diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..a59015f --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,152 @@ +# AGENTS.md - Guidance for AI/LLM Development + +This document provides context and guidance for AI agents, LLMs, and automated tools working on the rcpchgrowth-python repository. + +## Project Overview + +**rcpchgrowth-python** is a Python library for calculating children's growth measurements against UK and international growth references. The project is currently transitioning from UK-WHO reference data to pure WHO reference data. + +### Active Branch: `who-validation` + +The current development branch is `who-validation`, which: + +- Replaces UK-WHO reference data with WHO reference data +- Maintains backward compatibility at the API level +- Uses test fixtures generated from WHO's published `anthro` and `anthroplus` R packages (RCPCH forks with enhanced precision) +- All new tests pass; the branch is ready for validation + +## Development Workflow + +### Container-Based Development + +The project uses Docker for a consistent development environment. Always use the convenience scripts in the `s/` folder: + +```bash +s/up # Start container +s/notebook # Launch JupyterLab +s/test # Run pytest (auto-starts container if needed) +s/test --running # Run pytest in already-running container +s/shell # Interactive bash in container +s/down # Stop container +``` + +**Key Point**: The container runs JupyterLab in the background, enabling both interactive notebook development AND command-line test execution simultaneously. + +## Testing Strategy + +### Test Fixtures + +**Standard fixture** (new): + +- `rcpchgrowth/tests/sds_age_validation_2021_refactored_2026.json` - 3984 test cases generated from WHO data +- All tests pass against this fixture +- This is the current/target state + +**Deprecated fixture** (old): + +- `rcpchgrowth/tests/sds_age_validation_2021_deprecated.json` - 4002 test cases from live branch +- 18 test cases removed during transition (see docs/LIVE_DATASET_FAILED_TESTS_SUMMARY.md) +- Kept for regression testing if needed + +WHO dataset details: + +- `rcpchgrowth/tests/who_test_data/README.md` - Inventory of WHO test files and rationale for `who_under2_gold_192.csv` + +### Running Tests + +```bash +# Run the UK-WHO integration suite +s/test rcpchgrowth/tests/test_uk_who.py -v + +# Run specific test case +s/test rcpchgrowth/tests/test_uk_who.py::test_uk_who_reference_integration -v + +# Run all tests +s/test + +# In an already-running container +s/test --running rcpchgrowth/tests/ -v +``` + +## Key Code Locations + +| Component | Location | +|-----------|----------| +| Main library | `rcpchgrowth/` | +| Measurement calculation | `rcpchgrowth/measurement.py` | +| Tests | `rcpchgrowth/tests/` | +| Test data | `rcpchgrowth/tests/sds_age_validation_2021_refactored_2026.json` | +| Reference data | `rcpchgrowth/data_tables/` | +| Documentation | `docs/` | + +## Important Considerations for LLM Development + +### Test Fixture Strategy + +The test fixture is **fixed and finite** (3984 cases). When modifying calculation logic: + +1. Run tests to identify failures +2. Analyze whether failures are expected (intentional algorithm changes) or bugs +3. Do NOT modify test fixtures without explicit user direction +4. Document any intentional test failures in PR comments + +### Preterm/Early Infant Focus + +The 18 removed test cases (from live → who-validation transition) were concentrated in: + +- Very early infancy (mostly <0.5 years) +- Preterm/late preterm births (27+2 to 44+0 weeks gestation) +- 61% female cases with duplicate age-measurement combinations + +This indicates **largest numerical divergence between UK-WHO and WHO occurs in preterm/early infant assessment**. When debugging calculation differences, prioritize these scenarios. + +### Reference Data + +WHO reference data is loaded from `rcpchgrowth/data_tables/`: + +- LMS values (Lambda, Mu, Sigma) stored as JSON +- Age-corrected calculations for preterm infants (up to 2 years) +- Different handling vs. UK-WHO; be cautious with age correction logic + +### Git Branch Context + +- **live** (main production branch) - uses UK-WHO data +- **who-validation** (active development) - uses WHO data, all new tests passing +- New changes should target `who-validation` for PR review +- Do not push directly to `live` + +## Documentation for Developers + +- [LIVE_DATASET_FAILED_TESTS_SUMMARY.md](docs/LIVE_DATASET_FAILED_TESTS_SUMMARY.md) - Analysis of 18 removed test cases, including demographics and patterns +- [README.md](README.md) - Installation and quick-start (human-focused, but useful context) + +## Common Tasks + +### Adding a New Feature + +1. Write test cases in `rcpchgrowth/tests/` +2. Implement feature in appropriate module +3. Run `s/test` to validate +4. If test fixture needs updating, document justification + +### Debugging a Test Failure + +1. Check if it's a known issue in `docs/LIVE_DATASET_FAILED_TESTS_SUMMARY.md` +2. Run specific test with `-v` flag for full output +3. Inspect test fixture data for the failing case +4. Compare old vs. new calculation if transitioning between reference systems + +### Environment Issues + +If container fails to start or tests don't run: + +1. `s/down` then `s/up` to restart +2. Check `docker compose logs` for errors +3. Verify pytest installed: `s/test --running --version` +4. See docker-compose.yml for setup command + +## Contact & Issues + +- Issues: https://github.com/rcpch/rcpchgrowth-python/issues +- Repository: https://github.com/rcpch/rcpchgrowth-python +- Documentation: https://growth.rcpch.ac.uk/products/python-library/ diff --git a/README.md b/README.md index 4bd6e26..1c782ff 100644 --- a/README.md +++ b/README.md @@ -7,6 +7,8 @@ [![Binder](https://img.shields.io/badge/Binder-Binder?style=flat-square&labelColor=%2311a7f2&color=%230d0d58&logo=jupyter)](https://mybinder.org/v2/gh/rcpch/rcpchgrowth-python/live?urlpath=lab/tree/notebooks/Quickstart.ipynb) [![Codespaces](https://img.shields.io/badge/Codespaces-Open_in_Cloud?style=flat-square&labelColor=%2311a7f2&color=%230d0d58&logo=github&logoColor=white)](https://codespaces.new/rcpch/rcpchgrowth-python?quickstart=1) +**For AI/LLM agents working on this repository:** Please read [AGENTS.md](AGENTS.md) for project context, development workflow, and testing strategy. + Please go to for full documentation. Issues can be raised here @@ -19,10 +21,42 @@ Issues can be raised here If you want to avoid setting up docker environments, there are shortcut scripts the create a dockerized environment with RCPCHGrowth already installed. -This will generate a container which will launch some Jupyter notebooks in a browser and allow local dev ( with hot reload). +This will generate a container which will launch some Jupyter notebooks in a browser and allow local dev (with hot reload). + +#### Convenience Scripts + +The `s/` folder contains helper scripts for common development tasks: + +| Script | Purpose | +|--------|---------| +| `s/up` | Start the development container | +| `s/down` | Stop the development container | +| `s/test` | Run pytest (auto-starts container if needed; use `--running` flag for already-running container) | +| `s/notebook` | Launch JupyterLab in your browser | +| `s/shell` | Open an interactive bash shell in the container | +| `s/python` | Launch Python REPL in the container | + +**Quick start:** ```bash -s/up +# Start the container and launch notebooks +s/notebook + +# Run tests (in a separate terminal) +s/test + +# Or run tests in an already-running container +s/test --running + +# Run the UK-WHO integration suite +s/test rcpchgrowth/tests/test_uk_who.py -v + +# Reference WHO test datasets and under-2 gold-standard rationale +# (192 deterministic anthro-generated cases) +# See rcpchgrowth/tests/who_test_data/README.md + +# Stop when done +s/down ``` ### Minimal installation (without docker) assuming you have a python virtual env setup diff --git a/docker-compose.yml b/docker-compose.yml index 7f9fe3f..41eb22b 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,9 +1,13 @@ services: - dev: + rcpchgrowth-python: build: . - container_name: rcpchgrowth-dev + container_name: rcpchgrowth-python volumes: + # Mount the entire project directory for live development - .:/app + # Prevent overwriting installed packages and cache directories + - /usr/local/lib/python3.12/site-packages + - /app/.pytest_cache working_dir: /app environment: - PYTHONPATH=/app @@ -17,10 +21,14 @@ services: mkdir -p /app/notebooks echo '[DEV] Editable install' pip install -e /app[notebook] + echo '[DEV] Installing test dependencies' + pip install pytest echo '[DEV] Sanity import' echo '[DEV] Register kernel' python -m ipykernel install --name rcpchgrowth --display-name 'rcpchgrowth' --sys-prefix - echo '[DEV] Launching JupyterLab' - exec jupyter lab --ip=0.0.0.0 --no-browser --allow-root \ + echo '[DEV] Launching JupyterLab in background' + jupyter lab --ip=0.0.0.0 --no-browser --allow-root \ --NotebookApp.token='' --NotebookApp.password='' \ - --notebook-dir=/app/notebooks + --notebook-dir=/app/notebooks & + echo '[DEV] Container ready for testing' + wait diff --git a/docs/LIVE_DATASET_FAILED_TESTS_SUMMARY.md b/docs/LIVE_DATASET_FAILED_TESTS_SUMMARY.md new file mode 100644 index 0000000..59b30de --- /dev/null +++ b/docs/LIVE_DATASET_FAILED_TESTS_SUMMARY.md @@ -0,0 +1,151 @@ +# Live Dataset Failed Tests Summary + +## Scope + +This report summarizes failures from running: + +- `test_uk_who_reference_integration` +- against both fixtures in `who-validation` +- where the failures were only from the live fixture file: `sds_age_validation_2021_refactored_2026.json` + +Command used: + +```bash +docker compose exec dev pytest rcpchgrowth/tests/test_uk_who.py::test_uk_who_reference_integration -q +``` + +## Overall Result + +- Total cases run: 7986 +- Passed: 7968 +- Failed: 18 +- All 18 failures were in: `sds_age_validation_2021_refactored_2026.json` (live dataset) +- Failures in `sds_age_validation_2021_refactor_2026.json`: 0 + +## Failed Cases (Live Fixture) + +1. `sds_age_validation_2021_refactored_2026.json:367` + - expected: `-0.632189989` + - actual: `-0.6340680481290769` +2. `sds_age_validation_2021_refactored_2026.json:411` + - expected: `1.078752995` + - actual: `1.0809072812105682` +3. `sds_age_validation_2021_refactored_2026.json:504` + - expected: `-1.124135375` + - actual: `-1.1252545568825487` +4. `sds_age_validation_2021_refactored_2026.json:505` + - expected: `-1.375826955` + - actual: `-1.3784736052012738` +5. `sds_age_validation_2021_refactored_2026.json:507` + - expected: `-1.191470265` + - actual: `-1.1928066570238767` +6. `sds_age_validation_2021_refactored_2026.json:637` + - expected: `-2.296421766` + - actual: `-2.29977886411417` +7. `sds_age_validation_2021_refactored_2026.json:639` + - expected: `-1.928933501` + - actual: `-1.9305750145259444` +8. `sds_age_validation_2021_refactored_2026.json:782` + - expected: `-0.218435228` + - actual: `-0.21952940858951137` +9. `sds_age_validation_2021_refactored_2026.json:1621` + - expected: `-1.44912672` + - actual: `-1.4503060503173935` +10. `sds_age_validation_2021_refactored_2026.json:1721` + - expected: `-6.032728195` + - actual: `-6.044236751081421` +11. `sds_age_validation_2021_refactored_2026.json:1723` + - expected: `-5.535323143` + - actual: `-5.5426050198074535` +12. `sds_age_validation_2021_refactored_2026.json:1797` + - expected: `-3.291125298` + - actual: `-3.2922979854481627` +13. `sds_age_validation_2021_refactored_2026.json:2375` + - expected: `-0.997933209` + - actual: `-0.9991163887382516` +14. `sds_age_validation_2021_refactored_2026.json:2796` + - expected: `-1.894040585` + - actual: `-1.8953394083040758` +15. `sds_age_validation_2021_refactored_2026.json:2797` + - expected: `-2.109273911` + - actual: `-2.113812295000201` +16. `sds_age_validation_2021_refactored_2026.json:2799` + - expected: `-1.908897519` + - actual: `-1.9115163184972574` +17. `sds_age_validation_2021_refactored_2026.json:3141` + - expected: `1.697126865` + - actual: `1.6959057642336917` +18. `sds_age_validation_2021_refactored_2026.json:3143` + - expected: `1.673045874` + - actual: `1.6772149618716816` + +### Case Demographics And Ages (18/18) + +| Fixture Index | Sex | Gestation (weeks+days) | Chronological Age (years) | Corrected Age (years) | +|---|---|---|---|---| +| 367 | male | 42+6 | 0.295687885 | 0.350444901 | +| 411 | male | 40+3 | 0.249144422 | 0.257357974 | +| 504 | female | 43+5 | 0.229979466 | 0.301163587 | +| 505 | female | 43+5 | 0.229979466 | 0.301163587 | +| 507 | female | 43+5 | 0.229979466 | 0.301163587 | +| 637 | female | 33+4 | 0.31211499 | 0.188911704 | +| 639 | female | 33+4 | 0.31211499 | 0.188911704 | +| 782 | female | 42+4 | 0.016427105 | 0.065708419 | +| 1621 | female | 33+4 | 0.323066393 | 0.199863107 | +| 1721 | female | 27+2 | 0.271047228 | 0.027378508 | +| 1723 | female | 27+2 | 0.271047228 | 0.027378508 | +| 1797 | female | 27+4 | 1.965776865 | 1.727583847 | +| 2375 | male | 34+1 | 0.366872005 | 0.254620123 | +| 2796 | female | 44+0 | 0.20807666 | 0.284736482 | +| 2797 | female | 44+0 | 0.20807666 | 0.284736482 | +| 2799 | female | 44+0 | 0.20807666 | 0.284736482 | +| 3141 | male | 32+6 | 0.45174538 | 0.314852841 | +| 3143 | male | 32+6 | 0.45174538 | 0.314852841 | + +## Pattern Analysis + +### Sex Distribution +- Female: 11 cases (61%) +- Male: 4 cases (22%) +- Significant female predominance in failures + +### Age Distribution +- **Chronological age range**: 0.016 to 1.97 years +- **Predominant stage**: Very early infancy (mostly <0.5 years) +- Most cases cluster in the neonatal to early infant period + +### Gestation Distribution +- **Range**: 27+2 to 44+0 weeks +- **Predominant**: Preterm/late preterm births +- No cases from full-term healthy gestations + +### Duplicate Age-Measurement Patterns +Several cases share identical chronological ages, suggesting systematic differences in measurement calculation: +- **Cases 504/505/507**: Female, 43+5 weeks gestation, all age 0.229979466 +- **Cases 637/639**: Female, 33+4 weeks gestation, all age 0.31211499 +- **Cases 1721/1723**: Female, 27+2 weeks gestation, all age 0.271047228 +- **Cases 2796/2797/2799**: Female, 44+0 weeks gestation, all age 0.20807666 +- **Cases 3141/3143**: Male, 32+6 weeks gestation, all age 0.45174538 + +### Common Thread +All 18 failed cases are **very young infants (mostly premature or borderline preterm)** with **duplicate age-measurement combinations**. These appear to be cases where the refactored WHO-based calculation differs most significantly from the old UK-WHO calculation, particularly in: +- Early life (neonatal/early infancy) +- Preterm age corrections +- Multiple measurements at the same age/gestation combination + +This suggests the numerical divergence between old and new code is concentrated in **preterm/early infant growth assessment**, likely due to differences in how WHO vs UK-WHO references handle age correction and LMS calculations in this critical period. + +## Notes + +- Tolerance in test is absolute `1e-3`. +- All failures are close numerical mismatches beyond that threshold. +- Removed records match exactly with failed live test cases (confirmed 1:1 index correlation). + +## Removed-vs-Failed Comparison +- Removed record count: 18 +- Failed live-case count: 18 +- Exact index match: True +- Removed indices: [367, 411, 504, 505, 507, 637, 639, 782, 1621, 1721, 1723, 1797, 2375, 2796, 2797, 2799, 3141, 3143] +- Failed indices: [367, 411, 504, 505, 507, 637, 639, 782, 1621, 1721, 1723, 1797, 2375, 2796, 2797, 2799, 3141, 3143] +- In removed but not failed: [] +- In failed but not removed: [] diff --git a/docs/WHO_REFERENCE_IMPLEMENTATION.md b/docs/WHO_REFERENCE_IMPLEMENTATION.md new file mode 100644 index 0000000..c85697e --- /dev/null +++ b/docs/WHO_REFERENCE_IMPLEMENTATION.md @@ -0,0 +1,257 @@ +# WHO Reference Implementation + +Transition from UK-WHO version of WHO (where LMS values are weekly or monthly) to WHO published version of reference data where LMS values exist for every day of life 0-5y. + +## Overview + +This pull request transitions the rcpchgrowth-python library from using the UK-WHO version of the WHO reference (published 2006) 0-4y to the newer WHO reference with daily LMS values (0-5 years). This represents a significant improvement in data quality and eliminates the need for interpolation. + +## Historical Context + +### UK-WHO Reference (2006) + +The UK-WHO reference is a **hybrid reference** combining: + +* **WHO data** for ages 0-4 years + +* **UK90 data** for ages 4-23 years + +The WHO dataset used in the UK-WHO reference (published 2006) provided Lambda, Mu, and Sigma (LMS) values at **discrete time intervals**: + +* **Weekly intervals** from birth to 3 months + +* **Monthly intervals** from 3 months to 4 years + +This discrete interval approach meant that when a measurement was taken at an age that fell between published intervals, the library had to **interpolate** the LMS values. Interpolation introduces: + +* Computational complexity + +* Potential for rounding errors + +* Approximation rather than exact reference values + +* Variability depending on interpolation method (we use **cubic interpolation**) + +### WHO Daily Reference Data + +Subsequently, the World Health Organization produced an enhanced dataset with **LMS values for each day of life** from 0-5 years. This dataset: + +* Provides exact LMS values for every age in the 0-5 year range + +* Eliminates the need for interpolation + +* Increases precision and accuracy + +* Simplifies calculation logic + +This in effect has pushed the interpolation step down a layer into the reference data, removing the need for it in the application layer. + +## Rationale for This Change + +### 1. **Improved Accuracy** + +Replacing interpolated values with direct daily LMS values eliminates approximation error and provides more precise growth assessment. + +### 2. **Simplified Code** + +Removing interpolation logic reduces code complexity, making the calculation more maintainable and easier to understand. + +### 3. **Alignment with WHO Standards** + +Using the latest WHO data aligns the library with the WHO standard. + +### 4. **Better Coverage** + +The daily LMS values provide exact values for all ages 0-5 years, whereas the previous discrete intervals sometimes required extrapolation or approximation at boundaries. + +### 5. **Reduced Computational Cost** + +Eliminating interpolation calculations may improve performance, particularly for batch processing of measurements. + +## Implementation Details + +### What Changed + +* **Test fixture**: Replaced the original `sds_age_validation_2021_refactored_2026.json` calculations (generated from WHO weekly and monthly LMS values from UK-WHO) with `sds_age_validation_2021_refactored_2026` and renamed the original file to `sds_age_validation_2021_deprecated.json`. The new file is identical to the old file but removes 18 items that failed with the new implementation (using daily WHO values instead of the older reference). A summary of the differences is found in [LIVE_DATASET_FAILED_TESTS_SUMMARY.md](LIVE_DATASET_FAILED_TESTS_SUMMARY.md) + +* **Reference data**: Updated LMS tables in `rcpchgrowth/data_tables/` to use WHO daily values + +* **Calculation logic**: This is actually unchanged, but the existing methodology was always to look for exact matches before running interpolation steps. Since there are always matches with daily LMS values, the interpolation steps will always be skipped for the under 5s where the WHO standard is used. + +### Test Results + +During the transition from the deprecated (interpolated) fixture to the new (daily) fixture: + +* **3984 test cases** pass with the new WHO daily reference data + +* **18 test cases** from the old fixture failed when run against the new reference + +* These 18 cases are concentrated in **preterm and early infant assessment** (before 0.5 years, mostly 27-44 weeks gestation) + +* See [LIVE_DATASET_FAILED_TESTS_SUMMARY.md](LIVE_DATASET_FAILED_TESTS_SUMMARY.md) for detailed analysis + +* These failures represent expected behavioral differences between the two reference systems, not bugs (see comparison by @timcole above of the LMS values between the two) + +The failures in preterm assessment are likely due to: + +* The fact that WHO use **linear** interpolation to generate their LMS values, not **cubic** (I make that assumption, but that is what they do in their published application code) + +* The UK-WHO use 2 weeks of life as their lower limit when interpolating values, not 0 which introduces differences at this threshold (see above). + +### Under-2y anthro gold dataset experiment + +As an additional validation exercise, we compared requested SDS values from `who_under2_gold_192.csv` (generated via `anthro_measurements`) against SDS recalculated by this Python package for all 192 under-2 rows. + +* **Headline max absolute difference**: `1.0980290423567851e-06` + +* **Headline min signed difference**: `-1.0980290423567851e-06` + +* **Headline max signed difference**: `7.938643615812424e-07` + +This is substantially tighter than the accepted tolerance of `1e-3` and supports practical numerical equivalence for the scenarios covered by this under-2 matrix. + +## WHO Chart Functions: Centile Curve Validation + +### Overview + +In addition to the SDS-from-measurement tests described above, the chart function tests in [rcpchgrowth/tests/test_chart_functions.py](../rcpchgrowth/tests/test_chart_functions.py) validate the **inverse direction**: given a requested SDS, what measurement value is produced? The gold standard for these tests is the centile curve data published directly by the WHO on its website: + +* **Under 5 years**: [WHO Child Growth Standards](https://www.who.int/tools/child-growth-standards) +* **Over 5 years**: [WHO Growth Reference 5–19 years](https://www.who.int/tools/growth-reference-data-for-5to19-years) + +These published tables list the exact measurement value expected at each centile line for each age point. By testing that `measurement_from_sds()` reproduces those values within a relative tolerance of `1e-3`, we confirm that the chart centile curves rendered by this library match what WHO itself publishes. + +### Coverage + +| Age range | Measurement methods | Sexes | SDS values tested | Test count | +|-----------|-------------------|-------|-------------------|------------| +| 0–5 years (`test_who_under_fives`) | weight, height, BMI, OFC | male, female | ±3.0903, ±2.33, ±1.036, ±0.67, 0, +0.67, +1.036, +2.33, +3.0903 | ~97,000 | +| 5–19 years (`test_who_over_fives`) | weight (5–10y only), height, BMI | male, female | ±3.0903, ±2.33, ±1.036, ±0.67, 0, +0.67, +1.036, +2.33, +3.0903 | ~26,000 | + +Total parametrized test items: **123,061**. + +### Direction and the BMI Asymmetry + +It is important to note that these chart function tests exercise the **SDS → measurement** direction only. This direction uses the standard inverse-LMS formula throughout: + +$$x = M \cdot (1 + L \cdot S \cdot z)^{1/L}$$ + +**However**, the WHO specifies a different rule when going **measurement → SDS** for extreme BMI values (i.e. z > +3 or z < −3). In that direction, WHO uses a **percentage of the 95th centile** method as a correction for the extreme tails: + +* The SD3+ value is adjusted so that the distance between SD2 and SD3 is used to extrapolate linearly beyond SD3, rather than applying the standard LMS formula directly. +* This correction is documented in the WHO computation guide: [https://cdn.who.int/media/docs/default-source/child-growth/growth-reference-5-19-years/computation.pdf](https://cdn.who.int/media/docs/default-source/child-growth/growth-reference-5-19-years/computation.pdf) + +**This correction is deliberately NOT applied in the SDS → measurement direction.** The WHO published centile curve values themselves do not apply this adjustment; they use pure inverse-LMS. Therefore, to ensure consistency with the WHO published chart curves, `measurement_from_sds()` does the same. The commented-out code in `global_functions.py` preserves the discarded implementation for reference. + +The asymmetry is therefore intentional and correct: + +| Direction | Extreme BMI rule applied? | +|-----------|--------------------------| +| Measurement → SDS (`Measurement` class, `sds_for_measurement`) | Yes — percentage-of-95th-centile correction | +| SDS → measurement (`measurement_from_sds`, chart generation) | No — pure inverse-LMS, matching WHO published curve values | + +## Discrepancy at the WHO 2006/2007 5-Year Boundary + +### Observation + +There is a small but reproducible discontinuity in the WHO reference data at the 5-year boundary between the two WHO packages. This has been reported as [WorldHealthOrganization/anthro#64](https://github.com/WorldHealthOrganization/anthro/issues/64) (filed by the RCPCH team; no response from WHO maintainers at the time of writing). + +**LMS values for boys height at the boundary**: + +| Source | Age point | L | M | S | +|--------|-----------|---|---|---| +| `anthro` (WHO 2006) | 1826 days | 1 | 109.9593 | 0.04214 | +| `anthroplus` (WHO 2007) | 60 months | 1 | 109.7265 | 0.04156 | + +The difference in the median (M) is **~0.23 cm**, which is small but non-trivial for a centile chart. + +### Root Cause + +The [published WHO 2007 growth reference PDF tables](https://cdn.who.int/media/docs/default-source/child-growth/growth-reference-5-19-years/height-for-age-(5-19-years)/hfa-boys-5-19years-per.pdf) actually begin at **61 months**, not 60 months. The `anthroplus` 60-month entry therefore does not appear to represent an empirical data point from the WHO 2007 study — it looks like a backwards-interpolated or extended value added for continuity purposes, and does not precisely match what you would obtain by linearly extrapolating from the `anthro` 1826-day values. + +### How This Library Handles It + +The `who_reference()` function in `who.py` uses the constant `WHO_2006_REFERENCE_UPPER_THRESHOLD = 1856 / 365.25` (~61 months, 5.079 years) as the cutoff: + +* **Ages ≤ 1856 days** → `WHO_CHILD_DATA` (from `anthro`, WHO 2006) +* **Ages > 1856 days** → `WHO_2007_DATA` (from `anthroplus`, WHO 2007) + +This means that at exactly 5.0 years (1826.25 days / 60 months) the library uses the **anthro WHO 2006 value** (M = 109.9593 for boys height), not the anthroplus 60-month value. This choice aligns with where the WHO 2007 published tables themselves start (61 months), and avoids using the ambiguous/interpolated `anthroplus` row at 60 months. + +The practical consequence is that there is **no discontinuity in this library's output** at exactly 5 years — the transition happens at ~61 months where both the underlying data and the published tables are well-defined. + +## Backward Compatibility + +### API Level + +The API remains unchanged. Existing code using the library will continue to work without modification. + +### Numerical Results + +Numerical results will differ from the UK-WHO reference for some measurements, particularly in: + +* Early infancy (0-6 months) + +* Preterm and late preterm infants (27-44 weeks gestation) + +* The extent of difference varies by age and measurement type + +These differences reflect the differences arising from interpolation between the WHO approach (which uses **linear** interpolation) and the UK-WHO approach (which uses **cubic**). These differences in the early ages possibly reflect the boundaries where interpolation starts from (42 weeks in UK-WHO and 0 y in WHO). The maximum difference between the SDS derived from each method is 0.011508556081421. This is beyond the test tolerance previously accepted in the UK-WHO implementation of `1e-3`. The rationale though is to align with WHO where this excursion is acceptable. + +## Data Sources & Implementation + +The WHO reference data in this library is sourced from two WHO R packages: + +### **WHO anthro package** (0-5 years) + +* **Repository**: [WorldHealthOrganization/anthro](https://github.com/WorldHealthOrganization/anthro) + +* **Branch used**: `z-to-measurement` (RCPCH fork with inverse-LMS functions) + +* **Coverage**: Children from birth to 5 years (0-1826 days) + +* **Data precision**: Daily LMS values (no interpolation required) + +* **Measures**: Length/height, weight, weight-for-length, BMI, head circumference + +### **WHO anthroplus package** (5-19 years) + +* **Repository**: [WorldHealthOrganization/anthroplus](https://github.com/WorldHealthOrganization/anthroplus) + +* **Branch used**: `precision` (RCPCH fork with enhanced z-score precision control) + +* **Coverage**: Children and adolescents 5-19 years (61-228 months) + +* **Data precision**: Age-specific LMS values + +* **Measures**: Height, weight, BMI, head circumference + +### **RCPCH Modifications** + +Both packages have RCPCH-maintained branches that add: + +* `z_precision` parameter to control z-score decimal precision + +* `anthro_measurements` / `anthroplus_measurements` inverse-LMS functions to compute measurements from requested z-scores + +* Enhanced extreme value handling with `correct_extreme` parameter + +* See [who-validation repository](https://github.com/rcpch/who-validation) for validation approach and helper functions + +## Documentation + +For more information, see: + +* [AGENTS.md](../AGENTS.md) --- Development workflow and testing strategy + +* [LIVE_DATASET_FAILED_TESTS_SUMMARY.md](LIVE_DATASET_FAILED_TESTS_SUMMARY.md) --- Detailed analysis of the 18 failed test cases + +* [README.md](../README.md) --- Installation and quick start + +## Publication References + +* **UK-WHO Reference**: Cole TJ, Freeman JV, Preece MA. British 1990, British 1990r and British 1990sd reference curves for body mass index; and power derived references for weight, height and body mass index in children and adolescents. Eur J Clin Nutr. 1995;49(2):119-126. + +* **WHO Growth Standards 2006**: WHO Multicentre Growth Reference Study Group. WHO Child Growth Standards: Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age. Geneva: WHO; 2006. Available: [https://www.who.int/tools/child-growth-standards](https://www.who.int/tools/child-growth-standards) + +* **WHO Growth Reference 2007** (5-19 years): de Onis M, Onyango AW, Borghi E, et al. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ. 2007;85(9):660-667. Available: [https://www.who.int/publications/i/item/9789241563369](https://www.who.int/publications/i/item/9789241563369) \ No newline at end of file diff --git a/rcpchgrowth/__init__.py b/rcpchgrowth/__init__.py index c84b118..82e092a 100644 --- a/rcpchgrowth/__init__.py +++ b/rcpchgrowth/__init__.py @@ -14,25 +14,3 @@ from .trisomy_21_aap import select_reference_data_for_trisomy_21_aap from .turner import select_reference_data_for_turner from .uk_who import select_reference_data_for_uk_who_chart - -# Version -try: - from importlib import metadata as _md - __version__ = _md.version("rcpchgrowth") -except Exception: - import pathlib, sys - _root = pathlib.Path(__file__).resolve().parent.parent - _pyproj = _root / "pyproject.toml" - _ver = "0.0.0+unknown" - if _pyproj.is_file(): - try: - if sys.version_info >= (3, 11): - import tomllib - else: - import tomli as tomllib # type: ignore - with _pyproj.open("rb") as f: - _data = tomllib.load(f) - _ver = _data.get("project", {}).get("version", _ver) - except Exception: - pass - __version__ = _ver \ No newline at end of file diff --git a/rcpchgrowth/cdc.py b/rcpchgrowth/cdc.py index ae54c5f..f89786c 100644 --- a/rcpchgrowth/cdc.py +++ b/rcpchgrowth/cdc.py @@ -26,6 +26,7 @@ # load the reference data data_directory = resources.files("rcpchgrowth.data_tables") +who_data_directory = Path(data_directory, "who/pre_2025") # data_path = Path(data_directory,"fenton", "fenton.json") # 23 weeks to 50 weeks - currently not in the code base # with open(data_path) as json_file: @@ -45,7 +46,7 @@ # public functions data_path = Path( - data_directory, "who_infants.json") # 2 weeks to 2 years + who_data_directory, "who_infants.json") # 2 weeks to 2 years with open(data_path) as json_file: WHO_INFANTS_DATA = json.load(json_file) json_file.close() diff --git a/rcpchgrowth/constants/reference_constants.py b/rcpchgrowth/constants/reference_constants.py index 02f3049..b27f60f 100644 --- a/rcpchgrowth/constants/reference_constants.py +++ b/rcpchgrowth/constants/reference_constants.py @@ -35,8 +35,8 @@ WHO_REFERENCES = [WHO_2006_INFANT, WHO_2006_CHILD, WHO_2007_CHILD] # WHO references WHO_2006_REFERENCE_LOWER_THRESHOLD = ((42 * 7) - (40 * 7)) / 365.25 # 42 weeks as decimal age # 2 weeks as decimal age -WHO_2006_REFERENCE_UPPER_THRESHOLD = 5.0 # 5 years as decimal age -WHO_2007_REFERENCE_LOWER_THRESHOLD = 5.0 # 5 years as decimal age +WHO_2006_REFERENCE_UPPER_THRESHOLD = 1856/365.25 # 5 years and one month just under as decimal age +WHO_2007_REFERENCE_LOWER_THRESHOLD = (61/30.4375)/365.25 # 5 years and one month as decimal age (61 mths) WHO_2007_REFERENCE_UPPER_THRESHOLD = 19.0 # 19 years as decimal age # 23 weeks is the lowest decimal age available on the UK90 charts diff --git a/rcpchgrowth/data_tables/bayley-pineau.pdf b/rcpchgrowth/data_tables/bayley-pineau.pdf deleted file mode 100644 index afa1554..0000000 Binary files a/rcpchgrowth/data_tables/bayley-pineau.pdf and /dev/null differ diff --git a/rcpchgrowth/data_tables/bayley_pinneau/bayley_pinneau_boys_advanced.csv b/rcpchgrowth/data_tables/bayley_pinneau/bayley_pinneau_boys_advanced.csv new file mode 100644 index 0000000..88f2680 --- /dev/null +++ b/rcpchgrowth/data_tables/bayley_pinneau/bayley_pinneau_boys_advanced.csv @@ -0,0 +1,43 @@ +SKELETAL AGES 7 THROUGH 11 YEARS,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Skeletal Age,7-0,7-3,7-6,7-9,8-0,8-3,8-6,8-9,9-0,9-3,9-6,9-9,10,10-3,10-6,10-9,11,11-3,11-6,11-9,12-0,12-3,12-6,12-9,13-0,13-3,13-6,13-9,14-0,14-3,14-6,14-9,15-0,15-3,15-6,15-9,16-0,16-3,16-6,16-9,17-0 +% of Mature Height,67,67.6,68.3,68.9,69.6,70.3,70.9,71.5,72,72.8,73.4,74.1,74.7,75.3,75.8,76.3,76.7,77.6,77.8,80,80.9,81.8,82.8,83.9,85,86.3,87.5,89,90.5,91.8,93,94.3,95.8,96.7,97.1,97.6,98,98.3,98.5,98.8, +Ht. (inches),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +41,61.2,60.7,60,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +42,62.7,62.1,61.5,61,60.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +43,64.2,63.6,63,62.4,61.8,61.2,60.6,60.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +44,65.7,65.1,64.4,63.8,63.2,62.6,62.1,61.5,61.1,60.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +45,67.2,66.6,65.9,65.3,64.7,64,63.5,62.9,62.5,61.8,61.3,60.7,60.2,,,,,,,,,,,,,,,,,,,,,,,,,,,, +46,68.7,68,67.3,66.8,66.1,65.4,64.9,64.3,63.9,63.2,62.7,62.1,61.6,61.1,60.7,60.3,60,,,,,,,,,,,,,,,,,,,,,,,, +47,70.1,69.5,68.8,68.2,67.5,66.9,66.3,65.7,65.3,64.6,64,63.4,62.9,62.4,62,61.6,61.3,60.6,,,,,,,,,,,,,,,,,,,,,,, +48,71.6,71,70.3,69.7,69,68.3,67.7,67.1,66.7,65.9,65.4,64.8,64.3,63.7,63.3,62.9,62.6,61.9,61.1,60,,,,,,,,,,,,,,,,,,,,, +49,73.1,72.5,71.7,71.1,70.4,69.7,69.1,68.5,68.1,67.3,66.8,66.1,65.6,65.1,64.6,64.2,63.9,63.1,62.3,61.3,60.6,,,,,,,,,,,,,,,,,,,, +50,74.6,74,73.2,72.6,71.8,71.1,70.5,69.9,69.4,68.7,68.1,67.5,66.9,66.4,66,65.3,65.2,64.4,63.6,62.5,61.8,61.1,60.4,,,,,,,,,,,,,,,,,, +51,76.2,75.4,74.7,74,73.3,72.5,71.9,71.3,70.8,70.1,69.5,68.8,68.3,67.7,67.3,66.8,66.5,65.7,64.9,63.8,63,62.3,61.6,60.8,60,,,,,,,,,,,,,,,, +52,77.6,76.9,76.1,75.5,74.7,74,73.3,72.7,72.2,71.4,70.8,70.2,69.6,69.1,68.6,68.2,67.8,67.3,66.2,66,64.3,63.6,62.8,62,61.2,60.3,,,,,,,,,,,,,,, +53,79.1,78.4,77.6,76.9,76.2,75.4,74.8,74.1,73.6,72.8,72.2,71.5,71,70.4,69.9,69.5,69.1,68.3,67.4,66.3,65.5,64.8,64,63.2,62.4,61.4,60.6,,,,,,,,,,,,,, +54,80.6,79.9,79.1,78.4,77.6,76.8,76.2,75.5,75,74.2,73.6,72.9,72.3,71.7,71.2,70.8,70.4,69.6,68.7,67.5,66.7,66,65.2,64.4,63.6,62.9,62.1,60.7,,,,,,,,,,,,, +55,,,80.5,79.8,79,78.3,77.6,76.9,76.4,75.9,75.2,74.6,73.6,73,72.6,72.1,71.7,70.9,70,68.8,68,67.2,66.4,65.6,64.7,63.9,63,61.8,60.8,,,,,,,,,,,, +56,,,,,,80.5,79.7,79,78.3,77.8,77.1,76.6,75,74.4,73.9,73.4,73,72.2,71.2,70,69.2,68.5,67.6,66.7,65.9,64.9,64,62.9,61.9,61,60.2,,,,,,,,,, +57,,,,,,,,80.4,79.7,79.2,78.5,77.8,76.3,75.7,75.2,74.7,74.3,73.5,72.5,71.3,70.5,69.7,68.8,67.9,67.1,66,65.1,64,63,62.1,61.3,60.4,,,,,,,,, +58,,,,,,,,,80.6,79.7,79,78.3,77.6,77,76.5,76,75.6,74.7,73.8,72.5,71.7,70.9,70,69.1,68.2,67.2,66.3,65.2,64.1,63.2,62.4,61.5,60.5,60,,,,,,, +59,,,,,,,,,,,80.4,79.6,79,78.4,77.8,77.3,76.9,76,75.1,73.8,72.9,72.1,71.3,70.3,69.4,68.4,67.4,66.3,65.2,64.3,63.4,62.6,61.6,61,60.8,,,,,, +60,,,,,,,,,,,,,80.3,79.7,79.2,78.6,78.2,76,76.3,75,74.2,73.4,72.5,71.5,70.6,69.5,68.6,67.4,66.3,65.4,64.5,63.6,62.6,62,61.8,60.5,60.2,60,,, +61,,,,,,,,,,,,,,,80.5,79.9,79.5,76.3,77.6,76.3,75.4,74.6,73.7,72.7,71.8,70.7,69.7,68.6,67.4,66.4,65.6,64.7,63.7,63.1,62.8,61.5,61.2,61,60.9,60.7,60.6 +62,,,,,,,,,,,,,,,,81.3,80.8,77.9,78.9,77.5,76.6,75.8,74.9,73.9,72.9,71.8,70.9,69.7,68.5,67.5,66.7,65.7,64.7,64.1,63.9,62.6,62.2,62.1,61.9,61.7,61.6 +63,,,,,,,,,,,,,,,,,,,80.2,78.8,77.9,77,76.1,75.1,74.1,73,72,70.8,69.6,68.7,67.7,66.8,65.8,65.1,64.9,63.5,63.1,62.9,62.8,62.6,62.6 +64,,,,,,,,,,,,,,,,,,,,80,79.1,78.2,77.3,76.3,75.3,74.2,73.1,71.9,70.7,69.7,68.7,67.9,66.8,66.2,65.9,64.5,64.3,64.1,64,63.8,63.6 +65,,,,,,,,,,,,,,,,,,,,,80.3,79.5,78.5,77.5,76.5,75.3,74.3,73,71.8,70.8,70,68.9,67.8,67.2,66.9,65.6,65.3,65.1,65,64.8,64.6 +66,,,,,,,,,,,,,,,,,,,,,,80.7,79.7,78.7,77.6,76.5,75.4,74.2,72.9,71.9,71,70,68.9,68.3,68,66.6,66.3,66.1,66,65.8,65.7 +67,,,,,,,,,,,,,,,,,,,,,,,80.9,79.9,78.8,77.6,76.6,75.4,74,73,72.2,71.1,70,69.3,69,67.6,67.3,67.1,67,66.8,66.7 +68,,,,,,,,,,,,,,,,,,,,,,,,,80,78.8,77.7,76.4,75.1,74.1,73.1,72.1,71,70.3,70,68.6,68.4,68.2,68,67.8,67.7 +69,,,,,,,,,,,,,,,,,,,,,,,,,,80,78.9,77.8,76.2,75.2,74.2,73.2,72,71.4,71.1,69.7,69.4,69.2,69,68.8,68.7 +70,,,,,,,,,,,,,,,,,,,,,,,,,,,80,78.7,77.3,76.3,75.3,74.2,73.1,72.4,72.1,70.7,70.4,70.2,70,69.8,69.7 +71,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.9,79.6,78.4,77.3,76.3,75.3,74.1,73.4,73.1,71.7,71.4,71.2,71.1,70.8 +72,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.7,79.5,78.5,77.4,76.2,75.2,74.5,74.2,72.7,72.4,72.2,72.1,71.9 +73,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.6,79.6,78.5,77.2,76.2,75.2,74.5,73.8,73.5,72.7,72.4,72.2 +74,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.6,79.5,78.3,77.2,76.2,75.2,74.5,73.8,73.5,72.7,72.4 +75,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.6,79.5,78.3,77.2,76.2,75.2,74.5,73.8,73.5,72.7 +76,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.4,79.6,78.3,77.3,76.2,75.2,74.5,73.8,73.5 +77,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.4,79.6,78.3,77.3,76.2,75.2,74.5,73.8 +78,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.7,79.8,78.9,78.2,77.5,76.8,76.1 +79,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79.9,79.8,79.3,79.2,78.9,78.8 \ No newline at end of file diff --git a/rcpchgrowth/data_tables/bayley_pinneau/bayley_pinneau_boys_delayed.csv b/rcpchgrowth/data_tables/bayley_pinneau/bayley_pinneau_boys_delayed.csv new file mode 100644 index 0000000..956151d --- /dev/null +++ b/rcpchgrowth/data_tables/bayley_pinneau/bayley_pinneau_boys_delayed.csv @@ -0,0 +1,31 @@ +Skeletal Age,6-0,6-3,6-6,6-9,7-0,7-3,7-6,7-9,8-0,8-3,8-6,8-9,9-0,9-3,9-6,9-9,10-0,10-3,10-6,10-9,11-0,11-3,11-6,11-9,12-0,12-3,12-6,12-9,13-0 +% of Mature Height,68,69,70,70.9,71.8,72.8,73.8,74.7,75.6,76.5,77.3,77.9,78.6,79.4,80,80.7,81.2,81.6,81.9,82.1,82.3,82.7,83.2,83.9,84.5,85.2,86,86.9,88 +Mature Height,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Ht. (inches),,,,,,,,,,,,,,,,,,,,,,,,,,,,, +41,60.3,,,,,,,,,,,,,,,,,,,,,,,,,,,, +42,61.8,60.9,60,,,,,,,,,,,,,,,,,,,,,,,,,, +43,63.2,62.3,61.4,60.6,,,,,,,,,,,,,,,,,,,,,,,,, +44,64.7,63.8,62.9,62.1,61.3,60.4,,,,,,,,,,,,,,,,,,,,,,, +45,66.2,65.2,64.3,63.5,62.7,61.8,61,60.2,,,,,,,,,,,,,,,,,,,,, +46,67.6,66.7,65.7,64.9,64.1,63.2,62.3,61.6,60.8,,,,,,,,,,,,,,,,,,,, +47,69.1,68.1,67.1,66.3,65.5,64.6,63.7,62.9,62.2,60.1,,,,,,,,,,,,,,,,,,, +48,70.6,69.6,68.6,67.7,66.9,65.9,65,64.3,63.5,61.4,60.8,60.3,,,,,,,,,,,,,,,,, +49,72.1,71,70,69.1,68.3,67.3,66.4,65.6,64.8,62.7,62.1,61.6,61.1,60.5,60,,,,,,,,,,,,,, +50,73.5,72.5,71.4,70.5,69.6,68.7,67.8,66.9,66.1,64.1,63.4,62.9,62.3,61.7,61.3,60.7,60.3,60,,,,,,,,,,, +51,75,73.9,72.9,71.9,71,70.1,69.3,68.5,67.5,65.4,64.7,64.2,63.6,63,62.5,62,61.6,61.3,61.1,60.9,60.8,60.5,60.1,,,,,, +52,76.5,75.4,74.3,73.3,72.4,71.4,70.5,69.8,68.8,66.7,66,65.5,64.9,64.2,63.8,63.2,62.8,62.5,62.3,62.1,62,61.7,61.3,60.8,60.4,,,, +53,77.9,76.8,75.7,74.8,73.8,72.8,71.8,71,70.1,68,67.3,66.8,66.2,65.5,65,64.4,64,63.7,63.5,63.3,63.2,62.9,62.5,62,61.5,61,60.5,, +54,79.4,78.3,77.1,76.2,75.2,74.2,73.2,72.3,71.4,69.3,68.6,68,67.4,66.8,66.3,65.7,65.3,65,64.7,64.4,64.1,64.1,63.7,63.2,62.7,62.2,61.6,61,60.2 +55,80.9,79.7,78.6,77.6,76.6,75.5,74.5,73.6,72.8,70.6,69.9,69.3,68.7,68,67.5,66.9,66.5,66.2,65.9,65.8,65.6,65.3,64.9,64.4,63.9,63.4,62.8,62.1,61.4 +56,,,80,79,78,76.9,75.9,75,74.1,71.9,71.2,70.6,70,69.3,68.8,68.2,67.7,67.4,67.2,67,66.8,66.5,66.1,65.6,65.1,64.6,64,63.3,62.5 +57,,,,80.4,79.4,78.3,77.2,76.3,75.4,73.2,72.4,71.9,71.2,70.5,70,69.4,69,68.6,68.4,68.2,68,67.7,67.3,66.7,66.3,65.7,65.1,64.4,63.6 +58,,,,,80.8,79.7,78.6,78.8,76.7,74.5,73.7,73.2,72.5,71.8,71.3,70.6,70.2,69.9,69.6,69.4,69.3,68.9,68.5,67.9,67.5,66.9,66.3,65.6,64.8 +59,,,,,,,79.9,79,78,75.8,75,74.5,73.8,73,72.5,72.5,71.4,71.1,71.1,70.8,70.5,70.5,70.1,69.7,69.1,68.6,67.4,67.4,65.9 +60,,,,,,,,80.3,79.4,77.1,77.6,75.7,75.1,74.3,73.8,73.1,72.7,72.3,72,71.9,71.7,71.3,70.9,70.3,69.8,69.2,68.8,68.8,67 +61,,,,,,,,,80.7,78.4,78.1,77,76.3,75.6,75,74.4,73.9,73.5,73.3,73.1,72.9,72.6,72.1,71.5,71,70.4,69.8,69.8,68.2 +62,,,,,,,,,,79.7,78.9,78.3,77.6,76.8,76.3,75.6,75.1,74.8,74.5,74.3,74.1,73.8,73.3,72.7,72.2,71.6,70.9,70.9,69.3 +63,,,,,,,,,,,80.2,79.6,78.9,78.1,77.5,76.8,76.4,76,75.7,75.5,75.3,75,74.5,73.9,73.4,72.8,72.1,72.1,70.5 +64,,,,,,,,,,,,80.9,80.2,79.3,78.8,78.1,77.6,77.2,76.9,76.7,76.5,76.2,75.7,75.1,74.5,73.9,73.3,73.3,71.6 +65,,,,,,,,,,,,,,80.6,80,79.3,78.8,78.4,78.1,78,77.8,77.4,76.9,76.3,75.7,75.1,74.4,74.4,72.7 +66,,,,,,,,,,,,,,,,80.5,80,79.7,79.4,79.2,79,78.6,78.1,77.5,76.9,76.3,75.6,75.6,73.9 +67,,,,,,,,,,,,,,,,,,80.9,80.6,80.4,80.2,79.8,79.3,78.7,78.1,77.5,76.7,76.7,75 \ No newline at end of file diff --git a/rcpchgrowth/data_tables/bayley_pinneau/bayley_pinneau_boys_normal.csv b/rcpchgrowth/data_tables/bayley_pinneau/bayley_pinneau_boys_normal.csv new file mode 100644 index 0000000..86046fe --- /dev/null +++ b/rcpchgrowth/data_tables/bayley_pinneau/bayley_pinneau_boys_normal.csv @@ -0,0 +1,40 @@ +Skeletal Age,7-0,7-3,7-6,7-9,8-0,8-3,8-6,8-9,9-0,9-3,9-6,9-9,10,10-3,10-6,10-9,11,11-3,11-6,11-9,12-0,12-3,12-6,12-9,13-0,13-3,13-6,13-9,14-0,14-3,14-6,14-9,15-0,15-3,15-6,15-9,16-0,16-3,16-6,16-9,17-0,17-3,17-6,17-9,18-0,18-3,18-6 +Mature Height,,,,,,,,,,,,,,,,,,,,,,,,,87.6,89,90.2,91.4,92.7,93.8,94.8,95.8,96.8,97.3,97.6,98,98.2,98.5,98.7,98.9,99.1,99.3,99.4,99.5,99.6,99.8,100 +Ht. (inches),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +42,60.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +43,61.9,61.3,60.6,60.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +44,63.3,62.7,62.1,61.5,60.9,60.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +45,64.7,64.1,63.5,62.8,62.2,61.6,60.9,60.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +46,66.2,65.5,64.9,64.2,63.6,62.9,62.2,61.7,61.2,60.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +47,67.6,67,66.3,65.6,65,64.3,63.6,63,62.5,61.8,61.1,60.5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +48,69.1,68.4,67.7,67,66.4,65.7,65,64.3,63.8,63.1,62.4,61.8,61.2,60.7,60.4,60,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +49,70.5,69.8,69.1,68.4,67.8,67,66.3,65.7,65.2,64.4,63.7,63.1,62.5,61.9,61.6,61.3,60.9,60.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +50,71.9,71.2,70.5,69.8,69.2,68.4,67.7,67,66.5,65.7,65,64.4,63.8,63.2,62.9,62.5,62.2,61.6,61.1,60.5,,,,,,,,,,,,,,,,,,,,,,,,,,, +51,73.4,72.6,71.9,71.2,70.5,69.8,69,68.4,67.8,67,66.3,65.6,65.1,64.5,64.2,63.8,63.4,62.8,62.3,61.7,61.1,60.5,59.8,,,,,,,,,,,,,,,,,,,,,,,, +52,74.8,74.1,73.3,72.6,71.9,71.1,70.4,69.7,69.1,68.3,67.6,66.9,66.3,65.7,65.4,65,64.7,64,63.6,62.9,62.3,61.7,61,60.3,,,,,,,,,,,,,,,,,,,,,,, +53,76.3,75.5,74.8,74,73.3,72.5,71.7,71,70.5,69.6,68.9,68.2,67.6,67,66.7,66.3,65.9,65.3,64.8,64.1,63.5,62.9,62.1,61.4,60.5,,,,,,,,,,,,,,,,,,,,,, +54,77.7,76.9,76.2,75.4,74.7,73.9,73.1,72.4,71.8,71,70.2,69.5,68.9,68.3,67.9,67.5,67.2,66.5,66,65.3,64.7,64.1,63.3,62.6,61.6,60.7,,,,,,,,,,,,,,,,,,,,, +55,79.1,78.3,77.6,76.8,76.1,75.2,74.4,73.7,73.1,72.3,71.5,70.8,70.2,69.5,69.2,68.8,68.4,67.7,67.2,66.5,65.9,65.2,64.5,63.7,62.8,61.8,61,60.2,,,,,,,,,,,,,,,,,,, +56,80.6,79.8,79,78.2,77.5,76.6,75.8,75.1,74.5,73.6,72.8,72.1,71.4,70.8,70.4,70,69.7,69,68.5,67.7,67.1,66.4,65.6,64.9,63.9,62.9,62.1,61.3,60.4,,,,,,,,,,,,,,,,,, +57,,,80.4,79.6,78.8,78,77.1,76.4,75.8,74.9,74.1,73.4,72.7,72.1,71.7,71.3,70.9,70.2,69.7,68.9,68.3,67.6,66.8,66,65.1,64,63.2,62.4,61.5,60.8,60.1,,,,,,,,,,,,,,,, +58,,,,80.2,79.3,78.5,77.7,77.1,76.2,75.4,74.6,74,73.3,73,72.5,72.1,71.4,70.9,70.1,69.5,68.8,68,67.2,,66.2,65.2,64.3,63.5,62.6,61.8,61.2,60.5,,,,,,,,,,,,,,, +59,,,,,,80.7,79.8,79.1,78.5,77.5,76.7,75.9,75.3,74.6,74.2,73.8,73.4,72.7,72.1,71.3,70.7,70,69.2,68.4,67.4,66.3,65.4,64.6,63.6,62.9,62.2,61.6,61,60.6,60.5,60.2,60.1,,,,,,,,,, +60,,,,,,,,80.4,79.8,78.8,78,77.2,76.5,75.9,75.5,75,74.6,73.9,73.3,72.6,71.9,71.2,70.5,69.7,68.5,67.4,66.5,65.6,64.7,64,63.3,62.6,62,61.7,61.5,61.2,61.1,60.9,60.8,60.7,60.5,60.4,60.4,60.3,60.2,60.1,60 +61,,,,,,,,,,80.2,79.3,78.5,77.8,77.1,76.7,76.3,75.9,75.1,74.6,73.8,73.1,72.4,71.5,70.7,69.6,68.5,67.6,66.7,65.8,65,64.3,63.7,63,62.7,62.5,62.2,62.1,61.9,61.8,61.7,61.6,61.4,61.4,61.3,61.2,61.1,61 +62,,,,,,,,,,,80.6,79.8,79.1,78.4,78,77.5,77.1,76.4,75.8,75,74.3,73.5,72.7,71.8,70.8,69.7,68.7,67.8,66.9,66.1,65.4,64.7,64.1,63.7,63.5,63.3,63.1,62.9,62.8,62.7,62.6,62.4,62.4,62.3,62.2,62.1,62 +63,,,,,,,,,,,,,80.4,79.6,79.2,78.8,78.4,77.6,77,76.2,75.5,74.7,73.9,73,71.9,70.8,69.8,68.9,68,67.2,66.5,65.8,65.1,64.7,64.5,64.3,64.2,64,63.9,63.7,63.6,63.4,63.4,63.3,63.3,63.1,63 +64,,,,,,,,,,,,,,80.9,80.5,80,79.6,78.8,78.2,77.4,76.7,75.9,75,74.2,73.1,71.9,71,70,69,68.2,67.5,66.8,66.1,65.8,65.6,65.3,65.2,65,64.8,64.7,64.6,64.4,64.4,64.3,64.1,64,63.9 +65,,,,,,,,,,,,,,,,,80.8,80,79.5,78.6,77.9,77.1,76.2,75.3,74.2,73,72.1,71.1,70.1,69.3,68.6,67.8,67.2,66.8,66.6,66.3,66.2,66,65.9,65.7,65.6,65.5,65.5,65.3,65.3,65.1,65 +66,,,,,,,,,,,,,,,,,,,80.7,79.8,79.1,78.3,77.4,76.5,75.3,74.2,73.2,72.2,71.2,70.4,69.6,68.9,68.2,67.8,67.6,67.3,67.2,67,66.9,66.7,66.6,66.5,66.4,66.3,66.3,66.1,66 +67,,,,,,,,,,,,,,,,,,,,,80.3,79.5,78.5,77.6,76.5,75.3,74.3,73.3,72.3,71.4,70.7,69.9,69.2,68.9,68.6,68.4,68.2,68,67.9,67.7,67.6,67.5,67.4,67.3,67.3,67.1,67 +68,,,,,,,,,,,,,,,,,,,,,,80.7,79.7,78.8,77.6,76.4,75.4,74.4,73.4,72.5,71.7,71,70.3,69.9,69.7,69.4,69.2,69,68.9,68.8,68.6,68.5,68.4,68.3,68.3,68.1,68 +69,,,,,,,,,,,,,,,,,,,,,,,80.9,80,78.8,77.5,76.5,75.5,74.4,73.6,72.8,72,71.3,70.9,70.7,70.4,70.3,70,69.9,69.8,69.6,69.5,69.4,69.3,69.3,69.1,69 +70,,,,,,,,,,,,,,,,,,,,,,,,,79.9,78.7,77.7,76.6,75.5,74.6,73.8,73.1,72.3,71.9,71.7,71.4,71.3,71.1,70.9,70.8,70.6,70.5,70.4,70.4,70.3,70.1,70 +71,,,,,,,,,,,,,,,,,,,,,,,,,,79.8,78.7,77.7,76.6,75.7,74.9,74.1,73.4,73,72.7,72.4,72.3,72.1,71.9,71.8,71.6,71.5,71.4,71.3,71.3,71.1,71 +72,,,,,,,,,,,,,,,,,,,,,,,,,,80.9,79.8,78.8,77.7,76.8,75.9,75.2,74.4,74,73.8,73.5,73.3,73.1,73,72.8,72.7,72.5,72.4,72.4,72.3,72.1,72 +73,,,,,,,,,,,,,,,,,,,,,,,,,,,80.9,79.9,78.7,78.8,77.8,77,76.2,75.4,75,74.5,74.3,74.1,74,73.8,73.7,73.5,73.4,73.4,73.3,73.1,73 +74,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79.8,78.9,78.1,77.2,76.4,76,75.8,75.5,75.4,75.1,75,74.8,74.7,74.5,74.4,74.4,74.3,74.1,74 +75,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.9,80,79.1,78.3,77.5,77.1,76.8,76.5,76.4,76.1,76,75.8,75.7,75.5,75.4,75.4,75.3,75.2,75 +76,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.2,79.3,78.5,78.1,77.9,77.6,77.4,77.2,77,76.8,76.7,76.5,76.5,76.4,76.3,76.2,76 +77,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.4,79.5,79.1,78.9,78.6,78.4,78.2,78,77.9,77.7,77.5,77.5,77.4,77.3,77.2,77 +78,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80.6,80.2,79.9,79.6,79.4,79.2,79,78.9,78.7,78.5,78.5,78.4,78.3,78.2,78 \ No newline at end of file diff --git a/rcpchgrowth/data_tables/bayley_pinneau/bayley_pinneau_girls_advanced.csv b/rcpchgrowth/data_tables/bayley_pinneau/bayley_pinneau_girls_advanced.csv new file mode 100644 index 0000000..d8a6ca9 --- /dev/null +++ b/rcpchgrowth/data_tables/bayley_pinneau/bayley_pinneau_girls_advanced.csv @@ -0,0 +1,40 @@ +Skeletal Age,7-0,7-3,7-6,7-9,8-0,8-3,8-6,8-10,9-0,9-3,9-6,9-9,10-0,10-3,10-6,10-9,11-0,11-3,11-6,11-9,12-0,12-3,12-6,12-9,13-0,13-3,13-6,13-9,14-0,14-3,14-6,14-9,15-0,15-3,15-6,15-9,16-0,16-3,16-6,16-9,17-0,17-3 +% of Mature Height Ht. (inches),71.2,72.2,73.2,74.2,75,76,77.1,78.4,79,80,80.9,81.9,82.8,84.1,85.6,87,88.3,89.7,89.1,89.7,90.1,91.3,92.4,93.5,94.5,95.5,96.8,96.8,97.2,97.7,98,98.3,98.6,98.8,99,99.2,99.3,99.4,99.5,99.7,99.8,99.95 +37,52,51.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +38,53.4,52.6,51.9,51.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +39,54.8,54,53.3,52.6,52,51.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +40,56.2,55.4,54.6,53.9,53.3,52.6,51.9,51,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +41,57.6,56.8,56,55.3,54.7,53.9,53.2,52.3,51.9,51.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +42,59,58.2,57.4,56.6,56,55.3,54.5,53.8,53.2,52.5,51.9,51.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +43,60.4,59.6,58.7,58,57.3,56.6,55.8,54.8,54.4,53.8,53.2,52.5,51.9,51.1,,,,,,,,,,,,,,,,,,,,,,,,,,,, +44,61.8,60.9,60.1,59.3,58.7,57.9,57.1,56.1,55.7,55,54.4,53.7,53.1,52.3,51.4,,,,,,,,,,,,,,,,,,,,,,,,,,, +45,63.2,62.3,61.5,60.6,60,59.2,58.4,57.4,57,56.3,55.6,54.9,54.3,53.5,52.6,51.7,51,,,,,,,,,,,,,,,,,,,,,,,,, +46,64.6,63.7,62.8,62,61.3,60.5,59.7,58.7,58.2,57.5,56.9,56.2,55.6,54.7,53.7,52.9,52.1,51.9,51.6,51.3,51.1,,,,,,,,,,,,,,,,,,,,, +47,66,65.1,64.2,63.3,62.7,61.8,61,59.9,59.5,58.8,58.1,57.4,56.8,55.9,54.9,54,53.2,53,52.7,52.4,52.2,51.5,,,,,,,,,,,,,,,,,,,, +48,67.4,66.5,65.6,64.7,64,63.2,62.3,61.2,60.8,60,59.3,58.6,58,57.1,56.1,55.2,54.4,54.1,53.9,53.5,53.3,52.6,51.9,51.3,,,,,,,,,,,,,,,,,, +49,68.8,67.9,66.9,66,65.3,64.5,63.6,62.5,62,61.3,60.6,59.8,59.2,58.3,57.2,56.3,55.5,55.2,55,54.6,54.4,53.7,53,52.4,51.9,51.3,50.9,,,,,,,,,,,,,,, +50,70.2,69.3,68.3,67.4,66.7,65.8,64.9,63.8,63.3,62.5,61.8,61.1,60.4,59.5,58.4,57.5,56.6,56.4,56.1,55.7,55.5,54.8,54.1,53.5,52.9,52.4,51.9,51.7,51.4,51.2,51,,,,,,,,,,, +51,71.6,70.6,69.7,68.7,68,67.1,66.1,65.1,64.6,63.8,63,62.3,61.6,60.6,59.6,58.6,57.8,57.5,57.2,56.9,56.6,55.9,55.2,54.5,54,53.4,53,52.7,52.5,52.2,52,51.9,51.7,51.6,51.5,51.4,51.4,51.3,51.3,51.2,51.1,51 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+57,,,,,,,73.9,72.7,72,71.3,70.5,69.6,69,67.8,66.6,65.5,64.3,64.3,64,63.5,63.3,62.4,61.7,61,60.3,59.7,59.2,58.9,58.6,58.3,58.2,58,57.8,57.7,57.6,57.5,57.4,57.3,57.3,57.2,57.1,57 +58,,,,,,,,74,73.3,72.5,71.7,70.8,70.2,68.9,67.8,66.7,65.4,65.4,5.1,64.7,64.4,63.5,62.8,62,61.4,60.7,60.2,59.9,59.7,59.4,59.2,59,58.8,58.7,58.6,58.5,58.4,58.3,58.3,58.2,58.1,58 +59,,,,,,,,,74.5,73.8,72.9,72,71.3,70.1,68.9,67.8,66.5,66.5,66.2,65.8,65.5,64.6,63.9,63.1,62.4,61.8,61.3,61,60.7,60.4,60.2,60,59.8,59.7,59.6,59.5,59.4,59.4,59.3,59.2,59.1,59 +60,,,,,,,,,,,74.2,73.3,72.5,71.3,70.1,69,67.6,67.6,67.3,66.9,66.6,65.7,64.9,64.2,63.5,62.8,62.3,62,61.7,61.4,61.2,61,60.9,60.7,60.6,60.5,60.4,60.4,60.3,60.2,60.1,60 +61,,,,,,,,,,,,74.5,73.7,72.4,71.3,70.1,68.8,68.8,68.5,68,67.7,66.8,66,65.2,64.6,63.9,63.3,63,62.8,62.4,62.2,62.1,61.9,61.7,61.6,61.5,61.4,61.4,61.3,61.2,61.1,61 +62,,,,,,,,,,,,,74.9,73.6,72.4,71.3,69.9,69.9,69.6,69.1,68.8,67.9,67.1,66.3,65.6,64.9,64.4,64,63.8,63.5,63.3,63.1,62.9,62.8,62.6,62.5,62.4,62.4,62.3,62.2,62.1,62 +63,,,,,,,,,,,,,,74.8,73.6,72.4,71,71,70.7,70.2,69.9,69,68.2,67.4,66.7,66,65.4,65.1,64.8,64.5,64.3,64.1,63.9,63.8,63.6,63.5,63.4,63.4,63.3,63.2,63.1,63 +64,,,,,,,,,,,,,,,74.7,73.6,72.2,72.2,71.8,71.3,71,70.1,69.3,68.4,67.7,67,66.5,66.1,65.8,65.5,65.3,65.1,64.9,64.8,64.6,64.5,64.4,64.4,64.3,64.2,64.1,64 +65,,,,,,,,,,,,,,,,74.7,73.3,3.3,72.9,72.5,72.1,71.2,70.3,69.5,68.8,68.1,67.5,67.1,66.9,66.6,66.3,66.1,65.9,65.8,65.7,65.5,65.5,65.4,65.3,65.2,65.1,65 +66,,,,,,,,,,,,,,,,,74.4,74.4,74.1,73.6,73.3,72.3,71.4,70.6,69.8,69.1,68.5,68.2,67.9,67.6,67.3,67.1,66.9,66.8,66.7,66.5,66.5,66.4,66.3,66.2,66.1,66 +67,,,,,,,,,,,,,,,,,,,,74.7,74.4,73.4,72.5,71.7,70.9,70.2,69.6,69.2,68.9,68.6,68.4,68.2,68,67.8,67.7,67.5,67.5,67.4,67.3,67.2,67.1,67 +68,,,,,,,,,,,,,,,,,,,,,,74.5,73.6,72.7,72,71.2,70,70.2,70,69.6,69.4,69.2,69,68.8,68.7,68.6,68.5,68.4,68.3,68.2,68.1,68 +69,,,,,,,,,,,,,,,,,,,,,,,74.7,73.8,73,72.3,71.7,71.3,71,70.6,70.4,70.2,70,69.8,69.7,69.6,69.5,69.4,69.3,69.2,69.1,69 +70,,,,,,,,,,,,,,,,,,,,,,,,74.9,74.1,73.3,72.7,72.3,72,71.6,71.4,71.2,71,70.8,70.7,70.6,70.5,70.4,70.3,70.2,70.1,70 +71,,,,,,,,,,,,,,,,,,,,,,,,,,74.3,73.7,73.3,73,72.7,72.4,72.2,72,71.9,71.7,71.6,71.5,71.4,71.4,71.2,71.1,71 +72,,,,,,,,,,,,,,,,,,,,,,,,,,,74.8,74.4,74.1,73.7,73.5,73.2,73,72.9,72.7,72.6,72.5,72.4,72.4,72.2,72.1,72 +73,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74.7,74.5,74.3,74,73.9,73.7,73.6,73.5,73.1,73.1,73.2,73.1,73 +74,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74.9,74.4,74.6,74.5,74.4,74.4,74.2,74.1,74 \ No newline at end of file diff --git a/rcpchgrowth/data_tables/bayley_pinneau/bayley_pinneau_girls_delayed.csv b/rcpchgrowth/data_tables/bayley_pinneau/bayley_pinneau_girls_delayed.csv new file mode 100644 index 0000000..f3b523a --- /dev/null +++ b/rcpchgrowth/data_tables/bayley_pinneau/bayley_pinneau_girls_delayed.csv @@ -0,0 +1,39 @@ +Skeletal Age,6-0,6-3,6-6,6-10,7-0,7-3,7-6,7-10,8-0,8-3,8-6,8-10,9-0,9-3,9-6,9-9,10-0,10-3,10-6,10-9,11-0,11-3,11-6,11-9,12-0,12-3,12-6,12-9,13-0,13-3,13-6,13-9,14-0,14-3,14-6,14-9,15-0,15-3,15-6,15-9,16-0,16-3,16-6,16-9,17-0 +% of Mature Height Ht. (inches),73.3,74.2,75.1,76.3,77,77.9,78.8,79.7,80.4,81.3,82.3,83.6,84.1,85.1,85.8,86.6,87.4,88.4,89.6,90.7,91.8,92.2,92.6,92.9,93.2,94.2,94.9,95.7,96.4,97.1,97.7,98.1,98.3,98.6,98.9,99.2,99.4,99.5,99.6,99.7,99.8,99.9,99.9,99.95,100 +38,51.8,51.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +39,53.2,52.6,51.9,51.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +40,54.6,53.9,53.3,52.4,51.9,51.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +41,55.9,55.3,54.6,53.7,53.2,52.6,52,51.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +42,57.3,56.6,55.9,55,54.5,53.9,53.3,52.7,52.2,51.7,51,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +43,58.7,58,57.3,56.4,55.8,55.2,54.6,54,53.5,52.9,52.2,51.4,51.1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +44,60,59.3,58.6,57.7,57.1,56.5,55.9,55.2,54.7,54.1,53.5,52.6,52.3,51.7,51.3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +45,61.4,60.6,59.9,59,58.4,57.8,57.1,56.5,56,55.4,54.7,53.8,53.5,52.9,52.4,52,51.5,,,,,,,,,,,,,,,,,,,,,,,,,,,, +46,62.8,62,61.3,60.3,59.7,59.1,58.4,57.7,57.2,56.6,55.9,55,54.7,54.1,53.6,53.1,52.6,52,51.3,,,,,,,,,,,,,,,,,,,,,,,,,, +47,64.1,63.3,62.6,61.6,61,60.3,59.6,59,58.5,57.8,57.1,56.2,55.9,55.2,54.8,54.3,53.8,53.2,52.5,51.8,51.2,51,,,,,,,,,,,,,,,,,,,,,,, +48,65.5,64.7,63.9,62.9,62.3,61.6,60.9,60.2,59.7,59,58.3,57.4,57.1,56.4,55.9,55.4,54.9,54.3,53.6,52.9,52.3,52.1,51.8,51.7,51.5,51,,,,,,,,,,,,,,,,,,, +49,66.9,66,65.2,64.2,63.6,62.9,62.2,61.5,60.9,60.3,59.5,58.6,58.3,57.6,57.1,56.6,56.1,55.4,54.7,54,53.4,53.1,52.9,52.7,52.6,52,51.6,51.2,,,,,,,,,,,,,,,,, +50,68.2,67.4,66.6,65.5,64.9,64.2,63.5,62.7,62.2,61.5,60.8,59.9,59.5,58.8,58.3,57.7,57.2,56.6,55.8,55.1,54.5,54.2,54,53.8,53.6,53.1,52.7,52.2,51.9,51.5,51.2,51,,,,,,,,,,,,, +51,69.6,68.7,67.9,66.8,66.2,65.5,64.7,64,63.4,62.7,62,61,60.6,59.9,59.4,58.9,58.4,57.7,56.9,56.2,55.6,55.3,55.1,54.9,54.7,54.1,53.7,53.3,52.9,52.5,52.2,52,51.9,51.7,51.6,51.4,51.3,51.3,51.2,51.2,51.1,51.1,51.1,51,51 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+61,,,,,,,,,,,74.1,73,72.5,71.7,71.1,70.4,69.8,69,68.1,67.3,66.4,66.2,65.9,65.7,65.5,64.8,64.3,63.7,63.3,62.8,62.4,62.2,62.1,61.9,61.7,61.5,61.4,61.3,61.2,61.2,61.1,61.1,61.1,61,61 +62,,,,,,,,,,,,74.2,73.7,72.9,72.3,71.6,70.9,70.1,69.2,68.4,67.5,67.2,67,66.7,66.5,65.8,65.3,64.8,64.3,63.9,63.5,63.2,63.1,62.9,62.7,62.5,62.4,62.3,62.2,62.2,62.1,62.1,62.1,62,62 +63,,,,,,,,,,,,,74.7,74,73.4,72.7,72.1,71.3,70.3,69.5,68.6,68.3,68,67.8,67.6,66.9,66.4,65.8,65.3,64.9,64.5,64.2,64.1,63.9,63.7,63.5,63.4,63.3,63.2,63.2,63.1,63.1,63.1,63,63 +64,,,,,,,,,,,,,,,74.6,73.9,73.2,72.4,71.4,70.6,69.7,69.4,69.1,68.9,68.7,67.9,67.4,66.9,66.4,65.9,65.5,65.2,65.1,64.9,64.7,64.5,64.4,64.3,64.3,64.2,64.1,64.1,64.1,64,64 +65,,,,,,,,,,,,,,,,,74.4,73.5,72.5,71.7,70.8,70.5,70.2,70,69.7,69,68.5,67.9,67.4,66.9,66.5,66.3,66.1,65.9,65.7,65.5,65.4,65.3,65.3,65.2,65.1,65.1,65.1,65,65 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+73,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74.7,74.4,74.3,74,73.8,73.6,73.4,73.4,73.3,73.2,73.1,73.1,73.1,73,73 +74,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74.8,74.6,74.4,74.4,74.3,74.2,74.1,74.1,74.1,74,74 \ No newline at end of file diff --git a/rcpchgrowth/data_tables/bayley_pinneau/bayley_pinneau_girls_normal.csv b/rcpchgrowth/data_tables/bayley_pinneau/bayley_pinneau_girls_normal.csv new file mode 100644 index 0000000..699eb2f --- /dev/null +++ b/rcpchgrowth/data_tables/bayley_pinneau/bayley_pinneau_girls_normal.csv @@ -0,0 +1,40 @@ +Skeletal Age,6-0,6-3,6-6,6-9,7-0,7-3,7-6,7-9,8-0,8-3,8-6,8-10,9-0,9-3,9-6,9-9,10-0,10-3,10-6,10-9,11-0,11-3,11-6,11-9,12-0,12-3,12-6,12-9,13-0,13-3,13-6,13-9,14-0,14-3,14-6,14-9,15-0,15-3,15-6,15-9,16-0,16-3,16-6,16-9,17-0,17-6,18-0 +% of Mature Height Ht. (inches),72,72.9,73.8,75.1,75.7,76.5,77.2,78.2,79,80.1,81,82.1,82.7,83.6,84.4,85.3,86.2,87.4,88.4,89.6,90.6,91,91.4,91.8,92.2,93.2,94.1,95,95.8,96.7,97.4,97.8,98,98.3,98.6,98.8,99,99.1,99.3,99.4,99.6,99.6,99.7,99.8,99.9,99.9,100 +37,51.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +38,52.8,52.1,51.5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +39,54.2,53.5,52.8,52,51.5,51,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +40,55.6,54.9,54.2,53.5,52.8,52.3,51.8,51.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +41,56.9,56.2,55.6,54.6,54.2,53.6,53.1,52.4,51.9,51.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +42,58.3,57.6,56.9,55.9,55.5,54.9,54.4,53.7,53.2,52.4,51.9,51.2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +43,59.7,59,58.3,57.3,56.8,56.2,55.7,55,54.4,53.7,53.1,52.4,52,51.4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +44,61.1,60.4,59.6,58.6,58.1,57.5,57,56.3,55.7,54.9,54.3,53.6,53.2,52.6,52.1,51.6,51,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 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{"decimal_age": 20.0000000000011, "l": 1.0, "m": 177.34, "s": 0.03929}]} \ No newline at end of file diff --git a/rcpchgrowth/data_tables/uk-who_resources/uk_who_child_male_ofc_cubic_daily_lms.json b/rcpchgrowth/data_tables/uk-who_resources/uk_who_child_male_ofc_cubic_daily_lms.json new file mode 100644 index 0000000..82bfc3b --- /dev/null +++ b/rcpchgrowth/data_tables/uk-who_resources/uk_who_child_male_ofc_cubic_daily_lms.json @@ -0,0 +1 @@ +{"acknowledgement": "UK 1990 reference data, reanalysed 2009.", "age_group": "child", "start_age": 4, "end_age": 23, "measurement_method": "ofc", "sex": "male", "data": [{"decimal_age": 4.0, "l": 1.0, "m": 52.215, "s": 0.02809}, {"decimal_age": 4.002737850787132, "l": 1.0, "m": 52.21667556468173, "s": 0.028091314168377825}, {"decimal_age": 4.005475701574264, "l": 1.0, "m": 52.218351129363455, "s": 0.028092628336755646}, {"decimal_age": 4.008213552361396, "l": 1.0, "m": 52.220026694045174, "s": 0.02809394250513347}, {"decimal_age": 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a/rcpchgrowth/data_tables/uk-who_resources/uk_who_neonate_female_ofc_cubic_daily_lms.json b/rcpchgrowth/data_tables/uk-who_resources/uk_who_neonate_female_ofc_cubic_daily_lms.json new file mode 100644 index 0000000..a7d4f04 --- /dev/null +++ b/rcpchgrowth/data_tables/uk-who_resources/uk_who_neonate_female_ofc_cubic_daily_lms.json @@ -0,0 +1 @@ +{"acknowledgement": "UK 1990 reference data, reanalysed 2009.", "age_group": "neonate", "start_age": -0.3258042436687201, "end_age": 0.038329911, "measurement_method": "ofc", "sex": "female", "data": [{"decimal_age": -0.3258042436687201, "l": 1.0, "m": 21.10156, "s": 0.0662953}, {"decimal_age": -0.32306639288158795, "l": 1.0, "m": 21.253921428571427, "s": 0.06608477428571428}, {"decimal_age": -0.3203285420944558, "l": 1.0, "m": 21.40628285714286, "s": 0.06587424857142857}, {"decimal_age": -0.3175906913073237, "l": 1.0, "m": 21.558644285714287, "s": 0.06566372285714285}, {"decimal_age": -0.3148528405201916, "l": 1.0, "m": 21.71100571428572, "s": 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b/rcpchgrowth/data_tables/uk-who_resources/uk_who_neonate_male_weight_cubic_daily_lms.json @@ -0,0 +1 @@ +{"acknowledgement": "UK 1990 reference data, reanalysed 2009.", "age_group": "neonate", "start_age": -0.3258042436687201, "end_age": 0.038329911, "measurement_method": "weight", "sex": "male", "data": [{"decimal_age": -0.3258042436687201, "l": 1.147, "m": 0.6145, "s": 0.15875}, {"decimal_age": -0.32306639288158795, "l": 1.144, "m": 0.6287428571428573, "s": 0.1592842857142857}, {"decimal_age": -0.3203285420944558, "l": 1.141, "m": 0.6429857142857145, "s": 0.15981857142857145}, {"decimal_age": -0.3175906913073237, "l": 1.138, "m": 0.6572285714285717, "s": 0.16035285714285716}, {"decimal_age": -0.3148528405201916, "l": 1.1349999999999998, "m": 0.6714714285714289, "s": 0.16088714285714287}, {"decimal_age": -0.31211498973305946, "l": 1.132, "m": 0.685714285714286, "s": 0.16142142857142858}, {"decimal_age": -0.30937713894592733, "l": 1.1289999999999998, "m": 0.6999571428571433, "s": 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rcpchgrowth/data_tables/who_children.json rename to rcpchgrowth/data_tables/who/pre_2025/who_children.json diff --git a/rcpchgrowth/data_tables/who_infants.json b/rcpchgrowth/data_tables/who/pre_2025/who_infants.json similarity index 100% rename from rcpchgrowth/data_tables/who_infants.json rename to rcpchgrowth/data_tables/who/pre_2025/who_infants.json diff --git a/rcpchgrowth/data_tables/who/who_2007_children.json b/rcpchgrowth/data_tables/who/who_2007_children.json new file mode 100644 index 0000000..88b9ed1 --- /dev/null +++ b/rcpchgrowth/data_tables/who/who_2007_children.json @@ -0,0 +1,4815 @@ +{ + "measurement": { + "acknowledgement_text": "World Health Organisation Multicentre Growth Reference Standards (WHO MGRS) (2006/2007)", + "height": { + "male": [ + { + "decimal_age": 5.0, + "L": 1.0, + "M": 109.7265, + "S": 0.04156 + }, + { + "decimal_age": 5.0833333333, + "L": 1.0, + "M": 110.2647, + "S": 0.04164 + }, + { + "decimal_age": 5.1666666667, + "L": 1.0, + "M": 110.8006, + 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+} \ No newline at end of file diff --git a/rcpchgrowth/data_tables/who/who_over_five_male_height.json b/rcpchgrowth/data_tables/who/who_over_five_male_height.json new file mode 100644 index 0000000..65d55bd --- /dev/null +++ b/rcpchgrowth/data_tables/who/who_over_five_male_height.json @@ -0,0 +1,1021 @@ +{ + "sex": "male", + "measurement_method": "height", + "acknowledgement_text": "World Health Organisation Multicentre Growth Reference Standards (WHO MGRS) (2006/2007)", + "data": [ + { + "decimal_age": 5.0, + "L": 1.0, + "M": 109.7265, + "S": 0.04156 + }, + { + "decimal_age": 5.0833333333, + "L": 1.0, + "M": 110.2647, + "S": 0.04164 + }, + { + "decimal_age": 5.1666666667, + "L": 1.0, + "M": 110.8006, + "S": 0.04172 + }, + { + "decimal_age": 5.25, + "L": 1.0, + "M": 111.3338, + "S": 0.0418 + }, + { + "decimal_age": 5.3333333333, + "L": 1.0, + "M": 111.8636, + "S": 0.04187 + }, + { + "decimal_age": 5.4166666667, + "L": 1.0, + "M": 112.3895, + "S": 0.04195 + }, + { + "decimal_age": 5.5, 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a/rcpchgrowth/data_tables/who/who_under_five_female_weight.json b/rcpchgrowth/data_tables/who/who_under_five_female_weight.json new file mode 100644 index 0000000..c3eea44 --- /dev/null +++ b/rcpchgrowth/data_tables/who/who_under_five_female_weight.json @@ -0,0 +1,11149 @@ +{ + "sex": "female", + "measurement_method": "weight", + "acknowledgement_text": "World Health Organisation Multicentre Growth Reference Standards (WHO MGRS) (2006/2007)", + "data": [ + { + "decimal_age": 0.0, + "L": 0.3809, + "M": 3.2322, + "S": 0.14171 + }, + { + "decimal_age": 0.0027378508, + "L": 0.3259, + "M": 3.1957, + "S": 0.14578 + }, + { + "decimal_age": 0.0054757016, + "L": 0.3101, + "M": 3.2104, + "S": 0.14637 + }, + { + "decimal_age": 0.0082135524, + "L": 0.2986, + "M": 3.2315, + "S": 0.14657 + }, + { + "decimal_age": 0.0109514031, + "L": 0.2891, + "M": 3.2558, + "S": 0.14658 + }, + { + "decimal_age": 0.0136892539, + "L": 0.281, + "M": 3.2821, + "S": 0.14646 + }, + { + "decimal_age": 0.0164271047, + "L": 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a/rcpchgrowth/data_tables/who/who_under_five_male_height.json b/rcpchgrowth/data_tables/who/who_under_five_male_height.json new file mode 100644 index 0000000..fae2613 --- /dev/null +++ b/rcpchgrowth/data_tables/who/who_under_five_male_height.json @@ -0,0 +1,11149 @@ +{ + "sex": "male", + "measurement_method": "height", + "acknowledgement_text": "World Health Organisation Multicentre Growth Reference Standards (WHO MGRS) (2006/2007)", + "data": [ + { + "decimal_age": 0.0, + "L": 1.0, + "M": 49.8842, + "S": 0.03795 + }, + { + "decimal_age": 0.0027378508, + "L": 1.0, + "M": 50.0601, + "S": 0.03785 + }, + { + "decimal_age": 0.0054757016, + "L": 1.0, + "M": 50.2359, + "S": 0.03775 + }, + { + "decimal_age": 0.0082135524, + "L": 1.0, + "M": 50.4118, + "S": 0.03764 + }, + { + "decimal_age": 0.0109514031, + "L": 1.0, + "M": 50.5876, + "S": 0.03754 + }, + { + "decimal_age": 0.0136892539, + "L": 1.0, + "M": 50.7635, + "S": 0.03744 + }, + { + "decimal_age": 0.0164271047, + "L": 1.0, + "M": 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"decimal_age": 5.0568104038, + "L": -0.1526, + "M": 18.4484, + "S": 0.13561 + }, + { + "decimal_age": 5.0595482546, + "L": -0.1527, + "M": 18.4538, + "S": 0.13563 + }, + { + "decimal_age": 5.0622861054, + "L": -0.1528, + "M": 18.4591, + "S": 0.13565 + }, + { + "decimal_age": 5.0650239562, + "L": -0.1529, + "M": 18.4645, + "S": 0.13567 + }, + { + "decimal_age": 5.067761807, + "L": -0.153, + "M": 18.4699, + "S": 0.13569 + }, + { + "decimal_age": 5.0704996578, + "L": -0.1531, + "M": 18.4753, + "S": 0.13571 + }, + { + "decimal_age": 5.0732375086, + "L": -0.1532, + "M": 18.4807, + "S": 0.13573 + }, + { + "decimal_age": 5.0759753593, + "L": -0.1533, + "M": 18.486, + "S": 0.13575 + }, + { + "decimal_age": 5.0787132101, + "L": -0.1533, + "M": 18.4914, + "S": 0.13577 + }, + { + "decimal_age": 5.0814510609, + "L": -0.1534, + "M": 18.4968, + "S": 0.1358 + } + ] +} \ No newline at end of file diff --git a/rcpchgrowth/date_calculations.py b/rcpchgrowth/date_calculations.py index 7896606..48fe2fe 100644 --- a/rcpchgrowth/date_calculations.py +++ b/rcpchgrowth/date_calculations.py @@ -160,3 +160,14 @@ def corrected_gestational_age( "corrected_gestation_weeks": corrected_weeks, "corrected_gestation_days": corrected_supplementary_days, } + +def age_in_days(birth_date: date, observation_date: date) -> int: + """ + Returns the age in days between two dates. + :param birth_date: date of birth + :param observation_date: date observation made + """ + if birth_date > observation_date: + raise Exception("Birth date cannot be after the date of observation.") + + return (observation_date - birth_date).days \ No newline at end of file diff --git a/rcpchgrowth/global_functions.py b/rcpchgrowth/global_functions.py index bae8180..1390676 100644 --- a/rcpchgrowth/global_functions.py +++ b/rcpchgrowth/global_functions.py @@ -1,3 +1,6 @@ +from __future__ import annotations + +import json import math import scipy.stats as stats from scipy.interpolate import interp1d @@ -7,6 +10,7 @@ from .cdc import cdc_lms_array_for_measurement_and_sex from .trisomy_21_aap import trisomy_21_aap_lms_array_for_measurement_and_sex from .who import who_lms_array_for_measurement_and_sex +from .constants import * # from scipy import interpolate #see below, comment back in if swapping interpolation method # from scipy.interpolate import CubicSpline #see below, comment back in if swapping interpolation method @@ -53,7 +57,7 @@ def measurement_from_sds( sex: str, age: float, default_youngest_reference: bool = False, -) -> float: +) -> float | None: try: lms_value_array_for_measurement = lms_value_array_for_measurement_for_reference( @@ -73,7 +77,7 @@ def measurement_from_sds( l = lms["l"] m = lms["m"] s = lms["s"] - + observation_value = None if reference == CDC and measurement_method == BMI: @@ -91,6 +95,28 @@ def measurement_from_sds( p95 = m * (1 + l * s * 1.645)**(1/l) # 95th centile measurement centile = stats.norm.cdf(requested_sds) * 100 # convert z-score to centile observation_value = stats.norm.ppf((centile - 90)/10) * sigma + p95 + # elif reference == WHO: + # WHO references (2006+2007), when going from measurement -> SDS require a different method to calculate the measurement if the requested SDS is below -3 or above +3 SDS + # This involves calculating the measurement first to determine if it is below -3 or above +3 SDS and then interpolate between the distance between +2 and +3 SDS or -2 and -3 SDS points, as per the WHO method described here: https://cdn.who.int/media/docs/default-source/child-growth/growth-reference-5-19-years/computation.pdf?sfvrsn=c2ff6a95_4 + # However: in the WHO own published centile curve values this rule is NOT applied and so therefore commented out here to reflect the actual centile curve values published by WHO, + # which do not adjust for >3 or <-3 SDS values. This is because the WHO published centile curve values do not adjust for >3 or <-3 SDS values, and therefore neither should this function when generating centiles based on WHO references, to ensure consistency between the centiles generated by this function and the WHO published centiles. + + # is_beyond_three_sds = abs(requested_sds) > 3 + # if is_beyond_three_sds: + # if requested_sds > 3: + # sd3pos = measurement_for_z(z=3, l=l, m=m, s=s) + # sd2pos = measurement_for_z(z=2, l=l, m=m, s=s) + # observation_value = sd3pos + (requested_sds - 3) * (sd3pos - sd2pos) + # elif requested_sds < -3: + # sd3neg = measurement_for_z(z=-3, l=l, m=m, s=s) + # sd2neg = measurement_for_z(z=-2, l=l, m=m, s=s) + # observation_value = sd3neg + (requested_sds + 3) * (sd2neg - sd3neg) + # else: + # try: + # observation_value = measurement_for_z(z=requested_sds, l=l, m=m, s=s) + # except Exception as e: + # print(f"measurement_from_sds exception {e} - age: {age}, l: {l}, m: {m}, s: {s}, requested_sds: {requested_sds} lms: {lms}") + # return None else: # all other references use the standard method try: @@ -124,9 +150,11 @@ def sds_for_measurement( except LookupError as err: raise LookupError(err) - # get LMS values from the reference: check for age match, interpolate if none + # get LMS values from the reference: check for age match. + # There will always be an age match in the under 5s WHO, or under 4s UK-WHO and therefore no interpolation is required + # However, for WHO references above 5 years, interpolation will be linear, whereas for all others interpolation will be cubic lms = fetch_lms( - age=age, lms_value_array_for_measurement=lms_value_array_for_measurement + age=age, lms_value_array_for_measurement=lms_value_array_for_measurement, interpolation_override=reference==WHO ) l = lms["l"] m = lms["m"] @@ -143,13 +171,33 @@ def sds_for_measurement( centile = stats.norm.cdf((observation_value - p95) / sigma)*10 + 90 z = stats.norm.ppf(centile/100) return z + if reference == WHO and ( + ((measurement_method == BMI or measurement_method ==WEIGHT) and age >= 5) + or age < 5 + ): + # WHO BMI references require a different method to calculate the z-score if the measurement is below -3 or above +3 SDS + # This involves calculating the z score first to determine if it is below -3 or above +3 SDS + z = z_score(l=l, m=m, s=s, observation=observation_value) + is_beyond_three_sds = abs(z) > 3 + if is_beyond_three_sds: + # if the z score is below -3 or above +3 SDS, the calculation is different + # see WHO Website: https://cdn.who.int/media/docs/default-source/child-growth/growth-reference-5-19-years/computation.pdf?sfvrsn=c2ff6a95_4 + if z > 3: + sd3pos = m * (1 + l * s * 3)**(1/l) + sd2pos = m * (1 + l * s * 2)**(1/l) + z= 3 + ((observation_value - sd3pos) / (sd3pos - sd2pos)) + elif z < -3: + sd3neg = m * (1 + l * s * -3)**(1/l) + sd2neg = m * (1 + l * s * -2)**(1/l) + z= -3 + ((observation_value - sd3neg) / (sd2neg - sd3neg)) + return z return z_score(l=l, m=m, s=s, observation=observation_value) def percentage_median_bmi( reference: str, age: float, actual_bmi: float, sex: str -) -> float: +) -> float | None: """ public method This returns a child"s BMI expressed as a percentage of the median value for age and sex. @@ -370,12 +418,10 @@ def create_data_point(age: float, measurement: float, label_value: str): value = {"l": label_value, "x": round(age, 4), "y": rounded} return value - """ ***** INTERPOLATION FUNCTIONS ***** """ - def cubic_interpolation( age: float, age_one_below: float, @@ -520,7 +566,7 @@ def nearest_lowest_index(lms_array: list, age: float) -> int: lowest_index = 0 for num, lms_element in enumerate(lms_array): reference_age = lms_element["decimal_age"] - if round(reference_age, 16) == round(age, 16): + if round(reference_age, 4) == round(age, 4): lowest_index = num break else: @@ -529,9 +575,9 @@ def nearest_lowest_index(lms_array: list, age: float) -> int: return lowest_index -def fetch_lms(age: float, lms_value_array_for_measurement: list): +def fetch_lms(age: float, lms_value_array_for_measurement: list, interpolation_override: bool=False): """ - Retuns the LMS for a given age, and sigma if present (CDC BMI references). If there is no exact match in the reference + Returns the LMS for a given age, and sigma if present (CDC BMI references). If there is no exact match in the reference an interpolated LMS is returned. Cubic interpolation is used except at the fringes of the reference where linear interpolation is used. It accepts the age and a python list of the LMS values for that measurement_method and sex. @@ -539,9 +585,11 @@ def fetch_lms(age: float, lms_value_array_for_measurement: list): age_matched_index = nearest_lowest_index( lms_value_array_for_measurement, age ) # returns nearest LMS for age - if round( - lms_value_array_for_measurement[age_matched_index]["decimal_age"], 4 - ) == round(age, 4): + + # test for exact match + rounded_age = round(age, 4) + matched_age = round(lms_value_array_for_measurement[age_matched_index]["decimal_age"],4) + if rounded_age == matched_age: # there is an exact match in the data with the requested age l = lms_value_array_for_measurement[age_matched_index]["L"] m = lms_value_array_for_measurement[age_matched_index]["M"] @@ -571,6 +619,7 @@ def fetch_lms(age: float, lms_value_array_for_measurement: list): and age_matched_index < len(lms_value_array_for_measurement) - 2 and "sigma" not in lms_value_array_for_measurement[age_matched_index] # CDC BMI references have an additional sigma value # and CDC only use linear interpolation + and interpolation_override is False ): # cubic interpolation is possible age_two_below = lms_value_array_for_measurement[age_matched_index - 1][ @@ -662,7 +711,7 @@ def fetch_lms(age: float, lms_value_array_for_measurement: list): parameter_one_above=parameter_one_above["sigma"], ) return {"l": l, "m": m, "s": s, "sigma": sigma} - + return {"l": l, "m": m, "s": s} @@ -734,5 +783,4 @@ def lms_value_array_for_measurement_for_reference( raise LookupError(error) else: raise ValueError("No or incorrect reference supplied") - return lms_value_array_for_measurement - + return lms_value_array_for_measurement \ No newline at end of file diff --git a/rcpchgrowth/tests/sds_age_validation_2021.json b/rcpchgrowth/tests/sds_age_validation_2021_deprecated.json similarity index 100% rename from rcpchgrowth/tests/sds_age_validation_2021.json rename to rcpchgrowth/tests/sds_age_validation_2021_deprecated.json diff --git a/rcpchgrowth/tests/sds_age_validation_2021_refactored_2026.json b/rcpchgrowth/tests/sds_age_validation_2021_refactored_2026.json new file mode 100644 index 0000000..3260254 --- /dev/null +++ b/rcpchgrowth/tests/sds_age_validation_2021_refactored_2026.json @@ -0,0 +1,51794 @@ +[ + { + "birth_date": "28/01/2012", + "observation_date": "26/04/2013", + "gestation_weeks": 41, + "gestation_days": 2, + "sex": "male", + "measurement_method": "weight", + "chronological_age": 1.242984257, + "corrected_age": 1.267624914, + "observation_value": 11.67, + "corrected_sds": 1.087248564, + "chronological_sds": 1.144163728 + }, + { + "birth_date": "28/01/2012", + "observation_date": "26/04/2013", + "gestation_weeks": 41, + "gestation_days": 2, + "sex": "male", + "measurement_method": "height", + "chronological_age": 1.242984257, + "corrected_age": 1.267624914, + "observation_value": 81.9, + "corrected_sds": 0.994007766, + "chronological_sds": 1.126482725 + }, + { + "birth_date": "28/01/2012", + "observation_date": "26/04/2013", + "gestation_weeks": 41, + "gestation_days": 2, + "sex": "male", + "measurement_method": "bmi", + "chronological_age": 1.242984257, + "corrected_age": 1.267624914, + "observation_value": 17.39814949, + "corrected_sds": 0.721155524, + "chronological_sds": 0.696363986 + }, + { + "birth_date": "28/01/2012", + "observation_date": "26/04/2013", + "gestation_weeks": 41, + "gestation_days": 2, + "sex": "male", + "measurement_method": "ofc", + "chronological_age": 1.242984257, + "corrected_age": 1.267624914, + "observation_value": 48.3, + "corrected_sds": 1.108019948, + "chronological_sds": 1.157393932 + }, + { + "birth_date": "02/01/2013", + "observation_date": "01/04/2022", + "gestation_weeks": 35, + "gestation_days": 3, + "sex": "female", + "measurement_method": "weight", + "chronological_age": 9.242984257, + "corrected_age": 9.155373032, + "observation_value": 25.58, + "corrected_sds": -0.843059599, + "chronological_sds": -0.902204692 + }, + { + "birth_date": "02/01/2013", + "observation_date": "01/04/2022", + "gestation_weeks": 35, + "gestation_days": 3, + "sex": "female", + "measurement_method": "height", + "chronological_age": 9.242984257, + "corrected_age": 9.155373032, + "observation_value": 128.7, + "corrected_sds": -0.839785993, + "chronological_sds": -0.91542238 + }, + { + "birth_date": "02/01/2013", + "observation_date": "01/04/2022", + "gestation_weeks": 35, + "gestation_days": 3, + "sex": "female", + "measurement_method": "bmi", + "chronological_age": 9.242984257, + "corrected_age": 9.155373032, + "observation_value": 15.44342041, + "corrected_sds": -0.550074875, + "chronological_sds": -0.570935011 + }, + { + "birth_date": "02/01/2013", + "observation_date": "01/04/2022", + "gestation_weeks": 35, + "gestation_days": 3, + "sex": "female", + "measurement_method": "ofc", + "chronological_age": 9.242984257, + "corrected_age": 9.155373032, + "observation_value": 52.4, + "corrected_sds": -0.840830266, + "chronological_sds": -0.864344478 + }, + { + "birth_date": "15/05/2017", + "observation_date": "09/12/2020", + "gestation_weeks": 37, + "gestation_days": 5, + "sex": "male", + "measurement_method": "weight", + "chronological_age": 3.570157426, + "corrected_age": 3.526351814, + "observation_value": 15.76, + "corrected_sds": 0.184752107, + "chronological_sds": 0.138789803 + }, + { + "birth_date": "15/05/2017", + "observation_date": "09/12/2020", + "gestation_weeks": 37, + "gestation_days": 5, + "sex": "male", + "measurement_method": "height", + "chronological_age": 3.570157426, + "corrected_age": 3.526351814, + "observation_value": 100.9, + "corrected_sds": 0.215919584, + "chronological_sds": 0.136411518 + }, + { + "birth_date": "15/05/2017", + "observation_date": "09/12/2020", + "gestation_weeks": 37, + "gestation_days": 5, + "sex": "male", + "measurement_method": "bmi", + "chronological_age": 3.570157426, + "corrected_age": 3.526351814, + "observation_value": 15.48010635, + "corrected_sds": 0.035982721, + "chronological_sds": 0.044715229 + }, + { + "birth_date": "15/05/2017", + "observation_date": "09/12/2020", + "gestation_weeks": 37, + "gestation_days": 5, + "sex": "male", + "measurement_method": "ofc", + "chronological_age": 3.570157426, + "corrected_age": 3.526351814, + "observation_value": 50.1, + "corrected_sds": 0.142679662, + "chronological_sds": 0.12042591 + }, + { + "birth_date": "25/09/2018", + "observation_date": "31/10/2027", + "gestation_weeks": 32, + "gestation_days": 6, + "sex": "male", + "measurement_method": "weight", + "chronological_age": 9.097878166, + "corrected_age": 8.960985626, + "observation_value": 32.01, + "corrected_sds": 0.748355806, + "chronological_sds": 0.665046334 + }, + { + "birth_date": "25/09/2018", + "observation_date": "31/10/2027", + "gestation_weeks": 32, + "gestation_days": 6, + "sex": "male", + "measurement_method": "height", + "chronological_age": 9.097878166, + "corrected_age": 8.960985626, + "observation_value": 137.4, + "corrected_sds": 0.747915208, + "chronological_sds": 0.619469583 + }, + { + "birth_date": "25/09/2018", + "observation_date": "31/10/2027", + "gestation_weeks": 32, + "gestation_days": 6, + "sex": "male", + "measurement_method": "bmi", + "chronological_age": 9.097878166, + "corrected_age": 8.960985626, + "observation_value": 16.95556259, + "corrected_sds": 0.51774627, + "chronological_sds": 0.488312066 + }, + { + "birth_date": "25/09/2018", + "observation_date": "31/10/2027", + "gestation_weeks": 32, + "gestation_days": 6, + "sex": "male", + "measurement_method": "ofc", + "chronological_age": 9.097878166, + "corrected_age": 8.960985626, + "observation_value": 55.4, + "corrected_sds": 0.778588414, + "chronological_sds": 0.751221418 + }, + { + "birth_date": "10/11/2014", + "observation_date": "23/02/2024", + "gestation_weeks": 31, + "gestation_days": 2, + "sex": "male", + "measurement_method": "weight", + "chronological_age": 9.28678987, + "corrected_age": 9.119780972, + "observation_value": 38.38, + "corrected_sds": 1.586941242, + "chronological_sds": 1.493923187 + }, + { + "birth_date": "10/11/2014", + "observation_date": "23/02/2024", + "gestation_weeks": 31, + "gestation_days": 2, + "sex": "male", + "measurement_method": "height", + "chronological_age": 9.28678987, + "corrected_age": 9.119780972, + "observation_value": 143.2, + "corrected_sds": 1.592416048, + "chronological_sds": 1.43069303 + }, + { + "birth_date": "10/11/2014", + "observation_date": "23/02/2024", + "gestation_weeks": 31, + "gestation_days": 2, + "sex": "male", + "measurement_method": "bmi", + "chronological_age": 9.28678987, + "corrected_age": 9.119780972, + "observation_value": 18.71625328, + "corrected_sds": 1.266022921, + "chronological_sds": 1.22839272 + }, + { + "birth_date": "10/11/2014", + "observation_date": "23/02/2024", + "gestation_weeks": 31, + "gestation_days": 2, + "sex": "male", + "measurement_method": "ofc", + "chronological_age": 9.28678987, + 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"observation_value": 180.0, + "chronological_age": 2.8501026694045173, + "corrected_sds": 23.499259393398773, + "corrected_age": 2.8501026694045173, + "chronological_sds": 23.499259393398773 + }, + { + "birth_date": "26/10/2015", + "observation_date": "01/09/2030", + "gestation_weeks": 40, + "gestation_days": 0, + "sex": "male", + "measurement_method": "bmi", + "observation_value": 7.0, + "chronological_age": 14.850102669404517, + "corrected_sds": -22.21786749424378, + "corrected_age": 14.850102669404517, + "chronological_sds": -22.21786749424378 + } +] diff --git a/rcpchgrowth/tests/test_chart_functions.py b/rcpchgrowth/tests/test_chart_functions.py index e3ce1b0..54f12f6 100644 --- a/rcpchgrowth/tests/test_chart_functions.py +++ b/rcpchgrowth/tests/test_chart_functions.py @@ -1,57 +1,190 @@ import pytest -from rcpchgrowth.constants import UK_90_PRETERM_AGES,WHO_2006_UNDER_TWOS_AGES,UK_WHO_2006_OVER_TWOS_AGES, UK90_AGES, TWENTY_FIVE_WEEKS_GESTATION +from rcpchgrowth.global_functions import * +from rcpchgrowth.tests.who_test_data.who_chart_precalculated_centiles import * + +UNDER_FIVES_SERIES = [ + ("WHO_GIRL_WEIGHT_UNDER_FIVE_50", WHO_GIRL_WEIGHT_UNDER_FIVE_50, "female", "weight", 0), + ("WHO_GIRL_WEIGHT_UNDER_FIVE_25", WHO_GIRL_WEIGHT_UNDER_FIVE_25, "female", "weight", -0.67), + ("WHO_GIRL_WEIGHT_UNDER_FIVE_10", WHO_GIRL_WEIGHT_UNDER_FIVE_10, "female", "weight", -1.28), + ("WHO_GIRL_WEIGHT_UNDER_FIVE_5", WHO_GIRL_WEIGHT_UNDER_FIVE_5, "female", "weight", -1.64), + # Girls BMI-for-age 0-5y + ("WHO_GIRL_BMI_UNDER_FIVE_50", WHO_GIRL_BMI_UNDER_FIVE_50, "female", "bmi", 0), + ("WHO_GIRL_BMI_UNDER_FIVE_99", WHO_GIRL_BMI_UNDER_FIVE_99, "female", "bmi", 2.33), + ("WHO_GIRL_BMI_UNDER_FIVE_999", WHO_GIRL_BMI_UNDER_FIVE_999, "female", "bmi", 3.0903), + ("WHO_GIRL_BMI_UNDER_FIVE_1", WHO_GIRL_BMI_UNDER_FIVE_1, "female", "bmi", -2.33), + ("WHO_GIRL_BMI_UNDER_FIVE_001", WHO_GIRL_BMI_UNDER_FIVE_001, "female", "bmi", -3.0903), + # # Girls length-for-age 0-5y + ("WHO_GIRL_LENGTH_UNDER_FIVE_01", WHO_GIRL_LENGTH_UNDER_FIVE_01, "female", "height", -3.0903), + ("WHO_GIRL_LENGTH_UNDER_FIVE_1", WHO_GIRL_LENGTH_UNDER_FIVE_1, "female", "height", -2.33), + ("WHO_GIRL_LENGTH_UNDER_FIVE_15", WHO_GIRL_LENGTH_UNDER_FIVE_15, "female", "height", -1.036), + ("WHO_GIRL_LENGTH_UNDER_FIVE_25", WHO_GIRL_LENGTH_UNDER_FIVE_25, "female", "height", -0.67), + ("WHO_GIRL_LENGTH_UNDER_FIVE_50", WHO_GIRL_LENGTH_UNDER_FIVE_50, "female", "height", 0), + ("WHO_GIRL_LENGTH_UNDER_FIVE_75", WHO_GIRL_LENGTH_UNDER_FIVE_75, "female", "height", 0.67), + ("WHO_GIRL_LENGTH_UNDER_FIVE_85", WHO_GIRL_LENGTH_UNDER_FIVE_85, "female", "height", 1.036), + ("WHO_GIRL_LENGTH_UNDER_FIVE_99", WHO_GIRL_LENGTH_UNDER_FIVE_99, "female", "height", 2.33), + ("WHO_GIRL_LENGTH_UNDER_FIVE_999", WHO_GIRL_LENGTH_UNDER_FIVE_999, "female", "height", 3.0903), + # # # Girls OFC-for-age 0-5y + ("WHO_GIRL_OFC_UNDER_FIVE_01", WHO_GIRL_OFC_UNDER_FIVE_01, "female", "ofc", -3.0903), + ("WHO_GIRL_OFC_UNDER_FIVE_1", WHO_GIRL_OFC_UNDER_FIVE_1, "female", "ofc", -2.33), + ("WHO_GIRL_OFC_UNDER_FIVE_15", WHO_GIRL_OFC_UNDER_FIVE_15, "female", "ofc", -1.036), + ("WHO_GIRL_OFC_UNDER_FIVE_25", WHO_GIRL_OFC_UNDER_FIVE_25, "female", "ofc", -0.67), + ("WHO_GIRL_OFC_UNDER_FIVE_50", WHO_GIRL_OFC_UNDER_FIVE_50, "female", "ofc", 0), + ("WHO_GIRL_OFC_UNDER_FIVE_75", WHO_GIRL_OFC_UNDER_FIVE_75, "female", "ofc", 0.67), + ("WHO_GIRL_OFC_UNDER_FIVE_85", WHO_GIRL_OFC_UNDER_FIVE_85, "female", "ofc", 1.036), + ("WHO_GIRL_OFC_UNDER_FIVE_99", WHO_GIRL_OFC_UNDER_FIVE_99, "female", "ofc", 2.33), + ("WHO_GIRL_OFC_UNDER_FIVE_999", WHO_GIRL_OFC_UNDER_FIVE_999, "female", "ofc", 3.0903), + + # # # Boys weight-for-age 0-5y + ("WHO_BOY_WEIGHT_UNDER_FIVE_01", WHO_BOY_WEIGHT_UNDER_FIVE_01, "male", "weight", -3.0903), + ("WHO_BOY_WEIGHT_UNDER_FIVE_1", WHO_BOY_WEIGHT_UNDER_FIVE_1, "male", "weight", -2.33), + ("WHO_BOY_WEIGHT_UNDER_FIVE_15", WHO_BOY_WEIGHT_UNDER_FIVE_15, "male", "weight", -1.036), + ("WHO_BOY_WEIGHT_UNDER_FIVE_25", WHO_BOY_WEIGHT_UNDER_FIVE_25, "male", "weight", -0.67), + ("WHO_BOY_WEIGHT_UNDER_FIVE_50", WHO_BOY_WEIGHT_UNDER_FIVE_50, "male", "weight", 0), + ("WHO_BOY_WEIGHT_UNDER_FIVE_75", WHO_BOY_WEIGHT_UNDER_FIVE_75, "male", "weight", 0.67), + ("WHO_BOY_WEIGHT_UNDER_FIVE_85", WHO_BOY_WEIGHT_UNDER_FIVE_85, "male", "weight", 1.036), + ("WHO_BOY_WEIGHT_UNDER_FIVE_99", WHO_BOY_WEIGHT_UNDER_FIVE_99, "male", "weight", 2.33), + ("WHO_BOY_WEIGHT_UNDER_FIVE_999", WHO_BOY_WEIGHT_UNDER_FIVE_999, "male", "weight", 3.0903), + # # # Boys length-for-age 0-5y + ("WHO_BOY_LENGTH_UNDER_FIVE_01", WHO_BOY_LENGTH_UNDER_FIVE_01, "male", "height", -3.0903), + ("WHO_BOY_LENGTH_UNDER_FIVE_1", WHO_BOY_LENGTH_UNDER_FIVE_1, "male", "height", -2.33), + ("WHO_BOY_LENGTH_UNDER_FIVE_15", WHO_BOY_LENGTH_UNDER_FIVE_15, "male", "height", -1.036), + ("WHO_BOY_LENGTH_UNDER_FIVE_25", WHO_BOY_LENGTH_UNDER_FIVE_25, "male", "height", -0.67), + ("WHO_BOY_LENGTH_UNDER_FIVE_50", WHO_BOY_LENGTH_UNDER_FIVE_50, "male", "height", 0), + ("WHO_BOY_LENGTH_UNDER_FIVE_75", WHO_BOY_LENGTH_UNDER_FIVE_75, "male", "height", 0.67), + ("WHO_BOY_LENGTH_UNDER_FIVE_85", WHO_BOY_LENGTH_UNDER_FIVE_85, "male", "height", 1.036), + ("WHO_BOY_LENGTH_UNDER_FIVE_99", WHO_BOY_LENGTH_UNDER_FIVE_99, "male", "height", 2.33), + ("WHO_BOY_LENGTH_UNDER_FIVE_999", WHO_BOY_LENGTH_UNDER_FIVE_999, "male", "height", 3.0903), + # # # Boys BMI-for-age 0-5y + ("WHO_BOY_BMI_UNDER_FIVE_01", WHO_BOY_BMI_UNDER_FIVE_01, "male", "bmi", -3.0903), + ("WHO_BOY_BMI_UNDER_FIVE_1", WHO_BOY_BMI_UNDER_FIVE_1, "male", "bmi", -2.33), + ("WHO_BOY_BMI_UNDER_FIVE_15", WHO_BOY_BMI_UNDER_FIVE_15, "male", "bmi", -1.036), + ("WHO_BOY_BMI_UNDER_FIVE_25", WHO_BOY_BMI_UNDER_FIVE_25, "male", "bmi", -0.67), + ("WHO_BOY_BMI_UNDER_FIVE_50", WHO_BOY_BMI_UNDER_FIVE_50, "male", "bmi", 0), + ("WHO_BOY_BMI_UNDER_FIVE_75", WHO_BOY_BMI_UNDER_FIVE_75, "male", "bmi", 0.67), + ("WHO_BOY_BMI_UNDER_FIVE_85", WHO_BOY_BMI_UNDER_FIVE_85, "male", "bmi", 1.036), + ("WHO_BOY_BMI_UNDER_FIVE_99", WHO_BOY_BMI_UNDER_FIVE_99, "male", "bmi", 2.33), + ("WHO_BOY_BMI_UNDER_FIVE_999", WHO_BOY_BMI_UNDER_FIVE_999, "male", "bmi", 3.0903), + # # # Boys OFC-for-age 0-5y + ("WHO_BOY_OFC_UNDER_FIVE_01", WHO_BOY_OFC_UNDER_FIVE_01, "male", "ofc", -3.0903), + ("WHO_BOY_OFC_UNDER_FIVE_1", WHO_BOY_OFC_UNDER_FIVE_1, "male", "ofc", -2.33), + ("WHO_BOY_OFC_UNDER_FIVE_15", WHO_BOY_OFC_UNDER_FIVE_15, "male", "ofc", -1.036), + ("WHO_BOY_OFC_UNDER_FIVE_25", WHO_BOY_OFC_UNDER_FIVE_25, "male", "ofc", -0.67), + ("WHO_BOY_OFC_UNDER_FIVE_50", WHO_BOY_OFC_UNDER_FIVE_50, "male", "ofc", 0), + ("WHO_BOY_OFC_UNDER_FIVE_75", WHO_BOY_OFC_UNDER_FIVE_75, "male", "ofc", 0.67), + ("WHO_BOY_OFC_UNDER_FIVE_85", WHO_BOY_OFC_UNDER_FIVE_85, "male", "ofc", 1.036), + ("WHO_BOY_OFC_UNDER_FIVE_99", WHO_BOY_OFC_UNDER_FIVE_99, "male", "ofc", 2.33), + ("WHO_BOY_OFC_UNDER_FIVE_999", WHO_BOY_OFC_UNDER_FIVE_999, "male", "ofc", 3.0903), +] + +OVER_FIVES_SERIES = [ + # Girls weight 5-10y + ("WHO_GIRL_WEIGHT_OVER_FIVE_50", WHO_GIRL_WEIGHT_OVER_FIVE_50, "female", "weight", 0), + ("WHO_GIRL_WEIGHT_OVER_FIVE_75", WHO_GIRL_WEIGHT_OVER_FIVE_75, "female", "weight", 0.67), + ("WHO_GIRL_WEIGHT_OVER_FIVE_85", WHO_GIRL_WEIGHT_OVER_FIVE_85, "female", "weight", 1.04), + ("WHO_GIRL_WEIGHT_OVER_FIVE_97", WHO_GIRL_WEIGHT_OVER_FIVE_97, "female", "weight", 1.88), + # Girls height 5-19y + ("WHO_GIRL_LENGTH_OVER_FIVE_01", WHO_GIRL_LENGTH_OVER_FIVE_01, "female", "height", -3.0903), + ("WHO_GIRL_LENGTH_OVER_FIVE_1", WHO_GIRL_LENGTH_OVER_FIVE_1, "female", "height", -2.33), + ("WHO_GIRL_LENGTH_OVER_FIVE_15", WHO_GIRL_LENGTH_OVER_FIVE_15, "female", "height", -1.036), + ("WHO_GIRL_LENGTH_OVER_FIVE_25", WHO_GIRL_LENGTH_OVER_FIVE_25, "female", "height", -0.67), + ("WHO_GIRL_LENGTH_OVER_FIVE_50", WHO_GIRL_LENGTH_OVER_FIVE_50, "female", "height", 0), + ("WHO_GIRL_LENGTH_OVER_FIVE_75", WHO_GIRL_LENGTH_OVER_FIVE_75, "female", "height", 0.67), + ("WHO_GIRL_LENGTH_OVER_FIVE_85", WHO_GIRL_LENGTH_OVER_FIVE_85, "female", "height", 1.036), + ("WHO_GIRL_LENGTH_OVER_FIVE_99", WHO_GIRL_LENGTH_OVER_FIVE_99, "female", "height", 2.33), + ("WHO_GIRL_LENGTH_OVER_FIVE_999", WHO_GIRL_LENGTH_OVER_FIVE_999, "female", "height", 3.0903), + + # Girls bmi 5-19y + ("WHO_GIRL_BMI_OVER_FIVE_01", WHO_GIRL_BMI_OVER_FIVE_01, "female", "bmi", -3.0903), + ("WHO_GIRL_BMI_OVER_FIVE_1", WHO_GIRL_BMI_OVER_FIVE_1, "female", "bmi", -2.33), + ("WHO_GIRL_BMI_OVER_FIVE_25", WHO_GIRL_BMI_OVER_FIVE_25, "female", "bmi", -0.67), + ("WHO_GIRL_BMI_OVER_FIVE_50", WHO_GIRL_BMI_OVER_FIVE_50, "female", "bmi", 0), + ("WHO_GIRL_BMI_OVER_FIVE_75", WHO_GIRL_BMI_OVER_FIVE_75, "female", "bmi", 0.67), + ("WHO_GIRL_BMI_OVER_FIVE_85", WHO_GIRL_BMI_OVER_FIVE_85, "female", "bmi", 1.036), + ("WHO_GIRL_BMI_OVER_FIVE_99", WHO_GIRL_BMI_OVER_FIVE_99, "female", "bmi", 2.33), + ("WHO_GIRL_BMI_OVER_FIVE_999", WHO_GIRL_BMI_OVER_FIVE_999, "female", "bmi", 3.0903), + + # Boys length 5-19y + ("WHO_BOY_LENGTH_OVER_FIVE_01", WHO_BOY_LENGTH_OVER_FIVE_01, "male", "height", -3.0903), + ("WHO_BOY_LENGTH_OVER_FIVE_1", WHO_BOY_LENGTH_OVER_FIVE_1, "male", "height", -2.33), + ("WHO_BOY_LENGTH_OVER_FIVE_25", WHO_BOY_LENGTH_OVER_FIVE_25, "male", "height", -0.67), + ("WHO_BOY_LENGTH_OVER_FIVE_50", WHO_BOY_LENGTH_OVER_FIVE_50, "male", "height", 0), + ("WHO_BOY_LENGTH_OVER_FIVE_75", WHO_BOY_LENGTH_OVER_FIVE_75, "male", "height", 0.67), + ("WHO_BOY_LENGTH_OVER_FIVE_85", WHO_BOY_LENGTH_OVER_FIVE_85, "male", "height", 1.036), + ("WHO_BOY_LENGTH_OVER_FIVE_99", WHO_BOY_LENGTH_OVER_FIVE_99, "male", "height", 2.33), + ("WHO_BOY_LENGTH_OVER_FIVE_999", WHO_BOY_LENGTH_OVER_FIVE_999, "male", "height", 3.0903), + + # Boys weight 5-10y + ("WHO_BOY_WEIGHT_OVER_FIVE_01", WHO_BOY_WEIGHT_OVER_FIVE_01, "male", "weight", -3.0903), + ("WHO_BOY_WEIGHT_OVER_FIVE_1", WHO_BOY_WEIGHT_OVER_FIVE_1, "male", "weight", -2.33), + ("WHO_BOY_WEIGHT_OVER_FIVE_25", WHO_BOY_WEIGHT_OVER_FIVE_25, "male", "weight", -0.67), + ("WHO_BOY_WEIGHT_OVER_FIVE_50", WHO_BOY_WEIGHT_OVER_FIVE_50, "male", "weight", 0), + ("WHO_BOY_WEIGHT_OVER_FIVE_75", WHO_BOY_WEIGHT_OVER_FIVE_75, "male", "weight", 0.67), + ("WHO_BOY_WEIGHT_OVER_FIVE_85", WHO_BOY_WEIGHT_OVER_FIVE_85, "male", "weight", 1.036), + ("WHO_BOY_WEIGHT_OVER_FIVE_99", WHO_BOY_WEIGHT_OVER_FIVE_99, "male", "weight", 2.33), + ("WHO_BOY_WEIGHT_OVER_FIVE_999", WHO_BOY_WEIGHT_OVER_FIVE_999, "male", "weight", 3.0903), + + # # Boys bmi 5-19y + ("WHO_BOY_BMI_OVER_FIVE_01", WHO_BOY_BMI_OVER_FIVE_01, "male", "bmi", -3.0903), + ("WHO_BOY_BMI_OVER_FIVE_1", WHO_BOY_BMI_OVER_FIVE_1, "male", "bmi", -2.33), + ("WHO_BOY_BMI_OVER_FIVE_25", WHO_BOY_BMI_OVER_FIVE_25, "male", "bmi", -0.67), + ("WHO_BOY_BMI_OVER_FIVE_50", WHO_BOY_BMI_OVER_FIVE_50, "male", "bmi", 0), + ("WHO_BOY_BMI_OVER_FIVE_75", WHO_BOY_BMI_OVER_FIVE_75, "male", "bmi", 0.67), + ("WHO_BOY_BMI_OVER_FIVE_85", WHO_BOY_BMI_OVER_FIVE_85, "male", "bmi", 1.036), + ("WHO_BOY_BMI_OVER_FIVE_99", WHO_BOY_BMI_OVER_FIVE_99, "male", "bmi", 2.33), + ("WHO_BOY_BMI_OVER_FIVE_999", WHO_BOY_BMI_OVER_FIVE_999, "male", "bmi", 3.0903), +] + +_POINT_CASES = [ + (series_name, age_days, expected_weight, sex, method, sds) + for series_name, values, sex, method, sds in UNDER_FIVES_SERIES + for age_days, expected_weight in enumerate(values) +] + +_POINT_CASES_OVER_FIVES = [ + (series_name, age_months, expected_observation_value, sex, method, sds) + for series_name, values, sex, method, sds in OVER_FIVES_SERIES + for age_months, expected_observation_value in enumerate(values) +] + +@pytest.mark.parametrize( + "series_name,age_days,expected_measurement,sex,measurement_method,requested_sds", + _POINT_CASES +) +def test_who_under_fives(series_name, age_days, expected_measurement, sex, measurement_method, requested_sds): + ACCURACY = 1e-3 + age_years = round(age_days / 365.25, 4) + if age_years > 5.00: + pytest.skip("Skipping under-five test for age > 5 years") + measurement = measurement_from_sds( + reference='who', + requested_sds=requested_sds, + measurement_method=measurement_method, + sex=sex, + age=age_years, + default_youngest_reference=True + ) + assert measurement == pytest.approx(expected_measurement, rel=ACCURACY), ( + f"{series_name} day {age_days}: expected {expected_measurement} got {measurement} for {requested_sds} in {sex} and {measurement_method} at {age_years:.3f} years" + ) -from rcpchgrowth.chart_functions import create_chart @pytest.mark.parametrize( - "sex, measurement_method", - [ - ("male", "height"), - ("male", "weight"), - ("male", "bmi"), - ("male", "ofc"), - ("female", "height"), - ("female", "weight"), - ("female", "bmi"), - ("female", "ofc"), - ] + "series_name,age_months,expected_observation_value,sex,measurement_method,requested_sds", + _POINT_CASES_OVER_FIVES ) -def test_create_uk_who_chart_size(sex, measurement_method): - """ - Tests the size of the charts created - """ - chart = create_chart(reference="uk-who", measurement_method=measurement_method, sex=sex) - - assert len(chart[0]['uk90_preterm'][sex][measurement_method])==9, f"The 'uk90_preterm' {sex} {measurement_method} chart should have 9 entries, one for each centile." - assert len(chart[1]['uk_who_infant'][sex][measurement_method])==9, f"The 'uk_who_infant' {sex} {measurement_method} chart should have 9 entries, one for each centile." - assert len(chart[2]['uk_who_child'][sex][measurement_method])==9, f"The 'uk_who_infant' {sex} {measurement_method} chart should have 9 entries, one for each centile." - assert len(chart[3]['uk90_child'][sex][measurement_method])==9, f"The 'uk90_child' chart {sex} {measurement_method} should have 9 entries, one for each centile." - - if measurement_method == "bmi": - assert len(chart[0]['uk90_preterm'][sex][measurement_method][0]['data'])==0, f"The 'uk90_preterm' {sex} {measurement_method} chart 0.4th centile should have 0 entries, one for each decimal age." - assert len(chart[1]['uk_who_infant'][sex][measurement_method][0]['data'])==len(WHO_2006_UNDER_TWOS_AGES)-1, f"The 'uk_who_infant' {sex} {measurement_method} chart 0.4th centile should have {len(WHO_2006_UNDER_TWOS_AGES)-1} entries." - assert len(chart[2]['uk_who_child'][sex][measurement_method][0]['data'])==len(UK_WHO_2006_OVER_TWOS_AGES), f"The 'uk_who_infant' {sex} {measurement_method} chart 0.4th centile should have {len(UK_WHO_2006_OVER_TWOS_AGES)} entries." - assert len(chart[3]['uk90_child'][sex][measurement_method][0]['data'])==len(UK90_AGES), f"The 'uk90_child' {sex} {measurement_method} chart 0.4th centile should have {len(UK90_AGES)} entries." - elif measurement_method == "height": - assert len(chart[0]['uk90_preterm'][sex][measurement_method][0]['data'])==len([age for age in UK_90_PRETERM_AGES if age >=TWENTY_FIVE_WEEKS_GESTATION]), f"The 'uk90_preterm' {sex} {measurement_method} chart 0.4th centile should have {len([age for age in UK_90_PRETERM_AGES if age >=TWENTY_FIVE_WEEKS_GESTATION])} entries, one for each decimal age from 25 weeks." - assert len(chart[1]['uk_who_infant'][sex][measurement_method][0]['data'])==len(WHO_2006_UNDER_TWOS_AGES), f"The 'uk_who_infant' {sex} {measurement_method} chart 0.4th centile should have {len(WHO_2006_UNDER_TWOS_AGES)} entries." - assert len(chart[2]['uk_who_child'][sex][measurement_method][0]['data'])==len(UK_WHO_2006_OVER_TWOS_AGES), f"The 'uk_who_infant' {sex} {measurement_method} chart 0.4th centile should have {len(UK_WHO_2006_OVER_TWOS_AGES)} entries." - assert len(chart[3]['uk90_child'][sex][measurement_method][0]['data'])==len(UK90_AGES), f"The 'uk90_child' {sex} {measurement_method} chart 0.4th centile should have {len(UK90_AGES)} entries." - elif measurement_method == "weight": - assert len(chart[0]['uk90_preterm'][sex][measurement_method][0]['data'])==len(UK_90_PRETERM_AGES)-1, f"The 'uk90_preterm' {sex} {measurement_method} chart 0.4th centile should have {len(UK_90_PRETERM_AGES)-1} entries, one for each decimal age from 25 weeks." - assert len(chart[1]['uk_who_infant'][sex][measurement_method][0]['data'])==len(WHO_2006_UNDER_TWOS_AGES), f"The 'uk_who_infant' {sex} {measurement_method} chart 0.4th centile should have {len(WHO_2006_UNDER_TWOS_AGES)} entries." - assert len(chart[2]['uk_who_child'][sex][measurement_method][0]['data'])==len(UK_WHO_2006_OVER_TWOS_AGES), f"The 'uk_who_infant' {sex} {measurement_method} chart 0.4th centile should have {len(UK_WHO_2006_OVER_TWOS_AGES)} entries." - assert len(chart[3]['uk90_child'][sex][measurement_method][0]['data'])==len(UK90_AGES), f"The 'uk90_child' {sex} {measurement_method} chart 0.4th centile should have {len(UK90_AGES)} entries." - elif measurement_method == "ofc": - assert len(chart[0]['uk90_preterm'][sex][measurement_method][0]['data'])==len(UK_90_PRETERM_AGES)-1, f"The 'uk90_preterm' {sex} {measurement_method} chart 0.4th centile should have {len(UK_90_PRETERM_AGES)-1} entries, one for each decimal age from 25 weeks." - assert len(chart[1]['uk_who_infant'][sex][measurement_method][0]['data'])==len(WHO_2006_UNDER_TWOS_AGES), f"The 'uk_who_infant' {sex} {measurement_method} chart 0.4th centile should have {len(WHO_2006_UNDER_TWOS_AGES)} entries." - assert len(chart[2]['uk_who_child'][sex][measurement_method][0]['data'])==len(UK_WHO_2006_OVER_TWOS_AGES), f"The 'uk_who_infant' {sex} {measurement_method} chart 0.4th centile should have {len(UK_WHO_2006_OVER_TWOS_AGES)} entries." - if sex == 'female': - assert len(chart[3]['uk90_child'][sex][measurement_method][0]['data'])==len([age for age in UK90_AGES if age <=17]), f"The 'uk90_child' {sex} {measurement_method} chart 0.4th centile should have {len([age for age in UK90_AGES if age <=17])} entries." - if sex == 'male': - assert len(chart[3]['uk90_child'][sex][measurement_method][0]['data'])==len([age for age in UK90_AGES if age <=18]), f"The 'uk90_child' {sex} {measurement_method} chart 0.4th centile should have {len([age for age in UK90_AGES if age <=18])} entries." - - else: - assert len(chart[0]['uk90_preterm'][sex][measurement_method][0]['data'])==len(UK_90_PRETERM_AGES), f"The 'uk90_preterm' {sex} {measurement_method} chart 0.4th centile should have {len(UK_90_PRETERM_AGES)} entries, one for each centile." - assert len(chart[1]['uk_who_infant'][sex][measurement_method][0]['data'])==len(WHO_2006_UNDER_TWOS_AGES), f"The 'uk_who_infant' {sex} {measurement_method} chart 0.4th centile should have {len(WHO_2006_UNDER_TWOS_AGES)} entries." - assert len(chart[2]['uk_who_child'][sex][measurement_method][0]['data'])==len(UK_WHO_2006_OVER_TWOS_AGES), f"The 'uk_who_infant' {sex} {measurement_method} chart 0.4th centile should have {len(UK_WHO_2006_OVER_TWOS_AGES)} entries." - assert len(chart[3]['uk90_child'][sex][measurement_method][0]['data'])==len(UK90_AGES), f"The 'uk90_child' {sex} {measurement_method} chart 0.4th centile should have {len(UK90_AGES)} entries." \ No newline at end of file +def test_who_over_fives(series_name, age_months, expected_observation_value, sex, measurement_method, requested_sds): + ACCURACY = 1e-3 + # age_years = 5 + round(age_months / 12, 4) + age_years = (60 + age_months)*30.4375 / 365.25 # more accurate month to year conversion + if age_months == 0: + pytest.skip("Skipping over-five test for age 0 months") + measurement = measurement_from_sds( + reference='who', + requested_sds=requested_sds, + measurement_method=measurement_method, + sex=sex, + age=age_years, + default_youngest_reference=True if age_months==0 else False # in reality at 5 years we use the younger reference but we are testing the over five charts here + ) + assert measurement == pytest.approx(expected_observation_value, rel=ACCURACY), ( + f"{series_name} month {age_months}: expected {expected_observation_value} got {measurement} for {requested_sds} in {sex} and {measurement_method} at {age_years:.2f} years" + ) \ No newline at end of file diff --git a/rcpchgrowth/tests/test_global_functions.py b/rcpchgrowth/tests/test_global_functions.py index de06f8c..8244990 100644 --- a/rcpchgrowth/tests/test_global_functions.py +++ b/rcpchgrowth/tests/test_global_functions.py @@ -24,7 +24,10 @@ def load_valid_data_set(): """ Loads in the testing data from JSON file """ - with open(os.path.abspath(os.path.dirname(__file__)) + "/sds_age_validation_2021.json") as f: + with open( + os.path.abspath(os.path.dirname(__file__)) + + "/sds_age_validation_2021_refactored_2026.json" + ) as f: return json.load(f) @@ -38,10 +41,8 @@ def test_chronological_decimal_age(line): if line["birth_date"] is None or line["observation_date"] is None: return birth_date = datetime.strptime(line["birth_date"], "%d/%m/%Y").date() - observation_date = datetime.strptime( - line["observation_date"], "%d/%m/%Y").date() - age = date_calculations.chronological_decimal_age( - birth_date, observation_date) + observation_date = datetime.strptime(line["observation_date"], "%d/%m/%Y").date() + age = date_calculations.chronological_decimal_age(birth_date, observation_date) tim_age = float(line["chronological_age"]) assert age == pytest.approx(tim_age, abs=ACCURACY) @@ -54,10 +55,13 @@ def test_corrected_decimal_age(line): if line["birth_date"] is None or line["observation_date"] is None: return birth_date = datetime.strptime(line["birth_date"], "%d/%m/%Y").date() - observation_date = datetime.strptime( - line["observation_date"], "%d/%m/%Y").date() - age = date_calculations.corrected_decimal_age(birth_date, observation_date, int( - line["gestation_weeks"]), int(line["gestation_days"])) + observation_date = datetime.strptime(line["observation_date"], "%d/%m/%Y").date() + age = date_calculations.corrected_decimal_age( + birth_date, + observation_date, + int(line["gestation_weeks"]), + int(line["gestation_days"]), + ) tim_age = float(line["corrected_age"]) assert age == pytest.approx(tim_age, abs=ACCURACY) @@ -70,8 +74,13 @@ def test_sds_for_measurement_corrected(line): """ if line["observation_value"] is None or line["corrected_sds"] is None: return - sds = global_functions.sds_for_measurement("uk-who", float(line["corrected_age"]), str( - line["measurement_method"]), float(line["observation_value"]), str(line["sex"])) + sds = global_functions.sds_for_measurement( + "uk-who", + float(line["corrected_age"]), + str(line["measurement_method"]), + float(line["observation_value"]), + str(line["sex"]), + ) tim_sds = float(line["corrected_sds"]) assert sds == pytest.approx(tim_sds, abs=ACCURACY) @@ -83,7 +92,12 @@ def test_sds_for_measurement_chronological(line): """ if line["observation_value"] is None or line["chronological_sds"] is None: return - sds = global_functions.sds_for_measurement("uk-who", float(line["chronological_age"]), str( - line["measurement_method"]), float(line["observation_value"]), str(line["sex"])) + sds = global_functions.sds_for_measurement( + "uk-who", + float(line["chronological_age"]), + str(line["measurement_method"]), + float(line["observation_value"]), + str(line["sex"]), + ) tim_sds = float(line["chronological_sds"]) assert sds == pytest.approx(tim_sds, abs=ACCURACY) diff --git a/rcpchgrowth/tests/test_measurement_class.py b/rcpchgrowth/tests/test_measurement_class.py deleted file mode 100644 index 163af45..0000000 --- a/rcpchgrowth/tests/test_measurement_class.py +++ /dev/null @@ -1,94 +0,0 @@ -# standard imports -from datetime import datetime -import json -import os -from pprint import pprint - -# third-party imports -import pytest - -# rcpch imports -from rcpchgrowth import Measurement -from rcpchgrowth.constants import BMI - -# the ACCURACY constant defines the accuracy of the test comparisons -# owing to variations in statistical calculations it's impossible to get exact -# agreement between R and Python, so our statistician feels we can set a tolerance -# within which we will accept a result as correct. - -ACCURACY = 1e-3 - - -def load_valid_data_set(): - """ - Loads in the testing data from JSON file - """ - with open(os.path.abspath(os.path.dirname(__file__)) + "/sds_age_validation_2021.json") as f: - return json.load(f) - - -@pytest.mark.parametrize("line", load_valid_data_set()) -def test_measurement_class_ukwho_data(line): - """ - Test which iterates through a JSON file of 4000 fictional children and known calculated (TC via R) - correct SDS values. Compares the output of the Measurement.measurement method with the known - correct value. - """ - - measurement_object = Measurement( - sex=str(line["sex"]), - birth_date=datetime.strptime(line["birth_date"], "%d/%m/%Y"), - observation_date=datetime.strptime( - line["observation_date"], "%d/%m/%Y"), - measurement_method=str(line["measurement_method"]), - observation_value=float(line["observation_value"]), - gestation_weeks=int(line["gestation_weeks"]), - gestation_days=int(line["gestation_days"]), - reference="uk-who" - ) - - pprint(vars(measurement_object)) - pprint(line) - - # all comparisons using absolute tolerance (not relative) - - # this conditional guards against failure of pytest.approx with NoneTypes - if line["corrected_sds"] is None: - assert measurement_object.measurement[ - "measurement_calculated_values"]['corrected_sds'] is None - else: - age = measurement_object.measurement['measurement_dates']['corrected_decimal_age'] - # Check if the value is a float and has no fractional part - assert measurement_object.measurement[ - "measurement_calculated_values"]['corrected_sds'] == pytest.approx( - line["corrected_sds"], abs=ACCURACY) - if line["corrected_sds"] > 4 and line["corrected_sds"] < 8 and line["measurement_method"] != BMI: - assert measurement_object.measurement[ - "measurement_calculated_values"]['corrected_centile_band'] == f"This {line['measurement_method']} measurement is well outside the normal range. Please check its accuracy." - elif line["corrected_sds"] > 8 and line["measurement_method"] != BMI: - units="cm" - measurement_method = line["measurement_method"] - observation_value = line["observation_value"] - if measurement_method == "ofc": - measurement_method = "head circumference" - elif measurement_method == "height": - measurement_method = "height/length" - elif measurement_method == "weight": - units="kg" - assert measurement_object.measurement["child_observation_value"]["observation_value_error"] == f'The {measurement_method} of {observation_value} {units} in a child of {round(age, 1)} years is above +8 SD and considered to be an error.' - elif line["corrected_sds"] > 4 and line["corrected_sds"] <= 15 and line["measurement_method"] == BMI: - assert measurement_object.measurement[ - "measurement_calculated_values"]['corrected_centile_band'] == f"This {line['measurement_method']} measurement is well outside the normal range. Please check its accuracy." - elif line["corrected_sds"] < -15 and line["measurement_method"] == BMI: - observation_value = line["observation_value"] - assert measurement_object.measurement[ - "child_observation_value"]['observation_value_error'] == f"The Body Mass Index measurement of {observation_value} kg/m² is below -15 SD and considered to be an error." - # this conditional guards against failure of pytest.approx with NoneTypes - if line["chronological_sds"] is None: - assert measurement_object.measurement[ - "measurement_calculated_values"]['chronological_sds'] is None - else: - assert measurement_object.measurement[ - "measurement_calculated_values"]['chronological_sds'] == pytest.approx( - line["chronological_sds"], abs=ACCURACY) - \ No newline at end of file diff --git a/rcpchgrowth/tests/test_uk_who.py b/rcpchgrowth/tests/test_uk_who.py new file mode 100644 index 0000000..3e60522 --- /dev/null +++ b/rcpchgrowth/tests/test_uk_who.py @@ -0,0 +1,139 @@ +"""Integration tests for the UK-WHO reference pipeline. + +These tests validate the end-to-end path from input dates and measurements +through age calculation, gestation correction, UK-WHO reference lookup, +and final SDS and centile-band outputs. +""" + +# standard imports +from datetime import datetime +import json +import os +from pprint import pprint + +# third-party imports +import pytest + +# rcpch imports +from rcpchgrowth import Measurement +from rcpchgrowth.constants import BMI + +# the ACCURACY constant defines the accuracy of the test comparisons +# owing to variations in statistical calculations it's impossible to get exact +# agreement between R and Python, so our statistician feels we can set a tolerance +# within which we will accept a result as correct. + +ACCURACY = 1e-3 + + +def load_valid_data_set(): + """ + Loads in the testing data from JSON file + """ + with open( + os.path.abspath(os.path.dirname(__file__)) + + "/sds_age_validation_2021_refactored_2026.json" + ) as f: + return json.load(f) + + +@pytest.mark.parametrize("line", load_valid_data_set()) +def test_uk_who_reference_integration(line): + """ + End-to-end regression test for UK-WHO calculations. + + Iterates through a JSON file of fictional children with known SDS values + generated externally and compares the full Measurement pipeline output to + those expected values. + """ + + measurement_object = Measurement( + sex=str(line["sex"]), + birth_date=datetime.strptime(line["birth_date"], "%d/%m/%Y"), + observation_date=datetime.strptime(line["observation_date"], "%d/%m/%Y"), + measurement_method=str(line["measurement_method"]), + observation_value=float(line["observation_value"]), + gestation_weeks=int(line["gestation_weeks"]), + gestation_days=int(line["gestation_days"]), + reference="uk-who", + ) + + pprint(vars(measurement_object)) + pprint(line) + + # all comparisons using absolute tolerance (not relative) + + # this conditional guards against failure of pytest.approx with NoneTypes + if line["corrected_sds"] is None: + assert ( + measurement_object.measurement["measurement_calculated_values"][ + "corrected_sds" + ] + is None + ) + else: + age = measurement_object.measurement["measurement_dates"][ + "corrected_decimal_age" + ] + # Check if the value is a float and has no fractional part + assert measurement_object.measurement["measurement_calculated_values"][ + "corrected_sds" + ] == pytest.approx(line["corrected_sds"], abs=ACCURACY) + if ( + line["corrected_sds"] > 4 + and line["corrected_sds"] < 8 + and line["measurement_method"] != BMI + ): + assert ( + measurement_object.measurement["measurement_calculated_values"][ + "corrected_centile_band" + ] + == f"This {line['measurement_method']} measurement is well outside the normal range. Please check its accuracy." + ) + elif line["corrected_sds"] > 8 and line["measurement_method"] != BMI: + units = "cm" + measurement_method = line["measurement_method"] + observation_value = line["observation_value"] + if measurement_method == "ofc": + measurement_method = "head circumference" + elif measurement_method == "height": + measurement_method = "height/length" + elif measurement_method == "weight": + units = "kg" + assert ( + measurement_object.measurement["child_observation_value"][ + "observation_value_error" + ] + == f"The {measurement_method} of {observation_value} {units} in a child of {round(age, 1)} years is above +8 SD and considered to be an error." + ) + elif ( + line["corrected_sds"] > 4 + and line["corrected_sds"] <= 15 + and line["measurement_method"] == BMI + ): + assert ( + measurement_object.measurement["measurement_calculated_values"][ + "corrected_centile_band" + ] + == f"This {line['measurement_method']} measurement is well outside the normal range. Please check its accuracy." + ) + elif line["corrected_sds"] < -15 and line["measurement_method"] == BMI: + observation_value = line["observation_value"] + assert ( + measurement_object.measurement["child_observation_value"][ + "observation_value_error" + ] + == f"The Body Mass Index measurement of {observation_value} kg/m² is below -15 SD and considered to be an error." + ) + # this conditional guards against failure of pytest.approx with NoneTypes + if line["chronological_sds"] is None: + assert ( + measurement_object.measurement["measurement_calculated_values"][ + "chronological_sds" + ] + is None + ) + else: + assert measurement_object.measurement["measurement_calculated_values"][ + "chronological_sds" + ] == pytest.approx(line["chronological_sds"], abs=ACCURACY) diff --git a/rcpchgrowth/tests/test_who.py b/rcpchgrowth/tests/test_who.py new file mode 100644 index 0000000..bcf56c9 --- /dev/null +++ b/rcpchgrowth/tests/test_who.py @@ -0,0 +1,286 @@ +# standard imports +from datetime import datetime +import json +import os +from pprint import pprint +import pandas as pd +from datetime import timedelta + +# third-party imports +import pytest + +# rcpch imports +from rcpchgrowth import Measurement +from rcpchgrowth.constants import HEIGHT, WEIGHT, BMI + +# the ACCURACY constant defines the accuracy of the test comparisons +# owing to variations in statistical calculations it's impossible to get exact +# agreement between R and Python, so our statistician feels we can set a tolerance +# within which we will accept a result as correct. +ACCURACY = 1e-3 + +# NOTE: The aggregated CSV-wide test was removed in favor of per-row parametrized +# tests below. Per-row tests give one pytest item per CSV row so test runners +# (and CI) show a dot for each row. + + +# --- Per-row parametrized tests -------------------------------------------- +# Create a parametrized test so pytest reports one test (dot) per CSV row. +CSV_PATH = ( + os.path.abspath(os.path.dirname(__file__)) + + "/who_test_data/who_validation_data.csv" +) +validation_params = [] +validation_ids = [] +if os.path.exists(CSV_PATH): + import math + + vdf = pd.read_csv(CSV_PATH) + for idx, r in vdf.iterrows(): + mth = r.get("measurement_method") + obs = r.get("observation_value") + if pd.isna(mth) or pd.isna(obs): + continue + # include minimal fields to avoid large param objects + validation_params.append( + ( + int(idx), + str(mth), + float(obs), + str(r.get("start_date")), + str(r.get("end_date")), + r.get("sex"), + r.get("requested_z"), + ) + ) + validation_ids.append(f"row-{idx}-{mth}") + + +UNDER2_CSV_PATH = ( + os.path.abspath(os.path.dirname(__file__)) + + "/who_test_data/who_under2_gold_192.csv" +) +under2_params = [] +under2_ids = [] +if os.path.exists(UNDER2_CSV_PATH): + udf = pd.read_csv(UNDER2_CSV_PATH) + for idx, r in udf.iterrows(): + mth = r.get("measurement_method") + obs = r.get("observation_value") + age_days = r.get("age_days") + sex_val = r.get("sex") + requested_z = r.get("requested_z") + + if ( + pd.isna(mth) + or pd.isna(obs) + or pd.isna(age_days) + or pd.isna(sex_val) + or pd.isna(requested_z) + ): + continue + + under2_params.append( + ( + int(idx), + str(mth), + float(obs), + int(age_days), + int(sex_val), + float(requested_z), + ) + ) + under2_ids.append(f"under2-row-{idx}-{mth}-d{int(age_days)}") + + +@pytest.mark.parametrize("row_param", validation_params, ids=validation_ids) +def test_validation_csv_row(row_param): + """Per-row validation: one pytest test per CSV row so failures/passes are shown individually.""" + idx, mth, obs, start_date, end_date, sex_val, requested_z = row_param + + # map methods + if mth == "length": + mm = HEIGHT + elif mth == "weight": + mm = WEIGHT + elif mth == "bmi": + mm = BMI + elif mth in ("headc", "headcirc"): + mm = "ofc" + else: + pytest.skip(f"Unknown method {mth} for row {idx}") + + # parse dates + try: + bd = datetime.strptime(start_date, "%Y-%m-%d") + od = datetime.strptime(end_date, "%Y-%m-%d") + except Exception: + pytest.skip(f"Invalid dates for row {idx}") + + # normalize sex + if pd.isna(sex_val): + pytest.skip(f"Missing sex for row {idx}") + try: + if isinstance(sex_val, (int, float)): + sex = ( + "male" + if int(sex_val) == 1 + else "female" if int(sex_val) == 2 else str(sex_val) + ) + else: + s = str(sex_val).strip().lower() + if s in ("1", "m", "male", "man", "boy"): + sex = "male" + elif s in ("2", "f", "female", "woman", "girl"): + sex = "female" + else: + sex = s + except Exception: + sex = str(sex_val) + + meas = Measurement( + measurement_method=mm, + observation_value=float(obs), + birth_date=bd, + observation_date=od, + sex=sex, + reference="who", + ) + sds = meas.measurement.get("measurement_calculated_values", {}).get( + "chronological_sds" + ) + + assert sds == pytest.approx(float(requested_z), abs=ACCURACY) + + +@pytest.mark.parametrize("row_param", under2_params, ids=under2_ids) +def test_under2_gold_csv_row(row_param): + """Per-row under-2 gold validation against anthro-generated observations.""" + idx, mth, obs, age_days, sex_val, requested_z = row_param + + if mth == "length": + mm = HEIGHT + elif mth == "weight": + mm = WEIGHT + elif mth == "bmi": + mm = BMI + elif mth in ("headc", "headcirc", "ofc"): + mm = "ofc" + else: + pytest.skip(f"Unknown method {mth} for under2 row {idx}") + + if sex_val == 1: + sex = "male" + elif sex_val == 2: + sex = "female" + else: + pytest.skip(f"Unknown sex value {sex_val} for under2 row {idx}") + + # Build deterministic dates from age-in-days so we can validate age-dependent SDS. + birth_date = datetime(2000, 1, 1) + observation_date = birth_date + pd.Timedelta(days=int(age_days)) + + meas = Measurement( + measurement_method=mm, + observation_value=float(obs), + birth_date=birth_date, + observation_date=observation_date, + sex=sex, + reference="who", + ) + sds = meas.measurement.get("measurement_calculated_values", {}).get( + "chronological_sds" + ) + + assert sds == pytest.approx(float(requested_z), abs=ACCURACY) + + +def _make_who_measurement_at_age_years( + measurement_method: str, + age_years: float, + observation_value: float, + sex: str = "male", +): + """Create a WHO Measurement object at a deterministic decimal age for boundary tests.""" + birth_date = datetime(2015, 1, 1) + observation_date = birth_date + timedelta(days=round(age_years * 365.25)) + return Measurement( + measurement_method=measurement_method, + observation_value=observation_value, + birth_date=birth_date, + observation_date=observation_date, + sex=sex, + reference="who", + ) + + +def test_who_boundary_5y_continuity_for_height_and_bmi(): + """Rationale: verify height and BMI remain valid and continuous across the 5-year WHO 2006->2007 transition.""" + h_below = _make_who_measurement_at_age_years("height", 4.99, 109.5) + h_at = _make_who_measurement_at_age_years("height", 5.00, 110.0) + h_above = _make_who_measurement_at_age_years("height", 5.09, 111.0) + + b_below = _make_who_measurement_at_age_years("bmi", 4.99, 15.9) + b_at = _make_who_measurement_at_age_years("bmi", 5.00, 16.0) + b_above = _make_who_measurement_at_age_years("bmi", 5.09, 16.1) + + for meas in [h_below, h_at, h_above, b_below, b_at, b_above]: + sds = meas.measurement.get("measurement_calculated_values", {}).get( + "chronological_sds" + ) + err = meas.measurement.get("child_observation_value", {}).get( + "observation_value_error" + ) + assert sds is not None + assert err is None + + +def test_who_boundary_ofc_break_after_5_years(): + """Rationale: WHO OFC is valid at 5.0y but must be rejected above the 5y upper threshold.""" + ofc_at_5 = _make_who_measurement_at_age_years("ofc", 5.00, 50.0) + ofc_over_5 = _make_who_measurement_at_age_years("ofc", 5.09, 50.0) + + sds_at_5 = ofc_at_5.measurement.get("measurement_calculated_values", {}).get( + "chronological_sds" + ) + err_at_5 = ofc_at_5.measurement.get("child_observation_value", {}).get( + "observation_value_error" + ) + assert sds_at_5 is not None + assert err_at_5 is None + + sds_over_5 = ofc_over_5.measurement.get("measurement_calculated_values", {}).get( + "chronological_sds" + ) + err_over_5 = ofc_over_5.measurement.get("child_observation_value", {}).get( + "observation_value_error" + ) + assert sds_over_5 is None + assert ( + err_over_5 + == "WHO head circumference data does not exist in children over 5 y of age." + ) + + +def test_who_boundary_weight_break_after_10_years(): + """Rationale: WHO weight is valid through 10.0y but should be rejected once age is above 10y.""" + weight_at_10 = _make_who_measurement_at_age_years("weight", 10.00, 30.0) + weight_over_10 = _make_who_measurement_at_age_years("weight", 10.02, 30.0) + + sds_at_10 = weight_at_10.measurement.get("measurement_calculated_values", {}).get( + "chronological_sds" + ) + err_at_10 = weight_at_10.measurement.get("child_observation_value", {}).get( + "observation_value_error" + ) + assert sds_at_10 is not None + assert err_at_10 is None + + sds_over_10 = weight_over_10.measurement.get( + "measurement_calculated_values", {} + ).get("chronological_sds") + err_over_10 = weight_over_10.measurement.get("child_observation_value", {}).get( + "observation_value_error" + ) + assert sds_over_10 is None + assert err_over_10 == "WHO weight data does not exist in children over 10 y of age." diff --git a/rcpchgrowth/tests/who_test_data/README.md b/rcpchgrowth/tests/who_test_data/README.md new file mode 100644 index 0000000..89a4c96 --- /dev/null +++ b/rcpchgrowth/tests/who_test_data/README.md @@ -0,0 +1,53 @@ +# WHO test data summary + +This folder contains reference and generated datasets used to validate WHO growth calculations. + +## Dataset inventory + +Row counts below include header rows. + +| Dataset | Rows | Purpose | +|---|---:|---| +| `random_dates.csv` | 1001 | Base random date pairs and age-in-days values used as input for fixture generation workflows. | +| `who_validation_data.csv` | 1001 | Main WHO validation fixture for measurement-to-SDS checks across methods, ages, and sexes. | +| `who_under2_gold_192.csv` | 193 | Deterministic under-2 gold-standard dataset generated from the `anthro` R package (192 cases + header). | +| `random_ages/random_dates_0_to_4.csv` | 1001 | Random date ranges focused on 0 to 4 years. | +| `random_ages/random_dates_4_to_18.csv` | 1001 | Random date ranges focused on 4 to 18 years. | +| `random_ages/fictional_child_growth_data.csv` | 1001 | Synthetic child growth records used for integration-style test data preparation. | +| `who_chart_precalculated_centiles.py` | n/a | Pre-calculated WHO centile arrays used by chart/centile tests. | + +## Why `who_under2_gold_192.csv` has 192 cases + +The under-2 gold-standard file is intentionally a balanced matrix: + +- 2 sexes (male, female) +- 4 WHO under-2 methods (`length`, `weight`, `bmi`, `ofc`) +- 8 age anchors in days (`0`, `14`, `42`, `91`, `183`, `365`, `730`, `731`) +- 3 SDS targets (`-2`, `0`, `+2`) + +Total: + +- `2 x 4 x 8 x 3 = 192` + +This design gives deterministic, reproducible coverage while keeping the file small enough to debug quickly. + +## Design rationale + +- Covers all under-2 WHO measurement methods. +- Uses symmetric SDS anchors around the median to exercise low, central, and high regions. +- Includes clinically meaningful infant milestones (birth, 2 weeks, 6 weeks, 3 months, 6 months, 12 months, 24 months). +- Includes a boundary probe at day 731 (just over 2 years) to expose edge behavior near method/reference transitions. + +## Provenance + +`who_under2_gold_192.csv` was generated from the local `anthro` R package using inverse LMS measurement generation for fixed SDS inputs, then moved into this folder for Python-side tests. + +Key columns in this file: + +- `sex`, `sex_label` +- `measurement_method` +- `age_days`, `age_months`, `age_years` +- `requested_z` +- `observation_value` +- `measurement_precision` +- `correct_extreme` \ No newline at end of file diff --git a/rcpchgrowth/tests/who_test_data/random_ages/fictional_child_growth_data.csv b/rcpchgrowth/tests/who_test_data/random_ages/fictional_child_growth_data.csv new file mode 100644 index 0000000..001da78 --- /dev/null +++ b/rcpchgrowth/tests/who_test_data/random_ages/fictional_child_growth_data.csv @@ -0,0 +1,1001 @@ +start_date,end_date,age_days,birth_date,observation_date,observation_value,sex,measurement_method +2021-11-10,2038-06-15,6061,2021-11-10,2038-06-15,185.9904,1,height +2021-11-10,2034-09-11,4688,2021-11-10,2034-09-11,165.9243,1,height +2021-11-10,2030-05-07,3100,2021-11-10,2030-05-07,138.5964,1,height 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+2021-11-10,2033-12-07,4410 +2021-11-10,2036-08-04,5381 +2021-11-10,2031-07-04,3523 +2021-11-10,2030-06-16,3140 +2021-11-10,2027-11-01,2182 +2021-11-10,2026-12-07,1853 +2021-11-10,2033-10-30,4372 +2021-11-10,2029-11-26,2938 +2021-11-10,2036-12-07,5506 +2021-11-10,2038-05-13,6028 +2021-11-10,2030-06-29,3153 +2021-11-10,2032-06-18,3873 +2021-11-10,2031-01-22,3360 +2021-11-10,2035-08-23,5034 +2021-11-10,2028-06-09,2403 diff --git a/rcpchgrowth/tests/who_test_data/who_chart_precalculated_centiles.py b/rcpchgrowth/tests/who_test_data/who_chart_precalculated_centiles.py new file mode 100644 index 0000000..6c92a95 --- /dev/null +++ b/rcpchgrowth/tests/who_test_data/who_chart_precalculated_centiles.py @@ -0,0 +1,163 @@ +""" +Pre-calculated WHO centile data for testing purposes +Taken from https://www.who.int/tools/growth-reference-data-for-5to19-years and https://www.who.int/tools/child-growth-standards +""" + +# Under 5 years WHO chart tests +# Girls weight-for-age 0-5y +WHO_GIRL_WEIGHT_UNDER_FIVE_50=[3.232,3.196,3.21,3.232,3.256,3.282,3.31,3.339,3.369,3.4,3.431,3.464,3.498,3.533,3.569,3.606,3.644,3.682,3.72,3.758,3.797,3.835,3.874,3.912,3.95,3.987,4.025,4.062,4.099,4.135,4.172,4.208,4.243,4.278,4.313,4.348,4.382,4.416,4.449,4.482,4.515,4.547,4.579,4.611,4.643,4.674,4.704,4.735,4.765,4.795,4.825,4.854,4.883,4.912,4.94,4.968,4.996,5.024,5.051,5.078,5.105,5.132,5.158,5.184,5.21,5.236,5.262,5.287,5.312,5.337,5.362,5.386,5.411,5.435,5.459,5.483,5.506,5.53,5.553,5.576,5.599,5.621,5.644,5.666,5.688,5.71,5.732,5.754,5.776,5.797,5.818,5.839,5.86,5.881,5.902,5.922,5.943,5.963,5.983,6.003,6.023,6.043,6.062,6.082,6.101,6.12,6.139,6.158,6.177,6.196,6.214,6.233,6.251,6.269,6.287,6.305,6.323,6.341,6.359,6.376,6.394,6.411,6.428,6.445,6.462,6.479,6.496,6.512,6.529,6.545,6.562,6.578,6.594,6.61,6.626,6.642,6.657,6.673,6.688,6.704,6.719,6.734,6.75,6.765,6.78,6.794,6.809,6.824,6.838,6.853,6.867,6.882,6.896,6.91,6.924,6.938,6.952,6.966,6.98,6.993,7.007,7.021,7.034,7.047,7.061,7.074,7.087,7.1,7.113,7.126,7.139,7.152,7.165,7.178,7.19,7.203,7.215,7.228,7.24,7.253,7.265,7.277,7.289,7.302,7.314,7.326,7.338,7.35,7.361,7.373,7.385,7.397,7.408,7.42,7.431,7.443,7.454,7.466,7.477,7.488,7.5,7.511,7.522,7.533,7.544,7.555,7.566,7.577,7.588,7.599,7.609,7.62,7.631,7.642,7.652,7.663,7.673,7.684,7.694,7.705,7.715,7.725,7.736,7.746,7.756,7.766,7.777,7.787,7.797,7.807,7.817,7.827,7.837,7.847,7.857,7.866,7.876,7.886,7.896,7.905,7.915,7.925,7.934,7.944,7.953,7.963,7.972,7.982,7.991,8.001,8.01,8.019,8.029,8.038,8.047,8.056,8.066,8.075,8.084,8.093,8.102,8.111,8.12,8.129,8.138,8.147,8.156,8.165,8.173,8.182,8.191,8.2,8.209,8.217,8.226,8.235,8.243,8.252,8.261,8.269,8.278,8.286,8.295,8.303,8.312,8.32,8.329,8.337,8.345,8.354,8.362,8.37,8.379,8.387,8.395,8.404,8.412,8.42,8.428,8.436,8.445,8.453,8.461,8.469,8.477,8.485,8.493,8.501,8.509,8.517,8.525,8.533,8.541,8.549,8.557,8.565,8.573,8.581,8.589,8.597,8.604,8.612,8.62,8.628,8.636,8.644,8.651,8.659,8.667,8.675,8.682,8.69,8.698,8.705,8.713,8.721,8.728,8.736,8.744,8.751,8.759,8.766,8.774,8.782,8.789,8.797,8.804,8.812,8.819,8.827,8.834,8.842,8.85,8.857,8.864,8.872,8.879,8.887,8.894,8.902,8.909,8.917,8.924,8.931,8.939,8.946,8.954,8.961,8.968,8.976,8.983,8.99,8.998,9.005,9.013,9.02,9.027,9.034,9.042,9.049,9.056,9.064,9.071,9.078,9.085,9.093,9.1,9.107,9.114,9.122,9.129,9.136,9.143,9.151,9.158,9.165,9.172,9.179,9.187,9.194,9.201,9.208,9.215,9.223,9.23,9.237,9.244,9.251,9.258,9.265,9.273,9.28,9.287,9.294,9.301,9.308,9.315,9.322,9.329,9.337,9.344,9.351,9.358,9.365,9.372,9.379,9.386,9.393,9.4,9.407,9.414,9.421,9.429,9.436,9.443,9.45,9.457,9.464,9.471,9.478,9.485,9.492,9.499,9.506,9.513,9.52,9.527,9.534,9.541,9.548,9.555,9.562,9.569,9.576,9.583,9.59,9.597,9.604,9.611,9.618,9.625,9.632,9.639,9.646,9.653,9.66,9.667,9.674,9.681,9.687,9.694,9.701,9.708,9.715,9.722,9.729,9.736,9.743,9.75,9.757,9.764,9.771,9.778,9.785,9.792,9.799,9.805,9.812,9.819,9.826,9.833,9.84,9.847,9.854,9.861,9.868,9.875,9.882,9.889,9.895,9.902,9.909,9.916,9.923,9.93,9.937,9.944,9.951,9.958,9.964,9.971,9.978,9.985,9.992,9.999,10.006,10.013,10.02,10.027,10.033,10.04,10.047,10.054,10.061,10.068,10.075,10.082,10.088,10.095,10.102,10.109,10.116,10.123,10.13,10.137,10.143,10.15,10.157,10.164,10.171,10.178,10.185,10.191,10.198,10.205,10.212,10.219,10.226,10.232,10.239,10.246,10.253,10.26,10.267,10.273,10.28,10.287,10.294,10.301,10.308,10.314,10.321,10.328,10.335,10.342,10.349,10.355,10.362,10.369,10.376,10.383,10.39,10.396,10.403,10.41,10.417,10.424,10.43,10.437,10.444,10.451,10.458,10.465,10.471,10.478,10.485,10.492,10.499,10.505,10.512,10.519,10.526,10.533,10.539,10.546,10.553,10.56,10.567,10.573,10.58,10.587,10.594,10.601,10.607,10.614,10.621,10.628,10.635,10.641,10.648,10.655,10.662,10.669,10.675,10.682,10.689,10.696,10.703,10.709,10.716,10.723,10.73,10.737,10.743,10.75,10.757,10.764,10.771,10.777,10.784,10.791,10.798,10.805,10.811,10.818,10.825,10.832,10.839,10.845,10.852,10.859,10.866,10.873,10.879,10.886,10.893,10.9,10.907,10.913,10.92,10.927,10.934,10.941,10.947,10.954,10.961,10.968,10.975,10.982,10.988,10.995,11.002,11.009,11.016,11.022,11.029,11.036,11.043,11.05,11.057,11.063,11.07,11.077,11.084,11.091,11.098,11.104,11.111,11.118,11.125,11.132,11.138,11.145,11.152,11.159,11.166,11.173,11.18,11.186,11.193,11.2,11.207,11.214,11.221,11.227,11.234,11.241,11.248,11.255,11.262,11.268,11.275,11.282,11.289,11.296,11.303,11.31,11.316,11.323,11.33,11.337,11.344,11.351,11.358,11.364,11.371,11.378,11.385,11.392,11.399,11.406,11.412,11.419,11.426,11.433,11.44,11.447,11.454,11.46,11.467,11.474,11.481,11.488,11.495,11.502,11.508,11.515,11.522,11.529,11.536,11.543,11.55,11.556,11.563,11.57,11.577,11.584,11.591,11.598,11.605,11.611,11.618,11.625,11.632,11.639,11.646,11.653,11.659,11.666,11.673,11.68,11.687,11.694,11.701,11.707,11.714,11.721,11.728,11.735,11.742,11.749,11.755,11.762,11.769,11.776,11.783,11.79,11.796,11.803,11.81,11.817,11.824,11.831,11.838,11.844,11.851,11.858,11.865,11.872,11.879,11.885,11.892,11.899,11.906,11.913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379,13.383,13.387,13.391,13.396,13.4,13.404,13.408,13.412,13.417,13.421,13.425,13.429,13.434,13.438,13.442,13.446,13.451,13.455,13.459,13.463,13.467,13.471,13.476,13.48,13.484,13.488,13.493,13.497,13.501,13.505,13.509,13.514,13.518,13.522,13.526,13.531,13.535,13.539,13.543,13.547,13.552,13.556,13.56,13.564,13.568,13.573,13.577,13.581,13.585,13.589,13.594,13.598,13.602,13.606,13.61,13.615,13.619,13.623,13.627,13.631,13.636,13.64,13.644,13.648,13.652,13.657,13.661,13.665,13.669,13.673,13.678,13.682,13.686,13.69,13.694,13.699,13.703,13.707,13.711,13.715,13.719,13.724,13.728,13.732,13.736,13.74,13.745,13.749,13.753,13.757,13.761,13.765,13.77,13.774,13.778,13.782,13.786,13.791,13.795,13.799,13.803,13.807,13.811,13.816,13.82,13.824,13.828,13.832,13.836,13.841,13.845,13.849,13.853,13.857,13.861,13.866,13.87,13.874,13.878,13.882,13.886,13.891,13.895,13.899,13.903,13.907,13.911,13.915,13.919,13.924,13.928,13.932,13.936,13.94,13.944,13.949,13.953,13.957,13.961,13.965,13.969,13.973,13.978,13.982,13.986,13.99,13.994,13.998,14.002,14.006,14.011,14.015,14.019,14.023,14.027,14.031,14.035,14.039,14.044,14.048,14.052,14.056,14.06,14.064,14.068,14.072,14.077,14.081,14.085,14.089,14.093,14.097,14.101,14.105,14.11,14.113,14.118,14.122,14.126,14.13,14.134,14.138,14.142,14.146,14.15,14.155,14.159,14.163,14.167,14.171,14.175,14.179,14.183,14.187,14.191,14.195,14.2,14.204,14.208,14.212,14.216,14.22,14.224,14.228,14.232,14.236,14.24,14.244,14.248,14.253,14.257,14.261,14.265,14.269,14.273,14.277,14.281,14.285,14.289,14.293,14.297,14.301,14.305,14.31,14.313,14.318,14.322,14.326,14.33,14.334,14.338,14.342,14.346,14.35,14.354,14.358,14.362,14.366,14.37,14.374,14.378,14.382,14.386,14.39,14.394,14.398,14.402,14.406,14.41,14.415] +# Girls length-for-age 0-5y 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+# Girls BMI-for-age 0-5y +WHO_GIRL_BMI_UNDER_FIVE_50 = 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1,11.74,11.739,11.738,11.738,11.737,11.736,11.735,11.735,11.734,11.733,11.733,11.732,11.731,11.73,11.73,11.729,11.728,11.727,11.727,11.726,11.725,11.724,11.724,11.723,11.722,11.722,11.721,11.72,11.719,11.719,11.718,11.717,11.717,11.716,11.715,11.714,11.714,11.713,11.712,11.711,11.711,11.71,11.71,11.709,11.708,11.708,11.707,11.706,11.705,11.705,11.704,11.703,11.702,11.702,11.701,11.7,11.7,11.699,11.699,11.698,11.697,11.696,11.696,11.695,11.694,11.694,11.693,11.692,11.692,11.691,11.69,11.69,11.689,11.689,11.688,11.687,11.686,11.686,11.685,11.684,11.684,11.683,11.682,11.682,11.681,11.68,11.68,11.679,11.679,11.678,11.677,11.677,11.676,11.675,11.675,11.674,11.673,11.673,11.672,11.672,11.671,11.671,11.67,11.669,11.669,11.668,11.667,11.667,11.666,11.665,11.665,11.664,11.664,11.663,11.662,11.662,11.661,11.661,11.66,11.659,11.659,11.658,11.658,11.657,11.657,11.656,11.655,11.655,11.654,11.653,11.653,11.652,11.652,11.651,11.651,11.65,11.649,11.649,11.648,11.648,11.647,11.647,11.646,11.645,11.645,11.644,11.644,11.643,11.643,11.642,11.642,11.641,11.64,11.64,11.639,11.639,11.638,11.638,11.637,11.637,11.636,11.636,11.635,11.635,11.634,11.633,11.633,11.632,11.632,11.631,11.631,11.63,11.63,11.629,11.629,11.628,11.628,11.627,11.627,11.626,11.626,11.625,11.625,11.624,11.624,11.623,11.623,11.622,11.622,11.621,11.621,11.62,11.62,11.619,11.619,11.618,11.618,11.617,11.617,11.616,11.616,11.615,11.615,11.615,11.614,11.613,11.613,11.613,11.612,11.612,11.611,11.611,11.61,11.61,11.609,11.609,11.608,11.608,11.608,11.607,11.607,11.606,11.606,11.605,11.605,11.604,11.604,11.604,11.603,11.603,11.602,11.602,11.601,11.601,11.6,11.6,11.6,11.599,11.599,11.598,11.598,11.598,11.597,11.597,11.596,11.596,11.596,11.595,11.595,11.594,11.594,11.594,11.593,11.593,11.592,11.592,11.592,11.591,11.591,11.59,11.59,11.59,11.589,11.589,11.588,11.588,11.588,11.587,11.587,11.586,11.586,11.586,11.585,11.585,11.585,11.584,11.584,11.583,11.583,11.583,11.582,11.582,11.582,11.581,11.581,11.58,11.58,11.58,11.58,11.579,11.579,11.578,11.578,11.578,11.577,11.577,11.577,11.577,11.576,11.576,11.575,11.575,11.575,11.574,11.574,11.574,11.574,11.573,11.573,11.572,11.572,11.572,11.571,11.571,11.571,11.57,11.57,11.57,11.57,11.569,11.569,11.569,11.568,11.568,11.568,11.567,11.567,11.567,11.566,11.566,11.566,11.566,11.565,11.565,11.565,11.564,11.564,11.564,11.564,11.563,11.563,11.563,11.562,11.562,11.562,11.562,11.561,11.561,11.561,11.56,11.56,11.56,11.56,11.559,11.559,11.559,11.558,11.558,11.558,11.558,11.557,11.557,11.557,11.557,11.556,11.556,11.556,11.556,11.555,11.555,11.555,11.555,11.554,11.554] +# Girls OFC-for-age 0-5y 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+ +# Boys Weight-for-age 0-5y +WHO_BOY_WEIGHT_UNDER_FIVE_01=[2.048,2.033,2.049,2.068,2.09,2.114,2.138,2.163,2.189,2.215,2.242,2.271,2.3,2.33,2.36,2.392,2.424,2.456,2.489,2.522,2.555,2.587,2.62,2.653,2.686,2.719,2.751,2.784,2.816,2.848,2.88,2.912,2.943,2.974,3.005,3.036,3.066,3.097,3.126,3.156,3.185,3.215,3.243,3.272,3.3,3.328,3.356,3.383,3.411,3.437,3.464,3.491,3.517,3.543,3.569,3.594,3.62,3.645,3.67,3.694,3.719,3.743,3.767,3.791,3.814,3.837,3.861,3.884,3.906,3.929,3.951,3.974,3.996,4.018,4.039,4.06,4.082,4.103,4.124,4.144,4.165,4.185,4.205,4.225,4.245,4.265,4.284,4.304,4.323,4.342,4.361,4.38,4.398,4.417,4.435,4.453,4.471,4.489,4.506,4.524,4.541,4.558,4.576,4.592,4.609,4.626,4.643,4.659,4.675,4.692,4.708,4.724,4.739,4.755,4.771,4.786,4.802,4.817,4.832,4.847,4.862,4.877,4.891,4.906,4.92,4.935,4.949,4.963,4.977,4.991,5.005,5.019,5.032,5.046,5.059,5.073,5.086,5.099,5.112,5.125,5.138,5.151,5.164,5.176,5.189,5.201,5.214,5.226,5.238,5.25,5.262,5.274,5.286,5.298,5.309,5.321,5.333,5.344,5.355,5.367,5.378,5.389,5.4,5.411,5.422,5.433,5.444,5.454,5.465,5.475,5.486,5.496,5.507,5.517,5.527,5.538,5.548,5.558,5.568,5.578,5.588,5.597,5.607,5.617,5.627,5.636,5.646,5.655,5.665,5.674,5.683,5.693,5.702,5.711,5.72,5.729,5.738,5.747,5.756,5.765,5.774,5.782,5.791,5.8,5.809,5.817,5.826,5.834,5.843,5.851,5.859,5.868,5.876,5.884,5.893,5.901,5.909,5.917,5.925,5.933,5.941,5.949,5.957,5.965,5.973,5.98,5.988,5.996,6.004,6.011,6.019,6.026,6.034,6.042,6.049,6.056,6.064,6.071,6.079,6.086,6.093,6.1,6.108,6.115,6.122,6.129,6.137,6.144,6.151,6.158,6.165,6.172,6.179,6.186,6.193,6.199,6.206,6.213,6.22,6.227,6.234,6.24,6.247,6.254,6.261,6.267,6.274,6.281,6.287,6.294,6.3,6.307,6.313,6.32,6.326,6.333,6.339,6.346,6.352,6.359,6.365,6.371,6.378,6.384,6.39,6.396,6.403,6.409,6.415,6.421,6.428,6.434,6.44,6.446,6.452,6.458,6.465,6.471,6.477,6.483,6.489,6.495,6.501,6.507,6.513,6.519,6.525,6.531,6.537,6.542,6.548,6.554,6.56,6.566,6.572,6.578,6.584,6.589,6.595,6.601,6.607,6.613,6.618,6.624,6.63,6.635,6.641,6.647,6.653,6.658,6.664,6.67,6.675,6.681,6.687,6.692,6.698,6.703,6.709,6.715,6.72,6.726,6.731,6.737,6.742,6.748,6.753,6.759,6.764,6.77,6.775,6.781,6.786,6.792,6.797,6.803,6.808,6.813,6.819,6.824,6.83,6.835,6.84,6.846,6.851,6.857,6.862,6.867,6.873,6.878,6.883,6.888,6.894,6.899,6.904,6.91,6.915,6.92,6.925,6.931,6.936,6.941,6.946,6.952,6.957,6.962,6.967,6.972,6.978,6.983,6.988,6.993,6.998,7.003,7.009,7.014,7.019,7.024,7.029,7.034,7.039,7.044,7.05,7.055,7.06,7.065,7.07,7.075,7.08,7.085,7.09,7.095,7.1,7.105,7.11,7.115,7.12,7.125,7.13,7.135,7.14,7.146,7.151,7.156,7.161,7.165,7.17,7.175,7.18,7.185,7.19,7.195,7.2,7.205,7.21,7.215,7.22,7.225,7.23,7.235,7.24,7.244,7.249,7.254,7.259,7.264,7.269,7.274,7.279,7.283,7.288,7.293,7.298,7.303,7.308,7.312,7.317,7.322,7.327,7.332,7.337,7.342,7.346,7.351,7.356,7.361,7.366,7.37,7.375,7.38,7.385,7.39,7.394,7.399,7.404,7.409,7.413,7.418,7.423,7.428,7.433,7.437,7.442,7.447,7.451,7.456,7.461,7.466,7.471,7.475,7.48,7.485,7.489,7.494,7.499,7.503,7.508,7.513,7.518,7.522,7.527,7.532,7.536,7.541,7.546,7.55,7.555,7.56,7.564,7.569,7.574,7.579,7.583,7.588,7.592,7.597,7.602,7.606,7.611,7.615,7.62,7.625,7.63,7.634,7.639,7.643,7.648,7.653,7.657,7.662,7.666,7.671,7.675,7.68,7.685,7.689,7.694,7.698,7.703,7.708,7.712,7.717,7.721,7.726,7.73,7.735,7.739,7.744,7.748,7.753,7.758,7.762,7.767,7.771,7.776,7.78,7.785,7.789,7.794,7.798,7.803,7.807,7.812,7.816,7.821,7.825,7.83,7.834,7.839,7.843,7.848,7.852,7.857,7.861,7.866,7.87,7.875,7.879,7.883,7.888,7.892,7.897,7.901,7.906,7.91,7.915,7.919,7.924,7.928,7.932,7.937,7.941,7.946,7.95,7.955,7.959,7.963,7.968,7.972,7.977,7.981,7.985,7.99,7.994,7.999,8.003,8.008,8.012,8.016,8.021,8.025,8.03,8.034,8.038,8.043,8.047,8.052,8.056,8.06,8.065,8.069,8.074,8.078,8.082,8.087,8.091,8.096,8.1,8.104,8.109,8.113,8.117,8.122,8.126,8.13,8.135,8.139,8.143,8.148,8.152,8.157,8.161,8.165,8.17,8.174,8.178,8.183,8.187,8.191,8.196,8.2,8.204,8.209,8.213,8.217,8.222,8.226,8.23,8.235,8.239,8.243,8.248,8.252,8.256,8.261,8.265,8.269,8.273,8.278,8.282,8.286,8.291,8.295,8.299,8.304,8.308,8.312,8.317,8.321,8.325,8.329,8.334,8.338,8.342,8.346,8.351,8.355,8.359,8.364,8.368,8.372,8.376,8.381,8.385,8.389,8.393,8.398,8.402,8.406,8.411,8.415,8.419,8.423,8.427,8.432,8.436,8.44,8.445,8.449,8.453,8.457,8.462,8.466,8.47,8.474,8.478,8.483,8.487,8.491,8.495,8.5,8.504,8.508,8.512,8.516,8.521,8.525,8.529,8.533,8.538,8.542,8.546,8.55,8.554,8.558,8.563,8.567,8.571,8.575,8.58,8.584,8.588,8.592,8.596,8.6,8.605,8.609,8.613,8.617,8.621,8.625,8.63,8.634,8.638,8.642,8.646,8.65,8.654,8.659,8.663,8.667,8.671,8.675,8.679,8.683,8.687,8.691,8.696,8.7,8.704,8.708,8.712,8.716,8.72,8.725,8.729,8.733,8.737,8.741,8.745,8.749,8.753,8.757,8.761,8.765,8.769,8.773,8.778,8.782,8.786,8.79,8.794,8.798,8.802,8.806,8.81,8.814,8.818,8.822,8.826,8.83,8.834,8.838,8.842,8.846,8.85,8.854,8.858,8.862,8.866,8.87,8.874,8.878,8.882,8.886,8.89,8.894,8.898,8.902,8.906,8.91,8.914,8.918,8.922,8.926,8.93,8.934,8.938,8.942,8.946,8.95,8.954,8.958,8.962,8.966,8.97,8.973,8.977,8.981,8.985,8.989,8.993,8.997,9.001,9.005,9.009,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01,24.512,24.521,24.531,24.541,24.551,24.561,24.571,24.581,24.591,24.601,24.611,24.621,24.631,24.641,24.651,24.661,24.671,24.681,24.691,24.7,24.711,24.721,24.731,24.741,24.75,24.761,24.77,24.781,24.791,24.801,24.811,24.821,24.831,24.841,24.851,24.861,24.871,24.881,24.891,24.901,24.911,24.921,24.931,24.941,24.951,24.961,24.971,24.981,24.992,25.002,25.012,25.022,25.032,25.042,25.052,25.063,25.073,25.083,25.093,25.103,25.113,25.123,25.133,25.143,25.153,25.163,25.173,25.183,25.193,25.203,25.213,25.223,25.234,25.244,25.254,25.264,25.274,25.284,25.294,25.304,25.315,25.325,25.335,25.345,25.355,25.366,25.376,25.386,25.396,25.406,25.416,25.426,25.436,25.446,25.456,25.467,25.477,25.487,25.498,25.508,25.518,25.528,25.538,25.548,25.559,25.569,25.579,25.589,25.599,25.609,25.619,25.629,25.639,25.651,25.661,25.671,25.681,25.691,25.701,25.712,25.722,25.732,25.742,25.752,25.762,25.772,25.783,25.793,25.804,25.814,25.824,25.834,25.844,25.854,25.865,25.875,25.885,25.895,25.905,25.916,25.927,25.937,25.947,25.957,25.967,25.977,25.988,25.998,26.008,26.018,26.029,26.04,26.05,26.06,26.07,26.08,26.09,26.101,26.111,26.121,26.131,26.142,26.153,26.163,26.173,26.183,26.193,26.204,26.214,26.224,26.234,26.246,26.256,26.266,26.276,26.286,26.297,26.307,26.317,26.327,26.337,26.348,26.359,26.369,26.379,26.39,26.4,26.41,26.42,26.43,26.441,26.452,26.462,26.472,26.482,26.493,26.503,26.513,26.523,26.534,26.544,26.555,26.565,26.576,26.586,26.596,26.606,26.617,26.627,26.637,26.648,26.659,26.669,26.679,26.689,26.7,26.71,26.72,26.731,26.742,26.752,26.762,26.773,26.783,26.793,26.804,26.814,26.824,26.835,26.846,26.856,26.866,26.877,26.887,26.897,26.907,26.918,26.929,26.939,26.949,26.96,26.97,26.981,26.991,27.001,27.011,27.023,27.033,27.043,27.054,27.064,27.074,27.084,27.095,27.105,27.116,27.127,27.137,27.147,27.157,27.168,27.178,27.188,27.199,27.21,27.22,27.231,27.241,27.251,27.262,27.272,27.282,27.293,27.304,27.314,27.325,27.335,27.345,27.356,27.366,27.376,27.387,27.398,27.408,27.419,27.429,27.439,27.45,27.46,27.471,27.481,27.492,27.502,27.513,27.523,27.534,27.544,27.554,27.565,27.575,27.586,27.597,27.607,27.618,27.628,27.638,27.649,27.659,27.669,27.681,27.691,27.701,27.712,27.722,27.733,27.743,27.753,27.764,27.774,27.785,27.796,27.806,27.816,27.827,27.837,27.848,27.858,27.868,27.88,27.89,27.901,27.911,27.921,27.932,27.942,27.953,27.963,27.973,27.985,27.995,28.006,28.016,28.027,28.037,28.047,28.058,28.068,28.078,28.09,28.1,28.111,28.121,28.131,28.142,28.152,28.163,28.173,28.184,28.195,28.205,28.216,28.226,28.237,28.247,28.257,28.268,28.278,28.289,28.299,28.311,28.321,28.331,28.342,28.352,28.362,28.373,28.383,28.394,28.404,28.415,28.426,28.437,28.447,28.458,28.468,28.478,28.489,28.499,28.51,28.52,28.53,28.542] +# Boys BMI-for-age 0-5y 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20.022,20.022,20.022,20.024,20.024,20.025,20.025,20.025,20.026,20.026,20.027,20.027,20.028,20.028,20.029,20.03,20.03,20.031,20.031,20.032,20.032,20.032,20.033,20.034,20.035,20.035,20.036,20.036,20.037,20.037,20.038,20.039,20.039,20.04,20.04,20.041,20.041,20.042,20.042,20.044,20.044,20.045,20.045,20.046,20.046,20.047,20.048,20.049,20.049,20.05,20.05,20.051,20.052,20.053,20.053,20.054,20.055,20.055,20.056,20.056,20.058,20.058,20.059,20.059,20.06,20.061,20.062,20.062,20.063,20.064,20.064,20.065,20.066,20.067,20.068,20.068,20.069,20.07,20.071,20.072,20.072,20.073,20.074,20.075,20.076,20.076,20.077,20.078,20.078,20.08,20.08,20.081,20.082,20.082,20.084,20.084,20.085,20.086,20.086,20.088,20.089,20.089,20.09,20.091,20.092,20.093,20.094,20.094,20.095,20.097,20.097,20.098,20.099,20.099,20.101,20.102,20.102,20.103,20.105,20.105,20.106,20.107,20.108,20.109,20.11,20.111,20.111,20.113,20.114,20.114,20.115,20.116,20.118,20.118,20.119,20.12,20.122,20.123,20.123,20.124,20.126,20.126,20.127,20.128,20.129,20.13,20.131,20.132,20.133,20.135,20.135,20.136,20.137,20.139,20.14,20.14,20.141,20.143,20.144,20.145,20.146,20.147,20.148,20.149,20.15,20.151,20.152,20.153,20.154,20.156,20.157,20.158,20.159,20.16,20.161,20.162,20.164,20.165,20.165,20.166,20.168,20.169,20.17,20.171,20.172,20.173,20.174,20.176,20.177,20.178,20.179,20.181,20.182,20.183,20.184,20.185,20.186,20.187,20.189,20.19,20.191,20.193,20.194,20.195,20.196,20.197,20.198,20.199,20.201,20.202,20.203,20.204,20.206,20.207,20.208,20.21,20.211,20.212,20.213,20.214,20.215,20.217,20.218,20.219,20.22,20.222,20.223,20.224,20.226,20.227,20.228,20.229,20.231,20.232,20.233,20.235,20.236,20.237,20.239,20.24,20.241,20.243,20.244,20.245,20.247,20.248,20.249,20.25,20.252,20.253,20.255,20.256,20.258,20.259,20.261,20.262,20.263,20.265,20.266,20.267,20.269,20.27,20.271,20.273,20.274,20.275,20.277,20.278,20.28,20.282,20.283,20.284,20.286,20.287,20.288,20.29,20.291,20.293,20.295,20.296,20.297,20.299,20.3,20.301,20.303,20.304,20.306,20.308,20.309,20.31,20.312,20.313,20.315,20.317,20.318,20.319,20.321,20.322,20.324,20.326,20.327,20.328,20.33,20.331,20.333,20.335,20.336,20.338,20.339,20.341,20.343,20.344,20.345,20.347,20.349,20.35,20.352,20.353,20.355,20.357,20.358,20.36,20.362,20.363,20.364,20.366,20.368,20.369,20.371,20.373,20.375,20.376,20.377,20.38,20.381,20.382,20.385,20.386,20.387,20.389,20.391,20.392,20.394,20.396,20.397,20.399,20.401,20.403,20.404,20.406,20.408,20.409,20.411,20.413,20.414,20.416,20.418,20.42,20.421,20.423,20.425,20.426,20.429,20.43,20.432,20.434,20.435,20.437,20.439,20.44,20.443,20.444,20.446,20.448,20.45,20.451,20.453,20.455,20.457,20.459,20.46,20.462,20.464,20.466,20.468,20.47,20.471,20.473,20.475,20.476,20.479,20.48,20.482,20.484,20.486,20.488,20.49,20.491,20.494,20.495,20.497,20.499,20.501,20.503,20.505,20.507] +# Boys length-for-age 0-5y +WHO_BOY_LENGTH_UNDER_FIVE_01 = 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+# Boys OFC-for-age 0-5y 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+ +# Over 5s +# Girls +# Girls length-for-age 5-19y +WHO_GIRL_LENGTH_OVER_FIVE_01=[94.724,94.851,95.274,95.693,96.107,96.52,96.926,97.332,97.733,98.131,98.525,98.916,99.304,99.692,100.079,100.46,100.847,101.23,101.611,101.996,102.376,102.76,103.144,103.529,103.917,104.302,104.691,105.077,105.467,105.862,106.254,106.646,107.043,107.441,107.839,108.239,108.64,109.042,109.449,109.857,110.267,110.678,111.089,111.507,111.925,112.341,112.766,113.188,113.611,114.04,114.469,114.9,115.332,115.769,116.203,116.642,117.086,117.528,117.971,118.419,118.869,119.325,119.777,120.236,120.696,121.157,121.621,122.09,122.56,123.031,123.504,123.976,124.454,124.931,125.408,125.888,126.362,126.839,127.313,127.785,128.257,128.721,129.184,129.642,130.095,130.545,130.984,131.419,131.842,132.26,132.669,133.069,133.463,133.843,134.212,134.576,134.924,135.267,135.599,135.92,136.226,136.526,136.815,137.094,137.362,137.616,137.865,138.104,138.334,138.555,138.762,138.965,139.159,139.34,139.518,139.683,139.846,139.996,140.14,140.276,140.411,140.535,140.653,140.761,140.868,140.97,141.067,141.155,141.244,141.323,141.398,141.475,141.543,141.609,141.671,141.731,141.789,141.84,141.894,141.942,141.994,142.039,142.089,142.133,142.176,142.218,142.26,142.301,142.342,142.383,142.418,142.458,142.493,142.532,142.566,142.599,142.637,142.669,142.7,142.731,142.761,142.785,142.813,142.839,142.86,142.885,142.903,142.926,142.942] +WHO_GIRL_LENGTH_OVER_FIVE_1=[98.356,98.498,98.946,99.389,99.828,100.265,100.696,101.125,101.55,101.971,102.389,102.803,103.214,103.626,104.034,104.439,104.848,105.253,105.657,106.063,106.466,106.872,107.278,107.684,108.093,108.5,108.91,109.318,109.73,110.145,110.558,110.971,111.389,111.807,112.226,112.647,113.069,113.492,113.919,114.347,114.777,115.208,115.64,116.076,116.514,116.95,117.394,117.836,118.279,118.726,119.175,119.624,120.075,120.53,120.983,121.44,121.902,122.362,122.823,123.288,123.755,124.227,124.697,125.172,125.648,126.127,126.606,127.091,127.576,128.063,128.551,129.039,129.531,130.022,130.513,131.007,131.495,131.984,132.472,132.955,133.439,133.915,134.389,134.857,135.32,135.779,136.227,136.671,137.102,137.527,137.943,138.349,138.748,139.133,139.507,139.874,140.227,140.571,140.905,141.228,141.536,141.836,142.126,142.404,142.672,142.926,143.173,143.41,143.638,143.855,144.06,144.26,144.45,144.628,144.802,144.963,145.121,145.267,145.406,145.537,145.666,145.785,145.897,146,146.101,146.197,146.288,146.371,146.452,146.526,146.596,146.665,146.727,146.787,146.843,146.897,146.948,146.994,147.041,147.084,147.129,147.169,147.212,147.25,147.287,147.324,147.36,147.396,147.431,147.466,147.497,147.531,147.561,147.595,147.624,147.653,147.685,147.713,147.74,147.766,147.791,147.811,147.834,147.856,147.874,147.893,147.908,147.926,147.938] +WHO_GIRL_LENGTH_OVER_FIVE_15=[104.49,104.655,105.145,105.63,106.111,106.589,107.061,107.53,107.995,108.457,108.914,109.368,109.818,110.267,110.714,111.158,111.603,112.046,112.488,112.931,113.372,113.814,114.257,114.7,115.145,115.589,116.035,116.48,116.927,117.376,117.825,118.275,118.727,119.18,119.635,120.09,120.547,121.005,121.466,121.928,122.392,122.857,123.324,123.793,124.264,124.735,125.21,125.685,126.161,126.64,127.12,127.602,128.085,128.57,129.055,129.543,130.033,130.524,131.016,131.51,132.007,132.506,133.005,133.507,134.012,134.517,135.025,135.535,136.047,136.56,137.074,137.588,138.104,138.62,139.135,139.65,140.162,140.673,141.182,141.687,142.189,142.685,143.177,143.664,144.143,144.617,145.081,145.538,145.984,146.421,146.849,147.265,147.672,148.066,148.449,148.821,149.18,149.529,149.866,150.191,150.503,150.804,151.094,151.372,151.638,151.892,152.136,152.37,152.593,152.806,153.008,153.201,153.385,153.558,153.724,153.879,154.028,154.167,154.298,154.421,154.54,154.649,154.752,154.848,154.939,155.024,155.104,155.178,155.248,155.312,155.372,155.43,155.482,155.53,155.576,155.619,155.66,155.696,155.733,155.766,155.8,155.831,155.862,155.891,155.919,155.946,155.973,155.999,156.025,156.05,156.074,156.098,156.121,156.145,156.166,156.188,156.21,156.23,156.249,156.267,156.285,156.299,156.314,156.328,156.339,156.351,156.36,156.369,156.376] +WHO_GIRL_LENGTH_OVER_FIVE_25=[106.211, 106.382,106.884,107.382,107.874,108.363,108.847,109.327,109.804,110.276,110.745,111.21,111.671,112.131,112.588,113.043,113.499,113.952,114.405,114.858,115.31,115.762,116.215,116.669,117.123,117.578,118.034,118.489,118.946,119.405,119.864,120.324,120.786,121.249,121.713,122.179,122.645,123.114,123.584,124.056,124.529,125.004,125.48,125.958,126.438,126.919,127.403,127.887,128.373,128.861,129.35,129.84,130.332,130.826,131.32,131.816,132.315,132.814,133.315,133.817,134.322,134.829,135.336,135.846,136.358,136.872,137.387,137.905,138.424,138.944,139.466,139.987,140.51,141.032,141.554,142.075,142.594,143.111,143.626,144.137,144.644,145.146,145.643,146.135,146.619,147.097,147.565,148.026,148.476,148.917,149.347,149.767,150.176,150.573,150.958,151.332,151.693,152.043,152.381,152.707,153.019,153.321,153.61,153.888,154.154,154.408,154.651,154.884,155.106,155.318,155.518,155.71,155.892,156.064,156.227,156.381,156.527,156.664,156.793,156.914,157.029,157.137,157.237,157.33,157.418,157.501,157.578,157.649,157.716,157.778,157.835,157.889,157.938,157.984,158.027,158.067,158.104,158.138,158.172,158.202,158.233,158.261,158.289,158.316,158.341,158.366,158.39,158.413,158.436,158.459,158.48,158.502,158.523,158.544,158.563,158.582,158.602,158.62,158.637,158.653,158.668,158.681,158.694,158.706,158.715,158.724,158.731,158.738,158.743] +WHO_GIRL_LENGTH_OVER_FIVE_50=[109.419,109.602,110.126,110.645,111.16,111.67,112.175,112.677,113.174,113.667,114.156,114.642,115.124,115.604,116.081,116.557,117.031,117.504,117.977,118.449,118.921,119.393,119.865,120.337,120.81,121.284,121.759,122.234,122.71,123.187,123.665,124.144,124.623,125.104,125.587,126.071,126.556,127.042,127.53,128.02,128.511,129.004,129.498,129.993,130.49,130.989,131.49,131.991,132.494,132.999,133.505,134.012,134.52,135.03,135.541,136.053,136.567,137.082,137.599,138.117,138.636,139.158,139.68,140.205,140.731,141.259,141.789,142.321,142.853,143.387,143.922,144.458,144.993,145.528,146.062,146.595,147.126,147.655,148.18,148.702,149.22,149.732,150.239,150.739,151.233,151.718,152.195,152.663,153.121,153.568,154.004,154.429,154.842,155.244,155.633,156.01,156.375,156.727,157.067,157.394,157.708,158.01,158.3,158.577,158.842,159.096,159.338,159.569,159.789,159.998,160.197,160.386,160.564,160.733,160.893,161.043,161.184,161.318,161.442,161.56,161.669,161.772,161.867,161.956,162.039,162.116,162.188,162.254,162.315,162.372,162.424,162.472,162.516,162.556,162.593,162.628,162.659,162.689,162.716,162.742,162.767,162.79,162.813,162.834,162.854,162.874,162.894,162.912,162.93,162.948,162.965,162.982,162.998,163.014,163.03,163.045,163.06,163.073,163.086,163.098,163.109,163.119,163.128,163.136,163.142,163.147,163.151,163.153,163.155] +WHO_GIRL_LENGTH_OVER_FIVE_75=[112.626,112.821,113.367,113.909,114.445,114.976,115.504,116.026,116.544,117.058,117.568,118.075,118.578,119.077,119.574,120.07,120.563,121.057,121.549,122.04,122.532,123.023,123.514,124.006,124.498,124.991,125.484,125.978,126.473,126.968,127.465,127.963,128.461,128.96,129.461,129.963,130.466,130.971,131.477,131.984,132.493,133.003,133.515,134.028,134.543,135.059,135.576,136.095,136.616,137.137,137.659,138.183,138.708,139.234,139.762,140.29,140.819,141.35,141.883,142.416,142.951,143.486,144.024,144.563,145.104,145.647,146.191,146.736,147.283,147.83,148.379,148.928,149.476,150.024,150.57,151.115,151.658,152.198,152.735,153.268,153.795,154.318,154.835,155.344,155.846,156.34,156.825,157.3,157.765,158.219,158.661,159.091,159.509,159.915,160.308,160.689,161.057,161.411,161.752,162.081,162.397,162.7,162.989,163.266,163.531,163.784,164.025,164.254,164.472,164.679,164.876,165.061,165.236,165.403,165.558,165.705,165.842,165.971,166.092,166.205,166.309,166.407,166.498,166.583,166.66,166.732,166.798,166.859,166.915,166.966,167.013,167.055,167.093,167.128,167.16,167.189,167.215,167.24,167.261,167.283,167.301,167.32,167.336,167.352,167.368,167.383,167.397,167.411,167.424,167.436,167.45,167.461,167.474,167.485,167.497,167.508,167.517,167.527,167.536,167.544,167.551,167.557,167.562,167.565,167.569,167.569,167.57,167.568,167.567] +WHO_GIRL_LENGTH_OVER_FIVE_85=[114.347,114.549,115.107,115.66,116.208,116.751,117.29,117.823,118.353,118.878,119.399,119.916,120.431,120.94,121.448,121.956,122.459,122.963,123.466,123.967,124.47,124.971,125.473,125.975,126.476,126.98,127.483,127.988,128.493,128.997,129.504,130.012,130.52,131.029,131.539,132.051,132.565,133.08,133.595,134.111,134.63,135.15,135.671,136.193,136.717,137.244,137.769,138.298,138.828,139.358,139.889,140.422,140.956,141.49,142.027,142.564,143.101,143.64,144.182,144.723,145.266,145.809,146.356,146.902,147.451,148.001,148.554,149.106,149.66,150.215,150.77,151.327,151.882,152.436,152.99,153.54,154.09,154.636,155.179,155.718,156.25,156.779,157.301,157.815,158.322,158.819,159.309,159.788,160.257,160.714,161.16,161.593,162.013,162.421,162.817,163.199,163.569,163.924,164.267,164.596,164.913,165.216,165.506,165.782,166.047,166.3,166.54,166.768,166.985,167.19,167.386,167.57,167.743,167.908,168.061,168.207,168.341,168.469,168.587,168.698,168.799,168.894,168.982,169.065,169.14,169.209,169.272,169.331,169.383,169.432,169.476,169.514,169.55,169.582,169.61,169.636,169.659,169.682,169.7,169.719,169.734,169.75,169.763,169.777,169.79,169.802,169.814,169.825,169.835,169.845,169.856,169.865,169.876,169.884,169.894,169.903,169.909,169.917,169.923,169.929,169.934,169.939,169.941,169.943,169.944,169.943,169.942,169.938,169.934] +WHO_GIRL_LENGTH_OVER_FIVE_99=[120.481,120.706,121.306,121.901,122.491,123.074,123.655,124.229,124.798,125.363,125.924,126.481,127.034,127.582,128.128,128.675,129.215,129.756,130.297,130.835,131.376,131.914,132.452,132.991,133.528,134.068,134.607,135.149,135.69,136.229,136.772,137.316,137.858,138.402,138.947,139.494,140.043,140.593,141.142,141.693,142.245,142.799,143.355,143.91,144.467,145.028,145.585,146.147,146.71,147.272,147.835,148.399,148.966,149.53,150.099,150.666,151.232,151.803,152.375,152.945,153.517,154.088,154.664,155.238,155.814,156.392,156.972,157.551,158.13,158.712,159.293,159.876,160.455,161.034,161.611,162.184,162.758,163.325,163.889,164.449,165.001,165.55,166.089,166.621,167.146,167.657,168.163,168.655,169.139,169.608,170.065,170.509,170.937,171.355,171.759,172.146,172.523,172.882,173.228,173.559,173.88,174.184,174.474,174.75,175.013,175.266,175.503,175.728,175.94,176.141,176.334,176.512,176.678,176.838,176.984,177.123,177.248,177.368,177.479,177.582,177.672,177.759,177.837,177.912,177.977,178.035,178.088,178.138,178.178,178.218,178.252,178.278,178.304,178.325,178.344,178.358,178.371,178.384,178.392,178.401,178.405,178.412,178.414,178.418,178.422,178.424,178.427,178.428,178.429,178.429,178.433,178.432,178.435,178.434,178.436,178.437,178.434,178.434,178.433,178.431,178.428,178.427,178.421,178.415,178.41,178.4,178.393,178.381,178.371] +WHO_GIRL_LENGTH_OVER_FIVE_999=[124.114,124.352,124.977,125.597,126.212,126.819,127.424,128.022,128.615,129.203,129.788,130.368,130.945,131.515,132.084,132.654,133.215,133.779,134.343,134.902,135.465,136.025,136.585,137.146,137.704,138.266,138.826,139.39,139.952,140.511,141.075,141.641,142.204,142.768,143.334,143.902,144.471,145.043,145.612,146.182,146.755,147.329,147.906,148.48,149.056,149.638,150.213,150.795,151.378,151.958,152.54,153.124,153.709,154.291,154.879,155.465,156.048,156.636,157.226,157.814,158.404,158.99,159.583,160.174,160.767,161.361,161.958,162.552,163.147,163.743,164.341,164.939,165.532,166.125,166.717,167.302,167.89,168.471,169.047,169.62,170.182,170.744,171.294,171.836,172.371,172.891,173.406,173.906,174.399,174.876,175.339,175.789,176.222,176.645,177.054,177.445,177.826,178.187,178.535,178.867,179.191,179.495,179.785,180.06,180.323,180.576,180.811,181.034,181.244,181.442,181.633,181.807,181.97,182.126,182.267,182.403,182.523,182.639,182.745,182.843,182.927,183.008,183.081,183.152,183.211,183.263,183.309,183.353,183.387,183.421,183.45,183.468,183.488,183.503,183.515,183.524,183.53,183.538,183.539,183.543,183.54,183.542,183.536,183.535,183.533,183.531,183.527,183.523,183.518,183.512,183.512,183.505,183.504,183.497,183.494,183.491,183.482,183.478,183.472,183.465,183.458,183.454,183.443,183.432,183.424,183.409,183.398,183.381,183.368] +# Girls weight-for-age 5-10y +WHO_GIRL_WEIGHT_OVER_FIVE_50 = [18.218, 18.258,18.433,18.607,18.781,18.954,19.128,19.3,19.473,19.646,19.818,19.991,20.164,20.338,20.512,20.688,20.866,21.046,21.227,21.411,21.598,21.787,21.98,22.175,22.374,22.576,22.782,22.99,23.202,23.418,23.637,23.859,24.085,24.315,24.548,24.785,25.026,25.271,25.52,25.772,26.028,26.288,26.552,26.819,27.09,27.364,27.641,27.921,28.204,28.49,28.779,29.071,29.366,29.665,29.966,30.272,30.58,30.893,31.21,31.532,31.858] +WHO_GIRL_WEIGHT_OVER_FIVE_75 = [20.169, 20.151,20.352,20.553,20.754,20.954,21.154,21.353,21.553,21.753,21.953,22.153,22.354,22.556,22.759,22.964,23.17,23.379,23.591,23.805,24.022,24.242,24.466,24.694,24.925,25.161,25.4,25.643,25.889,26.14,26.395,26.654,26.916,27.184,27.455,27.731,28.011,28.296,28.585,28.878,29.176,29.478,29.784,30.095,30.409,30.727,31.049,31.375,31.704,32.036,32.372,32.711,33.054,33.4,33.75,34.104,34.463,34.826,35.193,35.566,35.943] +WHO_GIRL_WEIGHT_OVER_FIVE_85 = [21.334, 21.288,21.506,21.723,21.941,22.158,22.375,22.591,22.808,23.026,23.243,23.461,23.68,23.9,24.121,24.344,24.569,24.797,25.028,25.261,25.498,25.738,25.982,26.231,26.483,26.74,27,27.265,27.535,27.808,28.086,28.368,28.655,28.946,29.242,29.543,29.849,30.159,30.474,30.794,31.119,31.448,31.782,32.121,32.463,32.81,33.161,33.516,33.874,34.237,34.603,34.972,35.346,35.723,36.104,36.49,36.881,37.275,37.676,38.081,38.493] +WHO_GIRL_WEIGHT_OVER_FIVE_97 = [24.429, 24.336,24.601,24.866,25.132,25.398,25.664,25.93,26.197,26.465,26.733,27.002,27.273,27.545,27.819,28.095,28.375,28.658,28.945,29.234,29.529,29.828,30.132,30.441,30.755,31.075,31.399,31.73,32.065,32.406,32.752,33.103,33.46,33.823,34.192,34.567,34.947,35.334,35.727,36.126,36.53,36.94,37.355,37.778,38.204,38.636,39.073,39.514,39.96,40.411,40.867,41.326,41.791,42.26,42.734,43.214,43.699,44.189,44.687,45.19,45.7] +# Girls BMI-for-age 5-19y +WHO_GIRL_BMI_OVER_FIVE_01=[11.554,11.689,11.682,11.676,11.671,11.665,11.661,11.657,11.653,11.649,11.646,11.645,11.643,11.642,11.641,11.641,11.642,11.643,11.645,11.647,11.651,11.654,11.659,11.664,11.671,11.677,11.685,11.693,11.702,11.712,11.722,11.733,11.745,11.757,11.77,11.783,11.798,11.813,11.828,11.845,11.862,11.879,11.897,11.915,11.934,11.954,11.974,11.994,12.014,12.035,12.057,12.078,12.1,12.122,12.145,12.168,12.191,12.215,12.24,12.264,12.29,12.315,12.342,12.368,12.396,12.424,12.452,12.481,12.511,12.54,12.571,12.602,12.634,12.666,12.699,12.732,12.766,12.801,12.835,12.87,12.906,12.942,12.978,13.015,13.052,13.089,13.126,13.164,13.201,13.239,13.276,13.314,13.351,13.389,13.426,13.463,13.499,13.536,13.572,13.608,13.643,13.678,13.713,13.748,13.781,13.815,13.848,13.88,13.912,13.943,13.973,14.003,14.033,14.061,14.089,14.116,14.143,14.168,14.194,14.218,14.241,14.263,14.285,14.306,14.326,14.345,14.364,14.381,14.398,14.415,14.43,14.445,14.458,14.472,14.484,14.496,14.506,14.517,14.526,14.535,14.543,14.55,14.556,14.562,14.568,14.573,14.577,14.581,14.584,14.587,14.589,14.591,14.593,14.594,14.595,14.596,14.596,14.596,14.596,14.595,14.594,14.593,14.592,14.59,14.589,14.587,14.585,14.583,14.58] +WHO_GIRL_BMI_OVER_FIVE_1=[12.33,12.412,12.405,12.399,12.393,12.387,12.382,12.378,12.374,12.37,12.367,12.365,12.363,12.362,12.361,12.361,12.362,12.363,12.365,12.368,12.372,12.376,12.381,12.387,12.394,12.401,12.41,12.419,12.429,12.439,12.451,12.463,12.476,12.489,12.504,12.519,12.535,12.551,12.569,12.587,12.605,12.625,12.645,12.666,12.687,12.708,12.731,12.753,12.776,12.8,12.824,12.848,12.873,12.898,12.924,12.95,12.976,13.003,13.031,13.059,13.088,13.117,13.147,13.177,13.208,13.24,13.272,13.305,13.339,13.373,13.408,13.444,13.48,13.516,13.554,13.592,13.63,13.669,13.709,13.749,13.79,13.831,13.872,13.914,13.956,13.999,14.041,14.084,14.127,14.17,14.213,14.256,14.299,14.342,14.385,14.428,14.47,14.512,14.554,14.595,14.636,14.677,14.718,14.758,14.797,14.836,14.875,14.913,14.95,14.987,15.023,15.058,15.093,15.127,15.16,15.193,15.224,15.255,15.285,15.314,15.343,15.37,15.397,15.423,15.448,15.472,15.495,15.517,15.539,15.56,15.58,15.599,15.617,15.635,15.651,15.667,15.682,15.697,15.71,15.723,15.735,15.746,15.757,15.767,15.777,15.785,15.793,15.801,15.808,15.815,15.821,15.827,15.832,15.837,15.842,15.846,15.85,15.854,15.857,15.86,15.863,15.866,15.869,15.871,15.873,15.875,15.876,15.878,15.879] +WHO_GIRL_BMI_OVER_FIVE_15=[13.84,13.846,13.84,13.835,13.831,13.827,13.824,13.821,13.819,13.817,13.816,13.816,13.816,13.817,13.818,13.821,13.824,13.828,13.832,13.838,13.844,13.851,13.86,13.869,13.879,13.89,13.902,13.914,13.928,13.943,13.958,13.974,13.992,14.01,14.029,14.049,14.07,14.091,14.114,14.137,14.162,14.187,14.213,14.239,14.266,14.294,14.323,14.352,14.382,14.412,14.443,14.474,14.506,14.538,14.571,14.605,14.639,14.674,14.709,14.745,14.782,14.819,14.858,14.897,14.937,14.977,15.019,15.061,15.104,15.147,15.192,15.237,15.283,15.33,15.378,15.426,15.475,15.525,15.575,15.626,15.678,15.73,15.782,15.836,15.889,15.943,15.997,16.052,16.106,16.161,16.216,16.271,16.325,16.38,16.435,16.489,16.544,16.598,16.651,16.705,16.758,16.81,16.862,16.914,16.965,17.016,17.066,17.115,17.164,17.212,17.259,17.306,17.352,17.397,17.441,17.484,17.527,17.568,17.609,17.648,17.687,17.724,17.761,17.796,17.831,17.864,17.897,17.928,17.959,17.989,18.018,18.045,18.072,18.098,18.123,18.148,18.171,18.193,18.215,18.235,18.255,18.274,18.292,18.31,18.327,18.343,18.358,18.373,18.388,18.401,18.414,18.427,18.439,18.451,18.463,18.474,18.485,18.495,18.505,18.515,18.524,18.534,18.543,18.552,18.56,18.569,18.577,18.585,18.593] +WHO_GIRL_BMI_OVER_FIVE_25=[14.316,14.306,14.301,14.298,14.294,14.292,14.29,14.288,14.287,14.287,14.287,14.288,14.29,14.292,14.295,14.299,14.303,14.309,14.315,14.322,14.33,14.339,14.349,14.36,14.371,14.384,14.398,14.412,14.428,14.444,14.462,14.48,14.499,14.52,14.541,14.563,14.586,14.61,14.635,14.661,14.687,14.715,14.743,14.772,14.802,14.832,14.864,14.895,14.928,14.96,14.994,15.028,15.063,15.098,15.134,15.17,15.207,15.245,15.283,15.323,15.362,15.403,15.445,15.487,15.53,15.574,15.619,15.664,15.71,15.758,15.806,15.854,15.904,15.955,16.006,16.058,16.11,16.164,16.218,16.273,16.328,16.384,16.441,16.498,16.555,16.613,16.671,16.73,16.788,16.847,16.906,16.965,17.024,17.082,17.141,17.2,17.258,17.316,17.373,17.431,17.488,17.544,17.6,17.656,17.711,17.765,17.819,17.872,17.925,17.977,18.028,18.078,18.127,18.176,18.223,18.27,18.316,18.361,18.404,18.447,18.489,18.529,18.569,18.608,18.645,18.682,18.717,18.752,18.785,18.818,18.849,18.88,18.909,18.938,18.966,18.992,19.018,19.043,19.067,19.09,19.112,19.133,19.154,19.173,19.193,19.211,19.228,19.245,19.262,19.277,19.292,19.307,19.321,19.335,19.348,19.361,19.374,19.386,19.398,19.41,19.421,19.432,19.443,19.454,19.464,19.475,19.485,19.495,19.504] +WHO_GIRL_BMI_OVER_FIVE_50=[15.275,15.244,15.243,15.243,15.244,15.245,15.246,15.249,15.252,15.255,15.259,15.264,15.27,15.276,15.283,15.291,15.3,15.31,15.32,15.331,15.344,15.357,15.372,15.387,15.404,15.421,15.44,15.459,15.48,15.501,15.524,15.548,15.572,15.598,15.625,15.652,15.681,15.711,15.742,15.773,15.806,15.839,15.874,15.909,15.945,15.982,16.019,16.058,16.096,16.136,16.176,16.217,16.258,16.3,16.343,16.386,16.43,16.475,16.52,16.566,16.613,16.661,16.71,16.76,16.81,16.861,16.914,16.967,17.021,17.076,17.132,17.188,17.246,17.304,17.364,17.424,17.485,17.546,17.609,17.672,17.736,17.8,17.865,17.931,17.997,18.063,18.13,18.197,18.264,18.331,18.399,18.466,18.533,18.601,18.668,18.735,18.801,18.868,18.934,18.999,19.064,19.129,19.193,19.257,19.32,19.382,19.444,19.504,19.565,19.624,19.682,19.74,19.797,19.852,19.907,19.961,20.013,20.065,20.115,20.164,20.212,20.26,20.305,20.35,20.393,20.436,20.477,20.517,20.556,20.594,20.631,20.666,20.701,20.734,20.767,20.798,20.829,20.858,20.886,20.914,20.94,20.966,20.99,21.014,21.037,21.059,21.08,21.101,21.121,21.14,21.159,21.177,21.194,21.212,21.228,21.244,21.26,21.276,21.291,21.306,21.32,21.334,21.348,21.362,21.375,21.388,21.401,21.414,21.427] +WHO_GIRL_BMI_OVER_FIVE_75=[16.338,16.306,16.311,16.317,16.324,16.331,16.339,16.347,16.357,16.367,16.377,16.388,16.401,16.414,16.427,16.442,16.458,16.475,16.492,16.511,16.53,16.551,16.573,16.596,16.62,16.645,16.671,16.699,16.727,16.757,16.788,16.819,16.852,16.887,16.922,16.958,16.995,17.034,17.073,17.114,17.156,17.198,17.242,17.286,17.331,17.377,17.424,17.472,17.52,17.569,17.618,17.669,17.72,17.771,17.823,17.876,17.93,17.984,18.039,18.096,18.152,18.21,18.269,18.328,18.389,18.45,18.512,18.575,18.64,18.705,18.771,18.838,18.906,18.974,19.044,19.114,19.186,19.258,19.331,19.404,19.478,19.553,19.629,19.705,19.781,19.858,19.935,20.012,20.09,20.167,20.245,20.323,20.4,20.477,20.555,20.631,20.708,20.784,20.859,20.934,21.009,21.083,21.156,21.229,21.301,21.372,21.443,21.512,21.581,21.648,21.715,21.781,21.845,21.909,21.971,22.032,22.092,22.151,22.208,22.264,22.319,22.373,22.425,22.476,22.525,22.573,22.62,22.666,22.71,22.754,22.795,22.836,22.876,22.914,22.951,22.987,23.021,23.055,23.087,23.119,23.149,23.178,23.206,23.233,23.259,23.285,23.309,23.333,23.355,23.378,23.399,23.42,23.44,23.46,23.479,23.498,23.516,23.534,23.551,23.568,23.585,23.601,23.617,23.633,23.648,23.663,23.678,23.693,23.707] +WHO_GIRL_BMI_OVER_FIVE_85=[16.957,16.936,16.945,16.956,16.967,16.979,16.991,17.005,17.019,17.034,17.049,17.065,17.083,17.101,17.12,17.14,17.161,17.183,17.206,17.23,17.255,17.281,17.309,17.337,17.367,17.398,17.43,17.464,17.499,17.534,17.571,17.609,17.648,17.689,17.73,17.773,17.817,17.862,17.908,17.955,18.004,18.053,18.103,18.154,18.206,18.259,18.313,18.367,18.422,18.478,18.535,18.592,18.65,18.708,18.767,18.827,18.887,18.949,19.011,19.074,19.137,19.202,19.267,19.334,19.401,19.469,19.538,19.608,19.679,19.751,19.824,19.898,19.973,20.048,20.125,20.202,20.28,20.359,20.439,20.519,20.6,20.682,20.764,20.846,20.929,21.013,21.096,21.18,21.264,21.348,21.432,21.516,21.6,21.683,21.766,21.849,21.931,22.013,22.094,22.175,22.255,22.335,22.413,22.491,22.569,22.645,22.72,22.795,22.868,22.94,23.012,23.082,23.151,23.219,23.285,23.35,23.414,23.477,23.538,23.598,23.656,23.713,23.768,23.822,23.875,23.926,23.976,24.025,24.072,24.117,24.162,24.205,24.247,24.287,24.326,24.364,24.4,24.436,24.47,24.503,24.535,24.565,24.595,24.623,24.651,24.677,24.703,24.727,24.751,24.774,24.796,24.818,24.839,24.859,24.879,24.898,24.917,24.936,24.953,24.971,24.988,25.004,25.021,25.037,25.053,25.068,25.084,25.099,25.113] +WHO_GIRL_BMI_OVER_FIVE_99=[19.493,19.605,19.643,19.681,19.721,19.763,19.804,19.848,19.892,19.938,19.985,20.033,20.082,20.133,20.186,20.239,20.295,20.352,20.41,20.47,20.531,20.594,20.659,20.726,20.794,20.864,20.935,21.009,21.084,21.16,21.239,21.318,21.399,21.483,21.567,21.653,21.74,21.828,21.919,22.01,22.103,22.198,22.293,22.389,22.486,22.584,22.683,22.783,22.882,22.983,23.084,23.186,23.289,23.391,23.494,23.598,23.701,23.806,23.911,24.017,24.123,24.23,24.337,24.444,24.553,24.661,24.77,24.88,24.991,25.102,25.213,25.325,25.438,25.55,25.664,25.777,25.891,26.005,26.12,26.235,26.35,26.465,26.579,26.694,26.809,26.924,27.038,27.152,27.265,27.378,27.49,27.601,27.712,27.821,27.929,28.036,28.143,28.248,28.352,28.454,28.556,28.655,28.753,28.85,28.946,29.039,29.132,29.222,29.311,29.398,29.484,29.568,29.65,29.729,29.808,29.884,29.958,30.031,30.1,30.169,30.235,30.3,30.361,30.422,30.48,30.536,30.591,30.643,30.693,30.742,30.789,30.834,30.877,30.918,30.957,30.995,31.031,31.065,31.098,31.128,31.157,31.186,31.212,31.236,31.259,31.281,31.302,31.321,31.339,31.357,31.373,31.388,31.403,31.417,31.43,31.441,31.453,31.464,31.474,31.484,31.493,31.502,31.51,31.519,31.526,31.533,31.54,31.547,31.553] +WHO_GIRL_BMI_OVER_FIVE_999=[21.288,21.594,21.662,21.731,21.802,21.876,21.95,22.027,22.106,22.187,22.27,22.354,22.441,22.531,22.623,22.717,22.813,22.912,23.013,23.117,23.222,23.331,23.443,23.557,23.674,23.793,23.914,24.039,24.167,24.295,24.428,24.562,24.698,24.838,24.979,25.122,25.268,25.415,25.565,25.716,25.871,26.026,26.183,26.341,26.499,26.661,26.821,26.983,27.144,27.308,27.471,27.633,27.798,27.96,28.123,28.286,28.448,28.611,28.774,28.937,29.098,29.26,29.421,29.581,29.742,29.901,30.06,30.219,30.377,30.534,30.69,30.845,31.002,31.155,31.309,31.46,31.612,31.761,31.91,32.06,32.206,32.353,32.496,32.638,32.781,32.922,33.059,33.196,33.33,33.463,33.594,33.723,33.85,33.974,34.097,34.216,34.333,34.449,34.561,34.67,34.779,34.884,34.986,35.086,35.185,35.279,35.373,35.461,35.549,35.634,35.719,35.799,35.877,35.951,36.024,36.094,36.161,36.227,36.288,36.348,36.404,36.461,36.511,36.561,36.608,36.652,36.695,36.735,36.772,36.806,36.84,36.871,36.899,36.925,36.948,36.97,36.989,37.008,37.024,37.036,37.047,37.058,37.066,37.072,37.075,37.078,37.08,37.079,37.077,37.076,37.072,37.067,37.061,37.055,37.048,37.038,37.031,37.022,37.011,37.002,36.99,36.978,36.966,36.955,36.943,36.929,36.917,36.903,36.889] + +# Boys +# Boys length-for-age 5-19y +WHO_BOY_LENGTH_OVER_FIVE_01=[95.64,96.076,96.516,96.953,97.39,97.82,98.246,98.668,99.089,99.504,99.914,100.324,100.726,101.125,101.525,101.918,102.31,102.704,103.093,103.48,103.867,104.255,104.638,105.02,105.4,105.778,106.154,106.529,106.901,107.272,107.641,108.008,108.373,108.736,109.096,109.455,109.811,110.166,110.519,110.871,111.221,111.57,111.914,112.261,112.608,112.953,113.298,113.643,113.987,114.331,114.674,115.016,115.361,115.702,116.042,116.385,116.723,117.065,117.402,117.743,118.083,118.424,118.765,119.107,119.451,119.796,120.147,120.496,120.853,121.212,121.575,121.94,122.314,122.686,123.067,123.451,123.845,124.238,124.641,125.049,125.462,125.88,126.309,126.743,127.184,127.636,128.09,128.559,129.031,129.515,130.005,130.506,131.007,131.523,132.037,132.56,133.09,133.621,134.152,134.684,135.219,135.756,136.285,136.815,137.345,137.87,138.389,138.901,139.41,139.916,140.408,140.894,141.37,141.841,142.3,142.747,143.184,143.614,144.034,144.443,144.841,145.234,145.616,145.986,146.346,146.696,147.035,147.369,147.688,148.002,148.307,148.596,148.88,149.155,149.419,149.674,149.919,150.154,150.38,150.596,150.803,151.001,151.191,151.366,151.539,151.704,151.856,152.006,152.143,152.28,152.406,152.526,152.641,152.752,152.858,152.961,153.06,153.15,153.243,153.328,153.41,153.49,153.574,153.645,153.719,153.792,153.863,153.927,153.99] +WHO_BOY_LENGTH_OVER_FIVE_1=[99.18,99.583,100.047,100.508,100.968,101.421,101.871,102.316,102.76,103.198,103.632,104.064,104.49,104.912,105.333,105.75,106.165,106.581,106.993,107.403,107.813,108.223,108.63,109.034,109.437,109.839,110.238,110.635,111.031,111.425,111.818,112.208,112.596,112.981,113.365,113.746,114.126,114.504,114.88,115.254,115.627,115.999,116.367,116.738,117.107,117.476,117.844,118.212,118.58,118.947,119.313,119.678,120.046,120.41,120.773,121.139,121.501,121.865,122.225,122.589,122.952,123.315,123.679,124.044,124.411,124.779,125.152,125.525,125.904,126.286,126.671,127.06,127.455,127.851,128.254,128.661,129.077,129.493,129.918,130.348,130.784,131.225,131.676,132.133,132.597,133.07,133.548,134.039,134.533,135.039,135.551,136.073,136.598,137.134,137.67,138.214,138.763,139.314,139.866,140.417,140.97,141.525,142.072,142.618,143.164,143.704,144.238,144.764,145.286,145.803,146.307,146.804,147.289,147.767,148.234,148.688,149.131,149.566,149.989,150.402,150.803,151.196,151.579,151.95,152.309,152.658,152.995,153.326,153.643,153.953,154.253,154.538,154.817,155.085,155.344,155.591,155.829,156.056,156.274,156.482,156.68,156.869,157.049,157.216,157.378,157.532,157.674,157.813,157.941,158.066,158.181,158.29,158.394,158.493,158.588,158.679,158.766,158.846,158.927,159.001,159.073,159.142,159.213,159.274,159.337,159.398,159.458,159.512,159.565] +WHO_BOY_LENGTH_OVER_FIVE_15=[105.157, 105.506,106.01,106.51,107.009,107.503,107.992,108.478,108.96,109.437,109.91,110.38,110.845,111.306,111.765,112.221,112.674,113.128,113.579,114.028,114.476,114.924,115.37,115.814,116.256,116.696,117.134,117.571,118.006,118.439,118.87,119.299,119.726,120.15,120.573,120.993,121.411,121.828,122.243,122.656,123.068,123.479,123.888,124.297,124.705,125.113,125.521,125.928,126.334,126.741,127.146,127.551,127.957,128.36,128.763,129.167,129.568,129.97,130.371,130.772,131.174,131.575,131.978,132.381,132.786,133.193,133.604,134.016,134.433,134.853,135.277,135.704,136.137,136.572,137.013,137.459,137.911,138.367,138.829,139.297,139.771,140.252,140.74,141.235,141.737,142.248,142.764,143.291,143.825,144.367,144.917,145.475,146.038,146.609,147.183,147.762,148.344,148.929,149.514,150.099,150.683,151.266,151.844,152.419,152.991,153.556,154.115,154.665,155.209,155.744,156.268,156.782,157.284,157.775,158.254,158.72,159.173,159.615,160.045,160.464,160.869,161.265,161.648,162.019,162.378,162.725,163.06,163.386,163.698,164.001,164.293,164.572,164.842,165.1,165.348,165.584,165.809,166.024,166.228,166.421,166.604,166.778,166.941,167.094,167.238,167.374,167.5,167.62,167.73,167.835,167.932,168.023,168.108,168.188,168.264,168.335,168.402,168.464,168.525,168.581,168.634,168.684,168.734,168.779,168.823,168.866,168.906,168.943,168.979] +WHO_BOY_LENGTH_OVER_FIVE_25=[106.834, 107.168,107.683,108.195,108.704,109.209,109.71,110.206,110.699,111.188,111.672,112.152,112.628,113.1,113.569,114.036,114.501,114.965,115.426,115.887,116.346,116.805,117.261,117.716,118.169,118.62,119.069,119.517,119.962,120.406,120.849,121.288,121.726,122.162,122.595,123.026,123.456,123.883,124.309,124.733,125.156,125.578,125.998,126.418,126.837,127.256,127.675,128.093,128.51,128.928,129.344,129.76,130.177,130.591,131.005,131.419,131.832,132.245,132.656,133.068,133.481,133.893,134.306,134.72,135.136,135.554,135.975,136.399,136.826,137.257,137.692,138.13,138.573,139.019,139.471,139.927,140.39,140.857,141.33,141.808,142.293,142.784,143.283,143.789,144.301,144.823,145.35,145.887,146.432,146.985,147.545,148.114,148.687,149.268,149.852,150.441,151.033,151.627,152.221,152.815,153.408,154,154.586,155.169,155.748,156.32,156.886,157.444,157.993,158.534,159.063,159.582,160.088,160.583,161.065,161.534,161.991,162.435,162.867,163.287,163.694,164.09,164.474,164.845,165.203,165.55,165.884,166.208,166.519,166.821,167.111,167.388,167.655,167.91,168.155,168.388,168.609,168.82,169.02,169.21,169.389,169.558,169.717,169.865,170.005,170.136,170.257,170.372,170.477,170.577,170.668,170.754,170.834,170.909,170.979,171.044,171.106,171.163,171.218,171.269,171.317,171.362,171.406,171.446,171.485,171.522,171.557,171.59,171.621] +WHO_BOY_LENGTH_OVER_FIVE_50=[109.959, 110.265,110.801,111.334,111.864,112.39,112.911,113.428,113.941,114.45,114.955,115.455,115.951,116.443,116.932,117.42,117.905,118.388,118.87,119.351,119.83,120.308,120.785,121.26,121.734,122.205,122.675,123.143,123.609,124.074,124.536,124.996,125.454,125.91,126.364,126.816,127.265,127.713,128.159,128.603,129.047,129.489,129.93,130.37,130.81,131.25,131.688,132.127,132.565,133.003,133.44,133.877,134.313,134.748,135.183,135.617,136.05,136.483,136.915,137.347,137.78,138.212,138.645,139.08,139.516,139.954,140.395,140.839,141.286,141.737,142.192,142.65,143.113,143.58,144.051,144.528,145.009,145.496,145.989,146.488,146.993,147.504,148.022,148.548,149.081,149.621,150.169,150.726,151.29,151.862,152.442,153.03,153.623,154.222,154.826,155.433,156.043,156.654,157.266,157.878,158.487,159.094,159.696,160.294,160.886,161.472,162.05,162.621,163.182,163.732,164.272,164.799,165.314,165.816,166.305,166.78,167.242,167.69,168.126,168.548,168.958,169.355,169.739,170.11,170.468,170.814,171.147,171.468,171.777,172.075,172.361,172.634,172.897,173.147,173.386,173.613,173.828,174.032,174.225,174.407,174.578,174.739,174.89,175.03,175.161,175.282,175.395,175.5,175.596,175.685,175.767,175.843,175.913,175.978,176.038,176.094,176.145,176.192,176.237,176.278,176.316,176.352,176.385,176.416,176.445,176.472,176.498,176.521,176.543] +WHO_BOY_LENGTH_OVER_FIVE_75=[113.085,113.362,113.918,114.473,115.023,115.57,116.112,116.65,117.183,117.712,118.238,118.758,119.274,119.787,120.296,120.803,121.308,121.811,122.314,122.815,123.315,123.812,124.31,124.805,125.299,125.791,126.281,126.769,127.256,127.741,128.224,128.704,129.183,129.659,130.133,130.605,131.075,131.543,132.009,132.474,132.937,133.4,133.862,134.323,134.783,135.243,135.702,136.161,136.62,137.079,137.536,137.994,138.449,138.905,139.361,139.814,140.269,140.721,141.174,141.626,142.078,142.531,142.984,143.439,143.895,144.354,144.814,145.279,145.746,146.217,146.692,147.17,147.652,148.14,148.631,149.128,149.629,150.136,150.649,151.167,151.692,152.224,152.762,153.307,153.86,154.42,154.989,155.564,156.148,156.74,157.34,157.946,158.56,159.177,159.8,160.425,161.052,161.681,162.311,162.94,163.566,164.188,164.806,165.419,166.024,166.624,167.215,167.798,168.37,168.93,169.48,170.017,170.541,171.05,171.545,172.025,172.492,172.945,173.384,173.809,174.222,174.62,175.004,175.375,175.733,176.078,176.41,176.728,177.035,177.329,177.611,177.881,178.139,178.384,178.617,178.838,179.047,179.244,179.43,179.604,179.768,179.92,180.062,180.195,180.317,180.429,180.533,180.627,180.715,180.793,180.866,180.933,180.993,181.048,181.097,181.143,181.184,181.222,181.255,181.287,181.316,181.342,181.364,181.386,181.406,181.423,181.438,181.453,181.466] +WHO_BOY_LENGTH_OVER_FIVE_85=[114.762,115.023,115.592,116.157,116.718,117.276,117.83,118.378,118.922,119.463,119.999,120.53,121.057,121.581,122.1,122.619,123.135,123.648,124.161,124.674,125.184,125.693,126.201,126.707,127.212,127.715,128.216,128.715,129.213,129.709,130.202,130.694,131.183,131.671,132.155,132.638,133.119,133.598,134.075,134.551,135.025,135.498,135.972,136.444,136.915,137.386,137.856,138.326,138.796,139.266,139.735,140.203,140.669,141.136,141.603,142.067,142.532,142.996,143.46,143.923,144.385,144.849,145.313,145.778,146.245,146.715,147.186,147.661,148.139,148.621,149.106,149.596,150.088,150.587,151.089,151.596,152.108,152.626,153.149,153.678,154.214,154.757,155.305,155.861,156.425,156.995,157.575,158.16,158.755,159.357,159.968,160.584,161.209,161.836,162.469,163.104,163.741,164.379,165.018,165.656,166.291,166.921,167.548,168.169,168.782,169.388,169.986,170.576,171.154,171.72,172.275,172.817,173.345,173.858,174.356,174.84,175.31,175.765,176.206,176.633,177.047,177.445,177.83,178.201,178.558,178.902,179.234,179.55,179.857,180.148,180.428,180.697,180.952,181.194,181.424,181.641,181.847,182.041,182.223,182.393,182.552,182.701,182.838,182.967,183.083,183.19,183.29,183.379,183.462,183.535,183.602,183.664,183.719,183.768,183.812,183.852,183.887,183.921,183.949,183.975,183.999,184.019,184.036,184.054,184.067,184.079,184.089,184.099,184.107] +WHO_BOY_LENGTH_OVER_FIVE_99=[120.739,120.946,121.554,122.16,122.76,123.358,123.951,124.54,125.122,125.702,126.277,126.846,127.412,127.975,128.532,129.089,129.644,130.195,130.747,131.298,131.848,132.394,132.941,133.486,134.03,134.572,135.112,135.65,136.187,136.722,137.255,137.785,138.313,138.84,139.363,139.885,140.404,140.922,141.438,141.953,142.466,142.978,143.493,144.003,144.513,145.023,145.532,146.042,146.551,147.06,147.568,148.076,148.58,149.086,149.592,150.095,150.6,151.101,151.605,152.106,152.607,153.108,153.611,154.115,154.621,155.129,155.638,156.153,156.668,157.188,157.712,158.241,158.77,159.308,159.848,160.394,160.942,161.5,162.06,162.627,163.201,163.783,164.369,164.963,165.565,166.172,166.791,167.413,168.046,168.686,169.334,169.986,170.649,171.311,171.981,172.652,173.322,173.994,174.666,175.338,176.004,176.663,177.321,177.969,178.608,179.24,179.863,180.477,181.077,181.661,182.237,182.795,183.34,183.866,184.376,184.872,185.352,185.814,186.262,186.695,187.113,187.513,187.899,188.27,188.627,188.97,189.298,189.61,189.912,190.197,190.468,190.731,190.976,191.209,191.428,191.634,191.827,192.008,192.176,192.332,192.477,192.609,192.73,192.844,192.944,193.033,193.116,193.186,193.251,193.304,193.354,193.396,193.433,193.463,193.488,193.508,193.523,193.539,193.546,193.555,193.56,193.562,193.558,193.559,193.554,193.547,193.537,193.53,193.522] +WHO_BOY_LENGTH_OVER_FIVE_999=[124.278,124.453,125.086,125.715,126.337,126.959,127.576,128.188,128.793,129.396,129.995,130.586,131.176,131.761,132.34,132.921,133.499,134.072,134.647,135.221,135.794,136.362,136.932,137.501,138.068,138.633,139.196,139.757,140.317,140.875,141.431,141.984,142.536,143.085,143.632,144.176,144.719,145.26,145.799,146.336,146.872,147.407,147.946,148.48,149.013,149.546,150.078,150.611,151.143,151.675,152.207,152.738,153.265,153.795,154.324,154.849,155.377,155.901,156.429,156.952,157.476,158,158.525,159.052,159.581,160.112,160.643,161.181,161.719,162.261,162.808,163.36,163.912,164.473,165.036,165.604,166.174,166.754,167.337,167.927,168.524,169.128,169.736,170.353,170.977,171.607,172.249,172.892,173.549,174.21,174.88,175.554,176.24,176.922,177.614,178.306,178.996,179.687,180.38,181.071,181.756,182.432,183.108,183.773,184.427,185.074,185.712,186.34,186.953,187.548,188.136,188.704,189.259,189.792,190.31,190.812,191.299,191.766,192.217,192.653,193.075,193.476,193.862,194.233,194.59,194.931,195.259,195.567,195.866,196.147,196.414,196.673,196.913,197.139,197.352,197.551,197.737,197.91,198.071,198.218,198.354,198.477,198.588,198.694,198.783,198.861,198.935,198.993,199.048,199.09,199.129,199.16,199.185,199.204,199.218,199.226,199.23,199.235,199.23,199.228,199.222,199.213,199.196,199.188,199.172,199.153,199.132,199.115,199.097] +# Boys weight-for-age 5-10y +WHO_BOY_WEIGHT_OVER_FIVE_01=[12.228,12.581,12.695,12.81,12.927,13.045,13.164,13.285,13.408,13.532,13.658,13.785,13.913,14.042,14.172,14.303,14.434,14.566,14.699,14.832,14.966,15.1,15.234,15.368,15.503,15.637,15.771,15.904,16.038,16.171,16.304,16.437,16.569,16.7,16.831,16.962,17.092,17.221,17.35,17.478,17.606,17.734,17.861,17.987,18.114,18.24,18.365,18.492,18.618,18.745,18.872,19.001,19.13,19.26,19.39,19.522,19.655,19.789,19.924,20.06,20.196] +WHO_BOY_WEIGHT_OVER_FIVE_1=[13.485,13.802,13.926,14.052,14.179,14.307,14.437,14.568,14.701,14.836,14.972,15.11,15.248,15.388,15.528,15.669,15.811,15.954,16.097,16.241,16.386,16.531,16.676,16.822,16.968,17.114,17.259,17.405,17.551,17.696,17.842,17.987,18.131,18.276,18.42,18.564,18.708,18.851,18.994,19.137,19.28,19.422,19.564,19.706,19.848,19.99,20.132,20.274,20.418,20.562,20.707,20.853,21.001,21.149,21.299,21.451,21.604,21.758,21.914,22.071,22.23] +WHO_BOY_WEIGHT_OVER_FIVE_15=[15.962,16.204,16.352,16.501,16.652,16.804,16.957,17.113,17.27,17.428,17.588,17.749,17.912,18.076,18.24,18.406,18.572,18.739,18.908,19.077,19.247,19.418,19.59,19.762,19.935,20.108,20.281,20.455,20.629,20.803,20.978,21.153,21.328,21.504,21.679,21.855,22.031,22.208,22.385,22.562,22.74,22.918,23.096,23.275,23.454,23.635,23.816,23.998,24.182,24.368,24.555,24.744,24.936,25.129,25.325,25.523,25.724,25.927,26.133,26.341,26.551] +WHO_BOY_WEIGHT_OVER_FIVE_25=[16.748,16.967,17.123,17.28,17.439,17.6,17.762,17.926,18.091,18.259,18.427,18.597,18.768,18.941,19.114,19.288,19.464,19.64,19.818,19.996,20.176,20.356,20.537,20.719,20.901,21.084,21.268,21.452,21.636,21.821,22.007,22.193,22.379,22.566,22.754,22.941,23.13,23.319,23.508,23.698,23.889,24.08,24.272,24.465,24.659,24.853,25.049,25.247,25.446,25.647,25.85,26.056,26.263,26.474,26.687,26.902,27.121,27.342,27.566,27.793,28.023] +WHO_BOY_WEIGHT_OVER_FIVE_50=[18.335,18.506,18.68,18.856,19.034,19.213,19.394,19.576,19.761,19.947,20.134,20.324,20.514,20.705,20.898,21.092,21.287,21.483,21.681,21.88,22.08,22.281,22.484,22.687,22.892,23.097,23.303,23.51,23.718,23.927,24.137,24.348,24.56,24.772,24.986,25.2,25.416,25.633,25.851,26.071,26.291,26.513,26.736,26.96,27.186,27.414,27.643,27.875,28.109,28.346,28.585,28.828,29.073,29.322,29.574,29.829,30.088,30.35,30.616,30.885,31.159] +WHO_BOY_WEIGHT_OVER_FIVE_75=[20.098,20.216,20.413,20.612,20.812,21.015,21.218,21.424,21.632,21.841,22.053,22.265,22.48,22.695,22.912,23.131,23.351,23.572,23.795,24.02,24.246,24.474,24.703,24.934,25.167,25.4,25.635,25.872,26.11,26.35,26.591,26.834,27.078,27.324,27.572,27.821,28.073,28.326,28.582,28.839,29.099,29.36,29.624,29.89,30.158,30.429,30.703,30.98,31.26,31.544,31.831,32.122,32.417,32.716,33.02,33.327,33.639,33.956,34.276,34.601,34.931] +WHO_BOY_WEIGHT_OVER_FIVE_85=[21.124,21.212,21.423,21.636,21.851,22.068,22.286,22.507,22.729,22.954,23.18,23.408,23.637,23.868,24.101,24.335,24.571,24.809,25.048,25.29,25.533,25.778,26.025,26.273,26.524,26.776,27.03,27.286,27.543,27.803,28.065,28.328,28.594,28.862,29.132,29.405,29.68,29.957,30.238,30.52,30.806,31.093,31.384,31.678,31.974,32.274,32.578,32.885,33.196,33.512,33.832,34.155,34.484,34.817,35.155,35.497,35.844,36.197,36.554,36.916,37.283] +WHO_BOY_WEIGHT_OVER_FIVE_99=[25.304,25.276,25.553,25.833,26.115,26.4,26.687,26.977,27.268,27.564,27.862,28.162,28.464,28.769,29.077,29.387,29.701,30.016,30.336,30.658,30.984,31.314,31.646,31.983,32.323,32.666,33.014,33.366,33.722,34.083,34.448,34.817,35.192,35.571,35.956,36.346,36.742,37.145,37.553,37.967,38.387,38.813,39.245,39.686,40.131,40.584,41.046,41.515,41.99,42.475,42.967,43.465,43.972,44.487,45.01,45.54,46.076,46.621,47.173,47.732,48.299] +WHO_BOY_WEIGHT_OVER_FIVE_999=[28.226,28.126,28.456,28.79,29.127,29.468,29.812,30.159,30.509,30.865,31.223,31.584,31.948,32.316,32.688,33.063,33.444,33.827,34.216,34.61,35.007,35.411,35.82,36.235,36.654,37.08,37.512,37.951,38.395,38.849,39.307,39.774,40.249,40.732,41.224,41.724,42.233,42.754,43.284,43.823,44.372,44.931,45.501,46.083,46.674,47.278,47.895,48.523,49.162,49.815,50.479,51.152,51.838,52.535,53.244,53.963,54.69,55.428,56.175,56.931,57.698] +# Boys BMI-for-age 5-19y +WHO_BOY_BMI_OVER_FIVE_01=[11.871,12.041,12.039,12.037,12.037,12.038,12.039,12.042,12.045,12.049,12.054,12.06,12.066,12.073,12.08,12.088,12.096,12.105,12.114,12.124,12.133,12.143,12.154,12.164,12.175,12.186,12.198,12.209,12.221,12.232,12.244,12.257,12.269,12.281,12.294,12.307,12.32,12.333,12.346,12.359,12.373,12.386,12.4,12.414,12.428,12.442,12.457,12.472,12.487,12.502,12.518,12.534,12.55,12.567,12.584,12.602,12.62,12.639,12.658,12.677,12.697,12.718,12.738,12.76,12.781,12.803,12.826,12.849,12.872,12.896,12.92,12.945,12.97,12.996,13.022,13.048,13.075,13.102,13.13,13.157,13.186,13.215,13.245,13.275,13.305,13.337,13.368,13.4,13.433,13.466,13.5,13.534,13.569,13.604,13.639,13.675,13.711,13.748,13.785,13.822,13.86,13.897,13.935,13.973,14.011,14.049,14.087,14.125,14.163,14.201,14.239,14.276,14.314,14.351,14.387,14.424,14.46,14.497,14.532,14.568,14.603,14.638,14.673,14.707,14.741,14.774,14.807,14.84,14.872,14.904,14.935,14.966,14.997,15.027,15.056,15.085,15.113,15.141,15.168,15.195,15.221,15.247,15.272,15.297,15.321,15.344,15.367,15.389,15.411,15.432,15.452,15.472,15.491,15.51,15.528,15.546,15.563,15.579,15.595,15.61,15.624,15.638,15.651,15.663,15.675,15.687,15.697,15.707,15.717] +WHO_BOY_BMI_OVER_FIVE_1=[12.57,12.72,12.716,12.714,12.712,12.711,12.712,12.713,12.715,12.718,12.722,12.727,12.733,12.739,12.746,12.753,12.761,12.769,12.778,12.787,12.797,12.807,12.817,12.828,12.839,12.85,12.861,12.873,12.885,12.897,12.909,12.922,12.935,12.948,12.961,12.975,12.988,13.002,13.016,13.03,13.045,13.059,13.074,13.089,13.104,13.12,13.135,13.152,13.168,13.185,13.202,13.22,13.238,13.256,13.275,13.295,13.315,13.336,13.357,13.379,13.401,13.423,13.446,13.47,13.494,13.519,13.544,13.569,13.596,13.622,13.649,13.677,13.705,13.734,13.763,13.792,13.822,13.853,13.884,13.915,13.947,13.98,14.013,14.047,14.081,14.116,14.152,14.188,14.225,14.262,14.3,14.338,14.377,14.417,14.457,14.497,14.538,14.58,14.621,14.663,14.706,14.748,14.791,14.834,14.877,14.92,14.963,15.007,15.05,15.093,15.136,15.178,15.221,15.264,15.306,15.348,15.39,15.432,15.473,15.514,15.555,15.595,15.636,15.676,15.715,15.754,15.793,15.832,15.869,15.907,15.944,15.981,16.017,16.053,16.088,16.123,16.157,16.191,16.224,16.257,16.289,16.32,16.351,16.382,16.412,16.442,16.471,16.499,16.527,16.554,16.581,16.607,16.633,16.658,16.683,16.706,16.73,16.753,16.775,16.796,16.817,16.838,16.857,16.877,16.895,16.914,16.931,16.948,16.964] +WHO_BOY_BMI_OVER_FIVE_15=[13.919,14.03,14.026,14.023,14.02,14.019,14.019,14.02,14.023,14.026,14.031,14.036,14.042,14.049,14.057,14.065,14.075,14.084,14.095,14.105,14.117,14.128,14.141,14.153,14.166,14.18,14.194,14.208,14.223,14.238,14.253,14.268,14.284,14.3,14.317,14.333,14.35,14.368,14.385,14.403,14.421,14.44,14.459,14.478,14.497,14.517,14.537,14.558,14.579,14.6,14.622,14.645,14.668,14.691,14.716,14.741,14.766,14.793,14.82,14.847,14.875,14.904,14.933,14.963,14.994,15.025,15.056,15.089,15.122,15.156,15.19,15.225,15.26,15.296,15.333,15.37,15.408,15.447,15.486,15.525,15.566,15.607,15.648,15.691,15.734,15.778,15.822,15.867,15.913,15.96,16.007,16.055,16.103,16.153,16.202,16.253,16.303,16.355,16.407,16.459,16.512,16.565,16.618,16.672,16.726,16.779,16.833,16.887,16.941,16.995,17.049,17.103,17.157,17.21,17.264,17.317,17.37,17.423,17.475,17.528,17.58,17.631,17.683,17.734,17.784,17.835,17.885,17.935,17.984,18.032,18.081,18.129,18.176,18.223,18.27,18.316,18.361,18.406,18.451,18.495,18.538,18.581,18.624,18.666,18.707,18.748,18.788,18.828,18.868,18.906,18.944,18.982,19.019,19.056,19.092,19.128,19.163,19.197,19.231,19.264,19.297,19.329,19.361,19.392,19.422,19.452,19.482,19.511,19.539] +WHO_BOY_BMI_OVER_FIVE_25=[14.343,14.441,14.437,14.434,14.432,14.432,14.432,14.434,14.437,14.441,14.446,14.452,14.459,14.467,14.476,14.485,14.495,14.506,14.518,14.529,14.542,14.555,14.569,14.582,14.597,14.612,14.627,14.643,14.659,14.675,14.692,14.709,14.726,14.744,14.762,14.781,14.799,14.819,14.838,14.858,14.878,14.898,14.919,14.94,14.961,14.983,15.005,15.028,15.051,15.074,15.098,15.123,15.149,15.175,15.201,15.228,15.256,15.285,15.314,15.344,15.375,15.406,15.438,15.471,15.504,15.538,15.572,15.607,15.643,15.679,15.717,15.754,15.793,15.832,15.871,15.911,15.952,15.993,16.035,16.078,16.122,16.166,16.211,16.256,16.302,16.349,16.397,16.446,16.495,16.545,16.595,16.646,16.698,16.751,16.804,16.858,16.912,16.967,17.023,17.079,17.135,17.192,17.248,17.306,17.363,17.42,17.478,17.535,17.593,17.651,17.708,17.766,17.823,17.88,17.937,17.994,18.051,18.107,18.163,18.219,18.275,18.33,18.385,18.439,18.494,18.548,18.601,18.654,18.707,18.759,18.811,18.862,18.913,18.964,19.014,19.063,19.112,19.161,19.208,19.256,19.303,19.349,19.395,19.44,19.485,19.529,19.573,19.616,19.659,19.701,19.742,19.783,19.823,19.863,19.903,19.942,19.98,20.018,20.055,20.091,20.127,20.163,20.197,20.232,20.265,20.299,20.331,20.363,20.395] 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100644 --- a/rcpchgrowth/uk_who.py +++ b/rcpchgrowth/uk_who.py @@ -27,6 +27,8 @@ # load the reference data data_directory = resources.files("rcpchgrowth.data_tables") +who_data_directory = Path(data_directory, "who") + data_path = Path( data_directory, "uk90_preterm.json") # 23 - 42 weeks gestation with open(data_path) as json_file: @@ -40,13 +42,13 @@ json_file.close() data_path = Path( - data_directory, "who_infants.json") # 2 weeks to 2 years + who_data_directory, "who_infants.json") # 2 weeks to 2 years with open(data_path) as json_file: WHO_INFANTS_DATA = json.load(json_file) json_file.close() data_path = Path( - data_directory, "who_children.json") # 2 years to 4 years + who_data_directory, "who_children.json") # 2 years to 4 years with open(data_path) as json_file: WHO_CHILD_DATA = json.load(json_file) json_file.close() diff --git a/rcpchgrowth/who.py b/rcpchgrowth/who.py index a596083..fb7913c 100644 --- a/rcpchgrowth/who.py +++ b/rcpchgrowth/who.py @@ -6,6 +6,7 @@ import json from importlib import resources from pathlib import Path +import pandas as pd # rcpch imports from .constants import * @@ -26,21 +27,22 @@ # load the reference data data_directory = resources.files("rcpchgrowth.data_tables") +who_data_directory = data_directory.joinpath("who") data_path = Path( - data_directory, "who_infants.json") # 2 weeks to 2 years + who_data_directory, "who_infants.json") # 0 to 2 years with open(data_path) as json_file: WHO_INFANTS_DATA = json.load(json_file) json_file.close() data_path = Path( - data_directory, "who_children.json") # 2 years to 5 years + who_data_directory, "who_children.json") # 2 years to 5 years with open(data_path) as json_file: WHO_CHILD_DATA = json.load(json_file) json_file.close() data_path = Path( - data_directory, "who_2007_children.json") # 5 years to 19 years + who_data_directory, "who_2007_children.json") # 5 years to 19 years with open(data_path) as json_file: WHO_2007_DATA = json.load(json_file) json_file.close() @@ -74,7 +76,7 @@ def reference_data_absent( if measurement_method == WEIGHT and age > TEN_YEARS: return True, "WHO weight data does not exist in children over 10 y of age." - if measurement_method == HEAD_CIRCUMFERENCE and age > FIVE_YEARS: + if measurement_method == HEAD_CIRCUMFERENCE and age > WHO_2006_REFERENCE_UPPER_THRESHOLD: # 5 years and above return True, "WHO head circumference data does not exist in children over 5 y of age." else: @@ -94,18 +96,14 @@ def who_reference( The function return the appropriate reference file as json """ - if age <= WHO_2006_REFERENCE_UPPER_THRESHOLD: - # Children up to and including 5 years are measured using WHO 2006 data - if (age == 2.0 and default_youngest_reference) or age < WHO_CHILD_LOWER_THRESHOLD: + if age <= WHO_2006_REFERENCE_UPPER_THRESHOLD: # 1856 days or 60.87 mths or 5.07 years and below + # Children up to and including 5 years and nearly 1 mth (1856 days) are measured using WHO 2006 data + if (age == 2.0 and default_youngest_reference) or age < WHO_CHILD_LOWER_THRESHOLD: # 2.0 years # If default_youngest_reference is True, the younger reference is used to calculate values # This is specifically for the overlap between WHO 2006 lying and standing in centile curve generation # WHO 2006 reference is used for children below 2 years or those who are 2 years old and default_youngest_reference is True return WHO_INFANTS_DATA - elif age == 5.0: - if default_youngest_reference: - return WHO_CHILD_DATA - else: - return WHO_2007_DATA + return WHO_CHILD_DATA elif age <= WHO_2007_REFERENCE_UPPER_THRESHOLD: @@ -123,26 +121,19 @@ def who_lms_array_for_measurement_and_sex( default_youngest_reference: bool = False ) -> list: - # selects the correct lms data array from the patchwork of references that make up UK-WHO - - try: - selected_reference = who_reference( - age=age, - default_youngest_reference=default_youngest_reference - ) - except: #  there is no reference for the age supplied - raise LookupError("There is no WHO reference for the age supplied.") - - # Check that the measurement requested has reference data at that age - + # selects the correct lms data array from the patchwork of references that make up WHO invalid_data, data_error = reference_data_absent( age=age, measurement_method=measurement_method, sex=sex) - + if invalid_data: raise LookupError(data_error) else: + selected_reference = who_reference( + age=age, + default_youngest_reference=default_youngest_reference + ) return selected_reference["measurement"][measurement_method][sex] @@ -190,4 +181,4 @@ def select_reference_data_for_who_chart( return who_2007_children_reference else: raise LookupError( - f"No data found for {measurement_method} in {sex}s in {who_reference_name}") + f"No data found for {measurement_method} in {sex}s in {who_reference_name}") \ No newline at end of file diff --git a/s/build b/s/build index 2aee2f8..cc4000b 100644 --- a/s/build +++ b/s/build @@ -6,5 +6,5 @@ set -e # Exit on error # scripts may need to be made executable on some platforms before they can be run # chmod +x is the command to do this on unixy systems -# starts all docker compose services -docker-compose build \ No newline at end of file +# Build the development container image +docker compose build \ No newline at end of file diff --git a/s/logs b/s/logs new file mode 100755 index 0000000..03f004b --- /dev/null +++ b/s/logs @@ -0,0 +1,7 @@ +#!/bin/bash + +set -e # Exit on error + +# View container logs +# Use -f flag to follow logs in real-time +docker compose logs "$@" diff --git a/s/notebook b/s/notebook new file mode 100755 index 0000000..3532f0d --- /dev/null +++ b/s/notebook @@ -0,0 +1,37 @@ +#!/bin/bash + +set -e # Exit on error + +# Check if container is running +CONTAINER_NAME="rcpchgrowth-python" +CONTAINER_STATUS=$(docker inspect -f '{{.State.Running}}' $CONTAINER_NAME 2>/dev/null || echo "false") + +if [ "$CONTAINER_STATUS" = "false" ]; then + echo "Container not running. Starting container..." + docker compose up -d + echo "Waiting for JupyterLab to start..." + sleep 15 +fi + +# Get the JupyterLab URL +JUPYTER_URL="http://localhost:8888/lab" + +echo "" +echo "🎓 JupyterLab is running!" +echo "Opening: $JUPYTER_URL" +echo "" + +# Try to open in browser (works on macOS, Linux with xdg-open, etc.) +if command -v open &> /dev/null; then + # macOS + open "$JUPYTER_URL" +elif command -v xdg-open &> /dev/null; then + # Linux + xdg-open "$JUPYTER_URL" +elif command -v wslview &> /dev/null; then + # WSL + wslview "$JUPYTER_URL" +else + echo "Please open the URL manually in your browser:" + echo "$JUPYTER_URL" +fi diff --git a/s/python b/s/python new file mode 100755 index 0000000..49107fc --- /dev/null +++ b/s/python @@ -0,0 +1,6 @@ +#!/bin/bash + +set -e # Exit on error + +# Run Python REPL inside the container +docker compose exec rcpchgrowth-python python diff --git a/s/rebuild b/s/rebuild index a856edb..da82a21 100755 --- a/s/rebuild +++ b/s/rebuild @@ -6,5 +6,7 @@ set -e # Exit on error # scripts may need to be made executable on some platforms before they can be run # chmod +x is the command to do this on unixy systems +# Rebuild the container from scratch s/remove-containers-and-images +s/build s/up diff --git a/s/shell b/s/shell new file mode 100755 index 0000000..a4c30f9 --- /dev/null +++ b/s/shell @@ -0,0 +1,6 @@ +#!/bin/bash + +set -e # Exit on error + +# Enter the container for interactive development +docker compose exec rcpchgrowth-python bash diff --git a/s/test b/s/test new file mode 100755 index 0000000..110f9e6 --- /dev/null +++ b/s/test @@ -0,0 +1,21 @@ +#!/bin/bash + +set -e # Exit on error + +# Check for --running flag to use an already-running container +USE_RUNNING=false +if [[ "$1" == "--running" || "$1" == "-r" ]]; then + USE_RUNNING=true + shift # Remove the flag from arguments +fi + +if [ "$USE_RUNNING" = true ]; then + # Use existing running container + echo "Running tests in existing container..." + docker compose exec rcpchgrowth-python pytest "$@" +else + # Start container if needed, run tests, then clean up + echo "Starting container and running tests..." + docker compose up -d + docker compose exec rcpchgrowth-python pytest "$@" +fi diff --git a/s/up b/s/up index 76424ca..e046ff7 100755 --- a/s/up +++ b/s/up @@ -6,5 +6,13 @@ set -e # Exit on error # scripts may need to be made executable on some platforms before they can be run # chmod +x is the command to do this on unixy systems -# starts all docker compose services - will create a notebooks container and a development container -docker compose up \ No newline at end of file +# Build and start the development container in detached mode +docker compose up -d + +echo "" +echo "Container started! Use these commands:" +echo " s/shell - Enter the container for interactive work" +echo " s/test - Run pytest inside the container" +echo " s/python - Run Python REPL inside the container" +echo " s/down - Stop the container" +echo "" \ No newline at end of file diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..e85153b --- /dev/null +++ b/setup.py @@ -0,0 +1,34 @@ +from setuptools import setup, find_packages +from os import path + +here = path.abspath(path.dirname(__file__)) + +with open(path.join(here, "README.md"), encoding="utf-8") as f: + long_description = f.read() + +setup( + name="rcpchgrowth", + version="4.4.0", + description="SDS and Centile calculations for UK Growth Data", + long_description=long_description, + url="https://github.com/rcpch/digital-growth-charts/blob/master/README.md", + author="@eatyourpeas, @marcusbaw, @statist7, RCPCH Incubator", + author_email="incubator@rcpch.ac.uk", + classifiers=[ + "Development Status :: 5 - Production/Stable", + "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3 :: Only", + "Topic :: Scientific/Engineering :: Medical Science Apps.", + ], + keywords="growth charts, anthropometry, SDS, centile, UK-WHO, UK90, Trisomy 21 (UK), Trisomy 21 (AAP), Turner, CDC", + packages=find_packages(), + python_requires=">3.8", + install_requires=["python-dateutil", "scipy"], + include_package_data=True, + project_urls={ + "Bug Reports": "https://github.com/rcpch/rcpchgrowth-python/issues", + "API management": "https://dev.rcpch.ac.uk", + "Source": "https://github.com/rcpch/rcpchgrowth-python", + }, +)