Bayesian Model Graph

The standard Python distribution: http://www.python.org/

The NumPy scientific computing package: http://www.numpy.org/

Matplotlib: http://matplotlib.org/

Cython: http://cython.org/

The Boost C++ libraries: http://www.boost.org/

The odeint C++ library: http://headmyshoulder.github.io/odeint-v2/

PyUblas: http://www.wxpython.org/

First, download a release version of Boost from: http://www.boost.org/users/download/

Install Boost. I like to keep it local:

tar xvf boost_X_XX_X.tar.bz2
cd boost_X_XX_X
./bootstrap.sh --with-python=/opt/anaconda3/bin/python3.6 --with-python-version=3.6 --with-python-root=/opt/anaconda3/lib/python3.6
mkdir ${HOME}/pool
./b2 install --with-python --prefix=${HOME}/pool
cd ..

You may need to edit user-config.jam in $HOME to set Python configuration:

using python : 3.6 : /opt/anaconda3/bin/python3.6 : /opt/anaconda3/include/python3.6m : /opt/anaconda3/lib/python3.6 ;

Tell the dynamic linker about Boost (add to your .bashrc):

export LD_LIBRARY_PATH=${HOME}/pool/lib:${LD_LIBRARY_PATH}

Assuming you have Python installed on your system, make sure you also have the development libraries:

sudo apt-get install libpython-dev

Clone this repository:

git clone https://github.com/stuckeyr/msd.git

Either install the required libraries, preferably inside a virtualenv:

cd msd
pip install Cython ipython jupyter lmfit matplotlib numpy pymc scipy tqdm
cd ..

Or install using the requirements:

cd msd
pip install -r requirements.txt
cd ..

Clone the odeint source, and install (copy) into your Boost directory:

git clone https://github.com/headmyshoulder/odeint-v2.git
cp -r odeint-v2/include/boost/numeric/ $HOME/pool/include/boost/

Download PyUblas:

git clone http://git.tiker.net/trees/pyublas.git
cd pyublas

Create and Customize a Configuration File ".aksetup-defaults.py" in your $HOME directory with the following text:

BOOST_BINDINGS_INC_DIR = ['${HOME}/pool/include/boost-bindings']
BOOST_INC_DIR = ['${HOME}/pool/include']
BOOST_LIB_DIR = ['${HOME}/pool/lib']
BOOST_PYTHON_LIBNAME = ['boost_python3']

Build PyUblas:

python setup.py install --user
cd ..

Install (copy) the include files into your Boost directory:

cp -r pyublas/pyublas/include/pyublas/ ${HOME}/pool/include/

The instructions to install Pyublas are also here: http://documen.tician.de/pyublas/installing.html

Finally, build the Boost msd model, "msde":

cd msd
python setup-pyublas.py build_ext --inplace

If you encounter a compiler error: "... '_1' was not declared in this scope ...", add the following directive to ${HOME}/pool/include/boost/python/exception_translator.hpp and $HOME/pool/include/boost/python/iterator.hpp, after the include of boost/bind.hpp:

# include <boost/bind/placeholders.hpp>

Also, expand any reference to _1 and _2 with boost::placeholders::_1 and boost::placeholders::_2, respectively.

If that goes ok, you should have a shared object at msd/msdux*.so

In the same directory build the Cython extension:

python setup-cython.py build_ext --inplace

And build the Boost extension:

python setup-boost.py build_ext --inplace

Again, if that goes ok, you should have shared objects at msd/msdc*.so and msd/msdbx*.so

The best way to run the msd scripts is from within a Jupyter notebook:

jupyter notebook

You can view the msd notebook here.

If you want to run the notebook on a separate (local) computer, make sure you set the following in your ".jupyter/jupyter_notebook_config.py" first:

c.NotebookApp.port = 9999
c.NotebookApp.ip = '*'
c.NotebookApp.open_browser = False

In your web browser, go to the host and ip of the computer above.

Select the model to run:

MODEL = 'boost' # ['python', 'cython', 'boost']

From there, you can start by running the simulation:

PLOT_SIM = True
%run -i sim.py

The -i flag retains all variables in the global workspace.

Then try performing a linear regression:

%run -i reg.py

Next, do some iterative. Select the optimisation function:

OPTFUN = 'lmfit' # ['optimize', 'lmfit']

In order to see the system response from each iteration, set the following global variable:

PLOT_ESTIM = True

Then perform a nonlinear optimisation:

%run -i estim.py

Finally, run some Bayesian estimation algorithms:

%run -i bms.py

And plot some performance parameters:

%run -i bmsplot.py