This quote of B. Klemens¹ that I used in my previous post made me think a little bit:

"I spent much of my life ignoring the fundamentals of computing and just hacking together projects using the package or language of the month: C++, Mathematica, Octave, Perl, Python, Java, Scheme, S-PLUS, Stata, R, and probably a few others that I’ve forgotten."

Although much less skilled than M. Klemens, I have also wandered around the programming language landscape, out of curiosity, but also with particular needs in mind.

For example, I listed a set of features that I found useful for easily going from prototyping to operational code for remote sensing image processing. The main 3 bullet points were:

1. OTB accessibility, which meant C++, Python or Java (and its derivatives as Clojure);
2. Robust concurrent programming, which ruled out Python;
3. Having a REPL, which led me to say silly things and even develop working code.

Before going the OTB-from-LISP-through-bindings route, I had looked for C++ interpreters to come close to a REPL, some are available, but, at least at the time, I didn't manage to get one running.

Since I started using C++11 a little bit, I find more and more pleasant to write C++ even for small things that I would usually use Python for. Add to that all the new things in the standard library, like threads, regular expressions, tuples and proper random number generators, just to name a few, and here I am wondering how to make the edit/compile/run loop shorter.

B. Klemens proposes crazy things like compiling via cut/paste, but this is too extreme even for me. Since I use emacs, I can compile from within the editor just by hitting C-c C-c which asks for a command to compile. I usually do things like:

export ITK_AUTOLOAD_PATH="" && cd ~/Dev/builds/otb-bv/ && make \
    && ctest -I 8,8 -VV && gnuplot ~/Dev/otb-bv/data/plot_bvMultiT.gpl

I am nearly there, but not yet.

Remember that I discovered Klemens because I was looking for statistical libs after watching some videos of the Coursera Data Science specialization? One of the courses is about reproducible research and they show how you can mix together R code and Markdown to produce executable research papers.

When I saw this R Markdown approach, I thought, well, that's nice, but inferior to org-babel. The main advantage of org-babel with respect to R Markdown or IPython is that in org-mode you can use different languages in the same document.

In a recent research report written in org-mode and exported to PDF, I used, in the same document, Python, bash, gnuplot and maxima. This is a real use case. Of course, these are all interpreted languages, so it is easy to embed them in documents and talk to the interpreter.

Well, 15 years after I started using emacs and more than 6 years after discovering org-mode, I found that compiled languages can be used with org-babel. At least, decent compiled languages, since there is no mention of Objective-C or Java².

So how one does use C++ with org-babel? Just drop this into your emacs configuration:

Yes, don't forget the flag for C++11, since you want to be cool.

Now, let the fun begin. Open an org buffer and write:

#+begin_src C++ :includes <iostream>
std::cout << "Hello scripting world\n";
#+end_src

Go inside the code block and hit C-c C-c and you will magically see this appear just below the code block:

#+RESULTS:
: Hello scripting world

No problem. You are welcome. Good-bye.

If you want to go beyond the Hello world example keep reading.

You have noticed that the first line of the code block says it is C++ and gives information about the header files to include. If you need to include more than one header file³, just make a list of them (remember, emacs is a LISP machine):

#+begin_src C++ :includes (list "<iostream>" "<vector>")
std::vector<int> v{1,2,3};
for(auto& val : v) std::cout << val << "\n";
#+end_src

#+RESULTS:
| 1 |
| 2 |
| 3 |

Do you see the nice formatted results? This is an org-mode table. More fun with the standard library and tables:

#+begin_src C++ :includes (list "<iostream>" "<string>" "<map>")
std::map<std::string, int> m{{"pizza",1},{"muffin",2},{"cake",3}};
for(auto& val : m) std::cout << val.first << " " << val.second << "\n";
#+end_src

#+RESULTS:
| cake   | 3 |
| muffin | 2 |
| pizza  | 1 |

Sometimes, you may want to use other libraries than the C++ standard one⁴. Just give the header files as above and also the flags (here I split the header of the org-babel block in 2 lines for readability):

#+header: :includes (list "<gsl/gsl_fit.h>" "<stdio.h>") 
#+begin_src C++ :exports both :flags (list "-lgsl" "-lgslcblas")
int i, n = 4;
double x[4] = { 1970, 1980, 1990, 2000 };
double y[4] = {   12,   11,   14,   13 };
double w[4] = {  0.1,  0.2,  0.3,  0.4 };

double c0, c1, cov00, cov01, cov11, chisq;

gsl_fit_wlinear (x, 1, w, 1, y, 1, n, 
                 &c0, &c1, &cov00, &cov01, &cov11, 
                 &chisq);

printf ("# best fit: Y = %g + %g X\n", c0, c1);
printf ("# covariance matrix:\n");
printf ("# [ %g, %g\n#   %g, %g]\n", 
        cov00, cov01, cov01, cov11);
printf ("# chisq = %g\n", chisq);

for (i = 0; i < n; i++)
  printf ("data: %g %g %g\n", 
          x[i], y[i], 1/sqrt(w[i]));

printf ("\n");

for (i = -30; i < 130; i++)
  {
  double xf = x[0] + (i/100.0) * (x[n-1] - x[0]);
  double yf, yf_err;

  gsl_fit_linear_est (xf, 
                      c0, c1, 
                      cov00, cov01, cov11, 
                      &yf, &yf_err);

  printf ("fit: %g %g\n", xf, yf);
  printf ("hi : %g %g\n", xf, yf + yf_err);
  printf ("lo : %g %g\n", xf, yf - yf_err);
  }
#+end_src

#+RESULTS:
| #     | best       |    fit: |       Y | = | -106.6 | + | 0.06 | X |
| #     | covariance | matrix: |         |   |        |   |      |   |
| #     | [          |  39602, |   -19.9 |   |        |   |      |   |
| #     | -19.9,     |   0.01] |         |   |        |   |      |   |
| #     | chisq      |       = |     0.8 |   |        |   |      |   |

etc.

You may have noticed that we are really scripting like perl hackers, since there is no main() function in our code snippets. org-babel kindly generates all the boilerplate for us. Thank you.

But wait, what happens if I want to define or declare things outside main?

No problem, just tell org-babel not to generate a main():

#+begin_src C++ :includes <iostream> :main no
struct myStr{int i;};

int main()
{
myStr ms{2};
std::cout << "The value of the member is " << ms.i << std::endl;
}
#+end_src

#+RESULTS:
: The value of the member is 2

There are many other options you can play with and I suggest to read the documentation. Just 2 more useful things.

You saw that org-mode has tables. They are very powerful and can even be used as a spreadsheet. Let's see how they can be used as input to C++ code.

Let's start by giving a name to our table:

#+tblname: somedata
|  sqr | noise |
|------+-------|
|  0.0 |  0.23 |
|  1.0 |  1.31 |
|  4.0 |  4.61 |
|  9.0 |  9.05 |
| 16.0 | 16.55 |

Now, we can refer to it from org-babel using variables:

#+header: :exports results :includes <iostream>
#+begin_src C++ :var somedata=somedata :var somedata_cols=2 :var somedata_rows=5
for (int i=0; i<somedata_rows; i++)
  {
  for (int j=0; j<somedata_cols; j++) 
    {
    std::cout << somedata[i][j]/2.0 << "\t";
    }
  std::cout << "\n";
  }
#+end_src

#+RESULTS:
|   0 | 0.115 |
| 0.5 | 0.655 |
|   2 | 2.305 |
| 4.5 | 4.525 |
|   8 | 8.275 |

Once you have scripted your solution to save the world from hunger⁵, you may want to get it out from org-mode and upload it to github⁶. You can of course copy and paste the code, but since you are using a superior editor, you can ask it to do that⁷ for you. Just tell it the name of your file using the tangle option:

#+begin_src C++ :tangle MyApp.cpp :main no :includes <iostream>
namespace ns {
    class MyClass {};
}

int main(int argc, char* argv[])
{
ns::MyClass{};
std::cout << "hi" << std::endl;
return 0;
}
#+end_src

If you the call org-babel-tangle from this block, it will generate a file with the right name containing this:

#include <iostream>
namespace ns {
    class MyClass {};
}

int main(int argc, char* argv[])
{
ns::MyClass{};
std::cout << "hi" << std::endl;
return 0;
}

Since reproducible research in emacs has entered the realm of scientific journals⁸, I will end as follows:

In this article, we have shown the feasibility of coding in C++ without writing much boilerplate and going through the edit/compile/run loop. With the advent of modern C++ standards and richer standard libraries, C++ is a good candidate to quickly write throw-away code. The integration of C++ in org-babel gets C++ even closer to the tasks for which scripting languages are used for.

Future work will include learning to use the C++ standard library (data structures and algorithms), browsing the catalog of BOOST libraries, trying to get good C++ developers to use emacs and C++11, and find a generic variadic-template-based solution to world hunger.

C++ has the same tools as C for imperative programming. So there are the classical control flow mechanisms such as for and while loops, if statements, etc.

C++ supports procedural programming via functions, which are like C functions but with more strict type checking of the arguments. Unlike C, function arguments can be passed by reference, which is like passing a pointer to the object, but using the same syntax as if the object itself was passed. This allows to pass large objects without copying them as with pointers, but the programmer can choose to declare the reference as constant so that the object can't be modified inside the function.

C++ has classes which are composite types (that is, they can contain other objects) like C structs, but they can also have associated methods, or member functions. At first, object orientation in C++ can be seen very as similar to Java's or Python's in terms of what a class can contain and how it is used. The main difference is that C++ uses value semantics. In clear, when a variable of a given class is created in C++, it is a value, like an int or a double. In Java or Python, this is not the case. In these languages, what you get is a reference to an object, not the object itself, and therefore, you can't manage memory as you would want to and a garbage collector is needed in order to free memory when the object is not used anymore.

In C++ you can choose to have an object (the default behavior) and in this case, like an int or a double, it will be allocated in the stack and be deallocated automatically when it goes out of scope (typically at the end of the block, when a closing curly brace is found). So there is no need for garbage collector, since no garbage is generated. Java programmers will appreciate this joke …

On the other hand, you can choose to use pointers to the objects, and then the objects are allocated in the free store and the programmer is responsible for freeing the memory.

C++ offers multiple inheritance (unlike Java) and virtual functions (like Java).

C++ also offers operator overloading. This means that arithmetic operators like + for addition or * for multiplication can be redefined for each class. This is useful for instance for a matrix class. Other operators like [], -> can also be overloaded.

Generic programming can be used in C++ thanks to templates. Class templates are classes defined in terms of generic types. That is, a class can contain an object of type T which is not defined. All code, like member functions, etc. can be written in terms of this generic type.

When a programmer wants to use this class template, she has to say which is the concrete type that will be used instead of T. This is very useful when algorithms are exactly the same for different data types like integers, floating point numbers, etc. In languages which don't support generic programming, the same algorithm has to be rewritten for every different type, or the programmer has to chose the best type (for some definition of best) for which to write the algorithm.

There are also function templates, which are functions defined in terms of generic types.

The mechanism behind templates generates, at compile time, the code for the concrete types which will be as efficient as hand-written code for these types.

C++ comes with the Standard Template Library, the STL, which provides generic containers and generic algorithms. Generic containers are containers like vectors, lists, etc. whose elements are generic (like ints, doubles, strings or any other type or class). Generic algorithms are algorithms which operate on generic types (like ints, doubles, strings or any other type or class). These algorithms operate on ranges inside a container, so they are the same for a vector of ints or for a list of doubles. These ranges are defined by iterators, which are like pointers or coordinates inside the container.

In this way, if you have N types of containers and M different algorithms, you don't have to write NxM versions of the code, but just N+M. So less code to write and maintain. I am a huge fan of templates.

Functional programming is not just programming using functions, but rather using functions as first class citizens. That means being able to pass functions as arguments to other functions and return them as results.

In C, one can achieve this by using pointers to functions, but this is risky and ugly, in terms of function signature.

In C++ we can define functions as types, and therefore they can be used as arguments to and return values from other functions. The way of creating a function type (in C++ we call them function objects or functors) is to create a class and use operator overloading. In the same way as + can be overloaded in a matrix class, the () operator can be overloaded and therefore if myfunc is an object of the class F where the operator has been overloaded, we can use myfunc() like a normal function call.

And therefore, now we can define functions which take or return objects of class F and implement functional programming.

The STL uses extensively function objects in order to tune the behavior of algorithms. For example, the STL find_if algorithm returns the position of first element in a container for which a particular condition is true. It takes as one of its arguments the function which will be used to test if the elements of the container verify a given condition.

As far as I know, C++ offers the best trade-off between efficiency and abstraction level. Or as Bjarne Stroustrup said in a recent interview:

The essence of C++ is that it provides a direct map to hardware and offers mechanisms for very general zero-overhead abstraction.

Since C++ can be as efficient as C, I don't see any reason to use C for a new programming project. Learning C is still useful if one needs to modify existing C code. However, if one wants to use an existing C library in a new project, C++ seems a better choice.

The recent C++11 and C++14 versions of the language (which is by the way an ISO standard) have introduced many new features which make the use of C++ easier and this is giving a sort of renaissance to the language.

Nowadays, added to its classical uses in systems programming and scientific applications, C++ is starting to be used in mobile applications.

I have just recently had a look at the last C++ ISO standard (known as C++11). I was not aware of all the fancy features that have been included in the language.

Some of the most interesting features are:

• auto,
• range-based for loops,
• general constant expressions,
• uniform initialisation,
• type aliases,
• decltype,
• variadic templates,
• type safe threading,
• user-defined literals,
• move semantics

There are also new containers and algorithms in the STL.

During the end of the year break, I spent some time reading and watching on line presentations about the new C++. I recommend watching Bjarne Stroustrup's keynote at Going Native 2012 (link) and Scott Meyer's talk at Going Native 2013 where he even anticipates some C++14 features (link).

Stroustrup introduces his talk by saying:

I use C++11 freely. Examples include auto, general constant expressions, uniform initialisation, type aliases, type safe threading, and user-defined literals. C++11 features are only just starting to appear in production compilers, so some of my suggestions are conjecture. Developing a "modern style," however, is essential if we don't want to maintain newly-written 1970s and 1980s style code in 2020.

After listening at him, I realised that the most easy to use features alone could make C++ programming much more fun and simple. Since I have spent some time in the last 2 years flirting with functional and dynamic typed (or at least with type inference) languages (Scheme, Clojure, Python, SML), I wanted to see how using C++11 affects the style of OTB code.

As usual, GCC has been the first compiler to make available these new features (closely followed by Clang). C++11 is available in recent versions of GCC using -std=c++11. So I recompiled OTB with this flag and started modifying some of the Software Guide examples using C++11 features.

All in all, I can say that these features make OTB code shorter, clearer and maybe safer. I report some examples here.

I have only started to use this kind of code, but I think that once you start using auto and lambdas it must be very difficult to go back to C++98/03.

The only drawback with using C++11 is dropping compatibility with old compilers, but this is not an issue for most of the things I do. So I can start writing modern OTB code instead of poo (plain old OTB)!