def test_accumulate(self):
self.assertEqual(
[1, 3, 6, 10, 15],
list(iters.accumulate([1, 2, 3, 4, 5]))
)
self.assertEqual(
[1, 2, 6, 24, 120],
list(iters.accumulate([1, 2, 3, 4, 5], operator.mul))
)
def nd_matching_path_wt(G, path, dist):
'''given a path, and a graph, find the node which is *dist* distance along the path.
if no such path exists, return the surrounding nodes. '''
pathinfo = starmap(G.get_edge_data, slider(path, 2))
weights = map(_['weight'], pathinfo)
acc_weights = list(accumulate(weights))
if dist not in acc_weights:
#then it is between two nodes
idx=ilen(takewhile(_<dist, acc_weights))
return path[idx], path[idx+1]
return path[acc_weights.index(dist)]
def on_path_change(self, event):
notify('on_path_change:', event)
event_path = os.path.abspath(event.path)
nodes = {'/' + p for p in accumulate(event_path.split(os.sep),
lambda acc, x: os.path.join(acc, x))}
indices_to_paths = enumerate(os.path.abspath(p) for p in self.absolute_paths)
index, _ = next(ifilter(lambda (i, p): p in nodes, indices_to_paths))
for f, sect, items in _parse_files(_extract_files_from_paths([event_path])):
changes = self._sections[sect].set_layer((index, event_path), dict(items))
for k, v in changes.iteritems():
for path in self.ALL, sect, (sect, k):
if path in self.listeners:
for listener in self.listeners[path]:
listener(sect, k, v)
def on_path_change(self, event):
notify('on_path_change:', event)
event_path = os.path.abspath(event.path)
nodes = {
'/' + p
for p in accumulate(event_path.split(os.sep),
lambda acc, x: os.path.join(acc, x))
}
indices_to_paths = enumerate(
os.path.abspath(p) for p in self.absolute_paths)
index, _ = next(ifilter(lambda (i, p): p in nodes, indices_to_paths))
for f, sect, items in _parse_files(
_extract_files_from_paths([event_path])):
changes = self._sections[sect].set_layer((index, event_path),
dict(items))
for k, v in changes.iteritems():
for path in self.ALL, sect, (sect, k):
if path in self.listeners:
for listener in self.listeners[path]:
listener(sect, k, v)
def rolling_sum(elem_min, elem_max, length):
return st.lists(
st.integers(min_value=elem_min, max_value=elem_max),
max_size=length,
min_size=length).map(lambda xs: accumulate(xs, operator.add))
def rolling_sum(elem_min, elem_max, length):
return st.lists(
st.integers(min_value=elem_min,max_value=elem_max), max_size=length, min_size=length
).map(lambda xs: accumulate(xs, operator.add))
def most_even_chunk(string, group):
"""Divide a string into a list of strings as even as possible."""
counts = [0] + most_even(len(string), group)
indices = accumulate(counts)
slices = window(indices, 2)
return [string[slice(*one)] for one in slices]