feat(plotly): implement area-basic

Implement basic area chart for plotly library following KISS style guidelines.
Uses go.Scatter with fill='tozeroy' to create filled area under line.

- Uses Python Blue (#306998) from style guide
- Proper axis labels and title
- 4800x2700px output (1600x900 @ scale 3)

Author: github-actions[bot]

plots/plotly/scatter/area-basic/default.py

@@ -1,176 +1,46 @@
 """
 area-basic: Basic Area Chart
-Implementation for: plotly
-Variant: default
-Python: 3.10+
+Library: plotly
 """
 
-from typing import TYPE_CHECKING, Optional
-
 import pandas as pd
 import plotly.graph_objects as go
 
 
-if TYPE_CHECKING:
-    pass
-
-
-def create_plot(
-    data: pd.DataFrame,
-    x: str,
-    y: str,
-    title: Optional[str] = None,
-    xlabel: Optional[str] = None,
-    ylabel: Optional[str] = None,
-    color: str = "rgba(99, 110, 250, 0.5)",
-    line_color: Optional[str] = None,
-    line_width: float = 2.0,
-    fill_to: str = "tozeroy",
-    height: int = 900,
-    width: int = 1600,
-    **kwargs,
-) -> go.Figure:
-    """
-    Create a basic area chart showing quantitative data over a continuous interval.
-
-    The area between the line and the x-axis is filled with color, emphasizing
-    the magnitude of values. Ideal for showing trends and cumulative totals.
-
-    Args:
-        data: Input DataFrame with required columns
-        x: Column name for x-axis values (typically time or sequential data)
-        y: Column name for y-axis values (numeric)
-        title: Plot title (optional)
-        xlabel: Custom x-axis label (optional, defaults to column name)
-        ylabel: Custom y-axis label (optional, defaults to column name)
-        color: Fill color for the area with alpha (default: semi-transparent blue)
-        line_color: Color of the line at top of area (default: derived from fill color)
-        line_width: Width of the line (default: 2.0)
-        fill_to: Fill mode - 'tozeroy', 'tonexty', 'none' (default: 'tozeroy')
-        height: Figure height in pixels (default: 900)
-        width: Figure width in pixels (default: 1600)
-        **kwargs: Additional parameters passed to plotly Scatter trace
-
-    Returns:
-        Plotly Figure object
-
-    Raises:
-        ValueError: If data is empty
-        KeyError: If required columns not found
-
-    Example:
-        >>> data = pd.DataFrame({
-        ...     'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],
-        ...     'Sales': [100, 150, 130, 180, 200]
-        ... })
-        >>> fig = create_plot(data, x='Month', y='Sales', title='Monthly Sales')
-    """
-    # Input validation
-    if data.empty:
-        raise ValueError("Data cannot be empty")
-
-    # Check required columns
-    for col in [x, y]:
-        if col not in data.columns:
-            available = ", ".join(data.columns)
-            raise KeyError(f"Column '{col}' not found. Available columns: {available}")
-
-    # Derive line color from fill color if not provided
-    if line_color is None:
-        # Use a solid version of the fill color (darker)
-        line_color = "rgb(99, 110, 250)"
-
-    # Create the figure
-    fig = go.Figure()
-
-    # Add the area trace
-    fig.add_trace(
-        go.Scatter(
-            x=data[x],
-            y=data[y],
-            mode="lines",
-            fill=fill_to,
-            fillcolor=color,
-            line={"color": line_color, "width": line_width},
-            name=y,
-            hovertemplate=f"<b>{x}</b>: %{{x}}<br><b>{y}</b>: %{{y:,.2f}}<extra></extra>",
-            **kwargs,
-        )
+# Data
+data = pd.DataFrame(
+    {
+        "month": ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"],
+        "sales": [120, 135, 148, 162, 175, 195, 210, 198, 185, 170, 158, 190],
+    }
+)
+
+# Create figure
+fig = go.Figure()
+
+fig.add_trace(
+    go.Scatter(
+        x=data["month"],
+        y=data["sales"],
+        mode="lines",
+        fill="tozeroy",
+        fillcolor="rgba(48, 105, 152, 0.4)",
+        line={"color": "#306998", "width": 2},
+        name="Sales",
     )
-
-    # Update layout for professional appearance
-    fig.update_layout(
-        title={
-            "text": title or "Area Chart",
-            "font": {"size": 18, "family": "Arial, sans-serif"},
-            "x": 0.5,
-            "xanchor": "center",
-        },
-        xaxis={
-            "title": {"text": xlabel or x, "font": {"size": 14}},
-            "showgrid": True,
-            "gridcolor": "rgba(128, 128, 128, 0.3)",
-            "gridwidth": 1,
-            "zeroline": False,
-            "showline": True,
-            "linewidth": 1,
-            "linecolor": "rgba(128, 128, 128, 0.5)",
-        },
-        yaxis={
-            "title": {"text": ylabel or y, "font": {"size": 14}},
-            "showgrid": True,
-            "gridcolor": "rgba(128, 128, 128, 0.3)",
-            "gridwidth": 1,
-            "zeroline": True,
-            "zerolinewidth": 1,
-            "zerolinecolor": "rgba(128, 128, 128, 0.5)",
-            "showline": True,
-            "linewidth": 1,
-            "linecolor": "rgba(128, 128, 128, 0.5)",
-        },
-        plot_bgcolor="white",
-        paper_bgcolor="white",
-        height=height,
-        width=width,
-        showlegend=False,
-        hovermode="x unified",
-        hoverlabel={"bgcolor": "white", "font_size": 12, "font_family": "Arial, sans-serif"},
-        margin={"l": 80, "r": 40, "t": 80, "b": 60},
-    )
-
-    return fig
-
-
-if __name__ == "__main__":
-    import numpy as np
-
-    # Sample data: Monthly website traffic over a year
-    np.random.seed(42)
-
-    months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
-
-    # Generate realistic traffic pattern with seasonal variation
-    base_traffic = 10000
-    seasonal_factor = [0.8, 0.85, 0.95, 1.0, 1.1, 1.15, 1.2, 1.25, 1.1, 1.0, 0.9, 0.95]
-    noise = np.random.normal(0, 500, 12)
-
-    traffic = [int(base_traffic * sf + n) for sf, n in zip(seasonal_factor, noise, strict=False)]
-
-    data = pd.DataFrame({"Month": months, "Visitors": traffic})
-
-    # Create the area chart
-    fig = create_plot(
-        data,
-        x="Month",
-        y="Visitors",
-        title="Monthly Website Visitors (2024)",
-        xlabel="Month",
-        ylabel="Number of Visitors",
-        color="rgba(99, 110, 250, 0.4)",
-        line_color="rgb(99, 110, 250)",
-        line_width=2.5,
-    )
-
-    # Save as PNG
-    fig.write_image("plot.png", width=1600, height=900, scale=2)
-    print("Plot saved to plot.png")
+)
+
+# Layout
+fig.update_layout(
+    title={"text": "Monthly Sales", "font": {"size": 20}, "x": 0.5, "xanchor": "center"},
+    xaxis_title={"text": "Month", "font": {"size": 20}},
+    yaxis_title={"text": "Sales", "font": {"size": 20}},
+    template="plotly_white",
+    xaxis={"tickfont": {"size": 16}, "showgrid": True, "gridcolor": "rgba(0,0,0,0.1)"},
+    yaxis={"tickfont": {"size": 16}, "showgrid": True, "gridcolor": "rgba(0,0,0,0.1)"},
+    showlegend=False,
+    margin={"l": 80, "r": 40, "t": 80, "b": 80},
+)
+
+# Save
+fig.write_image("plot.png", width=1600, height=900, scale=3)