def Smooth(x,y):
"""return: smoothed funciton s(x) and estimation of sigma of y for one data point"""
#define segments to do spline smoothing
t=np.linspace(0,len(x),3)[1:-1]
sr = LSQUnivariateSpline(x, y.real, t)
si = LSQUnivariateSpline(x, y.imag, t)
return sr(x)+si(x)*1j, (sr.get_residual()/len(x))**0.5+(si.get_residual()/len(x))**0.5*1j
def Smooth(x,y):
"""return: smoothed funciton s(x) and estimation of sigma of y for one data point"""
#define segments to do spline smoothing
t=np.linspace(0,len(x),3)[1:-1]
sr = LSQUnivariateSpline(x, y.real, t)
si = LSQUnivariateSpline(x, y.imag, t)
return sr(x)+si(x)*1j, (sr.get_residual()/len(x))**0.5+(si.get_residual()/len(x))**0.5*1j
def fun_residuals(params, xnor, ynor, w, bbox, k, ext):
"""Compute fit residuals"""
spl = LSQUnivariateSpline(x=xnor,
y=ynor,
t=[item.value for item in params.values()],
w=w,
bbox=bbox,
k=k,
ext=ext,
check_finite=False)
return spl.get_residual()
def pval(self, index):
### In practice we'd implement some actual error scheme for this
knotsDel = np.delete(self.knots, index)
interpDel = LSQUnivariateSpline(self.x,
self.y,
knotsDel,
k=1,
w=self.weights)
resid = self.interp.get_residual()
residDel = interpDel.get_residual()
dof = len(self.x) - len(
self.knots
) - 2 # -2 because there are 2 parameters in a 1-knot k=1 fit.
dofdel = dof + 1
if dof == 0:
self.knotPvals[index] = -np.inf
else:
self.knotPvals[index] = abs(residDel / dofdel -
1) - abs(resid / dof - 1)
print(self.knotPvals[index], resid / dof, residDel / dofdel, resid,
dof)
class fitfunc:
def __init__(self, x, y, errors=None):
if errors is not None:
self.errors = errors
else:
grad = np.gradient(y)
ms = grad**2
ms2 = np.zeros(len(ms) + 4)
ms2[0] = ms[0]
ms2[1] = ms[0]
ms2[2:-2] = ms
ms2[-2] = ms[-1]
ms2[-1] = ms[-1]
ms = ms2
ms = np.convolve(ms, np.ones((5, )) / 5, mode='valid')
ms /= 2 # Correction for difference of uncorrelated errors
self.errors = np.sqrt(ms)
self.errors = np.copy(np.sqrt(np.abs(y)))
self.weights = self.errors**2
# Save inputs and enforce sorting along x
inds = np.argsort(x)
self.x = np.copy(x)[inds] + 1e-10 * np.array(range(
len(x))) # This prevents duplicates in x
self.y = np.copy(y)[inds]
# Setup knots
self.knots = np.copy(self.x[1:-1])
self.knotInd = np.array(range(len(self.knots)))
self.knotPvals = None
# Iteratively fit and remove knots
self.interp = None
self.fit()
self.pvals()
done = False
while not done and len(self.knots) > 2:
choice = np.random.choice(range(len(self.knots)),
len(self.knots),
replace=False)
for i in choice:
if self.knotPvals[i] < 0:
self.removeKnot(i)
self.fit()
self.pvals()
break
else:
done = True
self.interpDeriv = self.interp.derivative(1)
print('Converged with ', len(self.knots), 'knots.')
print(self.knotPvals, self.interp.get_residual())
def fit(self):
self.knotPvals = np.zeros(len(self.knots))
self.interp = LSQUnivariateSpline(self.x,
self.y,
self.knots,
k=1,
w=self.weights)
def pvals(self):
for i in range(len(self.knots)):
self.pval(i)
def pval(self, index):
### In practice we'd implement some actual error scheme for this
knotsDel = np.delete(self.knots, index)
interpDel = LSQUnivariateSpline(self.x,
self.y,
knotsDel,
k=1,
w=self.weights)
resid = self.interp.get_residual()
residDel = interpDel.get_residual()
dof = len(self.x) - len(
self.knots
) - 2 # -2 because there are 2 parameters in a 1-knot k=1 fit.
dofdel = dof + 1
if dof == 0:
self.knotPvals[index] = -np.inf
else:
self.knotPvals[index] = abs(residDel / dofdel -
1) - abs(resid / dof - 1)
print(self.knotPvals[index], resid / dof, residDel / dofdel, resid,
dof)
def removeKnot(self, index):
# Note that index can be a list of indices, or even
# a smart-indexing tool.
self.knots = np.delete(self.knots, index)
self.knotInd = np.delete(self.knotInd, index)
self.slope = None
self.intercept = None
self.knotPvals = None
def evalY(self, x):
return self.interp(x)
def evalDeriv(self, x):
return self.interpDeriv(x)
