######################################################## # Started Logging At: 2023-02-19 17:20:26 ######################################################## ######################################################## # # Started Logging At: 2023-02-19 17:20:26 ######################################################## print("test") get_ipython().run_line_magic('matplotlib', 'inline') import pylab as pl import numpy as np import os from astropy.utils.data import download_file from astropy.io import fits from spectral_cube import SpectralCube from astropy.visualization import simple_norm import shutil from reproject import reproject_from_healpix, reproject_to_healpix from astropy.wcs import WCS import matplotlib.pyplot as plt from astropy.visualization.wcsaxes.frame import EllipticalFrame from matplotlib.colors import rgb_to_hsv, hsv_to_rgb from astropy.convolution import convolve_fft, Gaussian2DKernel, Gaussian1DKernel import healpy def h_rot(rgb, rot): hsv = rgb_to_hsv(rgb) hsv[:,:,0] += rot # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) return rgb_scaled def make_rgb(rgb, basename, hsv_rotation=0, layernames='RGB', axlims=None, frame_class=EllipticalFrame): plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=frame_class) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) for ind, color in zip((0, 1, 2), (layernames)): rgbc = np.zeros_like(rgb) rgbc[:, :, ind] = rgb[:, :, ind] if hsv_rotation != 0: rgbc = h_rot(rgbc, hsv_rotation) ax.imshow(rgbc) pl.savefig(f'{basename}_{color}.png', bbox_inches='tight', transparent=True) if hsv_rotation != 0: rgb = h_rot(rgb, hsv_rotation) ax.imshow(rgb) if axlims is not None: ax.axis(axlims) pl.savefig(f'{basename}_RGB.png', bbox_inches='tight', transparent=True) target_header_medres = fits.Header.fromstring(""" NAXIS = 2 NAXIS1 = 960 NAXIS2 = 480 CTYPE1 = 'GLON-MOL' CRPIX1 = 480.5 CRVAL1 = 0.0 CDELT1 = -0.3375 CUNIT1 = 'deg ' CTYPE2 = 'GLAT-MOL' CRPIX2 = 240.5 CRVAL2 = 0.0 CDELT2 = 0.3375 CUNIT2 = 'deg ' COORDSYS= 'icrs ' """, sep='\n') target_header = fits.Header.fromstring(""" NAXIS = 2 NAXIS1 = 1920 NAXIS2 = 960 CTYPE1 = 'GLON-MOL' CRPIX1 = 960.5 CRVAL1 = 0.0 CDELT1 = -0.16875 CUNIT1 = 'deg ' CTYPE2 = 'GLAT-MOL' CRPIX2 = 480.5 CRVAL2 = 0.0 CDELT2 = 0.16875 CUNIT2 = 'deg ' COORDSYS= 'icrs ' """, sep='\n') def fix_nans(img): kernel = Gaussian2DKernel(2) sm = convolve_fft(img, kernel) img[np.isnan(img)] = sm[np.isnan(img)] return img def fix_rosat_nans(tb): kernel = Gaussian1DKernel(1) # tb is a 2D array, but we want to convolve in 1D sm = convolve_fft(tb, kernel.array[None, :]) tb[np.isnan(tb)] = sm[np.isnan(tb)] return tb fn_rosat5 = "RASS_SXRB_R5.fits" if not os.path.exists(fn_rosat5): rosat5 = download_file(f"https://www.jb.man.ac.uk/research/cosmos/rosat/{fn_rosat5}") shutil.move(rosat5, fn_rosat5) rosat5 = fits.open(fn_rosat5) (rosat5[1].data['COUNT_RATE'] == 0).sum() #[Out]# 2633 pl.imshow(rosat5[1].data['COUNT_RATE'], norm=simple_norm(rosat5[1].data['COUNT_RATE'], min_percent=1, max_percent=99, stretch='asinh')) #[Out]# rosat5[1].header['COORDSYS'] #[Out]# 'G' data_rosat5 = rosat5[1].data['COUNT_RATE'] data_rosat5 = fix_rosat_nans(np.where(data_rosat5==0, np.nan, data_rosat5)) pl.imshow(data_rosat5, norm=simple_norm(data_rosat5, min_percent=1, max_percent=99, stretch='asinh')) #[Out]# #img_rosat5, footprint = reproject_from_healpix(fn_rosat5, target_header) data_rosat5 = rosat5[1].data['COUNT_RATE'] data_rosat5 = fix_rosat_nans(np.where(data_rosat5==0, np.nan, data_rosat5)) img_rosat5, footprint = reproject_from_healpix((data_rosat5.ravel(), rosat5[1].header["COORDSYS"]), target_header, nested=rosat5[1].header["ORDERING"].lower() == "nested" ) #img_rosat5 = fix_rosat_nans(np.where(img_rosat5==0, np.nan, img_rosat5)) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame, ) ax.imshow(img_rosat5, norm=simple_norm(img_rosat5, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) #img_rosat5, footprint = reproject_from_healpix(fn_rosat5, target_header) data_rosat5 = rosat5[1].data['COUNT_RATE'] data_rosat5 = fix_rosat_nans(np.where(data_rosat5<=0, np.nan, data_rosat5)) img_rosat5, footprint = reproject_from_healpix((data_rosat5.ravel(), rosat5[1].header["COORDSYS"]), target_header, nested=rosat5[1].header["ORDERING"].lower() == "nested" ) #img_rosat5 = fix_rosat_nans(np.where(img_rosat5==0, np.nan, img_rosat5)) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame, ) ax.imshow(img_rosat5, norm=simple_norm(img_rosat5, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) np.min(data_rosat5[data_rosat5>=0]) #[Out]# 0.0 np.min(data_rosat5[data_rosat5>0]) #[Out]# 2.017121e-14 pl.hist(data_rosat5.ravel()) #[Out]# (array([3.14564e+06, 6.10000e+01, 9.00000e+00, 3.00000e+00, 8.00000e+00, #[Out]# 3.00000e+00, 2.00000e+00, 1.00000e+00, 0.00000e+00, 1.00000e+00]), #[Out]# array([-1.5533403e-09, 2.6890088e+04, 5.3780176e+04, 8.0670266e+04, #[Out]# 1.0756035e+05, 1.3445044e+05, 1.6134053e+05, 1.8823061e+05, #[Out]# 2.1512070e+05, 2.4201078e+05, 2.6890088e+05], dtype=float32), #[Out]# ) pl.hist(data_rosat5.ravel(), bins=np.logspace(-14, 6)) #[Out]# (array([1.000000e+00, 2.000000e+00, 4.000000e+00, 4.000000e+00, #[Out]# 4.000000e+00, 1.000000e+00, 0.000000e+00, 4.000000e+00, #[Out]# 4.000000e+00, 1.000000e+00, 0.000000e+00, 3.000000e+00, #[Out]# 3.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, #[Out]# 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, #[Out]# 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, #[Out]# 1.000000e+00, 3.000000e+00, 1.000000e+00, 7.000000e+00, #[Out]# 1.900000e+01, 4.400000e+01, 1.130000e+02, 2.730000e+02, #[Out]# 7.230000e+02, 1.817000e+03, 4.967000e+03, 1.511600e+04, #[Out]# 6.568700e+04, 3.409420e+05, 1.500620e+06, 1.012687e+06, #[Out]# 1.854940e+05, 8.410000e+03, 2.060000e+03, 5.230000e+02, #[Out]# 2.910000e+02, 7.900000e+01, 1.900000e+01, 5.000000e+00, #[Out]# 0.000000e+00]), #[Out]# array([1.00000000e-14, 2.55954792e-14, 6.55128557e-14, 1.67683294e-13, #[Out]# 4.29193426e-13, 1.09854114e-12, 2.81176870e-12, 7.19685673e-12, #[Out]# 1.84206997e-11, 4.71486636e-11, 1.20679264e-10, 3.08884360e-10, #[Out]# 7.90604321e-10, 2.02358965e-09, 5.17947468e-09, 1.32571137e-08, #[Out]# 3.39322177e-08, 8.68511374e-08, 2.22299648e-07, 5.68986603e-07, #[Out]# 1.45634848e-06, 3.72759372e-06, 9.54095476e-06, 2.44205309e-05, #[Out]# 6.25055193e-05, 1.59985872e-04, 4.09491506e-04, 1.04811313e-03, #[Out]# 2.68269580e-03, 6.86648845e-03, 1.75751062e-02, 4.49843267e-02, #[Out]# 1.15139540e-01, 2.94705170e-01, 7.54312006e-01, 1.93069773e+00, #[Out]# 4.94171336e+00, 1.26485522e+01, 3.23745754e+01, 8.28642773e+01, #[Out]# 2.12095089e+02, 5.42867544e+02, 1.38949549e+03, 3.55648031e+03, #[Out]# 9.10298178e+03, 2.32995181e+04, 5.96362332e+04, 1.52641797e+05, #[Out]# 3.90693994e+05, 1.00000000e+06]), #[Out]# ) pl.hist(data_rosat5.ravel(), bins=np.logspace(-14, 6), log=True) #[Out]# (array([1.000000e+00, 2.000000e+00, 4.000000e+00, 4.000000e+00, #[Out]# 4.000000e+00, 1.000000e+00, 0.000000e+00, 4.000000e+00, #[Out]# 4.000000e+00, 1.000000e+00, 0.000000e+00, 3.000000e+00, #[Out]# 3.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, #[Out]# 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, #[Out]# 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, #[Out]# 1.000000e+00, 3.000000e+00, 1.000000e+00, 7.000000e+00, #[Out]# 1.900000e+01, 4.400000e+01, 1.130000e+02, 2.730000e+02, #[Out]# 7.230000e+02, 1.817000e+03, 4.967000e+03, 1.511600e+04, #[Out]# 6.568700e+04, 3.409420e+05, 1.500620e+06, 1.012687e+06, #[Out]# 1.854940e+05, 8.410000e+03, 2.060000e+03, 5.230000e+02, #[Out]# 2.910000e+02, 7.900000e+01, 1.900000e+01, 5.000000e+00, #[Out]# 0.000000e+00]), #[Out]# array([1.00000000e-14, 2.55954792e-14, 6.55128557e-14, 1.67683294e-13, #[Out]# 4.29193426e-13, 1.09854114e-12, 2.81176870e-12, 7.19685673e-12, #[Out]# 1.84206997e-11, 4.71486636e-11, 1.20679264e-10, 3.08884360e-10, #[Out]# 7.90604321e-10, 2.02358965e-09, 5.17947468e-09, 1.32571137e-08, #[Out]# 3.39322177e-08, 8.68511374e-08, 2.22299648e-07, 5.68986603e-07, #[Out]# 1.45634848e-06, 3.72759372e-06, 9.54095476e-06, 2.44205309e-05, #[Out]# 6.25055193e-05, 1.59985872e-04, 4.09491506e-04, 1.04811313e-03, #[Out]# 2.68269580e-03, 6.86648845e-03, 1.75751062e-02, 4.49843267e-02, #[Out]# 1.15139540e-01, 2.94705170e-01, 7.54312006e-01, 1.93069773e+00, #[Out]# 4.94171336e+00, 1.26485522e+01, 3.23745754e+01, 8.28642773e+01, #[Out]# 2.12095089e+02, 5.42867544e+02, 1.38949549e+03, 3.55648031e+03, #[Out]# 9.10298178e+03, 2.32995181e+04, 5.96362332e+04, 1.52641797e+05, #[Out]# 3.90693994e+05, 1.00000000e+06]), #[Out]# ) pl.hist(data_rosat5.ravel(), bins=np.logspace(-14, 5), log=True) #[Out]# (array([1.000000e+00, 1.000000e+00, 3.000000e+00, 6.000000e+00, #[Out]# 4.000000e+00, 1.000000e+00, 0.000000e+00, 3.000000e+00, #[Out]# 3.000000e+00, 3.000000e+00, 0.000000e+00, 1.000000e+00, #[Out]# 3.000000e+00, 2.000000e+00, 0.000000e+00, 0.000000e+00, #[Out]# 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, #[Out]# 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, #[Out]# 0.000000e+00, 0.000000e+00, 2.000000e+00, 3.000000e+00, #[Out]# 2.000000e+00, 1.200000e+01, 3.000000e+01, 6.100000e+01, #[Out]# 1.380000e+02, 3.820000e+02, 8.700000e+02, 2.024000e+03, #[Out]# 5.799000e+03, 1.664800e+04, 6.976300e+04, 3.339790e+05, #[Out]# 1.405596e+06, 1.063946e+06, 2.224350e+05, 1.471300e+04, #[Out]# 2.274000e+03, 7.380000e+02, 3.110000e+02, 1.380000e+02, #[Out]# 2.100000e+01]), #[Out]# array([1.00000000e-14, 2.44205309e-14, 5.96362332e-14, 1.45634848e-13, #[Out]# 3.55648031e-13, 8.68511374e-13, 2.12095089e-12, 5.17947468e-12, #[Out]# 1.26485522e-11, 3.08884360e-11, 7.54312006e-11, 1.84206997e-10, #[Out]# 4.49843267e-10, 1.09854114e-09, 2.68269580e-09, 6.55128557e-09, #[Out]# 1.59985872e-08, 3.90693994e-08, 9.54095476e-08, 2.32995181e-07, #[Out]# 5.68986603e-07, 1.38949549e-06, 3.39322177e-06, 8.28642773e-06, #[Out]# 2.02358965e-05, 4.94171336e-05, 1.20679264e-04, 2.94705170e-04, #[Out]# 7.19685673e-04, 1.75751062e-03, 4.29193426e-03, 1.04811313e-02, #[Out]# 2.55954792e-02, 6.25055193e-02, 1.52641797e-01, 3.72759372e-01, #[Out]# 9.10298178e-01, 2.22299648e+00, 5.42867544e+00, 1.32571137e+01, #[Out]# 3.23745754e+01, 7.90604321e+01, 1.93069773e+02, 4.71486636e+02, #[Out]# 1.15139540e+03, 2.81176870e+03, 6.86648845e+03, 1.67683294e+04, #[Out]# 4.09491506e+04, 1.00000000e+05]), #[Out]# ) pl.hist(data_rosat5.ravel(), bins=np.logspace(-14, 5), log=True) pl.loglog(); #img_rosat5, footprint = reproject_from_healpix(fn_rosat5, target_header) data_rosat5 = rosat5[1].data['COUNT_RATE'] data_rosat5 = fix_rosat_nans(np.where(data_rosat5<=1e-8, np.nan, data_rosat5)) img_rosat5, footprint = reproject_from_healpix((data_rosat5.ravel(), rosat5[1].header["COORDSYS"]), target_header, nested=rosat5[1].header["ORDERING"].lower() == "nested" ) #img_rosat5 = fix_rosat_nans(np.where(img_rosat5==0, np.nan, img_rosat5)) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame, ) ax.imshow(img_rosat5, norm=simple_norm(img_rosat5, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) def fix_nans(img): kernel = Gaussian2DKernel(2) sm = convolve_fft(img, kernel) img[np.isnan(img)] = sm[np.isnan(img)] return img def fix_rosat_nans(tb): kernel = Gaussian1DKernel(2) # tb is a 2D array, but we want to convolve in 1D sm = convolve_fft(tb, kernel.array[None, :]) tb[np.isnan(tb)] = sm[np.isnan(tb)] return tb #img_rosat5, footprint = reproject_from_healpix(fn_rosat5, target_header) data_rosat5 = rosat5[1].data['COUNT_RATE'] data_rosat5 = fix_rosat_nans(np.where(data_rosat5<=1e-8, np.nan, data_rosat5)) img_rosat5, footprint = reproject_from_healpix((data_rosat5.ravel(), rosat5[1].header["COORDSYS"]), target_header, nested=rosat5[1].header["ORDERING"].lower() == "nested" ) #img_rosat5 = fix_rosat_nans(np.where(img_rosat5==0, np.nan, img_rosat5)) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame, ) ax.imshow(img_rosat5, norm=simple_norm(img_rosat5, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) def fix_nans(img): kernel = Gaussian2DKernel(2) sm = convolve_fft(img, kernel) img[np.isnan(img)] = sm[np.isnan(img)] return img def fix_rosat_nans(tb): kernel = Gaussian1DKernel(3) # tb is a 2D array, but we want to convolve in 1D sm = convolve_fft(tb, kernel.array[None, :]) tb[np.isnan(tb)] = sm[np.isnan(tb)] return tb #img_rosat5, footprint = reproject_from_healpix(fn_rosat5, target_header) data_rosat5 = rosat5[1].data['COUNT_RATE'] data_rosat5 = fix_rosat_nans(np.where(data_rosat5<=1e-8, np.nan, data_rosat5)) img_rosat5, footprint = reproject_from_healpix((data_rosat5.ravel(), rosat5[1].header["COORDSYS"]), target_header, nested=rosat5[1].header["ORDERING"].lower() == "nested" ) #img_rosat5 = fix_rosat_nans(np.where(img_rosat5==0, np.nan, img_rosat5)) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame, ) ax.imshow(img_rosat5, norm=simple_norm(img_rosat5, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) pl.hist(img_rosat5.ravel(), bins=np.logspace(-14, 5), log=True) pl.loglog(); #img_rosat5, footprint = reproject_from_healpix(fn_rosat5, target_header) data_rosat5 = rosat5[1].data['COUNT_RATE'] data_rosat5 = fix_rosat_nans(np.where(data_rosat5<=1e-8, np.nan, data_rosat5)) img_rosat5, footprint = reproject_from_healpix((data_rosat5.ravel(), rosat5[1].header["COORDSYS"]), target_header, nested=rosat5[1].header["ORDERING"].lower() == "nested" ) img_rosat5 = fix_rosat_nans(np.where(img_rosat5<=1e-9, np.nan, img_rosat5)) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame, ) ax.imshow(img_rosat5, norm=simple_norm(img_rosat5, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) #img_rosat5, footprint = reproject_from_healpix(fn_rosat5, target_header) data_rosat5 = rosat5[1].data['COUNT_RATE'] data_rosat5 = fix_rosat_nans(np.where(data_rosat5<=1e-8, np.nan, data_rosat5)) img_rosat5, footprint = reproject_from_healpix((data_rosat5.ravel(), rosat5[1].header["COORDSYS"]), target_header, nested=rosat5[1].header["ORDERING"].lower() == "nested" ) img_rosat5 = fix_nans(np.where(img_rosat5<=1e-9, np.nan, img_rosat5)) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame, ) ax.imshow(img_rosat5, norm=simple_norm(img_rosat5, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) def fix_nans(img): kernel = Gaussian2DKernel(2) sm = convolve_fft(img, kernel) img[np.isnan(img)] = sm[np.isnan(img)] return img def fix_rosat_nans(tb): #kernel = Gaussian1DKernel(3) kernel = Gaussian2DKernel(1) # convolving in 2D convolves adjacent scans # tb is a 2D array, but we want to convolve in 1D #sm = convolve_fft(tb, kernel.array[None, :]) sm = convolve_fft(tb, kernel) tb[np.isnan(tb)] = sm[np.isnan(tb)] return tb #img_rosat5, footprint = reproject_from_healpix(fn_rosat5, target_header) data_rosat5 = rosat5[1].data['COUNT_RATE'] data_rosat5 = fix_rosat_nans(np.where(data_rosat5<=1e-8, np.nan, data_rosat5)) img_rosat5, footprint = reproject_from_healpix((data_rosat5.ravel(), rosat5[1].header["COORDSYS"]), target_header, nested=rosat5[1].header["ORDERING"].lower() == "nested" ) img_rosat5 = fix_nans(np.where(img_rosat5<=1e-9, np.nan, img_rosat5)) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame, ) ax.imshow(img_rosat5, norm=simple_norm(img_rosat5, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_lfi30), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_hfi143), simple_norm(img_synchrotron, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_hfi857)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_Synchro', layernames=('X-Ray', 'Halpha', 'Synchrotron')) ######################################################## # Started Logging At: 2023-02-19 17:28:26 ######################################################## ######################################################## # # Started Logging At: 2023-02-19 17:28:26 ######################################################## print("test") get_ipython().run_line_magic('matplotlib', 'inline') import pylab as pl import numpy as np import os from astropy.utils.data import download_file from astropy.io import fits from spectral_cube import SpectralCube from astropy.visualization import simple_norm import shutil from reproject import reproject_from_healpix, reproject_to_healpix from astropy.wcs import WCS import matplotlib.pyplot as plt from astropy.visualization.wcsaxes.frame import EllipticalFrame from matplotlib.colors import rgb_to_hsv, hsv_to_rgb from astropy.convolution import convolve_fft, Gaussian2DKernel, Gaussian1DKernel import healpy def h_rot(rgb, rot): hsv = rgb_to_hsv(rgb) hsv[:,:,0] += rot # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) return rgb_scaled def make_rgb(rgb, basename, hsv_rotation=0, layernames='RGB', axlims=None, frame_class=EllipticalFrame): plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=frame_class) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) for ind, color in zip((0, 1, 2), (layernames)): rgbc = np.zeros_like(rgb) rgbc[:, :, ind] = rgb[:, :, ind] if hsv_rotation != 0: rgbc = h_rot(rgbc, hsv_rotation) ax.imshow(rgbc) pl.savefig(f'{basename}_{color}.png', bbox_inches='tight', transparent=True) if hsv_rotation != 0: rgb = h_rot(rgb, hsv_rotation) ax.imshow(rgb) if axlims is not None: ax.axis(axlims) pl.savefig(f'{basename}_RGB.png', bbox_inches='tight', transparent=True) target_header_medres = fits.Header.fromstring(""" NAXIS = 2 NAXIS1 = 960 NAXIS2 = 480 CTYPE1 = 'GLON-MOL' CRPIX1 = 480.5 CRVAL1 = 0.0 CDELT1 = -0.3375 CUNIT1 = 'deg ' CTYPE2 = 'GLAT-MOL' CRPIX2 = 240.5 CRVAL2 = 0.0 CDELT2 = 0.3375 CUNIT2 = 'deg ' COORDSYS= 'icrs ' """, sep='\n') target_header = fits.Header.fromstring(""" NAXIS = 2 NAXIS1 = 1920 NAXIS2 = 960 CTYPE1 = 'GLON-MOL' CRPIX1 = 960.5 CRVAL1 = 0.0 CDELT1 = -0.16875 CUNIT1 = 'deg ' CTYPE2 = 'GLAT-MOL' CRPIX2 = 480.5 CRVAL2 = 0.0 CDELT2 = 0.16875 CUNIT2 = 'deg ' COORDSYS= 'icrs ' """, sep='\n') def fix_nans(img): kernel = Gaussian2DKernel(2) sm = convolve_fft(img, kernel) img[np.isnan(img)] = sm[np.isnan(img)] return img def fix_rosat_nans(tb): #kernel = Gaussian1DKernel(3) kernel = Gaussian2DKernel(1) # convolving in 2D convolves adjacent scans # tb is a 2D array, but we want to convolve in 1D #sm = convolve_fft(tb, kernel.array[None, :]) sm = convolve_fft(tb, kernel) tb[np.isnan(tb)] = sm[np.isnan(tb)] return tb fn_rosat5 = "RASS_SXRB_R5.fits" if not os.path.exists(fn_rosat5): rosat5 = download_file(f"https://www.jb.man.ac.uk/research/cosmos/rosat/{fn_rosat5}") shutil.move(rosat5, fn_rosat5) rosat5 = fits.open(fn_rosat5) rosat5[1].header['COORDSYS'] #[Out]# 'G' data_rosat5 = rosat5[1].data['COUNT_RATE'] data_rosat5 = fix_rosat_nans(np.where(data_rosat5==0, np.nan, data_rosat5)) pl.hist(data_rosat5.ravel(), bins=np.logspace(-14, 5), log=True) pl.loglog(); pl.hist(img_rosat5.ravel(), bins=np.logspace(-14, 5), log=True) pl.loglog(); rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_synchrotron, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_synchrotron)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_Synchro', layernames=('X-Ray', 'Halpha', 'Synchrotron')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_Synchro', layernames=('X-Ray', 'Halpha', 'Synchrotron')) get_ipython().run_line_magic('pwd', '') #[Out]# '/orange/adamginsburg/cmz/DataSetVisualizations' get_ipython().run_line_magic('matplotlib', 'inline') import pylab as pl import numpy as np import os from astropy.utils.data import download_file from astropy.io import fits from spectral_cube import SpectralCube from astropy.visualization import simple_norm import shutil from reproject import reproject_from_healpix, reproject_to_healpix from astropy.wcs import WCS import matplotlib.pyplot as plt from astropy.visualization.wcsaxes.frame import EllipticalFrame from matplotlib.colors import rgb_to_hsv, hsv_to_rgb from astropy.convolution import convolve_fft, Gaussian2DKernel, Gaussian1DKernel import healpy def h_rot(rgb, rot): hsv = rgb_to_hsv(rgb) hsv[:,:,0] += rot # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) return rgb_scaled def make_rgb(rgb, basename, hsv_rotation=0, layernames='RGB', axlims=None, frame_class=EllipticalFrame): plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=frame_class) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) for ind, color in zip((0, 1, 2), (layernames)): rgbc = np.zeros_like(rgb) rgbc[:, :, ind] = rgb[:, :, ind] if hsv_rotation != 0: rgbc = h_rot(rgbc, hsv_rotation) ax.imshow(rgbc) pl.savefig(f'{basename}_{color}.png', bbox_inches='tight', transparent=True) if hsv_rotation != 0: rgb = h_rot(rgb, hsv_rotation) ax.imshow(rgb) if axlims is not None: ax.axis(axlims) pl.savefig(f'{basename}_RGB.png', bbox_inches='tight', transparent=True) target_header_medres = fits.Header.fromstring(""" NAXIS = 2 NAXIS1 = 960 NAXIS2 = 480 CTYPE1 = 'GLON-MOL' CRPIX1 = 480.5 CRVAL1 = 0.0 CDELT1 = -0.3375 CUNIT1 = 'deg ' CTYPE2 = 'GLAT-MOL' CRPIX2 = 240.5 CRVAL2 = 0.0 CDELT2 = 0.3375 CUNIT2 = 'deg ' COORDSYS= 'icrs ' """, sep='\n') target_header = fits.Header.fromstring(""" NAXIS = 2 NAXIS1 = 1920 NAXIS2 = 960 CTYPE1 = 'GLON-MOL' CRPIX1 = 960.5 CRVAL1 = 0.0 CDELT1 = -0.16875 CUNIT1 = 'deg ' CTYPE2 = 'GLAT-MOL' CRPIX2 = 480.5 CRVAL2 = 0.0 CDELT2 = 0.16875 CUNIT2 = 'deg ' COORDSYS= 'icrs ' """, sep='\n') def fix_nans(img): kernel = Gaussian2DKernel(2) sm = convolve_fft(img, kernel) img[np.isnan(img)] = sm[np.isnan(img)] return img def fix_rosat_nans(tb): #kernel = Gaussian1DKernel(3) kernel = Gaussian2DKernel(1) # convolving in 2D convolves adjacent scans # tb is a 2D array, but we want to convolve in 1D #sm = convolve_fft(tb, kernel.array[None, :]) sm = convolve_fft(tb, kernel) tb[np.isnan(tb)] = sm[np.isnan(tb)] return tb fn_rosat5 = "RASS_SXRB_R5.fits" if not os.path.exists(fn_rosat5): rosat5 = download_file(f"https://www.jb.man.ac.uk/research/cosmos/rosat/{fn_rosat5}") shutil.move(rosat5, fn_rosat5) rosat5 = fits.open(fn_rosat5) rosat5[1].header['COORDSYS'] #[Out]# 'G' data_rosat5 = rosat5[1].data['COUNT_RATE'] data_rosat5 = fix_rosat_nans(np.where(data_rosat5==0, np.nan, data_rosat5)) pl.hist(data_rosat5.ravel(), bins=np.logspace(-14, 5), log=True) pl.loglog(); pl.hist(img_rosat5.ravel(), bins=np.logspace(-14, 5), log=True) pl.loglog(); #img_rosat5, footprint = reproject_from_healpix(fn_rosat5, target_header) data_rosat5 = rosat5[1].data['COUNT_RATE'] data_rosat5 = fix_rosat_nans(np.where(data_rosat5<=1e-8, np.nan, data_rosat5)) img_rosat5, footprint = reproject_from_healpix((data_rosat5.ravel(), rosat5[1].header["COORDSYS"]), target_header, nested=rosat5[1].header["ORDERING"].lower() == "nested" ) img_rosat5 = fix_nans(np.where(img_rosat5<=1e-9, np.nan, img_rosat5)) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame, ) ax.imshow(img_rosat5, norm=simple_norm(img_rosat5, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) fn_halpha = "Halpha_map.fits" if not os.path.exists(fn_halpha): halpha = download_file(f"https://lambda.gsfc.nasa.gov/data/foregrounds/fink_halpha/{fn_halpha}") shutil.move(halpha, fn_halpha) halpha = fits.open(fn_halpha) #img_halpha, footprint = reproject_from_healpix(fn_halpha, target_header) img_halpha = halpha[0].data header = halpha[0].header plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(header), #frame_class=EllipticalFrame, ) ax.imshow(img_halpha, norm=simple_norm(img_halpha, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) fn_akari65 = "akari_mollweide_65_1_4096.fits" if not os.path.exists(fn_akari65): akari65 = download_file(f"https://lambda.gsfc.nasa.gov/data/foregrounds/akari/images/{fn_akari65}") shutil.move(akari65, fn_akari65) akari65 = fits.open(fn_akari65) #img_akari65, footprint = reproject_from_healpix(fn_akari65, target_header) img_akari65 = fix_nans(np.where(akari65[1].data == 0, np.nan, akari65[1].data)) header = akari65[1].header plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(header), #frame_class=EllipticalFrame, ) ax.imshow(img_akari65, norm=simple_norm(img_akari65, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) fn_akari90 = "akari_mollweide_WideS_1_4096.fits" if not os.path.exists(fn_akari90): akari90 = download_file(f"https://lambda.gsfc.nasa.gov/data/foregrounds/akari/images/{fn_akari90}") shutil.move(akari90, fn_akari90) akari90 = fits.open(fn_akari90) #img_akari90, footprint = reproject_from_healpix(fn_akari90, target_header) img_akari90 = fix_nans(np.where(akari90[1].data == 0, np.nan, akari90[1].data)) header = akari90[1].header plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(header), #frame_class=EllipticalFrame, ) ax.imshow(img_akari90, norm=simple_norm(img_akari90, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) fn_akari140 = "akari_mollweide_WideL_1_4096.fits" if not os.path.exists(fn_akari140): akari140 = download_file(f"https://lambda.gsfc.nasa.gov/data/foregrounds/akari/images/{fn_akari140}") shutil.move(akari140, fn_akari140) akari140 = fits.open(fn_akari140) #img_akari140, footprint = reproject_from_healpix(fn_akari140, target_header) img_akari140 = fix_nans(np.where(akari140[1].data == 0, np.nan, akari140[1].data)) header = akari140[1].header plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(header), #frame_class=EllipticalFrame, ) ax.imshow(img_akari140, norm=simple_norm(img_akari140, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) fn_akari160 = "akari_mollweide_160_1_4096.fits" if not os.path.exists(fn_akari160): akari160 = download_file(f"https://lambda.gsfc.nasa.gov/data/foregrounds/akari/images/{fn_akari160}") shutil.move(akari160, fn_akari160) akari160 = fits.open(fn_akari160) #img_akari160, footprint = reproject_from_healpix(fn_akari160, target_header) img_akari160 = fix_nans(np.where(akari160[1].data == 0, np.nan, akari160[1].data)) header = akari160[1].header plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(header), #frame_class=EllipticalFrame, ) ax.imshow(img_akari160, norm=simple_norm(img_akari160, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) #fn_chipass = "lambda_chipass_HPX_r10.fits" fn_chipass = "lambda_chipass_mollweide.fits" if not os.path.exists(fn_chipass): chipass = download_file(f"https://lambda.gsfc.nasa.gov/data/foregrounds/chipass/{fn_chipass}") shutil.move(chipass, fn_chipass) chipass = fits.open(fn_chipass) #img_chipass, footprint = reproject_from_healpix(fn_chipass, target_header) img_chipass = chipass[1].data img_chipass[img_chipass <= 0] = chipass[2].data[img_chipass <= 0] img_chipass[img_chipass <= 0] = np.nan header = chipass[1].header plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(header), #frame_class=EllipticalFrame, ) ax.imshow(img_chipass, norm=simple_norm(img_chipass, min_percent=1, max_percent=99.99, stretch='asinh')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) fn_sve1420 = "STOCKERT+VILLA-ELISA_1420MHz_1_256.fits" if not os.path.exists(fn_sve1420): sve1420 = download_file(f"https://lambda.gsfc.nasa.gov/data/foregrounds/reich_reich/{fn_sve1420}") shutil.move(sve1420, fn_sve1420) sve1420 = fits.open(fn_sve1420) img_sve1420, footprint = reproject_from_healpix(fn_sve1420, target_header) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) ax.imshow(img_sve1420, norm=simple_norm(img_sve1420, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) fn_lfi30 = 'LFI_SkyMap_030_1024_R2.01_full.fits' if not os.path.exists(fn_lfi30): lfi30 = download_file(f"https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/{fn_lfi30}") shutil.move(lfi30, fn_lfi30) lfi30 = fits.open(fn_lfi30) m30 = healpy.read_map(lfi30) healpy.mollview(m30, norm=simple_norm(m30, stretch='log'), cbar=False, title='') img_lfi30, footprint = reproject_from_healpix(fn_lfi_30, target_header) badinds = np.where(np.isnan(img_lfi30)) img_lfi30 = fix_nans(img_lfi30) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) ax.imshow(img_lfi30, norm=simple_norm(img_lfi30, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) fn_lfi30 = 'LFI_SkyMap_030_1024_R2.01_full.fits' if not os.path.exists(fn_lfi30): lfi30 = download_file(f"https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/{fn_lfi30}") shutil.move(lfi30, fn_lfi30) lfi30 = fits.open(fn_lfi30) m30 = healpy.read_map(lfi30) healpy.mollview(m30, norm=simple_norm(m30, stretch='log'), cbar=False, title='') img_lfi30, footprint = reproject_from_healpix(fn_lfi30, target_header) badinds = np.where(np.isnan(img_lfi30)) img_lfi30 = fix_nans(img_lfi30) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) ax.imshow(img_lfi30, norm=simple_norm(img_lfi30, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) fn_hfi143 = "HFI_SkyMap_143_2048_R2.02_full.fits" if not os.path.exists(fn_hfi143): hfi143 = download_file(f"https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/{fn_hfi143}") shutil.move(hfi143, fn_hfi143) hfi143 = fits.open(fn_hfi143) m143 = healpy.read_map(hfi143) healpy.mollview(m143, norm=simple_norm(m143, stretch='log'), cbar=False, title='') img_hfi143, footprint = reproject_from_healpix(fn_hfi143, target_header) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) ax.imshow(img_hfi143, norm=simple_norm(img_hfi143, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) fn_hfi857 = "HFI_SkyMap_857_2048_R2.02_full.fits" if not os.path.exists(fn_hfi857): hfi857 = download_file(f"https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/{fn_hfi857}") shutil.move(hfi857, fn_hfi857) hfi857 = fits.open(fn_hfi857) m857 = healpy.read_map(hfi857) healpy.mollview(m857, norm=simple_norm(m857, stretch='log'), cbar=False, title='') img_hfi857, footprint = reproject_from_healpix(fn_hfi857, target_header) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) ax.imshow(img_hfi857, norm=simple_norm(img_hfi857, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) rgb = np.array([simple_norm(img_lfi30, min_percent=0.01, max_percent=99.90, log_a=5e1, stretch='log')(img_lfi30), simple_norm(img_hfi143, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_hfi143), simple_norm(img_hfi857, min_percent=0.01, max_percent=99.90, log_a=5e1, stretch='log')(img_hfi857)]).T.swapaxes(0,1) make_rgb(rgb, 'Planck_RGB_30-143-857') fn_synchrotron = "COM_CompMap_Synchrotron-commander_0256_R2.00.fits" if not os.path.exists(fn_synchrotron): planck_synchrotron = download_file(f"https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/{fn_synchrotron}") shutil.move(planck_synchrotron, fn_synchrotron) planck_synchrotron = fits.open(fn_synchrotron) img_synchrotron, footprint = reproject_from_healpix(fn_synchrotron, target_header) img_synchrotron = fix_nans(img_synchrotron) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) ax.imshow(img_synchrotron, norm=simple_norm(img_synchrotron, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) fn_freefree = "COM_CompMap_freefree-commander_0256_R2.00.fits" if not os.path.exists(fn_freefree): planck_freefree = download_file(f"https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/{fn_freefree}") shutil.move(planck_freefree, fn_freefree) planck_freefree = fits.open(fn_freefree) img_freefree, footprint = reproject_from_healpix(fn_freefree, target_header) img_freefree = fix_nans(img_freefree) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) ax.imshow(img_freefree, norm=simple_norm(img_freefree, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) fn_CO21 = "COM_CompMap_CO21-commander_2048_R2.00.fits" if not os.path.exists(fn_CO21): planck_CO21 = download_file(f"https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/{fn_CO21}") shutil.move(planck_CO21, fn_CO21) planck_CO21 = fits.open(fn_CO21) img_CO21, footprint = reproject_from_healpix(fn_CO21, target_header) img_CO21 = fix_nans(img_CO21) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) ax.imshow(img_CO21, norm=simple_norm(img_CO21, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) fn_ThermalDust = "COM_CompMap_ThermalDust-commander_2048_R2.00.fits" if not os.path.exists(fn_ThermalDust): planck_ThermalDust = download_file(f"https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/{fn_ThermalDust}") shutil.move(planck_ThermalDust, fn_ThermalDust) planck_ThermalDust = fits.open(fn_ThermalDust) mThermalDust = healpy.read_map(planck_ThermalDust) healpy.mollview(mThermalDust, norm=simple_norm(mThermalDust, stretch='log'), cbar=False, title='') img_ThermalDust, footprint = reproject_from_healpix(fn_ThermalDust, target_header) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) ax.imshow(img_ThermalDust, norm=simple_norm(img_ThermalDust, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) fn_HI = 'LAB_fullvel.fits' if not os.path.exists(fn_HI): LAB_HI = download_file(f"https://lambda.gsfc.nasa.gov/data/foregrounds/HI/{fn_HI}") shutil.move(LAB_HI, fn_HI) LAB_HI = fits.open(fn_HI) mLAB_HI = healpy.read_map(LAB_HI) healpy.mollview(mLAB_HI, norm=simple_norm(mLAB_HI, stretch='asinh'), cbar=False, title='') fn_HI4PI = 'NHI_HPX.fits' if not os.path.exists(fn_HI4PI): HI4PI = download_file(f"https://lambda.gsfc.nasa.gov/data/foregrounds/HI4PI/{fn_HI4PI}") shutil.move(HI4PI, fn_HI4PI) HI4PI = fits.open(fn_HI4PI) mHI4PI = healpy.read_map(HI4PI, field=4) healpy.mollview(mHI4PI, norm=simple_norm(mHI4PI, stretch='asinh'), cbar=False, title='') healpy.cartview(mHI4PI, norm=simple_norm(mHI4PI, stretch='asinh'), cbar=False, title='', notext=True, lonra=[-20,20], latra=[-10,10]) img_HI4PI, footprint = reproject_from_healpix(fn_HI4PI, target_header, field=5) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) ax.imshow(img_HI4PI, norm=simple_norm(img_HI4PI, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) make_rgb(rgb, 'HI-CO-Dust') rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) make_rgb(rgb, 'HI-CO-Dust_m0.25', -0.25, layernames=('HI', 'CO', 'Dust')) # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust')) # zoom on lower-left corner rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) make_rgb(rgb, 'HI-CO-Dust_m0.35_zoom', -0.35, layernames=('HI', 'CO', 'Dust'), axlims=(240, 720, 120, 360), frame_class=None) # CMZoom rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) make_rgb(rgb, 'HI-CO-Dust_m0.35_zoomCMZ', -0.35, layernames=('HI', 'CO', 'Dust'), axlims=(240*2, 720*2, 120*2, 360*2), frame_class=None) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_synchrotron, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_synchrotron)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_Synchro', layernames=('X-Ray', 'Halpha', 'Synchrotron')) img_rosat5.shape, img_halpha.shape, img_synchrotron.shape #[Out]# ((960, 1920), (4320, 8640), (960, 1920)) #img_halpha, footprint = reproject_from_healpix(fn_halpha, target_header) # img_halpha = halpha[0].data # header = halpha[0].header img_halpha, footprint = reproject.reproject_interp(fn_halpha, target_header) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame, ) ax.imshow(img_halpha, norm=simple_norm(img_halpha, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) import os from astropy.utils.data import download_file from astropy.io import fits from spectral_cube import SpectralCube from astropy.visualization import simple_norm import shutil import reproject from reproject import reproject_from_healpix, reproject_to_healpix from astropy.wcs import WCS import matplotlib.pyplot as plt from astropy.visualization.wcsaxes.frame import EllipticalFrame from matplotlib.colors import rgb_to_hsv, hsv_to_rgb from astropy.convolution import convolve_fft, Gaussian2DKernel, Gaussian1DKernel import healpy # img_halpha = halpha[0].data # header = halpha[0].header img_halpha, footprint = reproject.reproject_interp(fn_halpha, target_header) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame, ) ax.imshow(img_halpha, norm=simple_norm(img_halpha, min_percent=1, max_percent=99.99, stretch='log')) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_synchrotron, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_synchrotron)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_Synchro', layernames=('X-Ray', 'Halpha', 'Synchrotron')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_Synchro', layernames=('X-Ray', 'Halpha', 'Synchrotron')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_Synchro', layernames=('X-Ray', 'Halpha', 'Synchrotron')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_synchrotron, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_synchrotron)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_Synchro', layernames=('X-Ray', 'Halpha', 'Synchrotron')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_Synchro', layernames=('X-Ray', 'Halpha', 'ThermalDust')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_ThermalDust', layernames=('X-Ray', 'Halpha', 'ThermalDust')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_CO, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_CO)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_CO', layernames=('X-Ray', 'Halpha', 'CO')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_freefree, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_freefree)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_freefree', layernames=('X-Ray', 'Halpha', 'freefree')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_HI4PI', layernames=('X-Ray', 'Halpha', 'HI4PI')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_CO21, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_CO21)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_CO21', layernames=('X-Ray', 'Halpha', 'CO21')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_HI4PI', layernames=('X-Ray', 'Halpha', 'HI4PI')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_freefree, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_freefree)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_freefree', layernames=('X-Ray', 'Halpha', 'freefree')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_CO21, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_CO21)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_CO21', layernames=('X-Ray', 'Halpha', 'CO21')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_ThermalDust', layernames=('X-Ray', 'Halpha', 'ThermalDust')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_synchrotron, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_synchrotron)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_Synchro', layernames=('X-Ray', 'Halpha', 'Synchrotron')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_HI4PI)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_HI4PI', layernames=('X-Ray', 'Halpha', 'HI4PI')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=1e2, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_freefree, min_percent=0.01, max_percent=99.99, log_a=1e2, stretch='log')(img_freefree)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_freefree', layernames=('X-Ray', 'Halpha', 'freefree')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=1e2, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_freefree, min_percent=0.01, max_percent=99.99, log_a=1e3, stretch='log')(img_freefree)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_freefree', layernames=('X-Ray', 'Halpha', 'freefree')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_HI4PI)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_HI4PI', layernames=('X-Ray', 'Halpha', 'HI4PI')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=2e2, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e2, stretch='log')(img_HI4PI)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_HI4PI', layernames=('X-Ray', 'Halpha', 'HI4PI')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=2e2, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e2, stretch='log')(img_HI4PI)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_HI4PI', layernames=('X-Ray', 'Halpha', 'HI4PI'), hsv_rotation=0.25) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=2e2, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e2, stretch='log')(img_HI4PI)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_HI4PI', layernames=('X-Ray', 'Halpha', 'HI4PI'), hsv_rotation=-0.25) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=2e2, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e2, stretch='log')(img_HI4PI)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_HI4PI', layernames=('X-Ray', 'Halpha', 'HI4PI'), hsv_rotation=-0.05) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=2e2, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e2, stretch='log')(img_HI4PI)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_HI4PI', layernames=('X-Ray', 'Halpha', 'HI4PI')) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=1e2, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_freefree, min_percent=0.01, max_percent=99.99, log_a=1e2, stretch='log')(img_freefree)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_freefree', layernames=('X-Ray', 'Halpha', 'freefree'), hsv_rotation=-0.25) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_freefree, min_percent=0.01, max_percent=99.99, log_a=1e2, stretch='log')(img_freefree)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_freefree', layernames=('X-Ray', 'Halpha', 'freefree'), hsv_rotation=-0.25) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_CO21, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_CO21)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_CO21', layernames=('X-Ray', 'Halpha', 'CO21'), hsv_rotation=-0.25) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_CO21, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_CO21', layernames=('X-Ray', 'Halpha', 'CO21'), hsv_rotation=-0.25) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=1e3, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_CO21, min_percent=0.01, max_percent=99.999, log_a=3e1, stretch='log')(img_CO21)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_CO21', layernames=('X-Ray', 'Halpha', 'CO21'), hsv_rotation=-0.25) rgb = np.array([simple_norm(img_rosat5, min_percent=0.01, max_percent=99.99, log_a=1e3, stretch='log')(img_rosat5), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_CO21, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21)]).T.swapaxes(0,1) make_rgb(rgb, 'XRay_Halpha_CO21', layernames=('X-Ray', 'Halpha', 'CO21'), hsv_rotation=-0.25) xray_synchro = (simple_norm(img_rosat5, min_percent=0.001, max_percent=99.999, log_a=1e3, stretch='linear')(img_rosat5) + simple_norm(img_synchrotron, min_percent=0.001, max_percent=99.999, log_a=1e3, stretch='linear')(img_synchrotron)) rgb = np.array([simple_norm(xray_synchro, min_percent=0.01, max_percent=99.99, log_a=1e3, stretch='log')(xray_synchro), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_CO21, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21)]).T.swapaxes(0,1) make_rgb(rgb, 'XRaySynchro_Halpha_CO21', layernames=('X-RaySynchro', 'Halpha', 'CO21'), hsv_rotation=-0.25) xray_synchro = (simple_norm(img_rosat5, min_percent=0.001, max_percent=99.999, stretch='linear')(img_rosat5) + simple_norm(img_synchrotron, min_percent=0.001, max_percent=99.999, stretch='linear')(img_synchrotron)) rgb = np.array([simple_norm(xray_synchro, min_percent=0.01, max_percent=99.99, log_a=1e2, stretch='log')(xray_synchro), simple_norm(img_halpha, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_halpha), simple_norm(img_CO21, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21)]).T.swapaxes(0,1) make_rgb(rgb, 'XRaySynchro_Halpha_CO21', layernames=('X-RaySynchro', 'Halpha', 'CO21'), hsv_rotation=-0.25) target_header_cmz = fits.Header.fromstring(""" NAXIS = 2 NAXIS1 = 1920 NAXIS2 = 960 CTYPE1 = 'GLON-MOL' CRPIX1 = 960.5 CRVAL1 = 0.0 CDELT1 = -0.05 CUNIT1 = 'deg ' CTYPE2 = 'GLAT-MOL' CRPIX2 = 480.5 CRVAL2 = 0.0 CDELT2 = 0.05 CUNIT2 = 'deg ' COORDSYS= 'icrs ' """, sep='\n') img_HI4PI_cmz, footprint = reproject_from_healpix(fn_HI4PI, target_header_cmz, field=5) img_ThermalDust_cmz, footprint = reproject_from_healpix(fn_ThermalDust, target_header_cmz) img_CO21_cmz, footprint = reproject_from_healpix(fn_CO21, target_header_cmz) rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) #[Out]# (240.0, 720.0) from astropy import units as u hi_cube_fn = '/orange/adamginsburg/cmz/hi/mcluregriffiths/GC.hi.tb.allgal.fits' #if os.path.exists("GC.hi.tb.allgal.fits.gz"): # hi_cube_fh = fits.open("GC.hi.tb.allgal.fits.gz") #else: # print("Not downloaded.") # #hi_cube_fh = download_file("http://www.atnf.csiro.au/research/HI/sgps/SGPS_1/GC.hi.tb.allgal.fits.gz") hi_cube = SpectralCube.read(hi_cube_fn, use_dask=True) peak_hi = hi_cube.max(axis=0) mean_hi = hi_cube.mean(axis=0) pl.imshow(peak_hi.value) #[Out]# pl.imshow(mean_hi.value, vmin=0) #[Out]# if os.path.exists("DHT02_Center_interp.fits"): cube_dame12co = SpectralCube.read('DHT02_Center_interp.fits', use_dask=True) else: pass peak_dameco = cube_dame12co.max(axis=0) mean_dameco = cube_dame12co.mean(axis=0) if os.path.exists('GalacticCenterMosaic_downsampled4x_2kms.fits'): co_cube = SpectralCube.read('GalacticCenterMosaic_downsampled4x_2kms.fits', use_dask=True) peak_co = co_cube.max(axis=0) mean_co = co_cube.mean(axis=0) rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(mean_hi.value, vmin=0, transform=ax.get_transform(hi_cube.wcs.celestial), cmap='gray') #[Out]# rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(mean_dameco.value, vmin=0, transform=ax.get_transform(cube_dame12co.wcs.celestial), cmap='gray') #[Out]# rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(peak_co.value, norm=simple_norm(peak_co.value, min_percent=0.1, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(co_cube.wcs.celestial), cmap='gray') #[Out]# atlasgal = fits.open('/orange/adamginsburg/galactic_plane_surveys/atlasgal/MOSAICS/apex_planck.fits') print(atlasgal[0].data.shape) rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=0.1, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap='gray') #[Out]# fn_meerkat = 'MeerKAT_Galactic_Centre_1284MHz-StokesI.fits' if os.path.exists(fn_meerkat): meerkat = download_file(f"https://archive-gw-1.kat.ac.za/public/repository/10.48479/fyst-hj47/data/{fn_meerkat}") shutil.move(meerkat, fn_meerkat) meerkat = fits.open(fn_meerkat) rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(meerkat[0].data, norm=simple_norm(meerkat[0].data, min_percent=0.1, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(meerkat[0].header)), cmap='gray') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(meerkat[0].data.squeeze(), norm=simple_norm(meerkat[0].data, min_percent=0.1, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(meerkat[0].header).celestial), cmap='gray') #[Out]# pl.imshow(meerkat[0].data) #[Out]# ax.imshow(meerkat[0].data.squeeze(), norm=simple_norm(meerkat[0].data, min_percent=0.1, max_percent=99.9, stretch='asinh'), #transform=ax.get_transform(WCS(meerkat[0].header).celestial), cmap='gray') #[Out]# pl.imshow(meerkat[0].data.squeeze(), norm=simple_norm(meerkat[0].data, min_percent=0.1, max_percent=99.9, stretch='asinh'), #transform=ax.get_transform(WCS(meerkat[0].header).celestial), cmap='gray') #[Out]# pl.imshow(meerkat[0].data.squeeze(), norm=simple_norm(meerkat[0].data, min_percent=0.1, max_percent=99.9, stretch='asinh'), #transform=ax.get_transform(WCS(meerkat[0].header).celestial), cmap='gray') #[Out]# rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(meerkat[0].data.squeeze(), norm=simple_norm(meerkat[0].data, min_percent=0.1, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(meerkat[0].header).celestial), cmap='gray') #[Out]# rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(meerkat[0].data.squeeze(), norm=simple_norm(meerkat[0].data, min_percent=0.1, max_percent=99.9, stretch='log'), transform=ax.get_transform(WCS(meerkat[0].header).celestial), cmap='gray') #[Out]# meerkat[0].data.shape #[Out]# (10527, 10121) meerkat[0].data[0,0] #[Out]# nan meerkat[0].header #[Out]# SIMPLE = T #[Out]# BITPIX = -64 #[Out]# NAXIS = 2 #[Out]# NAXIS1 = 10121 #[Out]# NAXIS2 = 10527 #[Out]# EXTEND = T / FITS dataset may contain extensions #[Out]# COMMENT FITS (Flexible Image Transport System) format is defined in 'Astronomy #[Out]# COMMENT and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H #[Out]# COMMENT -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- #[Out]# COMMENT MeerKAT Galactic Centre Survey #[Out]# COMMENT South African Radio Astronomy Observatory #[Out]# COMMENT Total intensity mosaic #[Out]# COMMENT Contact: ian.heywood@physics.ox.ac.uk #[Out]# COMMENT -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- #[Out]# CTYPE1 = 'RA---TAN' #[Out]# CTYPE2 = 'DEC--TAN' #[Out]# EQUINOX = 2000 #[Out]# CRVAL1 = 266.366790598 #[Out]# CRVAL2 = -29.2213778092 #[Out]# CRPIX1 = 5072.5 #[Out]# CRPIX2 = 5265.5 #[Out]# CDELT1 = -0.000305556 #[Out]# CDELT2 = 0.000305556 #[Out]# CTYPE3 = 'FREQ ' #[Out]# CRPIX3 = 1.0 #[Out]# CRVAL3 = 1283895507.8125 #[Out]# CDELT3 = 856000000.0 #[Out]# CUNIT3 = 'Hz ' #[Out]# CTYPE4 = 'STOKES ' #[Out]# CRPIX4 = 1.0 #[Out]# CRVAL4 = 1.0 #[Out]# CDELT4 = 1.0 #[Out]# CUNIT4 = '' #[Out]# BMAJ = 0.001111111111111111 #[Out]# BMIN = 0.001111111111111111 #[Out]# BPA = 0.0 #[Out]# TELESCOP= 'MeerKAT ' #[Out]# BUNIT = 'JY/BEAM ' plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(meerkat[0].data.squeeze(), norm=simple_norm(meerkat[0].data, min_percent=0.1, max_percent=99.9, stretch='log'), transform=ax.get_transform(WCS(meerkat[0].header).celestial), cmap='gray') #[Out]# plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.set_xlim(840, 1080) ax.set_ylim(360, 600) ax.imshow(meerkat[0].data.squeeze(), norm=simple_norm(meerkat[0].data, min_percent=0.1, max_percent=99.9, stretch='log'), transform=ax.get_transform(WCS(meerkat[0].header).celestial), cmap='gray') #[Out]# import platform; print(platform.platform()) import sys; print("Python", sys.version) import astropy; print("astropy", astropy.__version__) import numpy; print("Numpy", numpy.__version__) import erfa; print("pyerfa", erfa.__version__) import scipy; print("Scipy", scipy.__version__) import matplotlib; print("Matplotlib", matplotlib.__version__) rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) from matplotlib.colors import ListedColormap cmap = pl.cm.gray my_cmap = cmap(np.arange(cmap.N)) my_cmap[:,-1] = np.linspace(0, 1, cmap.N) my_cmap = ListedColormap(my_cmap) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=0.1, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=my_cmap) #[Out]# rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) from matplotlib.colors import ListedColormap cmap = pl.cm.gray my_cmap = cmap(np.arange(cmap.N)) my_cmap[:,-1] = np.linspace(0, 1, cmap.N) my_cmap = ListedColormap(my_cmap) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=1, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=my_cmap) #[Out]# rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) from matplotlib.colors import ListedColormap cmap = pl.cm.gray my_cmap = cmap(np.arange(cmap.N)) my_cmap[:,-1] = np.linspace(0, 1, cmap.N) my_cmap = ListedColormap(my_cmap) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=my_cmap) #[Out]# from matplotlib.colors import ListedColormap cmap = pl.cm.gray gray_transparent = cmap(np.arange(cmap.N)) gray_transparent[:,-1] = np.linspace(0, 1, cmap.N) gray_transparent = ListedColormap(gray_transparent) rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(mean_hi.value, vmin=0, transform=ax.get_transform(hi_cube.wcs.celestial), cmap=gray_transparent) #[Out]# rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(mean_dameco.value, vmin=0, transform=ax.get_transform(cube_dame12co.wcs.celestial), cmap=gray_transparent) #[Out]# from matplotlib.colors import ListedColormap cmap = pl.cm.gray gray_transparent = cmap(np.arange(cmap.N)) gray_transparent[:,-1] = np.linspace(0, 1, cmap.N) gray_transparent = ListedColormap(gray_transparent) cmap = pl.cm.Oranges orange_transparent = cmap(np.arange(cmap.N)) orange_transparent[:,-1] = np.linspace(0, 1, cmap.N) orange_transparent = ListedColormap(orange_transparent) rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(mean_hi.value, vmin=0, transform=ax.get_transform(hi_cube.wcs.celestial), cmap=orange_transparent) #[Out]# rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(mean_dameco.value, vmin=0, transform=ax.get_transform(cube_dame12co.wcs.celestial), cmap=orange_transparent) #[Out]# rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(peak_co.value, norm=simple_norm(peak_co.value, min_percent=0.1, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(co_cube.wcs.celestial), cmap=orange_transparent) #[Out]# rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) #[Out]# rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(mean_hi.value, transform=ax.get_transform(hi_cube.wcs.celestial), norm=simple_norm(mean_hi.value, min_percent=2, max_percent=99.9, stretch='asinh'), cmap=orange_transparent) #[Out]# rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(mean_dameco.value, norm=simple_norm(mean_dameco.value, min_percent=2, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(cube_dame12co.wcs.celestial), cmap=orange_transparent) #[Out]# rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(peak_co.value, norm=simple_norm(peak_co.value, min_percent=0.1, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(co_cube.wcs.celestial), cmap=orange_transparent) pl.savefig("HI4PI_CO_Dust_withJCMTCOOrange.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=gray_transparent) pl.savefig("HI4PI_CO_Dust_withATLASGALtransparent.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=0.1, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap='gray') pl.savefig("HI4PI_CO_Dust_withATLASGAL.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent.png", bbox_inches='tight') from astroquery.vizier import Vizier from astropy.coordinates import SkyCoord almaimf_cat = Vizier.get_catalogs('J/A+A/662/A9/objects')[0] almaimf_crds = SkyCoord(almaimf_cat['RAJ2000'], almaimf_cat['DEJ2000'], frame='fk5', unit=(u.h, u.deg)) rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) ax.scatter(crds.galactic.l, crds.galactic.b, marker='*', color='y', transform=ax.get_transform('galactic')) pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_ALMA-IMFtargets.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) ax.scatter(almaimf_crds.galactic.l, almaimf_crds.galactic.b, marker='*', color='y', transform=ax.get_transform('galactic')) pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_ALMA-IMFtargets.png", bbox_inches='tight') fn_meerkat = 'MeerKAT_Galactic_Centre_1284MHz-StokesI.fits' if os.path.exists(fn_meerkat): meerkat = download_file(f"https://archive-gw-1.kat.ac.za/public/repository/10.48479/fyst-hj47/data/{fn_meerkat}") shutil.move(meerkat, fn_meerkat) meerkat = fits.open(fn_meerkat) get_ipython().run_line_magic('ls', 'MeerKAT_Galactic_Centre_1284MHz-StokesI.fits') os.path.exists(fn_meerkat) #[Out]# True fn_meerkat = 'MeerKAT_Galactic_Centre_1284MHz-StokesI.fits' if not os.path.exists(fn_meerkat): meerkat = download_file(f"https://archive-gw-1.kat.ac.za/public/repository/10.48479/fyst-hj47/data/{fn_meerkat}") shutil.move(meerkat, fn_meerkat) meerkat = fits.open(fn_meerkat) rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(meerkat[0].data.squeeze(), norm=simple_norm(meerkat[0].data, min_percent=0.1, max_percent=99.9, stretch='log'), transform=ax.get_transform(WCS(meerkat[0].header).celestial), cmap='gray') #[Out]# rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) ax.scatter(almaimf_crds.galactic.l, almaimf_crds.galactic.b, marker='*', edgecolor='r', facecolor='none', transform=ax.get_transform('galactic')) pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_ALMA-IMFtargets.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) ax.scatter(almaimf_crds.galactic.l, almaimf_crds.galactic.b, marker='*', edgecolor='purple', facecolor='none', transform=ax.get_transform('galactic')) pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_ALMA-IMFtargets.png", bbox_inches='tight') almaimf_crds.galactic #[Out]# rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=0.1, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap='gray') pl.savefig("HI4PI_CO_Dust_withATLASGAL.png", bbox_inches='tight') target_header_innergal = fits.Header.fromstring(""" NAXIS = 2 NAXIS1 = 3840 NAXIS2 = 960 CTYPE1 = 'GLON-MOL' CRPIX1 = 1920.5 CRVAL1 = 0.0 CDELT1 = -0.05 CUNIT1 = 'deg ' CTYPE2 = 'GLAT-MOL' CRPIX2 = 480.5 CRVAL2 = 0.0 CDELT2 = 0.05 CUNIT2 = 'deg ' COORDSYS= 'icrs ' """, sep='\n') img_HI4PI_innergal, footprint = reproject_from_healpix(fn_HI4PI, target_header_innergal, field=5) img_ThermalDust_innergal, footprint = reproject_from_healpix(fn_ThermalDust, target_header_innergal) img_CO21_innergal, footprint = reproject_from_healpix(fn_CO21, target_header_innergal) rgb = np.array([simple_norm(img_HI4PI_innergal, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_innergal), simple_norm(img_CO21_innergal, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_innergal), simple_norm(img_ThermalDust_innergal, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_innergal)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_innergal),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) pl.savefig("HI4PI_CO_Dust_innergal_withATLASGALOrangetransparent.png", bbox_inches='tight') ax.scatter(almaimf_crds.galactic.l, almaimf_crds.galactic.b, marker='*', edgecolor='purple', facecolor='none', transform=ax.get_transform('galactic')) pl.savefig("HI4PI_CO_Dust_innergal_withATLASGALOrangetransparent_ALMA-IMFtargets.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_innergal, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_innergal), simple_norm(img_CO21_innergal, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_innergal), simple_norm(img_ThermalDust_innergal, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_innergal)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_innergal),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) #ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) pl.savefig("HI4PI_CO_Dust_innergal_withATLASGALOrangetransparent.png", bbox_inches='tight') ax.scatter(almaimf_crds.galactic.l, almaimf_crds.galactic.b, marker='*', edgecolor='purple', facecolor='none', transform=ax.get_transform('galactic')) pl.savefig("HI4PI_CO_Dust_innergal_withATLASGALOrangetransparent_ALMA-IMFtargets.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_innergal, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_innergal), simple_norm(img_CO21_innergal, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_innergal), simple_norm(img_ThermalDust_innergal, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_innergal)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_innergal),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(0, 3840) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) pl.savefig("HI4PI_CO_Dust_innergal_withATLASGALOrangetransparent.png", bbox_inches='tight') ax.scatter(almaimf_crds.galactic.l, almaimf_crds.galactic.b, marker='*', edgecolor='purple', facecolor='none', transform=ax.get_transform('galactic')) pl.savefig("HI4PI_CO_Dust_innergal_withATLASGALOrangetransparent_ALMA-IMFtargets.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_innergal, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_innergal), simple_norm(img_CO21_innergal, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_innergal), simple_norm(img_ThermalDust_innergal, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_innergal)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_innergal),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(540, 3840-540) ax.set_ylim(0, 960) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) pl.savefig("HI4PI_CO_Dust_innergal_withATLASGALOrangetransparent.png", bbox_inches='tight') ax.scatter(almaimf_crds.galactic.l, almaimf_crds.galactic.b, marker='*', edgecolor='purple', facecolor='none', transform=ax.get_transform('galactic')) pl.savefig("HI4PI_CO_Dust_innergal_withATLASGALOrangetransparent_ALMA-IMFtargets.png", bbox_inches='tight') def h_rot(rgb, rot): hsv = rgb_to_hsv(rgb) hsv[:,:,0] += rot # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) return rgb_scaled def make_rgb(rgb, basename, hsv_rotation=0, layernames='RGB', axlims=None, frame_class=EllipticalFrame): plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=frame_class) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) for ind, color in zip((0, 1, 2), (layernames)): rgbc = np.zeros_like(rgb) rgbc[:, :, ind] = rgb[:, :, ind] if hsv_rotation != 0: rgbc = h_rot(rgbc, hsv_rotation) ax.imshow(rgbc) pl.savefig(f'{basename}_{color}.png', bbox_inches='tight', transparent=True) if hsv_rotation != 0: rgb = h_rot(rgb, hsv_rotation) ax.imshow(rgb) if axlims is not None: ax.axis(axlims) pl.savefig(f'{basename}_RGB.png', bbox_inches='tight', transparent=True) return ax # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust')) rect = pl.Rectangle(0, 0, width=(3840-1080)*target_header_innergal['CDELT1'], height=960*target_header_innergal['CDELT2'], transform=pl.gca().get_transform('galactic')) ax.add_artist(rect) rect = pl.Rectangle((0, 0), width=(3840-1080)*target_header_innergal['CDELT1'], height=960*target_header_innergal['CDELT2'], transform=pl.gca().get_transform('galactic')) ax.add_artist(rect) rect = pl.Rectangle((0, 0), width=(3840-1080)*target_header_innergal['CDELT1'], height=960*target_header_innergal['CDELT2'], transform=ax.get_transform('galactic')) ax.add_artist(rect) #[Out]# # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax = make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust')) rect = pl.Rectangle((0, 0), width=(3840-1080)*target_header_innergal['CDELT1'], height=960*target_header_innergal['CDELT2'], transform=pl.gca().get_transform('galactic')) ax.add_artist(rect) #[Out]# # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax = make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust')) rect = pl.Rectangle(((3840-1080)*target_header_innergal['CDELT1']/2, 960*target_header_innergal['CDELT2']/2), width=(3840-1080)*target_header_innergal['CDELT1'], height=960*target_header_innergal['CDELT2'], facecolor='none', edgecolor='white', transform=pl.gca().get_transform('galactic')) ax.add_artist(rect) pl.savefig("HI-CO-Dust_m0.35_RGB_innergal_zoombox.png", bbox_inches='tight') # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax = make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust')) rect = pl.Rectangle(((3840-1080)*target_header_innergal['CDELT1']/2, -960*target_header_innergal['CDELT2']/2), width=(3840-1080)*target_header_innergal['CDELT1'], height=960*target_header_innergal['CDELT2'], facecolor='none', edgecolor='white', transform=pl.gca().get_transform('galactic')) ax.add_artist(rect) pl.savefig("HI-CO-Dust_m0.35_RGB_innergal_zoombox.png", bbox_inches='tight') # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax = make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust')) rect = pl.Rectangle((1080*target_header_innergal['CDELT1'], -960*target_header_innergal['CDELT2']/2), width=(3840-1080)*target_header_innergal['CDELT1'], height=960*target_header_innergal['CDELT2'], facecolor='none', edgecolor='white', transform=pl.gca().get_transform('galactic')) ax.add_artist(rect) pl.savefig("HI-CO-Dust_m0.35_RGB_innergal_zoombox.png", bbox_inches='tight') # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax = make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust')) rect = pl.Rectangle((-1080*target_header_innergal['CDELT1'], -960*target_header_innergal['CDELT2']/2), width=(3840-1080)*target_header_innergal['CDELT1'], height=960*target_header_innergal['CDELT2'], facecolor='none', edgecolor='white', transform=pl.gca().get_transform('galactic')) ax.add_artist(rect) pl.savefig("HI-CO-Dust_m0.35_RGB_innergal_zoombox.png", bbox_inches='tight') # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax = make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust')) rect = pl.Rectangle((-720*target_header_innergal['CDELT1'], -960*target_header_innergal['CDELT2']/2), width=(3840-1080)*target_header_innergal['CDELT1'], height=960*target_header_innergal['CDELT2'], facecolor='none', edgecolor='white', transform=pl.gca().get_transform('galactic')) ax.add_artist(rect) pl.savefig("HI-CO-Dust_m0.35_RGB_innergal_zoombox.png", bbox_inches='tight') ax.bbox #[Out]# ax.bbox.bbox ax.bbox.bounds #[Out]# (270.0, 125.0, 1510.0, 755.0) ax.get_xlim() #[Out]# (-0.5, 1919.5) ax.get_xlim() np.diff(ax.get_xlim())*target_header_innergal['CDELT1'] #[Out]# array([-96.]) import regions rect = regions.RectangleSkyRegion(coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=(ax.get_xlim()[1] - ax.get_xlim()[0])*target_header_innergal['CDELT1'], height=(ax.get_ylim()[1] - ax.get_ylim()[0])*target_header_innergal['CDELT2'], ) import regions rect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=(ax.get_xlim()[1] - ax.get_xlim()[0])*target_header_innergal['CDELT1'], height=(ax.get_ylim()[1] - ax.get_ylim()[0])*target_header_innergal['CDELT2'], ) rect import regions from astropy import coordinates rect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=(ax.get_xlim()[1] - ax.get_xlim()[0])*target_header_innergal['CDELT1'], height=(ax.get_ylim()[1] - ax.get_ylim()[0])*target_header_innergal['CDELT2'], ) rect import regions from astropy import coordinates rect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=(ax.get_xlim()[1] - ax.get_xlim()[0])*target_header_innergal['CDELT1']*u.deg, height=(ax.get_ylim()[1] - ax.get_ylim()[0])*target_header_innergal['CDELT2']*u.deg, ) rect import regions from astropy import coordinates rect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=np.abs((ax.get_xlim()[1] - ax.get_xlim()[0])*target_header_innergal['CDELT1'])*u.deg, height=(ax.get_ylim()[1] - ax.get_ylim()[0])*target_header_innergal['CDELT2']*u.deg, ) rect #[Out]# , width=96.0 deg, height=48.0 deg, angle=0.0 deg)> rgb = np.array([simple_norm(img_HI4PI_innergal, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_innergal), simple_norm(img_CO21_innergal, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_innergal), simple_norm(img_ThermalDust_innergal, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_innergal)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_innergal),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(540, 3840-540) ax.set_ylim(0, 960) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) pl.savefig("HI4PI_CO_Dust_innergal_withATLASGALOrangetransparent.png", bbox_inches='tight') ax.scatter(almaimf_crds.galactic.l, almaimf_crds.galactic.b, marker='*', edgecolor='purple', facecolor='none', transform=ax.get_transform('galactic')) pl.savefig("HI4PI_CO_Dust_innergal_withATLASGALOrangetransparent_ALMA-IMFtargets.png", bbox_inches='tight') import regions from astropy import coordinates rect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=np.abs((ax.get_xlim()[1] - ax.get_xlim()[0])*target_header_innergal['CDELT1'])*u.deg, height=(ax.get_ylim()[1] - ax.get_ylim()[0])*target_header_innergal['CDELT2']*u.deg, ) # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax = make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust')) prect = rect.to_pixel(ax.get_transform('world')) prect.plot(ax) #rect = pl.Rectangle((-720*target_header_innergal['CDELT1'], # -960*target_header_innergal['CDELT2']/2), # width=(3840-1080)*target_header_innergal['CDELT1'], # height=960*target_header_innergal['CDELT2'], # facecolor='none', # edgecolor='white', # transform=pl.gca().get_transform('galactic')) #ax.add_artist(rect) pl.savefig("HI-CO-Dust_m0.35_RGB_innergal_zoombox.png", bbox_inches='tight') ax.wcs #[Out]# WCS Keywords #[Out]# #[Out]# Number of WCS axes: 2 #[Out]# CTYPE : 'GLON-MOL' 'GLAT-MOL' #[Out]# CRVAL : 0.0 0.0 #[Out]# CRPIX : 960.5 480.5 #[Out]# PC1_1 PC1_2 : 1.0 0.0 #[Out]# PC2_1 PC2_2 : 0.0 1.0 #[Out]# CDELT : -0.16875 0.16875 #[Out]# NAXIS : 1920 960 # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax = make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust')) prect = rect.to_pixel(ax.wcs) prect.plot(ax) #rect = pl.Rectangle((-720*target_header_innergal['CDELT1'], # -960*target_header_innergal['CDELT2']/2), # width=(3840-1080)*target_header_innergal['CDELT1'], # height=960*target_header_innergal['CDELT2'], # facecolor='none', # edgecolor='white', # transform=pl.gca().get_transform('galactic')) #ax.add_artist(rect) pl.savefig("HI-CO-Dust_m0.35_RGB_innergal_zoombox.png", bbox_inches='tight') def h_rot(rgb, rot): hsv = rgb_to_hsv(rgb) hsv[:,:,0] += rot # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) return rgb_scaled def make_rgb(rgb, basename, hsv_rotation=0, layernames='RGB', do_layers=True, axlims=None, frame_class=EllipticalFrame): plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=frame_class) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) if do_layers: for ind, color in zip((0, 1, 2), (layernames)): rgbc = np.zeros_like(rgb) rgbc[:, :, ind] = rgb[:, :, ind] if hsv_rotation != 0: rgbc = h_rot(rgbc, hsv_rotation) ax.imshow(rgbc) pl.savefig(f'{basename}_{color}.png', bbox_inches='tight', transparent=True) if hsv_rotation != 0: rgb = h_rot(rgb, hsv_rotation) ax.imshow(rgb) if axlims is not None: ax.axis(axlims) pl.savefig(f'{basename}_RGB.png', bbox_inches='tight', transparent=True) return ax # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax = make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust'), do_layers=False) prect = rect.to_pixel(ax.wcs) prect.plot(ax=ax) #rect = pl.Rectangle((-720*target_header_innergal['CDELT1'], # -960*target_header_innergal['CDELT2']/2), # width=(3840-1080)*target_header_innergal['CDELT1'], # height=960*target_header_innergal['CDELT2'], # facecolor='none', # edgecolor='white', # transform=pl.gca().get_transform('galactic')) #ax.add_artist(rect) pl.savefig("HI-CO-Dust_m0.35_RGB_innergal_zoombox.png", bbox_inches='tight') # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax = make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust'), do_layers=False) prect = rect.to_pixel(ax.wcs) prect.visual['edgecolor'] = 'w' prect.plot(ax=ax) #rect = pl.Rectangle((-720*target_header_innergal['CDELT1'], # -960*target_header_innergal['CDELT2']/2), # width=(3840-1080)*target_header_innergal['CDELT1'], # height=960*target_header_innergal['CDELT2'], # facecolor='none', # edgecolor='white', # transform=pl.gca().get_transform('galactic')) #ax.add_artist(rect) pl.savefig("HI-CO-Dust_m0.35_RGB_innergal_zoombox.png", bbox_inches='tight') get_ipython().run_line_magic('pwd', '') #[Out]# '/orange/adamginsburg/cmz/DataSetVisualizations' rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=3*u.deg, height=2*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox.png", bbox_inches='tight') peak_co.wcs #[Out]# WCS Keywords #[Out]# #[Out]# Number of WCS axes: 2 #[Out]# CTYPE : 'GLON-SFL' 'GLAT-SFL' #[Out]# CRVAL : 360.0 0.0 #[Out]# CRPIX : 568.5 97.854521227825 #[Out]# PC1_1 PC1_2 : 1.0 0.0 #[Out]# PC2_1 PC2_2 : 0.0 1.0 #[Out]# CDELT : -0.010555555436732 0.010555555436732 #[Out]# NAXIS : 0 0 peak_co.shape #[Out]# (195, 1137) peak_hi.shape #[Out]# (1050, 1050) hnco_cube = SpectralCube.read("CMZ_3mm_HNCO.fits", use_dask=True) hnco_cube.shape #[Out]# (327, 165, 765) peak_hi.wcs.wcs.cdelt, peak_co.wcs.wcs.cdelt, atlasgal[0].header['CDELT1'] #[Out]# (array([-0.00972222, 0.00972222]), #[Out]# array([-0.01055556, 0.01055556]), #[Out]# -0.001666667) img_hi_cmz_hires, footprint = reproject_from_healpix(peak_hi.hdu, target_header_cmz_hires, field=5) img_agal_cmz, footprint = reproject_from_healpix(atlasgal, target_header_cmz) img_co_cmz, footprint = reproject_from_healpix(peak_co.hdu, target_header_cmz) target_header_cmz_hires = fits.Header.fromstring(""" NAXIS = 2 NAXIS1 = 1920 NAXIS2 = 960 CTYPE1 = 'GLON-CAR' CRPIX1 = 960.5 CRVAL1 = 0.0 CDELT1 = -0.01 CUNIT1 = 'deg ' CTYPE2 = 'GLAT-CAR' CRPIX2 = 480.5 CRVAL2 = 0.0 CDELT2 = 0.01 CUNIT2 = 'deg ' COORDSYS= 'icrs ' """, sep='\n') img_hi_cmz_hires, footprint = reproject_from_healpix(peak_hi.hdu, target_header_cmz_hires, field=5) img_agal_cmz, footprint = reproject_from_healpix(atlasgal, target_header_cmz) img_co_cmz, footprint = reproject_from_healpix(peak_co.hdu, target_header_cmz) img_hi_cmz_hires, footprint = reproject_from_healpix(peak_hi.hdulist, target_header_cmz_hires, field=5) img_agal_cmz, footprint = reproject_from_healpix(atlasgal, target_header_cmz) img_co_cmz, footprint = reproject_from_healpix(peak_co.hdulist, target_header_cmz) img_hi_cmz_hires, footprint = reproject_from_healpix(fits.HDUList([peak_hi.hdu]), target_header_cmz_hires, field=5) img_agal_cmz, footprint = reproject_from_healpix(atlasgal, target_header_cmz) img_co_cmz, footprint = reproject_from_healpix(peak_co.hdulist, target_header_cmz) img_hi_cmz_hires, footprint = reproject_from_healpix(peak_hi.hdu, target_header_cmz_hires, field=5) img_agal_cmz, footprint = reproject_from_healpix(atlasgal, target_header_cmz) img_co_cmz, footprint = reproject_from_healpix(peak_co.hdulist, target_header_cmz) img_hi_cmz_hires, footprint = reproject_interp(peak_hi.hdu, target_header_cmz_hires, field=5) img_agal_cmz, footprint = reproject_interp(atlasgal, target_header_cmz) img_co_cmz, footprint = reproject_interp(peak_co.hdulist, target_header_cmz) img_hi_cmz_hires, footprint = reproject.reproject_interp(peak_hi.hdu, target_header_cmz_hires, field=5) img_agal_cmz, footprint = reproject.reproject_interp(atlasgal, target_header_cmz) img_co_cmz, footprint = reproject.reproject_interp(peak_co.hdulist, target_header_cmz) img_hi_cmz_hires, footprint = reproject.reproject_interp(peak_hi.hdu, target_header_cmz_hires) img_agal_cmz, footprint = reproject.reproject_interp(atlasgal, target_header_cmz) img_co_cmz, footprint = reproject.reproject_interp(peak_co.hdulist, target_header_cmz) img_hi_cmz_hires, footprint = reproject.reproject_interp(peak_hi.hdu, target_header_cmz_hires) img_agal_cmz, footprint = reproject.reproject_interp(atlasgal, target_header_cmz) img_co_cmz, footprint = reproject.reproject_interp(peak_co.hdu, target_header_cmz) img_hi_cmz_hires, footprint = reproject.reproject_interp(peak_hi.hdu, target_header_cmz_hires) img_agal_cmz_hires, footprint = reproject.reproject_interp(atlasgal, target_header_cmz) img_co_cmz_hires, footprint = reproject.reproject_interp(peak_co.hdu, target_header_cmz) img_hi_cmz_hires, footprint = reproject.reproject_interp(peak_hi.hdu, target_header_cmz_hires) img_agal_cmz_hires, footprint = reproject.reproject_interp(atlasgal, target_header_cmz_hires) img_co_cmz_hires, footprint = reproject.reproject_interp(peak_co.hdu, target_header_cmz_hires) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) target_header_cmz_hires = fits.Header.fromstring(""" NAXIS = 2 NAXIS1 = 1200 NAXIS2 = 480 CTYPE1 = 'GLON-CAR' CRPIX1 = 600.5 CRVAL1 = 0.0 CDELT1 = -0.01 CUNIT1 = 'deg ' CTYPE2 = 'GLAT-CAR' CRPIX2 = 240.5 CRVAL2 = 0.0 CDELT2 = 0.01 CUNIT2 = 'deg ' COORDSYS= 'icrs ' """, sep='\n') img_hi_cmz_hires, footprint = reproject.reproject_interp(peak_hi.hdu, target_header_cmz_hires) img_agal_cmz_hires, footprint = reproject.reproject_interp(atlasgal, target_header_cmz_hires) img_co_cmz_hires, footprint = reproject.reproject_interp(peak_co.hdu, target_header_cmz_hires) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100) ax.set_ylim(100, 380) #[Out]# (100.0, 380.0) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100) ax.set_ylim(120, 360) #[Out]# (120.0, 360.0) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100) ax.set_ylim(140, 340) #[Out]# (140.0, 340.0) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=5e-1, stretch='log')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e3, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=5e-1, stretch='log')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e3, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); img_hi_cmz_hires, footprint = reproject.reproject_interp(peak_hi.hdu, target_header_cmz_hires) img_agal_cmz_hires, footprint = reproject.reproject_interp(atlasgal, target_header_cmz_hires) img_co_cmz_hires, footprint = reproject.reproject_interp(mean_co.hdu, target_header_cmz_hires) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=5e-1, stretch='log')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e3, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=5e-1, stretch='asinh')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e3, stretch='asinh')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); img_hi_cmz_hires, footprint = reproject.reproject_interp(mean_hi.hdu, target_header_cmz_hires) img_agal_cmz_hires, footprint = reproject.reproject_interp(atlasgal, target_header_cmz_hires) img_co_cmz_hires, footprint = reproject.reproject_interp(mean_co.hdu, target_header_cmz_hires) mean_hi.quicklook() peak_hi.quicklook() rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=5e-1, stretch='lienar')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e3, stretch='asinh')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e3, stretch='asinh')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=5, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e3, stretch='asinh')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=5, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e3, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=5, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e0, stretch='log')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e3, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=5, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=0.01, max_percent=99.99, log_a=1e3, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=5, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=1, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=1e3, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); img_hi_cmz_hires, footprint = reproject.reproject_interp(mean_hi.hdu, target_header_cmz_hires) img_agal_cmz_hires, footprint = reproject.reproject_interp(atlasgal, target_header_cmz_hires) img_co_cmz_hires, footprint = reproject.reproject_interp(mean_co.hdu, target_header_cmz_hires) for im in (img_hi_cmz_hires, img_agal_cmz_hires, img_co_cmz_hires): im[im==0] = np.nan rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=5, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=1, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=1e3, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=1, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=1, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=1e3, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=1, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=1, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e3, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=5, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e3, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.imshow(img_hi_cmz_hires, norm=simple_norm(img_hi_cmz_hires, min_percent=5, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires)) pl.imshow(img_hi_cmz_hires, norm=simple_norm(img_hi_cmz_hires, min_percent=5, max_percent=99.99, log_a=5e-1, stretch='linear')) #[Out]# pl.imshow(img_co_cmz_hires, norm=simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')) #[Out]# pl.imshow(img_agal_cmz_hires, norm=simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e3, stretch='log')) #[Out]# pl.imshow(img_agal_cmz_hires, norm=simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e3, stretch='asinh')) #[Out]# pl.imshow(img_hi_cmz_hires, norm=simple_norm(img_hi_cmz_hires, min_percent=10, max_percent=99.99, log_a=5e-1, stretch='linear')) #[Out]# rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=5, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e3, stretch='asinh')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.imshow(img_hi_cmz_hires, norm=simple_norm(img_hi_cmz_hires, min_percent=15, max_percent=99.99, log_a=5e-1, stretch='linear')) #[Out]# rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=15, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e3, stretch='asinh')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=15, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e3, stretch='asinh')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.15 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=15, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e3, stretch='asinh')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.25 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=15, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e3, stretch='asinh')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.0 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e3, stretch='asinh')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.0 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.imshow(img_hi_cmz_hires, norm=simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')) #[Out]# rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires)/2, simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e3, stretch='asinh')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.0 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); img_hi_cmz_hires, footprint = reproject.reproject_interp(mean_hi.hdu, target_header_cmz_hires) img_agal_cmz_hires, footprint = reproject.reproject_interp(atlasgal, target_header_cmz_hires) img_meerkat_cmz_hires, footprint = reproject.reproject_interp(meerkat, target_header_cmz_hires) img_co_cmz_hires, footprint = reproject.reproject_interp(mean_co.hdu, target_header_cmz_hires) for im in (img_hi_cmz_hires, img_agal_cmz_hires, img_co_cmz_hires): im[im==0] = np.nan img_hi_cmz_hires, footprint = reproject.reproject_interp(mean_hi.hdu, target_header_cmz_hires) img_agal_cmz_hires, footprint = reproject.reproject_interp(atlasgal, target_header_cmz_hires) img_meerkat_cmz_hires, footprint = reproject.reproject_interp((meerkat[0].data.squeeze(), WCS(meerkat[0].header).celestial), target_header_cmz_hires) img_co_cmz_hires, footprint = reproject.reproject_interp(mean_co.hdu, target_header_cmz_hires) for im in (img_hi_cmz_hires, img_agal_cmz_hires, img_co_cmz_hires): im[im==0] = np.nan pl.imshow(img_meerkat_hires, norm=simple_norm(img_meerkat_hires, min_percent=1, max_percent=99.99, log_a=5e-1, stretch='log')) pl.imshow(img_meerkat_cmz_hires, norm=simple_norm(img_meerkat_hires, min_percent=1, max_percent=99.99, log_a=5e-1, stretch='log')) pl.imshow(img_meerkat_cmz_hires, norm=simple_norm(img_meerkat_cmz_hires, min_percent=1, max_percent=99.99, log_a=5e-1, stretch='log')) #[Out]# hi_cube.spectral_slab(100*u.km/u.s, 300*u.km/u.s).mean(axis=0).quicklook() hi_cube.spectral_slab(-300*u.km/u.s, -100*u.km/u.s).mean(axis=0).quicklook() mean_hi_highv = (hi_cube.spectral_slab(-300*u.km/u.s, -100*u.km/u.s).mean(axis=0) + hi_cube.spectral_slab(100*u.km/u.s, 300*u.km/u.s).mean(axis=0)) img_hi_cmz_hires, footprint = reproject.reproject_interp(mean_hi_highv.hdu, target_header_cmz_hires) img_agal_cmz_hires, footprint = reproject.reproject_interp(atlasgal, target_header_cmz_hires) img_meerkat_cmz_hires, footprint = reproject.reproject_interp((meerkat[0].data.squeeze(), WCS(meerkat[0].header).celestial), target_header_cmz_hires) img_co_cmz_hires, footprint = reproject.reproject_interp(mean_co.hdu, target_header_cmz_hires) for im in (img_hi_cmz_hires, img_agal_cmz_hires, img_co_cmz_hires): im[im==0] = np.nan pl.imshow(img_meerkat_cmz_hires, norm=simple_norm(img_meerkat_cmz_hires, min_percent=1, max_percent=99.99, log_a=5e-1, stretch='log')) #[Out]# pl.imshow(img_hi_cmz_hires, norm=simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')) #[Out]# pl.imshow(img_co_cmz_hires, norm=simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')) #[Out]# pl.imshow(img_agal_cmz_hires, norm=simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e3, stretch='asinh')) #[Out]# rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires)/2, simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e3, stretch='asinh')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.0 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); mean_hi.quicklook() rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e3, stretch='asinh')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.0 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e3, stretch='asinh')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e4, stretch='asinh')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') pl.imshow(img_agal_cmz_hires, norm=simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e4, stretch='asinh')) #[Out]# pl.imshow(img_agal_cmz_hires, norm=simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e1, stretch='asinh')) #[Out]# pl.imshow(img_agal_cmz_hires, norm=simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e9, stretch='asinh')) #[Out]# pl.imshow(img_agal_cmz_hires, norm=simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e3, stretch='lpg')) pl.imshow(img_agal_cmz_hires, norm=simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e3, stretch='log')) #[Out]# pl.imshow(img_agal_cmz_hires, norm=simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e2, stretch='log')) #[Out]# rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e2, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') pl.imshow(img_agal_cmz_hires, norm=simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e1, stretch='log')) #[Out]# rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') pl.imshow(img_agal_cmz_hires, norm=simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=5e1, stretch='log')) #[Out]# pl.imshow(img_agal_cmz_hires, norm=simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=3e1, stretch='log')) #[Out]# rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=3e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=2e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += 0#-0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += 0#-0.35 # 0.25 = 90/360 hsv[:,:,0] -= hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += 0#-0.35 # 0.25 = 90/360 #hsv[:,:,0] -= hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += 0#-0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99., log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += 0#-0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99., log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.5, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.5, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += 0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.5, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += 0.55 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.5, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += 0.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=5e-1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.5, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += 0#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=1e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.5, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += 0#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=1e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.5, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.15#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=1e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.5, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.10#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=1e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.10#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=1e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.25#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=1e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=1e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.9, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.25#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=1e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.25#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=1e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=1e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=1e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=1e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=2e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=2e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=2e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=2e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=2e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') target_header_innercmz = fits.Header.fromstring(""" NAXIS = 2 NAXIS1 = 3000 NAXIS2 = 1000 CTYPE1 = 'GLON-CAR' CRPIX1 = 1500.5 CRVAL1 = 0.0 CDELT1 = -0.001 CUNIT1 = 'deg ' CTYPE2 = 'GLAT-CAR' CRPIX2 = 500.5 CRVAL2 = 0.0 CDELT2 = 0.001 CUNIT2 = 'deg ' COORDSYS= 'icrs ' """, sep='\n') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz_hires),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(100, 1100); ax.set_ylim(140, 340); pl.savefig("HI_CO_Dust_BigCMZ.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=12*u.deg, height=4.8*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox.png", bbox_inches='tight') from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes, inset_axes rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=12*u.deg, height=4.8*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) axins = inset_axes(ax, **zp['inset_pars'], axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=WCS(target_header_cmz_hires)) axins.imshow(rgb_scaled) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=12*u.deg, height=4.8*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) axins = inset_axes(ax, **zp['inset_pars'], axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=WCS(target_header_cmz_hires))) axins.imshow(rgb_scaled) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=12*u.deg, height=4.8*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) axins = inset_axes(ax, axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=WCS(target_header_cmz_hires))) axins.imshow(rgb_scaled) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=12*u.deg, height=4.8*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) axins = inset_axes(ax, width=2, height=2, axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=WCS(target_header_cmz_hires))) axins.imshow(rgb_scaled) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,2, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=12*u.deg, height=4.8*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) axins = inset_axes(ax, width=8, height=8, bbox_to_anchor=[0, 0.5,], bbox_transform=fig.transFigure, axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=WCS(target_header_cmz_hires))) axins.imshow(rgb_scaled) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,2, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=12*u.deg, height=4.8*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) axins = inset_axes(ax, width=8, height=8, bbox_to_anchor=[0, 0.5,], bbox_transform=fig.transFigure, axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=WCS(target_header_cmz_hires))) axins.imshow(rgb_scaled) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,2, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=12*u.deg, height=4.8*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) axins = inset_axes(ax, width=8, height=8, bbox_to_anchor=[0, 0.5,], bbox_transform=fig.transFigure, loc='lower left', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=WCS(target_header_cmz_hires))) axins.imshow(rgb_scaled) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,2, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=12*u.deg, height=4.8*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, 0.5,], bbox_transform=fig.transFigure, loc='lower left', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=WCS(target_header_cmz_hires))) axins.imshow(rgb_scaled) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,2, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=12*u.deg, height=4.8*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, 0.5,], bbox_transform=fig.transFigure, loc='lower center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=WCS(target_header_cmz_hires))) axins.imshow(rgb_scaled) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,2, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=12*u.deg, height=4.8*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, 0.5, 1, 0.5], bbox_transform=fig.transFigure, loc='lower center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=WCS(target_header_cmz_hires))) axins.imshow(rgb_scaled) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,2, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=12*u.deg, height=4.8*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, 0.25, 1, 0.5], bbox_transform=fig.transFigure, loc='lower center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=WCS(target_header_cmz_hires))) axins.imshow(rgb_scaled) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') def mark_inset_generic(axins, parent_ax, data, loc1=1, loc2=3, loc1in=None, loc2in=None, edgecolor='b', zorder1=100, zorder2=100, polyzorder=1): bl = axins.wcs.pixel_to_world(0, 0) br = axins.wcs.pixel_to_world(data.shape[1], 0) tl = axins.wcs.pixel_to_world(0, data.shape[0]) # x,y not y,x tr = axins.wcs.pixel_to_world(data.shape[1], data.shape[0]) # x,y not y,x fig = parent_ax.get_figure() frame = parent_ax.wcs.wcs.radesys.lower() frame = ax.wcs.world_axis_physical_types[0].split(".")[1] blt = bl.transform_to(frame) brt = br.transform_to(frame) tlt = tl.transform_to(frame) trt = tr.transform_to(frame) xys = [parent_ax.wcs.wcs_world2pix([[crd.spherical.lon.deg, crd.spherical.lat.deg]],0)[0] for crd in (trt, tlt, blt, brt, trt)] markinkwargs = dict(fc='none', ec=edgecolor) ppoly = Polygon(xys, fill=False, zorder=polyzorder, **markinkwargs) parent_ax.add_patch(ppoly) corners = parent_ax.transData.inverted().transform(xys) axcorners = [(1,1), (0,1), (0,0), (1,0)] corners = [(crd.spherical.lon.deg, crd.spherical.lat.deg) for crd in (trt, tlt, blt, brt)] if loc1in is None: loc1in = loc1 if loc2in is None: loc2in = loc2 con1 = ConnectionPatch(xyA=axcorners[loc1-1], coordsA='axes fraction', axesA=axins, xyB=corners[loc1in-1], coordsB=parent_ax.get_transform('world'), axesB=parent_ax, linestyle='-', color=edgecolor, zorder=zorder1) con2 = ConnectionPatch(xyA=axcorners[loc2-1], coordsA='axes fraction', axesA=axins, xyB=corners[loc2in-1], coordsB=parent_ax.get_transform('world'), axesB=parent_ax, linestyle='-', color=edgecolor, zorder=zorder2) fig.add_artist(con1) fig.add_artist(con2) return con1, con2 rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,2, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=12*u.deg, height=4.8*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, 0.25, 1, 0.5], bbox_transform=fig.transFigure, loc='lower center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=WCS(target_header_cmz_hires))) axins.imshow(rgb_scaled) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=img_hi_cmz_hires, loc1=4, edgecolor='w') pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') from mpl_toolkits.axes_grid1.inset_locator import TransformedBbox, BboxPatch, BboxConnector, Path from matplotlib.transforms import Bbox from matplotlib.patches import Polygon, PathPatch, ConnectionPatch def mark_inset_generic(axins, parent_ax, data, loc1=1, loc2=3, loc1in=None, loc2in=None, edgecolor='b', zorder1=100, zorder2=100, polyzorder=1): bl = axins.wcs.pixel_to_world(0, 0) br = axins.wcs.pixel_to_world(data.shape[1], 0) tl = axins.wcs.pixel_to_world(0, data.shape[0]) # x,y not y,x tr = axins.wcs.pixel_to_world(data.shape[1], data.shape[0]) # x,y not y,x fig = parent_ax.get_figure() frame = parent_ax.wcs.wcs.radesys.lower() frame = ax.wcs.world_axis_physical_types[0].split(".")[1] blt = bl.transform_to(frame) brt = br.transform_to(frame) tlt = tl.transform_to(frame) trt = tr.transform_to(frame) xys = [parent_ax.wcs.wcs_world2pix([[crd.spherical.lon.deg, crd.spherical.lat.deg]],0)[0] for crd in (trt, tlt, blt, brt, trt)] markinkwargs = dict(fc='none', ec=edgecolor) ppoly = Polygon(xys, fill=False, zorder=polyzorder, **markinkwargs) parent_ax.add_patch(ppoly) corners = parent_ax.transData.inverted().transform(xys) axcorners = [(1,1), (0,1), (0,0), (1,0)] corners = [(crd.spherical.lon.deg, crd.spherical.lat.deg) for crd in (trt, tlt, blt, brt)] if loc1in is None: loc1in = loc1 if loc2in is None: loc2in = loc2 con1 = ConnectionPatch(xyA=axcorners[loc1-1], coordsA='axes fraction', axesA=axins, xyB=corners[loc1in-1], coordsB=parent_ax.get_transform('world'), axesB=parent_ax, linestyle='-', color=edgecolor, zorder=zorder1) con2 = ConnectionPatch(xyA=axcorners[loc2-1], coordsA='axes fraction', axesA=axins, xyB=corners[loc2in-1], coordsB=parent_ax.get_transform('world'), axesB=parent_ax, linestyle='-', color=edgecolor, zorder=zorder2) fig.add_artist(con1) fig.add_artist(con2) return con1, con2 rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,2, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=12*u.deg, height=4.8*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, 0.25, 1, 0.5], bbox_transform=fig.transFigure, loc='lower center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=WCS(target_header_cmz_hires))) axins.imshow(rgb_scaled) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=img_hi_cmz_hires, loc1=4, edgecolor='w') pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,2, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) #cmzrect = regions.RectangleSkyRegion( # coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), # width=12*u.deg, # height=4.8*u.deg, #) #pcmzrect = cmzrect.to_pixel(ax.wcs) #pcmzrect.visual['edgecolor'] = 'w' #pcmzrect.plot(ax=ax) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(img_hi_cmz_hires), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(img_co_cmz_hires), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(img_agal_cmz_hires)]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, 0.25, 1, 0.5], bbox_transform=fig.transFigure, loc='lower center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=WCS(target_header_cmz_hires))) axins.imshow(rgb_scaled) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=img_hi_cmz_hires, loc1=4, edgecolor='w') pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,2, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.set_xlim(240*2, 720*2) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) #cmzrect = regions.RectangleSkyRegion( # coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), # width=12*u.deg, # height=4.8*u.deg, #) #pcmzrect = cmzrect.to_pixel(ax.wcs) #pcmzrect.visual['edgecolor'] = 'w' #pcmzrect.plot(ax=ax) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(np.nan_to_num(img_hi_cmz_hires)), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(np.nan_to_num(img_co_cmz_hires)), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(np.nan_to_num(img_agal_cmz_hires))]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, 0.25, 1, 0.5], bbox_transform=fig.transFigure, loc='lower center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=WCS(target_header_cmz_hires))) axins.imshow(rgb_scaled) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=img_hi_cmz_hires, loc1=4, edgecolor='w') pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,2, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) #cmzrect = regions.RectangleSkyRegion( # coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), # width=12*u.deg, # height=4.8*u.deg, #) #pcmzrect = cmzrect.to_pixel(ax.wcs) #pcmzrect.visual['edgecolor'] = 'w' #pcmzrect.plot(ax=ax) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(np.nan_to_num(img_hi_cmz_hires)), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(np.nan_to_num(img_co_cmz_hires)), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(np.nan_to_num(img_agal_cmz_hires))]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, 0.25, 1, 0.5], bbox_transform=fig.transFigure, loc='lower center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=WCS(target_header_cmz_hires))) axins.imshow(rgb_scaled) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=img_hi_cmz_hires, loc1=4, edgecolor='w') pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,2, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) #ax.set_xlim(240*2, 720*2) ax.set_xlim(0, 1920) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) #cmzrect = regions.RectangleSkyRegion( # coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), # width=12*u.deg, # height=4.8*u.deg, #) #pcmzrect = cmzrect.to_pixel(ax.wcs) #pcmzrect.visual['edgecolor'] = 'w' #pcmzrect.plot(ax=ax) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(np.nan_to_num(img_hi_cmz_hires)), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(np.nan_to_num(img_co_cmz_hires)), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(np.nan_to_num(img_agal_cmz_hires))]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, 0.25, 1, 0.5], bbox_transform=fig.transFigure, loc='lower center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=WCS(target_header_cmz_hires))) axins.imshow(rgb_scaled) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=img_hi_cmz_hires, loc1=4, edgecolor='w') pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') import PIL import pyavm rgb = PIL.open('gc_fullres_6.jpg') avm = pyavm.AVM('gc_fullres_6.jpg') wwavm = avm.celestial rgb = PIL.Image.open('gc_fullres_6.jpg') avm = pyavm.AVM('gc_fullres_6.jpg') wwavm = avm.celestial rgb = PIL.Image.open('gc_fullres_6.jpg') avm = pyavm.AVM.from_image('gc_fullres_6.jpg') wwavm = avm.wcs rgb = PIL.Image.open('gc_fullres_6.jpg') avm = pyavm.AVM.from_image('gc_fullres_6.jpg') wwavm = avm.to_wcs() ww = WCS('gc_fullres_6.wcs') ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(np.nan_to_num(img_hi_cmz_hires)), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(np.nan_to_num(img_co_cmz_hires)), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(np.nan_to_num(img_agal_cmz_hires))]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,1, projection=WCS(target_header_cmz_hires),) ax.imshow(rgb_scaled) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) rgb = PIL.Image.open('gc_fullres_6.jpg') ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, 0.25, 1, 0.5], bbox_transform=fig.transFigure, loc='lower center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=ww)) axins.imshow(rgb) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=rgb[:,:,0], loc1=4, edgecolor='w') pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(np.nan_to_num(img_hi_cmz_hires)), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(np.nan_to_num(img_co_cmz_hires)), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(np.nan_to_num(img_agal_cmz_hires))]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,1, projection=WCS(target_header_cmz_hires),) ax.imshow(rgb_scaled) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) rgb = np.array(PIL.Image.open('gc_fullres_6.jpg')) ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, 0.25, 1, 0.5], bbox_transform=fig.transFigure, loc='lower center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=ww)) axins.imshow(rgb) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=rgb[:,:,0], loc1=4, edgecolor='w') pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') np.array(PIL.Image.open('gc_fullres_6.jpg')).shape #[Out]# (3295, 6473, 3) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(np.nan_to_num(img_hi_cmz_hires)), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(np.nan_to_num(img_co_cmz_hires)), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(np.nan_to_num(img_agal_cmz_hires))]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,1, projection=WCS(target_header_cmz_hires),) ax.imshow(rgb_scaled) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) rgb = np.array(PIL.Image.open('gc_fullres_6.jpg')) ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, 0, 1, 0.5], bbox_transform=fig.transFigure, loc='lower center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=ww)) axins.imshow(rgb) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=rgb[:,:,0], loc2=2, edgecolor='w') pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(np.nan_to_num(img_hi_cmz_hires)), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(np.nan_to_num(img_co_cmz_hires)), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(np.nan_to_num(img_agal_cmz_hires))]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,1, projection=WCS(target_header_cmz_hires),) ax.imshow(rgb_scaled) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) rgb = np.array(PIL.Image.open('gc_fullres_6.jpg')) ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, 0.5, 1, 0.5], bbox_transform=fig.transFigure, loc='upper center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=ww)) axins.imshow(rgb[::-1,:,:]) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=rgb[:,:,0], loc2=2, edgecolor='w') pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(np.nan_to_num(img_hi_cmz_hires)), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(np.nan_to_num(img_co_cmz_hires)), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(np.nan_to_num(img_agal_cmz_hires))]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,1, projection=WCS(target_header_cmz_hires),) ax.imshow(rgb_scaled) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) rgb = np.array(PIL.Image.open('gc_fullres_6.jpg')) ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, -0.25, 1, 0.5], bbox_transform=fig.transFigure, loc='upper center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=ww)) axins.imshow(rgb[::-1,:,:]) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=rgb[:,:,0], loc2=2, edgecolor='w') pl.savefig("HI-CO-Dust_zoominto3color.png", bbox_inches='tight') rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=3e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.99, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) hsv = rgb_to_hsv(rgb) hsv[:,:,0] += -0.35 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,2, projection=WCS(target_header_cmz),) #frame_class=EllipticalFrame) ax.imshow(rgb_scaled) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) #ax.set_xlim(240*2, 720*2) ax.set_xlim(0, 1920) ax.set_ylim(120*2, 360*2) ax.imshow(atlasgal[0].data, norm=simple_norm(atlasgal[0].data, min_percent=5, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(WCS(atlasgal[0].header)), cmap=orange_transparent) #cmzrect = regions.RectangleSkyRegion( # coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), # width=12*u.deg, # height=4.8*u.deg, #) #pcmzrect = cmzrect.to_pixel(ax.wcs) #pcmzrect.visual['edgecolor'] = 'w' #pcmzrect.plot(ax=ax) rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(np.nan_to_num(img_hi_cmz_hires)), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(np.nan_to_num(img_co_cmz_hires)), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(np.nan_to_num(img_agal_cmz_hires))]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35#.45 # 0.25 = 90/360 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, 0.25, 1, 0.5], bbox_transform=fig.transFigure, loc='lower center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=WCS(target_header_cmz_hires))) axins.imshow(rgb_scaled) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=img_hi_cmz_hires, loc1=4, edgecolor='w') pl.savefig("HI4PI_CO_Dust_withATLASGALOrangetransparent_cmzbox_insetzoom.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(np.nan_to_num(img_hi_cmz_hires)), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(np.nan_to_num(img_co_cmz_hires)), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(np.nan_to_num(img_agal_cmz_hires))]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,1, projection=WCS(target_header_cmz_hires),) ax.imshow(rgb_scaled) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) rgb = np.array(PIL.Image.open('gc_fullres_6.jpg')) ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, -0.05, 1, 0.5], bbox_transform=fig.transFigure, loc='upper center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=ww)) axins.imshow(rgb[::-1,:,:]) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=rgb[:,:,0], loc2=2, edgecolor='w') pl.savefig("HI-CO-Dust_zoominto3color.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(np.nan_to_num(img_hi_cmz_hires)), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(np.nan_to_num(img_co_cmz_hires)), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(np.nan_to_num(img_agal_cmz_hires))]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,1, projection=WCS(target_header_cmz_hires),) ax.imshow(rgb_scaled) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) rgb = np.array(PIL.Image.open('gc_fullres_6.jpg')) ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, 0, 1, 0.5], bbox_transform=fig.transFigure, loc='upper center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=ww)) axins.imshow(rgb[::-1,:,:]) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=rgb[:,:,0], loc2=2, edgecolor='w') pl.savefig("HI-CO-Dust_zoominto3color.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(np.nan_to_num(img_hi_cmz_hires)), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(np.nan_to_num(img_co_cmz_hires)), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(np.nan_to_num(img_agal_cmz_hires))]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,1, projection=WCS(target_header_cmz_hires),) ax.imshow(rgb_scaled) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) rgb = np.array(PIL.Image.open('gc_fullres_6.jpg')) ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0, 0.2, 1, 0.5], bbox_transform=fig.transFigure, loc='upper center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=ww)) axins.imshow(rgb[::-1,:,:]) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=rgb[:,:,0], loc2=2, edgecolor='w') pl.savefig("HI-CO-Dust_zoominto3color.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(np.nan_to_num(img_hi_cmz_hires)), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(np.nan_to_num(img_co_cmz_hires)), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(np.nan_to_num(img_agal_cmz_hires))]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,1, projection=WCS(target_header_cmz_hires),) ax.imshow(rgb_scaled) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) rgb = np.array(PIL.Image.open('gc_fullres_6.jpg')) ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0.02, 0.2, 1, 0.5], bbox_transform=fig.transFigure, loc='upper center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=ww)) axins.imshow(rgb[::-1,:,:]) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=rgb[:,:,0], loc2=2, edgecolor='w') pl.savefig("HI-CO-Dust_zoominto3color.png", bbox_inches='tight') rgb = np.array([simple_norm(img_hi_cmz_hires, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(np.nan_to_num(img_hi_cmz_hires)), simple_norm(img_co_cmz_hires, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(np.nan_to_num(img_co_cmz_hires)), simple_norm(img_agal_cmz_hires, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(np.nan_to_num(img_agal_cmz_hires))]).T.swapaxes(0,1) hsv = rgb_to_hsv(np.nan_to_num(rgb)) hsv[:,:,0] += -0.35 hsv[:,:,0] = hsv[:,:,0] % 1 rgb_scaled = hsv_to_rgb(hsv) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,1, projection=WCS(target_header_cmz_hires),) ax.imshow(rgb_scaled) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) rgb = np.array(PIL.Image.open('gc_fullres_6.jpg')) ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0.01, 0.2, 1, 0.5], bbox_transform=fig.transFigure, loc='upper center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=ww)) axins.imshow(rgb[::-1,:,:]) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=rgb[:,:,0], loc2=2, edgecolor='w') pl.savefig("HI-CO-Dust_zoominto3color.png", bbox_inches='tight') # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax = make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust'), do_layers=False) prect = rect.to_pixel(ax.wcs) prect.visual['edgecolor'] = 'w' prect.plot(ax=ax) pl.savefig("HI-CO-Dust_m0.35_RGB_innergal_zoombox.png", bbox_inches='tight') ax.text(0, 20, "Galactic Plane") #[Out]# Text(0, 20, 'Galactic Plane') # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax = make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust'), do_layers=False) prect = rect.to_pixel(ax.wcs) prect.visual['edgecolor'] = 'w' prect.plot(ax=ax) pl.savefig("HI-CO-Dust_m0.35_RGB_innergal_zoombox.png", bbox_inches='tight') ax.text(0, 20, "Galactic Plane", transform=ax.get_transform('world'), color='w') #[Out]# Text(0, 20, 'Galactic Plane') # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax = make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust'), do_layers=False) prect = rect.to_pixel(ax.wcs) prect.visual['edgecolor'] = 'w' prect.plot(ax=ax) pl.savefig("HI-CO-Dust_m0.35_RGB_innergal_zoombox.png", bbox_inches='tight') ax.text(0, 23, "Galactic Plane", transform=ax.get_transform('world'), color='w', horizontalalignment='center') #[Out]# Text(0, 23, 'Galactic Plane') # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax = make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust'), do_layers=False) prect = rect.to_pixel(ax.wcs) prect.visual['edgecolor'] = 'w' prect.plot(ax=ax) pl.savefig("HI-CO-Dust_m0.35_RGB_innergal_zoombox.png", bbox_inches='tight') ax.text(0, 25, "Galactic Plane", transform=ax.get_transform('world'), color='w', horizontalalignment='center') cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=12*u.deg, height=4.8*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) ax.text(0, 5, "Central Molecular Zone", transform=ax.get_transform('world'), color='w', horizontalalignment='center') #[Out]# Text(0, 5, 'Central Molecular Zone') # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax = make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust'), do_layers=False) prect = rect.to_pixel(ax.wcs) prect.visual['edgecolor'] = 'w' prect.plot(ax=ax) pl.savefig("HI-CO-Dust_m0.35_RGB_innergal_zoombox.png", bbox_inches='tight') ax.text(0, 25, "Galactic Plane", transform=ax.get_transform('world'), color='w', horizontalalignment='center') cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=12*u.deg, height=4.8*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) ax.text(0, 5, "Central Molecular Zone", transform=ax.get_transform('world'), color='w', horizontalalignment='center') orionrect = regions.RectangleSkyRegion( coordinates.SkyCoord(209*u.deg, -19*u.deg, frame='galactic'), width=5*u.deg, height=5*u.deg, ) porionrect = orionrect.to_pixel(ax.wcs) porionrect.visual['edgecolor'] = 'w' porionrect.plot(ax=ax) ax.text(209, -19, "Orion", transform=ax.get_transform('world'), color='w', horizontalalignment='center') #[Out]# Text(209, -19, 'Orion') # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax = make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust'), do_layers=False) prect = rect.to_pixel(ax.wcs) prect.visual['edgecolor'] = 'w' prect.plot(ax=ax) pl.savefig("HI-CO-Dust_m0.35_RGB_innergal_zoombox.png", bbox_inches='tight') ax.text(0, 25, "Galactic Plane", transform=ax.get_transform('world'), color='w', horizontalalignment='center') cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=12*u.deg, height=4.8*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) ax.text(0, 5, "Central Molecular Zone", transform=ax.get_transform('world'), color='w', horizontalalignment='center') orionrect = regions.RectangleSkyRegion( coordinates.SkyCoord(209*u.deg, -19*u.deg, frame='galactic'), width=5*u.deg, height=5*u.deg, ) porionrect = orionrect.to_pixel(ax.wcs) porionrect.visual['edgecolor'] = 'w' porionrect.plot(ax=ax) ax.text(209, -15, "Orion", transform=ax.get_transform('world'), color='w', horizontalalignment='center') #[Out]# Text(209, -15, 'Orion') # different colors, overwrites previous rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax = make_rgb(rgb, 'HI-CO-Dust_m0.35', -0.35, layernames=('HI', 'CO', 'Dust'), do_layers=False) prect = rect.to_pixel(ax.wcs) prect.visual['edgecolor'] = 'w' prect.plot(ax=ax) pl.savefig("HI-CO-Dust_m0.35_RGB_innergal_zoombox.png", bbox_inches='tight') ax.text(0, 25, "Galactic Plane", transform=ax.get_transform('world'), color='w', horizontalalignment='center') cmzrect = regions.RectangleSkyRegion( coordinates.SkyCoord(0*u.deg, 0*u.deg, frame='galactic'), width=12*u.deg, height=4.8*u.deg, ) pcmzrect = cmzrect.to_pixel(ax.wcs) pcmzrect.visual['edgecolor'] = 'w' pcmzrect.plot(ax=ax) ax.text(0, 5, "Central Molecular Zone", transform=ax.get_transform('world'), color='w', horizontalalignment='center') orionrect = regions.RectangleSkyRegion( coordinates.SkyCoord(209*u.deg, -19*u.deg, frame='galactic'), width=5*u.deg, height=5*u.deg, ) porionrect = orionrect.to_pixel(ax.wcs) porionrect.visual['edgecolor'] = 'w' porionrect.plot(ax=ax) ax.text(209, -15, "Orion", transform=ax.get_transform('world'), color='w', horizontalalignment='center') pl.savefig("HI-CO-Dust_m0.35_RGB_labeled_zones.png", bbox_inches='tight') rgb = np.array(PIL.Image.open('gc_fullres_6.jpg')) ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces = SpectralCube.read('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') ax.imshow(aces[0].value, norm=simple_norm(aces[0].value, ), cmap=orange_transparent) #pl.savefig("gc_fullres_6_small_withACES.png", bbox_inches='tight') rgb = np.array(PIL.Image.open('gc_fullres_6.jpg')) ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mw = WCS(aces7m[0].header) ax.imshow(aces7m[0].data, transform=ax.get_transform(aces7mw), norm=simple_norm(aces7m[0].data, ), cmap=orange_transparent) #pl.savefig("gc_fullres_6_small_withACES.png", bbox_inches='tight') #[Out]# rgb = np.array(PIL.Image.open('gc_fullres_6.jpg')) ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mwcs = WCS(aces7m[0].header) #ax.imshow(aces7m[0].data, transform=ax.get_transform(aces7mwcs), # norm=simple_norm(aces7m[0].data, ), cmap=orange_transparent) #pl.savefig("gc_fullres_6_small_withACES.png", bbox_inches='tight') rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mwcs = WCS(aces7m[0].header) ax.imshow(aces7m[0].data, transform=ax.get_transform(aces7mwcs), norm=simple_norm(aces7m[0].data, ), cmap=orange_transparent) #pl.savefig("gc_fullres_6_small_withACES.png", bbox_inches='tight') #[Out]# rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mwcs = WCS(aces7m[0].header) aces7mrepr,_ = reproject.reproject_interp(aces7m, ww, output_footprint=rgb.shape[:2]) ax.imshow(aces7mrepr, norm=simple_norm(aces7mrepr, ), cmap=orange_transparent) #ax.imshow(aces7m[0].data, transform=ax.get_transform(aces7mwcs), # norm=simple_norm(aces7m[0].data, ), cmap=orange_transparent) #pl.savefig("gc_fullres_6_small_withACES.png", bbox_inches='tight') rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mwcs = WCS(aces7m[0].header) aces7mrepr,_ = reproject.reproject_interp(aces7m, ww, shape_out=rgb.shape[:2]) ax.imshow(aces7mrepr, norm=simple_norm(aces7mrepr, ), cmap=orange_transparent) #ax.imshow(aces7m[0].data, transform=ax.get_transform(aces7mwcs), # norm=simple_norm(aces7m[0].data, ), cmap=orange_transparent) #pl.savefig("gc_fullres_6_small_withACES.png", bbox_inches='tight') #[Out]# rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mwcs = WCS(aces7m[0].header) aces7mrepr,_ = reproject.reproject_interp(aces7m, ww, shape_out=rgb.shape[:2]) ax.imshow(aces7mrepr, norm=simple_norm(aces7mrepr, stretch='log'), cmap='gray') #ax.imshow(aces7m[0].data, transform=ax.get_transform(aces7mwcs), # norm=simple_norm(aces7m[0].data, ), cmap=orange_transparent) #pl.savefig("gc_fullres_6_small_withACES.png", bbox_inches='tight') #[Out]# rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mwcs = WCS(aces7m[0].header) aces7mrepr,_ = reproject.reproject_interp(aces7m, ww, shape_out=rgb.shape[:2]) ax.imshow(aces7mrepr, norm=simple_norm(aces7mrepr, stretch='log', max_percent=99.9, min_percent=2), cmap='gray') # doesn't work... #ax.imshow(aces7m[0].data, transform=ax.get_transform(aces7mwcs), # norm=simple_norm(aces7m[0].data, ), cmap=orange_transparent) #pl.savefig("gc_fullres_6_small_withACES.png", bbox_inches='tight') #[Out]# rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mwcs = WCS(aces7m[0].header) aces7mrepr,_ = reproject.reproject_interp(aces7m, ww, shape_out=rgb.shape[:2]) ax.imshow(aces7mrepr, norm=simple_norm(aces7mrepr, stretch='log', max_percent=99.96, min_percent=5), cmap='gray') # doesn't work... #ax.imshow(aces7m[0].data, transform=ax.get_transform(aces7mwcs), # norm=simple_norm(aces7m[0].data, ), cmap=orange_transparent) #pl.savefig("gc_fullres_6_small_withACES.png", bbox_inches='tight') #[Out]# rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mwcs = WCS(aces7m[0].header) aces7mrepr,_ = reproject.reproject_interp(aces7m, ww, shape_out=rgb.shape[:2]) ax.imshow(aces7mrepr, norm=simple_norm(aces7mrepr, stretch='log', max_percent=99.96, min_percent=5), cmap=orange_transparent) # doesn't work... #ax.imshow(aces7m[0].data, transform=ax.get_transform(aces7mwcs), # norm=simple_norm(aces7m[0].data, ), cmap=orange_transparent) #pl.savefig("gc_fullres_6_small_withACES.png", bbox_inches='tight') #[Out]# rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mwcs = WCS(aces7m[0].header) aces7mrepr,_ = reproject.reproject_interp(aces7m, ww, shape_out=rgb.shape[:2]) ax.imshow(aces7mrepr, norm=simple_norm(aces7mrepr, stretch='log', max_percent=99.96, min_cut=1e-5), cmap=orange_transparent) # doesn't work... #ax.imshow(aces7m[0].data, transform=ax.get_transform(aces7mwcs), # norm=simple_norm(aces7m[0].data, ), cmap=orange_transparent) #pl.savefig("gc_fullres_6_small_withACES.png", bbox_inches='tight') #[Out]# rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mwcs = WCS(aces7m[0].header) aces7mrepr,_ = reproject.reproject_interp(aces7m, ww, shape_out=rgb.shape[:2]) aces7mrepr[aces7mrepr < 0] = np.nan ax.imshow(aces7mrepr, norm=simple_norm(aces7mrepr, stretch='log', max_percent=99.96, min_percent=1), cmap=orange_transparent) # doesn't work... #ax.imshow(aces7m[0].data, transform=ax.get_transform(aces7mwcs), # norm=simple_norm(aces7m[0].data, ), cmap=orange_transparent) #pl.savefig("gc_fullres_6_small_withACES.png", bbox_inches='tight') #[Out]# from astropy.stats import mad_std mad_std(aces7m[0].data) #[Out]# nan from astropy.stats import mad_std mad_std(aces7m[0].data, ignore_nan=True) #[Out]# 0.0019418525725530408 rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces12m = fits.open('/orange/adamginsburg/ACES/mosaics/12m_continuum_mosaic.fits') aces12mwcs = WCS(aces12m[0].header) aces12mrepr,_ = reproject.reproject_interp(aces12m, ww, shape_out=rgb.shape[:2]) aces12mrepr[aces12mrepr < 0] = np.nan ax.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', max_percent=99.96, min_percent=1), cmap=orange_transparent) pl.savefig("gc_fullres_6_small_withACES12m.png", bbox_inches='tight') pl.imshow(aces12mrepr) #[Out]# pl.imshow(aces12m[0].data) #[Out]# pl.imshow(aces12m[0].data, norm=simple_norm(aces12m[0].data, stretch='log', min_percent=1, max_percent=99)) pl.colorbar() #[Out]# pl.imshow(aces12m[0].data, norm=simple_norm(aces12m[0].data, stretch='log', min_percent=6, max_percent=99)) pl.colorbar() #[Out]# pl.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', min_percent=6, max_percent=99)) pl.colorbar() #[Out]# pl.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# aces12mrepr[np.isfinite(aces12mrepr)] #[Out]# array([1.05734143e-04, 2.30151964e-05, 3.94036547e-05, ..., #[Out]# 3.01893825e-04, 2.15735682e-04, 2.65184245e-04]) pl.hist(aces12mrepr[np.isfinite(aces12mrepr)]) #[Out]# (array([1.406409e+06, 2.200000e+01, 5.000000e+00, 0.000000e+00, #[Out]# 1.000000e+00, 1.000000e+00, 0.000000e+00, 0.000000e+00, #[Out]# 1.000000e+00, 1.000000e+00]), #[Out]# array([1.25515907e-10, 2.87554229e-01, 5.75108457e-01, 8.62662686e-01, #[Out]# 1.15021691e+00, 1.43777114e+00, 1.72532537e+00, 2.01287960e+00, #[Out]# 2.30043383e+00, 2.58798806e+00, 2.87554229e+00]), #[Out]# ) pl.imshow(aces12mrepr)#, norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces12m = fits.open('/orange/adamginsburg/ACES/mosaics/12m_continuum_mosaic.fits') aces12mwcs = WCS(aces12m[0].header) aces12mrepr,_ = reproject.reproject_interp(aces12m, ww, shape_out=rgb.shape[:2]) #aces12mrepr[aces12mrepr < 0] = np.nan ax.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', max_percent=99.96, min_percent=1), cmap=orange_transparent) pl.savefig("gc_fullres_6_small_withACES12m.png", bbox_inches='tight') pl.imshow(aces12m[0].data, norm=simple_norm(aces12m[0].data, stretch='log', min_percent=6, max_percent=99)) pl.colorbar() #[Out]# pl.imshow(aces12mrepr)#, norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# pl.imshow(aces12mrepr < 0)#, norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# aces12mrepr < 0 #[Out]# array([[False, False, False, ..., False, False, False], #[Out]# [False, False, False, ..., False, False, False], #[Out]# [False, False, False, ..., False, False, False], #[Out]# ..., #[Out]# [False, False, False, ..., False, False, False], #[Out]# [False, False, False, ..., False, False, False], #[Out]# [False, False, False, ..., False, False, False]]) pl.imshow(aces12mrepr < 0)#, norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# pl.clf() pl.imshow(aces12mrepr < 0)#, norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# pl.figure(figsize=(20,10)) pl.imshow(aces12mrepr < 0)#, norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# pl.figure(figsize=(20,10)) pl.imshow(aces12mrepr[1000:2500,2000:6000] < 0)#, norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# pl.figure(figsize=(20,10)) pl.imshow(aces12mrepr[1000:2500,4000:6000] < 0)#, norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# pl.figure(figsize=(20,10)) pl.imshow(aces12mrepr[1500:1750,5000:1250] < 0)#, norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# pl.figure(figsize=(20,10)) pl.imshow(aces12mrepr[1500:1750,5000:6250] < 0)#, norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# pl.figure(figsize=(20,10)) pl.imshow(aces12mrepr[1500:1750,5000:5250] < 0)#, norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# pl.figure(figsize=(20,10)) pl.imshow(aces12mrepr < 0)#, norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# pl.figure(figsize=(20,10)) pl.imshow(aces12mrepr > 0)#, norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# from astropy.stats import mad_std mad_std(aces7m[0].data, ignore_nan=True) mad_std(aces12m[0].data, ignore_nan=True) #[Out]# 0.00015146579420586426 pl.figure(figsize=(20,10)) pl.imshow(aces12mrepr > 1e-4)#, norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# pl.figure(figsize=(20,10)) pl.imshow(aces12mrepr > 2e-4)#, norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# pl.figure(figsize=(20,10)) pl.imshow(aces12mrepr > 3e-4)#, norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces12m = fits.open('/orange/adamginsburg/ACES/mosaics/12m_continuum_mosaic.fits') aces12mwcs = WCS(aces12m[0].header) aces12mrepr,_ = reproject.reproject_interp(aces12m, ww, shape_out=rgb.shape[:2]) aces12mrepr[aces12mrepr < 2e-4] = np.nan ax.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', max_percent=99.96, min_percent=0.01), cmap=orange_transparent) pl.savefig("gc_fullres_6_small_withACES12m.png", bbox_inches='tight') pl.imshow(aces12m[0].data, norm=simple_norm(aces12m[0].data, stretch='log', min_percent=None, max_percent=None, min_cut=-0.0005, max_cut=0.005,)) pl.colorbar() #[Out]# aces12m[0].data[aces12m[0].data < -0.0005] = 0 pl.imshow(aces12m[0].data, norm=simple_norm(aces12m[0].data, stretch='log', min_percent=None, max_percent=None, min_cut=-0.0005, max_cut=0.005,), origin='lower') pl.colorbar() #[Out]# rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces12m = fits.open('/orange/adamginsburg/ACES/mosaics/12m_continuum_mosaic.fits') aces12m[0].data[aces12m[0].data < -0.0005] = 0 aces12mwcs = WCS(aces12m[0].header) aces12mrepr,_ = reproject.reproject_interp(aces12m, ww, shape_out=rgb.shape[:2]) aces12mrepr[aces12mrepr < 2e-4] = np.nan ax.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', max_percent=99.96, min_percent=0.01), cmap=orange_transparent) pl.savefig("gc_fullres_6_small_withACES12m.png", bbox_inches='tight') rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,2, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mwcs = WCS(aces7m[0].header) aces7mrepr,_ = reproject.reproject_interp(aces7m, ww, shape_out=rgb.shape[:2]) aces7mrepr[aces7mrepr < 0] = np.nan ax.imshow(aces7mrepr, norm=simple_norm(aces7mrepr, stretch='log', max_percent=99.96, min_percent=1), cmap=orange_transparent) aces12m = fits.open('/orange/adamginsburg/ACES/mosaics/12m_continuum_mosaic.fits') aces12mwcs = WCS(aces12m[0].header) aces12m[0].data[aces12m[0].data < -0.0005] = 0 axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0.01, 0.5, 1, 0.5], bbox_transform=fig.transFigure, loc='upper center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=aces12mwcs)) axins.imshow(aces12m[0].data, norm=simple_norm(aces12m[0].data, stretch='log', min_percent=None, max_percent=None, min_cut=-0.0005, max_cut=0.005,), ) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=aces12m[0].data, loc2=2, edgecolor='w') pl.savefig("gc_fullres_6_small_withACES7m_12minset.png", bbox_inches='tight') rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces12m = fits.open('/orange/adamginsburg/ACES/mosaics/12m_continuum_mosaic.fits') aces12m[0].data[aces12m[0].data < -0.0005] = 0 aces12mwcs = WCS(aces12m[0].header) aces12mrepr,_ = reproject.reproject_interp(aces12m, ww, shape_out=rgb.shape[:2]) aces12mrepr[aces12mrepr < 2e-4] = np.nan ax.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', max_percent=99.5, min_percent=0.01), cmap=orange_transparent) pl.savefig("gc_fullres_6_small_withACES12m.png", bbox_inches='tight') rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,2, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mwcs = WCS(aces7m[0].header) aces7mrepr,_ = reproject.reproject_interp(aces7m, ww, shape_out=rgb.shape[:2]) aces7mrepr[aces7mrepr < 0] = np.nan ax.imshow(aces7mrepr, norm=simple_norm(aces7mrepr, stretch='log', max_percent=99.96, min_percent=1), cmap=orange_transparent) aces12m = fits.open('/orange/adamginsburg/ACES/mosaics/12m_continuum_mosaic.fits') aces12mwcs = WCS(aces12m[0].header) aces12m[0].data[aces12m[0].data < -0.0005] = 0 axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0.01, 0.5, 1, 0.5], bbox_transform=fig.transFigure, loc='upper center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=aces12mwcs)) axins.imshow(aces12m[0].data, norm=simple_norm(aces12m[0].data, stretch='log', min_percent=None, max_percent=None, min_cut=-0.0005, max_cut=0.005,), cmap='gray', ) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=aces12m[0].data, loc1=4, edgecolor='w') pl.savefig("gc_fullres_6_small_withACES7m_12minset.png", bbox_inches='tight') pl.imshow(aces12mrepr)#, norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# pl.imshow(aces12mrepr, origin='lower', norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces12m = fits.open('/orange/adamginsburg/ACES/mosaics/12m_continuum_mosaic.fits') aces12m[0].data[aces12m[0].data < -0.0005] = 0 aces12mwcs = WCS(aces12m[0].header) aces12mrepr,_ = reproject.reproject_interp(aces12m, ww, shape_out=rgb.shape[:2]) aces12mrepr[aces12mrepr < -5e-4] = 0 ax.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', max_percent=99.5, min_percent=0.01), cmap=orange_transparent) pl.savefig("gc_fullres_6_small_withACES12m.png", bbox_inches='tight') pl.imshow(aces12mrepr, origin='lower', norm=simple_norm(aces12mrepr, stretch='log',))# min_percent=6, max_percent=99)) pl.colorbar() #[Out]# rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,2, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mwcs = WCS(aces7m[0].header) aces7mrepr,_ = reproject.reproject_interp(aces7m, ww, shape_out=rgb.shape[:2]) aces7mrepr[aces7mrepr < 0] = np.nan ax.imshow(aces7mrepr, norm=simple_norm(aces7mrepr, stretch='log', max_percent=99.96, min_percent=1), cmap=orange_transparent) aces12m = fits.open('/orange/adamginsburg/ACES/mosaics/12m_continuum_mosaic.fits') aces12mwcs = WCS(aces12m[0].header) aces12m[0].data[aces12m[0].data < -0.0005] = 0 axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0.01, 0.5, 1, 0.5], bbox_transform=fig.transFigure, loc='upper center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=aces12mwcs)) axins.imshow(aces12m[0].data, norm=simple_norm(aces12m[0].data, stretch='log', min_percent=None, max_percent=None, min_cut=-0.0005, max_cut=0.05,), cmap='gray', ) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=aces12m[0].data, loc1=4, edgecolor='w') pl.savefig("gc_fullres_6_small_withACES7m_12minset.png", bbox_inches='tight') pl.imshow(aces12mrepr, origin='lower', norm=simple_norm(aces12mrepr, stretch='log', min_percent=6, max_percent=99)) pl.colorbar() #[Out]# rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces12m = fits.open('/orange/adamginsburg/ACES/mosaics/12m_continuum_mosaic.fits') aces12m[0].data[aces12m[0].data < -0.0005] = 0 aces12mwcs = WCS(aces12m[0].header) aces12mrepr,_ = reproject.reproject_interp(aces12m, ww, shape_out=rgb.shape[:2]) aces12mrepr[aces12mrepr < -5e-4] = 0 ax.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', max_percent=99.5, min_cut=1e-4), cmap=orange_transparent) pl.savefig("gc_fullres_6_small_withACES12m.png", bbox_inches='tight') rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(2,1,2, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mwcs = WCS(aces7m[0].header) aces7mrepr,_ = reproject.reproject_interp(aces7m, ww, shape_out=rgb.shape[:2]) aces7mrepr[aces7mrepr < 0] = np.nan ax.imshow(aces7mrepr, norm=simple_norm(aces7mrepr, stretch='log', max_percent=99.96, min_percent=1), cmap=orange_transparent) aces12m = fits.open('/orange/adamginsburg/ACES/mosaics/12m_continuum_mosaic.fits') aces12mwcs = WCS(aces12m[0].header) aces12m[0].data[aces12m[0].data < -0.0005] = 0 axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0.01, 0.5, 1, 0.5], bbox_transform=fig.transFigure, loc='upper center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=aces12mwcs)) axins.imshow(aces12m[0].data, norm=simple_norm(aces12m[0].data, stretch='log', min_percent=None, max_percent=None, min_cut=-0.0005, max_cut=0.05,), cmap='gray', ) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=aces12m[0].data, loc1=4, edgecolor='y') pl.savefig("gc_fullres_6_small_withACES7m_12minset.png", bbox_inches='tight') pl.imshow(aces12mrepr, origin='lower', norm=simple_norm(aces12mrepr, stretch='log', min_cut=1e-4, max_percent=99.5)) pl.colorbar() #[Out]# orange_transparent.set_underer orange_transparent.set_under #[Out]# > orange_transparent.get orange_transparent.get_under() #[Out]# array([1. , 0.96078431, 0.92156863, 0. ]) orange_transparent.get_under() orange_transparent(0) #[Out]# (1.0, 0.9607843137254902, 0.9215686274509803, 0.0) orange_transparent.get_under(), orange_transparent(0) #[Out]# (array([1. , 0.96078431, 0.92156863, 0. ]), #[Out]# (1.0, 0.9607843137254902, 0.9215686274509803, 0.0)) orange_transparent.get_under(), orange_transparent(0), orange_transparent(1) #[Out]# (array([1. , 0.96078431, 0.92156863, 0. ]), #[Out]# (1.0, 0.9607843137254902, 0.9215686274509803, 0.0), #[Out]# (0.9998769703960015, #[Out]# 0.9589388696655133, #[Out]# 0.9180007689350249, #[Out]# 0.00392156862745098)) orange_transparent.get_under(), orange_transparent(0), orange_transparent(256) #[Out]# (array([1. , 0.96078431, 0.92156863, 0. ]), #[Out]# (1.0, 0.9607843137254902, 0.9215686274509803, 0.0), #[Out]# (0.4980392156862745, 0.15294117647058825, 0.01568627450980392, 1.0)) orange_transparent.get_under(), orange_transparent(0), orange_transparent(256), orange_transparent.get_bad() #[Out]# (array([1. , 0.96078431, 0.92156863, 0. ]), #[Out]# (1.0, 0.9607843137254902, 0.9215686274509803, 0.0), #[Out]# (0.4980392156862745, 0.15294117647058825, 0.01568627450980392, 1.0), #[Out]# array([0., 0., 0., 0.])) pl.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', max_percent=99.5, min_cut=1e-4), cmap=orange_transparent) #[Out]# from matplotlib.colors import ListedColormap cmap = pl.cm.gray gray_transparent = cmap(np.arange(cmap.N)) gray_transparent[:,-1] = np.linspace(0, 1, cmap.N) gray_transparent = ListedColormap(gray_transparent) cmap = pl.cm.Oranges orange_transparent = cmap(np.arange(cmap.N)) orange_transparent[:,-1] = np.linspace(0, 1, cmap.N) orange_transparent = ListedColormap(orange_transparent) cmap = pl.cm.Oranges_r orange_transparent_r = cmap(np.arange(cmap.N)) orange_transparent_r[:,-1] = np.linspace(0, 1, cmap.N) orange_transparent_r = ListedColormap(orange_transparent_r) pl.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', max_percent=99.5, min_cut=1e-4), cmap=orange_transparent_r) #[Out]# pl.imshow(aces12mrepr, cmap=orange_transparent_r) #[Out]# pl.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', max_percent=99.5, min_cut=-1e-4), cmap=orange_transparent_r) #[Out]# pl.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', max_percent=99.5, min_cut=1e-3), cmap=orange_transparent_r) #[Out]# pl.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', max_percent=99.5, min_cut=1e-5), cmap=orange_transparent_r) #[Out]# pl.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', max_percent=99.5, min_cut=1e-1), cmap=orange_transparent_r) #[Out]# pl.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', max_percent=99.5, min_cut=1e-1), cmap=orange_transparent_r) axes12mrepr[1500,3000] pl.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', max_percent=99.5, min_cut=1e-1), cmap=orange_transparent_r) aces12mrepr[1500,3000] #[Out]# 0.0001737762841247129 rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces12m = fits.open('/orange/adamginsburg/ACES/mosaics/12m_continuum_mosaic.fits') aces12m[0].data[aces12m[0].data < -0.0005] = 0 aces12mwcs = WCS(aces12m[0].header) aces12mrepr,_ = reproject.reproject_interp(aces12m, ww, shape_out=rgb.shape[:2]) aces12mrepr[aces12mrepr < -5e-4] = 0 aces12mrepr[aces12mrepr < 1e-4] = np.nan ax.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', max_percent=99.5, min_cut=1e-4), cmap=orange_transparent) pl.savefig("gc_fullres_6_small_withACES12m.png", bbox_inches='tight') rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mwcs = WCS(aces7m[0].header) aces7mrepr,_ = reproject.reproject_interp(aces7m, ww, shape_out=rgb.shape[:2]) aces7mrepr[aces7mrepr < 0] = np.nan ax.imshow(aces7mrepr, norm=simple_norm(aces7mrepr, stretch='log', max_percent=99.96, min_percent=1), cmap=orange_transparent) pl.savefig("gc_fullres_6_small_withACES7m.png", bbox_inches='tight') pl.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', max_percent=99.5, min_cut=1e-4), cmap=orange_transparent_r) #[Out]# pl.imshow(aces12mrepr, norm=simple_norm(aces12mrepr, stretch='log', max_percent=99.5, min_cut=1e-1), cmap=orange_transparent_r) aces12mrepr[1500,3000] #[Out]# 0.0001737762841247129 pl.imshow(aces12mrepr, origin='lower', norm=simple_norm(aces12mrepr, stretch='log', min_cut=1e-4, max_percent=99.5)) pl.colorbar() #[Out]# rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200, frameon=False) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mwcs = WCS(aces7m[0].header) aces7mrepr,_ = reproject.reproject_interp(aces7m, ww, shape_out=rgb.shape[:2]) aces7mrepr[aces7mrepr < 0] = np.nan ax.imshow(aces7mrepr, norm=simple_norm(aces7mrepr, stretch='log', max_percent=99.96, min_percent=1), cmap=orange_transparent) pl.savefig("gc_fullres_6_small_withACES7m.png", bbox_inches='tight') rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200, frameon=False) ax = plt.subplot(2,1,2, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mwcs = WCS(aces7m[0].header) aces7mrepr,_ = reproject.reproject_interp(aces7m, ww, shape_out=rgb.shape[:2]) aces7mrepr[aces7mrepr < 0] = np.nan ax.imshow(aces7mrepr, norm=simple_norm(aces7mrepr, stretch='log', max_percent=99.96, min_percent=1), cmap=orange_transparent) aces12m = fits.open('/orange/adamginsburg/ACES/mosaics/12m_continuum_mosaic.fits') aces12mwcs = WCS(aces12m[0].header) aces12m[0].data[aces12m[0].data < -0.0005] = 0 axins = inset_axes(ax, width=4, height=4, bbox_to_anchor=[0.01, 0.5, 1, 0.5], bbox_transform=fig.transFigure, loc='upper center', axes_class=astropy.visualization.wcsaxes.core.WCSAxes, axes_kwargs=dict(wcs=aces12mwcs)) axins.imshow(aces12m[0].data, norm=simple_norm(aces12m[0].data, stretch='log', min_percent=None, max_percent=None, min_cut=-0.0005, max_cut=0.05,), cmap='gray', ) axins.coords['glat'].set_ticklabel(visible=False) axins.coords['glon'].set_ticklabel(visible=False) axins.coords['glat'].set_ticks_visible(False) axins.coords['glon'].set_ticks_visible(False) mark_inset_generic(axins, ax, data=aces12m[0].data, loc1=4, edgecolor='y') pl.savefig("gc_fullres_6_small_withACES7m_12minset.png", bbox_inches='tight') rgb = np.array(PIL.Image.open('gc_fullres_6.jpg'))[::-1,:,:] ww = WCS(fits.Header.fromtextfile('gc_fullres_6.wcs')) fig = plt.figure(figsize=(10,5), dpi=200, frameon=False) ax = plt.subplot(1,1,1, projection=ww) ax.imshow(rgb) ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) ax.coords['glat'].set_ticks_visible(False) ax.coords['glon'].set_ticks_visible(False) aces7m = fits.open('/orange/adamginsburg/ACES/mosaics/7m_continuum_mosaic.fits') aces7mwcs = WCS(aces7m[0].header) aces7mrepr,_ = reproject.reproject_interp(aces7m, ww, shape_out=rgb.shape[:2]) aces7mrepr[aces7mrepr < 0] = np.nan ax.imshow(aces7mrepr, norm=simple_norm(aces7mrepr, stretch='log', max_percent=99.96, min_percent=1), cmap=orange_transparent) pl.savefig("gc_fullres_6_small_withACES7m.png", bbox_inches='tight', pad_inches=0) glimpsei4 = fits.open('/orange/adamginsburg/cmz/glimpse_data/GLM_00000+0000_mosaic_I4.fits') img_meerkat_ACES = reproject.reproject_interp(meerkat, aces12m[0].header) img_glimpse_ACES = reproject.reproject_interp(glimpsei4, aces12m[0].header) img_meerkat_ACES = reproject.reproject_interp(meerkat, aces12mwcs.celestial, shape_out=aces12m[0].data.shape) img_glimpse_ACES = reproject.reproject_interp(glimpsei4, aces12mwcs.celestial, shape_out=aces12m[0].data.shape) img_meerkat_ACES = reproject.reproject_interp((meerkat[0].data.squeeze(), WCS(meerkat[0].header).celestial), aces12mwcs.celestial, shape_out=aces12m[0].data.shape) img_glimpse_ACES = reproject.reproject_interp(glimpsei4, aces12mwcs.celestial, shape_out=aces12m[0].data.shape) rgb = np.array([simple_norm(img_meerkat_ACES, min_percent=25, max_percent=99.99, log_a=4e1, stretch='linear')(np.nan_to_num(img_meerkat_ACES)), simple_norm(aces12m[0].data, min_percent=5, max_percent=99.99, log_a=4e1, stretch='asinh')(np.nan_to_num(aces12m[0].data)), simple_norm(img_glimpse_ACES, min_percent=1, max_percent=99.99, log_a=4e1, stretch='log')(np.nan_to_num(img_glimpse_ACES))]).T.swapaxes(0,1) pl.imshow(rgb) rgb.shape #[Out]# (3295, 6473, 3) img_meerkat_ACES.shape, img_glimpse_ACES.shape, aces12m[0].data.shape img_meerkat_ACES,_ = reproject.reproject_interp((meerkat[0].data.squeeze(), WCS(meerkat[0].header).celestial), aces12mwcs.celestial, shape_out=aces12m[0].data.shape) img_glimpse_ACES,_ = reproject.reproject_interp(glimpsei4, aces12mwcs.celestial, shape_out=aces12m[0].data.shape)