feat: add area-basic implementation (9 libraries) (#508)

## Summary
Adds `area-basic` plot implementation.

### Libraries
- **Merged:** 9 (all libraries)
- **Not Feasible:** 0

### Links
- **Spec:** `specs/area-basic.md`
- **Parent Issue:** #201

---
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Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>

Author: 3 people

plots/altair/area/area-basic/default.py

@@ -0,0 +1,32 @@
+"""
+area-basic: Basic Area Chart
+Library: altair
+"""
+
+import altair as alt
+import pandas as pd
+
+
+# Data
+data = pd.DataFrame(
+    {
+        "month": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
+        "sales": [100, 150, 130, 180, 200, 220, 195, 240, 260, 230, 280, 310],
+    }
+)
+
+# Create chart with area and line
+chart = (
+    alt.Chart(data)
+    .mark_area(opacity=0.5, color="#306998", line={"color": "#306998", "strokeWidth": 2})
+    .encode(
+        x=alt.X("month:Q", title="Month", axis=alt.Axis(labelFontSize=16, titleFontSize=20)),
+        y=alt.Y("sales:Q", title="Sales", axis=alt.Axis(labelFontSize=16, titleFontSize=20)),
+    )
+    .properties(width=1600, height=900, title=alt.TitleParams(text="Basic Area Chart", fontSize=20))
+    .configure_view(strokeWidth=0)
+    .configure_axis(grid=True, gridOpacity=0.3)
+)
+
+# Save (1600 × 900 × 3 = 4800 × 2700)
+chart.save("plot.png", scale_factor=3.0)

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")

plots/highcharts/area/area-basic/default.py

@@ -0,0 +1,109 @@
+"""
+area-basic: Basic Area Chart
+Library: highcharts
+"""
+
+import tempfile
+import time
+import urllib.request
+from pathlib import Path
+
+from highcharts_core.chart import Chart
+from highcharts_core.options import HighchartsOptions
+from highcharts_core.options.series.area import AreaSeries
+from selenium import webdriver
+from selenium.webdriver.chrome.options import Options
+
+
+# Data - Monthly sales example
+sales = [100, 150, 130, 180, 200, 220, 195, 240, 280, 310, 290, 350]
+
+# Create chart
+chart = Chart(container="container")
+chart.options = HighchartsOptions()
+
+# Chart configuration
+chart.options.chart = {
+    "type": "area",
+    "width": 4800,
+    "height": 2700,
+    "backgroundColor": "#ffffff",
+    "style": {"fontFamily": "Arial, sans-serif"},
+}
+
+# Title
+chart.options.title = {"text": "Monthly Sales Trend", "style": {"fontSize": "48px"}}
+
+# Axes
+chart.options.x_axis = {
+    "title": {"text": "Month", "style": {"fontSize": "40px"}},
+    "categories": ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"],
+    "labels": {"style": {"fontSize": "32px"}, "enabled": True},
+    "gridLineWidth": 1,
+    "gridLineColor": "rgba(0, 0, 0, 0.1)",
+    "tickmarkPlacement": "on",
+}
+
+chart.options.y_axis = {
+    "title": {"text": "Sales ($)", "style": {"fontSize": "40px"}},
+    "labels": {"style": {"fontSize": "32px"}},
+    "gridLineWidth": 1,
+    "gridLineColor": "rgba(0, 0, 0, 0.1)",
+}
+
+# Legend
+chart.options.legend = {"enabled": False}
+
+# Create area series
+series = AreaSeries()
+series.data = sales
+series.name = "Sales"
+series.color = "#306998"
+series.fill_opacity = 0.5
+series.line_width = 4
+series.marker = {"enabled": True, "radius": 6, "fillColor": "#306998", "lineWidth": 2, "lineColor": "#ffffff"}
+
+chart.add_series(series)
+
+# Download Highcharts JS
+highcharts_url = "https://code.highcharts.com/highcharts.js"
+with urllib.request.urlopen(highcharts_url, timeout=30) as response:
+    highcharts_js = response.read().decode("utf-8")
+
+# Generate HTML with inline scripts
+html_str = chart.to_js_literal()
+html_content = f"""<!DOCTYPE html>
+<html>
+<head>
+    <meta charset="utf-8">
+    <script>{highcharts_js}</script>
+</head>
+<body style="margin:0;">
+    <div id="container" style="width: 4800px; height: 2700px;"></div>
+    <script>{html_str}</script>
+</body>
+</html>"""
+
+# Write temp HTML and take screenshot
+with tempfile.NamedTemporaryFile(mode="w", suffix=".html", delete=False, encoding="utf-8") as f:
+    f.write(html_content)
+    temp_path = f.name
+
+chrome_options = Options()
+chrome_options.add_argument("--headless")
+chrome_options.add_argument("--no-sandbox")
+chrome_options.add_argument("--disable-dev-shm-usage")
+chrome_options.add_argument("--disable-gpu")
+chrome_options.add_argument("--window-size=4800,2800")
+
+driver = webdriver.Chrome(options=chrome_options)
+driver.set_window_size(4800, 2800)
+driver.get(f"file://{temp_path}")
+time.sleep(5)
+
+# Get the container element and screenshot it directly
+container = driver.find_element("id", "container")
+container.screenshot("plot.png")
+driver.quit()
+
+Path(temp_path).unlink()

plots/letsplot/area/area-basic/default.py

@@ -0,0 +1,44 @@
+"""
+area-basic: Basic Area Chart
+Library: letsplot
+"""
+
+import pandas as pd
+from lets_plot import (
+    LetsPlot,
+    aes,
+    element_text,
+    geom_area,
+    geom_line,
+    ggplot,
+    ggsave,
+    ggsize,
+    labs,
+    scale_x_continuous,
+    theme,
+    theme_minimal,
+)
+
+
+LetsPlot.setup_html()
+
+# Data
+months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
+data = pd.DataFrame(
+    {"month_num": range(len(months)), "sales": [120, 135, 148, 162, 175, 195, 210, 198, 185, 170, 158, 190]}
+)
+
+# Plot
+plot = (
+    ggplot(data, aes(x="month_num", y="sales"))
+    + geom_area(fill="#306998", alpha=0.5)
+    + geom_line(color="#306998", size=2)
+    + scale_x_continuous(breaks=list(range(len(months))), labels=months)
+    + labs(x="Month", y="Sales", title="Basic Area Chart")
+    + theme_minimal()
+    + theme(axis_title=element_text(size=20), axis_text=element_text(size=16), plot_title=element_text(size=20))
+    + ggsize(1600, 900)
+)
+
+# Save - scale 3x to get 4800 × 2700 px
+ggsave(plot, "plot.png", path=".", scale=3)