def spline_derivatives(landscape,n_bins=100):
"""
:param landscape: original landscape
:param n_bins: number of bins for fitting
:return: tuple of (spline fit to A_z, split fit to A_dot, fit to A_ddot
"""
z = landscape.z
A_z = landscape.A_z
sort_idx = np.argsort(z)
z_sort = z[sort_idx]
A_sort = A_z[sort_idx]
knots = np.linspace(min(z_sort),max(z_sort),endpoint=True,num=n_bins)
spline_A_z = LSQUnivariateSpline(x=z_sort,y=A_sort,k=3,t=knots[1:-1])
A_dot_spline_z = spline_A_z.derivative(1)(z)
A_ddot_spline_z = spline_A_z.derivative(2)(z)
return spline_A_z,A_dot_spline_z,A_ddot_spline_z
def spline_derivatives(landscape, n_bins=100):
"""
:param landscape: original landscape
:param n_bins: number of bins for fitting
:return: tuple of (spline fit to A_z, split fit to A_dot, fit to A_ddot
"""
z = landscape.z
A_z = landscape.A_z
sort_idx = np.argsort(z)
z_sort = z[sort_idx]
A_sort = A_z[sort_idx]
knots = np.linspace(min(z_sort), max(z_sort), endpoint=True, num=n_bins)
spline_A_z = LSQUnivariateSpline(x=z_sort, y=A_sort, k=3, t=knots[1:-1])
A_dot_spline_z = spline_A_z.derivative(1)(z)
A_ddot_spline_z = spline_A_z.derivative(2)(z)
return spline_A_z, A_dot_spline_z, A_ddot_spline_z
class dataSmoother:
def __init__(self, file, knots=[]):
self.filename = file
with open(file, "r") as f:
inData = genfromtxt(f, float, comments='#').T
self.rho = inData[0]
self.orig_data = inData[1]
self.spline = LSQUnivariateSpline(self.rho, self.orig_data, knots, k=5)
def plot_orig(self):
fig = self._plot_base()
fig.scatter(self.rho, self.orig_data, color='red')
plt.show()
return fig
def plot(self):
fig = self._plot_base()
fig.scatter(self.rho, self.orig_data, color='red')
fig.plot(self.rho, self.spline(self.rho), color='black')
fig.scatter(self.rho, self.spline(self.rho), color='green', marker="x")
fig.legend(["Spline", "Original", "Cleaned"])
plt.show()
return fig
def save(self):
answer = ""
while answer not in ["y", "n"]:
answer = input("OK to continue [Y/N]? ").lower()
print("Backing up old file")
os.rename(self.filename, self.filename + '.bak')
contents = np.array([self.rho, self.spline(self.rho)]).T
savetxt(self.filename + "_clean", contents, delimiter=' ')
def get_spline(self):
return self.spline
def set_knots(self, knots):
self.spline = LSQUnivariateSpline(self.rho, self.orig_data, knots, k=5)
print("knots set")
def _plot_base(self):
plot = plt.figure()
fig = plot.add_subplot(111)
fig.set_xlabel(r'$\rho', fontsize=30)
fig.set_ylabel(r'Data', fontsize=30)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
return fig
def plot_derivative(self):
fig = self._plot_base()
fig.scatter(self.rho, self.spline.derivative()(self.rho), color="blue")
return fig
def spline(bin_size, offsets):
"""compute piecewise polynomial splines of degree three"""
x, y = zip(*offsets)
xs = np.arange(x[0], x[-1], 120)
# array of knots (with the start and end which is addes automatically 13 knots meanaing 12 pieces)
t = [offsets[0][0] + bin_size, offsets[0][0] + bin_size * 2,
offsets[0][0] + 3 * bin_size, offsets[0][0] + 4 * bin_size,
offsets[0][0] + 5 * bin_size, offsets[0][0] + 6 * bin_size,
offsets[0][0] + 7 * bin_size, offsets[0][0] + 8 * bin_size,
offsets[0][0] + 9 * bin_size, offsets[0][0] + 10 * bin_size,
offsets[0][0] + 11 * bin_size]
# compute a spline polynomial of degree 3 over 5 equal pieces for the y points over steps of 1 sec on the x axis.
try:
spl = LSQUnivariateSpline(x, y, t, w=None, bbox=[None, None], k=3)
except ValueError as e:
logging.error("ERROR: LSQUnivariateSpline ValueError failed with error {} and params x {} y {} t {} ".format(e, x, y, t))
raise
spl_deriv = spl.derivative(1) # derivative of degree one
orig_curve = spl(xs)
deriv_curve = spl_deriv(xs)
return orig_curve, deriv_curve, xs
class Tracker:
def __init__(self,
lb=-10,
ub=10,
budget=100,
adapt_options={},
n_knots=-1,
K=1,
lam=1E-2,
gamma=1E-2):
"""
An APP Tracker object.
Parameters:
lb (float): the lower bound for the range of possible STLE locations.
ub (float): the upper bound for the range of possible STLE locations.
budget (int): the number of dictionary data points.
adapt_options: dictionary of options to be passed to the nlopt optimizer. Options include 'xtol_rel', 'maxeval', 'maxtime', and 'algo'.
n_knots (int): number of knots for adaptive pressure profile. Defaults to -1 which chooses knots adaptively.
K (int): degree of adaptive spline fit. Defaults to K=1.
lam (float): L2 penalization parameter.
gamma (float): Adaptive pressure profile spline fit smoothness parameter.
Returns:
An object of the Tracker class.
"""
# fixed parameters of algorithm
self.lb = lb
self.ub = ub
self.budget = budget
self.n_knots = n_knots
self.K = K
self.lam = lam
self.gamma = gamma
self.i = 0 # internal counter for num processed timepoints
# estimated properties
self.est_adapt = None
self.p_mean = None
self.p_std = None
self.x1_d = None
self.p_d = None
self.delta_d = None
self.time_d = None
#optimizer
self.adapt = None
algo = nlopt.GN_CRS2_LM
self.optim_params_init = {
'maxeval': 100,
'xtol_rel': 1E-10,
'algo': algo,
'maxtime': -1
}
self.adapt_opt = self.init_opt(adapt_options, 1)
self.adapt_opt.set_lower_bounds(self.lb)
self.adapt_opt.set_upper_bounds(self.ub)
def make_adapt_design(self, x, update=False):
return x.reshape(-1)
def init_opt(self, new_options, npar):
ret_options = self.optim_params_init.copy()
for k, v in new_options.items():
ret_options[k] = v
opt = nlopt.opt(ret_options['algo'], npar)
opt.set_xtol_rel(ret_options['xtol_rel'])
opt.set_maxeval(ret_options['maxeval'])
opt.set_maxtime(ret_options['maxtime'])
return opt
def predict(self, x, p, base_method=None, std_x=True):
"""
Predict STLE location and update adaptive fit.
Parameters:
x (float): the streamwise locations of the pressure transducers.
p (float): the measure pressures.
base_method (function): a base method to produce a weak estimate. Defaults to pressure ratio method. Function takes two arguments x and p and produces a weak estimate (and a measurement of uncertainty).
std_x (boolean): Should fitting be done using standardized streamwise locations.
"""
self.i = self.i + 1
x_mean = 0
x_std = 1
if std_x:
x_mean = np.mean(x)
x_std = np.std(x)
x = (x - x_mean) / x_std
if base_method is None:
base_method = lambda x, p: self.pressure_ratio(x, p, pa=1)
# run base learner
x1_base, delta = base_method(x, p)
# update adaptive fit
self.update_adapt(x, p, x1_base, delta)
#initial guess for adaptive optimization (x0)
x0 = self.est_adapt
if x0 is None or ~np.isfinite(x0):
x0 = x1_base
# adaptive estimate
if self.adapt is not None:
self.est_adapt, adapt_cov = self.fit_adapt(x, p, x0)
else:
self.est_adapt = x0
adapt_cov = float('inf')
return {
'est': self.est_adapt * x_std + x_mean,
'est_cov': adapt_cov * x_std**2,
'base': x1_base * x_std + x_mean,
'delta': delta
}
def update_adapt(self, x, p, x1_base, delta):
# basic sanity check of input estimates
# don't update if base estimate doesn't exist
if ~np.isfinite(x1_base):
return
## DICTIONARY management
# add to dictionary
if self.x1_d is None: # need to create dictionary
self.x1_d = x - x1_base
self.p_d = p
self.delta_d = np.repeat(delta, len(x)) #uncertainty of base
self.time_d = np.repeat(self.i, len(x)) #time of point
self.x1_base = np.repeat(x1_base, len(x)) #base estimate of point
else:
self.x1_d = np.append(self.x1_d, x - x1_base)
self.p_d = np.append(self.p_d, p)
self.delta_d = np.append(self.delta_d, np.repeat(delta, len(x)))
self.time_d = np.append(self.time_d, np.repeat(self.i, len(x)))
self.x1_base = np.append(self.x1_base, np.repeat(x1_base, len(x)))
# if over budget prune
if self.p_d.shape[0] > self.budget:
N_rmv = len(self.x1_d) - self.budget
dff = np.diff(self.x1_d)
mdff = .5 * (dff[1:len(dff)] + dff[0:(len(dff) - 1)])
mdff = np.concatenate([[dff[0]], mdff, [dff[len(dff) - 1]]])
eps = self.delta_d / mdff # accuracy of base estimate / closeness to nearby points
rmv = np.where(
np.logical_or(eps >= -np.sort(-eps)[N_rmv - 1],
~np.isfinite(eps)))[0]
if len(rmv) > N_rmv:
rmv = np.random.choice(a=rmv, size=N_rmv, replace=False)
# remove pts from dictionary and assoc. information
self.x1_d = np.delete(self.x1_d, rmv, axis=0)
self.p_d = np.delete(self.p_d, rmv, axis=0)
self.delta_d = np.delete(self.delta_d, rmv, axis=0)
self.time_d = np.delete(self.time_d, rmv, axis=0)
self.x1_base = np.delete(self.x1_base, rmv, axis=0)
# sort and remove non-unique values (more stable fitting)
# remove non-unique from dict
ux, iux = np.unique(self.x1_d, return_index=True)
self.x1_d = ux
self.p_d = self.p_d[iux]
self.delta_d = self.delta_d[iux]
self.time_d = self.time_d[iux]
self.x1_base = self.x1_base[iux]
# sort dict
ordx = np.argsort(self.x1_d, axis=0).reshape(-1)
self.x1_d = self.x1_d[ordx]
self.p_d = self.p_d[ordx]
self.delta_d = self.delta_d[ordx]
self.time_d = self.time_d[ordx]
self.x1_base = self.x1_base[ordx]
## ADAPTIVE PROFILE fitting
# need more data than knots
if len(self.x1_d) <= self.K:
return
# determine location of knots based on some heuristics
if len(self.x1_d) > 5 and self.n_knots > 0:
l3len = int(np.ceil(len(self.x1_d) // 3))
if self.n_knots is not None:
qs = np.linspace(.25, .75, np.min([l3len, self.n_knots]))
else:
qs = np.linspace(.25, .75, l3len)
kseq = np.quantile(self.x1_d, qs)
self.kseq = kseq[1:(len(kseq) - 1)]
else:
self.kseq = []
self.x_design = self.make_adapt_design(self.x1_d, update=True)
self.p_mean = np.mean(self.p_d)
self.p_std = np.std(self.p_d)
self.p_z = (self.p_d - self.p_mean) / self.p_std
# fit model and its derivative
#just make sure everything is monotonic (for stability)
mono_fit = IsotonicRegression().fit(self.x_design, self.p_z)
mono_p = mono_fit.predict(self.x_design)
if self.n_knots < 0:
def err_mod(mod): #RSS for fit model
return np.sum((mod(self.x1_d) - self.p_z)**2) / len(self.p_z)
smooth_spl = UnivariateSpline(self.x_design,
mono_p,
k=self.K,
ext=0,
s=len(self.p_z) * self.gamma)
# some heuristics on what knots to keep
knts = smooth_spl.get_knots()
keep_knts = ~np.logical_or(knts < np.quantile(self.x1_d, .1),
knts > np.quantile(self.x1_d, .9))
knts = knts[keep_knts]
linear_mod = LSQUnivariateSpline(self.x_design,
mono_p,
k=self.K,
ext=0,
t=[])
self.adapt = LSQUnivariateSpline(self.x_design,
mono_p,
k=self.K,
ext=0,
t=knts)
adapt_err = err_mod(self.adapt)
lin_err = err_mod(linear_mod)
#if its not twice as good as a simple linear model just use the latter
if lin_err > 0 and adapt_err / lin_err > 0.5:
self.adapt = linear_mod
else: #n_knots > 0
self.adapt = LSQUnivariateSpline(self.x_design,
mono_p,
k=self.K,
ext=0,
t=self.kseq)
self.adapt_deriv = self.adapt.derivative(1)
def adapt_pred(self, x):
x_basis = self.make_adapt_design(x)
pred0 = self.adapt(x_basis)
return pred0
def fit_adapt(self, x, p, x0):
# set up optimization
obj = lambda par, grad: self.adapt_sqe(x, p, par)
self.adapt_opt.set_min_objective(obj)
x0 = np.max([self.lb, x0])
x0 = np.min([self.ub, x0])
# run optimization
x_hat = self.adapt_opt.optimize([x0])[0]
# calculate fitting sensitivity
drv1 = self.adapt_deriv(x - x_hat)
d2 = np.mean(drv1**2)
d2 += self.lam
sig2 = self.adapt_sqe(x, p, [x_hat], pen=False) / (len(x) - 1)
cov = .5 * sig2 / d2
return x_hat, cov
def adapt_sqe(self, x, p, cent, pen=True):
xc = x - cent
ft = self.adapt_pred(xc).reshape(-1)
p_z = (p - self.p_mean) / self.p_std
val = np.mean((p_z - ft)**2)
penalty = 0
if pen:
penalty = self.lam * np.abs(cent[0])**2
return val + penalty
# other helper functions
def pressure_rise(self, x, p, pct=.5):
try:
pmin = np.min(p)
delta = np.max(p) - pmin
p_q = pmin + delta * pct
i = np.min(np.where(np.diff((p >= p_q).astype(int)))[0]) + 1
a = (p[i] - p[i - 1]) / (x[i] - x[i - 1])
b = p[i] - a * x[i]
x_q = (p_q - b) / a
rat = np.max(p) / np.min(p)
if rat < 2:
x_q = float('Inf')
return x_q, rat
except Exception:
return float('Inf'), float('Inf')
def pressure_ratio(self, x, p, pa, ratio=2):
p = p / pa
p_q = ratio
ii = np.where(np.diff((p >= p_q).astype(int)))[0]
if len(ii) == 0:
return float('inf'), float('inf'),
i = np.min(ii) + 1
a = (p[i] - p[i - 1]) / (x[i] - x[i - 1])
b = p[i] - a * x[i]
x_q = (p_q - b) / a
delta = np.min(np.abs(x - x_q))
return x_q, delta
