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A dataclass representing a dedicated data analysis section that stores analysis results, plots, dataset information, and conclusions separately from text content.

Purpose

DataSection serves as a container for data analysis work within a larger document or project system. It manages the lifecycle of a data analysis section including versioning, status tracking, ownership, and storage of analysis artifacts like plots and conclusions. It provides serialization/deserialization capabilities for persistence and integrates with a DataAnalysisSession through a session ID reference.

Source Code

class DataSection:
    """Dedicated Data Analysis Section - separate from TextSection"""
    id: str
    owner: str  # User email/ID
    title: str
    description: str = ""
    
    # Content and versioning
    current_content: str = ""  # For summary/conclusions
    status: ContentStatus = ContentStatus.DRAFT
    
    # Timestamps
    created_at: datetime = None
    updated_at: datetime = None
    
    # Data Analysis specific fields
    analysis_session_id: str = None  # Link to DataAnalysisSession
    dataset_info: Dict[str, Any] = None  # Dataset metadata
    generated_plots: List[str] = None  # Plot file paths
    analysis_conclusions: str = ""  # Final analysis conclusions
    
    # Metadata
    tags: List[str] = None
    metadata: Dict[str, Any] = None
    
    def __post_init__(self):
        if self.created_at is None:
            self.created_at = datetime.now()
        if self.updated_at is None:
            self.updated_at = datetime.now()
        if self.dataset_info is None:
            self.dataset_info = {}
        if self.generated_plots is None:
            self.generated_plots = []
        if self.tags is None:
            self.tags = []
        if self.metadata is None:
            self.metadata = {}
    
    def to_dict(self) -> Dict:
        return {
            'id': self.id,
            'owner': self.owner,
            'title': self.title,
            'description': self.description,
            'current_content': self.current_content,
            'status': self.status.value,
            'created_at': self.created_at.isoformat(),
            'updated_at': self.updated_at.isoformat(),
            'analysis_session_id': self.analysis_session_id,
            'dataset_info': self.dataset_info,
            'generated_plots': self.generated_plots,
            'analysis_conclusions': self.analysis_conclusions,
            'tags': self.tags,
            'metadata': self.metadata
        }
    
    @classmethod
    def from_dict(cls, data: Dict) -> 'DataSection':
        return cls(
            id=data['id'],
            owner=data['owner'],
            title=data['title'],
            description=data.get('description', ''),
            current_content=data.get('current_content', ''),
            status=ContentStatus(data.get('status', ContentStatus.DRAFT.value)),
            created_at=datetime.fromisoformat(data['created_at']),
            updated_at=datetime.fromisoformat(data['updated_at']),
            analysis_session_id=data.get('analysis_session_id'),
            dataset_info=data.get('dataset_info', {}),
            generated_plots=data.get('generated_plots', []),
            analysis_conclusions=data.get('analysis_conclusions', ''),
            tags=data.get('tags', []),
            metadata=data.get('metadata', {})
        )

Parameters

Parameter Details

id: Unique identifier string for the data section, typically a UUID or similar unique string

owner: User email or ID string identifying who owns/created this data section

title: Display title string for the data section

description: Optional detailed description string of the data section's purpose (defaults to empty string)

current_content: String containing summary or conclusions text (defaults to empty string)

status: ContentStatus enum value indicating the section's workflow state (defaults to ContentStatus.DRAFT)

created_at: datetime object marking when the section was created (auto-set to current time if None)

updated_at: datetime object marking last modification time (auto-set to current time if None)

analysis_session_id: Optional string linking to a DataAnalysisSession ID for tracking the analysis workflow

dataset_info: Dictionary containing metadata about the dataset being analyzed (defaults to empty dict)

generated_plots: List of strings containing file paths to generated plot images (defaults to empty list)

analysis_conclusions: String containing final analysis conclusions and findings (defaults to empty string)

tags: List of string tags for categorization and search (defaults to empty list)

metadata: Dictionary for storing arbitrary additional metadata (defaults to empty dict)

Return Value

Instantiation returns a DataSection object with all attributes initialized. The to_dict() method returns a dictionary representation with datetime objects converted to ISO format strings and enums converted to their values. The from_dict() class method returns a new DataSection instance reconstructed from a dictionary.

Class Interface

Methods

Attributes

Dependencies

• datetime
• typing
• dataclasses

Required Imports

from datetime import datetime
from typing import List, Dict, Any
from dataclasses import dataclass

Conditional/Optional Imports

These imports are only needed under specific conditions:

from enum import Enum

Usage Example

from datetime import datetime
from typing import List, Dict, Any
from dataclasses import dataclass
from enum import Enum

class ContentStatus(Enum):
    DRAFT = 'draft'
    PUBLISHED = 'published'

@dataclass
class DataSection:
    # ... (class definition as provided)
    pass

# Create a new data section
section = DataSection(
    id='section-123',
    owner='user@example.com',
    title='Sales Analysis Q4 2023',
    description='Quarterly sales performance analysis'
)

# Add analysis results
section.analysis_session_id = 'session-456'
section.dataset_info = {'rows': 1000, 'columns': 15, 'source': 'sales_db'}
section.generated_plots = ['/plots/sales_trend.png', '/plots/regional_breakdown.png']
section.analysis_conclusions = 'Sales increased 15% YoY with strong performance in APAC region'
section.tags = ['sales', 'quarterly', 'performance']

# Serialize to dictionary for storage
data_dict = section.to_dict()
print(data_dict['status'])  # 'draft'

# Deserialize from dictionary
restored_section = DataSection.from_dict(data_dict)
print(restored_section.title)  # 'Sales Analysis Q4 2023'

Best Practices

• Always provide required fields (id, owner, title) when instantiating to avoid errors
• Use the from_dict() class method for deserialization rather than manual construction to ensure proper type conversions
• Update the updated_at timestamp manually when modifying the section content
• Store plot file paths as absolute paths or relative to a known base directory for portability
• Use the status field to track workflow states (draft, review, published, etc.)
• Link to DataAnalysisSession via analysis_session_id to maintain traceability of analysis work
• Leverage the metadata dictionary for extensibility without modifying the class structure
• Ensure ContentStatus enum is properly defined with all needed status values before using the class
• When persisting to database, use to_dict() to get a JSON-serializable representation
• The __post_init__ method automatically initializes mutable default values, preventing shared state issues

Tags

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