- name
- json-parser
- description
- Parse and validate JSON data from construction APIs, IoT sensors, and BIM exports. Transform nested JSON to flat DataFrames.
- homepage
- https://datadrivenconstruction.io
- metadata
- {"openclaw": {"emoji": "🏷️", "os": ["win32"], "homepage": "https://datadrivenconstruction.io", "requires": {"bins": ["python3"]}}}
JSON Parser for Construction Data
Overview
Construction systems increasingly use JSON for data exchange - from IoT sensors to BIM metadata exports. This skill handles parsing, validation, and flattening of JSON structures.
Python Implementation
import json
import pandas as pd
from typing import Dict, Any, List, Optional, Union
from dataclasses import dataclass
from pathlib import Path
@dataclass
class JSONParseResult:
"""Result of JSON parsing operation."""
success: bool
data: Any
errors: List[str]
record_count: int
class ConstructionJSONParser:
"""Parse JSON data from construction sources."""
def __init__(self):
self.errors: List[str] = []
def parse_file(self, file_path: str) -> JSONParseResult:
"""Parse JSON from file."""
try:
with open(file_path, 'r', encoding='utf-8') as f:
data = json.load(f)
return JSONParseResult(True, data, [], self._count_records(data))
except json.JSONDecodeError as e:
return JSONParseResult(False, None, [f"JSON Error: {e}"], 0)
except Exception as e:
return JSONParseResult(False, None, [str(e)], 0)
def parse_string(self, json_string: str) -> JSONParseResult:
"""Parse JSON from string."""
try:
data = json.loads(json_string)
return JSONParseResult(True, data, [], self._count_records(data))
except json.JSONDecodeError as e:
return JSONParseResult(False, None, [f"JSON Error: {e}"], 0)
def _count_records(self, data: Any) -> int:
"""Count records in data."""
if isinstance(data, list):
return len(data)
elif isinstance(data, dict):
return 1
return 0
def flatten_json(self, data: Dict, prefix: str = '') -> Dict[str, Any]:
"""Flatten nested JSON to single-level dict."""
flat = {}
for key, value in data.items():
new_key = f"{prefix}_{key}" if prefix else key
if isinstance(value, dict):
flat.update(self.flatten_json(value, new_key))
elif isinstance(value, list):
if all(isinstance(i, (str, int, float, bool, type(None))) for i in value):
flat[new_key] = value
else:
for i, item in enumerate(value):
if isinstance(item, dict):
flat.update(self.flatten_json(item, f"{new_key}_{i}"))
else:
flat[f"{new_key}_{i}"] = item
else:
flat[new_key] = value
return flat
def to_dataframe(self, data: Union[List[Dict], Dict]) -> pd.DataFrame:
"""Convert JSON data to DataFrame."""
if isinstance(data, list):
flat_records = [self.flatten_json(r) if isinstance(r, dict) else {'value': r} for r in data]
return pd.DataFrame(flat_records)
elif isinstance(data, dict):
if all(isinstance(v, list) for v in data.values()):
# Dict of lists - columnar format
return pd.DataFrame(data)
else:
flat = self.flatten_json(data)
return pd.DataFrame([flat])
return pd.DataFrame()
def extract_elements(self, data: Dict, path: str) -> List[Any]:
"""Extract elements using dot notation path."""
parts = path.split('.')
current = data
for part in parts:
if isinstance(current, dict) and part in current:
current = current[part]
elif isinstance(current, list) and part.isdigit():
current = current[int(part)]
else:
return []
return current if isinstance(current, list) else [current]
def validate_schema(self, data: Dict,
required_fields: List[str]) -> Dict[str, Any]:
"""Validate JSON against required fields."""
flat = self.flatten_json(data)
missing = [f for f in required_fields if f not in flat]
present = [f for f in required_fields if f in flat]
return {
'valid': len(missing) == 0,
'missing_fields': missing,
'present_fields': present,
'completeness': len(present) / len(required_fields) * 100
}
# BIM JSON Parser
class BIMJSONParser(ConstructionJSONParser):
"""Specialized parser for BIM JSON exports."""
def parse_bim_elements(self, data: Dict) -> pd.DataFrame:
"""Parse BIM elements from JSON export."""
elements = []
# Common BIM JSON structures
if 'elements' in data:
elements = data['elements']
elif 'objects' in data:
elements = data['objects']
elif 'entities' in data:
elements = data['entities']
elif isinstance(data, list):
elements = data
if not elements:
return pd.DataFrame()
# Flatten each element
flat_elements = []
for elem in elements:
if isinstance(elem, dict):
flat = self.flatten_json(elem)
flat_elements.append(flat)
return pd.DataFrame(flat_elements)
def extract_properties(self, element: Dict) -> Dict[str, Any]:
"""Extract properties from BIM element."""
props = {}
# Common property locations in BIM JSON
for key in ['properties', 'params', 'parameters', 'attributes']:
if key in element and isinstance(element[key], dict):
props.update(element[key])
return props
# IoT JSON Parser
class IoTJSONParser(ConstructionJSONParser):
"""Parser for IoT sensor data."""
def parse_sensor_reading(self, data: Dict) -> Dict[str, Any]:
"""Parse single sensor reading."""
return {
'sensor_id': data.get('sensor_id') or data.get('id'),
'timestamp': data.get('timestamp') or data.get('time'),
'value': data.get('value') or data.get('reading'),
'unit': data.get('unit', ''),
'location': data.get('location', '')
}
def parse_sensor_batch(self, data: List[Dict]) -> pd.DataFrame:
"""Parse batch of sensor readings."""
readings = [self.parse_sensor_reading(r) for r in data]
return pd.DataFrame(readings)Quick Start
parser = ConstructionJSONParser()
# Parse from file
result = parser.parse_file("bim_export.json")
if result.success:
df = parser.to_dataframe(result.data)
print(f"Loaded {len(df)} records")
# Flatten nested JSON
flat = parser.flatten_json(result.data)
# Extract specific path
elements = parser.extract_elements(result.data, "project.building.floors")Common Use Cases
1. BIM Metadata
bim_parser = BIMJSONParser()
result = bim_parser.parse_file("revit_export.json")
elements = bim_parser.parse_bim_elements(result.data)2. IoT Sensors
iot_parser = IoTJSONParser()
readings = iot_parser.parse_sensor_batch(sensor_data)3. API Response
parser = ConstructionJSONParser()
result = parser.parse_string(api_response)
df = parser.to_dataframe(result.data)Resources
- DDC Book: Chapter 2.1 - Semi-structured Data