Falcon debug#46
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Falcon's CSV doesn't carry per-spectrum precursor intensity, so convert_falcon_to_mscluster_format.py was hardcoding #PrecIntensity to 0 for every row. That zeroed out sum(precursor intensity) in clustersummary.tsv and turned every cell of featuretable_reformatted_precursorintensity.csv into 0.0 when the clustering tool was falcon. mscluster's binary writes #PrecIntensity itself from the input mzML, so the precursor-intensity feature table worked there. Look up the value from the original input mzML/mzXML the same way mscluster does, using ming_spectrum_library, and fall back to 0 only when the file or scan isn't available.
ming_spectrum_library.load_mzml_file unconditionally does float(activation["collision energy"]) and raises KeyError on any MS2 spectrum that lacks the "collision energy" CV term. That blew up parsing of an entire mzML in the lookup helper (verified against benchmark_featurefinding/MSV000080555/C7_RC7_01_8270.mzML) and silently fell back to 0 intensity for the whole file -- reintroducing the bug this change is supposed to fix. Read the mzML / mzXML directly with pyteomics in the helper and pull only the field we need (precursor "peak intensity" CV term for mzML, @precursorIntensity for mzXML). Verified on three benchmark files (C6_GC6_01_2916, C7_RC7_01_8270, WE_Bio_T1; 5357 MS2 spectra total) the lookup matches an independent pyteomics read exactly, including nonzero values such as 439017.71875.
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