Boutheine/hmmstr runtime optimizations - #12
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This PR introduces three major performance optimizations to HMMSTR: parallelized model building (build_all), parallelized peak calling (call_peaks and call_peaks_stranded), and a fix for the BAM ingestion bottleneck by replacing the lazy pool.imap() + worker wrapper with eager pool.apply_async(). The original code processed targets serially using pandas.apply(), leaving CPU cores idle; these changes enable full utilization of available cores by distributing independent per‑target work across workers. Additionally, I fixed a bug where import numpy as np was commented out (causing --stranded_report to crash), corrected --cluster_only behavior so counts files are preserved when needed, and removed unused imports for cleaner code.