GBTIDL FITS files¶
GBTIDL SDFITS sessions can be loaded as pyspeckit.ObsBlock
objects using the
GBTSession reader:
gbtsession = pyspeckit.readers.GBTSession('AGBTsession.fits')
API¶
GBTIDL SDFITS file¶
GBTIDL SDFITS files representing GBT observing sessions can be read into pyspeckit. Additional documentation is needed. Nodding reduction is supported, frequency switching is not.
- class pyspeckit.spectrum.readers.gbt.GBTSession(sdfitsfile)[source] [github] [bitbucket]¶
A class wrapping all of the above features
Load an SDFITS file or a pre-loaded FITS file
- load_target(target, **kwargs)[source] [github] [bitbucket]¶
Load a Target…
- reduce_all()[source] [github] [bitbucket]¶
- reduce_target(target, **kwargs)[source] [github] [bitbucket]¶
Reduce the data for a given object name
- class pyspeckit.spectrum.readers.gbt.GBTTarget(Session, target, **kwargs)[source] [github] [bitbucket]¶
A collection of ObsBlocks or Spectra
Container for the individual scans of a target from a GBT session
- reduce(obstype='nod', **kwargs)[source] [github] [bitbucket]¶
Reduce nodded observations (they should have been read in __init__)
- pyspeckit.spectrum.readers.gbt.average_IF(block, debug=False)[source] [github] [bitbucket]¶
Average the polarizations for each feed in each IF
- pyspeckit.spectrum.readers.gbt.average_pols(block)[source] [github] [bitbucket]¶
Average the polarizations for each feed in each IF
- pyspeckit.spectrum.readers.gbt.count_integrations(sdfitsfile, target)[source] [github] [bitbucket]¶
Return the number of integrations for a given target (uses one sampler; assumes same number for all samplers)
- pyspeckit.spectrum.readers.gbt.dcmeantsys(calon, caloff, tcal, debug=False)[source] [github] [bitbucket]¶
from GBTIDL’s dcmeantsys.py ; mean_tsys = tcal * mean(nocal) / (mean(withcal-nocal)) + tcal/2.0
- pyspeckit.spectrum.readers.gbt.find_feeds(block)[source] [github] [bitbucket]¶
Get a dictionary of the feed numbers for each sampler
- pyspeckit.spectrum.readers.gbt.find_matched_freqs(reduced_blocks, debug=False)[source] [github] [bitbucket]¶
Use frequency-matching to find which samplers observed the same parts of the spectrum
WARNING These IF numbers don’t match GBTIDL’s! I don’t know how to get those to match up!
- pyspeckit.spectrum.readers.gbt.find_pols(block)[source] [github] [bitbucket]¶
Get a dictionary of the polarization for each sampler
- pyspeckit.spectrum.readers.gbt.identify_samplers(block)[source] [github] [bitbucket]¶
Identify each sampler with an IF number, a feed number, and a polarization
- pyspeckit.spectrum.readers.gbt.list_targets(sdfitsfile, doprint=True)[source] [github] [bitbucket]¶
List the targets, their location on the sky…
- pyspeckit.spectrum.readers.gbt.read_gbt_scan(sdfitsfile, obsnumber=0)[source] [github] [bitbucket]¶
Read a single scan from a GBTIDL SDFITS file
- pyspeckit.spectrum.readers.gbt.read_gbt_target(sdfitsfile, objectname, verbose=False)[source] [github] [bitbucket]¶
Give an object name, get all observations of that object as an ‘obsblock’
- pyspeckit.spectrum.readers.gbt.reduce_gbt_target(sdfitsfile, objectname, nbeams, verbose=False)[source] [github] [bitbucket]¶
Wrapper - read an SDFITS file, get an object, reduce it (assuming nodded) and return it
- pyspeckit.spectrum.readers.gbt.reduce_nod(blocks, verbose=False, average=True, fdid=(1, 2))[source] [github] [bitbucket]¶
Do a nodded on/off observation given a dict of observation blocks as produced by read_gbt_target
- Parameters
fdid : 2-tuple
- pyspeckit.spectrum.readers.gbt.reduce_totalpower(blocks, verbose=False, average=True, fdid=1)[source] [github] [bitbucket]¶
Reduce a total power observation
- pyspeckit.spectrum.readers.gbt.round_to_resolution(frequency, resolution)[source] [github] [bitbucket]¶
kind of a hack, but round the frequency to the nearest integer multiple of the resolution, then multiply it back into frequency space
- pyspeckit.spectrum.readers.gbt.sigref(nod1, nod2, tsys_nod2)[source] [github] [bitbucket]¶
Signal-Reference (‘nod’) calibration ; ((dcsig-dcref)/dcref) * dcref.tsys see GBTIDL’s dosigref
- pyspeckit.spectrum.readers.gbt.totalpower(calon, caloff, average=True)[source] [github] [bitbucket]¶
Do a total-power calibration of an on/off data set (see dototalpower.pro in GBTIDL)
- pyspeckit.spectrum.readers.gbt.uniq(seq)[source] [github] [bitbucket]¶