Prospective Prediction Pipeline

"""
Directional Bearing Test
Extracts depth profiles along 8 compass bearings from a central point.
Tests whether a predicted bearing is the shallowest corridor.

Replace LATITUDE, LONGITUDE, BEARING_DEGREES, and DATA_FILE.
"""
import numpy as np
from scipy import ndimage
import rasterio

# ── Configuration ──
LAT, LON = LATITUDE, LONGITUDE    # Center point
PREDICTED_BEARING = BEARING_DEGREES   # Predicted shallowest (deg)
DATA_FILE = "DATA_FILE.tif"          # GeoTIFF bathymetry
TRANSECT_LEN_KM = 50                 # Transect length
N_POINTS = 200                       # Points per transect

# ── Extract 8 bearings ──
bearings = np.arange(0, 360, 45)
with rasterio.open(DATA_FILE) as src:
    for b in bearings:
        rad = np.radians(b)
        dists = np.linspace(0, TRANSECT_LEN_KM, N_POINTS)
        lats = LAT + (dists / 111.32) * np.cos(rad)
        lons = LON + (dists / (111.32 * np.cos(np.radians(LAT)))) * np.sin(rad)
        depths = [list(src.sample([(x, y)]))[0][0] for x, y in zip(lons, lats)]
        mean_d = np.nanmean(depths)
        print(f"Bearing {b:3d}°: mean depth = {mean_d:.1f}m")

# ── Rank test ──
# Compare predicted bearing rank against 8 bearings
# p-value = 1/8 = 0.125 for random correct pick

"""
Submerged Feature Detection
Runs a transect between two points and identifies ridges or
plateaux that break the depth gradient.

Replace START_LAT/LON, END_LAT/LON, and DATA_FILE.
"""
import numpy as np
from scipy.signal import find_peaks
import rasterio

START_LAT, START_LON = LATITUDE, LONGITUDE
END_LAT, END_LON = END_LATITUDE, END_LONGITUDE
DATA_FILE = "DATA_FILE.tif"
N_POINTS = 500

lats = np.linspace(START_LAT, END_LAT, N_POINTS)
lons = np.linspace(START_LON, END_LON, N_POINTS)

with rasterio.open(DATA_FILE) as src:
    depths = np.array([list(src.sample([(x, y)]))[0][0]
                       for x, y in zip(lons, lats)])

# ── Find ridges (local maxima = shallower points) ──
inverted = -depths
peaks, props = find_peaks(inverted, prominence=5, distance=20)
print(f"Found {len(peaks)} candidate ridges/features")
for i, p in enumerate(peaks):
    print(f"  Feature {i+1}: lat={lats[p]:.4f} lon={lons[p]:.4f} depth={depths[p]:.1f}m")

"""
Geomorphological Feature Matching
Compares DEM/bathymetry features against a predicted description.
Computes slope, aspect, and curvature to identify matching terrain.

Replace LATITUDE, LONGITUDE, RADIUS_KM, and DATA_FILE.
"""
import numpy as np
import rasterio
from rasterio.windows import from_bounds

LAT, LON = LATITUDE, LONGITUDE
RADIUS_KM = 10
DATA_FILE = "DATA_FILE.tif"

# ── Extract window around target ──
d = RADIUS_KM / 111.32
with rasterio.open(DATA_FILE) as src:
    win = from_bounds(LON - d, LAT - d, LON + d, LAT + d, src.transform)
    elev = src.read(1, window=win).astype(float)

# ── Compute terrain derivatives ──
dy, dx = np.gradient(elev)
slope = np.degrees(np.arctan(np.sqrt(dx**2 + dy**2)))
aspect = np.degrees(np.arctan2(-dx, dy)) % 360

print(f"Elevation range: {np.nanmin(elev):.1f} to {np.nanmax(elev):.1f}m")
print(f"Mean slope: {np.nanmean(slope):.2f}°")
print(f"Dominant aspect: {np.nanmedian(aspect):.0f}°")