def test_init_annotation_order():
num_peaks = 150
mz = np.random.uniform(100, 1400, num_peaks)
intensity = np.random.lognormal(0, 1, num_peaks)
annotation = [spectrum.FragmentAnnotation('y', i, 1, this_mz)
for i, this_mz in enumerate(mz)]
spec = spectrum.MsmsSpectrum('test_spectrum', 500, 2, mz, intensity,
annotation)
for this_mz, this_annotation in zip(spec.mz, spec.annotation):
assert this_mz == pytest.approx(this_annotation.calc_mz)
def test_mz_annotation_len():
num_peaks = 150
mz = np.random.uniform(100, 1400, num_peaks)
intensity = np.random.lognormal(0, 1, num_peaks)
annotation = [
spectrum.FragmentAnnotation(None, this_mz, str(this_mz))
for this_mz in mz[:100]
]
with pytest.raises(ValueError):
spectrum.MsmsSpectrum('test_spectrum', 500, 2, mz, intensity,
annotation)
def test_filter_intensity_keep_all():
num_peaks = 150
mz = np.random.uniform(100, 1400, num_peaks)
intensity = np.random.lognormal(0, 1, num_peaks)
annotation = [spectrum.FragmentAnnotation('y', i, 1, this_mz)
for i, this_mz in enumerate(mz)]
spec = spectrum.MsmsSpectrum('test_spectrum', 500, 2, mz, intensity,
annotation)
spec.filter_intensity()
assert len(spec.mz) == num_peaks
assert len(spec.intensity) == num_peaks
assert len(spec.annotation) == num_peaks
def test_set_mz_range_none():
num_peaks = 150
mz = np.random.uniform(100, 1400, num_peaks)
intensity = np.random.lognormal(0, 1, num_peaks)
annotation = [spectrum.FragmentAnnotation('y', i, 1, this_mz)
for i, this_mz in enumerate(mz)]
spec = spectrum.MsmsSpectrum('test_spectrum', 500, 2, mz, intensity,
annotation)
spec.set_mz_range(None, None)
assert len(spec.mz) == num_peaks
assert len(spec.intensity) == num_peaks
assert len(spec.annotation) == num_peaks
def test_annotate_mz_fragments_most_intense():
fragment_tol_mass = 1
fragment_tol_mode = 'Da'
mz, intensity = np.asarray([200, 200.5, 201.5]), np.asarray([10, 20, 30])
spec = spectrum.MsmsSpectrum('test_spectrum', 100, 1, mz, intensity)
fragment_mz = 200.15
charge = 1
spec.annotate_mz_fragment(fragment_mz, charge, fragment_tol_mass,
fragment_tol_mode, 'most_intense', 'hello')
assert spec.annotation[0] is None
assert spec.annotation[1] == spectrum.FragmentAnnotation(
charge, fragment_mz, 'hello')
assert spec.annotation[2] is None
def test_filter_intensity_max_num_peaks():
num_peaks = 150
mz = np.random.uniform(100, 1400, num_peaks)
intensity = np.random.lognormal(0, 1, num_peaks)
max_intensity = intensity.max()
annotation = [spectrum.FragmentAnnotation('y', i, 1, this_mz)
for i, this_mz in enumerate(mz)]
spec = spectrum.MsmsSpectrum('test_spectrum', 500, 2, mz, intensity,
annotation)
max_num_peaks = 50
spec.filter_intensity(max_num_peaks=max_num_peaks)
assert len(spec.mz) == max_num_peaks
assert len(spec.intensity) == max_num_peaks
assert len(spec.annotation) == max_num_peaks
assert spec.intensity.max() == pytest.approx(max_intensity)
def test_set_mz_range_truncate_right():
num_peaks = 150
mz = np.random.uniform(100, 1400, num_peaks)
intensity = np.random.lognormal(0, 1, num_peaks)
annotation = [spectrum.FragmentAnnotation('y', i, 1, this_mz)
for i, this_mz in enumerate(mz)]
spec = spectrum.MsmsSpectrum('test_spectrum', 500, 2, mz, intensity,
annotation)
min_mz, max_mz = 0, 1200
assert spec.mz.max() > max_mz
spec.set_mz_range(min_mz, max_mz)
assert len(spec.mz) < num_peaks
assert len(spec.intensity) < num_peaks
assert len(spec.annotation) < num_peaks
assert spec.mz.max() <= max_mz
def test_round_merge_len():
num_peaks = 10
mz = np.arange(1, num_peaks + 1) + np.random.uniform(-0.2, 0.2, num_peaks)
mz[4] = mz[3] + 0.0002
mz[5] = mz[3] + 0.0005
mz[7] = mz[8] - 0.00037
intensity = np.random.exponential(1, num_peaks)
annotation = [spectrum.FragmentAnnotation('y', i, 1, this_mz)
for i, this_mz in enumerate(mz)]
spec = spectrum.MsmsSpectrum('test_spectrum', 500, 2, mz, intensity,
annotation)
spec.round(1)
assert len(spec.mz) == len(mz) - 3
assert len(spec.mz) == len(spec.intensity)
assert len(spec.mz) == len(spec.annotation)
def test_round_no_merge():
num_peaks = 150
mz = np.arange(1, num_peaks + 1) + np.random.uniform(-0.49, 0.5, num_peaks)
intensity = np.random.exponential(1, num_peaks)
annotation = [spectrum.FragmentAnnotation('y', i, 1, this_mz)
for i, this_mz in enumerate(mz)]
spec = spectrum.MsmsSpectrum('test_spectrum', 500, 2, mz.copy(),
intensity.copy(), annotation.copy())
decimals = 0
spec.round(decimals)
assert len(spec.mz) == num_peaks
assert len(spec.intensity) == num_peaks
assert len(spec.annotation) == num_peaks
np.testing.assert_allclose(spec.mz, np.around(mz, decimals))
np.testing.assert_allclose(spec.intensity, intensity)
def test_remove_precursor_peak_none():
num_peaks = 150
mz = np.random.uniform(100, 1400, num_peaks)
fragment_tol_mass = np.random.uniform(0, 0.5)
fragment_tol_mode = 'Da'
precursor_mz = mz[np.random.randint(0, num_peaks)] + fragment_tol_mass * 2
intensity = np.random.lognormal(0, 1, num_peaks)
annotation = [spectrum.FragmentAnnotation('y', i, 1, this_mz)
for i, this_mz in enumerate(mz)]
spec = spectrum.MsmsSpectrum('test_spectrum', precursor_mz, 2, mz,
intensity, annotation)
spec.remove_precursor_peak(fragment_tol_mass, fragment_tol_mode)
assert len(spec.mz) == num_peaks
assert len(spec.intensity) == num_peaks
assert len(spec.annotation) == num_peaks
assert np.abs(precursor_mz - spec.mz).all() > fragment_tol_mass
def test_filter_intensity_remove_low_intensity():
num_peaks = 150
mz = np.random.uniform(100, 1400, num_peaks)
intensity = np.random.lognormal(0, 1, num_peaks)
max_intensity = intensity.max()
annotation = [spectrum.FragmentAnnotation('y', i, 1, this_mz)
for i, this_mz in enumerate(mz)]
spec = spectrum.MsmsSpectrum('test_spectrum', 500, 2, mz, intensity,
annotation)
min_intensity = 0.05
assert spec.intensity.min() < min_intensity * spec.intensity.max()
spec.filter_intensity(min_intensity=min_intensity)
assert len(spec.mz) < num_peaks
assert len(spec.intensity) < num_peaks
assert len(spec.annotation) < num_peaks
assert spec.intensity.max() == pytest.approx(max_intensity)
assert spec.intensity.min() >= min_intensity * max_intensity
def test_annotate_peaks_most_intense():
fragment_tol_mass = 0.02
fragment_tol_mode = 'Da'
peptide = 'YLYEIAR'
fragment_mz = np.asarray([fragment.calc_mz for fragment in
spectrum._get_theoretical_peptide_fragments(
peptide)])
mz = np.asarray([fragment_mz[0] - 0.01, fragment_mz[0] + 0.01])
intensity = np.asarray([10, 20])
charge = 2
spec = spectrum.MsmsSpectrum(
'test_spectrum', mass.calculate_mass(sequence=peptide,
charge=charge),
charge, mz, intensity, peptide=peptide)
spec.annotate_peaks(fragment_tol_mass, fragment_tol_mode,
peak_assignment='most_intense')
assert spec.annotation[0] is None
assert spec.annotation[1] == spectrum.FragmentAnnotation('b', 1, 1,
fragment_mz[0])
def test_remove_precursor_peak_isotope():
num_peaks = 150
mz = np.random.uniform(100, 1400, num_peaks)
fragment_tol_mass = np.random.uniform(0, 0.5)
fragment_tol_mode = 'Da'
precursor_mz = mz[np.random.randint(0, num_peaks)] + fragment_tol_mass / 2
precursor_charge = 3
mz[-1] = precursor_mz + 1 / precursor_charge
mz[-2] = precursor_mz + 2 / precursor_charge
intensity = np.random.lognormal(0, 1, num_peaks)
annotation = [spectrum.FragmentAnnotation('y', i, 1, this_mz)
for i, this_mz in enumerate(mz)]
spec = spectrum.MsmsSpectrum('test_spectrum', precursor_mz,
precursor_charge, mz, intensity, annotation)
spec.remove_precursor_peak(fragment_tol_mass, fragment_tol_mode, 2)
assert np.abs(precursor_mz - spec.mz).all() > fragment_tol_mass
assert len(spec.mz) <= num_peaks - 3
assert len(spec.intensity) <= num_peaks - 3
assert len(spec.annotation) <= num_peaks - 3
def test_round_merge_annotation():
num_peaks = 10
mz = np.arange(1, num_peaks + 1) + np.random.uniform(-0.2, 0.2, num_peaks)
mz[4] = mz[3] + 0.0002
mz[5] = mz[3] + 0.0005
mz[7] = mz[8] - 0.00037
intensity = np.arange(1, 11)
annotation = [None, None, None,
spectrum.FragmentAnnotation('b', 4, 1, mz[3]), None,
spectrum.FragmentAnnotation('b', 6, 1, mz[5]),
spectrum.FragmentAnnotation('b', 7, 1, mz[6]), None, None,
spectrum.FragmentAnnotation('b', 10, 1, mz[9])]
spec = spectrum.MsmsSpectrum('test_spectrum', 500, 2, mz.copy(), intensity,
annotation.copy())
spec.round(1, 'max')
np.testing.assert_array_equal(spec.annotation, [
None, None, None, spectrum.FragmentAnnotation('b', 6, 1, mz[5]),
spectrum.FragmentAnnotation('b', 7, 1, mz[6]), None,
spectrum.FragmentAnnotation('b', 10, 1, mz[9])])
def generate_mirror_figure(
spectrum_top: sus.MsmsSpectrum,
spectrum_bottom: sus.MsmsSpectrum,
extension: str,
**kwargs: Any,
) -> io.BytesIO:
"""
Generate a mirror plot of two spectra.
Parameters
----------
spectrum_top : sus.MsmsSpectrum
The spectrum to be plotted at the top of the mirror plot.
spectrum_bottom : sus.MsmsSpectrum
The spectrum to be plotted at the bottom of the mirror plot.
extension : str
Image format.
kwargs : Any
Plotting settings.
Returns
-------
io.BytesIO
Bytes buffer containing the mirror plot.
"""
usi1 = spectrum_top.identifier
usi2 = spectrum_bottom.identifier
fig, ax = plt.subplots(figsize=(kwargs["width"], kwargs["height"]))
# Determine cosine similarity and matching peaks.
if kwargs["cosine"]:
# Assign the matching peak annotations.
sim_score, peak_matches = similarity.cosine(
spectrum_top,
spectrum_bottom,
kwargs["fragment_mz_tolerance"],
kwargs["cosine"] == "shifted",
)
peak_matches = zip(*peak_matches)
else:
sim_score = 0
# Make sure that top and bottom spectra are colored..
peak_matches = [
np.arange(len(spectrum_top.annotation)),
np.arange(len(spectrum_bottom.annotation)),
]
if spectrum_top.peptide is None:
# Initialize the annotations as unmatched.
for annotation in spectrum_top.annotation:
if annotation is not None:
annotation.ion_type = "unmatched"
for annotation in spectrum_bottom.annotation:
if annotation is not None:
annotation.ion_type = "unmatched"
for peak_idx, spectrum, label in zip(peak_matches,
[spectrum_top, spectrum_bottom],
["top", "bottom"]):
for i in peak_idx:
if spectrum.annotation[i] is None:
spectrum.annotation[i] = sus.FragmentAnnotation(
0, spectrum.mz[i], "")
spectrum.annotation[i].ion_type = label
# Colors for mirror plot peaks (subject to change).
sup.colors["top"] = "#212121"
sup.colors["bottom"] = "#388E3C"
sup.colors["unmatched"] = "darkgray"
sup.colors[None] = "darkgray"
sup.mirror(
spectrum_top,
spectrum_bottom,
{
"annotate_ions": kwargs["annotate_peaks"],
"annot_kws": {
"rotation": kwargs["annotation_rotation"],
"clip_on": True,
},
"grid": kwargs["grid"],
},
ax=ax,
)
else:
sup.mirror(
spectrum_top,
spectrum_bottom,
{
"annot_kws": {
"rotation": kwargs["annotation_rotation"],
"clip_on": True,
},
"grid": kwargs["grid"],
},
ax=ax,
)
ax.set_xlim(kwargs["mz_min"], kwargs["mz_max"])
ax.set_ylim(-kwargs["max_intensity"] / 100, kwargs["max_intensity"] / 100)
if not kwargs["grid"]:
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.yaxis.set_ticks_position("left")
ax.xaxis.set_ticks_position("bottom")
text_y = 1.2 if kwargs["cosine"] else 1.15
for usi, spec, loc in zip([usi1, usi2], [spectrum_top, spectrum_bottom],
["Top", "Bottom"]):
title = ax.text(
0.5,
text_y,
f"{loc}: {usi}",
horizontalalignment="center",
verticalalignment="bottom",
fontsize="x-large",
fontweight="bold",
transform=ax.transAxes,
)
title.set_url(f"{USI_SERVER}mirror/?usi1={usi1}&usi2={usi2}")
text_y -= 0.04
subtitle = (f"Precursor $m$/$z$: "
f'{spec.precursor_mz:.{kwargs["annotate_precision"]}f} '
if spec.precursor_mz > 0 else "")
subtitle += f"Charge: {spec.precursor_charge}"
subtitle = ax.text(
0.5,
text_y,
subtitle,
horizontalalignment="center",
verticalalignment="bottom",
fontsize="large",
transform=ax.transAxes,
)
subtitle.set_url(f"{USI_SERVER}mirror/?usi1={usi1}&usi2={usi2}")
text_y -= 0.06
if kwargs["cosine"]:
subtitle_score = f"Cosine similarity = {sim_score:.4f}"
ax.text(
0.5,
text_y,
subtitle_score,
horizontalalignment="center",
verticalalignment="bottom",
fontsize="x-large",
fontweight="bold",
transform=ax.transAxes,
)
buf = io.BytesIO()
plt.savefig(buf, bbox_inches="tight", format=extension)
buf.seek(0)
fig.clear()
plt.close(fig)
gc.collect()
return buf
def _generate_mirror_figure(usi1: str, usi2: str, extension: str, **kwargs) \
-> io.BytesIO:
fig, ax = plt.subplots(figsize=(kwargs['width'], kwargs['height']))
annotate_peaks = kwargs['annotate_peaks']
kwargs['annotate_peaks'] = annotate_peaks[0]
spectrum_top = _prepare_spectrum(usi1, **kwargs)
kwargs['annotate_peaks'] = annotate_peaks[1]
spectrum_bottom = _prepare_spectrum(usi2, **kwargs)
fragment_mz_tolerance = kwargs['fragment_mz_tolerance']
if kwargs['cosine']:
# Initialize the annotations as unmatched.
if spectrum_top.annotation is None:
spectrum_top.annotation = np.full_like(spectrum_top.mz, None,
object)
if spectrum_bottom.annotation is None:
spectrum_bottom.annotation = np.full_like(spectrum_bottom.mz, None,
object)
for annotation in spectrum_top.annotation:
if annotation is not None:
annotation.ion_type = 'unmatched'
for annotation in spectrum_bottom.annotation:
if annotation is not None:
annotation.ion_type = 'unmatched'
# Assign the matching peak annotations.
similarity, peak_matches = cosine(spectrum_top, spectrum_bottom,
fragment_mz_tolerance,
kwargs['cosine'] == 'shifted')
for top_i, bottom_i in peak_matches:
if spectrum_top.annotation[top_i] is None:
spectrum_top.annotation[top_i] = sus.FragmentAnnotation(
0, spectrum_top.mz[top_i], '')
spectrum_top.annotation[top_i].ion_type = 'top'
if spectrum_bottom.annotation[bottom_i] is None:
spectrum_bottom.annotation[bottom_i] = sus.FragmentAnnotation(
0, spectrum_bottom.mz[bottom_i], '')
spectrum_bottom.annotation[bottom_i].ion_type = 'bottom'
else:
similarity = 0
# Colors for mirror plot peaks, subject to change.
sup.colors['top'] = '#212121'
sup.colors['bottom'] = '#388E3C'
sup.colors['unmatched'] = 'darkgray'
sup.mirror(spectrum_top,
spectrum_bottom, {
'annotate_ions': kwargs['annotate_peaks'],
'annot_kws': {
'rotation': kwargs['annotation_rotation'],
'clip_on': True
},
'grid': kwargs['grid']
},
ax=ax)
ax.set_xlim(kwargs['mz_min'], kwargs['mz_max'])
ax.set_ylim(-kwargs['max_intensity'], kwargs['max_intensity'])
if not kwargs['grid']:
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
text_y = 1.2 if kwargs['cosine'] else 1.15
for usi, spec, loc in zip([usi1, usi2], [spectrum_top, spectrum_bottom],
['Top', 'Bottom']):
title = ax.text(0.5,
text_y,
f'{loc}: {usi}',
horizontalalignment='center',
verticalalignment='bottom',
fontsize='x-large',
fontweight='bold',
transform=ax.transAxes)
title.set_url(f'{USI_SERVER}mirror/?usi1={usi1}&usi2={usi2}')
text_y -= 0.04
subtitle = (f'Precursor $m$/$z$: '
f'{spec.precursor_mz:.{kwargs["annotate_precision"]}f} '
if spec.precursor_mz > 0 else '')
subtitle += f'Charge: {spec.precursor_charge}'
subtitle = ax.text(0.5,
text_y,
subtitle,
horizontalalignment='center',
verticalalignment='bottom',
fontsize='large',
transform=ax.transAxes)
subtitle.set_url(f'{USI_SERVER}mirror/?usi1={usi1}&usi2={usi2}')
text_y -= 0.06
if kwargs['cosine']:
subtitle_score = f'Cosine similarity = {similarity:.4f}'
ax.text(0.5,
text_y,
subtitle_score,
horizontalalignment='center',
verticalalignment='bottom',
fontsize='x-large',
fontweight='bold',
transform=ax.transAxes)
buf = io.BytesIO()
plt.savefig(buf, bbox_inches='tight', format=extension)
buf.seek(0)
fig.clear()
plt.close(fig)
gc.collect()
return buf