def plot_co_spectra(results,):
filename_base = \
cloud_results['figure_dir'] + 'diagnostics/' + \
cloud_results['filename_extension'] + '_co_spectra'
from astropy.io import fits
from mycoords import make_velocity_axis
from myimage_analysis import bin_image
from myio import check_file
cloud = cloud_results['cloud']
co_filename = cloud.co_filename
if cloud_results['args']['bin_procedure'] in ('all', 'mle'):
co_filename = co_filename.replace('.fits', '_bin.fits')
exists = \
check_file(co_filename, clobber=False)
if not exists:
co_data, co_header = fits.getdata(co_filename,
header=True,
)
cloud.co_data, cloud.co_header = \
bin_image(co_data,
binsize=(1, cloud.binsize, cloud.binsize),
header=co_header,
statistic=np.nanmean)
fits.writeto(cloud.co_filename.replace('.fits', '_bin.fits'),
cloud.co_data,
cloud.co_header,
)
else:
cloud.co_data, cloud.co_header = \
fits.getdata(co_filename,
header=True,
)
cloud.co_vel_axis = make_velocity_axis(cloud.co_header)
# Derive relevant region
hi_mask = cloud.region_mask
av_data, av_header = fits.getdata(cloud.av_filename_bin, header=True)
cloud.load_region(cloud.region_filename, header=cloud.av_header)
cloud._derive_region_mask(av_data=av_data)
co_mask = cloud.region_mask
hi_mask = co_mask
cloudpy.plot_hi_spectrum(cloud,
filename=filename_base + '.png',
limits=[-50, 30, -10, 70],
plot_co=plot_co,
hi_mask=hi_mask,
co_mask=co_mask,
)
def plot_co_spectra(results, ):
filename_base = \
cloud_results['figure_dir'] + 'diagnostics/' + \
cloud_results['filename_extension'] + '_co_spectra'
from astropy.io import fits
from mycoords import make_velocity_axis
from myimage_analysis import bin_image
from myio import check_file
cloud = cloud_results['cloud']
co_filename = cloud.co_filename
if cloud_results['args']['bin_procedure'] in ('all', 'mle'):
co_filename = co_filename.replace('.fits', '_bin.fits')
exists = \
check_file(co_filename, clobber=False)
if not exists:
co_data, co_header = fits.getdata(
co_filename,
header=True,
)
cloud.co_data, cloud.co_header = \
bin_image(co_data,
binsize=(1, cloud.binsize, cloud.binsize),
header=co_header,
statistic=np.nanmean)
fits.writeto(
cloud.co_filename.replace('.fits', '_bin.fits'),
cloud.co_data,
cloud.co_header,
)
else:
cloud.co_data, cloud.co_header = \
fits.getdata(co_filename,
header=True,
)
cloud.co_vel_axis = make_velocity_axis(cloud.co_header)
# Derive relevant region
hi_mask = cloud.region_mask
av_data, av_header = fits.getdata(cloud.av_filename_bin, header=True)
cloud.load_region(cloud.region_filename, header=cloud.av_header)
cloud._derive_region_mask(av_data=av_data)
co_mask = cloud.region_mask
hi_mask = co_mask
cloudpy.plot_hi_spectrum(
cloud,
filename=filename_base + '.png',
limits=[-50, 30, -10, 70],
plot_co=plot_co,
hi_mask=hi_mask,
co_mask=co_mask,
)
def plot_hi_spectrum(cloud_results, plot_co=1):
filename_base = \
cloud_results['figure_dir'] + 'diagnostics/' + \
cloud_results['filename_extension'] + '_hi_spectrum'
from astropy.io import fits
from mycoords import make_velocity_axis
from myimage_analysis import bin_image
from myio import check_file
cloud = cloud_results['cloud']
if plot_co:
co_filename = cloud.co_filename
if cloud_results['args']['bin_procedure'] in ('all', 'mle'):
co_filename = co_filename.replace('.fits', '_bin.fits')
exists = \
check_file(co_filename, clobber=False)
if not exists:
co_data, co_header = fits.getdata(co_filename,
header=True,
)
cloud.co_data, cloud.co_header = \
bin_image(co_data,
binsize=(1, cloud.binsize, cloud.binsize),
header=co_header,
statistic=np.nanmean)
fits.writeto(cloud.co_filename.replace('.fits', '_bin.fits'),
cloud.co_data,
cloud.co_header,
)
else:
cloud.co_data, cloud.co_header = \
fits.getdata(co_filename,
header=True,
)
cloud.co_vel_axis = make_velocity_axis(cloud.co_header)
# Derive relevant region
if cloud_results['args']['bin_procedure'] in ('all', 'mle'):
av_filename = cloud.av_filename_bin
hi_filename = cloud.hi_filename_bin
else:
av_filename = cloud.av_filename
hi_mask = cloud.region_mask
av_data, av_header = fits.getdata(av_filename, header=True)
cloud.hi_data, cloud.hi_header = \
fits.getdata(hi_filename, header=True)
cloud.load_region(cloud.region_filename, header=av_header)
cloud._derive_region_mask(av_data=av_data)
co_mask = cloud.region_mask
hi_mask = co_mask
import matplotlib.pyplot as plt
plt.close(); plt.clf();
co = np.copy(cloud.co_data[30,:,:])
co[co_mask] = np.nan
plt.imshow(co, origin='lower')
plt.savefig('/usr/users/ezbc/Desktop/comap_' + cloud.region + '.png')
assert all((cloud.hi_data.shape, cloud.co_data.shape,
cloud.region_mask.shape))
cloudpy.plot_hi_spectrum(cloud,
filename=filename_base + '.png',
limits=[-50, 30, -10, 70],
plot_co=plot_co,
hi_mask=hi_mask,
co_mask=co_mask,
)
def plot_hi_spectrum(cloud_results, plot_co=1):
filename_base = \
cloud_results['figure_dir'] + 'diagnostics/' + \
cloud_results['filename_extension'] + '_hi_spectrum'
from astropy.io import fits
from mycoords import make_velocity_axis
from myimage_analysis import bin_image
from myio import check_file
cloud = cloud_results['cloud']
if plot_co:
co_filename = cloud.co_filename
if cloud_results['args']['bin_procedure'] in ('all', 'mle'):
co_filename = co_filename.replace('.fits', '_bin.fits')
exists = \
check_file(co_filename, clobber=False)
if not exists:
co_data, co_header = fits.getdata(
co_filename,
header=True,
)
cloud.co_data, cloud.co_header = \
bin_image(co_data,
binsize=(1, cloud.binsize, cloud.binsize),
header=co_header,
statistic=np.nanmean)
fits.writeto(
cloud.co_filename.replace('.fits', '_bin.fits'),
cloud.co_data,
cloud.co_header,
)
else:
cloud.co_data, cloud.co_header = \
fits.getdata(co_filename,
header=True,
)
cloud.co_vel_axis = make_velocity_axis(cloud.co_header)
# Derive relevant region
if cloud_results['args']['bin_procedure'] in ('all', 'mle'):
av_filename = cloud.av_filename_bin
hi_filename = cloud.hi_filename_bin
else:
av_filename = cloud.av_filename
hi_mask = cloud.region_mask
av_data, av_header = fits.getdata(av_filename, header=True)
cloud.hi_data, cloud.hi_header = \
fits.getdata(hi_filename, header=True)
cloud.load_region(cloud.region_filename, header=av_header)
cloud._derive_region_mask(av_data=av_data)
co_mask = cloud.region_mask
hi_mask = co_mask
import matplotlib.pyplot as plt
plt.close()
plt.clf()
co = np.copy(cloud.co_data[30, :, :])
co[co_mask] = np.nan
plt.imshow(co, origin='lower')
plt.savefig('/usr/users/ezbc/Desktop/comap_' + cloud.region + '.png')
assert all(
(cloud.hi_data.shape, cloud.co_data.shape, cloud.region_mask.shape))
cloudpy.plot_hi_spectrum(
cloud,
filename=filename_base + '.png',
limits=[-50, 30, -10, 70],
plot_co=plot_co,
hi_mask=hi_mask,
co_mask=co_mask,
)
