SVD low rank approximation for pictures (SVD image compression on Python3)

Well, tons of posts were written about it. It wont be "yet another one". But I implemented it anyway, just for fun. I wont explain theoretical part, but will suggest some links about it.

So good docs are:
http://timbaumann.info/svd-image-compression-demo/

Some good docs (thx Berkeley). This book is abandoned now, but it's good to read and store link for future reference.
http://inst.eecs.berkeley.edu/~ee127a/book/login/l_svd_low_rank.html
http://inst.eecs.berkeley.edu/~ee127a/book/login/l_svd_apps_image.html

Just nice presentation about PCA and SVD
http://math.arizona.edu/~brio/VIGRE/ThursdayTalk.pdf

and the source svd-img-compression.py

magic happens in np.dot() we pickup just take approx_rank singular values and discard others, so get some compression.

Result is below, on left side depicted original greyscale picture with rank equals 440 on a right side depicted low rank approximation with rank equals 50