Sure! To set the ticks, just, well… Set the ticks (see matplotlib.pyplot.xticks or ax.set_xticks). (Also, you don’t need to manually set the facecolor of the patches. You can just pass in a keyword argument.) For the rest, you’ll need to do some slightly more fancy things with the labeling, but matplotlib makes it fairly easy. … Read more
Did you mean something like this? it is HSV histogram showed as 3D graph V is ignored to get to 3D (otherwise it would be 4D graph …) if yes then this is how to do it (I do not use OpenCV so adjust it to your needs): convert source image to HSV compute histogram … Read more
Here you go! # create some data to work with x = rnorm(1000); # overlay histogram, empirical density and normal density p0 = qplot(x, geom = ‘blank’) + geom_line(aes(y = ..density.., colour=”Empirical”), stat=”density”) + stat_function(fun = dnorm, aes(colour=”Normal”)) + geom_histogram(aes(y = ..density..), alpha = 0.4) + scale_colour_manual(name=”Density”, values = c(‘red’, ‘blue’)) + theme(legend.position = c(0.85, … Read more
yes, and its quick and simple though very hidden: binwidth=5 bin(x,width)=width*floor(x/width) plot ‘datafile’ using (bin($1,binwidth)):(1.0) smooth freq with boxes check out help smooth freq to see why the above makes a histogram to deal with ranges just set the xrange variable.
This is not a completely responsive answer but it is very simple. It illustrates an alternate method to display marginal densities and also how to use alpha levels for graphical output that supports transparency: scatter <- qplot(x,y, data=xy) + scale_x_continuous(limits=c(min(x),max(x))) + scale_y_continuous(limits=c(min(y),max(y))) + geom_rug(col=rgb(.5,0,0,alpha=.2)) scatter
Here’s a nice easy way I found: h <- hist(g, breaks = 10, density = 10, col = “lightgray”, xlab = “Accuracy”, main = “Overall”) xfit <- seq(min(g), max(g), length = 40) yfit <- dnorm(xfit, mean = mean(g), sd = sd(g)) yfit <- yfit * diff(h$mids[1:2]) * length(g) lines(xfit, yfit, col = “black”, lwd = … Read more
If I understand your question correctly, then you probably want a density estimate along with the histogram: X <- c(rep(65, times=5), rep(25, times=5), rep(35, times=10), rep(45, times=4)) hist(X, prob=TRUE) # prob=TRUE for probabilities not counts lines(density(X)) # add a density estimate with defaults lines(density(X, adjust=2), lty=”dotted”) # add another “smoother” density Edit a long while … Read more
Here is an even simpler solution using base graphics and alpha-blending (which does not work on all graphics devices): set.seed(42) p1 <- hist(rnorm(500,4)) # centered at 4 p2 <- hist(rnorm(500,6)) # centered at 6 plot( p1, col=rgb(0,0,1,1/4), xlim=c(0,10)) # first histogram plot( p2, col=rgb(1,0,0,1/4), xlim=c(0,10), add=T) # second The key is that the colours are … Read more
hist(c(1,2,3,4,5), main=”Titre”, ylab = ‘Frequence’) This could however easily been solved by yourself with the help functions in R.
We can start by generating ranges required, and then map count function onto ranges to get list of partial functions, which finally can be applied on the given list. ranges lim n = takeWhile (\(x,y) -> x < lim) [(k*n, (k+1)*n – 1) | k <- [0..]] count (l,r) = length.filter (\x -> l <= … Read more