revanalyzer.REV_analyzer.REVAnalyzer

class revanalyzer.REV_analyzer.REVAnalyzer(metric, image, size, n_steps, sREV_max_step, datadir=None, outputdir='output')

Bases: object

analysis of representativity of a given image for a given scalar or vector metric.

Input:

metric (subclass of BasicMetric): metric to be analyzed;

image (str or numpy.ndarray): name of binary (‘uint8’) file or numpy.ndarray representing the image;

size (tuple (int, int, int)): linear image sizes in x, y and z directions;

n_steps (int): number of subsamples selection steps;

sREV_max_step (int): maximal step for which sREV and stationarity analysis can be performed;

datadir (str): path to the folder containing image, default: None;

outputdir (str): path to the output folder containing generated data, default: ‘output’.

Methods

`analyze`	Perform the analysis of representativity.
`analyze_stationarity`	Perform the analysis of stationarity.
`generate`	Generator of metric values for all selected subsamples.
`read`	Read the generated metric value for a given subsample.
`show`	Vizualize the vector metric for a specific subsample.
`show_results`	Visualization of REV analysis results.
`vectorize`	Vectorization of generated metric data using vetorizer.

analyze(dREV_threshold, sREV_threshold)

Perform the analysis of representativity.

Input:

dREV_threshold (float, <1): threshold to estimate dREV size;

sREV_threshold (float, <1): threshold to estimate sREV size.

analyze_stationarity(stationarity_threshold)

Perform the analysis of stationarity.

Input:

stationarity_threshold (float, <1): threshold to analyze stationarity.

Output

True or False: is image stationary.

generate(): Generator of metric values for all selected subsamples.

read(step, cut_id=0)

Read the generated metric value for a given subsample.

Input:

step (int): subsamples selection step;

cut_id (int: 0,..8): cut index .

Output

metric value (float or np.array(dtype=’float’)).

show(step, cut_id=0, nbins=None)

Vizualize the vector metric for a specific subsample.

Input:

step (int): subsamples selection step;

cut_id (int: 0,..8): cut index;

nbins (int): number of bins in histogram. For PNM-based metric only.

show_results(): Visualization of REV analysis results.

vectorize(): Vectorization of generated metric data using vetorizer. For vector metric only.