help="Path to TSV file containing initial F guess, can be single-point registration or multiple point correlation. Default = None")
parser.add_argument('-pfb',
'--phiFile-bin-ratio',
type=int,
default=1,
dest='PHIFILE_BIN_RATIO',
help="Ratio of binning level between loaded Phi file and current calculation. If the input Phi file has been obtained on a 500x500x500 image and now the calculation is on 1000x1000x1000, this should be 2. Default = 1")
parser.add_argument('-np',
'--number-of-processes',
default=None,
type=int,
dest='PROCESSES',
help="Number of parallel processes to use. Default = multiprocessing.cpu_count()")
parser.add_argument('-nr',
'--neighbourhood-radius-px',
type=float,
default=None,
dest='NEIGHBOUR_RADIUS',
help="Radius (in pixels) inside which to select neighbours for field interpolation. Excludes -nn option")
parser.add_argument('-nn',
'--number-of-neighbours',
type=int,
default=None,
dest='NUMBER_OF_NEIGHBOURS',
help="Number of neighbours for field interpolation. Default = None (radius mode is default)")
parser.add_argument('-lqc',
'--local-quadratic-coherency',
action="store_true",
dest='LQC',
help='Find incoherent points based on local quadratic coherency?')
parser.add_argument('-lqcf',
'--local-quadratic-coherency-fit',
action="store_true",
dest='LQCF',
help='Correct bad points based on a local quadratic coherency fit of good neighbours?')
parser.add_argument('-i',
'--interpolate-field',
action="store_true",
dest='CORRECT_FIELD',
help='Correct bad points based on an inverse distance interpolation of good neighbours?')
parser.add_argument('-rst',
'--return-status-threshold',
type=int,
default=-4,
dest='RETURN_STATUS_THRESHOLD',
help='Lowest return status value to preserve in input PhiField. Default = -4')
parser.add_argument('-dpt',
'--delta-phi-norm-threshold',
type=numpy.float,
default=0.001,
dest='DELTA_PHI_NORM_THRESHOLD',
help="Delta Phi norm threshold BELOW which to consider the point good. Only for a point with return status = 1 . Default = 0.001")
parser.add_argument('-pscct',
'--pixel-search-cc-threshold',
type=numpy.float,
default=0,
dest='PIXEL_SEARCH_CC_THRESHOLD',
help="Pixel search correlation coefficient threshold ABOVE which to consider the point good. Default = 0.9")
parser.add_argument('-mf',
'--median-filter',
action="store_true",
dest='CORRECT_MEDIAN_FILTER',
help="Activates an overall median filter on the input displacement field")
parser.add_argument('-mfr',
'--median-filter-radius',
type=int,
default=2,
dest='MEDIAN_FILTER_RADIUS',
help="Radius (in pixels) of median filter. Default = 2")
parser.add_argument('-od',
'--out-dir',
type=str,
default=None,
dest='OUT_DIR',
help='Output directory, default is the dirname of input file')
parser.add_argument('-pre',
'--prefix',
type=str,
default=None,
dest='PREFIX',
help='Prefix for output files (without extension). Default is basename of input file')
print("This function definitely needs a TSV Phi file input")
exit()
# If we have no out dir specified, deliver on our default promise -- this can't be done inline before since parser.parse_args() has not been run at that stage.
ifargs.OUT_DIRisNone:
args.OUT_DIR=os.path.dirname(args.PHIFILE.name)
# However if we have no dir, notice this and make it the current directory.