Base operator

Abstract base operator.

pydantic model tidyms2.core.operators.base.BaseOperator

Bases: ABC, BaseModel, Generic[ProcessType]

TidyMS base operator which all other operators inherit from.

Provides functionality to: - set default parameters using instrument type, separation type and polarity. - set parameters using a dictionary. - get default parameters.

field id: str = ''

The Operator id.

abstractmethod classmethod from_defaults(instrument, separation, polarity)

Create a new operator with sane defaults for the specified MS instrument, separation mode and polarity.

Parameters:

• instrument (MSInstrument) – The MS instrument used to measure the samples

• separation (SeparationMode) – The analytical method used for separation

• polarity (Polarity) – The polarity in which the samples where measured

Return type:

Self

Returns:

A new operator instance

check_status(status)

Raise an exception if data status is not compatible with operator required status.

Return type:

None

abstractmethod get_expected_status_in()

Get the expected sample status before applying the operator.

Return type:

TypeVar(ProcessType, SampleProcessStatus, AssayProcessStatus, DataMatrixProcessStatus)

abstractmethod get_expected_status_out()

Get the expected sample status after applying the operator.

Return type:

TypeVar(ProcessType, SampleProcessStatus, AssayProcessStatus, DataMatrixProcessStatus)

update_status(status_in)

Update the sample process status to the status after applying the operator.

Sample operators

Abstract sample operators.

pydantic model tidyms2.core.operators.sample.FeatureExtractor

Bases: SampleOperator

Extract features from ROIs.

Must implement the extract_rois method, which takes a single ROI and creates a list of features.

Provides descriptor based filtering of features using the bounds.

field bounds: dict[str, tuple[pydantic.NonNegativeFloat | None, pydantic.NonNegativeFloat | None]] = {}

Define valid boundaries for each feature descriptor. Boundaries are expressed by mapping descriptor names to a tuple lower and upper bounds. If only a lower/upper bound is required, None must be used (e.g. (None, 10.0) to use only an upper bound).

get_expected_status_in()

Get the expected status before performing ROI extraction.

Return type:

SampleProcessStatus

get_expected_status_out()

Get the expected status after performing ROI extraction.

Return type:

SampleProcessStatus

pydantic model tidyms2.core.operators.sample.FeatureTransformer

Bases: SampleOperator

Apply a transformation to individual features.

Must implement the transform_feature method, which takes a single feature and transform it inplace.

get_expected_status_in()

Get the expected status before performing ROI extraction.

Return type:

SampleProcessStatus

get_expected_status_out()

Get the expected status after performing ROI extraction.

Return type:

SampleProcessStatus

pydantic model tidyms2.core.operators.sample.RoiExtractor

Bases: SampleOperator

Extract ROIs from raw sample data.

get_expected_status_in()

Get the expected status before performing ROI extraction.

Return type:

SampleProcessStatus

get_expected_status_out()

Get the expected status after performing ROI extraction.

Return type:

SampleProcessStatus

pydantic model tidyms2.core.operators.sample.RoiTransformer

Bases: SampleOperator

Transform ROIs from raw sample data.

Must implement the transform_roi method, which takes a single ROI and transform it inplace.

If transform_roi returns None, the ROI is removed from the sample storage.

get_expected_status_in()

Get the expected status before performing ROI extraction.

Return type:

SampleProcessStatus

get_expected_status_out()

Get the expected status after performing ROI extraction.

Return type:

SampleProcessStatus

pydantic model tidyms2.core.operators.sample.SampleOperator

Bases: BaseOperator[SampleProcessStatus], Generic[RoiType, FeatureType]

Base operator for sample storage.

apply(data)

Apply the operator function to the data.

Return type:

None

Assay operators

Abstract assay operators.

pydantic model tidyms2.core.operators.assay.AnnotationPatcher

Bases: AssayOperator

Patches feature annotation data from an assay.

Must Implement the compute_patches method, which takes an assay storage and returns a list of patches that will be applied to the assay.

abstractmethod compute_patches(data)

Compute feature descriptor patches.

Return type:

list[AnnotationPatch]

get_expected_status_in()

Get expected status of input data.

Return type:

AssayProcessStatus

get_expected_status_out()

Get status of output data.

Return type:

AssayProcessStatus

pydantic model tidyms2.core.operators.assay.DescriptorPatcher

Bases: AssayOperator

Patches descriptor data from an assay.

Must Implement the compute_patches method, which takes an assay storage and returns a list of patches that will be applied to the assay.

abstractmethod compute_patches(data)

Compute feature descriptor patches.

Return type:

list[DescriptorPatch]

get_expected_status_in()

Get expected status of input data.

Return type:

AssayProcessStatus

get_expected_status_out()

Get status of output data.

Return type:

AssayProcessStatus

pydantic model tidyms2.core.operators.assay.MissingImputer

Bases: AssayOperator

Add values that will be used as fill in missing data matrix entries.

Must Implement the add_fill_values method, which takes an assay storage and returns a list of fill values.

abstractmethod compute_fill_values(data)

Compute fill values in a sample.

Parameters:

• sample – the sample to search missing values

• groups – the feature groups where the sample did not contribute a feature.

Return type:

list[FillValue]

get_expected_status_in()

Get expected status of input data.

Return type:

AssayProcessStatus

get_expected_status_out()

Get status of output data.

Return type:

AssayProcessStatus

Matrix operators

Abstract matrix operators.

pydantic model tidyms2.core.operators.matrix.ColumnFilter

Bases: MatrixOperator

Remove data matrix features based on a specific condition.

MUST implement the method _create_remove_list that takes the data matrix and produce a list of feature groups to remove.

field exclude: list[int] | None = None

A list of feature groups to ignore during filtering.

pydantic model tidyms2.core.operators.matrix.ColumnTransformer

Bases: MultiProcessTransformer

Transform a data matrix columns based on an arbitrary transformation.

MUST implement the method _transform_column that takes a feature vector and creates a transformed vector.

field exclude: list[int] | None = None

A list of feature groups to ignore during filtering.

pydantic model tidyms2.core.operators.matrix.MatrixOperator

Bases: BaseOperator[DataMatrixProcessStatus]

Base operator for data matrix transformation.

apply(data)

Apply the operator function to the data.

Return type:

None

pydantic model tidyms2.core.operators.matrix.MatrixTransformer

Bases: MatrixOperator

Apply an arbitrary transformation to a data matrix.

MUST implement transform_matrix which takes a data matrix and produces a 2D array that will replace the matrix data.

pydantic model tidyms2.core.operators.matrix.RowFilter

Bases: MatrixOperator

Remove data matrix samples based on a specific condition.

MUST implement the method _create_remove_list that takes the data matrix and produce a list of feature groups to remove.

field exclude: list[str] | None = None

A list of sample ids to ignore during filtering.

pydantic model tidyms2.core.operators.matrix.RowTransformer

Bases: MultiProcessTransformer

Transform a data matrix rows based on an arbitrary transformation.

MUST implement the method _transform_row that takes a sample vector and creates a transformed vector.

field exclude: list[str] | None = None

A list of sample ids to exclude from the transformation