def update_current_sums(curr_sums: dict, pub: Pub, auth: Author) -> dict:
"""
Updates temporary values needed to run greedy algorithm. Instead of recount
values in every loop iteration, we simply update them.
Args:
curr_sums: dictionary with values to update
contrib_sum: sum of contributions from all accepted publicatons (float)
monograph_sum: sum of contributions from all accepted monographs (float)
phd_and_outsiders: sum of contributions from all accepted phds' and
outsiders' publicatons (float)
pub: newly accepted publication
auth: publication's author
Returns:
dictionary with updated values
"""
monograph_sum = curr_sums["monograph_sum"]
phd_and_outsiders = curr_sums["phd_and_outsiders"]
if pub.is_monograph():
monograph_sum += pub.get_contribution()
if auth.is_phd_student() or not auth.is_in_n():
phd_and_outsiders += pub.get_contribution()
result = {
"contrib_sum": curr_sums["contrib_sum"] + pub.get_contribution(),
"monograph_sum": monograph_sum,
"phd_and_outsiders": phd_and_outsiders,
}
return result
def prepare_authors(data: dict) -> List[Author]:
"""
Prepares list of authors without their publications list
Args:
data: dictionary with keyes:
AUTHOR_ID: list of authors' ids
IS_EMPLOYEE: list that defines which authors are employees
IS_PHD_STUDENT: list that defines which authors are phd students
CONTRIBUTION: list of authors' contributions
IS_IN_N: list that defines which authors are in N
All keys are defined in settings.py
Returns:
List of authors
"""
result = []
for idx in range(len(data[AUTHOR_ID])):
author_id = data[AUTHOR_ID][idx]
is_emp = data[IS_EMPLOYEE][idx]
is_phd = data[IS_PHD_STUDENT][idx]
cont = data[CONTRIBUTION][idx]
in_n = data[IS_IN_N][idx]
result.append(Author(author_id, is_emp, is_phd, cont, in_n))
return result
def create_example_author(
a_id: str = AUTH_ID,
is_emp: bool = IS_EMP,
is_phd: bool = IS_PHD,
contrib: float = CONTRIB,
in_n: bool = IN_N,
):
return Author(a_id, is_emp, is_phd, contrib, in_n)
def prepare_test_authors(data: dict):
authors = []
for idx in range(len(data[AUTHOR_ID])):
a_id = data[AUTHOR_ID][idx]
is_emp = data[IS_EMPLOYEE][idx]
is_phd = data[IS_PHD_STUDENT][idx]
contrib = data[CONTRIBUTION][idx]
in_n = data[IS_IN_N][idx]
authors.append(Author(a_id, is_emp, is_phd, contrib, in_n))
return authors
def prepare_data_for_check_mono_limit_test():
"""
Prepares data that fails _Author__check_moographs_limit() test
"""
is_phd = False
is_emp = False
is_mon = True
tmp_sum = MONOGRAPH_COEFFICIENT * CONTRIB + 1.0
points = MONOGRAPH_LIMIT_MAX_POINTS / 2
author = Author(AUTH_ID, is_emp, is_phd, CONTRIB, CZYN)
return author, is_mon, tmp_sum, points
def check_full_author_limits(auth: Author, publications: List[Publication]):
if auth.is_employee():
publications_sum = 0
for pub in publications:
publications_sum += pub.get_contribution()
assert publications_sum <= auth.get_contribution(
) * PUBLICATIONS_COEFFICIENT
if auth.is_employee() and not auth.is_phd_student():
monograph_sum = 0
for pub in publications:
monograph_sum += pub.get_contribution() if pub.is_monograph(
) else 0.0
assert monograph_sum <= auth.get_contribution() * MONOGRAPH_COEFFICIENT
if auth.is_phd_student():
publications_sum = 0
for pub in publications:
publications_sum += pub.get_contribution()
assert publications_sum <= PUBLICATIONS_COEFFICIENT_FOR_PHD
return True
def prepare_complex_test_data():
publications = create_complex_publications_list()
author = Author("author", True, False, 0.5, 1)
author.load_publications(publications)
author.create_publications_ranking()
return author, publications
def test_update_contribution_with_value_smaller_than_MIN_CONTRIBUTION():
value = MIN_CONTRIBUTION - 0.1
assert Author._Author__update_contribution(value) == MIN_CONTRIBUTION
def test_update_contribution_with_value_greater_than_MAX_CONTRIBUTION():
value = MAX_CONTRIBUTION + 0.1
assert Author._Author__update_contribution(value) == MAX_CONTRIBUTION
def test_update_contribution():
value = (MAX_CONTRIBUTION + MIN_CONTRIBUTION) / 2
assert Author._Author__update_contribution(value) == value