def get_results(global_args):
import myio
print('\nPerforming analysis on ' + global_args['cloud_name'])
print('=======================' + '=' * len(global_args['cloud_name']))
# Get the results filename
filename_base, global_args = create_filename_base(global_args)
print('\n\tFilename base = \n\t' + filename_base)
results_dir = '/d/bip3/ezbc/multicloud/data/python_output/'
results_filename = results_dir + \
'bootstrap_results/' + filename_base + \
'_bootstrap_results.pickle'
global_args['results_filename'] = results_filename
exists = myio.check_file(results_filename)
# either load or perform analysis
if global_args['load'] and exists:
print('\n\tLoading results...')
results_dict = load_results(global_args['results_filename'])
else:
results_dict = run_cloud_analysis(global_args)
# derive col dens images and statistics on MC sim
#add_results_analysis(results_dict)
# calculate errors on dgrs and intercept
#results_dict['params_summary'] = calc_param_errors(results_dict)
return results_dict
def get_results(global_args):
import myio
print('\nPerforming analysis on ' + global_args['cloud_name'])
print('=======================' + '=' * len(global_args['cloud_name']))
# Get the results filename
filename_base, global_args = create_filename_base(global_args)
print('\n\tFilename base = \n\t' + filename_base)
results_dir = '/d/bip3/ezbc/multicloud/data/python_output/'
results_filename = results_dir + \
'bootstrap_results/' + filename_base + \
'_bootstrap_results.pickle'
global_args['results_filename'] = results_filename
exists = myio.check_file(results_filename)
# either load or perform analysis
if global_args['load'] and exists:
print('\n\tLoading results...')
results_dict = load_results(global_args['results_filename'])
else:
results_dict = run_cloud_analysis(global_args)
# derive col dens images and statistics on MC sim
#add_results_analysis(results_dict)
# calculate errors on dgrs and intercept
#results_dict['params_summary'] = calc_param_errors(results_dict)
return results_dict
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 perform_background_subtraction(av_filename,
background_mask=None,
background_dim=1,
background_filename=None,
background_init=None,
background_region_filename=None):
# Import external modules
# -----------------------
from myio import check_file
from mycoords import convert_limit_coordinates, get_pix_coords, \
hrs2degs, load_ds9_region
from myimage_analysis import fit_background
from astropy.io import fits
import mygeometry as myg
av_data, av_header = fits.getdata(av_filename, header=True)
if background_init is not None:
av_data = av_data - background_init
file_exists = check_file(background_filename, clobber=False)
if not file_exists:
props = {}
print('writing new background')
# Load background regions from ds9
props = load_ds9_region(props,
filename=background_region_filename,
header=av_header,
key='background_regions')
# Derive relevant region
background_mask = np.ones(av_data.shape)
for background_region in props['background_regions']:
background_vertices = \
props['background_regions']\
[background_region]['poly_verts']['pixel']
# block off region
background_mask_temp = ~np.logical_not(
myg.get_polygon_mask(av_data, background_vertices))
background_mask[background_mask_temp] = 0
background_mask = ~np.logical_not(background_mask)
# Fit the background
background = fit_background(av_data,
background_mask,
background_dim=background_dim)
fits.writeto(background_filename, background, av_header)
else:
background = fits.getdata(background_filename)
return background
def perform_background_subtraction(av_filename, background_mask=None,
background_dim=1, background_filename=None, background_init=None,
background_region_filename=None):
# Import external modules
# -----------------------
from myio import check_file
from mycoords import convert_limit_coordinates, get_pix_coords, \
hrs2degs, load_ds9_region
from myimage_analysis import fit_background
from astropy.io import fits
import mygeometry as myg
av_data, av_header = fits.getdata(av_filename,
header=True)
if background_init is not None:
av_data = av_data - background_init
file_exists = check_file(background_filename, clobber=False)
if not file_exists:
props = {}
print('writing new background')
# Load background regions from ds9
props = load_ds9_region(props,
filename=background_region_filename,
header=av_header,
key='background_regions')
# Derive relevant region
background_mask = np.ones(av_data.shape)
for background_region in props['background_regions']:
background_vertices = \
props['background_regions']\
[background_region]['poly_verts']['pixel']
# block off region
background_mask_temp = ~np.logical_not(myg.get_polygon_mask(av_data,
background_vertices))
background_mask[background_mask_temp] = 0
background_mask = ~np.logical_not(background_mask)
# Fit the background
background = fit_background(av_data, background_mask,
background_dim=background_dim)
fits.writeto(background_filename, background, av_header)
else:
background = fits.getdata(background_filename)
return background
def get_core_results(results, clobber=False):
import pickle
import myio
for cloud_name in results:
cloud_dict = results[cloud_name]
model_analysis_filename = \
cloud_dict['model_fitting']['results_filename']
exists = \
myio.check_file(model_analysis_filename,
clobber=clobber)
if not exists:
run_model_analysis(cloud_dict)
else:
with open(model_analysis_filename, 'rb') as f:
cloud_dict['model_fitting'] = pickle.load(f)
f.close()
def main():
from myimage_analysis import bin_image, calculate_nhi
from mycoords import make_velocity_axis
from astropy.io import fits
import numpy as np
import mystats
import myio
import pickle
# Location of models
#MODEL_DIR = '/d/bip3/ezbc/shield/749237_lowres/modeling_cmode1_3inc/'
MODEL_DIR = '/d/bip3/ezbc/shield/749237_lowres/modeling_highres_2/'
# If true, deletes files to be written
CLOBBER = 0
os.chdir(MODEL_DIR + 'models')
# Ddelete gipsy files, leaving only fits files
filename_delete_list = os.listdir('./')
for filename in filename_delete_list:
if '.image' in filename or '.descr' in filename:
os.system('rm -rf ' + filename)
# get leftover fits files
filename_init_list = os.listdir('./')
filename_list = []
for filename in filename_init_list:
if 'model' in filename and 'regrid' not in filename:
filename_list.append(filename)
#filename_list = filename_list[:5]
stats = {
'logL': np.empty(len(filename_list)),
'model_names': [],
'std': np.empty(len(filename_list)),
'mean_abs_resid': np.empty(len(filename_list)),
'sum_abs_resid': np.empty(len(filename_list)),
}
cube_name = '749237_rebin_cube_regrid.fits'
unbinned_cube_name = '749237_rebin_cube.fits'
cube_error_name = '749237_rebin_cube_error_regrid.fits'
data, header = \
fits.getdata(MODEL_DIR + \
cube_name,
header=True)
error = \
fits.getdata(MODEL_DIR + \
cube_error_name)
data_sum = np.nansum(data)
#binsize = 6
_, unbinned_header = fits.getdata(MODEL_DIR + '../' + \
unbinned_cube_name,
header=True)
beamsize = unbinned_header['BMAJ']
cdelt = np.abs(unbinned_header['CDELT1'])
binsize = int(beamsize / cdelt)
mask = (np.isnan(data) | np.isnan(error))
# Load the images into miriad
for i, model_name in enumerate(filename_list):
model_bin_name = model_name.replace('.FITS', '_regrid.FITS')
exists = myio.check_file(model_bin_name, clobber=CLOBBER)
if exists:
print('Loading cube:\n' + model_bin_name)
model_bin = fits.getdata(model_bin_name)
else:
print('Binning cube:\n' + model_name)
model = fits.getdata(model_name)
print('\tBinsize = ' + str(binsize))
# bin the model
model_bin = bin_image(model,
binsize=(1, binsize, binsize),
statistic=np.nanmean,
quick_bin=True
)
# normalize the model to have same sum as data
model_bin = model_bin / np.nansum(model_bin) * data_sum
#assert np.nansum(model_bin) == data_sum
# write the model to a file
fits.writeto(model_bin_name,
model_bin,
header,
clobber=CLOBBER)
residuals = model_bin[~mask] - data[~mask]
stats['model_names'].append(model_bin_name)
stats['logL'][i] = mystats.calc_logL(model_bin[~mask],
data[~mask],
data_error=error[~mask])
stats['std'][i] = np.nanstd(residuals)
stats['mean_abs_resid'][i] = np.nanmean(np.abs(residuals))
stats['sum_abs_resid'][i] = np.nansum(np.abs(residuals))
with open(MODEL_DIR + 'statistics.pickle', 'wb') as f:
pickle.dump(stats, f)
with open(MODEL_DIR + 'statistics.pickle', 'rb') as f:
stats = pickle.load(f)
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 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],
)
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 main():
from myimage_analysis import bin_image, calculate_nhi
from mycoords import make_velocity_axis
from astropy.io import fits
import numpy as np
import mystats
import myio
import pickle
# Location of models
#MODEL_DIR = '/d/bip3/ezbc/shield/749237_lowres/modeling_cmode1_3inc/'
MODEL_DIR = '/d/bip3/ezbc/shield/749237_lowres/modeling_highres_2/'
# If true, deletes files to be written
CLOBBER = 0
os.chdir(MODEL_DIR + 'models')
# Ddelete gipsy files, leaving only fits files
filename_delete_list = os.listdir('./')
for filename in filename_delete_list:
if '.image' in filename or '.descr' in filename:
os.system('rm -rf ' + filename)
# get leftover fits files
filename_init_list = os.listdir('./')
filename_list = []
for filename in filename_init_list:
if 'model' in filename and 'regrid' not in filename:
filename_list.append(filename)
#filename_list = filename_list[:5]
stats = {
'logL': np.empty(len(filename_list)),
'model_names': [],
'std': np.empty(len(filename_list)),
'mean_abs_resid': np.empty(len(filename_list)),
'sum_abs_resid': np.empty(len(filename_list)),
}
cube_name = '749237_rebin_cube_regrid.fits'
unbinned_cube_name = '749237_rebin_cube.fits'
cube_error_name = '749237_rebin_cube_error_regrid.fits'
data, header = \
fits.getdata(MODEL_DIR + \
cube_name,
header=True)
error = \
fits.getdata(MODEL_DIR + \
cube_error_name)
data_sum = np.nansum(data)
#binsize = 6
_, unbinned_header = fits.getdata(MODEL_DIR + '../' + \
unbinned_cube_name,
header=True)
beamsize = unbinned_header['BMAJ']
cdelt = np.abs(unbinned_header['CDELT1'])
binsize = int(beamsize / cdelt)
mask = (np.isnan(data) | np.isnan(error))
# Load the images into miriad
for i, model_name in enumerate(filename_list):
model_bin_name = model_name.replace('.FITS', '_regrid.FITS')
exists = myio.check_file(model_bin_name, clobber=CLOBBER)
if exists:
print('Loading cube:\n' + model_bin_name)
model_bin = fits.getdata(model_bin_name)
else:
print('Binning cube:\n' + model_name)
model = fits.getdata(model_name)
print('\tBinsize = ' + str(binsize))
# bin the model
model_bin = bin_image(model,
binsize=(1, binsize, binsize),
statistic=np.nanmean,
quick_bin=True)
# normalize the model to have same sum as data
model_bin = model_bin / np.nansum(model_bin) * data_sum
#assert np.nansum(model_bin) == data_sum
# write the model to a file
fits.writeto(model_bin_name, model_bin, header, clobber=CLOBBER)
residuals = model_bin[~mask] - data[~mask]
stats['model_names'].append(model_bin_name)
stats['logL'][i] = mystats.calc_logL(model_bin[~mask],
data[~mask],
data_error=error[~mask])
stats['std'][i] = np.nanstd(residuals)
stats['mean_abs_resid'][i] = np.nanmean(np.abs(residuals))
stats['sum_abs_resid'][i] = np.nansum(np.abs(residuals))
with open(MODEL_DIR + 'statistics.pickle', 'wb') as f:
pickle.dump(stats, f)
with open(MODEL_DIR + 'statistics.pickle', 'rb') as f:
stats = pickle.load(f)
