def on_press(event):
"""Handle keypresses"""
from interface import raw_input_no_history
import numpy
global img_ch0, img_rms, img_mean, img_gaus_mod, img_shap_mod
global pixels_per_beam, vmin, vmax, vmin_cur, vmax_cur, img_pi
global ch0min, ch0max, low, fig, images, src_list, srcid_cur
global markers
if event.key == '0':
print 'Resetting limits to defaults (%.4f -- %.4f Jy/beam)' \
% (pow(10, vmin)-low,
pow(10, vmax)-low)
axes_list = fig.get_axes()
for axindx, ax in enumerate(axes_list):
if images[axindx] != 'wavelets' and images[axindx] != 'seds':
im = ax.get_images()[0]
im.set_clim(vmin, vmax)
vmin_cur = vmin
vmax_cur = vmax
pl.draw()
if event.key == 'm':
# Modify scaling
# First check that there are images to modify
has_image = False
for im in images:
if isinstance(im, numpy.ndarray):
has_image = True
if not has_image:
return
minscl = 'a'
while isinstance(minscl, str):
try:
if minscl == '':
minscl = pow(10, vmin_cur) - low
break
minscl = float(minscl)
except ValueError:
prompt = "Enter min value (current = %.4f Jy/beam) : " % (
pow(10, vmin_cur) - low, )
try:
minscl = raw_input_no_history(prompt)
except RuntimeError:
print 'Sorry, unable to change scaling.'
return
minscl = N.log10(minscl + low)
maxscl = 'a'
while isinstance(maxscl, str):
try:
if maxscl == '':
maxscl = pow(10, vmax_cur) - low
break
maxscl = float(maxscl)
except ValueError:
prompt = "Enter max value (current = %.4f Jy/beam) : " % (
pow(10, vmax_cur) - low, )
try:
maxscl = raw_input_no_history(prompt)
except RuntimeError:
print 'Sorry, unable to change scaling.'
return
maxscl = N.log10(maxscl + low)
if maxscl <= minscl:
print 'Max value must be greater than min value!'
return
axes_list = fig.get_axes()
for axindx, ax in enumerate(axes_list):
if images[axindx] != 'wavelets' and images[axindx] != 'seds':
im = ax.get_images()[0]
im.set_clim(minscl, maxscl)
vmin_cur = minscl
vmax_cur = maxscl
pl.draw()
if event.key == 'c':
# Change source SED
# First check that SEDs are being plotted
has_sed = False
if 'seds' in images:
has_sed = True
if not has_sed:
return
srcid = 'a'
while isinstance(srcid, str):
try:
if srcid == '':
srcid = srcid_cur
break
srcid = int(srcid)
except ValueError:
prompt = "Enter source ID (current = %i) : " % (srcid_cur, )
try:
srcid = raw_input_no_history(prompt)
except RuntimeError:
print 'Sorry, unable to change source.'
return
ax_indx = images.index('seds')
sed_src = get_src(src_list, srcid)
if sed_src == None:
print 'Source not found!'
return
srcid_cur = srcid
axes_list = fig.get_axes()
for axindx, ax in enumerate(axes_list):
if images[axindx] == 'seds':
plot_sed(sed_src, ax)
pl.draw()
if event.key == 'i':
# Print info about visible region
has_image = False
axes_list = fig.get_axes()
# Get limits of visible region
for axindx, ax in enumerate(axes_list):
if images[axindx] != 'wavelets' and images[axindx] != 'seds':
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
has_image = True
break
if not has_image:
return
if xmin < 0:
xmin = 0
if xmax > img_ch0.shape[0]:
xmax = img_ch0.shape[0]
if ymin < 0:
ymin = 0
if ymax > img_ch0.shape[1]:
ymax = img_ch0.shape[1]
flux = N.nansum(img_ch0[xmin:xmax, ymin:ymax]) / pixels_per_beam
mask = N.isnan(img_ch0[xmin:xmax, ymin:ymax])
num_pix_unmasked = float(N.size(N.where(mask == False), 1))
mean_rms = N.nansum(img_rms[xmin:xmax, ymin:ymax]) / num_pix_unmasked
mean_map_flux = N.nansum(img_mean[xmin:xmax,
ymin:ymax]) / pixels_per_beam
if img_gaus_mod == None:
gaus_mod_flux = 0.0
else:
gaus_mod_flux = N.nansum(img_gaus_mod[xmin:xmax,
ymin:ymax]) / pixels_per_beam
print 'Visible region (%i:%i, %i:%i) :' % (xmin, xmax, ymin, ymax)
print ' ch0 flux density from sum of pixels ... : %f Jy'\
% (flux,)
print ' Background mean map flux density ...... : %f Jy'\
% (mean_map_flux,)
print ' Gaussian model flux density ........... : %f Jy'\
% (gaus_mod_flux,)
if img_shap_mod != None:
shap_mod_flux = N.nansum(img_shap_mod[xmin:xmax,
ymin:ymax]) / pixels_per_beam
print ' Shapelet model flux density ........... : %f Jy'\
% (shap_mod_flux,)
print ' Mean rms (from rms map) ............... : %f Jy/beam'\
% (mean_rms,)
if event.key == 'n':
# Show/Hide island numbers
if markers:
for marker in markers:
marker.set_visible(not marker.get_visible())
pl.draw()
def __call__(self, img):
mylog = mylogger.logging.getLogger("PyBDSM." + img.log + "Wavelet")
if img.opts.atrous_do:
if img.nisl == 0:
mylog.warning(
"No islands found. Skipping wavelet decomposition.")
img.completed_Ops.append('wavelet_atrous')
return
mylog.info(
"Decomposing gaussian residual image into a-trous wavelets")
bdir = img.basedir + '/wavelet/'
if img.opts.output_all:
if not os.path.isdir(bdir): os.makedirs(bdir)
if not os.path.isdir(bdir + '/residual/'):
os.makedirs(bdir + '/residual/')
if not os.path.isdir(bdir + '/model/'):
os.makedirs(bdir + '/model/')
dobdsm = img.opts.atrous_bdsm_do
filter = {
'tr': {
'size': 3,
'vec': [1. / 4, 1. / 2, 1. / 4],
'name': 'Triangle'
},
'b3': {
'size': 5,
'vec': [1. / 16, 1. / 4, 3. / 8, 1. / 4, 1. / 16],
'name': 'B3 spline'
}
}
if dobdsm: wchain, wopts = self.setpara_bdsm(img)
n, m = img.ch0_arr.shape
# Calculate residual image that results from normal (non-wavelet) Gaussian fitting
Op_make_residimage()(img)
resid = img.resid_gaus_arr
lpf = img.opts.atrous_lpf
if lpf not in ['b3', 'tr']: lpf = 'b3'
jmax = img.opts.atrous_jmax
l = len(filter[lpf]['vec']
) # 1st 3 is arbit and 2nd 3 is whats expected for a-trous
if jmax < 1 or jmax > 15: # determine jmax
# Check if largest island size is
# smaller than 1/3 of image size. If so, use it to determine jmax.
min_size = min(resid.shape)
max_isl_shape = (0, 0)
for isl in img.islands:
if isl.image.shape[0] * isl.image.shape[1] > max_isl_shape[
0] * max_isl_shape[1]:
max_isl_shape = isl.image.shape
if max_isl_shape != (
0, 0) and min(max_isl_shape) < min(resid.shape) / 3.0:
min_size = min(max_isl_shape) * 4.0
else:
min_size = min(resid.shape)
jmax = int(
floor(
log((min_size / 3.0 * 3.0 - l) /
(l - 1) + 1) / log(2.0) + 1.0)) + 1
if min_size * 0.55 <= (l + (l - 1) * (2**(jmax) - 1)):
jmax = jmax - 1
img.wavelet_lpf = lpf
img.wavelet_jmax = jmax
mylog.info("Using " + filter[lpf]['name'] +
' filter with J_max = ' + str(jmax))
img.atrous_islands = []
img.atrous_gaussians = []
img.atrous_sources = []
img.atrous_opts = []
img.resid_wavelets_arr = cp(img.resid_gaus_arr)
im_old = img.resid_wavelets_arr
total_flux = 0.0
ntot_wvgaus = 0
stop_wav = False
pix_masked = N.where(N.isnan(resid) == True)
jmin = 1
if img.opts.ncores is None:
numcores = 1
else:
numcores = img.opts.ncores
for j in range(jmin, jmax +
1): # extra +1 is so we can do bdsm on cJ as well
mylogger.userinfo(mylog, "\nWavelet scale #" + str(j))
im_new = self.atrous(im_old,
filter[lpf]['vec'],
lpf,
j,
numcores=numcores,
use_scipy_fft=img.opts.use_scipy_fft)
im_new[
pix_masked] = N.nan # since fftconvolve wont work with blanked pixels
if img.opts.atrous_sum:
w = im_new
else:
w = im_old - im_new
im_old = im_new
suffix = 'w' + ` j `
filename = img.imagename + '.atrous.' + suffix + '.fits'
if img.opts.output_all:
func.write_image_to_file('fits', filename, w, img, bdir)
mylog.info('%s %s' % ('Wrote ', img.imagename +
'.atrous.' + suffix + '.fits'))
# now do bdsm on each wavelet image.
if dobdsm:
wopts['filename'] = filename
wopts['basedir'] = bdir
box = img.rms_box[0]
y1 = (l + (l - 1) * (2**(j - 1) - 1))
bs = max(5 * y1, box) # changed from 10 to 5
if bs > min(n, m) / 2:
wopts['rms_map'] = False
wopts['mean_map'] = 'const'
wopts['rms_box'] = None
else:
wopts['rms_box'] = (bs, bs / 3)
if hasattr(img, '_adapt_rms_isl_pos'):
bs_bright = max(5 * y1, img.rms_box_bright[0])
if bs_bright < bs / 1.5:
wopts['adaptive_rms_box'] = True
wopts['rms_box_bright'] = (bs_bright,
bs_bright / 3)
else:
wopts['adaptive_rms_box'] = False
if j <= 3:
wopts['ini_gausfit'] = 'default'
else:
wopts['ini_gausfit'] = 'nobeam'
wid = (l + (l - 1) * (2**(j - 1) - 1)) # / 3.0
b1, b2 = img.pixel_beam()[0:2]
b1 = b1 * fwsig
b2 = b2 * fwsig
cdelt = img.wcs_obj.acdelt[:2]
wimg = Image(wopts)
wimg.beam = (sqrt(wid * wid + b1 * b1) * cdelt[0] * 2.0,
sqrt(wid * wid + b2 * b2) * cdelt[1] * 2.0,
0.0)
wimg.orig_beam = img.beam
wimg.pixel_beam = img.pixel_beam
wimg.pixel_beamarea = img.pixel_beamarea
wimg.log = 'Wavelet.'
wimg.basedir = img.basedir
wimg.extraparams['bbsprefix'] = suffix
wimg.extraparams['bbsname'] = img.imagename + '.wavelet'
wimg.extraparams['bbsappend'] = True
wimg.bbspatchnum = img.bbspatchnum
wimg.waveletimage = True
wimg.j = j
if hasattr(img, '_adapt_rms_isl_pos'):
wimg._adapt_rms_isl_pos = img._adapt_rms_isl_pos
self.init_image_simple(wimg, img, w, '.atrous.' + suffix)
for op in wchain:
op(wimg)
gc.collect()
if isinstance(op,
Op_islands) and img.opts.atrous_orig_isl:
if wimg.nisl > 0:
# Find islands that do not share any pixels with
# islands in original ch0 image.
good_isl = []
# Make original rank image boolean; rank counts from 0, with -1 being
# outside any island
orig_rankim_bool = N.array(img.pyrank + 1,
dtype=bool)
# Multiply rank images
old_islands = orig_rankim_bool * (wimg.pyrank +
1) - 1
# Exclude islands that don't overlap with a ch0 island.
valid_ids = set(old_islands.flatten())
for idx, wvisl in enumerate(wimg.islands):
if idx in valid_ids:
wvisl.valid = True
good_isl.append(wvisl)
else:
wvisl.valid = False
wimg.islands = good_isl
wimg.nisl = len(good_isl)
mylogger.userinfo(mylog,
"Number of islands found",
'%i' % wimg.nisl)
# Renumber islands:
for wvindx, wvisl in enumerate(wimg.islands):
wvisl.island_id = wvindx
if isinstance(op, Op_gausfit):
# If opts.atrous_orig_isl then exclude Gaussians outside of
# the original ch0 islands
nwvgaus = 0
if img.opts.atrous_orig_isl:
gaul = wimg.gaussians
tot_flux = 0.0
if img.ngaus == 0:
gaus_id = -1
else:
gaus_id = img.gaussians[-1].gaus_num
wvgaul = []
for g in gaul:
if not hasattr(g, 'valid'):
g.valid = False
if not g.valid:
try:
isl_id = img.pyrank[
int(g.centre_pix[0] + 1),
int(g.centre_pix[1] + 1)]
except IndexError:
isl_id = -1
if isl_id >= 0:
isl = img.islands[isl_id]
gcenter = (g.centre_pix[0] -
isl.origin[0],
g.centre_pix[1] -
isl.origin[1])
if not isl.mask_active[gcenter]:
gaus_id += 1
gcp = Gaussian(
img, g.parameters[:],
isl.island_id, gaus_id)
gcp.gaus_num = gaus_id
gcp.wisland_id = g.island_id
gcp.jlevel = j
g.valid = True
isl.gaul.append(gcp)
isl.ngaus += 1
img.gaussians.append(gcp)
nwvgaus += 1
tot_flux += gcp.total_flux
else:
g.valid = False
g.jlevel = 0
else:
g.valid = False
g.jlevel = 0
vg = []
for g in wimg.gaussians:
if g.valid:
vg.append(g)
wimg.gaussians = vg
mylogger.userinfo(
mylog, "Number of valid wavelet Gaussians",
str(nwvgaus))
else:
# Keep all Gaussians and merge islands that overlap
tot_flux = check_islands_for_overlap(img, wimg)
# Now renumber the islands and adjust the rank image before going to next wavelet image
renumber_islands(img)
total_flux += tot_flux
if img.opts.interactive and has_pl:
dc = '\033[34;1m'
nc = '\033[0m'
print dc + '--> Displaying islands and rms image...' + nc
if max(wimg.ch0_arr.shape) > 4096:
print dc + '--> Image is large. Showing islands only.' + nc
wimg.show_fit(rms_image=False,
mean_image=False,
ch0_image=False,
ch0_islands=True,
gresid_image=False,
sresid_image=False,
gmodel_image=False,
smodel_image=False,
pyramid_srcs=False)
else:
wimg.show_fit()
prompt = dc + "Press enter to continue or 'q' stop fitting wavelet images : " + nc
answ = raw_input_no_history(prompt)
while answ != '':
if answ == 'q':
img.wavelet_jmax = j
stop_wav = True
break
answ = raw_input_no_history(prompt)
if len(wimg.gaussians) > 0:
img.resid_wavelets_arr = self.subtract_wvgaus(
img.opts, img.resid_wavelets_arr, wimg.gaussians,
wimg.islands)
if img.opts.atrous_sum:
im_old = self.subtract_wvgaus(
img.opts, im_old, wimg.gaussians, wimg.islands)
if stop_wav == True:
break
pyrank = N.zeros(img.pyrank.shape, dtype=N.int32)
for i, isl in enumerate(img.islands):
isl.island_id = i
for g in isl.gaul:
g.island_id = i
for dg in isl.dgaul:
dg.island_id = i
pyrank[isl.bbox] += N.invert(isl.mask_active) * (i + 1)
pyrank -= 1 # align pyrank values with island ids and set regions outside of islands to -1
img.pyrank = pyrank
pdir = img.basedir + '/misc/'
img.ngaus += ntot_wvgaus
img.total_flux_gaus += total_flux
mylogger.userinfo(mylog,
"Total flux density in model on all scales",
'%.3f Jy' % img.total_flux_gaus)
if img.opts.output_all:
func.write_image_to_file('fits',
img.imagename + '.atrous.cJ.fits',
im_new, img, bdir)
mylog.info('%s %s' %
('Wrote ', img.imagename + '.atrous.cJ.fits'))
func.write_image_to_file(
'fits', img.imagename + '.resid_wavelets.fits',
(img.ch0_arr - img.resid_gaus_arr +
img.resid_wavelets_arr), img, bdir + '/residual/')
mylog.info('%s %s' %
('Wrote ', img.imagename + '.resid_wavelets.fits'))
func.write_image_to_file(
'fits', img.imagename + '.model_wavelets.fits',
(img.resid_gaus_arr - img.resid_wavelets_arr), img,
bdir + '/model/')
mylog.info('%s %s' %
('Wrote ', img.imagename + '.model_wavelets.fits'))
img.completed_Ops.append('wavelet_atrous')
def _run_op_list(img, chain):
"""Runs an Image object through chain of op's.
This is separate from execute() to allow other modules (such as
interface.py) to use it as well.
"""
from time import time
from types import ClassType, TypeType
from interface import raw_input_no_history
from gausfit import Op_gausfit
import mylogger
import gc
ops = []
stopat = img.opts.stop_at
# Make sure all op's are instances
for op in chain:
if isinstance(op, (ClassType, TypeType)):
ops.append(op())
else:
ops.append(op)
if stopat == 'read' and isinstance(op, Op_readimage): break
if stopat == 'isl' and isinstance(op, Op_islands): break
# Log all non-default parameters
mylog = mylogger.logging.getLogger("PyBDSF.Init")
mylog.info("PyBDSF version %s"
% (__version__, ))
par_msg = "Non-default input parameters:\n"
user_opts = img.opts.to_list()
for user_opt in user_opts:
k, v = user_opt
val = img.opts.__getattribute__(k)
if val != v._default and v.group() != 'hidden':
par_msg += ' %-20s = %s\n' % (k, repr(val))
mylog.info(par_msg[:-1]) # -1 is to trim final newline
# Run all op's
dc = '\033[34;1m'
nc = '\033[0m'
for op in ops:
if isinstance(op, Op_gausfit) and img.opts.interactive:
print dc + '--> Displaying islands and rms image...' + nc
if max(img.ch0_arr.shape) > 4096:
print dc + '--> Image is large. Showing islands only.' + nc
img.show_fit(rms_image=False, mean_image=False, ch0_image=False,
ch0_islands=True, gresid_image=False, sresid_image=False,
gmodel_image=False, smodel_image=False, pyramid_srcs=False)
else:
img.show_fit(rms_image=True, mean_image=True,
ch0_islands=True, gresid_image=False, sresid_image=False,
gmodel_image=False, smodel_image=False, pyramid_srcs=False)
prompt = dc + "Press enter to continue or 'q' to quit .. : " + nc
answ = raw_input_no_history(prompt)
while answ != '':
if answ == 'q':
return False
answ = raw_input_no_history(prompt)
op.__start_time = time()
op(img)
op.__stop_time = time()
gc.collect()
if img.opts.interactive and not img._pi:
print dc + 'Fitting complete. Displaying results...' + nc
if img.opts.shapelet_do:
show_smod = True
show_sres = True
else:
show_smod = False
show_sres = False
if img.opts.spectralindex_do:
show_spec = True
else:
show_spec = False
if max(img.ch0_arr.shape) > 4096:
print dc + '--> Image is large. Showing Gaussian residual image only.' + nc
img.show_fit(rms_image=False, mean_image=False, ch0_image=False,
ch0_islands=False, gresid_image=True, sresid_image=False,
gmodel_image=False, smodel_image=False, pyramid_srcs=False,
source_seds=show_spec)
else:
img.show_fit(smodel_image=show_smod, sresid_image=show_sres,
source_seds=show_spec)
if img.opts.print_timing:
print "="*36
print "%18s : %10s" % ("Module", "Time (sec)")
print "-"*36
for i, op in enumerate(chain):
if hasattr(op, '__start_time'):
print "%18s : %f" % (op.__class__.__name__,
(op.__stop_time - op.__start_time))
indx_stop = i
print "="*36
print "%18s : %f" % ("Total",
(chain[indx_stop].__stop_time - chain[0].__start_time))
# Log all internally derived parameters
mylog = mylogger.logging.getLogger("PyBDSF.Final")
par_msg = "Internally derived parameters:\n"
import inspect
import types
for attr in inspect.getmembers(img.opts):
if attr[0][0] != '_':
if isinstance(attr[1], (int, str, bool, float, types.NoneType, tuple, list)):
if hasattr(img, attr[0]):
used = img.__getattribute__(attr[0])
if used != attr[1] and isinstance(used, (int, str, bool, float,
types.NoneType, tuple,
list)):
par_msg += ' %-20s : %s\n' % (attr[0], repr(used))
mylog.info(par_msg[:-1]) # -1 is to trim final newline
return True
def on_press(event):
"""Handle keypresses"""
from interface import raw_input_no_history
import numpy
global img_ch0, img_rms, img_mean, img_gaus_mod, img_shap_mod
global pixels_per_beam, vmin, vmax, vmin_cur, vmax_cur, img_pi
global ch0min, ch0max, low, fig, images, src_list, srcid_cur
global markers
if event.key == '0':
print 'Resetting limits to defaults (%.4f -- %.4f Jy/beam)' \
% (pow(10, vmin)-low,
pow(10, vmax)-low)
axes_list = fig.get_axes()
for axindx, ax in enumerate(axes_list):
if images[axindx] != 'wavelets' and images[axindx] != 'seds':
im = ax.get_images()[0]
im.set_clim(vmin, vmax)
vmin_cur = vmin
vmax_cur = vmax
pl.draw()
if event.key == 'm':
# Modify scaling
# First check that there are images to modify
has_image = False
for im in images:
if isinstance(im, numpy.ndarray):
has_image = True
if not has_image:
return
minscl = 'a'
while isinstance(minscl, str):
try:
if minscl == '':
minscl = pow(10, vmin_cur) - low
break
minscl = float(minscl)
except ValueError:
prompt = "Enter min value (current = %.4f Jy/beam) : " % (pow(10, vmin_cur)-low,)
try:
minscl = raw_input_no_history(prompt)
except RuntimeError:
print 'Sorry, unable to change scaling.'
return
minscl = N.log10(minscl + low)
maxscl = 'a'
while isinstance(maxscl, str):
try:
if maxscl == '':
maxscl = pow(10, vmax_cur) - low
break
maxscl = float(maxscl)
except ValueError:
prompt = "Enter max value (current = %.4f Jy/beam) : " % (pow(10, vmax_cur)-low,)
try:
maxscl = raw_input_no_history(prompt)
except RuntimeError:
print 'Sorry, unable to change scaling.'
return
maxscl = N.log10(maxscl + low)
if maxscl <= minscl:
print 'Max value must be greater than min value!'
return
axes_list = fig.get_axes()
for axindx, ax in enumerate(axes_list):
if images[axindx] != 'wavelets' and images[axindx] != 'seds':
im = ax.get_images()[0]
im.set_clim(minscl, maxscl)
vmin_cur = minscl
vmax_cur = maxscl
pl.draw()
if event.key == 'c':
# Change source SED
# First check that SEDs are being plotted
has_sed = False
if 'seds' in images:
has_sed = True
if not has_sed:
return
srcid = 'a'
while isinstance(srcid, str):
try:
if srcid == '':
srcid = srcid_cur
break
srcid = int(srcid)
except ValueError:
prompt = "Enter source ID (current = %i) : " % (srcid_cur,)
try:
srcid = raw_input_no_history(prompt)
except RuntimeError:
print 'Sorry, unable to change source.'
return
ax_indx = images.index('seds')
sed_src = get_src(src_list, srcid)
if sed_src is None:
print 'Source not found!'
return
srcid_cur = srcid
axes_list = fig.get_axes()
for axindx, ax in enumerate(axes_list):
if images[axindx] == 'seds':
plot_sed(sed_src, ax)
pl.draw()
if event.key == 'i':
# Print info about visible region
has_image = False
axes_list = fig.get_axes()
# Get limits of visible region
for axindx, ax in enumerate(axes_list):
if images[axindx] != 'wavelets' and images[axindx] != 'seds':
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
has_image = True
break
if not has_image:
return
if xmin < 0:
xmin = 0
if xmax > img_ch0.shape[0]:
xmax = img_ch0.shape[0]
if ymin < 0:
ymin = 0
if ymax > img_ch0.shape[1]:
ymax = img_ch0.shape[1]
flux = N.nansum(img_ch0[xmin:xmax, ymin:ymax])/pixels_per_beam
mask = N.isnan(img_ch0[xmin:xmax, ymin:ymax])
num_pix_unmasked = float(N.size(N.where(mask == False), 1))
mean_rms = N.nansum(img_rms[xmin:xmax, ymin:ymax])/num_pix_unmasked
mean_map_flux = N.nansum(img_mean[xmin:xmax, ymin:ymax])/pixels_per_beam
if img_gaus_mod is None:
gaus_mod_flux = 0.0
else:
gaus_mod_flux = N.nansum(img_gaus_mod[xmin:xmax, ymin:ymax])/pixels_per_beam
print 'Visible region (%i:%i, %i:%i) :' % (xmin, xmax, ymin, ymax)
print ' ch0 flux density from sum of pixels ... : %f Jy'\
% (flux,)
print ' Background mean map flux density ...... : %f Jy'\
% (mean_map_flux,)
print ' Gaussian model flux density ........... : %f Jy'\
% (gaus_mod_flux,)
if img_shap_mod is not None:
shap_mod_flux = N.nansum(img_shap_mod[xmin:xmax, ymin:ymax])/pixels_per_beam
print ' Shapelet model flux density ........... : %f Jy'\
% (shap_mod_flux,)
print ' Mean rms (from rms map) ............... : %f Jy/beam'\
% (mean_rms,)
if event.key == 'n':
# Show/Hide island numbers
if markers:
for marker in markers:
marker.set_visible(not marker.get_visible())
pl.draw()
def _run_op_list(img, chain):
"""Runs an Image object through chain of op's.
This is separate from execute() to allow other modules (such as
interface.py) to use it as well.
"""
from time import time
from types import ClassType, TypeType
from interface import raw_input_no_history
from gausfit import Op_gausfit
import mylogger
import gc
ops = []
stopat = img.opts.stop_at
# Make sure all op's are instances
for op in chain:
if isinstance(op, (ClassType, TypeType)):
ops.append(op())
else:
ops.append(op)
if stopat == 'read' and isinstance(op, Op_readimage): break
if stopat == 'isl' and isinstance(op, Op_islands): break
# Log all non-default parameters
mylog = mylogger.logging.getLogger("PyBDSM.Init")
mylog.info("PyBDSM version %s (LUS revision %s)"
% (__version__, __revision__))
par_msg = "Non-default input parameters:\n"
user_opts = img.opts.to_list()
for user_opt in user_opts:
k, v = user_opt
val = img.opts.__getattribute__(k)
if val != v._default and v.group() != 'hidden':
par_msg += ' %-20s = %s\n' % (k, repr(val))
mylog.info(par_msg[:-1]) # -1 is to trim final newline
# Run all op's
dc = '\033[34;1m'
nc = '\033[0m'
for op in ops:
if isinstance(op, Op_gausfit) and img.opts.interactive:
print dc + '--> Displaying islands and rms image...' + nc
if max(img.ch0_arr.shape) > 4096:
print dc + '--> Image is large. Showing islands only.' + nc
img.show_fit(rms_image=False, mean_image=False, ch0_image=False,
ch0_islands=True, gresid_image=False, sresid_image=False,
gmodel_image=False, smodel_image=False, pyramid_srcs=False)
else:
img.show_fit(rms_image=True, mean_image=True,
ch0_islands=True, gresid_image=False, sresid_image=False,
gmodel_image=False, smodel_image=False, pyramid_srcs=False)
prompt = dc + "Press enter to continue or 'q' to quit .. : " + nc
answ = raw_input_no_history(prompt)
while answ != '':
if answ == 'q':
return False
answ = raw_input_no_history(prompt)
op.__start_time = time()
op(img)
op.__stop_time = time()
gc.collect()
if img.opts.interactive and not img._pi:
print dc + 'Fitting complete. Displaying results...' + nc
if img.opts.shapelet_do:
show_smod = True
show_sres = True
else:
show_smod = False
show_sres = False
if img.opts.spectralindex_do:
show_spec = True
else:
show_spec = False
if max(img.ch0_arr.shape) > 4096:
print dc + '--> Image is large. Showing Gaussian residual image only.' + nc
img.show_fit(rms_image=False, mean_image=False, ch0_image=False,
ch0_islands=False, gresid_image=True, sresid_image=False,
gmodel_image=False, smodel_image=False, pyramid_srcs=False,
source_seds=show_spec)
else:
img.show_fit(smodel_image=show_smod, sresid_image=show_sres,
source_seds=show_spec)
if img.opts.print_timing:
print "="*36
print "%18s : %10s" % ("Module", "Time (sec)")
print "-"*36
for i, op in enumerate(chain):
if hasattr(op, '__start_time'):
print "%18s : %f" % (op.__class__.__name__,
(op.__stop_time - op.__start_time))
indx_stop = i
print "="*36
print "%18s : %f" % ("Total",
(chain[indx_stop].__stop_time - chain[0].__start_time))
# Log all internally derived parameters
mylog = mylogger.logging.getLogger("PyBDSM.Final")
par_msg = "Internally derived parameters:\n"
import inspect
import types
for attr in inspect.getmembers(img.opts):
if attr[0][0] != '_':
if isinstance(attr[1], (int, str, bool, float, types.NoneType, tuple, list)):
if hasattr(img, attr[0]):
used = img.__getattribute__(attr[0])
if used != attr[1] and isinstance(used, (int, str, bool, float,
types.NoneType, tuple,
list)):
par_msg += ' %-20s : %s\n' % (attr[0], repr(used))
mylog.info(par_msg[:-1]) # -1 is to trim final newline
return True
def __call__(self, img):
mylog = mylogger.logging.getLogger("PyBDSM." + img.log + "Wavelet")
if img.opts.atrous_do:
if img.nisl == 0:
mylog.warning("No islands found. Skipping wavelet decomposition.")
img.completed_Ops.append('wavelet_atrous')
return
mylog.info("Decomposing gaussian residual image into a-trous wavelets")
bdir = img.basedir + '/wavelet/'
if img.opts.output_all:
if not os.path.isdir(bdir): os.makedirs(bdir)
if not os.path.isdir(bdir + '/residual/'): os.makedirs(bdir + '/residual/')
if not os.path.isdir(bdir + '/model/'): os.makedirs(bdir + '/model/')
dobdsm = img.opts.atrous_bdsm_do
filter = {'tr':{'size':3, 'vec':[1. / 4, 1. / 2, 1. / 4], 'name':'Triangle'},
'b3':{'size':5, 'vec':[1. / 16, 1. / 4, 3. / 8, 1. / 4, 1. / 16], 'name':'B3 spline'}}
if dobdsm: wchain, wopts = self.setpara_bdsm(img)
n, m = img.ch0_arr.shape
# Calculate residual image that results from normal (non-wavelet) Gaussian fitting
Op_make_residimage()(img)
resid = img.resid_gaus_arr
lpf = img.opts.atrous_lpf
if lpf not in ['b3', 'tr']: lpf = 'b3'
jmax = img.opts.atrous_jmax
l = len(filter[lpf]['vec']) # 1st 3 is arbit and 2nd 3 is whats expected for a-trous
if jmax < 1 or jmax > 15: # determine jmax
# Check if largest island size is
# smaller than 1/3 of image size. If so, use it to determine jmax.
min_size = min(resid.shape)
max_isl_shape = (0, 0)
for isl in img.islands:
if isl.image.shape[0] * isl.image.shape[1] > max_isl_shape[0] * max_isl_shape[1]:
max_isl_shape = isl.image.shape
if max_isl_shape != (0, 0) and min(max_isl_shape) < min(resid.shape) / 3.0:
min_size = min(max_isl_shape) * 4.0
else:
min_size = min(resid.shape)
jmax = int(floor(log((min_size / 3.0 * 3.0 - l) / (l - 1) + 1) / log(2.0) + 1.0)) + 1
if min_size * 0.55 <= (l + (l - 1) * (2 ** (jmax) - 1)): jmax = jmax - 1
img.wavelet_lpf = lpf
img.wavelet_jmax = jmax
mylog.info("Using " + filter[lpf]['name'] + ' filter with J_max = ' + str(jmax))
img.atrous_islands = []
img.atrous_gaussians = []
img.atrous_sources = []
img.atrous_opts = []
img.resid_wavelets_arr = cp(img.resid_gaus_arr)
im_old = img.resid_wavelets_arr
total_flux = 0.0
ntot_wvgaus = 0
stop_wav = False
pix_masked = N.where(N.isnan(resid) == True)
jmin = 1
if img.opts.ncores is None:
numcores = 1
else:
numcores = img.opts.ncores
for j in range(jmin, jmax + 1): # extra +1 is so we can do bdsm on cJ as well
mylogger.userinfo(mylog, "\nWavelet scale #" + str(j))
im_new = self.atrous(im_old, filter[lpf]['vec'], lpf, j, numcores=numcores, use_scipy_fft=img.opts.use_scipy_fft)
im_new[pix_masked] = N.nan # since fftconvolve wont work with blanked pixels
if img.opts.atrous_sum:
w = im_new
else:
w = im_old - im_new
im_old = im_new
suffix = 'w' + `j`
filename = img.imagename + '.atrous.' + suffix + '.fits'
if img.opts.output_all:
func.write_image_to_file('fits', filename, w, img, bdir)
mylog.info('%s %s' % ('Wrote ', img.imagename + '.atrous.' + suffix + '.fits'))
# now do bdsm on each wavelet image.
if dobdsm:
wopts['filename'] = filename
wopts['basedir'] = bdir
box = img.rms_box[0]
y1 = (l + (l - 1) * (2 ** (j - 1) - 1))
bs = max(5 * y1, box) # changed from 10 to 5
if bs > min(n, m) / 2:
wopts['rms_map'] = False
wopts['mean_map'] = 'const'
wopts['rms_box'] = None
else:
wopts['rms_box'] = (bs, bs/3)
if hasattr(img, '_adapt_rms_isl_pos'):
bs_bright = max(5 * y1, img.rms_box_bright[0])
if bs_bright < bs/1.5:
wopts['adaptive_rms_box'] = True
wopts['rms_box_bright'] = (bs_bright, bs_bright/3)
else:
wopts['adaptive_rms_box'] = False
if j <= 3:
wopts['ini_gausfit'] = 'default'
else:
wopts['ini_gausfit'] = 'nobeam'
wid = (l + (l - 1) * (2 ** (j - 1) - 1))# / 3.0
b1, b2 = img.pixel_beam()[0:2]
b1 = b1 * fwsig
b2 = b2 * fwsig
cdelt = img.wcs_obj.acdelt[:2]
wimg = Image(wopts)
wimg.beam = (sqrt(wid * wid + b1 * b1) * cdelt[0] * 2.0, sqrt(wid * wid + b2 * b2) * cdelt[1] * 2.0, 0.0)
wimg.orig_beam = img.beam
wimg.pixel_beam = img.pixel_beam
wimg.pixel_beamarea = img.pixel_beamarea
wimg.log = 'Wavelet.'
wimg.basedir = img.basedir
wimg.extraparams['bbsprefix'] = suffix
wimg.extraparams['bbsname'] = img.imagename + '.wavelet'
wimg.extraparams['bbsappend'] = True
wimg.bbspatchnum = img.bbspatchnum
wimg.waveletimage = True
wimg.j = j
if hasattr(img, '_adapt_rms_isl_pos'):
wimg._adapt_rms_isl_pos = img._adapt_rms_isl_pos
self.init_image_simple(wimg, img, w, '.atrous.' + suffix)
for op in wchain:
op(wimg)
gc.collect()
if isinstance(op, Op_islands) and img.opts.atrous_orig_isl:
if wimg.nisl > 0:
# Find islands that do not share any pixels with
# islands in original ch0 image.
good_isl = []
# Make original rank image boolean; rank counts from 0, with -1 being
# outside any island
orig_rankim_bool = N.array(img.pyrank + 1, dtype = bool)
# Multiply rank images
old_islands = orig_rankim_bool * (wimg.pyrank + 1) - 1
# Exclude islands that don't overlap with a ch0 island.
valid_ids = set(old_islands.flatten())
for idx, wvisl in enumerate(wimg.islands):
if idx in valid_ids:
wvisl.valid = True
good_isl.append(wvisl)
else:
wvisl.valid = False
wimg.islands = good_isl
wimg.nisl = len(good_isl)
mylogger.userinfo(mylog, "Number of islands found", '%i' %
wimg.nisl)
# Renumber islands:
for wvindx, wvisl in enumerate(wimg.islands):
wvisl.island_id = wvindx
if isinstance(op, Op_gausfit):
# If opts.atrous_orig_isl then exclude Gaussians outside of
# the original ch0 islands
nwvgaus = 0
if img.opts.atrous_orig_isl:
gaul = wimg.gaussians
tot_flux = 0.0
if img.ngaus == 0:
gaus_id = -1
else:
gaus_id = img.gaussians[-1].gaus_num
wvgaul = []
for g in gaul:
if not hasattr(g, 'valid'):
g.valid = False
if not g.valid:
try:
isl_id = img.pyrank[int(g.centre_pix[0] + 1), int(g.centre_pix[1] + 1)]
except IndexError:
isl_id = -1
if isl_id >= 0:
isl = img.islands[isl_id]
gcenter = (g.centre_pix[0] - isl.origin[0],
g.centre_pix[1] - isl.origin[1])
if not isl.mask_active[gcenter]:
gaus_id += 1
gcp = Gaussian(img, g.parameters[:], isl.island_id, gaus_id)
gcp.gaus_num = gaus_id
gcp.wisland_id = g.island_id
gcp.jlevel = j
g.valid = True
isl.gaul.append(gcp)
isl.ngaus += 1
img.gaussians.append(gcp)
nwvgaus += 1
tot_flux += gcp.total_flux
else:
g.valid = False
g.jlevel = 0
else:
g.valid = False
g.jlevel = 0
vg = []
for g in wimg.gaussians:
if g.valid:
vg.append(g)
wimg.gaussians = vg
mylogger.userinfo(mylog, "Number of valid wavelet Gaussians", str(nwvgaus))
else:
# Keep all Gaussians and merge islands that overlap
tot_flux = check_islands_for_overlap(img, wimg)
# Now renumber the islands and adjust the rank image before going to next wavelet image
renumber_islands(img)
total_flux += tot_flux
if img.opts.interactive and has_pl:
dc = '\033[34;1m'
nc = '\033[0m'
print dc + '--> Displaying islands and rms image...' + nc
if max(wimg.ch0_arr.shape) > 4096:
print dc + '--> Image is large. Showing islands only.' + nc
wimg.show_fit(rms_image=False, mean_image=False, ch0_image=False,
ch0_islands=True, gresid_image=False, sresid_image=False,
gmodel_image=False, smodel_image=False, pyramid_srcs=False)
else:
wimg.show_fit()
prompt = dc + "Press enter to continue or 'q' stop fitting wavelet images : " + nc
answ = raw_input_no_history(prompt)
while answ != '':
if answ == 'q':
img.wavelet_jmax = j
stop_wav = True
break
answ = raw_input_no_history(prompt)
if len(wimg.gaussians) > 0:
img.resid_wavelets_arr = self.subtract_wvgaus(img.opts, img.resid_wavelets_arr, wimg.gaussians, wimg.islands)
if img.opts.atrous_sum:
im_old = self.subtract_wvgaus(img.opts, im_old, wimg.gaussians, wimg.islands)
if stop_wav == True:
break
pyrank = N.zeros(img.pyrank.shape, dtype=N.int32)
for i, isl in enumerate(img.islands):
isl.island_id = i
for g in isl.gaul:
g.island_id = i
for dg in isl.dgaul:
dg.island_id = i
pyrank[isl.bbox] += N.invert(isl.mask_active) * (i + 1)
pyrank -= 1 # align pyrank values with island ids and set regions outside of islands to -1
img.pyrank = pyrank
pdir = img.basedir + '/misc/'
img.ngaus += ntot_wvgaus
img.total_flux_gaus += total_flux
mylogger.userinfo(mylog, "Total flux density in model on all scales" , '%.3f Jy' % img.total_flux_gaus)
if img.opts.output_all:
func.write_image_to_file('fits', img.imagename + '.atrous.cJ.fits',
im_new, img, bdir)
mylog.info('%s %s' % ('Wrote ', img.imagename + '.atrous.cJ.fits'))
func.write_image_to_file('fits', img.imagename + '.resid_wavelets.fits',
(img.ch0_arr - img.resid_gaus_arr + img.resid_wavelets_arr), img, bdir + '/residual/')
mylog.info('%s %s' % ('Wrote ', img.imagename + '.resid_wavelets.fits'))
func.write_image_to_file('fits', img.imagename + '.model_wavelets.fits',
(img.resid_gaus_arr - img.resid_wavelets_arr), img, bdir + '/model/')
mylog.info('%s %s' % ('Wrote ', img.imagename + '.model_wavelets.fits'))
img.completed_Ops.append('wavelet_atrous')
