CHAP.xrf package

This subpackage contains PipelineItems unique to SAXSWAXS data processing workflows.

Submodules

CHAP.xrf.processor module

Processors used only by XRF experiments.

class NXfluoProcessor(*, root: Annotated[Path, PathType(path_type=dir)] | None = '/home/runner/work/ChessAnalysisPipeline/ChessAnalysisPipeline/docs', inputdir: Annotated[Path, PathType(path_type=dir)] | None = None, outputdir: Annotated[Path, PathType(path_type=dir)] | None = None, interactive: bool | None = False, log_level: Literal['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'] | None = 'INFO')

Bases: Processor

When provided with a list of PipelineData that have the appropriate name fields, this Processor uses those data to construct a (not yet) standardized representation of fluorescence data in NeXus format.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

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

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Args:

self: The BaseModel instance. context: The context.

process(data)

Extract the contents of the input data, add a string to it, and return the amended value.

Parameters:

data (list[PipelineData]) – Input data.

Returns:

Processed data.

Return type:

str

class PyMcaProcessor(*, root: Annotated[Path, PathType(path_type=dir)] | None = '/home/runner/work/ChessAnalysisPipeline/ChessAnalysisPipeline/docs', inputdir: Annotated[Path, PathType(path_type=dir)] | None = None, outputdir: Annotated[Path, PathType(path_type=dir)] | None = None, interactive: bool | None = False, log_level: Literal['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'] | None = 'INFO')

Bases: Processor

Processor to use [PyMca](https://github.com/vasole/pymca) to analyze XRF spectra. Reads in XRF spectra, incident beam flux, and a PyMca fit parameter configuration. Returns a list of dictionaries returned by the PyMca fit routine – one dictionary for each XRF spectrum provided.

analyze_spectra(advanced_fit, mass_fraction_tool, spectra, flux)

analyze_spectrum(advanced_fit, mass_fraction_tool, spectrum, flux, index)

init_pymca()

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

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

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Args:

self: The BaseModel instance. context: The context.

pipeline_fields: dict

process(data)

Extract the contents of the input data, add a string to it, and return the amended value.

Parameters:

data (list[PipelineData]) – Input data.

Returns:

Processed data.

Return type:

str

pymca_config: dict

class PyMcaResultsProcessor(*, root: Annotated[Path, PathType(path_type=dir)] | None = '/home/runner/work/ChessAnalysisPipeline/ChessAnalysisPipeline/docs', inputdir: Annotated[Path, PathType(path_type=dir)] | None = None, outputdir: Annotated[Path, PathType(path_type=dir)] | None = None, interactive: bool | None = False, log_level: Literal['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'] | None = 'INFO')

Bases: Processor

Transforms a list of PyMca fit configuration result dictionaries (returned by PyMcaProcessor) into a basic NXentry structure.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

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

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Args:

self: The BaseModel instance. context: The context.

process(data)

Extract the contents of the input data, add a string to it, and return the amended value.

Parameters:

data (list[PipelineData]) – Input data.

Returns:

Processed data.

Return type:

str

CHAP.xrf.reader module

XRF Readers

class PyMcaFitConfigReader(*, root: Annotated[Path, PathType(path_type=dir)] | None = '/home/runner/work/ChessAnalysisPipeline/ChessAnalysisPipeline/docs', inputdir: Annotated[Path, PathType(path_type=dir)] | None = None, outputdir: Annotated[Path, PathType(path_type=dir)] | None = None, interactive: bool | None = False, log_level: Literal['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'] | None = 'INFO')

Bases: Reader

A file reader for PyMca5 fit parameter configuration (a PyMca5.PyMcaIO.ConfigDict object).

If run with `interactive == `:

• True: loads an existing parameter set from a file into the

  PyMca5 fit parameter configuration GUI. User may edit parameters in the GUI, optionally save the configuration to a new file, then click “OK” to return their edited configuration for later use by CHAP.xrf.processor tools.

• False: loads an existing parameter set from a file and

  returns it for later use by CHAP.xrf.processor tools.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

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

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Args:

self: The BaseModel instance. context: The context.

read()

Read and return the contents of filename as text.

Returns:

File content.

Return type:

str