feat(bokeh): implement line-basic

Add basic line plot implementation for bokeh library with:
- Line rendering with customizable color, width, and alpha
- Optional markers (scatter points) on data points
- Support for datetime x-axis
- Automatic data sorting for proper line connection
- Subtle grid styling (alpha=0.3)
- 16:9 aspect ratio (1600x900 pixels)
- Full input validation and type hints
- Google-style docstrings

Author: github-actions[bot]

plots/bokeh/line/line-basic/default.py

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+"""
+line-basic: Basic Line Plot
+Implementation for: bokeh
+Variant: default
+Python: 3.10+
+"""
+
+from typing import TYPE_CHECKING, Optional
+
+import numpy as np
+import pandas as pd
+from bokeh.models import ColumnDataSource
+from bokeh.plotting import figure
+
+
+if TYPE_CHECKING:
+    from bokeh.plotting import Figure
+
+
+def create_plot(
+    data: pd.DataFrame,
+    x: str,
+    y: str,
+    title: Optional[str] = None,
+    xlabel: Optional[str] = None,
+    ylabel: Optional[str] = None,
+    color: str = "steelblue",
+    line_width: float = 2.0,
+    marker: Optional[str] = None,
+    marker_size: float = 8,
+    alpha: float = 1.0,
+    width: int = 1600,
+    height: int = 900,
+    **kwargs,
+) -> "Figure":
+    """
+    Create a basic line plot visualizing trends over continuous or sequential data.
+
+    Args:
+        data: Input DataFrame with required columns
+        x: Column name for x-axis values (numeric or datetime)
+        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: Line color (default: "steelblue")
+        line_width: Width of the line (default: 2.0)
+        marker: Marker style for data points (optional, e.g., "circle", "square")
+        marker_size: Size of markers if enabled (default: 8)
+        alpha: Line transparency (default: 1.0)
+        width: Figure width in pixels (default: 1600)
+        height: Figure height in pixels (default: 900)
+        **kwargs: Additional parameters
+
+    Returns:
+        Bokeh 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({'x': [1, 2, 3, 4, 5], 'y': [2, 4, 3, 5, 6]})
+        >>> fig = create_plot(data, 'x', 'y')
+    """
+    # 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")
+
+    # Sort data by x to ensure proper line connection
+    plot_data = data[[x, y]].dropna().sort_values(by=x)
+
+    # Determine x-axis type
+    x_axis_type = "datetime" if pd.api.types.is_datetime64_any_dtype(plot_data[x]) else "auto"
+
+    # Create ColumnDataSource
+    source = ColumnDataSource(data={"x": plot_data[x], "y": plot_data[y]})
+
+    # Create figure
+    p = figure(
+        width=width,
+        height=height,
+        title=title or "Line Plot",
+        x_axis_type=x_axis_type,
+        toolbar_location="above",
+        tools="pan,wheel_zoom,box_zoom,reset,save",
+    )
+
+    # Plot line
+    p.line(x="x", y="y", source=source, line_color=color, line_width=line_width, line_alpha=alpha)
+
+    # Add markers if specified
+    if marker:
+        p.scatter(x="x", y="y", source=source, size=marker_size, color=color, alpha=alpha, marker=marker)
+
+    # Labels
+    p.xaxis.axis_label = xlabel or x
+    p.yaxis.axis_label = ylabel or y
+
+    # Styling
+    p.title.text_font_size = "14pt"
+    p.title.align = "center"
+    p.xaxis.axis_label_text_font_size = "12pt"
+    p.yaxis.axis_label_text_font_size = "12pt"
+    p.xgrid.grid_line_alpha = 0.3
+    p.ygrid.grid_line_alpha = 0.3
+    p.xgrid.grid_line_dash = [6, 4]
+    p.ygrid.grid_line_dash = [6, 4]
+
+    return p
+
+
+if __name__ == "__main__":
+    # Sample data for testing - simulating time series data
+    np.random.seed(42)
+    n_points = 50
+
+    # Create sequential x values
+    x_values = np.arange(n_points)
+    # Create y values with a trend and some noise
+    y_values = 10 + 0.5 * x_values + np.random.randn(n_points) * 2
+
+    data = pd.DataFrame({"Time": x_values, "Value": y_values})
+
+    # Create plot
+    fig = create_plot(
+        data,
+        x="Time",
+        y="Value",
+        title="Basic Line Plot Example",
+        xlabel="Time (units)",
+        ylabel="Measurement Value",
+        color="steelblue",
+        line_width=2.5,
+        marker="circle",
+        marker_size=6,
+    )
+
+    # Save as PNG using webdriver-manager for automatic chromedriver
+    from bokeh.io import export_png
+    from selenium import webdriver
+    from selenium.webdriver.chrome.options import Options
+    from selenium.webdriver.chrome.service import Service
+    from webdriver_manager.chrome import ChromeDriverManager
+
+    chrome_options = Options()
+    chrome_options.add_argument("--headless")
+    chrome_options.add_argument("--no-sandbox")
+    chrome_options.add_argument("--disable-dev-shm-usage")
+
+    service = Service(ChromeDriverManager().install())
+    driver = webdriver.Chrome(service=service, options=chrome_options)
+
+    export_png(fig, filename="plot.png", webdriver=driver)
+    driver.quit()
+    print("Plot saved to plot.png")

specs/line-basic.md

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+# line-basic: Basic Line Plot
+
+**Spec Version:** 1.0.0
+
+## Description
+
+A fundamental line plot that visualizes trends over continuous or sequential data. Ideal for showing how values change over time or across ordered categories, connecting data points with straight line segments.
+
+## Data Requirements
+
+- **x**: Numeric or datetime column for x-axis values (typically time or sequence)
+- **y**: Numeric column for y-axis values
+
+## Optional Parameters
+
+- `title`: Plot title (type: str, default: None)
+- `xlabel`: X-axis label (type: str, default: column name)
+- `ylabel`: Y-axis label (type: str, default: column name)
+- `color`: Line color (type: str, default: "steelblue")
+- `line_width`: Width of the line (type: float, default: 2.0)
+- `marker`: Marker style for data points (type: str, default: None)
+- `marker_size`: Size of markers if enabled (type: float, default: 8)
+- `alpha`: Line transparency (type: float, default: 1.0)
+
+## Quality Criteria
+
+- [ ] X and Y axes are labeled with column names or custom labels
+- [ ] Grid is visible but subtle (alpha <= 0.3)
+- [ ] Line is clearly visible with appropriate width
+- [ ] No overlapping axis labels or tick marks
+- [ ] Data points are connected in correct order
+- [ ] Figure uses 16:9 aspect ratio
+- [ ] Type hints and input validation present
+
+## Expected Output
+
+A line plot with data points connected by line segments. The x-axis represents the independent variable (often time or sequence), and the y-axis represents the dependent variable being measured. The line should be clearly visible against the grid background, with proper axis labels and title (if provided). The plot should handle various data sizes gracefully and maintain readability.
+
+## Tags
+
+line, trend, time-series, basic, 2d
+
+## Use Cases
+
+- Tracking stock prices over time
+- Monitoring temperature changes throughout the day
+- Displaying website traffic over months
+- Showing sales growth across quarters
+- Visualizing sensor readings over time
+- Analyzing experiment results over sequential trials