histogram

This question already has an answer here: Histogram with Logarithmic Scale and custom breaks 7 answers Hi I'm making histogram using R, but the number of Y axis is so large that I need to turn it into logarithmic.See below my script: hplot<-read.table("libl") hplot pdf("first_end") hist(hplot$V1, breaks=24, xlim=c(0,250000000), ylim=c(0,2000000),main="first end mapping", xlab="Coordinates") dev.off() So how should I change my script? thx You can save the histogram data to tweak it before plotting: set.seed(12345) x = rnorm(1000) hist.data = hist(x, plot=F) hist.data$counts = log10(hist.data

How do you use hist() to plot relative frequencies in R? If I do the following, I will get a density plot, but I want a relative frequency plot: a <- c(0,0,0,1,1,2) hist(a, freq=FALSE) I want to see a histogram with the following relative frequencies: .5 for 0 to 1, .33 for 1 to 2, and .166 for 2 to 3. you can try using the histogram() function in lattice a <- c(0,0,0,1,1,2) library(lattice) histogram(a) defaults to percent. I've added a new function to the HistogramTools package on CRAN, PlotRelativeFrequency() which takes a histogram object and generates a relative frequency histogram plot.

Anyone knows how to perform RGB histogram matching on two colored images? for example this is an image to be re-mapped: and this is a target image Then the RGB remapped image look like this here is what I did so far, in this code I took two color images im1 and im2 I took the im1 which is the one that has to be remapped then broke it up into its colors then I took each color of im1 and used histeq to match their histograms to each color in im2 . I don't know how to reconstruct the re-mapped image from the colors I matched, any help please that would be nice??: im1 = imread('Atlas-Mer.png');

I have set of value in float (always less than 0). Which I want to bin into histogram, i,e. each bar in histogram contain range of value [0,0.150) The data I have looks like this: 0.000 0.005 0.124 0.000 0.004 0.000 0.111 0.112 Whith my code below I expect to get result that looks like [0, 0.005) 5 [0.005, 0.011) 0 ...etc.. I tried to do do such binning with this code of mine. But it doesn't seem to work. What's the right way to do it? #! /usr/bin/env python import fileinput, math log2 = math.log(2) def getBin(x): return int(math.log(x+1)/log2) diffCounts = [0] * 5 for line in fileinput.input(

I have ~ 5 very large vectors (~ 108 MM entries) so any plot/stuff I do with them in R takes quite long time. I am trying to visualize their distribution (histogram), and was wondering what would be the best way to superimpose their histogram distributions in R without taking too long. I am thinking to first fit a distribution to the histogram, and then plot all the distribution line fits together in one plot. Do you have some suggestions on how to do that? Let us say my vectors are: x1, x2, x3, x4, x5. I am trying to use this code: Overlaying histograms with ggplot2 in R Example of the code I

I would like to know the difference between contrast stretching and histogram equalization . I have tried both using OpenCV and observed the results, but I still have not understood the main differences between the two techniques. Insights would be of much needed help. Lets Define Contrast first, Contrast is a measure of the “range” of an image; i.e. how spread its intensities are. It has many formal definitions one famous is Michelson’s: He says contrast = ( Imax - Imin )/( Imax + I min ) Contrast is strongly tied to an image’s overall visual quality. Ideally, we’d like images to use the