def update_bimet_result(metals, shape, num_atoms,
diameter, n_metal1,
CE, ordering, EE=None, nanop=None,
allow_insert=True, ensure_ce_min=True):
"""
Takes raw data and inserts the BimetallicResults datatable
- will update CE, EE, and ordering of an entry if necessary
Returns:
- BimetallicResults entry after successfully updating
"""
res = get_bimet_result(metals=metals,
shape=shape,
num_atoms=num_atoms,
n_metal1=n_metal1)
if res:
# if ensure_ce_min, do not overwrite a minimum CE structure
# with new data
if ensure_ce_min and res.CE < CE:
return
res.CE = CE
res.ordering = ordering
res.EE = EE
elif not allow_insert:
return
else:
metal1, metal2 = db_utils.sort_2metals(metals)
res = tbl.BimetallicResults(
metal1=metal1,
metal2=metal2,
shape=shape,
num_atoms=num_atoms,
diameter=diameter,
n_metal1=n_metal1,
n_metal2=num_atoms - n_metal1,
CE=CE,
ordering=ordering,
EE=EE)
if nanop:
nanop.bimetallic_results.append(res)
session.add(nanop)
# commit changes
session.add(res)
db_utils.commit_changes(session, raise_exception=True)
return res
def get_bimet_result(metals=None, shape=None, num_atoms=None, num_shells=None,
n_metal1=None, only_bimet=False,
lim=None, return_query=False, custom_filter=None,
return_list=False):
"""
Returns BimetallicResults entry that matches criteria
- if no criteria given, all data (up to <lim> amount)
returned
Kargs:
- metals (str || iterable): two metal elements
- shape (str): shape of NP
- num_atoms (int): number of atoms in NP
- num_shells (int): number of shells used to build NP
from structure_gen module
- n_metal1 (int): number of metal1 atoms in NP
- lim (int): max number of entries returned
(default: None = no limit)
- return_query (bool): if True, return query and
not results
- return_list (bool): if True, function always returns list, else
will only return list if (# results) != 1
Returns:
- (BimetallicResults)(s) if match is found else []
"""
if not num_atoms and num_shells:
if shape:
num_atoms = get_shell2num(shape, num_shells)
# if shape not defined, assume icosahedron shell->atoms mapping
else:
num_atoms = get_shell2num('icosahedron', num_shells)
if only_bimet:
only_bimet = db.and_(tbl.BimetallicResults.n_metal1 != 0,
tbl.BimetallicResults.n_metal2 != 0)
custom_filter = only_bimet
# get sorted metals
metal1, metal2 = db_utils.sort_2metals(metals)
return get_entry(tbl.BimetallicResults, metal1=metal1, metal2=metal2,
shape=shape, num_atoms=num_atoms, n_metal1=n_metal1,
lim=lim, return_query=return_query,
custom_filter=custom_filter, return_list=return_list)
def get_bimet_log(metals=None, shape=None, date=None, lim=None,
return_query=False):
"""
Returns BimetallicLog entry that matches criteria
- if no criteria given, all data (up to <lim> amount)
returned
Kargs:
- metals (str || iterable): two metal elements
- shape (str): shape of NP
- date (datetime.datetime): GA batch run completion time
- lim (int): max number of entries returned
(default: None = no limit)
- return_query (bool): if True, return query and
not results
Returns:
- (BimetallicResults)(s) if match is found else (None)
"""
# get sorted metals
metal1, metal2 = db_utils.sort_2metals(metals)
return get_entry(tbl.BimetallicLog, metal1=metal1, metal2=metal2,
shape=shape, date=date, lim=lim,
return_query=return_query)
def build_srf_plot(metals, shape, T=None):
"""
Creates a 3D surface plot from NP SQL database
- plots Size vs. Shape vs. Excess Energy (EE)
- can also use configurational entropy of mixing
to plot Size vs. Shape vs. delG = EE - T * delS(mix)
Args:
- metals (string || iterable): string(s) containing two metal elements
- shape (string): shape of the NP
KArgs:
T (float): if temperature is given, plot delG(mix)
(i.e. include configurational entropy)
(Default: None)
Returns:
- (plt.figure): figure of 3D surface plot
"""
metal1, metal2 = db_utils.sort_2metals(metals)
# build pd.DataFrame of all results that match criteria
runs = session.query(tbl.BimetallicResults.diameter,
(tbl.BimetallicResults.n_metal2 /
db.cast(tbl.BimetallicResults.num_atoms,
db.Float))
.label('comps'),
tbl.BimetallicResults.num_atoms,
tbl.BimetallicResults.EE) \
.filter(db.and_(tbl.BimetallicResults.metal1 == metal1,
tbl.BimetallicResults.metal2 == metal2,
tbl.BimetallicResults.shape == shape)) \
.statement
df = pd.read_sql(runs, session.bind)
# three parameters to plot
size = df.num_atoms.values
comps = df.comps.values
ees = df.EE.values
if T is not None:
# k_b T [eV] = (25.7 mEV at 298 K)
kt = 25.7E-3 * (T / 298.)
del_s = comps * np.ma.log(comps).filled(0) + \
(1 - comps) * np.ma.log(1 - comps).filled(0)
del_s *= -kt
ees -= del_s
# plots surface as heat map with warmer colors for larger EEs
colormap = plt.get_cmap('coolwarm')
normalize = matplotlib.colors.Normalize(vmin=ees.min(), vmax=-ees.min())
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
try:
ax.plot_trisurf(comps, size, ees,
cmap=colormap, norm=normalize, alpha=0.5)
except RuntimeError:
# if not enough data to create surface, make a scatter plot instead
ax.scatter3D(comps, size, ees,
cmap=colormap, norm=normalize)
# plot mins at each step
add_legend = True
for s in np.unique(size):
sizes = np.where(size == s)[0]
mini = np.where(ees == ees[sizes].min())[0][0]
color = 'orange' if ees[sizes].min() == ees.min() else 'pink'
ax.scatter3D(comps[mini], s, ees[mini], edgecolor='k',
color=color, s=50)
ax.set_xlabel('\n\n$X_{%s}$' % metal2)
ax.set_ylabel('\n\n$\\rm N_{atoms}$')
if T is not None:
ax.set_zlabel('\n\n$\\rm \\Delta$G (eV)')
ax.set_title('%iK\n%s %s %s' % (T, metal1, metal2, shape.title()))
else:
ax.set_zlabel('\n\nEE (eV)')
ax.set_title('%s %s %s' % (metal1, metal2, shape.title()))
return fig, ax
def build_new_structs_plot(metal_opts, shape_opts, pct=False,
cutoff_date=None):
"""
Uses BimetallicLog to create 2D line plot of
new structures found vs. datetime
Args:
- metal_opts (list): list of metal options
- shape_opts (list): list of shape options
Kargs:
- pct (bool): if True, y-axis = % new structures
else, y-axis = number of new structures
- cutoff_date (Datetime.Datetime): if given, will filter out runs
older than <cutoff_date>
Returns:
- (plt.Figure): 2D line plot object
"""
if isinstance(metal_opts, str):
metal_opts = [metal_opts]
if isinstance(shape_opts, str):
shape_opts = [shape_opts]
# if cutoff_date, create custom_filter
if isinstance(cutoff_date, datetime.datetime):
custom_filter = tbl.BimetallicLog.date >= cutoff_date
else:
custom_filter = None
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',
'#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']
fig, ax = plt.subplots()
i = 0
tot_lines = len(metal_opts) * len(shape_opts)
for j, m in enumerate(metal_opts):
metal1, metal2 = db_utils.sort_2metals(m)
for point, shape in zip(['o', 'x', '^', 's'], shape_opts):
# use abbreviated names for shapes
lbl_shape = shape.upper()[:3]
# pd.DataFrame of bimetallic log data
df = build_df(tbl.BimetallicLog, metals=m, shape=shape,
custom_filter=custom_filter)
x = df.date.values
label = '%s%s - %s' % (metal1, metal2, lbl_shape)
y = df.new_min_structs.values
if pct:
y = y / df.tot_structs.values[0]
ax.plot(x, y, '%s-' % point, label=label,
color=colors[j])
i += 1
if pct:
ax.yaxis.set_major_formatter(
matplotlib.ticker.FuncFormatter('{0:.0%}'.format))
ax.set_ylim(0, 1)
ax.set_ylabel('Percent Count')
else:
ax.set_ylabel('Total Count')
ax.legend(ncol=len(metal_opts))
ax.set_title('New Minimum Structures Found')
ax.set_xlabel('Batch Run Date')
# (month/day) x axis tick labels
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%m/%d'))
fig.tight_layout()
return fig