Bathymetry — depth distribution¶

Pull a GEBCO 2020 subset of a deep Atlantic abyssal plain and summarise its depth distribution: a histogram of cell depths plus the cumulative (hypsometric) curve. This is the quick statistical read a user makes to characterise a basin — modal depth, spread, deepest point.

In [ ]:

import tempfile

import matplotlib.pyplot as plt
import numpy as np
from pyramids.dataset import Dataset

from earthlens import EarthLens

import tempfile import matplotlib.pyplot as plt import numpy as np from pyramids.dataset import Dataset from earthlens import EarthLens

In [ ]:

out = tempfile.mkdtemp()
paths = EarthLens(
    data_source='bathymetry',
    dataset='gebco_2020',
    aoi=[-40.0, 20.0, -38.0, 22.0],
    path=out,
).download(progress_bar=False)

arr = np.asarray(Dataset.read_file(str(paths[0])).read_array(), dtype='float32')
arr = arr[0] if arr.ndim == 3 else arr
depths = arr[np.isfinite(arr) & (arr != 32767)]
print('cells', depths.size)
print('median depth', round(float(np.median(depths))), 'm')
print('deepest', round(float(depths.min())), 'm | shallowest', round(float(depths.max())), 'm')

out = tempfile.mkdtemp() paths = EarthLens( data_source='bathymetry', dataset='gebco_2020', aoi=[-40.0, 20.0, -38.0, 22.0], path=out, ).download(progress_bar=False) arr = np.asarray(Dataset.read_file(str(paths[0])).read_array(), dtype='float32') arr = arr[0] if arr.ndim == 3 else arr depths = arr[np.isfinite(arr) & (arr != 32767)] print('cells', depths.size) print('median depth', round(float(np.median(depths))), 'm') print('deepest', round(float(depths.min())), 'm | shallowest', round(float(depths.max())), 'm')

Histogram + hypsometric curve¶

The histogram shows how much sea floor sits at each depth band; the cumulative curve (right axis) reads off the fraction of the area shallower than a depth.

In [ ]:

fig, ax = plt.subplots(figsize=(7, 4))
ax.hist(depths, bins=40, color='#3182bd')
ax.set_xlabel('elevation (m)')
ax.set_ylabel('cell count', color='#3182bd')
ax.axvline(np.median(depths), color='crimson', ls='--', lw=1, label='median')

order = np.sort(depths)
cum = np.linspace(0, 100, order.size)
ax2 = ax.twinx()
ax2.plot(order, cum, color='k', lw=1.2)
ax2.set_ylabel('cumulative area (%)')
ax.set_title('GEBCO 2020 depth distribution — central Atlantic abyssal plain')
ax.legend(loc='upper left')
plt.show()

fig, ax = plt.subplots(figsize=(7, 4)) ax.hist(depths, bins=40, color='#3182bd') ax.set_xlabel('elevation (m)') ax.set_ylabel('cell count', color='#3182bd') ax.axvline(np.median(depths), color='crimson', ls='--', lw=1, label='median') order = np.sort(depths) cum = np.linspace(0, 100, order.size) ax2 = ax.twinx() ax2.plot(order, cum, color='k', lw=1.2) ax2.set_ylabel('cumulative area (%)') ax.set_title('GEBCO 2020 depth distribution — central Atlantic abyssal plain') ax.legend(loc='upper left') plt.show()