def detail_aggregate():
raw_query_params = request.args.copy()
agg, datatype, queries = parse_join_query(raw_query_params)
if not agg:
agg = 'day'
# if no obs_date given, default to >= 90 days ago
if not raw_query_params.get('obs_date__ge'):
six_months_ago = datetime.now() - timedelta(days=90)
raw_query_params['obs_date__ge'] = six_months_ago.strftime('%Y-%m-%d')
if not raw_query_params.get('obs_date__le'):
raw_query_params['obs_date__le'] = datetime.now().strftime('%Y-%m-%d')
mt = MasterTable.__table__
valid_query, base_clauses, resp, status_code = make_query(
mt, queries['base'])
# check for valid output format
if datatype not in VALID_DATA_TYPE:
valid_query = False
resp['meta']['status'] = 'error'
resp['meta']['message'] = "'%s' is an invalid output format" % datatype
resp = make_response(json.dumps(resp, default=dthandler), 400)
resp.headers['Content-Type'] = 'application/json'
# check for valid temporal aggregate
if agg not in VALID_AGG:
valid_query = False
resp['meta']['status'] = 'error'
resp['meta'][
'message'] = "'%s' is an invalid temporal aggregation" % agg
resp = make_response(json.dumps(resp, default=dthandler), 400)
resp.headers['Content-Type'] = 'application/json'
if valid_query:
time_agg = func.date_trunc(agg, mt.c['obs_date'])
base_query = session.query(time_agg, func.count(mt.c.dataset_row_id))
dname = raw_query_params.get('dataset_name')
try:
dataset = Table('dat_%s' % dname,
Base.metadata,
autoload=True,
autoload_with=engine,
extend_existing=True)
valid_query, detail_clauses, resp, status_code = make_query(
dataset, queries['detail'])
except:
valid_query = False
resp['meta']['status'] = 'error'
if not dname:
resp['meta']['message'] = "dataset_name' is required"
else:
resp['meta']['message'] = "unable to find dataset '%s'" % dname
resp = make_response(json.dumps(resp, default=dthandler), 400)
resp.headers['Content-Type'] = 'application/json'
if valid_query:
pk = [p.name for p in dataset.primary_key][0]
base_query = base_query.join(dataset,
mt.c.dataset_row_id == dataset.c[pk])
for clause in base_clauses:
base_query = base_query.filter(clause)
for clause in detail_clauses:
base_query = base_query.filter(clause)
values = [
r for r in base_query.group_by(time_agg).order_by(
time_agg).all()
]
# init from and to dates ad python datetimes
from_date = truncate(parse(raw_query_params['obs_date__ge']), agg)
if 'obs_date__le' in raw_query_params.keys():
to_date = parse(raw_query_params['obs_date__le'])
else:
to_date = datetime.now()
items = []
dense_matrix = []
cursor = from_date
v_index = 0
while cursor <= to_date:
if v_index < len(values) and \
values[v_index][0].replace(tzinfo=None) == cursor:
dense_matrix.append((cursor, values[v_index][1]))
v_index += 1
else:
dense_matrix.append((cursor, 0))
cursor = increment_datetime_aggregate(cursor, agg)
dense_matrix = OrderedDict(dense_matrix)
for k in dense_matrix:
i = {
'datetime': k,
'count': dense_matrix[k],
}
items.append(i)
if datatype == 'json':
resp['objects'] = items
resp['meta']['status'] = 'ok'
resp['meta']['query'] = raw_query_params
loc = resp['meta']['query'].get('location_geom__within')
if loc:
resp['meta']['query'][
'location_geom__within'] = json.loads(loc)
resp['meta']['query']['agg'] = agg
resp = make_response(json.dumps(resp, default=dthandler),
status_code)
resp.headers['Content-Type'] = 'application/json'
elif datatype == 'csv':
outp = StringIO()
writer = csv.DictWriter(outp, fieldnames=items[0].keys())
writer.writeheader()
writer.writerows(items)
resp = make_response(outp.getvalue(), status_code)
resp.headers['Content-Type'] = 'text/csv'
filedate = datetime.now().strftime('%Y-%m-%d')
resp.headers[
'Content-Disposition'] = 'attachment; filename=%s.csv' % (
filedate)
return resp
def detail_aggregate():
raw_query_params = request.args.copy()
agg, datatype, queries = parse_join_query(raw_query_params)
if not agg:
agg = 'day'
# if no obs_date given, default to >= 90 days ago
if not raw_query_params.get('obs_date__ge'):
six_months_ago = datetime.now() - timedelta(days=90)
raw_query_params['obs_date__ge'] = six_months_ago.strftime('%Y-%m-%d')
if not raw_query_params.get('obs_date__le'):
raw_query_params['obs_date__le'] = datetime.now().strftime('%Y-%m-%d')
mt = MasterTable.__table__
valid_query, base_clauses, resp, status_code = make_query(mt, queries['base'])
# check for valid output format
if datatype not in VALID_DATA_TYPE:
valid_query = False
resp['meta']['status'] = 'error'
resp['meta']['message'] = "'%s' is an invalid output format" % datatype
resp = make_response(json.dumps(resp, default=dthandler), 400)
resp.headers['Content-Type'] = 'application/json'
# check for valid temporal aggregate
if agg not in VALID_AGG:
valid_query = False
resp['meta']['status'] = 'error'
resp['meta']['message'] = "'%s' is an invalid temporal aggregation" % agg
resp = make_response(json.dumps(resp, default=dthandler), 400)
resp.headers['Content-Type'] = 'application/json'
if valid_query:
time_agg = func.date_trunc(agg, mt.c['obs_date'])
base_query = session.query(time_agg, func.count(mt.c.dataset_row_id))
dname = raw_query_params.get('dataset_name')
try:
dataset = Table('dat_%s' % dname, Base.metadata,
autoload=True, autoload_with=engine,
extend_existing=True)
valid_query, detail_clauses, resp, status_code = make_query(dataset, queries['detail'])
except:
valid_query = False
resp['meta']['status'] = 'error'
if not dname:
resp['meta']['message'] = "dataset_name' is required"
else:
resp['meta']['message'] = "unable to find dataset '%s'" % dname
resp = make_response(json.dumps(resp, default=dthandler), 400)
resp.headers['Content-Type'] = 'application/json'
if valid_query:
pk = [p.name for p in dataset.primary_key][0]
base_query = base_query.join(dataset, mt.c.dataset_row_id == dataset.c[pk])
for clause in base_clauses:
base_query = base_query.filter(clause)
for clause in detail_clauses:
base_query = base_query.filter(clause)
values = [r for r in base_query.group_by(time_agg).order_by(time_agg).all()]
# init from and to dates ad python datetimes
from_date = truncate(parse(raw_query_params['obs_date__ge']), agg)
if 'obs_date__le' in raw_query_params.keys():
to_date = parse(raw_query_params['obs_date__le'])
else:
to_date = datetime.now()
items = []
dense_matrix = []
cursor = from_date
v_index = 0
while cursor <= to_date:
if v_index < len(values) and \
values[v_index][0].replace(tzinfo=None) == cursor:
dense_matrix.append((cursor, values[v_index][1]))
v_index += 1
else:
dense_matrix.append((cursor, 0))
cursor = increment_datetime_aggregate(cursor, agg)
dense_matrix = OrderedDict(dense_matrix)
for k in dense_matrix:
i = {
'datetime': k,
'count': dense_matrix[k],
}
items.append(i)
if datatype == 'json':
resp['objects'] = items
resp['meta']['status'] = 'ok'
resp['meta']['query'] = raw_query_params
loc = resp['meta']['query'].get('location_geom__within')
if loc:
resp['meta']['query']['location_geom__within'] = json.loads(loc)
resp['meta']['query']['agg'] = agg
resp = make_response(json.dumps(resp, default=dthandler), status_code)
resp.headers['Content-Type'] = 'application/json'
elif datatype == 'csv':
outp = StringIO()
writer = csv.DictWriter(outp, fieldnames=items[0].keys())
writer.writeheader()
writer.writerows(items)
resp = make_response(outp.getvalue(), status_code)
resp.headers['Content-Type'] = 'text/csv'
filedate = datetime.now().strftime('%Y-%m-%d')
resp.headers['Content-Disposition'] = 'attachment; filename=%s.csv' % (filedate)
return resp
def dataset():
raw_query_params = request.args.copy()
# set default value for temporal aggregation
agg = raw_query_params.get('agg')
if not agg:
agg = 'day'
else:
del raw_query_params['agg']
# if no obs_date given, default to >= 90 days ago
if not raw_query_params.get('obs_date__ge'):
six_months_ago = datetime.now() - timedelta(days=90)
raw_query_params['obs_date__ge'] = six_months_ago.strftime('%Y-%m-%d')
if not raw_query_params.get('obs_date__le'):
raw_query_params['obs_date__le'] = datetime.now().strftime('%Y-%m-%d')
# set datatype
datatype = 'json'
if raw_query_params.get('data_type'):
datatype = raw_query_params['data_type']
del raw_query_params['data_type']
q = '''
SELECT m.dataset_name
FROM meta_master AS m
LEFT JOIN celery_taskmeta AS c
ON c.id = (
SELECT id FROM celery_taskmeta
WHERE task_id = ANY(m.result_ids)
ORDER BY date_done DESC
LIMIT 1
)
WHERE m.approved_status = 'true'
AND c.status = 'SUCCESS'
'''
with engine.begin() as c:
dataset_names = [d[0] for d in c.execute(q)]
raw_query_params['dataset_name__in'] = ','.join(dataset_names)
mt = MasterTable.__table__
valid_query, query_clauses, resp, status_code = make_query(
mt, raw_query_params)
# check for valid output format
if datatype not in VALID_DATA_TYPE:
valid_query = False
resp['meta']['status'] = 'error'
resp['meta']['message'] = "'%s' is an invalid output format" % datatype
resp = make_response(json.dumps(resp, default=dthandler), 400)
resp.headers['Content-Type'] = 'application/json'
# check for valid temporal aggregate
if agg not in VALID_AGG:
valid_query = False
resp['meta']['status'] = 'error'
resp['meta'][
'message'] = "'%s' is an invalid temporal aggregation" % agg
resp = make_response(json.dumps(resp, default=dthandler), 400)
resp.headers['Content-Type'] = 'application/json'
if valid_query:
time_agg = func.date_trunc(agg, mt.c['obs_date'])
base_query = session.query(time_agg, func.count(mt.c['obs_date']),
mt.c['dataset_name'])
base_query = base_query.filter(mt.c['current_flag'] == True)
for clause in query_clauses:
base_query = base_query.filter(clause)
base_query = base_query.group_by(mt.c['dataset_name'])\
.group_by(time_agg)\
.order_by(time_agg)
values = [o for o in base_query.all()]
# init from and to dates ad python datetimes
from_date = truncate(parse(raw_query_params['obs_date__ge']), agg)
if 'obs_date__le' in raw_query_params.keys():
to_date = parse(raw_query_params['obs_date__le'])
else:
to_date = datetime.now()
# build the response
results = sorted(values, key=itemgetter(2))
for k, g in groupby(results, key=itemgetter(2)):
d = {'dataset_name': k}
items = []
dense_matrix = []
cursor = from_date
v_index = 0
dataset_values = list(g)
while cursor <= to_date:
if v_index < len(dataset_values) and \
dataset_values[v_index][0].replace(tzinfo=None) == cursor:
dense_matrix.append((cursor, dataset_values[v_index][1]))
v_index += 1
else:
dense_matrix.append((cursor, 0))
cursor = increment_datetime_aggregate(cursor, agg)
dense_matrix = OrderedDict(dense_matrix)
for k in dense_matrix:
i = {
'datetime': k,
'count': dense_matrix[k],
}
items.append(i)
d['items'] = items
resp['objects'].append(d)
resp['meta']['query'] = raw_query_params
loc = resp['meta']['query'].get('location_geom__within')
if loc:
resp['meta']['query']['location_geom__within'] = json.loads(loc)
resp['meta']['query']['agg'] = agg
resp['meta']['status'] = 'ok'
if datatype == 'json':
resp = make_response(json.dumps(resp, default=dthandler),
status_code)
resp.headers['Content-Type'] = 'application/json'
elif datatype == 'csv':
# response format
# temporal_group,dataset_name_1,dataset_name_2
# 2014-02-24 00:00:00,235,653
# 2014-03-03 00:00:00,156,624
fields = ['temporal_group']
for o in resp['objects']:
fields.append(o['dataset_name'])
csv_resp = []
i = 0
for k, g in groupby(resp['objects'],
key=itemgetter('dataset_name')):
l_g = list(g)[0]
j = 0
for row in l_g['items']:
# first iteration, populate the first column with temporal_groups
if i == 0:
csv_resp.append([row['datetime']])
csv_resp[j].append(row['count'])
j += 1
i += 1
csv_resp.insert(0, fields)
csv_resp = make_csv(csv_resp)
resp = make_response(csv_resp, 200)
resp.headers['Content-Type'] = 'text/csv'
filedate = datetime.now().strftime('%Y-%m-%d')
resp.headers[
'Content-Disposition'] = 'attachment; filename=%s.csv' % (
filedate)
return resp
def dataset():
raw_query_params = request.args.copy()
# set default value for temporal aggregation
agg = raw_query_params.get('agg')
if not agg:
agg = 'day'
else:
del raw_query_params['agg']
# if no obs_date given, default to >= 90 days ago
if not raw_query_params.get('obs_date__ge'):
six_months_ago = datetime.now() - timedelta(days=90)
raw_query_params['obs_date__ge'] = six_months_ago.strftime('%Y-%m-%d')
if not raw_query_params.get('obs_date__le'):
raw_query_params['obs_date__le'] = datetime.now().strftime('%Y-%m-%d')
# set datatype
datatype = 'json'
if raw_query_params.get('data_type'):
datatype = raw_query_params['data_type']
del raw_query_params['data_type']
mt = MasterTable.__table__
valid_query, query_clauses, resp, status_code = make_query(mt,raw_query_params)
# check for valid output format
if datatype not in VALID_DATA_TYPE:
valid_query = False
resp['meta']['status'] = 'error'
resp['meta']['message'] = "'%s' is an invalid output format" % datatype
resp = make_response(json.dumps(resp, default=dthandler), 400)
resp.headers['Content-Type'] = 'application/json'
# check for valid temporal aggregate
if agg not in VALID_AGG:
valid_query = False
resp['meta']['status'] = 'error'
resp['meta']['message'] = "'%s' is an invalid temporal aggregation" % agg
resp = make_response(json.dumps(resp, default=dthandler), 400)
resp.headers['Content-Type'] = 'application/json'
if valid_query:
time_agg = func.date_trunc(agg, mt.c['obs_date'])
base_query = session.query(time_agg,
func.count(mt.c['obs_date']),
mt.c['dataset_name'])
base_query = base_query.filter(mt.c['current_flag'] == True)
for clause in query_clauses:
base_query = base_query.filter(clause)
base_query = base_query.group_by(mt.c['dataset_name'])\
.group_by(time_agg)\
.order_by(time_agg)
values = [o for o in base_query.all()]
# init from and to dates ad python datetimes
from_date = truncate(parse(raw_query_params['obs_date__ge']), agg)
if 'obs_date__le' in raw_query_params.keys():
to_date = parse(raw_query_params['obs_date__le'])
else:
to_date = datetime.now()
# build the response
results = sorted(values, key=itemgetter(2))
for k,g in groupby(results, key=itemgetter(2)):
d = {'dataset_name': k}
items = []
dense_matrix = []
cursor = from_date
v_index = 0
dataset_values = list(g)
while cursor <= to_date:
if v_index < len(dataset_values) and \
dataset_values[v_index][0].replace(tzinfo=None) == cursor:
dense_matrix.append((cursor, dataset_values[v_index][1]))
v_index += 1
else:
dense_matrix.append((cursor, 0))
cursor = increment_datetime_aggregate(cursor, agg)
dense_matrix = OrderedDict(dense_matrix)
for k in dense_matrix:
i = {
'datetime': k,
'count': dense_matrix[k],
}
items.append(i)
d['items'] = items
resp['objects'].append(d)
resp['meta']['query'] = raw_query_params
loc = resp['meta']['query'].get('location_geom__within')
if loc:
resp['meta']['query']['location_geom__within'] = json.loads(loc)
resp['meta']['query']['agg'] = agg
resp['meta']['status'] = 'ok'
if datatype == 'json':
resp = make_response(json.dumps(resp, default=dthandler), status_code)
resp.headers['Content-Type'] = 'application/json'
elif datatype == 'csv':
# response format
# temporal_group,dataset_name_1,dataset_name_2
# 2014-02-24 00:00:00,235,653
# 2014-03-03 00:00:00,156,624
fields = ['temporal_group']
for o in resp['objects']:
fields.append(o['dataset_name'])
csv_resp = []
i = 0
for k,g in groupby(resp['objects'], key=itemgetter('dataset_name')):
l_g = list(g)[0]
j = 0
for row in l_g['items']:
# first iteration, populate the first column with temporal_groups
if i == 0:
csv_resp.append([row['datetime']])
csv_resp[j].append(row['count'])
j += 1
i += 1
csv_resp.insert(0, fields)
csv_resp = make_csv(csv_resp)
resp = make_response(csv_resp, 200)
resp.headers['Content-Type'] = 'text/csv'
filedate = datetime.now().strftime('%Y-%m-%d')
resp.headers['Content-Disposition'] = 'attachment; filename=%s.csv' % (filedate)
return resp