feat(matplotlib): implement line-basic

Add basic line chart implementation for matplotlib library with:
- Line plot connecting data points with configurable style
- Marker support for data point visibility
- Customizable colors, line width, and transparency
- Proper axis labeling and grid
- Input validation and type hints

Author: github-actions[bot]

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

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+"""
+line-basic: Basic Line Chart
+Implementation for: matplotlib
+Variant: default
+Python: 3.10+
+"""
+
+from typing import TYPE_CHECKING, Optional
+
+import matplotlib.pyplot as plt
+import numpy as np
+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: Optional[str] = "o",
+    markersize: float = 6,
+    alpha: float = 0.8,
+    title: Optional[str] = None,
+    xlabel: Optional[str] = None,
+    ylabel: Optional[str] = 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
+    np.random.seed(42)
+
+    months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
+    base_values = [100, 120, 115, 130, 145, 160, 155, 170, 180, 175, 190, 210]
+    noise = np.random.randn(12) * 5
+
+    data = pd.DataFrame({"month": months, "sales": [base + n for base, n in zip(base_values, noise, strict=True)]})
+
+    # Create plot
+    fig = create_plot(data, "month", "sales", title="Monthly Sales Trend", xlabel="Month", ylabel="Sales ($)")
+
+    # Save for inspection - ALWAYS use 'plot.png' as filename
+    plt.savefig("plot.png", dpi=300, bbox_inches="tight")
+    print("Plot saved to plot.png")

specs/line-basic.md

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+# line-basic: Basic Line Chart
+
+**Spec Version:** 1.0.0
+
+## Description
+
+A fundamental line chart that displays data points connected by straight line segments. Ideal for visualizing trends over time or ordered categories, showing the progression and direction of data values.
+
+## Data Requirements
+
+- **x**: Numeric or datetime column for x-axis values (typically representing time or sequence)
+- **y**: Numeric column for y-axis values (the measurement or metric)
+
+## Optional Parameters
+
+- `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)
+
+## Quality Criteria
+
+- [x] X and Y axes are labeled with column names or custom labels
+- [x] Line clearly visible with appropriate width and color
+- [x] Grid visible but subtle (alpha ≤ 0.3)
+- [x] No overlapping axis labels or tick marks
+- [x] Data points optionally marked for clarity
+- [x] Appropriate figure size (16:9 aspect ratio)
+- [x] Type hints and validation present
+
+## Expected Output
+
+A clean line chart with data points connected by a continuous line. The plot should have clearly labeled axes, a subtle grid for readability, and optionally markers at each data point. The line should be clearly visible against the background, with sufficient contrast. The overall design should be professional and minimal, suitable for reports and presentations.
+
+## Tags
+
+line, trend, timeseries, basic, 2d
+
+## Use Cases
+
+- Tracking monthly sales figures over a year
+- Visualizing stock price movements over time
+- Monitoring temperature changes throughout a day
+- Displaying website traffic trends over weeks
+- Showing progress of a metric over time