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Install mopidy: ##N.B: You might need to do this as per instructions on `https://docs.mopidy.com/en/latest/installation/debian/`
`sudo apt-get update && sudo apt-get install mopidy` 

Set the mopidy to run automatically via `sudo systemctl enable mopidy`

Now we need to set up configuration files.

Since we're going to be running mopidy as a service, we need to edit `/etc/mopidy/mopidy.conf`(see https://docs.mopidy.com/en/latest/service/#service)

Add the following to to configuration file:

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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.framework import ops
from tensorflow.python.training import optimizer
import tensorflow as tf

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from __future__ import print_function
import tensorflow as tf
import numpy as np

from glob import glob
import os

import TensorflowUtils as utils
import read_MITSceneParsingData as scene_parsing
import datetime

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import scipy.io as sio
import numpy as np
import scipy.misc

mat_contents = sio.loadmat('LungTrainingData.mat')
#matrix is of the form [x,y,n]

#['CT2', 'Right2', 'Left2', '__header__', '__globals__', '__version__', 'Trachea2']
images = mat_contents['CT2']
left_lung = mat_contents['Left2']

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import glob
import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()
ax = plt.subplot(111)



def savitzky_golay(y, window_size, order, deriv=0, rate=1):

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from __future__ import division
import numpy as np
import tensorflow as tf

# Launch the graph
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
with tf.Session(config=config) as sess:

    NUM_EXAMPLES = 8096

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def combine_weights(in_list):
    """
    Returns a 1D tensor of the input list of (nested lists of) tensors, useful
    for doing things like comparing current weights with old weights for EWC.
    
    1.) For all elements in input list, (ln 3)
          if a list combine it recursively 
          else leave it alone
    2.) From resulting list, get all non-none elements and flatten them (ln 2)
    3.) If resulting list is empty return None (ln 1)

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using System.Collections;
using System.Collections.Generic;
using UnityEngine;
using UnityEngine.UI;

[ExecuteInEditMode]
public class DynBoneCopy : MonoBehaviour {

    public DynamicBone template;
    public bool Make_Permenant;

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import numpy as np
import tensorflow as tf

"""
# Generate some random data
# data   = n x m,  n = num examples, m = number features
# labels = n x c,  n = num examples, c = number classes
"""

KARPATHY_CONST = 0.00025

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========================================
A simple solution
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How To Make Your Own VR Headset

First, let's start with the basics. It's not a bad idea to make your own headset. The best way to make it is to buy an Oculus Rift.


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