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import matplotlib.pyplot as plt

def plot_polygon(vertices):
    plt.figure()
    for i in range(len(vertices)-1):
        vertex_set = [ vertices[i] ] + [ vertices[i+1] ]
        x, y = zip(*vertex_set)
        plt.plot(x,y)

    vertex_set = [ vertices[0] ] + [ vertices[-1] ]

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[
    //makes go to lines easier
		{ "keys": ["super+;"], "command": "show_overlay", "args": {"overlay": "goto", "text": ":"} },
		
		// makes bookmarking easier
		{ "keys": ["f5"], "command": "toggle_bookmark" },
		{ "keys": ["f6"], "command": "next_bookmark" },
		{ "keys": ["super+f6"], "command": "prev_bookmark" }
]

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# Load the data streamer:
ds = IOutils.data_streamer(num_sets='all')

# obtains the unique rows in a dataset
def unique_rows(data):
    uniq = np.unique(data.view(data.dtype.descr * data.shape[1]))
    return uniq.view(data.dtype).reshape(-1, data.shape[1])
    
X, Y = ds.next()  
Y = np.array(Y)

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# your polar functions  dr/dt and d\[Theta]/dt here:
field = {r (1 - r^2) (4 - r^2), 2 - r^2};

# Creates the stream plot
StreamPlot[
 Evaluate@TransformedField["Polar" -> "Cartesian", 
   field, {r, \[Theta]} -> {x, y}], {x, -3, 3}, {y, -3, 3}]

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apt-get update
apt-get install -y git
apt-get install -y tmux
apt-get install -y python-pip
apt-get install -y python-numpy python-scipy
apt-get install -y python-matplotlib
apt-get install -y python-pandas
pip install scikit-learn
apt-get install -y ipython
pip install -e git+https://github.com/Theano/Theano.git@15c90dd3#egg=Theano==0.8.git

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# time_values is a signal of shape: (time, n_channels)

# window data tensor is of shape: (time, window_size, n_channels)
# where window_size just holds all the data for the T-window_size data.

window_data_tensor = np.zeros((time_values.shape[0], window_size, time_values.shape[1]))
for t in range(1, time_values.shape[0]):
    if t <= window_size:
        window_data_tensor[t, :, :] = np.pad(time_values[:t, :], ((0, window_size-t), (0,0)), mode='constant')
    elif t > window_size and t < time_values.shape[0]-window_size+1:

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var q = require('q');
var request = require('request');

var context = {
	query: 'hello how are you?'
} // global context to be mutated by all "plugins"

function hello_plugin(context){
	var query = context.query
	var contains_hello = false;

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import numpy as np
import time
# the population:
global population

population =  np.random.normal(-10,10, size=(10**6, 3))

def sample(args):
	centre, radii = args
	# print centre, radii

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"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

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