def update_signal_ratio(self):
self.next_signal_magnitude = sum(f.total_signal for f in self.processor.feature_map)
self.total_signal_ratio = (self.last_signal_magnitude - self.next_signal_magnitude) / self.next_signal_magnitude
printer("Signal Ratio: %0.3e (%0.3e, %0.3e)" % (
self.total_signal_ratio, self.last_signal_magnitude, self.next_signal_magnitude))
self.last_signal_magnitude = self.next_signal_magnitude
return self.total_signal_ratio
def map_fits(self, report_interval=5):
i = 0
n = len(self.processor.feature_map)
interval = int(max(n // report_interval, 1000))
for feature in sorted(self.processor.feature_map, key=lambda x: x.mz, reverse=True):
fits = self.processor.charge_state_determination(
feature,
error_tolerance=self.error_tolerance,
charge_range=self.charge_range,
left_search=self.left_search,
right_search=self.right_search,
charge_carrier=self.charge_carrier,
truncate_after=self.truncate_after,
max_missed_peaks=self.max_missed_peaks,
threshold_scale=self.threshold_scale)
if self.debug:
print(feature, fits)
i += 1
if i % interval == 0:
printer("\t%0.1f%%" % ((100. * i) / n,))
self.all_fits = list(self.processor.dependence_network.dependencies)
self.disjoint_feature_clusters = self.processor.dependence_network.find_non_overlapping_intervals()
if self.relfitter is not None:
self.relations = self.relfitter.fit(
(d for cluster in self.disjoint_feature_clusters
for d in cluster), self.solutions)
self.relfitter.predict((d for cluster in self.disjoint_feature_clusters
for d in cluster))
printer("\tExtracting Fits")
self.fits = self.processor.select_best_disjoint_subgraphs(self.disjoint_feature_clusters)
def map_fits(self, report_interval=5):
i = 0
n = len(self.processor.feature_map)
interval = int(max(n // report_interval, 1000))
for feature in sorted(self.processor.feature_map, key=lambda x: x.mz, reverse=True):
fits = self.processor.charge_state_determination(
feature,
error_tolerance=self.error_tolerance,
charge_range=self.charge_range,
left_search=self.left_search,
right_search=self.right_search,
charge_carrier=self.charge_carrier,
truncate_after=self.truncate_after,
max_missed_peaks=self.max_missed_peaks,
threshold_scale=self.threshold_scale)
if self.debug:
print(feature, fits)
i += 1
if i % interval == 0:
printer("\t%0.1f%%" % ((100. * i) / n,))
self.all_fits = list(self.processor.dependence_network.dependencies)
self.disjoint_feature_clusters = self.processor.dependence_network.find_non_overlapping_intervals()
if self.relfitter is not None:
self.relations = self.relfitter.fit(
(d for cluster in self.disjoint_feature_clusters
for d in cluster), self.solutions)
self.relfitter.predict((d for cluster in self.disjoint_feature_clusters
for d in cluster))
printer("\tExtracting Fits")
self.fits = self.processor.select_best_disjoint_subgraphs(self.disjoint_feature_clusters)
def update_signal_ratio(self):
self.next_signal_magnitude = sum(f.total_signal for f in self.processor.feature_map)
self.total_signal_ratio = (self.last_signal_magnitude - self.next_signal_magnitude) / self.next_signal_magnitude
printer("Signal Ratio: %0.3e (%0.3e, %0.3e)" % (
self.total_signal_ratio, self.last_signal_magnitude, self.next_signal_magnitude))
self.last_signal_magnitude = self.next_signal_magnitude
return self.total_signal_ratio
def _map_precursors(self, error_tolerance):
printer("\tConstructing Precursor Seeds")
rt_map = RTMap(self.feature_map)
cache = dict()
seeds = set()
for key, pinfo in (self.precursor_map.mapping.items()):
time, ix = key
try:
candidates = cache[time]
except KeyError:
candidates = rt_map.rt_tree.contains_point(time)
cache[time] = candidates
mz = pinfo["mz"]
hits = [c.members[0] for c in candidates if c.contains_mz(mz, error_tolerance)]
seeds.update(hits)
return seeds
def _map_precursors(self, error_tolerance):
printer("\tConstructing Precursor Seeds")
rt_map = RTMap(self.feature_map)
cache = dict()
seeds = set()
for key, pinfo in (self.precursor_map.mapping.items()):
time, ix = key
try:
candidates = cache[time]
except KeyError:
candidates = rt_map.rt_tree.contains_point(time)
cache[time] = candidates
mz = pinfo["mz"]
hits = [c.members[0] for c in candidates if c.contains_mz(mz, error_tolerance)]
seeds.update(hits)
return seeds
def setup(self):
printer("Begin Iteration %d" % (self.iteration_count, ))
printer("Total Signal: %0.3e" %
(sum(f.total_signal for f in self.processor.feature_map), ))
self.processor.remove_peaks_below_threshold(self.minimum_intensity)
self.processor.build_dependence_network()
def setup(self):
printer("Begin Iteration %d" % (self.iteration_count,))
printer("Total Signal: %0.3e" % (sum(f.total_signal for f in self.processor.feature_map),))
self.processor.remove_peaks_below_threshold(self.minimum_intensity)
self.processor.build_dependence_network()