pycollisiondb 0.1.6

Valid Query Keywords

>>> pycoll.VALID_QUERY_KEYWORDS
(ˈpkˈ,
 ˈpksˈ,
 ˈreaction_textˈ,
 ˈreaction_textsˈ,
 ˈreactant1ˈ,
 ˈreactant2ˈ,
 ˈproduct1ˈ,
 ˈproduct2ˈ,
 ˈprocess_typesˈ,
 ˈmethodˈ,
 ˈdata_typeˈ,
 ˈreactantsˈ,
 ˈproductsˈ,
 ˈdoiˈ,
 'count_only')

>>> # Example
>>> pycoll = PyCollision.get_datasets(query={'rxns': ['H']})
---------------------------------------------------------------------------
PyCollisionDBKeywordError                 Traceback (most recent call last)
...
PyCollisionDBKeywordError: Invalid query keyword in dict_keys(['rxns'])

Please see the documentation for currently available choices or formats (if any) for query key values.

>>> # reaction_text is a string consisting of a single reaction,
>>> # while a comma-separated list of reactions can be searched with the reaction_texts key.
>>> # data_type can be specified as one of "cross section", "differential cross section" or "rate coefficient".
>>> query = {'reaction_text': "H+ + H 1s -> H+ + H+ + e-",
             'data_type': 'sigma'}
>>> pycoll = PyCollision.get_datasets(query=query)
---------------------------------------------------------------------------
PyCollisionDBConnectionError              Traceback (most recent call last)
...
PyCollisionDBConnectionError: Could not retrieve data: HTTP 400 (Bad Request) returned from https://db-amdis.org/collisiondb/api/

Accessing data from the datasets

>>> # Proton-impact ionization of H.
>>> query = {'reactants': ['H+', 'H 1s'],
              'process_types': ['HIN'],
              'data_type': 'cross section'}
>>> pycoll = PyCollision.get_datasets(query=query)

>>> # Datasets retrieved from the server as a dict keyed by pk ID.
>>> pycoll.datasets
{102737: D102737: H+ + H 1s → H+ + H+ + e-,
 107356: D107356: H+ + H 1s → H+ + H+ + e-,
 103103: D103103: H 1s + H+ → H+ + H+ + e-,
 103104: D103104: H 1s + H+ → H+ + H+ + e-}

>>> # A list of datasets pks identified by each distinct reaction text.
>>> pycoll.pks
defaultdict(list,
        {'H+ + H 1s → H+ + H+ + e-': [102737, 107356],
         'H 1s + H+ → H+ + H+ + e-': [103103, 103104]})

>>> # Summarize datasets for each distint reaction text.
>>> pycoll.summarize_datasets()
H+ + H 1s → H+ + H+ + e-
========================================================================
 qid: D102737
 process_types: ['HIN']
 data_type: cross section
 refs: {'B32': {'doi': '10.1016/j.adt.2019.05.002'}}
 qid: D107356
 process_types: ['HIN']
 data_type: cross section
 refs: {'B45': {'doi': '10.1140/epjd/e2019-100380-x'}}

H 1s + H+ → H+ + H+ + e-
========================================================================
 qid: D103103
 process_types: ['HIN']
 data_type: cross section
 refs: {'B33': {'doi': '10.1088/0022-3700/14/14/009'}}
 qid: D103104
 process_types: ['HIN']
 data_type: cross section
 refs: {'B34': {'doi': '10.1088/0022-3700/20/11/016'}}

>>> # Contents of the manifest.json file available in archive
>>> pycoll.manifest
 {'timestamp': '2022-12-08 16:00:32.768200+00:00',
  'uuid': '4d3bf0d4-c786-4712-b7a6-e5c04e67c89c',
  'GET_string': '',
  'ndatasets': 4,
  'datasets': {'D102737': 'H+ + H 1s → H+ + H+ + e-',
  'D107356': 'H+ + H 1s → H+ + H+ + e-',
  'D103103': 'H 1s + H+ → H+ + H+ + e-',
  'D103104': 'H 1s + H+ → H+ + H+ + e-'}}

>>> # Resolve the references for all the datasets into proper, citeable format.
>>> # this accesses the django-pyref API through the Server.
>>> pycoll.REFS_API_URL
'https://db-amdis.org/collisiondb/refs/api/'

>>> pycoll.resolve_refs()
>>> # a dict with bibliographic data identified by pk ID
>>> pycoll.refs
{
'B32': {'authors': 'H. Agueny, J. Petter Hansen, A. Dubois, A. Makhoute, A. Taoutioui, N. Sisourat',
 'title': 'Electron capture, ionization and excitation cross sections for keV collisions between fully stripped ions and atomic hydrogen in ground and excited states',
 'journal': 'Atomic Data and Nuclear Data Tables',
 'volume': '129-130',
 'page_start': '101281',
 'page_end': '',
 'article_number': '101281',
 'year': 2019,
 'note': '',
 'doi': '10.1016/j.adt.2019.05.002',
 'bibcode': '2019ADNDT.12901281A',
 'url': 'https://dx.doi.org/10.1016/j.adt.2019.05.002'},
'B33': {...},
'B34': {...},
'B45': {...}
}

More details

>>> # List of all datasets pks
>>> pycoll.all_pks
[102737, 107356, 103103, 103104]

>>> # Access to individual dataset via pk
>>> pycoll.datasets[102737].metadata
{'qid': 'D102737',
 'reaction': 'H+ + H 1s → H+ + H+ + e-',
 'process_types': {'HIN': 'Ionization'},
 'data_type': 'cross section',
 'refs': {'B32': {'doi': '10.1016/j.adt.2019.05.002'}},
 'json_comment': {'comment': 'Ionization cross sections in H+ + H collisions using a semiclassical close-coupling approach. Cross sections represent the average values of the results obtained with two basis sets and the uncertainties provide the estimate of convergence of the cross sections'},
 'json_data': {'method': 'CC',
 'columns': [{'name': 'E', 'units': 'eV.u-1'},
 {'name': 'sigma', 'units': 'cm2'}]}}

>>> # Prints the numerical values of datasets along with units.
>>> pycoll.datasets[102737].print_values()
E / eV.u-1 sigma / cm2
1000.0 5.471e-19
4000.0 1.96e-18
9000.0 1.235e-17
16000.0 4.05e-17
25000.0 9.192e-17
36000.0 1.405e-16
49000.0 1.628e-16
64000.0 1.605e-16
81000.0 1.45e-16
100000.0 1.248e-16

>>> # Metadata for all datasets can be accessed with a for loop.
>>> for ds in pycoll.datasets.values():
>>>     print(ds.metadata)