class PairingsBoard(models.TimeStampedModel):
"""
Keeps track of all pairings done in an activity.
"""
name = models.CodeschoolNameField()
members = models.ManyToManyField(
models.User,
related_name='pairing_boards',
)
class Kanban(models.TimeStampedModel):
"""
Represents a Kanban board activity.
"""
name = models.CodeschoolNameField()
members = models.ManyToManyField(
models.User,
related_name='kanban_boards',
)
class Feature(models.Model):
"""
A Feature in a sentiment board.
"""
board = models.ParentalKey(SentimentBoard, related_name='features')
name = models.CodeschoolNameField()
description = models.CodeschoolDescriptionField(blank=True)
image = models.ImageField(blank=True, null=True)
icon = models.CharField(max_length=50)
class Meta:
unique_together = [('board', 'name')]
class Sprint(models.TimeStampedModel):
"""
A sprint meta-data object that can be associated with many
projects/contexts.
"""
index = models.PositiveSmallIntegerField()
start_date = models.DateField(_('Starts'), )
due_date = models.DateField(_('Ends'), )
name = models.CodeschoolNameField(blank=True)
description = models.CodeschoolDescriptionField(blank=True)
def next_sprint(self, name='', description=''):
"""
Schedule a new sprint starting at the end of the current sprint and
keeping the same time-frame.
"""
raise NotImplementedError
class Task(models.TimeStampedModel):
"""
Represents a task in a Kanban activity.
"""
STATUS_TODO, STATUS_DOING, STATUS_DONE = range(3)
STATUS_CHOICES = [
(STATUS_TODO, _('To do')),
(STATUS_DOING, _('Doing')),
(STATUS_DONE, _('Done')),
]
name = models.CodeschoolNameField()
description = models.CodeschoolDescriptionField()
status = models.SmallIntegerField(
_('status'),
choices=STATUS_CHOICES,
)
members = models.ManyToManyField(
models.User,
related_name='kanban_tasks',
)
created_by = models.ForeignKey(
models.User,
related_name='created_tasks',
null=True,
blank=True,
)
assigned_to = models.ManyToManyField(
models.User,
related_name='assigned_tasks',
)
estimated_duration_hours = models.PositiveSmallIntegerField(
_('Estimated duration (hours)'),
default=0,
)
objects = TaskQuerySet.as_manager()
class SentimentBoard(models.TimeStampedModel):
"""
Represents sentiments of a group of users towards a group of
generic features.
This is usually used to communicate the level of competence of each member
in the team with different technologies.
"""
name = models.CodeschoolNameField()
members = models.ManyToManyField(
models.User,
related_name='sentiment_boards',
)
# Functions that define common features
feature_python = feature('Python')
feature_django = feature('Django')
feature_elm = feature('Elm')
feature_html5 = feature('HTML5')
feature_js = feature('JS', 'Javascript (ES5)')
feature_js6 = feature('ES6', 'Javascript ES6')
feature_css = feature('CSS')
class ActivityList(models.TimeStampedModel):
"""
List of activities.
"""
name = models.CodeschoolNameField()
slug = models.CodeschoolSlugField()
short_description = models.CodeschoolShortDescriptionField()
long_description = models.CodeschoolDescriptionField()
icon = models.CharField(
_('Optional icon'),
max_length=20,
default='code',
validators=[material_icon_validator],
help_text=_('Name of the material design icon. Check full list at: '
'https://material.io/icons'),
)
class Meta:
verbose_name = _('List of activities')
verbose_name_plural = _('Lists of activities')
objects = ActivityListManager()
def score_board_total(self) -> ScoreMap:
"""
Return a score board mapping with the total score for each user.
"""
board = self.score_board()
scores = ScoreMap(self.title)
for k, L in board.items():
scores[k] = sum(L)
return scores
def grades_as_csv(self) -> str:
"""
Return a string with CSV data for the all student submissions inside
the given section.
"""
return self.grades_as_dataframe().to_csv()
def grades_as_dataframe(self) -> pd.DataFrame:
"""
Return a Pandas dataframe with the grades for all students that
submited responses to questions in the given list.
"""
from codeschool.lms.activities.models import Progress
children = self.get_children()
children_id = children.values_list('id', flat=True)
cols = ('user__username', 'user__email', 'user__first_name',
'user__last_name', 'activity_page__title', 'given_grade_pc')
responses = (Progress.objects.filter(
activity_page__in=children_id).values_list(*cols))
responses = list(responses)
# User data
users_data = sorted({row[0:4] for row in responses})
users = pd.DataFrame(
users_data,
columns=['username', 'email', 'first_name', 'last_name'],
)
users.index = users.pop('username')
# Question data
df = pd.DataFrame(responses, columns=cols)
groups = df.groupby('activity_page__title')
by_question = {
name: group[['user__username', 'given_grade_pc']]
for name, group in groups
}
# Agregate question data
result = users.copy()
for name, df in by_question.items():
col = df['given_grade_pc']
col.index = df['user__username']
result[name] = col
return result