Download notebook.

SWOT L3 KaRIn Wind Wave Data Products

The SWOT KaRIn Level-3 Wind Wave product (L3_LR_WIND_WAVE) is an innovative product derived from the Unsmoothed L3_LR_SSH product based on the algorithm presented in Ardhuin et al., 2024. It takes advantage of the KaRIn Low Rate (LR) chain’s ability to resolve waves larger than about 500 meters in wavelength (about 18 s) and provides detailed information on the characteristics of these wave regimes, including significant wave height (SWH), dominant wavelength, and wave propagation direction. These regimes are typically associated with long-period swells and extreme events that play a critical role in ocean dynamics, coastal processes, and maritime operations.

SWOT L3_LR_WIND_WAVE product is organized in 2 subproducts:

Light L3_LR_WIND_WAVE (or lightweight), which includes the spectrum of KaRIn L3 250-m SSHA corrected from instrumental effects, expressed in both cartesian and polar coordinates, the swell partition of the spectrum and the wave parameters integrated over this partition for both WW3 model and KaRIn (significant wave height, wavelength and direction)
Extended L3_LR_WIND_WAVE, which includes the same variables as the light product below plus the WW3 spectrum in the same frequency grid as the KaRIn spectrum, the KaRIn transfer functions used for correction and some parameters derived from KaRIn observations (coherence, mean backscatter…)

Data can be explored at Swot LR L3 Wind Wave catalog.

For more information about this product, please consult its User handbook.

Tutorial Objectives

Download files via AVISO’s THREDDS Data Server
Present SWOT L3 Wind Wave data products (light and extended products)
Plot integrated wave parameters
Plot wave direction
Plot KaRIn Wave Spectrum

Imports + code

[1]:

import os
import re
from getpass import getpass
import requests as rq
from urllib.parse import urlparse

import xarray as xr
import matplotlib.pyplot as plt
from matplotlib import colors
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import numpy as np

[2]:

def http_download_file(dataset_url:str, output_path:str):
    response = rq.get(dataset_url, auth=(username, password))

    try:
        response.raise_for_status()
    except rq.exceptions.HTTPError as err:
        raise Exception(err)

    filename = os.path.basename(urlparse(dataset_url).path)
    file_path = os.path.join(output_path, filename)

    open(file_path, 'wb').write(response.content)
    return file_path

[3]:

def custom_ax(ax, crs=ccrs.PlateCarree(central_longitude=0), extent=None):
    ax.coastlines(resolution='50m')
    gl = ax.gridlines(
        crs=crs,
        draw_labels=True,
        linewidth=1,
        color='gray',
        alpha=0.8
    )
    gl.top_labels = gl.right_labels = False
    if extent:
        ax.set_extent(
            extent,
            crs=crs
        )
    return ax

[4]:

def get_mask_border(mask, fx1d, fy1d):
    import shapely.geometry
    import shapely.ops

    dfx = fx1d[1] - fx1d[0]
    dfy = fy1d[1] - fy1d[0]
    xbound = np.append(fx1d - dfx/2, fx1d[-1] + dfx/2)
    ybound = np.append(fy1d - dfy/2, fy1d[-1] + dfy/2)
    geoms = []
    for yidx, xidx in zip(*np.where(mask)):
        geoms.append(shapely.geometry.box(xbound[xidx], ybound[yidx], xbound[xidx+1], ybound[yidx+1]))
    full_geom = shapely.ops.unary_union(geoms)
    x,y = full_geom.exterior.xy
    return np.asarray(x), np.asarray(y)

def rotate_angle_from_NE_to_SWOT_ref_system(angle, track_angle):
    angle_swot = angle - track_angle
    angle_swot = (angle_swot + 180)%360 - 180
    return angle_swot

Download files from ODATIS TDS

2.1 Parameters

Define a local filepath to download files

[5]:

local_filepath='downloads'

2.2 Authentication parameters

Enter your AVISO+ credentials

[6]:

username = input("Enter username:")

Enter username: aviso-swot@altimetry.fr

[7]:

password = getpass(f"Enter password for {username}:")

Enter password for aviso-swot@altimetry.fr: ······

2.3 Download data

To find data for your study, please explore the Swot LR L3 Wind Wave catalog, and Swot LR L3 Unsmoothed catalog.

[8]:

aviso_tds_ww_url = "https://tds-odatis.aviso.altimetry.fr/thredds/fileServer/dataset-l3-swot-karin-wind-wave/v2_0/"

[9]:

swot_l3_ww_light_file = http_download_file(f"{aviso_tds_ww_url}/Light/cycle_006/SWOT_L3_LR_WIND_WAVE_006_567_20231122T183814_20231122T192938_v2.0.nc", local_filepath)

[10]:

swot_l3_ww_extended_file = http_download_file(f"{aviso_tds_ww_url}/Extended/cycle_006/SWOT_L3_LR_WIND_WAVE_Extended_006_567_20231122T183814_20231122T192938_v2.0.nc", local_filepath)

[11]:

aviso_tds_unsmoothed_url = "https://tds.aviso.altimetry.fr/thredds/fileServer/dataset-l3-swot-karin-nadir-validated/l3_lr_ssh/v2_0_1/Unsmoothed"

[12]:

swot_l3_unsmoothed_file = http_download_file(f"{aviso_tds_unsmoothed_url}/cycle_006/SWOT_L3_LR_SSH_Unsmoothed_006_567_20231122T183813_20231122T192940_v2.0.1.nc", local_filepath)

Discover the product

`Extended` product

The Extended product explores additional combinations of box and tile sizes

The tile size determines the dimensions (number of frequencies) of the spectrum. While Light files have a classic NetCDF structure, Extended files use a group and subgroup indexing approach. Each group corresponds to a specific tile size (in km). Within each tile group, there is a different subgroup per box size (in km). The structure of the Extended product is as follows:

40 km boxes with 10 km tiles
20 km boxes with 10 km tiles
10 km boxes with 5 km tiles:

The Extended product contains the same variables as the Light product plus additional ones useful for users desiring to have a deeper insight on the KaRIn measurement, namely:

Approximate KaRIn transfer functions
Phase of the cross-spectrum between SSH and NRCS (useful for removing 180° wave propagation ambiguity)
Coherence of SSH and NRCS spectra
Real part of SSH-NRCS cross-spectrum

`Extended` product content

Extended Netcdf file structure:

Group	Dimensions	Subgroup	Dimensions
tile_5km	nfy = 21 ; nfx = 20 ; nphi = 72 ; nf = 11 ;	box_40km	n_box = 986 ; nind = 4 ;
		box_10km	n_box = 27650 ; nind = 4 ;
tile_10km	nfy = 42 ; nfx = 40 ; nphi = 144 ; nf = 20 ;	box_20km	n_box = 5922 ; nind = 4 ;
		box_40km	n_box = 986 ; nind = 4 ;

Choose the combination of tile-box

[14]:

ds_tile = xr.open_dataset(swot_l3_ww_extended_file, decode_times = False, group='tile_10km')
ds_box = xr.open_dataset(swot_l3_ww_extended_file, decode_times = False, group='tile_10km/box_20km')

Plot map of integrated wave parameters

Parameter	Description	Unit
𝐻18	Significant wave height for waves longer than 18s	meter
𝐿18	Wavelength taken as the ratio of zeroth and first moment of the spectrum (m0/m1)	meter
ϕ_18	Swell propagation direction taken as the direction of the mean wavenumber vector	degree

[19]:

integrated_wparams = ['H18', 'L18', 'phi18']
integrated_wparams_titles = ['H18 (m)', 'L18 (m)', 'phi18 (deg)']

[20]:

central_lon = ds_box['longitude'].values[ds_box.sizes['n_box']//2]

f, axs = plt.subplots(
    1,
    len(integrated_wparams),
    figsize=(17,7),
    sharex=True,
    sharey=True,
    subplot_kw={'projection':ccrs.PlateCarree(central_longitude=central_lon)}
)

for i, v in enumerate(integrated_wparams):
    ax = axs[i]
    ax = custom_ax(ax)

    im = ax.scatter(
        ds_box.longitude,
        ds_box.latitude,
        c= ds_box[v],
        s=1,
        cmap='turbo',
        transform=ccrs.PlateCarree()
    )
    cbar = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
    ax.set_title(integrated_wparams_titles[i])

plt.tight_layout()
plt.show()

../_images/SWOT-Oceanography_ex_swot_l3_lr_ww_startup_38_0.png

Zoom into specific region: show 𝐻18, wave direction with arrows and quality flag

Show 𝐻18 with wave direction
Show quality flag
Select arbitrary point and show box indices

Geographical parameters

[21]:

lat_target = 20
lat_start_plot = lat_target - 2
lat_stop_plot = lat_target + 2

Subset data with latitude range

[22]:

lat=ds_box['latitude'].values
idx = np.where(
    (lat >= lat_start_plot) &
    (lat <= lat_stop_plot)
)
ds_sel = ds_box.isel(n_box=slice(idx[0][0], idx[0][-1]))

Update min and max longitudes for the plot

[23]:

lon_start_plot = np.min(ds_sel['longitude'].values)
lon_stop_plot = np.max(ds_sel['longitude'].values)

Select closest point to target latitude

[24]:

ind = np.argmin(abs(ds_sel.latitude.values - lat_target))

Quality flag

The quality flag is a bitwise flag

See User handbook (section 2.6)

Warning

The flag mask 16 is missing in the metadata. This will be fixed in the next version of the product

So we set flag_masks attribute with the missing value

[25]:

ds_sel.quality_flag.attrs['flag_masks'] = [0, 2, 4, 8, 16, 4096, 32768]

Display flag masks -> meanings

[26]:

dict(
        zip(
            ds_sel.quality_flag.attrs["flag_masks"],
            ds_sel.quality_flag.attrs["flag_meanings"].split(" "),
        )
)

[26]:

{0: 'good',
 2: 'suspect_energy_ratio',
 4: 'suspect_number_of_tiles',
 8: 'suspect_separated_mask_clusters',
 16: 'suspect_H18_model',
 4096: 'degraded_no_model',
 32768: 'bad_no_data'}

Discrete colormap values. Limits between 0 and 10 (higher values are possible but they are not present in this plot)

[27]:

flag_masks = [0, 2, 4, 6, 8, 10]

Plot

[28]:

central_lon = ds_sel['longitude'].values[ds_sel.sizes['n_box']//2]

f, axs = plt.subplots(
    1,
    2,
    figsize=(15,7),
    sharex=True,
    sharey=True,
    subplot_kw={'projection':ccrs.PlateCarree(central_longitude=central_lon)}
)

# Plot H18
ax = custom_ax(axs[0], extent=[lon_start_plot, lon_stop_plot, lat_start_plot, lat_stop_plot])

im = ax.scatter(
    ds_sel.longitude,
    ds_sel.latitude,
    c= ds_sel['H18'],
    s=50,
    cmap='turbo',
    transform=ccrs.PlateCarree()
)
cbar = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
ax.set_title('H18 (m)')

# Plot phi18: "TO" convention
ax.quiver(
    ds_sel['longitude'].values,
    ds_sel['latitude'].values,
    -np.sin(ds_sel['phi18'].values* (np.pi / 180)),
    -np.cos(ds_sel['phi18'].values* (np.pi / 180)),
    angles='uv',color='black',
    scale=15,
    transform=ccrs.PlateCarree()
)

# Plot flag
ax = custom_ax(axs[1], extent=[lon_start_plot, lon_stop_plot, lat_start_plot, lat_stop_plot])

# Discrete colormap. Limits between 0 and 10 (higher
# values are possible but they are not present in this
# plot)
cmap = plt.cm.nipy_spectral
vmin_cbar = 0
vmax_cbar = 10
norm = colors.BoundaryNorm(np.arange(vmin_cbar-0.5, vmax_cbar+2, 2), cmap.N)

im = ax.scatter(
    ds_sel.longitude,
    ds_sel.latitude,
    c= ds_sel['quality_flag'],
    s=50,
    cmap=cmap,
    norm=norm,
    transform=ccrs.PlateCarree()
)
cbar = plt.colorbar(
    im,
    ax=ax,
    ticks=flag_masks,
    fraction=0.046, pad=0.04
)

ax.set_title('Quality flag')

# Show selected point and respective box indices
ax.scatter(
    ds_sel.longitude[ind], ds_sel.latitude[ind],
    marker='x',
    s=100,
    c='k',
    transform=ccrs.PlateCarree()
)
ax.text(
    ds_sel.longitude[ind],
    ds_sel.latitude[ind]-0.04,
    '(%d,%d)'%(ds_sel['box_indy'].values[ind], ds_sel['box_indx'].values[ind]),
    verticalalignment='top',
    transform=ccrs.PlateCarree()
)

plt.tight_layout()
plt.show()

../_images/SWOT-Oceanography_ex_swot_l3_lr_ww_startup_58_0.png

Using box indices to get original SSHA

The variables box_indx and box_indy are provided to locate the position of each box within the swath, corresponding to the cross-track and along-track directions, respectively.

The box_indx values are defined as follows:

0–1 for 40 km boxes (left or right swath)
0-5 for 20 km boxes (0–2 for the left swath and 3–5 for the right swath in the satellite frame)
0–13 for 10 km boxes (0–6 for the left swath and 7–13 for the right swath in the satellite frame)

See User handbook (section 2.2)

Show box indices

[29]:

zoom_coords = [lon_start_plot, lon_stop_plot, lat_start_plot, lat_stop_plot]

central_lon = ds_sel['longitude'].values[ds_sel.sizes['n_box']//2]

f, ax = plt.subplots(
    1,
    1,
    figsize=(10,10),
    subplot_kw={'projection':ccrs.PlateCarree(central_longitude=central_lon)}
)

ax = custom_ax(ax, extent=zoom_coords)

im = ax.scatter(
    ds_sel.longitude,
    ds_sel.latitude,
    c= ds_sel['H18'],
    s=100,
    cmap='turbo',
    transform=ccrs.PlateCarree()
)
cbar = plt.colorbar(im, ax=ax,
    fraction=0.046, pad=0.04)
cbar.set_label('H18 (m)')

for box_y, box_x, lat, lon in zip(ds_sel['box_indy'].values, \
                                  ds_sel['box_indx'].values, \
                                  ds_sel['latitude'].values, \
                                  ds_sel['longitude'].values):
    if (zoom_coords[2]<=lat) & (zoom_coords[3]>=lat):
        ax.text(lon-0.25, lat-0.01,
                '(%d,%d)'%(box_y, box_x),
            verticalalignment='top',
            rotation = 20,
            transform=ccrs.PlateCarree())
    else:
        continue

ax.scatter(
    ds_sel.longitude[ind],
    ds_sel.latitude[ind],
    marker='x',
    s=100,
    c='k',
    transform=ccrs.PlateCarree()
)

ax.set_title('Box indices (box_indy, box_indx)')

plt.show()

../_images/SWOT-Oceanography_ex_swot_l3_lr_ww_startup_61_0.png

Get original SSHA

Plot SSH from L3 SSH product used for estimating spectra in L3 WW product

[30]:

box_indices = ds_sel['box_indices'][ind].values.astype(int)

[31]:

ds_ssh=xr.open_dataset(swot_l3_unsmoothed_file)[['latitude', 'longitude', 'ssha_unfiltered', 'quality_flag']]

[32]:

box_ssh =  ds_ssh.isel(num_lines=slice(box_indices[0],box_indices[1]), num_pixels=slice(box_indices[2],box_indices[3]))
box_ssh['longitude'] = (box_ssh['longitude'] + 180)%360 - 180

[33]:

box_ssh['ssha_filtered'] = box_ssh['ssha_unfiltered'].where(
            (box_ssh['quality_flag'] == 0)
            |(box_ssh['quality_flag'] == 10)
            |(box_ssh['quality_flag'] == 20))

fig = plt.figure(figsize=(10, 6))
ax = plt.axes(projection=ccrs.PlateCarree())

ax.add_feature(cfeature.COASTLINE, linewidth=0.5)
ax.add_feature(cfeature.BORDERS, linestyle=":", linewidth=0.5)
ax.add_feature(cfeature.LAND, facecolor="lightgray")

im = ax.pcolormesh(
    box_ssh.longitude,
    box_ssh.latitude,
    box_ssh['ssha_filtered'],
    transform=ccrs.PlateCarree()
)

cbar = plt.colorbar(im, orientation="vertical", ax=ax, shrink=0.7, label="SSHA (m)")

ax.set_title("SSHA box", fontsize=14)
ax.set_xlabel("Longitude (°)")
ax.set_ylabel("Latitude (°)")
xticks = np.arange(np.round(np.min(box_ssh.longitude),2),
                   np.round(np.max(box_ssh.longitude),2),
                   0.04)
yticks = np.arange(np.round(np.min(box_ssh.latitude),2),
                   np.round(np.max(box_ssh.latitude),2),
                   0.04)
ax.set_xticks(xticks)
ax.set_yticks(yticks)
ax.grid()

plt.show()

../_images/SWOT-Oceanography_ex_swot_l3_lr_ww_startup_66_0.png

Plot KaRIn spectrum

SSHA-based 2-dimensional wave spectra, for waves with periods longer than ~500 m (18 s)

Unsmoothed KaRIn data has a spatial posting of ~250 m so the smallest observable wavelength is ~500 m, which is equivalent to ~18 s through the wave dispersion equation. For this reason, integrated wave parameters are denoted with the suffix “18” as 𝐻18 (significant wave height), 𝐿18 (wavelength) and ϕ_18 (wave direction).

The estimation of the wave heights PSD starts with the computation of the KaRIn SSHA power spectral density. The estimation is performed for each spatial box.

See User handbook (section 2.3)

Plot KaRIn spectrum provided in cartesian coordinates

Express frequencies in km-1

[34]:

fx2 = ds_tile['fx2D'].values*1e3
fy2 = ds_tile['fy2D'].values*1e3

Get mask and integrated wave parameters

[35]:

xmask, ymask = get_mask_border(
    ds_sel['swell_mask'][ind].values,
    fx2[0,:],
    fy2[:,0]
)
L18 = ds_sel['L18'][ind].values*1e-3 # in km
phi18_NE = ds_sel['phi18'][ind].values
H18 = ds_sel['H18'][ind].values

Get wave direction in SWOT reference system

[36]:

phi18_SWOT = rotate_angle_from_NE_to_SWOT_ref_system(
    phi18_NE,
    ds_sel['track_angle'][ind].values
)

Plot

[37]:

fig, ax = plt.subplots(nrows=1, ncols=1,figsize=(4.1, 3.5))
im=ax.pcolormesh(
    fx2,
    fy2,
    10*np.log10(ds_sel['Efxfy_SWOT'][ind].values),
    cmap='viridis',
    rasterized=True
)
ax.set_xlabel("fx/cross-track [km-1]")
ax.set_ylabel("fy/along-track [km-1]")
cbar=plt.colorbar(im,ax=ax,label='dB [m^2.m^2]', shrink=0.8)

# Plot mask and swell barycenter
ax.plot(xmask, ymask, linewidth=2, color='k')
ax.scatter(
    1/L18*np.sin(np.deg2rad(phi18_SWOT)),
    1/L18*np.cos(np.deg2rad(phi18_SWOT)),
    marker = 'x',
    color = 'k'
)
info_sample_str = 'Cycle %d, Track %d, box_indx=%d, box_indy=%d, (lon,lat)=(%.3f,%.3f)\n'% (cycle_number,
                                                                 pass_number,
                                                                 ds_sel['box_indx'][ind].values,
                                                                 ds_sel['box_indy'][ind].values,
                                                                 ds_sel['longitude'][ind].values,
                                                                 ds_sel['latitude'][ind].values) + \
                    'L18 = %d m, H18 = %.3f m, phi18 = %d deg' % (L18*1e3,
                                                                    H18,
                                                                    phi18_NE
                                                                    )
ax.set_title('L3 WW KaRIn Wave Spectrum\n' + info_sample_str)
plt.show()

../_images/SWOT-Oceanography_ex_swot_l3_lr_ww_startup_76_0.png

Plot KaRIn spectrum provided in polar coordinates

[41]:

f =  ds_tile.f_vector.data
phi = ds_tile.phi_vector.data
S_fphi = ds_sel.isel(n_box=ind).E_f_phi_SWOT_masked.data

# Étendre les axes pour pcolormesh
F = np.linspace(f.min(), f.max(), len(f) + 1)
PHI = np.linspace(phi.min(), phi.max(), len(phi) + 1)
F_grid, PHI_grid = np.meshgrid(F, PHI, indexing="ij")

fig, ax = plt.subplots(subplot_kw={'projection': 'polar'}, figsize=(8, 8))

plot = ax.pcolormesh(PHI_grid, F_grid, np.log10(S_fphi), shading='flat', cmap='turbo')
plt.colorbar(plot, label="Spectral density (dB)")

f_ticks = f[1:]
T_ticks = 1 / f_ticks
ax.set_yticks(f_ticks)
ax.set_yticklabels([f"{T:.0f} m" for T in T_ticks])

ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)

ax.set_title("L3 WW KaRIn spectrum (wave section only)\nin polar coordinates", pad=20)

plt.show()

../_images/SWOT-Oceanography_ex_swot_l3_lr_ww_startup_78_0.png

Undo KaRIn instrument corrections (show original KaRIn spectrum)

At the end of the processing chain, the KaRIn SSHA power spectral density (i.e. the “wave spectrum”) is corrected by dividing it by an approximation of the KaRIn transfer function. This one accounts mainly for the azimuth point target response (PTR), the on-board averaging filters and the azimuth cutoff effect. This correction is done on the assumption that the KaRIn processing is linear and many other considerations have been omitted (see handbook). For this reason, the user should have this in mind when using KaRIn corrected wave spectra.

The corrected spectrum if provided in both the Light and the Extended files. In addition, the Extended product provides the KaRIn transfer function correction, so the user can undo the correction if necessary (note that the filter aliasing cannot be undone, but this should not change the interpretation of results).

[39]:

S_swot_original = ds_sel['Efxfy_SWOT'][ind].values * \
                  ds_tile['filter_OBP'].values * \
                  ds_tile['filter_PTR'].values * \
                  np.exp(-((fy2/1e3 * ds_sel['lambdac_model'][ind].values) ** 2)) # restore fy into m-1 (lambdac is given in m)

[40]:

fig, ax = plt.subplots(nrows=1, ncols=1,figsize=(4.1, 3.5))
im=ax.pcolormesh(
    fx2,
    fy2,
    10*np.log10(S_swot_original),
    cmap='viridis',
    rasterized=True
)
ax.set_xlabel("fx/cross-track [km-1]")
ax.set_ylabel("fy/along-track [km-1]")
cbar=plt.colorbar(
    im,
    ax=ax,
    label='dB [m^2.m^2]',
    shrink=0.8
)

info_sample_str = 'Cycle %d, Track %d, box_indx=%d, box_indy=%d, (lon,lat)=(%.3f,%.3f)\n'% (cycle_number,
                                                                 pass_number,
                                                                 ds_sel['box_indx'][ind].values,
                                                                 ds_sel['box_indy'][ind].values,
                                                                 ds_sel['longitude'][ind].values,
                                                                 ds_sel['latitude'][ind].values)
ax.set_title('SWOT original heights spectrum\n' + info_sample_str)
plt.show()

../_images/SWOT-Oceanography_ex_swot_l3_lr_ww_startup_81_0.png

[ ]:

Download notebook.

SWOT L3 KaRIn Wind Wave Data Products

Tutorial Objectives

Imports + code

Download files from ODATIS TDS

2.1 Parameters

2.2 Authentication parameters

2.3 Download data

Discover the product

`Light` product

`Light` product content

`Extended` product

`Extended` product content

Tile group

Box sub-group

Plot map of integrated wave parameters

Zoom into specific region: show 𝐻18, wave direction with arrows and quality flag

Geographical parameters

Quality flag

Plot

Using box indices to get original SSHA

Show box indices

Get original SSHA

Plot KaRIn spectrum

Plot KaRIn spectrum provided in cartesian coordinates

Plot KaRIn spectrum provided in polar coordinates

Undo KaRIn instrument corrections (show original KaRIn spectrum)

SWOT L3 KaRIn Wind Wave Data Products

Tutorial Objectives

Imports + code

Download files from ODATIS TDS

2.1 Parameters

2.2 Authentication parameters

2.3 Download data

Discover the product

Light product

Light product content

Extended product

Extended product content

Tile group

Box sub-group

Plot map of integrated wave parameters

Zoom into specific region: show 𝐻18, wave direction with arrows and quality flag

Geographical parameters

Quality flag

Plot

Using box indices to get original SSHA

Show box indices

Get original SSHA

Plot KaRIn spectrum

Plot KaRIn spectrum provided in cartesian coordinates

Plot KaRIn spectrum provided in polar coordinates

Undo KaRIn instrument corrections (show original KaRIn spectrum)

`Light` product

`Light` product content

`Extended` product

`Extended` product content