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.
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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
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load_target
(target, **kwargs)[source] [github] [bitbucket]¶ Load a Target…
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reduce_all
()[source] [github] [bitbucket]¶
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reduce_target
(target, **kwargs)[source] [github] [bitbucket]¶ Reduce the data for a given object name
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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
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reduce
(obstype='nod', **kwargs)[source] [github] [bitbucket]¶ Reduce nodded observations (they should have been read in __init__)
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pyspeckit.spectrum.readers.gbt.
average_IF
(block, debug=False)[source] [github] [bitbucket]¶ Average the polarizations for each feed in each IF
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pyspeckit.spectrum.readers.gbt.
average_pols
(block)[source] [github] [bitbucket]¶ Average the polarizations for each feed in each IF
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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)
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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
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pyspeckit.spectrum.readers.gbt.
find_feeds
(block)[source] [github] [bitbucket]¶ Get a dictionary of the feed numbers for each sampler
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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!
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pyspeckit.spectrum.readers.gbt.
find_pols
(block)[source] [github] [bitbucket]¶ Get a dictionary of the polarization for each sampler
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pyspeckit.spectrum.readers.gbt.
identify_samplers
(block)[source] [github] [bitbucket]¶ Identify each sampler with an IF number, a feed number, and a polarization
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pyspeckit.spectrum.readers.gbt.
list_targets
(sdfitsfile, doprint=True)[source] [github] [bitbucket]¶ List the targets, their location on the sky…
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pyspeckit.spectrum.readers.gbt.
read_gbt_scan
(sdfitsfile, obsnumber=0)[source] [github] [bitbucket]¶ Read a single scan from a GBTIDL SDFITS file
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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’
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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
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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
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pyspeckit.spectrum.readers.gbt.
reduce_totalpower
(blocks, verbose=False, average=True, fdid=1)[source] [github] [bitbucket]¶ Reduce a total power observation
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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
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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
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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)
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pyspeckit.spectrum.readers.gbt.
uniq
(seq)[source] [github] [bitbucket]¶