def load_and_convert_spectra(globname):
speclist = [
pyspeckit.Spectrum(fn) for fn in glob.glob(paths.spath(globname))
]
for sp in speclist:
sp.data -= np.nanpercentile(sp.data, 25)
spectra = pyspeckit.Spectra(speclist)
beam = radio_beam.Beam.from_fits_header(spectra.header)
# "baseline"
#spectra.data -= np.nanpercentile(spectra.data, 10)
spectra.data *= beam.jtok(spectra.xarr)
spectra.unit = 'K'
spectra.xarr.refX = 220 * u.GHz # hackalack
return spectra
def load_and_convert_spectra(globname):
import glob
import paths
from astropy import wcs
import pyspeckit
import radio_beam
speclist = [pyspeckit.Spectrum(fn) for fn in
glob.glob(paths.spath(globname))]
for sp in speclist:
sp.data -= np.nanpercentile(sp.data, 25)
spectra = pyspeckit.Spectra(speclist)
beam = radio_beam.Beam.from_fits_header(spectra.header)
# "baseline"
#spectra.data -= np.nanpercentile(spectra.data, 10)
spectra.data *= beam.jtok(spectra.xarr)
spectra.unit='K'
spectra.xarr.refX = 220*u.GHz # hackalack
return spectra
warnings.filterwarnings('ignore', category=MaskedArrayFutureWarning)
line_table = table.Table.read(paths.apath('full_line_table.csv'))
line_table.sort('Species')
regions = (pyregion.open(paths.rpath("cores.reg")))
# check that it will finish before running everything
for region in regions:
name = region.attr[1]['text']
if region.name == 'point':
# when we're using peak pixels from the photometry catalog, the 'point'
# objects can be skipped
continue
spectral_files = glob.glob(paths.spath('{0}_spw[0123]_peak.fits'.format(name)))
#background_spectral_files = glob.glob(paths.spath('{0}_spw[0123]_background_mean.fits'.format(name)))
assert len(spectral_files) == 4#len(background_spectral_files) == 4
vcen = velo_guesses.guesses[name]
for region in regions:
name = region.attr[1]['text']
if region.name == 'point':
# when we're using peak pixels from the photometry catalog, the 'point'
# objects can be skipped
continue
spectral_files = glob.glob(paths.spath('{0}_spw[0123]_peak.fits'.format(name)))
#background_spectral_files = glob.glob(paths.spath('{0}_spw[0123]_background_mean.fits'.format(name)))
assert len(spectral_files) == 4#len(background_spectral_files) == 4
spectra = pyspeckit.Spectra(spectral_files)
return myclass
pyspeckit.spectrum.fitters.default_Registry.add_fitter('hnco_absorption',hnco_absorption_fitter(),5)
if __name__ == "__main__" and False:
import glob
import pyspeckit
import paths
import radio_beam
target = 'e2nw'
spectra = pyspeckit.Spectra(glob.glob(paths.spath("*{0}*fits".format(target))))
beam = radio_beam.Beam.from_fits_header(spectra.header)
# "baseline"
spectra.data -= 0.15
spectra.data *= beam.jtok(spectra.xarr)
spectra.plotter()
spectra.specfit.Registry.add_fitter('hnco', hnco_fitter(), 4)
spectra.specfit(fittype='hnco', guesses=[55, 4, 200, 5e15],
limitedmin=[True]*4, limitedmax=[True]*4, limits=[(50,70),
(1,8),
(20,
1000),
(1e13,
1e18)])
(True, False), (True, False)],
parlimits=[(0, 0), (0, 0), (0, 0), (0, 0), (0, 0)],
shortvarnames=(r'\Delta x', r'\sigma', 'T_{ex}', 'N', 'T_{BG}'),
centroid_par='shift',
)
myclass.__name__ = "ch3cn_absorption"
return myclass
pyspeckit.spectrum.fitters.default_Registry.add_fitter(
'ch3cn_absorption', ch3cn_absorption_fitter(), 5)
if __name__ == "__main__":
sp = pyspeckit.Spectrum(paths.spath('e2e_radial_bin_0.00to0.38_spw2.fits'))
sp.specfit.register_fitter('13ch3cn_spw', ch3cn_spw_fitter(), 12)
sp.xarr.convert_to_unit(u.GHz)
sp.plotter(xmin=231.88, xmax=232.52)
F = False
T = True
sp.baseline.basespec[:] = 0.380
sp.baseline.baselinepars = [0, 0.380]
limits = [
(54, 59),
(1.5, 5),
] + [(0.01, 0.25)] * 9 + [(0.0, 0.01)]
fixed = [
F,
T,
] + [T] * 6 + [F] * 3 + [T]
line_table = table.Table.read(paths.apath('full_line_table.csv'))
line_table.sort('Species')
regions = (pyregion.open(paths.rpath("cores.reg")))
# check that it will finish before running everything
for region in regions:
name = region.attr[1]['text']
if region.name == 'point':
# when we're using peak pixels from the photometry catalog, the 'point'
# objects can be skipped
continue
spectral_files = glob.glob(
paths.spath('{0}_spw[0123]_peak.fits'.format(name)))
#background_spectral_files = glob.glob(paths.spath('{0}_spw[0123]_background_mean.fits'.format(name)))
assert len(spectral_files) == 4 #len(background_spectral_files) == 4
vcen = velo_guesses.guesses[name]
for region in regions:
name = region.attr[1]['text']
if region.name == 'point':
# when we're using peak pixels from the photometry catalog, the 'point'
# objects can be skipped
continue
spectral_files = glob.glob(
paths.spath('{0}_spw[0123]_peak.fits'.format(name)))
#background_spectral_files = glob.glob(paths.spath('{0}_spw[0123]_background_mean.fits'.format(name)))
assert len(spectral_files) == 4 #len(background_spectral_files) == 4
myvtbl = Table.read(paths.tpath('core_velocities.txt'),
# write as ascii.fixed_width
format='ascii.fixed_width', delimiter='|')
line_table = Table.read(paths.apath('full_line_table.csv'))
all_line_ids = {"{0}_{1}".format(row['Species'], row['Resolved QNs']):
(row['Freq-GHz'] if row['Freq-GHz']
else row['Meas Freq-GHz'])*u.GHz
for row in line_table}
all_line_ids.update(line_ids)
for row in myvtbl:
speclist = [pyspeckit.Spectrum(fn) for fn in
glob.glob(paths.spath("{0}_spw*_peak.fits".format(row['source'])))]
if len(speclist) == 0:
continue
for sp in speclist:
beam = radio_beam.Beam.from_fits_header(sp.header)
sp.data *= beam.jtok(sp.xarr)
sp.unit='K'
plot_whole_spectrum(speclist,
title=row['source'],
line_id=all_line_ids,
figname='fullspectra/ALLLINES_{0}.png'.format(row['source']),
velocity=row['velocity']*u.km/u.s,
)
import velo_guesses
import radio_beam
import warnings
from numpy.ma.core import MaskedArrayFutureWarning
warnings.filterwarnings('ignore', category=MaskedArrayFutureWarning)
line_table = table.Table.read(paths.apath('full_line_table.csv'))
line_table.sort('Species')
regions = (pyregion.open(paths.rpath("cores.reg")))
for region in regions:
name = region.attr[1]['text']
spectral_files = glob.glob(paths.spath('{0}_spw[0123]_mean.fits'.format(name)))
#background_spectral_files = glob.glob(paths.spath('{0}_spw[0123]_background_mean.fits'.format(name)))
assert len(spectral_files) == 4#len(background_spectral_files) == 4
spectra = pyspeckit.Spectra(spectral_files)
#bgspectra = pyspeckit.Spectra(background_spectral_files)
stats = spectra.stats()
err = stats['std'] # overly conservative guess
for sp in spectra:
sp.data -= np.nanpercentile(sp.data, 25)
#med = stats['median']
#if med < 0:
# med = 0
line_table.add_column(table.Column(name='{0}FittedAmplitude'.format(name), data=np.zeros(len(line_table))))
line_table.add_column(table.Column(name='{0}FittedCenter'.format(name), data=np.zeros(len(line_table))))
if __name__ == "__main__":
import glob
import pyspeckit
from astropy import wcs
from astropy import coordinates
import pyregion
import paths
import radio_beam
target = 'e2e'
spectra = pyspeckit.Spectra(
glob.glob(paths.spath("*{0}_spw[0-9]_mean.fits".format(target))))
beam = radio_beam.Beam.from_fits_header(spectra.header)
# "baseline"
spectra.data -= np.nanpercentile(spectra.data, 10)
spectra.data *= beam.jtok(spectra.xarr)
spectra.plotter()
spectra.specfit.Registry.add_fitter('ch3oh', ch3oh_fitter(), 4)
spectra.specfit(fittype='ch3oh',
guesses=[55, 4, 200, 5e15],
limitedmin=[True] * 4,
limitedmax=[True] * 4,
limits=[(50, 70), (1, 4), (20, 1000), (1e13, 1e18)])
ok = slaim['Species'] == 'CH3OHvt=0'
format='ascii.fixed_width',
delimiter='|')
line_table = Table.read(paths.apath('full_line_table.csv'))
all_line_ids = {
"{0}_{1}".format(row['Species'], row['Resolved QNs']):
(row['Freq-GHz'] if row['Freq-GHz'] else row['Meas Freq-GHz']) * u.GHz
for row in line_table
}
all_line_ids.update(line_ids)
for row in myvtbl:
speclist = [
pyspeckit.Spectrum(fn) for fn in glob.glob(
paths.spath("{0}_spw*_peak.fits".format(row['source'])))
]
if len(speclist) == 0:
continue
for sp in speclist:
beam = radio_beam.Beam.from_fits_header(sp.header)
sp.data *= beam.jtok(sp.xarr)
sp.unit = 'K'
plot_whole_spectrum(
speclist,
title=row['source'],
line_id=all_line_ids,
figname='fullspectra/ALLLINES_{0}.png'.format(row['source']),
velocity=row['velocity'] * u.km / u.s,
cubefn = paths.dpath('12m/fullcube_cutouts/e2cutout_full_W51_spw{0}_lines.fits'
.format(spw))
print(cubefn)
spectra = spectra_from_cubefn(cubefn, reg, bins_arcsec, coordinate)
pl.figure(1).clf()
for bins, (key, spectrum) in zip(bins_arcsec, spectra.items()):
include = np.isfinite(spectrum) & np.array([(bm.major < 1*u.arcsec) &
(bm.minor < 1*u.arcsec)
for bm in spectrum.beams])
avg_beam = spectral_cube.cube_utils.average_beams(spectrum.beams,
includemask=include)
spectrum.meta['beam'] = avg_beam
spectrum.write(paths.spath('e2e_radial_bin_{0:0.2f}to{1:0.2f}_spw{2}.fits'
.format(bins[0], bins[1], spw)),
overwrite=True
)
pl.plot(spectrum.spectral_axis.to(u.GHz).value, spectrum.value,
label='{0:0.1f}-{1:0.1f}'.format(*bins))
pl.xlabel("Frequency (GHz)")
pl.ylabel("Intensity (Jy)")
pl.gca().ticklabel_format(useOffset=False)
pl.gca().get_xaxis().get_major_formatter().set_scientific(False)
pl.gca().get_yaxis().get_major_formatter().set_scientific(False)
pl.legend(loc='best')
pl.savefig(paths.fpath('radial_spectra/e2e_radial_spectra_spw{0}.png'
from line_parameters import frequencies, freq_name_mapping, yoffset
cores = pyregion.open(paths.rpath('cores.reg'))
minvelo = 45*u.km/u.s
maxvelo = 90*u.km/u.s
data = {}
for corereg in cores:
name = corereg.attr[1]['text']
data[name] = {}
fn = "{name}_spw{ii}_mean.fits"
spectra = pyspeckit.Spectra([paths.spath(fn.format(name=name, ii=ii))
for ii in range(4)])
spectra.data[(233.84*u.GHz<spectra.xarr) & (spectra.xarr>234.036*u.GHz)] = np.nan
spectra.data[(230.00*u.GHz<spectra.xarr) & (spectra.xarr>230.523*u.GHz)] = np.nan
scaling = np.nanmax(spectra.data) - np.nanpercentile(spectra.data, 20)
assert not np.isnan(scaling)
print("Scaling for {fn} = {scaling}".format(fn=fn.format(name=name, ii=0), scaling=scaling))
fig = pl.figure(1)
fig.clf()
for spwnum,sp in enumerate(spectra):
if spwnum == 3:
# flag out the middle section where apparently many antennae have been flagged
sp.data[1600:1984] = np.nan
elif spwnum == 2:
'e2se': -0.005,
'north': 0.2,
}
velo = {'e8mm': 61*u.km/u.s,
'e2e': 56*u.km/u.s,
'ALMAmm14': 62*u.km/u.s,
'ALMAmm41': 55*u.km/u.s,
'e2nw': 55.626*u.km/u.s,
'e2se': 54*u.km/u.s,
'north': 58*u.km/u.s,
}
pl.figure(1).clf()
for target in snu_min:
files = glob.glob(paths.spath("*{0}*fits".format(target)))
if len(files) == 0:
print("No matches for {0}".format(target))
continue
spectra = pyspeckit.Spectra(files)
for ii in range(4):
spectra[ii].plotter(figure=pl.figure(1))
species_names = [x[0] for x in line_to_image_list]
frequencies = u.Quantity([float(x[1].strip("GHz")) for x in line_to_image_list],
unit=u.GHz)
spectra[ii].plotter.axis.set_ylim(snu_min[target],
spectra[ii].plotter.axis.get_ylim()[1])
if __name__ == "__main__":
import glob
import pyspeckit
from astropy import wcs
from astropy import coordinates
import pyregion
import paths
import radio_beam
target = 'e2e'
spectra = pyspeckit.Spectra(glob.glob(paths.spath("*{0}_spw[0-9]_mean.fits".format(target))))
beam = radio_beam.Beam.from_fits_header(spectra.header)
# "baseline"
spectra.data -= np.nanpercentile(spectra.data, 10)
spectra.data *= beam.jtok(spectra.xarr)
spectra.plotter()
spectra.specfit.Registry.add_fitter('ch3oh', ch3oh_fitter(), 4)
spectra.specfit(fittype='ch3oh', guesses=[55, 4, 200, 5e15],
limitedmin=[True]*4, limitedmax=[True]*4, limits=[(50,70),
(1,4),
(20,
1000),
(1e13,
1e18)])
for key in colnames
]
data_list.insert(0, names)
tbl = Table(data_list)
tbl.sort('SourceID')
tbl.write(paths.tpath("dendro_core_velocities_7m12mspectra.ipac"),
format="ascii.ipac")
# 12m only
data = {}
for row in pruned_ppcat:
name = row['_idx']
fn = paths.spath("dendro{name:03d}_spw{ii}_mean.fits")
result = spectral_overlays.spectral_overlays(
fn,
name=name,
freq_name_mapping=freq_name_mapping,
frequencies=frequencies,
yoffset=yoffset,
minvelo=minvelo,
maxvelo=maxvelo,
)
if result:
data[name] = result
firstentry = list(data.keys())[0]
colnames = list(data[firstentry].keys())
'north': 0.3,
'e2nw': 0.1,
}
velo = {
'e8mm': 61 * u.km / u.s,
'e2e': 56 * u.km / u.s,
'e2nw': 55.626 * u.km / u.s,
'ALMAmm14': 62 * u.km / u.s,
'north': 55 * u.km / u.s,
}
pl.figure(1).clf()
for target, species_list in spectra_to_species.items():
spectra = pyspeckit.Spectra(
glob.glob(paths.spath("*{0}*fits".format(target))))
for species_tuple in species_list:
species_name, chemid, tmax = species_tuple
for ii in range(4):
cat = Splatalogue.query_lines(spectra[ii].xarr.min(),
spectra[ii].xarr.max(),
chemical_name=chemid,
energy_max=tmax,
energy_type='eu_k',
noHFS=True,
line_lists=['SLAIM'])
spectra[ii].plotter(figure=pl.figure(1))
spectra[ii].plotter.axis.set_ylim(
snu_min[target], spectra[ii].plotter.axis.get_ylim()[1])
from line_parameters import frequencies, freq_name_mapping, yoffset
cores = pyregion.open(paths.rpath('cores.reg'))
minvelo = 45 * u.km / u.s
maxvelo = 90 * u.km / u.s
data = {}
for corereg in cores:
name = corereg.attr[1]['text']
data[name] = {}
fn = "{name}_spw{ii}_mean.fits"
spectra = pyspeckit.Spectra(
[paths.spath(fn.format(name=name, ii=ii)) for ii in range(4)])
spectra.data[(233.84 * u.GHz < spectra.xarr)
& (spectra.xarr > 234.036 * u.GHz)] = np.nan
spectra.data[(230.00 * u.GHz < spectra.xarr)
& (spectra.xarr > 230.523 * u.GHz)] = np.nan
scaling = np.nanmax(spectra.data) - np.nanpercentile(spectra.data, 20)
assert not np.isnan(scaling)
print("Scaling for {fn} = {scaling}".format(fn=fn.format(name=name, ii=0),
scaling=scaling))
fig = pl.figure(1)
fig.clf()
for spwnum, sp in enumerate(spectra):
if spwnum == 3:
# flag out the middle section where apparently many antennae have been flagged
data_list.insert(0, names)
tbl = Table(data_list)
tbl.sort('SourceID')
tbl.write(paths.tpath("core_velocities_7m12m_hires.ipac"), format="ascii.ipac",
overwrite=True)
# 12m only
data = {}
for corereg in cores:
name = corereg.attr[1]['text']
fn = paths.spath("{name}_spw{ii}_mean.fits")
result = spectral_overlays.spectral_overlays(fn, name=name,
freq_name_mapping=freq_name_mapping,
frequencies=frequencies,
yoffset=yoffset,
minvelo=minvelo,
maxvelo=maxvelo,
)
if result:
data[name] = result
firstentry = list(data.keys())[0]
colnames = list(data[firstentry].keys())
coltypes = {k:type(data[firstentry][k]) for k in colnames}
names = Column([name for name in data], name='SourceID')
import paths
import pyspeckit
import glob
import matplotlib
matplotlib.use('Qt4Agg')
import os
import pylab as pl
tbl = Table.read(paths.mpath('diffuse_lines_mainonly.ipac'),
format='ascii.ipac')
rfreq = tbl['FreqGHz']
rfreq[rfreq.mask] = tbl['MeasFreqGHz'][rfreq.mask]
freq = rfreq * (1-65*u.km/u.s/constants.c)
for fn in glob.glob(paths.spath("*.fits")):
sp = pyspeckit.Spectrum(fn)
sp.xarr.convert_to_unit('GHz')
sp.plotter(figure=pl.figure(1), clear=True)
sp.plotter.line_ids(tbl['Species'], freq*u.GHz, unit='GHz')
outname = os.path.splitext(os.path.split(fn)[1])[0]
sp.plotter.savefig(paths.sppath("{0}.png".format(outname)))
"""
import paths
import pyspeckit
import numpy as np
import glob
sp = pyspeckit.Spectrum('ionization_front_circle_SgrB2_12m_spw0_lines.fits')
sp.plotter()
spw))
print(cubefn)
spectra = spectra_from_cubefn(cubefn, reg, bins_arcsec, coordinate)
pl.figure(1).clf()
for bins, (key, spectrum) in zip(bins_arcsec, spectra.items()):
include = np.isfinite(spectrum) & np.array(
[(bm.major < 1 * u.arcsec) & (bm.minor < 1 * u.arcsec)
for bm in spectrum.beams])
avg_beam = spectral_cube.cube_utils.average_beams(
spectrum.beams, includemask=include)
spectrum.meta['beam'] = avg_beam
spectrum.write(paths.spath(
'e2e_radial_bin_{0:0.2f}to{1:0.2f}_spw{2}.fits'.format(
bins[0], bins[1], spw)),
overwrite=True)
pl.plot(spectrum.spectral_axis.to(u.GHz).value,
spectrum.value,
label='{0:0.1f}-{1:0.1f}'.format(*bins))
pl.xlabel("Frequency (GHz)")
pl.ylabel("Intensity (Jy)")
pl.gca().ticklabel_format(useOffset=False)
pl.gca().get_xaxis().get_major_formatter().set_scientific(False)
pl.gca().get_yaxis().get_major_formatter().set_scientific(False)
pl.legend(loc='best')
pl.savefig(
for key in colnames]
data_list.insert(0, names)
tbl = Table(data_list)
tbl.sort('SourceID')
tbl.write(paths.tpath("dendro_core_velocities_7m12mspectra.ipac"), format="ascii.ipac")
# 12m only
data = {}
for row in pruned_ppcat:
name = row['_idx']
fn = paths.spath("dendro{name:03d}_spw{ii}_mean.fits")
result = spectral_overlays.spectral_overlays(fn, name=name,
freq_name_mapping=freq_name_mapping,
frequencies=frequencies,
yoffset=yoffset,
minvelo=minvelo,
maxvelo=maxvelo,
)
if result:
data[name] = result
firstentry = list(data.keys())[0]
colnames = list(data[firstentry].keys())
coltypes = {k:type(data[firstentry][k]) for k in colnames}
names = Column([name for name in data], name='SourceID')
myvtbl = Table.read(paths.tpath('core_velocities.txt'),
# write as ascii.fixed_width
format='ascii.fixed_width', delimiter='|')
line_table = Table.read(paths.apath('full_line_table.csv'))
all_line_ids = {"{0}_{1}".format(row['Species'], row['Resolved QNs']):
(row['Freq-GHz'] if row['Freq-GHz']
else row['Meas Freq-GHz'])*u.GHz
for row in line_table}
all_line_ids.update(line_ids)
for row in myvtbl:
speclist = [pyspeckit.Spectrum(fn) for fn in
glob.glob(paths.spath("{0}_spw*fits".format(row['source'])))]
for sp in speclist:
beam = radio_beam.Beam.from_fits_header(sp.header)
sp.data *= beam.jtok(sp.xarr)
sp.unit='K'
plot_whole_spectrum(speclist,
title=row['source'],
line_id=all_line_ids,
figname='fullspectra/ALLLINES_{0}.png'.format(row['source']),
velocity=row['velocity']*u.km/u.s,
)
"e2nw": [("formamide", "NH2CHO", 500), ("methylformate", "CH3OCHO", 1500), ("dimethylether", "CH3OCH3", 1500)],
"ALMAmm14": [("methanol", "CH3OH", 500)],
}
snu_min = {"e8mm": 0.3, "e2e": 0.3, "ALMAmm14": 0.0, "north": 0.3, "e2nw": 0.1}
velo = {
"e8mm": 61 * u.km / u.s,
"e2e": 56 * u.km / u.s,
"e2nw": 55.626 * u.km / u.s,
"ALMAmm14": 62 * u.km / u.s,
"north": 55 * u.km / u.s,
}
pl.figure(1).clf()
for target, species_list in spectra_to_species.items():
spectra = pyspeckit.Spectra(glob.glob(paths.spath("*{0}*fits".format(target))))
for species_tuple in species_list:
species_name, chemid, tmax = species_tuple
for ii in range(4):
cat = Splatalogue.query_lines(
spectra[ii].xarr.min(),
spectra[ii].xarr.max(),
chemical_name=chemid,
energy_max=tmax,
energy_type="eu_k",
noHFS=True,
line_lists=["SLAIM"],
)
spectra[ii].plotter(figure=pl.figure(1))
import paths
import pyspeckit
import glob
import matplotlib
matplotlib.use('Qt4Agg')
import os
import pylab as pl
tbl = Table.read(paths.mpath('diffuse_lines_mainonly.ipac'),
format='ascii.ipac')
rfreq = tbl['FreqGHz']
rfreq[rfreq.mask] = tbl['MeasFreqGHz'][rfreq.mask]
freq = rfreq * (1 - 65 * u.km / u.s / constants.c)
for fn in glob.glob(paths.spath("*.fits")):
sp = pyspeckit.Spectrum(fn)
sp.xarr.convert_to_unit('GHz')
sp.plotter(figure=pl.figure(1), clear=True)
sp.plotter.line_ids(tbl['Species'], freq * u.GHz, unit='GHz')
outname = os.path.splitext(os.path.split(fn)[1])[0]
sp.plotter.savefig(paths.sppath("{0}.png".format(outname)))
"""
import paths
import pyspeckit
import numpy as np
import glob
sp = pyspeckit.Spectrum('ionization_front_circle_SgrB2_12m_spw0_lines.fits')
sp.plotter()
tbl = Table.read('/Users/adam/work/sgrb2/molecules/diffuse_lines_mainonly.ipac',format='ascii.ipac')
freq = tbl['FreqGHz']
