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function newCurve = spline3d(curve, dt) 
    % interpote a 3d curve using spline 
    % path 3*x 
    % newPath 3*x
    x = curve(1, :); 
    y = curve(2, :); 
    z = curve(3, :);
    t = cumsum([0;sqrt(diff(x(:)).^2 + diff(y(:)).^2 + diff(z(:)).^2)]); 
    sx = spline(t,x); 
    sy = spline(t,y); 

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function gplot3(A, xyz)
    % GPLOT3(A, xyz) is nearly the same as GPLOT(A, xy) except
    % that the xyz variable requires a third dimension.
    % This function takes an adjacency matrix and visualizes it
    % in 3D.
    [d e] = size(A);
    if d ~= e
        error('A matrix must be square.');
    end

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function quickPlot(varargin)
    % Quickly plot point clouds of data.
    % This works for up to 5 datasets of
    % 1D or 2D or 3D data, regardless of the
    % row-order or column-order format.
    %
    % Also works with 2D or 3D matrices.
    %
    % How to use this function:
    % --> Give it data. That's pretty much it.

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import urllib
import sys
import re

troll = "dQw4w9WgXcQ"

urlsource = sys.argv[1]

f = urllib.urlopen(urlsource)
s = f.readlines()

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In [1]: import numpy as np

In [2]: from scipy.spatial.distance import cdist

In [3]: from distlib import pairwise_cython_blas, pairwise_cython

In [4]: a = np.random.random(size=(1000,3))

In [5]: %timeit cdist(a,a)
100 loops, best of 3: 11.3 ms per loop

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import numpy as np
from collections import deque
import time
import threading
import matplotlib.pyplot as plt


def rollingFFT(s, n, dt):
    fy = np.fft.fft(s)
    # Frequencies associated with each samples

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'''
Convert Yelp Academic Dataset from JSON to CSV

Requires Pandas (https://pypi.python.org/pypi/pandas)

By Paul Butler, No Rights Reserved
'''

import json
import pandas as pd

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L1 cache reference                            0.5 ns
Branch mispredict                             5   ns
L2 cache reference                            7   ns             14x L1 cache
Mutex lock/unlock                            25   ns
Main memory reference                       100   ns             20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy              3,000   ns
Send 1K bytes over 1 Gbps network        10,000   ns    0.01 ms
Read 4K randomly from SSD               150,000   ns    0.15 ms
Read 1 MB sequentially from memory      250,000   ns    0.25 ms
Round trip within same datacenter       500,000   ns    0.5  ms

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# -*- coding: utf-8 -*-
#!/usr/bin/env python
import sys

class Polyomino(object):

    def __init__(self, iterable):
        self.squares = tuple(sorted(iterable))

    def __repr__(self):

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from numpy import *
from scipy.stats import beta


class BetaBandit(object):
    def __init__(self, num_options=2, prior=(1.0,1.0)):
        self.trials = zeros(shape=(num_options,), dtype=int)
        self.successes = zeros(shape=(num_options,), dtype=int)
        self.num_options = num_options
        self.prior = prior