python-pysph 0~20160514.git91867dc-4build1 / usr / lib / python2.7 / dist-packages / PySPH-1.0a4.dev0-py2.7-linux-x86_64.egg / pysph / solver / application.py

# Standard imports.
import glob
import inspect
import json
import logging
import os
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
from os.path import (abspath, basename, dirname, isdir, join, realpath,
    splitext)
import sys
import time

# PySPH imports.
from pysph.base.config import get_config
from pysph.base import utils
from pysph.base.nnps import BoxSortNNPS, LinkedListNNPS
from pysph.base import kernels
from pysph.solver.controller import CommandManager
from pysph.solver.utils import mkdir, load, get_files

# conditional parallel imports
from pysph import has_mpi, has_zoltan, in_parallel
if in_parallel():
    from pysph.parallel.parallel_manager import ZoltanParallelManagerGeometric
    import mpi4py.MPI as mpi

logger = logging.getLogger(__name__)


def is_overloaded_method(method):
    """Returns True if the given method is overloaded from any of its bases.
    """
    method_name = method.__name__
    self = method.__self__
    klass = self.__class__
    for base in klass.__bases__:
        if hasattr(base, method_name):
            if getattr(base, method_name) != getattr(klass, method_name):
                return True
    return False

def is_using_ipython():
    """Return True if the code is being run from an IPython session or
    notebook.
    """
    try:
        # If this is being run inside an IPython console or notebook
        # then this is defined.
        __IPYTHON__
    except NameError:
        return False
    else:
        return True

def list_all_kernels():
    """Return list of available kernels.
    """
    return [n for n in dir(kernels) if inspect.isclass(getattr(kernels, n))]


##############################################################################
# `Application` class.
##############################################################################
class Application(object):
    """ Class used by any SPH application.
    """

    def __init__(self, fname=None, domain=None):
        """ Constructor

        Parameters
        ----------
        fname : str
            file name to use for the output files.
        domain : pysph.nnps.DomainManager
            A domain manager to use. This is used for periodic domains etc.
        """
        self.domain = domain

        self.solver = None
        self.nnps = None
        self.scheme = None
        self.tools = []
        self.parallel_manager = None

        if fname is None:
            fname = self._guess_output_filename()

        self.fname = fname

        self.args = sys.argv[1:]

        # MPI related vars.
        self.comm = None
        self.num_procs = 1
        self.rank = 0
        if in_parallel():
            self.comm = comm = mpi.COMM_WORLD
            self.num_procs = comm.Get_size()
            self.rank = comm.Get_rank()

        self._log_levels = {'debug': logging.DEBUG,
                           'info': logging.INFO,
                           'warning': logging.WARNING,
                           'error': logging.ERROR,
                           'critical': logging.CRITICAL,
                           'none': None}

        self.output_dir = abspath(self._get_output_dir_from_fname())
        self.particles = []
        self.inlet_outlet = []

        self.initialize()
        self.scheme = self.create_scheme()
        self._setup_argparse()

    def _get_output_dir_from_fname(self):
        return self.fname + '_output'

    def _guess_output_filename(self):
        """Try to guess the output filename to use.
        """
        module = self.__module__.rsplit('.', 1)[-1]
        if is_using_ipython():
            return module
        else:
            if len(sys.argv[0]) == 0:
                return module
            else:
                return splitext(basename(abspath(sys.argv[0])))[0]

    def _setup_argparse(self):
        usage = '%(prog)s [options]'
        description = """
        Note that you may run this program via MPI and the run will be
        automatically parallelized.  To do this run::

         $ mpirun -n 4 /path/to/your/python %prog [options]

        Replace '4' above with the number of processors you have.
        Below are the options you may pass.

        """
        parser = ArgumentParser(
            usage=usage, description=description,
            formatter_class=ArgumentDefaultsHelpFormatter
        )
        self.arg_parse = parser

         # Add some default options.
         # -v
        valid_vals = "Valid values: %s"%self._log_levels.keys()
        parser.add_argument("-v", "--loglevel", action="store",
                          dest="loglevel",
                          default='info',
                          help="Log-level to use for log messages. " +
                               valid_vals)
        # --logfile

        parser.add_argument("--logfile", action="store",
                          dest="logfile",
                          default=None,
                          help="Log file to use for logging, set to "+
                               "empty ('') for no file logging.")
        # -l
        parser.add_argument("-l", "--print-log", action="store_true",
                          dest="print_log", default=False,
                          help="Print log messages to stderr.")
        # --final-time
        parser.add_argument("--tf", action="store",
                          type=float,
                          dest="final_time",
                          default=None,
                          help="Total time for the simulation.")
        # --timestep
        parser.add_argument("--timestep", action="store",
                          type=float,
                          dest="time_step",
                          default=None,
                          help="Timestep to use for the simulation.")
        # --max-steps
        parser.add_argument(
            "--max-steps", action="store", type=int, dest="max_steps",
            default=1<<31,
            help="Maximum number of iteration steps to take (defaults to a "
            "very large value)."
        )

        # --adaptive-timestep
        parser.add_argument("--adaptive-timestep", action="store_true",
                          dest="adaptive_timestep", default=None,
                          help="Use adaptive time stepping.")
        parser.add_argument("--no-adaptive-timestep", action="store_false",
                          dest="adaptive_timestep", default=None,
                          help="Do not use adaptive time stepping.")

        # --cfl
        parser.add_argument("--cfl", action="store", dest="cfl", type=float,
                          default=0.3,
                          help="CFL number for adaptive time steps")

        # -q/--quiet.
        parser.add_argument("-q", "--quiet", action="store_true",
                         dest="quiet", default=False,
                         help="Do not print any progress information.")

        # --disable-output
        parser.add_argument("--disable-output", action="store_true",
                         dest="disable_output", default=False,
                         help="Do not dump any output files.")

        # -o/ --fname
        parser.add_argument("-o", "--fname", action="store",
                          dest="fname", default=self.fname,
                          help="File name to use for output")

        # --pfreq.
        parser.add_argument("--pfreq", action="store",
                          dest="freq", default=None, type=int,
                          help="Printing frequency for the output")

        # --detailed-output.
        parser.add_argument("--detailed-output", action="store_true",
                         dest="detailed_output", default=None,
                         help="Dump detailed output.")

        # -z/--compress-output
        parser.add_argument(
            "-z", "--compress-output", action="store_true",
            dest="compress_output", default=False,
            help="Compress generated output files."
        )

        # --output-remote
        parser.add_argument("--output-dump-remote", action="store_true",
                          dest="output_dump_remote", default=False,
                          help="Save Remote particles in parallel")
        # -d/--directory
        parser.add_argument("-d", "--directory", action="store",
                          dest="output_dir",
                          default=self._get_output_dir_from_fname(),
                          help="Dump output in the specified directory.")

        # --openmp
        parser.add_argument("--openmp", action="store_true", dest="with_openmp",
                          default=None, help="Use OpenMP to run the "\
                            "simulation using multiple cores.")
        parser.add_argument("--no-openmp", action="store_false", dest="with_openmp",
                          default=None, help="Do not use OpenMP to run the "\
                            "simulation using multiple cores.")

        # --kernel
        all_kernels = list_all_kernels()
        parser.add_argument(
            "--kernel", action="store", dest="kernel", default=None,
             choices=all_kernels,
            help="Use specified kernel from %s"%all_kernels
        )

        # Restart options
        restart = parser.add_argument_group("Restart options",
                              "Restart options for PySPH")

        restart.add_argument("--restart-file", action="store", dest="restart_file",
                           default=None,
                           help=("""Restart a PySPH simulation using a specified file """)),

        restart.add_argument("--rescale-dt", action="store", dest="rescale_dt",
                           default=1.0, type=float,
                           help=("Scale dt upon restarting by a numerical constant"))


        # NNPS options
        nnps_options = parser.add_argument_group("NNPS",
                "Nearest Neighbor searching")

        # --nnps
        nnps_options.add_argument("--nnps", dest="nnps",
                                choices=['box', 'll'],
                                default='ll',
                                help="Use one of box-sort ('box') or "\
                                     "the linked list algorithm ('ll'). "
                                )

        # --fixed-h
        nnps_options.add_argument("--fixed-h", dest="fixed_h",
                                action="store_true", default=False,
                                help="Option for fixed smoothing lengths")

        nnps_options.add_argument("--cache-nnps", dest="cache_nnps",
                                action="store_true", default=False,
                        help="Option to enable the use of neighbor caching.")

        nnps_options.add_argument(
            "--sort-gids", dest="sort_gids", action="store_true",
            default=False, help="Sort neighbors by the GIDs to get "\
            "consistent results in serial and parallel (slows down a bit)."
        )


        # Zoltan Options
        zoltan = parser.add_argument_group("PyZoltan",
                             "Zoltan load balancing options")

        zoltan.add_argument("--with-zoltan", action="store_true",
                          dest="with_zoltan", default=True,
                          help=("""Use PyZoltan for dynamic load balancing """))

        zoltan.add_argument("--zoltan-lb-method", action="store",
                          dest="zoltan_lb_method", default="RCB",
                          help=("""Choose the Zoltan load balancnig method"""))

        # --rcb-lock
        zoltan.add_argument("--rcb-lock", action="store_true", dest="zoltan_rcb_lock_directions",
                          default=False,
                          help=("Lock the directions of the RCB cuts"))

        # rcb--reuse
        zoltan.add_argument("--rcb-reuse", action='store_true', dest="zoltan_rcb_reuse",
                          default=False,
                          help=("Reuse previous RCB cuts"))

        # rcb-rectilinear
        zoltan.add_argument("--rcb-rectilinear", action="store_true", dest='zoltan_rcb_rectilinear',
                          default=False,
                          help=("Produce nice rectilinear blocks without projections"))

        # rcb-set-direction
        zoltan.add_argument("--rcb-set-direction", action='store', dest="zoltan_rcb_set_direction",
                          default=0, type=int,
                          help=("Set the order of the RCB cuts"))

        zoltan.add_argument("--zoltan-weights", action="store_false",
                          dest="zoltan_weights", default=True,
                          help=("""Switch between using weights for input to Zoltan.
                          defaults to True"""))

        zoltan.add_argument("--ghost-layers", action='store', dest='ghost_layers',
                          default=3.0, type=float,
                          help=('Number of ghost cells to share for remote neighbors'))

        zoltan.add_argument("--lb-freq", action='store', dest='lb_freq',
                          default=10, type=int,
                          help=('The frequency for load balancing'))

        zoltan.add_argument("--zoltan-debug-level", action="store",
                          dest="zoltan_debug_level", default="0",
                          help=("""Zoltan debugging level"""))


        # Options to control parallel execution
        parallel_options=parser.add_argument_group("Parallel Options")

        # --update-cell-sizes
        parallel_options.add_argument("--update-cell-sizes", action='store_true',
                                    dest='update_cell_sizes', default=False,
                                    help=("Recompute cell sizes for binning in parallel"))

        # --parallel-scale-factor
        parallel_options.add_argument("--parallel-scale-factor", action="store",
                                    dest="parallel_scale_factor", default=2.0, type=float,
                                    help=("""Kernel scale factor for the parallel update"""))

        # --parallel-output-mode
        parallel_options.add_argument("--parallel-output-mode", action="store",
                            dest="parallel_output_mode", default=None,
                            help="""Use 'collected' to dump one output at
                          root or 'distributed' for every processor. """)



        # solver interfaces
        interfaces = parser.add_argument_group("Interfaces",
                                 "Add interfaces to the solver")

        interfaces.add_argument("--interactive", action="store_true",
                              dest="cmd_line", default=False,
                              help=("Add an interactive commandline interface "
                                    "to the solver"))

        interfaces.add_argument("--xml-rpc", action="store",
                dest="xml_rpc", metavar="[HOST:] PORT",
                              help=("Add an XML-RPC interface to the solver;"
                                      "HOST=0.0.0.0 by default"))

        interfaces.add_argument("--multiproc", action="store",
                              dest="multiproc", metavar='[[AUTHKEY@] HOST:] PORT[+] ',
                              default="pysph@0.0.0.0:8800+",
                              help=("Add a python multiprocessing interface "
                                    "to the solver; "
                                    "AUTHKEY=pysph, HOST=0.0.0.0, PORT=8800+ by"
                                    " default (8800+ means first available port "
                                    "number 8800 onwards)"))

        interfaces.add_argument("--no-multiproc", action="store_const",
                              dest="multiproc", const=None,
                              help=("Disable multiprocessing interface "
                                    "to the solver"))

          # User options.
        user_options = parser.add_argument_group("User",
                 "User defined command line arguments")
        if self.scheme is not None:
            self.scheme.add_user_options(user_options)
        self.add_user_options(user_options)

    def _parse_command_line(self, force=False):
        """If force is True, it will parse the arguments regardless of whether
        it is running in IPython or not.  This is handy when you want to parse
        the command line for a previously run case.
        """
        if is_using_ipython() and not force:
            # Don't parse the command line args.
            options  = self.arg_parse.parse_args([])
        else:
            options  = self.arg_parse.parse_args(self.args)

        self.options = options

        # save the path where we want to dump output
        self.output_dir = abspath(options.output_dir)
        mkdir(self.output_dir)
        if self.scheme is not None:
            self.scheme.consume_user_options(self.options)
        self.consume_user_options()
        self.configure_scheme()

    def _setup_logging(self):
        """Setup logging for the application.
        """
        options = self.options
        # Setup logging based on command line options.
        level = self._log_levels[options.loglevel]

        if level is None:
            return

        # logging setup
        logger.setLevel(level)

        filename = options.logfile
        # Setup the log file.
        if filename is None:
            filename = self.fname + '.log'

        if len(filename) > 0:
            lfn = os.path.join(self.output_dir,filename)
            format = '%(levelname)s|%(asctime)s|%(name)s|%(message)s'
            logging.basicConfig(level=level, format=format,
                                filename=lfn, filemode='a')
        if options.print_log:
            logger.addHandler(logging.StreamHandler())

    def _create_inlet_outlet(self, inlet_outlet_factory):
        """Create the inlets and outlets if needed.

        This method requires that the particles be already created.

        The `inlet_outlet_factory` is passed a dictionary of the particle
        arrays.  The factory should return a list of inlets and outlets.
        """
        if inlet_outlet_factory is not None:
            solver = self.solver
            particle_arrays = dict([(p.name, p) for p in self.particles])
            self.inlet_outlet = inlet_outlet_factory(particle_arrays)
            # Hook up the inlet/outlet's update method to be called after
            # each stage.
            for obj in self.inlet_outlet:
                solver.add_post_step_callback(obj.update)

    def _create_particles(self, particle_factory, *args, **kw):
        """ Create particles given a callable `particle_factory` and any
        arguments to it.
        """
        options = self.options
        rank = self.rank

        # particle array info that is used to create dummy particles
        # on non-root processors
        particles_info = {}

        # Only master actually calls the particle factory, the rest create
        # dummy particle arrays.
        if rank == 0:
            if options.restart_file is not None:
                # FIXME: not tested, probably does not work!
                solver = self.solver
                data = load(options.restart_file)

                arrays = data['arrays']
                solver_data = data['solver_data']

                # arrays and particles
                particles = []
                for array_name in arrays:
                    particles.append( arrays[array_name] )

                # save the particles list
                self.particles = particles

                # time, timestep and solver iteration count at restart
                t, dt, count = solver_data['t'], solver_data['dt'], solver_data['count']

                # rescale dt at restart
                dt *= options.rescale_dt
                solver.t, solver.dt, solver.count  = t, dt, count

            else:
                self.particles = particle_factory(*args, **kw)

            # get the array info which will be b'casted to other procs
            particles_info = utils.get_particles_info(self.particles)

        # Broadcast the particles_info to other processors for parallel runs
        if self.num_procs > 1:
            particles_info = self.comm.bcast(particles_info, root=0)

        # now all processors other than root create dummy particle arrays
        if rank != 0:
            self.particles = utils.create_dummy_particles(particles_info)

    def _configure(self):
        """Configures the application using the options from the
        command-line.
        """
        options = self.options
        # Setup configuration options.
        if options.with_openmp is not None:
            get_config().use_openmp = options.with_openmp
        # setup the solver using any options
        self.solver.setup_solver(options.__dict__)

        solver = self.solver

        # fixed smoothing lengths
        fixed_h = solver.fixed_h or options.fixed_h

        kernel = solver.kernel
        if options.kernel is not None:
            kernel = getattr(kernels, options.kernel)(dim=solver.dim)
            solver.kernel = kernel

        # This should be called before an NNPS is created as the particles are
        # changed after the initial load-balancing.
        self._setup_parallel_manager_and_initial_load_balance()

        if self.nnps is None:
            cache = options.cache_nnps

            # create the NNPS object
            if options.nnps == 'box':
                nnps = BoxSortNNPS(
                    dim=solver.dim, particles=self.particles,
                    radius_scale=kernel.radius_scale, domain=self.domain,
                    cache=cache, sort_gids=options.sort_gids
                )

            elif options.nnps == 'll':
                nnps = LinkedListNNPS(
                    dim=solver.dim, particles=self.particles,
                    radius_scale=kernel.radius_scale, domain=self.domain,
                    fixed_h=fixed_h, cache=cache,
                    sort_gids=options.sort_gids
                )
            self.nnps = nnps

        nnps = self.nnps
        # once the NNPS has been set-up, we set the default Solver
        # post-stage callback to the DomainManager.setup_domain
        # method. This method is responsible to computing the new cell
        # size and doing any periodicity checks if needed.
        solver.add_post_stage_callback( nnps.update_domain )

        # inform NNPS if it's working in parallel
        if self.num_procs > 1:
            nnps.set_in_parallel(True)

        dt = options.time_step
        if dt is not None:
            solver.set_time_step(dt)

        tf = options.final_time
        if tf is not None:
            solver.set_final_time(tf)

        solver.set_max_steps(self.options.max_steps)

        # Setup the solver output file name
        fname = options.fname

        if in_parallel():
            rank = self.rank
            if self.num_procs > 1:
                fname += '_' + str(rank)

        # set the rank for the solver
        solver.rank = self.rank
        solver.pid = self.rank
        solver.comm = self.comm

        # set the in parallel flag for the solver
        if self.num_procs > 1:
            solver.in_parallel = True

        # output file name
        solver.set_output_fname(fname)

        solver.set_compress_output(options.compress_output)
        # disable_output
        solver.set_disable_output(options.disable_output)

        # output print frequency
        if options.freq is not None:
            solver.set_print_freq(options.freq)

        # output printing level (default is not detailed)
        if options.detailed_output is not None:
            solver.set_output_printing_level(options.detailed_output)

        # solver output behaviour in parallel
        if options.output_dump_remote:
            solver.set_output_only_real( False )

        # output directory
        solver.set_output_directory(abspath(options.output_dir))
        self._message("Generating output in %s"%self.output_dir)

        # set parallel output mode
        if options.parallel_output_mode is not None:
            solver.set_parallel_output_mode(options.parallel_output_mode)

        # Set the adaptive timestep
        if options.adaptive_timestep is not None:
            solver.set_adaptive_timestep(options.adaptive_timestep)

            # set solver cfl number
            solver.set_cfl(options.cfl)


        # setup the solver. This is where the code is compiled
        solver.setup(
            particles=self.particles, equations=self.equations, nnps=nnps,
            kernel=kernel, fixed_h=fixed_h
        )

        # add solver interfaces
        self.command_manager = CommandManager(solver, self.comm)
        solver.set_command_handler(self.command_manager.execute_commands)

        if self.rank == 0:
            # commandline interface
            if options.cmd_line:
                from pysph.solver.solver_interfaces import CommandlineInterface
                self.command_manager.add_interface(CommandlineInterface().start)

            # XML-RPC interface
            if options.xml_rpc:
                from pysph.solver.solver_interfaces import XMLRPCInterface
                addr = options.xml_rpc
                idx = addr.find(':')
                host = "0.0.0.0" if idx == -1 else addr[:idx]
                port = int(addr[idx+1:])
                self.command_manager.add_interface(XMLRPCInterface((host,port)).start)

            # python MultiProcessing interface
            if options.multiproc:
                from pysph.solver.solver_interfaces import MultiprocessingInterface
                addr = options.multiproc
                idx = addr.find('@')
                authkey = "pysph" if idx == -1 else addr[:idx]
                addr = addr[idx+1:]
                idx = addr.find(':')
                host = "0.0.0.0" if idx == -1 else addr[:idx]
                port = addr[idx+1:]
                if port[-1] == '+':
                    try_next_port = True
                    port = port[:-1]
                else:
                    try_next_port = False
                port = int(port)

                interface = MultiprocessingInterface(
                    (host,port), authkey.encode(), try_next_port)

                self.command_manager.add_interface(interface.start)

                logger.info('started multiprocessing interface on %s'%(
                             interface.address,))

    def _setup_parallel_manager_and_initial_load_balance(self):
        """This will automatically distribute the particles among processors
        if this is a parallel run.
        """
        # Instantiate the Parallel Manager here and do an initial LB
        num_procs = self.num_procs
        options = self.options
        solver = self.solver
        comm = self.comm

        self.parallel_manager = None
        if num_procs > 1:
            options = self.options

            if options.with_zoltan:
                if not (has_zoltan() and has_mpi()):
                    raise RuntimeError("Cannot run in parallel!")

            else:
                raise ValueError("""Sorry. You're stuck with Zoltan for now
                use the option '--with-zoltan' for parallel runs
                """)

            # create the parallel manager
            obj_weight_dim = "0"
            if options.zoltan_weights:
                obj_weight_dim = "1"

            zoltan_lb_method = options.zoltan_lb_method
            zoltan_obj_wgt_dim = obj_weight_dim

            # ghost layers
            ghost_layers = options.ghost_layers

            # radius scale for the parallel update
            radius_scale = options.parallel_scale_factor*solver.kernel.radius_scale

            self.parallel_manager = pm = ZoltanParallelManagerGeometric(
                dim=solver.dim, particles=self.particles, comm=comm,
                lb_method=zoltan_lb_method,
                obj_weight_dim=obj_weight_dim,
                ghost_layers=ghost_layers,
                update_cell_sizes=options.update_cell_sizes,
                radius_scale=radius_scale,
                )

            ### ADDITIONAL LOAD BALANCING FUNCTIONS FOR ZOLTAN ###

            # RCB lock directions
            if options.zoltan_rcb_lock_directions:
                pm.set_zoltan_rcb_lock_directions()

            if options.zoltan_rcb_reuse:
                pm.set_zoltan_rcb_reuse()

            if options.zoltan_rcb_rectilinear:
                pm.set_zoltan_rcb_rectilinear_blocks()

            if options.zoltan_rcb_set_direction > 0:
                pm.set_zoltan_rcb_directions( str(options.zoltan_rcb_set_direction) )

            # set zoltan options
            pm.pz.Zoltan_Set_Param("DEBUG_LEVEL", options.zoltan_debug_level)
            pm.pz.Zoltan_Set_Param("DEBUG_MEMORY", "0")

            # do an initial load balance
            pm.update()
            pm.initial_update = False

            # set subsequent load balancing frequency
            lb_freq = options.lb_freq
            if lb_freq < 1 : raise ValueError("Invalid lb_freq %d"%lb_freq)
            pm.set_lb_freq( lb_freq )

            # wait till the initial partition is done
            comm.barrier()

        # set the solver's parallel manager
        solver.set_parallel_manager(self.parallel_manager)

    def _setup_solver_callbacks(self, obj):
        """Setup any solver callbacks given an object with any of `pre_step`,
        `post_step' and `post_stage`
        """
        if is_overloaded_method(obj.pre_step):
            self.solver.add_pre_step_callback(obj.pre_step)

        if is_overloaded_method(obj.post_stage):
            self.solver.add_post_stage_callback(obj.post_stage)

        if is_overloaded_method(obj.post_step):
            self.solver.add_post_step_callback(obj.post_step)

    def _message(self, msg):
        if self.options.quiet:
            return
        if self.num_procs == 1:
            logger.info(msg)
            print(msg)
        elif (self.num_procs > 1 and self.rank in (0,1)):
            s = "Rank %d: %s"%(self.rank, msg)
            logger.info(s)
            print(s)

    def _write_info(self, filename, **kw):
        """Write the information dictionary to given filename. Any extra
        keyword arguments are written to the file.
        """
        info = dict(
            fname=self.fname, output_dir=self.output_dir, args=self.args
        )
        info.update(kw)
        json.dump(info, open(filename, 'w'))

    ######################################################################
    # Public interface.
    ######################################################################
    def add_tool(self, tool):
        """Add a `Tool` instance to the application.
        """
        self._setup_solver_callbacks(tool)
        self.tools.append(tool)

    def dump_code(self, file):
        """Dump the generated code to given file.
        """
        file.write(self.solver.sph_eval.ext_mod.code)

    @property
    def info_filename(self):
        return abspath(join(self.output_dir, self.fname + '.info'))

    def initialize(self):
        """Called on the constructor, set constants etc. up here if needed.
        """
        pass

    @property
    def output_files(self):
        return get_files(self.output_dir, self.fname)

    def read_info(self, fname_or_dir):
        """Read the information from the given info file (or directory
        containing the info file, the first found info file will be used).
        """
        if isdir(fname_or_dir):
            fname_or_dir = glob.glob(join(fname_or_dir, "*.info"))[0]
        info_dir = dirname(fname_or_dir)
        with open(fname_or_dir, 'r') as f:
            info = json.load(f)
        self.fname = info.get('fname', self.fname)
        output_dir = info.get('output_dir', self.output_dir)
        if realpath(info_dir) != realpath(output_dir):
            # Happens if someone moved the directory!
            self.output_dir = info_dir
            info['output_dir'] = info_dir
        else:
            self.output_dir = output_dir

        self.args = info.get('args', self.args)
        self._parse_command_line(force=True)
        return info

    def run(self, argv=None):
        """Run the application.
        """
        if argv is not None:
            self.set_args(argv)

        if self.solver is None:
            start_time = time.time()

            self._parse_command_line()
            self._setup_logging()

            self.solver = self.create_solver()
            msg = "Solver is None, you may have forgotten to return it!"
            assert self.solver is not None, msg
            self.equations = self.create_equations()

            self._create_particles(self.create_particles)

            # This must be done before the initial load balancing
            # as the inlets will create new particles.
            if is_overloaded_method(self.create_inlet_outlet):
                self._create_inlet_outlet(self.create_inlet_outlet)

            if self.domain is None:
                self.domain = self.create_domain()

            self.nnps = self.create_nnps()

            self._configure()

            self._setup_solver_callbacks(self)
            for tool in self.create_tools():
                self.add_tool(tool)

            end_time = time.time()
            setup_duration = end_time - start_time
            self._message("Setup took: %.5f secs"%(setup_duration))
            self._write_info(
                self.info_filename, completed=False, cpu_time=0
            )

        start_time = time.time()
        self.solver.solve(not self.options.quiet)
        end_time = time.time()
        run_duration = end_time - start_time
        self._message("Run took: %.5f secs"%(run_duration))
        self._write_info(
            self.info_filename, completed=True, cpu_time=run_duration
        )

    def set_args(self, args):
        self.args = args

    def setup(self, solver, equations, nnps=None, inlet_outlet_factory=None,
              particle_factory=None, *args, **kwargs):
        """Setup the application's solver.

        This will parse the command line arguments (if this is not called from
        within an IPython notebook or shell) and then using those parameters
        and any additional parameters and call the solver's setup method.

        Parameters
        ----------
        solver: pysph.solver.solver.Solver
            The solver instance.

        equations: list
            A list of Groups/Equations.

        nnps: pysph.base.nnps.NNPS
            Optional NNPS instance. If None is given a default NNPS is created.

        inlet_outlet_factory: callable or None
            The `inlet_outlet_factory` is passed a dictionary of the particle
            arrays.  The factory should return a list of inlets and outlets.

        particle_factory : callable or None
            If supplied, particles will be created for the solver using the
            particle arrays returned by the callable. Else particles for the
            solver need to be set before calling this method

        args:
            extra positional arguments passed on to the `particle_factory`.

        kwargs:
            extra keyword arguments passed to the `particle_factory`.


        Examples
        --------

        >>> def create_particles():
        ...    ...
        ...
        >>> solver = Solver(...)
        >>> equations = [...]
        >>> app = Application()
        >>> app.setup(solver=solver, equations=equations,
        ...           particle_factory=create_particles)
        >>> app.run()
        """
        start_time = time.time()
        self.solver = solver
        self.equations = equations
        solver_opts = solver.get_options(self.arg_parse)
        self._parse_command_line()
        self._setup_logging()

        # Create particles either from scratch or restart
        self._create_particles(particle_factory, *args, **kwargs)

        # This must be done before the initial load balancing
        # as the inlets will create new particles.
        self._create_inlet_outlet(inlet_outlet_factory)
        if nnps is not None:
            self.nnps = nnps

        self._configure()

        end_time = time.time()
        setup_duration = end_time - start_time
        self._message("Setup took: %.5f secs"%(setup_duration))
        self._write_info(self.info_filename, completed=False, cpu_time=0)

    ######################################################################
    # User methods that could be overloaded.
    ######################################################################
    def add_user_options(self, group):
        """Add any user-defined options to the given option group.

        Note
        ----

        This uses the `argparse` module.
        """
        pass

    def configure_scheme(self):
        """This is called after ``consume_user_options`` is called.  One can
        configure the SPH scheme here as at this point all the command line
        options are known.
        """
        pass

    def consume_user_options(self):
        """This is called right after the command line arguments are parsed.

        All the parsed options are available in ``self.options`` and can be
        used in this method.

        This is meant to be overridden by users to setup any internal variables
        etc. that depend on the command line arguments passed.  Note that this
        method is called well before the solver or particles are created.
        """
        pass

    def create_domain(self):
        """Create a `pysph.nnps.DomainManager` and return it if needed.

        This is used for periodic domains etc.  Note that if the domain
        is passed to ``__init__``, then this method is not called.
        """
        return None

    def create_inlet_outlet(self, particle_arrays):
        """Create inlet and outlet objects and return them as a list.

        The method is passed a dictionary of particle arrays keyed on the name
        of the particle array.
        """
        pass

    def create_equations(self):
        """Create the equations to be used and return them.
        """
        if self.scheme is not None:
            return self.scheme.get_equations()
        else:
            msg = "Application.create_equations method must be overloaded."
            raise NotImplementedError(msg)

    def create_nnps(self):
        """Create any NNPS if desired and return it, else a default NNPS will
        be created automatically.
        """
        return None

    def create_particles(self):
        """Create particle arrays and return a list of them.
        """
        message = "Application.create_particles method must be overloaded."
        raise NotImplementedError(message)

    def create_scheme(self):
        """Create a suitable SPH scheme and return it.

        Note that this method is called after the arguments are all
        processed and after `consume_user_options` is called.
        """
        return None

    def create_solver(self):
        """Create the solver and return it.
        """
        if self.scheme is not None:
            return self.scheme.get_solver()
        else:
            msg = "Application.create_solver method must be overloaded."
            raise NotImplementedError(msg)

    def create_tools(self):
        """Create any tools and return a sequence of them.  This method is
        called after particles/inlets etc. are all setup, configured etc.
        """
        return []

    def pre_step(self, solver):
        """If overloaded, this is called automatically before each integrator
        step.  The method is passed the solver instance.
        """
        pass

    def post_stage(self, current_time, dt, stage):
        """If overloaded, this is called automatically after each integrator
        stage, i.e. if the integrator is a two stage integrator it will be
        called after the first and second stages.

        The method is passed (current_time, dt, stage).  See the the
        `Integrator.one_timestep` methods for examples of how this is called.
        """
        pass

    def post_step(self, solver):
        """If overloaded, this is called automatically after each integrator
        step.  The method is passed the solver instance.
        """
        pass

    def post_process(self, info_fname_or_directory):
        """Given an info filename or a directory containing the info file, read
        the information and do any post-processing of the results.  Please
        overload the method to perform any processing.

        The info file has a few useful attributes and can be read using the
        `read_info` method.

        The `output_files` property should provide the output files
        generated.
        """
        print('Overload this method to post-process the results.')