Algorithms that deal with low-discrepancy point sets.
Computes random permutation tables.
Takes a generator matrix c, a number of 1D samples to generate n, and stores the corresponding samples in memory at the location pointed to by p.
Takes two generator matrices c0 and c1, a number of 2D samples to generate n, and stores the corresponding samples in memory at the location pointed to by p.
Compute the inverse of the radical inverse function.
Map to an appropriate prime number and delegate to another function to compute the radical inverse.
The bits of an integer quantity can be efficiently reversed with a series of logical bit operations.
The bits of a 64-bit value can be reversed by reversing the two 32-bit components individually and then interchanging them.
Compute the radical inverse, but put each pixel through the permutation table for the given base.
Similar to van_der_corput(), but uses two generator matrices to generate the first two dimensions of Sobol’ points.
Returns the index of the _frame_th sample in the pixel p, if the sampling domain has be scaled to cover the pixel sampling area.
Takes different paths for 32- and 64-bit floating point values.
Takes a 64 bit index and 32x52 matrices to calculate sample values.
Generates a number of scrambled 1D sample values using the Gray code-based sampling machinery.