complex.cc

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/*    vspline - a set of generic tools for creation and evaluation      */
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/// \file complex.cc
///
/// \brief demonstrate use of b-spline over std::complex data
///
/// vspline handles std::complex data like pairs of the complex
/// type's value_type, and uses a vigra::TinyVector of two
/// simdized value_types as the vectorized type.
/// use examples.sh to compile with g++ and clang++, and all
/// available SIMD back-ends:
/// ./examples.sh complex.cc
 
#include <iostream>
#include <iomanip>
#include <assert.h>
#include <complex>
#include <vspline/multithread.h>
#include <vspline/vspline.h>
 
int main ( int argc , char * argv[] )
{
  // nicely formatted output
 
  std::cout << std::fixed << std::showpoint
            << std::showpos << std::setprecision(6) ;
 
  // create default b-spline over 100 values
 
  vspline::bspline < std::complex < float > , 1 > bsp ( 100 ) ;
  
  // get a vigra::MultiArrayView to the spline's 'core'. This is the
  // area corresponding with the original data and is filled with zero.
  
  auto v1 = bsp.core ;
  
  // we only set one single value in the middle of the array
 
  v1 [ 50 ] = std::complex<float> ( 3.3 , 2.2 ) ;
  
  // now we convert the original data to b-spline coefficients
  // by calling prefilter()
  
  bsp.prefilter() ;
 
  // and create an evaluator over the spline. Here we pass three
  // template arguments to the evaluator's type declaration:
  // - float for the 'incoming type' (coordinates)
  // - std::complex<float> for the 'outgoing type' (values)
  // - 4 for the vector width, just for this example
 
  typedef vspline::evaluator < float , std::complex<float> , 4 > ev_type ;
  
  // create the evalator
 
  auto ev = ev_type ( bsp ) ;
  
  // now we evaluate the spline in the region around the single
  // nonzero value in the input data and print argument and result
 
  float in ;
  std::complex<float> out ;
  
  for ( int k = -12 ; k < 13 ; k++ )
  {
    in = 50.0 + k * 0.1 ;
    
    // use ev's eval() method:
    ev.eval ( in , out ) ;
    std::cout << "1. ev(" << in << ") = " << out << std::endl ;
    
    // alternatively, use ev as a callable
    std::cout << "2. ev(" << in << ") = " << ev(in) << std::endl ;
    
    // ditto, but feed immediates. note we're passing float explicitly
    std::cout << "3. ev(" << in << ") = " << ev(50.0f + k * 0.1f) << std::endl ;
  }
  
  // repeat the example evaluation with vector data
 
  for ( int k = -12 ; k < 13 ; k++ )
  {
    // feed the evaluator with vectors. Note how we obtain the
    // type of vectorized data the evaluator will accept from
    // the evaluator, by querying for it's 'in_v' type.
    // in_v will be a vigra::TinyVector of two SIMD vectors,
    // where the type of the SIMD vectors depends on the SIMD
    // back-end. For simplicity's sake, we initialize the
    // vectorized argument to a uniform value.
    // Note how in the output, the different SIMD back-ends
    // produce visibly different output.
    
    typename ev_type::in_v vk ( 50.0 + k * 0.1 ) ;
 
    std::cout << "ev(" << vk << ") = " << ev(vk) << std::endl ;
  }
}