CCfits - HEASARC - NASA

3 Implementation Notes 8
3 Implementation Notes
This section comments on some of the design decisions for CCfits. We note the role of
cfitsio in CCfits as the underlying "engine," the use of the C++ standard library. We also
explain some of the choices made for standard library containers in the implementation
- all of which is hidden from the user [as it should be].
Most importantly, the library wraps rather than replaces the use of cfitsio library; it
does not perform direct disk I/O. The scheme is designed to retain the well-developed
facilities of cfitsio (in particular, the extended file syntax), and make them available to
C++ programmers in an OO framework. Some efficiency is lost over a ’pure’ C++ FI-
TS library, since the internal C implementation of many functions requires processing
if blocks or switch statements that could be recoded in C++ using templates. However,
we believe that the current version strikes a resonable compromise between developer
time, utility and efficiency.
The implementation of CCfits uses the C++ Standard Library containers and algorithms
[also referred to as the Standard Template Library, (STL)] and exception handling. -
Here is a summary of the rationale behind the implementation decisions made.
• HDUs are contained within a FITS object using a std::multimap object.
1. The map object constructs new array members on first reference
2. Objects stored in the map are sorted on entry and retrieved efficiently using
binary search techniques.
3. The pointer-to-HDU implementation allows for polymorphism: one set of
operations will process all HDU objects within the FITS file
4. String objects (char∗ ) are represented by the std::string class, which has a
rich public interface of search and manipulation facilities.
• Scalar column data [one entry per cell] are implemented using std::vector
objects.
• Vector column data [multiple and either fixed or variable numbers of entries per
cell] are implemented using std::vector objects. The std-
::valarray template is intended for optimized numeric processing. valarrays are
have the following desirable features:
1. they are dynamic, but designed to be allocated in full on construction rather
than dynamic resizing during operation: this is, what is usually needed in
FITS files.
2. They have built-in vectorized transcendental functions (e.g. std::valarray sin(const std::valarray& ).
3. They provide std::valarray apply(T f(const T&)) operation, to apply a
function f to each element
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