dummy.py

MAX_TRACKS = 20
MAX_VECTORS = 200
SHOP_ID = 1
MAX_SECONDS_IN_THE_PAST = 30 * 60

from random import randint
from sklearn.datasets import make_regression
import numpy as np
import pylab
import time

def create_dataset_vector():
    to = randint(1, MAX_VECTORS)
    X, Y = make_regression(n_samples=to, n_features=1, n_informative=1,\
                           random_state=0, noise=35)

    # Only one feature
    X = X.T[0]

    to_millis = int(round(time.time()))
    from_millis = to_millis - randint(0, MAX_SECONDS_IN_THE_PAST)

    timestamps = map(int, np.linspace(from_millis, to_millis, to))

    print "Created %s [X, Y, ts] vectors, with timestamp range from %s to %s" % (
        to, from_millis, to_millis
    )
    return [{'x': x, 'y': y, 'timestamp': timestamp}
            for x, y, timestamp
            in np.array([X, Y, timestamps]).T
            ]


def main():

    data = {
        'X8123': {
            'tracks': {
                '1': create_dataset_vector(),
                '2': create_dataset_vector()
            }
        }
    }

    from pymongo import MongoClient
    client = MongoClient('5.101.98.142', 49153)
    client.dummy.data.insert(data)
    #curve_data, t = make_s_curve(100)
    #curve_data = curve_data.T[1:3]

    #pylab.figure()
    #pylab.scatter(X, Y, label='noisy measurements')
    #pylab.show()



if __name__ == '__main__':
    main()