def errfunc(p, x, y):
# Assume we have loads of cal data so we can
# intepolate it to find values equiv to the
# experimental points (using numpys numpy.interp)
crit_stress = p[-1:]
crit_temp = p[-2:-1]
(xfine, yfine, sigma_p, zdiff, u_0, u_max, H_kp, Un, \
zdiff_kp, yderfine, sigma_b, sigma_p_index, kink_energy2) = kinker(x,y, G=60E9,silent=True,method=func,params=p[:-2] )
calc_y = (Un/(kink_energy2*2.0))*crit_temp
calc_x = sigma_b + crit_stress
# interpolate stresses at experimental data points. Note that we have to
# do this backwards, as kinker returns solutions for increing stress, which
# is decresing temp, and numpy.interp only likes things to increse (we check
# this condition). We don't have to do anything special about high T results
# as they get the low stress result (they are > max calc T) which is 0.1% of
# sigma_P (in kinker) and this is equal to crit_stress (see above).
if (np.all(np.diff(calc_y[::-1]) <= 0)):
raise ValueError("Cannot intepolate on temperature")
interp_calc_x = np.interp(T_n[::-1],calc_y[::-1],calc_x[::-1])
interp_calc_x = interp_calc_x[::-1] # Can we not do this in place above?
# This is useful for debugging - draws a graph to check the interp...
#interp_calc_y = T_n # Don't need this unless we are graphing.
#plt.figure(5)
#plt.plot(calc_y, calc_x, 'o', T_n, tau_n, '.', interp_calc_y, interp_calc_x, 'x')
#plt.legend(('fit to expt data', 'expt data', 'init model'))
#plt.title('Critical stress / kink energy')
#plt.xlabel('Tau* (MPa)')
#plt.ylabel('Un/2Uk*T_crit (K)')
#plt.show()
# Calculate error in stress.
error = (tau_n-interp_calc_x)
print "----------------------------------------"
print "kink energy (J)"
print kink_energy2
print "p stress (Pa)"
print sigma_p
print "Crit T (K)"
print crit_temp
print "Crit sigma (Pa)"
print crit_stress
print "Gnorm (Pa)"
print (np.sqrt(sum((error*error))))/len(error)
print "max error (Pa)"
print max(error)
print "time (s)"
print (time.time() - t0)
print "----------------------------------------"
return error
# Load data set, x is the displacment (u) and y is the energy (U)
#(x,y) = load_data(None) # For test data
basename = raw_input("Basename (input is basname.dat): ")
if (basename == ""):
basename = 'example'
filename = None
else:
filename = basename + '.dat'
(x,y) = pot.load_data(filename)
x_len = max(x)
# Solve for inital potential using bspline interpolation...
print "Solving kink nucleation problem with inital potential using bspline interp"
(xfine_interp, yfine_interp, sigma_p, zdiff, u_0, u_max, H_kp, Un, \
zdiff_kp, yderfine, sigma_b, sigma_p_index, kink_energy2) = kinker(x,y,G=60E9)
# Now do it again using a function...
(func, p0) = pot.choose_func(params=True)
# Fit this data to a sin function
print "Fitting initial potential to function"
opt_p = pot.fit_pot(p0, x, y, x_len, func)
# Initial solution with this function
(xfine_init, yfine, sigma_p, zdiff_init, u_0_init, u_max, H_kp, Un_init, \
zdiff_kp, yderfine_init, sigma_b_init, sigma_p_index, kink_energy2_init) = kinker(x,y,G=60E9,method=func,params=opt_p)
# Load experimental data
