Пример #1
0
def n_extensions(fitsfile):
  '''returns number of extension in a fits file
  
  Usage: n = n_extensions(filename)'''
  iraf.mscred(_doprint=0)
  result=iraf.mscextensions(fitsfile, output='none')
  return iraf.mscextensions.nimages
Пример #2
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def reproject_ota(img, ota, rad, decd):
    from pyraf import iraf
    image = odi.illcorpath+'illcor_'+ota+'.'+str(img[16:])
    imout = odi.reprojpath+'reproj_'+ota+'.'+str(img[16:])
    iraf.mscred(_doprint=0)
    iraf.clobber='no'
    iraf.unlearn(iraf.mscred.mscimage)
    iraf.mscred.mscimage.format='image'
    iraf.mscred.mscimage.pixmask='yes'
    iraf.mscred.mscimage.verbose='yes'
    iraf.mscred.mscimage.wcssour='parameters'
    iraf.mscred.mscimage.ref=''
    iraf.mscred.mscimage.ra=rad
    iraf.mscred.mscimage.dec=decd
    iraf.mscred.mscimage.scale=0.11
    iraf.mscred.mscimage.rotation=0.0
    iraf.mscred.mscimage.blank=-999
    iraf.mscred.mscimage.interpo='poly5'
    iraf.mscred.mscimage.minterp='poly5'
    iraf.mscred.mscimage.nxbl=4096
    iraf.mscred.mscimage.nybl=4096
    iraf.mscred.mscimage.fluxcon='yes'
    iraf.mscred.mscimage(image,imout)
    
    return
Пример #3
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def aperture_photometry(image,photfilesuffix,threshold=4.,wcs='logical',mode='small', telescope='Pairitel'):
  '''perform aperture photometry on a given image
  
  This procedure perfrom IRAF/DAOPHOT aperture phtometry on (all extensions of) a fits image.
  It sets some global daophot parameters, then perfroms daophot.daofind and daophot.phot
  The output .mag file is filtered for valid magnitudes and a mag.sex file computed, where
  all coordinates are transformed to sexagesimal coordinates.
  
  input: image: filename
  keywords:  threshold=4 :daofind detection threshold in sigma_sky
             wcs='logical' :working wcs system, can be  'logical','physical' or 'tv'
                        in any case an additional sexagesimal output file will be computed
         mode='small' : 'standard' = 'large' or 'psf' = 'small' choses a pre-defined aperture size for 
                        aperture photometry of standards or just as starting point for psf photometry
  '''
  print 'Performing aperture photometry on '+image
  if telescope == 'Pairitel':
      set_Pairitel_params()
  elif telescope == 'FLWO':
      set_FLWO_params()
  else:
      print 'No parameters found for this telescope!'
  
  iraf.mscred(_doprint=0)
  iraf.digiphot(_doprint=0)
  iraf.daophot(_doprint=0)
  iraf.daophot.verify = False
  iraf.daophot.wcsin = wcs
  iraf.daophot.wcsout = wcs
  iraf.daophot.wcspsf = wcs
  iraf.centerpars.cbox=2.*iraf.datapars.fwhmpsf
  iraf.fitskypars.annulus=20 # usually one would use 5.*iraf.datapars.fwhmpsf
  #annulus must be wide enough to have good sky statistics
  iraf.fitskypars.dannulus=10. # this is standard
  if 'standard'.find(mode.lower()) != -1 or 'large'.find(mode.lower()) != -1:
    iraf.photpars.aperture = 4.*iraf.datapars.fwhmpsf
    iraf.centerpars.calgorithm = 'centroid'
  elif 'small'.find(mode.lower()) != -1 or 'psf'.find(mode.lower()) != -1:
    iraf.photpars.aperture = 4. # close in for crowded regions
    iraf.centerpars.calgorithm = 'none'
  else:
    print '''use: aperture_photometry(image,threshold=4.,wcs="logical",mode="small")
    
    The following mode keywords are implemented: standard, large, psf, small'''
    raise NotImplementedError
  #for imagebi in fits_ext(image):
  sky,skydev=get_sky(image)
  iraf.datapars.datamin=sky-5*skydev
  iraf.datapars.sigma=skydev      
  #this could be changed to use Stdin and Stdout so that fewer files are written (saves space and time), but for debug purposes better keep all that
  iraf.daofind(image, output='default', verbose=False, verify=False, threshold=threshold) 
  iraf.daophot.phot(image, coords='default', output=image+photfilesuffix, verbose=False, verify=False, interactive=False, wcsout=wcs)
Пример #4
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def fix_wcs_full(img, coords='radec.coo', iters=1):
    print coords 
    iraf.mscred(_doprint=0)
    iraf.unlearn(iraf.mscred.msccmatch)
    # otaext = {'33':'[1]','34':'[2]','44':'[3]','43':'[4]','42':'[5]','32':'[6]','22':'[7]','23':'[8]','24':'[9]'}
    for i in range(iters):
        fix = iraf.msccmatch(input=img,coords=coords,usebpm='no',verbose='yes',nsearch=1000,search=10,rsearch=5.5,cfrac=.9,csig=1.0,nfit=3,rms=15,maxshif=50,fitgeom="general",update='yes',interac='no',fit='no',accept='yes', Stdout=1)
        print 'fixing WCS for',img+', iter ='+repr(i)
        print fix[-6]
        print fix[-5]
        print fix[-4]
        print fix[-3]
        print fix[-2]	
Пример #5
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def shift_wcs(image, shifttab, number):
  '''Shift wcs in image according to value in shifttab
  
  image: filename
  shifttab: structured record array with cols "from to rashift decshift"
  rashift and decshift in arcsec
  number: ID of image to chose record in shifttab
  
  This method applies the rashift and decshift specified in the record of shifttab with
  from <= number <= to
  
  from to rashift decshift
  90  105  46.7  16.0
  106 111  48.0  21.0
  112 127 -29.6  18.8
  '''
  iraf.mscred(_doprint=0)
  index=(shifttab['from']<=number)*(shifttab['to']>=number)
  iraf.mscwcs(image,ra_shift=shifttab['rashift'].compress(index)[0],dec_shift=shifttab['decshift'].compress(index)[0])
Пример #6
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def iqlfc(inpat,clobber=globclob,verbose=globver):

    """ intelligent processing of LFC imaging data """

    # Necessary packages
    if not iraf.mscred._loaded:
        iraf.mscred()

    # Defaults
    twait=30
    reduced={}
    filtkey="FILTER"
    trimsec="[1:2048,1:2048]"
    biasname="Bias.fits"
    biaspfx="b"
    flatpre="Flat-"
    flatpfx="f"
    statsec=""
    sigma=2.0
    satval=25000.0
    masksfx="mask"
    pix=0.378

    # Perform the LFC assembly
    allfiles=glob.glob(inpat)

    for file in allfiles:
        if not file.endswith('.fits'):
            os.rename(file,file+".fits")

    assembled={}
    allnames=[]
    for file in allfiles:
        if file.endswith('.fits'):
            (root,num,chip,ext)=file.split('.')
        else:
            (root,num,chip)=file.split('.')
        if not assembled.has_key(num):
            name="%s.%s" % (root,num)
            iraf.lfcassemble(name)
            assembled[num]=yes
            allnames.append(name)
Пример #7
0
def iqlfc(inpat, clobber=globclob, verbose=globver):
    """ intelligent processing of LFC imaging data """

    # Necessary packages
    if not iraf.mscred._loaded:
        iraf.mscred()

    # Defaults
    twait = 30
    reduced = {}
    filtkey = "FILTER"
    trimsec = "[1:2048,1:2048]"
    biasname = "Bias.fits"
    biaspfx = "b"
    flatpre = "Flat-"
    flatpfx = "f"
    statsec = ""
    sigma = 2.0
    satval = 25000.0
    masksfx = "mask"
    pix = 0.378

    # Perform the LFC assembly
    allfiles = glob.glob(inpat)

    for file in allfiles:
        if not file.endswith('.fits'):
            os.rename(file, file + ".fits")

    assembled = {}
    allnames = []
    for file in allfiles:
        if file.endswith('.fits'):
            (root, num, chip, ext) = file.split('.')
        else:
            (root, num, chip) = file.split('.')
        if not assembled.has_key(num):
            name = "%s.%s" % (root, num)
            iraf.lfcassemble(name)
            assembled[num] = yes
            allnames.append(name)
Пример #8
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def correct_wcs(image,clobber=False,minmag=13, radius=20.):
  '''correct wcs of fitsfile to 2MASS coordinates
  
  This method loads the 2MASS catalog in the region covered by the fits file
  and shifts,stretches, and rotates the wcs in the fits file to match the 2MASS coordinates
  The initial wcs needs to be approximately correct (within a few arcsec), otherwise 
  the matching will fail.
  image = filename
  clobber = overwrite existing 2MASS catalog file?
  minmag = mag of faintest object retrieved from 2MASS'''
  object = iraf.hselect(image+'[0]','OBJECT', 'yes', Stdout=1)[0]
  filter = iraf.hselect(image+'[0]','FILTER', 'yes', Stdout=1)[0]
  catalogue=os.path.join(os.path.dirname(image),"2mass_"+str(object)+'_'+str(filter)+'_'+str(minmag)+".cat")
  #Gererate catalogue, if it does not exist in this directory
  if clobber or not os.path.isfile(catalogue):
    get_catalogue(catalogue,iraf.hselect(image+'[0]','RA','yes', Stdout=1)[0],iraf.hselect(image+'[0]','DEC','yes', Stdout=1)[0], radius=radius, minmag=minmag)
  #cfrac: maximum fraction of targets which do not center correctly
  #       a large number is no problem here, since often there are a few 100 sources in the catalog to match.
  iraf.mscred(_doprint=0)
  #matchout=iraf.msccmatch(image,catalogue,interactive=False, Stdout=1,cfrac=0.95,rms=3)
  iraf.cd(os.path.dirname(image))
  matchout=iraf.msccmatch(os.path.basename(image),catalogue,interactive=False, Stdout=1,cfrac=0.9,rms=10,maxshif=100., nsearch=100, nfit=5, update='yes')
  try: print matchout[-5]
  except IndexError: print matchout
Пример #9
0
def reproject_ota(img, ota, rad, decd, wcsref):
    """
    Use the IRAF task ``mscimage`` in the ``mscred`` package to reproject
    an OTA to a reference tangent plane with constant pixel scale.

    Parameters
    ----------
    img : str
        Name of image being processed
    ota : str
        Name of current ``ota`` being processed in ``img``
    rad : float
        Reference Ra position for reprojection
    decd : float
        Reference Ra position for reprojection
    wcfreg : str
        Name of image and ota to be used as the reference image for ``mscimage``

    Note
    ----
    Nothing is returned by this function but the reprojected ota is saved to the
    ``repreopath``. The pipeline is setup to use OTA33 of
    the first image in the images list as the reference image for this function.

    Here is how the ``mscimage`` IRAF parameters are set:

    - iraf.mscred.mscimage.format='image'
    - iraf.mscred.mscimage.pixmask='yes'
    - iraf.mscred.mscimage.verbose='yes'
    - iraf.mscred.mscimage.wcssour='image'
    - iraf.mscred.mscimage.ref=wcsref
    - iraf.mscred.mscimage.ra=rad
    - iraf.mscred.mscimage.dec=decd
    - iraf.mscred.mscimage.scale=0.11
    - iraf.mscred.mscimage.rotation=0.0
    - iraf.mscred.mscimage.blank=-999
    - iraf.mscred.mscimage.interpo='poly5'
    - iraf.mscred.mscimage.minterp='poly5'
    - iraf.mscred.mscimage.nxbl=4096
    - iraf.mscred.mscimage.nybl=4096
    - iraf.mscred.mscimage.fluxcon='yes'
    - iraf.mscred.mscimage(image,imout)

    """
    from pyraf import iraf
    image = odi.illcorpath+'illcor_'+ota+'.'+img.stem()
    imout = odi.reprojpath+'reproj_'+ota+'.'+img.stem()
    iraf.mscred(_doprint=0)
    iraf.clobber='no'
    iraf.unlearn(iraf.mscred.mscimage)
    iraf.mscred.mscimage.format='image'
    iraf.mscred.mscimage.pixmask='yes'
    iraf.mscred.mscimage.verbose='yes'
    iraf.mscred.mscimage.wcssour='image'
    iraf.mscred.mscimage.ref=wcsref
    iraf.mscred.mscimage.ra=rad
    iraf.mscred.mscimage.dec=decd
    iraf.mscred.mscimage.scale=0.11
    iraf.mscred.mscimage.rotation=0.0
    iraf.mscred.mscimage.blank=-999
    iraf.mscred.mscimage.interpo='poly5'
    iraf.mscred.mscimage.minterp='poly5'
    iraf.mscred.mscimage.nxbl=4096
    iraf.mscred.mscimage.nybl=4096
    iraf.mscred.mscimage.fluxcon='yes'
    iraf.mscred.mscimage(image,imout)

    return
Пример #10
0
def fix_wcs_full(img, coords='radec.coo', iters=1):
    """
    Try to improve the WCS solution of a final stacked image.

    Parameters
    ----------
    img : str
        Name of image
    coords : str
        Name of coordinate file
    iter : int
        Number of desired iterations of  ``msccmatch``. It is still being
        tested, but one might be all that is necessary, especially if using the
        Gaia catalog.

    Note
    ----
    This function is set up to use the files in the ``illcor`` directory. The
    following are the parameters used by ``msccmatch``.

    - verbose='yes'
    - usebpm='no'
    - nsearch=250
    - search=30
    - rsearch=0.2
    - cfrac=.5
    - csig=0.1
    - nfit=5
    - rms=1.0
    - maxshif=5.0
    - fitgeom="general"
    - update='yes'
    - interac='yes'
    - fit='no',
    - accept='yes'
    - Stdout=1
    """
    print coords
    iraf.mscred(_doprint=0)
    iraf.unlearn(iraf.mscred.msccmatch)
    # otaext = {'33':'[1]','34':'[2]','44':'[3]','43':'[4]','42':'[5]','32':'[6]','22':'[7]','23':'[8]','24':'[9]'}
    for i in range(iters):
        fix = iraf.msccmatch(input=img,
                             coords=coords,
                             usebpm='no',
                             verbose='yes',
                             nsearch=250,
                             search=30,
                             rsearch=0.2,
                             cfrac=.5,
                             csig=0.1,
                             nfit=5,
                             rms=1.0,
                             maxshif=5.0,
                             fitgeom="general",
                             update='yes',
                             interac='yes',
                             fit='no',
                             accept='yes',
                             Stdout=1)
        print 'fixing WCS for',img.f+', iter ='+repr(i)
        print fix[-6]
        print fix[-5]
        print fix[-4]
        print fix[-3]
        print fix[-2]
Пример #11
0
def fix_wcs(img, ota, coords='radec.coo', iters=3):
    """
    Try to improve the WCS solution of an OTA using the IRAF task ``msccmatch``.
    This function will use the ``img.nofits()+'.'+ota+'.radec.coo'`` file produced
    by :py:func:`list_wcs_coords`.

    Parameters
    ----------
    img : str
        Name of image
    ota : str
        Name of OTA
    coords : str
        Name of coordinate file
    iter : int
        Number of desired iterations of  ``msccmatch``. It is still being
        tested, but one might be all that is necessary, especially if using the
        Gaia catalog.

    Note
    ----
    This function is set up to use the files in the ``illcor`` directory. The
    following are the parameters used by ``msccmatch``.

    - verbose='yes'
    - usebpm='no'
    - nsearch=250
    - search=30
    - rsearch=0.2
    - cfrac=.5
    - csig=0.1
    - nfit=5
    - rms=1.0
    - maxshif=5.0
    - fitgeom="general"
    - update='yes'
    - interac='yes'
    - fit='no',
    - accept='yes'
    - Stdout=1
    """
    image = odi.illcorpath+'illcor_'+ota+'.'+img.stem()
    iraf.mscred(_doprint=0)
    iraf.unlearn(iraf.mscred.msccmatch)

    for i in range(iters):
        fix = iraf.msccmatch(input=image,
                             coords=odi.coordspath+coords,
                             usebpm='no',
                             verbose='yes',
                             nsearch=250,
                             search=30,
                             rsearch=0.2,
                             cfrac=.5,
                             csig=0.1,
                             nfit=5,
                             rms=1.0,
                             maxshif=5.0,
                             fitgeom="general",
                             update='yes',
                             interac='yes',
                             fit='no',
                             accept='yes',
                             Stdout=1)
        print 'fixing WCS for',img.f+'['+ota+'], iter ='+repr(i)
        print fix[-6]
        print fix[-5]
        print fix[-4]
        print fix[-3]
        print fix[-2]
Пример #12
0
# On command line, use 'x' for multiplying instead of '*'
if operator == "x" : operator="*"


# Check for existing output file
if access(outfile,F_OK):
  print " Output file ", outfile, " already exists. Exiting ... \n"
  exit(1)

if access(outfile+'.fits',F_OK):
  print " Output file ", outfile+'.fits', " already exists. Exiting ... \n"
  exit(1)


# Start Pyraf
currentdir = getcwd()
chdir(logindir)
from pyraf import iraf
chdir(currentdir)

# Set some IRAF settings
iraf.set(imtype="fits")


# Load MSCRED
iraf.mscred(_doprint=0,Stdout="/dev/null")


# Do imarith while keeping FITS extensions
iraf.mscred.mscarith(file1,operator,file2,outfile,verbose='yes')
Пример #13
0
if operator == "x" : operator="*"


# Check for existing output file
if access(outfile,F_OK):
  print " Output file ", outfile, " already exists. Exiting ... \n"
  exit(1)

if access(outfile+'.fits',F_OK):
  print " Output file ", outfile+'.fits', " already exists. Exiting ... \n"
  exit(1)


# Start Pyraf
currentdir = getcwd()
chdir(logindir)
from pyraf import iraf
chdir(currentdir)

# Set some IRAF settings
iraf.set(imtype="fits")


# Load MSCRED
iraf.mscred(_doprint=0,Stdout="/dev/null")


# Do imarith while keeping FITS extensions
iraf.mscred.mscarith(file1,operator,file2,outfile,verbose='yes')