Understanding the inner workings of the radon function (from skimage)

[Kaloyan Marinov]

I am familiar with the mathematical theory of the continuous Radon 
transform, but am having trouble understanding its discrete implementation 
from skimage. Below I list my two questions on the topic.

*Question 1*

Let us define a matrix A as follows:

A = np.zeros( (4,4) )
A[1:3,1:3] = 1

Compute its Radon transform as follows:

radTranfOfA = skimage.transform.radon(A,[45])

This computed radTranfOfA to be the following array of shape (6,1):

array([[ 0.        ],
       [ 0.        ],
       [ 0.87867966],
       [ 2.42893219],
       [ 0.87867966],
       [ 0.        ]])

Could someone explain to me (preferably in a mathematically precise way) 
exactly how Python computed this?

*Question 2*

I have attempted to study the radon_transform.py source-code file from the 
skimage package, but the definition of the radon function there depends on 
a function called _warp_fast which is imported from _warps_cy. However, I 
cannot see the sourcecode for _warps_cy; could anyone suggest a way around 
this (or a precise mathematical explanation of what _warps_cy does and how 
its output is stored)?
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