SWAN procedural example¶

In this notebook we will use the SWAN Components and data objects to define a SWAN workspace

In [1]:

%load_ext autoreload
%autoreload 2

import shutil
from pathlib import Path
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
from cartopy import crs as ccrs

import warnings
warnings.filterwarnings("ignore")

%load_ext autoreload %autoreload 2 import shutil from pathlib import Path import numpy as np import xarray as xr import matplotlib.pyplot as plt from cartopy import crs as ccrs import warnings warnings.filterwarnings("ignore")

Workspace basepath¶

In [2]:

workdir = Path("example_procedural")
shutil.rmtree(workdir, ignore_errors=True)
workdir.mkdir()

workdir = Path("example_procedural") shutil.rmtree(workdir, ignore_errors=True) workdir.mkdir()

Model Grid¶

In [3]:

from rompy_swan.grid import SwanGrid

grid = SwanGrid(
    x0=110.0,
    y0=-35.2,
    rot=4.0,
    dx=0.5,
    dy=0.5,
    nx=15,
    ny=25,
)

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

from rompy_swan.grid import SwanGrid grid = SwanGrid( x0=110.0, y0=-35.2, rot=4.0, dx=0.5, dy=0.5, nx=15, ny=25, ) fig, ax = grid.plot(fscale=6)

Work with existing data¶

We will work with subsets of global bathymetry from Gebco, winds from ERA5 and spectral boundary from Oceanum available in Rompy. Some dummy interpolation routine is used to exemplify how existing xarray datasources could be processed and then provided to rompy to define the model config

In [4]:

from rompy.core.time import TimeRange
from rompy.core.types import DatasetCoords
from rompy_binary_datasources.source import SourceDataset
from rompy_swan.data import SwanDataGrid
from rompy_swan.boundary import Boundnest1

projection = ccrs.PlateCarree()

from rompy.core.time import TimeRange from rompy.core.types import DatasetCoords from rompy_binary_datasources.source import SourceDataset from rompy_swan.data import SwanDataGrid from rompy_swan.boundary import Boundnest1 projection = ccrs.PlateCarree()

In [5]:

def my_fancy_interpolation(
    dset: xr.Dataset,
    grid: SwanGrid,
    coords: DatasetCoords,
    buffer: float = 0.0,
) -> xr.Dataset:
    """Dummy interpolation function."""
    x0, y0, x1, y1 = grid.bbox(buffer)
    xarr = np.arange(x0, x1+grid.dx, grid.dx)
    yarr = np.arange(y0, y1+grid.dy, grid.dy)
    return dset.interp(**{coords.x: xarr, coords.y: yarr})

def my_fancy_interpolation( dset: xr.Dataset, grid: SwanGrid, coords: DatasetCoords, buffer: float = 0.0, ) -> xr.Dataset: """Dummy interpolation function.""" x0, y0, x1, y1 = grid.bbox(buffer) xarr = np.arange(x0, x1+grid.dx, grid.dx) yarr = np.arange(y0, y1+grid.dy, grid.dy) return dset.interp(**{coords.x: xarr, coords.y: yarr})

In [6]:

DATADIR = Path("../../../rompy-swan/tests/data")
display(sorted(DATADIR.glob("*")))

gebco = xr.open_dataset(DATADIR / "gebco-1deg.nc")
era5 = xr.open_dataset(DATADIR / "era5-20230101.nc")
oceanum = xr.open_dataset(DATADIR / "aus-20230101.nc")

DATADIR = Path("../../../rompy-swan/tests/data") display(sorted(DATADIR.glob("*"))) gebco = xr.open_dataset(DATADIR / "gebco-1deg.nc") era5 = xr.open_dataset(DATADIR / "era5-20230101.nc") oceanum = xr.open_dataset(DATADIR / "aus-20230101.nc")

[PosixPath('../../../rompy-swan/tests/data/aus-20230101.nc'),
 PosixPath('../../../rompy-swan/tests/data/catalog.yaml'),
 PosixPath('../../../rompy-swan/tests/data/era5-20230101.nc'),
 PosixPath('../../../rompy-swan/tests/data/gebco-1deg.nc'),
 PosixPath('../../../rompy-swan/tests/data/schism'),
 PosixPath('../../../rompy-swan/tests/data/wind.csv')]

Times¶

In [7]:

# Define a time object to run the model using the time range from ERA5 dataset

start, end = era5.time.to_index()[[0, -1]]
times = TimeRange(start=start, end=end, interval="1h")
print(times)

# Define a time object to run the model using the time range from ERA5 dataset start, end = era5.time.to_index()[[0, -1]] times = TimeRange(start=start, end=end, interval="1h") print(times)

	Start: 2023-01-01 00:00:00
	End: 2023-01-02 00:00:00
	Duration: 1 day
	Interval: 1:00:00
	Include End: True

Bathy¶

In [8]:

# Display the GEBCO dataset

display(gebco)
p = gebco.elevation.plot(figsize=(10, 5), cmap="terrain")

# Display the GEBCO dataset display(gebco) p = gebco.elevation.plot(figsize=(10, 5), cmap="terrain")

<xarray.Dataset> Size: 526kB
Dimensions:    (lat: 181, lon: 360)
Coordinates:
  * lon        (lon) int64 3kB 0 1 2 3 4 5 6 7 ... 353 354 355 356 357 358 359
  * lat        (lat) int64 1kB -90 -89 -88 -87 -86 -85 -84 ... 85 86 87 88 89 90
Data variables:
    elevation  (lat, lon) float64 521kB ...
Attributes:
    title:    Subset of the GEBCO 2020 grid for testing purposes

xarray.Dataset

• Dimensions:
  • lat: 181
  • lon: 360
• Coordinates: (2)
  • lon
    (lon)
    int64
    0 1 2 3 4 5 ... 355 356 357 358 359

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    axis :

    X

    sdn_parameter_urn :

    SDN:P01::ALONZZ01

    sdn_parameter_name :

    Longitude east

    sdn_uom_urn :

    SDN:P06::DEGE

    sdn_uom_name :

    Degrees east

    array([  0,   1,   2, ..., 357, 358, 359], shape=(360,))

  • lat
    (lat)
    int64
    -90 -89 -88 -87 -86 ... 87 88 89 90

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    sdn_parameter_urn :

    SDN:P01::ALATZZ01

    sdn_parameter_name :

    Latitude north

    sdn_uom_urn :

    SDN:P06::DEGN

    sdn_uom_name :

    Degrees north

    array([-90, -89, -88, -87, -86, -85, -84, -83, -82, -81, -80, -79, -78, -77,
           -76, -75, -74, -73, -72, -71, -70, -69, -68, -67, -66, -65, -64, -63,
           -62, -61, -60, -59, -58, -57, -56, -55, -54, -53, -52, -51, -50, -49,
           -48, -47, -46, -45, -44, -43, -42, -41, -40, -39, -38, -37, -36, -35,
           -34, -33, -32, -31, -30, -29, -28, -27, -26, -25, -24, -23, -22, -21,
           -20, -19, -18, -17, -16, -15, -14, -13, -12, -11, -10,  -9,  -8,  -7,
            -6,  -5,  -4,  -3,  -2,  -1,   0,   1,   2,   3,   4,   5,   6,   7,
             8,   9,  10,  11,  12,  13,  14,  15,  16,  17,  18,  19,  20,  21,
            22,  23,  24,  25,  26,  27,  28,  29,  30,  31,  32,  33,  34,  35,
            36,  37,  38,  39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,
            50,  51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
            64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
            78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90])

• Data variables: (1)
  • elevation
    (lat, lon)
    float64
    ...

    standard_name :

    height_above_reference_ellipsoid

    long_name :

    Elevation relative to sea level

    units :

    m

    sdn_parameter_urn :

    SDN:P01::BATHHGHT

    sdn_parameter_name :

    Sea floor height (above mean sea level) {bathymetric height}

    sdn_uom_urn :

    SDN:P06::ULAA

    sdn_uom_name :

    Metres

    [65160 values with dtype=float64]

• Indexes: (2)
  • lon
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           350, 351, 352, 353, 354, 355, 356, 357, 358, 359],
          dtype='int64', name='lon', length=360))

  • lat
    PandasIndex

    PandasIndex(Index([-90, -89, -88, -87, -86, -85, -84, -83, -82, -81,
           ...
            81,  82,  83,  84,  85,  86,  87,  88,  89,  90],
          dtype='int64', name='lat', length=181))

• Attributes: (1)

  title :

  Subset of the GEBCO 2020 grid for testing purposes

In [9]:

# Process gebco into the model bathy

dset = my_fancy_interpolation(gebco, grid, DatasetCoords(x="lon", y="lat"), buffer=1.0)
dset

# Process gebco into the model bathy dset = my_fancy_interpolation(gebco, grid, DatasetCoords(x="lon", y="lat"), buffer=1.0) dset

Out[9]:

<xarray.Dataset> Size: 5kB
Dimensions:    (lat: 30, lon: 21)
Coordinates:
  * lon        (lon) float64 168B 108.2 108.7 109.2 109.7 ... 117.2 117.7 118.2
  * lat        (lat) float64 240B -36.2 -35.7 -35.2 -34.7 ... -22.7 -22.2 -21.7
Data variables:
    elevation  (lat, lon) float64 5kB -5.443e+03 -5.552e+03 ... 333.9 346.7
Attributes:
    title:    Subset of the GEBCO 2020 grid for testing purposes

xarray.Dataset

• Dimensions:
  • lat: 30
  • lon: 21
• Coordinates: (2)
  • lon
    (lon)
    float64
    108.2 108.7 109.2 ... 117.7 118.2

    standard_name :

    longitude

    long_name :

    longitude

    units :

    degrees_east

    axis :

    X

    sdn_parameter_urn :

    SDN:P01::ALONZZ01

    sdn_parameter_name :

    Longitude east

    sdn_uom_urn :

    SDN:P06::DEGE

    sdn_uom_name :

    Degrees east

    array([108.162922, 108.662922, 109.162922, 109.662922, 110.162922, 110.662922,
           111.162922, 111.662922, 112.162922, 112.662922, 113.162922, 113.662922,
           114.162922, 114.662922, 115.162922, 115.662922, 116.162922, 116.662922,
           117.162922, 117.662922, 118.162922])

  • lat
    (lat)
    float64
    -36.2 -35.7 -35.2 ... -22.2 -21.7

    standard_name :

    latitude

    long_name :

    latitude

    units :

    degrees_north

    axis :

    Y

    sdn_parameter_urn :

    SDN:P01::ALATZZ01

    sdn_parameter_name :

    Latitude north

    sdn_uom_urn :

    SDN:P06::DEGN

    sdn_uom_name :

    Degrees north

    array([-36.2, -35.7, -35.2, -34.7, -34.2, -33.7, -33.2, -32.7, -32.2, -31.7,
           -31.2, -30.7, -30.2, -29.7, -29.2, -28.7, -28.2, -27.7, -27.2, -26.7,
           -26.2, -25.7, -25.2, -24.7, -24.2, -23.7, -23.2, -22.7, -22.2, -21.7])

• Data variables: (1)
  • elevation
    (lat, lon)
    float64
    -5.443e+03 -5.552e+03 ... 346.7

    standard_name :

    height_above_reference_ellipsoid

    long_name :

    Elevation relative to sea level

    units :

    m

    sdn_parameter_urn :

    SDN:P01::BATHHGHT

    sdn_parameter_name :

    Sea floor height (above mean sea level) {bathymetric height}

    sdn_uom_urn :

    SDN:P06::ULAA

    sdn_uom_name :

    Metres

    array([[-5.44333690e+03, -5.55203010e+03, -5.51244369e+03,
            -5.16607459e+03, -4.98063565e+03, -5.12815275e+03,
            -5.26401315e+03, -5.37575645e+03, -5.46007918e+03,
            -5.48767018e+03, -5.48030864e+03, -5.40063214e+03,
            -5.33507511e+03, -5.29873051e+03, -5.15867064e+03,
            -4.80402934e+03, -4.53275451e+03, -4.43396051e+03,
            -4.36858181e+03, -4.37233761e+03, -4.43275069e+03],
           [-5.48174021e+03, -5.56709226e+03, -5.53045000e+03,
            -5.24140795e+03, -4.98359843e+03, -4.79040738e+03,
            -4.67873520e+03, -4.73572125e+03, -4.80830313e+03,
            -4.91315198e+03, -4.91768694e+03, -4.71467774e+03,
            -4.46867165e+03, -4.13370725e+03, -3.79005251e+03,
            -3.42841791e+03, -3.13898421e+03, -2.99893036e+03,
            -2.91206937e+03, -2.93526162e+03, -3.03258501e+03],
           [-5.36776536e+03, -5.45166166e+03, -5.39673752e+03,
            -5.05460122e+03, -4.63601884e+03, -4.05927354e+03,
            -3.66337384e+03, -3.64163414e+03, -3.70276106e+03,
            -3.93533466e+03, -3.93694232e+03, -3.46069362e+03,
            -2.88378672e+03, -2.09862332e+03, -1.46811240e+03,
            -1.15756930e+03, -9.23004043e+02, -8.45632943e+02,
    ...
            -4.34846244e+03, -4.68482305e+03, -4.77194110e+03,
            -4.62750527e+03, -4.00399667e+03, -3.23228993e+03,
            -2.15396903e+03, -1.21748125e+03, -5.74438754e+02,
            -1.05447174e+02,  3.44222627e+00,  1.00844153e+02,
             1.74479103e+02,  2.53412629e+02,  3.43308629e+02,
             4.21966997e+02,  4.77375297e+02,  5.17342488e+02],
           [-3.73903434e+03, -4.00741114e+03, -4.27036950e+03,
            -4.52211740e+03, -4.73636365e+03, -4.87302095e+03,
            -4.84350990e+03, -4.47020530e+03, -3.85659019e+03,
            -2.74578529e+03, -1.71157177e+03, -8.35821765e+02,
            -2.00260509e+02, -6.16371095e+01,  5.55717162e+01,
             1.28474916e+02,  2.13744018e+02,  3.24597518e+02,
             4.11605805e+02,  4.49279605e+02,  4.68767626e+02],
           [-4.16772633e+03, -4.39071798e+03, -4.58527702e+03,
            -4.72101047e+03, -4.82205572e+03, -4.85133292e+03,
            -4.69269580e+03, -4.14527385e+03, -3.48418546e+03,
            -2.58792721e+03, -1.73280097e+03, -9.62774665e+02,
            -3.73438369e+02, -1.57940169e+02,  8.27680065e+00,
             7.25335006e+01,  1.46090556e+02,  2.38889556e+02,
             3.09416554e+02,  3.33863954e+02,  3.46746509e+02]])

• Indexes: (2)
  • lon
    PandasIndex

    PandasIndex(Index([108.1629223150705, 108.6629223150705, 109.1629223150705,
           109.6629223150705, 110.1629223150705, 110.6629223150705,
           111.1629223150705, 111.6629223150705, 112.1629223150705,
           112.6629223150705, 113.1629223150705, 113.6629223150705,
           114.1629223150705, 114.6629223150705, 115.1629223150705,
           115.6629223150705, 116.1629223150705, 116.6629223150705,
           117.1629223150705, 117.6629223150705, 118.1629223150705],
          dtype='float64', name='lon'))

  • lat
    PandasIndex

    PandasIndex(Index([              -36.2,               -35.7,               -35.2,
                         -34.7,               -34.2,               -33.7,
                         -33.2,               -32.7,               -32.2,
           -31.700000000000003, -31.200000000000003, -30.700000000000003,
           -30.200000000000003, -29.700000000000003, -29.200000000000003,
           -28.700000000000003, -28.200000000000003, -27.700000000000003,
           -27.200000000000003, -26.700000000000003, -26.200000000000003,
           -25.700000000000003, -25.200000000000003, -24.700000000000003,
           -24.200000000000003, -23.700000000000003, -23.200000000000003,
           -22.700000000000003, -22.200000000000003, -21.700000000000003],
          dtype='float64', name='lat'))

• Attributes: (1)

  title :

  Subset of the GEBCO 2020 grid for testing purposes

In [10]:

# Create and plot the data instance. This object will be provided to the SWAN Config through the DataInterface

bottom = SwanDataGrid(
    var="bottom",
    source=SourceDataset(obj=dset),
    z1="elevation",
    fac=-1,
    coords={"x": "lon", "y": "lat"},
    crop_data=False, # So data isn't cropped to model grid inside SwanConfig
)

fig, ax = bottom.plot(param="elevation", vmin=-5000, vmax=0, cmap="turbo_r", figsize=(5, 6))
grid.plot(ax=ax);

# Create and plot the data instance. This object will be provided to the SWAN Config through the DataInterface bottom = SwanDataGrid( var="bottom", source=SourceDataset(obj=dset), z1="elevation", fac=-1, coords={"x": "lon", "y": "lat"}, crop_data=False, # So data isn't cropped to model grid inside SwanConfig ) fig, ax = bottom.plot(param="elevation", vmin=-5000, vmax=0, cmap="turbo_r", figsize=(5, 6)) grid.plot(ax=ax);

Winds¶

In [11]:

# Display the ERA dataset

display(era5)

fu = era5.u10.plot(col="time", figsize=(15, 1.5), subplot_kws={"projection": projection}, cbar_kwargs={"label": "u (m/s)"})
fv = era5.v10.plot(col="time", figsize=(15, 1.5), subplot_kws={"projection": projection}, cbar_kwargs={"label": "v (m/s)"})
for f in [fu, fv]:
    f.map(lambda: plt.gca().coastlines())

# Display the ERA dataset display(era5) fu = era5.u10.plot(col="time", figsize=(15, 1.5), subplot_kws={"projection": projection}, cbar_kwargs={"label": "u (m/s)"}) fv = era5.v10.plot(col="time", figsize=(15, 1.5), subplot_kws={"projection": projection}, cbar_kwargs={"label": "v (m/s)"}) for f in [fu, fv]: f.map(lambda: plt.gca().coastlines())

<xarray.Dataset> Size: 214kB
Dimensions:    (time: 5, latitude: 37, longitude: 72)
Coordinates:
  * latitude   (latitude) float32 148B 90.0 85.0 80.0 75.0 ... -80.0 -85.0 -90.0
  * longitude  (longitude) float32 288B 0.0 5.0 10.0 15.0 ... 345.0 350.0 355.0
  * time       (time) datetime64[ns] 40B 2023-01-01 ... 2023-01-02
Data variables:
    u10        (time, latitude, longitude) float64 107kB ...
    v10        (time, latitude, longitude) float64 107kB ...
Attributes:
    Conventions:  CF-1.6
    history:      2023-06-10 00:03:38 GMT by grib_to_netcdf-2.25.1: /opt/ecmw...

xarray.Dataset

• Dimensions:
  • time: 5
  • latitude: 37
  • longitude: 72
• Coordinates: (3)
  • latitude
    (latitude)
    float32
    90.0 85.0 80.0 ... -85.0 -90.0

    array([ 90.,  85.,  80.,  75.,  70.,  65.,  60.,  55.,  50.,  45.,  40.,  35.,
            30.,  25.,  20.,  15.,  10.,   5.,   0.,  -5., -10., -15., -20., -25.,
           -30., -35., -40., -45., -50., -55., -60., -65., -70., -75., -80., -85.,
           -90.], dtype=float32)

  • longitude
    (longitude)
    float32
    0.0 5.0 10.0 ... 345.0 350.0 355.0

    array([  0.,   5.,  10.,  15.,  20.,  25.,  30.,  35.,  40.,  45.,  50.,  55.,
            60.,  65.,  70.,  75.,  80.,  85.,  90.,  95., 100., 105., 110., 115.,
           120., 125., 130., 135., 140., 145., 150., 155., 160., 165., 170., 175.,
           180., 185., 190., 195., 200., 205., 210., 215., 220., 225., 230., 235.,
           240., 245., 250., 255., 260., 265., 270., 275., 280., 285., 290., 295.,
           300., 305., 310., 315., 320., 325., 330., 335., 340., 345., 350., 355.],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    2023-01-01 ... 2023-01-02

    array(['2023-01-01T00:00:00.000000000', '2023-01-01T06:00:00.000000000',
           '2023-01-01T12:00:00.000000000', '2023-01-01T18:00:00.000000000',
           '2023-01-02T00:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (2)
  • u10
    (time, latitude, longitude)
    float64
    ...

    long_name :

    10 metre U wind component

    units :

    m s**-1

    valid_max :

    300.0

    valid_min :

    -300.0

    [13320 values with dtype=float64]

  • v10
    (time, latitude, longitude)
    float64
    ...

    long_name :

    10 metre V wind component

    units :

    m s**-1

    valid_max :

    300.0

    valid_min :

    -300.0

    [13320 values with dtype=float64]

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([ 90.0,  85.0,  80.0,  75.0,  70.0,  65.0,  60.0,  55.0,  50.0,  45.0,
            40.0,  35.0,  30.0,  25.0,  20.0,  15.0,  10.0,   5.0,   0.0,  -5.0,
           -10.0, -15.0, -20.0, -25.0, -30.0, -35.0, -40.0, -45.0, -50.0, -55.0,
           -60.0, -65.0, -70.0, -75.0, -80.0, -85.0, -90.0],
          dtype='float32', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([  0.0,   5.0,  10.0,  15.0,  20.0,  25.0,  30.0,  35.0,  40.0,  45.0,
            50.0,  55.0,  60.0,  65.0,  70.0,  75.0,  80.0,  85.0,  90.0,  95.0,
           100.0, 105.0, 110.0, 115.0, 120.0, 125.0, 130.0, 135.0, 140.0, 145.0,
           150.0, 155.0, 160.0, 165.0, 170.0, 175.0, 180.0, 185.0, 190.0, 195.0,
           200.0, 205.0, 210.0, 215.0, 220.0, 225.0, 230.0, 235.0, 240.0, 245.0,
           250.0, 255.0, 260.0, 265.0, 270.0, 275.0, 280.0, 285.0, 290.0, 295.0,
           300.0, 305.0, 310.0, 315.0, 320.0, 325.0, 330.0, 335.0, 340.0, 345.0,
           350.0, 355.0],
          dtype='float32', name='longitude'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-01-01 00:00:00', '2023-01-01 06:00:00',
                   '2023-01-01 12:00:00', '2023-01-01 18:00:00',
                   '2023-01-02 00:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (2)

  Conventions :

  CF-1.6

  history :

  2023-06-10 00:03:38 GMT by grib_to_netcdf-2.25.1: /opt/ecmwf/mars-client/bin/grib_to_netcdf.bin -S param -o /cache/data1/adaptor.mars.internal-1686355363.059632-31231-4-716aba91-9c1f-4cb0-85d0-16ceb0c26b80.nc /cache/tmp/716aba91-9c1f-4cb0-85d0-16ceb0c26b80-adaptor.mars.internal-1686355351.013629-31231-7-tmp.grib

In [12]:

# Process it into the model forcing

dset = my_fancy_interpolation(era5, grid, DatasetCoords(x="longitude", y="latitude"), buffer=1.0)
dset

# Process it into the model forcing dset = my_fancy_interpolation(era5, grid, DatasetCoords(x="longitude", y="latitude"), buffer=1.0) dset

Out[12]:

<xarray.Dataset> Size: 51kB
Dimensions:    (time: 5, latitude: 30, longitude: 21)
Coordinates:
  * time       (time) datetime64[ns] 40B 2023-01-01 ... 2023-01-02
  * longitude  (longitude) float64 168B 108.2 108.7 109.2 ... 117.2 117.7 118.2
  * latitude   (latitude) float64 240B -36.2 -35.7 -35.2 ... -22.7 -22.2 -21.7
Data variables:
    u10        (time, latitude, longitude) float64 25kB 3.054 3.084 ... 1.339
    v10        (time, latitude, longitude) float64 25kB 4.884 4.951 ... 0.9003
Attributes:
    Conventions:  CF-1.6
    history:      2023-06-10 00:03:38 GMT by grib_to_netcdf-2.25.1: /opt/ecmw...

xarray.Dataset

• Dimensions:
  • time: 5
  • latitude: 30
  • longitude: 21
• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    2023-01-01 ... 2023-01-02

    array(['2023-01-01T00:00:00.000000000', '2023-01-01T06:00:00.000000000',
           '2023-01-01T12:00:00.000000000', '2023-01-01T18:00:00.000000000',
           '2023-01-02T00:00:00.000000000'], dtype='datetime64[ns]')

  • longitude
    (longitude)
    float64
    108.2 108.7 109.2 ... 117.7 118.2

    array([108.162922, 108.662922, 109.162922, 109.662922, 110.162922, 110.662922,
           111.162922, 111.662922, 112.162922, 112.662922, 113.162922, 113.662922,
           114.162922, 114.662922, 115.162922, 115.662922, 116.162922, 116.662922,
           117.162922, 117.662922, 118.162922])

  • latitude
    (latitude)
    float64
    -36.2 -35.7 -35.2 ... -22.2 -21.7

    array([-36.2, -35.7, -35.2, -34.7, -34.2, -33.7, -33.2, -32.7, -32.2, -31.7,
           -31.2, -30.7, -30.2, -29.7, -29.2, -28.7, -28.2, -27.7, -27.2, -26.7,
           -26.2, -25.7, -25.2, -24.7, -24.2, -23.7, -23.2, -22.7, -22.2, -21.7])

• Data variables: (2)
  • u10
    (time, latitude, longitude)
    float64
    3.054 3.084 3.115 ... 2.082 1.339

    long_name :

    10 metre U wind component

    units :

    m s**-1

    valid_max :

    300.0

    valid_min :

    -300.0

    array([[[ 3.05406734,  3.08442334,  3.11477934, ...,  3.42997523,
              3.40515123,  3.38032723],
            [ 2.02745922,  2.04287522,  2.05829122, ...,  2.44683484,
              2.44877084,  2.45070684],
            [ 1.0008511 ,  1.0013271 ,  1.0018031 , ...,  1.46369444,
              1.49239044,  1.52108644],
            ...,
            [-3.39869218, -3.22623018, -3.05376818, ..., -0.4115609 ,
             -0.8750729 , -1.3385849 ],
            [-3.21607748, -3.05314548, -2.89021348, ..., -0.30344738,
             -0.74917938, -1.19491138],
            [-3.03346278, -2.88006078, -2.72665878, ..., -0.19533386,
             -0.62328586, -1.05123786]],
    
           [[ 3.56696104,  3.54759304,  3.52822504, ...,  3.86722608,
              4.10837808,  4.34953008],
            [ 2.74384988,  2.70505188,  2.66625388, ...,  2.78601324,
              3.06493524,  3.34385724],
            [ 1.92073872,  1.86251072,  1.80428272, ...,  1.70480039,
              2.02149239,  2.33818439],
    ...
            [-2.19217152, -1.93024152, -1.66831152, ...,  2.80878855,
              1.94261055,  1.07643255],
            [-1.76190573, -1.47092573, -1.17994573, ...,  3.40180597,
              2.53819797,  1.67458997],
            [-1.33163994, -1.01160994, -0.69157994, ...,  3.99482339,
              3.13378539,  2.27274739]],
    
           [[-1.24672642, -1.42658642, -1.60644642, ..., -1.26073352,
             -1.04990152, -0.83906952],
            [-1.90700676, -2.09171676, -2.27642676, ..., -1.97772168,
             -1.74131968, -1.50491768],
            [-2.56728711, -2.75684711, -2.94640711, ..., -2.69470983,
             -2.43273783, -2.17076583],
            ...,
            [-1.33056564, -1.00461164, -0.67865764, ...,  1.2409733 ,
              0.3903633 , -0.4602467 ],
            [-0.92305018, -0.56310618, -0.20316218, ...,  2.03251779,
              1.23605779,  0.43959779],
            [-0.51553472, -0.12160072,  0.27233328, ...,  2.82406227,
              2.08175227,  1.33944227]]], shape=(5, 30, 21))

  • v10
    (time, latitude, longitude)
    float64
    4.884 4.951 5.019 ... 1.778 0.9003

    long_name :

    10 metre V wind component

    units :

    m s**-1

    valid_max :

    300.0

    valid_min :

    -300.0

    array([[[4.88356179, 4.95115779, 5.01875379, ..., 4.90943125,
             4.79985525, 4.69027925],
            [5.0786504 , 5.1807064 , 5.2827624 , ..., 5.49437785,
             5.37279185, 5.25120585],
            [5.27373901, 5.41025501, 5.54677101, ..., 6.07932446,
             5.94572846, 5.81213246],
            ...,
            [9.39734418, 9.62032218, 9.84330018, ..., 4.53337248,
             3.72631448, 2.91925648],
            [9.38104521, 9.61145321, 9.84186121, ..., 4.55956504,
             3.67827704, 2.79698904],
            [9.36474624, 9.60258424, 9.84042224, ..., 4.58575759,
             3.63023959, 2.67472159]],
    
           [[5.48801128, 5.53226328, 5.57651528, ..., 5.11887975,
             4.98858375, 4.85828775],
            [5.69499962, 5.75327162, 5.81154362, ..., 5.46036237,
             5.34885637, 5.23735037],
            [5.90198796, 5.97427996, 6.04657196, ..., 5.80184499,
             5.70912899, 5.61641299],
    ...
            [9.03196617, 9.38474217, 9.73751817, ..., 2.7115134 ,
             2.0856734 , 1.4598334 ],
            [8.9127322 , 9.2495682 , 9.5864042 , ..., 2.88410324,
             2.23736324, 1.59062324],
            [8.79349824, 9.11439424, 9.43529024, ..., 3.05669309,
             2.38905309, 1.72141309]],
    
           [[5.98526781, 6.02446381, 6.06365981, ..., 4.77517889,
             4.66387089, 4.55256289],
            [6.22551532, 6.25792132, 6.29032732, ..., 4.82699794,
             4.68910994, 4.55122194],
            [6.46576284, 6.49137884, 6.51699484, ..., 4.87881699,
             4.71434899, 4.54988099],
            ...,
            [9.38537356, 9.65775356, 9.93013356, ..., 3.12724953,
             2.47717753, 1.82710553],
            [9.23130611, 9.48598611, 9.74066611, ..., 2.89148189,
             2.12758989, 1.36369789],
            [9.07723866, 9.31421866, 9.55119866, ..., 2.65571426,
             1.77800226, 0.90029026]]], shape=(5, 30, 21))

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-01-01 00:00:00', '2023-01-01 06:00:00',
                   '2023-01-01 12:00:00', '2023-01-01 18:00:00',
                   '2023-01-02 00:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • longitude
    PandasIndex

    PandasIndex(Index([108.1629223150705, 108.6629223150705, 109.1629223150705,
           109.6629223150705, 110.1629223150705, 110.6629223150705,
           111.1629223150705, 111.6629223150705, 112.1629223150705,
           112.6629223150705, 113.1629223150705, 113.6629223150705,
           114.1629223150705, 114.6629223150705, 115.1629223150705,
           115.6629223150705, 116.1629223150705, 116.6629223150705,
           117.1629223150705, 117.6629223150705, 118.1629223150705],
          dtype='float64', name='longitude'))

  • latitude
    PandasIndex

    PandasIndex(Index([              -36.2,               -35.7,               -35.2,
                         -34.7,               -34.2,               -33.7,
                         -33.2,               -32.7,               -32.2,
           -31.700000000000003, -31.200000000000003, -30.700000000000003,
           -30.200000000000003, -29.700000000000003, -29.200000000000003,
           -28.700000000000003, -28.200000000000003, -27.700000000000003,
           -27.200000000000003, -26.700000000000003, -26.200000000000003,
           -25.700000000000003, -25.200000000000003, -24.700000000000003,
           -24.200000000000003, -23.700000000000003, -23.200000000000003,
           -22.700000000000003, -22.200000000000003, -21.700000000000003],
          dtype='float64', name='latitude'))

• Attributes: (2)

  Conventions :

  CF-1.6

  history :

  2023-06-10 00:03:38 GMT by grib_to_netcdf-2.25.1: /opt/ecmwf/mars-client/bin/grib_to_netcdf.bin -S param -o /cache/data1/adaptor.mars.internal-1686355363.059632-31231-4-716aba91-9c1f-4cb0-85d0-16ceb0c26b80.nc /cache/tmp/716aba91-9c1f-4cb0-85d0-16ceb0c26b80-adaptor.mars.internal-1686355351.013629-31231-7-tmp.grib

In [13]:

# ERA5 has Latitude in reverse order, use filter to reverse it

from rompy.core.filters import Filter

# ERA5 has Latitude in reverse order, use filter to reverse it from rompy.core.filters import Filter

In [14]:

# Create and plot the data instance. This object will be provided to the SWAN Config through the DataInterface

wind = SwanDataGrid(
    var="wind",
    source=SourceDataset(obj=dset),
    z1="u10",
    z2="v10",
    coords={"x": "longitude", "y": "latitude"},
    crop_data=False, # So data isn't cropped to model grid inside SwanConfig
    filter=Filter(sort=dict(coords=["latitude"]))
)

fig, ax = wind.plot(param="u10", isel={"time": 0}, vmin=-5, vmax=5, cmap="RdBu_r", figsize=(5, 6))
grid.plot(ax=ax);

# Create and plot the data instance. This object will be provided to the SWAN Config through the DataInterface wind = SwanDataGrid( var="wind", source=SourceDataset(obj=dset), z1="u10", z2="v10", coords={"x": "longitude", "y": "latitude"}, crop_data=False, # So data isn't cropped to model grid inside SwanConfig filter=Filter(sort=dict(coords=["latitude"])) ) fig, ax = wind.plot(param="u10", isel={"time": 0}, vmin=-5, vmax=5, cmap="RdBu_r", figsize=(5, 6)) grid.plot(ax=ax);

Boundary¶

In [15]:

# Open and check the test spectra dataset

display(oceanum)

fig, ax = plt.subplots(figsize=(10, 5), subplot_kw={"projection": projection})
c = oceanum.isel(time=[0]).spec.hs()
p = ax.scatter(oceanum.lon, oceanum.lat, s=6, c=c, cmap="turbo", vmin=0, vmax=4)
ax.set_title(c.time.to_index().to_pydatetime()[0])
plt.colorbar(p, label=f"Hs (m)")
ax.coastlines()
grid.plot(ax=ax);

# Open and check the test spectra dataset display(oceanum) fig, ax = plt.subplots(figsize=(10, 5), subplot_kw={"projection": projection}) c = oceanum.isel(time=[0]).spec.hs() p = ax.scatter(oceanum.lon, oceanum.lat, s=6, c=c, cmap="turbo", vmin=0, vmax=4) ax.set_title(c.time.to_index().to_pydatetime()[0]) plt.colorbar(p, label=f"Hs (m)") ax.coastlines() grid.plot(ax=ax);

<xarray.Dataset> Size: 1MB
Dimensions:  (site: 412, time: 5, freq: 11, dir: 8)
Coordinates:
  * time     (time) datetime64[ns] 40B 2023-01-01 ... 2023-01-02
  * site     (site) int64 3kB 0 4 8 12 16 20 ... 1624 1628 1632 1636 1640 1644
  * freq     (freq) float32 44B 0.05417 0.05959 0.06555 ... 0.1161 0.1277 0.1405
  * dir      (dir) float32 32B 0.0 45.0 90.0 135.0 180.0 225.0 270.0 315.0
Data variables:
    lon      (site) float32 2kB ...
    lat      (site) float32 2kB ...
    efth     (time, site, freq, dir) float64 1MB ...
    dpt      (time, site) float32 8kB ...
    wspd     (time, site) float32 8kB ...
    wdir     (time, site) float32 8kB ...
Attributes: (12/16)
    product_name:           ww3.all_spec.nc
    area:                   Global 0.5 x 0.5 degree
    data_type:              OCO spectra 2D
    format_version:         1.1
    southernmost_latitude:  n/a
    northernmost_latitude:  n/a
    ...                     ...
    minimum_altitude:       n/a
    maximum_altitude:       n/a
    altitude_resolution:    n/a
    start_date:             2023-01-01 00:00:00
    stop_date:              2023-02-01 00:00:00
    field_type:             3-hourly

xarray.Dataset

• Dimensions:
  • site: 412
  • time: 5
  • freq: 11
  • dir: 8
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2023-01-01 ... 2023-01-02

    standard_name :

    time

    array(['2023-01-01T00:00:00.000000000', '2023-01-01T06:00:00.000000000',
           '2023-01-01T12:00:00.000000000', '2023-01-01T18:00:00.000000000',
           '2023-01-02T00:00:00.000000000'], dtype='datetime64[ns]')

  • site
    (site)
    int64
    0 4 8 12 16 ... 1632 1636 1640 1644

    array([   0,    4,    8, ..., 1636, 1640, 1644], shape=(412,))

  • freq
    (freq)
    float32
    0.05417 0.05959 ... 0.1277 0.1405

    standard_name :

    sea_surface_wave_frequency

    units :

    Hz

    array([0.054172, 0.059589, 0.065548, 0.072103, 0.079313, 0.087244, 0.095968,
           0.105565, 0.116122, 0.127734, 0.140507], dtype=float32)

  • dir
    (dir)
    float32
    0.0 45.0 90.0 ... 225.0 270.0 315.0

    standard_name :

    sea_surface_wave_from_direction

    units :

    degree

    array([  0.,  45.,  90., 135., 180., 225., 270., 315.], dtype=float32)

• Data variables: (6)
  • lon
    (site)
    float32
    ...

    standard_name :

    longitude

    units :

    degrees_east

    [412 values with dtype=float32]

  • lat
    (site)
    float32
    ...

    standard_name :

    latitude

    units :

    degrees_north

    [412 values with dtype=float32]

  • efth
    (time, site, freq, dir)
    float64
    ...

    standard_name :

    sea_surface_wave_directional_variance_spectral_density

    units :

    m2 s degree-1

    [181280 values with dtype=float64]

  • dpt
    (time, site)
    float32
    ...

    standard_name :

    sea_floor_depth_below_sea_surface

    units :

    m

    [2060 values with dtype=float32]

  • wspd
    (time, site)
    float32
    ...

    standard_name :

    wind_speed_at_10m_above_ground_level

    units :

    m s-1

    [2060 values with dtype=float32]

  • wdir
    (time, site)
    float32
    ...

    standard_name :

    wind_from_direction_at_10m_above_ground_level

    units :

    degree

    [2060 values with dtype=float32]

• Indexes: (4)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-01-01 00:00:00', '2023-01-01 06:00:00',
                   '2023-01-01 12:00:00', '2023-01-01 18:00:00',
                   '2023-01-02 00:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • site
    PandasIndex

    PandasIndex(Index([   0,    4,    8,   12,   16,   20,   24,   28,   32,   36,
           ...
           1608, 1612, 1616, 1620, 1624, 1628, 1632, 1636, 1640, 1644],
          dtype='int64', name='site', length=412))

  • freq
    PandasIndex

    PandasIndex(Index([0.05417170003056526, 0.05958886817097664, 0.06554775685071945,
           0.07210253179073334, 0.07931278645992279, 0.08724407106637955,
           0.09596847742795944, 0.10556533187627792, 0.11612186580896378,
           0.12773405015468597, 0.14050745964050293],
          dtype='float32', name='freq'))

  • dir
    PandasIndex

    PandasIndex(Index([0.0, 45.0, 90.0, 135.0, 180.0, 225.0, 270.0, 315.0], dtype='float32', name='dir'))

• Attributes: (16)

  product_name :

  ww3.all_spec.nc

  area :

  Global 0.5 x 0.5 degree

  data_type :

  OCO spectra 2D

  format_version :

  1.1

  southernmost_latitude :

  n/a

  northernmost_latitude :

  n/a

  latitude_resolution :

  n/a

  westernmost_longitude :

  n/a

  easternmost_longitude :

  n/a

  longitude_resolution :

  n/a

  minimum_altitude :

  n/a

  maximum_altitude :

  n/a

  altitude_resolution :

  n/a

  start_date :

  2023-01-01 00:00:00

  stop_date :

  2023-02-01 00:00:00

  field_type :

  3-hourly

In [16]:

# Create the boundary instance. This object will be provided to the SWAN Config through the BoundaryInterface

boundary_from_data = Boundnest1(
    id="westaus",
    source=SourceDataset(obj=oceanum),
    sel_method="idw",
    sel_method_kwargs={"tolerance": 4}, # points are sparse around the offshore boundary so make sure tolerance is appropriate
)

# Create the boundary instance. This object will be provided to the SWAN Config through the BoundaryInterface boundary_from_data = Boundnest1( id="westaus", source=SourceDataset(obj=oceanum), sel_method="idw", sel_method_kwargs={"tolerance": 4}, # points are sparse around the offshore boundary so make sure tolerance is appropriate )

In [17]:

# Generate the boundary data and plot them to check.
# This isn't necessary but it is useful for checking the generated boundary look okay

# Generate the boundary data
outfile, cmd = boundary_from_data.get(destdir=workdir, grid=grid, time=times)

# Read the boundary data into an xarray dataset
from wavespectra import read_swan

ds = read_swan(outfile)
display(ds)

# Plot the boundary data alongside the source dataset and the model grid
fig, ax = plt.subplots(figsize=(10, 5), subplot_kw={"projection": projection})

# Source dataset
c = oceanum.isel(time=[0]).spec.hs()
p = ax.scatter(oceanum.lon, oceanum.lat, s=20, c=c, marker="v", cmap="turbo", vmin=0, vmax=4)

# Generated boundary dataset
c = ds.isel(time=[0]).spec.hs()
p = ax.scatter(ds.lon, ds.lat, s=50, c=c, cmap="turbo", edgecolors="0.5", vmin=0, vmax=4)

# Model grid
grid.plot(ax=ax)

# Axis settings
ax.set_title(c.time.to_index().to_pydatetime()[0])
plt.colorbar(p, label=f"Hs (m)")
ax.coastlines()
ax.set_extent([ds.lon.min()-3, ds.lon.max()+3, ds.lat.min()-3, ds.lat.max()+3])

# Generate the boundary data and plot them to check. # This isn't necessary but it is useful for checking the generated boundary look okay # Generate the boundary data outfile, cmd = boundary_from_data.get(destdir=workdir, grid=grid, time=times) # Read the boundary data into an xarray dataset from wavespectra import read_swan ds = read_swan(outfile) display(ds) # Plot the boundary data alongside the source dataset and the model grid fig, ax = plt.subplots(figsize=(10, 5), subplot_kw={"projection": projection}) # Source dataset c = oceanum.isel(time=[0]).spec.hs() p = ax.scatter(oceanum.lon, oceanum.lat, s=20, c=c, marker="v", cmap="turbo", vmin=0, vmax=4) # Generated boundary dataset c = ds.isel(time=[0]).spec.hs() p = ax.scatter(ds.lon, ds.lat, s=50, c=c, cmap="turbo", edgecolors="0.5", vmin=0, vmax=4) # Model grid grid.plot(ax=ax) # Axis settings ax.set_title(c.time.to_index().to_pydatetime()[0]) plt.colorbar(p, label=f"Hs (m)") ax.coastlines() ax.set_extent([ds.lon.min()-3, ds.lon.max()+3, ds.lat.min()-3, ds.lat.max()+3])

<xarray.Dataset> Size: 273kB
Dimensions:  (time: 5, site: 77, freq: 11, dir: 8)
Coordinates:
  * time     (time) datetime64[ns] 40B 2023-01-01 ... 2023-01-02
  * site     (site) int64 616B 1 2 3 4 5 6 7 8 9 ... 69 70 71 72 73 74 75 76 77
  * freq     (freq) float64 88B 0.05417 0.05959 0.06555 ... 0.1161 0.1277 0.1405
  * dir      (dir) float64 64B 0.0 45.0 90.0 135.0 180.0 225.0 270.0 315.0
Data variables:
    efth     (time, site, freq, dir) float64 271kB 0.0 0.0 ... 5.238e-05
    lat      (site) float64 616B -35.2 -35.17 -35.13 -35.1 ... -34.2 -34.7 -35.2
    lon      (site) float64 616B 110.0 110.5 111.0 111.5 ... 109.9 110.0 110.0

xarray.Dataset

• Dimensions:
  • time: 5
  • site: 77
  • freq: 11
  • dir: 8
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2023-01-01 ... 2023-01-02

    array(['2023-01-01T00:00:00.000000000', '2023-01-01T06:00:00.000000000',
           '2023-01-01T12:00:00.000000000', '2023-01-01T18:00:00.000000000',
           '2023-01-02T00:00:00.000000000'], dtype='datetime64[ns]')

  • site
    (site)
    int64
    1 2 3 4 5 6 7 ... 72 73 74 75 76 77

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
           19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
           37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
           55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
           73, 74, 75, 76, 77])

  • freq
    (freq)
    float64
    0.05417 0.05959 ... 0.1277 0.1405

    array([0.05417, 0.05959, 0.06555, 0.0721 , 0.07931, 0.08724, 0.09597, 0.10557,
           0.11612, 0.12773, 0.14051])

  • dir
    (dir)
    float64
    0.0 45.0 90.0 ... 225.0 270.0 315.0

    array([  0.,  45.,  90., 135., 180., 225., 270., 315.])

• Data variables: (3)
  • efth
    (time, site, freq, dir)
    float64
    0.0 0.0 0.0 ... 0.006496 5.238e-05

    standard_name :

    sea_surface_wave_directional_variance_spectral_density

    units :

    m2 s degree-1

    _units :

    m^{2}.s.degree^{-1}

    _variable_name :

    VaDens

    array([[[[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
              4.13879808e-02, 1.97839296e-03, 0.00000000e+00],
             [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
              2.95571909e-01, 3.16542874e-04, 0.00000000e+00],
             [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
              6.72891015e-01, 3.16542874e-04, 0.00000000e+00],
             ...,
             [0.00000000e+00, 0.00000000e+00, 7.91357186e-05, ...,
              4.59778525e-02, 8.46752189e-03, 6.33085749e-04],
             [0.00000000e+00, 0.00000000e+00, 7.91357186e-05, ...,
              2.84097230e-02, 8.30925045e-03, 7.91357186e-05],
             [0.00000000e+00, 0.00000000e+00, 7.91357186e-05, ...,
              1.71724509e-02, 7.28048611e-03, 7.91357186e-05]],
    
            [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
              4.23931316e-02, 2.06022696e-03, 0.00000000e+00],
             [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
              2.98336711e-01, 3.16957993e-04, 0.00000000e+00],
             [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
              6.75516723e-01, 3.16957993e-04, 0.00000000e+00],
    ...
              4.26566896e-02, 4.87801560e-03, 6.74619179e-04],
             [0.00000000e+00, 0.00000000e+00, 2.64658293e-03, ...,
              2.65696169e-02, 6.01967883e-03, 1.03787566e-04],
             [0.00000000e+00, 0.00000000e+00, 2.43900780e-03, ...,
              1.54643473e-02, 5.86399748e-03, 5.18937830e-05]],
    
            [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
              1.78108924e-03, 1.30962444e-03, 0.00000000e+00],
             [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
              3.12738316e-02, 3.66694843e-04, 0.00000000e+00],
             [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
              1.91729018e-01, 1.04769955e-04, 0.00000000e+00],
             ...,
             [0.00000000e+00, 0.00000000e+00, 8.38159642e-04, ...,
              4.44224610e-02, 5.55280763e-03, 6.81004709e-04],
             [0.00000000e+00, 0.00000000e+00, 2.56686390e-03, ...,
              2.77116532e-02, 6.75766211e-03, 1.04769955e-04],
             [0.00000000e+00, 0.00000000e+00, 2.35732399e-03, ...,
              1.61345731e-02, 6.49573722e-03, 5.23849776e-05]]]],
          shape=(5, 77, 11, 8))

  • lat
    (site)
    float64
    -35.2 -35.17 -35.13 ... -34.7 -35.2

    standard_name :

    latitude

    units :

    degrees_north

    array([-35.2     , -35.165122, -35.130244, -35.095365, -35.060487,
           -35.025609, -34.990731, -34.955852, -34.920974, -34.886096,
           -34.851218, -34.816339, -34.781461, -34.746583, -34.711705,
           -34.212923, -33.714141, -33.215359, -32.716577, -32.217795,
           -31.719013, -31.220231, -30.721448, -30.222666, -29.723884,
           -29.225102, -28.72632 , -28.227538, -27.728756, -27.229974,
           -26.731192, -26.23241 , -25.733628, -25.234846, -24.736064,
           -24.237282, -23.7385  , -23.239718, -22.740936, -22.775814,
           -22.810693, -22.845571, -22.880449, -22.915327, -22.950206,
           -22.985084, -23.019962, -23.05484 , -23.089718, -23.124597,
           -23.159475, -23.194353, -23.229231, -23.728013, -24.226795,
           -24.725577, -25.224359, -25.723142, -26.221924, -26.720706,
           -27.219488, -27.71827 , -28.217052, -28.715834, -29.214616,
           -29.713398, -30.21218 , -30.710962, -31.209744, -31.708526,
           -32.207308, -32.70609 , -33.204872, -33.703654, -34.202436,
           -34.701218, -35.2     ])

  • lon
    (site)
    float64
    110.0 110.5 111.0 ... 110.0 110.0

    standard_name :

    longitude

    units :

    degrees_east

    array([110.      , 110.498782, 110.997564, 111.496346, 111.995128,
           112.49391 , 112.992692, 113.491474, 113.990256, 114.489038,
           114.98782 , 115.486602, 115.985384, 116.484166, 116.982948,
           116.94807 , 116.913192, 116.878314, 116.843435, 116.808557,
           116.773679, 116.738801, 116.703922, 116.669044, 116.634166,
           116.599288, 116.56441 , 116.529531, 116.494653, 116.459775,
           116.424897, 116.390018, 116.35514 , 116.320262, 116.285384,
           116.250505, 116.215627, 116.180749, 116.145871, 115.647089,
           115.148307, 114.649525, 114.150743, 113.651961, 113.153179,
           112.654396, 112.155614, 111.656832, 111.15805 , 110.659268,
           110.160486, 109.661704, 109.162922, 109.197801, 109.232679,
           109.267557, 109.302435, 109.337313, 109.372192, 109.40707 ,
           109.441948, 109.476826, 109.511705, 109.546583, 109.581461,
           109.616339, 109.651218, 109.686096, 109.720974, 109.755852,
           109.790731, 109.825609, 109.860487, 109.895365, 109.930244,
           109.965122, 110.      ])

• Indexes: (4)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-01-01 00:00:00', '2023-01-01 06:00:00',
                   '2023-01-01 12:00:00', '2023-01-01 18:00:00',
                   '2023-01-02 00:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • site
    PandasIndex

    PandasIndex(Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
           19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
           37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
           55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
           73, 74, 75, 76, 77],
          dtype='int64', name='site'))

  • freq
    PandasIndex

    PandasIndex(Index([0.05417, 0.05959, 0.06555,  0.0721, 0.07931, 0.08724, 0.09597, 0.10557,
           0.11612, 0.12773, 0.14051],
          dtype='float64', name='freq'))

  • dir
    PandasIndex

    PandasIndex(Index([0.0, 45.0, 90.0, 135.0, 180.0, 225.0, 270.0, 315.0], dtype='float64', name='dir'))

• Attributes: (0)

SWAN components¶

SWAN commands can be fully prescribed using what we define as "Components". Components are pydantic objects that describe the different sets of command instruction in SWAN with fields that matching command options and a render() method that returns the string to render in the INPUT command file.

The SwanConfig config class takes the components as fields organised as "group" components, a collection of individual components that are defined together and validated for consistency. These groups are structured similarly to the main groups of SWAN commands as defined by the subsections in Chapter 4 of the user manual.

In [18]:

from rompy_swan.config import SwanConfig

SwanConfig?

from rompy_swan.config import SwanConfig SwanConfig?

Init signature:
SwanConfig(
    *,
    model_type: Literal['swan', 'SWAN'] = 'swan',
    template: str = '/source/csiro/rompy-swan/src/rompy_swan/templates/swan',
    checkout: Optional[str] = 'main',
    cgrid: Union[rompy_swan.components.cgrid.REGULAR, rompy_swan.components.cgrid.CURVILINEAR, rompy_swan.components.cgrid.UNSTRUCTURED],
    startup: Optional[Annotated[rompy_swan.components.group.STARTUP, FieldInfo(annotation=NoneType, required=True, description='Startup components')]] = None,
    inpgrid: Optional[Annotated[Union[rompy_swan.components.group.INPGRIDS, rompy_swan.interface.DataInterface], FieldInfo(annotation=NoneType, required=True, description='Input grid components', discriminator='model_type')]] = None,
    boundary: Optional[Annotated[Union[rompy_swan.components.boundary.BOUNDSPEC, rompy_swan.components.boundary.BOUNDNEST1, rompy_swan.components.boundary.BOUNDNEST2, rompy_swan.components.boundary.BOUNDNEST3, rompy_swan.interface.BoundaryInterface], FieldInfo(annotation=NoneType, required=True, description='Boundary component', discriminator='model_type')]] = None,
    initial: Optional[Annotated[rompy_swan.components.boundary.INITIAL, FieldInfo(annotation=NoneType, required=True, description='Initial component')]] = None,
    physics: Optional[Annotated[rompy_swan.components.group.PHYSICS, FieldInfo(annotation=NoneType, required=True, description='Physics components')]] = None,
    prop: Optional[Annotated[rompy_swan.components.numerics.PROP, FieldInfo(annotation=NoneType, required=True, description='Propagation components')]] = None,
    numeric: Optional[Annotated[rompy_swan.components.numerics.NUMERIC, FieldInfo(annotation=NoneType, required=True, description='Numerics components')]] = None,
    output: Optional[Annotated[rompy_swan.components.group.OUTPUT, FieldInfo(annotation=NoneType, required=True, description='Output components')]] = None,
    lockup: Optional[Annotated[rompy_swan.components.group.LOCKUP, FieldInfo(annotation=NoneType, required=True, description='Output components')]] = None,
    **data: Any,
) -> None
Docstring:     
SWAN config class.

TODO: Combine boundary and inpgrid into a single input type.

Note
----
The `cgrid` is the only required field since it is used to define the swan grid
object which is passed to other components.
Init docstring:
Create a new model by parsing and validating input data from keyword arguments.

Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.

`self` is explicitly positional-only to allow `self` as a field name.
File:           /source/csiro/rompy-swan/src/rompy_swan/config.py
Type:           ModelMetaclass
Subclasses:     SwanConfigComponents

CGRID¶

In [19]:

from rompy_swan.components.cgrid import REGULAR
from rompy_swan.subcomponents.readgrid import GRIDREGULAR
from rompy_swan.subcomponents.spectrum import SPECTRUM

cgrid = REGULAR(
    grid=GRIDREGULAR(
        xp=grid.x0,
        yp=grid.y0,
        alp=grid.rot,
        xlen=grid.xlen,
        ylen=grid.ylen,
        mx=grid.nx -1,
        my=grid.ny -1,
    ),
    spectrum=SPECTRUM(
        mdc=36,
        flow=0.04,
        fhigh=1.0,
    ),
)

print(cgrid.render())

from rompy_swan.components.cgrid import REGULAR from rompy_swan.subcomponents.readgrid import GRIDREGULAR from rompy_swan.subcomponents.spectrum import SPECTRUM cgrid = REGULAR( grid=GRIDREGULAR( xp=grid.x0, yp=grid.y0, alp=grid.rot, xlen=grid.xlen, ylen=grid.ylen, mx=grid.nx -1, my=grid.ny -1, ), spectrum=SPECTRUM( mdc=36, flow=0.04, fhigh=1.0, ), ) print(cgrid.render())

CGRID REGULAR xpc=110.0 ypc=-35.2 alpc=4.0 xlenc=7.0 ylenc=12.0 mxc=14 myc=24 CIRCLE mdc=36 flow=0.04 fhigh=1.0

Startup¶

In [20]:

from rompy_swan.components.group import STARTUP
from rompy_swan.components.startup import PROJECT, SET, MODE, COORDINATES
from rompy_swan.subcomponents.startup import SPHERICAL

project = PROJECT(
    name="Test procedural",
    nr="run1",
    title1="Procedural definition of a Swan config with rompy",
)

set = SET(level=0.0, depmin=0.05, direction_convention="nautical")

mode = MODE(kind="nonstationary", dim="twodimensional")

coordinates = COORDINATES(kind=SPHERICAL())

startup = STARTUP(
    project=project,
    set=set,
    mode=mode,
    coordinates=coordinates,
)

print(startup.render())

from rompy_swan.components.group import STARTUP from rompy_swan.components.startup import PROJECT, SET, MODE, COORDINATES from rompy_swan.subcomponents.startup import SPHERICAL project = PROJECT( name="Test procedural", nr="run1", title1="Procedural definition of a Swan config with rompy", ) set = SET(level=0.0, depmin=0.05, direction_convention="nautical") mode = MODE(kind="nonstationary", dim="twodimensional") coordinates = COORDINATES(kind=SPHERICAL()) startup = STARTUP( project=project, set=set, mode=mode, coordinates=coordinates, ) print(startup.render())

PROJECT name='Test procedural' nr='run1' title1='Procedural definition of a Swan config with rompy'

SET level=0.0 depmin=0.05 NAUTICAL

MODE NONSTATIONARY TWODIMENSIONAL

COORDINATES SPHERICAL CCM

Input grids¶

We will prescribe input grids from our previously defined SwanDataGrid objects using the DataInterface object. This object is used by SwanConfig as an interface to pass around times and grids between model and data objects, create model input times and generate consistent CMD instructions.

In [21]:

from rompy_swan.interface import DataInterface

inpgrid = DataInterface(
    bottom=bottom,
    input=[wind],
)

inpgrid

from rompy_swan.interface import DataInterface inpgrid = DataInterface( bottom=bottom, input=[wind], ) inpgrid

Out[21]:

DataInterface(model_type='data_interface', bottom=SwanDataGrid(model_type='grid', id='data', source=SourceDataset(model_type='dataset', obj=<xarray.Dataset> Size: 5kB
Dimensions:    (lat: 30, lon: 21)
Coordinates:
  * lon        (lon) float64 168B 108.2 108.7 109.2 109.7 ... 117.2 117.7 118.2
  * lat        (lat) float64 240B -36.2 -35.7 -35.2 -34.7 ... -22.7 -22.2 -21.7
Data variables:
    elevation  (lat, lon) float64 5kB -5.443e+03 -5.552e+03 ... 333.9 346.7
Attributes:
    title:    Subset of the GEBCO 2020 grid for testing purposes), filter=Filter(sort={}, subset={}, crop={}, timenorm={}, rename={}, derived={}), variables=['elevation'], coords=DatasetCoords(t='time', x='lon', y='lat', z=None, s=None), crop_data=False, buffer=0.0, time_buffer=[0, 0], z1='elevation', z2=None, var=<GridOptions.BOTTOM: 'bottom'>, fac=-1.0), input=[SwanDataGrid(model_type='grid', id='data', source=SourceDataset(model_type='dataset', obj=<xarray.Dataset> Size: 51kB
Dimensions:    (time: 5, latitude: 30, longitude: 21)
Coordinates:
  * time       (time) datetime64[ns] 40B 2023-01-01 ... 2023-01-02
  * longitude  (longitude) float64 168B 108.2 108.7 109.2 ... 117.2 117.7 118.2
  * latitude   (latitude) float64 240B -36.2 -35.7 -35.2 ... -22.7 -22.2 -21.7
Data variables:
    u10        (time, latitude, longitude) float64 25kB 3.054 3.084 ... 1.339
    v10        (time, latitude, longitude) float64 25kB 4.884 4.951 ... 0.9003
Attributes:
    Conventions:  CF-1.6
    history:      2023-06-10 00:03:38 GMT by grib_to_netcdf-2.25.1: /opt/ecmw...), filter=Filter(sort={'coords': ['latitude']}, subset={}, crop={}, timenorm={}, rename={}, derived={}), variables=['u10', 'v10'], coords=DatasetCoords(t='time', x='longitude', y='latitude', z=None, s=None), crop_data=False, buffer=0.0, time_buffer=[0, 0], z1='u10', z2='v10', var=<GridOptions.WIND: 'wind'>, fac=1.0)])

Boundary¶

Boundary can be defined either from a SWAN boundary component or using the BoundaryInterface class which works in an analogous way to DataInterface.

Below we define a pure parametric boundary using the BOUNDSPEC component just to demonstrate it:

In [22]:

from rompy_swan.components.boundary import  BOUNDSPEC
from rompy_swan.subcomponents.boundary import SIDE, CONSTANTPAR
from rompy_swan.subcomponents.spectrum import SHAPESPEC, JONSWAP

shape = JONSWAP(gamma=3.3)

shapespec = SHAPESPEC(shape=shape, per_type="peak", dspr_type="degrees")

location = SIDE(side="west", direction="ccw")

data = CONSTANTPAR(hs=2.0, per=12.0, dir=255.0, dd=25.0)

boundary_parametric = BOUNDSPEC(shapespec=shapespec, location=location, data=data)

print(boundary_parametric.render())

from rompy_swan.components.boundary import BOUNDSPEC from rompy_swan.subcomponents.boundary import SIDE, CONSTANTPAR from rompy_swan.subcomponents.spectrum import SHAPESPEC, JONSWAP shape = JONSWAP(gamma=3.3) shapespec = SHAPESPEC(shape=shape, per_type="peak", dspr_type="degrees") location = SIDE(side="west", direction="ccw") data = CONSTANTPAR(hs=2.0, per=12.0, dir=255.0, dd=25.0) boundary_parametric = BOUNDSPEC(shapespec=shapespec, location=location, data=data) print(boundary_parametric.render())

BOUND SHAPESPEC JONSWAP gamma=3.3 PEAK DSPR DEGREES
BOUNDSPEC SIDE WEST CCW CONSTANT PAR hs=2.0 per=12.0 dir=255.0 dd=25.0

And here we define boundary from the data using the BoundaryInterface object which interfaces that with the time and grid objects within SwanConfig:

In [23]:

from rompy_swan.interface import BoundaryInterface

boundary_interface = BoundaryInterface(
    kind=boundary_from_data
)

boundary_interface

from rompy_swan.interface import BoundaryInterface boundary_interface = BoundaryInterface( kind=boundary_from_data ) boundary_interface

Out[23]:

BoundaryInterface(model_type='boundary_interface', kind=Boundnest1(model_type='boundnest1', id='westaus', source=SourceDataset(model_type='dataset', obj=<xarray.Dataset> Size: 1MB
Dimensions:  (site: 412, time: 5, freq: 11, dir: 8)
Coordinates:
  * time     (time) datetime64[ns] 40B 2023-01-01 ... 2023-01-02
  * site     (site) int64 3kB 0 4 8 12 16 20 ... 1624 1628 1632 1636 1640 1644
  * freq     (freq) float32 44B 0.05417 0.05959 0.06555 ... 0.1161 0.1277 0.1405
  * dir      (dir) float32 32B 0.0 45.0 90.0 135.0 180.0 225.0 270.0 315.0
Data variables:
    lon      (site) float32 2kB 108.0 108.0 108.0 108.0 ... 157.0 157.0 157.0
    lat      (site) float32 2kB -42.0 -34.0 -26.0 -18.0 ... -19.0 -12.0 -9.0
    efth     (time, site, freq, dir) float64 1MB ...
    dpt      (time, site) float32 8kB ...
    wspd     (time, site) float32 8kB ...
    wdir     (time, site) float32 8kB ...
Attributes: (12/16)
    product_name:           ww3.all_spec.nc
    area:                   Global 0.5 x 0.5 degree
    data_type:              OCO spectra 2D
    format_version:         1.1
    southernmost_latitude:  n/a
    northernmost_latitude:  n/a
    ...                     ...
    minimum_altitude:       n/a
    maximum_altitude:       n/a
    altitude_resolution:    n/a
    start_date:             2023-01-01 00:00:00
    stop_date:              2023-02-01 00:00:00
    field_type:             3-hourly), filter=Filter(sort={}, subset={}, crop={'time': Slice(start=Timestamp('2023-01-01 00:00:00'), stop=Timestamp('2023-01-02 00:00:00')), 'longitude': Slice(start=107.1629223150705, stop=118.98294835181876), 'latitude': Slice(start=-37.2, stop=-20.740936080673237)}, timenorm={}, rename={}, derived={}), variables=['efth', 'lon', 'lat'], coords=DatasetCoords(t='time', x='longitude', y='latitude', z=None, s=None), crop_data=True, buffer=2.0, time_buffer=[0, 0], spacing=None, sel_method='idw', sel_method_kwargs={'tolerance': 4}, grid_type='boundary_wave_station', rectangle='closed'))

Initial conditions¶

Components are available to represent the different initial conditions options in SWAN including DEFAULT, ZERO, PAR and HOTSTART

TODO: define an interface to define initial conditions.

In [24]:

from rompy_swan.components.boundary import INITIAL
from rompy_swan.subcomponents.boundary import DEFAULT

initial = INITIAL(kind=DEFAULT())

print(initial.render())

from rompy_swan.components.boundary import INITIAL from rompy_swan.subcomponents.boundary import DEFAULT initial = INITIAL(kind=DEFAULT()) print(initial.render())

INITIAL DEFAULT

Physics¶

The Components support every SWAN physics command option. They are prescribed in the SwanConfig using the PHYSICS group component.

In [25]:

from rompy_swan.components.group import PHYSICS
from rompy_swan.components.physics import GEN3, BREAKING_CONSTANT, FRICTION_RIPPLES, QUADRUPL, TRIAD
from rompy_swan.subcomponents.physics import WESTHUYSEN

gen = GEN3(source_terms=WESTHUYSEN(wind_drag="wu", cds2=5.0e-5, br=1.75e-3))

breaking = BREAKING_CONSTANT(alpha=1.0, gamma=0.73)

friction = FRICTION_RIPPLES(s=2.65, d=0.0001)

triad = TRIAD(itriad=1)

quad = QUADRUPL(iquad=2, lambd=0.25, cnl4=3.0e7, csh1=5.5, csh2=0.833, csh3=-1.25)

physics = PHYSICS(
    gen=gen,
    breaking=breaking,
    friction=friction,
    triad=triad,
    quadrupl=quad,
)

print(physics.render())

from rompy_swan.components.group import PHYSICS from rompy_swan.components.physics import GEN3, BREAKING_CONSTANT, FRICTION_RIPPLES, QUADRUPL, TRIAD from rompy_swan.subcomponents.physics import WESTHUYSEN gen = GEN3(source_terms=WESTHUYSEN(wind_drag="wu", cds2=5.0e-5, br=1.75e-3)) breaking = BREAKING_CONSTANT(alpha=1.0, gamma=0.73) friction = FRICTION_RIPPLES(s=2.65, d=0.0001) triad = TRIAD(itriad=1) quad = QUADRUPL(iquad=2, lambd=0.25, cnl4=3.0e7, csh1=5.5, csh2=0.833, csh3=-1.25) physics = PHYSICS( gen=gen, breaking=breaking, friction=friction, triad=triad, quadrupl=quad, ) print(physics.render())

GEN3 WESTHUYSEN cds2=5e-05 br=0.00175 DRAG WU

QUADRUPL iquad=2 lambda=0.25 cnl4=30000000.0 csh1=5.5 csh2=0.833 csh3=-1.25

BREAKING CONSTANT alpha=1.0 gamma=0.73

FRICTION RIPPLES S=2.65 D=0.0001

TRIAD itriad=1

Propagation scheme¶

In [26]:

from rompy_swan.components.numerics import PROP
from rompy_swan.subcomponents.numerics import BSBT

prop = PROP(scheme=BSBT())

print(prop.render())

from rompy_swan.components.numerics import PROP from rompy_swan.subcomponents.numerics import BSBT prop = PROP(scheme=BSBT()) print(prop.render())

PROP BSBT

Numerics¶

In [27]:

from rompy_swan.components.numerics import NUMERIC
from rompy_swan.subcomponents.numerics import STAT, STOPC, DIRIMPL

stopc = STOPC(dabs=0.02, drel=0.02, curvat=0.02, npnts=98, mode=STAT(mxitst=50))

dirimpl = DIRIMPL(cdd=0.5)

numeric = NUMERIC(stop=stopc, dirimpl=dirimpl)

print(numeric.render())

from rompy_swan.components.numerics import NUMERIC from rompy_swan.subcomponents.numerics import STAT, STOPC, DIRIMPL stopc = STOPC(dabs=0.02, drel=0.02, curvat=0.02, npnts=98, mode=STAT(mxitst=50)) dirimpl = DIRIMPL(cdd=0.5) numeric = NUMERIC(stop=stopc, dirimpl=dirimpl) print(numeric.render())

NUMERIC STOPC dabs=0.02 drel=0.02 curvat=0.02 npnts=98.0 STATIONARY mxitst=50 DIRIMPL cdd=0.5

Output¶

Output commands are defined in SwanConfig with the OUTPUT group component. Many validations are defined to ensure location and write components are prescribed correctly.

The output write components (and the lockup ones) require times, however we can skip defining times here as SwanConfig will ensure consistent times are defined for all time-dependant components.

In [28]:

from rompy_swan.components.group import OUTPUT
from rompy_swan.components.output import POINTS, QUANTITY, QUANTITIES, BLOCK, TABLE, SPECOUT
from rompy_swan.subcomponents.output import SPEC2D, ABS
from rompy_swan.subcomponents.time import TimeRangeOpen

points = POINTS(
    sname="pts",
    xp=[114.0, 112.5, 115.0],
    yp=[-34.0, -26.0, -30.0],
)

q1 = QUANTITY(output=["hsign"], hexp=50.0)
q2 = QUANTITY(output=["hsign", "tps"], fmin=0.04, fmax=0.3)
q3 = QUANTITY(output=["hswell"], fswell=0.125)
quantity = QUANTITIES(quantities=[q1, q2, q3])

block = BLOCK(
    sname="COMPGRID",
    fname="outgrid.nc",
    output=["depth", "wind", "hsign", "hswell", "dir", "tps"],
    times=TimeRangeOpen(tfmt=1, dfmt="min"), # Default times which will be overwritten
    idla=3,
)

table = TABLE(
    sname="pts",
    fname="outpts.txt",
    output=["time", "hsign", "dir", "tps", "tm01"],
    times=TimeRangeOpen(tfmt=1, dfmt="min"), # Default times which will be overwritten
)

specout = SPECOUT(
    sname="pts",
    fname="swanspec.nc",
    dim=SPEC2D(),
    freq=ABS(),
    times=TimeRangeOpen(tfmt=1, dfmt="min"), # Default times which will be overwritten
)

output = OUTPUT(
    points=points,
    quantity=quantity,
    block=block,
    table=table,
    specout=specout,
)

print(output.render())

from rompy_swan.components.group import OUTPUT from rompy_swan.components.output import POINTS, QUANTITY, QUANTITIES, BLOCK, TABLE, SPECOUT from rompy_swan.subcomponents.output import SPEC2D, ABS from rompy_swan.subcomponents.time import TimeRangeOpen points = POINTS( sname="pts", xp=[114.0, 112.5, 115.0], yp=[-34.0, -26.0, -30.0], ) q1 = QUANTITY(output=["hsign"], hexp=50.0) q2 = QUANTITY(output=["hsign", "tps"], fmin=0.04, fmax=0.3) q3 = QUANTITY(output=["hswell"], fswell=0.125) quantity = QUANTITIES(quantities=[q1, q2, q3]) block = BLOCK( sname="COMPGRID", fname="outgrid.nc", output=["depth", "wind", "hsign", "hswell", "dir", "tps"], times=TimeRangeOpen(tfmt=1, dfmt="min"), # Default times which will be overwritten idla=3, ) table = TABLE( sname="pts", fname="outpts.txt", output=["time", "hsign", "dir", "tps", "tm01"], times=TimeRangeOpen(tfmt=1, dfmt="min"), # Default times which will be overwritten ) specout = SPECOUT( sname="pts", fname="swanspec.nc", dim=SPEC2D(), freq=ABS(), times=TimeRangeOpen(tfmt=1, dfmt="min"), # Default times which will be overwritten ) output = OUTPUT( points=points, quantity=quantity, block=block, table=table, specout=specout, ) print(output.render())

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

QUANTITY HSIGN hexp=50.0

QUANTITY HSIGN TPS fmin=0.04 fmax=0.3

QUANTITY HSWELL fswell=0.125

BLOCK sname='COMPGRID' fname='outgrid.nc' LAYOUT idla=3 &
    DEPTH &
    WIND &
    HSIGN &
    HSWELL &
    DIR &
    TPS &
    OUTPUT tbegblk=19700101.000000 deltblk=60.0 MIN

TABLE sname='pts' fname='outpts.txt' &
    TIME &
    HSIGN &
    DIR &
    TPS &
    TM01 &
    OUTPUT tbegtbl=19700101.000000 delttbl=60.0 MIN

SPECOUT sname='pts' SPEC2D ABS fname='swanspec.nc' OUTPUT tbegspc=19700101.000000 deltspc=60.0 MIN

Lockup¶

The lockup components are prescribed to the SwanConfig class from the LOCKUP group component. similar to the output components, time-based fields do not need to be prescribed as they will be reset in the config class, however some time parameters such as tfmt and dfmt are maintained if defined so they could be defined here.

In [29]:

from rompy_swan.components.group import LOCKUP
from rompy_swan.components.lockup import COMPUTE_STAT, HOTFILE
from rompy_swan.subcomponents.time import NONSTATIONARY

hotfile = HOTFILE(fname="hotfile.swn", format="free")

compute = COMPUTE_STAT(
    times=NONSTATIONARY(tfmt=1, dfmt="hr"), # We use nonstationary times here to prescribe multiple STAT commands
    hotfile=hotfile,
    hottimes=[1, -1], # Output hotfile after the 2nd and last time steps
)

lockup = LOCKUP(compute=compute)

print(lockup.render())

from rompy_swan.components.group import LOCKUP from rompy_swan.components.lockup import COMPUTE_STAT, HOTFILE from rompy_swan.subcomponents.time import NONSTATIONARY hotfile = HOTFILE(fname="hotfile.swn", format="free") compute = COMPUTE_STAT( times=NONSTATIONARY(tfmt=1, dfmt="hr"), # We use nonstationary times here to prescribe multiple STAT commands hotfile=hotfile, hottimes=[1, -1], # Output hotfile after the 2nd and last time steps ) lockup = LOCKUP(compute=compute) print(lockup.render())

COMPUTE STATIONARY time=19700101.000000
COMPUTE STATIONARY time=19700101.010000
HOTFILE fname='hotfile_19700101T010000.swn' FREE
COMPUTE STATIONARY time=19700101.020000
COMPUTE STATIONARY time=19700101.030000
COMPUTE STATIONARY time=19700101.040000
COMPUTE STATIONARY time=19700101.050000
COMPUTE STATIONARY time=19700101.060000
COMPUTE STATIONARY time=19700101.070000
COMPUTE STATIONARY time=19700101.080000
COMPUTE STATIONARY time=19700101.090000
COMPUTE STATIONARY time=19700101.100000
COMPUTE STATIONARY time=19700101.110000
COMPUTE STATIONARY time=19700101.120000
COMPUTE STATIONARY time=19700101.130000
COMPUTE STATIONARY time=19700101.140000
COMPUTE STATIONARY time=19700101.150000
COMPUTE STATIONARY time=19700101.160000
COMPUTE STATIONARY time=19700101.170000
COMPUTE STATIONARY time=19700101.180000
COMPUTE STATIONARY time=19700101.190000
COMPUTE STATIONARY time=19700101.200000
COMPUTE STATIONARY time=19700101.210000
COMPUTE STATIONARY time=19700101.220000
COMPUTE STATIONARY time=19700101.230000
COMPUTE STATIONARY time=19700102.000000
HOTFILE fname='hotfile_19700102T000000.swn' FREE
STOP

Instantiate config¶

Note each field is optional so it is possible to skip defining a certain group component such as prop to allow using default options in SWAN.

In [30]:

config = SwanConfig(
    cgrid=cgrid,
    startup=startup,
    inpgrid=inpgrid,
    initial=initial,
    boundary=boundary_interface,
    physics=physics,
    prop=prop,
    numeric=numeric,
    output=output,
    lockup=lockup,
)

config = SwanConfig( cgrid=cgrid, startup=startup, inpgrid=inpgrid, initial=initial, boundary=boundary_interface, physics=physics, prop=prop, numeric=numeric, output=output, lockup=lockup, )

Generate workspace¶

In [31]:

from rompy.model import ModelRun
from rompy.core.time import TimeRange

run = ModelRun(
    run_id="run1",
    period=times,
    output_dir=str(workdir),
    config=config,
)

rundir = run()

from rompy.model import ModelRun from rompy.core.time import TimeRange run = ModelRun( run_id="run1", period=times, output_dir=str(workdir), config=config, ) rundir = run()

2025-09-18 21:17:29 [INFO] rompy.model         : ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
2025-09-18 21:17:29 [INFO] rompy.model         : ┃                      MODEL RUN CONFIGURATION                       ┃
2025-09-18 21:17:29 [INFO] rompy.model         : ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
2025-09-18 21:17:29 [INFO] rompy.model         :             Run ID : run1
2025-09-18 21:17:29 [INFO] rompy.model         :         Model Type : SwanConfig
2025-09-18 21:17:29 [INFO] rompy.model         :         Start Time : 2023-01-01T00:00:00
2025-09-18 21:17:29 [INFO] rompy.model         :           End Time : 2023-01-02T00:00:00
2025-09-18 21:17:29 [INFO] rompy.model         :           Duration : 1 day
2025-09-18 21:17:29 [INFO] rompy.model         :      Time Interval : 1:00:00
2025-09-18 21:17:29 [INFO] rompy.model         :   Output Directory : example_procedural
2025-09-18 21:17:29 [INFO] rompy.model         : ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
2025-09-18 21:17:29 [INFO] rompy.model         : ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
2025-09-18 21:17:29 [INFO] rompy.model         : 
2025-09-18 21:17:29 [INFO] root                : ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
2025-09-18 21:17:29 [INFO] root                : ┃                  MODEL CONFIGURATION (SwanConfig)                  ┃
2025-09-18 21:17:29 [INFO] root                : ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
2025-09-18 21:17:29 [INFO] root                : 
2025-09-18 21:17:29 [INFO] rompy.model         : SwanConfig:
2025-09-18 21:17:29 [INFO] rompy.model         :   ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
2025-09-18 21:17:29 [INFO] rompy.model         :   ┃                   SWAN COMPONENTS CONFIGURATION                    ┃
2025-09-18 21:17:29 [INFO] rompy.model         :   ┠━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┨
2025-09-18 21:17:29 [INFO] rompy.model         :     • Computational Grid: REGULAR
2025-09-18 21:17:29 [INFO] rompy.model         :         Resolution: 14x24 cells
2025-09-18 21:17:29 [INFO] rompy.model         :         Origin: (110.0, -35.2)
2025-09-18 21:17:29 [INFO] rompy.model         :         Rotation: 4.0°
2025-09-18 21:17:29 [INFO] rompy.model         :         Size: 7.0x12.0
2025-09-18 21:17:29 [INFO] rompy.model         :     • Startup: STARTUP
2025-09-18 21:17:29 [INFO] rompy.model         :     • Input Grid: DataInterface
2025-09-18 21:17:29 [INFO] rompy.model         :     • Boundary: BoundaryInterface
2025-09-18 21:17:29 [INFO] rompy.model         :     • Initial: INITIAL
2025-09-18 21:17:29 [INFO] rompy.model         :     • Physics: PHYSICS
2025-09-18 21:17:29 [INFO] rompy.model         :         Generation: GEN3
2025-09-18 21:17:29 [INFO] rompy.model         :         Breaking: BREAKING_CONSTANT
2025-09-18 21:17:29 [INFO] rompy.model         :         Friction: FRICTION_RIPPLES
2025-09-18 21:17:29 [INFO] rompy.model         :     • Propagation: PROP
2025-09-18 21:17:29 [INFO] rompy.model         :     • Numerics: NUMERIC
2025-09-18 21:17:29 [INFO] rompy.model         :     • Output: OUTPUT
2025-09-18 21:17:29 [INFO] rompy.model         :         Quantities: 3 output group(s)
2025-09-18 21:17:29 [INFO] rompy.model         :         Block output: Yes
2025-09-18 21:17:29 [INFO] rompy.model         :         Spectral output: Yes
2025-09-18 21:17:29 [INFO] rompy.model         :     • Lock-up: LOCKUP
2025-09-18 21:17:29 [INFO] rompy.model         :   
2025-09-18 21:17:29 [INFO] rompy.model         :     • Template: .../csiro/rompy-swan/src/rompy_swan/templates/swan
2025-09-18 21:17:29 [INFO] rompy.model         :   ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
2025-09-18 21:17:29 [INFO] rompy.model         : 
2025-09-18 21:17:29 [INFO] rompy.model         : ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
2025-09-18 21:17:29 [INFO] rompy.model         : ┃                     STARTING MODEL GENERATION                      ┃
2025-09-18 21:17:29 [INFO] rompy.model         : ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
2025-09-18 21:17:29 [INFO] rompy.model         : Preparing input files in example_procedural
2025-09-18 21:17:29 [INFO] rompy.model         : Processing model configuration...
2025-09-18 21:17:29 [INFO] rompy.model         : Running configuration callable...
2025-09-18 21:17:29 [INFO] rompy_swan.config   : ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
2025-09-18 21:17:29 [INFO] rompy_swan.config   : ┃                   PROCESSING SWAN CONFIGURATION                    ┃
2025-09-18 21:17:29 [INFO] rompy_swan.config   : ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
2025-09-18 21:17:29 [INFO] rompy_swan.config   : 
2025-09-18 21:17:29 [INFO] rompy_swan.config   : Configuration components:
2025-09-18 21:17:29 [INFO] rompy_swan.config   :   - CGRID: REGULAR
2025-09-18 21:17:29 [INFO] rompy_swan.config   :     ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
2025-09-18 21:17:29 [INFO] rompy_swan.config   :     ┃                         GRID CONFIGURATION                         ┃
2025-09-18 21:17:29 [INFO] rompy_swan.config   :     ┠━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┨
2025-09-18 21:17:29 [INFO] rompy_swan.config   :       • Type:       REG
2025-09-18 21:17:29 [INFO] rompy_swan.config   :       • Resolution: 14x24 cells
2025-09-18 21:17:29 [INFO] rompy_swan.config   :       • Origin:     (110.0, -35.2)
2025-09-18 21:17:29 [INFO] rompy_swan.config   :       • Rotation:   4.0°
2025-09-18 21:17:29 [INFO] rompy_swan.config   :       • Size:       7.0x12.0
2025-09-18 21:17:29 [INFO] rompy_swan.config   :     
2025-09-18 21:17:29 [INFO] rompy_swan.config   :       • Spectrum:
2025-09-18 21:17:29 [INFO] rompy_swan.config   :           Direction bins: 36
2025-09-18 21:17:29 [INFO] rompy_swan.config   :           Frequency range: 0.04 - 1.0 Hz
2025-09-18 21:17:29 [INFO] rompy_swan.config   :     ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
2025-09-18 21:17:29 [INFO] rompy_swan.config   :   - Startup: STARTUP
2025-09-18 21:17:29 [INFO] rompy_swan.config   :   - Input Grid: DataInterface
2025-09-18 21:17:29 [INFO] rompy_swan.config   :   - Boundary: BoundaryInterface
2025-09-18 21:17:29 [INFO] rompy_swan.config   :   - Physics: PHYSICS
2025-09-18 21:17:29 [INFO] rompy_swan.config   :   - Output: OUTPUT
2025-09-18 21:17:29 [INFO] rompy_swan.config   : Rendering SWAN configuration components
2025-09-18 21:17:29 [INFO] rompy_swan.data     : ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
2025-09-18 21:17:29 [INFO] rompy_swan.data     : ┃                      WRITING BOTTOM GRID DATA                      ┃
2025-09-18 21:17:29 [INFO] rompy_swan.data     : ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
2025-09-18 21:17:29 [INFO] rompy_swan.data     :   • Output file: example_procedural/run1/bottom.grd
2025-09-18 21:17:29 [INFO] rompy_swan.data     :   • Variable: elevation with shape (30, 21)
2025-09-18 21:17:29 [INFO] rompy_swan.data     :   • Scaling factor: -1.0
2025-09-18 21:17:29 [INFO] rompy_swan.data     : ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
2025-09-18 21:17:29 [INFO] rompy_swan.data     : ┃                      WRITING SWAN ASCII DATA                       ┃
2025-09-18 21:17:29 [INFO] rompy_swan.data     : ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
2025-09-18 21:17:29 [INFO] rompy_swan.data     : 
2025-09-18 21:17:29 [INFO] rompy_swan.data     :   • Completed in 0.01 seconds
2025-09-18 21:17:29 [INFO] rompy_swan.data     :   • File size: 0.00 MB
2025-09-18 21:17:29 [INFO] rompy_swan.data     : ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
2025-09-18 21:17:29 [INFO] rompy_swan.data     : ┃                       WRITING WIND GRID DATA                       ┃
2025-09-18 21:17:29 [INFO] rompy_swan.data     : ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
2025-09-18 21:17:29 [INFO] rompy_swan.data     :   • Output file: example_procedural/run1/wind.grd
2025-09-18 21:17:29 [INFO] rompy_swan.data     :   • Variable: u10 with shape (5, 30, 21)
2025-09-18 21:17:29 [INFO] rompy_swan.data     :   • Variable: v10 with shape (5, 30, 21)
2025-09-18 21:17:29 [INFO] rompy_swan.data     :   • Scaling factor: 1.0
2025-09-18 21:17:30 [INFO] rompy_swan.data     :   • Completed in 0.02 seconds
2025-09-18 21:17:30 [INFO] rompy_swan.data     :   • File size: 0.03 MB
2025-09-18 21:17:30 [INFO] rompy_swan.interface: Generating boundary file: example_procedural/run1/westaus.bnd
2025-09-18 21:17:30 [INFO] rompy_swan.config   : ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
2025-09-18 21:17:30 [INFO] rompy_swan.config   : ┃               SWAN CONFIGURATION RENDERING COMPLETE                ┃
2025-09-18 21:17:30 [INFO] rompy_swan.config   : ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
2025-09-18 21:17:30 [INFO] rompy_swan.config   : 
2025-09-18 21:17:30 [INFO] rompy.model         : Rendering model templates to example_procedural/run1...
2025-09-18 21:17:30 [INFO] rompy.core.render   : Template source: /source/csiro/rompy-swan/src/rompy_swan/templates/swan
2025-09-18 21:17:30 [INFO] rompy.core.render   : Output directory: example_procedural
2025-09-18 21:17:30 [INFO] rompy.core.render   : Using template version: main
2025-09-18 21:17:30 [INFO] rompy.core.render   :   • Locating template repository...
2025-09-18 21:17:30 [INFO] rompy.core.render   : Template repository located at: /source/csiro/rompy-swan/src/rompy_swan/templates/swan
2025-09-18 21:17:30 [INFO] rompy.core.render   :   • Generating files from template...
2025-09-18 21:17:30 [INFO] rompy.core.render   :   • Rendering time:      0.02 seconds
2025-09-18 21:17:30 [INFO] rompy.core.render   :   • Total process time:  0.02 seconds
2025-09-18 21:17:30 [INFO] rompy.core.render   :   • Files created:       4
2025-09-18 21:17:30 [INFO] rompy.core.render   :   • Output location:     /source/csiro/rompy-notebooks/notebooks/swan/example_procedural/run1
2025-09-18 21:17:30 [INFO] rompy.model         : 
2025-09-18 21:17:30 [INFO] rompy.model         : ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
2025-09-18 21:17:30 [INFO] rompy.model         : ┃                     MODEL GENERATION COMPLETE                      ┃
2025-09-18 21:17:30 [INFO] rompy.model         : ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
2025-09-18 21:17:30 [INFO] rompy.model         : Model files generated at: /source/csiro/rompy-notebooks/notebooks/swan/example_procedural/run1

Check the workspace¶

In [32]:

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

sorted(modeldir.glob("*"))

modeldir = Path(run.output_dir) / run.run_id sorted(modeldir.glob("*"))

Out[32]:

[PosixPath('example_procedural/run1/INPUT'),
 PosixPath('example_procedural/run1/bottom.grd'),
 PosixPath('example_procedural/run1/westaus.bnd'),
 PosixPath('example_procedural/run1/wind.grd')]

In [33]:

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

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

! Rompy SwanConfig
! Template: /source/csiro/rompy-swan/src/rompy_swan/templates/swan
! Generated: 2025-09-18 09:17:29.934680 on rguedes-XPS-13-9350 by rguedes


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

PROJECT name='Test procedural' nr='run1' title1='Procedural 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=7.0 ylenc=12.0 mxc=14 myc=24 CIRCLE mdc=36 flow=0.04 fhigh=1.0

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

INPGRID BOTTOM REG 108.1629223150705 -36.2 0.0 20 29 0.5 0.5 EXC -99.0
READINP BOTTOM -1.0 'bottom.grd' 3 FREE

INPGRID WIND REG 108.1629223150705 -36.2 0.0 20 29 0.5 0.5 EXC -99.0 NONSTATION 20230101.000000 6.00 HR
READINP WIND 1.0 'wind.grd' 3 0 1 0 FREE



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

BOUNDNEST1 NEST 'westaus.bnd' CLOSED

INITIAL DEFAULT


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

GEN3 WESTHUYSEN cds2=5e-05 br=0.00175 DRAG WU

QUADRUPL iquad=2 lambda=0.25 cnl4=30000000.0 csh1=5.5 csh2=0.833 csh3=-1.25

BREAKING CONSTANT alpha=1.0 gamma=0.73

FRICTION RIPPLES S=2.65 D=0.0001

TRIAD itriad=1


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

PROP BSBT

NUMERIC STOPC dabs=0.02 drel=0.02 curvat=0.02 npnts=98.0 STATIONARY mxitst=50 DIRIMPL cdd=0.5


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

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

QUANTITY HSIGN hexp=50.0

QUANTITY HSIGN TPS fmin=0.04 fmax=0.3

QUANTITY HSWELL fswell=0.125

BLOCK sname='COMPGRID' fname='outgrid.nc' LAYOUT idla=3 &
    DEPTH &
    WIND &
    HSIGN &
    HSWELL &
    DIR &
    TPS &
    OUTPUT tbegblk=20230101.000000 deltblk=60.0 MIN

TABLE sname='pts' fname='outpts.txt' &
    TIME &
    HSIGN &
    DIR &
    TPS &
    TM01 &
    OUTPUT tbegtbl=20230101.000000 delttbl=60.0 MIN

SPECOUT sname='pts' SPEC2D ABS fname='swanspec.nc' OUTPUT tbegspc=20230101.000000 deltspc=60.0 MIN


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

COMPUTE STATIONARY time=20230101.000000
COMPUTE STATIONARY time=20230101.010000
HOTFILE fname='hotfile_20230101T010000.swn' FREE
COMPUTE STATIONARY time=20230101.020000
COMPUTE STATIONARY time=20230101.030000
COMPUTE STATIONARY time=20230101.040000
COMPUTE STATIONARY time=20230101.050000
COMPUTE STATIONARY time=20230101.060000
COMPUTE STATIONARY time=20230101.070000
COMPUTE STATIONARY time=20230101.080000
COMPUTE STATIONARY time=20230101.090000
COMPUTE STATIONARY time=20230101.100000
COMPUTE STATIONARY time=20230101.110000
COMPUTE STATIONARY time=20230101.120000
COMPUTE STATIONARY time=20230101.130000
COMPUTE STATIONARY time=20230101.140000
COMPUTE STATIONARY time=20230101.150000
COMPUTE STATIONARY time=20230101.160000
COMPUTE STATIONARY time=20230101.170000
COMPUTE STATIONARY time=20230101.180000
COMPUTE STATIONARY time=20230101.190000
COMPUTE STATIONARY time=20230101.200000
COMPUTE STATIONARY time=20230101.210000
COMPUTE STATIONARY time=20230101.220000
COMPUTE STATIONARY time=20230101.230000
COMPUTE STATIONARY time=20230102.000000
HOTFILE fname='hotfile_20230102T000000.swn' FREE
STOP

Run the model¶

Redirect to avoid large output

In [34]:

!docker run  -v ./example_procedural/run1:/home oceanum/swan:4141 swan.exe > example_procedural/swan.log
!tail example_procedural/swan.log

!docker run -v ./example_procedural/run1:/home oceanum/swan:4141 swan.exe > example_procedural/swan.log !tail example_procedural/swan.log

 iteration    4; sweep 1
+iteration    4; sweep 2
+iteration    4; sweep 3
+iteration    4; sweep 4
 accuracy OK in  98.89 % of wet grid points ( 98.00 % required)

+SWAN is processing output request    1
+SWAN is processing output request    2
+SWAN is processing output request    3

Plot outputs¶

In [35]:

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, read_swan
from wavespectra.core.swan import read_tab

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

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, read_swan from wavespectra.core.swan import read_tab pd.set_option("display.notebook_repr_html", False)

In [36]:

sorted(modeldir.glob("*"))

sorted(modeldir.glob("*"))

Out[36]:

[PosixPath('example_procedural/run1/INPUT'),
 PosixPath('example_procedural/run1/PRINT'),
 PosixPath('example_procedural/run1/bottom.grd'),
 PosixPath('example_procedural/run1/hotfile_20230101T010000.swn'),
 PosixPath('example_procedural/run1/hotfile_20230102T000000.swn'),
 PosixPath('example_procedural/run1/norm_end'),
 PosixPath('example_procedural/run1/outgrid.nc'),
 PosixPath('example_procedural/run1/outpts.txt'),
 PosixPath('example_procedural/run1/swaninit'),
 PosixPath('example_procedural/run1/swanspec.nc'),
 PosixPath('example_procedural/run1/westaus.bnd'),
 PosixPath('example_procedural/run1/wind.grd')]

In [37]:

# Gridded output

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

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

Out[37]:

<xarray.Dataset> Size: 266kB
Dimensions:    (time: 25, yc: 25, xc: 15)
Coordinates:
  * time       (time) datetime64[ns] 200B 2023-01-01 ... 2023-01-02
    longitude  (yc, xc) float32 2kB ...
    latitude   (yc, xc) float32 2kB ...
Dimensions without coordinates: yc, xc
Data variables:
    depth      (time, yc, xc) float32 38kB ...
    xwnd       (time, yc, xc) float32 38kB ...
    ywnd       (time, yc, xc) float32 38kB ...
    hs         (time, yc, xc) float32 38kB ...
    hswe       (time, yc, xc) float32 38kB ...
    theta0     (time, yc, xc) float32 38kB ...
    tps        (time, yc, xc) float32 38kB ...
Attributes:
    Conventions:             CF-1.5
    History:                 Created with agioncmd version 1.5
    Directional_convention:  nautical
    project:                 Test procedural
    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: (7)
  • 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]

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

    units :

    m

    standard_name :

    sea_surface_swell_wave_significant_height

    long_name :

    wave height of swell part

    [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]

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

    units :

    s

    long_name :

    tps

    [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 procedural

  run :

  run1

In [38]:

# Spectra output

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

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

Out[38]:

<xarray.Dataset> Size: 379kB
Dimensions:  (site: 3, time: 25, freq: 35, dir: 36)
Coordinates:
  * time     (time) datetime64[ns] 200B 2023-01-01 ... 2023-01-02
  * freq     (freq) float32 140B 0.04 0.04397 0.04834 ... 0.8275 0.9097 1.0
  * dir      (dir) float32 144B 261.0 251.0 241.0 231.0 ... 291.0 281.0 271.0
  * site     (site) int64 24B 1 2 3
Data variables:
    lon      (site) float32 12B dask.array<chunksize=(3,), meta=np.ndarray>
    lat      (site) float32 12B dask.array<chunksize=(3,), meta=np.ndarray>
    efth     (time, site, freq, dir) float32 378kB dask.array<chunksize=(25, 3, 35, 36), meta=np.ndarray>
    dpt      (time, site) float32 300B dask.array<chunksize=(25, 3), meta=np.ndarray>
    wspd     (time, site) float32 300B dask.array<chunksize=(25, 3), meta=np.ndarray>
    wdir     (time, site) float32 300B 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 procedural
    model:                   41.41
    run:                     run1

xarray.Dataset

• Dimensions:
  • site: 3
  • time: 25
  • freq: 35
  • dir: 36
• Coordinates: (4)
  • 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]')

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

    units :

    s-1

    standard_name :

    wave_frequency

    flow :

    0.04

    fhigh :

    0.9999985

    msc :

    34

    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

    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

    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 procedural

  model :

  41.41

  run :

  run1

In [39]:

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

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

%
%
% Run:run1  Table:pts               SWAN version:41.41
%
%       Time               Hsig          Dir           TPsmoo        Tm01     
%       [ ]                [m]           [degr]        [sec]         [sec]    
%
20230101.000000           4.0560       222.149       14.4021       10.9046 
20230101.000000           2.9192       211.237       14.2411        8.8702 
20230101.000000          -9.0000      -999.000       -9.0000       -9.0000 
20230101.010000           4.0446       222.069       14.3827       10.8835 
20230101.010000           2.9188       211.452       14.2370        8.9171 
20230101.010000          -9.0000      -999.000       -9.0000       -9.0000 
20230101.020000           4.0369       222.077       14.3634       10.8677 
20230101.020000           2.9401       211.405       14.2326        8.9359 

Out[39]:

0

In [40]:

# 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()

# 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()

Out[40]:

                       Hsig      Dir   TPsmoo     Tm01
time                                                  
2023-01-01 00:00:00  4.0560  222.149  14.4021  10.9046
2023-01-01 01:00:00  4.0446  222.069  14.3827  10.8835
2023-01-01 02:00:00  4.0369  222.077  14.3634  10.8677
2023-01-01 03:00:00  4.0299  222.086  14.3448  10.8482
2023-01-01 04:00:00  4.0209  222.084  14.3272  10.8374

Plot model depth¶

In [41]:

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();

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¶

In [42]:

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

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

Plot gridded wind¶

In [43]:

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=200)
    ax.plot(dspec.isel(site=0).lon, dspec.isel(site=0).lat, "ok")

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=200) ax.plot(dspec.isel(site=0).lon, dspec.isel(site=0).lat, "ok")

Plot spectra¶

In [44]:

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

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

Plot timeseries¶

In [45]:

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()

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()

Plot hotfile¶

In [46]:

hotfiles = sorted(modeldir.glob(f"{run.config.lockup.compute.hotfile.fname.stem}*"))
hotfiles

hotfiles = sorted(modeldir.glob(f"{run.config.lockup.compute.hotfile.fname.stem}*")) hotfiles

Out[46]:

[PosixPath('example_procedural/run1/hotfile_20230101T010000.swn'),
 PosixPath('example_procedural/run1/hotfile_20230102T000000.swn')]

In [47]:

# Investigate why the option to read as grid doesn't work

dset = read_swan(str(hotfiles[-1]), as_site=False)
stats = dset.spec.stats(["hs", "tp", "dpm"]).chunk()

fig, [ax1, ax2, ax3] = plt.subplots(1, 3, figsize=(15, 5), subplot_kw=dict(projection=ccrs.PlateCarree()))
p = ax1.scatter(dset.lon, dset.lat, s=15, c=stats.hs, vmin=0, vmax=4, cmap="turbo")
plt.colorbar(p, label="Hs (m)")
p = ax2.scatter(dset.lon, dset.lat, s=15, c=stats.tp, vmin=12, vmax=16, cmap="viridis")
plt.colorbar(p, label="Tp (s)")
p = ax3.scatter(dset.lon, dset.lat, s=15, c=stats.dpm, vmin=0, vmax=360, cmap="hsv")
plt.colorbar(p, label="Dpm (deg)")
for ax in [ax1, ax2, ax3]:
    ax.coastlines();

# Investigate why the option to read as grid doesn't work dset = read_swan(str(hotfiles[-1]), as_site=False) stats = dset.spec.stats(["hs", "tp", "dpm"]).chunk() fig, [ax1, ax2, ax3] = plt.subplots(1, 3, figsize=(15, 5), subplot_kw=dict(projection=ccrs.PlateCarree())) p = ax1.scatter(dset.lon, dset.lat, s=15, c=stats.hs, vmin=0, vmax=4, cmap="turbo") plt.colorbar(p, label="Hs (m)") p = ax2.scatter(dset.lon, dset.lat, s=15, c=stats.tp, vmin=12, vmax=16, cmap="viridis") plt.colorbar(p, label="Tp (s)") p = ax3.scatter(dset.lon, dset.lat, s=15, c=stats.dpm, vmin=0, vmax=360, cmap="hsv") plt.colorbar(p, label="Dpm (deg)") for ax in [ax1, ax2, ax3]: ax.coastlines();