def run_once(num_elements):
import tee
import time
tic = time.clock()
# 0 means don't use PyTorch
with_pytorch = 0
num_scatterers = int(1e6)
tee.tee(f'{exe} {with_pytorch} {num_scatterers} {num_elements}')
return time.clock() - tic
def good_analysis(): sys.stdout = tee.tee(STDOUT, open(OUTDIR+'eg-appendix1b.out', 'w')) x, y = make_data() prior = gv.gvar(N * ['0(1)']) # prior for the fit fit = lsqfit.nonlinear_fit(data=(x, y), prior=prior, fcn=f) print fit.format(maxline=True) make_plot(x, y, fit) inputs = gv.BufferDict(prior=prior) for xi, yi in zip(x, y): inputs['y(%.2f)' % xi] = yi sys.stdout = tee.tee(STDOUT, open(OUTDIR+'eg-appendix1g.out', 'w')) inputs = dict(prior=prior, y=y) outputs = dict(p0=fit.p[0]) print gv.fmt_errorbudget(inputs=inputs, outputs=outputs) return fit
def good_analysis(plot=MAKE_PLOTS):
sys.stdout = tee.tee(STDOUT, open(OUTDIR + 'eg-appendix1b.out', 'w'))
x, y = make_data()
prior = gv.gvar(N * ['0(1)']) # prior for the fit
fit = lsqfit.nonlinear_fit(data=(x, y), prior=prior, fcn=f)
print fit.format(maxline=True)
if plot:
make_plot(x, y, fit, name='eg-appendix1b')
inputs = gv.BufferDict(prior=prior)
for xi, yi in zip(x, y):
inputs['y(%.2f)' % xi] = yi
sys.stdout = tee.tee(STDOUT, open(OUTDIR + 'eg-appendix1g.out', 'w'))
inputs = dict(prior=prior, y=y)
outputs = dict(p0=fit.p[0])
print gv.fmt_errorbudget(inputs=inputs, outputs=outputs)
return fit
def main():
gv.ranseed([2009,2010,2011,2012,2013]) # initialize random numbers (opt.)
x,y = make_data() # make fit data
p0 = None # make larger fits go faster (opt.)
sys_stdout = sys.stdout
for nexp in range(3,6):
prior = make_prior(nexp,x)
fit = lsqfit.nonlinear_fit(data=y,fcn=f,prior=prior,p0=p0) # ,svdcut=SVDCUT)
if fit.chi2/fit.dof<1.:
p0 = fit.pmean # starting point for next fit (opt.)
fit.check_roundoff()
if nexp == 4:
sys.stdout = tee.tee(sys.stdout,open("eg2.out","w"))
print '************************************* nexp =',nexp
print fit # print the fit results
E = fit.p['E'] # best-fit parameters
a = fit.p['a']
print 'E1/E0 =',E[1]/E[0],' E2/E0 =',E[2]/E[0]
print 'a1/a0 =',a[1]/a[0],' a2/a0 =',a[2]/a[0]
sys.stdout = sys_stdout
print
#
if DO_BOOTSTRAP:
Nbs = 10 # number of bootstrap copies
outputs = {'E1/E0':[], 'E2/E0':[], 'a1/a0':[],'a2/a0':[],'E1':[],'a1':[]} # results
for bsfit in fit.bootstrap_iter(n=Nbs):
E = bsfit.pmean['E'] # best-fit parameters
a = bsfit.pmean['a']
outputs['E1/E0'].append(E[1]/E[0]) # accumulate results
outputs['E2/E0'].append(E[2]/E[0])
outputs['a1/a0'].append(a[1]/a[0])
outputs['a2/a0'].append(a[2]/a[0])
outputs['E1'].append(E[1])
outputs['a1'].append(a[1])
# print E[:2]
# print a[:2]
# print bsfit.chi2/bsfit.dof
# extract means and standard deviations from the bootstrap output
for k in outputs:
outputs[k] = gv.gvar(np.mean(outputs[k]),np.std(outputs[k]))
print 'Bootstrap results:'
print 'E1/E0 =',outputs['E1/E0'],' E2/E1 =',outputs['E2/E0']
print 'a1/a0 =',outputs['a1/a0'],' a2/a0 =',outputs['a2/a0']
print 'E1 =',outputs['E1'],' a1 =',outputs['a1']
if DO_PLOT:
print fit.format(100) # print the fit results
import pylab as pp
from gvar import mean,sdev
fity = f(x,fit.pmean)
ratio = y/fity
pp.xlim(0,21)
pp.xlabel('x')
pp.ylabel('y/f(x,p)')
pp.errorbar(x=gv.mean(x),y=gv.mean(ratio),yerr=gv.sdev(ratio),fmt='ob')
pp.plot([0.0,21.0],[1.0,1.0])
pp.show()
def bad_analysis(): sys.stdout = tee.tee(STDOUT, open(OUTDIR+'eg-appendix1a.out', 'w')) x, y = make_data() p0 = np.ones(5.) # starting value for chi**2 minimization fit = lsqfit.nonlinear_fit(data=(x, y), p0=p0, fcn=f) print fit.format(maxline=True) make_plot(x, y, fit) return fit
def main():
sys_stdout = sys.stdout
# version 1 - relative errors
sys.stdout = tee.tee(sys_stdout, open("eg7a.out", "w"))
# fit data and prior
x = np.array([1., 2., 3., 4.])
y = np.array([3.4422, 1.2929, 0.4798, 0.1725])
prior = gv.gvar(['10(1)', '1.0(1)'])
# fit function
def fcn(x, p):
return p[0] * gv.exp(-p[1] * x)
# find optimal dy
def fitargs(z):
dy = y * z
newy = gv.gvar(y, dy)
return dict(data=(x, newy), fcn=fcn, prior=prior)
fit, z = lsqfit.empbayes_fit(0.001, fitargs)
print fit.format(True)
if MAKE_PLOT:
ratio = fit.y / fcn(x, fit.pmean)
plt.errorbar(x=fit.x, y=gv.mean(ratio), yerr=gv.sdev(ratio), c='b')
plt.plot([0.5, 4.5], [1.0, 1.0], c='r')
# version 2 - additive errors
sys.stdout = tee.tee(sys_stdout, open("eg7b.out", "w"))
def fitargs(z):
dy = np.ones_like(y) * z
newy = gv.gvar(y, dy)
return dict(data=(x, newy), fcn=fcn, prior=prior)
fit, z = lsqfit.empbayes_fit(0.001, fitargs)
print fit.format(True)
if MAKE_PLOT:
ratio = fit.y / fcn(x, fit.pmean)
plt.errorbar(x=fit.x + 0.1,
y=gv.mean(ratio),
yerr=gv.sdev(ratio),
c='g')
plt.show()
def bad_analysis():
sys.stdout = tee.tee(STDOUT, open(OUTDIR + 'eg-appendix1a.out', 'w'))
x, y = make_data()
p0 = np.ones(5.) # starting value for chi**2 minimization
fit = lsqfit.nonlinear_fit(data=(x, y), p0=p0, fcn=f)
print fit.format(maxline=True)
make_plot(x, y, fit, name='eg-appendix1a')
return fit
def prior_analysis():
x, y = make_data()
# loose prior
sys.stdout = tee.tee(STDOUT, open(OUTDIR + 'eg-appendix1d.out', 'w'))
prior = gv.gvar(91 * ['0(3)']) # prior for the fit
fit = lsqfit.nonlinear_fit(data=(x, y), prior=prior, fcn=f)
print fit.format(maxline=True)
# really loose prior
sys.stdout = tee.tee(STDOUT, open(OUTDIR + 'eg-appendix1h.out', 'w'))
prior = gv.gvar(91 * ['0(20)']) # prior for the fit
fit = lsqfit.nonlinear_fit(data=(x, y), prior=prior, fcn=f)
print fit.format(maxline=True)
make_plot(x, y, fit, xmax=0.96, name='eg-appendix1d')
# tight prior
sys.stdout = tee.tee(STDOUT, open(OUTDIR + 'eg-appendix1e.out', 'w'))
prior = gv.gvar(91 * ['0.0(3)']) # prior for the fit
fit = lsqfit.nonlinear_fit(data=(x, y), prior=prior, fcn=f)
print fit.format(maxline=True)
def prior_analysis(): x, y = make_data() # loose prior sys.stdout = tee.tee(STDOUT, open(OUTDIR+'eg-appendix1d.out', 'w')) prior = gv.gvar(91 * ['0(3)']) # prior for the fit fit = lsqfit.nonlinear_fit(data=(x, y), prior=prior, fcn=f) print fit.format(maxline=True) # really loose prior sys.stdout = tee.tee(STDOUT, open(OUTDIR+'eg-appendix1h.out', 'w')) prior = gv.gvar(91 * ['0(20)']) # prior for the fit fit = lsqfit.nonlinear_fit(data=(x, y), prior=prior, fcn=f) print fit.format(maxline=True) make_plot(x, y, fit, xmax=0.96) # tight prior sys.stdout = tee.tee(STDOUT, open(OUTDIR+'eg-appendix1e.out', 'w')) prior = gv.gvar(91 * ['0.0(3)']) # prior for the fit fit = lsqfit.nonlinear_fit(data=(x, y), prior=prior, fcn=f) print fit.format(maxline=True)
def test_fit(): sys.stdout = STDOUT fit = good_analysis() sp0 = [] q = [] sys.stdout = tee.tee(STDOUT, open(OUTDIR+'eg-appendix1f.out', 'w')) for sfit in fit.simulated_fit_iter(20): # print sfit.format(maxline=-1), sfit.p[0] - sfit.pexact[0] sp0.append(sfit.pmean[0]) q.append(sfit.Q) print(np.average(sp0), np.std(sp0), np.average(q))
def test_fit():
sys.stdout = STDOUT
fit = good_analysis(plot=False)
sp0 = []
q = []
sys.stdout = tee.tee(STDOUT, open(OUTDIR + 'eg-appendix1f.out', 'w'))
for sfit in fit.simulated_fit_iter(20):
# print sfit.format(maxline=-1), sfit.p[0] - sfit.pexact[0]
sp0.append(sfit.pmean[0])
q.append(sfit.Q)
print(np.average(sp0), np.std(sp0), np.average(q))
def run_test(self, testobj, fixtures=None):
'''
Run the given test.
This performs the bulkwork of the testing framework.
1. Handle setup for all fixtures required for the specific test.
2. Run the test.
3. Teardown the fixtures for the test which are tied locally to the\
test.
'''
outdir = test_results_output_path(testobj)
mkdir_p(outdir)
fstdout_name = joinpath(outdir, config.constants.system_err_name)
fstderr_name = joinpath(outdir, config.constants.system_out_name)
# Capture the output into a file.
with tee(fstderr_name, stderr=True, stdout=False),\
tee(fstdout_name, stderr=False, stdout=True):
return self._run_test(testobj, fstdout_name, fstderr_name,
fixtures)
def marginalized_analysis(): sys.stdout = tee.tee(STDOUT, open(OUTDIR+'eg-appendix1c.out', 'w')) x, y = make_data() prior = gv.gvar(91 * ['0(1)']) # prior for the fit ymod = y - (f(x, prior) - f(x, prior[:1])) priormod = prior[:1] fit = lsqfit.nonlinear_fit(data=(x, ymod), prior=priormod, fcn=f) print fit.format(maxline=True) sys.stdout = STDOUT print lsqfit.wavg(list(ymod) + list(priormod)) make_plot(x, ymod, fit, 'ymod(x)') inputs = dict(prior=prior, y0=y[0], y1=y[1], y2=y[2], y3=y[3], y4=y[4]) outputs = dict(p0=fit.p[0]) print gv.fmt_errorbudget(inputs=inputs, outputs=outputs) return fit
def marginalized_analysis():
sys.stdout = tee.tee(STDOUT, open(OUTDIR + 'eg-appendix1c.out', 'w'))
x, y = make_data()
prior = gv.gvar(91 * ['0(1)']) # prior for the fit
ymod = y - (f(x, prior) - f(x, prior[:1]))
priormod = prior[:1]
fit = lsqfit.nonlinear_fit(data=(x, ymod), prior=priormod, fcn=f)
print fit.format(maxline=True)
sys.stdout = STDOUT
print lsqfit.wavg(list(ymod) + list(priormod))
make_plot(x, ymod, fit, 'ymod(x)', name='eg-appendix1c')
inputs = dict(prior=prior, y0=y[0], y1=y[1], y2=y[2], y3=y[3], y4=y[4])
outputs = dict(p0=fit.p[0])
print gv.fmt_errorbudget(inputs=inputs, outputs=outputs)
return fit
def main():
# pendulum data exhibits experimental error in ability to measure theta
t = gv.gvar([
'0.10(1)', '0.20(1)', '0.30(1)', '0.40(1)', '0.50(1)',
'0.60(1)', '0.70(1)', '0.80(1)', '0.90(1)', '1.00(1)'
])
theta = gv.gvar([
'1.477(79)', '0.791(79)', '-0.046(79)', '-0.852(79)',
'-1.523(79)', '-1.647(79)', '-1.216(79)', '-0.810(79)',
'0.185(79)', '0.832(79)'
])
for t_n, theta_n in zip(t, theta):
print("{} {:>10}".format(t_n.fmt(2), theta_n.fmt(3)))
# prior: assume experimental error in ability to specify theta(0)
prior = gv.BufferDict()
prior['g/l'] = gv.gvar('40(20)')
prior['theta(0)'] = gv.gvar('1.571(50)')
prior['t'] = t
# fit function: use class Pendulum object to integrate pendulum motion
def fitfcn(p, t=None):
if t is None:
t = p['t']
pendulum = Pendulum(p['g/l'])
return pendulum(p['theta(0)'], t)
# do the fit and print results
fit = lsqfit.nonlinear_fit(data=theta, prior=prior, fcn=fitfcn)
sys.stdout = tee.tee(STDOUT, open('case-pendulum.out', 'w'))
print(fit.format(maxline=True))
sys.stdout = STDOUT
print('fit/exact for (g/l) =', fit.p['g/l'] / (2*np.pi) ** 2)
print('fit/exact for theta(0) =', fit.p['theta(0)'] / (np.pi / 2.))
if MAKE_PLOT:
# make figure (saved to file pendulum.pdf)
plt.figure(figsize=(4,3))
# start plot with data
plt.errorbar(
x=gv.mean(t), xerr=gv.sdev(t), y=gv.mean(theta), yerr=gv.sdev(theta),
fmt='b.',
)
# use best-fit function to add smooth curve for 100 points
t = np.linspace(0., 1.1, 100)
th = fitfcn(fit.p, t)
show_plot(t, th)
def main():
# pendulum data exhibits experimental error in ability to measure theta
t = gv.gvar([
'0.10(1)', '0.20(1)', '0.30(1)', '0.40(1)', '0.50(1)',
'0.60(1)', '0.70(1)', '0.80(1)', '0.90(1)', '1.00(1)'
])
theta = gv.gvar([
'1.477(79)', '0.791(79)', '-0.046(79)', '-0.852(79)',
'-1.523(79)', '-1.647(79)', '-1.216(79)', '-0.810(79)',
'0.185(79)', '0.832(79)'
])
for t_n, theta_n in zip(t, theta):
print("{} {:>10}".format(t_n.fmt(2), theta_n.fmt(3)))
# prior: assume experimental error in ability to specify theta(0)
prior = gv.BufferDict()
prior['g/l'] = gv.gvar('40(20)')
prior['theta(0)'] = gv.gvar('1.571(50)')
prior['t'] = t
# fit function: use class Pendulum object to integrate pendulum motion
def fitfcn(p, t=None):
if t is None:
t = p['t']
pendulum = Pendulum(p['g/l'])
return pendulum(p['theta(0)'], t)
# do the fit and print results
fit = lsqfit.nonlinear_fit(data=theta, prior=prior, fcn=fitfcn)
sys.stdout = tee.tee(STDOUT, open('case-pendulum.out', 'w'))
print(fit.format(maxline=True))
sys.stdout = STDOUT
print('fit/exact for (g/l) =', fit.p['g/l'] / (2*np.pi) ** 2)
print('fit/exact for theta(0) =', fit.p['theta(0)'] / (np.pi / 2.))
if MAKE_PLOT:
# make figure (saved to file pendulum.pdf)
plt.figure(figsize=(4,3))
# start plot with data
plt.errorbar(
x=gv.mean(t), xerr=gv.sdev(t), y=gv.mean(theta), yerr=gv.sdev(theta),
fmt='k.',
)
# use best-fit function to add smooth curve for 100 points
t = np.linspace(0., 1.1, 100)
th = fitfcn(fit.p, t)
show_plot(t, th)
def component(df, bill_components):
#df = component_type(df, bill_components)
df = connection_type(df, bill_components)
df = end_forms(df, bill_components)
df = sleeve.sleeve(df, bill_components, comp_sleeve)
df = adaptor.adaptor(df, bill_components, comp_adaptor)
df = boss.boss(df, bill_components, comp_boss)
df = elbow.elbow(df, bill_components, comp_elbow)
df = float_.float_(df, bill_components, comp_float)
df = hfl.hfl(df, bill_components, comp_hfl)
df = nut.nut(df, bill_components, comp_nut)
#df = other.other(df, bill_components, comp_other)
df = straight.straight(df, bill_components, comp_straight)
df = tee.tee(df, bill_components, comp_tee)
df = threaded.threaded(df, bill_components, comp_threaded)
return df
def main():
sys_stdout = sys.stdout
sys.stdout = tee.tee(sys.stdout, open("eg2.out", "w"))
x, y = make_data()
prior = make_prior(x)
fit = lsqfit.nonlinear_fit(prior=prior, data=y, fcn=fcn)
print(fit.format())
if MAKE_PLOT:
import matplotlib.pyplot as plt
plt.errorbar(x=gv.mean(x),
xerr=gv.sdev(x),
y=gv.mean(y),
yerr=gv.sdev(y),
fmt='ob')
# plot fit line
x = np.linspace(0.99 * min(x).mean, 1.01 * max(x).mean, 100)
p = dict(b=fit.pmean['b'], x=x)
y = fcn(p)
plt.xlabel('x')
plt.ylabel('y')
plt.plot(x, y, ':r')
plt.savefig('eg2.png', bbox_inches='tight')
plt.show()
def main():
### 1) least-squares fit to the data
x = np.array([
0.2, 0.4, 0.6, 0.8, 1., 1.2, 1.4, 1.6, 1.8, 2., 2.2, 2.4, 2.6, 2.8, 3.,
3.2, 3.4, 3.6, 3.8
])
y = gv.gvar([
'0.38(20)', '2.89(20)', '0.85(20)', '0.59(20)', '2.88(20)', '1.44(20)',
'0.73(20)', '1.23(20)', '1.68(20)', '1.36(20)', '1.51(20)', '1.73(20)',
'2.16(20)', '1.85(20)', '2.00(20)', '2.11(20)', '2.75(20)', '0.86(20)',
'2.73(20)'
])
prior = make_prior()
fit = lsqfit.nonlinear_fit(data=(x, y), prior=prior, fcn=fitfcn)
if LSQFIT_ONLY:
sys.stdout = tee.tee(STDOUT, open('case-outliers-lsq.out', 'w'))
elif not MULTI_W:
sys.stdout = tee.tee(STDOUT, open('case-outliers.out', 'w'))
print(fit)
# plot data
plt.errorbar(x, gv.mean(y), gv.sdev(y), fmt='o', c='b')
# plot fit function
xline = np.linspace(x[0], x[-1], 100)
yline = fitfcn(xline, fit.p)
plt.plot(xline, gv.mean(yline), 'k:')
yp = gv.mean(yline) + gv.sdev(yline)
ym = gv.mean(yline) - gv.sdev(yline)
plt.fill_between(xline, yp, ym, color='0.8')
plt.xlabel('x')
plt.ylabel('y')
plt.savefig('case-outliers1.png', bbox_inches='tight')
if LSQFIT_ONLY:
return
### 2) Bayesian integral with modified PDF
pdf = ModifiedPDF(data=(x, y), fcn=fitfcn, prior=prior)
# integrator for expectation values with modified PDF
expval = lsqfit.BayesIntegrator(fit, pdf=pdf)
# adapt integrator to pdf
expval(neval=1000, nitn=15)
# evaluate expectation value of g(p)
def g(p):
w = p['w']
c = p['c']
return dict(w=[w, w**2], mean=c, outer=np.outer(c, c))
results = expval(g, neval=1000, nitn=15, adapt=False)
print(results.summary())
# expval.map.show_grid(15)
if MULTI_W:
sys.stdout = tee.tee(STDOUT, open('case-outliers-multi.out', 'w'))
# parameters c[i]
mean = results['mean']
cov = results['outer'] - np.outer(mean, mean)
c = mean + gv.gvar(np.zeros(mean.shape), gv.mean(cov))
print('c =', c)
print(
'corr(c) =',
np.array2string(gv.evalcorr(c), prefix=10 * ' '),
'\n',
)
# parameter w
wmean, w2mean = results['w']
wsdev = gv.mean(w2mean - wmean**2)**0.5
w = wmean + gv.gvar(np.zeros(np.shape(wmean)), wsdev)
print('w =', w, '\n')
# Bayes Factor
print('logBF =', np.log(results.norm))
sys.stdout = STDOUT
if MULTI_W:
return
# add new fit to plot
yline = fitfcn(xline, dict(c=c))
plt.plot(xline, gv.mean(yline), 'r--')
yp = gv.mean(yline) + gv.sdev(yline)
ym = gv.mean(yline) - gv.sdev(yline)
plt.fill_between(xline, yp, ym, color='r', alpha=0.2)
plt.savefig('case-outliers2.png', bbox_inches='tight')
def main():
gd.ranseed([2009,2010,2011,2012]) # initialize random numbers (opt.)
x,y = make_data() # make fit data
p0 = None # make larger fits go faster (opt.)
for nexp in range(2,8):
if nexp == 2:
sys_stdout = sys.stdout
sys.stdout = tee.tee(sys_stdout, open("eg4GBF.out","w"))
print '************************************* nexp =',nexp
prior = make_prior(nexp)
fit = lsqfit.nonlinear_fit(data=(x,y),fcn=f,prior=prior,p0=p0)
print fit # print the fit results
# E = fit.p['E'] # best-fit parameters
# a = fit.p['a']
# print 'E1/E0 =',E[1]/E[0],' E2/E0 =',E[2]/E[0]
# print 'a1/a0 =',a[1]/a[0],' a2/a0 =',a[2]/a[0]
print
if nexp == 3:
sys.stdout = sys_stdout
if fit.chi2/fit.dof<1.:
p0 = fit.pmean # starting point for next fit (opt.)
if DO_ERRORBUDGET:
print E[1]/E[0]
print (E[1]/E[0]).partialsdev(fit.prior['E'])
print (E[1]/E[0]).partialsdev(fit.prior['a'])
print (E[1]/E[0]).partialsdev(y)
outputs = {'E1/E0':E[1]/E[0], 'E2/E0':E[2]/E[0],
'a1/a0':a[1]/a[0], 'a2/a0':a[2]/a[0]}
inputs = {'E':fit.prior['E'],'a':fit.prior['a'],'y':y}
sys.stdout = tee.tee(sys_stdout, open("eg4GBFb.out","w"))
print fit.fmt_values(outputs)
print fit.fmt_errorbudget(outputs,inputs)
sys.stdout = sys_stdout
if DO_EMPBAYES:
def fitargs(z,nexp=nexp,prior=prior,f=f,data=(x,y),p0=p0):
z = gd.exp(z)
prior['a'] = [gd.gvar(0.5,0.5*z[0]) for i in range(nexp)]
return dict(prior=prior,data=data,fcn=f,p0=p0)
##
z0 = [0.0]
fit,z = lsqfit.empbayes_fit(z0,fitargs,tol=1e-3)
sys.stdout = tee.tee(sys_stdout, open("eg4GBFa.out","w"))
print fit # print the optimized fit results
E = fit.p['E'] # best-fit parameters
a = fit.p['a']
print 'E1/E0 =',E[1]/E[0],' E2/E0 =',E[2]/E[0]
print 'a1/a0 =',a[1]/a[0],' a2/a0 =',a[2]/a[0]
print "prior['a'] =",fit.prior['a'][0]
sys.stdout = sys_stdout
print
if DO_PLOT:
import pylab as pp
from gvar import mean,sdev
fity = f(x,fit.pmean)
ratio = y/fity
pp.xlim(0,21)
pp.xlabel('x')
pp.ylabel('y/f(x,p)')
pp.errorbar(x=x,y=mean(ratio),yerr=sdev(ratio),fmt='ob')
pp.plot([0.0,21.0],[1.0,1.0])
pp.show()
def main():
sys_stdout = sys.stdout
sys.stdout = tee.tee(sys.stdout, open("eg3a.out","w"))
x, y = make_data()
prior = make_prior()
fit = lsqfit.nonlinear_fit(prior=prior, data=(x,y), fcn=fcn)
print fit
print 'p1/p0 =', fit.p[1] / fit.p[0], ' p3/p2 =', fit.p[3] / fit.p[2]
print 'corr(p0,p1) =', gv.evalcorr(fit.p[:2])[1,0]
if DO_PLOT:
plt.semilogx()
plt.errorbar(
x=gv.mean(x), xerr=gv.sdev(x), y=gv.mean(y), yerr=gv.sdev(y),
fmt='ob'
)
# plot fit line
xx = np.linspace(0.99 * gv.mean(min(x)), 1.01 * gv.mean(max(x)), 100)
yy = fcn(xx, fit.pmean)
plt.xlabel('x')
plt.ylabel('y')
plt.plot(xx, yy, ':r')
plt.savefig('eg3.png', bbox_inches='tight')
plt.show()
sys.stdout = sys_stdout
if DO_BOOTSTRAP:
gv.ranseed(123)
sys.stdout = tee.tee(sys_stdout, open('eg3c.out', 'w'))
print fit
print 'p1/p0 =', fit.p[1] / fit.p[0], ' p3/p2 =', fit.p[3] / fit.p[2]
print 'corr(p0,p1) =', gv.evalcorr(fit.p[:2])[1,0]
Nbs = 40
outputs = {'p':[], 'p1/p0':[], 'p3/p2':[]}
for bsfit in fit.bootstrap_iter(n=Nbs):
p = bsfit.pmean
outputs['p'].append(p)
outputs['p1/p0'].append(p[1] / p[0])
outputs['p3/p2'].append(p[3] / p[2])
print '\nBootstrap Averages:'
outputs = gv.dataset.avg_data(outputs, bstrap=True)
print gv.tabulate(outputs)
print 'corr(p0,p1) =', gv.evalcorr(outputs['p'][:2])[1,0]
# make histograms of p1/p0 and p3/p2
sys.stdout = sys_stdout
print
sys.stdout = tee.tee(sys_stdout, open('eg3d.out', 'w'))
print 'Histogram Analysis:'
count = {'p1/p0':[], 'p3/p2':[]}
hist = {
'p1/p0':gv.PDFHistogram(fit.p[1] / fit.p[0]),
'p3/p2':gv.PDFHistogram(fit.p[3] / fit.p[2]),
}
for bsfit in fit.bootstrap_iter(n=1000):
p = bsfit.pmean
count['p1/p0'].append(hist['p1/p0'].count(p[1] / p[0]))
count['p3/p2'].append(hist['p3/p2'].count(p[3] / p[2]))
count = gv.dataset.avg_data(count)
plt.rcParams['figure.figsize'] = [6.4, 2.4]
pltnum = 1
for k in count:
print k + ':'
print hist[k].analyze(count[k]).stats
plt.subplot(1, 2, pltnum)
plt.xlabel(k)
hist[k].make_plot(count[k], plot=plt)
if pltnum == 2:
plt.ylabel('')
pltnum += 1
plt.rcParams['figure.figsize'] = [6.4, 4.8]
plt.savefig('eg3d.png', bbox_inches='tight')
plt.show()
if DO_BAYESIAN:
gv.ranseed(123)
sys.stdout = tee.tee(sys_stdout, open('eg3e.out', 'w'))
print fit
expval = lsqfit.BayesIntegrator(fit)
# adapt integrator to PDF from fit
neval = 1000
nitn = 10
expval(neval=neval, nitn=nitn)
# <g(p)> gives mean and covariance matrix, and histograms
hist = [
gv.PDFHistogram(fit.p[0]), gv.PDFHistogram(fit.p[1]),
gv.PDFHistogram(fit.p[2]), gv.PDFHistogram(fit.p[3]),
]
def g(p):
return dict(
mean=p,
outer=np.outer(p, p),
count=[
hist[0].count(p[0]), hist[1].count(p[1]),
hist[2].count(p[2]), hist[3].count(p[3]),
],
)
# evaluate expectation value of g(p)
results = expval(g, neval=neval, nitn=nitn, adapt=False)
# analyze results
print('\nIterations:')
print(results.summary())
print('Integration Results:')
pmean = results['mean']
pcov = results['outer'] - np.outer(pmean, pmean)
print ' mean(p) =', pmean
print ' cov(p) =\n', pcov
# create GVars from results
p = gv.gvar(gv.mean(pmean), gv.mean(pcov))
print('\nBayesian Parameters:')
print(gv.tabulate(p))
# show histograms
print('\nHistogram Statistics:')
count = results['count']
for i in range(4):
print('p[{}] -'.format(i))
print(hist[i].analyze(count[i]).stats)
plt.subplot(2, 2, i + 1)
plt.xlabel('p[{}]'.format(i))
hist[i].make_plot(count[i], plot=plt)
if i % 2 != 0:
plt.ylabel('')
plt.savefig('eg3e.png', bbox_inches='tight')
plt.show()
if DO_SIMULATION:
gv.ranseed(1234)
sys.stdout = tee.tee(sys_stdout, open('eg3f.out', 'w'))
print(40 * '*' + ' real fit')
print(fit.format(True))
Q = []
p = []
for sfit in fit.simulated_fit_iter(n=3, add_priornoise=False):
print(40 * '=' + ' simulation')
print(sfit.format(True))
diff = sfit.p - sfit.pexact
print '\nsfit.p - pexact =', diff
print(gv.fmt_chi2(gv.chi2(diff)))
print
# omit constraint
sys.stdout = tee.tee(sys_stdout, open("eg3b.out", "w"))
prior = gv.gvar(4 * ['0(1)'])
prior[1] = gv.gvar('0(20)')
fit = lsqfit.nonlinear_fit(prior=prior, data=(x,y), fcn=fcn)
print fit
print 'p1/p0 =', fit.p[1] / fit.p[0], ' p3/p2 =', fit.p[3] / fit.p[2]
print 'corr(p0,p1) =', gv.evalcorr(fit.p[:2])[1,0]
def addnewtee(self, x, y):
self.sim.unitops.append(tee(len(self.sim.unitops), x, y, globe.TeeDefaultNOut))
def main():
gv.ranseed([2009,2010,2011,2012]) # initialize random numbers (opt.)
x,y = make_data() # make fit data
p0 = None # make larger fits go faster (opt.)
sys_stdout = sys.stdout
sys.stdout = tee.tee(sys.stdout, open("eg1.out","w"))
for nexp in range(3,20):
prior = make_prior(nexp)
fit = lsqfit.nonlinear_fit(data=(x,y),fcn=f,prior=prior,p0=p0) #, svdcut=SVDCUT)
if fit.chi2/fit.dof<1.:
p0 = fit.pmean # starting point for next fit (opt.)
if nexp in [8, 9, 10]:
print(".".center(73))
if nexp > 7 and nexp < 19:
continue
elif nexp not in [3]:
print("")
print '************************************* nexp =',nexp
print fit.format() # print the fit results
E = fit.p['E'] # best-fit parameters
a = fit.p['a']
print 'E1/E0 =',E[1]/E[0],' E2/E0 =',E[2]/E[0]
print 'a1/a0 =',a[1]/a[0],' a2/a0 =',a[2]/a[0]
# extra data 1
print '\n--------------------- fit with extra information'
sys.stdout = tee.tee(sys_stdout, open("eg1a.out", "w"))
def ratio(p):
return p['a'][1] / p['a'][0]
newfit = lsqfit.nonlinear_fit(data=gv.gvar(1,1e-5), fcn=ratio, prior=fit.p)
print (newfit)
# print(newfit.p['a'][1] / newfit.p['a'][0])
# print(fit.p['a'][1] / fit.p['a'][0])
# alternate method for extra data
sys.stdout = tee.tee(sys_stdout, open("eg1b.out", "w"))
fit.p['a1/a0'] = fit.p['a'][1] / fit.p['a'][0]
new_data = {'a1/a0' : gv.gvar(1,1e-5)}
new_p = lsqfit.wavg([fit.p, new_data])
print 'chi2/dof = %.2f\n' % (new_p.chi2 / new_p.dof)
print 'E:', new_p['E'][:4]
print 'a:', new_p['a'][:4]
print 'a1/a0:', new_p['a1/a0']
# # extra data 2
# sys.stdout = tee.tee(sys_stdout, open("eg1b.out", "w"))
# newfit = fit
# for i in range(1):
# print '\n--------------------- fit with %d extra data sets' % (i+1)
# x, ynew = make_data()
# prior = newfit.p
# newfit = lsqfit.nonlinear_fit(data=(x,ynew), fcn=f, prior=prior) # , svdcut=SVDCUT)
# print newfit
sys.stdout = sys_stdout
# def fcn(x, p):
# return f(x, p), f(x, p)
# prior = make_prior(nexp)
# fit = lsqfit.nonlinear_fit(data=(x, [y, ynew]), fcn=fcn, prior=prior, p0=newfit.pmean) # , svdcut=SVDCUT)
# print(fit)
if DO_BOOTSTRAP:
Nbs = 40 # number of bootstrap copies
outputs = {'E1/E0':[], 'E2/E0':[], 'a1/a0':[],'a2/a0':[],'E1':[],'a1':[]} # results
for bsfit in fit.bootstrap_iter(n=Nbs):
E = bsfit.pmean['E'] # best-fit parameters
a = bsfit.pmean['a']
outputs['E1/E0'].append(E[1]/E[0]) # accumulate results
outputs['E2/E0'].append(E[2]/E[0])
outputs['a1/a0'].append(a[1]/a[0])
outputs['a2/a0'].append(a[2]/a[0])
outputs['E1'].append(E[1])
outputs['a1'].append(a[1])
# print E[:2]
# print a[:2]
# print bsfit.chi2/bsfit.dof
# extract means and standard deviations from the bootstrap output
for k in outputs:
outputs[k] = gv.gvar(np.mean(outputs[k]),np.std(outputs[k]))
print 'Bootstrap results:'
print 'E1/E0 =',outputs['E1/E0'],' E2/E1 =',outputs['E2/E0']
print 'a1/a0 =',outputs['a1/a0'],' a2/a0 =',outputs['a2/a0']
print 'E1 =',outputs['E1'],' a1 =',outputs['a1']
if DO_PLOT:
import pylab as plt
ratio = y/fit.fcn(x,fit.pmean)
plt.xlim(0,21)
plt.xlabel('x')
plt.ylabel('y/f(x,p)')
plt.errorbar(x=x,y=gv.mean(ratio),yerr=gv.sdev(ratio),fmt='ob')
plt.plot([0.0,21.0],[1.0,1.0])
plt.show()
def main():
gv.ranseed([2009, 2010, 2011, 2012]) # initialize random numbers (opt.)
x, y = make_data() # make fit data
p0 = None # make larger fits go faster (opt.)
sys_stdout = sys.stdout
for nexp in range(3, 8):
prior = make_prior(nexp)
fit = lsqfit.nonlinear_fit(data=(x, y), fcn=f, prior=prior, p0=p0, svdcut=1e-15) # ,svdcut=SVDCUT)
if fit.chi2 / fit.dof < 1.0:
p0 = fit.pmean # starting point for next fit (opt.)
if nexp == 5:
sys.stdout = tee.tee(sys_stdout, open("eg3.out", "w"))
print "************************************* nexp =", nexp
print fit # print the fit results
E = fit.p["E"] # best-fit parameters
a = fit.p["a"]
print "E1/E0 =", E[1] / E[0], " E2/E0 =", E[2] / E[0]
print "a1/a0 =", a[1] / a[0], " a2/a0 =", a[2] / a[0]
# print E[1]-E[0], E[-1]-E[-2]
# print (E[1]/E[0]).partialsdev(fit.prior['E'])
# print (E[1]/E[0]).partialsdev(fit.prior['a'])
# print (E[1]/E[0]).partialsdev(fit.y)
sys.stdout = sys_stdout
print
# sys.stdout = tee.tee(sys_stdout, open("eg3a.out", "w"))
# for i in range(1):
# print '--------------------- fit with %d extra data sets' % (i+1)
# x, y = make_data(1)
# prior = fit.p
# fit = lsqfit.nonlinear_fit(data=(x,y),fcn=f1,prior=prior, svdcut=SVDCUT)
# print fit
sys.stdout = sys_stdout
if DO_BOOTSTRAP:
Nbs = 10 # number of bootstrap copies
outputs = {"E1/E0": [], "E2/E0": [], "a1/a0": [], "a2/a0": [], "E1": [], "a1": []} # results
for bsfit in fit.bootstrap_iter(n=Nbs):
E = bsfit.pmean["E"] # best-fit parameters
a = bsfit.pmean["a"]
outputs["E1/E0"].append(E[1] / E[0]) # accumulate results
outputs["E2/E0"].append(E[2] / E[0])
outputs["a1/a0"].append(a[1] / a[0])
outputs["a2/a0"].append(a[2] / a[0])
outputs["E1"].append(E[1])
outputs["a1"].append(a[1])
# print E[:2]
# print a[:2]
# print bsfit.chi2/bsfit.dof
# extract means and standard deviations from the bootstrap output
for k in outputs:
outputs[k] = gv.gvar(np.mean(outputs[k]), np.std(outputs[k]))
print "Bootstrap results:"
print "E1/E0 =", outputs["E1/E0"], " E2/E1 =", outputs["E2/E0"]
print "a1/a0 =", outputs["a1/a0"], " a2/a0 =", outputs["a2/a0"]
print "E1 =", outputs["E1"], " a1 =", outputs["a1"]
if DO_PLOT:
print fit.format(100) # print the fit results
import pylab as pp
from gvar import mean, sdev
fity = f(x, fit.pmean)
ratio = y / fity
pp.xlim(0, 21)
pp.xlabel("x")
pp.ylabel("y/f(x,p)")
pp.errorbar(x=x, y=mean(ratio), yerr=sdev(ratio), fmt="ob")
pp.plot([0.0, 21.0], [1.0, 1.0])
pp.show()
# print (y)
print()
log_prior = BufferDict()
log_prior['log(a)'] = log(gvar(0.02, 0.02))
sqrt_prior = BufferDict()
sqrt_prior['sqrt(a)'] = sqrt(gvar(0.02, 0.02))
prior = BufferDict(a=gvar(0.02, 0.02))
unif_prior = BufferDict()
unif_prior['f(a)'] = BufferDict.uniform('f', 0, 0.04)
stdout = sys.stdout
for p in [prior, log_prior, sqrt_prior, unif_prior]:
key = list(p.keys())[0].replace('(a)', '_a')
sys.stdout = tee.tee(sys_stdout, open("eg6-{}.out".format(key), "w"))
def fcn(p, N=len(y)):
return N * [p['a']]
f = nonlinear_fit(prior=p, fcn=fcn, data=(y))
print(f)
print("a =", f.p['a'])
sys.stdout = tee.tee(sys_stdout, open("eg6-erfinv.out", "w"))
prior = BufferDict()
prior['erfinv(50a-1)'] = gvar('0(1)') / sqrt(2)
def fcn(p, N=len(y)):
a = 0.02 + 0.02 * p['50a-1']
"--timesteps",
help="Number of timesteps.",
action="append",
dest="nlvar",
type=lambda x: "cntrl.nstlim=" + str(x))
parser.set_defaults(nlvar=[])
args = parser.parse_args(sys.argv[1:])
if args.runname is None:
args.runname = args.dataset
rundir = os.path.join(case.CASEROOT, 'run', args.runname, RUNID)
runlog = os.path.join(rundir, 'submission.log')
if not os.path.exists(rundir):
os.makedirs(rundir)
tee.tee(runlog, sys.stdout, sys.stderr)
lastrundir = os.path.join(case.CASEROOT, 'run', args.runname, 'latest')
dataset_matched = []
for dataset in datasets:
if dataset.startswith(args.dataset):
dataset_matched.append(dataset)
if len(dataset_matched) < 1:
print("No dataset matched for %s" % args.dataset)
sys.exit(1)
if len(dataset_matched) > 1:
print("Multiple dataset matched for %s, available datasets matched: %s" %
(args.dataset, str(dataset_matched)))
sys.exit(1)
indir = os.path.join(case.INPUTROOT, dataset_matched[0])
gv.ranseed(123456)
def f(x, p):
return p[0] + p[1] * np.exp(-p[2] * x)
p0 = [0.5, 0.4, 0.7]
N = 10000
x = np.linspace(0.2, 1.0, N)
y = make_fake_data(x, p0, f)
sys.stdout = tee.tee(sys_stdout, open('eg9a.out', 'w'))
print('x = [{} {} ... {}]'.format(x[0], x[1], x[-1]))
print('y = [{} {} ... {}]'.format(y[0], y[1], y[-1]))
print('corr(y[0],y[9999]) =', gv.evalcorr([y[0], y[-1]])[1, 0])
print()
# fit function and prior
def fcn(x, p):
return p[0] + p[1] * np.exp(-p[2] * x)
prior = gv.gvar(['0(1)', '0(1)', '0(1)'])
# Nstride fits, each to nfit data points
nfit = 100
help='exclude results with less than %(metavar)s duplicate files (implicit value: %(const)s)') parser_duplicates.set_defaults(func=duplicates) args = parser.parse_args() # find.wide option if args.func == find and args.wide: args.pattern = '*' + args.pattern + '*' # duplicates.filter option if args.func == duplicates and args.filter is None: args.filter = [] # log option if args.log_file: if args.no_stdout: sys.stdout = args.log_file else: sys.stdout = tee.tee( codecs.getwriter(sys.stdout.encoding)(sys.stdout, 'replace'), args.log_file ) elif args.no_stdout: sys.stdout = fake_fd() db = files_db.FilesDb(exclude_drives=args.exclude_drives, db_filepath=args.db_filepath) if args.drives and args.exclude_drives: args.drives = [drive for drive in args.drives if drive not in args.exclude_drives] _loaddb(db, args.quiet < 2) if args.updatedb and args.func != updatedb: updatedb(db, args) if args.quiet < 2: print args.func(db, args)
def copyfrom(self, simcopyfrom):
#//MemoryStream myStream = new MemoryStream();
#//simulation tempsim = new simulation();
#//BinaryFormatter bf = new BinaryFormatter();
#//bf.Serialize(myStream, simcopyfrom);
#//myStream.Seek(0, SeekOrigin.Begin);
#//tempsim = (simulation)bf.Deserialize(myStream);
#//myStream.Close();
#//unitops = tempsim.unitops;
#//streams = tempsim.streams;
#//pidcontrollers = tempsim.pidcontrollers;
if (len(self.unitops) == 0):
self.unitops = []
for i in range(len(simcopyfrom.unitops)):
#if simcopyfrom.unitops[i].objecttype == globe.objecttypes.CoolingTowerSimple:
# #obj = coolingtowersimple()
# obj.coolingtowersimplecopyconstructor(simcopyfrom.unitops[i])
# self.unitops.append(obj)
#elif simcopyfrom.unitops[i].objecttype == globe.objecttypes.CoolingTowerHeatExchangerSimple:
# obj = coolingtowerheatexchangersimple()
# obj.coolingtowerheatexchangersimplecopyconstructor(simcopyfrom.unitops[i])
# self.unitops.append(obj)
if simcopyfrom.unitops[
i].objecttype == globe.objecttypes.CoolingTower:
obj = coolingtower(0, 0, 0)
obj.copyfrom(simcopyfrom.unitops[i])
self.unitops.append(obj)
#elif simcopyfrom.unitops[i].objecttype == globe.objecttypes.DistillationColumn:
# obj = distillationcolumn()
# obj.copyfrom(simcopyfrom.unitops[i])
# self.unitops.append(obj)
#elif simcopyfrom.unitops[i].objecttype == globe.objecttypes.Flange:
# obj = flange()
# obj.flangecopyconstructor(simcopyfrom.unitops[i])
# self.unitops.append(obj)
#elif simcopyfrom.unitops[i].objecttype == globe.objecttypes.GasPipe:
# obj = gaspipe()
# obj.gaspipecopyconstructor(simcopyfrom.unitops[i])
# self.unitops.append(obj)
elif simcopyfrom.unitops[
i].objecttype == globe.objecttypes.HeatExchangerSimple:
obj = heatexchangersimple(0, 0, 0)
obj.copyfrom(simcopyfrom.unitops[i])
self.unitops.append(obj)
elif simcopyfrom.unitops[
i].objecttype == globe.objecttypes.Mixer:
obj = mixer(0, 0, 0, simcopyfrom.unitops[i].nin)
obj.copyfrom(simcopyfrom.unitops[i])
self.unitops.append(obj)
elif simcopyfrom.unitops[
i].objecttype == globe.objecttypes.Pump:
obj = pump(0.0, 0.0, 0.0, 0.0, 0.0, \
0.0, 0.0, 1)
obj.copyfrom(simcopyfrom.unitops[i])
self.unitops.append(obj)
elif simcopyfrom.unitops[
i].objecttype == globe.objecttypes.Tee:
obj = tee(0.0, 0.0, 0.0, simcopyfrom.unitops[i].nout)
obj.copyfrom(simcopyfrom.unitops[i])
self.unitops.append(obj)
elif simcopyfrom.unitops[
i].objecttype == globe.objecttypes.Valve:
obj = valve(0, 0.0, 0.0)
obj.copyfrom(simcopyfrom.unitops[i])
self.unitops.append(obj)
elif simcopyfrom.unitops[
i].objecttype == globe.objecttypes.Tank:
obj = tank(0, 0.0, 0.0)
obj.copyfrom(simcopyfrom.unitops[i])
self.unitops.append(obj)
else:
break
else:
for i in range(len(simcopyfrom.unitops)):
self.unitops[i].copyfrom(simcopyfrom.unitops[i])
if (len(self.streams) == 0):
self.streams = []
for i in range(len(simcopyfrom.streams)):
obj = stream(0, 0.0, 0.0, 0.0, 0.0)
obj.copyfrom(simcopyfrom.streams[i])
self.streams.append(obj)
else:
for i in range(len(simcopyfrom.streams)):
self.streams[i].copyfrom(simcopyfrom.streams[i])
#if (len(self.signals) == 0):
# self.signals = []
# for i in range(len(simcopyfrom.signals)):
# obj = signal()
# obj.copyfrom(simcopyfrom.signals[i])
# self.signals.append(obj)
#else:
# for i in range(len(simcopyfrom.signals)):
# self.signals[i].copyfrom(simcopyfrom.signals[i])
if (len(self.pidcontrollers) == 0):
self.pidcontrollers = []
for i in range(len(simcopyfrom.pidcontrollers)):
obj = pidcontroller()
obj.copyfrom(simcopyfrom.pidcontrollers[i])
self.pidcontrollers.append(obj)
else:
for i in range(len(simcopyfrom.pidcontrollers)):
self.pidcontrollers[i].copyfrom(simcopyfrom.pidcontrollers[i])
#if (len(self.blocks) == 0):
# self.blocks = []
# for i in range(len(simcopyfrom.blocks)):
# if simcopyfrom.blocks[i].objecttype == globe.objecttypes.ControlMVSignalSplitter:
# obj = controlmvsignalsplitter()
# obj.copyfrom(simcopyfrom.blocks[i])
# blocks.append(obj)
# else:
# break
#else:
# for i in range(len(simcopyfrom.blocks)):
# self.blocks[i].copyfrom(simcopyfrom.blocks[i])
#//public List<nmpc> nmpccontrollers; The nmpc controller(s) are not going to be copied at this point in time.
self.simi = simcopyfrom.simi #//Counting index for this class for simulation indexing for historisation and simulation.
import sys
import io
import subprocess
import case
sys.path.append(case.CASETOOL)
import filescan
import tee
#sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', 1)
BLDID = datetime.datetime.strftime(datetime.datetime.now(), "%y%m%d-%H%M%S")
bldlog = os.path.join(case.CASEROOT, "bld", "bldlog.%s" % BLDID)
objdir = os.path.join(case.CASEROOT, "bld", "obj")
if not os.path.exists(objdir):
os.makedirs(objdir)
tee.tee(bldlog, os.path.sys.stdout, sys.stderr)
os.environ["CASEROOT"] = case.CASEROOT
os.environ["PMEMDSRC"] = case.PMEMDSRC
os.environ["CASETOOL"] = case.CASETOOL
os.environ['BLDID'] = BLDID
os.environ["BINDIR"] = os.path.join(case.CASEROOT, "bld")
os.chdir(case.CASEROOT)
print("Commiting SourceMods before build...")
os.chdir("SourceMods")
os.system("git add -A .")
deleted_files = filescan.get_deleted('.')
os.system("git commit --allow-empty -m 'auto commit for build: %s'" % BLDID)
def main():
### 1) least-squares fit to the data
x = np.array([
0.2, 0.4, 0.6, 0.8, 1.,
1.2, 1.4, 1.6, 1.8, 2.,
2.2, 2.4, 2.6, 2.8, 3.,
3.2, 3.4, 3.6, 3.8
])
y = gv.gvar([
'0.38(20)', '2.89(20)', '0.85(20)', '0.59(20)', '2.88(20)',
'1.44(20)', '0.73(20)', '1.23(20)', '1.68(20)', '1.36(20)',
'1.51(20)', '1.73(20)', '2.16(20)', '1.85(20)', '2.00(20)',
'2.11(20)', '2.75(20)', '0.86(20)', '2.73(20)'
])
prior = make_prior()
fit = lsqfit.nonlinear_fit(data=(x, y), prior=prior, fcn=fitfcn, extend=True)
if LSQFIT_ONLY:
sys.stdout = tee.tee(STDOUT, open('case-outliers-lsq.out', 'w'))
elif not MULTI_W:
sys.stdout = tee.tee(STDOUT, open('case-outliers.out', 'w'))
print(fit)
# plot data
plt.errorbar(x, gv.mean(y), gv.sdev(y), fmt='o', c='b')
# plot fit function
xline = np.linspace(x[0], x[-1], 100)
yline = fitfcn(xline, fit.p)
plt.plot(xline, gv.mean(yline), 'k:')
yp = gv.mean(yline) + gv.sdev(yline)
ym = gv.mean(yline) - gv.sdev(yline)
plt.fill_between(xline, yp, ym, color='0.8')
plt.xlabel('x')
plt.ylabel('y')
plt.savefig('case-outliers1.png', bbox_inches='tight')
if LSQFIT_ONLY:
return
### 2) Bayesian integral with modified PDF
pdf = ModifiedPDF(data=(x, y), fcn=fitfcn, prior=prior)
# integrator for expectation values with modified PDF
expval = lsqfit.BayesIntegrator(fit, pdf=pdf)
# adapt integrator to pdf
expval(neval=1000, nitn=15)
# evaluate expectation value of g(p)
def g(p):
w = 0.5 + 0.5 * p['2w-1']
c = p['c']
return dict(w=[w, w**2], mean=c, outer=np.outer(c,c))
results = expval(g, neval=1000, nitn=15, adapt=False)
print(results.summary())
# expval.map.show_grid(15)
if MULTI_W:
sys.stdout = tee.tee(STDOUT, open('case-outliers-multi.out', 'w'))
# parameters c[i]
mean = results['mean']
cov = results['outer'] - np.outer(mean, mean)
c = mean + gv.gvar(np.zeros(mean.shape), gv.mean(cov))
print('c =', c)
print(
'corr(c) =',
np.array2string(gv.evalcorr(c), prefix=10 * ' '),
'\n',
)
# parameter w
wmean, w2mean = results['w']
wsdev = gv.mean(w2mean - wmean ** 2) ** 0.5
w = wmean + gv.gvar(np.zeros(np.shape(wmean)), wsdev)
print('w =', w, '\n')
# Bayes Factor
print('logBF =', np.log(expval.norm))
sys.stdout = STDOUT
if MULTI_W:
return
# add new fit to plot
yline = fitfcn(xline, dict(c=c))
plt.plot(xline, gv.mean(yline), 'r--')
yp = gv.mean(yline) + gv.sdev(yline)
ym = gv.mean(yline) - gv.sdev(yline)
plt.fill_between(xline, yp, ym, color='r', alpha=0.2)
plt.savefig('case-outliers2.png', bbox_inches='tight')
def main():
x, y = make_data() # make fit data
# y = gv.gvar(gv.mean(y), 0.75**2 * gv.evalcov(y))
p0 = None # make larger fits go faster (opt.)
sys_stdout = sys.stdout
sys.stdout = tee.tee(sys.stdout, open("eg1.out", "w"))
for nexp in range(1, 7):
prior = make_prior(nexp)
fit = lsqfit.nonlinear_fit(data=(x, y), fcn=fcn, prior=prior, p0=p0)
if fit.chi2 / fit.dof < 1.:
p0 = fit.pmean # starting point for next fit (opt.)
print '************************************* nexp =', nexp
print fit.format() # print the fit results
E = fit.p['E'] # best-fit parameters
a = fit.p['a']
if nexp > 2:
print 'E1/E0 =', E[1] / E[0], ' E2/E0 =', E[2] / E[0]
print 'a1/a0 =', a[1] / a[0], ' a2/a0 =', a[2] / a[0]
print
# error budget
outputs = {
'E1/E0': E[1] / E[0],
'E2/E0': E[2] / E[0],
'a1/a0': a[1] / a[0],
'a2/a0': a[2] / a[0]
}
inputs = {'E': fit.prior['E'], 'a': fit.prior['a'], 'y': y}
inputs = collections.OrderedDict()
inputs['a'] = fit.prior['a']
inputs['E'] = fit.prior['E']
inputs['y'] = fit.data[1]
print '================= Error Budget Analysis'
print fit.fmt_values(outputs)
print fit.fmt_errorbudget(outputs, inputs)
sys.stdout = sys_stdout
# print(gv.gvar(str(a[1])) / gv.gvar(str(a[0])) )
# print(gv.evalcorr([fit.p['a'][1], fit.p['E'][1]]))
# print(fit.format(True))
# redo fit with 4 parameters since that is enough
prior = make_prior(4)
fit = lsqfit.nonlinear_fit(data=(x, y), fcn=fcn, prior=prior, p0=fit.pmean)
sys.stdout = tee.tee(sys_stdout, open("eg1a.out", "w"))
print '--------------------- original fit'
print fit.format()
E = fit.p['E'] # best-fit parameters
a = fit.p['a']
print 'E1/E0 =', E[1] / E[0], ' E2/E0 =', E[2] / E[0]
print 'a1/a0 =', a[1] / a[0], ' a2/a0 =', a[2] / a[0]
print
# extra data 1
print '\n--------------------- new fit to extra information'
def ratio(p):
return p['a'][1] / p['a'][0]
newfit = lsqfit.nonlinear_fit(data=gv.gvar(1, 1e-5),
fcn=ratio,
prior=fit.p)
print(newfit.format())
E = newfit.p['E']
a = newfit.p['a']
print 'E1/E0 =', E[1] / E[0], ' E2/E0 =', E[2] / E[0]
print 'a1/a0 =', a[1] / a[0], ' a2/a0 =', a[2] / a[0]
if DO_PLOT:
import matplotlib.pyplot as plt
ratio = y / fit.fcn(x, fit.pmean)
plt.xlim(4, 15)
plt.ylim(0.95, 1.05)
plt.xlabel('x')
plt.ylabel('y / f(x,p)')
plt.yticks([0.96, 0.98, 1.00, 1.02, 1.04],
['0.96', '0.98', '1.00', '1.02', '1.04'])
plt.errorbar(x=x, y=gv.mean(ratio), yerr=gv.sdev(ratio), fmt='ob')
plt.plot([4.0, 21.0], [1.0, 1.0], 'b:')
plt.savefig('eg1.png', bbox_inches='tight')
plt.show()
# alternate method for extra data
sys.stdout = tee.tee(sys_stdout, open("eg1b.out", "w"))
fit.p['a1/a0'] = fit.p['a'][1] / fit.p['a'][0]
new_data = {'a1/a0': gv.gvar(1, 1e-5)}
new_p = lsqfit.wavg([fit.p, new_data])
print 'chi2/dof = %.2f\n' % (new_p.chi2 / new_p.dof)
print 'E:', new_p['E'][:4]
print 'a:', new_p['a'][:4]
print 'a1/a0:', new_p['a1/a0']
if DO_BAYES:
# Bayesian Fit
gv.ranseed([123])
prior = make_prior(4)
fit = lsqfit.nonlinear_fit(data=(x, y),
fcn=f,
prior=prior,
p0=fit.pmean)
sys.stdout = tee.tee(sys_stdout, open("eg1c.out", "w"))
# print fit
expval = lsqfit.BayesIntegrator(fit, limit=10.)
# adapt integrator to PDF
expval(neval=40000, nitn=10)
# calculate expectation value of function g(p)
fit_hist = gv.PDFHistogram(fit.p['E'][0])
def g(p):
parameters = [p['a'][0], p['E'][0]]
return dict(
mean=parameters,
outer=np.outer(parameters, parameters),
hist=fit_hist.count(p['E'][0]),
)
r = expval(g, neval=40000, nitn=10, adapt=False)
# print results
print r.summary()
means = r['mean']
cov = r['outer'] - np.outer(r['mean'], r['mean'])
print 'Results from Bayesian Integration:'
print 'a0: mean =', means[0], ' sdev =', cov[0, 0]**0.5
print 'E0: mean =', means[1], ' sdev =', cov[1, 1]**0.5
print 'covariance from Bayesian integral =', np.array2string(
cov, prefix=36 * ' ')
print
print 'Results from Least-Squares Fit:'
print 'a0: mean =', fit.p['a'][0].mean, ' sdev =', fit.p['a'][0].sdev
print 'E0: mean =', fit.p['E'][0].mean, ' sdev =', fit.p['E'][0].sdev
print 'covariance from least-squares fit =', np.array2string(
gv.evalcov([fit.p['a'][0], fit.p['E'][0]]),
prefix=36 * ' ',
precision=3)
sys.stdout = sys_stdout
# make histogram of E[0] probabilty
plt = fit_hist.make_plot(r['hist'])
plt.xlabel('$E_0$')
plt.ylabel('probability')
plt.savefig('eg1c.png', bbox_inches='tight')
# plt.show()
if DO_BOOTSTRAP:
Nbs = 40 # number of bootstrap copies
outputs = {
'E1/E0': [],
'E2/E0': [],
'a1/a0': [],
'a2/a0': [],
'E1': [],
'a1': []
} # results
for bsfit in fit.bootstrap_iter(n=Nbs):
E = bsfit.pmean['E'] # best-fit parameters
a = bsfit.pmean['a']
outputs['E1/E0'].append(E[1] / E[0]) # accumulate results
outputs['E2/E0'].append(E[2] / E[0])
outputs['a1/a0'].append(a[1] / a[0])
outputs['a2/a0'].append(a[2] / a[0])
outputs['E1'].append(E[1])
outputs['a1'].append(a[1])
# print E[:2]
# print a[:2]
# print bsfit.chi2/bsfit.dof
# extract means and standard deviations from the bootstrap output
for k in outputs:
outputs[k] = gv.gvar(np.mean(outputs[k]), np.std(outputs[k]))
print 'Bootstrap results:'
print 'E1/E0 =', outputs['E1/E0'], ' E2/E1 =', outputs['E2/E0']
print 'a1/a0 =', outputs['a1/a0'], ' a2/a0 =', outputs['a2/a0']
print 'E1 =', outputs['E1'], ' a1 =', outputs['a1']
def main():
gv.ranseed([2009,2010,2011,2012]) # initialize random numbers (opt.)
x,y = make_data() # make fit data
p0 = None # make larger fits go faster (opt.)
sys_stdout = sys.stdout
sys.stdout = tee.tee(sys.stdout, open("eg1.out","w"))
for nexp in range(1, 11):
prior = make_prior(nexp)
fit = lsqfit.nonlinear_fit(data=(x,y),fcn=f,prior=prior,p0=p0) #, svdcut=SVDCUT)
if fit.chi2/fit.dof<1.:
p0 = fit.pmean # starting point for next fit (opt.)
if nexp > 5 and nexp < 10:
print(".".center(73))
continue
elif nexp not in [1]:
print("")
print '************************************* nexp =',nexp
print fit.format() # print the fit results
E = fit.p['E'] # best-fit parameters
a = fit.p['a']
if nexp > 2:
print 'E1/E0 =',E[1]/E[0],' E2/E0 =',E[2]/E[0]
print 'a1/a0 =',a[1]/a[0],' a2/a0 =',a[2]/a[0]
# redo fit with 4 parameters since that is enough
prior = make_prior(4)
fit = lsqfit.nonlinear_fit(data=(x,y), fcn=f, prior=prior, p0=fit.pmean)
sys.stdout = sys_stdout
print fit
# extra data 1
print '\n--------------------- fit with extra information'
sys.stdout = tee.tee(sys_stdout, open("eg1a.out", "w"))
def ratio(p):
return p['a'][1] / p['a'][0]
newfit = lsqfit.nonlinear_fit(data=gv.gvar(1,1e-5), fcn=ratio, prior=fit.p)
print (newfit)
E = newfit.p['E']
a = newfit.p['a']
print 'E1/E0 =',E[1]/E[0],' E2/E0 =',E[2]/E[0]
print 'a1/a0 =',a[1]/a[0],' a2/a0 =',a[2]/a[0]
# alternate method for extra data
sys.stdout = tee.tee(sys_stdout, open("eg1b.out", "w"))
fit.p['a1/a0'] = fit.p['a'][1] / fit.p['a'][0]
new_data = {'a1/a0' : gv.gvar(1,1e-5)}
new_p = lsqfit.wavg([fit.p, new_data])
print 'chi2/dof = %.2f\n' % (new_p.chi2 / new_p.dof)
print 'E:', new_p['E'][:4]
print 'a:', new_p['a'][:4]
print 'a1/a0:', new_p['a1/a0']
if DO_BAYES:
# Bayesian Fit
gv.ranseed([123])
prior = make_prior(4)
fit = lsqfit.nonlinear_fit(data=(x,y), fcn=f, prior=prior, p0=fit.pmean)
sys.stdout = tee.tee(sys_stdout, open("eg1c.out", "w"))
# print fit
expval = lsqfit.BayesIntegrator(fit, limit=10.)
# adapt integrator to PDF
expval(neval=10000, nitn=10)
# calculate expectation value of function g(p)
fit_hist = gv.PDFHistogram(fit.p['E'][0])
def g(p):
parameters = [p['a'][0], p['E'][0]]
return dict(
mean=parameters,
outer=np.outer(parameters, parameters),
hist=fit_hist.count(p['E'][0]),
)
r = expval(g, neval=10000, nitn=10, adapt=False)
# print results
print r.summary()
means = r['mean']
cov = r['outer'] - np.outer(r['mean'], r['mean'])
print 'Results from Bayesian Integration:'
print 'a0: mean =', means[0], ' sdev =', cov[0,0]**0.5
print 'E0: mean =', means[1], ' sdev =', cov[1,1]**0.5
print 'covariance from Bayesian integral =', np.array2string(cov, prefix=36 * ' ')
print
print 'Results from Least-Squares Fit:'
print 'a0: mean =', fit.p['a'][0].mean, ' sdev =', fit.p['a'][0].sdev
print 'E0: mean =', fit.p['E'][0].mean, ' sdev =', fit.p['E'][0].sdev
print 'covariance from least-squares fit =', np.array2string(gv.evalcov([fit.p['a'][0], fit.p['E'][0]]), prefix=36*' ',precision=3)
sys.stdout = sys_stdout
# make histogram of E[0] probabilty
plt = fit_hist.make_plot(r['hist'])
plt.xlabel('$E_0$')
plt.ylabel('probability')
plt.savefig('eg1c.png', bbox_inches='tight')
# plt.show()
# # extra data 2
# sys.stdout = tee.tee(sys_stdout, open("eg1b.out", "w"))
# newfit = fit
# for i in range(1):
# print '\n--------------------- fit with %d extra data sets' % (i+1)
# x, ynew = make_data()
# prior = newfit.p
# newfit = lsqfit.nonlinear_fit(data=(x,ynew), fcn=f, prior=prior) # , svdcut=SVDCUT)
# print newfit
sys.stdout = sys_stdout
# def fcn(x, p):
# return f(x, p), f(x, p)
# prior = make_prior(nexp)
# fit = lsqfit.nonlinear_fit(data=(x, [y, ynew]), fcn=fcn, prior=prior, p0=newfit.pmean) # , svdcut=SVDCUT)
# print(fit)
if DO_BOOTSTRAP:
Nbs = 40 # number of bootstrap copies
outputs = {'E1/E0':[], 'E2/E0':[], 'a1/a0':[],'a2/a0':[],'E1':[],'a1':[]} # results
for bsfit in fit.bootstrap_iter(n=Nbs):
E = bsfit.pmean['E'] # best-fit parameters
a = bsfit.pmean['a']
outputs['E1/E0'].append(E[1]/E[0]) # accumulate results
outputs['E2/E0'].append(E[2]/E[0])
outputs['a1/a0'].append(a[1]/a[0])
outputs['a2/a0'].append(a[2]/a[0])
outputs['E1'].append(E[1])
outputs['a1'].append(a[1])
# print E[:2]
# print a[:2]
# print bsfit.chi2/bsfit.dof
# extract means and standard deviations from the bootstrap output
for k in outputs:
outputs[k] = gv.gvar(np.mean(outputs[k]),np.std(outputs[k]))
print 'Bootstrap results:'
print 'E1/E0 =',outputs['E1/E0'],' E2/E1 =',outputs['E2/E0']
print 'a1/a0 =',outputs['a1/a0'],' a2/a0 =',outputs['a2/a0']
print 'E1 =',outputs['E1'],' a1 =',outputs['a1']
if DO_PLOT:
import matplotlib.pyplot as plt
ratio = y / fit.fcn(x,fit.pmean)
plt.xlim(4, 21)
plt.xlabel('x')
plt.ylabel('y / f(x,p)')
plt.errorbar(x=x,y=gv.mean(ratio),yerr=gv.sdev(ratio),fmt='ob')
plt.plot([4.0, 21.0], [1.0, 1.0], 'b:')
plt.savefig('eg1.png', bbox_inches='tight')
plt.show()
def main():
gd.ranseed([2009,2010,2011,2012]) # initialize random numbers (opt.)
max_prior = make_prior(20) # maximum sized prior
p0 = None # make larger fits go faster (opt.)
sys_stdout = sys.stdout
if USE_SVD:
sys.stdout = tee.tee(sys_stdout,open("eg5a.out","w"))
for nexp in range(1,5):
print '************************************* nexp =',nexp
fit_prior = lsqfit.GPrior() # prior used in fit
ymod_prior = lsqfit.GPrior() # part of max_prior absorbed in ymod
for k in max_prior:
fit_prior[k] = max_prior[k][:nexp]
ymod_prior[k] = max_prior[k][nexp:]
x,y = make_data(ymod_prior) # make fit data
fit = lsqfit.nonlinear_fit(data=(x,y),fcn=f,prior=fit_prior,p0=p0,svdcut=SVDCUT,maxit=10000)
if nexp==4 and not USE_SVD:
sys.stdout = tee.tee(sys_stdout, open("eg5b.out", "w"))
print fit.format(100) # print the fit results
# if nexp>3:
# E = fit.p['E'] # best-fit parameters
# a = fit.p['a']
# print 'E1/E0 =',E[1]/E[0],' E2/E0 =',E[2]/E[0]
# print 'a1/a0 =',a[1]/a[0],' a2/a0 =',a[2]/a[0]
# E = fit.palt['E'] # best-fit parameters
# a = fit.palt['a']
# print 'E1/E0 =',E[1]/E[0],' E2/E0 =',E[2]/E[0]
# print 'a1/a0 =',a[1]/a[0],' a2/a0 =',a[2]/a[0]
print
if fit.chi2/fit.dof<1.:
p0 = fit.pmean # starting point for next fit (opt.)
E = fit.p['E'] # best-fit parameters
a = fit.p['a']
print 'E1/E0 =',(E[1]/E[0]).fmt(),' E2/E0 =',(E[2]/E[0]).fmt()
print 'a1/a0 =',(a[1]/a[0]).fmt(),' a2/a0 =',(a[2]/a[0]).fmt()
sys.stdout = sys_stdout
if DO_ERRORBUDGET:
if USE_SVD:
sys.stdout = tee.tee(sys_stdout,open("eg5d.out","w"))
outputs = {'E1/E0':E[1]/E[0], 'E2/E0':E[2]/E[0],
'a1/a0':a[1]/a[0], 'a2/a0':a[2]/a[0]}
inputs = {'E':max_prior['E'],'a':max_prior['a'],'svd':fit.svdcorrection}
print fit.fmt_values(outputs)
print fit.fmt_errorbudget(outputs,inputs)
sys.stdout = sys_stdout
outputs = {''}
if DO_BOOTSTRAP:
Nbs = 40 # number of bootstrap copies
outputs = {'E1/E0':[], 'E2/E0':[], 'a1/a0':[],'a2/a0':[],'E1':[],'a1':[]} # results
for bsfit in fit.bootstrap_iter(n=Nbs):
E = bsfit.pmean['E'] # best-fit parameters
a = bsfit.pmean['a']
outputs['E1/E0'].append(E[1]/E[0]) # accumulate results
outputs['E2/E0'].append(E[2]/E[0])
outputs['a1/a0'].append(a[1]/a[0])
outputs['a2/a0'].append(a[2]/a[0])
outputs['E1'].append(E[1])
outputs['a1'].append(a[1])
# print E[:2]
# print a[:2]
# print bsfit
# extract means and "standard deviations" from the bootstrap output
outputs = gd.dataset.avg_data(outputs,bstrap=True)
# for k in outputs:
# outputs[k] = gd.gvar(np.mean(outputs[k]),np.std(outputs[k]))
if USE_SVD:
sys.stdout = tee.tee(sys_stdout,open("eg5e.out","w"))
print 'Bootstrap results:'
print 'E1/E0 =',outputs['E1/E0'].fmt(),' E2/E1 =',outputs['E2/E0'].fmt()
print 'a1/a0 =',outputs['a1/a0'].fmt(),' a2/a0 =',outputs['a2/a0'].fmt()
print 'E1 =',outputs['E1'].fmt(),' a1 =',outputs['a1'].fmt()
def main():
gv.ranseed([2009,2010,2011,2012]) # initialize random numbers (opt.)
x,y = make_data() # make fit data
p0 = None # make larger fits go faster (opt.)
for nexp in range(3,5):
print '************************************* nexp =',nexp
prior = make_prior(nexp)
fit = lsqfit.nonlinear_fit(data=(x,y),fcn=f,prior=prior,p0=p0)
print fit # print the fit results
E = fit.p['E'] # best-fit parameters
a = fit.p['a']
print 'E1/E0 =',E[1]/E[0],' E2/E0 =',E[2]/E[0]
print 'a1/a0 =',a[1]/a[0],' a2/a0 =',a[2]/a[0]
print
if fit.chi2/fit.dof<1.:
p0 = fit.pmean # starting point for next fit (opt.)
sys_stdout = sys.stdout
if DO_ERRORBUDGET:
lines = [
"E = fit.p['E']",
"a = fit.p['a']",
"print(E[1] / E[0])",
"print((E[1] / E[0]).partialsdev(fit.prior['E']))",
"print((E[1] / E[0]).partialsdev(fit.prior['a']))",
"print((E[1] / E[0]).partialsdev(y))"
]
sys.stdout = tee.tee(sys_stdout, open("eg4c.out","w"))
for line in lines:
print ">>>", line
if line[:5] == "print":
print(eval(line[5:]))
# print E[1]/E[0]
# print (E[1]/E[0]).partialsdev(fit.prior['E'])
# print (E[1]/E[0]).partialsdev(fit.prior['a'])
# print (E[1]/E[0]).partialsdev(y)
outputs = {'E1/E0':E[1]/E[0], 'E2/E0':E[2]/E[0],
'a1/a0':a[1]/a[0], 'a2/a0':a[2]/a[0]}
inputs = {'E':fit.prior['E'],'a':fit.prior['a'],'y':y}
sys.stdout = tee.tee(sys_stdout, open("eg4b.out","w"))
print fit.fmt_values(outputs)
print fit.fmt_errorbudget(outputs,inputs)
sys.stdout = sys_stdout
if DO_SIMULATIONS:
# fit simulations
sys.stdout = tee.tee(sys_stdout, open("eg4d.out","w"))
for sfit in fit.simulated_fit_iter(3):
print '************************************* simulation'
print(sfit)
sE = sfit.p['E'] # best-fit parameters
sa = sfit.p['a']
E = sfit.pexact['E']
a = sfit.pexact['a']
print 'E1/E0 =', sE[1] / sE[0], ' E2/E0 =', sE[2] / sE[0]
print 'a1/a0 =', sa[1] / sa[0], ' a2/a0 =', sa[2] / sa[0]
print '\nSimulated Fit Values - Exact Values:'
print 'E1/E0:', (sE[1] / sE[0]) - (E[1] / E[0]),\
' E2/E0:', (sE[2] / sE[0]) - (E[2] / E[0])
print 'a1/a0:', (sa[1] / sa[0]) - (a[1] / a[0]),\
' a2/a0:', (sa[2] / sa[0]) - (a[2] / a[0])
# compute chi**2 comparing fit results to exact results
sim_results = [sE[0], sE[1], sa[0], sa[1]]
exact_results = [E[0], E[1], a[0], a[1]]
chi2 = gv.chi2(sim_results, exact_results, svdcut=1e-8)
print '\nParameter chi2/dof [dof] = %.2f' % (chi2/chi2.dof), '[%d]' % chi2.dof, ' Q = %.1f' % chi2.Q
print
sys.stdout = sys_stdout
if DO_EMPBAYES:
def fitargs(z,nexp=nexp,prior=prior,f=f,data=(x,y),p0=p0):
z = gv.exp(z)
prior['a'] = [gv.gvar(0.5,0.5*z[0]) for i in range(nexp)]
return dict(prior=prior,data=data,fcn=f,p0=p0)
##
z0 = [0.0]
fit,z = lsqfit.empbayes_fit(z0,fitargs,tol=1e-3)
sys.stdout = tee.tee(sys_stdout, open("eg4a.out","w"))
print fit # print the optimized fit results
E = fit.p['E'] # best-fit parameters
a = fit.p['a']
print 'E1/E0 =',E[1]/E[0],' E2/E0 =',E[2]/E[0]
print 'a1/a0 =',a[1]/a[0],' a2/a0 =',a[2]/a[0]
# print "prior['a'] =",fit.prior['a'][0]
sys.stdout = sys_stdout
print
if DO_PLOT:
import pylab as pp
from gvar import mean,sdev
fity = f(x,fit.pmean)
ratio = y/fity
pp.xlim(0,21)
pp.xlabel('x')
pp.ylabel('y/f(x,p)')
pp.errorbar(x=x,y=mean(ratio),yerr=sdev(ratio),fmt='ob')
pp.plot([0.0,21.0],[1.0,1.0])
pp.show()
def do_fit(svdcut=None, do_plot=False):
if svdcut is None:
svdcut = lsqfit.nonlinear_fit.set()['svdcut']
sys.stdout = tee.tee(sys_stdout, open('eg5a.out', 'w'))
default_svd = True
else:
default_svd = False
x, y = make_data()
prior = make_prior(
100) # 20 exponential terms in all (10 gives same result)
p0 = None
for nexp in range(1, 6):
# marginalize the last 100 - nexp terms
fit_prior = gv.BufferDict() # part of prior used in fit
ymod_prior = gv.BufferDict() # part of prior absorbed in ymod
for k in prior:
fit_prior[k] = prior[k][:nexp]
ymod_prior[k] = prior[k][nexp:]
ymod = y - fcn(x, ymod_prior)
# fit modified data with just nexp terms
fit = lsqfit.nonlinear_fit(data=(x, ymod),
prior=fit_prior,
fcn=fcn,
p0=p0,
tol=1e-10,
svdcut=svdcut)
if not default_svd and nexp == 5:
sys.stdout = tee.tee(sys_stdout, open('eg5b.out', 'w'))
print '************************************* nexp =', nexp
print fit.format(True)
p0 = fit.pmean
if do_plot:
import matplotlib.pyplot as plt
if nexp > 4:
continue
plt.subplot(2, 2, nexp)
if nexp not in [1, 3]:
plt.yticks([0.05, 0.10, 0.15, 0.20, 0.25], [])
else:
plt.ylabel('y')
if nexp not in [3, 4]:
plt.xticks([1.0, 1.5, 2.0, 2.5], [])
else:
plt.xlabel('x')
plt.errorbar(x=x, y=gv.mean(ymod), yerr=gv.sdev(ymod), fmt='bo')
plt.plot(x, y, '-r')
plt.plot(x, fcn(x, fit.pmean), ':k')
plt.text(1.75, 0.22, 'nexp = {}'.format(nexp))
if nexp == 4:
plt.savefig('eg5.png', bbox_inches='tight')
plt.show()
# print summary information and error budget
E = fit.p['E'] # best-fit parameters
a = fit.p['a']
outputs = {
'E1/E0': E[1] / E[0],
'E2/E0': E[2] / E[0],
'a1/a0': a[1] / a[0],
'a2/a0': a[2] / a[0]
}
inputs = {
'E prior': prior['E'],
'a prior': prior['a'],
'svd cut': fit.svdcorrection,
}
print(gv.fmt_values(outputs))
print(gv.fmt_errorbudget(outputs, inputs))
sys.stdout = sys_stdout
def main():
gv.ranseed(SEED)
y = exact(NSAMPLE)
ysamples = [yi for yi in gv.raniter(y, n=NSAMPLE)]
# above code (don't comment it out) generates the following
ysamples = [
[0.0092441016, 0.0068974057, 0.0051480509, 0.0038431422, 0.0028690492],
[0.0092477405, 0.0069030565, 0.0051531383, 0.0038455855, 0.0028700587],
[0.0092558569, 0.0069102437, 0.0051596569, 0.0038514537, 0.0028749153],
[0.0092294581, 0.0068865156, 0.0051395262, 0.003835656, 0.0028630454],
[0.009240534, 0.0068961523, 0.0051480046, 0.0038424661, 0.0028675632],
]
dstr = '['
for yi in ysamples:
dstr += ('[' + len(yi) * '{:10.8g},' + '],').format(*yi)
dstr += ']'
ysamples = eval(dstr)
print(np.array(ysamples).tolist())
s = gv.dataset.svd_diagnosis(ysamples)
# s.plot_ratio(show=True)
y = s.avgdata
x = np.array([15., 16., 17., 18., 19.])
def f(p):
return p['a'] * gv.exp(-p['b'] * x)
prior = gv.gvar(dict(a='0.75(5)', b='0.30(3)'))
sys_stdout = sys.stdout
sys.stdout = tee.tee(sys_stdout, open('eg10a.out', 'w'))
fit = lsqfit.nonlinear_fit(data=y, fcn=f, prior=prior, svdcut=0.0)
print(fit)
sys.stdout = tee.tee(sys_stdout, open('eg10b.out', 'w'))
fit = lsqfit.nonlinear_fit(data=y, fcn=f, prior=prior, svdcut=s.svdcut)
print(fit)
sys.stdout = tee.tee(sys_stdout, open('eg10c.out', 'w'))
fit = lsqfit.nonlinear_fit(data=y,
fcn=f,
prior=prior,
svdcut=s.svdcut,
add_svdnoise=True)
print(fit)
sys.stdout = tee.tee(sys_stdout, open('eg10d.out', 'w'))
yex = gv.gvar(gv.mean(y), gv.evalcov(exact(1.)))
fit = lsqfit.nonlinear_fit(data=yex, fcn=f, prior=prior, svdcut=0)
print(fit)
# fit.plot_residuals().show()
sys.stdout = tee.tee(sys_stdout, open('eg10e.out', 'w'))
fit = lsqfit.nonlinear_fit(data=y, fcn=f, prior=prior, svdcut=s.svdcut)
print(fit)
print('\n================ Add noise to prior, SVD')
noisyfit = lsqfit.nonlinear_fit(data=y,
prior=prior,
fcn=f,
svdcut=s.svdcut,
add_svdnoise=True,
add_priornoise=True)
print(noisyfit.format(True))
# save figures
fit.qqplot_residuals(plot=plt).savefig('eg10e1.png', bbox_inches='tight')
plt.cla()
noisyfit.qqplot_residuals(plot=plt).savefig('eg10e2.png',
bbox_inches='tight')
# true function
fm = 1. - .3 / m - .3 / m**2
plt.plot(m, fm, 'k--')
for s in data:
plt.plot()
ainv, am = param[s]
ms = ainv * am
d = gv.mean(data[s])
derr = gv.sdev(data[s])
col = next(coliter)
plt.errorbar(x=ms, y=d, yerr=derr, fmt=col + 'o')
plt.text(ms[-1] - 0.6, d[-1], s, color=col, fontsize='x-large')
fs = gv.mean(fm)
ams = m / ainv
idx = ams < am[-1]
ams = ams[idx]
fs = gv.mean(fm[idx])
for i, ci in enumerate(fit.p['c']):
fs += ci.mean * ams ** (2 * (i + 1))
plt.plot(m[idx], fs, col + ':')
plt.xlabel('m')
plt.ylabel('f')
plt.text(8, 0.65, 'f(m)', fontsize='x-large')
plt.savefig('eg-spline.png', bbox_inches='tight')
plt.show()
if __name__ == "__main__":
import tee
import sys
sys.stdout = tee.tee(sys.stdout, open('eg-spline.out', 'w'))
main()
