def check_description_unsing_offical_method():
t = Table()
while True:
Random_column_Unit = (''.join(
random.choice(ascii_uppercase) for i in range(1)))
Random_column_Name = (''.join(
random.choice(ascii_uppercase) for i in range(2)))
Random_description = (''.join(
random.choice(ascii_uppercase) for i in range(12)))
random_int = random.randint(0, 9)
random_int2 = random.randint(0, 9)
random_int3 = random.randint(0, 9)
t[Random_column_Name] = Column([random_int, random_int2, random_int3],
unit=Random_column_Unit,
description=Random_description)
t[Random_column_Name].description = Random_description
if (Random_description == t[Random_column_Name].description):
print Random_description, '==', t[Random_column_Name].description
else:
print Random_description, '!=', t[Random_column_Name].description
assert Random_description == t[Random_column_Name].description
print(t)
print 'Pass test for adding column: ', t[Random_column_Name]
return
def test_preserve_serialized_compatibility_mode(tmpdir):
test_file = str(tmpdir.join('test.hdf5'))
t1 = Table()
t1['a'] = Column(data=[1, 2, 3], unit="s")
t1['a'].meta['a0'] = "A0"
t1['a'].meta['a1'] = {"a1": [0, 1]}
t1['a'].format = '7.3f'
t1['a'].description = 'A column'
t1.meta['b'] = 1
t1.meta['c'] = {"c0": [0, 1]}
with catch_warnings() as w:
t1.write(test_file,
path='the_table',
serialize_meta=True,
overwrite=True,
compatibility_mode=True)
assert str(w[0].message).startswith(
"compatibility mode for writing is deprecated")
t2 = Table.read(test_file, path='the_table')
assert t1['a'].unit == t2['a'].unit
assert t1['a'].format == t2['a'].format
assert t1['a'].description == t2['a'].description
assert t1['a'].meta == t2['a'].meta
assert t1.meta == t2.meta
def test_preserve_serialized_compatibility_mode(tmpdir):
test_file = str(tmpdir.join('test.hdf5'))
t1 = Table()
t1['a'] = Column(data=[1, 2, 3], unit="s")
t1['a'].meta['a0'] = "A0"
t1['a'].meta['a1'] = {"a1": [0, 1]}
t1['a'].format = '7.3f'
t1['a'].description = 'A column'
t1.meta['b'] = 1
t1.meta['c'] = {"c0": [0, 1]}
with catch_warnings() as w:
t1.write(test_file, path='the_table', serialize_meta=True,
overwrite=True, compatibility_mode=True)
assert str(w[0].message).startswith(
"compatibility mode for writing is deprecated")
t2 = Table.read(test_file, path='the_table')
assert t1['a'].unit == t2['a'].unit
assert t1['a'].format == t2['a'].format
assert t1['a'].description == t2['a'].description
assert t1['a'].meta == t2['a'].meta
assert t1.meta == t2.meta
def test_preserve_serialized(tmpdir):
test_file = str(tmpdir.join('test.hdf5'))
t1 = Table()
t1['a'] = Column(data=[1, 2, 3], unit="s")
t1['a'].meta['a0'] = "A0"
t1['a'].meta['a1'] = {"a1": [0, 1]}
t1['a'].format = '7.3f'
t1['a'].description = 'A column'
t1.meta['b'] = 1
t1.meta['c'] = {"c0": [0, 1]}
t1.write(test_file, path='the_table', serialize_meta=True, overwrite=True)
t2 = Table.read(test_file, path='the_table')
assert t1['a'].unit == t2['a'].unit
assert t1['a'].format == t2['a'].format
assert t1['a'].description == t2['a'].description
assert t1['a'].meta == t2['a'].meta
assert t1.meta == t2.meta
# Check that the meta table is fixed-width bytes (see #11299)
h5 = h5py.File(test_file, 'r')
meta_lines = h5[meta_path('the_table')]
assert meta_lines.dtype.kind == 'S'
def test_metadata_very_large(tmpdir):
"""Test that very large datasets work"""
test_file = tmpdir.join('test.parquet')
t1 = Table()
t1['a'] = Column(data=[1, 2, 3], unit="s")
t1['a'].meta['a0'] = "A0"
t1['a'].meta['a1'] = {"a1": [0, 1]}
t1['a'].format = '7.3f'
t1['a'].description = 'A column'
t1.meta['b'] = 1
t1.meta['c'] = {"c0": [0, 1]}
t1.meta["meta_big"] = "0" * (2**16 + 1)
t1.meta["meta_biggerstill"] = "0" * (2**18)
t1.write(test_file, overwrite=True)
t2 = Table.read(test_file)
assert t1['a'].unit == t2['a'].unit
assert t1['a'].format == t2['a'].format
assert t1['a'].description == t2['a'].description
assert t1['a'].meta == t2['a'].meta
assert t1.meta == t2.meta
def test_preserve_serialized(tmpdir):
test_file = str(tmpdir.join('test.hdf5'))
t1 = Table()
t1['a'] = Column(data=[1, 2, 3], unit="s")
t1['a'].meta['a0'] = "A0"
t1['a'].meta['a1'] = {"a1": [0, 1]}
t1['a'].format = '7.3f'
t1['a'].description = 'A column'
t1.meta['b'] = 1
t1.meta['c'] = {"c0": [0, 1]}
t1.write(test_file, path='the_table', serialize_meta=True, overwrite=True)
t2 = Table.read(test_file, path='the_table')
assert t1['a'].unit == t2['a'].unit
assert t1['a'].format == t2['a'].format
assert t1['a'].description == t2['a'].description
assert t1['a'].meta == t2['a'].meta
assert t1.meta == t2.meta
def test_preserve_serialized(tmpdir):
test_file = str(tmpdir.join('test.hdf5'))
t1 = Table()
t1['a'] = Column(data=[1, 2, 3], unit="s")
t1['a'].meta['a0'] = "A0"
t1['a'].meta['a1'] = {"a1": [0, 1]}
t1['a'].format = '7.3f'
t1['a'].description = 'A column'
t1.meta['b'] = 1
t1.meta['c'] = {"c0": [0, 1]}
t1.write(test_file, path='the_table', serialize_meta=True, overwrite=True)
t2 = Table.read(test_file, path='the_table')
assert t1['a'].unit == t2['a'].unit
assert t1['a'].format == t2['a'].format
assert t1['a'].description == t2['a'].description
assert t1['a'].meta == t2['a'].meta
assert t1.meta == t2.meta
def check_description_unsing_offical_method():
t=Table()
while True:
Random_column_Unit=(''.join(random.choice(ascii_uppercase) for i in range(1)))
Random_column_Name=(''.join(random.choice(ascii_uppercase) for i in range(2)))
Random_description= (''.join(random.choice(ascii_uppercase) for i in range(12)))
random_int= random.randint(0,9)
random_int2= random.randint(0,9)
random_int3= random.randint(0,9)
t[Random_column_Name]= Column([random_int,random_int2,random_int3], unit= Random_column_Unit, description=Random_description)
t[Random_column_Name].description = Random_description
if (Random_description == t[Random_column_Name].description):
print Random_description, '==', t[Random_column_Name].description
else:
print Random_description, '!=', t[Random_column_Name].description
assert Random_description == t[Random_column_Name].description
print(t)
print 'Pass test for adding column: ', t[Random_column_Name]
return
def test_preserve_serialized_old_meta_format(tmpdir):
"""Test the old meta format
Only for some files created prior to v4.0, in compatibility mode.
"""
test_file = get_pkg_data_filename('data/old_meta_example.hdf5')
t1 = Table()
t1['a'] = Column(data=[1, 2, 3], unit="s")
t1['a'].meta['a0'] = "A0"
t1['a'].meta['a1'] = {"a1": [0, 1]}
t1['a'].format = '7.3f'
t1['a'].description = 'A column'
t1.meta['b'] = 1
t1.meta['c'] = {"c0": [0, 1]}
t2 = Table.read(test_file, path='the_table')
assert t1['a'].unit == t2['a'].unit
assert t1['a'].format == t2['a'].format
assert t1['a'].description == t2['a'].description
assert t1['a'].meta == t2['a'].meta
assert t1.meta == t2.meta
def test_preserve_serialized(tmpdir):
"""Test that writing/reading preserves unit/format/description."""
test_file = tmpdir.join('test.parquet')
t1 = Table()
t1['a'] = Column(data=[1, 2, 3], unit="s")
t1['a'].meta['a0'] = "A0"
t1['a'].meta['a1'] = {"a1": [0, 1]}
t1['a'].format = '7.3f'
t1['a'].description = 'A column'
t1.meta['b'] = 1
t1.meta['c'] = {"c0": [0, 1]}
t1.write(test_file, overwrite=True)
t2 = Table.read(test_file)
assert t1['a'].unit == t2['a'].unit
assert t1['a'].format == t2['a'].format
assert t1['a'].description == t2['a'].description
assert t1['a'].meta == t2['a'].meta
assert t1.meta == t2.meta
def test_metadata_very_large(tmpdir):
"""Test that very large datasets work, now!"""
test_file = str(tmpdir.join('test.hdf5'))
t1 = Table()
t1['a'] = Column(data=[1, 2, 3], unit="s")
t1['a'].meta['a0'] = "A0"
t1['a'].meta['a1'] = {"a1": [0, 1]}
t1['a'].format = '7.3f'
t1['a'].description = 'A column'
t1.meta['b'] = 1
t1.meta['c'] = {"c0": [0, 1]}
t1.meta["meta_big"] = "0" * (2 ** 16 + 1)
t1.meta["meta_biggerstill"] = "0" * (2 ** 18)
t1.write(test_file, path='the_table', serialize_meta=True, overwrite=True)
t2 = Table.read(test_file, path='the_table')
assert t1['a'].unit == t2['a'].unit
assert t1['a'].format == t2['a'].format
assert t1['a'].description == t2['a'].description
assert t1['a'].meta == t2['a'].meta
assert t1.meta == t2.meta
def add_ltemass(label='pcc_12',
n13cub=None,
i12cub=None,
i13cub=None,
n13cub_uc=None,
distpc=4.8e4,
co13toh2=5.0e6):
# Make the uncertainty input a list
if not isinstance(n13cub_uc, list): n13cub_uc = [n13cub_uc]
# Adopted parameters
dist = distpc * u.pc
# Get basic info from header
hd = getheader(n13cub)
deltav = np.abs(hd['cdelt3'] / 1000.)
pixdeg = np.abs(hd['cdelt2'])
pix2cm = (np.radians(pixdeg) * dist).to(u.cm)
ppbeam = np.abs((hd['bmaj'] * hd['bmin']) / (hd['cdelt1'] * hd['cdelt2']) *
2 * np.pi / (8 * np.log(2)))
osamp = np.sqrt(ppbeam)
# Total the LTE masses (and optionally, 12CO and 13CO fluxes)
d = Dendrogram.load_from(label + '_dendrogram.hdf5')
cat = Table.read(label + '_physprop.txt', format='ascii.ecsv')
srclist = cat['_idx'].tolist()
for col in [
'flux12', 'flux13', 'mlte', 'e_mlte', 'siglte', 'e_siglte',
'e_mlte_alt'
]:
newcol = Column(name=col, data=np.zeros(np.size(srclist)))
if col == 'flux12':
if i12cub is not None:
data, ihd = getdata(i12cub, header=True)
if 'RESTFREQ' in ihd.keys():
rfreq = ihd['RESTFREQ'] * u.Hz
elif 'RESTFRQ' in ihd.keys():
rfreq = ihd['RESTFRQ'] * u.Hz
newcol.description = '12CO flux within the structure'
else:
continue
elif col == 'flux13':
if i13cub is not None:
data, ihd = getdata(i13cub, header=True)
if 'RESTFREQ' in ihd.keys():
rfreq = ihd['RESTFREQ'] * u.Hz
elif 'RESTFRQ' in ihd.keys():
rfreq = ihd['RESTFRQ'] * u.Hz
newcol.description = '13CO flux within the structure'
else:
continue
elif col == 'mlte':
data = getdata(n13cub)
newcol.description = 'LTE mass using H2/13CO=' + str(co13toh2)
elif col == 'e_mlte':
data = getdata(n13cub_uc[0])
newcol.description = 'fractional unc in mlte'
elif col == 'siglte':
data = getdata(n13cub)
newcol.description = 'LTE mass divided by area in pc2'
elif col == 'e_siglte':
data = getdata(n13cub_uc[0])
newcol.description = 'fractional unc in siglte [same as e_lte]'
elif col == 'e_mlte_alt':
if len(n13cub_uc) > 1:
data = getdata(n13cub_uc[1])
newcol.description = 'fractional unc in mlte from alt approach'
else:
continue
for i, c in enumerate(srclist):
mask = d[c].get_mask()
if not col.startswith('e_'):
newcol[i] = np.nansum(data[np.where(mask)])
# nansum returns zero if all are NaN, want NaN
chknan = np.asarray(np.isnan(data[np.where(mask)]))
if chknan.all():
newcol[i] = np.nan
else:
newcol[i] = np.sqrt(np.nansum(data[np.where(mask)]**2)) * osamp
if col in ['flux12', 'flux13']:
# Convert from K*pix*ch to Jy*km/s
convfac = (1 * u.K).to(
u.Jy / u.deg**2, equivalencies=u.brightness_temperature(rfreq))
newcol *= deltav * convfac.value * (pixdeg)**2
newcol.unit = 'Jy km / s'
else:
# Multiply by channel width in km/s and area in cm^2 to get molecule number
newcol *= deltav * pix2cm.value**2
# Convert from molecule number to solar masses including He
newcol *= co13toh2 * 2 * 1.36 * const.m_p.value / const.M_sun.value
if col == 'mlte':
newcol.unit = 'solMass'
elif col == 'siglte':
newcol /= cat['area_pc2']
newcol.unit = 'solMass/pc2'
else:
newcol /= cat['mlte']
newcol.unit = ''
cat.add_column(newcol)
#cat.pprint(show_unit=True)
cat.write(label + '_physprop_add.txt', format='ascii.ecsv', overwrite=True)
return
def calc_phys_props(label='pcc_12',
cubefile=None,
dendrofile=None,
boot_iter=400,
efloor=0,
alphascale=1,
distpc=4.8e4,
copbcor=None,
conoise=None,
ancfile=None,
anclabel=None,
verbose=False,
clipping=False,
co13toh2=1e6,
n13cube=None,
n13errcube=None,
dendro_in=None):
if clipping:
clipstr = "_clipped"
else:
clipstr = ""
rmstorad = 1.91
alphaco = 4.3 * u.solMass * u.s / (u.K * u.km * u.pc**2) # Bolatto+ 13
dist = distpc * u.pc
as2 = 1 * u.arcsec**2
asarea = (as2 * dist**2).to(u.pc**2,
equivalencies=u.dimensionless_angles())
# ---- load the dendrogram and catalog
if dendrofile == None:
dendrofile = label + '_dendrogram.hdf5'
if dendro_in != None:
d = dendro_in
elif os.path.isfile(dendrofile):
print('Loading pre-existing dendrogram')
d = Dendrogram.load_from(dendrofile)
cat = Table.read(label + '_full_catalog' + clipstr + '.txt',
format='ascii.ecsv')
srclist = cat['_idx'].tolist()
# ---- load the cube and extract the metadata
cube, hd3 = getdata(cubefile, header=True)
metadata = {}
if hd3['BUNIT'].upper() == 'JY/BEAM':
metadata['data_unit'] = u.Jy / u.beam
elif hd3['BUNIT'].upper() == 'K':
metadata['data_unit'] = u.K
else:
print("\nWarning: Unrecognized brightness unit")
metadata['vaxis'] = 0
if 'RESTFREQ' in hd3.keys():
freq = hd3['RESTFREQ'] * u.Hz
elif 'RESTFRQ' in hd3.keys():
freq = hd3['RESTFRQ'] * u.Hz
cdelt1 = abs(hd3['cdelt1']) * 3600. * u.arcsec
cdelt2 = abs(hd3['cdelt2']) * 3600. * u.arcsec
# this assumes vel cube!
if hd3['ctype3'][0:4] == "FREQ":
nu0 = hd3['restfrq']
dnu = hd3['cdelt3']
deltav = 2.99792458e5 * np.absolute(dnu) / nu0 * u.km / u.s
else:
deltav = abs(hd3['cdelt3']) / 1000. * u.km / u.s
metadata['wavelength'] = freq.to(u.m, equivalencies=u.spectral())
metadata['spatial_scale'] = cdelt2
metadata['velocity_scale'] = deltav
bmaj = hd3['bmaj'] * 3600. * u.arcsec # FWHM
bmin = hd3['bmin'] * 3600. * u.arcsec # FWHM
metadata['beam_major'] = bmaj
metadata['beam_minor'] = bmin
ppbeam = np.abs(
(bmaj * bmin) / (cdelt1 * cdelt2) * 2 * np.pi / (8 * np.log(2)))
print("\nPixels per beam: {:.2f}".format(ppbeam))
# Assume every 2 channels have correlated noise
indfac = np.sqrt(ppbeam * 2)
# ---- read in ancillary files
if copbcor is not None and conoise is not None:
cube12, hd12 = getdata(copbcor, header=True)
ecube12, ehd12 = getdata(conoise, header=True)
if 'RESTFREQ' in hd12.keys():
freq12 = hd12['RESTFREQ'] * u.Hz
elif 'RESTFRQ' in hd12.keys():
freq12 = hd12['RESTFRQ'] * u.Hz
else:
print('Rest frequency missing from file ' + copbcor)
raise
if hd12['BUNIT'].upper() != 'K':
print('Non-Kelvin units not yet supported for copbcor')
raise
if ehd12['NAXIS'] == 2:
tmpcube = np.broadcast_to(ecube12, np.shape(cube12))
ecube12 = tmpcube
if ancfile is not None:
ancdata, anchd = getdata(ancfile, header=True)
# elliptical function
def w_ell(ratio):
return np.log(np.sqrt(ratio**2 - 1) + ratio) / np.sqrt(ratio**2 - 1)
# ---- call the bootstrapping routine
emaj, emin, epa, evrms, errms, eaxra, eflux, emvir, eemvir, ealpha, tb12, ancmean, ancrms = [
np.zeros(len(srclist)) for _ in range(13)
]
print("Calculating property errors... with %i iterations" % boot_iter)
print("....................\r", end='')
nsrc = len(srclist)
nsrcd = nsrc // 20
for j, clust in enumerate(srclist):
if j % nsrcd == 0 and j > 0:
print("+" * (j // nsrcd) + "." * ((nsrc - j) // nsrcd) + "\r",
end='')
if verbose: print(" cl", j, "/", len(srclist))
asgn = np.zeros(cube.shape)
asgn[d[clust].get_mask(shape=asgn.shape)] = 1
sindices = np.where(asgn == 1)
svalues = cube[sindices]
emmajs, emmins, emomvs, emom0s, pa = clustbootstrap(sindices,
svalues,
metadata,
boot_iter,
verbose=False)
# bin_list=np.linspace(np.floor(min(emmajs)),np.ceil(max(emmajs)),50)
# fig, axes = plt.subplots()
# axes.hist(emmajs, bin_list, normed=0, histtype='bar')
# plt.savefig('emmajs_histo.pdf', bbox_inches='tight')
bootmaj = np.asarray(emmajs) * u.arcsec
bootmajpc = (bootmaj * dist).to(u.pc,
equivalencies=u.dimensionless_angles())
bootmin = np.asarray(emmins) * u.arcsec
bootpa = np.asarray(pa) * u.deg
bootvrms = (np.asarray(emomvs) * u.m / u.s).to(u.km / u.s)
bootrrms = (np.sqrt(np.asarray(emmajs) * np.asarray(emmins)) *
u.arcsec * dist).to(u.pc,
equivalencies=u.dimensionless_angles())
bootaxrat = bootmin / bootmaj
bootflux = np.asarray(emom0s) * u.Jy # RI: cube assumed to be Jy
bootmvir = (5 * rmstorad * bootvrms**2 * bootrrms / const.G).to(
u.solMass)
# elliptical mvir:
bootemvir = (5 * rmstorad * bootvrms**2 * bootmajpc /
w_ell(1. / bootaxrat) / const.G).to(u.solMass)
bootmlum = alphaco * alphascale * deltav * asarea * (bootflux).to(
u.K, equivalencies=u.brightness_temperature(as2, freq))
bootalpha = bootmvir / bootmlum
emaj[j] = indfac * mad_std(bootmaj) / np.median(bootmaj)
emin[j] = indfac * mad_std(bootmin) / np.median(bootmin)
epa[j] = indfac * mad_std(bootpa) / np.median(bootpa)
evrms[j] = indfac * mad_std(bootvrms) / np.median(bootvrms)
errms[j] = indfac * mad_std(bootrrms) / np.median(bootrrms)
eaxra[j] = indfac * mad_std(bootaxrat) / np.median(bootaxrat)
emvir[j] = indfac * mad_std(bootmvir) / np.median(bootmvir)
eemvir[j] = indfac * mad_std(bootemvir) / np.median(bootemvir)
ealpha[j] = indfac * mad_std(bootalpha) / np.median(bootalpha)
if copbcor is not None and conoise is not None:
tb12[j] = np.nansum(asgn * cube12)
eflux[j] = indfac * np.sqrt(np.nansum(asgn * ecube12**2)) / tb12[j]
#print(j, len(svalues), tb12[j], etb12[j])
else:
eflux[j] = indfac * mad_std(bootflux) / np.median(bootflux)
if ancfile is not None:
if anchd['NAXIS'] == 2:
collapsedmask = np.amax(asgn, axis=0)
collapsedmask[collapsedmask == 0] = np.nan
ancmean[j] = np.nanmean(ancdata * collapsedmask)
ancrms[j] = np.sqrt(
np.nanmean((ancdata * collapsedmask)**2) - ancmean[j]**2)
else:
ancmean[j] = np.nanmean(ancdata * asgn)
ancrms[j] = np.sqrt(
np.nanmean((ancdata * asgn)**2) - ancmean[j]**2)
print()
# ---- report the median uncertainties
print("The median fractional error in rad_pc is {:2.4f}".format(
np.nanmedian(errms)))
print("The median fractional error in vrms_k is {:2.4f}".format(
np.nanmedian(evrms)))
print("The median fractional error in mlumco is {:2.4f}".format(
np.nanmedian(eflux)))
# ---- apply a floor if requested
if efloor > 0:
print("Applying a minimum fractional error of {:2.3f}".format(efloor))
errms[errms < efloor] = efloor
evrms[evrms < efloor] = efloor
eflux[eflux < efloor] = efloor
# ---- calculate the physical properties
rms_pc = (cat['radius'] * dist).to(u.pc,
equivalencies=u.dimensionless_angles())
maj_pc = (cat['major_sigma'] * dist).to(
u.pc, equivalencies=u.dimensionless_angles())
rad_pc = rmstorad * rms_pc
v_rms = cat['v_rms'].to(u.km / u.s)
axrat = cat['minor_sigma'] / cat['major_sigma']
axrat.unit = ""
ellarea = (cat['area_ellipse'] * dist**2).to(
u.pc**2, equivalencies=u.dimensionless_angles())
xctarea = (cat['area_exact'] * dist**2).to(
u.pc**2, equivalencies=u.dimensionless_angles())
if copbcor is not None and conoise is not None:
# Convert from K*pix*ch to Jy*km/s
#convfac = (1*u.K).to(u.Jy/u.arcsec**2, equivalencies=u.brightness_temperature(freq12))
#flux12 = tb12 * deltav.value * convfac.value * cdelt2.value**2
mlumco = alphaco * alphascale * tb12 * u.K * deltav * asarea * cdelt2.value**2
else:
# lumco = Luminosity in K km/s pc^2
lumco = deltav * asarea * (cat['flux']).to(
u.K, equivalencies=u.brightness_temperature(as2, freq))
mlumco = alphaco * alphascale * lumco
siglum = mlumco / xctarea
mvir = (5 * rmstorad * v_rms**2 * rms_pc / const.G).to(
u.solMass) # Rosolowsky+ 08
emvir = (5 * rmstorad * v_rms**2 * maj_pc / w_ell(1. / axrat) /
const.G).to(u.solMass) # Rosolowsky+ 08
sigvir = mvir / xctarea
sigevir = emvir / xctarea
alpha = mvir / mlumco
# ---- make the physical properties table
ptab = Table()
ptab['_idx'] = Column(srclist)
ptab['area_pc2'] = Column(xctarea,
description='projected area of structure')
ptab['rad_pc'] = Column(rad_pc, description='equivalent radius in pc')
ptab['e_rad_pc'] = Column(errms, description='frac error in radius')
ptab['vrms_k'] = Column(v_rms, description='rms linewidth in km/s')
ptab['e_vrms_k'] = Column(evrms, description='frac error in linewidth')
ptab['axratio'] = Column(axrat,
unit='',
description='minor to major axis ratio')
ptab['e_axratio'] = Column(eaxra, description='frac error in axis ratio')
if copbcor is not None and conoise is not None:
#ptab['flux12'] = Column(flux12, unit='Jy km / s', description='CO flux in structure')
#ptab['e_flux12'] = Column(eflux, description='frac error in CO flux')
ptab['mlumco'] = Column(mlumco,
description='CO-based mass with alphascale=' +
str(alphascale) + ' from ' +
os.path.basename(copbcor))
else:
ptab['mlumco'] = Column(
mlumco,
description='Mass from scaling luminosity with alphascale=' +
str(alphascale))
ptab['e_mlumco'] = Column(eflux, description='frac error in luminous mass')
ptab['siglum'] = Column(siglum,
description='average surface density from mlumco')
ptab['e_siglum'] = Column(eflux, description='same as e_mlumco')
ptab['mvir'] = Column(mvir, description='virial mass')
ptab['e_mvir'] = Column(emvir, description='frac error in virial mass')
ptab['emvir'] = Column(emvir, description='elliptical virial mass')
ptab['e_emvir'] = Column(
eemvir, description='frac error in elliptical virial mass')
ptab['sigvir'] = Column(sigvir, description='virial surface density')
ptab['e_sigvir'] = Column(emvir, description='same as e_mvir')
ptab['sigevir'] = Column(sigevir,
description='ell. virial surface density')
ptab['e_sigevir'] = Column(emvir, description='same as e_mvir')
ptab['alpha'] = Column(alpha, unit='', description='virial parameter')
ptab['e_alpha'] = Column(ealpha,
description='frac error in virial parameter')
if ancfile is not None:
if anclabel is None:
anclabel = ancimg.replace('.', '_').split('_')[1]
ptab[anclabel] = Column(ancmean, unit=anchd['BUNIT'])
ancferr = indfac * ancrms / ancmean
ptab['e_' + anclabel] = Column(ancferr)
from ellfit import ellfit
# add ellipse at half-max like in cprops
halfmax_ell_maj = np.zeros(len(srclist), dtype=np.float64)
halfmax_ell_min = np.zeros(len(srclist), dtype=np.float64)
halfmax_ell_pa = np.zeros(len(srclist), dtype=np.float64)
if clipping:
tmax = cat['tmax-tmin']
else:
tmax = cat['tmax']
for i, c in enumerate(srclist):
if tmax[i] > 0:
ind = d[c].indices()
half_ind_z = np.where(cube[ind] > 0.5 * cube[ind].max())[0]
z, y, x = ind
# unique set of 2-d indices for the 3-d clump
twod_id = x[half_ind_z] + y[half_ind_z] * (x[half_ind_z].max() + 1)
# half_twod_ind = np.unique(twod_id[half_ind],return_index=True)[1]
half_twod_ind = np.unique(twod_id, return_index=True)[1]
half_twod_x = x[half_twod_ind]
half_twod_y = y[half_twod_ind]
halfmax_ell_maj[i], halfmax_ell_min[i], halfmax_ell_pa[i] = ellfit(
half_twod_x, half_twod_y)
ptab['halfmax_ell_maj'] = Column(halfmax_ell_maj,
name='halfmax_ell_maj',
unit='pix')
ptab['halfmax_ell_min'] = Column(halfmax_ell_min,
name='halfmax_ell_min',
unit='pix')
ptab['halfmax_ell_pa'] = Column(halfmax_ell_min,
name='halfmax_ell_pa',
unit='rad')
# go ahead and add lte mass here to have all this in one place
if n13cube != None:
if os.path.exists(n13cube):
print("adding MLTE from " + n13cube)
srclist = ptab['_idx'].tolist()
newcol = Column(name="mlte", data=np.zeros(np.size(srclist)))
data = fits.getdata(n13cube)
cubehdr = fits.getheader(n13cube)
dx = cubehdr['cdelt2'] * 3600 / 206265 * dist.value # pc
nu0 = cubehdr['restfrq']
if cubehdr['ctype3'] == 'VRAD' or cubehdr['ctype3'][0:3] == 'VEL':
dv = cubehdr['cdelt3'] / 1000
else:
dnu = cubehdr['cdelt3']
dv = 2.99792458e5 * np.absolute(dnu) / nu0
newcol.description = 'LTE mass using H2/13CO=' + str(co13toh2)
if os.path.exists(n13errcube):
e_newcol = Column(name="e_mlte",
data=np.zeros(np.size(srclist)))
uncert2 = fits.getdata(n13errcube)**2
e_newcol.description = 'LTE mass uncert.'
for i, c in enumerate(srclist):
mask = d[c].get_mask()
if clipping:
cmin = np.nanmin(data[np.where(mask)])
newcol[i] = np.nansum(data[np.where(mask)] - cmin)
else:
newcol[i] = np.nansum(data[np.where(mask)])
# nansum returns zero if all are NaN, want NaN
chknan = np.asarray(np.isnan(data[np.where(mask)]))
if chknan.all():
newcol[i] = np.nan
if os.path.exists(n13errcube):
e_newcol[i] = np.nansum(uncert2[np.where(mask)])
# nansum returns zero if all are NaN, want NaN
chknan = np.asarray(np.isnan(uncert2[np.where(mask)]))
if chknan.all():
e_newcol[i] = np.nan
# Multiply by channel width in km/s and area in cm^2 to get molecule number
newcol *= dv * (dx * 3.09e18)**2
# Convert from molecule number to solar masses including He
newcol *= co13toh2 * 2 * 1.36 * 1.66e-24 / 1.99e33
newcol.unit = 'solMass'
ptab['mlte'] = newcol
if os.path.exists(n13errcube):
e_newcol = np.sqrt(e_newcol)
e_newcol *= dv * (dx * 3.09e18)**2
# Convert from molecule number to solar masses including He
e_newcol *= co13toh2 * 2 * 1.36 * 1.66e-24 / 1.99e33
e_newcol.unit = 'solMass'
ptab['e_mlte'] = e_newcol
ptab.write(label + '_physprop' + clipstr + '.txt',
format='ascii.ecsv',
overwrite=True)
