def _set_stats(self):
self.critical_stats = Stat()
self.all_stats = Stat()
for suite in self.suites:
suite.set_status()
self._add_suite_to_stats(suite)
for test in self.tests:
self._add_test_to_stats(test)
def __init__(self, name, source=None, parent=None):
_TestAndSuiteHelper.__init__(self, name, parent)
self.source = source is not None and utils.normpath(source) or None
self.metadata = utils.NormalizedDict()
self.suites = []
self.tests = []
self.critical = _Critical()
self.critical_stats = Stat()
self.all_stats = Stat()
if parent:
parent.suites.append(self)
def __init__(self, source, destination, network, timeout, debug=False):
self.src_host, self.src_port = source
self.dst_host, self.dst_port = destination
self.socket = network
self.stat = Stat()
self.timeout = timeout
self.debug = debug
class DataHandler:
# composition
calculator = Stat()
@classmethod
def get_raw_data(filename):
"""
get_raw_data(filname) -> dict
"""
workbook = op.load_workbook(filename)
ac = workbook.active
g = ac.rows
student = dict()
for name, score in g:
student[name.value] = score.value
return student
def __init__(self):
# 아직 인스턴스가 만들어지지 않았는데 생성자가 인스턴스 메소드를 호출하는게 논리적으로 맞지 않으므로 수정함
# 물론 self.으로해도 잘 작동함
self.year_class = filename.split('_')[1]
self.raw_data = DataHandler.get_raw_data(filename)
self.scores = list(self.raw_data.values())
self.cache = {}
def get_average(self):
self.calculator.get_average()
return avg
def get_std_dev(self):
pass
def __init__(self):
# publishing as training data
self.pub_teleop_vel = rospy.Publisher('train/cmd_vel',
Twist,
queue_size=1)
self.pub_image = rospy.Publisher('train/image_raw',
Image,
queue_size=1)
self.pub_intention = rospy.Publisher('train/intention',
String,
queue_size=1)
self.name = "TeleController"
# used for quatitive results
self.stat = Stat(self.name)
class DataHandler():
#compostion
calculater = Stat()
@classmethod
def get_raw_data(cls, filename):
raw_data = {}
wb = load_workbook(filename)
ws = wb.active
g = ws.rows
for name_cell, scroll_cell in g:
raw_data[name_cell.value] = scroll_cell.value
return raw_data
def __init__(self, filename):
self.year_class = filename.split('_')[1]
self.raw_data = DataHandler.get_raw_data(filename)
self.scores = list(self.raw_data.values())
self.cache = {}
class DataHandler:
evaluator = Stat()
@classmethod
def get_data_from_excel(cls, filename):
"""
get_data_from_excel(filename)->{'name1' : 'score1', 'name2' : 'score2', ...}
엑셀파일에서 데이터를 가져옵니다.
반환값은 key가 학생 이름이고 value가 점수인 딕셔너리입니다.
"""
raw_data = {}
wb = load_workbook(filename)
ws = wb.active
g = ws.rows
for name_cell, score_cell in g:
raw_data[name_cell.value] = score_cell.value
return raw_data
def __init__(self, filename, year_class):
self.rawdata = DataHandler.get_data_from_excel(filename)
self.year_class = year_class
self.cache = {}
def get_scores(self):
if 'scores' not in self.cache:
self.cache['scores'] = list(self.rawdata.values())
return self.cache.get('scores')
def get_average(self):
if 'average' not in self.cache:
self.cache['average'] = self.evaluator.get_average(
self.get_scores())
return self.cache.get('average')
def get_variance(self):
if 'variance' not in self.cache:
self.cache['variance'] = self.evaluator.get_variance(
self.get_scores(), self.get_average())
return self.cache.get('variance')
def get_standard_deviation(self):
if 'standard_deviation' not in self.cache:
self.cache['standard_deviation'] = self.evaluator.get_std_dev(
self.get_variance())
return self.cache.get('standard_deviation')
def evaluate_class(self, total_avrg, sd):
"""
evaluate_class(total_avrg, sd)->None
total_avrg : 학년 전체 성적 평균
sd : 원하는 표준편차 기준
"""
avrg = self.get_average()
std_dev = self.get_standard_deviation()
if avrg < 50 and std_dev > 20:
print('성적이 너무 저조하고 학생들의 실력 차이가 너무 크다.')
elif avrg > 50 and std_dev > 20:
print('성적은 평균 이상이지만 학생들의 실력 차이가 크다. 주의 요망!')
elif avrg < 50 and std_dev < 20:
print('학생들의 실력 차이는 크지 않지만 성적이 너무 저조하다. 주의 요망!')
elif avrg > 50 and std_dev < 20:
print('성적도 평균 이상이고 학생들의 실력 차이도 크지 않다.')
def get_evaluation(self, total_avrg, sd=20):
print('*' * 50)
print('{} 반 성적 분석 결과'.format(self.year_class))
print('{} 반의 평균은 {}점이고 분산은 {}이며 표준편차는 {}이다.'.format(
self.year_class, self.get_average(), self.get_variance(),
self.get_standard_deviation()))
print('*' * 50)
print('{}반 종합 평가'.format(self.year_class))
print('*' * 50)
self.evaluate_class(total_avrg, sd)
