Plotting

Plotting graphs works the same way for both Spec2Vec and MS2DeepScore.

Molecule Plot

The following example will plot the structures of the molecules.

from omigami.plotting import MoleculePlotter
from omigami.spectra_matching import Spec2Vec

client = Spec2Vec()
mgf_file_path = "path_to_file.mgf"
best_matches = client.match_spectra(mgf_file_path, n_best=10)

plotter = MoleculePlotter()
plots, legends = plotter.plot_molecule_structure(
    spectra_matches=best_matches[1],
    representation="smiles",
    draw_indices=True,
    img_size=(600, 600),
    substructure_highlight="C(=O)"
)
first_match = list(plots.values())[0]
first_match
print(legends[0])

ClassyFire Plot

The following code allows us to plot the results of the Classyfire model API.

from omigami.plotting import MoleculePlotter
plotter = MoleculePlotter()
plotter.plot_classyfire_result(best_matches[1], color="green")

NPClassifier Plot

Furthermore, Omigami provides the possibility to use the NPClassifier API.

from omigami.plotting import MoleculePlotter
plotter = MoleculePlotter()
plotter.plot_NPclassifier_result(best_matches[1], color="orange")

Spectra Comparison Mirror Plot

The following code snippet will plot the Spectra Comparison Mirror Plot. It takes two spectra returned and creates a mirror plot with spectrum 1 on top (blue), and spectrum 2 on the bottom (red).

from omigami.plotting import SpectraComparisonPlotter
from omigami.utilities import SpectrumDataFrameHelper
from matchms.importing import load_from_mgf

plotter = SpectraComparisonPlotter()

# lets load in memory our input spectra so we can compare input and output (matches)
input_spectra = list(load_from_mgf(mgf_file_path))

input_df = SpectrumDataFrameHelper.from_spectrum(input_spectra[0])

spectrum_match_id = best_matches[0].index[0]
output_df = SpectrumDataFrameHelper.from_gnps_id(spectrum_match_id)

plot = plotter.mirror_plot(
    spectrum_1=input_df,
    spectrum_2=output_df,
    labels=["Input Spectrum", "First Spectrum Match"],
    display_limits=(0,500)
)