def query():
"""
given {start_time, end_time, grid_id [optional]}
return -> [{grid_id, volume}, ...]
"""
start_time = datetime.strptime(request.query.get('start_time'), '%Y-%m-%dT%H:%M:%SZ')
end_time = datetime.strptime(request.query.get('end_time'), '%Y-%m-%dT%H:%M:%SZ')
grid_id = request.query.get('grid_id')
mg = MongoDB()
mg.connect()
print('querying grid volumes...')
# if grid_id:
# results = mg.group_by([{'$match': {'created_at': {'$gt': start_time, '$lt': end_time}, 'grid_id': grid_id}},
# {'$group': {'_id': '$grid_id', 'count': {'$sum': 1}}}
# ])
# else:
# results = mg.group_by([{'$match': {'created_at': {'$gt': start_time, '$lt': end_time}}},
# {'$group': {'_id': '$grid_id', 'count': {'$sum': 1}}}
# ])
# group and count distinct user:
results = mg.group_by([ {'$match': {'created_at': {'$gt': start_time, '$lt': end_time}}},
{'$group': {'_id':{ 'grid_id':'$grid_id', 'user_id':'$user_id' }, 'count': {'$sum': 1} } },
{'$group': {'_id': '$_id.grid_id', 'count': {'$sum': 1}}}
])
ret = []
for result in results:
ret.append({'grid_id': result['_id'], 'volume': result['count']})
response.content_type = 'application/json'
return json_dumps(ret, indent=2)
def all_grids():
mg = MongoDB()
mg.connect()
griddb = GridDB()
print('querying grid volumes...')
results = mg.group_by([{'$match': {'created_at': {'$gt': datetime.strptime('2012-10-15T20:00:02Z', '%Y-%m-%dT%H:%M:%SZ'),
'$lt': datetime.strptime('2012-11-15T20:00:02Z', '%Y-%m-%dT%H:%M:%SZ')}}}]) # print(results)
griddb.add(results)
ret = Grid.get_raw_pandas_ts(results, 'D')
STL.seasonal_decomposition(ret)
def single_grid(grid_id):
mg = MongoDB()
mg.connect()
start_time = datetime.strptime('2012-10-15T20:00:02Z', '%Y-%m-%dT%H:%M:%SZ')
end_time = datetime.strptime('2012-11-15T20:00:02Z', '%Y-%m-%dT%H:%M:%SZ')
print('querying grid volumes...')
# results = mg.group_by([{'$match': {'created_at': {'$gt': start_time, '$lt': end_time}, 'grid_id': grid_id}},
# {'$group': {'_id': '$grid_id', 'count': {'$sum': 1}}}])
results = mg.group_by([{'$match': {'created_at': {'$gt': datetime.strptime('2012-10-15T00:00:00Z', '%Y-%m-%dT%H:%M:%SZ'),
'$lt': datetime.strptime('2012-11-15T00:00:00Z', '%Y-%m-%dT%H:%M:%SZ')},
'grid_id': grid_id}}]) # print(results)
print(results)
ret = Grid.get_raw_pandas_ts(results, 'H')
# print('------------')
# print(ret)
print(STL.seasonal_decomposition(ret))
print("tweets loaded")
# for grid_id in grid_db.grid_cache:
# print(grid_id, grid_db.grid_cache[grid_id].get_ts(grid_db.get_tweets(), 'H'))
exit()
mg = MongoDB()
mg.connect()
print("querying grid volumes...")
results = mg.group_by(
[
{
"$match": {
"created_at": {
"$gt": datetime.strptime("2012-10-15T20:00:02Z", "%Y-%m-%dT%H:%M:%SZ"),
"$lt": datetime.strptime("2012-10-22T20:00:02Z", "%Y-%m-%dT%H:%M:%SZ"),
}
}
}
]
) # print(results)
# results = mg.group_by([{'$match': {'created_at': {'$gt': datetime.strptime('2012-10-15T20:00:02Z', '%Y-%m-%dT%H:%M:%SZ'),
# '$lt': datetime.strptime('2012-11-15T20:00:02Z', '%Y-%m-%dT%H:%M:%SZ')}, 'grid_id': '34013011500'}}]) # print(results)
ret = Grid.get_raw_pandas_ts(results, "D")
# print('ret: ', ret)
# for result in Grid.get_ts(results, 'H'):
# print(result[0])
# # print('result: ', result)
# for tup in result:
# obj = []
