def generate(cls, _xmodule_instance_args, _entry_id, course_id, task_input,
action_name):
"""
For a given `course_id`, generate a CSV file containing
all student answers to a given problem, and store using a `ReportStore`.
"""
start_time = time()
start_date = datetime.now(UTC)
num_reports = 1
task_progress = TaskProgress(action_name, num_reports, start_time)
current_step = {'step': 'Calculating students answers to problem'}
task_progress.update_task_state(extra_meta=current_step)
# Compute result table and format it
problem_location = task_input.get('problem_location')
student_data = list_problem_responses(course_id, problem_location)
features = ['username', 'state']
header, rows = format_dictlist(student_data, features)
task_progress.attempted = task_progress.succeeded = len(rows)
task_progress.skipped = task_progress.total - task_progress.attempted
rows.insert(0, header)
current_step = {'step': 'Uploading CSV'}
task_progress.update_task_state(extra_meta=current_step)
# Perform the upload
problem_location = re.sub(r'[:/]', '_', problem_location)
csv_name = 'student_state_from_{}'.format(problem_location)
upload_csv_to_report_store(rows, csv_name, course_id, start_date)
return task_progress.update_task_state(extra_meta=current_step)
def upload_students_csv(_xmodule_instance_args, _entry_id, course_id, task_input, action_name):
"""
For a given `course_id`, generate a CSV file containing profile
information for all students that are enrolled, and store using a
`ReportStore`.
"""
start_time = time()
start_date = datetime.now(UTC)
task_progress = TaskProgress(action_name, CourseEnrollment.num_enrolled_in(course_id), start_time)
current_step = {'step': 'Calculating Profile Info'}
task_progress.update_task_state(extra_meta=current_step)
# compute the student features table and format it
query_features = task_input.get('features')
student_data = enrolled_students_features(course_id, query_features)
header, rows = format_dictlist(student_data, query_features)
task_progress.attempted = task_progress.succeeded = len(rows)
task_progress.skipped = task_progress.total - task_progress.attempted
rows.insert(0, header)
current_step = {'step': 'Uploading CSV'}
task_progress.update_task_state(extra_meta=current_step)
# Perform the upload
upload_csv_to_report_store(rows, 'student_profile_info', course_id, start_date)
return task_progress.update_task_state(extra_meta=current_step)
def upload_proctored_exam_results_report(_xmodule_instance_args, _entry_id, course_id, _task_input, action_name): # pylint: disable=invalid-name
"""
For a given `course_id`, generate a CSV file containing
information about proctored exam results, and store using a `ReportStore`.
"""
start_time = time()
start_date = datetime.now(UTC)
num_reports = 1
task_progress = TaskProgress(action_name, num_reports, start_time)
current_step = {'step': 'Calculating info about proctored exam results in a course'}
task_progress.update_task_state(extra_meta=current_step)
# Compute result table and format it
query_features = _task_input.get('features')
student_data = get_proctored_exam_results(course_id, query_features)
header, rows = format_dictlist(student_data, query_features)
task_progress.attempted = task_progress.succeeded = len(rows)
task_progress.skipped = task_progress.total - task_progress.attempted
rows.insert(0, header)
current_step = {'step': 'Uploading CSV'}
task_progress.update_task_state(extra_meta=current_step)
# Perform the upload
upload_csv_to_report_store(rows, 'proctored_exam_results_report', course_id, start_date)
return task_progress.update_task_state(extra_meta=current_step)
def test_format_dictlist(self):
dictlist = [
{
'label1': 'value-1,1',
'label2': 'value-1,2',
'label3': 'value-1,3',
'label4': 'value-1,4',
},
{
'label1': 'value-2,1',
'label2': 'value-2,2',
'label3': 'value-2,3',
'label4': 'value-2,4',
}
]
features = ['label1', 'label4']
header, datarows = format_dictlist(dictlist, features)
ideal_header = ['label1', 'label4']
ideal_datarows = [['value-1,1', 'value-1,4'],
['value-2,1', 'value-2,4']]
self.assertEqual(header, ideal_header)
self.assertEqual(datarows, ideal_datarows)
def upload_students_csv(_xmodule_instance_args, _entry_id, course_id,
task_input, action_name):
"""
For a given `course_id`, generate a CSV file containing profile
information for all students that are enrolled, and store using a
`ReportStore`.
"""
start_time = time()
start_date = datetime.now(UTC)
task_progress = TaskProgress(action_name,
CourseEnrollment.num_enrolled_in(course_id),
start_time)
current_step = {'step': 'Calculating Profile Info'}
task_progress.update_task_state(extra_meta=current_step)
# compute the student features table and format it
query_features = task_input.get('features')
student_data = enrolled_students_features(course_id, query_features)
header, rows = format_dictlist(student_data, query_features)
task_progress.attempted = task_progress.succeeded = len(rows)
task_progress.skipped = task_progress.total - task_progress.attempted
rows.insert(0, header)
current_step = {'step': 'Uploading CSV'}
task_progress.update_task_state(extra_meta=current_step)
# Perform the upload
upload_csv_to_report_store(rows, 'student_profile_info', course_id,
start_date)
return task_progress.update_task_state(extra_meta=current_step)
def upload_may_enroll_csv(_xmodule_instance_args, _entry_id, course_id,
task_input, action_name):
"""
For a given `course_id`, generate a CSV file containing
information about students who may enroll but have not done so
yet, and store using a `ReportStore`.
"""
start_time = time()
start_date = datetime.now(UTC)
num_reports = 1
task_progress = TaskProgress(action_name, num_reports, start_time)
current_step = {'step': 'Calculating info about students who may enroll'}
task_progress.update_task_state(extra_meta=current_step)
# Compute result table and format it
query_features = task_input.get('features')
student_data = list_may_enroll(course_id, query_features)
header, rows = format_dictlist(student_data, query_features)
task_progress.attempted = task_progress.succeeded = len(rows)
task_progress.skipped = task_progress.total - task_progress.attempted
rows.insert(0, header)
current_step = {'step': 'Uploading CSV'}
task_progress.update_task_state(extra_meta=current_step)
# Perform the upload
upload_csv_to_report_store(rows, 'may_enroll_info', course_id, start_date)
return task_progress.update_task_state(extra_meta=current_step)
def upload_may_enroll_csv(_xmodule_instance_args, _entry_id, course_id, task_input, action_name):
"""
For a given `course_id`, generate a CSV file containing
information about students who may enroll but have not done so
yet, and store using a `ReportStore`.
"""
start_time = time()
start_date = datetime.now(UTC)
num_reports = 1
task_progress = TaskProgress(action_name, num_reports, start_time)
current_step = {'step': 'Calculating info about students who may enroll'}
task_progress.update_task_state(extra_meta=current_step)
# Compute result table and format it
query_features = task_input.get('features')
student_data = list_may_enroll(course_id, query_features)
header, rows = format_dictlist(student_data, query_features)
task_progress.attempted = task_progress.succeeded = len(rows)
task_progress.skipped = task_progress.total - task_progress.attempted
rows.insert(0, header)
current_step = {'step': 'Uploading CSV'}
task_progress.update_task_state(extra_meta=current_step)
# Perform the upload
upload_csv_to_report_store(rows, 'may_enroll_info', course_id, start_date)
return task_progress.update_task_state(extra_meta=current_step)
def generate(cls, _xmodule_instance_args, _entry_id, course_id, task_input, action_name):
"""
For a given `course_id`, generate a CSV file containing
all student answers to a given problem, and store using a `ReportStore`.
"""
start_time = time()
start_date = datetime.now(UTC)
num_reports = 1
task_progress = TaskProgress(action_name, num_reports, start_time)
current_step = {'step': 'Calculating students answers to problem'}
task_progress.update_task_state(extra_meta=current_step)
# Compute result table and format it
problem_location = task_input.get('problem_location')
student_data = list_problem_responses(course_id, problem_location)
features = ['username', 'state']
header, rows = format_dictlist(student_data, features)
task_progress.attempted = task_progress.succeeded = len(rows)
task_progress.skipped = task_progress.total - task_progress.attempted
rows.insert(0, header)
current_step = {'step': 'Uploading CSV'}
task_progress.update_task_state(extra_meta=current_step)
# Perform the upload
problem_location = re.sub(r'[:/]', '_', problem_location)
csv_name = 'student_state_from_{}'.format(problem_location)
upload_csv_to_report_store(rows, csv_name, course_id, start_date)
return task_progress.update_task_state(extra_meta=current_step)
def test_format_dictlist(self):
dictlist = [
{
'label1': 'value-1,1',
'label2': 'value-1,2',
'label3': 'value-1,3',
'label4': 'value-1,4',
},
{
'label1': 'value-2,1',
'label2': 'value-2,2',
'label3': 'value-2,3',
'label4': 'value-2,4',
}
]
features = ['label1', 'label4']
header, datarows = format_dictlist(dictlist, features)
ideal_header = ['label1', 'label4']
ideal_datarows = [['value-1,1', 'value-1,4'],
['value-2,1', 'value-2,4']]
self.assertEqual(header, ideal_header)
self.assertEqual(datarows, ideal_datarows)
def generate(cls, _xmodule_instance_args, _entry_id, course_id, task_input,
action_name):
"""
For a given `course_id`, generate a CSV file containing
all student answers to a given problem, and store using a `ReportStore`.
"""
start_time = time()
start_date = datetime.now(UTC)
num_reports = 1
task_progress = TaskProgress(action_name, num_reports, start_time)
current_step = {'step': 'Calculating students answers to problem'}
task_progress.update_task_state(extra_meta=current_step)
problem_locations = task_input.get('problem_locations')
problem_types_filter = task_input.get('problem_types_filter')
filter_types = None
if problem_types_filter:
filter_types = problem_types_filter.split(',')
# Compute result table and format it
student_data, student_data_keys = cls._build_student_data(
user_id=task_input.get('user_id'),
course_key=course_id,
usage_key_str_list=problem_locations.split(','),
filter_types=filter_types,
)
for data in student_data:
for key in student_data_keys:
data.setdefault(key, '')
header, rows = format_dictlist(student_data, student_data_keys)
task_progress.attempted = task_progress.succeeded = len(rows)
task_progress.skipped = task_progress.total - task_progress.attempted
rows.insert(0, header)
current_step = {'step': 'Uploading CSV'}
task_progress.update_task_state(extra_meta=current_step)
# Perform the upload
# Limit problem locations string to 200 characters in case a large number of
# problem locations are selected.
problem_location = re.sub(r'[:/]', '_', problem_locations)[:200]
csv_name = 'student_state_from_{}'.format(problem_location)
report_name = upload_csv_to_report_store(rows, csv_name, course_id,
start_date)
current_step = {'step': 'CSV uploaded', 'report_name': report_name}
return task_progress.update_task_state(extra_meta=current_step)
def generate(cls, _xmodule_instance_args, _entry_id, course_id, task_input, action_name):
"""
For a given `course_id`, generate a CSV file containing
all student answers to a given problem, and store using a `ReportStore`.
"""
start_time = time()
start_date = datetime.now(UTC)
num_reports = 1
task_progress = TaskProgress(action_name, num_reports, start_time)
current_step = {'step': 'Calculating students answers to problem'}
task_progress.update_task_state(extra_meta=current_step)
problem_locations = task_input.get('problem_locations')
problem_types_filter = task_input.get('problem_types_filter')
filter_types = None
if problem_types_filter:
filter_types = problem_types_filter.split(',')
# Compute result table and format it
student_data, student_data_keys = cls._build_student_data(
user_id=task_input.get('user_id'),
course_key=course_id,
usage_key_str_list=problem_locations.split(','),
filter_types=filter_types,
)
for data in student_data:
for key in student_data_keys:
data.setdefault(key, '')
header, rows = format_dictlist(student_data, student_data_keys)
task_progress.attempted = task_progress.succeeded = len(rows)
task_progress.skipped = task_progress.total - task_progress.attempted
rows.insert(0, header)
current_step = {'step': 'Uploading CSV'}
task_progress.update_task_state(extra_meta=current_step)
# Perform the upload
# Limit problem locations string to 200 characters in case a large number of
# problem locations are selected.
problem_location = re.sub(r'[:/]', '_', problem_locations)[:200]
csv_name = 'student_state_from_{}'.format(problem_location)
report_name = upload_csv_to_report_store(rows, csv_name, course_id, start_date)
current_step = {'step': 'CSV uploaded', 'report_name': report_name}
return task_progress.update_task_state(extra_meta=current_step)
def upload_proctored_exam_results_report(_xmodule_instance_args, _entry_id, course_id, _task_input, action_name):
"""
For a given `course_id`, generate a CSV file containing
information about proctored exam results, and store using a `ReportStore`.
"""
start_time = time()
start_date = datetime.now(UTC)
num_reports = 1
task_progress = TaskProgress(action_name, num_reports, start_time)
current_step = {'step': 'Calculating info about proctored exam results in a course'}
task_progress.update_task_state(extra_meta=current_step)
# Compute result table and format it
query_features = [
'course_id',
'provider',
'track',
'exam_name',
'username',
'email',
'attempt_code',
'allowed_time_limit_mins',
'is_sample_attempt',
'started_at',
'completed_at',
'status',
'review_status',
'Suspicious Count',
'Suspicious Comments',
'Rules Violation Count',
'Rules Violation Comments'
]
student_data = get_proctored_exam_results(course_id, query_features)
header, rows = format_dictlist(student_data, query_features)
task_progress.attempted = task_progress.succeeded = len(rows)
task_progress.skipped = task_progress.total - task_progress.attempted
rows.insert(0, header)
current_step = {'step': 'Uploading CSV'}
task_progress.update_task_state(extra_meta=current_step)
# Perform the upload
upload_csv_to_report_store(rows, 'proctored_exam_results_report', course_id, start_date)
return task_progress.update_task_state(extra_meta=current_step)
def upload_proctored_exam_results_report(_xmodule_instance_args, _entry_id, course_id, _task_input, action_name):
"""
For a given `course_id`, generate a CSV file containing
information about proctored exam results, and store using a `ReportStore`.
"""
start_time = time()
start_date = datetime.now(UTC)
num_reports = 1
task_progress = TaskProgress(action_name, num_reports, start_time)
current_step = {'step': 'Calculating info about proctored exam results in a course'}
task_progress.update_task_state(extra_meta=current_step)
# Compute result table and format it
query_features = [
'course_id',
'exam_name',
'username',
'email',
'attempt_code',
'allowed_time_limit_mins',
'is_sample_attempt',
'started_at',
'completed_at',
'status',
'review_status',
'Suspicious Count',
'Suspicious Comments',
'Rules Violation Count',
'Rules Violation Comments'
]
student_data = get_proctored_exam_results(course_id, query_features)
header, rows = format_dictlist(student_data, query_features)
task_progress.attempted = task_progress.succeeded = len(rows)
task_progress.skipped = task_progress.total - task_progress.attempted
rows.insert(0, header)
current_step = {'step': 'Uploading CSV'}
task_progress.update_task_state(extra_meta=current_step)
# Perform the upload
upload_csv_to_report_store(rows, 'proctored_exam_results_report', course_id, start_date)
return task_progress.update_task_state(extra_meta=current_step)
def push_students_csv_to_s3(_xmodule_instance_args, _entry_id, course_id,
task_input, _action_name):
"""
For a given `course_id`, generate a CSV file containing profile
information for all students that are enrolled, and store using a
`ReportStore`.
"""
# compute the student features table and format it
query_features = task_input.get('features')
student_data = enrolled_students_features(course_id, query_features)
header, rows = format_dictlist(student_data, query_features)
rows.insert(0, header)
# Perform the upload
upload_csv_to_report_store(rows, 'student_profile_info', course_id,
datetime.now(UTC))
return UPDATE_STATUS_SUCCEEDED
def test_format_dictlist_empty(self):
header, datarows = format_dictlist([], [])
self.assertEqual(header, [])
self.assertEqual(datarows, [])
def test_format_dictlist_empty(self):
header, datarows = format_dictlist([], [])
self.assertEqual(header, [])
self.assertEqual(datarows, [])
