def __init__(self,
name: str,
subtrees: List[TMTree],
authors: str = '',
doi: str = '',
citations: int = 0,
by_year: bool = True,
all_papers: bool = False) -> None:
"""Initialize a new PaperTree with the given <name> and <subtrees>,
<authors> and <doi>, and with <citations> as the size of the data.
If <all_papers> is True, then this tree is to be the root of the paper
tree. In that case, load data about papers from DATA_FILE to build the
tree.
If <all_papers> is False, Do NOT load new data.
<by_year> indicates whether or not the first level of subtrees should be
the years, followed by each category, subcategory, and so on. If
<by_year> is False, then the year in the dataset is simply ignored.
"""
if all_papers:
tree_inf = _load_papers_to_dict(by_year)
tree_dict = _build_tree_from_dict(tree_inf)
TMTree.__init__(self, name, tree_dict, citations)
self._authors, self._doi = authors, doi
else:
TMTree.__init__(self, name, subtrees, citations)
self._authors, self._doi = authors, doi
def __init__(self, name: str, subtrees: List[TMTree], authors: str = '',
doi: str = '', citations: int = 0, by_year: bool = True,
all_papers: bool = False) -> None:
"""Initialize a new PaperTree with the given <name> and <subtrees>,
<authors> and <doi>, and with <citations> as the size of the data.
If <all_papers> is True, then this tree is to be the root of the paper
tree. In that case, load data about papers from DATA_FILE to build the
tree.
If <all_papers> is False, Do NOT load new data.
<by_year> indicates whether or not the first level of subtrees should be
the years, followed by each category, subcategory, and so on. If
<by_year> is False, then the year in the dataset is simply ignored.
"""
# TODO: Complete this initializer. Your implementation must not
# TODO: duplicate anything done in the superclass initializer.
self._doi = doi
self._authors = authors
TMTree.__init__(self, name, subtrees, citations)
if all_papers:
paper_dict = _load_papers_to_dict(by_year)
self._subtrees = _build_tree_from_dict(paper_dict)
if not self._subtrees:
self.data_size = citations
else:
self.data_size = 0
for t in self._subtrees:
self.data_size += t.data_size
t._parent_tree = self
def __init__(self,
name: str,
subtrees: List[TMTree],
authors: str = '',
doi: str = '',
citations: int = 0,
by_year: bool = True,
all_papers: bool = False) -> None:
"""Initialize a new PaperTree with the given <name> and <subtrees>,
<authors> and <doi>, and with <citations> as the size of the data.
If <all_papers> is True, then this tree is to be the root of the paper
tree. In that case, load data about papers from DATA_FILE to build the
tree.
If <all_papers> is False, Do NOT load new data.
<by_year> indicates whether or not the first level of subtrees should be
the years, followed by each category, subcategory, and so on. If
<by_year> is False, then the year in the dataset is simply ignored.
"""
all_sub_tree = [] # all the sub-folders of this folder
all_data = _load_papers_to_dict(by_year)
if not all_papers:
self._authors = authors
self._doi = doi
TMTree.__init__(self, name, subtrees, citations)
else:
for key in all_data:
temp_subtree = _build_tree_from_dict(all_data[key])
all_sub_tree.append(
PaperTree(key, temp_subtree, by_year=by_year))
self._authors = authors
self._doi = doi
TMTree.__init__(self, name, all_sub_tree)
def __init__(self,
name: str,
subtrees: List[TMTree],
authors: str = '',
doi: str = '',
citations: int = 0,
by_year: bool = True,
all_papers: bool = False) -> None:
self._doi = doi
self._authors = authors
if all_papers:
if by_year:
tree = _build_tree_from_dict(_load_papers_to_dict(True))
TMTree.__init__(self, name, tree, citations)
else:
tree = _build_tree_from_dict(_load_papers_to_dict(False))
TMTree.__init__(self, name, tree, citations)
else:
TMTree.__init__(self, name, subtrees, citations)
"""Initialize a new PaperTree with the given <name> and <subtrees>,
def __init__(self,
name: str,
subtrees: List[TMTree],
authors: str = '',
doi: str = '',
citations: int = 0,
by_year: bool = True,
all_papers: bool = False) -> None:
"""Initialize a new PaperTree with the given <name> and <subtrees>,
<authors> and <doi>, and with <citations> as the size of the data.
If <all_papers> is True, then this tree is to be the root of the paper
tree. In that case, load data about papers from DATA_FILE to build the
tree.
If <all_papers> is False, Do NOT load new data.
<by_year> indicates whether or not the first level of subtrees should be
the years, followed by each category, subcategory, and so on. If
<by_year> is False, then the year in the dataset is simply ignored.
"""
if subtrees == []:
TMTree.__init__(self, name, subtrees,
citations) # i.e our file is a
self._doi = doi
self._authors = authors
# self._citation = citations ### Data_size
self._by_year = by_year
if not all_papers and subtrees != []:
TMTree.__init__(self, name, subtrees, citations)
self._doi = doi
self._authors = authors
self._by_year = by_year
if all_papers:
x = _get_paper_list(by_year)
subtrees = _build_tree_from_dict(x)
TMTree.__init__(self, name, subtrees, citations)
self._doi = doi
self._authors = authors
self._by_year = by_year
