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create_3_colour_image.py
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import os
import astropy.units as u
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from astropy.coordinates import SkyCoord
from astropy.io import fits
from astropy.wcs import WCS
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
from reproject import reproject_interp
matplotlib.rcParams['mathtext.fontset'] = 'stix'
matplotlib.rcParams['font.family'] = 'STIXGeneral'
matplotlib.rcParams['font.size'] = 14
os.chdir('/Users/williams/Documents/phangs/jwst_early_science')
high_pass_jwst = True
data_dir = 'data'
plot_dir = 'plots'
pmin, pmax = 1, 99
# hdu = fits.open('/Users/williams/Documents/phangs/muse/DR2.1/NGC0628_MAPS.fits')
# hdu['HA6562_FLUX'].writeto(os.path.join(data_dir, 'muse_ha.fits'), overwrite=True)
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
jwst_file_name = os.path.join('data',
'ngc0628_miri_f2100w_anchored.fits',
)
alma_file_name = os.path.join('data', 'ngc0628_12m+7m+tp_co21_strict_mom0.fits')
muse_file_name = os.path.join('data',
'muse_ha.fits',
# 'ngc0628_mips24_image_v5-0.fits'
)
spur1_file_name = os.path.join('data',
'spur1.fits',
)
spur2_file_name = os.path.join('data',
'spur2.fits',
)
environment_file_name = os.path.join('data', 'NGC0628_simple.fits')
centre = SkyCoord(24.173855, 15.783643, unit=(u.deg, u.deg))
pa = 20.7
size = (0.04 * u.deg, 0.08 * u.deg)
# size = (0.5 * u.deg, 0.5 * u.deg)
w = WCS(jwst_file_name)
x_cen, y_cen = w.all_world2pix(centre.ra, centre.dec, 1)
# Rotate (-5000, 0), (5000, 0) by the PA to plot on later
# x_pa = np.array([-5000, 5000], dtype=float)
# y_pa = np.array([np.abs(x_pa[0] / np.tan(np.radians(pa))), - x_pa[1] / np.tan(np.radians(pa))])
#
# x_pa += x_cen
# y_pa += y_cen
# We'll regrid everything to the JWST data
jwst_hdu = fits.open(jwst_file_name)
jwst_sci = jwst_hdu[0]
jwst_sci.data[jwst_sci.data == 0] = np.nan
jwst_w = WCS(jwst_sci)
alma_hdu = fits.open(alma_file_name)
muse_hdu = fits.open(muse_file_name)
environment_hdu = fits.open(environment_file_name)
spur1_hdu = fits.open(spur1_file_name)
spur2_hdu = fits.open(spur2_file_name)
alma_reproj = reproject_interp(alma_hdu,
jwst_sci.header,
return_footprint=False)
muse_reproj = reproject_interp(muse_hdu[1],
jwst_sci.header,
return_footprint=False)
env_reproj = reproject_interp(environment_hdu[0],
jwst_sci.header,
return_footprint=False,
order='nearest-neighbor')
spur1_reproj = reproject_interp(spur1_hdu[0],
jwst_sci.header,
return_footprint=False)
spur2_reproj = reproject_interp(spur2_hdu[0],
jwst_sci.header,
return_footprint=False)
env_reproj[env_reproj != 6] = np.nan
env_reproj[env_reproj == 6] = 1
ii, jj = np.meshgrid(np.arange(env_reproj.shape[1]), np.arange(env_reproj.shape[0]))
# Create some masks to show observation extent
jwst_fov = ~np.isnan(jwst_sci.data)
muse_fov = ~np.isnan(muse_reproj)
alma_fov = ~np.isnan(alma_reproj)
vmin_jwst, vmax_jwst = 0, 7
jwst_rescale = jwst_sci.data - vmin_jwst
jwst_rescale /= (vmax_jwst - vmin_jwst)
vmin_muse, vmax_muse = 0, 5000
muse_reproj -= vmin_muse
muse_reproj /= (vmax_muse - vmin_muse)
vmin_alma, vmax_alma = 0, 6
alma_reproj -= vmin_alma
alma_reproj /= (vmax_alma - vmin_alma)
# nan_idx = np.where((np.isnan(jwst_rescale)) | (np.isnan(alma_reproj)) | (np.isnan(muse_reproj)))
# jwst_rescale[nan_idx] = np.nan
# muse_reproj[nan_idx] = np.nan
# alma_reproj[nan_idx] = np.nan
rgb = np.dstack((jwst_rescale,
muse_reproj,
alma_reproj))
plot_name = os.path.join(plot_dir, 'jwst_muse_alma')
plt.figure(figsize=(10, 10))
ax = plt.subplot(projection=jwst_w)
plt.imshow(rgb,
origin='lower')
xlim = plt.xlim()
ylim = plt.ylim()
plt.contourf(ii, jj, env_reproj,
levels=1,
colors='white',
alpha=0.2,
)
plt.contour(ii, jj, jwst_fov,
levels=1,
colors='red',
alpha=0.5,
)
plt.contour(ii, jj, muse_fov,
levels=1,
colors='green',
alpha=0.5,
)
plt.contour(ii, jj, alma_fov,
levels=1,
colors='blue',
alpha=0.5,
)
# Plot on the position angle line
# plt.plot(x_pa, y_pa, c='white', lw=2)
plt.plot([2700, 2700], [100, 300], c='white', lw=2)
plt.plot([2700, 2700 - 200 * np.tan(np.radians(pa))], [100, 300], c='white', lw=2)
plt.text(2900, 200, 'PA',
c='white',
ha='right', va='top',
bbox=dict(facecolor='black', edgecolor='black', alpha=1),
fontsize=18, fontweight='bold',
)
plt.xlim(xlim)
plt.ylim(ylim)
# Plot on spur contours
spur1_mask = np.array(spur1_reproj > 0.2, dtype=int)
spur2_mask = np.array(spur2_reproj > 0.5, dtype=int)
plt.contour(ii, jj, spur1_mask,
levels=1,
colors='orange',
# lw=1,
)
plt.contour(ii, jj, spur2_mask,
levels=1,
colors='cyan',
# lw=1,
)
lon = ax.coords[0]
lat = ax.coords[1]
lon.display_minor_ticks(True)
lat.display_minor_ticks(True)
plt.grid(zorder=99, alpha=0.5)
# plt.text(0.05, 0.95,
# r'\textcolor{red}{MIRI 21$\mu$m}' + '\n' +
# r'\textcolor{green}{MUSE H$\alpha$}' + '\n' +
# r'\textcolor{blue}{ALMA CO}',
# ha='left', va='top',
# bbox=dict(facecolor='black', edgecolor='white', alpha=1),
# transform=ax.transAxes)
plt.text(0.05, 0.95,
r'MIRI 21$\mu$m',
c='r',
ha='left', va='top',
bbox=dict(facecolor='black', edgecolor='black', alpha=1),
fontsize=18, fontweight='bold',
transform=ax.transAxes)
plt.text(0.05, 0.90,
r'MUSE H$\alpha$',
c='g',
ha='left', va='top',
bbox=dict(facecolor='black', edgecolor='black', alpha=1),
fontsize=18, fontweight='bold',
transform=ax.transAxes)
plt.text(0.05, 0.85,
r'ALMA CO',
c='b',
ha='left', va='top',
bbox=dict(facecolor='black', edgecolor='black', alpha=1),
fontsize=18, fontweight='bold',
transform=ax.transAxes)
plt.text(0.95, 0.95,
'NGC 628',
c='white',
ha='right', va='top',
bbox=dict(facecolor='black', edgecolor='white', alpha=0.75),
transform=ax.transAxes,
fontsize=18, fontweight='bold',
)
pix_scale = np.abs(jwst_hdu[0].header['CDELT1'])
kpc_scalebar = np.degrees(1e3 / 10e6) / pix_scale
# Add on scalebar
scalebar = AnchoredSizeBar(ax.transData,
kpc_scalebar, '1 kpc', 'lower left',
pad=0.5,
borderpad=1,
sep=4,
# color='white',
frameon=True,
size_vertical=1,
)
ax.add_artist(scalebar)
# ax.arrow(24.18, 15.751, kpc_scalebar, 0,
# head_width=0, head_length=0,
# fc='white', ec='white', width=0.0005,
# transform=ax.get_transform('icrs'),
# zorder=1000)
# plt.text(24.196, 15.75, '1 kpc',
# color='white', rotation=0,
# transform=ax.get_transform('icrs'),
# zorder=1001)
plt.xlabel('RA (J2000)')
plt.ylabel('Dec (J2000)')
plt.tight_layout()
# plt.show()
plt.savefig(plot_name + '.png', bbox_inches='tight')
plt.savefig(plot_name + '.pdf', bbox_inches='tight')
plt.close()
# plt.show()
plot_name = os.path.join(plot_dir, 'jwst_alma_zoom')
# jwst_cutout = Cutout2D(jwst_sci.data,
# position=centre,
# size=size,
# wcs=jwst_w)
#
# zero_idx = np.where(jwst_cutout.data == 0)
#
# if high_pass_jwst:
# jwst_cutout.data = butterworth(jwst_cutout.data)
#
# jwst_cutout.data[zero_idx] = np.nan
#
# vmin_jwst, vmax_jwst = np.nanpercentile(jwst_cutout.data, [pmin, pmax])
#
# # jwst_norm = matplotlib.colors.Normalize(vmin=vmin_jwst,
# # vmax=vmax_jwst,
# # )
# jwst_norm = matplotlib.colors.PowerNorm(1, vmin=vmin_jwst, vmax=vmax_jwst)
#
# alma_reproj = reproject_interp(alma_hdu[0],
# jwst_cutout.wcs.to_header(),
# shape_out=jwst_cutout.data.shape,
# return_footprint=False)
# alma_reproj[alma_reproj == 0] = np.nan
# # alma_norm = matplotlib.colors.Normalize(vmin=np.nanpercentile(alma_reproj, 3),
# # vmax=np.nanpercentile(alma_reproj, 97),
# # )
# vmin_alma, vmax_alma = np.nanpercentile(alma_reproj, [pmin, pmax])
#
# alma_norm = matplotlib.colors.PowerNorm(0.5, vmin=vmin_alma, vmax=vmax_alma)
#
# muse_reproj = reproject_interp(muse_hdu[1], # [1] for real MUSE
# jwst_cutout.wcs.to_header(),
# shape_out=jwst_cutout.data.shape,
# return_footprint=False)
# muse_reproj[muse_reproj == 0] = np.nan
#
# vmin_muse, vmax_muse = np.nanpercentile(muse_reproj, [pmin, pmax])
#
# muse_norm = matplotlib.colors.PowerNorm(0.5, vmin=vmin_muse, vmax=vmax_muse)
#
# # muse_norm = matplotlib.colors.Normalize(vmin=np.nanpercentile(muse_reproj, 1),
# # vmax=np.nanpercentile(muse_reproj, 99),
# # )
#
# env_reproj = reproject_interp(environment_hdu[0],
# jwst_cutout.wcs.to_header(),
# shape_out=jwst_cutout.data.shape,
# return_footprint=False,
# order='nearest-neighbor')
# env_reproj[env_reproj != 6] = np.nan
# env_reproj[env_reproj == 6] = 1
#
# ii, jj = np.meshgrid(np.arange(env_reproj.shape[1]), np.arange(env_reproj.shape[0]))
#
# # rgb = make_lupton_rgb(jwst_cutout.data,
# # alma_reproj,
# # alma_reproj,
# # # Q=10,
# # stretch=0.5,
# # )
# rgb = np.dstack((jwst_norm(jwst_cutout.data),
# muse_norm(muse_reproj),
# # alma_norm(alma_reproj),
# alma_norm(alma_reproj)))
#
# plt.figure(figsize=(8, 4))
#
# ax = plt.subplot(projection=jwst_cutout.wcs)
# plt.imshow(rgb,
# origin='lower')
#
# plt.contourf(ii, jj, env_reproj,
# levels=1,
# colors='white',
# alpha=0.25,
# )
#
# plt.xlabel('RA (J2000)')
# plt.ylabel('Dec (J2000)')
#
# # plt.text(0.95, 0.95,
# # 'Test',
# # ha='right', va='top',
# # bbox=dict(facecolor='white', edgecolor='black', alpha=1),
# # transform=ax.transAxes)
#
# plt.tight_layout()
#
# plt.savefig(plot_name + '.pdf', bbox_inches='tight')
# plt.savefig(plot_name + '.png', bbox_inches='tight')
#
# # plt.show()
# plt.close()
#
jwst_hdu.close()
alma_hdu.close()
muse_hdu.close()
spur1_hdu.close()
spur2_hdu.close()
environment_hdu.close()
print('Complete!')