######################################################## # Started Logging At: 2023-02-18 10:10:25 ######################################################## ######################################################## # # Started Logging At: 2023-02-18 10:10:25 ######################################################## ######################################################## # Started Logging At: 2023-02-18 19:51:56 ######################################################## ######################################################## # # Started Logging At: 2023-02-18 19:51:56 ######################################################## print("test") get_ipython().run_line_magic('matplotlib', 'inline') import pylab as pl pl.rcParams['figure.facecolor']='w' 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 import healpy target_header = fits.Header.fromstring(""" NAXIS = 2 NAXIS1 = 480 NAXIS2 = 240 CTYPE1 = 'GLON-MOL' CRPIX1 = 240.5 CRVAL1 = 0.0 CDELT1 = -0.675 CUNIT1 = 'deg ' CTYPE2 = 'GLAT-MOL' CRPIX2 = 120.5 CRVAL2 = 0.0 CDELT2 = 0.675 CUNIT2 = 'deg ' COORDSYS= 'icrs ' """, sep='\n') fn_lfi_30 = 'LFI_SkyMap_030_1024_R2.01_full.fits' if not os.path.exists(fn_lfi_30): lfi_30 = download_file(f"https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/{fn_lfi_30}") shutil.move(lfi_30, fn_lfi_30) lfi_30 = fits.open(fn_lfi_30) m30 = healpy.read_map(lfi_30) healpy.mollview(m30, norm=simple_norm(m30, stretch='log'), cbar=False, title='') array, footprint = reproject_from_healpix(fn_lfi_30, target_header) from astropy.wcs import WCS import matplotlib.pyplot as plt from astropy.visualization.wcsaxes.frame import EllipticalFrame plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) ax.imshow(array, norm=simple_norm(array, 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) fn = "HFI_SkyMap_143_2048_R2.02_full.fits" if not os.path.exists(fn): hfi_143 = download_file(f"https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/{fn}") shutil.move(hfi_143, fn) hfi_143 = fits.open(fn) m143 = healpy.read_map(lfi_143) healpy.mollview(m143, norm=simple_norm(m143, stretch='log'), cbar=False, title='') m143 = healpy.read_map(hfi_143) healpy.mollview(m143, norm=simple_norm(m143, stretch='log'), cbar=False, title='') fn = "HFI_SkyMap_857_2048_R2.02_full.fits" if not os.path.exists(fn): hfi_857 = download_file(f"https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/{fn}") shutil.move(hfi_857, fn) hfi_857 = fits.open(fn) m857 = healpy.read_map(hfi_857) healpy.mollview(m857, norm=simple_norm(m857, stretch='log'), cbar=False, title='') fn = "COM_CompMap_CO21-commander_2048_R2.00.fits" if not os.path.exists(fn): planck_CO21 = download_file(f"https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/{fn}") shutil.move(planck_CO21, fn) planck_CO21 = fits.open(fn) mCO21 = healpy.read_map(planck_CO21) healpy.mollview(mCO21, norm=simple_norm(mCO21, stretch='log', min_cut=0, max_cut=1), min=np.percentile(mCO21, 1), bgcolor=pl.cm.viridis(0), cbar=False, title='') fn = "COM_CompMap_ThermalDust-commander_2048_R2.00.fits" if not os.path.exists(fn): planck_ThermalDust = download_file(f"https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/{fn}") shutil.move(planck_ThermalDust, fn) planck_ThermalDust = fits.open(fn) mThermalDust = healpy.read_map(planck_ThermalDust) healpy.mollview(mThermalDust, norm=simple_norm(mThermalDust, stretch='log'), cbar=False, title='') fn = 'LAB_fullvel.fits' if not os.path.exists(fn): LAB_HI = download_file(f"https://lambda.gsfc.nasa.gov/data/foregrounds/HI/{fn}") shutil.move(LAB_HI, fn) LAB_HI = fits.open(fn) mLAB_HI = healpy.read_map(LAB_HI) healpy.mollview(mLAB_HI, norm=simple_norm(mLAB_HI, stretch='asinh'), cbar=False, title='') fn = 'NHI_HPX.fits' if not os.path.exists(fn): HI4PI = download_file(f"https://lambda.gsfc.nasa.gov/data/foregrounds/HI4PI/{fn}") shutil.move(HI4PI, fn) HI4PI = fits.open(fn) HI4PI[1].data.s 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 import healpy img_hfi143, footprint = reproject_from_healpix(fn_hfi_143, 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) 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) fn_hfi143 = "HFI_SkyMap_143_2048_R2.02_full.fits" if not os.path.exists(fn_hfi143): hfi_143 = download_file(f"https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/{fn}") shutil.move(hfi_143, fn) hfi_143 = fits.open(fn) 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) 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}") shutil.move(hfi857, fn) hfi857 = fits.open(fn) 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) m857 = healpy.read_map(hfi857) healpy.mollview(m857, norm=simple_norm(m857, stretch='log'), cbar=False, title='') 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}") 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) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) ax.imshow([img_lfi30, img_hfi143, img_hfi857]) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) img_lfi30, footprint = reproject_from_healpix(fn_lfi_30, 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_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) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) ax.imshow([img_lfi30, img_hfi143, img_hfi857]) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) rgb = np.array([img_lfi30, img_hfi143, img_hfi857]) rgb.shape #[Out]# (3, 240, 480) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([img_lfi30, img_hfi143, img_hfi857]).T.swapaxes(0,1) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([simple_norm(img_lfi30, min_percent=1, max_percent=99.99, stretch='log')(img_lfi30), simple_norm(img_hfi143, min_percent=1, max_percent=99.99, stretch='log')(img_hfi143), simple_norm(img_hfi857, min_percent=1, max_percent=99.99, stretch='log')(img_hfi857)]).T.swapaxes(0,1) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([simple_norm(img_lfi30, min_percent=1, max_percent=99.99, stretch='asinh')(img_lfi30), simple_norm(img_hfi143, min_percent=1, max_percent=99.99, stretch='asinh')(img_hfi143), simple_norm(img_hfi857, min_percent=1, max_percent=99.99, stretch='asinh')(img_hfi857)]).T.swapaxes(0,1) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) 0.3375*2 #[Out]# 0.675 target_header = 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 = 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') fn_lfi_30 = 'LFI_SkyMap_030_1024_R2.01_full.fits' if not os.path.exists(fn_lfi_30): lfi_30 = download_file(f"https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/{fn_lfi_30}") shutil.move(lfi_30, fn_lfi_30) lfi_30 = fits.open(fn_lfi_30) m30 = healpy.read_map(lfi_30) healpy.mollview(m30, norm=simple_norm(m30, stretch='log'), cbar=False, title='') img_lfi30, footprint = reproject_from_healpix(fn_lfi_30, 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_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) 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}") 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) 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}") 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) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([simple_norm(img_lfi30, min_percent=1, max_percent=99.99, stretch='asinh')(img_lfi30), simple_norm(img_hfi143, min_percent=1, max_percent=99.99, stretch='asinh')(img_hfi143), simple_norm(img_hfi857, min_percent=1, max_percent=99.99, stretch='asinh')(img_hfi857)]).T.swapaxes(0,1) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) fn = "COM_CompMap_CO21-commander_2048_R2.00.fits" if not os.path.exists(fn): planck_CO21 = download_file(f"https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/{fn}") shutil.move(planck_CO21, fn) planck_CO21 = fits.open(fn) mCO21 = healpy.read_map(planck_CO21) healpy.mollview(mCO21, norm=simple_norm(mCO21, stretch='log', min_cut=0, max_cut=1), min=np.percentile(mCO21, 1), bgcolor=pl.cm.viridis(0), cbar=False, title='') fn = "COM_CompMap_ThermalDust-commander_2048_R2.00.fits" if not os.path.exists(fn): planck_ThermalDust = download_file(f"https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/{fn}") shutil.move(planck_ThermalDust, fn) planck_ThermalDust = fits.open(fn) mThermalDust = healpy.read_map(planck_ThermalDust) healpy.mollview(mThermalDust, norm=simple_norm(mThermalDust, stretch='log'), cbar=False, title='') fn = 'LAB_fullvel.fits' if not os.path.exists(fn): LAB_HI = download_file(f"https://lambda.gsfc.nasa.gov/data/foregrounds/HI/{fn}") shutil.move(LAB_HI, fn) LAB_HI = fits.open(fn) mLAB_HI = healpy.read_map(LAB_HI) healpy.mollview(mLAB_HI, norm=simple_norm(mLAB_HI, stretch='asinh'), cbar=False, title='') fn = 'NHI_HPX.fits' if not os.path.exists(fn): HI4PI = download_file(f"https://lambda.gsfc.nasa.gov/data/foregrounds/HI4PI/{fn}") shutil.move(HI4PI, fn) HI4PI = fits.open(fn) 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}") shutil.move(planck_CO21, fn_CO21) planck_CO21 = fits.open(fn_CO21) img_CO21, footprint = reproject_from_healpix(fn_CO21, 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_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) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([simple_norm(img_lfi30, min_percent=1, max_percent=99.9, stretch='asinh')(img_lfi30), simple_norm(img_hfi143, min_percent=1, max_percent=99.99, stretch='asinh')(img_hfi143), simple_norm(img_hfi857, min_percent=1, max_percent=99.99, stretch='asinh')(img_hfi857)]).T.swapaxes(0,1) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([simple_norm(img_lfi30, min_percent=1, max_percent=99.9, stretch='asinh')(img_lfi30), simple_norm(img_hfi143, min_percent=1, max_percent=99.99, stretch='asinh')(img_hfi143), simple_norm(img_hfi857, min_percent=1, max_percent=99.9, stretch='asinh')(img_hfi857)]).T.swapaxes(0,1) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([simple_norm(img_lfi30, min_percent=1, max_percent=99.9, stretch='log')(img_lfi30), simple_norm(img_hfi143, min_percent=1, max_percent=99.99, stretch='log')(img_hfi143), simple_norm(img_hfi857, min_percent=1, max_percent=99.9, stretch='log')(img_hfi857)]).T.swapaxes(0,1) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([simple_norm(img_lfi30, min_percent=0.01, max_percent=99.9, stretch='log')(img_lfi30), simple_norm(img_hfi143, min_percent=0.01, max_percent=99.99, stretch='log')(img_hfi143), simple_norm(img_hfi857, min_percent=0.01, max_percent=99.9, stretch='log')(img_hfi857)]).T.swapaxes(0,1) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) get_ipython().run_line_magic('pinfo', 'simple_norm') plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([simple_norm(img_lfi30, min_percent=0.01, max_percent=99.9, log_a=1e4, stretch='log')(img_lfi30), simple_norm(img_hfi143, min_percent=0.01, max_percent=99.99, log_a=1e4, stretch='log')(img_hfi143), simple_norm(img_hfi857, min_percent=0.01, max_percent=99.9, log_a=1e4, stretch='log')(img_hfi857)]).T.swapaxes(0,1) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([simple_norm(img_lfi30, min_percent=0.01, max_percent=99.9, log_a=1e2, stretch='log')(img_lfi30), simple_norm(img_hfi143, min_percent=0.01, max_percent=99.99, log_a=1e2, stretch='log')(img_hfi143), simple_norm(img_hfi857, min_percent=0.01, max_percent=99.9, log_a=1e2, stretch='log')(img_hfi857)]).T.swapaxes(0,1) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([simple_norm(img_lfi30, min_percent=0.01, max_percent=99.90, log_a=1e1, stretch='log')(img_lfi30), simple_norm(img_hfi143, min_percent=0.01, max_percent=99.99, log_a=1e1, stretch='log')(img_hfi143), simple_norm(img_hfi857, min_percent=0.01, max_percent=99.90, log_a=1e1, stretch='log')(img_hfi857)]).T.swapaxes(0,1) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) 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=1e1, 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) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) 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=3e1, 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) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) 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) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(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}") shutil.move(planck_ThermalDust, fn_ThermalDust) planck_ThermalDust = fits.open(fn_ThermalDust) img_CO21, footprint = reproject_from_healpix(fn_CO21, 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_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) 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) 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) 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]) from astropy import units as u if os.path.exists("GC.hi.tb.allgal.fits.gz"): hi_cube_fh = fits.open("GC.hi.tb.allgal.fits.gz") else: 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_fh, use_dask=True) 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 import healpy target_header = 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') 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) 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) 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) 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) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) 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) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(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) mCO21 = healpy.read_map(planck_CO21) healpy.mollview(mCO21, norm=simple_norm(mCO21, stretch='log', min_cut=0, max_cut=1), min=np.percentile(mCO21, 1), bgcolor=pl.cm.viridis(0), cbar=False, title='') img_CO21, footprint = reproject_from_healpix(fn_CO21, 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_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) 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) 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) 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) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.90, log_a=5e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e1, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) hsv = rgb_to_hsv(rgb_scaled) 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), 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) img_HI4PI, footprint = reproject_from_healpix(fn_HI4PI, field=4, 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_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) img_HI4PI, footprint = reproject_from_healpix(fn_HI4PI, target_header, field=4, ) 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) img_HI4PI, footprint = reproject_from_healpix(fn_HI4PI, target_header, field=4) 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) img_HI4PI, footprint = reproject_from_healpix(fn_HI4PI, target_header, field=3) 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) 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) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.90, log_a=5e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.99, log_a=2e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e1, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) hsv = rgb_to_hsv(rgb_scaled) 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), 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) 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), 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) 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), 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) 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), 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) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.90, log_a=5e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.99, log_a=5e1, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=5e1, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.90, log_a=5e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.99, log_a=1e2, stretch='log')(img_CO21), simple_norm(img_ThermalDust, min_percent=0.01, max_percent=99.90, log_a=1e3, stretch='log')(img_ThermalDust)]).T.swapaxes(0,1) ax.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.90, log_a=5e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.99, log_a=1e2, 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.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.90, log_a=5e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.95, log_a=1e2, 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.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.90, log_a=5e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.95, log_a=5e1, 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.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) rgb = np.array([simple_norm(img_HI4PI, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_HI4PI), simple_norm(img_CO21, min_percent=0.01, max_percent=99.95, log_a=5e1, 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.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) 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=5e1, 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.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) 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.imshow(rgb) #ax.coords.grid(color='white') ax.coords['glat'].set_ticklabel(visible=False) ax.coords['glon'].set_ticklabel(visible=False) 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), 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) 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), 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) 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), 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) 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), 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) 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), 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) pl.hist(img_CO21) #[Out]# (array([[ 0., 26., 0., ..., 0., 0., 0.], #[Out]# [ 0., 40., 0., ..., 0., 0., 0.], #[Out]# [ 0., 50., 0., ..., 0., 0., 0.], #[Out]# ..., #[Out]# [ 0., 50., 0., ..., 0., 0., 0.], #[Out]# [ 0., 40., 0., ..., 0., 0., 0.], #[Out]# [ 0., 26., 0., ..., 0., 0., 0.]]), #[Out]# array([-127.75212994, -32.64492164, 62.46228667, 157.56949498, #[Out]# 252.67670328, 347.78391159, 442.8911199 , 537.9983282 , #[Out]# 633.10553651, 728.21274481, 823.31995312]), #[Out]# ) pl.hist(img_CO21.ravel()) #[Out]# (array([4.00000e+00, 3.61716e+05, 4.00000e+02, 1.70000e+01, 5.00000e+00, #[Out]# 3.00000e+00, 3.00000e+00, 4.00000e+00, 1.00000e+00, 3.00000e+00]), #[Out]# array([-127.75212994, -32.64492164, 62.46228667, 157.56949498, #[Out]# 252.67670328, 347.78391159, 442.8911199 , 537.9983282 , #[Out]# 633.10553651, 728.21274481, 823.31995312]), #[Out]# ) img_CO21.max() #[Out]# nan def fix_nans(img): from astropy.convolution import convolve_fft, Gaussian2DKernel kernel = Gaussian2DKernel(3) sm = convolve_fft(img, kernel) img[np.isnan(img)] = sm[np.isnan(img)] return img img_lfi30, footprint = fix_nans(reproject_from_healpix(fn_lfi_30, 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_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) def fix_nans(img): from astropy.convolution import convolve_fft, Gaussian2DKernel kernel = Gaussian2DKernel(3) print(kernel.shape, img.shape) sm = convolve_fft(img, kernel) img[np.isnan(img)] = sm[np.isnan(img)] return img img_lfi30, footprint = fix_nans(reproject_from_healpix(fn_lfi_30, 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_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) img_lfi30, footprint = reproject_from_healpix(fn_lfi_30, target_header) 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) def fix_nans(img): from astropy.convolution import convolve_fft, Gaussian2DKernel kernel = Gaussian2DKernel(3) sm = convolve_fft(img, kernel) img[np.isnan(img)] = sm[np.isnan(img)] return img img_lfi30.max() #[Out]# 0.18311390533020222 img_lfi30.shape #[Out]# (480, 960) img_lfi30[240, 480] #[Out]# 0.08952149020673798 img_lfi30[240-2:240+2, 480-2:480+2] #[Out]# array([[0.04775085, 0.06265866, 0.05720012, 0.04026645], #[Out]# [0.09307519, 0.14043916, 0.12252462, 0.06566105], #[Out]# [0.07767489, 0.11543623, 0.08952149, 0.05108677], #[Out]# [0.02620783, 0.03635891, 0.03243179, 0.02894827]]) img_lfi30[240-2:240+2, :5] #[Out]# array([[0.0009288 , 0.0008832 , 0.00076149, 0.00077466, 0.00085711], #[Out]# [0.00077153, 0.00073054, 0.00057023, 0.00057254, 0.00074503], #[Out]# [0.00070947, 0.0007683 , 0.00065404, 0.00061427, 0.00066693], #[Out]# [0.0004135 , 0.00055419, 0.00055852, 0.00061204, 0.00069123]]) img_lfi30[240-2:240+2, -5:] #[Out]# array([[0.00103351, 0.00089888, 0.00078025, 0.00085016, 0.00078458], #[Out]# [0.00088877, 0.00070945, 0.00056769, 0.00063693, 0.0006909 ], #[Out]# [0.00073597, 0.00059486, 0.0006377 , 0.00061823, 0.00052619], #[Out]# [0.00074094, 0.00060941, 0.00045995, 0.00046481, 0.00041411]]) np.where(np.isnan(img_lfi30)) #[Out]# (array([], dtype=int64), array([], dtype=int64)) img_lfi30, footprint = reproject_from_healpix(fn_lfi_30, target_header) print(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) img_lfi30, footprint = reproject_from_healpix(fn_lfi_30, target_header) badinds = np.where(np.isnan(img_lfi30)) print(img_lfi30[badinds]) img_lfi30 = fix_nans(img_lfi30) print(img_lfi30[badinds]) 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) 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 import healpy def fix_nans(img): kernel = Gaussian2DKernel(5) sm = convolve_fft(img, kernel) img[np.isnan(img)] = sm[np.isnan(img)] return img def fix_nans(img): kernel = Gaussian2DKernel(5) sm = convolve_fft(img, kernel) img[np.isnan(img)] = sm[np.isnan(img)] return img img_lfi30, footprint = reproject_from_healpix(fn_lfi_30, target_header) badinds = np.where(np.isnan(img_lfi30)) print(img_lfi30[badinds]) img_lfi30 = fix_nans(img_lfi30) print(img_lfi30[badinds]) 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) img_lfi30, footprint = reproject_from_healpix(fn_lfi_30, target_header) badinds = np.where(np.isnan(img_lfi30)) print(img_lfi30[badinds]) img_lfi30 = fix_nans(img_lfi30) print(img_lfi30[badinds+1]) print(img_lfi30[badinds]) 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) img_lfi30, footprint = reproject_from_healpix(fn_lfi_30, target_header) badinds = np.where(np.isnan(img_lfi30)) print(img_lfi30[badinds]) img_lfi30 = fix_nans(img_lfi30) print(img_lfi30[badinds[0]-1, badinds[1]-1]) print(img_lfi30[badinds]) 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) pl.imshow(img_lfi30) #[Out]# pl.imshow(img_lfi30, norm=simple_norm(img_lfi30, min_percent=1, max_percent=99.99, stretch='log')) #[Out]# pl.figure(figsize=(15,8)) pl.imshow(img_lfi30, norm=simple_norm(img_lfi30, min_percent=1, max_percent=99.99, stretch='log')) #[Out]# 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) ax.coords['glat'] #[Out]# axg = ax.coords['glat'] 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) 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) 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) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) 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) ax.imshow(rgb) #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_CO21, footprint = reproject_from_healpix(fn_CO21, 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_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) 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) 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) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) 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.imshow(rgb) #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) 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), 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) 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), 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) 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), 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(-90, 90) ax.set_ylim(-20, 20) #[Out]# (-20.0, 20.0) 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),) #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(-90, 90) ax.set_ylim(-20, 20) #[Out]# (-20.0, 20.0) 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),) #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:-240) ax.set_ylim(120:-120) 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),) #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, 720) ax.set_ylim(120, 360) #[Out]# (120.0, 360.0) 960*2 #[Out]# 1920 0.16775*2 #[Out]# 0.3355 0.16875*2 #[Out]# 0.3375 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(5) sm = convolve_fft(img, kernel) img[np.isnan(img)] = sm[np.isnan(img)] return img 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_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) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) 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) ax.imshow(rgb) #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) mCO21 = healpy.read_map(planck_CO21) healpy.mollview(mCO21, norm=simple_norm(mCO21, stretch='log', min_cut=0, max_cut=1), min=np.percentile(mCO21, 1), bgcolor=pl.cm.viridis(0), cbar=False, title='') img_CO21, footprint = reproject_from_healpix(fn_CO21, 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_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) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) 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.imshow(rgb) #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) 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), 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) 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), 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) 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),) #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, 720) ax.set_ylim(120, 360) #[Out]# (120.0, 360.0) 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) def fix_nans(img): kernel = Gaussian2DKernel(2) sm = convolve_fft(img, kernel) img[np.isnan(img)] = sm[np.isnan(img)] return img 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) 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),) #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) 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=2e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.90, log_a=5e2, stretch='log')(img_ThermalDust_cmz)]).T.swapaxes(0,1) 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=2e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.90, log_a=2e1, stretch='log')(img_CO21_cmz), simple_norm(img_ThermalDust_cmz, min_percent=0.01, max_percent=99.90, 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) 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=2e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=2e1, 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) 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=2e1, stretch='log')(img_HI4PI_cmz), simple_norm(img_CO21_cmz, min_percent=0.01, max_percent=99.99, log_a=2e1, 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) rgb = np.array([simple_norm(img_HI4PI_cmz, min_percent=0.01, max_percent=99.99, log_a=2e1, 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) 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) os.path.exists("GC.hi.tb.allgal.fits.gz") #[Out]# True from astropy import units as u os.path.exists("GC.hi.tb.allgal.fits.gz") #[Out]# True if os.path.exists("GC.hi.tb.allgal.fits.gz"): hi_cube_fh = fits.open("GC.hi.tb.allgal.fits.gz") else: #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_fh, use_dask=True) 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_fh, use_dask=True) get_ipython().run_line_magic('ls', '-lhrt *fits *fits.gz') get_ipython().run_line_magic('ls', '-lh /orange/adamginsburg/cmz/hi/mcluregriffiths/GC.hi.tb.allgal.fits.gz') hi_cube_fn = '/orange/adamginsburg/cmz/hi/mcluregriffiths/GC.hi.tb.allgal.fits.gz' 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) 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) fits.open(hi_cube_fn fits.open(hi_cube_fn) #[Out]# [] fits.open(hi_cube_fn)[0].data.shape #[Out]# (1, 800, 1050, 1050) 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) pl.imshow(hi_cube.max(axis=0)) #[Out]# pl.imshow(hi_cube.mean(axis=0)) #[Out]# peak_hi = hi_cube.max(axis=0) pl.imshow(peak_hi.value) #[Out]# peak_hi = hi_cube.max(axis=0) mean_hi = hi_cube.mean(axis=0) pl.imshow(mean_hi) #[Out]# if os.path.exists("DHT02_Center_interp.fits"): cube_12co = SpectralCube.read('DHT02_Center_interp.fits', use_dask=True) else: pass pl.imshow(mean_hi.value) #[Out]# pl.imshow(mean_hi.value, vmin=0) #[Out]# if os.path.exists("DHT02_Center_interp.fits"): cube_12co = SpectralCube.read('DHT02_Center_interp.fits', use_dask=True) else: pass peak_co = co_cube.max(axis=0) mean_co = co_cube.mean(axis=0) peak_co = cube_12co.max(axis=0) mean_co = cube_12co.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, vmin=0, transform=ax.get_transform(hi_cube.wcs.celestial)) #[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, vmin=0, transform=ax.get_transform(hi_cube.wcs.celestial)) #[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, 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_co.value, vmin=0, transform=ax.get_transform(cube_12co.wcs.celestial), cmap='gray') #[Out]# 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) 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) 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(mean_co.value, vmin=0, transform=ax.get_transform(co_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(max_co.value, vmin=0, transform=ax.get_transform(co_cube.wcs.celestial), 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(peak_co.value, vmin=0, transform=ax.get_transform(co_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(peak_co.value, norm=simple_norm(peak_co.value, min_percent=0.1, max_percent=99.9, stretch='asinh'), vmin=0, transform=ax.get_transform(co_cube.wcs.celestial), 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(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/AG-Laboca-Planck.4.5.fits') 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(peak_co.value, min_percent=0.1, max_percent=99.9, stretch='asinh'), transform=ax.get_transform(atlasgal[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(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]# 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]# plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) 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) ax.imshow(rgb) #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.savefig('Planck_RGB_30-143-857.png', bbox_inches='tight') def make_rgb(rgb, basename): plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) #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), ('RGB')): rgbc = np.zeros_like(rgb) rgbc[:, :, ind] = rgb[:, :, ind] ax.imshow(rgbc) pl.savefig(f'{basename}_{color}.png', bbox_inches='tight') ax.imshow(rgb) pl.savefig(f'{basename}_RGB.png', bbox_inches='tight') make_rgb(rgb, 'Planck_RGB_30-143-857') get_ipython().run_line_magic('pwd', '') #[Out]# '/orange/adamginsburg/cmz/DataSetVisualizations' 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') get_ipython().run_line_magic('matplotlib', 'inline') import pylab as pl pl.rcParams['figure.facecolor']='none' import numpy as np 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) def make_rgb(rgb, basename): plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) #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), ('RGB')): rgbc = np.zeros_like(rgb) rgbc[:, :, ind] = rgb[:, :, ind] ax.imshow(rgbc) pl.savefig(f'{basename}_{color}.png', bbox_inches='tight', transparent=True) ax.imshow(rgb) pl.savefig(f'{basename}_RGB.png', bbox_inches='tight', transparent=True) 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') get_ipython().run_line_magic('matplotlib', 'inline') import pylab as pl import numpy as np 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') 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): plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) #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), ('RGB')): 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) pl.savefig(f'{basename}_RGB.png', bbox_inches='tight', transparent=True) 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', -0.25) 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', -0.25, layernames=('HI', 'CO', 'Dust')) 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'): plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) #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) pl.savefig(f'{basename}_RGB.png', bbox_inches='tight', transparent=True) 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', -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', -0.35, layernames=('HI', 'CO', 'Dust')) 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): plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) #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) # 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', -0.35, layernames=('HI', 'CO', 'Dust'), axlims=(240, 720, 120, 360)) 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')) # 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_zoom', -0.35, layernames=('HI', 'CO', 'Dust'), axlims=(240, 720, 120, 360)) # 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_zoomCMZ', -0.35, layernames=('HI', 'CO', 'Dust'), axlims=(240*2, 720*2, 120*2, 360*2)) 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) # 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_zoom', -0.35, layernames=('HI', 'CO', 'Dust'), axlims=(240, 720, 120, 360), frame_class=None) # 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_zoomCMZ', -0.35, layernames=('HI', 'CO', 'Dust'), axlims=(240*2, 720*2, 120*2, 360*2), frame_class=None) fn_chipass = "lambda_chipass_HPX_r10.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) plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(target_header), frame_class=EllipticalFrame) ax.imshow(img_chipass, norm=simple_norm(img_chipass, 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" 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) chipass #[Out]# [] fn_chipass = "lambda_chipass_HPX_r10.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) chipass[0].data #[Out]# array([[ 4.0326079e+03, 4.0384629e+03, 4.0515142e+03, ..., #[Out]# nan, nan, nan], #[Out]# [ 4.0275764e+03, 4.0395645e+03, 4.0547297e+03, ..., #[Out]# nan, nan, nan], #[Out]# [ 4.0134336e+03, 4.0256304e+03, 4.0496831e+03, ..., #[Out]# nan, nan, nan], #[Out]# ..., #[Out]# [ nan, nan, nan, ..., #[Out]# -1.0000000e+30, -1.0000000e+30, -1.0000000e+30], #[Out]# [ nan, nan, nan, ..., #[Out]# -1.0000000e+30, -1.0000000e+30, -1.0000000e+30], #[Out]# [ nan, nan, nan, ..., #[Out]# -1.0000000e+30, -1.0000000e+30, -1.0000000e+30]], dtype=float32) #img_chipass, footprint = reproject_from_healpix(fn_chipass, target_header) img_chipass = chipass[0].data header = chipass[0].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='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_chipass, footprint = reproject_from_healpix(fn_chipass, target_header) img_chipass = chipass[0].data header = chipass[0].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='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_zea.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[0].data header = chipass[0].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='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 = akari65[0].data header = akari65[0].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) chipass #[Out]# [, , , , ] chipass[0].data chipass[1].data #[Out]# array([[0., 0., 0., ..., 0., 0., 0.], #[Out]# [0., 0., 0., ..., 0., 0., 0.], #[Out]# [0., 0., 0., ..., 0., 0., 0.], #[Out]# ..., #[Out]# [0., 0., 0., ..., 0., 0., 0.], #[Out]# [0., 0., 0., ..., 0., 0., 0.], #[Out]# [0., 0., 0., ..., 0., 0., 0.]], dtype=float32) chipass[1].header #[Out]# XTENSION= 'IMAGE ' / IMAGE extension #[Out]# BITPIX = -32 / Number of bits per data pixel #[Out]# NAXIS = 2 / Number of data axes #[Out]# NAXIS1 = 2048 / #[Out]# NAXIS2 = 2048 / #[Out]# PCOUNT = 0 / No Group Parameters #[Out]# GCOUNT = 1 / One Data Group #[Out]# EXTNAME = 'TEMPERATURE_NORTH' / #[Out]# CTYPE1 = 'GLON-ZEA' / Coordinate Type #[Out]# CTYPE2 = 'GLAT-ZEA' / Coordinate Type #[Out]# EQUINOX = 2000.00 / Equinox of Ref. Coord. #[Out]# CDELT1 = -0.0791293606162 / Degrees / Pixel #[Out]# CDELT2 = 0.0791293606162 / Degrees / Pixel #[Out]# CROTA2 = 0.00000 / Rotation Angle (Degrees) #[Out]# CRPIX1 = 1024.50 / Reference Pixel in X #[Out]# CRPIX2 = 1024.50 / Reference Pixel in Y #[Out]# CRVAL1 = 270.0000000000 / Galactic longitude of reference pixel #[Out]# CRVAL2 = 90.0000000000 / Galactic latitude of reference pixel #[Out]# LONPOLE = 0.00000000000 / Native longitude of Galactic pole #[Out]# LATPOLE = 90.00000000000 / Galactic latitude of native pole #[Out]# PV2_1 = 0.00000000000 /Projection parameter 1 #[Out]# HISTORY PUTAST: Apr 1 15:55:31 2014 World Coordinate System parameters written #img_chipass, footprint = reproject_from_healpix(fn_chipass, target_header) img_chipass = chipass[1].data 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='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_chipass, footprint = reproject_from_healpix(fn_chipass, target_header) img_chipass = chipass[2].data 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='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_chipass, footprint = reproject_from_healpix(fn_chipass, target_header) img_chipass = chipass[3].data 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='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_chipass, footprint = reproject_from_healpix(fn_chipass, target_header) img_chipass = chipass[4].data 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='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_chipass, footprint = reproject_from_healpix(fn_chipass, target_header) img_chipass = chipass[5].data 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='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_chipass, footprint = reproject_from_healpix(fn_chipass, target_header) img_chipass = chipass[1].data 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='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) chipass #[Out]# [, , , , ] chipass.fileinfo() chipass.info() #img_chipass, footprint = reproject_from_healpix(fn_chipass, target_header) img_chipass = chipass[1].data 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='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_chipass, footprint = reproject_from_healpix(fn_chipass, target_header) img_chipass = chipass[1].data 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='linear')) #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_chipass, footprint = reproject_from_healpix(fn_chipass, target_header) img_chipass = chipass[1].data header = chipass[1].header plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(header), #frame_class=EllipticalFrame, ) img_chipass[img_chipass == 0] = np.nan ax.imshow(img_chipass, norm=simple_norm(img_chipass, min_percent=1, max_percent=99.99, stretch='linear')) #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_chipass, footprint = reproject_from_healpix(fn_chipass, target_header) img_chipass = chipass[1].data header = chipass[1].header plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(header), #frame_class=EllipticalFrame, ) img_chipass[img_chipass == 0] = np.nan 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) pl.hist(img_chipass.ravel()) #[Out]# (array([1384040., 0., 0., 0., 0., 0., #[Out]# 0., 0., 0., 1910053.]), #[Out]# array([-1.1000000e+30, -9.9000001e+29, -8.8000000e+29, -7.6999999e+29, #[Out]# -6.5999998e+29, -5.5000001e+29, -4.4000000e+29, -3.2999999e+29, #[Out]# -2.2000000e+29, -1.1000000e+29, 4.8841652e+04], dtype=float32), #[Out]# ) #img_chipass, footprint = reproject_from_healpix(fn_chipass, target_header) img_chipass = chipass[1].data header = chipass[1].header plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(header), #frame_class=EllipticalFrame, ) img_chipass[img_chipass <= 0] = np.nan 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_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 header = chipass[1].header plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(header), #frame_class=EllipticalFrame, ) img_chipass[img_chipass <= 0] = np.nan 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) akari65.info() #img_akari65, footprint = reproject_from_healpix(fn_akari65, target_header) img_akari65 = 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) #img_chipass, footprint = reproject_from_healpix(fn_chipass, target_header) img_chipass = chipass[1].data + chipass[2].data header = chipass[1].header plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(header), #frame_class=EllipticalFrame, ) img_chipass[img_chipass <= 0] = np.nan 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) #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_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) halpha.info() #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_akari90 = "akari_mollweide_65_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 = 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_65_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 = 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_akari140 = "akari_mollweide_140_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) 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) 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 = 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 = 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 = 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) #img_akari140, footprint = reproject_from_healpix(fn_akari140, target_header) img_akari140 = fix_nans(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) #img_akari160, footprint = reproject_from_healpix(fn_akari160, target_header) img_akari160 = fix_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) #img_akari160, footprint = reproject_from_healpix(fn_akari160, target_header) img_akari160 = fix_nans(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) np.where(0, np.nan, akari160[1].data) #[Out]# array([[0., 0., 0., ..., 0., 0., 0.], #[Out]# [0., 0., 0., ..., 0., 0., 0.], #[Out]# [0., 0., 0., ..., 0., 0., 0.], #[Out]# ..., #[Out]# [0., 0., 0., ..., 0., 0., 0.], #[Out]# [0., 0., 0., ..., 0., 0., 0.], #[Out]# [0., 0., 0., ..., 0., 0., 0.]], dtype=float32) np.where(akari160[1].data == 0, np.nan, akari160[1].data) #[Out]# array([[nan, nan, nan, ..., nan, nan, nan], #[Out]# [nan, nan, nan, ..., nan, nan, nan], #[Out]# [nan, nan, nan, ..., nan, nan, nan], #[Out]# ..., #[Out]# [nan, nan, nan, ..., nan, nan, nan], #[Out]# [nan, nan, nan, ..., nan, nan, nan], #[Out]# [nan, nan, nan, ..., nan, nan, nan]], dtype=float32) #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) #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) #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) #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_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_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_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_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) #img_rosat5, footprint = reproject_from_healpix(fn_rosat5, target_header) img_rosat5 = rosat5[0].data header = rosat5[0].header plt.figure(figsize=(10,5), dpi=200) ax = plt.subplot(1,1,1, projection=WCS(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) img_rosat5, footprint = reproject_from_healpix(fn_rosat5, 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_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) img_rosat5, footprint = reproject_from_healpix(fn_rosat5, target_header) img_rosat5 = fix_nans(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) img_rosat5, footprint = reproject_from_healpix(fn_rosat5, target_header) img_rosat5 = fix_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) rosat5 #[Out]# [, ] rosat5[1].data #[Out]# FITS_rec([([1.49588661e+01, 4.50610781e+00, 3.18872452e+01, 2.26882534e+01, 3.05574856e+01, 3.63407288e+01, 2.73925896e+01, 2.68128586e+01, 4.95065765e+01, 3.38679466e+01, 6.54470673e+01, 5.83736153e+01, 2.70152435e+01, 3.39361801e+01, 3.92069817e+01, 3.30878220e+01, 2.37086945e+01, 3.43528328e+01, 1.19546394e+01, 2.23490562e+01, 4.15421638e+01, 2.45013199e+01, 6.50889130e+01, 4.76067047e+01, 2.20920601e+01, 3.59367142e+01, 2.13649063e+01, 1.97959137e+01, 6.02897568e+01, 5.40676384e+01, 4.78777390e+01, 4.83310699e+01, 8.77415848e+01, 8.22610474e+01, 9.46918030e+01, 9.71712112e+01, 6.78699951e+01, 7.34317627e+01, 9.92845993e+01, 8.70716248e+01, 7.44542389e+01, 1.18527954e+02, 4.41554413e+01, 8.35297241e+01, 1.18426956e+02, 7.23794403e+01, 8.66300659e+01, 7.10720825e+01, 4.78165932e+01, 5.31340179e+01, 5.29759750e+01, 6.05060883e+01, 6.61408463e+01, 5.55218811e+01, 6.64393692e+01, 4.08559608e+01, 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44.62439 , 51.57036 , 56.10117 , 43.202614 , 46.195206 , 50.33249 , 48.939873 , 45.155678 , 45.97621 , 49.38446 , 51.970074 , 46.1127 , 50.30184 , 52.964905 , 46.639336 , 51.750618 , 46.857628 , 32.14327 , 35.442703 , 42.354324 , 58.265488 , 43.099995 , 43.89454 , 44.032223 , 44.950356 , 39.895596 , 50.98379 , 37.645493 , 41.133575 , 73.90623 , 81.330284 , 72.34819 , 71.01854 , 62.839813 , 63.686935 , 60.30968 , 69.640564 , 57.405205 , 37.731834 , 42.252735 , 36.592167 , 49.50071 , 55.256668 , 49.049362 , 52.043762 , 46.45038 , 47.835384 , 39.69285 , 39.628426 , 44.85772 , 47.424828 , 41.164886 , 44.86007 , 40.781357 , 24.218351 , 46.31053 , 33.94258 , 29.359615 , 47.922897 , 37.713264 , 46.4178 , 47.593674 , 46.14177 , 45.99185 , 42.41765 , 52.92478 , 49.715347 , 41.485138 , 36.48903 , 49.91107 , 40.71142 , 41.97397 , 22.152142 , 32.058933 , 31.874352 , 26.748848 , 33.83281 , 280.30722 , 426.14224 , 82.43856 , 123.50722 , 223.78543 , 223.7387 , 181.96736 , 115.29849 , 55.454414 , 61.43376 , 45.486343 , 57.3091 , 83.74479 , 75.95681 , 59.25836 , 63.95688 , 154.7099 , 113.23793 , 114.825294 , 105.26855 , 80.908844 , 54.14392 , 51.819397 , 46.379555 , 76.752 , 61.026756 , 57.023643 , 57.33246 , 55.599297 , 40.508957 , 33.939953 , 26.94374 , 53.635178 , 56.092247 , 63.16092 , 58.36767 , 58.356777 , 60.650368 , 62.205555 , 60.650906 , 62.697548 , 62.175236 , 52.280693 , 57.559074 , 63.03345 , 60.03308 , 57.45453 , 60.316406 , 59.204475 , 54.63536 , 56.45726 , 55.93508 , 41.38755 , 31.176508 , 54.227097 , 43.605995 , 56.54606 , 54.543144 , 56.52119 , 54.419533 , 54.814056 , 53.55475 , 53.433014 , 51.24292 , 61.727905 , 42.49389 , 47.36116 , 37.07341 , 37.965874 , 49.12359 , 39.597294 , 49.291435 , 45.373634 , 44.08856 , 40.35388 , 41.81449 , 47.604115 , 47.724 , 43.951973 , 39.813423 , 56.086548 , 49.624733 , 55.645477 , 49.186577 , 41.095078 , 38.445877 , 42.0204 , 38.881218 , 47.38359 , 51.151726 , 39.752544 , 45.954906 , 44.54148 , 39.47713 , 38.809563 , 39.531406 , 37.2416 , 44.024403 , 37.124756 , 48.139854 , 46.08344 , 41.108494 , 51.668304 , 47.816833 , 42.82386 , 45.93914 , 43.65377 , 41.853447 , 48.067047 , 44.190784 , 41.926807 , 42.737915 , 44.332146 , 52.920097 , 52.95943 , 55.413383 , 45.43018 , 47.14216 , 59.54947 , 54.853333 , 47.81308 , 57.340446 , 40.406273 , 48.522156 , 62.501328 , 59.093586 , 54.712067 , 56.07914 , 56.507385 , 47.803917 , 58.56971 , 53.883286 , 47.74604 , 51.187836 , 48.357758 , 45.15863 , 56.84028 , 58.986748 , 53.776432 , 60.5478 , 57.72293 , 50.043343 , 60.854836 , 57.942856 , 48.428253 , 53.498703 , 44.19846 , 66.17857 , 63.77753 , 57.869743 , 71.34563 , 56.066357 , 44.15204 , 55.305855 , 48.602066 , 48.798546 , 62.33149 , 43.465393 , 53.80806 , 49.272427 , 53.170036 , 57.071243 , 42.489517 , 52.322006 , 59.309322 , 61.09706 , 57.04164 , 49.813866 , 38.063293 , 43.15879 , 34.362144 , 41.774498 , 48.753975 , 37.863743 , 46.50918 , 42.05266 , 50.983223 , 50.934372 , 35.372616 , 40.937317 , 55.561977 , 51.676643 , 49.96461 , 50.602806 , 35.65706 , 45.456856 , 39.230488 , 45.126087 , 48.587185 , 53.505005 , 51.39447 , 58.597996 , 46.19631 , 38.727913 , 38.2628 , 38.219017 , 39.787354 , 36.251648 , 28.658325 , 31.67987 , 40.405277 , 36.36742 , 50.124084 , 46.201843 , 26.258986 , 32.342113 , 41.03243 , 49.22628 , 36.117622 , 37.810814 , 36.994724 , 40.54229 , 37.50735 , 47.762295 , 34.935104 , 37.707928 , 39.984375 , 40.97791 , 43.11862 , 47.50637 , 37.426598 , 34.177353 , 43.787964 , 39.298416 , 53.308075 , 51.714035 , 52.482834 , 53.5535 , 49.904175 , 49.001213 , 51.647076 , 50.53378 , 58.638657 , 54.210533 , 58.587265 , 46.69799 , 48.657646 , 49.081306 , 45.91445 , 43.185314 , 51.01272 , 51.57059 , 52.00461 , 49.357574 , 45.363262 , 39.50448 , 42.18577 , 40.816193 , 48.680428 , 48.160034 , 40.97574 , 45.47532 , 45.638443 , 45.420006 , 41.823357 , 41.318665 ])], #[Out]# dtype=(numpy.record, [('COUNT_RATE', '>f4', (1024,)), ('UNCERTAINTY', '>f4', (1024,))])) Table.read(rosat5[1]) from astropy.table import Table Table.read(rosat5[1]) #[Out]# #[Out]# COUNT_RATE UNCERTAINTY #[Out]# 1e-6cts/s/arcmin^2 1e-6cts/s/arcmin^2 #[Out]# float32[1024] float32[1024] #[Out]# ----------------------- ----------------------- #[Out]# 14.958866 .. 58.415848 19.011818 .. 28.460255 #[Out]# 62.673065 .. 60.186466 32.041504 .. 28.01587 #[Out]# 10.5905695 .. 94.80371 12.187382 .. 39.026306 #[Out]# 32.967834 .. 56.428974 21.9021 .. 28.817657 #[Out]# 72.75197 .. 87.31131 33.346313 .. 33.238495 #[Out]# 36.20376 .. 26.717072 24.28534 .. 21.23656 #[Out]# 82.89493 .. 88.49844 32.796776 .. 47.470535 #[Out]# 60.6135 .. 60.351604 28.448719 .. 26.880974 #[Out]# 38.209084 .. 183.72794 31.345615 .. 56.02453 #[Out]# 101.79762 .. 142.78143 40.50649 .. 48.845844 #[Out]# 99.59178 .. 80.51065 42.924076 .. 49.80906 #[Out]# 98.79138 .. 129.61674 40.677322 .. 56.16407 #[Out]# 35.059433 .. 47.050846 24.11045 .. 27.760788 #[Out]# 114.304756 .. 38.494015 55.952724 .. 36.094067 #[Out]# 112.58261 .. 117.62329 43.007744 .. 50.137115 #[Out]# 32.144665 .. 72.97708 24.171602 .. 37.571392 #[Out]# 141.15579 .. 91.37555 45.766117 .. 37.673832 #[Out]# 77.14352 .. 65.63304 28.782593 .. 26.605944 #[Out]# 114.881836 .. 82.07925 43.532364 .. 31.18754 #[Out]# 108.87489 .. 83.95593 40.38899 .. 30.690243 #[Out]# 112.71868 .. 86.67385 33.312813 .. 27.705826 #[Out]# ... ... #[Out]# 76.23253 .. 14.997907 51.840633 .. 13.496582 #[Out]# 116.460594 .. 60.498974 51.14092 .. 40.21457 #[Out]# 70.26988 .. 84.630486 75.66973 .. 37.617374 #[Out]# 84.46112 .. 108.07146 76.45401 .. 40.363525 #[Out]# 34.791843 .. 53.556923 25.840935 .. 34.178246 #[Out]# 76.062515 .. 44.18604 35.61315 .. 30.095467 #[Out]# 160.69247 .. 188.87988 100.324745 .. 128.20787 #[Out]# 184.60493 .. 143.00357 118.64194 .. 87.377075 #[Out]# 68.41747 .. 41.578835 66.96457 .. 35.2046 #[Out]# 188.50446 .. 127.42395 124.601494 .. 52.250854 #[Out]# -9.584647 .. 197.63092 0.0 .. 83.57472 #[Out]# 38.556202 .. 170.70604 37.384476 .. 78.87565 #[Out]# 211.48671 .. 210.8453 103.01068 .. 63.52465 #[Out]# 189.54306 .. 98.107864 84.24194 .. 48.275185 #[Out]# -6.4734406 .. 156.17572 2.6622245 .. 48.880753 #[Out]# 10.24295 .. 148.95036 16.85659 .. 47.719643 #[Out]# 83.439 .. 72.40684 35.617798 .. 33.66597 #[Out]# 106.09712 .. 115.04104 40.513523 .. 42.47626 #[Out]# 83.612114 .. 99.19212 44.541756 .. 42.27456 #[Out]# 155.01724 .. 135.8976 54.48588 .. 50.81183 #[Out]# 100.036606 .. 124.6382 39.600113 .. 48.066788 #[Out]# 163.3219 .. 83.52938 53.79322 .. 41.318665 rosat5[1]['COUNT_RATE'] rosat5[1].data['COUNT_RATE'] #[Out]# array([[ 14.958866 , 4.506108 , 31.887245 , ..., 46.68386 , #[Out]# 72.64604 , 58.415848 ], #[Out]# [ 62.673065 , 66.026405 , 63.172585 , ..., 31.61976 , #[Out]# 54.86081 , 60.186466 ], #[Out]# [ 10.5905695, 5.727805 , 22.463112 , ..., 105.26994 , #[Out]# 34.33767 , 94.80371 ], #[Out]# ..., #[Out]# [155.01724 , 216.68222 , 80.588684 , ..., 108.199265 , #[Out]# 136.41353 , 135.8976 ], #[Out]# [100.036606 , 67.71236 , 141.21446 , ..., 161.0857 , #[Out]# 93.71858 , 124.6382 ], #[Out]# [163.3219 , 123.849075 , 136.8566 , ..., 102.29584 , #[Out]# 85.671196 , 83.52938 ]], dtype=float32) pl.imshow(rosat5[1].data['COUNT_RATE']) #[Out]# 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[0].data np.isnan(rosat5[1].data['COUNT_RATE']).sum() #[Out]# 0 (rosat5[1].data['COUNT_RATE'] == 0).sum() #[Out]# 2633 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 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) 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) img_rosat5, footprint = reproject_from_healpix(fn_rosat5, target_header) 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(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 #img_rosat5, footprint = reproject_from_healpix(fn_rosat5, target_header) img_rosat5, footprint = reproject_from_healpix(fn_rosat5, target_header) 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_rosat = rosat5[1].data['COUNT_RATE'] data_rosat = fix_rosat_nans(np.where(data_rosat5==0, np.nan, data_rosat5)) img_rosat5, footprint = reproject_from_healpix(data_rosat, target_header) #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, target_header) #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) rosat5[1].header['COORDSYS'] #[Out]# 'G' #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, rosat5[1].header["COORDSYS"]), target_header) #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, 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)