from mendeleev import Element, get_session
# I had to follow the instruction given in the comments in this program unfortunately.
# I would have gotten only the most necessary ions and the required data! -_-
session = get_session()
#Henter ut ione-radius-data fra mendeleev
ans = session.query(Element).filter(Element.ionic_radii != None).order_by(
Element.atomic_number)
for a in ans:
print(a.symbol, a.name, a.ionic_radii)
element_symbols_needed = ['Mg', 'Ca', 'Ba', 'Pb']
#skal fylle data i en dictionarie
#skriv din egen kode her
ionic_dict = {a.symbol: a.ionic_radii for a in ans}
#leser ut ioniske radiuser for allem elementer som har ladning = 2+ og koordinering = VI
#skriv din egen kode her
filtered_ionic_dict = {}
for key in ionic_dict.keys():
for ionic_radius in ionic_dict[key]:
if (ionic_radius.charge == 2 and ionic_radius.coordination == 'VI'):
filtered_ionic_dict[key] = ionic_radius
#printer ut for de 4 elementene vi trenger data for - sjekket med boka at dette stemmer
#skriv din egen kode her
necessary_ionic_dict = {
k: v
for k, v in filtered_ionic_dict.items() if k in element_symbols_needed
}
def session():
return get_session()
def get_neutral_data():
'''
Get extensive set of data from multiple database tables as pandas.DataFrame
'''
elements = get_table('elements')
series = get_table('series')
groups = get_table('groups')
elements = pd.merge(elements,
series,
left_on='series_id',
right_on='id',
how='left',
suffixes=('', '_series'))
elements = pd.merge(elements,
groups,
left_on='group_id',
right_on='group_id',
how='left',
suffixes=('', '_group'))
elements.rename(columns={'color': 'series_colors'}, inplace=True)
en_scales = [
'allred-rochow', 'cottrell-sutton', 'gordy', 'martynov-batsanov',
'mulliken', 'nagle', 'sanderson'
]
for scale in en_scales:
elements['en_' + scale] = [
element(row.symbol).electronegativity(scale=scale)
for i, row in elements.iterrows()
]
for attr in ['hardness', 'softness']:
elements[attr] = [
getattr(element(row.symbol), attr)()
for i, row in elements.iterrows()
]
elements['mass'] = [
element(row.symbol).mass_str() for i, row in elements.iterrows()
]
elements.loc[:, 'zeff_slater'] = elements.apply(
lambda x: get_zeff(x['atomic_number'], method='slater'), axis=1)
elements.loc[:, 'zeff_clementi'] = elements.apply(
lambda x: get_zeff(x['atomic_number'], method='clementi'), axis=1)
session = get_session()
engine = get_engine()
query = session.query(IonizationEnergy).\
filter(IonizationEnergy.degree == 1).\
filter(IonizationEnergy.atomic_number.in_(list(range(1, 119))))
out = pd.read_sql_query(query.statement.compile(dialect=sqlite.dialect()),
engine)
out = out[['atomic_number', 'energy']]
out.columns = ['atomic_number', 'ionization_energy']
elements = pd.merge(elements, out, on='atomic_number', how='left')
return elements
def get_neutral_data():
"""
Get extensive set of data from multiple database tables as pandas.DataFrame
"""
elements = get_table("elements")
series = get_table("series")
groups = get_table("groups")
elements = pd.merge(
elements,
series,
left_on="series_id",
right_on="id",
how="left",
suffixes=("", "_series"),
)
elements = pd.merge(
elements,
groups,
left_on="group_id",
right_on="group_id",
how="left",
suffixes=("", "_group"),
)
elements.rename(columns={"color": "series_colors"}, inplace=True)
en_scales = [
"allred-rochow",
"cottrell-sutton",
"gordy",
"martynov-batsanov",
"mulliken",
"nagle",
"sanderson",
]
for scale in en_scales:
elements["en_" + scale] = [
element(row.symbol).electronegativity(scale=scale)
for i, row in elements.iterrows()
]
for attr in ["hardness", "softness"]:
elements[attr] = [
getattr(element(row.symbol), attr)()
for i, row in elements.iterrows()
]
elements["mass"] = [
element(row.symbol).mass_str() for i, row in elements.iterrows()
]
elements.loc[:, "zeff_slater"] = elements.apply(
lambda x: get_zeff(x["atomic_number"], method="slater"), axis=1)
elements.loc[:, "zeff_clementi"] = elements.apply(
lambda x: get_zeff(x["atomic_number"], method="clementi"), axis=1)
session = get_session()
engine = get_engine()
query = (session.query(IonizationEnergy).filter(
IonizationEnergy.degree == 1).filter(
IonizationEnergy.atomic_number.in_(list(range(1, 119)))))
out = pd.read_sql_query(query.statement.compile(dialect=sqlite.dialect()),
engine)
out = out[["atomic_number", "energy"]]
out.columns = ["atomic_number", "ionization_energy"]
elements = pd.merge(elements, out, on="atomic_number", how="left")
return elements