feat(altair): implement line-basic

Simplified implementation following KISS principle:
- Removed function wrapping
- Removed type hints and docstrings
- Sequential script structure
- Uses data from spec example

Author: github-actions[bot]

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

@@ -1,166 +1,35 @@
 """
-line-basic: Basic Line Chart
+line-basic: Basic Line Plot
 Library: altair
 """
 
-from typing import TYPE_CHECKING
-
 import altair as alt
 import pandas as pd
 
 
-if TYPE_CHECKING:
-    from altair import Chart
-
-# Style guide colors
-PYTHON_BLUE = "#306998"
-
-
-def create_plot(
-    data: pd.DataFrame,
-    x: str,
-    y: str,
-    title: str | None = None,
-    xlabel: str | None = None,
-    ylabel: str | None = None,
-    color: str = PYTHON_BLUE,
-    linewidth: float = 2,
-    marker: str | None = None,
-    marker_size: int = 60,
-    alpha: float = 1.0,
-    linestyle: str = "solid",
-    **kwargs,
-) -> "Chart":
-    """
-    Create a basic line chart showing trends over a continuous axis.
-
-    Args:
-        data: Input DataFrame with required columns
-        x: Column name for x-axis values (typically time or sequence)
-        y: Column name for y-axis values
-        title: Plot title (default: None)
-        xlabel: Custom x-axis label (default: uses column name)
-        ylabel: Custom y-axis label (default: uses column name)
-        color: Line color (default: Python Blue #306998)
-        linewidth: Width of the line (default: 2)
-        marker: Marker style - 'circle', 'square', 'diamond', etc. (default: None)
-        marker_size: Size of markers if enabled (default: 60)
-        alpha: Transparency level for the line (default: 1.0)
-        linestyle: Line style - 'solid', 'dashed', 'dotted' (default: 'solid')
-        **kwargs: Additional parameters for altair chart configuration
-
-    Returns:
-        Altair Chart object
-
-    Raises:
-        ValueError: If data is empty
-        KeyError: If required columns not found
-
-    Example:
-        >>> data = pd.DataFrame({
-        ...     'month': [1, 2, 3, 4, 5, 6],
-        ...     'sales': [100, 150, 130, 180, 200, 190]
-        ... })
-        >>> chart = create_plot(data, x='month', y='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}")
-
-    # Sort data by x-axis to ensure proper line connections
-    plot_data = data.sort_values(by=x).copy()
+# Data
+data = pd.DataFrame({"time": [1, 2, 3, 4, 5, 6, 7], "value": [10, 15, 13, 18, 22, 19, 25]})
 
-    # Determine x encoding type based on data
-    x_dtype = plot_data[x].dtype
-    if pd.api.types.is_datetime64_any_dtype(x_dtype):
-        x_encoding = f"{x}:T"
-    else:
-        x_encoding = f"{x}:Q"
-
-    # Map linestyle to altair strokeDash
-    stroke_dash_map = {"solid": [], "dashed": [8, 4], "dotted": [2, 2]}
-    stroke_dash = stroke_dash_map.get(linestyle, [])
-
-    # Create the line chart
-    line = (
-        alt.Chart(plot_data)
-        .mark_line(color=color, strokeWidth=linewidth, opacity=alpha, strokeDash=stroke_dash)
-        .encode(
-            x=alt.X(
-                x_encoding,
-                title=xlabel or x,
-                axis=alt.Axis(labelAngle=0, labelLimit=200, labelFontSize=16, titleFontSize=20),
-            ),
-            y=alt.Y(
-                f"{y}:Q",
-                title=ylabel or y,
-                scale=alt.Scale(zero=False),
-                axis=alt.Axis(labelFontSize=16, titleFontSize=20),
-            ),
-            tooltip=[
-                alt.Tooltip(x_encoding, title=xlabel or x),
-                alt.Tooltip(f"{y}:Q", title=ylabel or y, format=".2f"),
-            ],
-        )
-    )
-
-    # Add markers if specified
-    if marker:
-        points = (
-            alt.Chart(plot_data)
-            .mark_point(color=color, size=marker_size, filled=True, shape=marker, opacity=alpha)
-            .encode(
-                x=alt.X(x_encoding),
-                y=alt.Y(f"{y}:Q"),
-                tooltip=[
-                    alt.Tooltip(x_encoding, title=xlabel or x),
-                    alt.Tooltip(f"{y}:Q", title=ylabel or y, format=".2f"),
-                ],
-            )
-        )
-        chart_base = line + points
-    else:
-        chart_base = line
-
-    # Apply properties and configuration
-    # Target: 4800 × 2700 px with scale_factor=3.0 -> 1600 × 900 base
-    title_config = alt.TitleParams(text=title, fontSize=20, anchor="middle") if title else None
-    chart = (
-        chart_base.properties(width=1600, height=900, title=title_config)
-        .configure_view(strokeWidth=0)
-        .configure_axis(grid=True, gridOpacity=0.3, gridDash=[3, 3], domainWidth=1, tickWidth=1)
+# Create line chart
+line = (
+    alt.Chart(data)
+    .mark_line(strokeWidth=2, color="#306998")
+    .encode(
+        x=alt.X("time:Q", title="Time", axis=alt.Axis(labelFontSize=16, titleFontSize=20)),
+        y=alt.Y("value:Q", title="Value", axis=alt.Axis(labelFontSize=16, titleFontSize=20)),
     )
+)
 
-    return chart
+# Add points on the line for clarity
+points = alt.Chart(data).mark_point(size=100, color="#306998", filled=True).encode(x="time:Q", y="value:Q")
 
+# Combine and configure chart
+chart = (
+    (line + points)
+    .properties(width=1600, height=900, title=alt.Title("Basic Line Plot", fontSize=20))
+    .configure_view(strokeWidth=0)
+    .configure_axis(grid=True, gridOpacity=0.3)
+)
 
-if __name__ == "__main__":
-    # Sample data for testing - monthly sales trend
-    sample_data = pd.DataFrame(
-        {
-            "Month": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
-            "Sales": [120, 150, 170, 165, 180, 220, 250, 240, 210, 190, 180, 200],
-        }
-    )
-
-    # Create plot with markers
-    chart = create_plot(
-        sample_data,
-        x="Month",
-        y="Sales",
-        title="Monthly Sales Trend (2024)",
-        xlabel="Month",
-        ylabel="Sales (thousands)",
-        linewidth=2.5,
-        marker="circle",
-        marker_size=80,
-    )
-
-    # Save as PNG - scale_factor=3.0 gives 4800 × 2700 px
-    chart.save("plot.png", scale_factor=3.0)
-    print("Plot saved to plot.png")
+# Save as PNG (1600 × 900 at scale 3 = 4800 × 2700 px)
+chart.save("plot.png", scale_factor=3.0)