Complex

fields (for FFT operations)

using Complex = std::complex<double>;
pfc::DiscreteField<Complex, 3> kspace_field(
    {64, 64, 33},  // FFT size (nz/2+1 for real-to-complex)
    {0, 0, 0},
    {0.0, 0.0, 0.0},
    {1.0, 1.0, 1.0}
);
 
// Initialize complex field
kspace_field.apply([](double kx, double ky, double kz) {
    double k2 = kx*kx + ky*ky + kz*kz;
    return Complex(std::exp(-k2/10.0), 0.0);
});

Performance Considerations:

• Direct data access via get_data() for maximum performance
• apply() has iterator overhead but ensures coordinate correctness
• Nearest-neighbor interpolation is O(1)
• Coordinate transformations are cheap (simple arithmetic)

Memory Layout:

• Underlying storage is std::vector<T> (contiguous, cache-friendly)
• Row-major order (last index varies fastest)
• Compatible with MPI subdomain data

Common Use Cases:

• Initial condition setup with coordinate-space functions
• Post-processing and analysis with interpolation
• Coordinate-aware field manipulation
• Testing and validation (analytical comparisons)

Note

Geometry (origin, discretization, bounds) is immutable after construction

Only nearest-neighbor interpolation supported (no linear/cubic)

For MPI subdomains, use offset parameter to specify global position

Warning

interpolate() returns reference - ensure coordinate is in bounds! Use inbounds() first if uncertain

See also

Array<T,D> for underlying storage

MultiIndex<D> for index iteration

world.hpp for coordinate system abstraction

Model::get_real_field() for integration with simulation fields