is it possible to do 'stateless' programming in matlab / how to avoid checking data integrity?

Question

My day to day work flow is something like this:

• acquire raw data (~50GB)
• parse raw data timing-information and build raw data structure (struct / object) from timing-information (what event occurred when, in which order, in what file, what other events occurred at the same time, etc ...)
• load only the necessary parts of raw data into struct / object as selected from previous timing information (basically this is a way to sub-select data)
• for each raw data chunk, calculate / extract certain metrics like RMS of signal, events where data > threshold, d' / z-score, and save them with struct / object
• given the the previously calculated metrics, load some raw-data of same time episodes from different data channel and compare certain things, etc ...
• visualize results x, y, z

I have two ways of dealing with this kind of data / workflow:

1. use struct()
2. use objects

There are certain advantages / disadvantages to both cases:

1. struct:

   • can add properties / fields on the fly
   • have to check for state of struct every single time that I pass a struct to a function
   • keep re-writing certain functions because every time that I change the struct slightly I a) tend to forget that a function already exists for it or b) I write a new version that handles a special case of the struct state.

2. objects:

   • using 'get.property()' methods, I can check the state of a property before it get's accessed inside a function / method -> allows to do data consistency checks.
   • I always know which methods work with my object, since they are part of the object definition.
   • need to clear classes every time I add a new property or method - very annoying!

Now my question is: how do other people deal with this kind of situation? how do you organize your data? in structs? in objects? how do you handle state checks? is there a way to do 'stateless' programming in matlab?

Answer 1

I like to use objects. You don't need to call clear classes on every change. It is enough to delete all instances of the "old" object.

Two very powerful additions I inherit often are handle and dynamicprops.

1. Handle makes the object behave as handle. Very nice to come around matlabs copy-on-change behavior.
2. Dynamic props - to do some meta programming.

About the consistency checks - why no do them when you use set.property?

---

Edit 1:

a simplified class that uses the database:

classdef measurement
   class
   id
properties (SetAccess = private)

end
methods
function obj = measurement(varargin)
  obj.id = varargin{1};
end

    function cs = get.class(obj)
       if isempty(obj.id)
                    cs = '';
                    return
       end
       mc = mydb.local; % use some tricks here to keep the connection open
         tmp = mym(mc,...
                    'SELECT class FROM measurements WHERE id = {Si}'...
                    ,obj.id);
         cs = tmp{1};
     end
end

---

Edit 2: Example for Event - Observer

classdef ObservableClass < handle

    properties 
        ListObservers=[]
        data
    end

    methods
        function obj = ObservableClass(varargin)
            obj.data = rand(100,2);
        end

        function addObserver(obj,observer)
            obj.ListObservers = [obj.ListObservers,observer];
        end

        function fireUpdate(obj)
            for i=1:numel(obj.ListObservers)
                obj.ListObservers(i).update();
            end
        end

        function set.data(obj,newData)
            obj.data = newData;
            obj.fireUpdate;
        end
    end
end

and a listener:

 classdef ObservingPlot
    properties
        fig
        observedClass
    end

    methods
        function obj = ObservingPlot(varargin)
            obj.observedClass = varargin{1};
            obj.createPlot;
            obj.observedClass.addObserver(obj);
        end

        function createPlot(obj)
            obj.fig=figure;
            plot(obj.observedClass.data);
        end

        function update(obj)
            gcf(obj.fig)
            clf
            plot(obj.observedClass.data);
        end
    end

end

The example:

a = ObservableClass()
b = ObservingPlot(a)

you can then observe when you do a: a.data=rand(100,3) - the plot will change immediatly.

---

Edit 3: a simple saving class

classdef SavingClass < handle

    properties 
        saveName
        data
    end

    methods
        function set.data(obj,input)
            if isempty(obj.saveName)
                obj.saveName = [tempname '.mat'];
            end
            save(obj.saveName,'input')
        end

        function out = get.data(obj)            
                out = [];
               if exist(obj.saveName,'file')                   
                   tmp = load(obj.saveName);
                   out = tmp.input;
               end
        end
    end

end

Example:

a = SavingClass;
b=rand(1000,1000);
a.data = b;

look at `whos':

Name         Size                Bytes  Class          Attributes

  a            1x1                    60  SavingClass              
  ans          1x5                    10  char                     
  b         1000x1000            8000000  double          

although you can do calculations like d = a.data-b - a takes just 60 bytes in memory - as opposed to the ~8 MB of b.

---

Edit 4: trick for often changing functions. When you put the logic in external commands matlab will not complain when you change the function definition there.

classdef MyOftenEditedClass < handle

    properties
        a
    end

    methods
        function set.a(obj,val)
            mySetFunctionA(obj,val)
        end

        function out=get.a(obj)
            out = myGetFunctionA(obj);
        end
    end

end