... casa::Stack< elem > . ... Stack (const Stack < elem > &other) . ... Add an element to the top of the stack. void . ... Retreive the top element on the stack. const elem & . ... template<class elem> . ... References casa::False , casa::Stack< elem >::topOfStack , and casa::True . ... const elem& casa::Stack < elem >::top . ... Referenced by casa::Stack< elem >::empty() , casa::Stack< elem >::pop() , casa::Stack< elem >::popVal() , casa::Stack< elem >::push() , and casa::Stack< elem >::top() . ...

... Bojan Nikolic web pages (r. 329) . Out-Of-Focus (OOF) Holography . ... The architecture of the OOF data reduction software can be summarised in following points: . ... A well defined set of external libraries is used and generally newer versions of these libraries are adopted quickly after release . ... Routines for simulating OOF data-sets . ... OOF ? ... Description of the OOF software architecture . ... OOF Reduction Software . ... Copyright 2005 -- 2016, Bojan Nikolic feedback welcome ...

Stellarium 0.12.3 . ... Classes . ... FileљStructure . ... StelSphericalIndex Class Reference . ... voidљ . ... Insert the given object in the StelSphericalIndex . ... template<class FuncObject > . ... processIntersectingRegions (const SphericalRegionP &region, FuncObject &func) const . Process all the objects intersecting the given region using the passed function object. ... processContainedRegions (const SphericalRegionP &region, FuncObject &func) const . ... void StelSphericalIndex::clear . ...

. This diagram illustrates how the definition of a pixel depends on the users distance from the viewing surface (paper or screen). The image depicts the user looking at two planes, one 28 inches (71 cm) from the user, the second 140 inches (3.5 m) from the user. An expanding cone is projected from the user's eye onto each plane. Where the cone strikes the first plane, the projected pixel is .28 mm high. Where the cone strikes the second plane, the projected pixel is 1.4mm high. Return to image.

... The first of the equations defines the position of the spectral orders, m , in the raw image, while the second equation gives, for each order, the dispersion relation in one dimension. ... where the coefficients are computed using least squares techniques on a grid , i.e. sample number and line number of points located within the spectral orders of the image. These points in the grid are found automatically by an order--following algorithm, normally using the FLAT or STD image. ...

... Documentation for the Python objects defined by the project is divided into separate pages for each package, module, and class. The API