thearn

import numpy as np


def low_rank_approx(SVD=None, A=None, r=1):
    """
    Computes an r-rank approximation of a matrix
    given the component u, s, and v of it's SVD

    Requires: numpy
    """

thearn

from numpy.fft import fft, ifft, fft2, ifft2, fftshift
import numpy as np

def fft_convolve2d(x,y):
    """ 2D convolution, using FFT"""
    fr = fft2(x)
    fr2 = fft2(np.flipud(np.fliplr(y)))
    m,n = fr.shape
    cc = np.real(ifft2(fr*fr2))
    cc = np.roll(cc, -m/2+1,axis=0)

thearn

import base64
import time
import urllib2

import cv2
import numpy as np


"""
Examples of objects for image frame aquisition from both IP and

thearn

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import random
import pickle
import matplotlib.dates as mdates
from matplotlib.ticker import LinearLocator

thearn

import numpy as np
import openmdao.api as om

class SIRvec(om.ExplicitComponent):
    """Basic epidemiological infection model
       S (suceptible), I (infected), R (recovered).
    """

    def initialize(self):
        self.options.declare('num_nodes', types=int)

thearn

import numpy as np
import openmdao.api as om

class SIRvec(om.ExplicitComponent):
    """Basic epidemiological infection model
       S (suceptible), I (infected), R (recovered).
    """

    def initialize(self):
        self.options.declare('num_nodes', types=int)

thearn

thearn

import pickle
import gzip

def save(object, filename, protocol = 0):
        """Saves a compressed object to disk
        """
        file = gzip.GzipFile(filename, 'wb')
        file.write(pickle.dumps(object, protocol))
        file.close()

thearn

import time
import Image
import ImageGrab

while True:
    time.sleep(0.5)
    t = str(time.time()).replace('.','-')
    tt = time.time()
    img=ImageGrab.grab()
    img = img.resize((800,600))

thearn

    4   21  894
  419  794  987
  424  797  125
  651  305  558
  655  631  963
    2  628  436
  736   50  363
  657  707  408
  252  705   98
  532  173  878