Source code for wavespectra.construct.ochihubble

import numpy as np
from typing_extensions import deprecated

from wavespectra.construct.helpers import (
    spread,
    check_coordinates,
    arrange_inputs,
    make_dataset,
)

gamma = (
    lambda x: np.sqrt(2.0 * np.pi / x)
    * ((x / np.exp(1.0)) * np.sqrt(x * np.sinh(1.0 / x))) ** x
)




@deprecated(
    "All functions and classes in wavespectra.construct will be replaced in "
    "**4.0.0** by a new spectral reconstruction api",
)
def ochihubble(
    hs,
    tp,
    L,
    dp,
    dspr,
    freqs=np.arange(0.02, 1.0, 0.02),
    dirs=np.arange(0, 360, 10),
    coordinates=[("part", [0, 1])],
    sumpart=True,
):
    """Construct OCHIHUBBLE spectra."""
    check_coordinates(hs, coordinates)

    # Arrange inputs
    hs_m, tp_m, l_m, dp_m, dspr_m = arrange_inputs(hs, tp, L, dp, dspr)
    w = 2 * np.pi * freqs.reshape((-1, 1))

    # Create 1D spectra
    w0 = 2 * np.pi / tp_m
    B = np.maximum(l_m, 0.01) + 0.25
    A = 0.5 * np.pi * hs_m ** 2 * ((B * w0 ** 4) ** l_m / gamma(l_m))
    a = np.minimum((w0 / w) ** 4, 100.0)
    S = A * np.exp(-B * a) / (np.power(w, 4.0 * B))

    # Apply spreading
    G1 = spread(dp_m, dspr_m, dirs)
    spec = S * G1

    # sum partitions
    if sumpart:
        idimpart = [i for i, t in enumerate(coordinates) if t[0] == "part"]
        if idimpart:
            spec = np.sum(spec, axis=idimpart[0])
            coordinates.pop(idimpart[0])

    return make_dataset(spec, freqs, dirs, coordinates)