import re
from typing import List
import pybtex.errors
from pybtex.database.input import bibtex
from tqdm import tqdm
pybtex.errors.set_strict_mode(False)
SNAPSHOT_NAME = "mp_time_summary.json"
DUMMY_SNAPSHOT_NAME = "mp_dummy_time_summary.json"
noble = ["He", "Ar", "Ne", "Kr", "Xe", "Og", "Rn"]
# fmt: off
radioactive = ["U", "Th", "Ra", "Pu", "Po", "Rn", "Cm", "At", "Bk", "Fr", "Ac", "Am", "Bh", "Cf", "Np", "Ts", "Tc", "Md", "Lr", "Fm", "Hs", "Mt", "No", "Pm", "Rf", "Sg", "Ds", "Cn", "Rg", "Lv", "Og", "Fl", "Nh", "Db", "Es", "Mc", "Pa", "Bi", "Cs"] # noqa: E501
# fmt: on
[docs]
def get_discovery_dict(references: List[dict]) -> List[dict]:
"""Get a dictionary containing earliest bib info for each MP entry.
Modified from source:
"How do I do a time-split of Materials Project entries? e.g. pre-2018 vs. post-2018"
https://matsci.org/t/42584/4?u=sgbaird, answer by @Joseph_Montoya, Materials Project Alumni
Parameters
----------
provenance_results : List[dict]
List of references results, e.g. taken from from the ``ProvenanceRester`` API
results (:func:`mp_api.materials.provenance`)
Returns
-------
discovery, List[dict]
Dictionary containing earliest bib info for each MP entry with keys: ``["year",
"authors", "num_authors"]``
Examples
--------
>>> with MPRester(api_key) as mpr:
... provenance_results = mpr.materials.provenance.search(num_sites=(1, 4), elements=["V"])
>>> discovery = get_discovery_dict(provenance_results)
[{'year': 1963, 'authors': ['Raub, E.', 'Fritzsche, W.'], 'num_authors': 2}, {'year': 1925, 'authors': ['Becker, K.', 'Ebert, F.'], 'num_authors': 2}, {'year': 1965, 'authors': ['Giessen, B.C.', 'Grant, N.J.'], 'num_authors': 2}, {'year': 1957, 'authors': ['Philip, T.V.', 'Beck, P.A.'], 'num_authors': 2}, {'year': 1963, 'authors': ['Darby, J.B.jr.'], 'num_authors': 1}, {'year': 1977, 'authors': ['Aksenova, T.V.', 'Kuprina, V.V.', 'Bernard, V.B.', 'Skolozdra, R.V.'], 'num_authors': 4}, {'year': 1964, 'authors': ['Maldonado, A.', 'Schubert, K.'], 'num_authors': 2}, {'year': 1962, 'authors': ['Darby, J.B.jr.', 'Lam, D.J.', 'Norton, L.J.', 'Downey, J.W.'], 'num_authors': 4}, {'year': 1925, 'authors': ['Becker, K.', 'Ebert, F.'], 'num_authors': 2}, {'year': 1959, 'authors': ['Dwight, A.E.'], 'num_authors': 1}] # noqa: E501
"""
discovery = []
for refs in tqdm(references):
parser = bibtex.Parser()
refs = "".join(refs)
refs = parser.parse_string(refs)
entries = refs.entries
entries_by_year = [
(int(entry.fields["year"]), entry)
for _, entry in entries.items()
if "year" in entry.fields and re.match(r"\d{4}", entry.fields["year"])
]
if entries_by_year:
entries_by_year = sorted(entries_by_year, key=lambda x: x[0])
first_report = {
"year": entries_by_year[0][0],
"authors": entries_by_year[0][1].persons["author"],
}
first_report["authors"] = [str(auth) for auth in first_report["authors"]]
first_report["num_authors"] = len(first_report["authors"])
discovery.append(first_report)
else:
discovery.append(dict(year=None, authors=None, num_authors=None))
return discovery
# def encode_dataframe(df):
# jsonpickle_pandas.register_handlers()
# return jsonpickle.encode(df)
# def decode_dataframe_from_string(string):
# jsonpickle_pandas.register_handlers()
# return jsonpickle.decode(string, classes=[Structure])
# %% Code graveyard