"""Extract study identifiers and results"""

tree = etree.parse(file_path)

study_robs = []

studies = []

# Find out NTC******** ids in the XMLs. Turns out they appear as study_id

# xpatheval = etree.XPathEvaluator(tree)

# matches = xpatheval("//*[contains(*,'NCT')]")

# for match in matches:

# print(file_path)

# print(match.tag)

# print(match.attrib)

# print(match.text)

quality_item_data_entries = tree.findall('//QUALITY_ITEM_DATA_ENTRY')

# Get risk of bias

for quality_item_data_entry in quality_item_data_entries:

# Get the results from a QUALITY_ITEM_DATA_ENTRY element

study_rob = {}

study_rob['study_id'] = quality_item_data_entry.attrib['STUDY_ID']

study_rob['modified'] = quality_item_data_entry.attrib.get('MODIFIED', '')

study_rob['result'] = quality_item_data_entry.attrib['RESULT']

study_rob['group_id'] = quality_item_data_entry.attrib.get('GROUP_ID', '')

study_rob['group_name'] = ''

for description in quality_item_data_entry.iter('P'):

study_rob['result_description'] = description.text

# Get info about the rob from the parent QUALITY_ITEM element

quality_item = quality_item_data_entry.getparent().getparent()

study_rob['rob_id'] = quality_item.attrib['ID']

study_rob['rob_name'] = quality_item.findtext('NAME')

rob_description = quality_item.find('DESCRIPTION/P')

study_rob['rob_description'] = rob_description.text

for group in quality_item.iter('QUALITY_ITEM_DATA_ENTRY_GROUP'):

group_id = group.attrib.get('ID')

if group_id == study_rob['group_id']:

study_rob['group_name'] = group.findtext('NAME')

study_robs.append(study_rob)

included_studies = tree.find('//INCLUDED_STUDIES')

#Get references

for study in included_studies.iter('STUDY'):

study_info = {}

study_info['file'] = file_path

study_info['id'] = study.attrib['ID']

corresponding_robs = [rob for rob in study_robs

if rob['study_id'] == study_info['id']]

study_info['robs'] = corresponding_robs

study_info['study_type'] = study.attrib['DATA_SOURCE']

study_info['references'] = []

for reference in study.iter('REFERENCE'):

ref = {}

ref['type'] = reference.attrib['TYPE']

ref['authors'] = reference.findtext('AU') or ''

ref['title'] = reference.findtext('TI') or ''

ref['source'] = reference.findtext('SO') or ''

ref['year'] = reference.findtext('YR') or ''

ref['vl'] = reference.findtext('VL') or ''

ref['no'] = reference.findtext('NO') or ''

ref['pg'] = reference.findtext('PG') or ''

ref['country'] = reference.findtext('CY') or ''

ref['identifiers'] = []

for identifier in reference.iter('IDENTIFIER'):

ident = deepcopy(identifier.attrib)

ident = { key.lower(): ident[key] for key in ident

if key not in ['MODIFIED', 'MODIFIED_BY'] }

ref['identifiers'].append(ident)

study_info['references'].append(ref)

studies.append(study_info)

with open(filename, 'w') as csvfile:

writer = csv.DictWriter(csvfile, fieldnames=headers, extrasaction='ignore', quoting=csv.QUOTE_ALL)

writer.writeheader()

for file_studies in all_studies:

for study in file_studies:

study_info = { key: study[key] for key in study

if key not in ['robs', 'references'] }

for rob in study[result_key]:

rob.update(study_info)

"""Recursively converts dictionary keys to strings."""

if not isinstance(dictionary, dict):

return dictionary

return dict((str(k), convert_keys_to_string(v))