msnpy package

Subpackages

Submodules

msnpy.msnpy module

msnpy.processing module

msnpy.processing.assign_precursor(peaklist: dimspy.models.peaklist.PeakList, header_frag: str, tolerance: float = 0.5)[source]
Parameters

  • peaklist –

  • header_frag –

  • tolerance –

Returns

Return type

msnpy.processing.create_graphs_from_scan_ids(scan_dependents: list, scan_events: dict, ion_injection_times: dict)[source]

Create Directed Graph from scan dependent relationships

Parameters

  • scan_dependents –

  • scan_events –

  • ion_injection_times –

Returns

Return type

msnpy.processing.create_spectral_trees(trees: Sequence[networkx.classes.ordered.OrderedDiGraph], peaklists: Sequence[dimspy.models.peaklist.PeakList])[source]
Parameters

  • trees – list of NetworkX graphs

  • peaklists – list of PeakList objects

Returns

Return type

Sequence[nx.OrderedDiGraph]

msnpy.processing.create_templates(graphs: list, nh: int)[source]

Create a ‘master’ graph that include all the experimental trees Loop through all the subgraphs/graphs

Parameters

  • graphs –

  • nh –

Returns

Return type

msnpy.processing.group_by_template(graphs: list, templates: list)[source]
Parameters

  • graphs –

  • templates –

Returns

Return type

msnpy.processing.group_scans(filename: str, nh: int = 2, min_replicates: int = 1, report: str = None, max_injection_time: float = None, merge_ms1: bool = False, split: bool = False, remove: bool = True)[source]
Parameters

  • filename –

  • nh –

  • min_replicates –

  • report –

  • max_injection_time –

  • merge_ms1 –

  • split –

  • remove –

Returns

msnpy.processing.hdf5_peaklists_to_txt(filename: str, path_out: str, delimiter: str = '\t')[source]
Parameters

  • filename –

  • path_out –

  • delimiter –

msnpy.processing.merge_ms1_scans(graphs: list)[source]
Parameters

graphs –

Returns

Return type

msnpy.processing.mz_tolerance(mz: float, tol: float, unit: str = 'ppm')[source]
Parameters

  • mz – mz value

  • tol – tolerance

  • unit – ppm or da

Returns

Return type

float

msnpy.processing.process_scans(filename: str, groups: list, function_noise: str, snr_thres: float, ppm: float, min_fraction: float = None, rsd_thres: float = None, normalise: bool = False, ringing_thres: float = None, exclusion_list: dict = {}, report: str = None, block_size: int = 5000, ncpus: int = None)[source]
Parameters

  • filename –

  • groups –

  • function_noise –

  • snr_thres –

  • ppm –

  • min_fraction –

  • rsd_thres –

  • normalise –

  • ringing_thres –

  • exclusion_list –

  • report –

  • block_size – number of peaks in each clustering block.

  • ncpus – number of CPUs for parallel clustering. Default = None, indicating using as many as possible

Returns

List of (average) PeakList objects (DIMSpy)

Return type

Sequence[PeakList]

Module contents