Source code for holoviews.core.traversal
"""
Advanced utilities for traversing nesting/hierarchical Dimensioned
objects either to inspect the structure of their declared dimensions
or mutate the matching elements.
"""
from collections import defaultdict
from operator import itemgetter
from .dimension import Dimension
from .util import merge_dimensions
try:
import itertools.izip as zip
except ImportError:
pass
def create_ndkey(length, indexes, values):
key = [None] * length
for i, v in zip(indexes, values):
key[i] = v
return tuple(key)
[docs]def unique_dimkeys(obj, default_dim='Frame'):
"""
Finds all common dimension keys in the object including subsets of
dimensions. If there are is no common subset of dimensions, None
is returned.
Returns the list of dimensions followed by the list of unique
keys.
"""
from .ndmapping import NdMapping, item_check
from .spaces import HoloMap
key_dims = obj.traverse(lambda x: (tuple(x.kdims),
list(x.data.keys())), (HoloMap,))
if not key_dims:
return [Dimension(default_dim)], [(0,)]
dim_groups, keys = zip(*sorted(key_dims, key=lambda x: -len(x[0])))
dgroups = [frozenset(d.name for d in dg) for dg in dim_groups]
subset = all(g1 <= g2 or g1 >= g2 for g1 in dgroups for g2 in dgroups)
# Find unique keys
if subset:
dims = merge_dimensions(dim_groups)
all_dims = sorted(dims, key=lambda x: dim_groups[0].index(x))
else:
# Handle condition when HoloMap/DynamicMap dimensions do not overlap
hmaps = obj.traverse(lambda x: x, ['HoloMap'])
if hmaps:
raise ValueError('When combining HoloMaps into a composite plot '
'their dimensions must be subsets of each other.')
dimensions = merge_dimensions(dim_groups)
dim_keys = {}
for dims, keys in key_dims:
for key in keys:
for d, k in zip(dims, key):
dim_keys[d.name] = k
if dim_keys:
keys = [tuple(dim_keys.get(dim.name) for dim in dimensions)]
else:
keys = []
return merge_dimensions(dim_groups), keys
ndims = len(all_dims)
unique_keys = []
for group, keys in zip(dim_groups, keys):
dim_idxs = [all_dims.index(dim) for dim in group]
for key in keys:
padded_key = create_ndkey(ndims, dim_idxs, key)
matches = [item for item in unique_keys
if padded_key == tuple(k if k is None else i
for i, k in zip(item, padded_key))]
if not matches:
unique_keys.append(padded_key)
with item_check(False):
sorted_keys = NdMapping({key: None for key in unique_keys},
kdims=all_dims).data.keys()
return all_dims, list(sorted_keys)
def bijective(keys):
ndims = len(keys[0])
if ndims <= 1:
return True
for idx in range(ndims):
getter = itemgetter(*(i for i in range(ndims) if i != idx))
store = []
for key in keys:
subkey = getter(key)
if subkey in store:
return False
store.append(subkey)
return True
[docs]def hierarchical(keys):
"""
Iterates over dimension values in keys, taking two sets
of dimension values at a time to determine whether two
consecutive dimensions have a one-to-many relationship.
If they do a mapping between the first and second dimension
values is returned. Returns a list of n-1 mappings, between
consecutive dimensions.
"""
ndims = len(keys[0])
if ndims <= 1:
return True
dim_vals = list(zip(*keys))
combinations = (zip(*dim_vals[i:i+2])
for i in range(ndims-1))
hierarchies = []
for combination in combinations:
hierarchy = True
store1 = defaultdict(list)
store2 = defaultdict(list)
for v1, v2 in combination:
if v2 not in store2[v1]:
store2[v1].append(v2)
previous = store1[v2]
if previous and previous[0] != v1:
hierarchy = False
break
if v1 not in store1[v2]:
store1[v2].append(v1)
hierarchies.append(store2 if hierarchy else {})
return hierarchies