海洋中尺度涡识别算法几何特征的代码

海洋中尺度涡识别算法主要是基于涡旋的几何特征进行识别，一些常用的几何特征包括涡旋的中心位置、涡旋的半径、涡旋的面积、涡旋的强度等等。这里提供一份基于 Python 的海洋中尺度涡识别算法的代码，该代码主要是基于涡旋的面积和强度进行涡识别。

import numpy as np
from skimage import measure

def detect_eddies(lon, lat, sst, threshold, min_eddy_size, max_eddy_size):
    """Detect eddies in sea surface temperature (SST) data.

    Args:
        lon: 1D array, longitudes.
        lat: 1D array, latitudes.
        sst: 2D array, SST data.
        threshold: float, threshold to determine eddy boundaries.
        min_eddy_size: float, minimum eddy size.
        max_eddy_size: float, maximum eddy size.

    Returns:
        eddy_centers: list of tuples, coordinates of eddy centers.
        eddy_radii: list of floats, radii of eddies.
        eddy_areas: list of floats, areas of eddies.
        eddy_strengths: list of floats, strengths of eddies.
    """

    # Calculate gradients of SST data.
    dx, dy = np.gradient(sst)
    dxx, dxy = np.gradient(dx)
    dyx, dyy = np.gradient(dy)

    # Calculate the Rossby number.
    rossby_number = (dx*dyy - dy*dxx) / (dx**2 + dy**2)

    # Find eddy boundaries.
    eddy_boundaries = measure.find_contours(np.abs(rossby_number), threshold)

    # Extract eddies.
    eddy_centers = []
    eddy_radii = []
    eddy_areas = []
    eddy_strengths = []
    for boundary in eddy_boundaries:
        # Calculate centroid of eddy.
        y, x = np.mean(boundary, axis=0)
        eddy_center = (lon[int(y)], lat[int(x)])
        eddy_centers.append(eddy_center)

        # Calculate radius of eddy.
        eddy_radius = np.sqrt(measure.inertia_tensor(boundary)[0, 0])
        eddy_radii.append(eddy_radius)

        # Calculate area of eddy.
        eddy_area = measure.area(boundary)
        eddy_areas.append(eddy_area)

        # Calculate strength of eddy.
        eddy_strength = np.mean(sst[np.round(boundary[:, 0]).astype(int), np.round(boundary[:, 1]).astype(int)])
        eddy_strengths.append(eddy_strength)

    # Filter eddies by size.
    eddy_centers = np.array(eddy_centers)
    eddy_radii = np.array(eddy_radii)
    eddy_areas = np.array(eddy_areas)
    eddy_strengths = np.array(eddy_strengths)
    indices = np.logical_and(min_eddy_size <= eddy_areas, eddy_areas <= max_eddy_size)
    eddy_centers = eddy_centers[indices]
    eddy_radii = eddy_radii[indices]
    eddy_areas = eddy_areas[indices]
    eddy_strengths = eddy_strengths[indices]

    return eddy_centers, eddy_radii, eddy_areas, eddy_strengths

该代码使用了 Scikit-image 库中的 `measure` 模块，该模块提供了一些计算几何特征的函数。具体来说，该代码首先计算了 SST 数据的梯度，并通过梯度计算了 Rossby 数，然后使用 `measure.find_contours` 函数找到了涡旋的边界，接着通过计算涡旋的几何特征（中心位置、半径、面积、强度）来提取涡旋。最后，该代码对涡旋进行了大小过滤，只保留了面积在指定范围内的涡旋。