import re
import glob
from line_to_image_list import line_to_image_list
frequencies = u.Quantity(
[float(row[1].strip('GHz')) for row in line_to_image_list], u.GHz)
name_to_freq = {
row[0]: frq
for frq, row in zip(frequencies, line_to_image_list)
}
freq_to_name = {
frq: row[0]
for frq, row in zip(frequencies, line_to_image_list)
}
hnco = Vamdc.query_molecule('Isocyanic acid HNCO')
rt = hnco.data['RadiativeTransitions']
frqs = u.Quantity([
(float(rt[key].FrequencyValue) * u.MHz).to(u.GHz, u.spectral())
for key in rt
])
frqs_to_ids = {frq: key for frq, key in zip(frqs, rt)}
upperStateRefs = [rt[key].UpperStateRef for key in rt]
degeneracies = [
int(hnco.data['States'][upperStateRef].TotalStatisticalWeight)
for upperStateRef in upperStateRefs
]
einsteinAij = u.Quantity([float(rt[key].TransitionProbabilityA) for key in rt],
1 / u.s)
from vamdclib import specmodel
from astropy import modeling
import re
import glob
from line_to_image_list import line_to_image_list
frequencies = u.Quantity([float(row[1].strip('GHz'))
for row in line_to_image_list], u.GHz)
name_to_freq = {row[0]:frq for frq, row in zip(frequencies, line_to_image_list)}
freq_to_name = {frq:row[0] for frq, row in zip(frequencies, line_to_image_list)}
linere = re.compile("W51_b6_12M.(.*).image.pbcor")
ch3oh = Vamdc.query_molecule('CH3OH')
rt = ch3oh.data['RadiativeTransitions']
frqs = u.Quantity([(float(rt[key].FrequencyValue)*u.MHz).to(u.GHz,
u.spectral())
for key in rt])
frqs_to_ids = {frq: key for frq,key in zip(frqs, rt)}
upperStateRefs = [rt[key].UpperStateRef for key in rt]
degeneracies = [int(ch3oh.data['States'][upperStateRef].TotalStatisticalWeight)
for upperStateRef in upperStateRefs]
einsteinAij = u.Quantity([float(rt[key].TransitionProbabilityA) for key in rt], 1/u.s)
# http://www.astro.uni-koeln.de/cdms/catalog#equations
# the units are almost certainly wrong; I don't know how to compute line strength
# from aij =(
def get_Aul_(frqs):
def get_Aul(freq):
closest = np.nanargmin(np.abs(freq - frqs))
assert frqs[closest] - freq < 1 * u.MHz
return einsteinAij[closest]
return get_Aul
if __name__ == "__main__":
all_lines = {**lines.disk_lines, **lines.absorbers}
frequencies = u.Quantity([all_lines[x] for x in all_lines if 'Cl' in x])
kcl35 = Vamdc.query_molecule('KCl-35')
rt35 = kcl35.data['RadiativeTransitions']
frqs = u.Quantity([
(float(rt35[key].FrequencyValue) * u.MHz).to(u.GHz, u.spectral())
for key in rt35
])
upperStateRefs = [rt35[key].UpperStateRef for key in rt35]
degeneracies = [
int(kcl35.data['States'][upperStateRef].TotalStatisticalWeight)
for upperStateRef in upperStateRefs
]
einsteinAij = u.Quantity(
[float(rt35[key].TransitionProbabilityA) for key in rt35], 1 / u.s)
tbl = table.Table.read(paths.tpath('fitted_stacked_lines.txt'),
import numpy as np
import paths
import pylab as pl
from astroquery.vamdc import Vamdc
from vamdclib import specmodel
ch3oh = Vamdc.query_molecule("CH3OH")
temperatures = np.linspace(10, 300)
partition_func = [
specmodel.calculate_partitionfunction(ch3oh.data["States"], temperature=tex)["XCDMS-149"] for tex in temperatures
]
pl.matplotlib.rc_file("pubfiguresrc")
pl.figure(1).clf()
pl.plot(temperatures, partition_func)
pl.xlabel("T (K)")
pl.ylabel("$Q_{rot}$")
pl.savefig(paths.fpath("chemistry/ch3oh_partition_function.png"))
scaling = 1e12 * u.cm**-2
pl.plot(rslt['upperstateenergy'].data,
np.log10(rslt['upperlevelpop'].data * scaling.value),
'.',
label=None,
alpha=0.15)
#pl.plot(rslt[vzero]['upperstateenergy'].data, np.log10(rslt[vzero]['upperlevelpop'].data), 'o', label=None)
#pl.plot(rslt[vone]['upperstateenergy'].data, np.log10(rslt[vone]['upperlevelpop'].data), 'o', label=None)
#pl.plot(rslt[vzero & obs]['upperstateenergy'].data, np.log10(rslt[vzero & obs]['upperlevelpop'].data), 'o', label=None)
#pl.plot(rslt[vone & obs]['upperstateenergy'].data, np.log10(rslt[vone & obs]['upperlevelpop'].data), 'o', label=None)
#pl.plot(rslt[vtwo]['upperstateenergy'].data, np.log10(rslt[vtwo]['upperlevelpop'].data), 'o', label=None)
#pl.plot(rslt[vtwo & obs]['upperstateenergy'].data, np.log10(rslt[vtwo & obs]['upperlevelpop'].data), 'o', label=None)
#pl.plot(rslt[Jeight]['upperstateenergy'].data, np.log10(rslt[Jeight]['upperlevelpop'].data), 's', label=None)
nacl = Vamdc.query_molecule('NaCl$')
fit_tex(u.Quantity(rslt[vone & obs]['upperstateenergy'], u.K),
rslt[vone & obs]['upperlevelpop'] * scaling,
molecule=nacl,
plot=True,
min_nupper=1e-50)
fit_tex(u.Quantity(rslt[Jeight]['upperstateenergy'], u.K),
rslt[Jeight]['upperlevelpop'] * scaling,
molecule=nacl,
plot=True,
min_nupper=1e-50,
color='k')
pl.legend(loc='upper right')
pl.xlabel("E$_U$ [K]")
from astroquery.vamdc import Vamdc
from vamdclib import specmodel
from astropy import modeling
import re
import glob
from line_to_image_list import line_to_image_list
frequencies = u.Quantity([float(row[1].strip('GHz'))
for row in line_to_image_list], u.GHz)
name_to_freq = {row[0]:frq for frq, row in zip(frequencies, line_to_image_list)}
freq_to_name = {frq:row[0] for frq, row in zip(frequencies, line_to_image_list)}
hnco = Vamdc.query_molecule('Isocyanic acid HNCO')
rt = hnco.data['RadiativeTransitions']
frqs = u.Quantity([(float(rt[key].FrequencyValue)*u.MHz).to(u.GHz,
u.spectral())
for key in rt])
frqs_to_ids = {frq: key for frq,key in zip(frqs, rt)}
upperStateRefs = [rt[key].UpperStateRef for key in rt]
degeneracies = [int(hnco.data['States'][upperStateRef].TotalStatisticalWeight)
for upperStateRef in upperStateRefs]
einsteinAij = u.Quantity([float(rt[key].TransitionProbabilityA) for key in rt], 1/u.s)
# http://www.astro.uni-koeln.de/cdms/catalog#equations
# the units are almost certainly wrong; I don't know how to compute line strength
# from aij =(