histogram

问题 I'm trying to implement the histogram equalization using this code: clc A=input('please enter image adress','s'); Iimg=imread(A); Iimg1=double(Iimg); histi=imhist(Iimg); mmax=max(Iimg1(:)); h=histi/numel(Iimg1) cdf=cumsum(h) cdf=cdf*double(mmax); c=uint8(cdf); subplot(1,3,1) bar(c) subplot(1,3,2) imhist(Iimg) subplot(1,3,3) imhist(histeq(Iimg)) Is my code wrong? I don't get expected results. 回答1: I get this from your code: What is wrong? unless your image is rgb , the code works (I used

问题 I posted a question earlier ( Pandas-ipython, how to create new data frames with drill down capabilities ) and it was pointed out that it is possibly too broad so I have some more specific questions that may be easier to respond to and help me get a start with graphing data. I have decided to try creating some visualizations of my data using Pandas (or any package accessible through ipython). The first, obvious, problem I run into is how can I filter on certain conditions. For example I type

问题 I would like to include a 3D dynamic (i.e. one can change its perspective just by moving the plot) histogram widget in a R Shiny application. Unfortunately I didn't find any until now. So far the results of my searches: with threejs (e.g. here on CRAN and there on GitHub) one can use many different representations (scatterplots, surfaces, etc.) but no 3D histogram. plot3D and plot3Drgl don't have any R Shiny counterpart. Unless something already exists my intention is to create an HTMLWidget

问题 Is there a way to create a 2D histogram, in Python, where each bin is coloured by standard deviation of points in each bin rather than by point density? For example, I have a set of 2D data that looks as follows: But I'd like to turn this into a 2D histogram where I expect the purity to decrease in the middle of the gradient-like structure. I realise there's no code in this question but if I could be pointed in the right direction to do this efficiently I's be very grateful. Perhaps there is

问题 I want to plot a barplot like in figure. I want each bin to be coloured based on the sum of the value in the other columns. . I made a reproducible example here. library(reshape) library(ggplot2) values= replicate(4, diff(c(0, sort(runif(92)), 1))) colnames(values) = c("A","B","C","D") counts = sample(10:100, 93, replace=T) df = data.frame(cbind(values,"count"=counts)) mdf = melt(df,id="count") mdf = mdf %>% mutate(binCounts = cut(count, breaks = seq(0, 100, by = 5))) plot = ggplot(mdf) +

问题 I am trying to write a program for histogram calculation. So i have created a random array of 10 numbers treating it as the image array in 1D. The following is my renderscript and Java code. I want the histogram o be returned as an array with counts of each possible no generated but it only returns the first address of that number encountered. Any help would be much appreciated! Java code: public class Histcal extends Activity { private int[] image; private int[] luminance; private float[]

问题 This question already has answers here : In matplotlib.pyplot, how to plot two datasets using interleaved bar graphs? (4 answers) Closed 2 years ago . I have these two dictionaries that I want to plot in the same bar chart using Matplotlib: accuracy_pre = {"Random Forest": rf_accuracy_score, "Logistic Regression": log_accuracy_score, "KNN": knn_accuracy_score} accuracy_post = {"Random Forest": gs_rf_accuracy_score, "Logistic Regression": gs_logmodel_accuracy_score, "KNN": knn_accuracy_score

问题 t.dat file looks like this: 260.37 260.04 261.53 261.32 260.19 260.49 260.43 260.59 260.26 260.68 260.28 259.93 260.82 259.50 260.29 260.52 260.30 259.91 262.24 260.58 260.74 260.22 261.66 260.31 260.99 259.79 260.90 259.88 260.19 261.50 259.32 260.79 259.94 260.35 260.03 260.07 261.86 261.09 260.60 260.15 75.17 75.16 75.33 75.31 75.34 75.04 75.49 75.25 75.27 75.32 75.10 75.75 75.58 74.86 75.19 75.44 75.29 75.31 75.55 75.91 75.39 75.65 75.85 75.67 75.62 74.87 75.64 75.69 75.13 77.76 75.31 74

问题 I know how to fit the data entering an histogram with a normal distribution using the SCipy library (Fitting a histogram with python) but how could I do the same if on top of having data I have an array of weights having the same dimension. Is there a proper function for that or should I create a second array fed by the data and weighting it myself? Cheers. Edit: This is pretty much already answered here: Weighted standard deviation in NumPy? 回答1: just use the weights paramater on scipy

问题 I am trying to get a D3JS histogram working with an ordinal scale (in this case, student grade symbols (A+, A, B ... F). Here is a minimum working example: <!DOCTYPE html> <meta charset="utf-8"> <body> <script src="http://d3js.org/d3.v3.min.js"></script> <script> var studentSymbols = ["F", "E", "D", "C", "C", "C", "C", "B", "B", "A", "A+"]; var x = d3.scale.ordinal() .domain(["F", "E", "D", "C", "B", "A", "A+"]) .rangeRoundBands([0, width]); // Generate a histogram using twenty uniformly