How to create a histogram without using the matlab function
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How to create a histogram without using the matlab hist function.
Given scattered data x = randn(1,100); y = randn(1,100);
with coresponding phase p = randn(1,100), having phase phase = exp(1i*p);
would like to create uniform grid, add the phase for the data points that are inside each bin.
This is to create a na intensity plot or coherent sum of the scattered data.
7 个评论
Image Analyst
2020-2-28
编辑：Image Analyst
2020-2-28
Why would you want to do that? What's wrong with using the built-in function that you bought along with MATLAB??? Which array (x, y, p, or phase) do you want to take the histogram of? Why not use histogram2()?
Guillaume
2020-2-28
In addition to what has been said, it would be useful to know which 'matlab function' has been banned. There are many matlab functions that can be used to generate histograms: hist, histc, histogram, histcounts, histogram2, histcounts2, accumarray, imhist, probably more...
Image Analyst
2020-2-29
Why not
[counts, edges] = histcounts(phases, numberOfBins);
We're not sure what you want if it's not a count of the number of occurrences of values in certain ranges (bins). That is what a histogram is. How can you say you want a histogram but say you're struggling when the functions count the occurrences? That's what a histogram is.
Guillaume
2020-3-1
@Ole, can you give a formal mathematical definition of what it is you want to calculate for a bin, because as you can see we're a bit confused.
Ole
2020-3-1
采纳的回答
Guillaume
2020-3-1
As Steven said, use discretize to find the bin indices and then one of the many aggregation functions in matlab. With 3 vector inputs, I'd use the older accumarray:
%demo data and bin definitions
x = randn(1, 100);
y = randn(1, 100);
p = randn(1, 100);
phase = exp(1i*p);
xedges = [-Inf, -3:3, +Inf]; %there will be one less bin that there are edges
yedges = [-Inf, -3:3, +Inf]; %see documentation of discretize
%histogram building
destrow = discretize(y, ybins);
destcol = discretize(x, xbins);
phasehistogram = accummaray([destrow, destcol], phase, [numel(ybins), numel(xbins)] - 1);
Or you could put the vectors in a table, and call groupsummary which would do the binning and summing for you:
%demo data and bin definitions
x = randn(1, 100);
y = randn(1, 100);
p = randn(1, 100);
phase = exp(1i*p);
xedges = [-Inf, -3:3, +Inf]; %there will be one less bin that there are edges
yedges = [-Inf, -3:3, +Inf]; %see documentation of discretize
%table construction and histogram:
phasetable = table(x, y, phase);
phasehistogram = groupsummary(phasetable, {'x', 'y'}, {xedges, yedges}, 'sum', 'phase');
更多回答（1 个）
Steven Lord
2020-2-28
1 个投票
Consider using discretize to bin the data then passing that grouping information into groupsummary or splitapply.
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