stock-data-skill
OpenClaw Skill for stock data analysis
Read and parse XML from construction systems - P6 schedules, BSDD exports, IFC-XML, COBie-XML. Convert to pandas DataFrames.
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。
SkillHub CLI
skillhub install datadrivenconstruction/xml-reader打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。
将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。
XML is used in construction for P6 schedules (XER), IFC-XML, COBie-XML, and buildingSMART Data Dictionary exports. This skill parses XML and converts to structured DataFrames.
import xml.etree.ElementTree as ET
import pandas as pd
from typing import Dict, Any, List, Optional, Union
from dataclasses import dataclass
from pathlib import Path
import re
@dataclass
class XMLElement:
"""Parsed XML element."""
tag: str
attributes: Dict[str, str]
text: Optional[str]
children: List['XMLElement']
class ConstructionXMLReader:
"""Parse XML from construction systems."""
def __init__(self):
self.namespaces: Dict[str, str] = {}
def parse_file(self, file_path: str) -> ET.Element:
"""Parse XML file and return root element."""
tree = ET.parse(file_path)
root = tree.getroot()
# Extract namespaces
self._extract_namespaces(root)
return root
def parse_string(self, xml_string: str) -> ET.Element:
"""Parse XML from string."""
root = ET.fromstring(xml_string)
self._extract_namespaces(root)
return root
def _extract_namespaces(self, root: ET.Element):
"""Extract namespace mappings."""
# Find namespace declarations
for attr, value in root.attrib.items():
if attr.startswith('{'):
ns = attr[1:attr.index('}')]
self.namespaces[root.tag.split('}')[0][1:]] = ns
def find_elements(self, root: ET.Element,
tag: str,
namespace: str = None) -> List[ET.Element]:
"""Find all elements with given tag."""
if namespace:
tag = f"{{{namespace}}}{tag}"
return root.findall(f".//{tag}")
def element_to_dict(self, element: ET.Element,
include_children: bool...
这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。
# XML Reader for Construction Data
## Overview
XML is used in construction for P6 schedules (XER), IFC-XML, COBie-XML, and buildingSMART Data Dictionary exports. This skill parses XML and converts to structured DataFrames.
## Python Implementation
```python
import xml.etree.ElementTree as ET
import pandas as pd
from typing import Dict, Any, List, Optional, Union
from dataclasses import dataclass
from pathlib import Path
import re
@dataclass
class XMLElement:
"""Parsed XML element."""
tag: str
attributes: Dict[str, str]
text: Optional[str]
children: List['XMLElement']
class ConstructionXMLReader:
"""Parse XML from construction systems."""
def __init__(self):
self.namespaces: Dict[str, str] = {}
def parse_file(self, file_path: str) -> ET.Element:
"""Parse XML file and return root element."""
tree = ET.parse(file_path)
root = tree.getroot()
# Extract namespaces
self._extract_namespaces(root)
return root
def parse_string(self, xml_string: str) -> ET.Element:
"""Parse XML from string."""
root = ET.fromstring(xml_string)
self._extract_namespaces(root)
return root
def _extract_namespaces(self, root: ET.Element):
"""Extract namespace mappings."""
# Find namespace declarations
for attr, value in root.attrib.items():
if attr.startswith('{'):
ns = attr[1:attr.index('}')]
self.namespaces[root.tag.split('}')[0][1:]] = ns
def find_elements(self, root: ET.Element,
tag: str,
namespace: str = None) -> List[ET.Element]:
"""Find all elements with given tag."""
if namespace:
tag = f"{{{namespace}}}{tag}"
return root.findall(f".//{tag}")
def element_to_dict(self, element: ET.Element,
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