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def pca(X):
  """
  Features are in columns
  """
  
  # Data matrix X, assumes 0-centered
  n, m = X.shape
  assert np.allclose(X.mean(axis=0), np.zeros(m))
  
  # Compute covariance matrix

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fig = figure(1);
set(gcf,'units','points','position',[0,0,1200,500])

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sudo apt-get install liblapack-dev
sudo apt-get install libblas-dev
sudo apt-get install libboost-dev

sudo apt-get install libarmadillo-dev

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export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/pylon5/lib64

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(sudo) find /search/directory -type f -name "filename_to.find"

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Ctrl+Alt+F1

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conda install pip
source activate base (or env...)
pip install [package]

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			// subtract IR from BGR
			Vec3u tmp;
			for (int i = 0; i < ir.rows; i++) {
				for (int j = 0; j < ir.cols; j++) {
					tmp = bgr.at<Vec3u>(i,j);
					tmp[0] = tmp[0] - ir.at<ushort>(i,j);
					tmp[1] = tmp[1] - ir.at<ushort>(i,j);
					tmp[2] = tmp[2] - ir.at<ushort>(i,j);

					bgr.at<Vec3u>(i, j) = tmp;

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def print_object(some_object):
    # Check if the object is printable...
    try:
        printable = str(some_object)
    except TypeError:
        print("unprintable object")
    else:
        print(printable)
        
        

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def colored_noise_generator(D, lamb, dt):
    """
    An iterable generator function for colored noise.

    :param D: Amplitude of the noise
    :param lamb: Reciprocal of the characteristic time
    :param dt: Time step
    :return : yields the successive value
    """