Python NDG.filter方法代码示例

# 需要导入模块: from starutil import NDG [as 别名]
# 或者: from starutil.NDG import filter [as 别名]
   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) )

#  The next stuff we do independently for each subarray.
   qmos = {}
   umos = {}
   for a in ('S4A','S4B','S4C','S4D','S8A','S8B','S8C','S8D'):

#  Get an NDG object that contains the cleaned Q maps for the current
#  subarray.
      qarray = qcont.filter(a)

#  If any data was found for the current subarray...
      if qarray != None:

#  There seems to be a tendency for each bolometer to have its own fixed
#  bias in Q and U. We now try to remove these biases by removing the Q and
#  U values that are common to each image (as opposed to astronomical Q/U
#  variations, which are  fixed on the sky and so will vary from image to
#  image as the focal plane is moved on the sky). First we find the mean Q
#  value in each bolometer by averaging the Q images, aligned in PIXEL (i.e.
#  bolomter) coords. The mean Q value per bolometer is put in qcom.sdf.
         msg_out( "Removing background Q level from {0} bolometers...".format(a))
         qcom = NDG(1)
         qcom.comment = "qcom"
         invoke("$KAPPA_DIR/wcsframe {0} PIXEL".format(qarray))

# 需要导入模块: from starutil import NDG [as 别名]
# 或者: from starutil.NDG import filter [as 别名]
      if indata != fred:
         raise UsageError("\n\nThe directory specified by parameter RESTART ({0}) "
                          "refers to different time-series data".format(restart) )
      msg_out( "Re-using data in {0}".format(restart) )

#  Initialise the starlink random number seed to a known value so that
#  results are repeatable.
   os.environ["STAR_SEED"] = "65"

#  Flat field the supplied template data
   ff = loadndg( "FF" )
   if not ff:
      ff = NDG(indata)
      msg_out( "Flatfielding template data...")
      invoke("$SMURF_DIR/flatfield in={0} out={1}".format(indata,ff) )
      ff = ff.filter()
      savendg( "FF", ff  )
   else:
      msg_out( "Re-using old flatfielded template data...")

#  If required, create new artificial I, Q and U maps.
   if newart:
      msg_out( "Creating new artificial I, Q and U maps...")

#  Get the parameters defining the artificial data
      ipeak = parsys["IPEAK"].value
      ifwhm = parsys["IFWHM"].value
      pol = parsys["POL"].value

#  Determine the spatial extent of the data on the sky.
      invoke("$SMURF_DIR/makemap in={0} out=! config=def".format(ff))

# 需要导入模块: from starutil import NDG [as 别名]
# 或者: from starutil.NDG import filter [as 别名]
   invoke("$POLPACK_DIR/polext in={0} angrot=90".format(uart) )
   invoke("$KAPPA_DIR/setunits ndf={0} units=pW".format(iart) )
   invoke("$KAPPA_DIR/setunits ndf={0} units=pW".format(qart) )
   invoke("$KAPPA_DIR/setunits ndf={0} units=pW".format(uart) )

#  If required, create an artificial common-mode (i.e. unpolarised emission
#  from the sky) for each sub-scan/grid-point.
   if incom:

#  First flat-field all the INCOM files.
      cff = NDG.load( "CFF" )
      if not cff:
         cff = NDG(incom)
         msg_out( "Flatfielding common-mode data...")
         invoke("$SMURF_DIR/flatfield in={0} out={1}".format(incom,cff) )
         cff = cff.filter()
         cff.save( "CFF" )
      else:
         msg_out( "Re-using old flatfielded common-mode data...")

#  Process each sub-scan separately as they may have different lengths.
      cfactor = parsys["CFACTOR"].value
      com = NDG.load( "COM" )
      if not com:
         msg_out( "Creating new artificial common-mode signals...")

         comlen = 0
         totcom = NDG(1)
         lbnd = []
         ubnd = []