histogram

问题 I am working on a scientific work project, where i need to get some image standards. To do this, i need a code that generate 3 Histograms from a normal .JPEG .PNG OR .BMP image. The first histogram its the HUE Histogram, the second is SATURATION histogram and the third the LUMINOSITY or INTENSITY histogram. Im working with HSL and HSI, so the third histogram both LUMINOSITY or INTENSITY would be useful. I would prefer a Java or C# implementation, if theres exists one. Does anyone knows if

问题 I'm at very beginning with R programming. I'm using RStudio for an exam, and I have to represent graphically the results of some calculations on a dataset. I have a structure like that: and what I was thinking to do was make some histograms with the 3 values of the mean for each row, and the same for median and trimmed mean. First question: Is this a correct way to represent this kind of data graphically? Or there are some better plot. Second question: Could someone give me the code to draw a

问题 Sample data df <- data.frame(id = rep(1:6, each = 50), x = rnorm(50*6, mean = 10, sd = 5), y = rnorm(50*6, mean = 20, sd = 10), z = rnorm(50*6, mean = 30, sd = 15)) ggplot(df, aes(x)) + geom_histogram() + facet_wrap(~id) How do I show x, y, z in the same plot for each id in different colours 回答1: It's best to reshape data from wide to long first, and then add a fill aesthetic to map what (i.e. x , y , z ) to different fill colours: library(tidyverse) df %>% gather(what, val, -id) %>% ggplot

问题 Is there a way that compares histograms but for example white color to be excluded and so white color doesn't affect onto the comparison. 回答1: White pixels have Saturation , S = 0 . So, it is very easy to remove the white pixels from being counted while creating histogram. Do the following: Convert your image from BGR to HSV Then split your HSV image into three individual channels i.e. H , S and V Then, access each pixel of channel S and if the pixel value = 0 (means S = 0) then it mean that

问题 I made this code RendBio= RtBio m<-mean(RtBio) std<-sqrt(var(RtBio)) hist(RendBio, density=20, prob=TRUE, xlab="Rendimientos Bio Pappel", ylim=c(0, 20), main="normal curve over histogram", col="brown1") curve(dnorm(x, mean=m, sd=std), col="darkgoldenrod1", lwd=2, add=TRUE, yaxt="n") When I run the code there are lines in my histogram but I'd like it to be filled with e solid color. Is there a way to do this? 回答1: If you want transparency then you need to use an argument to rgb with the same

问题 I made this code RendBio= RtBio m<-mean(RtBio) std<-sqrt(var(RtBio)) hist(RendBio, density=20, prob=TRUE, xlab="Rendimientos Bio Pappel", ylim=c(0, 20), main="normal curve over histogram", col="brown1") curve(dnorm(x, mean=m, sd=std), col="darkgoldenrod1", lwd=2, add=TRUE, yaxt="n") When I run the code there are lines in my histogram but I'd like it to be filled with e solid color. Is there a way to do this? 回答1: If you want transparency then you need to use an argument to rgb with the same

问题 I'm making histograms using pandas and I find this approach convenient. For example if I do: df['plotvar'].hist(by='Zone') I get But now I want to add the 95%CI on each of these subgroups, and of course the intervals are different for each group. I could do it just using plt.axvline in matplotlib, but not sure how to do it when I've made the original plots using pandas. TIA for any inputs/suggestions. edit: I shoudl add that I already know what the 95%CI values are. This is just a plotting

问题 In a file with the data in the following structure: # [year] "Town A" population "Town B" population "Town C" population # [year2] "Town A" population "Town B" population "Town C" population # [year3] "Town A" population "Town B" population "Town C" population I have managed to create a histogram using the following: set style data histogram set style histogram columnstacked p 'file.dat' index '[year]' u 2:key(1) ,\ '' index '[year2]' u 2,\ '' index '[year3]' u 2; The previous settings

问题 I have a dataframe which consists of a mix of text and numerical data, with some values of -999 representing missing or invalid data. As a toy example, let's say it looks like this: import pandas as pd import matplotlib.pyplot as plt dictOne = {'Name':['First', 'Second', 'Third', 'Fourth', 'Fifth', 'Sixth', 'Seventh', 'Eighth', 'Ninth'], "A":[1, 2, -3, 4, 5, -999, 7, -999, 9], "B":[4, 5, 6, 5, 3, -999, 2, 9, 5], "C":[7, -999, 10, 5, 8, 6, 8, 2, 4]} df2 = pd.DataFrame(dictOne) df2.hist('C',

问题 I am making a figure with 3 subplots and some of the histogram bins are appearing to be different sizes, despite them all being equal width. My goal is to create a histogram with equal width bars. I am plotting data from three different data frames df1,df2,df3 and each gets its own axis. The first two data frames ( df1,df2 ) have 12 values, while the third ( df3 ) has 21 values. A minimal working example: import pandas as pd import matplotlib.pyplot as plt import numpy as np #Data df1 = pd