def check(name,data,target,origin=False,tol=2e-16):
"""
name data set name
data [y,x]
target [p,dp] but low to high rather than high to low
"""
p = wpolyfit(data[:,1],data[:,0],degree=target.shape[0]-1,origin=origin)
Ep,Edp = N.flipud(target).T
show_result(name,p.coeff,p.std,Ep,Edp,tol=tol)
def check_uncertainty(n=10000):
"""
This function computes a number of fits to simulated data
to determine how well the values and uncertainties reported
by the wpolyfit solver correspond to individual fits of the data.
For large N the reported parameters do indeed converge to the mean
parameter values for fits to resampled data. Reported parameter
uncertainty estimates are not supported by MC.
"""
## x y dy
data = N.matrix("""
0.0013852 0.2144023 0.020470;
0.0018469 0.2516856 0.022868;
0.0023087 0.3070443 0.026362;
0.0027704 0.3603186 0.029670;
0.0032322 0.4260864 0.033705;
0.0036939 0.4799956 0.036983
""").A
x,y,dy=data[:,0],data[:,1],data[:,2]
if True: # simpler system to analyze
x = N.linspace(2,4,12)
y = 3*x+5
dy = y
p = wpolyfit(x,y,dy=dy,degree=1)
P=N.empty((2,n),'d')
for i in xrange(n):
#pi = N.polyfit(x,N.random.normal(y,dy),degree=1)
pi = wpolyfit(x,N.random.normal(y,dy),dy=dy,degree=1)
P[:,i] = pi.coeff
#print "P",P
Ep,Edp = N.mean(P,1),N.std(P,1)
show_result("uncertainty check",p.coeff,p.std,Ep,Edp)
if False:
import pylab
pylab.hist(P[0,:])
pylab.show()
""" # Not yet converted from octave
