General ----------------------------------------- We have chosen to release our full-sky WISE 12 micron dust map as a set of 430 so-called "WSSA" tiles, each in FITS format. The WSSA tiling is analogous but not identical to that of the IRAS Sky Survey Atlas (ISSA). The ISSA tile centers and orientations are based on B1950 celestial coordinates, whereas WSSA tile astrometry has been precessed to J2000. For instance, ISSA tile centers trace out a series of isolatitude rings in B1950 celestial coordinates, while WSSA tile centers trace out a series of isolatitude rings in J2000 celestial coordinates. Thus ISSA and WSSA tiles do not cover identical astrometric footprints. As stated in the headers/docstrings of the WSSA software tools, input coordinates are expected to be J2000 RA/Dec. Tile extension and mask bit description tables are appended below. Because the WSSA tile footprints are not mutually exclusive, there exists some freedom in precisely how to interpolate values off of this set of tiles. The WSSA software tools solve this problem by using the HEALPix pixel indices corresponding to user-input coordinates to hash from coordinates to WSSA tile number, using a precomputed correspondence between HEALPix indices and tile numbers. Notes ------------------------------------------ We have conducted a thorough comparison of the values output by the IDL/Python software tools, and verified that both return identical values for identical (RA, Dec) input coordinates. Users who choose to perform their own custom interpolation off of the WSSA tiles may obtain slightly different values. The WSSA tools assume a full data download (all 430 tiles), and have not been tested or optimized for the case of partial download. Software Download ----------------------------------------- After unpacking wssa_utils.tar.gz, you should see the following directory structure. $ tree -d wssa_utils wssa_utils |-- etc |-- pro `-- python wssa_utils/pro contains the IDL WSSA tools. wssa_utils/python contains the Python WSSA tools. The wssa_utils/etc subdirectory contains auxiliary files which store the central coordinates of the tiles and a lookup table between HEALPix pixel index and WSSA tile number. Environment ------------------------------------------ Both the Python and IDL implementations require that the WISE_TILE environment variable be set to the directory containing the WSSA tiles and that the WISE_DATA environment variable be set to the wssa_utils/etc directory. IDL ----------------------------------------- Include wssa_utils/pro in your IDL_PATH. IDL> vals = wssa_getval(ra, dec) ra, dec should be arrays of equal length or single numerical values. See the header documentation for further details. The IDL implementation has been tested with IDL 7.1.1, 8.1, 8.2.2 and IDLUTILS v5_4_24. Python ----------------------------------------- Include wssa_utils/python in your PYTHONPATH. >> from wssa_utils import wssa_getval >> vals = wssa_getval(ra, dec) ra, dec should be numpy arrays. Single numerical values or lists whose elements are of numerical type will be automatically converted to numpy arrays. See the docstrings for further details. The Python implementation has been tested with Python 2.7.1, NumPy 1.6.0, SciPy 0.9.0, and PyFITS 2.4.0. Tile Extensions and Mask Bits ----------------------------------------- The wssa_getval exten keyword allows users to sample different tile extensions. The available extensions are given below, and the mask bits for the bit-mask extensions are also listed. Meisner & Finkbeiner Table 3 -------------------------- extension name description ----------- ------ ------------- 0 'clean' cleaned co-add 1 'dirt' dirty co-add 2 'cov' integer coverage 3 'min' minimum value image 4 'max' maximum value image 5 'amsk' AND bit-mask 6 'omsk' OR bit-mask 7 'art' transient artifact image Meisner & Finkbeiner Table 2 -------------------------- bit description ----- ------------- 0 saturated point source core 1 point source ghost 2 bright region of point source profile 3 first latent of point source 4 PSF subtraction residual interpolated over 5 bright region of point source ghost 6 SSO interpolation 7 resolved compact source 8 second latent of point source 9 third latent of point source 10 fourth latent of point source 11 bright SSO ghost 12 bright SSO latent 13 point source diffraction spike 14 saturated pixel not in static mask 15 Moon contamination 16 RC3 optical galaxy 17 big object (M31, LMC, SMC) 18 Solar System planet 19 reference comparison failure 20 line-like defect 21 low integer frame coverage 22 ecliptic plane