Downloading: s3://natera-rnd-pltf-dev-nextflow-scratch-01/work/dd/85bf851b0fa4f35aa52ccaa1470df6/1029_0I-N1-BDNA-1_B23KGCJLT4_1_alleles.tsv.gz
  Downloading: s3://natera-rnd-pltf-dev-nextflow-scratch-01/work/stage-02f6c14b-6a09-4281-a8fa-f6a36499687f/ad/a3f996b40cf1f958af55481d7b613c/test_pairs.tsv
  Downloading: s3://natera-rnd-pltf-dev-nextflow-scratch-01/work/f9/bcd86fad8936e42a2fc76ccfa1328a/.command.sh
  Downloading: s3://natera-rnd-pltf-dev-nextflow-scratch-01/work/5b/f05173814a7fe2515878e24151ee79/1029_0I-T1-TDNA-1_A23KCGYLT4_1_normalized.parquet
  Downloading: s3://natera-platform-sandbox/pipeline-resources/ensembl/Homo_sapiens.GRCh38.110.gtf.gz
  Downloading: s3://natera-rnd-pltf-dev-nextflow-scratch-01/work/f9/bcd86fad8936e42a2fc76ccfa1328a/.command.run
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  Downloading: s3://natera-rnd-pltf-dev-nextflow-scratch-01/work/c7/1c144a65e51c6b036f2334b12f3254/1029_0I-T1-TDNA-1_A23KCGYLT4_1_alleles.tsv.gz
  Downloading: s3://ai-pipelines-experiment/somatic-cnv/altera_genes_hg38.bed
==> STAGING COMPLETE (9 inputs)

mkdir -p failed for path /.config/matplotlib: [Errno 13] Permission denied: '/.config'
Matplotlib created a temporary cache directory at /tmp/matplotlib-ud7vg0y2 because there was an issue with the default path (/.config/matplotlib); it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.
Processing 1 T/N pair(s)...
  Enabled: mode=ascn, joint-seg, purity-estimation, adaptive-pen, weighted-seg, fit=clonal-decomp
  Loading gene annotations from Homo_sapiens.GRCh38.110.gtf.gz...
  Loaded 62062 gene annotations
  [1/1] 1029_0I... MAD=0.3548, pen_mult=3.15  fit_multiclone_select: n_peaks=6, n_segs=190, dipLogR=-0.5180
  Greedy search: 6 model combinations
  n_clones=1 best[0]: sum_rho=0.6342, cost=0.08075, ICL=-1156.91, converged=True, n_iter=3, init=[0.6493], phi=[0.6342]
  n_clones=2 best[0,1]: sum_rho=0.6391, cost=0.04588, ICL=-1172.23, converged=True, n_iter=3, init=[0.2745, 0.6493], phi=[0.2667, 0.3724]
  n_clones=3 best[0,1,2]: sum_rho=0.6396, cost=0.04000, ICL=-1182.19, converged=True, n_iter=7, init=[0.2144, 0.2745, 0.6493], phi=[0.2061, 0.0738, 0.3597]
  n_clones=4 best[0,1,2,3]: sum_rho=0.7045, cost=0.02762, ICL=-1246.81, converged=True, n_iter=23, init=[0.2144, 0.2745, 0.3347, 0.6493], phi=[0.2334, 0.0747, 0.0563, 0.3400]
  n_clones=5 best[0,1,2,3,4]: sum_rho=0.7352, cost=0.02432, ICL=-1266.65, converged=True, n_iter=8, init=[0.2144, 0.2745, 0.3347, 0.6493, 0.7174], phi=[0.2139, 0.0725, 0.0394, 0.3133, 0.0961]
  n_clones=6 best[0,1,2,3,4,5]: sum_rho=0.7317, cost=0.02409, ICL=-1264.90, converged=True, n_iter=15, init=[0.2144, 0.2745, 0.3347, 0.5471, 0.6493, 0.7174], phi=[0.2149, 0.0743, 0.0438, 0.2048, 0.1029, 0.0909]
  SELECTED (best-ICL): n_clones=5, ICL=-1266.65, purity=0.7352 (6 models evaluated)
 190 segments, 190 events, 21048 genes
Wrote 190 segments (190 events) to 1029_0I_segments.tsv
Wrote 1 metrics rows to 1029_0I_metrics.tsv
Wrote 21048 gene entries to 1029_0I_gene_cnv.tsv.gz
Wrote somatic VCF to 1029_0I_somatic.vcf

Single-sample analysis...
  1029_0I (tumor)...  fit_multiclone_select: n_peaks=7, n_segs=264, dipLogR=-0.5140
  Greedy search: 7 model combinations
  n_clones=1 best[0]: sum_rho=0.6322, cost=0.09752, ICL=-1654.52, converged=True, n_iter=2, init=[0.6533], phi=[0.6322]
  n_clones=2 best[0,1]: sum_rho=0.6420, cost=0.05442, ICL=-1679.25, converged=True, n_iter=4, init=[0.2745, 0.6533], phi=[0.2598, 0.3822]
  n_clones=3 best[0,1,2]: sum_rho=0.6413, cost=0.04247, ICL=-1723.59, converged=True, n_iter=8, init=[0.1884, 0.2745, 0.6533], phi=[0.1714, 0.1096, 0.3603]
  n_clones=4 best[0,1,2,3]: sum_rho=0.6415, cost=0.04070, ICL=-1724.08, converged=True, n_iter=6, init=[0.1884, 0.2745, 0.3467, 0.6533], phi=[0.1832, 0.0970, 0.0610, 0.3003]
  n_clones=5 best[0,1,2,3,4]: sum_rho=0.6416, cost=0.03981, ICL=-1722.91, converged=True, n_iter=6, init=[0.1884, 0.2745, 0.3467, 0.5872, 0.6533], phi=[0.1902, 0.0882, 0.0708, 0.2380, 0.0544]
  n_clones=6 best[0,1,2,3,4,5]: sum_rho=0.7212, cost=0.02912, ICL=-1807.26, converged=True, n_iter=8, init=[0.1884, 0.2745, 0.3467, 0.5872, 0.6533, 0.7154], phi=[0.1791, 0.0953, 0.0804, 0.2417, 0.0691, 0.0557]
  n_clones=7 best[0,1,2,3,4,5,6]: sum_rho=0.7399, cost=0.02838, ICL=-1812.43, converged=True, n_iter=33, init=[0.1884, 0.2745, 0.3467, 0.4689, 0.5872, 0.6533, 0.7154], phi=[0.1943, 0.1019, 0.0948, 0.1171, 0.1110, 0.0814, 0.0394]
  SELECTED (best-ICL): n_clones=7, ICL=-1812.43, purity=0.7399 (7 models evaluated)
 264 segments, 264 events
  1029_0I (blood)... 99 segments, 37 events
Single-sample analysis complete.