Defining the midpoint of a colormap in matplotlib

Defining the midpoint of a colormap in matplotlib

In Matplotlib, you can define the midpoint of a colormap by creating a custom colormap (colormap segment) that places a specific color at the midpoint of the colormap. You can do this using the ListedColormap class from Matplotlib's matplotlib.colors module. Here's a step-by-step example:

import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap # Define your existing colormap (e.g., 'viridis') existing_cmap = plt.get_cmap('viridis') # Define the color you want at the midpoint midpoint_color = 'orange' # Define the midpoint value (between 0 and 1) midpoint = 0.5 # Create a custom colormap with the specified midpoint color cmap = ListedColormap([existing_cmap(0.0), midpoint_color, existing_cmap(1.0)]) # Generate some example data data = np.random.rand(10, 10) # Create a plot using the custom colormap plt.imshow(data, cmap=cmap) # Add a colorbar for reference plt.colorbar() plt.show()

In this example:

  1. We start with an existing colormap, in this case, 'viridis' (you can choose any colormap you prefer).

  2. We specify the color we want at the midpoint of the colormap ('orange' in this case).

  3. We define the midpoint value (a value between 0 and 1) where the midpoint color will be placed.

  4. We create a custom colormap (cmap) using ListedColormap. The custom colormap is defined as a list of colors, including the colors at the extremes (0.0 and 1.0) and the midpoint color.

  5. We generate some example data and create a plot using the custom colormap.

  6. Finally, we add a colorbar to the plot to show the colormap.

This allows you to define the midpoint of a colormap with the specified color. Adjust the existing_cmap, midpoint_color, and midpoint values to suit your specific colormap and midpoint color requirements.

Examples

  1. Defining midpoint of colormap in Matplotlib example:

    • Description: Learn how to set the midpoint of a colormap in Matplotlib for data visualization.
    import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import LinearSegmentedColormap def custom_cmap(colors, position=None, bit=False): bit_rgb = np.linspace(0, 1, 256) if position == None: position = np.linspace(0, 1, len(colors)) else: if len(position) != len(colors): raise ValueError("position length must be the same as colors") elif position[0] != 0 or position[-1] != 1: raise ValueError("position must start with 0 and end with 1") for i in range(len(colors)): colors[i] = (position[i],) + colors[i] cdict = {'red': [], 'green': [], 'blue': []} for pos, c in colors: cdict['red'].append((pos, c[0], c[0])) cdict['green'].append((pos, c[1], c[1])) cdict['blue'].append((pos, c[2], c[2])) cmap = LinearSegmentedColormap('custom_cmap', cdict, 256) if bit: return cmap, bit_rgb else: return cmap cmap = custom_cmap([(0, 0, 1), (0.5, 0.5, 0.5), (1, 0, 0)]) plt.imshow(np.random.rand(25, 25), cmap=cmap) plt.colorbar() plt.show()

  2. Setting colormap midpoint in Matplotlib:

    • Description: Set the midpoint of a colormap in Matplotlib for better visualization of data.
    import numpy as np import matplotlib.pyplot as plt data = np.random.rand(10,10) plt.imshow(data, cmap='RdYlBu', vmin=0, vmax=1) # RdYlBu is the colormap, vmin and vmax set the color range plt.colorbar() plt.show()

  3. Custom midpoint for colormap in Matplotlib:

    • Description: Customize the midpoint of a colormap in Matplotlib using normalization.
    import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import Normalize data = np.random.rand(10,10) norm = Normalize(vmin=0, vmax=1) cmap = plt.cm.RdYlBu cmap.set_bad('white') # Set the color for invalid values plt.imshow(data, cmap=cmap, norm=norm) plt.colorbar() plt.show()

  4. Adjusting midpoint of colormap in Matplotlib:

    • Description: Adjust the midpoint of a colormap in Matplotlib by specifying normalization range.
    import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import Normalize data = np.random.rand(10,10) midpoint = 0.5 # Set the midpoint value norm = Normalize(vmin=0, vmax=1) norm_midpoint = (midpoint - 0.5) / (1 - 0.5) # Calculate normalized midpoint cmap = plt.cm.RdYlBu cmap.set_bad('white') plt.imshow(data, cmap=cmap, norm=norm, alpha=0.8) plt.colorbar() plt.show()

  5. Defining custom midpoint for Matplotlib colormap:

    • Description: Define a custom midpoint for a colormap in Matplotlib to enhance data visualization.
    import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import LinearSegmentedColormap def custom_cmap(cmap_name, midpoint=0.5): cmap = plt.get_cmap(cmap_name) start, end = 0.0, 1.0 cdict = { 'red': [], 'green': [], 'blue': [], 'alpha': [] } cdict['red'].append((start, cmap(0.0)[0], cmap(midpoint)[0])) cdict['green'].append((start, cmap(0.0)[1], cmap(midpoint)[1])) cdict['blue'].append((start, cmap(0.0)[2], cmap(midpoint)[2])) cdict['red'].append((midpoint, cmap(midpoint)[0], cmap(1.0)[0])) cdict['green'].append((midpoint, cmap(midpoint)[1], cmap(1.0)[1])) cdict['blue'].append((midpoint, cmap(midpoint)[2], cmap(1.0)[2])) cdict['alpha'].append((start, cmap(0.0)[3], cmap(1.0)[3])) cdict['alpha'].append((midpoint, cmap(midpoint)[3], cmap(1.0)[3])) new_cmap = LinearSegmentedColormap(cmap_name + "_shifted", cdict) return new_cmap data = np.random.rand(10, 10) cmap = custom_cmap('RdYlBu') plt.imshow(data, cmap=cmap) plt.colorbar() plt.show()

More Tags

default-constructor angular2-aot media-queries onmouseover integer external-tools horizontal-scrolling underline angular2-compiler onpause

More Python Questions

More Biology Calculators

More Date and Time Calculators

More Stoichiometry Calculators

More Various Measurements Units Calculators