chore: merge feature branch

Author: MarkusNeusinger

.github/workflows/ci-plottest.yml

@@ -66,8 +66,8 @@ jobs:
           echo "📦 Installing: uv sync $EXTRAS"
           uv sync $EXTRAS
 
-      - name: Setup Chrome for Highcharts
-        if: steps.detect_libs.outputs.has_plots == 'true' && contains(steps.detect_libs.outputs.libraries, 'highcharts')
+      - name: Setup Chrome for Selenium-based libraries
+        if: steps.detect_libs.outputs.has_plots == 'true' && (contains(steps.detect_libs.outputs.libraries, 'highcharts') || contains(steps.detect_libs.outputs.libraries, 'bokeh'))
         uses: browser-actions/setup-chrome@v1
         with:
           chrome-version: stable

plots/bokeh/varea/area-basic/default.py

@@ -1,164 +1,55 @@
 """
 area-basic: Basic Area Chart
-Implementation for: bokeh
-Variant: default
-Python: 3.10+
+Library: bokeh
 """
 
-from typing import TYPE_CHECKING, Optional
-
 import pandas as pd
+from bokeh.io import export_png
 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,
-    fill_alpha: float = 0.5,
-    line_color: Optional[str] = None,
-    title: Optional[str] = None,
-    x_label: Optional[str] = None,
-    y_label: Optional[str] = None,
-    width: int = 1600,
-    height: int = 900,
-    **kwargs,
-) -> "Figure":
-    """
-    Create a basic filled area chart using bokeh.
-
-    A simple filled area chart showing a single data series over time or
-    sequential x-values. The area between the data line and the baseline
-    (zero) is filled with a semi-transparent color.
-
-    Args:
-        data: Input DataFrame with x and y columns
-        x: Column name for x-axis values
-        y: Column name for y-axis values
-        fill_alpha: Transparency of the filled area (default: 0.5)
-        line_color: Color of the line and fill (default: bokeh blue)
-        title: Chart title (optional)
-        x_label: Label for x-axis (optional, defaults to column name)
-        y_label: Label for y-axis (optional, defaults to column name)
-        width: Figure width in pixels (default: 1600)
-        height: Figure height in pixels (default: 900)
-        **kwargs: Additional parameters passed to figure
-
-    Returns:
-        Bokeh Figure object
-
-    Raises:
-        ValueError: If data is empty or fill_alpha is out of range
-        KeyError: If required columns not found
-
-    Example:
-        >>> data = pd.DataFrame({
-        ...     'Month': [1, 2, 3, 4, 5, 6],
-        ...     'Sales': [100, 120, 90, 140, 160, 130]
-        ... })
-        >>> fig = create_plot(data, x='Month', y='Sales', title='Monthly Sales')
-    """
-    # Input validation
-    if data.empty:
-        raise ValueError("Data cannot be empty")
-
-    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}")
-
-    if not 0 <= fill_alpha <= 1:
-        raise ValueError(f"fill_alpha must be between 0 and 1, got {fill_alpha}")
-
-    # Set default color (bokeh blue)
-    color = line_color or "#1f77b4"
-
-    # Sort data by x to ensure proper area rendering
-    plot_data = data[[x, y]].dropna().sort_values(by=x).reset_index(drop=True)
-
-    # Create ColumnDataSource
-    source = ColumnDataSource(data={"x": plot_data[x], "y": plot_data[y], "y0": [0] * len(plot_data)})
-
-    # Create figure
-    p = figure(
-        width=width,
-        height=height,
-        title=title or "Area Chart",
-        x_axis_label=x_label or x,
-        y_axis_label=y_label or y,
-        toolbar_location="above",
-        tools="pan,wheel_zoom,box_zoom,reset,save",
-        **kwargs,
-    )
-
-    # Draw the filled area from baseline (0) to y values
-    p.varea(x="x", y1="y0", y2="y", source=source, fill_color=color, fill_alpha=fill_alpha)
-
-    # Draw line on top for better visibility
-    p.line(x="x", y="y", source=source, line_color=color, line_width=2)
-
-    # Styling
-    p.title.text_font_size = "14pt"
-    p.title.align = "center"
-
-    # Grid styling - subtle
-    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]
-
-    # Axis styling
-    p.xaxis.axis_label_text_font_size = "12pt"
-    p.yaxis.axis_label_text_font_size = "12pt"
-    p.xaxis.major_label_text_font_size = "10pt"
-    p.yaxis.major_label_text_font_size = "10pt"
-
-    return p
-
-
-if __name__ == "__main__":
-    import numpy as np
-
-    # Sample data: Monthly website traffic over a year
-    np.random.seed(42)
-    months = list(range(1, 13))
-    base_traffic = [1000, 1100, 1050, 1200, 1400, 1600, 1500, 1550, 1700, 1650, 1800, 2000]
-    noise = np.random.normal(0, 50, 12)
-    traffic = [max(0, int(b + n)) for b, n in zip(base_traffic, noise, strict=False)]
-
-    data = pd.DataFrame({"Month": months, "Visitors": traffic})
-
-    # Create plot
-    fig = create_plot(
-        data,
-        x="Month",
-        y="Visitors",
-        title="Monthly Website Traffic",
-        x_label="Month",
-        y_label="Visitors (thousands)",
-        fill_alpha=0.5,
-    )
-
-    # 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")
+# 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 x positions for categorical months
+x = list(range(len(data["month"])))
+y = data["sales"].tolist()
+
+source = ColumnDataSource(data={"x": x, "y": y, "month": data["month"]})
+
+# Create figure
+p = figure(
+    width=4800,
+    height=2700,
+    title="Monthly Sales Volume",
+    x_axis_label="Month",
+    y_axis_label="Sales ($)",
+    x_range=(-0.5, len(x) - 0.5),
+)
+
+# Plot area (varea fills from y1 to y2)
+p.varea(x="x", y1=0, y2="y", source=source, fill_alpha=0.7, fill_color="#306998")
+
+# Add line on top for clearer boundary
+p.line(x="x", y="y", source=source, line_width=2, line_color="#306998")
+
+# Configure x-axis ticks to show month labels
+p.xaxis.ticker = x
+p.xaxis.major_label_overrides = dict(enumerate(data["month"]))
+
+# 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
+
+# Save
+export_png(p, filename="plot.png")

pyproject.toml

@@ -75,7 +75,7 @@ plotting = [
 lib-matplotlib = ["matplotlib>=3.9.0", "numpy>=1.26.0", "pandas>=2.2.0"]
 lib-seaborn = ["seaborn>=0.13.0", "matplotlib>=3.9.0", "numpy>=1.26.0", "pandas>=2.2.0"]
 lib-plotly = ["plotly>=5.18.0", "kaleido>=0.2.1", "numpy>=1.26.0", "pandas>=2.2.0"]
-lib-bokeh = ["bokeh>=3.4.0", "numpy>=1.26.0", "pandas>=2.2.0"]
+lib-bokeh = ["bokeh>=3.4.0", "numpy>=1.26.0", "pandas>=2.2.0", "selenium>=4.15.0", "webdriver-manager>=4.0.0"]
 lib-altair = ["altair>=5.2.0", "vl-convert-python>=1.3.0", "numpy>=1.26.0", "pandas>=2.2.0"]
 lib-plotnine = ["plotnine>=0.13.0", "numpy>=1.26.0", "pandas>=2.2.0"]
 lib-pygal = ["pygal>=3.0.0", "cairosvg>=2.7.0", "pandas>=2.2.0"]