def test_mle_derivation(self, ):
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_almost_equal
from myimage_analysis import calculate_nhi
dgr = 0.1 # cm^2 10^-20 mag
intercept = 1 # mag
width = 20 # km/s
vel_range = (self.cloud.vel_center - width / 2.0,
self.cloud.vel_center + width / 2.0)
nhi_image = calculate_nhi(cube=self.cloud.hi_data,
velocity_axis=self.cloud.hi_vel_axis,
velocity_range=vel_range)
# Create mock Av_data
if 0:
av_data_mock = dgr * nhi_image + intercept
self.cloud.av_data = av_data_mock
self.cloud.run_analysis(
region_filename=self.region_filename,
region=self.region)
print('\nSaving cloud...')
cloudpy.save(self.cloud, self.cloud_filename)
else:
self.cloud = cloudpy.load(self.cloud_filename)
dgr_mle = self.cloud.props['dust2gas_ratio_max']['value']
intercept_mle = self.cloud.props['intercept_max']['value']
width_mle = self.cloud.props['hi_velocity_width_max']['value']
assert_almost_equal(dgr_mle, dgr, decimal=1)
assert_almost_equal(intercept_mle, intercept, decimal=1)
assert_almost_equal(width_mle, width, decimal=-1)
def test_mle_derivation(self,):
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_almost_equal
from myimage_analysis import calculate_nhi
dgr = 0.1 # cm^2 10^-20 mag
intercept = 1 # mag
width = 20 # km/s
vel_range = (self.cloud.vel_center - width / 2.0,
self.cloud.vel_center + width / 2.0)
nhi_image = calculate_nhi(cube=self.cloud.hi_data,
velocity_axis=self.cloud.hi_vel_axis,
velocity_range=vel_range)
# Create mock Av_data
if 0:
av_data_mock = dgr * nhi_image + intercept
self.cloud.av_data = av_data_mock
self.cloud.run_analysis(region_filename=self.region_filename,
region=self.region)
print('\nSaving cloud...')
cloudpy.save(self.cloud, self.cloud_filename)
else:
self.cloud = cloudpy.load(self.cloud_filename)
dgr_mle = self.cloud.props['dust2gas_ratio_max']['value']
intercept_mle = self.cloud.props['intercept_max']['value']
width_mle = self.cloud.props['hi_velocity_width_max']['value']
assert_almost_equal(dgr_mle, dgr, decimal=1)
assert_almost_equal(intercept_mle, intercept, decimal=1)
assert_almost_equal(width_mle, width, decimal=-1)
def test_run_anaylsis(self):
self.cloud.run_analysis(region_filename=self.region_filename,
region=self.region)
props, iter_vars = self.cloud.props, self.cloud.iter_vars
print('\nSaving props...')
cloudpy.save(props,
'/d/bip3/ezbc/multicloud/data/props.pickle')
cloudpy.save(iter_vars,
'/d/bip3/ezbc/multicloud/data/iter_vars.pickle')
print('\nSaving cloud...')
cloudpy.save(self.cloud, self.cloud_filename)
def test_run_anaylsis(self):
self.cloud.run_analysis(region_filename=self.region_filename,
region=self.region)
props, iter_vars = self.cloud.props, self.cloud.iter_vars
print('\nSaving props...')
cloudpy.save(props,
'/d/bip3/ezbc/multicloud/data/props.pickle')
cloudpy.save(iter_vars,
'/d/bip3/ezbc/multicloud/data/iter_vars.pickle')
print('\nSaving cloud...')
cloudpy.save(self.cloud, self.cloud_filename)
def run_cloud_analysis(args,
cloud_name = 'perseus',
region = None,
load = False,
data_type = 'planck'):
if 1:
cloud_name = args['cloud_name']
region = args['region']
load = args['load']
data_type = args['data_type']
background_subtract = args['background_subtract']
# define directory locations
# --------------------------
figure_dir = \
'/d/bip3/ezbc/' + cloud_name + '/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/'
# define filename
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'
if cloud_name == 'perseus' and data_type == 'lee12':
av_filename = av_dir + \
cloud_name + '_av_lee12_iris_regrid_planckres.fits'
av_error_filename = None
av_error = 0.1
if background_subtract:
av_background = 0.5
else:
av_background = None
if data_type == 'planck':
av_filename = av_dir + \
cloud_name + '_av_planck_tau353_5arcmin.fits'
av_error_filename = av_dir + \
cloud_name + '_av_error_planck_tau353_5arcmin.fits'
av_error = None
if 0:
av_error_filename = None
av_error = 1
av_background = None
if cloud_name == 'perseus' and data_type == 'planck_lee12mask':
av_filename = av_dir + \
cloud_name + '_av_planck_tau353_5arcmin_lee12mask.fits'
av_error_filename = av_dir + \
cloud_name + '_av_error_planck_tau353_5arcmin.fits'
av_error = None
av_background = None
if data_type == 'k09':
av_filename = av_dir + \
cloud_name + '_av_k09_regrid_planckres.fits'
av_error_filename = None
av_error = 0.4
av_background = 0.0
# Name of diagnostic files
if background_subtract:
background_name = '_backsub'
else:
background_name = ''
if args['bin_image']:
bin_name = '_binned'
args['bin_procedure'] = 'all'
else:
bin_name = ''
args['bin_procedure'] = 'none'
if args['fixed_width'] is None:
width_name = ''
init_vel_width = args['init_vel_width']
vel_center_gauss_fit_kwargs = None
else:
if args['fixed_width'] == 'gaussfit':
if args['cloud_name'] == 'perseus':
guesses = (28, 3, 5,
2, -20, 20)
ncomps = 2
elif args['cloud_name'] == 'taurus':
guesses = (28, 3, 5,
5, -30, 20,
3, -15, 5,
)
ncomps = 3
elif args['cloud_name'] == 'california':
guesses = (50, 3, 5,
20, -10, 10,
3, -45, 10,
#2, -20, 20,
)
ncomps = 3
vel_center_gauss_fit_kwargs = {'guesses': guesses,
'ncomps': ncomps,
#'width_scale': 2,
}
else:
vel_center_gauss_fit_kwargs = None
width_name = '_fixedwidth'
init_vel_width = args['fixed_width']
if args['use_weights']:
weights_name = '_weights'
weights_filename = av_dir + \
cloud_name + '_bin_weights.fits'
else:
weights_name = ''
weights_filename = None
if args['region'] is None:
region_name = ''
region = cloud_name
else:
region_name = '_region' + args['region']
region = cloud_name + args['region']
if args['av_mask_threshold'] is not None:
avthres_name = '_avthres'
else:
avthres_name = ''
if not args['use_intercept']:
intercept_name = '_noint'
else:
intercept_name = ''
if args['recalculate_likelihoods']:
error_name = '_errorrecalc'
else:
error_name = ''
if args['subtract_comps']:
compsub_name = '_compsub'
else:
compsub_name = ''
filename_extension = cloud_name + '_' + data_type + background_name + \
bin_name + weights_name + '_' + args['likelihood_resolution'] + \
'res' + region_name + width_name + avthres_name + \
intercept_name + error_name + compsub_name
# Plot args
residual_hist_filename_base = figure_dir + 'diagnostics/residuals/' + \
filename_extension + '_residual_hist'
residual_map_filename_base = figure_dir + 'diagnostics/residuals/' + \
filename_extension + '_residual_map'
likelihood_filename_base = figure_dir + 'diagnostics/likelihoods/' + \
filename_extension + '_likelihood'
av_bin_map_filename_base = figure_dir + 'diagnostics/maps/' + \
filename_extension + '_bin_map'
plot_args = {
'residual_hist_filename_base': residual_hist_filename_base,
'residual_map_filename_base': residual_map_filename_base,
'likelihood_filename_base': likelihood_filename_base,
'av_bin_map_filename_base' : av_bin_map_filename_base,
}
if 0:
import os
os.system('rm -rf ' + hi_error_filename)
region_filename = region_dir + 'multicloud_divisions.reg'
cloud_filename = \
'/d/bip3/ezbc/multicloud/data/python_output/' + \
filename_extension + \
'.pickle'
cloud_likelihood_filename = \
'/d/bip3/ezbc/multicloud/data/python_output/' + \
filename_extension + \
'_likelihoods.npy'
props_filename = \
'/d/bip3/ezbc/multicloud/data/python_output/' + \
filename_extension + \
'_props.pickle'
# background filenames
background_filename = av_dir + filename_extension + '_background.fits'
background_region_filename = background_region_dir + cloud_name + \
'_background.reg'
mask_filename = av_dir + filename_extension + '_mask.fits'
# Lee+12 testing
if args['fixed_width'] == 20 and args['data_type'] == 'lee12':
vel_center = 5
else:
vel_center = None
diagnostic_filename = \
'/d/bip3/ezbc/multicloud/data/python_output/diagnostics/' + \
filename_extension + '_diagnostic.txt'
if args['likelihood_resolution'] == 'fine':
if args['fixed_width'] is not None:
width_grid = np.array((args['fixed_width'],))
else:
width_grid = np.arange(1, 70, 2*0.16667)
#width_grid = np.arange(30, 70, 2*0.16667)
dgr_grid = np.arange(0.0, 0.2, 2e-4)
#dgr_grid = np.arange(0.05, 0.15, 2e-4)
intercept_grid = np.arange(-1, 1, 0.005)
#intercept_grid = np.arange(-1, 0., 0.005)
elif args['likelihood_resolution'] == 'coarse':
if args['fixed_width'] is not None:
width_grid = np.array((args['fixed_width'],))
else:
width_grid = np.arange(1, 50, 2*0.16667)
#width_grid = np.arange(100, 101, 1)
dgr_grid = np.arange(0.000, 0.6, 3e-3)
if args['use_intercept']:
intercept_grid = np.arange(-4, 4, 0.1)
else:
intercept_grid = np.array((0,))
#dgr_grid = np.arange(0.1, 0.5, 3e-3)
#intercept_grid = np.arange(-5, 1, 0.1)
#intercept_grid = np.array((0.9,))
else:
raise ValueError('likelihood_resolution should be either ' + \
'"coarse" or "fine"')
# Define number of pixels in each bin
# size of binned pixel in degrees * number of arcmin / degree * number of
# arcmin / pixel
binsize = 0.5 * 60.0 / 5.0
if load:
try:
if not args['load_props']:
print('\nAttempting to load cloud from file \n' + \
cloud_filename)
cloud = cloudpy.load(cloud_filename,
binary_likelihood_filename=cloud_likelihood_filename,
load_fits=True)
cloudpy.save(cloud.props, props_filename)
props = cloud.props
else:
print('\nAttempting to load cloud props from file \n' + \
props_filename)
cloud = None
props = cloudpy.load(props_filename)
run_analysis = False
except (EOFError, IOError):
print('\nLoading failed, performing analysis')
run_analysis = True
else:
run_analysis = True
if run_analysis:
print('\n\nPerforming analysis on ' + cloud_name)
if cloud_name == 'california':
if args['background_subtract']:
print('\nPerforming a background subtraction...')
av_filename_back = av_filename.replace('.fits', '_bin.fits')
av_background = perform_background_subtraction(av_filename_back,
background_dim=2,
#background_init=0.9,
background_filename=\
background_filename,
background_region_filename=\
background_region_filename)
#intercept_grid = np.arange(0, 1, 1)
av_background = 0.9
#results['av_background'] = av_background
cloud = cloudpy.Cloud(av_filename,
hi_filename,
av_error_filename=av_error_filename,
av_error=av_error,
av_background=av_background,
mask_filename=mask_filename,
hi_error_filename=hi_error_filename,
cloud_prop_filename=prop_filename,
dgr_grid=dgr_grid,
intercept_grid=intercept_grid,
width_grid=width_grid,
residual_width_scale=3,
threshold_delta_dgr=0.001,
#threshold_delta_dgr=1,
hi_noise_vel_range=[90,110],
vel_range_diff_thres=2,
init_vel_width=init_vel_width,
vel_center=vel_center,
vel_center_gauss_fit_kwargs=\
vel_center_gauss_fit_kwargs,
subtract_comps=args['subtract_comps'],
verbose=True,
clobber_likelihoods=True,
recalculate_likelihoods=\
args['recalculate_likelihoods'],
binsize=binsize,
use_bin_weights=args['use_weights'],
use_only_binned_data=args['bin_image'],
bin_procedure=args['bin_procedure'],
pixel_mask_increase_fraction=0.05,
binned_data_filename_ext=\
args['binned_data_filename_ext'],
weights_filename=weights_filename,
#diagnostic_filename=diagnostic_filename,
av_mask_threshold=args['av_mask_threshold'],
plot_args=plot_args,
perform_parent_iterations=0,
)
cloud.run_analysis(region_filename=region_filename,
region=region)
print('\nSaving cloud to file \n' + cloud_filename)
cloudpy.save(cloud,
cloud_filename,
binary_likelihood_filename=cloud_likelihood_filename,
write_fits=False)
cloudpy.save(cloud.props, props_filename)
props = cloud.props
cloud.co_filename = co_filename
results = {}
results['cloud'] = cloud
results['cloud_name'] = cloud_name
results['props'] = props
results['args'] = args
results['filename_extension'] = filename_extension
results['figure_dir'] = figure_dir
return results
def setup(self):
# define directory locations
# --------------------------
self.output_dir = '/d/bip3/ezbc/perseus/data/python_output/nhi_av/'
self.figure_dir = \
'/d/bip3/ezbc/perseus/figures/'
self.av_dir = '/d/bip3/ezbc/perseus/data/av/'
self.hi_dir = '/d/bip3/ezbc/perseus/data/hi/'
self.co_dir = '/d/bip3/ezbc/perseus/data/co/'
self.core_dir = \
'/d/bip3/ezbc/perseus/data/python_output/core_properties/'
self.property_dir = '/d/bip3/ezbc/perseus/data/python_output/'
self.region_dir = '/d/bip3/ezbc/multicloud/data/python_output/'
self.likelihood_dir = \
'/d/bip3/ezbc/perseus/data/python_output/nhi_av/'
# define filenames
self.prop_filename = self.property_dir + \
'perseus_global_properties.txt'
self.av_filename = self.av_dir + \
'perseus_av_planck_tau353_5arcmin.fits'
self.av_error_filename = self.av_dir + \
'perseus_av_error_planck_tau353_5arcmin.fits'
self.hi_filename = self.hi_dir + \
'perseus_hi_galfa_cube_regrid_planckres.fits'
self.hi_error_filename = self.hi_dir + \
'perseus_hi_galfa_cube_regrid_planckres_noise.fits'
self.region = 'perseus'
self.region_filename = self.region_dir + 'multicloud_divisions.reg'
self.cloud_filename = \
'/d/bip3/ezbc/multicloud/data/cloud.pickle'
# Plot args
residual_hist_filename_base = self.figure_dir + \
'diagnostics/residuals/' + \
self.region + '_residual_hist'
residual_map_filename_base = self.figure_dir + 'diagnostics/residuals/' + \
self.region + '_residual_map'
likelihood_filename_base = self.figure_dir + 'diagnostics/likelihoods/' + \
self.region + '_likelihood'
av_bin_map_filename_base = self.figure_dir + 'diagnostics/maps/' + \
self.region + '_bin_map'
plot_args = {
'residual_hist_filename_base': residual_hist_filename_base,
'residual_map_filename_base': residual_map_filename_base,
'likelihood_filename_base': likelihood_filename_base,
'av_bin_map_filename_base': av_bin_map_filename_base,
}
width_grid = np.arange(1, 75, 6 * 0.16667)
dgr_grid = np.arange(0.001, 0.3, 5e-3)
intercept_grid = np.arange(-2, 2, 0.1)
self.width_grid = width_grid
self.dgr_grid = dgr_grid
self.intercept_grid = intercept_grid
# Define number of pixels in each bin
self.binsize = 1.0 * 60.0 / 5.0
self.cloud = cloudpy.Cloud(
self.av_filename,
self.hi_filename,
av_error_filename=self.av_error_filename,
hi_error_filename=self.hi_error_filename,
cloud_prop_filename=self.prop_filename,
dgr_grid=self.dgr_grid,
intercept_grid=self.intercept_grid,
width_grid=self.width_grid,
residual_width_scale=3.0,
threshold_delta_dgr=0.0001,
hi_noise_vel_range=[90, 110],
vel_range_diff_thres=10,
init_vel_range=[-50, 50],
verbose=True,
clobber_likelihoods=True,
binsize=self.binsize,
plot_args=plot_args,
)
if 0:
cloudpy.save(
self.cloud,
self.cloud_filename.replace('cloud.pickle',
'cloud_init.pickle'))
def setup(self):
# define directory locations
# --------------------------
self.output_dir = '/d/bip3/ezbc/perseus/data/python_output/nhi_av/'
self.figure_dir = \
'/d/bip3/ezbc/perseus/figures/'
self.av_dir = '/d/bip3/ezbc/perseus/data/av/'
self.hi_dir = '/d/bip3/ezbc/perseus/data/hi/'
self.co_dir = '/d/bip3/ezbc/perseus/data/co/'
self.core_dir = \
'/d/bip3/ezbc/perseus/data/python_output/core_properties/'
self.property_dir = '/d/bip3/ezbc/perseus/data/python_output/'
self.region_dir = '/d/bip3/ezbc/multicloud/data/python_output/'
self.likelihood_dir = \
'/d/bip3/ezbc/perseus/data/python_output/nhi_av/'
# define filenames
self.prop_filename = self.property_dir + \
'perseus_global_properties.txt'
self.av_filename = self.av_dir + \
'perseus_av_planck_tau353_5arcmin.fits'
self.av_error_filename = self.av_dir + \
'perseus_av_error_planck_tau353_5arcmin.fits'
self.hi_filename = self.hi_dir + \
'perseus_hi_galfa_cube_regrid_planckres.fits'
self.hi_error_filename = self.hi_dir + \
'perseus_hi_galfa_cube_regrid_planckres_noise.fits'
self.region = 'perseus'
self.region_filename = self.region_dir + 'multicloud_divisions.reg'
self.cloud_filename = \
'/d/bip3/ezbc/multicloud/data/cloud.pickle'
# Plot args
residual_hist_filename_base = self.figure_dir + \
'diagnostics/residuals/' + \
self.region + '_residual_hist'
residual_map_filename_base = self.figure_dir + 'diagnostics/residuals/' + \
self.region + '_residual_map'
likelihood_filename_base = self.figure_dir + 'diagnostics/likelihoods/' + \
self.region + '_likelihood'
av_bin_map_filename_base = self.figure_dir + 'diagnostics/maps/' + \
self.region + '_bin_map'
plot_args = {
'residual_hist_filename_base': residual_hist_filename_base,
'residual_map_filename_base': residual_map_filename_base,
'likelihood_filename_base': likelihood_filename_base,
'av_bin_map_filename_base' : av_bin_map_filename_base,
}
width_grid = np.arange(1, 75, 6*0.16667)
dgr_grid = np.arange(0.001, 0.3, 5e-3)
intercept_grid = np.arange(-2, 2, 0.1)
self.width_grid = width_grid
self.dgr_grid = dgr_grid
self.intercept_grid = intercept_grid
# Define number of pixels in each bin
self.binsize = 1.0 * 60.0 / 5.0
self.cloud = cloudpy.Cloud(self.av_filename,
self.hi_filename,
av_error_filename=self.av_error_filename,
hi_error_filename=self.hi_error_filename,
cloud_prop_filename=self.prop_filename,
dgr_grid=self.dgr_grid,
intercept_grid=self.intercept_grid,
width_grid=self.width_grid,
residual_width_scale=3.0,
threshold_delta_dgr=0.0001,
hi_noise_vel_range=[90,110],
vel_range_diff_thres=10,
init_vel_range=[-50,50],
verbose=True,
clobber_likelihoods=True,
binsize=self.binsize,
plot_args=plot_args,
)
if 0:
cloudpy.save(self.cloud,
self.cloud_filename.replace('cloud.pickle',
'cloud_init.pickle'))
def run_cloud_analysis(args,
cloud_name='perseus',
region=None,
load=False,
data_type='planck'):
if 1:
cloud_name = args['cloud_name']
region = args['region']
load = args['load']
data_type = args['data_type']
background_subtract = args['background_subtract']
# define directory locations
# --------------------------
figure_dir = \
'/d/bip3/ezbc/' + cloud_name + '/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/'
# define filename
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'
if cloud_name == 'perseus' and data_type == 'lee12':
av_filename = av_dir + \
cloud_name + '_av_lee12_iris_regrid_planckres.fits'
av_error_filename = None
av_error = 0.1
if background_subtract:
av_background = 0.5
else:
av_background = None
if data_type == 'planck':
av_filename = av_dir + \
cloud_name + '_av_planck_tau353_5arcmin.fits'
av_error_filename = av_dir + \
cloud_name + '_av_error_planck_tau353_5arcmin.fits'
av_error = None
if 0:
av_error_filename = None
av_error = 1
av_background = None
if cloud_name == 'perseus' and data_type == 'planck_lee12mask':
av_filename = av_dir + \
cloud_name + '_av_planck_tau353_5arcmin_lee12mask.fits'
av_error_filename = av_dir + \
cloud_name + '_av_error_planck_tau353_5arcmin.fits'
av_error = None
av_background = None
if data_type == 'k09':
av_filename = av_dir + \
cloud_name + '_av_k09_regrid_planckres.fits'
av_error_filename = None
av_error = 0.4
av_background = 0.0
# Name of diagnostic files
if background_subtract:
background_name = '_backsub'
else:
background_name = ''
if args['bin_image']:
bin_name = '_binned'
args['bin_procedure'] = 'all'
else:
bin_name = ''
args['bin_procedure'] = 'none'
if args['fixed_width'] is None:
width_name = ''
init_vel_width = args['init_vel_width']
vel_center_gauss_fit_kwargs = None
else:
if args['fixed_width'] == 'gaussfit':
if args['cloud_name'] == 'perseus':
guesses = (28, 3, 5, 2, -20, 20)
ncomps = 2
elif args['cloud_name'] == 'taurus':
guesses = (
28,
3,
5,
5,
-30,
20,
3,
-15,
5,
)
ncomps = 3
elif args['cloud_name'] == 'california':
guesses = (
50,
3,
5,
20,
-10,
10,
3,
-45,
10,
#2, -20, 20,
)
ncomps = 3
vel_center_gauss_fit_kwargs = {
'guesses': guesses,
'ncomps': ncomps,
#'width_scale': 2,
}
else:
vel_center_gauss_fit_kwargs = None
width_name = '_fixedwidth'
init_vel_width = args['fixed_width']
if args['use_weights']:
weights_name = '_weights'
weights_filename = av_dir + \
cloud_name + '_bin_weights.fits'
else:
weights_name = ''
weights_filename = None
if args['region'] is None:
region_name = ''
region = cloud_name
else:
region_name = '_region' + args['region']
region = cloud_name + args['region']
if args['av_mask_threshold'] is not None:
avthres_name = '_avthres'
else:
avthres_name = ''
if not args['use_intercept']:
intercept_name = '_noint'
else:
intercept_name = ''
if args['recalculate_likelihoods']:
error_name = '_errorrecalc'
else:
error_name = ''
if args['subtract_comps']:
compsub_name = '_compsub'
else:
compsub_name = ''
filename_extension = cloud_name + '_' + data_type + background_name + \
bin_name + weights_name + '_' + args['likelihood_resolution'] + \
'res' + region_name + width_name + avthres_name + \
intercept_name + error_name + compsub_name
# Plot args
residual_hist_filename_base = figure_dir + 'diagnostics/residuals/' + \
filename_extension + '_residual_hist'
residual_map_filename_base = figure_dir + 'diagnostics/residuals/' + \
filename_extension + '_residual_map'
likelihood_filename_base = figure_dir + 'diagnostics/likelihoods/' + \
filename_extension + '_likelihood'
av_bin_map_filename_base = figure_dir + 'diagnostics/maps/' + \
filename_extension + '_bin_map'
plot_args = {
'residual_hist_filename_base': residual_hist_filename_base,
'residual_map_filename_base': residual_map_filename_base,
'likelihood_filename_base': likelihood_filename_base,
'av_bin_map_filename_base': av_bin_map_filename_base,
}
if 0:
import os
os.system('rm -rf ' + hi_error_filename)
region_filename = region_dir + 'multicloud_divisions.reg'
cloud_filename = \
'/d/bip3/ezbc/multicloud/data/python_output/' + \
filename_extension + \
'.pickle'
cloud_likelihood_filename = \
'/d/bip3/ezbc/multicloud/data/python_output/' + \
filename_extension + \
'_likelihoods.npy'
props_filename = \
'/d/bip3/ezbc/multicloud/data/python_output/' + \
filename_extension + \
'_props.pickle'
# background filenames
background_filename = av_dir + filename_extension + '_background.fits'
background_region_filename = background_region_dir + cloud_name + \
'_background.reg'
mask_filename = av_dir + filename_extension + '_mask.fits'
# Lee+12 testing
if args['fixed_width'] == 20 and args['data_type'] == 'lee12':
vel_center = 5
else:
vel_center = None
diagnostic_filename = \
'/d/bip3/ezbc/multicloud/data/python_output/diagnostics/' + \
filename_extension + '_diagnostic.txt'
if args['likelihood_resolution'] == 'fine':
if args['fixed_width'] is not None:
width_grid = np.array((args['fixed_width'], ))
else:
width_grid = np.arange(1, 70, 2 * 0.16667)
#width_grid = np.arange(30, 70, 2*0.16667)
dgr_grid = np.arange(0.0, 0.2, 2e-4)
#dgr_grid = np.arange(0.05, 0.15, 2e-4)
intercept_grid = np.arange(-1, 1, 0.005)
#intercept_grid = np.arange(-1, 0., 0.005)
elif args['likelihood_resolution'] == 'coarse':
if args['fixed_width'] is not None:
width_grid = np.array((args['fixed_width'], ))
else:
width_grid = np.arange(1, 50, 2 * 0.16667)
#width_grid = np.arange(100, 101, 1)
dgr_grid = np.arange(0.000, 0.6, 3e-3)
if args['use_intercept']:
intercept_grid = np.arange(-4, 4, 0.1)
else:
intercept_grid = np.array((0, ))
#dgr_grid = np.arange(0.1, 0.5, 3e-3)
#intercept_grid = np.arange(-5, 1, 0.1)
#intercept_grid = np.array((0.9,))
else:
raise ValueError('likelihood_resolution should be either ' + \
'"coarse" or "fine"')
# Define number of pixels in each bin
# size of binned pixel in degrees * number of arcmin / degree * number of
# arcmin / pixel
binsize = 0.5 * 60.0 / 5.0
if load:
try:
if not args['load_props']:
print('\nAttempting to load cloud from file \n' + \
cloud_filename)
cloud = cloudpy.load(
cloud_filename,
binary_likelihood_filename=cloud_likelihood_filename,
load_fits=True)
cloudpy.save(cloud.props, props_filename)
props = cloud.props
else:
print('\nAttempting to load cloud props from file \n' + \
props_filename)
cloud = None
props = cloudpy.load(props_filename)
run_analysis = False
except (EOFError, IOError):
print('\nLoading failed, performing analysis')
run_analysis = True
else:
run_analysis = True
if run_analysis:
print('\n\nPerforming analysis on ' + cloud_name)
if cloud_name == 'california':
if args['background_subtract']:
print('\nPerforming a background subtraction...')
av_filename_back = av_filename.replace('.fits', '_bin.fits')
av_background = perform_background_subtraction(av_filename_back,
background_dim=2,
#background_init=0.9,
background_filename=\
background_filename,
background_region_filename=\
background_region_filename)
#intercept_grid = np.arange(0, 1, 1)
av_background = 0.9
#results['av_background'] = av_background
cloud = cloudpy.Cloud(av_filename,
hi_filename,
av_error_filename=av_error_filename,
av_error=av_error,
av_background=av_background,
mask_filename=mask_filename,
hi_error_filename=hi_error_filename,
cloud_prop_filename=prop_filename,
dgr_grid=dgr_grid,
intercept_grid=intercept_grid,
width_grid=width_grid,
residual_width_scale=3,
threshold_delta_dgr=0.001,
#threshold_delta_dgr=1,
hi_noise_vel_range=[90,110],
vel_range_diff_thres=2,
init_vel_width=init_vel_width,
vel_center=vel_center,
vel_center_gauss_fit_kwargs=\
vel_center_gauss_fit_kwargs,
subtract_comps=args['subtract_comps'],
verbose=True,
clobber_likelihoods=True,
recalculate_likelihoods=\
args['recalculate_likelihoods'],
binsize=binsize,
use_bin_weights=args['use_weights'],
use_only_binned_data=args['bin_image'],
bin_procedure=args['bin_procedure'],
pixel_mask_increase_fraction=0.05,
binned_data_filename_ext=\
args['binned_data_filename_ext'],
weights_filename=weights_filename,
#diagnostic_filename=diagnostic_filename,
av_mask_threshold=args['av_mask_threshold'],
plot_args=plot_args,
perform_parent_iterations=0,
)
cloud.run_analysis(region_filename=region_filename, region=region)
print('\nSaving cloud to file \n' + cloud_filename)
cloudpy.save(cloud,
cloud_filename,
binary_likelihood_filename=cloud_likelihood_filename,
write_fits=False)
cloudpy.save(cloud.props, props_filename)
props = cloud.props
cloud.co_filename = co_filename
results = {}
results['cloud'] = cloud
results['cloud_name'] = cloud_name
results['props'] = props
results['args'] = args
results['filename_extension'] = filename_extension
results['figure_dir'] = figure_dir
return results