9/17/2023 0 Comments Pyplot scatter plot color by value![]() ![]() Smap = plt.cm.ScalarMappable(cmap='viridis', norm=norm)Ĭbar = fig.colorbar(smap, ax=axs, fraction=0.1, shrink = 0.8)Ĭbar.ax. Norm = plt.Normalize(np.min(all_data), np.max(all_data))Īxs.scatter(x, y, c=t1, cmap='viridis', norm=norm)Īxs.scatter(x**2, y, c=t2, cmap='viridis', norm=norm) # Create custom Normalise object using the man and max data values across both subplots to ensure colors are consistent on both plots Then a colorbar object can be created from a ScalarMappable() object, which maps between scalar values and colors. In this case, a Normalize() object needs to be created using the minimum and maximum data values across both plots. Sometimes it is preferable to have a single colorbar to indicate data values visualised on multiple subplots. Good examples can be found here for a single subplot colorbar and here for 2 subplots 1 colorbar. fig, ax = plt.subplots() or ax = fig.add_subplot(111)), adding a colorbar can be a bit more involved. Note that if you are using figures and subplots explicitly (e.g. You can add a colorbar by using plt.scatter(x, y, c=t, cmap='viridis') Here's an example with the new 1.5 colormap viridis: import numpy as np Plt.scatter(x, y, c=t, cmap="cmap_name_r") So either plt.scatter(x, y, c=t, cmap=cm.cmap_name_r) Also know that you can reverse a colormap by simply calling it as cmap_name_r. There is a reference page of colormaps showing what each looks like. Importing matplotlib.cm is optional as you can call colormaps as cmap="cmap_name" just as well. Plt.scatter(x, y, c=t, cmap=cm.cmap_name) You can change the colormap by adding import matplotlib.cm as cm The plotting routine will scale the colormap such that the minimum/maximum values in c correspond to the bottom/top of the colormap. it doesn't need to be sorted or integers as in these examples. Note that the array you pass as c doesn't need to have any particular order or type, i.e. Perhaps an easier-to-understand example is the slightly simpler import numpy as np ![]() The above code means that we are setting the color of the scatter plot as red.Here you are setting the color based on the index, t, which is just an array of. To set the colors of a scatter plot, we need to set the argument color or simply c to the pyplot.scatter() function.įor example, take a look at the code below: plt.scatter(x, y, color = 'red') Setting colors to the multiple scatter plot If you want to set only one of the boundaries of the axis and let the other boundary unchanged, you can choose one or more of the following statements. By default, pyplot returned orange and blue. Note: Notice that the two plots in the figure above gave two different colors. ![]() Line 16: The pyplot.show() function is used, which tells pyplot to display both the scatter plots. pyplot.scatter(x,y2) is used to create a scatter plot of x and y2. Lines 12 to 13: The array y2 is created, which contains the y-coordinates for the second scatter plot. pyplot.scatter(x,y1) is used to create a scatter plot of x and y1. Lines 8 to 9: The array y1 is created, which contains the y-coordinates for the first scatter plot. Line 5: The array x is created, containing the x-coordinates common to both plots. Line 2: The numpy module is imported, which will be used to create arrays. Line 1: In matplotlib, the pyplot module is imported, which will be used to create plots. ![]()
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