cpp-matplotlib

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# Contents

* [About](#about)
* [Usage](#usage)
* [Prereqs](#prereqs)
* [Building](#building)
* [Running the Example](#running-the-example)

# About

An easy-to-use **C++11** library for simple plotting from C++ via a ZeroMQ
bridge to an [IPython](http://ipython.org/) kernel.

It provides the ability to send [NumPy](http://www.numpy.org/) array
compatible data to an IPython kernel session as well as execute arbitrary
code.

Execution of arbitrary code is "protected" by IPython's kernel HMAC signing
mechanism: only code signed by a secret key provided by IPython will run.  As
of 2015-03-19 it has been tested with IPython version 1.2.1 on Ubuntu 14.04.

# Usage

First, create an IPython kernel session

```
$ ipython kernel --pylab
NOTE: When using the `ipython kernel` entry point, Ctrl-C will not work.

... blah blah blah ...

To connect another client to this kernel, use:
    --existing kernel-NNN.json
```

It is important to remember that NNN in the last line, which is the PID of the
kernel.  This JSON file is stored somewhere in your $HOME, exactly where can
vary.  Find it with <tt>find ~/ -name kernel-NNN.json</tt>.

Here we create some 1D data and plot it.  The numpy.array "MyData" will be
available for working with in the IPython session, even after the C++ program
finishes.

All library code lives in the <tt>cppmpl</tt> namespace.

```c++
#include "cpp_mpl.hpp"

int main() {
  // ...

cppmpl::CppMatplotlib mpl{"/path/to/kernel-NNN.json"};
  mpl.Connect();

// Create a nice curve  
  std::vector<cppmpl::NumpyArray::dtype> raw_data;
  double x = 0.0;
  while (x < 3.14159 * 4) {
    raw_data.push_back(std::sin(x));
    x += 0.05;
  }

// Send it to IPython for plotting
  cppmpl::NumpyArray data("MyData", raw_data);
  mpl.SendData(data);
  mpl.RunCode("plot(MyData)\n"
              "title('f(x) = sin(x)')\n"
              "xlabel('x')\n"
              "ylabel('f(x)')\n");

// NOTE: if you want to store the python in an external file, use the 
  // convenience function LoadFile("my_code.py"), as in, 
  // mpl.RunCode(cppmpl::LoadFile("plotting_code.py"));

// ...
}
```

And the result is ![Screenshot](https://raw.githubusercontent.com/daemacles/cpp-matplotlib/master/screenshot.png)

See [src/main.cc](src/main.cc) for a complete program.

To work with "MyData" you can connect to the kernel using an IPython console,
notebook, or qtconsole:

```
$ ipython console --existing kernel-NNN.json
Python 2.7.6 (default, Mar 22 2014, 22:59:56) 
Type "copyright", "credits" or "license" for more information.

IPython 1.2.1 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object', use 'object??' for extra details.

In [1]: MyData *= 4

In [84]: print MyData[9]
[ 1.73986214]
```

## Compiling / Linking

When compiling you must link against <tt>libcpp_mpl.so</tt>, which, assuming
you've installed it to somewhere in your library path:

```
$ g++ my_prog.cc -std=c++11 -lcpp_mpl -lyour_other_libs
```

# Prereqs

## Ubuntu 14.04

sudo apt-get install ipython python-matplotlib libzmq3-dev \
                         libjsoncpp-dev uuid-dev libssl-dev

# Building

# git clone this repository to cpp-matplotlib/
    cd cpp-matplotlib
    mkdir build
    cd build
    cmake ..
    make

## Installing

make install

By default installs to /usr/local.  Don't forget to run <tt>sudo ldconfig</tt>
to update the library cache.

## Generating the documentation

cd cpp-matplotlib
    doxygen Doxyfile
    # open html/index.html

# Running the Example
    
In terminal 1:

ipython kernel --pylab
    # this will print out the kernel PID to connect to, NNN below.

In terminal 2:

# Once per kernel invocation:
    export KERNEL_CONFIG=`find ~/ -name kernel-NNN.json`

# Each time you run the program
    build/cpp-matplotlib-example $KERNEL_CONFIG

In terminal 3 (if desired):

ipython console --existing kernel-NNN.json

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