revanalyzer.metrics.pnm.BasicPNMMetric

class revanalyzer.metrics.pnm.BasicPNMMetric(vectorizer, n_threads, resolution, show_time)

Bases: BasicMetric

Base class of PNM-based metrics. (Don’t use it directly but derive from it).

Input:

vectorizer (PNMVectorizer object): vectorizer to be used for a vector metric.

n_threads (int): number threads used for data generation;

resolution (float): resolution of studied sample;

show_time (bool): Added to monitor time cost for large images.

Methods

generate	Generates PNM metric for a specific subsample.
read	Read the metric data generated for a specific subsample.
show	Vizualize the vector metric for a specific subsample.
vectorize	Vectorize the vector metric values for a given pair of subsample.

generate(cut_name, gendatadir)

Generates PNM metric for a specific subsample.

Input:

cut_name (str): name of subsample;

gendatadir (str): folder with generated PNM data.

Output:

df (pandas.DataFrame): data frame with pnm statistics.

read(inputdir, step, cut_id)

Read the metric data generated for a specific subsample.

Input:

inputdir (str): path to the folder containing image;

step (int): subsamples selection step;

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

Output:

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

show(inputdir, step, cut_id, nbins, metric_name)

Vizualize the vector metric for a specific subsample.

Input:

inputdir (str): path to the folder containing generated metric data for subcubes;

step (int): subsamples selection step;

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

nbins (int): number of bins in histogram;

metric_name(str): name of metric.

vectorize(v1, v2)

Vectorize the vector metric values for a given pair of subsample. Makes normalization to voxels and calls the vectorizer function.

Input:

v1 (list(dtype = float)): data for the first subsample;

v2 (list(dtype = float)): data for the second subsample.

Output:

(list(dtype = float), list(dtype = float), float) - a tuple, in which the first two elements are vectorized metric values for a given pair of subsamples, and the last one is the normalized distance between these vectors.