207 lines
6.0 KiB
Python
207 lines
6.0 KiB
Python
import struct
|
||
import pandas as pd
|
||
import geopandas as gpd
|
||
from datetime import datetime, timedelta
|
||
import json
|
||
from datetime import datetime, timedelta
|
||
import numpy as np
|
||
from shapely.geometry import mapping
|
||
from color import create_color_gradient_mapping, get_color_from_value1
|
||
|
||
|
||
x_min = 10
|
||
x_max = 3500
|
||
a = 0.25
|
||
n = 19
|
||
val = (np.linspace((x_min**a), (x_max**a), n)) ** (1 / a)
|
||
|
||
|
||
hours = 168
|
||
|
||
# 生成颜色映射
|
||
color_mapping = create_color_gradient_mapping(val)
|
||
|
||
|
||
def convert_coordinates(coord_list):
|
||
result = []
|
||
for coord in coord_list:
|
||
result.extend([coord[0], coord[1], 0])
|
||
return result
|
||
|
||
|
||
def read_binary_file(file_path):
|
||
|
||
df = pd.DataFrame()
|
||
|
||
with open(file_path, "rb") as file:
|
||
index = 0
|
||
|
||
# 读取前8个字节，表示数据列数
|
||
column_bytes = file.read(8)
|
||
if not column_bytes:
|
||
return # 文件结束
|
||
column_count = struct.unpack("<d", column_bytes)[0]
|
||
column_count_int = int(column_count)
|
||
print(f"Column Count: {column_count_int}")
|
||
|
||
# 读取接下来的8个字节，表示日期
|
||
date_bytes = file.read(8)
|
||
date = struct.unpack("<d", date_bytes)[0]
|
||
print(f"Date: {date}")
|
||
|
||
while True:
|
||
# print("index", index)
|
||
if index > hours:
|
||
break
|
||
time_bytes = file.read(8)
|
||
time = struct.unpack("<d", time_bytes)[0]
|
||
# print(f"time: {time}")
|
||
|
||
# 读取数据矩阵
|
||
data_size = column_count_int * 8 # 每个数据点8个字节
|
||
data_bytes = file.read(data_size)
|
||
data_matrix = struct.unpack(f"<{column_count_int}d", data_bytes)
|
||
data_matrix = [data_matrix]
|
||
new_df = pd.DataFrame(data_matrix)
|
||
df = pd.concat([df, new_df], ignore_index=True)
|
||
# print(f"Data Matrix: {data_matrix}")
|
||
index = index + 1
|
||
|
||
return df
|
||
|
||
|
||
def read_shp(shpfile_path, level=4):
|
||
river_data = gpd.read_file(shpfile_path)
|
||
return river_data[river_data["Type"] == level]
|
||
|
||
|
||
def get_color(runoff, min_runoff=0, max_runoff=10):
|
||
"""根据径流量获取颜色，从蓝到红的渐变"""
|
||
normalized = (runoff - min_runoff) / (max_runoff - min_runoff)
|
||
red = int(normalized * 255)
|
||
blue = int((1 - normalized) * 255)
|
||
return [red, 0, blue, 255]
|
||
|
||
|
||
def format_time(dt):
|
||
"""格式化时间为ISO字符串"""
|
||
return dt.strftime("%Y-%m-%dT%H:%M:%SZ")
|
||
|
||
|
||
def generate_river_czml(river_df, runoff_df, level=4, start_time=None):
|
||
"""
|
||
生成河网径流时序变化的CZML文件
|
||
|
||
Parameters:
|
||
-----------
|
||
river_df : GeoDataFrame
|
||
河网数据，包含geometry列(LINESTRING)
|
||
runoff_df : DataFrame
|
||
径流数据，shape为(时间数, 河段数)
|
||
start_time : datetime, optional
|
||
起始时间，默认为'2010-07-20 00:00:00'
|
||
|
||
Returns:
|
||
--------
|
||
list : CZML文档
|
||
"""
|
||
|
||
if start_time is None:
|
||
start_time = datetime(2010, 7, 20)
|
||
|
||
# 创建CZML文档
|
||
czml = [
|
||
{
|
||
"id": "document",
|
||
"name": "River Flow Visualization",
|
||
"version": "1.0",
|
||
"clock": {
|
||
"interval": f"{format_time(start_time)}/{format_time(start_time + timedelta(hours=hours))}",
|
||
"currentTime": format_time(start_time),
|
||
"multiplier": 3600, # 1小时/秒
|
||
"range": "LOOP_STOP",
|
||
"step": "SYSTEM_CLOCK_MULTIPLIER",
|
||
},
|
||
}
|
||
]
|
||
|
||
print(river_df.index)
|
||
|
||
# 为每条河段创建一个entity
|
||
for idx in river_df.index:
|
||
river_id = river_df.loc[idx, "Index"]
|
||
print("river_index", river_id)
|
||
coords = mapping(river_df.loc[idx, "geometry"])["coordinates"]
|
||
# new_coords = [coord for point in coords for coord in point]
|
||
# print(new_coords)
|
||
new_coords = []
|
||
for coord in coords:
|
||
new_coords.extend([[round(x, 5), round(y, 5), 0] for x, y in coord])
|
||
|
||
# 获取该河段的所有时间点的径流数据
|
||
river_runoff = runoff_df.iloc[:, idx - 1].values
|
||
max_runoff = max(river_runoff)
|
||
|
||
# # 创建时间序列的颜色数据
|
||
color_property = {"epoch": format_time(start_time), "rgba": []}
|
||
|
||
for hour, runoff in enumerate(river_runoff):
|
||
time_offset = hour * 3600 # 转换为秒
|
||
value = round(runoff / 86400, 3)
|
||
# print("value", value)
|
||
color = get_color_from_value1(abs(value), color_mapping)
|
||
color_property["rgba"].extend([time_offset] + list(color))
|
||
|
||
# 创建河段实体
|
||
entity = {
|
||
"id": f"{river_id}",
|
||
"name": f"River Segment {idx}",
|
||
"polyline": {
|
||
"positions": {
|
||
"cartographicDegrees": [
|
||
coord for point in new_coords for coord in point
|
||
]
|
||
},
|
||
"material": {"solidColor": {"color": color_property}},
|
||
"width": level * 2,
|
||
"clampToGround": True, # 贴地显示
|
||
"classificationType": "BOTH", # 同时在地形和3D切片上显示
|
||
"zIndex": 1, # 控制叠加顺序
|
||
"distanceDisplayCondition": {
|
||
"nearFar": [0, 1000000] # 显示距离范围（米）
|
||
},
|
||
"heightReference": "CLAMP_TO_GROUND", # 确保贴地
|
||
},
|
||
}
|
||
|
||
czml.append(entity)
|
||
|
||
return czml
|
||
|
||
|
||
def save_czml(czml_data, filename):
|
||
"""保存CZML数据到文件"""
|
||
with open(filename, "w") as f:
|
||
json.dump(czml_data, f, indent=2)
|
||
|
||
|
||
# 使用示例
|
||
"""
|
||
# 生成CZML数据
|
||
czml_data = generate_river_czml(river_df, runoff_df)
|
||
|
||
# 保存到文件
|
||
save_czml(czml_data, 'river_flow.czml')
|
||
"""
|
||
|
||
|
||
if __name__ == "__main__":
|
||
level = 4
|
||
# 读取文件
|
||
river_df = read_shp("lz.in/1.geojson", level)
|
||
runoff_df = read_binary_file("lz.out/lz.rivqdown.dat")
|
||
czml_data = generate_river_czml(river_df, runoff_df, level)
|
||
save_czml(czml_data, f"river_flow_{level}.czml")
|
||
|
||
print(f"CZML文件已生成：river_flow_{level}.czml")
|