def get_data_sqla(projections, sliders_dict, quantities, plot_info):
"""Query database using SQLAlchemy.
Note: For efficiency, this uses the the sqlalchemy.sql interface which does
not go via the (more convenient) ORM.
"""
from import_db import automap_table, engine
from sqlalchemy.sql import select, and_
Table = automap_table(engine)
selections = []
for label in projections:
selections.append(getattr(Table, label))
filters = []
for k, v in sliders_dict.items():
if isinstance(v, RangeSlider):
if not v.value == quantities[k]['range']:
f = getattr(Table, k).between(v.value[0], v.value[1])
filters.append(f)
elif isinstance(v, CheckboxButtonGroup):
if not len(v.active) == len(v.labels):
f = getattr(Table, k).in_([v.tags[i] for i in v.active])
filters.append(f)
s = select(selections).where(and_(*filters))
#s = select(selections)
results = engine.connect().execute(s).fetchall()
nresults = len(results)
if not results:
plot_info.text = "No matching Complexes found."
return data_empty
elif nresults > max_points:
results = results[:max_points]
plot_info.text = "{} Complexes found.\nPlotting {}...".format(
nresults, max_points)
else:
plot_info.text = "{} Complexes found.\nPlotting {}...".format(
nresults, nresults)
# x,y position
x, y, clrs, names, filenames = zip(*results)
x = list(map(float, x))
y = list(map(float, y))
if projections[2] == 'bond_type':
#clrs = map(lambda clr: bondtypes.index(clr), clrs)
clrs = list(map(str, clrs))
else:
clrs = list(map(float, clrs))
return dict(x=x, y=y, filename=filenames, color=clrs, name=names)
def get_sqlite_data(name, plot_info):
"""Query the sqlite database"""
from import_db import automap_table, engine
from sqlalchemy.orm import sessionmaker
# configure Session class with desired options
Session = sessionmaker(bind=engine)
session = Session()
Table = automap_table(engine)
query = session.query(Table).filter_by(name=str(name))
nresults = query.count()
if nresults == 0:
plot_info.text = "No matching COF found."
return None
return query.one()
def get_data_sqla(projections, sliders_dict, quantities, plot_info):
"""Query database using SQLAlchemy.
Note: For efficiency, this uses the the sqlalchemy.sql interface which does
not go via the (more convenient) ORM.
"""
from import_db import automap_table, engine
from sqlalchemy.sql import select, and_
Table = automap_table(engine)
selections = []
for label in projections:
selections.append(getattr(Table, label))
filters = []
for k, v in sliders_dict.items():
if isinstance(v, RangeSlider):
if not v.value == quantities[k]["range"]:
f = getattr(Table, k).between(v.value[0], v.value[1])
filters.append(f)
elif isinstance(v, CheckboxButtonGroup):
if not len(v.active) == len(v.labels):
f = getattr(Table, k).in_([v.tags[i] for i in v.active])
filters.append(f)
s = select(selections).where(and_(*filters))
results = engine.connect().execute(s).fetchall()
nresults = len(results)
if not results:
plot_info.text = "No matching structure found."
return data_empty
elif nresults > max_points:
results = results[:max_points]
plot_info.text = "{} frameworks found.\nPlotting {}...".format(
nresults, max_points)
else:
plot_info.text = "{} frameworks found.\nPlotting {}...".format(
nresults, nresults)
# x,y position
x, y, clrs, sampled, names, filenames = zip(*results)
x = list(map(float, x))
y = list(map(float, y))
sampled_ = [20 if s == "sampled" else 10 for s in sampled]
lw = [2 if s == "sampled" else 0.1 for s in sampled]
if projections[2] == "group":
# clrs = map(lambda clr: bondtypes.index(clr), clrs)
clrs = list(clrs)
# df = pd.DataFrame({
# 'x': x,
# 'y': y,
# 'filename': filenames,
# 'name': names,
# 'color': clrs
# })
#
# my_own_order = ['COFs', 'MOFs', 'zeolites', 'sampled']
# my_own_order_dict = {key: i for i, key in enumerate(my_own_order)}
# inv_my_own_order_dict = {v: k for k, v in my_own_order_dict.items()}
# df['color_mapped'] = df['color'].map(my_own_order_dict)
# df.sort_values(by=['color_mapped'], inplace=True)
# x = df['x'].astype(float).to_list()
# y = df['y'].astype(float).to_list()
# filenames = df['filename'].to_list()
# clrs = df['color'].to_list()
# names = df['name'].to_list()
else:
clrs = list(map(float, clrs))
return dict(x=x,
y=y,
filename=filenames,
color=clrs,
sampled=sampled_,
name=names,
lw=lw)
def get_data_sqla(projections, sliders_dict, quantities, plot_info):
"""Query database using SQLAlchemy.
Note: For efficiency, this uses the the sqlalchemy.sql interface which does
not go via the (more convenient) ORM.
"""
from import_db import automap_table, engine
from sqlalchemy.sql import select, and_
Table = automap_table(engine, table_name='mofs')
selections = []
for label in projections:
selections.append(getattr(Table, label))
filters = []
for k, v in sliders_dict.items():
if isinstance(v, RangeSlider):
if not v.value == quantities[k]['range']:
f = getattr(Table, k).between(v.value[0], v.value[1])
filters.append(f)
elif isinstance(v, CheckboxButtonGroup):
if not len(v.active) == len(v.labels):
f = getattr(Table, k).in_([v.tags[i] for i in v.active])
filters.append(f)
# Leopold: Some structures have void_fraction = -1
# Pete: Likely, some structures do not have measurable pores using zeo++.
# This doesn't necessarily mean 0 uptake however, as zeo++ uses hard
# spheres to measure pore space, while a lennard-jones function governs the
# adsorption measured by GCMC.
# The selectivity ratio would be high in these cases, as even slight CO2
# adsorption but significantly less N2 will yield a high selectivity. I
# think I filtered these cases out of the plot, as they would be
# uninteresting from a materials design point of view.
filters.append(Table.void_fraction >= 0)
s = select(selections).where(and_(*filters))
results = engine.connect().execute(s).fetchall()
nresults = len(results)
if not results:
plot_info.text = "No matching MOFs found."
return data_empty
elif nresults > max_points:
results = results[:max_points]
plot_info.text = "{} MOFs found.\nPlotting {}...".format(
nresults, max_points)
else:
plot_info.text = "{} MOFs found.\nPlotting {}...".format(
nresults, nresults)
# x,y position
x, y, clrs, names, filenames = zip(*results)
x = list(map(float, x))
y = list(map(float, y))
if projections[2] == 'bond_type':
#clrs = map(lambda clr: bondtypes.index(clr), clrs)
clrs = list(map(str, clrs))
else:
clrs = list(map(float, clrs))
return dict(x=x, y=y, filename=filenames, color=clrs, name=names)
