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
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
