MATLAB Code Implementation for Contour Plots with Value Labels

Creating contour plots with value labels in MATLAB is a common data visualization requirement, particularly when dealing with terrain data, temperature distributions, or any scalar field. This guide demonstrates how to implement annotated contour plots and ensure consistent scaling across multiple subplots for proper comparison. ### 1. Creating Basic Contour Plots The `contour` or `contourf` functions generate contour plots, with the former drawing only lines and the latter filling areas with colors. You can control the density of contour lines by specifying level parameters. To display values on the lines, set the `'ShowText'` option to `'on'` during plotting. For example, `contour(X,Y,Z, 'ShowText','on')` annotates each contour line with its corresponding value. The function automatically calculates optimal positions for text labels based on contour curvature. ### 2. Uniform Scaling Across Multiple Subplots When displaying multiple related contour plots (such as data comparisons at different time points), maintaining identical color scales is crucial. Implement this through: Manual level specification: Define identical contour level vectors for all subplots (e.g., `levels = -10:2:10`) using the `contour(X,Y,Z,levels)` syntax to ensure consistent segmentation. Fixed colormap ranges: Use the `caxis` or `clim` functions to unify color axis ranges across all subplots (e.g., `clim([minVal, maxVal])`). This ensures color-to-value mappings remain comparable. ### 3. Optimizing Annotations and Styling Label positioning: Prevent overlapping text labels by adjusting the `'LabelSpacing'` parameter in `contour` functions or manually refining label positions using the `clabel` function with custom location arguments. Color bar integration: Add a `colorbar` to display the relationship between colors and values, which becomes particularly intuitive in filled contour plots (`contourf`). The colorbar automatically adapts to the specified `clim` range. ### 4. Implementation Example For comparing two temperature distribution datasets, create side-by-side subplots using `subplot(1,2,1)` and `subplot(1,2,2)`. After generating contour plots for each dataset, enforce consistent scaling by applying identical `levels` vectors and `clim` values. Finally, add a shared `colorbar` to one subplot using `colorbar('Position', [position_vector])` for precise placement. These techniques enable clear data detail presentation while maintaining comparability across multiple plots, making them particularly valuable for scientific publications and engineering reports. The implementation leverages MATLAB's built-in contour plotting functions with parameter optimization for professional visualization outputs.