import argparse import configparser import logging import pathlib import matplotlib.pyplot as plt import numpy as np from scipy import interpolate from .lambert import Lambert parser = argparse.ArgumentParser(description="Pre-process bathymetry") parser.add_argument("-v", "--verbose", action="count", default=0) args = parser.parse_args() logging.basicConfig(level=max((10, 20 - 10 * args.verbose))) log = logging.getLogger("bathy") log.info("Starting bathymetry pre-processing") config = configparser.ConfigParser() config.read("config.ini") bathy_inp = pathlib.Path(config.get("bathy", "sub")) hires_inp = pathlib.Path(config.get("bathy", "hires")) bathy_out = pathlib.Path(config.get("bathy", "out")) log.info(f"Loading bathymetry from {bathy_inp}") bathy_curvi = np.load(bathy_inp) projection = Lambert() bathy = np.stack( ( *projection.cartesian(bathy_curvi[:, 0], bathy_curvi[:, 1]), bathy_curvi[:, 2], ), axis=1, ) log.debug(f"Cartesian bathy: {bathy}") artha_curvi = np.array( (config.getfloat("artha", "lon"), config.getfloat("artha", "lat")) ) buoy_curvi = np.array( (config.getfloat("buoy", "lon"), config.getfloat("buoy", "lat")) ) artha = np.asarray(projection.cartesian(*artha_curvi)) buoy = np.asarray(projection.cartesian(*buoy_curvi)) D = np.diff(np.stack((artha, buoy)), axis=0) x = np.arange( -150, np.sqrt((D**2).sum()) + 150, config.getfloat("bathy", "step", fallback=1), ) theta = np.angle(D.dot((1, 1j))) coords = artha + (x * np.stack((np.cos(theta), np.sin(theta)))).T log.info("Interpolating bathymetry in 1D") z = interpolate.griddata(bathy[:, :2], bathy[:, 2], coords) log.debug(f"z: {z}") _hires = np.genfromtxt(hires_inp)[::-1] bathy_hires = np.stack( ( np.linspace( 0, (_hires.size - 1) * config.getfloat("bathy", "hires_step"), _hires.size, ), _hires, ), axis=1, ) del _hires log.debug(f"Bathy hires: {bathy_hires}") z_cr = 5 hires_crossing = np.diff(np.signbit(bathy_hires[:, 1] - z_cr)).nonzero()[0][-1] log.debug(f"Hires crossing: {hires_crossing}") z_crossing = np.diff(np.signbit(z - z_cr)).nonzero()[0][-1] log.debug(f"Z crossing: {z_crossing}") x_min_hires = x[z_crossing] + ( bathy_hires[:, 0].min() - bathy_hires[hires_crossing, 0] ) x_max_hires = x[z_crossing] + ( bathy_hires[:, 0].max() - bathy_hires[hires_crossing, 0] ) log.debug(f"Replacing range: [{x_min_hires},{x_max_hires}]") flt_x = (x > x_min_hires) & (x < x_max_hires) z[flt_x] = interpolate.griddata( (bathy_hires[:, 0],), bathy_hires[:, 1], (x[flt_x] - x[z_crossing] + bathy_hires[hires_crossing, 0]), )