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问题:
In maptplotlib, one can create a heatmap representation of a correlation matrix using the imshow function. By definition, such a matrix is symmetrical around its main diagonal, therefore there is no need to present both the upper and lower triangles. For example:
The above example was taken from this site Unfortunately, I couldn't figure out how to do this in matplotlib. Setting upper/lower part of the matrix to None results in black triangle. I have googled for "matplotlib missing values", but couldn't find anything helpful
回答1:
The problem with the answer provided by doug is that it relies on the fact that the colormap maps zero values to white. This means that colormaps that do not include white color are not useful. The key for solution is cm.set_bad function. You mask the unneeded parts of the matrix with None or with NumPy masked arrays and set_bad to white, instead of the default black. Adopting doug's example we get the following:
import numpy as NP from matplotlib import pyplot as PLT from matplotlib import cm as CM A = NP.random.randint(10, 100, 100).reshape(10, 10) mask = NP.tri(A.shape[0], k=-1) A = NP.ma.array(A, mask=mask) # mask out the lower triangle fig = PLT.figure() ax1 = fig.add_subplot(111) cmap = CM.get_cmap('jet', 10) # jet doesn't have white color cmap.set_bad('w') # default value is 'k' ax1.imshow(A, interpolation="nearest", cmap=cmap) ax1.grid(True) PLT.show()
回答2:
import numpy as NP from matplotlib import pyplot as PLT from matplotlib import cm as CM A = NP.random.randint(10, 100, 100).reshape(10, 10) # create an upper triangular 'matrix' from A A2 = NP.triu(A) fig = PLT.figure() ax1 = fig.add_subplot(111) # use dir(matplotlib.cm) to get a list of the installed colormaps # the "_r" means "reversed" and accounts for why zero values are plotted as white cmap = CM.get_cmap('gray_r', 10) ax1.imshow(A2, interpolation="nearest", cmap=cmap) ax1.grid(True) PLT.show()
plot http://img444.imageshack.us/img444/9585/cmapgrayr.png
回答3:
回答4:
With seaborn and numpy, a quick solution is:
import matplotlib.pyplot as plt import seabon as sns # Say your matrix object (e.g. np.array) is corr_mat # Get the upper triangle without the diagonal corr_mat = np.triu(corr_mat, k=1) # Plot the heatmap ax = sns.heatmap(corr_mat)
Please, refer to seaborn online document for makeup.
