Sampler Single Peak [HSD13]

Description

The sampler from [HSD13] with a single peak profile.

For a more precise description of this sampler and its performances in terms of aliasing and discrepancy, please refer to the following web bundle https://liris.cnrs.fr/ldbn/HTML_bundle/index.html.

Files

src/samplers/SinglePeak.cpp  
include/utk/samplers/SinglePeak.hpp
externals/SinglePeak/*

Usage

Exe C++ Python

SinglePeak sampler
Usage: ./SinglePeak [OPTIONS]

Options:
  -h,--help                   Print this help message and exit
  -n UINT REQUIRED            Number of points
  -d UINT                     UNUSED ! Here for compatibility with others.
  -s,--seed UINT              Seed (unspecified means 'random')
  -m UINT [1]                 Number of pointsets
  -o,--out TEXT [out.dat]     Output file (format). {i} splits outputs in multiple files and token is replaced by index.
  --criticalFreq FLOAT [36.5] 
                              Critical frequency
  --smoothing FLOAT [8]       Smoothing
  --peakPower FLOAT [2]       Peak power
  --peakSmoothing FLOAT [2]   Peak smoothing
  --silent                    Silence UTK logs

#include <utk/utils/PointsetIO.hpp>
#include <utk/utils/Pointset.hpp>
#include <utk/samplers/SamplerSinglePeak.hpp>

int main()
{
    utk::Pointset<double> pts;

    utk::SamplerSinglePeak step(
      36.5, /* critical frequency */
      8,    /* smoothing */
      2,    /* peak smoothing */
      2,    /* peak power */
    );
    step.setRandomSeed(args->seed);
    // Check for no errors
    if (step.generateSamples(pts, 1024 /* Number of points */))
    {
        write_text_pointset("step.dat", pts);
    }
}

import pyutk

sp = pyutk.SinglePeak(
  36.5, # critical frequency
  8,    # smoothing
  2,    # peak smoothing
  2,    # peak power
)
samples = sp.sample(1024) # This is a numpy array !

Results

License

See Licence.md file. Author code from has no licence but is provided with following header.

//
// Source code for the paper, edited to fit the utk framework
//
// D. Heck and T. Schloemer and O. Deussen, "Blue Noise Sampling with
// Controlled Aliasing", ACM Trans. Graph., 2013, in press
//
// Copyright (C) 2012,2013 Daniel Heck and Thomas Schloemer
//