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See the recommended documentation of this function

Scilab manual >> Graphics Library > 2d_plot > histplot

# histplot

plot a histogram

### Calling Sequence

```histplot(n, data, <opt_args>)
histplot(x, data, <opt_args>)```

### Arguments

n

positive integer (number of classes)

x

increasing vector defining the classes (`x` may have at least 2 components)

data

vector (datas to be analysed)

<opt_args>

This represents a sequence of statements `key1=value1,key2=value2` ,... where `key1`, `key2,...` can be any optional plot2d parameter (```style,strf,leg, rect,nax, logflag,frameflag, axesflag```) or normalization. For this last one the corresponding value must be a boolean scalar (default value %t).

### Description

This function plot an histogram of the `data` vector using the classes `x`. When the number `n` of classes is provided instead of `x`, the classes are choosen equally spaced and x(1) = min(data) < x(2) = x(1) + dx < ... < x(n+1) = max(data) with dx = (x(n+1)-x(1))/n.

The classes are defined by C1 = [x(1), x(2)] and Ci = ( x(i), x(i+1)] for i >= 2. Noting Nmax the total number of `data` (Nmax = length(data)) and Ni the number of `data` components falling in Ci, the value of the histogram for x in Ci is equal to Ni/(Nmax (x(i+1)-x(i))) when `normalization` is true (default case) and else, simply equal to Ni. When normalization occurs the histogram verifies:

```x(n+1)
/
|   h(x) dx = 1,  when x(1)<=min(data) and max(data) <= x(n+1))
/
x(1)```

Any plot2d (optional) parameter may be provided; for instance to plot an histogram with the color number 2 (blue if std colormap is used) and to restrict the plot inside the rectangle [-3,3]x[0,0.5], you may use `histplot(n,data, style=2, rect=[-3,0,3,0.5])`.

Enter the command `histplot()` to see a demo.

### Examples

```// example #1: variations around an histogram of a gaussian random sample
d=rand(1,10000,'normal');  // the gaussian random sample
clf();histplot(20,d)
clf();histplot(20,d,normalization=%f)
clf();histplot(20,d,leg='rand(1,10000,''normal'')',style=5)
clf();histplot(20,d,leg='rand(1,10000,''normal'')',style=16, rect=[-3,0,3,0.5]);

// example #2: histogram of a binomial (B(6,0.5)) random sample
d = grand(1000,1,"bin", 6, 0.5);
c = linspace(-0.5,6.5,8);
clf()
subplot(2,1,1)
histplot(c, d, style=2)
xtitle("normalized histogram")
subplot(2,1,2)
histplot(c, d, normalization=%f, style=5)
xtitle("non normalized histogram")

// example #3: histogram of an exponential random sample
lambda = 2;
X = grand(100000,1,"exp", 1/lambda);
Xmax = max(X);
clf()
histplot(40, X, style=2)
x = linspace(0,max(Xmax),100)';
plot2d(x,lambda*exp(-lambda*x),strf="000",style=5)
legend(["exponential random sample histogram" "exact density curve"]);```