feat: add box-basic implementation (9 libraries)

plots/altair/boxplot/box-basic/default.py

@@ -1,179 +1,45 @@
 """
 box-basic: Basic Box Plot
-Implementation for: altair
-Variant: default
-Python: 3.10+
+Library: altair
 """
 
-from typing import TYPE_CHECKING, Optional
-
 import altair as alt
 import numpy as np
 import pandas as pd
 
 
-if TYPE_CHECKING:
-    from altair import Chart
-
-
-def create_plot(
-    data: pd.DataFrame,
-    values: str,
-    groups: str,
-    title: Optional[str] = None,
-    xlabel: Optional[str] = None,
-    ylabel: Optional[str] = None,
-    color_scheme: str = "set2",
-    width: int = 800,
-    height: int = 450,
-    **kwargs,
-) -> Chart:
-    """
-    Create a basic box plot showing statistical distribution of multiple groups using altair.
-
-    Args:
-        data: Input DataFrame with required columns
-        values: Column name containing numeric values
-        groups: Column name containing group categories
-        title: Plot title (optional)
-        xlabel: Custom x-axis label (optional, defaults to groups column name)
-        ylabel: Custom y-axis label (optional, defaults to values column name)
-        color_scheme: Color scheme for boxes (default: 'set2')
-        width: Figure width in pixels (default: 800)
-        height: Figure height in pixels (default: 450)
-        **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({
-        ...     'Group': ['A', 'A', 'B', 'B', 'C', 'C'],
-        ...     'Value': [1, 2, 2, 3, 3, 4]
-        ... })
-        >>> chart = create_plot(data, values='Value', groups='Group')
-    """
-    # Input validation
-    if data.empty:
-        raise ValueError("Data cannot be empty")
-
-    # Check required columns
-    for col in [values, groups]:
-        if col not in data.columns:
-            available = ", ".join(data.columns)
-            raise KeyError(f"Column '{col}' not found. Available columns: {available}")
-
-    # Create the box plot using Altair's mark_boxplot
-    base = (
-        alt.Chart(data)
-        .mark_boxplot(
-            extent=1.5,  # 1.5 * IQR for whiskers
-            outliers=True,
-            size=40,
-            opacity=0.7,
-        )
-        .encode(
-            x=alt.X(
-                f"{groups}:N",
-                title=xlabel or groups,
-                axis=alt.Axis(labelAngle=0 if data[groups].nunique() <= 5 else -45, labelLimit=200),
-            ),
-            y=alt.Y(f"{values}:Q", title=ylabel or values, scale=alt.Scale(zero=False)),
-            color=alt.Color(
-                f"{groups}:N",
-                scale=alt.Scale(scheme=color_scheme),
-                legend=None,  # Hide legend as it's redundant with x-axis
-            ),
-            tooltip=[
-                alt.Tooltip(f"{groups}:N", title="Group"),
-                alt.Tooltip(f"count({values}):Q", title="Count"),
-                alt.Tooltip(f"min({values}):Q", title="Min", format=".2f"),
-                alt.Tooltip(f"q1({values}):Q", title="Q1", format=".2f"),
-                alt.Tooltip(f"median({values}):Q", title="Median", format=".2f"),
-                alt.Tooltip(f"q3({values}):Q", title="Q3", format=".2f"),
-                alt.Tooltip(f"max({values}):Q", title="Max", format=".2f"),
-            ],
-        )
-    )
-
-    # Add sample size annotations
-    text = (
-        alt.Chart(data)
-        .mark_text(align="center", baseline="top", dy=10, fontSize=10, opacity=0.7)
-        .encode(x=alt.X(f"{groups}:N"), y=alt.Y(f"min({values}):Q"), text=alt.Text("count():Q", format="d"))
-        .transform_aggregate(count="count()", groupby=[groups])
-    )
-
-    # Combine box plot with annotations
-    chart = (
-        (base + text)
-        .properties(
-            width=width,
-            height=height,
-            title=alt.TitleParams(text=title or "Box Plot Distribution", fontSize=16, anchor="middle"),
-        )
-        .configure_view(strokeWidth=0)
-        .configure_axis(grid=True, gridOpacity=0.3, gridDash=[3, 3], domainWidth=1, tickWidth=1)
-        .configure_boxplot(
-            median={"color": "red", "strokeWidth": 2},
-            box={"strokeWidth": 1.5},
-            outliers={"fill": "red", "fillOpacity": 0.5, "size": 50},
-        )
-    )
-
-    return chart
-
-
-if __name__ == "__main__":
-    # Sample data for testing with different distributions per group
-    np.random.seed(42)  # For reproducibility
-
-    # Generate sample data with 4 groups
-    data_dict = {"Group": [], "Value": []}
-
-    # Group A: Normal distribution, mean=50, std=10
-    group_a_data = np.random.normal(50, 10, 40)
-    # Add some outliers
-    group_a_data = np.append(group_a_data, [80, 85, 15])
-
-    # Group B: Normal distribution, mean=60, std=15
-    group_b_data = np.random.normal(60, 15, 35)
-    # Add outliers
-    group_b_data = np.append(group_b_data, [100, 10])
-
-    # Group C: Normal distribution, mean=45, std=8
-    group_c_data = np.random.normal(45, 8, 45)
-
-    # Group D: Skewed distribution
-    group_d_data = np.random.gamma(2, 2, 30) + 40
-    # Add outliers
-    group_d_data = np.append(group_d_data, [75, 78, 20])
-
-    # Combine all data
-    for group, values in zip(
-        ["Group A", "Group B", "Group C", "Group D"],
-        [group_a_data, group_b_data, group_c_data, group_d_data],
-        strict=False,
-    ):
-        data_dict["Group"].extend([group] * len(values))
-        data_dict["Value"].extend(values)
-
-    data = pd.DataFrame(data_dict)
-
-    # Create plot
-    chart = create_plot(
-        data,
-        values="Value",
-        groups="Group",
-        title="Statistical Distribution Comparison Across Groups",
-        ylabel="Measurement Value",
-        xlabel="Categories",
+# Data
+np.random.seed(42)
+data = pd.DataFrame(
+    {
+        "group": ["A"] * 50 + ["B"] * 50 + ["C"] * 50 + ["D"] * 50,
+        "value": np.concatenate(
+            [
+                np.random.normal(50, 10, 50),
+                np.random.normal(60, 15, 50),
+                np.random.normal(45, 8, 50),
+                np.random.normal(70, 20, 50),
+            ]
+        ),
+    }
+)
+
+# Color palette from style guide
+colors = ["#306998", "#FFD43B", "#DC2626", "#059669"]
+
+# Create chart
+chart = (
+    alt.Chart(data)
+    .mark_boxplot(size=60, outliers={"size": 8})
+    .encode(
+        x=alt.X("group:N", title="Group", axis=alt.Axis(labelFontSize=16, titleFontSize=20)),
+        y=alt.Y("value:Q", title="Value", axis=alt.Axis(labelFontSize=16, titleFontSize=20)),
+        color=alt.Color("group:N", scale=alt.Scale(domain=["A", "B", "C", "D"], range=colors), legend=None),
     )
+    .properties(width=1600, height=900, title=alt.Title("Basic Box Plot", fontSize=20))
+    .configure_view(strokeWidth=0)
+    .configure_axis(grid=True, gridOpacity=0.3)
+)
 
-    # Save as PNG
-    chart.save("plot.png", scale_factor=2.0)
-    print("Plot saved to plot.png")
+# Save (4800 × 2700 px via scale_factor=3)
+chart.save("plot.png", scale_factor=3.0)