def get_questions_list(assets):
global kobo
if kobo is None:
printMsgAndQuit(
"Error in get_merged_questions: KoboExtractor not initialised.")
questions_list = []
for asset in assets:
asset_questions = kobo.get_questions(asset=asset,
unpack_multiples=True)
if asset['uid'] == 'aAYAW5qZHEcroKwvEq8pRb':
# Special treatment for interview version
# Move S40/residence, S40/residence_99 to S10/residence, S10/residence_99
# Move S10/email to S70/email
asset_questions['groups']['S10']['questions'][
'residence'] = asset_questions['groups']['S40']['questions'][
'residence']
asset_questions['groups']['S10']['questions'][
'residence_99'] = asset_questions['groups']['S40'][
'questions']['residence_99']
asset_questions['groups']['S70']['questions'][
'email'] = asset_questions['groups']['S10']['questions'][
'email']
del asset_questions['groups']['S40']['questions']['residence']
del asset_questions['groups']['S40']['questions']['residence_99']
del asset_questions['groups']['S10']['questions']['email']
questions_list.append(asset_questions)
return questions_list
def get_choice_lists(assets):
global kobo
if kobo is None:
printMsgAndQuit(
"Error in get_choice_lists: KoboExtractor not initialised.")
def merge_dicts(d1, d2):
"""
Modifies d1 in-place to contain values from d2. If any value
in d1 is a dictionary (or dict-like), *and* the corresponding
value in d2 is also a dictionary, then merge them in-place.
"""
for k, v2 in d2.items():
v1 = d1.get(k) # returns None if v1 has no value for this key
if (isinstance(v1, dict) and isinstance(v2, dict)):
merge_dicts(v1, v2)
else:
d1[k] = v2
# Create dict of of choice options in the form of choice_lists[list_name][answer_code] = answer_label
# Merge the choice lists into one, with the first versions taking precedence
choice_lists = {}
for asset in reversed(assets):
merge_dicts(choice_lists, kobo.get_choices(asset))
return choice_lists
def get_contact_values(labeled_results, flat_questions, value_type):
if value_type != "labels" and value_type != "codes":
printMsgAndQuit("Invalid value_type for get_data_values().")
contact_upload_values = []
for labeled_result in labeled_results:
if '/'.join([GROUP_CODES['intro'], QUESTION_CODES['interviewer']
]) in labeled_result['results']:
interviewer = labeled_result['results']['/'.join(
[GROUP_CODES['intro'],
QUESTION_CODES['interviewer']])]['answer_code']
else:
interviewer = ''
contact_values = [
labeled_result['meta']['_submission_time'], FORMULAS['datetime'],
labeled_result['meta']['_id'],
getUniqueId(labeled_result), interviewer
]
for question in flat_questions:
new_values = []
new_values.append(get_answer(question, labeled_result, value_type))
if question['group_code'].startswith(GROUP_CODES['contact']):
contact_values += new_values
contact_upload_values.append(contact_values)
return contact_upload_values
def get_data_values(labeled_results, flat_questions, value_type):
if value_type != "labels" and value_type != "codes":
printMsgAndQuit("Invalid value_type for get_data_values().")
upload_values = []
for labeled_result in labeled_results:
# Determine version
version, version_ = get_version(labeled_result)
result_values = [
labeled_result['meta']['_submission_time'], FORMULAS['datetime'],
FORMULAS['date'], version_, version, labeled_result['meta']['_id']
]
for question in flat_questions:
new_values = []
new_values.append(get_answer(question, labeled_result, value_type))
if not question['group_code'].startswith(GROUP_CODES['contact']):
result_values += new_values
# Add the unique ID and row # of last response
if question['group_code'] == INSERT_UNIQUE_ID_AFTER[
'group'] and question['code'] == INSERT_UNIQUE_ID_AFTER[
'question']:
result_values.append(getUniqueId(labeled_result))
result_values.append(FORMULAS['latest_response'])
upload_values.append(result_values)
return upload_values
def get_assets(asset_uids):
global kobo
if kobo is None:
printMsgAndQuit("Error in get_assets: KoboExtractor not initialised.")
assets = []
for asset_uid in asset_uids:
assets.append(kobo.get_asset(asset_uid))
return assets
def get_labeled_result(new_data, choice_lists, questions, questions_list):
global kobo
if kobo is None:
printMsgAndQuit(
"Error in get_choice_lists: KoboExtractor not initialised.")
labeled_results = []
for i in range(0, len(new_data)):
for result in new_data[i]['results']:
labeled_results.append(
kobo.label_result(unlabeled_result=result,
choice_lists=choice_lists,
questions=questions_list[i],
unpack_multiples=True))
return sorted(labeled_results,
key=lambda result: result['meta']['_submission_time'])
def get_asset_data(asset_uids, last_submit_time):
global kobo
if kobo is None:
printMsgAndQuit(
"Error in get_asset_data: KoboExtractor not initialised.")
new_data = []
for asset_uid in asset_uids:
asset_data = kobo.get_data(asset_uid, submitted_after=last_submit_time)
#asset_data = kobo.get_data(asset_uid, query='{"_id":80353595}') # Get one of Heiko's responses for v34
if asset_uid == 'aAYAW5qZHEcroKwvEq8pRb':
# Special treatment to re-arrange the interview version
asset_data = move_question(asset_data, 'S40/residence',
'S10/residence')
asset_data = move_question(asset_data, 'S40/residence_99',
'S10/residence_99')
asset_data = move_question(asset_data, 'S10/email', 'S70/email')
new_data.append(asset_data)
return new_data