feat(matplotlib): implement pie-basic

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

@@ -3,167 +3,43 @@
 Library: matplotlib
 """
 
-from typing import TYPE_CHECKING
-
 import matplotlib.pyplot as plt
 import pandas as pd
 
 
-if TYPE_CHECKING:
-    from matplotlib.figure import Figure
-
-
-# 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[float, float] = (10, 8),
-    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,
-) -> "Figure":
-    """
-    Create a basic pie chart showing proportions of categorical data.
-
-    Args:
-        data: Input DataFrame containing category and value columns.
-        category: Column name for category labels.
-        value: Column name for numeric values.
-        figsize: Figure size as (width, height). Defaults to (10, 8).
-        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. Defaults to None.
-        shadow: Add shadow effect for 3D appearance. Defaults to False.
-        labels: Custom labels for slices. Defaults to category values.
-        legend: Display legend. Defaults to True.
-        legend_loc: Legend location. Defaults to 'best'.
-        **kwargs: Additional parameters passed to ax.pie().
-
-    Returns:
-        Matplotlib Figure object.
-
-    Raises:
-        ValueError: If data is empty or values contain negatives or sum to zero.
-        KeyError: If required columns are not found in data.
-
-    Example:
-        >>> data = pd.DataFrame({
-        ...     'category': ['A', 'B', 'C'],
-        ...     'value': [30, 50, 20]
-        ... })
-        >>> fig = create_plot(data, 'category', 'value', title='Distribution')
-    """
-    # 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}")
-
-    values = data[value]
-    if (values < 0).any():
-        raise ValueError("Values cannot be negative for pie charts")
-
-    if values.sum() == 0:
-        raise ValueError("Values cannot all be zero")
-
-    # Prepare data
-    categories = data[category].tolist()
-    pie_values = values.tolist()
-    slice_labels = labels if labels is not None else categories
-
-    # Use PyPlots colors if not provided
-    if colors is None:
-        n_slices = len(pie_values)
-        colors = (PYPLOTS_COLORS * ((n_slices // len(PYPLOTS_COLORS)) + 1))[:n_slices]
-
-    # Create figure
-    fig, ax = plt.subplots(figsize=figsize)
-
-    # Configure text properties for style guide compliance
-    textprops = {"fontsize": 14, "fontfamily": ["Inter", "DejaVu Sans", "Arial", "Helvetica", "sans-serif"]}
-
-    # Plot pie chart
-    wedges, texts, autotexts = ax.pie(
-        pie_values,
-        labels=slice_labels if not legend else None,
-        colors=colors,
-        startangle=startangle,
-        autopct=autopct,
-        explode=explode,
-        shadow=shadow,
-        textprops=textprops,
-        **kwargs,
-    )
-
-    # Style percentage labels
-    for autotext in autotexts:
-        autotext.set_fontsize(14)
-        autotext.set_fontweight("bold")
-
-    # Ensure circular shape (equal aspect ratio)
-    ax.set_aspect("equal")
-
-    # Add title if provided
-    if title:
-        ax.set_title(
-            title,
-            fontsize=20,
-            fontweight="semibold",
-            fontfamily=["Inter", "DejaVu Sans", "Arial", "Helvetica", "sans-serif"],
-            pad=20,
-        )
+# Data
+data = pd.DataFrame(
+    {"category": ["Product A", "Product B", "Product C", "Product D", "Other"], "value": [35, 25, 20, 15, 5]}
+)
 
-    # Add legend if requested
-    if legend:
-        ax.legend(
-            wedges,
-            slice_labels,
-            loc=legend_loc,
-            fontsize=16,
-            frameon=True,
-            facecolor="white",
-            edgecolor="black",
-            framealpha=1.0,
-        )
+# Colors from style guide
+colors = ["#306998", "#FFD43B", "#DC2626", "#059669", "#8B5CF6"]
 
-    # Tight layout
-    plt.tight_layout()
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
 
-    return fig
+wedges, texts, autotexts = ax.pie(
+    data["value"],
+    labels=data["category"],
+    colors=colors,
+    autopct="%1.1f%%",
+    startangle=90,
+    explode=[0.02] * len(data),
+    wedgeprops={"linewidth": 2, "edgecolor": "white"},
+)
 
+# Style the text
+for text in texts:
+    text.set_fontsize(20)
+for autotext in autotexts:
+    autotext.set_fontsize(16)
+    autotext.set_color("white")
+    autotext.set_fontweight("bold")
 
-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]}
-    )
+ax.set_title("Basic Pie Chart", fontsize=20, fontweight="bold", pad=20)
 
-    # Create plot
-    fig = create_plot(sample_data, "category", "value", title="Market Share Distribution")
+# Equal aspect ratio ensures circular pie
+ax.set_aspect("equal")
 
-    # Save - ALWAYS use 'plot.png'!
-    plt.savefig("plot.png", dpi=300, bbox_inches="tight", facecolor="white")
-    print("Plot saved to plot.png")
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")