def update(source, data):
""" Update source with data
This checks a few things first
1. If the data is the same, then don't update
2. If numpy is available and the data is numeric, then convert to numpy
arrays
3. If profiling then perform the update in another callback
"""
if (not np or
not any(isinstance(v, np.ndarray) for v in source.data.values())):
if source.data == data:
return
if np and len(data[first(data)]) > 10:
d = {}
for k, v in data.items():
if type(v) is not np.ndarray and isinstance(v[0], Number):
d[k] = np.array(v)
else:
d[k] = v
else:
d = data
if PROFILING:
curdoc().add_next_tick_callback(lambda: source.data.update(d))
else:
source.data.update(d)
def find_unbalanced(self):
node = self
while True:
grouped_children = node.grouped('children')
unbalanced = cc.pipe(grouped_children,
cc.valfilter(lambda x: len(x) == 1),
lambda x: cc.first(x.values())[0])
if unbalanced.children_are_balanced:
return unbalanced
node = unbalanced
x[1], lambda x: x.split(','), cc.map(str.strip), list))), list)
tree_val_dict = cc.pipe(
data_input, cc.map(cc.first),
cc.map(lambda x: [tree_val_re.match(x).group(y) for y in (1, 2)]), dict,
cc.valmap(int))
tree_mapping_dict = cc.pipe(
data_input, cc.map(lambda x: (tree_val_re.match(x[0]).group(1), x[1])),
dict)
root = cc.pipe(
tree_mapping_dict.keys(),
cc.filter(lambda x: x not in cc.concat(tree_mapping_dict.values())),
cc.first)
tree = Tree(root, tree_mapping_dict, tree_val_dict)
unbalanced = tree.find_unbalanced()
unbalanced_self_weight = unbalanced.weight - sum(x.weight
for x in unbalanced.children)
unbalanced_grouped_siblings = unbalanced.grouped('siblings')
balanced_weight = cc.first(
cc.valfilter(lambda x: len(x) > 1, unbalanced_grouped_siblings).keys())
unbalanced_weight = cc.first(
cc.valfilter(lambda x: len(x) == 1, unbalanced_grouped_siblings).keys())
weight_offset = balanced_weight - unbalanced_weight
pp(unbalanced_self_weight + weight_offset)
