feat(matplotlib): implement line-basic

plots/matplotlib/plot/line-basic/default.py

@@ -1,125 +1,25 @@
 """
-line-basic: Basic Line Chart
-Implementation for: matplotlib
-Variant: default
-Python: 3.10+
-
-Generated by AI workflow.
+line-basic: Basic Line Plot
+Library: matplotlib
 """
 
-from typing import TYPE_CHECKING
-
 import matplotlib.pyplot as plt
 import pandas as pd
 
 
-if TYPE_CHECKING:
-    from matplotlib.figure import Figure
-
-
-def create_plot(
-    data: pd.DataFrame,
-    x: str,
-    y: str,
-    figsize: tuple[float, float] = (16, 9),
-    color: str = "steelblue",
-    linewidth: float = 2.0,
-    marker: str | None = "o",
-    markersize: float = 6,
-    alpha: float = 0.8,
-    title: str | None = None,
-    xlabel: str | None = None,
-    ylabel: str | None = None,
-    linestyle: str = "-",
-    **kwargs,
-) -> "Figure":
-    """
-    Create a basic line chart visualizing the trend of data points.
-
-    Args:
-        data: Input DataFrame with required columns
-        x: Column name for x-axis values
-        y: Column name for y-axis values
-        figsize: Figure size as (width, height) tuple (default: (16, 9))
-        color: Line color (default: "steelblue")
-        linewidth: Width of the line (default: 2.0)
-        marker: Marker style for data points (default: "o")
-        markersize: Size of markers (default: 6)
-        alpha: Transparency level (default: 0.8)
-        title: Plot title (default: None)
-        xlabel: X-axis label (default: uses column name)
-        ylabel: Y-axis label (default: uses column name)
-        linestyle: Line style (default: "-" solid)
-        **kwargs: Additional parameters passed to plot function
-
-    Returns:
-        Matplotlib Figure object
-
-    Raises:
-        ValueError: If data is empty
-        KeyError: If required columns not found
-        TypeError: If y column contains non-numeric data
-
-    Example:
-        >>> data = pd.DataFrame({'month': [1, 2, 3], 'sales': [100, 150, 130]})
-        >>> fig = create_plot(data, 'month', '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}")
-
-    # Check if y column is numeric
-    if not pd.api.types.is_numeric_dtype(data[y]):
-        raise TypeError(f"Column '{y}' must contain numeric data")
-
-    # Create figure
-    fig, ax = plt.subplots(figsize=figsize)
-
-    # Plot data
-    ax.plot(
-        data[x],
-        data[y],
-        color=color,
-        linewidth=linewidth,
-        marker=marker,
-        markersize=markersize,
-        alpha=alpha,
-        linestyle=linestyle,
-        **kwargs,
-    )
-
-    # Labels and title
-    ax.set_xlabel(xlabel or x)
-    ax.set_ylabel(ylabel or y)
-
-    if title:
-        ax.set_title(title)
-
-    # Apply styling
-    ax.grid(True, alpha=0.3)
-
-    # Layout
-    plt.tight_layout()
-
-    return fig
-
-
-if __name__ == "__main__":
-    # Sample data for testing - monthly sales data
-    months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
-    sales_values = [100, 120, 115, 130, 145, 160, 155, 170, 180, 175, 190, 210]
+# Data
+data = pd.DataFrame({"time": [1, 2, 3, 4, 5, 6, 7], "value": [10, 15, 13, 18, 22, 19, 25]})
 
-    data = pd.DataFrame({"month": months, "sales": sales_values})
+# Plot
+fig, ax = plt.subplots(figsize=(16, 9))
+ax.plot(data["time"], data["value"], color="#306998", linewidth=2, marker="o", markersize=6)
 
-    # Create plot
-    fig = create_plot(data, "month", "sales", title="Monthly Sales Trend", xlabel="Month", ylabel="Sales ($)")
+# Labels and styling
+ax.set_xlabel("Time", fontsize=20)
+ax.set_ylabel("Value", fontsize=20)
+ax.set_title("Basic Line Plot", fontsize=20)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, alpha=0.3)
 
-    # Save for inspection - ALWAYS use 'plot.png' as filename
-    plt.savefig("plot.png", dpi=300, bbox_inches="tight")
-    print("Plot saved to plot.png")
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")