Implemented and Debugged Python Data Processing Modules

• Day: 2025-09-15
• Time: 14:50 to 16:30
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Data Processing, Automation, Debugging, CLI

Description

Session Goal: The session aimed to implement and debug Python modules for data processing, specifically focusing on L3 selection, publishing, and EDA command troubleshooting.

Key Activities:

• Developed two Python modules: select_l3_daily for filtering and scoring units, and publish_l2 for mirroring validated MDX files.
• Conducted an end-to-end pilot for data ingestion and processing, including setting up a workflow for data ingestion, processing, and digest generation.
• Troubleshot issues with EDA commands in a Python CLI, addressing command naming conventions and ensuring non-empty input.
• Debugged a KeyError in the eda-tagpairs-from-units function due to an empty units file.
• Created bootstrap commands for generating tag-pair and session digests, bypassing existing pipeline issues.
• Resolved an error in event loading by applying code patches for compatibility with dataclass instances and dictionaries.
• Fixed a schema mismatch in the Unit dataclass by implementing a patch in the bags_pipeline/quick.py file.
• Developed a playbook for log processing, transforming trial-and-error methods into a streamlined workflow.

Achievements:

• Successfully implemented and debugged multiple Python modules and workflows for data processing.
• Enhanced the robustness of the data processing pipeline through targeted debugging and error resolution.

Pending Tasks:

• Further testing and validation of the implemented modules and workflows in a production environment.

Evidence

• source_file=2025-09-15.sessions.jsonl, line_number=1, event_count=0, session_id=34a1e3be98528b371e8e399493cc5c98426e451d3020f70d75df50dea413e2f7
• event_ids: []