darsia.ConcentrationAnalysis#

class ConcentrationAnalysis(base=None, signal_reduction=None, balancing=None, restoration=None, model=None, labels=None, **kwargs)[source]#

Bases: object

Class providing the capabilities to determine concentration/saturation profiles based on image comparison, and interpretation of intensity maps.

Set up a concentration analysis.

Each processing stage defaults to the identity when its argument is None.

Parameters:

basedarsia.Image or list of darsia.Image, optional

Baseline image(s); several images define a cleaning filter.

signal_reductiondarsia.SignalReduction, optional

Reduction from multi-channel to scalar data.

balancingdarsia.Model, optional

Operator balancing the signal, e.g. across facies.

restorationdarsia.TVD, optional

Regulariser applied to the scalar signal.

modeldarsia.Model, optional

Conversion of the signal to a physical quantity.

labelsdarsia.Image, optional

Labelled image of the domain; None means a homogeneous medium.

**kwargs

Tuning parameters:

"diff option"          "positive" | "negative" | "absolute"
                       | "plain"   (how image differences are
                       formed)
"restoration -> model" if True, restoration runs before the
                       model conversion
"verbosity"            0 (none) | 1 (final only) | 2 (all
                       intermediate results)

Methods

`find_cleaning_filter`([baseline_images])	Determine structural noise by studying a series of baseline images.
`read_cleaning_filter_from_file`(path)	Read cleaning filter from file.
`update`([base, mask])	Update of the baseline image or parameters.
`write_cleaning_filter_to_file`(path_to_filter)	Store cleaning filter to file.

Attributes

`base`	Baseline image.
`signal_reduction`	Reduction to scalar signal.
`balancing`	Balancing for heterogeneous signals.
`model`	Signal to data conversion model.
`restoration`	Restoration model.
`labels`	Indicator for heterogeneous image.
`first_restoration_then_model`	Option for defining order of routines.
`mask`	Mask.
`verbosity`	Fetch verbosity.

find_cleaning_filter(baseline_images=None)[source]#

Determine structural noise by studying a series of baseline images. The resulting cleaning filter will be used prior to the conversion of signal to concentration. The cleaning filter should be understood as thresholding mask.

Parameters:

baseline_imageslist of images: Series of baseline_images; default: use internally available baseline images.

read_cleaning_filter_from_file(path)[source]#

Read cleaning filter from file.

Parameters:

pathstr or Path: Path to cleaning filter array.

update(base=None, mask=None)[source]#

Update of the baseline image or parameters.

Parameters:

baseImage, optional: Image array.
masknp.ndarray, optional: Boolean mask, detecting which pixels will be considered, all other will be ignored in the analysis.

write_cleaning_filter_to_file(path_to_filter)[source]#

Store cleaning filter to file.

Parameters:

path_to_filterstr or Path: Path for storage of the cleaning filter.

balancing#: Balancing for heterogeneous signals.

base#: Baseline image.

first_restoration_then_model#: Option for defining order of routines.

labels#: Indicator for heterogeneous image.

mask#: Mask.

model#: Signal to data conversion model.

restoration#: Restoration model.

signal_reduction#: Reduction to scalar signal.

verbosity#: Fetch verbosity. With increasing number, more intermediate results are displayed. Useful for parameter tuning.