Reputation: 56
I want to change the y axis of the histogram to show percentages ranging from 0 to 1. this is what I've tried, but it doesn't seem to be working.
myTolerance=1e-12; % in erg units.
nbins=50;
for j=1:ntM/100:ntM
H = histfit(Wkinet(abs(Wkinet(:,j))>myTolerance, j) * erg2eV, nbins);
%Select from column j all rows in column j whose absolute values are
%greater than the tolerance.
H(1).delete; %%Remove bins, only keep the fit.
set(gca, 'YScale', 'log');
set(gca, 'XScale', 'log'); % Make logarithmic X
yt = get(gca, 'YTick');
set(gca, 'YTick', yt, 'YTickLabel',
yt/numel(Wkinet(abs(Wkinet(:,j))>myTolerance)))
pause;
end
Upvotes: 1
Views: 756
Reputation: 60484
Just to simplify the discussion below, the line
H = histfit(Wkinet(abs(Wkinet(:,j))>myTolerance, j) * erg2eV, nbins);
is equivalent to
data = Wkinet(abs(Wkinet(:,j))>myTolerance, j) * erg2eV;
H = histfit(data, nbins);
This means below we'll assume data
is a vector.
histfit
computes and plots a histogram though histogram
, then fits a function to it through fitdist
. Since you don't want to plot the histogram itself, just stick to fitdist
:
pd = fitdist(data,'Normal'); % this is the default distribution used in `histfit`, is it correct?
x = linspace(min(data),max(data),200); % 200 points in the graph, you might want to change this?
y = pdf(pd,x);
plot(x,y);
Now it's simple to normalize the plot however we want. For example set the first element to 1:
pd = fitdist(data,'Normal');
x = linspace(min(data),max(data),200);
y = pdf(pd,x);
y = y/y(1); % <<< Normalize
plot(x,y);
Upvotes: 2
Reputation: 630
You can set limits on your y-axis using
ylim([1e-3 1]) %lower limit is nonzero since it's plotted on log scale
or
set(gca, 'ylim', [1e-3 1])
Upvotes: 0