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* * * [ Matplotlib Reference Documentation](https://example.com/matplotlib/matplotlib-apiref.html) `contour()` draws contour lines, `contourf()` draws filled contours. Both are used to visualize 2D scalar fields (such as topographic maps, temperature fields, pressure fields, etc.). ## Function Definition ### pyplot Interface matplotlib.pyplot.contour(*args, **kwargs) matplotlib.pyplot.contourf(*args, **kwargs) ### Axes Interface Axes.contour(X, Y, Z, levels=None, **kwargs)Axes.contourf(X, Y, Z, levels=None, **kwargs) ## Parameter Description | Parameter | Type | Description | | --- | --- | --- | | X, Y | 2D array-like or 1D array | x and y coordinates of grid points. If 1D arrays are passed, they are automatically expanded via meshgrid | | Z | 2D array-like | Function values (heights) at each grid point, shape same as X, Y | | levels | int or array-like | Contour levels: integer means auto-generate N lines, array means specific level values | | colors | color or list | Contour line colors (for contour) | | cmap | str or Colormap | Color mapping (for contourf), e.g., 'viridis', 'terrain' | | alpha | float | Transparency 0-1 | | linewidths | float or list | Line width (for contour) | | linestyles | str or list | Line style (for contour), e.g., 'solid', 'dashed' | | extend | str | Color handling for values beyond levels range: 'neither'/'both'/'min'/'max' | | antialiased | bool | Anti-aliasing (for contourf), default True | ## clabel() Additional Notes `clabel()` is used to add numeric labels on contour lines. Axes.clabel(CS, levels=None, **kwargs) * * * ## Usage Examples ### Example 1: Basic Contour + Filled Contour Comparison ## Example import matplotlib.pyplot as plt import numpy as np # Create 2D grid and function values x = np.linspace(-3,3,100) y = np.linspace(-3,3,100) X, Y = np.meshgrid(x, y) Z = np.sin(X) * np.cos(Y)# 2D function fig,(ax1, ax2)= plt.subplots(1,2, figsize=(12,5), layout='constrained') # Left plot: contour lines (contour) cs1 = ax1.contour(X, Y, Z, levels=10, cmap='viridis') ax1.clabel(cs1, inline=True, fontsize=8)# Add labels ax1.set_title('contour() - Line Contours') # Right plot: filled contours (contourf) cs2 = ax2.contourf(X, Y, Z, levels=15, cmap='RdYlBu') fig.colorbar(cs2, ax=ax2, label='Value') # Overlay boundary lines ax2.contour(X, Y, Z, levels=15, colors='black', linewidths=0.3) ax2.set_title('contourf() - Filled Contours') for ax in[ax1, ax2]: ax.set_xlabel('X') ax.set_ylabel('Y') plt.show() ### Example 2: Custom Levels ## Example import matplotlib.pyplot as plt import numpy as np x = np.linspace(-5,5,150) y = np.linspace(-5,5,150) X, Y = np.meshgrid(x, y) # Gaussian hill Z = np.exp(-((X-1)**2 + Y**2) / 4) + np.exp(-((X+1)**2 + Y**2) / 3) fig, ax = plt.subplots(figsize=(7,5), layout='constrained') # Custom levels: 0.1 to 1.0, spacing 0.1 custom_levels = np.arange(0.1,1.1,0.1) cs = ax.contourf(X, Y, Z, levels=custom_levels, cmap='YlOrRd', extend='both') cbar = fig.colorbar(cs, ax=ax, label='Height') ax.contour(X, Y, Z, levels=custom_levels, colors='black', linewidths=0.5) ax.set_title('Custom Levels Contour') ax.set_xlabel('X') ax.set_ylabel('Y') plt.show() ### Example 3: Terrain Style (terrain colormap) ## Example import matplotlib.pyplot as plt import numpy as np x = np.linspace(-4,4,200) y = np.linspace(-4,4,200) X, Y = np.meshgrid(x, y) # Simulate terrain: two peaks + one valley Z =3 * np.exp(-((X+2)**2 + Y**2) / 3) + 2 * np.exp(-((X-1)**2 + (Y-1)**2) / 2) - 1 * np.exp(-((X+0.5)**2 + (Y-2)**2) / 1.5) fig, ax = plt.subplots(figsize=(8,6), layout='constrained') # terrain colormap for terrain map simulation cs = ax.contourf(X, Y, Z, levels=20, cmap='terrain', extend='both') fig.colorbar(cs, ax=ax, label='Elevation', shrink=0.8) ax.contour(X, Y, Z, levels=20, colors='black', linewidths=0.3, alpha=0.4) ax.set_title('Terrain-style Contour Map') ax.set_xlabel('X (km)') ax.set_ylabel('Y (km)') plt.show() print("tutorial: terrain contour displayed") * * * ## FAQ ### When to use contour vs contourf? `contour()`: Suitable for viewing clear boundary lines, such as pressure isolines. `contourf()`: Suitable for displaying continuously changing fields, such as temperature distribution. Common practice is to overlay both: contourf for color fill, contour for boundary lines. ### Do X and Y have to be 2D? No. If 1D arrays are passed as X and Y, matplotlib will automatically call `np.meshgrid(X, Y)` to generate a 2D grid. Z must match the shape after meshgrid. [ Matplotlib Reference Documentation](https://example.com/matplotlib