Notes on setting up MongoDB to act as a place for us to store processed features.
Will be using to do this PyMongo and a MongoDB instance on mongolab to do this. Hopefully, these notes should be enough for everyone else to get the hang of it as well.
We might have problems with the space available on mongolab for our purposes. In that case, everyone might have to have a database running locally or we could have one running in the forum and instances running in other locations synced through Google Drive.
Have installed the package python-pymongo 2.7.2-1 from the official Arch Linux repos.
Created an account called Neuroglycerin and a deployment database on there called features.
I will have to create users for everyone that wants access to this sandbox database. Created myself a user and am connecting to it as a client below.
In [1]:
import pymongo
In [38]:
# reading in my user name and password from file so you can't read it
with open("/home/gavin/.neuromongo.user") as f:
details = f.readlines()
# little list comprehension to strip out \ns
details = [x.strip("\n") for x in details]
In [39]:
# define a client instance
client = pymongo.MongoClient('mongodb://{0}:{1}@ds063859.mongolab.com:63859/features'.format(*details))
In [46]:
db = client['features']
In [47]:
collection = db['Dog_1']
So, the structure I wanted from the database, from the email, was:
As I can only think in Python, this is how it should be constructed(dictionaries are {}):
{subject_key:{feature_tag:{description:"string description", feature_data:data, ...}, ...}, ...}
But, the collection of features doesn't act like a dictionary, it appears to be more like a list. So, the structure becomes:
{subject_key:[name:"feature name", description:"string description", feature_data:data, ...], ...}
So, I'll build that structure and put in a dummy feature.
In [48]:
import numpy as np
First, actually, I'll have to define how the processed feature data should be structured to take into account mapping from the segment files:
"data":{"preictal/interictal/test":{"1":[value, ...], ...}, ...}
Note that inside the preictal/interictal/test dictionary the data is indexed by an integer, but that this has to be formatted to a string for the database to operate (which is annoying).
In [49]:
preictal,interictal,test = {},{},{}
# filling dummy data
for i in range(1,600):
si = str(i)
if i <= 480:
interictal[si] = [np.random.rand()]
if i <= 24:
preictal[si] = [np.random.rand()]
if i <= 502:
test[si] = [np.random.rand()]
In [50]:
data = {"preictal":preictal,"interictal":interictal,"test":test}
In [57]:
dummy = {"name":"dummy", "description":"A useless dummy feature.", "data":data}
In [78]:
# then we just insert this into our Dog_1 collection
dummy_id = collection.insert(dummy)
In [77]:
dummy = {"name":"the dummy feature", "description":"A useless dummy feature.", "data":data}
In [78]:
# then we just insert this into our Dog_1 collection
dummy_id = collection.insert(dummy)
And that's it, it's online, I can see it in the web dashboard and everything. Can even list the collection here:
In [60]:
db.collection_names()
Out[60]:
['system.indexes', 'system.users', 'Dog_1']
In [68]:
collection.find_one({"name":"dummy"})
Out[68]:
{'_id': ObjectId('542883972a0c4e0963a26ad9'),
'name': 'dummy',
'data': {'test': {'186': [0.3964093126393088],
'90': [0.17411617316575023],
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'96': [0.9685633478573],
'437': [0.9026742829853768],
'24': [0.6972407871371145],
'45': [0.5674501229108595],
'409': [0.988793151052608],
'67': [0.4861746306595661],
'450': [0.46625589030003],
'158': [0.5803645541152124],
'322': [0.9222198642489626],
'365': [0.07150769526308876],
'91': [0.1673085049943861],
'145': [0.3432536873514689],
'348': [0.7789222008640231],
'267': [0.5904998424659462],
'15': [0.2833140718036006],
'1': [0.46082900406327765],
'426': [0.48314984962888385],
'196': [0.6209209346552017],
'147': [0.5432794313899858],
'119': [0.5376580405865439],
'281': [0.9618578227788076],
'387': [0.30565248799475564],
'354': [0.462787661944006]},
'preictal': {'19': [0.5751373782630792],
'2': [0.32036944199450523],
'4': [0.982187171857782],
'13': [0.950038017344332],
'21': [0.224641541534928],
'24': [0.7855452393806281],
'18': [0.24721538682439104],
'16': [0.8714695635751384],
'22': [0.8646691776166601],
'7': [0.716693491388096],
'20': [0.6254787648791436],
'14': [0.6484754472953268],
'9': [0.6211821535086461],
'3': [0.5419671435228954],
'6': [0.34701091763113157],
'17': [0.5289655826833892],
'10': [0.9245822446174692],
'1': [0.09873317265533987],
'12': [0.5867740670618006],
'8': [0.5211401849999311],
'11': [0.18748837706706922],
'5': [0.7084182630281614],
'15': [0.9417857549148925],
'23': [0.12522105286576235]}},
'description': 'A useless dummy feature.'}
In [79]:
for feature in collection.find():
print(feature["name"])
dummy
the dummy feature
In [80]:
# first find it's id
dummyid = collection.find_one({"name":"the dummy feature"})['_id']
# then remove it
collection.remove(spec_or_id=dummyid)
Out[80]:
{'err': None,
'n': 1,
'ok': 1.0,
'connectionId': 155160,
'lastOp': Timestamp(1411941997, 1)}
Checking if it's still there by looking through all the documents in this collection:
In [81]:
for feature in collection.find():
print(feature["name"])
dummy
Content source: Neuroglycerin/hail-seizure
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