Direct Copy Optimization

Note

This feature was introduced in v1.0.0

Direct copy optimization allows C code to directly copy data into a Python data structure. This allows for less overhead when calling specific functions that load data into some container. This overhead can be nontrivial for functions that have to convert many native C types to Python types (e.g. forcedimension_core.dhd.getPositionAndOrientationFrame() must perform 12 float allocations and copies).

Functions which support direct copy optimization are loaded under a special module called direct in the forcedimension_core.dhd, forcedimension_core.dhd.expert, and forcedimension_core.drd modules.

These functions require that you use the special containers in forcedimension_core.containers. Below is a brief example (omitting error checking for brevity).

import forcedimension_core.containers as containers
import forcedimension_core.dhd as dhd

dhd.open()

pos = containers.Vector3()

# Equivalent to: dhd.getPosition(out=pos)
dhd.direct.getPosition(out=pos)

Which containers should you use when? Direct copy optimized functions hint at which container to use in the “See Also” section of their docstring. These containers are memory safe. If an improper container is used, ctypes will raise a ctypes.ArgumentError.

There are also containers which subclass NumPy’s numpy.ndarray available if NumPy is installed (NumPy 1.20+ required). See Installation for more details.

Theory of Operation

Note

This section is left for library developers and higher level users. It requires a knowledge of how pointers in C work to fully understand.

Under the hood, direct copy optimization is nothing special. The below image shows how forcedimension_core.dhd.getPosition() works.

Each arrow represents a copy in the in the diagram The library invokes the C function _libdhd.dhdGetPosition() and stores the data from it in ctypes.c_double buffers called px, py, pz. Then, it copies the data to a the out container. As we can see, it would be faster if we could copy the data directly into a Python container.

The code for it is shown below (without docstrings), noting the overhead.

def getPosition(out: MutableArray[int, float], ID: int = -1) -> int:
  # Extra allocation here
  px = c_double()
  py = c_double()
  pz = c_double()

  # px, py, and pz are pased byref and data is copied into them
  err = _runtime._libdhd.dhdGetPosition(px, py, pz, ID)

  # Extra copy here
  out[0] = px.value
  out[1] = py.value
  out[2] = pz.value

  return err

In general, _libdhd.dhdGetPosition() needs float pointers to the data you wish to copy to. The solution then, is to use a type, which can directly pass pointers to _libdhd.dhdGetPosition() like array.array. 3rd party libraries like NumPy also have this capability. Below we show a bare-bones version of the library implementation.

class Vector3(array):
    def __new__(cls, initializer: Iterable[float] = (0., 0., 0.)):
      if isinstance(initializer, array):
        return initializer

      arr = super(Vector3, cls).__new__(cls, 'd', initializer)

      if len(arr) != 3:
        raise ValueError()

      return arr

    def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)

      # Get a pointer to the front of the array
      ptr = self.buffer_info()[0]

      self._ptrs = (
          ctypes.cast(ptr, c_double_ptr),  # 0th element
          ctypes.cast(ptr + self.itemsize, c_double_ptr),  # 1st element
          ctypes.cast(ptr + 2 * self.itemsize, c_double_ptr),  # 2nd element
      )

    @property
    def ptrs(self) -> Tuple[c_double_ptr, c_double_ptr, c_double_ptr]:
      return self._ptrs


def getPosition(out: SupportsPtrs3[c_double], ID: int = -1) -> int:
  return _runtime._libdhd.dhdGetPosition(*out.ptrs, ID)

forcedimension_core.containers.Vector3 simply adds an additional property to a Python array.array called ptrs. Now _libdhd.dhdGetPosition() can be given pointers to the memory inside the container itself, saving us an allocation and a copy.