def main(RootDir, Data_Dir, StartSite, RunSite, NumStarts, RunStarts,
         ReleaseLength, TrajectoryRunLength, StartTimeFiles, TrajectoriesPath,
         NumLEs, MapFileName, refloat, current_files, wind_files,
         diffusion_coef, model_timestep, windage_range, windage_persist,
         OutputTimestep):

    timingRecord = open(os.path.join(RootDir, "timing.txt"), "w")
    count = len(StartTimeFiles) * len(RunStarts)
    timingRecord.write("This file tracks the time to process " + str(count) +
                       " gnome runs")

    # model timing
    release_duration = timedelta(hours=ReleaseLength)
    run_time = timedelta(hours=TrajectoryRunLength)

    # initiate model
    model = Model(duration=run_time, time_step=model_timestep, uncertain=False)

    # determine boundary for model
    print "Adding the map:", MapFileName
    mapfile = get_datafile(os.path.join(Data_Dir, MapFileName))
    # model.map = MapFromBNA(mapfile, refloat_halflife=refloat) no, model map needs to inclde mudflats. later

    # loop through seasons
    for Season in StartTimeFiles:
        timer1 = datetime.now()

        SeasonName = Season[1]
        start_times = open(Season[0], 'r').readlines()[:NumStarts]
        SeasonTrajDir = os.path.join(RootDir, TrajectoriesPath, SeasonName)
        if not os.path.isdir(SeasonTrajDir):
            print "Creating directory: ", SeasonTrajDir
            make_dir(SeasonTrajDir)
        print "  Season:", SeasonName

        # get and parse start times in this season
        start_dt = []
        for start_time in start_times:
            start_time = [int(i) for i in start_time.split(',')]
            start_time = datetime(start_time[0], start_time[1], start_time[2],
                                  start_time[3], start_time[4])
            start_dt.append(start_time)

        ## loop through start times
        for time_idx in RunStarts:
            timer2 = datetime.now()

            gc.collect()
            model.movers.clear()

            ## set the start location
            start_time = start_dt[time_idx]
            end_time = start_time + run_time
            model.start_time = start_time
            print "  ", start_time, "to", end_time

            ## get a list of the only data files needed for the start time (less data used)
            ## note: requires data files in year increments
            #Todo: needs fixing before real run
            years = range(start_time.year, end_time.year + 1)
            years = [str(i) for i in years]
            wind = [s for s in wind_files if any(xs in s for xs in years)]
            current = [
                s for s in current_files if any(xs in s for xs in years)
            ]

            #Todo: add mudflats. Does it work like this?
            topology = {'node_lon': 'x', 'node_lat': 'y'}

            ## add wind movers
            w_mover = PyWindMover(filename=wind)
            model.movers += w_mover

            ## add current movers
            current_mover = gs.GridCurrent.from_netCDF(current,
                                                       grid_topology=topology)
            c_mover = PyCurrentMover(current=current_mover)
            model.movers += c_mover

            tideflat = Matroos_Mudflats(current, grid_topology=topology)
            land_map = gs.MapFromBNA(mapfile)
            model.map = TideflatMap(land_map, tideflat)

            ## add diffusion
            model.movers += RandomMover(diffusion_coef=diffusion_coef)

            ## loop through start locations
            timer3 = datetime.now()

            #Todo: can it deal with the test.location.txt file??
            start_position = [float(i) for i in StartSite.split(',')]

            OutDir = os.path.join(RootDir, TrajectoriesPath, SeasonName,
                                  'pos_%03i' % (RunSite + 1))
            make_dir(OutDir)

            print "    ", RunSite, time_idx
            print "    Running: start time:", start_time,
            print "at start location:", start_position

            ## set the spill to the location
            spill = surface_point_line_spill(
                num_elements=NumLEs,
                start_position=(start_position[0], start_position[1], 0.0),
                release_time=start_time,
                end_release_time=start_time + release_duration,
                windage_range=windage_range,
                windage_persist=windage_persist)

            # print "adding netcdf output"
            netcdf_output_file = os.path.join(
                OutDir,
                'pos_%03i-t%03i_%08i.nc' %
                (RunSite + 1, time_idx, int(start_time.strftime('%y%m%d%H'))),
            )
            model.outputters.clear()
            model.outputters += NetCDFOutput(
                netcdf_output_file,
                output_timestep=timedelta(hours=OutputTimestep))

            model.spills.clear()
            model.spills += spill

            model.full_run(rewind=True)

            timer4 = datetime.now()
            diff = round((timer4 - timer3).total_seconds() / 60, 2)
            timingRecord.write("\t\t" + str(RunSite) + " took " + str(diff) +
                               " minutes to complete")
        diff = round((timer4 - timer1).total_seconds() / 3600, 2)
        count = len(RunStarts)
        timingRecord.write("\t" + str(SeasonName) + " took " + str(diff) +
                           " hours to finish " + str(count) + " Gnome runs")
    #OutDir.close
    timingRecord.close
def make_model(images_dir=os.path.join(base_dir, 'images')):
    print 'initializing the model'

    # set up the modeling environment
    start_time = datetime(2016, 9, 23, 0, 0)
    model = Model(start_time=start_time,
                  duration=timedelta(days=2),
                  time_step=30 * 60,
                  uncertain=False)

    print 'adding the map'
    model.map = GnomeMap()  # this is a "water world -- no land anywhere"

    # renderere is only top-down view on 2d -- but it's something
    renderer = Renderer(output_dir=images_dir,
                        image_size=(1024, 768),
                        output_timestep=timedelta(hours=1),
                        )
    renderer.viewport = ((196.14, 71.89), (196.18, 71.93))

    print 'adding outputters'
    model.outputters += renderer

    # Also going to write the results out to a netcdf file
    netcdf_file = os.path.join(base_dir, 'script_arctic_plume.nc')
    scripting.remove_netcdf(netcdf_file)

    model.outputters += NetCDFOutput(netcdf_file,
                                     which_data='most',
                                     # output most of the data associated with the elements
                                     output_timestep=timedelta(hours=2))

    print "adding Horizontal and Vertical diffusion"

    # Horizontal Diffusion
    model.movers += RandomMover(diffusion_coef=500)
    # vertical diffusion (different above and below the mixed layer)
    model.movers += RandomMover3D(vertical_diffusion_coef_above_ml=5,
                                        vertical_diffusion_coef_below_ml=.11,
                                        mixed_layer_depth=10)

    print 'adding Rise Velocity'
    # droplets rise as a function of their density and radius
    model.movers += TamocRiseVelocityMover()

    print 'adding a circular current and eastward current'
    fn = 'hycom_glb_regp17_2016092300_subset.nc'
    fn_ice = 'hycom-cice_ARCu0.08_046_2016092300_subset.nc'
    iconc = IceConcentration.from_netCDF(filename=fn_ice)
    ivel = IceVelocity.from_netCDF(filename=fn_ice, grid = iconc.grid)
    ic = IceAwareCurrent.from_netCDF(ice_concentration = iconc, ice_velocity= ivel, filename=fn)

    model.movers += PyCurrentMover(current = ic)
    model.movers += SimpleMover(velocity=(0., 0., 0.))
    model.movers += constant_wind_mover(20, 315, units='knots')

    # Now to add in the TAMOC "spill"
    print "Adding TAMOC spill"

    model.spills += tamoc_spill.TamocSpill(release_time=start_time,
                                        start_position=(196.16, 71.91, 40.0),
                                        num_elements=1000,
                                        end_release_time=start_time + timedelta(days=1),
                                        name='TAMOC plume',
                                        TAMOC_interval=None,  # how often to re-run TAMOC
                                        )

    model.spills[0].data_sources['currents'] = ic

    return model
Beispiel #3
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def make_model(images_dir=os.path.join(base_dir, 'images')):
    print 'initializing the model'

    # set up the modeling environment
    start_time = datetime(2004, 12, 31, 13, 0)
    model = Model(start_time=start_time,
                  duration=timedelta(days=3),
                  time_step=30 * 60,
                  uncertain=False)

    print 'adding the map'
    model.map = GnomeMap()  # this is a "water world -- no land anywhere"

    # renderere is only top-down view on 2d -- but it's something
    renderer = Renderer(
        output_dir=images_dir,
        size=(1024, 768),
        output_timestep=timedelta(hours=1),
    )
    renderer.viewport = ((-.15, -.35), (.15, .35))

    print 'adding outputters'
    model.outputters += renderer

    # Also going to write the results out to a netcdf file
    netcdf_file = os.path.join(base_dir, 'script_plume.nc')
    scripting.remove_netcdf(netcdf_file)

    model.outputters += NetCDFOutput(
        netcdf_file,
        which_data='most',
        # output most of the data associated with the elements
        output_timestep=timedelta(hours=2))

    print "adding Horizontal and Vertical diffusion"

    # Horizontal Diffusion
    # model.movers += RandomMover(diffusion_coef=5)
    # vertical diffusion (different above and below the mixed layer)
    model.movers += RandomVerticalMover(vertical_diffusion_coef_above_ml=5,
                                        vertical_diffusion_coef_below_ml=.11,
                                        mixed_layer_depth=10)

    print 'adding Rise Velocity'
    # droplets rise as a function of their density and radius
    model.movers += RiseVelocityMover()

    print 'adding a circular current and eastward current'
    # This is .3 m/s south
    model.movers += PyCurrentMover(current=vg,
                                   default_num_method='Trapezoid',
                                   extrapolate=True)
    model.movers += SimpleMover(velocity=(0., -0.1, 0.))

    # Now to add in the TAMOC "spill"
    print "Adding TAMOC spill"

    model.spills += tamoc_spill.TamocSpill(
        release_time=start_time,
        start_position=(0, 0, 1000),
        num_elements=1000,
        end_release_time=start_time + timedelta(days=1),
        name='TAMOC plume',
        TAMOC_interval=None,  # how often to re-run TAMOC
    )

    return model
if not os.path.exists(newpath):
    os.makedirs(newpath)

print 'init model'
model = gs.Model(start_time=start_time,
                 duration=model_duration,
                 time_step=gs.minutes(1))

print 'Loading current'
topology = {'grid_type': 'rgrid', 'node_lon': 'x', 'node_lat': 'y'}
current = gs.GridCurrent.from_netCDF(os.path.join(data_dir, currentfile),
                                     grid_topology=topology)
angle = TimeseriesData.constant(name='angle', data=17.0, units='degrees')

current.angle = angle
current_mover = PyCurrentMover(current=current)

print 'Adding  map'

tideflat = Matroos_Mudflats(os.path.join(data_dir, currentfile),
                            grid_topology=topology)

mapfile = 'Waddensea_ijsselmeer_6.bna'
land_map = gs.MapFromBNA(mapfile)

model.map = TideflatMap(land_map, tideflat)

print 'Adding current'
model.movers += current_mover

print 'Adding RandomMover'
def make_model(images_dir=os.path.join(base_dir, 'images')):
    print 'initializing the model'

    # set up the modeling environment
    start_time = datetime(2016, 9, 18, 1, 0)
    model = Model(start_time=start_time,
                  duration=timedelta(days=3),
                  time_step=30 * 60,
                  uncertain=False)

    print 'adding the map'
    model.map = GnomeMap()  # this is a "water world -- no land anywhere"

    # renderere is only top-down view on 2d -- but it's something
    renderer = Renderer(
        output_dir=images_dir,
        image_size=(1024, 768),
        output_timestep=timedelta(hours=1),
    )
    renderer.viewport = ((-87.295, 27.795), (-87.705, 28.205))

    print 'adding outputters'
    model.outputters += renderer

    # Also going to write the results out to a netcdf file
    netcdf_file = os.path.join(base_dir, 'gulf_tamoc.nc')
    scripting.remove_netcdf(netcdf_file)

    model.outputters += NetCDFOutput(
        netcdf_file,
        which_data='most',
        # output most of the data associated with the elements
        output_timestep=timedelta(hours=2))

    print "adding Horizontal and Vertical diffusion"

    # Horizontal Diffusion
    #model.movers += RandomMover(diffusion_coef=100000)
    # vertical diffusion (different above and below the mixed layer)
    model.movers += RandomMover3D(vertical_diffusion_coef_above_ml=50,
                                  vertical_diffusion_coef_below_ml=10,
                                  horizontal_diffusion_coef_above_ml=100000,
                                  horizontal_diffusion_coef_below_ml=100,
                                  mixed_layer_depth=10)

    print 'adding Rise Velocity'
    # droplets rise as a function of their density and radius
    model.movers += TamocRiseVelocityMover()

    print 'adding the 3D current mover'
    gc = GridCurrent.from_netCDF('HYCOM_3d.nc')

    model.movers += PyCurrentMover('HYCOM_3d.nc')
    #    model.movers += SimpleMover(velocity=(0., 0, 0.))
    model.movers += constant_wind_mover(10, 315, units='knots')

    # Wind from a buoy
    #w = Wind(filename='KIKT.osm')
    #model.movers += WindMover(w)

    # Now to add in the TAMOC "spill"
    print "Adding TAMOC spill"

    model.spills += tamoc_spill.TamocSpill(
        release_time=start_time,
        start_position=(-87.5, 28.0, 1000),
        num_elements=1000,
        end_release_time=start_time + timedelta(days=2),
        name='TAMOC plume',
        TAMOC_interval=None,  # how often to re-run TAMOC
    )

    model.spills[0].data_sources['currents'] = gc

    return model