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Raw OLAP data formats
OLAP produces several data formats, which are intended to be replaced by their final format, such as HDF5. The formats below are not officially supported and subject to change without notice.
Beamformed Data
Beamformed data can be recorded as either complex voltages (yielding X and Y polarisations) or one or more stokes. In either case, a sequence of blocks will be stored, each of which consists of a header and data. The header is defined as:
struct header { uint32 sequence_number; /* big endian */ char padding[508]; };
in which sequence_number starts at 0, and is increased by 1 for every block. Missing sequence numbers implies missing data. The padding can have any value and is to be ignored.
Complex Voltages
Each (pencil) beam produces two files: one containing the X polarisation, and one containing the Y polarisation. The names of these files adhere to the following scheme:
Lxxxxx_Byyy_S0-bf.raw | X polarisations of beam yyy of observation xxxxx |
Lxxxxx_Byyy_S1-bf.raw | Y polarisations of beam yyy of observation xxxxx |
Each file is a sequence of blocks of the following structure:
struct block { struct header header; /* big endian */ fcomplex voltages[SAMPLES|2][SUBBANDS][CHANNELS]; }
Stokes
Each (pencil) beam produces one or four files: one containing the Stokes I (power) values, and optionally three files for Stokes Q, U, and V, respectively. The names of these files adhere to the following scheme:
Lxxxxx_Byyy_S0-bf.raw | Stokes I of beam yyy of observation xxxxx |
Lxxxxx_Byyy_S1-bf.raw | Stokes Q of beam yyy of observation xxxxx |
Lxxxxx_Byyy_S2-bf.raw | Stokes U of beam yyy of observation xxxxx |
Lxxxxx_Byyy_S3-bf.raw | Stokes V of beam yyy of observation xxxxx |
Currently (release 2010-09-20), the Stokes U and V are multiplied by a factor of 1/2, but that will be changed in a subsequent release.
Each file is a sequence of blocks of the following structure:
struct block { struct header header; /* big endian */ float stokes[SAMPLES|2][SUBBANDS][CHANNELS]; }
Types and constants
Types
A 'float' is a 32-bit IEEE floating point number. An 'fcomplex' is a complex number defined as
struct fcomplex { float real; float imag; };
Constants
Constants can be computed using the parset file. Below is a translation between the C constants used above and their respective parset keys:
SAMPLES | The number of time samples in a block | OLAP.CNProc.integrationSteps |
SUBBANDS | The number of subbands (beamlets) specified | len(Oberservation.subbandList) |
CHANNELS | The number of channels per subband | Observation.channelsPerSubband |
Useful routines
The following routines might be useful when reading raw OLAP data.
Byte swapping
Needed if you read data on a machine which used a different endianness. Typically, x86 machines (intel, amd) are little-endian, while the rest (sparc, powerpc, including the BlueGene/P) is big-endian.
uint32 swap_uint32( uint32 x ) { union { char c[4]; uint32 i; } src,dst; src.i = x; dst.c[0] = src.c[3]; dst.c[1] = src.c[2]; dst.c[2] = src.c[1]; dst.c[3] = src.c[0]; return dst.i; } /* Do NOT take a float as an argument. An incorrectly read float (because it has the wrong endianness) is subject to modification by the platform/compiler (normalisation etc). */ float swap_float( char *x ) { union { char c[4]; float f; } dst; dst.c[0] = x[3]; dst.c[1] = x[2]; dst.c[2] = x[1]; dst.c[3] = x[0]; return dst.f; }
Variable-sized arrays
Since the dimensions of the arrays produced by OLAP depend on the parset, it's handy to have access to arrays with variable size. The easiest way is to use C++ and the boost library (which is often installed by default):
#include "boost/multi_array.hpp" int main() { /* create an array of floats with 2 dimensions, and initialise it to have dimensions [2][3] */ boost::multi_array<float,2> myarray(boost::extents[2][3]); /* getting and setting is the same as with regular C arrays */ myarray[1][2] = 1.0; }
See also http://www.boost.org/doc/libs/1_43_0/libs/multi_array/doc/user.html
Another method is rolling out your own multi-dimensional array, although you'll have to customise your code for each number of dimensions in order to keep your code readable. For example:
struct myarray { float *data; unsigned max1,max2; }; /* return myarray[one][two] */ float get( struct myarray *array, int one, int two ) { return *(myarray.data + one * myarray.max2 + two); } /* set myarray[one][two] to value */ void set( struct myarray *array, int one, int two, float value ) { *(myarray.data + one * myarray.max2 + two) = value; } int main() { /* create an array of floats */ struct array myarray; /* allocate the array with dimensions [2][3] */ myarray.max1 = 2; myarray.max2 = 3; myarray.data = malloc( myarray.max1 * myarray.max2 * sizeof *myarray ); /* emulate myarray[1][2] = 1.0 */ set(&myarray,1,2,1.0); /* free the array */ free( myarray ); }