def project_information(request):
try:
data = load_encoded_json(request.body)
project_ids = data['project_ids']
return get_project_information(project_ids)
except KeyError:
raise BadRequest('project_ids is required')
def project_management_information(project_id):
project = Project.objects.get(id=project_id)
project_information = get_project_information(project.id)
project_information['project']['status'] = dict(
Project.STATUS_CHOICES).get(project.status, None)
project_information['project']['admin_url'] = urljoin(
settings.ORCHESTRA_URL,
urlresolvers.reverse(
'admin:orchestra_project_change',
args=(project_id,)))
for slug, task in project_information['tasks'].items():
task['admin_url'] = urljoin(
settings.ORCHESTRA_URL,
urlresolvers.reverse(
'admin:orchestra_task_change',
args=(task['id'],)))
for assignment in task['assignments']:
assignment['admin_url'] = urljoin(
settings.ORCHESTRA_URL,
urlresolvers.reverse(
'admin:orchestra_taskassignment_change',
args=(assignment['id'],)))
for iteration in assignment['iterations']:
iteration['admin_url'] = urljoin(
settings.ORCHESTRA_URL,
urlresolvers.reverse(
'admin:orchestra_iteration_change',
args=(iteration['id'],)))
if not iteration['end_datetime']:
iteration['end_datetime'] = timezone.now().isoformat()
return project_information
def project_management_information(project_id):
project = Project.objects.get(id=project_id)
project_information = get_project_information([project.id])
project_information[project.id]['project']['status'] = dict(
Project.STATUS_CHOICES).get(project.status, None)
project_information[project.id]['project']['admin_url'] = urljoin(
settings.ORCHESTRA_URL,
urlresolvers.reverse('admin:orchestra_project_change',
args=(project_id, )))
for slug, task in project_information[project.id]['tasks'].items():
task['admin_url'] = urljoin(
settings.ORCHESTRA_URL,
urlresolvers.reverse('admin:orchestra_task_change',
args=(task['id'], )))
for assignment in task['assignments']:
assignment['admin_url'] = urljoin(
settings.ORCHESTRA_URL,
urlresolvers.reverse('admin:orchestra_taskassignment_change',
args=(assignment['id'], )))
for iteration in assignment['iterations']:
iteration['admin_url'] = urljoin(
settings.ORCHESTRA_URL,
urlresolvers.reverse('admin:orchestra_iteration_change',
args=(iteration['id'], )))
if not iteration['end_datetime']:
iteration['end_datetime'] = timezone.now().isoformat()
return project_information
def test_get_project_information(self):
projects = Project.objects.all()[:2]
projects_info = get_project_information([p.pk for p in projects])
a_project_info_key = list(projects_info.keys())[0]
a_project_info = projects_info[a_project_info_key]
self.assertTrue(isinstance(a_project_info['project'], dict))
self.assertTrue(isinstance(a_project_info['tasks'], dict))
self.assertTrue(isinstance(a_project_info['steps'], list))
def project_information(request):
try:
data = load_encoded_json(request.body)
project_id = data['project_id']
return get_project_information(project_id)
except KeyError:
raise BadRequest('project_id is required')
except Project.DoesNotExist:
raise BadRequest('No project for given id')
def project_information(request):
try:
# TODO(marcua): Add checking for json.loads exceptions to all
# endpoints.
return get_project_information(json.loads(request.body.decode())["project_id"])
except KeyError:
raise BadRequest("project_id is required")
except Project.DoesNotExist:
raise BadRequest("No project for given id")
def project_information(request):
try:
data = load_encoded_json(request.body)
project_id = data['project_id']
return get_project_information(project_id)
except KeyError:
raise BadRequest('project_id is required')
except Project.DoesNotExist:
raise BadRequest('No project for given id')
def project_information(request):
try:
# TODO(marcua): Add checking for json.loads exceptions to all
# endpoints.
return get_project_information(
json.loads(request.body.decode())['project_id'])
except KeyError:
raise BadRequest('project_id is required')
except Project.DoesNotExist:
raise BadRequest('No project for given id')
def work_time_df(projects, human_only=True, complete_tasks_only=True):
"""
Return projects' task assignment iteration timing information.
The returned dataframe's schema is:
project_id: the project's ID
project_description: the project's human readable description
task_id: the task's ID
task_step_slug: the task's step slug
assignment_level: a 1-indexed assignment counter (1 is entry-level, 2
is first review, etc.)
worker: the username of the worker on that assignment
iteration: a work iteration for the assignment:
- Iteration 0 represents the period from previous submission to
reviewer assignment:
- If this is the first assignment, 'previous submission'
means 'task creation'.
- For iteration 0, work time will always be 0
- If this task is auto-assigned, iteration 0's calendar time
will be close to 0.
- Iterations N > 0 represent the period between a previous worker's
input (submit/accept/reject) and the worker's submission.
start_datetime: the time the iteration started.
end_datetime: the time the iteration ended.
calendar_time: the wallclock time it took for the worker to complete
that iteration.
work_time: the amount of work time the worker reported dedicating to
that iteration (0 for iteration 0).
Args:
projects (django.db.models.query.QuerySet):
An iterable of projects to include in the report.
Returns:
df (pandas.DataFrame):
A DataFrame with timing information.
"""
# TODO(marcua): `get_project_information` per project means we do random
# access all over the database. Ideally, we'd have a sequential scan
# version of this.
time_data = list(itertools.chain.from_iterable(
project_time_row_generator(
get_project_information(project.id),
human_only, complete_tasks_only)
for project in projects))
# Ensure that even an empty DataFrame has the correct columns
df = DataFrame(time_data, columns=DATAFRAME_COLUMNS)
# Pandas treats all non-primitives as strings, so we explicitly cast
# datetimes as datetimes instead of strings.
df['start_datetime'] = df['start_datetime'].astype('datetime64[ns]')
df['end_datetime'] = df['end_datetime'].astype('datetime64[ns]')
return df
def work_time_df(projects, human_only=True, complete_tasks_only=True):
"""
Return projects' task assignment iteration timing information.
The returned dataframe's schema is:
project_id: the project's ID
project_description: the project's human readable description
task_id: the task's ID
task_step_slug: the task's step slug
assignment_level: a 1-indexed assignment counter (1 is entry-level, 2
is first review, etc.)
worker: the username of the worker on that assignment
iteration: a work iteration for the assignment:
- Iteration 0 represents the period from previous submission to
reviewer assignment:
- If this is the first assignment, 'previous submission'
means 'task creation'.
- For iteration 0, work time will always be 0
- If this task is auto-assigned, iteration 0's calendar time
will be close to 0.
- Iterations N > 0 represent the period between a previous worker's
input (submit/accept/reject) and the worker's submission.
start_datetime: the time the iteration started.
end_datetime: the time the iteration ended.
calendar_time: the wallclock time it took for the worker to complete
that iteration.
work_time: the amount of work time the worker reported dedicating to
that iteration (0 for iteration 0).
Args:
projects (django.db.models.query.QuerySet):
An iterable of projects to include in the report.
Returns:
df (pandas.DataFrame):
A DataFrame with timing information.
"""
# TODO(marcua): `get_project_information` per project means we do random
# access all over the database. Ideally, we'd have a sequential scan
# version of this.
time_data = list(
itertools.chain.from_iterable(
project_time_row_generator(get_project_information(project.id),
human_only, complete_tasks_only)
for project in projects))
# Ensure that even an empty DataFrame has the correct columns
df = DataFrame(time_data, columns=DATAFRAME_COLUMNS)
# Pandas treats all non-primitives as strings, so we explicitly cast
# datetimes as datetimes instead of strings.
df['start_datetime'] = df['start_datetime'].astype('datetime64[ns]')
df['end_datetime'] = df['end_datetime'].astype('datetime64[ns]')
return df
def work_time_df(projects, human_only=True, complete_tasks_only=True):
"""
Return projects' task assignment iteration timing information.
The returned dataframe's schema is:
project_id: the project's ID
task_id: the task's ID
assignment_level: a 1-indexed assignment counter (1 is entry-level, 2
is first review, etc.)
worker: the username of the worker on that assignment
iteration: a work iteration for the assignment:
- Iteration 0 represents the period from previous submission to
reviewer assignment:
- If this is the first assignment, 'previous submission'
means 'task creation'.
- For iteration 0, work time will always be 0
- If this task is auto-assigned, iteration 0's calendar time
will be close to 0.
- Iterations N > 0 represent the period between a previous worker's
input (submit/accept/reject) and the worker's submission.
start_datetime: the time the iteration started.
calendar_time: the wallclock time it took for the worker to complete
that iteration.
work_time: the amount of work time the worker reported dedicating to
that iteration (0 for iteration 0).
Args:
projects (django.db.models.query.QuerySet):
An iterable of projects to include in the report.
Returns:
df (pandas.DataFrame):
A DataFrame with timing information.
"""
# TODO(marcua): `get_project_information` per project means we do random
# access all over the database. Ideally, we'd have a sequential scan
# version of this.
time_data = list(
itertools.chain.from_iterable(
project_time_row_generator(get_project_information(project.id), human_only, complete_tasks_only)
for project in projects
)
)
df = DataFrame(time_data)
return df
def project_management_information(project_id):
project = Project.objects.get(id=project_id)
df = work_time_df([project], human_only=False, complete_tasks_only=False)
project_information = get_project_information(project.id)
project_information['project']['status'] = dict(
Project.STATUS_CHOICES).get(project.status, None)
project_information['project']['admin_url'] = urljoin(
settings.ORCHESTRA_URL,
urlresolvers.reverse('admin:orchestra_project_change',
args=(project_id, )))
for slug, task in project_information['tasks'].items():
task['admin_url'] = urljoin(
settings.ORCHESTRA_URL,
urlresolvers.reverse('admin:orchestra_task_change',
args=(task['id'], )))
for assignment in task['assignments']:
assignment['admin_url'] = urljoin(
settings.ORCHESTRA_URL,
urlresolvers.reverse('admin:orchestra_taskassignment_change',
args=(assignment['id'], )))
iterations = df[(df.worker == assignment['worker']['username'])
& (df.task_id == task['id'])]
iterations = iterations[['start_datetime', 'end_datetime']]
assignment['iterations'] = []
for idx, info in iterations.T.items():
iteration = info.to_dict()
assignment['iterations'].append(iteration)
if assignment['status'] == 'Processing':
last_iteration_end = assignment['start_datetime']
last_assignment = last_snapshotted_assignment(task['id'])
if last_assignment and len(assignment['iterations']) > 1:
last_iteration_end = (
last_assignment.snapshots['snapshots'][-1]['datetime'])
assignment['iterations'].append({
'start_datetime': last_iteration_end,
'end_datetime': timezone.now()
})
if task['status'] in ('Awaiting Processing', 'Pending Review'):
last_assignment_end = task['start_datetime']
last_assignment = last_snapshotted_assignment(task['id'])
if last_assignment:
last_assignment_end = (
last_assignment.snapshots['snapshots'][-1]['datetime'])
task['assignments'].append({
'iterations': [{
'start_datetime': last_assignment_end,
'end_datetime': timezone.now()
}],
'snapshots':
empty_snapshots(),
'start_datetime':
last_assignment_end,
'status':
'Processing',
'task':
task['id'],
'worker': {
'id': None,
'username': None
},
})
return project_information