def setpara_bdsm(self, img):
from types import ClassType, TypeType
chain = [
Op_preprocess,
Op_rmsimage(),
Op_threshold(),
Op_islands(),
Op_gausfit(),
Op_gaul2srl(),
Op_make_residimage()
]
opts = img.opts.to_dict()
if img.opts.pi_thresh_isl != None:
opts['thresh_isl'] = img.opts.pi_thresh_isl
if img.opts.pi_thresh_pix != None:
opts['thresh_pix'] = img.opts.pi_thresh_pix
opts['thresh'] = 'hard'
opts['polarisation_do'] = False
opts['filename'] = ''
opts['detection_image'] = ''
ops = []
for op in chain:
if isinstance(op, (ClassType, TypeType)):
ops.append(op())
else:
ops.append(op)
return ops, opts
def setpara_bdsm(self, img):
from types import ClassType, TypeType
chain = [
Op_preprocess,
Op_rmsimage(),
Op_threshold(),
Op_islands(),
Op_gausfit(),
Op_gaul2srl(),
Op_make_residimage()
]
opts = {'thresh': 'hard'}
opts['thresh_pix'] = img.thresh_pix
opts['kappa_clip'] = 3.0
opts['rms_map'] = img.opts.rms_map
opts['mean_map'] = img.opts.mean_map
opts['thresh_isl'] = img.opts.thresh_isl
opts['minpix_isl'] = 6
opts['savefits_rmsim'] = False
opts['savefits_meanim'] = False
opts['savefits_rankim'] = False
opts['savefits_normim'] = False
opts['polarisation_do'] = False
opts['aperture'] = None
opts['group_by_isl'] = img.opts.group_by_isl
opts['quiet'] = img.opts.quiet
opts['ncores'] = img.opts.ncores
opts['flag_smallsrc'] = False
opts['flag_minsnr'] = 0.2
opts['flag_maxsnr'] = 1.2
opts['flag_maxsize_isl'] = 2.5
opts['flag_bordersize'] = 0
opts['flag_maxsize_bm'] = 50.0
opts['flag_minsize_bm'] = 0.2
opts['flag_maxsize_fwhm'] = 0.5
opts['bbs_patches'] = img.opts.bbs_patches
opts['filename'] = ''
opts['output_all'] = img.opts.output_all
opts['verbose_fitting'] = img.opts.verbose_fitting
opts['split_isl'] = False
opts['peak_fit'] = True
opts['peak_maxsize'] = 30.0
opts['detection_image'] = ''
opts['verbose_fitting'] = img.opts.verbose_fitting
ops = []
for op in chain:
if isinstance(op, (ClassType, TypeType)):
ops.append(op())
else:
ops.append(op)
return ops, opts
def rms_spectrum(self, img, image):
from rmsimage import Op_rmsimage
global bar1
mylog = img.mylog
nchan = image.shape[0]
rms_map = img.use_rms_map
if img.opts.kappa_clip is None:
kappa = -img.pixel_beamarea()
else:
kappa = img.opts.kappa_clip
map_opts = (kappa, img.rms_box, img.opts.spline_rank)
if rms_map:
rms_spec = N.zeros(image.shape, dtype=N.float32)
mean = N.zeros(image.shape[1:], dtype=N.float32)
rms = N.zeros(image.shape[1:], dtype=N.float32)
median_rms = N.zeros(nchan)
for ichan in range(nchan):
if bar1.started:
bar1.increment()
dumi = Op_rmsimage()
Op_rmsimage.map_2d(dumi, image[ichan], mean, rms, None, *map_opts)
rms_spec[ichan,:,:] = rms
median_rms[ichan] = N.median(rms)
else:
rms_spec = N.zeros(image.shape, dtype=N.float32)
for ichan in range(nchan):
if bar1.started:
bar1.increment()
rms_spec[ichan,:,:] = img.channel_clippedrms[ichan]
median_rms = rms_spec
str1 = " ".join(["%9.4e" % n for n in img.channel_clippedrms])
if rms_map:
mylog.debug('%s %s ' % ('Median rms of channels : ', str1))
mylog.info('RMS image made for each channel')
else:
mylog.debug('%s %s ' % ('RMS of channels : ', str1))
mylog.info('Clipped rms calculated for each channel')
return rms_spec
def setpara_bdsm(self, img, det_file):
from types import ClassType, TypeType
chain=[Op_readimage(), Op_collapse(), Op_preprocess, Op_rmsimage(),
Op_threshold(), Op_islands()]
opts = img.opts.to_dict()
opts['filename'] = det_file
opts['detection_image'] = ''
opts['polarisation_do'] = False
ops = []
for op in chain:
if isinstance(op, (ClassType, TypeType)):
ops.append(op())
else:
ops.append(op)
return ops, opts
from make_residimage import Op_make_residimage
from output import Op_outlist
from shapefit import Op_shapelets
from gaul2srl import Op_gaul2srl
from spectralindex import Op_spectralindex
from polarisation import Op_polarisation
from wavelet_atrous import Op_wavelet_atrous
from psf_vary import Op_psf_vary
from cleanup import Op_cleanup
from _version import __version__
import gc
default_chain = [Op_readimage(),
Op_collapse(),
Op_preprocess(),
Op_rmsimage(),
Op_threshold(),
Op_islands(),
Op_gausfit(),
Op_wavelet_atrous(),
Op_shapelets(),
Op_gaul2srl(),
Op_spectralindex(),
Op_polarisation(),
Op_make_residimage(),
Op_psf_vary(),
Op_outlist(),
Op_cleanup()
]
fits_chain = default_chain # for legacy scripts