def abscal(uv_files, pol=-5):
pol2str = {-5:'xx', -6:'yy'}
polstr = pol2str[pol]
# echo info
echo("Running abscal on field {} in JD {} for pol {}".format(field, JD, pol), type=1)
# get source info
echo("getting source info", type=1)
(lst, jd, utc_range, utc_center, source_files, source_utc_range) = source2file(source_ra, duration=duration, offset=0.0, start_jd=JD,
jd_files=uv_files, get_filetimes=True, verbose=True)
utc_center = "'" + '/'.join(utc_center.split('/')[:-1]) + ' ' + utc_center.split('/')[-1] + "'"
source_files = list(source_files)
Nsf = len(source_files)
# make flux model
echo("making flux model", type=1)
cmd = "casa --nologger --nocrashreport --nogui --agg -c {} --image --freqs 100,200,1024 --cell 150arcsec --imsize 512".format(complist)
out = subprocess.call(cmd, shell=True, stdout=abs_out, stderr=abs_err)
echo("flux model exit {}".format(out))
# apply PB correction to flux model
echo("applying PB to flux model")
cmd = "pbcorr.py --beamfile {} --outdir ./ --pol {} --time {} --ext pbcorr --lon {} --lat {} {} --multiply {}.cl.fits"\
"".format(beamfile, pol, utc_center, lon, lat, overwrite, field)
out = subprocess.call(cmd, shell=True, stdout=abs_out, stderr=abs_err)
echo("flux model PB corr exit {}".format(out))
# import to CASA image
echo("importing model file to CASA image format")
cmd = '''casa --nologger --nocrashreport --nogui --agg -c ''' \
'''"importfits('{}.cl.pbcorr.fits', '{}.cl.pbcorr.image', overwrite=True)"'''.format(field, field)
out = subprocess.call(cmd, shell=True, stdout=abs_out, stderr=abs_err)
echo("flux model CASA import exit {}".format(out))
modelfile = field + '.cl.pbcorr.image'
if rfi_flag:
echo("RFI flagging data", type=1)
for i, sf in enumerate(source_files):
cmd = "xrfi_run.py --algorithm 'xrfi' --kf_size 13 --summary {}".format(sf)
out = subprocess.call(cmd, shell=True, stdout=abs_out, stderr=abs_err)
echo("RFI flag on {} exit {}".format(sf, out))
echo("applying RFI flags")
for i, sf in enumerate(source_files):
cmd = "xrfi_apply.py --ext R {} --flag_file {}.flags.npz {}".format(overwrite, sf, sf)
out = subprocess.call(cmd, shell=True, stdout=abs_out, stderr=abs_err)
echo("RFI apply on {} exit {}".format(sf, out))
# update source file(s) paths
for i in range(Nsf): source_files[i] = source_files[i] + 'R'
# convert to uvfits
echo("converting miriad to uvfits", type=1)
for i, sf in enumerate(source_files):
cmd = "miriad_to_uvfits.py {} {}".format(overwrite, sf)
out = subprocess.call(cmd, shell=True, stdout=abs_out, stderr=abs_err)
echo("miriad_to_uvfits on {} exit {}".format(sf, out))
source_uvfits = map(lambda x: x+'.uvfits', source_files)
# renumber ants
if renumber_uvfits:
echo("renumbering uvfits", type=1)
for i, sf in enumerate(source_uvfits):
cmd = "renumber_ants.py --overwrite {} {}".format(sf, sf)
out = subprocess.call(cmd, shell=True, stdout=abs_out, stderr=abs_err)
echo("renumber_ants on {} exit {}".format(sf, out))
# combine uvfits
if len(source_uvfits) > 1:
echo("combining uvfits", type=1)
cmd = "combine_uvfits.py {} {}".format(overwrite, ' '.join(source_uvfits))
out = subprocess.call(cmd, shell=True, stdout=abs_out, stderr=abs_err)
echo("combine uvfits exit {}".format(out))
source_uvfile = source_files[0]
source_uvfits = source_uvfits[0]
# absolute calibration
echo("absolutely calibrating", type=1)
cmd = "casa --nologger --nocrashreport --nogui --agg -c sky_image.py " \
"--msin {} --source {} --model_im {} --refant {} {} --imsize {} --pxsize {} --niter {} --timerange {} " \
"--KGcal --KGsnr {} --Acal --Asnr {} --BPcal --BPsnr {} --image_mfs --image_model --plot_uvdist" \
"".format(source_uvfits, source, modelfile, refant, ex_ants, imsize, pxsize, niter, source_utc_range,
KGsnr, Asnr, BPsnr)
if casa_rflag:
cmd += ' --rflag'
out = subprocess.call(cmd, shell=True, stdout=abs_out, stderr=abs_err)
echo("abscal exit {}".format(out))
# convert to calfits
echo("converting CASA caltables to calfits", type=1)
calfits_fname = source_uvfile + '.ms.abs.calfits'
dly_file = source_uvfile + '.ms.K.cal.npz'
phs_file = source_uvfile + '.ms.Gphs.cal.npz'
amp_file = source_uvfile + '.ms.Gamp.cal.npz'
bp_file = source_uvfile + '.ms.B.cal.npz'
cmd = "skynpz2calfits.py --fname {} --uv_file {} --dly_file {} --phs_file {} --amp_file {} --bp_file {} " \
"--plot_bp --plot_phs --plot_amp --plot_dlys --bp_medfilt --medfilt_kernel 13 {} " \
"--bp_gp_max_dly {} --bp_gp_thin 4 {}".format(calfits_fname, source_uvfile, dly_file, phs_file, amp_file,
bp_file, bp_gp_smooth, bp_gp_mx_dly, overwrite)
out = subprocess.call(cmd, shell=True, stdout=abs_out, stderr=abs_err)
echo("skynpz2calfits exit {}".format(out))
# primary beam correct MFS image
echo("pb correct mfs image", type=1)
cmd = "pbcorr.py --beamfile {} --pol {} --time {} --ext pbcorr --lon {} --lat {} --spec_cube {} {}" \
"".format(beamfile, pol, utc_center, lon, lat, overwrite, source_uvfile+".ms.{}.fits".format(source))
out = subprocess.call(cmd, shell=True, stdout=abs_out, stderr=abs_err)
echo("pb correction exit {}".format(out))
# plot source and model source
source_im = source_uvfile + ".ms.{}".format(source)
model_im = source_uvfile + ".ms.model.{}".format(source)
vmin = -5
vmax = 40
fig = plt.figure(figsize=(14,10))
f1 = aplpy.FITSFigure(source_im+".fits", figure=fig, subplot=[0.0, 0.55, 0.45, 0.45])
f1.show_colorscale(cmap='nipy_spectral', vmin=vmin, vmax=vmax)
f1.add_grid()
f1.add_beam()
f1.recenter(source_ra, source_dec, width=15, height=15)
f1.add_colorbar()
f1.colorbar.set_axis_label_text("Jy/beam")
f1.set_title("{} {} MFS Image".format(source, polstr))
f2 = aplpy.FITSFigure(source_im+".pb.fits", figure=fig, subplot=[0.55, 0.55, 0.45, 0.45])
f2.show_colorscale(cmap='nipy_spectral', vmin=0, vmax=1)
f2.add_grid()
f2.recenter(source_ra, source_dec, width=15, height=15)
f2.add_colorbar()
f2.colorbar.set_axis_label_text("PB Beam Response")
f2.set_title("Primary Beam")
f3 = aplpy.FITSFigure(source_im+".pbcorr.fits", figure=fig, subplot=[0.0, 0.0, 0.45, 0.45])
f3.show_colorscale(cmap='nipy_spectral', vmin=vmin, vmax=vmax)
f3.add_grid()
f3.add_beam()
f3.recenter(source_ra, source_dec, width=15, height=15)
f3.add_colorbar()
f3.colorbar.set_axis_label_text("Jy/beam")
f3.set_title("{} {} MFS Image + PB Correction".format(source, polstr))
f4 = aplpy.FITSFigure(model_im+".fits", figure=fig, subplot=[0.55, 0.0, 0.45, 0.45])
f4.show_colorscale(cmap='nipy_spectral', vmin=vmin, vmax=vmax)
f4.add_grid()
f4.add_beam()
f4.recenter(source_ra, source_dec, width=15, height=15)
f4.add_colorbar()
f4.colorbar.set_axis_label_text("Jy/beam")
f4.set_title("Model MFS Image")
fig.savefig("{}/{}_{}_MFS.png".format(data_path, source, polstr), dpi=150, bbox_inches='tight')
plt.close()
return calfits_fname
'13CO_m331_m338_m345.png', '13CO_346_333_345.png', '13CO_452_439_426.png',
'13CO_505_492_479.png', '13CO_532_519_505.png', '13CO_592_578_565.png'
]
# Legend labels
lab_blue = ['-34.5', '31.9', '42.6', '47.9', '50.5', '56.5']
lab_green = ['-33.8', '33.3', '43.9', '49.2', '51.9', '57.8']
lab_red = ['-33.1', '34.6', '45.2', '50.5', '53.2', '59.2']
for png in pngs:
t = os.path.splitext(png)
filename = str(t[0])
index = pngs.index(png)
if Path('./../rgb_figs/' + filename + '.pdf').exists() == False:
f = aplpy.FITSFigure(png)
f.show_rgb()
f.add_scalebar(0.1 * pc_sc)
f.scalebar.show(0.1 * pc_sc)
f.scalebar.set_corner('bottom left')
f.scalebar.set_color('white')
f.scalebar.set_label('0.1 pc')
f.scalebar.set_font(size=15)
f.scalebar.set_linewidth(2)
f.axis_labels.set_font(size=16)
f.ticks.set_color('white')
f.tick_labels.set_font(size=15)
f.axis_labels.set_xtext('Right Ascension (J2000)')
f.axis_labels.set_ytext('Declination (J2000)')
f.axis_labels.set_ypad(-2)
f.show_regions('./../../brick_regions_new.reg')
-5.412955261, -5.461988054, -5.510106789, -5.555931851, -5.605577113,
-5.6519109, -5.701555547, -5.752730272, -5.801156183, -5.84927762,
-5.897401317, -5.940943148, -5.986011267, -6.029551074, -6.068507208,
-6.103567778, -6.135131552, -6.166692778, -6.195094167, -6.219704167,
-6.240522195, -6.261336664
] * u.deg)
path4 = Path(fk, width=6 * u.arcmin)
slice3 = extract_pv_slice(
'/Users/shuokong/GoogleDrive/13co/products/regrid_12co_specsmooth_0p25_mask_imfit_13co_pix_2_Tmb.fits',
path4)
slice3.writeto('my_slice.fits')
import aplpy
import matplotlib.pyplot as plt
fig = plt.figure()
gc = aplpy.FITSFigure('my_slice.fits', dimensions=[0, 1], figure=fig, hdu=0)
gc.show_colorscale(aspect='auto')
gc.ticks.set_xspacing(21.)
gc.ticks.set_minor_frequency(7)
gc.axis_labels.set_ytext('Velocity (km/s)')
gc.ticks.show()
gc.ticks.set_color('black')
gc.ticks.set_length(10)
gc.ticks.set_linewidth(2)
gc.add_colorbar()
gc.set_theme('publication')
gc.colorbar.set_width(0.2)
gc.colorbar.set_axis_label_text('K')
plt.savefig('pvKirkcores.pdf', bbox_inches='tight')
def aplpy_moments_grid(fitsimages, scaling, **kwargs):
"""
aplpy_moments_grid: plot moment maps side by side
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This is being tested at the moment. No description yet.
"""
print("--> plotting map grid of these maps: ", fitsimages)
# get keyword arguments
main_figsize = kwargs.get('figsize', (8.27, 11.69)) # default: A4
recenter = kwargs.get('recenter', None) # default: do not recenter
contours = kwargs.get('contour', ['' for im in fitsimages]) # default: no contours
contours = kwargs.get('contours', ['' for im in fitsimages]) # default: no contours
labels = kwargs.get('labels', [im[:-5].replace('_','$\_$') for im in fitsimages]) # default: file name without ending
label_kwargs = kwargs.get('label_kwargs', {}) # default: no kwargs
scalebar = kwargs.get('scalebar', None) # default: not displayed
beam = kwargs.get('beam', None) # default: not displayed
out = kwargs.get('out', 'moments.pdf') # default: generic figure name
# check inputs
if not ( len(contours) == len(fitsimages) ):
raise ValueError("Number of contours dows not match number of images.")
# set up main figure containing all subplots and calculate number of rows
main_fig = __plt__.figure(figsize = main_figsize)
ncols = 3.0
nrows = len(fitsimages)/3
# loop over rows if more than 3 images are given
for row in __np__.arange(len(fitsimages)/3):
this_row_fits = fitsimages[3*row:3*row+3]
this_row_contours = contours[3*row:3*row+3]
this_row_label = labels[3*row:3*row+3]
# loop over moments 0 to 2
for mom in [0,1,2]:
this_panel_fits = this_row_fits[mom]
this_panel_contours = this_row_contours[mom]
this_panel_label = this_row_label[mom]
this_panel_scaling = scaling[mom]
this_panel_size = (nrows,ncols,row*ncols+mom+1)
# plot image
fig = __aplpy__.FITSFigure(this_panel_fits,
figure = main_fig,
subplot = this_panel_size
)
fig.show_colorscale(cmap = this_panel_scaling[4],
vmin = this_panel_scaling[0],
vmax = this_panel_scaling[1],
stretch = this_panel_scaling[3]
)
# recenter image
if recenter is not None:
if (len(recenter) == 2):
fig.recenter(recenter[0].ra.degree, recenter[0].dec.degree, radius=recenter[1].to(__u__.degree).value)
elif (len(recenter) == 3):
fig.recenter(recenter[0].ra.degree, recenter[0].dec.degree, width=recenter[1].to(__u__.degree).value, height=recenter[2].to(__u__.degree).value)
else:
raise SyntaxWarning("--> Specify center as SkyCoord(x,y) and either radius or width, height. Not recentering")
# overplot contours
if not this_panel_contours is '':
if ( len(this_panel_contours) == 3 ):
fig.show_contour(data = this_panel_contours[0],
levels = this_panel_contours[1],
colors = this_panel_contours[2]
)
else:
raise SyntaxWarning("--> Wrong number or format of contour parameters. Not plotting contours")
# panel label
if not this_panel_label is None:
fig.add_label(0.5,0.9, this_panel_label.replace('_','$\_$'), color='black', relative=True, size=ap._velo_fontsize, **label_kwargs)
# colorbar settings
# show colorbars only in top images
if ( row == 0 ):
fig.add_colorbar()
fig.colorbar.set_location('top')
fig.colorbar.set_width(0.2)
fig.colorbar.set_axis_label_text(this_panel_scaling[2])
fig.colorbar.set_axis_label_font(size=ap._colorbar_fontsize)
fig.colorbar.set_font(size=ap._colorbar_fontsize)
fig.colorbar.set_frame_color(ap._frame_color)
# set up panel ticks + labels
fig.axis_labels.hide()
fig.tick_labels.hide()
fig.ticks.show()
fig.ticks.set_xspacing(ap.ticks_xspacing.to(__u__.degree).value)
fig.ticks.set_yspacing(ap.ticks_yspacing.to(__u__.degree).value)
fig.ticks.set_minor_frequency(ap.ticks_minor_frequency)
fig.ticks.set_color(ap._ticks_color)
fig.frame.set_color(ap._frame_color)
# add axis label and scale bar if bottom left plot
if ( row == nrows-1 ) and ( mom == 0 ):
fig.axis_labels.show()
fig.tick_labels.show()
fig.tick_labels.set_xformat(ap.tick_label_xformat)
fig.tick_labels.set_yformat(ap.tick_label_yformat)
fig.tick_labels.set_font(size=ap._tick_label_fontsize)
fig.ticks.show()
fig.ticks.set_xspacing(ap.ticks_xspacing.to(__u__.degree).value)
fig.ticks.set_yspacing(ap.ticks_yspacing.to(__u__.degree).value)
fig.ticks.set_minor_frequency(ap.ticks_minor_frequency)
fig.ticks.set_color(ap._ticks_color)
fig.axis_labels.set_font(size=ap._tick_label_fontsize)
# add beam
if not beam is None:
fig.add_beam()
fig.beam.show()
fig.beam.set_corner(beam)
fig.beam.set_frame(ap._beam_frame)
fig.beam.set_color(ap._beam_color)
# add scalebar
if not scalebar is None:
fig.add_scalebar(length = scalebar[0].to(__u__.degree).value,
label = scalebar[1],
corner = scalebar[2],
frame = ap._scalebar_frame
)
fig.scalebar.set_font(size = ap._scalebar_fontsize)
fig.scalebar.set_linestyle(ap._scalebar_linestyle)
fig.scalebar.set_linewidth(ap._scalebar_linewidth)
fig.scalebar.set_color(ap._scalebar_color)
__mpl__.pyplot.subplots_adjust(wspace=0.001, hspace=0.001)
# __mpl__.pyplot.tight_layout()
fig.save(out, dpi=300, transparent=True)
print("--> saved file as "+out)
def show_overlay(lofarhdu,opthdu,ra,dec,size,firsthdu=None,rms_use=None,bmaj=None,bmin=None,bpa=None,title=None,save_name=None,plotpos=None,block=True,interactive=False,plot_coords=True,overlay_cat=None,lw=1.0,show_lofar=True,no_labels=False,show_grid=True,overlay_region=None,overlay_scale=1.0,circle_radius=None,coords_color='white',coords_lw=1,coords_ra=None,coords_dec=None,marker_ra=None,marker_dec=None,marker_color='white',marker_lw=3,noisethresh=1,lofarlevel=2.0,first_color='lightgreen',drlimit=500,interactive_handler=None,peak=None):
if lofarhdu is None:
print 'LOFAR HDU is missing, not showing it'
show_lofar=False
try:
from matplotlib.cbook import MatplotlibDeprecationWarning
import warnings
warnings.simplefilter('ignore', MatplotlibDeprecationWarning)
except:
print 'Cannot hide warnings'
print '========== Doing overlay at',ra,dec,'with size',size,'==========='
print '========== Title is',title,'=========='
if coords_ra is None:
coords_ra=ra
coords_dec=dec
if show_lofar:
if peak is None:
lofarmax=np.nanmax(lofarhdu[0].data)
else:
print 'Using user-specified peak flux of',peak
lofarmax=peak
if rms_use is None:
rms_use=find_noise_area(lofarhdu,ra,dec,size)[1]
print 'Using LOFAR rms',rms_use
print lofarmax/drlimit,rms_use*lofarlevel
minlevel=max([lofarmax/drlimit,rms_use*lofarlevel])
levels=minlevel*2.0**np.linspace(0,14,30)
hdu=opthdu
mean,noise,vmax=find_noise_area(hdu,ra,dec,size)
print 'Optical parameters are',mean,noise,vmax
f = aplpy.FITSFigure(hdu,north=True)
print 'centring on',ra,dec,size
f.recenter(ra,dec,width=size,height=size)
f.show_colorscale(vmin=mean+noisethresh*noise,vmax=vmax,stretch='log')
#f.show_colorscale(vmin=0,vmax=1e-3)
#f.show_colorscale(vmin=0,vmax=1.0)
if bmaj is not None:
f._header['BMAJ']=bmaj
f._header['BMIN']=bmin
f._header['BPA']=bpa
if show_lofar: f.show_contour(lofarhdu,colors='yellow',linewidths=lw, levels=levels)
if firsthdu is not None:
firstrms=find_noise_area(firsthdu,ra,dec,size)[1]
print 'Using FIRST rms',firstrms
firstlevels=firstrms*3*2.0**np.linspace(0,14,30)
f.show_contour(firsthdu,colors=first_color,linewidths=lw, levels=firstlevels)
if bmaj is not None:
f.add_beam()
f.beam.set_corner('bottom left')
f.beam.set_edgecolor('red')
f.beam.set_facecolor('white')
if plot_coords:
f.show_markers(coords_ra,coords_dec,marker='+',facecolor=coords_color,edgecolor=coords_color,linewidth=coords_lw,s=1500,zorder=100)
if marker_ra is not None:
f.show_markers(marker_ra,marker_dec,marker='x',facecolor=marker_color,edgecolor=marker_color,linewidth=marker_lw,s=1500,zorder=100)
if plotpos is not None:
if not isinstance(plotpos,list):
plotpos=[(plotpos,'x'),]
for t,marker in plotpos:
if len(t)>0:
f.show_markers(t['ra'],t['dec'],marker=marker,facecolor='white',edgecolor='white',linewidth=2,s=750)
if circle_radius is not None:
f.show_circles([ra,],[dec,],[circle_radius,],facecolor='none',edgecolor='cyan',linewidth=5)
if overlay_cat is not None:
t=overlay_cat
if len(t)>0:
f.show_ellipses(t['RA'],t['DEC'],t['Maj']*2/overlay_scale,t['Min']*2/overlay_scale,angle=90+t['PA'],edgecolor='red',linewidth=3,zorder=100)
if overlay_region is not None:
f.show_regions(overlay_region)
if no_labels:
f.axis_labels.hide()
f.tick_labels.hide()
if show_grid:
f.add_grid()
f.grid.show()
f.grid.set_xspacing(1.0/60.0)
f.grid.set_yspacing(1.0/60.0)
if title is not None:
plt.title(title)
plt.tight_layout()
if save_name is None:
plt.show(block=block)
else:
plt.savefig(save_name)
def onclick(event):
xp=event.xdata
yp=event.ydata
xw,yw=f.pixel2world(xp,yp)
if event.button==2:
print title,xw,yw
if interactive:
fig=plt.gcf()
if interactive_handler is None:
cid = fig.canvas.mpl_connect('button_press_event', onclick)
else:
I=interactive_handler(f)
fig.canvas.mpl_connect('button_press_event', I.onclick)
return f
(xcenter, ycenter), (mincolor, maxcolor)):
import os
import aplpy
import matplotlib as mpl
import matplotlib.pyplot as plt
lletter = [
'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',
'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'
]
colors = ['red', 'blue', 'green', 'white']
ypanels = 1
xpanels = 1
wid = (xmax - xmin) * cellsize / 3600.
hei = (ymax - ymin) * cellsize / 3600.
fig = plt.figure(figsize=(10., 10. / wid * hei))
ff = aplpy.FITSFigure(imagein, figure=fig)
ff.recenter(xcenter, ycenter, width=wid, height=hei)
ff.set_theme('publication')
ff.set_system_latex(True)
ff.set_tick_labels_font(size='x-small')
ff.set_axis_labels_font(size='small')
ff.tick_labels.set_yformat('dd.d')
ff.tick_labels.set_xformat('ddd.d')
ff.show_colorscale(vmin=mincolor,
vmax=maxcolor,
cmap='gray',
stretch='sqrt',
interpolation='none')
ff.add_colorbar()
for c in range(len(maskin)):
ff.show_contour(maskin[c], levels=1, colors=colors[c], linewidths=0.4)
#! /usr/bin/env python
# non-interactive mode
import matplotlib
matplotlib.use('Agg')
import aplpy
#ff = 'IRC_79448.fits'
ff = 'IRC_79448.wt.fits'
f = aplpy.FITSFigure(ff,slices=[0])
f.set_title("IRC_79448")
f.show_grayscale()
f.show_colorscale(aspect='auto')
f.show_contour(ff, colors='white', levels=[24.0], smooth=3)
rpd = 57.2958
#f.show_circles(146.9892/rpd,13.27877/rpd,0.003993056/rpd)
f.show_circles([48],[48],2,coords_frame='pixel')
#f.add_beam()
f.add_colorbar()
f.set_nan_color('white')
f.save('IRC_79448.wt.png')
# 1. An $r$-band DECaLS fits image of HCG 16.
# 2. A combined $grz$ jpeg image from DECaLS covering exactly the same field.
#
# These files were downloaded directly from the [DECaLS public website](http://legacysurvey.org/). The exact parameters defining the region and pixel size of these images is contained in the [pipeline.yml](pipeline.yml) file.
#
# 3. Moment 0 and 1 maps of each candidate tidal dwarf galaxy.
#
# The moment 0 and 1 maps of the galaxies were generated in the *imaging* step of the workflow using CASA. The exact steps are included in the [imaging.py](casa/imaging.py) script. The masks used to make these moment maps were constructed manually using the [SlicerAstro](http://github.com/Punzo/SlicerAstro) software package. They were downloaded along with the raw data from the EUDAT service [B2SHARE](http://b2share.eudat.eu) at the beginnning of the workflow execution. The exact location of the data are given in the [pipeline.yml](pipeline.yml) file.
# Make moment 0 contour overlays and moment 1 maps.
# In[ ]:
#Initialise figure using DECaLS r-band image
f = aplpy.FITSFigure(r_image_decals,figsize=(6.,4.3),dimensions=[0,1])
#Display DECaLS grz image
f.show_rgb(grz_image_decals)
#Recentre and resize
f.recenter(32.356, -10.125, radius=1.5/60.)
#Overlay HI contours
f.show_contour(data='NW_clump'+'_mom0th.fits',dimensions=[0,1],slices=[0],
colors='lime',levels=numpy.arange(0.1,5.,0.05))
#Add grid lines
f.add_grid()
f.grid.set_color('black')
import os
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
import sys
hdu1 = fits.open('mom1_12co_pix_2_Tmb.fits')[0]
xcenter = 84.
ycenter = -6.
wid = 1.5
hei = 2.4
xpanels = 1
ypanels = 1
fig = plt.figure(figsize=(3 * xpanels * 1.1 * (wid / (wid + hei)) * 10.,
3 * ypanels / 1.1 * (hei / (wid + hei)) * 10.))
ff = aplpy.FITSFigure(hdu1, figure=fig)
ff.recenter(xcenter, ycenter, width=wid, height=hei)
ff.set_theme('publication')
#ff.set_system_latex(True)
maxcolor = np.nanmax(hdu1.data)
ff.show_colorscale(cmap='jet', vmin=5, vmax=13, stretch='linear')
ff.show_regions('olay2.reg')
#ff.show_contour(mask_hdu, levels=1, colors='yellow', linewidths=0.1)
ff.add_colorbar()
ff.colorbar.set_font(size=12)
ff.colorbar.set_pad(0.5)
ff.colorbar.set_axis_label_text('km s$^{-1}$')
ff.colorbar.set_font(size=12)
ff.set_tick_labels_font(size=12)
ff.set_axis_labels_font(size=12)
ff.add_scalebar(0.286, corner='bottom right',
peak = 1
histogram = 0
#hdu1 = fits.open('mask_regrid_peak_regrid_12CO_specsmooth.fits')[0]
hdu1 = fits.open('subregion_mask_regrid_peak_regrid_12CO_specsmooth.fits')[0]
if peak == 1:
xcenter = 84
ycenter = -6
wid = 1.5
hei = 2.4
xpanels = 1
ypanels = 1
#fig=plt.figure(figsize=(5*xpanels*1.*(wid/(wid+hei))*10.,5*ypanels/1.1*(hei/(wid+hei))*10.))
#ff = aplpy.FITSFigure(hdu1, figure=fig)
ff = aplpy.FITSFigure(hdu1)
#ff.recenter(xcenter,ycenter,width=wid,height=hei)
ff.set_theme('publication')
ff.set_title('NRO45', fontsize=20)
#ff.set_system_latex(True)
maxcolor = np.nanmax(hdu1.data)
ff.show_colorscale(cmap='gist_heat', vmin=0, vmax=maxcolor, stretch='sqrt')
ff.axis_labels.hide()
ff.tick_labels.hide()
ff.ticks.hide()
#ff.show_regions('olay.reg')
#ff.show_contour(mask_hdu, levels=1, colors='yellow', linewidths=0.1)
#ff.add_colorbar()
#ff.colorbar.set_font(size=16)
#ff.colorbar.set_pad(0.5)
ff.set_tick_labels_font(size='large')
