def load_node_single(node_dict):
contained = node_dict.get("contained", [])
contained = load_contained(contained)
node = IntervalTreeNode(
node_dict['center'], left=None, contained=contained,
right=None)
node.start = node_dict['start']
node.end = node_dict['end']
return node
def make_rt_tree(intervals):
temp = []
for interval in intervals:
mz, rt = interval
rt.members.append(mz)
temp.append(rt)
tree = IntervalTreeNode.build(temp)
return tree
def make_rt_tree(intervals):
temp = []
for interval in intervals:
mz, rt = interval
rt.members.append(mz)
temp.append(rt)
tree = IntervalTreeNode.build(temp)
return tree
def to_deconvoluted_peak_set(self, time_bound=None):
if self.deconvoluted_features is None:
raise ValueError(
"Must first deconvolute IMS features before converting to a time-binned peak set!"
)
if time_bound is not None:
cft = IntervalTreeNode.build([
Interval(f.start_time, f.end_time, [f])
for f in self.deconvoluted_features
])
features = chain.from_iterable(cft.overlaps(*time_bound))
else:
features = self.deconvoluted_features
return features_to_peak_set(features)
def make_rt_tree(intervals):
nested = nest_2d_intervals(intervals)
tree = IntervalTreeNode.build(nested)
return tree
def make_rt_tree(intervals):
nested = nest_2d_intervals(intervals)
tree = IntervalTreeNode.build(nested)
return tree