Diagnosed Git and EPH preprocessing cleanup

3 min read

Diagnosed Git and EPH preprocessing cleanup

  • Day: 2026-06-14
  • Time: 11:50 to 12:10
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: In Progress
  • Priority: HIGH
  • Assignee: Matías Nehuen Iglesias
  • Tags: Git, History-Rewrite, Preprocessing, Eph, Pandas, Debugging

Description

Session Goal

Investigate two parallel troubleshooting threads: (1) a GitHub push failure caused by an accidentally committed nested bare repository / large blob in local history, and (2) validation and recovery of the legacy EPH preprocessing pipeline used to generate training sets.

Key Activities

  • Diagnosed the Git rejection as a nested bare repository tracked under clean-repo.[[git]]/objects/pack/..., not a normal project file.
  • Compared remediation paths: quick removal from the latest commit vs. full history rewrite with [[git]] filter-repo when the bad path exists in earlier commits.
  • Outlined a safe cleanup workflow: collapse local commits onto the remote baseline, remove the accidental repo, add ignore rules for generated artifacts / Git internals, and verify the branch no longer contains oversized blobs.
  • Noted that clean-repo.[[git]] is absent from the current tree but still present in local commit history, with origin/master still at a93fd4d5, making a local history rewrite the cleanest fix.
  • For the data pipeline, proposed isolating the migrated preprocess.py and running a legacy local version that reads .txt files from microdatos-EPH-INDEC and writes outputs to a new folder to avoid overwriting validated CSVs.
  • Defined a legacy preprocessing workflow for EPH household/individual microdata: limited-column reads, merge without _x/_y contamination, rename EPH variables to Censo equivalents, deflate monetary variables, and create income indicators.
  • Added a year-by-year audit routine and a shell runbook to execute the preprocessing job, capture logs, inspect errors, and audit generated training CSVs for 2010-2025.
  • Debugged CSV header issues by checking raw file headers directly in binary/text mode and counting delimiters to distinguish display truncation from actual source corruption.
  • Concluded that corrupted headers likely originate in the source .txt files rather than [[pandas]].read_csv, and proposed scanning affected quarters and comparing against remote versions.
  • Identified a Python indentation/tab issue in the legacy script and provided a rerun procedure: normalize tabs to spaces, compile-check, and rerun the preprocessing job.

Achievements

  • Clarified the root cause of the Git failure and established that history rewriting is likely required rather than a simple working-tree cleanup.
  • Established a safe validation path for the EPH preprocessing migration that preserves current outputs until the new pipeline is verified.
  • Narrowed the CSV/header problem to source-file corruption, reducing uncertainty around pandas display or parsing settings.
  • Produced an operational recovery plan for rerunning the legacy preprocessing script after fixing indentation.

Pending Tasks

  • Rewrite or surgically clean Git history to remove the nested bare repository and any large blobs, then verify the push succeeds.
  • Run the legacy EPH preprocessing pipeline end-to-end for 2010-2025 and audit the outputs.
  • Inspect affected local EPH .txt source files, identify corrupted quarters, and replace them with clean remote copies if needed.
  • Fix the Python indentation issue in the preprocessing script and rerun validation.
  • Confirm generated training CSVs are clean before overwriting or migrating the main pipeline outputs.

Evidence

  • source_file=2026-06-14.sessions.jsonl, line_number=4, event_count=0, session_id=ef0612462fd3c2ff9d19f6aa05bd1b5864225356c29cbfc6394c2ff97443c352
  • event_ids: []

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