def cross_match_CSAR(getsources_core_catalog = '/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo/+catalogs/L1157.sw.final.reliable.ok.cat', CSAR_catalog = '/mnt/scratch-lustre/jkeown/DS9_regions/L1157/CSAR/CEPl1157_CSAR.dat', high_res_coldens_image = '/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cep1157_255_mu.image.resamp.fits', CSAR_core_indices='L1157_matched_CSAR_cores.dat'):
# Import getsources "good core" data table
cores_array1 = numpy.loadtxt(getsources_core_catalog,comments='!')
good_core_indices = good_cores_getsources.get_good_cores(getsources_core_catalog)
cores_array = cores_array1[numpy.array(good_core_indices)]
### Import the CSAR catalog
### ***MAKE SURE FIRST TWO COLUMNS OF "CSAR_catalog" FILE ARE X_POSITION AND Y_POSITION OF SOURCE IN DECIMAL DEGREES***
CSAR_array = numpy.loadtxt(CSAR_catalog,comments='#')
#print CSAR_array
CSAR_positions = numpy.column_stack((CSAR_array[:,0], CSAR_array[:,1]))
#print CSAR_positions
w = wcs.WCS(high_res_coldens_image)
pos_pix = w.wcs_world2pix(CSAR_positions, 1)
### Loop through the potential matched cores identified in the step above.
counter = 0
matched_cores = []
for line in cores_array:
x_coor = str(line[3])
y_coor = str(line[4])
### Create a DS9 region string for the core's getsources ellipse,
### from which a mask will be created.
region = ('fk5;ellipse(' + x_coor + ', ' + y_coor + ', ' + str((line[50]/2.0)/3600.) + ', ' +
str((line[51]/2.0)/3600.) + ', ' + str(line[52]+90.0)+')')
r = pyregion.parse(region)
f=fits.open(high_res_coldens_image)
mymask = r.get_mask(hdu=f[0])
f.close()
newmask=mymask
### Set all values outside the core's ellipse to zero,
### all values inside the ellipse are set to one.
newmask=numpy.where(newmask==0,0,1)
mask_shape = numpy.shape(newmask)
### Loop through the CSAR catalog
### If any CSAR cores fall within a getsources core's ellipse,
### store the getsources core's index
match_counter=0
for i in pos_pix:
ypos = int(round(i[1],0))-1
xpos = int(round(i[0],0))-1
if ypos<=mask_shape[0] and xpos<=mask_shape[1]:
if newmask[ypos][xpos]==1 and match_counter==0:
matched_cores.append(counter)
# match_counter prevents counting indices twice
# if two CSAR cores fall within getsources ellipse
match_counter+=1
#print len(matched_cores)
counter += 1
print 'CSAR_matched:total ratio =' + str(round(float(len(matched_cores)) / float(len(cores_array[:,0])),3))
### Save the CSAR matched core indices to a file
#numpy.savetxt(CSAR_core_indices, matched_cores, fmt='%i')
return numpy.array(matched_cores)
def make_DS9_regions_good_cores(getsources_core_catalog = '/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo/+catalogs/L1157.sw.final.reliable.ok.cat', YSO_catalog = '/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo_proto/+catalogs/L1157.sw.final.reliable.ok.cat', DS9_region_directory = '/mnt/scratch-lustre/jkeown/DS9_regions/HGBS_pipeline/L1157/', catalog_type=False, cross_matched_core_indices=False, candidate_prestellar_indices=False, robust_prestellar_indices=False):
if catalog_type=='all_cores':
# Import getsources "good core" data table
cores_array1 = numpy.loadtxt(getsources_core_catalog,comments='!')
good_core_indices = good_cores_getsources.get_good_cores(getsources_core_catalog)
cores_array = cores_array1[numpy.array(good_core_indices)]
save_file_name = '_all_cores_good.reg'
new_line = ' #color=green width=2\n'
elif catalog_type=='proto':
# Import the raw getsources protostar catalog
protostar_array1 = numpy.loadtxt(YSO_catalog,comments='!', unpack=True)
# Find the indices of the "good" protostars that pass HGBS selection criteria
good_proto_indices = good_protostars_getsources.get_good_protostars(YSO_catalog)
# Create new array of only the "good" protostars to be used for our analysis
# Not sure why the transpose below is needed, but it seems the protostar_array1
# is imported transposed. This doesn't seem to be the case for the core_array1
cores_array = protostar_array1.T[numpy.array(good_proto_indices)]
save_file_name = '_proto_good.reg'
new_line = ' #color=red width=2\n'
elif catalog_type=='proto_cores':
cores_array1 = numpy.loadtxt(getsources_core_catalog,comments='!')
good_core_indices = good_cores_getsources.get_good_cores(getsources_core_catalog)
cores_array = cores_array1[numpy.array(good_core_indices)]
cores_array = cores_array[numpy.array(cross_matched_core_indices)]
save_file_name = '_protostellar_cores.reg'
new_line = ' #color=yellow width=2\n'
elif catalog_type=='starless_cores':
cores_array1 = numpy.loadtxt(getsources_core_catalog,comments='!')
good_core_indices = good_cores_getsources.get_good_cores(getsources_core_catalog)
cores_array = cores_array1[numpy.array(good_core_indices)]
indices = numpy.arange(len(cores_array[:,0]))
for i in cross_matched_core_indices:
indices_minus_protostellar_cores = filter(lambda a: a!=i,indices)
indices = indices_minus_protostellar_cores
cores_array = cores_array[numpy.array(indices)]
save_file_name = '_starless_cores.reg'
new_line = ' #color=magenta width=2\n'
elif catalog_type=='prestellar_candidates':
cores_array1 = numpy.loadtxt(getsources_core_catalog,comments='!')
good_core_indices = good_cores_getsources.get_good_cores(getsources_core_catalog)
cores_array = cores_array1[numpy.array(good_core_indices)]
cores_array = cores_array[numpy.array(candidate_prestellar_indices[0])]
save_file_name = '_prestellar_candidates.reg'
new_line = ' #color=brown width=2\n'
elif catalog_type=='prestellar_robust':
cores_array1 = numpy.loadtxt(getsources_core_catalog,comments='!')
good_core_indices = good_cores_getsources.get_good_cores(getsources_core_catalog)
cores_array = cores_array1[numpy.array(good_core_indices)]
cores_array = cores_array[numpy.array(robust_prestellar_indices[0])]
save_file_name = '_prestellar_robust.reg'
new_line = ' #color=blue width=2\n'
header = 'Region file format: DS9 version 4.1 \nglobal color=green dashlist=8 3 width=2 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1 \nfk5'
ellipse = []
for i in cores_array[:,0]:
ellipse.append('ellipse')
number = [1, 2, 3, 4, 5, 6, 7]
A = [cores_array[:,14], cores_array[:,23], cores_array[:,32], cores_array[:,41], cores_array[:,50], cores_array[:,59], cores_array[:,68]]
B = [cores_array[:,15], cores_array[:,24], cores_array[:,33], cores_array[:,42], cores_array[:,51], cores_array[:,60], cores_array[:,69]]
T = [cores_array[:,16], cores_array[:,25], cores_array[:,34], cores_array[:,43], cores_array[:,52], cores_array[:,61], cores_array[:,70]]
f = 0
for wavelength in number:
string = (numpy.array(A[f])/2.)/3600. #Divide by two because DS9 takes radius rather than FWHM (or diameter) &&& convert arcseconds to degrees
string2 = []
for i in string:
string2.append(str(i))
#string3 = []
#for i in string2:
# string3.append(i+'"')
AFWHM = string2
string = (numpy.array(B[f])/2.)/3600.
string2 = []
for i in string:
string2.append(str(i))
#string3 = []
#for i in string2:
# string3.append(i+'"')
BFWHM = string2
# theta is given as degrees E of N in getsources, but degrees ccw from the +ve x-axis in ds9.
THEP_adjusted = numpy.array(T[f]) + 90.0
# Must divide FWHM by 2 because DS9 region files use a radius as input rather than diameter or "full width"
data = numpy.column_stack((ellipse, cores_array[:,3], cores_array[:,4], AFWHM, BFWHM, THEP_adjusted))
wave = ['070', '160', '165', '250', '255', '350', '500']
numpy.savetxt(DS9_region_directory + wave[f]+save_file_name, data, delimiter=' ', fmt = '%s', header=header, newline=new_line)
f = f + 1
# Write over the third line in the created files
# If this is not added, the "fk5" string remains commented out in the header
files = ['070' + save_file_name,'160' + save_file_name,'165' + save_file_name, '250' + save_file_name,'255' + save_file_name,'350' + save_file_name,'500' + save_file_name]
for i in files:
with open(DS9_region_directory + i, 'r') as file:
data = file.readlines()
data[2] = "fk5 \n"
with open(DS9_region_directory + i, 'w') as file:
file.writelines(data)
def make_DS9_regions_good_cores(
getsources_core_catalog='/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo/+catalogs/L1157.sw.final.reliable.ok.cat',
YSO_catalog='/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo_proto/+catalogs/L1157.sw.final.reliable.ok.cat',
DS9_region_directory='/mnt/scratch-lustre/jkeown/DS9_regions/HGBS_pipeline/L1157/',
catalog_type=False,
cross_matched_core_indices=False,
candidate_prestellar_indices=False,
robust_prestellar_indices=False):
if catalog_type == 'all_cores':
# Import getsources "good core" data table
cores_array1 = numpy.loadtxt(getsources_core_catalog, comments='!')
good_core_indices = good_cores_getsources.get_good_cores(
getsources_core_catalog)
cores_array = cores_array1[numpy.array(good_core_indices)]
save_file_name = '_all_cores_good.reg'
new_line = ' #color=green width=2\n'
elif catalog_type == 'proto':
# Import the raw getsources protostar catalog
protostar_array1 = numpy.loadtxt(YSO_catalog,
comments='!',
unpack=True)
# Find the indices of the "good" protostars that pass HGBS selection criteria
good_proto_indices = good_protostars_getsources.get_good_protostars(
YSO_catalog)
# Create new array of only the "good" protostars to be used for our analysis
# Not sure why the transpose below is needed, but it seems the protostar_array1
# is imported transposed. This doesn't seem to be the case for the core_array1
cores_array = protostar_array1.T[numpy.array(good_proto_indices)]
save_file_name = '_proto_good.reg'
new_line = ' #color=red width=2\n'
elif catalog_type == 'proto_cores':
cores_array1 = numpy.loadtxt(getsources_core_catalog, comments='!')
good_core_indices = good_cores_getsources.get_good_cores(
getsources_core_catalog)
cores_array = cores_array1[numpy.array(good_core_indices)]
cores_array = cores_array[numpy.array(cross_matched_core_indices)]
save_file_name = '_protostellar_cores.reg'
new_line = ' #color=yellow width=2\n'
elif catalog_type == 'starless_cores':
cores_array1 = numpy.loadtxt(getsources_core_catalog, comments='!')
good_core_indices = good_cores_getsources.get_good_cores(
getsources_core_catalog)
cores_array = cores_array1[numpy.array(good_core_indices)]
indices = numpy.arange(len(cores_array[:, 0]))
for i in cross_matched_core_indices:
indices_minus_protostellar_cores = filter(lambda a: a != i,
indices)
indices = indices_minus_protostellar_cores
cores_array = cores_array[numpy.array(indices)]
save_file_name = '_starless_cores.reg'
new_line = ' #color=magenta width=2\n'
elif catalog_type == 'prestellar_candidates':
cores_array1 = numpy.loadtxt(getsources_core_catalog, comments='!')
good_core_indices = good_cores_getsources.get_good_cores(
getsources_core_catalog)
cores_array = cores_array1[numpy.array(good_core_indices)]
cores_array = cores_array[numpy.array(candidate_prestellar_indices[0])]
save_file_name = '_prestellar_candidates.reg'
new_line = ' #color=brown width=2\n'
elif catalog_type == 'prestellar_robust':
cores_array1 = numpy.loadtxt(getsources_core_catalog, comments='!')
good_core_indices = good_cores_getsources.get_good_cores(
getsources_core_catalog)
cores_array = cores_array1[numpy.array(good_core_indices)]
cores_array = cores_array[numpy.array(robust_prestellar_indices[0])]
save_file_name = '_prestellar_robust.reg'
new_line = ' #color=blue width=2\n'
header = 'Region file format: DS9 version 4.1 \nglobal color=green dashlist=8 3 width=2 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1 \nfk5'
ellipse = []
for i in cores_array[:, 0]:
ellipse.append('ellipse')
number = [1, 2, 3, 4, 5, 6, 7]
A = [
cores_array[:, 14], cores_array[:, 23], cores_array[:, 32],
cores_array[:, 41], cores_array[:, 50], cores_array[:, 59],
cores_array[:, 68]
]
B = [
cores_array[:, 15], cores_array[:, 24], cores_array[:, 33],
cores_array[:, 42], cores_array[:, 51], cores_array[:, 60],
cores_array[:, 69]
]
T = [
cores_array[:, 16], cores_array[:, 25], cores_array[:, 34],
cores_array[:, 43], cores_array[:, 52], cores_array[:, 61],
cores_array[:, 70]
]
f = 0
for wavelength in number:
string = (
numpy.array(A[f]) / 2.
) / 3600. #Divide by two because DS9 takes radius rather than FWHM (or diameter) &&& convert arcseconds to degrees
string2 = []
for i in string:
string2.append(str(i))
#string3 = []
#for i in string2:
# string3.append(i+'"')
AFWHM = string2
string = (numpy.array(B[f]) / 2.) / 3600.
string2 = []
for i in string:
string2.append(str(i))
#string3 = []
#for i in string2:
# string3.append(i+'"')
BFWHM = string2
# theta is given as degrees E of N in getsources, but degrees ccw from the +ve x-axis in ds9.
THEP_adjusted = numpy.array(T[f]) + 90.0
# Must divide FWHM by 2 because DS9 region files use a radius as input rather than diameter or "full width"
data = numpy.column_stack(
(ellipse, cores_array[:, 3], cores_array[:, 4], AFWHM, BFWHM,
THEP_adjusted))
wave = ['070', '160', '165', '250', '255', '350', '500']
numpy.savetxt(DS9_region_directory + wave[f] + save_file_name,
data,
delimiter=' ',
fmt='%s',
header=header,
newline=new_line)
f = f + 1
# Write over the third line in the created files
# If this is not added, the "fk5" string remains commented out in the header
files = [
'070' + save_file_name, '160' + save_file_name, '165' + save_file_name,
'250' + save_file_name, '255' + save_file_name, '350' + save_file_name,
'500' + save_file_name
]
for i in files:
with open(DS9_region_directory + i, 'r') as file:
data = file.readlines()
data[2] = "fk5 \n"
with open(DS9_region_directory + i, 'w') as file:
file.writelines(data)
def auto_zoomed_cores(region='Aquila', distance=260.):
print region
getsources_core_catalog = '/mnt/scratch-lustre/jkeown/GS_Extractions/GS-Extractions/' + region + '/' + region + '.sw.final.reliable.ok.cat'
core_figure_directory = '/mnt/scratch-lustre/jkeown/JCMT_GBS_Jybeam/JCMT_pipeline/Figures/' + region + '/core_figures/'
JCMT_450um_image = '/mnt/scratch-lustre/jkeown/GS_Extractions/GS-Extractions/' + region + '/' + region + '_450_Jybeam.m.fits'
JCMT_850um_image = '/mnt/scratch-lustre/jkeown/GS_Extractions/GS-Extractions/' + region + '/' + region + '_850_Jybeam.m.fits'
# Import getsources "good core" data table
cores_array1 = numpy.loadtxt(getsources_core_catalog, comments='!')
good_core_indices = good_cores_getsources.get_good_cores(region=region)
cores_array = cores_array1[numpy.array(good_core_indices)]
if region == 'PipeE1':
cores_array = numpy.array(cores_array1)
z = numpy.zeros(len(cores_array))
cores_array = numpy.stack((cores_array, z))
core_center_RA = cores_array[:, 3]
core_center_Dec = cores_array[:, 4]
core_index = 1
for line in cores_array:
x_coor = str(line[3])
y_coor = str(line[4])
AFWHM_array = [line[14], line[23]] # 450, 850
BFWHM_array = [line[15], line[24]]
Theta_array = [line[16], line[25]]
SIG_array = [line[8], line[17]]
images_array = [JCMT_450um_image, JCMT_850um_image]
wavelengths = ['450', '850']
maximums = numpy.zeros(len(AFWHM_array))
minimums = numpy.zeros(len(AFWHM_array))
counter = 0
for i in wavelengths:
### Create a DS9 region string for the core's getsources ellipse,
### from which a mask will be created.
region1 = ('fk5;ellipse(' + x_coor + ', ' + y_coor + ', ' +
str(AFWHM_array[counter] / 3600.) + ', ' +
str(BFWHM_array[counter] / 3600.) + ', ' +
str(Theta_array[counter] + 90.) + ')')
r = pyregion.parse(region1)
f = fits.open(images_array[counter])
header_primary = fits.getheader(images_array[counter])
data = fits.getdata(images_array[counter])
mymask = r.get_mask(hdu=f[0])
newmask = numpy.where(mymask != 0)
maximums[counter] = max(data[newmask])
minimums[counter] = min(data[newmask])
region2 = ('fk5;box(' + x_coor + ', ' + y_coor + ', ' + str(0.04) +
', ' + str(0.04) + ', ' + str(0.0) + ')')
r = pyregion.parse(region2)
mymask = r.get_mask(hdu=f[0])
newmask = numpy.where(mymask != 0)
f.close()
newmask2 = numpy.where(mymask == 0, 0, data)
fits.writeto(region + '_' + i + '_contour_mask.fits',
newmask2,
header_primary,
clobber=True)
counter += 1
microns = ['mu_450', 'mu_850']
if maximums[0] < 0.:
maximums[0] = 10.0
if minimums[0] < 0.:
minimums[0] = -5.0
v_max = maximums
v_min = minimums
contour_levels = [(maximums[0] * 0.3, maximums[0] * 0.5,
maximums[0] * 0.7, maximums[0] * 0.9),
(maximums[1] * 0.3, maximums[1] * 0.5,
maximums[1] * 0.7, maximums[1] * 0.9)]
fig = plt.figure() #figsize=(8,5.5)
f = 0
for i in wavelengths:
microns[f] = aplpy.FITSFigure(region + '_' + i +
'_contour_mask.fits',
figure=fig,
subplot=(2, 1, f + 1))
microns[f].recenter(line[3], line[4], 0.01)
microns[f].show_contour(region + '_' + i + '_contour_mask.fits',
colors=('white', 'white', 'white', 'grey'),
levels=contour_levels[f],
overlap=True)
microns[f].show_colorscale(cmap='gist_stern',
vmin=v_min[f],
vmax=v_max[f])
#microns[f].show_regions('/mnt/scratch-lustre/jkeown/DS9_regions/HGBS_pipeline/L1157/L1157_core_SED/255_all_cores_good.reg')
microns[f].add_colorbar()
microns[f].colorbar.set_axis_label_text('Jy/beam')
scale = str('{:.2f}'.format(
2 * (distance * numpy.tan(numpy.radians(0.5 * (1.0 / 60.))))))
if f == 0:
microns[f].add_scalebar(
(1. / 60.),
path_effects=[
Patheffects.withStroke(linewidth=3, foreground='white')
])
microns[f].scalebar.show(
1. / 60.) # length in degrees (one arcminute)
microns[f].scalebar.set_corner('top left')
microns[f].scalebar.set_label('1' + "'" + ' = ' + scale +
' pc')
if SIG_array[f] > 5.:
line_type = 'solid'
else:
line_type = 'dashed'
microns[f].show_ellipses(line[3],
line[4],
AFWHM_array[f] / 3600.,
BFWHM_array[f] / 3600.,
Theta_array[f] + 90.,
color='#00FF00',
zorder=10,
linewidth=1.0,
linestyle=line_type)
#microns[f].show_markers(line[3], line[4], c='#00FF00', marker='x', zorder=10, linewidths=1.5,s=20)
#microns[f].show_markers(line[3], line[4], c='black', marker='x', zorder=9, linewidths=2.0,s=30)
microns[f].tick_labels.hide()
microns[f].axis_labels.hide()
#microns[f].set_title('L' + name)
microns[f].add_label(0.3,
0.1,
i + ' $\mu$' + 'm',
relative=True,
path_effects=[
Patheffects.withStroke(linewidth=3,
foreground='white')
])
f = f + 1
fig.subplots_adjust(hspace=0.05, wspace=0.35)
fig.canvas.draw()
fig.suptitle('Core Number ' + str(core_index) + ' - RA: ' +
str(line[3]) + ' Dec: ' + str(line[4]))
fig.savefig(core_figure_directory + 'core' + str(core_index) + '.pdf')
print 'Core Number = ' + str(core_index)
for i in range(2):
microns[i].close()
core_index = core_index + 1
def cross_match(getsources_core_catalog = '/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo/+catalogs/L1157.sw.final.reliable.ok.cat', YSO_catalog = '/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo_proto/+catalogs/L1157.sw.final.reliable.ok.cat', high_res_coldens_image = '/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cep1157_255_mu.image.resamp.fits', proto_core_indices='L1157_matched_protostellar_cores.dat'):
# Import getsources "good core" data table
cores_array1 = numpy.loadtxt(getsources_core_catalog,comments='!')
good_core_indices = good_cores_getsources.get_good_cores(getsources_core_catalog)
cores_array = cores_array1[numpy.array(good_core_indices)]
NO, XCO_P, YCO_P, WCS_ACOOR, WCS_DCOOR, SIG_GLOB, FG, GOOD, SIG_MONO01, FM01, FXP_BEST01, FXP_ERRO01, FXT_BEST01, FXT_ERRO01, AFWH01, BFWH01, THEP01, SIG_MONO02, FM02, FXP_BEST02, FXP_ERRO02, FXT_BEST02, FXT_ERRO02, AFWH02, BFWH02, THEP02, SIG_MONO03, FM03, FXP_BEST03, FXP_ERRO03, FXT_BEST03, FXT_ERRO03, AFWH03, BFWH03, THEP03, SIG_MONO04, FM04, FXP_BEST04, FXP_ERRO04, FXT_BEST04, FXT_ERRO04, AFWH04, BFWH04, THEP04, SIG_MONO05, FM05, FXP_BEST05, FXP_ERRO05, FXT_BEST05, FXT_ERRO05, AFWH05, BFWH05, THEP05, SIG_MONO06, FM06, FXP_BEST06, FXP_ERRO06, FXT_BEST06, FXT_ERRO06, AFWH06, BFWH06, THEP06, SIG_MONO07, FM07, FXP_BEST07, FXP_ERRO07, FXT_BEST07, FXT_ERRO07, AFWH07, BFWH07, THEP07 = numpy.loadtxt(YSO_catalog,comments='!', unpack=True)
good_proto_indices = good_protostars_getsources.get_good_protostars(YSO_catalog)
#print len(good_proto_indices)
### Loop through entire core catalog to find cores that are close to YSOs.
### This step significantly reduces the time it would take to run through
### entire catalog.
potential_matches = []
count = 0
for line in cores_array:
match_counter=0
for i in good_proto_indices:
distance = ((line[3]-WCS_ACOOR[i])**2 + (line[4]-WCS_DCOOR[i])**2)**0.5
if distance < 200.0/3600. and match_counter==0:
# matched_counter prevents counting indices twice
# if two YSO candidates fall within getsources ellipse
potential_matches.append(count)
match_counter+=1
count += 1
#print len(potential_matches)
### Loop through the potential matched cores identified in the step above.
matched_cores = []
matched_cores_proto_index = []
for value in potential_matches:
line = cores_array[value]
x_coor = str(line[3])
y_coor = str(line[4])
### Create a DS9 region string for the core's getsources ellipse,
### from which a mask will be created.
region = ('fk5;ellipse(' + x_coor + ', ' + y_coor + ', ' + str((line[50]/2.0)/3600.) + ', ' +
str((line[51]/2.0)/3600.) + ', ' + str(line[52]+90.0)+')')
r = pyregion.parse(region)
f=fits.open(high_res_coldens_image)
mymask = r.get_mask(hdu=f[0])
f.close()
newmask=mymask
### Set all values outside the core's ellipse to zero,
### all valus inside the ellipse are set to one.
newmask=numpy.where(newmask==0,0,1)
### Loop through the 70 micron point sources
### If any fall within a core's ellipse, store that core's index
match_counter=0
good_protostar_index = 0
for i in good_proto_indices:
if newmask[int(round(YCO_P[i],0))-1][int(round(XCO_P[i],0))-1]!=0 and match_counter==0:
matched_cores.append(value)
matched_cores_proto_index.append(good_protostar_index)
# matched_counter prevents counting indices twice
# if two YSO candidates fall within getsources ellipse
match_counter+=1
good_protostar_index+=1
else:
good_protostar_index+=1
#print len(matched_cores)
#print matched_cores
#print matched_cores_proto_index
### Save the protostellar core indices to a file
#numpy.savetxt(proto_core_indices, matched_cores, fmt='%i')
# Return indices of matched cores and protostars from the "good" lists of each
# i.e., The indices are the index from the "good" lists and not the unprocessed original, total getsources list
return numpy.column_stack((numpy.array(matched_cores), numpy.array(matched_cores_proto_index)))
def auto_zoomed_cores(getsources_core_catalog = '/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo/+catalogs/L1157.sw.final.reliable.ok.cat', YSO_catalog = '/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo_proto/+catalogs/L1157.sw.final.reliable.ok.cat', core_figure_directory = '/mnt/scratch-lustre/jkeown/DS9_regions/HGBS_pipeline/L1157/core_figures/', Herschel_70um_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cepL1157_070_mu_emission.image.resamp.fits', Herschel_160um_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cepL1157_160_mu_emission.image.resamp.fits', Herschel_250um_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cep1157_250_mu.image.resamp.fits', Herschel_350um_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cep1157_350_mu.image.resamp.fits', Herschel_500um_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cep1157_500_mu.image.resamp.fits', Herschel_coldens_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cep1157_255_mu.image.resamp.fits', distance=325.):
# Import getsources "good core" data table
cores_array1 = numpy.loadtxt(getsources_core_catalog,comments='!')
good_core_indices = good_cores_getsources.get_good_cores(getsources_core_catalog)
cores_array = cores_array1[numpy.array(good_core_indices)]
core_center_RA = cores_array[:,3]
core_center_Dec = cores_array[:,4]
core_index = 1
for line in cores_array:
x_coor = str(line[3])
y_coor = str(line[4])
AFWHM_array = [line[14], line[23], line[41], line[59], line[68], line[50]] # 70,160,250,350,500,coldens
BFWHM_array = [line[15], line[24], line[42], line[60], line[69], line[51]]
Theta_array = [line[16], line[25], line[43], line[61], line[70], line[52]]
SIG_array = [line[8], line[17], line[35], line[53], line[62], line[44]]
images_array = [Herschel_70um_image, Herschel_160um_image, Herschel_250um_image,
Herschel_350um_image, Herschel_500um_image, Herschel_coldens_image]
wavelengths = ['070', '160', '250', '350', '500', '255']
maximums = numpy.zeros(len(AFWHM_array))
minimums = numpy.zeros(len(AFWHM_array))
counter = 0
for i in wavelengths:
### Create a DS9 region string for the core's getsources ellipse,
### from which a mask will be created.
region = ('fk5;box(' + x_coor + ', ' + y_coor + ', ' + str(0.05) + ', ' +
str(0.05) + ', ' + str(0.0)+')')
r = pyregion.parse(region)
f = fits.open(images_array[counter])
header_primary = fits.getheader(images_array[counter])
data = fits.getdata(images_array[counter])
mymask = r.get_mask(hdu=f[0])
newmask=numpy.where(mymask!=0)
maximums[counter] = max(data[newmask])
minimums[counter] = min(data[newmask])
f.close()
newmask2=numpy.where(mymask==0, 0, data)
fits.writeto(i + '_contour_mask.fits', newmask2, header_primary, clobber=True)
counter+=1
microns = ['mu_070', 'mu_160', 'mu_250', 'mu_350', 'mu_500', 'mu_255']
v_max = maximums
v_min = minimums
contour_levels = [(maximums[0]*0.3,maximums[0]*0.5,maximums[0]*0.7,maximums[0]*0.9),
(maximums[1]*0.3,maximums[1]*0.5,maximums[1]*0.7,maximums[1]*0.9),
(maximums[2]*0.3,maximums[2]*0.5,maximums[2]*0.7,maximums[2]*0.9),
(maximums[3]*0.3,maximums[3]*0.5,maximums[3]*0.7,maximums[3]*0.9),
(maximums[4]*0.3,maximums[4]*0.5,maximums[4]*0.7,maximums[4]*0.9),
(maximums[5]*0.3,maximums[5]*0.5,maximums[5]*0.7,maximums[5]*0.9)]
fig = plt.figure(figsize=(8,5.5))
f=0
for i in wavelengths:
microns[f]=aplpy.FITSFigure(i + '_contour_mask.fits', figure=fig, subplot=(2,3,f+1))
microns[f].recenter(line[3],line[4],0.025)
microns[f].show_contour(i + '_contour_mask.fits', colors=('white', 'white', 'white','grey'), levels=contour_levels[f], overlap=True)
microns[f].show_colorscale(cmap='gist_stern', vmin=v_min[f], vmax=v_max[f])
#microns[f].show_regions('/mnt/scratch-lustre/jkeown/DS9_regions/HGBS_pipeline/L1157/L1157_core_SED/255_all_cores_good.reg')
microns[f].add_colorbar()
if f==2 or f==5:
microns[f].colorbar.set_axis_label_text('MJy/sr')
scale = str('{:.2f}'.format(2*(distance*numpy.tan(numpy.radians(0.5*(1.0/60.))))))
if f==0:
microns[f].add_scalebar((1./60.), path_effects=[Patheffects.withStroke(linewidth=3, foreground='white')])
microns[f].scalebar.show(1./60.) # length in degrees (one arcminute)
microns[f].scalebar.set_corner('top left')
microns[f].scalebar.set_label('1' + "'" + ' = ' + scale + ' pc')
if SIG_array[f]>5.:
line_type = 'solid'
else:
line_type = 'dashed'
microns[f].show_ellipses(line[3], line[4], AFWHM_array[f]/3600., BFWHM_array[f]/3600., Theta_array[f]+90., color='#00FF00', zorder=10, linewidth=1.0, linestyle=line_type)
#microns[f].show_markers(line[3], line[4], c='#00FF00', marker='x', zorder=10, linewidths=1.5,s=20)
#microns[f].show_markers(line[3], line[4], c='black', marker='x', zorder=9, linewidths=2.0,s=30)
microns[f].tick_labels.hide()
microns[f].axis_labels.hide()
#microns[f].set_title('L' + name)
if i=='255':
microns[f].add_label(0.3,0.1,'N$_{H2}$',relative=True, path_effects=[Patheffects.withStroke(linewidth=3, foreground='white')])
else:
microns[f].add_label(0.3,0.1,i + ' $\mu$' + 'm',relative=True, path_effects=[Patheffects.withStroke(linewidth=3, foreground='white')])
f=f+1
fig.subplots_adjust(hspace=0.05, wspace=0.35)
fig.canvas.draw()
fig.suptitle('Core Number ' + str(core_index) + ' - RA: ' + str(line[3]) + ' Dec: ' + str(line[4]))
fig.savefig(core_figure_directory + 'core' +str(core_index) + '.pdf')
print 'Core Number = ' + str(core_index)
for i in range(6):
microns[i].close()
core_index = core_index + 1
def core_mass_fits(region_name='Aquila',
T_Dust=15.0,
T_Dust_err=3.0,
distance=260.,
Dunham_YSOs_file='Dunham_YSOs.dat'):
getsources_core_catalog = '/mnt/scratch-lustre/jkeown/GS_Extractions/GS-Extractions/' + region_name + '/' + region_name + '.sw.final.reliable.ok.cat'
SED_figure_directory = '/mnt/scratch-lustre/jkeown/JCMT_GBS_Jybeam/JCMT_pipeline/Figures/' + region_name + '/'
# These are the values in each column of "cores_array" and "protostar_array"
#NO, XCO_P, YCO_P, WCS_ACOOR, WCS_DCOOR, SIG_GLOB, FG, GOOD, SIG_MONO01, FM01, FXP_BEST01, FXP_ERRO01, FXT_BEST01, FXT_ERRO01, AFWH01, BFWH01, THEP01, SIG_MONO02, FM02, FXP_BEST02, FXP_ERRO02, FXT_BEST02, FXT_ERRO02, AFWH02, BFWH02, THEP02
# These are the values in each column of the "additional" "cores_array" and "protostar_array"
# NO XCO_P YCO_P PEAK_SRC01 PEAK_BGF01 CONV_SRC01 CONV_BGF01 PEAK_SRC02 PEAK_BGF02 CONV_SRC02 CONV_BGF02
# Import the raw getsources core catalog (includes "bad" sources that don't pass HGBS selection criteria)
cores_array1 = numpy.loadtxt(getsources_core_catalog, comments='!')
# Find the indices of the "good" cores that pass HGBS selection criteria
good_core_indices = good_cores_getsources.get_good_cores(region_name)
# Create new array of only the "good" cores to be used for our analysis
cores_array = cores_array1[numpy.array(good_core_indices)]
if region_name == 'PipeE1':
cores_array = numpy.array(cores_array1)
print cores_array
z = numpy.zeros(len(cores_array))
cores_array = numpy.stack((cores_array, z))
print cores_array
# Calculate the deconvolved core radii
AFWH05 = cores_array[:, 23]
BFWH05 = cores_array[:, 24]
A = numpy.float64(((((AFWH05) / 60.) / 60.) * numpy.pi) / 180.) #radians
A1 = numpy.float64(numpy.tan(A / 2.) * 2. * distance) #pc
B = numpy.float64(((((BFWH05) / 60.) / 60.) * numpy.pi) / 180.) #radians
B1 = numpy.float64(numpy.tan(B / 2.) * 2. * distance) #pc
FWHM_mean = mstats.gmean([A1, B1])
HPBW = numpy.float64(((((14.1) / 60.) / 60.) * numpy.pi) / 180.) #radians
HPBW1 = numpy.float64(numpy.tan(HPBW / 2.) * 2. * distance) #pc
R_deconv = ((FWHM_mean**2.0) - (HPBW1**2.0))**0.5 #pc
R_deconv = numpy.where(((FWHM_mean**2.0) - (HPBW1**2.0)) <= 0., FWHM_mean,
R_deconv)
resolved = numpy.where(((FWHM_mean**2.0) - (HPBW1**2.0)) <= 0., 0, 1)
# Calculate the Bonnor-Ebert masses of each core based on their R_deconvolved
c_s = 0.2 #km/s
G = 4.302 * 10**-3 #pc/M_solar (km/s)^2
M_BE = (2.4 * R_deconv * (c_s**2.)) / G #M_solar
#M_BE = numpy.where(((FWHM_mean**2.0) - (HPBW1**2.0))<0, 9999., M_BE)
# Define a function that produces a Flux given wavelength, Temp, and Mass
# We will input wavelength then find T and M using least squares minimization below
def core_mass(wavelength, T, M):
#wavelength input in microns, Temp in Kelvin, Mass in M_solar
#returns S_v (i.e., Flux) in units of Jy
D = distance #parsecs to cloud
wavelength_mm = numpy.array(wavelength) * 10.**-3.
exponent = 1.439 * (wavelength_mm**-1) * ((T / 10.)**-1)
aaa = (0.12 * (numpy.exp(exponent) - 1.0))**-1.0
bbb = (0.1 * ((numpy.array(wavelength) / 300.)**-2.0)) / 0.01
ccc = (D / 100.)**-2
ddd = wavelength_mm**-3.
return M * aaa * bbb * ccc * ddd
# Define another function that calculates Mass directly from wavelength, Temp, and Flux
# This will be used to find the Mass of cores that don't have reliable least-squares fits
def core_mass_from_flux(wavelength, T, S_v):
#wavelength input in microns, Temp in Kelvin, Mass in M_solar
#returns S_v (i.e., Flux) in units of Jy
D = distance #parsecs to cloud
wavelength_mm = wavelength * 10.**-3.
exponent = 1.439 * (wavelength_mm**-1) * ((T / 10.)**-1)
aaa = 0.12 * (numpy.exp(exponent) - 1.0)
bbb = ((0.1 * ((wavelength / 300.)**-2.0)) / 0.01)**-1.0
ccc = (D / 100.)**2.0
ddd = wavelength_mm**3.0
return S_v * aaa * bbb * ccc * ddd
# Define another function that calculates Mass uncertainty due to temp
def core_mass_err_dT(wavelength, T, S_v, dT):
#wavelength input in microns, Temp in Kelvin, Mass in M_solar
#returns S_v (i.e., Flux) in units of Jy
D = distance #parsecs to cloud
wavelength_mm = wavelength * 10.**-3.
exponent = 1.439 * (wavelength_mm**-1) * ((T / 10.)**-1)
aaa = 0.12 * (numpy.exp(exponent))
bbb = ((0.1 * ((wavelength / 300.)**-2.0)) / 0.01)**-1.0
ccc = (D / 100.)**2.0
ddd = wavelength_mm**3.0
eee = 1.439 * 10 * (wavelength_mm**-1) * (T**-2.)
return S_v * aaa * bbb * ccc * ddd * dT * eee
# Define another function that calculates Mass uncertainty due to flux
def core_mass_err_dS_v(wavelength, T, S_v, dS_v):
#wavelength input in microns, Temp in Kelvin, Mass in M_solar
#returns S_v (i.e., Flux) in units of Jy
D = distance #parsecs to cloud
wavelength_mm = wavelength * 10.**-3.
exponent = 1.439 * (wavelength_mm**-1) * ((T / 10.)**-1)
aaa = 0.12 * (numpy.exp(exponent) - 1.0)
bbb = ((0.1 * ((wavelength / 300.)**-2.0)) / 0.01)**-1.0
ccc = (D / 100.)**2.0
ddd = wavelength_mm**3.0
return aaa * bbb * ccc * ddd * dS_v
# Create some empty arrays to which we will append accepted values
Masses = []
Temps = []
Masses_err = []
Temps_err = []
counter = 0
# Loop through all the "good" cores
for NO, XCO_P, YCO_P, WCS_ACOOR, WCS_DCOOR, SIG_GLOB, FG, GOOD, SIG_MONO01, FM01, FXP_BEST01, FXP_ERRO01, FXT_BEST01, FXT_ERRO01, AFWH01, BFWH01, THEP01, SIG_MONO02, FM02, FXP_BEST02, FXP_ERRO02, FXT_BEST02, FXT_ERRO02, AFWH02, BFWH02, THEP02 in cores_array:
#flux_err_run1 = [FXT_BEST01/SIG_MONO01, FXT_BEST02/SIG_MONO02] ## Should these be FXP_BEST?
# Find the longest significant wavelength and corresponding flux
wave = 850.
flux_fit = FXT_BEST02 # Fluxes are in mJy, so multiple by 10^3 to get Jy
flux_fit_err = FXT_ERRO02 # Fluxes are in mJy, so multiple by 10^3 to get Jy
# Find the mass corresponding to that flux measurement
# ***This uses the median of the best-fit Temps from the cores with
# reliable SED fits (i.e., those that pass the test above)
Mass_fit = core_mass_from_flux(wave, T_Dust, flux_fit)
# Can add more uncertainties (e.g., calibration, etc.) below
Mass_error = (
core_mass_err_dT(wave, T_Dust, flux_fit, T_Dust_err) +
core_mass_err_dS_v(wave, T_Dust, flux_fit, flux_fit_err)**2.0)**0.5
# Store the Mass with uncertainties
# Need to perform a more in-depth error analysis
Masses.append(Mass_fit)
Masses_err.append(Mass_error)
Temps.append(T_Dust)
Temps_err.append(T_Dust_err)
#Replace nans if they exist in the M_BE array
if len(cores_array) > 1:
where_are_nans = numpy.isnan(M_BE)
M_BE[where_are_nans] = 9999
# Calculate the alpha_BE ratio to determine prestellar cores
alpha_BE = numpy.array(M_BE) / numpy.array(Masses)
#alpha_BE = numpy.where(((FWHM_mean**2.0) - (HPBW1**2.0))<0, 9999., alpha_BE)
# Create an array indicating a core as candidate(1)/robust(2) prestellar
candidate_array = numpy.where(alpha_BE <= 5.0, 1, 0)
#candidate_array = numpy.where(alpha_BE<=alpha_factor, 1, 0)
robust_candidate_array = numpy.where(alpha_BE <= 2.0, 2, candidate_array)
# Remove protostars from the alpha_BE array and find the indices of the remaining
# candidate/robust prestellar cores
robust_prestellar_indices = numpy.where(alpha_BE <= 2.0)
candidate_prestellar_indices = numpy.where(alpha_BE <= 5.0)
Masses2 = numpy.array(Masses)
# Find the final list of prestellar candidate/robust Masses with protostars removed
prestellar_candidates = Masses2[numpy.array(
candidate_prestellar_indices[0])]
prestellar_robust = Masses2[numpy.array(robust_prestellar_indices[0])]
print 'prestellar candidates: ' + str(len(prestellar_candidates))
print 'robust prestellar candidates: ' + str(len(prestellar_robust))
# Plot Mass versus Radius and save the figure
fig = plt.figure()
plt.scatter(R_deconv, Masses, label='starless')
if len(Masses) > 1:
plt.scatter(R_deconv[numpy.array(candidate_prestellar_indices[0])],
prestellar_candidates,
color='red',
label='candidate')
plt.scatter(R_deconv[numpy.array(robust_prestellar_indices[0])],
prestellar_robust,
color='green',
label='robust')
plt.yscale('log')
plt.xscale('log')
#plt.legend()
plt.title(region_name + ' Cores')
plt.ylabel("Mass, M (M$_\odot$)")
plt.xlabel("Deconvolved FWHM size, R (pc)")
#plt.xlim([10**-3, 2*10**-1])
#plt.ylim([10**-3, 10**2])
fig.savefig(SED_figure_directory + 'mass_vs_radius_' + region_name +
'_Feb2018.png')
Cloud, Name, Av, alpha, T_bol, L_bol, alphaPrime, TbolPrime, LbolPrime, likelyAGB, Dunham_RA, Dunham_DEC, Class = numpy.loadtxt(
Dunham_YSOs_file,
delimiter=',',
unpack=True,
dtype=[('Cloud', 'S30'), ('Name', 'S40'), ('Av', float),
('alpha', float), ('T_bol', float), ('L_bol', float),
('alphaPrime', float), ('TbolPrime', float),
('LbolPrime', float), ('likelyAGB', 'S1'), ('Dunham_RA', float),
('Dunham_DEC', float), ('Class', 'S10')])
#Dunham_indices = numpy.where(Cloud==cloud_name)
Spitzer_YSOs_RA = Dunham_RA
Spitzer_YSOs_DEC = Dunham_DEC
Spitzer_YSOs_Name = Name
potential_matches = []
YSO_matches = []
count = 0
for line in cores_array:
match_counter = 0
YSO_index = 0
for RA, DEC in zip(Spitzer_YSOs_RA, Spitzer_YSOs_DEC):
distance = ((line[3] - RA)**2 + (line[4] - DEC)**2)**0.5
if distance < 6.0 / 3600. and match_counter == 0:
# matched_counter prevents counting indices twice
# if two YSO candidates fall within getsources ellipse
potential_matches.append(count)
match_counter += 1
YSO_matches.append(YSO_index)
YSO_index += 1
count += 1
Spitzer_column = numpy.zeros(len(cores_array[:, 0]), dtype='S50')
Spitzer_column[numpy.arange(0, len(cores_array[:, 0]))] = 'None'
if len(potential_matches) > 0:
Spitzer_column[numpy.array(potential_matches)] = Spitzer_YSOs_Name[
numpy.array(YSO_matches)]
# Save a text file with RA and Dec of the "good" cores
# This is needed for the SIMBAD cross-match
numpy.savetxt(SED_figure_directory + region_name + '_SIMBAD_RA_DEC.dat',
zip(cores_array[:, 3], cores_array[:, 4]))
# Cross-match cores with SIMBAD catalog
print "Cross-matching SIMBAD catalog:"
RA, Dec = numpy.loadtxt(SED_figure_directory + region_name +
'_SIMBAD_RA_DEC.dat',
unpack=True)
Simbad.ROW_LIMIT = 1
results = []
if len(cores_array) == 1:
result_table = Simbad.query_region(astropy.coordinates.SkyCoord(
ra=RA, dec=Dec, unit=(u.deg, u.deg)),
radius=6. * u.arcsec)
if result_table != None:
results.append(result_table['MAIN_ID'][0].replace(" ", "_"))
else:
results.append('None')
# Cross-match cores with NED catalog
print "Cross-matching NED catalog:"
Ned.ROW_LIMIT = 1
results2 = []
result_table_value = 'Yes'
try:
result_table = Ned.query_region(astropy.coordinates.SkyCoord(
ra=RA, dec=Dec, unit=(u.deg, u.deg)),
radius=6. * u.arcsec)
except astroquery.exceptions.RemoteServiceError:
result_table_value = None
if result_table_value != None:
results2.append(result_table['Object Name'][0].replace(" ", "_"))
else:
results2.append('None')
else:
for i, j in zip(RA, Dec):
result_table = Simbad.query_region(astropy.coordinates.SkyCoord(
ra=i, dec=j, unit=(u.deg, u.deg)),
radius=6. * u.arcsec)
if result_table != None:
results.append(result_table['MAIN_ID'][0].replace(" ", "_"))
else:
results.append('None')
# Cross-match cores with NED catalog
print "Cross-matching NED catalog:"
Ned.ROW_LIMIT = 1
results2 = []
for i, j in zip(RA, Dec):
result_table_value = 'Yes'
try:
result_table = Ned.query_region(astropy.coordinates.SkyCoord(
ra=i, dec=j, unit=(u.deg, u.deg)),
radius=6. * u.arcsec)
except astroquery.exceptions.RemoteServiceError:
result_table_value = None
if result_table_value != None:
results2.append(result_table['Object Name'][0].replace(
" ", "_"))
else:
results2.append('None')
running_number = numpy.arange(len(cores_array[:, 0])) + 1
header1 = 'running_NO, getsources_NO, XCO_P, YCO_P, WCS_ACOOR, WCS_DCOOR, SIG_GLOB, FG, GOOD, SIG_MONO01, FM01, FXP_BEST01, FXP_ERRO01, FXT_BEST01, FXT_ERRO01, AFWH01, BFWH01, THEP01, SIG_MONO02, FM02, FXP_BEST02, FXP_ERRO02, FXT_BEST02, FXT_ERRO02, AFWH02, BFWH02, THEP02, R_deconv, R_fwhm_mean, resolved, Mass, M_err, Temp, T_err, alpha_BE, SIMBAD_match, NED_match, Spitzer_match'
# Append the Radius, Mass, Temperature, alpha_BE, etc. arrays as columns
# onto the "good cores" array and save as a .dat file
numpy.savetxt(
SED_figure_directory + region_name + '_good_sources_Feb2018.dat',
numpy.column_stack((running_number, cores_array, numpy.array(R_deconv),
numpy.array(FWHM_mean), numpy.array(resolved),
numpy.array(Masses), numpy.array(Masses_err),
numpy.array(Temps), numpy.array(Temps_err),
numpy.array(alpha_BE), numpy.array(results),
numpy.array(results2), Spitzer_column)),
fmt='%s',
header=header1)
def core_mass_fits(region_name = 'L1157', cloud_name = 'Cepheus', distance = 325, getsources_core_catalog = '/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo/+catalogs/L1157.sw.final.reliable.ok.cat', getsources_additional_catalog = '/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo/+catalogs/L1157.sw.final.reliable.add.ok.cat', YSO_catalog = '/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo_proto/+catalogs/L1157.sw.final.reliable.ok.cat', YSO_additional_catalog = '/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo_proto/+catalogs/L1157.sw.final.reliable.add.ok.cat', high_res_coldens_image = '/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cep1157_255_mu.image.resamp.fits', SED_figure_directory = '/mnt/scratch-lustre/jkeown/DS9_regions/HGBS_pipeline/L1157/L1157_core_SED/', CSAR_catalog = '/mnt/scratch-lustre/jkeown/DS9_regions/L1157/CSAR/CEPl1157_CSAR.dat', Dunham_YSOs_file = 'Dunham_YSOs.dat'):
# These are the values in each column of "cores_array" and "protostar_array"
#NO, XCO_P, YCO_P, WCS_ACOOR, WCS_DCOOR, SIG_GLOB, FG, GOOD, SIG_MONO01, FM01, FXP_BEST01, FXP_ERRO01, FXT_BEST01, FXT_ERRO01, AFWH01, BFWH01, THEP01, SIG_MONO02, FM02, FXP_BEST02, FXP_ERRO02, FXT_BEST02, FXT_ERRO02, AFWH02, BFWH02, THEP02, SIG_MONO03, FM03, FXP_BEST03, FXP_ERRO03, FXT_BEST03, FXT_ERRO03, AFWH03, BFWH03, THEP03, SIG_MONO04, FM04, FXP_BEST04, FXP_ERRO04, FXT_BEST04, FXT_ERRO04, AFWH04, BFWH04, THEP04, SIG_MONO05, FM05, FXP_BEST05, FXP_ERRO05, FXT_BEST05, FXT_ERRO05, AFWH05, BFWH05, THEP05, SIG_MONO06, FM06, FXP_BEST06, FXP_ERRO06, FXT_BEST06, FXT_ERRO06, AFWH06, BFWH06, THEP06, SIG_MONO07, FM07, FXP_BEST07, FXP_ERRO07, FXT_BEST07, FXT_ERRO07, AFWH07, BFWH07, THEP07
# These are the values in each column of the "additional" "cores_array" and "protostar_array"
# NO XCO_P YCO_P PEAK_SRC01 PEAK_BGF01 CONV_SRC01 CONV_BGF01 PEAK_SRC02 PEAK_BGF02 CONV_SRC02 CONV_BGF02 PEAK_SRC03 PEAK_BGF03 CONV_SRC03 CONV_BGF03 PEAK_SRC04 PEAK_BGF04 CONV_SRC04 CONV_BGF04 PEAK_SRC05 PEAK_BGF05 CONV_SRC05 CONV_BGF05 PEAK_SRC06 PEAK_BGF06 CONV_SRC06 CONV_BGF06 PEAK_SRC07 PEAK_BGF07 CONV_SRC07 CONV_BGF07
# Import the raw getsources core catalog (includes "bad" sources that don't pass HGBS selection criteria)
cores_array1 = numpy.loadtxt(getsources_core_catalog,comments='!')
# Find the indices of the "good" cores that pass HGBS selection criteria
good_core_indices = good_cores_getsources.get_good_cores(getsources_core_catalog)
# Create new array of only the "good" cores to be used for our analysis
cores_array = cores_array1[numpy.array(good_core_indices)]
# Import the raw "additional" getsources catalog
additional_cores_array1 = numpy.loadtxt(getsources_additional_catalog,comments='!')
# Create another new array of only the "good" cores from the getsources "additional" catalog
additional_cores_array = additional_cores_array1[numpy.array(good_core_indices)]
# Import the raw getsources protostar catalog
protostar_array1 = numpy.loadtxt(YSO_catalog,comments='!', unpack=True)
# Find the indices of the "good" protostars that pass HGBS selection criteria
good_proto_indices = good_protostars_getsources.get_good_protostars(YSO_catalog)
# Create new array of only the "good" protostars to be used for our analysis
# Not sure why the transpose below is needed, but it seems the protostar_array1
# is imported transposed. This doesn't seem to be the case for the core_array1
protostar_array = protostar_array1.T[numpy.array(good_proto_indices)]
# Pull the 70micron flux and error columns to be used later
flux_70um = protostar_array[:,12]
flux_70um_err = protostar_array[:,13]
# Import the raw getsources protostar catalog
additional_protostar_array1 = numpy.loadtxt(YSO_additional_catalog,comments='!', unpack=True)
# Create another new array of only the "good" YSOs from the getsources "additional" catalog
additional_protostar_array = additional_protostar_array1.T[numpy.array(good_proto_indices)]
# Cross-match the good cores and protostars arrays to find protostars that fall within a core's ellipse
print "Cross-matching Core and Protostar Catalogs:"
protostellar_core_indices = proto_core_cross_match.cross_match(getsources_core_catalog, YSO_catalog, high_res_coldens_image)
# The first column of protostellar_core_indices is the core's index in cores_array
cross_matched_core_indices = numpy.array(protostellar_core_indices[:,0])
# The second column of protostellar_core_indices is the protostar's index in protostar_array
cross_matched_proto_indices = numpy.array(protostellar_core_indices[:,1])
# Replace the 70 micron flux measurements of protostellar cores with the protostar extraction measurements
if len(cross_matched_core_indices)>0:
for i,j in zip(cross_matched_core_indices, cross_matched_proto_indices):
cores_array[i][10]=protostar_array[j][10]
cores_array[i][11]=protostar_array[j][11]
cores_array[i][12]=protostar_array[j][12]
cores_array[i][13]=protostar_array[j][13]
additional_cores_array[i][3]=additional_protostar_array[j][3]
additional_cores_array[i][4]=additional_protostar_array[j][4]
additional_cores_array[i][5]=additional_protostar_array[j][5]
additional_cores_array[i][6]=additional_protostar_array[j][6]
# Calculate the deconvolved core radii
AFWH05 = cores_array[:,50]
BFWH05 = cores_array[:,51]
A = numpy.float64(((((AFWH05)/60.)/60.)*numpy.pi)/180.) #radians
A1 = numpy.float64(numpy.tan(A/2.)*2.*distance) #pc
B = numpy.float64(((((BFWH05)/60.)/60.)*numpy.pi)/180.) #radians
B1 = numpy.float64(numpy.tan(B/2.)*2.*distance) #pc
FWHM_mean = mstats.gmean([A1,B1])
HPBW = numpy.float64(((((18.2)/60.)/60.)*numpy.pi)/180.) #radians
HPBW1 = numpy.float64(numpy.tan(HPBW/2.)*2.*distance) #pc
R_deconv = ((FWHM_mean**2.0) - (HPBW1**2.0))**0.5 #pc
R_deconv = numpy.where(((FWHM_mean**2.0) - (HPBW1**2.0))<=0., FWHM_mean, R_deconv)
# Calculate the Bonnor-Ebert masses of each core based on their R_deconvolved
c_s = 0.2 #km/s at 10K
G = 4.302*10**-3 #pc/M_solar (km/s)^2
M_BE = (2.4*R_deconv*(c_s**2.))/G #M_solar
M_BE = numpy.where(((FWHM_mean**2.0) - (HPBW1**2.0))<0, 9999., M_BE)
# Define a function that produces a Flux given wavelength, Temp, and Mass
# We will input wavelength then find T and M using least squares minimization below
def core_mass(wavelength, T, M):
#wavelength input in microns, Temp in Kelvin, Mass in M_solar
#returns S_v (i.e., Flux) in units of Jy
D = distance #parsecs to cloud
wavelength_mm = numpy.array(wavelength)*10.**-3.
exponent = 1.439*(wavelength_mm**-1)*((T/10.)**-1)
aaa = (0.12*(numpy.exp(exponent)-1.0))**-1.0
bbb = (0.1*((numpy.array(wavelength)/300.)**-2.0))/0.01
ccc = (D/100.)**-2
ddd = wavelength_mm**-3.
return M*aaa*bbb*ccc*ddd
# Define another function that calculates Mass directly from wavelength, Temp, and Flux
# This will be used to find the Mass of cores that don't have reliable least-squares fits
def core_mass_from_flux(wavelength, T, S_v):
#wavelength input in microns, Temp in Kelvin, Mass in M_solar
#returns S_v (i.e., Flux) in units of Jy
D = distance #parsecs to cloud
wavelength_mm = wavelength*10.**-3.
exponent = 1.439*(wavelength_mm**-1)*((T/10.)**-1)
aaa = 0.12*(numpy.exp(exponent)-1.0)
bbb = ((0.1*((wavelength/300.)**-2.0))/0.01)**-1.0
ccc = (D/100.)**2.0
ddd = wavelength_mm**3.0
return S_v*aaa*bbb*ccc*ddd
# Define another function that calculates Mass uncertainty due to temp
def core_mass_err_dT(wavelength, T, S_v, dT):
#wavelength input in microns, Temp in Kelvin, Mass in M_solar
#returns S_v (i.e., Flux) in units of Jy
D = distance #parsecs to cloud
wavelength_mm = wavelength*10.**-3.
exponent = 1.439*(wavelength_mm**-1)*((T/10.)**-1)
aaa = 0.12*(numpy.exp(exponent))
bbb = ((0.1*((wavelength/300.)**-2.0))/0.01)**-1.0
ccc = (D/100.)**2.0
ddd = wavelength_mm**3.0
eee = 1.439*10*(wavelength_mm**-1)*(T**-2.)
return S_v*aaa*bbb*ccc*ddd*dT*eee
# Define another function that calculates Mass uncertainty due to flux
def core_mass_err_dS_v(wavelength, T, S_v, dS_v):
#wavelength input in microns, Temp in Kelvin, Mass in M_solar
#returns S_v (i.e., Flux) in units of Jy
D = distance #parsecs to cloud
wavelength_mm = wavelength*10.**-3.
exponent = 1.439*(wavelength_mm**-1)*((T/10.)**-1)
aaa = 0.12*(numpy.exp(exponent)-1.0)
bbb = ((0.1*((wavelength/300.)**-2.0))/0.01)**-1.0
ccc = (D/100.)**2.0
ddd = wavelength_mm**3.0
return aaa*bbb*ccc*ddd*dS_v
# Create some empty arrays to which we will append accepted values
Temps = []
Masses = []
Temps_err = []
Masses_err = []
counter=0
not_accepted_counter = []
protostellar_core_counter = 0
mean_dust_Temp = 10.0
mean_dust_Temp_err = 4.0
# Designate the initial Temp and Mass guess for the least-squares fitting below
guess = (20.0, 0.7)
print "Begin SED-fitting:"
# Loop through all the "good" cores
for NO, XCO_P, YCO_P, WCS_ACOOR, WCS_DCOOR, SIG_GLOB, FG, GOOD, SIG_MONO01, FM01, FXP_BEST01, FXP_ERRO01, FXT_BEST01, FXT_ERRO01, AFWH01, BFWH01, THEP01, SIG_MONO02, FM02, FXP_BEST02, FXP_ERRO02, FXT_BEST02, FXT_ERRO02, AFWH02, BFWH02, THEP02, SIG_MONO03, FM03, FXP_BEST03, FXP_ERRO03, FXT_BEST03, FXT_ERRO03, AFWH03, BFWH03, THEP03, SIG_MONO04, FM04, FXP_BEST04, FXP_ERRO04, FXT_BEST04, FXT_ERRO04, AFWH04, BFWH04, THEP04, SIG_MONO05, FM05, FXP_BEST05, FXP_ERRO05, FXT_BEST05, FXT_ERRO05, AFWH05, BFWH05, THEP05, SIG_MONO06, FM06, FXP_BEST06, FXP_ERRO06, FXT_BEST06, FXT_ERRO06, AFWH06, BFWH06, THEP06, SIG_MONO07, FM07, FXP_BEST07, FXP_ERRO07, FXT_BEST07, FXT_ERRO07, AFWH07, BFWH07, THEP07 in cores_array:
not_accepted = False
N_SED_counter = 0
fig = plt.figure()
# Proceed with starless core SED-fitting procedure
# Determine how many bands the core has significant flux measurements
if SIG_MONO01 > 5.0:
N_SED_counter+=1
if SIG_MONO02 > 5.0:
N_SED_counter+=1
if SIG_MONO04 > 5.0:
N_SED_counter+=1
if SIG_MONO06 > 5.0:
N_SED_counter+=1
if SIG_MONO07 > 5.0:
N_SED_counter+=1
# If the core has more than three bands in which it is significant,
# and the 350um Flux is higher than the 500um flux, fit the SED
# over the 70-500um bands
if N_SED_counter>=3 and FXT_BEST06>FXT_BEST07:
flux_run1 = [FXT_BEST01, FXT_BEST02, FXT_BEST04, FXT_BEST06, FXT_BEST07]
flux_err_run1 = [FXT_ERRO01, FXT_ERRO02, FXT_ERRO04, FXT_ERRO06, FXT_ERRO07]
flux_run2 = [FXT_BEST02, FXT_BEST04, FXT_BEST06, FXT_BEST07]
flux_err_run2 = [FXT_ERRO02, FXT_ERRO04, FXT_ERRO06, FXT_ERRO07]
wavelength_run1=[70., 160.,250.,350.,500.]
wavelength_run2=[160.,250.,350.,500.]
try:
popt,pcov = curve_fit(core_mass, wavelength_run1, flux_run1, p0=guess, sigma=flux_err_run1)
except RuntimeError:
popt = [-9999., -9999.]
# Perform a second round of least squares fitting (without 70um point)
try:
popt2, pcov2 = curve_fit(core_mass, wavelength_run2, flux_run2, p0=guess, sigma=flux_err_run2)
except RuntimeError:
popt2 = [-1., -1.]
# If the best fit Mass from the two fits varies by more than a factor of 2,
# calculate the mass from the flux of the longest wavelength with significant flux
if (popt2[1]/popt[1]) > 2.0 or (popt2[1]/popt[1]) < 0.5:
not_accepted = True
not_accepted_counter.append("no_SED_fit")
# Store fillers for Mass and T
# Will re-calculate these values below using a median core dust temp
# from the reliable SED fits
Masses.append(9999)
Temps.append(9999)
Temps_err.append(9999)
Masses_err.append(9999)
else:
not_accepted_counter.append(" ")
# This means the SED fit is reliable
# Store the best-fit T and M (from second run) to corresponding arrays
Temps.append(popt2[0])
Masses.append(popt2[1])
# Find the uncertainty on T and M from the square root of the diagonal
# terms in the covariance matrix and store in arrays
Temps_err.append(pcov2[0][0]**0.5)
Masses_err.append(pcov2[1][1]**0.5)
else:
# This means the SED is not reliable and we will instead get the Mass
# directly from the longest significant wavelength's flux measurement
not_accepted = True
not_accepted_counter.append("no_SED_fit")
# Store fillers for Mass and T
# Will re-calculate these values below using a median core dust temp
# from the reliable SED fits
Masses.append(9999)
Temps.append(9999)
Temps_err.append(9999)
Masses_err.append(9999)
# Plot the fluxes of all Herschel bands regardless of significance
wavelength3=[70., 160.,250.,350.,500.]
flux3 = [FXT_BEST01, FXT_BEST02, FXT_BEST04, FXT_BEST06, FXT_BEST07]
flux_err3 = [FXT_ERRO01, FXT_ERRO02, FXT_ERRO04, FXT_ERRO06, FXT_ERRO07]
plt.errorbar(wavelength3,flux3,yerr=flux_err3,ecolor='r',fmt='o')
plt.yscale('log')
plt.xscale('log')
plt.xlabel("Wavelength ($\mu$m)")
plt.ylabel("Flux density (Jy)")
# If the SED fit is reliable, also plot the best fit line to the SED points
wavelength2=[160.,250.,350.,500.]
if not_accepted==False:
plt.plot(np.linspace(wavelength2[0]-40,wavelength2[3]+100, 50),core_mass(np.linspace(wavelength2[0]-40,wavelength2[3]+100, 50), popt2[0], popt2[1]), color="green")
plt.title('Core ' + str(counter+1) + ' \n' + 'T$_{dust}$ (K) = ' + str("{0:.2f}".format(round(popt2[0],2))) + ' $\pm$ ' + str("{0:.2f}".format(round(pcov2[0][0],3))) + ', Mass (M$_\odot$) = ' + str("{0:.2f}".format(round(popt2[1],2))) + ' $\pm$ ' + str("{0:.2f}".format(round(pcov2[1][1],3))))
# Save the plots as a PDF in specified SED_figure_directory
# The core number (in terms of the "good" sources) is added to file name
fig.savefig(SED_figure_directory + 'core' + str(counter+1) + '.pdf')
plt.close(fig)
# Keep track of the core's that have been fit
counter=counter+1
print "Core Number " + str(counter) + " of " + str(len(cores_array))
redo_indices = numpy.where(numpy.array(Temps_err)==9999)
print "Need to re-calculate Mass and Temp for " + str(len(redo_indices[0])) + " Cores"
Temps = numpy.array(Temps)
mean_dust_Temp = numpy.median(numpy.delete(Temps, redo_indices))
mean_dust_Temp_err = numpy.std(numpy.delete(Temps, redo_indices))
print "We will assume a core Temp of " + str(mean_dust_Temp) + " +/- " + str(mean_dust_Temp_err) + " for those cores"
redo_indices = redo_indices[0]
redo_counter = 0
for NO, XCO_P, YCO_P, WCS_ACOOR, WCS_DCOOR, SIG_GLOB, FG, GOOD, SIG_MONO01, FM01, FXP_BEST01, FXP_ERRO01, FXT_BEST01, FXT_ERRO01, AFWH01, BFWH01, THEP01, SIG_MONO02, FM02, FXP_BEST02, FXP_ERRO02, FXT_BEST02, FXT_ERRO02, AFWH02, BFWH02, THEP02, SIG_MONO03, FM03, FXP_BEST03, FXP_ERRO03, FXT_BEST03, FXT_ERRO03, AFWH03, BFWH03, THEP03, SIG_MONO04, FM04, FXP_BEST04, FXP_ERRO04, FXT_BEST04, FXT_ERRO04, AFWH04, BFWH04, THEP04, SIG_MONO05, FM05, FXP_BEST05, FXP_ERRO05, FXT_BEST05, FXT_ERRO05, AFWH05, BFWH05, THEP05, SIG_MONO06, FM06, FXP_BEST06, FXP_ERRO06, FXT_BEST06, FXT_ERRO06, AFWH06, BFWH06, THEP06, SIG_MONO07, FM07, FXP_BEST07, FXP_ERRO07, FXT_BEST07, FXT_ERRO07, AFWH07, BFWH07, THEP07 in cores_array[redo_indices]:
# Find the longest significant wavelength and corresponding flux
if SIG_MONO07>5.0:
wave = 500.
flux_fit = FXT_BEST07
flux_fit_err = FXT_ERRO07
elif SIG_MONO06>5.0:
wave = 350.
flux_fit = FXT_BEST06
flux_fit_err = FXT_ERRO06
elif SIG_MONO04>5.0:
wave = 250.
flux_fit = FXT_BEST04
flux_fit_err = FXT_ERRO04
elif SIG_MONO02>5.0:
wave = 160.
flux_fit = FXT_BEST02
flux_fit_err = FXT_ERRO02
# Find the mass corresponding to that flux measurement
# ***This uses the median of the best-fit Temps from the cores with
# reliable SED fits (i.e., those that pass the test above)
Mass_fit = core_mass_from_flux(wave, mean_dust_Temp, flux_fit)
# Can add more uncertainties (e.g., calibration, etc.) below
Mass_error = (core_mass_err_dT(wave, mean_dust_Temp, flux_fit, mean_dust_Temp_err) +
core_mass_err_dS_v(wave, mean_dust_Temp, flux_fit, flux_fit_err)**2.0)**0.5
# Store the Mass and T with uncertainties
# Need to perform a more in-depth error analysis
Masses[redo_indices[redo_counter]] = Mass_fit
Temps[redo_indices[redo_counter]] = mean_dust_Temp
Temps_err[redo_indices[redo_counter]] = mean_dust_Temp_err
Masses_err[redo_indices[redo_counter]] = Mass_error
fig = plt.figure()
wavelength3=[70., 160.,250.,350.,500.]
flux3 = [FXT_BEST01, FXT_BEST02, FXT_BEST04, FXT_BEST06, FXT_BEST07]
flux_err3 = [FXT_ERRO01, FXT_ERRO02, FXT_ERRO04, FXT_ERRO06, FXT_ERRO07]
plt.errorbar(wavelength3,flux3,yerr=flux_err3,ecolor='r',fmt='o')
plt.yscale('log')
plt.xscale('log')
plt.xlabel("Wavelength ($\mu$m)")
plt.ylabel("Flux density (Jy)")
plt.title('Core ' + str(redo_indices[redo_counter]+1) + ' **Not Accepted** ' + ' \n' + 'T$_{dust}$ (K) = ' + str("{0:.2f}".format(round(mean_dust_Temp,2))) + "$\pm$" + str("{0:.2f}".format(round(mean_dust_Temp_err,2))) + ', Mass (M$_\odot$) = ' + str("{0:.2f}".format(round(Mass_fit,2))) + "$\pm$" + str("{0:.2f}".format(round(Mass_error,2))))
fig.savefig(SED_figure_directory + 'core' + str(redo_indices[redo_counter]+1) + '_NA.pdf')
plt.close(fig)
redo_counter+=1
print "Re-calculating Core " + str(redo_counter) + " of " + str(len(redo_indices))
unreliable = float(len(numpy.where(numpy.array(not_accepted_counter)=="no_SED_fit")[0]))
print 'Fraction of Unreliable SED fits: ' + str(round((unreliable/float(len(cores_array))),3))
not_accepted_counter = numpy.where(numpy.array(not_accepted_counter) == "no_SED_fit", not_accepted_counter, "None")
#Replace nans if they exist in the M_BE array
where_are_nans = numpy.isnan(M_BE)
M_BE[where_are_nans] = 9999
# Calculate the alpha_BE ratio to determine prestellar cores
alpha_BE = numpy.array(M_BE)/numpy.array(Masses)
alpha_BE = numpy.where(((FWHM_mean**2.0) - (HPBW1**2.0))<0, 9999., alpha_BE)
# Create an array indicating a core as candidate(1)/robust(2) prestellar
candidate_array = numpy.where(alpha_BE<=5.0, 1, 0)
robust_candidate_array = numpy.where(alpha_BE<=2.0, 2, candidate_array)
# Identify protostars in the array with the number (3)
if len(cross_matched_core_indices)>0:
robust_candidate_array[cross_matched_core_indices]=3
# Remove protostars from the alpha_BE array and find the indices of the remaining
# candidate/robust prestellar cores
robust_prestellar_indices = numpy.where(numpy.delete(alpha_BE,cross_matched_core_indices)<=2.0)
candidate_prestellar_indices = numpy.where(numpy.delete(alpha_BE,cross_matched_core_indices)<=5.0)
# Remove protostars from the Mass and Radius arrays
# (we only want to plot starless cores in the Mass vs. Radius plot)
R_deconv_minus_protos=numpy.delete(R_deconv, cross_matched_core_indices)
Masses_minus_protos=numpy.delete(Masses, cross_matched_core_indices)
# Find the final list of prestellar candidate/robust Masses with protostars removed
prestellar_candidates = Masses_minus_protos[numpy.array(candidate_prestellar_indices[0])]
prestellar_robust = Masses_minus_protos[numpy.array(robust_prestellar_indices[0])]
print 'prestellar candidates: ' + str(len(prestellar_candidates))
print 'robust prestellar candidates: ' + str(len(prestellar_robust))
# Plot Mass versus Radius and save the figure
fig = plt.figure()
plt.scatter(R_deconv_minus_protos,Masses_minus_protos, label='starless')
plt.scatter(R_deconv_minus_protos[numpy.array(candidate_prestellar_indices[0])],prestellar_candidates, color='red', label='candidate')
plt.scatter(R_deconv_minus_protos[numpy.array(robust_prestellar_indices[0])],prestellar_robust, color='green', label='robust')
plt.yscale('log')
plt.xscale('log')
#plt.legend()
plt.title(region_name + ' Cores')
plt.ylabel("Mass, M (M$_\odot$)")
plt.xlabel("Deconvolved FWHM size, R (pc)")
plt.xlim([10**-3, 2*10**-1])
plt.ylim([10**-3, 10**2])
fig.savefig(SED_figure_directory + 'mass_vs_radius_' + region_name + '.png')
# Append the Radius, Mass, Temperature, alpha_BE, etc. arrays as columns
# onto the "good cores" array and save as a .dat file
numpy.savetxt(SED_figure_directory + region_name +'_good_sources.dat', numpy.column_stack((cores_array,numpy.array(R_deconv),numpy.array(FWHM_mean),numpy.array(Masses), numpy.array(Masses_err),numpy.array(Temps), numpy.array(Temps_err),numpy.array(alpha_BE),numpy.array(robust_candidate_array))))
# Save a text file with RA and Dec of the "good" cores
# This is needed for the SIMBAD cross-match
numpy.savetxt(SED_figure_directory + region_name +'_SIMBAD_RA_DEC.dat', zip(cores_array[:,3],cores_array[:,4]))
# Create the catalog of good cores; includes flux measurments, positions, etc.
make_catalog.make_catalog(region_name=region_name, cloud_name=cloud_name, distance=distance, additional_cores_array=additional_cores_array, good_cores_array = cores_array, cross_matched_core_indices=cross_matched_core_indices, cross_matched_proto_indices=cross_matched_proto_indices, alpha_BE=alpha_BE, getsources_core_catalog = getsources_core_catalog, R_deconv=R_deconv, FWHM_mean = FWHM_mean, Masses=Masses, Masses_err = Masses_err, Temps=Temps, Temps_err=Temps_err, not_accepted_counter = not_accepted_counter, CSAR_catalog = CSAR_catalog, high_res_coldens_image = high_res_coldens_image, SED_figure_directory=SED_figure_directory, Dunham_YSOs_file=Dunham_YSOs_file)
make_CMF.CMF_plotter(region=region_name, Masses_minus_protos=Masses_minus_protos, prestellar_candidates=prestellar_candidates, prestellar_robust=prestellar_robust, SED_figure_directory=SED_figure_directory)
# Create plot of column density PDF
#make_plots.coldense_vs_cores(region_name=region_name, SED_figure_directory=SED_figure_directory, high_res_coldens_image=high_res_coldens_image)
# Create histogram of background column densities for the prestellar cores
make_plots.bg_coldense_plotter(region_name=region_name, SED_figure_directory=SED_figure_directory)
# Create histogram of core dust temperatures from the cores with reliable SED fits
make_plots.core_temp_plotter(region_name=region_name, SED_figure_directory=SED_figure_directory)
# Create histogram of core radii for the starless core population
make_plots.core_size_plotter(region_name=region_name, SED_figure_directory=SED_figure_directory)
# Create DS9 region files for the good core and proto catalogs at all wavelengths
make_DS9_region.make_DS9_regions_good_cores(getsources_core_catalog=getsources_core_catalog, YSO_catalog = YSO_catalog, DS9_region_directory = SED_figure_directory, catalog_type='all_cores', cross_matched_core_indices=cross_matched_core_indices, candidate_prestellar_indices=candidate_prestellar_indices, robust_prestellar_indices=robust_prestellar_indices)
make_DS9_region.make_DS9_regions_good_cores(getsources_core_catalog=getsources_core_catalog, YSO_catalog = YSO_catalog, DS9_region_directory = SED_figure_directory, catalog_type='proto', cross_matched_core_indices=cross_matched_core_indices, candidate_prestellar_indices=candidate_prestellar_indices, robust_prestellar_indices=robust_prestellar_indices)
make_DS9_region.make_DS9_regions_good_cores(getsources_core_catalog=getsources_core_catalog, YSO_catalog = YSO_catalog, DS9_region_directory = SED_figure_directory, catalog_type='prestellar_candidates', cross_matched_core_indices=cross_matched_core_indices, candidate_prestellar_indices=candidate_prestellar_indices, robust_prestellar_indices=robust_prestellar_indices)
make_DS9_region.make_DS9_regions_good_cores(getsources_core_catalog=getsources_core_catalog, YSO_catalog = YSO_catalog, DS9_region_directory = SED_figure_directory, catalog_type='prestellar_robust', cross_matched_core_indices=cross_matched_core_indices, candidate_prestellar_indices=candidate_prestellar_indices, robust_prestellar_indices=robust_prestellar_indices)
if len(cross_matched_core_indices)>0:
make_DS9_region.make_DS9_regions_good_cores(getsources_core_catalog=getsources_core_catalog, YSO_catalog = YSO_catalog, DS9_region_directory = SED_figure_directory, catalog_type='proto_cores', cross_matched_core_indices=cross_matched_core_indices, candidate_prestellar_indices=candidate_prestellar_indices, robust_prestellar_indices=robust_prestellar_indices)
make_DS9_region.make_DS9_regions_good_cores(getsources_core_catalog=getsources_core_catalog, YSO_catalog = YSO_catalog, DS9_region_directory = SED_figure_directory, catalog_type='starless_cores', cross_matched_core_indices=cross_matched_core_indices, candidate_prestellar_indices=candidate_prestellar_indices, robust_prestellar_indices=robust_prestellar_indices)
make_DS9_region.make_DS9_regions_CSAR(CSAR_catalog = CSAR_catalog, DS9_region_directory=SED_figure_directory)
def core_mass_fits(
region_name='L1157',
cloud_name='Cepheus',
distance=325,
getsources_core_catalog='/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo/+catalogs/L1157.sw.final.reliable.ok.cat',
getsources_additional_catalog='/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo/+catalogs/L1157.sw.final.reliable.add.ok.cat',
YSO_catalog='/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo_proto/+catalogs/L1157.sw.final.reliable.ok.cat',
YSO_additional_catalog='/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo_proto/+catalogs/L1157.sw.final.reliable.add.ok.cat',
high_res_coldens_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cep1157_255_mu.image.resamp.fits',
SED_figure_directory='/mnt/scratch-lustre/jkeown/DS9_regions/HGBS_pipeline/L1157/L1157_core_SED/',
CSAR_catalog='/mnt/scratch-lustre/jkeown/DS9_regions/L1157/CSAR/CEPl1157_CSAR.dat',
Dunham_YSOs_file='Dunham_YSOs.dat'):
# These are the values in each column of "cores_array" and "protostar_array"
#NO, XCO_P, YCO_P, WCS_ACOOR, WCS_DCOOR, SIG_GLOB, FG, GOOD, SIG_MONO01, FM01, FXP_BEST01, FXP_ERRO01, FXT_BEST01, FXT_ERRO01, AFWH01, BFWH01, THEP01, SIG_MONO02, FM02, FXP_BEST02, FXP_ERRO02, FXT_BEST02, FXT_ERRO02, AFWH02, BFWH02, THEP02, SIG_MONO03, FM03, FXP_BEST03, FXP_ERRO03, FXT_BEST03, FXT_ERRO03, AFWH03, BFWH03, THEP03, SIG_MONO04, FM04, FXP_BEST04, FXP_ERRO04, FXT_BEST04, FXT_ERRO04, AFWH04, BFWH04, THEP04, SIG_MONO05, FM05, FXP_BEST05, FXP_ERRO05, FXT_BEST05, FXT_ERRO05, AFWH05, BFWH05, THEP05, SIG_MONO06, FM06, FXP_BEST06, FXP_ERRO06, FXT_BEST06, FXT_ERRO06, AFWH06, BFWH06, THEP06, SIG_MONO07, FM07, FXP_BEST07, FXP_ERRO07, FXT_BEST07, FXT_ERRO07, AFWH07, BFWH07, THEP07
# These are the values in each column of the "additional" "cores_array" and "protostar_array"
# NO XCO_P YCO_P PEAK_SRC01 PEAK_BGF01 CONV_SRC01 CONV_BGF01 PEAK_SRC02 PEAK_BGF02 CONV_SRC02 CONV_BGF02 PEAK_SRC03 PEAK_BGF03 CONV_SRC03 CONV_BGF03 PEAK_SRC04 PEAK_BGF04 CONV_SRC04 CONV_BGF04 PEAK_SRC05 PEAK_BGF05 CONV_SRC05 CONV_BGF05 PEAK_SRC06 PEAK_BGF06 CONV_SRC06 CONV_BGF06 PEAK_SRC07 PEAK_BGF07 CONV_SRC07 CONV_BGF07
# Import the raw getsources core catalog (includes "bad" sources that don't pass HGBS selection criteria)
cores_array1 = numpy.loadtxt(getsources_core_catalog, comments='!')
# Find the indices of the "good" cores that pass HGBS selection criteria
good_core_indices = good_cores_getsources.get_good_cores(
getsources_core_catalog)
# Create new array of only the "good" cores to be used for our analysis
cores_array = cores_array1[numpy.array(good_core_indices)]
# Import the raw "additional" getsources catalog
additional_cores_array1 = numpy.loadtxt(getsources_additional_catalog,
comments='!')
# Create another new array of only the "good" cores from the getsources "additional" catalog
additional_cores_array = additional_cores_array1[numpy.array(
good_core_indices)]
# Import the raw getsources protostar catalog
protostar_array1 = numpy.loadtxt(YSO_catalog, comments='!', unpack=True)
# Find the indices of the "good" protostars that pass HGBS selection criteria
good_proto_indices = good_protostars_getsources.get_good_protostars(
YSO_catalog)
# Create new array of only the "good" protostars to be used for our analysis
# Not sure why the transpose below is needed, but it seems the protostar_array1
# is imported transposed. This doesn't seem to be the case for the core_array1
protostar_array = protostar_array1.T[numpy.array(good_proto_indices)]
# Pull the 70micron flux and error columns to be used later
flux_70um = protostar_array[:, 12]
flux_70um_err = protostar_array[:, 13]
# Import the raw getsources protostar catalog
additional_protostar_array1 = numpy.loadtxt(YSO_additional_catalog,
comments='!',
unpack=True)
# Create another new array of only the "good" YSOs from the getsources "additional" catalog
additional_protostar_array = additional_protostar_array1.T[numpy.array(
good_proto_indices)]
# Cross-match the good cores and protostars arrays to find protostars that fall within a core's ellipse
print "Cross-matching Core and Protostar Catalogs:"
protostellar_core_indices = proto_core_cross_match.cross_match(
getsources_core_catalog, YSO_catalog, high_res_coldens_image)
# The first column of protostellar_core_indices is the core's index in cores_array
cross_matched_core_indices = numpy.array(protostellar_core_indices[:, 0])
# The second column of protostellar_core_indices is the protostar's index in protostar_array
cross_matched_proto_indices = numpy.array(protostellar_core_indices[:, 1])
# Replace the 70 micron flux measurements of protostellar cores with the protostar extraction measurements
if len(cross_matched_core_indices) > 0:
for i, j in zip(cross_matched_core_indices,
cross_matched_proto_indices):
cores_array[i][10] = protostar_array[j][10]
cores_array[i][11] = protostar_array[j][11]
cores_array[i][12] = protostar_array[j][12]
cores_array[i][13] = protostar_array[j][13]
additional_cores_array[i][3] = additional_protostar_array[j][3]
additional_cores_array[i][4] = additional_protostar_array[j][4]
additional_cores_array[i][5] = additional_protostar_array[j][5]
additional_cores_array[i][6] = additional_protostar_array[j][6]
# Calculate the deconvolved core radii
AFWH05 = cores_array[:, 50]
BFWH05 = cores_array[:, 51]
A = numpy.float64(((((AFWH05) / 60.) / 60.) * numpy.pi) / 180.) #radians
A1 = numpy.float64(numpy.tan(A / 2.) * 2. * distance) #pc
B = numpy.float64(((((BFWH05) / 60.) / 60.) * numpy.pi) / 180.) #radians
B1 = numpy.float64(numpy.tan(B / 2.) * 2. * distance) #pc
FWHM_mean = mstats.gmean([A1, B1])
HPBW = numpy.float64(((((18.2) / 60.) / 60.) * numpy.pi) / 180.) #radians
HPBW1 = numpy.float64(numpy.tan(HPBW / 2.) * 2. * distance) #pc
R_deconv = ((FWHM_mean**2.0) - (HPBW1**2.0))**0.5 #pc
R_deconv = numpy.where(((FWHM_mean**2.0) - (HPBW1**2.0)) <= 0., FWHM_mean,
R_deconv)
# Calculate the Bonnor-Ebert masses of each core based on their R_deconvolved
c_s = 0.2 #km/s at 10K
G = 4.302 * 10**-3 #pc/M_solar (km/s)^2
M_BE = (2.4 * R_deconv * (c_s**2.)) / G #M_solar
M_BE = numpy.where(((FWHM_mean**2.0) - (HPBW1**2.0)) < 0, 9999., M_BE)
# Define a function that produces a Flux given wavelength, Temp, and Mass
# We will input wavelength then find T and M using least squares minimization below
def core_mass(wavelength, T, M):
#wavelength input in microns, Temp in Kelvin, Mass in M_solar
#returns S_v (i.e., Flux) in units of Jy
D = distance #parsecs to cloud
wavelength_mm = numpy.array(wavelength) * 10.**-3.
exponent = 1.439 * (wavelength_mm**-1) * ((T / 10.)**-1)
aaa = (0.12 * (numpy.exp(exponent) - 1.0))**-1.0
bbb = (0.1 * ((numpy.array(wavelength) / 300.)**-2.0)) / 0.01
ccc = (D / 100.)**-2
ddd = wavelength_mm**-3.
return M * aaa * bbb * ccc * ddd
# Define another function that calculates Mass directly from wavelength, Temp, and Flux
# This will be used to find the Mass of cores that don't have reliable least-squares fits
def core_mass_from_flux(wavelength, T, S_v):
#wavelength input in microns, Temp in Kelvin, Mass in M_solar
#returns S_v (i.e., Flux) in units of Jy
D = distance #parsecs to cloud
wavelength_mm = wavelength * 10.**-3.
exponent = 1.439 * (wavelength_mm**-1) * ((T / 10.)**-1)
aaa = 0.12 * (numpy.exp(exponent) - 1.0)
bbb = ((0.1 * ((wavelength / 300.)**-2.0)) / 0.01)**-1.0
ccc = (D / 100.)**2.0
ddd = wavelength_mm**3.0
return S_v * aaa * bbb * ccc * ddd
# Define another function that calculates Mass uncertainty due to temp
def core_mass_err_dT(wavelength, T, S_v, dT):
#wavelength input in microns, Temp in Kelvin, Mass in M_solar
#returns S_v (i.e., Flux) in units of Jy
D = distance #parsecs to cloud
wavelength_mm = wavelength * 10.**-3.
exponent = 1.439 * (wavelength_mm**-1) * ((T / 10.)**-1)
aaa = 0.12 * (numpy.exp(exponent))
bbb = ((0.1 * ((wavelength / 300.)**-2.0)) / 0.01)**-1.0
ccc = (D / 100.)**2.0
ddd = wavelength_mm**3.0
eee = 1.439 * 10 * (wavelength_mm**-1) * (T**-2.)
return S_v * aaa * bbb * ccc * ddd * dT * eee
# Define another function that calculates Mass uncertainty due to flux
def core_mass_err_dS_v(wavelength, T, S_v, dS_v):
#wavelength input in microns, Temp in Kelvin, Mass in M_solar
#returns S_v (i.e., Flux) in units of Jy
D = distance #parsecs to cloud
wavelength_mm = wavelength * 10.**-3.
exponent = 1.439 * (wavelength_mm**-1) * ((T / 10.)**-1)
aaa = 0.12 * (numpy.exp(exponent) - 1.0)
bbb = ((0.1 * ((wavelength / 300.)**-2.0)) / 0.01)**-1.0
ccc = (D / 100.)**2.0
ddd = wavelength_mm**3.0
return aaa * bbb * ccc * ddd * dS_v
# Create some empty arrays to which we will append accepted values
Temps = []
Masses = []
Temps_err = []
Masses_err = []
counter = 0
not_accepted_counter = []
protostellar_core_counter = 0
mean_dust_Temp = 10.0
mean_dust_Temp_err = 4.0
# Designate the initial Temp and Mass guess for the least-squares fitting below
guess = (20.0, 0.7)
print "Begin SED-fitting:"
# Loop through all the "good" cores
for NO, XCO_P, YCO_P, WCS_ACOOR, WCS_DCOOR, SIG_GLOB, FG, GOOD, SIG_MONO01, FM01, FXP_BEST01, FXP_ERRO01, FXT_BEST01, FXT_ERRO01, AFWH01, BFWH01, THEP01, SIG_MONO02, FM02, FXP_BEST02, FXP_ERRO02, FXT_BEST02, FXT_ERRO02, AFWH02, BFWH02, THEP02, SIG_MONO03, FM03, FXP_BEST03, FXP_ERRO03, FXT_BEST03, FXT_ERRO03, AFWH03, BFWH03, THEP03, SIG_MONO04, FM04, FXP_BEST04, FXP_ERRO04, FXT_BEST04, FXT_ERRO04, AFWH04, BFWH04, THEP04, SIG_MONO05, FM05, FXP_BEST05, FXP_ERRO05, FXT_BEST05, FXT_ERRO05, AFWH05, BFWH05, THEP05, SIG_MONO06, FM06, FXP_BEST06, FXP_ERRO06, FXT_BEST06, FXT_ERRO06, AFWH06, BFWH06, THEP06, SIG_MONO07, FM07, FXP_BEST07, FXP_ERRO07, FXT_BEST07, FXT_ERRO07, AFWH07, BFWH07, THEP07 in cores_array:
not_accepted = False
N_SED_counter = 0
fig = plt.figure()
# Proceed with starless core SED-fitting procedure
# Determine how many bands the core has significant flux measurements
if SIG_MONO01 > 5.0:
N_SED_counter += 1
if SIG_MONO02 > 5.0:
N_SED_counter += 1
if SIG_MONO04 > 5.0:
N_SED_counter += 1
if SIG_MONO06 > 5.0:
N_SED_counter += 1
if SIG_MONO07 > 5.0:
N_SED_counter += 1
# If the core has more than three bands in which it is significant,
# and the 350um Flux is higher than the 500um flux, fit the SED
# over the 70-500um bands
if N_SED_counter >= 3 and FXT_BEST06 > FXT_BEST07:
flux_run1 = [
FXT_BEST01, FXT_BEST02, FXT_BEST04, FXT_BEST06, FXT_BEST07
]
flux_err_run1 = [
FXT_ERRO01, FXT_ERRO02, FXT_ERRO04, FXT_ERRO06, FXT_ERRO07
]
flux_run2 = [FXT_BEST02, FXT_BEST04, FXT_BEST06, FXT_BEST07]
flux_err_run2 = [FXT_ERRO02, FXT_ERRO04, FXT_ERRO06, FXT_ERRO07]
wavelength_run1 = [70., 160., 250., 350., 500.]
wavelength_run2 = [160., 250., 350., 500.]
try:
popt, pcov = curve_fit(core_mass,
wavelength_run1,
flux_run1,
p0=guess,
sigma=flux_err_run1)
except RuntimeError:
popt = [-9999., -9999.]
# Perform a second round of least squares fitting (without 70um point)
try:
popt2, pcov2 = curve_fit(core_mass,
wavelength_run2,
flux_run2,
p0=guess,
sigma=flux_err_run2)
except RuntimeError:
popt2 = [-1., -1.]
# If the best fit Mass from the two fits varies by more than a factor of 2,
# calculate the mass from the flux of the longest wavelength with significant flux
if (popt2[1] / popt[1]) > 2.0 or (popt2[1] / popt[1]) < 0.5:
not_accepted = True
not_accepted_counter.append("no_SED_fit")
# Store fillers for Mass and T
# Will re-calculate these values below using a median core dust temp
# from the reliable SED fits
Masses.append(9999)
Temps.append(9999)
Temps_err.append(9999)
Masses_err.append(9999)
else:
not_accepted_counter.append(" ")
# This means the SED fit is reliable
# Store the best-fit T and M (from second run) to corresponding arrays
Temps.append(popt2[0])
Masses.append(popt2[1])
# Find the uncertainty on T and M from the square root of the diagonal
# terms in the covariance matrix and store in arrays
Temps_err.append(pcov2[0][0]**0.5)
Masses_err.append(pcov2[1][1]**0.5)
else:
# This means the SED is not reliable and we will instead get the Mass
# directly from the longest significant wavelength's flux measurement
not_accepted = True
not_accepted_counter.append("no_SED_fit")
# Store fillers for Mass and T
# Will re-calculate these values below using a median core dust temp
# from the reliable SED fits
Masses.append(9999)
Temps.append(9999)
Temps_err.append(9999)
Masses_err.append(9999)
# Plot the fluxes of all Herschel bands regardless of significance
wavelength3 = [70., 160., 250., 350., 500.]
flux3 = [FXT_BEST01, FXT_BEST02, FXT_BEST04, FXT_BEST06, FXT_BEST07]
flux_err3 = [
FXT_ERRO01, FXT_ERRO02, FXT_ERRO04, FXT_ERRO06, FXT_ERRO07
]
plt.errorbar(wavelength3, flux3, yerr=flux_err3, ecolor='r', fmt='o')
plt.yscale('log')
plt.xscale('log')
plt.xlabel("Wavelength ($\mu$m)")
plt.ylabel("Flux density (Jy)")
# If the SED fit is reliable, also plot the best fit line to the SED points
wavelength2 = [160., 250., 350., 500.]
if not_accepted == False:
plt.plot(np.linspace(wavelength2[0] - 40, wavelength2[3] + 100,
50),
core_mass(
np.linspace(wavelength2[0] - 40, wavelength2[3] + 100,
50), popt2[0], popt2[1]),
color="green")
plt.title('Core ' + str(counter + 1) + ' \n' +
'T$_{dust}$ (K) = ' +
str("{0:.2f}".format(round(popt2[0], 2))) + ' $\pm$ ' +
str("{0:.2f}".format(round(pcov2[0][0], 3))) +
', Mass (M$_\odot$) = ' +
str("{0:.2f}".format(round(popt2[1], 2))) + ' $\pm$ ' +
str("{0:.2f}".format(round(pcov2[1][1], 3))))
# Save the plots as a PDF in specified SED_figure_directory
# The core number (in terms of the "good" sources) is added to file name
fig.savefig(SED_figure_directory + 'core' + str(counter + 1) +
'.pdf')
plt.close(fig)
# Keep track of the core's that have been fit
counter = counter + 1
print "Core Number " + str(counter) + " of " + str(len(cores_array))
redo_indices = numpy.where(numpy.array(Temps_err) == 9999)
print "Need to re-calculate Mass and Temp for " + str(len(
redo_indices[0])) + " Cores"
Temps = numpy.array(Temps)
mean_dust_Temp = numpy.median(numpy.delete(Temps, redo_indices))
mean_dust_Temp_err = numpy.std(numpy.delete(Temps, redo_indices))
print "We will assume a core Temp of " + str(
mean_dust_Temp) + " +/- " + str(
mean_dust_Temp_err) + " for those cores"
redo_indices = redo_indices[0]
redo_counter = 0
for NO, XCO_P, YCO_P, WCS_ACOOR, WCS_DCOOR, SIG_GLOB, FG, GOOD, SIG_MONO01, FM01, FXP_BEST01, FXP_ERRO01, FXT_BEST01, FXT_ERRO01, AFWH01, BFWH01, THEP01, SIG_MONO02, FM02, FXP_BEST02, FXP_ERRO02, FXT_BEST02, FXT_ERRO02, AFWH02, BFWH02, THEP02, SIG_MONO03, FM03, FXP_BEST03, FXP_ERRO03, FXT_BEST03, FXT_ERRO03, AFWH03, BFWH03, THEP03, SIG_MONO04, FM04, FXP_BEST04, FXP_ERRO04, FXT_BEST04, FXT_ERRO04, AFWH04, BFWH04, THEP04, SIG_MONO05, FM05, FXP_BEST05, FXP_ERRO05, FXT_BEST05, FXT_ERRO05, AFWH05, BFWH05, THEP05, SIG_MONO06, FM06, FXP_BEST06, FXP_ERRO06, FXT_BEST06, FXT_ERRO06, AFWH06, BFWH06, THEP06, SIG_MONO07, FM07, FXP_BEST07, FXP_ERRO07, FXT_BEST07, FXT_ERRO07, AFWH07, BFWH07, THEP07 in cores_array[
redo_indices]:
# Find the longest significant wavelength and corresponding flux
if SIG_MONO07 > 5.0:
wave = 500.
flux_fit = FXT_BEST07
flux_fit_err = FXT_ERRO07
elif SIG_MONO06 > 5.0:
wave = 350.
flux_fit = FXT_BEST06
flux_fit_err = FXT_ERRO06
elif SIG_MONO04 > 5.0:
wave = 250.
flux_fit = FXT_BEST04
flux_fit_err = FXT_ERRO04
elif SIG_MONO02 > 5.0:
wave = 160.
flux_fit = FXT_BEST02
flux_fit_err = FXT_ERRO02
# Find the mass corresponding to that flux measurement
# ***This uses the median of the best-fit Temps from the cores with
# reliable SED fits (i.e., those that pass the test above)
Mass_fit = core_mass_from_flux(wave, mean_dust_Temp, flux_fit)
# Can add more uncertainties (e.g., calibration, etc.) below
Mass_error = (core_mass_err_dT(wave, mean_dust_Temp, flux_fit,
mean_dust_Temp_err) +
core_mass_err_dS_v(wave, mean_dust_Temp, flux_fit,
flux_fit_err)**2.0)**0.5
# Store the Mass and T with uncertainties
# Need to perform a more in-depth error analysis
Masses[redo_indices[redo_counter]] = Mass_fit
Temps[redo_indices[redo_counter]] = mean_dust_Temp
Temps_err[redo_indices[redo_counter]] = mean_dust_Temp_err
Masses_err[redo_indices[redo_counter]] = Mass_error
fig = plt.figure()
wavelength3 = [70., 160., 250., 350., 500.]
flux3 = [FXT_BEST01, FXT_BEST02, FXT_BEST04, FXT_BEST06, FXT_BEST07]
flux_err3 = [
FXT_ERRO01, FXT_ERRO02, FXT_ERRO04, FXT_ERRO06, FXT_ERRO07
]
plt.errorbar(wavelength3, flux3, yerr=flux_err3, ecolor='r', fmt='o')
plt.yscale('log')
plt.xscale('log')
plt.xlabel("Wavelength ($\mu$m)")
plt.ylabel("Flux density (Jy)")
plt.title('Core ' + str(redo_indices[redo_counter] + 1) +
' **Not Accepted** ' + ' \n' + 'T$_{dust}$ (K) = ' +
str("{0:.2f}".format(round(mean_dust_Temp, 2))) + "$\pm$" +
str("{0:.2f}".format(round(mean_dust_Temp_err, 2))) +
', Mass (M$_\odot$) = ' +
str("{0:.2f}".format(round(Mass_fit, 2))) + "$\pm$" +
str("{0:.2f}".format(round(Mass_error, 2))))
fig.savefig(SED_figure_directory + 'core' +
str(redo_indices[redo_counter] + 1) + '_NA.pdf')
plt.close(fig)
redo_counter += 1
print "Re-calculating Core " + str(redo_counter) + " of " + str(
len(redo_indices))
unreliable = float(
len(numpy.where(numpy.array(not_accepted_counter) == "no_SED_fit")[0]))
print 'Fraction of Unreliable SED fits: ' + str(
round((unreliable / float(len(cores_array))), 3))
not_accepted_counter = numpy.where(
numpy.array(not_accepted_counter) == "no_SED_fit",
not_accepted_counter, "None")
#Replace nans if they exist in the M_BE array
where_are_nans = numpy.isnan(M_BE)
M_BE[where_are_nans] = 9999
# Calculate the alpha_BE ratio to determine prestellar cores
alpha_BE = numpy.array(M_BE) / numpy.array(Masses)
alpha_BE = numpy.where(((FWHM_mean**2.0) - (HPBW1**2.0)) < 0, 9999.,
alpha_BE)
# Create an array indicating a core as candidate(1)/robust(2) prestellar
candidate_array = numpy.where(alpha_BE <= 5.0, 1, 0)
robust_candidate_array = numpy.where(alpha_BE <= 2.0, 2, candidate_array)
# Identify protostars in the array with the number (3)
if len(cross_matched_core_indices) > 0:
robust_candidate_array[cross_matched_core_indices] = 3
# Remove protostars from the alpha_BE array and find the indices of the remaining
# candidate/robust prestellar cores
robust_prestellar_indices = numpy.where(
numpy.delete(alpha_BE, cross_matched_core_indices) <= 2.0)
candidate_prestellar_indices = numpy.where(
numpy.delete(alpha_BE, cross_matched_core_indices) <= 5.0)
# Remove protostars from the Mass and Radius arrays
# (we only want to plot starless cores in the Mass vs. Radius plot)
R_deconv_minus_protos = numpy.delete(R_deconv, cross_matched_core_indices)
Masses_minus_protos = numpy.delete(Masses, cross_matched_core_indices)
# Find the final list of prestellar candidate/robust Masses with protostars removed
prestellar_candidates = Masses_minus_protos[numpy.array(
candidate_prestellar_indices[0])]
prestellar_robust = Masses_minus_protos[numpy.array(
robust_prestellar_indices[0])]
print 'prestellar candidates: ' + str(len(prestellar_candidates))
print 'robust prestellar candidates: ' + str(len(prestellar_robust))
# Plot Mass versus Radius and save the figure
fig = plt.figure()
plt.scatter(R_deconv_minus_protos, Masses_minus_protos, label='starless')
plt.scatter(R_deconv_minus_protos[numpy.array(
candidate_prestellar_indices[0])],
prestellar_candidates,
color='red',
label='candidate')
plt.scatter(R_deconv_minus_protos[numpy.array(
robust_prestellar_indices[0])],
prestellar_robust,
color='green',
label='robust')
plt.yscale('log')
plt.xscale('log')
#plt.legend()
plt.title(region_name + ' Cores')
plt.ylabel("Mass, M (M$_\odot$)")
plt.xlabel("Deconvolved FWHM size, R (pc)")
plt.xlim([10**-3, 2 * 10**-1])
plt.ylim([10**-3, 10**2])
fig.savefig(SED_figure_directory + 'mass_vs_radius_' + region_name +
'.png')
# Append the Radius, Mass, Temperature, alpha_BE, etc. arrays as columns
# onto the "good cores" array and save as a .dat file
numpy.savetxt(
SED_figure_directory + region_name + '_good_sources.dat',
numpy.column_stack(
(cores_array, numpy.array(R_deconv), numpy.array(FWHM_mean),
numpy.array(Masses), numpy.array(Masses_err), numpy.array(Temps),
numpy.array(Temps_err), numpy.array(alpha_BE),
numpy.array(robust_candidate_array))))
# Save a text file with RA and Dec of the "good" cores
# This is needed for the SIMBAD cross-match
numpy.savetxt(SED_figure_directory + region_name + '_SIMBAD_RA_DEC.dat',
zip(cores_array[:, 3], cores_array[:, 4]))
# Create the catalog of good cores; includes flux measurments, positions, etc.
make_catalog.make_catalog(
region_name=region_name,
cloud_name=cloud_name,
distance=distance,
additional_cores_array=additional_cores_array,
good_cores_array=cores_array,
cross_matched_core_indices=cross_matched_core_indices,
cross_matched_proto_indices=cross_matched_proto_indices,
alpha_BE=alpha_BE,
getsources_core_catalog=getsources_core_catalog,
R_deconv=R_deconv,
FWHM_mean=FWHM_mean,
Masses=Masses,
Masses_err=Masses_err,
Temps=Temps,
Temps_err=Temps_err,
not_accepted_counter=not_accepted_counter,
CSAR_catalog=CSAR_catalog,
high_res_coldens_image=high_res_coldens_image,
SED_figure_directory=SED_figure_directory,
Dunham_YSOs_file=Dunham_YSOs_file)
make_CMF.CMF_plotter(region=region_name,
Masses_minus_protos=Masses_minus_protos,
prestellar_candidates=prestellar_candidates,
prestellar_robust=prestellar_robust,
SED_figure_directory=SED_figure_directory)
# Create plot of column density PDF
#make_plots.coldense_vs_cores(region_name=region_name, SED_figure_directory=SED_figure_directory, high_res_coldens_image=high_res_coldens_image)
# Create histogram of background column densities for the prestellar cores
make_plots.bg_coldense_plotter(region_name=region_name,
SED_figure_directory=SED_figure_directory)
# Create histogram of core dust temperatures from the cores with reliable SED fits
make_plots.core_temp_plotter(region_name=region_name,
SED_figure_directory=SED_figure_directory)
# Create histogram of core radii for the starless core population
make_plots.core_size_plotter(region_name=region_name,
SED_figure_directory=SED_figure_directory)
# Create DS9 region files for the good core and proto catalogs at all wavelengths
make_DS9_region.make_DS9_regions_good_cores(
getsources_core_catalog=getsources_core_catalog,
YSO_catalog=YSO_catalog,
DS9_region_directory=SED_figure_directory,
catalog_type='all_cores',
cross_matched_core_indices=cross_matched_core_indices,
candidate_prestellar_indices=candidate_prestellar_indices,
robust_prestellar_indices=robust_prestellar_indices)
make_DS9_region.make_DS9_regions_good_cores(
getsources_core_catalog=getsources_core_catalog,
YSO_catalog=YSO_catalog,
DS9_region_directory=SED_figure_directory,
catalog_type='proto',
cross_matched_core_indices=cross_matched_core_indices,
candidate_prestellar_indices=candidate_prestellar_indices,
robust_prestellar_indices=robust_prestellar_indices)
make_DS9_region.make_DS9_regions_good_cores(
getsources_core_catalog=getsources_core_catalog,
YSO_catalog=YSO_catalog,
DS9_region_directory=SED_figure_directory,
catalog_type='prestellar_candidates',
cross_matched_core_indices=cross_matched_core_indices,
candidate_prestellar_indices=candidate_prestellar_indices,
robust_prestellar_indices=robust_prestellar_indices)
make_DS9_region.make_DS9_regions_good_cores(
getsources_core_catalog=getsources_core_catalog,
YSO_catalog=YSO_catalog,
DS9_region_directory=SED_figure_directory,
catalog_type='prestellar_robust',
cross_matched_core_indices=cross_matched_core_indices,
candidate_prestellar_indices=candidate_prestellar_indices,
robust_prestellar_indices=robust_prestellar_indices)
if len(cross_matched_core_indices) > 0:
make_DS9_region.make_DS9_regions_good_cores(
getsources_core_catalog=getsources_core_catalog,
YSO_catalog=YSO_catalog,
DS9_region_directory=SED_figure_directory,
catalog_type='proto_cores',
cross_matched_core_indices=cross_matched_core_indices,
candidate_prestellar_indices=candidate_prestellar_indices,
robust_prestellar_indices=robust_prestellar_indices)
make_DS9_region.make_DS9_regions_good_cores(
getsources_core_catalog=getsources_core_catalog,
YSO_catalog=YSO_catalog,
DS9_region_directory=SED_figure_directory,
catalog_type='starless_cores',
cross_matched_core_indices=cross_matched_core_indices,
candidate_prestellar_indices=candidate_prestellar_indices,
robust_prestellar_indices=robust_prestellar_indices)
make_DS9_region.make_DS9_regions_CSAR(
CSAR_catalog=CSAR_catalog, DS9_region_directory=SED_figure_directory)
def auto_zoomed_cores(
getsources_core_catalog='/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo/+catalogs/L1157.sw.final.reliable.ok.cat',
YSO_catalog='/mnt/scratch-lustre/jkeown/Getsources/Extract/cep1157/120115_flat/combo_proto/+catalogs/L1157.sw.final.reliable.ok.cat',
core_figure_directory='/mnt/scratch-lustre/jkeown/DS9_regions/HGBS_pipeline/L1157/core_figures/',
Herschel_70um_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cepL1157_070_mu_emission.image.resamp.fits',
Herschel_160um_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cepL1157_160_mu_emission.image.resamp.fits',
Herschel_250um_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cep1157_250_mu.image.resamp.fits',
Herschel_350um_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cep1157_350_mu.image.resamp.fits',
Herschel_500um_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cep1157_500_mu.image.resamp.fits',
Herschel_coldens_image='/mnt/scratch-lustre/jkeown/Getsources/Prepare/Images/cep1157/080615/cep1157_255_mu.image.resamp.fits',
distance=325.):
# Import getsources "good core" data table
cores_array1 = numpy.loadtxt(getsources_core_catalog, comments='!')
good_core_indices = good_cores_getsources.get_good_cores(
getsources_core_catalog)
cores_array = cores_array1[numpy.array(good_core_indices)]
core_center_RA = cores_array[:, 3]
core_center_Dec = cores_array[:, 4]
core_index = 1
for line in cores_array:
x_coor = str(line[3])
y_coor = str(line[4])
AFWHM_array = [
line[14], line[23], line[41], line[59], line[68], line[50]
] # 70,160,250,350,500,coldens
BFWHM_array = [
line[15], line[24], line[42], line[60], line[69], line[51]
]
Theta_array = [
line[16], line[25], line[43], line[61], line[70], line[52]
]
SIG_array = [line[8], line[17], line[35], line[53], line[62], line[44]]
images_array = [
Herschel_70um_image, Herschel_160um_image, Herschel_250um_image,
Herschel_350um_image, Herschel_500um_image, Herschel_coldens_image
]
wavelengths = ['070', '160', '250', '350', '500', '255']
maximums = numpy.zeros(len(AFWHM_array))
minimums = numpy.zeros(len(AFWHM_array))
counter = 0
for i in wavelengths:
### Create a DS9 region string for the core's getsources ellipse,
### from which a mask will be created.
region = ('fk5;box(' + x_coor + ', ' + y_coor + ', ' + str(0.05) +
', ' + str(0.05) + ', ' + str(0.0) + ')')
r = pyregion.parse(region)
f = fits.open(images_array[counter])
header_primary = fits.getheader(images_array[counter])
data = fits.getdata(images_array[counter])
mymask = r.get_mask(hdu=f[0])
newmask = numpy.where(mymask != 0)
maximums[counter] = max(data[newmask])
minimums[counter] = min(data[newmask])
f.close()
newmask2 = numpy.where(mymask == 0, 0, data)
fits.writeto(i + '_contour_mask.fits',
newmask2,
header_primary,
clobber=True)
counter += 1
microns = ['mu_070', 'mu_160', 'mu_250', 'mu_350', 'mu_500', 'mu_255']
v_max = maximums
v_min = minimums
contour_levels = [(maximums[0] * 0.3, maximums[0] * 0.5,
maximums[0] * 0.7, maximums[0] * 0.9),
(maximums[1] * 0.3, maximums[1] * 0.5,
maximums[1] * 0.7, maximums[1] * 0.9),
(maximums[2] * 0.3, maximums[2] * 0.5,
maximums[2] * 0.7, maximums[2] * 0.9),
(maximums[3] * 0.3, maximums[3] * 0.5,
maximums[3] * 0.7, maximums[3] * 0.9),
(maximums[4] * 0.3, maximums[4] * 0.5,
maximums[4] * 0.7, maximums[4] * 0.9),
(maximums[5] * 0.3, maximums[5] * 0.5,
maximums[5] * 0.7, maximums[5] * 0.9)]
fig = plt.figure(figsize=(8, 5.5))
f = 0
for i in wavelengths:
microns[f] = aplpy.FITSFigure(i + '_contour_mask.fits',
figure=fig,
subplot=(2, 3, f + 1))
microns[f].recenter(line[3], line[4], 0.025)
microns[f].show_contour(i + '_contour_mask.fits',
colors=('white', 'white', 'white', 'grey'),
levels=contour_levels[f],
overlap=True)
microns[f].show_colorscale(cmap='gist_stern',
vmin=v_min[f],
vmax=v_max[f])
#microns[f].show_regions('/mnt/scratch-lustre/jkeown/DS9_regions/HGBS_pipeline/L1157/L1157_core_SED/255_all_cores_good.reg')
microns[f].add_colorbar()
if f == 2 or f == 5:
microns[f].colorbar.set_axis_label_text('MJy/sr')
scale = str('{:.2f}'.format(
2 * (distance * numpy.tan(numpy.radians(0.5 * (1.0 / 60.))))))
if f == 0:
microns[f].add_scalebar(
(1. / 60.),
path_effects=[
Patheffects.withStroke(linewidth=3, foreground='white')
])
microns[f].scalebar.show(
1. / 60.) # length in degrees (one arcminute)
microns[f].scalebar.set_corner('top left')
microns[f].scalebar.set_label('1' + "'" + ' = ' + scale +
' pc')
if SIG_array[f] > 5.:
line_type = 'solid'
else:
line_type = 'dashed'
microns[f].show_ellipses(line[3],
line[4],
AFWHM_array[f] / 3600.,
BFWHM_array[f] / 3600.,
Theta_array[f] + 90.,
color='#00FF00',
zorder=10,
linewidth=1.0,
linestyle=line_type)
#microns[f].show_markers(line[3], line[4], c='#00FF00', marker='x', zorder=10, linewidths=1.5,s=20)
#microns[f].show_markers(line[3], line[4], c='black', marker='x', zorder=9, linewidths=2.0,s=30)
microns[f].tick_labels.hide()
microns[f].axis_labels.hide()
#microns[f].set_title('L' + name)
if i == '255':
microns[f].add_label(0.3,
0.1,
'N$_{H2}$',
relative=True,
path_effects=[
Patheffects.withStroke(
linewidth=3, foreground='white')
])
else:
microns[f].add_label(0.3,
0.1,
i + ' $\mu$' + 'm',
relative=True,
path_effects=[
Patheffects.withStroke(
linewidth=3, foreground='white')
])
f = f + 1
fig.subplots_adjust(hspace=0.05, wspace=0.35)
fig.canvas.draw()
fig.suptitle('Core Number ' + str(core_index) + ' - RA: ' +
str(line[3]) + ' Dec: ' + str(line[4]))
fig.savefig(core_figure_directory + 'core' + str(core_index) + '.pdf')
print 'Core Number = ' + str(core_index)
for i in range(6):
microns[i].close()
core_index = core_index + 1