matchms.Spectrum module

class matchms.Spectrum.Spectrum(mz: array, intensities: array, metadata: dict | None = None, metadata_harmonization: bool = True)

Bases: object

Container for a collection of peaks, losses and metadata.

Spectrum peaks are stored as Fragments object which can be addressed calling spectrum.peaks and contains m/z values and the respective peak intensities.

Spectrum metadata is stored as Metadata object which can be addressed by spectrum.metadata.

Code example

import numpy as np
from matchms import Scores, Spectrum
from matchms.similarity import CosineGreedy

spectrum = Spectrum(mz=np.array([100, 150, 200.]),
                    intensities=np.array([0.7, 0.2, 0.1]),
                    metadata={"id": 'spectrum1',
                              "precursor_mz": 222.333,
                              "peak_comments": {200.: "the peak at 200 m/z"}})

print(spectrum)
print(spectrum.peaks.mz[0])
print(spectrum.peaks.intensities[0])
print(spectrum.get('id'))
print(spectrum.peak_comments.get(200))

Should output

Spectrum(precursor m/z=222.33, 3 fragments between 100.0 and 200.0)
100.0
0.7
spectrum1
the peak at 200 m/z

peaks

Peaks of spectrum

Type:

Fragments

losses

Losses of spectrum, the difference between the precursor and all peaks.

Can be filled with

from matchms import Fragments
spectrum.losess = Fragments(mz=np.array([50.]), intensities=np.array([0.1]))

Type:

Fragments or None

metadata

Dict of metadata with for example the scan number of precursor m/z.

Type:

dict

__init__(mz: array, intensities: array, metadata: dict | None = None, metadata_harmonization: bool = True)

Parameters:

• mz – Array of m/z for the peaks

• intensities – Array of intensities for the peaks

• metadata – Dictionary with for example the scan number of precursor m/z.

• metadata_harmonization (bool, optional) – Set to False if default metadata filters should not be applied. The default is True.

clone()

Return a deepcopy of the spectrum instance.

compute_losses(loss_mz_from: float = 0.0, loss_mz_to: float = None) → Fragments | None

This will compute the “losses”, i.e. the differences between the precursor_mz and the individual fragment m/z values. Only losses between loss_mz_from and loss_mz_to will be kept.

Parameters:

• loss_mz_from – Float value to set the minimum acceptable loss value. Default is 0.0.

• loss_mz_to – Float value to set the maximum acceptable loss value. Default is None which means that the los_mz_to will be set to the spectrum’s precursor_mz.

get(key: str, default=None)

Retrieve value from metadata dict. Shorthand for

val = self.metadata[key]

metadata_dict(export_style: str = 'matchms') → dict

Convert spectrum metadata to Python dictionary.

Parameters:

export_style – Converts the keys to the required export style. One of [“matchms”, “massbank”, “nist”, “riken”, “gnps”]. Default is “matchms”

metadata_hash()

Return a (truncated) sha256-based hash which is generated based on the spectrum metadata. Spectra with same metadata results in same metadata_hash.

plot(figsize=(8, 6), dpi=200, **kwargs)

Plot to visually inspect a spectrum run spectrum.plot()

Example of a spectrum plotted using spectrum.plot() ..

plot_against(other_spectrum, figsize=(8, 6), dpi=200, **spectrum_kws)

Compare two spectra visually in a mirror plot.

To visually compare the peaks of two spectra run spectrum.plot_against(other_spectrum)

Example of a mirror plot comparing two spectra spectrum.plot_against() ..

set(key: str, value)

Set value in metadata dict. Shorthand for

self.metadata[key] = val

spectrum_hash()

Return a (truncated) sha256-based hash which is generated based on the spectrum peaks (mz:intensity pairs). Spectra with same peaks will results in same spectrum_hash.

to_dict(export_style: str = 'matchms') → dict

Return a dictionary representation of a spectrum.

Parameters:

export_style – Converts the keys to the required export style. One of [“matchms”, “massbank”, “nist”, “riken”, “gnps”]. Default is “matchms”

classmethod update_peak_comments_mz_tolerance(mz_tolerance: float)

Change current peak comment m/z tolerance to mz_tolerance.