feat(bokeh): implement line-basic (#374)

## Summary
Implements `line-basic` for **bokeh** library.

**Parent Issue:** #205
**Sub-Issue:** #232
**Base Branch:** `plot/line-basic`

## Implementation
- `plots/bokeh/line/line-basic/default.py`

## Changes
- KISS style: simple sequential script (no functions, no classes)
- Uses ColumnDataSource for data management
- Output: 4800x2700 PNG with proper styling (20pt title/labels, 16pt
ticks)
- Uses Python Blue (#306998) color from style guide
- Includes line with markers at data points

## Latest Fix (afa562e)
- **Removed webdriver-manager dependency**: Uses system chromedriver
directly instead of webdriver-manager
- **Added better headless flags**: `--headless=new` and `--disable-gpu`
for improved compatibility
- This approach is more reliable in CI environments where
Chrome/Chromium and chromedriver are pre-installed

---------

Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: Markus Neusinger <2921697+MarkusNeusinger@users.noreply.github.com>

Author: 3 people

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

@@ -1,154 +1,50 @@
 """
 line-basic: Basic Line Plot
-Implementation for: bokeh
-Variant: default
-Python: 3.10+
+Library: bokeh
 """
 
-from typing import TYPE_CHECKING, Optional
-
-import numpy as np
 import pandas as pd
+from bokeh.io import export_png
 from bokeh.models import ColumnDataSource
 from bokeh.plotting import figure
+from selenium import webdriver
+from selenium.webdriver.chrome.options import Options
+from selenium.webdriver.chrome.service import Service
 
 
-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]
+# Data
+data = pd.DataFrame({"time": [1, 2, 3, 4, 5, 6, 7], "value": [10, 15, 13, 18, 22, 19, 25]})
 
-    return p
+source = ColumnDataSource(data={"x": data["time"], "y": data["value"]})
 
+# Create figure
+p = figure(width=4800, height=2700, title="Basic Line Plot", x_axis_label="Time", y_axis_label="Value")
 
-if __name__ == "__main__":
-    from bokeh.io import export_png
+# Plot line
+p.line(x="x", y="y", source=source, line_width=2, line_color="#306998")
 
-    # Sample data for testing - simulating time series data
-    np.random.seed(42)
-    n_points = 50
+# Add markers at data points
+p.scatter(x="x", y="y", source=source, size=8, color="#306998")
 
-    # 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
+# Styling
+p.title.text_font_size = "20pt"
+p.xaxis.axis_label_text_font_size = "20pt"
+p.yaxis.axis_label_text_font_size = "20pt"
+p.xaxis.major_label_text_font_size = "16pt"
+p.yaxis.major_label_text_font_size = "16pt"
+p.grid.grid_line_alpha = 0.3
 
-    data = pd.DataFrame({"Time": x_values, "Value": y_values})
+# Setup Chrome/Chromium webdriver for PNG export
+chrome_options = Options()
+chrome_options.add_argument("--headless=new")
+chrome_options.add_argument("--no-sandbox")
+chrome_options.add_argument("--disable-dev-shm-usage")
+chrome_options.add_argument("--disable-gpu")
 
-    # 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,
-    )
+# Use system chromedriver (pre-installed on GitHub Actions runners)
+service = Service()
+driver = webdriver.Chrome(service=service, options=chrome_options)
 
-    # Save as PNG
-    export_png(fig, filename="plot.png")
-    print("Plot saved to plot.png")
+# Save
+export_png(p, filename="plot.png", webdriver=driver)
+driver.quit()