.. image:: http://img.shields.io/github/release/josh146/pyctqw.png?style=flat :target: https://github.com/josh146/pyctqw/releases/latest

.. image:: http://img.shields.io/pypi/v/pyCTQW.png?style=flat :target: https://pypi.python.org/pypi/pyCTQW

.. image:: http://img.shields.io/pypi/dm/pyCTQW.png?style=flat :target: https://pypi.python.org/pypi/pyCTQW

Fortran library and Python module to calculate continuous-time quantum walks

This is intended to provide a framework to quickly and easily work with quantum walkers, take advantage of high performance computing, and allow easy visualisation.

For more information on how to use this package, please see the online documentation <http://pyctqw.readthedocs.org>_ and the following peer-reviewed article:

Josh A. Izaac and Jingbo B. Wang. pyCTQW: a continuous-time quantum walk simulator on distributed memory computers. Computer Physics Communications, September 2014. doi:10.1016/j.cpc.2014.09.011 <http://dx.doi.org/10.1016/j.cpc.2014.09.011>_

Features

Fortran and Python bindings (in the form of a library and module respectively)
Supports MPI through the use of the PETSc and SLEPc high-performance sparse
matrix libraries (CUDA support planned)
Has in-built support for infinite-line hamiltonians
Import and export matrices/states in binary or text
Can import custom adjancency matrices
Supports both one and two walkers (non-interacting)
Import and export matrices/states in binary, text or matlab format
Python module supports plotting and visualisation using matplotlib and networkx
Entanglement calculations
Ability to place diagonal defects/barriers on graph vertices
MPI graph isomorphism methods, for both 2 and 3 interacting particles

TODO

CUDA support
Calculation of transmission
Add fallback modes when PETSc/SLEPc are not present

Dependencies

In addition to an MPI implementation (e.g. MPICH <http://www.mpich.org/>_ or Open MPI <http://www.open-mpi.org/>_), the python pyCTQW.MPI module depends on the following components:

Python <http://www.python.org/>_ >= 2.7
NumPy <http://www.numpy.org/>_ >= 1.6.0
PETSc <http://www.mcs.anl.gov/petsc/>_ >= 3.4.2
SLEPc <http://www.grycap.upv.es/slepc/>_ >= 3.4.1
petsc4py 3.4 or petsc4py-dev <https://bitbucket.org/petsc/petsc4py>_
mpi4py <http://mpi4py.scipy.org/>_ (recommended, used for some plotting)
matplotlib <http://matplotlib.org/>_ (recommended, for node plotting and graph visualisation)
SciPy <http://www.scipy.org/>_ (recommended, for some I/O operations)
NetworkX <http://networkx.github.io/>_ (recommended, graph visualisation)

Installation using `pip`

After ensuring NumPy and petsc4py are installed (and all PETSc, SLEPc and MPI environment variables are properly set), pyCTQW can be installed using pip:

.. code-block:: bash

$ pip install pyCTQW

Installation from source code

Alternatively, the source code can be downloaded and compiled manually:

Ensure all dependencies required above are installed
Extract the pyCTQW folder, and cd into the extracted directory:

.. code-block:: bash
```
 $ tar xvzf pyctqw-1.1.0.tar.gz
 $ cd pyctqw-1.1.0
```
Ensure that your PETSc and SLEPc environment variables are correctly set; for example,

.. code-block:: bash
```
 $ export PETSC_DIR=/path/to/petsc
 $ export PETSC_ARCH=linux-gnu
 $ export SLEPC_DIR=/path/to/slepc
```
If you are unsure what your PETSc or SLEPc variables should be, please refer to their documentation.

.. important:: If you plan to install pyCTQW.MPI using root to a system directory, the PETSc and SLEPc environment variables must be available to the root user.
Compile the Python module pyCTQW.MPI by running

.. code-block:: bash
```
 $ python setup.py build
```
System-wide install:

.. code-block:: bash
```
 $ sudo -E python setup.py install
```
where the command -E ensures that the environment variables set in step 3 are passed to the root.

.. note:: If you do not have root access, or the above command does not appear to work, you can install the package locally by running
```
 .. code-block:: bash
     
     $ python setup.py install --user
```
Now, have a go running some of the examples!

Optional: compiling `libctqwMPI`

In addition to an MPI implementation (e.g. MPICH <http://www.mpich.org/>_ or Open MPI <http://www.open-mpi.org/>_), the Fortran library libctqwMPI depends on the following components:

PETSc <http://www.mcs.anl.gov/petsc/>_ >= 3.4.2
SLEPc <http://www.grycap.upv.es/slepc/>_ >= 3.4.1

Once these dependencies are installed, simply open a terminal in the root directory of pyCTQW-X.Y and run

.. code-block:: bash

$ make fortran [options]

where available options include

.. list-table:: :widths: 3 3 30 :header-rows: 1

* - Option
  - Values
  - Description

* - ``shared_lib``
  - 0 (default), 1
  - whether to build ``libctqwMPI`` as a shared library (``shared_lib=1``, producing ``libctqwMPI.so``) or a static library (``shared_lib=0`` (default), producing ``libctqwMPI.a``).

    If built as a shared library, compiled programs will be smaller, but ``libctqwMPI.so`` will need to be added to a directory used by ``ld`` (either by setting the environment variable ``LD_LIBRARY_PATH`` or by placing ``libctqwMPI.so`` in ``/usr/local/lib`` etc).

The fortran library (libctqwMPI.so or libctqwMPI.a) can be found in the pyCTQW-X.Y/lib directory, with required module files found in the pyCTQW-X.Y/include directory.

Optional: build documentation

If Sphinx <http://sphinx-doc.org/>_ is installed, the documentation can be compiled by running

.. code-block:: bash

$ pip install -r docs/requirements.txt
$ make docs

Documentation

For more information on how to use this package, please see the online documentation <http://pyctqw.readthedocs.org>_

Acknowledgements

The graph isomorphism subroutine GraphISCert uses the external subroutine d_refsor, a highly optimised Fortran sorting implementation written by Michel Olagnon and part of the ORDERPACK 2.0 <http://www.fortran-2000.com/rank/>_ suite of ranking and sorting algorithms for Fortran 90.