def predict_collision(self, other: "Particle") -> float:
"""
Returns the time in the future when self collides with the Particle given
Returns nan if there is no collision
"""
v = other.velocity - self.velocity
o = other.origin - self.origin
return quad_formula(
length(v)**2, 2 * dot(v, o),
length(o)**2 - (self.radius + other.radius)**2, True)
def onUnitCell(self, unitcell):
block = self._currentblock
av, bv, cv = unitcell.getCellVectors()
from numpy.linalg import norm as length
block['_cell_length_a'] = str(length(av))
block['_cell_length_b'] = str(length(bv))
block['_cell_length_c'] = str(length(cv))
block['_cell_angle_alpha'] = str(angle(bv, cv))
block['_cell_angle_beta'] = str(angle(cv, av))
block['_cell_angle_gamma'] = str(angle(av, bv))
block['_cell_volume'] = str(unitcell.getVolume())
id2site = unitcell._siteIds
labels = id2site.keys()
sites = id2site.values()
positions = [site.getPosition() for site in sites]
xs = [str(p[0]) for p in positions]
ys = [str(p[1]) for p in positions]
zs = [str(p[2]) for p in positions]
symbols = [site.getAtom().symbol for site in sites]
cols = [labels, xs, ys, zs, symbols]
block.AddCifItem((
[[
'_atom_site_label',
'_atom_site_fract_x',
'_atom_site_fract_y',
'_atom_site_fract_z',
'_atom_site_type_symbol',
]],
[cols],
))
return
def check_system(self) -> dict:
"""Returns a report of any errors in the system"""
out_of_bounds = [] # a list of particles outside of the bounds
intersecting = [
] # a list of pairs of particles which are intersecting
for particle, other in generate_pairs(self.particles):
if length(other.origin -
particle.origin) < other.radius + particle.radius:
intersecting.append((particle, other))
for particle in self.particles:
for i, bound in enumerate(self.bounds):
if particle.origin[i] > max(
bound) - particle.radius or particle.origin[i] < min(
bound) + particle.radius:
out_of_bounds.append(particle)
break
return {"out_of_bounds": out_of_bounds, "intersecting": intersecting}
def check_collision(self, other: "Particle") -> bool:
"""Returns True if this Particle is intersecting another particle"""
return length(self.origin - other.origin) <= self.radius + other.radius
def normalized(vec: np.ndarray) -> np.ndarray:
"""Returns the unit/normalized vector of the vector given"""
return vec / length(vec)
def fun_jac(z):
## Squared reliability index
fun = z.dot(z)
jac = 2 * z * length(z)
return (fun, jac)
def eval_form_pma(
model,
betas=None,
cons=None,
df_corr=None,
df_det=None,
append=True,
tol=1e-3,
n_maxiter=25,
n_restart=1,
verbose=False,
):
r"""Tail quantile via FORM PMA
Approximate the desired tail quantiles using the performance measure approach (PMA) of the first-order reliability method (FORM) [1]. Select limit states to minimize at desired quantile with `betas`. Provide confidence levels `cons` and estimator covariance `df_corr` to compute with margin in beta [2].
Note that under the performance measure approach, the optimized limit state value `g` is sought to be non-negative $g \geq 0$. This is usually included as a constraint in optimization, which can be accomplished in by using ``gr.eval_form_pnd()` *within* a model definition---see the Examples below for more details.
Args:
model (gr.Model): Model to analyze
betas (dict): Target reliability indices;
key = limit state name; must be in model.out
value = reliability index; beta = Phi^{-1}(reliability)
cons (dict or None): Target confidence levels;
key = limit state name; must be in model.out
value = confidence level, \in (0, 1)
df_corr (DataFrame or None): Sampling distribution covariance entries; parameters with no information assumed to be known exactly.
df_det (DataFrame): Deterministic levels for evaluation; use "nom" for nominal deterministic levels.
n_maxiter (int): Maximum iterations for each optimization run
n_restart (int): Number of restarts (== number of optimization runs)
append (bool): Append MPP results for random values?
verbose (bool): Print optimization results?
Returns:
DataFrame: Results of MPP search
Notes:
Since FORM PMA relies on optimization over the limit state, it is often beneficial to scale your limit state to keep values near unity.
References:
Tu, Choi, and Park, "A new study on reliability-based design optimization," Journal of Mechanical Design, 1999
del Rosario, Fenrich, and Iaccarino, "Fast precision margin with the first-order reliability method," AIAA Journal, 2019
Examples::
import grama as gr
from grama.models import make_cantilever_beam
md_beam = make_cantilever_beam()
## Evaluate the reliability of specified designs
(
md_beam
>> gr.ev_form_pma(
# Specify target reliability
betas=dict(g_stress=3, g_disp=3),
# Analyze three different thicknesses
df_det=gr.df_make(t=[2, 3, 4], w=3)
)
)
## Build a nested model for optimization under uncertainty
md_opt = (
gr.Model("Beam Optimization")
>> gr.cp_vec_function(
fun=lambda df: gr.df_make(c_area=df.w * df.t),
var=["w", "t"],
out=["c_area"],
name="Area objective",
)
>> gr.cp_vec_function(
fun=lambda df: gr.eval_form_pma(
md_beam,
betas=dict(g_stress=3, g_disp=3),
df_det=df,
append=False,
)
var=["w", "t"],
out=["g_stress", "g_disp"],
name="Reliability constraints",
)
>> gr.cp_bounds(w=(2, 4), t=(2, 4))
)
# Run the optimization
(
md_opt
>> gr.ev_min(
out_min="c_area",
out_geq=["g_stress", "g_disp"],
)
)
"""
## Check invariants
invariants_eval_model(model)
invariants_eval_df(df_corr, arg_name="df_corr", acc_none=True)
invariants_eval_df(df_det, arg_name="df_det", valid_str=["nom"])
if betas is None:
raise ValueError(
"Must provide `betas` keyword argument to define reliability targets"
)
if not set(betas.keys()).issubset(set(model.out)):
raise ValueError("betas.keys() must be subset of model.out")
if not cons is None:
if not (set(cons.keys()) == set(betas.keys())):
raise ValueError("cons.keys() must be same as betas.keys()")
else:
if df_corr is None:
raise ValueError("Must provide df_corr is using cons")
raise NotImplementedError
df_det = model.var_outer(
DataFrame(data=zeros((1, model.n_var_rand)), columns=model.var_rand),
df_det=df_det,
)
df_det = df_det[model.var_det]
df_return = DataFrame()
for ind in range(df_det.shape[0]):
## Loop over objectives
for key in betas.keys():
## Temp dataframe
df_inner = df_det.iloc[[ind]].reset_index(drop=True)
## Construct lambdas
def objective(z):
## Transform: standard normal-to-random variable
df_norm = DataFrame(data=[z], columns=model.var_rand)
df_rand = model.norm2rand(df_norm)
df = model.var_outer(df_rand, df_det=df_inner)
df_res = eval_df(model, df=df)
g = df_res[key].iloc[0]
# return (g, jac)
return g
def con_beta(z):
return z.dot(z) - (betas[key])**2
## Use conservative direction for initial guess
signs = array(
[model.density.marginals[k].sign for k in model.var_rand])
if length(signs) > 0:
z0 = betas[key] * signs / length(signs)
else:
z0 = betas[key] * ones(model.n_var_rand) / sqrt(
model.n_var_rand)
## Minimize
res_all = []
for jnd in range(n_restart):
res = minimize(
objective,
z0,
args=(),
method="SLSQP",
jac=False,
tol=tol,
options={
"maxiter": n_maxiter,
"disp": False
},
constraints=[{
"type": "eq",
"fun": con_beta
}],
)
# Append only a successful result
if res["status"] == 0:
res_all.append(res)
# Set a random start; repeat
z0 = multivariate_normal([0] * model.n_var_rand,
eye(model.n_var_rand))
z0 = z0 / length(z0) * betas[key]
# Choose value among restarts
n_iter_total = sum([res_all[i].nit for i in range(len(res_all))])
if len(res_all) > 0:
i_star = argmin([res.fun for res in res_all])
x_star = res_all[i_star].x
fun_star = res_all[i_star].fun
if verbose:
print("out = {}: Optimization successful".format(key))
print("n_iter = {}".format(res_all[i_star].nit))
print("n_iter_total = {}".format(n_iter_total))
else:
## WARNING
x_star = [NaN] * model.n_var_rand
fun_star = NaN
if verbose:
print("out = {}: Optimization unsuccessful".format(key))
print("n_iter = {}".format(res_all[i_star].nit))
print("n_iter_total = {}".format(n_iter_total))
## Extract results
if append:
df_inner = concat(
(
df_inner,
model.norm2rand(
DataFrame(data=[x_star], columns=model.var_rand)),
),
axis=1,
sort=False,
)
df_inner[key] = [fun_star]
df_return = concat((df_return, df_inner), axis=0, sort=False)
if not append:
df_return = (df_return.groupby(model.var_det).agg(
{s: max
for s in betas.keys()}).reset_index())
return df_return
def eval_form_ria(
model,
limits=None,
cons=None,
df_corr=None,
df_det=None,
append=True,
tol=1e-3,
n_maxiter=25,
n_restart=1,
verbose=False,
):
r"""Tail reliability via FORM RIA
Approximate the desired tail probability using the reliability index approach (RIA) of the first-order reliability method (FORM) [1]. Select limit states to analyze with list input `limits`. Provide confidence levels `cons` and estimator covariance `df_corr` to compute with margin in beta [2].
Note that the reliability index approach (RIA) is generally less stable than the performance measure approach (PMA). Consider using ``gr.eval_form_pma()`` instead, particularly when using FORM to optimize a design.
Args:
model (gr.Model): Model to analyze
limits (list): Target limit states; must be in model.out; limit state assumed to be critical at g == 0.
cons (dict or None): Target confidence levels;
key = limit state name; must be in model.out
value = confidence level, \in (0, 1)
df_corr (DataFrame or None): Sampling distribution covariance entries; parameters with no information assumed to be known exactly.
df_det (DataFrame): Deterministic levels for evaluation; use "nom" for nominal deterministic levels.
n_maxiter (int): Maximum iterations for each optimization run
n_restart (int): Number of restarts (== number of optimization runs)
append (bool): Append MPP results for random values?
verbose (bool): Print optimization results?
Returns:
DataFrame: Results of MPP search
Notes:
Since FORM RIA relies on optimization over the limit state, it is often beneficial to scale your limit state to keep values near unity.
References:
[1] Tu, Choi, and Park, "A new study on reliability-based design optimization," Journal of Mechanical Design, 1999
[2] del Rosario, Fenrich, and Iaccarino, "Fast precision margin with the first-order reliability method," AIAA Journal, 2019
Examples::
import grama as gr
from grama.models import make_cantilever_beam
md_beam = make_cantilever_beam()
## Evaluate the reliability of specified designs
(
md_beam
>> gr.ev_form_ria(
# Specify limit states to analyze
limits=("g_stress", "g_disp"),
# Analyze three different thicknesses
df_det=gr.df_make(t=[2, 3, 4], w=3)
)
)
"""
## Check invariants
invariants_eval_model(model)
invariants_eval_df(df_corr, arg_name="df_corr", acc_none=True)
invariants_eval_df(df_det, arg_name="df_det", valid_str=["nom"])
if limits is None:
raise ValueError(
"Must provide `limits` keyword argument to define reliability targets"
)
if not set(limits).issubset(set(model.out)):
raise ValueError("`limits` must be subset of model.out")
if not cons is None:
if not (set(cons.keys()) == set(limits)):
raise ValueError("cons.keys() must be same as limits")
else:
if df_corr is None:
raise ValueError("Must provide df_corr is using cons")
raise NotImplementedError
df_det = model.var_outer(
DataFrame(data=zeros((1, model.n_var_rand)), columns=model.var_rand),
df_det=df_det,
)
df_det = df_det[model.var_det]
# df_return = DataFrame(columns=model.var_rand + model.var_det + limits)
df_return = DataFrame()
for ind in range(df_det.shape[0]):
## Loop over objectives
for key in limits:
## Temp dataframe
df_inner = df_det.iloc[[ind]].reset_index(drop=True)
## Construct lambdas
def fun_jac(z):
## Squared reliability index
fun = z.dot(z)
jac = 2 * z * length(z)
return (fun, jac)
def con_limit(z):
## Transform: standard normal-to-random variable
df_norm = DataFrame(data=[z], columns=model.var_rand)
df_rand = model.norm2rand(df_norm)
df = model.var_outer(df_rand, df_det=df_inner)
## Eval limit state
df_res = eval_df(model, df=df)
g = df_res[key].iloc[0]
return g
## Use conservative direction for initial guess
signs = array(
[model.density.marginals[k].sign for k in model.var_rand])
if length(signs) > 0:
z0 = signs / length(signs)
else:
z0 = ones(model.n_var_rand) / sqrt(model.n_var_rand)
## Minimize
res_all = []
for jnd in range(n_restart):
res = minimize(
fun_jac,
z0,
args=(),
method="SLSQP",
jac=True,
tol=tol,
options={
"maxiter": n_maxiter,
"disp": False
},
constraints=[{
"type": "eq",
"fun": con_limit
}],
)
# Append only a successful result
if res["status"] == 0:
res_all.append(res)
# Set a random start; repeat
z0 = multivariate_normal([0] * model.n_var_rand,
eye(model.n_var_rand))
z0 = z0 / length(z0)
# Choose value among restarts
n_iter_total = sum([res_all[i].nit for i in range(len(res_all))])
if len(res_all) > 0:
i_star = argmin([res.fun for res in res_all])
x_star = res_all[i_star].x
fun_star = sqrt(res_all[i_star].fun)
if verbose:
print("out = {}: Optimization successful".format(key))
print("n_iter = {}".format(res_all[i_star].nit))
print("n_iter_total = {}".format(n_iter_total))
else:
## WARNING
x_star = [NaN] * model.n_var_rand
fun_star = NaN
if verbose:
print("out = {}: Optimization unsuccessful".format(key))
print("n_iter = {}".format(res_all[i_star].nit))
print("n_iter_total = {}".format(n_iter_total))
## Extract results
if append:
df_inner = concat(
(
df_inner,
model.norm2rand(
DataFrame(data=[x_star], columns=model.var_rand)),
),
axis=1,
sort=False,
)
df_inner["beta_" + key] = [fun_star]
df_return = concat((df_return, df_inner), axis=0, sort=False)
if not append:
df_return = (
df_return.groupby(model.var_det) \
.agg({"beta_" + s: max for s in limits}).reset_index()
)
return df_return
def eval_form_pma(
model,
betas=None,
cons=None,
df_corr=None,
df_det=None,
append=True,
tol=1e-3,
maxiter=25,
nrestart=1,
):
r"""Tail quantile via FORM PMA
Approximate the desired tail quantiles using the performance measure
approach (PMA) of the first-order reliability method (FORM) [1]. Select
limit states to minimize at desired quantile with `betas`. Provide
confidence levels `cons` and estimator covariance `df_corr` to compute with
margin in beta [2].
Args:
model (gr.Model): Model to analyze
betas (dict): Target reliability indices;
key = limit state name; must be in model.out
value = reliability index; beta = Phi^{-1}(reliability)
cons (dict or None): Target confidence levels;
key = limit state name; must be in model.out
value = confidence level, \in (0, 1)
df_corr (DataFrame or None): Sampling distribution covariance entries;
parameters with no information assumed to be known exactly.
df_det (DataFrame): Deterministic levels for evaluation; use "nom"
for nominal deterministic levels.
append (bool): Append MPP results for random values?
Returns:
DataFrame: Results of MPP search
Notes:
- Since FORM PMA relies on optimization over the limit state, it is
often beneficial to scale your limit state to keep values near unity.
References:
- [1] Tu, Choi, and Park, "A new study on reliability-based design optimization," Journal of Mechanical Design, 1999
- [2] del Rosario, Fenrich, and Iaccarino, "Fast precision margin with the first-order reliability method," AIAA Journal, 2019
"""
## Check invariants
if betas is None:
raise ValueError(
"Must provide `betas` keyword argument to define reliability targets"
)
if not set(betas.keys()).issubset(set(model.out)):
raise ValueError("betas.keys() must be subset of model.out")
if not cons is None:
if not (set(cons.keys()) == set(betas.keys())):
raise ValueError("cons.keys() must be same as betas.keys()")
else:
if df_corr is None:
raise ValueError("Must provide df_corr is using cons")
raise NotImplementedError
df_det = model.var_outer(
DataFrame(data=zeros((1, model.n_var_rand)), columns=model.var_rand),
df_det=df_det,
)
df_det = df_det[model.var_det]
# df_return = DataFrame(columns=model.var_rand + model.var_det + list(betas.keys()))
df_return = DataFrame()
for ind in range(df_det.shape[0]):
## Loop over objectives
for key in betas.keys():
## Temp dataframe
df_inner = df_det.iloc[[ind]].reset_index(drop=True)
## Construct lambdas
def objective(z):
## Transform: standard normal-to-random variable
df_norm = DataFrame(data=[z], columns=model.var_rand)
df_rand = model.norm2rand(df_norm)
df = model.var_outer(df_rand, df_det=df_inner)
df_res = gr.eval_df(model, df=df)
g = df_res[key].iloc[0]
# return (g, jac)
return g
def con_beta(z):
return z.dot(z) - (betas[key]) ** 2
## Use conservative direction for initial guess
signs = array([
model.density.marginals[k].sign for k in model.var_rand
])
if length(signs) > 0:
z0 = betas[key] * signs / length(signs)
else:
z0 = betas[key] * ones(model.n_var_rand) / sqrt(model.n_var_rand)
## Minimize
res_all = []
for jnd in range(nrestart):
res = minimize(
objective,
z0,
args=(),
method="SLSQP",
jac=False,
tol=tol,
options={"maxiter": maxiter, "disp": False},
constraints=[{"type": "eq", "fun": con_beta}],
)
# Append only a successful result
if res["status"] == 0:
res_all.append(res)
# Set a random start; repeat
z0 = multivariate_normal([0] * model.n_var_rand, eye(model.n_var_rand))
z0 = z0 / length(z0) * betas[key]
# Choose value among restarts
if len(res_all) > 0:
i_star = argmin([res.fun for res in res_all])
x_star = res_all[i_star].x
fun_star = res_all[i_star].fun
else:
## WARNING
x_star = [NaN] * model.n_var_rand
fun_star = NaN
## Extract results
if append:
df_inner = concat(
(df_inner, DataFrame(data=[x_star], columns=model.var_rand)),
axis=1,
sort=False,
)
df_inner[key] = [fun_star]
df_return = concat((df_return, df_inner), axis=0, sort=False)
return df_return
