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.. _astroquery.linelists.cdms:
******************************************************************************************
Cologne Database for Molecular Spectroscopy (CDMS) Queries (``astroquery.linelists.cdms``)
******************************************************************************************
Getting Started
===============
The CDMS module provides a query interface for the `Search and Conversion Form
of the Cologne Database for Molecular Spectroscopy
<https://cdms.astro.uni-koeln.de/cgi-bin/cdmssearch>`_. The module outputs the results that
would arise from the browser form using similar search criteria as the ones
found in the form, and presents the output as a `~astropy.table.Table`. The
module is similar in spirit and content to the JPLSpec module.
Examples
========
Querying the catalog
--------------------
The default option to return the query payload is set to ``False``. In the
following examples we have explicitly set it to False and True to show the what
each setting yields:
.. doctest-remote-data::
>>> from astroquery.linelists.cdms import CDMS
>>> import astropy.units as u
>>> response = CDMS.query_lines(min_frequency=100 * u.GHz,
... max_frequency=1000 * u.GHz,
... min_strength=-500,
... molecule="028503 CO",
... get_query_payload=False)
>>> response.pprint(max_width=120)
FREQ ERR LGINT DR ELO GUP MOLWT TAG QNFMT Ju Ku vu F1u F2u F3u Jl Kl vl F1l F2l F3l name Lab
MHz MHz nm2 MHz 1 / cm u
----------- ------ ------- --- -------- --- ----- --- ----- --- --- --- --- --- --- --- --- --- --- --- --- ------- ----
115271.2018 0.0005 -5.0105 2 0.0 3 28 503 101 1 -- -- -- -- -- 0 -- -- -- -- -- CO, v=0 True
230538.0 0.0005 -4.1197 2 3.845 5 28 503 101 2 -- -- -- -- -- 1 -- -- -- -- -- CO, v=0 True
345795.9899 0.0005 -3.6118 2 11.535 7 28 503 101 3 -- -- -- -- -- 2 -- -- -- -- -- CO, v=0 True
461040.7682 0.0005 -3.2657 2 23.0695 9 28 503 101 4 -- -- -- -- -- 3 -- -- -- -- -- CO, v=0 True
576267.9305 0.0005 -3.0118 2 38.4481 11 28 503 101 5 -- -- -- -- -- 4 -- -- -- -- -- CO, v=0 True
691473.0763 0.0005 -2.8193 2 57.6704 13 28 503 101 6 -- -- -- -- -- 5 -- -- -- -- -- CO, v=0 True
806651.806 0.005 -2.6716 2 80.7354 15 28 503 101 7 -- -- -- -- -- 6 -- -- -- -- -- CO, v=0 True
921799.7 0.005 -2.559 2 107.6424 17 28 503 101 8 -- -- -- -- -- 7 -- -- -- -- -- CO, v=0 True
The following example, with ``get_query_payload = True``, returns the payload:
.. doctest-remote-data::
>>> response = CDMS.query_lines(min_frequency=100 * u.GHz,
... max_frequency=1000 * u.GHz,
... min_strength=-500,
... molecule="028503 CO",
... get_query_payload=True)
>>> print(response)
{'MinNu': 100.0, 'MaxNu': 1000.0, 'UnitNu': 'GHz', 'StrLim': -500, 'temp': 300, 'logscale': 'yes', 'mol_sort_query': 'tag', 'sort': 'frequency', 'output': 'text', 'but_action': 'Submit', 'Molecules': '028503 CO'}
The units of the columns of the query can be displayed by calling
``response.info``:
.. doctest-remote-data::
>>> response = CDMS.query_lines(min_frequency=100 * u.GHz,
... max_frequency=1000 * u.GHz,
... min_strength=-500,
... molecule="028503 CO",
... get_query_payload=False)
>>> print(response.info)
<Table length=8>
name dtype unit class n_bad
----- ------- ------- ------------ -----
FREQ float64 MHz Column 0
ERR float64 MHz Column 0
LGINT float64 nm2 MHz Column 0
DR int64 Column 0
ELO float64 1 / cm Column 0
GUP int64 Column 0
MOLWT int64 u Column 0
TAG int64 Column 0
QNFMT int64 Column 0
Ju int64 Column 0
Ku int64 MaskedColumn 8
vu int64 MaskedColumn 8
F1u int64 MaskedColumn 8
F2u int64 MaskedColumn 8
F3u int64 MaskedColumn 8
Jl int64 Column 0
Kl int64 MaskedColumn 8
vl int64 MaskedColumn 8
F1l int64 MaskedColumn 8
F2l int64 MaskedColumn 8
F3l int64 MaskedColumn 8
name str7 Column 0
Lab bool Column 0
These come in handy for converting to other units easily, an example using a
simplified version of the data above is shown below:
.. doctest-remote-data::
>>> print(response['FREQ', 'ERR', 'ELO'])
FREQ ERR ELO
MHz MHz 1 / cm
----------- ------ --------
115271.2018 0.0005 0.0
230538.0 0.0005 3.845
345795.9899 0.0005 11.535
461040.7682 0.0005 23.0695
576267.9305 0.0005 38.4481
691473.0763 0.0005 57.6704
806651.806 0.005 80.7354
921799.7 0.005 107.6424
>>> response['FREQ'].quantity
<Quantity [115271.2018, 230538. , 345795.9899, 461040.7682, 576267.9305,
691473.0763, 806651.806 , 921799.7 ] MHz>
>>> response['FREQ'].to('GHz')
<Quantity [115.2712018, 230.538 , 345.7959899, 461.0407682, 576.2679305,
691.4730763, 806.651806 , 921.7997 ] GHz>
The parameters and response keys are described in detail under the
Reference/API section.
Looking Up More Information from the partition function file
------------------------------------------------------------
If you have found a molecule you are interested in, the ``tag`` column in the
results provides enough information to access specific molecule information
such as the partition functions at different temperatures. Keep in mind that a
negative ``tag`` value signifies that the line frequency has been measured in the
laboratory but not in space
.. doctest-remote-data::
>>> import matplotlib.pyplot as plt
>>> from astroquery.linelists.cdms import CDMS
>>> result = CDMS.get_species_table()
>>> mol = result[result['tag'] == 28503]
>>> mol.pprint(max_width=160)
tag molecule Name #lines lg(Q(1000)) lg(Q(500)) lg(Q(300)) ... lg(Q(9.375)) lg(Q(5.000)) lg(Q(2.725)) Ver. Documentation Date of entry Entry
----- -------- --------- ------ ----------- ---------- ---------- ... ------------ ------------ ------------ ---- ------------- ------------- -----------
28503 CO, v=0 CO, v = 0 95 2.5595 2.2584 2.0369 ... 0.5733 0.3389 0.1478 1 e028503.cat Oct. 2000 w028503.cat
One of the advantages of using CDMS is the availability in the catalog of the
partition function at different temperatures for the molecules (just like for
JPL). As a continuation of the example above, an example that accesses and
plots the partition function against the temperatures found in the metadata is
shown below:
.. doctest-skip::
>>> import numpy as np
>>> keys = [k for k in mol.keys() if 'lg' in k]
>>> temp = np.array([float(k.split('(')[-1].split(')')[0]) for k in keys])
>>> part = list(mol[keys][0])
>>> plt.scatter(temp, part)
>>> plt.xlabel('Temperature (K)')
>>> plt.ylabel('Partition Function Value')
>>> plt.title('Partition Function vs Temperature')
.. plot::
:context: reset
import numpy as np
import matplotlib.pyplot as plt
from astroquery.linelists.cdms import CDMS
result = CDMS.get_species_table()
mol = result[result['tag'] == 28503] # do not include signs of tag for this
keys = [k for k in mol.keys() if 'lg' in k]
temp = np.array([float(k.split('(')[-1].split(')')[0]) for k in keys])
part = list(mol[keys][0])
plt.scatter(temp,part)
plt.xlabel('Temperature (K)')
plt.ylabel('Partition Function Value')
plt.title('Partition Function vs Temperature')
For non-linear molecules like H2CO, curve fitting methods can be used to
calculate production rates at different temperatures with the proportionality:
``a*T**(3./2.)``. Calling the process above for the H2CO molecule (instead of
for the CO molecule) we can continue to determine the partition function at
other temperatures using curve fitting models:
.. doctest-skip::
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from astroquery.linelists.cdms import CDMS
>>> from scipy.optimize import curve_fit
...
>>> result = CDMS.get_species_table()
>>> mol = result[result['tag'] == 30501] #do not include signs of tag for this
...
>>> def f(T, a):
return np.log10(a*T**(1.5))
>>> keys = [k for k in mol.keys() if 'lg' in k]
>>> def tryfloat(x):
... try:
... return float(x)
... except:
... return np.nan
>>> temp = np.array([float(k.split('(')[-1].split(')')[0]) for k in keys])
>>> part = np.array([tryfloat(x) for x in mol[keys][0]])
>>> param, cov = curve_fit(f, temp[np.isfinite(part)], part[np.isfinite(part)])
>>> print(param)
# array([0.51865074])
>>> x = np.linspace(2.7,500)
>>> y = f(x,param[0])
>>> plt.scatter(temp,part,c='r')
>>> plt.plot(x,y,'k')
>>> plt.title('Partition Function vs Temperature')
>>> plt.xlabel('Temperature')
>>> plt.ylabel('Log10 of Partition Function')
.. plot::
:context: reset
import numpy as np
import matplotlib.pyplot as plt
from astroquery.linelists.cdms import CDMS
from scipy.optimize import curve_fit
result = CDMS.get_species_table()
mol = result[result['tag'] == 30501] # do not include signs of tag for this
def f(T, a):
return np.log10(a*T**(1.5))
keys = [k for k in mol.keys() if 'lg' in k]
def tryfloat(x):
try:
return float(x)
except:
return np.nan
temp = np.array([float(k.split('(')[-1].split(')')[0]) for k in keys])
part = np.array([tryfloat(x) for x in mol[keys][0]])
param, cov = curve_fit(f, temp[np.isfinite(part)], part[np.isfinite(part)])
x = np.linspace(2.7,500)
y = f(x,param[0])
plt.clf()
plt.scatter(temp,part,c='r',label='CDMS Data')
plt.plot(x,y,'k',label='Fitted')
inds = np.argsort(temp)
interp_Q = np.interp(x, temp[inds], 10**part[inds])
plt.plot(x, np.log10(interp_Q), label='Interpolated', linewidth=0.75)
plt.title('Partition Function vs Temperature')
plt.xlabel('Temperature')
plt.ylabel('Log10 of Partition Function')
plt.legend(loc='best')
We can then compare linear interpolation to the fitted interpolation above:
.. doctest-skip::
>>> interp_Q = np.interp(x, temp, 10**part)
>>> plt.plot(x, (10**y-interp_Q)/10**y)
>>> plt.xlabel("Temperature")
>>> plt.ylabel("Fractional difference between linear and fitted")
.. plot::
:context: reset
import numpy as np
import matplotlib.pyplot as plt
from astroquery.linelists.cdms import CDMS
from scipy.optimize import curve_fit
result = CDMS.get_species_table()
mol = result[result['tag'] == 30501] # do not include signs of tag for this
def f(T, a):
return np.log10(a*T**(1.5))
keys = [k for k in mol.keys() if 'lg' in k]
def tryfloat(x):
try:
return float(x)
except:
return np.nan
temp = np.array([float(k.split('(')[-1].split(')')[0]) for k in keys])
part = np.array([tryfloat(x) for x in mol[keys][0]])
param, cov = curve_fit(f, temp[np.isfinite(part)], part[np.isfinite(part)])
x = np.linspace(2.7,500)
y = f(x,param[0])
inds = np.argsort(temp)
interp_Q = np.interp(x, temp[inds], 10**part[inds])
plt.clf()
plt.plot(x, (10**y-interp_Q)/10**y)
plt.xlabel("Temperature")
plt.ylabel("Fractional difference between linear and fitted")
Linear interpolation is a good approximation, in this case, for any moderately
high temperatures, but is increasingly poor at lower temperatures.
It can be valuable to check this for any given molecule.
Querying the Catalog with Regexes and Relative names
----------------------------------------------------
The regular expression parsing is analogous to that in the JPLSpec module.
Troubleshooting
===============
If you are repeatedly getting failed queries, or bad/out-of-date results, try clearing your cache:
.. code-block:: python
>>> from astroquery.linelists.cdms import CDMS
>>> CDMS.clear_cache()
If this function is unavailable, upgrade your version of astroquery.
The ``clear_cache`` function was introduced in version 0.4.7.dev8479.
Reference/API
=============
.. automodapi:: astroquery.linelists.cdms
:no-inheritance-diagram:
|
