print("total stars in clusters: ", allclusters.sum())
if name in ('M', 'N'):
NandM += mask
if name == "M":
maskM = mask
elif name == "N":
maskN = mask
rowmax = max([hii_only_inferred_mass, core_inferred_mass]) * u.M_sun
total_picking_max += rowmax
row = [
name,
ncores,
nhii,
latex_info.round_to_n(mass, 2) * u.M_sun,
latex_info.round_to_n(inferred_mass, 2) * u.M_sun,
latex_info.round_to_n(rowmax.value, 2) * u.M_sun,
latex_info.round_to_n(hii_only_inferred_mass, 2) * u.M_sun,
latex_info.round_to_n(core_inferred_mass, 2) * u.M_sun,
schmiedeke_summary_table[sst_mask]['M∗ initial'] * u.M_sun,
schmiedeke_summary_table[sst_mask]['M∗ all'] * 1e3 * u.M_sun,
latex_info.round_to_n(inferred_mass / sgrb2_age_myr / 1e6, 2) *
u.M_sun / u.yr,
]
if name == 'Total':
totalrow = row
totalrow[5] = latex_info.round_to_n(total_picking_max.value,
2) * u.M_sun
else:
'$\\sigma_{MAD}$',
'$\\theta_{req}$',
'\\sigma_{req}$',
'$\\sigma_{MAD}/\\sigma_{req}$',
# '$\\theta_{req}/\\theta_{maj}$',
"$\Omega_{syn}^{1/2}/\Omega_{req}^{1/2}$",
'DR$_{pre}$',
'DR$_{post}$',
'DR$_{post}$/DR$_{pre}$'
]
# convert to mJy
fwtbl['$\sigma_{MAD}$'] *= 1000
formats = {
key: lambda x: strip_trailing_zeros('{0:0.3f}'.format(round_to_n(x, 2)))
for key in float_cols
}
fwtbl.write('selfcal_summary.ecsv', format='ascii.ecsv', overwrite=True)
# caption needs to be *before* preamble.
#latexdict['caption'] = 'Continuum Source IDs and photometry'
latexdict['header_start'] = '\label{tab:selfcal}' #\n\\footnotesize'
latexdict[
'preamble'] = '\caption{Selfcal Summary}\n\\resizebox{\\textwidth}{!}{'
latexdict['col_align'] = 'l' * len(fwtbl.columns)
latexdict['tabletype'] = 'table*'
latexdict['tablefoot'] = (
"}\par\n"
"$n_{sc}$ is the number of self-calibration iterations adopted. "
'Muno_xray_ID',
'Muno_xray_Counts',
'peak_mass_20K',
'peak_col_20K',
'McGrath_V_H2O',
'McGrath_matchdistance',
'nn11',
'Muno_xray_matchdistance',
]:
cont_tbl.remove_column(colname)
formats = {
'Coordinates':
lambda x: x.to_string('hmsdms', sep=":"),
'$S_{\\nu,max}$':
lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x, 2))),
'$S_{\\nu,tot}$':
lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x, 2))),
'$\sigma_{bg}$':
lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x, 2))),
'$\\alpha$':
lambda x: '-' if np.isnan(x) else strip_trailing_zeros('{0:0.2f}'.format(
round_to_n(x, 2))),
'$E(\\alpha)$':
lambda x: '-' if np.isnan(x) else strip_trailing_zeros('{0:0.3f}'.format(
round_to_n(x, 2))),
'$N(\hh)_{40 K}$':
format_float,
'$M_{40K}$':
lambda x: '-' if np.isnan(x) else strip_trailing_zeros('{0:0.2f}'.format(
round_to_n(x, 2))),
def make_latex_table(releasename='February2021', savename='selfcal_summary'):
if datetime.datetime.today() > datetime.datetime(
year=2021, month=1, day=10):
result = requests.get(
f'https://data.rc.ufl.edu/secure/adamginsburg/ALMA-IMF/{releasename}Release/tables/metadata_sc.ecsv',
auth=('almaimf', keyring.get_password('almaimf', 'almaimf')))
with open(f'{releasename}_metadata_sc.ecsv', 'w') as fh:
fh.write(result.text)
result = requests.get(
'https://data.rc.ufl.edu/secure/adamginsburg/ALMA-IMF/tables/bandpass_fraction.ecsv',
auth=('almaimf', keyring.get_password('almaimf', 'almaimf')))
with open('bandpass_fraction.ecsv', 'w') as fh:
fh.write(result.text)
bp_tbl = Table.read('bandpass_fraction.ecsv')
bp_tbl['band'] = [f'B{b}' for b in bp_tbl['band']]
bp_tbl.rename_column('field', 'region')
bp_tbl = table.join(bp_tbl.group_by('config').groups[0],
bp_tbl.group_by('config').groups[1],
keys=('region', 'band'))
bp_tbl.rename_column('bwfrac_1', '12Mlong_frac')
bp_tbl.rename_column('bwfrac_2', '12Mshort_frac')
bp_tbl.remove_column('config_1')
bp_tbl.remove_column('config_2')
tbl = table.join(Table.read(f'{releasename}_metadata_sc.ecsv'),
bp_tbl,
keys=('region', 'band'))
bad = np.array([('diff' in x) or ('noco' in x) for x in tbl['filename']])
# downselect
keep = ((tbl['suffix'] == 'finaliter') & (tbl['robust'] == 'r0.0') &
(~tbl['pbcor']) & (~tbl['bsens']) & (~tbl['nobright']) & (~bad))
wtbl = tbl[keep]
assert not any(np.isnan(wtbl['dr_improvement']))
print(len(wtbl))
print(wtbl)
# strip preceding "sc" from selfcal numbers
wtbl['selfcaliter'] = Column(data=[
row['selfcaliter'][2:] + ("a" if row['has_amp'] else "")
for row in wtbl
])
# SensVsReq can be populated with either pre- or post-; we want post
wtbl['SensVsReqPost'] = wtbl['mad_sample_post'] / wtbl['Req_Sens']
wtbl['SensVsReqPre'] = wtbl['mad_sample_pre'] / wtbl['Req_Sens']
wtbl['mad_sample_post'].unit = u.mJy / u.beam
wtbl['mad_sample_pre'].unit = u.mJy / u.beam
# convert 'peak' to mJy
# ("imstats" uses Jy, "compare" uses mJy)
wtbl['peak'] *= 1e3
wtbl['peak'].unit = u.mJy / u.beam
cols_to_keep = {
'region': 'Region',
'band': 'Band',
'selfcaliter': '$n_{sc}$',
'bmaj': r'$\theta_{maj}$',
'bmin': r'$\theta_{min}$',
'bpa': 'BPA',
'Req_Res': r"$\theta_{req}$",
'BeamVsReq': r"$\Omega_{syn}^{1/2}/\Omega_{req}^{1/2}$",
#'peak/mad': "DR",
'peak': '$S_{peak}$',
'mad_sample_post': '$\sigma_{MAD}$',
'Req_Sens': r"$\sigma_{req}$",
'SensVsReqPost': r"$\sigma_{MAD}/\sigma_{req}$",
'dr_pre': "DR$_{pre}$",
'dr_post': "DR$_{post}$",
'dr_improvement': "DR$_{post}$/DR$_{pre}$"
}
units = {
'$S_{peak}$': (u.mJy / u.beam).to_string(u.format.LatexInline),
'$\sigma_{MAD}$': (u.mJy / u.beam).to_string(u.format.LatexInline),
'$\sigma_{req}$': (u.mJy / u.beam).to_string(u.format.LatexInline),
r'$\theta_{req}$': u.arcsec.to_string(u.format.LatexInline),
r'$\theta_{maj}$': u.arcsec.to_string(u.format.LatexInline),
r'$\theta_{min}$': u.arcsec.to_string(u.format.LatexInline),
r'PA': u.deg.to_string(u.format.LatexInline),
}
latexdict['units'] = units
fwtbl = wtbl[list(cols_to_keep.keys())]
for old, new in cols_to_keep.items():
if old in wtbl.colnames:
#wtbl[old].meta['description'] = description[old]
fwtbl.rename_column(old, new)
if new in units:
fwtbl[new].unit = units[new]
float_cols = [
'$\\theta_{maj}$',
'$\\theta_{min}$',
'BPA',
'$S_{peak}$',
'$\\sigma_{MAD}$',
'$\\theta_{req}$',
'\\sigma_{req}$',
'$\\sigma_{MAD}/\\sigma_{req}$',
# '$\\theta_{req}/\\theta_{maj}$',
"$\Omega_{syn}^{1/2}/\Omega_{req}^{1/2}$",
'DR$_{pre}$',
'DR$_{post}$',
'DR$_{post}$/DR$_{pre}$'
]
# ALREADY IN mJy
# convert to mJy
#fwtbl['$\sigma_{MAD}$'] *= 1000
formats = {
key: lambda x: strip_trailing_zeros('{0:0.3f}'.format(round_to_n(x, 2),
nsf=2))
for key in float_cols
}
formats = {
key: lambda x: str(sigfig.round(str(x), sigfigs=2))
for key in float_cols
}
fwtbl.write('selfcal_summary.ecsv', format='ascii.ecsv', overwrite=True)
# caption needs to be *before* preamble.
#latexdict['caption'] = 'Continuum Source IDs and photometry'
latexdict['header_start'] = '\label{tab:selfcal}' #\n\\footnotesize'
latexdict[
'preamble'] = '\caption{Selfcal Summary}\n\\resizebox{\\textwidth}{!}{'
latexdict['col_align'] = 'l' * len(fwtbl.columns)
latexdict['tabletype'] = 'table*'
latexdict['tablefoot'] = (
"}\par\n"
"$n_{sc}$ is the number of self-calibration iterations adopted. "
"Those with a final iteration of amplitude self-calibration are denoted with the `a' suffix. "
"$\\theta_{maj}, \\theta_{min}$, and BPA give the major and minor full-width-half-maxima (FWHM) of the synthesized beams. "
"$\\theta_{req}$ is the requested beam size, "
"and $\\Omega_{syn}^{1/2}/\\Omega_{req}^{1/2}$ gives the ratio of the synthesized to the "
"requested beam area; larger numbers imply poorer resolution. "
"$\sigma_{MAD}$ and $\sigma_{req}$ are the measured and requested "
"RMS sensitivity, respectively, and $\sigma_{MAD}/\sigma_{req}$ is the excess noise "
"in the image over that requested. $\sigma_{MAD}$ is measured on the \\texttt{cleanest} images. "
"$DR_{pre}$ and $DR_{post}$ are the dynamic range, $S_{peak} / \sigma_{MAD}$, for the "
"pre- and post-self-calibration data; $DR_{post}/DR_{pre}$ gives the improvement "
"factor.")
fwtbl.sort(['Region', 'Band'])
fwtbl.write(f"../datapaper/{savename}.tex",
formats=formats,
overwrite=True,
latexdict=latexdict)
return fwtbl
"and \\texttt{-} is indeterminate (either $20<T<50$K "
"or no measurement). "
"In column 3, \\texttt{c} indicates compact sources, "
"and \\texttt{-} indicates a diffuse source."
)
#latexdict['tablefoot'] = ('\par\nJy-Kelvin gives the conversion factor from Jy '
# 'to Kelvin given the synthesized beam size and '
# 'observation frequency.')
latexdict['col_align'] = 'l'*len(tbl.columns)
#latexdict['tabletype'] = 'longtable'
#latexdict['tabulartype'] = 'longtable'
latexdict['units'] = {}
formats={'RA': lambda x: '{0:0.4f}'.format(x),
'Dec': lambda x: '{0:0.4f}'.format(x),
'$S_{\\nu}(0.2\\arcsec)$': lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x,2))),
'$S_{\\nu}(0.4\\arcsec)$': lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x,2))),
'$T_{B,max}(\mathrm{line})$': lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x,2))),
'$T_{B,max}(\mathrm{line+cont})$': lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x,2))),
'$T_{B,max}$': lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x,2))),
'M$(T_B, 0.2\\arcsec)$': lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x,2))),
'M$(T_B, \mathrm{peak})$': lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x,2))),
}
tbl_towrite.write('../paper1/photometry_table.tex',
latexdict=latexdict, format='ascii.latex', overwrite=True,
formats=formats)
latexdict['caption'] = 'Continuum Source IDs and photometry Part 2'
latexdict['header_start'] = '\label{tab:photometry2}'
tbl_towrite[35:].write('../paper1/photometry_table_2.tex',
if 'FWHM' in key_:
newname = "$\mathrm{FWHM}$(" + key_.split("FWHM")[1] + ")"
if 'AMPLITUDE' in key_:
#newname = key_.replace("AMPLITUDE","Peak")
del depree_merged_tex[key]
continue
depree_merged_tex.rename_column(key, pfx + newname)
merged_tex_ok = (np.isfinite(depree_merged_tex['RA']) &
(np.isfinite(depree_merged_tex['V$_\\mathrm{LSR}$(41)'])
| np.isfinite(depree_merged_tex['V$_\\mathrm{LSR}$(52)'])
| np.isfinite(depree_merged_tex['V$_\\mathrm{LSR}$(66)'])))
depree_merged_tex = depree_merged_tex[merged_tex_ok]
formats = {
key: (lambda x: (strip_trailing_zeros('{0:0.1f}'.format(round_to_n(x, 2)))
if not np.isnan(x) else '-')) if key[0] == 'e' else
(lambda x: (strip_trailing_zeros('{0:0.3f}'.format(round_to_n(x, 3)))
if not np.isnan(x) else '-'))
for key in depree_merged_tex.colnames
}
del formats['Source']
del formats['RA']
del formats['Dec']
coords_column = coordinates.SkyCoord(depree_merged_tex['RA'],
depree_merged_tex['Dec'],
frame='fk5',
unit=(u.deg, u.deg))
depree_merged_tex.remove_column('RA')
depree_merged_tex.remove_column('Dec')
tbl['Disk PA'] = tbl['Disk PA'].to(u.deg)
formats = {
'Pt Position':
lambda x: "-" if np.isnan(x.ra) else "{0:0.4} {1:0.3}".format(
x.ra.hms.s - 14, x.dec.dms.s + 30),
'Pt RA':
lambda x: "-" if np.isnan(x) else "{0:0.3}".format(x - 14),
'Pt Dec':
lambda x: "-" if np.isnan(x) else "{0:0.3}".format(x + 30),
'ePt RA':
lambda x: "-" if np.isnan(x) else "{0:0.4}".format(x),
'ePt Dec':
lambda x: "-" if np.isnan(x) else "{0:0.3}".format(x),
'Pt Amp':
lambda x: ('{0:0.2f}'.format(round_to_n(x, 2))),
'ePt Amp':
lambda x: ('{0:0.2f}'.format(round_to_n(x, 2))),
'Pt Width':
lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x, 2))),
'Disk PA':
lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x, 2))),
'Disk FWHM':
lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x, 2))),
'Disk Radius':
lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x, 2))),
'Total Flux':
lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x, 2))),
'Pt Flux':
lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x, 2))),
'Pt \%':
'$f_{BW,cleanest}$',
#'$\\theta_{req}$',
#'\\sigma_{req}$',
#'$\\sigma_{req}/\\sigma_{MAD}$',
#'$\\theta_{req}/\\theta_{maj}$',
#'DR$_{cleanest}$',
#'DR$_{bsens}$',
#'DR$_{bsens}$/DR$_{cleanest}$'
]
# convert to mJy
wtbl['$\sigma_{MAD}$(bsens)'] *= 1000
wtbl['$\sigma_{MAD}$(cleanest)'] *= 1000
formats = {key: lambda x: ('{0:0.2f}'.format(round_to_n(x,2)))
for key in float_cols}
wtbl.write('bsens_cleanest_diff.ecsv', format='ascii.ecsv', overwrite=True)
# caption needs to be *before* preamble.
#latexdict['caption'] = 'Continuum Source IDs and photometry'
latexdict['header_start'] = '\label{tab:bsens_cleanest}'#\n\\footnotesize'
latexdict['preamble'] = '\caption{Best Sensitivity vs Cleanest Continuum comparison}\n\\resizebox{\\textwidth}{!}{'
latexdict['col_align'] = 'l'*len(wtbl.columns)
latexdict['tabletype'] = 'table*'
latexdict['tablefoot'] = ("}\par\n"
"Like Table \\ref{tab:selfcal}, but comparing the cleanest and bsens data. "
def make_latex_table_overview(releasename='June2021', savename='res_sens'):
if datetime.datetime.today() > datetime.datetime(
year=2021, month=1, day=10):
result = requests.get(
f'https://bio.rc.ufl.edu/secure/adamginsburg/ALMA-IMF/{releasename}Release/tables/metadata_bsens_cleanest.ecsv',
auth=('almaimf', keyring.get_password('almaimf', 'almaimf')))
with open(f'{releasename}_metadata_bsens_cleanest.ecsv', 'w') as fh:
fh.write(result.text)
result = requests.get(
'https://bio.rc.ufl.edu/secure/adamginsburg/ALMA-IMF/tables/bandpass_fraction.ecsv',
auth=('almaimf', keyring.get_password('almaimf', 'almaimf')))
with open('bandpass_fraction.ecsv', 'w') as fh:
fh.write(result.text)
result = requests.get(
'https://data.rc.ufl.edu/secure/adamginsburg/ALMA-IMF/May2021Release/tables/uvspacings.ecsv',
auth=('almaimf', keyring.get_password('almaimf', 'almaimf')))
with open('uvspacings.ecsv', 'w') as fh:
fh.write(result.text)
bp_tbl = Table.read('bandpass_fraction.ecsv')
bp_tbl['band'] = [f'B{b}' for b in bp_tbl['band']]
bp_tbl.rename_column('field', 'region')
bp_tbl = table.join(bp_tbl.group_by('config').groups[0],
bp_tbl.group_by('config').groups[1],
keys=('region', 'band'))
bp_tbl.rename_column('bwfrac_1', '12Mlong_frac')
bp_tbl.rename_column('bwfrac_2', '12Mshort_frac')
bp_tbl.remove_column('config_1')
bp_tbl.remove_column('config_2')
uvtbl = Table.read('uvspacings.ecsv')
tbl = table.join(Table.read(f'{releasename}_metadata_bsens_cleanest.ecsv'),
bp_tbl,
keys=('region', 'band'))
tbl = table.join(tbl, uvtbl)
bad = np.array(['diff' in x for x in tbl['filename']])
# downselect (need bsens=true to avoid duplication - but be wary in case you remove duplicates upstream!)
keep = ((tbl['suffix'] == 'finaliter') & (tbl['robust'] == 'r0.0') &
(~tbl['pbcor']) & (tbl['bsens']) & (~tbl['nobright']) & (~bad))
wtbl = tbl[keep]
print(len(wtbl))
print(wtbl)
wtbl['selfcaliter'] = Column(
data=[int(x[2:]) for x in wtbl['selfcaliter']])
wtbl['bsens_div_cleanest_mad'] = wtbl['mad_sample_bsens'] / wtbl[
'mad_sample_cleanest']
wtbl['bsens_div_cleanest_max'] = wtbl['max_bsens'] / wtbl['max_cleanest']
wtbl['bsens_mad_div_req'] = wtbl['mad_sample_bsens'] / wtbl[
'Req_Sens'] # * 1e3
wtbl['max_bsens'].unit = u.mJy / u.beam
wtbl['max_cleanest'].unit = u.mJy / u.beam
cols_to_keep = {
'region': 'Region',
'FOV_B3': 'FOV(B3)',
'FOV_B6': 'FOV(B6)'
}
for band in ('B3', 'B6'):
cols_to_keep[f'bmaj_{band}'] = f'$\\theta_{{maj}}({band})$'
cols_to_keep[f'bmin_{band}'] = f'$\\theta_{{min}}({band})$'
cols_to_keep[
f'mad_sample_bsens_{band}'] = f'$\sigma_{{MAD}}$(bsens,{band})'
cols_to_keep[
f'mad_sample_cleanest_{band}'] = f'$\sigma_{{MAD}}$(cleanest,{band})'
cols_to_keep[f'10%_{band}'] = f'LAS$_{{10\%}}$({band})'
# Region &
# FOV$_{B6}$ & $\theta_{maj}\times\theta_{min}(B6)$ & $\sigma_{MAD}(cleanest)$ & $\sigma_{MAD}(bsens)$ & LAS(5\%, B6) &
# FOV$_{B3}$ & $\theta_{maj}\times\theta_{min}(B3)$ & $\sigma_{MAD}(cleanest)$ & $\sigma_{MAD}(bsens)$ & LAS(5\%, B3) \\
cols = ['bmaj', 'bmin', '10%', 'mad_sample_bsens', 'mad_sample_cleanest']
wtbl.add_index('region')
wtbl.add_index('band')
rows = []
for region in set(wtbl['region']):
row = {
f'{key}_{band}': wtbl.loc['band', band].loc['region', region][key]
for key in cols for band in ('B3', 'B6')
}
row['region'] = region
row['FOV_B3'] = FOV[(region, 'B3')]
row['FOV_B6'] = FOV[(region, 'B6')]
rows.append(row)
mtbl = Table(rows)
units = {
'$S_{peak}(bsens)$': (u.mJy / u.beam).to_string(u.format.LatexInline),
'$S_{peak}(cleanest)$':
(u.mJy / u.beam).to_string(u.format.LatexInline),
'$\sigma_{MAD}(bsens,B6)$':
(u.mJy / u.beam).to_string(u.format.LatexInline),
'$\sigma_{MAD}(cleanes,B6)$':
(u.mJy / u.beam).to_string(u.format.LatexInline),
'$\sigma_{MAD}(bsens,B3)$':
(u.mJy / u.beam).to_string(u.format.LatexInline),
'$\sigma_{MAD}(cleanest,B3)$':
(u.mJy / u.beam).to_string(u.format.LatexInline),
#'$\sigma_{req}$':u.mJy.to_string(u.format.LatexInline),
#r'$\theta_{req}$':u.arcsec.to_string(u.format.LatexInline),
r'$\theta_{maj}(B3)$':
u.arcsec.to_string(u.format.LatexInline),
r'$\theta_{min}(B3)$':
u.arcsec.to_string(u.format.LatexInline),
r'$\theta_{maj}(B6)$':
u.arcsec.to_string(u.format.LatexInline),
r'$\theta_{min}(B6)$':
u.arcsec.to_string(u.format.LatexInline),
'LAS$_{10\%}$(B3)':
u.arcsec.to_string(u.format.LatexInline),
'LAS$_{10\%}$(B6)':
u.arcsec.to_string(u.format.LatexInline),
#r'PA':u.deg.to_string(u.format.LatexInline),
}
latexdict['units'] = units
mtbl.add_column(col=[
f'${major:0.2f}\\times{minor:0.2f}$'
for major, minor in zip(mtbl['bmaj_B3'], mtbl['bmin_B3'])
],
name='Res. B3')
mtbl.add_column(col=[
f'${major:0.2f}\\times{minor:0.2f}$'
for major, minor in zip(mtbl['bmaj_B6'], mtbl['bmin_B6'])
],
name='Res. B6')
column_order = [
'region',
]
for band in ('B3', 'B6'):
column_order += [
f'FOV_{band}', f'Res. {band}', f'mad_sample_cleanest_{band}',
f'mad_sample_bsens_{band}', f'10%_{band}'
]
mtbl = mtbl[column_order]
for old, new in cols_to_keep.items():
if old in mtbl.colnames:
#mtbl[old].meta['description'] = description[old]
mtbl.rename_column(old, new)
if new in units:
mtbl[new].unit = units[new]
for colname in [
'$\sigma_{MAD}$(bsens,B3)',
'$\sigma_{MAD}$(cleanest,B3)',
'$\sigma_{MAD}$(bsens,B6)',
'$\sigma_{MAD}$(cleanest,B6)',
]:
mtbl[colname].unit = u.mJy / u.beam
for colname in ('FOV(B3)', 'FOV(B6)', 'Res. B3', 'Res. B6'):
mtbl[colname].unit = u.arcsec
float_cols = [
'$\\theta_{maj}(B3)$',
'$\\theta_{min}(B3)$',
'$\\theta_{maj}(B6)$',
'$\\theta_{min}(B6)$',
'LAS$_{10\%}$(B3)',
'LAS$_{10\%}$(B6)',
'BPA',
'$S_{peak}$(bsens)',
'$S_{peak}$(cleanest)',
'$\\frac{S_{peak}(\mathrm{bsens})}{S_{peak}(\mathrm{cleanest})}$',
'$\\sigma_{MAD}$(bsens,B3)',
'$\\sigma_{MAD}$(cleanest,B3)',
'$\\sigma_{MAD}$(bsens,B6)',
'$\\sigma_{MAD}$(cleanest,B6)',
'$\\frac{\sigma_{MAD}(\mathrm{bsens})}{\sigma_{MAD}(\mathrm{cleanest})}$',
'Requested $\sigma$',
'$\sigma_{\mathrm{bsens}}/\sigma_{\mathrm{req}}$',
'$f_{BW,cleanest}$',
#'$\\theta_{req}$',
#'\\sigma_{req}$',
#'$\\sigma_{req}/\\sigma_{MAD}$',
#'$\\theta_{req}/\\theta_{maj}$',
#'DR$_{cleanest}$',
#'DR$_{bsens}$',
#'DR$_{bsens}$/DR$_{cleanest}$'
]
# convert to mJy
# mtbl['$\sigma_{MAD}$(bsens)'] *= 1000
# mtbl['$\sigma_{MAD}$(cleanest)'] *= 1000
formats = {
key: lambda x: strip_trailing_zeros(
('{0:0.3f}'.format(round_to_n(x, 2))))
for key in float_cols
}
formats = {
key: lambda x: str(sigfig.round(str(x), sigfigs=2))
for key in float_cols
}
mtbl.write('overviewpapertble.ecsv', format='ascii.ecsv', overwrite=True)
# caption needs to be *before* preamble.
#latexdict['caption'] = 'Continuum Source IDs and photometry'
latexdict['header_start'] = (
'\label{tab:sensitivity_scale_overview}\n\n'
' & \multicolumn{5}{c}{B3} & \multicolumn{5}{c}{B6}\\\\')
latexdict[
'preamble'] = '\caption{Overview of sensitivity and angular scales}\n\\resizebox{\\textwidth}{!}{'
latexdict['col_align'] = 'l|lllll|lllll|'
latexdict['tabletype'] = 'table*'
latexdict['tablefoot'] = ("}\par\n" "")
mtbl.sort(['Region'])
mtbl.write(f"../overviewpaper/tables/{savename}.tex",
formats=formats,
overwrite=True,
latexdict=latexdict)
with open(f"../overviewpaper/tables/{savename}.tex", 'r') as fh:
lines = fh.readlines()
with open(f"../overviewpaper/tables/{savename}.tex", 'w') as fh:
for row in lines:
if 'FOV(B3)' in row:
row = row.replace('(B3)', '').replace('(B6)', '').replace(
',B3', '').replace(',B6',
'').replace(" B3",
"").replace(" B6", "")
fh.write(row)
return mtbl
def make_latex_table(releasename='February2021',
savename='bsens_cleanest_diff'):
if datetime.datetime.today() > datetime.datetime(
year=2021, month=1, day=10):
result = requests.get(
f'https://bio.rc.ufl.edu/secure/adamginsburg/ALMA-IMF/{releasename}Release/tables/metadata_bsens_cleanest.ecsv',
auth=('almaimf', keyring.get_password('almaimf', 'almaimf')))
with open(f'{releasename}_metadata_bsens_cleanest.ecsv', 'w') as fh:
fh.write(result.text)
result = requests.get(
'https://bio.rc.ufl.edu/secure/adamginsburg/ALMA-IMF/tables/bandpass_fraction.ecsv',
auth=('almaimf', keyring.get_password('almaimf', 'almaimf')))
with open('bandpass_fraction.ecsv', 'w') as fh:
fh.write(result.text)
bp_tbl = Table.read('bandpass_fraction.ecsv')
bp_tbl['band'] = [f'B{b}' for b in bp_tbl['band']]
bp_tbl.rename_column('field', 'region')
bp_tbl = table.join(bp_tbl.group_by('config').groups[0],
bp_tbl.group_by('config').groups[1],
keys=('region', 'band'))
bp_tbl.rename_column('bwfrac_1', '12Mlong_frac')
bp_tbl.rename_column('bwfrac_2', '12Mshort_frac')
bp_tbl.remove_column('config_1')
bp_tbl.remove_column('config_2')
tbl = table.join(Table.read(f'{releasename}_metadata_bsens_cleanest.ecsv'),
bp_tbl,
keys=('region', 'band'))
bad = np.array(['diff' in x for x in tbl['filename']])
# downselect (need bsens=true to avoid duplication - but be wary in case you remove duplicates upstream!)
keep = ((tbl['suffix'] == 'finaliter') & (tbl['robust'] == 'r0.0') &
(~tbl['pbcor']) & (tbl['bsens']) & (~tbl['nobright']) & (~bad))
wtbl = tbl[keep]
print(len(wtbl))
print(wtbl)
wtbl['selfcaliter'] = Column(
data=[int(x[2:]) for x in wtbl['selfcaliter']])
wtbl['bsens_div_cleanest_mad'] = wtbl['mad_sample_bsens'] / wtbl[
'mad_sample_cleanest']
wtbl['bsens_div_cleanest_max'] = wtbl['max_bsens'] / wtbl['max_cleanest']
wtbl['bsens_mad_div_req'] = wtbl['mad_sample_bsens'] / wtbl[
'Req_Sens'] # * 1e3
wtbl['max_bsens'].unit = u.mJy / u.beam
wtbl['max_cleanest'].unit = u.mJy / u.beam
cols_to_keep = {
'region': 'Region',
'band': 'Band',
#'selfcaliter':'$n_{sc}$',
#'bmaj':r'$\theta_{maj}$',
#'bmin':r'$\theta_{min}$',
#'bpa':'BPA',
#'Req_Res': r"$\theta_{req}$",
#'BeamVsReq': r"$\theta_{req}/\theta_{maj}$",
#'peak/mad': "DR",
#'peak':'$S_{peak}$',
'mad_sample_bsens': '$\sigma_{MAD}$(bsens)',
'mad_sample_cleanest': '$\sigma_{MAD}$(cleanest)',
'bsens_div_cleanest_mad':
'$\\frac{\sigma_{MAD}(\mathrm{bsens})}{\sigma_{MAD}(\mathrm{cleanest})}$',
'12Mshort_frac': '$f_{BW,cleanest}$',
'max_bsens': '$S_{peak}$(bsens)',
'max_cleanest': '$S_{peak}$(cleanest)',
'bsens_div_cleanest_max':
'$\\frac{S_{peak}(\mathrm{bsens})}{S_{peak}(\mathrm{cleanest})}$',
'Req_Sens': 'Requested $\sigma$',
'bsens_mad_div_req': '$\sigma_{\mathrm{bsens}}/\sigma_{\mathrm{req}}$',
#'dr_cleanest': "DR$_{cleanest}$",
#'dr_bsens': "DR$_{bsens}$",
}
units = {
'$S_{peak}(bsens)$': (u.mJy / u.beam).to_string(u.format.LatexInline),
'$S_{peak}(cleanest)$':
(u.mJy / u.beam).to_string(u.format.LatexInline),
'$\sigma_{MAD}(bsens)$':
(u.mJy / u.beam).to_string(u.format.LatexInline),
'$\sigma_{MAD}(cleanest)$':
(u.mJy / u.beam).to_string(u.format.LatexInline),
'Requested $\sigma$': (u.mJy / u.beam).to_string(u.format.LatexInline),
#'$\sigma_{req}$':u.mJy.to_string(u.format.LatexInline),
#r'$\theta_{req}$':u.arcsec.to_string(u.format.LatexInline),
#r'$\theta_{maj}$':u.arcsec.to_string(u.format.LatexInline),
#r'$\theta_{min}$':u.arcsec.to_string(u.format.LatexInline),
#r'PA':u.deg.to_string(u.format.LatexInline),
}
latexdict['units'] = units
wtbl = wtbl[list(cols_to_keep.keys())]
for old, new in cols_to_keep.items():
if old in wtbl.colnames:
#wtbl[old].meta['description'] = description[old]
wtbl.rename_column(old, new)
if new in units:
wtbl[new].unit = units[new]
for colname in [
'$\sigma_{MAD}$(bsens)',
'$\sigma_{MAD}$(cleanest)',
]:
wtbl[colname].unit = u.mJy / u.beam
for colname in [
'$S_{peak}$(bsens)',
'$S_{peak}$(cleanest)',
]:
wtbl[colname].unit = u.mJy / u.beam
float_cols = [
'$\\theta_{maj}$',
'$\\theta_{min}$',
'BPA',
'$S_{peak}$(bsens)',
'$S_{peak}$(cleanest)',
'$\\frac{S_{peak}(\mathrm{bsens})}{S_{peak}(\mathrm{cleanest})}$',
'$\\sigma_{MAD}$(bsens)',
'$\\sigma_{MAD}$(cleanest)',
'$\\frac{\sigma_{MAD}(\mathrm{bsens})}{\sigma_{MAD}(\mathrm{cleanest})}$',
'Requested $\sigma$',
'$\sigma_{\mathrm{bsens}}/\sigma_{\mathrm{req}}$',
'$f_{BW,cleanest}$',
#'$\\theta_{req}$',
#'\\sigma_{req}$',
#'$\\sigma_{req}/\\sigma_{MAD}$',
#'$\\theta_{req}/\\theta_{maj}$',
#'DR$_{cleanest}$',
#'DR$_{bsens}$',
#'DR$_{bsens}$/DR$_{cleanest}$'
]
# convert to mJy
# wtbl['$\sigma_{MAD}$(bsens)'] *= 1000
# wtbl['$\sigma_{MAD}$(cleanest)'] *= 1000
formats = {
key: lambda x: strip_trailing_zeros(
('{0:0.3f}'.format(round_to_n(x, 2))))
for key in float_cols
}
formats = {
key: lambda x: str(sigfig.round(str(x), sigfigs=2))
for key in float_cols
}
wtbl.write('bsens_cleanest_diff.ecsv', format='ascii.ecsv', overwrite=True)
# caption needs to be *before* preamble.
#latexdict['caption'] = 'Continuum Source IDs and photometry'
latexdict[
'header_start'] = '\label{tab:bsens_cleanest}' #\n\\footnotesize'
latexdict[
'preamble'] = '\caption{Best Sensitivity vs Cleanest Continuum comparison}\n\\resizebox{\\textwidth}{!}{'
latexdict['col_align'] = 'l' * len(wtbl.columns)
latexdict['tabletype'] = 'table*'
latexdict['tablefoot'] = (
"}\par\n"
"Like Table \\ref{tab:selfcal}, but comparing the cleanest and bsens data. "
"$\sigma_{MAD}$(bsens) and $\sigma_{MAD}$(cleanest) are the "
"standard deviation error estimates computed from a signal-free "
"region in the map using the Median Absolute Deviation as a "
"robust estimator. Their ratio shows that the broader included "
"bandwidth increases sensitivity; $f_{BW,cleanest}$ specifies "
"the fraction of the total bandwidth that was incorporated into "
"the cleanest images. "
"$S_{peak}$ is the peak intensity in the images.")
wtbl.sort(['Region', 'Band'])
wtbl.write(f"../datapaper/{savename}.tex",
formats=formats,
overwrite=True,
latexdict=latexdict)
return wtbl
tbl['ePt Amp'].unit = u.mJy
tbl['Total Flux'] *= 1000
tbl['Total Flux'].unit = u.mJy
tbl['Pt Width'].unit = u.au
tbl['Pt Flux'] *= 1000
tbl['Pt Flux'].unit = u.mJy
tbl['Disk PA'] = tbl['Disk PA'].to(u.deg)
formats = {'Pt Position': lambda x: "-" if np.isnan(x.ra) else "{0:0.4} {1:0.3}".format(x.ra.hms.s-14, x.dec.dms.s+30),
'Pt RA': lambda x: "-" if np.isnan(x) else "{0:0.3}".format(x-14),
'Pt Dec': lambda x: "-" if np.isnan(x) else "{0:0.3}".format(x+30),
'ePt RA': lambda x: "-" if np.isnan(x) else "{0:0.4}".format(x),
'ePt Dec': lambda x: "-" if np.isnan(x) else "{0:0.3}".format(x),
'Pt Amp': lambda x: ('{0:0.2f}'.format(round_to_n(x,2))),
'ePt Amp': lambda x: ('{0:0.2f}'.format(round_to_n(x,2))),
'Pt Width': lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x,2))),
'Disk PA': lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x,2))),
'Disk FWHM': lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x,2))),
'Disk Radius': lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x,2))),
'Total Flux': lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x,2))),
'Pt Flux': lambda x: strip_trailing_zeros('{0:0.2f}'.format(round_to_n(x,2))),
'Pt \%': lambda x: strip_trailing_zeros('{0:0.5g}\%'.format(round_to_n(x,2)*100)),
}
latexdict = latex_info.latexdict.copy()
latexdict['header_start'] = '\label{tab:continuum_fit_parameters}'
latexdict['caption'] = 'Continuum Fit Parameters'
latexdict['preamble'] = '\centering'