def merge_answers(self, question, answers, merge_to=None):
"""
Merge one or more answer values together, keeping the sequence of answers the same. References to the
merged answers in criteria or caveats will be replaced with a reference to the 'merge_to' answer.
examples:
self.merge_answers(53, ['low-medium', 'medium'], merge_to='medium')
will change all entries with 'low-medium' answer values to 'medium'.
self.merge_answers(53, ['low-medium', 'medium'], merge_to='high')
will change all entries with 'low-medium' or 'medium' answer values to 'high'.
If merge_to is omitted, defaults to the first answer listed in the answers argument.
self.merge_answers(53, ['low-medium', 'medium'])
will change all entries with 'medium' answer values to 'low-medium'.
In all cases the merged answers are subsequently deleted.
pandas was a really terrible choice, internally.
:param question:
:param answers:
:param merge_to:
:return:
"""
if isinstance(answers, str):
answers = [answers]
cur = self._questions[question].valid_answers
ans_ind = [indices(cur, lambda x: x == ans)[0] for ans in answers]
if merge_to is None:
merge_ind = ans_ind[0]
else:
merge_ind = indices(cur, lambda x: x == merge_to)[0]
mapping = range(len(cur))
for i in mapping:
if i in ans_ind:
mapping[i] = merge_ind
print ("Merging answers into %s:" % cur[merge_ind])
for i in ans_ind:
print (" %s" % cur[i])
if ifinput("Really continue?", "y") != "y":
print ("NOT merged.")
return
new_cri, new_cav = self._remap_answers(question, mapping)
self._criteria = new_cri
self._caveats = new_cav
for i in ans_ind:
if i != merge_ind:
self.delete_answer(question, cur[i])
def merge_answers(self, question, answers, merge_to=None):
"""
Merge one or more answer values together, keeping the sequence of answers the same. References to the
merged answers in criteria or caveats will be replaced with a reference to the 'merge_to' answer.
examples:
self.merge_answers(53, ['low-medium', 'medium'], merge_to='medium')
will change all entries with 'low-medium' answer values to 'medium'.
self.merge_answers(53, ['low-medium', 'medium'], merge_to='high')
will change all entries with 'low-medium' or 'medium' answer values to 'high'.
If merge_to is omitted, defaults to the first answer listed in the answers argument.
self.merge_answers(53, ['low-medium', 'medium'])
will change all entries with 'medium' answer values to 'low-medium'.
In all cases the merged answers are subsequently deleted.
pandas was a really terrible choice, internally.
:param question:
:param answers:
:param merge_to:
:return:
"""
if isinstance(answers, str):
answers = [answers]
cur = self._questions[question].valid_answers
ans_ind = [indices(cur, lambda x: x == ans)[0] for ans in answers]
if merge_to is None:
merge_ind = ans_ind[0]
else:
merge_ind = indices(cur, lambda x: x == merge_to)[0]
mapping = range(len(cur))
for i in mapping:
if i in ans_ind:
mapping[i] = merge_ind
print('Merging answers into %s:' % cur[merge_ind])
for i in ans_ind:
print(' %s' % cur[i])
new_cri, new_cav = self._remap_answers(question, mapping)
self._criteria = new_cri
self._caveats = new_cav
for i in ans_ind:
if i != merge_ind:
self.delete_answer(question, cur[i])
def _cri_for_record(self, index, record="question", answer=None):
fieldname = {"question": "QuestionID", "target": "TargetID"}[record]
criteria = self._criteria[self._criteria[fieldname] == index].copy()
# TODO: make this return 'satisfies' entries as well
if answer is not None:
if record == "target":
print ("Ignoring answer value for target-based query")
else:
answer_value = indices(self._questions[index].valid_answers, lambda x: x == answer)
if len(answer_value) != 1:
print ("Got spurious answer matching results: %s" % answer_value)
else:
# filter to only passing answers
criteria = criteria[criteria["Threshold"] <= answer_value]
# re-encode answer value with answer text (and call it Answer Value)
self._replace_field_with_answer(criteria)
return criteria
def _cri_for_record(self, index, record='question', answer=None):
fieldname = {'question': 'QuestionID', 'target': 'TargetID'}[record]
criteria = self._criteria[self._criteria[fieldname] == index].copy()
# TODO: make this return 'satisfies' entries as well
if answer is not None:
if record == 'target':
print('Ignoring answer value for target-based query')
else:
answer_value = indices(self._questions[index].valid_answers,
lambda x: x == answer)
if len(answer_value) != 1:
print('Got spurious answer matching results: %s' %
answer_value)
else:
# filter to only passing answers
criteria = criteria[criteria['Threshold'] >= answer_value]
# re-encode answer value with answer text (and call it Answer Value)
self._replace_field_with_answer(criteria)
return criteria
def __init__(self):
"""
:return:
"""
spreadsheet_data = load_default_set() # TODO: make this flexible
attribute_set = spreadsheet_data.Attributes
note_set = spreadsheet_data.Notations
t_a_targets = []
t_a_attrs = []
q_a_questions = []
q_a_attrs = []
cri_questions = []
cri_thresholds = []
cri_targets = []
cav_questions = []
cav_targets = []
cav_answers = []
cav_notes = []
# create mapping of dict keys to series
q_index = 0
q_dict = dict()
questions = spreadsheet_data.Questions.iterkeys()
for k in sorted(questions):
if k not in q_dict:
# if it's not mapped, map it and all its synonyms
q_dict[k] = q_index
v = spreadsheet_data.Questions[k]
if len(v.synonyms) > 0:
for syn in v.synonyms:
q_dict[syn] = q_index
q_index += 1
else:
# already in the dict- nothing to do
pass
# create target_enum
target_enum = []
t_dict = dict()
targets = spreadsheet_data.Targets.iterkeys()
for k in sorted(targets):
t_index = len(target_enum)
v = spreadsheet_data.Targets[k]
target_enum.append(MsspTarget(v))
t_dict[k] = t_index
for a in v.attrs:
t_a_targets.append(t_index)
t_a_attrs.append(attribute_set.get_index(a))
# create question_enum
question_enum = []
for i in range(0, q_index):
question_enum.append(MsspQuestion())
# populate question_enum, criteria, caveats
for k, v in spreadsheet_data.Questions.iteritems():
q_i = q_dict[k]
question_enum[q_i].append(v, q_dict)
my_valid_answers = [
cast_answer(ans) for ans in question_enum[q_i].valid_answers
]
for attr in v.attrs:
q_a_questions.append(q_i)
q_a_attrs.append(attribute_set.get_index(attr))
for cross_index, element in v.criteria_mappings:
# criteria_mappings is (cross-index, element)
# lookup target index
t_i = t_dict[(v.selector, cross_index)]
# lookup threshold
answer = cast_answer(
element.text) # convert to 'Yes' / 'No' if applicable
thresh = indices(my_valid_answers, lambda z: answer == z)
if len(thresh) == 0:
print "QuestionID {0}, TargetID {1}, text {2}: no threshold found.".format(
q_i, t_i, element.text)
print " valid answers: %s" % my_valid_answers
thresh = [None]
cri_questions.append(q_i)
cri_targets.append(t_i)
cri_thresholds.append(thresh[0])
for answer, mapping in v.caveat_mappings:
# caveat_mappings is (answer, (cross-index, element))
cross_index, element = mapping
# lookup target index
t_i = t_dict[(v.selector, cross_index)]
# lookup answer sense
answer = cast_answer(
answer) # convert to 'Yes' / 'No' if applicable
ans_i = indices(my_valid_answers, lambda z: answer == z)
n_i = note_set.get_index(element)
cav_questions.append(q_i)
cav_targets.append(t_i)
if len(ans_i) > 0:
cav_answers.append(ans_i[0])
else:
print "QuestionID {0}, TargetID {1}, cast answer '{2}' unparsed.".format(
q_i, t_i, answer)
print " valid answers: %s" % my_valid_answers
cav_answers.append(None)
cav_notes.append(n_i)
# create pandas tables
question_attributes = pd.DataFrame({
"QuestionID": q_a_questions,
"AttributeID": q_a_attrs
}).drop_duplicates()
target_attributes = pd.DataFrame({
"TargetID": t_a_targets,
"AttributeID": t_a_attrs
})
criteria = pd.DataFrame({
"QuestionID": cri_questions,
"Threshold": cri_thresholds,
"TargetID": cri_targets
})
caveats = pd.DataFrame({
"QuestionID": cav_questions,
"TargetID": cav_targets,
"Answer": cav_answers,
"NoteID": cav_notes
})
super(XlsImporter,
self).__init__(attribute_set, note_set, question_enum,
target_enum, question_attributes,
target_attributes, criteria, caveats,
spreadsheet_data.colormap)
def __init__(self, file_ref):
"""
Constructs an MsspDataStore object from a collection of JSON dictionaries.
:param file_ref: file or directory containing the json data
:return: an MsspDataStore
"""
json_in = read_json(file_ref)
# first thing to do is build the attribute and note lists
colormap = pd.DataFrame(json_in['colormap'])
attribute_set = SemanticElementSet.from_json(json_in['attributes'])
note_set = SemanticElementSet.from_json(json_in['notes'], colormap=colormap)
question_enum = [None] * (1 + max([i['QuestionID'] for i in json_in['questions']]))
target_enum = [None] * (1 + max([i['TargetID'] for i in json_in['targets']]))
t_a_targets = []
t_a_attrs = []
q_a_questions = []
q_a_attrs = []
cri_questions = []
cri_thresholds = []
cri_targets = []
cav_questions = []
cav_targets = []
cav_answers = []
cav_notes = []
for t in json_in['targets']:
# need to preserve IDs because of criteria and caveat maps
t_index = t['TargetID']
try:
target_enum[t_index] = MsspTarget.from_json(t)
except IndexError:
print 'Index error at {0}'.format(t_index)
print t
# add _attributes to element set and build mapping
for a in t['Attributes']:
# a is a text string
t_a_targets.append(t_index)
t_a_attrs.append(uuid.UUID(a))
for q in json_in['questions']:
# need to preserve IDs because of criteria and caveat maps
q_index = q['QuestionID']
question_enum[q_index] = MsspQuestion.from_json(q)
# add _attributes to element set and build mapping
for a in q['Attributes']:
# a is a text string
q_a_questions.append(q_index)
q_a_attrs.append(uuid.UUID(a))
for cri in json_in['criteria']:
# the threshold is a literal entry from the question's valid_answers-
# needs to be converted into an index
q_index = cri['QuestionID']
t_index = cri['TargetID']
thresh = indices(question_enum[q_index].valid_answers, lambda k: cri['Threshold'] == k)
if len(thresh) == 0:
print "QuestionID {0}, TargetID {1}, text {2}: no threshold found.".format(
q_index, t_index, cri['Threshold'])
thresh = [None]
cri_questions.append(q_index)
cri_targets.append(t_index)
cri_thresholds.append(thresh[0])
_cav_detect_flag = False
for cav in json_in['caveats']:
# the answer is a literal entry from the question's valid answers-
# needs to be converted into an index.
# the color needs to be converted into a colormap RGB.
q_index = cav['QuestionID']
t_index = cav['TargetID']
if question_enum[q_index] is None:
print "Question {0} is none! Skipping this entry".format(q_index)
elif 'Answers' in cav: # new way
for ans in cav['Answers']:
if 'NoteID' in ans:
note_id = uuid.UUID(ans['NoteID'])
ans_i = indices(question_enum[q_index].valid_answers, lambda k: ans['Answer'] == k)
if len(ans_i) == 0:
print "QuestionID {0}, TargetID {1}, valid answer '{2}' unparsed.".format(
q_index, t_index, ans['Answer'])
ans_i = [None]
cav_questions.append(q_index)
cav_targets.append(t_index)
cav_answers.append(ans_i[0])
cav_notes.append(note_id)
else: # old way
# the answer is a literal entry from the question's valid answers-
# needs to be converted into an index.
# the color needs to be converted into a colormap RGB.
if _cav_detect_flag is False:
print 'Loading old-style Caveats'
_cav_detect_flag = True
q_index = cav['QuestionID']
t_index = cav['TargetID']
note_id = uuid.UUID(cav['NoteID'])
if question_enum[q_index] is None:
print "Question {0} is none!".format(q_index)
ans_i = [None]
else:
ans_i = indices(question_enum[q_index].valid_answers, lambda k: cav['Answer'] == k)
if len(ans_i) == 0:
print "QuestionID {0}, TargetID {1}, valid answer '{2}' unparsed.".format(
q_index, t_index, cav['Answer'])
ans_i = [None]
cav_questions.append(q_index)
cav_targets.append(t_index)
cav_answers.append(ans_i[0])
cav_notes.append(note_id)
# create pandas tables
question_attributes = pd.DataFrame(
{
"QuestionID": q_a_questions,
"AttributeID": q_a_attrs
}
).drop_duplicates()
target_attributes = pd.DataFrame(
{
"TargetID": t_a_targets,
"AttributeID": t_a_attrs
}
)
criteria = pd.DataFrame(
{
"QuestionID": cri_questions,
"Threshold": cri_thresholds,
"TargetID": cri_targets
}
)
caveats = pd.DataFrame(
{
"QuestionID": cav_questions,
"TargetID": cav_targets,
"Answer": cav_answers,
"NoteID": cav_notes
}
)
super(JsonImporter, self).__init__(
attribute_set, note_set, question_enum, target_enum,
question_attributes, target_attributes, criteria, caveats,
colormap)
def __init__(self):
"""
:return:
"""
spreadsheet_data = load_default_set() # TODO: make this flexible
attribute_set = SemanticElementSet.from_element_set(spreadsheet_data.Attributes)
note_set = SemanticElementSet.from_element_set(spreadsheet_data.Notations)
t_a_targets = []
t_a_attrs = []
q_a_questions = []
q_a_attrs = []
cri_questions = []
cri_thresholds = []
cri_targets = []
cav_questions = []
cav_targets = []
cav_answers = []
cav_notes = []
# create mapping of dict keys to series
q_index = 0
q_dict = dict()
questions = spreadsheet_data.Questions.iterkeys()
for k in sorted(questions):
if k not in q_dict:
# if it's not mapped, map it and all its synonyms
q_dict[k] = q_index
v = spreadsheet_data.Questions[k]
if len(v.synonyms) > 0:
for syn in v.synonyms:
q_dict[syn] = q_index
q_index += 1
else:
# already in the dict- nothing to do
pass
# create target_enum
target_enum = []
t_dict = dict()
targets = spreadsheet_data.Targets.iterkeys()
for k in sorted(targets):
t_index = len(target_enum)
v = spreadsheet_data.Targets[k]
target_enum.append(MsspTarget(v))
t_dict[k] = t_index
for a in v.attrs:
t_a_targets.append(t_index)
t_a_attrs.append(attribute_set.get_index(a))
# create question_enum
question_enum = []
for i in range(0, q_index):
question_enum.append(MsspQuestion())
# populate question_enum, criteria, caveats
for k, v in spreadsheet_data.Questions.iteritems():
q_i = q_dict[k]
question_enum[q_i].append(v, q_dict)
my_valid_answers = [cast_answer(ans) for ans in question_enum[q_i].valid_answers]
for attr in v.attrs:
q_a_questions.append(q_i)
q_a_attrs.append(attribute_set.get_index(attr))
for cross_index, element in v.criteria_mappings:
# criteria_mappings is (cross-index, element)
# lookup target index
t_i = t_dict[(v.selector, cross_index)]
# lookup threshold
answer = cast_answer(element.text) # convert to 'Yes' / 'No' if applicable
thresh = indices(my_valid_answers, lambda z: answer == z)
if len(thresh) == 0:
print "QuestionID {0}, TargetID {1}, text {2}: no threshold found.".format(
q_i, t_i, element.text)
print " valid answers: %s" % my_valid_answers
thresh = [None]
cri_questions.append(q_i)
cri_targets.append(t_i)
cri_thresholds.append(thresh[0])
for answer, mapping in v.caveat_mappings:
# caveat_mappings is (answer, (cross-index, element))
cross_index, element = mapping
# lookup target index
t_i = t_dict[(v.selector, cross_index)]
# lookup answer sense
answer = cast_answer(answer) # convert to 'Yes' / 'No' if applicable
ans_i = indices(my_valid_answers, lambda z: answer == z)
n_i = note_set.get_index(element)
cav_questions.append(q_i)
cav_targets.append(t_i)
if len(ans_i) > 0:
cav_answers.append(ans_i[0])
else:
print "QuestionID {0}, TargetID {1}, cast answer '{2}' unparsed.".format(
q_i, t_i, answer)
print " valid answers: %s" % my_valid_answers
cav_answers.append(None)
cav_notes.append(n_i)
# create pandas tables
question_attributes = pd.DataFrame(
{
"QuestionID": q_a_questions,
"AttributeID": q_a_attrs
}
).drop_duplicates()
target_attributes = pd.DataFrame(
{
"TargetID": t_a_targets,
"AttributeID": t_a_attrs
}
)
criteria = pd.DataFrame(
{
"QuestionID": cri_questions,
"Threshold": cri_thresholds,
"TargetID": cri_targets
}
)
caveats = pd.DataFrame(
{
"QuestionID": cav_questions,
"TargetID": cav_targets,
"Answer": cav_answers,
"NoteID": cav_notes
}
)
super(XlsImporter, self).__init__(
attribute_set, note_set, question_enum, target_enum,
question_attributes, target_attributes, criteria, caveats,
spreadsheet_data.colormap)
def __init__(self, file_ref):
"""
Constructs an MsspDataStore object from a collection of JSON dictionaries.
:param file_ref: file or directory containing the json data
:return: an MsspDataStore
"""
json_in = read_json(file_ref)
# first thing to do is build the attribute and note lists
attribute_set = ElementSet()
note_set = ElementSet()
colormap = pd.DataFrame(json_in['colormap'])
question_enum = [None] * (1 + max([i['QuestionID'] for i in json_in['questions']]))
target_enum = [None] * (1 + max([i['TargetID'] for i in json_in['targets']]))
t_a_targets = []
t_a_attrs = []
q_a_questions = []
q_a_attrs = []
cri_questions = []
cri_thresholds = []
cri_targets = []
cav_questions = []
cav_targets = []
cav_answers = []
cav_notes = []
for t in json_in['targets']:
# need to preserve IDs because of criteria and caveat maps
t_index = t['TargetID']
try:
target_enum[t_index] = MsspTarget.from_json(t)
except IndexError:
print 'Index error at {0}'.format(t_index)
print t
# add _attributes to element set and build mapping
for a in t['Attributes']:
# a is a text string
a_index = attribute_set.add_element(Element(a)) # don't care about attribute colors
t_a_targets.append(t_index)
t_a_attrs.append(a_index)
for q in json_in['questions']:
# need to preserve IDs because of criteria and caveat maps
q_index = q['QuestionID']
question_enum[q_index] = MsspQuestion.from_json(q)
# add _attributes to element set and build mapping
for a in q['Attributes']:
# a is a text string
a_index = attribute_set.add_element(Element(a)) # don't care about attribute colors
q_a_questions.append(q_index)
q_a_attrs.append(a_index)
for cri in json_in['criteria']:
# the threshold is a literal entry from the question's valid_answers-
# needs to be converted into an index
q_index = cri['QuestionID']
t_index = cri['TargetID']
thresh = indices(question_enum[q_index].valid_answers, lambda k: cri['Threshold'] == k)
if len(thresh) == 0:
print "QuestionID {0}, TargetID {1}, text {2}: no threshold found.".format(
q_index, t_index, cri['Threshold'])
thresh = [None]
cri_questions.append(q_index)
cri_targets.append(t_index)
cri_thresholds.append(thresh[0])
for cav in json_in['caveats']:
# the answer is a literal entry from the question's valid answers-
# needs to be converted into an index.
# the color needs to be converted into a colormap RGB.
q_index = cav['QuestionID']
t_index = cav['TargetID']
rgb = colormap[colormap['ColorName'] == cav['Color']]['RGB'].iloc[0]
note_id = note_set.add_element(Element(cav['Note'], 0, rgb)) # add to note set or find if exists
if question_enum[q_index] is None:
print "Question {0} is none!".format(q_index)
ans_i = [None]
else:
ans_i = indices(question_enum[q_index].valid_answers, lambda k: cav['Answer'] == k)
if len(ans_i) == 0:
print "QuestionID {0}, TargetID {1}, valid answer '{2}' unparsed.".format(
q_index, t_index, cav['Answer'])
ans_i = [None]
cav_questions.append(q_index)
cav_targets.append(t_index)
cav_answers.append(ans_i[0])
cav_notes.append(note_id)
# create pandas tables
question_attributes = pd.DataFrame(
{
"QuestionID": q_a_questions,
"AttributeID": q_a_attrs
}
).drop_duplicates()
target_attributes = pd.DataFrame(
{
"TargetID": t_a_targets,
"AttributeID": t_a_attrs
}
)
criteria = pd.DataFrame(
{
"QuestionID": cri_questions,
"Threshold": cri_thresholds,
"TargetID": cri_targets
}
)
caveats = pd.DataFrame(
{
"QuestionID": cav_questions,
"TargetID": cav_targets,
"Answer": cav_answers,
"NoteID": cav_notes
}
)
super(JsonImporter, self).__init__(
attribute_set, note_set, question_enum, target_enum,
question_attributes, target_attributes, criteria, caveats,
colormap)
def __init__(self, file_ref):
"""
Constructs an MsspDataStore object from a collection of JSON dictionaries.
:param file_ref: file or directory containing the json data
:return: an MsspDataStore
"""
json_in = read_json(file_ref)
# first thing to do is build the attribute and note lists
attribute_set = ElementSet()
note_set = ElementSet()
colormap = pd.DataFrame(json_in['colormap'])
question_enum = [None] * (
1 + max([i['QuestionID'] for i in json_in['questions']]))
target_enum = [None] * (
1 + max([i['TargetID'] for i in json_in['targets']]))
t_a_targets = []
t_a_attrs = []
q_a_questions = []
q_a_attrs = []
cri_questions = []
cri_thresholds = []
cri_targets = []
cav_questions = []
cav_targets = []
cav_answers = []
cav_notes = []
for t in json_in['targets']:
# need to preserve IDs because of criteria and caveat maps
t_index = t['TargetID']
try:
target_enum[t_index] = MsspTarget.from_json(t)
except IndexError:
print 'Index error at {0}'.format(t_index)
print t
# add _attributes to element set and build mapping
for a in t['Attributes']:
# a is a text string
a_index = attribute_set.add_element(
Element(a)) # don't care about attribute colors
t_a_targets.append(t_index)
t_a_attrs.append(a_index)
for q in json_in['questions']:
# need to preserve IDs because of criteria and caveat maps
q_index = q['QuestionID']
question_enum[q_index] = MsspQuestion.from_json(q)
# add _attributes to element set and build mapping
for a in q['Attributes']:
# a is a text string
a_index = attribute_set.add_element(
Element(a)) # don't care about attribute colors
q_a_questions.append(q_index)
q_a_attrs.append(a_index)
for cri in json_in['criteria']:
# the threshold is a literal entry from the question's valid_answers-
# needs to be converted into an index
q_index = cri['QuestionID']
t_index = cri['TargetID']
thresh = indices(question_enum[q_index].valid_answers,
lambda k: cri['Threshold'] == k)
if len(thresh) == 0:
print "QuestionID {0}, TargetID {1}, text {2}: no threshold found.".format(
q_index, t_index, cri['Threshold'])
thresh = [None]
cri_questions.append(q_index)
cri_targets.append(t_index)
cri_thresholds.append(thresh[0])
for cav in json_in['caveats']:
# the answer is a literal entry from the question's valid answers-
# needs to be converted into an index.
# the color needs to be converted into a colormap RGB.
q_index = cav['QuestionID']
t_index = cav['TargetID']
rgb = colormap[colormap['ColorName'] ==
cav['Color']]['RGB'].iloc[0]
note_id = note_set.add_element(Element(
cav['Note'], 0, rgb)) # add to note set or find if exists
if question_enum[q_index] is None:
print "Question {0} is none!".format(q_index)
ans_i = [None]
else:
ans_i = indices(question_enum[q_index].valid_answers,
lambda k: cav['Answer'] == k)
if len(ans_i) == 0:
print "QuestionID {0}, TargetID {1}, valid answer '{2}' unparsed.".format(
q_index, t_index, cav['Answer'])
ans_i = [None]
cav_questions.append(q_index)
cav_targets.append(t_index)
cav_answers.append(ans_i[0])
cav_notes.append(note_id)
# create pandas tables
question_attributes = pd.DataFrame({
"QuestionID": q_a_questions,
"AttributeID": q_a_attrs
}).drop_duplicates()
target_attributes = pd.DataFrame({
"TargetID": t_a_targets,
"AttributeID": t_a_attrs
})
criteria = pd.DataFrame({
"QuestionID": cri_questions,
"Threshold": cri_thresholds,
"TargetID": cri_targets
})
caveats = pd.DataFrame({
"QuestionID": cav_questions,
"TargetID": cav_targets,
"Answer": cav_answers,
"NoteID": cav_notes
})
super(JsonImporter,
self).__init__(attribute_set, note_set, question_enum,
target_enum, question_attributes,
target_attributes, criteria, caveats, colormap)