plot_heatmap
Generate publication-quality heatmaps to visualize matrix data like correlation matrices, confusion matrices, or any 2D datasets with customizable annotations and styling.
Instructions
Create a heatmap for visualizing matrix data.
This tool generates a heatmap with optional annotations, ideal for correlation matrices, confusion matrices, or any 2D data.
Args: data: For direct input, 2D list (matrix). For file input, column name. data_input: Optional. {"file_path": "path/to/file.csv"} or {"data": {...}} x_labels: Optional labels for x-axis (columns) y_labels: Optional labels for y-axis (rows) annotate: If True, show values in each cell style: Optional. {"title": "...", "xlabel": "...", "ylabel": "...", "colormap": "viridis"} output: Optional. {"format": "png/pdf/svg", "width": 15, "height": 10, "dpi": 300}
Returns: PIL Image object or bytes containing the plot
Examples: Correlation matrix: >>> plot_heatmap( ... data=[[1.0, 0.8, 0.3], [0.8, 1.0, 0.5], [0.3, 0.5, 1.0]], ... x_labels=["A", "B", "C"], ... y_labels=["A", "B", "C"], ... annotate=True, ... style={"title": "Correlation Matrix", "colormap": "RdBu"} ... )
From file:
>>> plot_heatmap(
... data="matrix",
... data_input={"file_path": "data_matrix.csv"},
... style={"colormap": "plasma"}
... )Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| data_input | No | ||
| x_labels | No | ||
| y_labels | No | ||
| annotate | No | ||
| style | No | ||
| output | No |