SWAN declarative example

In this notebook we will run SWAN entirely from a model runtime and config entirely declared in a yaml file. We only plot some model settings to visualise before creating the model workspace

%load_ext autoreload
%autoreload 2

from pathlib import Path
import yaml

import warnings
warnings.filterwarnings('ignore')

Instantiate model

Use a fully-defined config from yaml to instantiate ModelRun with the runtime parameters and the config definition

# Uncoment below to view the contents of the yaml file

# !cat example_declarative.yml

from rompy.model import ModelRun

conf = yaml.load(open("example_declarative.yml"), Loader=yaml.Loader)
run = ModelRun(**conf)
run

ModelRun(run_id='run1', period=TimeRange(start=datetime.datetime(2023, 1, 1, 0, 0), end=datetime.datetime(2023, 1, 2, 0, 0), duration=datetime.timedelta(days=1), interval=datetime.timedelta(seconds=3600), include_end=True), output_dir='example_declarative', config=SwanConfigComponents(model_type='swanconfig', template='/source/csiro/rompy/rompy/templates/swancomp', checkout='main', cgrid=REGULAR(model_type='regular', spectrum=SPECTRUM(model_type='spectrum', mdc=36, flow=0.04, fhigh=1.0, msc=None, dir1=None, dir2=None), grid=GRIDREGULAR(model_type='gridregular', xp=110.0, yp=-35.2, alp=4.0, xlen=7.5, ylen=12.5, mx=14, my=24, suffix='c')), startup=STARTUP(model_type='startup', project=PROJECT(model_type='project', name='Test declarative', nr='run1', title1='Declarative definition of a Swan config with rompy', title2=None, title3=None), set=SET(model_type='set', level=0.0, nor=None, depmin=0.05, maxmes=None, maxerr=None, grav=None, rho=None, cdcap=None, inrhog=None, hsrerr=None, direction_convention='nautical', pwtail=None, froudmax=None, icewind=None), mode=MODE(model_type='mode', kind='nonstationary', dim='twodimensional'), coordinates=COORDINATES(model_type='coordinates', kind=SPHERICAL(model_type='spherical', projection='ccm'), reapeating=False)), inpgrid=DataInterface(model_type='data_interface', bottom=SwanDataGrid(model_type='data_grid', id='data', source=SourceIntake(model_type='intake', dataset_id='gebco', catalog_uri='../../tests/data/catalog.yaml', kwargs={}), filter=Filter(sort={}, subset={}, crop={}, timenorm={}, rename={}, derived={}), variables=['elevation'], coords=DatasetCoords(t='time', x='lon', y='lat', z='depth'), crop_data=True, buffer=1.0, z1='elevation', z2=None, var=<GridOptions.BOTTOM: 'bottom'>, fac=-1.0), input=[SwanDataGrid(model_type='data_grid', id='data', source=SourceIntake(model_type='intake', dataset_id='era5', catalog_uri='../../tests/data/catalog.yaml', kwargs={}), filter=Filter(sort={'coords': ['latitude']}, subset={}, crop={}, timenorm={}, rename={}, derived={}), variables=['u10', 'v10'], coords=DatasetCoords(t='time', x='longitude', y='latitude', z='depth'), crop_data=True, buffer=2.0, z1='u10', z2='v10', var=<GridOptions.WIND: 'wind'>, fac=1.0)]), boundary=None, initial=INITIAL(model_type='initial', kind=DEFAULT(model_type='default')), physics=PHYSICS(model_type='physics', gen=GEN3(model_type='gen3', source_terms=WESTHUYSEN(model_type='westhuysen', wind_drag='wu', agrow=False, a=None, cds2=None, br=None)), sswell=None, negatinp=None, wcapping=None, quadrupl=QUADRUPL(model_type='quadrupl', iquad=2, lambd=None, cnl4=None, csh1=None, csh2=None, csh3=None), breaking=BREAKING_CONSTANT(model_type='constant', alpha=None, gamma=0.73), friction=FRICTION_MADSEN(model_type='madsen', kn=0.05), triad=TRIAD(model_type='triad', itriad=None, trfac=None, cutfr=None, a=None, b=None, ucrit=None, urslim=None), vegetation=None, mud=None, sice=None, turbulence=None, bragg=None, limiter=None, obstacle=None, setup=None, diffraction=None, surfbeat=None, scat=None, deactivate=None), prop=PROP(model_type='prop', scheme=BSBT(model_type='bsbt')), numeric=NUMERIC(model_type='numeric', stop=STOPC(model_type='stopc', dabs=0.05, drel=0.05, curvat=0.05, npnts=95.0, mode=NONSTAT(model_type='nonstat', mxitns=3), limiter=None), dirimpl=None, sigimpl=None, ctheta=None, csigma=None, setup=None), output=OUTPUT(model_type='output', frame=None, group=None, curve=None, ray=None, isoline=None, points=POINTS(model_type='points', sname='pts', xp=[114.0, 112.5, 115.0], yp=[-34.0, -26.0, -30.0]), ngrid=None, quantity=QUANTITIES(model_type='quantities', quantities=[QUANTITY(model_type='quantity', output=[<BlockOptions.DEPTH: 'depth'>, <BlockOptions.HSIGN: 'hsign'>, <BlockOptions.TPS: 'tps'>, <BlockOptions.DIR: 'dir'>, <BlockOptions.TM01: 'tm01'>], short=None, long=None, lexp=None, hexp=None, excv=-9.0, power=None, ref=None, fswell=None, noswll=None, fmin=None, fmax=None, coord=None), QUANTITY(model_type='quantity', output=[<BlockOptions.HSWELL: 'hswell'>], short=None, long=None, lexp=None, hexp=None, excv=None, power=None, ref=None, fswell=0.125, noswll=None, fmin=None, fmax=None, coord=None)]), output_options=None, block=BLOCK(model_type='block', sname='COMPGRID', fname='swangrid.nc', times=TimeRangeOpen(model_type='open', tbeg=datetime.datetime(1970, 1, 1, 0, 0), delt=datetime.timedelta(seconds=3600), tfmt=1, dfmt='hr', suffix='blk'), header=None, idla=None, output=[<BlockOptions.DEPTH: 'depth'>, <BlockOptions.WIND: 'wind'>, <BlockOptions.HSIGN: 'hsign'>, <BlockOptions.TPS: 'tps'>, <BlockOptions.DIR: 'dir'>], unit=None), table=TABLE(model_type='table', sname='pts', fname='swantable.txt', times=TimeRangeOpen(model_type='open', tbeg=datetime.datetime(1970, 1, 1, 0, 0), delt=datetime.timedelta(seconds=3600), tfmt=1, dfmt='hr', suffix='tbl'), format='header', output=[<BlockOptions.TIME: 'time'>, <BlockOptions.HSIGN: 'hsign'>, <BlockOptions.HSWELL: 'hswell'>, <BlockOptions.DIR: 'dir'>, <BlockOptions.TPS: 'tps'>, <BlockOptions.TM01: 'tm01'>]), specout=SPECOUT(model_type='specout', sname='pts', fname='swanspec.nc', times=TimeRangeOpen(model_type='open', tbeg=datetime.datetime(1970, 1, 1, 0, 0), delt=datetime.timedelta(seconds=3600), tfmt=1, dfmt='hr', suffix='spc'), dim=SPEC2D(model_type='spec2d'), freq=ABS(model_type='abs')), nestout=None, test=None), lockup=LOCKUP(model_type='lockup', compute=COMPUTE_NONSTAT(model_type='nonstat', times=NONSTATIONARY(model_type='nonstationary', tbeg=datetime.datetime(1970, 1, 1, 0, 0), delt=datetime.timedelta(seconds=3600), tfmt=1, dfmt='hr', suffix='c', tend=datetime.datetime(1970, 1, 2, 0, 0)), hotfile=HOTFILE(model_type='hotfile', fname=PosixPath('hotfile.txt'), format='free'), hottimes=[-1], suffix='_%Y%m%dT%H%M%S', initstat=True))))

QC config

Plot model grid and data to QC before generating the workspace

# Model grid

fig, ax = run.config.grid.plot(fscale=6)

# Model bathy

bottom = run.config.inpgrid.bottom
bottom._filter_grid(run.config.grid) # This isn't necessary since cropping is done by the SwanConfigComponents, it is just for plotting
fig, ax = bottom.plot(param="elevation", vmin=-5000, vmax=0, cmap="turbo_r", figsize=(5, 6))
fig, ax = run.config.grid.plot(ax=ax)

# Model winds

wind = run.config.inpgrid.input[0]
wind._filter_grid(run.config.grid) # This isn't necessary since cropping is done by the SwanConfigComponents, it is just for plotting
fig, ax = wind.plot(param="u10", isel={"time": 0}, vmin=-5, vmax=5, cmap="RdBu_r", figsize=(5, 6))
fig, ax = run.config.grid.plot(ax=ax)

Run the model

run()

INFO:rompy.model:
INFO:rompy.model:-----------------------------------------------------
INFO:rompy.model:Model settings:
INFO:rompy.model:
run_id: run1
period:
        Start: 2023-01-01 00:00:00
        End: 2023-01-02 00:00:00
        Duration: 1 day, 0:00:00
        Interval: 1:00:00
        Include End: True

output_dir: example_declarative
config: <class 'rompy.swan.config.SwanConfigComponents'>

INFO:rompy.model:-----------------------------------------------------
INFO:rompy.model:Generating model input files in example_declarative
INFO:rompy.swan.data:   Writing bottom to example_declarative/run1/bottom.grd
INFO:rompy.swan.data:   Writing wind to example_declarative/run1/wind.grd
INFO:rompy.model:
INFO:rompy.model:Successfully generated project in example_declarative
INFO:rompy.model:-----------------------------------------------------

'/source/csiro/rompy/notebooks/swan/example_declarative/run1'

Check the workspace

modeldir = Path(run.output_dir) / run.run_id

sorted(modeldir.glob("*"))

[PosixPath('example_declarative/run1/INPUT'),
 PosixPath('example_declarative/run1/PRINT'),
 PosixPath('example_declarative/run1/bottom.grd'),
 PosixPath('example_declarative/run1/hotfile_20230102T000000.txt'),
 PosixPath('example_declarative/run1/norm_end'),
 PosixPath('example_declarative/run1/swangrid.nc'),
 PosixPath('example_declarative/run1/swaninit'),
 PosixPath('example_declarative/run1/swanspec.nc'),
 PosixPath('example_declarative/run1/swantable.txt'),
 PosixPath('example_declarative/run1/wind.grd')]

input = modeldir / "INPUT"
print(input.read_text())

! Rompy SwanConfig
! Template: /source/csiro/rompy/rompy/templates/swancomp
! Generated: 2023-11-09 19:09:33.215731 on rafael-XPS by rguedes


! Startup -------------------------------------------------------------------------------------------------------------------------------------------------------------------------

PROJECT name='Test declarative' nr='run1' title1='Declarative definition of a Swan config with rompy'

SET level=0.0 depmin=0.05 NAUTICAL

MODE NONSTATIONARY TWODIMENSIONAL

COORDINATES SPHERICAL CCM

! Computational Grid --------------------------------------------------------------------------------------------------------------------------------------------------------------


CGRID REGULAR xpc=110.0 ypc=-35.2 alpc=4.0 xlenc=7.5 ylenc=12.5 mxc=14 myc=24 CIRCLE mdc=36 flow=0.04 fhigh=1.0

! Input Grids ---------------------------------------------------------------------------------------------------------------------------------------------------------------------

INPGRID BOTTOM REG 109.0 -36.0 0.0 9 14 1.0 1.0 EXC -99.0
READINP BOTTOM -1.0 'bottom.grd' 3 FREE

INPGRID WIND REG 110.0 -35.0 0.0 1 2 5.0 5.0 EXC -99.0 NONSTATION 20230101.000000 6.0 HR
READINP WIND 1.0 'wind.grd' 3 0 1 0 FREE



! Boundary and Initial conditions -------------------------------------------------------------------------------------------------------------------------------------------------



INITIAL DEFAULT


! Physics -------------------------------------------------------------------------------------------------------------------------------------------------------------------------

GEN3 WESTHUYSEN DRAG WU

QUADRUPL iquad=2

BREAKING CONSTANT gamma=0.73

FRICTION MADSEN kn=0.05

TRIAD


! Numerics ------------------------------------------------------------------------------------------------------------------------------------------------------------------------

PROP BSBT

NUMERIC STOPC dabs=0.05 drel=0.05 curvat=0.05 npnts=95.0 NONSTATIONARY mxitns=3


! Output --------------------------------------------------------------------------------------------------------------------------------------------------------------------------

POINTS sname='pts' &
    xp=114.0 yp=-34.0 &
    xp=112.5 yp=-26.0 &
    xp=115.0 yp=-30.0

QUANTITY DEPTH HSIGN TPS DIR TM01 excv=-9.0

QUANTITY HSWELL fswell=0.125

BLOCK sname='COMPGRID' fname='swangrid.nc' &
    DEPTH &
    WIND &
    HSIGN &
    TPS &
    DIR &
    OUTPUT tbegblk=20230101.000000 deltblk=1.0 HR

TABLE sname='pts' HEADER fname='swantable.txt' &
    TIME &
    HSIGN &
    HSWELL &
    DIR &
    TPS &
    TM01 &
    OUTPUT tbegtbl=20230101.000000 delttbl=1.0 HR

SPECOUT sname='pts' SPEC2D ABS fname='swanspec.nc' OUTPUT tbegspc=20230101.000000 deltspc=1.0 HR


! Lockup --------------------------------------------------------------------------------------------------------------------------------------------------------------------------

COMPUTE STATIONARY time=20230101.000000
COMPUTE NONSTATIONARY tbegc=20230101.000000 deltc=1.0 HR tendc=20230102.000000
HOTFILE fname='hotfile_20230102T000000.txt' FREE
STOP

Run the model

!docker run  -v ./example_declarative/run1:/home oceanum/swan:4141 swan.exe

 SWAN is preparing computation

 iteration    1; sweep 1
+iteration    1; sweep 2
+iteration    1; sweep 3
+iteration    1; sweep 4
 not possible to compute, first iteration

 iteration    2; sweep 1
+iteration    2; sweep 2
+iteration    2; sweep 3
+iteration    2; sweep 4
 accuracy OK in  45.28 % of wet grid points ( 95.00 % required)

 iteration    3; sweep 1
+iteration    3; sweep 2
+iteration    3; sweep 3
+iteration    3; sweep 4
 accuracy OK in   0.79 % of wet grid points ( 95.00 % required)

 iteration    4; sweep 1
+iteration    4; sweep 2
+iteration    4; sweep 3
+iteration    4; sweep 4
 accuracy OK in  56.70 % of wet grid points ( 95.00 % required)

 iteration    5; sweep 1
+iteration    5; sweep 2
+iteration    5; sweep 3
+iteration    5; sweep 4
 accuracy OK in  20.87 % of wet grid points ( 95.00 % required)

 iteration    6; sweep 1
+iteration    6; sweep 2
+iteration    6; sweep 3
+iteration    6; sweep 4
 accuracy OK in  85.83 % of wet grid points ( 95.00 % required)

 iteration    7; sweep 1
+iteration    7; sweep 2
+iteration    7; sweep 3
+iteration    7; sweep 4
 accuracy OK in  96.86 % of wet grid points ( 95.00 % required)

+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.010000   , step      1; iteration    1; sweep 1
+time 20230101.010000   , step      1; iteration    1; sweep 2
+time 20230101.010000   , step      1; iteration    1; sweep 3
+time 20230101.010000   , step      1; iteration    1; sweep 4
+time 20230101.010000   , step      1; iteration    2; sweep 1
+time 20230101.010000   , step      1; iteration    2; sweep 2
+time 20230101.010000   , step      1; iteration    2; sweep 3
+time 20230101.010000   , step      1; iteration    2; sweep 4
+time 20230101.010000   , step      1; iteration    3; sweep 1
+time 20230101.010000   , step      1; iteration    3; sweep 2
+time 20230101.010000   , step      1; iteration    3; sweep 3
+time 20230101.010000   , step      1; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.020000   , step      2; iteration    1; sweep 1
+time 20230101.020000   , step      2; iteration    1; sweep 2
+time 20230101.020000   , step      2; iteration    1; sweep 3
+time 20230101.020000   , step      2; iteration    1; sweep 4
+time 20230101.020000   , step      2; iteration    2; sweep 1
+time 20230101.020000   , step      2; iteration    2; sweep 2
+time 20230101.020000   , step      2; iteration    2; sweep 3
+time 20230101.020000   , step      2; iteration    2; sweep 4
+time 20230101.020000   , step      2; iteration    3; sweep 1
+time 20230101.020000   , step      2; iteration    3; sweep 2
+time 20230101.020000   , step      2; iteration    3; sweep 3
+time 20230101.020000   , step      2; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.030000   , step      3; iteration    1; sweep 1
+time 20230101.030000   , step      3; iteration    1; sweep 2
+time 20230101.030000   , step      3; iteration    1; sweep 3
+time 20230101.030000   , step      3; iteration    1; sweep 4
+time 20230101.030000   , step      3; iteration    2; sweep 1
+time 20230101.030000   , step      3; iteration    2; sweep 2
+time 20230101.030000   , step      3; iteration    2; sweep 3
+time 20230101.030000   , step      3; iteration    2; sweep 4
+time 20230101.030000   , step      3; iteration    3; sweep 1
+time 20230101.030000   , step      3; iteration    3; sweep 2
+time 20230101.030000   , step      3; iteration    3; sweep 3
+time 20230101.030000   , step      3; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.040000   , step      4; iteration    1; sweep 1
+time 20230101.040000   , step      4; iteration    1; sweep 2
+time 20230101.040000   , step      4; iteration    1; sweep 3
+time 20230101.040000   , step      4; iteration    1; sweep 4
+time 20230101.040000   , step      4; iteration    2; sweep 1
+time 20230101.040000   , step      4; iteration    2; sweep 2
+time 20230101.040000   , step      4; iteration    2; sweep 3
+time 20230101.040000   , step      4; iteration    2; sweep 4
+time 20230101.040000   , step      4; iteration    3; sweep 1
+time 20230101.040000   , step      4; iteration    3; sweep 2
+time 20230101.040000   , step      4; iteration    3; sweep 3
+time 20230101.040000   , step      4; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.050000   , step      5; iteration    1; sweep 1
+time 20230101.050000   , step      5; iteration    1; sweep 2
+time 20230101.050000   , step      5; iteration    1; sweep 3
+time 20230101.050000   , step      5; iteration    1; sweep 4
+time 20230101.050000   , step      5; iteration    2; sweep 1
+time 20230101.050000   , step      5; iteration    2; sweep 2
+time 20230101.050000   , step      5; iteration    2; sweep 3
+time 20230101.050000   , step      5; iteration    2; sweep 4
+time 20230101.050000   , step      5; iteration    3; sweep 1
+time 20230101.050000   , step      5; iteration    3; sweep 2
+time 20230101.050000   , step      5; iteration    3; sweep 3
+time 20230101.050000   , step      5; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.060000   , step      6; iteration    1; sweep 1
+time 20230101.060000   , step      6; iteration    1; sweep 2
+time 20230101.060000   , step      6; iteration    1; sweep 3
+time 20230101.060000   , step      6; iteration    1; sweep 4
+time 20230101.060000   , step      6; iteration    2; sweep 1
+time 20230101.060000   , step      6; iteration    2; sweep 2
+time 20230101.060000   , step      6; iteration    2; sweep 3
+time 20230101.060000   , step      6; iteration    2; sweep 4
+time 20230101.060000   , step      6; iteration    3; sweep 1
+time 20230101.060000   , step      6; iteration    3; sweep 2
+time 20230101.060000   , step      6; iteration    3; sweep 3
+time 20230101.060000   , step      6; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.070000   , step      7; iteration    1; sweep 1
+time 20230101.070000   , step      7; iteration    1; sweep 2
+time 20230101.070000   , step      7; iteration    1; sweep 3
+time 20230101.070000   , step      7; iteration    1; sweep 4
+time 20230101.070000   , step      7; iteration    2; sweep 1
+time 20230101.070000   , step      7; iteration    2; sweep 2
+time 20230101.070000   , step      7; iteration    2; sweep 3
+time 20230101.070000   , step      7; iteration    2; sweep 4
+time 20230101.070000   , step      7; iteration    3; sweep 1
+time 20230101.070000   , step      7; iteration    3; sweep 2
+time 20230101.070000   , step      7; iteration    3; sweep 3
+time 20230101.070000   , step      7; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.080000   , step      8; iteration    1; sweep 1
+time 20230101.080000   , step      8; iteration    1; sweep 2
+time 20230101.080000   , step      8; iteration    1; sweep 3
+time 20230101.080000   , step      8; iteration    1; sweep 4
+time 20230101.080000   , step      8; iteration    2; sweep 1
+time 20230101.080000   , step      8; iteration    2; sweep 2
+time 20230101.080000   , step      8; iteration    2; sweep 3
+time 20230101.080000   , step      8; iteration    2; sweep 4
+time 20230101.080000   , step      8; iteration    3; sweep 1
+time 20230101.080000   , step      8; iteration    3; sweep 2
+time 20230101.080000   , step      8; iteration    3; sweep 3
+time 20230101.080000   , step      8; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.090000   , step      9; iteration    1; sweep 1
+time 20230101.090000   , step      9; iteration    1; sweep 2
+time 20230101.090000   , step      9; iteration    1; sweep 3
+time 20230101.090000   , step      9; iteration    1; sweep 4
+time 20230101.090000   , step      9; iteration    2; sweep 1
+time 20230101.090000   , step      9; iteration    2; sweep 2
+time 20230101.090000   , step      9; iteration    2; sweep 3
+time 20230101.090000   , step      9; iteration    2; sweep 4
+time 20230101.090000   , step      9; iteration    3; sweep 1
+time 20230101.090000   , step      9; iteration    3; sweep 2
+time 20230101.090000   , step      9; iteration    3; sweep 3
+time 20230101.090000   , step      9; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.100000   , step     10; iteration    1; sweep 1
+time 20230101.100000   , step     10; iteration    1; sweep 2
+time 20230101.100000   , step     10; iteration    1; sweep 3
+time 20230101.100000   , step     10; iteration    1; sweep 4
+time 20230101.100000   , step     10; iteration    2; sweep 1
+time 20230101.100000   , step     10; iteration    2; sweep 2
+time 20230101.100000   , step     10; iteration    2; sweep 3
+time 20230101.100000   , step     10; iteration    2; sweep 4
+time 20230101.100000   , step     10; iteration    3; sweep 1
+time 20230101.100000   , step     10; iteration    3; sweep 2
+time 20230101.100000   , step     10; iteration    3; sweep 3
+time 20230101.100000   , step     10; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.110000   , step     11; iteration    1; sweep 1
+time 20230101.110000   , step     11; iteration    1; sweep 2
+time 20230101.110000   , step     11; iteration    1; sweep 3
+time 20230101.110000   , step     11; iteration    1; sweep 4
+time 20230101.110000   , step     11; iteration    2; sweep 1
+time 20230101.110000   , step     11; iteration    2; sweep 2
+time 20230101.110000   , step     11; iteration    2; sweep 3
+time 20230101.110000   , step     11; iteration    2; sweep 4
+time 20230101.110000   , step     11; iteration    3; sweep 1
+time 20230101.110000   , step     11; iteration    3; sweep 2
+time 20230101.110000   , step     11; iteration    3; sweep 3
+time 20230101.110000   , step     11; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.120000   , step     12; iteration    1; sweep 1
+time 20230101.120000   , step     12; iteration    1; sweep 2
+time 20230101.120000   , step     12; iteration    1; sweep 3
+time 20230101.120000   , step     12; iteration    1; sweep 4
+time 20230101.120000   , step     12; iteration    2; sweep 1
+time 20230101.120000   , step     12; iteration    2; sweep 2
+time 20230101.120000   , step     12; iteration    2; sweep 3
+time 20230101.120000   , step     12; iteration    2; sweep 4
+time 20230101.120000   , step     12; iteration    3; sweep 1
+time 20230101.120000   , step     12; iteration    3; sweep 2
+time 20230101.120000   , step     12; iteration    3; sweep 3
+time 20230101.120000   , step     12; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.130000   , step     13; iteration    1; sweep 1
+time 20230101.130000   , step     13; iteration    1; sweep 2
+time 20230101.130000   , step     13; iteration    1; sweep 3
+time 20230101.130000   , step     13; iteration    1; sweep 4
+time 20230101.130000   , step     13; iteration    2; sweep 1
+time 20230101.130000   , step     13; iteration    2; sweep 2
+time 20230101.130000   , step     13; iteration    2; sweep 3
+time 20230101.130000   , step     13; iteration    2; sweep 4
+time 20230101.130000   , step     13; iteration    3; sweep 1
+time 20230101.130000   , step     13; iteration    3; sweep 2
+time 20230101.130000   , step     13; iteration    3; sweep 3
+time 20230101.130000   , step     13; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.140000   , step     14; iteration    1; sweep 1
+time 20230101.140000   , step     14; iteration    1; sweep 2
+time 20230101.140000   , step     14; iteration    1; sweep 3
+time 20230101.140000   , step     14; iteration    1; sweep 4
+time 20230101.140000   , step     14; iteration    2; sweep 1
+time 20230101.140000   , step     14; iteration    2; sweep 2
+time 20230101.140000   , step     14; iteration    2; sweep 3
+time 20230101.140000   , step     14; iteration    2; sweep 4
+time 20230101.140000   , step     14; iteration    3; sweep 1
+time 20230101.140000   , step     14; iteration    3; sweep 2
+time 20230101.140000   , step     14; iteration    3; sweep 3
+time 20230101.140000   , step     14; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.150000   , step     15; iteration    1; sweep 1
+time 20230101.150000   , step     15; iteration    1; sweep 2
+time 20230101.150000   , step     15; iteration    1; sweep 3
+time 20230101.150000   , step     15; iteration    1; sweep 4
+time 20230101.150000   , step     15; iteration    2; sweep 1
+time 20230101.150000   , step     15; iteration    2; sweep 2
+time 20230101.150000   , step     15; iteration    2; sweep 3
+time 20230101.150000   , step     15; iteration    2; sweep 4
+time 20230101.150000   , step     15; iteration    3; sweep 1
+time 20230101.150000   , step     15; iteration    3; sweep 2
+time 20230101.150000   , step     15; iteration    3; sweep 3
+time 20230101.150000   , step     15; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.160000   , step     16; iteration    1; sweep 1
+time 20230101.160000   , step     16; iteration    1; sweep 2
+time 20230101.160000   , step     16; iteration    1; sweep 3
+time 20230101.160000   , step     16; iteration    1; sweep 4
+time 20230101.160000   , step     16; iteration    2; sweep 1
+time 20230101.160000   , step     16; iteration    2; sweep 2
+time 20230101.160000   , step     16; iteration    2; sweep 3
+time 20230101.160000   , step     16; iteration    2; sweep 4
+time 20230101.160000   , step     16; iteration    3; sweep 1
+time 20230101.160000   , step     16; iteration    3; sweep 2
+time 20230101.160000   , step     16; iteration    3; sweep 3
+time 20230101.160000   , step     16; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.170000   , step     17; iteration    1; sweep 1
+time 20230101.170000   , step     17; iteration    1; sweep 2
+time 20230101.170000   , step     17; iteration    1; sweep 3
+time 20230101.170000   , step     17; iteration    1; sweep 4
+time 20230101.170000   , step     17; iteration    2; sweep 1
+time 20230101.170000   , step     17; iteration    2; sweep 2
+time 20230101.170000   , step     17; iteration    2; sweep 3
+time 20230101.170000   , step     17; iteration    2; sweep 4
+time 20230101.170000   , step     17; iteration    3; sweep 1
+time 20230101.170000   , step     17; iteration    3; sweep 2
+time 20230101.170000   , step     17; iteration    3; sweep 3
+time 20230101.170000   , step     17; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.180000   , step     18; iteration    1; sweep 1
+time 20230101.180000   , step     18; iteration    1; sweep 2
+time 20230101.180000   , step     18; iteration    1; sweep 3
+time 20230101.180000   , step     18; iteration    1; sweep 4
+time 20230101.180000   , step     18; iteration    2; sweep 1
+time 20230101.180000   , step     18; iteration    2; sweep 2
+time 20230101.180000   , step     18; iteration    2; sweep 3
+time 20230101.180000   , step     18; iteration    2; sweep 4
+time 20230101.180000   , step     18; iteration    3; sweep 1
+time 20230101.180000   , step     18; iteration    3; sweep 2
+time 20230101.180000   , step     18; iteration    3; sweep 3
+time 20230101.180000   , step     18; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.190000   , step     19; iteration    1; sweep 1
+time 20230101.190000   , step     19; iteration    1; sweep 2
+time 20230101.190000   , step     19; iteration    1; sweep 3
+time 20230101.190000   , step     19; iteration    1; sweep 4
+time 20230101.190000   , step     19; iteration    2; sweep 1
+time 20230101.190000   , step     19; iteration    2; sweep 2
+time 20230101.190000   , step     19; iteration    2; sweep 3
+time 20230101.190000   , step     19; iteration    2; sweep 4
+time 20230101.190000   , step     19; iteration    3; sweep 1
+time 20230101.190000   , step     19; iteration    3; sweep 2
+time 20230101.190000   , step     19; iteration    3; sweep 3
+time 20230101.190000   , step     19; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.200000   , step     20; iteration    1; sweep 1
+time 20230101.200000   , step     20; iteration    1; sweep 2
+time 20230101.200000   , step     20; iteration    1; sweep 3
+time 20230101.200000   , step     20; iteration    1; sweep 4
+time 20230101.200000   , step     20; iteration    2; sweep 1
+time 20230101.200000   , step     20; iteration    2; sweep 2
+time 20230101.200000   , step     20; iteration    2; sweep 3
+time 20230101.200000   , step     20; iteration    2; sweep 4
+time 20230101.200000   , step     20; iteration    3; sweep 1
+time 20230101.200000   , step     20; iteration    3; sweep 2
+time 20230101.200000   , step     20; iteration    3; sweep 3
+time 20230101.200000   , step     20; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.210000   , step     21; iteration    1; sweep 1
+time 20230101.210000   , step     21; iteration    1; sweep 2
+time 20230101.210000   , step     21; iteration    1; sweep 3
+time 20230101.210000   , step     21; iteration    1; sweep 4
+time 20230101.210000   , step     21; iteration    2; sweep 1
+time 20230101.210000   , step     21; iteration    2; sweep 2
+time 20230101.210000   , step     21; iteration    2; sweep 3
+time 20230101.210000   , step     21; iteration    2; sweep 4
+time 20230101.210000   , step     21; iteration    3; sweep 1
+time 20230101.210000   , step     21; iteration    3; sweep 2
+time 20230101.210000   , step     21; iteration    3; sweep 3
+time 20230101.210000   , step     21; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.220000   , step     22; iteration    1; sweep 1
+time 20230101.220000   , step     22; iteration    1; sweep 2
+time 20230101.220000   , step     22; iteration    1; sweep 3
+time 20230101.220000   , step     22; iteration    1; sweep 4
+time 20230101.220000   , step     22; iteration    2; sweep 1
+time 20230101.220000   , step     22; iteration    2; sweep 2
+time 20230101.220000   , step     22; iteration    2; sweep 3
+time 20230101.220000   , step     22; iteration    2; sweep 4
+time 20230101.220000   , step     22; iteration    3; sweep 1
+time 20230101.220000   , step     22; iteration    3; sweep 2
+time 20230101.220000   , step     22; iteration    3; sweep 3
+time 20230101.220000   , step     22; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230101.230000   , step     23; iteration    1; sweep 1
+time 20230101.230000   , step     23; iteration    1; sweep 2
+time 20230101.230000   , step     23; iteration    1; sweep 3
+time 20230101.230000   , step     23; iteration    1; sweep 4
+time 20230101.230000   , step     23; iteration    2; sweep 1
+time 20230101.230000   , step     23; iteration    2; sweep 2
+time 20230101.230000   , step     23; iteration    2; sweep 3
+time 20230101.230000   , step     23; iteration    2; sweep 4
+time 20230101.230000   , step     23; iteration    3; sweep 1
+time 20230101.230000   , step     23; iteration    3; sweep 2
+time 20230101.230000   , step     23; iteration    3; sweep 3
+time 20230101.230000   , step     23; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3
+time 20230102.000000   , step     24; iteration    1; sweep 1
+time 20230102.000000   , step     24; iteration    1; sweep 2
+time 20230102.000000   , step     24; iteration    1; sweep 3
+time 20230102.000000   , step     24; iteration    1; sweep 4
+time 20230102.000000   , step     24; iteration    2; sweep 1
+time 20230102.000000   , step     24; iteration    2; sweep 2
+time 20230102.000000   , step     24; iteration    2; sweep 3
+time 20230102.000000   , step     24; iteration    2; sweep 4
+time 20230102.000000   , step     24; iteration    3; sweep 1
+time 20230102.000000   , step     24; iteration    3; sweep 2
+time 20230102.000000   , step     24; iteration    3; sweep 3
+time 20230102.000000   , step     24; iteration    3; sweep 4
+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3

Plot outputs

import os
import numpy as np
import pandas as pd
import xarray as xr
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from wavespectra import read_ncswan
from wavespectra.core.swan import read_tab

pd.set_option("display.notebook_repr_html", False)

modeldir = Path(run.output_dir) / run.run_id

sorted(modeldir.glob("*"))

[PosixPath('example_declarative/run1/INPUT'),
 PosixPath('example_declarative/run1/PRINT'),
 PosixPath('example_declarative/run1/bottom.grd'),
 PosixPath('example_declarative/run1/hotfile_20230102T000000.txt'),
 PosixPath('example_declarative/run1/norm_end'),
 PosixPath('example_declarative/run1/swangrid.nc'),
 PosixPath('example_declarative/run1/swaninit'),
 PosixPath('example_declarative/run1/swanspec.nc'),
 PosixPath('example_declarative/run1/swantable.txt'),
 PosixPath('example_declarative/run1/wind.grd')]

# Gridded output

dsgrid = xr.open_dataset(modeldir / run.config.output.block.fname)
dsgrid

<xarray.Dataset>
Dimensions:    (time: 25, yc: 25, xc: 15)
Coordinates:
  * time       (time) datetime64[ns] 2023-01-01 ... 2023-01-02
    longitude  (yc, xc) float32 ...
    latitude   (yc, xc) float32 ...
Dimensions without coordinates: yc, xc
Data variables:
    depth      (time, yc, xc) float32 ...
    xwnd       (time, yc, xc) float32 ...
    ywnd       (time, yc, xc) float32 ...
    hs         (time, yc, xc) float32 ...
    tps        (time, yc, xc) float32 ...
    theta0     (time, yc, xc) float32 ...
Attributes:
    Conventions:             CF-1.5
    History:                 Created with agioncmd version 1.5
    Directional_convention:  nautical
    project:                 Test declarative
    run:                     run1

xarray.Dataset

• Dimensions:
  • time: 25
  • yc: 25
  • xc: 15
• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    2023-01-01 ... 2023-01-02

    standard_name :

    time

    long_name :

    time

    array(['2023-01-01T00:00:00.000000000', '2023-01-01T01:00:00.000000000',
           '2023-01-01T02:00:00.000000000', '2023-01-01T03:00:00.000000000',
           '2023-01-01T04:00:00.000000000', '2023-01-01T05:00:00.000000000',
           '2023-01-01T06:00:00.000000000', '2023-01-01T07:00:00.000000000',
           '2023-01-01T08:00:00.000000000', '2023-01-01T09:00:00.000000000',
           '2023-01-01T10:00:00.000000000', '2023-01-01T11:00:00.000000000',
           '2023-01-01T12:00:00.000000000', '2023-01-01T13:00:00.000000000',
           '2023-01-01T14:00:00.000000000', '2023-01-01T15:00:00.000000000',
           '2023-01-01T16:00:00.000000000', '2023-01-01T17:00:00.000000000',
           '2023-01-01T18:00:00.000000000', '2023-01-01T19:00:00.000000000',
           '2023-01-01T20:00:00.000000000', '2023-01-01T21:00:00.000000000',
           '2023-01-01T22:00:00.000000000', '2023-01-01T23:00:00.000000000',
           '2023-01-02T00:00:00.000000000'], dtype='datetime64[ns]')

  • longitude
    (yc, xc)
    float32
    ...

    units :

    degrees_east

    long_name :

    longitude

    standard_name :

    longitude

    [375 values with dtype=float32]

  • latitude
    (yc, xc)
    float32
    ...

    units :

    degrees_north

    long_name :

    latitude

    standard_name :

    latitude

    [375 values with dtype=float32]

• Data variables: (6)
  • depth
    (time, yc, xc)
    float32
    ...

    units :

    m

    standard_name :

    sea_floor_depth_below_sea_level

    long_name :

    depth below mean sea level

    [9375 values with dtype=float32]

  • xwnd
    (time, yc, xc)
    float32
    ...

    units :

    m s-1

    standard_name :

    eastward_wind

    long_name :

    U-Component of Wind

    [9375 values with dtype=float32]

  • ywnd
    (time, yc, xc)
    float32
    ...

    units :

    m s-1

    standard_name :

    northward_wind

    long_name :

    V-Component of Wind

    [9375 values with dtype=float32]

  • hs
    (time, yc, xc)
    float32
    ...

    units :

    m

    standard_name :

    sea_surface_wave_significant_height

    long_name :

    hs

    [9375 values with dtype=float32]

  • tps
    (time, yc, xc)
    float32
    ...

    units :

    s

    long_name :

    tps

    [9375 values with dtype=float32]

  • theta0
    (time, yc, xc)
    float32
    ...

    units :

    degrees

    standard_name :

    sea_surface_wave_from_direction

    long_name :

    theta0

    [9375 values with dtype=float32]

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-01-01 00:00:00', '2023-01-01 01:00:00',
                   '2023-01-01 02:00:00', '2023-01-01 03:00:00',
                   '2023-01-01 04:00:00', '2023-01-01 05:00:00',
                   '2023-01-01 06:00:00', '2023-01-01 07:00:00',
                   '2023-01-01 08:00:00', '2023-01-01 09:00:00',
                   '2023-01-01 10:00:00', '2023-01-01 11:00:00',
                   '2023-01-01 12:00:00', '2023-01-01 13:00:00',
                   '2023-01-01 14:00:00', '2023-01-01 15:00:00',
                   '2023-01-01 16:00:00', '2023-01-01 17:00:00',
                   '2023-01-01 18:00:00', '2023-01-01 19:00:00',
                   '2023-01-01 20:00:00', '2023-01-01 21:00:00',
                   '2023-01-01 22:00:00', '2023-01-01 23:00:00',
                   '2023-01-02 00:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (5)

  Conventions :

  CF-1.5

  History :

  Created with agioncmd version 1.5

  Directional_convention :

  nautical

  project :

  Test declarative

  run :

  run1

# Spectra output

dspec = read_ncswan(modeldir / run.config.output.specout.fname)
dspec

<xarray.Dataset>
Dimensions:  (time: 25, site: 3, freq: 35, dir: 36)
Coordinates:
  * time     (time) datetime64[ns] 2023-01-01 2023-01-01T01:00:00 ... 2023-01-02
  * freq     (freq) float32 0.04 0.04397 0.04834 0.05314 ... 0.8275 0.9097 1.0
  * dir      (dir) float32 261.0 251.0 241.0 231.0 ... 301.0 291.0 281.0 271.0
  * site     (site) int64 1 2 3
Data variables:
    lon      (site) float32 dask.array<chunksize=(3,), meta=np.ndarray>
    lat      (site) float32 dask.array<chunksize=(3,), meta=np.ndarray>
    efth     (time, site, freq, dir) float32 dask.array<chunksize=(25, 3, 35, 36), meta=np.ndarray>
    dpt      (time, site) float32 dask.array<chunksize=(25, 3), meta=np.ndarray>
    wspd     (time, site) float32 dask.array<chunksize=(25, 3), meta=np.ndarray>
    wdir     (time, site) float32 dask.array<chunksize=(25, 3), meta=np.ndarray>
Attributes:
    Conventions:             CF-1.5
    History:                 Created with agioncmd version 1.5
    Directional_convention:  nautical
    project:                 Test declarative
    model:                   41.41
    run:                     run1

xarray.Dataset

• Dimensions:
  • time: 25
  • site: 3
  • freq: 35
  • dir: 36
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2023-01-01 ... 2023-01-02

    standard_name :

    time

    array(['2023-01-01T00:00:00.000000000', '2023-01-01T01:00:00.000000000',
           '2023-01-01T02:00:00.000000000', '2023-01-01T03:00:00.000000000',
           '2023-01-01T04:00:00.000000000', '2023-01-01T05:00:00.000000000',
           '2023-01-01T06:00:00.000000000', '2023-01-01T07:00:00.000000000',
           '2023-01-01T08:00:00.000000000', '2023-01-01T09:00:00.000000000',
           '2023-01-01T10:00:00.000000000', '2023-01-01T11:00:00.000000000',
           '2023-01-01T12:00:00.000000000', '2023-01-01T13:00:00.000000000',
           '2023-01-01T14:00:00.000000000', '2023-01-01T15:00:00.000000000',
           '2023-01-01T16:00:00.000000000', '2023-01-01T17:00:00.000000000',
           '2023-01-01T18:00:00.000000000', '2023-01-01T19:00:00.000000000',
           '2023-01-01T20:00:00.000000000', '2023-01-01T21:00:00.000000000',
           '2023-01-01T22:00:00.000000000', '2023-01-01T23:00:00.000000000',
           '2023-01-02T00:00:00.000000000'], dtype='datetime64[ns]')

  • freq
    (freq)
    float32
    0.04 0.04397 0.04834 ... 0.9097 1.0

    standard_name :

    sea_surface_wave_frequency

    units :

    Hz

    array([0.04    , 0.043972, 0.048338, 0.053138, 0.058415, 0.064215, 0.070592,
           0.077602, 0.085307, 0.093778, 0.103091, 0.113327, 0.124581, 0.136951,
           0.15055 , 0.1655  , 0.181934, 0.2     , 0.21986 , 0.241692, 0.265691,
           0.292074, 0.321077, 0.35296 , 0.388008, 0.426537, 0.468892, 0.515452,
           0.566636, 0.622902, 0.684756, 0.752752, 0.827499, 0.909669, 0.999999],
          dtype=float32)

  • dir
    (dir)
    float32
    261.0 251.0 241.0 ... 281.0 271.0

    standard_name :

    sea_surface_wave_from_direction

    units :

    degree

    array([261.      , 251.      , 241.      , 231.      , 221.      , 211.      ,
           201.      , 191.      , 181.      , 171.      , 160.99998 , 151.      ,
           141.      , 131.      , 120.999985, 110.999985, 100.99999 ,  90.99999 ,
            80.99999 ,  70.999985,  60.999985,  50.999985,  40.99999 ,  30.999989,
            20.99999 ,  10.999992,   0.999994, 350.99997 , 340.99997 , 330.99997 ,
           320.99997 , 310.99997 , 300.99997 , 290.99997 , 281.      , 271.      ],
          dtype=float32)

  • site
    (site)
    int64
    1 2 3

    standard_name :

    site

    units :

    array([1, 2, 3])

• Data variables: (6)
  • lon
    (site)
    float32
    dask.array<chunksize=(3,), meta=np.ndarray>

    standard_name :

    longitude

    units :

    degrees_east

    Array Chunk Bytes 12 B 12 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lat
    (site)
    float32
    dask.array<chunksize=(3,), meta=np.ndarray>

    standard_name :

    latitude

    units :

    degrees_north

    Array Chunk Bytes 12 B 12 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • efth
    (time, site, freq, dir)
    float32
    dask.array<chunksize=(25, 3, 35, 36), meta=np.ndarray>

    standard_name :

    sea_surface_wave_directional_variance_spectral_density

    units :

    m2 s degree-1

    Array Chunk Bytes 369.14 kiB 369.14 kiB Shape (25, 3, 35, 36) (25, 3, 35, 36) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

  • dpt
    (time, site)
    float32
    dask.array<chunksize=(25, 3), meta=np.ndarray>

    standard_name :

    sea_floor_depth_below_sea_surface

    units :

    m

    Array Chunk Bytes 300 B 300 B Shape (25, 3) (25, 3) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wspd
    (time, site)
    float32
    dask.array<chunksize=(25, 3), meta=np.ndarray>

    standard_name :

    wind_speed_at_10m_above_ground_level

    units :

    m s-1

    Array Chunk Bytes 300 B 300 B Shape (25, 3) (25, 3) Dask graph 1 chunks in 8 graph layers Data type float32 numpy.ndarray

  • wdir
    (time, site)
    float32
    dask.array<chunksize=(25, 3), meta=np.ndarray>

    standard_name :

    wind_from_direction_at_10m_above_ground_level

    units :

    degree

    Array Chunk Bytes 300 B 300 B Shape (25, 3) (25, 3) Dask graph 1 chunks in 8 graph layers Data type float32 numpy.ndarray

• Indexes: (4)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-01-01 00:00:00', '2023-01-01 01:00:00',
                   '2023-01-01 02:00:00', '2023-01-01 03:00:00',
                   '2023-01-01 04:00:00', '2023-01-01 05:00:00',
                   '2023-01-01 06:00:00', '2023-01-01 07:00:00',
                   '2023-01-01 08:00:00', '2023-01-01 09:00:00',
                   '2023-01-01 10:00:00', '2023-01-01 11:00:00',
                   '2023-01-01 12:00:00', '2023-01-01 13:00:00',
                   '2023-01-01 14:00:00', '2023-01-01 15:00:00',
                   '2023-01-01 16:00:00', '2023-01-01 17:00:00',
                   '2023-01-01 18:00:00', '2023-01-01 19:00:00',
                   '2023-01-01 20:00:00', '2023-01-01 21:00:00',
                   '2023-01-01 22:00:00', '2023-01-01 23:00:00',
                   '2023-01-02 00:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • freq
    PandasIndex

    PandasIndex(Index([ 0.03999999910593033,   0.0439719632267952,  0.04833833873271942,
            0.05313829332590103, 0.058414872735738754,  0.06421541422605515,
            0.07059194892644882,  0.07760166376829147,  0.08530744165182114,
            0.09377838671207428,  0.10309050232172012,  0.11332729458808899,
            0.12458059191703796,  0.13695132732391357,   0.1505504697561264,
             0.1655000001192093,  0.18193399906158447,   0.1999998688697815,
            0.21985965967178345,  0.24169152975082397,  0.26569128036499023,
            0.29207417368888855,   0.3210768699645996,   0.3529595136642456,
             0.3880080580711365,     0.42653688788414,  0.46889159083366394,
             0.5154520869255066,   0.5666360259056091,   0.6229023933410645,
             0.6847560405731201,   0.7527517080307007,    0.827499270439148,
             0.9096691608428955,   0.9999985098838806],
          dtype='float32', name='freq'))

  • dir
    PandasIndex

    PandasIndex(Index([             261.0,              251.0,              241.0,
                        231.0,              221.0,              211.0,
                        201.0,              191.0,              181.0,
                        171.0, 160.99998474121094,              151.0,
                        141.0,              131.0, 120.99998474121094,
           110.99998474121094, 100.99999237060547,  90.99999237060547,
            80.99999237060547,  70.99998474121094,  60.99998474121094,
            50.99998474121094,   40.9999885559082, 30.999988555908203,
           20.999990463256836, 10.999992370605469, 0.9999943375587463,
            350.9999694824219,  340.9999694824219,  330.9999694824219,
            320.9999694824219,  310.9999694824219,  300.9999694824219,
            290.9999694824219,              281.0,              271.0],
          dtype='float32', name='dir'))

  • site
    PandasIndex

    PandasIndex(Index([1, 2, 3], dtype='int64', name='site'))

• Attributes: (6)

  Conventions :

  CF-1.5

  History :

  Created with agioncmd version 1.5

  Directional_convention :

  nautical

  project :

  Test declarative

  model :

  41.41

  run :

  run1

os.system(f"head -n 15 {modeldir / run.config.output.table.fname}")

%
%
% Run:run1  Table:pts               SWAN version:41.41
%
%       Time               Hsig          Hswell        Dir           TPsmoo        Tm01
%       [ ]                [m]           [m]           [degr]        [sec]         [sec]
%
20230101.000000          0.61126       0.00002       183.186        3.6992        2.7202
20230101.000000          1.07937       0.03900       176.467        4.9832        3.7063
20230101.000000         -9.00000      -9.00000        -9.000       -9.0000       -9.0000
20230101.010000          0.61275       0.00002       182.850        3.7210        2.7083
20230101.010000          1.07086       0.03935       176.350        5.0704        3.6928
20230101.010000         -9.00000      -9.00000        -9.000       -9.0000       -9.0000
20230101.020000          0.61664       0.00002       182.787        3.7145        2.7083
20230101.020000          1.06328       0.03973       176.565        5.0631        3.6886

0

# Timeseries output (keep 1st site only)

df = read_tab(modeldir / run.config.output.table.fname)

df["time"] = df.index
df = df.drop_duplicates("time", keep="first").drop("time", axis=1)
df.head()

                        Hsig   Hswell      Dir  TPsmoo    Tm01
time
2023-01-01 00:00:00  0.61126  0.00002  183.186  3.6992  2.7202
2023-01-01 01:00:00  0.61275  0.00002  182.850  3.7210  2.7083
2023-01-01 02:00:00  0.61664  0.00002  182.787  3.7145  2.7083
2023-01-01 03:00:00  0.61837  0.00001  182.792  3.7198  2.7177
2023-01-01 04:00:00  0.62287  0.00001  182.762  3.7230  2.7247

Plot model depth

fig, ax = plt.subplots(subplot_kw=dict(projection=ccrs.PlateCarree()))
p = dsgrid.depth.isel(time=0, drop=True).plot(ax=ax, x="longitude", y="latitude")
ax.coastlines();

Plot gridded Hs

f = dsgrid.hs.isel(time=slice(0, -1, 3)).plot(
    x="longitude",
    y="latitude",
    col="time",
    col_wrap=4,
    vmin=0,
    vmax=2,
    cmap="turbo",
    subplot_kws=dict(projection=ccrs.PlateCarree()),
)
f.map(lambda: plt.gca().coastlines());

Plot gridded wind

u = dsgrid.xwnd.isel(time=slice(0, -1, 3))
v = dsgrid.ywnd.isel(time=slice(0, -1, 3))
f = np.sqrt(u ** 2 + v ** 2).plot(
    x="longitude",
    y="latitude",
    col="time",
    col_wrap=4,
    vmin=2,
    vmax=12,
    cmap="jet",
    cbar_kwargs={"label": "Wind speed (m/s)"},
    subplot_kws=dict(projection=ccrs.PlateCarree()),
)
for ax, time in zip(f.axs.flat, u.time):
    ax.coastlines()
    ax.quiver(u.longitude, u.latitude, u.sel(time=time), v.sel(time=time), scale=150)
    ax.plot(dspec.isel(site=0).lon, dspec.isel(site=0).lat, "ok")

Plot spectra

p = dspec.isel(site=0, time=slice(0, -1, 3)).spec.plot(col="time", col_wrap=4)

Plot timeseries

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))

df.Hsig.plot(ax=ax1, label="From table", linewidth=5)
dspec.isel(site=0).spec.hs().to_pandas().plot(ax=ax1, label="From spectra")
ax1.set_ylabel("Hs (m)")
l = ax1.legend()

df.TPsmoo.plot(ax=ax2, label="From table", linewidth=5)
dspec.isel(site=0).spec.tp(smooth=True).to_pandas().plot(ax=ax2, label="From spectra")
ax2.set_ylabel("Tp (s)")
l = ax2.legend()