mikeio

st=>start: 输入 
mikeio=>operation: 使用mikeio读取dfsu/mesh文件
geopandas=>operation: 使用geopandas转换成shp/geojson
e=>end: 完成
st->mikeio->geopandas->e

mesh to shp

from mikeio import Mesh
import pandas as pd
import geopandas as gpd

# 文件
filename = r"XC-BE.mesh"
msh = Mesh(filename)

# 获取shp对象
shp = msh.to_shapely()
buffer = shp.buffer(0)

# 导出shp文件
gdf = gpd.GeoSeries([buffer])
gdf.to_file('111.shp')

dfsu to shp

pandas

from typing import Optional, List, Union
import numpy as np
from mikeio import Dfsu
import pandas as pd
import geopandas as gpd

def dfsu_to_shp(
    dfsu_file: str,
    out_file: str,
    data_items: Optional[List[Union[str, int]]] = None,
    time_step: Optional[int] = None,
    value_filter: Optional[List[float]] = None,
    value_field: str = "value",
) -> str:
    """将DFSU文件中的标量场数据导出为Shapefile
    
    Args:
        dfsu_file: 输入DFSU文件路径
        out_file: 输出Shapefile路径
        data_items: 数据项名称/索引，默认读取第一个数据项
        time_step: 时间步索引，默认最后一步
        value_filter: 过滤特定数值范围（闭区间）
        value_field: 数据值字段名称

    Returns:
        成功返回输出路径，失败返回空字符串
    """
    try:
        # 读取DFSU文件
        dfs = Dfsu(dfsu_file)
        
        # 设置默认参数
        data_items = data_items or [0]
        time_step = time_step if time_step is not None else dfs.n_timesteps - 1
        
        # 读取数据
        dataset = dfs.read(items=data_items, time=time_step)
        values = np.round(dataset[0].values, 3)
        
        # 构建数据框
        df = pd.DataFrame({
            value_field: values,
            "coord_x": dfs.element_coordinates[:, 0],
            "coord_y": dfs.element_coordinates[:, 1]
        })
        
        # 数值过滤
        if value_filter and len(value_filter) == 2:
            min_val, max_val = sorted(value_filter)
            df = df[(df[value_field] >= min_val) & (df[value_field] <= max_val)]
        
        # 生成地理数据框
        gdf = gpd.GeoDataFrame(
            df[[value_field]],
            geometry=gpd.points_from_xy(df.coord_x, df.coord_y),
            crs=dfs.projection_string
        )
        
        # 导出Shapefile
        gdf.to_file(out_file)
        return out_file

    except Exception as e:
        print(f"导出失败: {str(e)}")
        return ""

if __name__ == "__main__":
    tar = r"D:\filed.dfsu"

    dfsu_to_shp(tar, "tar.shp", ["Total water depth"], 228)

• geopandas（可能有BUG)

from mikeio import Dfsu,Dfs0,Mesh
import pandas as pd
import geopandas as gpd


filename = "xxx.dfsu"
item = 'Current speed'
step = -1

df = Dfsu(filename)
data = dfsu.read()

item_data = data[item][setp]
item_dataframe = pd.DataFrame({'sp': item_data})

# 获取shp格式
shp = dfsu.to_shapely()
poly_list = [e for e in shp] #???

# 转换为地理数据格式
gdf = gpd.GeoDataFrame(item_dataframe, geometry=poly_list)

# 导出shp
gdf.to_file("xxx.shp")

shp to geojson

• 使用python转换

# shp to GeoJson
import geopandas as gpd
data = gpd.read_file('中国省级区域20200720.shp')
data.to_file("中国省级区域20200720.json", driver='GeoJSON', encoding="utf-8")

# GeoJson to shp
data = gpd.read_file('中国省级区域20200720.json')
data.to_file('中国省级区域20200720', driver='ESRI Shapefile', encoding='utf-8')

• 使用arcmap转换

在arcgis里面使用工具把shp数据处理成geoJSON数据。使用工具为arcgis工具箱 Conversion Tools->JSON->Features To JSON

读取最后一次setp数据

dfs = mikeio.open(dfsu_file)

# 因为数组索引从0开始，所以-1进行修正
last_setp = int(dfs.n_timesteps) - 1
items = ["Current speed", "Current direction"]

all_data = dfs.read(items=items, time=last_setp)