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152
mcp/router.py
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152
mcp/router.py
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from fastmcp import FastMCP, Context
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from typing import Optional, Dict, Any
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from ..postgresql.database import get_database_instance
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from ..postgresql.scada_info import ScadaRepository
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from ..postgresql.scheme import SchemeRepository
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# 创建 MCP 服务器实例
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mcp = FastMCP("TJWater PostgreSQL Service", description="访问水务系统 PostgreSQL 数据库操作")
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# 数据库连接辅助函数
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async def get_database_connection(db_name: Optional[str] = None, ctx: Context = None):
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"""获取数据库连接，支持通过参数指定数据库名称"""
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if ctx:
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await ctx.info(f"连接到数据库: {db_name or '默认数据库'}")
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instance = await get_database_instance(db_name)
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async with instance.get_connection() as conn:
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yield conn
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@mcp.tool
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async def get_scada_info(db_name: Optional[str] = None, ctx: Context = None) -> Dict[str, Any]:
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"""
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查询所有 SCADA 信息
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Args:
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db_name: 可选的数据库名称，为空时使用默认数据库
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ctx: MCP 上下文，用于日志记录
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"""
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try:
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if ctx:
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await ctx.info("查询 SCADA 信息...")
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async for conn in get_database_connection(db_name, ctx):
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scada_data = await ScadaRepository.get_scadas(conn)
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if ctx:
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await ctx.info(f"检索到 {len(scada_data)} 条 SCADA 记录")
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return {"success": True, "data": scada_data, "count": len(scada_data)}
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except Exception as e:
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error_msg = f"查询 SCADA 信息时发生错误: {str(e)}"
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if ctx:
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await ctx.error(error_msg)
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return {"success": False, "error": error_msg}
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@mcp.tool
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async def get_scheme_list(db_name: Optional[str] = None, ctx: Context = None) -> Dict[str, Any]:
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"""
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查询所有方案信息
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Args:
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db_name: 可选的数据库名称，为空时使用默认数据库
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ctx: MCP 上下文，用于日志记录
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"""
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try:
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if ctx:
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await ctx.info("查询方案信息...")
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async for conn in get_database_connection(db_name, ctx):
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scheme_data = await SchemeRepository.get_schemes(conn)
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if ctx:
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await ctx.info(f"检索到 {len(scheme_data)} 条方案记录")
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return {"success": True, "data": scheme_data, "count": len(scheme_data)}
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except Exception as e:
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error_msg = f"查询方案信息时发生错误: {str(e)}"
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if ctx:
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await ctx.error(error_msg)
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return {"success": False, "error": error_msg}
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@mcp.tool
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async def get_burst_locate_results(db_name: Optional[str] = None, ctx: Context = None) -> Dict[str, Any]:
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"""
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查询所有爆管定位结果
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Args:
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db_name: 可选的数据库名称，为空时使用默认数据库
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ctx: MCP 上下文，用于日志记录
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"""
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try:
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if ctx:
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await ctx.info("查询爆管定位结果...")
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async for conn in get_database_connection(db_name, ctx):
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burst_data = await SchemeRepository.get_burst_locate_results(conn)
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if ctx:
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await ctx.info(f"检索到 {len(burst_data)} 条爆管记录")
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return {"success": True, "data": burst_data, "count": len(burst_data)}
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except Exception as e:
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error_msg = f"查询爆管定位结果时发生错误: {str(e)}"
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if ctx:
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await ctx.error(error_msg)
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return {"success": False, "error": error_msg}
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@mcp.tool
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async def get_burst_locate_result_by_incident(
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burst_incident: str,
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db_name: Optional[str] = None,
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ctx: Context = None
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) -> Dict[str, Any]:
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"""
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根据 burst_incident 查询爆管定位结果
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Args:
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burst_incident: 爆管事件标识符
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db_name: 可选的数据库名称，为空时使用默认数据库
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ctx: MCP 上下文，用于日志记录
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"""
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try:
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if ctx:
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await ctx.info(f"查询爆管事件 {burst_incident} 的结果...")
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async for conn in get_database_connection(db_name, ctx):
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result = await SchemeRepository.get_burst_locate_result_by_incident(
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conn, burst_incident
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)
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if ctx:
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await ctx.info("检索到爆管事件数据")
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return result
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except Exception as e:
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error_msg = f"根据 burst_incident 查询爆管定位结果时发生错误: {str(e)}"
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if ctx:
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await ctx.error(error_msg)
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return {"success": False, "error": error_msg}
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# 添加静态配置资源
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@mcp.resource("config://database/supported_databases")
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def get_supported_databases():
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"""列出支持的数据库配置"""
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return ["default", "backup", "analytics"]
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@mcp.resource("config://api/version")
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def get_api_version():
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"""获取 API 版本"""
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return "1.0.0"
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if __name__ == "__main__":
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mcp.run()
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@@ -412,11 +412,11 @@ def flushing_analysis(
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############################################################
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def contaminant_simulation(
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name: str,
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modify_pattern_start_time: str,
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modify_total_duration: int = 900,
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source: str = None,
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concentration: float = None,
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source_pattern: str = None,
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modify_pattern_start_time: str, # 模拟开始时间,格式为'2024-11-25T09:00:00+08:00'
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modify_total_duration: int = 900, # 模拟总历时，秒
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source: str = None,# 污染源节点ID
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concentration: float = None, # 污染源浓度，单位mg/L
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source_pattern: str = None, # 污染源时间变化模式名称
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scheme_Name: str = None,
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) -> None:
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"""
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@@ -425,7 +425,7 @@ def contaminant_simulation(
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:param modify_pattern_start_time: 模拟开始时间,格式为'2024-11-25T09:00:00+08:00'
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:param modify_total_duration: 模拟总历时，秒
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:param source: 污染源所在的节点ID
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:param concentration: 污染源位置处的浓度，单位mg/L，即默认的污染模拟setting为concentration
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:param concentration: 污染源位置处的浓度，单位mg/L，即默认的污染模拟setting为concentration（应改为 Set point booster）
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:param source_pattern: 污染源的时间变化模式，若不传入则默认以恒定浓度持续模拟，时间长度等于duration;
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若传入，则格式为{1.0,0.5,1.1}等系数列表pattern_step模拟等于模型的hydraulic time step
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:param scheme_Name: 方案名称
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BIN
requirements.txt
BIN
requirements.txt
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168
requirements2.txt
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168
requirements2.txt
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annotated-doc==0.0.4
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annotated-types==0.7.0
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anyio==4.8.0
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attrs==25.4.0
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Authlib==1.6.6
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beartype==0.22.9
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beautifulsoup4==4.13.4
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cachetools==6.2.4
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certifi==2024.8.30
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cffi==2.0.0
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chardet==5.2.0
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charset-normalizer==3.4.0
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click==8.1.8
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cloudpickle==3.1.2
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colorama==0.4.6
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contourpy==1.3.2
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cryptography==46.0.3
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cycler==0.12.1
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cyclopts==4.5.0
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Cython==3.0.12
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diskcache==5.6.3
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distro==1.9.0
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dnspython==2.8.0
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docstring_parser==0.17.0
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docutils==0.21.2
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dotenv==0.9.9
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ecos==2.0.14
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email-validator==2.3.0
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esda==2.7.0
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et_xmlfile==2.0.0
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exceptiongroup==1.3.1
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fakeredis==2.33.0
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fastapi==0.128.0
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fastmcp==2.9.2
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fonttools==4.58.0
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future==0.18.3
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GeoAlchemy2==0.17.1
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geopandas==1.0.1
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greenlet==3.1.1
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h11==0.14.0
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httpcore==1.0.7
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httpx==0.28.1
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httpx-sse==0.4.3
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idna==3.10
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importlib_metadata==8.7.1
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influxdb-client==1.48.0
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iniconfig==2.0.0
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jaraco.classes==3.4.0
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jaraco.context==6.1.0
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jaraco.functools==4.4.0
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jeepney==0.9.0
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Jinja2==3.1.6
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jiter==0.8.2
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joblib==1.5.0
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jsonschema==4.26.0
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jsonschema-path==0.3.4
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jsonschema-specifications==2025.9.1
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keyring==25.7.0
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Kivy==2.3.0
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Kivy-Garden==0.1.5
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kiwisolver==1.4.8
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libpysal==4.13.0
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lupa==2.6
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mapclassify==2.8.1
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markdown-it-py==3.0.0
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MarkupSafe==3.0.3
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matplotlib==3.10.3
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mcp==1.9.4
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mdurl==0.1.2
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more-itertools==10.8.0
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msgpack==1.1.0
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networkx==3.4.2
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numexpr==2.14.1
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numpy==1.26.2
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openai==1.63.0
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openapi-pydantic==0.5.1
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openpyxl==3.1.5
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opentelemetry-api==1.39.1
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opentelemetry-exporter-prometheus==0.60b1
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opentelemetry-instrumentation==0.60b1
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opentelemetry-sdk==1.39.1
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opentelemetry-semantic-conventions==0.60b1
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osqp==1.0.5
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packaging==25.0
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pandas==2.2.3
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pathable==0.4.4
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pathvalidate==3.3.1
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pillow==11.2.1
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pipdeptree==2.30.0
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platformdirs==4.3.8
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pluggy==1.5.0
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pointpats==2.5.1
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prometheus_client==0.24.1
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psycopg==3.2.5
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psycopg-binary==3.2.5
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psycopg-pool==3.3.0
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psycopg2==2.9.10
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PuLP==3.1.1
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py-key-value-aio==0.3.0
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py-key-value-shared==0.3.0
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py-linq==1.4.0
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pyarmor==9.0.7
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pyarmor.cli.core==7.6.4
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pybind11-stubgen==2.5.1
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pycparser==2.23
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pydantic==2.10.6
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pydantic-settings==2.12.0
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pydantic_core==2.27.2
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pydevd-pycharm==243.16718.36
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pydocket==0.16.6
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Pygments==2.18.0
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PyJWT==2.10.1
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pykalman==0.10.2
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# pymetis @ file:///D:/bld/pymetis_1762455149640/work 通过 conda 安装 pymetis,避免编译问题 conda install -c conda-forge pymetis
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pynvim==0.5.0
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pyogrio==0.11.0
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pyparsing==3.2.3
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pyperclip==1.11.0
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pyproj==3.7.1
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pytest==8.3.5
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python-dateutil==2.9.0.post0
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python-dotenv==1.2.1
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python-json-logger==4.0.0
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python-multipart==0.0.20
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pytz==2025.2
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PyYAML==6.0.3
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pyzmq==26.2.1
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reactivex==4.0.4
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redis==5.2.1
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referencing==0.36.2
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requests==2.32.3
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rich==14.2.0
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rich-rst==1.3.2
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rpds-py==0.30.0
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rtree==1.4.0
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schedule==1.2.2
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scikit-base==0.12.6
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scikit-fuzzy==0.5.0
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scikit-learn==1.6.1
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scikit-survival==0.26.0
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scipy==1.15.2
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SecretStorage==3.5.0
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setuptools==80.7.1
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shapely==2.1.0
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shellingham==1.5.4
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six==1.17.0
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sniffio==1.3.1
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sortedcontainers==2.4.0
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soupsieve==2.7
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spaghetti==1.7.6
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spopt==0.6.1
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SQLAlchemy==2.0.41
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sse-starlette==3.0.4
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starlette==0.50.0
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threadpoolctl==3.6.0
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tqdm==4.67.1
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typer==0.21.1
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typing-inspection==0.4.0
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typing_extensions==4.12.2
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tzdata==2025.2
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urllib3==2.2.3
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uvicorn==0.34.0
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websockets==16.0
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wheel==0.45.1
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wntr==1.3.2
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wrapt==1.17.3
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zipp==3.23.0
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zmq==0.0.0
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Reference in New Issue
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