New answer: It seems we really want to have a 3D Tetris game here 😉 So here is a way to plot cubes of different color to fill the space given by the arrays (x,y,z). from mpl_toolkits.mplot3d import Axes3D import numpy as np import matplotlib.pyplot as plt import matplotlib.cm import matplotlib.colorbar import matplotlib.colors def cuboid_data(center, … Read more
There are two options: First, heatmap is an Axes level figure, so you could set up a main large main heatmap axes for the correlation matrix and flank it with heatmaps that you then pass class colors to yourself. This will be a little bit of work, but gives you lots of control over how … Read more
This feature has just been added in the recent version of Seaborn 0.7.1. From Seaborn update history: The annot parameter of heatmap() now accepts a rectangular dataset in addition to a boolean value. If a dataset is passed, its values will be used for the annotations, while the main dataset will be used for the … Read more
imshow can take an array of [r, g, b] entries. So you can convert the absolute values to intensities and phases – to hues. I will use as an example complex numbers, because for it it makes the most sense. If needed, you can always add numpy arrays Z = X + 1j * Y. … Read more
A heatmap alternative: library(gplots) # need data as matrix mm <- as.matrix(testdf, ncol = 3) heatmap.2(x = mm, Rowv = FALSE, Colv = FALSE, dendrogram = “none”, cellnote = mm, notecol = “black”, notecex = 2, trace = “none”, key = FALSE, margins = c(7, 11)) In heatmap.2 the side of the plot the axis … Read more
If you don’t want hexagons, you can use numpy’s histogram2d function: import numpy as np import numpy.random import matplotlib.pyplot as plt # Generate some test data x = np.random.randn(8873) y = np.random.randn(8873) heatmap, xedges, yedges = np.histogram2d(x, y, bins=50) extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]] plt.clf() plt.imshow(heatmap.T, extent=extent, origin=’lower’) plt.show() This makes a 50×50 heatmap. … Read more
The imshow() function with parameters interpolation=’nearest’ and cmap=’hot’ should do what you want. Please review the interpolation parameter details, and see Interpolations for imshow and Image antialiasing. import matplotlib.pyplot as plt import numpy as np a = np.random.random((16, 16)) plt.imshow(a, cmap=’hot’, interpolation=’nearest’) plt.show()
One option is to use densCols() to extract kernel densities at each point. Mapping those densities to the desired color ramp, and plotting points in order of increasing local density gets you a plot much like those in the linked article. ## Data in a data.frame x1 <- rnorm(n=1E3, sd=2) x2 <- x1*1.2 + rnorm(n=1E3, … Read more
log simply takes the logarithm (base e, by default) of each element of the vector. scale, with default settings, will calculate the mean and standard deviation of the entire vector, then “scale” each element by those values by subtracting the mean and dividing by the sd. (If you use scale(x, scale=FALSE), it will only subtract … Read more