aim2dat.fct.fingerprint

Module to compute a fingerprint for spectra.

Module Contents

Classes

FunctionDiscretizationFingerprint

Fingerprint for functions based on the DOS-Fingerprint presented in
class aim2dat.fct.fingerprint.FunctionDiscretizationFingerprint(grid, **kwargs)[source]

Fingerprint for functions based on the DOS-Fingerprint presented in doi:10.1038/s41597-022-01754-z.

Overview

Methods

calculate_fingerprint(x_values, y_values, label)

Calculate the fingerprint.

compare_fingerprints(label_1, label_2)

Compare two fingerprints that are stored in the internal memory.

plot_fingerprint(x_values, y_values)

Plot the discretized function and the corresponding grid.
calculate_fingerprint(x_values: numpy.array, y_values: numpy.array, label: str = None) → numpy.array[source]

Calculate the fingerprint.

Parameters:

  • x_values (np.array) – x-values of the function.

  • y_values (np.array) – y-values of the function. In case it’s a 2D-array, each row will be interpreted as a dataset and the fingerprint is calculated by concatenating the individual fingerprints.

  • label (str) – Label for the internal memory. Defaults to None.

Returns:

np.array – The discretized fingerprint.

compare_fingerprints(label_1: str, label_2: str) → float[source]

Compare two fingerprints that are stored in the internal memory.

Parameters:

  • label_1 (str) – Label of the first fingerprint.

  • label_2 (str) – Label of the second fingerprint.

Returns:

float – Similarity measure.

plot_fingerprint(x_values: numpy.array, y_values: numpy.array) → matplotlib.pyplot.Figure[source]

Plot the discretized function and the corresponding grid.

Parameters:

  • x_values (np.array) – x-values of the function.

  • y_values (np.array) – y-values of the function.

Returns:

plt.Figure – Plot of the discretized function.