Profiles¶

This notebook demonstrates profile and multi-profile analysis using bathy.

In [1]:

import bathy

import bathy

Load example data¶

Load the built-in NE Atlantic sample dataset from the GEBCO 2025 grid.

GEBCO Compilation Group (2025) GEBCO 2025 Grid (doi:10.5285/37c52e96-24ea-67ce-e063-7086abc05f29)

In [2]:

data = bathy.sample_data()
data

data = bathy.sample_data() data

Out[2]:

<xarray.DataArray 'elevation' (lat: 839, lon: 1645)> Size: 3MB
[1380155 values with dtype=int16]
Coordinates:
  * lat      (lat) float64 7kB 46.05 46.05 46.06 46.06 ... 49.53 49.54 49.54
  * lon      (lon) float64 13kB -12.08 -12.08 -12.07 ... -5.24 -5.235 -5.231
Attributes:
    standard_name:       height_above_mean_sea_level
    long_name:           Elevation relative to sea level
    units:               m
    grid_mapping:        crs
    sdn_parameter_urn:   SDN:P01::BATHHGHT
    sdn_parameter_name:  Sea floor height (above mean sea level) {bathymetric...
    sdn_uom_urn:         SDN:P06::ULAA
    sdn_uom_name:        Metres

xarray.DataArray

'elevation'

• lat: 839
• lon: 1645

• ...

  [1380155 values with dtype=int16]

• Coordinates: (2)
  • lat
    (lat)
    float64
    46.05 46.05 46.06 ... 49.54 49.54

    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

    _CoordinateAxisType :

    Lat

    array([46.047917, 46.052083, 46.05625 , ..., 49.53125 , 49.535417, 49.539583],
          shape=(839,))

  • lon
    (lon)
    float64
    -12.08 -12.08 ... -5.235 -5.231

    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

    _CoordinateAxisType :

    Lon

    array([-12.08125 , -12.077083, -12.072917, ...,  -5.239583,  -5.235417,
            -5.23125 ], shape=(1645,))

• Attributes: (8)

  standard_name :

  height_above_mean_sea_level

  long_name :

  Elevation relative to sea level

  units :

  m

  grid_mapping :

  crs

  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

In [3]:

# You can also download directly:
# data = bathy.load_gebco_opendap(region="north_atlantic", save_path="gebco.nc")
# data = bathy.load_bathymetry("local_file.nc", lon_range=(-12, -5), lat_range=(46, 50))

# You can also download directly: # data = bathy.load_gebco_opendap(region="north_atlantic", save_path="gebco.nc") # data = bathy.load_bathymetry("local_file.nc", lon_range=(-12, -5), lat_range=(46, 50))

Single profile¶

In [4]:

# Create a profile
prof = bathy.extract_profile(
    data, (-11.0, 47.5), (-6.5, 47.5), point_spacing=500.0, name="East-West Profile"
)

print(f"Distance: {prof.distances[-1]:.1f} m")
print(f"Points: {len(prof.distances)}")

bathy.plot_profile(prof)

# Create a profile prof = bathy.extract_profile( data, (-11.0, 47.5), (-6.5, 47.5), point_spacing=500.0, name="East-West Profile" ) print(f"Distance: {prof.distances[-1]:.1f} m") print(f"Points: {len(prof.distances)}") bathy.plot_profile(prof)

Distance: 338998.7 m
Points: 679

Out[4]:

(<Figure size 1200x500 with 1 Axes>,
 [<Axes: xlabel='Distance (km)', ylabel='Elevation (m)'>])

Profile with smoothing¶

In [5]:

# Compare different smoothing levels
bathy.plot_profile(prof, smooth=3.0)

# Compare different smoothing levels bathy.plot_profile(prof, smooth=3.0)

Out[5]:

(<Figure size 1200x500 with 1 Axes>,
 [<Axes: xlabel='Distance (km)', ylabel='Elevation (m)'>])

Canyon identification¶

In [6]:

# Identify canyons
canyons = bathy.get_canyons(prof, prominence=500)
canyons

# Identify canyons canyons = bathy.get_canyons(prof, prominence=500) canyons

Out[6]:

shape: (5, 8)

floor_distance	floor_elevation	shoulder_elevation	width_start	width_end	width	depth	cross_sectional_area
f64	f64	f64	f64	f64	f64	f64	f64
233999.084103	-3651.0	-2869.0	218364.224665	238499.06649	20134.841825	782.0	5.3984e6
242999.048876	-3744.0	-2869.0	238499.06649	246036.21171	7537.14522	875.0	3.8544e6
260498.98038	-2750.0	-1268.0	254499.003864	263966.475833	9467.471969	1482.0	6.4808e6
267498.952981	-1986.0	-1176.0	264998.962766	271354.904967	6355.9422	810.0	3.0960e6
284998.884485	-1298.0	-590.0	282498.89427	288030.451566	5531.557296	708.0	1.8041e6

Visualise canyons¶

In [7]:

# Plot with canyons highlighted
bathy.plot_canyons(prof, canyons)

# Plot with canyons highlighted bathy.plot_canyons(prof, canyons)

Out[7]:

(<Figure size 1200x500 with 1 Axes>,
 [<Axes: xlabel='Distance (km)', ylabel='Elevation (m)'>])

Knickpoint detection¶

Identify knickpoints (abrupt slope changes) along a profile.

In [8]:

# Detect and plot knickpoints (slope breaks)
kp = bathy.knickpoints(prof, smooth=2)
bathy.plot_knickpoints(prof, kp)

# Detect and plot knickpoints (slope breaks) kp = bathy.knickpoints(prof, smooth=2) bathy.plot_knickpoints(prof, kp)

Out[8]:

(<Figure size 1200x500 with 1 Axes>,
 [<Axes: xlabel='Distance (km)', ylabel='Elevation (m)'>])

Multiple profiles¶

Compare multiple parallel profiles.

In [9]:

# Create multiple profiles
profile_list = []

for lat in [46.5, 47.5, 48.5, 49.0]:
    p = bathy.extract_profile(
        data, (-11.0, lat), (-6.5, lat), num_points=200, name=f"{lat}°N"
    )
    profile_list.append(p)

print(f"Created {len(profile_list)} profiles")

# Create multiple profiles profile_list = [] for lat in [46.5, 47.5, 48.5, 49.0]: p = bathy.extract_profile( data, (-11.0, lat), (-6.5, lat), num_points=200, name=f"{lat}°N" ) profile_list.append(p) print(f"Created {len(profile_list)} profiles")

Created 4 profiles

In [10]:

bathy.plot_profiles_map(profile_list, bathymetry_data=data)

bathy.plot_profiles_map(profile_list, bathymetry_data=data)

Out[10]:

(<Figure size 1000x800 with 1 Axes>,
 <Axes: xlabel='Longitude (°)', ylabel='Latitude (°)'>)

Compare Profiles¶

In [11]:

bathy.plot_profiles(profile_list)

bathy.plot_profiles(profile_list)

Out[11]:

(<Figure size 1200x600 with 1 Axes>,
 [<Axes: xlabel='Distance (km)', ylabel='Elevation (m)'>])

Compare statistics¶

In [12]:

bathy.compare_stats(profile_list)

bathy.compare_stats(profile_list)

Out[12]:

shape: (7, 5)

statistic	46.5°N	47.5°N	48.5°N	49.0°N
str	f64	f64	f64	f64
"total_distance_m"	345384.94263	338998.673124	332508.392432	329224.855211
"min_elevation_m"	-4846.0	-4554.0	-3163.0	-710.0
"max_elevation_m"	-4636.0	-209.0	-118.0	-112.0
"mean_elevation_m"	-4729.405	-2748.175	-569.345	-153.4
"median_elevation_m"	-4730.0	-2830.5	-165.5	-147.0
"std_elevation_m"	51.95932	1399.6607	731.354453	60.702553
"elevation_range_m"	210.0	4345.0	3045.0	598.0

Perpendicular cross-sections¶

Create cross-sections perpendicular to a profile at regular intervals.

In [13]:

# Create a profile
main_profile = bathy.extract_profile(
    data, (-11.0, 47.5), (-6.5, 49.0), point_spacing=1000.0, name="Main Profile"
)

# Create cross-sections every 20 km with 30 km total width
x_sections = bathy.cross_sections(
    data, main_profile, interval_m=20000, section_width_m=30000, num_points=50
)

print(f"Created {len(x_sections)} cross-sections")

bathy.plot_profiles_map(x_sections, bathymetry_data=data, main_profile=main_profile)

# Create a profile main_profile = bathy.extract_profile( data, (-11.0, 47.5), (-6.5, 49.0), point_spacing=1000.0, name="Main Profile" ) # Create cross-sections every 20 km with 30 km total width x_sections = bathy.cross_sections( data, main_profile, interval_m=20000, section_width_m=30000, num_points=50 ) print(f"Created {len(x_sections)} cross-sections") bathy.plot_profiles_map(x_sections, bathymetry_data=data, main_profile=main_profile)

Created 19 cross-sections

Out[13]:

(<Figure size 1000x800 with 1 Axes>,
 <Axes: xlabel='Longitude (°)', ylabel='Latitude (°)'>)

In [14]:

# Plot cross-sections in grid with main profile intersection marked
bathy.plot_profiles_grid(x_sections[:6], cols=3, main_profile=main_profile)

# Plot cross-sections in grid with main profile intersection marked bathy.plot_profiles_grid(x_sections[:6], cols=3, main_profile=main_profile)

Out[14]:

(<Figure size 2100x700 with 6 Axes>,
 array([<Axes: title={'center': 'Section_1_at_0m (30.0 km)'}, xlabel='Distance (km)', ylabel='Elevation (m)'>,
        <Axes: title={'center': 'Section_2_at_20000m (30.0 km)'}, xlabel='Distance (km)', ylabel='Elevation (m)'>,
        <Axes: title={'center': 'Section_3_at_40000m (30.0 km)'}, xlabel='Distance (km)', ylabel='Elevation (m)'>,
        <Axes: title={'center': 'Section_4_at_60000m (30.0 km)'}, xlabel='Distance (km)', ylabel='Elevation (m)'>,
        <Axes: title={'center': 'Section_5_at_80000m (30.0 km)'}, xlabel='Distance (km)', ylabel='Elevation (m)'>,
        <Axes: title={'center': 'Section_6_at_100000m (30.0 km)'}, xlabel='Distance (km)', ylabel='Elevation (m)'>],
       dtype=object))

File import¶

Load profiles from a vector file of linestrings. This requires your own data files — the example below uses the Global Submarine Canyon Database from Harris & Whiteway (2011) and a local GEBCO NetCDF.

Harris, P.T. and Whiteway, T. (2011). Global distribution of large submarine canyons: Geomorphic differences between active and passive continental margins. Marine Geology, 285(1-4), 69-86.

In [15]:

# Load bathymetry data for the Celtic margin
data_clipped = bathy.load_bathymetry(
    "../data/gebco_2025_bathymetry.nc", lon_range=(-12, -4), lat_range=(46, 52)
)

# Load profiles from Global Canyons shapefile
canyon_profiles = bathy.profiles_from_file(
    data_clipped,
    "../data/shp/Global_Canyons.shp",
    id_column="NAME",
)

print(f"Loaded {len(canyon_profiles)} canyon profiles")

# Plot first 10 profiles in a grid
bathy.plot_profiles_grid(canyon_profiles[:10])

# Load bathymetry data for the Celtic margin data_clipped = bathy.load_bathymetry( "../data/gebco_2025_bathymetry.nc", lon_range=(-12, -4), lat_range=(46, 52) ) # Load profiles from Global Canyons shapefile canyon_profiles = bathy.profiles_from_file( data_clipped, "../data/shp/Global_Canyons.shp", id_column="NAME", ) print(f"Loaded {len(canyon_profiles)} canyon profiles") # Plot first 10 profiles in a grid bathy.plot_profiles_grid(canyon_profiles[:10])

Loaded 63 canyon profiles

Out[15]:

(<Figure size 1400x1750 with 10 Axes>,
 array([<Axes: title={'center': 'Feature_4768 (20.8 km)'}, xlabel='Distance (km)', ylabel='Elevation (m)'>,
        <Axes: title={'center': 'Feature_4769 (66.6 km)'}, xlabel='Distance (km)', ylabel='Elevation (m)'>,
        <Axes: title={'center': 'Feature_4770 (52.1 km)'}, xlabel='Distance (km)', ylabel='Elevation (m)'>,
        <Axes: title={'center': 'Feature_4775 (19.6 km)'}, xlabel='Distance (km)', ylabel='Elevation (m)'>,
        <Axes: title={'center': 'Feature_4776 (49.1 km)'}, xlabel='Distance (km)', ylabel='Elevation (m)'>,
        <Axes: title={'center': 'Feature_4777 (80.0 km)'}, xlabel='Distance (km)', ylabel='Elevation (m)'>,
        <Axes: title={'center': 'Feature_4778 (18.1 km)'}, xlabel='Distance (km)', ylabel='Elevation (m)'>,
        <Axes: title={'center': 'Feature_4779 (36.9 km)'}, xlabel='Distance (km)', ylabel='Elevation (m)'>,
        <Axes: title={'center': 'Feature_4780 (23.5 km)'}, xlabel='Distance (km)', ylabel='Elevation (m)'>,
        <Axes: title={'center': 'Feature_4781 (8.2 km)'}, xlabel='Distance (km)', ylabel='Elevation (m)'>],
       dtype=object))

Visualise profiles on a map¶

In [16]:

# Plot canyon locations on map
bathy.plot_profiles_map(canyon_profiles, bathymetry_data=data_clipped)

# Plot canyon locations on map bathy.plot_profiles_map(canyon_profiles, bathymetry_data=data_clipped)

Out[16]:

(<Figure size 1000x800 with 1 Axes>,
 <Axes: xlabel='Longitude (°)', ylabel='Latitude (°)'>)