Histograms with Colors by Values
xxxxxxxxxxdist = RealDistribution('gaussian', 1)data = [dist.get_random_element() for _ in range(1000)]n, x, y = 30, [], []sdata = sorted(data)dx = (sdata[-1] - sdata[0])/nx = [[sdata[0]+dx*i, sdata[0]+dx*(i+1)] for i in range(n)]i, c = 0, 0while i < n - 1: if sdata[0] > x[i+1][1]: y.append(0); i += 1; c = 0 elif sdata[0] > x[i][1]: y.append(c); i += 1; c = 0 else: sdata = sdata[1:]; c += 1y.append(1000-sum(y))col = lambda i, y: colormaps.RdBu(int(255*y[i]/max(y)))[:3]g = Graphics(); p = .02for i in range(n): g += polygon([[x[i][0]+p, 0], [x[i][0]+p, y[i]], [x[i][1]-p, y[i]], [x[i][1]-p, 0]], color=col(i,y), aspect_ratio=.05, frame=True)save(g, "SagePlots.png")show(g)xxxxxxxxxx%%rdata <- rnorm(1000)n <- 30h <- hist(data, breaks=n, plot=FALSE)pal <- colorRamp(c("midnightblue", "steelblue", "bisque", "firebrick"))fcol <- function(x) {rgb(pal((x - min(x)) / diff(range(x))), max=255)}fn<-'Rplots.png'png(fn, pointsize=12, bg='whitesmoke')plot(h, col=fcol(h$density))dev.off()xxxxxxxxxximport numpy as npimport matplotlib.pyplot as pltfrom matplotlib.colors import LinearSegmentedColormapdata = np.random.randn(1000)n = 30hist, bins = np.histogram(data, bins=n, density=True)colors = ['midnightblue', 'steelblue', 'bisque', 'firebrick']cmap = LinearSegmentedColormap.from_list('custom_cmap', colors)plt.bar(bins[:-1], hist, width=np.diff(bins)*.9, color=cmap((hist-hist.min())/(hist.max()-hist.min())))plt.savefig('PythonPlots.png')plt.show()