The similarity score for MS/MS spectral comparison
You can find the detail reference here: https://daphnis.readthedocs.io/en/master/
Python 3.7, numpy>=1.17.4, scipy>=1.3.2
cython>=0.29.13 (Not required but highly recommended)
# The command below is not required but strongly recommended, as it will compile the cython code to run faster
python setup.py build_ext --inplaceBefore calculating spectral similarity, it's highly recommended to remove spectral noise. For example, peaks have intensity less than 1% maximum intensity can be removed to improve identificaiton performance.
import numpy as np
import spectral_similarity
spec_query = np.array([[69.071, 7.917962], [86.066, 1.021589], [86.0969, 100.0]], dtype=np.float32)
spec_reference = np.array([[41.04, 37.16], [69.07, 66.83], [86.1, 999.0]], dtype=np.float32)
# Get entropy distance.
print('-' * 30)
similarity = spectral_similarity.similarity(spec_query, spec_reference, method="entropy",
ms2_da=0.05)
print("Entropy similarity:{}.".format(similarity))
# The output should be: Entropy similarity:0.9082203404798316.
# Get dynamic weighted entropy distance.
print('-' * 30)
similarity = spectral_similarity.similarity(spec_query, spec_reference, method="unweighted_entropy",
ms2_da=0.05)
print("Unweighted entropy similarity:{}.".format(similarity))
# The output should be: Unweighted entropy similarity:0.9826668790176113.
# Get all similarity.
print('-' * 30)
all_dist = spectral_similarity.all_similarity(spec_query, spec_reference, ms2_da=0.05)
for dist_name in all_dist:
method_name = spectral_similarity.methods_name[dist_name]
print("Method name: {}, similarity score:{}.".format(method_name, all_dist[dist_name]))
# A list of different spectral similarity will be shown."entropy": Entropy distance
"unweighted_entropy": Unweighted entropy distance
"euclidean": Euclidean distance
"manhattan": Manhattan distance
"chebyshev": Chebyshev distance
"squared_euclidean": Squared Euclidean distance
"fidelity": Fidelity distance
"matusita": Matusita distance
"squared_chord": Squared-chord distance
"bhattacharya_1": Bhattacharya 1 distance
"bhattacharya_2": Bhattacharya 2 distance
"harmonic_mean": Harmonic mean distance
"probabilistic_symmetric_chi_squared": Probabilistic symmetric χ2 distance
"ruzicka": Ruzicka distance
"roberts": Roberts distance
"intersection": Intersection distance
"motyka": Motyka distance
"canberra": Canberra distance
"baroni_urbani_buser": Baroni-Urbani-Buser distance
"penrose_size": Penrose size distance
"mean_character": Mean character distance
"lorentzian": Lorentzian distance
"penrose_shape": Penrose shape distance
"clark": Clark distance
"hellinger": Hellinger distance
"whittaker_index_of_association": Whittaker index of association distance
"symmetric_chi_squared": Symmetric χ2 distance
"pearson_correlation": Pearson/Spearman Correlation Coefficient
"improved_similarity": Improved Similarity
"absolute_value": Absolute Value Distance
"dot_product": Dot-Product (cosine)
"dot_product_reverse": Reverse dot-Product (cosine)
"spectral_contrast_angle": Spectral Contrast Angle
"wave_hedges": Wave Hedges distance
"cosine": Cosine distance
"jaccard": Jaccard distance
"dice": Dice distance
"inner_product": Inner Product distance
"divergence": Divergence distance
"avg_l": Avg (L1, L∞) distance
"vicis_symmetric_chi_squared_3": Vicis-Symmetric χ2 3 distance
"ms_for_id_v1": MSforID distance version 1
"ms_for_id": MSforID distance
"weighted_dot_product": Weighted dot product distance"