def __call__(self, img):
mylog = mylogger.logging.getLogger("PyBDSM.Cleanup")
### plotresults for all gaussians together
if img.opts.plot_allgaus and has_pl:
pl.figure()
pl.title('All gaussians including wavelet images')
allgaus = img.gaussians
if hasattr(img, 'atrous_gaussians'):
for gg in img.atrous_gaussians:
allgaus += gg
for g in allgaus:
ellx, elly = func.drawellipse(g)
pl.plot(ellx, elly, 'r')
from math import log10
bdir = img.basedir + '/misc/'
if not os.path.isdir(bdir): os.makedirs(bdir)
im_mean = img.clipped_mean
im_rms = img.clipped_rms
low = 1.1*abs(img.min_value)
low1 = 1.1*abs(N.min(im_mean-im_rms*5.0))
if low1 > low: low = low1
vmin = log10(im_mean-im_rms*5.0 + low)
vmax = log10(im_mean+im_rms*15.0 + low)
im = N.log10(img.ch0_arr + low)
pl.imshow(N.transpose(im), origin='lower', interpolation='nearest',vmin=vmin, vmax=vmax, \
cmap=cm.gray); pl.colorbar()
pl.savefig(bdir+'allgaussians.png')
pl.close()
img.completed_Ops.append('cleanup')
def make_casa_str(img, glist):
"""Makes a list of string entries for a casa clean box file."""
import functions as func
outstr_list = []
sep = ' '
scale = 2.0
for gindx, g in enumerate(glist[0]):
x, y = g.centre_pix
xsize, ysize, ang = g.size_pix # FWHM
ellx, elly = func.drawellipse(g)
blc = [int(min(ellx)), int(min(elly))]
trc = [int(max(ellx)), int(max(elly))]
blc[0] -= (x - blc[0]) * scale
blc[1] -= (y - blc[1]) * scale
trc[0] += (trc[0] - x) * scale
trc[1] += (trc[1] - y) * scale
# Format is: <id> <blcx> <blcy> <trcx> <trcy>
# Note that we use gindx rather than g.gaus_num so that
# all Gaussians will have a unique id, even if wavelet
# Gaussians are included.
outstr_list.append(str(gindx+1) + sep + str(blc[0]) + sep +
str(blc[1]) + sep + str(trc[0]) + sep +
str(trc[1]) +'\n')
return outstr_list
def make_casa_str(img, glist):
"""Makes a list of string entries for a casa region file."""
import functions as func
outstr_list = ['#CRTFv0 CASA Region Text Format version 0\n']
sep = ' '
scale = 2.0 # scale box to 2 times FWHM of Gaussian
for gindx, g in enumerate(glist[0]):
x, y = g.centre_pix
ellx, elly = func.drawellipse(g)
blc = [min(ellx), min(elly)]
trc = [max(ellx), max(elly)]
blc[0] -= (x - blc[0]) * scale
blc[1] -= (y - blc[1]) * scale
trc[0] += (trc[0] - x) * scale
trc[1] += (trc[1] - y) * scale
blc_sky = img.pix2sky(blc)
trc_sky = img.pix2sky(trc)
blc_sky_str = convert_radec_str(blc_sky[0], blc_sky[1])
trc_sky_str = convert_radec_str(trc_sky[0], trc_sky[1])
# Format is: box [ [<blcx>, <blcy>], [<trcx>, <trcy>] ]
# Note that we use gindx rather than g.gaus_num so that
# all Gaussians will have a unique id, even if wavelet
# Gaussians are included.
outstr_list.append('box [[' + ', '.join(blc_sky_str) + '], [' +
', '.join(trc_sky_str) + ']] coord=J2000\n')
return outstr_list
def __call__(self, img):
mylog = mylogger.logging.getLogger("PyBDSM.Cleanup")
### plotresults for all gaussians together
if img.opts.plot_allgaus and has_pl:
pl.figure()
pl.title('All gaussians including wavelet images')
allgaus = img.gaussians
if hasattr(img, 'atrous_gaussians'):
for gg in img.atrous_gaussians:
allgaus += gg
for g in allgaus:
ellx, elly = func.drawellipse(g)
pl.plot(ellx, elly, 'r')
from math import log10
bdir = img.basedir + '/misc/'
if not os.path.isdir(bdir): os.makedirs(bdir)
im_mean = img.clipped_mean
im_rms = img.clipped_rms
low = 1.1 * abs(img.min_value)
low1 = 1.1 * abs(N.min(im_mean - im_rms * 5.0))
if low1 > low: low = low1
vmin = log10(im_mean - im_rms * 5.0 + low)
vmax = log10(im_mean + im_rms * 15.0 + low)
im = N.log10(img.ch0_arr + low)
pl.imshow(N.transpose(im), origin='lower', interpolation='nearest',vmin=vmin, vmax=vmax, \
cmap=cm.gray)
pl.colorbar()
pl.savefig(bdir + 'allgaussians.png')
pl.close()
img.completed_Ops.append('cleanup')
def plotresults(img,
ch0_image=True,
rms_image=True,
mean_image=True,
ch0_islands=True,
gresid_image=True,
sresid_image=False,
gmodel_image=True,
smodel_image=False,
pyramid_srcs=False,
source_seds=False,
ch0_flagged=False,
pi_image=False,
psf_major=False,
psf_minor=False,
psf_pa=False,
broadcast=False):
"""Show the results of a fit."""
global img_ch0, img_rms, img_mean, img_gaus_mod, img_shap_mod
global img_gaus_resid, img_shap_resid, pixels_per_beam, pix2sky
global vmin, vmax, vmin_cur, vmax_cur, ch0min, ch0max, img_pi
global low, fig, images, src_list, srcid_cur, sky2pix, markers
global img_psf_maj, img_psf_min, img_psf_pa, do_broadcast, samp_client
global samp_key, samp_gaul_table_url, samp_srl_table_url
if not has_pl:
print "\033[31;1mWARNING\033[0m: Matplotlib not found. Plotting is disabled."
return
if hasattr(img, 'samp_client'):
samp_client = img.samp_client
samp_key = img.samp_key
if hasattr(img, 'samp_srl_table_url'):
samp_srl_table_url = img.samp_srl_table_url
else:
samp_srl_table_url = None
if hasattr(img, 'samp_gaul_table_url'):
samp_gaul_table_url = img.samp_gaul_table_url
else:
samp_gaul_table_url = None
else:
samp_clent = None
samp_key = None
samp_srl_table_url = None
samp_gaul_table_url = None
do_broadcast = broadcast
# Define the images. The images are used both by imshow and by the
# on_press() and coord_format event handlers
pix2sky = img.pix2sky
sky2pix = img.sky2pix
gfactor = 2.0 * N.sqrt(2.0 * N.log(2.0))
pixels_per_beam = 2.0 * N.pi * (img.beam2pix(img.beam)[0] *
img.beam2pix(img.beam)[1]) / gfactor**2
# Construct lists of images, titles, etc.
images = []
titles = []
names = []
markers = []
img_gaus_mod = None # default needed for key press event
img_shap_mod = None # default needed for key press event
if ch0_image:
img_ch0 = img.ch0_arr
images.append(img_ch0)
titles.append('Original (ch0) Image\n(arbitrary logarithmic scale)')
names.append('ch0')
if ch0_islands:
img_ch0 = img.ch0_arr
images.append(img_ch0)
if hasattr(img, 'ngaus'):
if hasattr(img, 'ch0_pi_arr'):
ch0_str = 'Islands (hatched boundaries; red = PI only) and\nGaussians'
else:
ch0_str = 'Islands (hatched boundaries) and\nGaussians'
if hasattr(img, 'atrous_gaussians'):
ch0_str += ' (red = wavelet)'
titles.append(ch0_str)
else:
titles.append('Islands (hatched boundaries)')
names.append('ch0')
if ch0_flagged:
if not hasattr(img, 'ngaus'):
print 'Image was not fit with Gaussians. Skipping display of flagged Gaussians.'
else:
img_ch0 = img.ch0_arr
images.append(img_ch0)
titles.append('Flagged Gaussians')
names.append('ch0')
if pi_image:
if not hasattr(img, 'ch0_pi_arr'):
print 'Polarization module not run. Skipping PI image.'
else:
img_pi = img.ch0_pi_arr
images.append(img_pi)
titles.append('Polarized Intensity Image')
names.append('ch0_pi')
if rms_image:
img_rms = img.rms_arr
images.append(img_rms)
titles.append('Background rms Image')
names.append('rms')
if gresid_image:
if not hasattr(img, 'ngaus'):
print 'Image was not fit with Gaussians. Skipping residual Gaussian image.'
else:
img_gaus_resid = img.resid_gaus_arr
images.append(img_gaus_resid)
titles.append('Gaussian Residual Image')
names.append('gaus_resid')
if gmodel_image:
if not hasattr(img, 'ngaus'):
print 'Image was not fit with Gaussians. Skipping model Gaussian image.'
else:
img_gaus_mod = img.model_gaus_arr
images.append(img_gaus_mod)
titles.append('Gaussian Model Image')
names.append('gaus_mod')
if mean_image:
img_mean = img.mean_arr
images.append(img_mean)
titles.append('Background mean Image')
names.append('mean')
if sresid_image:
if img.opts.shapelet_do == False:
print 'Image was not decomposed into shapelets. Skipping residual shapelet image.'
else:
img_shap_resid = img.ch0_arr - img.model_shap_arr
images.append(img_shap_resid)
titles.append('Shapelet Residual Image')
names.append('shap_resid')
if smodel_image:
if img.opts.shapelet_do == False:
print 'Image was not decomposed into shapelets. Skipping model shapelet image.'
else:
img_shap_mod = img.model_shap_arr
images.append(img_shap_mod)
titles.append('Shapelet Model Image')
names.append('shap_mod')
if source_seds:
if img.opts.spectralindex_do == False:
print 'Source SEDs were not fit. Skipping source SED plots.'
else:
src_list = img.sources
sed_src = get_src(src_list, 0)
if sed_src == None:
print 'No sources found. Skipping source SED plots.'
else:
images.append('seds')
titles.append('')
names.append('seds')
srcid_cur = 0
if pyramid_srcs:
if img.opts.atrous_do == False:
print 'Image was not decomposed into wavelets. Skipping wavelet images.'
else:
# Get the unique j levels and store them. Only make subplots for
# occupied j levels
print 'Pyramidal source plots not yet supported.'
# j_list = []
# for p in img.pyrsrcs:
# for l in p.jlevels:
# j_list.append(l)
# j_set = set(j_list)
# j_with_gaus = list(j_set)
# index_first_waveplot = len(images)
# for i in range(len(j_with_gaus)):
# images.append('wavelets')
# names.append('pyrsrc'+str(i))
if psf_major or psf_minor or psf_pa:
if img.opts.psf_vary_do == False:
print 'PSF variation not calculated. Skipping PSF variation images.'
else:
if psf_major:
img_psf_maj = img.psf_vary_maj_arr * fwsig
images.append(img_psf_maj)
titles.append('PSF Major Axis FWHM (pixels)')
names.append('psf_maj')
if psf_minor:
img_psf_min = img.psf_vary_min_arr * fwsig
images.append(img_psf_min)
titles.append('PSF Minor Axis FWHM (pixels)')
names.append('psf_min')
if psf_pa:
img_psf_pa = img.psf_vary_pa_arr
images.append(img_psf_pa)
titles.append('PSF Pos. Angle FWhM (degrees)')
names.append('psf_pa')
if images == []:
print 'No images to display.'
return
im_mean = img.clipped_mean
im_rms = img.clipped_rms
if img.resid_gaus == None:
low = 1.1 * abs(img.min_value)
else:
low = N.max(
[1.1 * abs(img.min_value), 1.1 * abs(N.nanmin(img.resid_gaus))])
if low <= 0.0:
low = 1E-6
vmin_est = im_mean - im_rms * 5.0 + low
if vmin_est <= 0.0:
vmin = N.log10(low)
else:
vmin = N.log10(vmin_est)
vmax = N.log10(im_mean + im_rms * 30.0 + low)
ch0min = vmin
ch0max = N.log10(img.max_value + low)
vmin_cur = vmin
vmax_cur = vmax
origin = 'lower'
colours = ['m', 'b', 'c', 'g', 'y', 'k'] # reserve red ('r') for wavelets
styles = ['-', '-.', '--']
print '=' * 72
print 'NOTE -- With the mouse pointer in plot window:'
print ' Press "i" ........ : Get integrated flux densities and mean rms'
print ' values for the visible portion of the image'
print ' Press "m" ........ : Change min and max scaling values'
print ' Press "n" ........ : Show / hide island IDs'
print ' Press "0" ........ : Reset scaling to default'
if 'seds' in images:
print ' Press "c" ........ : Change source for SED plot'
if ch0_islands and hasattr(img, 'ngaus'):
print ' Click Gaussian ... : Print Gaussian and source IDs (zoom_rect mode, '
print ' toggled with the "zoom" button and indicated in '
print ' the lower right corner, must be off)'
if 'seds' in images:
print ' The SED plot will also show the chosen source.'
print '_' * 72
if len(images) > 1:
numx = 2
else:
numx = 1
numy = int(N.ceil(float(len(images)) / float(numx)))
fig = pl.figure(figsize=(max(15, 10.0 * float(numy) / float(numx)), 10.0))
fig.canvas.set_window_title('PyBDSM Fit Results for ' + img.filename)
gray_palette = cm.gray
gray_palette.set_bad('k')
for i, image in enumerate(images):
if image != 'wavelets' and image != 'seds':
if i == 0:
cmd = 'ax' + str(i+1) + ' = pl.subplot(' + str(numx) + \
', ' + str(numy) + ', ' + str(i+1) + ')'
else:
cmd = 'ax' + str(i+1) + ' = pl.subplot(' + str(numx) + \
', ' + str(numy) + ', ' + str(i+1) + ', sharex=ax1' + \
', sharey=ax1)'
exec cmd
if 'PSF' in titles[i]:
im = image
else:
im = N.log10(image + low)
if 'Islands' in titles[i]:
island_offsets_x = []
island_offsets_y = []
border_color = []
ax = pl.gca()
for iisl, isl in enumerate(img.islands):
xb, yb = isl.border
if hasattr(isl, '_pi'):
for c in range(len(xb)):
border_color.append('r')
else:
for c in range(len(xb)):
border_color.append('#afeeee')
island_offsets_x += xb.tolist()
island_offsets_y += yb.tolist()
marker = ax.text(N.max(xb) + 2,
N.max(yb),
str(isl.island_id),
color='#afeeee',
clip_on=True)
marker.set_visible(not marker.get_visible())
markers.append(marker)
# draw the gaussians with one colour per source or island
# (if gaul2srl was not run)
if hasattr(img, 'nsrc'):
nsrc = len(isl.sources)
for isrc in range(nsrc):
col = colours[isrc % 6]
style = styles[isrc / 6 % 3]
src = isl.sources[isrc]
for g in src.gaussians:
if hasattr(g, 'valid'):
valid = g.valid
else:
valid = True
if g.jlevel == 0 and valid and g.gaus_num >= 0:
gidx = g.gaus_num
e = Ellipse(xy=g.centre_pix,
width=g.size_pix[0],
height=g.size_pix[1],
angle=g.size_pix[2] + 90.0)
ax.add_artist(e)
e.set_picker(3)
e.set_clip_box(ax.bbox)
e.set_facecolor(col)
e.set_alpha(0.5)
e.gaus_id = gidx
e.src_id = src.source_id
e.jlevel = g.jlevel
e.isl_id = g.island_id
e.tflux = g.total_flux
e.pflux = g.peak_flux
e.centre_sky = g.centre_sky
if len(img.islands) > 0:
island_offsets = zip(N.array(island_offsets_x),
N.array(island_offsets_y))
isl_borders = collections.AsteriskPolygonCollection(
4,
offsets=island_offsets,
color=border_color,
transOffset=ax.transData,
sizes=(10.0, ))
ax.add_collection(isl_borders)
if hasattr(img, 'gaussians'):
for atrg in img.gaussians:
if atrg.jlevel > 0 and atrg.gaus_num >= 0:
col = 'r'
style = '-'
gidx = atrg.gaus_num
e = Ellipse(xy=atrg.centre_pix,
width=atrg.size_pix[0],
height=atrg.size_pix[1],
angle=atrg.size_pix[2] + 90.0)
ax.add_artist(e)
e.set_picker(3)
e.set_clip_box(ax.bbox)
e.set_edgecolor(col)
e.set_facecolor('none')
e.set_alpha(0.8)
e.gaus_id = gidx
e.src_id = atrg.source_id
e.jlevel = atrg.jlevel
e.isl_id = atrg.island_id
e.tflux = atrg.total_flux
e.pflux = atrg.peak_flux
e.centre_sky = atrg.centre_sky
if 'Flagged' in titles[i]:
for iisl, isl in enumerate(img.islands):
ax = pl.gca()
style = '-'
for ig, g in enumerate(isl.fgaul):
col = colours[ig % 6]
ellx, elly = func.drawellipse(g)
gline, = ax.plot(ellx,
elly,
color=col,
linestyle=style,
picker=3)
gline.flag = g.flag
if 'PSF' in titles[i]:
cmd = 'ax' + str(i+1) + ".imshow(N.transpose(im), origin=origin, "\
"interpolation='nearest', cmap=gray_palette)"
else:
cmd = 'ax' + str(i+1) + ".imshow(N.transpose(im), origin=origin, "\
"interpolation='nearest',vmin=vmin, vmax=vmax, cmap=gray_palette)"
exec cmd
cmd = 'ax' + str(i +
1) + '.format_coord = format_coord_' + names[i]
exec cmd
pl.title(titles[i])
elif image == 'seds':
cmd = 'ax' + str(i+1) + ' = pl.subplot(' + str(numx) + \
', ' + str(numy) + ', ' + str(i+1) + ')'
exec cmd
ax = pl.gca()
plot_sed(sed_src, ax)
elif image == 'wavelets':
if i == index_first_waveplot:
for j in range(len(j_with_gaus)):
cmd = 'ax' + str(j+i+1) + ' = pl.subplot(' + str(numx) + \
', ' + str(numy) + ', ' + str(j+i+1) + ', sharex=ax1, '+\
'sharey=ax1)'
exec cmd
pl.title('Pyramidal Sources for\nWavelet Scale J = ' +
str(j_with_gaus[j]))
for pyr in img.pyrsrcs:
for iisl, isl in enumerate(pyr.islands):
jj = pyr.jlevels[iisl]
jindx = j_with_gaus.index(jj)
col = colours[pyr.pyr_id % 6]
ind = N.where(~isl.mask_active)
cmd = "ax" + str(jindx + index_first_waveplot + 1) + \
".plot(ind[0]+isl.origin[0], "\
"ind[1]+isl.origin[1], '.', color=col)"
exec cmd
fig.canvas.mpl_connect('key_press_event', on_press)
fig.canvas.mpl_connect('pick_event', on_pick)
pl.show()
pl.close('all')
def _flag_gaussian(self, g, beam, thr, peak, shape, opts, mask, image, size_bms):
"""The actual flagging routine. See above for description.
"""
from math import sqrt, sin, cos, log, pi
from const import fwsig
import functions as func
import scipy.ndimage as nd
A, x1, x2, s1, s2, th = g
s1, s2 = map(abs, [s1, s2])
flag = 0
if N.any(N.isnan(g)) or s1 == 0.0 or s2 == 0.0:
return -1
if s1 < s2: # s1 etc are sigma
ss1 = s2
ss2 = s1
th1 = divmod(th+90.0, 180)[1]
else:
ss1 = s1
ss2 = s2
th1 = divmod(th, 180)[1]
th1 = th1/180.0*pi
if ss1 > 1e4 and ss2 > 1e4:
xbox = 1e9; ybox = 1e9
else:
xbox = 2.0*(abs(ss1*cos(th1)*cos(th1))+abs(ss2*ss2/ss1*sin(th1)*sin(th1)))/ \
sqrt(cos(th1)*cos(th1)+ss2*ss2/ss1/ss1*sin(th1)*sin(th1))
ybox = 2.0*(abs(ss1*sin(th1)*sin(th1))+abs(ss2*ss2/ss1*cos(th1)*cos(th1)))/ \
sqrt(sin(th1)*sin(th1)+ss2*ss2/ss1/ss1*cos(th1)*cos(th1))
### now check all conditions
border = opts.flag_bordersize
x1ok = True
x2ok = True
flagmax = False
if A < opts.flag_minsnr*thr: flag += 1
if A > opts.flag_maxsnr*peak:
flag += 2
flagmax = True
if x1 - border < 0 or x1 + border + 1 > shape[0]:
flag += 4
x1ok = False
if x2 - border < 0 or x2 + border + 1 > shape[1]:
flag += 8
x2ok = False
if x1ok and x2ok:
if not flagmax:
# Check image value at Gaussian center
im_val_at_cen = nd.map_coordinates(image, [N.array([x1]), N.array([x2])])
if A > opts.flag_maxsnr*im_val_at_cen:
flag += 2
borx1_1 = x1 - border
if borx1_1 < 0: borx1_1 = 0
borx1_2 = x1 + border + 1
if borx1_2 > shape[0]: borx1_2 = shape[0]
if N.any(mask[borx1_1:borx1_2, x2]):
flag += 4
borx2_1 = x2 - border
if borx2_1 < 0: borx2_1 = 0
borx2_2 = x2 + border + 1
if borx2_2 > shape[1]: borx2_2 = shape[1]
if N.any(mask[x1, borx2_1:borx2_2]):
flag += 8
if xbox > opts.flag_maxsize_isl*shape[0]: flag += 16
if ybox > opts.flag_maxsize_isl*shape[1]: flag += 32
if s1*s2 > opts.flag_maxsize_bm*beam[0]*beam[1]: flag += 64
if opts.flag_smallsrc:
if s1*s2 < opts.flag_minsize_bm*beam[0]*beam[1]: flag += 128
if not opts.flag_smallsrc:
if s1*s2 == 0.: flag += 128
if ss1/ss2 > 2.0:
# Only check for fairly elliptical Gaussians, as this condition
# is unreliable for more circular ones.
ellx, elly = func.drawellipse([A, x1, x2, s1*opts.flag_maxsize_fwhm,
s2*opts.flag_maxsize_fwhm, th])
pt1 = [N.min(ellx), elly[N.argmin(ellx)]]
pt2 = [ellx[N.argmax(elly)], N.max(elly)]
pt3 = [N.max(ellx), elly[N.argmax(ellx)]]
pt4 = [ellx[N.argmin(elly)], N.min(elly)]
extremes = [pt1, pt2, pt3, pt4]
for pt in extremes:
if pt[0] < 0 or pt[0] >= shape[0] or pt[1] < 0 or pt[1] >= shape[1]:
flag += 256
break
elif mask[tuple(pt)]:
flag += 256
break
return flag
def plotresults(img, ch0_image=True, rms_image=True, mean_image=True,
ch0_islands=True, gresid_image=True, sresid_image=False,
gmodel_image=True, smodel_image=False, pyramid_srcs=False,
source_seds=False, ch0_flagged=False, pi_image=False,
psf_major=False, psf_minor=False, psf_pa=False, broadcast=False):
"""Show the results of a fit."""
global img_ch0, img_rms, img_mean, img_gaus_mod, img_shap_mod
global img_gaus_resid, img_shap_resid, pixels_per_beam, pix2sky
global vmin, vmax, vmin_cur, vmax_cur, ch0min, ch0max, img_pi
global low, fig, images, src_list, srcid_cur, sky2pix, markers
global img_psf_maj, img_psf_min, img_psf_pa, do_broadcast, samp_client
global samp_key, samp_gaul_table_url, samp_srl_table_url
if not has_pl:
print "\033[31;1mWARNING\033[0m: Matplotlib not found. Plotting is disabled."
return
if hasattr(img, 'samp_client'):
samp_client = img.samp_client
samp_key = img.samp_key
if hasattr(img, 'samp_srl_table_url'):
samp_srl_table_url = img.samp_srl_table_url
else:
samp_srl_table_url = None
if hasattr(img, 'samp_gaul_table_url'):
samp_gaul_table_url = img.samp_gaul_table_url
else:
samp_gaul_table_url = None
else:
samp_clent = None
samp_key = None
samp_srl_table_url = None
samp_gaul_table_url = None
do_broadcast = broadcast
# Define the images. The images are used both by imshow and by the
# on_press() and coord_format event handlers
pix2sky = img.pix2sky
sky2pix = img.sky2pix
gfactor = 2.0 * N.sqrt(2.0 * N.log(2.0))
pixels_per_beam = 2.0 * N.pi * (img.beam2pix(img.beam)[0]
* img.beam2pix(img.beam)[1]) / gfactor**2
# Construct lists of images, titles, etc.
images = []
titles = []
names = []
markers = []
img_gaus_mod = None # default needed for key press event
img_shap_mod = None # default needed for key press event
if ch0_image:
img_ch0 = img.ch0_arr
images.append(img_ch0)
titles.append('Original (ch0) Image\n(arbitrary logarithmic scale)')
names.append('ch0')
if ch0_islands:
img_ch0 = img.ch0_arr
images.append(img_ch0)
if hasattr(img, 'ngaus'):
if hasattr(img, 'ch0_pi_arr'):
ch0_str = 'Islands (hatched boundaries; red = PI only) and\nGaussians'
else:
ch0_str = 'Islands (hatched boundaries) and\nGaussians'
if hasattr(img, 'atrous_gaussians'):
ch0_str += ' (red = wavelet)'
titles.append(ch0_str)
else:
titles.append('Islands (hatched boundaries)')
names.append('ch0')
if ch0_flagged:
if not hasattr(img, 'ngaus'):
print 'Image was not fit with Gaussians. Skipping display of flagged Gaussians.'
else:
img_ch0 = img.ch0_arr
images.append(img_ch0)
titles.append('Flagged Gaussians')
names.append('ch0')
if pi_image:
if not hasattr(img, 'ch0_pi_arr'):
print 'Polarization module not run. Skipping PI image.'
else:
img_pi = img.ch0_pi_arr
images.append(img_pi)
titles.append('Polarized Intensity Image')
names.append('ch0_pi')
if rms_image:
img_rms = img.rms_arr
images.append(img_rms)
titles.append('Background rms Image')
names.append('rms')
if gresid_image:
if not hasattr(img, 'ngaus'):
print 'Image was not fit with Gaussians. Skipping residual Gaussian image.'
else:
img_gaus_resid = img.resid_gaus_arr
images.append(img_gaus_resid)
titles.append('Gaussian Residual Image')
names.append('gaus_resid')
if gmodel_image:
if not hasattr(img, 'ngaus'):
print 'Image was not fit with Gaussians. Skipping model Gaussian image.'
else:
img_gaus_mod = img.model_gaus_arr
images.append(img_gaus_mod)
titles.append('Gaussian Model Image')
names.append('gaus_mod')
if mean_image:
img_mean = img.mean_arr
images.append(img_mean)
titles.append('Background mean Image')
names.append('mean')
if sresid_image:
if img.opts.shapelet_do == False:
print 'Image was not decomposed into shapelets. Skipping residual shapelet image.'
else:
img_shap_resid = img.ch0_arr - img.model_shap_arr
images.append(img_shap_resid)
titles.append('Shapelet Residual Image')
names.append('shap_resid')
if smodel_image:
if img.opts.shapelet_do == False:
print 'Image was not decomposed into shapelets. Skipping model shapelet image.'
else:
img_shap_mod = img.model_shap_arr
images.append(img_shap_mod)
titles.append('Shapelet Model Image')
names.append('shap_mod')
if source_seds:
if img.opts.spectralindex_do == False:
print 'Source SEDs were not fit. Skipping source SED plots.'
else:
src_list = img.sources
sed_src = get_src(src_list, 0)
if sed_src is None:
print 'No sources found. Skipping source SED plots.'
else:
images.append('seds')
titles.append('')
names.append('seds')
srcid_cur = 0
if pyramid_srcs:
if img.opts.atrous_do == False:
print 'Image was not decomposed into wavelets. Skipping wavelet images.'
else:
# Get the unique j levels and store them. Only make subplots for
# occupied j levels
print 'Pyramidal source plots not yet supported.'
# j_list = []
# for p in img.pyrsrcs:
# for l in p.jlevels:
# j_list.append(l)
# j_set = set(j_list)
# j_with_gaus = list(j_set)
# index_first_waveplot = len(images)
# for i in range(len(j_with_gaus)):
# images.append('wavelets')
# names.append('pyrsrc'+str(i))
if psf_major or psf_minor or psf_pa:
if img.opts.psf_vary_do == False:
print 'PSF variation not calculated. Skipping PSF variation images.'
else:
if psf_major:
img_psf_maj = img.psf_vary_maj_arr*fwsig
images.append(img_psf_maj)
titles.append('PSF Major Axis FWHM (pixels)')
names.append('psf_maj')
if psf_minor:
img_psf_min = img.psf_vary_min_arr*fwsig
images.append(img_psf_min)
titles.append('PSF Minor Axis FWHM (pixels)')
names.append('psf_min')
if psf_pa:
img_psf_pa = img.psf_vary_pa_arr
images.append(img_psf_pa)
titles.append('PSF Pos. Angle FWhM (degrees)')
names.append('psf_pa')
if images == []:
print 'No images to display.'
return
im_mean = img.clipped_mean
im_rms = img.clipped_rms
if img.resid_gaus is None:
low = 1.1*abs(img.min_value)
else:
low = N.max([1.1*abs(img.min_value),1.1*abs(N.nanmin(img.resid_gaus))])
if low <= 0.0:
low = 1E-6
vmin_est = im_mean - im_rms*5.0 + low
if vmin_est <= 0.0:
vmin = N.log10(low)
else:
vmin = N.log10(vmin_est)
vmax = N.log10(im_mean + im_rms*30.0 + low)
ch0min = vmin
ch0max = N.log10(img.max_value + low)
vmin_cur = vmin
vmax_cur = vmax
origin = 'lower'
colours = ['m', 'b', 'c', 'g', 'y', 'k'] # reserve red ('r') for wavelets
styles = ['-', '-.', '--']
print '=' * 72
print 'NOTE -- With the mouse pointer in plot window:'
print ' Press "i" ........ : Get integrated flux densities and mean rms'
print ' values for the visible portion of the image'
print ' Press "m" ........ : Change min and max scaling values'
print ' Press "n" ........ : Show / hide island IDs'
print ' Press "0" ........ : Reset scaling to default'
if 'seds' in images:
print ' Press "c" ........ : Change source for SED plot'
if ch0_islands and hasattr(img, 'ngaus'):
print ' Click Gaussian ... : Print Gaussian and source IDs (zoom_rect mode, '
print ' toggled with the "zoom" button and indicated in '
print ' the lower right corner, must be off)'
if 'seds' in images:
print ' The SED plot will also show the chosen source.'
print '_' * 72
if len(images) > 1:
numx = 2
else:
numx = 1
numy = int(N.ceil(float(len(images))/float(numx)))
fig = pl.figure(figsize=(max(15, 10.0*float(numy)/float(numx)), 10.0))
fig.canvas.set_window_title('PyBDSM Fit Results for '+ img.filename)
gray_palette = cm.gray
gray_palette.set_bad('k')
for i, image in enumerate(images):
if image != 'wavelets' and image != 'seds':
if i == 0:
cmd = 'ax' + str(i+1) + ' = pl.subplot(' + str(numx) + \
', ' + str(numy) + ', ' + str(i+1) + ')'
else:
cmd = 'ax' + str(i+1) + ' = pl.subplot(' + str(numx) + \
', ' + str(numy) + ', ' + str(i+1) + ', sharex=ax1' + \
', sharey=ax1)'
exec cmd
if 'PSF' in titles[i]:
im = image
else:
im = N.log10(image + low)
if 'Islands' in titles[i]:
island_offsets_x = []
island_offsets_y = []
border_color = []
ax = pl.gca()
for iisl, isl in enumerate(img.islands):
xb, yb = isl.border
if hasattr(isl, '_pi'):
for c in range(len(xb)):
border_color.append('r')
else:
for c in range(len(xb)):
border_color.append('#afeeee')
island_offsets_x += xb.tolist()
island_offsets_y += yb.tolist()
marker = ax.text(N.max(xb)+2, N.max(yb), str(isl.island_id),
color='#afeeee', clip_on=True)
marker.set_visible(not marker.get_visible())
markers.append(marker)
# draw the gaussians with one colour per source or island
# (if gaul2srl was not run)
if hasattr(img, 'nsrc'):
nsrc = len(isl.sources)
for isrc in range(nsrc):
col = colours[isrc % 6]
style = styles[isrc/6 % 3]
src = isl.sources[isrc]
for g in src.gaussians:
if hasattr(g, 'valid'):
valid = g.valid
else:
valid = True
if g.jlevel == 0 and valid and g.gaus_num >= 0:
gidx = g.gaus_num
e = Ellipse(xy=g.centre_pix, width=g.size_pix[0],
height=g.size_pix[1], angle=g.size_pix[2]+90.0)
ax.add_artist(e)
e.set_picker(3)
e.set_clip_box(ax.bbox)
e.set_facecolor(col)
e.set_alpha(0.5)
e.gaus_id = gidx
e.src_id = src.source_id
e.jlevel = g.jlevel
e.isl_id = g.island_id
e.tflux = g.total_flux
e.pflux = g.peak_flux
e.centre_sky = g.centre_sky
if len(img.islands) > 0:
island_offsets = zip(N.array(island_offsets_x), N.array(island_offsets_y))
isl_borders = collections.AsteriskPolygonCollection(4, offsets=island_offsets, color=border_color,
transOffset=ax.transData, sizes=(10.0,))
ax.add_collection(isl_borders)
if hasattr(img, 'gaussians'):
for atrg in img.gaussians:
if atrg.jlevel > 0 and atrg.gaus_num >= 0:
col = 'r'
style = '-'
gidx = atrg.gaus_num
e = Ellipse(xy=atrg.centre_pix, width=atrg.size_pix[0], height=atrg.size_pix[1], angle=atrg.size_pix[2]+90.0)
ax.add_artist(e)
e.set_picker(3)
e.set_clip_box(ax.bbox)
e.set_edgecolor(col)
e.set_facecolor('none')
e.set_alpha(0.8)
e.gaus_id = gidx
e.src_id = atrg.source_id
e.jlevel = atrg.jlevel
e.isl_id = atrg.island_id
e.tflux = atrg.total_flux
e.pflux = atrg.peak_flux
e.centre_sky = atrg.centre_sky
if 'Flagged' in titles[i]:
for iisl, isl in enumerate(img.islands):
ax = pl.gca()
style = '-'
for ig, g in enumerate(isl.fgaul):
col = colours[ig % 6]
ellx, elly = func.drawellipse(g)
gline, = ax.plot(ellx, elly, color = col,
linestyle = style, picker=3)
gline.flag = g.flag
if 'PSF' in titles[i]:
cmd = 'ax' + str(i+1) + ".imshow(N.transpose(im), origin=origin, "\
"interpolation='nearest', cmap=gray_palette)"
else:
cmd = 'ax' + str(i+1) + ".imshow(N.transpose(im), origin=origin, "\
"interpolation='nearest',vmin=vmin, vmax=vmax, cmap=gray_palette)"
exec cmd
cmd = 'ax' + str(i+1) + '.format_coord = format_coord_'+names[i]
exec cmd
pl.title(titles[i])
elif image == 'seds':
cmd = 'ax' + str(i+1) + ' = pl.subplot(' + str(numx) + \
', ' + str(numy) + ', ' + str(i+1) + ')'
exec cmd
ax = pl.gca()
plot_sed(sed_src, ax)
elif image == 'wavelets':
if i == index_first_waveplot:
for j in range(len(j_with_gaus)):
cmd = 'ax' + str(j+i+1) + ' = pl.subplot(' + str(numx) + \
', ' + str(numy) + ', ' + str(j+i+1) + ', sharex=ax1, '+\
'sharey=ax1)'
exec cmd
pl.title('Pyramidal Sources for\nWavelet Scale J = ' +
str(j_with_gaus[j]))
for pyr in img.pyrsrcs:
for iisl, isl in enumerate(pyr.islands):
jj = pyr.jlevels[iisl]
jindx = j_with_gaus.index(jj)
col = colours[pyr.pyr_id % 6]
ind = N.where(~isl.mask_active)
cmd = "ax" + str(jindx + index_first_waveplot + 1) + \
".plot(ind[0]+isl.origin[0], "\
"ind[1]+isl.origin[1], '.', color=col)"
exec cmd
fig.canvas.mpl_connect('key_press_event', on_press)
fig.canvas.mpl_connect('pick_event', on_pick)
pl.show()
pl.close('all')
