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import math
from optparse import OptionParser
from NISTModels import Models, ReadNistData
from lmfit import Parameters, minimize
def ndig(a, b):
"""Precision for NIST values."""
return round(-math.log10((abs(abs(a)-abs(b)) + 1.e-15) / abs(b)))
ABAR = ' |----------------+----------------+------------------+-------------------|'
def Compare_NIST_Results(DataSet, myfit, params, NISTdata):
buff = [' ======================================',
' %s: ' % DataSet,
' | Parameter Name | Value Found | Certified Value | # Matching Digits |']
buff.append(ABAR)
val_dig_min = 200
err_dig_min = 200
fmt = ' | %s | % -.7e | % -.7e | %2i |'
for i in range(NISTdata['nparams']):
parname = 'b%i' % (i+1)
par = params[parname]
thisval = par.value
certval = NISTdata['cert_values'][i]
vdig = ndig(thisval, certval)
pname = (parname + ' value ' + ' '*14)[:14]
buff.append(fmt % (pname, thisval, certval, vdig))
val_dig_min = min(val_dig_min, vdig)
thiserr = par.stderr
certerr = NISTdata['cert_stderr'][i]
if thiserr is not None and myfit.errorbars:
edig = ndig(thiserr, certerr)
ename = (parname + ' stderr' + ' '*14)[:14]
buff.append(fmt % (ename, thiserr, certerr, edig))
err_dig_min = min(err_dig_min, edig)
buff.append(ABAR)
sumsq = NISTdata['sum_squares']
try:
chi2 = myfit.chisqr
buff.append(' | Sum of Squares | %.7e | %.7e | %2i |'
% (chi2, sumsq, ndig(chi2, sumsq)))
except Exception:
pass
buff.append(ABAR)
if not myfit.errorbars:
buff.append(' | * * * * COULD NOT ESTIMATE UNCERTAINTIES * * * * |')
err_dig_min = 0
if err_dig_min < 199:
buff.append(' Worst agreement: %i digits for value, %i digits for error '
% (val_dig_min, err_dig_min))
else:
buff.append(' Worst agreement: %i digits' % (val_dig_min))
return val_dig_min, '\n'.join(buff)
def NIST_Dataset(DataSet, method='leastsq', start='start2',
plot=False, verbose=False):
NISTdata = ReadNistData(DataSet)
resid, npar, dimx = Models[DataSet]
y = NISTdata['y']
x = NISTdata['x']
params = Parameters()
for i in range(npar):
pname = 'b%i' % (i+1)
pval1 = NISTdata[start][i]
params.add(pname, value=pval1)
myfit = minimize(resid, params, method=method, args=(x,), kws={'y': y})
digs, buff = Compare_NIST_Results(DataSet, myfit, myfit.params, NISTdata)
if verbose:
print(buff)
return digs > 1
def build_usage():
modelnames = []
ms = ''
for d in sorted(Models.keys()):
ms = ms + ' %s ' % d
if len(ms) > 55:
modelnames.append(ms)
ms = ' '
modelnames.append(ms)
modelnames = '\n'.join(modelnames)
usage = """
=== Test Fit to NIST StRD Models ===
usage:
------
python fit_NIST.py [options] Model Start
where Start is one of 'start1','start2' or 'cert', for different
starting values, and Model is one of
%s
if Model = 'all', all models and starting values will be run.
options:
--------
-m name of fitting method. One of:
leastsq, nelder, powell, lbfgsb, bfgs,
tnc, cobyla, slsqp, cg, newto-cg
leastsq (Levenberg-Marquardt) is the default
""" % modelnames
return usage
############################
def run_interactive():
usage = build_usage()
parser = OptionParser(usage=usage, prog="fit-NIST.py")
parser.add_option("-m", "--method", dest="method",
metavar='METH',
default='leastsq',
help="set method name, default = 'leastsq'")
(opts, args) = parser.parse_args()
dset = ''
start = 'start2'
if len(args) > 0:
dset = args[0]
if len(args) > 1:
start = args[1]
if dset.lower() == 'all':
tpass = 0
tfail = 0
failures = []
dsets = sorted(Models.keys())
for dset in dsets:
for start in ('start1', 'start2', 'cert'):
if NIST_Dataset(dset, method=opts.method, start=start,
plot=False, verbose=True):
tpass += 1
else:
tfail += 1
failures.append(" %s (starting at '%s')" % (dset, start))
print('--------------------------------------')
print(' Fit Method: %s ' % opts.method)
print(' Final Results: %i pass, %i fail.' % (tpass, tfail))
print(' Tests Failed for:\n %s' % '\n '.join(failures))
print('--------------------------------------')
elif dset not in Models:
print(usage)
else:
return NIST_Dataset(dset, method=opts.method,
start=start, plot=False, verbose=True)
def RunNIST_Model(model):
out1 = NIST_Dataset(model, start='start1', plot=False, verbose=False)
out2 = NIST_Dataset(model, start='start2', plot=False, verbose=False)
print("NIST Test", model, out1, out2)
assert(out1 or out2)
return out1 or out2
def test_Bennett5():
return RunNIST_Model('Bennett5')
def test_BoxBOD():
return RunNIST_Model('BoxBOD')
def test_Chwirut1():
return RunNIST_Model('Chwirut1')
def test_Chwirut2():
return RunNIST_Model('Chwirut2')
def test_DanWood():
return RunNIST_Model('DanWood')
def test_ENSO():
return RunNIST_Model('ENSO')
def test_Eckerle4():
return RunNIST_Model('Eckerle4')
def test_Gauss1():
return RunNIST_Model('Gauss1')
def test_Gauss2():
return RunNIST_Model('Gauss2')
def test_Gauss3():
return RunNIST_Model('Gauss3')
def test_Hahn1():
return RunNIST_Model('Hahn1')
def test_Kirby2():
return RunNIST_Model('Kirby2')
def test_Lanczos1():
return RunNIST_Model('Lanczos1')
def test_Lanczos2():
return RunNIST_Model('Lanczos2')
def test_Lanczos3():
return RunNIST_Model('Lanczos3')
def test_MGH09():
return RunNIST_Model('MGH09')
def test_MGH10():
return RunNIST_Model('MGH10')
def test_MGH17():
return RunNIST_Model('MGH17')
def test_Misra1a():
return RunNIST_Model('Misra1a')
def test_Misra1b():
return RunNIST_Model('Misra1b')
def test_Misra1c():
return RunNIST_Model('Misra1c')
def test_Misra1d():
return RunNIST_Model('Misra1d')
def test_Nelson():
return RunNIST_Model('Nelson')
def test_Rat42():
return RunNIST_Model('Rat42')
def test_Rat43():
return RunNIST_Model('Rat43')
def test_Roszman1():
return RunNIST_Model('Roszman1')
def test_Thurber():
return RunNIST_Model('Thurber')
if __name__ == '__main__':
run_interactive()
|
