im = mplim4.Image()
frame = fig.add_subplot(2, 3, 5)
Diff = D.real - mplim.data
f = maputils.FITSimage("m101.fits", externaldata=Diff)
mplim5 = f.Annotatedimage(frame, cmap="spectral")
im = mplim5.Image()
frame.text(0.5,
yshift,
"M101 - inv. FFT",
ha='center',
va='center',
transform=frame.transAxes)
s = "Residual with min=%.1g max=%.1g" % (Diff.min(), Diff.max())
frame.text(0.5,
yshift - 0.08,
s,
ha='center',
va='center',
transform=frame.transAxes,
fontsize=8)
mplim.interact_imagecolors()
mplim2.interact_imagecolors()
mplim3.interact_imagecolors()
mplim4.interact_imagecolors()
mplim5.interact_imagecolors()
maputils.showall()
graticule2.setp_axislabel(plotaxis='left', xpos=labelx)
# fig 3. Velocity - Dec (Version without offsets)
frame3 = fig.add_subplot(4,1,3)
mplim3 = fitsobj.Annotatedimage(frame3)
mplim3.Image()
graticule3 = mplim3.Graticule(offsety=False)
graticule3.setp_axislabel(plotaxis='left', xpos=labelx)
graticule3.setp_ticklabel(plotaxis="left", fmt='DMs')
# fig 4. Velocity - R.A.
frame4 = fig.add_subplot(4,1,4)
fitsobj.set_imageaxes('vel','ra')
mplim4 = fitsobj.Annotatedimage(frame4)
mplim4.Image()
graticule4 = mplim4.Graticule(offsety=False)
graticule4.setp_axislabel(plotaxis='left', xpos=labelx)
graticule4.setp_ticklabel(plotaxis="left", fmt='HMs')
graticule4.Insidelabels(wcsaxis=0, constval='20h34m',
rotation=90, fontsize=10,
color='r', ha='right')
graticule4.Insidelabels(wcsaxis=1, fontsize=10, fmt="%.2f", color='y')
mplim4.Minortickmarks(graticule4)
#Apply new aspect ratio for the XV maps
mplim2.set_aspectratio(newaspect)
mplim3.set_aspectratio(newaspect)
mplim4.set_aspectratio(newaspect)
maputils.showall()
def show_image(self, beam=None, step=None, chunk=None, major=None, minor=None, finaltype=None, type=None, imin=None, imax=None):
'''
Function to show an image in the notebook
beam (string): The beam number to show the image for. 'iterate' will iterate over beams.
step (string): The step of the pipeline to show the image for. No default. selfcal and final are allowed.
chunk (string): The frequency chunk to show the image for. 'iterate' will iterate over chunks. No default.
major(string or int): Major iteration of clean to display. Default is last iteration. pm is also possible. Only for selfcal, not for final step. 'iterate' will iterate over images for major cycles.
minor (string or int): Minor iteration of clean to display. Default is last iteration. 'iterate' will iterate over images for minor cycles.
finaltype(string): Type of final image to show. mf and stack are possible.
type(string): Type of image to show. mask, beam, image, residual, and in case of step=final final are possible. 'all' will show image, mask, residual, and model in one plot.
imin(float): Minimum cutoff in the image.
imax(float): Maximum cutoff in the image.
return (string): String with a path and image name to use for displaying.
'''
subs.setinit.setinitdirs(self)
self.manage_tempdir() # Check if the temporary directory exists and if not create it
self.clean_tempdir() # Remove any temorary files from the temporary directory
char_set = string.ascii_uppercase + string.digits # Create a charset for random image name generation
if any(it == 'iterate' for it in [beam, chunk, major, minor]):
if beam == 'iterate':
print('### Not implemented yet ###')
elif chunk == 'iterate':
imagelist = glob.glob(self.selfcaldir + '/*/' + str(major).zfill(2) + '/' + str(type) + '_' + str(minor).zfill(2))
plottext = 'Frequency \nchunk'
elif major == 'iterate':
imagelist = glob.glob(self.selfcaldir + '/' + str(chunk).zfill(2) + '/*/' + str(type) + '_' + str(minor).zfill(2))
plottext = 'Major \ncycle'
elif minor == 'iterate':
imagelist = glob.glob(self.selfcaldir + '/' + str(chunk).zfill(2) + '/' + str(major).zfill(2) + '/' + str(type) + '_*')
plottext = 'Minor \ncycle'
images = []
for image in imagelist:
self.fitsimage = self.tempdir + '/' + ''.join(random.sample(char_set * 8, 8)) + '.fits'
fits = lib.miriad('fits')
fits.op = 'xyout'
fits.in_ = image
fits.out = self.fitsimage
fits.go()
images.append(maputils.FITSimage(self.fitsimage))
# Do the autoscaling of the image contrast
if imin == None or imax == None:
imagedata = pyfits.open(self.fitsimage)[0].data
imagestd = np.nanstd(imagedata)
if imin == None:
imin = -1*imagestd
else:
pass
if imax == None:
imax = 3*imagestd
else:
pass
else:
pass
print('Minimum/maximum colour range for image: ' + str(imin) + '/' + str(imax))
# Draw the plot
fig = plt.figure(figsize=(12, 10))
frame = fig.add_axes([0.1, 0.15, 0.85, 0.8])
frame.set_title(str(type).capitalize())
# Add the slider for selecting the image
slider_ax = fig.add_axes([0.103, 0.05, 0.672, 0.03])
slider = Slider(slider_ax, plottext, 0, len(images)-1, valinit = 0, valfmt='%1.0f', dragging=False, color='black')
# Define the attributes for the plot
image_object = images[int(round(slider.val))].Annotatedimage(frame, cmap='gist_gray', clipmin=imin, clipmax=imax)
image_object.Image()
image_object.Graticule()
image_object.Colorbar()
image_object.interact_toolbarinfo()
image_object.interact_imagecolors()
image_object.plot()
fig.canvas.draw()
# Update the plot if the slider is clicked
def slider_on_changed(val):
image_object = images[int(round(slider.val))].Annotatedimage(frame, cmap='gist_gray', clipmin=imin, clipmax=imax)
image_object.Image()
image_object.interact_toolbarinfo()
image_object.interact_imagecolors()
image_object.plot()
fig.canvas.draw()
slider.on_changed(slider_on_changed)
plt.show()
else:
if type == 'all': # Make a 2x2 plot of image, residual, mask, and model of a cycle
rawimage = self.get_image(beam, step, chunk, major, minor, finaltype, 'image')
rawresidual = self.get_image(beam, step, chunk, major, minor, finaltype, 'residual')
rawmask = self.get_image(beam, step, chunk, major, minor, finaltype, 'mask')
rawmodel = self.get_image(beam, step, chunk, major, minor, finaltype, 'model')
self.fitsimage = self.tempdir + '/' + ''.join(random.sample(char_set * 8, 8)) + '.fits'
self.fitsresidual = self.tempdir + '/' + ''.join(random.sample(char_set * 8, 8)) + '.fits'
self.fitsmask = self.tempdir + '/' + ''.join(random.sample(char_set * 8, 8)) + '.fits'
self.fitsmodel = self.tempdir + '/' + ''.join(random.sample(char_set * 8, 8)) + '.fits'
fits = lib.miriad('fits')
fits.op = 'xyout'
fits.in_ = rawimage
fits.out = self.fitsimage
fits.go()
fits.in_ = rawresidual
fits.out = self.fitsresidual
fits.go()
fits.in_ = rawmask
fits.out = self.fitsmask
fits.go()
regrid = lib.miriad('regrid')
regrid.in_ = rawmodel
tempregrid = self.tempdir + '/' + ''.join(random.sample(char_set * 8, 8))
regrid.out = tempregrid
regrid.axes = '1,2'
regrid.tin = rawimage
regrid.go()
fits.in_ = tempregrid
fits.out = self.fitsmodel
fits.go()
im = maputils.FITSimage(self.fitsimage)
imdata = pyfits.open(self.fitsimage)[0].data
imstd = np.nanstd(imdata)
re = maputils.FITSimage(self.fitsresidual)
redata = pyfits.open(self.fitsresidual)[0].data
restd = np.nanstd(redata)
ma = maputils.FITSimage(self.fitsmask)
madata = pyfits.open(self.fitsmask)[0].data
mastd = np.nanstd(madata)
mo = maputils.FITSimage(self.fitsmodel)
modata = pyfits.open(self.fitsmodel)[0].data
mostd = np.nanstd(modata)
fig = plt.figure(figsize=(12, 10))
frame1 = fig.add_axes([0.1,0.58,0.4,0.4])
frame1.set_title('Image')
frame2 = fig.add_axes([0.59,0.58,0.4,0.4])
frame2.set_title('Residual')
frame3 = fig.add_axes([0.1, 0.12, 0.4, 0.4])
frame3.set_title('Mask')
frame4 = fig.add_axes([0.59, 0.12, 0.4, 0.4])
frame4.set_title('Model')
annim1 = im.Annotatedimage(frame1, cmap='gist_gray')
annim2 = re.Annotatedimage(frame2, cmap='gist_gray')
annim3 = ma.Annotatedimage(frame3, cmap='gist_gray')
annim4 = mo.Annotatedimage(frame4, cmap='gist_gray')
annim1.Image(); annim2.Image(); annim3.Image(); annim4.Image()
if imin == None: # Set the displayed colour range if not set
immin, remin, mamin, momin = -1*imstd, -1*restd, -1*mastd, -1*mostd
else:
immin, remin, mamin, momin = imin, imin, imin, imin
if imax == None:
immax, remax, mamax, momax = 3*imstd, 3*restd, mastd, mostd
else:
immax, remax, mamax, momax = imax, imax, imax, imax
print('Minimum colour range for image: ' + str(immin) + ' residual: ' + str(remin) + ' mask: ' + str(mamin) + ' model: ' + str(momin))
print('Maximum colour range for image: ' + str(immax) + ' residual: ' + str(remax) + ' mask: ' + str(mamax) + ' model: ' + str(momax))
annim1.set_norm(clipmin=immin, clipmax=immax); annim2.set_norm(clipmin=remin, clipmax=remax); annim3.set_norm(clipmin=0.0, clipmax=mamax); annim4.set_norm(clipmin=0.0, clipmax=momax)
annim1.Colorbar(); annim2.Colorbar(); annim3.Colorbar(); annim4.Colorbar()
annim1.Graticule(); annim2.Graticule(); annim3.Graticule(); annim4.Graticule()
annim1.interact_toolbarinfo(); annim2.interact_toolbarinfo(); annim3.interact_toolbarinfo(); annim4.interact_toolbarinfo()
annim1.interact_imagecolors(); annim2.interact_imagecolors(); annim3.interact_imagecolors(); annim4.interact_imagecolors()
tdict = dict(color='g', fontsize=10, va='bottom', ha='left')
fig.text(0.01, 0.01, annim1.get_colornavigation_info(), tdict)
maputils.showall()
else: # Make a simple plot of one specified image
rawimage = self.get_image(beam, step, chunk, major, minor, finaltype, type)
self.fitsimage = self.tempdir + '/' + ''.join(random.sample(char_set * 8, 8)) + '.fits'
fits = lib.miriad('fits')
fits.op = 'xyout'
fits.in_ = rawimage
fits.out = self.fitsimage
fits.go()
f = maputils.FITSimage(self.fitsimage)
imdata = pyfits.open(self.fitsimage)[0].data
imstd = np.nanstd(imdata)
fig = plt.figure(figsize=(12,10))
frame = fig.add_axes([0.1, 0.15, 0.85, 0.8])
annim = f.Annotatedimage(frame, cmap='gist_gray')
annim.Image()
if imin == None: # Set the displayed colour range if not set
imin = -1*imstd
if imax == None:
imax = 3*imstd
print('Minimum/maximum colour range for image: ' + str(imin) + '/' + str(imax))
annim.set_norm(clipmin=imin,clipmax=imax)
annim.Colorbar()
annim.Graticule()
annim.interact_toolbarinfo()
annim.interact_imagecolors()
units = 'unknown'
if 'BUNIT' in f.hdr:
units = f.hdr['BUNIT']
helptext = "File: [%s] Data units: [%s]\n" % (rawimage, units)
helptext += annim.get_colornavigation_info()
tdict = dict(color='g', fontsize=10, va='bottom', ha='left')
fig.text(0.01, 0.01, helptext, tdict)
maputils.showall()