feat(plotly): implement box-basic

plots/plotly/box/box-basic/default.py

@@ -1,206 +1,53 @@
 """
 box-basic: Basic Box Plot
-Implementation for: plotly
-Variant: default
-Python: 3.10+
+Library: plotly
 """
 
-from typing import TYPE_CHECKING, Optional
-
 import numpy as np
 import pandas as pd
-import plotly.express as px
-
-
-if TYPE_CHECKING:
-    from plotly.graph_objects import Figure
-
-
-def create_plot(
-    data: pd.DataFrame,
-    values: str,
-    groups: str,
-    title: Optional[str] = None,
-    xlabel: Optional[str] = None,
-    ylabel: Optional[str] = None,
-    color_discrete_sequence: Optional[list] = None,
-    height: int = 900,
-    width: int = 1600,
-    showlegend: bool = False,
-    **kwargs,
-) -> Figure:
-    """
-    Create an interactive box plot showing statistical distribution of multiple groups using plotly.
-
-    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_discrete_sequence: List of colors for each box (optional)
-        height: Figure height in pixels (default: 900)
-        width: Figure width in pixels (default: 1600)
-        showlegend: Whether to show legend (default: False)
-        **kwargs: Additional parameters passed to plotly box trace
-
-    Returns:
-        Plotly Figure 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]
-        ... })
-        >>> fig = 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}")
-
-    # Use plotly.express for easier box plot creation
-    fig = px.box(
-        data,
-        x=groups,
-        y=values,
-        color=groups,
-        color_discrete_sequence=color_discrete_sequence or px.colors.qualitative.Set2,
-        notched=False,
-        points="outliers",  # Show only outliers as points
-        **kwargs,
-    )
-
-    # Update traces for better styling
-    fig.update_traces(
-        boxmean="sd",  # Show mean and standard deviation
-        marker={"size": 8, "opacity": 0.5, "line": {"width": 1}},
-        line={"width": 1.5},
-        fillcolor=None,
-        opacity=0.7,
-    )
-
-    # Update layout
-    fig.update_layout(
-        title={
-            "text": title or "Box Plot Distribution",
-            "font": {"size": 16, "family": "Arial, sans-serif"},
-            "x": 0.5,
-            "xanchor": "center",
-        },
-        xaxis={
-            "title": xlabel or groups,
-            "gridcolor": "lightgray",
-            "gridwidth": 0.5,
-            "showgrid": False,
-            "zeroline": False,
-        },
-        yaxis={
-            "title": ylabel or values,
-            "gridcolor": "lightgray",
-            "gridwidth": 0.5,
-            "showgrid": True,
-            "zeroline": True,
-            "zerolinewidth": 1,
-            "zerolinecolor": "lightgray",
-        },
-        plot_bgcolor="white",
-        paper_bgcolor="white",
-        height=height,
-        width=width,
-        showlegend=showlegend,
-        hovermode="x unified",
-        hoverlabel={"bgcolor": "white", "font_size": 12, "font_family": "Arial, sans-serif"},
-    )
-
-    # Add annotations with sample sizes
-    group_counts = data.groupby(groups)[values].count()
-    annotations = []
-    for _i, (group_name, count) in enumerate(group_counts.items()):
-        annotations.append(
-            {
-                "x": group_name,
-                "y": data[data[groups] == group_name][values].min() - (data[values].max() - data[values].min()) * 0.05,
-                "text": f"n={count}",
-                "showarrow": False,
-                "font": {"size": 10, "color": "gray"},
-                "xanchor": "center",
-                "yanchor": "top",
-            }
-        )
-
-    fig.update_layout(annotations=annotations)
-
-    # Update hover template for better information
-    fig.update_traces(
-        hovertemplate="<b>%{x}</b><br>"
-        + "Max: %{y}<br>"
-        + "Q3: %{upperfence}<br>"
-        + "Median: %{median}<br>"
-        + "Q1: %{lowerfence}<br>"
-        + "Min: %{y}<br>"
-        + "<extra></extra>"
-    )
-
-    return fig
-
-
-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
-    fig = create_plot(
-        data,
-        values="Value",
-        groups="Group",
-        title="Statistical Distribution Comparison Across Groups",
-        ylabel="Measurement Value",
-        xlabel="Categories",
+import plotly.graph_objects as go
+
+
+# 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),
+            ]
+        ),
+    }
+)
+
+# Colors from style guide
+colors = ["#306998", "#FFD43B", "#DC2626", "#059669"]
+
+# Create figure
+fig = go.Figure()
+
+# Add box trace for each group
+for i, group in enumerate(["A", "B", "C", "D"]):
+    group_data = data[data["group"] == group]["value"]
+    fig.add_trace(
+        go.Box(y=group_data, name=f"Group {group}", marker_color=colors[i], boxpoints="outliers", line_width=2)
     )
 
-    # Save as PNG
-    fig.write_image("plot.png", width=1600, height=900, scale=2)
-    print("Plot saved to plot.png")
+# Layout
+fig.update_layout(
+    title={"text": "Basic Box Plot", "font": {"size": 40}, "x": 0.5, "xanchor": "center"},
+    xaxis_title={"text": "Group", "font": {"size": 32}},
+    yaxis_title={"text": "Value", "font": {"size": 32}},
+    template="plotly_white",
+    showlegend=False,
+    font={"size": 26},
+    xaxis={"tickfont": {"size": 26}},
+    yaxis={"tickfont": {"size": 26}, "gridwidth": 1, "gridcolor": "rgba(0,0,0,0.1)"},
+)
+
+# Save
+fig.write_image("plot.png", width=1600, height=900, scale=3)