def list_issues():
cur = issues.find()
data = paginate_cursor(cur)
add_motions(data['results'])
return jsonify(data)
def list_issues():
cur = issues.find()
data = paginate_cursor(cur)
add_motions(data['results'])
return jsonify(data)
def generate_stances(blocs=[], issue_ids=[], filters={}):
keys = set(blocs)
_filt = {}
if len(issue_ids):
_filt = {'_id': {'$in': [ObjectId(i) for i in issue_ids]}}
issue_objs = list(issues.find(_filt))
motion_issues = defaultdict(list)
for issue in issue_objs:
for motion in issue.get('motions', []):
motion_issues[motion['motion_id']].append((issue, motion))
spec = dict(filters)
spec['motion.motion_id'] = {'$in': motion_issues.keys()}
keys.add('motion.motion_id')
options = get_options()
data = {}
# Aggregate on the server.
for cell in votes.group(keys, spec, {"votes": {}, 'num_votes': 0}, REDUCE):
# Aggregate by issue locally.
for issue, mdata in motion_issues.get(cell.get('motion.motion_id')):
# Output cell key.
key = repr([issue.get('_id')] + [cell.get(k) for k in set(blocs)])
if not key in data:
data[key] = {
'issue': issue,
'counts': defaultdict(int),
'weighted': defaultdict(int),
'bloc': {},
'stats': {
'num_motions': 0,
'num_votes': 0,
'max_score': 0,
'min_score': 0
}
}
for option in options:
v = cell.get('votes').get(option, 0)
data[key]['counts'][option] += v
data[key]['weighted'][option] += v * get_weight(mdata, option)
for k, v in cell.items():
if k in blocs:
data[key]['bloc'][k] = v
data[key]['stats']['num_motions'] += 1
data[key]['stats']['num_votes'] += cell['num_votes']
weights = map(lambda x: get_weight(mdata, x), options)
data[key]['stats']['max_score'] += cell['num_votes'] * max(weights)
data[key]['stats']['min_score'] += cell['num_votes'] * min(weights)
blocs = []
for bloc in data.values():
# determine the full range of available values:
value_range = bloc['stats']['max_score'] - bloc['stats']['min_score']
# sum up 'yes' and 'no' values:
bloc_value = sum(bloc['weighted'].values())
# make the bloc value fall in between 0 and value_range
normalized_value = bloc_value + (bloc['stats']['min_score'] * -1)
# calculate a score based on the normalized value and the value range
bloc['match'] = normalized_value / max(1, value_range)
blocs.append(bloc)
return blocs
def generate_stances(blocs=[], issue_ids=[], filters={}):
keys = set(blocs)
_filt = {}
if len(issue_ids):
_filt = {'_id': {'$in': [ObjectId(i) for i in issue_ids]}}
issue_objs = list(issues.find(_filt))
motion_issues = defaultdict(list)
for issue in issue_objs:
for motion in issue.get('motions', []):
motion_issues[motion['motion_id']].append((issue, motion))
spec = dict(filters)
spec['motion.motion_id'] = {'$in': motion_issues.keys()}
keys.add('motion.motion_id')
options = get_options()
data = {}
# Aggregate on the server.
for cell in votes.group(keys, spec, {"votes": {}, 'num_votes': 0}, REDUCE):
# Aggregate by issue locally.
for issue, mdata in motion_issues.get(cell.get('motion.motion_id')):
# Output cell key.
key = repr([issue.get('_id')] + [cell.get(k) for k in set(blocs)])
if not key in data:
data[key] = {
'issue': issue,
'counts': defaultdict(int),
'weighted': defaultdict(int),
'bloc': {},
'stats': {
'num_motions': 0,
'num_votes': 0,
'max_score': 0,
'min_score': 0
}
}
for option in options:
v = cell.get('votes').get(option, 0)
data[key]['counts'][option] += v
data[key]['weighted'][option] += v * get_weight(mdata, option)
for k, v in cell.items():
if k in blocs:
data[key]['bloc'][k] = v
data[key]['stats']['num_motions'] += 1
data[key]['stats']['num_votes'] += cell['num_votes']
weights = map(lambda x: get_weight(mdata, x), options)
data[key]['stats']['max_score'] += cell['num_votes'] * max(weights)
data[key]['stats']['min_score'] += cell['num_votes'] * min(weights)
blocs = []
for bloc in data.values():
# determine the full range of available values:
value_range = bloc['stats']['max_score'] - bloc['stats']['min_score']
# sum up 'yes' and 'no' values:
bloc_value = sum(bloc['weighted'].values())
# make the bloc value fall in between 0 and value_range
normalized_value = bloc_value + (bloc['stats']['min_score'] * -1)
# calculate a score based on the normalized value and the value range
bloc['match'] = normalized_value / max(1, value_range)
blocs.append(bloc)
return blocs