追逐台风卡努项目源码及数据集

import netCDF4 as nc

import numpy as np

import matplotlib.pyplot as plt

import cartopy.crs as ccrs

import cartopy.feature as cfeature

from matplotlib.patches import Rectangle, Circle

from matplotlib.offsetbox import OffsetImage, AnnotationBbox

import os

import matplotlib

seapress = seapress / 100 # 转换为百帕

# 提取经度和纬度信息

lons = dataset.variables['longitude'][:]

lats = dataset.variables['latitude'][:]

# 提取时间变量

times = dataset.variables['time'][:]

time_units = dataset.variables['time'].units

calendar = dataset.variables['time'].calendar if hasattr(dataset.variables['time'], 'calendar') else

'standard'

time_vals = nc.num2date(times, units=time_units, calendar=calendar)

line_color = (68 / 255, 65 / 255, 65 / 255, 0.84)

# 确保目录存在 替换为自己的保存路径

save_dir = 'C:/Users/Mayn/Desktop/台风实验/台风实验/image'

if not os.path.exists(save_dir):

os.makedirs(save_dir)

# 寻找最低点的函数

def detect_low_pressure_center(msl_data):

min_val = np.min(msl_data)

min_idx = np.unravel_index(np.argmin(msl_data), msl_data.shape)

return min_val, min_idx

# 定义函数以查找 3x3 网格内的下一个最低点

def find_min_in_3x3_region(msl_data, center_idx):

x, y = center_idx

# 确保搜索范围在数据范围内

x_max = min(msl_data.shape[0] - 1, x + 1)

y_min = max(0, y - 1)

# 返回在原始数据中的实际索引

return min_val, (x_min + min_idx[0], y_min + min_idx[1])

initial_min_val, initial_min_idx = detect_low_pressure_center(seapress_2d_0)

# 记录当前最低点位置

current_min_idx = initial_min_idx

# 遍历每个时间片

for t in range(len(times)):

# 使用 3x3 网格找到最低点

# 更新当前最低点的位置

current_min_idx = min_idx

# 绘制图形

fig = plt.figure(figsize=(10, 6))