Alayna  Rippin

Alayna Rippin

1597345200

Structures in MATLAB/Octave

Structures, or structs, are a basic data type in MATLAB/Octave that can be used to organize and combine multiple properties into one common data structure. The properties on a struct, also referred to as fields, can be of different types and of varying sizes. Each of the properties on a struct can then be accessed and operated on as they normally would. Structs have a multitude of uses in MATLAB/Octave and are very much akin to dictionaries for those that are more familiar with Python.

In this article, I introduce the basics of structs and then share what I believe to be some of the most useful “tips” for working with them. In particular, I cover the following topics:

  • Dynamic property access and the _filednames() _function
  • Working with arrays of structs
  • Considerations for writing efficient code using structs

Source code for all of the examples can be found in the GitHub repository. It is important to note that MATLAB and Octave display structs quite differently, so you may see some differences depending on which application you run the code with. In this tutorial, I have run all of the examples with Octave to make things as accessible as possible.

The Basics

Structs in MATLAB/Octave are managed dynamically, so we can add and remove new properties at any time. The easiest way to create a struct is to use the struct() command to create an empty struct and the dot operator to populate it. Here is a simple example.

s = struct(); % create an empty struct
s.val1 = 100; % add a scalar
s.val2 = 0 : 25 : 100; % add a vectorize
s.val3 = randn( 2, 3, 5 ); % add an ND array
s.char1 = 'This is a character string'; % add a character string
s.cell1 = { 100, 300, '5', 'abc' };  % add a cell array
disp( s );
scalar structure containing the fields:
val1 =  100
    val2 =
0    25    50    75   100
val3 =
ans(:,:,1) =
-0.49715   0.32621  -0.81624
      -0.34628   1.85946   0.29556
ans(:,:,2) =
-0.32521   0.44471   2.40743
      -0.97223  -0.24240   0.39241
ans(:,:,3) =
-1.63085  -0.34078   0.46239
       0.48026  -0.45361   1.62214
ans(:,:,4) =

       0.523431  -0.576770  -0.028377
      -1.035780   0.093593  -2.001485
ans(:,:,5) =
0.30492  -2.05388   0.13755
      -0.98662  -1.36354  -0.43432
char1 = This is a character string
    cell1 =
    {
      [1,1] =  100
      [1,2] =  300
      [1,3] = 5
      [1,4] = abc
    }

We can now access any of the newly added properties using the corresponding field names. For example, let’s access the 2nd element of “val2”.

>> disp( s.val2(2) );

25

Or we could access the last element of “cell1”.

>> disp( s.cell1{end} );

abc

In fact, we can now access and modify all of the properties just like we would if they weren’t on the struct. Let’s add a new value to “cell1”.

>> s.cell1{ end+1 } = 'I am new!';
>> disp( s.cell1 );

{
  [1,1] =  100
  [1,2] =  300
  [1,3] = 5
  [1,4] = abc
  [1,5] = I am new!
}

Or let’s remove some values from “val2”.

>> disp( s.val2 );

0    25    50    75   100
>> s.val2( 2:3 ) = [];
>> disp( s.val2 );
0    75   100

Finally, using the rmfield() function, we can completely remove fields from a struct. For example, let’s create a new struct named “s2” and remove all of the fields except for “val1”. The syntax for this is: sOut = rmfield( s, { ‘name1’, ‘name2’, etc… } ), where “sOut” could be “s” if we wanted to overwrite the original struct, and the cell array contains the name of all fields that we would like to remove.

>> s2 = rmfield( s, { 'val2', 'val3', 'char1', 'cell1' } );
>> disp( s2 );

scalar structure containing the fields:
val1 =  100

This was a rather fast walk through of the most basic uses of structs. If you are brand new to structs, it is probably worth spending a little bit of time looking through the MATLAB documentation. The rest of this article will touch on some of the more advanced, or perhaps just lest obvious, ways of working with structs.

Dynamically accessing properties and the fieldnames() function

Another very useful feature of structs is that you can build arrays of them. These arrays behave mostly like any other array, however, there is some additional care that must be taken to populate them. In particular, each element in an array of structs must have all of the same properties. They can be empty, but all of the properties must exist before multiple structures can be concatenated into an array.

To start with a simple example, lets set the second element of “s3” to another copy of itself.

>> s3(2) = s3;
>> disp( s3 );

1x2 struct array containing the fields:
noodles
    dexter
    ron
    greg

Now we can see that there is a 1x2 array of structs. Note that this worked since both of the structs had exactly the same properties.

Structure arrays act just like any other arrays and can be accessed using (). As an example, let’s update the “noodles” field on the first struct to be equal to twice the current value.

>> s3(1).noodles = s3(1).noodles * 2;
>> disp( s3(1).noodles );

62
>> disp( s3(2).noodles );
31

We can also remove elements from a structure by setting an element to an empty value.

>> s3(1) = [];
>> disp( s3 );

scalar structure containing the fields:
noodles =  31
    dexter =  11
    ron =  46
    greg =  28

Note that the above output shows that we once again have a scalar structure. since we have removed the first element of the structure array.

Finally, it can also be convenient to collect values across an array of structs. For example, let’s say we had an array of structs containing images with geo-tags and we were interested in looking at the locations for all of the images. We can collect these values into an array by capturing the struct output between square brackets [].

Let’s demonstrate this with another example. First, we will build 10 structs, each with “img”, “lat’, and “lon” properties. Note that within the loop, a temporary variable is used to fully populate each struct before it is added to the array. This is actually unnecessary in this case since I am pre-allocating the structure memory by looping backwards; however, in general, it is a good practice because it will ensure that all of the fields exist before you attempt to concatenate the structures.

#coding #matlab #programming #octave #visual studio code #visual studio

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Buddha Community

Structures in MATLAB/Octave
Alayna  Rippin

Alayna Rippin

1597345200

Structures in MATLAB/Octave

Structures, or structs, are a basic data type in MATLAB/Octave that can be used to organize and combine multiple properties into one common data structure. The properties on a struct, also referred to as fields, can be of different types and of varying sizes. Each of the properties on a struct can then be accessed and operated on as they normally would. Structs have a multitude of uses in MATLAB/Octave and are very much akin to dictionaries for those that are more familiar with Python.

In this article, I introduce the basics of structs and then share what I believe to be some of the most useful “tips” for working with them. In particular, I cover the following topics:

  • Dynamic property access and the _filednames() _function
  • Working with arrays of structs
  • Considerations for writing efficient code using structs

Source code for all of the examples can be found in the GitHub repository. It is important to note that MATLAB and Octave display structs quite differently, so you may see some differences depending on which application you run the code with. In this tutorial, I have run all of the examples with Octave to make things as accessible as possible.

The Basics

Structs in MATLAB/Octave are managed dynamically, so we can add and remove new properties at any time. The easiest way to create a struct is to use the struct() command to create an empty struct and the dot operator to populate it. Here is a simple example.

s = struct(); % create an empty struct
s.val1 = 100; % add a scalar
s.val2 = 0 : 25 : 100; % add a vectorize
s.val3 = randn( 2, 3, 5 ); % add an ND array
s.char1 = 'This is a character string'; % add a character string
s.cell1 = { 100, 300, '5', 'abc' };  % add a cell array
disp( s );
scalar structure containing the fields:
val1 =  100
    val2 =
0    25    50    75   100
val3 =
ans(:,:,1) =
-0.49715   0.32621  -0.81624
      -0.34628   1.85946   0.29556
ans(:,:,2) =
-0.32521   0.44471   2.40743
      -0.97223  -0.24240   0.39241
ans(:,:,3) =
-1.63085  -0.34078   0.46239
       0.48026  -0.45361   1.62214
ans(:,:,4) =

       0.523431  -0.576770  -0.028377
      -1.035780   0.093593  -2.001485
ans(:,:,5) =
0.30492  -2.05388   0.13755
      -0.98662  -1.36354  -0.43432
char1 = This is a character string
    cell1 =
    {
      [1,1] =  100
      [1,2] =  300
      [1,3] = 5
      [1,4] = abc
    }

We can now access any of the newly added properties using the corresponding field names. For example, let’s access the 2nd element of “val2”.

>> disp( s.val2(2) );

25

Or we could access the last element of “cell1”.

>> disp( s.cell1{end} );

abc

In fact, we can now access and modify all of the properties just like we would if they weren’t on the struct. Let’s add a new value to “cell1”.

>> s.cell1{ end+1 } = 'I am new!';
>> disp( s.cell1 );

{
  [1,1] =  100
  [1,2] =  300
  [1,3] = 5
  [1,4] = abc
  [1,5] = I am new!
}

Or let’s remove some values from “val2”.

>> disp( s.val2 );

0    25    50    75   100
>> s.val2( 2:3 ) = [];
>> disp( s.val2 );
0    75   100

Finally, using the rmfield() function, we can completely remove fields from a struct. For example, let’s create a new struct named “s2” and remove all of the fields except for “val1”. The syntax for this is: sOut = rmfield( s, { ‘name1’, ‘name2’, etc… } ), where “sOut” could be “s” if we wanted to overwrite the original struct, and the cell array contains the name of all fields that we would like to remove.

>> s2 = rmfield( s, { 'val2', 'val3', 'char1', 'cell1' } );
>> disp( s2 );

scalar structure containing the fields:
val1 =  100

This was a rather fast walk through of the most basic uses of structs. If you are brand new to structs, it is probably worth spending a little bit of time looking through the MATLAB documentation. The rest of this article will touch on some of the more advanced, or perhaps just lest obvious, ways of working with structs.

Dynamically accessing properties and the fieldnames() function

Another very useful feature of structs is that you can build arrays of them. These arrays behave mostly like any other array, however, there is some additional care that must be taken to populate them. In particular, each element in an array of structs must have all of the same properties. They can be empty, but all of the properties must exist before multiple structures can be concatenated into an array.

To start with a simple example, lets set the second element of “s3” to another copy of itself.

>> s3(2) = s3;
>> disp( s3 );

1x2 struct array containing the fields:
noodles
    dexter
    ron
    greg

Now we can see that there is a 1x2 array of structs. Note that this worked since both of the structs had exactly the same properties.

Structure arrays act just like any other arrays and can be accessed using (). As an example, let’s update the “noodles” field on the first struct to be equal to twice the current value.

>> s3(1).noodles = s3(1).noodles * 2;
>> disp( s3(1).noodles );

62
>> disp( s3(2).noodles );
31

We can also remove elements from a structure by setting an element to an empty value.

>> s3(1) = [];
>> disp( s3 );

scalar structure containing the fields:
noodles =  31
    dexter =  11
    ron =  46
    greg =  28

Note that the above output shows that we once again have a scalar structure. since we have removed the first element of the structure array.

Finally, it can also be convenient to collect values across an array of structs. For example, let’s say we had an array of structs containing images with geo-tags and we were interested in looking at the locations for all of the images. We can collect these values into an array by capturing the struct output between square brackets [].

Let’s demonstrate this with another example. First, we will build 10 structs, each with “img”, “lat’, and “lon” properties. Note that within the loop, a temporary variable is used to fully populate each struct before it is added to the array. This is actually unnecessary in this case since I am pre-allocating the structure memory by looping backwards; however, in general, it is a good practice because it will ensure that all of the fields exist before you attempt to concatenate the structures.

#coding #matlab #programming #octave #visual studio code #visual studio

navin prakash

1613201688

The Eight Benefits of MATLAB

The eight benefits of MATLAB are listed below:

  1. Ease of Use:
    MATLAB is a language that has been interpreted. Programs can be readily written and updated with the built-in integrated programming environment and debugger.
  2. Platform Independence:
    MATLAB is helped on several different computer systems, it offers a great measure of independence from the framework. This language is helped on Windows, Linux, Unix, and Macintosh. Programs written on any system will operate on all of the other systems.
  3. Device-Independent Plotting:
    MATLAB has many integral planning and imaging commands, unlike other programming languages. You can view the plots and photos on any graphical output system assisted by the machine running MATLAB. This skill makes MATLAB an excellent data visualization tool.
  4. Complete collection of abilities:
    To visualize science and engineering information, MATLAB has all the graphical functions required. It provides two-dimensional and three-dimensional diagram representation features, three-dimensional volume visualization, motion, interactive diagram development tools, and the ability to export to some of the most common graphic types. Diagrams can be modified by adding multi-axis, adjusting row colors and markers, inserting annotations, LaTeX expressions, legends, as well as other choices for plotting.
  5. Operating numerical analysis:
    The statistical analysis relies on approximation instead of the reliability, you see in symbolic mathematics. It is difficult to perform some building construction activities without implementing the mathematical solution, and astronomers also seem to make heavy use of everything. You probably won’t see the application of numerical analysis by a carpenter, but you’ll see developers who will need to do so.
  6. Getting involved in science:
    To test new hypotheses, MATLAB is likely to be using it. MATLAB helps you do a “what if” analysis when applying to science, which enables you to confirm a theory’s viability.
    Technology is being used in several various ways, of course. You may be interested in the health sector, for example, and then use research to find the cure for the Ebola virus. A computer scientist might watch for a different approach to use machine technology to assist those with accessibility requirements.
  7. Engaging mathematics:
    Some persons just liked to run with mathematics. This is the reason that there are so many theorems sufficient to address current problems. These people are involved with math in a manner that few others can easily comprehend. MATLAB makes it possible to play with math, to develop innovative methods of just using numbers to needed to execute useful tasks.
  8. Examining research:
    A researcher should persuade peers after a question is posed and a reply is given that the explanation is right and then viable to put into effect. MATLAB helps you to validate the answer and check that it does, as the researcher says, work effectively. The researcher may use MATLAB to further describe precisely how the answer is used after a response is confirmed.
    If you are interesting more learn Matlab Training in Chennai with placement assistance. FITA Academy is the best Training Institute for Matlab Course in Chennai with excellent knowledge.

#matlab #matlab course in chennai #matlab training in chennai #matlab training #education

Dylan  Iqbal

Dylan Iqbal

1644246022

Machine Learning in MatLab/Octave

Octave Tutorial Machine Learning

Here I teach Octave, which is a language commonly used when teaching Machine Learning and it is also basically a Free Version of Matlab! I'll cover basically a 400 page book in one video.

All of the Files used are here : https://github.com/derekbanas/OctaveTutorial 

#machinelearning #octave #matlab

Enos  Prosacco

Enos Prosacco

1594019855

Figure Properties in MATLAB

In this tutorial, we are going to walk through two basic functions, get() and set(), that can be used to manipulate the properties of graphic objects in MATLAB. Specifically, we will be focusing on Figures; however, these methods can be applied to many different objects.

#data-visualization #octave #matlab #coding-tips

Brain  Crist

Brain Crist

1594724400

Plot Legends in MATLAB

Plot legends are essential for properly annotating your figures. Luckily, MATLAB/Octave include the legend() function which provides some flexible and easy-to-use options for generating legends. In this article, I cover the basic use of the legend() function, as well as some special cases that I tend to use regularly.

The source code for the included examples can be found in the GitHub repository.

Basic Use of Plot Legends

The _legend() _function in MATLAB/Octave allows you to add descriptive labels to your plots. The simplest way to use the function is to pass in a character string for each line on the plot. The basic syntax is: legend( ‘Description 1’, ‘Description 2’, … ).

For the examples in this section, we will generate a sample figure using the following code.

x = 0 : 0.1 : ( 2pi ); 
plot( x, sin( x ), 'rx', 'linewidth', 2 ); 
hold on; 
plot( x, cos( x/3 ) - 0.25, 'bs', 'linewidth', 2 ); 
plot( x, cos( 4x )/3, '^g', 'linewidth', 2 );
hold off;
xlim( [ 0, 2*pi ] );
grid on;
title( 'Sample Plot' );
set( gca, 'fontsize', 16 );

A legend can be added with the following command.

legend( 'Line 1', 'Line 2', 'Line 3' );

In addition to specifying the labels as individual character strings, it is often convenient to collect the strings in a cell array. This is most useful when you are programmatically creating the legend string.

legStr = { 'Line 1', 'Line 2', 'Line 3' };
legend( legStr );

For convenience, this method will be used for the rest of the examples. The resulting figure looks like this.

Image for post

Image by author

By default the legend has been placed in the upper, right corner; however, the ‘location’ keyword can be used to adjust where it is displayed. The location is selected using a convention based on the cardinal directions. The following keywords can be used: north, south, east, west, northeast, northwest, southeast, southwest. The following code snippet would place the legend in the center top of the plot.

legend( legStr, 'location', 'north' );

Similarly, all of the other keywords can be used to place the legend around the plot. The following animation illustrates all of the different keywords.

Image for post

Image by author

The keyword ‘outside’ can also be appended to all of the locations to place the legend outside of the plot. The next image shows an example with the legend on the east outside.

legend( legStr, 'location', 'eastoutside' );

Image for post

#matlab #data-visualization #octave #coding