cat.replace("&", "&amp;")
+    parallel_svg += f'''
+    <rect x="{lx:.0f}" y="{legend_y:.0f}" width="50" height="50" fill="{color}" stroke="none"/>
+    <text x="{lx + 70:.0f}" y="{legend_y + 38:.0f}" text-anchor="start"
+          font-size="40" font-family="DejaVu Sans, sans-serif" fill="#333333">{cat_escaped}</text>'''
+
+# Add subtitle
+parallel_svg += f'''
+    <text x="2400" y="{chart_height + margin_top + 200:.0f}" text-anchor="middle"
+          font-size="36" font-style="italic" font-family="DejaVu Sans, sans-serif"
+          fill="#666666">Customer Purchase Journey Flows by Product Category</text>'''
+
+parallel_svg += "\n</g>"
+
+# Insert elements before closing </svg> tag
+svg_with_parallel = base_svg.replace("</svg>", f"{parallel_svg}\n</svg>")
+
+# Save SVG
+with open("plot.svg", "w") as f:
+    f.write(svg_with_parallel)
+
+# Render to PNG
+cairosvg.svg2png(bytestring=svg_with_parallel.encode("utf-8"), write_to="plot.png")
+
+# Save HTML for interactive version
+with open("plot.html", "w") as f:
+    f.write("""<!DOCTYPE html>
+<html>
+<head>
+    <title>parallel-categories-basic · pygal · pyplots.ai</title>
+    <style>
+        body { margin: 0; padding: 20px; background: #f5f5f5; font-family: sans-serif; }
+        .container { max-width: 100%; margin: 0 auto; }
+        object { width: 100%; height: auto; }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <object type="image/svg+xml" data="plot.svg">
+            Parallel categories diagram not supported
+        </object>
+    </div>
+</body>
+</html>""")

plots/parallel-categories-basic/metadata/pygal.yaml

@@ -0,0 +1,27 @@
+library: pygal
+specification_id: parallel-categories-basic
+created: '2025-12-30T21:54:55Z'
+updated: '2025-12-30T22:03:41Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20606635585
+issue: 0
+python_version: 3.13.11
+library_version: 3.1.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/parallel-categories-basic/pygal/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/parallel-categories-basic/pygal/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/parallel-categories-basic/pygal/plot.html
+quality_score: 90
+review:
+  strengths:
+  - Excellent parallel categories visualization with clear flow ribbons connecting
+    four dimensions
+  - Colorblind-safe palette with distinct colors for each product category
+  - Well-proportioned ribbons showing customer journey flows with appropriate opacity
+  - Clean layout with dimension labels at top and category labels positioned outside
+    bars
+  - Realistic e-commerce scenario with plausible purchase journey data
+  weaknesses:
+  - Middle dimension category labels (Channel, Payment) are positioned below bars
+    but could overlap with ribbons in denser visualizations
+  - Cancelled outcome label at bottom right is quite small and harder to read than
+    other labels