SWAN BOUNDNEST1¶
This notebooks shows examples of how to prescribe BOUNDNEST1 spectral boundaries using rompy boundary data objects.
Boundary of type BOUNDNEST1 can be specified from existing spectral data using
the rompy_swan.boundary.Boundnest1 object. Spectra are interpolated or nearest-selected
from a source spectra dataset along the model grid and saved as a single SWAN SPEC2D
ASCII file.
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%load_ext autoreload
%autoreload 2
import os
from pathlib import Path
from tempfile import mkdtemp
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import wavespectra
from rompy.core.source import SourceFile
from rompy.core.time import TimeRange
from rompy_swan.grid import SwanGrid
from rompy_swan.boundary import Boundnest1
import warnings
warnings.filterwarnings('ignore')
%load_ext autoreload
%autoreload 2
import os
from pathlib import Path
from tempfile import mkdtemp
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import wavespectra
from rompy.core.source import SourceFile
from rompy.core.time import TimeRange
from rompy_swan.grid import SwanGrid
from rompy_swan.boundary import Boundnest1
import warnings
warnings.filterwarnings('ignore')
Helper functions¶
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def plot_boundary_side(dset, bnd, grid, time):
"""Plot side boundary stats.
This function plots a snapshot of wave statistics from the source spectra dataset
and the boundary wave stats generated for a grid from those spectra points.
Parameters
----------
dset: xr.Dataset
Source spectra dataset used to define boundary from.
bnd: xr.Dataset
Boundary dataset read from the SWAN ASCII file.
grid: SwanGrid
Grid object defining the SWAN domain.
time: Datatime | str
Time to plot.
"""
# xb, yb = bnd._boundary_points(grid)
xb = grid.x[:, 0]
yb = grid.y[:, 0]
fig, axs = plt.subplots(1, 3, subplot_kw=dict(projection=ccrs.PlateCarree()), figsize=(15, 3.5))
# Hs
ax = axs[0]
p = ax.scatter(dset.lon, dset.lat, s=50, c=dset.sel(time=time).spec.hs(), cmap="turbo", vmin=2.0, vmax=3.5)
plt.colorbar(p, label="Hs (m)")
ax.scatter(bnd.lon, bnd.lat, s=40, c=bnd.sel(time=time).spec.hs(), marker="s", edgecolor="k", linewidth=0.5, cmap="turbo", vmin=2.0, vmax=3.5)
# Tp
ax = axs[1]
p = ax.scatter(dset.lon, dset.lat, s=50, c=dset.sel(time=time).spec.tp(), cmap="viridis", vmin=13.8, vmax=14.5)
plt.colorbar(p, label="Tp (s)")
ax.scatter(bnd.lon, bnd.lat, s=40, c=bnd.sel(time=time).spec.tp(), marker="s", edgecolor="k", linewidth=0.5, cmap="viridis", vmin=13.8, vmax=14.5)
# Dpm
ax = axs[2]
p = ax.scatter(dset.lon, dset.lat, s=50, c=dset.sel(time=time).spec.dpm(), cmap="hsv", vmin=220, vmax=225)
plt.colorbar(p, label="Dpm (deg)")
ax.scatter(bnd.lon, bnd.lat, s=40, c=bnd.sel(time=time).spec.dpm(), marker="s", edgecolor="k", linewidth=0.5, cmap="hsv", vmin=220, vmax=225)
for ax in axs:
grid.plot(ax=ax, fscale=5)
ax.set_extent([110.5, 115.5, -29.5, -23.5])
ax.set_title(time)
ax.coastlines()
def plot_boundary_side(dset, bnd, grid, time):
"""Plot side boundary stats.
This function plots a snapshot of wave statistics from the source spectra dataset
and the boundary wave stats generated for a grid from those spectra points.
Parameters
----------
dset: xr.Dataset
Source spectra dataset used to define boundary from.
bnd: xr.Dataset
Boundary dataset read from the SWAN ASCII file.
grid: SwanGrid
Grid object defining the SWAN domain.
time: Datatime | str
Time to plot.
"""
# xb, yb = bnd._boundary_points(grid)
xb = grid.x[:, 0]
yb = grid.y[:, 0]
fig, axs = plt.subplots(1, 3, subplot_kw=dict(projection=ccrs.PlateCarree()), figsize=(15, 3.5))
# Hs
ax = axs[0]
p = ax.scatter(dset.lon, dset.lat, s=50, c=dset.sel(time=time).spec.hs(), cmap="turbo", vmin=2.0, vmax=3.5)
plt.colorbar(p, label="Hs (m)")
ax.scatter(bnd.lon, bnd.lat, s=40, c=bnd.sel(time=time).spec.hs(), marker="s", edgecolor="k", linewidth=0.5, cmap="turbo", vmin=2.0, vmax=3.5)
# Tp
ax = axs[1]
p = ax.scatter(dset.lon, dset.lat, s=50, c=dset.sel(time=time).spec.tp(), cmap="viridis", vmin=13.8, vmax=14.5)
plt.colorbar(p, label="Tp (s)")
ax.scatter(bnd.lon, bnd.lat, s=40, c=bnd.sel(time=time).spec.tp(), marker="s", edgecolor="k", linewidth=0.5, cmap="viridis", vmin=13.8, vmax=14.5)
# Dpm
ax = axs[2]
p = ax.scatter(dset.lon, dset.lat, s=50, c=dset.sel(time=time).spec.dpm(), cmap="hsv", vmin=220, vmax=225)
plt.colorbar(p, label="Dpm (deg)")
ax.scatter(bnd.lon, bnd.lat, s=40, c=bnd.sel(time=time).spec.dpm(), marker="s", edgecolor="k", linewidth=0.5, cmap="hsv", vmin=220, vmax=225)
for ax in axs:
grid.plot(ax=ax, fscale=5)
ax.set_extent([110.5, 115.5, -29.5, -23.5])
ax.set_title(time)
ax.coastlines()
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datadir = Path("../../../../rompy-swan/tests/data")
modeldir = Path("model")
modeldir.mkdir(exist_ok=True)
datadir = Path("../../../../rompy-swan/tests/data")
modeldir = Path("model")
modeldir.mkdir(exist_ok=True)
Define rompy objects¶
Create instances of source, time and grid objects to use with the boundary classes
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# source defines the dataset to be used to create the model boundary
source = SourceFile(
uri=datadir / "aus-20230101.nc",
kwargs=dict(engine="netcdf4"),
)
dset = source.open()
dset
# source defines the dataset to be used to create the model boundary
source = SourceFile(
uri=datadir / "aus-20230101.nc",
kwargs=dict(engine="netcdf4"),
)
dset = source.open()
dset
Out[4]:
<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-hourlyIn [5]:
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# times prescribe the times over which the model will run
times = TimeRange(start="2023-01-01T00", end="2023-01-02T00", interval="6h")
print(times)
# times prescribe the times over which the model will run
times = TimeRange(start="2023-01-01T00", end="2023-01-02T00", interval="6h")
print(times)
Start: 2023-01-01 00:00:00 End: 2023-01-02 00:00:00 Duration: 1 day Interval: 6:00:00 Include End: True
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# grid defines the model grid
grid = SwanGrid(x0=112, y0=-29, dx=0.5, dy=0.5, nx=8, ny=9, rot=20)
print(grid)
# grid defines the model grid
grid = SwanGrid(x0=112, y0=-29, dx=0.5, dy=0.5, nx=8, ny=9, rot=20)
print(grid)
SwanGrid: REG, 8x9
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# Plot model grid and boundary points in the source spectra
fig, ax = grid.plot(fscale=5)
ax.plot(dset.lon, dset.lat, "ok")
ax.set_extent([108, 120, -35.5, -19])
# Plot model grid and boundary points in the source spectra
fig, ax = grid.plot(fscale=5)
ax.plot(dset.lon, dset.lat, "ok")
ax.set_extent([108, 120, -35.5, -19])
Boundnest1 with IDW interpolation at all boundary points¶
Create BOUNDNEST1 type SWAN boundary by interpolating from the source spectra
dataset along the model domain using the Inverse Distance Weighting (idw) sel
method from wavespectra. Boundary data are defined at all points along the grid
boundary given the spacing parameter isn't prescribed
⚠️ When using IDW, missing values are returned for locations that do not have three or
more sites withing tolerance in the source dataset. This is okay for land locations
but will be problematic for locations expecting an open boundary. Use a larger
tolerance when encountering this problem.
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workspace = Path(mkdtemp(dir="model"))
bnd = Boundnest1(
id="wa",
source=source,
sel_method="idw",
sel_method_kwargs={"tolerance": 2.0},
rectangle="closed",
)
filename, cmd = bnd.get(destdir=workspace, grid=grid, time=times)
display(cmd)
os.system(f"head -n 80 {filename}");
workspace = Path(mkdtemp(dir="model"))
bnd = Boundnest1(
id="wa",
source=source,
sel_method="idw",
sel_method_kwargs={"tolerance": 2.0},
rectangle="closed",
)
filename, cmd = bnd.get(destdir=workspace, grid=grid, time=times)
display(cmd)
os.system(f"head -n 80 {filename}");
"BOUNDNEST1 NEST 'wa.bnd' CLOSED"
SWAN 1 Swan standard spectral file
$ Created by wavespectra
$
TIME time-dependent data
1 time coding option
LONLAT locations in spherical coordinates
31 number of locations
112.000000 -29.000000
112.469846 -28.828990
112.939693 -28.657980
113.409539 -28.486970
113.879385 -28.315960
114.349232 -28.144950
114.819078 -27.973940
115.288924 -27.802929
115.117914 -27.333083
114.946904 -26.863237
114.775894 -26.393391
114.604884 -25.923544
114.433874 -25.453698
114.262864 -24.983852
114.091854 -24.514005
113.920844 -24.044159
113.450997 -24.215169
112.981151 -24.386179
112.511305 -24.557189
112.041458 -24.728199
111.571612 -24.899209
111.101766 -25.070219
110.631919 -25.241230
110.802929 -25.711076
110.973940 -26.180922
111.144950 -26.650768
111.315960 -27.120615
111.486970 -27.590461
111.657980 -28.060307
111.828990 -28.530154
112.000000 -29.000000
AFREQ absolute frequencies in Hz
11 number of frequencies
0.05417
0.05959
0.06555
0.07210
0.07931
0.08724
0.09597
0.10557
0.11612
0.12773
0.14051
NDIR spectral nautical directions in degr
8 number of directions
0.0000
45.0000
90.0000
135.0000
180.0000
225.0000
270.0000
315.0000
QUANT
1 number of quantities in table
VaDens variance densities in m2/Hz/degr
m2/Hz/degr unit
-99 exception value
20230101.000000 date and time
FACTOR
3.77111971E-05
0 0 0 0 324 732 4 0
0 0 0 0 306 2831 2 0
0 0 0 0 211 8155 0 0
0 0 0 0 150 9998 0 0
0 0 0 0 124 5261 2 0
0 0 0 1 86 2541 3 5
0 0 0 11 143 1321 5 12
0 0 2 71 351 919 3 9
0 0 9 188 966 735 1 9
0 0 21 350 1357 565 0 2
0 0 18 687 1325 363 1 0
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# Load the boundary created as a dataset
ds = wavespectra.read_swan(filename, as_site=True)
display(ds)
# Plot boundary stats calculated from ds
for time in ds.time.to_index()[::2]:
plot_boundary_side(dset, ds, grid, time)
# Load the boundary created as a dataset
ds = wavespectra.read_swan(filename, as_site=True)
display(ds)
# Plot boundary stats calculated from ds
for time in ds.time.to_index()[::2]:
plot_boundary_side(dset, ds, grid, time)
<xarray.Dataset> Size: 110kB
Dimensions: (time: 5, site: 31, freq: 11, dir: 8)
Coordinates:
* time (time) datetime64[ns] 40B 2023-01-01 ... 2023-01-02
* site (site) int64 248B 1 2 3 4 5 6 7 8 9 ... 23 24 25 26 27 28 29 30 31
* 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 109kB 0.0 0.0 0.0 ... 4.87e-05 0.0
lat (site) float64 248B -29.0 -28.83 -28.66 ... -28.06 -28.53 -29.0
lon (site) float64 248B 112.0 112.5 112.9 113.4 ... 111.7 111.8 112.0
Boundnest1 with nearest selection at all boundary points¶
Create BOUNDNEST1 type SWAN boundary by nearest-selecting from the source spectra
dataset along the model domain using the Nearest Neighbour (nearest) sel
method from wavespectra. Boundary data are defined at all points along the grid
boundary given the spacing parameter isn't prescribed
⚠️ When using nearest, exceptions are raised for locations that do not have at least one
site placed withing tolerance in the source dataset. Use a larger tolerance when
encountering this problem.
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workspace = Path(mkdtemp(dir="model"))
bnd = Boundnest1(
id="wa",
source=source,
sel_method="nearest",
sel_method_kwargs={"tolerance": 2.5},
rectangle="closed",
)
filename, cmd = bnd.get(destdir=workspace, grid=grid, time=times)
display(cmd)
os.system(f"head -n 80 {filename}");
workspace = Path(mkdtemp(dir="model"))
bnd = Boundnest1(
id="wa",
source=source,
sel_method="nearest",
sel_method_kwargs={"tolerance": 2.5},
rectangle="closed",
)
filename, cmd = bnd.get(destdir=workspace, grid=grid, time=times)
display(cmd)
os.system(f"head -n 80 {filename}");
"BOUNDNEST1 NEST 'wa.bnd' CLOSED"
SWAN 1 Swan standard spectral file
$ Created by wavespectra
$
TIME time-dependent data
1 time coding option
LONLAT locations in spherical coordinates
31 number of locations
112.000000 -29.000000
112.469846 -28.828990
112.939693 -28.657980
113.409539 -28.486970
113.879385 -28.315960
114.349232 -28.144950
114.819078 -27.973940
115.288924 -27.802929
115.117914 -27.333083
114.946904 -26.863237
114.775894 -26.393391
114.604884 -25.923544
114.433874 -25.453698
114.262864 -24.983852
114.091854 -24.514005
113.920844 -24.044159
113.450997 -24.215169
112.981151 -24.386179
112.511305 -24.557189
112.041458 -24.728199
111.571612 -24.899209
111.101766 -25.070219
110.631919 -25.241230
110.802929 -25.711076
110.973940 -26.180922
111.144950 -26.650768
111.315960 -27.120615
111.486970 -27.590461
111.657980 -28.060307
111.828990 -28.530154
112.000000 -29.000000
AFREQ absolute frequencies in Hz
11 number of frequencies
0.05417
0.05959
0.06555
0.07210
0.07931
0.08724
0.09597
0.10557
0.11612
0.12773
0.14051
NDIR spectral nautical directions in degr
8 number of directions
0.0000
45.0000
90.0000
135.0000
180.0000
225.0000
270.0000
315.0000
QUANT
1 number of quantities in table
VaDens variance densities in m2/Hz/degr
m2/Hz/degr unit
-99 exception value
20230101.000000 date and time
FACTOR
4.32390256E-05
0 0 0 0 361 699 5 0
0 0 0 0 365 2990 2 0
0 0 0 0 289 7579 0 0
0 0 0 0 190 9998 0 0
0 0 0 0 137 5204 2 0
0 0 0 0 88 2514 2 5
0 0 0 2 113 1270 5 14
0 0 2 19 167 745 2 12
0 0 12 130 333 555 2 12
0 0 35 315 625 407 0 2
0 0 28 567 807 301 2 0
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# Load the boundary created as a dataset
ds = wavespectra.read_swan(filename, as_site=True)
display(ds)
# Plot boundary stats calculated from ds
for time in ds.time.to_index()[::2]:
plot_boundary_side(dset, ds, grid, time)
# Load the boundary created as a dataset
ds = wavespectra.read_swan(filename, as_site=True)
display(ds)
# Plot boundary stats calculated from ds
for time in ds.time.to_index()[::2]:
plot_boundary_side(dset, ds, grid, time)
<xarray.Dataset> Size: 110kB
Dimensions: (time: 5, site: 31, freq: 11, dir: 8)
Coordinates:
* time (time) datetime64[ns] 40B 2023-01-01 ... 2023-01-02
* site (site) int64 248B 1 2 3 4 5 6 7 8 9 ... 23 24 25 26 27 28 29 30 31
* 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 109kB 0.0 0.0 0.0 ... 0.0 0.0
lat (site) float64 248B -29.0 -28.83 -28.66 ... -28.06 -28.53 -29.0
lon (site) float64 248B 112.0 112.5 112.9 113.4 ... 111.7 111.8 112.0
Boundnest1 with spacing defined from the parent dataset¶
Create BOUNDNEST1 type SWAN boundary by interpolating from the source spectra
dataset along the model domain at locations along the grid boundary defined acording
to the minimum spacing between points in the source dataset.
⚠️ When using any custom spacing, it is possible there won't be boundary points defined
at some of the grid vertices if the grid sides are not divisible by the spacing.
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workspace = Path(mkdtemp(dir="model"))
bnd = Boundnest1(
id="wa",
source=source,
sel_method="idw",
sel_method_kwargs={"tolerance": 2.0},
rectangle="closed",
spacing="parent",
)
filename, cmd = bnd.get(destdir=workspace, grid=grid, time=times)
display(cmd)
os.system(f"head -n 80 {filename}");
workspace = Path(mkdtemp(dir="model"))
bnd = Boundnest1(
id="wa",
source=source,
sel_method="idw",
sel_method_kwargs={"tolerance": 2.0},
rectangle="closed",
spacing="parent",
)
filename, cmd = bnd.get(destdir=workspace, grid=grid, time=times)
display(cmd)
os.system(f"head -n 80 {filename}");
"BOUNDNEST1 NEST 'wa.bnd' CLOSED"
SWAN 1 Swan standard spectral file
$ Created by wavespectra
$
TIME time-dependent data
1 time coding option
LONLAT locations in spherical coordinates
22 number of locations
112.000000 -29.000000
112.664463 -28.758155
113.328926 -28.516310
113.993389 -28.274466
114.657852 -28.032621
115.276771 -27.769539
115.034926 -27.105076
114.793081 -26.440612
114.551237 -25.776150
114.309392 -25.111687
114.067547 -24.447223
113.659445 -24.139300
112.994982 -24.381145
112.330519 -24.622990
111.666056 -24.864835
111.001593 -25.106679
110.739214 -25.536019
110.981059 -26.200482
111.222903 -26.864945
111.464748 -27.529408
111.706593 -28.193871
111.948438 -28.858334
AFREQ absolute frequencies in Hz
11 number of frequencies
0.05417
0.05959
0.06555
0.07210
0.07931
0.08724
0.09597
0.10557
0.11612
0.12773
0.14051
NDIR spectral nautical directions in degr
8 number of directions
0.0000
45.0000
90.0000
135.0000
180.0000
225.0000
270.0000
315.0000
QUANT
1 number of quantities in table
VaDens variance densities in m2/Hz/degr
m2/Hz/degr unit
-99 exception value
20230101.000000 date and time
FACTOR
3.77111971E-05
0 0 0 0 324 732 4 0
0 0 0 0 306 2831 2 0
0 0 0 0 211 8155 0 0
0 0 0 0 150 9998 0 0
0 0 0 0 124 5261 2 0
0 0 0 1 86 2541 3 5
0 0 0 11 143 1321 5 12
0 0 2 71 351 919 3 9
0 0 9 188 966 735 1 9
0 0 21 350 1357 565 0 2
0 0 18 687 1325 363 1 0
FACTOR
3.34683442E-05
0 0 0 0 305 762 4 0
0 0 0 0 275 2743 1 0
0 0 0 0 171 8603 0 0
0 0 0 0 135 9998 0 0
0 0 0 0 126 5349 2 0
0 0 0 2 93 2589 3 6
0 0 0 17 173 1393 5 12
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# Load the boundary created as a dataset
ds = wavespectra.read_swan(filename, as_site=True)
display(ds)
# Plot boundary stats calculated from ds
for time in ds.time.to_index()[::2]:
plot_boundary_side(dset, ds, grid, time)
# Load the boundary created as a dataset
ds = wavespectra.read_swan(filename, as_site=True)
display(ds)
# Plot boundary stats calculated from ds
for time in ds.time.to_index()[::2]:
plot_boundary_side(dset, ds, grid, time)
<xarray.Dataset> Size: 78kB
Dimensions: (time: 5, site: 22, freq: 11, dir: 8)
Coordinates:
* time (time) datetime64[ns] 40B 2023-01-01 ... 2023-01-02
* site (site) int64 176B 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22
* 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 77kB 0.0 0.0 0.0 ... 4.817e-05 0.0
lat (site) float64 176B -29.0 -28.76 -28.52 ... -27.53 -28.19 -28.86
lon (site) float64 176B 112.0 112.7 113.3 114.0 ... 111.5 111.7 111.9
Boundnest1 with custom spacing¶
Create BOUNDNEST1 type SWAN boundary by interpolating from the source spectra
dataset along the model domain at locations along the grid boundary defined custom
spacing parameter.
⚠️ When using any custom spacing, it is possible there won't be boundary points defined
at some of the grid vertices if the grid sides are not divisible by the spacing.
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workspace = Path(mkdtemp(dir="model"))
bnd = Boundnest1(
id="wa",
source=source,
sel_method="idw",
sel_method_kwargs={"tolerance": 2.0},
rectangle="closed",
spacing=1.0,
)
filename, cmd = bnd.get(destdir=workspace, grid=grid, time=times)
display(cmd)
os.system(f"head -n 80 {filename}");
workspace = Path(mkdtemp(dir="model"))
bnd = Boundnest1(
id="wa",
source=source,
sel_method="idw",
sel_method_kwargs={"tolerance": 2.0},
rectangle="closed",
spacing=1.0,
)
filename, cmd = bnd.get(destdir=workspace, grid=grid, time=times)
display(cmd)
os.system(f"head -n 80 {filename}");
"BOUNDNEST1 NEST 'wa.bnd' CLOSED"
SWAN 1 Swan standard spectral file
$ Created by wavespectra
$
TIME time-dependent data
1 time coding option
LONLAT locations in spherical coordinates
16 number of locations
112.000000 -29.000000
112.939693 -28.657980
113.879385 -28.315960
114.819078 -27.973940
115.117914 -27.333083
114.775894 -26.393391
114.433874 -25.453698
114.091854 -24.514005
113.450997 -24.215169
112.511305 -24.557189
111.571612 -24.899209
110.631919 -25.241230
110.973940 -26.180922
111.315960 -27.120615
111.657980 -28.060307
112.000000 -29.000000
AFREQ absolute frequencies in Hz
11 number of frequencies
0.05417
0.05959
0.06555
0.07210
0.07931
0.08724
0.09597
0.10557
0.11612
0.12773
0.14051
NDIR spectral nautical directions in degr
8 number of directions
0.0000
45.0000
90.0000
135.0000
180.0000
225.0000
270.0000
315.0000
QUANT
1 number of quantities in table
VaDens variance densities in m2/Hz/degr
m2/Hz/degr unit
-99 exception value
20230101.000000 date and time
FACTOR
3.77111971E-05
0 0 0 0 324 732 4 0
0 0 0 0 306 2831 2 0
0 0 0 0 211 8155 0 0
0 0 0 0 150 9998 0 0
0 0 0 0 124 5261 2 0
0 0 0 1 86 2541 3 5
0 0 0 11 143 1321 5 12
0 0 2 71 351 919 3 9
0 0 9 188 966 735 1 9
0 0 21 350 1357 565 0 2
0 0 18 687 1325 363 1 0
FACTOR
2.99598167E-05
0 0 0 0 283 800 4 0
0 0 0 0 255 2828 1 0
0 0 0 0 157 8520 0 0
0 0 0 0 121 9998 0 0
0 0 0 0 112 5290 2 0
0 0 0 1 82 2559 3 6
0 0 0 14 153 1356 6 11
0 0 1 86 429 1042 5 6
0 0 6 183 1269 894 1 7
0 0 12 306 1714 735 1 1
0 0 10 633 1590 479 1 0
FACTOR
1.74700485E-05
In [15]:
Copied!
# Load the boundary created as a dataset
ds = wavespectra.read_swan(filename, as_site=True)
display(ds)
# Plot boundary stats calculated from ds
for time in ds.time.to_index()[::2]:
plot_boundary_side(dset, ds, grid, time)
# Load the boundary created as a dataset
ds = wavespectra.read_swan(filename, as_site=True)
display(ds)
# Plot boundary stats calculated from ds
for time in ds.time.to_index()[::2]:
plot_boundary_side(dset, ds, grid, time)
<xarray.Dataset> Size: 57kB
Dimensions: (time: 5, site: 16, freq: 11, dir: 8)
Coordinates:
* time (time) datetime64[ns] 40B 2023-01-01 ... 2023-01-02
* site (site) int64 128B 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
* 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 56kB 0.0 0.0 0.0 ... 4.87e-05 0.0
lat (site) float64 128B -29.0 -28.66 -28.32 ... -27.12 -28.06 -29.0
lon (site) float64 128B 112.0 112.9 113.9 114.8 ... 111.3 111.7 112.0
