def conform_envelopes(experimental, base_theoretical, minimum_theoretical_abundance=0.05):
total = 0
n_missing = 0
i = 0
cleaned_eid = []
for peak in experimental:
if peak is None:
peak = FittedPeak(base_theoretical[i].mz, 1, 1, -1, -1, 0, 1, 0, 0)
if base_theoretical[i].intensity > minimum_theoretical_abundance:
n_missing += 1
total += peak.intensity
cleaned_eid.append(peak)
i += 1
tid = []
for peak in base_theoretical:
peak = peak.clone()
peak.intensity *= total
tid.append(peak)
return cleaned_eid, tid, n_missing
def average_fitted_peak(peak_cluster, divisor=False):
if divisor:
scale = divisor
else:
scale = 1
weighted_mz = peak_cluster.weighted_mz()
total_intensity = peak_cluster.intensity
signal_to_noise = sum(p.signal_to_noise * p.intensity for p in peak_cluster) / total_intensity
fwhm = sum(p.full_width_at_half_max * p.intensity for p in peak_cluster) / total_intensity
area = sum(p.area * p.intensity for p in peak_cluster) / total_intensity
return FittedPeak(weighted_mz, total_intensity / scale, signal_to_noise, -1, -1, fwhm, area)
def conform_envelopes(experimental, base_theoretical, minimum_theoretical_abundance=0.05):
total = 0
n_missing = 0
i = 0
cleaned_eid = []
for peak in experimental:
if peak is None:
peak = FittedPeak(base_theoretical[i].mz, 1, 1, -1, -1, 0, 1, 0, 0)
if base_theoretical[i].intensity > minimum_theoretical_abundance:
n_missing += 1
total += peak.intensity
cleaned_eid.append(peak)
i += 1
tid = []
for peak in base_theoretical:
peak = peak.clone()
peak.intensity *= total
tid.append(peak)
return cleaned_eid, tid, n_missing
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_profile(points, fwhm):
peaks = []
i = 0
for point in points:
tid = peptide.isotopic_cluster(point[0], point[1], truncate_after=0.99)
for tp in tid:
fp = FittedPeak(tp.mz, tp.intensity * point[2], 0, i, i, fwhm,
tp.intensity * point[2])
peaks.append(fp)
mz = np.array([0])
intensity = np.array([0])
for p in peaks:
x, y = gaussian_shape(p)
mz = np.concatenate([mz, [x[0] - 0.0001], x, [x[-1] + 0.0001]])
intensity = np.concatenate([intensity, [0], y, [0]])
return mz, intensity
def _find_next_putative_peak(self,
mz,
charge,
step=1,
tolerance=ERROR_TOLERANCE):
"""
Recalibrates the current peak location given the position of the **next** putative peak
in a theoretical isotopic cluster.
Suppose that the peak at `mz` is roughly in the neighborhood of a real isotopic peak,
but the alignment is bad, so it won't make a good starting point for the search for the
rest of the peaks in its cluster under a stringent error tolerance.
However, if we're willing to search for the **next** putative peak with a more permissive error
tolerance, which we expect will be properly aligned with the rest of its isotopic cluster,
we can recalibrate the proper starting peak's mz and use that for isotopic cluster fitting.
Parameters
----------
mz : float
Starting m/z value to search from
charge : int
Charge state to use when calculating the step size in m/z
step : int, optional
The number of steps into the putative isotopic cluster to take. Defaults to 1
tolerance : float, optional
The error tolerance to accept for finding supporting peaks.
Returns
-------
list
"""
shift = isotopic_shift(charge)
next_peak = mz + (shift * step)
peaklist_slice = self.between(next_peak - (next_peak * tolerance),
next_peak + (next_peak * tolerance))
candidates = []
for forward in peaklist_slice:
prev_peak_mz = forward.mz - (shift * step)
dummy_peak = FittedPeak(prev_peak_mz, 1.0, 1.0, -1, 0, 0, 0)
candidates.append((dummy_peak, charge))
return candidates
def has_peak(self, mz, error_tolerance):
"""Query :attr:`peaklist` for a peak at `mz` within `error_tolerance` ppm. If a peak
is not found, this method returns a placeholder peak.
Parameters
----------
mz : float
The m/z to search for a peak at
error_tolerance : float
The parts-per-million error tolerance to search with
Returns
-------
FittedPeak
A peak from :attr:`peaklist` if present, else a placeholder peak.
"""
peak = self.peaklist.has_peak(mz, error_tolerance)
if peak is None or peak.intensity < self.minimum_intensity:
return FittedPeak(mz, 1.0, 1.0, -1, 0, 0, 0)
return peak
def envelope_to_peak_list(envelope):
return [FittedPeak(e[0], e[1], 0, 0, 0, 0, 0, 0, 0) for e in envelope]
import unittest
import numpy as np
from ms_peak_picker import FittedPeak
from brainpy._c.isotopic_distribution import TheoreticalPeak as Peak
from ms_deisotope.scoring import (PenalizedMSDeconVFitter, MSDeconVFitter,
DotProductFitter, ScaledGTestFitter,
GTestFitter, LeastSquaresFitter)
experimental = [
FittedPeak(mz=739.920,
intensity=8356.829,
signal_to_noise=100.000,
peak_count=30,
index=30588,
full_width_at_half_max=0.050,
area=425.445),
FittedPeak(mz=740.255,
intensity=8006.456,
signal_to_noise=100.000,
peak_count=31,
index=30622,
full_width_at_half_max=0.051,
area=416.640),
FittedPeak(mz=740.589,
intensity=4970.605,
signal_to_noise=100.000,
peak_count=32,
index=30655,
