| """Independent integrity, design and regression checks for the clonal reanalysis.""" |
| import hashlib |
| import importlib.metadata |
| import json |
| from pathlib import Path |
| import platform |
| import numpy as np |
| import pandas as pd |
| from scipy.stats import spearmanr |
| from sklearn.linear_model import LinearRegression |
|
|
| ROOT=Path(__file__).resolve().parents[1] |
|
|
| def main(): |
| source=ROOT/'external_data/clonal_common_garden_20261007' |
| out=ROOT/'data/derived/clonal_common_garden_20261007' |
| manifest=json.loads((source/'download_manifest.json').read_text()) |
| payload=(source/manifest['file']).read_bytes() |
| assert hashlib.sha256(payload).hexdigest()==manifest['sha256'] |
| assert hashlib.md5(payload).hexdigest()==manifest['md5'] |
| d=pd.read_csv(source/manifest['file']);d.columns=d.columns.str.strip() |
| assert len(d)==400 and not d.duplicated(['Common_garden','Block','Provenance']).any() |
| traits={'ramets':'Peak number of ramet','emergence':'Ramet emergence day'} |
| c=pd.read_csv(out/'garden_origin_trait_summary.csv') |
| assert len(c)==48 and c.groupby('trait')['observed'].sum().to_dict()=={'emergence':338,'ramets':344} |
| adjustment=pd.read_csv(out/'block_sensitivity_origin_means.csv').query("scenario == 'block_adjusted_additive'") |
| max_error=0. |
| |
| |
| for trait,col in traits.items(): |
| for garden,g in d.groupby('Common_garden'): |
| obs=g.dropna(subset=[col]);cols=['Provenance','Block'] |
| X=pd.get_dummies(obs[cols].astype(str),drop_first=False,dtype=float) |
| model=LinearRegression().fit(X,obs[col]) |
| query=g[cols].copy();Z=pd.get_dummies(query.astype(str),drop_first=False,dtype=float).reindex(columns=X.columns,fill_value=0.) |
| query['prediction']=model.predict(Z) |
| expected=query.groupby('Provenance').prediction.mean().sort_index() |
| actual=adjustment[(adjustment.trait==trait)&(adjustment.garden==garden)].set_index('origin').estimate.sort_index() |
| err=float(np.max(np.abs(expected.to_numpy()-actual.to_numpy())));max_error=max(max_error,err) |
| assert err<1e-8 |
| clines=pd.read_csv(out/'latitude_clines_exact_permutation.csv') |
| for row in clines.itertuples(): |
| sub=c[(c.trait==row.trait)&(c.garden==row.garden)] |
| assert abs(spearmanr(sub.latitude,sub['mean']).statistic-row.spearman_rho)<1e-12 |
| assert row.maxT_adjusted_p+1e-12>=row.exact_two_sided_p |
| assert abs(row.exact_two_sided_p*40320-round(row.exact_two_sided_p*40320))<1e-8 |
| draws=pd.read_csv(out/'garden_contrast_bootstrap_draws.csv.gz') |
| assert draws.groupby(['trait','scheme','contrast']).size().eq(5000).all() |
| assert draws.groupby(['trait','scheme','contrast']).ngroups==12 |
| preds=pd.read_csv(out/'leave_origin_out_predictions.csv') |
| assert len(preds)==144 and not preds.duplicated(['trait','model','held_out_origin','garden']).any() |
| for row in preds[preds.model=='garden_mean'].itertuples(): |
| train=c[(c.trait==row.trait)&(c.garden==row.garden)&(c.origin!=row.held_out_origin)] |
| assert len(train)==7 and abs(train['mean'].mean()-row.prediction)<1e-10 |
| folds=pd.read_csv(out/'fold_manifest.csv') |
| assert len(folds)==8 |
| for row in folds.itertuples(): |
| assert len(row.training_origins.split(';'))==7 and row.held_out_origin not in row.training_origins.split(';') |
| reference=pd.read_csv(out/'block_adjusted_latitude_clines.csv') |
| z=reference[(reference.trait=='emergence')&(reference.garden=='Zhanjiang')].iloc[0] |
| assert np.isclose(z.linear_slope_per_latitude_degree,-.04261836847818077,atol=1e-8) |
| result={'source_digest_verified':True,'unique_record_keys':400,'source_populations':8,'trait_cells':48, |
| 'independent_block_regression_max_abs_difference':max_error,'exact_permutation_quantization_checked':True, |
| 'bootstrap_groups':12,'draws_per_group':5000,'whole_origin_folds':8,'prediction_rows':144, |
| 'interpretation':'Checks verify numerical implementation and data bookkeeping, not unknown design metadata or biological causality.'} |
| (out/'verification.json').write_text(json.dumps(result,indent=2)+'\n') |
| packages=['numpy','pandas','scipy','matplotlib','scikit-learn','requests','huggingface_hub'] |
| (out/'software_versions.json').write_text(json.dumps({'python':platform.python_version(),'packages':{p:importlib.metadata.version(p) for p in packages}},indent=2)+'\n') |
| print(json.dumps(result,indent=2)) |
|
|
| if __name__=='__main__':main() |
|
|
