def run_analysis(cloud_name):
from astropy.io import fits
from myimage_analysis import calculate_nhi, calc_region_mask
from mycoords import make_velocity_axis
from mystats import calc_symmetric_error, calc_logL
import myio
# define directory locations
# --------------------------
figure_dir = \
'/d/bip3/ezbc/multicloud/figures/'
av_dir = '/d/bip3/ezbc/' + cloud_name + '/data/av/'
hi_dir = '/d/bip3/ezbc/' + cloud_name + '/data/hi/'
co_dir = '/d/bip3/ezbc/' + cloud_name + '/data/co/'
core_dir = \
'/d/bip3/ezbc/' + cloud_name + '/data/python_output/core_properties/'
property_dir = '/d/bip3/ezbc/' + cloud_name + '/data/python_output/'
region_dir = '/d/bip3/ezbc/multicloud/data/python_output/'
background_region_dir = '/d/bip3/ezbc/' + cloud_name + \
'/data/python_output/ds9_regions/'
likelihood_dir = \
'/d/bip3/ezbc/' + cloud_name + '/data/python_output/nhi_av/'
results_dir = '/d/bip3/ezbc/multicloud/data/python_output/'
# define filenames
prop_filename = property_dir + \
cloud_name + '_global_properties.txt'
hi_filename = hi_dir + \
cloud_name + '_hi_galfa_cube_regrid_planckres.fits'
hi_error_filename = hi_dir + \
cloud_name + '_hi_galfa_cube_regrid_planckres_noise.fits'
co_filename = co_dir + \
cloud_name + '_co_cfa_cube_regrid_planckres.fits'
av_planck_filename = av_dir + \
cloud_name + '_av_planck_tau353_5arcmin.fits'
av_error_filename = av_dir + \
cloud_name + '_av_error_planck_tau353_5arcmin.fits'
av_k09_filename = av_dir + \
cloud_name + '_av_k09_regrid_planckres.fits'
if cloud_name == 'perseus':
av_lee12_filename = av_dir + \
cloud_name + '_av_lee12_2mass_regrid_planckres.fits'
else:
av_lee12_filename = None
av_error = 0.4
av_background = 0.0
# Get data
av_data_pl, av_header_pl = fits.getdata(av_planck_filename, header=True)
av_data_k09, av_header_k09 = fits.getdata(av_k09_filename, header=True)
if cloud_name == 'perseus':
av_data_lee12, av_header_lee12 = fits.getdata(av_lee12_filename,
header=True)
else:
av_data_lee12, av_header_lee12 = None, None
# mask data
region_filename = region_dir + 'multicloud_divisions.reg'
region_mask = calc_region_mask(region_filename,
av_data_pl,
av_header_pl,
region_name=cloud_name)
av_data_pl[region_mask] = np.nan
av_data_k09[region_mask] = np.nan
if cloud_name == 'perseus':
av_data_lee12[region_mask] = np.nan
#if debugging:
if 0:
import matplotlib.pyplot as plt
plt.close()
plt.clf()
plt.imshow(av_data, origin='lower')
plt.savefig('/usr/users/ezbc/Desktop/avmap.png')
#hi_data, hi_header = fits.getdata(hi_filename, header=True)
#co_data, co_header = fits.getdata(co_filename, header=True)
#hi_vel_axis = make_velocity_axis(hi_header)
filename = figure_dir + 'av/' + cloud_name + '_planck_vs_k09.png'
plot_planck_vs_2mass(
av_data_k09,
av_data_pl,
filename=filename,
title=cloud_name,
fit_labels=[r'$A_{V,{\rm Planck}}$', r'$A_{V,{\rm K+09}}$'],
labels=[
r'$A_{V,{\rm K+09\ 2MASS}}$ [mag]', r'$A_{V,{\rm Planck}}$ [mag]'
],
limits=[-1, 20, -1, 20])
if cloud_name == 'perseus':
filename = figure_dir + 'av/' + cloud_name + '_lee12_vs_k09.png'
plot_planck_vs_2mass(
av_data_k09,
av_data_lee12,
filename=filename,
title=cloud_name,
fit_labels=[r'$A_{V,{\rm Lee+12}}$', r'$A_{V,{\rm K+09}}$'],
labels=[
r'$A_{V,{\rm K+09\ 2MASS}}$ [mag]',
r'$A_{V,{\rm Lee+12\ 2MASS}}$ [mag]'
],
limits=[-1, 12, -1, 12])
filename = figure_dir + 'av/' + cloud_name + '_planck_vs_lee12.png'
plot_planck_vs_2mass(
av_data_lee12,
av_data_pl,
filename=filename,
title=cloud_name,
fit_labels=[r'$A_{V,{\rm Lee+12}}$',
r'$A_{V,{\rm Planck}}$'][::-1],
labels=[
r'$A_{V,{\rm Planck}}$ [mag]',
r'$A_{V,{\rm Lee+12\ 2MASS}}$ [mag]'
][::-1],
limits=[-1, 12, -1, 12])
def main():
# define constants
DIR_FIGURES = '/d/bip3/ezbc/multicloud/figures/'
DIR_RESULTS = '/d/bip3/ezbc/multicloud/data/python_output/bootstrap_results/'
DIR_AV = '/d/bip3/ezbc/multicloud/data/av/'
DIR_BETA = '/d/bip3/ezbc/multicloud/data/dust_temp/'
DIR_REGION = '/d/bip3/ezbc/multicloud/data/python_output/regions/'
FILENAME_EXT = '_planck_noint_gaussrange_isotropic_bootstrap_results.pickle'
FILENAME_PLOT_BASE = DIR_FIGURES + 'dust/av_vs_beta'
PLOT_FILETYPES = ['png', 'pdf']
CLOUD_NAMES = ['california', 'perseus', 'taurus']
beta, beta_header = fits.getdata(DIR_BETA + \
'multicloud_dust_beta_5arcmin.fits',
header=True)
temp, temp_header = fits.getdata(DIR_BETA + \
'multicloud_dust_temp_5arcmin.fits',
header=True)
av, av_header = fits.getdata(DIR_AV + \
#'multicloud_av_planck_5arcmin.fits',
'multicloud_av_k09_nan_regrid_planckres.fits',
header=True)
region_filename = DIR_REGION + 'multicloud_divisions.reg'
# get the data needed to plot
plot_dict = {}
for cloud_name in CLOUD_NAMES:
plot_dict[cloud_name] = {}
cloud_dict = plot_dict[cloud_name]
# get mask for data
region_mask = calc_region_mask(region_filename,
av,
av_header,
region_name=cloud_name)
# load the analysis
results = load_results(DIR_RESULTS + cloud_name + FILENAME_EXT)
hi_sd = results['data_products']['hi_sd']
h2_sd = results['data_products']['h2_sd']
cloud_dict['contour_image'] = None# hi_sd
cloud_dict['contours'] = [4, 8]
cloud_dict['h2_sd'] = h2_sd
cloud_dict['header'] = results['data']['av_header']
cloud_dict['beta'] = beta[~region_mask]
cloud_dict['av'] = av[~region_mask]
if cloud_name == 'california':
plot_dict[cloud_name]['limits'] = [75, 60, 30, 38]
if cloud_name == 'perseus':
plot_dict[cloud_name]['limits'] = [61, 45, 24, 36]
if cloud_name == 'taurus':
plot_dict[cloud_name]['limits'] = [75, 57, 20, 33]
# plot the 3-panel H2 surface density map
for filetype in PLOT_FILETYPES:
plot_av_vs_beta_grid(plot_dict,
filename=FILENAME_PLOT_BASE + '.' + filetype,
poly_fit=True,
#vlimits=[-0.1, 50],
)
def run_analysis(cloud_name):
from astropy.io import fits
from myimage_analysis import calculate_nhi, calc_region_mask
from mycoords import make_velocity_axis
from mystats import calc_symmetric_error, calc_logL
import myio
# define directory locations
# --------------------------
figure_dir = \
'/d/bip3/ezbc/multicloud/figures/'
av_dir = '/d/bip3/ezbc/' + cloud_name + '/data/av/'
hi_dir = '/d/bip3/ezbc/' + cloud_name + '/data/hi/'
co_dir = '/d/bip3/ezbc/' + cloud_name + '/data/co/'
core_dir = \
'/d/bip3/ezbc/' + cloud_name + '/data/python_output/core_properties/'
property_dir = '/d/bip3/ezbc/' + cloud_name + '/data/python_output/'
region_dir = '/d/bip3/ezbc/multicloud/data/python_output/'
background_region_dir = '/d/bip3/ezbc/' + cloud_name + \
'/data/python_output/ds9_regions/'
likelihood_dir = \
'/d/bip3/ezbc/' + cloud_name + '/data/python_output/nhi_av/'
results_dir = '/d/bip3/ezbc/multicloud/data/python_output/'
# define filenames
prop_filename = property_dir + \
cloud_name + '_global_properties.txt'
hi_filename = hi_dir + \
cloud_name + '_hi_galfa_cube_regrid_planckres.fits'
hi_error_filename = hi_dir + \
cloud_name + '_hi_galfa_cube_regrid_planckres_noise.fits'
co_filename = co_dir + \
cloud_name + '_co_cfa_cube_regrid_planckres.fits'
av_planck_filename = av_dir + \
cloud_name + '_av_planck_tau353_5arcmin.fits'
av_error_filename = av_dir + \
cloud_name + '_av_error_planck_tau353_5arcmin.fits'
av_k09_filename = av_dir + \
cloud_name + '_av_k09_regrid_planckres.fits'
if cloud_name == 'perseus':
av_lee12_filename = av_dir + \
cloud_name + '_av_lee12_2mass_regrid_planckres.fits'
else:
av_lee12_filename = None
av_error = 0.4
av_background = 0.0
# Get data
av_data_pl, av_header_pl = fits.getdata(av_planck_filename, header=True)
av_data_k09, av_header_k09 = fits.getdata(av_k09_filename, header=True)
if cloud_name == 'perseus':
av_data_lee12, av_header_lee12 = fits.getdata(av_lee12_filename,
header=True)
else:
av_data_lee12, av_header_lee12 = None, None
# mask data
region_filename = region_dir + 'multicloud_divisions.reg'
region_mask = calc_region_mask(region_filename,
av_data_pl,
av_header_pl,
region_name=cloud_name)
av_data_pl[region_mask] = np.nan
av_data_k09[region_mask] = np.nan
if cloud_name == 'perseus':
av_data_lee12[region_mask] = np.nan
#if debugging:
if 0:
import matplotlib.pyplot as plt
plt.close(); plt.clf()
plt.imshow(av_data, origin='lower')
plt.savefig('/usr/users/ezbc/Desktop/avmap.png')
#hi_data, hi_header = fits.getdata(hi_filename, header=True)
#co_data, co_header = fits.getdata(co_filename, header=True)
#hi_vel_axis = make_velocity_axis(hi_header)
filename = figure_dir + 'av/' + cloud_name + '_planck_vs_k09.png'
plot_planck_vs_2mass(av_data_k09, av_data_pl,
filename=filename,
title=cloud_name,
fit_labels=[r'$A_{V,{\rm Planck}}$',
r'$A_{V,{\rm K+09}}$'],
labels=[r'$A_{V,{\rm K+09\ 2MASS}}$ [mag]',
r'$A_{V,{\rm Planck}}$ [mag]'],
limits=[-1, 20, -1, 20])
if cloud_name == 'perseus':
filename = figure_dir + 'av/' + cloud_name + '_lee12_vs_k09.png'
plot_planck_vs_2mass(av_data_k09, av_data_lee12,
filename=filename,
title=cloud_name,
fit_labels=[r'$A_{V,{\rm Lee+12}}$',
r'$A_{V,{\rm K+09}}$'],
labels=[r'$A_{V,{\rm K+09\ 2MASS}}$ [mag]',
r'$A_{V,{\rm Lee+12\ 2MASS}}$ [mag]'],
limits=[-1, 12, -1, 12])
filename = figure_dir + 'av/' + cloud_name + '_planck_vs_lee12.png'
plot_planck_vs_2mass(av_data_lee12, av_data_pl,
filename=filename,
title=cloud_name,
fit_labels=[r'$A_{V,{\rm Lee+12}}$',
r'$A_{V,{\rm Planck}}$'][::-1],
labels=[r'$A_{V,{\rm Planck}}$ [mag]',
r'$A_{V,{\rm Lee+12\ 2MASS}}$ [mag]'][::-1],
limits=[-1, 12, -1, 12])
def run_cloud_analysis(global_args, ):
from astropy.io import fits
from myimage_analysis import calculate_nhi, calc_region_mask
import myimage_analysis as myia
from mycoords import make_velocity_axis
from mystats import calc_symmetric_error, calc_logL
import os
import myio
import pickle
import mystats
cloud_name = global_args['cloud_name']
region = global_args['region']
load = global_args['load']
data_type = global_args['data_type']
background_subtract = global_args['background_subtract']
# define directory locations
# --------------------------
figure_dir = \
'/d/bip3/ezbc/multicloud/figures/'
av_dir = '/d/bip3/ezbc/' + cloud_name + '/data/av/'
dust_temp_dir = '/d/bip3/ezbc/' + cloud_name + '/data/dust_temp/'
hi_dir = '/d/bip3/ezbc/' + cloud_name + '/data/hi/'
co_dir = '/d/bip3/ezbc/' + cloud_name + '/data/co/'
core_dir = \
'/d/bip3/ezbc/' + cloud_name + '/data/python_output/core_properties/'
property_dir = '/d/bip3/ezbc/' + cloud_name + '/data/python_output/'
region_dir = '/d/bip3/ezbc/multicloud/data/python_output/regions/'
background_region_dir = '/d/bip3/ezbc/' + cloud_name + \
'/data/python_output/ds9_regions/'
results_dir = '/d/bip3/ezbc/multicloud/data/python_output/'
av_filename = av_dir + \
cloud_name + '_av_planck_tau353_5arcmin.fits'
av_data, av_header = fits.getdata(av_filename, header=True)
# define filenames
prop_filename = property_dir + \
cloud_name + '_global_properties.txt'
hi_filename = hi_dir + \
cloud_name + '_hi_galfa_cube_regrid_planckres.fits'
hi_dr1_filename = hi_dir + \
cloud_name + '_hi_galfa_dr1_cube_regrid_planckres.fits'
hi_error_filename = hi_dir + \
cloud_name + '_hi_galfa_cube_regrid_planckres_noise.fits'
co_filename = co_dir + \
cloud_name + '_co_cfa_cube_regrid_planckres.fits'
# Get the filename base to differentiate between different parameters
filename_base, global_args = create_filename_base(global_args)
# set up plotting variables
plot_kwargs = {
'figure_dir': figure_dir,
'cloud_name': cloud_name,
'filename_base': filename_base,
'plot_diagnostics': global_args['plot_diagnostics'],
#'av_nhi_contour': av_nhi_contour,
'av_nhi_contour': True,
'av_nhi_limits': [0, 20, -1, 9],
#'av_nhi_limits': None,
}
# mask data
region_filename = region_dir + 'multicloud_divisions.reg'
region_mask = calc_region_mask(region_filename,
av_data,
av_header,
region_name=global_args['region_name'])
# Load HI and CO cubes
hi_data, hi_header = fits.getdata(hi_filename, header=True)
hi_dr1_data, hi_dr1_header = fits.getdata(hi_dr1_filename, header=True)
co_data, co_header = fits.getdata(co_filename, header=True)
#hi_data[:, region_mask] = np.nan
#hi_dr1_data[:, region_mask] = np.nan
#co_data[:, region_mask] = np.nan
hi_vel_axis = make_velocity_axis(hi_header)
co_vel_axis = make_velocity_axis(co_header)
# Load HI error
if global_args['clobber_hi_error']:
print('\n\tCalculating HI noise cube...')
os.system('rm -rf ' + hi_error_filename)
hi_data_error = \
myia.calculate_noise_cube(cube=hi_data,
velocity_axis=hi_vel_axis,
velocity_noise_range=[-110,-90, 90,110],
Tsys=30.0,
filename=hi_error_filename)
else:
hi_data_error = fits.getdata(hi_error_filename)
# Derive N(HI)
# -------------------------------------------------------------------------
# get fit kwargs
gauss_fit_kwargs, ncomps_in_cloud = get_gauss_fit_kwargs(global_args)
# derive spectra or load
spectra_filename = results_dir + 'spectra/' + global_args['cloud_name'] + \
'_spectra.pickle'
spectra_dr1_filename = results_dir + 'spectra/' + \
global_args['cloud_name'] + \
'_spectra_dr1.pickle'
load_spectra = myio.check_file(spectra_filename,
clobber=global_args['clobber_spectra'])
if load_spectra:
hi_spectrum, hi_std_spectrum, co_spectrum = \
myio.load_pickle(spectra_filename)
hi_dr1_spectrum, hi_std_dr1_spectrum, co_spectrum = \
myio.load_pickle(spectra_dr1_filename)
else:
print('\n\tCalculating spectra...')
if global_args['smooth_hi_to_co_res']:
from astropy.convolution import Gaussian2DKernel, convolve
# Create kernel
# one pix = 5 arcmin, need 8.4 arcmin for CO res
# The beamsize is the FWHM. The convolution kernel needs the
# standard deviation
hi_res = 1.0
co_res = 8.4 / 5.0
width = (co_res**2 - hi_res**2)**0.5
std = width / 2.355
g = Gaussian2DKernel(width)
# Convolve data
hi_data_co_res = np.zeros(hi_data.shape)
for i in xrange(hi_data.shape[0]):
hi_data_co_res[i, :, :] = \
convolve(hi_data[i, :, :], g, boundary='extend')
hi_dr1_data_co_res = np.zeros(hi_dr1_data.shape)
for i in xrange(hi_dr1_data.shape[0]):
hi_dr1_data_co_res[i, :, :] = \
convolve(hi_dr1_data[i, :, :], g, boundary='extend')
hi_spectrum = myia.calc_spectrum(hi_data_co_res)
hi_std_spectrum = myia.calc_spectrum(hi_data_co_res,
statistic=np.nanstd)
hi_dr1_spectrum = myia.calc_spectrum(hi_dr1_data_co_res)
hi_std_dr1_spectrum = myia.calc_spectrum(hi_dr1_data_co_res,
statistic=np.nanstd)
co_spectrum = myia.calc_spectrum(co_data)
myio.save_pickle(spectra_filename,
(hi_spectrum, hi_std_spectrum, co_spectrum))
myio.save_pickle(spectra_dr1_filename,
(hi_dr1_spectrum, hi_std_dr1_spectrum, co_spectrum))
if global_args['hi_range_calc'] == 'gaussian':
velocity_range, gauss_fits, comp_num, hi_range_error = \
calc_hi_vel_range(hi_spectrum,
hi_vel_axis,
gauss_fit_kwargs,
co_spectrum=co_spectrum,
co_vel_axis=co_vel_axis,
ncomps=ncomps_in_cloud,
)
global_args['vel_range_error'] = hi_range_error
velocity_range_dr1, gauss_fits_dr1, comp_num_dr1, hi_range_error_dr1 = \
calc_hi_vel_range(hi_dr1_spectrum,
hi_vel_axis,
gauss_fit_kwargs,
co_spectrum=co_spectrum,
co_vel_axis=co_vel_axis,
ncomps=ncomps_in_cloud,
)
else:
velocity_range = [-5, 15]
gauss_fits = None
comp_num = None
hi_range_kwargs = {
'velocity_range': velocity_range,
'gauss_fits': gauss_fits,
'comp_num': comp_num,
'hi_range_error': hi_range_error,
'vel_range': velocity_range,
'gauss_fit_kwargs': gauss_fit_kwargs,
}
# plot the results
# --------------------------------------------------------------------------
filename = plot_kwargs['figure_dir'] + \
'spectra/' + plot_kwargs['filename_base'] + \
'_spectra_dr2.png'
print('Saving\neog ' + filename + ' &')
plot_spectra(
hi_spectrum,
hi_vel_axis,
hi_std_spectrum=hi_std_spectrum,
gauss_fits=gauss_fits,
comp_num=comp_num,
co_spectrum=co_spectrum,
co_vel_axis=co_vel_axis,
vel_range=velocity_range,
filename=filename,
limits=[-50, 30, -10, 70],
)
# DR1 data
filename = plot_kwargs['figure_dir'] + \
'spectra/' + plot_kwargs['filename_base'] + \
'_spectra_dr1.png'
print('Saving\neog ' + filename + ' &')
plot_spectra(
hi_dr1_spectrum,
hi_vel_axis,
hi_std_spectrum=hi_std_dr1_spectrum,
gauss_fits=gauss_fits_dr1,
comp_num=comp_num_dr1,
co_spectrum=co_spectrum,
co_vel_axis=co_vel_axis,
vel_range=velocity_range_dr1,
filename=filename,
limits=[-50, 30, -10, 70],
)
velocity_range = [0, 15]
velocity_range_dr1 = [0, 15]
# use the vel range to derive N(HI)
nhi_image, nhi_image_error = \
calculate_nhi(cube=hi_data,
velocity_axis=hi_vel_axis,
velocity_range=velocity_range,
noise_cube=hi_data_error,
return_nhi_error=True,
)
# use the vel range to derive N(HI)
nhi_image_dr1 = \
calculate_nhi(cube=hi_dr1_data,
velocity_axis=hi_vel_axis,
velocity_range=velocity_range_dr1,
)
# mask for erroneous pixels
mask_nhi = (nhi_image < 0) & (nhi_image_dr1 < 0)
nhi_image[mask_nhi] = np.nan
nhi_image_dr1[mask_nhi] = np.nan
# Plot residuals between nhi maps
filename = plot_kwargs['figure_dir'] + \
'maps/' + plot_kwargs['filename_base'] + \
'_nhi_dr2_dr1_residuals.png'
print('Saving\neog ' + filename + ' &')
plot_nhi_image(
nhi_image=nhi_image / nhi_image_dr1,
header=hi_header,
limits=[65, 45, 25, 35],
filename=filename,
show=0,
cb_text='DR2 / DR1',
#hi_vlimits=[0.91, 0.93],
)
def run_cloud_analysis(global_args,):
from astropy.io import fits
from myimage_analysis import calculate_nhi, calc_region_mask
import myimage_analysis as myia
from mycoords import make_velocity_axis
from mystats import calc_symmetric_error, calc_logL
import os
import myio
import pickle
import mystats
cloud_name = global_args['cloud_name']
region = global_args['region']
load = global_args['load']
data_type = global_args['data_type']
background_subtract = global_args['background_subtract']
# define directory locations
# --------------------------
figure_dir = \
'/d/bip3/ezbc/multicloud/figures/'
av_dir = '/d/bip3/ezbc/' + cloud_name + '/data/av/'
dust_temp_dir = '/d/bip3/ezbc/' + cloud_name + '/data/dust_temp/'
hi_dir = '/d/bip3/ezbc/' + cloud_name + '/data/hi/'
co_dir = '/d/bip3/ezbc/' + cloud_name + '/data/co/'
core_dir = \
'/d/bip3/ezbc/' + cloud_name + '/data/python_output/core_properties/'
property_dir = '/d/bip3/ezbc/' + cloud_name + '/data/python_output/'
region_dir = '/d/bip3/ezbc/multicloud/data/python_output/regions/'
background_region_dir = '/d/bip3/ezbc/' + cloud_name + \
'/data/python_output/ds9_regions/'
results_dir = '/d/bip3/ezbc/multicloud/data/python_output/'
av_filename = av_dir + \
cloud_name + '_av_planck_tau353_5arcmin.fits'
av_data, av_header = fits.getdata(av_filename, header=True)
# define filenames
prop_filename = property_dir + \
cloud_name + '_global_properties.txt'
hi_filename = hi_dir + \
cloud_name + '_hi_galfa_cube_regrid_planckres.fits'
hi_dr1_filename = hi_dir + \
cloud_name + '_hi_galfa_dr1_cube_regrid_planckres.fits'
hi_error_filename = hi_dir + \
cloud_name + '_hi_galfa_cube_regrid_planckres_noise.fits'
co_filename = co_dir + \
cloud_name + '_co_cfa_cube_regrid_planckres.fits'
# Get the filename base to differentiate between different parameters
filename_base, global_args = create_filename_base(global_args)
# set up plotting variables
plot_kwargs = {
'figure_dir': figure_dir,
'cloud_name': cloud_name,
'filename_base': filename_base,
'plot_diagnostics': global_args['plot_diagnostics'],
#'av_nhi_contour': av_nhi_contour,
'av_nhi_contour': True,
'av_nhi_limits': [0, 20, -1, 9],
#'av_nhi_limits': None,
}
# mask data
region_filename = region_dir + 'multicloud_divisions.reg'
region_mask = calc_region_mask(region_filename,
av_data,
av_header,
region_name=global_args['region_name'])
# Load HI and CO cubes
hi_data, hi_header = fits.getdata(hi_filename, header=True)
hi_dr1_data, hi_dr1_header = fits.getdata(hi_dr1_filename, header=True)
co_data, co_header = fits.getdata(co_filename, header=True)
#hi_data[:, region_mask] = np.nan
#hi_dr1_data[:, region_mask] = np.nan
#co_data[:, region_mask] = np.nan
hi_vel_axis = make_velocity_axis(hi_header)
co_vel_axis = make_velocity_axis(co_header)
# Load HI error
if global_args['clobber_hi_error']:
print('\n\tCalculating HI noise cube...')
os.system('rm -rf ' + hi_error_filename)
hi_data_error = \
myia.calculate_noise_cube(cube=hi_data,
velocity_axis=hi_vel_axis,
velocity_noise_range=[-110,-90, 90,110],
Tsys=30.0,
filename=hi_error_filename)
else:
hi_data_error = fits.getdata(hi_error_filename)
# Derive N(HI)
# -------------------------------------------------------------------------
# get fit kwargs
gauss_fit_kwargs, ncomps_in_cloud = get_gauss_fit_kwargs(global_args)
# derive spectra or load
spectra_filename = results_dir + 'spectra/' + global_args['cloud_name'] + \
'_spectra.pickle'
spectra_dr1_filename = results_dir + 'spectra/' + \
global_args['cloud_name'] + \
'_spectra_dr1.pickle'
load_spectra = myio.check_file(spectra_filename,
clobber=global_args['clobber_spectra'])
if load_spectra:
hi_spectrum, hi_std_spectrum, co_spectrum = \
myio.load_pickle(spectra_filename)
hi_dr1_spectrum, hi_std_dr1_spectrum, co_spectrum = \
myio.load_pickle(spectra_dr1_filename)
else:
print('\n\tCalculating spectra...')
if global_args['smooth_hi_to_co_res']:
from astropy.convolution import Gaussian2DKernel, convolve
# Create kernel
# one pix = 5 arcmin, need 8.4 arcmin for CO res
# The beamsize is the FWHM. The convolution kernel needs the
# standard deviation
hi_res = 1.0
co_res = 8.4 / 5.0
width = (co_res**2 - hi_res**2)**0.5
std = width / 2.355
g = Gaussian2DKernel(width)
# Convolve data
hi_data_co_res = np.zeros(hi_data.shape)
for i in xrange(hi_data.shape[0]):
hi_data_co_res[i, :, :] = \
convolve(hi_data[i, :, :], g, boundary='extend')
hi_dr1_data_co_res = np.zeros(hi_dr1_data.shape)
for i in xrange(hi_dr1_data.shape[0]):
hi_dr1_data_co_res[i, :, :] = \
convolve(hi_dr1_data[i, :, :], g, boundary='extend')
hi_spectrum = myia.calc_spectrum(hi_data_co_res)
hi_std_spectrum = myia.calc_spectrum(hi_data_co_res,
statistic=np.nanstd)
hi_dr1_spectrum = myia.calc_spectrum(hi_dr1_data_co_res)
hi_std_dr1_spectrum = myia.calc_spectrum(hi_dr1_data_co_res,
statistic=np.nanstd)
co_spectrum = myia.calc_spectrum(co_data)
myio.save_pickle(spectra_filename,
(hi_spectrum, hi_std_spectrum, co_spectrum))
myio.save_pickle(spectra_dr1_filename,
(hi_dr1_spectrum, hi_std_dr1_spectrum, co_spectrum))
if global_args['hi_range_calc'] == 'gaussian':
velocity_range, gauss_fits, comp_num, hi_range_error = \
calc_hi_vel_range(hi_spectrum,
hi_vel_axis,
gauss_fit_kwargs,
co_spectrum=co_spectrum,
co_vel_axis=co_vel_axis,
ncomps=ncomps_in_cloud,
)
global_args['vel_range_error'] = hi_range_error
velocity_range_dr1, gauss_fits_dr1, comp_num_dr1, hi_range_error_dr1 = \
calc_hi_vel_range(hi_dr1_spectrum,
hi_vel_axis,
gauss_fit_kwargs,
co_spectrum=co_spectrum,
co_vel_axis=co_vel_axis,
ncomps=ncomps_in_cloud,
)
else:
velocity_range = [-5, 15]
gauss_fits = None
comp_num = None
hi_range_kwargs = {
'velocity_range': velocity_range,
'gauss_fits': gauss_fits,
'comp_num': comp_num,
'hi_range_error': hi_range_error,
'vel_range': velocity_range,
'gauss_fit_kwargs': gauss_fit_kwargs,
}
# plot the results
# --------------------------------------------------------------------------
filename = plot_kwargs['figure_dir'] + \
'spectra/' + plot_kwargs['filename_base'] + \
'_spectra_dr2.png'
print('Saving\neog ' + filename + ' &')
plot_spectra(hi_spectrum,
hi_vel_axis,
hi_std_spectrum=hi_std_spectrum,
gauss_fits=gauss_fits,
comp_num=comp_num,
co_spectrum=co_spectrum,
co_vel_axis=co_vel_axis,
vel_range=velocity_range,
filename=filename,
limits=[-50, 30, -10, 70],
)
# DR1 data
filename = plot_kwargs['figure_dir'] + \
'spectra/' + plot_kwargs['filename_base'] + \
'_spectra_dr1.png'
print('Saving\neog ' + filename + ' &')
plot_spectra(hi_dr1_spectrum,
hi_vel_axis,
hi_std_spectrum=hi_std_dr1_spectrum,
gauss_fits=gauss_fits_dr1,
comp_num=comp_num_dr1,
co_spectrum=co_spectrum,
co_vel_axis=co_vel_axis,
vel_range=velocity_range_dr1,
filename=filename,
limits=[-50, 30, -10, 70],
)
velocity_range = [0, 15]
velocity_range_dr1 = [0, 15]
# use the vel range to derive N(HI)
nhi_image, nhi_image_error = \
calculate_nhi(cube=hi_data,
velocity_axis=hi_vel_axis,
velocity_range=velocity_range,
noise_cube=hi_data_error,
return_nhi_error=True,
)
# use the vel range to derive N(HI)
nhi_image_dr1 = \
calculate_nhi(cube=hi_dr1_data,
velocity_axis=hi_vel_axis,
velocity_range=velocity_range_dr1,
)
# mask for erroneous pixels
mask_nhi = (nhi_image < 0) & (nhi_image_dr1 < 0)
nhi_image[mask_nhi] = np.nan
nhi_image_dr1[mask_nhi] = np.nan
# Plot residuals between nhi maps
filename = plot_kwargs['figure_dir'] + \
'maps/' + plot_kwargs['filename_base'] + \
'_nhi_dr2_dr1_residuals.png'
print('Saving\neog ' + filename + ' &')
plot_nhi_image(nhi_image=nhi_image / nhi_image_dr1,
header=hi_header,
limits=[65, 45, 25, 35],
filename=filename,
show=0,
cb_text='DR2 / DR1',
#hi_vlimits=[0.91, 0.93],
)
