To program the functions `meanstats`

and `stdevstats`

that
you created in Build Model with MATLAB Function in a Chart,
follow these steps:

Open the chart in the model

`call_stats_function_stateflow`

.In the chart, open the function

`meanstats`

.The function editor appears with the header:

function meanout = meanstats(vals)

This header is taken from the function label in the chart. You can edit the header directly in the editor, and your changes appear in the chart after you close the editor.

On the line after the function header, enter:

%#codegen

The

`%#codegen`

compilation directive helps detect compile-time violations of syntax and semantics in MATLAB^{®}functions supported for code generation.Enter a line space and this comment:

% Calculates the statistical mean for vals

Add the line:

len = length(vals);

The function

`length`

is an example of a built-in MATLAB function that is supported for code generation. You can call this function directly to return the vector length of its argument`vals`

. When you build a simulation target, the function length is implemented with generated C code. Functions supported for code generation appear in Functions and Objects Supported for C/C++ Code Generation — Alphabetical List.The variable

`len`

is an example of implicitly declared local data. It has the same size and type as the value assigned to it — the value returned by the function`length`

, a scalar`double`

. To learn more about declaring variables, see Data Definition Basics.The MATLAB function treats implicitly declared local data as temporary data, which exists only when the function is called and disappears when the function exits. You can declare local data for a MATLAB function in a chart to be persistent by using the

`persistent`

construct.Enter this line to calculate the value of

`meanout`

:meanout = avg(vals,len);

The function

`meanstats`

stores the mean of`vals`

in the Stateflow^{®}data`meanout`

. Because these data are defined for the parent Stateflow chart, you can use them directly in the MATLAB function.Two-dimensional arrays with a single row or column of elements are treated as vectors or matrices in MATLAB functions. For example, in

`meanstats`

, the argument`vals`

is a four-element vector. You can access the fourth element of this vector with the matrix notation`vals(4,1)`

or the vector notation`vals(4)`

.The MATLAB function uses the functions

`avg`

and`sum`

to compute the value of`mean`

.`sum`

is a function supported for code generation.`avg`

is a local function that you define later. When resolving function names, MATLAB functions in your chart look for local functions first, followed by functions supported for code generation.**Note:**If you call a function that the MATLAB function cannot resolve as a local function or a function for code generation, you must declare the function to be extrinsic.Now enter this statement:

coder.extrinsic('plot');

Enter this line to plot the input values in

`vals`

against their vector index.plot(vals,'-+');

Recall that you declared

`plot`

to be an extrinsic function because it is not supported for code generation. When the MATLAB function encounters an extrinsic function, it sends the call to the MATLAB workspace for execution during simulation.Now, define the local function

`avg`

, as follows:function mean = avg(array,size) mean = sum(array)/size;

The header for

`avg`

defines two arguments,`array`

and`size`

, and a single return value,`mean`

. The local function`avg`

calculates the average of the elements in`array`

by dividing their sum by the value of argument`size`

.The complete code for the function

`meanstats`

looks like this:function meanout = meanstats(vals) %#codegen % Calculates the statistical mean for vals len = length(vals); meanout = avg(vals,len); coder.extrinsic('plot'); plot(vals,'-+'); function mean = avg(array,size) mean = sum(array)/size;

Save the model.

Back in the chart, open the function

`stdevstats`

and add code to compute the standard deviation of the values in`vals`

. The complete code should look like this:function stdevout = stdevstats(vals) %#codegen % Calculates the standard deviation for vals len = length(vals); stdevout = sqrt(sum(((vals-avg(vals,len)).^2))/len); function mean = avg(array,size) mean = sum(array)/size;

Save the model again.

Was this topic helpful?