Blood manifest: 182 samples
Loaded 85,974,252 rows from 182 blood samples in 104.0s
GC reference: 472,386 probes
Sex chrom homology mask: 1494 probes
Pass 1: 4-feat blood GAM (16 workers)...
20/182 blood samples, 26s, R²=0.4345
40/182 blood samples, 42s, R²=0.3945
60/182 blood samples, 55s, R²=0.4234
80/182 blood samples, 68s, R²=0.4159
100/182 blood samples, 83s, R²=0.4164
120/182 blood samples, 97s, R²=0.4687
140/182 blood samples, 111s, R²=0.4476
160/182 blood samples, 126s, R²=0.3971
180/182 blood samples, 140s, R²=0.4567
182/182 blood samples, 142s, R²=0.5254
Sex: 111 female, 71 male, 0 unknown
/usr/local/bin/build_norm_reference.py:247: RuntimeWarning: All-NaN slice encountered
probe_coverage_median = np.nanmedian(blood_matrix, axis=1)
/usr/local/lib/python3.12/site-packages/numpy/lib/_nanfunctions_impl.py:1593: RuntimeWarning: All-NaN slice encountered
return fnb._ureduce(a,
Probe coverage: 472,145/472,386 valid probes
Preliminary 5-feat GAM on blood (16 workers)...
20/182 blood prelim 5-feat, 40s, R²=0.9446
40/182 blood prelim 5-feat, 60s, R²=0.9443
60/182 blood prelim 5-feat, 83s, R²=0.9483
80/182 blood prelim 5-feat, 103s, R²=0.9413
100/182 blood prelim 5-feat, 124s, R²=0.9506
120/182 blood prelim 5-feat, 143s, R²=0.9413
140/182 blood prelim 5-feat, 162s, R²=0.9493
160/182 blood prelim 5-feat, 187s, R²=0.9521
180/182 blood prelim 5-feat, 206s, R²=0.9497
182/182 blood prelim 5-feat, 208s, R²=0.9426
/usr/local/bin/build_norm_reference.py:335: RuntimeWarning: All-NaN slice encountered
prelim_median = np.nanmedian(prelim_matrix, axis=1)
/usr/local/lib/python3.12/site-packages/numpy/lib/_nanfunctions_impl.py:1593: RuntimeWarning: All-NaN slice encountered
return fnb._ureduce(a,
Probe mask: 20,974 masked (4.4%) — 11,691 low cov, 13,908 high var, 3,413 extreme
Wrote batch_reference.parquet (472,386 probes) to ./batch_reference.parquet
Wrote sex_calls.csv (182 samples) to ./sex_calls.csv
Wrote feature_stats.csv (182 samples) to ./feature_stats.csv
Build reference complete in 1711.2s total