AWE is Copyright (C) 2013- The University of Notre Dame. This software is distributed under the GNU General Public License. See the file COPYING for details.
AWE uses Work Queue, which is part of the Cooperating Computing Tools (CCTools) package, for dispatching jobs for execution on allocated resources. Documentation on downloading, installing, and using Work Queue can be found here.
The software requirements of AWE are summarized below along with how AWE finds and accesses them:
| Software | Version | Must be present in environment variable |
|---|---|---|
| Python | 2.6 or 2.7 | PATH and PYTHONPATH |
| GROMACS | 4.5 or higher | PATH |
| GROMACS XTC Library | 1.1 or higher | C_INCLUDE_PATH and LD_LIBRARY_PATH |
| Numpy | 1.5 or higher | PYTHONPATH |
| Prody | 0.9.4 or higher | PYTHONPATH |
| GNU Scientific Library | 1.15 or higher | C_INCLUDE_PATH and LD_LIBRARY_PATH |
| Matplotlib | 1.1.0 or higher | PYTHONPATH |
First, determine the location where AWE is to be installed. For example:
% export AWE_INSTALL_PATH=$HOME/aweCompile and install AWE in the location pointed by $AWE_INSTALL_PATH using:
% cd cctools-xxx-src % cd apps/awe % ./configure --prefix $AWE_INSTALL_PATH % make installNext, set PATH to include the installed AWE binaries:
% export PATH=${AWE_INSTALL_PATH}/bin:${PATH}
Finally, set PYTHONPATH to include the installed AWE Python modules:
% export PYTHONPATH=${AWE_INSTALL_PATH}/lib/python2.6/site-packages:${PYTHONPATH}
Note that the AWE Python modules will be built for the version of Python
accessible in your installed environment. The installation script creates a
directory (under $AWE_INSTALL_PATH/lib) named with the version of Python for
which the modules are built and copies the modules to this directory. So if your
environment has a Python version different from 2.6, replace the version string
accordingly when setting PYTHONPATH.
You can check if AWE was correctly installed by running:
% awe-verify
% cd $HOME % mkdir awe-alanine % cd awe-alanine
To run AWE to sample a protein molecule, you will need to have the files describing the topology of atoms in that molecule, the coordinates of the walkers, and the coordinates of the cells. In addition, AWE transfers the executables from the GROMACS package required for running the simulations of each walker.
These files and executables can be fetched to the current working directory by running
% awe-prepare
This will create two directories named awe-generic-data and awe-instance-data. awe-generic-data will contain files that all AWE runs will require, such as the task executables and Gromacs forcefield files. awe-instance-data will contain files that are particular to a protein system such as the state definitions, initial protein coordinates, etc. Note that awe-prepare, by default, will transfer the files for the Alanine Dipeptide protein molecule. Further, awe-prepare will also copy the example program awe-ala.py provided in the AWE source that samples the state transitions for the Alanine Dipeptide protein.
To run the example in awe-ala.py, do
% python awe-ala.py
You will see this output right away:
Running on port 9123... Loading cells and walkersThe AWE master program successfully started and began loading the cells and walkers for running the simulations. After that, the master waits for workers to connect so it can dispatch the simulation tasks for execution by the connected workers. Now, start a worker for this master on the same machine:
% work_queue_worker localhost 9123However, to run a really large sampling, you will need to run as many workers as possible. A simple (but tiresome) way of doing so is to ssh into several machines and manually run work_queue_worker as above. But, if you have access to a batch system like Condor or SGE, you can use them to start many workers with a single submit command.
We have provided some scripts to make this easy. For example, to submit 10 workers to your local Condor pool:
% condor_submit_workers master.somewhere.edu 9123 10 Submitting job(s).......... Logging submit event(s).......... 10 job(s) submitted to cluster 298.Or, to submit 10 worker processes to your SGE cluster:
% sge_submit_workers master.somewhere.edu 9123 10
Your job 1054781 ("worker.sh") has been submitted
Your job 1054782 ("worker.sh") has been submitted
Your job 1054783 ("worker.sh") has been submitted
...
Once the workers begin running, the AWE master can dispatch tasks to
each one very quickly. It's ok if a machine running a worker crashes
or is turned off; the work will be silently sent elsewhere to run.
When the AWE master process completes, your workers will still be available, so you can either run another master with the same workers, remove them from the batch system, or wait for them to expire. If you do nothing for 15 minutes, they will automatically exit.
% python awe-rama-ala.py -w cell-weights.csv -p awe-instance-data/topol.pdb -c awe-instance-data/cells.dat -n 100where, -w specifies the csv file recording cell weights, -p the structure file of simulation target, -c the data file recording coordinates of cell centers, and -n the number of cells This produces an output file named awe-rama-ala.png that contains the Ramachandran plot.
To generate and visualize forward and backward fluxes from the output file color-transition-matrix.dat, use the script awe-flux.py.
% python awe-flux.py -i color-transition-matrix.csv -l 0.01where -i specifies the data file recording transitions at every iteration, -l specifies the scale iteration length to actual unit and (nano/pico/femto-seconds). This will produce the following outputs:
Finally, to generate transition probability matrix from AWE output, run the script awe-transMatrix.py
% python awe-transMatrix.py -p walker-history.dat -w walker-weights.dat -t 1 -n 100where -p specifies the data file recording dependencies of walkers, -w specifies the data file recording weights and cell ID of walkers, -t specifies the time lag (number of iterations) for calculating, transition matrix, and -n specifies the number of cells
This prints the matrix to a file called trans-probability-matrix.csv.