def _draw_peak_pair(pair, edge_color='red', peak_color='orange', alpha=0.8, fontsize=12, label=None, rotation=45,
ax=None, **kwargs):
p1, p2 = pair
ax.plot((p1.mz, p2.mz), (p1.intensity, p2.intensity),
color=edge_color, alpha=alpha, **kwargs)
kwargs.setdefault("clip_on", False)
clip_on = kwargs['clip_on']
draw_peaklist(pair, ax=ax, alpha=0.4, color=peak_color)
if label:
midx = (p1.mz + p2.mz) / 2
# interpolate the midpoint's height
midy = (p1.intensity * (p2.mz - midx) +
p2.intensity * (midx - p1.mz)) / (p2.mz - p1.mz)
# find the angle of the line connecting the two peaks
xlo = min(p1.mz, p2.mz)
xhi = max(p1.mz, p2.mz)
adj = xhi - xlo
ylo = min(p1.intensity, p2.intensity)
yhi = max(p1.intensity, p2.intensity)
opp = yhi - ylo
hypot = np.hypot(adj, opp) # pylint: disable=assignment-from-no-return
rotation = np.arccos(adj / hypot) # pylint: disable=assignment-from-no-return
if isinstance(label, (list, tuple)):
label = '-'.join(map(str, label))
else:
label = str(label)
ax.text(midx, midy, label, fontsize=fontsize,
ha='center', va='bottom', rotation=rotation, clip_on=clip_on)
def _draw_peak_pair(pair, edge_color='red', peak_color='orange', alpha=0.8, fontsize=12, label=None, rotation=45,
ax=None, **kwargs):
p1, p2 = pair
ax.plot((p1.mz, p2.mz), (p1.intensity, p2.intensity),
color=edge_color, alpha=alpha, **kwargs)
kwargs.setdefault("clip_on", False)
clip_on = kwargs['clip_on']
draw_peaklist(pair, ax=ax, alpha=0.4, color=peak_color)
if label:
midx = (p1.mz + p2.mz) / 2
# interpolate the midpoint's height
midy = (p1.intensity * (p2.mz - midx) +
p2.intensity * (midx - p1.mz)) / (p2.mz - p1.mz)
# find the angle of the line connecting the two peaks
xlo = min(p1.mz, p2.mz)
xhi = max(p1.mz, p2.mz)
adj = xhi - xlo
ylo = min(p1.intensity, p2.intensity)
yhi = max(p1.intensity, p2.intensity)
opp = yhi - ylo
hypot = np.hypot(adj, opp) # pylint: disable=assignment-from-no-return
rotation = np.arccos(adj / hypot) # pylint: disable=assignment-from-no-return
if isinstance(label, (list, tuple)):
label = '-'.join(map(str, label))
else:
label = str(label)
ax.text(midx, midy, label, fontsize=fontsize,
ha='center', va='bottom', rotation=rotation, clip_on=clip_on)
def annotate_isotopic_peaks(scan, ax=None, color_cycle=None, **kwargs):
'''Mark distinct isotopic peaks from the :class:`~.DeconvolutedPeakSet`
in ``scan``.
.. plot::
:include-source:
import ms_deisotope
from ms_deisotope import plot
from ms_deisotope.test.common import datafile
reader = ms_deisotope.MSFileLoader(datafile("20150710_3um_AGP_001_29_30.mzML.gz"))
bunch = next(reader)
bunch.precursor.pick_peaks()
bunch.precursor.deconvolute(
scorer=ms_deisotope.PenalizedMSDeconVFitter(20., 2.0),
averagine=ms_deisotope.glycopeptide, use_quick_charge=True)
ax = plot.draw_peaklist(bunch.precursor, color='black')
ax = plot.annotate_isotopic_peaks(bunch.precursor, ax=ax)
ax.set_xlim(1160, 1165)
ax.figure.set_figwidth(12)
Parameters
----------
scan: ScanBase
The scan to annotate
color_cycle: :class:`~.Iterable`
An iterable to draw isotopic cluster colors from
ax: :class:`matplotlib._axes.Axes`
An :class:`~.Axes` object to draw the plot on
Returns
-------
:class:`matplotlib._axes.Axes`
'''
from .peak_set import DeconvolutedPeakSet
if ax is None:
_, ax = plt.subplots(1)
if color_cycle is None:
color_cycle = _default_color_cycle()
color_cycle = itertools.cycle(color_cycle)
if isinstance(scan, DeconvolutedPeakSet):
peaks = scan
else:
peaks = getattr(scan, "deconvoluted_peak_set", [])
peaks = sorted(peaks, key=lambda x: x.mz)
for peak in peaks:
color = next(color_cycle)
draw_peaklist(peak.envelope, ax=ax, color=color, alpha=0.75, **kwargs)
ax.scatter(*zip(*peak.envelope), color=color, alpha=0.75)
return ax
def annotate_isotopic_peaks(scan, ax=None, color_cycle=None, **kwargs):
'''Mark distinct isotopic peaks from the :class:`~.DeconvolutedPeakSet`
in ``scan``.
.. plot::
:include-source:
import ms_deisotope
from ms_deisotope import plot
from ms_deisotope.test.common import datafile
reader = ms_deisotope.MSFileLoader(datafile("20150710_3um_AGP_001_29_30.mzML.gz"))
bunch = next(reader)
bunch.precursor.pick_peaks()
bunch.precursor.deconvolute(
scorer=ms_deisotope.PenalizedMSDeconVFitter(20., 2.0),
averagine=ms_deisotope.glycopeptide, use_quick_charge=True)
ax = plot.draw_peaklist(bunch.precursor, color='black')
ax = plot.annotate_isotopic_peaks(bunch.precursor, ax=ax)
ax.set_xlim(1160, 1165)
ax.figure.set_figwidth(12)
Parameters
----------
scan: ScanBase
The scan to annotate
color_cycle: :class:`~.Iterable`
An iterable to draw isotopic cluster colors from
ax: :class:`matplotlib._axes.Axes`
An :class:`~.Axes` object to draw the plot on
Returns
-------
:class:`matplotlib._axes.Axes`
'''
from .peak_set import DeconvolutedPeakSet
if ax is None:
_, ax = plt.subplots(1)
if color_cycle is None:
color_cycle = _default_color_cycle()
color_cycle = itertools.cycle(color_cycle)
if isinstance(scan, DeconvolutedPeakSet):
peaks = scan
else:
peaks = getattr(scan, "deconvoluted_peak_set", [])
for peak in peaks:
color = next(color_cycle)
draw_peaklist(peak.envelope, ax=ax, color=color, alpha=0.75, **kwargs)
ax.scatter(*zip(*peak.envelope), color=color, alpha=0.75)
return ax
def annotate_isotopic_peaks(scan, ax=None, color_cycle=None, **kwargs):
from .peak_set import DeconvolutedPeakSet
if ax is None:
fig, ax = plt.subplots(1)
if color_cycle is None:
color_cycle = _default_color_cycle()
color_cycle = itertools.cycle(color_cycle)
if isinstance(scan, DeconvolutedPeakSet):
peaks = scan
else:
peaks = getattr(scan, "deconvoluted_peak_set", [])
for peak in peaks:
color = next(color_cycle)
draw_peaklist(peak.envelope, ax=ax, color=color, alpha=0.75, **kwargs)
ax.scatter(*zip(*peak.envelope), color=color, alpha=0.75)
return ax
def annotate_scan(scan, products, nperrow=4, ax=None):
if ax is None:
figure = plt.figure()
else:
figure = ax.figure
n = len(products)
if n > 0:
gs = gridspec.GridSpec(1 + max(int(math.ceil(n / float(nperrow))), 1),
nperrow)
else:
gs = gridspec.GridSpec(1 + (n / nperrow), nperrow)
ax = figure.add_subplot(gs[0, :])
if scan.is_profile:
draw_raw(scan.arrays, ax=ax)
if scan.peak_set is None:
scan.pick_peaks()
draw_peaklist(scan.peak_set, ax=ax, lw=0.5, alpha=0.75)
if scan.deconvoluted_peak_set:
draw_peaklist(scan.deconvoluted_peak_set, ax=ax, lw=0.5, alpha=0.75)
ax.set_title(scan.id)
k = -1
# for the ith row of the grid
for i in range(int(n // nperrow) + 1):
# for the jth column of the row
for j in range(nperrow):
# the kth MS^n scan
k += 1
try:
product_scan = products[k]
except IndexError:
# the number of MS^n scans is not a multiple of nperrow
# so the last row has fewer than nperrow
break
# add the subplot from the grid spec using i + 1 instead of i
# because the MS1 scan is drawn across the row at i = 0
ax = figure.add_subplot(gs[i + 1, j])
if scan.is_profile:
draw_raw(scan.arrays, ax=ax, alpha=0.8)
# obtain the interval around the precursor
pinfo = product_scan.precursor_information
if not product_scan.isolation_window.is_empty():
lower, upper = (product_scan.isolation_window.lower_bound - 2,
product_scan.isolation_window.upper_bound + 2)
else:
lower = pinfo.mz - 4
upper = pinfo.mz + 4
try:
peak = max(scan.peak_set.between(lower + 1.2, upper - 1.2),
key=lambda x: x.intensity)
local_intensity = peak.intensity
except ValueError:
local_intensity = 1e3
# get the monoisotopic peak for the precursor, or the isolation center depending
# upon whether the precursor has been deconvoluted and what the instrument reports
if pinfo.extracted_charge != 0:
target_mz = pinfo.extracted_mz
else:
target_mz = pinfo.mz
draw_peaklist(scan.peak_set, ax=ax, alpha=0.5, lw=0.5)
if scan.deconvoluted_peak_set:
draw_peaklist(scan.deconvoluted_peak_set.between(lower - 1.2,
upper + 1.2,
use_mz=True),
ax=ax,
alpha=0.9,
color='blue')
ax.set_ylim(0, local_intensity * 1.25)
ax.set_xlim(lower, upper)
upper_intensity = local_intensity
# draw the precursor isolation annotations
ax.vlines(target_mz,
0,
upper_intensity * 1.5,
alpha=0.50,
color='red',
lw=1)
if product_scan.isolation_window.lower != 0:
ax.vlines(product_scan.isolation_window.lower_bound,
0,
upper_intensity * 1.5,
linestyle='--',
alpha=0.5)
if product_scan.isolation_window.upper != 0:
ax.vlines(product_scan.isolation_window.upper_bound,
0,
upper_intensity * 1.5,
linestyle='--',
alpha=0.5)
ax.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
ax.set_ylabel("")
ax.set_xlabel("")
if pinfo.extracted_charge == 0:
if pinfo.charge == "ChargeNotProvided":
charge = '?'
else:
charge = str(pinfo.charge)
else:
charge = str(pinfo.extracted_charge)
ax.set_title("%0.3f @ %s" % (target_mz, charge))
fig = figure
# this should probably be a function of figwidth and number of rows
fig.set_figheight(fig.get_figheight() * 2)
fig.tight_layout()
return ax
def annotate_scan_single(scan, product_scan, ax=None, standalone=True):
if ax is None:
fig, ax = plt.subplots(1)
if scan.is_profile:
draw_raw(scan.arrays, ax=ax)
if scan.peak_set is None:
scan.pick_peaks()
draw_peaklist(scan.peak_set, ax=ax, lw=0.5, alpha=0.75)
if scan.deconvoluted_peak_set:
draw_peaklist(scan.deconvoluted_peak_set, ax=ax, lw=0.5, alpha=0.75)
pinfo = product_scan.precursor_information
if not product_scan.isolation_window.is_empty():
lower, upper = (product_scan.isolation_window.lower_bound - 2,
product_scan.isolation_window.upper_bound + 2)
else:
lower = pinfo.mz - 4
upper = pinfo.mz + 4
peak_set = scan.peak_set
try:
peak = max(peak_set.between(lower + 1.2, upper - 1.2),
key=lambda x: x.intensity)
local_intensity = peak.intensity
except ValueError:
local_intensity = 1e3
# get the monoisotopic peak for the precursor, or the isolation center depending
# upon whether the precursor has been deconvoluted and what the instrument reports
if pinfo.extracted_charge != 0:
target_mz = pinfo.extracted_mz
else:
target_mz = pinfo.mz
draw_peaklist(scan.peak_set, ax=ax, alpha=0.5, lw=0.5)
if scan.deconvoluted_peak_set:
draw_peaklist(scan.deconvoluted_peak_set.between(lower - 1.2,
upper + 1.2,
use_mz=True),
ax=ax,
alpha=0.9,
color='blue')
ax.set_ylim(0, local_intensity * 1.25)
ax.set_xlim(lower, upper)
upper_intensity = local_intensity
# draw the precursor isolation annotations
ax.vlines(target_mz,
0,
upper_intensity * 1.5,
alpha=0.50,
color='red',
lw=1)
if product_scan.isolation_window.lower != 0:
ax.vlines(product_scan.isolation_window.lower_bound,
0,
upper_intensity * 1.5,
linestyle='--',
alpha=0.5)
if product_scan.isolation_window.upper != 0:
ax.vlines(product_scan.isolation_window.upper_bound,
0,
upper_intensity * 1.5,
linestyle='--',
alpha=0.5)
ax.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
ax.set_ylabel("")
ax.set_xlabel("")
if pinfo.extracted_charge == 0:
if pinfo.charge == "ChargeNotProvided":
charge = '?'
else:
charge = str(pinfo.charge)
else:
charge = str(pinfo.extracted_charge)
ax.set_title("%0.3f @ %s" % (target_mz, charge))
return ax
def annotate_scan(scan, products, nperrow=4, ax=None, label=True):
'''Given an MS1 :class:`~.ScanBase` ``scan`` and a :class:`~.Sequence` of
:class:`~.ScanBase` product scans, draw the MS1 spectrum in full profile,
and then in a subplot grid below it, draw a zoomed-in view of the MS1 spectrum
surrounding the area around each precursor ion that gave rise to the scans in
``products``, with monoisotopic peaks and isolation windows marked.
.. plot::
:include-source:
import ms_deisotope
from ms_deisotope import plot
from ms_deisotope.test.common import datafile
reader = ms_deisotope.MSFileLoader(datafile("20150710_3um_AGP_001_29_30.mzML.gz"))
bunch = next(reader)
bunch.precursor.pick_peaks()
bunch.precursor.deconvolute(
scorer=ms_deisotope.PenalizedMSDeconVFitter(20., 2.0),
averagine=ms_deisotope.glycopeptide, use_quick_charge=True)
ax = plot.annotate_scan(bunch.precursor, bunch.products, nperrow=2)
ax.figure.set_figwidth(12)
ax.figure.set_figheight(16)
Parameters
----------
scan: ScanBase
The precursor MS1 scan
products: :class:`~.Sequence` of :class:`~.ScanBase`
The collection of MSn scans based upon ``scan``
nperrow: int
The number of precursor ion subplots to draw per row
of the grid. Defaults to :const:`4`.
ax: :class:`matplotlib._axes.Axes`
An :class:`~.Axes` object to use to find the figure to
draw the plot on.
Returns
-------
:class:`matplotlib._axes.Axes`:
The axes of the full MS1 profile plot
'''
if ax is None:
figure = plt.figure()
else:
figure = ax.figure
n = len(products)
if n > 0:
gs = gridspec.GridSpec(1 + max(int(math.ceil(n / float(nperrow))), 1), nperrow)
else:
gs = gridspec.GridSpec(1 + (n / nperrow), nperrow)
ax = figure.add_subplot(gs[0, :])
if scan.is_profile:
draw_raw(scan.arrays, ax=ax)
if scan.peak_set is None:
scan.pick_peaks()
draw_peaklist(scan.peak_set, ax=ax, lw=0.5, alpha=0.75)
if scan.deconvoluted_peak_set:
draw_peaklist(scan.deconvoluted_peak_set, ax=ax, lw=0.5, alpha=0.75)
ax.set_title(scan.id)
k = -1
# for the ith row of the grid
for i in range(int(n // nperrow) + 1):
# for the jth column of the row
for j in range(nperrow):
# the kth MS^n scan
k += 1
try:
product_scan = products[k]
except IndexError:
# the number of MS^n scans is not a multiple of nperrow
# so the last row has fewer than nperrow
break
# add the subplot from the grid spec using i + 1 instead of i
# because the MS1 scan is drawn across the row at i = 0
ax = figure.add_subplot(gs[i + 1, j])
if scan.is_profile:
draw_raw(scan.arrays, ax=ax, alpha=0.8)
# obtain the interval around the precursor
pinfo = product_scan.precursor_information
if not product_scan.isolation_window.is_empty():
lower, upper = (product_scan.isolation_window.lower_bound - 2,
product_scan.isolation_window.upper_bound + 2)
else:
lower = pinfo.mz - 4
upper = pinfo.mz + 4
try:
peak = max(scan.peak_set.between(lower - 1.2, upper + 1.2), key=lambda x: x.intensity)
local_intensity = peak.intensity
except ValueError:
if scan.deconvoluted_peak_set:
try:
peak = max(scan.deconvoluted_peak_set.between(lower - 1.2, upper + 1.2, use_mz=True),
key=lambda x: x.intensity)
local_intensity = peak.intensity
except ValueError:
local_intensity = 1e3
else:
local_intensity = 1e3
# get the monoisotopic peak for the precursor, or the isolation center depending
# upon whether the precursor has been deconvoluted and what the instrument reports
if pinfo.extracted_charge != 0:
target_mz = pinfo.extracted_mz
else:
target_mz = pinfo.mz
draw_peaklist(scan.peak_set, ax=ax, alpha=0.5, lw=0.5)
if scan.deconvoluted_peak_set:
draw_peaklist(
scan.deconvoluted_peak_set.between(
lower - 1.2, upper + 1.2, use_mz=True),
ax=ax, alpha=0.9, color='blue')
if label:
label_peaks(scan, lower, upper, ax=ax,
is_deconvoluted=bool(scan.deconvoluted_peak_set))
ax.set_ylim(0, local_intensity * 1.25)
ax.set_xlim(lower, upper)
upper_intensity = local_intensity
# draw the precursor isolation annotations
ax.vlines(target_mz, 0, upper_intensity * 1.5, alpha=0.50,
color='red', lw=1)
if product_scan.isolation_window.lower != 0:
ax.vlines(product_scan.isolation_window.lower_bound, 0,
upper_intensity * 1.5, linestyle='--', alpha=0.5)
if product_scan.isolation_window.upper != 0:
ax.vlines(product_scan.isolation_window.upper_bound, 0,
upper_intensity * 1.5, linestyle='--', alpha=0.5)
ax.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
ax.set_ylabel("")
ax.set_xlabel("")
if pinfo.extracted_charge == 0:
if pinfo.charge == "ChargeNotProvided":
charge = '?'
else:
charge = str(pinfo.charge)
else:
charge = str(pinfo.extracted_charge)
ax.set_title("%0.3f @ %s" % (target_mz, charge))
fig = figure
# this should probably be a function of figwidth and number of rows
fig.set_figheight(fig.get_figheight() * 2)
fig.tight_layout()
return ax
def label_peaks(scan, min_mz=None, max_mz=None, ax=None, is_deconvoluted=None, threshold=None, **kwargs):
"""Label a region of the peak list, marking centroids with their m/z or mass. If the peaks
of `scan` have been deconvoluted, the most abundant peak will be annotated with
"<neutral mass> (<charge>)", otherwise just "<m/z>".
Parameters
----------
scan : :class:`~.ScanBase`
The scan to annotate
min_mz : float, optional
The minimum m/z to annotate
max_mz : float, optional
The maximum m/z to annotate
ax: :class:`matplotlib._axes.Axes`
An :class:`~.Axes` object to draw the plot on
is_deconvoluted : bool, optional
Whether or not to always use :attr:`Scan.deconvoluted_peak_set`
threshold : float, optional
The intensity threshold under which peaks will be ignored
Returns
-------
ax: :class:`matplotlib._axes.Axes`
The axes the plot was drawn on
annotations: :class:`list` of :class:`matplotlib.text.Text`
The list of :class:`matplotlib.text.Text` annotations
"""
if ax is None:
# when no axes are provided, draw all the peak data
_, ax = plt.subplots(1)
if scan.is_profile:
draw_raw(scan, ax)
if scan.peak_set is not None:
draw_peaklist(scan.peak_set, ax)
if scan.deconvoluted_peak_set is not None:
draw_peaklist(scan.deconvoluted_peak_set, ax)
if is_deconvoluted is None:
is_deconvoluted = scan.deconvoluted_peak_set is not None
if min_mz is None:
min_mz = 0
if max_mz is None:
if is_deconvoluted:
max_mz = max(peak.mz for peak in scan.deconvoluted_peak_set)
else:
max_mz = max(peak.mz for peak in scan.peak_set)
annotations = []
# select the peak sub range
if is_deconvoluted:
subset = scan.deconvoluted_peak_set.between(
min_mz, max_mz, use_mz=True)
else:
subset = scan.peak_set.between(
min_mz, max_mz)
if not subset:
return
# guess the threshold
if threshold is None:
threshold = 0.0
if is_deconvoluted:
threshold_list = ([max(i.intensity for i in p.envelope)
for p in subset])
else:
threshold_list = ([p.intensity for p in subset])
if threshold_list:
threshold = np.mean(threshold_list)
threshold_list = [v > threshold for v in threshold_list]
if threshold_list:
threshold = np.mean(threshold_list)
# draw the actual labels
kwargs.setdefault("clip_on", True)
kwargs.setdefault("fontsize", 10)
if is_deconvoluted:
for peak in subset:
if peak.intensity > threshold:
label = '%0.2f (%d)' % (peak.neutral_mass, peak.charge)
# set the y-position to the highest peak in the isotopic
# pattern
pt = max(peak.envelope, key=lambda x: x.intensity)
y = pt.intensity * 1.05
# set the x-position to the weighted average m/z in the
# isotopic pattern
x = np.average(
[p.mz for p in peak.envelope],
weights=[p.intensity for p in peak.envelope])
annotations.append(
ax.text(x, y, label, ha='center', **kwargs))
else:
for peak in subset:
if peak.intensity > threshold:
label = "%0.2f" % (peak.mz, )
y = peak.intensity * 1.05
x = peak.mz
annotations.append(
ax.text(x, y, label, ha='center', **kwargs))
return annotations, ax
def annotate_scan_single(scan, product_scan, ax=None, label=True, standalone=True):
'''Draw a zoomed-in view of the MS1 spectrum ``scan`` surrounding the
area around each precursor ion that gave rise to ``product_scan``
with monoisotopic peaks and isolation windows marked.
.. plot::
:include-source:
import ms_deisotope
from ms_deisotope import plot
from ms_deisotope.test.common import datafile
reader = ms_deisotope.MSFileLoader(datafile("20150710_3um_AGP_001_29_30.mzML.gz"))
bunch = next(reader)
bunch.precursor.pick_peaks()
bunch.precursor.deconvolute(
scorer=ms_deisotope.PenalizedMSDeconVFitter(20., 2.0),
averagine=ms_deisotope.glycopeptide, use_quick_charge=True)
ax = plot.annotate_scan_single(bunch.precursor, bunch.products[0])
ax.figure.set_figwidth(12)
Parameters
----------
scan: ScanBase
The MS1 scan to annotate
product_scan: ScanBase
The product scan to annotate the precursor ion of
ax: :class:`matplotlib._axes.Axes`
An :class:`~.Axes` object to draw the plot on
Returns
-------
:class:`matplotlib._axes.Axes`
'''
if ax is None:
_, ax = plt.subplots(1)
if scan.is_profile:
draw_raw(scan.arrays, ax=ax)
if scan.peak_set is None:
scan.pick_peaks()
draw_peaklist(scan.peak_set, ax=ax, lw=0.5, alpha=0.75)
if scan.deconvoluted_peak_set:
draw_peaklist(scan.deconvoluted_peak_set, ax=ax, lw=0.5, alpha=0.75)
pinfo = product_scan.precursor_information
if not product_scan.isolation_window.is_empty():
lower, upper = (product_scan.isolation_window.lower_bound - 2,
product_scan.isolation_window.upper_bound + 2)
else:
lower = pinfo.mz - 4
upper = pinfo.mz + 4
peak_set = scan.peak_set
try:
peak = max(peak_set.between(lower - 1.2, upper + 1.2), key=lambda x: x.intensity)
local_intensity = peak.intensity
except ValueError:
if scan.deconvoluted_peak_set:
try:
peak = max(scan.deconvoluted_peak_set.between(lower - 1.2, upper + 1.2, use_mz=True),
key=lambda x: x.intensity)
local_intensity = peak.intensity
except ValueError:
local_intensity = 1e3
else:
local_intensity = 1e3
# get the monoisotopic peak for the precursor, or the isolation center depending
# upon whether the precursor has been deconvoluted and what the instrument reports
if pinfo.extracted_charge != 0:
target_mz = pinfo.extracted_mz
else:
target_mz = pinfo.mz
draw_peaklist(scan.peak_set, ax=ax, alpha=0.5, lw=0.5)
if scan.deconvoluted_peak_set:
draw_peaklist(
scan.deconvoluted_peak_set.between(
lower - 1.2, upper + 1.2, use_mz=True),
ax=ax, alpha=0.9, color='blue')
if label:
label_peaks(scan, lower, upper, ax=ax,
is_deconvoluted=bool(scan.deconvoluted_peak_set))
ax.set_ylim(0, local_intensity * 1.25)
ax.set_xlim(lower, upper)
upper_intensity = local_intensity
# draw the precursor isolation annotations
ax.vlines(target_mz, 0, upper_intensity * 1.5, alpha=0.50,
color='red', lw=1)
if product_scan.isolation_window.lower != 0:
ax.vlines(product_scan.isolation_window.lower_bound, 0,
upper_intensity * 1.5, linestyle='--', alpha=0.5)
if product_scan.isolation_window.upper != 0:
ax.vlines(product_scan.isolation_window.upper_bound, 0,
upper_intensity * 1.5, linestyle='--', alpha=0.5)
ax.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
ax.set_ylabel("")
ax.set_xlabel("")
if pinfo.extracted_charge == 0:
if pinfo.charge == "ChargeNotProvided":
charge = '?'
else:
charge = str(pinfo.charge)
else:
charge = str(pinfo.extracted_charge)
ax.set_title("%0.3f @ %s" % (target_mz, charge))
return ax
def label_peaks(scan, min_mz=None, max_mz=None, ax=None, is_deconvoluted=None, threshold=None, **kwargs):
"""Label a region of the peak list, marking centroids with their m/z or mass. If the peaks
of `scan` have been deconvoluted, the most abundant peak will be annotated with
"<neutral mass> (<charge>)", otherwise just "<m/z>".
Parameters
----------
scan : :class:`~.ScanBase`
The scan to annotate
min_mz : float, optional
The minimum m/z to annotate
max_mz : float, optional
The maximum m/z to annotate
ax: :class:`matplotlib._axes.Axes`
An :class:`~.Axes` object to draw the plot on
is_deconvoluted : bool, optional
Whether or not to always use :attr:`Scan.deconvoluted_peak_set`
threshold : float, optional
The intensity threshold under which peaks will be ignored
Returns
-------
ax: :class:`matplotlib._axes.Axes`
The axes the plot was drawn on
annotations: :class:`list` of :class:`matplotlib.text.Text`
The list of :class:`matplotlib.text.Text` annotations
"""
if ax is None:
# when no axes are provided, draw all the peak data
_, ax = plt.subplots(1)
if scan.is_profile:
draw_raw(scan, ax)
if scan.peak_set is not None:
draw_peaklist(scan.peak_set, ax)
if scan.deconvoluted_peak_set is not None:
draw_peaklist(scan.deconvoluted_peak_set, ax)
if is_deconvoluted is None:
is_deconvoluted = scan.deconvoluted_peak_set is not None
if min_mz is None:
min_mz = 0
if max_mz is None:
if is_deconvoluted:
max_mz = max(peak.mz for peak in scan.deconvoluted_peak_set)
else:
max_mz = max(peak.mz for peak in scan.peak_set)
annotations = []
# select the peak sub range
if is_deconvoluted:
subset = scan.deconvoluted_peak_set.between(
min_mz, max_mz, use_mz=True)
else:
subset = scan.peak_set.between(
min_mz, max_mz)
if not subset:
return
# guess the threshold
if threshold is None:
threshold = 0.0
if is_deconvoluted:
threshold_list = ([max(i.intensity for i in p.envelope)
for p in subset])
else:
threshold_list = ([p.intensity for p in subset])
if threshold_list:
threshold = np.mean(threshold_list)
threshold_list = [v > threshold for v in threshold_list]
if threshold_list:
threshold = np.mean(threshold_list)
# draw the actual labels
kwargs.setdefault("clip_on", True)
kwargs.setdefault("fontsize", 10)
if is_deconvoluted:
for peak in subset:
if peak.intensity > threshold:
label = '%0.2f (%d)' % (peak.neutral_mass, peak.charge)
# set the y-position to the highest peak in the isotopic
# pattern
pt = max(peak.envelope, key=lambda x: x.intensity)
y = pt.intensity * 1.05
# set the x-position to the weighted average m/z in the
# isotopic pattern
x = np.average(
[p.mz for p in peak.envelope],
weights=[p.intensity for p in peak.envelope])
annotations.append(
ax.text(x, y, label, ha='center', **kwargs))
else:
for peak in subset:
if peak.intensity > threshold:
label = "%0.2f" % (peak.mz, )
y = peak.intensity * 1.05
x = peak.mz
annotations.append(
ax.text(x, y, label, ha='center', **kwargs))
return annotations, ax
def annotate_scan(scan, products, nperrow=4, ax=None, label=True):
'''Given an MS1 :class:`~.ScanBase` ``scan`` and a :class:`~.Sequence` of
:class:`~.ScanBase` product scans, draw the MS1 spectrum in full profile,
and then in a subplot grid below it, draw a zoomed-in view of the MS1 spectrum
surrounding the area around each precursor ion that gave rise to the scans in
``products``, with monoisotopic peaks and isolation windows marked.
.. plot::
:include-source:
import ms_deisotope
from ms_deisotope import plot
from ms_deisotope.test.common import datafile
reader = ms_deisotope.MSFileLoader(datafile("20150710_3um_AGP_001_29_30.mzML.gz"))
bunch = next(reader)
bunch.precursor.pick_peaks()
bunch.precursor.deconvolute(
scorer=ms_deisotope.PenalizedMSDeconVFitter(20., 2.0),
averagine=ms_deisotope.glycopeptide, use_quick_charge=True)
ax = plot.annotate_scan(bunch.precursor, bunch.products, nperrow=2)
ax.figure.set_figwidth(12)
ax.figure.set_figheight(16)
Parameters
----------
scan: ScanBase
The precursor MS1 scan
products: :class:`~.Sequence` of :class:`~.ScanBase`
The collection of MSn scans based upon ``scan``
nperrow: int
The number of precursor ion subplots to draw per row
of the grid. Defaults to :const:`4`.
ax: :class:`matplotlib._axes.Axes`
An :class:`~.Axes` object to use to find the figure to
draw the plot on.
Returns
-------
:class:`matplotlib._axes.Axes`:
The axes of the full MS1 profile plot
'''
if ax is None:
figure = plt.figure()
else:
figure = ax.figure
n = len(products)
if n > 0:
gs = gridspec.GridSpec(1 + max(int(math.ceil(n / float(nperrow))), 1), nperrow)
else:
gs = gridspec.GridSpec(1 + (n / nperrow), nperrow)
ax = figure.add_subplot(gs[0, :])
if scan.is_profile:
draw_raw(scan.arrays, ax=ax)
if scan.peak_set is None:
scan.pick_peaks()
draw_peaklist(scan.peak_set, ax=ax, lw=0.5, alpha=0.75)
if scan.deconvoluted_peak_set:
draw_peaklist(scan.deconvoluted_peak_set, ax=ax, lw=0.5, alpha=0.75)
ax.set_title(scan.id)
k = -1
# for the ith row of the grid
for i in range(int(n // nperrow) + 1):
# for the jth column of the row
for j in range(nperrow):
# the kth MS^n scan
k += 1
try:
product_scan = products[k]
except IndexError:
# the number of MS^n scans is not a multiple of nperrow
# so the last row has fewer than nperrow
break
# add the subplot from the grid spec using i + 1 instead of i
# because the MS1 scan is drawn across the row at i = 0
ax = figure.add_subplot(gs[i + 1, j])
if scan.is_profile:
draw_raw(scan.arrays, ax=ax, alpha=0.8)
# obtain the interval around the precursor
pinfo = product_scan.precursor_information
if not product_scan.isolation_window.is_empty():
lower, upper = (product_scan.isolation_window.lower_bound - 2,
product_scan.isolation_window.upper_bound + 2)
else:
lower = pinfo.mz - 4
upper = pinfo.mz + 4
try:
peak = max(scan.peak_set.between(lower + 1.2, upper - 1.2), key=lambda x: x.intensity)
local_intensity = peak.intensity
except ValueError:
local_intensity = 1e3
# get the monoisotopic peak for the precursor, or the isolation center depending
# upon whether the precursor has been deconvoluted and what the instrument reports
if pinfo.extracted_charge != 0:
target_mz = pinfo.extracted_mz
else:
target_mz = pinfo.mz
draw_peaklist(scan.peak_set, ax=ax, alpha=0.5, lw=0.5)
if scan.deconvoluted_peak_set:
draw_peaklist(
scan.deconvoluted_peak_set.between(
lower - 1.2, upper + 1.2, use_mz=True),
ax=ax, alpha=0.9, color='blue')
if label:
label_peaks(scan, lower, upper, ax=ax,
is_deconvoluted=bool(scan.deconvoluted_peak_set))
ax.set_ylim(0, local_intensity * 1.25)
ax.set_xlim(lower, upper)
upper_intensity = local_intensity
# draw the precursor isolation annotations
ax.vlines(target_mz, 0, upper_intensity * 1.5, alpha=0.50,
color='red', lw=1)
if product_scan.isolation_window.lower != 0:
ax.vlines(product_scan.isolation_window.lower_bound, 0,
upper_intensity * 1.5, linestyle='--', alpha=0.5)
if product_scan.isolation_window.upper != 0:
ax.vlines(product_scan.isolation_window.upper_bound, 0,
upper_intensity * 1.5, linestyle='--', alpha=0.5)
ax.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
ax.set_ylabel("")
ax.set_xlabel("")
if pinfo.extracted_charge == 0:
if pinfo.charge == "ChargeNotProvided":
charge = '?'
else:
charge = str(pinfo.charge)
else:
charge = str(pinfo.extracted_charge)
ax.set_title("%0.3f @ %s" % (target_mz, charge))
fig = figure
# this should probably be a function of figwidth and number of rows
fig.set_figheight(fig.get_figheight() * 2)
fig.tight_layout()
return ax
def annotate_scan_single(scan, product_scan, ax=None, label=True, standalone=True):
'''Draw a zoomed-in view of the MS1 spectrum ``scan`` surrounding the
area around each precursor ion that gave rise to ``product_scan``
with monoisotopic peaks and isolation windows marked.
.. plot::
:include-source:
import ms_deisotope
from ms_deisotope import plot
from ms_deisotope.test.common import datafile
reader = ms_deisotope.MSFileLoader(datafile("20150710_3um_AGP_001_29_30.mzML.gz"))
bunch = next(reader)
bunch.precursor.pick_peaks()
bunch.precursor.deconvolute(
scorer=ms_deisotope.PenalizedMSDeconVFitter(20., 2.0),
averagine=ms_deisotope.glycopeptide, use_quick_charge=True)
ax = plot.annotate_scan_single(bunch.precursor, bunch.products[0])
ax.figure.set_figwidth(12)
Parameters
----------
scan: ScanBase
The MS1 scan to annotate
product_scan: ScanBase
The product scan to annotate the precursor ion of
ax: :class:`matplotlib._axes.Axes`
An :class:`~.Axes` object to draw the plot on
Returns
-------
:class:`matplotlib._axes.Axes`
'''
if ax is None:
_, ax = plt.subplots(1)
if scan.is_profile:
draw_raw(scan.arrays, ax=ax)
if scan.peak_set is None:
scan.pick_peaks()
draw_peaklist(scan.peak_set, ax=ax, lw=0.5, alpha=0.75)
if scan.deconvoluted_peak_set:
draw_peaklist(scan.deconvoluted_peak_set, ax=ax, lw=0.5, alpha=0.75)
pinfo = product_scan.precursor_information
if not product_scan.isolation_window.is_empty():
lower, upper = (product_scan.isolation_window.lower_bound - 2,
product_scan.isolation_window.upper_bound + 2)
else:
lower = pinfo.mz - 4
upper = pinfo.mz + 4
peak_set = scan.peak_set
try:
peak = max(peak_set.between(lower + 1.2, upper - 1.2), key=lambda x: x.intensity)
local_intensity = peak.intensity
except ValueError:
local_intensity = 1e3
# get the monoisotopic peak for the precursor, or the isolation center depending
# upon whether the precursor has been deconvoluted and what the instrument reports
if pinfo.extracted_charge != 0:
target_mz = pinfo.extracted_mz
else:
target_mz = pinfo.mz
draw_peaklist(scan.peak_set, ax=ax, alpha=0.5, lw=0.5)
if scan.deconvoluted_peak_set:
draw_peaklist(
scan.deconvoluted_peak_set.between(
lower - 1.2, upper + 1.2, use_mz=True),
ax=ax, alpha=0.9, color='blue')
if label:
label_peaks(scan, lower, upper, ax=ax,
is_deconvoluted=bool(scan.deconvoluted_peak_set))
ax.set_ylim(0, local_intensity * 1.25)
ax.set_xlim(lower, upper)
upper_intensity = local_intensity
# draw the precursor isolation annotations
ax.vlines(target_mz, 0, upper_intensity * 1.5, alpha=0.50,
color='red', lw=1)
if product_scan.isolation_window.lower != 0:
ax.vlines(product_scan.isolation_window.lower_bound, 0,
upper_intensity * 1.5, linestyle='--', alpha=0.5)
if product_scan.isolation_window.upper != 0:
ax.vlines(product_scan.isolation_window.upper_bound, 0,
upper_intensity * 1.5, linestyle='--', alpha=0.5)
ax.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
ax.set_ylabel("")
ax.set_xlabel("")
if pinfo.extracted_charge == 0:
if pinfo.charge == "ChargeNotProvided":
charge = '?'
else:
charge = str(pinfo.charge)
else:
charge = str(pinfo.extracted_charge)
ax.set_title("%0.3f @ %s" % (target_mz, charge))
return ax