def get_spectral_limits(cube,
freq_range=None,
vel_range=None,
chan_range=None,
chan_halfwidth=None,
vlsr=None,
linefreq=None):
# Turn everything into frequency or velocity
spec_unit = cube.spectral_axis.unit
rangefmt = '{0.value[0]:.4f} {0.value[1]:.4f} {0.unit}'
if freq_range is not None:
LOG.info('Using frequency range = %s',
rangefmt.format(freq_range.to(u.GHz)))
if spec_unit.is_equivalent(freq_range.unit):
return freq_range[0], freq_range[1]
else:
# Convert to spectral units
restfreq = get_restfreq(cube)
freq_to_vel = u.doppler_radio(restfreq)
lim = freq_range.to(spec_unit, equivalencies=freq_to_vel)
return np.min(lim), np.max(lim)
elif vel_range is not None:
LOG.info('Using velocity range = %s',
rangefmt.fmt(vel_range.to(u.km / u.s)))
if spec_unit.is_equivalent(vel_range.unit):
return vel_range[0], vel_range[1]
else:
# Convert to spectral units
restfreq = get_restfreq(cube)
freq_to_vel = u.doppler_radio(restfreq)
lim = vel_range.to(spec_unit, equivalencies=freq_to_vel)
return np.min(lim), np.max(lim)
elif chan_range is not None:
LOG.info('Channel range = %i %i', *chan_range)
return sorted(chan_range)
elif chan_halfwidth is not None and linefreq and vlsr:
# Get spectral axis
spaxis = cube.spectral_axis
restfreq = get_restfreq(cube)
# Convert to velocity from line
vel_to_freq = u.doppler_radio(restfreq)
spaxis = spaxis.to(linefreq.unit, equivalencies=vel_to_freq)
freq_to_vel = u.doppler_radio(linefreq)
spaxis = spaxis.to(vlsr.unit, equivalencies=freq_to_vel)
spaxis = spaxis - vlsr
# Closest value to vlsr
ind = np.nanargmin(np.abs(spaxis.value))
chmin = ind - chan_halfwidth
chmax = ind + chan_halfwidth
if chmin < 0:
chmin = 0
if chmax >= len(spaxis):
chmax = len(spaxis) - 1
LOG.info('Using channel range = %i %i', chmin, chmax)
return chmin, chmax
else:
LOG.info('No spectral limit')
return None, None
def test_byhand_vrad():
# VRAD
CRVAL3F = 1.37847121643E+09
CDELT3F = 9.764775E+04
RESTFRQR= 1.420405752E+09
CRVAL3R = 8.85075090419E+06
CDELT3R = -2.0609645E+04
CUNIT3R = 'm/s'
CUNIT3F = 'Hz'
crvalf = CRVAL3F * u.Unit(CUNIT3F)
crvalv = CRVAL3R * u.Unit(CUNIT3R)
restfreq = RESTFRQR * u.Unit(CUNIT3F)
cdeltf = CDELT3F * u.Unit(CUNIT3F)
cdeltv = CDELT3R * u.Unit(CUNIT3R)
# (Pdb) crval_in,crval_lin1,crval_lin2,crval_out
# (<Quantity 1378471216.43 Hz>, <Quantity 1378471216.43 Hz>, <Quantity 8850750.904040769 m / s>, <Quantity 8850750.904040769 m / s>)
# (Pdb) cdelt_in, cdelt_lin1, cdelt_lin2, cdelt_out
# (<Quantity 97647.75 Hz>, <Quantity 97647.75 Hz>, <Quantity -20609.645482954576 m / s>, <Quantity -20609.645482954576 m / s>)
crvalv_computed = crvalf.to(CUNIT3R, u.doppler_radio(restfreq))
cdeltv_computed = -(cdeltf / restfreq)*constants.c
assert_allclose(crvalv_computed, crvalv, rtol=1.e-3)
assert_allclose(cdeltv_computed, cdeltv, rtol=1.e-3)
crvalf_computed = crvalv_computed.to(CUNIT3F, u.doppler_radio(restfreq))
cdeltf_computed = -(cdeltv_computed/constants.c) * restfreq
assert_allclose(crvalf_computed, crvalf, rtol=1.e-3)
assert_allclose(cdeltf_computed, cdeltf, rtol=1.e-3)
def test_byhand_vrad():
# VRAD
CRVAL3F = 1.37847121643E+09
CDELT3F = 9.764775E+04
RESTFRQR= 1.420405752E+09
CRVAL3R = 8.85075090419E+06
CDELT3R = -2.0609645E+04
CUNIT3R = 'm/s'
CUNIT3F = 'Hz'
crvalf = CRVAL3F * u.Unit(CUNIT3F)
crvalv = CRVAL3R * u.Unit(CUNIT3R)
restfreq = RESTFRQR * u.Unit(CUNIT3F)
cdeltf = CDELT3F * u.Unit(CUNIT3F)
cdeltv = CDELT3R * u.Unit(CUNIT3R)
# (Pdb) crval_in,crval_lin1,crval_lin2,crval_out
# (<Quantity 1378471216.43 Hz>, <Quantity 1378471216.43 Hz>, <Quantity 8850750.904040769 m / s>, <Quantity 8850750.904040769 m / s>)
# (Pdb) cdelt_in, cdelt_lin1, cdelt_lin2, cdelt_out
# (<Quantity 97647.75 Hz>, <Quantity 97647.75 Hz>, <Quantity -20609.645482954576 m / s>, <Quantity -20609.645482954576 m / s>)
crvalv_computed = crvalf.to(CUNIT3R, u.doppler_radio(restfreq))
cdeltv_computed = -(cdeltf / restfreq)*constants.c
assert_allclose(crvalv_computed, crvalv, rtol=1.e-3)
assert_allclose(cdeltv_computed, cdeltv, rtol=1.e-3)
crvalf_computed = crvalv_computed.to(CUNIT3F, u.doppler_radio(restfreq))
cdeltf_computed = -(cdeltv_computed/constants.c) * restfreq
assert_allclose(crvalf_computed, crvalf, rtol=1.e-3)
assert_allclose(cdeltf_computed, cdeltf, rtol=1.e-3)
def closest_spectral_channel(self, value, rest_frequency=None):
"""
Find the index of the closest spectral channel to the specified
spectral coordinate.
Parameters
----------
value : :class:`~astropy.units.Quantity`
The value of the spectral coordinate to search for.
rest_frequency : :class:`~astropy.units.Quantity`
The rest frequency for any Doppler conversions
"""
# TODO: we have to not compute this every time
spectral_axis = self.spectral_axis
try:
value = value.to(spectral_axis.unit, equivalencies=u.spectral())
except u.UnitsError:
if value.unit.is_equivalent(spectral_axis.unit, equivalencies=u.doppler_radio(None)):
if rest_frequency is None:
raise u.UnitsError("{0} cannot be converted to {1} without a "
"rest frequency".format(value.unit, spectral_axis.unit))
else:
try:
value = value.to(spectral_axis.unit,
equivalencies=u.doppler_radio(rest_frequency))
except u.UnitsError:
raise u.UnitsError("{0} cannot be converted to {1}".format(value.unit, spectral_axis.unit))
else:
raise u.UnitsError("'value' should be in frequency equivalent or velocity units (got {0})".format(value.unit))
# TODO: optimize the next line - just brute force for now
return np.argmin(np.abs(spectral_axis - value))
def freqs_to_vel(center_freq, fs, sc):
"""Convert frequency to radial Doppler velocity.
Accounts for movement of the earth relative to the sun and the sun relative to galactic center.
Args:
center_freq: frequency at zero velocity
fs: Quantity object representing frequencies
sc: SkyCoord object representing one or many coordinates (must have obstime and location set)
"""
# Convert from earth reference frame to solar reference frame using
# https://docs.astropy.org/en/stable/coordinates/velocities.html#radial-velocity-corrections
# Then convert from solar reference frame to Galactic Standard of Rest using
# https://docs.astropy.org/en/stable/generated/examples/coordinates/rv-to-gsr.html
pos_gal = sc.galactic
v_to_bary = pos_gal.radial_velocity_correction(kind='barycentric')
# Calculate the sun's velocity projected in the observing direction.
v_sun = Galactocentric().galcen_v_sun.to_cartesian()
cart_data = pos_gal.data.to_cartesian()
unit_vector = cart_data / cart_data.norm()
v_proj = v_sun.dot(unit_vector)
doppler_shift = u.doppler_radio(center_freq)
v_local = fs.to(u.km/u.s, doppler_shift)
v_bary = v_local + v_to_bary + v_local * v_to_bary / c
# v_bary is now barycentric; now we need to remove the solar system motion as well
return (v_bary + v_proj)
def convert_chan_freq_vel(chan_res):
"""
Takes an input channel resolution, either in freq or velocity
Will return matching resolution in other dimensions
Presumes radio definition; can consider as a param for future
Inputs:
chan_res (Quantity): Input resolution, with units
Outputs:
new_res (Quantity): Output resolution, with units, opp of input
"""
#define conversion
restfreq = 1420.405752 * u.MHz
freq_to_vel = u.doppler_radio(restfreq)
#check if in frequency; if so, get vel
if 'Hz' in chan_res.unit.to_string():
offsetfreq = restfreq - chan_res
vel = restfreq.to(u.km / u.s, equivalencies=freq_to_vel)
offsetvel = offsetfreq.to(u.km / u.s, equivalencies=freq_to_vel)
new_res = offsetvel - vel
else:
print("Not frequency unit, presuming velocity")
offsetfreq = chan_res.to(u.kHz, equivalencies=freq_to_vel)
new_res = restfreq - offsetfreq
return new_res
def velocity_axis(self):
"""Obtain spectral axis en velocity units."""
if self.spectral_axis.unit.is_equivalent(u.km / u.s):
return self.spectral_axis
else:
equiv = u.doppler_radio(self.restfreq)
return self.spectral_axis.to(u.km / u.s, equivalencies=equiv)
def test_convert_to_unit(run_with_assert=False):
for from_unit, to_unit, _, __ in u.doppler_optical(1 * u.Hz):
sp = SpectroscopicAxis(np.linspace(-100, 100, 100),
unit=from_unit,
refX=23.2 * u.Hz,
velocity_convention='optical')
sp.convert_to_unit(to_unit)
if (run_with_assert):
assert sp.unit == to_unit
for from_unit, to_unit, _, __ in u.doppler_radio(1 * u.eV):
sp = SpectroscopicAxis(np.linspace(-100, 100, 100),
unit=from_unit,
refX=23.2 * u.Hz,
velocity_convention='optical')
sp.convert_to_unit(to_unit)
if (run_with_assert):
assert sp.unit == to_unit
for from_unit, to_unit, _, __ in u.doppler_relativistic(1 * u.Angstrom):
sp = SpectroscopicAxis(np.linspace(-100, 100, 100),
unit=from_unit,
refX=23.2 * u.Hz,
velocity_convention='relativistic')
sp.convert_to_unit(to_unit)
if (run_with_assert):
assert sp.unit == to_unit
def f_kernel(self):
"""
Returns a function defining the beam amplitude as a function of
position.
The model implemented is based on an interpolation of an image of the
WSRT beam, re-scaled according to declination and frequency.
Returns
-------
Callable accepting 2 arguments (both float or array) and returning a
float or array of corresponding size.
"""
if self.dec <= 0. * U.deg:
raise ValueError('WSRT beam requires positive declination.')
freq = self.vel.to(U.GHz, equivalencies=U.doppler_radio(f_HI))
bheader, bdata = self._load_beamfile()
centroid = self._centroid()
bpx_ra = np.abs(bheader['CDELT1'] * U.deg).to(U.arcsec)
bpx_dec = np.abs(bheader['CDELT2'] * U.deg).to(U.arcsec)
dRAs = np.arange(-(bdata.shape[0] // 2), bdata.shape[0] // 2 + 1) \
* bpx_ra * (centroid[2] / freq).to(U.dimensionless_unscaled)
dDecs = np.arange(-(bdata.shape[1] // 2), bdata.shape[1] // 2 + 1) \
* bpx_dec * np.sin(self.dec) / np.sin(centroid[1]) \
* (centroid[2] / freq)\
.to(U.dimensionless_unscaled)
interpolator = RectBivariateSpline(
dRAs, dDecs, bdata[..., 0], kx=1, ky=1, s=0)
# RectBivariateSpline is a wrapper around Fortran code and causes a
# transpose
return lambda x, y: interpolator(y, x, grid=False).T
def kernel_size_px(self):
"""
Returns a 2-tuple specifying the half-size of the beam image to be
initialized, in pixels.
The interpolation may become unstable if the image is severely
undersampled, pixel sizes of more than 8 arcsec are not recommended.
Returns
-------
out : 2-tuple, each element an integer.
"""
if self.px_size > 12. * U.arcsec:
warnings.warn(
"Using WSRT beam with datacube pixel size >> 8 arcsec,"
" beam interpolation may fail.")
freq = self.vel.to(U.GHz, equivalencies=U.doppler_radio(f_HI))
bheader, bdata = self._load_beamfile()
centroid = self._centroid()
aspect_x, aspect_y = np.floor(bdata.shape[0] // 2 *
np.sin(self.dec)), bdata.shape[1] // 2
aspect_x = aspect_x * np.abs((bheader['CDELT1'] * U.deg)).to(U.arcsec)\
* (centroid[2] / freq).to(U.dimensionless_unscaled)
aspect_y = aspect_y * (bheader['CDELT2'] * U.deg).to(U.arcsec) \
* (centroid[2] / freq).to(U.dimensionless_unscaled)
return tuple([
(a.to(U.pix, U.pixel_scale(self.px_size / U.pix))).value + 1
for a in (aspect_x, aspect_y)
])
def __init__(self,
species: str,
qns: str,
restfreq: u.Quantity,
obsfreq: Optional[u.Quantity] = None,
vlsr: Optional[u.Quantity] = None,
eup: Optional[u.Quantity] = None,
logaij: Optional[u.Quantity] = None) -> None:
"""Initiate a transition object.
Args:
species: chemical formula.
qns: quantum numbers.
restfreq: transition rest frequency.
obsfreq: optional; observed frequency.
vlsr: optional; used to determine `obsfreq` if not given.
eup: optional; upper level energy.
logaij: optional; log of the Aij coeficient.
"""
self.species = species
self.qns = qns
self.restfreq = restfreq.to(u.GHz)
self.obsfreq = obsfreq
if self.obsfreq is None and vlsr is not None:
equiv = u.doppler_radio(self.restfreq)
self.obsfreq = to_rest_freq(self.restfreq, -vlsr, equiv)
try:
self.obsfreq = self.obsfreq.to(u.GHz)
except AttributeError:
pass
self.eup = eup
self.logaij = logaij
def redshift_frequency(f, vlsr):
"""Assumes (for now) rest frequency (f) in GHz and velocity of the source (vlsr) in km/s."""
f_rest = f * u.GHz # rest frequency
vlsr = vlsr * u.km / u.s # velocity of the source
radio_equiv = u.doppler_radio(f_rest)
f_shifted = vlsr.to(u.GHz, equivalencies=radio_equiv)
return f_shifted.value
def make_synthspec_cold(
velo=np.linspace(-25, 25, 251),
tkin=25,
column=13,
width=0.5,
vcen=0.0,
lines=('oneone', 'twotwo', 'fourfour'),
):
velo = u.Quantity(velo, u.km / u.s)
spectra_list = []
for line in lines:
cfrq = ammonia_constants.freq_dict[line] * u.Hz
frq = velo.to(u.GHz, u.doppler_radio(cfrq))
xarr = units.SpectroscopicAxis(frq, refX=cfrq)
data = ammonia.cold_ammonia(xarr,
tkin=tkin,
width=width,
ntot=column,
xoff_v=vcen)
sp = Spectrum(xarr=xarr, data=data)
spectra_list.append(sp)
return Spectra(spectra_list)
def col_d_tot(self):
###--return total column density of the line species (cm^-2)--###
lwd = np.abs(self.lw)
#channel width in Hz
ftov = u.doppler_radio(self.ln[self.ll][2]*u.Hz)
lw = self.ln[self.ll][2]- \
(lwd*(u.km/u.s)).to(u.Hz,equivalencies=ftov).value
#upper column density
to = h.value/k_B.value*self.ln[self.ll][2]
ex = to/self.Tx
n_u = 8*np.pi*self.ln[self.ll][2]**2/(c.value*100.)**2* \
self.f(ex)/self.ln[self.ll][0]* \
np.sum(self.tau())*lw
#alternatively, one may use d(nu)/nu_0 = dv/c in linewidth:
#n_u = 8*np.pi*self.ln[self.ll][2]/(c.value*100.)*\
# self.f(ex)/self.ln[self.ll][0]* \
# np.sum(self.tau())*lwd
#partition function, approximated by integral
eu = k_B.value*self.Tx/h.value/self.ln[self.ll][1]
zz = eu/(self.ln[self.ll][3]*2+1)* \
np.exp(self.ln[self.ll][3]*(self.ln[self.ll][3]+1)/eu)
return n_u*zz
def get_molecule(args: argparse.Namespace) -> Molecule:
"""Generate a `Molecule` from argparse.
Requires the argparse to have `cube` and `log` attributes. The `vlsr`
attribute is also needed but does not need to be initialized.
"""
# Frequency ranges
spectral_axis = args.cube.spectral_axis
obs_freq_range = spectral_axis[[0,-1]]
args.log.info((f'Observed freq. range: {obs_freq_range[0].value} '
f'{obs_freq_range[1].value} {obs_freq_range[1].unit}'))
if args.vlsr is not None:
equiv = u.doppler_radio(get_restfreq(args.cube))
rest_freq_range = to_rest_freq(obs_freq_range, args.vlsr, equiv)
args.log.info((f'Rest freq. range: {rest_freq_range[0].value} '
f'{rest_freq_range[1].value} {rest_freq_range[1].unit}'))
else:
args.log.warn('Could not determine rest frequency, using observed')
rest_freq_range = obs_freq_range
# Create molecule
if args.onlyj:
filter_out = ['F', 'K']
else:
filter_out = None
mol = Molecule.from_query(f' {args.molecule[0]} ', rest_freq_range,
vlsr=args.vlsr, filter_out=filter_out,
line_lists=args.line_lists, qns=args.qns)
mol.reduce_qns()
args.log.info(f'Number of transitions: {len(mol.transitions)}')
return mol
def sio_model(xarr, vcen, width, tex, column, background=None, tbg=2.73):
if hasattr(tex, 'unit'):
tex = tex.value
if hasattr(tbg, 'unit'):
tbg = tbg.value
if hasattr(column, 'unit'):
column = column.value
if column < 25:
column = 10**column
if hasattr(vcen, 'unit'):
vcen = vcen.value
if hasattr(width, 'unit'):
width = width.value
if background is not None:
tbg = background
# assume equal-width channels
kwargs = dict(rest=ref_freq)
equiv = u.doppler_radio(**kwargs)
channelwidth = np.abs(xarr[1].to(u.Hz, equiv) -
xarr[0].to(u.Hz, equiv)).value
velo = xarr.to(u.km / u.s, equiv).value
mol_model = np.zeros_like(xarr).value
freqs_ = freqs.to(u.Hz).value
Q = m.calculate_partitionfunction(result.data['States'],
temperature=tex)[sio.Id]
jnu_bg = lte_molecule.Jnu_cgs(xarr.to(u.Hz).value, tbg)
bg_model = np.ones_like(xarr).value * jnu_bg
for voff, A, g, nu, eu in zip(vdiff, aij, deg, freqs_, EU):
tau_per_dnu = lte_molecule.line_tau_cgs(tex, column, Q, g, nu, eu,
10**A)
s = np.exp(-(velo - vcen - voff)**2 /
(2 * width**2)) * tau_per_dnu / channelwidth
jnu_mol = lte_molecule.Jnu_cgs(nu, tex)
# the "emission" model is generally zero everywhere, so we can just
# add to it as we move along
mol_model = mol_model + jnu_mol * (1 - np.exp(-s))
# background is assumed to start as a uniform value, then each
# absorption line multiplies to reduce it. s is zero for most velocities,
# so this is mostly bg_model *= 1
bg_model *= np.exp(-s)
if background:
# subtract jnu_bg because we *must* rezero for the case of
# having multiple components, otherwise the backgrounds add,
# which is nonsense
model = bg_model + mol_model - jnu_bg
else:
model = mol_model
return model
def closest_spectral_channel(self, value, rest_frequency=None):
"""
Find the index of the closest spectral channel to the specified
spectral coordinate.
Parameters
----------
value : :class:`~astropy.units.Quantity`
The value of the spectral coordinate to search for.
rest_frequency : :class:`~astropy.units.Quantity`
The rest frequency for any Doppler conversions
"""
# TODO: we have to not compute this every time
spectral_axis = self.spectral_axis
try:
value = value.to(spectral_axis.unit, equivalencies=u.spectral())
except u.UnitsError:
if value.unit.is_equivalent(spectral_axis.unit,
equivalencies=u.doppler_radio(None)):
if rest_frequency is None:
raise u.UnitsError(
"{0} cannot be converted to {1} without a "
"rest frequency".format(value.unit,
spectral_axis.unit))
else:
try:
value = value.to(
spectral_axis.unit,
equivalencies=u.doppler_radio(rest_frequency))
except u.UnitsError:
raise u.UnitsError(
"{0} cannot be converted to {1}".format(
value.unit, spectral_axis.unit))
else:
raise u.UnitsError(
"'value' should be in frequency equivalent or velocity units (got {0})"
.format(value.unit))
# TODO: optimize the next line - just brute force for now
return np.argmin(np.abs(spectral_axis - value))
def get_spectral_equivalencies(restfreqs: list,
keys: Optional[list] = None) -> dict:
"""Get spectral equivalencies from rest frequencies.
Args:
restfreqs: rest frequencies.
keys: optional; keys for the output dictionary.
Returns:
A dictionary with the astropy equivalency functions.
"""
# Define keys
if keys is None:
keys = range(len(restfreqs))
# Process restfreqs
if len(restfreqs) == 1:
equiv = {key: u.doppler_radio(restfreqs[0]) for key in keys}
else:
if len(keys) != len(restfreqs):
raise ValueError('Size of keys and restfreqs do not match')
aux = zip(keys, restfreqs)
equiv = {key: u.doppler_radio(restfreq) for key, restfreq in aux}
return equiv
def _preproc(args: argparse.Namespace) -> None:
"""Prepare args inputs for analysis."""
# Equivalencies
if args.restfreq:
if args.table is not None and 'spw' in args.table[0].dtype.names:
spws = np.unique(args.table[0]['spw'])
args.equivalencies = get_spectral_equivalencies(args.restfreq,
keys=spws)
else:
args.equivalencies['all'] = u.doppler_radio(args.restfreq[0])
# Define filename
if args.filename is not None:
args.filename = args.filename[0]
else:
args.filename = pathlib.Path('./results.ecsv').resolve()
def spectral_velocities(data, wcs=None, fqs=None, fqis=None, restfrq=None):
"""
Get the spectral velocities from frequencies fqs given a rest
frequency (by default search for it in the WCS). If fqs is None,
then frequencies indices (fqis) need to be given.
Parameters
----------
data : (M,N) or (M,N,Z) numpy.ndarray or astropy.nddata.NDData or astropy.nddata.NDDataRef
Astronomical data cube.
wcs : astropy.wcs.wcs.WCS
World Coordinate System to use.
fqs : astropy.units.quantity.Quantity
Array of frequencies with units.
fqis : list of integers
Array of frequencies indices
restfrq : astropy.units.quantity.Quantity
Rest frequency
Returns
-------
result: astropy.units.quantity.Quantity
Array of Spectral velocities.
"""
if wcs is None:
log.error("A world coordinate system (WCS) is needed")
return None
if data.ndim != 3:
log.error("Not spectral axis found.")
return None
if restfrq is None:
restfrq = wcs.wcs.restfrq * u.Hz
if fqs is None:
dim = wcs.wcs.spec
if fqis is None:
# Semi Hardconded...
fqis = np.arange(data.shape[data.ndim - dim - 1])
idx = np.zeros((fqis.size, data.ndim))
idx[:, dim] = fqis
vals = wcs.all_pix2world(idx, 0)
fqs = vals[:, dim] * u.Hz
eq = u.doppler_radio(restfrq)
return fqs.to(u.km / u.s, equivalencies=eq)
def convert_vel_freq(quantity, restfreq=1420.405752 * u.MHz):
"""
Take an input quantity that is either vel or freq and return the other
Default for HI line
Use radio convention (could set as param)
"""
freq_to_vel = u.doppler_radio(restfreq)
if 'Hz' in quantity.unit.to_string():
new_quantity = quantity.to(u.km / u.s, equivalencies=freq_to_vel)
else:
#presume in vel, use try/except in case
try:
new_quantity = quantity.to(u.MHz, equivalencies=freq_to_vel)
except:
print("Units of input not recognized")
new_quantity = np.nan
return new_quantity
def make_synthspec_cold(velo=np.linspace(-25, 25, 251), tkin=25,
column=13, width=0.5, vcen=0.0,
lines=('oneone', 'twotwo', 'fourfour'),
):
velo = u.Quantity(velo, u.km/u.s)
spectra_list = []
for line in lines:
cfrq = ammonia_constants.freq_dict[line]*u.Hz
frq = velo.to(u.GHz, u.doppler_radio(cfrq))
xarr = units.SpectroscopicAxis(frq, refX=cfrq)
data = ammonia.cold_ammonia(xarr, tkin=tkin, width=width, ntot=column,
xoff_v=vcen)
sp = Spectrum(xarr=xarr, data=data)
spectra_list.append(sp)
return Spectra(spectra_list)
def ch3cn_model(xarr, vcen, width, tex, column, background=None, tbg=2.73):
if hasattr(tex,'unit'):
tex = tex.value
if hasattr(tbg,'unit'):
tbg = tbg.value
if hasattr(column, 'unit'):
column = column.value
if column < 25:
column = 10**column
if hasattr(vcen, 'unit'):
vcen = vcen.value
if hasattr(width, 'unit'):
width = width.value
# assume equal-width channels
kwargs = dict(rest=ref_freq)
equiv = u.doppler_radio(**kwargs)
channelwidth = np.abs(xarr[1].to(u.Hz, equiv) - xarr[0].to(u.Hz, equiv)).value
velo = xarr.to(u.km/u.s, equiv).value
model = np.zeros_like(xarr).value
freqs_ = freqs.to(u.Hz).value
Q = m.calculate_partitionfunction(result.data['States'],
temperature=tex)[ch3cn.Id]
for voff, A, g, nu, eu in zip(vdiff, aij, deg, freqs_, EU):
tau_per_dnu = lte_molecule.line_tau_cgs(tex,
column,
Q,
g,
nu,
eu,
10**A)
s = np.exp(-(velo-vcen-voff)**2/(2*width**2))*tau_per_dnu/channelwidth
jnu = (lte_molecule.Jnu_cgs(nu, tex)-lte_molecule.Jnu_cgs(nu, tbg))
model = model + jnu*(1-np.exp(-s))
if background is not None:
return background-model
return model
def __init__(self, mode='relativistic'):
# HI restframe
self.nu0 = 1420.4058
self.nu0_u = self.nu0 * u.MHz
# full mode for Minkowski space time
if mode.lower() == 'relativistic':
self.v_frame = u.doppler_relativistic(self.nu0_u)
# radio definition
elif mode.lower() == 'radio':
self.v_frame = u.doppler_radio(self.nu0_u)
# velo = c * (1. - nu/nu0)
# optical definition
elif mode.lower() == 'optical':
self.v_frame = u.doppler_optical(self.nu0_u)
self.mode = mode
return None
def spectral_velocities(data,wcs=None,fqs=None,fqis=None,restfrq=None):
"""
Get the spectral velocities from frequencies fqs given a rest
frequency (by default search for it in the WCS). If fqs is None,
then frequencies indices (fqis) need to be given.
Parameters
----------
data : (M,N) or (M,N,Z) numpy.ndarray or astropy.nddata.NDData or astropy.nddata.NDDataRef
Astronomical data cube.
wcs : astropy.wcs.wcs.WCS
World Coordinate System to use.
fqs : astropy.units.quantity.Quantity
Array of frequencies with units.
fqis : list of integers
Array of frequencies indices
restfrq : astropy.units.quantity.Quantity
Rest frequency
Returns
-------
result: astropy.units.quantity.Quantity
Array of Spectral velocities.
"""
if wcs is None:
log.error("A world coordinate system (WCS) is needed")
return None
if restfrq is None:
restfrq=wcs.wcs.restfrq*u.Hz
if fqs is None:
if fqis is None:
return None
dim=wcs.wcs.spec
idx=np.zeros((fqis.size,data.ndim))
idx[:,dim]=fqis
vals=wcs.all_pix2world(idx,0)
fqs=vals[:,dim]*u.Hz
eq=u.doppler_radio(restfrq)
return fqs.to(u.km/u.s, equivalencies=eq)
def get_spectrum(self, region_pix):
# import image
print("\t>>> Importing fits image...")
hdul = fits.open(self.name)
data_image = hdul[0].data[0]
# get info from header
beam_maj = hdul[0].header["BMAJ"]
beam_min = hdul[0].header["BMIN"]
beam_units = hdul[0].header["BUNIT"]
delta_ra = hdul[0].header["CDELT1"]
delta_dec = hdul[0].header["CDELT2"]
delta_freq = hdul[0].header["CDELT3"]
initial_freq = hdul[0].header["CRVAL3"]
freq_units = hdul[0].header["CUNIT3"]
channels = hdul[0].header["NAXIS3"]
rest_freq = hdul[0].header["RESTFRQ"]
hdul.close()
# calculate beam
beam_area = (np.pi / (4 * np.log(2)) * beam_maj * beam_min * 3600**2
) # arcsec^2
pix_area = abs(delta_ra * delta_dec) * 3600**2 # arcsec^2
beam_pix = beam_area / pix_area # pixels
# calculate sum inside mask
print("\t>>> Taking spectrum...")
spectrum = []
for channel in data_image:
intensity = np.nansum(channel * self.mask)
spectrum.append(intensity)
# generate freq column
freq = (np.arange(initial_freq, initial_freq + channels * delta_freq,
delta_freq)[:channels] * 10**-9 * u.GHz)
# generate velocity column
radio_equiv = u.doppler_radio(rest_freq * 10**-9 * u.GHz)
velocity = freq.to(u.km / u.s, equivalencies=radio_equiv)
# generate channel column
chan = np.arange(channels)
# generate pixels column
pix = np.full(channels, region_pix)
return chan, pix, freq.value, velocity.value, spectrum, beam_area, beam_pix
def test_convert_to_unit(run_with_assert=False):
for from_unit, to_unit, _, __ in u.doppler_optical(1 * u.Hz):
sp = SpectroscopicAxis(
np.linspace(-100, 100, 100), unit=from_unit, refX=23.2 * u.Hz, velocity_convention="optical"
)
sp.convert_to_unit(to_unit)
if run_with_assert:
assert sp.unit == to_unit
for from_unit, to_unit, _, __ in u.doppler_radio(1 * u.eV):
sp = SpectroscopicAxis(
np.linspace(-100, 100, 100), unit=from_unit, refX=23.2 * u.Hz, velocity_convention="optical"
)
sp.convert_to_unit(to_unit)
if run_with_assert:
assert sp.unit == to_unit
for from_unit, to_unit, _, __ in u.doppler_relativistic(1 * u.Angstrom):
sp = SpectroscopicAxis(
np.linspace(-100, 100, 100), unit=from_unit, refX=23.2 * u.Hz, velocity_convention="relativistic"
)
sp.convert_to_unit(to_unit)
if run_with_assert:
assert sp.unit == to_unit
def _init_vel(self):
"""
Converts the spectral coordinate to (radio) velocities.
Currently assumes that the spectral coordinate is in units of Hz.
FIXME: read unit from fits file
FIXME: stolen from here ... https://github.com/astropy/astropy/issues/4529
mabe not the best way to go
FIXME: maybe could be generalized
"""
wcsvel=wcs.WCS(self.header)
xv = numpy.repeat(0, self.header['NAXIS3'])
yv = numpy.repeat(0, self.header['NAXIS3'])
zv = numpy.arange(self.header['NAXIS3'])
wx, wy, wz = wcsvel.wcs_pix2world(xv, yv, zv, 0)
freq_to_vel = u.doppler_radio(self.header['RESTFRQ']*u.Hz)
vel=(wz * u.Hz).to(u.km / u.s, equivalencies=freq_to_vel)
return vel
def frequencies(self):
if self._frequencies is None:
specax = self.wcs.wcs.spec
channels = np.arange(self.data.shape[::-1][specax])
spec = self.spec_wcs.wcs_pix2world(channels, 0)[0]
specc = spec * self.axis_units[specax]
# Convert radio or optical velocities to frequencies
if 'VRAD' in self.wcs.wcs.ctype[specax]:
eq = u.doppler_radio(self.rest_frequency)
elif 'VOPT' in self.wcs.wcs.ctype[specax]:
eq = u.doppler_optical(self.rest_frequency)
elif 'FREQ' in self.wcs.wcs.ctype[specax]:
eq = []
else:
raise AttributeError(
'Unsupported spectral type {:s} in header.'.format(self.wcs.wcs.ctype[specax]))
self._frequencies = specc.to(u.Hz, eq)
return self._frequencies
def test_equivalencies():
"""
Testing spectral equivalencies
"""
# range in "RADIO" with "100 * u.GHz" as rest frequancy
range = u.Quantity([-318 * u.km / u.s, -320 * u.km / u.s])
# range in freq
r1 = range.to("GHz", equivalencies=u.doppler_radio(100 * u.GHz))
# round conversion for "doppler_z"
r2 = r1.to("km/s", equivalencies=doppler_z(100 * u.GHz))
r3 = r2.to("GHz", equivalencies=doppler_z(100*u.GHz))
assert_quantity_allclose(r1, r3)
# round conversion for "doppler_beta"
r2 = r1.to("km/s", equivalencies=doppler_beta(100 * u.GHz))
r3 = r2.to("GHz", equivalencies=doppler_beta(100 * u.GHz))
assert_quantity_allclose(r1, r3)
# round conversion for "doppler_gamma"
r2 = r1.to("km/s", equivalencies=doppler_gamma(100 * u.GHz))
r3 = r2.to("GHz", equivalencies=doppler_gamma(100 * u.GHz))
assert_quantity_allclose(r1, r3)
def test_equivalencies():
"""
Testing spectral equivalencies
"""
# range in "RADIO" with "100 * u.GHz" as rest frequancy
range = u.Quantity([-318 * u.km / u.s, -320 * u.km / u.s])
# range in freq
r1 = range.to("GHz", equivalencies=u.doppler_radio(100 * u.GHz))
# round conversion for "doppler_z"
r2 = r1.to("km/s", equivalencies=doppler_z(100 * u.GHz))
r3 = r2.to("GHz", equivalencies=doppler_z(100 * u.GHz))
assert_quantity_allclose(r1, r3)
# round conversion for "doppler_beta"
r2 = r1.to("km/s", equivalencies=doppler_beta(100 * u.GHz))
r3 = r2.to("GHz", equivalencies=doppler_beta(100 * u.GHz))
assert_quantity_allclose(r1, r3)
# round conversion for "doppler_gamma"
r2 = r1.to("km/s", equivalencies=doppler_gamma(100 * u.GHz))
r3 = r2.to("GHz", equivalencies=doppler_gamma(100 * u.GHz))
assert_quantity_allclose(r1, r3)
def convert_to_ms(q):
return q.to(U.m / U.s, equivalencies=U.doppler_radio(HIfreq))
def find_linefree_freq(ms_name, vel_map, spw_dict,
vel_width=40 * u.km / u.s,
field_name='M33',
pb_size=0 * u.deg,
debug_printing=False):
'''
Use a velocity map (e.g., from HI) to define line-free channels
in an MS.
Parameters
----------
ms_name : str
Name of MS file to open.
vel_map : spectral_cube.Projection
Line velocity map to use extents from.
spw_dict : dict
Dictionary of SPWs numbers (as keys) and the
rest frequency. Continuum SPWs can be set to
the brightest line (e.g., CO) to exclude those
channels.
'''
try:
# CASA 6
import casatools
# iatool = casatools.image()
tb = casatools.table()
except ImportError:
try:
from taskinit import tbtool
# iatool = iatool()
tb = tbtool()
except ImportError:
raise ImportError("Could not import CASA (casac).")
# Get channel frequencies.
tb.open(os.path.join(ms_name, 'SPECTRAL_WINDOW'))
chanfreqs = tb.getvarcol('CHAN_FREQ')
tb.close()
# Get science field positions to define enclosing box of mosaic.
tb.open(os.path.join(ms_name, 'FIELD'))
field_names = tb.getcol('NAME')
phase_dirns = tb.getcol('PHASE_DIR').squeeze()
tb.close()
# Screen out non-science fields
selected_fields = np.array([True if field_name in name else False
for name in field_names])
ras, decs = (phase_dirns * u.rad).to(u.deg)
ras = ras[selected_fields]
decs = decs[selected_fields]
pointings = SkyCoord(ras, decs, frame='icrs')
# Make bounding box.
min_ra = pointings.ra.min() - 0.5 * pb_size.to(u.deg)
max_ra = pointings.ra.max() + 0.5 * pb_size.to(u.deg)
min_dec = pointings.dec.min() - 0.5 * pb_size.to(u.deg)
max_dec = pointings.dec.max() + 0.5 * pb_size.to(u.deg)
vel_map_box = vel_map.subimage(ylo=min_dec, yhi=max_dec,
xlo=max_ra, xhi=min_ra).to(u.km / u.s)
if vel_map_box.ndim != 2:
raise ValueError("Spatial slicing failed. Don't think this should happen?")
linefree_range = dict.fromkeys(spw_dict.keys())
for line_name in spw_dict:
spw_props = spw_dict[line_name]
spw_num = spw_props['spw_num']
restfreq = spw_props['restfreq'].to(u.Hz)
# Check if a velocity padding is defined
if 'vel_pad' in spw_props:
vel_pad = spw_props['vel_pad']
else:
vel_pad = vel_width
if debug_printing:
print('Velocity padding used for {0} is {1}'.format(spw_num,
vel_pad))
# Get the channel frequencies
key_name = "r{0}".format(int(spw_num) + 1)
chanfreqs_spw = chanfreqs[key_name].squeeze() * u.Hz
# Convert velocity extrema to freq.
vel_min = np.nanmin(vel_map_box.quantity) - vel_pad.to(u.km / u.s) / 2.
vel_max = np.nanmax(vel_map_box.quantity) + vel_pad.to(u.km / u.s) / 2.
freq_min = vel_min.to(u.Hz, u.doppler_radio(restfreq))
freq_max = vel_max.to(u.Hz, u.doppler_radio(restfreq))
if debug_printing:
print("Min velocity of spw {}".format(chanfreqs_spw.to(u.km / u.s, u.doppler_radio(restfreq)).min()))
print("Max velocity of spw {}".format(chanfreqs_spw.to(u.km / u.s, u.doppler_radio(restfreq)).max()))
print("Min velocity of line-free {}".format(vel_min))
print("Max velocity of line-free {}".format(vel_max))
# Switch if needed
if freq_max < freq_min:
freq_max, freq_min = freq_min, freq_max
line_chans = np.logical_and(chanfreqs_spw > freq_min,
chanfreqs_spw < freq_max)
if debug_printing:
print("Valid line channels found: {0}".format(line_chans))
if not line_chans.any():
raise ValueError("Invalid range found for {}".format(line_name))
chan_min = np.where(line_chans)[0].min()
chan_max = np.where(line_chans)[0].max()
# Make the line-free channel ranges
linefree_range[line_name] = []
if debug_printing:
print("Chan min/max for {0} are {1}/{2}".format(spw_num, chan_min,
chan_max))
if chan_min > 0:
lows = [0, chan_min]
linefree_range[line_name].append(lows)
if chan_max < chanfreqs_spw.size - 1:
highs = [chan_max, chanfreqs_spw.size]
linefree_range[line_name].append(highs)
return linefree_range
"J3-2_38": 0.016574,
"J3-2_39": 0.009776,
"J3-2_40": 0.000995,
"J3-2_41": 0.000491,
"J3-2_42": 0.000067,
"J3-2_43": 0.000039,
"J3-2_44": 0.000010,
}
# freq_dict = {
# 'J2-1': (voff_lines_dict['J2-1']*u.km/u.s).to(u.GHz, equivalencies=u.doppler_radio(freq_dict_cen['J2-1']*u.Hz)).value,
# 'J3-2': (voff_lines_dict['J3-2']*u.km/u.s).to(u.GHz, equivalencies=u.doppler_radio(freq_dict_cen['J3-2']*u.Hz)).value,
# }
# Get frequency dictionary in Hz based on the offset velocity and rest frequency
conv_J21 = u.doppler_radio(freq_dict_cen["J2-1"] * u.Hz)
conv_J32 = u.doppler_radio(freq_dict_cen["J3-2"] * u.Hz)
freq_dict = {
name: ((voff_lines_dict[name] * u.km / u.s).to(u.Hz, equivalencies=conv_J21).value)
for name in voff_lines_dict.keys()
if "J2-1" in name
}
freq_dict.update(
{
name: ((voff_lines_dict[name] * u.km / u.s).to(u.Hz, equivalencies=conv_J32).value)
for name in voff_lines_dict.keys()
if "J3-2" in name
}
)
# I don't know yet how to use this parameter... in CLASS it does not exist
from __future__ import print_function
import pyspeckit
from astropy import units as u
sp = pyspeckit.Spectrum('G203.04+1.76_h2co.fits',wcstype='D')
sp.xarr.center_frequency._unit = u.Hz
sp.xarr = sp.xarr.as_unit('km/s', equivalencies=u.doppler_radio(sp.xarr.center_frequency))
sp.specfit(fittype='formaldehyde',multifit=True,usemoments=True,guesses=[-0.6,4,0.2],equivalencies=u.doppler_optical(sp.xarr.center_frequency))
sp.plotter(figure=2)
sp.crop(-5*u.km/u.s,15*u.km/u.s)
sp.specfit(fittype='formaldehyde',multifit=True,usemoments=True,guesses=[-0.6,4,0.2])
sp.specfit(fittype='formaldehyde',multifit=True,usemoments=True,guesses=[-0.6,4,0.2])
sp.specfit.plot_fit(show_components=True)
sp.plotter.savefig("h2co_11_h2cofit.png")
print("Line integral (Formaldehyde,11): ",sp.specfit.integral())
print("Direct Line integral (Formaldehyde,11): ",sp.specfit.integral(direct=True,return_error=True))
sp2 = pyspeckit.Spectrum('G203.04+1.76_h2co.fits',wcstype='V')
sp2.xarr.convert_to_unit('km/s')
sp2.crop(-5*u.km/u.s,15*u.km/u.s)
sp2.plotter(figure=3)
sp2.specfit.peakbgfit(negamp=True, vheight=False)
sp2.specfit.peakbgfit(negamp=True, vheight=False)
sp2.plotter.savefig("h2co_11_gaussfit.png")
print("Line integral (Gaussian,11): ",sp2.specfit.integral())
print("Direct Line integral (Gaussian,11): ",sp2.specfit.integral(direct=True,return_error=True))
sp22g = pyspeckit.Spectrum('G203.04+1.76_h2co_Tastar.fits',wcstype='V')
sp22g.specfit.peakbgfit(negamp=True)
sp22g.xarr = sp22g.xarr.as_unit('km/s')
sp22g.plotter(figure=5)
'p-1_01-1_11_03': 0.417,
'p-1_01-1_11_04': 0.083,
'p-1_01-1_11_05': 0.139,
'p-1_01-1_11_06': 0.111,
####### para-NH2D J=1_01-0_00
'p-1_01-0_00_01': 0.111,
'p-1_01-0_00_02': 0.556,
'p-1_01-0_00_03': 0.333,
}
# freq_dict = {
# 'J2-1': (voff_lines_dict['J2-1']*u.km/u.s).to(u.GHz, equivalencies=u.doppler_radio(freq_dict_cen['J2-1']*u.Hz)).value,
# 'J3-2': (voff_lines_dict['J3-2']*u.km/u.s).to(u.GHz, equivalencies=u.doppler_radio(freq_dict_cen['J3-2']*u.Hz)).value,
# }
# Get offset velocity dictionary in km/s based on the lines frequencies and rest frequency
conv_o1_1=u.doppler_radio(freq_dict_cen['o-1_01-1_11']*u.Hz)
conv_p1_1=u.doppler_radio(freq_dict_cen['p-1_01-1_11']*u.Hz)
conv_o1_0=u.doppler_radio(freq_dict_cen['o-1_01-0_00']*u.Hz)
conv_p1_0=u.doppler_radio(freq_dict_cen['p-1_01-0_00']*u.Hz)
voff_lines_dict = {
name: ((freq_dict[name]*u.Hz).to(u.km/u.s, equivalencies=conv_o1_1).value) for name in freq_dict.keys() if "o-1_01-1_11" in name
}
voff_lines_dict.update({
name: ((freq_dict[name]*u.Hz).to(u.km/u.s, equivalencies=conv_p1_1).value) for name in freq_dict.keys() if "p-1_01-1_11" in name
})
voff_lines_dict.update({
name: ((freq_dict[name]*u.Hz).to(u.km/u.s, equivalencies=conv_p1_1).value) for name in freq_dict.keys() if "o-1_01-0_00" in name
})
voff_lines_dict.update({
name: ((freq_dict[name]*u.Hz).to(u.km/u.s, equivalencies=conv_p1_1).value) for name in freq_dict.keys() if "p-1_01-0_00" in name
import matplotlib
import numpy as np
from astropy import units as u
from distutils.version import StrictVersion
if not 'savedir' in globals():
savedir = ''
# load a FITS-compliant spectrum
spec = pyspeckit.Spectrum('10074-190_HCOp.fits')
# The units are originally frequency (check this by printing spec.xarr.units).
# I want to know the velocity. Convert!
# Note that this only works because the reference frequency is set in the header
# this is no longer necessary! #spec.xarr.frequency_to_velocity()
# Default conversion is to m/s, but we traditionally work in km/s
spec.xarr = spec.xarr.as_unit('km/s', equivalencies=u.doppler_radio(spec.xarr.center_frequency))
# plot it up!
spec.plotter()
# Subtract a baseline (the data is only 'mostly' reduced)
spec.baseline(interactive=True)
# specify x points in data units. We need to transform them to axis units
# because the axis are not consistently generated by mpl
xpoints = [-270,0,50,218,218]
ypoints = [0]*5
buttons = [1,1,1,1,2]
transform = spec.plotter.axis.transData.transform_point
# this absolutely ridiculous line is to deal with scope changes from py2->py3
# http://stackoverflow.com/questions/13905741/accessing-class-variables-from-a-list-comprehension-in-the-class-definition#comment19179733_13913933
def xy_(transform, xpoints, ypoints):
return [transform((xp,yp)) for xp,yp in zip(xpoints,ypoints)]
point = SkyCoord(hdu[0].header['OBSRA'] * u.deg,
hdu[0].header['OBSDEC'] * u.deg)
point_string = point.to_string(style='hmsdms')
# hdu[0].header['CRVAL5'] = hdu[0].header['OBSRA']
# hdu[0].header['CRVAL6'] = hdu[0].header['OBSDEC']
# The ref freq is also confusing. It's the frequency in the rest frame
# So it needs to be altered to be in the observed frame.
# GILDAS appears to have this conversion built-in everywhere
restfreq = hdu[0].header['RESTFREQ'] * u.Hz
rest_cent_freq = hdu[0].header['CRVAL4'] * u.Hz
source_vel = hdu[0].header['VELO-LSR'] * u.m / u.s
# Calculate the frequency shift
del_f = rest_cent_freq - source_vel.to(u.Hz, u.doppler_radio(restfreq))
# Adjust CRVAL4 to the observed frame frequency
hdu[0].header['CRVAL4'] -= del_f.value
# Need to assign the SD visibilities to an ant pair
# CASA doesn't barf when doing this, but I can't be sure it worked in
# any useful way. So don't use this for any science products, just as
# a check against the GILDAS imaging
if 'merged' in pref:
raise ValueError("This does not work. Don't import merged files "
"into CASA!")
baselines = hdu[0].data['BASELINE']
baselines[np.where(baselines == 0)] = baselines[0]
hdu[0].data['BASELINE'] = baselines
def convert_to_Hz(q):
return q.to(U.Hz, equivalencies=U.doppler_radio(HIfreq))
from astropy import units as u rest = 4829.66*u.MHz chwid = (7.8125*u.kHz) chan0 = 4824.861*u.MHz vlsr = 42.27*u.km/u.s firstchan = (((35*u.km/u.s-vlsr).to(u.MHz, u.doppler_radio(rest)) - chan0) / chwid).decompose() lastchan = (((80*u.km/u.s-vlsr).to(u.MHz, u.doppler_radio(rest)) - chan0) / chwid).decompose() print firstchan, lastchan print "Frequency at channel 550: ",(550*chwid + chan0).to(u.GHz) print "Frequency at channel 0: ",(0*chwid + chan0).to(u.GHz) print "Frequency at channel 1024: ",(1024*chwid + chan0).to(u.GHz) print "Frequency at channel 512: ",(512*chwid + chan0).to(u.GHz)," and according to CASA: ",4.82886*u.GHz print "total bw: ",1024*chwid
extent=[minfrq, maxfrq, nfields * nconfigs, 0],
interpolation='nearest',
cmap='gnuplot')
ax.set_aspect((maxfrq - minfrq) * 2 / (nfields * nconfigs))
xmin, xmax = ax.get_xlim()
ax.hlines(np.arange(nfields) * 3, xmin, xmax, color='w', linestyle='-')
for linename, linefrq in lines_to_overplot.items():
linefrq = u.Quantity(linefrq).to(u.GHz)
linefrqval = linefrq.value
if (minfrq < linefrqval) & (maxfrq > linefrqval):
for fieldnum, field in fields_and_numbers:
vlsr = u.Quantity(field_vlsr[field])
shifted_frq = vlsr.to(u.GHz,
u.doppler_radio(linefrq)).value
if (minfrq < shifted_frq) & (maxfrq > shifted_frq):
ax.vlines(shifted_frq,
fieldnum * nconfigs,
(fieldnum + 1) * nconfigs,
color='b')
pl.figure(fig.number)
pl.tight_layout()
pl.subplots_adjust(wspace=0.05, hspace=0)
fig.text(0.5, xlabel_offset[band], 'Frequency (GHz)', ha='center')
fig.savefig(
f"{basepath}/paper_figures/continuum_selection_regions_band{band}.png",
bbox_inches='tight')
from __future__ import print_function
import pyspeckit
import numpy as np
from astropy import units as u
from pyspeckit.spectrum.models import ammonia
xarr = np.linspace(-40, 40, 300) * u.km/u.s
oneonemod = ammonia.ammonia(xarr.to(u.GHz, u.doppler_radio(ammonia.freq_dict['oneone']*u.Hz)),)
twotwomod = ammonia.ammonia(xarr.to(u.GHz, u.doppler_radio(ammonia.freq_dict['twotwo']*u.Hz)),)
sp11 = pyspeckit.Spectrum(xarr=xarr, data=oneonemod, unit=u.K,
xarrkwargs={'refX': ammonia.freq_dict['oneone']*u.Hz},
header={})
sp22 = pyspeckit.Spectrum(xarr=xarr, data=twotwomod, unit=u.K,
xarrkwargs={'refX': ammonia.freq_dict['twotwo']*u.Hz},
header={})
input_dict={'oneone':sp11, 'twotwo':sp22,}
spf, specout = pyspeckit.wrappers.fitnh3.fitnh3tkin(input_dict, dobaseline=False)
print(specout.specfit.modelpars)
print(specout.specfit.parinfo)
spf2, specout2 = pyspeckit.wrappers.fitnh3.fitnh3tkin(input_dict,
dobaseline=True,
baselinekwargs={'exclude':[-30,30]*u.km/u.s})
print(specout.specfit.modelpars)
print(specout.specfit.parinfo)
def calculate_syn(linedata=None,
ntot=None,
tex=None,
source_size=None,
beam_size=None,
species=None,
fwhm=None,
usetau=True,
returnjy=True,
spectra=None,
fluxcol=None,
ilims=[None,None],
vsys=None,
modelname="model_flux_1",
dnu=0.001 * u.GHz,
verbose=True,
qrot_method='cdms',
use_ilims=True
):
# Step 0, sort table according to frequency
linedata.sort('freq_rest')
# Step 1, calculate integrated synthetic line fluxes
# from Ntot and Tex for the given lines in input "model"
# This function will calculate Jy if returnjy=True
# and then use the input beam and source size to also
# account for beam dilution. This is total integrated fluxes.
linemodel = utils.calc_line_fluxes(linedata=linedata,
ntot=ntot,
tex=tex,
source=source_size,
beam=beam_size,
species=species,
fwhm=fwhm,
usetau=True,
ilims=ilims,
returnjy=True,
verbose=verbose,
qrot_method=qrot_method,
)
# Step 2, integrate the observed spectrum around frequency for
# each line. This step is not crucial, but for fitting Ntot or Tex
# it is useful. However, be careful to compare the same intensity.
# i.e. integrated vs. fitted Gaussian etc.
linemodel = utils.integrate_all_lines(spectra=spectra,
model=linemodel,
linedata=linedata,
fluxcol=fluxcol,
ilims=ilims,
vsys=vsys,
verbose=verbose,
)
# Step 3, check the model for line blends
linemodel = utils.check_for_blend(model=linemodel,
dnu=dnu,
verbose=verbose)
if linemodel.meta['blends_present']:
if verbose:
print('Blends present, process blends.')
# Step 4, calculate a summed integrated synthetic line flux
linemodel = utils.process_model_blends(model=linemodel)
# Step 5, calculate the synthetic spectrum and put it in
# a new column named as the input parameter "modelname".
# note that linemodel_syn is different from the linemodel
# data structure.
spectra_syn, linemodel_syn = utils.calc_synthetic_spectrum(model=linemodel,
spectra=spectra,
fluxcol=fluxcol,
modelname=modelname,
fwhm=fwhm,
verbose=verbose,
)
linemodel.meta['ilims'] = ilims
# add a column with velocity for each line
# makes plotting some what easier
try:
linemodel_syn['vel_rest'] = [i.quantity.to(u.km/u.s,
equivalencies=u.doppler_radio(j)) for (i,j) in zip(
linemodel_syn['freq_rest'],
linemodel['freq_rest'].quantity)]
except(AttributeError): # if theres only one entry
if verbose:
print('WARN:Only one line, ugly hack ahead. Check units.')
linemodel_syn['vel_rest'] = [(i*u.GHz).to(u.km/u.s,
equivalencies=u.doppler_radio(j*u.GHz)) for (i,j) in zip(
linemodel_syn['freq_rest'],
linemodel['freq_rest'])]
return spectra_syn, linemodel_syn, linemodel
def radio_velocities(self):
if self._radio_velocities is None:
rad_eq = u.doppler_radio(self.rest_frequency)
self._radio_velocities = self.frequencies.to(u.km / u.s, rad_eq)
return self._radio_velocities