Module riid.data

This sub-package contains all utilities for synthesizing, reading, writing, and converting data.

# Copyright 2021 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
# Under the terms of Contract DE-NA0003525 with NTESS,
# the U.S. Government retains certain rights in this software.
"""This sub-package contains all utilities for synthesizing, reading, writing, and converting data.
"""
import numpy as np


def get_expected_spectra(seeds: np.ndarray, expected_counts: np.ndarray) -> np.ndarray:
    """Multiply a 1-D array of expected counts by either a 1-D array or 2-D
    matrix of seed spectra.

    The dimension(s) of the seed array(s), `seeds`, is expanded to be `(m, n, 1)` where:

    - m = # of seeds
    - n = # of channels

    and the final dimension is added in order to facilitate proper broadcasting.
    The dimension of the `expected_counts` must be 1, but the length `p` can be
    any positive number.

    The resulting expected spectra will be of shape `(m x p, n)`.
    This represents the same number of channels `n`, but each expected count
    value, of which there are `p`, will be me multiplied through each seed spectrum,
    of which there are `m`.
    All expected spectra matrices for each seed are then concatenated together
    (stacked), eliminating the 3rd dimension.
    """
    if expected_counts.ndim != 1:
        raise ValueError("Expected counts array must be 1-D.")
    if expected_counts.shape[0] == 0:
        raise ValueError("Expected counts array cannot be empty.")
    if seeds.ndim > 2:
        raise InvalidSeedError("Seeds array must be 1-D or 2-D.")

    expected_spectra = np.concatenate(
        seeds * expected_counts[:, np.newaxis, np.newaxis]
    )

    return expected_spectra


class InvalidSeedError(Exception):
    """Seed spectra data structure is not 1- or 2-dimensional."""
    pass

Sub-modules

riid.data.converters

This module contains utilities for converting known datasets into SampleSets.

riid.data.labeling

This module contains utility functions for managing ground truth information.

riid.data.sampleset

This module contains the SampleSet class and other SampleSet-related functions.

riid.data.synthetic

This module contains utilities for synthesizing gamma spectra.

Functions

def get_expected_spectra(seeds: numpy.ndarray, expected_counts: numpy.ndarray) ‑> numpy.ndarray

Multiply a 1-D array of expected counts by either a 1-D array or 2-D matrix of seed spectra.

The dimension(s) of the seed array(s), seeds, is expanded to be (m, n, 1) where:

• m = # of seeds
• n = # of channels

and the final dimension is added in order to facilitate proper broadcasting. The dimension of the expected_counts must be 1, but the length p can be any positive number.

The resulting expected spectra will be of shape (m x p, n). This represents the same number of channels n, but each expected count value, of which there are p, will be me multiplied through each seed spectrum, of which there are m. All expected spectra matrices for each seed are then concatenated together (stacked), eliminating the 3rd dimension.

Expand source code Browse git

def get_expected_spectra(seeds: np.ndarray, expected_counts: np.ndarray) -> np.ndarray:
    """Multiply a 1-D array of expected counts by either a 1-D array or 2-D
    matrix of seed spectra.

    The dimension(s) of the seed array(s), `seeds`, is expanded to be `(m, n, 1)` where:

    - m = # of seeds
    - n = # of channels

    and the final dimension is added in order to facilitate proper broadcasting.
    The dimension of the `expected_counts` must be 1, but the length `p` can be
    any positive number.

    The resulting expected spectra will be of shape `(m x p, n)`.
    This represents the same number of channels `n`, but each expected count
    value, of which there are `p`, will be me multiplied through each seed spectrum,
    of which there are `m`.
    All expected spectra matrices for each seed are then concatenated together
    (stacked), eliminating the 3rd dimension.
    """
    if expected_counts.ndim != 1:
        raise ValueError("Expected counts array must be 1-D.")
    if expected_counts.shape[0] == 0:
        raise ValueError("Expected counts array cannot be empty.")
    if seeds.ndim > 2:
        raise InvalidSeedError("Seeds array must be 1-D or 2-D.")

    expected_spectra = np.concatenate(
        seeds * expected_counts[:, np.newaxis, np.newaxis]
    )

    return expected_spectra

Classes

class InvalidSeedError (*args, **kwargs)

Seed spectra data structure is not 1- or 2-dimensional.

Expand source code Browse git

class InvalidSeedError(Exception):
    """Seed spectra data structure is not 1- or 2-dimensional."""
    pass

Ancestors

• builtins.Exception
• builtins.BaseException