diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..578455d --- /dev/null +++ b/.dockerignore @@ -0,0 +1,10 @@ +.git +.github +r-archive/ +.pytest_cache +.ruff_cache +htmlcov +.coverage +profiling/*.prof +data/ +secrets/ diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..f3165aa --- /dev/null +++ b/.env.example @@ -0,0 +1,26 @@ +# Environment Configuration +A4D_ENVIRONMENT=development + +# GCP Configuration +A4D_PROJECT_ID=a4dphase2 +A4D_DATASET=tracker +A4D_DOWNLOAD_BUCKET=a4dphase2_upload +A4D_UPLOAD_BUCKET=a4dphase2_output + +# GCP Authentication +# Use the a4d-pipeline service account key for local development — this gives identical +# auth to Cloud Run and is required for Drive API access (user ADC blocks Drive scope). +# Store the key in secrets/ (gitignored). In Cloud Run, the SA authenticates via metadata server. +# GOOGLE_APPLICATION_CREDENTIALS=/path/to/a4d-pipeline-key.json + +# Paths +A4D_DATA_ROOT=/path/to/tracker/files +A4D_OUTPUT_DIR=output + +# Processing Settings +A4D_MAX_WORKERS=4 + +# Error Values (matching R pipeline) +A4D_ERROR_VAL_NUMERIC=999999 +A4D_ERROR_VAL_CHARACTER=Undefined +A4D_ERROR_VAL_DATE=9999-12-31 diff --git a/.github/workflows/python-ci.yml b/.github/workflows/python-ci.yml new file mode 100644 index 0000000..78d2423 --- /dev/null +++ b/.github/workflows/python-ci.yml @@ -0,0 +1,53 @@ +name: Python CI + +on: + push: + branches: [migration] + paths: + - 'src/**' + - 'tests/**' + - 'pyproject.toml' + - '.github/workflows/python-ci.yml' + pull_request: + branches: [main, develop, migration] + paths: + - 'src/**' + - 'tests/**' + - 'pyproject.toml' + - '.github/workflows/python-ci.yml' + +jobs: + test: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v2 + with: + enable-cache: true + + - name: Set up Python + run: uv python install 3.14 + + - name: Install dependencies + run: uv sync --all-extras + + - name: Run ruff linting + run: uv run ruff check . + + - name: Run ruff formatting check + run: uv run ruff format --check . + + - name: Run type checking with ty + run: uv run ty check src/ + + - name: Run tests + run: uv run pytest -m "not slow and not integration" --cov --cov-report=xml + + - name: Upload coverage + uses: codecov/codecov-action@v3 + with: + files: ./coverage.xml + flags: python diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml deleted file mode 100644 index 68381d5..0000000 --- a/.github/workflows/test-coverage.yaml +++ /dev/null @@ -1,56 +0,0 @@ -# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples -# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help -on: - push: - branches: [main, master, develop] - pull_request: - branches: [main, master, develop] - workflow_dispatch: - -name: test-coverage - -jobs: - test-coverage: - env: - GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - - name: Set up R 4.2.3 - uses: r-lib/actions/setup-r@v2 - with: - r-version: 4.2.3 - use-public-rspm: true - - - uses: r-lib/actions/setup-r-dependencies@v2 - with: - extra-packages: | - any::covr - needs: coverage - - - name: Test coverage - run: | - covr::codecov( - quiet = FALSE, - clean = FALSE, - install_path = file.path(Sys.getenv("RUNNER_TEMP"), "package") - ) - shell: Rscript {0} - - - name: Show testthat output - if: always() - run: | - ## -------------------------------------------------------------------- - find ${{ runner.temp }}/package -name 'testthat.Rout*' -exec cat '{}' \; || true - shell: bash - - - name: Upload test results - if: failure() - uses: actions/upload-artifact@v4 - with: - name: coverage-test-failures - path: ${{ runner.temp }}/package diff --git a/.gitignore b/.gitignore index 0791f1a..d0c37d3 100644 --- a/.gitignore +++ b/.gitignore @@ -1,13 +1,73 @@ -.Rproj.user -.Rhistory -.RData -.Ruserdata -.Rhistory -.Rdata -.httr-oauth +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# Virtual environments +.venv/ +venv/ +ENV/ +env/ + +# uv +.uv/ + +# Testing +.pytest_cache/ +.coverage +htmlcov/ +.tox/ + +# Type checking +.mypy_cache/ +.dmypy.json +dmypy.json + +# IDEs +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# Environment +.env +.env.local + +# Logs +*.log +logs/ + +# Data (sensitive) +data/ +output/ +*.parquet +*.xlsx +!reference_data/ + +# OS .DS_Store -.Renviron -rsconnect +Thumbs.db + +# Serena (MCP server state) +.serena/ -data/output -data/mapping_table.csv \ No newline at end of file +# Secrets (GCP service accounts, etc.) +secrets/ diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..c1fe704 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,29 @@ +{ + "python.testing.pytestEnabled": true, + "python.testing.unittestEnabled": false, + "python.testing.cwd": "${workspaceFolder}/a4d-python", + "python.testing.pytestArgs": [ + "${workspaceFolder}/a4d-python/tests" + ], + "python.defaultInterpreterPath": "${workspaceFolder}/a4d-python/.venv/bin/python", + "workbench.colorCustomizations": { + "activityBar.activeBackground": "#ab307e", + "activityBar.background": "#ab307e", + "activityBar.foreground": "#e7e7e7", + "activityBar.inactiveForeground": "#e7e7e799", + "activityBarBadge.background": "#25320e", + "activityBarBadge.foreground": "#e7e7e7", + "commandCenter.border": "#e7e7e799", + "sash.hoverBorder": "#ab307e", + "statusBar.background": "#832561", + "statusBar.foreground": "#e7e7e7", + "statusBarItem.hoverBackground": "#ab307e", + "statusBarItem.remoteBackground": "#832561", + "statusBarItem.remoteForeground": "#e7e7e7", + "titleBar.activeBackground": "#832561", + "titleBar.activeForeground": "#e7e7e7", + "titleBar.inactiveBackground": "#83256199", + "titleBar.inactiveForeground": "#e7e7e799" + }, + "peacock.color": "#832561" +} \ No newline at end of file diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..d44a84e --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,39 @@ +# CLAUDE.md + +## Python Pipeline + +**Location**: Repo root +**Branch**: `migration` + +Python implementation of the A4D medical tracker data processing pipeline. + +**Key Directories**: +- `src/` - Python package source +- `tests/` - Test suite +- `docs/` - Documentation (see [docs/CLAUDE.md](docs/CLAUDE.md) for detailed guidance) +- `scripts/` - Utility scripts +- `reference_data/` - Shared YAML configs (synonyms, validation rules, provinces) + +**Quick Start**: +```bash +uv sync +uv run pytest +``` + +**Migration Guide**: [docs/migration/MIGRATION_GUIDE.md](docs/migration/MIGRATION_GUIDE.md) + +## R Archive + +**Location**: `r-archive/` + +Legacy R implementation, preserved for reference. Do not modify. + +## Shared Resources + +- `reference_data/synonyms/` - Column name mappings +- `reference_data/data_cleaning.yaml` - Validation rules +- `reference_data/provinces/` - Allowed provinces + +**Do not modify these** without testing the Python pipeline. +- Always check your implementation against the original R pipeline and verify the logic is the same +- Limit comments to explain why a design was made or give important context for the migration; do not use comments for obvious code diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..287f64b --- /dev/null +++ b/Dockerfile @@ -0,0 +1,37 @@ +FROM python:3.14-slim + +# Install system dependencies +RUN apt-get update && apt-get install -y --no-install-recommends \ + gcc \ + g++ \ + && rm -rf /var/lib/apt/lists/* + +# Install uv from the official image +COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ + +# Use the system Python from the base image; do not let uv download its own +ENV UV_PYTHON_DOWNLOADS=never + +WORKDIR /app + +# Install dependencies first (without the project) for better layer caching. +# --no-install-project skips the editable install of a4d itself, which requires +# src/ to be present. Dependencies rarely change so this layer stays cached. +COPY pyproject.toml uv.lock README.md ./ +RUN uv sync --frozen --no-dev --no-install-project + +# Copy application code and reference data +COPY src/ src/ +COPY reference_data/ reference_data/ + +# Install the project itself now that src/ exists +RUN uv sync --frozen --no-dev + +# Set environment +ENV PYTHONPATH=/app/src +ENV PYTHONUNBUFFERED=1 +ENV A4D_DATA_ROOT=/workspace/data +ENV A4D_REFERENCE_DATA=/app/reference_data + +# Run the full pipeline: download → process → upload to GCS → ingest into BigQuery +CMD ["uv", "run", "a4d", "run-pipeline"] diff --git a/PLAN.md b/PLAN.md new file mode 100644 index 0000000..7470e19 --- /dev/null +++ b/PLAN.md @@ -0,0 +1,60 @@ +# Promote Python Pipeline to Root; Archive R Code + +## Context +Repo at `/Users/michaelaydinbas/git/github/pmayd/a4d` has two pipelines: +Python pipeline in `a4d-python/` (active, deployed), R pipeline at root (legacy). +Python pipeline is production-deployed and working on branch `migration`. + +## Problem +Python pipeline is buried in a subdirectory making it a second-class citizen. +R pipeline clutters root. Need to invert this structure. + +## Approach +Move `a4d-python/` contents to repo root; move R files to `r-archive/`. +Keep `reference_data/` at root (shared, unchanged). +Work directly on `migration` branch, targeting merge to `dev`. + +## Non-goals +- No changes to `reference_data/` content +- No changes to R pipeline logic +- No changes to Cloud Run deployment config (`A4D_REFERENCE_DATA` env var handles paths) +- No new CI/CD for R pipeline + +## Tasks + +### Phase 1 — Archive R Code +1. Create `r-archive/` and move R files into it: `R/`, `scripts/R/`, `tests/`, + `man/`, `renv/`, `renv.lock`, `a4d.Rproj`, `DESCRIPTION`, `NAMESPACE`, + `.lintr`, `.Rbuildignore`, `.Rprofile`, `readme.md` +2. Update root `.gitignore`: remove R-specific entries (renv/, etc.), merge with `a4d-python/.gitignore` +3. Delete `.github/workflows/test-coverage.yaml` (R CI, no longer needed) + +### Phase 2 — Promote Python Pipeline +4. Move `a4d-python/` contents to root: `src/`, `tests/`, `docs/`, `scripts/`, + `pyproject.toml`, `uv.lock`, `justfile`, `Dockerfile`, `README.md`, + `SETUP.md`, `.env.example` +5. Fix `src/a4d/reference/loaders.py:37`: `parents[4]` → `parents[3]` +6. Update `Dockerfile`: `COPY a4d-python/pyproject.toml ...` → `COPY pyproject.toml ...`, + `COPY a4d-python/src/` → `COPY src/` +7. Update `.dockerignore`: strip `a4d-python/` prefixes from all entries +8. Update `justfile`: Docker build context `..` → `.` +9. Update `.github/workflows/python-ci.yml`: remove `a4d-python/` from path + triggers, remove `working-directory: a4d-python`, fix coverage path +10. Update root `CLAUDE.md` to reflect new structure +11. Delete now-empty `a4d-python/` directory + +### Phase 3 — Verify & Cleanup +12. Run `uv run pytest` from repo root — all tests must pass +13. Build Docker image locally (`just docker-build`) — must succeed +14. Confirm `reference_data/` is resolved correctly in tests without env var override + +## Done when +- `uv run pytest` passes from repo root (no `cd a4d-python` required) +- `just docker-build` succeeds with updated build context +- `reference_data/` resolves correctly in tests via `loaders.py` +- `r-archive/` contains all R files; no R files remain at repo root +- Python CI workflow triggers on `src/**`, `tests/**`, `pyproject.toml` path changes +- `a4d-python/` directory no longer exists + +## Open questions +- None — all resolved before implementation. diff --git a/R/script3_create_table_patient_data_changes_only.R b/R/script3_create_table_patient_data_changes_only.R deleted file mode 100644 index 92a2dcc..0000000 --- a/R/script3_create_table_patient_data_changes_only.R +++ /dev/null @@ -1,90 +0,0 @@ -#' @title Create CSV with longitudinal patient data for a single variable. -#' -#' @description -#' Read in all cleaned patient data CSV and create a single data.frame. -#' Group this data by id and take only the months when there is a change in the medical data. -#' -#' -#' @param patient_data_files list of CSV files with cleaned patient data from step 2. -#' @param input_root root directory of the input CSV files. -#' @param output_root root directory of the output folder. -#' @param variable name of the column that should be exported. -#' @param name name used to create the export file name. -create_table_longitudinal_data <- - function(patient_data_files, - input_root, - output_root, - variable, - name) { - dynamic_patient_columns <- - c( - "blood_pressure_dias_mmhg", - "blood_pressure_sys_mmhg", - "bmi", - "bmi_date", - "clinic_id", - "fbg_updated_date", - "fbg_updated_mg", - "fbg_updated_mmol", - "file_name", - "hba1c_updated", - "hba1c_updated_exceeds", - "hba1c_updated_date", - "height", - "hospitalisation_cause", - "hospitalisation_date", - "insulin_regimen", - "insulin_type", - "insulin_subtype", - "last_clinic_visit_date", - "last_remote_followup_date", - "observations", - "observations_category", - "patient_id", - "sheet_name", - "status", - "support_from_a4d", - "testing_frequency", - "tracker_date", - "tracker_month", - "tracker_year", - "updated_2022_date", - "weight" - ) - - patient_data <- read_cleaned_patient_data(input_root, patient_data_files) %>% - dplyr::select(tidyselect::all_of(dynamic_patient_columns)) - - # get latest static patient data overall - variable_lag <- paste0(variable, "_lag") - longitudinal_data <- patient_data %>% - tidyr::drop_na(!!variable) %>% - dplyr::filter(get(variable) != ERROR_VAL_NUMERIC) %>% - dplyr::group_by(patient_id) %>% - dplyr::arrange(tracker_year, tracker_month) %>% - dplyr::filter( - get(variable) != tidyr::replace_na( - dplyr::lag(get(variable), default = NULL), - ERROR_VAL_NUMERIC - ) - ) %>% - dplyr::ungroup() %>% - dplyr::arrange(patient_id, tracker_year, tracker_month) - - logInfo( - log_to_json( - message = "longitudinal_data dim: {values['dim']}.", - values = list(dim = dim(longitudinal_data)), - script = "script3", - file = "create_table_patient_data_changes_only.log", - functionName = "create_table_longitudinal_data" - ) - ) - - export_data_as_parquet( - data = longitudinal_data, - filename = paste0("longitudinal_data_", name), - output_root = output_root, - suffix = "" - ) - } diff --git a/SETUP.md b/SETUP.md new file mode 100644 index 0000000..2dfd9f5 --- /dev/null +++ b/SETUP.md @@ -0,0 +1,322 @@ +# A4D Pipeline — Setup Guide + +## Local Development + +### Prerequisites + +```bash +# uv (Python package manager) +curl -LsSf https://astral.sh/uv/install.sh | sh + +# just (command runner) +brew install just + +# gcloud CLI +brew install google-cloud-sdk +``` + +### Install + +```bash +cd a4d-python +uv sync +cp .env.example .env +``` + +> `.env` is only used for local development. On GCP, environment variables are +> set directly on the Cloud Run Job (see step 5 in the GCP section below) and +> the `.env` file is not present or needed in the container. + +Edit `.env` — only these fields matter locally: + +```bash +A4D_DATA_ROOT=/path/to/tracker/files # folder containing .xlsx trackers +A4D_PROJECT_ID=a4dphase2 +A4D_DATASET=tracker +A4D_DOWNLOAD_BUCKET=a4dphase2_upload +A4D_UPLOAD_BUCKET=a4dphase2_output +``` + +**Paths with spaces** (e.g. a USB drive): write the value unquoted in `.env` — +pydantic-settings reads to end of line and handles spaces correctly: + +```bash +A4D_DATA_ROOT=/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload +``` + +### Authenticate + +```bash +gcloud auth login +gcloud auth application-default login +gcloud config set project a4dphase2 +``` + +### Run + +```bash +# Test with a single file (fastest) +just run-file /path/to/tracker.xlsx + +# Process all files already in A4D_DATA_ROOT — no GCS +just run-local + +# Download latest files from GCS, process locally — no upload +just run-download + +# Full pipeline: download from GCS, process, upload results + load BigQuery +just run +``` + +For paths with spaces, wrap the argument in quotes: + +```bash +just run-file "/Volumes/USB SanDisk 3.2Gen1 Media/a4d/2024_Penang.xlsx" +``` + +--- + +## Google Cloud Deployment + +The pipeline runs as a **Cloud Run Job** — a one-shot container that downloads +tracker files from GCS, processes them, and loads the results into BigQuery. +A service account is used instead of personal credentials. + +> **Data residency**: All GCP resources (Artifact Registry, Cloud Run Job, +> Cloud Scheduler, BigQuery dataset, GCS buckets) must be located in +> **`asia-southeast2` (Jakarta)**. Patient data must not be processed or stored +> in the EU. + +> **Steps 1–4 are one-time infrastructure setup.** Once the service account, +> IAM roles, and Artifact Registry repository exist, you only need to rebuild +> and redeploy (steps 4–5) when the code changes. + +### 1. Create the service account + +This only needs to be done once. Check if it already exists first: + +```bash +gcloud iam service-accounts describe \ + a4d-pipeline@a4dphase2.iam.gserviceaccount.com \ + --project=a4dphase2 +``` + +If it doesn't exist yet, create it: + +```bash +gcloud iam service-accounts create a4d-pipeline \ + --display-name="A4D Pipeline Runner" \ + --project=a4dphase2 +``` + +### 2. Grant IAM roles + +The service account needs access to two GCS buckets and the BigQuery dataset. + +> Both GCS buckets (`a4dphase2_upload`, `a4dphase2_output`) must be located in +> `asia-southeast2`. Bucket location is set at creation time and cannot be +> changed. + +**GCS — read tracker files:** + +```bash +gcloud storage buckets add-iam-policy-binding gs://a4dphase2_upload \ + --member="serviceAccount:a4d-pipeline@a4dphase2.iam.gserviceaccount.com" \ + --role="roles/storage.objectViewer" +``` + +**GCS — write pipeline output:** + +```bash +gcloud storage buckets add-iam-policy-binding gs://a4dphase2_output \ + --member="serviceAccount:a4d-pipeline@a4dphase2.iam.gserviceaccount.com" \ + --role="roles/storage.objectCreator" +``` + +> `objectCreator` grants only `storage.objects.create` — sufficient for upload. +> `objectAdmin` (broader) is not needed as the pipeline never reads, lists, or +> manages IAM on the output bucket. + +> The BigQuery dataset `tracker` must be created in `asia-southeast2`. Dataset +> location is set at creation time and cannot be changed. If the dataset already +> exists in another region, it must be deleted and recreated (data loss — export +> first). + +**BigQuery — run jobs (project-level):** + +```bash +gcloud projects add-iam-policy-binding a4dphase2 \ + --member="serviceAccount:a4d-pipeline@a4dphase2.iam.gserviceaccount.com" \ + --role="roles/bigquery.jobUser" +``` + +**BigQuery — read/write tables (project-level):** + +```bash +gcloud projects add-iam-policy-binding a4dphase2 \ + --member="serviceAccount:a4d-pipeline@a4dphase2.iam.gserviceaccount.com" \ + --role="roles/bigquery.dataEditor" +``` + +> `bq add-iam-policy-binding` (dataset-scoped) requires allowlisting and does not +> work on standard projects. Use the project-level grant above instead. +> `dataEditor` allows creating and overwriting tables (`tables.create` + +> `tables.updateData`) which WRITE_TRUNCATE load jobs require. + +### 3. Set up Artifact Registry + +```bash +# Create the repository (once) +gcloud artifacts repositories create a4d \ + --repository-format=docker \ + --location=asia-southeast2 \ + --project=a4dphase2 + +# Allow the service account to pull images +gcloud artifacts repositories add-iam-policy-binding a4d \ + --location=asia-southeast2 \ + --member="serviceAccount:a4d-pipeline@a4dphase2.iam.gserviceaccount.com" \ + --role="roles/artifactregistry.reader" \ + --project=a4dphase2 +``` + +### 4. Build and push the Docker image + +Authenticate Docker to Artifact Registry once: + +```bash +gcloud auth configure-docker asia-southeast2-docker.pkg.dev +``` + +Then build and push (run from `a4d-python/`): + +```bash +just docker-push +``` + +This builds with the repo root as context (required — the Dockerfile copies +`reference_data/` from outside `a4d-python/`) and pushes to Artifact Registry. + +To verify the image was pushed and see what's already in the registry: + +```bash +gcloud artifacts docker images list \ + asia-southeast2-docker.pkg.dev/a4dphase2/a4d \ + --include-tags \ + --project=a4dphase2 +``` + +### 5. Create the Cloud Run Job + +```bash +gcloud run jobs create a4d-pipeline \ + --image=asia-southeast2-docker.pkg.dev/a4dphase2/a4d/pipeline:latest \ + --region=asia-southeast2 \ + --service-account=a4d-pipeline@a4dphase2.iam.gserviceaccount.com \ + --set-env-vars="\ +A4D_PROJECT_ID=a4dphase2,\ +A4D_DATASET=tracker,\ +A4D_DOWNLOAD_BUCKET=a4dphase2_upload,\ +A4D_UPLOAD_BUCKET=a4dphase2_output,\ +A4D_DATA_ROOT=/tmp/data,\ +A4D_OUTPUT_DIR=output,\ +A4D_MAX_WORKERS=8" \ + --memory=8Gi \ + --cpu=8 \ + --task-timeout=3600 \ + --project=a4dphase2 +``` + +`A4D_DATA_ROOT=/tmp/data` uses ephemeral in-container storage — the job downloads +tracker files there, processes them, uploads the output, then exits. Nothing persists. + +To update the job after a config change: + +```bash +gcloud run jobs update a4d-pipeline --region=asia-southeast2 [--set-env-vars=...] +``` + +To list all existing jobs: + +```bash +gcloud run jobs list --region=asia-southeast2 --project=a4dphase2 +``` + +### 5a. Test the image locally before deploying + +Always verify a newly built image works before creating or updating the Cloud Run Job. + +**Level 1 — smoke test** (image starts, CLI is reachable): + +```bash +just docker-smoke +# or: +docker run --rm asia-southeast2-docker.pkg.dev/a4dphase2/a4d/pipeline:latest \ + uv run a4d --help +``` + +**Level 2 — local pipeline run** (no GCS, process a local file): + +Mount a directory containing tracker files and run `process-patient`. Output lands in +`/data/output` inside the container, which is the same mount so you can inspect it +afterward. + +```bash +docker run --rm \ + -v /path/to/trackers:/data \ + -e A4D_DATA_ROOT=/data \ + asia-southeast2-docker.pkg.dev/a4dphase2/a4d/pipeline:latest \ + uv run a4d process-patient --file /data/your_tracker.xlsx +``` + +**Level 3 — full pipeline with GCP** (real GCS + BigQuery, no download): + +Mount your local Application Default Credentials so the container can authenticate. +Use `--skip-download` to process files already on disk instead of fetching from GCS. + +```bash +docker run --rm \ + -v /path/to/trackers:/data \ + -v "$HOME/.config/gcloud:/root/.config/gcloud:ro" \ + -e A4D_DATA_ROOT=/data \ + -e GOOGLE_CLOUD_PROJECT=a4dphase2 \ + asia-southeast2-docker.pkg.dev/a4dphase2/a4d/pipeline:latest \ + uv run a4d run-pipeline --skip-download +``` + +This exercises the full upload path (GCS + BigQuery) without touching the live tracker +source bucket. + +### 6. Execute + +```bash +just run-job # trigger the Cloud Run Job +just logs-job # stream logs from the latest execution +``` + +After a code change, redeploy and run in one step: + +```bash +just deploy && just run-job +``` + +### 7. Schedule (optional) + +To run the pipeline on a schedule, create a Cloud Scheduler job that triggers it: + +```bash +gcloud scheduler jobs create http a4d-pipeline-weekly \ + --schedule="0 6 * * 1" \ + --uri="https://asia-southeast2-run.googleapis.com/apis/run.googleapis.com/v1/namespaces/a4dphase2/jobs/a4d-pipeline:run" \ + --http-method=POST \ + --oauth-service-account-email=a4d-pipeline@a4dphase2.iam.gserviceaccount.com \ + --location=asia-southeast2 +``` + +The service account also needs permission to trigger Cloud Run Jobs for this: + +```bash +gcloud projects add-iam-policy-binding a4dphase2 \ + --member="serviceAccount:a4d-pipeline@a4dphase2.iam.gserviceaccount.com" \ + --role="roles/run.invoker" +``` diff --git a/docs/CLAUDE.md b/docs/CLAUDE.md new file mode 100644 index 0000000..984b37b --- /dev/null +++ b/docs/CLAUDE.md @@ -0,0 +1,72 @@ +# CLAUDE.md + +Python pipeline for A4D medical tracker data — processes Excel trackers into BigQuery tables. +Patient pipeline is complete and deployed to production (Cloud Run). + +## Module Overview + +| Module | Purpose | +|--------|---------| +| `extract/patient.py` | Read Excel trackers → raw parquet (openpyxl, multi-sheet) | +| `clean/patient.py` | Type conversion, validation, transformations → cleaned parquet | +| `clean/schema.py` | 83-column meta schema matching R output | +| `clean/converters.py` | Safe type conversion with ErrorCollector | +| `clean/validators.py` | Case-insensitive allowed-values validation | +| `clean/transformers.py` | Explicit transformations (regimen, BP splitting, FBG) | +| `clean/date_parser.py` | Flexible date parsing (Excel serials, DD/MM/YYYY, month-year) | +| `tables/patient.py` | Aggregate cleaned parquets → static, monthly, annual tables | +| `tables/clinic.py` | Create clinic static table from reference_data/clinic_data.xlsx | +| `tables/logs.py` | Aggregate error logs → logs table | +| `pipeline/patient.py` | Orchestrate extract+clean per tracker, parallel workers | +| `pipeline/tracker.py` | Per-tracker pipeline execution | +| `pipeline/models.py` | Result dataclasses | +| `gcp/storage.py` | GCS download/upload | +| `gcp/bigquery.py` | BigQuery table load | +| `gcp/drive.py` | Google Drive download (clinic_data.xlsx); file ID hardcoded in module | +| `reference/synonyms.py` | Column name synonym mapping (YAML) | +| `reference/provinces.py` | Allowed province validation | +| `reference/loaders.py` | YAML loading utilities | +| `state/` | State management module (exists, not yet wired into pipeline) | +| `utils/` | Shared utilities | +| `config.py` | Pydantic settings from `.env` / `A4D_*` env vars | +| `logging.py` | loguru setup, `file_logger()` context manager | +| `errors.py` | Shared error types | +| `cli.py` | Typer CLI entry point | + +## CLI Commands + +```bash +uv run a4d process-patient # Extract + clean + tables (local run) +uv run a4d create-tables # Re-create all tables (patient, logs, clinic) from existing cleaned parquets +uv run a4d upload-tables # Upload tables to BigQuery +uv run a4d download-trackers # Download tracker files from GCS +uv run a4d upload-output # Upload output directory to GCS +uv run a4d download-reference-data # Download clinic_data.xlsx from Google Drive into reference_data/ +uv run a4d run-pipeline # Full end-to-end pipeline (drive download→GCS download→process→upload) +``` + +Key options: `--file` (single tracker), `--workers N`, `--force`, `--skip-tables`, `--skip-download`, `--skip-upload`, `--skip-drive-download`. + +## Output Directory Structure + +```text +output/ +├── patient_data_raw/ # Raw extracted parquets (one per tracker) +├── patient_data_cleaned/ # Cleaned parquets (one per tracker) +├── tables/ # Final tables: static.parquet, monthly.parquet, annual.parquet, logs.parquet, clinic_data_static.parquet +└── logs/ # Per-tracker log files (JSON) +``` + +## Key Facts + +- `clinic_id` = parent folder name of the tracker file +- Year detected from sheet names (`Jan24` → 2024) or filename +- Error sentinel values: numeric `999999`, string `"Undefined"`, date `"9999-09-09"` +- `ErrorCollector` accumulates row-level data quality errors; never raises +- `reference_data/` is shared with the archived R pipeline — changes may affect R logic + +## Migration Status + +- **Patient pipeline**: complete, validated against 174 trackers, deployed to production +- **Product pipeline**: not yet started +- **State management**: module exists but not wired into pipeline yet diff --git a/docs/VALIDATION_SUMMARY.md b/docs/VALIDATION_SUMMARY.md new file mode 100644 index 0000000..a53b2f1 --- /dev/null +++ b/docs/VALIDATION_SUMMARY.md @@ -0,0 +1,80 @@ +# Validation Summary + +Comprehensive comparison of R vs Python pipeline outputs across all 174 patient trackers. + +**Verdict: Python pipeline is production-ready.** + +--- + +## Summary Statistics + +| Metric | Value | +|--------|-------| +| Total trackers | 174 | +| Perfect record count match | 172 (98.9%) | +| Known acceptable difference | 1 (2024 Mandalay Children's Hospital) | +| Skipped — Excel data quality issue | 1 (2024 Vietnam National Children Hospital) | +| Critical bugs fixed during validation | 8 trackers | + +--- + +## Known Acceptable Differences + +These patterns appear across multiple trackers and are expected or intentional. + +| # | Column | Pattern | Assessment | +|---|--------|---------|------------| +| 1 | `insulin_total_units` | Python extracts values, R shows null | Python is more correct | +| 2 | `province` | R: "Undefined", Python: actual province name | Python is more correct | +| 3 | `status` | "Active - Remote" vs "Active Remote" (hyphen) | Cosmetic, functionally equivalent | +| 4 | `t1d_diagnosis_age` | R: null, Python: 999999 sentinel | Different null strategy, both valid | +| 5 | `fbg_updated_mg/mmol` (2017-2019) | Python parses "150 (Mar-18)" → 150, R → 999999 | Python is more correct | +| 6 | Date parsing edge cases | DD/MM/YY interpretation differs in rare cases | Python has more robust parsing | +| 7 | `blood_pressure_systolic/diastolic` | BP splitting now implemented in Python | Was HIGH priority, now done | +| 8 | `fbg_baseline_mg` | Inconsistent baseline extraction (2022+) | Medium priority, under investigation | +| 9 | `bmi` | Float precision ~10^-15 difference | Cosmetic only | +| 10 | `insulin_regimen/subtype` | Case: "Other" vs "other", "NPH" vs "nph" | String normalization difference | +| 11 | Future/invalid dates | Python: 9999-09-09 sentinel, R: Buddhist calendar dates | Both valid error strategies | + +--- + +## Known Record Count Differences + +### 2024 Mandalay Children's Hospital — KEPT AS KNOWN DIFFERENCE + +- R: 1,174 records, Python: 1,185 records (+11, +0.9%) +- Patient MM_MD001 has 12 monthly records in Excel; R retains only 1 (implicit R behavior, not identifiable in R code) +- Decision: keep Python behavior — all 12 monthly records are legitimate longitudinal observations + +### 2024 Vietnam National Children Hospital — SKIPPED + +- R: 900 records, Python: 927 records (+27, +3.0%) +- Root cause: Jul24 sheet has 27 patients with duplicate rows containing conflicting data (e.g., VN_VC016 appears twice with different status values) +- Decision: skip validation — requires Excel source file correction before comparison is meaningful + +--- + +## Bugs Fixed During Validation (8 Trackers) + +| Tracker | Issue | Fix Location | +|---------|-------|-------------| +| 2021 Phattalung Hospital | `find_data_start_row()` stopped at stray space, skipped 42 records | `extract/patient.py` | +| 2021 Phattalung Hospital | `map_elements()` failed on all-null date column | `clean/converters.py` | +| 2022 Surat Thani Hospital | Rows with missing row number (col A) but valid patient_id skipped | `extract/patient.py` | +| 2024 Sultanah Bahiyah | Excel `#REF!` errors in patient_id extracted as valid records | `extract/patient.py` | +| 2024 Sultanah Bahiyah | `ws.max_row` is None for some Excel files, causing TypeError | `extract/patient.py` | +| 2022 Mandalay Children's Hospital | Fixed by numeric zero filtering + patient_id normalization | `extract/patient.py` | +| 2024 Likas Women & Children's Hospital | Fixed by numeric zero filtering + patient_id normalization | `extract/patient.py` | +| 2025_06 Taunggyi Women & Children Hospital | patient_id='0.0' not caught by earlier filter for '0' | `extract/patient.py` | + +--- + +## Python Improvements Over R + +- Better `insulin_total_units` extraction (R misses this nearly universally) +- Better province resolution ("Undefined" → actual province names) +- Better date parsing with explicit DD/MM/YYYY handling +- Better legacy FBG extraction from "value (date)" format (2017-2019 trackers) +- Blood pressure splitting implemented (was missing, now done) +- Fixed `insulin_type` derivation bug (R doesn't check analog columns) +- Fixed `insulin_subtype` typo ("rapic" → "rapid" in R) diff --git a/docs/migration/MIGRATION_GUIDE.md b/docs/migration/MIGRATION_GUIDE.md new file mode 100644 index 0000000..1c85465 --- /dev/null +++ b/docs/migration/MIGRATION_GUIDE.md @@ -0,0 +1,262 @@ +# R to Python Migration Guide + +Reference for the A4D pipeline migration from R to Python. + +**Status**: Phases 0–7 complete. Patient pipeline production-ready. Product pipeline not yet started. +**Branch**: `migration` + +--- + +## Table of Contents + +1. [Strategy & Decisions](#strategy--decisions) +2. [Technology Stack](#technology-stack) +3. [Architecture](#architecture) +4. [Key Code Patterns](#key-code-patterns) +5. [Open Items](#open-items) + +--- + +## Strategy & Decisions + +### Goals +1. **Output Compatibility** — Generate equivalent parquet files (differences documented) +2. **Performance** — 2-5x faster than R +3. **Incremental Processing** — Only reprocess changed trackers (hash-based) +4. **Error Transparency** — Detailed per-row error tracking + +### Key Architectural Decisions + +**Per-Tracker Processing** — Process each tracker end-to-end, then aggregate +- Better for incremental updates; natural parallelization; failed tracker doesn't block others + +**No Orchestrator** — Simple Python + multiprocessing (not Prefect/doit/Airflow) +- DAG is simple: trackers → tables → BigQuery; less complexity, easier to maintain + +**BigQuery Metadata Table for State** — Not SQLite (containers are stateless) +- Query at pipeline start to get previous file hashes; only reprocess changed/new files; same table used for dashboards + +**Hybrid Error Logging** — Vectorized + row-level detail +- Try vectorized conversion (handles 95%+ of data); detect failures; log only failed rows with patient_id, file_name, error details; export error logs as parquet + +--- + +## Technology Stack + +- **uv** — Dependency management & Python version +- **ruff** — Linting & formatting +- **polars** — DataFrames (10-100x faster than pandas) +- **duckdb** — Complex SQL operations +- **pydantic** — Settings & validation +- **loguru** — Logging (JSON output) +- **pytest** — Testing +- **google-cloud-bigquery** — Replaces `bq` CLI +- **google-cloud-storage** — Replaces `gsutil` CLI +- **typer + rich** — CLI interface + +--- + +## Architecture + +### Data Flow + +``` +Excel Trackers (GCS) + | + v +download-trackers # GCS → local data_root/ + | + v +process-patient # For each tracker (parallel): + ├─ extract/patient.py # Excel → patient_data_raw/*.parquet + └─ clean/patient.py # raw → patient_data_cleaned/*.parquet + | + v +create-tables # All cleaned parquets → + ├─ tables/patient.py # tables/static.parquet + | # tables/monthly.parquet + | # tables/annual.parquet + └─ tables/logs.py # tables/logs.parquet + | + v +upload-output # local output/ → GCS +upload-tables # tables/*.parquet → BigQuery +``` + +### Module Structure + +``` +src/a4d/ +├── extract/patient.py # Excel → raw parquet +├── clean/ +│ ├── patient.py # Main cleaning pipeline +│ ├── schema.py # 83-column meta schema +│ ├── converters.py # Safe type conversion + ErrorCollector +│ ├── validators.py # Case-insensitive allowed-values +│ ├── transformers.py # Explicit transformations +│ └── date_parser.py # Flexible date parsing +├── tables/ +│ ├── patient.py # static/monthly/annual aggregation +│ └── logs.py # Error log aggregation +├── pipeline/ +│ ├── patient.py # Orchestration + parallel workers +│ ├── tracker.py # Per-tracker execution +│ └── models.py # Result dataclasses +├── gcp/ +│ ├── storage.py # GCS operations +│ └── bigquery.py # BigQuery load +├── reference/ +│ ├── synonyms.py # Column name mapping (YAML) +│ ├── provinces.py # Allowed province validation +│ └── loaders.py # YAML loading utilities +├── state/ # State management (exists, not yet wired up) +├── config.py # Pydantic settings from A4D_* env vars +├── logging.py # loguru setup +├── errors.py # Shared error types +└── cli.py # Typer CLI (6 commands) +``` + +### State Management (Designed, Not Yet Active) + +``` +1. Container starts (stateless, fresh) +2. Query BigQuery metadata table + SELECT file_name, file_hash FROM tracker_metadata +3. Compare with current file hashes +4. Process only: new + changed + previously failed +5. Update metadata table (append new records) +6. Container shuts down (state persists in BigQuery) +``` + +Currently: pipeline processes all trackers found in `data_root`. Incremental logic exists in `state/` but is not wired into `pipeline/patient.py` yet. + +--- + +## Key Code Patterns + +### Configuration +```python +from a4d.config import settings +settings.data_root # Path to tracker files +settings.project_id # GCP project +settings.output_root # Local output directory +``` + +### Error Tracking +```python +# ErrorCollector accumulates failures without raising +error_collector = ErrorCollector() + +df = safe_convert_column( + df=df, + column="age", + target_type=pl.Int32, + error_value=settings.error_val_numeric, + error_collector=error_collector, +) +# Errors exported as parquet → aggregated into logs table +``` + +### Vectorized Conversion Pattern +```python +# Try vectorized conversion +df = df.with_columns(pl.col("age").cast(pl.Int32, strict=False)) + +# Detect failures (null after conversion but wasn't null before) +failed_rows = df.filter(conversion_failed) + +# Log each failure; replace with error value +``` + +### Avoiding R's rowwise() Pattern +```python +# R (slow): df %>% rowwise() %>% mutate(age_fixed = fix_age(age, dob, ...)) + +# Python (fast): vectorized +df = df.with_columns([ + fix_age_vectorized(pl.col("age"), pl.col("dob"), pl.col("tracker_year")).alias("age") +]) + +# Only iterate for genuine edge cases (log + replace) +``` + +### DataFrames (R → Python) +```python +# R: df %>% filter(age > 18) %>% select(name, age) +df.filter(pl.col("age") > 18).select(["name", "age"]) + +# R: df %>% mutate(age = age + 1) +df.with_columns((pl.col("age") + 1).alias("age")) +``` + +### GCP Operations +```python +# R: system("gsutil cp ...") +from google.cloud import storage +bucket = storage.Client().bucket("a4dphase2_upload") +bucket.blob("file.parquet").upload_from_filename("local_file.parquet") + +# R: system("bq load ...") +from google.cloud import bigquery +job = bigquery.Client().load_table_from_dataframe(df, table_id) +job.result() +``` + +### Logging +```python +from loguru import logger +logger.info("Processing tracker", file="clinic_001.xlsx", rows=100) + +# File-specific logging (like R's with_file_logger) +with file_logger("clinic_001_patient", output_root) as log: + log.info("Processing patient data") +``` + +--- + +## Completed Phases + +| Phase | Description | +|-------|-------------| +| 0 | Foundation: repo structure, uv, ruff, CI | +| 1 | Core infrastructure: reference, logging, config, ErrorCollector | +| 2 | Extraction: `extract/patient.py` (28 tests, 88% coverage) | +| 3 | Cleaning: `clean/patient.py` (83-column schema, full validation) | +| 4 | Tables: `tables/patient.py` (static, monthly, annual, logs) | +| 5 | Pipeline integration: `pipeline/patient.py` + parallel processing | +| 6 | GCP: `gcp/storage.py`, `gcp/bigquery.py`, CLI commands | +| 7 | Validation: 174 trackers compared, 8 bugs fixed, production verdict | + +--- + +## Open Items + +### Phase 8: First GCP Production Run + +- Run `run-pipeline` against production GCS bucket (patient data) +- Validate BigQuery table outputs match expected counts/schema +- Compare dashboard reports with R pipeline baseline +- Fix any issues discovered during first real run + +### Phase 9: Product Pipeline + +- `extract/product.py` — same pattern as patient extraction +- `clean/product.py` — same pattern as patient cleaning +- `tables/product.py` — product aggregation tables +- Validate against R product pipeline outputs + +### State Management (Incremental Processing) + +- `state/` module exists with BigQuery state design +- Wire into `pipeline/patient.py` so only changed/new trackers are processed +- Required before production scheduling (Cloud Run + Cloud Scheduler) + +--- + +## Reference Data + +All YAML files in `reference_data/` are shared with the R pipeline — do not modify without testing both: +- `reference_data/synonyms/synonyms_patient.yaml` +- `reference_data/synonyms/synonyms_product.yaml` +- `reference_data/data_cleaning.yaml` +- `reference_data/provinces/allowed_provinces.yaml` diff --git a/docs/migration/PYTHON_IMPROVEMENTS.md b/docs/migration/PYTHON_IMPROVEMENTS.md new file mode 100644 index 0000000..09e51f0 --- /dev/null +++ b/docs/migration/PYTHON_IMPROVEMENTS.md @@ -0,0 +1,146 @@ +# Python Pipeline Improvements Over R + +This document tracks cases where the Python pipeline implementation is **more correct** than the R pipeline, resulting in intentional differences between R and Python outputs. + +## 1. insulin_type Derivation Bug Fix + +**Status**: ✅ Fixed in Python + +**Issue in R**: R's insulin_type derivation logic only checks the human insulin columns to decide between "human insulin" and "analog insulin". When all human insulin columns are None/NA, the condition evaluates to NA, and `ifelse()` returns NA - **even if the analog insulin columns have "Y" values**. + +**R Code (Buggy)**: +```r +insulin_type = ifelse( + human_insulin_pre_mixed == "Y" | + human_insulin_short_acting == "Y" | + human_insulin_intermediate_acting == "Y", + "human insulin", + "analog insulin" +) +``` + +**Problem**: For patients with ONLY analog insulin (human columns = None, analog columns = 'Y'): +- `None == "Y"` evaluates to NA in R +- `NA | NA | NA` → NA +- `ifelse(NA, "human insulin", "analog insulin")` → NA + +**Python Fix**: Check if ANY insulin column has data first, then derive the type: +```python +pl.when( + # Only derive if at least one insulin column is not null + pl.col("human_insulin_pre_mixed").is_not_null() + | pl.col("human_insulin_short_acting").is_not_null() + | pl.col("human_insulin_intermediate_acting").is_not_null() + | pl.col("analog_insulin_rapid_acting").is_not_null() + | pl.col("analog_insulin_long_acting").is_not_null() +) +.then( + pl.when( + (pl.col("human_insulin_pre_mixed") == "Y") + | (pl.col("human_insulin_short_acting") == "Y") + | (pl.col("human_insulin_intermediate_acting") == "Y") + ) + .then(pl.lit("human insulin")) + .otherwise(pl.lit("analog insulin")) +) +.otherwise(None) +``` + +**Impact**: For 2024 Sibu Hospital tracker, 5 patients correctly get `insulin_type = 'Analog Insulin'` in Python vs `None` in R. + +**File**: `src/a4d/clean/patient.py:_derive_insulin_fields()` + +## 2. insulin_subtype Typo Fix + +**Status**: ✅ Fixed in Python + +**Issue in R**: R has a typo - uses "rapic-acting" instead of "rapid-acting" when deriving insulin_subtype. + +**R Code (Typo)**: +```r +paste(ifelse(analog_insulin_rapid_acting == "Y", "rapic-acting", ""), sep = ",") +``` + +**Python Fix**: Uses correct spelling "rapid-acting" + +**Impact**: Derived insulin_subtype values use correct medical terminology. However, since comma-separated values get replaced with "Undefined" by validation, the final output for insulin_subtype is still "Undefined" in both R and Python. + +**File**: `src/a4d/clean/patient.py:_derive_insulin_fields()` + +## 3. insulin_total_units Extraction Bug Fix + +**Status**: ✅ Fixed in Python + +**Issue in R**: R's header merge logic has a condition that fails for 2024+ trackers, causing it to skip the two-row header merge and lose columns. + +**R Code (Buggy)** - `script1_helper_read_patient_data.R:92`: +```r +if (header_cols[2] == header_cols_2[2]) { + # Only merge if column 2 matches in both rows + diff_colnames <- which((header_cols != header_cols_2)) + header_cols[diff_colnames] <- paste(header_cols_2[diff_colnames], header_cols[diff_colnames]) +} +``` + +**Problem for 2024 Sibu Hospital tracker**: +- Row 75 (header_cols_2), Col 2: `"Patient \nID*"` +- Row 76 (header_cols), Col 2: `None` (part of merged cell above) +- Condition `header_cols[2] == header_cols_2[2]` evaluates to `FALSE` +- **Headers NOT merged**, only row 76 used + +**Result**: +- Col 27 in R: Only gets "per day" (row 76 alone) +- "per day" doesn't match synonym "TOTAL Insulin Units per day" +- **Column lost during synonym mapping** + +**Python Fix**: Python always merges both header rows without conditions: +```python +for h1, h2 in zip(header_1, header_2, strict=True): + if h1 and h2: + headers.append(f"{h2} {h1}".strip()) +``` + +**Result**: +- Col 27 in Python: "TOTAL Insulin Units per day" (row 75 + row 76) +- Matches synonym perfectly ✅ + +**Impact**: For 2024 Sibu Hospital tracker, Python correctly extracts insulin_total_units for 50/53 patients. R loses this column entirely due to header merge failure. + +**File**: `src/a4d/extract/patient.py:merge_headers()` + +## 4. BMI Float Precision + +**Status**: ℹ️ Negligible difference + +**Observation**: Minor floating point precision differences at the ~10^-15 level. + +**Example**: +- R: `19.735976492259113` +- Python: `19.73597649225911` + +**Cause**: Different floating point arithmetic between R and Python/Polars. + +**Impact**: Negligible - differences are below any meaningful precision threshold for BMI measurements. + +## Summary + +| Issue | R Behavior | Python Behavior | Classification | +|-------|-----------|-----------------|----------------| +| insulin_type derivation | Bug - returns None for analog-only patients (doesn't check analog columns) | Correct derivation (checks all insulin columns) | **Python Fix** | +| insulin_subtype typo | "rapic-acting" (typo) | "rapid-acting" (correct spelling) | **Python Fix** | +| insulin_total_units extraction | Not extracted (header merge fails for 2024+ trackers) | Correctly extracted (unconditional header merge) | **Python Fix** | +| BMI precision | 16 decimal places | 14-15 decimal places | **Negligible** | + +## Migration Validation Status + +✅ **Schema**: 100% match (83 columns, all types correct) +✅ **Extraction**: Improved (unconditional header merge fixes insulin_total_units) +✅ **Cleaning**: Improved (fixes insulin_type derivation bug, corrects insulin_subtype typo) +ℹ️ **Precision**: Acceptable float differences (~10^-15 for BMI) + +**All 3 value differences are Python improvements over R bugs.** + +The Python pipeline is production-ready with significant improvements over the R pipeline: +1. **More robust header parsing** - No conditional merge that fails on 2024+ trackers +2. **Better null handling** - Correctly checks all insulin columns before derivation +3. **Correct terminology** - Uses proper medical terms ("rapid-acting" not "rapic-acting") diff --git a/internship_scope.docx b/internship_scope.docx new file mode 100644 index 0000000..6eef0f8 Binary files /dev/null and b/internship_scope.docx differ diff --git a/justfile b/justfile new file mode 100644 index 0000000..8c9d005 --- /dev/null +++ b/justfile @@ -0,0 +1,216 @@ +# a4d Python Pipeline - Development Commands + +# Default recipe (show available commands) +default: + @just --list + +PROJECT := "a4dphase2" +DATASET := "tracker" +REGISTRY := "asia-southeast2-docker.pkg.dev/a4dphase2/a4d/pipeline" +GIT_SHA := `git rev-parse --short HEAD` +IMAGE := REGISTRY + ":latest" +IMAGE_SHA := REGISTRY + ":" + GIT_SHA + +# ── Environment ─────────────────────────────────────────────────────────────── + +# Install dependencies and sync environment +sync: + uv sync --all-extras + +# Update dependencies +update: + uv lock --upgrade + +# Show project info +info: + @echo "Python version:" + @uv run python --version + @echo "\nInstalled packages:" + @uv pip list + +# Clean cache and build artifacts +clean: + rm -rf .ruff_cache + rm -rf .pytest_cache + rm -rf htmlcov + rm -rf .coverage + rm -rf dist + rm -rf build + rm -rf src/*.egg-info + find . -type d -name __pycache__ -exec rm -rf {} + + find . -type f -name "*.pyc" -delete + +# ── Code Quality ────────────────────────────────────────────────────────────── + +# Format code with ruff +format: + uv run ruff format . + +# Check code formatting without modifying files +format-check: + uv run ruff format --check . + +# Auto-fix linting issues +fix: + uv run ruff check --fix . + +# Run ruff linting +lint: + uv run ruff check . + +# Run type checking with ty +check: + uv run ty check src/ + +# Run all CI checks (format, lint, type, test) +ci: format-check lint check test + +# ── Testing ─────────────────────────────────────────────────────────────────── + +# Run unit tests (skip slow/integration) +test: + uv run pytest -m "not slow" + +# Run tests without coverage (faster, fail fast) +test-fast: + uv run pytest -m "not slow" --no-cov -x + +# Run all tests including slow/integration +test-all: + uv run pytest + +# Run integration tests only +test-integration: + uv run pytest -m integration + +# Install pre-commit hooks +hooks: + uv run pre-commit install + +# Run pre-commit on all files +hooks-run: + uv run pre-commit run --all-files + +# ── Local Pipeline ──────────────────────────────────────────────────────────── + +# Process a single tracker file (no GCS) +run-file FILE: + uv run a4d process-patient --file "{{FILE}}" + +# Process local files only, no GCS (use files already in data_root) +# Optionally pass a path: just run-local --data-root /path/to/trackers +run-local *ARGS: + uv run a4d process-patient {{ARGS}} + +# Create tables from existing cleaned parquet files +create-tables INPUT: + uv run a4d create-tables --input "{{INPUT}}" + +# Download from GCS, process locally, no upload +run-download *ARGS: + uv run a4d run-pipeline --skip-upload {{ARGS}} + +# Full pipeline: download from GCS, process, upload to GCS + BigQuery +run *ARGS: + uv run a4d run-pipeline {{ARGS}} + +# ── Docker ──────────────────────────────────────────────────────────────────── + +# --provenance=false: suppress BuildKit attestation manifests so the registry +# shows one image entry instead of three (image + attestation + index) +# Build Docker image tagged as :latest and : +docker-build: + docker build --provenance=false --platform=linux/amd64 \ + -t {{IMAGE}} \ + -t {{IMAGE_SHA}} \ + -f Dockerfile . + +# Smoke test: verify the image starts and the CLI is reachable +docker-smoke: + docker run --rm {{IMAGE}} uv run a4d --help + +# Push both :latest and : tags to Artifact Registry +docker-push: docker-build + docker push {{IMAGE}} + docker push {{IMAGE_SHA}} + @echo "Pushed: {{IMAGE}} and {{IMAGE_SHA}}" + +# Delete all images from Artifact Registry except :latest +docker-clean: + #!/usr/bin/env bash + set -euo pipefail + LATEST=$(gcloud artifacts docker images describe {{IMAGE}} \ + --project={{PROJECT}} --format="value(image_summary.digest)") + echo "Keeping: {{IMAGE}} ($LATEST)" + gcloud artifacts docker images list {{REGISTRY}} \ + --include-tags --project={{PROJECT}} \ + --format="value(digest)" \ + | while read -r digest; do + if [ "$digest" != "$LATEST" ]; then + echo "Deleting $digest..." + gcloud artifacts docker images delete "{{REGISTRY}}@$digest" \ + --project={{PROJECT}} --quiet --delete-tags 2>/dev/null || true + fi + done + echo "Done." + +# List images in Artifact Registry with tags and digests +docker-list: + gcloud artifacts docker images list {{REGISTRY}} \ + --include-tags \ + --project={{PROJECT}} + +# ── GCP / Cloud Run ─────────────────────────────────────────────────────────── + +# Creates dated snapshots e.g. patient_data_static_20260227 with 7-day expiry. +# Snapshot all BigQuery pipeline tables (safe to run before deploy) +backup-bq: + #!/usr/bin/env bash + set -euo pipefail + DATE=$(date +%Y%m%d) + EXPIRY="TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)" + TABLES="patient_data_static patient_data_monthly patient_data_annual" + for TABLE in $TABLES; do + if bq show --quiet {{PROJECT}}:{{DATASET}}.${TABLE} 2>/dev/null; then + SNAP="${TABLE}_${DATE}" + echo "Snapshotting ${TABLE} -> ${SNAP}..." + bq query --use_legacy_sql=false --project_id={{PROJECT}} \ + "CREATE SNAPSHOT TABLE \`{{PROJECT}}.{{DATASET}}.${SNAP}\` + CLONE \`{{PROJECT}}.{{DATASET}}.${TABLE}\` + OPTIONS(expiration_timestamp = ${EXPIRY})" + else + echo "Skipping ${TABLE} (does not exist yet)" + fi + done + echo "Done. Snapshots expire in 7 days." + +# Build, push and update the Cloud Run Job to use the latest image +deploy: docker-push + gcloud run jobs update a4d-pipeline \ + --image={{IMAGE}} \ + --region=asia-southeast2 + +# Execute the Cloud Run Job +run-job: + gcloud run jobs execute a4d-pipeline --region=asia-southeast2 + +# Stream logs from the Cloud Run Job (Ctrl-C to stop) +logs-job: + gcloud beta logging tail 'resource.type="cloud_run_job" AND resource.labels.job_name="a4d-pipeline"' \ + --project={{PROJECT}} \ + --format="value(textPayload)" + +# Show current resource settings (CPU, memory, timeout, parallelism) for the Cloud Run Job +job-settings: + gcloud run jobs describe a4d-pipeline \ + --region=asia-southeast2 \ + --project={{PROJECT}} \ + --format="yaml(spec.template.spec.template.spec.containers[0].resources, spec.template.spec.template.spec.timeoutSeconds, spec.template.spec.parallelism, spec.template.spec.taskCount)" + +# Roll back Cloud Run Job to a specific git SHA +# Usage: just rollback abc1234 +rollback SHA: + gcloud run jobs update a4d-pipeline \ + --image={{REGISTRY}}:{{SHA}} \ + --region=asia-southeast2 + @echo "Rolled back to {{REGISTRY}}:{{SHA}}" diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..44f2033 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,82 @@ +[project] +name = "a4d" +version = "2.0.0" +description = "A4D Medical Tracker Data Processing Pipeline (Python)" +readme = "README.md" +requires-python = ">=3.14" +authors = [ + {name = "Michael Aydinbas", email = "michael.aydinbas@gmail.com"} +] +license = {text = "MIT"} + +dependencies = [ + "polars>=0.20.0", + "pydantic>=2.6.0", + "pydantic-settings>=2.2.0", + "pandera[polars]>=0.18.0", + "loguru>=0.7.0", + "openpyxl>=3.1.0", + "google-cloud-bigquery>=3.17.0", + "google-cloud-storage>=2.14.0", + "pyyaml>=6.0", + "typer>=0.9.0", + "rich>=13.7.0", + "tqdm>=4.66.0", + "python-dateutil>=2.8.0", + "fastexcel>=0.16.0", +] + + +[dependency-groups] +dev = [ + "pre-commit>=4.3.0", + "pytest>=8.4.2", + "pytest-cov>=7.0.0", + "pytest-mock>=3.15.1", + "ruff>=0.14.1", + "ty>=0.0.1a23", +] + +[project.scripts] +a4d = "a4d.cli:main" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.ruff] +line-length = 100 +target-version = "py314" +lint.select = [ + "E", # pycodestyle errors + "W", # pycodestyle warnings + "F", # pyflakes + "I", # isort + "N", # pep8-naming + "UP", # pyupgrade + "B", # flake8-bugbear + "A", # flake8-builtins + "C4", # flake8-comprehensions + "PT", # flake8-pytest-style +] + +[tool.ruff.lint.per-file-ignores] +"__init__.py" = ["F401"] # Allow unused imports in __init__.py + +[tool.pytest.ini_options] +testpaths = ["tests"] +python_files = ["test_*.py"] +python_functions = ["test_*"] +markers = [ + "slow: marks tests as slow (deselected by default)", + "integration: marks tests as integration tests requiring real tracker files", + "e2e: marks tests as end-to-end tests (extraction + cleaning)", +] +addopts = [ + "--cov=src/a4d", + "--cov-report=term-missing", + "--cov-report=html", +] +filterwarnings = [ + "ignore::RuntimeWarning:google_crc32c", +] diff --git a/.Rbuildignore b/r-archive/.Rbuildignore similarity index 100% rename from .Rbuildignore rename to r-archive/.Rbuildignore diff --git a/.Rprofile b/r-archive/.Rprofile similarity index 100% rename from .Rprofile rename to r-archive/.Rprofile diff --git a/.lintr b/r-archive/.lintr similarity index 100% rename from .lintr rename to r-archive/.lintr diff --git a/DESCRIPTION b/r-archive/DESCRIPTION similarity index 100% rename from DESCRIPTION rename to r-archive/DESCRIPTION diff --git a/NAMESPACE b/r-archive/NAMESPACE similarity index 100% rename from NAMESPACE rename to r-archive/NAMESPACE diff --git a/R/a4d-package.R b/r-archive/R/a4d-package.R similarity index 100% rename from R/a4d-package.R rename to r-archive/R/a4d-package.R diff --git a/R/helper_main.R b/r-archive/R/helper_main.R similarity index 100% rename from R/helper_main.R rename to r-archive/R/helper_main.R diff --git a/R/helper_product_data.R b/r-archive/R/helper_product_data.R similarity index 100% rename from R/helper_product_data.R rename to r-archive/R/helper_product_data.R diff --git a/R/logger.R b/r-archive/R/logger.R similarity index 100% rename from R/logger.R rename to r-archive/R/logger.R diff --git a/R/read_product_data.R b/r-archive/R/read_product_data.R similarity index 100% rename from R/read_product_data.R rename to r-archive/R/read_product_data.R diff --git a/R/script1_get_tracker_year.R b/r-archive/R/script1_get_tracker_year.R similarity index 100% rename from R/script1_get_tracker_year.R rename to r-archive/R/script1_get_tracker_year.R diff --git a/R/script1_helper_read_patient_data.R b/r-archive/R/script1_helper_read_patient_data.R similarity index 100% rename from R/script1_helper_read_patient_data.R rename to r-archive/R/script1_helper_read_patient_data.R diff --git a/R/script1_process_patient_data.R b/r-archive/R/script1_process_patient_data.R similarity index 100% rename from R/script1_process_patient_data.R rename to r-archive/R/script1_process_patient_data.R diff --git a/R/script1_process_product_data.R b/r-archive/R/script1_process_product_data.R similarity index 100% rename from R/script1_process_product_data.R rename to r-archive/R/script1_process_product_data.R diff --git a/R/script1_process_tracker_file.R b/r-archive/R/script1_process_tracker_file.R similarity index 100% rename from R/script1_process_tracker_file.R rename to r-archive/R/script1_process_tracker_file.R diff --git a/R/script1_read_patient_data.R b/r-archive/R/script1_read_patient_data.R similarity index 100% rename from R/script1_read_patient_data.R rename to r-archive/R/script1_read_patient_data.R diff --git a/R/script2_helper_dates.R b/r-archive/R/script2_helper_dates.R similarity index 100% rename from R/script2_helper_dates.R rename to r-archive/R/script2_helper_dates.R diff --git a/R/script2_helper_patient_data_fix.R b/r-archive/R/script2_helper_patient_data_fix.R similarity index 98% rename from R/script2_helper_patient_data_fix.R rename to r-archive/R/script2_helper_patient_data_fix.R index 278ab1c..d18ef7f 100644 --- a/R/script2_helper_patient_data_fix.R +++ b/r-archive/R/script2_helper_patient_data_fix.R @@ -176,6 +176,15 @@ parse_dates <- function(date) { return(lubridate::NA_Date_) } + # Handle Excel serial numbers (e.g., "45341.0", "39920.0") + # Excel stores dates as days since 1899-12-30 + numeric_date <- suppressWarnings(as.numeric(date)) + if (!is.na(numeric_date) && numeric_date > 1 && numeric_date < 100000) { + # This is likely an Excel serial number + excel_origin <- as.Date("1899-12-30") + return(excel_origin + as.integer(numeric_date)) + } + parsed_date <- suppressWarnings(lubridate::as_date(date)) if (is.na(parsed_date)) { diff --git a/R/script2_process_patient_data.R b/r-archive/R/script2_process_patient_data.R similarity index 100% rename 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b/r-archive/man/testit.Rd similarity index 100% rename from man/testit.Rd rename to r-archive/man/testit.Rd diff --git a/man/update_receivedfrom.Rd b/r-archive/man/update_receivedfrom.Rd similarity index 100% rename from man/update_receivedfrom.Rd rename to r-archive/man/update_receivedfrom.Rd diff --git a/man/wide_cells_2_rows.Rd b/r-archive/man/wide_cells_2_rows.Rd similarity index 100% rename from man/wide_cells_2_rows.Rd rename to r-archive/man/wide_cells_2_rows.Rd diff --git a/man/with_file_logger.Rd b/r-archive/man/with_file_logger.Rd similarity index 100% rename from man/with_file_logger.Rd rename to r-archive/man/with_file_logger.Rd diff --git a/readme.html b/r-archive/readme.html similarity index 100% rename from readme.html rename to r-archive/readme.html diff --git a/r-archive/readme.md b/r-archive/readme.md new file mode 100644 index 0000000..077cca5 --- /dev/null +++ b/r-archive/readme.md @@ -0,0 +1,142 @@ +## Welcome! + +Below is a description of the folders and the files we find in these + +- **R**: Contains the scripts to extract, and preprocess the patient and the product data. + - *patient_tracker_extract_helper* : Helper functions to extract the data for the patients (needed to run the *patient_tracker_extract* script) + - *helper_product_data*: Helper functions to extract the data for the products + - *patient_tracker_extract*: Script that reads in the different raw (Excel) trackers for each clinic and year and extracts the data into an machine readable table. + - *patient_tracker_format*: Script that reads in the output of *patient_tracker_extract* and reformats columns according to the codebook indications, performs checks and removes duplicates (i.e., patients whose information is copied across months but remains unchanged). Returns a dataframe that is ready to input in the database and another dataframe indicating the locations of errors or non-readable data. +- **4ADMonthlyTrackerCodebook**: Codebook containing the information on the variables that we are extracting from the trackers (patient and product data). Also contains tabs listing the different labels that one variable may have together with its standardized formulation. + +## Setup + +We use the [renv](https://rstudio.github.io/renv/index.html) package to manage dependencies. + +This project was setup with `renv` and uses a local `.Rprofile` file that activates `renv`. +The first time you open this project in RStudio, this will check if `renv` is already installed, and, if that is not the case, install it. + +You are then informed about the difference between your local R environment and the packages used for this project. +Check your console in RStudio after opening. +You should see something like: + +```R +* One or more packages recorded in the lockfile are not installed. +* Use `renv::status()` for more details. +``` + +You can use `renv::status()` to see which packages will be installed. +Once you are ready, run + +```R +renv::restore() +``` + +and `renv` will install all packages with the version as stated in the `renv.lock` file. + +### Updating the Lockfile + +See [collaborating](https://rstudio.github.io/renv/articles/collaborating.html) for the full details. + +While working on a project, you or your collaborators may need to update or install new packages in your project. +When this occurs, you’ll also want to ensure your collaborators are then using the same newly-installed packages. +In general, the process looks like this: + +- A user installs, or updates, one or more packages in their local project library; +- That user calls `renv::snapshot()` to update the `renv.lock` lockfile; +- That user then shares the updated version of `renv.lock` with their collaborators (meaning that this file is commited and pushed via `git`); +- Other collaborators then call `renv::restore()` to install the packages specified in the newly-updated lockfile. + +If you want to add another package to this project, install the package with `renv::install()` instead of `package.install()`. + +**Note**: Not all packages are locked by `renv`. +For example, if you want to preview this Readme with RStudio, RStudio will likely ask to install or update additional packages like `markdown`. +That is ok and intended because these additional packages are not used by the project code so it is up to you to install them or not. + +### Loading the code + +Once you have installed the dependencies with `renv::restore()` you can go on and load our "package". + +However, you need one more dependency, and that is [devtools](https://devtools.r-lib.org/). +This is because `renv` does only lock required packages that are needed to run the code, not packages to develop the code (like `devtools`). + +So make sure to run + +```r +install.packages("devtools") +``` + +It is also possible that this package was already downloaded because we have this code in the `.Rprofile` file that is executed automatically when RStudio is opened: + +```r +if (interactive()) { + require("devtools", quietly = TRUE) + # automatically attaches usethis + devtools::load_all() +} +``` + +Now you have access to `devtools`, which is in fact a set of packages that are installed, like `usethis` and `roxygen2`. + +To load all functions within the `./R` folder, run + +```r +devtools::load_all() +``` + +This will make available the `a4d` package in the global environment, giving you access to all functions within it, as well as the core packages of `tidyverse` because it is listed at "Depends" in the DESCRIPTION file. + +You can now go ahead and run one of the two main scripts: +- `run_a4d_patient_data.R` +- `run_a4d_product_data.R` + +### Loading the data + +We will all have the encrypted data stored on different folders within our computers. +To account for the different file paths for every user and to speed the selection of the tracker files (where the data is stored), there is the following solution: + +- Run `usethis::edit_r_environ()` + - This should open the following file: `.Renviron` +- Add the following line: + - `A4D_DATA_ROOT = "your_path"` + - Replace `"your_path"` with the path to your A4D tracker files + - E.g. `A4D_DATA_ROOT = "D:/A4D"` +- Save the `.Renviron` file +- You are good to go and will not need to re-select the folder containing the tracker files when running `select_A4D_directory()`. + This function will now get the correct path from the `.Renviron` file. + +## Development workflow + +For a short overview, see [cheatsheets](https://devtools.r-lib.org/#cheatsheet). + +If you want to change any (code) file, add new files or delete existing files, please follow these steps: + +1. only once: `git clone` this repository +2. switch to the develop branch: `git checkout develop` or `git switch develop` +3. update develop: `git pull` +4. create a new branch: `git checkout -b -` (or create the branch in GitHub and just switch to it, no `-b` needed then) +5. do your code changes, create new R files with `usethis::use_r()` and new test files with `usethis::use_test()` +6. load and test/execute your code changes: `devtools::load_all()` +7. run the tests: `devtools::test()` + a. Fix any problems until all tests are green and make sure your changes do not break other code +8. check the package: `devtools::check()` + a. Fix any problems until all checks are green +9. document your functions: use roxygen documentation by comments starting with `#'` +10. update documentation: `devtools:document()` +11. optional: add additional documentation to the README or create a RMarkdown file with examples +12. get latest changes from develop: `git merge develop` + a. if there are any conflicts, solve them +13. create a (final) commit with all your changes: `git add ` and `git commit -m""` +14. push your changes: `git push` (for a newly created local branch, you will need to set an up-stream first, just follow the instructions on the command line) +15. Check the GitHub workflows (cicd pipelines) for your branch and fix any problems +16. Create a PR with develop as target +17. Again, check the GitHub workflows for your PR and fix any problems + +In addition to this general workflow, there are some additional steps required if you made use of external packages not yet stored in `renv.lock`: + +1. install the package: Use `renv::install()` if you want to use this package only for development, or `usethis::use_package()` to add the package to the DESCRIPTION file + a. if you want to update a package, you can also use `renv::install()`, without arguments it will update all listed packages in the lock file +2. use the package with the `package::fun()` syntax in your code +3. use `renv::snapshot()` to update the `renv.lock` +4. make sure to add the `renv.lock` file with your PR + diff --git a/renv.lock b/r-archive/renv.lock similarity index 100% rename from renv.lock rename to r-archive/renv.lock diff --git a/renv/.gitignore b/r-archive/renv/.gitignore similarity index 100% rename from renv/.gitignore rename to r-archive/renv/.gitignore diff --git a/renv/activate.R b/r-archive/renv/activate.R similarity index 100% rename from renv/activate.R rename to r-archive/renv/activate.R diff --git a/renv/settings.json b/r-archive/renv/settings.json similarity index 100% rename from renv/settings.json rename to r-archive/renv/settings.json diff --git a/scripts/gcp/copy_data_back_to_storage.sh b/r-archive/scripts-root/gcp/copy_data_back_to_storage.sh similarity index 100% rename from scripts/gcp/copy_data_back_to_storage.sh rename to r-archive/scripts-root/gcp/copy_data_back_to_storage.sh diff --git a/scripts/gcp/copy_data_from_storage.sh b/r-archive/scripts-root/gcp/copy_data_from_storage.sh similarity index 100% rename from scripts/gcp/copy_data_from_storage.sh rename to r-archive/scripts-root/gcp/copy_data_from_storage.sh diff --git a/r-archive/scripts-root/gcp/deploy.sh b/r-archive/scripts-root/gcp/deploy.sh new file mode 100755 index 0000000..ffa5542 --- /dev/null +++ b/r-archive/scripts-root/gcp/deploy.sh @@ -0,0 +1,77 @@ +#!/bin/bash +# Build the Docker image, push it to Artifact Registry, and deploy the A4D +# Python pipeline as a Cloud Run Job that can be triggered manually. +# +# The Docker image is built from the repo root (to include reference_data/) +# using a4d-python/Dockerfile as the build file. +# +# Prerequisites: +# - gcloud CLI authenticated with sufficient permissions +# - Docker installed and running +# - Service account "${SERVICE_ACCOUNT}" created with the following roles: +# roles/storage.objectViewer (read source files from GCS) +# roles/storage.objectCreator (write output files to GCS) +# roles/bigquery.dataEditor (write tables to BigQuery) +# roles/bigquery.jobUser (run BigQuery load jobs) +# +# Authentication inside the container uses Workload Identity / ADC via the +# Cloud Run service account — no JSON key file is required. +# +# Usage (run from the repo root): +# PROJECT_ID=my-project SERVICE_ACCOUNT=sa@my-project.iam.gserviceaccount.com \ +# bash scripts/gcp/deploy.sh +# +# To run the pipeline after deployment: +# gcloud run jobs execute a4d-pipeline \ +# --region=${REGION} --project=${PROJECT_ID} --wait + +set -euo pipefail + +PROJECT_ID="${PROJECT_ID:-a4d-315220}" +REGION="${REGION:-europe-west1}" +REPOSITORY="a4d" +IMAGE_NAME="pipeline" +JOB_NAME="a4d-pipeline" +SERVICE_ACCOUNT="${SERVICE_ACCOUNT:-a4d-pipeline@${PROJECT_ID}.iam.gserviceaccount.com}" +IMAGE_URI="${REGION}-docker.pkg.dev/${PROJECT_ID}/${REPOSITORY}/${IMAGE_NAME}" + +echo "==> Configuring Docker authentication for Artifact Registry..." +gcloud auth configure-docker "${REGION}-docker.pkg.dev" --quiet + +echo "==> Creating Artifact Registry repository (skipped if it already exists)..." +gcloud artifacts repositories create "${REPOSITORY}" \ + --repository-format=docker \ + --location="${REGION}" \ + --project="${PROJECT_ID}" \ + --quiet 2>/dev/null || true + +echo "==> Building Docker image: ${IMAGE_URI}" +# Build context is the repo root so that reference_data/ can be copied into the image. +docker build \ + --cache-from "${IMAGE_URI}" \ + -f a4d-python/Dockerfile \ + -t "${IMAGE_URI}" \ + . + +echo "==> Pushing Docker image to Artifact Registry..." +docker push "${IMAGE_URI}" + +echo "==> Deploying Cloud Run Job: ${JOB_NAME}" +gcloud run jobs deploy "${JOB_NAME}" \ + --image="${IMAGE_URI}" \ + --region="${REGION}" \ + --project="${PROJECT_ID}" \ + --service-account="${SERVICE_ACCOUNT}" \ + --memory=8Gi \ + --cpu=4 \ + --max-retries=0 \ + --task-timeout=3h \ + --set-env-vars="A4D_PROJECT_ID=${PROJECT_ID},A4D_ENVIRONMENT=production,A4D_DATA_ROOT=/workspace/data" + +echo "" +echo "==> Deployment complete." +echo "" +echo "To run the pipeline manually, execute:" +echo " gcloud run jobs execute ${JOB_NAME} \\" +echo " --region=${REGION} --project=${PROJECT_ID} --wait" + diff --git a/scripts/gcp/ingest_data.sh b/r-archive/scripts-root/gcp/ingest_data.sh similarity index 100% rename from scripts/gcp/ingest_data.sh rename to r-archive/scripts-root/gcp/ingest_data.sh diff --git a/scripts/gcp/setup.sh b/r-archive/scripts-root/gcp/setup.sh similarity index 100% rename from scripts/gcp/setup.sh rename to r-archive/scripts-root/gcp/setup.sh diff --git a/scripts/logs.ipynb b/r-archive/scripts-root/logs.ipynb similarity index 100% rename from scripts/logs.ipynb rename to r-archive/scripts-root/logs.ipynb diff --git a/scripts/python/.gitignore b/r-archive/scripts-root/python/.gitignore similarity index 100% rename from scripts/python/.gitignore rename to r-archive/scripts-root/python/.gitignore diff --git a/scripts/python/README.md b/r-archive/scripts-root/python/README.md similarity index 100% rename from scripts/python/README.md rename to r-archive/scripts-root/python/README.md diff --git a/scripts/python/a4d_replacer_tool.spec b/r-archive/scripts-root/python/a4d_replacer_tool.spec similarity index 100% rename from scripts/python/a4d_replacer_tool.spec rename to r-archive/scripts-root/python/a4d_replacer_tool.spec diff --git a/scripts/python/main.py b/r-archive/scripts-root/python/main.py similarity index 97% rename from scripts/python/main.py rename to r-archive/scripts-root/python/main.py index 8b485a7..dbc0571 100644 --- a/scripts/python/main.py +++ b/r-archive/scripts-root/python/main.py @@ -1,3 +1,11 @@ +# /// script +# requires-python = ">=3.13" +# dependencies = [ +# "click>=8.3.1", +# "openpyxl>=3.1.5", +# "pandas>=3.0.1", +# ] +# /// """ Small cli helper tool to replace patient names with patient ids in excel files. This script is used to replace patient names with patient ids in excel files. diff --git a/scripts/python/poetry.lock b/r-archive/scripts-root/python/poetry.lock similarity index 100% rename from scripts/python/poetry.lock rename to r-archive/scripts-root/python/poetry.lock diff --git a/scripts/python/pyproject.toml b/r-archive/scripts-root/python/pyproject.toml similarity index 94% rename from scripts/python/pyproject.toml rename to r-archive/scripts-root/python/pyproject.toml index a21275c..67b264f 100644 --- a/scripts/python/pyproject.toml +++ b/r-archive/scripts-root/python/pyproject.toml @@ -7,7 +7,7 @@ readme = "README.md" package-mode = false [tool.poetry.dependencies] -python = ">=3.10,<3.13" +python = ">=3.10,<3.14" pandas = "^2.2.1" openpyxl = "^3.1.5" click = "^8.1.7" diff --git a/scripts/python/sort_yaml.py b/r-archive/scripts-root/python/sort_yaml.py similarity index 100% rename from scripts/python/sort_yaml.py rename to r-archive/scripts-root/python/sort_yaml.py diff --git a/scripts/R/pre_commit.R b/r-archive/scripts/R/pre_commit.R similarity index 100% rename from scripts/R/pre_commit.R rename to r-archive/scripts/R/pre_commit.R diff --git a/scripts/R/run_pipeline.R b/r-archive/scripts/R/run_pipeline.R similarity index 83% rename from scripts/R/run_pipeline.R rename to r-archive/scripts/R/run_pipeline.R index 5c161da..e34d49c 100644 --- a/scripts/R/run_pipeline.R +++ b/r-archive/scripts/R/run_pipeline.R @@ -31,19 +31,21 @@ upload_data <- function(bucket, data_dir) { print("Finished uploading data to GCP Storage") } -ingest_data <- function(project_id, cluster_fields, dataset, table, source) { - print("Deleting old table in GCP Big Query") - command <- paste( - "bq rm", - "-f", - "-t", - paste0(project_id, ":", dataset, ".", table) - ) - cat(command) - exit_code <- system(command) - if (exit_code != 0) { - paste("Error while executing", command) - stop("Error during ingesting data") +ingest_data <- function(project_id, cluster_fields, dataset, table, source, delete=T) { + if (delete) { + print("Deleting old table in GCP Big Query") + command <- paste( + "bq rm", + "-f", + "-t", + paste0(project_id, ":", dataset, ".", table) + ) + cat(command) + exit_code <- system(command) + if (exit_code != 0) { + paste("Error while executing", command) + stop("Error during ingesting data") + } } print("Ingesting data to GCP Big Query") @@ -102,20 +104,14 @@ ingest_data( table = "patient_data_static", source = file.path(table_dir, "patient_data_static.parquet") ) -ingest_data( - project_id = config$project_id, - cluster_fields = "clinic_id,patient_id,tracker_date", - dataset = config$dataset, - table = "patient_data_hba1c", - source = file.path(table_dir, "longitudinal_data_hba1c.parquet") -) -ingest_data( - project_id = config$project_id, - cluster_fields = "clinic_id,product_released_to,product_table_year,product_table_month", - dataset = config$dataset, - table = "product_data", - source = file.path(table_dir, "product_data.parquet") -) +# NOTE: product data ingestion is deliberately skipped until the product pipeline is finalized +# ingest_data( +# project_id = config$project_id, +# cluster_fields = "clinic_id,product_released_to,product_table_year,product_table_month", +# dataset = config$dataset, +# table = "product_data", +# source = file.path(table_dir, "product_data.parquet") +# ) ingest_data( project_id = config$project_id, cluster_fields = "clinic_id", diff --git a/scripts/R/run_script_1_extract_raw_data.R b/r-archive/scripts/R/run_script_1_extract_raw_data.R similarity index 100% rename from scripts/R/run_script_1_extract_raw_data.R rename to r-archive/scripts/R/run_script_1_extract_raw_data.R diff --git a/scripts/R/run_script_2_clean_data.R b/r-archive/scripts/R/run_script_2_clean_data.R similarity index 100% rename from scripts/R/run_script_2_clean_data.R rename to r-archive/scripts/R/run_script_2_clean_data.R diff --git a/scripts/R/run_script_3_create_tables.R b/r-archive/scripts/R/run_script_3_create_tables.R similarity index 85% rename from scripts/R/run_script_3_create_tables.R rename to r-archive/scripts/R/run_script_3_create_tables.R index 8a27014..9b86568 100644 --- a/scripts/R/run_script_3_create_tables.R +++ b/r-archive/scripts/R/run_script_3_create_tables.R @@ -100,48 +100,6 @@ main <- function() { output_root = paths$output_root ) - logfile <- "table_longitudinal_data_hba1c" - with_file_logger(logfile, - { - tryCatch( - { - create_table_longitudinal_data( - patient_data_files, - file.path(paths$output_root, "patient_data_cleaned"), - paths$tables, - "hba1c_updated", - "hba1c" - ) - }, - error = function(e) { - logError( - log_to_json( - "Could not create table for longitudinal patient data. Error = {values['e']}.", - values = list(e = e$message), - script = "script3", - file = "run_script_3_create_tables.R", - errorCode = "critical_abort", - functionName = "create_table_longitudinal_data" - ) - ) - }, - warning = function(w) { - logWarn( - log_to_json( - "Could not create table for longitudinal patient data. Warning = {values['w']}.", - values = list(w = w$message), - script = "script3", - file = "run_script_3_create_tables.R", - warningCode = "critical_abort", - functionName = "create_table_longitudinal_data" - ) - ) - } - ) - }, - output_root = paths$output_root - ) - logfile <- "table_patient_data_annual" with_file_logger(logfile, { diff --git a/scripts/R/run_script_4_create_logs_table.R b/r-archive/scripts/R/run_script_4_create_logs_table.R similarity index 100% rename from scripts/R/run_script_4_create_logs_table.R rename to r-archive/scripts/R/run_script_4_create_logs_table.R diff --git a/scripts/R/run_script_5_create_metadata_table.R b/r-archive/scripts/R/run_script_5_create_metadata_table.R similarity index 100% rename from scripts/R/run_script_5_create_metadata_table.R rename to r-archive/scripts/R/run_script_5_create_metadata_table.R diff --git a/tests/testthat.R b/r-archive/tests/testthat.R similarity index 100% rename from tests/testthat.R rename to r-archive/tests/testthat.R diff --git a/tests/testthat/helper-compute-balance.R b/r-archive/tests/testthat/helper-compute-balance.R similarity index 100% rename from tests/testthat/helper-compute-balance.R rename to r-archive/tests/testthat/helper-compute-balance.R diff --git a/tests/testthat/helper-data-extract-country-clinic-code.R b/r-archive/tests/testthat/helper-data-extract-country-clinic-code.R similarity index 100% rename from tests/testthat/helper-data-extract-country-clinic-code.R rename to r-archive/tests/testthat/helper-data-extract-country-clinic-code.R diff --git a/tests/testthat/test-compute-balance.R b/r-archive/tests/testthat/test-compute-balance.R similarity index 100% rename from tests/testthat/test-compute-balance.R rename to r-archive/tests/testthat/test-compute-balance.R diff --git a/tests/testthat/test-data_cleaning_conifg.R b/r-archive/tests/testthat/test-data_cleaning_conifg.R similarity index 100% rename from tests/testthat/test-data_cleaning_conifg.R rename to r-archive/tests/testthat/test-data_cleaning_conifg.R diff --git a/tests/testthat/test-get_tracker_year.R b/r-archive/tests/testthat/test-get_tracker_year.R similarity index 100% rename from tests/testthat/test-get_tracker_year.R rename to r-archive/tests/testthat/test-get_tracker_year.R diff --git a/tests/testthat/test-helper_clean_data.R b/r-archive/tests/testthat/test-helper_clean_data.R similarity index 100% rename from tests/testthat/test-helper_clean_data.R rename to r-archive/tests/testthat/test-helper_clean_data.R diff --git a/tests/testthat/test-helper_dates.R b/r-archive/tests/testthat/test-helper_dates.R similarity index 100% rename from tests/testthat/test-helper_dates.R rename to r-archive/tests/testthat/test-helper_dates.R diff --git a/tests/testthat/test-helper_patient_data_fix.R b/r-archive/tests/testthat/test-helper_patient_data_fix.R similarity index 100% rename from tests/testthat/test-helper_patient_data_fix.R rename to r-archive/tests/testthat/test-helper_patient_data_fix.R diff --git a/tests/testthat/test-read_column_synonyms.R b/r-archive/tests/testthat/test-read_column_synonyms.R similarity index 100% rename from tests/testthat/test-read_column_synonyms.R rename to r-archive/tests/testthat/test-read_column_synonyms.R diff --git a/tests/testthat/test_data/synonyms/synonyms_patient.yaml b/r-archive/tests/testthat/test_data/synonyms/synonyms_patient.yaml similarity index 100% rename from tests/testthat/test_data/synonyms/synonyms_patient.yaml rename to r-archive/tests/testthat/test_data/synonyms/synonyms_patient.yaml diff --git a/readme.md b/readme.md index 077cca5..3614b12 100644 --- a/readme.md +++ b/readme.md @@ -1,142 +1,225 @@ -## Welcome! +# A4D Data Processing Pipeline (Python) -Below is a description of the folders and the files we find in these +Python implementation of the A4D medical tracker data processing pipeline. -- **R**: Contains the scripts to extract, and preprocess the patient and the product data. - - *patient_tracker_extract_helper* : Helper functions to extract the data for the patients (needed to run the *patient_tracker_extract* script) - - *helper_product_data*: Helper functions to extract the data for the products - - *patient_tracker_extract*: Script that reads in the different raw (Excel) trackers for each clinic and year and extracts the data into an machine readable table. - - *patient_tracker_format*: Script that reads in the output of *patient_tracker_extract* and reformats columns according to the codebook indications, performs checks and removes duplicates (i.e., patients whose information is copied across months but remains unchanged). Returns a dataframe that is ready to input in the database and another dataframe indicating the locations of errors or non-readable data. -- **4ADMonthlyTrackerCodebook**: Codebook containing the information on the variables that we are extracting from the trackers (patient and product data). Also contains tabs listing the different labels that one variable may have together with its standardized formulation. +## Migration Status -## Setup +🚧 **Active Development** - Migrating from R to Python -We use the [renv](https://rstudio.github.io/renv/index.html) package to manage dependencies. +See [Migration Documentation](../MIGRATION_OVERVIEW.md) for details. -This project was setup with `renv` and uses a local `.Rprofile` file that activates `renv`. -The first time you open this project in RStudio, this will check if `renv` is already installed, and, if that is not the case, install it. +## Features -You are then informed about the difference between your local R environment and the packages used for this project. -Check your console in RStudio after opening. -You should see something like: +- ✅ **Incremental Processing** - Only process changed tracker files +- ✅ **Parallel Execution** - Process multiple trackers concurrently +- ✅ **Stateless GCP Deployment** - Uses BigQuery for state management +- ✅ **Comprehensive Error Tracking** - Detailed error logs per patient/tracker +- ✅ **High Performance** - Built on Polars (10-100x faster than pandas) -```R -* One or more packages recorded in the lockfile are not installed. -* Use `renv::status()` for more details. +## Quick Start + +### Installation + +```bash +# Install uv (if not already installed) +curl -LsSf https://astral.sh/uv/install.sh | sh + +# Install just (optional, for convenient commands) +# macOS: brew install just +# Other: https://github.com/casey/just + +# Install dependencies +just sync +# or: uv sync --all-extras ``` -You can use `renv::status()` to see which packages will be installed. -Once you are ready, run +### Configuration + +Create a `.env` file: -```R -renv::restore() +```bash +A4D_ENVIRONMENT=development +A4D_DATA_ROOT=/path/to/tracker/files +A4D_PROJECT_ID=a4dphase2 +A4D_DATASET=tracker +A4D_DOWNLOAD_BUCKET=a4dphase2_upload +A4D_UPLOAD_BUCKET=a4dphase2_output ``` -and `renv` will install all packages with the version as stated in the `renv.lock` file. +### Running the Pipeline + +```bash +# Full pipeline +just run +# or: uv run python scripts/run_pipeline.py + +# With options +just run --max-workers 8 +just run --force # Reprocess all files +just run --skip-upload # Local testing +``` + +## Architecture + +``` +Pipeline Flow: +1. Query BigQuery metadata → determine changed files +2. Process changed trackers in parallel (extract → clean → validate) +3. Aggregate individual parquets → final tables +4. Upload to BigQuery +5. Update metadata table +``` + +## Project Structure + +``` +a4d-python/ +├── src/a4d/ # Main package +│ ├── config.py # Pydantic settings +│ ├── logging.py # loguru configuration +│ ├── extract/ # Data extraction (Script 1) +│ ├── clean/ # Data cleaning (Script 2) +│ ├── tables/ # Table creation (Script 3) +│ ├── gcp/ # BigQuery & GCS integration +│ ├── state/ # State management +│ └── utils/ # Utilities +├── tests/ # Test suite +├── scripts/ # CLI scripts +└── pyproject.toml # Dependencies +``` + +## Development + +### Common Commands + +```bash +# Show all available commands +just + +# Run all CI checks (format, lint, type, test) +just ci + +# Run tests with coverage +just test -### Updating the Lockfile +# Run tests without coverage (faster) +just test-fast -See [collaborating](https://rstudio.github.io/renv/articles/collaborating.html) for the full details. +# Format code +just format -While working on a project, you or your collaborators may need to update or install new packages in your project. -When this occurs, you’ll also want to ensure your collaborators are then using the same newly-installed packages. -In general, the process looks like this: +# Lint code +just lint -- A user installs, or updates, one or more packages in their local project library; -- That user calls `renv::snapshot()` to update the `renv.lock` lockfile; -- That user then shares the updated version of `renv.lock` with their collaborators (meaning that this file is commited and pushed via `git`); -- Other collaborators then call `renv::restore()` to install the packages specified in the newly-updated lockfile. +# Auto-fix linting issues +just fix -If you want to add another package to this project, install the package with `renv::install()` instead of `package.install()`. +# Type checking with ty +just check -**Note**: Not all packages are locked by `renv`. -For example, if you want to preview this Readme with RStudio, RStudio will likely ask to install or update additional packages like `markdown`. -That is ok and intended because these additional packages are not used by the project code so it is up to you to install them or not. +# Clean build artifacts +just clean +``` + +### Running Tests + +```bash +# All tests with coverage +just test +# or: uv run pytest --cov -### Loading the code +# Fast tests (no coverage) +just test-fast +# or: uv run pytest -x -Once you have installed the dependencies with `renv::restore()` you can go on and load our "package". +# Specific test file +uv run pytest tests/test_extract/test_patient.py +``` -However, you need one more dependency, and that is [devtools](https://devtools.r-lib.org/). -This is because `renv` does only lock required packages that are needed to run the code, not packages to develop the code (like `devtools`). +### Code Quality -So make sure to run +```bash +# Run all checks (what CI runs) +just ci -```r -install.packages("devtools") +# Individual checks +just lint # Linting +just format # Format code +just format-check # Check formatting without changes +just check # Type checking with ty +just fix # Auto-fix linting issues ``` -It is also possible that this package was already downloaded because we have this code in the `.Rprofile` file that is executed automatically when RStudio is opened: +### Pre-commit Hooks + +```bash +# Install hooks +just hooks +# or: uv run pre-commit install -```r -if (interactive()) { - require("devtools", quietly = TRUE) - # automatically attaches usethis - devtools::load_all() -} +# Run manually on all files +just hooks-run +# or: uv run pre-commit run --all-files ``` -Now you have access to `devtools`, which is in fact a set of packages that are installed, like `usethis` and `roxygen2`. +### Docker + +```bash +# Build Docker image +just docker-build -To load all functions within the `./R` folder, run +# Run container locally +just docker-run -```r -devtools::load_all() +# Or manually: +docker build -t a4d-python:latest . +docker run --rm --env-file .env -v $(pwd)/output:/app/output a4d-python:latest ``` -This will make available the `a4d` package in the global environment, giving you access to all functions within it, as well as the core packages of `tidyverse` because it is listed at "Depends" in the DESCRIPTION file. - -You can now go ahead and run one of the two main scripts: -- `run_a4d_patient_data.R` -- `run_a4d_product_data.R` - -### Loading the data - -We will all have the encrypted data stored on different folders within our computers. -To account for the different file paths for every user and to speed the selection of the tracker files (where the data is stored), there is the following solution: - -- Run `usethis::edit_r_environ()` - - This should open the following file: `.Renviron` -- Add the following line: - - `A4D_DATA_ROOT = "your_path"` - - Replace `"your_path"` with the path to your A4D tracker files - - E.g. `A4D_DATA_ROOT = "D:/A4D"` -- Save the `.Renviron` file -- You are good to go and will not need to re-select the folder containing the tracker files when running `select_A4D_directory()`. - This function will now get the correct path from the `.Renviron` file. - -## Development workflow - -For a short overview, see [cheatsheets](https://devtools.r-lib.org/#cheatsheet). - -If you want to change any (code) file, add new files or delete existing files, please follow these steps: - -1. only once: `git clone` this repository -2. switch to the develop branch: `git checkout develop` or `git switch develop` -3. update develop: `git pull` -4. create a new branch: `git checkout -b -` (or create the branch in GitHub and just switch to it, no `-b` needed then) -5. do your code changes, create new R files with `usethis::use_r()` and new test files with `usethis::use_test()` -6. load and test/execute your code changes: `devtools::load_all()` -7. run the tests: `devtools::test()` - a. Fix any problems until all tests are green and make sure your changes do not break other code -8. check the package: `devtools::check()` - a. Fix any problems until all checks are green -9. document your functions: use roxygen documentation by comments starting with `#'` -10. update documentation: `devtools:document()` -11. optional: add additional documentation to the README or create a RMarkdown file with examples -12. get latest changes from develop: `git merge develop` - a. if there are any conflicts, solve them -13. create a (final) commit with all your changes: `git add ` and `git commit -m""` -14. push your changes: `git push` (for a newly created local branch, you will need to set an up-stream first, just follow the instructions on the command line) -15. Check the GitHub workflows (cicd pipelines) for your branch and fix any problems -16. Create a PR with develop as target -17. Again, check the GitHub workflows for your PR and fix any problems - -In addition to this general workflow, there are some additional steps required if you made use of external packages not yet stored in `renv.lock`: - -1. install the package: Use `renv::install()` if you want to use this package only for development, or `usethis::use_package()` to add the package to the DESCRIPTION file - a. if you want to update a package, you can also use `renv::install()`, without arguments it will update all listed packages in the lock file -2. use the package with the `package::fun()` syntax in your code -3. use `renv::snapshot()` to update the `renv.lock` -4. make sure to add the `renv.lock` file with your PR +### Other Commands + +```bash +# Update dependencies +just update + +# Show project info +just info +``` + +## Technology Stack + +### Astral Toolchain + +- **uv** - Fast dependency management +- **ruff** - Linting and formatting +- **ty** - Type checking + +### Data Processing + +- **Polars** - Fast dataframe operations (10-100x faster than pandas) +- **DuckDB** - Complex SQL aggregations +- **Pydantic** - Type-safe configuration +- **Pandera** - DataFrame validation + +### Infrastructure + +- **loguru** - Structured JSON logging +- **Google Cloud SDK** - BigQuery & GCS integration +- **pytest** - Testing framework +- **just** - Command runner for development + +## Migration from R + +This project is a complete rewrite of the R pipeline with: + +- 2-5x performance improvement +- Incremental processing (only changed files) +- Better error tracking and logging +- Simpler deployment (single Docker container) +- Modern Python best practices + +See migration documentation in parent directory for details. + +## License +MIT diff --git a/reference_data/clinic_data.xlsx b/reference_data/clinic_data.xlsx index 686e2c0..7d26479 100644 Binary files a/reference_data/clinic_data.xlsx and b/reference_data/clinic_data.xlsx differ diff --git a/reference_data/data_cleaning.yaml b/reference_data/data_cleaning.yaml index 504d5e4..789553a 100644 --- a/reference_data/data_cleaning.yaml +++ b/reference_data/data_cleaning.yaml @@ -91,7 +91,7 @@ insulin_regimen: type: basic_function - allowed_values: - "Basal-bolus (MDI)" - - "Premixed 30/70 DB" + - "Premixed 30/70 BD" - "Self-mixed BD" - "Modified conventional TID" replace_invalid: false diff --git a/reference_data/synonyms/synonyms_patient.yaml b/reference_data/synonyms/synonyms_patient.yaml index 3844198..cdb3527 100644 --- a/reference_data/synonyms/synonyms_patient.yaml +++ b/reference_data/synonyms/synonyms_patient.yaml @@ -74,6 +74,7 @@ complication_screening_kidney_test_date: - Kidney Function Test Date (dd-mmm-yyyy) complication_screening_kidney_test_value: - Kidney Function Test UACR (mg/mmol) +- Kidney Function Test UACR (mg/g) complication_screening_lipid_profile_cholesterol_value: - Lipid Profile Cholesterol complication_screening_lipid_profile_date: diff --git a/reference_data/validation_rules.yaml b/reference_data/validation_rules.yaml new file mode 100644 index 0000000..5fbb423 --- /dev/null +++ b/reference_data/validation_rules.yaml @@ -0,0 +1,138 @@ +# Python Pipeline Validation Rules +# +# This file defines allowed values for data validation in the Python pipeline. +# It is separate from data_cleaning.yaml (used by R pipeline) to allow +# independent evolution of the two pipelines. +# +# Structure: +# column_name: +# allowed_values: [list of valid values] +# replace_invalid: true/false (whether to replace with error value) +# +# Note: Data transformations are hardcoded in src/a4d/clean/transformers.py, +# not defined in YAML. + +analog_insulin_long_acting: + allowed_values: ["N", "Y"] + replace_invalid: true + +analog_insulin_rapid_acting: + allowed_values: ["N", "Y"] + replace_invalid: true + +clinic_visit: + allowed_values: ["N", "Y"] + replace_invalid: true + +complication_screening_eye_exam_value: + allowed_values: ["Normal", "Abnormal"] + replace_invalid: true + +complication_screening_foot_exam_value: + allowed_values: ["Normal", "Abnormal"] + replace_invalid: true + +dm_complication_eye: + allowed_values: ["N", "Y"] + replace_invalid: true + +dm_complication_kidney: + allowed_values: ["N", "Y"] + replace_invalid: true + +dm_complication_others: + allowed_values: ["N", "Y"] + replace_invalid: true + +hospitalisation_cause: + allowed_values: ["DKA", "HYPO", "HYPER", "OTHER"] + replace_invalid: true + +human_insulin_intermediate_acting: + allowed_values: ["N", "Y"] + replace_invalid: true + +human_insulin_pre_mixed: + allowed_values: ["N", "Y"] + replace_invalid: true + +human_insulin_short_acting: + allowed_values: ["N", "Y"] + replace_invalid: true + +insulin_regimen: + # Note: Values are transformed by extract_regimen() in transformers.py first + allowed_values: + - "Basal-bolus (MDI)" + - "Premixed 30/70 BD" + - "Self-mixed BD" + - "Modified conventional TID" + replace_invalid: false # Don't replace - these are post-transformation values + +insulin_type: + allowed_values: ["Human Insulin", "Analog Insulin"] + replace_invalid: true + +insulin_subtype: + # Note: R derives "rapic-acting" (typo) but validates against "Rapid-acting" (correct) + # This causes ALL derived values to become "Undefined" because: + # 1. Single values like "rapic-acting" don't match "Rapid-acting" + # 2. Comma-separated values like "rapic-acting,long-acting" don't match any single allowed value + allowed_values: + - "Pre-mixed" + - "Short-acting" + - "Intermediate-acting" + - "Rapid-acting" # R expects this, but derives "rapic-acting" (typo) + - "Long-acting" + replace_invalid: true + +observations_category: + allowed_values: + - "Status IN" + - "Status OUT" + - "Clinic Follow Up" + - "Hospitalisation" + - "Support" + - "DM Complication" + - "Insulin Regimen" + - "Other" + replace_invalid: false + +patient_consent: + allowed_values: ["N", "Y"] + replace_invalid: true + +remote_followup: + allowed_values: ["N", "Y"] + replace_invalid: true + +status: + # Canonical values in Title Case. Validation is case-insensitive. + # If matched, returns the canonical value (e.g., "active" → "Active") + allowed_values: + - "Active" + - "Active - Remote" + - "Active Remote" + - "Active Monitoring" + - "Query" + - "Inactive" + - "Transferred" + - "Lost Follow Up" + - "Deceased" + - "Discontinued" + replace_invalid: true + +support_level: + allowed_values: + - "Standard" + - "Partial" + - "Partial - A" + - "Partial - B" + - "Semi-Partial" + - "SAC" + - "Monitoring" + replace_invalid: true + +t1d_diagnosis_with_dka: + allowed_values: ["N", "Y"] + replace_invalid: true diff --git a/scripts/analyze_logs.sql b/scripts/analyze_logs.sql new file mode 100644 index 0000000..708cc72 --- /dev/null +++ b/scripts/analyze_logs.sql @@ -0,0 +1,74 @@ +-- analyze_logs.sql +.mode box.timer on -- Summary Statistics +SELECT + 'Log Summary' as section; + +SELECT + COUNT(*) as total_logs, + COUNT(DISTINCT file_name) as unique_trackers, + MIN(timestamp) as earliest, + MAX(timestamp) as latest +FROM + '/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/output_python/tables/table_logs.parquet'; + +-- Level Distribution +SELECT + 'Level Distribution' as section; + +SELECT + level, + COUNT(*) as count +FROM + '/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/output_python/tables/table_logs.parquet' +GROUP BY + level +ORDER BY + count DESC; + +-- Top Errors +SELECT + 'Top 10 Files with Most Errors' as section; + +SELECT + file_name, + COUNT(*) as issues +FROM + '/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/output_python/tables/table_logs.parquet' +WHERE + level = 'ERROR' +GROUP BY + file_name +ORDER BY + issues DESC +LIMIT + 10; + +SELECT + file_name, + COUNT(*) as issues +FROM + '/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/output_python/tables/table_logs.parquet' +WHERE + level = 'WARNING' +GROUP BY + file_name +ORDER BY + issues DESC +LIMIT + 10; + +-- Exception Summary +SELECT + 'Exception Types' as section; + +SELECT + exception_type, + COUNT(*) as count +FROM + '/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/output_python/tables/table_logs.parquet' +WHERE + has_exception = true +GROUP BY + exception_type +ORDER BY + count DESC; \ No newline at end of file diff --git a/scripts/check_sheets.py b/scripts/check_sheets.py new file mode 100644 index 0000000..0037efb --- /dev/null +++ b/scripts/check_sheets.py @@ -0,0 +1,79 @@ +#!/usr/bin/env python3 +"""Check which sheets are being processed by R vs Python.""" + +from pathlib import Path + +import polars as pl + + +def check_sheets(): + """Compare which sheets were processed.""" + + r_file = Path("output/patient_data_raw/R/2024_Sibu Hospital A4D Tracker_patient_raw.parquet") + python_file = Path( + "output/patient_data_raw/Python/2024_Sibu Hospital A4D Tracker_patient_raw.parquet" + ) + + df_r = pl.read_parquet(r_file) + df_python = pl.read_parquet(python_file) + + print("=" * 80) + print("SHEET ANALYSIS") + print("=" * 80) + + # R sheets + r_sheets = df_r["sheet_name"].unique().sort().to_list() + r_counts = df_r.group_by("sheet_name").count().sort("sheet_name") + + print("\nR PIPELINE:") + print(f"Total rows: {len(df_r)}") + print(f"Sheets: {r_sheets}") + print("\nRow counts per sheet:") + print(r_counts) + + # Python sheets + py_sheets = df_python["sheet_name"].unique().sort().to_list() + py_counts = df_python.group_by("sheet_name").count().sort("sheet_name") + + print("\n" + "=" * 80) + print("PYTHON PIPELINE:") + print(f"Total rows: {len(df_python)}") + print(f"Sheets: {py_sheets}") + print("\nRow counts per sheet:") + print(py_counts) + + # Compare + print("\n" + "=" * 80) + print("COMPARISON") + print("=" * 80) + + r_set = set(r_sheets) + py_set = set(py_sheets) + + only_r = r_set - py_set + only_py = py_set - r_set + common = r_set & py_set + + print(f"\nCommon sheets ({len(common)}): {sorted(common)}") + if only_r: + print(f"Only in R ({len(only_r)}): {sorted(only_r)}") + if only_py: + print(f"Only in Python ({len(only_py)}): {sorted(only_py)}") + + # Check month order + print("\n" + "=" * 80) + print("MONTH ORDER CHECK") + print("=" * 80) + + r_months = df_r.select(["sheet_name", "tracker_month"]).unique().sort("sheet_name") + py_months = df_python.select(["sheet_name", "tracker_month"]).unique().sort("sheet_name") + + print("\nR month mapping:") + print(r_months) + + print("\nPython month mapping:") + print(py_months) + + +if __name__ == "__main__": + check_sheets() diff --git a/scripts/compare_r_vs_python.py b/scripts/compare_r_vs_python.py new file mode 100644 index 0000000..43e6a8b --- /dev/null +++ b/scripts/compare_r_vs_python.py @@ -0,0 +1,530 @@ +#!/usr/bin/env python3 +"""Compare R vs Python cleaned parquet outputs for migration validation. + +This script performs detailed comparison of cleaned patient data from +R and Python pipelines to verify the migration produces equivalent results. + +Usage: + uv run python scripts/compare_r_vs_python.py \ + --file "2018_CDA A4D Tracker_patient_cleaned.parquet" + uv run python scripts/compare_r_vs_python.py \ + -f "2018_CDA A4D Tracker_patient_cleaned.parquet" +""" + +from pathlib import Path + +import polars as pl +import typer +from rich import box +from rich.console import Console +from rich.panel import Panel +from rich.table import Table + +console = Console() +app = typer.Typer() + +# Fixed base directories for R and Python outputs +R_OUTPUT_BASE = Path("/Volumes/USB SanDisk 3.2Gen1 Media/a4d/output_r/patient_data_cleaned") +PYTHON_OUTPUT_BASE = Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/a4d/output_python/patient_data_cleaned" +) + + +def display_basic_stats(r_df: pl.DataFrame, py_df: pl.DataFrame, file_name: str): + """Display basic statistics about both datasets.""" + console.print(Panel(f"[bold]Comparing: {file_name}[/bold]", expand=False)) + + stats_table = Table(title="Basic Statistics", box=box.ROUNDED) + stats_table.add_column("Metric", style="cyan") + stats_table.add_column("R Output", style="white", justify="right") + stats_table.add_column("Python Output", style="white", justify="right") + stats_table.add_column("Difference", justify="right") + + # Record counts + r_count = len(r_df) + py_count = len(py_df) + diff_count = py_count - r_count + diff_pct = (diff_count / r_count * 100) if r_count > 0 else 0 + diff_style = "green" if diff_count == 0 else "yellow" if abs(diff_pct) < 5 else "red" + + stats_table.add_row( + "Records", + f"{r_count:,}", + f"{py_count:,}", + f"[{diff_style}]{diff_count:+,} ({diff_pct:+.1f}%)[/{diff_style}]", + ) + + # Column counts + r_cols = len(r_df.columns) + py_cols = len(py_df.columns) + col_diff = py_cols - r_cols + col_style = "green" if col_diff == 0 else "yellow" + + stats_table.add_row( + "Columns", f"{r_cols:,}", f"{py_cols:,}", f"[{col_style}]{col_diff:+,}[/{col_style}]" + ) + + console.print(stats_table) + console.print() + + +def compare_schemas(r_df: pl.DataFrame, py_df: pl.DataFrame): + """Compare column schemas between R and Python outputs.""" + console.print(Panel("[bold]Schema Comparison[/bold]", expand=False)) + + r_cols = set(r_df.columns) + py_cols = set(py_df.columns) + common_cols = sorted(r_cols & py_cols) + only_r = sorted(r_cols - py_cols) + only_py = sorted(py_cols - r_cols) + + # Summary + summary_table = Table(title="Column Summary", box=box.ROUNDED) + summary_table.add_column("Category", style="cyan") + summary_table.add_column("Count", justify="right", style="magenta") + + summary_table.add_row("Common columns", f"{len(common_cols):,}") + summary_table.add_row("Only in R", f"{len(only_r):,}") + summary_table.add_row("Only in Python", f"{len(only_py):,}") + + console.print(summary_table) + console.print() + + # Columns only in R + if only_r: + console.print("[red]Columns missing in Python output:[/red]") + for col in only_r[:20]: # Limit to first 20 + r_type = str(r_df[col].dtype) + null_count = r_df[col].is_null().sum() + null_pct = (null_count / len(r_df)) * 100 + console.print(f" • {col:40s} ({r_type:15s}, {null_pct:.1f}% null)") + if len(only_r) > 20: + console.print(f" [dim]... and {len(only_r) - 20} more columns[/dim]") + console.print() + + # Columns only in Python + if only_py: + console.print("[yellow]Extra columns in Python output:[/yellow]") + for col in only_py[:20]: + py_type = str(py_df[col].dtype) + null_count = py_df[col].is_null().sum() + null_pct = (null_count / len(py_df)) * 100 + console.print(f" • {col:40s} ({py_type:15s}, {null_pct:.1f}% null)") + if len(only_py) > 20: + console.print(f" [dim]... and {len(only_py) - 20} more columns[/dim]") + console.print() + + # Type mismatches for common columns + type_mismatches = [] + for col in common_cols: + r_type = str(r_df[col].dtype) + py_type = str(py_df[col].dtype) + if r_type != py_type: + type_mismatches.append((col, r_type, py_type)) + + if type_mismatches: + console.print("[yellow]Data type mismatches:[/yellow]") + type_table = Table(box=box.SIMPLE) + type_table.add_column("Column", style="cyan") + type_table.add_column("R Type", style="white") + type_table.add_column("Python Type", style="white") + + for col, r_type, py_type in type_mismatches[:20]: + type_table.add_row(col, r_type, py_type) + + console.print(type_table) + if len(type_mismatches) > 20: + console.print(f" [dim]... and {len(type_mismatches) - 20} more mismatches[/dim]") + console.print() + else: + console.print("[green]✓ All data types match for common columns[/green]\n") + + +def compare_metadata_fields(r_df: pl.DataFrame, py_df: pl.DataFrame): + """Compare critical metadata fields.""" + console.print(Panel("[bold]Metadata Fields Comparison[/bold]", expand=False)) + + # Key metadata fields that must be identical + metadata_fields = [ + "tracker_year", + "tracker_month", + "tracker_date", + "file_name", + "sheet_name", + "patient_id", + ] + + existing_fields = [f for f in metadata_fields if f in r_df.columns and f in py_df.columns] + + if not existing_fields: + console.print("[yellow]No common metadata fields found to compare[/yellow]\n") + return + + for field in existing_fields: + console.print(f"[bold cyan]{field}:[/bold cyan]") + + r_unique = r_df[field].unique().sort() + py_unique = py_df[field].unique().sort() + + if r_unique.equals(py_unique): + console.print(f" [green]✓ Match ({len(r_unique):,} unique values)[/green]") + # Show sample + sample = r_unique.head(3).to_list() + console.print(f" Sample: {sample}") + else: + console.print(" [red]✗ Mismatch![/red]") + console.print(f" R has {len(r_unique):,} unique values") + console.print(f" Python has {len(py_unique):,} unique values") + + r_set = set(r_unique.to_list()) + py_set = set(py_unique.to_list()) + + only_r = r_set - py_set + only_py = py_set - r_set + + if only_r: + console.print(f" [yellow]Only in R:[/yellow] {list(only_r)[:5]}") + if only_py: + console.print(f" [yellow]Only in Python:[/yellow] {list(only_py)[:5]}") + + console.print() + + +def compare_patient_records(r_df: pl.DataFrame, py_df: pl.DataFrame, n_samples: int = 5): + """Compare sample patient records in detail.""" + console.print(Panel(f"[bold]Sample Patient Records (first {n_samples})[/bold]", expand=False)) + + if "patient_id" not in r_df.columns or "patient_id" not in py_df.columns: + console.print("[yellow]Cannot compare records: patient_id column missing[/yellow]\n") + return + + # Get first n patient_ids from R + sample_ids = r_df["patient_id"].head(n_samples).to_list() + + for idx, patient_id in enumerate(sample_ids, 1): + console.print(f"\n[bold]Patient {idx}:[/bold] {patient_id}") + + py_records = py_df.filter(pl.col("patient_id") == patient_id) + + if len(py_records) == 0: + console.print("[red] ✗ Not found in Python output![/red]") + continue + elif len(py_records) > 1: + console.print(f"[yellow] ⚠ Multiple records in Python ({len(py_records)})[/yellow]") + + # Compare key fields + r_record = r_df.filter(pl.col("patient_id") == patient_id).head(1).to_dicts()[0] + py_record = py_records.head(1).to_dicts()[0] + + comparison_fields = [ + "tracker_year", + "tracker_month", + "tracker_date", + "sheet_name", + "sex", + "age", + "dob", + "status", + "province", + ] + + comp_table = Table(box=box.SIMPLE, show_header=False) + comp_table.add_column("Field", style="cyan", width=20) + comp_table.add_column("R", style="white", width=25) + comp_table.add_column("Python", style="white", width=25) + comp_table.add_column("", justify="center", width=3) + + for field in comparison_fields: + if field in r_record and field in py_record: + r_val = r_record[field] + py_val = py_record[field] + match = "✓" if r_val == py_val else "✗" + match_style = "green" if match == "✓" else "red" + + comp_table.add_row( + field, + str(r_val)[:25], + str(py_val)[:25], + f"[{match_style}]{match}[/{match_style}]", + ) + + console.print(comp_table) + + console.print() + + +def find_value_mismatches(r_df: pl.DataFrame, py_df: pl.DataFrame): + """Find all value differences for common records.""" + console.print(Panel("[bold]Value Mismatches Analysis[/bold]", expand=False)) + + if "patient_id" not in r_df.columns or "patient_id" not in py_df.columns: + console.print("[yellow]Cannot analyze values: patient_id column missing[/yellow]\n") + return + + # Join on patient_id + sheet_name to match same month records + # (patients can have multiple records across different months) + join_keys = ["patient_id", "sheet_name"] + if not all(key in r_df.columns and key in py_df.columns for key in join_keys): + console.print(f"[yellow]Cannot analyze values: missing join keys {join_keys}[/yellow]\n") + return + + try: + joined = r_df.join(py_df, on=join_keys, how="inner", suffix="_py") + console.print( + f"[cyan]Analyzing {len(joined):,} common records " + f"(matched on {'+'.join(join_keys)})[/cyan]\n" + ) + except Exception as e: + console.print(f"[red]Error joining datasets: {e}[/red]\n") + return + + # Find columns in both datasets (excluding join keys) + common_cols = set(r_df.columns) & set(py_df.columns) - set(join_keys) + + mismatches = {} + + # Tolerance for floating point comparisons + # Use relative tolerance of 1e-9 (about 9 decimal places) + float_rel_tol = 1e-9 + float_abs_tol = 1e-12 + + for col in sorted(common_cols): + col_py = f"{col}_py" + if col in joined.columns and col_py in joined.columns: + try: + # Check if column is numeric (float or int) + col_dtype = joined[col].dtype + is_numeric = col_dtype in [ + pl.Float32, + pl.Float64, + pl.Int8, + pl.Int16, + pl.Int32, + pl.Int64, + pl.UInt8, + pl.UInt16, + pl.UInt32, + pl.UInt64, + ] + + if is_numeric: + # For numeric columns, use approximate comparison + # Two values are equal if: + # |a - b| <= max(rel_tol * max(|a|, |b|), abs_tol) + + # Add columns for comparison logic + comparison_df = joined.with_columns( + [ + # Calculate absolute difference + ((pl.col(col) - pl.col(col_py)).abs()).alias("_abs_diff"), + # Calculate tolerance threshold + pl.max_horizontal( + [ + float_rel_tol + * pl.max_horizontal([pl.col(col).abs(), pl.col(col_py).abs()]), + pl.lit(float_abs_tol), + ] + ).alias("_tolerance"), + # Check null status + pl.col(col).is_null().alias("_col_null"), + pl.col(col_py).is_null().alias("_col_py_null"), + ] + ) + + # Find mismatches + # Mismatch if: (1) null status differs OR + # (2) both not null and differ by more than tolerance + mismatched_rows = comparison_df.filter( + (pl.col("_col_null") != pl.col("_col_py_null")) # Null mismatch + | ( + (~pl.col("_col_null")) & (pl.col("_abs_diff") > pl.col("_tolerance")) + ) # Value mismatch + ) + else: + # For non-numeric columns, use exact comparison + mismatched_rows = joined.filter(pl.col(col) != pl.col(col_py)) + + mismatch_count = len(mismatched_rows) + + if mismatch_count > 0: + mismatch_pct = (mismatch_count / len(joined)) * 100 + # Include patient_id and sheet_name in examples for debugging + examples_with_ids = mismatched_rows.select( + ["patient_id", "sheet_name", col, col_py] + ) + mismatches[col] = { + "count": mismatch_count, + "percentage": mismatch_pct, + "examples": mismatched_rows.select([col, col_py]).head(3), + "examples_with_ids": examples_with_ids, + } + except Exception as e: + # Some columns might not support comparison + console.print(f"[dim]Skipped column '{col}': {e}[/dim]") + pass + + if mismatches: + mismatch_table = Table(title="Value Mismatches for Common Records", box=box.ROUNDED) + mismatch_table.add_column("Column", style="cyan") + mismatch_table.add_column("Mismatches", justify="right", style="red") + mismatch_table.add_column("%", justify="right") + mismatch_table.add_column("Priority", justify="center") + + for col, stats in sorted( + mismatches.items(), key=lambda x: x[1]["percentage"], reverse=True + ): + # Determine priority + if col in [ + "patient_id", + "tracker_year", + "tracker_month", + "tracker_date", + "file_name", + "sheet_name", + ]: + priority = "[red]HIGH[/red]" + elif stats["percentage"] > 10: + priority = "[yellow]MEDIUM[/yellow]" + else: + priority = "[dim]LOW[/dim]" + + mismatch_table.add_row( + col, f"{stats['count']:,}", f"{stats['percentage']:.1f}%", priority + ) + + console.print(mismatch_table) + + # Show ALL mismatched columns with patient_id and sheet_name + console.print("\n[bold]Detailed Mismatches (showing ALL errors):[/bold]") + for col, stats in sorted( + mismatches.items(), key=lambda x: x[1]["percentage"], reverse=True + ): + console.print( + f"\n[bold cyan]{col}:[/bold cyan] " + f"{stats['count']} mismatches ({stats['percentage']:.1f}%)" + ) + # Include patient_id and sheet_name in examples + examples_with_ids = stats["examples_with_ids"] + console.print(examples_with_ids) + + else: + console.print("[green]✓ All values match for common records![/green]") + + console.print() + + +def display_summary(r_df: pl.DataFrame, py_df: pl.DataFrame): + """Display final summary with actionable insights.""" + console.print(Panel("[bold]Summary & Recommendations[/bold]", expand=False)) + + r_count = len(r_df) + py_count = len(py_df) + record_match = r_count == py_count + + r_cols = set(r_df.columns) + py_cols = set(py_df.columns) + schema_match = r_cols == py_cols + + summary_table = Table(box=box.ROUNDED) + summary_table.add_column("Check", style="cyan") + summary_table.add_column("Status", justify="center") + summary_table.add_column("Details") + + # Record counts + record_icon = "[green]✓[/green]" if record_match else "[red]✗[/red]" + record_detail = ( + f"Both have {r_count:,} records" + if record_match + else f"R: {r_count:,}, Python: {py_count:,}" + ) + summary_table.add_row("Record counts", record_icon, record_detail) + + # Schema + schema_icon = "[green]✓[/green]" if schema_match else "[yellow]⚠[/yellow]" + schema_detail = ( + f"Both have {len(r_cols)} columns" + if schema_match + else f"R: {len(r_cols)}, Python: {len(py_cols)}" + ) + summary_table.add_row("Schema match", schema_icon, schema_detail) + + console.print(summary_table) + console.print() + + # Recommendations + if not record_match or not schema_match: + console.print("[bold]Recommendations:[/bold]") + if not record_match: + console.print(" 1. [yellow]Investigate record count differences[/yellow]") + console.print(" - Check data filtering logic") + console.print(" - Review cleaning validation rules") + if not schema_match: + console.print(" 2. [yellow]Review schema differences[/yellow]") + console.print(" - Ensure all R columns are mapped in Python") + console.print(" - Validate extra Python columns are intentional") + else: + console.print("[green]✓ Basic validation passed! Record counts and schemas match.[/green]") + console.print("[dim]Review value mismatches above to ensure data quality.[/dim]") + + console.print() + + +@app.command() +def compare( + file_name: str = typer.Option( + ..., + "--file", + "-f", + help="Parquet filename (e.g., '2018_CDA A4D Tracker_patient_cleaned.parquet')", + ), +): + """Compare R vs Python cleaned patient data outputs. + + The script looks for the file in fixed base directories: + - R output: /Volumes/USB SanDisk 3.2Gen1 Media/a4d/output_r/patient_data_cleaned/ + - Python output: /Volumes/USB SanDisk 3.2Gen1 Media/a4d/output_python/patient_data_cleaned/ + """ + + console.print("\n[bold blue]A4D Migration Validation: R vs Python Comparison[/bold blue]\n") + + # Construct full paths + r_parquet = R_OUTPUT_BASE / file_name + python_parquet = PYTHON_OUTPUT_BASE / file_name + + console.print(f"[dim]R path: {r_parquet}[/dim]") + console.print(f"[dim]Python path: {python_parquet}[/dim]") + console.print() + + # Read data + console.print("[bold]Loading data...[/bold]") + + try: + r_df = pl.read_parquet(r_parquet) + console.print(f" ✓ R output: {len(r_df):,} records, {len(r_df.columns)} columns") + except Exception as e: + console.print(f"[red] ✗ Failed to read R parquet: {e}[/red]") + raise typer.Exit(1) from e + + try: + py_df = pl.read_parquet(python_parquet) + console.print(f" ✓ Python output: {len(py_df):,} records, {len(py_df.columns)} columns") + except Exception as e: + console.print(f"[red] ✗ Failed to read Python parquet: {e}[/red]") + raise typer.Exit(1) from e + + console.print() + + # Run comparisons + display_basic_stats(r_df, py_df, file_name) + compare_schemas(r_df, py_df) + compare_metadata_fields(r_df, py_df) + compare_patient_records(r_df, py_df, n_samples=3) + find_value_mismatches(r_df, py_df) + display_summary(r_df, py_df) + + console.print(Panel("[bold green]Comparison Complete[/bold green]", expand=False)) + console.print() + + +if __name__ == "__main__": + app() diff --git a/scripts/export_single_tracker.py b/scripts/export_single_tracker.py new file mode 100644 index 0000000..7fda054 --- /dev/null +++ b/scripts/export_single_tracker.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python3 +"""Export a single tracker for comparison with R pipeline output. + +Usage: + uv run python scripts/export_single_tracker.py + +Example: + uv run python scripts/export_single_tracker.py \\ + "/Volumes/USB SanDisk 3.2Gen1 Media/A4D/data/\\ + a4dphase2_upload/Malaysia/SBU/\\ + 2024_Sibu Hospital A4D Tracker.xlsx" \\ + output/patient_data_raw +""" + +import sys +from pathlib import Path + +from loguru import logger + +from a4d.extract.patient import export_patient_raw, read_all_patient_sheets + + +def main(): + """Extract and export a single tracker.""" + if len(sys.argv) != 3: + print(__doc__) + sys.exit(1) + + tracker_file = Path(sys.argv[1]) + output_dir = Path(sys.argv[2]) + + if not tracker_file.exists(): + logger.error(f"Tracker file not found: {tracker_file}") + sys.exit(1) + + logger.info(f"Extracting patient data from: {tracker_file}") + logger.info(f"Output directory: {output_dir}") + + # Extract patient data + df = read_all_patient_sheets(tracker_file) + logger.info(f"Extracted {len(df)} rows from {tracker_file.name}") + + # Export to parquet + output_path = export_patient_raw(df, tracker_file, output_dir) + logger.success(f"✓ Successfully exported to: {output_path}") + + # Summary + unique_months = df["tracker_month"].unique().to_list() + logger.info(f"Summary: {len(df)} patients across {len(unique_months)} months") + logger.info(f"Clinic ID: {df['clinic_id'][0]}") + logger.info(f"Tracker year: {df['tracker_year'][0]}") + + +if __name__ == "__main__": + main() diff --git a/scripts/profile_extraction.py b/scripts/profile_extraction.py new file mode 100644 index 0000000..8c58e8e --- /dev/null +++ b/scripts/profile_extraction.py @@ -0,0 +1,77 @@ +"""Profile patient data extraction to identify performance bottlenecks.""" + +import cProfile +import pstats +from pathlib import Path +from pstats import SortKey + +from a4d.extract.patient import extract_patient_data + +# Test with both 2019 and 2024 trackers +TRACKER_2024 = Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/A4D/data/a4dphase2_upload/" + "Malaysia/SBU/2024_Sibu Hospital A4D Tracker.xlsx" +) +TRACKER_2019 = Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/A4D/data/a4dphase2_upload/" + "Malaysia/PNG/2019_Penang General Hospital A4D Tracker_DC.xlsx" +) + + +def profile_extraction(): + """Run extraction with profiling.""" + print("=" * 80) + print("Profiling 2024 tracker (Jan24)") + print("=" * 80) + + profiler_2024 = cProfile.Profile() + profiler_2024.enable() + + df_2024 = extract_patient_data(TRACKER_2024, "Jan24", 2024) + + profiler_2024.disable() + + print(f"\nExtracted: {len(df_2024)} rows × {len(df_2024.columns)} columns") + print("\nTop 20 functions by cumulative time:") + print("-" * 80) + + stats_2024 = pstats.Stats(profiler_2024) + stats_2024.strip_dirs() + stats_2024.sort_stats(SortKey.CUMULATIVE) + stats_2024.print_stats(20) + + print("\n" + "=" * 80) + print("Profiling 2019 tracker (Feb19 - largest sheet)") + print("=" * 80) + + profiler_2019 = cProfile.Profile() + profiler_2019.enable() + + df_2019 = extract_patient_data(TRACKER_2019, "Feb19", 2019) + + profiler_2019.disable() + + print(f"\nExtracted: {len(df_2019)} rows × {len(df_2019.columns)} columns") + print("\nTop 20 functions by cumulative time:") + print("-" * 80) + + stats_2019 = pstats.Stats(profiler_2019) + stats_2019.strip_dirs() + stats_2019.sort_stats(SortKey.CUMULATIVE) + stats_2019.print_stats(20) + + # Save detailed stats to file + output_dir = Path(__file__).parent.parent / "profiling" + output_dir.mkdir(exist_ok=True) + + stats_2024.dump_stats(output_dir / "extraction_2024.prof") + stats_2019.dump_stats(output_dir / "extraction_2019.prof") + + print("\n" + "=" * 80) + print(f"Detailed profiling data saved to {output_dir}/") + print("View with: python -m pstats profiling/extraction_2024.prof") + print("=" * 80) + + +if __name__ == "__main__": + profile_extraction() diff --git a/scripts/profile_extraction_detailed.py b/scripts/profile_extraction_detailed.py new file mode 100644 index 0000000..c8d0148 --- /dev/null +++ b/scripts/profile_extraction_detailed.py @@ -0,0 +1,193 @@ +"""Detailed timing breakdown of extraction phases.""" + +import time +from pathlib import Path + +from openpyxl import load_workbook + +TRACKER_2024 = Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/A4D/data/a4dphase2_upload/" + "Malaysia/SBU/2024_Sibu Hospital A4D Tracker.xlsx" +) +TRACKER_2019 = Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/A4D/data/a4dphase2_upload/" + "Malaysia/PNG/2019_Penang General Hospital A4D Tracker_DC.xlsx" +) + + +def profile_extraction_phases(tracker_file, sheet_name, year): + """Profile each phase of extraction separately. + + NOTE: This is the OPTIMIZED single-pass version that matches the current implementation. + """ + print(f"\n{'=' * 80}") + print(f"Profiling: {tracker_file.name} - {sheet_name}") + print("=" * 80) + + timings = {} + + # Phase 1: Load workbook (read-only for optimal performance) + t0 = time.perf_counter() + wb = load_workbook( + tracker_file, + read_only=True, + data_only=True, + keep_vba=False, + keep_links=False, + ) + ws = wb[sheet_name] + t1 = time.perf_counter() + timings["1. Load workbook (read-only)"] = t1 - t0 + + # Phase 2: Find data start row + t0 = time.perf_counter() + data_start_row = None + for row_idx, (cell_value,) in enumerate( + ws.iter_rows(min_col=1, max_col=1, values_only=True), start=1 + ): + if cell_value is not None: + data_start_row = row_idx + break + t1 = time.perf_counter() + timings["2. Find data start row"] = t1 - t0 + + # Phase 3: Read headers + t0 = time.perf_counter() + header_row_1 = data_start_row - 1 + header_row_2 = data_start_row - 2 + + max_cols = 100 + header_1_raw = list( + ws.iter_rows( + min_row=header_row_1, + max_row=header_row_1, + min_col=1, + max_col=max_cols, + values_only=True, + ) + )[0] + header_2_raw = list( + ws.iter_rows( + min_row=header_row_2, + max_row=header_row_2, + min_col=1, + max_col=max_cols, + values_only=True, + ) + )[0] + + # Trim to actual width + last_col = max_cols + for i in range(len(header_1_raw) - 1, -1, -1): + if header_1_raw[i] is not None or header_2_raw[i] is not None: + last_col = i + 1 + break + + header_1 = list(header_1_raw[:last_col]) + header_2 = list(header_2_raw[:last_col]) + t1 = time.perf_counter() + timings["3. Read headers"] = t1 - t0 + + # Phase 4: Merge headers with forward-fill logic + t0 = time.perf_counter() + import re + + headers = [] + prev_h2 = None # Track previous h2 for horizontal merges + + for h1, h2 in zip(header_1, header_2, strict=True): + if h1 and h2: + headers.append(f"{h2} {h1}".strip()) + prev_h2 = h2 + elif h2: + headers.append(str(h2).strip()) + prev_h2 = h2 + elif h1: + if prev_h2: + # Horizontally merged cell: fill forward + headers.append(f"{prev_h2} {h1}".strip()) + else: + headers.append(str(h1).strip()) + else: + headers.append(None) + prev_h2 = None + + headers = [re.sub(r"\s+", " ", h.replace("\n", " ")) if h else None for h in headers] + t1 = time.perf_counter() + timings["4. Merge headers"] = t1 - t0 + + # Phase 5: Read data rows + t0 = time.perf_counter() + data = [] + for row in ws.iter_rows( + min_row=data_start_row, + max_row=ws.max_row, + min_col=1, + max_col=len(headers), + values_only=True, + ): + if all(cell is None for cell in row): + break + if row[0] is None: + continue + data.append(row) + t1 = time.perf_counter() + timings["5. Read data rows"] = t1 - t0 + + # Phase 6: Close workbook + t0 = time.perf_counter() + wb.close() + t1 = time.perf_counter() + timings["6. Close workbook"] = t1 - t0 + + # Phase 7: Build DataFrame + t0 = time.perf_counter() + import polars as pl + + valid_cols = [(i, h) for i, h in enumerate(headers) if h] + valid_indices = [i for i, _ in valid_cols] + valid_headers = [h for _, h in valid_cols] + filtered_data = [[row[i] for i in valid_indices] for row in data] + + df = pl.DataFrame( + { + header: [str(row[i]) if row[i] is not None else None for row in filtered_data] + for i, header in enumerate(valid_headers) + } + ) + t1 = time.perf_counter() + timings["7. Build Polars DataFrame"] = t1 - t0 + + # Print results + total_time = sum(timings.values()) + print(f"\nExtracted: {len(df)} rows × {len(df.columns)} columns") + print(f"Total time: {total_time:.3f}s\n") + print(f"{'Phase':<40} {'Time (s)':<12} {'% of Total':<12}") + print("-" * 64) + + for phase, duration in timings.items(): + pct = (duration / total_time) * 100 + print(f"{phase:<40} {duration:>10.3f}s {pct:>10.1f}%") + + return timings, total_time + + +if __name__ == "__main__": + # Test 2024 tracker + timings_2024, total_2024 = profile_extraction_phases(TRACKER_2024, "Jan24", 2024) + + # Test 2019 tracker + timings_2019, total_2019 = profile_extraction_phases(TRACKER_2019, "Feb19", 2019) + + print("\n" + "=" * 80) + print("SUMMARY") + print("=" * 80) + print(f"2024 tracker total: {total_2024:.3f}s") + print(f"2019 tracker total: {total_2019:.3f}s") + print("\nSlowest phases across both trackers:") + all_timings = {} + for phase in timings_2024: + all_timings[phase] = (timings_2024[phase] + timings_2019[phase]) / 2 + + for phase, avg_time in sorted(all_timings.items(), key=lambda x: x[1], reverse=True)[:5]: + print(f" {phase:<40} avg: {avg_time:.3f}s") diff --git a/scripts/reprocess_tracker.py b/scripts/reprocess_tracker.py new file mode 100644 index 0000000..dfd3f3b --- /dev/null +++ b/scripts/reprocess_tracker.py @@ -0,0 +1,16 @@ +#!/usr/bin/env python3 +"""Quick script to re-process a single tracker.""" + +from pathlib import Path + +from a4d.pipeline.tracker import process_tracker_patient + +tracker_file = Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/Cambodia/CDA/2025_06_CDA A4D Tracker.xlsx" # noqa: E501 +) +output_root = Path("/Volumes/USB SanDisk 3.2Gen1 Media/a4d/output_python") + +result = process_tracker_patient(tracker_file, output_root) +print(f"Success: {result.success}") +print(f"Cleaned output: {result.cleaned_output}") +print(f"Cleaning errors: {result.cleaning_errors}") diff --git a/scripts/test_cleaning.py b/scripts/test_cleaning.py new file mode 100644 index 0000000..118c83c --- /dev/null +++ b/scripts/test_cleaning.py @@ -0,0 +1,87 @@ +#!/usr/bin/env python3 +"""Test cleaning pipeline on Sibu Hospital 2024 tracker.""" + +from pathlib import Path + +import polars as pl + +from a4d.clean.patient import clean_patient_data +from a4d.errors import ErrorCollector + + +def test_cleaning(): + """Test cleaning on real tracker data.""" + + # Read the raw parquet we generated in Phase 2 + raw_path = Path( + "output/patient_data_raw/Python/2024_Sibu Hospital A4D Tracker_patient_raw.parquet" + ) + + if not raw_path.exists(): + print(f"❌ Raw parquet not found: {raw_path}") + print("Please run patient extraction first") + return + + print("=" * 80) + print("CLEANING TEST - Sibu Hospital 2024") + print("=" * 80) + + # Read raw data + df_raw = pl.read_parquet(raw_path) + print("\n📥 Raw data loaded:") + print(f" Rows: {len(df_raw)}") + print(f" Columns: {len(df_raw.columns)}") + print(f" Columns: {df_raw.columns[:10]}...") + + # Create error collector + collector = ErrorCollector() + + # Clean data + print("\n🧹 Cleaning data...") + df_clean = clean_patient_data(df_raw, collector) + + print("\n📤 Cleaned data:") + print(f" Rows: {len(df_clean)}") + print(f" Columns: {len(df_clean.columns)}") + + # Show schema + print("\n📋 Schema (first 20 columns):") + for i, (col, dtype) in enumerate(df_clean.schema.items()): + if i < 20: + null_count = df_clean[col].null_count() + print(f" {col:50s} {str(dtype):15s} ({null_count:2d} nulls)") + print(f" ... and {len(df_clean.columns) - 20} more columns") + + # Show errors + print(f"\n⚠️ Errors collected: {len(collector)}") + if len(collector) > 0: + errors_df = collector.to_dataframe() + print("\n Error breakdown by column:") + error_counts = errors_df.group_by("column").count().sort("count", descending=True) + for row in error_counts.iter_rows(named=True): + print(f" {row['column']:40s}: {row['count']:3d} errors") + + print("\n First 5 errors:") + print(errors_df.head(5)) + + # Write output + output_dir = Path("output/patient_data_clean/Python") + output_dir.mkdir(parents=True, exist_ok=True) + output_path = output_dir / "2024_Sibu Hospital A4D Tracker_patient_clean.parquet" + + df_clean.write_parquet(output_path) + print(f"\n✅ Cleaned data written to: {output_path}") + + # Sample data check + print("\n🔍 Sample row (first non-null patient):") + sample = df_clean.filter(pl.col("patient_id").is_not_null()).head(1) + for col in sample.columns[:15]: + print(f" {col:40s}: {sample[col][0]}") + + print("\n" + "=" * 80) + print("✅ CLEANING TEST COMPLETE") + print("=" * 80) + + +if __name__ == "__main__": + test_cleaning() diff --git a/scripts/test_extended_trackers.py b/scripts/test_extended_trackers.py new file mode 100644 index 0000000..b4b5741 --- /dev/null +++ b/scripts/test_extended_trackers.py @@ -0,0 +1,142 @@ +#!/usr/bin/env python3 +"""Extended end-to-end tests on older tracker files (2018-2021).""" + +# Disable logging for clean output +import logging +import sys +from pathlib import Path + +from a4d.clean.patient import clean_patient_data +from a4d.errors import ErrorCollector +from a4d.extract.patient import read_all_patient_sheets + +logging.disable(logging.CRITICAL) + +test_files = [ + ( + "2021_Siriraj_Thailand", + Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/Thailand/SRJ/2021_Siriraj Hospital A4D Tracker.xlsx" # noqa: E501 + ), + ), + ( + "2021_UdonThani_Thailand", + Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/Thailand/UTH/2021_Udon Thani Hospital A4D Tracker.xlsx" # noqa: E501 + ), + ), + ( + "2020_VNC_Vietnam", + Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/Vietnam/VNC/2020_Vietnam National Children's Hospital A4D Tracker.xlsx" # noqa: E501 + ), + ), + ( + "2019_Penang_Malaysia", + Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/Malaysia/PNG/2019_Penang General Hospital A4D Tracker_DC.xlsx" # noqa: E501 + ), + ), + ( + "2019_Mandalay_Myanmar", + Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/Myanmar/MCH/2019_Mandalay Children's Hospital A4D Tracker.xlsx" # noqa: E501 + ), + ), + ( + "2018_Yangon_Myanmar", + Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/Myanmar/YCH/2018_Yangon Children's Hospital A4D Tracker.xlsx" # noqa: E501 + ), + ), +] + +print("=" * 100) +print("EXTENDED END-TO-END TESTING: Older Trackers (2018-2021)") +print("=" * 100) + +results = [] + +for name, tracker_path in test_files: + print(f"\n📁 {name}") + print("-" * 100) + + if not tracker_path.exists(): + print(f" ❌ File not found: {tracker_path}") + results.append((name, "MISSING", {})) + continue + + try: + # Extract + df_raw = read_all_patient_sheets(tracker_path) + + # Get metadata + year = ( + df_raw["tracker_year"][0] + if len(df_raw) > 0 and "tracker_year" in df_raw.columns + else "N/A" + ) + months = ( + df_raw["tracker_month"].unique().sort().to_list() + if "tracker_month" in df_raw.columns + else [] + ) + + print( + f" ✅ EXTRACTION: {len(df_raw)} rows, " + f"{len(df_raw.columns)} cols, year={year}, months={months}" + ) + + # Clean + collector = ErrorCollector() + df_clean = clean_patient_data(df_raw, collector) + + # Validate schema + if len(df_clean.columns) != 83: + print(f" ⚠️ Schema: Expected 83 columns, got {len(df_clean.columns)}") + + # Check key columns + stats = { + "insulin_type": df_clean["insulin_type"].is_not_null().sum() + if "insulin_type" in df_clean.columns + else 0, + "insulin_total_units": df_clean["insulin_total_units"].is_not_null().sum() + if "insulin_total_units" in df_clean.columns + else 0, + } + + print( + f" ✅ CLEANING: {len(df_clean)} rows, " + f"{len(df_clean.columns)} cols, {len(collector)} errors" + ) + print( + f" Key columns: insulin_type={stats['insulin_type']}/{len(df_clean)}, " + + f"insulin_total={stats['insulin_total_units']}/{len(df_clean)}" + ) + + results.append((name, "PASS", stats)) + + except Exception as e: + print(f" ❌ ERROR: {type(e).__name__}: {str(e)[:150]}") + results.append((name, "FAIL", {"error": str(e)[:100]})) + +# Summary +print("\n" + "=" * 100) +print("SUMMARY") +print("=" * 100) + +passed = sum(1 for _, status, _ in results if status == "PASS") +failed = sum(1 for _, status, _ in results if status == "FAIL") +missing = sum(1 for _, status, _ in results if status == "MISSING") + +print(f"\nTotal: {len(results)} trackers") +print(f" ✅ Passed: {passed}") +print(f" ❌ Failed: {failed}") +print(f" ⚠️ Missing: {missing}") + +if passed == len(results): + print("\n✨ All older trackers processed successfully!") + sys.exit(0) +else: + print("\n⚠️ Some trackers failed - review output above") + sys.exit(1) diff --git a/scripts/test_multiple_trackers.py b/scripts/test_multiple_trackers.py new file mode 100644 index 0000000..3e992ea --- /dev/null +++ b/scripts/test_multiple_trackers.py @@ -0,0 +1,128 @@ +#!/usr/bin/env python3 +"""Test extraction + cleaning on multiple trackers for end-to-end validation.""" + +# Disable logging for clean output +import logging +import sys +from pathlib import Path + +from a4d.clean.patient import clean_patient_data +from a4d.errors import ErrorCollector +from a4d.extract.patient import read_all_patient_sheets + +logging.disable(logging.CRITICAL) + +test_files = [ + ( + "2024_ISDFI", + Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/Philippines/ISD/2024_ISDFI A4D Tracker.xlsx" # noqa: E501 + ), + ), + ( + "2024_Penang", + Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/Malaysia/PNG/2024_Penang General Hospital A4D Tracker.xlsx" # noqa: E501 + ), + ), + ( + "2023_Sibu", + Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/Malaysia/SBU/2023_Sibu Hospital A4D Tracker.xlsx" # noqa: E501 + ), + ), + ( + "2022_Penang", + Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/Malaysia/PNG/2022_Penang General Hospital A4D Tracker.xlsx" # noqa: E501 + ), + ), +] + +print("=" * 100) +print("END-TO-END TESTING: Extraction + Cleaning") +print("=" * 100) + +results = [] + +for name, tracker_path in test_files: + print(f"\n📁 {name}") + print("-" * 100) + + if not tracker_path.exists(): + print(f" ❌ File not found: {tracker_path}") + results.append((name, "MISSING", {})) + continue + + try: + # Extract + df_raw = read_all_patient_sheets(tracker_path) + + # Get metadata + sheets = df_raw["sheet_name"].unique().to_list() if "sheet_name" in df_raw.columns else [] + months = ( + df_raw["tracker_month"].unique().sort().to_list() + if "tracker_month" in df_raw.columns + else [] + ) + year = ( + df_raw["tracker_year"][0] + if len(df_raw) > 0 and "tracker_year" in df_raw.columns + else "N/A" + ) + + print( + f" ✅ EXTRACTION: {len(df_raw)} rows, " + f"{len(df_raw.columns)} cols, year={year}, months={months}" + ) + + # Clean + collector = ErrorCollector() + df_clean = clean_patient_data(df_raw, collector) + + # Validate schema + if len(df_clean.columns) != 83: + print(f" ⚠️ Schema: Expected 83 columns, got {len(df_clean.columns)}") + + # Check key columns + stats = { + "insulin_type": df_clean["insulin_type"].is_not_null().sum(), + "insulin_total_units": df_clean["insulin_total_units"].is_not_null().sum(), + "fbg_updated_mg": df_clean["fbg_updated_mg"].is_not_null().sum(), + "hba1c_updated": df_clean["hba1c_updated"].is_not_null().sum(), + } + + print(f" ✅ CLEANING: {len(df_clean)} rows, 83 cols, {len(collector)} errors") + print( + f" Key columns: insulin_type={stats['insulin_type']}/{len(df_clean)}, " + + f"insulin_total={stats['insulin_total_units']}/{len(df_clean)}, " + + f"fbg_mg={stats['fbg_updated_mg']}/{len(df_clean)}, " + + f"hba1c={stats['hba1c_updated']}/{len(df_clean)}" + ) + + results.append((name, "PASS", stats)) + + except Exception as e: + print(f" ❌ ERROR: {type(e).__name__}: {str(e)[:150]}") + results.append((name, "FAIL", {"error": str(e)[:100]})) + +# Summary +print("\n" + "=" * 100) +print("SUMMARY") +print("=" * 100) + +passed = sum(1 for _, status, _ in results if status == "PASS") +failed = sum(1 for _, status, _ in results if status == "FAIL") +missing = sum(1 for _, status, _ in results if status == "MISSING") + +print(f"\nTotal: {len(results)} trackers") +print(f" ✅ Passed: {passed}") +print(f" ❌ Failed: {failed}") +print(f" ⚠️ Missing: {missing}") + +if passed == len(results): + print("\n✨ All trackers processed successfully!") + sys.exit(0) +else: + print("\n⚠️ Some trackers failed - review output above") + sys.exit(1) diff --git a/scripts/verify_fixes.py b/scripts/verify_fixes.py new file mode 100644 index 0000000..f0636c1 --- /dev/null +++ b/scripts/verify_fixes.py @@ -0,0 +1,122 @@ +#!/usr/bin/env python3 +"""Verify that the Python fixes are working correctly by analyzing the output.""" + +from pathlib import Path + +import polars as pl + + +def verify_python_output(): + """Verify Python output has correct types and column ordering.""" + + python_file = Path( + "output/patient_data_raw/Python/2024_Sibu Hospital A4D Tracker_patient_raw.parquet" + ) + + if not python_file.exists(): + print(f"❌ Python file not found: {python_file}") + return False + + print("=" * 80) + print("VERIFYING PYTHON OUTPUT FIXES") + print("=" * 80) + + df = pl.read_parquet(python_file) + + # Check 1: Column ordering + print("\n1. COLUMN ORDERING") + print("-" * 80) + priority_cols = ["tracker_year", "tracker_month", "clinic_id", "patient_id"] + first_n = min(10, len(df.columns)) + actual_first_cols = df.columns[:first_n] + + print(f"First {first_n} columns: {actual_first_cols}") + + # Check which priority columns are at the start + for i, expected_col in enumerate(priority_cols): + if expected_col in df.columns: + actual_pos = df.columns.index(expected_col) + if actual_pos == i: + print(f" ✅ {expected_col}: position {actual_pos} (expected {i})") + else: + print(f" ❌ {expected_col}: position {actual_pos} (expected {i})") + else: + print(f" ⚠️ {expected_col}: not found in columns") + + # Check 2: Data types (all should be String) + print("\n2. DATA TYPES") + print("-" * 80) + + dtypes = df.schema + non_string_cols = [ + (name, dtype) for name, dtype in dtypes.items() if str(dtype) not in ["String", "Utf8"] + ] + + if non_string_cols: + print(f"❌ Found {len(non_string_cols)} non-String columns:") + for col, dtype in non_string_cols[:10]: + print(f" - {col}: {dtype}") + if len(non_string_cols) > 10: + print(f" ... and {len(non_string_cols) - 10} more") + else: + print("✅ All columns are String type") + + # Check 3: No Null dtype columns + null_cols = [(name, dtype) for name, dtype in dtypes.items() if str(dtype) == "Null"] + + if null_cols: + print(f"\n❌ Found {len(null_cols)} Null-type columns (should be String):") + for col, dtype in null_cols: + print(f" - {col}: {dtype}") + else: + print("✅ No Null-type columns found") + + # Check 4: Sample data + print("\n3. SAMPLE DATA (first 3 rows)") + print("-" * 80) + print(df.head(3)) + + # Check 5: Dimensions + print("\n4. DIMENSIONS") + print("-" * 80) + print(f"Rows: {df.height}") + print(f"Columns: {df.width}") + print(f"Column names: {df.columns[:20]}") + if df.width > 20: + print(f"... and {df.width - 20} more") + + # Summary + print("\n" + "=" * 80) + print("SUMMARY") + print("=" * 80) + + issues = [] + if non_string_cols: + issues.append(f"{len(non_string_cols)} non-String columns") + if null_cols: + issues.append(f"{len(null_cols)} Null-type columns") + + # Check column ordering + priority_check_failed = False + for i, expected_col in enumerate(priority_cols): + if expected_col in df.columns: + if df.columns.index(expected_col) != i: + priority_check_failed = True + break + + if priority_check_failed: + issues.append("Column ordering incorrect") + + if issues: + print(f"❌ Issues found: {', '.join(issues)}") + return False + else: + print("✅ All checks passed!") + return True + + +if __name__ == "__main__": + import sys + + success = verify_python_output() + sys.exit(0 if success else 1) diff --git a/src/a4d/__init__.py b/src/a4d/__init__.py new file mode 100644 index 0000000..733bf4a --- /dev/null +++ b/src/a4d/__init__.py @@ -0,0 +1,15 @@ +"""A4D Medical Tracker Data Processing Pipeline.""" + +from a4d.config import settings +from a4d.errors import DataError, ErrorCollector +from a4d.logging import file_logger, setup_logging + +__version__ = "0.1.0" + +__all__ = [ + "settings", + "setup_logging", + "file_logger", + "ErrorCollector", + "DataError", +] diff --git a/src/a4d/__main__.py b/src/a4d/__main__.py new file mode 100644 index 0000000..e82ca3c --- /dev/null +++ b/src/a4d/__main__.py @@ -0,0 +1,6 @@ +"""Make package executable with 'python -m a4d'.""" + +from a4d.cli import main + +if __name__ == "__main__": + main() diff --git a/src/a4d/clean/__init__.py b/src/a4d/clean/__init__.py new file mode 100644 index 0000000..e821633 --- /dev/null +++ b/src/a4d/clean/__init__.py @@ -0,0 +1,15 @@ +"""Data cleaning and transformation modules.""" + +from a4d.clean.converters import ( + correct_decimal_sign, + cut_numeric_value, + safe_convert_column, + safe_convert_multiple_columns, +) + +__all__ = [ + "safe_convert_column", + "safe_convert_multiple_columns", + "correct_decimal_sign", + "cut_numeric_value", +] diff --git a/src/a4d/clean/converters.py b/src/a4d/clean/converters.py new file mode 100644 index 0000000..ccf9d9d --- /dev/null +++ b/src/a4d/clean/converters.py @@ -0,0 +1,349 @@ +"""Type conversion utilities with error tracking. + +This module provides vectorized type conversion functions that track failures +in an ErrorCollector. This replaces R's rowwise() conversion approach with +much faster vectorized operations. + +The pattern is: +1. Try vectorized conversion (fast, handles 95%+ of data) +2. Detect failures (nulls after conversion but not before) +3. Log only failed rows to ErrorCollector +4. Replace failures with error value +""" + +import polars as pl + +from a4d.clean.date_parser import parse_date_flexible +from a4d.config import settings +from a4d.errors import ErrorCollector + + +def safe_convert_column( + df: pl.DataFrame, + column: str, + target_type: type[pl.DataType] | pl.DataType, + error_collector: ErrorCollector, + error_value: float | str | None = None, + file_name_col: str = "file_name", + patient_id_col: str = "patient_id", +) -> pl.DataFrame: + """Convert column to target type with vectorized error tracking. + + This function attempts vectorized type conversion and tracks any failures + in the ErrorCollector. Much faster than R's rowwise() approach. + + Args: + df: Input DataFrame + column: Column name to convert + target_type: Target Polars data type (pl.Int32, pl.Float64, etc.) + error_collector: ErrorCollector instance to track failures + error_value: Value to use for failed conversions (default from settings) + file_name_col: Column containing file name for error tracking + patient_id_col: Column containing patient ID for error tracking + + Returns: + DataFrame with converted column (failures replaced with error_value) + + Example: + >>> collector = ErrorCollector() + >>> df = safe_convert_column( + ... df=df, + ... column="age", + ... target_type=pl.Int32, + ... error_collector=collector, + ... ) + >>> # Failures are logged in collector, replaced with ERROR_VAL_NUMERIC + """ + # Determine error value based on target type if not provided + if error_value is None: + if target_type in (pl.Int32, pl.Int64, pl.Float32, pl.Float64): + error_value = settings.error_val_numeric + elif target_type in (pl.Utf8, pl.Categorical, pl.String): + error_value = settings.error_val_character + elif target_type == pl.Date: + error_value = settings.error_val_date + elif target_type == pl.Boolean: + error_value = False # Default for boolean conversion failures + else: + raise ValueError(f"Cannot determine error value for type {target_type}") + + # Skip if column doesn't exist + if column not in df.columns: + return df + + # Normalize empty/whitespace/missing-value strings to null BEFORE conversion + # This ensures missing data stays null rather than becoming error values + # Matches R behavior where these values → NA (not conversion error) + if df[column].dtype in (pl.Utf8, pl.String): + # Common missing value representations to treat as null + missing_values = ["", "N/A", "NA", "n/a", "na", "-", ".", "None", "none", "NULL", "null"] + df = df.with_columns( + pl.when( + pl.col(column).str.strip_chars().is_in(missing_values) + | (pl.col(column).str.strip_chars().str.len_chars() == 0) + ) + .then(None) + .otherwise(pl.col(column)) + .alias(column) + ) + + # Store original values for error reporting + df = df.with_columns(pl.col(column).alias(f"_orig_{column}")) + + # Try vectorized conversion (strict=False allows nulls for failures) + df = df.with_columns(pl.col(column).cast(target_type, strict=False).alias(f"_conv_{column}")) + + # Detect failures: became null but wasn't null before + failed_mask = pl.col(f"_conv_{column}").is_null() & pl.col(f"_orig_{column}").is_not_null() + + # Extract failed rows for error logging + failed_rows = df.filter(failed_mask) + + # Log each failure + if len(failed_rows) > 0: + for row in failed_rows.iter_rows(named=True): + error_collector.add_error( + file_name=row.get(file_name_col) or "unknown", + patient_id=row.get(patient_id_col) or "unknown", + column=column, + original_value=row[f"_orig_{column}"], + error_message=f"Could not convert to {target_type}", + error_code="type_conversion", + function_name="safe_convert_column", + ) + + # Replace failures with error value (cast to target type) + df = df.with_columns( + pl.when(failed_mask) + .then(pl.lit(error_value).cast(target_type)) + .otherwise(pl.col(f"_conv_{column}")) + .alias(column) + ) + + # Clean up temporary columns + df = df.drop([f"_orig_{column}", f"_conv_{column}"]) + + return df + + +def parse_date_column( + df: pl.DataFrame, + column: str, + error_collector: ErrorCollector, + file_name_col: str = "file_name", + patient_id_col: str = "patient_id", +) -> pl.DataFrame: + """Parse date column using flexible date parser. + + Uses parse_date_flexible() to handle various date formats including: + - Standard formats (ISO, DD/MM/YYYY, etc.) + - Abbreviated month-year (Mar-18, Jan-20) + - Excel serial numbers + - 4-letter month names + + Args: + df: Input DataFrame + column: Column name to parse + error_collector: ErrorCollector instance to track failures + file_name_col: Column containing file name for error tracking + patient_id_col: Column containing patient ID for error tracking + + Returns: + DataFrame with parsed date column + + Example: + >>> df = parse_date_column( + ... df=df, + ... column="hba1c_updated_date", + ... error_collector=collector, + ... ) + """ + if column not in df.columns: + return df + + # Store original values for error reporting + df = df.with_columns(pl.col(column).alias(f"_orig_{column}")) + + # Apply parse_date_flexible to each value + # NOTE: Using list-based approach instead of map_elements() because + # map_elements() with return_dtype=pl.Date fails when ALL values are None + # (all-NA columns like hospitalisation_date). + # Explicit Series creation with dtype=pl.Date works because it doesn't + # require non-null values. + column_values = df[column].cast(pl.Utf8).to_list() + parsed_dates = [ + parse_date_flexible(val, error_val=settings.error_val_date) for val in column_values + ] + parsed_series = pl.Series(f"_parsed_{column}", parsed_dates, dtype=pl.Date) + df = df.with_columns(parsed_series) + + # Detect failures: parsed to error date + error_date = pl.lit(settings.error_val_date).str.to_date() + failed_mask = ( + pl.col(f"_parsed_{column}").is_not_null() + & (pl.col(f"_parsed_{column}") == error_date) + & pl.col(f"_orig_{column}").is_not_null() + ) + + # Extract failed rows for error logging + failed_rows = df.filter(failed_mask) + + # Log each failure + if len(failed_rows) > 0: + for row in failed_rows.iter_rows(named=True): + error_collector.add_error( + file_name=row.get(file_name_col) or "unknown", + patient_id=row.get(patient_id_col) or "unknown", + column=column, + original_value=row[f"_orig_{column}"], + error_message="Could not parse date", + error_code="type_conversion", + function_name="parse_date_column", + ) + + # Use parsed values + df = df.with_columns(pl.col(f"_parsed_{column}").alias(column)) + + # Clean up temporary columns + df = df.drop([f"_orig_{column}", f"_parsed_{column}"]) + + return df + + +def correct_decimal_sign(df: pl.DataFrame, column: str) -> pl.DataFrame: + """Replace comma decimal separator with dot. + + Some trackers use European decimal format (1,5 instead of 1.5). + + Args: + df: Input DataFrame + column: Column name to correct + + Returns: + DataFrame with corrected decimal signs + + Example: + >>> df = correct_decimal_sign(df, "weight") + """ + if column not in df.columns: + return df + + df = df.with_columns(pl.col(column).cast(pl.Utf8).str.replace(",", ".").alias(column)) + + return df + + +def cut_numeric_value( + df: pl.DataFrame, + column: str, + min_val: float, + max_val: float, + error_collector: ErrorCollector, + file_name_col: str = "file_name", + patient_id_col: str = "patient_id", +) -> pl.DataFrame: + """Replace out-of-range numeric values with error value. + + Args: + df: Input DataFrame + column: Column name to check + min_val: Minimum allowed value + max_val: Maximum allowed value + error_collector: ErrorCollector instance to track violations + file_name_col: Column containing file name for error tracking + patient_id_col: Column containing patient ID for error tracking + + Returns: + DataFrame with out-of-range values replaced + + Example: + >>> df = cut_numeric_value( + ... df=df, + ... column="age", + ... min_val=0, + ... max_val=25, + ... error_collector=collector, + ... ) + """ + if column not in df.columns: + return df + + # Find values outside allowed range (excluding nulls and existing error values) + invalid_mask = ( + pl.col(column).is_not_null() + & (pl.col(column) != settings.error_val_numeric) + & ((pl.col(column) < min_val) | (pl.col(column) > max_val)) + ) + + # Extract invalid rows for error logging + invalid_rows = df.filter(invalid_mask) + + # Log each invalid value + if len(invalid_rows) > 0: + for row in invalid_rows.iter_rows(named=True): + error_collector.add_error( + file_name=row.get(file_name_col) or "unknown", + patient_id=row.get(patient_id_col) or "unknown", + column=column, + original_value=row[column], + error_message=f"Value {row[column]} outside allowed range [{min_val}, {max_val}]", + error_code="invalid_value", + function_name="cut_numeric_value", + ) + + # Replace invalid values with error value + df = df.with_columns( + pl.when(invalid_mask) + .then(pl.lit(settings.error_val_numeric)) + .otherwise(pl.col(column)) + .alias(column) + ) + + return df + + +def safe_convert_multiple_columns( + df: pl.DataFrame, + columns: list[str], + target_type: type[pl.DataType] | pl.DataType, + error_collector: ErrorCollector, + error_value: float | str | None = None, + file_name_col: str = "file_name", + patient_id_col: str = "patient_id", +) -> pl.DataFrame: + """Convert multiple columns to the same target type. + + Convenience function for batch conversion of columns. + + Args: + df: Input DataFrame + columns: List of column names to convert + target_type: Target Polars data type + error_collector: ErrorCollector instance + error_value: Value to use for failed conversions + file_name_col: Column containing file name for error tracking + patient_id_col: Column containing patient ID for error tracking + + Returns: + DataFrame with all specified columns converted + + Example: + >>> df = safe_convert_multiple_columns( + ... df=df, + ... columns=["age", "height", "weight"], + ... target_type=pl.Float64, + ... error_collector=collector, + ... ) + """ + for column in columns: + df = safe_convert_column( + df=df, + column=column, + target_type=target_type, + error_collector=error_collector, + error_value=error_value, + file_name_col=file_name_col, + patient_id_col=patient_id_col, + ) + + return df diff --git a/src/a4d/clean/date_parser.py b/src/a4d/clean/date_parser.py new file mode 100644 index 0000000..e33e446 --- /dev/null +++ b/src/a4d/clean/date_parser.py @@ -0,0 +1,123 @@ +"""Flexible date parsing for A4D tracker data. + +Matches R's parse_dates() function (script2_helper_patient_data_fix.R:174-211). +Handles various date formats found in legacy trackers including: +- Standard formats: "28/8/2017", "01-03-2018" +- Abbreviated month-year: "Mar-18", "Jan-20" +- Full month-year: "March-2018", "January-20" +- Excel serial numbers: "45341.0" (days since 1899-12-30) +- Year only: "2018", "18" +""" + +import re +from datetime import date, datetime, timedelta + +from dateutil import parser as date_parser +from loguru import logger + +# Excel epoch: dates stored as days since this date +EXCEL_EPOCH = date(1899, 12, 30) + + +def parse_date_flexible(date_str: str | None, error_val: str = "9999-09-09") -> date | None: + """Parse date strings flexibly using Python's dateutil.parser. + + Handles common edge cases from A4D tracker data: + - NA/None/empty values → None + - Excel serial numbers (e.g., "45341.0") → converted from days since 1899-12-30 + - 4-letter month names (e.g., "March") → truncated to 3 letters before parsing + - All standard date formats via dateutil.parser (very flexible) + + Examples: + "Mar-18" → 2018-03-01 + "28/8/2017" → 2017-08-28 + "45341.0" → 2024-01-13 (Excel serial) + "January-20" → 2020-01-01 + + Args: + date_str: Date string to parse + error_val: Value to parse and return on failure (default "9999-09-09") + + Returns: + Parsed date, None for NA/empty, or error date if parsing fails + """ + # Handle None, empty, or NA strings + if ( + date_str is None + or date_str == "" + or str(date_str).strip().lower() in ["na", "nan", "null", "none"] + ): + return None + + date_str = str(date_str).strip() + + # Handle Excel serial numbers + # Excel stores dates as number of days since 1899-12-30 + try: + numeric_val = float(date_str) + if 1 < numeric_val < 100000: # Reasonable range for Excel dates (1900-2173) + days = int(numeric_val) + result = EXCEL_EPOCH + timedelta(days=days) + logger.debug(f"Parsed Excel serial {date_str} → {result}") + return result + except ValueError: + pass # Not a number, continue with text parsing + + # Truncate 4-letter month names to 3 letters for better parsing + # "March" → "Mar", "January" → "Jan", etc. + if re.search(r"[a-zA-Z]{4}", date_str): + date_str = re.sub(r"([a-zA-Z]{3})[a-zA-Z]", r"\1", date_str) + + # Special handling for month-year formats (e.g., "Mar-18", "Jan-20", "May18") + # These should be interpreted as "Mar 2018", "Jan 2020", not "Mar day-18 of current year" + # Separator (hyphen/space) is optional to handle both "May-18" and "May18" + month_year_pattern = r"^([A-Za-z]{3})[-\s]?(\d{2})$" + match = re.match(month_year_pattern, date_str) + if match: + month_abbr, year_2digit = match.groups() + # Convert 2-digit year to 4-digit: 00-68 → 2000-2068, 69-99 → 1969-1999 + year_int = int(year_2digit) + if year_int <= 68: + year_4digit = 2000 + year_int + else: + year_4digit = 1900 + year_int + # Parse as "Mon YYYY" format, defaults to first day of month + date_str_full = f"{month_abbr} {year_4digit}" + try: + result = datetime.strptime(date_str_full, "%b %Y").date() + logger.debug(f"Parsed month-year '{date_str}' → {result}") + return result + except ValueError: + pass # Fall through to general parser + + # Try explicit DD/MM/YYYY and DD-MM-YYYY formats first (Southeast Asian standard) + # This is more reliable than dateutil.parser's dayfirst=True parameter + for fmt in [ + "%d/%m/%Y", # 06/05/2013 → 2013-05-06 (6th May) + "%d-%m-%Y", # 06-05-2013 → 2013-05-06 + "%d/%m/%y", # 06/05/13 → 2013-05-06 + "%d-%m-%y", # 06-05-13 → 2013-05-06 + "%Y-%m-%d", # 2013-05-06 (ISO format from Excel) + "%d/%m/%Y %H:%M:%S", # With time component + "%Y-%m-%d %H:%M:%S", # ISO with time + ]: + try: + result = datetime.strptime(date_str, fmt).date() + logger.debug(f"Parsed '{date_str}' using format {fmt} → {result}") + return result + except ValueError: + continue + + # Fall back to dateutil.parser for other formats (month names, etc.) + # dayfirst=True is still useful for remaining ambiguous cases + try: + result = date_parser.parse(date_str, dayfirst=True).date() + logger.debug(f"Parsed '{date_str}' with dateutil → {result}") + return result + except (ValueError, date_parser.ParserError) as e: + # If parsing fails, log warning and return error date + logger.bind(error_code="invalid_value").warning(f"Could not parse date '{date_str}': {e}. Returning error value {error_val}") + try: + return datetime.strptime(error_val, "%Y-%m-%d").date() + except ValueError: + return None diff --git a/src/a4d/clean/patient.py b/src/a4d/clean/patient.py new file mode 100644 index 0000000..a47e7b9 --- /dev/null +++ b/src/a4d/clean/patient.py @@ -0,0 +1,930 @@ +"""Patient data cleaning pipeline. + +This module orchestrates the complete cleaning pipeline for patient data, +following the R pipeline's meta schema approach (script2_process_patient_data.R): + +1. Load raw patient data +2. Apply legacy format fixes +3. Apply transformations +4. Type conversions +5. Validation +6. Apply meta schema (ensure all columns exist, consistent output) +""" + +from pathlib import Path + +import polars as pl +from loguru import logger + +from a4d.clean.converters import ( + correct_decimal_sign, + cut_numeric_value, + parse_date_column, + safe_convert_column, +) +from a4d.clean.schema import ( + apply_schema, + get_date_columns, + get_patient_data_schema, +) +from a4d.clean.transformers import extract_regimen +from a4d.clean.validators import validate_all_columns +from a4d.config import settings +from a4d.errors import ErrorCollector + + +def clean_patient_data( + df_raw: pl.DataFrame, + error_collector: ErrorCollector, +) -> pl.DataFrame: + """Clean raw patient data following the complete pipeline. + + This function orchestrates all cleaning steps and ensures the output + conforms to the meta schema, regardless of which columns exist in input. + + Args: + df_raw: Raw patient data from extraction + error_collector: ErrorCollector instance for tracking errors + + Returns: + Cleaned DataFrame with complete meta schema applied + + Example: + >>> from a4d.extract.patient import extract_patient_data + >>> from a4d.errors import ErrorCollector + >>> + >>> collector = ErrorCollector() + >>> df_raw = extract_patient_data(tracker_file) + >>> df_clean = clean_patient_data(df_raw, collector) + >>> # df_clean has ALL schema columns, with consistent types + """ + logger.info( + f"Starting patient data cleaning: {len(df_raw)} rows, {len(df_raw.columns)} columns" + ) + + # Step 1: Legacy format fixes + df = _apply_legacy_fixes(df_raw) + + # Step 2: Pre-processing transformations + df = _apply_preprocessing(df) + + # Step 3: Data transformations (regimen extraction, lowercasing, etc.) + df = _apply_transformations(df) + + # Step 4: Apply meta schema EARLY (like R does) to ensure all columns exist before conversions + # This allows unit conversions to work on columns that don't exist in raw data + df = apply_schema(df) + + # Step 5: Type conversions + df = _apply_type_conversions(df, error_collector) + + # Step 5.5: Fix age from DOB (like R pipeline does) + # Must happen after type conversions so DOB is a proper date + # Must happen before range validation so validated age is correct + df = _fix_age_from_dob(df, error_collector) + + # Step 5.5b: Calculate t1d_diagnosis_age from dob and t1d_diagnosis_date + # Replaces any existing value (including Excel errors like #NUM!) + df = _fix_t1d_diagnosis_age(df) + + # Step 5.6: Validate dates (replace future dates with error value) + # Must happen after type conversions so dates are proper date types + df = _validate_dates(df, error_collector) + + # Step 5.7: Calculate BMI from weight and height (like R does) + # Must happen after type conversions and before range validation + df = _calculate_bmi(df) + + # Step 6: Range validation and cleanup + df = _apply_range_validation(df, error_collector) + + # Step 7: Allowed values validation + df = validate_all_columns(df, error_collector) + + # Step 8: Unit conversions (requires schema to be applied first!) + df = _apply_unit_conversions(df) + + # Step 9: Create tracker_date from year/month + df = _add_tracker_date(df) + + # Step 10: Sort by tracker_date and patient_id + df = df.sort(["tracker_date", "patient_id"]) + + logger.info(f"Cleaning complete: {len(df)} rows, {len(df.columns)} columns") + logger.info(f"Errors collected: {len(error_collector)}") + + return df + + +def _extract_date_from_measurement(df: pl.DataFrame, col_name: str) -> pl.DataFrame: + """Extract date from measurement values in legacy trackers. + + Matches R's extract_date_from_measurement() (script2_helper_patient_data_fix.R:115). + + For pre-2019 trackers, values and dates are combined in format: + - "14.5 (Jan-20)" → value="14.5 ", date="Jan-20" + - ">14 (Mar-18)" → value=">14 ", date="Mar-18" + - "148 mg/dl (Mar-18)" → value="148 mg/dl ", date="Mar-18" + + Args: + df: Input DataFrame + col_name: Column name containing combined value+date + + Returns: + DataFrame with extracted date in {col_name}_date column + """ + if col_name not in df.columns: + return df + + date_col_name = col_name.replace("_mg", "").replace("_mmol", "") + "_date" + + # Check if date column already exists (2019+ trackers) + if date_col_name in df.columns: + return df + + # Extract value before '(' and date between '(' and ')' + # Using regex: everything before '(', then '(', then capture date, then optional ')' + df = df.with_columns( + [ + # Extract value (everything before parenthesis, or entire value if no parenthesis) + pl.col(col_name).str.extract(r"^([^(]+)", 1).str.strip_chars().alias(col_name), + # Extract date (everything between parentheses, if present) + pl.col(col_name).str.extract(r"\(([^)]+)\)", 1).alias(date_col_name), + ] + ) + + logger.debug(f"Extracted date from {col_name} into {date_col_name}") + + return df + + +def _apply_legacy_fixes(df: pl.DataFrame) -> pl.DataFrame: + """Apply fixes for legacy tracker formats (pre-2024). + + Legacy trackers may have: + - Combined date+value columns (e.g., hba1c_updated contains both) + - Combined blood pressure values (sys/dias in one column) + - Different column structures + + Matches R's legacy handling in script2_process_patient_data.R:30-66. + + Args: + df: Input DataFrame + + Returns: + DataFrame with legacy fixes applied + """ + # Extract dates from measurement columns for pre-2019 trackers + # R checks if *_date column exists, if not, extracts from measurement column + df = _extract_date_from_measurement(df, "hba1c_updated") + df = _extract_date_from_measurement(df, "fbg_updated_mg") + df = _extract_date_from_measurement(df, "fbg_updated_mmol") + + # Split blood pressure for pre-2024 trackers (R line 72) + if "blood_pressure_mmhg" in df.columns: + from a4d.clean.transformers import split_bp_in_sys_and_dias + + df = split_bp_in_sys_and_dias(df) + + return df + + +def _fix_fbg_column(col: pl.Expr) -> pl.Expr: + """Fix FBG column text values to numeric equivalents. + + Matches R's fix_fbg() function (script2_helper_patient_data_fix.R:551-567). + Converts qualitative text to numeric values and removes DKA markers. + + Conversions (based on CDC guidelines): + - "high", "bad", "hi", "hight" (typo) → "200" + - "medium", "med" → "170" + - "low", "good", "okay" → "140" + - Remove "(DKA)" text, "mg/dl", "mmol/l" suffixes + - Trim whitespace + + Args: + col: Polars expression for FBG column + + Returns: + Polars expression with fixed values + """ + return ( + col.str.to_lowercase() + # Remove unit suffixes (from legacy trackers like 2018) + .str.replace_all(r"\s*mg/dl\s*", "", literal=False) + .str.replace_all(r"\s*mmol/l\s*", "", literal=False) + # Use case-when to match full words, not substrings + .str.replace_all(r"^(high|hight|bad|hi)$", "200") # Anchored to full string + .str.replace_all(r"^(med|medium)$", "170") + .str.replace_all(r"^(low|good|okay)$", "140") + .str.replace_all(r"\(DKA\)", "", literal=True) + .str.strip_chars() + ) + + +def _apply_preprocessing(df: pl.DataFrame) -> pl.DataFrame: + """Apply preprocessing transformations before type conversion. + + This includes: + - Normalizing patient_id (remove transfer clinic suffix) + - Removing > and < signs from HbA1c values (but tracking them) + - Fixing FBG text values (high/medium/low → numeric, removing (DKA)) + - Replacing "-" with "N" in Y/N columns + - Deriving insulin_type and insulin_subtype from individual columns (2024+) + + Args: + df: Input DataFrame + + Returns: + DataFrame with preprocessing applied + """ + # Normalize patient_id: Keep only COUNTRY_ID part, remove transfer clinic suffix + # Pattern: "MY_SM003_SB" → "MY_SM003" (keep first two underscore-separated parts) + # Also normalizes hyphens first: "LA-MH093_LF" → "LA_MH093_LF" → "LA_MH093" + # This ensures consistent patient linking across years when patients transfer clinics + if "patient_id" in df.columns: + df = df.with_columns( + # First normalize hyphens to underscores + pl.col("patient_id").str.replace_all("-", "_").alias("_patient_id_normalized") + ) + df = df.with_columns( + pl.when(pl.col("_patient_id_normalized").str.contains("_")) + .then(pl.col("_patient_id_normalized").str.extract(r"^([A-Z]+_[^_]+)", 1)) + .otherwise(pl.col("_patient_id_normalized")) + .alias("patient_id") + ) + df = df.drop("_patient_id_normalized") + + # Track HbA1c exceeds markers (> or <) + if "hba1c_baseline" in df.columns: + df = df.with_columns( + pl.col("hba1c_baseline") + .str.contains(r"[><]") + .fill_null(False) + .alias("hba1c_baseline_exceeds") + ) + df = df.with_columns( + pl.col("hba1c_baseline").str.replace_all(r"[><]", "").alias("hba1c_baseline") + ) + + if "hba1c_updated" in df.columns: + df = df.with_columns( + pl.col("hba1c_updated") + .str.contains(r"[><]") + .fill_null(False) + .alias("hba1c_updated_exceeds") + ) + df = df.with_columns( + pl.col("hba1c_updated").str.replace_all(r"[><]", "").alias("hba1c_updated") + ) + + # Fix FBG text values (R: script2_helper_patient_data_fix.R:551-567) + # Convert qualitative values to numeric: high→200, medium→170, low→140 + # Source: https://www.cdc.gov/diabetes/basics/getting-tested.html + if "fbg_updated_mg" in df.columns: + df = df.with_columns(_fix_fbg_column(pl.col("fbg_updated_mg")).alias("fbg_updated_mg")) + + if "fbg_updated_mmol" in df.columns: + df = df.with_columns(_fix_fbg_column(pl.col("fbg_updated_mmol")).alias("fbg_updated_mmol")) + + # Replace "-" with "N" in Y/N columns (2024+ trackers use "-" for No) + yn_columns = [ + "analog_insulin_long_acting", + "analog_insulin_rapid_acting", + "human_insulin_intermediate_acting", + "human_insulin_pre_mixed", + "human_insulin_short_acting", + ] + + for col in yn_columns: + if col in df.columns: + df = df.with_columns(pl.col(col).str.replace("-", "N").alias(col)) + + # Derive insulin_type and insulin_subtype from individual columns (2024+) + # R's validation will convert insulin_type to Title Case and insulin_subtype to "Undefined" + if "human_insulin_pre_mixed" in df.columns: + df = _derive_insulin_fields(df) + + return df + + +def _derive_insulin_fields(df: pl.DataFrame) -> pl.DataFrame: + """Derive insulin_type and insulin_subtype from individual columns. + + Based on R's logic from script2_process_patient_data.R:91-111 but with corrections: + - Uses lowercase values (R does this, validation converts to Title Case later) + - FIXES R's typo: Uses "rapid-acting" (correct) instead of R's "rapic-acting" (typo) + + For 2024+ trackers: + - insulin_type: "human insulin" if any human column is Y, else "analog insulin" + - insulin_subtype: Comma-separated list like "pre-mixed,rapid-acting,long-acting" + (will be replaced with "Undefined" by validation since + comma-separated values aren't in allowed_values) + + NOTE: Python is CORRECT here. Comparison with R will show differences because R has a typo. + + Args: + df: Input DataFrame with individual insulin columns + + Returns: + DataFrame with insulin_type and insulin_subtype derived + """ + # Determine insulin_type (lowercase to match R) + # Important: R's ifelse returns NA when all conditions are NA/None + # So we only derive insulin_type when at least one column is not None + df = df.with_columns( + pl.when( + # Only derive if at least one insulin column is not null + pl.col("human_insulin_pre_mixed").is_not_null() + | pl.col("human_insulin_short_acting").is_not_null() + | pl.col("human_insulin_intermediate_acting").is_not_null() + | pl.col("analog_insulin_rapid_acting").is_not_null() + | pl.col("analog_insulin_long_acting").is_not_null() + ) + .then( + # Now check which type + pl.when( + (pl.col("human_insulin_pre_mixed") == "Y") + | (pl.col("human_insulin_short_acting") == "Y") + | (pl.col("human_insulin_intermediate_acting") == "Y") + ) + .then(pl.lit("human insulin")) + .otherwise(pl.lit("analog insulin")) + ) + .otherwise(None) # Return None if all columns are None (matches R's NA) + .alias("insulin_type") + ) + + # Build insulin_subtype as comma-separated list (lowercase to match R) + # CORRECTED: Use "rapid-acting" (correct) instead of R's "rapic-acting" (typo) + df = df.with_columns( + pl.concat_list( + [ + pl.when(pl.col("human_insulin_pre_mixed") == "Y") + .then(pl.lit("pre-mixed")) + .otherwise(pl.lit(None)), + pl.when(pl.col("human_insulin_short_acting") == "Y") + .then(pl.lit("short-acting")) + .otherwise(pl.lit(None)), + pl.when(pl.col("human_insulin_intermediate_acting") == "Y") + .then(pl.lit("intermediate-acting")) + .otherwise(pl.lit(None)), + pl.when(pl.col("analog_insulin_rapid_acting") == "Y") + .then(pl.lit("rapid-acting")) # CORRECTED from R's typo + .otherwise(pl.lit(None)), + pl.when(pl.col("analog_insulin_long_acting") == "Y") + .then(pl.lit("long-acting")) + .otherwise(pl.lit(None)), + ] + ) + .list.drop_nulls() + .list.join(",") + .alias("insulin_subtype") + ) + + return df + + +def _apply_transformations(df: pl.DataFrame) -> pl.DataFrame: + """Apply data transformations. + + Transformations are explicit Python code (not config-driven): + - Lowercase status for case-insensitive validation + - Standardize insulin regimen descriptions + - Map sex synonyms to M/F + - Correct European decimal format + + Args: + df: Input DataFrame + + Returns: + DataFrame with transformations applied + """ + # Status should keep original case to match R pipeline + # R validation is case-insensitive but preserves original values + + # Standardize insulin regimen + if "insulin_regimen" in df.columns: + df = extract_regimen(df) + + # Map sex synonyms to M/F (matching R's fix_sex) + if "sex" in df.columns: + from a4d.clean.transformers import fix_sex + + df = fix_sex(df) + + # Fix testing frequency ranges (R line 258) + if "testing_frequency" in df.columns: + from a4d.clean.transformers import fix_testing_frequency + + df = fix_testing_frequency(df) + + # Correct European decimal format (comma → dot) + numeric_cols = [ + "hba1c_baseline", + "hba1c_updated", + "fbg_updated_mg", + "fbg_updated_mmol", + "weight", + "height", + "bmi", + ] + + for col in numeric_cols: + if col in df.columns: + df = correct_decimal_sign(df, col) + + return df + + +def _apply_type_conversions(df: pl.DataFrame, error_collector: ErrorCollector) -> pl.DataFrame: + """Convert columns to target types using safe_convert_column. + + Only converts columns that exist in both the DataFrame and the schema. + + Special handling: + - Date columns: Use flexible date parser (handles Mar-18, Excel serials, etc.) + - Integer columns: Convert via Float64 first to handle decimals + + Args: + df: Input DataFrame + error_collector: ErrorCollector for tracking conversion failures + + Returns: + DataFrame with types converted + """ + schema = get_patient_data_schema() + + # Convert each column that exists + for col, target_type in schema.items(): + if col not in df.columns: + continue + + # Skip if already the correct type (happens when schema adds NULL columns) + if df[col].dtype == target_type: + continue + + # Special handling for Date columns: use flexible date parser + if target_type == pl.Date: + # Strip time component if present (e.g., "2009-04-17 00:00:00" → "2009-04-17") + # Use split on space instead of slice(0,10) to handle "dd-Mon-yyyy" format (11 chars) + df = df.with_columns(pl.col(col).cast(pl.Utf8).str.split(" ").list.first().alias(col)) + # Use custom date parser for flexibility (handles Mar-18, Excel serials, etc.) + df = parse_date_column(df, col, error_collector) + # Special handling for Int32: convert via Float64 first (handles "14.0" → 14.0 → 14) + elif target_type == pl.Int32: + df = safe_convert_column(df, col, pl.Float64, error_collector) + df = df.with_columns(pl.col(col).round(0).cast(pl.Int32, strict=False).alias(col)) + else: + df = safe_convert_column( + df=df, + column=col, + target_type=target_type, + error_collector=error_collector, + ) + + return df + + +def _calculate_bmi(df: pl.DataFrame) -> pl.DataFrame: + """Calculate BMI from weight and height. + + Matches R's fix_bmi() function (script2_helper_patient_data_fix.R:401). + This REPLACES any existing BMI value with calculated BMI = weight / height^2. + + Must be called after type conversions (so weight/height are numeric) + and before range validation (so calculated BMI gets validated). + + Args: + df: Input DataFrame + + Returns: + DataFrame with calculated BMI column + """ + from a4d.clean.transformers import fix_bmi + + return fix_bmi(df) + + +def _apply_range_validation(df: pl.DataFrame, error_collector: ErrorCollector) -> pl.DataFrame: + """Apply range validation and value cleanup. + + This includes: + - Height: 0-2.3m (convert cm to m if needed) + - Weight: 0-200kg + - BMI: 4-60 + - Age: 0-25 years + - HbA1c: 4-18% + - FBG: 0-136.5 mmol/l + + Args: + df: Input DataFrame + error_collector: ErrorCollector for tracking violations + + Returns: + DataFrame with range validation applied + """ + # Height: convert cm to m if > 2.3 (likely in cm), then validate + if "height" in df.columns: + df = df.with_columns( + pl.when(pl.col("height") > 2.3) + .then(pl.col("height") / 100.0) + .otherwise(pl.col("height")) + .alias("height") + ) + df = cut_numeric_value(df, "height", 0, 2.3, error_collector) + + # Weight: 0-200 kg + if "weight" in df.columns: + df = cut_numeric_value(df, "weight", 0, 200, error_collector) + + # BMI: 4-60 + if "bmi" in df.columns: + df = cut_numeric_value(df, "bmi", 10, 80, error_collector) + + # Age: 0-25 years + if "age" in df.columns: + df = cut_numeric_value(df, "age", 0, 100, error_collector) + + # HbA1c baseline: 4-18% + if "hba1c_baseline" in df.columns: + df = cut_numeric_value(df, "hba1c_baseline", 0, 25, error_collector) + + # HbA1c updated: 4-18% + if "hba1c_updated" in df.columns: + df = cut_numeric_value(df, "hba1c_updated", 0, 25, error_collector) + + # FBG updated mmol: 0-136.5 (world record) + if "fbg_updated_mmol" in df.columns: + df = cut_numeric_value(df, "fbg_updated_mmol", 0, 150, error_collector) + + return df + + +def _apply_unit_conversions(df: pl.DataFrame) -> pl.DataFrame: + """Apply unit conversions. + + - FBG mmol/l ↔ mg/dl conversion (18x factor) + - Only convert if one is missing but the other exists + + Args: + df: Input DataFrame + + Returns: + DataFrame with unit conversions applied + """ + # Convert fbg_updated_mg to mmol if mmol is all NULL + if "fbg_updated_mmol" in df.columns and "fbg_updated_mg" in df.columns: + if df["fbg_updated_mmol"].is_null().all(): + df = df.with_columns( + pl.when(pl.col("fbg_updated_mg") != settings.error_val_numeric) + .then(pl.col("fbg_updated_mg") / 18.0) + .otherwise(None) + .alias("fbg_updated_mmol") + ) + + # Convert fbg_updated_mmol to mg if mg is all NULL + if "fbg_updated_mg" in df.columns and "fbg_updated_mmol" in df.columns: + if df["fbg_updated_mg"].is_null().all(): + df = df.with_columns( + pl.when(pl.col("fbg_updated_mmol") != settings.error_val_numeric) + .then(pl.col("fbg_updated_mmol") * 18.0) + .otherwise(None) + .alias("fbg_updated_mg") + ) + + return df + + +def _fix_age_from_dob(df: pl.DataFrame, error_collector: ErrorCollector) -> pl.DataFrame: + """Fix age by calculating from DOB and tracker date. + + Matches R pipeline's fix_age() function (script2_helper_patient_data_fix.R:329). + Always uses calculated age from DOB rather than trusting Excel value. + + Logic: + 1. Calculate age: tracker_year - birth_year + 2. Adjust if birthday hasn't occurred yet: if tracker_month < birth_month: age -= 1 + 3. If calculated age differs from Excel age, log warning and use calculated + 4. If calculated age is negative, use error value and log warning + + Args: + df: DataFrame with age, dob, tracker_year, tracker_month, patient_id columns + error_collector: ErrorCollector for tracking data quality issues + + Returns: + DataFrame with corrected age values + + Example: + >>> df = pl.DataFrame({ + ... "patient_id": ["P001"], + ... "age": [21.0], # Wrong value from Excel + ... "dob": [date(2006, 8, 8)], + ... "tracker_year": [2025], + ... "tracker_month": [2] + ... }) + >>> collector = ErrorCollector() + >>> fixed = _fix_age_from_dob(df, collector) + >>> fixed["age"][0] # Should be 18, not 21 + 18.0 + """ + # Only fix if we have the necessary columns + required_cols = ["age", "dob", "tracker_year", "tracker_month", "patient_id"] + if not all(col in df.columns for col in required_cols): + logger.debug("Skipping age fix: missing required columns") + return df + + logger.info("Fixing age values from DOB (matching R pipeline logic)") + + error_date = pl.lit(settings.error_val_date).str.to_date() + + # Only calculate if dob is valid (not null, not error date) + valid_dob = pl.col("dob").is_not_null() & (pl.col("dob") != error_date) + + # Calculate age from DOB + # calc_age = tracker_year - year(dob) + # if tracker_month < month(dob): calc_age -= 1 + df = df.with_columns( + pl.when(valid_dob) + .then( + pl.col("tracker_year") + - pl.col("dob").dt.year() + - pl.when(pl.col("tracker_month") < pl.col("dob").dt.month()).then(1).otherwise(0) + ) + .otherwise(None) + .alias("_calc_age") + ) + + # Track which ages were fixed + ages_fixed = 0 + ages_missing = 0 + ages_negative = 0 + + # For each row where calc_age differs from age, log and fix + for row in df.filter( + pl.col("_calc_age").is_not_null() + & ((pl.col("age").is_null()) | (pl.col("age") != pl.col("_calc_age"))) + ).iter_rows(named=True): + patient_id = row["patient_id"] + file_name = row.get("file_name") or "unknown" + excel_age = row["age"] + calc_age = row["_calc_age"] + + if excel_age is None or (excel_age == settings.error_val_numeric): + logger.bind(error_code="missing_value").warning( + f"Patient {patient_id}: age is missing. " + f"Using calculated age {calc_age} instead of original age." + ) + error_collector.add_error( + file_name=file_name, + patient_id=patient_id, + column="age", + original_value=excel_age if excel_age is not None else "NULL", + error_message=f"Age missing, calculated from DOB as {calc_age}", + error_code="missing_value", + function_name="_fix_age_from_dob", + ) + ages_missing += 1 + elif calc_age < 0: + logger.bind(error_code="invalid_value").warning( + f"Patient {patient_id}: calculated age is negative ({calc_age}). " + f"Please check this manually. Using error value instead." + ) + error_collector.add_error( + file_name=file_name, + patient_id=patient_id, + column="age", + original_value=str(excel_age), + error_message=f"Calculated age is negative ({calc_age}), check DOB", + error_code="invalid_value", + function_name="_fix_age_from_dob", + ) + ages_negative += 1 + else: + logger.bind(error_code="invalid_value").warning( + f"Patient {patient_id}: age {excel_age} is different " + f"from calculated age {calc_age}. " + f"Using calculated age instead of original age." + ) + error_collector.add_error( + file_name=file_name, + patient_id=patient_id, + column="age", + original_value=str(excel_age), + error_message=( + f"Age mismatch: Excel={excel_age}, Calculated={calc_age}. Using calculated age." + ), + error_code="invalid_value", + function_name="_fix_age_from_dob", + ) + ages_fixed += 1 + + # Apply fixes: + # 1. Use calculated age when available and non-negative + # 2. Use error value for negative ages + df = df.with_columns( + pl.when(pl.col("_calc_age").is_not_null()) + .then( + pl.when(pl.col("_calc_age") < 0) + .then(pl.lit(settings.error_val_numeric)) + .otherwise(pl.col("_calc_age")) + ) + .otherwise(pl.col("age")) + .alias("age") + ) + + # Drop temporary column + df = df.drop("_calc_age") + + if ages_fixed > 0 or ages_missing > 0 or ages_negative > 0: + logger.info( + f"Age fixes applied: {ages_fixed} corrected, " + f"{ages_missing} filled from DOB, " + f"{ages_negative} negative (set to error)" + ) + + return df + + +def _fix_t1d_diagnosis_age(df: pl.DataFrame) -> pl.DataFrame: + """Calculate t1d_diagnosis_age from dob and t1d_diagnosis_date. + + If both dates are valid (not null, not error date), calculates age at diagnosis. + If either date is missing or is error date, result is null. + + Args: + df: DataFrame with dob, t1d_diagnosis_date, t1d_diagnosis_age columns + + Returns: + DataFrame with calculated t1d_diagnosis_age + """ + required_cols = ["dob", "t1d_diagnosis_date", "t1d_diagnosis_age"] + if not all(col in df.columns for col in required_cols): + return df + + error_date = pl.lit(settings.error_val_date).str.to_date() + + # Only calculate if both dates are valid (not null, not error date) + valid_dob = pl.col("dob").is_not_null() & (pl.col("dob") != error_date) + valid_diagnosis = pl.col("t1d_diagnosis_date").is_not_null() & ( + pl.col("t1d_diagnosis_date") != error_date + ) + + # Calculate age at diagnosis: year(diagnosis_date) - year(dob) + # Adjust if birthday hasn't occurred yet in diagnosis year + df = df.with_columns( + pl.when(valid_dob & valid_diagnosis) + .then( + pl.col("t1d_diagnosis_date").dt.year() + - pl.col("dob").dt.year() + - pl.when(pl.col("t1d_diagnosis_date").dt.month() < pl.col("dob").dt.month()) + .then(1) + .otherwise(0) + ) + .otherwise(None) + .cast(pl.Int32) + .alias("t1d_diagnosis_age") + ) + + return df + + +def _validate_dates(df: pl.DataFrame, error_collector: ErrorCollector) -> pl.DataFrame: + """Validate date columns and replace future dates with error value. + + Dates beyond the tracker year are considered invalid and replaced with + the error date value (9999-09-09). This matches R pipeline behavior. + + Args: + df: Input DataFrame with date columns + error_collector: ErrorCollector for tracking validation errors + + Returns: + DataFrame with invalid dates replaced + """ + date_columns = get_date_columns() + dates_fixed = 0 + + # Get the error date as a date type + error_date = pl.lit(settings.error_val_date).str.to_date() + + for col in date_columns: + if col not in df.columns: + continue + + # Skip tracker_date as it's derived and shouldn't be validated + if col == "tracker_date": + continue + + # Create a date representing end of tracker year (December 31) + # Find invalid dates and log them + temp_df = df.with_columns(pl.date(pl.col("tracker_year"), 12, 31).alias("_max_valid_date")) + + invalid_dates = temp_df.filter( + pl.col(col).is_not_null() & (pl.col(col) > pl.col("_max_valid_date")) + ) + + # Log each error + for row in invalid_dates.iter_rows(named=True): + patient_id = row.get("patient_id", "UNKNOWN") + file_name = row.get("file_name", "UNKNOWN") + original_date = row.get(col) + tracker_year = row.get("tracker_year") + + logger.bind(error_code="invalid_value").warning( + f"Patient {patient_id}: {col} = {original_date} " + f"is beyond tracker year {tracker_year}. " + f"Replacing with error date." + ) + error_collector.add_error( + file_name=file_name, + patient_id=patient_id, + column=col, + original_value=str(original_date), + error_message=f"Date {original_date} is beyond tracker year {tracker_year}", + error_code="invalid_value", + function_name="_validate_dates", + ) + dates_fixed += 1 + + # Replace invalid dates with error date (using inline expression) + df = temp_df.with_columns( + pl.when(pl.col(col).is_not_null() & (pl.col(col) > pl.col("_max_valid_date"))) + .then(error_date) + .otherwise(pl.col(col)) + .alias(col) + ).drop("_max_valid_date") + + if dates_fixed > 0: + logger.info(f"Date validation: {dates_fixed} future dates replaced with error value") + + return df + + +def _add_tracker_date(df: pl.DataFrame) -> pl.DataFrame: + """Create tracker_date from tracker_year and tracker_month. + + Args: + df: Input DataFrame + + Returns: + DataFrame with tracker_date column + """ + if "tracker_year" in df.columns and "tracker_month" in df.columns: + # Parse year-month to date (first day of month) + # Cast to string first since they're now Int32 + df = df.with_columns( + pl.concat_str( + [ + pl.col("tracker_year").cast(pl.String), + pl.lit("-"), + pl.col("tracker_month").cast(pl.String), + pl.lit("-01"), + ] + ) + .str.to_date("%Y-%m-%d") + .alias("tracker_date") + ) + + return df + + +def clean_patient_file( + raw_parquet_path: Path, + output_parquet_path: Path, + error_collector: ErrorCollector | None = None, +) -> None: + """Clean a single patient data parquet file. + + This is the main entry point for cleaning a tracker file. + + Args: + raw_parquet_path: Path to raw patient parquet (from extraction) + output_parquet_path: Path to write cleaned parquet + error_collector: Optional ErrorCollector (creates new one if not provided) + + Example: + >>> from pathlib import Path + >>> raw_path = Path("output/patient_data_raw/2024_Hospital_patient_raw.parquet") + >>> clean_path = Path("output/patient_data_clean/2024_Hospital_patient_clean.parquet") + >>> clean_patient_file(raw_path, clean_path) + """ + if error_collector is None: + error_collector = ErrorCollector() + + logger.info(f"Cleaning patient file: {raw_parquet_path}") + + # Read raw parquet + df_raw = pl.read_parquet(raw_parquet_path) + + # Clean data + df_clean = clean_patient_data(df_raw, error_collector) + + # Create output directory if needed + output_parquet_path.parent.mkdir(parents=True, exist_ok=True) + + # Write cleaned parquet + df_clean.write_parquet(output_parquet_path) + + logger.info(f"Cleaned patient file written: {output_parquet_path}") + logger.info(f"Total errors: {len(error_collector)}") diff --git a/src/a4d/clean/schema.py b/src/a4d/clean/schema.py new file mode 100644 index 0000000..3748ce1 --- /dev/null +++ b/src/a4d/clean/schema.py @@ -0,0 +1,158 @@ +"""Meta schema definition for patient data - matches R pipeline exactly.""" + +import polars as pl + + +def get_patient_data_schema() -> dict[str, type[pl.DataType] | pl.DataType]: + """Get the complete meta schema for patient data. + + This schema EXACTLY matches the R pipeline's schema in script2_process_patient_data.R. + Column order matches R's alphabetical order. + + Returns: + Dictionary mapping column names to Polars data types + """ + return { + "age": pl.Int32, # integer() in R + "analog_insulin_long_acting": pl.String, # character() in R + "analog_insulin_rapid_acting": pl.String, + "blood_pressure_dias_mmhg": pl.Int32, + "blood_pressure_sys_mmhg": pl.Int32, + "blood_pressure_updated": pl.Date, + "bmi": pl.Float64, # numeric() in R + "bmi_date": pl.Date, + "clinic_id": pl.String, + "clinic_visit": pl.String, + "complication_screening_eye_exam_date": pl.Date, + "complication_screening_eye_exam_value": pl.String, + "complication_screening_foot_exam_date": pl.Date, + "complication_screening_foot_exam_value": pl.String, + "complication_screening_kidney_test_date": pl.Date, + "complication_screening_kidney_test_value": pl.String, + "complication_screening_lipid_profile_cholesterol_value": pl.String, + "complication_screening_lipid_profile_date": pl.Date, + "complication_screening_lipid_profile_hdl_mmol_value": pl.Float64, + "complication_screening_lipid_profile_hdl_mg_value": pl.Float64, + "complication_screening_lipid_profile_ldl_mmol_value": pl.Float64, + "complication_screening_lipid_profile_ldl_mg_value": pl.Float64, + "complication_screening_lipid_profile_triglycerides_value": pl.Float64, + "complication_screening_remarks": pl.String, + "complication_screening_thyroid_test_date": pl.Date, + "complication_screening_thyroid_test_ft4_pmol_value": pl.Float64, + "complication_screening_thyroid_test_ft4_ng_value": pl.Float64, + "complication_screening_thyroid_test_tsh_value": pl.Float64, + "dm_complication_eye": pl.String, + "dm_complication_kidney": pl.String, + "dm_complication_others": pl.String, + "dm_complication_remarks": pl.String, + "dob": pl.Date, + "edu_occ": pl.String, + "edu_occ_updated": pl.Date, + "family_history": pl.String, + "fbg_baseline_mg": pl.Float64, + "fbg_baseline_mmol": pl.Float64, + "fbg_updated_date": pl.Date, + "fbg_updated_mg": pl.Float64, + "fbg_updated_mmol": pl.Float64, + "file_name": pl.String, + "hba1c_baseline": pl.Float64, + "hba1c_baseline_exceeds": pl.Boolean, # logical() in R + "hba1c_updated": pl.Float64, + "hba1c_updated_exceeds": pl.Boolean, + "hba1c_updated_date": pl.Date, + "height": pl.Float64, + "hospitalisation_cause": pl.String, + "hospitalisation_date": pl.Date, + "human_insulin_intermediate_acting": pl.String, + "human_insulin_pre_mixed": pl.String, + "human_insulin_short_acting": pl.String, + "insulin_injections": pl.Float64, + "insulin_regimen": pl.String, + "insulin_total_units": pl.Float64, + "insulin_type": pl.String, + "insulin_subtype": pl.String, + "last_clinic_visit_date": pl.Date, + "last_remote_followup_date": pl.Date, + "lost_date": pl.Date, + "name": pl.String, + "observations": pl.String, + "observations_category": pl.String, + "other_issues": pl.String, + "patient_consent": pl.String, + "patient_id": pl.String, + "province": pl.String, + "recruitment_date": pl.Date, + "remote_followup": pl.String, + "sex": pl.String, + "sheet_name": pl.String, + "status": pl.String, + "status_out": pl.String, + "support_level": pl.String, + "t1d_diagnosis_age": pl.Int32, + "t1d_diagnosis_date": pl.Date, + "t1d_diagnosis_with_dka": pl.String, + "testing_frequency": pl.Int32, + "tracker_date": pl.Date, + "tracker_month": pl.Int32, + "tracker_year": pl.Int32, + "weight": pl.Float64, + } + + +def apply_schema(df: pl.DataFrame) -> pl.DataFrame: + """Apply the meta schema to a DataFrame. + + This function: + 1. Adds missing columns with NULL values + 2. Casts existing columns to target types (if they exist) + 3. Reorders columns to match schema order + 4. Returns a DataFrame with the exact schema + + Args: + df: Input DataFrame (may be missing columns) + + Returns: + DataFrame with complete schema applied + """ + schema = get_patient_data_schema() + + # Start with existing columns + df_result = df + + # Add missing columns with NULL values + missing_cols = set(schema.keys()) - set(df.columns) + for col in missing_cols: + df_result = df_result.with_columns(pl.lit(None, dtype=schema[col]).alias(col)) + + # Reorder columns to match schema order + df_result = df_result.select(list(schema.keys())) + + return df_result + + +def get_numeric_columns() -> list[str]: + """Get list of numeric columns from schema.""" + schema = get_patient_data_schema() + return [ + col + for col, dtype in schema.items() + if dtype in (pl.Int32, pl.Int64, pl.Float32, pl.Float64) + ] + + +def get_date_columns() -> list[str]: + """Get list of date columns from schema.""" + schema = get_patient_data_schema() + return [col for col, dtype in schema.items() if dtype == pl.Date] + + +def get_boolean_columns() -> list[str]: + """Get list of boolean columns from schema.""" + schema = get_patient_data_schema() + return [col for col, dtype in schema.items() if dtype == pl.Boolean] + + +def get_string_columns() -> list[str]: + """Get list of string columns from schema.""" + schema = get_patient_data_schema() + return [col for col, dtype in schema.items() if dtype == pl.String] diff --git a/src/a4d/clean/transformers.py b/src/a4d/clean/transformers.py new file mode 100644 index 0000000..d20a55a --- /dev/null +++ b/src/a4d/clean/transformers.py @@ -0,0 +1,385 @@ +"""Data transformation functions for cleaning. + +This module provides transformation functions that are applied before validation. +These functions standardize values, fix legacy formats, and normalize data. + +Transformations are referenced in reference_data/data_cleaning.yaml with +type: basic_function. +""" + +import polars as pl + +from a4d.config import settings + + +def extract_regimen(df: pl.DataFrame, column: str = "insulin_regimen") -> pl.DataFrame: + """Extract and standardize insulin regimen values. + + This function applies regex pattern matching to standardize insulin regimen + descriptions into canonical forms. Matches are case-insensitive. + + Transformations: + - Contains "basal" → "Basal-bolus (MDI)" + - Contains "premixed" → "Premixed 30/70 BD" + - Contains "self-mixed" → "Self-mixed BD" + - Contains "conventional" → "Modified conventional TID" + + Args: + df: Input DataFrame + column: Column name to transform (default: "insulin_regimen") + + Returns: + DataFrame with standardized insulin regimen values + + Example: + >>> df = extract_regimen(df) + >>> # "Basal-bolus" → "Basal-bolus (MDI)" + >>> # "PREMIXED 30/70" → "Premixed 30/70 BD" + """ + if column not in df.columns: + return df + + # Apply regex transformations in order (matching R's behavior) + df = df.with_columns( + pl.col(column) + .str.to_lowercase() + .str.replace(r"^.*basal.*$", "Basal-bolus (MDI)") + .str.replace(r"^.*premixed.*$", "Premixed 30/70 BD") + .str.replace(r"^.*self-mixed.*$", "Self-mixed BD") + .str.replace(r"^.*conventional.*$", "Modified conventional TID") + .alias(column) + ) + + return df + + +def fix_sex(df: pl.DataFrame, column: str = "sex") -> pl.DataFrame: + """Map sex synonyms to canonical values (M/F) or error value. + + Matches R's fix_sex() function behavior: + - Female synonyms: female, girl, woman, fem, feminine, f → "F" + - Male synonyms: male, boy, man, masculine, m → "M" + - Anything else → "Undefined" (error value) + + Args: + df: Input DataFrame + column: Column name to transform (default: "sex") + + Returns: + DataFrame with sex values normalized to M/F or Undefined + + Example: + >>> df = fix_sex(df) + >>> # "Female" → "F" + >>> # "MALE" → "M" + >>> # "invalid" → "Undefined" + """ + if column not in df.columns: + return df + + # Define synonyms matching R's fix_sex function + synonyms_female = ["female", "girl", "woman", "fem", "feminine", "f"] + synonyms_male = ["male", "boy", "man", "masculine", "m"] + + # Build expression using pl.when().then().when().then()... chain + # Start with null/empty handling + expr = pl.when(pl.col(column).is_null() | (pl.col(column) == "")).then(None) + + # Add female synonyms + for synonym in synonyms_female: + expr = expr.when(pl.col(column).str.to_lowercase() == synonym).then(pl.lit("F")) + + # Add male synonyms + for synonym in synonyms_male: + expr = expr.when(pl.col(column).str.to_lowercase() == synonym).then(pl.lit("M")) + + # Default: anything else becomes Undefined + expr = expr.otherwise(pl.lit(settings.error_val_character)) + + df = df.with_columns(expr.alias(column)) + + return df + + +def fix_bmi(df: pl.DataFrame) -> pl.DataFrame: + """Calculate BMI from weight and height. + + Matches R's fix_bmi() function behavior: + - If weight or height is null → BMI becomes null + - If weight or height is error value → BMI becomes error value + - Otherwise: BMI = weight / height^2 + + Height is converted from cm to m if > 50 (R's transform_cm_to_m threshold). + This ensures correct BMI regardless of whether height is in cm or m. + + This calculation REPLACES any existing BMI value, matching R's behavior. + + Args: + df: Input DataFrame (must have weight and height columns) + + Returns: + DataFrame with calculated BMI column + + Example: + >>> df = fix_bmi(df) + >>> # weight=70, height=1.75 → bmi=22.86 + >>> # weight=30.7, height=135.5 (cm) → height_m=1.355, bmi=16.72 + """ + if "weight" not in df.columns or "height" not in df.columns: + return df + + # Convert height from cm to m if > 50 (R's transform_cm_to_m threshold) + height_m = ( + pl.when(pl.col("height") > 50).then(pl.col("height") / 100.0).otherwise(pl.col("height")) + ) + + # Calculate BMI: weight / height^2 + # Match R's case_when logic exactly + df = df.with_columns( + pl.when(pl.col("weight").is_null() | pl.col("height").is_null()) + .then(None) + .when( + (pl.col("weight") == settings.error_val_numeric) + | (pl.col("height") == settings.error_val_numeric) + ) + .then(pl.lit(settings.error_val_numeric)) + .otherwise(pl.col("weight") / height_m.pow(2)) + .alias("bmi") + ) + + return df + + +def str_to_lower(df: pl.DataFrame, column: str) -> pl.DataFrame: + """Convert column values to lowercase. + + This is used for case-insensitive validation. For example, the "status" + column may have mixed case values like "Active", "ACTIVE", "active" which + should all be normalized to lowercase before validation. + + Args: + df: Input DataFrame + column: Column name to transform + + Returns: + DataFrame with lowercase column values + + Example: + >>> df = str_to_lower(df, "status") + >>> # "ACTIVE" → "active" + >>> # "Inactive" → "inactive" + """ + if column not in df.columns: + return df + + df = df.with_columns(pl.col(column).str.to_lowercase().alias(column)) + + return df + + +def apply_transformation( + df: pl.DataFrame, + column: str, + function_name: str, +) -> pl.DataFrame: + """Apply a named transformation function to a column. + + This is the dispatcher function that maps function names from + data_cleaning.yaml to actual transformation functions. + + Args: + df: Input DataFrame + column: Column name to transform + function_name: Name of transformation function (from YAML) + + Returns: + DataFrame with transformation applied + + Raises: + ValueError: If function_name is not recognized + + Example: + >>> df = apply_transformation(df, "status", "stringr::str_to_lower") + >>> df = apply_transformation(df, "insulin_regimen", "extract_regimen") + """ + # Map R function names to Python implementations + function_mapping = { + "extract_regimen": lambda df, col: extract_regimen(df, col), + "stringr::str_to_lower": lambda df, col: str_to_lower(df, col), + "str_to_lower": lambda df, col: str_to_lower(df, col), + } + + if function_name not in function_mapping: + raise ValueError(f"Unknown transformation function: {function_name}") + + return function_mapping[function_name](df, column) + + +def correct_decimal_sign_multiple( + df: pl.DataFrame, + columns: list[str], +) -> pl.DataFrame: + """Replace comma decimal separator with dot for multiple columns. + + Some trackers use European decimal format (1,5 instead of 1.5). + This function fixes that for multiple numeric columns. + + Args: + df: Input DataFrame + columns: List of column names to correct + + Returns: + DataFrame with corrected decimal signs + + Example: + >>> df = correct_decimal_sign_multiple(df, ["weight", "height", "hba1c"]) + """ + from a4d.clean.converters import correct_decimal_sign + + for column in columns: + df = correct_decimal_sign(df, column) + + return df + + +def replace_range_with_mean(x: str) -> float: + """Calculate mean of a range string. + + Matches R's replace_range_with_mean() function behavior. + Splits string on "-", converts parts to numeric, returns mean. + + Args: + x: Range string (e.g., "0-2", "2-3") + + Returns: + Mean of the range values + + Example: + >>> replace_range_with_mean("0-2") + 1.0 + >>> replace_range_with_mean("2-3") + 2.5 + """ + parts = x.split("-") + numbers = [float(p) for p in parts] + return sum(numbers) / len(numbers) + + +def fix_testing_frequency(df: pl.DataFrame) -> pl.DataFrame: + """Fix testing_frequency column by replacing ranges with mean values. + + Matches R's fix_testing_frequency() function behavior: + - Replaces ranges like "0-2" with mean "1" + - Preserves null and empty values as null + - Logs warning when ranges are detected + + Args: + df: Input DataFrame + + Returns: + DataFrame with testing_frequency ranges replaced by mean values + + Example: + >>> df = fix_testing_frequency(df) + >>> # "0-2" → "1" + >>> # "2-3" → "2.5" + >>> # "2" → "2" (unchanged) + """ + if "testing_frequency" not in df.columns: + return df + + from loguru import logger + + # Track if we logged warnings + has_ranges = False + + def fix_value(value: str | None) -> str | None: + """Fix a single testing_frequency value.""" + nonlocal has_ranges + + if value is None or value == "": + return None + + if "-" in value: + has_ranges = True + + try: + mean_value = replace_range_with_mean(value) + # Return as string, remove trailing .0 for whole numbers + if mean_value == int(mean_value): + return str(int(mean_value)) + return str(mean_value) + except Exception: + # If replacement fails, return None + return None + + return value + + # Apply transformation + df = df.with_columns( + pl.col("testing_frequency") + .map_elements(fix_value, return_dtype=pl.String) + .alias("testing_frequency") + ) + + # Log warning if any ranges were found + if has_ranges: + logger.bind(error_code="invalid_value").warning("Found ranges in testing_frequency column. Replacing with mean values.") + + return df + + +def split_bp_in_sys_and_dias(df: pl.DataFrame) -> pl.DataFrame: + """Split blood_pressure_mmhg into systolic and diastolic columns. + + Matches R's split_bp_in_sys_and_dias() function behavior: + - Splits "120/80" format into two columns + - Invalid formats (without "/") are replaced with error value + - Logs warning for invalid values + + Args: + df: Input DataFrame with blood_pressure_mmhg column + + Returns: + DataFrame with blood_pressure_sys_mmhg and blood_pressure_dias_mmhg columns + + Example: + >>> df = split_bp_in_sys_and_dias(df) + >>> # "96/55" → sys="96", dias="55" + >>> # "96" → sys="999999", dias="999999" (invalid) + """ + if "blood_pressure_mmhg" not in df.columns: + return df + + from loguru import logger + + # First, replace invalid values (those without "/") with error format + error_val_int = int(settings.error_val_numeric) + df = df.with_columns( + pl.when(~pl.col("blood_pressure_mmhg").str.contains("/", literal=True)) + .then(pl.lit(f"{error_val_int}/{error_val_int}")) + .otherwise(pl.col("blood_pressure_mmhg")) + .alias("blood_pressure_mmhg") + ) + + # Check if any invalid values were found + error_pattern = f"{error_val_int}/{error_val_int}" + has_errors = df.filter(pl.col("blood_pressure_mmhg") == error_pattern).height > 0 + + if has_errors: + logger.bind(error_code="invalid_value").warning( + "Found invalid values for column blood_pressure_mmhg " + f"that do not follow the format X/Y. " + f"Values were replaced with {error_val_int}." + ) + + # Split the column + df = df.with_columns( + pl.col("blood_pressure_mmhg").str.split("/").list.get(0).alias("blood_pressure_sys_mmhg"), + pl.col("blood_pressure_mmhg").str.split("/").list.get(1).alias("blood_pressure_dias_mmhg"), + ) + + # Drop the original combined column + df = df.drop("blood_pressure_mmhg") + + return df diff --git a/src/a4d/clean/validators.py b/src/a4d/clean/validators.py new file mode 100644 index 0000000..f279d52 --- /dev/null +++ b/src/a4d/clean/validators.py @@ -0,0 +1,423 @@ +"""Schema and validation utilities for data cleaning. + +This module provides functions for validating DataFrame columns against +allowed values defined in reference_data/validation_rules.yaml. + +The validation pattern is: +1. Load validation rules from YAML +2. Check column values against allowed values +3. Log invalid values to ErrorCollector +4. Replace invalid values with error value (if configured) + +Note: Data transformations are NOT in the YAML - they are hardcoded in +transformers.py for better type safety and maintainability. +""" + +import re +from typing import Any + +import polars as pl + +from a4d.config import settings +from a4d.errors import ErrorCollector +from a4d.reference.loaders import get_reference_data_path, load_yaml + + +def sanitize_str(text: str) -> str: + """Sanitize string for case-insensitive matching. + + Matches R's sanitize_str function: + 1. Convert to lowercase + 2. Remove spaces + 3. Remove special characters (keep only alphanumeric) + + Args: + text: String to sanitize + + Returns: + Sanitized string + + Example: + >>> sanitize_str("Active - Remote") + 'activeremote' + >>> sanitize_str("Lost Follow Up") + 'lostfollowup' + """ + if not isinstance(text, str): + return text + return re.sub(r"[^a-z0-9]", "", text.lower()) + + +def load_validation_rules() -> dict[str, Any]: + """Load validation rules from validation_rules.yaml. + + Returns: + Dictionary mapping column names to their validation rules. + Structure: {column_name: {allowed_values: [...], replace_invalid: bool}} + + Example: + >>> rules = load_validation_rules() + >>> rules["status"]["allowed_values"] + ['active', 'inactive', ...] + >>> rules["status"]["replace_invalid"] + True + """ + yaml_path = get_reference_data_path("validation_rules.yaml") + return load_yaml(yaml_path) + + +def validate_allowed_values( + df: pl.DataFrame, + column: str, + allowed_values: list[str], + error_collector: ErrorCollector, + replace_invalid: bool = True, + file_name_col: str = "file_name", + patient_id_col: str = "patient_id", +) -> pl.DataFrame: + """Validate column against allowed values with case-insensitive matching. + + Matches R's validation behavior: + 1. Sanitize both input values and allowed values for matching + 2. If matched, replace with canonical value from allowed_values + 3. If not matched, replace with error value (if replace_invalid=True) + + Args: + df: Input DataFrame + column: Column name to validate + allowed_values: List of canonical allowed values (e.g., ["Active", "Inactive"]) + error_collector: ErrorCollector instance to track violations + replace_invalid: If True, replace invalid values with error value + file_name_col: Column containing file name for error tracking + patient_id_col: Column containing patient ID for error tracking + + Returns: + DataFrame with values normalized to canonical form or replaced + + Example: + >>> collector = ErrorCollector() + >>> df = validate_allowed_values( + ... df=df, + ... column="status", + ... allowed_values=["Active", "Inactive"], # Canonical forms + ... error_collector=collector, + ... ) + >>> # "active", "ACTIVE", "Active" all become "Active" + """ + if column not in df.columns: + return df + + # Create mapping: {sanitized → canonical} like R does + # E.g., {"active": "Active", "activeremote": "Active - Remote"} + canonical_mapping = {sanitize_str(val): val for val in allowed_values} + + # Get unique non-null values from the column + col_values = df.filter(pl.col(column).is_not_null()).select(column).unique() + + # Track which values need replacement and their canonical forms + value_replacements = {} # {original → canonical or error_value} + + for row in col_values.iter_rows(named=True): + original_val = row[column] + + # Skip if already the error value + if original_val == settings.error_val_character: + value_replacements[original_val] = original_val + continue + + # Sanitize and lookup + sanitized = sanitize_str(original_val) + + if sanitized in canonical_mapping: + # Valid - replace with canonical value + value_replacements[original_val] = canonical_mapping[sanitized] + else: + # Invalid - log error + error_collector.add_error( + file_name="unknown", # Will be filled in bulk operations + patient_id="unknown", + column=column, + original_value=original_val, + error_message=f"Value '{original_val}' not in allowed values: {allowed_values}", + error_code="invalid_value", + function_name="validate_allowed_values", + ) + + if replace_invalid: + value_replacements[original_val] = settings.error_val_character + else: + value_replacements[original_val] = original_val + + # Apply all replacements at once using pl.when().then() chain + # This ensures we replace with canonical values even if they match + if value_replacements: + expr = pl.col(column) + for original, replacement in value_replacements.items(): + expr = pl.when(pl.col(column) == original).then(pl.lit(replacement)).otherwise(expr) + + df = df.with_columns(expr.alias(column)) + + return df + + +def validate_column_from_rules( + df: pl.DataFrame, + column: str, + rules: dict[str, Any], + error_collector: ErrorCollector, + file_name_col: str = "file_name", + patient_id_col: str = "patient_id", +) -> pl.DataFrame: + """Validate column using rules from validation_rules.yaml. + + Args: + df: Input DataFrame + column: Column name to validate + rules: Validation rules for this column (from validation_rules.yaml) + Structure: {allowed_values: [...], replace_invalid: bool} + error_collector: ErrorCollector instance + file_name_col: Column containing file name for error tracking + patient_id_col: Column containing patient ID for error tracking + + Returns: + DataFrame with column validated and cleaned + + Example: + >>> rules = load_validation_rules() + >>> collector = ErrorCollector() + >>> df = validate_column_from_rules( + ... df=df, + ... column="status", + ... rules=rules["status"], + ... error_collector=collector, + ... ) + """ + if column not in df.columns: + return df + + # Extract validation parameters from simplified rules + allowed_values = rules.get("allowed_values", []) + replace_invalid = rules.get("replace_invalid", True) + + df = validate_allowed_values( + df=df, + column=column, + allowed_values=allowed_values, + error_collector=error_collector, + replace_invalid=replace_invalid, + file_name_col=file_name_col, + patient_id_col=patient_id_col, + ) + + return df + + +def validate_province( + df: pl.DataFrame, + error_collector: ErrorCollector, + file_name_col: str = "file_name", + patient_id_col: str = "patient_id", +) -> pl.DataFrame: + """Validate province column against allowed provinces from YAML. + + Uses the shared allowed_provinces.yaml file to validate province values. + Matches R's behavior: sanitizes values for comparison and sets invalid + provinces to "Undefined". + + Args: + df: Input DataFrame + error_collector: ErrorCollector instance + file_name_col: Column containing file name for error tracking + patient_id_col: Column containing patient ID for error tracking + + Returns: + DataFrame with province validated + + Example: + >>> collector = ErrorCollector() + >>> df = validate_province(df, collector) + """ + from a4d.reference.provinces import load_canonical_provinces + + if "province" not in df.columns: + return df + + # Load canonical province names (with proper casing) for validation + allowed_provinces = load_canonical_provinces() + + # Use generic validator with loaded provinces + df = validate_allowed_values( + df=df, + column="province", + allowed_values=allowed_provinces, + error_collector=error_collector, + replace_invalid=True, + file_name_col=file_name_col, + patient_id_col=patient_id_col, + ) + + return df + + +def validate_all_columns( + df: pl.DataFrame, + error_collector: ErrorCollector, + file_name_col: str = "file_name", + patient_id_col: str = "patient_id", +) -> pl.DataFrame: + """Validate all columns that have rules in data_cleaning.yaml. + + Args: + df: Input DataFrame + error_collector: ErrorCollector instance + file_name_col: Column containing file name for error tracking + patient_id_col: Column containing patient ID for error tracking + + Returns: + DataFrame with all columns validated + + Example: + >>> collector = ErrorCollector() + >>> df_clean = validate_all_columns(df, collector) + >>> len(collector) # Number of validation errors found + """ + rules = load_validation_rules() + + for column, column_rules in rules.items(): + if column in df.columns: + df = validate_column_from_rules( + df=df, + column=column, + rules=column_rules, + error_collector=error_collector, + file_name_col=file_name_col, + patient_id_col=patient_id_col, + ) + + # Validate province separately (not in validation_rules.yaml) + df = validate_province( + df=df, + error_collector=error_collector, + file_name_col=file_name_col, + patient_id_col=patient_id_col, + ) + + # Fix patient_id LAST (other functions use it for logging) + df = fix_patient_id( + df=df, + error_collector=error_collector, + patient_id_col=patient_id_col, + ) + + return df + + +def fix_patient_id( + df: pl.DataFrame, + error_collector: ErrorCollector, + patient_id_col: str = "patient_id", +) -> pl.DataFrame: + """Validate and fix patient ID format. + + Matches R's fix_id() function behavior: + - Valid format: XX_YY### (e.g., "KD_EW004") + - 2 uppercase letters, underscore, 2 uppercase letters, 3 digits + - Normalizes hyphens to underscores: "KD-EW004" → "KD_EW004" + - Truncates if > 8 characters: "KD_EW004XY" → "KD_EW004" + - Replaces with error value if ≤ 8 chars and invalid format + + This function should be called LAST in the validation pipeline because + other functions use patient_id for error logging. + + Args: + df: Input DataFrame + error_collector: ErrorCollector for tracking validation errors + patient_id_col: Column name for patient ID (default: "patient_id") + + Returns: + DataFrame with validated/fixed patient IDs + + Example: + >>> df = fix_patient_id(df, error_collector) + >>> # "KD_EW004" → "KD_EW004" (valid) + >>> # "KD-EW004" → "KD_EW004" (normalized) + >>> # "KD_EW004XY" → "KD_EW004" (truncated) + >>> # "INVALID" → "Other" (replaced) + """ + import re + + from a4d.config import settings + + if patient_id_col not in df.columns: + return df + + # Store original values for error reporting + original_col = f"{patient_id_col}_original" + df = df.with_columns(pl.col(patient_id_col).alias(original_col)) + + # Valid format: XX_YY### (2 letters, underscore, 2 letters, 3 digits) + valid_pattern = re.compile(r"^[A-Z]{2}_[A-Z]{2}\d{3}$") + + def fix_single_id(patient_id: str | None) -> str | None: + """Fix a single patient ID value.""" + if patient_id is None: + return None + + # Step 1: Replace hyphens with underscores + patient_id = patient_id.replace("-", "_") + + # Step 2: Check if it matches the valid pattern + if valid_pattern.match(patient_id): + return patient_id + + # Step 3: Invalid format - either truncate or replace + if len(patient_id) > 8: + # Truncate to 8 characters + return patient_id[:8] + else: + # Replace with error value + return settings.error_val_character + + # Apply transformation + df = df.with_columns( + pl.col(patient_id_col) + .map_elements(fix_single_id, return_dtype=pl.String) + .alias(patient_id_col) + ) + + # Now collect errors for changed values + for row in df.iter_rows(named=True): + original = row[original_col] + fixed = row[patient_id_col] + + if original != fixed and original is not None: + # Normalize original to check if it's just hyphen replacement + normalized = original.replace("-", "_") + + if normalized != fixed: + # Not just normalization - either truncation or replacement + if len(original.replace("-", "_")) > 8: + # Truncation + error_collector.add_error( + file_name="", + patient_id=original, + column=patient_id_col, + original_value=original, + error_message="Patient ID truncated (length > 8)", + error_code="invalid_value", + ) + else: + # Replacement + error_collector.add_error( + file_name="", + patient_id=original, + column=patient_id_col, + original_value=original, + error_message="Invalid patient ID format (expected XX_YY###)", + error_code="invalid_value", + ) + + # Drop the temporary column + df = df.drop(original_col) + + return df diff --git a/src/a4d/cli.py b/src/a4d/cli.py new file mode 100644 index 0000000..c4b0a96 --- /dev/null +++ b/src/a4d/cli.py @@ -0,0 +1,740 @@ +"""Command-line interface for A4D pipeline.""" + +import warnings +from datetime import datetime +from pathlib import Path +from typing import Annotated + +import polars as pl +import typer +from rich.console import Console +from rich.table import Table + +from a4d.pipeline.patient import ( + discover_tracker_files, + process_patient_tables, + run_patient_pipeline, +) +from a4d.tables.logs import create_table_logs + +# google-crc32c has no pre-built C wheel for Python 3.14 yet; the pure-Python +# fallback is correct, just slightly slower. Suppress the noisy runtime warning +# before any google SDK calls are made (those happen lazily inside commands). +warnings.filterwarnings( + "ignore", message="As the c extension couldn't be imported", category=RuntimeWarning +) + +app = typer.Typer( + name="a4d", help="A4D medical tracker data processing pipeline", no_args_is_help=True +) + +console = Console() + + +def _display_tables_summary(tables: dict[str, Path]) -> None: + """Display summary table of created tables with record counts. + + Args: + tables: Dictionary mapping table name to output path + """ + if not tables: + return + + console.print("\n[bold green]Created Tables:[/bold green]") + tables_table = Table(title="Created Tables") + tables_table.add_column("Table", style="cyan") + tables_table.add_column("Path", style="green") + tables_table.add_column("Records", justify="right", style="magenta") + + # Add patient tables first, then logs table + for name in ["static", "monthly", "annual"]: + if name in tables: + path = tables[name] + try: + df = pl.read_parquet(path) + record_count = f"{len(df):,}" + except Exception: + record_count = "?" + tables_table.add_row(name, str(path.name), record_count) + + # Add logs table last + if "logs" in tables: + path = tables["logs"] + try: + df = pl.read_parquet(path) + record_count = f"{len(df):,}" + except Exception: + record_count = "?" + tables_table.add_row("logs", str(path.name), record_count) + + console.print(tables_table) + console.print() + + +@app.command("process-patient") +def process_patient_cmd( + file: Annotated[ + Path | None, + typer.Option( + "--file", + "-f", + help="Process specific tracker file (if not set, processes all files in data_root)", + ), + ] = None, + workers: Annotated[ + int | None, + typer.Option( + "--workers", "-w", help="Number of parallel workers (default: A4D_MAX_WORKERS)" + ), + ] = None, + skip_tables: Annotated[ + bool, typer.Option("--skip-tables", help="Skip table creation (only extract + clean)") + ] = False, + force: Annotated[ + bool, typer.Option("--force", help="Force reprocessing (ignore existing outputs)") + ] = False, + data_root: Annotated[ + Path | None, + typer.Option( + "--data-root", "-d", help="Directory containing tracker files (default: from config)" + ), + ] = None, + output_root: Annotated[ + Path | None, typer.Option("--output", "-o", help="Output directory (default: from config)") + ] = None, +): + """Process patient data pipeline. + + \b + Output is always cleaned before each run so tables reflect only the + current run's files. + + Examples: + # Process all trackers in data_root (from config) + uv run a4d process-patient + + # Process all trackers in a specific directory + uv run a4d process-patient --data-root /path/to/trackers + + # Process specific file + uv run a4d process-patient --file /path/to/tracker.xlsx + + # Parallel processing with 8 workers + uv run a4d process-patient --workers 8 + + # Just extract + clean, skip tables + uv run a4d process-patient --skip-tables + """ + from a4d.config import settings as _settings + + console.print("\n[bold blue]A4D Patient Pipeline[/bold blue]\n") + + if file: + tracker_files = [file] + data_root_display = f"{file} (single file)" + elif data_root: + tracker_files = discover_tracker_files(data_root) + if not tracker_files: + console.print(f"[bold red]Error: No tracker files found in {data_root}[/bold red]\n") + raise typer.Exit(1) + data_root_display = str(data_root) + else: + tracker_files = None # pipeline uses settings.data_root + data_root_display = str(_settings.data_root) + + _output_root = output_root or _settings.output_root + _workers = workers if workers is not None else _settings.max_workers + + console.print(f"Data root: {data_root_display}") + console.print(f"Output root: {_output_root}") + console.print(f"Workers: {_workers}") + if skip_tables: + console.print("Tables: skipped") + if force: + console.print("Force: yes") + console.print() + + # Step 1: Extract + clean (table creation handled below for visible progress) + console.print("[bold]Step 1/3:[/bold] Extracting and cleaning tracker files...") + try: + result = run_patient_pipeline( + tracker_files=tracker_files, + max_workers=_workers, + output_root=output_root, + skip_tables=True, # tables created below with console feedback + force=force, + clean_output=True, + show_progress=True, + console_log_level="ERROR", + ) + except Exception as e: + console.print(f"\n[bold red]Error: {e}[/bold red]\n") + raise typer.Exit(1) from e + + # Step 2+3: Table and log creation with console feedback + tables: dict[str, Path] = {} + if not skip_tables and result.successful_trackers > 0: + cleaned_dir = _output_root / "patient_data_cleaned" + tables_dir = _output_root / "tables" + logs_dir = _output_root / "logs" + + console.print("[bold]Step 2/3:[/bold] Creating patient tables...") + try: + tables = process_patient_tables(cleaned_dir, tables_dir) + except Exception as e: + console.print(f"[bold red]Error creating tables: {e}[/bold red]") + + if logs_dir.exists(): + console.print("[bold]Step 3/3:[/bold] Creating logs table...") + try: + logs_table_path = create_table_logs(logs_dir, tables_dir) + tables["logs"] = logs_table_path + except Exception as e: + console.print(f"[bold red]Error creating logs table: {e}[/bold red]") + elif skip_tables: + console.print("[dim]Steps 2–3: Skipped (--skip-tables)[/dim]") + + # Display results + console.print("\n[bold]Pipeline Results[/bold]\n") + + # Calculate error statistics + total_errors = sum(tr.cleaning_errors for tr in result.tracker_results) + files_with_errors = sum(1 for tr in result.tracker_results if tr.cleaning_errors > 0) + + summary_table = Table(title="Summary") + summary_table.add_column("Metric", style="cyan") + summary_table.add_column("Value", style="green") + + summary_table.add_row("Total Trackers", str(result.total_trackers)) + summary_table.add_row("Successful", str(result.successful_trackers)) + summary_table.add_row("Failed", str(result.failed_trackers)) + summary_table.add_row("Tables Created", str(len(tables))) + summary_table.add_row("", "") # Spacer + summary_table.add_row("Data Quality Errors", f"{total_errors:,}") + summary_table.add_row("Files with Errors", str(files_with_errors)) + + console.print(summary_table) + + # Show error type breakdown if there are errors + if total_errors > 0: + console.print("\n[bold yellow]Error Type Breakdown:[/bold yellow]") + + # Aggregate error types across all trackers + error_type_totals: dict[str, int] = {} + for tr in result.tracker_results: + if tr.error_breakdown: + for error_type, count in tr.error_breakdown.items(): + error_type_totals[error_type] = error_type_totals.get(error_type, 0) + count + + # Create frequency table + error_type_table = Table() + error_type_table.add_column("Error Type", style="yellow") + error_type_table.add_column("Count", justify="right", style="red") + error_type_table.add_column("Percentage", justify="right", style="cyan") + + # Sort by count (descending) + sorted_error_types = sorted(error_type_totals.items(), key=lambda x: x[1], reverse=True) + + for error_type, count in sorted_error_types: + percentage = (count / total_errors) * 100 + error_type_table.add_row(error_type, f"{count:,}", f"{percentage:.1f}%") + + console.print(error_type_table) + + # Show failed trackers if any + if result.failed_trackers > 0: + console.print("\n[bold yellow]Failed Trackers:[/bold yellow]") + failed_table = Table() + failed_table.add_column("File", style="red") + failed_table.add_column("Error") + + for tr in result.tracker_results: + if not tr.success: + failed_table.add_row( + tr.tracker_file.name, + str(tr.error)[:100], # Truncate long errors + ) + + console.print(failed_table) + + # Show top files with most data quality errors (if any) + if total_errors > 0: + console.print("\n[bold yellow]Top Files by Error Count:[/bold yellow]") + # Sort by error count (descending) and take top 10 + files_by_errors = sorted( + [ + (tr.tracker_file.name, tr.cleaning_errors) + for tr in result.tracker_results + if tr.cleaning_errors > 0 + ], + key=lambda x: x[1], + reverse=True, + )[:10] + + errors_table = Table() + errors_table.add_column("File", style="yellow") + errors_table.add_column("Errors", justify="right", style="red") + + for filename, error_count in files_by_errors: + errors_table.add_row(filename, f"{error_count:,}") + + console.print(errors_table) + + # Show created tables + _display_tables_summary(tables) + + # Exit status + if result.success: + console.print("\n[bold green]✓ Pipeline completed successfully![/bold green]\n") + raise typer.Exit(0) + else: + console.print( + f"\n[bold red]✗ Pipeline completed with {result.failed_trackers} failures[/bold red]\n" + ) + raise typer.Exit(1) + + +@app.command("create-tables") +def create_tables_cmd( + input_dir: Annotated[ + Path, typer.Option("--input", "-i", help="Directory containing cleaned parquet files") + ], + output_dir: Annotated[ + Path | None, + typer.Option( + "--output", "-o", help="Output directory for tables (default: input_dir/tables)" + ), + ] = None, +): + """Create final tables from existing cleaned parquet files. + + This command creates the patient tables (static, monthly, annual) and logs table + from existing cleaned parquet files, without running the full pipeline. + + Useful for: + - Re-creating tables after fixing table creation logic + - Creating tables from manually cleaned data + - Testing table creation independently + + \\b + Examples: + # Create tables from existing output + uv run a4d create-tables --input output/patient_data_cleaned + + # Specify custom output directory + uv run a4d create-tables --input output/patient_data_cleaned --output custom_tables + """ + console.print("\n[bold blue]A4D Table Creation[/bold blue]\n") + + # Determine output directory + if output_dir is None: + output_dir = input_dir.parent / "tables" + + console.print(f"Input directory: {input_dir}") + console.print(f"Output directory: {output_dir}\n") + + # Find cleaned parquet files + cleaned_files = list(input_dir.glob("*_patient_cleaned.parquet")) + if not cleaned_files: + console.print( + f"[bold red]Error: No cleaned parquet files found in {input_dir}[/bold red]\n" + ) + raise typer.Exit(1) + + console.print(f"Found {len(cleaned_files)} cleaned parquet files\n") + + try: + from a4d.tables.clinic import create_table_clinic_static + + console.print("[bold]Creating tables...[/bold]") + + # Create patient tables + tables = process_patient_tables(input_dir, output_dir) + + # Create logs table separately (operational data) + logs_dir = input_dir.parent / "logs" + if logs_dir.exists(): + console.print(" • Creating logs table...") + logs_table_path = create_table_logs(logs_dir, output_dir) + tables["logs"] = logs_table_path + else: + console.print(f" [yellow]Warning: Logs directory not found at {logs_dir}[/yellow]") + + # Create clinic static table (reads reference_data/clinic_data.xlsx) + console.print(" • Creating clinic static table...") + clinic_table_path = create_table_clinic_static(output_dir) + tables["clinic_data_static"] = clinic_table_path + + # Display results + console.print("\n[bold green]✓ Tables created successfully![/bold green]") + _display_tables_summary(tables) + + except Exception as e: + console.print(f"\n[bold red]Error creating tables: {e}[/bold red]\n") + raise typer.Exit(1) from e + + +@app.command("upload-tables") +def upload_tables_cmd( + tables_dir: Annotated[ + Path, + typer.Option("--tables-dir", "-t", help="Directory containing parquet table files"), + ], + dataset: Annotated[ + str | None, + typer.Option("--dataset", "-d", help="BigQuery dataset name (default: from config)"), + ] = None, + project_id: Annotated[ + str | None, + typer.Option("--project", "-p", help="GCP project ID (default: from config)"), + ] = None, + append: Annotated[ + bool, + typer.Option("--append", help="Append to existing tables instead of replacing"), + ] = False, +): + """Upload pipeline output tables to BigQuery. + + Loads parquet files from the tables directory into the configured + BigQuery dataset. By default, existing tables are replaced (matching + the R pipeline behavior). + + \b + Examples: + # Upload tables from default output directory + uv run a4d upload-tables --tables-dir output/tables + + # Upload to a specific dataset + uv run a4d upload-tables --tables-dir output/tables --dataset tracker_dev + + # Append instead of replace + uv run a4d upload-tables --tables-dir output/tables --append + """ + from a4d.gcp.bigquery import load_pipeline_tables + + console.print("\n[bold blue]A4D BigQuery Upload[/bold blue]\n") + console.print(f"Tables directory: {tables_dir}") + + if not tables_dir.exists(): + console.print(f"[bold red]Error: Directory not found: {tables_dir}[/bold red]\n") + raise typer.Exit(1) + + try: + results = load_pipeline_tables( + tables_dir=tables_dir, + dataset=dataset, + project_id=project_id, + replace=not append, + ) + + if results: + result_table = Table(title="Uploaded Tables") + result_table.add_column("Table", style="cyan") + result_table.add_column("Rows", justify="right", style="green") + result_table.add_column("Status", style="green") + + for table_name, job in results.items(): + result_table.add_row( + table_name, + f"{job.output_rows:,}" if job.output_rows else "?", + "✓", + ) + + console.print(result_table) + console.print( + f"\n[bold green]✓ Uploaded {len(results)} tables to BigQuery[/bold green]\n" + ) + else: + console.print("[bold yellow]No tables found to upload[/bold yellow]\n") + + except Exception as e: + console.print(f"\n[bold red]Error: {e}[/bold red]\n") + raise typer.Exit(1) from e + + +@app.command("download-trackers") +def download_trackers_cmd( + destination: Annotated[ + Path, + typer.Option("--destination", "-d", help="Local directory to download files to"), + ], + bucket: Annotated[ + str | None, + typer.Option("--bucket", "-b", help="GCS bucket name (default: from config)"), + ] = None, +): + """Download tracker files from Google Cloud Storage. + + \b + Examples: + # Download to local directory + uv run a4d download-trackers --destination /data/trackers + + # Download from specific bucket + uv run a4d download-trackers --destination /data/trackers --bucket my-bucket + """ + from a4d.gcp.storage import download_tracker_files + + console.print("\n[bold blue]A4D Tracker Download[/bold blue]\n") + console.print(f"Destination: {destination}") + + try: + downloaded = download_tracker_files(destination=destination, bucket_name=bucket) + console.print(f"\n[bold green]✓ Downloaded {len(downloaded)} files[/bold green]\n") + except Exception as e: + console.print(f"\n[bold red]Error: {e}[/bold red]\n") + raise typer.Exit(1) from e + + +@app.command("upload-output") +def upload_output_cmd( + source_dir: Annotated[ + Path, + typer.Option("--source", "-s", help="Output directory to upload"), + ], + bucket: Annotated[ + str | None, + typer.Option("--bucket", "-b", help="GCS bucket name (default: from config)"), + ] = None, + prefix: Annotated[ + str, + typer.Option("--prefix", help="Prefix for uploaded blob names"), + ] = "", +): + """Upload pipeline output to Google Cloud Storage. + + \b + Examples: + # Upload output directory + uv run a4d upload-output --source output/ + + # Upload with prefix + uv run a4d upload-output --source output/ --prefix 2024-01 + """ + from a4d.gcp.storage import upload_output + + console.print("\n[bold blue]A4D Output Upload[/bold blue]\n") + console.print(f"Source: {source_dir}") + + if not source_dir.exists(): + console.print(f"[bold red]Error: Directory not found: {source_dir}[/bold red]\n") + raise typer.Exit(1) + + try: + uploaded = upload_output(source_dir=source_dir, bucket_name=bucket, prefix=prefix) + console.print(f"\n[bold green]✓ Uploaded {len(uploaded)} files to GCS[/bold green]\n") + except Exception as e: + console.print(f"\n[bold red]Error: {e}[/bold red]\n") + raise typer.Exit(1) from e + + +@app.command("download-reference-data") +def download_reference_data_cmd() -> None: + """Download reference data files from Google Drive. + + Downloads clinic_data.xlsx from Google Drive into the reference_data/ + directory. Uses Application Default Credentials with Drive readonly scope. + + The service account must have at least Viewer access to the file. + """ + from a4d.gcp.drive import download_clinic_data + from a4d.reference.loaders import find_reference_data_dir + + console.print("\n[bold blue]A4D Reference Data Download[/bold blue]\n") + + reference_dir = find_reference_data_dir() + console.print(f"Destination: {reference_dir}\n") + + try: + console.print("Downloading clinic_data.xlsx from Google Drive...") + path = download_clinic_data(reference_dir) + size_kb = path.stat().st_size / 1024 + console.print(f" [bold green]✓[/bold green] clinic_data.xlsx ({size_kb:.1f} KB) -> {path}\n") + except Exception as e: + console.print(f" [bold red]✗ Download failed: {e}[/bold red]\n") + raise typer.Exit(1) from e + + +@app.command("run-pipeline") +def run_pipeline_cmd( + workers: Annotated[ + int | None, + typer.Option( + "--workers", "-w", help="Number of parallel workers (default: A4D_MAX_WORKERS)" + ), + ] = None, + force: Annotated[ + bool, typer.Option("--force", help="Force reprocessing (ignore existing outputs)") + ] = False, + skip_download: Annotated[ + bool, + typer.Option("--skip-download", help="Skip GCS download (use files already in data_root)"), + ] = False, + skip_upload: Annotated[ + bool, + typer.Option("--skip-upload", help="Skip GCS and BigQuery upload steps"), + ] = False, + skip_drive_download: Annotated[ + bool, + typer.Option( + "--skip-drive-download", + help="Skip Google Drive download of reference data (clinic_data.xlsx)", + ), + ] = False, +): + """Run the full end-to-end A4D pipeline. + + Executes all pipeline stages in sequence: + 0. Download reference data (clinic_data.xlsx) from Google Drive + 1. Download tracker files from Google Cloud Storage + 2. Extract and clean all tracker files + 3. Create final tables (static, monthly, annual, clinic) + 4. Upload output files to Google Cloud Storage + 5. Ingest tables into BigQuery + + All configuration is read from environment variables (A4D_*) or a .env file. + + \b + Examples: + # Full pipeline (download + process + upload) + uv run a4d run-pipeline + + # Download latest files, process locally, skip upload + uv run a4d run-pipeline --skip-upload + + # Process local files only, no download or upload + uv run a4d run-pipeline --skip-download --skip-upload + + # Skip Drive download if clinic_data.xlsx is already current + uv run a4d run-pipeline --skip-drive-download + """ + from a4d.config import settings + from a4d.gcp.bigquery import load_pipeline_tables + from a4d.gcp.drive import download_clinic_data + from a4d.gcp.storage import download_tracker_files, upload_output + from a4d.reference.loaders import find_reference_data_dir + from a4d.tables.clinic import create_table_clinic_static + + _workers = workers if workers is not None else settings.max_workers + run_ts = datetime.now().strftime("%Y/%m/%d/%H%M%S") + + console.print("\n[bold blue]A4D Full Pipeline[/bold blue]\n") + console.print(f"Data root: {settings.data_root}") + console.print(f"Output root: {settings.output_root}") + console.print(f"Workers: {_workers}") + console.print(f"Project: {settings.project_id}") + console.print(f"Dataset: {settings.dataset}") + console.print(f"Drive: {'yes' if not skip_drive_download else 'skipped (--skip-drive-download)'}") + console.print(f"Download: {'yes' if not skip_download else 'skipped (--skip-download)'}") + console.print(f"Upload: {'yes' if not skip_upload else 'skipped (--skip-upload)'}") + console.print() + + # Step 0 – Download reference data from Google Drive + if not skip_drive_download: + console.print("[bold]Step 0/5:[/bold] Downloading reference data from Google Drive...") + try: + reference_dir = find_reference_data_dir() + path = download_clinic_data(reference_dir) + size_kb = path.stat().st_size / 1024 + console.print(f" ✓ clinic_data.xlsx ({size_kb:.1f} KB)\n") + except Exception as e: + console.print(f"\n[bold red]Error downloading reference data: {e}[/bold red]\n") + raise typer.Exit(1) from e + else: + console.print("[bold]Step 0/5:[/bold] Skipping Drive download (--skip-drive-download)\n") + + # Step 1 – Download tracker files from GCS + if not skip_download: + console.print("[bold]Step 1/5:[/bold] Downloading tracker files from GCS...") + try: + downloaded = download_tracker_files(destination=settings.data_root) + console.print(f" ✓ Downloaded {len(downloaded)} files\n") + except Exception as e: + console.print(f"\n[bold red]Error during download: {e}[/bold red]\n") + raise typer.Exit(1) from e + else: + console.print("[bold]Step 1/5:[/bold] Skipping GCS download (--skip-download)\n") + + # Step 2+3 – Extract, clean and build tables + console.print("[bold]Steps 2–3/5:[/bold] Processing tracker files...\n") + try: + result = run_patient_pipeline( + max_workers=_workers, + force=force, + show_progress=True, + console_log_level="WARNING", + ) + + console.print( + f" ✓ Processed {result.total_trackers} trackers " + f"({result.successful_trackers} ok, {result.failed_trackers} failed)\n" + ) + + if result.failed_trackers > 0: + console.print("[bold yellow]Failed trackers:[/bold yellow]") + for tr in result.tracker_results: + if not tr.success: + console.print(f" • {tr.tracker_file.name}: {tr.error}") + console.print() + + if not result.success: + console.print("[bold red]✗ Pipeline failed – aborting upload steps[/bold red]\n") + raise typer.Exit(1) + + except Exception as e: + console.print(f"\n[bold red]Error during processing: {e}[/bold red]\n") + raise typer.Exit(1) from e + + tables_dir = settings.output_root / "tables" + logs_dir = settings.output_root / "logs" + + # Clinic static table — independent of tracker processing, always created + console.print("[bold]Step 3b/5:[/bold] Creating clinic static table...") + try: + create_table_clinic_static(tables_dir) + console.print(" ✓ Clinic static table created\n") + except Exception as e: + console.print(f" [bold red]Error creating clinic static table: {e}[/bold red]\n") + raise typer.Exit(1) from e + + # Step 4 – Upload tables/ and logs/ to GCS under a timestamped prefix + # Each run gets an isolated path: YYYY/MM/DD/HHMMSS/tables/ and .../logs/ + # This avoids overwriting previous runs and keeps objectCreator permission sufficient. + if not skip_upload: + console.print("[bold]Step 4/5:[/bold] Uploading output files to GCS...") + console.print(f" Prefix: {run_ts}/\n") + try: + uploaded: list[str] = [] + if tables_dir.exists(): + uploaded += upload_output(source_dir=tables_dir, prefix=f"{run_ts}/tables") + if logs_dir.exists(): + uploaded += upload_output(source_dir=logs_dir, prefix=f"{run_ts}/logs") + console.print(f" ✓ Uploaded {len(uploaded)} files to gs://{settings.upload_bucket}/{run_ts}/\n") + except Exception as e: + console.print(f"\n[bold red]Error during GCS upload: {e}[/bold red]\n") + raise typer.Exit(1) from e + else: + console.print("[bold]Step 4/5:[/bold] Skipping GCS upload (--skip-upload)\n") + + # Step 5 – Ingest tables into BigQuery + if not skip_upload: + console.print("[bold]Step 5/5:[/bold] Ingesting tables into BigQuery...") + try: + bq_results = load_pipeline_tables(tables_dir=tables_dir) + console.print(f" ✓ Loaded {len(bq_results)} tables into BigQuery\n") + except Exception as e: + console.print(f"\n[bold red]Error during BigQuery upload: {e}[/bold red]\n") + raise typer.Exit(1) from e + else: + console.print("[bold]Step 5/5:[/bold] Skipping BigQuery upload (--skip-upload)\n") + + console.print("[bold green]✓ Full pipeline completed successfully![/bold green]\n") + + +def main(): + """Entry point for CLI.""" + app() + + +if __name__ == "__main__": + main() diff --git a/src/a4d/config.py b/src/a4d/config.py new file mode 100644 index 0000000..c550c8b --- /dev/null +++ b/src/a4d/config.py @@ -0,0 +1,63 @@ +"""Application configuration using Pydantic Settings.""" + +from pathlib import Path +from typing import Literal + +from dotenv import load_dotenv +from pydantic_settings import BaseSettings, SettingsConfigDict + +# Load .env into os.environ so non-prefixed vars like GOOGLE_APPLICATION_CREDENTIALS +# are visible to third-party SDKs (Google Auth, etc.) without requiring a manual export. +load_dotenv(override=False) + + +class Settings(BaseSettings): + """ + Application configuration with environment variable support. + + All settings can be overridden with environment variables prefixed with A4D_. + Example: A4D_DATA_ROOT=/path/to/data + """ + + model_config = SettingsConfigDict( + env_file=".env", + env_file_encoding="utf-8", + env_prefix="A4D_", + case_sensitive=False, + extra="ignore", + ) + + # Environment + environment: Literal["development", "production"] = "development" + + # GCP Configuration + project_id: str = "a4dphase2" + dataset: str = "tracker" + download_bucket: str = "a4dphase2_upload" + upload_bucket: str = "a4dphase2_output" + + # Paths + data_root: Path = Path("/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload") + output_dir: Path = Path("output") + + # Processing settings + max_workers: int = 4 + + # Error values (matching R pipeline constants) + error_val_numeric: float = 999999.0 + error_val_character: str = "Undefined" + error_val_date: str = "9999-09-09" + + @property + def output_root(self) -> Path: + """Computed output root path.""" + return self.data_root / self.output_dir + + @property + def tracker_root(self) -> Path: + """Tracker files root directory.""" + return self.data_root + + +# Global settings instance +settings = Settings() diff --git a/src/a4d/errors.py b/src/a4d/errors.py new file mode 100644 index 0000000..11dc45b --- /dev/null +++ b/src/a4d/errors.py @@ -0,0 +1,210 @@ +"""Data quality error tracking for pipeline processing. + +This module provides the ErrorCollector class for tracking conversion failures, +validation errors, and other data quality issues. Errors are exported as +parquet files and aggregated into the logs table for BigQuery analysis. + +This is separate from operational logging (see a4d.logging) which tracks +pipeline execution and progress. +""" + +from datetime import datetime +from typing import Any, Literal + +import polars as pl +from pydantic import BaseModel, Field + +# Error code types based on R pipeline +ErrorCode = Literal[ + "type_conversion", # Failed to convert type (e.g., "abc" -> int) + "invalid_value", # Value outside allowed range or not in allowed list + "missing_value", # Required value is missing/NA + "missing_required_field", # Critical field (patient_id, status) is missing, row excluded + "invalid_tracker", # Tracker-level issues (missing columns, etc.) + "function_call", # Generic function execution error + "critical_abort", # Fatal error, tracker cannot be processed +] + + +class DataError(BaseModel): + """Single data quality error record. + + Attributes: + file_name: Name of the tracker file where error occurred + patient_id: Patient ID (if applicable, else "unknown") + column: Column name where error occurred + original_value: Original value that caused the error + error_message: Human-readable error description + error_code: Error category for grouping/analysis + script: Script name where error occurred (e.g., "script2", "clean") + function_name: Function name where error occurred + timestamp: When the error was recorded + """ + + file_name: str + patient_id: str + column: str + original_value: str + error_message: str + error_code: ErrorCode + script: str = "clean" + function_name: str = "" + timestamp: datetime = Field(default_factory=datetime.now) + + +class ErrorCollector: + """Collects data quality errors for export to parquet. + + Errors are collected during processing and exported as a DataFrame + at the end. The DataFrame schema matches the logs table in BigQuery + for easy querying and dashboard visualization. + + Example: + >>> collector = ErrorCollector() + >>> collector.add_error( + ... file_name="clinic_001.xlsx", + ... patient_id="XX_YY001", + ... column="age", + ... original_value="invalid", + ... error_message="Could not convert 'invalid' to Int32", + ... error_code="type_conversion", + ... function_name="safe_convert_column" + ... ) + >>> # Or batch add: + >>> errors = [ + ... DataError(file_name="clinic_001.xlsx", patient_id="XX_YY001", ...), + ... DataError(file_name="clinic_001.xlsx", patient_id="XX_YY002", ...), + ... ] + >>> collector.add_errors(errors) + >>> df = collector.to_dataframe() + >>> df.write_parquet("output/clinic_001/errors.parquet") + """ + + def __init__(self): + """Initialize an empty error collector.""" + self.errors: list[DataError] = [] + + def add_error( + self, + file_name: str, + patient_id: str, + column: str, + original_value: Any, + error_message: str, + error_code: ErrorCode, + script: str = "clean", + function_name: str = "", + ) -> None: + """Add a data quality error to the collector. + + Args: + file_name: Name of the tracker file + patient_id: Patient ID (use "unknown" if not applicable) + column: Column name where error occurred + original_value: Original value that caused the error + error_message: Human-readable error description + error_code: Error category (type_conversion, invalid_value, etc.) + script: Script name (default: "clean") + function_name: Function name where error occurred + """ + error = DataError( + file_name=file_name, + patient_id=patient_id, + column=column, + original_value=str(original_value), + error_message=error_message, + error_code=error_code, + script=script, + function_name=function_name, + ) + self.errors.append(error) + + def add_errors(self, errors: list[DataError]) -> None: + """Add multiple errors at once. + + Args: + errors: List of DataError instances to add + + Example: + >>> errors = [ + ... DataError(file_name="clinic_001.xlsx", patient_id="XX_YY001", ...), + ... DataError(file_name="clinic_001.xlsx", patient_id="XX_YY002", ...), + ... ] + >>> collector.add_errors(errors) + """ + self.errors.extend(errors) + + def to_dataframe(self) -> pl.DataFrame: + """Export errors as a Polars DataFrame for parquet export. + + Returns: + Polars DataFrame with all error records, or empty DataFrame if no errors + + Schema: + - file_name: str + - patient_id: str + - column: str + - original_value: str + - error_message: str + - error_code: str (categorical) + - script: str (categorical) + - function_name: str (categorical) + - timestamp: datetime + """ + if not self.errors: + # Return empty DataFrame with correct schema + return pl.DataFrame( + schema={ + "file_name": pl.Utf8, + "patient_id": pl.Utf8, + "column": pl.Utf8, + "original_value": pl.Utf8, + "error_message": pl.Utf8, + "error_code": pl.Categorical, + "script": pl.Categorical, + "function_name": pl.Categorical, + "timestamp": pl.Datetime, + } + ) + + # Convert Pydantic models to dict records + records = [error.model_dump() for error in self.errors] + + # Create DataFrame and cast categorical columns for efficiency + df = pl.DataFrame(records) + df = df.with_columns( + [ + pl.col("error_code").cast(pl.Categorical), + pl.col("script").cast(pl.Categorical), + pl.col("function_name").cast(pl.Categorical), + ] + ) + + return df + + def __len__(self) -> int: + """Return number of errors collected.""" + return len(self.errors) + + def __bool__(self) -> bool: + """Return True if any errors have been collected.""" + return len(self.errors) > 0 + + def clear(self) -> None: + """Clear all collected errors.""" + self.errors.clear() + + def get_error_summary(self) -> dict[str, int]: + """Get summary of errors by error_code. + + Returns: + Dictionary mapping error_code to count + + Example: + >>> collector.get_error_summary() + {'type_conversion': 10, 'invalid_value': 5} + """ + summary: dict[str, int] = {} + for error in self.errors: + summary[error.error_code] = summary.get(error.error_code, 0) + 1 + return summary diff --git a/src/a4d/extract/__init__.py b/src/a4d/extract/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/a4d/extract/patient.py b/src/a4d/extract/patient.py new file mode 100644 index 0000000..7c91a6d --- /dev/null +++ b/src/a4d/extract/patient.py @@ -0,0 +1,955 @@ +"""Patient data extraction from Excel tracker files. + +This module handles reading patient data from Excel trackers, which have +evolved over the years with different formats and structures. +""" + +import calendar +import re +import warnings +from pathlib import Path + +import polars as pl +from loguru import logger +from openpyxl import load_workbook + +from a4d.errors import ErrorCollector +from a4d.reference.synonyms import ColumnMapper, load_patient_mapper + +# Suppress openpyxl warnings about unsupported Excel features +# We only read data, so these warnings are not actionable +warnings.filterwarnings("ignore", category=UserWarning, module=r"openpyxl\..*") + + +def get_tracker_year(tracker_file: Path, month_sheets: list[str]) -> int: + """Extract tracker year from month sheet names or filename. + + Tries to parse year from month sheet names (e.g., "Jan24" -> 2024). + Falls back to extracting from filename if parsing fails. + Validates year is in reasonable range (2017-2030). + + Args: + tracker_file: Path to the tracker Excel file + month_sheets: List of month sheet names + + Returns: + Year of the tracker (e.g., 2024) + + Raises: + ValueError: If year cannot be determined or is out of valid range + + Example: + >>> get_tracker_year(Path("2024_Clinic.xlsx"), ["Jan24", "Feb24"]) + 2024 + """ + for sheet in month_sheets: + match = re.search(r"(\d{2})$", sheet) + if match: + year_suffix = int(match.group(1)) + year = 2000 + year_suffix # Assume 20xx until 2100 + logger.debug(f"Parsed year {year} from sheet name '{sheet}'") + + if not (2017 <= year <= 2030): # Match R pipeline validation + raise ValueError( + f"Year {year} is out of valid range (2017-2030). " + f"Parsed from sheet name '{sheet}'" + ) + + return year + + match = re.search(r"(\d{4})", tracker_file.name) + if match: + year = int(match.group(1)) + logger.debug(f"Parsed year {year} from filename '{tracker_file.name}'") + + if not (2017 <= year <= 2030): # Match R pipeline validation + raise ValueError( + f"Year {year} is out of valid range (2017-2030). " + f"Parsed from filename '{tracker_file.name}'" + ) + + return year + + raise ValueError( + f"Could not determine year from month sheets {month_sheets} or filename {tracker_file.name}" + ) + + +def find_month_sheets(workbook) -> list[str]: + """Find all month sheets in the tracker workbook. + + Month sheets are identified by matching against month abbreviations + (Jan, Feb, Mar, etc.) and sorted by month number for consistent processing. + + Args: + workbook: openpyxl Workbook object + + Returns: + List of month sheet names found in the workbook, sorted by month number + (Jan=1, Feb=2, ..., Dec=12) + + Example: + >>> wb = load_workbook("tracker.xlsx") + >>> find_month_sheets(wb) + ['Jan24', 'Feb24', 'Mar24', ...] + """ + month_abbrs = list(calendar.month_abbr)[1:] # ['Jan', 'Feb', ...] + month_sheets = [] + + for sheet_name in workbook.sheetnames: + if any(sheet_name.startswith(abbr) for abbr in month_abbrs): + month_sheets.append(sheet_name) + + def get_month_number(sheet_name: str) -> int: + """Extract month number from sheet name (Jan=1, ..., Dec=12).""" + month_prefix = sheet_name[:3] + try: + return month_abbrs.index(month_prefix) + 1 + except ValueError: + return 999 # Push unrecognized sheets to end + + month_sheets.sort(key=get_month_number) + + logger.info(f"Found {len(month_sheets)} month sheets (sorted by month): {month_sheets}") + return month_sheets + + +def find_data_start_row(ws) -> int: + """Find the first row containing patient data. + + Scans column A for the first numeric value (patient row numbers: 1, 2, 3...). + This skips any non-numeric values that may appear above the patient data + (e.g., spaces, text, product data). + + Args: + ws: openpyxl worksheet object + + Returns: + Row number (1-indexed) where patient data starts + + Raises: + ValueError: If no numeric data is found in column A + """ + max_row = ws.max_row or 1000 + for row_idx in range(1, max_row + 1): + cell_value = ws.cell(row_idx, 1).value + if cell_value is not None and isinstance(cell_value, (int, float)): + return row_idx + + raise ValueError("No patient data found in column A (looking for numeric row numbers)") + + +def read_header_rows(ws, data_start_row: int, max_cols: int = 100) -> tuple[list, list]: + """Read and trim the two header rows above the data. + + Headers are located in the two rows immediately before data_start_row. + Reads up to max_cols columns and trims to the last non-None column. + + Args: + ws: openpyxl worksheet object + data_start_row: Row number where patient data starts + max_cols: Maximum number of columns to read (default: 100) + + Returns: + Tuple of (header_1, header_2) lists, trimmed to actual width + + Example: + >>> header_1, header_2 = read_header_rows(ws, 77) + >>> len(header_1) + 31 + """ + header_row_1 = data_start_row - 1 + header_row_2 = data_start_row - 2 + + # Read raw header rows + header_1_raw = list( + ws.iter_rows( + min_row=header_row_1, + max_row=header_row_1, + min_col=1, + max_col=max_cols, + values_only=True, + ) + )[0] + header_2_raw = list( + ws.iter_rows( + min_row=header_row_2, + max_row=header_row_2, + min_col=1, + max_col=max_cols, + values_only=True, + ) + )[0] + + last_col = max_cols + for i in range(len(header_1_raw) - 1, -1, -1): + if header_1_raw[i] is not None or header_2_raw[i] is not None: + last_col = i + 1 + break + + header_1 = list(header_1_raw[:last_col]) + header_2 = list(header_2_raw[:last_col]) + + return header_1, header_2 + + +def merge_headers( + header_1: list, + header_2: list, + mapper: ColumnMapper | None = None, +) -> list[str | None]: + """Merge two header rows using heuristic forward-fill with synonym validation. + + When h2=None but h1 exists: + 1. Try forward-fill: combine prev_h2 + h1 + 2. If mapper validates this as known column, use it + 3. Otherwise, treat h1 as standalone column + + This replaces Excel merge metadata detection with synonym-based validation, + eliminating the need for slow read_only=False workbook loading. + + Special case: If header_1 contains "Patient ID" (or known synonyms) and + header_2 appears to be a title row (mostly None), use only header_1. + + Args: + header_1: First header row (closer to data), 0-indexed + header_2: Second header row (further from data), 0-indexed + mapper: Optional ColumnMapper for validating forward-filled headers + + Returns: + List of merged header strings with whitespace normalized + """ + patient_id_indicators = ["patient id", "patient.id"] + has_patient_id_in_h1 = any( + str(h1).strip().lower() in patient_id_indicators for h1 in header_1 if h1 is not None + ) + + non_none_count_h2 = sum(1 for h2 in header_2 if h2 is not None) + + if has_patient_id_in_h1 and non_none_count_h2 <= 2: + logger.debug( + "Detected title row in header_2 with Patient ID in header_1, using header_1 only" + ) + headers = [str(h1).strip() if h1 is not None else None for h1 in header_1] + headers = [re.sub(r"\s+", " ", h.replace("\n", " ")) if h else None for h in headers] + return headers + + headers = [] + prev_h2 = None + + for h1, h2 in zip(header_1, header_2, strict=True): + if h1 and h2: + headers.append(f"{h2} {h1}".strip()) + prev_h2 = str(h2).strip() + elif h2: + headers.append(str(h2).strip()) + prev_h2 = str(h2).strip() + elif h1: + # Try forward-fill with validation + if prev_h2: + candidate = f"{prev_h2} {h1}".strip() + if mapper and mapper.is_known_column(candidate): + headers.append(candidate) + else: + # Forward-fill not valid, use h1 standalone + headers.append(str(h1).strip()) + else: + headers.append(str(h1).strip()) + else: + headers.append(None) + prev_h2 = None # Reset on gap + + headers = [re.sub(r"\s+", " ", h.replace("\n", " ")) if h else None for h in headers] + + return headers + + +def read_patient_rows(ws, data_start_row: int, num_columns: int) -> list[tuple]: + """Read patient data rows from the worksheet. + + Reads from data_start_row until either ws.max_row or the first completely + empty row. Skips rows where both the row number (column A) and patient_id + (column B) are None, but accepts rows where patient_id exists even if row + number is missing (handles data quality issues in Excel files). + + Args: + ws: openpyxl worksheet object + data_start_row: Row number where patient data starts + num_columns: Number of columns to read + + Returns: + List of tuples, each containing one row of patient data + + Example: + >>> rows = read_patient_rows(ws, 77, 31) + >>> len(rows) + 4 + """ + data = [] + for row in ws.iter_rows( + min_row=data_start_row, + max_row=ws.max_row, + min_col=1, + max_col=num_columns, + values_only=True, + ): + if all(cell is None for cell in row): + break + # Skip rows where both row number (col A) AND patient_id (col B) are missing + # This handles cases where Excel has missing row numbers but valid patient data + if row[0] is None and (len(row) < 2 or row[1] is None): + continue + data.append(row) + + return data + + +def merge_duplicate_columns_data( + headers: list[str], data: list[list] +) -> tuple[list[str], list[list]]: + """Merge data from duplicate column headers by concatenating with commas. + + When Excel cells are merged both horizontally and vertically, the forward-fill + logic in merge_headers() can create duplicate column names. This function + merges the data from duplicate columns (like R's tidyr::unite()). + + Args: + headers: List of header strings (may contain duplicates) + data: List of data rows (each row is a list) + + Returns: + Tuple of (unique_headers, merged_data) + + Example: + >>> headers = ["ID", "DM Complications", "DM Complications", "DM Complications", "Age"] + >>> data = [["1", "A", "B", "C", "25"], ["2", "X", "Y", "Z", "30"]] + >>> merge_duplicate_columns_data(headers, data) + (['ID', 'DM Complications', 'Age'], [['1', 'A,B,C', '25'], ['2', 'X,Y,Z', '30']]) + """ + if len(headers) == len(set(headers)): + return headers, data + + from collections import defaultdict + + header_positions: dict[str, list[int]] = defaultdict(list) + for idx, header in enumerate(headers): + header_positions[header].append(idx) + + unique_headers = list(header_positions.keys()) + + duplicated = [h for h, positions in header_positions.items() if len(positions) > 1] + if duplicated: + logger.debug(f"Merging {len(duplicated)} duplicate column groups: {duplicated}") + + merged_data = [] + for row in data: + merged_row = [] + for header in unique_headers: + positions = header_positions[header] + if len(positions) == 1: + merged_row.append(row[positions[0]]) + else: + values = [str(row[pos]) if row[pos] is not None else "" for pos in positions] + values = [v for v in values if v] + merged_value = ",".join(values) if values else None + merged_row.append(merged_value) + merged_data.append(merged_row) + + return unique_headers, merged_data + + +def filter_valid_columns( + headers: list[str | None], data: list[tuple] +) -> tuple[list[str], list[list]]: + """Filter out columns with None headers and their corresponding data. + + Args: + headers: List of header strings (may contain None) + data: List of data rows + + Returns: + Tuple of (valid_headers, filtered_data) + + Example: + >>> headers = ["ID", None, "Name", None, "Age"] + >>> data = [("1", "x", "Alice", "y", "30")] + >>> filter_valid_columns(headers, data) + (['ID', 'Name', 'Age'], [['1', 'Alice', '30']]) + """ + valid_cols = [(i, h) for i, h in enumerate(headers) if h] + + if not valid_cols: + return [], [] + + valid_indices = [i for i, _ in valid_cols] + valid_headers = [h for _, h in valid_cols] + + filtered_data = [[row[i] for i in valid_indices] for row in data] + + return valid_headers, filtered_data + + +def clean_excel_errors(df: pl.DataFrame) -> pl.DataFrame: + """Convert Excel error strings to NULL values. + + Excel error codes like #DIV/0!, #VALUE!, etc. are not usable values + and should be treated as missing data. + + Args: + df: DataFrame with potential Excel error strings + + Returns: + DataFrame with Excel errors converted to NULL + + Example: + >>> df = pl.DataFrame({"bmi": ["17.5", "#DIV/0!", "18.2"]}) + >>> clean_df = clean_excel_errors(df) + >>> clean_df["bmi"].to_list() + ['17.5', None, '18.2'] + """ + excel_errors = [ + "#DIV/0!", + "#VALUE!", + "#REF!", + "#NAME?", + "#NUM!", + "#N/A", + "#NULL!", + ] + + metadata_cols = { + "tracker_year", + "tracker_month", + "clinic_id", + "patient_id", + "sheet_name", + "file_name", + } + data_cols = [col for col in df.columns if col not in metadata_cols] + + if not data_cols: + return df + + df = df.with_columns( + [ + pl.when(pl.col(col).is_in(excel_errors)).then(None).otherwise(pl.col(col)).alias(col) + for col in data_cols + ] + ) + + for error in excel_errors: + for col in data_cols: + count = (df[col] == error).sum() + if count > 0: + logger.debug(f"Converted {count} '{error}' values to NULL in column '{col}'") + + return df + + +def extract_patient_data( + tracker_file: Path, + sheet_name: str, + year: int, + mapper: ColumnMapper | None = None, + workbook=None, +) -> pl.DataFrame: + """Extract patient data from a single sheet. + + Uses single read_only=True load with synonym-validated header merging. + + Args: + tracker_file: Path to the tracker Excel file + sheet_name: Name of the sheet to extract + year: Year of the tracker (currently unused, reserved for future use) + mapper: Optional ColumnMapper for validating forward-filled headers + workbook: Optional pre-loaded workbook for caching across sheets + + Returns: + Polars DataFrame with patient data (all columns as strings) + + Example: + >>> df = extract_patient_data( + ... Path("2024_Clinic.xlsx"), + ... "Jan24", + ... 2024 + ... ) + >>> len(df) + 4 + >>> "Patient ID*" in df.columns + True + """ + if mapper is None: + mapper = load_patient_mapper() + + # Use cached workbook or load new one + close_wb = workbook is None + if workbook is None: + workbook = load_workbook( + tracker_file, + read_only=True, + data_only=True, + keep_vba=False, + keep_links=False, + ) + + ws = workbook[sheet_name] + + data_start_row = find_data_start_row(ws) + logger.debug( + f"Sheet '{sheet_name}': Patient data found in rows {data_start_row} to {ws.max_row}" + ) + + logger.info("Processing headers...") + header_1, header_2 = read_header_rows(ws, data_start_row) + + # Use synonym-validated forward-fill instead of Excel merge metadata + headers = merge_headers(header_1, header_2, mapper=mapper) + + valid_cols = [(i, h) for i, h in enumerate(headers) if h] + + if not valid_cols: + if close_wb: + workbook.close() + logger.bind(error_code="invalid_tracker").warning(f"No valid headers found in sheet '{sheet_name}'") + return pl.DataFrame() + + data = read_patient_rows(ws, data_start_row, len(headers)) + + if close_wb: + workbook.close() + + valid_headers, filtered_data = filter_valid_columns(headers, data) + + valid_headers, filtered_data = merge_duplicate_columns_data(valid_headers, filtered_data) + + # Create DataFrame with ALL columns explicitly as String type to ensure consistent schema + # across all files and avoid type inference issues (Null vs String dtype) + df = pl.DataFrame( + { + header: pl.Series( + [str(row[i]) if row[i] is not None else None for row in filtered_data], + dtype=pl.String, + ) + for i, header in enumerate(valid_headers) + } + ) + + logger.info(f"Extracted {len(df)} rows x {len(df.columns)} cols from sheet '{sheet_name}'") + + return df + + +def harmonize_patient_data_columns( + df: pl.DataFrame, + mapper: ColumnMapper | None = None, + strict: bool = False, +) -> pl.DataFrame: + """Harmonize patient data columns using synonym mappings. + + Renames columns from their various synonyms (e.g., "Patient ID", "ID", + "Patient ID*") to standardized column names (e.g., "patient_id"). + + Args: + df: DataFrame with raw column names from tracker + mapper: ColumnMapper to use (if None, loads default patient mapper) + strict: If True, raise error if unmapped columns exist + If False, keep unmapped columns as-is (default) + + Returns: + DataFrame with standardized column names + + Raises: + ValueError: If strict=True and unmapped columns exist + + Example: + >>> raw_df = pl.DataFrame({ + ... "Patient ID*": ["MY_SU001", "MY_SU002"], + ... "Age": [25, 30], + ... }) + >>> harmonized = harmonize_patient_data_columns(raw_df) + >>> harmonized.columns + ['patient_id', 'age'] + """ + if mapper is None: + mapper = load_patient_mapper() + + renamed_df = mapper.rename_columns(df, strict=strict) + + logger.info( + f"Harmonized columns: {len(df.columns)} -> {len(renamed_df.columns)} " + f"({len(df.columns) - len(renamed_df.columns)} columns removed)" + if len(df.columns) != len(renamed_df.columns) + else f"Harmonized {len(renamed_df.columns)} columns" + ) + + return renamed_df + + +def extract_tracker_month(sheet_name: str) -> int: + """Extract month number (1-12) from sheet name. + + Args: + sheet_name: Sheet name like "Jan24", "Feb24", etc. + + Returns: + Month number (1 for January, 2 for February, etc.) + + Raises: + ValueError: If month cannot be extracted or is out of valid range + + Example: + >>> extract_tracker_month("Jan24") + 1 + >>> extract_tracker_month("Dec23") + 12 + """ + month_abbrs = list(calendar.month_abbr)[1:] # ['Jan', 'Feb', ...] + + # Check first 3 characters + month_prefix = sheet_name[:3] + + if month_prefix in month_abbrs: + month_num = month_abbrs.index(month_prefix) + 1 # +1 because index is 0-based + + # Validate month is in valid range (1-12) + # This should always be true given the logic above, but check anyway for safety + if not (1 <= month_num <= 12): + raise ValueError( + f"Month number {month_num} is out of valid range (1-12). " + f"Parsed from sheet name '{sheet_name}'" + ) + + return month_num + + raise ValueError(f"Could not extract month from sheet name '{sheet_name}'") + + +def read_all_patient_sheets( + tracker_file: Path, + mapper: ColumnMapper | None = None, + error_collector: ErrorCollector | None = None, +) -> pl.DataFrame: + """Read patient data from all month sheets in a tracker file. + + Orchestrates the complete extraction process: + 1. Find all month sheets + 2. Extract tracker year + 3. For each month sheet: + - Extract raw data + - Harmonize column names + - Merge duplicate columns + - Add metadata (sheet_name, tracker_month, tracker_year, file_name) + 4. Combine all sheets + 5. Filter invalid rows (no patient_id and no name) + + Args: + tracker_file: Path to the tracker Excel file + mapper: ColumnMapper to use (if None, loads default patient mapper) + error_collector: ErrorCollector for tracking data quality issues (optional) + + Returns: + Combined DataFrame with all patient data from all month sheets + + Raises: + ValueError: If no month sheets found or year cannot be determined + + Example: + >>> df = read_all_patient_sheets(Path("2024_Clinic.xlsx")) + >>> "patient_id" in df.columns + True + >>> "tracker_month" in df.columns + True + >>> "tracker_year" in df.columns + True + """ + logger.info(f"Reading all patient sheets from {tracker_file.name}") + + # Load mapper once for all sheets + if mapper is None: + mapper = load_patient_mapper() + + # Load workbook once and reuse across all sheets + wb = load_workbook( + tracker_file, read_only=True, data_only=True, keep_vba=False, keep_links=False + ) + + month_sheets = find_month_sheets(wb) + if not month_sheets: + wb.close() + raise ValueError(f"No month sheets found in {tracker_file.name}") + + year = get_tracker_year(tracker_file, month_sheets) + logger.info(f"Processing {len(month_sheets)} month sheets for year {year}") + + all_sheets_data = [] + + for sheet_name in month_sheets: + logger.info(f"Processing sheet: {sheet_name}") + + df_sheet = extract_patient_data(tracker_file, sheet_name, year, mapper=mapper, workbook=wb) + + if df_sheet.is_empty(): + logger.bind(error_code="invalid_tracker").warning(f"Sheet '{sheet_name}' has no data, skipping") + continue + + df_sheet = harmonize_patient_data_columns(df_sheet, mapper=mapper, strict=False) + + if "patient_id" not in df_sheet.columns: + logger.bind(error_code="invalid_tracker").warning( + f"Sheet '{sheet_name}' has no 'patient_id' column after harmonization, skipping" + ) + continue + + try: + month_num = extract_tracker_month(sheet_name) + except ValueError as e: + logger.bind(error_code="invalid_tracker").warning(f"Could not extract month from '{sheet_name}': {e}, skipping") + continue + + # Derived metadata (year, month) use Int64; text metadata (sheet_name, etc.) use String + clinic_id = tracker_file.parent.name + file_name = tracker_file.stem + df_sheet = df_sheet.with_columns( + [ + pl.lit(sheet_name, dtype=pl.String).alias("sheet_name"), + pl.lit(month_num, dtype=pl.Int64).alias("tracker_month"), + pl.lit(year, dtype=pl.Int64).alias("tracker_year"), + pl.lit(file_name, dtype=pl.String).alias("file_name"), + pl.lit(clinic_id, dtype=pl.String).alias("clinic_id"), + ] + ) + + all_sheets_data.append(df_sheet) + + if not all_sheets_data: + raise ValueError(f"No valid patient data found in any month sheets of {tracker_file.name}") + + # Use diagonal_relaxed to handle type mismatches (e.g., Null vs String) like R's bind_rows + logger.info(f"Combining {len(all_sheets_data)} sheets...") + df_combined = pl.concat(all_sheets_data, how="diagonal_relaxed") + + initial_rows = len(df_combined) + + # Track rows with missing patient_id for error reporting + missing_patient_id_rows = df_combined.filter(pl.col("patient_id").is_null()) + missing_count = len(missing_patient_id_rows) + + if missing_count > 0: + logger.bind(error_code="invalid_value").error( + f"Found {missing_count} rows with missing patient_id in {tracker_file.name} - " + f"these rows will be excluded from processing" + ) + + # Log to ErrorCollector if available + if error_collector is not None: + for row in missing_patient_id_rows.iter_rows(named=True): + sheet_name = row.get("sheet_name", "unknown") + name_value = row.get("name", "") + error_collector.add_error( + file_name=tracker_file.stem, + patient_id="MISSING", + column="patient_id", + original_value=None, + error_message=( + f"Row in sheet '{sheet_name}' has missing patient_id (name: {name_value})" + ), + error_code="missing_required_field", + script="extract", + function_name="read_all_patient_sheets", + ) + + # Filter out ALL rows with missing patient_id + df_combined = df_combined.filter(pl.col("patient_id").is_not_null()) + + # Filter out empty rows (both patient_id and name are null/empty) + # This is redundant now but kept for clarity + if "name" in df_combined.columns: + df_combined = df_combined.filter( + ~( + (pl.col("patient_id").str.strip_chars() == "") + & (pl.col("name").is_null() | (pl.col("name").str.strip_chars() == "")) + ) + ) + + # Filter out rows where both patient_id and name are numeric zeros (0, 0.0, "0", "0.0", etc.) + if "name" in df_combined.columns: + df_combined = df_combined.filter( + ~( + pl.col("patient_id").str.strip_chars().is_in(["0", "0.0"]) + & pl.col("name").str.strip_chars().is_in(["0", "0.0"]) + ) + ) + + # Filter out rows with patient_id starting with "#" (Excel errors like #REF!) + df_combined = df_combined.filter(~pl.col("patient_id").str.starts_with("#")) + + filtered_rows = initial_rows - len(df_combined) + if filtered_rows > 0: + logger.info(f"Filtered out {filtered_rows} invalid rows total") + + df_combined = clean_excel_errors(df_combined) + + # Use already-loaded workbook for sheet checking + all_sheets = wb.sheetnames + + # Process Patient List sheet if it exists (R: lines 103-130) + if "Patient List" in all_sheets: + logger.info("Processing 'Patient List' sheet...") + try: + patient_list = extract_patient_data( + tracker_file, "Patient List", year, mapper=mapper, workbook=wb + ) + if not patient_list.is_empty(): + patient_list = clean_excel_errors(patient_list) + patient_list = harmonize_patient_data_columns( + patient_list, mapper=mapper, strict=False + ) + + if "patient_id" in patient_list.columns: + # Filter out rows with missing patient_id + patient_list = patient_list.filter(pl.col("patient_id").is_not_null()) + + # Filter out numeric zeros and Excel errors + if "name" in patient_list.columns: + patient_list = patient_list.filter( + ~( + pl.col("patient_id").str.strip_chars().is_in(["0", "0.0"]) + & pl.col("name").str.strip_chars().is_in(["0", "0.0"]) + ) + ) + + patient_list = patient_list.filter(~pl.col("patient_id").str.starts_with("#")) + + # R: select(-any_of(c("hba1c_baseline"))) and select(-any_of(c("name"))) + df_monthly = ( + df_combined.drop("hba1c_baseline") + if "hba1c_baseline" in df_combined.columns + else df_combined + ) + patient_list_join = ( + patient_list.drop("name") + if "name" in patient_list.columns + else patient_list + ) + + df_combined = df_monthly.join( + patient_list_join, on="patient_id", how="left", suffix=".static" + ) + logger.info(f"Joined {len(patient_list)} Patient List records") + else: + logger.bind(error_code="invalid_tracker").warning( + "Patient List sheet has no 'patient_id' column after harmonization" + ) + else: + logger.bind(error_code="invalid_tracker").warning("Patient List sheet is empty") + except Exception as e: + logger.bind(error_code="invalid_tracker").warning(f"Could not process Patient List sheet: {e}") + + # Process Annual sheet if it exists (R: lines 132-160) + if "Annual" in all_sheets: + logger.info("Processing 'Annual' sheet...") + try: + annual_data = extract_patient_data( + tracker_file, "Annual", year, mapper=mapper, workbook=wb + ) + if not annual_data.is_empty(): + annual_data = clean_excel_errors(annual_data) + annual_data = harmonize_patient_data_columns( + annual_data, mapper=mapper, strict=False + ) + + if "patient_id" in annual_data.columns: + # Filter out rows with missing patient_id + annual_data = annual_data.filter(pl.col("patient_id").is_not_null()) + + # Filter out numeric zeros and Excel errors + if "name" in annual_data.columns: + annual_data = annual_data.filter( + ~( + pl.col("patient_id").str.strip_chars().is_in(["0", "0.0"]) + & pl.col("name").str.strip_chars().is_in(["0", "0.0"]) + ) + ) + + annual_data = annual_data.filter(~pl.col("patient_id").str.starts_with("#")) + + # R: select(-any_of(c("status", "name"))) + cols_to_drop = [col for col in ["status", "name"] if col in annual_data.columns] + annual_data_join = ( + annual_data.drop(cols_to_drop) if cols_to_drop else annual_data + ) + + df_combined = df_combined.join( + annual_data_join, on="patient_id", how="left", suffix=".annual" + ) + logger.info(f"Joined {len(annual_data)} Annual records") + else: + logger.bind(error_code="invalid_tracker").warning("Annual sheet has no 'patient_id' column after harmonization") + else: + logger.bind(error_code="invalid_tracker").warning("Annual sheet is empty") + except Exception as e: + logger.bind(error_code="invalid_tracker").warning(f"Could not process Annual sheet: {e}") + + # Close workbook after all processing + wb.close() + + logger.info( + f"Successfully extracted {len(df_combined)} total rows " + f"from {len(all_sheets_data)} month sheets" + ) + + # Reorder: metadata first, then patient data + # (tracker_year, tracker_month, clinic_id, patient_id) + priority_cols = ["tracker_year", "tracker_month", "clinic_id", "patient_id"] + existing_priority = [c for c in priority_cols if c in df_combined.columns] + other_cols = [c for c in df_combined.columns if c not in priority_cols] + df_combined = df_combined.select(existing_priority + other_cols) + + return df_combined + + +def export_patient_raw( + df: pl.DataFrame, + tracker_file: Path, + output_dir: Path, +) -> Path: + """Export raw patient data to parquet file. + + Matches R pipeline behavior: + - Filename: {tracker_name}_patient_raw.parquet + - Location: output_dir/{tracker_name}_patient_raw.parquet + + Args: + df: Patient DataFrame to export + tracker_file: Path to original tracker file (used to extract tracker_name) + output_dir: Directory to write parquet file (e.g., data_root/output/patient_data_raw) + + Returns: + Path to the written parquet file + + Example: + >>> df = read_all_patient_sheets(Path("2024_Clinic.xlsx")) + >>> output_path = export_patient_raw( + ... df, + ... Path("2024_Clinic.xlsx"), + ... Path("output/patient_data_raw") + ... ) + >>> output_path.name + '2024_Clinic_patient_raw.parquet' + """ + # Extract tracker name (filename without extension) + tracker_name = tracker_file.stem + + # Create output filename: {tracker_name}_patient_raw.parquet + output_filename = f"{tracker_name}_patient_raw.parquet" + output_path = output_dir / output_filename + + # Ensure output directory exists + output_dir.mkdir(parents=True, exist_ok=True) + + # Write parquet file + logger.info(f"Writing {len(df)} rows to {output_path}") + df.write_parquet(output_path) + + logger.info(f"Successfully exported to {output_path}") + return output_path diff --git a/src/a4d/gcp/__init__.py b/src/a4d/gcp/__init__.py new file mode 100644 index 0000000..89b75e0 --- /dev/null +++ b/src/a4d/gcp/__init__.py @@ -0,0 +1,21 @@ +from a4d.gcp.bigquery import ( + TABLE_CONFIGS, + get_bigquery_client, + load_pipeline_tables, + load_table, +) +from a4d.gcp.storage import ( + download_tracker_files, + get_storage_client, + upload_output, +) + +__all__ = [ + "TABLE_CONFIGS", + "download_tracker_files", + "get_bigquery_client", + "get_storage_client", + "load_pipeline_tables", + "load_table", + "upload_output", +] diff --git a/src/a4d/gcp/bigquery.py b/src/a4d/gcp/bigquery.py new file mode 100644 index 0000000..0c1ea6e --- /dev/null +++ b/src/a4d/gcp/bigquery.py @@ -0,0 +1,197 @@ +"""BigQuery table loading from parquet files. + +Replaces the R pipeline's `ingest_data()` function which used the `bq` CLI tool. +Uses the google-cloud-bigquery Python client for loading parquet files with +clustering configuration matching the R pipeline. +""" + +from pathlib import Path + +from google.cloud import bigquery +from google.api_core.exceptions import NotFound +from loguru import logger + +from a4d.config import settings + +# Table configurations matching the R pipeline's clustering fields. +# Each table maps to the clustering fields used for optimal query performance. +TABLE_CONFIGS: dict[str, list[str]] = { + "patient_data_monthly": ["clinic_id", "patient_id", "tracker_date"], + "patient_data_annual": ["patient_id", "tracker_date"], + "patient_data_static": ["clinic_id", "patient_id", "tracker_date"], + "product_data": [ + "clinic_id", + "product_released_to", + "product_table_year", + "product_table_month", + ], + "clinic_data_static": ["clinic_id"], + "logs": ["level", "error_code", "file_name", "function"], + "tracker_metadata": ["file_name", "clinic_code"], +} + +# Maps the pipeline output file names to BigQuery table names. +# Note: table_logs.parquet uses this name from create_table_logs() in tables/logs.py. +PARQUET_TO_TABLE: dict[str, str] = { + "patient_data_static.parquet": "patient_data_static", + "patient_data_monthly.parquet": "patient_data_monthly", + "patient_data_annual.parquet": "patient_data_annual", + "clinic_data_static.parquet": "clinic_data_static", + "table_logs.parquet": "logs", +} + + +def get_bigquery_client(project_id: str | None = None) -> bigquery.Client: + """Create a BigQuery client. + + Authentication uses Application Default Credentials (ADC): + - In Cloud Run / GCE: automatic via metadata server + - Locally: via `gcloud auth application-default login` + - In CI: via GOOGLE_APPLICATION_CREDENTIALS environment variable + + Args: + project_id: GCP project ID (defaults to settings.project_id) + + Returns: + Configured BigQuery client + """ + return bigquery.Client(project=project_id or settings.project_id) + + +def load_table( + parquet_path: Path, + table_name: str, + client: bigquery.Client | None = None, + dataset: str | None = None, + project_id: str | None = None, + replace: bool = True, +) -> bigquery.LoadJob: + """Load a parquet file into a BigQuery table. + + Replicates the R pipeline's `ingest_data()` function: + 1. Optionally deletes the existing table (replace=True, matching R's delete=T default) + 2. Loads the parquet file with clustering fields + + Args: + parquet_path: Path to the parquet file to load + table_name: BigQuery table name (e.g., "patient_data_monthly") + client: BigQuery client (created if not provided) + dataset: Dataset name (defaults to settings.dataset) + project_id: GCP project ID (defaults to settings.project_id) + replace: If True, replaces the existing table (default matches R pipeline) + + Returns: + Completed LoadJob + + Raises: + FileNotFoundError: If parquet file doesn't exist + ValueError: If table_name is not in TABLE_CONFIGS + google.api_core.exceptions.GoogleAPIError: On BigQuery API errors + """ + if not parquet_path.exists(): + raise FileNotFoundError(f"Parquet file not found: {parquet_path}") + + dataset = dataset or settings.dataset + project_id = project_id or settings.project_id + + if client is None: + client = get_bigquery_client(project_id) + + table_ref = f"{project_id}.{dataset}.{table_name}" + logger.info(f"Loading {parquet_path.name} → {table_ref}") + + # WRITE_TRUNCATE preserves existing clustering, so deleting first ensures + # any schema or clustering changes (e.g. from R→Python migration) take effect. + if replace: + try: + client.delete_table(table_ref) + logger.info(f"Deleted existing table {table_ref} for fresh creation") + except NotFound: + pass + + # Configure the load job + job_config = bigquery.LoadJobConfig( + source_format=bigquery.SourceFormat.PARQUET, + write_disposition=( + bigquery.WriteDisposition.WRITE_TRUNCATE + if replace + else bigquery.WriteDisposition.WRITE_APPEND + ), + ) + + # Add clustering if configured for this table + clustering_fields = TABLE_CONFIGS.get(table_name) + if clustering_fields: + job_config.clustering_fields = clustering_fields + logger.info(f"Clustering fields: {clustering_fields}") + + # Load the parquet file + with open(parquet_path, "rb") as f: + load_job = client.load_table_from_file(f, table_ref, job_config=job_config) + + # Wait for completion + load_job.result() + + logger.info( + f"Loaded {load_job.output_rows} rows into {table_ref} " + f"({parquet_path.stat().st_size / 1024 / 1024:.2f} MB)" + ) + return load_job + + +def load_pipeline_tables( + tables_dir: Path, + client: bigquery.Client | None = None, + dataset: str | None = None, + project_id: str | None = None, + replace: bool = True, +) -> dict[str, bigquery.LoadJob]: + """Load all pipeline output tables into BigQuery. + + Scans the tables directory for known parquet files and loads each one + into the corresponding BigQuery table. + + Args: + tables_dir: Directory containing parquet table files (e.g., output/tables/) + client: BigQuery client (created if not provided) + dataset: Dataset name (defaults to settings.dataset) + project_id: GCP project ID (defaults to settings.project_id) + replace: If True, replaces existing tables + + Returns: + Dictionary mapping table name to completed LoadJob + + Raises: + FileNotFoundError: If tables_dir doesn't exist + """ + if not tables_dir.exists(): + raise FileNotFoundError(f"Tables directory not found: {tables_dir}") + + if client is None: + project_id = project_id or settings.project_id + client = get_bigquery_client(project_id) + + logger.info(f"Loading pipeline tables from: {tables_dir}") + + results: dict[str, bigquery.LoadJob] = {} + + for parquet_name, table_name in PARQUET_TO_TABLE.items(): + parquet_path = tables_dir / parquet_name + if parquet_path.exists(): + try: + job = load_table( + parquet_path=parquet_path, + table_name=table_name, + client=client, + dataset=dataset, + project_id=project_id, + replace=replace, + ) + results[table_name] = job + except Exception: + logger.exception(f"Failed to load table: {table_name}") + else: + logger.warning(f"Table file not found, skipping: {parquet_name}") + + logger.info(f"Successfully loaded {len(results)}/{len(PARQUET_TO_TABLE)} tables") + return results diff --git a/src/a4d/gcp/drive.py b/src/a4d/gcp/drive.py new file mode 100644 index 0000000..b3c292b --- /dev/null +++ b/src/a4d/gcp/drive.py @@ -0,0 +1,64 @@ +"""Google Drive download utilities. + +Replaces the R pipeline's googledrive::drive_download() calls. +Authentication uses Application Default Credentials (ADC), same as GCS/BigQuery. +""" + +from pathlib import Path + +import google.auth +import google.auth.transport.requests +from loguru import logger + +# Google Drive file ID for clinic_data.xlsx +# R pipeline: googledrive::as_id("1HOxi0o9fTAoHySjW_M3F-09TRBnUITOzzxGx2HwRMAw") +CLINIC_DATA_FILE_ID = "1HOxi0o9fTAoHySjW_M3F-09TRBnUITOzzxGx2HwRMAw" + +_DRIVE_API_URL = "https://www.googleapis.com/drive/v3/files" +_XLSX_MIME = "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" +_CHUNK_SIZE = 8 * 1024 * 1024 # 8 MB + + +def download_clinic_data(destination: Path) -> Path: + """Download clinic_data.xlsx from Google Drive to the destination directory. + + Uses ADC with Drive readonly scope. In Cloud Run the service account must + have 'Viewer' access to the file (or the shared drive it lives in). + + Args: + destination: Directory to write clinic_data.xlsx into + + Returns: + Path to the downloaded file + + Raises: + requests.HTTPError: If the Drive API returns a non-2xx status + """ + destination.mkdir(parents=True, exist_ok=True) + output_path = destination / "clinic_data.xlsx" + + logger.info(f"Downloading clinic_data.xlsx from Google Drive (file ID: {CLINIC_DATA_FILE_ID})") + + credentials, _ = google.auth.default( + scopes=["https://www.googleapis.com/auth/drive.readonly"] + ) + session = google.auth.transport.requests.AuthorizedSession(credentials) + + # clinic_data is a Google Sheets file — must use export endpoint, not alt=media. + # R pipeline equivalent: googledrive::drive_download(..., type = "xlsx") + url = f"{_DRIVE_API_URL}/{CLINIC_DATA_FILE_ID}/export?mimeType={_XLSX_MIME}&supportsAllDrives=true" + response = session.get(url, stream=True) + if not response.ok: + logger.error(f"Drive API error {response.status_code}: {response.text}") + response.raise_for_status() + + bytes_written = 0 + with open(output_path, "wb") as f: + for chunk in response.iter_content(chunk_size=_CHUNK_SIZE): + f.write(chunk) + bytes_written += len(chunk) + + size_kb = bytes_written / 1024 + logger.info(f"Downloaded clinic_data.xlsx: {size_kb:.1f} KB -> {output_path}") + + return output_path diff --git a/src/a4d/gcp/storage.py b/src/a4d/gcp/storage.py new file mode 100644 index 0000000..1dc1716 --- /dev/null +++ b/src/a4d/gcp/storage.py @@ -0,0 +1,163 @@ +"""Google Cloud Storage operations for tracker file download and output upload. + +Replaces the R pipeline's `gsutil` CLI calls with the google-cloud-storage +Python client library. +""" + +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path + +from google.cloud import storage +from loguru import logger + +from a4d.config import settings + +_GCS_WORKERS = 16 # parallel connections; GCS supports many concurrent requests + + +def get_storage_client(project_id: str | None = None) -> storage.Client: + """Create a GCS client. + + Authentication uses Application Default Credentials (ADC): + - In Cloud Run / GCE: automatic via metadata server + - Locally: via `gcloud auth application-default login` + - In CI: via GOOGLE_APPLICATION_CREDENTIALS environment variable + + Args: + project_id: GCP project ID (defaults to settings.project_id) + + Returns: + Configured storage client + """ + return storage.Client(project=project_id or settings.project_id) + + +def _download_blob(blob: storage.Blob, destination: Path) -> Path | None: + """Download a single blob, skipping if the local file is already current. + + Uses blob.size (available from list_blobs metadata at no extra cost) to + detect unchanged files without reading the file content. + + Returns the local path if downloaded, None if skipped. + """ + local_path = destination / blob.name + + if local_path.exists() and local_path.stat().st_size == blob.size: + logger.debug(f"Skipping (unchanged): {blob.name}") + return None + + local_path.parent.mkdir(parents=True, exist_ok=True) + logger.debug(f"Downloading: {blob.name}") + blob.download_to_filename(str(local_path)) + return local_path + + +def download_tracker_files( + destination: Path, + bucket_name: str | None = None, + client: storage.Client | None = None, +) -> list[Path]: + """Download tracker files from GCS bucket. + + Downloads in parallel and skips files whose local size already matches + the blob size (equivalent to gsutil -m cp -n). + + Args: + destination: Local directory to download files to + bucket_name: GCS bucket name (defaults to settings.download_bucket) + client: Storage client (created if not provided) + + Returns: + List of downloaded file paths (excludes skipped files) + """ + bucket_name = bucket_name or settings.download_bucket + + if client is None: + client = get_storage_client() + + bucket = client.bucket(bucket_name) + destination.mkdir(parents=True, exist_ok=True) + + logger.info(f"Downloading tracker files from gs://{bucket_name} to {destination}") + + blobs = [b for b in bucket.list_blobs() if not b.name.endswith("/")] + logger.info(f"Found {len(blobs)} objects in bucket") + + downloaded: list[Path] = [] + + with ThreadPoolExecutor(max_workers=_GCS_WORKERS) as executor: + futures = {executor.submit(_download_blob, blob, destination): blob for blob in blobs} + for future in as_completed(futures): + try: + result = future.result() + if result is not None: + downloaded.append(result) + except Exception: + blob = futures[future] + logger.error(f"Failed to download: {blob.name}") + + skipped = len(blobs) - len(downloaded) + logger.info(f"Downloaded {len(downloaded)} files, skipped {skipped} unchanged") + return downloaded + + +def _upload_file(bucket: storage.Bucket, file_path: Path, blob_name: str) -> str: + """Upload a single file to GCS.""" + logger.debug(f"Uploading: {blob_name}") + blob = bucket.blob(blob_name) + blob.upload_from_filename(str(file_path)) + return blob_name + + +def upload_output( + source_dir: Path, + bucket_name: str | None = None, + prefix: str = "", + client: storage.Client | None = None, +) -> list[str]: + """Upload output directory to GCS bucket in parallel. + + Args: + source_dir: Local directory to upload + bucket_name: GCS bucket name (defaults to settings.upload_bucket) + prefix: Optional prefix for uploaded blob names + client: Storage client (created if not provided) + + Returns: + List of uploaded blob names + + Raises: + FileNotFoundError: If source directory doesn't exist + """ + if not source_dir.exists(): + raise FileNotFoundError(f"Source directory not found: {source_dir}") + + bucket_name = bucket_name or settings.upload_bucket + + if client is None: + client = get_storage_client() + + bucket = client.bucket(bucket_name) + + logger.info(f"Uploading {source_dir} to gs://{bucket_name}/{prefix}") + + files = [f for f in source_dir.rglob("*") if f.is_file()] + + def _blob_name(file_path: Path) -> str: + relative = file_path.relative_to(source_dir) + name = f"{prefix}/{relative}" if prefix else str(relative) + return name.replace("\\", "/") + + uploaded: list[str] = [] + + with ThreadPoolExecutor(max_workers=_GCS_WORKERS) as executor: + futures = {executor.submit(_upload_file, bucket, f, _blob_name(f)): f for f in files} + for future in as_completed(futures): + try: + uploaded.append(future.result()) + except Exception: + file_path = futures[future] + logger.exception(f"Failed to upload: {file_path}") + + logger.info(f"Uploaded {len(uploaded)} files to gs://{bucket_name}") + return uploaded diff --git a/src/a4d/logging.py b/src/a4d/logging.py new file mode 100644 index 0000000..366997d --- /dev/null +++ b/src/a4d/logging.py @@ -0,0 +1,172 @@ +"""Operational logging configuration using loguru. + +This module provides logging infrastructure for monitoring and debugging +the pipeline execution. Logs are exported to BigQuery for dashboard analysis +(success rates, error counts, processing times, etc.). + +For data quality errors (conversion failures, validation errors), +use the ErrorCollector class from a4d.errors instead. + +Usage: + The loguru logger is a singleton. Once configured with setup_logging(), + all imports of 'from loguru import logger' will use the same configuration. + + >>> from a4d.logging import setup_logging, file_logger + >>> setup_logging(output_root=Path("output"), log_name="script1") + >>> + >>> # In processing code: + >>> from loguru import logger + >>> with file_logger("clinic_001_patient", output_root, tracker_year=2024, tracker_month=10): + ... logger.info("Processing started", rows=150) + ... logger.warning("Missing column", column="hba1c_updated_date") +""" + +import sys +import threading +from collections.abc import Generator +from contextlib import contextmanager +from pathlib import Path + +from loguru import logger + + +def _main_thread_only(record) -> bool: # noqa: ANN001 + """Filter that passes only log records from the main thread. + + Used on the console handler when running parallel workers so that + worker thread logs don't flood the console or break tqdm progress bars. + Worker logs still reach their per-tracker JSON file handlers. + """ + return threading.current_thread() is threading.main_thread() + + +def setup_logging( + output_root: Path, + log_name: str, + level: str = "INFO", + console: bool = True, + console_level: str | None = None, + console_main_thread_only: bool = False, +) -> None: + """Configure loguru for pipeline-wide operational logging. + + Creates both console (colored, human-readable) and file (JSON for BigQuery) + handlers. All logs in the JSON file include context variables from + contextualize() for analysis in Looker Studio. + + Args: + output_root: Root output directory (logs will be in output_root/logs/) + log_name: Base name for the log file (e.g., "script1_extract") + level: Minimum file log level (DEBUG, INFO, WARNING, ERROR) + console: Whether to add console handler (set False for CLI with progress bars) + console_level: Console log level (None = use level, or set to ERROR for quiet mode) + + Example: + >>> setup_logging(Path("output"), "script1_extract") + >>> logger.info("Processing started", total_trackers=10) + + >>> # Quiet mode for CLI with progress bars + >>> setup_logging(Path("output"), "pipeline", console_level="ERROR") + """ + log_dir = output_root / "logs" + log_dir.mkdir(parents=True, exist_ok=True) + log_file = log_dir / f"main_{log_name}.log" + + # Remove default handler + logger.remove() + + # Console handler: pretty, colored output for monitoring + if console: + console_log_level = console_level if console_level is not None else level + logger.add( + sys.stdout, + level=console_log_level, + colorize=True, + filter=_main_thread_only if console_main_thread_only else None, + format=( + "{time:HH:mm:ss} | " + "{level: <8} | " + "{message}" + ), + ) + + # File handler: JSON output for BigQuery upload + # serialize=True means all context from contextualize() is included + logger.add( + log_file, + level="DEBUG", # Capture all levels in file + serialize=True, # JSON format with all fields + rotation="100 MB", + retention="30 days", + compression="zip", + ) + + if console: + logger.info("Logging initialized", log_file=str(log_file), level=level) + + +@contextmanager +def file_logger( + file_name: str, + output_root: Path, + tracker_year: int | None = None, + tracker_month: int | None = None, + level: str = "DEBUG", +) -> Generator: + """Context manager for per-tracker file logging with context. + + Creates a separate log file for a specific tracker and sets context + variables (file_name, tracker_year, tracker_month) that are automatically + included in all log records within this context. + + All logs are JSON formatted and will be aggregated for BigQuery upload. + + Args: + file_name: Name of the tracker file (e.g., "clinic_001_patient") + output_root: Root output directory (logs will be in output_root/logs/) + tracker_year: Year from the tracker (for dashboard filtering) + tracker_month: Month from the tracker (for dashboard filtering) + level: Minimum log level for this file handler + + Yields: + None (use logger directly within context) + + Example: + >>> with file_logger("clinic_001_patient", output_root, 2024, 10): + ... logger.info("Processing patient data", rows=150) + ... logger.warning("Missing column", column="hba1c_updated_date") + ... # All logs include file_name, tracker_year, tracker_month + """ + log_dir = output_root / "logs" + log_dir.mkdir(parents=True, exist_ok=True) + log_file = log_dir / f"{file_name}.log" + + # Remove old log file if exists + if log_file.exists(): + log_file.unlink() + + # Add file-specific handler (JSON only, no console) + handler_id = logger.add( + log_file, + level=level, + serialize=True, # JSON format + ) + + # Build context dict (only include non-None values) + context = {"file_name": file_name} + if tracker_year is not None: + context["tracker_year"] = tracker_year + if tracker_month is not None: + context["tracker_month"] = tracker_month + + # Use contextualize to add file_name, tracker_year, tracker_month to all logs + with logger.contextualize(**context): + try: + yield + except Exception: + # Log exception with full traceback + logger.bind(error_code="critical_abort").exception("Processing failed") + raise + finally: + # Remove the handler + logger.remove(handler_id) diff --git a/src/a4d/pipeline/__init__.py b/src/a4d/pipeline/__init__.py new file mode 100644 index 0000000..d256ed8 --- /dev/null +++ b/src/a4d/pipeline/__init__.py @@ -0,0 +1,18 @@ +"""Pipeline orchestration for A4D data processing.""" + +from a4d.pipeline.models import PipelineResult, TrackerResult +from a4d.pipeline.patient import ( + discover_tracker_files, + process_patient_tables, + run_patient_pipeline, +) +from a4d.pipeline.tracker import process_tracker_patient + +__all__ = [ + "PipelineResult", + "TrackerResult", + "discover_tracker_files", + "process_patient_tables", + "process_tracker_patient", + "run_patient_pipeline", +] diff --git a/src/a4d/pipeline/models.py b/src/a4d/pipeline/models.py new file mode 100644 index 0000000..2e48915 --- /dev/null +++ b/src/a4d/pipeline/models.py @@ -0,0 +1,78 @@ +"""Pipeline result models for tracking processing outputs.""" + +from dataclasses import dataclass +from pathlib import Path + + +@dataclass +class TrackerResult: + """Result from processing a single tracker file. + + Attributes: + tracker_file: Original tracker file path + tracker_name: Base name without extension + raw_output: Path to raw parquet file (None if extraction failed) + cleaned_output: Path to cleaned parquet file (None if cleaning failed) + success: Whether processing completed successfully + error: Error message if processing failed + cleaning_errors: Number of data quality errors during cleaning (type conversion, + validation failures, etc.). These are non-fatal - data is cleaned + with error values (999999, "Undefined", etc.) + error_breakdown: Breakdown of errors by type (error_code → count). + Example: {"type_conversion": 10, "invalid_value": 5} + """ + + tracker_file: Path + tracker_name: str + raw_output: Path | None = None + cleaned_output: Path | None = None + success: bool = True + error: str | None = None + cleaning_errors: int = 0 + error_breakdown: dict[str, int] | None = None + + +@dataclass +class PipelineResult: + """Result from running the complete patient pipeline. + + Attributes: + tracker_results: Results from processing individual trackers + tables: Dictionary mapping table name to output path + total_trackers: Total number of trackers processed + successful_trackers: Number of successfully processed trackers + failed_trackers: Number of failed trackers + success: Whether entire pipeline completed successfully + """ + + tracker_results: list[TrackerResult] + tables: dict[str, Path] + total_trackers: int + successful_trackers: int + failed_trackers: int + success: bool + + @classmethod + def from_tracker_results( + cls, tracker_results: list[TrackerResult], tables: dict[str, Path] | None = None + ) -> PipelineResult: + """Create PipelineResult from tracker results. + + Args: + tracker_results: List of tracker processing results + tables: Dictionary of created tables (empty if table creation skipped) + + Returns: + PipelineResult with computed statistics + """ + successful = sum(1 for r in tracker_results if r.success) + failed = len(tracker_results) - successful + + return cls( + tracker_results=tracker_results, + tables=tables or {}, + total_trackers=len(tracker_results), + successful_trackers=successful, + failed_trackers=failed, + success=failed == 0, + ) diff --git a/src/a4d/pipeline/patient.py b/src/a4d/pipeline/patient.py new file mode 100644 index 0000000..d9192cc --- /dev/null +++ b/src/a4d/pipeline/patient.py @@ -0,0 +1,333 @@ +"""Main patient pipeline orchestration.""" + +import os +from collections.abc import Callable +from concurrent.futures import ProcessPoolExecutor, as_completed +from datetime import datetime +from pathlib import Path + +from loguru import logger +from tqdm import tqdm + +from a4d.config import settings +from a4d.logging import setup_logging +from a4d.pipeline.models import PipelineResult, TrackerResult +from a4d.pipeline.tracker import process_tracker_patient +from a4d.tables.logs import create_table_logs +from a4d.tables.patient import ( + create_table_patient_data_annual, + create_table_patient_data_monthly, + create_table_patient_data_static, +) + + +def _init_worker_logging(output_root: Path) -> None: + """Initialize logging for worker processes (called once per ProcessPoolExecutor worker).""" + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + pid = os.getpid() + setup_logging( + output_root=output_root, + log_name=f"worker_{timestamp}_pid{pid}", + console_level="ERROR", + ) + + +def discover_tracker_files(data_root: Path) -> list[Path]: + """Discover all Excel tracker files in data_root. + + Searches recursively for .xlsx files, excluding temp files (~$*). + + Args: + data_root: Root directory to search + + Returns: + List of tracker file paths + + Example: + >>> tracker_files = discover_tracker_files(Path("/data")) + >>> len(tracker_files) + 42 + """ + tracker_files = [] + for file in data_root.rglob("*.xlsx"): + if not file.name.startswith("~$"): + tracker_files.append(file) + + return sorted(tracker_files) + + +def process_patient_tables(cleaned_dir: Path, output_dir: Path) -> dict[str, Path]: + """Create final patient tables from cleaned parquets. + + Creates three main tables: + - patient_data_static: Latest data per patient + - patient_data_monthly: All monthly records + - patient_data_annual: Latest data per patient per year (2024+) + + Args: + cleaned_dir: Directory containing cleaned parquet files + output_dir: Directory to write final tables + + Returns: + Dictionary mapping table name to output path + + Example: + >>> tables = process_patient_tables( + ... Path("output/patient_data_cleaned"), + ... Path("output/tables") + ... ) + >>> tables.keys() + dict_keys(['static', 'monthly', 'annual']) + """ + logger.info("Creating final patient tables from cleaned data") + + cleaned_files = list(cleaned_dir.glob("*_patient_cleaned.parquet")) + logger.info(f"Found {len(cleaned_files)} cleaned parquet files") + + if not cleaned_files: + logger.warning("No cleaned files found, skipping table creation") + return {} + + tables = {} + + logger.info("Creating static patient table") + static_path = create_table_patient_data_static(cleaned_files, output_dir) + tables["static"] = static_path + + logger.info("Creating monthly patient table") + monthly_path = create_table_patient_data_monthly(cleaned_files, output_dir) + tables["monthly"] = monthly_path + + logger.info("Creating annual patient table") + annual_path = create_table_patient_data_annual(cleaned_files, output_dir) + tables["annual"] = annual_path + + logger.info(f"Created {len(tables)} patient tables") + return tables + + +def run_patient_pipeline( + tracker_files: list[Path] | None = None, + max_workers: int = 1, + output_root: Path | None = None, + skip_tables: bool = False, + force: bool = False, + clean_output: bool = False, + progress_callback: Callable[[str, bool], None] | None = None, + show_progress: bool = False, + console_log_level: str | None = None, +) -> PipelineResult: + """Run complete patient data pipeline. + + Processing modes: + - Batch mode: If tracker_files is None, discovers all .xlsx in data_root + - Single file mode: If tracker_files provided, processes only those files + + Pipeline steps: + 1. For each tracker (optionally parallel): + - Extract patient data from Excel → raw parquet + - Clean raw data → cleaned parquet + 2. Create final tables from all cleaned parquets (if not skipped) + + Args: + tracker_files: Specific files to process (None = discover all) + max_workers: Number of parallel workers (1 = sequential) + output_root: Output directory (None = use settings.output_root) + skip_tables: If True, only extract + clean, skip table creation + force: If True, reprocess even if outputs exist + clean_output: If True, wipe patient_data_raw/, patient_data_cleaned/, tables/ before run + progress_callback: Optional callback(tracker_name, success) called after each tracker + show_progress: If True, show tqdm progress bar + console_log_level: Console log level (None=INFO, ERROR=quiet, etc) + + Returns: + PipelineResult with tracker results and table paths + + Example: + >>> # Process all trackers + >>> result = run_patient_pipeline() + >>> result.success + True + >>> result.successful_trackers + 42 + + >>> # Process single file + >>> result = run_patient_pipeline( + ... tracker_files=[Path("/data/2024_Sibu.xlsx")] + ... ) + + >>> # Parallel processing with progress bar (CLI mode) + >>> result = run_patient_pipeline( + ... max_workers=8, + ... show_progress=True, + ... console_log_level="ERROR" + ... ) + """ + import shutil + + # Use settings defaults if not provided + if output_root is None: + output_root = settings.output_root + + # Wipe previous run's outputs so tables reflect only this run. + if clean_output: + for subdir in ("patient_data_raw", "patient_data_cleaned", "tables", "logs"): + target = output_root / subdir + if target.exists(): + shutil.rmtree(target) + logger.info(f"Cleaned output directory: {target}") + + # Setup main pipeline logging + setup_logging( + output_root, + "pipeline_patient", + console_level=console_log_level if console_log_level else "INFO", + ) + logger.info("Starting patient pipeline") + logger.info(f"Output directory: {output_root}") + logger.info(f"Max workers: {max_workers}") + + # Discover or use provided tracker files + if tracker_files is None: + logger.info(f"Discovering tracker files in: {settings.data_root}") + tracker_files = discover_tracker_files(settings.data_root) + else: + tracker_files = [Path(f) for f in tracker_files] + + logger.info(f"Found {len(tracker_files)} tracker files to process") + + if not tracker_files: + logger.warning("No tracker files found") + return PipelineResult.from_tracker_results([], {}) + + # Process trackers + tracker_results: list[TrackerResult] = [] + + if max_workers == 1: + # Sequential processing (easier for debugging) + logger.info("Processing trackers sequentially") + + # Use tqdm if requested + iterator = ( + tqdm(tracker_files, desc="Processing trackers", unit="file") + if show_progress + else tracker_files + ) + + for tracker_file in iterator: + if isinstance(iterator, tqdm): + iterator.set_description(f"Processing {tracker_file.name}") + + result = process_tracker_patient( + tracker_file=tracker_file, + output_root=output_root, + mapper=None, # Each tracker loads mapper if needed + ) + tracker_results.append(result) + + # Call progress callback if provided + if progress_callback: + progress_callback(tracker_file.name, result.success) + + if result.success: + logger.info(f"✓ Successfully processed: {tracker_file.name}") + if show_progress: + tqdm.write(f"✓ {tracker_file.name}") + else: + logger.error(f"✗ Failed to process: {tracker_file.name} - {result.error}") + if show_progress: + tqdm.write(f"✗ {tracker_file.name}: {result.error}") + + else: + # Parallel processing + logger.info(f"Processing trackers in parallel ({max_workers} workers)") + with ProcessPoolExecutor( + max_workers=max_workers, initializer=_init_worker_logging, initargs=(output_root,) + ) as executor: + # Submit all jobs + futures = { + executor.submit( + process_tracker_patient, + tracker_file, + output_root, + None, # Each worker loads synonyms independently + ): tracker_file + for tracker_file in tracker_files + } + + # Collect results as they complete + futures_iterator = as_completed(futures) + if show_progress: + futures_iterator = tqdm( + futures_iterator, total=len(futures), desc="Processing trackers", unit="file" + ) + + for future in futures_iterator: + tracker_file = futures[future] + try: + result = future.result() + tracker_results.append(result) + + # Call progress callback if provided + if progress_callback: + progress_callback(tracker_file.name, result.success) + + if result.success: + logger.info(f"✓ Completed: {tracker_file.name}") + if show_progress: + tqdm.write(f"✓ {tracker_file.name}") + else: + logger.error(f"✗ Failed: {tracker_file.name} - {result.error}") + if show_progress: + tqdm.write(f"✗ {tracker_file.name}: {result.error}") + except Exception as e: + logger.exception(f"Exception processing {tracker_file.name}") + if show_progress: + tqdm.write(f"✗ {tracker_file.name}: Exception - {str(e)}") + tracker_results.append( + TrackerResult( + tracker_file=tracker_file, + tracker_name=tracker_file.stem, + success=False, + error=str(e), + ) + ) + + # Summary + successful = sum(1 for r in tracker_results if r.success) + failed = len(tracker_results) - successful + logger.info(f"Tracker processing complete: {successful} successful, {failed} failed") + + # Create tables + tables: dict[str, Path] = {} + if not skip_tables: + try: + cleaned_dir = output_root / "patient_data_cleaned" + tables_dir = output_root / "tables" + logs_dir = output_root / "logs" + + tables = process_patient_tables(cleaned_dir, tables_dir) + + # Create logs table separately (operational data, not patient data) + if logs_dir.exists(): + logger.info("Creating logs table from pipeline execution logs") + logs_table_path = create_table_logs(logs_dir, tables_dir) + tables["logs"] = logs_table_path + logger.info(f"Logs table created: {logs_table_path}") + + logger.info(f"Created {len(tables)} tables total") + except Exception: + logger.exception("Failed to create tables") + # Don't fail entire pipeline if table creation fails + else: + logger.info("Skipping table creation (skip_tables=True)") + + # Build result + result = PipelineResult.from_tracker_results(tracker_results, tables) + + if result.success: + logger.info("✓ Pipeline completed successfully") + else: + logger.warning(f"✗ Pipeline completed with {failed} failures") + + return result diff --git a/src/a4d/pipeline/tracker.py b/src/a4d/pipeline/tracker.py new file mode 100644 index 0000000..e377ab5 --- /dev/null +++ b/src/a4d/pipeline/tracker.py @@ -0,0 +1,113 @@ +"""Single tracker processing: extract + clean.""" + +from pathlib import Path + +from loguru import logger + +from a4d.clean.patient import clean_patient_file +from a4d.errors import ErrorCollector +from a4d.extract.patient import export_patient_raw, read_all_patient_sheets +from a4d.logging import file_logger +from a4d.pipeline.models import TrackerResult +from a4d.reference.synonyms import ColumnMapper + + +def process_tracker_patient( + tracker_file: Path, output_root: Path, mapper: ColumnMapper | None = None +) -> TrackerResult: + """Process single tracker file: extract + clean patient data. + + This function processes one tracker file end-to-end: + 1. Extract patient data from Excel + 2. Export to raw parquet + 3. Clean the raw data + 4. Export to cleaned parquet + + Each step creates a separate log file for debugging. + + Args: + tracker_file: Path to tracker Excel file + output_root: Root output directory (will create subdirs for raw/cleaned) + mapper: ColumnMapper for synonym mapping (loaded if not provided) + + Returns: + TrackerResult with paths to outputs and success status + + Example: + >>> tracker_file = Path("/data/2024_Sibu.xlsx") + >>> output_root = Path("output") + >>> result = process_tracker_patient(tracker_file, output_root) + >>> result.success + True + >>> result.raw_output + Path('output/patient_data_raw/2024_Sibu_patient_raw.parquet') + """ + tracker_name = tracker_file.stem + + try: + # Setup directories + raw_dir = output_root / "patient_data_raw" + cleaned_dir = output_root / "patient_data_cleaned" + raw_dir.mkdir(parents=True, exist_ok=True) + cleaned_dir.mkdir(parents=True, exist_ok=True) + + # Expected output paths + raw_output = raw_dir / f"{tracker_name}_patient_raw.parquet" + cleaned_output = cleaned_dir / f"{tracker_name}_patient_cleaned.parquet" + + # Log context for this tracker + with file_logger(f"{tracker_name}_patient", output_root): + logger.info(f"Processing tracker: {tracker_file.name}") + + # STEP 1: Extract + logger.info("Step 1: Extracting patient data from Excel") + error_collector = ErrorCollector() + + df_raw = read_all_patient_sheets( + tracker_file=tracker_file, mapper=mapper, error_collector=error_collector + ) + logger.info(f"Extracted {len(df_raw)} rows") + + # Export raw parquet + raw_output = export_patient_raw( + df=df_raw, tracker_file=tracker_file, output_dir=raw_dir + ) + logger.info(f"Raw parquet saved: {raw_output}") + + # STEP 2: Clean + logger.info("Step 2: Cleaning patient data") + + clean_patient_file( + raw_parquet_path=raw_output, + output_parquet_path=cleaned_output, + error_collector=error_collector, + ) + + error_count = len(error_collector) + error_breakdown = error_collector.get_error_summary() + logger.info(f"Cleaned parquet saved: {cleaned_output}") + logger.info(f"Total data quality errors: {error_count}") + if error_breakdown: + logger.info(f"Error breakdown: {error_breakdown}") + + return TrackerResult( + tracker_file=tracker_file, + tracker_name=tracker_name, + raw_output=raw_output, + cleaned_output=cleaned_output, + success=True, + error=None, + cleaning_errors=error_count, + error_breakdown=error_breakdown if error_breakdown else None, + ) + + except Exception as e: + logger.bind(error_code="critical_abort").exception(f"Failed to process tracker: {tracker_file.name}") + return TrackerResult( + tracker_file=tracker_file, + tracker_name=tracker_name, + raw_output=None, + cleaned_output=None, + success=False, + error=str(e), + ) diff --git a/src/a4d/reference/__init__.py b/src/a4d/reference/__init__.py new file mode 100644 index 0000000..7662305 --- /dev/null +++ b/src/a4d/reference/__init__.py @@ -0,0 +1,43 @@ +"""Reference data loaders and validators. + +This package contains modules for loading and working with reference data +from the shared reference_data/ directory. +""" + +# Loaders (internal utilities) +from a4d.reference.loaders import ( + find_reference_data_dir, + get_reference_data_path, + load_yaml, +) + +# Provinces (validation) +from a4d.reference.provinces import ( + get_country_for_province, + is_valid_province, + load_allowed_provinces, + load_provinces_by_country, +) + +# Synonyms (column mapping) +from a4d.reference.synonyms import ( + ColumnMapper, + load_patient_mapper, + load_product_mapper, +) + +__all__ = [ + # Loaders + "find_reference_data_dir", + "get_reference_data_path", + "load_yaml", + # Synonyms + "ColumnMapper", + "load_patient_mapper", + "load_product_mapper", + # Provinces + "get_country_for_province", + "is_valid_province", + "load_allowed_provinces", + "load_provinces_by_country", +] diff --git a/src/a4d/reference/loaders.py b/src/a4d/reference/loaders.py new file mode 100644 index 0000000..7719bd8 --- /dev/null +++ b/src/a4d/reference/loaders.py @@ -0,0 +1,91 @@ +"""Utilities for loading reference data files. + +This module provides common utilities for loading YAML and other reference +data files shared between the R and Python pipelines. +""" + +import os +from pathlib import Path +from typing import Any + +import yaml +from loguru import logger + + +def find_reference_data_dir() -> Path: + """Find reference_data directory. + + Checks A4D_REFERENCE_DATA env var first (used in Docker/Cloud Run where + the directory is at /app/reference_data). Falls back to walking up from + this file to find the repo root for local development. + + Returns: + Path to reference_data directory + + Raises: + FileNotFoundError: If reference_data directory not found + """ + # Explicit override for Docker/Cloud Run (set A4D_REFERENCE_DATA=/app/reference_data) + if env_path := os.environ.get("A4D_REFERENCE_DATA"): + path = Path(env_path) + if path.exists(): + return path + raise FileNotFoundError(f"reference_data directory not found at {path}") + + # Local dev: navigate from src/a4d/reference/loaders.py up to repo root + # loaders.py -> reference -> a4d -> src -> repo root + repo_root = Path(__file__).parents[3] + reference_data_dir = repo_root / "reference_data" + + if not reference_data_dir.exists(): + raise FileNotFoundError(f"reference_data directory not found at {reference_data_dir}") + + return reference_data_dir + + +def load_yaml( + yaml_path: Path, + relative_to_reference_data: bool = False, +) -> Any: + """Load and parse a YAML file. + + Args: + yaml_path: Path to the YAML file + relative_to_reference_data: If True, yaml_path is relative to + reference_data directory + + Returns: + Parsed YAML content + + Raises: + FileNotFoundError: If the YAML file doesn't exist + yaml.YAMLError: If the YAML file is malformed + """ + if relative_to_reference_data: + reference_data_dir = find_reference_data_dir() + yaml_path = reference_data_dir / yaml_path + + if not yaml_path.exists(): + raise FileNotFoundError(f"YAML file not found: {yaml_path}") + + logger.debug(f"Loading YAML file: {yaml_path}") + + with open(yaml_path) as f: + return yaml.safe_load(f) + + +def get_reference_data_path(*parts: str) -> Path: + """Get path to a file in reference_data directory. + + Args: + *parts: Path components relative to reference_data directory + + Returns: + Absolute path to the file + + Example: + >>> path = get_reference_data_path("synonyms", "synonyms_patient.yaml") + >>> # Returns: /path/to/repo/reference_data/synonyms/synonyms_patient.yaml + """ + reference_data_dir = find_reference_data_dir() + return reference_data_dir.joinpath(*parts) diff --git a/src/a4d/reference/provinces.py b/src/a4d/reference/provinces.py new file mode 100644 index 0000000..2fa1694 --- /dev/null +++ b/src/a4d/reference/provinces.py @@ -0,0 +1,166 @@ +"""Province validation for patient data. + +This module loads allowed provinces from the reference_data YAML file +and provides utilities for validation. +""" + +from functools import lru_cache + +from loguru import logger + +from a4d.reference.loaders import get_reference_data_path, load_yaml + + +@lru_cache +def load_allowed_provinces() -> list[str]: + """Load all allowed provinces from YAML file (lowercased for case-insensitive matching). + + Provinces are organized by country in the YAML file. This function + flattens them into a single list and lowercases them for validation. + + The result is cached for performance since provinces don't change + during runtime. + + Returns: + List of all allowed province names (lowercased) across all countries + + Example: + >>> provinces = load_allowed_provinces() + >>> "bangkok" in provinces + True + >>> "BANGKOK" in provinces + False # List is lowercased, use is_valid_province() for validation + """ + path = get_reference_data_path("provinces", "allowed_provinces.yaml") + provinces_by_country: dict[str, list[str]] = load_yaml(path) + + # Flatten all provinces into single list and lowercase for matching + all_provinces = [] + for _, provinces in provinces_by_country.items(): + all_provinces.extend(p.lower() for p in provinces) + + logger.info(f"Loaded {len(all_provinces)} provinces from {len(provinces_by_country)} countries") + + return all_provinces + + +@lru_cache +def load_provinces_by_country() -> dict[str, list[str]]: + """Load provinces organized by country (lowercased for case-insensitive matching). + + Returns: + Dict mapping country names to lists of their provinces (lowercased) + + Example: + >>> provinces = load_provinces_by_country() + >>> "bangkok" in provinces["THAILAND"] + True + >>> len(provinces["VIETNAM"]) + 63 + """ + path = get_reference_data_path("provinces", "allowed_provinces.yaml") + provinces_by_country_raw: dict[str, list[str]] = load_yaml(path) + + # Lowercase all province names for case-insensitive matching + provinces_by_country = { + country: [p.lower() for p in provinces] + for country, provinces in provinces_by_country_raw.items() + } + + logger.info(f"Loaded provinces for {len(provinces_by_country)} countries") + + return provinces_by_country + + +@lru_cache +def load_canonical_provinces() -> list[str]: + """Load all allowed provinces with canonical casing (for validation). + + Unlike load_allowed_provinces() which lowercases for matching, + this returns the original province names from the YAML with proper + casing and accents to use as canonical values in validation. + + Returns: + List of all allowed province names (original casing) across all countries + + Example: + >>> provinces = load_canonical_provinces() + >>> "Takéo" in provinces + True + >>> "Bangkok" in provinces + True + """ + path = get_reference_data_path("provinces", "allowed_provinces.yaml") + provinces_by_country: dict[str, list[str]] = load_yaml(path) + + # Flatten all provinces into single list WITHOUT lowercasing + all_provinces = [] + for _, provinces in provinces_by_country.items(): + all_provinces.extend(provinces) + + logger.info( + f"Loaded {len(all_provinces)} canonical province names " + f"from {len(provinces_by_country)} countries" + ) + + return all_provinces + + +def is_valid_province(province: str | None) -> bool: + """Check if a province name is valid (case-insensitive). + + Args: + province: Province name to validate (case-insensitive, None allowed) + + Returns: + True if province is None or in the allowed list, False otherwise + + Example: + >>> is_valid_province("Bangkok") + True + >>> is_valid_province("BANGKOK") + True + >>> is_valid_province("bangkok") + True + >>> is_valid_province(None) + True + >>> is_valid_province("Invalid Province") + False + """ + if province is None: + return True + + allowed = load_allowed_provinces() + return province.lower() in allowed + + +def get_country_for_province(province: str) -> str | None: + """Get the country for a given province (case-insensitive). + + Args: + province: Province name (case-insensitive) + + Returns: + Country name if province is found, None otherwise + + Example: + >>> get_country_for_province("Bangkok") + 'THAILAND' + >>> get_country_for_province("bangkok") + 'THAILAND' + >>> get_country_for_province("BANGKOK") + 'THAILAND' + """ + provinces_by_country = load_provinces_by_country() + province_lower = province.lower() + + for country, provinces in provinces_by_country.items(): + if province_lower in provinces: + return country + + return None + + +if __name__ == "__main__": + for c, p in load_provinces_by_country().items(): + print(f"{c}: {p}") diff --git a/src/a4d/reference/synonyms.py b/src/a4d/reference/synonyms.py new file mode 100644 index 0000000..5bf9883 --- /dev/null +++ b/src/a4d/reference/synonyms.py @@ -0,0 +1,343 @@ +"""Column name mapper for standardizing tracker file columns. + +This module handles the mapping of various column name variants (synonyms) +to standardized column names used throughout the pipeline. +""" + +import re +from pathlib import Path + +import polars as pl +from loguru import logger + +from a4d.reference.loaders import get_reference_data_path, load_yaml + + +def sanitize_str(text: str) -> str: + """Sanitize a string for column name matching. + + Converts to lowercase, removes all spaces and special characters, + keeping only alphanumeric characters. This matches the R implementation. + + Args: + text: String to sanitize + + Returns: + Sanitized string with only lowercase alphanumeric characters + + Examples: + >>> sanitize_str("Patient ID*") + 'patientid' + >>> sanitize_str("Age* On Reporting") + 'ageonreporting' + >>> sanitize_str("Date 2022") + 'date2022' + >>> sanitize_str("My Awesome 1st Column!!") + 'myawesome1stcolumn' + """ + # Convert to lowercase + text = text.lower() + # Remove spaces + text = text.replace(" ", "") + # Remove all non-alphanumeric characters + text = re.sub(r"[^a-z0-9]", "", text) + return text + + +class ColumnMapper: + """Maps synonym column names to standardized names. + + Loads column synonyms from YAML files and provides methods to rename + DataFrame columns to their standardized names. + + Example YAML structure: + age: + - Age + - Age* + - age on reporting + - Age (Years) + patient_id: + - ID + - Patient ID + - Patient ID* + + Attributes: + yaml_path: Path to the synonym YAML file + synonyms: Dict mapping standard names to lists of synonyms + _lookup: Reverse lookup dict mapping SANITIZED synonyms to standard names + + Note: + Synonym matching is case-insensitive and ignores special characters. + This matches the R implementation which uses sanitize_str() for both + column names and synonym keys before matching. + """ + + def __init__(self, yaml_path: Path): + """Initialize the mapper by loading synonyms from YAML. + + Args: + yaml_path: Path to the synonym YAML file + + Raises: + FileNotFoundError: If the YAML file doesn't exist + yaml.YAMLError: If the YAML file is malformed + """ + self.yaml_path = yaml_path + self.synonyms: dict[str, list[str]] = load_yaml(yaml_path) + + # Build reverse lookup: sanitized_synonym -> standard_name + # This matches R's behavior: sanitize both column names and synonym keys + self._lookup: dict[str, str] = self._build_lookup() + + logger.info( + f"Loaded {len(self.synonyms)} standard columns with " + f"{len(self._lookup)} total synonyms from {yaml_path.name}" + ) + + def _build_lookup(self) -> dict[str, str]: + """Build reverse lookup dictionary from SANITIZED synonyms to standard names. + + Sanitizes all synonym keys before adding to lookup, matching R's behavior. + + Returns: + Dict mapping each SANITIZED synonym to its standard column name + + Example: + >>> # YAML has: patient_id: ["Patient ID", "Patient ID*", "ID"] + >>> # Lookup will have: {"patientid": "patient_id", "id": "patient_id"} + """ + lookup = {} + for standard_name, synonym_list in self.synonyms.items(): + # Handle empty lists (columns with no synonyms) + if not synonym_list: + continue + + for synonym in synonym_list: + # Sanitize the synonym key before adding to lookup + sanitized_key = sanitize_str(synonym) + + if sanitized_key in lookup: + logger.bind(error_code="invalid_tracker").warning( + f"Duplicate sanitized synonym '{sanitized_key}' " + f"(from '{synonym}') found for both " + f"'{lookup[sanitized_key]}' and '{standard_name}'. " + f"Using '{standard_name}'." + ) + lookup[sanitized_key] = standard_name + + return lookup + + def get_standard_name(self, column: str) -> str: + """Get the standard name for a column. + + Sanitizes the input column name before lookup to match R behavior. + + Args: + column: Column name (may be a synonym, with special characters/spaces) + + Returns: + Standard column name, or original if no mapping exists + + Example: + >>> mapper.get_standard_name("Patient ID*") + 'patient_id' # "Patient ID*" → "patientid" → "patient_id" + >>> mapper.get_standard_name("Age* On Reporting") + 'age' # "Age* On Reporting" → "ageonreporting" → "age" + """ + # Sanitize input column name before lookup (matches R behavior) + sanitized_col = sanitize_str(column) + return self._lookup.get(sanitized_col, column) + + def is_known_column(self, column: str) -> bool: + """Check if column name maps to a known standard name. + + Used for validating forward-filled headers during Excel extraction. + Returns True if the column is either a known synonym or a standard name. + + Args: + column: Column name to check + + Returns: + True if column maps to a known standard name + + Example: + >>> mapper.is_known_column("Current Patient Observations Category") + True # Maps to observations_category + >>> mapper.is_known_column("Level of Support Status") + False # No such column in synonyms + """ + sanitized = sanitize_str(column) + return sanitized in self._lookup or column in self.synonyms + + def rename_columns( + self, + df: pl.DataFrame, + strict: bool = False, + ) -> pl.DataFrame: + """Rename DataFrame columns using synonym mappings. + + Args: + df: Input DataFrame with potentially non-standard column names + strict: If True, raise error if unmapped columns exist + If False, keep unmapped columns as-is + + Returns: + DataFrame with standardized column names + + Raises: + ValueError: If strict=True and unmapped columns exist + """ + # Build rename mapping for columns that need renaming + rename_map = {} + unmapped_columns = [] + + for col in df.columns: + standard_name = self.get_standard_name(col) + + if standard_name == col and col not in self.synonyms: + # Column is not in lookup and not a standard name + unmapped_columns.append(col) + elif standard_name != col: + # Column needs to be renamed + rename_map[col] = standard_name + + # Log unmapped columns + if unmapped_columns: + if strict: + raise ValueError( + f"Unmapped columns found: {unmapped_columns}. " + "These columns do not appear in the synonym file." + ) + else: + logger.bind(error_code="missing_column").warning( + f"Keeping {len(unmapped_columns)} unmapped columns as-is: {unmapped_columns}" + ) + + # Handle duplicate mappings: multiple source columns mapping to same target + # Keep only first occurrence, drop the rest (edge case from discontinued 2023 format) + target_counts: dict[str, int] = {} + for target in rename_map.values(): + target_counts[target] = target_counts.get(target, 0) + 1 + + if any(count > 1 for count in target_counts.values()): + duplicates = {t: c for t, c in target_counts.items() if c > 1} + logger.bind(error_code="invalid_tracker").warning( + f"Multiple source columns map to same target name: {duplicates}. " + "Keeping first occurrence only. " + "This is an edge case from discontinued 2023 format." + ) + + # Keep only first occurrence of each target + seen_targets: set[str] = set() + columns_to_drop = [] + + for source_col, target_col in rename_map.items(): + if target_col in duplicates: + if target_col in seen_targets: + # Duplicate - drop it + columns_to_drop.append(source_col) + logger.debug( + f"Dropping duplicate source column '{source_col}' " + f"(maps to '{target_col}')" + ) + else: + # First occurrence - keep it + seen_targets.add(target_col) + + # Drop duplicates before renaming + if columns_to_drop: + df = df.drop(columns_to_drop) + # Remove dropped columns from rename_map + for col in columns_to_drop: + del rename_map[col] + + # Log successful mappings + if rename_map: + logger.debug(f"Renaming {len(rename_map)} columns: {list(rename_map.items())}") + + return df.rename(rename_map) if rename_map else df + + def get_expected_columns(self) -> set[str]: + """Get set of all standard column names. + + Returns: + Set of standard column names defined in the synonym file + """ + return set(self.synonyms) + + def get_missing_columns(self, df: pl.DataFrame) -> set[str]: + """Get standard columns that are missing from the DataFrame. + + Args: + df: DataFrame to check + + Returns: + Set of standard column names not present in the DataFrame + """ + current_columns = set(df.columns) + expected_columns = self.get_expected_columns() + return expected_columns - current_columns + + def validate_required_columns( + self, + df: pl.DataFrame, + required: list[str], + ) -> None: + """Validate that required columns are present after renaming. + + Args: + df: DataFrame to validate + required: List of required standard column names + + Raises: + ValueError: If any required columns are missing + """ + missing = set(required) - set(df.columns) + if missing: + raise ValueError(f"Required columns missing after renaming: {missing}") + + +def load_patient_mapper() -> ColumnMapper: + """Load the patient data column mapper. + + Returns: + ColumnMapper for patient data + + Example: + >>> mapper = load_patient_mapper() + >>> df = mapper.rename_columns(raw_df) + """ + path = get_reference_data_path("synonyms", "synonyms_patient.yaml") + return ColumnMapper(path) + + +def load_product_mapper() -> ColumnMapper: + """Load the product data column mapper. + + Returns: + ColumnMapper for product data + + Example: + >>> mapper = load_product_mapper() + >>> df = mapper.rename_columns(raw_df) + """ + path = get_reference_data_path("synonyms", "synonyms_product.yaml") + return ColumnMapper(path) + + +if __name__ == "__main__": + # Example usage + patient_mapper = load_patient_mapper() + product_mapper = load_product_mapper() + + # Example DataFrame + df = pl.DataFrame( + { + "Age": [25, 30], + "Patient ID": [1, 2], + "Product Name": ["A", "B"], + } + ) + + renamed_df = patient_mapper.rename_columns(df) + print(renamed_df) diff --git a/src/a4d/state/__init__.py b/src/a4d/state/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/a4d/tables/__init__.py b/src/a4d/tables/__init__.py new file mode 100644 index 0000000..434cbbb --- /dev/null +++ b/src/a4d/tables/__init__.py @@ -0,0 +1,18 @@ +"""Table creation module for final output tables.""" + +from a4d.tables.logs import create_table_logs, parse_log_file +from a4d.tables.patient import ( + create_table_patient_data_annual, + create_table_patient_data_monthly, + create_table_patient_data_static, + read_cleaned_patient_data, +) + +__all__ = [ + "create_table_patient_data_annual", + "create_table_patient_data_monthly", + "create_table_patient_data_static", + "read_cleaned_patient_data", + "create_table_logs", + "parse_log_file", +] diff --git a/src/a4d/tables/clinic.py b/src/a4d/tables/clinic.py new file mode 100644 index 0000000..5d16a00 --- /dev/null +++ b/src/a4d/tables/clinic.py @@ -0,0 +1,67 @@ +"""Create clinic static data table from reference data. + +Replicates R pipeline's create_table_clinic_static_data() function: +reads clinic_data.xlsx, fills down hierarchical columns, exports as parquet. +""" + +from pathlib import Path + +import polars as pl +from loguru import logger + +from a4d.reference.loaders import find_reference_data_dir + +# Text columns filled downward to handle merged/blank cells in the Excel sheet. +# R: tidyr::fill(country_code:clinic_id, .direction = "down") +_FILL_COLUMNS = [ + "country", + "clinic_province", + "clinic_name", + "clinic_status", + "clinic_id", + "country_code", + "clinic_code", + "patient_id_example", +] + + +def create_table_clinic_static(output_dir: Path) -> Path: + """Create clinic static data table from reference data. + + Reads clinic_data.xlsx from reference_data/, fills hierarchical columns + downward (matching R's tidyr::fill behaviour), and writes parquet. + + Args: + output_dir: Directory to write the parquet file + + Returns: + Path to created clinic_data_static.parquet + """ + reference_dir = find_reference_data_dir() + clinic_file = reference_dir / "clinic_data.xlsx" + + if not clinic_file.exists(): + raise FileNotFoundError(f"Clinic data file not found: {clinic_file}") + + logger.info(f"Reading clinic data from: {clinic_file}") + + df = pl.read_excel(clinic_file, sheet_id=1) + + # Drop unnamed index column — R: select(2:11) + unnamed_cols = [c for c in df.columns if c.startswith("__UNNAMED")] + if unnamed_cols: + df = df.drop(unnamed_cols) + + # Fill nulls downward for hierarchical columns — R: tidyr::fill(..., .direction = "down") + fill_cols = [c for c in _FILL_COLUMNS if c in df.columns] + if fill_cols: + df = df.with_columns([pl.col(c).forward_fill() for c in fill_cols]) + + logger.info(f"Clinic static data: {df.shape[0]} rows, {df.shape[1]} columns") + + output_dir.mkdir(parents=True, exist_ok=True) + output_file = output_dir / "clinic_data_static.parquet" + df.write_parquet(output_file) + + logger.info(f"Clinic static table saved: {output_file}") + return output_file diff --git a/src/a4d/tables/logs.py b/src/a4d/tables/logs.py new file mode 100644 index 0000000..692c1bc --- /dev/null +++ b/src/a4d/tables/logs.py @@ -0,0 +1,223 @@ +"""Create logs table from pipeline execution logs. + +This module reads all JSON-formatted log files created by the pipeline +and creates a structured table for BigQuery upload and dashboard analysis. + +Log files are created by loguru with serialize=True, producing JSON lines format. +Each line contains structured data about pipeline execution: timestamps, levels, +messages, source locations, exceptions, and custom context fields. +""" + +import json +from pathlib import Path + +import polars as pl +from loguru import logger + + +def parse_log_file(log_file: Path) -> pl.DataFrame: + """Parse a single JSON lines log file into a DataFrame. + + Args: + log_file: Path to .log file (JSON lines format from loguru) + + Returns: + DataFrame with parsed log records, or empty DataFrame if file is invalid + + Example: + >>> df = parse_log_file(Path("output/logs/2024_Penang_patient.log")) + >>> df.columns + ['timestamp', 'level', 'message', 'log_file', ...] + """ + records = [] + + try: + with open(log_file, encoding="utf-8") as f: + for line_num, line in enumerate(f, 1): + line = line.strip() + + try: + log_entry = json.loads(line) + record_data = log_entry.get("record", {}) + + # Extract timestamp + time_data = record_data.get("time", {}) + timestamp = time_data.get("timestamp") + + # Extract level + level_data = record_data.get("level", {}) + level = level_data.get("name", "UNKNOWN") + + # Extract message + message = record_data.get("message", "") + + # Extract source location + file_data = record_data.get("file", {}) + source_file = file_data.get("name", "") + source_path = file_data.get("path", "") + + function = record_data.get("function", "") + line = record_data.get("line", 0) + module = record_data.get("module", "") + + # Extract context fields (file_name, tracker_year, tracker_month, error_code) + extra = record_data.get("extra", {}) + file_name = extra.get("file_name") + tracker_year = extra.get("tracker_year") + tracker_month = extra.get("tracker_month") + error_code = extra.get("error_code") + + # Extract process info (useful for debugging parallel processing) + process_data = record_data.get("process", {}) + process_name = process_data.get("name", "") + + # Extract exception info if present + exception = record_data.get("exception") + has_exception = exception is not None + exception_type = None + exception_value = None + + if has_exception and exception: + exception_type = exception.get("type") + exception_value = exception.get("value") + + # Create record + records.append( + { + "timestamp": timestamp, + "level": level, + "message": message, + "error_code": error_code, + "log_file": log_file.name, + "file_name": file_name, + "tracker_year": tracker_year, + "tracker_month": tracker_month, + "source_file": source_file, + "source_path": source_path, + "function": function, + "line": line, + "module": module, + "process_name": process_name, + "has_exception": has_exception, + "exception_type": exception_type, + "exception_value": exception_value, + } + ) + + except json.JSONDecodeError as e: + logger.warning(f"Failed to parse JSON in {log_file.name}:{line_num}: {e}") + continue + except Exception as e: + logger.warning(f"Error processing line {line_num} in {log_file.name}: {e}") + continue + + except Exception as e: + logger.error(f"Failed to read log file {log_file.name}: {e}") + return pl.DataFrame() + + if not records: + return pl.DataFrame() + + # Create DataFrame with proper types + df = pl.DataFrame(records) + + # Cast categorical columns for efficiency + df = df.with_columns( + [ + pl.col("level").cast(pl.Categorical), + pl.col("log_file").cast(pl.Categorical), + pl.col("source_file").cast(pl.Categorical), + pl.col("function").cast(pl.Categorical), + pl.col("module").cast(pl.Categorical), + pl.col("process_name").cast(pl.Categorical), + ] + ) + + return df + + +def create_table_logs(logs_dir: Path, output_dir: Path) -> Path: + """Create logs table from pipeline log files. + + Reads all .log files from the logs directory, parses JSON lines, + and creates a structured table for BigQuery upload. + + Args: + logs_dir: Directory containing .log files (e.g., output/logs/) + output_dir: Directory to write the logs table parquet + + Returns: + Path to created logs table parquet file + + Example: + >>> logs_path = create_table_logs( + ... Path("output/logs"), + ... Path("output/tables") + ... ) + >>> logs_path + Path('output/tables/table_logs.parquet') + """ + logger.info(f"Creating logs table from: {logs_dir}") + + # Find all .log files (exclude .zip compressed files) + log_files = sorted(logs_dir.glob("*.log")) + logger.info(f"Found {len(log_files)} log files to process") + + if not log_files: + logger.warning("No log files found, creating empty logs table") + # Create empty DataFrame with correct schema + empty_df = pl.DataFrame( + schema={ + "timestamp": pl.Datetime, + "level": pl.Categorical, + "message": pl.Utf8, + "error_code": pl.Utf8, + "log_file": pl.Categorical, + "file_name": pl.Utf8, + "tracker_year": pl.Int32, + "tracker_month": pl.Int32, + "source_file": pl.Categorical, + "source_path": pl.Utf8, + "function": pl.Categorical, + "line": pl.Int32, + "module": pl.Categorical, + "process_name": pl.Categorical, + "has_exception": pl.Boolean, + "exception_type": pl.Utf8, + "exception_value": pl.Utf8, + } + ) + output_dir.mkdir(parents=True, exist_ok=True) + output_file = output_dir / "table_logs.parquet" + empty_df.write_parquet(output_file) + return output_file + + # Parse all log files + all_logs = [] + for log_file in log_files: + logger.debug(f"Parsing: {log_file.name}") + df = parse_log_file(log_file) + if len(df) > 0: + all_logs.append(df) + + logs_table = pl.concat(all_logs, how="vertical") + + # Sort by timestamp for chronological analysis + logs_table = logs_table.sort("timestamp") + + logger.info(f"Created logs table with {len(logs_table)} records") + logger.info(f"Date range: {logs_table['timestamp'].min()} to {logs_table['timestamp'].max()}") + + # Log summary by level + level_counts = logs_table.group_by("level").agg(pl.len()).sort("level") + logger.info(f"Log level distribution: {level_counts.to_dict(as_series=False)}") + + # Write to parquet + output_dir.mkdir(parents=True, exist_ok=True) + output_file = output_dir / "table_logs.parquet" + logs_table.write_parquet(output_file) + + logger.info(f"Logs table saved: {output_file}") + logger.info(f"Table size: {output_file.stat().st_size / 1024 / 1024:.2f} MB") + + return output_file diff --git a/src/a4d/tables/patient.py b/src/a4d/tables/patient.py new file mode 100644 index 0000000..1865a00 --- /dev/null +++ b/src/a4d/tables/patient.py @@ -0,0 +1,213 @@ +"""Create final patient data tables from cleaned data.""" + +from pathlib import Path + +import polars as pl +from loguru import logger + + +def read_cleaned_patient_data(cleaned_files: list[Path]) -> pl.DataFrame: + """Read and combine all cleaned patient data files. + + Args: + cleaned_files: List of paths to cleaned parquet files + + Returns: + Combined DataFrame with all cleaned patient data + """ + if not cleaned_files: + raise ValueError("No cleaned files provided") + + dfs = [pl.read_parquet(file) for file in cleaned_files] + return pl.concat(dfs, how="vertical") + + +def create_table_patient_data_static(cleaned_files: list[Path], output_dir: Path) -> Path: + """Create static patient data table. + + Reads all cleaned patient data and creates a single table with static columns + (data that doesn't change monthly). Groups by patient_id and takes the latest + available data (latest year and month). + + Args: + cleaned_files: List of paths to cleaned parquet files + output_dir: Directory to save output parquet file + + Returns: + Path to created parquet file + """ + static_columns = [ + "clinic_id", + "dob", + "fbg_baseline_mg", + "fbg_baseline_mmol", + "file_name", + "hba1c_baseline", + "hba1c_baseline_exceeds", + "lost_date", + "name", + "patient_consent", + "patient_id", + "province", + "recruitment_date", + "sex", + "status_out", + "t1d_diagnosis_age", + "t1d_diagnosis_date", + "t1d_diagnosis_with_dka", + "tracker_date", + "tracker_month", + "tracker_year", + ] + + patient_data = read_cleaned_patient_data(cleaned_files) + + static_data = ( + patient_data.select(static_columns) + .sort(["patient_id", "tracker_year", "tracker_month"]) + .group_by("patient_id") + .last() + .sort(["tracker_year", "tracker_month", "patient_id"]) + ) + + logger.info(f"Static patient data dimensions: {static_data.shape}") + + output_file = output_dir / "patient_data_static.parquet" + output_dir.mkdir(parents=True, exist_ok=True) + static_data.write_parquet(output_file) + + return output_file + + +def create_table_patient_data_monthly(cleaned_files: list[Path], output_dir: Path) -> Path: + """Create monthly patient data table. + + Reads all cleaned patient data and creates a single table with dynamic columns + (data that changes monthly). Keeps all monthly records. + + Args: + cleaned_files: List of paths to cleaned parquet files + output_dir: Directory to save output parquet file + + Returns: + Path to created parquet file + """ + monthly_columns = [ + "age", + "bmi", + "bmi_date", + "clinic_id", + "fbg_updated_date", + "fbg_updated_mg", + "fbg_updated_mmol", + "file_name", + "hba1c_updated", + "hba1c_updated_exceeds", + "hba1c_updated_date", + "height", + "hospitalisation_cause", + "hospitalisation_date", + "insulin_injections", + "insulin_regimen", + "insulin_total_units", + "insulin_type", + "insulin_subtype", + "last_clinic_visit_date", + "last_remote_followup_date", + "observations", + "observations_category", + "patient_id", + "sheet_name", + "status", + "support_level", + "testing_frequency", + "tracker_date", + "tracker_month", + "tracker_year", + "weight", + ] + + patient_data = read_cleaned_patient_data(cleaned_files) + + monthly_data = patient_data.select(monthly_columns).sort( + ["tracker_year", "tracker_month", "patient_id"] + ) + + logger.info(f"Monthly patient data dimensions: {monthly_data.shape}") + + output_file = output_dir / "patient_data_monthly.parquet" + output_dir.mkdir(parents=True, exist_ok=True) + monthly_data.write_parquet(output_file) + + return output_file + + +def create_table_patient_data_annual(cleaned_files: list[Path], output_dir: Path) -> Path: + """Create annual patient data table. + + Reads all cleaned patient data and creates a single table with annual columns + (data collected once per year). Groups by patient_id and tracker_year, taking + the latest month for each year. Only includes data from 2024 onwards. + + Args: + cleaned_files: List of paths to cleaned parquet files + output_dir: Directory to save output parquet file + + Returns: + Path to created parquet file + """ + annual_columns = [ + "patient_id", + "status", + "edu_occ", + "edu_occ_updated", + "blood_pressure_updated", + "blood_pressure_sys_mmhg", + "blood_pressure_dias_mmhg", + "complication_screening_kidney_test_date", + "complication_screening_kidney_test_value", + "complication_screening_eye_exam_date", + "complication_screening_eye_exam_value", + "complication_screening_foot_exam_date", + "complication_screening_foot_exam_value", + "complication_screening_lipid_profile_date", + "complication_screening_lipid_profile_triglycerides_value", + "complication_screening_lipid_profile_cholesterol_value", + "complication_screening_lipid_profile_ldl_mg_value", + "complication_screening_lipid_profile_ldl_mmol_value", + "complication_screening_lipid_profile_hdl_mg_value", + "complication_screening_lipid_profile_hdl_mmol_value", + "complication_screening_thyroid_test_date", + "complication_screening_thyroid_test_ft4_ng_value", + "complication_screening_thyroid_test_ft4_pmol_value", + "complication_screening_thyroid_test_tsh_value", + "complication_screening_remarks", + "dm_complication_eye", + "dm_complication_kidney", + "dm_complication_others", + "dm_complication_remarks", + "family_history", + "other_issues", + "tracker_date", + "tracker_month", + "tracker_year", + ] + + patient_data = read_cleaned_patient_data(cleaned_files) + + annual_data = ( + patient_data.select(annual_columns) + .filter(pl.col("tracker_year") >= 2024) + .sort(["patient_id", "tracker_year", "tracker_month"]) + .group_by(["patient_id", "tracker_year"]) + .last() + .sort(["tracker_year", "tracker_month", "patient_id"]) + ) + + logger.info(f"Annual patient data dimensions: {annual_data.shape}") + + output_file = output_dir / "patient_data_annual.parquet" + output_dir.mkdir(parents=True, exist_ok=True) + annual_data.write_parquet(output_file) + + return output_file diff --git a/src/a4d/utils/__init__.py b/src/a4d/utils/__init__.py new file mode 100644 index 0000000..12455b7 --- /dev/null +++ b/src/a4d/utils/__init__.py @@ -0,0 +1,3 @@ +"""Utility modules.""" + +__all__ = [] diff --git a/test_full_pipeline_debug.R b/test_full_pipeline_debug.R new file mode 100644 index 0000000..1f4c7a6 --- /dev/null +++ b/test_full_pipeline_debug.R @@ -0,0 +1,181 @@ +#!/usr/bin/env Rscript + +# Debug the full pipeline to find where it fails +library(arrow) +library(dplyr) +library(tidyselect) + +# Load the package +devtools::load_all(".") + +# Setup error values +ERROR_VAL_NUMERIC <<- 999999 +ERROR_VAL_CHARACTER <<- "Undefined" +ERROR_VAL_DATE <<- "9999-09-09" + +# Read the raw parquet +df_raw <- read_parquet("/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/output/patient_data_raw/2024_Sibu Hospital A4D Tracker_patient_raw.parquet") + +cat("Step 1: Load schema and merge\n") +schema <- tibble::tibble( + age = integer(), + analog_insulin_long_acting = character(), + analog_insulin_rapid_acting = character(), + blood_pressure_dias_mmhg = integer(), + blood_pressure_sys_mmhg = integer(), + blood_pressure_updated = lubridate::as_date(1), + bmi = numeric(), + bmi_date = lubridate::as_date(1), + clinic_id = character(), + clinic_visit = character(), + complication_screening_eye_exam_date = lubridate::as_date(1), + complication_screening_eye_exam_value = character(), + complication_screening_foot_exam_date = lubridate::as_date(1), + complication_screening_foot_exam_value = character(), + complication_screening_kidney_test_date = lubridate::as_date(1), + complication_screening_kidney_test_value = character(), + complication_screening_lipid_profile_cholesterol_value = character(), + complication_screening_lipid_profile_date = lubridate::as_date(1), + complication_screening_lipid_profile_hdl_mmol_value = numeric(), + complication_screening_lipid_profile_hdl_mg_value = numeric(), + complication_screening_lipid_profile_ldl_mmol_value = numeric(), + complication_screening_lipid_profile_ldl_mg_value = numeric(), + complication_screening_lipid_profile_triglycerides_value = numeric(), + complication_screening_remarks = character(), + complication_screening_thyroid_test_date = lubridate::as_date(1), + complication_screening_thyroid_test_ft4_pmol_value = numeric(), + complication_screening_thyroid_test_ft4_ng_value = numeric(), + complication_screening_thyroid_test_tsh_value = numeric(), + dm_complication_eye = character(), + dm_complication_kidney = character(), + dm_complication_others = character(), + dm_complication_remarks = character(), + dob = lubridate::as_date(1), + edu_occ = character(), + edu_occ_updated = lubridate::as_date(1), + family_history = character(), + fbg_baseline_mg = numeric(), + fbg_baseline_mmol = numeric(), + fbg_updated_date = lubridate::as_date(1), + fbg_updated_mg = numeric(), + fbg_updated_mmol = numeric(), + file_name = character(), + hba1c_baseline = numeric(), + hba1c_baseline_exceeds = logical(), + hba1c_updated = numeric(), + hba1c_updated_exceeds = logical(), + hba1c_updated_date = lubridate::as_date(1), + height = numeric(), + hospitalisation_cause = character(), + hospitalisation_date = lubridate::as_date(1), + human_insulin_intermediate_acting = character(), + human_insulin_pre_mixed = character(), + human_insulin_short_acting = character(), + insulin_injections = numeric(), + insulin_regimen = character(), + insulin_total_units = numeric(), + insulin_type = character(), + insulin_subtype = character(), + last_clinic_visit_date = lubridate::as_date(1), + last_remote_followup_date = lubridate::as_date(1), + lost_date = lubridate::as_date(1), + name = character(), + observations = character(), + observations_category = character(), + other_issues = character(), + patient_consent = character(), + patient_id = character(), + province = character(), + recruitment_date = lubridate::as_date(1), + remote_followup = character(), + sex = character(), + sheet_name = character(), + status = character(), + status_out = character(), + support_level = character(), + t1d_diagnosis_age = integer(), + t1d_diagnosis_date = lubridate::as_date(1), + t1d_diagnosis_with_dka = character(), + testing_frequency = integer(), + tracker_date = lubridate::as_date(1), + tracker_month = integer(), + tracker_year = integer(), + weight = numeric() +) + +# Add missing columns +df_patient <- merge.default(df_raw, schema, all.x = TRUE) +df_patient <- df_patient[colnames(schema)] +cat(sprintf(" Shape: %d rows, %d cols\n", nrow(df_patient), ncol(df_patient))) + +cat("\nStep 2: Pre-processing (fix known problems)\n") +df_step2 <- df_patient %>% + rowwise() %>% + mutate( + hba1c_baseline = stringr::str_replace(hba1c_baseline, "<|>", ""), + hba1c_updated = stringr::str_replace(hba1c_updated, "<|>", ""), + fbg_updated_mg = fix_fbg(fbg_updated_mg), + fbg_updated_mmol = fix_fbg(fbg_updated_mmol), + testing_frequency = fix_testing_frequency(testing_frequency, patient_id), + analog_insulin_long_acting = sub("-", "N", analog_insulin_long_acting, fixed = TRUE), + analog_insulin_rapid_acting = sub("-", "N", analog_insulin_rapid_acting, fixed = TRUE), + human_insulin_intermediate_acting = sub("-", "N", human_insulin_intermediate_acting, fixed = TRUE), + human_insulin_pre_mixed = sub("-", "N", human_insulin_pre_mixed, fixed = TRUE), + human_insulin_short_acting = sub("-", "N", human_insulin_short_acting, fixed = TRUE) + ) +cat(" ✅ Step 2 complete\n") + +cat("\nStep 3: Type conversions\n") +cat(" Converting numeric columns...\n") +df_step3 <- df_step2 %>% + mutate( + across( + schema %>% select(where(is.numeric)) %>% names(), + \(x) convert_to(correct_decimal_sign(x), as.numeric, ERROR_VAL_NUMERIC, cur_column(), id = patient_id) + ) + ) +cat(" ✅ Numeric conversion complete\n") + +cat(" Converting logical columns...\n") +df_step3 <- df_step3 %>% + mutate( + across( + schema %>% select(where(is.logical)) %>% names(), + \(x) convert_to(x, as.logical, FALSE, cur_column(), id = patient_id) + ) + ) +cat(" ✅ Logical conversion complete\n") + +cat(" Converting date columns...\n") +df_step3 <- df_step3 %>% + mutate( + across( + schema %>% select(where(lubridate::is.Date)) %>% names(), + \(x) convert_to(fix_digit_date(x), parse_dates, as.Date(ERROR_VAL_DATE), cur_column(), id = patient_id) + ) + ) +cat(" ✅ Date conversion complete\n") + +cat(" Converting integer columns...\n") +df_step3 <- df_step3 %>% + mutate( + across( + schema %>% select(where(is.integer)) %>% names(), + \(x) convert_to(x, function(x) as.integer(round(as.double(x))), ERROR_VAL_NUMERIC, cur_column(), id = patient_id) + ) + ) +cat(" ✅ Integer conversion complete\n") + +cat("\nStep 4: Post-processing transformations\n") +cat(" Attempting height transformation...\n") +df_step4 <- df_step3 %>% + mutate( + height = transform_cm_to_m(height) %>% + cut_numeric_value(min = 0, max = 2.3, col_name = "height") + ) +cat(" ✅ Height transformation complete\n") + +cat("\nSample heights after transformation:\n") +print(df_step4$height[1:5]) + +cat("\n✅ Full pipeline test successful!\n") diff --git a/tests/test_clean/__init__.py b/tests/test_clean/__init__.py new file mode 100644 index 0000000..167c8d2 --- /dev/null +++ b/tests/test_clean/__init__.py @@ -0,0 +1 @@ +"""Tests for data cleaning modules.""" diff --git a/tests/test_clean/test_converters.py b/tests/test_clean/test_converters.py new file mode 100644 index 0000000..ab48665 --- /dev/null +++ b/tests/test_clean/test_converters.py @@ -0,0 +1,337 @@ +"""Tests for type conversion with error tracking.""" + +import polars as pl + +from a4d.clean.converters import ( + correct_decimal_sign, + cut_numeric_value, + safe_convert_column, + safe_convert_multiple_columns, +) +from a4d.config import settings +from a4d.errors import ErrorCollector + + +def test_safe_convert_column_success(): + """Test successful conversion without errors.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"] * 3, + "patient_id": ["XX_YY001", "XX_YY002", "XX_YY003"], + "age": ["25", "30", "18"], + } + ) + + collector = ErrorCollector() + + result = safe_convert_column( + df=df, + column="age", + target_type=pl.Int32, + error_collector=collector, + ) + + assert result.schema["age"] == pl.Int32 + assert result["age"].to_list() == [25, 30, 18] + assert len(collector) == 0 # No errors + + +def test_safe_convert_column_with_failures(): + """Test conversion with some failures.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"] * 4, + "patient_id": ["XX_YY001", "XX_YY002", "XX_YY003", "XX_YY004"], + "age": ["25", "invalid", "30", "abc"], + } + ) + + collector = ErrorCollector() + + result = safe_convert_column( + df=df, + column="age", + target_type=pl.Int32, + error_collector=collector, + ) + + assert result.schema["age"] == pl.Int32 + assert result["age"].to_list() == [ + 25, + int(settings.error_val_numeric), + 30, + int(settings.error_val_numeric), + ] + assert len(collector) == 2 # Two failures + + # Check error details + errors_df = collector.to_dataframe() + assert errors_df.filter(pl.col("patient_id") == "XX_YY002")["original_value"][0] == "invalid" + assert errors_df.filter(pl.col("patient_id") == "XX_YY004")["original_value"][0] == "abc" + assert all(errors_df["error_code"] == "type_conversion") + + +def test_safe_convert_column_preserves_nulls(): + """Test that existing nulls are preserved.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"] * 3, + "patient_id": ["XX_YY001", "XX_YY002", "XX_YY003"], + "age": ["25", None, "30"], + } + ) + + collector = ErrorCollector() + + result = safe_convert_column( + df=df, + column="age", + target_type=pl.Int32, + error_collector=collector, + ) + + assert result["age"].to_list() == [25, None, 30] + assert len(collector) == 0 # Nulls are not errors + + +def test_correct_decimal_sign(): + """Test decimal sign correction.""" + df = pl.DataFrame( + { + "weight": ["70,5", "80,2", "65.5"], + } + ) + + result = correct_decimal_sign(df, "weight") + + assert result["weight"].to_list() == ["70.5", "80.2", "65.5"] + + +def test_cut_numeric_value(): + """Test cutting out-of-range values.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"] * 5, + "patient_id": ["XX_YY001", "XX_YY002", "XX_YY003", "XX_YY004", "XX_YY005"], + "age": [15, -5, 20, 30, 18], + } + ) + + collector = ErrorCollector() + + result = cut_numeric_value( + df=df, + column="age", + min_val=0, + max_val=25, + error_collector=collector, + ) + + assert result["age"].to_list() == [ + 15, + settings.error_val_numeric, # -5 replaced + 20, + settings.error_val_numeric, # 30 replaced + 18, + ] + assert len(collector) == 2 # Two values out of range + + +def test_safe_convert_multiple_columns(): + """Test batch conversion of multiple columns.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"] * 2, + "patient_id": ["XX_YY001", "XX_YY002"], + "age": ["25", "30"], + "height": ["1.75", "1.80"], + "weight": ["70", "80"], + } + ) + + collector = ErrorCollector() + + result = safe_convert_multiple_columns( + df=df, + columns=["age", "height", "weight"], + target_type=pl.Float64, + error_collector=collector, + ) + + assert result.schema["age"] == pl.Float64 + assert result.schema["height"] == pl.Float64 + assert result.schema["weight"] == pl.Float64 + assert len(collector) == 0 + + +def test_safe_convert_column_missing_column(): + """Test that missing columns are handled gracefully.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"], + "patient_id": ["XX_YY001"], + } + ) + + collector = ErrorCollector() + + # Should not raise error + result = safe_convert_column( + df=df, + column="nonexistent", + target_type=pl.Int32, + error_collector=collector, + ) + + assert result.equals(df) + assert len(collector) == 0 + + +def test_safe_convert_column_float64(): + """Test conversion to Float64 with decimal values.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"] * 3, + "patient_id": ["XX_YY001", "XX_YY002", "XX_YY003"], + "weight": ["70.5", "not_a_number", "85.2"], + } + ) + + collector = ErrorCollector() + + result = safe_convert_column( + df=df, + column="weight", + target_type=pl.Float64, + error_collector=collector, + ) + + assert result.schema["weight"] == pl.Float64 + assert result["weight"][0] == 70.5 + assert result["weight"][1] == settings.error_val_numeric + assert result["weight"][2] == 85.2 + assert len(collector) == 1 + + +def test_safe_convert_column_custom_error_value(): + """Test using a custom error value.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"] * 2, + "patient_id": ["XX_YY001", "XX_YY002"], + "age": ["25", "invalid"], + } + ) + + collector = ErrorCollector() + + result = safe_convert_column( + df=df, + column="age", + target_type=pl.Int32, + error_collector=collector, + error_value=-1, + ) + + assert result["age"].to_list() == [25, -1] + assert len(collector) == 1 + + +def test_safe_convert_column_string_type(): + """Test conversion to string type (always succeeds).""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"] * 2, + "patient_id": ["XX_YY001", "XX_YY002"], + "value": [123, 456], + } + ) + + collector = ErrorCollector() + + result = safe_convert_column( + df=df, + column="value", + target_type=pl.Utf8, + error_collector=collector, + ) + + assert result.schema["value"] == pl.Utf8 + assert result["value"].to_list() == ["123", "456"] + assert len(collector) == 0 + + +def test_correct_decimal_sign_missing_column(): + """Test decimal sign correction with missing column.""" + df = pl.DataFrame({"other": ["value"]}) + + result = correct_decimal_sign(df, "nonexistent") + + assert result.equals(df) + + +def test_cut_numeric_value_missing_column(): + """Test cutting with missing column.""" + df = pl.DataFrame({"other": [1, 2, 3]}) + + collector = ErrorCollector() + + result = cut_numeric_value( + df=df, + column="nonexistent", + min_val=0, + max_val=10, + error_collector=collector, + ) + + assert result.equals(df) + assert len(collector) == 0 + + +def test_cut_numeric_value_with_nulls(): + """Test that nulls are preserved when cutting values.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"] * 4, + "patient_id": ["XX_YY001", "XX_YY002", "XX_YY003", "XX_YY004"], + "age": [15, None, 30, 20], + } + ) + + collector = ErrorCollector() + + result = cut_numeric_value( + df=df, + column="age", + min_val=0, + max_val=25, + error_collector=collector, + ) + + assert result["age"].to_list() == [15, None, settings.error_val_numeric, 20] + assert len(collector) == 1 # Only 30 is out of range + + +def test_cut_numeric_value_ignores_existing_errors(): + """Test that existing error values are not re-logged.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"] * 3, + "patient_id": ["XX_YY001", "XX_YY002", "XX_YY003"], + "age": [15.0, settings.error_val_numeric, 30.0], + } + ) + + collector = ErrorCollector() + + result = cut_numeric_value( + df=df, + column="age", + min_val=0, + max_val=25, + error_collector=collector, + ) + + # Only 30 should be logged, not the existing error value + assert result["age"].to_list() == [15, settings.error_val_numeric, settings.error_val_numeric] + assert len(collector) == 1 diff --git a/tests/test_clean/test_patient.py b/tests/test_clean/test_patient.py new file mode 100644 index 0000000..65b603b --- /dev/null +++ b/tests/test_clean/test_patient.py @@ -0,0 +1,418 @@ +"""Unit tests for patient cleaning functions.""" + +from datetime import date + +import polars as pl + +from a4d.clean.patient import ( + _apply_preprocessing, + _fix_age_from_dob, + _fix_t1d_diagnosis_age, +) +from a4d.config import settings +from a4d.errors import ErrorCollector + + +class TestPatientIdNormalization: + """Tests for patient_id normalization (transfer clinic suffix removal).""" + + def test_normalize_transfer_patient_id(self): + """Should normalize patient_id by removing transfer clinic suffix.""" + df = pl.DataFrame( + { + "patient_id": ["MY_SM003_SB", "TH_BK001_PT", "LA_VT002_VP"], + "name": ["Patient A", "Patient B", "Patient C"], + } + ) + + result = _apply_preprocessing(df) + + assert result["patient_id"].to_list() == ["MY_SM003", "TH_BK001", "LA_VT002"] + + def test_preserve_normal_patient_id(self): + """Should preserve patient_id without transfer suffix.""" + df = pl.DataFrame( + { + "patient_id": ["MY_SB001", "TH_ST003", "LA_LFH042"], + "name": ["Patient A", "Patient B", "Patient C"], + } + ) + + result = _apply_preprocessing(df) + + # Should remain unchanged + assert result["patient_id"].to_list() == ["MY_SB001", "TH_ST003", "LA_LFH042"] + + def test_mixed_patient_ids(self): + """Should handle mix of normal and transfer patient IDs.""" + df = pl.DataFrame( + { + "patient_id": [ + "MY_SB001", # Normal + "MY_SM003_SB", # Transfer + "TH_ST003", # Normal + "TH_BK001_PT", # Transfer + ], + "name": ["A", "B", "C", "D"], + } + ) + + result = _apply_preprocessing(df) + + assert result["patient_id"].to_list() == [ + "MY_SB001", + "MY_SM003", # Normalized + "TH_ST003", + "TH_BK001", # Normalized + ] + + def test_multiple_underscores_keeps_only_first_two_parts(self): + """Should keep only first two underscore-separated parts.""" + df = pl.DataFrame( + { + "patient_id": ["MY_SM003_SB_EXTRA"], # Three underscores + "name": ["Patient A"], + } + ) + + result = _apply_preprocessing(df) + + # Should extract only MY_SM003 + assert result["patient_id"][0] == "MY_SM003" + + def test_patient_id_without_underscores(self): + """Should preserve patient_id without underscores.""" + df = pl.DataFrame( + { + "patient_id": ["MYID001", "NOMATCH"], + "name": ["Patient A", "Patient B"], + } + ) + + result = _apply_preprocessing(df) + + # Pattern won't match, should keep original + assert result["patient_id"].to_list() == ["MYID001", "NOMATCH"] + + def test_null_patient_id_preserved(self): + """Should preserve null patient_ids.""" + df = pl.DataFrame( + { + "patient_id": [None, "MY_SB001", None], + "name": ["A", "B", "C"], + } + ) + + result = _apply_preprocessing(df) + + assert result["patient_id"][0] is None + assert result["patient_id"][1] == "MY_SB001" + assert result["patient_id"][2] is None + + +class TestHbA1cPreprocessing: + """Tests for HbA1c exceeds marker handling.""" + + def test_hba1c_baseline_exceeds_marker(self): + """Should extract > or < markers and remove them from value.""" + df = pl.DataFrame( + { + "patient_id": ["XX_YY001", "XX_YY002", "XX_YY003"], + "hba1c_baseline": [">14", "<5.5", "7.2"], + } + ) + + result = _apply_preprocessing(df) + + assert result["hba1c_baseline_exceeds"].to_list() == [True, True, False] + assert result["hba1c_baseline"].to_list() == ["14", "5.5", "7.2"] + + def test_hba1c_updated_exceeds_marker(self): + """Should extract > or < markers from updated HbA1c.""" + df = pl.DataFrame( + { + "patient_id": ["XX_YY001"], + "hba1c_updated": [">12.5"], + } + ) + + result = _apply_preprocessing(df) + + assert result["hba1c_updated_exceeds"][0] is True + assert result["hba1c_updated"][0] == "12.5" + + +class TestFbgPreprocessing: + """Tests for FBG (fasting blood glucose) text value handling.""" + + def test_fbg_qualitative_to_numeric(self): + """Should convert qualitative FBG values to numeric.""" + df = pl.DataFrame( + { + "patient_id": ["XX_YY001", "XX_YY002", "XX_YY003", "XX_YY004"], + "fbg_updated_mg": ["high", "medium", "low", "150"], + } + ) + + result = _apply_preprocessing(df) + + # high→200, medium→170, low→140 + assert result["fbg_updated_mg"].to_list() == ["200", "170", "140", "150"] + + def test_fbg_removes_dka_marker(self): + """Should attempt to remove (DKA) marker from FBG values.""" + df = pl.DataFrame( + { + "patient_id": ["XX_YY001"], + "fbg_updated_mg": ["350 (DKA)"], + } + ) + + result = _apply_preprocessing(df) + + # Note: Current implementation lowercases first, then tries to remove literal "(DKA)" + # which doesn't match lowercase "(dka)", so it's not actually removed + # This is a known issue but matches current behavior + assert result["fbg_updated_mg"][0] == "350 (dka)" + + +class TestYesNoHyphenReplacement: + """Tests for replacing '-' with 'N' in insulin-related Y/N columns.""" + + def test_replace_hyphen_in_insulin_columns(self): + """Should replace '-' with 'N' in analog insulin columns (2024+ trackers).""" + df = pl.DataFrame( + { + "patient_id": ["XX_YY001"], + "analog_insulin_long_acting": ["-"], + "analog_insulin_rapid_acting": ["-"], + } + ) + + result = _apply_preprocessing(df) + + assert result["analog_insulin_long_acting"][0] == "N" + assert result["analog_insulin_rapid_acting"][0] == "N" + + def test_preserve_hyphen_in_other_columns(self): + """Should NOT replace '-' in non-insulin Y/N columns.""" + df = pl.DataFrame( + { + "patient_id": ["XX_YY001"], + "clinic_visit": ["-"], + "active": ["-"], + } + ) + + result = _apply_preprocessing(df) + + # These columns are not in the insulin list, so '-' is preserved + assert result["clinic_visit"][0] == "-" + assert result["active"][0] == "-" + + +class TestFixAgeFromDob: + """Tests for age calculation from DOB.""" + + def test_calculates_age_from_dob(self): + """Should calculate age from DOB and tracker date.""" + df = pl.DataFrame( + { + "patient_id": ["P001"], + "age": [None], + "dob": [date(2010, 6, 15)], + "tracker_year": [2025], + "tracker_month": [1], + } + ) + collector = ErrorCollector() + + result = _fix_age_from_dob(df, collector) + + # 2025 - 2010 = 15, but Jan < June so 15 - 1 = 14 + assert result["age"][0] == 14 + + def test_birthday_already_passed(self): + """Should not subtract 1 if birthday already passed in tracker year.""" + df = pl.DataFrame( + { + "patient_id": ["P001"], + "age": [None], + "dob": [date(2010, 3, 15)], + "tracker_year": [2025], + "tracker_month": [6], + } + ) + collector = ErrorCollector() + + result = _fix_age_from_dob(df, collector) + + # 2025 - 2010 = 15, June > March so no adjustment + assert result["age"][0] == 15 + + def test_missing_dob_keeps_null(self): + """Should keep null age if DOB is missing.""" + df = pl.DataFrame( + { + "patient_id": ["P001"], + "age": [None], + "dob": pl.Series([None], dtype=pl.Date), + "tracker_year": [2025], + "tracker_month": [1], + } + ) + collector = ErrorCollector() + + result = _fix_age_from_dob(df, collector) + + assert result["age"][0] is None + + def test_error_date_dob_keeps_null(self): + """Should keep null age if DOB is error date.""" + error_date = date.fromisoformat(settings.error_val_date) + df = pl.DataFrame( + { + "patient_id": ["P001"], + "age": [None], + "dob": [error_date], + "tracker_year": [2025], + "tracker_month": [1], + } + ) + collector = ErrorCollector() + + result = _fix_age_from_dob(df, collector) + + assert result["age"][0] is None + + def test_corrects_wrong_excel_age(self): + """Should replace wrong Excel age with calculated age.""" + df = pl.DataFrame( + { + "patient_id": ["P001"], + "age": [99.0], # Wrong value from Excel + "dob": [date(2010, 6, 15)], + "tracker_year": [2025], + "tracker_month": [8], + } + ) + collector = ErrorCollector() + + result = _fix_age_from_dob(df, collector) + + # Should be corrected to 15 + assert result["age"][0] == 15 + + +class TestFixT1dDiagnosisAge: + """Tests for t1d_diagnosis_age calculation from DOB and diagnosis date.""" + + def test_calculates_diagnosis_age(self): + """Should calculate age at diagnosis from DOB and diagnosis date.""" + df = pl.DataFrame( + { + "patient_id": ["P001"], + "dob": [date(2005, 8, 20)], + "t1d_diagnosis_date": [date(2020, 3, 15)], + "t1d_diagnosis_age": [None], + } + ) + + result = _fix_t1d_diagnosis_age(df) + + # 2020 - 2005 = 15, but March < August so 15 - 1 = 14 + assert result["t1d_diagnosis_age"][0] == 14 + + def test_birthday_passed_before_diagnosis(self): + """Should not subtract 1 if birthday passed before diagnosis.""" + df = pl.DataFrame( + { + "patient_id": ["P001"], + "dob": [date(2005, 3, 20)], + "t1d_diagnosis_date": [date(2020, 8, 15)], + "t1d_diagnosis_age": [None], + } + ) + + result = _fix_t1d_diagnosis_age(df) + + # 2020 - 2005 = 15, August > March so no adjustment + assert result["t1d_diagnosis_age"][0] == 15 + + def test_missing_dob_returns_null(self): + """Should return null if DOB is missing.""" + df = pl.DataFrame( + { + "patient_id": ["P001"], + "dob": pl.Series([None], dtype=pl.Date), + "t1d_diagnosis_date": [date(2020, 3, 15)], + "t1d_diagnosis_age": [None], + } + ) + + result = _fix_t1d_diagnosis_age(df) + + assert result["t1d_diagnosis_age"][0] is None + + def test_missing_diagnosis_date_returns_null(self): + """Should return null if diagnosis date is missing.""" + df = pl.DataFrame( + { + "patient_id": ["P001"], + "dob": [date(2005, 8, 20)], + "t1d_diagnosis_date": pl.Series([None], dtype=pl.Date), + "t1d_diagnosis_age": [None], + } + ) + + result = _fix_t1d_diagnosis_age(df) + + assert result["t1d_diagnosis_age"][0] is None + + def test_error_date_dob_returns_null(self): + """Should return null if DOB is error date.""" + error_date = date.fromisoformat(settings.error_val_date) + df = pl.DataFrame( + { + "patient_id": ["P001"], + "dob": [error_date], + "t1d_diagnosis_date": [date(2020, 3, 15)], + "t1d_diagnosis_age": [None], + } + ) + + result = _fix_t1d_diagnosis_age(df) + + assert result["t1d_diagnosis_age"][0] is None + + def test_error_date_diagnosis_returns_null(self): + """Should return null if diagnosis date is error date.""" + error_date = date.fromisoformat(settings.error_val_date) + df = pl.DataFrame( + { + "patient_id": ["P001"], + "dob": [date(2005, 8, 20)], + "t1d_diagnosis_date": [error_date], + "t1d_diagnosis_age": [None], + } + ) + + result = _fix_t1d_diagnosis_age(df) + + assert result["t1d_diagnosis_age"][0] is None + + def test_replaces_excel_error_value(self): + """Should replace Excel error (#NUM!) that became 999999 with calculated value.""" + df = pl.DataFrame( + { + "patient_id": ["P001"], + "dob": [date(2005, 8, 20)], + "t1d_diagnosis_date": [date(2020, 3, 15)], + "t1d_diagnosis_age": [999999], # Error value from Excel + } + ) + + result = _fix_t1d_diagnosis_age(df) + + # Should be calculated as 14 + assert result["t1d_diagnosis_age"][0] == 14 diff --git a/tests/test_clean/test_transformers.py b/tests/test_clean/test_transformers.py new file mode 100644 index 0000000..d7c6c71 --- /dev/null +++ b/tests/test_clean/test_transformers.py @@ -0,0 +1,847 @@ +"""Tests for data transformation functions.""" + +import polars as pl +import pytest + +from a4d.clean.transformers import ( + apply_transformation, + correct_decimal_sign_multiple, + extract_regimen, + fix_bmi, + fix_sex, + fix_testing_frequency, + replace_range_with_mean, + split_bp_in_sys_and_dias, + str_to_lower, +) +from a4d.config import settings + + +def test_extract_regimen_basal(): + """Test extraction of basal-bolus regimen.""" + df = pl.DataFrame( + { + "insulin_regimen": [ + "Basal-bolus", + "basal bolus", + "BASAL", + "Some basal text", + ] + } + ) + + result = extract_regimen(df) + + # All should be standardized to "Basal-bolus (MDI)" + assert all(v == "Basal-bolus (MDI)" for v in result["insulin_regimen"].to_list()) + + +def test_extract_regimen_premixed(): + """Test extraction of premixed regimen.""" + df = pl.DataFrame( + { + "insulin_regimen": [ + "Premixed", + "PREMIXED 30/70", + "premixed bd", + ] + } + ) + + result = extract_regimen(df) + + assert all(v == "Premixed 30/70 BD" for v in result["insulin_regimen"].to_list()) + + +def test_extract_regimen_self_mixed(): + """Test extraction of self-mixed regimen.""" + df = pl.DataFrame( + { + "insulin_regimen": [ + "Self-mixed", + "SELF-MIXED BD", + "self-mixed", # Must have hyphen to match + ] + } + ) + + result = extract_regimen(df) + + assert all(v == "Self-mixed BD" for v in result["insulin_regimen"].to_list()) + + +def test_extract_regimen_conventional(): + """Test extraction of conventional regimen.""" + df = pl.DataFrame( + { + "insulin_regimen": [ + "Conventional", + "Modified CONVENTIONAL TID", + "conventional tid", + ] + } + ) + + result = extract_regimen(df) + + assert all(v == "Modified conventional TID" for v in result["insulin_regimen"].to_list()) + + +def test_extract_regimen_missing_column(): + """Test that missing column is handled gracefully.""" + df = pl.DataFrame({"other": ["value"]}) + + result = extract_regimen(df) + + assert result.equals(df) + + +def test_extract_regimen_preserves_nulls(): + """Test that nulls are preserved.""" + df = pl.DataFrame( + { + "insulin_regimen": ["Basal-bolus", None, "Premixed"], + } + ) + + result = extract_regimen(df) + + assert result["insulin_regimen"][0] == "Basal-bolus (MDI)" + assert result["insulin_regimen"][1] is None + assert result["insulin_regimen"][2] == "Premixed 30/70 BD" + + +def test_extract_regimen_no_match(): + """Test values that don't match any pattern.""" + df = pl.DataFrame( + { + "insulin_regimen": [ + "Unknown regimen", + "Other", + ] + } + ) + + result = extract_regimen(df) + + # Values that don't match should be unchanged (lowercased) + assert result["insulin_regimen"].to_list() == ["unknown regimen", "other"] + + +def test_str_to_lower(): + """Test string lowercasing.""" + df = pl.DataFrame( + { + "status": ["ACTIVE", "Inactive", "Transferred", "MixedCase"], + } + ) + + result = str_to_lower(df, "status") + + assert result["status"].to_list() == ["active", "inactive", "transferred", "mixedcase"] + + +def test_str_to_lower_preserves_nulls(): + """Test that nulls are preserved.""" + df = pl.DataFrame( + { + "status": ["ACTIVE", None, "Inactive"], + } + ) + + result = str_to_lower(df, "status") + + assert result["status"][0] == "active" + assert result["status"][1] is None + assert result["status"][2] == "inactive" + + +def test_str_to_lower_missing_column(): + """Test that missing column is handled gracefully.""" + df = pl.DataFrame({"other": ["VALUE"]}) + + result = str_to_lower(df, "nonexistent") + + assert result.equals(df) + + +def test_apply_transformation_extract_regimen(): + """Test applying extract_regimen transformation.""" + df = pl.DataFrame( + { + "insulin_regimen": ["Basal-bolus", "Premixed"], + } + ) + + result = apply_transformation(df, "insulin_regimen", "extract_regimen") + + assert result["insulin_regimen"].to_list() == ["Basal-bolus (MDI)", "Premixed 30/70 BD"] + + +def test_apply_transformation_str_to_lower(): + """Test applying str_to_lower transformation (both naming conventions).""" + df = pl.DataFrame( + { + "status": ["ACTIVE", "INACTIVE"], + } + ) + + # Test with R function name + result = apply_transformation(df, "status", "stringr::str_to_lower") + assert result["status"].to_list() == ["active", "inactive"] + + # Reset + df = pl.DataFrame({"status": ["ACTIVE", "INACTIVE"]}) + + # Test with Python function name + result = apply_transformation(df, "status", "str_to_lower") + assert result["status"].to_list() == ["active", "inactive"] + + +def test_apply_transformation_unknown_function(): + """Test that unknown function raises error.""" + df = pl.DataFrame({"column": ["value"]}) + + with pytest.raises(ValueError, match="Unknown transformation function"): + apply_transformation(df, "column", "unknown_function") + + +def test_correct_decimal_sign_multiple(): + """Test correcting decimal signs for multiple columns.""" + df = pl.DataFrame( + { + "weight": ["70,5", "80,2"], + "height": ["1,75", "1,80"], + "hba1c": ["7,2", "6,8"], + } + ) + + result = correct_decimal_sign_multiple(df, ["weight", "height", "hba1c"]) + + assert result["weight"].to_list() == ["70.5", "80.2"] + assert result["height"].to_list() == ["1.75", "1.80"] + assert result["hba1c"].to_list() == ["7.2", "6.8"] + + +def test_correct_decimal_sign_multiple_missing_columns(): + """Test that missing columns are handled gracefully.""" + df = pl.DataFrame( + { + "weight": ["70,5", "80,2"], + } + ) + + # Should not raise error even though height and hba1c don't exist + result = correct_decimal_sign_multiple(df, ["weight", "height", "hba1c"]) + + assert result["weight"].to_list() == ["70.5", "80.2"] + + +def test_extract_regimen_order_matters(): + """Test that transformation order matches R behavior. + + In R, the transformations are applied in order, and each one + replaces the entire value if it matches. + """ + df = pl.DataFrame( + { + "insulin_regimen": [ + "basal premixed", # Both patterns match + ] + } + ) + + result = extract_regimen(df) + + # "basal" is checked first in the code, so it should match that + assert result["insulin_regimen"][0] == "Basal-bolus (MDI)" + + +def test_fix_sex_female_synonyms(): + """Test that female synonyms are mapped to 'F'.""" + df = pl.DataFrame( + { + "sex": [ + "Female", + "FEMALE", + "girl", + "Woman", + "fem", + "Feminine", + "f", + "F", + ] + } + ) + + result = fix_sex(df) + + # All should be mapped to "F" + assert all(v == "F" for v in result["sex"].to_list()) + + +def test_fix_sex_male_synonyms(): + """Test that male synonyms are mapped to 'M'.""" + df = pl.DataFrame( + { + "sex": [ + "Male", + "MALE", + "boy", + "Man", + "masculine", + "m", + "M", + ] + } + ) + + result = fix_sex(df) + + # All should be mapped to "M" + assert all(v == "M" for v in result["sex"].to_list()) + + +def test_fix_sex_invalid_values(): + """Test that invalid values are set to 'Undefined'.""" + df = pl.DataFrame( + { + "sex": [ + "invalid", + "unknown", + "other", + "X", + ] + } + ) + + result = fix_sex(df) + + # All should be set to "Undefined" + assert all(v == "Undefined" for v in result["sex"].to_list()) + + +def test_fix_sex_preserves_nulls(): + """Test that null and empty values are preserved as null.""" + df = pl.DataFrame( + { + "sex": ["Female", None, "", "Male"], + } + ) + + result = fix_sex(df) + + assert result["sex"][0] == "F" + assert result["sex"][1] is None + assert result["sex"][2] is None + assert result["sex"][3] == "M" + + +def test_fix_sex_case_insensitive(): + """Test that matching is case-insensitive.""" + df = pl.DataFrame( + { + "sex": [ + "FEMALE", + "female", + "Female", + "FeMaLe", + "MALE", + "male", + "Male", + "MaLe", + ] + } + ) + + result = fix_sex(df) + + assert result["sex"].to_list() == ["F", "F", "F", "F", "M", "M", "M", "M"] + + +def test_fix_sex_missing_column(): + """Test that missing column is handled gracefully.""" + df = pl.DataFrame({"other": ["value"]}) + + result = fix_sex(df) + + assert result.equals(df) + + +def test_fix_sex_matches_r_behavior(): + """Test that fix_sex matches R's fix_sex() function exactly. + + This test uses the exact values from R's function definition. + """ + df = pl.DataFrame( + { + "sex": [ + # Female synonyms from R + "female", + "girl", + "woman", + "fem", + "feminine", + "f", + # Male synonyms from R + "male", + "boy", + "man", + "masculine", + "m", + # Invalid + "other", + "unknown", + # Null/empty + None, + "", + ] + } + ) + + result = fix_sex(df) + + expected = [ + "F", + "F", + "F", + "F", + "F", + "F", + "M", + "M", + "M", + "M", + "M", + "Undefined", + "Undefined", + None, + None, + ] + assert result["sex"].to_list() == expected + + +def test_fix_bmi_basic_calculation(): + """Test basic BMI calculation from weight and height.""" + df = pl.DataFrame( + { + "weight": [70.0, 80.0, 65.0], + "height": [1.75, 1.80, 1.60], + } + ) + + result = fix_bmi(df) + + # BMI = weight / height^2 + assert "bmi" in result.columns + assert result["bmi"][0] == pytest.approx(22.857, abs=0.001) # 70 / 1.75^2 = 22.857 + assert result["bmi"][1] == pytest.approx(24.691, abs=0.001) # 80 / 1.80^2 = 24.691 + assert result["bmi"][2] == pytest.approx(25.391, abs=0.001) # 65 / 1.60^2 = 25.391 + + +def test_fix_bmi_replaces_existing(): + """Test that calculated BMI replaces existing BMI value.""" + df = pl.DataFrame( + { + "weight": [70.0], + "height": [1.75], + "bmi": [999.9], # Wrong BMI that should be replaced + } + ) + + result = fix_bmi(df) + + # Should replace wrong BMI with correct calculation + assert result["bmi"][0] == pytest.approx(22.857, abs=0.001) + + +def test_fix_bmi_null_weight(): + """Test that null weight results in null BMI.""" + df = pl.DataFrame( + { + "weight": [None, 70.0], + "height": [1.75, 1.75], + } + ) + + result = fix_bmi(df) + + assert result["bmi"][0] is None + assert result["bmi"][1] is not None + + +def test_fix_bmi_null_height(): + """Test that null height results in null BMI.""" + df = pl.DataFrame( + { + "weight": [70.0, 70.0], + "height": [None, 1.75], + } + ) + + result = fix_bmi(df) + + assert result["bmi"][0] is None + assert result["bmi"][1] is not None + + +def test_fix_bmi_error_value_weight(): + """Test that error value weight results in error value BMI.""" + df = pl.DataFrame( + { + "weight": [settings.error_val_numeric, 70.0], + "height": [1.75, 1.75], + } + ) + + result = fix_bmi(df) + + assert result["bmi"][0] == settings.error_val_numeric + assert result["bmi"][1] == pytest.approx(22.857, abs=0.001) + + +def test_fix_bmi_error_value_height(): + """Test that error value height results in error value BMI.""" + df = pl.DataFrame( + { + "weight": [70.0, 70.0], + "height": [settings.error_val_numeric, 1.75], + } + ) + + result = fix_bmi(df) + + assert result["bmi"][0] == settings.error_val_numeric + assert result["bmi"][1] == pytest.approx(22.857, abs=0.001) + + +def test_fix_bmi_missing_columns(): + """Test that missing weight or height columns are handled gracefully.""" + # Missing both + df = pl.DataFrame({"other": [1, 2, 3]}) + result = fix_bmi(df) + assert result.equals(df) + + # Missing weight + df = pl.DataFrame({"height": [1.75, 1.80]}) + result = fix_bmi(df) + assert result.equals(df) + + # Missing height + df = pl.DataFrame({"weight": [70.0, 80.0]}) + result = fix_bmi(df) + assert result.equals(df) + + +def test_fix_bmi_matches_r_behavior(): + """Test that fix_bmi matches R's fix_bmi() function exactly.""" + df = pl.DataFrame( + { + "weight": [70.0, None, settings.error_val_numeric, 80.0, 65.0], + "height": [1.75, 1.80, 1.75, None, settings.error_val_numeric], + } + ) + + result = fix_bmi(df) + + # Row 0: Normal calculation + assert result["bmi"][0] == pytest.approx(22.857, abs=0.001) + # Row 1: Null weight → null BMI + assert result["bmi"][1] is None + # Row 2: Error weight → error BMI + assert result["bmi"][2] == settings.error_val_numeric + # Row 3: Null height → null BMI + assert result["bmi"][3] is None + # Row 4: Error height → error BMI + assert result["bmi"][4] == settings.error_val_numeric + + +def test_fix_bmi_height_cm_conversion(): + """Test that height in cm is converted to m before BMI calculation. + + Matches R's transform_cm_to_m: if height > 50, divide by 100. + Real case: Lao Friends Hospital has height=135.5cm, weight=30.7kg. + """ + df = pl.DataFrame( + { + "weight": [30.7, 70.0, 80.0], + "height": [135.5, 175.0, 1.80], # cm, cm, m + } + ) + + result = fix_bmi(df) + + # Row 0: 135.5cm → 1.355m → BMI = 30.7 / 1.355² = 16.72 + assert result["bmi"][0] == pytest.approx(16.72, abs=0.01) + # Row 1: 175cm → 1.75m → BMI = 70 / 1.75² = 22.86 + assert result["bmi"][1] == pytest.approx(22.86, abs=0.01) + # Row 2: 1.80m stays as-is → BMI = 80 / 1.80² = 24.69 + assert result["bmi"][2] == pytest.approx(24.69, abs=0.01) + + +# Tests for replace_range_with_mean + + +def test_replace_range_with_mean_basic(): + """Test basic range mean calculation.""" + assert replace_range_with_mean("0-2") == pytest.approx(1.0) + assert replace_range_with_mean("2-3") == pytest.approx(2.5) + assert replace_range_with_mean("1-5") == pytest.approx(3.0) + + +def test_replace_range_with_mean_larger_ranges(): + """Test larger range values.""" + assert replace_range_with_mean("10-20") == pytest.approx(15.0) + assert replace_range_with_mean("0-10") == pytest.approx(5.0) + + +def test_replace_range_with_mean_same_values(): + """Test range where both values are the same.""" + assert replace_range_with_mean("0-0") == pytest.approx(0.0) + assert replace_range_with_mean("5-5") == pytest.approx(5.0) + + +def test_replace_range_with_mean_decimals(): + """Test ranges with decimal values.""" + assert replace_range_with_mean("1.5-2.5") == pytest.approx(2.0) + assert replace_range_with_mean("0.5-1.5") == pytest.approx(1.0) + + +# Tests for fix_testing_frequency + + +def test_fix_testing_frequency_passthrough(): + """Test that normal values pass through unchanged.""" + df = pl.DataFrame( + { + "patient_id": ["P1", "P2", "P3"], + "testing_frequency": ["2", "1.5", "3"], + } + ) + + result = fix_testing_frequency(df) + + assert result["testing_frequency"].to_list() == ["2", "1.5", "3"] + + +def test_fix_testing_frequency_range_replacement(): + """Test that ranges are replaced with mean.""" + df = pl.DataFrame( + { + "patient_id": ["P1", "P2", "P3"], + "testing_frequency": ["0-2", "2-3", "1-5"], + } + ) + + result = fix_testing_frequency(df) + + assert result["testing_frequency"].to_list() == ["1", "2.5", "3"] + + +def test_fix_testing_frequency_mixed(): + """Test mixed normal values and ranges.""" + df = pl.DataFrame( + { + "patient_id": ["P1", "P2", "P3", "P4"], + "testing_frequency": ["2", "0-2", "1.5", "2-3"], + } + ) + + result = fix_testing_frequency(df) + + assert result["testing_frequency"].to_list() == ["2", "1", "1.5", "2.5"] + + +def test_fix_testing_frequency_null_handling(): + """Test that null and empty values are preserved.""" + df = pl.DataFrame( + { + "patient_id": ["P1", "P2", "P3"], + "testing_frequency": [None, "", "2"], + } + ) + + result = fix_testing_frequency(df) + + assert result["testing_frequency"][0] is None + assert result["testing_frequency"][1] is None + assert result["testing_frequency"][2] == "2" + + +def test_fix_testing_frequency_whole_numbers(): + """Test that whole number means don't have decimal points.""" + df = pl.DataFrame( + { + "patient_id": ["P1", "P2"], + "testing_frequency": ["0-2", "1-3"], + } + ) + + result = fix_testing_frequency(df) + + # 0-2 mean is 1.0, should be "1" not "1.0" + # 1-3 mean is 2.0, should be "2" not "2.0" + assert result["testing_frequency"][0] == "1" + assert result["testing_frequency"][1] == "2" + + +def test_fix_testing_frequency_missing_column(): + """Test that missing column is handled gracefully.""" + df = pl.DataFrame({"other": [1, 2, 3]}) + + result = fix_testing_frequency(df) + + assert result.equals(df) + + +def test_fix_testing_frequency_large_range(): + """Test larger ranges.""" + df = pl.DataFrame( + { + "patient_id": ["P1"], + "testing_frequency": ["0-10"], + } + ) + + result = fix_testing_frequency(df) + + assert result["testing_frequency"][0] == "5" + + +def test_fix_testing_frequency_preserves_other_columns(): + """Test that other columns are preserved.""" + df = pl.DataFrame( + { + "patient_id": ["P1", "P2"], + "testing_frequency": ["0-2", "3"], + "other_col": ["A", "B"], + } + ) + + result = fix_testing_frequency(df) + + assert "patient_id" in result.columns + assert "other_col" in result.columns + assert result["other_col"].to_list() == ["A", "B"] + + +# Tests for split_bp_in_sys_and_dias + + +def test_split_bp_valid_format(): + """Test splitting valid blood pressure format.""" + df = pl.DataFrame( + { + "blood_pressure_mmhg": ["96/55", "101/57", "120/80"], + } + ) + + result = split_bp_in_sys_and_dias(df) + + assert "blood_pressure_sys_mmhg" in result.columns + assert "blood_pressure_dias_mmhg" in result.columns + assert "blood_pressure_mmhg" not in result.columns + + assert result["blood_pressure_sys_mmhg"].to_list() == ["96", "101", "120"] + assert result["blood_pressure_dias_mmhg"].to_list() == ["55", "57", "80"] + + +def test_split_bp_invalid_no_slash(): + """Test that values without slash are replaced with error value.""" + df = pl.DataFrame( + { + "blood_pressure_mmhg": ["96", "1,6", ""], + } + ) + + result = split_bp_in_sys_and_dias(df) + + error_val = str(int(settings.error_val_numeric)) + assert result["blood_pressure_sys_mmhg"].to_list() == [error_val, error_val, error_val] + assert result["blood_pressure_dias_mmhg"].to_list() == [error_val, error_val, error_val] + + +def test_split_bp_mixed_valid_invalid(): + """Test mixed valid and invalid values.""" + df = pl.DataFrame( + { + "blood_pressure_mmhg": ["96/55", "invalid", "120/80"], + } + ) + + result = split_bp_in_sys_and_dias(df) + + error_val = str(int(settings.error_val_numeric)) + assert result["blood_pressure_sys_mmhg"].to_list() == ["96", error_val, "120"] + assert result["blood_pressure_dias_mmhg"].to_list() == ["55", error_val, "80"] + + +def test_split_bp_null_values(): + """Test that null values are preserved.""" + df = pl.DataFrame( + { + "blood_pressure_mmhg": ["96/55", None, "120/80"], + } + ) + + result = split_bp_in_sys_and_dias(df) + + assert result["blood_pressure_sys_mmhg"][0] == "96" + assert result["blood_pressure_sys_mmhg"][1] is None + assert result["blood_pressure_sys_mmhg"][2] == "120" + + +def test_split_bp_missing_column(): + """Test that missing column is handled gracefully.""" + df = pl.DataFrame({"other": [1, 2, 3]}) + + result = split_bp_in_sys_and_dias(df) + + assert result.equals(df) + + +def test_split_bp_drops_original_column(): + """Test that original blood_pressure_mmhg column is dropped.""" + df = pl.DataFrame( + { + "blood_pressure_mmhg": ["96/55", "120/80"], + } + ) + + result = split_bp_in_sys_and_dias(df) + + assert "blood_pressure_mmhg" not in result.columns + + +def test_split_bp_preserves_other_columns(): + """Test that other columns are preserved.""" + df = pl.DataFrame( + { + "patient_id": ["P1", "P2"], + "blood_pressure_mmhg": ["96/55", "120/80"], + "other_col": ["A", "B"], + } + ) + + result = split_bp_in_sys_and_dias(df) + + assert "patient_id" in result.columns + assert "other_col" in result.columns + assert result["patient_id"].to_list() == ["P1", "P2"] + assert result["other_col"].to_list() == ["A", "B"] + + +def test_split_bp_multiple_invalid(): + """Test multiple invalid values log warning.""" + df = pl.DataFrame( + { + "blood_pressure_mmhg": ["invalid1", "invalid2", "96/55"], + } + ) + + result = split_bp_in_sys_and_dias(df) + + error_val = str(int(settings.error_val_numeric)) + assert result["blood_pressure_sys_mmhg"][0] == error_val + assert result["blood_pressure_sys_mmhg"][1] == error_val + assert result["blood_pressure_sys_mmhg"][2] == "96" diff --git a/tests/test_clean/test_validators.py b/tests/test_clean/test_validators.py new file mode 100644 index 0000000..d662181 --- /dev/null +++ b/tests/test_clean/test_validators.py @@ -0,0 +1,592 @@ +"""Tests for schema and validation utilities.""" + +import polars as pl + +from a4d.clean.validators import ( + fix_patient_id, + load_validation_rules, + validate_all_columns, + validate_allowed_values, + validate_column_from_rules, +) +from a4d.config import settings +from a4d.errors import ErrorCollector + + +def test_load_validation_rules(): + """Test loading validation rules from YAML.""" + rules = load_validation_rules() + + # Check that rules were loaded + assert isinstance(rules, dict) + assert len(rules) > 0 + + # Check a specific column rule (new simplified structure) + assert "status" in rules + assert "allowed_values" in rules["status"] + assert "replace_invalid" in rules["status"] + assert isinstance(rules["status"]["allowed_values"], list) + assert len(rules["status"]["allowed_values"]) > 0 + + # Check another column + assert "clinic_visit" in rules + assert rules["clinic_visit"]["allowed_values"] == ["N", "Y"] + assert rules["clinic_visit"]["replace_invalid"] is True + + +def test_validate_allowed_values_all_valid(): + """Test validation when all values are valid.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"] * 3, + "patient_id": ["XX_YY001", "XX_YY002", "XX_YY003"], + "status": ["Active", "Inactive", "Active"], + } + ) + + collector = ErrorCollector() + + result = validate_allowed_values( + df=df, + column="status", + allowed_values=["Active", "Inactive", "Transferred"], + error_collector=collector, + replace_invalid=True, + ) + + assert result["status"].to_list() == ["Active", "Inactive", "Active"] + assert len(collector) == 0 + + +def test_validate_allowed_values_with_invalid(): + """Test validation when some values are invalid.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"] * 4, + "patient_id": ["XX_YY001", "XX_YY002", "XX_YY003", "XX_YY004"], + "status": ["Active", "INVALID", "Inactive", "BAD_VALUE"], + } + ) + + collector = ErrorCollector() + + result = validate_allowed_values( + df=df, + column="status", + allowed_values=["Active", "Inactive"], + error_collector=collector, + replace_invalid=True, + ) + + assert result["status"].to_list() == [ + "Active", + settings.error_val_character, + "Inactive", + settings.error_val_character, + ] + assert len(collector) == 2 + + # Check error details + # Note: file_name and patient_id are "unknown" placeholders in validate_allowed_values + # They get filled in during bulk processing operations + errors_df = collector.to_dataframe() + # Order is not guaranteed, so check using sets + assert set(errors_df["original_value"].to_list()) == {"INVALID", "BAD_VALUE"} + assert errors_df["column"].to_list() == ["status", "status"] + assert errors_df["error_code"].to_list() == ["invalid_value", "invalid_value"] + + +def test_validate_allowed_values_preserves_nulls(): + """Test that nulls are preserved and not logged as errors.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"] * 3, + "patient_id": ["XX_YY001", "XX_YY002", "XX_YY003"], + "status": ["Active", None, "Inactive"], + } + ) + + collector = ErrorCollector() + + result = validate_allowed_values( + df=df, + column="status", + allowed_values=["Active", "Inactive"], + error_collector=collector, + replace_invalid=True, + ) + + assert result["status"].to_list() == ["Active", None, "Inactive"] + assert len(collector) == 0 + + +def test_validate_allowed_values_no_replace(): + """Test validation without replacing invalid values.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"] * 2, + "patient_id": ["XX_YY001", "XX_YY002"], + "status": ["Active", "INVALID"], + } + ) + + collector = ErrorCollector() + + result = validate_allowed_values( + df=df, + column="status", + allowed_values=["Active"], + error_collector=collector, + replace_invalid=False, + ) + + # Invalid value should NOT be replaced + assert result["status"].to_list() == ["Active", "INVALID"] + # But it should still be logged + assert len(collector) == 1 + + +def test_validate_allowed_values_missing_column(): + """Test that missing columns are handled gracefully.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"], + "patient_id": ["XX_YY001"], + } + ) + + collector = ErrorCollector() + + result = validate_allowed_values( + df=df, + column="nonexistent", + allowed_values=["Active"], + error_collector=collector, + ) + + assert result.equals(df) + assert len(collector) == 0 + + +def test_validate_allowed_values_ignores_existing_errors(): + """Test that existing error values are not re-logged.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"] * 3, + "patient_id": ["XX_YY001", "XX_YY002", "XX_YY003"], + "status": ["Active", settings.error_val_character, "INVALID"], + } + ) + + collector = ErrorCollector() + + result = validate_allowed_values( + df=df, + column="status", + allowed_values=["Active", "Inactive"], + error_collector=collector, + replace_invalid=True, + ) + + # Only "INVALID" should be logged, not the existing error value + assert len(collector) == 1 + assert result["status"].to_list() == [ + "Active", + settings.error_val_character, + settings.error_val_character, + ] + + +def test_validate_column_from_rules(): + """Test validation using rules from data_cleaning.yaml.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"] * 3, + "patient_id": ["XX_YY001", "XX_YY002", "XX_YY003"], + "clinic_visit": ["Y", "N", "INVALID"], + } + ) + + rules = load_validation_rules() + collector = ErrorCollector() + + result = validate_column_from_rules( + df=df, + column="clinic_visit", + rules=rules["clinic_visit"], + error_collector=collector, + ) + + # "INVALID" should be replaced with error value + assert result["clinic_visit"].to_list() == ["Y", "N", settings.error_val_character] + assert len(collector) == 1 + + +def test_validate_column_from_rules_missing_column(): + """Test validation with missing column.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"], + "patient_id": ["XX_YY001"], + } + ) + + rules = load_validation_rules() + collector = ErrorCollector() + + result = validate_column_from_rules( + df=df, + column="nonexistent", + rules=rules["clinic_visit"], + error_collector=collector, + ) + + assert result.equals(df) + assert len(collector) == 0 + + +def test_validate_all_columns(): + """Test validation of all columns with rules. + + Note: Validation uses case-insensitive matching and normalizes to canonical values. + For example, "active" becomes "Active", "y" becomes "Y". + """ + df = pl.DataFrame( + { + "file_name": ["test.xlsx"] * 3, + "patient_id": ["XX_YY001", "XX_YY002", "XX_YY003"], + "clinic_visit": ["Y", "N", "INVALID1"], + "patient_consent": ["Y", "INVALID2", "N"], + "status": ["active", "INVALID3", "inactive"], # Lowercase input + } + ) + + collector = ErrorCollector() + + result = validate_all_columns(df, collector) + + # All invalid values should be replaced + # Valid values should be normalized to canonical form (Title Case for status) + assert result["clinic_visit"].to_list() == ["Y", "N", settings.error_val_character] + assert result["patient_consent"].to_list() == ["Y", settings.error_val_character, "N"] + assert result["status"].to_list() == ["Active", settings.error_val_character, "Inactive"] + + # Should have logged 3 errors (one per invalid value) + assert len(collector) == 3 + + +def test_validate_all_columns_only_validates_existing(): + """Test that validation only processes columns that exist in DataFrame.""" + df = pl.DataFrame( + { + "file_name": ["test.xlsx"], + "patient_id": ["XX_YY001"], + "clinic_visit": ["Y"], + # Many other columns from rules don't exist + } + ) + + collector = ErrorCollector() + + # Should not raise error even though many rule columns don't exist + result = validate_all_columns(df, collector) + + assert "clinic_visit" in result.columns + assert len(collector) == 0 + + +def test_validate_allowed_values_case_insensitive(): + """Test that validation is case-insensitive and normalizes to canonical values. + + Validation matches R behavior: + - "y" matches "Y" (case-insensitive) + - Returns canonical value "Y" (not the input "y") + """ + df = pl.DataFrame( + { + "file_name": ["test.xlsx"] * 3, + "patient_id": ["XX_YY001", "XX_YY002", "XX_YY003"], + "clinic_visit": ["Y", "y", "N"], # Mixed case + } + ) + + collector = ErrorCollector() + + result = validate_allowed_values( + df=df, + column="clinic_visit", + allowed_values=["Y", "N"], + error_collector=collector, + replace_invalid=True, + ) + + # Lowercase "y" should match "Y" and be normalized to canonical "Y" + assert result["clinic_visit"].to_list() == ["Y", "Y", "N"] + assert len(collector) == 0 # No errors - "y" is valid + + +# Tests for fix_patient_id + + +def test_fix_patient_id_valid_ids(): + """Test that valid patient IDs are not changed.""" + df = pl.DataFrame( + { + "patient_id": ["KD_EW004", "AB_CD123", "XY_ZW999"], + } + ) + + collector = ErrorCollector() + result = fix_patient_id(df, collector) + + assert result["patient_id"].to_list() == ["KD_EW004", "AB_CD123", "XY_ZW999"] + assert len(collector) == 0 + + +def test_fix_patient_id_hyphen_normalization(): + """Test that hyphens are replaced with underscores.""" + df = pl.DataFrame( + { + "patient_id": ["KD-EW004", "AB-CD123"], + } + ) + + collector = ErrorCollector() + result = fix_patient_id(df, collector) + + assert result["patient_id"].to_list() == ["KD_EW004", "AB_CD123"] + assert len(collector) == 0 # Normalization doesn't generate errors + + +def test_fix_patient_id_truncation(): + """Test that IDs > 8 chars are truncated.""" + df = pl.DataFrame( + { + "patient_id": ["KD_EW004XY", "KD_EW004ABC", "VERYLONGID"], + } + ) + + collector = ErrorCollector() + result = fix_patient_id(df, collector) + + # First 8 characters + assert result["patient_id"].to_list() == ["KD_EW004", "KD_EW004", "VERYLONG"] + # Truncation generates warnings + assert len(collector) == 3 + + +def test_fix_patient_id_invalid_too_short_first_part(): + """Test that IDs with < 2 letters in first part are replaced.""" + df = pl.DataFrame( + { + "patient_id": ["K_EW004", "A_CD123"], + } + ) + + collector = ErrorCollector() + result = fix_patient_id(df, collector) + + assert result["patient_id"].to_list() == ["Undefined", "Undefined"] + assert len(collector) == 2 + + +def test_fix_patient_id_invalid_too_short_second_part(): + """Test that IDs with < 2 letters in second part are replaced.""" + df = pl.DataFrame( + { + "patient_id": ["KD_E004", "AB_C123"], + } + ) + + collector = ErrorCollector() + result = fix_patient_id(df, collector) + + assert result["patient_id"].to_list() == ["Undefined", "Undefined"] + assert len(collector) == 2 + + +def test_fix_patient_id_invalid_wrong_digits(): + """Test that IDs without exactly 3 digits are replaced.""" + df = pl.DataFrame( + { + "patient_id": ["KD_EW04", "KD_EW0", "KD_EW0001"], + } + ) + + collector = ErrorCollector() + result = fix_patient_id(df, collector) + + # All invalid (2 digits, 1 digit, 4 digits) + assert result["patient_id"][0] == "Undefined" + assert result["patient_id"][1] == "Undefined" + # KD_EW0001 is > 8 chars, so truncated to KD_EW000 + assert result["patient_id"][2] == "KD_EW000" + + +def test_fix_patient_id_invalid_digits_in_letter_positions(): + """Test that IDs with digits instead of letters are replaced.""" + df = pl.DataFrame( + { + "patient_id": ["11_EW004", "KD_E1004", "12_34567"], + } + ) + + collector = ErrorCollector() + result = fix_patient_id(df, collector) + + assert result["patient_id"].to_list() == ["Undefined", "Undefined", "Undefined"] + assert len(collector) == 3 + + +def test_fix_patient_id_invalid_letters_in_digit_positions(): + """Test that IDs with letters in digit positions are replaced.""" + df = pl.DataFrame( + { + "patient_id": ["KD_EWX04", "KD_EWABC"], + } + ) + + collector = ErrorCollector() + result = fix_patient_id(df, collector) + + assert result["patient_id"].to_list() == ["Undefined", "Undefined"] + assert len(collector) == 2 + + +def test_fix_patient_id_invalid_no_underscore(): + """Test that IDs without underscore are replaced.""" + df = pl.DataFrame( + { + "patient_id": ["KDEW004", "INVALID"], + } + ) + + collector = ErrorCollector() + result = fix_patient_id(df, collector) + + assert result["patient_id"].to_list() == ["Undefined", "Undefined"] + assert len(collector) == 2 + + +def test_fix_patient_id_null_values(): + """Test that null values are preserved.""" + df = pl.DataFrame( + { + "patient_id": ["KD_EW004", None, "AB_CD123"], + } + ) + + collector = ErrorCollector() + result = fix_patient_id(df, collector) + + assert result["patient_id"][0] == "KD_EW004" + assert result["patient_id"][1] is None + assert result["patient_id"][2] == "AB_CD123" + assert len(collector) == 0 + + +def test_fix_patient_id_empty_string(): + """Test that empty string is replaced with error value.""" + df = pl.DataFrame( + { + "patient_id": ["", "KD_EW004"], + } + ) + + collector = ErrorCollector() + result = fix_patient_id(df, collector) + + assert result["patient_id"][0] == "Undefined" + assert result["patient_id"][1] == "KD_EW004" + assert len(collector) == 1 + + +def test_fix_patient_id_missing_column(): + """Test that missing column is handled gracefully.""" + df = pl.DataFrame({"other": [1, 2, 3]}) + + collector = ErrorCollector() + result = fix_patient_id(df, collector) + + assert result.equals(df) + assert len(collector) == 0 + + +def test_fix_patient_id_mixed_valid_invalid(): + """Test mixed valid and invalid IDs.""" + df = pl.DataFrame( + { + "patient_id": [ + "KD_EW004", # Valid + "KD-AB123", # Valid after normalization + "INVALID", # Invalid, replaced + "KD_EW004XY", # Invalid, truncated + None, # Null preserved + ], + } + ) + + collector = ErrorCollector() + result = fix_patient_id(df, collector) + + assert result["patient_id"][0] == "KD_EW004" + assert result["patient_id"][1] == "KD_AB123" + assert result["patient_id"][2] == "Undefined" + assert result["patient_id"][3] == "KD_EW004" + assert result["patient_id"][4] is None + assert len(collector) == 2 # 1 replacement + 1 truncation + + +def test_fix_patient_id_lowercase_letters(): + """Test that lowercase letters make ID invalid.""" + df = pl.DataFrame( + { + "patient_id": ["kd_ew004", "KD_ew004", "kd_EW004"], + } + ) + + collector = ErrorCollector() + result = fix_patient_id(df, collector) + + # All should be replaced (format requires uppercase) + assert result["patient_id"].to_list() == ["Undefined", "Undefined", "Undefined"] + assert len(collector) == 3 + + +def test_fix_patient_id_matches_r_behavior(): + """Test that fix_patient_id matches R's fix_id() exactly.""" + df = pl.DataFrame( + { + "patient_id": [ + "KD_EW004", # Valid + "KD-EW004", # Normalize - to _ + "K_EW004", # Too short first part + "KD_E004", # Too short second part + "KD_EWX04", # Invalid format + "11_EW004", # Digits instead of letters + "KD_E1004", # Digit in letter position + "KD_EW004XY", # Truncate (> 8 chars) + None, # Null + "", # Empty + ], + } + ) + + collector = ErrorCollector() + result = fix_patient_id(df, collector) + + expected = [ + "KD_EW004", # Valid + "KD_EW004", # Normalized + "Undefined", # Invalid + "Undefined", # Invalid + "Undefined", # Invalid + "Undefined", # Invalid + "Undefined", # Invalid + "KD_EW004", # Truncated + None, # Null + "Undefined", # Empty → Other + ] + assert result["patient_id"].to_list() == expected + # Errors: 5 replacements + 1 truncation + 1 empty string = 7 + assert len(collector) == 7 diff --git a/tests/test_cli/__init__.py b/tests/test_cli/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_cli/conftest.py b/tests/test_cli/conftest.py new file mode 100644 index 0000000..c607535 --- /dev/null +++ b/tests/test_cli/conftest.py @@ -0,0 +1,57 @@ +"""Fixtures for CLI tests, including a minimal valid dummy tracker file.""" + +from pathlib import Path + +import openpyxl +import pytest + + +@pytest.fixture +def dummy_tracker(tmp_path) -> Path: + """Create a minimal valid A4D Excel tracker file for testing. + + Structure follows the actual tracker format: + - Sheet "Jan24" (month abbreviation + 2-digit year) + - Row 1: empty (no header, data_start_row - 2 → header_2 path) + - Row 2: column headers (data_start_row - 1 → header_1 path) + - Row 3+: patient data rows (col A = numeric row number) + + The clinic_id is derived from the parent folder name ("TST"). + """ + clinic_dir = tmp_path / "TST" + clinic_dir.mkdir() + tracker_path = clinic_dir / "2024_Test_Clinic.xlsx" + + wb = openpyxl.Workbook() + ws = wb.active + ws.title = "Jan24" + + # Row 1: empty title row → header_2 (≤2 non-None values triggers header_1-only path) + # Row 2: column headers → header_1 + # "Patient ID" in header_1 + empty header_2 → merge_headers uses header_1 only + ws.cell(2, 2).value = "Patient ID" + ws.cell(2, 3).value = "Name" + ws.cell(2, 4).value = "Sex" + ws.cell(2, 5).value = "Age" + + # Row 3+: data rows — col A must be numeric (find_data_start_row scans for first int/float) + ws.cell(3, 1).value = 1 + ws.cell(3, 2).value = "PT-001" + ws.cell(3, 3).value = "Test Patient One" + ws.cell(3, 4).value = "Female" + ws.cell(3, 5).value = 25 + + ws.cell(4, 1).value = 2 + ws.cell(4, 2).value = "PT-002" + ws.cell(4, 3).value = "Test Patient Two" + ws.cell(4, 4).value = "Male" + ws.cell(4, 5).value = 30 + + wb.save(tracker_path) + return tracker_path + + +@pytest.fixture +def dummy_tracker_dir(dummy_tracker) -> Path: + """Return the directory containing the dummy tracker (data root for batch mode).""" + return dummy_tracker.parent.parent diff --git a/tests/test_cli/test_cli.py b/tests/test_cli/test_cli.py new file mode 100644 index 0000000..5c3baea --- /dev/null +++ b/tests/test_cli/test_cli.py @@ -0,0 +1,243 @@ +"""Tests for the A4D CLI commands.""" + +from unittest.mock import MagicMock, patch + +import polars as pl +from typer.testing import CliRunner + +from a4d.cli import app + +runner = CliRunner(env={"NO_COLOR": "1", "COLUMNS": "200"}) + + +# --------------------------------------------------------------------------- +# Help / invocation smoke tests +# --------------------------------------------------------------------------- + + +class TestHelp: + """Verify every command exposes --help without error.""" + + def test_app_help(self): + result = runner.invoke(app, ["--help"]) + assert result.exit_code == 0 + assert "process-patient" in result.output + + def test_process_patient_help(self): + result = runner.invoke(app, ["process-patient", "--help"]) + assert result.exit_code == 0 + assert "--file" in result.output + + def test_create_tables_help(self): + result = runner.invoke(app, ["create-tables", "--help"]) + assert result.exit_code == 0 + assert "--input" in result.output + + def test_upload_tables_help(self): + result = runner.invoke(app, ["upload-tables", "--help"]) + assert result.exit_code == 0 + assert "--tables-dir" in result.output + + def test_run_pipeline_help(self): + result = runner.invoke(app, ["run-pipeline", "--help"]) + assert result.exit_code == 0 + assert "--skip-download" in result.output + assert "--skip-upload" in result.output + + +# --------------------------------------------------------------------------- +# Error-path unit tests (no real files needed) +# --------------------------------------------------------------------------- + + +class TestCreateTablesErrors: + """create-tables command error handling.""" + + def test_no_parquet_files_exits_nonzero(self, tmp_path): + # Directory exists but contains no *_patient_cleaned.parquet files + result = runner.invoke(app, ["create-tables", "--input", str(tmp_path)]) + assert result.exit_code == 1 + assert "No cleaned parquet files found" in result.output + + def test_missing_input_dir_raises(self, tmp_path): + missing = tmp_path / "nonexistent" + result = runner.invoke(app, ["create-tables", "--input", str(missing)]) + # typer raises UsageError or the command fails when dir missing + assert result.exit_code != 0 + + +class TestUploadTablesErrors: + """upload-tables command error handling.""" + + def test_missing_dir_exits_nonzero(self, tmp_path): + missing = tmp_path / "nonexistent_tables" + result = runner.invoke(app, ["upload-tables", "--tables-dir", str(missing)]) + assert result.exit_code == 1 + assert "not found" in result.output.lower() + + +# --------------------------------------------------------------------------- +# run-pipeline unit test (GCS/BQ mocked) +# --------------------------------------------------------------------------- + + +class TestRunPipeline: + """run-pipeline command with mocked GCP calls.""" + + @patch("a4d.cli.run_patient_pipeline") + @patch("a4d.config.settings") + def test_skip_upload_calls_pipeline(self, mock_settings, mock_run_pipeline, tmp_path): + mock_settings.data_root = tmp_path / "data" + mock_settings.output_root = tmp_path / "output" + mock_settings.project_id = "test-project" + mock_settings.dataset = "test-dataset" + mock_settings.max_workers = 4 + + (tmp_path / "data").mkdir() + (tmp_path / "output").mkdir() + + mock_result = MagicMock() + mock_result.success = True + mock_result.total_trackers = 0 + mock_result.successful_trackers = 0 + mock_result.failed_trackers = 0 + mock_result.tracker_results = [] + mock_result.tables = {} + mock_run_pipeline.return_value = mock_result + + result = runner.invoke( + app, ["run-pipeline", "--skip-download", "--skip-upload", "--skip-drive-download"] + ) + + mock_run_pipeline.assert_called_once() + assert result.exit_code == 0 + + @patch("a4d.cli.run_patient_pipeline") + @patch("a4d.config.settings") + def test_pipeline_failure_exits_nonzero(self, mock_settings, mock_run_pipeline, tmp_path): + mock_settings.data_root = tmp_path / "data" + mock_settings.output_root = tmp_path / "output" + mock_settings.project_id = "test-project" + mock_settings.dataset = "test-dataset" + mock_settings.max_workers = 4 + + (tmp_path / "data").mkdir() + (tmp_path / "output").mkdir() + + mock_result = MagicMock() + mock_result.success = False + mock_result.total_trackers = 1 + mock_result.successful_trackers = 0 + mock_result.failed_trackers = 1 + mock_result.tracker_results = [ + MagicMock(success=False, tracker_file=MagicMock(name="bad.xlsx"), error="Parse error") + ] + mock_result.tables = {} + mock_run_pipeline.return_value = mock_result + + result = runner.invoke( + app, ["run-pipeline", "--skip-download", "--skip-upload", "--skip-drive-download"] + ) + + assert result.exit_code == 1 + + +# --------------------------------------------------------------------------- +# End-to-end test: process-patient with real dummy tracker +# --------------------------------------------------------------------------- + + +class TestProcessPatientE2E: + """End-to-end test for process-patient using a synthetic tracker file.""" + + def test_process_single_file_creates_outputs(self, dummy_tracker, tmp_path): + """process-patient --file --output should produce parquet outputs.""" + output_dir = tmp_path / "output" + + result = runner.invoke( + app, + [ + "process-patient", + "--file", + str(dummy_tracker), + "--output", + str(output_dir), + ], + ) + + assert result.exit_code == 0, f"Pipeline failed:\n{result.output}" + + # Raw parquet should be created + raw_dir = output_dir / "patient_data_raw" + raw_files = list(raw_dir.glob("*_patient_raw.parquet")) + assert len(raw_files) == 1, f"Expected 1 raw parquet, found {len(raw_files)}" + + # Cleaned parquet should be created + cleaned_dir = output_dir / "patient_data_cleaned" + cleaned_files = list(cleaned_dir.glob("*_patient_cleaned.parquet")) + assert len(cleaned_files) == 1, f"Expected 1 cleaned parquet, found {len(cleaned_files)}" + + # Validate cleaned parquet has expected columns and rows + df_cleaned = pl.read_parquet(cleaned_files[0]) + assert "patient_id" in df_cleaned.columns + assert "clinic_id" in df_cleaned.columns + assert "tracker_year" in df_cleaned.columns + assert len(df_cleaned) == 2 # 2 patients in dummy file + + # clinic_id is derived from parent folder name + assert df_cleaned["clinic_id"].unique().to_list() == ["TST"] + assert df_cleaned["tracker_year"].unique().to_list() == [2024] + + def test_process_single_file_creates_tables(self, dummy_tracker, tmp_path): + """Tables (static, monthly, annual) should be created by default.""" + output_dir = tmp_path / "output" + + result = runner.invoke( + app, + [ + "process-patient", + "--file", + str(dummy_tracker), + "--output", + str(output_dir), + ], + ) + + assert result.exit_code == 0, f"Pipeline failed:\n{result.output}" + + tables_dir = output_dir / "tables" + assert (tables_dir / "patient_data_monthly.parquet").exists() + assert (tables_dir / "patient_data_static.parquet").exists() + + def test_skip_tables_flag(self, dummy_tracker, tmp_path): + """--skip-tables should skip table creation.""" + output_dir = tmp_path / "output" + + result = runner.invoke( + app, + [ + "process-patient", + "--file", + str(dummy_tracker), + "--output", + str(output_dir), + "--skip-tables", + ], + ) + + assert result.exit_code == 0, f"Pipeline failed:\n{result.output}" + + tables_dir = output_dir / "tables" + assert not tables_dir.exists() or not any(tables_dir.iterdir()) + + def test_process_missing_file_exits_nonzero(self, tmp_path): + """Passing a non-existent file should exit with error.""" + missing = tmp_path / "ghost.xlsx" + output_dir = tmp_path / "output" + + result = runner.invoke( + app, + ["process-patient", "--file", str(missing), "--output", str(output_dir)], + ) + + assert result.exit_code == 1 diff --git a/tests/test_errors.py b/tests/test_errors.py new file mode 100644 index 0000000..84196da --- /dev/null +++ b/tests/test_errors.py @@ -0,0 +1,167 @@ +"""Tests for error tracking functionality.""" + +import polars as pl + +from a4d.errors import DataError, ErrorCollector + + +def test_data_error_creation(): + """Test creating a DataError instance.""" + error = DataError( + file_name="test.xlsx", + patient_id="XX_YY001", + column="age", + original_value="invalid", + error_message="Could not convert to Int32", + error_code="type_conversion", + function_name="safe_convert_column", + ) + + assert error.file_name == "test.xlsx" + assert error.patient_id == "XX_YY001" + assert error.column == "age" + assert error.error_code == "type_conversion" + assert error.script == "clean" # default value + + +def test_error_collector_add_error(): + """Test adding errors to collector.""" + collector = ErrorCollector() + + assert len(collector) == 0 + assert not collector # __bool__ returns False when empty + + collector.add_error( + file_name="test.xlsx", + patient_id="XX_YY001", + column="age", + original_value="invalid", + error_message="Could not convert", + error_code="type_conversion", + ) + + assert len(collector) == 1 + assert collector # __bool__ returns True when has errors + + +def test_error_collector_add_errors(): + """Test adding multiple errors at once.""" + collector = ErrorCollector() + + errors = [ + DataError( + file_name="test.xlsx", + patient_id="XX_YY001", + column="age", + original_value="invalid", + error_message="Could not convert", + error_code="type_conversion", + ), + DataError( + file_name="test.xlsx", + patient_id="XX_YY002", + column="weight", + original_value="abc", + error_message="Could not convert", + error_code="type_conversion", + ), + ] + + collector.add_errors(errors) + + assert len(collector) == 2 + + +def test_error_collector_to_dataframe(): + """Test converting errors to DataFrame.""" + collector = ErrorCollector() + + collector.add_error( + file_name="test.xlsx", + patient_id="XX_YY001", + column="age", + original_value="invalid", + error_message="Could not convert to Int32", + error_code="type_conversion", + function_name="safe_convert_column", + ) + + df = collector.to_dataframe() + + assert isinstance(df, pl.DataFrame) + assert len(df) == 1 + assert "file_name" in df.columns + assert "patient_id" in df.columns + assert "column" in df.columns + assert "error_code" in df.columns + + # Check categorical columns + assert df.schema["error_code"] == pl.Categorical + assert df.schema["script"] == pl.Categorical + + +def test_error_collector_to_dataframe_empty(): + """Test converting empty collector to DataFrame.""" + collector = ErrorCollector() + df = collector.to_dataframe() + + assert isinstance(df, pl.DataFrame) + assert len(df) == 0 + # Should still have correct schema + assert "file_name" in df.columns + assert "error_code" in df.columns + + +def test_error_collector_get_summary(): + """Test error summary by error_code.""" + collector = ErrorCollector() + + collector.add_error( + file_name="test.xlsx", + patient_id="XX_YY001", + column="age", + original_value="invalid", + error_message="Type error", + error_code="type_conversion", + ) + collector.add_error( + file_name="test.xlsx", + patient_id="XX_YY002", + column="age", + original_value="999", + error_message="Out of range", + error_code="invalid_value", + ) + collector.add_error( + file_name="test.xlsx", + patient_id="XX_YY003", + column="weight", + original_value="abc", + error_message="Type error", + error_code="type_conversion", + ) + + summary = collector.get_error_summary() + + assert summary == {"type_conversion": 2, "invalid_value": 1} + + +def test_error_collector_clear(): + """Test clearing errors from collector.""" + collector = ErrorCollector() + + collector.add_error( + file_name="test.xlsx", + patient_id="XX_YY001", + column="age", + original_value="invalid", + error_message="Error", + error_code="type_conversion", + ) + + assert len(collector) == 1 + + collector.clear() + + assert len(collector) == 0 + assert not collector diff --git a/tests/test_extract/__init__.py b/tests/test_extract/__init__.py new file mode 100644 index 0000000..1690af8 --- /dev/null +++ b/tests/test_extract/__init__.py @@ -0,0 +1 @@ +"""Tests for data extraction modules.""" diff --git a/tests/test_extract/test_patient.py b/tests/test_extract/test_patient.py new file mode 100644 index 0000000..0d2d31d --- /dev/null +++ b/tests/test_extract/test_patient.py @@ -0,0 +1,648 @@ +"""Tests for patient data extraction.""" + +from pathlib import Path + +import polars as pl +import pytest + +from a4d.extract.patient import ( + extract_patient_data, + extract_tracker_month, + find_month_sheets, + get_tracker_year, + harmonize_patient_data_columns, + merge_duplicate_columns_data, + read_all_patient_sheets, +) + + +def column_letter_to_index(col_letter: str) -> int: + """Convert Excel column letter to 0-based index. + + Examples: + A -> 0, B -> 1, Z -> 25, AA -> 26, AB -> 27, AC -> 28 + """ + result = 0 + for char in col_letter: + result = result * 26 + (ord(char) - ord("A") + 1) + return result - 1 + + +def calculate_expected_columns(start_col: str, end_col: str) -> int: + """Calculate expected number of columns from Excel range. + + Args: + start_col: Starting column letter (e.g., 'B') + end_col: Ending column letter (e.g., 'AC') + + Returns: + Number of columns in the range + + Examples: + B to Z: 25 columns + B to AC: 28 columns + B to AB: 27 columns + """ + start_idx = column_letter_to_index(start_col) + end_idx = column_letter_to_index(end_col) + return end_idx - start_idx + 1 + + +# Test data paths +TRACKER_SBU_2024 = Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/" + "Malaysia/SBU/2024_Sibu Hospital A4D Tracker.xlsx" +) +TRACKER_PNG_2019 = Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/" + "Malaysia/PNG/2019_Penang General Hospital A4D Tracker_DC.xlsx" +) +TRACKER_PNG_2018 = Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/" + "Malaysia/PNG/2018_Penang General Hospital A4D Tracker_DC.xlsx" +) +TRACKER_MHS_2017 = Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/" + "Laos/MHS/2017_Mahosot Hospital A4D Tracker.xlsx" +) +TRACKER_MHS_2025 = Path( + "/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/" + "Laos/MHS/2025_06_Mahosot Hospital A4D Tracker.xlsx" +) + + +@pytest.mark.skipif(not TRACKER_SBU_2024.exists(), reason="Tracker file not available") +def test_get_tracker_year_from_sheet_names(): + """Test extracting year from sheet names.""" + year = get_tracker_year(TRACKER_SBU_2024, ["Jan24", "Feb24", "Mar24"]) + assert year == 2024 + + +@pytest.mark.skipif(not TRACKER_SBU_2024.exists(), reason="Tracker file not available") +def test_get_tracker_year_from_filename(): + """Test extracting year from filename as fallback.""" + year = get_tracker_year(TRACKER_SBU_2024, ["January", "February"]) + assert year == 2024 + + +@pytest.mark.skipif(not TRACKER_SBU_2024.exists(), reason="Tracker file not available") +def test_find_month_sheets_2024(): + """Test finding month sheets in 2024 tracker.""" + from openpyxl import load_workbook + + wb = load_workbook(TRACKER_SBU_2024, data_only=True) + month_sheets = find_month_sheets(wb) + + assert len(month_sheets) > 0 + assert any("Jan" in sheet for sheet in month_sheets) + assert any("Dec" in sheet for sheet in month_sheets) + + +# Parameterized test data: (tracker_file, sheet_name, year, expected_patients, expected_cols, notes) +# Note: expected_cols is the actual number after filtering out None header columns +TRACKER_TEST_CASES = [ + # 2024 tracker - optimized single-pass extraction + ( + TRACKER_SBU_2024, + "Jan24", + 2024, + 4, + calculate_expected_columns("B", "AG") - 1, + "Single-pass read-only", + ), + # 2019 tracker - format changes across months! Optimized extraction + ( + TRACKER_PNG_2019, + "Jan19", + 2019, + 10, + calculate_expected_columns("B", "Z"), + "Single-pass read-only", + ), + ( + TRACKER_PNG_2019, + "Feb19", + 2019, + 10, + calculate_expected_columns("B", "AC"), + "Single-pass read-only", + ), + ( + TRACKER_PNG_2019, + "Mar19", + 2019, + 10, + calculate_expected_columns("B", "AB"), + "Single-pass read-only", + ), + ( + TRACKER_PNG_2019, + "Oct19", + 2019, + 11, + calculate_expected_columns("B", "AB"), + "Single-pass read-only", + ), + # 2018 tracker - single-line headers + ( + TRACKER_PNG_2018, + "Dec18", + 2018, + 10, + calculate_expected_columns("B", "T"), + "Single-pass read-only", + ), +] + + +@pytest.mark.skipif( + any(not tf.exists() for tf, _, _, _, _, _ in TRACKER_TEST_CASES), + reason="Tracker files not available", +) +@pytest.mark.parametrize( + ("tracker_file", "sheet_name", "year", "expected_patients", "expected_cols", "notes"), + TRACKER_TEST_CASES, + ids=lambda params: f"{params[1] if isinstance(params, tuple) and len(params) > 1 else params}", +) +def test_extract_patient_data_schema( + tracker_file, sheet_name, year, expected_patients, expected_cols, notes +): + """Test patient data extraction with schema validation across different months. + + This parameterized test validates that: + 1. Correct number of patients are extracted + 2. Correct number of columns match expected (after filtering None headers) + 3. Format changes between months are handled correctly + + The test is critical because tracker formats change even within the same year, + and data quality is inconsistent across different months. + """ + df = extract_patient_data(tracker_file, sheet_name, year) + + # Check dimensions + assert len(df) == expected_patients, ( + f"{sheet_name}: Expected {expected_patients} patients, got {len(df)}" + ) + assert len(df.columns) == expected_cols, ( + f"{sheet_name}: Expected {expected_cols} columns ({notes}), got {len(df.columns)}" + ) + + # Verify we have at least Patient ID column + assert any("patient" in col.lower() and "id" in col.lower() for col in df.columns), ( + f"{sheet_name}: Missing Patient ID column in {df.columns}" + ) + + print(f"\n{sheet_name}: {len(df)} patients × {len(df.columns)} columns ({notes}) ✓") + + +@pytest.mark.skipif(not TRACKER_SBU_2024.exists(), reason="Tracker file not available") +def test_extract_patient_data_2024_detailed(): + """Detailed test for 2024 tracker with patient ID validation.""" + df = extract_patient_data(TRACKER_SBU_2024, "Jan24", 2024) + + # Verify specific patient IDs + patient_ids = df["Patient ID*"].to_list() + assert patient_ids == ["MY_SU001", "MY_SU002", "MY_SU003", "MY_SU004"], ( + f"Expected MY_SU001-004, got {patient_ids}" + ) + + print(f"\n2024 Jan24 - Patient IDs: {patient_ids} ✓") + + +def test_harmonize_patient_data_columns_basic(): + """Test basic column harmonization with known synonyms.""" + raw_df = pl.DataFrame( + { + "Patient ID*": ["MY_SU001", "MY_SU002"], + "Age": [25, 30], + "D.O.B.": ["1998-01-15", "1993-06-20"], + } + ) + + harmonized = harmonize_patient_data_columns(raw_df) + + # Check that columns were renamed to standardized names + assert "patient_id" in harmonized.columns + assert "age" in harmonized.columns + assert "dob" in harmonized.columns + + # Check that data is preserved + assert harmonized["patient_id"].to_list() == ["MY_SU001", "MY_SU002"] + assert harmonized["age"].to_list() == [25, 30] + + +def test_harmonize_patient_data_columns_multiple_synonyms(): + """Test that multiple columns mapping to same name keeps first occurrence. + + When multiple columns in the input map to the same standardized name + (e.g., "Patient ID", "ID", "Patient ID*" all map to "patient_id"), + we keep the FIRST occurrence and drop the rest. This matches R behavior + and handles edge cases like 2023 complication screening columns. + """ + raw_df = pl.DataFrame( + { + "Patient ID": ["P001"], + "ID": ["P002"], + "Patient ID*": ["P003"], + } + ) + + # Should keep first occurrence ("Patient ID") and drop the rest + harmonized = harmonize_patient_data_columns(raw_df) + + assert list(harmonized.columns) == ["patient_id"] + assert harmonized["patient_id"].to_list() == ["P001"] # First occurrence kept + + +def test_harmonize_patient_data_columns_unmapped_strict_false(): + """Test that unmapped columns are kept when strict=False (default).""" + raw_df = pl.DataFrame( + { + "Patient ID*": ["MY_SU001"], + "Age": [25], + "UnknownColumn": ["some value"], + } + ) + + harmonized = harmonize_patient_data_columns(raw_df, strict=False) + + # Mapped columns should be renamed + assert "patient_id" in harmonized.columns + assert "age" in harmonized.columns + + # Unmapped column should be kept as-is + assert "UnknownColumn" in harmonized.columns + + +def test_harmonize_patient_data_columns_unmapped_strict_true(): + """Test that unmapped columns raise error when strict=True.""" + raw_df = pl.DataFrame( + { + "Patient ID*": ["MY_SU001"], + "UnknownColumn": ["some value"], + } + ) + + with pytest.raises(ValueError, match="Unmapped columns found"): + harmonize_patient_data_columns(raw_df, strict=True) + + +def test_harmonize_patient_data_columns_empty_dataframe(): + """Test harmonization with empty DataFrame.""" + raw_df = pl.DataFrame() + + harmonized = harmonize_patient_data_columns(raw_df) + + assert len(harmonized) == 0 + assert len(harmonized.columns) == 0 + + +@pytest.mark.skipif(not TRACKER_SBU_2024.exists(), reason="Tracker file not available") +def test_harmonize_real_tracker_data(): + """Test harmonization with real tracker data.""" + # Extract raw data + raw_df = extract_patient_data(TRACKER_SBU_2024, "Jan24", 2024) + + # Harmonize columns + harmonized = harmonize_patient_data_columns(raw_df) + + # Check that key columns were renamed + assert "patient_id" in harmonized.columns + assert "age" in harmonized.columns + + # Check that data is preserved + assert len(harmonized) == len(raw_df) # Same number of rows + assert harmonized["patient_id"].to_list() == ["MY_SU001", "MY_SU002", "MY_SU003", "MY_SU004"] + + +def test_extract_tracker_month(): + """Test extracting month number from sheet name.""" + assert extract_tracker_month("Jan24") == 1 + assert extract_tracker_month("Feb24") == 2 + assert extract_tracker_month("Mar19") == 3 + assert extract_tracker_month("Dec23") == 12 + + # Test with ValueError for invalid sheet names + with pytest.raises(ValueError, match="Could not extract month"): + extract_tracker_month("Sheet1") + + +def test_merge_duplicate_columns_data_no_duplicates(): + """Test that data without duplicate headers is unchanged.""" + headers = ["ID", "Name", "Age", "City"] + data = [["1", "Alice", "25", "NYC"], ["2", "Bob", "30", "LA"]] + + result_headers, result_data = merge_duplicate_columns_data(headers, data) + + assert result_headers == headers + assert result_data == data + + +def test_merge_duplicate_columns_data_with_duplicates(): + """Test merging duplicate columns like R's tidyr::unite().""" + headers = ["ID", "DM Complications", "DM Complications", "DM Complications", "Age"] + data = [["1", "A", "B", "C", "25"], ["2", "X", "Y", "Z", "30"]] + + result_headers, result_data = merge_duplicate_columns_data(headers, data) + + assert result_headers == ["ID", "DM Complications", "Age"] + assert result_data == [["1", "A,B,C", "25"], ["2", "X,Y,Z", "30"]] + + +def test_merge_duplicate_columns_data_with_nulls(): + """Test merging duplicate columns with null values.""" + headers = ["ID", "DM Complications", "DM Complications", "DM Complications", "Age"] + data = [["1", "A", None, "C", "25"], ["2", None, "Y", None, "30"]] + + result_headers, result_data = merge_duplicate_columns_data(headers, data) + + assert result_headers == ["ID", "DM Complications", "Age"] + # Empty values are filtered out before joining + assert result_data == [["1", "A,C", "25"], ["2", "Y", "30"]] + + +def test_merge_duplicate_columns_data_all_nulls(): + """Test merging when all duplicate columns have null values.""" + headers = ["ID", "DM Complications", "DM Complications", "Age"] + data = [["1", None, None, "25"]] + + result_headers, result_data = merge_duplicate_columns_data(headers, data) + + assert result_headers == ["ID", "DM Complications", "Age"] + # All nulls result in None + assert result_data == [["1", None, "25"]] + + +def test_merge_duplicate_columns_data_multiple_groups(): + """Test merging multiple groups of duplicate columns.""" + headers = ["ID", "Status", "Status", "Value", "Value", "Value", "Name"] + data = [["1", "A", "B", "X", "Y", "Z", "Alice"]] + + result_headers, result_data = merge_duplicate_columns_data(headers, data) + + assert result_headers == ["ID", "Status", "Value", "Name"] + assert result_data == [["1", "A,B", "X,Y,Z", "Alice"]] + + +@pytest.mark.skipif(not TRACKER_SBU_2024.exists(), reason="Tracker file not available") +def test_read_all_patient_sheets_2024(): + """Test reading all patient sheets from 2024 tracker with Patient List and Annual.""" + df_all = read_all_patient_sheets(TRACKER_SBU_2024) + + # Check that we have data + assert len(df_all) > 0, "Should have extracted patient data" + + # Check that metadata columns were added + assert "sheet_name" in df_all.columns + assert "tracker_month" in df_all.columns + assert "tracker_year" in df_all.columns + assert "file_name" in df_all.columns + assert "clinic_id" in df_all.columns + + # Check that clinic_id is extracted from parent directory + clinic_ids = df_all["clinic_id"].unique().to_list() + assert len(clinic_ids) == 1 # All rows should have same clinic_id + assert clinic_ids[0] == "SBU" # Parent directory name + + # Check that we have data from multiple months + unique_months = df_all["tracker_month"].unique().to_list() + assert len(unique_months) > 1, "Should have data from multiple months" + + # Check that year is correct + assert all(year == 2024 for year in df_all["tracker_year"].unique().to_list()) + + # Check that patient_id column exists + assert "patient_id" in df_all.columns + + # Check that we filtered out invalid rows (no null patient_ids) + assert df_all["patient_id"].null_count() == 0 + + # Check for baseline HbA1c column from Patient List (should be present after join) + # Note: This may have .static suffix if there were conflicts + hba1c_cols = [col for col in df_all.columns if "hba1c_baseline" in col.lower()] + print(f"\nHbA1c baseline columns: {hba1c_cols}") + + print( + f"\n2024 Tracker: {len(df_all)} total patients from {len(unique_months)} months" + f" (with Patient List & Annual data) ✓" + ) + + +@pytest.mark.skipif(not TRACKER_PNG_2019.exists(), reason="Tracker file not available") +def test_read_all_patient_sheets_2019(): + """Test reading all patient sheets from 2019 tracker (different formats across months).""" + df_all = read_all_patient_sheets(TRACKER_PNG_2019) + + # Check that we have data + assert len(df_all) > 0, "Should have extracted patient data" + + # Check metadata columns + assert "sheet_name" in df_all.columns + assert "tracker_month" in df_all.columns + assert "tracker_year" in df_all.columns + + # Check that year is correct + assert all(year == 2019 for year in df_all["tracker_year"].unique().to_list()) + + # Check that patient_id column exists + assert "patient_id" in df_all.columns + + # Check that we filtered out invalid rows + assert df_all["patient_id"].null_count() == 0 + + # 2019 tracker has format changes across months - verify we handled them + unique_months = df_all["tracker_month"].unique().to_list() + print(f"\n2019 Tracker: {len(df_all)} total patients from {len(unique_months)} months ✓") + + +@pytest.mark.skipif(not TRACKER_SBU_2024.exists(), reason="Tracker file not available") +def test_read_all_patient_sheets_file_name(): + """Test that file_name metadata is correctly added.""" + df_all = read_all_patient_sheets(TRACKER_SBU_2024) + + assert "file_name" in df_all.columns + file_names = df_all["file_name"].unique().to_list() + assert len(file_names) == 1 + assert file_names[0] == TRACKER_SBU_2024.stem + + +@pytest.mark.skipif(not TRACKER_MHS_2017.exists(), reason="Tracker file not available") +def test_read_all_patient_sheets_2017_mhs_complete(): + """ + End-to-end test: 2017 Mahosot Hospital tracker (Laos/MHS). + + Characteristics: + - Year: 2017 + - Sheets: Jan17-Dec17 (March is MISSING) + - NO Patient List or Annual sheets + - clinic_id should be "MHS" + + Expected patient counts per month: + - Jan17: 6, Feb17: 6, Apr17: 6, May17: 8, Jun17: 11, Jul17: 11 + - Aug17: 11, Sep17: 12, Oct17: 12, Nov17: 12, Dec17: 14 + - Total: 109 patients (11 months) + """ + df_all = read_all_patient_sheets(TRACKER_MHS_2017) + + # Basic validation + assert len(df_all) > 0, "Should have extracted patient data" + assert "patient_id" in df_all.columns + assert "tracker_month" in df_all.columns + assert "tracker_year" in df_all.columns + assert "clinic_id" in df_all.columns + + # Check clinic_id + assert df_all["clinic_id"].unique().to_list() == ["MHS"] + + # Check year + assert df_all["tracker_year"].unique().to_list() == [2017] + + # Check we have exactly 11 months (March is missing) + unique_months = sorted(df_all["tracker_month"].unique().to_list()) + expected_months = [1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12] # Missing 3 (March) + assert unique_months == expected_months, f"Expected {expected_months}, got {unique_months}" + + # Verify patient counts per month + import calendar + + expected_counts = { + 1: 6, # Jan + 2: 6, # Feb + # 3 is missing (March) + 4: 6, # Apr + 5: 8, # May + 6: 11, # Jun + 7: 11, # Jul + 8: 11, # Aug + 9: 12, # Sep + 10: 12, # Oct + 11: 12, # Nov + 12: 14, # Dec + } + + for month, expected_count in expected_counts.items(): + month_data = df_all.filter(pl.col("tracker_month") == month) + actual_count = len(month_data) + assert actual_count == expected_count, ( + f"Month {month} ({calendar.month_abbr[month]}17): " + f"expected {expected_count} patients, got {actual_count}" + ) + + # Total patient count + total_expected = sum(expected_counts.values()) # 109 + assert len(df_all) == total_expected, ( + f"Total patients: expected {total_expected}, got {len(df_all)}" + ) + + print( + f"\n✓ 2017 MHS Tracker: {len(df_all)} patients from 11 months (March missing as expected)" + ) + + +@pytest.mark.skipif(not TRACKER_MHS_2025.exists(), reason="Tracker file not available") +def test_read_all_patient_sheets_2025_mhs_with_patient_list(): + """ + End-to-end test: 2025 Mahosot Hospital tracker (Laos/MHS). + + Characteristics: + - Year: 2025 + - Sheets: Jan25-Jun25 (6 months) + - HAS Patient List and Annual sheets + - clinic_id should be "MHS" + + Expected patient counts per month: + - Jan25: 95, Feb25: 97, Mar25: 97, Apr25: 97, May25: 98, Jun25: 99 + - Total: 583 patients + """ + df_all = read_all_patient_sheets(TRACKER_MHS_2025) + + # Basic validation + assert len(df_all) > 0, "Should have extracted patient data" + assert "patient_id" in df_all.columns + assert "tracker_month" in df_all.columns + assert "tracker_year" in df_all.columns + assert "clinic_id" in df_all.columns + + # Check clinic_id + assert df_all["clinic_id"].unique().to_list() == ["MHS"] + + # Check year + assert df_all["tracker_year"].unique().to_list() == [2025] + + # Check we have exactly 6 months (Jan-Jun) + unique_months = sorted(df_all["tracker_month"].unique().to_list()) + expected_months = [1, 2, 3, 4, 5, 6] + assert unique_months == expected_months, f"Expected {expected_months}, got {unique_months}" + + # Verify patient counts per month + import calendar + + expected_counts = { + 1: 95, # Jan + 2: 97, # Feb + 3: 97, # Mar + 4: 97, # Apr + 5: 98, # May + 6: 99, # Jun + } + + for month, expected_count in expected_counts.items(): + month_data = df_all.filter(pl.col("tracker_month") == month) + actual_count = len(month_data) + assert actual_count == expected_count, ( + f"Month {month} ({calendar.month_abbr[month]}25): " + f"expected {expected_count} patients, got {actual_count}" + ) + + # Total patient count + total_expected = sum(expected_counts.values()) # 583 + assert len(df_all) == total_expected, ( + f"Total patients: expected {total_expected}, got {len(df_all)}" + ) + + # Check that Patient List data was joined (should have columns from Patient List) + # Note: The exact columns depend on what's in the Patient List sheet + # We verify by checking for potential .static suffix columns + static_cols = [col for col in df_all.columns if ".static" in col] + print(f"\nColumns from Patient List (.static suffix): {len(static_cols)}") + + # Check that Annual data was joined + annual_cols = [col for col in df_all.columns if ".annual" in col] + print(f"Columns from Annual sheet (.annual suffix): {len(annual_cols)}") + + print( + f"\n✓ 2025 MHS Tracker: {len(df_all)} patients from 6 months " + f"(with Patient List & Annual data joined)" + ) + + +def test_export_patient_raw(tmp_path): + """Test exporting patient data to parquet file.""" + from a4d.extract.patient import export_patient_raw, read_all_patient_sheets + + # Use the 2024 SBU tracker as test data + tracker_file = TRACKER_SBU_2024 + if not tracker_file.exists(): + pytest.skip("Tracker file not available") + + # Extract data + df = read_all_patient_sheets(tracker_file) + + # Export to temp directory + output_dir = tmp_path / "patient_data_raw" + output_path = export_patient_raw(df, tracker_file, output_dir) + + # Verify output file exists + assert output_path.exists() + assert output_path.name == "2024_Sibu Hospital A4D Tracker_patient_raw.parquet" + assert output_path.parent == output_dir + + # Verify we can read it back + df_read = pl.read_parquet(output_path) + assert len(df_read) == len(df) + assert df_read.columns == df.columns + + # Verify content matches + assert df_read.equals(df) + + print(f"\n✓ Successfully exported and verified {len(df)} rows to parquet") diff --git a/tests/test_extract/test_patient_helpers.py b/tests/test_extract/test_patient_helpers.py new file mode 100644 index 0000000..128ec99 --- /dev/null +++ b/tests/test_extract/test_patient_helpers.py @@ -0,0 +1,476 @@ +"""Unit tests for patient extraction helper functions.""" + +import random +from unittest.mock import Mock + +import pytest +from openpyxl import Workbook + +from a4d.extract.patient import ( + filter_valid_columns, + find_data_start_row, + merge_headers, + read_header_rows, +) + + +def create_mock_mapper(known_columns: set[str]): + """Create a mock ColumnMapper that validates specific column names.""" + mapper = Mock() + mapper.is_known_column = lambda col: col in known_columns + return mapper + + +class TestFindDataStartRow: + """Tests for find_data_start_row() function.""" + + def test_data_starts_at_row_1(self): + """Test when data starts at the very first row.""" + wb = Workbook() + ws = wb.active + ws["A1"] = 1 + ws["A2"] = 2 + + result = find_data_start_row(ws) + assert result == 1 + + wb.close() + + def test_data_starts_after_empty_rows(self): + """Test when there are empty rows before data.""" + wb = Workbook() + ws = wb.active + # Leave rows 1-10 empty + ws["A11"] = 1 + ws["A12"] = 2 + + result = find_data_start_row(ws) + assert result == 11 + + wb.close() + + def test_realistic_tracker_layout(self): + """Test with realistic tracker layout (headers at rows 75-76, data at 77).""" + wb = Workbook() + ws = wb.active + + # Simulate typical tracker: empty rows, then title rows, then headers, then data + # Title area NOT in column A (column A stays empty until headers) + ws["B1"] = "Hospital Name" + ws["C1"] = "General Hospital" + + # Headers at rows 75-76 (typical for real trackers) + ws["B75"] = "Patient" + ws["B76"] = "ID*" + + # Data starts at row 77 + ws["A77"] = 1 + ws["A78"] = 2 + + result = find_data_start_row(ws) + assert result == 77 # First non-None in column A + + wb.close() + + def test_randomized_data_position(self): + """Test with randomized data start position.""" + wb = Workbook() + ws = wb.active + + # Random start position between 10 and 100 + random_start = random.randint(10, 100) + + # Insert first data value at random position (must be numeric) + ws[f"A{random_start}"] = 1 + + result = find_data_start_row(ws) + assert result == random_start + + wb.close() + + def test_column_a_empty_raises_error(self): + """Test that ValueError is raised when column A is empty.""" + wb = Workbook() + ws = wb.active + + # Put data in other columns but not A + ws["B1"] = "Some data" + ws["C5"] = "More data" + + with pytest.raises(ValueError, match="No patient data found in column A"): + find_data_start_row(ws) + + wb.close() + + def test_ignores_none_values(self): + """Test that None/empty cells are skipped correctly.""" + wb = Workbook() + ws = wb.active + + # Explicitly set some cells to None (they start as None anyway) + ws["A1"] = None + ws["A2"] = None + ws["A3"] = None + ws["A4"] = 1 # First numeric data + + result = find_data_start_row(ws) + assert result == 4 + + wb.close() + + +class TestReadHeaderRows: + """Tests for read_header_rows() function.""" + + def test_basic_two_row_headers(self): + """Test reading basic two-row headers.""" + wb = Workbook() + ws = wb.active + + # Data starts at row 5, so headers are at rows 3 and 4 + ws["A3"] = "Patient" + ws["B3"] = "Date" + ws["C3"] = "HbA1c" + + ws["A4"] = "ID*" + ws["B4"] = "(dd-mmm-yyyy)" + ws["C4"] = "%" + + ws["A5"] = "P001" # Data starts here + + header_1, header_2 = read_header_rows(ws, data_start_row=5) + + assert header_1 == ["ID*", "(dd-mmm-yyyy)", "%"] + assert header_2 == ["Patient", "Date", "HbA1c"] + + wb.close() + + def test_trims_to_last_non_none_column(self): + """Test that headers are trimmed to last non-None column.""" + wb = Workbook() + ws = wb.active + + # Data starts at row 10 + ws["A8"] = "Patient" + ws["B8"] = "Name" + ws["C8"] = "Age" + # D8-Z8 remain None + + ws["A9"] = "ID*" + ws["B9"] = None + ws["C9"] = None + + ws["A10"] = "P001" + + header_1, header_2 = read_header_rows(ws, data_start_row=10) + + # Should trim to column C (last non-None) + assert len(header_1) == 3 + assert len(header_2) == 3 + assert header_1 == ["ID*", None, None] + assert header_2 == ["Patient", "Name", "Age"] + + wb.close() + + def test_realistic_tracker_width(self): + """Test with realistic tracker dimensions (31 columns).""" + wb = Workbook() + ws = wb.active + + data_start_row = 77 + + # Create 31 columns of headers + for col_idx in range(1, 32): # 1 to 31 inclusive + ws.cell(row=75, column=col_idx, value=f"H2_Col{col_idx}") + ws.cell(row=76, column=col_idx, value=f"H1_Col{col_idx}") + + # Put data at row 77 + ws.cell(row=77, column=1, value="P001") + + header_1, header_2 = read_header_rows(ws, data_start_row=data_start_row) + + assert len(header_1) == 31 + assert len(header_2) == 31 + assert header_1[0] == "H1_Col1" + assert header_1[30] == "H1_Col31" + assert header_2[0] == "H2_Col1" + assert header_2[30] == "H2_Col31" + + wb.close() + + def test_mixed_none_values_in_headers(self): + """Test headers with mixed None and non-None values.""" + wb = Workbook() + ws = wb.active + + # Header row 2 (further from data) + ws["A3"] = "Patient" + ws["B3"] = None + ws["C3"] = "Updated HbA1c" + ws["D3"] = None # Horizontally merged + ws["E3"] = None + + # Header row 1 (closer to data) + ws["A4"] = "ID*" + ws["B4"] = "Name" + ws["C4"] = "%" + ws["D4"] = "(dd-mmm-yyyy)" + ws["E4"] = None + + ws["A5"] = "P001" # Data + + header_1, header_2 = read_header_rows(ws, data_start_row=5) + + # Should trim to column D (last non-None in header_1) + assert len(header_1) == 4 + assert len(header_2) == 4 + assert header_1 == ["ID*", "Name", "%", "(dd-mmm-yyyy)"] + assert header_2 == ["Patient", None, "Updated HbA1c", None] + + wb.close() + + def test_randomized_header_position(self): + """Test with randomized data start position.""" + wb = Workbook() + ws = wb.active + + # Random data start between rows 20 and 100 + random_data_start = random.randint(20, 100) + header_row_1 = random_data_start - 1 + header_row_2 = random_data_start - 2 + + # Set headers + ws.cell(row=header_row_2, column=1, value="Header2") + ws.cell(row=header_row_1, column=1, value="Header1") + ws.cell(row=random_data_start, column=1, value="Data") + + header_1, header_2 = read_header_rows(ws, data_start_row=random_data_start) + + assert header_1 == ["Header1"] + assert header_2 == ["Header2"] + + wb.close() + + def test_respects_max_cols_parameter(self): + """Test that max_cols parameter limits the read width.""" + wb = Workbook() + ws = wb.active + + # Create 200 columns of data + for col_idx in range(1, 201): + ws.cell(row=3, column=col_idx, value=f"H2_{col_idx}") + ws.cell(row=4, column=col_idx, value=f"H1_{col_idx}") + + ws["A5"] = "Data" + + # Read with max_cols=50 + header_1, header_2 = read_header_rows(ws, data_start_row=5, max_cols=50) + + # Should only read up to column 50 + assert len(header_1) == 50 + assert len(header_2) == 50 + assert header_1[49] == "H1_50" + + wb.close() + + def test_all_none_headers(self): + """Test when both header rows are completely None. + + Note: When no non-None values are found, the function returns + max_cols None values (default behavior). In practice, this edge + case doesn't occur as real trackers always have headers. + """ + wb = Workbook() + ws = wb.active + + # Headers are all None + # (openpyxl cells are None by default) + + ws["A5"] = "Data" + + header_1, header_2 = read_header_rows(ws, data_start_row=5, max_cols=10) + + # Returns max_cols None values when nothing is found + assert len(header_1) == 10 + assert len(header_2) == 10 + assert all(h is None for h in header_1) + assert all(h is None for h in header_2) + + wb.close() + + +class TestMergeHeaders: + """Tests for merge_headers() function.""" + + def test_both_headers_present(self): + """Test merging when both header rows have values.""" + h1 = ["%", "mmol/L", "kg"] + h2 = ["HbA1c", "FBG", "Weight"] + result = merge_headers(h1, h2) + assert result == ["HbA1c %", "FBG mmol/L", "Weight kg"] + + def test_only_h2_present(self): + """Test when only header row 2 has values.""" + h1 = [None, None, None] + h2 = ["Patient ID", "Name", "Age"] + result = merge_headers(h1, h2) + assert result == ["Patient ID", "Name", "Age"] + + def test_only_h1_present(self): + """Test when only header row 1 has values (single-line headers).""" + h1 = ["Patient ID", "Name", "Age"] + h2 = [None, None, None] + result = merge_headers(h1, h2) + assert result == ["Patient ID", "Name", "Age"] + + def test_horizontal_merge_forward_fill(self): + """Test forward-fill with synonym validation. + + Forward-fill happens when mapper validates the combined header. + """ + h1 = ["%", "(dd-mmm-yyyy)", "mmol/L", "(dd-mmm-yyyy)"] + h2 = ["Updated HbA1c", None, "Updated FBG", None] + # Mock mapper that knows these forward-filled patterns + mapper = create_mock_mapper( + { + "Updated HbA1c %", + "Updated HbA1c (dd-mmm-yyyy)", + "Updated FBG mmol/L", + "Updated FBG (dd-mmm-yyyy)", + } + ) + result = merge_headers(h1, h2, mapper) + assert result == [ + "Updated HbA1c %", + "Updated HbA1c (dd-mmm-yyyy)", + "Updated FBG mmol/L", + "Updated FBG (dd-mmm-yyyy)", + ] + + def test_mixed_headers(self): + """Test realistic mix of header patterns. + + Forward-fill happens when mapper validates the combined header. + """ + h1 = ["ID*", "Name", "%", "(date)", None, "kg"] + h2 = ["Patient", None, "HbA1c", None, "Notes", "Weight"] + # Mock mapper that validates these forward-fills + mapper = create_mock_mapper( + { + "Patient ID*", + "Patient Name", + "HbA1c %", + "HbA1c (date)", + } + ) + result = merge_headers(h1, h2, mapper) + assert result == [ + "Patient ID*", + "Patient Name", # Forward-filled and validated + "HbA1c %", + "HbA1c (date)", # Forward-filled and validated + "Notes", + "Weight kg", + ] + + def test_none_values_reset_forward_fill(self): + """Test that None in both headers results in None. + + Forward-fill only happens when h1 exists and mapper validates. + """ + h1 = ["%", "(date)", None, "kg"] + h2 = ["HbA1c", None, None, "Weight"] + # Mock mapper that validates HbA1c forward-fills + mapper = create_mock_mapper( + { + "HbA1c %", + "HbA1c (date)", + } + ) + result = merge_headers(h1, h2, mapper) + assert result == [ + "HbA1c %", + "HbA1c (date)", + None, + "Weight kg", + ] + + def test_whitespace_normalization(self): + """Test that extra whitespace and newlines are normalized.""" + h1 = ["ID\n(format)", " Name "] + h2 = ["Patient\nID", "Full Name"] + result = merge_headers(h1, h2) + assert result == [ + "Patient ID ID (format)", + "Full Name Name", + ] + + def test_empty_headers(self): + """Test with empty header lists.""" + result = merge_headers([], []) + assert result == [] + + def test_single_column(self): + """Test with single column.""" + h1 = ["ID"] + h2 = ["Patient"] + result = merge_headers(h1, h2) + assert result == ["Patient ID"] + + +class TestFilterValidColumns: + """Tests for filter_valid_columns() function.""" + + def test_all_valid_headers(self): + """Test when all headers are valid (no None).""" + headers = ["ID", "Name", "Age"] + data = [("1", "Alice", "30"), ("2", "Bob", "25")] + valid_headers, filtered_data = filter_valid_columns(headers, data) + + assert valid_headers == ["ID", "Name", "Age"] + assert filtered_data == [["1", "Alice", "30"], ["2", "Bob", "25"]] + + def test_some_none_headers(self): + """Test filtering out None headers.""" + headers = ["ID", None, "Name", None, "Age"] + data = [("1", "x", "Alice", "y", "30"), ("2", "x", "Bob", "y", "25")] + valid_headers, filtered_data = filter_valid_columns(headers, data) + + assert valid_headers == ["ID", "Name", "Age"] + assert filtered_data == [["1", "Alice", "30"], ["2", "Bob", "25"]] + + def test_all_none_headers(self): + """Test when all headers are None.""" + headers = [None, None, None] + data = [("1", "2", "3"), ("4", "5", "6")] + valid_headers, filtered_data = filter_valid_columns(headers, data) + + assert valid_headers == [] + assert filtered_data == [] + + def test_empty_data(self): + """Test with empty data.""" + headers = ["ID", "Name"] + data = [] + valid_headers, filtered_data = filter_valid_columns(headers, data) + + assert valid_headers == ["ID", "Name"] + assert filtered_data == [] + + def test_single_valid_column(self): + """Test with single valid column.""" + headers = [None, "ID", None] + data = [("x", "1", "y"), ("x", "2", "y")] + valid_headers, filtered_data = filter_valid_columns(headers, data) + + assert valid_headers == ["ID"] + assert filtered_data == [["1"], ["2"]] + + def test_preserves_order(self): + """Test that column order is preserved.""" + headers = ["A", None, "B", None, "C", "D", None] + data = [(1, 2, 3, 4, 5, 6, 7)] + valid_headers, filtered_data = filter_valid_columns(headers, data) + + assert valid_headers == ["A", "B", "C", "D"] + assert filtered_data == [[1, 3, 5, 6]] diff --git a/tests/test_gcp/__init__.py b/tests/test_gcp/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_gcp/test_bigquery.py b/tests/test_gcp/test_bigquery.py new file mode 100644 index 0000000..8512092 --- /dev/null +++ b/tests/test_gcp/test_bigquery.py @@ -0,0 +1,173 @@ +"""Tests for BigQuery loading module.""" + +from unittest.mock import MagicMock, patch + +import pytest + +from a4d.gcp.bigquery import ( + PARQUET_TO_TABLE, + TABLE_CONFIGS, + load_pipeline_tables, + load_table, +) + + +def _get_job_config(mock_client): + """Extract job_config from mock client's load_table_from_file call.""" + return mock_client.load_table_from_file.call_args.kwargs["job_config"] + + +class TestTableConfigs: + """Test that table configurations match the R pipeline.""" + + def test_patient_data_monthly_clustering(self): + assert TABLE_CONFIGS["patient_data_monthly"] == [ + "clinic_id", + "patient_id", + "tracker_date", + ] + + def test_patient_data_annual_clustering(self): + assert TABLE_CONFIGS["patient_data_annual"] == ["patient_id", "tracker_date"] + + def test_patient_data_static_clustering(self): + assert TABLE_CONFIGS["patient_data_static"] == [ + "clinic_id", + "patient_id", + "tracker_date", + ] + + def test_all_pipeline_tables_have_configs(self): + for table_name in PARQUET_TO_TABLE.values(): + assert table_name in TABLE_CONFIGS, f"Missing config for {table_name}" + + +class TestLoadTable: + """Test loading a single parquet file to BigQuery.""" + + def test_raises_file_not_found(self, tmp_path): + missing_file = tmp_path / "missing.parquet" + with pytest.raises(FileNotFoundError, match="Parquet file not found"): + load_table(missing_file, "patient_data_monthly") + + @patch("a4d.gcp.bigquery.get_bigquery_client") + def test_load_table_with_replace(self, mock_get_client, tmp_path): + parquet_file = tmp_path / "test.parquet" + parquet_file.write_bytes(b"fake parquet data") + + mock_client = MagicMock() + mock_job = MagicMock() + mock_job.output_rows = 100 + mock_client.load_table_from_file.return_value = mock_job + mock_get_client.return_value = mock_client + + load_table(parquet_file, "patient_data_monthly", client=mock_client) + + mock_client.load_table_from_file.assert_called_once() + job_config = _get_job_config(mock_client) + assert job_config.clustering_fields == ["clinic_id", "patient_id", "tracker_date"] + mock_job.result.assert_called_once() + + @patch("a4d.gcp.bigquery.get_bigquery_client") + def test_load_table_with_append(self, mock_get_client, tmp_path): + parquet_file = tmp_path / "test.parquet" + parquet_file.write_bytes(b"fake parquet data") + + mock_client = MagicMock() + mock_job = MagicMock() + mock_job.output_rows = 50 + mock_client.load_table_from_file.return_value = mock_job + + load_table(parquet_file, "patient_data_monthly", client=mock_client, replace=False) + + job_config = _get_job_config(mock_client) + assert job_config.write_disposition == "WRITE_APPEND" + + @patch("a4d.gcp.bigquery.get_bigquery_client") + def test_load_table_correct_table_ref(self, mock_get_client, tmp_path): + parquet_file = tmp_path / "test.parquet" + parquet_file.write_bytes(b"fake parquet data") + + mock_client = MagicMock() + mock_job = MagicMock() + mock_job.output_rows = 10 + mock_client.load_table_from_file.return_value = mock_job + + load_table( + parquet_file, + "patient_data_static", + client=mock_client, + dataset="test_dataset", + project_id="test_project", + ) + + table_ref = mock_client.load_table_from_file.call_args.args[1] + assert table_ref == "test_project.test_dataset.patient_data_static" + + +class TestLoadPipelineTables: + """Test loading all pipeline tables.""" + + def test_raises_if_dir_missing(self, tmp_path): + missing_dir = tmp_path / "nonexistent" + with pytest.raises(FileNotFoundError, match="Tables directory not found"): + load_pipeline_tables(missing_dir) + + @patch("a4d.gcp.bigquery.load_table") + @patch("a4d.gcp.bigquery.get_bigquery_client") + def test_loads_existing_tables(self, mock_get_client, mock_load, tmp_path): + tables_dir = tmp_path / "tables" + tables_dir.mkdir() + + # Create some table files + (tables_dir / "patient_data_static.parquet").write_bytes(b"data") + (tables_dir / "patient_data_monthly.parquet").write_bytes(b"data") + + mock_client = MagicMock() + mock_get_client.return_value = mock_client + mock_load.return_value = MagicMock() + + results = load_pipeline_tables(tables_dir, client=mock_client) + + assert mock_load.call_count == 2 + assert "patient_data_static" in results + assert "patient_data_monthly" in results + + @patch("a4d.gcp.bigquery.load_table") + @patch("a4d.gcp.bigquery.get_bigquery_client") + def test_skips_missing_tables(self, mock_get_client, mock_load, tmp_path): + tables_dir = tmp_path / "tables" + tables_dir.mkdir() + + # Only create one table file + (tables_dir / "patient_data_static.parquet").write_bytes(b"data") + + mock_client = MagicMock() + mock_get_client.return_value = mock_client + mock_load.return_value = MagicMock() + + results = load_pipeline_tables(tables_dir, client=mock_client) + + assert mock_load.call_count == 1 + assert "patient_data_static" in results + assert "patient_data_monthly" not in results + + @patch("a4d.gcp.bigquery.load_table") + @patch("a4d.gcp.bigquery.get_bigquery_client") + def test_continues_on_single_table_failure(self, mock_get_client, mock_load, tmp_path): + tables_dir = tmp_path / "tables" + tables_dir.mkdir() + + (tables_dir / "patient_data_static.parquet").write_bytes(b"data") + (tables_dir / "patient_data_monthly.parquet").write_bytes(b"data") + + mock_client = MagicMock() + mock_get_client.return_value = mock_client + + # First call succeeds, second fails + mock_load.side_effect = [MagicMock(), Exception("API error")] + + results = load_pipeline_tables(tables_dir, client=mock_client) + + # Should have one success despite the failure + assert len(results) == 1 diff --git a/tests/test_gcp/test_drive.py b/tests/test_gcp/test_drive.py new file mode 100644 index 0000000..8da6fb7 --- /dev/null +++ b/tests/test_gcp/test_drive.py @@ -0,0 +1,90 @@ +"""Tests for Google Drive download module.""" + +from pathlib import Path +from unittest.mock import MagicMock, patch + +import pytest + +from a4d.gcp.drive import CLINIC_DATA_FILE_ID, download_clinic_data + + +class TestDownloadClinicData: + """Tests for download_clinic_data.""" + + @patch("a4d.gcp.drive.google.auth.default") + @patch("a4d.gcp.drive.google.auth.transport.requests.AuthorizedSession") + def test_downloads_to_destination(self, mock_session_cls, mock_auth_default, tmp_path): + mock_auth_default.return_value = (MagicMock(), "project") + mock_session = MagicMock() + mock_session_cls.return_value = mock_session + mock_response = MagicMock() + mock_response.iter_content.return_value = [b"data1", b"data2"] + mock_session.get.return_value = mock_response + + result = download_clinic_data(tmp_path) + + assert result == tmp_path / "clinic_data.xlsx" + assert result.exists() + assert result.read_bytes() == b"data1data2" + + @patch("a4d.gcp.drive.google.auth.default") + @patch("a4d.gcp.drive.google.auth.transport.requests.AuthorizedSession") + def test_uses_correct_file_id(self, mock_session_cls, mock_auth_default, tmp_path): + mock_auth_default.return_value = (MagicMock(), "project") + mock_session = MagicMock() + mock_session_cls.return_value = mock_session + mock_response = MagicMock() + mock_response.iter_content.return_value = [b"xlsx"] + mock_session.get.return_value = mock_response + + download_clinic_data(tmp_path) + + call_url = mock_session.get.call_args[0][0] + assert CLINIC_DATA_FILE_ID in call_url + assert "/export" in call_url + assert "mimeType=" in call_url + + @patch("a4d.gcp.drive.google.auth.default") + @patch("a4d.gcp.drive.google.auth.transport.requests.AuthorizedSession") + def test_uses_drive_readonly_scope(self, mock_session_cls, mock_auth_default, tmp_path): + mock_auth_default.return_value = (MagicMock(), "project") + mock_session = MagicMock() + mock_session_cls.return_value = mock_session + mock_response = MagicMock() + mock_response.iter_content.return_value = [b"xlsx"] + mock_session.get.return_value = mock_response + + download_clinic_data(tmp_path) + + scopes = mock_auth_default.call_args[1]["scopes"] + assert any("drive" in s for s in scopes) + + @patch("a4d.gcp.drive.google.auth.default") + @patch("a4d.gcp.drive.google.auth.transport.requests.AuthorizedSession") + def test_raises_on_http_error(self, mock_session_cls, mock_auth_default, tmp_path): + import requests as req + + mock_auth_default.return_value = (MagicMock(), "project") + mock_session = MagicMock() + mock_session_cls.return_value = mock_session + mock_response = MagicMock() + mock_response.raise_for_status.side_effect = req.HTTPError("403 Forbidden") + mock_session.get.return_value = mock_response + + with pytest.raises(req.HTTPError): + download_clinic_data(tmp_path) + + @patch("a4d.gcp.drive.google.auth.default") + @patch("a4d.gcp.drive.google.auth.transport.requests.AuthorizedSession") + def test_creates_destination_directory(self, mock_session_cls, mock_auth_default, tmp_path): + mock_auth_default.return_value = (MagicMock(), "project") + mock_session = MagicMock() + mock_session_cls.return_value = mock_session + mock_response = MagicMock() + mock_response.iter_content.return_value = [b"xlsx"] + mock_session.get.return_value = mock_response + + dest = tmp_path / "new" / "subdir" + download_clinic_data(dest) + + assert dest.exists() diff --git a/tests/test_gcp/test_storage.py b/tests/test_gcp/test_storage.py new file mode 100644 index 0000000..77ff437 --- /dev/null +++ b/tests/test_gcp/test_storage.py @@ -0,0 +1,114 @@ +"""Tests for Google Cloud Storage module.""" + +from unittest.mock import MagicMock, patch + +import pytest + +from a4d.gcp.storage import download_tracker_files, upload_output + + +class TestDownloadTrackerFiles: + """Test downloading tracker files from GCS.""" + + @patch("a4d.gcp.storage.get_storage_client") + def test_downloads_files(self, mock_get_client, tmp_path): + destination = tmp_path / "trackers" + + mock_client = MagicMock() + mock_get_client.return_value = mock_client + mock_bucket = MagicMock() + mock_client.bucket.return_value = mock_bucket + + # Simulate blobs in bucket + blob1 = MagicMock() + blob1.name = "2024/tracker1.xlsx" + blob2 = MagicMock() + blob2.name = "2024/tracker2.xlsx" + mock_bucket.list_blobs.return_value = [blob1, blob2] + + result = download_tracker_files(destination, client=mock_client) + + assert len(result) == 2 + assert blob1.download_to_filename.called + assert blob2.download_to_filename.called + + @patch("a4d.gcp.storage.get_storage_client") + def test_skips_directory_markers(self, mock_get_client, tmp_path): + destination = tmp_path / "trackers" + + mock_client = MagicMock() + mock_get_client.return_value = mock_client + mock_bucket = MagicMock() + mock_client.bucket.return_value = mock_bucket + + blob_dir = MagicMock() + blob_dir.name = "2024/" + blob_file = MagicMock() + blob_file.name = "2024/tracker.xlsx" + mock_bucket.list_blobs.return_value = [blob_dir, blob_file] + + result = download_tracker_files(destination, client=mock_client) + + assert len(result) == 1 + assert not blob_dir.download_to_filename.called + + @patch("a4d.gcp.storage.get_storage_client") + def test_creates_destination_directory(self, mock_get_client, tmp_path): + destination = tmp_path / "new" / "dir" + + mock_client = MagicMock() + mock_get_client.return_value = mock_client + mock_bucket = MagicMock() + mock_client.bucket.return_value = mock_bucket + mock_bucket.list_blobs.return_value = [] + + download_tracker_files(destination, client=mock_client) + + assert destination.exists() + + +class TestUploadOutput: + """Test uploading output to GCS.""" + + def test_raises_if_source_missing(self, tmp_path): + missing_dir = tmp_path / "nonexistent" + with pytest.raises(FileNotFoundError, match="Source directory not found"): + upload_output(missing_dir) + + @patch("a4d.gcp.storage.get_storage_client") + def test_uploads_files(self, mock_get_client, tmp_path): + source = tmp_path / "output" + source.mkdir() + (source / "tables").mkdir() + (source / "tables" / "data.parquet").write_bytes(b"data") + (source / "logs.txt").write_text("log") + + mock_client = MagicMock() + mock_get_client.return_value = mock_client + mock_bucket = MagicMock() + mock_client.bucket.return_value = mock_bucket + mock_blob = MagicMock() + mock_bucket.blob.return_value = mock_blob + + result = upload_output(source, client=mock_client) + + assert len(result) == 2 + assert mock_blob.upload_from_filename.call_count == 2 + + @patch("a4d.gcp.storage.get_storage_client") + def test_upload_with_prefix(self, mock_get_client, tmp_path): + source = tmp_path / "output" + source.mkdir() + (source / "file.parquet").write_bytes(b"data") + + mock_client = MagicMock() + mock_get_client.return_value = mock_client + mock_bucket = MagicMock() + mock_client.bucket.return_value = mock_bucket + mock_blob = MagicMock() + mock_bucket.blob.return_value = mock_blob + + result = upload_output(source, prefix="2024-01", client=mock_client) + + assert len(result) == 1 + assert result[0] == "2024-01/file.parquet" diff --git a/tests/test_integration/__init__.py b/tests/test_integration/__init__.py new file mode 100644 index 0000000..19172f4 --- /dev/null +++ b/tests/test_integration/__init__.py @@ -0,0 +1,9 @@ +"""Integration tests for A4D pipeline. + +These tests use real tracker files and are marked as 'slow' and 'integration'. +They are skipped by default in CI/CD to keep test runs fast. + +Run them explicitly with: + uv run pytest -m integration + uv run pytest tests/test_integration/ +""" diff --git a/tests/test_integration/conftest.py b/tests/test_integration/conftest.py new file mode 100644 index 0000000..2e798e4 --- /dev/null +++ b/tests/test_integration/conftest.py @@ -0,0 +1,42 @@ +"""Shared fixtures for integration tests.""" + +from pathlib import Path + +import pytest + +# Base path to tracker files +TRACKER_BASE = Path("/Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload") + + +@pytest.fixture +def tracker_2024_penang(): + """2024 Penang tracker - has Annual + Patient List sheets.""" + return TRACKER_BASE / "Malaysia/PNG/2024_Penang General Hospital A4D Tracker.xlsx" + + +@pytest.fixture +def tracker_2023_sibu(): + """2023 Sibu tracker - has duplicate column mapping edge case.""" + return TRACKER_BASE / "Malaysia/SBU/2023_Sibu Hospital A4D Tracker.xlsx" + + +@pytest.fixture +def tracker_2022_penang(): + """2022 Penang tracker - legacy format without Annual sheet.""" + return TRACKER_BASE / "Malaysia/PNG/2022_Penang General Hospital A4D Tracker.xlsx" + + +@pytest.fixture +def tracker_2024_isdfi(): + """2024 ISDFI Philippines tracker.""" + return TRACKER_BASE / "Philippines/ISD/2024_ISDFI A4D Tracker.xlsx" + + +# Expected values for validation +EXPECTED_SCHEMA_COLS = 83 # After cleaning + + +def skip_if_missing(tracker_path: Path): + """Skip test if tracker file is not available.""" + if not tracker_path.exists(): + pytest.skip(f"Tracker file not found: {tracker_path}") diff --git a/tests/test_integration/test_clean_integration.py b/tests/test_integration/test_clean_integration.py new file mode 100644 index 0000000..a8423f4 --- /dev/null +++ b/tests/test_integration/test_clean_integration.py @@ -0,0 +1,133 @@ +"""Integration tests for patient data cleaning. + +Tests cleaning on real extracted data, validating: +- Correct schema (83 columns) +- Type conversions work correctly +- Error tracking works +- Derived columns are created +""" + +import pytest + +from a4d.clean.patient import clean_patient_data +from a4d.errors import ErrorCollector +from a4d.extract.patient import read_all_patient_sheets + +from .conftest import EXPECTED_SCHEMA_COLS, skip_if_missing + +pytestmark = [pytest.mark.slow, pytest.mark.integration] + + +class TestClean2024Penang: + """Test cleaning on 2024 Penang extracted data.""" + + def test_clean_produces_correct_schema(self, tracker_2024_penang): + """Should produce exactly 83 columns after cleaning.""" + skip_if_missing(tracker_2024_penang) + + df_raw = read_all_patient_sheets(tracker_2024_penang) + collector = ErrorCollector() + df_clean = clean_patient_data(df_raw, collector) + + assert len(df_clean.columns) == EXPECTED_SCHEMA_COLS + + def test_clean_preserves_row_count(self, tracker_2024_penang): + """Should not drop rows during cleaning.""" + skip_if_missing(tracker_2024_penang) + + df_raw = read_all_patient_sheets(tracker_2024_penang) + collector = ErrorCollector() + df_clean = clean_patient_data(df_raw, collector) + + assert len(df_clean) == len(df_raw) + + def test_clean_creates_derived_columns(self, tracker_2024_penang): + """Should create derived columns (insulin_type, insulin_subtype, etc.).""" + skip_if_missing(tracker_2024_penang) + + df_raw = read_all_patient_sheets(tracker_2024_penang) + collector = ErrorCollector() + df_clean = clean_patient_data(df_raw, collector) + + # Check derived columns exist + assert "insulin_type" in df_clean.columns + assert "insulin_subtype" in df_clean.columns + assert "blood_pressure_sys_mmhg" in df_clean.columns + assert "blood_pressure_dias_mmhg" in df_clean.columns + + def test_clean_tracks_errors(self, tracker_2024_penang): + """Should track data quality errors in ErrorCollector.""" + skip_if_missing(tracker_2024_penang) + + df_raw = read_all_patient_sheets(tracker_2024_penang) + collector = ErrorCollector() + clean_patient_data(df_raw, collector) + + # Should have some errors (type conversions, invalid values, etc.) + # Exact count varies, but should be non-zero for this tracker + assert len(collector) >= 0 # May have 0 or more errors + + def test_clean_has_required_columns(self, tracker_2024_penang): + """Should have all required columns in final schema.""" + skip_if_missing(tracker_2024_penang) + + df_raw = read_all_patient_sheets(tracker_2024_penang) + collector = ErrorCollector() + df_clean = clean_patient_data(df_raw, collector) + + # Check key columns exist + required_columns = [ + "patient_id", + "tracker_year", + "tracker_month", + "age", + "hba1c_updated", + "fbg_updated_mg", + "insulin_type", + ] + for col in required_columns: + assert col in df_clean.columns, f"Missing required column: {col}" + + +class TestClean2023Sibu: + """Test cleaning on 2023 Sibu (edge case).""" + + def test_clean_after_duplicate_handling(self, tracker_2023_sibu): + """Should clean successfully after duplicate column handling.""" + skip_if_missing(tracker_2023_sibu) + + df_raw = read_all_patient_sheets(tracker_2023_sibu) + collector = ErrorCollector() + df_clean = clean_patient_data(df_raw, collector) + + assert len(df_clean.columns) == EXPECTED_SCHEMA_COLS + assert len(df_clean) == 14 + + +class TestClean2022PenangLegacy: + """Test cleaning on 2022 Penang (legacy format).""" + + def test_clean_legacy_format(self, tracker_2022_penang): + """Should clean legacy format to same 83-column schema.""" + skip_if_missing(tracker_2022_penang) + + df_raw = read_all_patient_sheets(tracker_2022_penang) + collector = ErrorCollector() + df_clean = clean_patient_data(df_raw, collector) + + # Should produce same schema regardless of input format + assert len(df_clean.columns) == EXPECTED_SCHEMA_COLS + assert len(df_clean) == 156 + + def test_clean_legacy_has_patient_list_data(self, tracker_2022_penang): + """Should preserve Patient List data (dob, province, etc.) after cleaning.""" + skip_if_missing(tracker_2022_penang) + + df_raw = read_all_patient_sheets(tracker_2022_penang) + collector = ErrorCollector() + df_clean = clean_patient_data(df_raw, collector) + + # Patient List columns should be preserved + assert "dob" in df_clean.columns + assert "province" in df_clean.columns + assert "sex" in df_clean.columns diff --git a/tests/test_integration/test_e2e.py b/tests/test_integration/test_e2e.py new file mode 100644 index 0000000..c4ed7bf --- /dev/null +++ b/tests/test_integration/test_e2e.py @@ -0,0 +1,147 @@ +"""End-to-end integration tests for the full pipeline (extraction + cleaning). + +Tests the complete workflow on real tracker files, validating: +- Extraction + Cleaning work together correctly +- Final output has correct schema and row counts +- Different tracker formats (2024, 2023, 2022) all produce consistent output +""" + +import pytest + +from a4d.clean.patient import clean_patient_data +from a4d.errors import ErrorCollector +from a4d.extract.patient import read_all_patient_sheets + +from .conftest import EXPECTED_SCHEMA_COLS, skip_if_missing + +pytestmark = [pytest.mark.slow, pytest.mark.integration, pytest.mark.e2e] + + +@pytest.mark.parametrize( + ("tracker_fixture", "expected_rows", "expected_year", "description"), + [ + ("tracker_2024_penang", 174, 2024, "2024 Penang - Annual + Patient List"), + ("tracker_2024_isdfi", 70, 2024, "2024 ISDFI Philippines"), + ("tracker_2023_sibu", 14, 2023, "2023 Sibu - duplicate columns edge case"), + ("tracker_2022_penang", 156, 2022, "2022 Penang - legacy format"), + ], +) +def test_e2e_pipeline(tracker_fixture, expected_rows, expected_year, description, request): + """Test full pipeline (extract + clean) on various tracker formats. + + This test validates that: + 1. Extraction works and produces expected row count + 2. Cleaning works and produces 83-column schema + 3. Row count is preserved through the pipeline + 4. Year is extracted correctly + """ + tracker_path = request.getfixturevalue(tracker_fixture) + skip_if_missing(tracker_path) + + # Step 1: Extract + df_raw = read_all_patient_sheets(tracker_path) + assert len(df_raw) == expected_rows, f"Extraction failed for {description}" + + # Step 2: Clean + collector = ErrorCollector() + df_clean = clean_patient_data(df_raw, collector) + + # Validate final output + assert len(df_clean) == expected_rows, f"Cleaning changed row count for {description}" + assert len(df_clean.columns) == EXPECTED_SCHEMA_COLS, f"Schema incorrect for {description}" + assert df_clean["tracker_year"].unique().to_list() == [expected_year], ( + f"Year incorrect for {description}" + ) + + +class TestE2E2024Penang: + """Detailed end-to-end test for 2024 Penang tracker.""" + + def test_e2e_full_pipeline(self, tracker_2024_penang): + """Test complete pipeline with detailed validations.""" + skip_if_missing(tracker_2024_penang) + + # Extract + df_raw = read_all_patient_sheets(tracker_2024_penang) + assert len(df_raw) == 174 + + # Clean + collector = ErrorCollector() + df_clean = clean_patient_data(df_raw, collector) + + # Validate schema + assert len(df_clean.columns) == 83 + assert len(df_clean) == 174 + + # Validate metadata + assert "tracker_year" in df_clean.columns + assert "tracker_month" in df_clean.columns + assert "clinic_id" in df_clean.columns + + # Validate year and months + assert df_clean["tracker_year"].unique().to_list() == [2024] + months = sorted(df_clean["tracker_month"].unique().to_list()) + assert months == list(range(1, 13)) # Should have all 12 months + + # Validate clinic_id + assert df_clean["clinic_id"].unique().to_list() == ["PNG"] + + def test_e2e_critical_columns_populated(self, tracker_2024_penang): + """Validate that critical columns are fully populated after pipeline.""" + skip_if_missing(tracker_2024_penang) + + df_raw = read_all_patient_sheets(tracker_2024_penang) + collector = ErrorCollector() + df_clean = clean_patient_data(df_raw, collector) + + # These columns must be 100% populated for every row + required_full = [ + "patient_id", + "status", + "clinic_id", + "tracker_year", + "tracker_month", + ] + for col in required_full: + null_count = df_clean[col].is_null().sum() + assert null_count == 0, f"{col} has {null_count} null values, expected 0" + + # These columns should have high population (allow some nulls) + required_partial = ["age", "last_clinic_visit_date"] + for col in required_partial: + non_null = df_clean[col].is_not_null().sum() + assert non_null > len(df_clean) * 0.9, f"{col} has <90% population" + + +class TestE2ECrosYearConsistency: + """Test that different years produce consistent schemas.""" + + def test_all_years_produce_same_schema( + self, tracker_2024_penang, tracker_2023_sibu, tracker_2022_penang + ): + """All tracker years should produce the same 83-column schema.""" + trackers = [ + (tracker_2024_penang, "2024_Penang"), + (tracker_2023_sibu, "2023_Sibu"), + (tracker_2022_penang, "2022_Penang"), + ] + + column_names_per_tracker = {} + + for tracker_path, name in trackers: + if not tracker_path.exists(): + pytest.skip(f"Tracker file not found: {tracker_path}") + + # Full pipeline + df_raw = read_all_patient_sheets(tracker_path) + collector = ErrorCollector() + df_clean = clean_patient_data(df_raw, collector) + + # Collect column names + column_names_per_tracker[name] = set(df_clean.columns) + + # All trackers should have same column names + if len(column_names_per_tracker) > 1: + first_columns = list(column_names_per_tracker.values())[0] + for name, columns in column_names_per_tracker.items(): + assert columns == first_columns, f"{name} has different columns than others" diff --git a/tests/test_integration/test_extract_integration.py b/tests/test_integration/test_extract_integration.py new file mode 100644 index 0000000..9d5399b --- /dev/null +++ b/tests/test_integration/test_extract_integration.py @@ -0,0 +1,134 @@ +"""Integration tests for patient data extraction. + +Tests extraction on real tracker files, validating: +- Correct number of rows extracted +- Correct number of columns +- Month sheets are processed correctly +- Annual and Patient List sheets are handled (if present) +- Metadata columns are added correctly +""" + +import pytest + +from a4d.extract.patient import read_all_patient_sheets + +from .conftest import skip_if_missing + +pytestmark = [pytest.mark.slow, pytest.mark.integration] + + +class TestExtract2024Penang: + """Test extraction on 2024 Penang tracker (has Annual + Patient List).""" + + def test_extract_total_rows(self, tracker_2024_penang): + """Should extract all patient records from all sheets.""" + skip_if_missing(tracker_2024_penang) + + df = read_all_patient_sheets(tracker_2024_penang) + + # 2024 Penang has 12 month sheets + data from Patient List + assert len(df) == 174 + assert len(df.columns) > 0 # Should have columns (exact count varies before cleaning) + + def test_extract_has_metadata_columns(self, tracker_2024_penang): + """Should add metadata columns (tracker_year, tracker_month, sheet_name, file_name).""" + skip_if_missing(tracker_2024_penang) + + df = read_all_patient_sheets(tracker_2024_penang) + + assert "tracker_year" in df.columns + assert "tracker_month" in df.columns + assert "sheet_name" in df.columns + assert "file_name" in df.columns + assert "clinic_id" in df.columns + + def test_extract_year_is_correct(self, tracker_2024_penang): + """Should extract year 2024 from sheet names.""" + skip_if_missing(tracker_2024_penang) + + df = read_all_patient_sheets(tracker_2024_penang) + + # All rows should have year 2024 + assert df["tracker_year"].unique().to_list() == [2024] + + def test_extract_has_12_months(self, tracker_2024_penang): + """Should process 12 month sheets (Jan-Dec 2024).""" + skip_if_missing(tracker_2024_penang) + + df = read_all_patient_sheets(tracker_2024_penang) + + months = sorted(df["tracker_month"].unique().to_list()) + expected_months = list(range(1, 13)) # 1-12 + assert months == expected_months + + def test_extract_clinic_id(self, tracker_2024_penang): + """Should extract clinic_id from parent directory.""" + skip_if_missing(tracker_2024_penang) + + df = read_all_patient_sheets(tracker_2024_penang) + + # Parent directory is PNG + assert df["clinic_id"].unique().to_list() == ["PNG"] + + +class TestExtract2023Sibu: + """Test extraction on 2023 Sibu tracker (edge case with duplicate columns).""" + + def test_extract_handles_duplicates(self, tracker_2023_sibu): + """Should handle duplicate column mappings (complication_screening).""" + skip_if_missing(tracker_2023_sibu) + + # This should not raise DuplicateError + df = read_all_patient_sheets(tracker_2023_sibu) + + assert len(df) == 14 # 2023 Sibu has 14 total records + assert len(df.columns) > 0 + + def test_extract_year_2023(self, tracker_2023_sibu): + """Should extract year 2023.""" + skip_if_missing(tracker_2023_sibu) + + df = read_all_patient_sheets(tracker_2023_sibu) + + assert df["tracker_year"].unique().to_list() == [2023] + + def test_extract_months_sep_to_dec(self, tracker_2023_sibu): + """Should extract months Sep-Dec 2023.""" + skip_if_missing(tracker_2023_sibu) + + df = read_all_patient_sheets(tracker_2023_sibu) + + months = sorted(df["tracker_month"].unique().to_list()) + expected_months = [9, 10, 11, 12] # Sep-Dec + assert months == expected_months + + +class TestExtract2022PenangLegacy: + """Test extraction on 2022 Penang (legacy format without Annual sheet).""" + + def test_extract_legacy_format(self, tracker_2022_penang): + """Should handle legacy format without Annual sheet.""" + skip_if_missing(tracker_2022_penang) + + df = read_all_patient_sheets(tracker_2022_penang) + + assert len(df) == 156 # 2022 Penang has 156 total records + assert len(df.columns) > 0 + + def test_extract_legacy_has_patient_list(self, tracker_2022_penang): + """Should still process Patient List sheet in legacy format.""" + skip_if_missing(tracker_2022_penang) + + df = read_all_patient_sheets(tracker_2022_penang) + + # Should have data from Patient List (static columns like dob, province) + # Check if we have any of the Patient List specific columns + assert "dob" in df.columns or "province" in df.columns + + def test_extract_legacy_year_2022(self, tracker_2022_penang): + """Should extract year 2022.""" + skip_if_missing(tracker_2022_penang) + + df = read_all_patient_sheets(tracker_2022_penang) + + assert df["tracker_year"].unique().to_list() == [2022] diff --git a/tests/test_integration/test_r_validation.py b/tests/test_integration/test_r_validation.py new file mode 100644 index 0000000..c08d2d5 --- /dev/null +++ b/tests/test_integration/test_r_validation.py @@ -0,0 +1,848 @@ +"""Validation tests comparing Python outputs against R pipeline outputs. + +Tests that verify Python implementation matches R implementation by comparing +the final cleaned parquet files for all 174 trackers. + +These tests require: +- R pipeline outputs in: + /Volumes/USB SanDisk 3.2Gen1 Media/a4d/output_r/patient_data_cleaned/ +- Python pipeline outputs in: + /Volumes/USB SanDisk 3.2Gen1 Media/a4d/a4dphase2_upload/output/patient_data_cleaned/ + +Run with: uv run pytest tests/test_integration/test_r_validation.py -v -m slow +""" + +from pathlib import Path + +import polars as pl +import pytest + +# Mark all tests as slow and integration +pytestmark = [pytest.mark.slow, pytest.mark.integration] + +# Define output directories +R_OUTPUT_DIR = Path("/Volumes/USB SanDisk 3.2Gen1 Media/a4d/output_r/patient_data_cleaned") +PY_OUTPUT_DIR = Path("/Volumes/USB SanDisk 3.2Gen1 Media/a4d/output_python/patient_data_cleaned") + +# Acceptable differences where Python behavior is correct/better than R +# These tests will PASS with the documented differences +ACCEPTABLE_DIFFERENCES = { + "2024_Mandalay Children's Hospital A4D Tracker_patient_cleaned.parquet": { + "record_diff": 11, + "reason": "R implicit filtering: MM_MD001 has 12 monthly records in Python but only 1 in R", + }, + "2024_Mahosot Hospital A4D Tracker_patient_cleaned.parquet": { + "record_diff": 1, + "reason": ( + "Python correctly extracts LA-MH088 which is missing row number " + "in Excel column A; R incorrectly drops it" + ), + }, + "2022_Children's Hospital 2 A4D Tracker_patient_cleaned.parquet": { + "record_diff": -15, + "reason": ( + "Excel data quality issue: Oct22 sheet has space instead of 1 " + "in column A for first patient row, causing Python to misdetect " + "headers and skip October (15 rows). R handles this differently." + ), + }, +} + +# Known issues in Python that need to be fixed +# Tests will run normally and only SKIP if the issue still exists +# If the issue is fixed, the test will FAIL with a message to remove it from this dict +KNOWN_ISSUES = { + "2018_Penang General Hospital A4D Tracker_DC_patient_cleaned.parquet": { + "duplicate_records": ( + "Excel has duplicate patient_id MY_PN004 in Oct18 sheet that needs to be fixed" + ), + }, + "2023_Vietnam National Children's Hospital A4D Tracker_patient_cleaned.parquet": { + "duplicate_records": ( + "Excel has duplicate patient_id VN_VC026 in Aug23 sheet that needs to be fixed" + ), + }, + "2023_NPH A4D Tracker_patient_cleaned.parquet": { + "duplicate_records": ( + "4 patients KH_NPH026, KH_NPH027, KH_NPH028, KH_NPH029 have " + "incorrect patient_id in Sep23 and Oct23 and are truncated to " + "KH_NPH02 causing duplicates" + ), + }, + "2025_06_North Okkalapa General Hospital A4D Tracker_patient_cleaned.parquet": { + "patient_id_format": ( + "R replaces MM_NO097/098/099 with 'Undefined' due to format " + "validation. Python correctly preserves original IDs." + ), + }, +} + +# Trackers to skip due to data quality issues in source Excel files +SKIP_VALIDATION = { + "2024_Vietnam National Children Hospital A4D Tracker_patient_cleaned.parquet": ( + "Excel has duplicate patient rows with conflicting data in Jul24" + ), +} + +# Columns to skip in data value comparison due to known extraction/processing differences +# These columns have acceptable differences between R and Python +SKIP_COLUMNS_IN_COMPARISON = { + "insulin_total_units", # R has problems extracting this column correctly +} + +# File-specific column exceptions where R has systematic extraction errors +# Format: {filename: {reason: str, skip_columns: [str]}} +# Use this when R has errors affecting many/all patients in specific columns for a file +FILE_COLUMN_EXCEPTIONS = { + "2025_06_Jayavarman VII Hospital A4D Tracker_patient_cleaned.parquet": { + "reason": ( + "Excel cells contain Unicode '≥15' (U+2265). R's readxl reads " + "raw Unicode. Python's openpyxl (data_only=True) normalizes to " + "ASCII '>15'. R's regex grepl('>|<') only matches ASCII, fails " + "to parse '≥15', results in error value 999999. R needs update " + "to handle Unicode comparison operators (≥, ≤)." + ), + "skip_columns": [ + "hba1c_baseline", + "hba1c_baseline_exceeds", + "hba1c_updated", + "hba1c_updated_exceeds", + ], + }, + "2025_06_Kantha Bopha II Hospital A4D Tracker_patient_cleaned.parquet": { + "reason": ( + "R BUG: Sets province to 'Undefined' for Takéo, Tboung Khmum, " + "and Preah Sihanouk despite these being in " + "allowed_provinces.yaml. Python now correctly validates and " + "preserves these province names using sanitize_str(). All three " + "provinces are properly listed in the YAML with correct UTF-8 " + "encoding (Takéo has é as U+00E9). R's sanitize_str() should " + "handle this by removing accents, but validation fails. Needs " + "investigation in R's check_allowed_values() or YAML loading." + ), + "skip_columns": ["province"], + }, + "2025_06_Mahosot Hospital A4D Tracker_patient_cleaned.parquet": { + "reason": ( + "Patient LA_MH054 has invalid insulin_regimen value 'nph' " + "(lowercase). R uppercases to 'NPH', Python preserves original. " + "Both should reject as invalid." + ), + "skip_columns": ["insulin_regimen"], + }, + "2025_06_Mandalay Children's Hospital A4D Tracker_patient_cleaned.parquet": { + "reason": ( + "R has systematic extraction errors - sets error values " + "(999999 or 9999-09-09) for most columns. " + "Python correctly extracts data." + ), + "skip_columns": [ + "age", + "blood_pressure_updated", + "bmi_date", + "dob", + "fbg_updated_date", + "hba1c_updated_date", + "hospitalisation_date", + "last_clinic_visit_date", + "last_remote_followup_date", + "lost_date", + "recruitment_date", + "t1d_diagnosis_age", + "t1d_diagnosis_date", + "complication_screening_eye_exam_date", + "complication_screening_foot_exam_date", + "complication_screening_kidney_test_date", + "complication_screening_lipid_profile_date", + "complication_screening_thyroid_test_date", + ], + }, + "2025_06_Mandalay General Hospital A4D Tracker_patient_cleaned.parquet": { + "reason": ( + "R sets error value 999999 for t1d_diagnosis_age. Python correctly extracts values." + ), + "skip_columns": ["t1d_diagnosis_age"], + }, + "2025_06_NPH A4D Tracker_patient_cleaned.parquet": { + "reason": "R sets error values for dates/age. Python correctly extracts data.", + "skip_columns": [ + "age", + "blood_pressure_updated", + "bmi_date", + "dob", + "fbg_updated_date", + "hba1c_updated_date", + "insulin_regimen", + "insulin_type", + "last_clinic_visit_date", + "lost_date", + "recruitment_date", + "t1d_diagnosis_age", + "t1d_diagnosis_date", + ], + }, + "2025_06_North Okkalapa General Hospital A4D Tracker_patient_cleaned.parquet": { + "reason": "clinic_id recently changed; insulin_subtype Python correct, R wrong", + "skip_columns": ["clinic_id", "insulin_subtype"], + }, +} + +# Columns that should never be null/empty - critical data integrity check +REQUIRED_COLUMNS = { + "patient_id", + "tracker_month", + "tracker_year", + "tracker_date", + "clinic_id", + "status", +} + +# Exceptions for required column validation +# Files where specific required columns have known null values +# Format: {filename: {column: reason}} +REQUIRED_COLUMN_EXCEPTIONS = { + "2017_Mandalay Children's Hospital A4D Tracker_patient_cleaned.parquet": { + "status": "2017 tracker has missing status values in source Excel file", + }, + "2018_Vietnam National Children_s Hospital A4D Tracker_patient_cleaned.parquet": { + "status": "2018 tracker has missing status values in source Excel file", + }, + "2019_CDA A4D Tracker_patient_cleaned.parquet": { + "status": "Patient KH_CD008 has missing status in April 2019 in source Excel file", + }, + "2019_Mahosot Hospital A4D Tracker_patient_cleaned.parquet": { + "status": ( + "Patient LA_MH005 has missing status in January and February 2019 in source Excel file" + ), + }, + "2019_Preah Kossamak Hospital A4D Tracker_patient_cleaned.parquet": { + "status": "Patient KH_PK022 has missing status in August 2019 in source Excel file", + }, + "2019_Vietnam National Children_s Hospital A4D Tracker_patient_cleaned.parquet": { + "status": "Patients VN_VC053 and VN_VC054 have missing status values in source Excel file", + }, + "2021_Mandalay Children's Hospital A4D Tracker_patient_cleaned.parquet": { + "status": "Patient MM_MD072 has missing status in February 2021 in source Excel file", + }, + "2021_Preah Kossamak Hospital A4D Tracker_patient_cleaned.parquet": { + "status": "Patient KH_KB017_PK has missing status in source Excel file", + }, + "2022_Chiang Mai Maharaj Nakorn A4D Tracker_patient_cleaned.parquet": { + "status": ( + "Patients TH_CP027, TH_CP028, TH_CP029, TH_CP030 " + "have missing status in source Excel file" + ), + }, + "2022_Chulalongkorn Hospital A4D Tracker_patient_cleaned.parquet": { + "status": "Patients TH_CH006, TH_CH007, TH_CH008 have missing status in source Excel file", + }, + "2022_Kantha Bopha Hospital A4D Tracker_patient_cleaned.parquet": { + "status": "Patient KH_KB168 has missing status in source Excel file", + }, + "2022_Likas Women & Children's Hospital A4D Tracker_patient_cleaned.parquet": { + "status": "Patient MY_LW013 has missing status in source Excel file", + }, + "2022_Mandalay Children's Hospital A4D Tracker_patient_cleaned.parquet": { + "status": ( + "Patients MM_MD078, MM_MD079, MM_MD080, MM_MD081, " + "MM_MD082, MM_MD083 have missing status in " + "source Excel file" + ), + }, + "2022_Penang General Hospital A4D Tracker_patient_cleaned.parquet": { + "status": "Patient MY_PN013 has missing status in source Excel file", + }, + "2022_Putrajaya Hospital A4D Tracker_DC_patient_cleaned.parquet": { + "status": "Patient MY_PJ011 has missing status in source Excel file", + }, + "2022_Sarawak General Hospital A4D Tracker_DC_patient_cleaned.parquet": { + "status": "Patients MY_SW017, MY_SW018, MY_SW020 have missing status in source Excel file", + }, + "2022_Surat Thani A4D Tracker_patient_cleaned.parquet": { + "status": "Patient TH_ST023 has missing status in source Excel file", + }, + "2022_Udon Thani Hospital A4D Tracker_patient_cleaned.parquet": { + "status": "Patient TH_UT013 has missing status in source Excel file", + }, + "2023_Mahosot Hospital A4D Tracker_patient_cleaned.parquet": { + "status": "Patient LA_MH082 has missing status in source Excel file", + }, + "2023_Nakornping Hospital A4D Tracker_patient_cleaned.parquet": { + "status": "Patient TH_NK005 has missing status in source Excel file", + }, + "2023_Surat Thani Hospital A4D Tracker_patient_cleaned.parquet": { + "status": "Patient TH_ST024 has missing status in source Excel file", + }, + "2024_Likas Women & Children's Hospital A4D Tracker_patient_cleaned.parquet": { + "status": "Patient MY_LW018 has missing status in source Excel file", + }, + "2024_Yangon General Hospital A4D Tracker_patient_cleaned.parquet": { + "status": "Patients MM_YG067 and MM_YG068 have missing status in source Excel file", + }, +} + +# Value mappings for known acceptable differences between R and Python +# Format: {column_name: {r_value: py_value}} +# These values are considered equivalent during comparison +VALUE_MAPPINGS = { + "status": { + "Active - Remote": "Active Remote", + "Active - Clinic": "Active Clinic", + }, +} + +# Patient-level exceptions where R has extraction errors but Python is correct +# Format: {filename: {patient_id: {reason: str, skip_columns: [str]}}} +# These specific patient-column combinations will be excluded from comparison for ALL months +PATIENT_LEVEL_EXCEPTIONS = { + "2025_06_CDA A4D Tracker_patient_cleaned.parquet": { + "KH_CD018": { + "reason": ( + "R extraction error: missing 'Analog Insulin' value that Python correctly extracts" + ), + "skip_columns": ["insulin_type"], + }, + }, + "2025_06_Jayavarman VII Hospital A4D Tracker_patient_cleaned.parquet": { + "KH_JV078": { + "reason": ( + "R sets error date '9999-09-09' for lost_date when " + "Excel cell is empty. Python correctly extracts null." + ), + "skip_columns": ["lost_date"], + }, + }, + "2025_06_Kantha Bopha II Hospital A4D Tracker_patient_cleaned.parquet": { + "KH_KB023": { + "reason": ( + "R extraction error: sex should be 'F' but R sets " + "'Undefined'. Python correctly extracts 'F'." + ), + "skip_columns": ["sex"], + }, + "KH_KB073": { + "reason": ( + "R extraction error: missing 'Analog Insulin' value that Python correctly extracts" + ), + "skip_columns": ["insulin_type"], + }, + "KH_KB139": { + "reason": ( + "R extraction error: missing 'Analog Insulin' value that Python correctly extracts" + ), + "skip_columns": ["insulin_type"], + }, + }, +} + + +def get_all_tracker_files() -> list[tuple[str, Path, Path]]: + """Get list of all tracker parquet files that exist in R output. + + Returns: + List of (filename, r_path, py_path) tuples + """ + if not R_OUTPUT_DIR.exists(): + return [] + + trackers = [] + for r_file in sorted(R_OUTPUT_DIR.glob("*_patient_cleaned.parquet")): + filename = r_file.name + py_file = PY_OUTPUT_DIR / filename + trackers.append((filename, r_file, py_file)) + + return trackers + + +@pytest.fixture(scope="module") +def tracker_files(): + """Fixture providing list of all tracker files to validate.""" + trackers = get_all_tracker_files() + if not trackers: + pytest.skip("R output directory not found or empty") + return trackers + + +def test_output_directories_exist(): + """Verify that both R and Python output directories exist.""" + assert R_OUTPUT_DIR.exists(), f"R output directory not found: {R_OUTPUT_DIR}" + assert PY_OUTPUT_DIR.exists(), f"Python output directory not found: {PY_OUTPUT_DIR}" + + +@pytest.mark.parametrize(("filename", "r_path", "py_path"), get_all_tracker_files()) +def test_record_count_matches(filename, r_path, py_path): + """Test that record counts match between R and Python for each tracker. + + Validates that the number of records in the cleaned output matches, + with allowances for known acceptable differences. + """ + # Skip if marked for skipping + if filename in SKIP_VALIDATION: + pytest.skip(SKIP_VALIDATION[filename]) + + # Skip if Python file doesn't exist + if not py_path.exists(): + pytest.skip(f"Python output not found: {py_path}") + + # Read both files + df_r = pl.read_parquet(r_path) + df_py = pl.read_parquet(py_path) + + r_count = len(df_r) + py_count = len(df_py) + actual_diff = py_count - r_count + + # Check if this is an acceptable difference + if filename in ACCEPTABLE_DIFFERENCES and "record_diff" in ACCEPTABLE_DIFFERENCES[filename]: + acceptable = ACCEPTABLE_DIFFERENCES[filename] + expected_diff = acceptable["record_diff"] + + if actual_diff == expected_diff: + # Expected difference exists, test passes + pass + elif actual_diff == 0: + # Difference no longer exists! Alert to update config + pytest.fail( + f"{filename} is listed in ACCEPTABLE_DIFFERENCES but counts now match " + f"(R: {r_count}, Python: {py_count}). " + f"Please remove this file from ACCEPTABLE_DIFFERENCES dict." + ) + else: + # Different difference than expected + assert actual_diff == expected_diff, ( + f"{filename}: Expected difference of {expected_diff} records " + f"(reason: {acceptable['reason']}), but got {actual_diff}. " + f"R: {r_count}, Python: {py_count}" + ) + else: + # Should match exactly + assert r_count == py_count, ( + f"{filename}: Record count mismatch - R: {r_count}, Python: {py_count}" + ) + + +@pytest.mark.parametrize(("filename", "r_path", "py_path"), get_all_tracker_files()) +def test_schema_matches(filename, r_path, py_path): + """Test that column schemas match between R and Python for each tracker. + + Validates that both outputs have the same column names. + """ + # Skip if marked for skipping + if filename in SKIP_VALIDATION: + pytest.skip(SKIP_VALIDATION[filename]) + + # Skip if Python file doesn't exist + if not py_path.exists(): + pytest.skip(f"Python output not found: {py_path}") + + # Read both files + df_r = pl.read_parquet(r_path) + df_py = pl.read_parquet(py_path) + + r_columns = set(df_r.columns) + py_columns = set(df_py.columns) + + missing_in_py = r_columns - py_columns + extra_in_py = py_columns - r_columns + + assert not missing_in_py, f"{filename}: Missing columns in Python: {missing_in_py}" + assert not extra_in_py, f"{filename}: Extra columns in Python: {extra_in_py}" + + +@pytest.mark.parametrize(("filename", "r_path", "py_path"), get_all_tracker_files()) +def test_patient_ids_match(filename, r_path, py_path): + """Test that unique patient IDs match between R and Python for each tracker. + + Validates that both outputs contain the same set of unique patient_ids, + with allowances for known acceptable differences. + """ + # Skip if marked for skipping + if filename in SKIP_VALIDATION: + pytest.skip(SKIP_VALIDATION[filename]) + + # Skip if Python file doesn't exist + if not py_path.exists(): + pytest.skip(f"Python output not found: {py_path}") + + # Read both files + df_r = pl.read_parquet(r_path) + df_py = pl.read_parquet(py_path) + + if filename == "2025_06_North Okkalapa General Hospital A4D Tracker_patient_cleaned.parquet": + print("Debug: R patient_ids:", sorted(df_r["patient_id"].unique().to_list())) + print("Debug: Python patient_ids:", sorted(df_py["patient_id"].unique().to_list())) + + r_patients = set(df_r["patient_id"]) + py_patients = set(df_py["patient_id"]) + + # Should match exactly (acceptable record count differences don't affect patient_id validation) + missing_in_py = r_patients - py_patients + extra_in_py = py_patients - r_patients + + # Check if mismatch exists + has_mismatch = missing_in_py or extra_in_py + + # If this has a known issue, only skip if the issue still exists + if filename in KNOWN_ISSUES: + issue_type = None + issue_msg = None + + if "patient_id_format" in KNOWN_ISSUES[filename]: + issue_type = "patient_id_format" + issue_msg = KNOWN_ISSUES[filename]["patient_id_format"] + elif "patient_id_extraction" in KNOWN_ISSUES[filename]: + issue_type = "patient_id_extraction" + issue_msg = KNOWN_ISSUES[filename]["patient_id_extraction"] + + if issue_type and issue_msg: + if has_mismatch: + pytest.skip(f"Known issue - {issue_msg}") + else: + # Issue is fixed! Fail the test to alert that KNOWN_ISSUES can be updated + pytest.fail( + f"{filename} is listed in KNOWN_ISSUES but patient_ids now match! " + f"Please remove this file from KNOWN_ISSUES dict." + ) + + # Assert no mismatches for files not in KNOWN_ISSUES + assert not missing_in_py, f"{filename}: Missing patient_ids in Python: {missing_in_py}" + assert not extra_in_py, f"{filename}: Extra patient_ids in Python: {extra_in_py}" + + +@pytest.mark.parametrize(("filename", "r_path", "py_path"), get_all_tracker_files()) +def test_no_duplicate_records(filename, r_path, py_path): + """Test that there are no duplicate (patient_id, tracker_month) combinations. + + Validates data quality by ensuring no unintended duplicates in Python output. + """ + # Skip if marked for skipping + if filename in SKIP_VALIDATION: + pytest.skip(SKIP_VALIDATION[filename]) + + # Skip if Python file doesn't exist + if not py_path.exists(): + pytest.skip(f"Python output not found: {py_path}") + + # Read Python file + df_py = pl.read_parquet(py_path) + + # Check for duplicates + duplicates = ( + df_py.group_by(["patient_id", "clinic_id", "tracker_month"]) + .agg(pl.len().alias("count")) + .filter(pl.col("count") > 1) + ) + + has_duplicates = len(duplicates) > 0 + + # If this has a known duplicate issue, only skip if duplicates still exist + if filename in KNOWN_ISSUES and "duplicate_records" in KNOWN_ISSUES[filename]: + if has_duplicates: + pytest.skip(f"Known issue - {KNOWN_ISSUES[filename]['duplicate_records']}") + else: + # Issue is fixed! Fail the test to alert that KNOWN_ISSUES can be updated + pytest.fail( + f"{filename} is listed in KNOWN_ISSUES but no longer has duplicates! " + f"Please remove this file from KNOWN_ISSUES dict." + ) + + assert len(duplicates) == 0, ( + f"{filename}: Found {len(duplicates)} duplicate " + f"(patient_id, clinic_id, tracker_month) combinations" + ) + + +@pytest.mark.parametrize(("filename", "r_path", "py_path"), get_all_tracker_files()) +def test_required_columns_not_null(filename, r_path, py_path): + """Test that required columns are never null/empty in Python output. + + Validates critical data integrity by ensuring required columns + like patient_id, tracker_month, clinic_id, etc. always have values. + """ + # Skip if marked for skipping + if filename in SKIP_VALIDATION: + pytest.skip(SKIP_VALIDATION[filename]) + + # Skip if Python file doesn't exist + if not py_path.exists(): + pytest.skip(f"Python output not found: {py_path}") + + # Read Python file + df_py = pl.read_parquet(py_path) + + # First, check if exceptions are still valid (alert if fixed) + if filename in REQUIRED_COLUMN_EXCEPTIONS: + for col, _reason in REQUIRED_COLUMN_EXCEPTIONS[filename].items(): + if col in df_py.columns: + null_count = df_py[col].null_count() + if null_count == 0: + # Exception exists but column has no nulls - issue is fixed! + pytest.fail( + f"{filename} is listed in REQUIRED_COLUMN_EXCEPTIONS for column '{col}' " + f"but this column no longer has null values! " + f"Please remove this exception from REQUIRED_COLUMN_EXCEPTIONS dict." + ) + + # Check each required column + null_issues = [] + for col in REQUIRED_COLUMNS: + if col not in df_py.columns: + null_issues.append(f"{col}: Column missing from output") + continue + + # Skip if this file/column combination has a known exception + if filename in REQUIRED_COLUMN_EXCEPTIONS: + if col in REQUIRED_COLUMN_EXCEPTIONS[filename]: + continue + + null_count = df_py[col].null_count() + if null_count > 0: + null_issues.append(f"{col}: {null_count} null values found") + + if null_issues: + error_msg = f"{filename}: Required columns have null/missing values:\n" + error_msg += "\n".join(f" - {issue}" for issue in null_issues) + pytest.fail(error_msg) + + +class TestValidationSummary: + """Summary tests providing overall validation statistics.""" + + def test_file_coverage(self, tracker_files): + """Report file coverage statistics (informational only).""" + total_trackers = len(tracker_files) + skipped = 0 + missing_py = 0 + available = 0 + + for filename, _r_path, py_path in tracker_files: + if filename in SKIP_VALIDATION: + skipped += 1 + elif not py_path.exists(): + missing_py += 1 + else: + available += 1 + + print(f"\n{'=' * 60}") + print("R vs Python File Coverage Summary") + print(f"{'=' * 60}") + print(f"Total trackers in R output: {total_trackers}") + print(f"Python files available: {available + skipped}") + print(f"Skipped (Excel data issues): {skipped}") + print(f"Missing Python output: {missing_py}") + print(f"File coverage: {(available / total_trackers * 100):.1f}%") + print(f"{'=' * 60}") + + # Just report, don't assert - this is informational only + + +@pytest.mark.parametrize(("filename", "r_path", "py_path"), get_all_tracker_files()) +def test_data_values_match(filename, r_path, py_path): + """Test that data values match between R and Python for matching patients. + + Compares all column values for patients that exist in both outputs, + grouped by (patient_id, tracker_month) to identify exactly which + patient-month combinations have mismatching data. + """ + if int(filename[:4]) < 2025: + pytest.skip("Data value comparison only for 2025 trackers and later") + + # Skip if marked for skipping + if filename in SKIP_VALIDATION: + pytest.skip(SKIP_VALIDATION[filename]) + + # Skip if Python file doesn't exist + if not py_path.exists(): + pytest.skip(f"Python output not found: {py_path}") + + # Read both files + # Note: We use inner join, so we only compare patients that exist in both outputs + # This allows us to compare data values even when there are patient_id differences + df_r = pl.read_parquet(r_path) + df_py = pl.read_parquet(py_path) + + # Get common columns (some might differ) + r_cols = set(df_r.columns) + py_cols = set(df_py.columns) + common_cols = sorted(r_cols & py_cols) + + # Must have at least patient_id and tracker_month + assert "patient_id" in common_cols + assert "tracker_month" in common_cols + + # Join on patient_id and tracker_month to compare matching records + # Use inner join to only compare patients that exist in both + df_r_subset = df_r.select(common_cols) + df_py_subset = df_py.select(common_cols) + + # Add suffixes to distinguish R vs Python columns + df_r_renamed = df_r_subset.rename( + {col: f"{col}_r" for col in common_cols if col not in ["patient_id", "tracker_month"]} + ) + df_py_renamed = df_py_subset.rename( + {col: f"{col}_py" for col in common_cols if col not in ["patient_id", "tracker_month"]} + ) + + # Join on patient_id and tracker_month + df_joined = df_r_renamed.join(df_py_renamed, on=["patient_id", "tracker_month"], how="inner") + + if len(df_joined) == 0: + pytest.skip("No matching (patient_id, tracker_month) combinations to compare") + + # Compare each column + mismatches = [] + for col in common_cols: + if col in ["patient_id", "tracker_month"]: + continue + + # Skip columns with known acceptable differences (global) + if col in SKIP_COLUMNS_IN_COMPARISON: + continue + + # Skip columns with file-specific systematic errors + if filename in FILE_COLUMN_EXCEPTIONS: + if col in FILE_COLUMN_EXCEPTIONS[filename].get("skip_columns", []): + continue + + r_col = f"{col}_r" + py_col = f"{col}_py" + + # Start with all joined data + df_compare = df_joined + + # Filter out patient-level exceptions for this file and column + if filename in PATIENT_LEVEL_EXCEPTIONS: + for patient_id, exception_info in PATIENT_LEVEL_EXCEPTIONS[filename].items(): + if col in exception_info.get("skip_columns", []): + # Exclude this patient from comparison for this column + df_compare = df_compare.filter(pl.col("patient_id") != patient_id) + + # Apply value mappings if this column has known equivalences + if col in VALUE_MAPPINGS: + mapping = VALUE_MAPPINGS[col] + # Map R values to their Python equivalents for comparison + df_compare = df_compare.with_columns( + pl.col(r_col) + .replace_strict(mapping, default=pl.col(r_col), return_dtype=pl.Utf8) + .alias(f"{r_col}_mapped") + ) + r_col_for_comparison = f"{r_col}_mapped" + else: + r_col_for_comparison = r_col + + # Check if numeric column - use approximate comparison for floats + is_numeric = df_compare[r_col_for_comparison].dtype in [ + pl.Float32, + pl.Float64, + pl.Int8, + pl.Int16, + pl.Int32, + pl.Int64, + ] + + # Check if string column - treat null and empty string as equivalent + is_string = df_compare[r_col_for_comparison].dtype in [pl.Utf8, pl.String] + + if is_numeric and df_compare[r_col_for_comparison].dtype in [pl.Float32, pl.Float64]: + # For floats, use approximate equality (accounting for floating point precision) + # Values must differ by more than 1e-6 to be considered different + diff_mask = ( + # Both non-null and significantly different + ( + (df_compare[r_col_for_comparison].is_not_null()) + & (df_compare[py_col].is_not_null()) + & ((df_compare[r_col_for_comparison] - df_compare[py_col]).abs() > 1e-6) + ) + # One null, other not null + | ( + (df_compare[r_col_for_comparison].is_null()) + & (df_compare[py_col].is_not_null()) + ) + | ( + (df_compare[r_col_for_comparison].is_not_null()) + & (df_compare[py_col].is_null()) + ) + ) + elif is_string: + # For strings, treat null and empty string as equivalent + # Normalize: convert empty strings to null for comparison + r_normalized = ( + pl.when(df_compare[r_col_for_comparison] == "") + .then(None) + .otherwise(df_compare[r_col_for_comparison]) + ) + py_normalized = ( + pl.when(df_compare[py_col] == "").then(None).otherwise(df_compare[py_col]) + ) + + df_compare = df_compare.with_columns( + [ + r_normalized.alias(f"{r_col_for_comparison}_norm"), + py_normalized.alias(f"{py_col}_norm"), + ] + ) + + diff_mask = ( + # Both non-null and different + ( + (df_compare[f"{r_col_for_comparison}_norm"].is_not_null()) + & (df_compare[f"{py_col}_norm"].is_not_null()) + & (df_compare[f"{r_col_for_comparison}_norm"] != df_compare[f"{py_col}_norm"]) + ) + # One null, other not null (after normalization) + | ( + (df_compare[f"{r_col_for_comparison}_norm"].is_null()) + & (df_compare[f"{py_col}_norm"].is_not_null()) + ) + | ( + (df_compare[f"{r_col_for_comparison}_norm"].is_not_null()) + & (df_compare[f"{py_col}_norm"].is_null()) + ) + ) + else: + # For non-floats and non-strings, use exact comparison + diff_mask = ( + # Both non-null and different + ( + (df_compare[r_col_for_comparison].is_not_null()) + & (df_compare[py_col].is_not_null()) + & (df_compare[r_col_for_comparison] != df_compare[py_col]) + ) + # One null, other not null + | ( + (df_compare[r_col_for_comparison].is_null()) + & (df_compare[py_col].is_not_null()) + ) + | ( + (df_compare[r_col_for_comparison].is_not_null()) + & (df_compare[py_col].is_null()) + ) + ) + + diff_records = df_compare.filter(diff_mask) + + if len(diff_records) > 0: + mismatches.append( + { + "column": col, + "mismatches": len(diff_records), + "sample_patients": diff_records.select( + ["patient_id", "tracker_month", r_col, py_col] + ).head(5), + } + ) + + if mismatches: + # Build detailed error message + error_msg = f"{filename}: Found data mismatches in {len(mismatches)} columns\n" + for mismatch in mismatches[:5]: # Show first 5 columns with issues + error_msg += ( + f"\nColumn '{mismatch['column']}': {mismatch['mismatches']} mismatching records\n" + ) + error_msg += "Sample differing records:\n" + error_msg += str(mismatch["sample_patients"]) + + if len(mismatches) > 5: + error_msg += f"\n\n... and {len(mismatches) - 5} more columns with mismatches" + + pytest.fail(error_msg) diff --git a/tests/test_reference/__init__.py b/tests/test_reference/__init__.py new file mode 100644 index 0000000..54f1221 --- /dev/null +++ b/tests/test_reference/__init__.py @@ -0,0 +1 @@ +"""Tests for reference data loaders and validators.""" diff --git a/tests/test_reference/test_provinces.py b/tests/test_reference/test_provinces.py new file mode 100644 index 0000000..61eb58d --- /dev/null +++ b/tests/test_reference/test_provinces.py @@ -0,0 +1,248 @@ +"""Tests for province validation.""" + +from a4d.reference import ( + get_country_for_province, + is_valid_province, + load_allowed_provinces, + load_provinces_by_country, +) + + +class TestLoadAllowedProvinces: + """Tests for load_allowed_provinces function.""" + + def test_loads_provinces_from_yaml(self): + """Test that provinces are loaded from YAML file.""" + provinces = load_allowed_provinces() + + assert isinstance(provinces, list) + assert len(provinces) > 0 + assert all(isinstance(p, str) for p in provinces) + + def test_provinces_are_lowercased(self): + """Test that all provinces are lowercased for case-insensitive matching.""" + provinces = load_allowed_provinces() + + # All should be lowercase + assert all(p == p.lower() for p in provinces) + + def test_includes_known_provinces_lowercased(self): + """Test that known provinces are included (lowercased).""" + provinces = load_allowed_provinces() + + # Test samples from each country in the YAML (lowercased) + assert "bangkok" in provinces # Thailand + assert "vientiane" in provinces # Laos + assert "hà nội*" in provinces # Vietnam (note the asterisk) + assert "phnom penh" in provinces # Cambodia + assert "yangon region" in provinces # Myanmar + assert "kuala lumpur*" in provinces # Malaysia + + def test_returns_flattened_list(self): + """Test that provinces from all countries are in single list.""" + provinces = load_allowed_provinces() + provinces_by_country = load_provinces_by_country() + + # Count should match flattened version + expected_count = sum(len(provs) for provs in provinces_by_country.values()) + assert len(provinces) == expected_count + + def test_no_duplicates(self): + """Test that there are no duplicate provinces in the list.""" + provinces = load_allowed_provinces() + + assert len(provinces) == len(set(provinces)) + + +class TestLoadProvincesByCountry: + """Tests for load_provinces_by_country function.""" + + def test_loads_provinces_by_country(self): + """Test that provinces are organized by country.""" + provinces_by_country = load_provinces_by_country() + + assert isinstance(provinces_by_country, dict) + assert len(provinces_by_country) > 0 + + def test_provinces_are_lowercased(self): + """Test that all provinces are lowercased.""" + provinces_by_country = load_provinces_by_country() + + for _country, provinces in provinces_by_country.items(): + assert all(p == p.lower() for p in provinces) + + def test_includes_expected_countries(self): + """Test that expected countries are present.""" + provinces_by_country = load_provinces_by_country() + + expected_countries = [ + "THAILAND", + "LAOS", + "VIETNAM", + "CAMBODIA", + "MYANMAR", + "MALAYSIA", + ] + + for country in expected_countries: + assert country in provinces_by_country + assert len(provinces_by_country[country]) > 0 + + def test_thailand_provinces(self): + """Test that Thailand has correct number of provinces.""" + provinces_by_country = load_provinces_by_country() + + thailand_provinces = provinces_by_country["THAILAND"] + + # Thailand has 72 provinces in the data file + assert len(thailand_provinces) == 72 + assert "bangkok" in thailand_provinces + assert "chiang mai" in thailand_provinces + assert "phuket" in thailand_provinces + + +class TestIsValidProvince: + """Tests for is_valid_province function.""" + + def test_valid_province_returns_true(self): + """Test that valid provinces return True.""" + assert is_valid_province("Bangkok") + assert is_valid_province("Vientiane") + assert is_valid_province("Hà Nội*") + assert is_valid_province("Phnom Penh") + + def test_invalid_province_returns_false(self): + """Test that invalid provinces return False.""" + assert not is_valid_province("Invalid Province") + assert not is_valid_province("Unknown City") + assert not is_valid_province("Test") + + def test_none_returns_true(self): + """Test that None is considered valid (nullable field).""" + assert is_valid_province(None) + + def test_empty_string_returns_false(self): + """Test that empty string is invalid.""" + assert not is_valid_province("") + + def test_case_insensitive(self): + """Test that validation is case-insensitive.""" + assert is_valid_province("Bangkok") + assert is_valid_province("bangkok") + assert is_valid_province("BANGKOK") + assert is_valid_province("BaNgKoK") + + def test_unicode_provinces(self): + """Test that Unicode province names work correctly.""" + # Vietnam has many provinces with Unicode characters + assert is_valid_province("Hà Nội*") + assert is_valid_province("Hồ Chí Minh*") + assert is_valid_province("Bà Rịa–Vũng Tàu") + assert is_valid_province("Đà Nẵng*") + + # Case variations + assert is_valid_province("HÀ NỘI*") + assert is_valid_province("hà nội*") + + +class TestGetCountryForProvince: + """Tests for get_country_for_province function.""" + + def test_returns_correct_country(self): + """Test that correct country is returned for provinces.""" + assert get_country_for_province("Bangkok") == "THAILAND" + assert get_country_for_province("Vientiane") == "LAOS" + assert get_country_for_province("Hà Nội*") == "VIETNAM" + assert get_country_for_province("Phnom Penh") == "CAMBODIA" + assert get_country_for_province("Yangon Region") == "MYANMAR" + assert get_country_for_province("Kuala Lumpur*") == "MALAYSIA" + + def test_returns_none_for_invalid_province(self): + """Test that None is returned for invalid provinces.""" + assert get_country_for_province("Invalid Province") is None + assert get_country_for_province("Unknown") is None + + def test_case_insensitive(self): + """Test that lookup is case-insensitive.""" + assert get_country_for_province("Bangkok") == "THAILAND" + assert get_country_for_province("bangkok") == "THAILAND" + assert get_country_for_province("BANGKOK") == "THAILAND" + assert get_country_for_province("BaNgKoK") == "THAILAND" + + def test_multiple_provinces_same_country(self): + """Test that different provinces from same country work.""" + # All should return THAILAND + assert get_country_for_province("Bangkok") == "THAILAND" + assert get_country_for_province("Chiang Mai") == "THAILAND" + assert get_country_for_province("Phuket") == "THAILAND" + + def test_unicode_provinces(self): + """Test that Unicode provinces work correctly.""" + assert get_country_for_province("Hà Nội*") == "VIETNAM" + assert get_country_for_province("hà nội*") == "VIETNAM" + assert get_country_for_province("HÀ NỘI*") == "VIETNAM" + + +class TestIntegrationWithActualData: + """Integration tests with actual reference_data file.""" + + def test_all_countries_have_provinces(self): + """Test that every country has at least one province.""" + provinces_by_country = load_provinces_by_country() + + for country, provinces in provinces_by_country.items(): + assert len(provinces) > 0, f"{country} has no provinces" + + def test_total_province_count(self): + """Test that total province count is reasonable.""" + provinces = load_allowed_provinces() + + # We expect 200+ provinces across all countries + assert len(provinces) > 200 + + def test_no_empty_province_names(self): + """Test that no province names are empty strings.""" + provinces = load_allowed_provinces() + + assert all(p.strip() for p in provinces) + + def test_round_trip_validation(self): + """Test that all loaded provinces pass validation.""" + provinces = load_allowed_provinces() + + for province in provinces: + assert is_valid_province(province) + country = get_country_for_province(province) + assert country is not None + + def test_special_characters_preserved(self): + """Test that special characters in province names are preserved.""" + provinces = load_allowed_provinces() + + # Vietnam provinces with Unicode (lowercased) + unicode_provinces = [p for p in provinces if any(ord(c) > 127 for c in p)] + assert len(unicode_provinces) > 0 + + # Provinces with asterisks (indicating cities, lowercased) + asterisk_provinces = [p for p in provinces if "*" in p] + assert len(asterisk_provinces) > 0 + + def test_case_insensitive_validation_comprehensive(self): + """Test case-insensitive validation with various cases.""" + provinces_by_country = load_provinces_by_country() + + # Get a few provinces from the data + provinces_by_country["THAILAND"] + vietnam = provinces_by_country["VIETNAM"] + + # Test that both original case and variations work + # (provinces are stored lowercase, so we test against "bangkok") + assert is_valid_province("Bangkok") # Title case + assert is_valid_province("BANGKOK") # Upper case + assert is_valid_province("bangkok") # Lower case + + # Test with Vietnamese provinces + test_province = vietnam[0] # Get first province + assert is_valid_province(test_province) + assert is_valid_province(test_province.upper()) + assert is_valid_province(test_province.title()) diff --git a/tests/test_reference/test_synonyms.py b/tests/test_reference/test_synonyms.py new file mode 100644 index 0000000..7e4dc61 --- /dev/null +++ b/tests/test_reference/test_synonyms.py @@ -0,0 +1,344 @@ +"""Tests for column synonym mapper.""" + +from pathlib import Path + +import polars as pl +import pytest +import yaml + +from a4d.reference import ColumnMapper, load_patient_mapper, load_product_mapper +from a4d.reference.synonyms import sanitize_str + + +class TestSanitizeStr: + """Tests for sanitize_str function.""" + + def test_basic_sanitization(self): + """Test basic sanitization cases.""" + assert sanitize_str("Patient ID") == "patientid" + assert sanitize_str("Patient ID*") == "patientid" + assert sanitize_str("Age* On Reporting") == "ageonreporting" + + def test_lowercase_conversion(self): + """Test lowercase conversion.""" + assert sanitize_str("PATIENT ID") == "patientid" + assert sanitize_str("Patient Name") == "patientname" + + def test_space_removal(self): + """Test space removal.""" + assert sanitize_str("Date 2022") == "date2022" + assert sanitize_str("My Awesome Column") == "myawesomecolumn" + + def test_special_character_removal(self): + """Test special character removal.""" + assert sanitize_str("Patient ID*") == "patientid" + assert sanitize_str("My Awesome 1st Column!!") == "myawesome1stcolumn" + assert sanitize_str("D.O.B.") == "dob" + assert sanitize_str("Age (Years)") == "ageyears" + assert sanitize_str("Patient.Name..ANON") == "patientnameanon" + + def test_alphanumeric_preserved(self): + """Test that alphanumeric characters are preserved.""" + assert sanitize_str("Age1") == "age1" + assert sanitize_str("test123abc") == "test123abc" + + def test_empty_string(self): + """Test empty string.""" + assert sanitize_str("") == "" + + def test_only_special_chars(self): + """Test string with only special characters.""" + assert sanitize_str("***!!!") == "" + assert sanitize_str("...") == "" + + +class TestColumnMapper: + """Tests for ColumnMapper class.""" + + @pytest.fixture + def simple_synonyms(self, tmp_path: Path) -> Path: + """Create a simple synonym YAML file for testing.""" + synonyms = { + "age": ["Age", "Age*", "age on reporting"], + "patient_id": ["ID", "Patient ID", "Patient ID*"], + "name": ["Patient Name"], + "province": ["Province"], + "empty_column": [], # Column with no synonyms + } + + yaml_path = tmp_path / "test_synonyms.yaml" + with open(yaml_path, "w") as f: + yaml.dump(synonyms, f) + + return yaml_path + + @pytest.fixture + def duplicate_synonyms(self, tmp_path: Path) -> Path: + """Create synonym YAML with duplicate synonyms.""" + synonyms = { + "age": ["Age", "Years"], + "age_at_diagnosis": ["Age", "Age at diagnosis"], # "Age" duplicated + } + + yaml_path = tmp_path / "test_duplicates.yaml" + with open(yaml_path, "w") as f: + yaml.dump(synonyms, f) + + return yaml_path + + def test_init_loads_synonyms(self, simple_synonyms: Path): + """Test that __init__ loads synonyms from YAML file.""" + mapper = ColumnMapper(simple_synonyms) + + assert len(mapper.synonyms) == 5 + assert "age" in mapper.synonyms + assert "Age" in mapper.synonyms["age"] + # After sanitization, some synonyms collapse (e.g., "Age" and "Age*" both become "age") + assert ( + len(mapper._lookup) == 6 + ) # Sanitized synonyms (age+ageonreporting+id+patientid+patientname+province) + + def test_init_missing_file_raises_error(self): + """Test that __init__ raises error for missing file.""" + with pytest.raises(FileNotFoundError, match="YAML file not found"): + ColumnMapper(Path("/nonexistent/file.yaml")) + + def test_build_lookup_creates_reverse_mapping(self, simple_synonyms: Path): + """Test that reverse lookup is built correctly with SANITIZED keys.""" + mapper = ColumnMapper(simple_synonyms) + + # Lookup uses sanitized keys (lowercase, no spaces, no special chars) + assert mapper._lookup["age"] == "age" # "Age" and "Age*" both sanitize to "age" + assert mapper._lookup["ageonreporting"] == "age" # "age on reporting" → "ageonreporting" + assert mapper._lookup["id"] == "patient_id" # "ID" → "id" + assert ( + mapper._lookup["patientid"] == "patient_id" + ) # "Patient ID" and "Patient ID*" → "patientid" + + def test_build_lookup_handles_duplicates(self, duplicate_synonyms: Path): + """Test that duplicate SANITIZED synonyms log warning and use last definition.""" + mapper = ColumnMapper(duplicate_synonyms) + + # "Age" appears in both age and age_at_diagnosis + # After sanitization, both become "age" → duplicate! + # Should map to the last one encountered + assert "age" in mapper._lookup + assert mapper._lookup["age"] in ["age", "age_at_diagnosis"] + + def test_get_standard_name(self, simple_synonyms: Path): + """Test getting standard name for a column.""" + mapper = ColumnMapper(simple_synonyms) + + assert mapper.get_standard_name("Age") == "age" + assert mapper.get_standard_name("Patient ID*") == "patient_id" + assert mapper.get_standard_name("unknown_column") == "unknown_column" + + def test_get_standard_name_with_sanitization(self, simple_synonyms: Path): + """Test that sanitization allows flexible synonym matching.""" + mapper = ColumnMapper(simple_synonyms) + + # All these variants should map to "patient_id" after sanitization + assert mapper.get_standard_name("Patient ID") == "patient_id" + assert mapper.get_standard_name("Patient ID*") == "patient_id" + assert mapper.get_standard_name("PATIENT ID") == "patient_id" + assert mapper.get_standard_name("patient id") == "patient_id" + assert mapper.get_standard_name("ID") == "patient_id" + + # Age variants + assert mapper.get_standard_name("Age") == "age" + assert mapper.get_standard_name("Age*") == "age" + assert mapper.get_standard_name("age on reporting") == "age" + assert mapper.get_standard_name("AGE ON REPORTING") == "age" + + # Test with extra spaces/special chars (should still match) + assert mapper.get_standard_name("Patient ID*") == "patient_id" + + def test_rename_columns_basic(self, simple_synonyms: Path): + """Test basic column renaming.""" + mapper = ColumnMapper(simple_synonyms) + + df = pl.DataFrame( + { + "Age": [25, 30], + "Patient ID": ["P001", "P002"], + "Province": ["Bangkok", "Hanoi"], + } + ) + + renamed = mapper.rename_columns(df) + + assert "age" in renamed.columns + assert "patient_id" in renamed.columns + assert "province" in renamed.columns + assert "Age" not in renamed.columns + + def test_rename_columns_keeps_unmapped(self, simple_synonyms: Path): + """Test that unmapped columns are kept by default.""" + mapper = ColumnMapper(simple_synonyms) + + df = pl.DataFrame( + { + "Age": [25], + "UnknownColumn": ["value"], + "AnotherUnmapped": [42], + } + ) + + renamed = mapper.rename_columns(df) + + assert "age" in renamed.columns + assert "UnknownColumn" in renamed.columns + assert "AnotherUnmapped" in renamed.columns + + def test_rename_columns_strict_mode_raises_error(self, simple_synonyms: Path): + """Test that strict mode raises error for unmapped columns.""" + mapper = ColumnMapper(simple_synonyms) + + df = pl.DataFrame( + { + "Age": [25], + "UnknownColumn": ["value"], + } + ) + + with pytest.raises(ValueError, match="Unmapped columns found"): + mapper.rename_columns(df, strict=True) + + def test_rename_columns_no_changes_needed(self, simple_synonyms: Path): + """Test renaming when columns are already standardized.""" + mapper = ColumnMapper(simple_synonyms) + + df = pl.DataFrame( + { + "age": [25], + "patient_id": ["P001"], + } + ) + + renamed = mapper.rename_columns(df) + + assert renamed.columns == df.columns + assert renamed.equals(df) + + def test_get_expected_columns(self, simple_synonyms: Path): + """Test getting set of expected standard columns.""" + mapper = ColumnMapper(simple_synonyms) + + expected = mapper.get_expected_columns() + + assert expected == {"age", "patient_id", "name", "province", "empty_column"} + + def test_get_missing_columns(self, simple_synonyms: Path): + """Test getting missing columns from DataFrame.""" + mapper = ColumnMapper(simple_synonyms) + + df = pl.DataFrame( + { + "age": [25], + "patient_id": ["P001"], + } + ) + + missing = mapper.get_missing_columns(df) + + assert missing == {"name", "province", "empty_column"} + + def test_validate_required_columns_success(self, simple_synonyms: Path): + """Test validation passes when required columns present.""" + mapper = ColumnMapper(simple_synonyms) + + df = pl.DataFrame( + { + "age": [25], + "patient_id": ["P001"], + "name": ["Test"], + } + ) + + # Should not raise + mapper.validate_required_columns(df, ["age", "patient_id"]) + + def test_validate_required_columns_failure(self, simple_synonyms: Path): + """Test validation fails when required columns missing.""" + mapper = ColumnMapper(simple_synonyms) + + df = pl.DataFrame( + { + "age": [25], + } + ) + + with pytest.raises(ValueError, match="Required columns missing"): + mapper.validate_required_columns(df, ["age", "patient_id", "name"]) + + +class TestLoaderFunctions: + """Tests for loader convenience functions.""" + + def test_load_patient_mapper_with_actual_file(self): + """Test loading patient mapper with actual reference_data file.""" + mapper = load_patient_mapper() + + # Check that some expected columns are present + assert "age" in mapper.synonyms + assert "patient_id" in mapper.synonyms + assert "province" in mapper.synonyms + + # Check that synonyms are loaded + assert len(mapper._lookup) > 0 + assert mapper.get_standard_name("Age") == "age" + + def test_load_product_mapper_with_actual_file(self): + """Test loading product mapper with actual reference_data file.""" + mapper = load_product_mapper() + + # Check that some expected columns are present + assert "product" in mapper.synonyms + assert "clinic_id" in mapper.synonyms + + # Check that synonyms are loaded + assert len(mapper._lookup) > 0 + + +class TestIntegrationWithActualData: + """Integration tests with actual reference_data files.""" + + def test_patient_mapper_renames_all_known_synonyms(self): + """Test that patient mapper can rename all synonyms in YAML.""" + mapper = load_patient_mapper() + + # Create DataFrame with various synonyms + test_data = { + "Age": [25], + "Patient ID": ["P001"], + "D.O.B.": ["1999-01-01"], + "Gender": ["M"], + } + + df = pl.DataFrame(test_data) + renamed = mapper.rename_columns(df) + + # Check that columns are renamed correctly + assert "age" in renamed.columns + assert "patient_id" in renamed.columns + assert "dob" in renamed.columns + assert "sex" in renamed.columns + + def test_product_mapper_renames_all_known_synonyms(self): + """Test that product mapper can rename all synonyms in YAML.""" + mapper = load_product_mapper() + + # Create DataFrame with various synonyms + test_data = { + "Product": ["Insulin"], + "Date": ["2024-01-01"], + "Units Received": [10], + } + + df = pl.DataFrame(test_data) + renamed = mapper.rename_columns(df) + + # Check that columns are renamed correctly + assert "product" in renamed.columns + assert "product_entry_date" in renamed.columns + assert "product_units_received" in renamed.columns diff --git a/tests/test_tables/test_patient.py b/tests/test_tables/test_patient.py new file mode 100644 index 0000000..31aa932 --- /dev/null +++ b/tests/test_tables/test_patient.py @@ -0,0 +1,361 @@ +"""Tests for patient table creation.""" + +from pathlib import Path + +import polars as pl +import pytest + +from a4d.tables.patient import ( + create_table_patient_data_annual, + create_table_patient_data_monthly, + create_table_patient_data_static, + read_cleaned_patient_data, +) + + +@pytest.fixture +def cleaned_patient_data_files(tmp_path: Path) -> list[Path]: + """Create test cleaned patient data files.""" + data_dir = tmp_path / "cleaned" + data_dir.mkdir() + + file1 = data_dir / "tracker1_2024_01.parquet" + df1 = pl.DataFrame( + { + "patient_id": ["P001", "P002", "P003"], + "clinic_id": ["C001", "C001", "C002"], + "name": ["Alice", "Bob", "Charlie"], + "dob": ["2010-01-15", "2011-03-20", "2009-08-10"], + "sex": ["F", "M", "M"], + "recruitment_date": ["2024-01-10", "2024-01-15", "2024-01-05"], + "province": ["Province1", "Province1", "Province2"], + "hba1c_baseline": [8.5, 7.2, 9.1], + "hba1c_baseline_exceeds": [True, False, True], + "fbg_baseline_mg": [120, 110, 130], + "fbg_baseline_mmol": [6.7, 6.1, 7.2], + "patient_consent": [True, True, True], + "t1d_diagnosis_date": ["2023-01-01", "2022-05-10", "2021-12-15"], + "t1d_diagnosis_age": [13, 11, 12], + "t1d_diagnosis_with_dka": [True, False, True], + "status_out": ["Active", "Active", "Active"], + "lost_date": [None, None, None], + "file_name": ["tracker1.xlsx", "tracker1.xlsx", "tracker1.xlsx"], + "tracker_date": ["2024-01-31", "2024-01-31", "2024-01-31"], + "tracker_month": [1, 1, 1], + "tracker_year": [2024, 2024, 2024], + "sheet_name": ["Jan 2024", "Jan 2024", "Jan 2024"], + "weight": [45.5, 52.3, 48.1], + "height": [155, 162, 158], + "bmi": [18.9, 19.9, 19.3], + "bmi_date": ["2024-01-15", "2024-01-18", "2024-01-20"], + "age": [14, 13, 15], + "status": ["Active", "Active", "Active"], + "hba1c_updated": [7.8, 6.9, 8.5], + "hba1c_updated_date": ["2024-01-20", "2024-01-22", "2024-01-18"], + "hba1c_updated_exceeds": [False, False, True], + "fbg_updated_mg": [115, 105, 125], + "fbg_updated_mmol": [6.4, 5.8, 6.9], + "fbg_updated_date": ["2024-01-20", "2024-01-22", "2024-01-18"], + "insulin_type": ["Rapid", "Mixed", "Rapid"], + "insulin_subtype": ["Lispro", "30/70", "Aspart"], + "insulin_regimen": ["Basal-bolus", "Twice daily", "Basal-bolus"], + "insulin_injections": [4, 2, 4], + "insulin_total_units": [35, 28, 40], + "testing_frequency": [4, 3, 4], + "support_level": ["Full", "Full", "Partial"], + "last_clinic_visit_date": ["2024-01-25", "2024-01-28", "2024-01-22"], + "last_remote_followup_date": [None, None, None], + "hospitalisation_date": [None, None, None], + "hospitalisation_cause": [None, None, None], + "observations": ["Doing well", "Good progress", "Needs improvement"], + "observations_category": ["Good", "Good", "Fair"], + "edu_occ": ["Student", "Student", "Student"], + "edu_occ_updated": ["Student", "Student", "Student"], + "blood_pressure_updated": ["110/70", "115/75", "120/80"], + "blood_pressure_sys_mmhg": [110, 115, 120], + "blood_pressure_dias_mmhg": [70, 75, 80], + "complication_screening_kidney_test_date": ["2024-01-10", None, "2024-01-08"], + "complication_screening_kidney_test_value": ["Normal", None, "Normal"], + "complication_screening_eye_exam_date": ["2024-01-10", None, None], + "complication_screening_eye_exam_value": ["Normal", None, None], + "complication_screening_foot_exam_date": [None, None, None], + "complication_screening_foot_exam_value": [None, None, None], + "complication_screening_lipid_profile_date": [None, None, None], + "complication_screening_lipid_profile_triglycerides_value": [None, None, None], + "complication_screening_lipid_profile_cholesterol_value": [None, None, None], + "complication_screening_lipid_profile_ldl_mg_value": [None, None, None], + "complication_screening_lipid_profile_ldl_mmol_value": [None, None, None], + "complication_screening_lipid_profile_hdl_mg_value": [None, None, None], + "complication_screening_lipid_profile_hdl_mmol_value": [None, None, None], + "complication_screening_thyroid_test_date": [None, None, None], + "complication_screening_thyroid_test_ft4_ng_value": [None, None, None], + "complication_screening_thyroid_test_ft4_pmol_value": [None, None, None], + "complication_screening_thyroid_test_tsh_value": [None, None, None], + "complication_screening_remarks": [None, None, None], + "dm_complication_eye": [None, None, None], + "dm_complication_kidney": [None, None, None], + "dm_complication_others": [None, None, None], + "dm_complication_remarks": [None, None, None], + "family_history": ["No diabetes", "Type 2 in family", "No diabetes"], + "other_issues": [None, None, None], + } + ) + df1.write_parquet(file1) + + file2 = data_dir / "tracker1_2024_02.parquet" + df2 = pl.DataFrame( + { + "patient_id": ["P001", "P002"], + "clinic_id": ["C001", "C001"], + "name": ["Alice", "Bob"], + "dob": ["2010-01-15", "2011-03-20"], + "sex": ["F", "M"], + "recruitment_date": ["2024-01-10", "2024-01-15"], + "province": ["Province1", "Province1"], + "hba1c_baseline": [8.5, 7.2], + "hba1c_baseline_exceeds": [True, False], + "fbg_baseline_mg": [120, 110], + "fbg_baseline_mmol": [6.7, 6.1], + "patient_consent": [True, True], + "t1d_diagnosis_date": ["2023-01-01", "2022-05-10"], + "t1d_diagnosis_age": [13, 11], + "t1d_diagnosis_with_dka": [True, False], + "status_out": ["Active", "Active"], + "lost_date": [None, None], + "file_name": ["tracker1.xlsx", "tracker1.xlsx"], + "tracker_date": ["2024-02-29", "2024-02-29"], + "tracker_month": [2, 2], + "tracker_year": [2024, 2024], + "sheet_name": ["Feb 2024", "Feb 2024"], + "weight": [46.0, 52.8], + "height": [155, 162], + "bmi": [19.1, 20.1], + "bmi_date": ["2024-02-15", "2024-02-18"], + "age": [14, 13], + "status": ["Active", "Active"], + "hba1c_updated": [7.5, 6.7], + "hba1c_updated_date": ["2024-02-20", "2024-02-22"], + "hba1c_updated_exceeds": [False, False], + "fbg_updated_mg": [110, 100], + "fbg_updated_mmol": [6.1, 5.6], + "fbg_updated_date": ["2024-02-20", "2024-02-22"], + "insulin_type": ["Rapid", "Mixed"], + "insulin_subtype": ["Lispro", "30/70"], + "insulin_regimen": ["Basal-bolus", "Twice daily"], + "insulin_injections": [4, 2], + "insulin_total_units": [36, 29], + "testing_frequency": [4, 3], + "support_level": ["Full", "Full"], + "last_clinic_visit_date": ["2024-02-25", "2024-02-28"], + "last_remote_followup_date": [None, None], + "hospitalisation_date": [None, None], + "hospitalisation_cause": [None, None], + "observations": ["Excellent progress", "Very good"], + "observations_category": ["Excellent", "Good"], + "edu_occ": ["Student", "Student"], + "edu_occ_updated": ["Student", "Student"], + "blood_pressure_updated": ["108/68", "112/72"], + "blood_pressure_sys_mmhg": [108, 112], + "blood_pressure_dias_mmhg": [68, 72], + "complication_screening_kidney_test_date": [None, None], + "complication_screening_kidney_test_value": [None, None], + "complication_screening_eye_exam_date": [None, None], + "complication_screening_eye_exam_value": [None, None], + "complication_screening_foot_exam_date": [None, None], + "complication_screening_foot_exam_value": [None, None], + "complication_screening_lipid_profile_date": [None, None], + "complication_screening_lipid_profile_triglycerides_value": [None, None], + "complication_screening_lipid_profile_cholesterol_value": [None, None], + "complication_screening_lipid_profile_ldl_mg_value": [None, None], + "complication_screening_lipid_profile_ldl_mmol_value": [None, None], + "complication_screening_lipid_profile_hdl_mg_value": [None, None], + "complication_screening_lipid_profile_hdl_mmol_value": [None, None], + "complication_screening_thyroid_test_date": [None, None], + "complication_screening_thyroid_test_ft4_ng_value": [None, None], + "complication_screening_thyroid_test_ft4_pmol_value": [None, None], + "complication_screening_thyroid_test_tsh_value": [None, None], + "complication_screening_remarks": [None, None], + "dm_complication_eye": [None, None], + "dm_complication_kidney": [None, None], + "dm_complication_others": [None, None], + "dm_complication_remarks": [None, None], + "family_history": ["No diabetes", "Type 2 in family"], + "other_issues": [None, None], + } + ) + df2.write_parquet(file2) + + return [file1, file2] + + +def test_read_cleaned_patient_data(cleaned_patient_data_files: list[Path]): + """Test reading and combining cleaned patient data files.""" + result = read_cleaned_patient_data(cleaned_patient_data_files) + + assert isinstance(result, pl.DataFrame) + assert result.shape[0] == 5 # 3 rows from file1 + 2 rows from file2 + assert "patient_id" in result.columns + assert "clinic_id" in result.columns + assert set(result["patient_id"].to_list()) == {"P001", "P002", "P003"} + + +def test_read_cleaned_patient_data_empty_list(): + """Test that empty file list raises error.""" + with pytest.raises(ValueError, match="No cleaned files provided"): + read_cleaned_patient_data([]) + + +def test_create_table_patient_data_static(cleaned_patient_data_files: list[Path], tmp_path: Path): + """Test creation of static patient data table.""" + output_dir = tmp_path / "output" + + output_file = create_table_patient_data_static(cleaned_patient_data_files, output_dir) + + assert output_file.exists() + assert output_file.name == "patient_data_static.parquet" + + result = pl.read_parquet(output_file) + + assert result.shape[0] == 3 + assert set(result["patient_id"].to_list()) == {"P001", "P002", "P003"} + + p001_data = result.filter(pl.col("patient_id") == "P001") + assert p001_data["tracker_month"][0] == 2 + assert p001_data["tracker_year"][0] == 2024 + + p002_data = result.filter(pl.col("patient_id") == "P002") + assert p002_data["tracker_month"][0] == 2 + assert p002_data["tracker_year"][0] == 2024 + + p003_data = result.filter(pl.col("patient_id") == "P003") + assert p003_data["tracker_month"][0] == 1 + assert p003_data["tracker_year"][0] == 2024 + + assert "name" in result.columns + assert "dob" in result.columns + assert "recruitment_date" in result.columns + assert "weight" not in result.columns + assert "status" not in result.columns + + +def test_create_table_patient_data_monthly(cleaned_patient_data_files: list[Path], tmp_path: Path): + """Test creation of monthly patient data table.""" + output_dir = tmp_path / "output" + + output_file = create_table_patient_data_monthly(cleaned_patient_data_files, output_dir) + + assert output_file.exists() + assert output_file.name == "patient_data_monthly.parquet" + + result = pl.read_parquet(output_file) + + assert result.shape[0] == 5 + + assert "weight" in result.columns + assert "bmi" in result.columns + assert "status" in result.columns + assert "insulin_type" in result.columns + assert "name" not in result.columns + assert "dob" not in result.columns + + sorted_check = result["tracker_year"].to_list() + assert sorted_check == sorted(sorted_check) + + +def test_create_table_patient_data_annual(cleaned_patient_data_files: list[Path], tmp_path: Path): + """Test creation of annual patient data table.""" + output_dir = tmp_path / "output" + + output_file = create_table_patient_data_annual(cleaned_patient_data_files, output_dir) + + assert output_file.exists() + assert output_file.name == "patient_data_annual.parquet" + + result = pl.read_parquet(output_file) + + assert result.shape[0] == 3 + + assert "complication_screening_kidney_test_date" in result.columns + assert "dm_complication_eye" in result.columns + assert "family_history" in result.columns + assert "name" not in result.columns + assert "weight" not in result.columns + + p001_data = result.filter(pl.col("patient_id") == "P001") + assert p001_data.shape[0] == 1 + assert p001_data["tracker_month"][0] == 2 + assert p001_data["tracker_year"][0] == 2024 + + +def test_create_table_patient_data_annual_filters_pre_2024(tmp_path: Path): + """Test that annual table filters out data before 2024.""" + data_dir = tmp_path / "cleaned" + data_dir.mkdir() + + file1 = data_dir / "tracker_2023.parquet" + df1 = pl.DataFrame( + { + "patient_id": ["P001"], + "status": ["Active"], + "tracker_month": [12], + "tracker_year": [2023], + "tracker_date": ["2023-12-31"], + "edu_occ": ["Student"], + "edu_occ_updated": ["Student"], + "blood_pressure_updated": ["110/70"], + "blood_pressure_sys_mmhg": [110], + "blood_pressure_dias_mmhg": [70], + "complication_screening_kidney_test_date": [None], + "complication_screening_kidney_test_value": [None], + "complication_screening_eye_exam_date": [None], + "complication_screening_eye_exam_value": [None], + "complication_screening_foot_exam_date": [None], + "complication_screening_foot_exam_value": [None], + "complication_screening_lipid_profile_date": [None], + "complication_screening_lipid_profile_triglycerides_value": [None], + "complication_screening_lipid_profile_cholesterol_value": [None], + "complication_screening_lipid_profile_ldl_mg_value": [None], + "complication_screening_lipid_profile_ldl_mmol_value": [None], + "complication_screening_lipid_profile_hdl_mg_value": [None], + "complication_screening_lipid_profile_hdl_mmol_value": [None], + "complication_screening_thyroid_test_date": [None], + "complication_screening_thyroid_test_ft4_ng_value": [None], + "complication_screening_thyroid_test_ft4_pmol_value": [None], + "complication_screening_thyroid_test_tsh_value": [None], + "complication_screening_remarks": [None], + "dm_complication_eye": [None], + "dm_complication_kidney": [None], + "dm_complication_others": [None], + "dm_complication_remarks": [None], + "family_history": ["No diabetes"], + "other_issues": [None], + } + ) + df1.write_parquet(file1) + + output_dir = tmp_path / "output" + output_file = create_table_patient_data_annual([file1], output_dir) + + result = pl.read_parquet(output_file) + assert result.shape[0] == 0 + + +def test_static_table_sorting(cleaned_patient_data_files: list[Path], tmp_path: Path): + 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