Matplotlib imshow - Change default colour normalisation

Matplotlib imshow - Change default colour normalisation

In Matplotlib's imshow function, you can change the default color normalization (colormap scaling) using the norm parameter. The norm parameter allows you to specify a custom normalization function for the color data.

Here's an example of how you can change the default color normalization using the norm parameter:

import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import Normalize # Create a sample image data data = np.random.random((10, 10)) # Create a custom normalization function (e.g., logarithmic) class LogNorm(Normalize): def __call__(self, value, clip=None): result, is_scalar = self.process_value(value) self.autoscale_None(result) result = np.ma.log(result) return result # Create a figure and axes fig, ax = plt.subplots() # Use the imshow function with the custom normalization im = ax.imshow(data, cmap='viridis', norm=LogNorm()) # Add colorbar for reference cbar = plt.colorbar(im) plt.show()

In this example, a custom normalization class LogNorm is created by inheriting from Normalize. This class applies a logarithmic scaling to the color data. Then, when you use imshow, you pass norm=LogNorm() to apply the custom normalization.

You can create other custom normalization functions to suit your specific needs. Remember that you can also use predefined normalization classes like matplotlib.colors.Normalize, matplotlib.colors.LogNorm, and others provided by Matplotlib.

Note that this approach changes the normalization for a specific imshow call. If you want to change the default color normalization for all imshow calls globally in your script or notebook, you might need to explore advanced configuration options or use a custom context manager to temporarily set the default normalization.

Examples

  1. "Matplotlib imshow custom color map" Description: Explore how to create a custom color map for imshow in Matplotlib to change default color normalization.

    import matplotlib.pyplot as plt import numpy as np # Generate random data data = np.random.rand(10, 10) # Create a custom colormap custom_cmap = plt.cm.get_cmap('viridis', 10) # Example: viridis colormap with 10 levels # Plot the data using imshow with custom colormap plt.imshow(data, cmap=custom_cmap) plt.colorbar() # Add colorbar for reference plt.show()

  2. "Matplotlib imshow set vmin and vmax" Description: Learn how to set minimum and maximum values for color normalization in imshow to customize the color range.

    import matplotlib.pyplot as plt import numpy as np # Generate random data data = np.random.rand(10, 10) # Define vmin and vmax for custom color range vmin = 0.2 vmax = 0.8 # Plot the data using imshow with custom vmin and vmax plt.imshow(data, vmin=vmin, vmax=vmax) plt.colorbar() # Add colorbar for reference plt.show()

  3. "Matplotlib imshow normalize data" Description: Understand how to normalize data before plotting with imshow to adjust color representation.

    import matplotlib.pyplot as plt import numpy as np # Generate random data data = np.random.randn(10, 10) # Normalize data to [0, 1] range normalized_data = (data - np.min(data)) / (np.max(data) - np.min(data)) # Plot the normalized data using imshow plt.imshow(normalized_data, cmap='viridis') plt.colorbar() # Add colorbar for reference plt.show()

  4. "Matplotlib imshow logarithmic scale" Description: Implement logarithmic scale for color normalization in imshow to handle data with wide dynamic range.

    import matplotlib.pyplot as plt import numpy as np # Generate random data with wide dynamic range data = np.random.exponential(scale=100, size=(10, 10)) # Plot the data using imshow with logarithmic scale plt.imshow(data, norm=plt.LogNorm()) plt.colorbar() # Add colorbar for reference plt.show()

  5. "Matplotlib imshow reverse color map" Description: Reverse the color map for imshow to invert the default color scheme.

    import matplotlib.pyplot as plt import numpy as np # Generate random data data = np.random.rand(10, 10) # Plot the data using imshow with reversed color map plt.imshow(data, cmap='viridis_r') # '_r' suffix reverses the colormap plt.colorbar() # Add colorbar for reference plt.show()

  6. "Matplotlib imshow set color bar ticks" Description: Set custom ticks for the color bar in imshow to specify specific values or intervals.

    import matplotlib.pyplot as plt import numpy as np # Generate random data data = np.random.rand(10, 10) # Plot the data using imshow and set custom color bar ticks plt.imshow(data, cmap='viridis') cbar = plt.colorbar() cbar.set_ticks([0, 0.5, 1]) # Set custom ticks plt.show()

  7. "Matplotlib imshow set color bar labels" Description: Assign labels to the color bar ticks in imshow for better interpretation of color values.

    import matplotlib.pyplot as plt import numpy as np # Generate random data data = np.random.rand(10, 10) # Plot the data using imshow and set color bar labels plt.imshow(data, cmap='viridis') cbar = plt.colorbar() cbar.set_ticklabels(['Low', 'Medium', 'High']) # Set custom tick labels plt.show()

  8. "Matplotlib imshow normalize to specific range" Description: Normalize data to a specific range before plotting with imshow to control color mapping.

    import matplotlib.pyplot as plt import numpy as np # Generate random data data = np.random.randn(10, 10) # Define specific range for normalization desired_min = -1 desired_max = 1 # Normalize data to the specific range normalized_data = np.clip(data, desired_min, desired_max) # Plot the normalized data using imshow plt.imshow(normalized_data, cmap='viridis') plt.colorbar() # Add colorbar for reference plt.show()

  9. "Matplotlib imshow adjust color intensity" Description: Adjust color intensity for imshow visualization to enhance contrast or highlight specific features.

    import matplotlib.pyplot as plt import numpy as np # Generate random data data = np.random.rand(10, 10) # Adjust color intensity by scaling the data intensity_factor = 2.0 adjusted_data = data * intensity_factor # Plot the adjusted data using imshow plt.imshow(adjusted_data, cmap='viridis') plt.colorbar() # Add colorbar for reference plt.show()

  10. "Matplotlib imshow set alpha value" Description: Set alpha (transparency) value for imshow to control the opacity of the plotted image.

    import matplotlib.pyplot as plt import numpy as np # Generate random data data = np.random.rand(10, 10) # Set alpha value for transparency alpha_value = 0.5 # Plot the data using imshow with alpha value plt.imshow(data, cmap='viridis', alpha=alpha_value) plt.colorbar() # Add colorbar for reference plt.show()

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