matchms.similarity.spectrum_similarity_functions module

matchms.similarity.spectrum_similarity_functions.collect_peak_pairs(spec1: ndarray, spec2: ndarray, tolerance: float, shift: float = 0, mz_power: float = 0.0, intensity_power: float = 1.0)

Find matching pairs between two spectra.

Parameters:

• spec1 – Spectrum peaks and intensities as numpy array.

• spec2 – Spectrum peaks and intensities as numpy array.

• tolerance – Peaks will be considered a match when <= tolerance appart.

• shift – Shift spectra peaks by shift. The default is 0.

• mz_power – The power to raise mz to in the cosine function. The default is 0, in which case the peak intensity products will not depend on the m/z ratios.

• intensity_power – The power to raise intensity to in the cosine function. The default is 1.

Returns:

matching_pairs – Array of found matching peaks.

Return type:

numpy array

matchms.similarity.spectrum_similarity_functions.filter_noise(mz: ndarray, intensities: ndarray, noise_cutoff: float) → ndarray

Filter noise from spectra by removing peaks with intensity below a certain cutoff.

Parameters:

• mz – Array of m/z values. Must be the same length as intensities.

• intensities – Array of intensity values. Must be the same length as mz.

• noise_cutoff – Peaks with intensity below this cutoff (relative to the maximum intensity) will be removed.

matchms.similarity.spectrum_similarity_functions.find_matches(spec1_mz: ndarray, spec2_mz: ndarray, tolerance: float, shift: float = 0) → List

Faster search for matching peaks. Makes use of the fact that spec1 and spec2 contain ordered peak m/z (from low to high m/z).

Parameters:

• spec1_mz – Spectrum peak m/z values as numpy array. Peak mz values must be ordered.

• spec2_mz – Spectrum peak m/z values as numpy array. Peak mz values must be ordered.

• tolerance – Peaks will be considered a match when <= tolerance appart.

• shift – Shift peaks of second spectra by shift. The default is 0.

Returns:

List containing entries of type (idx1, idx2).

Return type:

matches

matchms.similarity.spectrum_similarity_functions.number_matching(numbers_1, numbers_2, tolerance)

Find all pairs between numbers_1 and numbers_2 which are within tolerance.

matchms.similarity.spectrum_similarity_functions.number_matching_ppm(numbers_1, numbers_2, tolerance_ppm)

Find all pairs between numbers_1 and numbers_2 which are within tolerance.

matchms.similarity.spectrum_similarity_functions.number_matching_symmetric(numbers_1, tolerance)

Find all pairs between numbers_1 and numbers_1 which are within tolerance.

matchms.similarity.spectrum_similarity_functions.number_matching_symmetric_ppm(numbers_1, tolerance_ppm)

Find all pairs between numbers_1 and numbers_1 which are within tolerance.

matchms.similarity.spectrum_similarity_functions.score_best_matches(matching_pairs: ndarray, spec1: ndarray, spec2: ndarray, mz_power: float = 0.0, intensity_power: float = 1.0) → tuple[float, int]

Calculate cosine-like score by multiplying matches. Does require a sorted list of matching peaks (sorted by intensity product).