ms2pip.spectrum

MS2 spectrum handling.

class ms2pip.spectrum.Spectrum(*, mz, intensity, annotations=None, identifier=None, peptidoform=None, precursor_mz=None, precursor_charge=None, retention_time=None, mass_tolerance=None, mass_tolerance_unit=None)[source]

Bases: BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

  • mz (ndarray)

  • intensity (ndarray)

  • annotations (ndarray | None)

  • identifier (str | None)

  • peptidoform (Annotated[Peptidoform | None, BeforeValidator(func=~ms2pip.spectrum._coerce_peptidoform, json_schema_input_type=PydanticUndefined)])

  • precursor_mz (float | None)

  • precursor_charge (int | None)

  • retention_time (float | None)

  • mass_tolerance (float | None)

  • mass_tolerance_unit (str | None)

model_config = {'arbitrary_types_allowed': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

property tic

Total ion current.

remove_reporter_ions(label_type=None)[source]

Set the intensity of reporter ions to 0.

Return type:

None

remove_precursor(tolerance=0.02)[source]

Set the intensity of the precursor peak to 0.

Return type:

None

tic_norm()[source]

Normalize spectrum to total ion current.

Return type:

None

log2_transform()[source]

Log2-transform spectrum.

Return type:

None

inverse_log2_transform()[source]

Undo log2 transformation of intensities (inverse of log2_transform()).

Return type:

None

clip_intensity(min_intensity=0.0)[source]

Clip intensity values.

Return type:

None

to_spectrum_utils()[source]

Convert to spectrum_utils.spectrum.MsmsSpectrum.

Notes

  • Requires spectrum_utils to be installed.

  • If the precursor_mz or precursor_charge attributes are not set, the theoretical m/z and precursor charge of the peptidoform attribute are used, if present. Otherwise, ValueError is raised.

class ms2pip.spectrum.ObservedSpectrum(*, mz, intensity, annotations=None, identifier=None, peptidoform=None, precursor_mz=None, precursor_charge=None, retention_time=None, mass_tolerance=None, mass_tolerance_unit=None)[source]

Bases: Spectrum

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

  • mz (ndarray)

  • intensity (ndarray)

  • annotations (ndarray | None)

  • identifier (str | None)

  • peptidoform (Annotated[Peptidoform | None, BeforeValidator(func=~ms2pip.spectrum._coerce_peptidoform, json_schema_input_type=PydanticUndefined)])

  • precursor_mz (float | None)

  • precursor_charge (int | None)

  • retention_time (float | None)

  • mass_tolerance (float | None)

  • mass_tolerance_unit (str | None)

model_config = {'arbitrary_types_allowed': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class ms2pip.spectrum.PredictedSpectrum(*, mz, intensity, annotations=None, identifier=None, peptidoform=None, precursor_mz=None, precursor_charge=None, retention_time=None, mass_tolerance=0.001, mass_tolerance_unit='Da')[source]

Bases: Spectrum

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

  • mz (ndarray)

  • intensity (ndarray)

  • annotations (ndarray | None)

  • identifier (str | None)

  • peptidoform (Annotated[Peptidoform | None, BeforeValidator(func=~ms2pip.spectrum._coerce_peptidoform, json_schema_input_type=PydanticUndefined)])

  • precursor_mz (float | None)

  • precursor_charge (int | None)

  • retention_time (float | None)

  • mass_tolerance (float | None)

  • mass_tolerance_unit (str | None)

model_config = {'arbitrary_types_allowed': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].