def convert_map_to_scan_peak_list(feature_map, peak_loader, time_precision=4, deconvoluted=True):
metadata_map = {}
scan_accumulator = defaultdict(list)
for scan_id, metadata in peak_loader.extended_index.ms1_ids.items():
metadata_map[round(metadata["scan_time"], time_precision)] = metadata
for feature in feature_map:
for node in feature:
scan_accumulator[round(node.time, time_precision)].extend(node.members)
packed = []
for key, peaks in sorted(scan_accumulator.items(), key=lambda x: x[0]):
template = peak_loader.get_scan_by_time(key)
if deconvoluted:
peak_set = PeakSet([])
deconvoluted_peak_set = DeconvolutedPeakSet(peaks)
else:
peak_set = PeakSet(peaks)
deconvoluted_peak_set = DeconvolutedPeakSet([])
peak_set.reindex()
deconvoluted_peak_set.reindex()
scan = ProcessedScan(
template.id, template.title, None, template.ms_level, template.scan_time,
template.index, peak_set, deconvoluted_peak_set, template.polarity,
None)
packed.append(scan)
return packed
def _simplify_peak_set(peaks, bin_width=5.0):
bin_edges = np.arange(0, peaks[-1].mz + bin_width, bin_width)
bins = []
for i, bin_edge in enumerate(bin_edges, 1):
if i == len(bin_edges):
next_edge = bin_edges[-1] + bin_width
else:
next_edge = bin_edges[i]
subset = peaks.between(bin_edge, next_edge)
bins.append(subset)
thresholds = []
reduced_subsets = {}
k = 0
for b in bins:
if len(b) > 0:
bin_intensities = np.array([p.intensity for p in b])
thresholds.append(np.max(bin_intensities) / 3.)
for p in b:
if p.intensity > thresholds[-1]:
reduced_subsets[p.peak_count] = p
k += (bin_intensities > thresholds[-1]).sum()
else:
thresholds.append(0.0)
subset_peaks = PeakSet(sorted(reduced_subsets.values(),
key=lambda x: x.mz)).clone()
subset_peaks.reindex()
return PeakIndex(np.array([]), np.array([]), subset_peaks)
def _pick_peaks_vendor(self, scan, *args, **kwargs):
scan_info = Business.Scan.FromFile(self._source, scan.scan_number + 1)
if scan_info.HasCentroidStream:
stream = self._source.GetCentroidStream(scan.scan_number + 1, 0)
mzs = stream.Masses
intens = stream.Intensities
peaks = PeakSet([simple_peak(mzs[i], intens[i], 0.001) for i in range(len(mzs))])
peaks.reindex()
arrays = self._scan_arrays(scan)
return PeakIndex(arrays[0], arrays[1], peaks)
else:
raise NotImplementedError()
def deserialize_peak_set(scan_dict):
mz_array = scan_dict['m/z array']
intensity_array = scan_dict['intensity array']
n = len(scan_dict['m/z array'])
peaks = []
for i in range(n):
peak = FittedPeak(
mz_array[i], intensity_array[i], 1, i, i,
0, intensity_array[i], 0, 0)
peaks.append(peak)
peak_set = PeakSet(peaks)
peak_set.reindex()
return PeakIndex(np.array([]), np.array([]), peak_set)
def _make_scan(self, data):
scan = super(ProcessedMzMLDeserializer, self)._make_scan(data)
if scan.precursor_information:
scan.precursor_information.default()
selected_ion_dict = self._get_selected_ion(data)
scan.precursor_information.orphan = selected_ion_dict.get(
"ms_deisotope:orphan") == "true"
scan.precursor_information.defaulted = selected_ion_dict.get(
"ms_deisotope:defaulted") == "true"
scan.annotations['precursor purity'] = data.get(
'precursor purity', 0)
if "isotopic envelopes array" in data:
scan.peak_set = PeakIndex(np.array([]), np.array([]), PeakSet([]))
scan.deconvoluted_peak_set = self.deserialize_deconvoluted_peak_set(
data)
if self.has_extended_index(
) and scan.id in self.extended_index.ms1_ids:
chosen_indices = self.extended_index.ms1_ids[
scan.id]['msms_peaks']
for ix in chosen_indices:
scan.deconvoluted_peak_set[ix].chosen_for_msms = True
else:
scan.peak_set = self.deserialize_peak_set(data)
scan.deconvoluted_peak_set = None
packed = scan.pack()
return packed
def envelopes_to_peak_set(self):
"""Convert a set of deconvoluted peaks with fitted isotopic envelopes into a
set of centroids representing those envelope peaks.
Returns
-------
:class:`ms_peak_picker.PeakSet`
"""
peaks = []
for peak in self:
for point in peak.envelope:
peaks.append(
simple_peak(point.mz, point.intensity, peak.full_width_at_half_max))
new_peak_set = FittedPeakSet(peaks)
new_peak_set.reindex()
return new_peak_set
def convert(self, fitted=True, deconvoluted=True):
precursor_information = self.precursor_information.convert(
) if self.precursor_information is not None else None
session = object_session(self)
conn = session.connection()
if fitted:
q = conn.execute(select([FittedPeak.__table__]).where(
FittedPeak.__table__.c.scan_id == self.id)).fetchall()
peak_set_items = list(
map(make_memory_fitted_peak, q))
peak_set = PeakSet(peak_set_items)
peak_set._index()
peak_index = PeakIndex(np.array([], dtype=np.float64), np.array(
[], dtype=np.float64), peak_set)
else:
peak_index = PeakIndex(np.array([], dtype=np.float64), np.array(
[], dtype=np.float64), PeakSet([]))
if deconvoluted:
q = conn.execute(select([DeconvolutedPeak.__table__]).where(
DeconvolutedPeak.__table__.c.scan_id == self.id)).fetchall()
deconvoluted_peak_set_items = list(
map(make_memory_deconvoluted_peak, q))
deconvoluted_peak_set = DeconvolutedPeakSet(
deconvoluted_peak_set_items)
deconvoluted_peak_set._reindex()
else:
deconvoluted_peak_set = DeconvolutedPeakSet([])
info = self.info or {}
scan = ProcessedScan(
self.scan_id, self.title, precursor_information, int(self.ms_level),
float(self.scan_time), self.index, peak_index, deconvoluted_peak_set,
activation=info.get('activation'))
return scan
def deserialize_peak_set(scan_dict):
mz_array = scan_dict['m/z array']
intensity_array = scan_dict['intensity array']
n = len(scan_dict['m/z array'])
peaks = []
for i in range(n):
peak = FittedPeak(mz_array[i], intensity_array[i], 1, i, i, 0,
intensity_array[i], 0, 0)
peaks.append(peak)
peak_set = PeakSet(peaks)
peak_set.reindex()
return PeakIndex(np.array([]), np.array([]), peak_set)
def parse(self):
path = self.path
for line in open(path):
if not line:
break
if re.match(r"\d+\.?[0-9]*", line):
mz, intensity = map(float, re.findall(r"\d+\.?[0-9]*", line))
self.peaklist.append(
deconvolution.FittedPeak(mz, intensity, 0, 0, 0, 0, 0))
self.peaklist = PeakIndex(np.array([]), np.array([]),
PeakSet(self.peaklist))
def convert_map_to_scan_peak_list(feature_map,
peak_loader,
time_precision=4,
deconvoluted=True):
metadata_map = {}
scan_accumulator = defaultdict(list)
for scan_id, metadata in peak_loader.extended_index.ms1_ids.items():
metadata_map[round(metadata["scan_time"], time_precision)] = metadata
for feature in feature_map:
for node in feature:
scan_accumulator[round(node.time,
time_precision)].extend(node.members)
packed = []
for key, peaks in sorted(scan_accumulator.items(), key=lambda x: x[0]):
template = peak_loader.get_scan_by_time(key)
if deconvoluted:
peak_set = PeakSet([])
deconvoluted_peak_set = DeconvolutedPeakSet(peaks)
else:
peak_set = PeakSet(peaks)
deconvoluted_peak_set = DeconvolutedPeakSet([])
peak_set.reindex()
deconvoluted_peak_set.reindex()
scan = ProcessedScan(template.id, template.title, None,
template.ms_level, template.scan_time,
template.index, peak_set, deconvoluted_peak_set,
template.polarity, None)
packed.append(scan)
return packed
def _make_scan(self, data):
scan = super(ProcessedMzMLDeserializer, self)._make_scan(data)
if scan.precursor_information:
scan.precursor_information.default()
if "isotopic envelopes array" in data:
scan.peak_set = PeakIndex(np.array([]), np.array([]), PeakSet([]))
scan.deconvoluted_peak_set = deserialize_deconvoluted_peak_set(
data)
if scan.id in self.extended_index.ms1_ids:
chosen_indices = self.extended_index.ms1_ids[
scan.id]['msms_peaks']
for ix in chosen_indices:
scan.deconvoluted_peak_set[ix].chosen_for_msms = True
else:
scan.peak_set = deserialize_peak_set(data)
scan.deconvoluted_peak_set = None
return scan.pack()
def prepare_peaklist(peaks):
'''Ensure ``peaks`` is a :class:`~.PeakSet` object,
converting from other compatible types as needed. Additionally, make a deep
copy of the peaks as signal subtraction methods will modify peaks in place.
This function ensures that any of the following common input types are coerced
to the appropriate type:
1. :class:`ms_peak_picker.PeakSet` will be copied and indexed
2. :class:`ms_peak_picker.PeakIndex` will have its peaks extracted and copied
3. Any other *sequence* of :class:`PeakLike` objects (objects having an mz and
intensity attribute) will be converted into a :class:`ms_peak_picker.PeakSet`
4. Any *sequence* of :class:`tuple` or :class:`list` having at least two entries
will be converted into a :class:`ms_peak_picker.PeakSet` with the m/z value
of each peak being the the `p[0]` of each entry and the intensity `p[1]`. Any
other entries will be ignored.
Parameters
----------
peaks: Sequence
Any sequence of :class:`~.FittedPeak` objects, objects
with ``mz`` and ``intensity`` attributes, or :class:`list` / :class:`tuple`
objects containing paired values for ``mz`` and ``intensity``
Returns
-------
:class:`~.PeakSet`
'''
if isinstance(peaks, PeakIndex):
peaks = PeakSet(peaks.peaks).clone()
else:
peaks = tuple(peaks)
if len(peaks) == 0:
return PeakSet([])
if not isinstance(peaks[0], FittedPeak):
if is_peak(peaks[0]):
peaks = [simple_peak(p.mz, p.intensity, 0.01) for p in peaks]
elif isinstance(peaks[0], (list, tuple)):
peaks = [simple_peak(p[0], p[1], 0.01) for p in peaks]
else:
raise TypeError("Cannot convert peaks into a PeakSet")
peaks = PeakSet(peaks).clone()
peaks.reindex()
return peaks
def make_peak_index(fitted_peaks):
ps = PeakSet(fitted_peaks)
ps._index()
return PeakIndex(np.array([], dtype=float), np.array([], dtype=float), ps)
def make_peak_index(fitted_peaks):
ps = PeakSet(fitted_peaks)
ps._index()
return ps
def make_peak_index(fitted_peaks):
ps = PeakSet(fitted_peaks)
ps._index()
return PeakIndex(np.array([], dtype=float), np.array([], dtype=float), ps)
def pick_peaks(self, *args, **kwargs):
"""A wrapper around :func:`ms_peak_picker.pick_peaks` which will populate the
:attr:`peak_set` attribute of this scan.
Parameters
----------
fit_type : str, optional
The name of the peak model to use. One of "quadratic", "gaussian", "lorentzian", or "apex"
signal_to_noise_threshold : int, optional
Minimum signal-to-noise measurement to accept a peak
intensity_threshold : int, optional
Minimum intensity measurement to accept a peak
threshold_data : bool, optional
Whether to apply thresholds to the data
target_envelopes : list, optional
A sequence of (start m/z, end m/z) pairs, limiting peak picking to only those intervals
transforms : list, optional
A list of :class:`scan_filter.FilterBase` instances or callable that
accepts (mz_array, intensity_array) and returns (mz_array, intensity_array) or
`str` matching one of the premade names in `scan_filter.filter_register`
verbose : bool, optional
Whether to log extra information while picking peaks
start_mz : float, optional
A minimum m/z value to start picking peaks from
stop_mz : float, optional
A maximum m/z value to stop picking peaks after
*args :
Passed along to :func:`ms_peak_picker.pick_peaks`
**kwargs :
Passed along to :func:`ms_peak_picker.pick_peaks`
Returns
-------
Scan
Returns self
"""
# Check to see if the user requested one of the ms_peak_picker fits or wanted
# to use the vendor peak picker if provided.
fit_type_k = kwargs.get("fit_type")
if len(args) > 0:
fit_type_a = args[0]
else:
fit_type_a = None
if fit_type_k == 'vendor' or fit_type_a == 'vendor':
try:
peaks = self.source._pick_peaks_vendor(self._data, *args,
**kwargs)
self.peak_set = peaks
return self
except NotImplementedError:
pass
# Prepare the peak picking parameters
mzs, intensities = self.arrays
if len(mzs) == 0:
self.peak_set = PeakIndex(mzs, intensities, PeakSet([]))
return self
if self.is_profile:
peak_mode = 'profile'
else:
peak_mode = 'centroid'
kwargs.setdefault('peak_mode', peak_mode)
self.peak_set = pick_peaks(mzs, intensities, *args, **kwargs)
return self
