histogram

I want to calculate 3D histogram of my Cielab image in python. I am using openCV to calculate my histogram. I want to compare images using compareHist function of openCV, thats why I am using openCV to compute 3D histogram of my image. I tried with the following variables: i_lab = image.copy() i_lab = i_lab.astype(np.uint8) Range_hist = [[0, 100], [-100, 100], [-100, 100]] hist_1 = cv2.calcHist([i_lab], [[0], [1], [2]], None, [[20], [20], [20]], Range_hist) But it gives error SystemError: error return without exception set Please tell me what am I doing wrong and if it is possible to compute

Exploring a new data set: What is the easiest, quickest way to visualise many (all) variables? Ideally, the output shows the histograms next to each other with minimal clutter and maximum information. Key to this question is flexibility and stability to deal with large and different data sets. I'm using RStudio and usually deal with large and messy survey data. One example which comes out of the box of Hmisc and works quite well here is: library(ggplot2) str(mpg) library(Hmisc) hist.data.frame(mpg) Unfortunately, somewhere else I run into problems with data lables (Error in plot.new() : figure

I have a numpy array results that looks like [ 0. 2. 0. 0. 0. 0. 3. 0. 0. 0. 0. 0. 0. 0. 0. 2. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 2. 0. 3. 1. 0. 0. 2. 2. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 2. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 3. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 2. 2.] I would like to plot a histogram of it. I have tried import matplotlib.pyplot as plt plt.hist(results, bins=range(5)) plt.show() This gives me a histogram with the x-axis labelled 0.0 0.5 1.0 1.5 2.0 2.5 3.0. 3.5 4.0 . I would like the x-axis to be

I have a problem with making a histogram when some of my data contains "not a number" values. I can get rid of the error by using nan_to_num from numpy, but than i get a lot of zero values which mess up the histogram as well. pylab.figure() pylab.hist(numpy.nan_to_num(A)) pylab.show() So the idea would be to make another array in which all the nan values are gone, or to just mask them in the histogram in some way (preferrably with some builtin method). Remove np.nan values from your array using A[~np.isnan(A)] , this will select all entries in A which values are not nan , so they will be

So basically I want to count the number of occurrences a floating point appears in a given list. For example: a list of grades (all scores out of 100) are inputted by the user and they are sorted in groups of ten. How many times do scores from 0-10, 10-20, 20-30.. etc) appear? Like test score distribution. I know I can use the count function but since I'm not looking for specific numbers I'm having trouble. Is there a away to combine the count and range? Thanks for any help. To group the data, divide it by the interval width. To count the number in each group, consider using collections