def test_empty(self):
peaks = []
x, y = reprofile(peaks)
repicked = pick_peaks(x, y)
assert len(repicked) == 0
peaks = self.make_data()
peaks[0].full_width_at_half_max = 10
x, y = reprofile(peaks, max_fwhm=1.0)
repicked = pick_peaks(x, y)
assert len(repicked) == 0
def make_scan(self):
peaks = []
tids, ions = self.make_tids()
list(map(peaks.extend, tids))
peaks.sort(key=lambda x: x.mz)
mz = np.array([0])
intensity = np.array([0])
fpeaks = []
for p in peaks:
fpeaks.append(FittedPeak(
mz=p.mz, intensity=p.intensity, signal_to_noise=p.intensity, peak_count=-1, index=-1,
full_width_at_half_max=fwhm, area=p.intensity))
mz, intensity = reprofile(fpeaks)
scan = common.Scan(
{
"id": "test-scan",
"index": 0,
"m/z array": mz,
"intensity array": intensity,
"ms level": 1,
"scan time": 0.0,
"profile spectrum": "",
"negative scan": "",
},
mzml.MzMLDataInterface())
return scan
def make_scan(self):
peaks = []
tids, ions = self.make_tids()
list(map(peaks.extend, tids))
peaks.sort(key=lambda x: x.mz)
mz = np.array([0])
intensity = np.array([0])
fpeaks = []
for p in peaks:
fpeaks.append(FittedPeak(
mz=p.mz, intensity=p.intensity, signal_to_noise=p.intensity, peak_count=-1, index=-1,
full_width_at_half_max=fwhm, area=p.intensity))
mz, intensity = reprofile(fpeaks)
scan = common.Scan(
{
"id": "test-scan",
"index": 0,
"m/z array": mz,
"intensity array": intensity,
"ms level": 1,
"scan time": 0.0,
"profile spectrum": "",
"negative scan": "",
},
mzml.MzMLDataInterface())
return scan
def _scan_arrays(self, scan):
if scan.is_combined():
mzs, intensities = self.read_spectrum(
scan.frame.id, scan.start_scan, scan.end_scan)
if len(mzs) == 0:
return np.array([], dtype=float), np.array([], dtype=float)
sort_mask = np.argsort(mzs)
mzs = mzs[sort_mask]
intensities = intensities[sort_mask]
centroids = pick_peaks(mzs, intensities, peak_mode="centroid")
if centroids is None:
return np.array([], dtype=float), np.array([], dtype=float)
mzs, intensities = reprofile(
centroids, dx=self._scan_merging_parameters['dx'],
override_fwhm=self._scan_merging_parameters['fwhm'])
return mzs, intensities
else:
mzs, intensities = self.read_spectrum(scan.frame.id, scan.start_scan, scan.end_scan)
return mzs, intensities
def reprofile(self, max_fwhm=0.2, dx=0.01, model_cls=None):
"""Use the picked peaks in :attr:`peak_set` to create a new
profile mass spectrum using a peak shape model.
Parameters
----------
max_fwhm : float, optional
Maximum peak width above which peaks will be ignored
dx : float, optional
The distance between each new point in m/z space in the
reprofiled spectrum
model_cls : ms_peak_picker.peak_statistics.PeakShapeModel, optional
The peak shape model to use to generate the profile data from
the centroided peaks. Defaults a Gaussian model
Returns
-------
Scan
A shallow copy of this scan with its :attr:`arrays` replaced with
the new reprofiled arrays
Raises
------
ValueError
A scan that has not been centroided and is already in profile mode
must have its peaks picked before it can be reprofiled.
"""
if self.peak_set is None and self.is_profile:
raise ValueError(
"Cannot reprofile a scan that has not been centroided")
elif self.peak_set is None and not self.is_profile:
self.pick_peaks()
if not self.peak_set:
arrays = (np.array([], dtype=float), np.array([], dtype=float))
else:
arrays = reprofile(self.peak_set, max_fwhm, dx, model_cls)
scan = WrappedScan(self._data,
self.source,
arrays,
list(self.product_scans),
is_profile=True,
annotations=self._external_annotations)
return scan
def reprofile(self, max_fwhm=0.2, dx=0.01, model_cls=None):
"""Use the picked peaks in :attr:`peak_set` to create a new
profile mass spectrum using a peak shape model.
Parameters
----------
max_fwhm : float, optional
Maximum peak width above which peaks will be ignored
dx : float, optional
The distance between each new point in m/z space in the
reprofiled spectrum
model_cls : ms_peak_picker.peak_statistics.PeakShapeModel, optional
The peak shape model to use to generate the profile data from
the centroided peaks. Defaults a Gaussian model
Returns
-------
Scan
A shallow copy of this scan with its :attr:`arrays` replaced with
the new reprofiled arrays
Raises
------
ValueError
A scan that has not been centroided and is already in profile mode
must have its peaks picked before it can be reprofiled.
"""
if self.peak_set is None and self.is_profile:
raise ValueError(
"Cannot reprofile a scan that has not been centroided")
elif self.peak_set is None and not self.is_profile:
self.pick_peaks()
if not self.peak_set:
arrays = (np.array([], dtype=float), np.array([], dtype=float))
else:
arrays = reprofile(self.peak_set, max_fwhm, dx, model_cls)
scan = WrappedScan(
self._data, self.source, arrays,
list(self.product_scans), is_profile=True,
annotations=self._external_annotations)
return scan
def make_data(self):
peak = make_peak(200, 1e4)
x = np.arange(0, 1000, 0.01)
y = (np.random.random(x.size) + 1) * 100
y[19700:20300] += reprofile([peak])[1]
return x, y
def test_reprofile(self):
peaks = self.make_data()
x, y = reprofile(peaks)
repicked = pick_peaks(x, y)
diff = repicked[0].mz - peaks[0].mz
assert abs(diff) < 1e-3