I have a couple of MongoDB documents wherein one my the fields is best represented as a matrix (numpy array). I would like to save this document to MongoDB, how do I do this?
{
'name' : 'subject1',
'image_name' : 'blah/foo.png',
'feature1' : np.array(...)
}
For a 1D numpy array, you can use lists:
# serialize 1D array x
record['feature1'] = x.tolist()
# deserialize 1D array x
x = np.fromiter( record['feature1'] )
For multidimensional data, I believe you'll need to use pickle and pymongo.binary.Binary:
# serialize 2D array y
record['feature2'] = pymongo.binary.Binary( pickle.dumps( y, protocol=2) ) )
# deserialize 2D array y
y = pickle.loads( record['feature2'] )
pickle.dumps(y, protocol=2), which results in a more compact and fast binary representation of the data.
binary.Binary has been moved to bson
TypeError: Required argument 'dtype' (pos 2) not found
cPickle by @AlexGaudio is relevant for Python 2 only. Nowadays (Python 3), the accelerated libraries are used by defaultThe code pymongo.binary.Binary(...) didnt work for me, may be we need to use bson as @tcaswell suggested.
Anyway here is one solution for multi-dimensional numpy array
>>from bson.binary import Binary
>>import pickle
# convert numpy array to Binary, store record in mongodb
>>record['feature2'] = Binary(pickle.dumps(npArray, protocol=2), subtype=128 )
# get record from mongodb, convert Binary to numpy array
>> npArray = pickle.loads(record['feature2'])
Having said that, the credit goes to MongoWrapper used the code written by them.
We've built an open source library for storing numeric data (Pandas, numpy, etc.) in MongoDB:
https://github.com/manahl/arctic
Best of all it's really easy to use, pretty fast and supports data versioning, multiple data libraries and more.
I know this is an old question but here is an elegant solution which works in new versions of pymongo:
import pickle
from bson.binary import Binary, USER_DEFINED_SUBTYPE
from bson.codec_options import TypeCodec, TypeRegistry, CodecOptions
import numpy as np
class NumpyCodec(TypeCodec):
python_type = np.ndarray
bson_type = Binary
def transform_python(self, value):
return Binary(pickle.loads(value), USER_DEFINED_SUBTYPE)
def transform_bson(self, value):
if value.subtype == USER_DEFINED_SUBTYPE:
return pickle.dumps(value, protocol=2)
return value
def get_codec_options():
numpy_codec = NumpyCodec()
type_registry = TypeRegistry([numpy_codec])
codec_options = CodecOptions(type_registry=type_registry)
return codec_options
def get_collection(name, db):
codec_options = get_codec_options()
return db.get_collection(name, codec_options=codec_options)
Then you can get you collection this way:
from pymongo import MongoClient
client = MongoClient()
db = client['my_db']
my_collection = get_collection('my_collection', db)
Afterwards, you just insert and find with Numpy arrays in your database transparently.
Have you tried Monary?
They have examples on the site
Have you try MongoWrapper, i think it simple :
import monogowrapper as mdb
db = mdb.MongoWrapper(dbName='test',
collectionName='test_collection',
hostname="localhost",
port="27017")
my_dict = {"name": "Important experiment",
"data":np.random.random((100,100))}
The dictionary's just as you'd expect it to be:
print my_dict
{'data': array([[ 0.773217, 0.517796, 0.209353, ..., 0.042116, 0.845194,
0.733732],
[ 0.281073, 0.182046, 0.453265, ..., 0.873993, 0.361292,
0.551493],
[ 0.678787, 0.650591, 0.370826, ..., 0.494303, 0.39029 ,
0.521739],
...,
[ 0.854548, 0.075026, 0.498936, ..., 0.043457, 0.282203,
0.359131],
[ 0.099201, 0.211464, 0.739155, ..., 0.796278, 0.645168,
0.975352],
[ 0.94907 , 0.363454, 0.912208, ..., 0.480943, 0.810243,
0.217947]]),
'name': 'Important experiment'}
db.save(my_dict)
my_loaded_dict = db.load({"name":"Important experiment"})
pickle)?