Python starutil.msg_out函数代码示例

def cleanup():
   global retain
   ParSys.cleanup()
   if retain:
      msg_out( "Retaining temporary files in {0}".format(NDG.tempdir))
   else:
      NDG.cleanup()

def cleanup():
   global retain
   try:
      starutil.ParSys.cleanup()
      if retain:
         msg_out( "Retaining temporary files in {0}".format(NDG.tempdir))
      else:
         NDG.cleanup()
   except:
      pass

def run_calcqu(input_data,config,harmonic):
    #  The following call to SMURF:CALCQU creates two HDS container files -
    #  one holding a set of Q NDFs and the other holding a set of U NDFs. Create
    #  these container files in the NDG temporary directory.
    qcont = NDG(1)
    qcont.comment = "qcont"
    ucont = NDG(1)
    ucont.comment = "ucont"

    msg_out( "Calculating Q and U values for each bolometer...")
    invoke("$SMURF_DIR/calcqu in={0} config=\"{1}\" lsqfit=no outq={2} outu={3} "
           "harmonic={4} fix".format(input_data,starutil.shell_quote(config),
                                     qcont,ucont,harmonic) )
    return (qcont,ucont)

def cleanup():
   global retain, new_ext_ndfs, new_lut_ndfs, new_noi_ndfs
   try:
      starutil.ParSys.cleanup()
      if retain:
         msg_out( "Retaining EXT, LUT and NOI models in {0} and temporary files in {1}".format(os.getcwd(),NDG.tempdir))
      else:
         NDG.cleanup()
         for ext in new_ext_ndfs:
            os.remove( ext )
         for lut in new_lut_ndfs:
            os.remove( lut )
         for noi in new_noi_ndfs:
            os.remove( noi )
         for res in qua:
            os.remove( res )
   except:
      pass

def force_flat( ins, masks ):
   """

   Forces the background regions to be flat in a set of Q or U images.

   Invocation:
      result = force_flat( ins, masks )

   Arguments:
      in = NDG
         An NDG object specifying a group of Q or U images from which
         any low frequency background structure is to be removed.
      masks = NDG
         An NDG object specifying a corresponding group of Q or U images
         in which source pixels are bad. These are only used to mask the
         images specified by "in". It should have the same size as "in".

   Returned Value:
      A new NDG object containing the group of corrected Q or U images.

   """

#  How many NDFs are we processing?
   nndf = len( ins )

#  Blank out sources by copy the bad pixels from "mask" into "in".
   msg_out( "   masking...")
   qm = NDG( ins )
   invoke( "$KAPPA_DIR/copybad in={0} ref={1} out={2}".format(ins,masks,qm) )

#  Smooth the blanked NDFs using a 3 pixel Gaussian. Set wlim so that
#  small holes are filled in by the smoothing process.
   msg_out( "   smoothing...")
   qs = NDG( ins )
   invoke( "$KAPPA_DIR/gausmooth in={0} out={1} fwhm=3 wlim=0.5".format(qm,qs) )

#  Fill remaining big holes using artifical data.
   msg_out( "   filling...")
   qf = NDG( ins )
   invoke( "$KAPPA_DIR/fillbad in={0} out={1} niter=10 size=10 variance=no".format(qs,qf) )

#  Subtract the filled low frequency data form the original to create the
#  returned images.
   msg_out( "   removing low frequency background structure...")
   result = NDG( ins )
   invoke( "$KAPPA_DIR/sub in1={0} in2={1} out={2}".format(ins,qf,result) )

   return result

#  See if temp files are to be retained.
   retain = parsys["RETAIN"].value

#  See statistical debiasing is to be performed.
   debias = parsys["DEBIAS"].value

#  See if we should convert pW to Jy.
   jy = parsys["JY"].value

#  Determine the waveband and get the corresponding FCF values with and
#  without POL2 in the beam.
   try:
      filter = int( float( starutil.get_fits_header( qin[0], "FILTER", True )))
   except NoValueError:
      filter = 850
      msg_out( "No value found for FITS header 'FILTER' in {0} - assuming 850".format(qin[0]))

   if filter == 450:
      fcf1 = 962.0
      fcf2 = 491.0
   elif filter == 850:
      fcf1 = 725.0
      fcf2 = 537.0
   else:
      raise starutil.InvalidParameterError("Invalid FILTER header value "
             "'{0} found in {1}.".format( filter, qin[0] ) )

#  Remove any spectral axes
   qtrim = NDG(qin)
   invoke( "$KAPPA_DIR/ndfcopy in={0} out={1} trim=yes".format(qin,qtrim) )
   utrim = NDG(uin)

#  See if temp files are to be retained.
   retain = parsys["RETAIN"].value

#  The following call to SMURF:CALCQU creates two HDS container files -
#  one holding a set of Q NDFs and the other holding a set of U NDFs. Create
#  these container files in the NDG temporary directory.
   qcont = NDG(1)
   qcont.comment = "qcont"
   ucont = NDG(1)
   ucont.comment = "ucont"

#  Create a set of Q images and a set of U images. These are put into the HDS
#  container files "q_TMP.sdf" and "u_TMP.sdf". Each image contains Q or U
#  values derived from a short section of raw data during which each bolometer
#  moves less than half a pixel.
   msg_out( "Calculating Q and U values for each bolometer...")
   invoke("$SMURF_DIR/calcqu in={0} config={1} outq={2} outu={3} fix".
          format(indata,config,qcont,ucont) )

#  Remove spikes from the Q and U images. The cleaned NDFs are written to
#  temporary NDFs specified by two new NDG objects "qff" and "uff", which
#  inherit their size from the existing groups "qcont" and "ucont".
   msg_out( "Removing spikes from bolometer Q and U values...")
   qff = NDG(qcont)
   qff.comment = "qff"
   uff = NDG(ucont)
   uff.comment = "uff"
   invoke( "$KAPPA_DIR/ffclean in={0} out={1} box=3 clip=\[2,2,2\]"
           .format(qcont,qff) )
   invoke( "$KAPPA_DIR/ffclean in={0} out={1} box=3 clip=\[2,2,2\]"
           .format(ucont,uff) )

def remove_corr( ins, masks ):
   """

   Masks the supplied set of Q or U images and then looks for and removes
   correlated components in the background regions.

   Invocation:
      result = remove_corr( ins, masks )

   Arguments:
      ins = NDG
         An NDG object specifying a group of Q or U images from which
         correlated background components are to be removed.
      masks = NDG
         An NDG object specifying a corresponding group of Q or U images
         in which source pixels are bad. These are only used to mask the
         images specified by "in". It should have the same size as "in".

   Returned Value:
      A new NDG object containing the group of corrected Q or U images.

   """

#  How many NDFs are we processing?
   nndf = len( ins )

#  Blank out sources by copy the bad pixels from "mask" into "in". We refer
#  to "q" below, but the same applies whether processing Q or U.
   msg_out( "   masking...")
   qm = NDG( ins )
   invoke( "$KAPPA_DIR/copybad in={0} ref={1} out={2}".format(ins,masks,qm) )

#  Find the most correlated pair of imagtes. We use the basic correlation
#  coefficient calculated by kappa:scatter for this.
   msg_out( "   Finding most correlated pair of images...")
   cmax = 0
   for i in range(0,nndf-1):
      for j in range(i + 1,nndf):
         invoke( "$KAPPA_DIR/scatter in1={0} in2={1} device=!".format(qm[i],qm[j]) )
         c = starutil.get_task_par( "corr", "scatter" )
         if abs(c) > abs(cmax):
            cmax = c
            cati = i
            catj = j

   if abs(cmax) < 0.3:
      msg_out("   No correlated images found!")
      return ins

   msg_out( "   Correlation for best pair of images = {0}".format( cmax ) )

#  Find images that are reasonably correlated to the pair found above,
#  and coadd them to form a model for the correlated background
#  component. Note, the holes left by the masking are filled in by the
#  coaddition using background data from other images.
   msg_out( "   Forming model...")

#  Form the average of the two most correlated images, first normalising
#  them to a common scale so that they both have equal weight.
   norm = "{0}/norm".format(NDG.tempdir)
   if not normer( qm[cati], qm[catj], 0.3, norm ):
      norm = qm[cati]

   mslist = NDG( [ qm[catj], norm ] )
   ave = "{0}/ave".format(NDG.tempdir)
   invoke( "$CCDPACK_DIR/makemos in={0} method=mean genvar=no usevar=no out={1}".format(mslist,ave) )

#  Loop round each image finding the correlation factor of the image and
#  the above average image.
   temp = "{0}/temp".format(NDG.tempdir)
   nlist = []
   ii = 0
   for i in range(0,nndf):
      c = blanker( qm[i], ave, temp )

#  If the correlation is high enough, normalize the image to the average
#  image and then include the normalised image in the list of images to be
#  coadded to form the final model.
      if abs(c) > 0.3:
         tndf = "{0}/t{1}".format(NDG.tempdir,ii)
         ii += 1
         invoke( "$KAPPA_DIR/normalize in1={1} in2={2} out={0} device=!".format(tndf,temp,ave))
         nlist.append( tndf )

   if ii == 0:
      msg_out("   No secondary correlated images found!")
      return ins

   msg_out("   Including {0} secondary correlated images in the model.".format(ii) )

#  Coadded the images created above to form the model of the correlated
#  background component. Fill any remaining bad pixels with artificial data.
   model = "{0}/model".format(NDG.tempdir)
   included = NDG( nlist )
   invoke( "$CCDPACK_DIR/makemos in={0} method=mean usevar=no genvar=no out={1}".format( included, temp ) )
   invoke( "$KAPPA_DIR/fillbad in={1} variance=no out={0} size=10 niter=10".format(model,temp) )

#  Now estimate how much of the model is present in each image and remove it.
   msg_out("   Removing model...")
   temp2 = "{0}/temp2".format(NDG.tempdir)
#.........这里部分代码省略.........

#  Do not use more com files for each sub-array than are needed.
      remlist = []
      for subarr in ( "s8a", "s8b", "s8c", "s8d", "s4a", "s4b", "s4c", "s4d" ):
         nin = 0
         for ndf in indata:
            if subarr in ndf:
               nin += 1

         ncom = 0
         for ndf in incom:
            if subarr in ndf:
               ncom += 1
               if ncom > nin:
                  remlist.append( ndf )

      msg_out("Ignoring {0} surplus files in INCOM".format(len(remlist) ))
      for ndf in remlist:
        incom.remove( ndf )

#  See if new artificial I, Q and U maps are to be created.
   newart = parsys["NEWART"].value

#  If not, set the ART parameters to indicate that the specified NDFs
#  must already exist.
   if not newart:
      parsys["ARTI"].exists = True
      parsys["ARTQ"].exists = True
      parsys["ARTU"].exists = True
   else:
      parsys["ARTI"].exists = False
      parsys["ARTQ"].exists = False

def get_filtered_skydip_data(qarray,uarray,clip,a):
    """

    This function takes q and u array data (output from calcqu), applies ffclean to remove spikes
    and puts in numpy array variable
    It borrows (copies) heavily from pol2cat.py (2015A)

    Invocation:
        ( qdata_total,qvar_total,udata_total,uvar_total,elevation,opacity_term,bad_pixel_ref ) = ...
            get_filtered_skydip_data(qarray,uarray,clip,a)

    Arguments:
        qarray = An NDF of Q array data (output from calcqu).
        uarray = An NDF of U array data (output form calcqu).
        clip = The sigma cut for ffclean.
           a = A string indicating the array (eg. 'S8A').

    Returned Value:
        qdata_total = A numpy array with the cleaned qarray data.
        qvar_total = A numpy array with the qarray variance data.
        udata_total = A numpy array with the cleaned uarray data.
        uvar_total = A numpy array with the uarray variance data.
        elevation = A numpy array with the elevation data
        opacity_term = A numpy array with the opacity brightness term (1-exp(-tau*air_mass))
            Here tau is calculated using the WVM data as input.

    """

    #  Remove spikes from the Q images for the current subarray. The cleaned NDFs
    #  are written to temporary NDFs specified by the new NDG object "qff", which
    #  inherit its size from the existing group "qarray"".
    msg_out( "Removing spikes from {0} bolometer Q values...".format(a))
    qff = NDG(qarray)
    qff.comment = "qff"
    invoke( "$KAPPA_DIR/ffclean in={0} out={1} genvar=yes box=3 clip=\[{2}\]".format(qarray,qff,clip) )

    #  Remove spikes from the U images for the current subarray. The cleaned NDFs
    #  are written to temporary NDFs specified by the new NDG object "uff", which
    #  inherit its size from the existing group "uarray"".
    msg_out( "Removing spikes from {0} bolometer U values...".format(a))
    uff = NDG(uarray)
    uff.comment = "uff"
    invoke( "$KAPPA_DIR/ffclean in={0} out={1} genvar=yes box=3 clip=\[{2}\]"
            .format(uarray,uff,clip) )

    elevation = []
    opacity_term = []
    for stare in range(len(qff[:])):
    # Stack Q data in numpy array
        # Get elevation information
        elevation.append(numpy.array( float( invoke( "$KAPPA_DIR/fitsmod ndf={0} edit=print keyword=ELSTART".format( qff[ stare ] ) ) ) ) )
        # Get Tau (Opacity) information
        tau_temp = numpy.array( float( invoke( "$KAPPA_DIR/fitsmod ndf={0} edit=print keyword=WVMTAUST".format( qff[ stare ] ) ) ) )
        # Convert to obs band.
        if '4' in a:
             tau_temp = 19.04*(tau_temp-0.018) # Eq from Dempsey et al
        elif '8' in a:
             tau_temp = 5.36*(tau_temp-0.006) # Eq from Dempsey et al.
        opacity_term.append(1-numpy.exp(-1*tau_temp/numpy.sin(numpy.radians(elevation[-1]))))
        invoke( "$KAPPA_DIR/ndftrace {0} quiet".format(qff[ stare ]))
        nx = get_task_par( "dims(1)", "ndftrace" )
        ny = get_task_par( "dims(2)", "ndftrace" )
        qdata_temp = numpy.reshape( Ndf( qff[ stare ] ).data, (ny,nx))
        qdata_temp[numpy.abs(qdata_temp)>1e300] = numpy.nan;
        if stare == 0:
            qdata_total = qdata_temp
        else:
            qdata_total = numpy.dstack((qdata_total,qdata_temp))
        qvar_temp = numpy.reshape( Ndf( qff[ stare ] ).var, (ny,nx))
        qdata_temp[numpy.abs(qvar_temp)>1e300] = numpy.nan;
        if stare == 0:
            qvar_total = qvar_temp
        else:
            qvar_total = numpy.dstack((qvar_total,qvar_temp))
        # Stack U data in numpy array
        invoke( "$KAPPA_DIR/ndftrace {0} quiet".format(uff[ stare ]))
        nx = get_task_par( "dims(1)", "ndftrace" )
        ny = get_task_par( "dims(2)", "ndftrace" )
        udata_temp = numpy.reshape( Ndf( uff[ stare ] ).data, (ny,nx))
        udata_temp[numpy.abs(udata_temp)>1e300] = numpy.nan;
        if stare == 0:
            udata_total = udata_temp
        else:
            udata_total = numpy.dstack((udata_total,udata_temp))
        uvar_temp = numpy.reshape( Ndf( uff[ stare ] ).var, (ny,nx))
        udata_temp[numpy.abs(uvar_temp)>1e300] = numpy.nan;
        if stare == 0:
            uvar_total = uvar_temp
        else:
            uvar_total = numpy.dstack((uvar_total,uvar_temp))

    # Create bad pixel reference.
    bad_pixel_ref = NDG(1)
    invoke( "$KAPPA_DIR/copybad in={0} ref={1} out={2}".format(qff,uff,bad_pixel_ref))
    return( qdata_total,qvar_total,udata_total,uvar_total,elevation,opacity_term,bad_pixel_ref )

         else:
            tile_dict[ jsatile ] = tile

#  Create a list holding the paths to the tile NDFs that intersect
#  the required region.
      ntile = 0
      used_tile_list = []
      for jsatile in jsatile_list:
         key = str(jsatile)
         if key in tile_dict and tile_dict[ key ]:
            used_tile_list.append( tile_dict[ key ] )
            ntile += 1

#  Create an NDG holding the group of tile NDFs.
      if ntile > 0:
         msg_out( "{0} of the supplied tiles intersect the requested region.".format(ntile) )
         used_tiles = NDG( used_tile_list )
      else:
         raise starutil.InvalidParameterError( "None of the supplied JSA tiles "
                                               "intersect the requested region" )

#  If we are using all tiles, just use the supplied group of tiles. Use
#  the middle supplied tile as the reference.
   else:
      used_tiles = tiles
      jsatile = int( len(tiles)/2 )
      jsatile = starutil.get_fits_header( tiles[ jsatile ], "TILENUM" )

#  Paste these tile NDFs into a single image. This image still uses the
#  JSA all-sky pixel grid. If we have only a single tile, then just use
#  it as it is.

#  Initialise the parameters to hold any values supplied on the command
#  line. This automatically adds definitions for the additional parameters
#  "MSG_FILTER", "ILEVEL", "GLEVEL" and "LOGFILE".
   parsys = starutil.ParSys( params )

#  It's a good idea to get parameter values early if possible, in case
#  the user goes off for a coffee whilst the script is running and does not
#  see a later parameter prompt or error.
   restart = parsys["RESTART"].value
   if restart == None:
      retain = parsys["RETAIN"].value
   else:
      retain = True
      NDG.tempdir = restart
      NDG.overwrite = True
      msg_out( "Re-starting using data in {0}".format(restart) )

   indata = parsys["IN"].value
   outdata = parsys["OUT"].value
   niter = parsys["NITER"].value
   pixsize = parsys["PIXSIZE"].value
   config = parsys["CONFIG"].value
   ref = parsys["REF"].value
   mask2 = parsys["MASK2"].value
   mask3 = parsys["MASK3"].value
   extra = parsys["EXTRA"].value
   itermap = parsys["ITERMAP"].value

#  See if we are using pre-cleaned data, in which case there is no need
#  to export the cleaned data on the first iteration.
   if invoke( "$KAPPA_DIR/configecho name=doclean config={0} "

                        ipprms_pol_screen[row_val,col_val] = ipprms.x[0]
                        ipprms_Co[row_val,col_val] = ipprms.x[1]
                        ipprms_dc_Q[row_val,col_val] = ipprms.x[2]
                        ipprms_dc_U[row_val,col_val] = ipprms.x[3]
                        chi2Vals[row_val,col_val] = ipprms.fun
                    else:
                        returnCode[row_val,col_val] = False

            # Write NDFs.
            out_p0 = write_ip_NDF(ip_prms['Pf_'+a[-1]],bad_pixel_ref)
            out_p1 = write_ip_NDF(ipprms_pol_screen,bad_pixel_ref)
            out_c0 = write_ip_NDF(ipprms_Co,bad_pixel_ref)
            out_angc = write_ip_NDF(ip_prms['Theta_ip_'+a[-1]],bad_pixel_ref)

            # Fill any bad pixels with smooth function to match surrounding pixels
            msg_out( "Filling in bad pixel values for {0} bolometer IP parameters...".format(a))
            out_p0_filled = NDG(1)
            invoke( "$KAPPA_DIR/fillbad in={0} out={1} variance=true niter=10 size=15".format(out_p0,out_p0_filled) )
            out_p1_filled = NDG(1)
            invoke( "$KAPPA_DIR/fillbad in={0} out={1} variance=true niter=10 size=15".format(out_p1,out_p1_filled) )
            out_c0_filled = NDG(1)
            invoke( "$KAPPA_DIR/fillbad in={0} out={1} variance=true niter=10 size=15".format(out_c0,out_c0_filled) )
            out_angc_filled = NDG(1)
            invoke( "$KAPPA_DIR/fillbad in={0} out={1} variance=true niter=10 size=15".format(out_angc,out_angc_filled) )

            # Copy individual NDFs to single output.
            invoke( "$KAPPA_DIR/ndfcopy {0} {1}".format(out_p0,outdata+'_preclean.'+str.lower(a)+'p0'))
            invoke( "$KAPPA_DIR/ndfcopy {0} {1}".format(out_p1,outdata+'_preclean.'+str.lower(a)+'p1'))
            invoke( "$KAPPA_DIR/ndfcopy {0} {1}".format(out_c0,outdata+'_preclean.'+str.lower(a)+'c0'))
            invoke( "$KAPPA_DIR/ndfcopy {0} {1}".format(out_angc,outdata+'_preclean.'+str.lower(a)+'angc'))

def pca( indata, ncomp ):
   """

   Identifies and returns the strongest PCA components in a 3D NDF.

   Invocation:
      result = pca( indata, ncomp )

   Arguments:
      indata = NDG
         An NDG object specifying a single 3D NDF. Each plane in the cube
         is a separate image, and the images are compared using PCA.
      ncomp = int
         The number of PCA components to include in the returned NDF.

   Returned Value:
      A new NDG object containing a single 3D NDF containing just the
      strongest "ncomp" PCA components found in the input NDF.

   """

   msg_out( "   finding strongest {0} components using Principal Component Analysis...".format(ncomp) )

#  Get the shape of the input NDF.
   invoke( "$KAPPA_DIR/ndftrace {0} quiet".format(indata) )
   nx = get_task_par( "dims(1)", "ndftrace" )
   ny = get_task_par( "dims(2)", "ndftrace" )
   nz = get_task_par( "dims(3)", "ndftrace" )

#  Fill any bad pixels.
   tmp = NDG(1)
   invoke( "$KAPPA_DIR/fillbad in={0} out={1} variance=no niter=10 size=10".format(indata,tmp) )

#  Read the planes from the supplied NDF. Note, numpy axis ordering is the
#  reverse of starlink axis ordering. We want a numpy array consisting of
#  "nz" elements, each being a vectorised form of a plane from the 3D NDF.
   ndfdata = numpy.reshape( Ndf( tmp[0] ).data, (nz,nx*ny) )

#  Normalize each plane to a mean of zero and standard deviation of 1.0
   means = []
   sigmas = []
   newdata = []
   for iplane in range(0,nz):
      plane = ndfdata[ iplane ]
      mn = plane.mean()
      sg = math.sqrt( plane.var() )
      means.append( mn )
      sigmas.append( sg )

      if sg > 0.0:
         newdata.append( (plane-mn)/sg )

   newdata= numpy.array( newdata )

#  Transpose as required by MDP.
   pcadata = numpy.transpose( newdata )

#  Find the required number of PCA components (these are the strongest
#  components).
   pca = mdp.nodes.PCANode( output_dim=ncomp )
   comp = pca.execute( pcadata )

#  Re-project the components back into the space of the input 3D NDF.
   ip = numpy.dot( comp, pca.get_recmatrix() )

#  Transpose the array so that each row is an image.
   ipt = numpy.transpose(ip)

#  Normalise them back to the original scales.
   jplane = 0
   newdata = []
   for iplane in range(0,nz):
      if sigmas[ iplane ] > 0.0:
         newplane = sigmas[ iplane ] * ipt[ jplane ] + means[ iplane ]
         jplane += 1
      else:
         newplane = ndfdata[ iplane ]
      newdata.append( newplane )
   newdata= numpy.array( newdata )

#  Dump the re-projected images out to a 3D NDF.
   result = NDG(1)
   indf = ndf.open( result[0], 'WRITE', 'NEW' )
   indf.new('_DOUBLE', 3, numpy.array([1,1,1]),numpy.array([nx,ny,nz]))
   ndfmap = indf.map( 'DATA', '_DOUBLE', 'WRITE' )
   ndfmap.numpytondf( newdata )
   indf.annul()

#  Uncomment to dump the components.
#   msg_out( "Dumping PCA comps to {0}-comps".format(result[0]) )
#   compt = numpy.transpose(comp)
#   indf = ndf.open( "{0}-comps".format(result[0]), 'WRITE', 'NEW' )
#   indf.new('_DOUBLE', 3, numpy.array([1,1,1]),numpy.array([nx,ny,ncomp]))
#   ndfmap = indf.map( 'DATA', '_DOUBLE', 'WRITE' )
#   ndfmap.numpytondf( compt )
#   indf.annul()

   return result

#  Fixed clump size (FWHM in pixels on all axes)
clump_fwhm = 10

#  Initial mean clump separation in pixels
clump_separation = clump_fwhm/2.0

#  Do tests for 5 different separations
for isep in range(0, 1):

#  Initial peak value
   peak_value = noise*0.5

#  Do tests for 5 different peak values
   for ipeak in range(0, 1):
      starutil.msg_out( ">>> Doing sep={0} and peak={1}....".format(clump_separation,peak_value))

#  Get the dimensions of a square image that would be expected to
#  contain the target number of clumps at the current separation.
      npix = int( clump_separation*math.sqrt( nclump_target ) )

#  Create a temporary file containing circular clumps of constant size
#  and shape (except for the effects of noise).
      model = NDG(1)
      out = NDG(1)
      outcat = NDG.tempfile(".fit")
      invoke( "$CUPID_DIR/makeclumps angle=\[0,0\] beamfwhm=0 deconv=no "
              "fwhm1=\[{0},0\] fwhm2=\[{0},0\] lbnd=\[1,1\] ubnd=\[{1},{1}\] "
              "model={2} nclump={3} out={4} outcat={5} pardist=normal "
              "peak = \[{6},0\] rms={7} trunc=0.1".
               format(clump_fwhm,npix,model,nclump_target,out,outcat,