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chore: archive unused files and move legacy code to can_delete

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archive/ — unused reference files (no active code references):
  - LookupSearchTermCleanedDrugName.csv, condition_directorate_mapping.csv
  - na_directory_rows.csv (diagnostic output), ta-recommendations.xlsx
  - snomed_indication_mapping_query.sql (source for embedded SQL)
  - IMPROVEMENT_RECOMMENDATIONS.md, power query.pq

archive/can_delete/ — legacy code and logs safe to remove:
  - dashboard_gui.py (replaced by Reflex app)
  - pathways_app_old.py.bak (old backup)
  - Ralph loop iteration logs (iterations 2-8)
This commit is contained in:
Andrew Charlwood
2026-02-06 01:01:02 +00:00
parent a31907aa1f
commit bb93c1673e
23 changed files with 121509 additions and 0 deletions
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archive/can_delete/iteration_6.log
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I'll start by reading the required files in order to understand the current state of the project.
Now let me check the recent git log and read the current state of `refresh_pathways.py` since Task 3.1 is next.
Reading progress.txt, IMPLEMENTATION_PLAN.md, and guardrails.md. I can see:
**Status**: Phase 1 (Tasks 1.1, 1.2, 1.3) and Phase 2 (Tasks 2.1, 2.2) are all complete. The next task is **Task 3.1: Update `refresh_pathways.py` indication processing to use `assign_drug_indications()`**.
**Why Task 3.1**: This is the first task in Phase 3 and the natural next step. All Phase 1 & 2 dependencies are complete (the query returns all matches, drug mapping is loaded, and `assign_drug_indications()` exists). Now I need to wire them together in the pipeline.
The current indication processing block (lines 361-518) uses the old approach:
- Line 424-428: `dict(zip(...))` which only keeps the LAST match per patient
- Lines 430-452: Manual indication_df building per UPID (no drug awareness)
I need to replace this with the new drug-aware approach:
1. Load drug mapping via `load_drug_indication_mapping()`
2. Pass `earliest_hcd_date` to `get_patient_indication_groups()`
3. Call `assign_drug_indications()` to get (modified_df, indication_df)
4. Use modified_df for pathway processing
Let me first mark Task 3.1 as in-progress, then look at the `assign_drug_indications` function signature to ensure I wire it correctly.
Reading the relevant function signatures from diagnosis_lookup.py.
Good. I now have a clear picture of the function signatures:
- `load_drug_indication_mapping(csv_path=None)`  `(fragment_to_search_terms, search_term_to_fragments)`
- `assign_drug_indications(df, gp_matches_df, search_term_to_fragments)`  `(modified_df, indication_df)`
- `indication_df` is already indexed by modified UPID with a `'Directory'` column ÔÇö exactly what `process_indication_pathway_for_date_filter` expects
Now I'll update `refresh_pathways.py`. The key changes to the `elif current_chart_type == "indication":` block (lines 361-518):
1. Import `assign_drug_indications` and `load_drug_indication_mapping` at the top
2. Load drug mapping
3. Pass `earliest_hcd_date` to `get_patient_indication_groups()`
4. Call `assign_drug_indications()` instead of the manual dict(zip) approach
5. Use modified_df for pathway processing (instead of original df)
6. indication_df is already in the right format ÔÇö no need for the manual building code
Let me first update the imports, then replace the indication processing block.
Now replace the indication processing block. The old code spans from the `elif current_chart_type == "indication":` block at line 361 to the end of the try/except at line 518. I need to replace the inner logic (lines 362-512) with the new drug-aware approach.
Let me replace the entire indication block. The key section to replace is from `elif current_chart_type == "indication":` through the end of the inner try block.
Now let me verify the file looks correct by reading the updated indication block.
The code looks correct. Let me also verify the imports at the top are clean.
Good, imports look clean. Now let me run the validation checks.
All validation checks pass:
- `py_compile` ÔÇö no syntax errors
- Individual imports ÔÇö `assign_drug_indications`, `get_patient_indication_groups`, `load_drug_indication_mapping` all import successfully
- Full module import ÔÇö `cli.refresh_pathways` imports without error
Now let me update IMPLEMENTATION_PLAN.md to mark Task 3.1 subtasks as complete, then update progress.txt and commit.
Now update progress.txt with the iteration log.
Now let me commit the changes.
I'll only stage the files I actually changed in this iteration: `cli/refresh_pathways.py`, `IMPLEMENTATION_PLAN.md`, and `progress.txt`.
Good ÔÇö 3 files, net reduction of 7 lines in refresh_pathways.py (cleaner code), plus progress notes.
Now update the commit hash in progress.txt.
**Task 3.1 complete.** Here's what was done:
**Changes to `cli/refresh_pathways.py`:**
- Added imports for `assign_drug_indications` and `load_drug_indication_mapping`
- Replaced the old indication processing block (~90 lines  ~60 lines) with drug-aware matching:
1. Loads drug mapping from DimSearchTerm.csv
2. Computes `earliest_hcd_date` from HCD data to restrict GP code window
3. Gets ALL GP matches per patient (with code_frequency) via updated query
4. Calls `assign_drug_indications()` to cross-reference drugs against GP diagnoses
5. Passes `modified_df` (with indication-aware UPIDs like `RMV12345|rheumatoid arthritis`) to pathway processing
6. Uses `indication_df` directly ÔÇö already in the correct format (indexed by modified UPID, `Directory` column)
**Validation:** `py_compile` passed, all imports verified, full module import OK.
**Commits:** `920570b` (code) + `73088b0` (progress docs)
**Next task:** 3.2 ÔÇö Run `python -m cli.refresh_pathways --chart-type indication --dry-run -v` to test the integrated pipeline with live Snowflake data.
**Task 3.1 complete.** Here's what was done:
**Changes to `cli/refresh_pathways.py`:**
- Added imports for `assign_drug_indications` and `load_drug_indication_mapping`
- Replaced the old indication processing block (~90 lines  ~60 lines) with drug-aware matching:
1. Loads drug mapping from DimSearchTerm.csv
2. Computes `earliest_hcd_date` from HCD data to restrict GP code window
3. Gets ALL GP matches per patient (with code_frequency) via updated query
4. Calls `assign_drug_indications()` to cross-reference drugs against GP diagnoses
5. Passes `modified_df` (with indication-aware UPIDs like `RMV12345|rheumatoid arthritis`) to pathway processing
6. Uses `indication_df` directly ÔÇö already in the correct format (indexed by modified UPID, `Directory` column)
**Validation:** `py_compile` passed, all imports verified, full module import OK.
**Commits:** `920570b` (code) + `73088b0` (progress docs)
**Next task:** 3.2 ÔÇö Run `python -m cli.refresh_pathways --chart-type indication --dry-run -v` to test the integrated pipeline with live Snowflake data.