def GroupByLevel(NTree, sdict):
#Levels = [NTree.subtrees.keys()]
Levels = EqualLevels(NTree.subtrees.keys(), sdict)
LowerLevels = [GroupByLevel(t, sdict) for t in NTree.subtrees.values()]
if len(LowerLevels) > 0:
h = max([len(l) for l in LowerLevels])
for i in range(h):
New = utils.uniqify(utils.listunion([l[i] for l in LowerLevels
if len(l) > i]))
if len(New) > 0:
Levels += [New]
return Levels
def GroupByLevel(NTree, sdict):
#Levels = [NTree.subtrees.keys()]
Levels = EqualLevels(NTree.subtrees.keys(), sdict)
LowerLevels = [GroupByLevel(t, sdict) for t in NTree.subtrees.values()]
if len(LowerLevels) > 0:
h = max([len(l) for l in LowerLevels])
for i in range(h):
New = utils.uniqify(
utils.listunion([l[i] for l in LowerLevels if len(l) > i]))
if len(New) > 0:
Levels += [New]
return Levels
def deletecols(self, cols):
"""
Delete columns and/or colors.
Method wraps::
tabular.spreadsheet.deletecols(self, cols)
"""
deletenames = utils.uniqify(utils.listunion([[c] if c in
self.dtype.names else self.coloring[c] for c in cols]))
return spreadsheet.deletecols(self,deletenames)
def __getitem__(self, ind):
"""
Returns a subrectangle of the table.
The representation of the subrectangle depends on `type(ind)`. Also,
whether the returned object represents a new independent copy of the
subrectangle, or a "view" into this self object, depends on
`type(ind)`.
* If you pass the name of an existing coloring, you get a tabarray
consisting of copies of columns in that coloring.
* If you pass a list of existing coloring names and/or column names,
you get a tabarray consisting of copies of columns in the list
(name of coloring is equivalent to list of names of columns in that
coloring; duplicate columns are deleted).
* If you pass a :class:`numpy.ndarray`, you get a tabarray consisting
a subrectangle of the tabarray, as handled by
:func:`numpy.ndarray.__getitem__`:
* if you pass a 1D NumPy ndarray of booleans of `len(self)`,
the rectangle contains copies of the rows for which the
corresponding entry is `True`.
* if you pass a list of row numbers, you get a tabarray
containing copies of these rows.
"""
if ind in self.coloring.keys():
return self[self.coloring[ind]]
elif isinstance(ind, list) and self.dtype.names and \
all([a in self.dtype.names or a in self.coloring.keys()
for a in ind]) and \
set(self.coloring.keys()).intersection(ind):
ns = utils.uniqify(
utils.listunion(
[[a] if a in self.dtype.names else self.coloring[a]
for a in ind]))
return self[ns]
else:
D = np.ndarray.__getitem__(self, ind)
if isinstance(D, np.ndarray) and not (D.dtype.names is None):
D = D.view(tabarray)
D.coloring = dict([(
k,
list(
set(self.coloring[k]).intersection(set(D.dtype.names)))
) for k in self.coloring.keys() if len(
set(self.coloring[k]).intersection(set(D.dtype.names))) > 0
])
return D
def __getitem__(self, ind):
"""
Returns a subrectangle of the table.
The representation of the subrectangle depends on `type(ind)`. Also,
whether the returned object represents a new independent copy of the
subrectangle, or a "view" into this self object, depends on
`type(ind)`.
* If you pass the name of an existing coloring, you get a tabarray
consisting of copies of columns in that coloring.
* If you pass a list of existing coloring names and/or column names,
you get a tabarray consisting of copies of columns in the list
(name of coloring is equivalent to list of names of columns in that
coloring; duplicate columns are deleted).
* If you pass a :class:`numpy.ndarray`, you get a tabarray consisting
a subrectangle of the tabarray, as handled by
:func:`numpy.ndarray.__getitem__`:
* if you pass a 1D NumPy ndarray of booleans of `len(self)`,
the rectangle contains copies of the rows for which the
corresponding entry is `True`.
* if you pass a list of row numbers, you get a tabarray
containing copies of these rows.
"""
if ind in self.coloring.keys():
return self[self.coloring[ind]]
elif isinstance(ind,list) and \
all([a in self.dtype.names or a in self.coloring.keys()
for a in ind]) and \
set(self.coloring.keys()).intersection(ind):
ns = utils.uniqify(utils.listunion([[a] if a in self.dtype.names
else self.coloring[a] for a in ind]))
return self[ns]
else:
D = np.ndarray.__getitem__(self,ind)
if isinstance(D,np.ndarray) and not D.dtype.names is None:
D = D.view(tabarray)
D.coloring = dict([(k,
list(set(self.coloring[k]).intersection(set(D.dtype.names))))
for k in self.coloring.keys() if
len(set(self.coloring[k]).intersection(set(D.dtype.names))) > 0
and len(set(D.dtype.names).difference(self.coloring[k])) > 0])
return D
def __init__(self, names, sdict):
self.topnodes = set(names).difference(utils.listunion(sdict.values()))
self.subtrees = {}
SK = sdict.keys() ; SK.sort()
done = []
for (i,s) in enumerate(SK):
if not any([set(sdict[s]) < set(sdict[ss]) for ss in SK]):
subsets = [ss for ss in sdict.keys()
if set(sdict[ss]) < set(sdict[s])]
newsdict = dict([(ss, list(set(sdict[ss]).difference(done)))
for ss in subsets])
newnames = list(set(sdict[s]))#.difference(done))
if len(newnames) > 0:
self.subtrees[s] = NameTree(newnames, newsdict)
done += newnames
self.weight = (sum([t.weight for t in self.subtrees.values()]) +
len(self.topnodes))
def Testlistunion():
Input = [[2, 3, 4], [4, 5, 6], [6, 4, 2]]
Output = [2, 3, 4, 4, 5, 6, 6, 4, 2]
assert (Output == listunion(Input))
