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Vagrant.configure("2") do |config|
  config.vm.box = "ubuntu/trusty64"
  config.vm.provider "virtualbox" do |vb|
    vb.memory="2048"
  end
  config.vm.network :forwarded_port, host: 8888, guest: 8888,
  	auto_correct: true
  config.vm.provision :shell, inline: <<-SHELL
sudo apt-get update
sudo apt-get install -y ipython-notebook

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from oii.ifcb2.session import session

from oii.ifcb2.identifiers import parse_pid, get_timestamp
from oii.ifcb2.orm import Bin, TimeSeries

page_size = 5000

tses = session.query(TimeSeries).all()

for ts in tses:

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import numpy as np
from scipy import ndimage as ndi

# lookup tables for bwmorph_thin

G123_LUT = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,
       0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0,
       1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,

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import numpy as np

def makeGaussian(size, fwhm = 3, center=None):
    """ Make a square gaussian kernel.

    size is the length of a side of the square
    fwhm is full-width-half-maximum, which
    can be thought of as an effective radius.
    """

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import numpy as np
from sklearn import datasets

D = datasets.load_digits()

from sklearn.ensemble import RandomForestClassifier

split = D.target.size // 2
rfc = RandomForestClassifier()
rfc.fit(D.data[:split], D.target[:split])

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