feat(plotly): implement pie-basic

Simplified implementation following KISS pattern:
- Removed function-based approach, using sequential code
- Uses PyPlots color palette from style guide
- Proper 16:9 aspect ratio (4800x2700px via scale=3)
- Clean Plotly white template with legend

Author: github-actions[bot]

plots/plotly/pie/pie-basic/default.py

@@ -3,215 +3,42 @@
 Library: plotly
 """
 
-from typing import TYPE_CHECKING
-
 import pandas as pd
 import plotly.graph_objects as go
 
 
-if TYPE_CHECKING:
-    pass
-
-# PyPlots.ai color palette
-PYPLOTS_COLORS = [
-    "#306998",  # Python Blue
-    "#FFD43B",  # Python Yellow
-    "#DC2626",  # Signal Red
-    "#059669",  # Teal Green
-    "#8B5CF6",  # Violet
-    "#F97316",  # Orange
-]
-
-
-def create_plot(
-    data: pd.DataFrame,
-    category: str,
-    value: str,
-    figsize: tuple[int, int] = (1600, 900),
-    title: str | None = None,
-    colors: list[str] | None = None,
-    startangle: float = 90,
-    autopct: str = "%1.1f%%",
-    explode: list[float] | None = None,
-    shadow: bool = False,
-    labels: list[str] | None = None,
-    legend: bool = True,
-    legend_loc: str = "best",
-    **kwargs,
-) -> go.Figure:
-    """
-    Create a basic pie chart for categorical data composition.
-
-    A fundamental pie chart that visualizes proportions and percentages of
-    categorical data as slices of a circular chart. Each slice represents
-    a category's share of the whole, making it ideal for showing composition
-    and distribution across a small number of categories.
-
-    Args:
-        data: Input DataFrame containing the data to plot.
-        category: Column name for category names (slice labels).
-        value: Column name for numeric values (slice sizes).
-        figsize: Figure size as (width, height) in pixels. Defaults to (1600, 900).
-        title: Plot title. Defaults to None.
-        colors: Custom color palette for slices. Defaults to PyPlots palette.
-        startangle: Starting angle for first slice in degrees. Defaults to 90.
-        autopct: Format string for percentage labels. Defaults to '%1.1f%%'.
-        explode: Offset distances for each slice (0-0.1 typical). Defaults to None.
-        shadow: Add shadow effect (not fully supported in Plotly). Defaults to False.
-        labels: Custom labels (defaults to category names). Defaults to None.
-        legend: Display legend. Defaults to True.
-        legend_loc: Legend location (Plotly uses different positioning). Defaults to 'best'.
-        **kwargs: Additional parameters passed to go.Pie.
-
-    Returns:
-        Plotly Figure object containing the pie chart.
-
-    Raises:
-        ValueError: If data is empty or contains negative values.
-        KeyError: If required columns are not found in data.
-
-    Example:
-        >>> data = pd.DataFrame({
-        ...     'category': ['Product A', 'Product B', 'Product C', 'Product D'],
-        ...     'value': [35, 25, 20, 20]
-        ... })
-        >>> fig = create_plot(data, 'category', 'value', title='Market Share')
-    """
-    # Input validation
-    if data.empty:
-        raise ValueError("Data cannot be empty")
-
-    for col in [category, value]:
-        if col not in data.columns:
-            available = ", ".join(data.columns)
-            raise KeyError(f"Column '{col}' not found. Available: {available}")
-
-    # Validate non-negative values
-    if (data[value] < 0).any():
-        raise ValueError("Pie chart values must be non-negative")
-
-    # Handle case where all values sum to zero
-    if data[value].sum() == 0:
-        raise ValueError("Pie chart values cannot all be zero")
-
-    # Get data values
-    categories = labels if labels is not None else data[category].tolist()
-    values = data[value].tolist()
+# Data
+data = pd.DataFrame(
+    {"category": ["Product A", "Product B", "Product C", "Product D", "Other"], "value": [35, 25, 20, 15, 5]}
+)
 
-    # Set colors - use custom colors or default PyPlots palette
-    color_sequence = colors if colors is not None else PYPLOTS_COLORS
-    # Extend colors if needed
-    while len(color_sequence) < len(values):
-        color_sequence = color_sequence + PYPLOTS_COLORS
-    slice_colors = color_sequence[: len(values)]
+# Color palette from style guide
+colors = ["#306998", "#FFD43B", "#DC2626", "#059669", "#8B5CF6"]
 
-    # Handle explode/pull parameter
-    pull_values = explode if explode is not None else [0] * len(values)
-    # Ensure pull_values has correct length
-    if len(pull_values) < len(values):
-        pull_values = list(pull_values) + [0] * (len(values) - len(pull_values))
-
-    # Create figure
-    fig = go.Figure()
-
-    # Build texttemplate from autopct format
-    # Convert matplotlib format to plotly format
-    if "%%" in autopct:
-        # Parse the format string - e.g., '%1.1f%%' -> '%{percent:.1%}'
-        try:
-            # Extract precision from format like '%1.1f%%'
-            import re
-
-            match = re.search(r"%(\d+)\.(\d+)f%%", autopct)
-            if match:
-                precision = int(match.group(2))
-                text_template = f"%{{percent:.{precision}%}}"
-            else:
-                text_template = "%{percent:.1%}"
-        except Exception:
-            text_template = "%{percent:.1%}"
-    else:
-        text_template = "%{percent:.1%}"
-
-    # Add pie trace
-    fig.add_trace(
+# Create plot
+fig = go.Figure(
+    data=[
         go.Pie(
-            labels=categories,
-            values=values,
-            marker={"colors": slice_colors, "line": {"color": "white", "width": 2}},
-            textinfo="percent",
-            texttemplate=text_template,
-            textfont={"size": 14, "family": "Inter, DejaVu Sans, Arial, sans-serif"},
+            labels=data["category"],
+            values=data["value"],
+            marker={"colors": colors, "line": {"color": "white", "width": 2}},
+            textinfo="label+percent",
+            textfont={"size": 16},
             textposition="inside",
             insidetextorientation="horizontal",
-            pull=pull_values,
-            rotation=startangle,
-            showlegend=legend,
             hovertemplate="<b>%{label}</b><br>Value: %{value}<br>Percentage: %{percent}<extra></extra>",
-            **kwargs,
+            rotation=90,
         )
-    )
-
-    # Configure legend position based on legend_loc
-    legend_config = {
-        "font": {"size": 16, "family": "Inter, DejaVu Sans, Arial, sans-serif"},
-        "bgcolor": "rgba(255, 255, 255, 1)",
-        "bordercolor": "rgba(0, 0, 0, 0.3)",
-        "borderwidth": 1,
-    }
-
-    # Map matplotlib legend locations to Plotly positions
-    if legend_loc in ["right", "center right"]:
-        legend_config.update({"x": 1.02, "y": 0.5, "xanchor": "left", "yanchor": "middle"})
-    elif legend_loc in ["left", "center left"]:
-        legend_config.update({"x": -0.15, "y": 0.5, "xanchor": "right", "yanchor": "middle"})
-    elif legend_loc in ["upper right"]:
-        legend_config.update({"x": 1.02, "y": 1, "xanchor": "left", "yanchor": "top"})
-    elif legend_loc in ["upper left"]:
-        legend_config.update({"x": -0.15, "y": 1, "xanchor": "right", "yanchor": "top"})
-    elif legend_loc in ["lower right"]:
-        legend_config.update({"x": 1.02, "y": 0, "xanchor": "left", "yanchor": "bottom"})
-    elif legend_loc in ["lower left"]:
-        legend_config.update({"x": -0.15, "y": 0, "xanchor": "right", "yanchor": "bottom"})
-    else:
-        # Default 'best' - place on the right
-        legend_config.update({"x": 1.02, "y": 0.5, "xanchor": "left", "yanchor": "middle"})
-
-    # Update layout with styling
-    fig.update_layout(
-        title={
-            "text": title,
-            "x": 0.5,
-            "xanchor": "center",
-            "font": {"size": 20, "family": "Inter, DejaVu Sans, Arial, sans-serif", "weight": 600},
-        }
-        if title
-        else None,
-        template="plotly_white",
-        width=figsize[0],
-        height=figsize[1],
-        showlegend=legend,
-        legend=legend_config if legend else None,
-        margin={"l": 40, "r": 150 if legend else 40, "t": 80 if title else 40, "b": 40},
-        paper_bgcolor="white",
-        plot_bgcolor="white",
-    )
-
-    # Ensure pie chart is circular (equal aspect ratio)
-    fig.update_layout(yaxis={"scaleanchor": "x", "scaleratio": 1})
-
-    return fig
-
-
-if __name__ == "__main__":
-    # Sample data for testing
-    sample_data = pd.DataFrame(
-        {"category": ["Product A", "Product B", "Product C", "Product D", "Other"], "value": [35, 25, 20, 15, 5]}
-    )
-
-    # Create plot
-    fig = create_plot(sample_data, "category", "value", title="Market Share Distribution")
-
-    # Save
-    fig.write_image("plot.png", width=1600, height=900, scale=2)
-    print("Plot saved to plot.png")
+    ]
+)
+
+# Layout
+fig.update_layout(
+    title={"text": "Basic Pie Chart", "font": {"size": 20}, "x": 0.5, "xanchor": "center"},
+    legend={"font": {"size": 16}, "orientation": "v", "yanchor": "middle", "y": 0.5, "xanchor": "left", "x": 1.02},
+    template="plotly_white",
+    margin={"l": 50, "r": 150, "t": 80, "b": 50},
+)
+
+# Save (4800 x 2700 px using scale=3 with 1600x900 base)
+fig.write_image("plot.png", width=1600, height=900, scale=3)