Reconstructed notebook pipeline for investment project recovery

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Reconstructed notebook pipeline for investment project recovery

  • Day: 2026-06-18
  • Time: 11:55 to 12:05
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: In Progress
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Notebooks, Pipeline-Recovery, Matching, Regression, Manifest, Data-Pipeline

Description

Session Goal

Reconstruct the investment-project notebook bundle into a coherent, recoverable pipeline and identify how the current notebook state maps onto a modular production architecture.

Key Activities

  • Reviewed the notebook bundle as a staged workflow rather than isolated files.
  • Mapped the pipeline into three main layers: source ingestion, spatial treatment construction, and categorization/scaffolding.
  • Distinguished the missing downstream components, especially matching, outcome merging, and regression stages.
  • Framed the remaining work as a downstream analysis contract, emphasizing extraction of each notebook’s read/write responsibilities and execution stage before judging analytical results.
  • Proposed a manifest-first recovery approach to preserve the current state of the project before refactoring notebook logic into modular components.
  • Reconstructed the empirical chain for the matching/regression workflow, including panel construction, diagnostics, and prototype regression notebooks, while separating canonical notebooks from exploratory or broken ones.

Achievements

  • Clarified the notebook architecture and the role of each stage in the broader data pipeline.
  • Identified gaps in the downstream analysis chain and the need for explicit notebook contracts.
  • Established a recovery strategy centered on a manifest and modular pipeline design.
  • Improved traceability between notebook artifacts and the intended production workflow.

Pending Tasks

  • Extract the read/write contract and stage for each notebook.
  • Recover or replace missing downstream matching, outcome-merging, and regression logic.
  • Build the notebook-to-product manifest to preserve the current state.
  • Modularize the workflow so preprocessing, matching, diagnostics, and regression can run as a coherent pipeline.

Evidence

  • source_file=2026-06-18.sessions.jsonl, line_number=2, event_count=0, session_id=d5216c671c09fc2678e6cc0bcdbdbda4b3d7e8e2a148c9e1e854c07bbd456216
  • event_ids: []

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