ExportIOConfig

class pyprophet._config.ExportIOConfig(infile: str, outfile: str, subsample_ratio: float, level: str, context: str, export_format: Literal['matrix', 'legacy_merged', 'legacy_split', 'parquet', 'parquet_split', 'library'] = 'legacy_merged', out_type: Literal['tsv', 'csv'] = 'tsv', transition_quantification: bool = False, max_transition_pep: float = 0.7, ipf: Literal['peptidoform', 'augmented', 'disable'] = 'peptidoform', ipf_max_peptidoform_pep: float = 0.4, max_rs_peakgroup_qvalue: float = 0.05, peptide: bool = True, max_global_peptide_qvalue: float = 0.01, protein: bool = True, max_global_protein_qvalue: float = 0.01, test: bool = False, use_alignment: bool = True, max_alignment_pep: float = 0.7, top_n: int = 3, consistent_top: bool = True, normalization: Literal['none', 'median', 'medianmedian', 'quantile'] = 'none', compression_method: Literal['none', 'snappy', 'gzip', 'brotli', 'zstd'] = 'zstd', compression_level: int = 11, split_transition_data: bool = True, split_runs: bool = False, include_transition_data: bool = True, pqp_file: str | None = None, rt_calibration: bool = True, im_calibration: bool = True, intensity_calibration: bool = True, min_fragments: int = 4, keep_decoys: bool = False, rt_unit: Literal['iRT', 'RT'] = 'iRT', exclude_decoys: bool = True)[source]

Bases: BaseIOConfig

Configuration for exporting results to various formats.

export_format

Format for exporting results. - “matrix”: Single TSV file with merged results in matrix format. - “legacy_merged”: Single TSV file with merged results. - “legacy_split”: Split TSV files for each run. - “parquet”: Single Parquet file with merged results. - “parquet_split”: Split Parquet files for each run. - “library” : .tsv library file

Type:: Literal[“legacy_merged”, “legacy_split”, “parquet”, “split_parquet”]

out_type

Output file type for exported results.

Type:: Literal[“tsv”, “csv”]

transition_quantification

Report aggregated transition-level quantification.

Type:: bool

max_transition_pep

Maximum PEP to retain scored transitions for quantification (requires transition-level scoring).

Type:: float

ipf

Should IPF results be reported if present? - “peptidoform”: Report results on peptidoform-level, - “augmented”: Augment OpenSWATH results with IPF scores, - “disable”: Ignore IPF results’

Type:: Literal[“peptidoform”, “augmented”, “disable”]

ipf_max_peptidoform_pep

IPF: Filter results to maximum run-specific peptidoform-level PEP.

Type:: float

max_rs_peakgroup_qvalue

Filter results to maximum run-specific peak group-level q-value.

Type:: float

peptide

Append peptide-level error-rate estimates if available.

Type:: bool

max_global_peptide_qvalue

Filter results to maximum global peptide-level q-value.

Type:: float

protein

Append protein-level error-rate estimates if available.

Type:: bool

max_global_protein_qvalue

Filter results to maximum global protein-level q-value.

Type:: float

use_alignment

Use alignment results to recover peaks with good alignment scores if alignment data is present (default: True).

Type:: bool

max_alignment_pep

Maximum PEP to consider for good alignments when use_alignment is True (default: 0.7).

Type:: float

# Quantification matrix options

top_n

Number of top intense features to use for summarization

Type:: int

consistent_top

Whether to use same top features across all runs

Type:: bool

normalization

Normalization method

Type:: Literal[“none”, “median”, “medianmedian”, “quantile”]

test

bool = False: Whether to enable test mode with deterministic behavior, test mode will sort libraries by precursor, fragmentType, fragmentSeriesNumber and fragmentCharge

Type:: bool

# OSW: Export to parquet

compression_method

Compression method for parquet files.

Type:: Literal[“none”, “snappy”, “gzip”, “brotli”, “zstd”]

compression_level

Compression level for parquet files (0-9).

Type:: int

split_transition_data

Split precursor data and transition data into separate files.

Type:: bool

split_runs

Split data by runs

Type:: bool

# SqMass: Export to parquet

pqp_file

Path to PQP file for precursor/transition mapping.

Type:: Optional[str]

# Export to library

rt_calibration

If True, will use emperical RT values as oppose to the original library RT values

Type:: bool

im_calibration

If True, will use emperical IM values as oppose to the original library IM values

Type:: bool

intensity_calibration

If True, will use emperical intensity values as oppose to the original library intensity values

Type:: bool

min_fragments

Minimum number of fragments required to include the peak group in the library, only relevant if intensity_calibration is True

Type:: int

keep_decoys

Whether to keep decoy entries in the library, will only keep decoys that pass the thresholds specified

Type:: bool

rt_unit

Unit of retention time in the library, only relevant if rt_calibration is True. If “iRT” is selected, the retention times will be scaled to the iRT scale (0-100) in the library

Type:: Literal[“iRT”, “RT”], default = ‘iRT’

__eq__(other): Return self==value.

__hash__ = None

__init__(infile: str, outfile: str, subsample_ratio: float, level: str, context: str, export_format: Literal['matrix', 'legacy_merged', 'legacy_split', 'parquet', 'parquet_split', 'library'] = 'legacy_merged', out_type: Literal['tsv', 'csv'] = 'tsv', transition_quantification: bool = False, max_transition_pep: float = 0.7, ipf: Literal['peptidoform', 'augmented', 'disable'] = 'peptidoform', ipf_max_peptidoform_pep: float = 0.4, max_rs_peakgroup_qvalue: float = 0.05, peptide: bool = True, max_global_peptide_qvalue: float = 0.01, protein: bool = True, max_global_protein_qvalue: float = 0.01, test: bool = False, use_alignment: bool = True, max_alignment_pep: float = 0.7, top_n: int = 3, consistent_top: bool = True, normalization: Literal['none', 'median', 'medianmedian', 'quantile'] = 'none', compression_method: Literal['none', 'snappy', 'gzip', 'brotli', 'zstd'] = 'zstd', compression_level: int = 11, split_transition_data: bool = True, split_runs: bool = False, include_transition_data: bool = True, pqp_file: str | None = None, rt_calibration: bool = True, im_calibration: bool = True, intensity_calibration: bool = True, min_fragments: int = 4, keep_decoys: bool = False, rt_unit: Literal['iRT', 'RT'] = 'iRT', exclude_decoys: bool = True) → None

__repr__(): Return repr(self).